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Transposable elements ( TEs ) have been active in the mammalian genome for millions of years and the silencing of these elements in the germline is important for the survival of the host . Mice carrying reporter transgenes can be used to model transcriptional silencing . A mutagenesis screen for modifiers of epigenetic gene silencing produced a line with a mutation in Trim33; the mutants displayed increased expression of the reporter transgene . ChIP-seq of Trim33 in testis revealed 9 , 109 peaks , mostly at promoters . This is the first report of ChIP-seq for Trim33 in any tissue . Comparison with ENCODE datasets showed that regions of high read density for Trim33 had high read density for histone marks associated with transcriptional activity and mapping to TE consensus sequences revealed Trim33 enrichment at RLTR10B , the LTR of one of the youngest retrotransposons in the mouse genome , MMERVK10C . We identified consensus sequences from the 266 regions at which Trim33 ChIP-seq peaks overlapped RLTR10B elements and found a match to the A-Myb DNA-binding site . We found that TRIM33 has E3 ubiquitin ligase activity for A-MYB and regulates its abundance . RNA-seq revealed that mice haploinsufficient for Trim33 had altered expression of a small group of genes in the testis and the gene with the most significant increase was found to be transcribed from an upstream RLTR10B . These studies provide the first evidence that A-Myb has a role in the actions of Trim33 and suggest a role for both A-Myb and Trim33 in the arms race between the transposon and the host . This the first report of any factor specifically regulating RLTR10B and adds to the current literature on the silencing of MMERVK10C retrotransposons . This is also the first report that A-Myb has a role in the transcription of any retrotransposon .
Approximately half of the genomes of humans and mice are made up of transposable elements ( TEs ) and numerous pathways , both genetic and epigenetic , have evolved to repress their expression . Each TE has had a period of transpositional activity during which it spreads through the genome and this is dependent on its transcriptional activity in the germline . It has been suggested that during periods of epigenetic reprogramming , such as occurs in the germline , when DNA methylation is low , other mechanisms are used to suppress the expression of recently transposed retrotransposons [1] . Because of the difficulty of mapping individual repeats back to the genome , the epigenetic and transcriptional state of retrotransposons has been difficult to study . Improved next generation sequencing chemistry , resulting in longer reads , is helping to overcome this problem and bioinformatics tools are being refined to deal with repetitive elements . Unbiased genetic screens for modifiers of epigenetic gene silencing have been carried out in a number of model organisms , including the mouse , and have provided a valuable tool in the identification of the proteins responsible for mediating transcriptional silencing of inserted reporter transgenes [2 , 3] . The silencing of transgenes is thought to mimic , in many ways , that of retrotransposons [4 , 5] . We have carried out a dominant mouse screen using a line containing a GFP transgene array that undergoes stochastic silencing in erythroid cells and the alleles identified are termed Modifiers of murine metastable epialleles Dominant ( MommeDs ) [4] . The underlying genes responsible for many but not all of the MommeD lines have been identified and reported; MommeD1 , 2 , 4 , 5 , 8–10 , 12–14 , 16–19 , 23 , 27 , 28 , 30–40 , 42 [3] , MommeD6 , 8 [6] and the results of the screen have recently been reviewed [7] . The genes encode known epigenetic modifiers , including DNA methyltransferases , chromatin remodellers , histone methyltransferases , histone deacetylases and some previously uncharacterised proteins , such as Smchd1 and Rlf . Here we report , for the first time , the mutation underlying MommeD44 and the consequences of this mutation on the transcriptome in the mouse . MommeD44 heterozygous mutants showed an increase in the expression of the reporter transgene compared to that of their wildtype littermates . The underlying N-ethyl-N-nitrosourea-induced mutation was found to have produced a null allele of the gene Trim33 , Tripartite motif containing 33 , also known as Tif1γ , which codes for a protein that contains a tripartite motif with ubiquitin ligase activity and two chromatin binding domains; a bromodomain and a PHD ( plant homodomain ) finger . The function of Trim33 remains poorly understood . In cell culture , Trim33 can act as transcriptional coregulator via the TGFβ pathway [8] and mice homozygous for a null allele die during embryonic development [9] . While few functional studies have been carried out in whole animals , it has been shown that Trim33 can act in combination with the close homologues , Trim24 and Trim28; Lox-Cre knockdown of Trim33 in the liver of mice null for Trim24 results in increased expression of the VL30 class of retrotransposons [10] and hepatocellular carcinoma [11] . Unlike Trim28 , which is known to recruit chromatin modifying protein complexes to the 5’ UTR of ERVs [12] , the mechanism by which Trim33 acts has been unclear . Trim33 has been shown to be highly expressed in the spermatogonia and primary spermatocytes of the testis [13] but its role in these cells is unknown . We have carried out ChIP-seq in adult testis and show widespread binding to active gene promoters , as well as to the members of the RLTR10B class of retrotransposon that are marked by H3K27ac . Using publically available datasets , we find considerable overlap between Trim33 and A-Myb binding sites . Transcriptome analysis , using heterozygous MommeD44 mice , demonstrates a role for Trim33 as a dominant transcriptional repressor of RLTR10B elements in the testis and a combination of bioinformatics and biochemical studies suggests that this repression involves the A-Myb transcription factor . This is the first report of ChIP-seq for Trim33 and the first report of a function for Trim33 in the testis .
The experimental pipeline for the screen has been described previously [3] . The mutagenesis was carried out in the FVB/NJ background and the MommeD44 founder was identified because it showed an increase in the proportion of red blood cells expressing the GFP reporter ( Fig 1A ) . As such , MommeD44 was classified as a suppressor of variegation . This readout was used to maintain the MommeD44 allele for five generations prior to performing genetic linkage and exome sequencing . Mapping was carried out following a F2 backcross to Line3C ( a C57BL/6J strain carrying the same GFP transgene array at the same location ) , using the general method described previously[6] . Using the Illumina GoldenGate SNP genotyping assay , we identified an interval on Chr3 between 75Mb and 155Mb ( S1 Fig ) . Fine mapping , using additional mice , reduced the interval to Chr3 between 99Mb and 109Mb ( Fig 1B ) . To identify the underlying mutation , whole exome deep sequencing of DNA was carried out and variants were called within the linked interval . An exonic mutation was identified in the Trim33 gene , which is located in the linked interval . The A → T mutation changes a lysine to a premature stop codon ( Fig 1C ) . We designate this allele Trim33MommeD44 and genotyping mice from the colony showed that the presence of the mutation correlated well with the altered GFP profile ( S2 Fig ) . RTqPCR was carried out to determine the effect of the mutation on the level of Trim33 mRNA ( Fig 1D ) . The study was carried out in testis because Trim33 is expressed in that tissue at much higher levels than in any other tissue in the adult [13] . Heterozygotes have approximately half the Trim33 mRNA level seen in wildtypes , suggesting that the mutant transcript undergoes nonsense mediated mRNA decay . Using an antibody that binds specifically to Trim33 ( S3A Fig ) , a similar decrease in the level of Trim33 protein was found in heterozygous mutant testis ( S3B and S3C Fig ) and no band can be seen in homozygous mutant embryos ( S3D Fig ) . We examined the viability of homozygous mutants . As expected , embryos homozygous for the mutation were grossly abnormal at E10 . 5 and were not recovered after this stage ( Fig 1E ) . The ratios of heterozygous to wildtype offspring suggest no loss of heterozygotes . At three weeks , heterozygotes showed no difference in body weight when compared to wildtypes ( Fig 1F ) . The heterozygous males were fertile and sections from adult testis showed no obvious abnormalities ( S4 Fig ) . To identify the binding sites of Trim33 , across the genome , the same anti-Trim33 antibody ( Bethyl Laboratories A301-060A ) was used to perform ChIP in adult testis followed by high throughput sequencing of Trim33-bound DNA . Approximately 40 million reads were generated for both Trim33 chromatin immunoprecipitated DNA and an Input control and approximately 30 million of these uniquely aligned to the genome in both cases ( S1 Table ) . Peaks were called in the aligned sequence data using the program MACS [14] . This method looks for significant enrichments in the ChIP-seq data file when compared to the Input data file . A total of 9109 peaks were identified across the genome ( S2 Table ) . Trim33 enrichment was validated using ChIP qPCR at all loci tested ( four of four ) ( S5 Fig ) . The majority of the 9109 peaks occurred within 2Kb from a RefSeq transcription start site ( TSS ) ( Fig 2A ) . Furthermore , when read density was calculated across the gene body unit for all RefSeq genes , Trim33 was found to be enriched over the transcription start site ( Fig 2B ) . Using the ENCODE testis dataset , we found that regions of high read density for Trim33 had high read density for H3K4me3 and H3K27ac ( Fig 2C ) . These histone modifications are generally associated with actively transcribed genes and this has been reported specifically in spermatogonial cells [15] . There were a significant proportion of RefSeq genes with both H3K27ac and H3K4me3 that did not have Trim33 ChIP-seq peaks , suggesting that not all genes that are transcriptionally active in the testis are bound by Trim33 . Using the GREAT gene ontology tool [16] , the 9109 Trim33 peaks were interrogated for association with genes of particular function . The peaks were significantly enriched adjacent to genes that are classified by the MGI expression database as “the TS28_primary spermatocyte” ( Fig 2D ) . This is equivalent to an adult animal’s primary spermatocytes . This was expected , as the majority of Trim33 peaks were located at promoters with active histone marks and likely to be expressed in testis . Primary spermatocytes represent the majority of the cells of the testis . Trim33 was identified in a screen for genes involved in silencing of a GFP transgene , a locus that had been inserted into the genome . This raised the possibility that it might have a role in regulating other elements recently integrated into the genome , including LINE-elements and LTR-containing retrotransposons . Using the Trim33 ChIP-seq data , we mapped reads to consensus sequences for each repeat annotation from the rodent Repbase repository , which groups elements by sequence , e . g . IAPEZI , IAPLTR1_Mm , IAPLTR2b etc . Of the approximately 1600 repeat consensus sequences , we considered only the 309 that had a RPKM ( reads per kilobase per million ) >10 in testis ( S3 Table ) . Only 20 classes showed a fold change ( over Input ) of greater than or equal to 1 . 25 ( Fig 3A and S3 Table ) . We found that Trim33 was enriched almost seven-fold at RLTR10B and to a lesser extent ( two-fold ) at RLTR10B2 . These are among the youngest retrotransposon elements in the genome [17] and belong to the ERVK family of repetitive elements . Using the RepeatMasker locations of all RLTR10B and RLTR10B2 elements , we found that 35% of the former and 12% of the latter overlapped with Trim33 ChIP-seq peaks ( Fig 3B ) . Some reads were likely to have aligned poorly at individual RLTR10B elements due to their repetitive nature . After filtering out reads with a Bowtie2 mapping quality score of below 20 , a large proportion of reads still remained at these sites suggesting that they were uniquely mapped . Importantly , many reads overlapped with non-repeat derived flanking sequences to which short deep sequencing reads are more readily mapped ( Fig 3C ) . Fortuitously , ChIP qPCR validations , discussed in the previous section , included a ChIP-seq peak overlapping a RLTR10B element located in an intron of the gene Fgf2 ( S5 Fig ) . Trim33 lacks a classic DNA binding domain , suggesting other mechanisms are required to provide specificity . Studies in other tissues/cell types have found that Trim33 uses either Smad4 or β-catenin to anchor to DNA [8] . To address how Trim33 might be directed to DNA in testis , we used the MEME program to identify any consensus sequences from the Trim33 binding peaks with a high significance , P value < 1 . 0e-20 . These peaks ( N = 2338 ) generally had high enrichment over Input ( 99% were at least 5 fold enriched ) and these were the most likely to represent direct interactions between Trim33 and the transcription factors to which it binds . The three most highly enriched motifs are found in RLTR10B elements , as expected . Using the JASPAR CORE 2014 database of transcription factor DNA-binding sites , a match was found between an RLTR10B MEME-generated consensus sequence and the Myb consensus DNA binding site ( Fig 3D ) . The Myb consensus sequence emerged from 502 individual sites . Several other DNA binding factors were identified with lesser significance , however , no match was found with Smad or β-catenin binding sites ( S6 Fig ) . As ChIP-seq has been carried out for A-Myb ( also known as Mybl1 ) in testis [18] , we performed unsupervised hierarchical clustering of ChIP-seq read densities for Trim33 , A-Myb and two Encode histone marks , H3K27ac and H3K27me3 ( representing active and repressive chromatin , respectively ) at RLTR10B elements ( Fig 3E ) . We found good overlap between those RLTR10B elements that bind Trim33 , those that bind A-Myb and those enriched for H3K27ac . The repressive chromatin mark H3K27me3 was not enriched at RLTR10B elements . This lack of H3K27me3 enrichment at RLTR10B elements has been reported in ES cells [19] . Given the similar profiles of Trim33 and A-Myb at RLTR10B elements , we compared sites of Trim33 enrichment and those of A-Myb enrichment across the entire genome and found that approximately half of all A-Myb peaks overlapped with Trim33 peaks and approximately one fifth of Trim33 peaks overlap with A-Myb peaks ( Fig 3F ) . These findings suggest that A-Myb has a role in the actions of Trim33 across the genome in testis . RLTR10B elements were clustered by similarity of the Trim33 and A-Myb read density and those with a high read density for both were found to be enriched for the JASPAR database Myb consensus DNA-binding sequence ( S7A Fig ) . RLTR10B elements that bind Trim33 and A-Myb account for 78% ( 390/502 ) of the MYB sites identified in our Trim33 ChIP-seq motif analysis . RLTR10B with no enrichment for Trim33 and A-Myb were not enriched for the Myb DNA-binding sequence ( S7A Fig ) . This supports a role for A-Myb in the function of Trim33 in transcriptional regulation of RLTR10B-containing retrotransposons in the testis . Furthermore , several Trim33 ChIP-seq datasets have recently been carried out in lymphoid cell lines that do not express A-Myb [20] . Reanalysis of these datasets revealed little affinity of Trim33 for RLTR10B elements in these cell types ( S7B Fig ) . The RLTR10B consensus sequence has four tandem sites matching the JASPAR CORE database Myb consensus binding site located at the 5’ region of the RLTR10B consensus sequence ( S8 Fig ) . Among the broader category of 447 ERV2 elements , 12 contain the consensus Myb binding site but in all cases these are interspersed across the element ( S8 Fig ) . Given that Trim33 binds to chromatin and functions as a transcriptional repressor via its ubiquitin ligase activity [21] , it seemed likely that A-Myb is a testis-specific target of Trim33 ubiquitination . Two ubiquitination sites have been identified on the A-Myb protein , the amino acids K79 and K140 [22] . Combinations of tagged A-MYB , ubiquitin and TRIM33 were overexpressed in HEK293 cells; A-MYB ubiquitination was substantially increased in the presence of TRIM33 ( Fig 4A ) . Knock down of TRIM33 using shRNA decreased the level of ubiquitinated A-MYB ( Fig 4B ) . To demonstrate the functional significance of this , A-MYB levels were monitored in the presence of cycloheximide , which prevents protein translation . In the presence of ubiquitin alone , A-MYB survived to 10 hours whereas in the presence of ubiquitin and TRIM33 , the levels of A-MYB survived to only 6 hour time point ( Fig 4C ) . In an attempt to identify an interaction between the co-expressed TRIM33 and A-MYB , we carried out immunoprecipitation for A-MYB ( using the FLAG tag ) followed by Western blotting for TRIM33 ( using the GFP tag ) . TRIM33 could be detected following immunoprecipitation with anti-FLAG ( A-MYB ) but not following immunoprecipitation with anti-IgG ( Fig 4D ) . In the absence of a spermatogonial cell line , immortalized MEFs were used to measure the expression of RLTR10B elements following overexpression of A-MYB and ubiquitin . Overexpression of A-MYB resulted in a small increase in RLTR10B expression and this effect was reversed after co-expression with ubiquitin ( S9 Fig ) , consistent with our hypothesis . We were keen to identify loci that might be sensitive to haploinsufficiency of Trim33 in the testis and that overlapped with Trim33 enrichment . We purified RNA from the testis of wildtype ( n = 3 ) and heterozygous Trim33MommeD44/+ ( n = 4 ) mice and carried out RNA-seq . Using the R package DESeq and Ensembl transcript annotations ( NCBI build 37 ) , we identified 39 genes that were significantly differentially expressed ( Fig 5A and S4 Table ) . Trim33 was ranked third in this list and was 0 . 68 fold down-regulated , close to expectation . The majority of the changes showed increased expression levels in the mutants , consistent with a role for Trim33 as a repressor of transcription in this tissue . Using RTqPCR we were able to validate the majority of those tested ( 9 of 11 ) ( S10 Fig ) . However , only eight of the 39 showed overlap with Trim33 binding peaks . Interestingly , the most significantly deregulated gene , Nmnat3 , upregulated in the mutants by two-fold , and validated by RTqPCR ( S10 Fig ) has an upstream RLTR10B element bound by Trim33 ( Fig 5B ) . In the testis , reads for the Nmnat3 gene start at this element and read across into exon2 ( the first coding exon ) showing that transcription initiates in the repeat element ( S11 Fig ) . Clonal bisulphite sequencing inside this RLTR10B showed no change in DNA methylation in testis of MommeD44 heterozygotes compared to that of wildtype mice ( S12 Fig ) , despite a two-fold change in transcription . We used Cufflinks to establish transcript annotations guided by both the raw RNA-seq data and existing annotations for the mouse mm9 genome to identify other upstream RLTR10Bs that initiate transcription of an adjacent gene . This set was used to identify transcripts that were significantly differentially expressed and linked to a RLTR10B or RLTR10B2 . Ten were found ( S5 Table ) , including Nmnat3 . A comparison was made between the Trim33 ChIP-seq data mapped against the consensus sequences for each repeat class , described above , and the RNA-seq mapped against the same consensus sequences ( Fig 5C ) . The RLTR10B and RLTR10B2 elements both deviate from all other elements with respect to Trim33 binding , as expected . A slight increase can be seen in expression of the consensus RLTR10B element in heterozygotes compared to wildtypes ( fold change = 1 . 33 , p value adjusted for multiple testing = 0 . 079 ) ( Fig 5C ) . We did not expect a large increase in expression of this class in the RNA-seq data as the majority of RLTR10B elements do not have A-Myb sites and are not bound by Trim33 ( S7A Fig ) . This increase was validated using RTqPCR with primers designed over the Myb consensus binding sequences but not with primers designed elsewhere in the RLTR10B consensus sequence ( S13A Fig ) , suggesting that the changes seen are limited to those RLTR10B elements with multiple Myb binding sites at the 5’ end . A second individual transcript which was upregulated in heterozygous MommeD44 mice and overlapping with an RLTR10B element , the Vmn1r181 gene ( S5 Table ) , was validated using two primers; one annealing upstream of the normal reading frame in the ectopic transcript and the other inside the normal gene reading frame ( S13B Fig ) . RLTR10B expression was measured in E9 . 5 embryos , in which A-Myb is not expressed . No change was seen across wildtype , heterozygous or homozygous MommeD44 embryos , consistent with an A-Myb independent function for Trim33 at RLTR10B in whole embryos ( S13C Fig ) . Interestingly , Nmnat3 has been shown to be down regulated ten-fold in the testis of mice with reduced A-Myb function [18] . Using their raw RNA-seq data and the consensus RLTR10B sequence , expression of RLTR10Bs was found to be decreased two-fold in mice heterozygous for a mutation in A-Myb and ten-fold in mice homozygous for the mutation ( S14 Fig ) . These findings are consistent with a central role for A-Myb in transcriptional activity of the RLTR10B elements .
Transposable elements have been a driving force in the structure and evolution of the mammalian genome . Deep sequencing of 17 mouse genomes has revealed over 100 , 000 transposable element variants that have survived selection over the past 2 million years of Mus lineage evolution [23] . From an inferred evolutionary history of TE family activity , ERVs and in particular ERVKs , appear to be expanding rapidly in the mouse [24] . The ERVK class , also known as ERV2 , contains IAPs , Etn/MusD and RLTR10B-containing retrotransposons such as MMERVK10C . The vast majority of the expansion of LTR retrotransposons is thought to have occurred in the paternal germline [23] . This is consistent with findings from a study in which IAP-GFP transgenes were inserted into the mouse genome . Expression of these transgenes was found to be limited to the male germline [25] . Most studies that have investigated the transcriptional regulation of the ERV2 class of repeats have focussed on piRNA pathways [23] . Directed mutagenesis in mice has identified nine proteins with a key role in protecting the male germline against retrotransposition of IAPs , Etn/MusD and MMERVK10C elements; Dnmt3L , Dnmt3a , Miwi2 , Mili , Mael , Tdrd1 , Tdrd9 , Gasz , Tex19 . 1 and most function in the piRNA pathway [23] . Several groups have reported on factors that silence MMERVK10Cs in the germline [26 , 27] but the mechanisms of silencing of RLTR10Bs has not been understood prior to our studies . Here we describe the first factor involved in silencing of the RLTR10Bs elements . Trim33 has been identified in a number of screens designed to find genes involved in transcriptional silencing . It was found in a RNA inhibition ( RNAi ) screen carried out in a human cell line to find proteins required for transgene silencing [28] . The fly homologue of Trim33 , Bonus , was identified as a suppressor of variegation ( i . e . reduced levels of Bonus result in an increased proportion of cells expressing the reporter transgene ) when tested for its ability to alter transcription of a variegating reporter locus [29] . Here we report , the identification of Trim33 in a mutagenesis screen for modifiers of transcriptional silencing in the mouse . In this screen it also behaves as a suppressor of variegation . Trim33 was identified in a zebrafish screen for genes involved in the development of the haematopoietic system [30 , 31] . It is known to be required for development of ectoderm in Xenopus [32] . Knockout of Trim33 in the mouse has been shown to result in embryonic lethality as a consequence of excessive TGFβ/Nodal signalling [33] . Our findings of embryonic lethality of embryos homozygous for the TrimMommed44 allele are consistent with this report . Trim33 is not thought to bind DNA directly and available evidence suggests that its transcriptional effects occur via DNA-binding cofactors . For example , the role of Trim33 in TGF-beta signalling in cell lines has been shown to involve the Smad transcription factors that bind DNA in a sequence-specific manner [8 , 34] . [It has been suggested that the mechanism by which Trim33 inhibits TGF-beta signalling during the development of ectoderm in Xenopus involves the ubiquitination of Smad4 [32] . The model proposes that Trim33 inhibits expression of the target locus by destabilising the Smad cofactor . ] The role of the chromatin binding domains ( PHD and Bromodomain ) of Trim33 remains unclear . Previous studies , following overexpression of the chromatin binding domains in somatic cell lines , mapped the binding specificity to H3K4me0 and acetylated lysine residues [8 , 21] . However , our ChIP-seq studies , carried out in vivo , found that most Trim33 peaks in testis overlap with peaks for H3K4me3 and H3K27ac and presumably not for H3K4me0 . Others have suggested that the PHD and Bromodomain of Trim33 are required to activate its ubiquitin ligase activity [21] . Multiple mouse models of cancer have demonstrated a role for Trim33 as a tumour suppressor [11 , 35 , 36] . Trim33 was found to act as a tumour suppressor in the pancreas of mice and humans [35] . The mechanism underlying the tumour suppressor function is thought to be Smad4-independent [37] . Recent studies have found that TRIM33 abolishes tumour cell proliferation and tumorigenesis by degrading nuclear β-catenin via ubiquitination [36] . Taken together , these findings indicate Trim33 can be considered a corepressor of transcription that functions by ubiquitinating DNA-binding cofactors and that it has important roles in development and cancer . Trim33 is expressed an order of magnitude higher in the testis than in any other adult mouse tissue [13] . It is expressed in spermatogonia , preleptotene spermatocytes and round spermatids but its function in these cells is unknown . It is also expressed in one class of the somatic cells in the testis , the Sertoli cells , but these are much less abundant than the germ cells . Since it has been identified in a number of screens for gene silencing and since the silencing of retrotransposons is a critical function of the germline , we hypothesised that it might have a role in this process . Our study is the first report of ChIP-seq for Trim33 in any tissue . We show that Trim33 binds many active gene promoters in the testis ( i . e . those promoters marked by H3K4me3 and H3K27ac ) . Trim33 also shows enrichment at RLTR10B elements , a subgroup of ERVK LTR retrotransposons , consistent with our hypothesis . RLTR10Bs are among the chronologically youngest retrotransposable elements in the mouse genome , ranked 5th out of 546 elements analysed [17] . The only groups considered younger than RLTR10Bs are IAPs and these are known to have retrotranspositional activity in the mouse [38] . In general , the youngest retrotransposable elements will have accumulated less defects ( i . e . mutation of DNA binding sites ) and will have retained the ability to be expressed . Knowing that haploinsufficiency for Trim33 was sufficiently disruptive to increase expression of the reporter transgene in our mutagenesis screen , we were keen to test whether it would alter the expression of other genes in the testis . RNA-seq suggests no dramatic effect across the genome but confirms a role for Trim33 in silencing RLTR10B . The fact that the transgene and the RLTR10B elements , but not other Trim33-bound loci , are sensitive to Trim33 dosage is not understood . It might be that other Trim family members ( and other factors ) can compensate for reduced levels of Trim33 at most loci . It is likely that in the complete absence of Trim33 , many of the Trim33 bound loci would be affected but the early demise of the homozygous embryo precludes testing . We have identified a role for Trim33 in binding and silencing the RLTR10B-containing class of retrotransposon in testis . Many other groups of retrotransposons , including active IAP elements , are enriched for the same histone marks found on RLTR10B elements [19] but are not bound by Trim33 , suggesting that Trim33 does not bind via its bromodomain or PHD finger . On the assumption that Trim33 binds DNA via a transcription factor , we searched for a transcription factor consensus binding site and found four separate Myb binding sites at the 5’ end of the RLTR10B consensus sequence and the individual RLTR10B upstream of Nmnat3 . This tandem arrangement has previously been shown to increase the affinity for Myb binding to DNA [39] A-Myb is a master regulator of male meiosis and is expressed specifically during spermatogenesis [40] but has not previously been reported to bind retrotransposons . Given the overlapping ChIP-seq signal between A-Myb and Trim33 at these repeats ( Fig 3E ) , it is likely that Trim33 functions to repress transcription at these sites via A-Myb . The Momme screen reporter transgene contains an AACTGTCT element in the HS40 alpha globin hypersensitive site and this fits the MYB binding consensus site–AACTG ( C/T ) C ( A/T ) . A-MYB has been shown to act as a transcriptional activator in peripheral blood cells [41] . It is reasonable to suggest that Trim33 acts as a transcriptional repressor of the reporter transgene via ubiquitination of A-Myb ( or another target of Trim33 ubiquitination ) , although we have no direct evidence of this . Myb family members are subject to several types of post-translational modifications , including phosphorylation , acetylation , and ubiquitination . Furthermore , the ubiquitination of B-Myb and C-Myb has been shown to inhibit the transcriptional activation functions of these two factors [42 , 43] . Trim28 , like Trim33 , can ubiquitinate proteins and has been shown to bind to C-Myb and repress its ability to function as a transcriptional activator [44] . Here , we have demonstrated that Trim33 can ubiquitinate A-Myb and that this regulates the abundance of A-Myb , probably by the ubiquitination–proteasome pathway as has been suggested for Trim33 mediated degradation of β-catenin [36] . Publically available datasets show that reduction in A-Myb in mouse testis results in a decrease in expression of Nmnat3 [18] . We have reanalysed their datasets to search for effects on repeats and find dramatically decreased expression of RLTR10Bs ( S14 Fig ) . In addition , the cell types in the adult testis that specifically express high levels of Trim33 overlap with those that express high levels of A-Myb [40] . Given that Trim33 can ubiquitinate and regulate the abundance of A-Myb , a simple model of the mechanism by which Trim33 silences RLTR10B elements in the germline would include the ubiquitination of A-Myb ( Fig 6 ) . Transcriptional activity at retrotransposons is species specific , consistent with rapid evolution of retroviral subtypes [23] . It is likely that a subset of RLTR10Bs have recently evolved Myb DNA binding sites to capitalise on the critical role that the A-Myb transcription factor has in gene expression in germ cells in order to ensure their continued retrotransposition . Suppression of A-Myb by Trim33 provides a plausible mechanism by which the host keeps retrotransposition in check .
The ENU screen was carried out in Line3 , a transgenic line that is homozygous for the GFP transgene and on an FVB/NJ inbred background , as previously described [4] . Maintenance of the MommeD44 allele was carried out on the Line3 background . The Line3C was used for linkage studies and was produced by crossing Line3 to C57BL/6J for 10 generations and selecting for homozygosity of the GFP transgene . All breeding crosses and experimental procedures using the MommeD44 allele were carried out using mice at least 5 generations from the founder mouse . Procedures were approved by the Animal Ethics Committee of LaTrobe University , under approval numbers AEC 12–74 and AEC 12–75 . All mice sacrificed in the study were anesthetized using isoflurane and euthanised by cervical dislocation . The expression vector encoding A-MYB-FLAG was purchased from OriGene ( Rockville , MD , USA ) . TRIM33-GFP was obtained from DR Kyle Miller ( Addgene plasmid #65399 ) . Knockdown shRNA for Trim33 were obtained from Dr Joan Massague ( Addgene plasmid # 15728 ) . PCR was carried out using DNA from tail tips using the following conditions: 94 degrees Celsius 6 minutes , 30x cycles with 94 degrees Celsius 30 seconds , 60 degrees Celsius 45 seconds 72 degrees Celsius 45 seconds , followed by a final annealing step of 72 degrees Celsius 6 minutes . All primers used in the study are described in S6 Table . Flow cytometry of blood from 3 week old mice collected in FACSFlow Sheath Fluid ( BD Biosciences ) was carried out and analysed on a Guava easyCyte HT ( Merck/Millipore , Darmstadt , Germany ) and with the Guava InCyte software , respectively . Erythrocyte green fluorescence ( 525nm ) was recorded and a GFP-positive gate was set to exclude 99% of wildtype erythrocytes . MommeD44 heterozygous mice were backcrossed twice to Line3C ( see above ) and phenotyped for GFP expression by flow cytometry . DNA from tail tissue collected during flow cytometry procedures was used to perform linkage analysis . The Illumina GoldenGate genotyping assay ( Mouse Medium Density Linkage Panel ) was used with 10 wildtype and 13 heterozygous mice . MommeD44 wildtype samples should only have heterozygous C57BL/6J SNPs surrounding the causative mutation and MommeD44 mutants should have FVB and C57BL/6J SNPs at this interval . The Mouse Medium Density Linkage panel contains 766 measurable SNPs between C57BL/6J and FVB/NJ . Samples were genotyped following the Illumina protocol and genotype calls were made using the Genotyping module of the GenomeStudio v1 . 1 software . Only samples with a call rate >95 were accepted . The linked interval was identified based on a peak in the LOD score . Fine mapping was carried out using primers amplifying C57BL/6J or FVB SNP loci that could be cut with restriction enzymes to determine genotype . Of the 95 F2 backcrosses , 8 mice had SNP profiles that were inconsistent with the mapping and were excluded . This small ( <10% ) error rate in phenotyping is commonly encountered in this screen [4] . Exome capture was performed using the Roche NimbleGen reagents ( SeqCap EZ Mouse Exome , version Beta 2 , 110603_MM9_exome_rebal_2EZ_HX1 , Madison , WI , USA ) as per the Illumina optimized Roche NimbleGen SeqCap User’s Guide ( version 1 . 0 ) and using a Bioruptor ( Diagenode , Liège , Belgium ) for DNA fragmentation . Libraries were sequenced using the illumina GAIIx platform and reads were aligned to the mouse ( build 37 , mm9 ) genome using bwa aln and bwa sampe programs [45] . For further details see [3] . As control sequence , that lacked MommeD44 ENU mutations , other MommeD lines previously identified in the screen and described elsewhere were used [3] . Varscan output was scanned manually for likely heterozygous mutations that could be validated in additional MommeD44 mutant mice . Sanger Sequencing or restriction enzyme digest with the enzyme Mse1 ( New England Biolabs ) that specifically cuts at the ENU mutation site in the Trim33MommeD44 allele was used . Timed matings between heterozygous mutant females and heterozygous mutant males were set up and the detection of a vaginal plug was counted as 0 . 5 dpc . Genotyping was carried out using DNA extracted from embryos . Bisulphite conversion was carried out on 1ug of DNA extracted from whole testis using the EpiTect Bisulphite Kit ( Qiagen , Doncaster , VIC , Australia ) according to the manufacturer’s instructions . The bisulphite conversion rate was at least 99% and sequences were analysed using the BiQ Analyser software . Oligonucleotides are provided in S6 Table . PCR cycling conditions were as follows: 95 degrees Celsius 10 minutes , 30x cycles with 95 degrees Celsius 15 seconds 55 degrees Celsius 15 seconds , 72 degrees Celsius 30 seconds . PCR products were cloned using a pGEM-T Easy Vector ( Promega , Alexandria , NSW , Australia ) and sequenced using The BigDye Terminator v3 . 1 Cycle Sequencing Kit ( Life technologies , Mulgrave , Victoria , Australia ) as per kit instructions . HEK293T and mouse embryonic fibroblasts were cultured in DMEM supplemented with 10% fetal calf serum ( Invitrogen , Scoresby , VIC , Australia ) at 37ᴼC in a humidified 10% CO2 incubator . For transient expression of cDNA or RNAi vectors , HEK293 or MEF cells were transfected with expression plasmids using the Fugene 6 reagent ( 11814443001 , Roche , Castle Hill , NSW , Australia ) following the manufacturer’s instructions . For A-MYB survival assays , cells were transfected with indicated plasmids for 48 hours and then treated with cycloheximide to a final concentration of 10 ug/ml . Cells were lysed at indicated time points and A-MYB protein levels were assessed by Western blotting . Whole-cell extract was prepared from testis of adult ( 12 week old ) mice in 8M urea lysis reducing buffer ( 8 M urea , 1/10 vol . glycerol , 1/20 vol . 20% SDS , 1/2 , 000 vol . 1 M dithiothreitol , 1/100 vol . 1 M Tris , pH 6 . 8 ) . For ubiquitination assays , HEK293T cells were lysed in RIPA buffer ( Tris 50mM , NaCl 150mM , NP-40 1% , DOC 0 . 5% , SDS 0 . 1% ) , diluted 1:10 in ONYX buffer ( Tris 20mM , NaCL 135mM , MgCl2 1 . 5mM , EGTA 1mM , Triton X-100 1% , Glycerol 10% ) and incubated overnight with anti-FLAG antibody at 4ᴼC . Following incubation , antibodies were washed in ONYX buffer and proteins were eluted in 8M urea lysis buffer . For protein co-immunoprecipitations the Nuclear Complex Co-IP Kit ( 54001 , Active Motif , Carlsbad , CA , USA ) was used as per manufacturer’s instructions . BCA ( Thermo Scientific , Waltham , MA , USA ) was used to quantify protein and total lysates were separated according to size on polyacrylamide gels ( Biorad , Gladesville , New South Wales , Australia ) . Antibodies used for western blotting were rabbit polyclonal anti-Trim33 ( A301-060A , Bethyl Laboratories , Montgomery , TX , USA ) , rabbit polyclonal anti-A-Myb ( HPA008791 , Sigma Aldrich , Castle Hill , NSW , Australia ) , mouse monoclonal anti-GFP ( 11814460001 , Roche , Castle Hill , NSW , Australia ) , mouse monoclonal anti-HA ( 6E2 , Cell Signaling , Boston , MA , USA ) , mouse monoclonal anti-Gapdh ( MAB374 , Merck/Millipore , Darmstadt , Germany ) mouse monoclonal anti-HSP70 ( MA3-028 , Scoresby VIC , Australia ) and rabbit polyclonal anti-γ-Tubulin ( T5192 , Sigma Aldrich , Castle Hill , NSW , Australia ) . Anti-Flag was a gift from Dr Lorraine O'Reilly at the Walter and Eliza Hall Institute of Medical Research . Antibodies used for immunoprecipitation were bead-conjugated anti-FLAG ( M8823 , Sigma Aldrich , Castle Hill , NSW , Australia ) and anti-IgG ( sc-2345 , Santa Cruz Biotechnology , Dallas , TX , USA ) . Total RNA was extracted from snap frozen tissues or cells using TRIzole reagent ( Life technologies , Mulgrave , Victoria , Australia ) according to manufacturer instructions . cDNA synthesis was carried out from total RNA using the QuantiTect Reverse Transcription Kit ( Qiagen , Doncaster , VIC , Australia ) and RTqPCR was performed with the QuantiTect SYBR Green reagent ( Qiagen , Doncaster , VIC , Australia ) with primers designed to span exon junctions in mRNA ( S6 Table ) . Samples were run on the CFX384 Touch Real-Time PCR Detection System ( Biorad , Gladesville , New South Wales , Australia ) , with the following conditions: 95 degrees Celsius 10 minutes , 39x cycles with 95 degrees Celsius 15 seconds then 60 degrees Celsius 1 minute , with a final step of 95 degrees Celsius 15 seconds . Each experimental sample consisted of three technical replicates and reverse transcriptase negative samples; a melt curve analysis was carried out after each run to confirm unique PCR product amplification . Relative cDNA abundance was calculated using the delta delta CT method normalizing to housekeeper gene expression indicated in the figures . Statistical analysis was performed using Student’s t test . RNA sequencing was carried out from total RNA submitted to the Australia Genome Research Facility ( AGRF , Parkville , Victoria , Australia ) . At least 20 million 100bp single end reads were generated on an Illumina HiSeq platform for each sample , using libraries generated using the illumina TruSeq RNA Sample Preparation kit ( Illumina , San Diego , CA , USA ) . Initial QC was performed by AGRF . Reads were aligned to the mouse genome ( NCBI 37 , mm9 ) using the program Tophat ( version 2 . 0 . 11 ) [46] with the following parameters: -I 100000—library-type = fr-unstranded—read-edit-dist 3—no-coverage-search—read-mismatches 3 . Read counts for gene exons were extracted using the program htseq-count ( version 0 . 6 . 1 ) [47] with the options -s no -m intersection-strict and using gene annotations from Ensembl ( release 67 ) . Differential gene expression was assessed using the R-package DEseq [48] , with default parameters . Genes were considered differentially expressed when an adjusted p value of at least 0 . 05 . Where indicated in the text , the program Cufflinks ( version 2 . 2 . 1 ) [49] was used to estimate differential gene expression of transcripts by creating annotations based on mapped reads . Mapped RNA-seq reads ( above ) were used to create transcript annotations using default settings for each sample , these were merged and then used to estimated differential expression of transcripts . Testis tissue from three 12 week old wildtype mice were snap-frozen and sent to Active Motif for ChIP , library preparation , sequencing and initial data analysis . The rabbit polyclonal anti-Trim33 ( A301-060A , Bethyl Laboratories , Montgomery , TX , USA ) was used for ChIP . Sequencing was carried out for 75mer read lengths on the NextSeq 500 platform ( Illumina , San Diego , CA , USA ) . Reads were aligned using the BWA program [45] with default settings . Peak calling was done with MACS ( Version 1 . 4 . 2 ) [14] by first filtering out duplicate reads and reads with a mapping quality of less than 25 , then using default parameters and the following options -s 75—bw 200 -m 10 30 –p 0 . 0000001 ( S1 Table ) . All heat plot and read tag density figures were generated using the seqMiner program ( version 1 . 3 . 3 ) [50] using default parameters and ChIP-seq data aligned to the mouse genome ( NCBI 37 , mm9 ) with the program Bowtie2 ( version 2 . 2 . 2 ) [51] with default settings . Heat plots were generated by subsampling all datasets to approximately 16 million reads . In the case of publically available ENCODE datasets , aligned reads in bam format were downloaded and were subsampled to approximately 16 million reads or in the case of H3K27ac , two biological replicates were combined to generate a data file with 16 million reads . The data sets supporting the results of this article are available in the Gene Expression Omnibus ( GEO ) repository , [GSE68617] . Motif discovery and enrichment was performed with highly significant Trim33 peaks ( P value <1 . 0d-20 , region summit +- 650bp ) using the MEME-ChIP ( version 4 . 10 . 0 ) [52] and MEME suite programs MEME , DREAME , CentriMo and Tomtom with default settings . Gene ontology analysis was carried out using Trim33 peak locations across the genome with the GREAT tool ( version 2 . 0 ) [16] with default settings . To estimate enrichment of repeat elements for ChIP-seq and RNA-seq datasets reads were mapped to a repeat assembly file containing a single FASTA entry for each repeat type defined in the rodent repeat sequences RepBase database [53] ( update 20 . 02 ) . The Bowtie2 ( version 2 . 2 . 2 ) [51] aligner was used to map reads aligning to each FASTA entry using default settings and RPKM values were extracted from the number of reads aligned at each entry and the library size for each data file . The data sets supporting the results of this article are available in the NCBI Gene Expression Omnibus under the accession code GSE68617 . | Almost half of the genomes of humans and mice are made up of transposable elements . During host evolution , subsets of these elements have periods of transpositional activity during which they spread throughout the genome . This is dependent on the transcriptional activity of these elements in the cells that contribute to the germline . Hosts have evolved pathways to silence their expression . A number of Trim family proteins have been found to have a role in silencing transposable elements , and it was previously shown that Trim33 shared this function in liver . However , the function of Trim33 in other tissues is poorly understood . Here we report a role for Trim33 in silencing a specific subset of retrotransposons that contain RLTR10B LTRs , in the germline . We also show the transcription factor , A-Myb , is responsible for activating transcription of these elements and it is likely that a subset of RLTR10Bs have recently evolved Myb DNA binding sites to capitalise on the critical role that the A-Myb transcription factor has in germ cells . Suppression of A-Myb activity by Trim33 provides a plausible mechanism by which the host keeps transposons in check . |
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Derived large-mouthed snakes ( macrostomatans ) possess numerous specializations in their skull and lower jaws that allow them to consume large vertebrate prey . In contrast , basal snakes lack these adaptations and feed primarily on small prey items . The sequence of osteological and behavioral modifications involved in the evolution of the macrostomatan condition has remained an open question because of disagreement about the origin and interrelationships of snakes , the paucity of well-preserved early snake fossils on many continental landmasses , and the lack of information about the feeding ecology of early snakes . We report on a partial skeleton of a new 3 . 5-m-long snake , Sanajeh indicus gen . et sp . nov . , recovered from Upper Cretaceous rocks of western India . S . indicus was fossilized in association with a sauropod dinosaur egg clutch , coiled around an egg and adjacent to the remains of a ca . 0 . 5-m-long hatchling . Multiple snake-egg associations at the site strongly suggest that S . indicus frequented nesting grounds and preyed on hatchling sauropods . We interpret this pattern as “ethofossil” preservation of feeding behavior . S . indicus lacks specializations of modern egg-eaters and of macrostomatans , and skull and vertebral synapomorphies place it in an intermediate position in snake phylogeny . Sanajeh and its large-bodied madtsoiid sister taxa Yurlunggur camfieldensis and Wonambi naracoortensis from the Neogene of Australia show specializations for intraoral prey transport but lack the adaptations for wide gape that characterize living macrostomatan snakes . The Dholi Dungri fossils are the second definitive association between sauropod eggs and embryonic or hatchling remains . New fossils from western India provide direct evidence of feeding ecology in a Mesozoic snake and demonstrate predation risks for hatchling sauropod dinosaurs . Our results suggest that large body size and jaw mobility afforded some non-macrostomatan snakes a greater diversity of prey items than previously suspected on the basis of extant basal snakes .
Snakes are limbless reptiles that first appeared in the fossil record in the middle of the Cretaceous , approximately 98 million years ago [1] . Most species of living snakes are macrostomatans , which consume large prey items using a specialized gape achieved via a posteriorly displaced jaw joint , increased cranial kinesis , and an elongated skull and lower jaws . The evolution of large-gape feeding in macrostomatans has remained controversial owing to the scarcity of Cretaceous snake specimens preserving cranial and postcranial remains . Phylogenetic interpretation of these early snake fossils as either basal to all living snakes or to its subgroup Macrostomata has polarized views on snake origins , interrelationships , and ancestral habitat [2]–[6] . Here we describe an articulated snake fossil from uppermost Cretaceous horizons of Indo-Pakistan that is among the first such known from the subcontinent prior to the Miocene [7] . The new snake is preserved in an extraordinary setting—within a sauropod dinosaur nesting ground in association with eggs and a hatchling ( Figures 1 and 2 ) . The new fossils provide the first evidence , to our knowledge , of snake predation on hatchling dinosaurs and a rare example of non-dinosaurian predation on dinosaurs [8] , [9] . Below we describe this new snake and its association with a sauropod egg clutch , resolve its phylogenetic relationships to other early snakes , and explore its implications for the evolution of wide-gape feeding in snakes and predation risks on sauropod dinosaurs .
Squamata Oppel 1811 Serpentes Linnaeus 1758 Alethinophidia Nopcsa 1923 Madtsoiidae Hoffstetter 1961 urn:lsid:zoobank . org:act:AB09F42A-6E4E-4F96-8B32-60D4B9FA6FD6 urn:lsid:zoobank . org:act:45E1476C-0BC1-4892-B4A9-B4D16530B43F The skull and partial vertebral column of Sanajeh were found in articulation ( Figures 1 and 2 ) . On the basis of the length of the nearly complete skull ( 95 mm ) , we estimate total body length to be 3 . 5 m ( Figure S7; Text S3 ) . Most of the jaws , palate , and braincase are preserved ( Figures 3 and S8 ) . The braincase is elongate , and its lateral surface bears two prominent openings that are separated by the I-shaped prootic ( Figure 3D and 3E ) . These two openings , the trigeminal foramen and the juxtastapedial recess , house the cranial nerves associated with the jaws and the ear , respectively . The trigeminal foramen is the more anteriorly positioned of the two openings . It is bordered almost completely by the prootic but receives a small contribution to its anterior margin from the parietal . Like scolecophidians , Dinilysia , Najash [4] , and the Australian madtsoiids Wonambi [15] and Yurlunggur [16] , the trigeminal foramen is undivided . In alethinophidians , a laterosphenoid ossification subdivides the trigeminal foramen , separating maxillary and mandibular branches of cranial nerve V [17] . The more posterior , larger opening in the lateral wall of the braincase is the juxtastapedial recess , which is formed by the prootic and otooccipital . The juxtastapedial recess is subdivided into the fenestra ovalis , which houses the footplate of the stapes , and the recessus scalae tympani . The narrow crista interfenestralis separates these two openings and extends as an accessory process onto the ventral aspect of the skull ( Figure 3C ) . Posteriorly , the juxtastapedial recess is bordered by a thick crista tuberalis , which begins on the otooccipital and extends posteroventrally to form the posterolateral corner of the ventral braincase . The architecture of the neurovascular openings within the recessus scalae tympani could not be examined , because this region is broken away on the left side of the skull and obscured by the supratemporal on the right . The short , broad supratemporal would have overlain the dorsal surface of the skull roof in articulation , as it does in macrostomatan snakes ( Figure 3E ) . As in basal alethinophidian snakes such as Xenopeltis , the supratemporal has a wide articular surface for the quadrate on its lateral margin and a very short , free-ending posterior margin that does not extend posteriorly beyond the otic capsule . Importantly , the position of the quadrate articular facet , which is on the lateral surface of the supratemporal and located dorsal to the juxtastapedial recess , suggests that the jaw joint of Sanajeh was positioned lateral to the posterior margin of the braincase , as it is in basal snakes . A prominent sagittal crest formed by the basioccipital and parabasisphenoid is present on the ventral aspect of the braincase ( Figure 3C ) . This crest served as the insertion surface for muscles that moved the toothed bones of the palate ( m . protractor pterygoidei ) [18] . Paired parabasisphenoid processes project ventrally from the anterior end of this crest , as in Wonambi , boines , and pythonids . Arcuate crests extend posterolaterally from the posterior end of the crest , as in Yurlunggur , Wonambi , and some macrostomatans . A conspicuous opening for the posterior opening of the Vidian canal is preserved on the parabasisphenoid , but its anterior opening was not preserved . An enclosed Vidian canal is unique to squamates and carries the internal carotid artery and a branch of cranial nerve VII [17] . The facial and palatal portions of the skull are not as well preserved as the braincase and skull roof , but they are complete enough to estimate total skull length to be 95 mm . The maxilla is nearly complete and has a relatively short narial region . Its short , recurved anterior process and prominent dorsal process resemble those of anilioids . The dentary bears a single mental foramen , located near its anterior end , and a long posterior dentigerous process . Dentary teeth are broad and only slightly recurved , a condition more similar to anilioids than macrostomatans ( Figure 3A ) . The vertebral column of Sanajeh is represented by precloacal vertebrae ( Figures 4 and S9; Text S4 ) . The wedge-and-notch zygosphene-zygantrum articulations are well developed , and the zygantrum is flanked by small parazygantral foramina on the posterior surface of the neural arch , as in Najash [4] and taxa referred to Madtsoiidae . The neural spines of Sanajeh are thin and strongly posteriorly angled , overhanging the shallow embayment between the postzygapophyses . Shallow fossae are present on either side of the neural spine . The prezygapophyses lack accessory processes , and the rib articulations ( synapophyses ) extend laterally beyond the margins of the prezygapophyses , both of which are characters present in madtsoiids [16] . The skeleton of Sanajeh was preserved in close association with three sauropod eggs of the oospecies Megaloolithus dhoridungriensis [19] and a partial sauropod hatchling ( Figures 1 , 2 , and S10; Text S5 ) . The eggs represent part of a single clutch , which typically contains six to 12 eggs at Dholi Dungri . No nest structure is preserved at Dholi Dungri nor any other Indian egg locality , owing to extensive postburial pedogenic modification of the entombing sediments [19] . The high porosity of the eggs at Dholi Dungri suggests that they were incubated in a nest covered by a layer of either vegetation or loose sediment [20] . The skull of Sanajeh rests atop a coil of the vertebral column , which wraps around three sides of a crushed egg ( Figures 1 and 2 ) . The two other eggs are uncrushed and unhatched , and we infer that the crushed egg encircled by the snake was exited by the sauropod hatchling found adjacent to it . The sauropod hatchling is represented by a portion of the left side of the anterior thorax , a partial shoulder girdle , and a partial forelimb preserved in anatomical articulation ( Figure 5 ) . The hatchling bones are not completely ossified , but they can be confidently attributed to a sauropod dinosaur on the basis of the presence of a relatively large acromial region on the proximal scapula and a straight-shafted humerus [21] . The hatchling almost certainly is a titanosaur because no other sauropod lineage has been recovered from uppermost Cretaceous sediments in Indo-Pakistan or elsewhere [22] . The Dholi Dungri specimen is only the second definitive association between sauropod bones and eggs [23] . Multiple lines of evidence suggest that the snake-dinosaur association preserved at Dholi Dungri was the result of preservation of organisms “caught in the act” rather than a postmortem accumulation of independently transported elements . First , the pose of the snake with its skull resting atop a coil encircling a crushed egg is not likely to have resulted from the transport of two unassociated remains . Second , the high degree of articulation of the snake , hatchling , and crushed egg , as well as the excellent preservation of delicate cranial elements and intact , relatively undeformed eggs rule out substantial transport and are indicative of relatively rapid and deep burial . Third , our sedimentological analysis indicates that the site was located adjacent to a paleotopographic high that could have been the source of rapid sedimentation pulses as a result of storm-induced debris flows ( see Text S2 ) . Fourth , at least three individual snake specimens were found associated with sauropod eggs , suggesting active habitation of nests rather than postmortem transport . The three associations of Sanajeh bones and Megaloolithus eggs found over a 25-m2 area , together with the sedimentological and taphonomic profile of the site , suggest “ethofossil” preservation—i . e . , a record of typical behavior rather than of aberrant behavior or a fatal mistake [24] . We infer that Sanajeh actively frequented sauropod nesting environments and predated upon sauropod hatchlings . It is unlikely that Sanajeh consumed large , intact , rigid sauropod eggs ( 16 cm diameter , 2 , 145-cm3 volume ) , which greatly exceed its gape , because it lacks the cranial and vertebral adaptations for consumption of large eggs present in oophagous macrostomatans [25] , [26] . However , it is possible that Sanajeh consumed contents of the sauropod eggs in a fashion resembling the non-macrostomatan snake Loxocemus bicolor , which is known to break eggs of the Olive Ridley sea turtle ( Lepidochelys olivacea ) by constriction and then ingest shell and contents with minimal loss [27] . In addition , L . bicolor is known to consume both eggs and hatchlings of the lizards Ctenosaura and Iguana [28] , [29] and has a relatively flexible prey restraint repertoire [30] . Given the presence of theropod dinosaur eggs and smaller reptile eggs at the site ( unpublished data ) , it is possible that a broad range of prey items supported a nest-plundering feeding strategy for S . indicus . A phylogenetic analysis of 116 characters in 23 fossil and recent snake taxa resolves S . indicus as the sister taxon to the late Cenozoic Australian snakes Wonambi and Yurlunggur ( Figure 6 ) . The latter have been referred to as madtsoiids [15] , [16] , and we apply this name to the clade uniting Sanajeh , Wonambi , and Yurlunggur but note that additional phylogenetic investigation is needed to resolve whether this clade includes the giant , fragmentary South American , African , and Malagasy species that originally formed the basis for the group ( e . g . , Madtsoia bai and M . madagascariensis ) , or the numerous Cretaceous and Paleogene taxa that have subsequently been assigned to it based on vertebral morphology [31] , [32] . Lengthy ghost lineages preceded the appearance of Wonambi and Yurlunggur in the fossil record , consistent with their hypothesized early origin on Gondwana [33] . Morphology of the braincase and mandibular suspensorium resolve the madtsoiids Sanajeh , Yurlunggur , and Wonambi as phylogenetically intermediate between narrow-gaped anilioids and wide-gaped macrostomatans . Our analysis does not support the proposition that the Australian madtsoiids Wonambi and Yurlunggur are closely related to the South American snakes Dinilysia and Najash , which are here resolved as basal snakes [34] . Although previous phylogenetic studies placed Yurlunggur and Wonambi as either basal snakes or derived macrostomatans [6] , [16] , the shortest trees for these alternative arrangements each require 21 additional evolutionary steps ( Figure S12; see Text S6 ) . We found only weak support for the monophyly of Anilioidea , which is not supported by molecular studies [35] . We found relatively strong support , in contrast , for a derived position for the limbed , marine pachyophiids , whose position is uncertain in other analyses [2]–[6] . Our phylogenetic analysis has important implications for the evolution of feeding in snakes ( Figure 6 ) . Basal snakes , which include scolecophidians and anilioids ( uropeltines , Anomochilus , Cylindrophis , Anilius ) , possess a narrow oral gape and limited kinesis of the palatal bones . Their prey items are generally restricted to ant and termite larvae ( scolecophidians ) or annelids and small-bodied , often elongate limbless vertebrates such as amphisbaenians and caecilians ( anilioids ) . This feeding ecology has been hypothesized to represent the plesiomorphic condition for snakes [36] . Conversely , derived macrostomatan snakes ( boids , pythonids , caenophidians ) evolved a specialized wide oral gape that allows them to consume a variety of relatively large-bodied prey items . Osteological specializations facilitating wide gape feeding in macrostomatans include posterior displacement of the jaw joint via an elongate , free-ending posterior process of the supratemporal bone , elongation of the lower jaws , and increased mobility of the tooth-bearing bones of the upper and lower jaws [36] . The evolutionary transition from narrow-gape feeding to wide-gape macrostomy has remained controversial owing to disagreement about the interrelationships of snakes and paucity of well-preserved fossils and ecological data for basal and early appearing snake taxa . Sanajeh possesses cranial characters that , combined with its depositional context and ecological associations , shed light on this transition ( Figure 6 ) . The short supratemporal and inferred broad , short quadrate indicate a narrow oral gape comparable to that of anilioids and Xenopeltis . However , the large insertion for m . protractor pterygoidei indicates powerful movement of the palatopterygoid bar during intraoral prey manipulation , as in derived macrostomatans [18] . The presence of a long posterior articular process of the dentary indicates extensive flexure of the intramandibular joint during intraoral prey transport , a condition Wonambi , Yurlunggur , and Sanajeh share with macrostomatans . Together , basicranial , mandibular , and suspensorial morphology indicate that expanded oral kinesis and complex intraoral mobility allowing for efficient intake of a variety of prey types and shapes preceded the evolution of a wide gape in snakes ( Figure 6 ) . Large body size combined with intraoral kinesis may have been a strategy that allowed gape-limited snakes such as Sanajeh , Yurlunggur , and Wonambi to consume large prey . On the basis of the feeding ecology of Sanajeh and the basal position of the large-bodied Dinilysia , we conclude that the high prey specificity and reduced cranial kinesis observed in extant basal snakes may not result from plesiomorphic gape-width restrictions , but may be specializations associated with fossoriality—especially miniaturization and habitat limitations on prey diversity [36] . Squamates ( e . g . , Sphaerodactylus ariasae , 0 . 014 kg ) and sauropod dinosaurs ( e . g . , Brachiosaurus brancai , 38 , 000 kg ) bracket reptile body mass range , which spans six orders of magnitude [37] , [38] . The large body size ( 20–25 m ) attained by the two titanosaur genera recognized from Indo-Pakistan , Isisaurus and Jainosaurus [39] , may have been an effective deterrent to predators , but hatchlings were likely vulnerable to predation by organisms too small to prey upon adults . Large numbers of offspring [40] and accelerated growth rates [41] , [42] may have offset losses of hatchlings to snake predation . “Ethofossil” preservation at Dholi Dungri captured an early instant in sauropod ontogeny when a 3 . 5-m-long snake maintained a body size advantage . Although at least one of the titanosaur species from India bore osteoderms , these elements probably did not form a shield of armor [43] and have never been recorded in hatchlings [44] , which are poorly ossified . Because living derived macrostomatan snakes of comparable length ingest prey weighing much less than 10 kg [45] , titanosaur hatchlings were probably free of risk of predation by Sanajeh-sized snakes before the end of their first year of growth .
The specimen described in this paper was discovered by one of us ( DMM ) in 1984 near the village of Dholi Dungri in western India . The specimen was collected using hand tools and removed as a series of blocks . The specimen was covered with a preservative but not subjected to chemical or mechanical preparation at the time . The initial descriptive paper [14] , written well before the specimen was prepared , interpreted the specimen as a hatchling sauropod dinosaur preserved inside a nest ( Figure S2 ) . Although identification of sauropod egg and hatchling sauropod limb bones was correct , the vertebrae were incorrectly identified . S . L . Jain [12] was the first to correctly identify the vertebrae preserved on the main block as pertaining to a snake , an observation that went largely unnoticed and was never followed by detailed study . In 2001 , one of us ( JAW ) reexamined the specimen and independently arrived at the same conclusion that Jain made 12 years earlier . Further study in the GSI collections by DMM and JAW uncovered a block that had been collected with the original specimen but was never described and , as a consequence , had been dissociated from it . That block has a snap-fit on the other blocks and preserves vertebrae that complete the snake's loop around the crushed egg ( Figures S3 and S4; Text S1 ) . Additional field reconnaissance conducted by the authors in 2007 relocated the original site at Dholi Dungri and collected additional geological and paleontological data . These included a detailed stratigraphic and sedimentological investigation of the site ( Text S2 ) and the discovery of multiple associations between Sanajeh and sauropod eggs . In 2004 , the specimen was brought to the University of Michigan Museum of Paleontology , where it was prepared using a combination of chemical and mechanical techniques . The original lacquer preservative was removed from each block using Zip-Strip and then subjected to 3% formic acid for approximately 2–3 h , which weakened calcareous cement . Each block was then mechanically prepared using a micro-airscribe and needles to uncover the “up” surface of the bones . The blocks were fit together as they were found in the field and then molded and cast . The snake braincase and sauropod scapula and humerus were then fully freed from the matrix . The electronic version of this document does not represent a published work according to the International Code of Zoological Nomenclature ( ICZN ) , and hence the nomenclatural acts contained in the electronic version are not available under that Code from the electronic edition . Therefore , a separate edition of this document was produced by a method that assures numerous identical and durable copies , and those copies were simultaneously obtainable ( from the publication date noted on the first page of this article ) for the purpose of providing a public and permanent scientific record , in accordance with Article 8 . 1 of the Code . The separate print-only edition is available on request from PLoS by sending a request to PLoS Biology , 185 Berry Street , Suite 3100 , San Francisco , CA 94107 , USA along with a check for $10 ( to cover printing and postage ) payable to “Public Library of Science . ” The online version of the article is archived and available from the following digital repositories: PubMedCentral ( www . pubmedcentral . nih . gov/ ) , LOCKSS ( http://www . lockss . org/lockss/ ) , and Deep Blue at the University of Michigan ( http://deepblue . lib . umich . edu/ ) . In addition , the genus and species names established herein have been registered in ZooBank , the proposed online registration system for the ICZN . The ZooBank LSIDs ( Life Science Identifiers ) can be resolved and the associated information viewed through any standard web browser by appending the LSID to the prefix “http://zoobank . org/” . The LSID for genus is: AB09F42A-6E4E-4F96-8B32-60D4B9FA6FD6 and the LSID for the species is:45E1476C-0BC1-4892-B4A9-B4D16530B43F . We estimated body size of S . indicus by constructing a regression model of total body length onto skull length for crown-group snakes ( Figure S7; see Text S3 ) . Characters used in this analysis come from evaluation of the two most recent comprehensive morphological analyses of snake phylogeny [4] , [16] and original specimen observations by JJH ( see Text S6 for examined specimens ) . We used Amphisbaenia and Varanoidea as alternative outgroups to snakes on the basis of the most recent comprehensive analysis of squamate relationships [46] . We derived our phylogeny using a heuristic parsimony search in PAUP* 4 . 0 b using 10 , 000 random addition sequence replications . For additional information about the analysis , matrix , character list , and constraint trees , please see Text S6 . | Snakes first appear in the fossil record towards the end of the dinosaur era , approximately 98 million years ago . Snake fossils from that time are fragmentary , usually consisting of parts of the backbone . Relatively complete snake fossils preserving skulls and occasionally hindlimbs are quite rare and have only been found in marine sediments in Afro-Arabia and Europe or in terrestrial sediments in South America . Early snake phylogeny remains controversial , in part because of the paucity of early fossils . We describe a new 3 . 5-m-long snake from the Late Cretaceous of western India that is preserved in an extraordinary setting—within a sauropod dinosaur nest , coiled around an egg and adjacent the remains of a ca . 0 . 5-m-long hatchling . Other snake-egg associations at the same site suggest that the new snake frequented nesting grounds and preyed on hatchling sauropods . We named this new snake Sanajeh indicus because of its provenance and its somewhat limited oral gape . Sanajeh broadens the geographical distribution of early snakes and helps resolve their phylogenetic affinities . We conclude that large body size and jaw mobility afforded some early snakes a greater diversity of prey items than previously suspected . |
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Essential aspects of the innate immune response to microbial infection appear to be conserved between insects and mammals . Although signaling pathways that activate NF-κB during innate immune responses to various microorganisms have been studied in detail , regulatory mechanisms that control other immune responses to fungal infection require further investigation . To identify new Drosophila genes involved in antifungal immune responses , we selected genes known to be differentially regulated in SL2 cells by microbial cell wall components and tested their roles in antifungal defense using mutant flies . From 130 mutant lines , sixteen mutants exhibited increased sensitivity to fungal infection . Examination of their effects on defense against various types of bacteria and fungi revealed nine genes that are involved specifically in defense against fungal infection . All of these mutants displayed defects in phagocytosis or activation of antimicrobial peptide genes following infection . In some mutants , these immune deficiencies were attributed to defects in hemocyte development and differentiation , while other mutants showed specific defects in immune signaling required for humoral or cellular immune responses . Our results identify a new class of genes involved in antifungal immune responses in Drosophila .
Innate immunity is the first line of defense in multicellular organisms , and effectively prevents or limits infection after exposure to microbes [1] . The innate immune response to microbes triggers diverse humoral and cellular activities via signal transduction pathways that exhibit transphyletic conservation in animals [2]–[4] . In mammals , the adaptive immune system is recruited for complete elimination of microbes or microbial debris after initial neutralization or clearance by the innate immune system . However , Drosophila relies on humoral and cellular innate immune responses for protection against the barrage of microbes that thrive in its habitats [3]–[6] . A hallmark of the humoral response in Drosophila is the massive synthesis of antimicrobial peptides ( AMPs ) after immune challenge . AMPs are produced primarily by the fat body , the anatomical equivalent of the mammalian liver , and are secreted into the hemolymph where they directly kill invading microorganisms [6] . Genetic analysis has shown that AMP genes are regulated by various immunogenes through the Toll and Imd pathways [3] , [6] . The Toll pathway is activated by both Gram-positive bacteria and fungi . Recognition of microbial components triggers proteolytic cleavage of the Toll ligand Spatzle ( Spz ) leading to activation of the Rel proteins , Dif and Dorsal [7]–[10] . In contrast , the Imd pathway mainly responds to Gram-negative bacteria and controls the expression of specific AMP genes by activating Relish [9] , [11] , [12] . In addition to strong antimicrobial activities provided by the humoral response , cell-mediated defenses also play an important role in the elimination of apoptosed cells and invading microbes or parasites [13]–[18] . The Drosophila hemocyte population consists of three cell types: plasmatocytes , crystal cells , and lamellocytes [19] , [20] . Plasmatocytes represent 90–95% of all mature Drosophila hemocytes and function in the phagocytic removal of dead cells and microbial pathogens [15] , [16] . Crystal cells , which constitute approximately 5% of the hemocyte population , are non-phagocytic cells that facilitate innate immune responses and promote wound healing through the process of melanization [15] , [17] , [21] . Lamellocytes are relatively large ( 15–40 µm ) , flat , adherent cells that facilitate the encapsulation and neutralization of objects too large to be engulfed by plasmatocytes [18] . These hemocytes are activated by microbial molecules through the same pattern recognition receptors as in the fat body , but the mechanisms leading to the activation of cellular immune responses are not fully understood . Significant effort has focused on identifying components of the signaling pathways involved in regulating the innate immune response . Previous studies have identified a number of genes that are differentially regulated in hematocytes during microbial infection [22] , [23] . However , the role of these genes in the immune response is only known for a few of them . To evaluate the role of these genes in antifungal immune responses , we examined the effect of individual mutations on the immune response of flies against Beauveria bassiana infection , and identified 16 mutants with increased sensitivity to B . bassiana . Examination of the sensitivities of these mutants to infection with several types of bacteria identified several mutants that were required mainly for defense against fungal infection . Examination of cellular immune responses revealed that transcription factors involved in chromatin remodeling or lineage specific differentiation were required for proper hemocyte development . Mutation of genes involved in cytoskeletal remodeling caused a strong defect in phagocytosis , while Trx-2 and DDB1 were required for development of functional crystal cells . The screen also identified several novel genes required for activation of antimicrobial peptide genes , indicating their involvement in signaling during pathogen specific immune responses . The distinct requirement of these genes for defense against different microbial infections also reveals the complexity of innate immune responses designed to compete with diverse offensive mechanisms used by microbes . In this paper , we present new findings on the regulation of cellular and humoral immune responses of Drosophila against fungal infection .
Previously we identified genes that were differentially induced in SL2 cells after treatment with LPS/PGN or curdlan using Drosophila cDNA microarrays [24] . These LPS/PGN-or curdlan-induced genes are probably involved in diverse immune responses , such as activation of signaling pathways downstream of pathogen associated molecular pattern recognition receptors , induction of phagocytosis , and differentiation into a specialized immune effector cell type . Because these immune responses require crosstalk between different cell types in a physiological condition , expression profile analysis of SL2 cells alone may not provide a complete picture of gene regulation during infection . However , because SL2 cells display important characteristics of macrophages in an in vitro assay , we assumed that their expression pattern may reflect regulatory mechanism underlying some immune responses of macrophages . To identify key regulators of innate immunity , we obtained mutants of the genes that are differentially regulated following treatment with microbial components , and monitored their requirement for defense against infection . Out of 5 , 405 genes screened on the microarray , 231 and 1 , 151 genes were induced more than 1 . 6 fold after the LPS/PGN or curdlan treatment of SL2 cells , respectively . A search for congenic EP ( Enhancer-Promoter ) lines in which these differentially regulated genes were disrupted by a P-element insertion identified 130 lines ( 110 and 20 lines with a P-element inserted at the untranslated and coding regions of the differentially regulated genes , respectively ) from the GenExel library ( Daejeon , Korea ) . The P-element insertion positions of all the GenExel EP lines were confirmed twice independently by direct sequencing of the inverse PCR fragment amplified with P-element specific primers ( data not shown ) . These results suggest that most of the defects associated with the EP lines are related to disruption of the candidate genes . About one-third ( 47 lines ) of the EP lines obtained were homozygous lethal , indicating that the P-element insertion effectively disrupted function of the target genes . None of the 83 homozygote viable EP lines showed obvious developmental abnormalities . These results indicated that the EP lines could be used to screen for genes involved specifically in defense against microbial infection . Therefore , adult homozygote flies were screened for survival after infection with entomopathogenic fungi ( B . bassiana ) ( Table S1 ) . Although a developmental defect caused by heterozygocity of a gene is rare , functional insufficiency of a heterozygote is often observed under strong environmental stress such as infection , and can influence survival of the heterozygotes as shown in the study of Dif 1 heterozygotes [8] . Based on this assumption , adult heterozygote flies were monitored for survival after fungal infection in the case of the homozygous lethal lines . To identify EP lines with a compromised defense against fungal infection , 30 adult flies from each of the 130 lines were pricked on the leg disc with a needle dipped into a concentrated solution of live B . bassiana , and the survival rate was followed over a six day period at 25°C . The septic infection with B . bassiana resulted in approximately 10% mortality in the wild type flies . Under the same infection conditions , most of the mutant flies showed similar levels of survival ( Figure 1A , Table S1 ) . However , 16 mutant lines , including six heterozygote flies ( Pcl , DDB1 , shg , Rab6 , CG6181 , and CG7263 ) , were significantly more sensitive to fungal infection ( p<0 . 002 ) ( Figure 1A ) . In these cases , death was clearly associated with uncontrolled fungal growth , as the dead flies were covered with fungal hyphae ( Figure S1 ) . To confirm the defects of the 16 lines , we first compared their survival rates after fungi infection with wild type and spz mutant as negative and positive controls , respectively , in three independent experiments . The repeated experiments revealed that the 16 EP lines had a clear defect in survival ( Figure 1B ) . We next examined the survival rates after natural infection with B . bassiana to rule out the possibility that reduced viability resulted from septic injury rather than from fungal infection . When the flies were raised after being covered with spores for 1 min , the 16 mutant lines showed remarkably less survival comparable to that of the spz mutant , while wild type showed only a minor decrease in survival ( Figure 1C ) . This result indicated that we have identified Drosophila mutants that have a reduced ability to defend against B . bassiana infection . To confirm that the increased sensitivity of these mutants to fungal infection is caused by specific disruption of the candidate genes by the P-element , we excised the P-element from the mutant flies by crossing with P[ry+Δ2–3] ( 99B ) Sb/TM6B , TB . After excising the P-element from the germ cells , white-eye progeny were established as homozygous lines for all mutants . Excision of the P-element in each line was confirmed by PCR with primers specific to one end of the P-element ( PF ) and to target sequences surrounding the P-element insertion sites ( F and R ) ( Figure 2A ) . The PF and R primer pairs amplified specific fragments ( fragment II ) from the P-element mutant lines confirming the mutation sites , but failed to amplify this fragment in any of the excised lines . On the other hand , F and R primer pairs that amplify the undisrupted target gene sequences ( fragment I ) failed to amplify specific fragments from the homozygous mutant flies and produced reduced levels of the amplification products from the heterozygous mutants ( Pcl , DDB1 , shg , Rab6 , CG6181 , and CG7263 ) . These PCR primers specifically amplified products from all of the excised lines ( Figure 2B ) . To confirm that these excised lines did not contain a small deletion or insertion at the P-element insertion sites , we cloned fragment I amplified from each excision line and sequenced them together with fragment II obtained from the corresponding P-element insertion mutants . This sequencing analysis confirmed that precise excision lines had been obtained for all mutants except CG12004 and CG6181 ( data not shown ) . After obtaining the precise excision lines for all mutants , we examined whether excision of the P-element from the mutants could revert their sensitivity to fungal infection to that of wild type flies . As shown in Figure 2C , similar survival rates ( 90% ) were observed in both the wild type and the precise excision homozygous lines following fungal infection that caused complete death of the spz mutant ( Figure 2C ) . In addition to rescuing the increased lethality following infection by precise excision of the P-element , we tested whether overexpression of disrupted genes with the EP promoter inserted in front of the coding region could reverse the mutant phenotype . Half of the mutants contained a Gal4-dependent promoter ( EP element ) at the 5′ UTR in a forward orientation to the disrupted gene . Heat shock in combination with an hs-Gal4 driver induced overexpression of the disrupted gene in this half of the EP mutant lines ( Table S2 ) . Thus , we generated flies carrying a copy of hs-Gal4 driver and homo- or heterozygous P-element insertions , depending on the corresponding mutant configuration used for the screen . Quantitative RT-PCR analysis of the mutant EP lines revealed that the disrupted gene transcript was significantly less than that in the wild type . However , heat shock treatment ( 1 h at 37°C ) in the presence of the hs-Gal4 driver activated transcription of the target genes above the level observed in wild type flies ( Figure S2 ) . Consistent with this observation , lethality of the mutant lines reverted completely to wild type levels ( Figure 2D ) . These results demonstrate that the genes identified from our screen are required for Drosophila antifungal immunity . These genes identified in our screen encode proteins from many different functional classes including transcription factors involved in chromatin remodeling or lineage specific transcription ( spen , Pcl , CG12744 , jumeaux , inv , and Lmpt ) , cytoskeletal regulation ( coro , shg , loco , and Rab6 ) , DNA fragmentation , apoptosis and redox signaling ( CG7263 , DDB1 and Trx-2 ) , along with a few genes ( CG6181 , CG12004 and JhI-21 ) of unknown function . Therefore , genes involved in immune responses ranging from development to cell movement were identified in this fungal defense screen . To determine whether reduced survival of the mutant flies resulted from defective immune responses specifically to fungal infection , we examined the effect of these mutations on wound healing and defense against bacterial infection . When mutant flies were pricked with a sterile tungsten needle , the majority of the flies survived the wounding and only the spen mutant showed a minor decrease in survival ( Figure 3A ) , suggesting that the reduced survival rates of these mutants , except spen , were caused by a defective defense against microbial infection . Septic infection with Gram-negative bacteria does not normally affect the viability of wild type flies . However , loss of a major antibacterial gene , such as imd , severely reduces survival following infection with Gram-negative bacteria . When the mutant flies were tested for susceptibility to Ecc-15 infection , most showed no significant defect in survival . However , spen and imd mutants were highly sensitive to infection . Interestingly , imd was not required for defense against Micrococcus luteus ( Gram-positive bacteria ) [9] , [25] . On the other hand loss of spz caused a minor defect in immune response against M . luteus infection as was shown [9] , [26] . Similar infection analysis with M . luteus showed significantly more lethality in spen , CG12744 , and CG12004 than in spz mutants without affecting survival in most of the other mutants ( Figure 3B , 3C ) . These results indicate that most of the genes except spen are not required to defend against Gram-negative bacteria , while two novel genes ( CG122744 and CG12004 ) are required to defend against Gram-positive bacterial infection . We also tested survival of the mutant flies after Staphylococcus aureus infection . In addition to the three mutants susceptible to M . luteus infection , jumeaux , Lmpt , shg , and Trx-2 mutants were highly susceptible to S . aureus infection ( Figure 3D ) . This result indicated that more sophisticated immune responses are required to control the highly pathogenic S . aureus . Therefore , of the 16 genes found to be essential for anti-fungal defense , spen appears to be required for general immune responses , while nine genes ( Pcl , inv , DDB1 , coro , loco , Rab6 , JhI-21 , CG6181 , and CG7263 ) are specifically required for anti-fungal defense . The other six genes ( CG12744 , jumeaux , Lmpt , Trx-2 , shg , and CG12004 ) are differentially required , to defend against Gram-positive bacteria , depending on the pathogenic activities of the infecting bacteria . Because flies utilize several defense mechanisms against microbial infection , Gram-negative bacteria may be easily cured even if some mechanisms are not functional , while both cellular and humoral defenses may be needed to eradicate highly pathogenic microbes such as S . aureus and fungi . To determine whether the immune response was defective in each mutant , particularly in adults , we first examined the synthesis of diverse antimicrobial peptides ( AMPs ) in response to fungal infection . Quantitative RT-PCR analysis of five major AMP transcripts ( AttA , CecA2 , Dpt , Drom , and Def ) revealed very low AMP transcript levels that are comparable to those in wild type flies prior to fungal infection in all of the mutants , indicating no major defect in the regulation of basal AMP expression in the mutants ( data not shown ) . When the flies were challenged with fungal spores , all the five AMP genes were highly induced in wild type flies , and the expression of these genes was strongly reduced or abolished by mutation of the Toll-dependent transcription factor , Dif . Under the same infection condition , most of the mutant flies were defective in activation of certain types of AMP gene expression , and different AMP genes appear to require different genes for their activation in response to fungal infection ( Figure 4 ) . AttA , Drom , and Dpt synthesis in response to fungal infection was not affected in most of the mutants . However , mutations in Trx-2 , coro , CG6181 and spen caused moderate defects in their activation . In contrast , the induction of CecA2 and Def by fungal infection was significantly reduced in most of the mutants analyzed . CecA2 expression was defective in most of the mutants except DDB1 . In particular , CecA2 expression was completely abolished in JhI-21 and CG6181 mutants , and was highly repressed in spen and jumeaux mutants . Activation of Def expression was affected in most of the mutants except CG12744 , jumeaux , and CG7263 , with the most severe defects found in Trx-2 , CG12004 , and JhI-21 mutants . Therefore , spen , Trx-2 , coro , and CG6181 appear to be required to activate most of the antimicrobial peptide genes upon fungal infection , while CG12004 and JhI-21 appear to be required to activate Def and CecA2 , respectively . However , DDB1 does not seem to be required to activate AMP expression induced by fungal infection . Along with the humoral response , which is mediated mainly by the synthesis of specific antimicrobial peptides , the phagocytosis of invading microbes by hemocytes is another major defense mechanism of adult flies . Hemocytes are mostly sessile and cannot easily be removed from adult flies . However , these cells can be observed through the cuticle , and clusters of hemocytes are present under the dorsal surface of the abdomen , along the dorsal vessel [27] , [28] . To assay the phagocytic activities of mutant hemocytes in vivo , wild type and mutant adult male flies were infected with Alexa Fluor 488-labeled spores of B . bassiana , and the level of fluorescence from phagocytosed spores was measured after quenching the signal from spores outside the hemocytes ( Figure 5 , A and C ) . Wild type flies showed a strong fluorescence signal from the phagocytosed spores; however , eleven ( spen , Pcl , CG12744 , Lmpt , coro , shg , loco , Rab6 , CG12004 , JhI-21 , and CG7263 ) of the sixteen EP mutants had a weak fluorescence signal , indicating that the mutant hemocytes were defective in uptake of the spores . To determine whether the reduction in phagocytosed spores in some of the mutant flies resulted from the reduced hemocytes , we measured the number of hemocytes present under the dorsal surface of the abdomen of each of the mutant flies . Hemocytes were visualized by injecting India ink , and the amount of black particles taken up by each mutant hemocyte was quantified . India ink staining revealed that most of the mutants contained hemocytes that were comparable to or even higher ( spen , jumeaux , CG12004 , JhI-21 , and CG7263 ) than wild type ( Figure 5B ) . Therefore , the reduced fluorescent signals appear to reflect defective phagocytosis rather than fewer hemocytes in the mutants . When the fluorescent signal of the phagocytosis assay was normalized to the number of hemocytes estimated by India ink staining , we observed a moderate defect in jumeaux mutant in addition to the eleven mutants that showed clear phagocytic defects ( Figure 5D ) . In addition , these fluorescent signals appeared to depend on the phagocytotic machinery of the hemocytes since injection of excessive latex beads competed out the signal completely ( Figure 5C ) . Therefore , in addition to obvious phagocytotic components ( cytoskeletal regulators; coro , shg , loco , and rab6 ) , genes in diverse categories , such as transcription factors ( spen , Pcl , CG12744 , and Lmpt ) , cell death regulators ( CG7263 ) , and other novel factors ( CG12004 and JhI-21 ) , appear to be required to phagocytose fungal spores . We next examined whether similar genes are required to phagocytose bacteria . The E . coli phagocytosis signal was strongly reduced in flies carrying a mutation in the cytoskeletal regulators ( coro , shg , loco , and rab6 ) or in some of the genes required to phagocytose fungal spores ( spen , Pcl , JhI-21 , and CG7263 ) . In addition , CG6181 appeared to be required specifically for E . coli phagocytosis ( Figure 5 , E and F ) . In addition to genes required to phagocytose E . coli , phagocytosis of S . aureus requires additional genes that function as transcription factors ( CG12744 , jumeaux , and Lmpt ) or as a redox regulator ( Trx-2 ) ( Figure 5G , 5H ) . These results indicate that genes involved in cytoskeletal and cell death regulation , along with spen ( chromatin regulator ) and JhI-21 ( transporter induced by juvenile hormone ) , are generally required for phagocytosis of diverse microorganisms . In contrast , jumeaux and Trx-2 are required to specifically phagocytose S . aureus , which is known to utilize diverse immune evading mechanisms [29]–[31] . Therefore , hemocytes appear to require genes involved in diverse cellular functions to mediate a proper cellular immune response against fungal and bacterial infection . The analysis of hemocytes in adult flies revealed that some mutants are defective in the activation of both phagocytosis and AMP synthesis , and showed an abnormal number of hemocytes . This observation suggested that some of the immune defects were caused by inappropriate hematopoiesis . To test this idea we examined whether hemocyte development in these mutants occurred normally . We first compared the number of circulating plasmatocytes in third instar larvae of mutant and wild type flies . Since the number of circulating hemocytes increases rapidly during development , we staged the wandering larvae according to the presence or absence of food in the gut [32] . Because the mutants showed no obvious developmental defects or delay , this method enabled us to measure the circulating hemocytes of each mutant at a comparable developmental stage . However , we cannot rule out the possibility that hemocyte development in certain mutant larvae was affected in some degree by the mutations . When we counted the circulating hemocytes , late third instar larvae of six mutants ( spen , Pcl , jumeaux , CG12004 , JhI-21 , and CG7263 ) displayed a 2- to 6-fold increase in the number of plasmatocytes ( Figure 6A ) , which is consistent with the higher number of hemocytes observed in the adult of the same mutants . Therefore , the defective immune responses observed in many mutants appear to be related to abnormal plasmatocyte proliferation . The defects in plasmatocyte proliferation in some of the immune compromised mutant flies prompted us to examine the effect of the mutations on crystal cell development . To measure the number of crystal cells in the larvae of each mutant , third instar larvae were heated to 60°C for 10 min to induce blackening of mature crystal cells . spen , Pcl , CG12744 , Trx-2 , and DDB1 mutant larvae showed fewer crystal cells than did wild type larvae ( Figure 6B ) . We also tested the functional activity of crystal cells in each mutant by injuring third instar larvae with a clean needle and measuring the level of melanization in each mutant larva . Strong melanization at the injury site was observed in wild type larvae and most of the mutant larvae . However , spen , Pcl , CG12744 , Trx-2 , and DDB1 mutant larvae showed much less melanization induced by injury , consistent with their defects in crystal cell proliferation ( Figure 6C , Figure S3 ) . Therefore , spen and Pcl , which are involved in chromatin regulation , appear to function in the development of both plasmocytes and crystal cells . It is intriguing that genes involved in the recognition of damaged DNA ( DDB1 ) , redox regulation ( Trx-2 ) , and a novel transcription factor ( CG12744 ) are also required for proper crystal cell development .
The immune system employs multiple layers of defense against pathogens and it is difficult for most invading bacteria to overcome these redundant host defense barriers . However , fungi are largely opportunists , causing infection when any of host defenses are breached . Beauveria bassiana is an entomopathogenic fungus that causes a disease in insects known as white muscadine disease . Unlike bacterial pathogens , once inside the insect it produces a toxin that weakens the host immune system . To search for important factors within the entire Drosophila immune system that are required for antifungal defense , we screened for genes specifically required for survival following B . bassiana infection and identified several genes involved in diverse aspects of cellular and humoral immune responses ( summarized in Table 1 ) . Although some of the mutants showed general immune defects and were susceptible to both fungal and bacterial infection , most of the other mutants exhibited distinct immune defects and were susceptible only to fungal or to highly pathogenic bacterial infection . This increased susceptibility specifically to fungal infection might result from defects in defenses against fungal-specific pathogenic molecules , but it is also possible that anti-fungal responses require more diverse immune defense mechanisms than bacterial infection , such that mutants with specific defects could overcome bacterial infection using other functional immune responses . Inappropriate development of plasmocytes and crystal cells appears to be one of the main causes of the immune defects in the mutants identified in this screen . Spen and Pcl play essential roles in the chromatin modification needed for hemocyte development [33]–[35] . Mutations in these genes must prevent progenitor hemocytes from differentiating into functional plasmocytes or crystal cells , and cause pleiotrophic defects in diverse aspects of immune function . Pcl appears to be less important for bacterial infection than does Spen , but the difference may be due to different degrees of gene inactivation in the Pcl heterozygotes vs . spen homozygotes , rather than from differences in regulatory function . A similar explanation could be applied to fungal specific defects of the other heterozygote mutants . In addition to chromatin regulators , it is intriguing that CG12744 and Jumeaux are required specifically for the development of crystal cells and plasmocytes , respectively . CG12744 is a novel transcription factor; in contrast , Jumeaux is a transcription factor expressed in embryonic CNS and is required in neuronal development [36] . How these transcription factors regulate the development of specific hemocytes is not known , but their expression pattern and requirement in a specific blood cell type suggest a role in the maturation of distinct types of hemocytes . Crystal cell differentiation also requires Trx-2 ( thoredoxin-2 ) and DDB1 ( Damaged DNA Binding protein 1 ) . Trx-2 regulates redox signaling , which is essential for the activation of immune effector functions [30] , [31] , [37] and the melanization reaction . The misregulation of redox signals by the loss of Trx-2 may affect early steps in the signal transduction pathway induced by pathogen recognition , causing diverse defects in immune function . DDB1 is involved in the recognition of damaged DNA in dying cells or in invading pathogens and is required for plasmocyte development [38] . However , how DDB1 affects crystal cell function is not known . In addition to transcription factors , cytoskeletal regulators are another major group of genes required to defend against infection . Coro has F-actin binding activity and is required for membrane trafficking [39] . Shg is a Drosophila Cadherin and is required for cell motility and adhesion [40] , [41] . Loco and Rab6 are involved in asymmetric cell division and vesicle transport , respectively [42] , [43] . Therefore , these proteins must be required for cytoskeletal rearrangement during phagocytosis . It is interesting that these mutants also showed defects in AMP synthesis . Efficient recognition of pathogens or subsequent signaling may require cytoskeletal rearrangement . We also identified several novel genes , whose function in innate immunity has not been previously suggested . CG12004 is a novel protein without known protein motifs , but it appears to play an important role in plasmocyte development . JhI-21 is a cationic amino acid transporter induced by juvenile hormone [44] . It is required for plasmocyte development and affects their phagocytosis and AMP synthesis . CG6181 is a novel protein and CG7263 are known to be involved in apoptosis [45] . Recently , endocytic degradation by apoptosis was suggested to play essential roles in defense against pathogenic microbes that can escape from endosomes to cytoplasm [46] . Several novel genes identified from this screen appear to have essential roles in defense against both fungi and bacteria , indicating their roles in the regulation of primary immune responses . The putative functions of these newly identified genes ( Spen , CG12744 , Jumeaux , Lmpt , Trx-2 , Shg , and CG12004 ) as transcription factors , redox regulator , or cell adhesion molecule hints at their role in regulating immune responses . Therefore , further study of these genes will provide important insight into regulatory mechanism of the Drosophila immune system . Our results showed that complex immune reactions are required to defend against fungal infection in Drosophila , and identified key regulatory components involved in these immune reactions . These findings increase our understanding of the mechanisms underlying cellular and humoral aspects of Drosophila antifungal immunity , and have significant implications in the treatment of human diseases caused by fungi .
Drosophila melanogaster strains were cultured on a standard cornmeal-yeast medium at 25°C and 60% humidity . Mutant flies containing a P-element at the translated/untranslated region of the candidate genes ( Table S1 ) were purchased from GenExel ( Daejeon Korea ) . Because the GenExel EP lines contain Gal4 binding sites , overexpression of Gal4 can induce strong expression of adjacent endogenous genes in which an EP element is inserted at the 5′ UTR in a forward orientation [47] . To activate transcription of P-element inserted genes from the EP promoter , we crossed mutant flies containing a P-element at the 5′ UTR in a forward orientation with hs-Gal4 driver ( Bloomington Stock Center ) . For homozygous viable lines , we generated flies carrying a homozygous P-element inserted chromosome in addition to a hs-Gal4 driver . Overexpression of target genes was achieved by heat shocking the adult flies for 1 h at 37°C , and these flies were used for infection one day after a heat shock . W1118 was used as a wild type stock and P[ry+Δ2–3]sb/TM6B , TB was used as a genomic transposase source . The Imd and spzrm7 were a gift from Dr . Won-Jae Lee , and Dif 2 was a gift from Dr . Kwang-Min Choe . Beauveria bassiana from three day cultures ( per 1 . 0 L distilled water: Dextrose 10 g , Peptone 2 . 5 g , Yeast extract 5 g , 25°C ) . Staphylococcus aureus ( per 1 . 0 L distilled water: Trypticase soy broth 30 g , 37°C ) , Micrococcus luteus and Erwinia carotovora carotovar-15 ( per 1 . 0 L distilled water: Beef extract 3 . 0 g , Peptone 5 . 0 g pH 6 . 8 , 30°C ) from overnight cultures were recovered by centrifuging at 6 , 000 rpm for 10 min at 25°C . The supernatants were discarded and the pellets were resuspended in corresponding fresh culture media . Septic injury was performed by pricking the leg disc of adult flies with a tungsten needle previously dipped into a concentrated B . bassiana or by injecting diluted bacteria ( OD = 0 . 1 , 55 nl ) into the ventral lateral side with a thin needle using a Picospritzer III injector ( Parker Hannifin , USA ) . Natural infections with B . bassiana were performed by shaking anesthetized flies for 60 sec in a Petri dish containing a sporulating fungal culture [7] . Survival rates of flies after pathogen infection were measured under identical conditions for each genotype tested . Groups of 30 adults , aged 2–4 days , were septically injured , maintained at 25°C , and transferred to a fresh vial every three days . Fewer than five percent of the total flies tested died within three hours after infection and these flies were not considered in the analyses . Revertants for each P-element insertion mutant were generated through precise excision of the P-element by crossing with flies containing the Δ2–3 transposase , as described by Robertson et al . [48] . Excision allele identity was confirmed by PCR and direct sequencing of the excision sites . Approximately 10–15 adult flies were placed in a 1 . 5 ml centrifuge tube and frozen in liquid nitrogen for 5 min . The frozen flies were homogenized with a small pestle and genomic DNA was isolated with a G-spin™ Genomic DNA Extraction kit ( Intron , Gyeonggi-do , Korea ) . The oligonucleotide primers used in PCR amplifications , with each sequence shown in 5′ to 3′ orientation , are described in Table S3 . The standard thermal profile for PCR amplifications was 30 cycles of denaturation at 95°C for 1 min , annealing at 50°C for 1 min , and extension at 72°C for 1 min . Adult males were challenged with live B . bassiana spores and incubated at 25°C for 6 h . Total RNA was isolated from 8–10 adult flies with TRIzol ( Invitrogen , Carlsbad , CA ) and used for cDNA synthesis with Superscript II reverse transcriptase ( Invitrogen , Carlsbad , CA ) . Target cDNAs were measured by real time PCR using a LightCycler 480 ( Roche , Basel , Switzerland ) . PCR reactions contained 1×SYBR Green mix ( Applied Biosystems , Foster City , CA ) and were analyzed with LightCycler 480 software 4 ( Roche ) . All results were normalized to the level of RpL32 mRNA in each sample . Primers used are shown in Table S3 . In vivo phagocytosis assays of adult flies were performed following the procedure of Elrod-Erickson et al . and Brandt et al . [28] , [49] . Groups of 3–5 day-old adult males were injected with Alexa Fluor 488-labeled heat killed spores of B . bassiana , fluorescein conjugated E . coli ( K-12 ) BioParticles , and fluorescein conjugated S . aureus BioParticles ( 1 mg/ml , 50–60 nl ) ( Molecular Probes , Invitrogen ) on the ventral lateral side with a thin needle using a Picospritzer III injector . Flies were incubated for 1 h at 25°C to permit phagocytosis of the spores or bacteria , followed by injection of excess Trypan blue ( 0 . 4% , 220 nl ) to quench extracellular fluorescence . Phagocyte ablation experiments were performed as described by Kocks et al . [50] . CML latex beads ( 1 . 0 µm diameter , Molecular Probes ) were washed in PBS and concentrated in PBS to 8% solids . Beads ( 100 nl ) were injected 24 hours before the phagocytosis test . Phagocytosis of India ink was observed as described in Rutschmann et al . [10] . India ink carbon particles ( Pébéo , Gemenos , France ) ( diluted 1/50 in PBS , 90 nl ) were injected on the ventral lateral side with a thin needle using a Picospritzer III injector ( Parker Hannifin ) . The phagocytosis of India ink by the sessile blood cells was observed 2 h later . Phagocytosed signals were observed under a Zeiss Axioplan 2 microscope ( Zeiss ) . Fluorescence particles and Indian ink around the dorsal vessel was quantified from raw unaltered pictures using Image J software ( NIH , Bethesda , MD ) . Before the software was used to count the area of particles , each image was converted to a 32-bit grey scale image and was thresholded to highlight the particles . The phagocytic index was expressed as area of the signal corresponding to the sum of the encircled areas . Larvae were staged according to procedures described in Zettervall et al . [32] . Emptying of the gut marks the difference between early- and late-wandering third instar larvae , therefore a red household food dye was added to the food to allow visualization of the gut contents . The six homo-lethal alleles were maintained as heterozygotes balanced with either the second chromosome balancer CyO or with the third chromosome balancer Ubx . Precisely staged late-wandering third instar larvae were rinsed well in PBS ( 137 mM NaCl , 2 . 7 mM KCl , 6 . 7 mM Na2HPO4 , and 1 . 5 mM KH2PO4 ) and blotted on Kimwipes to remove excess PBS before bleeding . The larval cuticle was ripped gently near the posterior end while submerging the larva in 20 µl PBS . The hemocytes were transferred to a Neubauer improved hemocytometer ( Marienfeld ) to determine plasmocyte number . To quantify crystal cells , late-wandering third instar larvae were heated at 60°C for 10 min in a water bath to induce blackening of mature crystal cells and blackened crystal cells in the last two posterior dorsal segments of third instar larvae were counted under a dissecting microscope . For melanization reactions , third instar larvae were pricked with a clean standard needle and the reaction was observed 2 h after injury . Melanization signals were quantified from raw unaltered pictures using Image Pro Plus 4 . 5 software ( Media Cybernetics , Silver Spring , USA ) . The melanization index was expressed as [area]×[mean intensity] of encircled areas . | The innate immune response is the first line of defense against microbial infections in insects and mammals . In Drosophila , multiple defense mechanisms that contribute to the innate immune response include antimicrobial peptides ( AMP ) , reactive oxygen species , phagocytosis and melanization . A search for genes involved in these immune processes identified sixteen mutants that exhibited increased lethality after infection . The diverse functions annotated to these genes indicate the complexity of the regulatory mechanisms required for defense against fungal infection . Lineage specific transcription factors and chromatin modifiers appeared to be required for proper development of functional hemocytes , while cytoskeletal regulators were required for phagocytotic activities of hemocytes . In addition , we identified several genes involved in the immune signaling required for AMP synthesis or melanization . These results may lay the foundation for defining a new class of genes that are involved in humoral and cellular antifungal immune responses . |
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A fundamental observation of comparative genomics is that the distribution of evolution rates across the complete sets of orthologous genes in pairs of related genomes remains virtually unchanged throughout the evolution of life , from bacteria to mammals . The most straightforward explanation for the conservation of this distribution appears to be that the relative evolution rates of all genes remain nearly constant , or in other words , that evolutionary rates of different genes are strongly correlated within each evolving genome . This correlation could be explained by a model that we denoted Universal PaceMaker ( UPM ) of genome evolution . The UPM model posits that the rate of evolution changes synchronously across genome-wide sets of genes in all evolving lineages . Alternatively , however , the correlation between the evolutionary rates of genes could be a simple consequence of molecular clock ( MC ) . We sought to differentiate between the MC and UPM models by fitting thousands of phylogenetic trees for bacterial and archaeal genes to supertrees that reflect the dominant trend of vertical descent in the evolution of archaea and bacteria and that were constrained according to the two models . The goodness of fit for the UPM model was better than the fit for the MC model , with overwhelming statistical significance , although similarly to the MC , the UPM is strongly overdispersed . Thus , the results of this analysis reveal a universal , genome-wide pacemaker of evolution that could have been in operation throughout the history of life .
Genome-wide analysis of distances between orthologous genes in pairs of organisms from a broad range of taxa belonging to all three domains of life ( bacteria , archaea and eukaryotes ) revealed striking similarity between the distributions of these distances . All these distributions are approximately lognormal , span a range of three to four order of magnitude and are nearly identical in shape , up to a scaling factor [1]–[3] . Although many different explanations are possible of this remarkable conservation of evolutionary rate distribution across the entire spectrum of life , the simplest underlying model is that all genes evolve at approximately constant rates relative to each other , i . e . the changes in the gene-specific rates of evolution are strongly correlated genome-wide . This general model of evolution can be denoted Universal PaceMaker ( UPM ) of genome evolution: all genes in evolving genomes , in each evolving lineage , change their evolutionary rate ( approximately ) in unison although the pacemakers of different lineages need not to be synchronized . The existence of UPM is compatible with the considerable amount of available data on fast-evolving and slow-evolving organismal lineages , primarily different groups of mammals [4] , [5] . Conceivably , lineage-specific accelerations and decelerations of evolution can be caused by changes in the effective population size , and such rate changes are indeed expected to equally affect all genes in evolving genomes . The evolutionary rate has also been linked with other biological features of animals that are collectively denoted life history [5] . For instance , a genome-wide comparison of the evolutionary rates in the human and mouse lineages has shown that the number of fixed mutations per unit time is about twofold greater in rodents than it is in primates , with the implication that a lineage-specific , genome-wide change of evolutionary rate occurred after the separation of these lineages [6] . In the same vein , a genome-wide analysis of ratios between the evolutionary rates of orthologous genes in triplets of related bacterial , archaeal and mammalian species revealed near constancy of these ratios , with only a small percentage of gene-specific deviations that were attributed to functional diversification of individual genes [7] . A systematic study of densely populated phylogenetic trees for 44 mammalian genes has demonstrated clade-specific slowdown of evolution occurring independently in several orders including primates and whales [8] . Multiple studies of mitochondrial DNA evolution that used extensive samples from numerous taxa also detected consistent lineage-specific rates that differed by as much as an order of magnitude between animal taxa [9] , [10] . However , in other analyses , striking differences between lineages in the relative rates of evolution of different genes have been discovered , casting doubt on the universality of lineage-specific rates , leading to the idea of ‘erratic evolution’ [11] , [12] . The plausibility of the UPM notwithstanding , the genome-wide correlations between the evolutionary rates of individual genes also could be explained within the concept of molecular clock which is one of the central tenets of molecular evolution . In 1962 Zuckerkandl and Pauling discovered that the number of differences between homologous proteins is roughly proportional to the divergence time separating the corresponding species [13] , [14] . This phenomenon became known as Molecular Clock ( MC ) and has been validated by multiple independent observations [15]–[18] . The MC is the basis of molecular dating whereby the age of an evolutionary event , usually the split between lineages ( such as for example humans and chimpanzee ) , is estimated from the sequence divergence using calibration with dates known from fossil record [19]–[22] . From the phylogenetic point of view , when genes evolve along a rooted tree under the MC , branch lengths are proportional to the time between speciation ( or duplication ) events and the distances from each internal tree node to all descendant leaves are the same ( ultrametric tree ) up to the precision of the estimation ( the latter being determined by sampling error which is inevitable in comparison of finite-length sequences ) . Over the 50 years that elapsed since the seminal finding of Zuckerkandl and Pauling , the MC has been shown to be substantially overdispersed , i . e . the differences between the root to tip distances in many or most subtrees of a given tree usually greatly exceed the expectation from sampling error , under the assumption of a Poisson mutational process [23]–[26] . Notably , the overdispersion of the MC has been shown to be lineage-specific: the MC in lineages with large effective population sizes is overdispersed to a greater extent than the MC in lineages with small populations implying that deviations from the MC are controlled by selection [27] . The demonstration of the overdispersion of the MC inspired the relaxed MC model which is a compromise between an unconstrained tree with arbitrary branch lengths and an MC tree [28] , [29] . Under the relaxed MC , the evolutionary rate is allowed to change from branch to branch but this change is presumed to be gradual so that related lineages evolve at similar rates . The relaxed MC model underlies most of the modern methods of molecular dating . The strict MC implies that all orthologous genes present in a group of organisms and sharing the same evolutionary history evolve in a fully coherent manner even if at different rates . Indeed , if the divergence between gene sequences is solely determined by the divergence time and gene-specific evolution rate , phylogenetic trees reconstructed from different genes will have the same topology and nearly identical branch lengths up to a scaling factor which is equal to the relative evolution rate . Under the MC model , the differences between the corresponding branch lengths in different gene trees are due solely to the sampling error which arises from stochastic factors and is expected to be uncorrelated between trees . The relaxed MC model allows greater , non-random deviations in the lengths of corresponding branches but to our knowledge , the possibility that these evolution rate changes are correlated between genes has not been explicitly considered . The MC implies the constancy of gene-specific relative evolution rates , with deviations caused by overdispersion . However , the inverse is not true: the deviations of the absolute evolution rates from the clock could be arbitrarily high ( hence no MC ) but , if they apply to all genes in the genome to the same degree , the relative evolutionary rates would remain approximately the same throughout the entire course of evolution and in all lineages . In other words , the conservation of the evolutionary rate distribution follows from a model of evolution that is more general and less constrained than the MC , namely the UPM model . Here we sought to determine which of the two models of gene evolution , the MC and or the UPM , better fits the empirical data . To this end , we performed comparative analysis of phylogenetic trees for a genome-wide set of prokaryotic gene families and compared the goodness of fit for the two models . The results show that the UPM model is a better fit than the MC model for the evolution of prokaryotes . These findings are compatible with the previously observed accelerations and decelerations of evolution in individual evolving lineages . However , we show that synchronous , genome-wide change of evolutionary rates is a universal trend of genome evolution that appears to pervade the entire history of life .
Our data set consisted of the “forest” of phylogenetic trees reconstructed for 6901 orthologous gene families representing 41 archaeal and 59 bacterial genomes [30] ( see Supporting Text S1 ) . Although horizontal gene transfer is widespread in the evolution of prokaryotes [31] , [32] , the tree-like statistical trend is detectable in the genome-wide data set and moreover dominates the evolution of ( nearly ) ubiquitous gene families [30] , [33] . We encapsulate this trend in a rooted supertree ( ST ) that reflects the prevalent vertical descent in the evolution of archaea and bacteria ( see Supporting Text S1 ) . Each individual original gene tree ( GT ) is compared to the ST and reduced to the maximum agreement subtree ( MAST ) , i . e . the largest set of leaves whose phylogeny fits the ST topology . Removal of discordant nodes and edges leads to collapse of several edges of the original GT into a single edge ( Figure 1 ) ; then , the length of the newly created GT edge is the sum of the original contributing GT edges . Likewise , when a GT is mapped to the ST , several adjacent ST edges could correspond to a single edge in the reduced GT , forming a composite edge . Under both the MC and the UPM models , we assume that the lengths of the ST edges determine the expected lengths of the corresponding GT edges . For the MC model , edge lengths correspond to time intervals between speciation events , the ST is strictly ultrametric , and gene-specific evolutionary rates are measured in substitutions per site per time unit . Under the UPM model , edge lengths represent arbitrarily defined “ticks” of the universal pacemaker ( internal time ) , and gene-specific evolutionary rates are measured in substitutions per site per pacemaker unit of internal time . Formally:where li , k is the length of the i-th edge of the k-th GT , tj is length of the j-th ( possibly composite ) ST edge corresponding to the i-th edge of the k-th GT , rk is the gene-specific evolution rate , and εi , k is the multiplicative error factor for the given edge . We further assume that the error is random , independent for branches both within and between GTs , and comes from a lognormal distribution with the mean of 1 and an arbitrary variance , translating to a model with an additive normally distributed deviation in the logarithmic scale . Because the distributions of evolutionary rates tend to follow symmetric bell-shaped curves in log scale [3] , [34] , the assumption of a multiplicative , log-normally distributed deviation seems natural . First , we seek to find the set of ST edge lengths t and gene rates r that provides the best fit to the entire set of GTs . Under the assumption of a normally distributed deviation , the likelihood function for the set of GTs given t and r iswhere n is the total number of edges in the set of GTs and E2 is the sum of squares of deviations between the expected and observed edge lengths in the logarithmic scale:where the summation for i is done over the edges of a given GT and the summation for k is done over all GTs ( see Supporting Text S2 ) . Thus , finding the maximum likelihood solution for {t , r} is equivalent to finding the minimum of E2 . For the MC model , the ST edge lengths t are constrained by the ultrametricity requirement , whereas for the UPM model , ST edge lengths are unconstrained . For the analyzed set of 100 genomes , there is a choice of several possible ST topologies , produced using different methods ( see Methods and Supporting Figure S1 ) . We mapped all original GTs onto each of these STs and obtained reduced GTs that corresponded to the respective MASTs . The GTs that yielded MASTs with fewer than 10 leaves were discarded . The ST topology derived from the concatenated alignments of ribosomal proteins provided the maximum total number of leaves in the resulting set of reduced GTs and accordingly was chosen for further analysis . Altogether , we obtained 2294 reduced GTs with MAST size greater or equal to 10 species including 44 , 889 leaves and 82 , 896 edges . This set of trees was fit to an ultrametricity-constrained ST ( MC model ) and an unconstrained ST ( UPM model ) ( Table 1 , see Supporting Text S3 for details ) . We then compared the MC and UPM models in terms of the goodness of fit to the data . Obviously , the residual sum of squares is lower for the UPM model because it involves independent optimization of all 198 ST edge lengths , whereas under the MC model the edge lengths are subject to 99 ultrametricity constraints . To account for the difference in the numbers of degrees of freedom , we employed the Akaike Information Criterion ( AIC ) and the Bayesian Information Criterion ( BIC ) to compare the MC and UPM models . Under the assumption of normally distributed deviations:andwhere E2MC and E2UPM are the residual sums of squares for the MC and UPM models , respectively , n is the total number of GT edges and Δd is the difference in the number of parameters optimized in the process of fitting ( in our case Δd = −99 ) . Because lower AIC values correspond to better quality of fit , negative ΔAIC would indicate preference for the MC model whereas a positive ΔAIC would indicate support for the UPM model . The relative likelihood weight of the suboptimal model can be estimated as 1/exp ( |ΔAIC|/2 ) . The same calculations were repeated for smaller , more conservative subsets of gene families with MAST>20 and MAST>30 and also using BIC to compare the fit to the UPM and MC models ( Table 1 ) . Overall , the results presented in Table 1 reveal overwhelming support of the UPM model over the MC model . The only exception is the ΔBIC value for MAST>30 that weakly supports the MC model . This outcome is predictable given the much larger number of parameters in the UPM model , the small number of trees in this subset and the heavier penalty that BIC imposes on parameter-rich models [35] . Thus , the results show that the evolutionary rates tend to change synchronously for the majority ( if not all ) of the genes in evolving genomes although the rate of the UPM relative to the astronomical time differs for different lineages . The results of this analysis show that the apparent genome-wide constancy of the relative rates of gene evolution across vast spans of life's history ( Figure 2A ) is not a trivial consequence of MC but at least in part results from a distinct , fundamental evolutionary phenomenon , the UPM ( Figure 2B ) . The difference between the UPM and MC models is highly significant but small in magnitude . Root mean square deviation ( r . m . s . d . ) of GT edges from the expectations derived from UMP ST is large ( a factor of 2 . 45 ) and only slightly less that the r . m . s . d for the MC ST ( a factor of 2 . 48 ) . Thus , similar to MC , the UPM appears to be substantially overdispersed . To assess the robustness of the finding that UPM fits the GTs better than MC , we isolated the contributions of individual trees to the E2MC and E2UPM ( E2MC , k and E2UPM , k respectively ) , took 1000 bootstrap samples of the set of GTs and computed ΔAIC values for each sample . All 1000 ΔAIC values obtained for the resampled sets were positive ( in the range of 1511 to 2147 ) , providing 100% support to the superiority of the UPM model and ensuring that this result is consistent for the majority of the GTs and is not determined by a small number of strongly biased trees ( see Supporting Text S3 and Supporting Figure S2 for details ) . The distribution of the E2MC , k/E2UPM , k ratios ( Figure 3 ) shows a strong bias toward values greater than unity ( 73% of the GTs ) , supporting the robustness of this result . The E2MC , k/E2UPM , k ratio characterizes the degree to which the k-th GT favors the UPM model . Linear model analysis shows that this value is significantly and independently influenced by the average goodness of fit to the ST ( p-value ≪0 . 001; Figure 4 ) , the fraction of the original GT leaves remaining in the MAST with ST ( p-value ≪0 . 001; Supporting Figure S3 ) and the number of the original GT leaves ( p-value ≪0 . 001; Supporting Figure S3 ) . Thus , the GTs that retain a greater number of leaves in the MAST , fit the ST better and are wider distributed among prokaryotes , typically show the strongest preference for the UPM model over the MC model . These three factors together explain ∼9% of the variance in ln ( E2MC , k/E2UPM , k ) . Neither the relative evolution rate nor the functional class of the gene significantly impact the degree of preference of UPM over MC ( see Supporting Text S3 and Supporting Figure S3 for details ) . Interpreting these findings in terms closer to biology , widely-distributed genes that are subject to relatively little horizontal transfer or sporadic changes of evolution rate that reduce the fit to ST appear to make the greatest contribution to the UPM . These observations imply that the UPM is indeed a fundamental feature of genome evolution , at least in prokaryotes . The distribution of estimated relative evolution rates ( Figure 5 ) spans values within a range slightly greater than an order of magnitude ( 0 . 26 to 4 . 58 ) . This range is considerably more narrow than the range of rates measured over short evolutionary distances [3] , [34] . Accelerations and decelerations of the UPM are likely to average out over long intervals of evolution , reducing the observed differences between genes . A logical extension of the UPM is a Multiple PaceMakers ( MPM ) whereby a number of uncorrelated pacemakers ‘guide’ their own sets of trees . In the extreme case , the number of PMs is equal to the number of GTs so that the individual GTs would be completely uncorrelated . We sought to explore this case in order to determine how well such a degenerate MPM ( dMPM ) model fits the data compared to the UPM and MC . Formally , under the basic assumptions of this work , the log likelihood of dMPM is infinite because the E2 value is estimated as the sum of squared differences between the observed and the expected edge lengths . Under dMPM , each edge is equal to its own expectation sothat E2 = 0 . However , this logic assumes that the tree edge length is measured precisely and is not subject to any error , whereas the E2 value is dominated by deviations of individual GTs from the universal standard ( MC or UPM ) . This assumption is obviously unrealistic , so to assess the likelihood of the dMPM , one needs to introduce the edge length estimate error explicitly . To obtain the lower limit on the E2 value induced by the inherent sampling fluctuations , one should note that the sum of the lengths of the 49 , 981 edges in 967 trees ( MAST size ≥20 ) is 13 , 018 . 5 ( substitutions per site ) , on average 0 . 26 per edge . With the typical prokaryotic protein length being ∼200 amino acids [36] , this translates into the average of ∼52 substitutions per tree branch . Assuming that substitutions are generated by a Poisson-type random process , one expects the standard deviation of approximately and the “mean” error of the observed value on the order of ( 52+ ) /52 = 1 . 14 or 0 . 13 log units per branch . Multiplying the square of this value by 49 , 981 edges , we obtain the E2 value estimate of 843 . 0 , much lower than 35065 . 0 for UPM . It should be noted that the use of the average gene length and the average number of substitutions per branch comprises the ‘best-case scenario’ because variations in both would necessarily introduce larger deviations which would increase the E2 value . To calculate the ΔAIC value , one needs to obtain the difference in the degrees of freedom between the UPM and dMPM models . The UPM model uses the estimates of 198 individual edge lengths in one UPM tree plus 967 GT rates; the dMPM model requires 967±198 edge length estimates and no GT rates , yielding Δd = −190 , 301 . Plugging these values into the equation for ΔAIC , one gets the difference of −194 , 269 in the UPM-dMPM comparison . Thus , the dMPM model is less likely than the UPM model by 83 , 370 orders of magnitude , an obvious indication that the assumption of completely uncorrelated rate changes does not fit the data . More specifically , the data would support no more than 476 pacemakers for 967 GTs under ideal conditions ( each GT follows its PM perfectly , so the E2 value remains to be solely determined by sampling fluctuations ) . Thus , the actual number of distinct pacemakers is expected to be much lower . The results of the genome-wide comparison of phylogenetic trees of prokaryote genes described here show that the UPM model fits the data substantially better than the MC model . These findings have no bearing on the validity of the MC but show that a more general conservation principle ( the UPM ) is sufficient to explain the observed correlations between gene-specific evolutionary rates . It seems a natural possibility that UPM is instigated by shifts in population dynamics of evolving lineages , with changes affecting all genes in the same direction and to a similar degree . In principle , UPM reflects the well-known phenomenon of lineage-specific acceleration-deceleration of evolution . However , to our knowledge , the previous studies on this phenomenon have focused primarily on mammals and to a lesser extent other vertebrates [4] , [5] . Here we show that the UPM can explain the correlations between the evolutionary rates of prokaryote genes on the whole genome scale and over time intervals that span effectively the entire history of life on earth . The discovery of the UPM opens up several areas of further inquiry . We show here that an unconstrained model of evolution ( dMPM ) does not fit the data but it remains to be determined whether or not distinct pacemakers govern the evolution of different classes of genes . The biological connotations of the UPM are of major interest . Mapping UPM shifts to specific stages of the evolution of life , changes in the life style and population structure of organisms as well as to the geological record could become an important direction of future research .
Three distinct supertrees ( STs ) were tested for the purpose of representing the vertical inheritance trend in the analyzed set of GTs . The first supertree ( ST1 ) was from [30] ( originally computed using the CLANN program [37]; the second supertree ( ST2 ) was computed using the quartet supertree method [38] for all species quartets in the complete set of GTs the third supertree ( ST3 ) was derived from a tree of concatenated sequences of ( nearly ) universal ribosomal proteins [39] . Maximum Agreement Subtrees ( MAST ) between the supertree ( ST ) and any given gene tree ( GT ) were computed using the agree program of the PAUP* package [40] . The set of MASTs with the analyzed GTs was computed for each of these STs , yielding a total of 43 , 068 MAST leaves for ST1 , 43 , 411 MAST leaves for ST2 and 44 , 889 MAST leaves for ST3 ( MAST ≥10 for each ST ) . Accordingly , ST3 was used for all further analyses as the topology that best represented the entire set of GTs . To perform the LS optimization of the ST edge lengths and the GT relative evolution rates , we used the function fmin_slsqp ( ) that is part of the scipy . optimize package of Python which minimizes a function using sequential least squares programming . The function also adopts a set of constraints that are necessary for the calculation . In both the MC and the UPM models , both the ST edges and the GT rates were constrained to positive values . For the UPM model , the distances from a node to any leaf in a subtree under that node were set equal for all subtrees . It can be shown by induction that this constraint implies an ultrametric tree . Thus , we have a constraint for every internal node; in a rooted binary tree with m leaves , there are m−1 such nodes . Consider a rooted supertree ( ST ) with a fixed topology . The ST encompasses a set of edges e defined by the ST topology and a set of unknown edge lengths t . Consider a set of unrooted GTs reduced to MAST with the given ST . Each GT encompasses a set of edges with known edge lengths and an unknown gene-specific evolution rate ( bk , lk and rk for the k-th GT , respectively ) . Each edge of each GT uniquely maps to an ST path ej , that is a subset of adjacent edges in the ST ( bk , i≡ej where ej⊆e for the i-th edge of the k-th GT ) . Let be the length of the path ej . We assume that the length of the i-th edge of the k-th GT is related to the length of the corresponding ST path ej:where εi , k is the multiplicative deviation factor for the given edge . We further assume that the deviation is random , independent for branches both within and between GTs , and comes from a lognormal distribution with the mean of 1 and an arbitrary variance , translating to a model with an additive normally distributed deviation in the logarithmic scale ( i . e . ln εi , k∼N ( 0 , σ2 ) ) . Given t and r , the expectation for the logarithm of the length of the i-th edge of the k-th GT is:and the likelihood of observing the length li , k is:where E2i , k = ( ln li , k−ln tj−ln rk ) 2 . For all observed edge lengths in all GTs ( l ) , the likelihood function isIn the logarithmic scale:where n is the total number of GT edges ( ) . Designating the residual sum of squares and substituting the estimate for σ2for large n , we obtain:Because n is constant for a given data set , finding the maximum of L ( l | t , r ) is equivalent to finding the minimum of E2 . Least Squares ( LS ) is called linear if the residuals are linear for all unknowns . Linear LS can be represented in a matrix format which has a closed form solution ( given that the columns of the matrix are linearly independent ) . However , our formulation requires taking logs over sums of unknowns in the case where a GT edge corresponds to a path in ST ( ) . Then , the problem becomes non-linear with respect to LS and can be solved only using numerical algorithms where the solution is obtained by iteratively refining the parameter values . This approach requires supplying initial values for the parameters . The goodness of the initial value estimation is critical for the convergence time of the iterative method and the risk of being trapped in local maximum points . We employed the following strategy for determining the initial values: For each ST edge , we computed the mean value of the sum over all GT edges that uniquely correspond to the given edge . Therefore , if we assign one gene a specific rate value ( e . g . the length of some edge ) , we obtain initial rate values for all genes . It can be easily shown that , if there are no errors in rates ( i . e . σ2 = 0 ) , the above procedure yields the accurate ( ML ) values for all unknowns . | A central concept of evolution is Molecular Clock according to which each gene evolves at a characteristic , near constant rate . Numerous studies support the Molecular Clock hypothesis in principle but also show that the clock is indeed very approximate . Genome-wide comparative analysis of phylogenetic trees described here reveals a distinct , more general feature of genome evolution that we called Universal Pacemaker . Under this model , when the rate of evolution changes , the change occurs synchronously in many if not all genes in the evolving genome . In other words , the relative rates of gene evolution remain constant across long evolutionary spans: if a gene is slow relative to the rest of the genes in the given lineage , it is always slow , and if it evolves fast , it is always fast . We show here that the Universal Pacemaker model fits the available data much better than the traditional Molecular Clock model . These findings are compatible with the previously observed accelerations and decelerations of evolution in individual lineages but we show that synchronous , genome-wide change of evolutionary rates is a global feature of genome evolution that appears to pervade the entire history of life . |
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Characterizing the parasite dynamics and population structure provides useful information to understand the dynamic of transmission and to better target control interventions . Despite considerable efforts for its control , vivax malaria remains a major health problem in Peru . In this study , we have explored the population genetics of Plasmodium vivax isolates from Iquitos , the main city in the Peruvian Amazon , and 25 neighbouring peri-urban as well as rural villages along the Iquitos-Nauta Road . From April to December 2008 , 292 P . vivax isolates were collected and successfully genotyped using 14 neutral microsatellites . Analysis of the molecular data revealed a similar proportion of monoclonal and polyclonal infections in urban areas , while in rural areas monoclonal infections were predominant ( p = 0 . 002 ) . Multiplicity of infection was higher in urban ( MOI = 1 . 5–2 ) compared to rural areas ( MOI = 1 ) ( p = 0 . 003 ) . The level of genetic diversity was similar in all areas ( He = 0 . 66–0 . 76 , p = 0 . 32 ) though genetic differentiation between areas was substantial ( PHIPT = 0 . 17 , p<0 . 0001 ) . Principal coordinate analysis showed a marked differentiation between parasites from urban and rural areas . Linkage disequilibrium was detected in all the areas ( IAs = 0 . 08–0 . 49 , for all p<0 . 0001 ) . Gene flow among the areas was stablished through Bayesian analysis of migration models . Recent bottleneck events were detected in 4 areas and a recent parasite expansion in one of the isolated areas . In total , 87 unique haplotypes grouped in 2 or 3 genetic clusters described a sub-structured parasite population . Our study shows a sub-structured parasite population with clonal propagation , with most of its components recently affected by bottleneck events . Iquitos city is the main source of parasite spreading for all the peripheral study areas . The routes of transmission and gene flow and the reduction of the parasite population described are important from the public health perspective as well for the formulation of future control policies .
According to the World Health Organization ( WHO ) , Plasmodium vivax caused about 14 . 2 million malaria cases outside sub-Saharan Africa in 2013 [1] . Despite considerable efforts , Asian and South American countries are still far from achieving malaria elimination [2] . In Peru , the vast majority of malaria cases ( 76% of 64 , 673 ) was reported in the Amazon basin area ( Loreto region ) for 2014 and about 83% of them are due to P . vivax [3] . Many P . vivax infections are asymptomatic and undetectable by microscopy , providing a potentially important reservoir sustaining local transmission [4–7] . In addition , multiple infections recur even after the administration of the WHO-recommended radical cure treatment against blood- and hepatic-parasite stages ( chloroquine and primaquine ) [5 , 6 , 8] . To understand the epidemiology , distribution and transmission dynamics of P . vivax and thus improve its control , it is necessary to unravel the parasite population genetics and dynamics [9 , 10] . Such information would be extremely useful for the monitoring and evaluation of control activities , both in the short and long term [10–12] . The extreme genetic variations in the P . vivax populations has already been reported from several endemic areas [10 , 13] . In the Peruvian Amazon , the few observations available on P . vivax population genetics were collected in small areas ( dispersed villages and communities ) and reported heterogeneous and clonal parasite populations [6 , 14–16] . Hereby , we report the genetic diversity and population genetics of the P . vivax parasite population from the most important urban city in the Peruvian Amazon and 25 villages located around and along the Iquitos-Nauta road .
The clinical isolates were collected during an initial screening of the study sites in the Peruvian Amazon ( April 2008 ) followed by active case detection of all fever cases ( April to December 2008 ) during a longitudinal study assessing the efficacy of the recommended radical cure treatment for P . vivax malaria infection ( chloroquine 25 mg/kg/day for 3 days and primaquine 0 . 5 mg//kg/day for 7 days ) [17] . The study was conducted both within Iquitos city and in 25 neighbouring villages ( Loreto region ) , some of them along the Iquitos-Nauta Road . Villages were geographically stratified in 5 study areas ( A1- A5 ) ( Fig 1A and S1 Table ) : A1 ( 3–7 km northwest from Iquitos city and only accessible by boat ) , A2 ( Iquitos city and peripheral villages ) , A3 ( villages situated along the Iquitos-Nauta Road , 11–13 km southwest from Iquitos city ) , A4 ( villages situated 21 km southwest of Iquitos city and 2–9 km far from the Iquitos-Nauta Road ) and A5 ( villages situated 26–58 km southwest from Iquitos city , most of them along the Iquitos-Nauta road ) . Participants’ demographics are described in S1 Table . In Fig 1B , we describe the main human mobility patterns in the study areas . Most human mobility occurs around A2 ( Iquitos city , economic centre and big markets ) . Indeed , some people commute every day to A2 but live in A1 or A3 , others travel from A5 to A2 during the weekends crossing the Nanay river or come from A4 a couple of times per year navigating along the Itaya river to sell their goods . Some sporadic movement between A1 and A5 , crossing the Nanay river , is observed . The study population consisted primarily of mestizos of low socioeconomic status . Villages located along Iquitos-Nauta road or next to the river had no electricity and in most cases drinking water was taken from the river or natural springs . Malaria transmission is perennial with peaks from November to May ( rainy season ) [18] , and the majority of malaria cases are due to P . vivax . Recurrent sub-patent and asymptomatic infections are frequent [5 , 6] . Anopheles darlingi is the main anthropophilic and exo/endophilic vector [19] . Malaria prevention and control activities were conducted before the sample collection in Loreto region including all our study areas but in A4 ( S1 Table ) as part of PAMAFRO ( Malaria Control Program in Andean-country Border Regions ) which started in 2005 [20] . Patients were examined daily during the treatment and followed up weekly with blood sampling according to WHO guidelines for P . vivax drug efficacy during the first 28 days [WHO , 2009]; and systematic monthly follow-up was carried out thereafter . For the purpose of the current study , all D0 ( before- ) and D1 ( after treatment ) isolates were analysed , including a blood sample for microscopy ( thick and thick film ) and a blood spot on filter paper ( BSFP ) ( Whatman grade 3 , Whatman , Springfield Mill , USA ) . The study was approved by the Ethical Boards of Universidad Peruana Cayetano Heredia , Peru ( Project PVIVAX-UPCH , SIDISI code: 053256 ) , the institutional review board of the Institute of Tropical Medicine Antwerp and the University Hospital of Antwerp , Belgium . Adult participants provided informed written consent , and a parent or guardian of any child participant provided informed written consent on their behalf . All slides were read by microscopy ( thin and thick smear ) to confirm P . vivax infection and estimate the parasite density ( number of asexual parasites for 200 white blood cells ( WBC ) assuming 8000 WBC/μl ) [6 , 21] . Parasite and human DNA was extracted using the Chelex method [22] . P . vivax mono-infections were confirmed by species-specific PCR ( ssPCR ) [23] and genotyped using a panel of 14 well-described microsatellite markers ( MS ) , namely MS1 , MS2 , MS3 , MS4 , MS5 , MS6 , MS7 , MS8 , MS9 , MS10 , MS12 , MS15 , MS16 and MS20 [24 , 25] . Briefly , the DNA extracted from each sample was used to perform a separate PCR for each MS . The forward primer of each MS was labelled with a fluorophore to identify the size of the amplicon through capillary electrophoresis in a 3730 XL ABI sequencer ( Applied Biosystems , Foster City , CA , USA ) [6] . MS PCR was repeated for those isolates with no MS PCR amplification . Some isolates from A1 and A4 were previously analysed and already published [6 , 14] . The allele fragment sizes recovered from the capillary electrophoresis were determined using Genemapper ( Applied Biosystems , Foster City , CA , USA ) . Only fragments with ≥100 relative fluorescence units ( RFU ) were considered as ‘real’ alleles . In case of the presence of two or more alleles , only alleles with RFU ≥30% of the dominant allele RFU were considered for further analysis . The genetic and statistical analysis were performed mainly at study areas level but whenever feasible also at the village level to assess the influence of individual villages on the areas . SPSS for Windows v . 20 ( IBM Corp . , NY ) was used to perform non-genetic statistical analysis .
Overall we have analysed 292 P . vivax isolates ( out of 302 ) with genotyping success on 56 . 2% ( out of 292 isolates ) with all 14 loci , 62 . 3% with 11–13 loci and 81 . 5% with 10 or less loci ( efficiency of PCR amplification for each locus tabulated in S2 Table ) . Most of the isolates , 62 . 7% ( 183/292 ) were monoclonal infections and the polyclonal infections carried a minimum of 2 or 3 different haplotypes ( 34 . 6% and 2 . 7% , respectively ) . The proportion of mono/polyclonal infections significantly differed between areas ( p = 0 . 002 ) , i . e . A1 and A4 presented higher frequency of monoclonal infections while in A2 and A3 the proportions of mono/polyclonal infections were similar ( Fig 2A ) . The median MOI was higher in A2 ( MOI = 1 . 5 ) and A3 ( MOI = 2 ) compared to the other areas ( MOI = 1 ) ( p = 0 . 003 ) . The overall genetic diversity estimates among the areas described a median He = 0 . 74 ( range 0 . 66–0 . 76 ) , median allelic richness = 4 . 6 alleles ( range 3 . 6–4 . 9 ) , and did not differ significantly between areas ( He: p = 0 . 32 ) ; allelic richness: p = 0 . 17 ) , Fig 2B ) . Private alleles were less in A3 and almost absent in A5 than in the other areas ( Fig 2B ) . The level of polymorphism ( He ) of each locus is presented in S2 Table . The AMOVA revealed that most of the genetic variation of the parasite population relied within areas ( 83% ) ( Table 1 ) , though genetic differentiation between areas was also observed ( PHIPT = 0 . 17 , p = 0 . 0001 ) . Pairwise calculations of the genetic differentiation and PCoA between areas and/or villages showed that parasites in the geographically isolated areas A1 and A4 were differentiated compared to those circulating in the other three areas with direct access to the Iquitos-Nauta road ( Table 2 and Fig 3A ) . The first two coordinates of the PCoA explained 59 . 8% of the total variance pointing out clustering of parasites in villages from A1 ( median PHIPT 0 . 02 within A1 ) , presence of related parasites in A2 , A3 and A5 and genetic differentiation of parasites from San Carlos village ( A4 ) ( Fig 3A ) . When considering parasite populations per village , most of the genetic variation actually lied within villages ( 73% of the total genetic variation ) with a high differentiation among the parasites within the villages ( PHIPT = 0 . 27 , p = 0 . 0001 ) , while little differentiation was found between the villages ( PHIPR = 0 . 06 , p = 0 . 004 ) ( Table 1 ) . The PCoA at the individual haplotype level confirmed genetic differentiation among parasites within the same areas even within the same village: i . e . two different groups of haplotypes in San Carlos village ( Fig 3B ) . The genetic distance of the parasite population was not correlated with geographic distance between villages ( using PHIPT matrix Rxy = -0 . 41 p = 0 . 17 ) ( scatterplot of the genetic and geographic distances in S1 Fig ) . In all study areas except for A5 , multilocus linkage disequilibrium ( LD ) was found when all isolates ( MOI≥1 ) or only monoclonal ( MOI = 1 ) isolates were analysed ( IAs = 0 . 08–0 . 49 , for all areas except A5: p<0 . 0001 ) ( Table 3 ) . LD remained in those areas when only unique haplotypes were considered for the analysis ( IAs = 0 . 08–0 . 17 , p<0 . 0001 ) . The number of isolates decreased drastically when only monoclonal infections were considered ( in A3 , from 10 polyclonal isolates to 3 monoclonal isolates ) . Further LD analysis was performed at village-level only for those villages with more than four isolates . LD was found in villages within A1 ( MN , SR and LP , IAs = 0 . 18–0 . 21 p<0 . 0001 ) ; A2 ( VS , IAs = 0 . 38 p<2 . 0x10-04 ) ; A3 ( VA , IAs = 0 . 39 p<1 . 0x10-05 ) and A4 ( SC , IAs = 0 . 52 p<0 . 0002 ) . In contrast to the multilocus LD analysis , the pairwise LD analysis was performed by including isolates with missing alleles and showed presence of LD in all areas . The pairwise LD was found mainly between loci located in different contigs therefore the LD found among loci within the same contigs did not alter the outcome ( Fig 4 ) . 87 unique haplotypes were found ( 52 . 9% of them in A1 ) in 128 isolates without allelic missing data ( Fig 5A; list of haplotypes in S3 Table ) . The two most frequent haplotypes were found 18 and 11 times ( both in A4 ) , followed by 11 haplotypes found 2–4 times ( 10 from A1 and 1 from A3 ) and 74 haplotypes were present only once ( Fig 5A ) . Haplotypes were not shared between areas and only shared within the villages of A1 . The haplotypes were grouped into 9 to 13 genetic clusters when the eBURST criteria was set to 8 to 13 loci with identical alleles . When the criteria was set to 7 loci , eBURST assigned the parasite population into 3 genetic clusters ( PHIPT among clusters = 0 . 25 , p = 0 . 001 ) and 6 singletons , where one cluster accounted for 91% ( 116/128 ) of the isolates . The K-means clustering divided the population into 2 and 3 genetic clusters and significant genetic differentiation between clusters was found ( Rho 0 . 35 and 0 . 23 , respectively ) . STRUCTURE analysis was performed to infer cluster assignment including only isolates with up to 3 missing alleles and with known haplotypes . Using STRUCTURE results HARVESTER predicted the most likely number of clusters being K = 2 , followed by K = 3 , and K = 7 ( Fig 6 ) . Using a threshold of 85% for the assignment of group representatives to each cluster , 70–63% of the isolates were assigned to clusters for K = 2 , K = 3 and K = 7 and the remaining isolates were assigned as admixed parasites ( Fig 5B ) . The AMOVA analysis revealed high differentiation between these genetic clusters ( PHIPT = 0 . 26–0 . 59 ) ( Table 1 ) which were graphically displayed using PCoA ( Fig 5C ) . When the number of clusters was set to K = 2 , parasites from all areas belonged to cluster 2 ( K2: He = 0 . 76; IAs = 0 . 06 p<0 . 0001 ) while 18 isolates from San Carlos village belonged to cluster 1 ( K1: all the isolates shared the same haplotype ) ( Fig 5B ) . Hierarchical structure was subsequently found within cluster 2 where two sub-clusters were found ( PHIPT = 0 . 20 , p<0 . 0001 ) , A1 contained one cluster and hybrid samples while the other cluster was present in all the other areas ( S2 Fig ) . When K = 3 , the former 18 isolates from San Carlos and 11 additional isolates from San Carlos were classified as admixed parasites where their genetic composition shared part of the cluster 1 or 2 with a third cluster . No parasites with >85% ancestry belonging to the third cluster were found within our study . Cluster 1 and 2 contained parasites from all areas ( He = 0 . 77 and 0 . 35; IAs = 0 . 05 and 0 . 11 , for both p<0 . 0001 ) . The extent of genetic diversity and LD within the clusters decreased when K = 7 ( He varied from no diversity to 0 . 74 , median He = 0 . 35 ) and linkage disequilibrium was found for all the clusters ( p<0 . 01 ) . The minimum spanning tree in Fig 5A displayed the phylogenetic relationship among the parasites coloured by its geographic origin . Besides most A1’s parasites were related to other A1’s parasite ( probably due to its larger sample size ) , all the rest of A1 parasites shared a phylogenetic relationship with parasites from other areas . Among A2 and A3 haplotypes , most A2 haplotypes diverged from A3 haplotypes but almost none from A2 to A3 . By the way , the two largest clusters of haplotypes from A4 have diverged from parasites similar to the ones found in A3 while all A5 parasites have diverged from parasites from other areas . The gene flow was assessed evaluating parasite migration models which relied on the combined knowledge of the genetic structuring of the parasite population and the known human mobility patterns ( Figs 1B and 7A ) . Thirteen migration models were evaluated through Bayesian analysis ( marginal likelihoods and LBF of all the models tabulated in S4 Table ) . The high gene flow rates among all areas denoted the model XIII as the best model ( log mL = -47467 . 4 , prob>0 . 99 ) , which describes a single panmictic population ( random mating among the parasites from all the five areas ) with an effective population size of 6 , 891 haplotypes ( credibility interval 95% 4 , 144–9 , 640 ) . Considering the genetic substructuring and asymmetric human mobilization observed for most of the areas which contrast with the panmictic model , the 2nd and 3rd best models which consider 3 and 5 populations with asymmetric migration were also explored in detail ( Fig 7B ) . The 3-population model XI suggests A2 , A3 and A5 as a panmictic unit with unequal migration among A1 to A2/3/5 and few parasite moving from A2/3/5 to A4 ( Nm<1 ) . The 5-population model III adds unidirectional gene flow from A1 to A5 which is in line with the phylogenetic results ( Figs 5A and 7B ) . For Model III the highest rate of gene flow was found between the A2 and A3 , areas which contain parasites with common genetic characteristics ( PCoA and cluster analysis ) , have the highest multiplicity of infection rates compared to the other areas and is consistent with the current people’s mobilization patterns ( Figs 1B , 2A , 3A and 7B ) . Other models were evaluated were A4 was treated as a fully isolated area or with bidirectional gene flow but these models had the lowest probabilities ( Fig 7A and S4 Table ) . However , model V described unidirectional gene flow from A3 to A4 with a high number of migrants ( Nm = 5 . 6 ) probably due to related parasite among A3 and A4 . BOTTLENECK analysis using 10 polymorphic markers showed a significant number of microsatellites had an excess of He in areas A1 , A2 and A3 under IAM ( p<0 . 002 ) and TPM ( only for A2 and A3 , p<0 . 04 ) , indicating a recent bottleneck event and a deficiency of He for A4 under SMM ( p = 0 . 002 ) , possibly indicating a rapid expansion ( S3 Fig ) . However , A1 also presented contrasting He deficiency under SMM ( p = 0 . 002 ) . A5 was not included in the analysis ( n = 4 ) . Similar results were obtained when the analysis was performed separately for perfect and imperfect microsatellites as shown in S3 Fig The presence of excess and deficiency of He in A1 was further investigated by grouping the isolates by village ( whenever the sample size was sufficiently large ) : excess of He in all villages was found but in addition deficit was also found in Manacamiri village ( MA: IAM for imperfect MS: excess p = 0 . 008 and deficit p = 0 . 04; SMM for perfect MS: excess p = 0 . 06 and deficit p = 0 . 03 ) .
MOI , the average number of distinct parasites infecting an individual in a specific area , has been used as a proxy of malaria transmission [12] , and it provides information about the configuration of the parasite population [10] . Overall , monoclonal P . vivax infections were the most frequent , confirming previous studies from the Peruvian Amazon [6 , 14 , 51] . Areas with some degree of isolation , such as areas A1 and A4 , had mainly monoclonal infections , whereas the areas close to the Iquitos-Nauta road ( A2 and A3 ) had polyclonal infections with 2 or 3 different clones and a similar proportion of monoclonal and polyclonal infections . This difference may be explained by a higher mobility rate of the people from A2 and A3 leading to a higher probability of being infected with distinct parasites . In areas with limited gene flow , A1 and A4 , frequent mating between genetically identical or very related parasites may increase the odds that a person is re-infected with the same “clone” , which would result in a low MOI [52] . Moreover , the MOI will be affected by the patterns of hypnozoite activation where the probability of having a homologous activation may be higher in areas with one or few circulating clones compared to areas where unrelated clones are circulating . Previously , varying levels of genetic diversity have been reported in endemic settings of the Peruvian Amazon revealing different transmission patterns [6 , 14 , 15 , 51] . Multivariate analysis ( AMOVA ) indicated that the major source of the genetic variation was due to variation within villages instead of between areas . The greater genetic variation within the villages may be explained by the coexistence of different haplotypes within the villages as results of gene flow , genetic drift and/or a large hidden P . vivax reservoir but also by the high mutability of the microsatellites [53] or overestimation of the number of different haplotypes . The degree of polymorphism of the microsatellites or genotyping errors due to technical artefacts ( false alleles ) may influence the accuracy of defining the presence of one or more haplotypes within an infection [54] . In our study the levels of genetic diversity remained at intermediate levels with no substantial differences between areas . The high rates of gene flow found may have increased the levels of He . In the present study we reported coexistence of LD and extensive diversity also reported in other studies [55–57] . In the case of repeat-sequence in tandems like the microsatellites , the level of genetic polymorphism ( ‘genetic diversity’ ) may be maintained or increased due to the appearance of new alleles by mutational events during replication in the host cells without affecting the LD [55 , 57] . Considering long-term P . vivax infections in the Peruvian Amazon the rate of allele mutations occurring within the host needs to be further explored . On the other hand , the extent of LD could be overestimated due to some of the recruited vivax patients were living in the same household [57] . The presence of LD and relatively high genetic diversity may be also favoured by a scenario where inbreeding of few sympatric divergent parasites is frequent [58] . Similarly to other P . vivax populations from South America [11 , 53 , 58–62] , the presence of LD and low MOI indicated a clonal propagation type in our study population . The low malaria transmission and/or the long-term vivax infections in the Peruvian Amazon may also favour the predominant clonal propagation [6] . Passive clonality due to restrained diversity and low gene flow , a different scenario to what we found in the present study , may not be the only type of clonal propagation occurring in the study areas . Tibayrenc and Ayala ( 2014 ) coined the ‘in-built , active clonality’ whereas P . falciparum despite the possibility of recombination with sympatric unrelated clones P . falciparum would prefer self-fertilization in order to gain biological and evolutionary adaptations to its environment [52] . The high rate of gene flow and high variability within the study villages drive us to consider that P . vivax may have also in-built , active clonal behaviour in order to take advantage during its adaptation to the environment/hosts . Further study is needed to verify the in-built clonal behaviour and its impact on the epidemiology . Genetic clustering approaches revealed the presence of at least two or three independent parasite clusters in our study population . We rerun STRUCTURE within one cluster of the cluster when K = 2 to: ( 1 ) look for hierarchical structure and; ( 2 ) to avoid misleading cluster assignment [37] . Sub-structured parasite population was confirmed in areas A1 and A4 and possibly misleading initial STRUCTURE assignment of the clusters ( this is also graphically detected in Fig 5C ) . The cluster 1 , which contained 18 isolates carrying the same haplotype , may influence the misleading assignment: STRUCTURE tends to group strongly related samples into one cluster and the rest of samples are assigned in a large cluster [37] . Little to moderate differentiation among the parasites was found in the urban areas A2 , A3 and A5 ( “urban cluster” ) where continuous human mobilization and parasite gene flow on the Iquitos-Nauta Road occur . Conversely , different clusters of parasites were found circulating in the area A1 and A4 increasing the levels of genetic differentiation . In the San Carlos village ( A4 ) was detected a group of parasites circulating only within this village ( Fig 6 ) . Major divergence of these parasites may have occurred due to the limited gene flow , genetic drift , bottleneck events , selection and/or recombination with imported parasites not being sampled in this study . The LD and the deficiency of He detected in A4 confirmed the rapid expansion of these clones with clustered transmission previously described after 2-year follow up [6] . Determining the transmission patterns is a priority for the implementation of control and elimination programs [63] . Our initial analysis revealed no genetic isolation of the study areas despite the geographic distance indicating that exist gene flow . Overall , few private alleles were found among areas where A3 and A5 had the lowest numbers supporting gene flow among the areas where A3 and A5 are beneficiated with more immigrants ( parasites ) . However due to sample size bias on the calculation of private alleles we further used a Bayes approach where our sample size was not anymore an issue . We evaluated 13 migration models , using a Bayes approach based on the coalescence theory , which were proposed under the assumptions of current human mobilization and the genetic structuring data . The panmictic model was inferred as the best model , which is in line with the AMOVA results ( less genetic differentiation among areas compared to within the areas ) . However , due to geographic constrains it is unlikely that people mobilizes to every area while parasites mate randomly . Possibly our genetic data was not strong enough to distinguish the best model: i . e . recent bottleneck events may affect the analysis [45]; however still we were able to recover two models that proposed a better explanation regarding the transmission dynamics and genetic structuring . Both models ( XI and III ) agreed that area A2 ( for model XI: A2/3/5 ) is the source of parasites spreading for all the other areas: A2 comprises Iquitos city , place where people from A1 and A3 transmutes to every day , crossing the Nanay river by boat or going by car/bus/motorbike through the Iquitos-Nauta Road , for economic activities ( Figs 1A and 1B and 7B ) . The influx of people from A1 to A2 is drives most of the parasite influx among these areas where people from A1 may be infected in A2 then importing parasites from A1 to A2 when they return to their households . Model XI described a panmixia between A2 , A3 and A while the model III showed that most of migration occurs between A2 and A3 , favoured by the proximity and the road ( no geographic barrier ) in line with the shared genetic parasite characteristics found previously . In line to the phylogenetic analysis , the model III described that A5 have influx of parasites from A2 which could occur during the visit of people from A5 to A1 on weekends to sell their products in the markets of Iquitos . Model III also described influx of parasites from A1 to A5 despite the Nanay river isolate both areas . High rates of parasite migration from A2 to A3 and A5 is in line with low ( or none ) number of private alleles recorded for A3 and A5 . Regarding to area A4 , is it known that occasionally people from A4 ( especially from San Carlos ) travels through the Itaya river to the A2 to sell their products and that may explain the importation of parasites from A2 to A4 . As mentioned before , some parasites from A4 are related to parasites from A3 , which in turn are highly related to A2 parasites . The relatedness of A4 and A3 parasites ( Network analysis ) and migration of parasites from A3 to A4 ( referred only in model IV ) may have occur by unknown human migration patterns or events where the vector mobilizes among these areas or genetic divergence of A3 parasites into the current A4 parasites . We have documented parasite transmission from A2 to the other areas but not significant immigration of parasites towards A2 which explains why A2 has the smallest population size ( number of haplotypes ) compared to the other areas ( S5 Table ) and why we still found significant pairwise genetic differentiation against A1 and A4 . In addition to the low rate of immigrants to A2 , recent bottleneck events may have also negatively affected the effective population size in A2 . By describing the parasite structure , genetic diversity and dynamics , population genetics can also contribute in assessing the impact of an intervention [10] . Before our sample collection in 2008 , the PAMAFRO project which involved campaigns of malaria prevention and control program with active case detection and treatment as well as distribution of insecticide-treated mosquito nets was carried out in Loreto ( including all our study areas except for area A4 ) , resulting in a 49% drop of the incidence of clinical vivax malaria from 2005 to 2008 [20] . The expected impact of the intervention on the parasite population besides lowering the malaria incidence would be a reduction of the effective parasite population size , the so called bottleneck effect . Since no data prior to the intervention on the effective parasite population size were available , we performed a retrospective analysis looking for recent bottleneck effect . A parasite population having experienced a recent bottleneck shows a faster decline in the number of alleles compared to a He reduction because rare alleles will be lost with little influence on the He [47] . The predominant clonal propagation found in this study did not affect the Bottleneck analysis since moderate He and significant intra-area genetic variation were found . Only areas with n>15 isolates ( S3 Fig ) were used for the analysis to increase the resolution power as described by Luikart et al . [64] . In this study , bottleneck events were detected in all areas where control interventions were implemented . San Carlos village did not benefit of any control activity before the sample collection , and no bottleneck but rather a rapid clonal expansion was observed . This is the first report of bottleneck events for P . vivax population in the Peruvian Amazon following the implementation of prevention and control activities . Noteworthy that the reduction of malaria cases in Peru lasted only until 2011 coinciding with the finalization of the PAMAFRO project and since then there has been an steady increase of malaria cases: i . e . Loreto region reported 11 , 779 vivax malaria cases in 2011 and 60 , 566 in 2014 [3 , 65] . Further and continuous monitoring of the population structure and dynamics of the parasite population is necessary to understand the factors that are involved in the evolution of malaria in the Peruvian Amazon . The detection of recent bottlenecks in the parasite population could be used as complementary tool to measure the efficacy and impact of malaria control programs . In conclusion , we have elucidated the population genetics of Plasmodium vivax in a large geographical area in and around Iquitos , the main socio-economic capital city of the Peruvian Amazon . We have shown the use of a Bayes approach to infer the gene flow pattern among our study areas and the detection of the reduction of the population size as a result of a control program . The knowledge about the routes of malaria transmission ( gene flow ) and the effect of control policies on the parasite population is a priority from the public health perspective as well for the formulation of future control policies and assessment of current control/elimination strategies . | We present the population genetics of malaria vivax parasites in a large area of the Peruvian Amazon . Our results showed that the parasite population had a predominant clonal propagation , reproducing themselves with identically or closely related parasites; therefore , the same genetic characteristics are maintained in the offspring . The clonal propagation may favour the higher levels of genetic differentiation among the parasites from isolated areas compared to areas where human migration is common . The patterns of gene flow have been established , finding Iquitos city as a reservoir of parasite genetic variability . Moreover , a recent reduction of the parasite population was observed in areas where recent control activities were performed . This research provides a picture of the nature and dynamics of the parasite population which have a significant impact in the malaria epidemiology; therefore , this knowledge is crucial for the development of efficient control policies . |
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Although there have been great advances in our understanding of the bacterial cytoskeleton , major gaps remain in our knowledge of its importance to virulence . In this study we have explored the contribution of the bacterial cytoskeleton to the ability of Salmonella to express and assemble virulence factors and cause disease . The bacterial actin-like protein MreB polymerises into helical filaments and interacts with other cytoskeletal elements including MreC to control cell-shape . As mreB appears to be an essential gene , we have constructed a viable ΔmreC depletion mutant in Salmonella . Using a broad range of independent biochemical , fluorescence and phenotypic screens we provide evidence that the Salmonella pathogenicity island-1 type three secretion system ( SPI1-T3SS ) and flagella systems are down-regulated in the absence of MreC . In contrast the SPI-2 T3SS appears to remain functional . The phenotypes have been further validated using a chemical genetic approach to disrupt the functionality of MreB . Although the fitness of ΔmreC is reduced in vivo , we observed that this defect does not completely abrogate the ability of Salmonella to cause disease systemically . By forcing on expression of flagella and SPI-1 T3SS in trans with the master regulators FlhDC and HilA , it is clear that the cytoskeleton is dispensable for the assembly of these structures but essential for their expression . As two-component systems are involved in sensing and adapting to environmental and cell surface signals , we have constructed and screened a panel of such mutants and identified the sensor kinase RcsC as a key phenotypic regulator in ΔmreC . Further genetic analysis revealed the importance of the Rcs two-component system in modulating the expression of these virulence factors . Collectively , these results suggest that expression of virulence genes might be directly coordinated with cytoskeletal integrity , and this regulation is mediated by the two-component system sensor kinase RcsC .
Salmonellae remain major global pathogens causing a broad spectrum of disease ranging from gastroenteritis to typhoid fever [1] , [2] . The emergence of multidrug resistant salmonellae is complicating the management of disease [3] , [4] . Hence , there is an urgent need to identify novel bacterial targets for the development of new antimicrobial agents or vaccines to combat infection . The view that bacteria do not possess a cytoskeleton has radically changed in recent years with the discovery of intracellular filamentous protein assemblies with cell-shape defining function [5] . Although there is little primary sequence identity between eukaryotic cytoskeletal proteins and those in prokaryotes , proteins with actin- and tubulin-like structural motifs have been identified in bacteria . Bacterial cytokinesis is dependent on FtsZ which contains a structural fold mirroring tubulin . FtsZ displays similar dynamic properties to tubulin and is able to polymerise unidirectionally in a GTP-dependent manner to produce polymeric filaments [6] , [7] . Polymers of FtsZ are able to assemble into a transient helical structure and subsequently form a ring-like structure around the circumference of the mid-cell [8] . This Z-ring is required for recruiting proteins for the assembly of the cell division complex [8] . The intermediate filament-like protein crescentin determines the vibroid shape of Caulobacter crescentus cells [9] . The bacterial proteins MreB , Mbl , and ParM display the structural and dynamic properties of eukaryotic actin [10] . Amongst these proteins , MreB is the most homologous to actin in terms of primary sequence , structure , and size [11] , [12] . The most conserved region of this actin-superfamily is the ATPase domain . MreB can polymerise into helical filamentous structures important for cell morphology . Live cell microscopy in Bacillus subtilis revealed that MreB forms large cables which follow a helical path close to the cytoplasmic membrane [5] . An equivalent MreB protein has been found in Escherichia coli . When MreB is depleted , rod-shaped B . subtilis and E . coli cells become spherical [5] , [13]–[15] . In C . crescentus MreB has been implicated to play a role in the control of cell polarity [16] . In rod-shaped bacteria the MreB polymeric structures control the localisation of cell wall growth by providing a scaffold for enzymes involved in cell wall assembly [17] . The MreB operon in E . coli and B . subtilis encodes for a number of additional genes , which do not possess any similarity to actin [18] . These include the cellular membrane proteins MreC and MreD , which also have a helical disposition . MreC forms a dimer and interestingly in C . crescentus MreC is localised in spirals in the periplasm [19] . Recent studies by Rothfield and colleagues provide convincing evidence to suggest that in E . coli MreB , MreC and MreD form helical structures independently of each other [20] . Using affinity purification and bacterial two hybrid assays , MreC and MreD appear to interact together [13] . In E . coli there is evidence to suggest that MreB interacts with MreC , but this may not be the case in Rhodobacter sphaeroides or C . crescentus [21] . As well as playing a key role in cell morphogenesis , MreB also has a pivotal function in chromosome segregation [22]–[24] . Adding the MreB inhibitor A22 [S- ( 3 , 4-Dichlorobenzyl ) isothiourea] to synchronised cultures of C . crescentus inhibited segregation of GFP-tagged chromosomal origins [22] . However MreB may not function in chromosome segregation in Bacillus [15] . Recently another helically distributed cytoplasmic membrane protein which interacts with MreB named RodZ has been identified [25]–[27] . Cellular components including the RNA degradosome and lipopolysaccharide have also been identified to be localised in helical structures within the cell [28] , [29] . In spite of these major advances in our understanding of the structure and organization of the bacterial cytoskeleton , there are major gaps in our knowledge of its role in bacterial pathogenicity . In this study we wished to gain insights into understanding the function of the bacterial cytoskeleton in the pathogenicity of Salmonella .
The in vivo experiments were covered by a Project License granted by the Home Office under the Animal ( Scientific Procedures ) Act 1986 . This license was approved locally by the University of Cambridge Ethical Review Committee . S . Typhimurium SL1344 and mutant derivatives used in this study are described in Table 1 . Strains were routinely grown in Luria-Bertani ( LB ) broth with appropriate antibiotics at the following concentrations: ( kanamycin 50 µg ml−1 ) , ampicillin ( 100 µg ml−1 or 30 µg ml−1 for pNDM220 ) . A22 ( Calbiochem ) was added at 10 µg ml−1 . Bacteria were grown overnight in 5 ml LB , before 25 µl of culture was used to inoculate 25 ml of fresh LB in a 250 ml flask and grown at 37°C shaking ( 200 rpm ) unless otherwise stated . ΔmreC was maintained in media containing 100 µM IPTG , however for phenotypic testing this was removed unless otherwise mentioned . For the SPI-1 T3S studies cells were grown overnight in LB before subculturing 1/100 into 25 ml fresh LB and growing at 37°C for approximately 5 hrs with good aeration until OD600nm∼1 . 2 in 250 ml flasks [30] . For the SPI-2 T3S studies cells were grown in SPI-2 induction media ( 100 mM Tris-base , 0 . 1% w/v casamino acids , 0 . 1% w/v glycerol , 10 µM MgSO4 , 40 µg ml−1 histidine , pH 5 . 8 ) . Cells were grown overnight in LB before subculturing 1/100 in 25 ml SPI-2 inducing media before growing for 16 h at 37°C in 250 ml flasks before sampling . Cells were inoculated from a fresh LB plate onto the semi-solid motility agar ( 10 g l−1 Bacto-tryptone , 5 g l−1 NaCl , 3 g l−1 agar ) and incubated upright for a minimum of 5 h . Distinct zones of cell motility were measured and compared to WT SL1344 and non-motile SL1344 strains . Chromosomal gene deletions were constructed using the lambda Red method as described previously [31] , before transducing the mutation into a genetically clean parent strain using bacteriophage P22int . In the case of ΔmreC and ΔmreD the mutations were transduced into a parent strain containing pTK521 ( lac-mreBCD E . coli ) to complement the mutation in the presence of 100 µM isopropyl beta-D-1-thiogalactopyranoside ( IPTG ) . Gene deletion primers typically encompassed the first and final 20 bases of the coding sequence of the respective gene were synthesised . However , as the mreC and mreD gene coding sequences overlap by a single base , to ensure only a single coding sequence was disrupted the respective mreC 3′ primer and mreD 5′ primer were moved internally into their coding sequence such as to produce no overlapping mutations . Gene deletions for the two-component systems ( ΔqseF , ΔphoBR , ΔyjiGH , ΔbaeSR , ΔbasSR , ΔhydH , ΔqseBC ΔtctDE , ΔcpxAR , ΔrcaA , ΔrcsB , ΔrcsC , ΔrcsD , ΔrcsDB , and ΔrcsCBD ) , were constructed in SL1344 WT using classical lambda Red methods before transducing into the ΔmreC strain using bacteriophage P22int . Primers are listed in Table 2 . GFP was amplified from pZEP08 and cloned along with a new multiple cloning site into the EcoRI and HindIII sites of pBR322 to create pBR322GFP . mreB along with its natural promoter was amplified from genomic DNA and cloned into the EcoRI and XbaI sites of pBR322GFP , before the mreB-gfp fusion was subcloned from the pBR322mreB-gfp into pNDM220 using the EcoRI and BamHI sites . Flagella and SPI1 transcriptional reporter plasmids were transformed into SL1344 and ΔmreC mutant cells . Expression from the lux transcriptional reporters was measured during the growth cycle of 10−3 diluted overnight cultures cells grown in microtitre plates ( 200 µl total volume ) for a minimum of 15 h at 37°C with periodic shaking . Optical density ( 600nm ) and relative luminescence was measured at 15 minute intervals using a Tecan Infinity200 luminometer . Samples were tested in triplicate , and repeated at least 3 times . The hilA and rcsC open reading frames were amplified from SL1344 genomic DNA and cloned into the EcoRI and XbaI or the EcoRI and HindIII sites of pBAD24 to create pBADhilA and pBADrcsC respectively . Whole cell total protein samples were obtained by pelleting an appropriate volume of bacterial culture , followed by resuspension in SDS-loading buffer and boiling for 10 mins . Culture supernatants were filter sterilized ( 0 . 22 µm ) and proteins were ammonium sulphate precipitated ( 4 g 10 ml−1 supernatant ) overnight at 4°C . Precipitated secreted proteins were resuspended in H2O and then combined with an equal volume of sample buffer ( Biorad ) . Whole cell and culture supernatant samples were run on 12% SDS/PAGE and transferred on Protran nitrocellulose transfer membranes ( Schleicher & Schuell ) using a wet transfer apparatus ( Biorad ) . Western blot analysis was performed using polyclonal SipA , SipB , SipC or PrgH for testing SPI-1 T3S functionality , coupled with a goat anti-mouse horseradish peroxidase-labelled secondary antibody ( Dako Cytomation ) . Detection was carried out using 4-chloro-1-naphthol ( Sigma ) according to the manufacturer's instructions . Female C57BL/6 mice were purchased from Harlan Olac Ltd . , ( Blackthorn , Bicester , UK ) . Mice were used when over eight weeks of age . Bacterial suspensions for injection were grown for 16 h as a stationary culture at 37oC in LB broth . Bacteria were diluted in PBS prior to injection into a lateral tail vein . Mice were killed by cervical dislocation and the livers and spleens aseptically removed . Each organ was homogenised ( separately ) in a Seward Stomacher 80 Biomaster ( Seward ) in 10 ml of distilled water and viable bacterial counts in the homogenate were assayed on pour plates of LB agar . Representative bacterial colonies were kept and re-tested for phenotypic changes . Wild type Salmonella SJW1103 cells with chromosomal N-terminal GFP fusion to fliG ( YVM004 ) [32] were P22 transduced with the mreC::kan mutation to create YVM004 ΔmreC . This strain , along with the WT control , was subsequently transduced with a chromosomally-based inducible flhDC locus derived from TH2919 [33] . Cells were grown to the appropriate growth phase ( mid-log for SPI-1 and flagella , or stationary phase for SPI-2 ) in relevant media ( LB or SPI-2 inducing media ) . Flagella visualisation strains ( fliG-gfp ) , were mounted on 1% agarose beds for imaging . Samples for visualising the type 3 secretion apparatus were fixed in 4% paraformaldehyde diluted in PBS for 1 h before washing for 15 minutes in three changes of PBS . Samples were incubated with either αSipA , αSipB , αSipC , αSipD ( SPI-1 ) or αSseB ( SPI-2 ) antibodies diluted 1∶1000 in PBS for 3 h with gentle agitation . Samples were subsequently washed in PBS before incubating in 1∶1000 Alexa Fluor 488 conjugated goat anti-rabbit antibody ( Invitrogen-Molecular Probes , Paisley , U . K . ) , washed for 30 mins in fresh PBS before mounting onto agarose beds . Half of each organ was fixed overnight in 4% paraformaldehyde diluted in PBS , washed for 90 min in three changes of PBS and then immersed in 20% sucrose ( in PBS ) for 16 h at 4oC before being embedded in Optimal Cutting Temperature ( OCT ) ( Raymond A Lamb Ltd , Eastbourne , U . K . ) in cryomoulds ( Park Scientific , Northampton , U . K . ) . Samples were frozen and stored at -80oC . 30 µm sections were cut , blocked and permeabilised for 10 min in a permeabilising solution containing 10% normal goat serum and 0 . 02% Saponin in PBS ( Sigma , Poole , UK ) . Sections were stained with 1∶1000 dilution of rat anti-mouse CD18+ monoclonal antibody ( clone M18/2 , BD Pharmingen ) , together with a 1∶500 dilution of rabbit anti-LPS O4 agglutinating serum ( Remel Europe Ltd ) , for 16 h at 4oC . Subsequently , sections were washed in PBS then incubated with 1∶200 Alexa Fluor 568-conjugated goat anti-rat antibody ( Invitrogen-Molecular Probes , Paisley , U . K . ) and a 1∶1000 dilution of Alexa Fluor 488-conjugated goat anti-rabbit antibody ( Invitrogen-Molecular Probes , Paisley , U . K . ) . All sections were mounted onto Vectabond-treated glass slides ( Vector Laboratories Ltd . ) using Vectashield containing DAPI ( Vector Laboratories Ltd . ) . All phase contrast and fluorescence images were captured using an Andor iXonEM+ 885 EMCCD camera coupled to a Nikon Ti-E microscope using a 100x/NA 1 . 4 oil immersion objective . Images were acquired with NIS-ELEMENTS software ( Nikon ) and processed using ImageJ . Fluorescence images were deconvolved using Huygens Deconvolution software ( Scientific Volume Imaging ) . Cell measurements were taken on a Nikon Ti-E microscope with NIS-ELEMENTS software . Immunofluorescence images from tissue sections were analysed multi-colour fluorescence microscopy ( MCFM ) using a Leica DM6000B Fluorescence microscope running FW4000 acquisition software . The effect of Salmonella infection on transepithelial resistance ( TER ) was determined for differentiated Caco-2 cells as previously described [34] . Briefly , the Caco-2 cells were grown on transwell inserts ( Corning , UK ) until differentiated ( 12–14 days ) , before the transepithelial resistance was measured for each well . Salmonella strains were then added to the cells at a multiplicity of infection ( MOI ) of 20 , and the cells incubated for 4 h . TER measurements were taken every hour and the results given as a ratio of TER ( t ) / ( t0 ) to show the percentage change in TER over the course of the experiment . Data were collated and analysed for statistical differences ( Student's t-test ) in Minitab . Samples for the assay of translocated effector proteins were isolated from differentiated Caco-2 cells grown in 6 well plates after infection with an MOI of 20 for 4 h . Excess bacteria were washed off before the cells were solubilised in 0 . 01% Triton X-100 and centrifuged to remove bacteria and host cell membranes . The host cell cytoplasmic fractions were analysed by western blotting with αSipB antibody .
We wished to identify and characterise putative Salmonella cytoskeletal gene homologues . A BLAST search of the S . Typhimurium genome sequence database ( www . ncbi . nlm . nih . gov ) [35] for the known E . coli actin-homologue MreB identified a putative mre operon of high sequence identity . Comparison of the Salmonella genes to those of E . coli showed 100% ( mreB ) , 88% ( mreC ) and 94% ( mreD ) homology at amino acid level , comparisons of these same genes to those in B . subtilis revealed sequence homologies of 57% , 24% and 27% respectively . In order to determine the localisation of MreB in Salmonella , vectors expressing N and C terminal fusions of MreB to GFP were used . The N-terminal fusion plasmid has already been described [36] , and we constructed a C-terminal fusion vector . Both constructs revealed a helical distribution of MreB along the long axis of the cell . The helices were discerned by assembling a series of z-stack images taken in successive planes by using Metamorph imaging and Huygens deconvolution software ( Figure 1A ) . The mreB gene appears to be essential in bacteria including Salmonella ( data not shown ) , and ΔmreB viable cells often contain compensatory mutations [37] . Each of the components of the cytoskeletal complex , for example MreB , MreC , or MreD , are essential for its function . As an alternative strategy to study the function of the cytoskeleton we therefore generated a mreC depletion strain under conditions designed to minimise selective pressures for undefined secondary compensatory mutations [37] . Using the lambda Red one-step gene disruption method , we successfully constructed a mreC::kan mutant in the S . Typhimurium wild-type strain SL1344 [31] . This mutation leaves intact the first gene in the operon mreB . Using bacteriophage P22int the mreC::kan mutation was then transduced into a genetically “clean” SL1344 strain harbouring plac-mre operon ( pTK521 ) [14] and the resulting strain designated ΔmreC . The plac-mre operon is a low copy number plasmid expressing the mre operon from the IPTG-inducible lac promoter . The identity of the mutation was confirmed by PCR and DNA sequencing . Expression of MreC was assessed by western blotting in the mutant strains , revealing no detectable levels MreC unless complementation was induced ( Figure S1 ) . In addition to the ΔmreC mutant , the lambda Red method was used to generate ΔmreD . When the morphology of the ΔmreC mutant was examined microscopically , the cells were no longer rod-shaped but spherical ( Figure 1B ) . Upon the addition of IPTG the morphology of the ΔmreC strain was restored to the wild-type rod shape . Under microscopic examination the ΔmreD mutant displays a similar morphological phenotype to the ΔmreC . WT cells were measured to be on 1 . 61 ( +/−0 . 49 ) µm in length and 0 . 75 ( +/− 0 . 17 ) µm in width , whereas the ΔmreC cells were 2 . 03 ( +/−0 . 60 ) µm in length and 1 . 21 ( +/−0 . 41 ) µm in width . Complementation of the ΔmreC mutant with 100 µM IPTG resulted in wild type shaped cells 1 . 82 ( +/−0 . 44 ) µm in length and 0 . 78 ( +/−0 . 24 ) µm in width . Measurements were taken from a minimum of 350 cells per strain . Growth rates of the strains were determined in LB media at 37°C revealing a ∼50% increase in the lag phase of the ΔmreC mutants ( Figure S2 ) , which subsequently grow at a comparable rate to that of the wild type or complemented mutant strains during log phase . The motility phenotype of ΔmreC was examined on semi-solid agar plates . In contrast to the isogenic parent , the ΔmreC cells were no longer motile . Surprisingly , this motility defect has not been reported in either E . coli or B . subtilis . Cellular and secreted proteins of the parent SL1344 and ΔmreC were examined by SDS-PAGE and western blotting using antibodies directed against the phase-1 and phase-2 flagellin subunits FliC and FljB . Neither of these subunits were present in either the secreted or cellular proteins , explaining the inability of the cells to swim ( data not shown ) . The non-motile phenotype was fully complementable in trans upon the addition of IPTG to the mutant strain harbouring pTK521 ( Figure S3 ) . We observed that the Salmonella ΔmreC depletion strain was non-motile and failed to express flagella subunits FliC or FljB . The regulation and assembly of flagella in Salmonella is complex . Flagella genes are arranged into 14 operons and their transcription is organised into a regulatory hierarchy of early ( class I ) , middle ( class II ) , and late genes ( class III ) [38] . The class I flhDC operon is the master regulator , with FlhD and FlhC forming a heterotetramer that is required for transcriptional activation of the class II genes , which encode the hook-basal body complexes and the alternative sigma factor FliA ( sigma28 ) . FliA alone or with FlhDC , activates expression of the class III operon genes , which encode the filament protein , hook-associated proteins , motor proteins , and chemotaxis proteins [39] , [40] . The class III genes are further subdivided into fliA-independent expression class IIIa or class IIIb [41] . In order to systematically investigate the mechanistic basis for the ΔmreC motility phenotype we have taken selected class I , II , and III regulated flagella gene promoter fusions to a luciferase reporter gene , and monitored their expression by luminescence in wild type and ΔmreC strains . Constructs with flhD ( class I ) , fliA , flgA , ( class II ) , and fliC ( class III ) promoters fused to the luciferase reporter gene were used . The reporter plasmid pSB401 has a promoterless luxCDABE operon and was used as a control . The class I flhD promoter displayed a reduction in the level of expression in ΔmreC compared to the wild-type strain suggesting the class I promoter has reduced activity . Notably greater changes in the expression profiles occur in other class II and class III genes . The class II promoters for the operons encoding the transcriptional regulators fliAZY and flgAM display significant reductions in expression levels in ΔmreC ( Figure 2 ) . As predicted from the western blotting data expression of the fliC class III promoter was significantly reduced . Collectively , the promoter-reporter activity data can account for the motility defect . Type 3 secretion systems are essential for the virulence of a range of pathogens including Salmonella [42] , [43] . The secretion apparatus assembles into a supramolecular needle-complex . Secreted effector proteins in the bacterial cytoplasm traverse through the needle-complex and the bacterial multi-membrane envelope , directly into host cells [44]–[46] . The apparatus anchors to the cell envelope via a multi-ring base . Salmonella possess two T3SS's encoded by pathogenicity islands ( SPI's ) . The SPI-1 T3SS is important for invasion of intestinal epithelial cells and the SPI-2 T3SS plays a central role in survival within the hostile environment of a macrophage . The SPI-1 T3S system translocates virulence effector proteins into the cytosol of epithelial cells resulting in rearrangements of the actin cytoskeleton which enable Salmonella to invade [47] . To investigate whether the mreC mutation has an impact on SPI-1 T3S , we used western blotting to determine the presence and functionality of the system using antibodies to an apparatus protein PrgH as well as the effector proteins SipA and SipC , in both SL1344 and ΔmreC . In contrast to the wild-type SL1344 , the T3S structural and effector proteins were not expressed in the cellular or secreted fractions from the ΔmreC depletion mutant ( Figure 3A ) . This suggests that SPI-1 T3S in the ΔmreC mutant is not fully functional . The expression and secretion phenotypes were fully complementable in trans upon the addition of IPTG ( data not shown ) . The functional assembly of SPI-1 T3SS was also confirmed using transepithelial resistance ( TER ) assays in differentiated Caco-2 cells , showing a reduced ability to disrupt epithelial tight junctions in the ΔmreC mutant compared to the wild type strain ( Figure 4 ) . To further assess the disruption of the functionality of the SPI-1 T3S , a translocation assay was performed in Caco-2 cells infected with the strains . Host cell cytoplasmic proteins were probed for the bacterial effector protein SipB using western blotting ( Figure S4 ) . This revealed the inability of the ΔmreC mutants to infect host epithelia and disrupt their tight junctions . In addition , ΔmreC was fully complementable in this assay following IPTG induction . The SPI-2 T3SS is pivotal for the establishment of the Salmonella containing vacuole ( SCV ) inside macrophages and subsequent survival [43] . We next investigated the effect of the ΔmreC mutation on the functionality of the SPI-2 T3SS . The strains were grown under SPI-2 inducing conditions and the T3S of the translocon protein SseB monitored . SseB together with SseC and SseD function as a translocon for other effector proteins and SseB is normally found associated with the outer surface of Salmonella . Thus membrane fractions were purified to monitor expression and T3S by western blotting . This revealed that in contrast to the SPI-2 negative control ( ssaV ) , SseB was secreted and associated with the bacterial membrane surface in both the wild-type and ΔmreC strains ( Figure 3A ) . This provides qualitative evidence to suggest that in contrast to the SPI-1 T3SS , the SPI-2 T3SS appears to remain functional . Several environmental signals and transcriptional factors modulate expression of the SPI-1 T3SS . We wished to understand the mechanistic basis by which expression of the SPI1-T3SS is down-regulated . Within SPI-1 there are key transcriptional activators which regulate expression of SPI-1 genes: HilC , HilD , HilA , and InvF . Both HilC and HilD activate expression of SPI-1 genes by binding upstream of the master regulatory gene hilA to induce its expression[48] . HilA binds and activates promoters of SPI-1 operon genes encoding the type 3 secretory apparatus , several secreted effectors , and the transcriptional regulator InvF . InvF activates expression of effector genes inside SPI1 and also effector genes outside SPI-1 such as sopB and sopE [47] . Expression of selected SPI-1 T3SS genes was monitored using transcriptional promoter reporters in ΔmreC , using constructs harbouring the hilA , hilC , hilD , invF and sopB promoters fused to the promoterless luxCDABE operon that produces light in response to gene expression [49]–[51] . Each construct was introduced into both wild-type SL1344 and ΔmreC depletion mutant , and the level of expression of the promoters in these strains monitored by luminescence assays . WT SL1344 and ΔmreC cells harbouring pCS26 or pSB401 vectors alone were used as controls , and did not produce any luminescence as expected . The reporter assays revealed that the SPI-1 transcription factor gene promoters for hilA , hilC , hilD , and invF were completely inactive in ΔmreC in contrast to the wild-type strain . However the promoter of sopB located in SPI-5 remained active but its activity was marginally lower than in the wild-type strain ( Figure 3B ) . The regulation of many T3SS genes often require multiple signals for maximal expression and clearly other signals remain in the ΔmreC depletion mutant which drive expression of the SopB in SPI-5 . Expression of SPI-2 T3SS genes were monitored using a transcriptional reporter for the SPI-2 gene ssaG , whose promoter was cloned upstream of the luxCDABE luciferase operon in the plasmid pMK1-lux [52] . The construct was transformed into wild-type SL1344 and ΔmreC , and the luminescence and OD600 measured during growth in SPI-2 inducing conditions ( Figure 3B ) . The ssaG promoter remains active in the ΔmreC mutant although expression appears to be delayed , and is marginally less than in WT . This evidence supports the western blot data with αSseB and suggests that in contrast to the SPI-1 T3SS , the SPI-2 T3SS remains functional in the absence of the cytoskeleton . Two-component regulatory systems are vital in sensing environmental and cell surface signals , enabling bacteria to rapidly adapt to ever changing conditions [53] , [54] . These signals are detected by histidine protein sensor kinases , which subsequently transfer phosphate groups to an aspartate residue in the response regulator proteins , thus modulating their regulatory activities . The environmental signals are thus transmitted by a phosphorelay system to regulate gene expression . In order to identify putative regulators of the ΔmreC observed phenotypes , we have constructed knockout mutations in a range of two-component systems . As an initial screen , a panel of nine separate two-component system mutant strains were constructed as double mutants with ΔmreC . One two-component system sensor kinase mutation ΔrcsC resulted in recovery of SPI-1 effector expression in the ΔmreC background as judged by western blotting using αSipC sera ( Figure 5 panels A and B ) . Interestingly the amount of SipC protein expressed and secreted from the cell was less than the wild-type suggesting there are additional repressors continuing to operate ( Figure 5 panels A and B and Figure S5 ) . Furthermore , disruption of rcsC also significantly de-repressed motility ( Figure 6 and Figure S6 ) in a ΔmreC mutant similar to SPI-1 expression , again suggesting there are additional repressors involved . Expression of the RcsC protein in trans was able to restore the phenotype of ΔmreC ΔrcsC back to the equivalent of a ΔmreC strain , with respect to repressing SPI-1 type 3 secretion and motility . These complementation studies provide further evidence supporting the regulatory role of RcsC in the ΔmreC phenotypes ( Figure S7 ) . Rcs is a highly complex multi-component phosphorelay system and was originally identified in regulating genes involved in capsule synthesis in Escherichia coli [55] , [56] . The RcsC sensor kinase phosphorylates RcsD , which subsequently phopshorylates the DNA binding response regulator RcsB . The unstable RcsA protein and additional auxillary proteins can also interact and regulate RcsB . The Rcs system is involved in down-regulating the expression of flagella , SPI1-T3S and increasing biofilm formation [57] . We therefore also constructed ΔmreC ΔrcsB , ΔmreC ΔrcsD , ΔmreC ΔrcsDB and ΔmreC ΔrcsCBD mutants , which however did not restore either SPI-T3S or motility ( Figures 5 , 6 , and S6 ) . We propose that in the absence of RcsC signalling , phosphorylated levels of RcsB are depleted enabling de-repression of FlhDC and motility . The presence of RcsDB appears essential for restoring motility in the absence of RcsC [55] . The functionality of SPI-1 T3SS in the ΔmreC ΔrcsC and ΔmreC ΔrcsDB mutants were assessed in a TER assay , which revealed partial restoration of tight junction disruption in the ΔmreC ΔrcsC mutant , but not in the ΔmreC ΔrcsDB ( Figure S8 ) . It has been suggested that the outer membrane protein RcsF may perceive some of the environmental signals necessary to activate the Rcs phosphorelay system . To investigate this we constructed a ΔmreC ΔrcsF mutant which failed to restore motility or SPI-1 T3S and appeared phenotypically identical to ΔmreC ( Figure 5 , S6 ) . This would suggest that RcsF is not involved in the observed ΔmreC phenotypes . Furthermore as the auxillary protein RcsA can interact and regulate RcsB , we therefore disrupted the rcsA gene in ΔmreC and which also resulted in no impact on the observed phenotypes ( Figure 5 , S6 ) . In summary , we propose that RscC is sensing cell surface perturbations [58] in ΔmreC , resulting from a disrupted cytoskeleton , and subsequently down-regulating the expression of SPI-1 T3S and motility . This signalling appears to be independent of both RcsF and RcsA . A cell permeable compound named A22 [S- ( 3 , 4-Dichlorobenzyl ) isothiourea] has been demonstrated to perturb MreB function [59] . As an alternative approach to genetically disrupting the essential gene mreB , we exposed wild-type Salmonella cultures to A22 and observed a morphological change from rod to spherical-shaped cells . In addition we phenotypically screened and tested A22-treated cells for motility and T3S . The A22-treated cells were phenotypically identical to ΔmreC with respect to cell shape , motility , SPI-1 T3S , and also SPI-2 T3S ( data not shown ) . The effects of A22 were completely reversible following its removal ( data not shown ) . Thus the chemical genetic inactivation of MreB , independently corroborates the phenotypic observations made with ΔmreC . The ΔmreC defect clearly has an impact on the expression of important virulence determinants of Salmonella in vitro . We therefore wished to investigate the contribution of the bacterial cytoskeleton on the virulence of Salmonella in vivo using the mouse model . We observed that the SPI-1 T3SS in ΔmreC is completely down-regulated , and as this virulence system is important for infection through the oral route of inoculation the strain would be attenuated . We therefore explored the colonization of ΔmreC using the intravenous route allowing us to examine the impact of the host on the further down-stream stages of infection . Groups of 5 female C57/BL6 mice were inoculated intravenously with circa 103 colony forming units of either control SL1344 or ΔmreC . The times taken for clinical symptoms to appear were determined . Viable bacterial numbers in the spleen and liver for SL1344 were determined at days 1 and 4 , and ΔmreC at days 1 , 4 , 7 , and 10 . The in vivo bacterial net growth curves vividly demonstrate two clear phenotypic effects upon the growth of ΔmreC compared to the wild-type . Firstly , there is a greater initial kill of ΔmreC , and this is secondly followed by a slower net growth rate . However , in spite of the reduced growth rate of ΔmreC , the bacterial numbers steadily increase over 6 days . This eventually causes the onset of clinical symptoms necessitating termination of the experiment at day 10 ( Figure 7 ) . During these stages Salmonella infect and multiply within macrophages and the SPI-2 T3SS is essential for survival . Thus providing further evidence to support the presence of a functional SPI-2 T3SS in ΔmreC . Collectively , these observations imply the mreC defect reduces the virulence of the strain , but does not completely abrogate its ability to multiply and cause disease systemically in vivo . Strains recovered from in vivo passage were tested for changes in morphology , motility and T3S , and were found to be identical to the input strain . Furthermore the in vivo morphology of the strain within livers and spleens was determined by immunofluorescence microscopy . Sections of livers and spleens were taken and stained as described in the materials and methods . Figure 8 demonstrates the Salmonella ΔmreC mutant strain retains the round morphology in vivo compared to the rod shaped wild-type control . Collectively these data suggests that the mutation has remained stable during the in vivo passage for the virulence phenotypes tested . The regulation and assembly of SPI-1 T3SS and flagella are complex . When the bacterial cytoskeleton is disrupted both the SPI-1 T3SS and flagella expression are down-regulated . A hypothesis is that the integrity of the cytoskeleton is essential for the correct assembly of these complex macromolecular structures and in its absence the SPI-1 and flagella gene expression are down-regulated to conserve resources . Alternatively , in the absence of a functional cytoskeleton the bacterial cell is stressed and shuts down the expression of energetically expensive “non-essential” machinery . To test these ideas we wished to force on the expression of SPI-1 T3S and flagella genes , and examine whether these systems are correctly assembled and functional . We therefore expressed in trans from heterologous inducible promoters either the flagella master regulator FlhDC or the SPI-1 T3S regulator HilA in a panel of strains including ΔmreC . Strikingly , expression of FlhDC restored both the expression and assembly of flagella on the cell surface as determined by fluorescence microscopy ( Figure 9A ) and motility assays ( data not shown ) in ΔmreC . Furthermore , expression of HilA in trans up-regulated expression of the SPI-T3SS and its assembly on the cell surface as determined immunofluorescence microscopy ( Figure 9B ) western blotting with αSipB antibody ( Figure S9 ) or functionally by TER measurements ( Figure 4 ) . In contrast to SPI-1 T3SS and flagella , the expression of the SPI-2 T3SS was not turned off in the ΔmreC mutant as shown in ( Figure 9C ) . Interestingly , in WT cells the SPI-1 T3S apparatus and flagella appear to be present in around six to eight copies mainly along the long axis of the cell . In marked contrast the SPI-2 apparatus is typically present in one or two copies located at the poles of the bacterial cell [42] , whereas their localisation appears less clear in the ΔmreC mutant , possibly due to perturbations in the cell envelope and the indistinct cell polarity in these cells caused by disruption of the cytoskeleton . The complementation of the functional assembly of SPI-1 T3SS was also confirmed using TER assays , where the levels of decrease in resistance after infection with ΔmreC strain reverted to that of the parent strain upon induction of the transcriptional regulator hilA ( Figure 9B and S9 ) , or complementation of the ΔmreC mutation ( Figure 4 ) . Taken together the data support the notion that the cytoskeleton is not required for the correct assembly of these virulence factors but essential for their expression .
Bacterial cells possess dynamic cytoskeletons composed of diverse classes of self-assembling polymeric proteins . Some of these proteins resemble eukaryotic actin , tubulin , and intermediate filaments both structurally and functionally [5] , [7] , [11] , [12] . The bacterial tubulin FtsZ plays a key role in cell division . Bacterial actins provide vital functions in maintaining cell morphology , segregating DNA , and positioning bacterial organelles . It has recently been demonstrated in Helicobacter pylori , that MreB is essential not for cell shape but for maintenance of the full enzymatic activity of urease , an essential virulence factor [60] . Furthermore the MreB cytoskeleton is also essential for the polar localisation of pili in Pseudomonas aeruginosa [61] . Using a variety of approaches we have demonstrated the importance of the bacterial cytoskeleton in the pathogenicity of Salmonella . MreC and MreD form a complex in the cytoplasmic membrane , which subsequently interacts with MreB . The mreB gene appears to be essential in many organisms including as we discovered in Salmonella . Viable mreB mutants often contain compensatory changes in other genes e . g . ftsZ which compensate for the lethality of the mreB lesion [37] . As an alternative strategy to investigate the function of the bacterial cytoskeleton and avoid these deleterious effects , we carefully constructed depletion mutants of mreC in strains harbouring a single-copy plasmid expressing the MreB operon from the lac promoter . In addition we confirmed the phenotypic effects of the mreC genetic lesion by disrupting the functions of MreB using a chemical genetics approach and inactivating MreB with A22 . Removal of the gratuitous inducer IPTG from the growth medium of the ΔmreC depletion mutant resulted in cells changing from rod to a spherical shaped morphology . Using fluorescence microscopy MreB was observed to be no longer distributed in a helical fashion throughout the cell but rather diffusely throughout the cytoplasm ( data not shown ) . Presumably MreB polymers are no longer able to contact the cytoplasmic membrane via MreD attachment sites resulting in mis-assembly of the entire cytoskeleton . In growing cells , this disruption of the cytoskeleton leads to loss of the rod-shape . We next examined the motility of ΔmreC depletion strain to assess the functionality of flagella . The strains were non-motile and western blotting revealed absence of the flagellin filament subunit proteins FliC and FljB in both secreted and also cytoplasmic protein fractions , suggesting expression of these alternative subunits had been switched off . Flagella gene expression is complex and involves a regulatory hierarchy of Class I , Class II , and Class III genes [38] . The class I flhDC operon is the master regulator , and FlhDC complex is required for transcriptional activation of the class II genes including the specialized flagellar sigma factor FliA . FliA alone or with FlhDC complex , activates expression of the class III operon genes encoding motor proteins , hook-associated proteins , the filament protein , and chemotaxis proteins [39] , [40] . Expression of the FlhDC complex was reduced but still appeared comparable between the wild-type and the ΔmreC suggesting changes in the promoter activity of flhDC alone are not responsible for the observed phenotype . Class II gene expression was significantly reduced . Expression of the Class III gene fliC was completely down-regulated confirming the western blot observations . Hence these independent observations are in accordance with the ΔmreC motility data . Thus in the absence of the cytoskeleton expression of class II and class III flagella genes appears to be down-regulated . Expression of the SPI-1 T3S system is essential for invasion of intestinal epithelial cells and the SPI-2 T3SS plays a central role in survival within the hostile environment of a macrophage [43] . Western blotting revealed the SPI-1 T3S structural protein PrgH and the effectors SipA and SipC were no longer expressed or secreted in the ΔmreC depletion mutant . The phenoptype was fully complementable by the addition of IPTG . Several environmental signals and transcriptional factors modulate expression of the SPI-1 and SPI-2 T3SS [43] , [45] , [62] . We wished to understand the mechanistic basis by which expression of the SPI1-T3SS is down-regulated . Within SPI-1 there are key transcriptional activators which regulate expression of SPI-1 genes: HilC , HilD , HilA , and InvF . Using promoter-luciferase transcriptional reporter assays it was revealed that the SPI-1 transcription factor gene promoters for hilA , hilC , hilD , and invF were completely inactive in ΔmreC , in marked contrast to the control wild-type strain . Surprisingly , the promoter of sopB located outside of SPI-1 in SPI-5 remained active but its activity was marginally lower than in the wild-type strain . The regulation of many T3SS genes often require the input of multiple signals for maximal expression and clearly other signals remain in the ΔmreC depletion mutant which drive expression of the SopB in SPI-5 . It therefore appears that the SPI-1 T3SS is completely down-regulated in the absence of an cytoskeleton by an unidentified regulatory factor . In contrast , the SPI-2 T3SS remains functional as evidenced by western blotting with SseB antibody and promoter-reporter assays . This is further corroborated with the in vivo evidence that following systemic inoculation , ΔmreC is able to survive and multiply within the host . This takes place within the hostile environment of the macrophage where SPI-2 T3S is essential for biogenesis of the Salmonella containing vacuole and survival [43] , [63] , [64] . We wished to gain further insights into the mechanistic basis of the down-regulation of both SPI- T3SS and motility in ΔmreC . Two-component systems play an essential role in sensing and responding to environmental and cell surface signals [54] . To investigate if two-component systems contribute to the regulation of the ΔmreC phenotypes , we constructed a panel of separate two-component system mutant strains in an ΔmreC background . The double mutants were screened for recovery of motility and expression of the SPI-1 T3SS . A mutation in the rcsC sensor kinase gene resulted in significant but not complete recovery of both motility and expression of the SPI-1 T3SS . The Rcs phosphorelay system regulates a broad range of genes from capsule synthesis in E . coli to increasing biofilm formation [58] . RcsC also plays an important role in repressing expression of flagella and SPI-1 T3SS in Salmonella Typhi [57] . The RcsC sensor kinase normally phosphorylates RcsD , which subsequently phosphorylates the DNA binding response regulator RcsB . However , in ΔmreC ΔrcsDB and ΔmreC ΔrcsCBD there was no restoration of either motility or expression of the SPI-1 T3SS suggesting that RcsC signals repression and requires the presence of rcsDB to mediate this effect . We propose that in ΔmreC , the sensor kinase RscC detects cell surface perturbations and down-regulates expression of flagella and the SPI-1 T3S apparatus [58] . This signalling is independent of both the outer membrane lipoprotein RcsF sensor and the auxilliary regulatory protein RcsA . There are a number of explanations to provide a bacterial rational for this shutdown in expression . In the absence of a functional cytoskeleton the flagella and SPI-1 T3SS are either not being correctly assembled , triggering a feedback loop to repress expression , or alternatively are down-regulated to prevent the cell from wasting valuable resources under these conditions . To test the assembly idea , we forced on the expression of flagella and SPI-1 T3SS genes by expressing the regulators flhDC or hilA in trans in ΔmreC . Using independent methods we observed the correct assembly and function of these macromolecular machines suggesting the cytoskeleton is not essential for functionality . The cytoskeleton could also have a role in sensing cellular stress , as has recently been suggested by Chiu and colleagues [65] . They propose that the integrity of the cytoskeleton may be exploited by the cell to monitor oxidative stress and physiological status . If the cytoskeleton disintegrates in the absence of MreC , this may be sensed by the cell leading to a shut-down of the SPI-1 T3S apparatus and down-regulation of flagella protein expression . We have provided mechanistic insights into the regulation of motility and SPI-1 T3S in ΔmreC . We have identified the two-component system sensor RcsC as an important regulator controlling expression of these systems , presumably as a consequence of sensing membrane perturbations brought about by the disruption of the cytoskeleton [58] . With a non-functional SPI-1 T3SS , we would expect the ΔmreC would be attenuated in mice when administered by the oral route as it is unable to invade intestinal epithelial cells by the SPI-1 T3SS . We therefore explored the colonization of ΔmreC in vivo using the intravenous route of inoculation [66] . This provides an opportunity to examine the impact of ΔmreC on the down-stream stages of infection . Salmonella infect and multiply within macrophages during the systemic stages of infection . Survival within the hostile environment of the macrophage would require a functional SPI-2 T3SS in the Salmonella-containing vacuole to remodel the host cell environment and survive attack from reactive oxygen free radicals [64] , [67] , [68] . By examining the in vivo net bacterial growth curves within livers and spleens two clear phenotypic effects were revealed with ΔmreC compared to the wild-type . Greater initial killing of ΔmreC is followed by a slower net growth rate with the bacterial numbers steadily increasing over six days . Clinical symptoms begin to appear and by day ten these symptoms necessitate termination of the experiment . The phenotypic data clearly imply the ΔmreC defect reduces the colonization of Salmonella , but does not completely abrogate its ability to multiply and cause disease systemically in vivo . This would suggest that the second T3S in Salmonella encoded on SPI-2 remains sufficiently functional to permit growth in the absence of the cytoskeleton . In the absence of an intact cytoskeleton in ΔmreC the expression of the SPI-1 T3SS and flagella are clearly down-regulated . Strikingly however , the SPI-2 T3SS appears to remain functional contributing to the virulence of the ΔmreC strain observed in vivo . A possible explanation could be that the regulation of the SPI-2 T3SS is co-ordinated independently of the integrity of the cytoskeleton in contrast to flagella and SPI-1 T3SS . Collectively these data highlight the importance of the bacterial cytoskeleton in the ability of Salmonella to cause disease , and may provide opportunities for the development of new antimicrobials to target the cytoskeleton . | Salmonella are major global pathogens responsible for causing food-borne disease . In recent years the existence of a cytoskeleton in prokaryotes has received much attention . In this study the Salmonella cytoskeleton has been genetically disrupted , causing changes in morphology , motility and expression of key virulence factors . We provide evidence that the sensory protein RcsC detects changes at the cell surface caused by the disintegration of the bacterial cytoskeleton and modulates expression of key virulence factors . This study provides insights into the importance of the integrity of the bacterial cytoskeleton in the ability of Salmonella to cause disease , and thus may provide a novel target for antimicrobial drugs or vaccines . |
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Most of our understanding of plant genome structure and evolution has come from the careful annotation of small ( e . g . , 100 kb ) sequenced genomic regions or from automated annotation of complete genome sequences . Here , we sequenced and carefully annotated a contiguous 22 Mb region of maize chromosome 4 using an improved pseudomolecule for annotation . The sequence segment was comprehensively ordered , oriented , and confirmed using the maize optical map . Nearly 84% of the sequence is composed of transposable elements ( TEs ) that are mostly nested within each other , of which most families are low-copy . We identified 544 gene models using multiple levels of evidence , as well as five miRNA genes . Gene fragments , many captured by TEs , are prevalent within this region . Elimination of gene redundancy from a tetraploid maize ancestor that originated a few million years ago is responsible in this region for most disruptions of synteny with sorghum and rice . Consistent with other sub-genomic analyses in maize , small RNA mapping showed that many small RNAs match TEs and that most TEs match small RNAs . These results , performed on ∼1% of the maize genome , demonstrate the feasibility of refining the B73 RefGen_v1 genome assembly by incorporating optical map , high-resolution genetic map , and comparative genomic data sets . Such improvements , along with those of gene and repeat annotation , will serve to promote future functional genomic and phylogenomic research in maize and other grasses .
The systematic genetic improvement of crop species achieved by plant breeders has been one of the great achievements of modern agriculture [1] . Agricultural systems face considerable challenges because inputs such as chemical fertilizers , pesticides , herbicides , water and arable land are becoming less available , affordable , or sustainable . In addition , because crops are adapted to relatively stable weather patterns , global climate change promises to disrupt crop production . Finally , agriculture now is being asked to provide not only food , feed , and fiber to a growing world population , but also to contribute substantially more to world fuel supplies . An enhanced understanding of basic crop biology is required to efficiently design and develop crops that can produce the higher sustainable yields with reduced inputs that are needed to satisfy current and future demands . Maize has been , and continues to be , an important model system for basic biological research [2] . Because maize also is a crop , the resulting biological understanding is readily translated into crop improvement . In addition , knowledge gained from maize can be used to improve its relatives , including sorghum , sugarcane , and small grains . The bulk of the maize genome is composed of highly repetitive transposable elements ( TEs ) , that were first discovered in maize [3] . This initial TE identification was due partly to the ease with which associations with mutant phenotypes and high levels of TE activity could be made . Maize also was the first organism in which the quantitative contributions of TEs to genome structure were appreciated [4]–[8] , leading to the current understanding that the major determinants of plant genome size are different rates of amplification and removal of TEs [9] , [10] . TEs are largely responsible for the exceptionally high rates of rearrangement of both intergenic and genic DNA in plant genomes , observations first made in maize [11]–[14] . Thus , we now know that plant genome organization is primarily an outcome of the specificities and vagaries of TE action , and maize provides an excellent genetic platform for TE discovery and study . Besides TEs , the maize genome is also unique in its recent polyploid origin . The cereals , such as maize , rice , sorghum , and wheat , shared a common ancestor some 50 million years ago ( MYA ) [15] . Their genomes are highly syntenic [16] , [17] and the ancestor genome experienced an ancient whole genome duplication approximately 50–70 MYA [15] , [18] . Although the maize genome is genetically and physically diploid with ten pairs of chromosomes , its genome contains a whole genome duplication resulting from the hybridization of two related maize progenitors [16] , [19] , [20] . Alignments of orthologous sorghum regions to two maize homoeologous regions indicated that sorghum and the two maize progenitors diverged at the same time about 11 . 9 MYA . The two maize progenitors may have hybridized as recently as 4 . 8 MYA [21] . The Maize Genome Sequencing Consortium ( MGSC ) was funded to provide draft quality sequence across the vast majority of the genome and finished high quality sequence ( fewer than 1 error per 10 , 000 bases in genes and regulatory elements ) in low-copy-number regions ( e . g . , genes and associated regulatory regions ) . To sequence the maize B73 genome , a minimum tiling path ( MTP ) of Bacterial Artificial Chromosome ( BAC ) and fosmid clones with known locations on the physical map [22] was sequenced to 4–6× fold coverage , assembled , sequence improved in low-copy regions , and annotated via automated approaches resulting in the first reference genome sequence for maize ( i . e . B73 RefGen_v1 ) [23] . Over 32 , 000 genes were predicted , 99 . 8% of which could be placed on the integrated physical , genetic and optical maps . In addition , nearly 85% of the genome sequence was shown to consist of >1 , 000 transposable element families dispersed non-randomly across the genome . Here , we report a detailed analysis of a contiguous ∼22-Mb region of the maize B73 genome , with sequence quality that has been further improved beyond the maize reference genome , by the incorporation of additional shotgun sequence and integration with the maize optical map , which serves as a pilot for the future whole genome analysis . The resultant analysis provides the most comprehensive study to date of a single region of the maize genome ( ∼1% of the 2300 Mb genome ) . These analyses demonstrate how additional automated and manual sequence improvement and annotation would affect the extraction of important biological information from the maize genome .
Contig 182 in the B73 physical map of the maize genome [24] is located on chromosome 4 ( Chr4 ) , and was selected for analysis due to its large contiguous size ( ∼22 Mb ) and exceptional colinearity with rice Chr2 ( Figure 1C–1E ) . Many interesting genes have been identified in this region ( Table S1 ) , such as rf2b ( a paralog of a nuclear restorer of cytoplasmic male sterility that encodes an aldehyde dehydrogenase ) , opaque endosperm 1 ( o1 ) , dek31 ( mutations in which result in defective kernels ) , nitrite reductase 2 ( nii2 ) , gl4 ( a gene involved in the accumulation of cuticular waxes ) , and QTL related to ear length , diameter , grain yield , kernel length , weight , oil/protein/starch content , pest resistance and disease resistance [25]–[34] . Although several genes in this region have been cloned and functionally characterized , e . g . , nii2 [35] , rf2b [36] , and gl4 [37] , none of the QTL have been functionally characterized . Starting with the sequence-ready physical map [24] , we selected a MTP of 176 BAC clones ( Table S2 ) across contig 182 using the MTP analysis function of the Fingerprinted Contigs ( FPC ) program [38] . Standard shotgun sequencing protocols were employed for each BAC , and assembled sequences ( ∼4–6× redundancy ) underwent K-mer analysis to identify repeats [39] . The remaining low-copy-number regions were finished to high quality . Pseudomolecules were constructed using BAC end sequences , overlap and scaffold information , and were adjusted and validated by alignment with the maize B73 optical map . ( Figure 1A and 1B; [40]; see Materials and Methods section for details . ) The final sequence contained 21 , 702 , 972 bp in 907 uninterrupted sequence blocks , herein referred to as accelerated region 182 ( AR182 ) . The contig N50 is 57 , 261 bp , and the scaffold N50 is 160 , 621 bp . In this region , there are 178 genetic markers ( Table S3 ) from bin 4 . 06 to bin 4 . 08 in the IBM2 2008 Neighbors maize genetic map ( http://www . maizegdb . org/map . php ) —a consensus map compiled from all available maize mapping populations . Among the 150 markers with sequence information , 124 were identified in AR182 , and 18 were located in flanking contig 181 ( 19 markers ) or neighboring contig 197 ( 1 marker ) . Of the remaining eight markers , all were placed in other regions of the maize genome . Seven of these eight markers are multiple copy RFLP markers and could not be detected on maize Chr4 at e−5 , perhaps because these restriction fragment length polymorphism ( RFLP ) markers were incorrectly mapped , or are not present in the B73 genome . Two companion studies [41] , [42] used resequencing and comparative genome hybridization to demonstrate that maize exhibits high frequencies of haplotype-specific sequences ( Presence/Absence Variation ) . Many of these PAVs may have arisen via a consequence of the movement of transposable elements carrying genes or gene fragments . This finding , in combination with our use of a consensus map derived from multiple mapping populations may explain the absence of the eight genetic markers in AR182 . Among the 13 framework markers with solid genetic positions , 12 had corresponding sequences in AR182 . With the exception of two adjoining markers ( umc104a and mmp147 ) with switched positions , all other markers had the same order in the physical map as in the genetic map . The ratio of genetic to physical distance across AR182 averaged 4 . 4 cM/Mb ( Table S4 ) , somewhat lower than the previously-estimated genome average of 5 . 5 cm/Mb [24] . Transposable elements ( TEs ) are the most numerous and unstable components of the maize genome , and of all other complex plant genomes studied to date . In addition , TEs significantly complicate genome assembly and annotation because they are often repetitive , can be located in and around genes , and often encode ORFs that are easily mistaken for standard plant genes [43] . Because many of these TEs , especially the long terminal repeat ( LTR ) retrotransposons , are large and very similar in sequence due to their recent amplification , repetitive TEs are a major source of gaps and misassembled contigs in complex plant genomes . The simplest way to minimize the negative impact of TEs on gene discovery and annotation is to initially describe all of the TEs in a region . This allows TEs to be computationally masked , thereby providing a residual sequence that can be carefully analyzed . Structure-based searches are especially useful for the discovery of novel TEs , especially given that many are both low in copy number and represented in EST libraries . TE and other repeats were sought within the assembled sequence of AR182 by several independent approaches . Repeats per se were identified using an oligonucleotide counter that searched for the representation of all possible 20-mers in 1 , 124 , 441 whole genome shotgun reads ( 1 , 088 , 525 , 270 nucleotides; ∼0 . 45 genome equivalents [39] ) . Repeats also were found by homology to known repeats in the MIPS REdata database ( v4 . 3 ) [44] and TE exemplar databases [45] . Finally , structure-based searches were employed to identify novel TEs , including those that exist in low copy numbers . These structure-based search processes rely on the unique characteristics of particular classes of TEs , especially their end structures , but require significant manual curation to confirm the validity of any candidate TEs that are identified . The most abundant repeats identified were the LTR retrotransposons , which were found to constitute about 74 . 6% of the assembled sequence . The identified LTR retrotransposons were divided into 237 families . Intact elements ( i . e . with 2 LTRs and the appropriate internal sequences ) were found in this region for 47 of these families . One hundred and eighty-one of these families were represented in maize EST libraries ( data not shown ) . The specific elements present , their copy numbers and their relative coverage on AR182 are provided in Table S5 . As seen in earlier studies of maize [4] and other large plant genomes [46]–[49] , most of these elements are inserted into each other in nested arrangements with the oldest elements at the base of the stacks ( e . g . Figure S1A and Figure S2A ) . Two other classes of retroelements , LINEs and SINEs , were located in this region , providing 1 . 1% and 0 . 03% of the assembled sequence , respectively ( Table S6 ) . In AR182 , Copia-like retrotransposons were found to be over-represented ( 29 . 2% ) relative to the entire maize genome ( 23 . 7% ) , while Gypsy-like retrotransposons were found to be under-represented ( 38 . 9 vs 46 . 4%; Table 1 ) . These results agree with earlier studies [50] , [51] showing that different LTR retrotransposons preferentially accumulate in different areas of the maize genome . Although all of these high-copy-number LTR retrotransposons appear to prefer to insert into each other rather than into genes , they also distinguish LTR retrotransposon clusters that are near genes and those that are in largely gene-free regions like pericentromeric heterochromatin . In yeast , this class of elements finds insertion sites by association between the element-encoded integrase and specific heterochromatin proteins [52] . The presence of chromodomains in some , but not all , plant LTR retrotransposons [53] suggests a similar targeting mechanism . DNA transposons also were well represented in this region ( Table 1 ) , including 92 CACTA elements ( 66 families ) , 420 hAT elements ( 178 families ) , 744 MITEs ( 182 families ) , 163 MULEs ( 88 families ) and 1 , 149 mostly fragmented Helitrons ( 6 families ) , and each class comprised between 1–3% of AR182 . Few of these elements are likely to be autonomous ( encoding all the functions needed for transposition ) . For seven of the CACTA families , we found at least one copy with intact open reading frames . Four Helitrons were found to contain apparently full-length Rep/helicase genes with protein products believed to be necessary for transposition . Unlike the highly abundant LTR retrotransposons , the MITEs , Helitrons , CACTAs and MULEs primarily were found to be associated with genes ( Figure S3 ) . This is also the case for small SINE retroelements , as most copies present in the AR182 region were found in gene introns . The preferential insertion and/or retention of these lower-copy-number elements in these presumably euchromatic regions has the advantage of maintaining their potential for expression . However , by locating in recombinationally active regions near genes [54] , [55] their potential to contribute to genome rearrangements is increased . Perhaps the most amazing characteristic of the maize genome is the incredible number of gene fragments that are found inside TEs . Several classes of TEs have been found to acquire and transpose fragments of normal cellular genes , with MULEs and Helitrons particularly active in this regard [14] , [56] . AR182 was found to contain 20 LTR retrotransposons with apparent gene fragment insertions , plus 9 MULEs , 5 CACTA TEs , and 187 Helitrons with one or more acquired gene fragments ( Table S7 ) . The capture of gene fragments by LTR retrotransposons and CACTA elements has been reported before [57]–[60] , but the extent has not been known for any plant genome . The analysis of AR182 demonstrated that this is a common phenomenon in maize . In purely automated genome annotations , most or all of these fragments would have been counted as genes . Hence , in this region , 1 , 009 rather than 544 genes would have initially been predicted , and extrapolations to the entire maize genome nearly would have doubled overall gene content . Combining this error with the common error of annotating TE-encoded transposition genes as standard plant genes principally is responsible for the two-fold or more errors in gene content that have sometimes occurred in plant genome analysis [43] . Beyond the complications they create for gene discovery and annotation , the gene fragments within TEs also generate many questions about their possible contributions to host cell biology . Although the rapid rate of removal of unselected DNA from plant nuclear genomes [9] suggests that the great majority of the gene fragments and multi-gene chimeras within TEs rapidly become extinct , even the rare creation of a novel gene by the process of exon shuffling [61] could have enormous biological significance . Many cases of “transposon domestication” [62] , where all or part of a TE has been co-opted by the host organism to perform an important biological function now have been reported . The acquisition of gene fragments from multiple loci , and their fusion with each other and with standard TE proteins , should only increase the potential for valuable novelty and domestication . Equally important , the epigenetic silencing of TEs by siRNAs [63] , [64] predicts that many of the gene fragments inside TEs could contribute to the pool of siRNAs , and thereby acquire regulatory roles over the genes from which they were derived . Perhaps this is the mechanism of origin of some microRNAs , as fragments created by TEs that have evolved to encode specific small RNAs that regulate the source gene [65] . The distributions of these TEs across the region appeared uneven when viewed at the level of the entire AR182 ( Figure 2 ) . Among LTR retrotransposons , the concentrations of Gypsy- and Copia-like elements were correlated inversely . On a smaller scale , specific TE arrangements were found to be highly non-random . LTR retrotransposons primarily were inserted into each other and away from most genes , while DNA transposons , such as CACTAs , Helitrons , and MULEs , or small retroelements such as SINEs , were near genes . It should be noted that novel TEs that are low in copy number or have no intact copies here or elsewhere in the maize genome will still have been missed in this annotation process , so it is expected that this will cause some under-estimate of TE number and an over-estimate of gene number . Annotation of protein-coding genes was based predominantly on extrinsic evidence , using a gene building process adapted from Ensembl [66] , [67] . Sources of evidence included sequences from maize full-length cDNAs [68] ( http://www . maizecdna . org/ ) as well as ESTs and proteins . Ab initio predictions were included only where they did not overlap with evidence-based genes , or where overlap allowed extension of coding sequences . Although known repeats were masked prior to annotation , additional measures ( see Materials and Methods ) were needed to screen TEs , a common source of false positive predictions in plants [43] . Manual methods also were used to identify and remedy falsely split or fused gene models , though these were relatively rare . The resulting gene set includes 544 annotated loci , of which 514 were evidence-based , including 160 by full-length cDNAs ( Table S8 ) . Overall , AR182 has a gene density of 25 genes per Mb . Gene content in the 2045 Mb RefGen_v1 whole genome assembly was estimated at between ∼37 , 000 and ∼39 , 000 , giving a gene density of 18 to 19 genes/Mb [23] . Hence , AR182 is relatively gene-rich compared to the genome overall . Seven pairs of genes were found to be overlapping , and this conclusion is supported by full-length cDNA or protein homologs in other species . In rice , the presence of overlapping genes is relatively common and most are caused by transcripts using the promoter or enhancer of LTRs in a retrotransposon ( Wei and Wing , unpublished ) . Given the large number of LTR retrotransposons in maize , it would not be surprising if the observation of overlapping genes is be common in maize . Among the non-overlapping genes , the intergenic spaces in 246 ( 45 . 3% ) of the 543 gene spaces were less than 10 kb while 240 ( 44 . 2% ) genes were separated by more than 20 kb . Fifty-four of the intergenic regions were greater than 100 kb , with the largest being 530 kb ( Figure S4 ) . Most of these large intergenic regions are filled with nested LTR retrotransposons ( Figure S1 and Figure S2 ) . Gene , exon , and intron lengths , as well as number of exons per gene , were found to be within previously estimated ranges [69] , [70] , as shown in Table 2 . To make comparisons with other cereals , we selected 341 ortholog sets having three-way colinearity within syntenic regions of maize , sorghum and rice ( Table 2 ) . Exon lengths were relatively invariable across species , consistent with previous findings [70] , [71] . This contrasts with introns , which averaged 229 bp , 361 bp , and 498 bp for rice , sorghum , and maize , respectively . Haberer et al . [70] had previously reported this trend and also found examples of introns harboring TEs , suggesting that such insertions were responsible for inflated intron sizes in maize , which is consistent with earlier reports of TE and retrotransposon insertions within maize introns [72] , [73] . To further examine this hypothesis , we directly compared orthologous introns among maize , sorghum , and rice . Introns were paired based on their conserved position between flanking mapped exons ( see Materials and Methods ) . When introns of less than 1 kb were considered , lengths between pairs were strongly correlated ( Figure S5 ) . The correlation was greater between maize-sorghum than between maize-rice , consistent with their more recent divergence . However , maize had more large introns , leading to discrepancies in paired intron lengths . For example , 2 . 6% of maize introns were observed to be larger than 3 kb , whereas this number was only 0 . 47% in sorghum and 0 . 17% in rice ( Figure S6 ) . Length discrepancies in which the maize intron exceeded the length of its cross-species partner by more than 1 kb occurred in 4 . 7% of mapped intron pairs , whereas the reverse was true in only 0 . 55% of cases ( Figure S7 ) . Figure S8 shows a clear linear relationship between length discrepancies in positionally conserved introns and repeat content within such maize introns . All told , about 2 . 4% of maize introns harbor repetitive sequences exceeding 1 kb or greater ( an example of nested LTR retrotransposons in an intron shown in Figure S9 ) and 11% of intron-containing maize genes have at least one intron with this characteristic . That these genes are active is strongly indicated by evidence derived from GenBank mRNAs/full-length cDNAs . Besides these protein-coding genes , five miRNA genes in four families were computationally identified . The overall density of miRNAs in this region is 3 fold higher than the average genome distribution and all 5 genes have evidence of expression based on small RNA libraries [74] . Previous studies have shown that extensive genetic colinearity and synteny exist among the maize , rice and sorghum genomes [16] , [24] , [75]–[81] . All those studies were based on either genetic markers or short contiguous sequence analysis . In this study , four sequence-to-sequence comparisons were performed among the three species , including maize-rice , maize-sorghum , rice-sorghum , and maize-maize analysis using BLASTZ [82] and the Synteny Mapping and Analysis Program ( SyMAP , [83] ) . AR182 on maize Chr4 was found to align with rice Chr2 ( 29 , 020 , 340–35 , 806 , 283; Figure 1E ) and sorghum Chr4 ( 57 , 193 , 840–60 , 617 , 265 and 63 , 725 , 383–67 , 939 , 454; Figure 1E ) , and maize Chr5 from part of ctg250 to ctg254 ( [24]; Figure S10; clone list in Table S9 ) . While Figure 1E shows a pairwise pseudomolecule-to-pseudomolecule comparison of sequences , Figure 3 shows a comparative map based on homologous genes within these regions . The map in Figure 3 uses rice as a common reference because rice has been consistently identified as containing a relatively stable genome that closely resembles the ancestral state [78] . In the syntenic regions , there were annotations of 544 maize genes , 825 rice genes , and 847 sorghum genes . A higher level of synteny was observed between rice and sorghum than between maize and rice . Indeed , 686 ( 83 . 2% ) of the 825 rice genes in the corresponding region were found to be syntenic to sorghum , while 375 ( 45 . 5% ) of the rice genes were syntenic to the maize region . The same was true in that 685 ( 80 . 9% ) of the 847 sorghum genes were syntenic to rice , while 362 ( 66 . 5% ) of the 544 maize genes were syntenic to rice . Direct comparisons between maize and sorghum in AR182 revealed that 394 ( 72 . 4% ) maize genes were syntenic to sorghum , while 396 ( 46 . 8% ) sorghum genes were syntenic maize genes ( Figure S11 ) . Of course , any false positive gene annotations of TEs as genes in any of these regions [43] would be perceived as having non-syntenic relationships . It should be noted that the selected AR182 region is highly collinear with rice , however , at the whole genome level , maize is probably less syntenic with rice than estimated here . All five of the miRNA genes were found to be syntenic ( Figure 3B ) to a corresponding region in rice and sorghum . Four of the genes also were retained on the homeologous arm . Two hundred and forty-one genes maize genes ( 44 . 3% ) from AR182 were syntenic to its homeologous region on Chr5 . This result is quite different from a previous study that showed only 20–28% of the genes located on duplicated and sequenced regions of Chr1S and 9L [69] were syntenic . At the genome level , 25% of the conserved maize genes maintained their homeologous copy [23] . These results suggest that the degree of genome “fractionation” ( i . e . , loss of one homeologous copy from the ancestral Zea tetraploid formed 5–12 MYA [21] ) can be very different in various regions of the genome . As expected , 337 ( 75% ) of the 450 rice genes that are not syntenic to AR182 were observed to be syntenic and colinear in the corresponding maize Chr5 region . In total , 726 ( 88% ) of the 825 rice genes are syntenic to at least one of the two maize syntenic regions . These data strongly support previous proposals that deletion of redundant homologous maize genes is the major factor that disrupts colinearity between maize and other species [81] , [84] . Comparisons between maize-maize , maize-rice , maize-sorghum , and rice-sorghum revealed several rearrangements . Regions syntenic to AR182 from both maize and sorghum contain large inversion breakpoints that formed independently after the maize-sorghum lineage split ( Figure 1E ) . By contrast , colinearity between the maize homeologous region in Chr5 and the rice genome spans the entire region , with no apparent rearrangement ( Figure S10 ) , indicating that the inversion on maize Chr4 occurred after the ancestral Zea tetraploidization . Inversions in both maize and sorghum extend beyond the region under study . For sorghum , the inversion breakpoints occur at ∼57 . 1 and ∼63 . 7 Mb . Because the first breakpoint lies outside AR182 , the inversion introduces an ∼3 . 1 Mb flanking sequence , bearing some 375 genes , for which homologous genes are absent from the other genomes within the scope of the region . For maize Chr4 , the first inversion breakpoint is at ∼8 . 5 Mb , while the second occurs downstream within ctg184 ( not shown ) . This left a gap in rice within which ∼68 genes map to ctg184 rather than AR182 . Additional , possibly overlapping , inversions occur within maize Chr4 , ∼2 . 9 to 4 . 4 Mb , and this also arose after the whole genome allotetraploidization . Finally , a smaller inversion is conserved in both sorghum and the two homologous regions of maize , corresponding to coordinates ∼34 . 6 to 34 . 7 Mb in rice . This rearrangement occurred after the rice-sorghum/maize lineage split but its lineage of origin is unclear . By using rice as a reference genome , one can infer the timing of each rearrangement . All of the rearrangements were observed to be specific to each genome and none were shared among the genomes . Previous studies showed that rice diverged from maize and sorghum about 50–70 million years ago , the ancestors of maize and sorghum diverged about 12 MYA , and the two ancestors of current maize hybridized about 4 . 8 MYA [15] , [21] . Combining the evolutionary data of the species with comparisons in AR182-rice-sorghum and maize AR182-rice-maize Chr5 , one can infer that these inversions occurred after lineage divergence . The maize Chr5 region demonstrates perfect synteny to rice and therefore preserves the original order and orientation of the ancestors of maize and sorghum . The sorghum genome experienced the inversion after divergence with the ancestors of maize , while the two larger inversions in AR182 of maize Chr4 perhaps arose during genome shuffling after the tetraploid progenitor of maize originated . In sequence divergence ( Ks ) analysis ( see below ) , indistinguishable distances were observed between sorghum/maize and maize/maize homeologes , indicating a very similar date of lineage divergence with ancestral maize duplication; consistent with the ∼12 MYA timing predicted in a previous study [21] . Extensive tandem gene duplication has been found in Arabidopsis ( 17%; [85] ) and rice ( 14–29%; [86] ) . In AR182 , 51 ( 8 . 1% of the total ) genes were found to be involved in 14 tandem duplication clusters with 2–19 genes in each cluster . Most ( 9 ) of the clusters have only two genes . The largest gene family in the region is the 19-member DUF1754 superfamily . This gene family is present in most eukaryotic genomes , including those in mammals , birds , fish , insects , fungi and plants . The biological function of the DUF1754 superfamily is unknown . There is one gene copy in most species ( such as human , chimpanzee , chicken , rice and Arabidopsis ) , two copies in several others ( mouse , sorghum , and popular ) , and seven copies in the bovine genome . The gene was not detected in nematodes . The 19 members in AR182 are distributed in a 1 . 16 Mb region and are interrupted by twelve other genes . Additionally , there are two other family members in maize , located on Chr3 and 8 . Interestingly , 8 of the 14 gene clusters are not syntenic with either rice or sorghum . In the corresponding co-linear rice region , there are 105 genes ( 10 . 6% ) involved in 33 duplication clusters with each cluster varying from 2 to 8 genes . Nineteen of the 33 clusters involved only 2 genes and 20 of the 33 clusters have no syntenic relationships to maize AR182 . Ninety-two ( 10 . 0% ) of the sorghum genes were observed to be involved in 37 tandem duplication clusters , with 2 to 7 genes in each cluster . Twenty-six of the 37 sorghum gene clusters have 2 genes and 12 of the 37 clusters have no syntenic relationships with maize . The synteny data for tandem gene duplication in rice , sorghum , and maize indicate that most of the tandem duplication occurred after lineage divergence , in agreement with previous studies in Drosophila that tandem duplicated genes tend to be younger with lower survivorships [87] . We are aware of at least two possible processes that would result in non-synteny: gene mobilization from one location to a new location and corresponding gene loss in the other species . Because most genes that are non-syntenic relative to rice are also non-syntenic relative to sorghum , the more parsimonious explanation is that these non-syntenic maize genes were mobilized from elsewhere in the genome . As shown above , mobilization of genes , particularly by transposons such as Helitrons and Pack-MULEs , frequently result in fragmentation of the amplified/transposed copy [14] , [88] . To examine this phenomenon , we calculated the ratio of the CDS ( coding sequence ) length of the maize gene to that of its best scoring rice or sorghum homolog ( ortholog for syntenic relationships ) . While syntenic genes have a single CDS ratio peak centered at one , non-syntenic loci have a bimodal distribution , with a second peak centered at 0 . 4 , indicative of frequent truncation ( Figure 4 ) . As relates to sorghum , 68% of non-syntenic maize genes have a CDS ratio of less than 0 . 8 whereas only 14% of syntenic loci do . Thus , a substantial proportion of non-syntenic genes are fragmented , consistent with a mechanism of gene mobilization and the likelihood that these are truncated pseudogenes [51] . To further characterize these , synonymous ( Ks ) and non-synonymous ( ka ) mutation rates ( Ks ) were measured relative to their best-scoring homologs in sorghum . For this analysis , six potential false-negative syntenic genes were identified by TBLASTN alignment to sorghum , possibly missed due to omission of these genes in the sorghum annotation . Figure 5 shows distributions of Ks and Ka , for maize loci , stratified by synteny relationship and by evidence of truncation using a CDS length ratio threshold of 0 . 8 . Large differences were seen between syntenic genes and non-syntenic genes for characteristics of both Ka and Ks . The Mann-Whitney test [89] for non-parametric data showed that these differences are significant . The median Ks is 0 . 2352 ( 95%CI 0 . 2131 to 0 . 2674 ) for syntenic loci compared to 0 . 9769 ( 95% CI 0 . 7153–1 . 5543 ) for non-syntenic loci ( P<0 . 0001 ) . Ks was significantly different even when considering only genes with a CDS length ratio ≥0 . 8 , For this class the median Ks for syntenic loci was 0 . 2326 ( 95%CI 0 . 2130 to 0 . 2681 ) , compared to 2 . 0389 ( 95% CI 0 . 3114 to 3 . 7455 ) for non-syntenic genes ( P<0 . 0001 ) . Thus , truncation itself is not associated with elevated Ks values . Indeed , the Ks for non-syntenic loci having a CDS length ratio <0 . 8 ( median = 0 . 9064 ( 95% CI 0 . 5168 to 1 . 2777 ) is not significantly different from those having CDS length ratio ≥0 . 8 ( P = 0 . 6310 ) . Because Ks approximates mutation rate [90] , [91] , this result suggests that non-syntenic mappings have a more ancient relationship than do the orthologous relationships found in syntenic genes . The rate of non-synonymous mutation ( Ka ) likewise is elevated among non-syntenic genes . The median Ka for syntenic loci is 0 . 0442 ( 95%CI 0 . 0411 to 0 . 04889 ) compared to 0 . 2965 ( 95%CI 0 . 2426 to 0 . 3981 ) for non-syntenic loci ( P<0 . 0001 ) . It is clear that non-syntenic loci have vastly different properties compared to syntenic genes and that the identified sorghum homologs of non-syntenic maize genes cannot be regarded as orthologs . To determine the extent to which the sequence of AR182 may contribute to , or interact with , the small RNA population expressed by the whole maize genome , five small RNA libraries representing different maize tissues and genetic backgrounds were analyzed ( see Materials and Methods for details ) . Three libraries ( B73-zma1 , B73-zma2 and B73-zma3 ) were constructed using small RNA fractions from young leaves , immature ears and immature tassels , respectively , of a B73 genotype . The remaining two libraries ( K55-wt and K55-mop1 ) were previously described by Nobuta et al . [92] and include small RNAs from immature ears of wild-type and mop1-1 maize , respectively , in a K55 background . The mop1 gene was shown to encode an ortholog of Arabidopsis RNA-DEPENDENT RNA POLYMERASE 2 ( RDR2 ) and is required for the establishment of paramutation and the maintenance of transcriptional silencing of transposons and transgenes [93] , [94] . On average , the proportion of distinct small RNAs matching the sequence of AR182 at least once was 12% per library , corresponding to a range of ∼147 , 000 to ∼380 , 000 different small RNA sequences ( Table S10 ) . The leaf tissue libraries exhibited the lowest complexity , with approximately half the rate of matched , distinct sequences compared to any other sample ( which all represented reproductive organs ) and a distinct to total reads ratio of 16% in this library ( compared to more than 30% in the other libraries ) . All of the libraries exhibited a similar pattern of size distribution with two prominent peaks at 22 nt and 24 nt respectively ( Figure S12 ) ; as expected , in this contig , small RNAs in K55-mop1 presented a strikingly lower proportion of repeat-associated 24-mers compared to K55-wt . Moreover , consistent with prior reports , the 22-nt class predominantly associated with high-copy repeats was more abundant than the 21-mers , both when distinct numbers and total abundances were taken into account [92] . Based on these observations , the population of small RNAs matched to AR182 demonstrated small RNA match rates and patterns consistent with other analyses of sub-genomic portions of the maize genome [92] . Among the small RNAs matching to AR182 , 54% had more than two hits in a set of 60 Mb of maize contigs ( including this contig from Chr 4 , plus two other contigs from chr 1 , and 9 ) , suggesting that most small RNAs may be derived from repetitive elements . First , 25–38% of the unique signatures from each library were found to match tandem repeats ( Table 3 ) , which are known substrates for small RNA biosynthesis [95] . Next , to investigate in detail the fraction of small RNA originating from transposons , five principal families of DNA transposons were examined . These families included Harbinger , hAT , En-Spm , MuDR , two superfamilies of LTR retrotransposons ( Copia and Gypsy ) , and a family of non-LTR retrotransposons ( LINE1 ) that were mapped and annotated on the chromosome . The data from K55-mop1 was made comparable with the other libraries by dividing the abundance of all small RNAs by 5 . 3 , the average overall enrichment observed for miRNAs in the mop1-1 mutant [92] . All the classes of repetitive elements analyzed expressed larger small RNA populations in the reproductive organs compared to leaves and showed a reduction in the mop1-1 mutant , relative to wild type ( Figure S13A and S13B ) . Unique small RNAs related to the En-Spm and MuDR families were significantly the most frequent among the DNA transposons , irrespective of the tissue and the genetic background . This is consistent with the finding that the mop1 mutation can reverse the methylation status and silencing of Mutator elements in maize [96] , probably via a reduction of the corresponding siRNA population [92] . Interestingly , the expected decrease of distinct signatures in K55-mop1 compared to K55-wt was more remarkable for MuDR than for En-Spm , in particular when the total abundances were considered ( 77% reduction vs 50% for the two families , respectively ) . However , the size distributions of the two populations were very similar , both involving a majority of 24-mers in wild type that are expected to be reduced in a mop1-1 background . Discrepancies between the small RNAs of the two varieties ( K55 vs B73 ) also were observed . Ears from K55 showed slightly higher small RNA abundances for MuDR and En-Spm than the equivalent tissue of B73 ( Figure 6C ) . Besides , a much more significant difference was observed in the opposite direction for the hAT family , which was more abundant in the small RNAs of B73 ears ( 2∶1 ) . Further investigations are required to clarify to what extent this phenomenon is determined by different genetic backgrounds , environmental effects , or an imperfect correspondence between the developmental stages of the two samples . LTR retrotransposons were the most prominent repeat class matched by small RNAs , consistent with their large proportion in the genome . Non-redundant small RNAs mapped within these elements were , when averaged across the libraries , 38-fold more numerous than those matching to DNA transposons . Accordingly , the sum of their abundances was 15-fold greater than the total abundance of DNA transposon-specific small RNAs , after a normalization based on the number of copies in the available contigs . Since the disparity can be only partially accounted for by the difference in unit length between the two classes of repetitive elements , this observation suggested a more pronounced tendency of LTR retrotransposon sequences to be processed into small RNAs , possibly because their replication cycle involves an RNA intermediate . One unexpected observation was that , in every sample , the LTR retrotransposons of the Copia superfamily in AR182 were represented by much fewer and less abundant small RNAs than the elements of the Gypsy superfamily ( Figure S13 ) . Because the difference in the total nucleotide length covered by the two superfamilies was negligible , this result suggests that Copia elements are less prone to provide templates for small RNA biogenesis . Nevertheless , considering the prominent role of siRNAs in the transcriptional silencing of transposable elements , the observed pattern of small RNA generation is not sufficient to explain the very low transcript level reported for most families of Copia LTR retrotransposons [97] . A total of ∼28 , 000 distinct signatures per library were found to match the gene space of AR182 . This corresponded to 20% of the set of sRNAs originating from transposable elements , but within the overall length of the 544 genes analyzed , the density of distinct sRNAs was 10-fold larger compared to those in repeats and the mean total abundance per library ( ∼84 , 000 TPM ) was not less than 25% of those from transposons . We noticed that many of the genic small RNAs matched an average of more than two genomic locations , possibly indicating either ( 1 ) sequence conservation of paralogs , or ( 2 ) mis-annotation of repetitive elements . A separate analysis of exons and introns revealed a strong bias for small RNAs accumulating in the latter , with introns having five times as many distinct small RNAs as exons , including a four-fold larger total abundance after correcting for hits to the contigs ( Table S11 ) . Further analysis demonstrated that 64% of the intronic small RNAs matched to identifiable repetitive elements . The impact of repetitive elements on the small RNAs in proximity to gene promoters and trailers ( upstream and downstream of annotated genes ) also was analyzed . The upstream sequences of the 544 genes were investigated in 50 bp windows starting from the putative transcription start site . While the occurrence of TEs gradually increased from 2 . 7% to 12% between 1 and 200 bp upstream of the genes and again from 12% to 17% between 250 and 400 bp , the number of distinct signatures matching to the first region ( 1 to 200 bp ) was limited and rapidly increased in the second genomic interval ( 250 to 400 bp ) . The same pattern was apparent and even more evident when the total abundances of the matching sRNAs were analyzed ( Figure S14 ) . Moreover , after correcting the abundances for the hits to the contigs , a comparison to a region further upstream revealed that the 1–200 bp interval was predominately matched by low-copy-number signatures . The analysis of downstream sequences showed a very similar profile , indicating a general paucity of small RNAs relative to the occurrence of TEs in the flanking regions next to the gene boundaries . However , this reduced set of small RNAs is only observable over a short distance both in 3′ gene trailers and 5′ gene promoters . All small RNA data from this analysis can be accessed at http://mpss . udel . edu/maize/ . Consistent with the additional data used to construct the pseudomolecule ( i . e . overlapping sequences , and ordering and manually orienting sequence contigs based on optical map evidence ) and the degree of manual annotation/curation it received , AR182 exhibits significant improvement ( see Materials and Methods ) as compared to AGP182 , the corresponding sequence in B73 RefGen_v1 [23] , ( Table 4 ) . The total sequence contig number was reduced from 1170 to 907 whereas the average size of each contig increased from 18 , 923 bp to 23 , 860 bp . Similarly , the number of scaffolds was reduced from 544 in the highly automated AGP182 assembly to 440 in AR182 , and the average scaffold size increased from 40 , 819 bp in AGP182 to 49 , 238 in AR182 . The use of the enhanced AR182 sequence led to slight but detectable differences in the annotation of repetitive elements compared to AGP182 ( Table 1 ) . While the identified coverage of all TE types was similar between AR182 and AGP182 , they appeared less fragmented on AR182 in comparison to AGP182 , a finding that is likely due to the improved assembly of AR182 . Because the same databases were used to RepeatMask AR182 and AGP182 , any difference between them can be attributed to the differences in the level of sequence assembly and improvement . For example , from the comparisons of nested TE insertions ( Figure S1A versus Figure S1B and Figure S2A versus Figure S2B ) , more complete LTR elements could be detected on AR182 . This more complete description of TEs will improve detection sensitivity and characterization of TEs in future projects , and by extension improve the specificity of gene annotations as well . In the MGSP , the low-copy regions of the genome were finished at high quality . Indeed , with the exception of two 1-bp mismatches , and a 1-bp gap in three genes ( highlighted in Table S8 ) , the sequences of all predicted genes from AGP182 were identical to sequences present in AR182 . These variations however had no effect on the three open reading frames and protein translations , except for a single amino acid substitution in gene ZmAcc7g20000928 . Although the current draft sequence is of tremendous utility to the maize/plant genetics research community as it stands today , like any genome sequence and annotation , it could be improved by the application of additional time , resources , new methods , technologies , analysis tools , etc . This manuscript attempts to quantify the benefits of doing so to a reasonable approximation for a small region of the maize genome . Our results demonstrate the feasibility of refining the B73 RefGen_v1 genome assembly by incorporating optical map , high-resolution genetic map , and comparative genomic data sets . Such improvements , along with those of gene and repeat annotation , will serve to promote future functional genomic and phylogenomic research in maize and other grasses .
BAC shotgun sequencing ( 176 BACs total ) and finishing were performed using standard and previously published protocols [23] , [98] . Each BAC received two 384-well paired end sequences , which resulted in ∼4–6× coverage , depending on the BAC insert size . Sequence data for each BAC was assembled , confirmed using BAC end sequence , checked for minimum coverage standards , and sent for automated sequence improvement . Prior to sequence improvement , fosmid end sequences [22] , [23] , were added to the assemblies to enhance order and orientation . Consensus sequence data were evaluated by K-mer analysis [39] to determine repeat content . Automated improvement involved directed sequencing reactions across all gaps and low quality areas within non-repetitive regions of the sequence . Following automated sequence improvement , additional data , in the form of cDNA sequences and sequences from subtractive libraries using methyl-filtered DNA and high Cot techniques [7] available from GenBank , were incorporated into the assemblies . Manual improvement was performed on non-repetitive regions only , using guidelines established by the MGSC ( see supplemental material in [23] ) . Improved sequence was submitted to GenBank as phase-I improved ( HTGS_IMPROVED ) . Trace files from the 176 BAC shotgun sequencing projects ( Table S2 ) were downloaded from the NCBI Trace archive . Trace files from 5–8 BACs were pooled and assembled with Phrap ( http://phrap . org ) . Sequence contigs in each assembly were divided into different groups according to their BAC origin . Based on their positions in the physical map , BESs of related BACs were used to set the order and orientation of these sequence contigs . Within each BAC sequence , paired-end sequences were used to order and orient two contigs ( >9 reads and >2 kb ) . Sequences from each assembly were exported to the ALL_AGI_CTG_SEQ database . Concurrently , sequences from the same 176 BACs from NCBI CoreNucleotide database were downloaded . These NCBI sequences were split into pieces according to gaps ( 100 Ns ) and defined as the Genome Center at Washington University ( GCWU ) sequences . A Mega BLAST search ( identity >98% ) was then run using each GCWU sequence as a query against the ALL_AGI_CTG_SEQ database . If AGI sequences were found , the GCWU sequences were ordered and oriented according to the AGI assembly and were recorded in the final pseudomolecule as a fragment . If no AGI sequence was found , then the sequence was disregarded . The pseuodomolecule was further adjusted by comparing it with the maize Optical Map [40] . Regions of disparity called by the Optical Map were manually curated and modified for accurate ordering and orientation and contaminated sequence removal . Extra sequences present in the pseudomolecule , but not on the Optical Map , were used as query sequences to search against the maize genome sequence using BLASTN . In most cases , these sequences hit other genomic regions , indicating slight sequence contamination , and were removed from the pseudomolecule . Although maize-rice synteny was also used to order and/or orient some fragments , these refinements were then validated by comparisons to the Optical Map . Finally , gaps were filled between sequence fragments by using a series of Ns ( 50 Ns to fill gaps between ordered and oriented contigs; a pair of 60 Ns to tag internal fragments that were ordered but not oriented; a pair of 100 Ns to tag internal fragments that were neither ordered nor oriented; and 80 Ns inside a pair of 100 Ns to connect two fragments ( or blocks ) with unknown order and orientation ) . Comparisons with the Optical Map showed that ∼600 kb of sequence was missing from the AR182 assembly , stemming largely from misassembly of nearly identical retrotransposon LTRs . The sequence order could not be determined for two large nested retrotransposon insertion complexes ( regions from 14 to 14 . 3 Mb , and 19 . 0 to 19 . 4 Mb ) ; however , only a single gene in each region was identified ( Figure 2 ) . Otherwise , gap sizes should be minimal ( <500 bp in size based on the resolution of the Optical Map [40] ) . In total , the AR182 pseudomolecule contained 21 , 702 , 972 bp of sequence , composed of 907 sequence fragments and 906 gaps . Repetitive elements were masked by RepeatMasker [103] using version 4 . 3 of the MIPS REdat database of plant repeats [44] and TE exemplar databases [45] . Protein-coding genes were annotated using a modification of the Ensembl evidence-based gene-build pipeline [66] , [67] . The following available evidence was used: 11 , 742 cDNAs from the maize full-length cDNA sequencing project [108] , 359 , 942 Swiss-Prot proteins from all species , 62 , 242 GenBank proteins from plant species , 1 , 462 , 607 ESTs and 18 , 181 other mRNAs from maize , 1 , 217 , 859 ESTs and 72 , 919 other mRNAs from rice , 2 , 448 , 641 ESTs and 14 , 015 other mRNAs from other monocot species . Putative genes were filtered using a minimum translation length of 30 amino acids . Ab initio prediction by Fgenesh [109] was used to supplement evidence-based models . Where a Fgenesh prediction overlapped with a partial evidence-based model ( i . e . those lacking a start or stop codon ) the models were combined to extend the coding sequence . Fgenesh models were included “as is” where no overlap was found with an evidence-based model . Resultant models were further screened for transposable elements by BLASTP alignment to NCBI GenPept and comparison of aligned subjects to a curated list of transposable elements derived from the same database . Misannotations caused by fusion of separate genes into one model or splitting of genes into multiple models were detected by TBLASTN alignment using rice or sorghum proteins as the query . Such models were reannotated using GeneMark [110] . For the additional evidence of the 115 AR182-unique genes , we used the most recent ( up to July 17 , 2009 ) EST/fl-cDNA and nr database from GenBank , the MSU rice annotation 6 . 0 ( http://rice . plantbiology . msu . edu/ ) , and the JGI sorghum genome annotation ( http://genome . jgi-psf . org/Sorbi1/Sorbi1 . home . html ) . DNA/amino acid sequences of the 115 genes were used as queries against the above databases in either BLASTn or tBLASTn searches . All sequences in this analysis were masked as above prior to alignment with BLASTZ [82] and SyMAP ( Synteny Mapping and Analysis Program;[83] ) . For Oryza sativa ssp . japonica we used the TIGR release 5 assembly [111] . For Sorghum bicolor we used the Sorbi1 assembly [71] . To identify the maize homeologous region we used physically-anchored BAC sequences and annotations from release 4a . 53 of the Maize Genome Project [23] . To reduce the effects of overlapping BAC's , the annotated genes were screened for redundancy . The rice-only hypothetical genes ( totaling 168 genes ) and sorghum low-confidence genes ( 175 genes ) were excluded in the final statistics due to the high potential of annotation error . Syntenic regions were defined as maximally-scoring , colinear chains as described [112] . Putative orthologs within these regions were identified as best reciprocal hits using BLASTP . Additional confidence in ortholog assignment was provided by filtering for colinearity . Reciprocal best hits were deemed colinear if separated by no more than 500 kb in either of the genomes being compared . This method is conservative since only a subset of lineage-specific duplications in the region can have a reciprocal best hit . In addition , it should be noted that this method would not distinguish misassignment due to reciprocal loss of adjacent paralogous genes [21] . TBLASTN was used to search of duplicated genes in the maize Chr5-related region using the AR182 set of 544 genes as queries against the Chr5-related BAC sequences at a cutoff of e-10 . Manual inspection was performed to ensure gene colinearity . Exons within coding sequences were aligned in a pairwise manner between othologous genes using BLASTN . Positive mappings were assigned to those exons having an e-value threshold of 1e−5 , at least 80% identity , and matching orientation . Intron mappings were assigned when both flanking exons of one gene mapped to the flanking exons of its ortholog , and such exons occurred in the same order relative to their respective coding sequences . Eighty-eight percent of maize exons mapped to rice and 94% mapped to sorghum . Identical exon/intron structures were found in 51% of rice-maize orthologs and in 66% of sorghum-maize orthologs . Overall , we recovered 1 , 268 intron pairs between maize and sorghum and 1 , 114 pairs between maize and rice . Annotation of miRNA genes on AR182 was performed as identified as described in [74] . Annotations were similarly performed on BAC clone sequences present in the maize chromosome 5 homeologous region [23] For rice we used TIGR Release 5 [111] and for sorghum we used the Sorbi1 assembly [71] . Amino acid sequences were translated from coding sequences and aligned in a pairwise fashion using CLUSTALW version 1 . 83 [113] . Alignments were mapped to coding sequence coordinates and alignment gaps were removed using methods available in the BioPerl toolkit [114] . Rates of non-synomous ( Ka ) and synonymous ( Ks ) substitutions were estimated with codeml of the PAML package version 3 . 15 [115] using the F3X4 codon frequency model . Differences in the distributions of divergence rates were evaluated for significance using the non-parametric Mann-Whitney test [89] as implemented in the MedCalc statistical software package ( MedCalc Software , version 9 . 3 . 8 . 0 , Mariakerke , Belgium ) . All the small RNA libraries were generated by ligation of the small RNA fraction to 5′ and 3′ adaptors followed by RT-PCR amplification and sequencing with Illumina's SBS technology . While the sequencing of K55-wt and K55-mop1 libraries was performed on multiple flow cell channels , yielding 5 . 6 and 7 . 2 million signatures respectively , the libraries from the B73 genotype were processed on single channels , resulting in an average of 4 . 2 million signatures each . The abundance of each sequence was normalized to 5 million [units of transcripts per 5 million ( TP5M ) ] in all the libraries . | Maize is a major cereal crop and key experimental system for eukaryotic biology . Previous investigations of the maize genome at the sequence level have primarily focused on analyses of genome survey sequences and BAC contigs . Here we used a comprehensive set of resources to construct an ordered and oriented 22-Mb sequence from chromosome 4 that represents 1% of the maize genome . Genome annotation revealed the presence of 544 genes that are interspersed with transposable elements ( TEs ) , which occupy 83 . 8% of the sequence . Fifty-one genes were involved in 14 tandem gene clusters and most appear to have arisen after lineage divergence . TEs , especially helitrons , were found to contain gene fragments and were widely distributed in gene-rich regions . Large inversions and unequal gene deletion between the two homoeologous maize regions were the main contributors to synteny disruption among maize , sorghum , and rice . We also show that small RNAs are primarily associated with TEs across the region . Comparison of this ordered and oriented sequence with the corresponding uncurated region in the whole genome sequence of maize resulted in improvements in TE annotation that will ultimately enhance detection sensitivity and characterization of TEs . Doing so is likely to improve the specificity of gene annotations . |
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Plasmodium undergoes one round of multiplication in the liver prior to invading erythrocytes and initiating the symptomatic blood phase of the malaria infection . Productive hepatocyte infection by sporozoites leads to the generation of thousands of merozoites capable of erythrocyte invasion . Merozoites are released from infected hepatocytes as merosomes , packets of hundreds of parasites surrounded by host cell membrane . Intravital microscopy of green fluorescent protein–expressing P . yoelii parasites showed that the majority of merosomes exit the liver intact , adapt a relatively uniform size of 12–18 μm , and contain 100–200 merozoites . Merosomes survived the subsequent passage through the right heart undamaged and accumulated in the lungs . Merosomes were absent from blood harvested from the left ventricle and from tail vein blood , indicating that the lungs effectively cleared the blood from all large parasite aggregates . Accordingly , merosomes were not detectable in major organs such as brain , kidney , and spleen . The failure of annexin V to label merosomes collected from hepatic effluent indicates that phosphatidylserine is not exposed on the surface of the merosome membrane suggesting the infected hepatocyte did not undergo apoptosis prior to merosome release . Merosomal merozoites continued to express green fluorescent protein and did not incorporate propidium iodide or YO-PRO-1 indicating parasite viability and an intact merosome membrane . Evidence of merosomal merozoite infectivity was provided by hepatic effluent containing merosomes being significantly more infective than blood with an identical low-level parasitemia . Ex vivo analysis showed that merosomes eventually disintegrate inside pulmonary capillaries , thus liberating merozoites into the bloodstream . We conclude that merosome packaging protects hepatic merozoites from phagocytic attack by sinusoidal Kupffer cells , and that release into the lung microvasculature enhances the chance of successful erythrocyte invasion . We believe this previously unknown part of the plasmodial life cycle ensures an effective transition from the liver to the blood phase of the malaria infection .
Two billion people , more than one third of the world's population , live at risk for malaria and about 1 billion are infected . Each year there are 300 million to 500 million new cases with 2–3 million deaths , the vast majority young children in Africa . We are now forty years past the discovery that radiation-attenuated sporozoites protect against malaria [1] , but we still lack an efficient malaria vaccine to combat this deadly parasitic disease , and drug resistance is wide-spread [2] . The malaria infection begins with the introduction of sporozoites from the bite of an infected Anopheles mosquito [3 , 4] . The sporozoites travel to the liver and develop in hepatocytes to large exoerythrocytic forms ( EEFs ) [5 , 6] . Schizogonic division of the EEF then results in the formation of thousands of first-generation merozoites , which are responsible for the initiation of clinical malaria . Merozoites have a short life span and must infect erythrocytes immediately after release into the bloodstream [7] . Merozoites are also highly susceptible to phagocytosis and must therefore avoid contact with macrophages [8] . Acute danger of phagocytic elimination is presented in the form of Kupffer cells [8] , the resident phagocytes of the liver that comprise by far the largest population of tissue macrophages of the body [9] . Kupffer cells are predominantly located at sinusoidal bifurcations , largely within and often spanning the sinusoidal lumen [9–11] , thereby presenting significant obstacles for non-self particulate material . This strategic position of Kupffer cells makes it difficult for free merozoites to exit the liver without being trapped by these surveillance cells of the innate immune system . The first evidence suggesting that merozoites can be released from hepatocytes as clusters , held together by host cell cytoplasm , was presented several decades ago in Garnham's ultrastructural examination of Plasmodium yoelii–infected murine livers and described in more detail in Meis' extensive electron microscopic studies on P . berghei infection of the mouse [5 , 12 , 13] . More recently , we and others reported that merozoites are released as “extrusomes” or “merosomes” that contain hundreds to thousands of parasites [14 , 15] ( reviewed in [16 , 17] ) . Our initial intravital observations using green fluorescent P . yoelii and BALB/c mice revealed extensive movement within EEFs nearing completion of merozoite maturation culminating in budding and release of merosomes into the hepatic bloodstream [14] . An elegant series of in vitro studies described the differentiation of P . berghei merozoites in the human hepatoma cell line HepG2 [15] . While developing into hepatic schizonts , the intracellular parasites prevent the initiation of a death program in their host cells , but leave them to die once merozoite formation is complete . Underlying molecular details remain to be determined , but the data suggest that host cell death in this in vitro model shares more features with autophagy than apoptosis or necrosis [18] . However , information on the viability of hepatocytes releasing merozoites into the sinusoidal blood is lacking to date . Because P . yoelii infection of the mouse represents an accepted model closely reflecting human malaria [19] , we used a variety of microscopic techniques to study the dynamics of merosome budding from infected hepatocytes and the fate of hepatic merozoites in the body . Confocal images provided measurements of merosome volume , merosomal merozoite content , and EEF volume , and appropriate mathematic processing of these data allowed us to calculate the number of hepatic merozoites produced by P . yoelii sporozoites in the murine host . Using intravital and ex vivo microscopy , we found that the vast majority of hepatic P . yoelii merozoites leave the liver camouflaged as merosomes , disseminate within the cardiovascular system , and arrest in the lungs . Molecular markers revealed that merosomal merozoites remain viable and infectious until being released into the pulmonary microcirculation . In contrast , various in vivo and ex vivo assays suggest that unreleased merozoites and the exhausted host cell eventually succumb to necrosis . The resulting inflammatory stimulus attracts neutrophils , and mononuclear phagocytes thus give rise to the formation of microgranulomata . Overall , this systematic temporal and quantitative analysis indicates that merosome formation and release by host hepatocytes , merosome transport to and sequestration in the lungs , and release of merozoites into the pulmonary microvasculature are parts of a previously unrecognized phase of the Plasmodium life cycle .
P . yoelii–infected mice have been suggested to represent a suitable model for human malaria [20] . We also consider P . yoelii an appropriate rodent model for liver stage analysis because it induces less inflammation in murine livers than P . berghei and produces more EEFs [21] , which in addition are generally larger and contain more merozoites [12 , 22 , 23] ( Table S1 ) . While available for other species such as P . berghei , information is scarce regarding ultrastructural changes during P . yoelii EEF maturation in the liver and the subsequent release of first generation merozoites [12 , 24] . To help fill this gap and to expand our previous investigation of Plasmodium merosomes in live mice [14] , we used several light and electron microscopy techniques to examine this process . Mature Plasmodium EEFs contained thousands of merozoites enclosed in a parasitophorous vacuole ( PV ) . Up to the final developmental stage and onset of merozoite release , infected hepatocytes remained in close contact with neighboring uninfected parenchymal ( Figure 1A and 1B ) and sinusoidal cells ( Figure 1C ) . Shortly before merosome formation , the PV membrane ( PVM ) disintegrated so that host cytoplasm contained a mixture of mature merozoites , morphologically intact hepatocyte organelles ( Figure 1D ) , parasite remnant bodies ( or pseudocytomeres [5] ) , and parasite stroma left over from schizogonic merozoite formation ( Figure 1E ) . Some of the sinusoids adjacent to infected hepatocytes remained filled with erythrocytes indicating preservation of function , but others were compressed by the expanding parasite and lacked erythrocytes suggesting local obstruction of blood flow ( Figure 1C ) . To calculate the merozoite content of mature EEFs ( see below ) , we needed accurate measurements of the EEF size . Compared to tissue sections , intravital microscopy of green fluorescent protein ( GFP ) Plasmodium yoelii parasite ( PyGFP ) –infected mouse livers ( Figure 1F ) offered the advantage of examining live tissue within an intact animal , thus avoiding artifacts associated with both fresh and fixed sections . Mature EEFs within the liver typically have a slightly ellipsoid shape with the minimum and maximum diameters ranging from 40 to 75 μm ( with averages of 49 . 2 ± 10 . 3 μm to 55 . 6 ± 9 . 0 μm ) , respectively ( n = 16 ) . Detailed intravital examination of 30 mice at times ranging from 30 to 74 h after intravenous infection with PyGFP sporozoites allowed us to follow the complex series of events involved in merozoite liberation from hepatocytes . We monitored more than 60 EEFs over this period and observed the earliest merosome budding at 46 h ( Figure 2A , Videos S1–S3 ) , a time in general agreement with earlier work reporting the first appearance of P . yoelii in the blood at 45 . 5 h [12] . Of these 60 EEFs , 20 reached maturity during the observation period and released merozoites , while the rest remained immature . The majority ( 13 ) of these 20 EEFs released merozoites by merosome formation . Merosome formation continued until 56 h after infection , thus confirming the asynchronous nature of P . yoelii EEF maturation , a common observation in Plasmodium-infected livers [5 , 25] . Because we infected by intravenous sporozoite injection , the well-known slow release of sporozoites from the mosquito bite site [3 , 26] alone cannot account for the asynchronicity observed here . For individual EEFs , the process of merosome budding and release lasted several hours during which time the host cell gradually decreased in size and separated from neighboring cells ( Figure 2B ) . In addition to fully formed green fluorescent merozoites , released merosomes contained non-fluorescent remnant bodies and host cell organelles , thus providing further evidence that merosome budding occurs after rupture of the PVM . Eventually , the host cell membrane appeared to lose its integrity and allowed some leftover merozoites to enter the bloodstream singly and without protection by a merosomal membrane ( Figure 2C and 2D , Video S4 ) . GFP radiated out from the disintegrating EEF into the surrounding tissue , implying that parasite antigens and host cell cytoplasm were set free as well . Indeed , electron microscopic examination showed free mitochondria in the sinusoidal lumen ( Figure 2E ) . Size and shape of these organelles revealed hepatocyte origin . Eventually , inflammatory cells were attracted to the site of the disintegrating EEF . During phagocytic removal of debris from dead merozoites and host cells , neutrophil granulocytes and mononuclear phagocytes transformed the site of the former EEF into a small granuloma ( Figure S1 ) , a structure commonly reported at late stages of Plasmodium liver infection [5 , 8 , 27–31] . Thus , merosome formation in the liver occurs over a period of about 10 h and is followed by disintegration of the host cell and some leftover parasites , clearance of the remains by infiltrating phagocytes , and production of a small granuloma . Merozoites were also liberated by a less frequent mechanism . Starting earlier than merosome formation ( 42 h post inoculation ) , some infected hepatocytes rapidly discharged their content of merozoites and cell organelles by a mechanism appearing to involve rupture of the cell membrane ( Figure 3A–3E , Video S5 ) . In some cases , the process was complete in as little as 5 min; in others it lasted as long as 60 min . Of the EEFs rupturing in this manner , 80% harbored mature merozoites , but 20% had a homogeneous cytoplasm; thus , schizogony had not even begun ( Figure S2 ) . Occasionally , electron micrographs showed immature merozoites incompletely separated from remnant bodies yet released into the sinusoidal bloodstream ( Figure 3F ) . This apparent rupture-release left large faintly fluorescent EEF ghosts at the site of the former host cell . Because our intravital observations were based on confocal microscopy , we considered the possibility of phototoxicity playing a role in this rupture-release mechanism . However , since EEF ghosts identical to those resulting from observed rupture were detectable at the very beginning of intravital examination , we could reject that possibility . Because the EEFs did not decrease in volume prior to transformation into a ghost , and we did not find erythrocytes associated with these ghosts , we suspect that the remains of the host cell cytoskeleton , the surrounding extracellular matrix , and/or the sinusoidal cell layer resealed the ghost after merozoite release; thus , preventing the formation of hemorrhages . Similar to the end of the merosome release mechanism ( see above ) , EEF ghosts were infiltrated by inflammatory cells that gave rise to small granulomata . When we combine results from intravital microscopy , showing that both mature and immature EEFs undergo this rapid decay , with our electron microscopy data , showing that some of the rupturing EEFs were immature , we conclude that this rapid release process is a result of abortive EEF development that , in the absence of host cell membrane protection , exposes the parasites to Kupffer cell phagocytosis . To demonstrate that merozoites within merosomes are alive and to help exclude the possibility that merosome release represents an abnormal development , we injected infected mice with markers that reveal cell viability in vivo . At points ranging from 51 to 74 h post inoculation , mice were injected with a mix of the membrane-permeable DNA stain Hoechst 33342 and the dead cell marker propidium iodide ( PI ) . Subsequent intravital confocal microscopy revealed that PI does not enter merosomes or intact EEFs ( Figure 4A and 4B ) , but does stain some of the merozoites left behind in EEF ghosts and also in EEFs that had disintegrated after merosome budding ( Figure 4C and 4D ) . These findings support the interpretations above in that they suggest that merozoites that fail to exit the host cell eventually succumb to necrosis . Efforts to determine the mode by which merosomes breach the sinusoidal cell layer failed so far due to insufficient numbers of suitable events for analysis . We suspect that budding occurs through the endothelial fenestration rather by a paracellular route , because of the extreme natural variability of the diameter of the fenestrae in response to changes in blood pressure and other physiologic stimuli . Interestingly , mature EEFs were frequently surrounded by a layer of flattened cells that had incorporated the dead cell stain PI ( Figure 4A and 4B ) . Perhaps the death of these cells is due to extreme compression by the extensive expansion of the EEF during the final stage of development . Occasionally , merosomes were found budding into such dead cells , but the immobility of the parasites indicated that they were trapped ( Figure 4A ) . When hepatic merosomes initially bud from infected hepatocytes ( Figure 5 ) , they are highly variable in size and contain hundreds to thousands of mature merozoites , while merosomes in blood draining from the liver were smaller and more uniform in size . Intravital microscopy showed very large merosomes moving far more slowly than small ones , which leave the liver lobules at a velocity close to that of blood cells ( unpublished data ) . We frequently observed merosomes hindering the free flow of the blood as they moved along a sinusoid ( Figure 5C–5G ) as well as being hindered by the vascular architecture . The speed of merosome transport at any instant depended on the diameter and local structure of the sinusoid as well as the size of the merosome . We recorded large merosomes being arrested at sinusoidal bifurcations where they occasionally even reversed direction of movement ( Figure 5A and 5B and Videos S6 and S7 ) . Because morphological measurements taken in vitro are subject to artifact and do not reveal in vivo dynamics , we sought a better understanding of sinusoidal architecture using intravital analysis of uninfected transgenic Tie2-GFP mice that have fluorescent vascular endothelia [32] . We found sinusoidal diameters to range from 3 . 4 μm to 14 . 1 μm ( 6 . 7 ± 1 . 9 μm; n = 94 ) under normal blood pressure conditions . Although large merosomes greatly exceed this size range , their considerable deformability allowed them to gradually wind their way towards the central vein and exit the liver without rupture and release of merozoites , a process aided by resizing ( Figure 5A and 5B ) . We occasionally observed large merosomes subdividing into smaller ones while traveling through sinusoids ( Videos S8 and S9 ) , but we suspect that shear forces associated with the faster blood velocity in larger vessels caused merosomes in the hepatic effluent and inside lung capillaries to be generally smaller and uniform in size compared to those in the liver . The importance of mechanical forces for resizing is demonstrated by another set of experiments in which PyGFP-infected mouse livers were removed from the animals and analyzed ex vivo by confocal microscopy , i . e . , in the absence of blood flow . The sinusoids of such livers contained merosomes of an unusually large size ( Figure S3A ) . When livers were perfused with medium prior to ex vivo confocal microscopy , the sinusoids contained even larger merosomes ( Figure S3B ) . We contend that lack of blood flow prevents subdivision of large merosomes into smaller ones and that liver perfusion hastened merosome budding and liberation from the host cell . Merosome formation results in packaging a mixture of parasites , remnant bodies , and host cell cytoplasm within host cell membrane for release into the sinusoidal lumen ( Figures 5H and 6A ) . Ultrastructurally , the merosomal matrix contained well-preserved merozoites and morphologically intact host cell mitochondria ( Figure 6B ) suggesting that these organelles are viable at the time of merosome budding . Merosomes also typically contained remnant bodies ( Figure 6A ) suggesting that these leftovers from EEF schizogony represent a natural component of the merosomal cytoplasm . In the absence of better viability markers , we interpret the presence of MSP-1 on the surface of merozoites in both mature EEFs and merosomes ( Figure 6D and 6E ) to indicate intactness and complete differentiation of the parasites , and propose that merosomes are linked to productive infection of erythrocytes . Disintegration of the PVM prior to merosome formation indicates the merosome membrane is derived from hepatocyte cell membrane . Asialoglycoprotein receptor 1 ( ASGR1 ) , a protein expressed only on parenchymal liver cells [33–37] , was detectable by immunofluorescence lining the basal hepatocyte surface within the space of Disse ( Figure 6C ) . ASGR1 clearly surrounded mature EEFs ( Figure 6D ) , but it was absent from the merosomal membrane ( Figure 6E ) . The lack of this hepatocyte surface protein could be due to dedifferentiation of the infected host cell or modification by the intracellular parasite at late stages of EEF maturation . However , because the ASGR1 label was located predominantly in the space of Disse rather than on the hepatocyte membrane , more work is needed to define the composition of the merosome membrane . Because intravital observations showed that merosomes remain intact during transport towards the central vein , we examined the hepatic venous effluent for membrane-enveloped parasites . To do this , we opened the inferior Vena cava at its point of entry into the diaphragm and collected blood from the peritoneal cavity . Thick smears were prepared from 5 μl blood , and the concentrations of venous merosomes from three separate experiments were measured . In hepatic venous blood collected 52 h post infection with 2 . 5 × 106 sporozoites , we found 28 . 7 ± 4 . 3 merosomes per μl , and 69% of these merosomes contained between 100 and 200 merozoites ( unpublished data ) . While much information is available on P . berghei–infected HepG2 cells [15] , in vivo data on the molecular composition of the merosome membrane , for example phosphatidylserine ( PS ) exposure , are lacking to date . To obtain more detail on merosome structure , another set of experiments was performed in which the parasite material available for examination was enhanced by liver perfusion . Beginning 52 h after infection with PyGFP or wild-type ( wt ) P . yoelii , livers were perfused with culture medium and the perfusate collected . Cells were immobilized by attachment to Alcian blue–treated glass-bottom dishes and immediately examined by confocal microscopy using conditions that maintain viability . Perfusate merosomes typically adapted a spherical shape in vitro ( Figure 7A ) and 3-D images from confocal stacks demonstrated a relatively uniform size containing several hundred merozoites . Labeling by the phospholipid marker FM 4–64 FX verified that the parasites were held together by a membrane ( Figure 7B ) . Immediately after harvesting , the majority of the parasites appeared viable and merosome membranes were negative for annexin V labeling ( Figure 7C ) ; thus they do not display PS that targets cells for phagocytosis . However , with increased time in vitro , the presence of PS gradually became apparent ( Figure 7D ) . Merozoites in freshly isolated merosomes did not stain with the dead cell marker PI , but those that became positive for PS also lost the ability to exclude PI ( Figure 7D ) . A further viability assessment utilized YO-PRO-1 , a DNA stain that selectively passes through the ( intact ) plasma membrane of apoptotic cells . Again , merozoites in freshly isolated merosomes did not label with YO-PRO-1 , but as time in vitro progressed , they began to incorporate YO-PRO-1 along with PI ( Figure 7E and 7F ) . Within roughly 60 min of in vitro examination , all merosomes were positive for annexin V , YO-PRO-1 , and PI . Attempts to quantify the time course of these processes more precisely were prevented by the sensitivity of the merosomes to the various steps of isolation from the liver and concentration by centrifugation . Taken together , these results suggest that P . yoelii merosomes leaving the liver contain viable merozoites , and , similar to P . berghei–infected HepG2 cells [15] , lack PS as a membrane marker that signals “eat-me” to phagocytes . Considering that Kupffer cells are located largely within and often spanning the sinusoidal lumen , thus presenting a significant obstacle for non-self particulate material and damaged host cells [9–11] , the lack of PS on the merosome membrane is likely critical for merozoite escape from this defense mechanism of the host . Because the entire hepatic effluent must pass through the right ventricle and the pulmonary microcirculation before reaching any other capillary bed , we suspected merosomes might sequester in the lungs . To address this , we used ex vivo confocal microscopy to examine the alveolar microvasculature immediately after lung removal while the tissues were intact and the cells alive . At time points from 46 to 58 h after inoculation with PyGFP sporozoites , we found numerous intact merosomes as well as individual parasites ( Figure 8A–8C ) . We did not find pulmonary merosomes earlier than 46 h post inoculation nor later than 65 h , timing consistent with our observation that merosome release begins and ends at roughly 46 h and 56 h . MSP-1 labeling confirmed the maturity of merozoites , both those within merosomes and those already released into pulmonary capillaries ( Figure 8D–8G and Video S10 ) . The small liver stage protein UIS-4 , which localizes to the PVM [38] , was not detected ( unpublished data ) ; thus providing more evidence that the PVM is not involved in merosome formation [5] . As for merosomes in the liver , pulmonary merosomes were negative by immunofluorescence for the hepatocyte receptor ASGR1 ( Figure 8F and 8G ) . In confocal images we often observed an asymmetric arrangement of individual merozoites in relation to lung merosomes and the pattern suggested some of the merosomes were in the process of disintegrating and releasing merozoites into the pulmonary microvasculature just as blood circulation was stopped by lung removal ( Figure 8B ) . Electron microscopy supports the notion of merozoite release by merosomal membrane degradation . Pulmonary merosomes typically contained morphologically well-preserved merozoites , but the cytoplasmic matrix was swollen , and host cell organelles were clearly degenerating . The membrane of lung merosomes was frequently disrupted or barely detectable ( Figure 8H ) suggesting that free merozoites found in nearby pulmonary microvasculature had just been released before fixation ( Figure 8I ) . The presence of erythrocytes containing newly invaded merozoites ( Figure 8J ) supports the notion that blood infection occurred in the lungs . To determine whether merosomes leaving the liver can pass through the lungs and disseminate throughout the body , we analyzed thick smears of blood collected from the aorta and tail vein for merosomes . We also used intravital microscopy , ex vivo imaging , and immunofluorescence microscopy to examine capillary beds of spleen , kidney , and brain of the same mice . While individual small parasites were occasionally detectable , merosomes were completely absent from aorta and tail vein blood and the microcirculation of these organs ( unpublished data ) . These results demonstrate effective retention of hepatic merosomes in the lungs . At 52 h after infection with PyGFP , mice were injected with Hoechst 33342 and PI , and the lungs were removed and analyzed ex vivo . Confocal microscopy revealed that pulmonary merosomes and free merozoites excluded PI ( Figure 9 ) and were also TUNEL-negative ( unpublished data ) ; thus providing evidence of viability . Because infectivity is the ultimate criterion for viability , we tested merosomal merozoites for their ability to induce a parasitemia in naïve mice . However , interpretation of results from inoculation with blood containing merosomes is complicated by the presence of infected erythrocytes . To circumvent this , we initially attempted to eliminate parasitized erythrocytes using selective hypotonic lysis , but this also affected the integrity of the merosomes . Our solution was to control for infected erythrocytes by comparing the infectivity of two types of blood taken from the same mouse: hepatic effluent ( with merosomes and some infected erythrocytes ) and tail vein blood ( without merosomes , but with the same number of infected erythrocytes ) . At 52 h after intravenous infection with 2 . 5 × 106 wt P . yoelii sporozoites , hepatic effluent and tail vein blood samples were collected for inoculation into other mice . Parasitemia and merosome concentration were determined by analysis of thin and thick blood smears , respectively . Preliminary studies showed that the parasitemia in recipient mice injected with hepatic effluent blood rose significantly faster compared to control mice injected with tail vein blood ( unpublished data ) suggesting that merosomes exiting the liver are infectious . This conclusion can be confirmed once a method for merosome purification is available . Because of the large number of parasites and their high packing density , counting merozoites in EEFs is not feasible , although estimates have been published [6 , 22 , 23] . We therefore used measurements obtained from merosomes to calculate the number of merozoites contained in mature P . yoelii EEFs . To do this , we first quantified the merozoite content of a subset of smaller merosomes . We found that merosomes with a diameter of 13 . 4 ± 2 . 0 μm contained 134 . 7 ± 51 . 6 merozoites . Then , we determined the average effective volume merozoites take up inside merosomes . P . berghei merozoites measure 1 . 0–1 . 2 × 1 . 5–1 . 7 μm [39–41] , but because the parasites are embedded in cytoplasm that also contains parasite remnant bodies and host cell organelles , the effective volume the parasites occupy is larger than their actual volume of 0 . 78–1 . 23 μm3 ( based on the ellipsoid volume v = 4/3 π r1 r2 r3 ) . Using a mathematical algorithm for optimal packing of small spheres in a large sphere [42] , we found that P . yoelii merozoites have an effective diameter of roughly 2 . 2 μm and occupy an effective volume of 5 . 56 μm3 in merosomes . Because intravital and electron microscopy showed that the merozoite packing density and the composition of the cytoplasm was basically identical in merosomes compared to mature EEFs ( after rupture of the PVM and mixing of parasites and host cell organelles ) , we then used the sphere packing algorithm to determine the merozoite content of mature P . yoelii EEFs . Based on the measured EEF diameter of 40–75 μm ( see above ) , the effective merozoite diameter of 2 . 2 μm , and assuming a round EEF shape , we calculated that P . yoelii sporozoites produce 4 , 200–29 , 000 hepatic merozoites ( Table S1 ) .
We present here a new model for the transition from the liver to the blood phase of the malaria life cycle ( Figure 10 ) : large merosomes of various sizes bud from infected hepatocytes , enter the hepatic circulation , exit the liver intact , subdivide into smaller more uniform sizes , but otherwise withstand bloodstream shear forces during passage through the right ventricle , and accumulate in the lungs where the merosomes disintegrate and release merozoites to initiate the erythrocytic phase of the malaria cycle . While EEF of avian and reptilian malaria parasites develop in the reticulo-endothelial or hematopoietic systems [43–45] , a major evolutionary change occurred with the mammalian malaria parasites , whose EEF mature in hepatocytes . Perhaps the nutritionally rich and immunologically privileged hepatic environment offers advantages , but it also presents a problem for merozoites released from EEFs into hepatic sinusoids: unless they invade an erythrocyte very quickly they face a gauntlet of highly phagocytic Kupffer cells . The location of most EEFs in the periportal area of the liver lobule [46] means they must travel almost the full length of the sinusoid and pass by a large complement of Kupffer cells before escaping into relative safety outside the liver . As proposed previously by us and others [14 , 15] , our premise is that evolution produced a countermeasure to this threat: release of merozoites within large packets that are initially hidden from the host's innate immune system by envelopment with a hepatocyte-derived membrane . Here we show that merosomes are delivered to the pulmonary microcirculation where they are released . We propose that release of merozoites into the lung microvasculature rather than into larger blood vessels is advantageous , because the low macrophage density and the reduced blood velocity with reduced shear forces will enhance the ability of merozoites to invade erythrocytes . Merosome disintegration in the lungs appears to be the predominant mechanism of merozoite liberation into the bloodstream for the following reasons: ( 1 ) In confirmation of previous reports on the asynchronous nature of EEF maturation [5 , 25] , we observed P . yoelii merosome formation in the liver from 46 h to 56 h after sporozoite infection . Assuming a 10-h window of merosome release , roughly 3 ml total blood volume in a 40 g mouse , and a 100% rate of sporozoite infection and EEF development , 2 . 5 million sporozoites would generate 4 , 167 maturing EEFs per minute , corresponding to 1 . 4 merosome-releasing EEFs per μl blood . ( 2 ) Assuming that extrahepatic merosomes contain on average 150 merozoites , the roughly 29 merosomes we found per μl venous liver blood should have contained 4 , 350 merozoites . Since P . yoelii EEFs contain 4 , 200–29 , 000 merozoites ( Table S1 ) , up to 74% of the total number of merozoites released by 1 . 4 EEFs per min and μl would have been enclosed in merosomes . ( 3 ) A large number of merosomes was arrested in alveolar capillaries suggesting that many merosomes withstand the shear forces inside the central cardiovascular system . Together , these data indicate that a major proportion of the merosome population arrives intact in the lungs and then gradually disintegrates , thus liberating merozoites into the microvasculature . Pulmonary merosomes were detectable in the lungs at least up to 58 h after infection , i . e . , beyond the period of release from the liver ( 46–56 h ) , suggesting that they remained intact for at least many minutes . Similar to hepatic merosomes , which appeared to be infectious and did not stain with annexin V , YO-PRO-1 , or PI , pulmonary merozoites were ultrastructurally well preserved , TUNEL-negative , and did not incorporate PI . Together , these data suggest that merosomal merozoites remain viable until their release into the pulmonary microvasculature . Based on the above assumptions , we propose that merozoite liberation in the lungs represents an integral part of the Plasmodium life cycle . Further support for our premise was found in the following observations and suggestions derived from them . The notion that merozoites shuttled out of the liver within merosomes that are protected from phagocytosis by Kupffer cells [8] was confirmed by demonstrating that murine Kupffer cells do not phagocytoze PyGFP merosomes in vitro ( unpublished data ) , in agreement with the finding that P . berghei merosomes are not ingested by a murine macrophage cell line in vitro [15] . Trager and Jensen's finding that P . falciparum merozoite invasion is enhanced by lack of flow and dense erythrocyte packing [47 , 48] supports our hypothesis that merozoites released within capillary beds have a better chance to invade erythrocytes than those released into larger vessels . We can imagine that capillary occlusion by arrested merosomes could be helpful by causing local stagnation of the pulmonary blood flow . We can also speculate that merosome arrest in lung septal capillaries allows Plasmodium to exploit the unique microenvironment of the blood-air barrier . Virtually nothing is known about the biology of the first-generation ( hepatic ) merozoites , but perhaps transient residence in the lungs provides these parasites with time and a suitable microenvironment to gain infectivity for erythrocytes . The well-oxygenated milieu of the terminal airways and the anastomozed nature of the pulmonary microvasculature [49] likely allow local occlusion of septal capillaries by merosomes without causing the necrotic tissue damage associated with infarction of microvessels in other organs . Many aspects of the process of merosome formation and release we describe are in agreement with earlier work , but others are not . For example , we found that similar to P . berghei–infected HepG2 cells , which detach in toto from the culture vessel after merozoite differentiation is complete [15] , merosomes exiting P . yoelii–infected mouse livers contain viable merozoites and initially do not expose PS on their surface . This confirms earlier predictions [14 , 15] that merozoites are safely shuttled out of the liver disguised as merosomes . The presence of intact mitochondria in mature EEFs indicates that Plasmodium liver stages are able to manipulate hepatocytes in a way that useful organelles ( such as mitochondria as a source of energy ) are preserved , even after merosome budding . Our interpretation , namely that Plasmodium controls certain host cell functions to the last minute , differs from the P . berghei HepG2 cell model , in which the parasites induce death and detachment of their host cells followed by merosome budding [15] . Further , the cell membrane of P . yoelii–infected hepatocytes remains in close apposition to that of neighboring parenchymal and endothelial cells until the very end of EEF differentiation , i . e . , up to the onset of merosome budding , as reported [5 , 12 , 13 , 50 , 51] . As merosomes are produced , the host cell gradually decreases in size and loses contact with neighboring cells as reported [15] . We observed that after releasing merosomes over several hours , the exhausted host cell eventually disintegrates . Some free merozoites still escaped and entered the sinusoidal lumen , thus being exposed to attack by Kupffer cells . In contrast , others proposed that the remaining host cell remnant is rapidly expelled in toto from the tissue with the resulting void immediately filled by neighboring cells [15 , 18] . We find that the necrotic remnant attracts neutrophils and mononuclear phagocytes , which eventually produce a small granuloma . Such granulomata are a frequent observation in P . yoelii– and particularly in P . berghei–infected mouse livers [5 , 8 , 27–31] . Rather than the void created by expulsion of an EEF being filled quickly , our in vivo observations suggest that hours , if not days , are required for phagocytic removal of parasite and host cell debris with subsequent repair of the structural damage before normal tissue architecture is restored . Although we found merosome formation to be the predominant mode of merozoite release from the liver , we observed a less frequent but still common alternative: EEFs undergoing what we interpret as decay . This alternative process of EEF ghost formation was rapid and typically complete within minutes to an hour . In contrast to merozoite release by merosome formation , ghost-forming EEFs did not detach from the surrounding tissue . EEF decay was accompanied by leakage of GFP into the surrounding tissue suggesting damage to the host cell membrane . It occurred in immature EEFs ( recognizable by a homogeneous green fluorescent cytoplasm ) and also in mature EEFs ( containing fully formed merozoites ) without merosome formation regardless of maturity . Sometimes it was found as early as 42 h after sporozoite infection , hours before merozoite differentiation begins . The end result of this alternative process was the formation of large faintly fluorescent EEF ghosts containing some cellular debris and a few dead merozoites . We interpret this rapid conversion of EEFs to ghosts as abortive liver stage development . Merozoite content of EEFs has historically been difficult to estimate due to the large number of parasites and their high packing density . Based on measurements of the size and merozoite content of small merosomes combined with size measurements of EEFs and an appropriate mathematical algorithm [42] , we were able to calculate the number of merozoites in an EEF ( Table S2 ) . Under intravital imaging conditions , mature P . yoelii EEFs measured 40–75 μm and the calculated space effectively occupied by a merozoite is a sphere of 2 . 2 μm diameter . Using this effective size , we calculated that individual P . yoelii sporozoites produce roughly 4 , 200–29 , 000 merozoites per EEF . This number is in general agreement with older estimates of EEF merozoite content [5 , 12 , 22 , 23 , 52–57] ( Table S1 ) . An exception is P . falciparum , which produces considerably larger numbers of hepatic merozoites , most likely because of the small size of the parasites . As far as we know , our analysis of the number of merozoites produced in hepatocytes is the first such analysis based on actual merozoite counts and host cell measurements . Precision is limited by variations in measurements , but basing calculations on direct in vivo measurements enhances accuracy . Earlier studies conducted by us and others had suggested that merosome budding may precede completion of merozoite differentiation [14 , 15] . One factor that helped lead to this interpretation is that GFP expressed in the parasite stroma can obscure the parasites in mature EEFs . We now show that prior to merosome formation , the signal of the stromal GFP fluorescence equaled that of the merozoite cytoplasm , thus preventing clear definition of parasites enmeshed in the stroma . At the onset of merosome budding , the stromal GFP emission signal decreased abruptly thus revealing the presence of the already formed fluorescent parasites ( Figure 3A–3E and Video 5 ) . Two factors contribute to this reduction in fluorescence of material surrounding the parasites: dilution and loss of cytosolic GFP . Dilution of GFP results from PV disassembly and mixing of fluorescent parasite stroma with non-fluorescent host cytoplasm . Loss of GFP is caused by leakage of the fluorochrome into the environment . In agreement with reports that the hepatocyte membrane becomes permeable at late stages of infection with P . berghei [5] , we found that merosome-forming EEFs are typically surrounded by a halo of green fluorescence . Optimization of the imaging conditions allowed us to visualize the parasites inside mature EEFs and revealed that merosomes always contain mature merozoites . Thus , merozoites maturation precedes merosome formation . Depending on the approach used for measurement , the reported diameters of hepatic and pulmonary capillaries vary greatly . For example , when measured in perfusion-fixed liver tissue , the sinusoidal diameter ranged from 4–6 μm to 9–12 μm [58–60] . A crucially important factor is the pressure applied during perfusion fixation , because the sinusoidal diameter is known to vary with changes in blood pressure [61 , 62] . To determine the sinusoidal diameter under normal blood pressure conditions , we used live Tie2-GFP mice [32] , whose fluorescent endothelia clearly delineate the boundaries of the sinusoidal lumen [31] . In agreement with earlier in vivo microscopic studies , which reported a diameter of 6 μm for portal sinusoids and 7 μm for central sinusoids [58] , we found by intravital imaging that liver sinusoids measure 6 . 7 ± 1 . 9 μm in diameter . Similar differences between fixed and live specimens were reported for the size of alveolar capillaries . While vascular casts of the lung suggested that alveolar capillaries measure 6 . 69 ± 1 . 39 μm in diameter [63] , intravital measurements determined a functional diameter of only 1–4 μm [64 , 65] . Regardless which liver sinusoid and lung capillary measurements are relied upon and regardless of the drastic reduction in merosome size after leaving the liver , merosomes still exceed the size of the lumen of the microvasculature of both liver and lung . Since even the largest merosomes were eventually transported out of the liver , the much smaller extrahepatic merosomes would be expected to be malleable enough to be able to pass though the pulmonary capillary bed . Therefore it is somewhat surprising that the lungs effectively clear the blood of all merosomes , so virtually none were detectable in arterial blood harvested from the left ventricle , in the capillary beds of spleen , brain and kidney , or in tail vein blood . The fact that the velocity in pulmonary capillaries is somewhat higher than hepatic sinusoids [66–69] makes this more unexpected . Consequently , the possibility of a receptor-mediated mechanism for pulmonary merosome arrest cannot be excluded .
Anopheles stephensi mosquitoes were used to propagate wild-type P . yoelii ( strain 17 XNL ) or PyGFP [14 , 70] . Sporozoites were purified from the salivary glands of female A . stephensi mosquitoes [71] . Mice were ( 1 ) Balb/c ( Taconic Farms , Incorporated ) , ( 2 ) Swiss Webster ( Taconic Farms , Incorporated ) , or ( 3 ) Tie2-GFP mice , a transgenic strain that expresses GFP in vascular endothelial cells under control of the Tie2 promoter ( STOCK Tg ( TIE2GFP ) 287Sato/J; Jackson Laboratory ) [31 , 32] . Animals were maintained and used in accordance with recommendations in the guide for the Care and Use of Laboratory Animals . Mice were inoculated into the tail vein with 0 . 3–1 . 5 × 106 PyGFP sporozoites . At 30–66 h p . i . , the animals were surgically prepared for intravital imaging of liver and spleen as described [31] and anesthetized by intraperitoneal injection of a cocktail of 50 mg/kg ketamine ( Ketaset , Fort Dodge Animal Health ) , 10 mg/kg xylazine ( Rompun , Bayer ) , and 1 . 7 mg/kg acepromazine ( Boehringer Ingelheim Vetmedica ) . Reinjection of the anesthetics at 30-min intervals allowed intravital microscopic examination of the animals for at least 3 h [31] . After surgical preparation for intravital imaging , mice were placed onto the stage of an inverted Zeiss DMIRE2 microscope , equipped with a temperature-controlled Ludin chamber , and analyzed with a Leica TCS SP2 AOBS confocal microscope . Appropriate laser lines were used to excite GFP , various other fluorochromes , and the natural autofluorescence of the mouse tissues . Laser power was reduced to a minimum to avoid phototoxicity and bleaching . These optimized conditions allowed continuous scanning of live PyGFP for a period of up to 6 h without any apparent effect on viability . To assess parasite and host cell viability , some mice were i . v . injected with 1–2 μg/ml of the membrane-permeable nuclear dye Hoechst 33342 prior to confocal microscopy . Other mice received 1 μg/ml PI in addition to detect dead host cells and/or parasites . Mice were intravenously inoculated with 3 ×106 purified wt P . yoelii or 1 × 106 PyGFP salivary gland sporozoites and various organs were removed at 52 h after infection . Tissue slices were snap-frozen in liquid nitrogen or fixed with PBS containing 4% paraformaldehyde for immunofluorescence labeling of cryosections and with PBS containing 4% paraformaldehyde and 1% glutaraldehyde for electron microscopic examination [72 , 73] . At 30–66 h after infection with PyGFP , major organs such as spleen , brain , kidney , or lung were removed , placed into glass-bottom dishes , and kept moist with medium for confocal microscopy analysis . Blood was harvested from ( 1 ) the terminal hepatic vein , ( 2 ) the aorta , or ( 3 ) a tail vein . To increase the probability of detection , ten aliquots of 5 μl blood from each of these sites were spread over an area of 1 cm2 , allowed to dry , and stained with Giemsa without prior fixation . Merosomes were counted and expressed as average number ± STD . In parallel , the number of merozoites per merosome was determined accordingly . Two days after infection with 1 . 5 × 106 wt P . yoelii sporozoites , hepatic effluent and tail vein blood was harvested from the same animal and parasitemia and merosome content were determined using thin and thick blood smears , respectively . 20-μl hepatic effluent , containing 1 × 105 infected erythrocytes plus 167 merosomes , or tail vein blood containing the same number of infected erythrocytes but no merosomes , was intravenously inoculated into Swiss Webster mice ( three mice per group ) and the parasitemia was monitored daily by Giemsa staining . To improve the recovery of parasite material from the liver , merosomes were dislodged from hepatic sinusoids by perfusing mouse livers via the portal vein with oxygenated medium at 5 ml/min for 10–30 min . The effluent was collected in two fractions: fraction 1 was collected from the Vena cava inferior and contained mainly red blood cells; fraction 2 was collected from the Vena cava superior after ligation of the Vena cava inferior . The cells were washed and allowed to settle onto cover slips or glass-bottom dishes ( WillCo Wells ) treated with Alcian blue [74] for live cell imaging . Nuclei of merosomes were visualized with the membrane permeable nucleic acid stains Hoechst 33342 ( 1–2 μg/ml ) or SYTO-64 . Nuclei of dead parasites were determined with membrane impermeable PI ( 1 μg/ml ) . Merosome membranes were stained with 5 μg/ml FM 4–64 FX ( Molecular Probes ) . Annexin V Alexa Fluor 488 conjugate or YO-PRO-1 ( 0 . 1 μM ) were used to detect evidence of programmed cell death in live merosomes . Tissue sections were stained with a BrdU TUNEL assay kit ( Molecular Probes ) according to manufacturer's guidelines . Alcian blue–immobilized PyGFP merosomes were fixed and labeled with the red nuclear dye SYTO-64 . 3-D stacks were scanned by confocal microscopy and the number of merozoite nuclei was counted using a 3-D object count plug-in of ImageJ ( NIH freeware ) . Merozoite number and merosome diameter were then entered into a formula for efficient packing of equal small spheres in a large sphere ( n = 0 . 7405 [1–2D] / D3 + 1 / [2D2]; D = dmerozoite / dliver stage ) [42] to determine the effective diameter/volume merozoites occupy inside merosomes . Based on these calculations and the diameter of PyGFP liver stages measured by intravital microscopy , the merozoite content of P . yoelii liver stages was estimated in relation to size . Frozen sections of 10-μm thickness were prepared with a Reichert-Jung Frigocut cryostat . Parasites were labeled with a mAb directed against the P . yoelii merozoite surface protein MSP-1 , a kind gift from W . Bergman [75] . A rabbit antiserum , which was originally generated against the PVM-associated protein from P . berghei , but exhibits cross-reactivity with P . yoelii UIS4 [38 , 76] , was used to label the PV in P . yoelii–infected hepatocytes . Affinity-purified goat IgG against the murine asialoglycoprotein receptor ASGR1 was from R&D Systems . Incubation with the primary antibodies was followed with protein A conjugated to fluorescein isothiocyanate ( PA-FITC; Molecular Probes ) , anti-goat IgG conjugated to Texas Red ( GAR-TR; Molecular Probes ) , or goat anti-rabbit IgG conjugated to Texas Red ( GAM-TX; Molecular Probes ) in color-matching fluorochrome combinations . In case of a single FITC label , the specimens were counterstained with 0 . 1% Evans blue in PBS . Immunofluorescence-labeled frozen tissue sections were examined by confocal microscopy . Mouse liver or lung tissue was fixed with 1% glutaraldehyde and 4% paraformaldehyde in PBS , post-fixed with 1% osmium tetroxide and 1 . 5% potassium hexacyanoferrate , stained en bloc with 1% uranyl acetate , dehydrated in ethanol , and embedded in Epon as described [72 , 73] . Semithin sections were cut with an RMC MT-7 ultramicrotome and photographs were taken with Kodak Ektachrome 160T slide film using a Nikon FX-35DX/UFX-DX camera/exposure system . Thin sections were post-stained with uranyl acetate and lead citrate and viewed with a Zeiss EM 910 electron microscope [73] . Electron microscopy negatives and Ektachrome slides were scanned with a Hewlett Packard Scanjet 5370C . All digital , electron , or confocal microscopy images were processed using Image-Pro Plus ( Media Cybernetics ) , Adobe Photoshop ( Adobe ) , and AutoDeBlur ( AutoQuant Imaging , Incorporated ) software . | The malaria parasite Plasmodium undergoes one large round of multiplication in the liver before beginning the blood phase of the life cycle , the phase that causes the typical episodes of fever and chills . Using intravital microscopy and fluorescent parasites , we studied the mode and dynamics of parasite release from the liver , a critical stage in the malaria life cycle . Earlier work had indicated that infected liver cells could release packets of dozens to hundreds of parasites enveloped by host cell membrane , structures now known as merosomes . We report here that this is the predominant mechanism of parasite release from the liver . The host-derived merosome membrane lacks a marker for phagocytic engulfment , thus allowing safe passage through the gauntlet of Kupffer cells , highly active liver macrophages . Merosomes remain intact during passage through the heart and become sequestered within lung capillaries where the membrane eventually disintegrates liberating the parasites into the lung circulation . We propose that this previously unknown part of the life cycle of Plasmodium facilitates red blood cell invasion , thus jump-starting the blood phase of the life cycle and the onset of clinical malaria . |
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Like human immunodeficiency virus type 1 ( HIV-1 ) , simian immunodeficiency virus of chimpanzees ( SIVcpz ) can cause CD4+ T cell loss and premature death . Here , we used molecular surveillance tools and mathematical modeling to estimate the impact of SIVcpz infection on chimpanzee population dynamics . Habituated ( Mitumba and Kasekela ) and non-habituated ( Kalande ) chimpanzees were studied in Gombe National Park , Tanzania . Ape population sizes were determined from demographic records ( Mitumba and Kasekela ) or individual sightings and genotyping ( Kalande ) , while SIVcpz prevalence rates were monitored using non-invasive methods . Between 2002–2009 , the Mitumba and Kasekela communities experienced mean annual growth rates of 1 . 9% and 2 . 4% , respectively , while Kalande chimpanzees suffered a significant decline , with a mean growth rate of −6 . 5% to −7 . 4% , depending on population estimates . A rapid decline in Kalande was first noted in the 1990s and originally attributed to poaching and reduced food sources . However , between 2002–2009 , we found a mean SIVcpz prevalence in Kalande of 46 . 1% , which was almost four times higher than the prevalence in Mitumba ( 12 . 7% ) and Kasekela ( 12 . 1% ) . To explore whether SIVcpz contributed to the Kalande decline , we used empirically determined SIVcpz transmission probabilities as well as chimpanzee mortality , mating and migration data to model the effect of viral pathogenicity on chimpanzee population growth . Deterministic calculations indicated that a prevalence of greater than 3 . 4% would result in negative growth and eventual population extinction , even using conservative mortality estimates . However , stochastic models revealed that in representative populations , SIVcpz , and not its host species , frequently went extinct . High SIVcpz transmission probability and excess mortality reduced population persistence , while intercommunity migration often rescued infected communities , even when immigrating females had a chance of being SIVcpz infected . Together , these results suggest that the decline of the Kalande community was caused , at least in part , by high levels of SIVcpz infection . However , population extinction is not an inevitable consequence of SIVcpz infection , but depends on additional variables , such as migration , that promote survival . These findings are consistent with the uneven distribution of SIVcpz throughout central Africa and explain how chimpanzees in Gombe and elsewhere can be at equipoise with this pathogen .
Until recently , simian immunodeficiency virus ( SIVcpz ) of chimpanzees ( Pan troglodytes ) , the immediate precursor to human immunodeficiency virus type 1 ( HIV-1 ) , was assumed to be non-pathogenic in its natural host [1] . However , a long-term natural history study of infected apes in Gombe National Park revealed that SIVcpz is quite pathogenic , causing CD4+ T cell depletion , lymphatic tissue destruction and premature death [2] . Chimpanzees are already highly endangered and face severe pressure from hunting , habitat destruction , and other diseases [3]–[5] . In this study , we thus asked what additional impact SIVcpz pathogenicity may have on chimpanzee survival at the population level . Most primate species naturally infected with SIV appear not to develop immunodeficiency , although only African green monkeys ( Chlorocebus spp . ) and sooty mangabeys ( Cercocebus atys ) have been studied in detail [1] . In the latter species , SIV infection is common and widespread throughout the natural habitat [6]–[9] . This is not true for wild-living chimpanzees , where extensive sampling across Africa has shown a rather uneven distribution of SIVcpz , with high prevalence rates in some communities and rare or absent infection in others [10]–[12] . Within the range of the eastern chimpanzee ( Pan troglodytes schweinfurthii ) , SIVcpz has been documented at several locations in the Democratic Republic of Congo [12] , [13]; however , extensive molecular epidemiological studies of chimpanzee communities in Uganda and Rwanda failed to detect evidence of infection [10] , [14] . Similarly , in Tanzania SIVcpz was found in Gombe , but not in Mahale Mountains National Park [10] , [14] . The reasons for this uneven distribution remain unclear; however , one possibility is that infected populations go extinct as a consequence of SIVcpz infection , with habitat subsequently colonized by chimpanzees from uninfected populations . To examine whether SIVcpz has a negative impact on chimpanzee population growth , we obtained detailed demographic and prevalence data from Gombe National Park , Tanzania . Gombe is home to three chimpanzee communities ( Figure 1 ) and is the only site where habituated chimpanzees are endemically infected with SIVcpz , thus making it the only location where these studies could be conducted . Observational studies of the Kasekela and Mitumba chimpanzees began in 1960 and 1985 , and both communities were habituated to close-range observation by the mid-1960s and mid-1990s , respectively [15] . The Kalande community has not been habituated , but has been continuously monitored since 1999 [16] . Non-invasive SIVcpz surveys were initiated in 2000 , but did not include sufficiently large numbers of individuals until 2002 [10] . We thus examined data for the 8-year time period for which we have both demographic and SIVcpz prevalence data ( 2002–2009 ) . This represented a sufficiently long timescale to document both new infections and deaths of infected individuals . To gain a longer-term perspective on demographic patterns , we also examined demographic data going back to 1998 , the earliest year for which reliable population size estimates for all three communities are available . In a previous study , we reported the molecular epidemiology of SIVcpz infection in Kasekela and Mitumba [2] . In the present study , we focused on the unhabituated Kalande community , for three reasons . First , an initial survey conducted in 2002 showed that Kalande chimpanzees exhibited a high SIVcpz prevalence [10] . Second , molecular epidemiological evidence suggested that Kalande played a role in the spread of the SIVcpz infection in Gombe [2] . Finally , Kalande appeared to have undergone a substantial population decline [15] . From the late 1960s through the early 1980s , Kalande was likely comparable in size to the Kasekela community , based on the number of males ( 7–9 adult males in Kalande compared to 7–8 in Kasekela ) and the size of territory that each community controlled [17] . By the late 1990s , however , Kasekela chimpanzees were traveling deep into former Kalande territory . The fact that they encountered little resistance suggested that the Kalande community had undergone a decline , which was confirmed by subsequent population surveys [16] , [18] . Concern that the Kalande community was declining prompted a survey in 1998 , followed by regular monitoring starting in 1999 . Initially , the decline was attributed to human impacts , including habitat loss and poaching [15] , [18] . However , an unusually high SIVcpz prevalence in Kalande [10] , combined with the now recognized excess mortality associated with this infection [2] , suggested that SIVcpz might have contributed to the decline of this community . Following up on these earlier observations , we have in the present study examined the decline of the Kalande chimpanzees in greater detail . First , we examined whether their decline continued in more recent years , by estimating their annual population size using a combination of nest transect data , visual observations , and non-invasively collected genetic data to infer kinship relations . Second , we used microsatellite data to examine kin relationships of presumed Kalande emigrants . Third , we constructed viral phylogenies to examine the role of Kalande chimpanzees in inter-community transmission of SIVcpz . Fourth , we tested whether habitat loss , rather than disease , was responsible for Kalande's most recent decline by compiling data from vegetation plots and comparing the food abundance in the ranges of each community . Finally , we used empirically determined demographic parameters as well as deterministic and stochastic mathematical models to gain a more general understanding of the impact of SIVcpz associated excess mortality on chimpanzee population size . Collectively , our studies confirmed that the Kalande community has experienced a catastrophic population decline and suggested that this decline was caused , at least in part , by high levels of SIVcpz infection . However , we also found that population extinction is not an inevitable consequence of SIVcpz infection . Stochastic modeling revealed that intercommunity migration can counteract the negative effects of SIVcpz and rescue declining populations . Testing various realistic conditions , we found that SIVcpz frequently went extinct rather than its primate host . These results have important implications for chimpanzee conservation .
Noninvasive SIVcpz testing was initiated in Gombe in 2000 , after the first infected chimpanzee ( Ch-006 ) was identified [19] . Subsequent studies documented additional infections and showed that all three communities harbored SIVcpz [10] . By 2001 , many Kasekela and Mitumba chimpanzees were sampled at least once every year [2] . Sampling in Kalande was more sporadic due to the non-habituated nature of this community ( Figure S1 ) , but over the past eight years 341 fecal samples have been collected from 26 different individuals ( median 4 . 5 samples per individual; range 1 to 75; Tables 1 and S1 ) . Five resident Kalande chimpanzees were followed continuously over six years ( Figure S1 ) , one of whom ( Ch-100 ) became newly infected during the course of the study ( Table S1 ) . Like in Mitumba and Kasekela , SIVcpz infection in Kalande was determined non-invasively , by documenting virus specific antibodies and/or nucleic acids in fecal samples [2] , [10] . The number of sampled individuals was determined by microsatellite analyses ( Tables S1 and S2 ) . Figure 2 compares the SIVcpz prevalence rates in Mitumba , Kasekela and Kalande with the corresponding population sizes over an eight-year observation period . Although fewer in number , Kalande apes consistently exhibited a much higher biannual prevalence rate ( mean = 46 . 1±7 . 6% ) than either Mitumba ( mean = 12 . 7±4 . 9% ) or Kasekela apes ( mean = 12 . 1±3 . 2% ) ( Figure 2C ) . Community had a statistically significant effect on prevalence , with Kalande chimpanzees exhibiting significantly higher infection rates than Mitumba or Kasekela ( Kruskal-Wallis test , P<0 . 001 ) . The same relationships were also observed when annual prevalence rates were used , indicating that the statistical significance was not the result of an inflated sample size . The population sizes of the habituated Kasekela and Mitumba communities have been monitored closely since the 1980s and are based on detailed daily observational records [15] , [17] , [18] . Regular monitoring of the Kalande population did not begin until 1999 , but intermittent population data are available from as early as 1968 , due to attempts to habituate this community for observation . In 1969 , C . Gale identified more than 20 individuals and estimated that the total population size of Kalande was approximately 40 individuals ( unpublished data from the Gombe Stream Research Center ) . In 1999 , E . Greengrass observed large parties of 20 or more individuals in Kalande and estimated a total population of 30 individuals ( unpublished data ) . Between 2000 and 2002 , F . Grossman saw parties of up to 16 individuals and identified a total of 30 distinct Kalande apes ( unpublished data ) . Nest transect data for these years yielded smaller numbers , as only weaned individuals make nests , with an estimated 14 . 7 weaned individuals in the population in 1999–2000 [16] , and 17 weaned individuals in 2000–2002 ( 95% CI: 12 . 1–24 . 6 ) . As monitoring continued , the Kalande chimpanzees became more habituated , with the median distance from observers decreasing from 43 m in 2004 to 13 m in 2009 . While most of the Kalande chimpanzees remained wary , observers were generally able to determine the age-sex class of individuals , and visually identify and name at least 14 regularly seen apes ( additional individuals were named only after emigrating to other communities ) . Despite a growing acceptance of observers , the number of chimpanzees seen at any given time decreased . During 290 observations in the time from January 2002 to June 2009 , the mean party size was 2 . 98±1 . 63 , and the maximum party size only nine chimpanzees . When , as is common in measures of party composition , dependent offspring were not included , the mean party size was 1 . 89±1 . 11 ( range = 1 to 6 ) . These parties were not only smaller than those observed earlier in the same community , but also much smaller than those seen in Kasekela , where the mean party size for 2002–2007 was 10 . 3±9 . 2 ( dependent offspring not included; range = 1 to 38; unpublished data from the Gombe Stream Research Center ) . More recently , we have estimated the Kalande population size by using sightings , genetic data , and inferences about age and residence patterns ( Tables S3 and S4 ) . Microsatellite analyses revealed maternal , paternal , and sibling relationships and thus confirmed the Kalande origin of eight Kasekela and Mitumba immigrants ( Figure 3 , Tables S5 and S6 ) . Of the 26 individuals initially sampled in Kalande , three were subsequently sampled after they transferred to a habituated community , including two to Kasekela ( Ch-071 , Ch-099 ) and one to Mitumba ( Ch-098 ) . Five others were not sampled in Kalande , but were found to have either mothers ( Ch-021 , Ch-079 , Ch-101 ) or other close kin ( Ch-029 , Ch-033 ) in Kalande , supporting the view that they originated there ( Table S5 ) . For three additional individuals , who immigrated to Kasekela ( Ch-022 , Ch-105 ) and Mitumba ( Ch-076 ) in recent years ( 2000 , 2004 , and 2001 , respectively ) , we did not find close genetic ties to Kalande , but we inferred that they came from Kalande because their origin in a habituated community could be ruled out . We have thus genetically identified 34 individuals of known ( n = 26 ) , highly likely ( n = 5 ) , or inferred ( n = 3 ) Kalande origin . In combination , these data have yielded minimum and maximum population estimates for Kalande . As shown in Figure 2B , the Kalande community has declined substantially . Estimates based on inferred community membership yielded a 1998 population size of 19–43 individuals . By 2002 , when genetic sampling and more regular observations provided a narrower range of estimates , approximately 28–36 individuals lived in Kalande . By the beginning of 2009 , only 14–18 individuals remained . The observed decrease in Kalande party sizes corresponds to documented losses from the community through death and emigration ( Tables S3 and S4 ) . In 2002 alone , six to ten individuals died , leaving the community with only a single adult male . Moreover , the largest number of adult males observed in Kalande since 1998 ( n = 4 ) was much lower than the number of adult males seen during intercommunity interactions in the 1970s ( n = 7–9 ) [17] , suggesting that the entire community was larger in the 1970s than it was in the late 1990s . Of the 52 chimpanzees known or suspected to have resided in Kalande ( 1998–2009 ) , only 14–18 were still living in Kalande at the beginning of 2009 . Eleven had emigrated , 10–22 died , and 4 were of unknown status ( Table S4 ) . For 34 of the 52 chimpanzees , fecal samples were available for genotype and SIVcpz status determinations . Five of these sampled individuals are known to have died ( Ch-021 , Ch-033 , Ch-085 , Ch-087 , Ch-099 ) , and an additional 9 have not been sampled since 2005 ( Ch-070 , Ch-081 , Ch-082 , Ch-083 , Ch-084 , Ch-091 , Ch-092 , Ch-095 , Ch-107 ) and are thus presumed to have died ( Figure S1 ) . Of these 14 individuals , seven ( 50% ) were infected with SIVcpz at the time of their last sample , including three ( Ch-021 , Ch-033 , Ch-099 ) of the five known dead ( 60% ) and four ( Ch-070 , Ch-082 , Ch-091 , Ch-107 ) of the 9 presumed dead ( 44% ) ( Figure S1; Table S4 ) . Three of the known dead emigrated from Kalande to Kasekela where they died of unknown causes ( Ch-033 ) , the consequences of a spinal cord injury ( Ch-099 ) [2] , and an AIDS-like illness ( Ch-021 ) ( K . Terio , personal communication ) . An additional female ( Ch-036 ) , who immigrated into Kasekela in 1997 , possibly from Kalande , also died of an AIDS-like illness [2] . During the years in which we were able to determine SIVcpz prevalence ( 2002–2009 ) , the Kalande community exhibited a negative annual growth rate , using both minimum ( mean ± standard = −6 . 5±14 . 1% ) and maximum ( −7 . 4±8% ) community size estimates . In contrast , the Mitumba and Kasekela communities , which had lower SIVcpz prevalence rates , both grew ( Mitumba: 1 . 9±8 . 4%; Kasekela: 2 . 4±5 . 3% ) ( Figure 2D ) . The difference in annual population growth among these three communities was statistically significant , but only for the maximum estimate of the Kalande community ( Kruskal-Wallis test , n = 8 years for each of 3 communities; P<0 . 05 ) . From the start of 2002 through the end of 2009 , Mitumba increased from 22 to 25 individuals and Kasekela increased from 51 to 61 individuals ( Figure 2A ) . Much of this growth , however , consisted of immigrating females and their offspring , who transferred from Kalande to Kasekela ( Ch-021 , Ch-071 , Ch-079 , Ch-099 , Ch-101 , Ch-105 , with one infant subsequently born to each of Ch-021 , Ch-071 , Ch-099 , and Ch-105 ) ; from Kalande to Mitumba ( Ch-098 ) ; and from Mitumba to Kasekela ( Ch-080 , Ch-096 ) . Some of these individuals ( Ch-099 and her infant , and the infants of Ch-021 and Ch-071 ) died by the end of 2009 . Excluding the surviving 2002–2009 immigrants and their offspring , the Mitumba community grew by one , while the Kasekela community neither grew nor declined . The net population growth in the communities with lower SIVcpz prevalence was thus largely due to female immigration from Kalande . In contrast , Kalande suffered a net population decline , even accounting for female migration , which comprised 11 of the 21–33 departures from Kalande . The remaining departures from Kalande resulted from known ( 10 ) or suspected ( 12 ) death ( Figure S1 , Table S4 ) . To compare the evolutionary relationships of SIVcpz strains in Kalande to those in Kasekela and Mitumba , we amplified a 477-bp pol fragment from two chimpanzees ( Ch-086 , Ch-100 ) , one of whom ( Ch-086 ) had not previously been characterized . Attempts to amplify SIVcpz sequences from Ch-091 , Ch-107 , Ch-118 and Ch-121 remained unsuccessful , most likely due to sample degradation . A phylogenetic tree of SIVcpz pol sequences from Gombe is shown in Figure 4 , with viruses color-coded according to their current or most recent community ( Mitumba , blue; Kasekela , brown; Kalande , orange ) . Attempts to amplify a pol region from Ch-033 were unsuccessful and the phylogenetic position of this ape's virus ( TAN16 ) is approximated based its gp41/nef region [2] . The analysis shows that all Gombe viruses form a monophyletic lineage , and that viruses from Mitumba , Kasekela and Kalande are interspersed , indicating inter-community transfers . Interestingly , all viruses , except TAN13 from Ch-080 , appear to have their roots in Kalande ( indicated by orange colored branches ) . This includes TAN5 , TAN6 , and TAN9 , which are derived from females who are known ( Ch-071 , Ch-099 ) or inferred ( Ch-022 ) to have immigrated from Kalande . It also includes all viruses from Mitumba , which fall within a cluster of Kalande viruses . Finally , it includes TAN14 , TAN10 , TAN11 and TAN12 , which cluster distal to TAN16 from a female ( Ch-033 ) inferred to have originated in Kalande . Based on these data , it seems clear that most extant viruses in Kasekela and Mitumba trace back to the Kalande community , with movement of infected females representing the main mode of inter-community transmission . Interestingly , TAN18 identified in a resident Kalande female ( Ch-089 ) likely originated in Kasekela . Ch-089 visited Kasekela in 2003–2004 where she became pregnant , based on paternity analysis of her son ( Ch-118 ) , and presumably also acquired SIVcpz . Thus , in this case , SIVcpz was likely transmitted back to Kalande , again through the movement of a female . The dispersal of infected females from Kalande was accelerated in 2002 when Kalande suffered an especially devastating series of deaths . Up to 10 individuals were last observed in 2002 , including two adult males identified both visually and genetically ( Ch-085 , Ch-087 ) , four apes known only from genotyped samples ( Ch-070 , Ch-081 , Ch-082 , Ch-083 ) and four known only visually ( KLAM2 , KLAF4 , KLSM2 , BB-089 ) . Of these , Ch-087 was likely killed by people [18] , Ch-085 suffered from severe diarrhea when last seen and likely died from causes unrelated to SIVcpz , and KLAF4 and KLSM2 died from respiratory disease [18] . Of six genotyped individuals , two were positive for SIVcpz when last tested ( Ch-070 and Ch-082 ) . Thus , while SIVcpz may have contributed to 2 of 6 ( 33% ) known cases of death in 2002 , the remaining mortality was likely caused by other factors . Interestingly , there was an outbreak of respiratory disease in Kalande , which appears to be linked to an outbreak first reported in Kasekela . Between September 7 and 30 of 2002 , 24 of 51 Kasekela chimpanzees were seen with symptoms of respiratory illness . Subsequently , between October 5 and 15 , four Kalande apes were seen coughing severely , of which two ( KLAF4 and KLSM2 ) died , with their bodies found on October 10 and 11 , respectively . Mitumba also appears to have been affected by this outbreak , with 5 of 23 Mitumba chimpanzees seen with symptoms of respiratory disease from late September to mid-October . After these deaths , several mothers either visited ( Ch-086 , Ch-089 , Ch-093 ) or permanently transferred ( Ch-099 ) into Kasekela . Two of these females ( Ch-086 , Ch-099 ) were already SIVcpz infected , while Ch-089 apparently became infected during her visit in Kasekela . Adult females usually do not transfer once they have settled and reproduced in a community [20] . These individuals thus seem to represent extraordinary cases where the decline of their community prompted their emigration into neighboring communities . It is thus likely that the dramatic decline of Kalande chimpanzees in 2002 increased the inter-community spread of SIVcpz . Over the years , Kalande chimpanzees suffered a reduction of available habitat both by deforestation of land outside the park [15] and through loss of territory to the Kasekela community . Since this habitat loss and consequent reduced food availability could have contributed to the community's decline [15] , we compared the abundance of chimpanzee food plants within the ranges of all three Gombe communities . We conducted a series of vegetation plots placed throughout the park , in which we determined the basal area of chimpanzee food trees ( defined as the total area covered by the cross-sections of tree trunks measured at breast height [21] and counted the number of stems of smaller food plants . Because ranging data for the unhabituated Kalande chimpanzees are limited , we used two estimates of their range , a minimal range and a likely range ( see Methods ) , which measured 597 ha and 1182 ha respectively . Because observational data for each year yielded only a limited number of locations for Kalande chimpanzees , we calculated a single 2002–2009 range using all locations . For comparison , earlier qualitative estimates for the Kalande range , based on small samples of sightings , were ∼900 ha for 1999–2000 ( E . Greengrass , unpublished data ) and ∼984 ha for 2000–2002 ( F . Grossmann , unpublished data ) . The 2007 ranges for Mitumba and Kasekela covered 544 ha and 1649 ha respectively . We found that the estimated total basal area of chimpanzee food trees in Kalande ( 5 , 420–10 , 050 m2 ) was intermediate between that of Mitumba ( 5 , 403 m2 ) and Kasekela ( 19 , 155 m2 ) ( Figure 5A ) . However , because few chimpanzees lived in Kalande , the per capita abundance of food trees was actually highest in Kalande ( Mitumba: 235 m2; Kasekela: 325 m2; Kalande: 356–648 m2 ) ( Figure 5B ) . In addition to fruit and leaves from trees , chimpanzees obtain much of their food from smaller plants , including vines and shrubs . The range of estimates for the abundance of these plants in Kalande ( 1 , 432 , 000–2 , 841 , 000 stems ) encompassed the estimate for Mitumba ( 2 , 820 , 000 stems ) , but was much lower than in Kasekela ( 8 , 485 , 000 stems ) ( Figure 5A ) . However , as with the food trees , the small population in Kalande resulted in relatively high per capita estimates of smaller plants ( 92 , 000–183 , 000 stems ) , with the upper end of this range exceeding the estimates for both Mitumba ( 123 , 000 stems ) and Kasekela ( 144 , 000 stems ) ( Figure 5B ) . Thus , while the density of food species was lower , the large size of the Kalande range resulted in an overall per capita abundance of food species that was comparable to , or larger than , those of the other Gombe communities . The predicted population size for this range , assuming levels of per capita food abundance comparable to those in Mitumba , was 24–43 individuals based on food trees , and 12–23 individuals based on the abundance of other food plants . The estimated population of the Kalande community in 2002 ( 32–37 individuals ) was thus within the range of the population predicted from the abundance of food trees , but larger than the population based on the abundance of other plants . To estimate the impact of SIVcpz infection on chimpanzee population growth , we employed two sets of models: deterministic calculations of critical prevalence and event-based stochastic simulations . Deterministic calculations tend to provide an upper bound for population growth , since stochastic factors generally reduce population growth . However , stochastic simulations incorporate random processes likely to occur in real populations , particularly the small populations typical of chimpanzee communities . The deterministic model indicated that the critical prevalence of SIVcpz ( i . e . , the prevalence below which the population does not decline ) depends greatly on the mortality multiplier , ρ , which reflects the excess mortality due to SIVcpz infection ( Figure 6 ) . We previously found that SIVcpz infection increases the mortality hazard 10–16 fold , depending on assumptions concerning the death and infection status of certain individuals [2] . Because the 95% confidence intervals for these estimates were wide ( e . g . , 2 . 8–34 . 3 for the lower estimate [2] ) , we considered a range of estimates for ρ . When SIVcpz doubles mortality ( ρ = 2 ) , the prevalence can be 17% and the population will not decline . When ρ = 5 , the prevalence can only be 6 . 4% , and when ρ = 10 , the maximum prevalence compatible with population persistence is 3 . 4% . Thus , if the actual value for ρ is indeed in the range of 10–16 , even relatively low prevalence rates of SIVcpz are predicted to lead to population decline . SIVcpz infection may affect population growth not only by increasing mortality , but also by reducing fertility . Infected females studied to date experienced a reduction in fertility , α , of 50% [2] . Because this estimate is based on a small sample size , we examined the impact of three different values of α on the critical prevalence ( Figure 6 ) . We found that the effect of SIVcpz infection on fertility affects the critical prevalence only if the mortality multiplier is low ( Figure 6 ) . The critical prevalence determination rests on the assumption of a large population size . It is well known in conservation ecology that small population size , and the stochastic variation introduced by demographic “sampling” from such populations , can have a powerful effect on population outcomes [22] . Compared to the assumptions of the deterministic model , the typical chimpanzee community is “small , ” especially when considered in isolation from other communities . To investigate the effects of SIVcpz infection in such populations , we developed stochastic simulations that incorporated both demography and infection dynamics . For the stochastic simulations , we used our best estimates of epidemiological and demographic parameters . We used two methods for estimating τ , the probability of transmission of SIVcpz per coital act: ( i ) the transmission probability for HIV-1 estimated by Gray and colleagues using sero-discordant human partnerships in Rakai , Uganda [23] , and ( ii ) the transmission probability for SIVcpz based on the following parameters calculated from data from the Kasekela chimpanzees: the basic reproduction number , R0; the average copulation rate for each sex; the median number of susceptible males and females; the baseline mortality rate for each sex; and the SIVcpz associated mortality multiplier , ρ ( see Methods for details ) . As for the deterministic model , we used a range of values for ρ , calculating τ based on both high ( ρ = 10 ) and low ( ρ = 5 ) estimates . The resulting values for SIVcpz ( τ = 0 . 00077–0 . 0015 ) bracket the value for HIV-1 ( τ = 0 . 0011 ) . In addition , calculations from annual incidence/prevalence ratios observed in the Kasekela community among sexually active individuals yielded estimates consistent with these values . Simulations started with a population based on data from the Kasekela community during years ( 2002–2007 ) for which we have demographic , epidemiological and mating data . We chose Kasekela as a model community both because this is the community for which we have the most detailed data , and because Kasekela's size in these years ( median = 55 . 5 individuals , range = 51–61 ) is close to the average size of chimpanzee communities in other long-term studies ( median = 47 . 1 , range = 10–144 , n = 8 [24] ) . We ran simulations for 12 sets of starting conditions , with varying combinations of the following parameters: migration both in and out of the community ( allowed or not allowed ) ; the percentage of incoming females infected with SIVcpz ( pF = 5% or 30% ) , transmissibility ( τ = 0 . 00077 , 0 . 0011 , or 0 . 0015 ) ; and the mortality multiplier ( ρ = 5 or 10 ) . For each set of starting conditions , we ran 10 , 000 replicate simulations over a 100-year time frame , and calculated the percentage of chimpanzee populations that persisted 100 years , the percentage of those surviving populations in which the virus persisted , the population growth rate of the surviving chimpanzee populations , and the mean time of extinction for those chimpanzee populations that did not persist ( Table 2 ) . Overall , within the range of our starting conditions , chimpanzee populations usually persisted , whereas the virus frequently became extinct . Figure 7 depicts the results of two example runs of the stochastic simulation , showing the number of infectious males and females , the SIVcpz prevalence , as well as the number of susceptible individuals over time . In Figure 7A , the virus persisted for over 40 years , driving the chimpanzee population to extinction by year 65 , while in Figure 7B the virus went extinct by year 10 and the chimpanzee population persisted . For both chimpanzees and virus , the particular pattern of persistence varied greatly depending on the values chosen for the other parameters , as did the growth rate of surviving chimpanzee populations . In particular , migration had a profound effect on the qualitative results . Simulations with migration resulted in high persistence of the chimpanzee population ( median = 93% , range = 66–99% ) , low persistence of the virus ( median = 10% , range = 2–39% ) , and robust population growth rates ( median = 0 . 021 , range = 0 . 011–0 . 033; Table 2 ) . In contrast , simulations without migration resulted in lower persistence of chimpanzee population ( median = 62% , range = 40–71% ) , and greater persistence of the virus ( median = 33% , range = 18–59% ) , and those chimpanzee populations that did not go extinct suffered negative population growth rates ( median = −0 . 019 , range = −0 . 020–−0 . 018; Table 2 ) . This latter point indicates that while the populations did not go extinct within the time bounds of the simulation , they were almost certain to go extinct shortly thereafter . For chimpanzee populations that went extinct within the 100-year time frame , the time to extinction was fairly uniformly distributed across this time span . While migration reduced the overall probability of extinction , those populations that did go extinct tended to do so sooner in simulations with migration ( median = 39 years , range = 23–52 ) than without migration ( median = 50 years , range = 43–56 ) . Whether incoming females had a low or high probability of being infected with SIVcpz had a negligible effect on chimpanzee population persistence ( median persistence = 93% for pF = 5% and 30% ) and viral persistence ( pF = 5% , median persistence = 13% vs . pF = 30% , median persistence = 10% ) , but did result in substantially slower population growth for surviving chimpanzee populations ( pF = 5% , median growth = 0 . 028 vs . pF = 30% , median growth = 0 . 017 ) . The effect of the SIV-excess mortality multiplier ( ρ ) had a modest and perhaps counter-intuitive effect on both chimpanzee and viral persistence . For example , in simulations that included migration , chimpanzee population persistence was higher when SIVcpz-induced mortality was high ( ρ = 10 , median persistence = 99% vs . ρ = 5 , median persistence = 77% ) . In contrast , viral persistence was lower when SIVcpz-induced mortality was high ( ρ = 10 , median persistence = 3 . 1% vs . ρ = 5 , median persistence = 26% ) . These patterns are interrelated , because higher mortality causes infected individuals to exit the population more quickly . In a small population , even a small number of such exits can lead to viral extinction . With lower disease-induced mortality , the sojourn time of infectious individuals is longer , leading to more potential infection .
In this study , we examined the impact of SIVcpz infection on chimpanzee population growth in three Gombe communities . We found that a high prevalence of SIVcpz was associated with population decline: the Kalande community , which exhibited a SIVcpz prevalence of ∼40% to 50% for at least a decade , suffered a population decline during this same observation period . In contrast , the Mitumba and Kasekela communities , which exhibited lower prevalence rates , experienced mean population growth of 1 . 9% and 2 . 4% , respectively . These growth rates were influenced by the movement of females among the various communities . Controlling for female migration , Kalande still declined , Mitumba grew by one individual , and Kasekela remained stable . Consistent with this , our deterministic demographic calculations predict that even modest levels of SIVcpz prevalence can lead to population decline . The decline of the Kalande community resulted from both death ( 10–22 individuals ) and emigration ( 11 individuals ) . Of 34 individuals whose infection status was known , 5 are known to have died , and an additional 9 have not been sampled since 2005 and are thus presumed to have died ( Figure S1 ) . Of these 14 individuals , seven ( 50% ) were infected with SIVcpz at the time of their last sample . Although the extent to which SIVcpz contributed directly to the mortality in Kalande remains unclear in the absence of necropsy data , any virus induced increase in mortality is likely to have amplified the factors promoting emigration from Kalande . Emigration of adolescent females is typical of chimpanzees [25] and by itself is unlikely to lead to population decline , as it is usually matched by immigration from other communities . However , emigration in Kalande was exacerbated by two factors: emigration of mothers with dependent offspring , and lack of immigration from other communities . Mothers with dependent offspring rarely emigrate , presumably due to risk of infanticide by males in the new community [26] , [27] . “Mass transfer” of females with dependent offspring occurred in Mahale , when the K-group declined to a single adult male [28] . A similar process occurred in Kalande . The death of adult males in 2002 likely prompted females , including mothers with dependent offspring , to search for a community with more males , and thus more mates and improved protection . While only one mother ( Ch-099 ) and her offspring ( Ch-071 , Ch-101 ) departed Kalande permanently , several other mothers are known ( Ch-086 ) or suspected ( Ch-089 , Ch-093 ) to have visited Kasekela , and Ch-089 apparently visited long enough to conceive an infant and acquire SIVcpz infection . Moreover , immigration has failed to replace the emigrants . A decline in adult males since the 1980s is likely to have led to a decreased ability to defend territory , a decreased home range size , and a decreased ability to attract new females . Whether SIVcpz contributed directly to the deaths of some of the Kalande males that appeared to have precipitated the subsequent wave of female emigration is not known . One of the males , Ch-087 , who was killed by people , was negative for SIVcpz . A second male , Ch-085 , who died of disease , was also negative for SIVcpz . The infection status of the remaining two males , KLAM2 and KLSM2 ( Table S3 ) , could not be determined because neither appears to have been sampled . It is possible that one of them was Ch-081 , who was SIVcpz negative when last sampled . However , it is equally possible that Ch-081 represents another adult male , such as KLAM4 , who was seen as recently as 2010 and also has not yet been sampled . Thus , while two of the four males who died in 2002 were SIVcpz negative , we do not know the infection status of the other two . Given the high infection rates in Kalande , it is thus possible that SIVcpz contributed to their death . In addition to SIVcpz , chimpanzees in Gombe face other threats . In particular , poaching and loss of habitat outside the park represent significant hazards of survival , particularly for the Mitumba and Kalande communities , as these communities border dense human settlements [15] , [18] . Individuals from both communities are suspected to have been killed by people [15] , although conclusive evidence exists for only one of these cases [15] . Habitat loss occurred through the conversion of forest and woodland habitats adjacent to the park to human croplands and settlements [15] . Estimates of the overall abundance of trees , shrubs and vines that provide food for chimpanzees , however , indicate that the current Kalande population is low compared to its potential food supply ( Figure 5 ) . The Kalande chimpanzees live in the southern half of Gombe , which compared to the rest of the park has less evergreen forest and more open woodland and grassland [15] ( Figure 1 ) . These more open habitats provide a lower density of food plants , especially vines and shrubs . Nonetheless , the likely range of Kalande ( 1182 ha ) is twice that of Mitumba ( 544 ha ) , and thus contains a total food supply that exceeds that of Mitumba . It therefore seems likely that the Kalande range could support at least as many chimpanzees as the Mitumba range ( n = 25 ) , rather than the much smaller number of apes currently found in Kalande ( n = 14–18 ) . Data on habitat quality for years preceding 2007 are not available . Thus , habitat loss may have contributed to the early decline of the Kalande community . However , the current abundance of food plants within the Kalande range suggests that the more recent decline of this community's population cannot be explained by habitat loss alone . This view is further supported by C . Gale's observations from 1968–69 , when the Kalande community numbered at least 40 individuals , frequently foraged in large parties , and apparently found ample food within the same valleys used by the community in more recent years ( unpublished data from the Gombe Stream Research Centre ) . Phylogenetic analysis of SIVcpz sequences revealed the likely direction of viral spread both within and between communities . All but one of the major viral lineages in Gombe are plausibly rooted in Kalande , suggesting a key role for Kalande in the spread of SIVcpz infection . The increase in emigration and visiting of neighboring communities by females that followed the death of four Kalande males in 2002 thus likely increased the spread of the virus throughout Gombe . In contrast to Kalande , the Mitumba and Kasekela communities did not decline during the study period , despite having infection rates well above the predicted critical prevalence . The persistence of these communities could be interpreted to mean that we have assumed an overly high estimate of ρ , the increase in mortality caused by SIVcpz infection . However , recent findings render this explanation unlikely . Since our initial report [2] , the Kasekela community has experienced three additional SIVcpz-related deaths ( Ch-021 and her infant , and Ch-033 ) , while none of the uninfected individuals died or disappeared . Moreover , one of the infected chimpanzees ( Ch-021 ) whose body was recovered suffered from CD4+ T cell depletion and immune system destruction ( K . Terio , personal communication ) . Finally , because chimpanzees mature and reproduce slowly , even a very low value for ρ is predicted to lead to a population decline at the observed prevalences . Instead , we believe that two other factors are responsible for the absence of a population decline in Mitumba and Kasekela . First , much of the observed growth in these communities depended on immigrants and their offspring . Excluding the surviving 2002–2009 immigrants and their offspring , the Mitumba community grew by one , while the Kasekela community neither grew nor declined in numbers . Second , the current infections in Mitumba and Kasekela are largely the result of very recent transmission events . In humans , the median time from HIV infection to death in untreated patients in rural Uganda without access to anti-retroviral therapy was 9 years [29] . The current infections may therefore take several more years before they have a measurable impact on population growth . Deterministic calculations make the assumption of a large population size , which does not apply to most chimpanzee communities . To investigate the effects of SIVcpz infection on more representative populations , we developed event-driven stochastic simulations that incorporated both demography and infection dynamics . For these analyses , we first estimated the probability of SIVcpz transmission per coital act for Gombe chimpanzees using mortality , mating and SIVcpz prevalence data from the best-studied Kasekela community . Using conservative values of excess mortality , we estimated the corresponding SIVcpz transmission probabilities to be 0 . 0015 ( ρ = 5 ) and 0 . 0008 ( ρ = 10 ) . Interestingly , these values bracket the transmission probability of HIV-1 reported for discordant human couples ( 0 . 0011 ) [23] , and thus suggest that the biology of HIV-1 and SIVcpz heterosexual transmission is fundamentally similar . We also incorporated intercommunity migration into the stochastic model , including both immigration and dispersal of females . The stochastic modeling revealed that under most simulated conditions a large proportion of SIVcpz infected communities survived . This was the case both in the presence and absence of migration , although in the presence of migration the fraction of persisting communities was considerably larger ( Table 2 ) . Using a transmission probability of 0 . 0015 , a mortality multiplier of 10 , and a 30% infection probability in immigrating females , we found that in the presence of migration 99% of simulated populations persisted , with only 2% still harboring SIVcpz . In the absence of migration , only 71% of simulated populations persisted , with 18% still harboring SIVcpz ( Table 2 ) . Moreover , under the conditions tested , we uniformly found that simulations that included migration had positive mean growth rates for surviving populations , whereas in the absence of migration , even those populations that survived 100 years had negative mean growth rates and were thus at risk of going extinct . These data thus indicate that intercommunity migration protects against SIVcpz-driven extinction . Interestingly , whether incoming females had a low or high probability of being infected with SIVcpz had a negligible effect on both chimpanzee population and viral persistence . This is because the product of the probability of a given female migrating and the probability of that female being infected ( assuming they are independent events ) is small , regardless whether the assumed infection rates are high or low . Allowing migration led to a substantially higher rate of chimpanzee population persistence and a lower rate of viral persistence . These two outcomes are clearly related . A population in which all infections have died out but which loses all breeding females , can be rescued from extinction ( at least immediate extinction ) by an uninfected immigrant . This protective effect likely results from the fact that even with a 30% probability of immigrating females being infected , the majority of immigrating females are uninfected . Migration also allows infected females to emigrate , providing an additional mechanism ( beyond death ) for reducing prevalence . We also explored the impact of transmission probabilities ( τ ) that were higher than those empirically determined . For example , when τ was three times higher than our highest estimated value , less than 10% of the simulated chimpanzee populations persisted for the duration of simulation ( not shown ) . Overall , higher values of τ revealed three consistent trends: ( i ) chimpanzee population persistence decreased , ( ii ) viral persistence increased , ( iii ) the effect of the mortality multiplier ( ρ ) on chimpanzee/viral persistence became attenuated . While such transmission probabilities are perhaps implausibly high , there are conditions such as acute-to-acute infection or genital ulcer disease that are known to greatly increase the transmission probability of HIV-1 [30] . Indeed , the sudden appearance of new infections in Mitumba ( 2003 ) and Kasekela ( 2005 ) suggested a series of acute-to-acute transmissions in both communities [2] . Thus , increased transmissibility of SIVcpz would be expected to have a negative impact on the survival of infected communities . Our mathematical models thus lead to three major conclusions . First , SIVcpz infection can cause serious population decline . Second , depending on the population structure and transmission dynamics , SIVcpz may be more likely to go extinct than its chimpanzee host . In fact , most infections are predicted by our models to die out under current epidemiological conditions . Third , intercommunity migration is a key parameter that increases survival of infected populations , even when the immigrating females have a chance of being infected with SIVcpz . In conclusion , we found that a chimpanzee community in which roughly half of all members harbored SIVcpz suffered a dramatic population decline , and that even moderate levels of SIVcpz infection are predicted to result in negative population growth . At the same time , simulations indicated that in representative populations , the virus frequently died out before the host population , especially when intercommunity migration was incorporated into the model . Thus , population extinction is not an inevitable consequence of SIVcpz infection . Instead , the fate of an infected community depends on the particular epidemiological and ecological circumstances that are unique to that community . These results are consistent with the uneven distribution of SIVcpz infection in wild chimpanzee populations: infected communities either become extinct , after which the vacated habitat may be colonized by uninfected chimpanzees , or the virus goes extinct , allowing the previously infected population to recover . The finding that SIVcpz is pathogenic in chimpanzees reveals yet another challenge to chimpanzee conservation . This challenge is sobering given that few options exist for intervention . For a number of reasons , including the need for daily drug administration , treating wild chimpanzees with antiretroviral therapy is currently not feasible . Although the persistence of endemically infected chimpanzee populations across central Africa [10] , [11] would argue that this threat is not catastrophic , the long-term consequences of SIVcpz infection in the context of other survival hazards remains to be determined . Also , while little can be done to control the virus spread within a population , it is clear that the risk of introducing SIVcpz infection into a naïve population has to be taken into account in conservation planning . For example , it is critically important that efforts to reintroduce chimpanzees to the wild include SIVcpz screening to prevent the release of infected individuals . Additionally , the risk of exposing uninfected communities to SIVcpz should also be taken into account in the cost-benefit analysis when planning corridors between isolated chimpanzee populations .
Gombe National Park is located in northwestern Tanzania , along the eastern shore of Lake Tanganyika . The park's southern border is located 15 km north of Kigoma . The park covers 35 km2 of rugged terrain , rising from the lakeshore in the west ( 770 meters above sea level; m . a . s . l . ) to the crest of the rift escarpment in the east ( 1300 to 1600 m . a . s . l . ) [15] , [17] , [18] . As of January 2009 , the park provides habitat for 96–100 chimpanzees in three communities: Mitumba ( 25 ) , Kasekela ( 57 ) , and Kalande ( 14–18 ) . Most research has focused on the Kasekela community , which Goodall began studying in 1960 [17] . Efforts to habituate the Mitumba community began in the 1980s and by the mid-1990s most Mitumba chimpanzees could be observed within a distance of 20–30 meters [18] . Efforts to habituate the Kalande community started with a six-month project by C . Gale ( December 1968–June 1969 ) , followed by additional attempts in the 1970s and 1980s , which were not successful . However , a monitoring program initiated by E . Greengrass ( February 1999–August 2000 ) and F . Grossmann ( September 2000–March 2002 ) has continued to the present . In this program , researchers have not attempted to habituate chimpanzees , but have instead focused on nest transect surveys ( 1999–2002 ) , monitoring of phenology trails ( 2002 - present ) , and opportunistic sightings of chimpanzees and other wildlife ( 1999 - present ) . Since 2002 , Tanzanian field assistants trained by Greengrass and Grossmann have continued the monitoring , conducting regular searches of the area for chimpanzees and other wildlife . Fecal and urine sample collection in Gombe began in 2000 , with collection of feces starting in Kalande in late 2001 . For habituated apes , fecal and urine samples were collected under direct observation [2] , [10]; however , this was not possible for most Kalande apes , who were sampled by collecting stool from the forest floor near night nests . When possible , field assistants also collected samples during direct observation , but because of the brief observation times at Kalande , only few such opportunities occurred . Fecal samples ( ∼20 g ) were placed into 50 ml conical tubes , and mixed with equal amounts of RNAlater ( Ambion ) . If the sample was collected under direct observation , the name ( if known ) or age-sex class was recorded . Time , date , location , and name of collector were also recorded . Specimens from Kasekela were frozen on the day of collection , while specimens from Mitumba and Kalande remained at ambient temperature until transported to the field lab in Kasekela ( usually within one week of collection ) . Samples were shipped at ambient temperatures , then stored at −80°C upon receipt . Between 2000 and 2009 , a total of 1 , 536 fecal samples were collected from all three Gombe communities , 1 , 153 of which have been reported previously [2] . During the same time period , 341 fecal samples were collected from 26 individuals who resided in Kalande ( Table S1 ) . Three Kalande apes transferred to Kasekela or Mitumba during the study years and were previously reported ( Ch-071 , Ch-098 , Ch-099 ) . A fourth female , Ch-108 , was sampled in Kasekela , but was apparently visiting rather than transferring , as she has since been sampled in Kalande . All individuals were identified by microsatellite genotyping . A median 4 . 5 samples were collected for each Kalande chimpanzee ( range = 1–75 ) . Table 1 summarizes the number of samples collected in Kalande for each year since 2001 . Fecal DNA was extracted as described previously [2] , [10] , [11] and quantified using real-time PCR [31] . All individuals for whom fecal DNA was available were microsatellite genotyped at autosomal loci as well as typed for sex and mitochondrial haplotype [32] , [33] . A total of 116 individuals from the three communities were genotyped at a minimum of 8 of 11 microsatellite loci and were tested for relatedness . We used the likelihood-based program CERVUS 2 . 0 [34] to identify parent-offspring relationships ( Tables S5 and S6 ) . We first examined individuals within the same mitochondrial haplotype for mother-offspring relationships since mitochondrial DNA is matrilineally inherited . Females were only considered candidate mothers if they shared at least one microsatellite allele at each locus . Simulations were run using 100 , 000 cycles , 1% error rate , and confidence levels of 80% and 95% . The sampling proportions for the simulations were determined by including all genotyped females of a given haplotype with an additional 50% unsampled female candidates included to account for any ungenotyped females from the Kalande community . When a probable mother-offspring relationship was identified , we used the possible mother as the “known parent” in CERVUS to identify potential fathers amongst all sampled males ( n = 49 ) from the three communities using the same simulation conditions . A male was only considered a probable father if , given the genotype of the corresponding mother , he did not have microsatellite allelic mismatches with the genotype of the presumed offspring ( Table S6 ) . In some cases , CERVUS assigned a particular candidate as “most likely , ” even though a statistically significant parent was not identified . To further validate parent-offspring relationships and identify siblings we also used the microsatellite genotypes to perform KINSHIP analyses [35] ( Tables S5 and S6 ) . These analyses tested whether dyads were maternally or paternally related compared to the null hypothesis that they were unrelated . We used KINSHIP to calculate a likelihood ratio for the primary ( related ) and null hypotheses for each dyad . Given the availability of long-term demographic data in Gombe , we were able to include the identity of known mothers and fathers for numerous individuals within the population , which improved the likelihood calculations . Nonetheless , when individuals did not have identified parents , KINSHIP was unable to differentiate between maternal and paternal lineages among autosomal loci . We also used KINSHIP to estimate the relatedness of individuals , R , defined as the probability that the same allele found in two individuals is identical by descent , taking into account the frequency of the allele in the population [35] . For diploid , sexually reproducing species , R should be 0 . 5 for parent-offspring and full-sibling relationships , and 0 . 25 for half-sibling and grandparent-grandoffspring relationships . Departures from these expected values may occur when calculating R from a relatively small number of loci , such as the 8 to 11 loci that were used here ( which were nonetheless sufficient to correctly assign close relationships , e . g . , parent , half-sibling; [36] ) . Thus , we obtained calculated estimates for R that were close to ( but not precisely equal to ) 0 . 5 for parental relationships ( mother-offspring: n = 12 , median = 0 . 43 , range = 0 . 21–0 . 62; father-offspring: n = 7 , median = 0 . 40 , range = 0 . 24–0 . 74 ) and close to zero for unrelated individuals ( n = 17 , median = 0 . 06 , range = −0 . 22–0 . 38 ) . Tables S5 and S6 summarize all CERVUS and KINSHIP results , with particular focus on SIVcpz-infected chimpanzees from Kalande . These results are conservative in that we only report results for dyads that are ( i ) within the same mitochondrial haplotype and also lack microsatellite allelic mismatches; and/or ( ii ) significant relationships from CERVUS and the corresponding KINSHIP analyses for these dyads; as well as ( iii ) results for any dyad for which KINSHIP found a strongly significant relationship ( P<0 . 001 ) . Finally , we included results for dyads suspected to be related based on other significant dyadic relationships ( i . e . , if individuals A and B were related and individuals B and C were related , then we also reported results for individuals A and C ) . All fecal samples were screened for the presence of SIVcpz specific antibodies by enhanced chemiluminescent Western blot analysis [2] , [9] , [10] . Sample integrity was confirmed using an IgG control . SIVcpz sequences were amplified from Kalande apes Ch-100 and Ch-086 as previously described [2] , [9] , [10] . Briefly , fecal RNA was extracted using the RNAqueous Midi-kit ( Ambion ) . Reverse transcription polymerase chain reaction ( RT-PCR ) amplification was the performed using the following primers: PTS-midpol-F1 ( 5′-CWAAYCAACAAGCAGARYTATGGGC-3′ ) , CPZ-pol-R1 ( 5′-ACBACYGCNCCTTCHCCTTC-3′ ) , PTS-midpolF2 ( 5′CAAAGTGACTCYCCCATAGTAGAG-3′ ) , and PTS-midpol-R2 ( 5′-CCCAATCCCCCCTTTTCTTTTAAAATT-3′ ) . RT-PCR products were gel purified and sequenced directly . The newly derived SIVcpz sequences are available at GenBank under accession numbers GU992204 ( TAN7 ) and GU992204 ( TAN21 ) . To determine the evolutionary relationships of the Gombe viruses to each other and to SIVcpzPts reference strains , a phylogenetic tree was constructed from available pol nucleotide sequences ( 477 bp ) . These included previously reported sequences from Gombe ( TAN1 , GenBank accession number AF447763; TAN2 , DQ374657; TAN3 , DQ374658; TAN5 , FJ895394; TAN6 , FJ895395; TAN8 , FJ895403; TAN9 , FJ895405; TAN10 , FJ895398; TAN11 , FJ895399; TAN12 , FJ895400; TAN13 , FJ895393; TAN14 , FJ895397; TAN15 , FJ895404; TAN18 , FJ895396; TAN19 , FJ895402; and TAN20 , FJ895401 ) , newly derived sequences from Gombe ( TAN7 , GU992204; TAN21 , GU992204 ) and two SIVcpzPts strains from the Democratic Republic of Congo that served as an outgroup ( ANT , U42720; BF1167 , FJ869116 ) . Nucleotide sequences were aligned using CLUSTAL W [37]; sites that could not be aligned unambiguously were excluded . Trees were inferred by Bayesian methods [38] . SIVcpz prevalence rates were determined for Kasekela , Mitumba , and Kalande apes separately from 2002 to 2009 . For this analysis , individuals were considered SIVcpz positive if they had detectable antibodies in their urine or feces as determined by Western blot analysis [2] , [10] . A positive Western blot is diagnostic of SIVcpz infection , except for nursing infants who may contain maternal antibodies in their feces [2] . Prevalence rates were calculated semiannually by dividing the number of positive individuals by the total number of apes tested in each community . Since SIVcpz infection is a chronic , life-long infection , we could infer the infection status for a number of missing time points , using the following guidelines: ( i ) if an individual was infected or uninfected before and after a missing time point , we inferred the same status for the missing time point; ( ii ) if an individual died after testing positive for SIVcpz , we assumed the individual was infected for all time points between the positive sample and death; ( iii ) if data from missing years could not be inferred according to these guidelines , the individual was omitted from prevalence calculations for that time period . Annual population estimates for the Mitumba and Kasekela communities were based on detailed demographic records of known individuals , combined with genotyping to track individuals moving between communities . We used two methods to estimate the number of individuals in the Kalande community: ( i ) nest transects and ( ii ) a table of annual membership , based on visual identification of individuals and genetic markers . Habitat quality was estimated using 91 20×20 m vegetation plots distributed across the park ( 30 plots each in Mitumba and Kalande , and 31 in Kasekela ) , following methods described previously [44] . Plots were stratified according to vegetation class ( evergreen forest/vine tangle , thicket woodland , open woodland , grassland ) , which was determined by remote sensing and confirmed by on-the-ground classification . Within each plot , the Diameter at Breast Height ( DBH , with breast height = 137 cm ) for each tree with DBH≥10 cm was measured and used to calculate the tree's basal area . Each tree was identified to local name and , when possible , species . Within a 5×5 m subset of each plot , smaller plants including shrubs and vines were identified and the number of individual stems for each species counted ( up to 20 ) . Long-term feeding records of the Kasekela community were used to determine whether chimpanzees regularly consumed the fruits , leaves , or other parts of each plant species . A relational database ( Microsoft Access ) was used to calculate the mean basal area of chimpanzee food trees within each vegetation class in the range of each community . For Mitumba and Kasekela , we estimated the community range for 2007 using the 99% minimum convex polygon ( MCP ) enclosing all observed locations with BIOTAS 1 . 01 ( Ecological Software Solutions , LLC ) . Because the Kalande community is not habituated , information on ranging behavior was much more limited . We therefore used two estimates , the minimal range and the likely range . For the minimal range , we used 69 GPS locations where observers have seen Kalande chimpanzees ( 2002–2009 ) , as well locations of three of the four intergroup encounters with the Kasekela chimpanzees recorded from 2000 on . To be conservative , we excluded one brief encounter with one lone female on 07 November 2003 , as this encounter occurred further north than other encounters and may have involved an immigrating female outside of her normal range . Second , for the likely range , we included locations of chimpanzee nests found outside the park , adjacent to the Kalande range ( 2000–2006 ) , and the locations of all 10 intergroup encounters between Kalande and Kasekela from 1998 on , including the 07 November 2003 encounter . We then used ArcGIS 9 . 3 ( ESRI ) to join these ranges with a vegetation class layer to determine the total extent of each vegetation class for each community . The total basal area of chimpanzee food trees for each community was estimated as the sum of the extent of each vegetation class in each community times the mean basal area of chimpanzee food trees in that vegetation class in that community . The total number of stems of smaller food plants per community was likewise calculated . Vegetation cover was derived using ERDAS , Inc software from a 4 m multispectral IKONOS satellite image acquired on July 30 , 2000 . The image was georeferenced using GPS ground control points and a digital elevation model . A 3×3 low-pass filter was applied and a Normalized Difference Vegetation Index ( NDVI ) was computed . Vegetation types were classified by identifying a range of NDVI values that represented a particular vegetation class using data collected on the ground in 2002 [45] . The accuracy of the vegetation map was estimated using 20×20 m vegetation plots , which revealed that 92% of evergreen forest/vine tangle plots and 70% of thicket woodland plots were correctly classified . Newly derived SIVcpz sequences have been deposited in GenBank under accession numbers GU992204 and GU992205 . | It is now well established that human immunodeficiency virus type 1 ( HIV-1 ) , which causes acquired immune deficiency syndrome ( AIDS ) , emerged following cross-species transmission of a simian immunodeficiency virus that infects chimpanzees ( SIVcpz ) . Although SIVcpz was originally believed not to be pathogenic , a recent study conducted in Gombe National Park , Tanzania , found that infected chimpanzees can develop AIDS-like symptoms and have a high mortality . To examine the impact of SIVcpz on chimpanzee survival , we examined and then modeled the effect of virus infection on chimpanzee population growth . We studied three communities , including the little-studied Kalande community , located in the south of the park . We found that 46% of Kalande apes were infected with SIVcpz , compared to less than 13% of the other communities . Interestingly , the communities with lower infection rates grew , while Kalande suffered a catastrophic decline . Mathematical modeling indicated that chimpanzee populations infected with SIVcpz are likely to decline , although intercommunity migration counteracted this negative effect , allowing many simulated populations to persist . These results suggest that SIVcpz infection , together with other variables , contributed to the Kalande population decline . However , population extinction is not a necessary consequence of SIVcpz infection , but can be counteracted by other factors , such as migration , that promote population survival . These findings are consistent with the uneven distribution of SIVcpz across Africa and may explain how chimpanzees as a species have survived this pathogen . |
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Although leptospirosis is traditionally considered a disease of rural , agricultural and flooded environments , Leptospira spp . are found in a range of habitats and infect numerous host species , with rodents among the most significant reservoirs and vectors . To explore the local ecology of Leptospira spp . in a city experiencing rapid urbanization , we assessed Leptospira prevalence in rodents from three locations in Malaysian Borneo with differing levels of anthropogenic influence: 1 ) high but stable influence ( urban ) ; 2 ) moderate yet increasing ( developing ) ; and 3 ) low ( rural ) . A total of 116 urban , 122 developing and 78 rural rodents were sampled , with the majority of individuals assigned to either the Rattus rattus lineage R3 ( n = 165 ) or Sundamys muelleri ( n = 100 ) . Leptospira spp . DNA was detected in 31 . 6% of all rodents , with more urban rodents positive ( 44 . 8% ) , than developing ( 32 . 0% ) or rural rodents ( 28 . 1% ) , and these differences were statistically significant . The majority of positive samples were identified by sequence comparison to belong to known human pathogens L . interrogans ( n = 57 ) and L . borgpetersenii ( n = 38 ) . Statistical analyses revealed that both Leptospira species occurred more commonly at sites with higher anthropogenic influence , particularly those with a combination of commercial and residential activity , while L . interrogans infection was also associated with low forest cover , and L . borgpetersenii was more likely to be identified at sites without natural bodies of water . This study suggests that some features associated with urbanization may promote the circulation of Leptospira spp . , resulting in a potential public health risk in cities that may be substantially underestimated .
Leptospirosis is the most widespread zoonotic disease globally , with over a million cases of severe disease and around 60 , 000 deaths reported annually [1] . Occurring in a wide variety of environmental settings , and with the greatest impact on public health in tropical and subtropical regions , it is a significantly under-diagnosed disease due to its broad clinical picture and symptoms that are common to several other diseases [2] . Leptospirosis is caused by spirochaetes of the genus Leptospira , of which 22 species and >300 serovars are currently recognized . Ten species have been definitively associated with severe human disease , whilst a further five have been linked to milder disease [3] . In addition , 12 novel species have recently been identified from tropical soils , although none have yet been associated with disease [4] . Human infection with Leptospira spp . occurs via several routes , including through direct contact with urine or tissues from infected animals , or indirectly through contamination of ( usually humid ) environments with infected urine . The two species responsible for the majority of human infections , L . interrogans and L . borgpetersenii , differ in their transmission routes; L . interrogans remains viable for extended periods in aquatic or humid environments , whilst L . borgpetersenii , which has lost several genes related to environmental sensing , now relies primarily on direct transmission between hosts [5] . These differences impact the ability of each species to persist in the environment and have led to differences in distribution and zoonotic potential [6] . As such , whilst exposure to wetlands has traditionally been considered a significant risk factor for this disease , Leptospira spp . have been detected in a number of environments , including cities [7–10] . Although relatively little is known about the ecology and epidemiology of Leptospira spp . in urban environments , zoonotic transmission has been repeatedly documented and often associated with poor sanitation and slum conditions [11–14] . By 2050 , 66% of the global human population is predicted to reside in urban environments and as such , the majority of human-wildlife interactions are likely to occur in these areas [15] . Critically , features of the urban environment can impact disease dynamics in wildlife hosts and increase the frequency of human exposure to zoonotic pathogens . Indeed , Leptospira spp . , infection prevalence has been found to be higher in wildlife occupying urban habitats than natural environments , and this trend appears to be particularly significant for rodents [16] . Several species of rodent , including Rattus norvegicus , R . rattus and R . exulans , appear to benefit from urbanization and thrive in city environments , resulting in regular human exposure to these species and their excreta [17 , 18] . Despite the obvious risks posed by urban rodent infestation , the distribution , prevalence , diversity and dynamics of Leptospira spp . in urban populations remains largely unknown , impacting the ability of local authorities to develop effective prevention and control strategies . In Southeast Asia , the number of reported cases and outbreaks of leptospirosis has increased dramatically in recent years , due in part to improvements in diagnosis and surveillance , but also as a result of the rapid environmental changes occurring in this region [19–21] . At least six zoonotic species have been detected in Southeast Asian rodents to date: L . borgpetersenii , L . interrogans , L . kirschneri , L . weilli , L . noguchii and L . wolfii [6 , 22] . In Malaysia , the annual number of reported cases increased more than 14-fold between 2004 and 2012 , which led to the classification of leptospirosis as a mandatory notifiable disease at the end of 2010 [23] . Although many recent Malaysian outbreaks have been associated with outdoor recreational activities , human infections have also been documented in urban environments [24] . Some studies have begun to assess the prevalence of Leptospira spp . in urban reservoir species in Southeast Asia [22 , 25] ) , but none have yet compared how distribution and transmission varies with the degree of anthropogenic influence across an urban landscape . In this study , we screened native and invasive rodents found in urban , developing and rural locations around the city of Kuching , Sarawak for Leptospira spp . , to begin to explore how urbanization effects the presence and prevalence of Leptospira in Malaysian Borneo .
This study was approved by the CSIRO Australian Animal Health Laboratory’s Animal Ethics Committee ( #1750 ) and the Sarawak Forests Department ( Permit: NCCD . 907 . 4 . 4 ( JLD . 12 ) -131 ) . For this study , sites were considered to be a circle with a 110 m radius centered at the point where GPS coordinates were taken during rodent trapping . All site-specific environmental variables were measured or estimated over the complete circle . The 110 m radius was chosen to correspond with the approximate home range of R . rattus that has been estimated under similar environmental conditions [26] . As home range data is not available for the other rodent species studied , we used the R . rattus estimate to delineate sites throughout the study . To classify the degree of urbanization and the intensity of anthropogenic influence at each site , the following estimates of land use were considered: 1 ) Mean forest cover was estimated using QGIS v 2 . 14 . 0 and previously published forest cover and loss datasets at the Landsat pixel scale . Mean estimates were ranked and grouped into tertiles , which were categorized as minimal , moderate or maximal forest cover ( https://earthenginepartners . appspot . com/science-2013-global-forest ) [21] . 2 ) Dominant land-cover type ( gray , green or gray/green interface ) was determined by assessing the proportion of vegetated ( forest , scrub , etc . ) or impervious ( buildings , roads , etc . ) space within and around each site using QGIS ( as above ) and ground-truthing . Gray sites were considered to be completely within and primarily surrounded by human infrastructure , green sites were those dominated by unmanaged vegetation , gray interface sites were within human infrastructure but adjacent to substantial vegetation , and green interface sites were within managed/unkempt vegetation and adjacent to human infrastructure . Other site-specific environmental features recorded included the presence or absence of a natural water body at a site , and the local environment in which individual rodents were caught , referred to as ‘trap location’ . Trap locations were recorded as: 1 ) inside domestic dwellings , 2 ) household gardens and yards , 3 ) forests , 4 ) sewers , and 5 ) scrub ( areas of vegetation dominated by unkempt bushes and grasses ) . Where buildings were present at a site , the relative condition ( i . e . , poor , fair , good , excellent ) and type of building ( s ) ( i . e . , residential , mixed commercial/residential , institutional ) were also recorded . Rodents were collected from multiple sites between September 2015 and April 2016 at each of the three locations described above . At each site , multiple wire mesh traps ( ~30cm x 14cm ) were baited with meat and banana , placed at intervals >1m for between one and seven nights , and checked every morning . Trapping effort varied substantially between sites in an effort to collect equal numbers of animals/species/location . Rodents were euthanized by over-anesthetization in isoflurane , followed by bilateral thoracotomy . Sex , reproductive status , weight ( as a proxy for age ) and tentative species assignment ( by morphological assessment ) were recorded , and tissues were collected and frozen directly on dry ice . The species identity of each animal was confirmed by sequencing the product of a PCR assay using primers BatL5310 and R6036R , which amplify 726bp of the cytochrome oxidase I gene [27] . Approximately 30mg of rodent kidney was homogenized in 600ml of Buffer RLT Plus ( Qiagen ) containing 1% β-mercaptoethanol using the TissueLyser II ( Qiagen ) , and a 5mm stainless steel bead . Homogenized tissue was clarified by centrifugation and the resultant supernatant transferred to a new tube and used for DNA extraction with the AllPrep DNA/RNA mini Kit ( Qiagen ) , as per the manufacturer’s instructions . DNA quantity and quality were assessed using a NanoDrop ( Thermo Scientific ) , diluted to <400ng/ul , and subjected to six previously described PCR assays targeting the rpoB , flaB and 16S rRNA genes [28–34] . Multiple PCR assays were chosen to maximize the probability of detecting any and all Leptospira spp . DNA present , including both pathogenic and non-pathogenic species . Samples were considered positive if they produced a visible band on an electrophoresis gel that could be confirmed as Leptospira spp . by Sanger sequencing ( conventional PCRs ) , or if they demonstrated a Ct value of 35 or lower by Leptospira-specific TaqMan PCR . The resultant sequences ( S1 Appendix ) were trimmed for quality and length and subjected to BLAST ( http://blast . ncbi . nlm . nih . gov/Blast . cgi ) analysis to assess sequence similarity and determine putative species [3] . Sequences were considered to belong to a species if they shared ≥99% nucleotide similarity with publicly available sequences from verified species . Statistical analyses were conducted for all Leptospira spp . , as well as for L . interrogans and L . borgpetersenii separately , due to documented differences in transmission routes [5] . We further considered all rodent hosts collectively to avoid conflating Leptospira ecology with rodent ecology , as no evidence exists at present to suggest that these rodent species differ in competence [6] . Chi squared tests were used to assess differences in Leptospira prevalence in rodents between all three locations ( i . e . , urban , developing , and rural ) , as well as between each pair of locations . To interrogate the relationships between site-specific environmental variables , the GoodmanKruskal package ( version 0 . 01 ) implemented in R was used to run Goodman and Kruskal’s tau ( τ ) statistic ( https://CRAN . R-project . org/package=GoodmanKruskal ) [35] . This test measures the strength of associations between categorical data , with values ranging from −1 ( perfectly negative association ) to +1 ( perfectly positive association ) . A multivariate analysis of mixed data was also performed using the package PCAmixdata ( version 3 . 1 ) implemented in R [36] . To examine links between the probability of Leptospira infection of rodents and site-specific environmental variables , a generalized linear ( mixed ) model ( GLMM or GLM ) with logit function was created using the lme4 package implemented in R ( version 1 . 1–15 ) [37] . Analyses were performed using two initial models with explanatory variables chosen according to the strength of association identified in the Goodman and Kruskal’s tau statistics , and the results of the multivariate analysis . The first model ( a GLMM , henceforth referred to as the global model ) , included the following explanatory variables: site location , trap location , forest cover , dominant land-cover type and waterbody , with no interactions added among independent variables and rodent species as a random effect . The second model ( a GLM , referred to as the built environment model ) was intended to investigate aspects of the built environment that may be relevant to Leptospira prevalence , and included site location , trap location , building type and building condition , with no interactions among them . Only the infection status of rodents trapped in sites with buildings present were included in this model . Support for competing models was evaluated using the Akaike information criterion adjusted for small sample sizes ( AICc ) in the package AICcmodavg ( version 2 . 1–1 ) , and Akaike weights wr [38] . Selection of the best models was made using the R package glmulti ( version 1 . 0 . 7 ) [39] . The three top best models selected for the global and built environment modes are given in S2 Appendix and S3 Appendix , respectively .
A total of 316 animals were caught across all locations . Of these , nine species from four genera were identified by COI sequence analysis , with most individuals classified as S . muelleri ( n = 100 individuals ) or as R . rattus R3 ( n = 165 ) , one of the lineages within the R . rattus super-group ( Table 1 ) [40 , 41] . A total of 31 . 6% of all animals were positive for Leptospira spp . , and Leptospira spp . prevalence varied significantly by site location , with rodents from urban and developing locations more likely to be infected than rural rodents ( Fig 2; S2 Appendix ) . Sequence analysis revealed the presence of two distinct Leptospira species: L . borgpetersenii and L . interrogans . L . borgpetersenii was identified in 38 rodents ( six S . muelleri and 32 Rattus spp . ) , whilst L . interrogans was identified from 57 rodents , comprising Maxomys ochraceiventer ( N = 1 ) , M . whiteheadii ( N = 1 ) , S . muelleri ( N = 15 ) , and Rattus spp . ( N = 40 ) . Sequence information could not be obtained from five samples , which were positive only by a qPCR assay that yielded amplicons too small to sequence [34] . Goodman and Kruskal’s τ and our multivariate analysis showed moderate positive associations between pairs of environmental variables , but as all values were <0 . 60 , they were not considered to be fully redundant in this case ( S3 Appendix ) ( Fig 3 ) . As a result , no variables were excluded from the subsequent analyses . Of the variables considered in the global GLMM , trap location , forest cover , dominant land-cover type and water body were each important in explaining the infection of rodents with Leptospira ( S4 Appendix ) . Significant associations were identified in the first top model between infection with any Leptospira species and sites characterized by minimal forest cover ( P = 0 . 001 ) , and the absence of natural water bodies ( P = 0 . 01 ) , which may be reflective of the association between minimal forest cover and L . interrogans , and the absence of a natural water body with L . borgpetersenii ( Table 2 ) . The built environment GLM further investigated the effects of building type , building condition , trap location and site location on rodent infection with Leptospira ( S5 Appendix ) . The first top model demonstrated that individual rodents trapped in or near buildings with mixed commercial and residential uses were more likely to be infected by both Leptospira species ( P = 0 . 001 , Table 3 ) . Moreover , the presence of institutional buildings within a site ( e . g . , churches , schools , etc . ) also appeared to increase the risk of rodent infection by L . interrogans ( P = 0 . 038 , Table 3 ) .
The results of this study reveal that the prevalence of Leptospira spp . in rodents is influenced by a number of environmental factors , and that these may vary depending on the species of Leptospira considered . Overall , Leptospira prevalence increased with increasing anthropogenic influence across the landscape , with a significantly higher proportion of infected rodents observed at the urban location . In particular , L . borgpetersenii was most commonly found at sites without a natural body of water , whilst L . interrogans infection was most prevalent among rodents inhabiting sites with low forest cover . For sites within the built environment , the type of buildings present was also found to have an impact on the prevalence of both Leptospira species . The overall prevalence of pathogenic Leptospira spp . in rodents in this study was slightly higher ( 32% ) than those observed in other studies from the Southeast Asian region ( 6–27% ) , and was also higher than previously identified from rodents trapped in urban areas of Sarawak ( 5 . 6% , N = 107 rodents ) [6 , 22 , 42–46] . While the discrepancy between our results and those of Pui et al . [22] are not straightforward to explain , they may be due to differences in sampling sites , organs tested and laboratory methodology . Pui et al . , cultured all samples prior to detection with a single PCR assay , unlike the direct-detection methodology with multiple primer sets applied in the current study . Although the approach of Pui et al . is common , growing Leptospira in vitro is well-known to be challenging and can be biased by species and serovar , which may result in a lower reported prevalence . Rural habitats have repeatedly been associated with an increased risk of leptospirosis due to associations with some types of agriculture ( e . g . rice farming ) and outdoor recreational activities [47 , 48] . As a result , the majority of research on the ecology and distribution of Leptospira spp . in rodents has been performed in rural environments , and the ecological drivers and risk factors for zoonotic infection in these habitats are relatively well documented . In contrast , Leptospira ecology in urban environments has received considerably less attention , despite the abundance of rodents and other potential hosts in urban environments , and clear evidence of human infection [49–52] . In addition , and as observed in this study , most surveys of urban rats have found a high prevalence of pathogenic Leptospira spp . [42 , 53–56] . The high population densities that rodents can reach in urban areas and the resulting frequency of human-rodent contact suggests that a real risk of human infection is present , even when infection prevalence in rodents is low [57 , 58] . From the results of this study we are unable to determine if the Leptospira spp . carried by rodents in and around Kuching are associated with human infection , or what the relevant risk factors for zoonotic transmission may be . However , previous work has assessed serovar diversity in both soil and rodents in urban Sarawak , and predominantly identified L . interrogans serovar Icterohaemorrhagiae [22] . This serovar is commonly associated with rodents and has been linked to human disease in Sarawak and other regions of Malaysia [59–62] . In addition , serovar Sarawak ( Lepto 175 ) has also been detected in both humans and rodents in Sarawak but has not yet been confirmed as an agent of human disease [25 , 62 , 63] . Taken together this suggests that rodents , including the species sampled in this study , are likely a source of human infection in Sarawak and the Southeast Asian region . In recent years , several leptospirosis outbreaks in Malaysia have been linked to outdoor activities ( e . g . hiking , water-sports ) in natural environments [23 , 42] . Reflective of this risk , both species of Leptospira were detected in vegetated areas across the landscape in this study . Infected rodents were detected in disturbed forests , recreational parks and vacant lots , all of which are utilized to varying degrees by people , and which provide additional interfaces for human exposure to Leptospira . Across our study sites , city parks are used extensively for sporting and social activities , vacant lots for edible plant foraging and small-scale fruit and vegetable cultivation ( personal observation ) , and disturbed forests for farming , hunting and foraging , as well as recreational activities [64 , 65] . In contrast , we identified an unexpected association between the presence of L . interrogans and reduced forest cover at a site , which may indicate that transmission is favored in more cleared ( and disturbed ) habitats [6 , 66] . However , this trend may be related to the ecology of the dominant rodent species assessed in this study . Across Southeast Asia , both members of the R . rattus super-group and S . muelleri are often found at higher abundance in disturbed and urban habitats compared to more pristine , forested habitats [67 , 68] . It is therefore possible that rodent population density , which was not measured here but did appear low in forested areas , is a factor that inhibits Leptospira spp . transmission . Alternatively , the lower infection prevalence observed at sites characterized by high forest cover may simply be a result of the relatively small number of rodents trapped at these sites . Rodent abundance may also be related to the association we identified between the presence of buildings with mixed commercial and residential uses , and an increased prevalence of both Leptospira investigated here . This type of building , which often has a shop , restaurant or market on the ground floor and higher-density accommodation above , is the primary building type found in the center of Kuching and at focal points of human activity in its suburbs . The disposal of waste from these premises is often informal and directed towards the sewerage system , providing an ample source of food for rodents . As sewers also provide access to water and shelter from most predators , they are regularly favored by urban rodents such as R . norvegicus , which can become extremely abundant in urban environments [55] . The high rodent population densities that can occur in such settings may promote the circulation of Leptospira and increase the risk of zoonotic transmission in urban environments . Indeed , living close to open sewers has been identified as a risk factor for human Leptospira infection in Salvador , Brazil , and occupational risks have been identified for town cleaners and sewage workers in other cities [24 , 69–71] . The drivers behind the association between L . interrogans positive rodents and sites with institutional buildings is less clear , although this may be an artifact of our analysis as there were only four sites in this category . However , it is worth noting that as none of these sites were rural , this association may also be reflective of benefits related to higher levels of urbanization . It is surprising that the presence of L . borgpetersenii at a site was significantly more likely if natural bodies of water were absent , and may reflect the evolution of this species towards direct transmission between hosts [5] . This is reflected in the findings of another Southeast Asian study , which found L . borgpetersenii to be abundant in both dry and humid habitats , with the highest prevalence in non-floodable lands such as orchards , plantations and shrubby wasteland [6] . However , it may be worth noting that although sewers were accounted for by the variable ‘trap location’ in our analyses , they were not part of the variable ‘water body’ due to their artificial nature and the inconsistent presence of water in this environment . As approximately half of all Leptospira-positive rodents ( 51/100 ) were trapped in sewers , their exclusion from this category may have influenced these results . In addition , ‘trap location’ was not selected in the best top model for L . borgpetersenii ( Table 2 ) , but it was selected in the second and third top models , indicating that it may have some influence on L . borgpetersenii infection prevalence ( S4 Appendix ) . This study focused on identifying environmental factors that influence the prevalence of Leptospira spp . in urbanizing environments; however , the environment can both directly and indirectly influence the circulation of Leptospira ( i . e . by shaping host ecology ) . While we are unable to distinguish between these two modes of action in this study , our choice to explore the ecology of Leptospira across all rodents collectively is supported by several factors: 1 ) Leptospira are host generalists; 2 ) rodent species are not known to differ in competence; 3 ) a similar ecological study of Leptospira in Thailand found no impact of rodent species [6]; 4 ) individuals from R . rattus R3 and S . muelleri ( comprising 84% of all captures in this study ) were both found in urban , developing and rural locations , including at some of the same sites . However , infection prevalence did vary between these two species , with 42 . 5% of R . rattus R3 and 23 . 0% of S . muelleri individuals infected , suggesting that either the former is more likely to become infected , or that this species prefers to inhabit environments that promote the circulation of Leptospira . For example , R . rattus R3 was commonly caught in sewers in our study ( 73/165 captures ) , and large numbers of animals were often observed at these sites , suggestive of high population density . These conditions may promote the circulation of Leptospira , particularly for species such as L . borgpetersenii , which rely on direct transmission between hosts [5] . The number of reported cases of leptospirosis in Malaysia has increased considerably in recent years [23] . Although many cases are still documented in rural areas , zoonotic transmission is also clearly a feature of urban living , with some occupations ( i . e . garbage collectors , town cleaners ) associated with a higher risk of infection [24 , 69] . The high prevalence of Leptospira observed here and the importance of rodents as sources for human disease , suggests that the ecology and dynamics of rodent-associated transmission in urban Kuching warrants further study and may be required to prevent ongoing human disease . With the increasing loss of natural habitats and continuing urbanization occurring across the globe , the majority of zoonotic transmission events are anticipated to occur in urban settings . It is therefore essential to develop a thorough understanding of the drivers of pathogen transmission and zoonotic infection that occur in the ecologically and demographically complex urban environment . | Leptospirosis is a significant zoonotic disease that is found in a range of environments worldwide , most notably tropical regions prone to flooding . The bacterial agents of this disease , Leptospira spp . , are most often associated with rodents , including species frequently found in urban areas . In cities , rodent populations are often larger and denser than those found in natural environments , which can lead to higher rates of contact with people and impact human disease risk . To investigate the impacts of urbanization on Leptospira spp . , we sampled rodents at locations with differing levels of human influence , from highly urbanized to rural , surrounding a city in Malaysian Borneo . We found that 31 . 6% of all rodents were positive for Leptospira spp . DNA , and that two primary species were present , L . interrogans and L . borgpetersenii , both of which are known human pathogens . Statistical analyses revealed that infected animals were more common in areas with higher levels of human influence , and were more likely to occur at sites with limited forest cover , and mixed commercial and residential activity . Our study adds to a growing body of evidence suggesting that there is a significant yet underappreciated risk of leptospirosis for people living in urban environments . |
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Although systemic immunity is critical to the process of tumor rejection , cancer research has largely focused on immune cells in the tumor microenvironment . To understand molecular changes in the patient systemic response ( SR ) to the presence of BC , we profiled RNA in blood and matched tumor from 173 patients . We designed a system ( MIxT , Matched Interactions Across Tissues ) to systematically explore and link molecular processes expressed in each tissue . MIxT confirmed that processes active in the patient SR are especially relevant to BC immunogenicity . The nature of interactions across tissues ( i . e . which biological processes are associated and their patterns of expression ) varies highly with tumor subtype . For example , aspects of the immune SR are underexpressed proportionally to the level of expression of defined molecular processes specific to basal tumors . The catalog of subtype-specific interactions across tissues from BC patients provides promising new ways to tackle or monitor the disease by exploiting the patient SR .
Breast cancer ( BC ) research has largely focused on understanding the intrinsic properties of the primary tumor in order to therapeutically target key molecular components that drive progression within the tumor epithelial cells [1] . For example , tamoxifen and trastuzumab target the estrogen and human epidermal growth factor receptors ( ER , HER2 ) whose expression levels in tumors define the traditional clinical subtypes of BC . The vast majority of BC-related genomic studies have focused on bulk tumor samples that are expected to be enriched for neoplastic epithelial cells [2] . These efforts have produced subtyping schemes that classify patients into groups based on the similarity of expression of diverse molecular markers and processes [3–9] and generated gene signatures that can predict patient prognosis and benefit from therapy [10–13] . Cancers however are much more than an autonomous mass of epithelial cells . They constitute multicellular systems capable of bidirectional interactions with neighboring non-malignant cells and extracellular components i . e . the tumor microenvironment [14–16] . Tumor-microenvironmental interactions are necessary for tumor progression and drug sensitivity [16 , 17] and are becoming better understood [18–21] . In fact , several genomics studies of the BC microenvironment , including our efforts , show that the microenvironment reflects its tumor and harbors prognostic information [22–24] . However , we also recently established that the primary tumor and its microenvironment does not harbor accurate prognostic signals in approximately 20% of BC patients [9] . Specifically , these patients are consistently misclassified by all hallmarks of breast tumors defining tumor epithelial cells ( such as proliferation and ER status ) and their microenvironment ( such as the infiltration of immune cells , angiogenesis and fibroblast activation ) . The systemic response ( SR ) in cancer patients refers here to the perturbations that occur in peripheral blood cells , which include immune effector cells and circulate throughout the body . The fact that a tumor exerts systemic effects ( via eg soluble or exosomal factors ) may provide an explanation for the clinical observation that patients with one tumor have an increased risk of developing several independent tumors , and that removal of primary cancer improves the survival of patients with distant metastases at the time of diagnosis [25] . In addition , since ER positive ( ER+ ) BC tends to recur as long as 10–15 years after surgical removal of the tumor , it is important to understand systemic factors governing late recurrence and therapeutic approaches that target beyond the tumor site . In fact , there is a rapidly increasing understanding of the various means a tumor employs to favor metastasis in distant organs [26 , 27] . For example , an “instigating” BC can exploit the patient SR so that otherwise-indolent disseminated tumor cells become activated [27–32] . The SR has also been investigated in BC at time of diagnosis . Specifically , our recent comparison of blood profiles of BC patients and matched controls yielded a gene signature that reports the presence of BC [33] . This diagnostic signature is specific to BC ( i . e . the test classifies women with carcinoma other than breast as negative ) , and the composition of genes and enriched pathways in the signature suggest that a cytostatic immune-related signal in the SR of patients is associated with the presence of a tumor . Finally , recent evidence demonstrates that engagement of systemic immunity is critical to the process of tumor rejection in genetically engineered mouse models [34] . This study is the first large-scale genomics effort to study the molecular relationships between patient SR and primary tumor . We generated and analyzed expression profiles from peripheral blood and matched tumor cells in 173 BC patients . First , our results highlight how the patient SR is especially relevant to BC immunogenicity . Second , we present a novel tool entitled Matched Interactions across Tissues ( MIxT ) that starts by identifying sets of genes tightly co-expressed across all patients in each tissue . Then , MIxT identifies which of these gene sets and pathways expressed in one tissue are associated with gene sets and pathways in the second tissue by determining if their expression patterns in tumor and in the patient SR are tightly correlated . We find that there are very few such associations when all BC are considered . However , we do identify biological processes with significant associations between tumor and patient SR when we stratify our analysis by BC subtype . That is , we identify molecular processes in the tumor that are tightly co-expressed with ( different ) molecular processes in the SR across patients of a specific subtype . In particular , we detail how several tumor-permissive signals are associated between the tumor and SR of basal BC patients .
The Norwegian Women and Cancer ( NOWAC ) is a prospective population-based cohort that tracks 34% of all Norwegian women born between 1943–57 . In collaboration with all major hospitals in Norway , we collected blood samples and matched tumor from women with an abnormal lesion , at the time of the diagnostic biopsy or at surgery , before surgery and any treatment ( N ~ 300 , S1 Text ) . RNA preservation for blood samples obtained followed our methodology previously described [33 , 35] and detailed in S1 Text . RNA profiles from blood and tumor cells were generated using Illumina Beadarrays and data were processed following careful procedures ( S1 Text , S1A Fig ) . After quality control , our study retained matched blood ( SR ) and tumor profiles of 173 BC patients diagnosed with invasive ductal carcinoma , and blood profiles of 282 control women ( ie . women with no history of cancer with the exception of basal-cell and cervical carcinoma , which are both very common; Fig 1A ) . The controls are used to determine what constitutes a “normal” SR . BC patients and controls are comparable in terms of age , weight and menopausal status ( Fig 1B ) . Several groups including ours have defined intra- and inter- individual variability of blood gene expression in healthy individuals [35–38] . All together , these studies demonstrate that intra-individual changes that can occur between blood draws are strikingly smaller than the variation observed among samples collected from different individuals . In this study , most women were 50 year-old or older and postmenopausal at time of sampling . Each profile measures the expression of 16 , 782 unique genes ( S1 Text , S1A Fig ) . Almost all BC ( 95 . 4% ) are early-stage disease ( stage I or II ) . Several tumor RNA-based subtyping tools were applied including PAM50 [5] that defines the intrinsic subtypes including luminal A ( lumA ) , luminal B ( lumB ) , normal-like ( normalL ) , basal-like ( basalL ) , and her2-enriched ( her2E ) . The hybrid subtyping scheme partitions ER+ tumors according to their intrinsic subtype and partitions ER- tumors according to their HER2 status [9] ( S1 Text , S1B and S1C Fig ) . In our dataset , all intrinsic luminals ( lumA and lumB ) and most normalL tumors ( 85 . 2% ) are ER+; however , ~40% of basalL and ~50% of her2E BC are ER+ ( Fig 1C , S1 Table ) . We also applied the Cartes d’Identité des Tumeurs ( CIT ) [8] subtyping scheme , which includes a ‘molecular-\ apocrine’ ( mApo ) subtype enriched for ER-/HER2+ tumors ( 78 . 6% ) and the highly immunogenic ER+ luminal C ( lumC ) subtype enriched for ER+/basalL ( 39 . 1% ) . Fig 1C and S1 Table depict the relationships between these three schemes . Although the IntClust ( IC ) subtyping scheme [6] is based on gene expression and DNA copy number profiles simultaneously , subtypes can be inferred using a reported RNA-based surrogate algorithm [7 , 39] . S1 Table reports when subtypes from other schemes are enriched in each IC subtype . Most notably , IC1 and IC9 are enriched for CIT lumB; IC3 , IC7 and IC8 are enriched for lumA; IC4+ is enriched for normalL and at lesser extent CIT lumC , IC5 enriched for mApo-her2E-HER2+ , and IC10 enriched for basalL and ER-/HER2- . IC2 , IC4- , and IC6 include very few patients ( n < 10 ) and were therefore not further considered in our downstream analyses . Restricting our attention to tumor profiles , we performed sparse hierarchical clustering with complete linkage using a permutation approach to select the tuning parameter that weights each gene to compute the dissimilarity matrix [40] . The resulting clusters were strongly associated with BC subtypes for all three RNA-based schemes ( Fig 1D upper ) , which confirms that the transcriptional fingerprint of BC subtypes are also ubiquitous in our tumor samples . When restricting our attention to SR profiles , this unsupervised analysis does not identify patient clusters enriched for any given subtype across the three schemes ( Fig 1D lower ) , suggesting that the transcriptional fingerprint of BC subtypes is not the predominant signal in the patient SR . We then asked if there are genes in the patient SR whose expression covaries with the state of the pathological variables ER and HER2 measured in the primary tumor . Although both are key drivers in BC , neither was found to be associated with individual gene expression changes in the patient SR ( limma , linear models for microarray data , false discovery rate , fdr ≤ 0 . 2 , Fig 1E; S1 Text ) . Similarly , we asked if there are genes in the SR that are markers of tumor subtype ( n patients > 10 ) . For the intrinsic , hybrid , and IntClust subtypes , only the ubiquitin ligase RFWD3 is highly expressed uniquely in the SR of lumA patients , and TIMP3 , an inhibitor of matrix metalloproteinases , is highly expressed uniquely in ER+/her2E patients ( Fig 1E , S2 Fig ) . For the CIT subtypes [8] , we found 70 univariate gene markers in the SR of patients of the lumC subtype . The genes are primarily involved in general cellular processes such as protein processing or transcription in blood cells ( fdr ≤ 0 . 2 , Fig 1E , S3 Fig ) . The lumC subtype is defined by strong activation of several immune pathways at the site of ER+ tumor ( i . e . antigen presentation and processing pathway , hematopoietic cell lineage , NK cell mediated cytotoxicity , T-cell receptor signaling and Toll-like receptor signaling ) [8] , suggesting that the SR is informative in cases where the primary tumor exhibits strong immune properties . To compare genome-wide molecular changes in tumor and SR across patients , we used WGCNA-based clustering to define sets of tightly co-expressed genes ( termed modules ) in tumor and blood , respectively [41] ( S1 Text ) . Briefly , we opted for a distance measure based on topological overlap , which considers the correlation between two genes and their respective correlations with neighboring genes [42] ( S1 Text ) . The WGCNA cut and merge routine [43] after clustering identified 19 and 23 modules in the patient tumor and SR , respectively ( S4 Fig; S1 Text ) . Each of these modules can be considered as a unique and stable pattern of expression shared by a significant number of genes . Modules of the primary tumor are enriched for genes from a broad range of BC hallmarks including angiogenesis ( salmon module ) , extracellular matrix reorganization ( greenyellow ) , proliferation ( green ) , and immune response ( brown and darkturquoise ) ( S2 and S3 Tables , S1 Text ) . For example , the proliferation tumor module is enriched for mitotic cell cycle-related genes ( green , n = 1064 genes; weight01 Fisher test [44] , p-value < 2e-17 ) including the well-known marker of proliferation MKI67 , 12 serine/threonine kinases that are used in the calculation of the mitotic kinase score ( MKS ) [45] , and several components of the Minichromosome Maintenance Complex ( MCM ) . Modules of the patient SR are often enriched for genes involved in either general cellular processes such as translation ( black ) and transcription ( grey60 ) , or immune-related processes such as inflammatory response ( brown , green ) , B-cell response ( saddlebrown ) , innate immune response ( greenyellow ) ( S4 and S5 Tables ) . Thus , seven SR modules are enriched in genes that are specifically expressed in immune cells [46] ( “iris” signature set in S5 Table; Fisher’s Exact Test FET fdr < 0 . 05 ) . We constructed a web-based system to visualize gene expression networks , heatmaps and pathway analyses of the modules in each tissue at http://mixt-blood-tumor . bci . mcgill . ca . In a network , genes are represented by nodes ( colored by their module membership ) that are connected by edges whose length corresponds to their level of co-expression across patients [47] . When selecting only strong gene-gene correlations ( topological overlap > 0 . 1 ) and removing isolated nodes , the SR network has ~20% more genes than the tumor network ( Fig 2A and 2B ) . Moreover , the SR network has approximately twice as many edges ( 89 , 465 connections between genes ) than the tumor network ( 50 , 617 connections between genes ) . Thus , the underlying patterns of expression of the tumor genes ( and modules ) are more dissimilar from each other than the patterns of expression of the SR genes ( and modules ) . In both tissues , the edges that span between modules reflect natural overlaps between cellular process ( Fig 2A and 2B ) . For example in tumors , angiogenesis-related genes of the salmon module are strongly co-expressed with genes of the greenyellow module involved in extracellular matrix remodeling . In blood , modules enriched for genes involved in general cellular processes such as translation ( black ) , RNA processing ( violet ) , and RNA splicing ( darkred ) are also heavily connected to each other . We first investigated the relationships between the expression pattern of each module and patient clinicopathological attributes . Towards this end , each gene of a module is used to rank the patient samples ( S1 Text ) . In particular , the sum of gene ranks ( ranksum ) for each patient provides a linear ordering of the patient samples . Association tests then compare the ranksum values of patients with the attribute of interest eg tumor subtype ( S1 Text ) . When we consider tumor modules , the expression pattern of the green module ( S5A Fig ) , previously established to be enriched for proliferation-related genes ( S2 Table ) , ranks basalL , her2E and lumB tumors significantly higher than lumA and normalL tumors ( ANOVA p-value < 1e-34 , S5B Fig ) . In fact , we observe that the expression pattern of nearly every module is associated with BC subtype ( 15 of 19 modules , Fig 2C , fdr ≤ 0 . 15 ) . Moreover , many tumor modules are associated with the proliferative state of the tumor encoded into the MKS score [45] ( Pearson correlation , fdr ≤ 0 . 15 ) or with ER status ( ER+ vs ER- , t-test , fdr ≤ 0 . 15 ) , two variables that are strongly embedded in the definition of BC subtypes ( Fig 2C ) . These results are consistent with our previous claim that patient subtype is a predominant signal in the primary tumor . Several tumor modules are associated with HER2 status of the tumor , however there are fewer such modules ( n = 6 ) when compared with the proliferative state or ER status of the tumor ( Fig 2C ) , suggesting that transcriptional fingerprint of HER2 is not as ubiquitous in tumor samples . A small number of modules are associated with the lumC subtype , including the brown module enriched for T-cell and inflammatory response genes ( S2 Table ) . This is again consistent with the fact that this is a highly immunogenic subtype [8] ( lumC versus not lumC , t-test , fdr ≤ 0 . 15 , Fig 2C ) . HER2 status , the lumC subtype and tumor size are all associated with modules of the patient SR ( Fig 2D , t-test fdr ≤ 0 . 15 ) . Although we did not find univariate gene markers in blood associated with HER2 status , the saddlebrown SR module is significantly underexpressed in patients with HER2+ tumors compared to other BC subtypes and controls ( fdr = 0 . 07 , S6A Fig ) and is enriched for genes involved in B-cell receptor signaling and proliferation ( including BLK , CXCR5 , CD19 , CD79A , CD79B and FCRL5; S4 and S5 Tables ) . Four SR modules are associated with the immunogenic lumC subtype; one of these modules are also associated with tumor size ( Fig 2D , S6B and S6C Fig ) . Among the 70 univariate gene markers in blood of lumC tumors identified earlier , 31 are included in the darkgreen SR module predominantly underexpressed in lumC patients in comparison to other BC subtypes ( fdr = 0 . 02 , S6B Fig ) . In fact , all four SR modules associated with the lumC subtype are underexpressed compared to other BC subtypes and control samples ( S6B and S6C Fig ) . This includes the purple module highly enriched for genes involved in T-cell ( thymus ) homing ( CCR7 , LTA , LTB , VEGFB , HAPLN3 , SLC7A6 , SIRPG , BCL11B0 ) and activation ( CD47 , TNFRSF25 , MAL , LDLRAP1 , CD40LG ) which are underexpressed in lumC patients ( fdr = 0 . 04 , S6B Fig ) . Genes in the cyan modules are also found underexpressed in patients with large ( > 2cm ) tumors compared to other BC patients and controls ( Fig 2D , S6C Fig ) . Finally , specifically for patients with large tumors , both the darkgrey module , which is enriched for MYC target genes , and the greenyellow module , which is enriched for genes involved in the lymphoid cell-mediated immunity ( including GZMH , GZMB , GZMM , KLRD1 , PRF1 , KLRG1 , and GNLY; S4 and S5 Tables ) , are underexpressed compared to the remaining BC patients and controls . Together these results indicate that distinct SR are detected in BC patients with HER2+ , lumC and/or large tumors , and that overall the patient immune response is underexpressed compared to patients of other subtypes and controls . These results also highlight the importance of distinct immune components for each of these disease groups . In particular , patients with HER2+ tumors exhibit low expression of genes specifically expressed in B-cell compared to patients with other BC subtypes . Patients with lumC tumors exhibit low expression of genes involved in T-cell homing and function compared to patients with other BC subtypes . Patients with large tumors ( >2cm ) exhibit low expression of genes involved in lymphoid cell-mediated immunity compared to patients with smaller tumors . Our analysis to this point identified modules within each tissue independently . Our focus here is on the relationships between tissues by asking if specific biologies in one tissue are correlated with ( possibly distinct ) biologies in the second tissue . To do this , we constructed a software entitled MIxT ( Matched Interactions across Tissues ) that contains the computational and statistical methods for identifying and exploring associations between modules across tissues ( http://mixt-blood-tumor . bci . mcgill . ca ) . Using MIxT , we first ask if genes that are tightly co-expressed in the primary tumor are also tightly co-expressed in the SR , and vice versa ( Fig 3A , S1 Text ) by investigating the gene overlap between tumor and SR modules ( Fisher’s Exact Test FET , fdr < 0 . 01 ) . Genes that retain strong co-expression across patients regardless of tissue type are likely to be involved in the same biological functions in both tissues as a “system-wide” response to the presence of the disease ( even if patterns of gene expression across tissues might differ ) . Most modules , regardless of tissue , have significant overlap with three to five modules in the other tissue ( Fig 3A ) . In some cases , it appears that a single ( large ) module in one tissue is in large part the union of several smaller modules from the other tissue . For example , the brown tumor module has 2765 genes including many involved in immune-related processes ( T-cell costimulation , the IFN-gamma pathway and inflammation , S2 and S3 Tables ) . All of these genes/processes show very strong co-expression in the tumor however , in the SR , these genes divide into four distinct patterns of co-expression ( Fig 3A ) , captured by four different modules: brown ( inflammation ) , greenyellow ( cytolysis and innate immune response ) , saddlebrown ( B-cell ) and pink ( TNFA inflammatory response ) ( S4 and S5 Tables ) . Of note , MIxT identifies three modules in each tissue ( SR and tumor ) that do not have significant overlap with any module in the other tissue ( Fig 3A ) . For tumors , this includes the purple module enriched for genes involved in estrogen response , the lightcyan module enriched for genes involved in hemidesmosome assembly and cytoarchitecture , and the greenyellow module enriched for genes involved in ECM organization ( Fig 3A , S2 and S3 Tables ) . For the SR , this includes the turquoise module enriched for genes expressed in erythrocytes and involved in hemoglobin production , the purple module enriched for genes in translational termination , and the green module enriched for genes involved in inflammation and specifically expressed in myeloid cells ( Fig 3A , S4 Table ) . This suggests that these processes and responses are either specific to a tissue type ( eg ECM organization specific to tumor , and hemoglobin production specific to blood cells ) or that the co-expression of genes involved in a defined process is unique to a particular tissue ( eg genes specifically co-expressed in peripheral myeloid cells ) . There is only one instance where a single tumor module has significant overlap with only a single SR module: darkturquoise modules of size = 86 and 97 genes in SR and tumor , respectively with 50 common genes , including 20 involved in the type 1 IFN signaling pathway ( S2 and S4 Tables ) . Although these two “mirrored” modules share many genes , their patterns of expression are significantly different between the two matched tissues ( Fig 3B , correlation between ranksums p-value > 0 . 05; S1 Text ) , hinting at a non-concordant expression of the local ( in tumor ) and systemic ( in blood ) IFN-1 mediated signals . Whereas the previous section considers interactions defined by a large number of shared genes between a tumor and a SR module , we also examined more general notions of interactions in MIxT . Here we identify tumor and SR modules that have similar expression patterns ( ie both modules linearly order the patients in very similar manner in both tissues ) but do not necessarily share any genes in common . More specifically , MIxT derives estimates of significance for interactions using a random permutation approach based on the Pearson correlation between ranksums of gene expression in modules across tissues ( S1 Text ) . This type of interaction detects a biological process or response in the primary tumor that is tightly correlated ( or anti-correlated ) with a ( possibly distinct ) biological process or response in the SR , and vice versa . The specific expression pattern in the tissues allows us to then postulate the functional nature of the interaction across tissues . MIxT identified only one tumor module ( of 19 ) that interacts with only a single SR module ( of 23 ) across all patients ( MIxT statistic; p-value < 0 . 005 ) . The paucity of pan-BC interactions across tissues suggest the need to stratify by patient subtype . After stratification for each of the five subtyping schemes ( clinical , PAM50 , hybrid , CIT , and Intclust ) ( Fig 4A ) , we identified 53 interactions involving 15 tumor modules and 19 SR modules ( MIxT statistic; p-value < 0 . 005; Fig 4B , S7 Fig ) . Tumor and SR modules are indicated in columns and rows of Fig 4B , respectively . A non-empty cell corresponds to a significant interaction with color used to indicate in which subtype the association is found , grouping together similar subtypes across schemes ( eg basalL tumors of the pam50 and CIT schemes ) . Nearly all interactions are significant in only a single subtype ( four exceptions indicated by orange arrows , Fig 4B ) . For some subtypes , a single stimulus in the tumor affects several biological processes in the patient SR . For example , within the ER+/HER2- subtype and only within this subtype , the pink tumor module , enriched for genes involved in alternative splicing , is associated with three SR modules , enriched for a diverse range of biological processes ( orange rectangle in Fig 4B ) . The brown tumor module , which is enriched for genes involved in immune processes ( S2 Table ) , has several interactions with SR modules across several subtypes ( orange rectangle in Fig 4B ) . This includes interactions specific to normalL , lumB and IC9 but also several distinct interactions within the ER-/HER2- and basal subtypes . This suggests that immune signals expressed in tumor are associated with changes in expression of different molecular processes in the patient SR for a broad range of subtypes . As alluded to earlier , only a few interactions are significant in two distinct subtypes simultaneously . For example , the brown tumor module is associated with green SR module in both ER-/HER2- and lumB although the directionality of the association differs between the two cases . More specifically , patients with high ranksums in the brown tumor module have low ranksums according to the green SR module , if the patient is of the ER-/HER2- subtype ( Fig 5A , 5C and 5E , MIxT statistic , p-value = 0 . 004 ) . At the same time , patients with high ranksums in the brown tumor module have high ranksum with respect to the green SR module , if the patient is of the lumB subtype ( Fig 5B , 5D and 5F MIxT statistic , p-value < 0 . 004 ) . In this manner the direction of correlation between the biological processes of the brown tumor module and of the green SR module is determined by the subtype of the patient . For the brown tumor module in both subtypes , patients with a high ranksum ( on the left of the ordering in Fig 5B or 5C for both subtypes ) have the strongest immune signals in the tumors . This is because most of the immune-related genes in this brown module ( within the red sidebar in Fig 5B and 5C , S3 Table ) have highest expression in these patients . This includes genes involved in T-cell stimulation ( incl . CD3 , CD4 , CD5 , ICOS , several HLA-DR , -DP , -DQ ) , IFNɣ signaling ( IFNG , IRF1-5 , ICAM1 , IFI30 , HLA-A -B -C ) and inflammation ( incl . several interleukins , chemokines ) . For the green SR module in both subtypes , a high ranksum indicates an inflammatory SR ( patients on the right in Fig 5E for ER-/HER2- , and patients on the left in Fig 5F for lumB ) . This is because almost every inflammation-related genes ( incl . IFNAR1 , IL15 , TLR2 , IL18RAP , RNF144B ) , and B-cell proliferation genes ( incl . BCL6 , IL13RA1 , MIF , IRS2 ) ( within the red sidebar in Fig 5E and 5F , S5 Table ) have highest expression in these patients . Thus , ER-/HER2- patients with low immune activity at the tumor site have a high inflammatory SR ( right side of Fig 5C and 5E ) . In fact , the level of the inflammatory response in these BC patients is higher than healthy controls ( Fig 5I , t-test , p < 0 . 001 ) . However , for the lumB subtype , the relationship between tumor and SR is reversed . Here , it is the patients that have high immune activity at the tumor site that have a high inflammatory SR ( left side Fig 5D and 5F ) . In fact , the CIT subtyping scheme calls these patients on the left side as belonging to the lumC subtype ( Fig 5H ) , the highly immunogenic ER+ subtype . In these lumB patients the inflammatory response is also higher than in healthy controls ( t-test , p-value < 0 . 01; Fig 5J ) . Altogether these results indicate that a high inflammatory SR is observed in both ER-/HER2- and ER+/lumB patients but increase in systemic inflammation is associated with distinct immune activity at the tumor site depending on subtype . Three tumor modules are enriched for genes within amplicons prevalent in BC [48] ( highlighted in orange in Fig 4B , S3 Table ) . Two modules , the darkgrey and turquoise tumor modules , contain 68 genes ( of 110 ) and 48 genes ( on 71 ) located within the 16p11-13 amplicon highly prevalent in luminal tumors [48] , respectively ( S3 Table ) . The darkgrey module interacts with two distinct SR modules for the lumA and ER+/HER2+ subtype , respectively ( S8A and S8B Fig ) . Tumors of both subtypes that over-express genes in the darkgrey module ( left hand side S8C and S8D Fig ) are likely amplified in 16p13 . In these patients , the presence of this amplification is correlated with changes in expression of specific processes within the patient SR and these processes are distinct depending on subtype ( S8E and S8F Fig , p < 0 . 005 in both cases ) . S8G and S8H Fig depicts associations between the presence of this amplification and patient clinico-pathological attributes . For example , in ER+/HER2+ patients ( S8H Fig ) , the presence of 16p13 amplification is correlated with the luminal score of the tumor . In the lumA subtype , patients with the highest expression of the lightyellow SR module are significantly different than healthy controls ( S8I Fig ) , and in the ER+/HER2+ subtype , patients with the lowest expression of the salmon module are significantly different than healthy controls ( S8J Fig ) . The third module enriched for genes involved in BC amplifications is the darkgreen tumor module . This module contains 43 ( of 99 ) genes within the 8q23-24 amplicon prevalent in basal and her2E tumors [48] ( S3 Table ) . Most associations with patient SR modules are specific to the basalL subtype ( Fig 4B ) and again suggest that basalL tumors that harbor this amplification have concomitant changes in expression of specific molecular processes in patient SR . Approximately one-fourth of the interactions identified by MIxT are specific to ER-/HER2- , IC10 and basalL subtypes , indicating that the tumor and SR interact strongly in this family of BCs ( Fig 4B ) . We study two tumor modules in greater depth here: the brown immune-enriched module and the darkgreen 8q-enriched module , and their interactions with SR modules in basalL patients ( Fig 6A–6C ) . Here the brown tumor module interacts with one ( tan ) SR module enriched for genes involved in TOR signaling and cell proliferation ( Fig 6A and 6B ) . BasalL patients with low immune activity at their tumor site ( right side of brown tumor module ) have low expression of the tan SR module , and this expression is significantly lower than healthy controls ( boxplots in Fig 6B , t-test p < 0 . 0005 ) . The darkgreen tumor module interacts with four SR modules in basalL patients ( Fig 6A and 6C ) . High expression of genes in 8q is associated with high expression of the green SR module . This module is enriched for genes involved in inflammation . For the remaining three SR modules associated with the 8q-enriched tumor module , almost all genes in these modules are underexpressed when 8q genes are highly expressed ( ie . the patient orderings are reversed compared to the darkgreen tumor module ) . These SR modules contain genes involved in general cellular processes of blood cells ( RNA/protein processing , cell proliferation; darkgreen module ) , genes involved in cytolysis and lymphoid cell-mediated immunity ( greenyellow module ) , and MYC and CD5 target genes ( darkgrey module ) ( Fig 6A–6C , S5 Table ) . The increase in inflammatory SR and the decrease in the three other molecular processes in the SR of basalL patients whose tumor is amplified on 8q are all significantly different from how these processes are expressed in healthy controls ( boxplots in Fig 6C ) . Overall , we identified one distinct signature in the SR of basalL patients with low immune activity at their tumor site and several immuno-suppressive signals in the SR of basalL patients whose tumor is amplified on 8q .
Molecular profiles of peripheral blood cells and matched tumors were generated and compared for a large cohort of BC patients part of the NOWAC study . The NOWAC consortium provides a highly curated population-based study with extensive gene expression profiling across several tissues from BC patients and controls [35 , 49] . A careful design and our extensive experience in blood-based expression profiles enable a detailed molecular description of the patient SR to the presence of BC where blood molecular profiles represent effectively an “averaging” over the transcriptional programs of the different types of cells in blood . We first asked if the SR could provide accurate univariate markers of tumoral properties such as ER status or subtype . Although thousands of transcripts are differentially expressed in tumors between ER+ and ER- BC [9 , 50] , there is no gene in SR that can reliably predict ER status of the primary tumor . Moreover , the SR does not inform on the intrinsic BC subtype of the tumor such as lumA , lumB or basalL subtype or on IntClust subtypes . Interestingly , univariate markers in the patient SR were only identified for the CIT lumC subtype defined as particularly immunogenic ER+ tumors [8] , suggesting that the SR is informative in cases where the primary tumor exhibits strong immune properties . This is consistent with previous reports that uses blood transcriptomics as a gateway into the patient immune system [51–53] and which is extensively used in the context of autoimmune and infectious diseases [54–56] . This result suggests that it is also applicable in cancer such as BC . To further investigate the molecular changes in the patient SR , we extended our analyses to multivariate approaches where genes are combined into sets or “modules” . In particular , we performed cluster analysis to partition the genes of both tumor and SR profiles into modules with each module representing a distinct pattern of expression across patients . Our user-friendly website ( www . mixt-blood-tumor . bci . mcgill . ca ) provides access to these modules built in each tissue , enables investigation of their expression profiles in each tissue and allow user-defined queries of gene , gene sets , and pathway of interest . Further , our MIxT approach estimates gene module expression in both tissues and find significant associations between modules across tissues in a representative cohort of BC patients . In our dataset , the primary tumor and SR have approximately the same number of modules ( 19 and 23 , respectively ) but their gene composition is qualitatively different . Not surprisingly , many modules in tumors were enriched for genes involved in hallmarks of cancer , while SR modules were enriched for either general cellular processes or specific immune responses . Only one module involved in the IFN-I pathway is highly conserved in both tumor and SR , although the common genes had markedly different expression patterns between the two tissues . This is important as it establishes that genes , whose expression patterns may act as good markers in the primary tumor , are not necessarily expressed in the same manner within blood cells . Our multivariate approach was able to identify modules from the patient SR that could reliably identify not only lumC but also HER2+ and large ( > 2cm ) tumors . These three cases are among the most immunogenic subtypes of BC and are of relatively poor prognosis . For these patients , gene expression in blood cells is mostly decreased compared to other BC and controls . This result also highlights the importance of distinct immune components of the SR for each of these disease groups: B-cells for HER2+ tumors , T-cells for lumC , and aspects of the cellular immune response for large tumors . Interestingly , a previous study showed that her2E tumors have the highest B-cell infiltration and expression of B-cell receptor gene segments , although this was not predictive of improved patient survival [57] . Our study finds an impaired systemic B-cell response specifically in HER2+ patients , consistent with an inefficient anti-tumoral response in these patients , potentially due to a dysfunctional antigen receptor response and cell development . We could also speculate that the dysfunctional thymic T-cell homing signature in lumC patients reflects the well-documented effect of estrogen on thymic T lymphopoiesis [58–61] in patients diagnosed with a highly immunogenic ER+ tumor . These associations would certainly require validation in follow-up studies . Finally , MIxT focuses on molecular associations between tissues and provides a holistic view of molecular changes in BC patients . Although the focus here is towards gene expression of blood and matched tumor , our approach could be extended to multiple tissues ( eg . blood-microenvironment-tumor ) or other levels of molecular data ( eg . DNA level somatic aberrations , gene and miRNA expression , epigenetic profiles ) . Interestingly , associations between BC tumor and patient SR are heavily dependent on subtype . Only one interaction between tumor and patient SR is identified when all BC patients are considered in the analysis but many are identified when we first stratify patients by BC subtype . This is perhaps not surprising given that there is a great deal of molecular heterogeneity between BC subtypes making “one SR fitting all” highly unlikely . We identified molecular stimuli in tumors that change patient SR in multiple ways only for patients within a particular subtype . For example , expression of genes involved in alternative splicing in ER+/HER2- tumors is associated with changes in expression of multiple processes in SR of patients and those associations are observed only within this specific subtype . Of note , immune signals measured at the tumor site are associated with distinct SR across a broad range of subtypes . Immune-related processes are known to be more or less expressed within every subtypes and have prognostic capacity in almost all subtypes [9] . Here we show that a change in immune activity at the tumor site is not associated with equal SR across subtypes . Furthermore , high immune signals in tumor is associated with the patient inflammatory SR in opposite ways depending if the patient is ER-/HER2- or lumB . The high inflammatory SR in ER-/HER2- patients ( with low immune activity at the tumor site ) and in lumB patients ( with high immune activity at the tumor site ) were both significantly different from how systemic inflammation is “normally” expressed in controls . Finally , we identify other examples of interactions between tumor and patient SR that occur in subtype-specific fashions . In particular , three tumor modules were enriched for genes in known large-scale BC amplicons ( 16p11-13 , 8q23-24 ) . The expression of these genes changes in a coordinated manner from high to low , suggesting that these genes measure amplification of the corresponding region in BC tumors . In turn , these patterns of expression were associated with distinct SR depending on subtypes highlighting the significance of each amplicon in defining patient SR for particular BC subtypes ( eg 16p13 in lumA and ER+/HER2+ , and 8q23-24 in basalL and her2E ) . Of note , these patterns of expression also define patients with particular clinico-pathological characteristics . For example , ER+/HER2+ tumors that do not highly express the genes on 16p have a lower luminal score than ER+/HER2+ tumors that highly express the genes on 16p . When we restrict our attention to basalL patients , we observe that both the immune-related module and the presence of a 8q23-24 amplification is associated with the patient SR . In fact , the subset of basal patients with 8q23-24 amplification exhibit high inflammatory SR and underexpress genes involved in general cellular proliferation of blood cells , in immune cytolysis , and in MYC and CD5 targets . Together , our matched profiles offer a detailed map of tumor-permissive SR particularly relevant for basalL tumors amplified on 8q and highlight a signature in the SR of basalL patients with low immune activity at their tumor site . This is especially interesting in the context of BC-immunotherapy combination or for monitoring response to these therapies . Overall , our study set the groundwork for further investigation of promising new ways to tackle and monitor the disease by looking outside the tumor and exploiting the patient SR .
Tumor and blood samples were obtained as part of the NOWAC study [49 , 62] with approval from Regional Committees for Medical and Health Research Ethics in Norway . Between 2006–10 , we collected blood and biopsy samples from BC cases at time of diagnosis , and blood samples from selected age-matched blood controls together with associated lifestyle and clinicopathologic data ( S1 Text ) . In total , and after data preprocessing , profiles include 16 , 792 unique genes expressed in primary tumors and blood from 173 BC patients , and in blood from 290 controls ( S1A Fig ) . We used ER status as measured by IHC and HER2 status measured by FISH or IHC where available . When unavailable , ER and HER2 status was determined using gene expression of the ESR1 gene and 6 gene members of the HER2 amplicon , respectively [9 , 63] ( S1 Text , S1B and S1C Fig ) . In addition , we calculated the HER2 score ( HER2S ) and the luminal score ( LUMS ) as the average expression of the HER2 amplicon gene members and the pam50 luminal genes , respectively . A proliferation score was calculated similarly using 12 mitotic kinases to produce the Mitotic kinase gene expression score ( MKS ) [45] . Samples were labeled according to our subtyping schemes from the literature: PAM50 [5] , hybrid [9] , CIT [8] , IntClust [7 , 39] ( S1 Text ) . Lists of differentially expressed genes in SR according to subtypes were obtained using the R/Bioconductor package Limma [64] . Whenever p-values were adjusted for multiple testing , the false discovery rate [65] was controlled at the reported level ( S1 Text ) . An unsigned weighted co-expression network was constructed independently in each tissue ( SR and tumor ) using the R/Bioconductor package WGCNA [41] ( S1 Text ) . First , a matrix of pairwise correlations between all pairs of genes is constructed across blood and tumor samples , respectively . Next , the adjacency matrix is obtained by raising the co-expression measure to the power β = 6 ( default value ) . Based on the resulting adjacency matrix , we calculate the topological overlap , which is a robust and biologically meaningful measure of network interconnectedness [42] ( that is , the strength of two genes’ co-expression relationship with respect to all other genes in the network ) . Genes with highly similar co-expression relationships are grouped together by performing average linkage hierarchical clustering on the topological overlap . The Dynamic Hybrid Tree Cut algorithm [43] cuts the hierarchal clustering tree , and modules are defined as branches from the tree cutting . Modules in each network were annotated based on Gene Ontology biological processes ( weight01 Fisher test [44] ) , MSigDB [66] and other curated signatures relevant to immune and blood cell responses [33 , 46 , 52] ( S1 Text ) Our approach maps samples to a linear ordering based on expression of genes within a given module or signature of interest ( S1 Text ) . In an univariate fashion , each gene within a given module/signature is used to rank all patients based on their expression . For each patient , the ranks of all k genes from the signature are summed and patients are then linearly ordered from right to left according to this ranksum vector . To identify the left and right boundaries of the low and high regions within the observed linear ordering , we delimit the region of independance ( ROI95 ) for each module . Briefly , we compute ( n = 10K times ) the position of an artificial patient within the observed linear ordering by summing the randomized ranks over all k genes in the module ( S1 Text ) . The ROI95 is defined as the region that contains 95% of the randomly generated samples . The three defined categories of patients correspond to those patients that have high ranskums of the module/signature ( high category ) , low ranksums of the module/signature ( low category ) , and a set of patients where the expression of the genes within the module/signature lose their pattern of pairwise correlation ( mid category ) . Using gene ranksums to capture module expression , we asked how modules are associated with patients’ clinical attributes and how they are associated across tissues . Pearson correlation and Analysis of Variance ( ANOVA ) was used to test association between a given module and continuous patient attributes ( eg . age , weight , MKS , LUMS ) and between a given module and categorical patient attributes ( eg . ER , HER2 , subtypes , lymph node status ) , respectively ( S1 Text ) . For each variable . we computed empirical p-values after permuting clinical labels 1000 times . For each variable , we perform a total of 42 association tests ( 23 blood modules + 19 tumor modules ) and used false discovery rate [65] to correct for multiple testing for each variable independently or for each “family” of tests when dependent variables are very similar ( S1 Text ) . Interactions between modules across tissues are identified using a random permutation approach based on the Pearson correlation between ranksums of gene expression in modules across tissues ( S1 Text ) . ANOVA was used to compare SR module expression between BC patients ( assigned to a given tumor module ROI95 categories ) and controls . | We present a novel system ( MIxT ) to identify genes and pathways in the primary tumor that are tightly linked to genes and pathways in the patient systemic response ( SR ) . These results suggest new ways to tackle and monitor the disease by looking outside the tumor and exploiting the patient SR . |
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The parasitoid wasp Nasonia vitripennis is an emerging genetic model for functional analysis of DNA methylation . Here , we characterize genome-wide methylation at a base-pair resolution , and compare these results to gene expression across five developmental stages and to methylation patterns reported in other insects . An accurate assessment of DNA methylation across the genome is accomplished using bisulfite sequencing of adult females from a highly inbred line . One-third of genes show extensive methylation over the gene body , yet methylated DNA is not found in non-coding regions and rarely in transposons . Methylated genes occur in small clusters across the genome . Methylation demarcates exon-intron boundaries , with elevated levels over exons , primarily in the 5′ regions of genes . It is also elevated near the sites of translational initiation and termination , with reduced levels in 5′ and 3′ UTRs . Methylated genes have higher median expression levels and lower expression variation across development stages than non-methylated genes . There is no difference in frequency of differential splicing between methylated and non-methylated genes , and as yet no established role for methylation in regulating alternative splicing in Nasonia . Phylogenetic comparisons indicate that many genes maintain methylation status across long evolutionary time scales . Nasonia methylated genes are more likely to be conserved in insects , but even those that are not conserved show broader expression across development than comparable non-methylated genes . Finally , examination of duplicated genes shows that those paralogs that have lost methylation in the Nasonia lineage following gene duplication evolve more rapidly , show decreased median expression levels , and increased specialization in expression across development . Methylation of Nasonia genes signals constitutive transcription across developmental stages , whereas non-methylated genes show more dynamic developmental expression patterns . We speculate that loss of methylation may result in increased developmental specialization in evolution and acquisition of methylation may lead to broader constitutive expression .
DNA methylation is an important epigenetic modification found in many plants and animals [1]–[5] . In mammals , DNA methylation is associated with important epigenetic processes such as genomic imprinting [6] , histone modifications and X chromosome inactivation [7] , [8] , and plays an important role in brain development [9] . Clusters of CpG sites ( CpG islands or CGIs ) are often found in the 5′ regulatory regions including the promoter regions in mammals [10] , [11] . Methylation at the promoter will typically result in silencing of the gene [12] . The promoters of transposable elements ( TEs ) are also often repressed by DNA methylation [13] . Non-CpG methylation has been observed in mammals , with high percentages in embryonic stem cells [14] . DNA methylation is also widespread in invertebrates [4] , [15]–[26] . In contrast with mammals , methylation typically occurs over gene bodies , and is correlated with elevated gene expression [4] , [15] , [16] , [18] , [19] , [22] , [27] , rather than gene inactivation . Consistent with gene activation , several studies of invertebrate methylation have reported that methylated genes tend to have “house-keeping functions” , whereas non-methylated genes are more tissue-specific [18] , [28] , [29] . DNA methylation is not universal among invertebrates [30] , [31] . For example , the fruit fly Drosophila melanogaster lacks DNA methylation in adults due to the loss of two of three DNA methyltranferases ( Dnmt1 and Dnmt3 ) , and the reported DNA methylation found in early embryonic stages [32] , [33] may be due to bisulfite conversion artifacts [31] . Nevertheless , in insects a combination of insect genome sequencing , identification of a full complement of DNMTs , and indirect or direct quantification of methylation , has uncovered genome-wide methylation in many species . A common indirect computational method for identifying genome-wide methylation is gene specific depletion of expected frequencies of CpG relative to observed ( CpG O/E ) , which occurs in methylated genes due to mutational biases of methylated C to T [16] . This approach yielded evidence of genome-wide methylation in a number of insects , including the honeybee Apis mellifera , parasitoid wasp Nasonia vitripennis , pea aphid Acrythiosiphon pisum , and others [16] , [26] , [34]–[36] . Direct methods that have been used to quantify genome-wide methylation in insects include methylation sensitive restriction enzymes [37] and methylated DNA immunoprecipitation ( MeDIP ) [21] . However , to achieve single-base resolution of methylation in the genome requires bisulfite conversion coupled with high throughput sequencing , which has so far only been reported for honeybee ( Apis mellifera ) [15] , [19] , silkworm ( Bombyx mori ) [15] , [22] and ants Camponotus floridanus , Harpegnathos saltator [18] and Solenopsis invicta [38] . Most of the work on arthropod DNA methylation has focused on the social insects ( honeybees and ants ) where alternative castes drive an interest in developmental processes that modulate caste determination [18] , [28] , [29] . Investigations in honeybee and ants have suggested an association between alternate splicing and methylation [18] , [39] . N . vitripennis is a non-social haplodiploid parasitoid wasp with a well annotated reference genome [34] , [40] , [41] . Prior studies have revealed DNA methylation in Nasonia [20] and the presence of requisite DNA methyltransferases , including three members of Dnmt1 [34] . Here , we report findings of a whole-genome bisulfite sequencing ( WGBS-seq ) study that provides base-pair resolution of the genome of Nasonia vitripennis , a non-social Hymenopteran species [34] , [40] , [41] . The highly inbred strain of Nasonia used here allows for precisely mapping of WGBS-seq reads and CpG methylation calls to the genome without the complications caused by SNP variation found within heterologous DNA samples from variable strains or populations . We analyze whole genome patterns of DNA methylation in N . vitripennis , including the relationship between methylation , gene expression , expression breadth , and gene length , clustering of methylated CpG sites and methylated genes in the genome , patterns of methylation among transposons , non-CpG methylation , methylome comparisons with Apis , and changes in gene expression correlated with changes in methylation among paralogs in the Nasonia lineage . The Nasonia methylome helps to shed light on the function ( s ) and evolution of DNA methylation in insects .
To profile the Nasonia methylome , we performed Illumina whole-genome bisulfite sequencing ( WGBS-seq ) in adult female samples with 25× haploid genome coverage ( Figure S1 ) and 16 . 2× average CpG coverage ( Figure S2 ) . From the control lambda DNA alignments , the bisulfite conversion efficiency was 99 . 7% ( Table S1 ) , indicating highly efficient conversion . Additional quality control metrics and procedures to assure the high quality of this methylome are described in Materials and Methods . Across the 8 million CpG sites in the Nasonia genome covered by our data , the average percentage methylation is 1 . 45% , and 1 . 6% of sites are defined as methylated CpG sites ( mCpG ) based on our criteria of the site having at least 10× coverage and >10% methylation ( see Materials and Methods , Table 1 and Table S2 ) . The percentage of methylation is not uniform across mCpG sites – those with 100% methylated sites are highly enriched , and the distribution is biased toward highly methylated sites with >75% methylation ( Figure S3 ) . In other words , CpG sites tend to either be largely non-methylated or highly methylated . We established that genome-wide bisulfite sequencing correctly identifies methylated and non-methylated CpGs by sequencing multiple clones from bisulfite converted DNA from three randomly chosen methylated genes and three non-methylated genes ( Figures S4 S5 , S6 , S7 , S8 , S9 and Text S1 ) . Below we describe some of the striking patterns observed in the methylome of Nasonia . We next investigated associations between DNA methylation and gene expression , using a combination of RNA-seq data from adult females and genome-wide tiling microarray data from five different developmental stages: early embryo , late embryo , larva , pupa , and adult ( Figure S15 and Dataset S1 , See Materials and Methods ) . Here , we compare expression patterns across developmental stages , and also examine copies of duplicated genes that differ in their methylation status .
In this study , we profiled the genome-wide methylation at base-pair resolution in Nasonia and found several striking features . First , 1 . 6% of covered CpG sites are methylated in the Nasonia genome , and the methylated CpGs are clustered along the genome . As found in several other invertebrates [15] , [18] , [19] , DNA methylation is located mainly in the gene bodies in Nasonia , with coding genes falling into two distinct groups: around 30% of genes are methylated and show strong CpG methylation in 5′ exons , while DNA methylation is largely absent in the remaining genes . To compare the global methylation level across hymenopteran species , we calculated the percentage of methylated CpGs ( mC/C ) in Nasonia , Apis and ants ( Text S7 ) . Although it is difficult to compare genome-wide methylation levels due to differences in methodology , it appears that Nasonia ( 1 . 6% ) has a higher overall methylation level than is found in honeybees ( 0 . 8% ) or ants ( 1 . 05% in Camponotus and 0 . 68% in Harpegnathos ) . Unlike mammals , where methylation is associated with suppression of transposon gene expression , with rare exceptions TEs are not methylated in Nasonia . The finding is in concordance with honeybee TE methylation profile [19] , and suggests that DNA methylation is not required for TE repression in insects . In ants , TE methylation is at the genomic background level , but certain types of TE are hypermethylated and the pattern is species-specific [18] . In our data , we found five retrotransposon families with >5% methylation across CpG sites . The top three methylated TE types ( SNAKEHEAD , GYPSY and SPRINGER ) are highly expressed in the adult female RNA-seq data ( Table S4 ) , suggesting that DNA methylation may actually enhance expression of these elements . We do not know how this is accomplished , but it is possible that certain TEs may contain ( or land near ) sequence signals that promote DNA methylation . But globally the vast majority of TEs show no methylation in Nasonia . Close examination of methylation in coding genes revealed a striking matching of methylation with the transcription unit . Methylation is low in 5′ UTR and increases rapidly near the transcription start site . Methylation is then consistently higher on exons and decreases significantly on introns , resulting in a clear delineation of exon-intron boundaries by methylation “tagging” . Finally , at least for methylated genes <1 kbp in length , methylation also declines significantly in the 3′UTR ( after the stop codon ) . These patterns across the gene region suggest that DNA methylation provides “tags” that mark exons and targets introns for excision during transcription , but also that mark location of translational start and stop , even though translation occurs in the cytoplasm and is not directly associated with the DNA . If methylation affects the rate of transcription , then it is possible that methylation-induced transcriptional pausing at the exon-intron boundary could play a role in splicing [47] . However , how would the DNA methylation signal result in tagging of mature mRNA to demarcate translational initiation and termination ? One possibility is through directing mRNA base modifications . For example , in mammals methylation of the N6 position of adenosine ( m6A ) has been shown to accumulate at stop codons and 3′UTR [48] , suggesting a possible signal for translation termination . It has been hypothesized that in insects DNA methylation regulates alternative splicing [19]; however , a direct causal relationship between methylation and differential splicing remains unsubstantiated . In Nasonia , we found no global correlation between methylation status and alternative splicing , although methylation changes across exon/intron boundaries suggested a potential link between DNA methylation and splicing . We should emphasize that DNA methylation is not required for either intron splicing or coding region demarcation , as non-methylated genes show both . Nevertheless , it is possible that methylation expedites these signals for a subset of methylated house-keeping genes , which we have shown to be expressed constitutively and at higher levels . Investigating these mechanisms is an interesting avenue for future research . In Nasonia , the exon-intron pattern is augmented by a strong 5′ bias in level of methylation . The majority of DNA methylation was within the first 1 kbp coding exons and clearly drops beyond that in Nasonia , although an exon-intron distinction is still discernible in larger genes . A similar 5′-biased DNA methylation pattern has been observed in ants [18] . Studies in honeybee have reported a negative correlation between gene length and methylation status [49] and we observed the same pattern in Nasonia when the methylation percentage across the entire gene was used; however , this pattern disappears in Nasonia when the score of methylation level is restricted to the first 1 kbp of the coding region . We found little evidence for non-CpG methylation in Nasonia , but were able to confirm a single case . Therefore , non-CpG methylation is present , but it is extremely rare in Nasonia . Most candidate non-CpG methylation sites were located in genes nested in CpG methylation clusters . These findings suggest that non-CpG methylation may result from the inaccurate methylation at non-CpG sites by the CpG methylation machinery . It may strengthen the CpG methylation cluster , but the biological significance remains an open question . In mammals , DNA methylation at promoter regions is often associated with suppression of gene expression [50] , [51] . However , in insects , DNA methylation has been shown to be positively correlated with expression level in silkworm and ants [18] , [22] . Here , we also observed a strong positive correlation between methylation and gene expression level; however , methylation is more strongly associated with constitutive expression across development independent of expression level . The distribution of expression levels for methylated genes is unimodal , matching the high expression class . Non-methylated genes show a bimodal distribution , with a mixture of both low and moderate expression , indicating DNA methylation is not the only factor affecting expression level . Other epigenetic marks such as histone modifications are likely to play a role in expression differences among non-methylated genes . By comparing gene expression levels across five developmental stages , we found that methylated genes show more even expression across stages , and this pattern applies to both highly- and moderately-expressed methylated genes . The finding complements studies in honeybee , which found methylated genes to be expressed across multiple tissues , whereas non-methylated genes showed a more spatially restricted expression pattern [28] , [29] . In both cases , methylation appears to be more prevalent in genes that are constitutively expressed across development and tissue types . GO-term analysis showed that methylated genes in Nasonia are enriched for genes with housekeeping functions , as observed in honeybee and ants [18] , [19] , [21] . Furthermore , genes methylated in Nasonia tend to be more evolutionarily conserved , as also found in recent studies in ants and other invertebrates [27] , [52] . Housekeeping genes tend to be expressed in most tissue and cell types , which may explain the low expression variability for methylated genes across stages . Further support for the role of methylation in constitutive expression of genes comes from the study of duplicated genes that have lost methylation relative to their paralog in the Nasonia lineage . Comparing non-methylated and methylated paralogs reveals both a marked median reduction in expression level , and evolution toward more developmental stage-specific expression patterns in the non-methylated genes . Functional category enrichment analysis showed that methylated genes are enriched for basic cellular functions , such as transcription and translation , as also found in honeybee and ants [18] , [19] , [21] . Our comparative genomic analysis also shows that many genes have maintained their methylation status across the long evolutionary time scale from Apis to Nasonia . This probably reflects the role of methylation in constitutive expression of basal housekeeping genes . We also find that methylated genes are enriched among the class of genes that are conserved among insects , while non-methylated genes are enriched among Hymenoptera-specific and Nasonia-specific genes . Nevertheless , methylated genes are expressed more broadly across development than are non-methylated genes for each of these conservation categories . Even the more recently evolved “Nasonia-specific” methylated genes show broad expression across developmental stages ( median 4 . 60 ) , considerably greater than for non-methylated genes ( median 2 . 5 ) . This suggests that broader constitutive expression is a hallmark of methylated genes whether they are conserved or recently evolved . Bisulfite sequencing and expression profiling in our study were done on whole insects . Therefore , it could be argued that the correlation between methylation status and expression level occurs because genes that are methylated in more tissues show both higher levels of methylation and higher expression . In other words , tissue specific changes in methylation regulate tissue-specific gene expression , and this creates a correlation between methylation status and gene expression in whole animals . Although a possibility , we found that among methylated genes there is no correlation between level of methylation and level of expression ( Figure 3B ) , which would be expected if the proportion of tissues in which the gene is methylated was driving the pattern . Future work will help resolve whether some genes are being differentially regulated by changes in methylation status . However , it appears that in general DNA methylation is a hallmark of genes that are constitutively “turned on” , at least across developmental stages . In some eusocial organisms such as honeybee and ants , DNA methylation was shown to be related to caste determination [18] , [19] , [53] . In Nasonia , we have no evidence as yet that changes in methylation regulate specific developmental programs . In contrast , the general data reported above suggest that its primary role is in maintaining constitutive ( and perhaps higher ) expression of a subset of important cellular “house-keeping” genes , whereas non-methylated genes are more involved in stage-specific differences in expression . Investigating the role of methylation in epigenetic processes ( e . g . sexual differentiation , tissue-specific gene expression ) will motivate the future study of establishment , maintenance , epigenetic reprogramming and interactors of DNA methylation in Nasonia and other insects . Comparison among closely related Nasonia also provides the opportunity to study the microevolution of DNA methylation . In addition , the ability to genetically dissect species differences in Nasonia through inter-fertile crosses [41] , [54] , [55] could provide tools for the genetic investigation of cis-regulatory mechanisms of DNA methylation .
Genomic DNA samples were extracted from a pool of 50 24 h adult females from the standard N . vitripennis strain AsymCX using DNeasy Blood & Tissue Kit ( Qiagen , CA ) . This is the same strain used for the Nasonia genome project [34] and is cured of the intracellular bacterium Wolbachia . For RNA-seq , total RNA samples were extracted from adult females ∼24 h following eclosion from pupation , using RNeasy Plus mini kit ( Qiagen , CA ) following the manufacturer's protocol . The DNA , RNA concentration and the A260 nm/A280 nm absorption ratios were measured by NanoDrop ND-1000 Spectrophotometer ( Thermo Scientific , DE ) to assess quality . RNA integrity was checked using the Agilent 2100 Bioanalyzer ( Agilent Technologies , CA ) . All of the samples had a RIN ( RNA integrity number ) in the range 9 . 8–10 . 0 ( RINmax = 10 . 0 ) . For tiling microarrays , total RNA was extracted from samples of 5 different life stages , 0–10 h embryos , 18–30 h embryos , 51–57 h larvae , day yellow pupae ( little to no red eye pigment ) , and 1 day post eclosion adults . To generate the samples , mated females were first singly given two Sarcophaga bullata hosts for 48 h and then given one host for 6 hours , with access to the host restricted to one end for ease of embryo collection . Embryos or larvae were then collected from the hosts . Under this experimental design , females typically produce 85–95% female offspring , and these percentages were confirmed using control hosts where the offspring were permitted to complete development . Therefore , the wasps from these samples are predominantly female , although individual embryos or larvae were not sexed . For pupal collections , hosts were opened and female pupae from the “yellow pupal” stage were collected . Adult females were collected for RNA extraction ∼24 h after eclosion from the pupal stage . Six replicates per sample were used , averaging 400 individuals per replicate for embryos , 300 for larvae , 20 for pupae and 20 for adults . Samples were extracted in Trizol ( Invitrogen , cat#15596-026 ) and then sent to the Indiana University Center for Genomics and Bioinformatics for sample preparation and tiling microarray analysis using previously published methods . 20 µg of female Nasonia genomic DNA and 5 µg non-methylated control lambda DNA ( catalog #: D1521 , Promega , WI ) were sheared by Covaris S2 system ( Covaris , MA ) for 480 second with 10% duty cycle , level 5 intensity and 200 cycles per burst . The DNA fragments were purified with Zymo DNA Clean & Concentrator-5 columns ( Zymo Research , CA ) , size-selected for 130–180 bp with E-Gel system ( Life technologies , CA ) and QIAquick Gel Extraction Kit ( Qiagen , CA ) , end-repaired with NEBnext end repair module and NEBnext dA tailing module ( New England Biolabs Inc . , MA ) , ligated with Illumina methylated PE adapter oligo ( part #1005560 , Illumina , CA ) and then purified with Agencourt AMPure XP beads ( Beckman Coulter , CA ) . We performed bisulfite conversion on purified Nasonia adult DNA and lambda control DNA using Qiagen EpiTect Bisulfite kit with 2× bisulfite conversion cycles to improve the conversion efficiency and then purified the elute by AMPure XP beads . The purified converted DNA was amplified with PfuTurbo Cx Hotstart DNA Polymerase ( Agilent Technologies , CA ) using 15 cycles . The final libraries were purified again using AMPure XP beads and the library concentration was measured by Qubit ( Life technologies , CA ) . The library size distribution was checked by Agilent 2100 Bioanalyzer ( Agilent Technologies , CA ) . We mixed 0 . 5% of the lambda control DNA library in the Nasonia DNA WGBS-seq library , and performed Illumina short-read sequencing in one 84 bp lane on Genome Analyzer IIx ( GAIIx ) and one 101 bp paired-end lane on HiSeq2000 instrument . Image analysis and base calling were performed by the Illumina instrument software . In total , we obtained 27 , 766 , 713 reads from the GAIIx lane and 89 , 739 , 445 reads from the HiSeq2000 lane . Illumina WGBS-seq data have been deposited in GEO under accession no . GSE43423 . The mRNA-Seq library was made from 3 . 5 µg total RNA samples from 24 h adult females , using TruSeq RNA Sample Preparation Kits v2 ( Illumina Inc . , CA ) . The library was sequenced on an Illumina HiSeq2000 instrument and we obtained 65 , 334 , 896 reads . IIlumina RNA-seq data in this study have been deposited in GEO under accession no . GSE43422 . The Illumina quality score and nucleotide distribution were checked by the FASTX toolkits ( http://hannonlab . cshl . edu/fastx_toolkit/index . html ) . The adapter sequences were removed from the raw reads by custom scripts ( 0 . 7% in GAIIx lane and 0 . 9% in HiSeq lane ) . To include only high quality bases in our analysis , the sequence reads were trimmed to 75 bp . After trimming , the GAIIx and HiSeq ( read 1 only ) data gave us 8 . 75 Gbp of sequences or 25× coverage of the haploid genome , assuming 350 Mbp genome size . We first aligned the reads to the plus and minus strands of non-methylated lambda genome ( NCBI reference sequence NC_001416 ) with all Cs converted to Ts , using BWA with 4 mismatches [56] . A total of 746 , 736 ( 0 . 64% ) reads were uniquely mapped to the lambda genome without indels , resulting 1155× coverage of lambda genome . We estimated the unconverted Cs to be 0 . 31% by subtracting the background T→C sequence error from the remaining unconverted Cs , therefore the final bisulfite conversion efficiency , at 99 . 69% , was ideal for downstream analysis . The Illumina sequencing error rates for each type of nucleotide in the GAIIx and HiSeq lane were also estimated from the lambda control alignments ( Table S1 ) . From the N . vitripennis reference scaffolds [34] , we built C→T converted reference genomes for both the Watson ( + ) and Crick ( − ) strand separately , with all Cs in CpGs context remains Cs ( meth_genome ) and all CpG Cs converted to Ts ( unmeth_genome ) . The rest Illumina sequencing reads were aligned to the converted genomes with BWA [56] with a maximum of 4 mismatches , and summarized in a single BAM file ( Figure S1 ) . We tested 4 , 6 , 8 and 10 mismatches and found 4 mismatches will give the best mapping percentage without ambiguity due to reduced genome complexity after bisulfite conversion . ∼80% of the reads could be mapped to the converted Nasonia reference genome . To get accurate methylation estimation , we only used uniquely mapped reads without any indel ( 60% of total reads ) for the methylation quantification . CpG methylation percentages were estimated from the proportion of remaining Cs in CpG context ( Table S2 ) . Non-CpG methylation was also quantified ( Table S5 ) . We aligned adult female RNA-seq reads to the Nasonia reference scaffolds using TopHat v1 . 4 . 1 [57] with a maximum of three mismatches . 94% of the reads were uniquely mapped to the genome . Total expression level ( FPKM: Fragments Per Kilobase-pair of exon Model ) was calculated using Cufflinks v1 . 3 . 0 [58] based on all mapped reads from the TopHat alignments . The multiple mapped reads were weighted using the “-u” parameter in Cufflinks . The RNA-seq alignments were viewed in the IGV browser [59] , [60] . Among the 14 , 024 , 488 CpG sites in Nasonia haploid genome , we covered >90% with 2 or more uniquely aligned reads and >55% with 10 or more reads . The average coverage at CpG sites is 16 . 2× ( Figure S2 ) . To obtain accurate quantification of the methylation percentages , we only included ∼8 M CpGs sites with 10 or more coverage ( covered CpGs ) . To quantify the CpG methylation levels , we used two metrics: percentage of methylated CpGs ( percentage of mCpGs ) and average methylation percentage in covered CpGs ( methylation percentage ) . Methylated CpGs ( mCpGs ) are defined as CpG sites with >10% methylated Cs and ≥10 coverage . This definition requires at least two unconverted C containing reads to call a site methylated , therefore a single T→C Illumina sequence error will not results a spurious methylated site . Methylation percentage is the average methylated percentage over all CpGs in a particular region , which is the total number of unconverted Cs divided by the total number of reads at CpG sites . The methylated CpG sites were annotated using both the Nasonia OGS1 . 2 ( official gene set ) and OGS2 gene models [42] . OGS2 gene models incorporated both whole genome tiling expression array and RNA-seq data from multiple tissues at multiple developmental time points , proving high quality support for 5′- and 3′-UTR annotation . Among the 14 , 024 , 488 CpGs , 1 , 159 , 303 were located in overlapped gene models and were excluded from the analysis . To determine the gene methylation status , we calculated the percentage of mCpG among the covered CpG sites ( depth ≥10 ) in both the first 1 kbp coding region and in the entire transcript region . Since the majority of the mCpGs are located in the first 1 kbp coding region and the methylation level is under the UTR level beyond 2 kbp ( Figure 1G ) , long genes with heavy methylation at the beginning will be averaged out if the entire transcript length was used . Therefore , we inferred the gene methylation status using the percentage of mCpG in the first 1 kbp coding region . Because single or sparse mCpG could be spuriously generated by T→C sequencing error , local incomplete bisulfite conversion or alignment problems , we applied arbitrary cut-off and genes with at least four covered CpGs and >10% mCpG in the first 1 kbp coding region are classified as methylated genes; genes with ≤10% mCpG are defined as non-methylated genes . To quantify the DNA methylation in repetitive elements and retrotransposons , we built a non-redundant repeat sequence database for the repeat library and retroid elements annotation from the Nasonia genome project [34] . From the 1195 sequences in the repeat library , 763 that are >100 bp in length and contain 4 or more CpGs were kept . Simple repeats and STRs were excluded from the analysis . The longest element in each of the 76 retroid families was included in repetitive elements database . We aligned the unmapped and non-uniquely mapped WGBS-seq reads to the database , and quantified the methylation percentage at CpG positions . Elements with average read depth four or more were included in the analysis . To search for mammalian type CpG islands ( CGIs ) in the Nasonia genome , we ran predictions of CGIs in the Nasonia genome using the same criteria as in mammals [10]: GC percent >50% , CpG O/E ( observed/expected CpGs ) ratio >0 . 6 , and greater than 200 bp in length . 9 , 265 CGIs were found in the Nasonia genome . We define methylated CpG clusters ( mCpGCLs ) as regions with >80% methylated CpGs and >40% average methylation percentage , and we found 5 , 440 mCpGCLs in the Nasonia genome . To determine whether the methylated genes are clustered or randomly distributed in the Nasonia genome , we analyzed the frequency and distance between neighboring gene pairs ( MM: methylated-methylated; MN: methylated-nonmethylated; NM: nonmethylated-methylated; NN: nonmethylated-nonmethylated ) , as well as the consecutive runs of methylated genes . Scaffold rather than the chromosomal locations were used for the analysis because neighboring genes on two different scaffolds are not in proximity . To eliminate the effect of short scaffolds with few genes in them , only the top 100 largest scaffolds were included for the analysis , containing 11 , 683 genes with methylation status . To confirm methylation status of individual genes , DNA from 20 pooled 24–27 h virgin Nasonia vitripennis ( strain Asymcx ) females was extracted using the Qiagen DNeasy Blood and Tissue Kit ( Cat No . 69504 ) . The bisulfite conversion was performed by the Qiagen EpiTect Bisulfite Kit ( Cat No . 59104 ) with 1 . 5 µg of starting DNA . Bisulfite PCR primers for six selected genes were designed using Methyl Primer Express software v1 . 0 ( Applied Biosystems by Life Technologies , CA ) . The amplified PCR product was gel purified and cloned using Promega pGEM-T Easy Vector System II ( Cat No . A1380 ) . Direct PCR from the E . coli “white” colonies with T7 and SP6 primers was used to select colonies with the right insert size , which were then inoculated in LB broth with ampicillin and the plasmid was extracted using the QIAprep Miniprep kit ( Cat No . 27104 ) . Prism BigDye Terminator v3 . 1 Cycle Sequencing Ready Run Kit ( Applied Biosystems ) was used to prepare the products for sequencing . BigDye clean-up was completed using ABgene Dye Terminator Removal Kit ( Cat No . AB-0943 ) . Sequencing was completed at the Function Genomic Center at the University of Rochester . We used NimbleGen high-density 2 ( HD2 ) arrays for transcriptome investigations . The custom 4-array ( chip ) set consisted of 8 . 4 million isothermal long-oligonucleotide probes that are 50–60 nt in length and that span the Nasonia genome sequence at overlapping intervals of 33 bp , on average . Each slide contained 27 , 000 Markov model random probes that are not represented in the genome for setting background level thresholds . All probes were designed using NimbleGen's ArrayScribe software and the quality assurance tests of the probes were conducted using Indiana University's Centre for Genomics and Bioinformatics in-house algorithms . Signal to background ratios were determined by first calling probes that fluoresced at intensities greater than 99% of the random probes' signal intensities; therefore only 1% of fluorescing probes are likely to be false positives . The arrays reliably produced high signal to background ratios; log2 ratios of eight were observed for signal over background . We conducted three replicates each using RNA from independent biological extractions of female early embryo ( 0–10 h ) , late embryo ( 18–30 h ) , 1st instar larvae , and pupae . Additional experiments were performed comparing transcription in testis and the female reproductive tract . Samples were prepared at 25°C as follows: Approximately 100 N . vitripennis ( AsymCX ) virgins were collected as black pupae . After eclosion , females were provided with males and allowed to mate overnight . Females were initially provisions 15–20 Sarchophaga bullata hosts in groups of 20 females for 24 h to induce production of eggs . The hosts were then removed and females were left overnight ( ∼18 h ) . Mated females produced 85% female progeny under the design used here , and therefore the embryo and larval collections are predominantly female offspring . To collect embryos , individual females were given access to a host at one end ( to restrict the oviposition site ) and allowed to lay eggs for 6–10 h before being removed . Embryos were then harvested immediately ( early embryos ) , 18 h later ( late embryos ) , or 51 h later ( 1st instar larvae ) . All embryos and larvae were collected in an RNase free environment . The host was cracked open and the “cap” removed to expose the embryo . Dissecting needles were used to gently scrape embryos from the surface of the host and transfer them into a 1 . 5 ml tube pre-chilled on dry ice . Samples were stored at −80°C . If at any time the host was punctured or embryos were exposed to host hemolymph , they were discarded . Estimates of the number of embryos per replicate ( three per life stage/sex ) were recorded; early embryos ranged from 300–900 , late embryo 140–500 , 1st instar larvae 245–520 . Since sex cannot be determined at larval stage , some of the mated female hostings were allowed to mature to adulthood then males and females were counted to determine the sex ratio . Early larvae showed an average of 82 . 9% females and late larvae had an average of 84 . 2% . Pupae collections were made among the progeny of mated females provided with hosts for 48 hrs . They were sorted by sex and stage ( early yellow , red-eye , half black , and black pupae ) . Equal numbers ( S20 ) of pupae from each stage were then pooled prior to RNA extraction . Female reproductive tracts ( 30 per replicate ) were removed from 1–3 days post eclosion virgin females and transferred to a tube on dry ice prior to RNA extraction . Tissue was disrupted and homogenized using Trizol reagent ( Invitrogen ) , and extracted RNA was purified using the Qiagen RNeasy protocol with optimal , on column DNase treatment from specific tissues . Beginning with at least 0 . 5 µg of total RNA ( for early to late embryo ) or at least 1 . 0 µg ( for other tissue types ) , a single round of amplification using MessageAmp II aRNA kit ( Ambion ) produced between 30 and 45 µg of cRNA for embryo RNA and greater than 100 µg for all other tissue types . Starting with 10 µg of cRNA , double strand cDNA synthesis was carried out using the Invitrogen SuperScript Double-Stranded cDNA Synthesis kit using random hexamer primer followed by DNA labeling using 1 O . D . CY-labeled random nonomer primer and 100 U Klenow fragment ( 3>5 exo ) per 1 µg double-stranded cDNA . The use of random primers ensured that all transcripts hybridize to the array , which contains probes designed solely from a single strand of the DNA sequence . Both sexes for each tissue type were alternatively labelled and a dye-swap was included among the replicate experiments . Dual-color hybridization , post-hybridization washing and scanning were done according to the manufacturer's instructions . Images were acquired using a GenePix 4200A scanner with GenePix 6 . 0 software . The data from these arrays were extracted using the software NimbleScan 2 . 4 ( Roche NimbleGen ) . The normalized tiling array data can be found in Dataset S1 . The data analysis was performed using the statistical software package R ( http://www . r-project . org/ ) and Bioconductor ( http://www . bioconductor . org/ ) [61] . The signal distributions across chips , samples and replicates were adjusted to be equal according to the mean fluorescence of the random probes on each array . All probes including random probes were quantile normalized across replicates . Scores were assigned for each predicted OGS v2 gene , for each sample , based on the median log2 fluorescence over background intensity of probes falling within the boundaries of the largest gene transcript . The genes were deemed to be transcribed only when greater than ½ or their tiled length was expressed . On average , the 23 , 161 interrogated genes were tiled by 95 . 4±1 . 1 probes . Genes validated by tiling array or EST data are available online at http://www . hymenopteragenome . org/nasonia/ ? q=sequencing_and_analysis_consortium_datasets . We used two methods to obtain the alternative splicing status for Nasonia transcripts . First , we used the alternative splicing status from the OGS2 gene models with good intron information support . Genes with more than one OGS2 transcripts per gene were considered as alternatively spliced genes , and genes with a single form in OGS2 were considered as non-spliced genes . We also inferred the alternative splicing status from the adult female RNA-seq data using Cufflinks software . Moderately and highly expressed genes with expression level FPKM>2 were included in the study because sufficient RNA-seq coverage is needed to detect the alter-spliced forms in the RNA-seq data . Genes with the percentage of second most abundant forms greater than 10% were considered as alternatively spliced genes . For inference about conservation of methylation status of genes , loci were called Nasonia-specific if they did not have a homolog in OrthoDB BLASTp homolog ( 1e-5 ) to a database containing Human , Mouse , Xenopus , Apis mellifera , Drosophila melanogaster , and Anopheles gambiae . Arthropod-specific loci were those Nasonia sequences that had strong BLASTp hits ( 1e-5 ) to Apis mellifera , Drosophila melanogaster , Anopheles gambiae , but had no homology to proteins from Human , Mouse or Xenopus . GO term enrichment analysis was performed using blast2go [62] with the Nasonia OGS2 protein sequences and a BLASTp cut-off score of 1E-3 for assigning terms . Enrichment was determined using Fisher exact test as implemented by blast2go , with the cut-off for enrichment set to a 5% false discovery rate . The background gene set was restricted to the 17726 Nasonia genes with a known adult female methylation status as determined by bisulfite sequencing . For enrichment across different expression levels , genes were divided into low ( 9–11 ) , medium ( 11–13 ) and high expression ( 13–15 ) based on median array expression ( Table S11 , S12 , S13 ) , with the background restricted to all genes with known methylation status that fell within that expression range . For GO-term analysis of genes with conserved methylation status between Apis and Nasonia , 1∶1 orthologs were selected based on their known methylation status for Apis ( taken from [15] ) . The orthology status for thirteen Hymenoptera insect species ( Acromyrmex echinatior , Apis florea , Apis mellifera , Atta cephalotes , Bombus impatiens , Bombus terrestris , Camponotus floridanus , Harpegnathos saltator , Linepithema humile , Megachile rotundata , Nasonia vitripennis , Pogonomyrmex barbatus , and Solenopsis invicta ) was obtained from OrthoDB [46] . The updated Official Gene Set 2 . 0 ( OGS2 ) for Nasonia vitripennis was used in this analysis ( http://arthropods . eugenes . org/genes2/nasonia/ ) . The honeybee methylation status was from Zemach et al . 2010 [15] . The nucleotide substitution rates between three Nasonia species ( N . longicornis , N . giraulti and N . vitripennis ) were from the Nasonia genome project [34] . Analysis of paralogs that had undergone changes in methylation status was accomplished by first identifying all genes that had 1∶1 orthologs in thirteen sequenced hymenopteran genomes , but are duplicated in N . vitripennis , using the OrthoDB database [46] . These were then divided into categories based on methylation status . Rates of evolution of the Nasonia genes relative to the Apis orthologs were measured by comparing pairwise distances of protein alignments scores obtained from the AllAll tool ( available at http://www . cbrg . ethz . ch/services/AllAll ) . Median expression level , range in expression and largest difference in expression were calculated using tiling microarray data . The logistic regression analysis of the effect of expression level and expression breadth on gene methylation status was performed using the LOGISTIC procedure in SAS 9 . 1 ( Text S5 ) . The statistical software R ( version 2 . 13 . 0 , www . r-project . org ) was used for the rest of the statistical tests . Comparisons between matched gene samples were conducted using the Wilcoxon Matched-Pairs Signed Ranks Test ( WMSRT ) implemented in wilcox . test ( ) function in the stats package . The test P-value of unimodality of gene expression distribution for methylated and non-methylated genes was calculated using the Hartigans' dip test for unimodality ( dip package ) . | Insects use methylation to modulate genome function in a different manner from vertebrates . Here , we quantified the global methylation profile in a parasitic wasp species , Nasonia vitripennis , a model with some advantages over ant and honeybee for functional and genetic analyses of methylation , such as short generation time , inbred lines , and inter-fertile species . Using a highly inbred line permitted us to precisely characterize DNA methylation , which is compared to gene expression variation across developmental stages , and contrasted to other insect species . DNA methylation is almost exclusively on the 5′-most 1 kbp coding exons , and ∼1/3 of protein coding genes are methylated . Methylated genes tend to occur in small clusters in the genome . Unlike many organisms , Nasonia leaves nearly all transposable element genes non-methylated . Methylated genes exhibit more uniform expression across developmental stages for both moderately and highly expressed genes , suggesting that DNA methylation is marking the genes for constitutive expression . Among pairs of differentially methylated duplicated genes , the paralogs that lose DNA methylation after duplication in the Nasonia lineage show lower expression and greater specialization of expression . Finally , by comparative analysis , we show that methylated genes are more conserved at three different time scales during evolution . |
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To cooperatively carry large food items to the nest , individual ants conform their efforts and coordinate their motion . Throughout this expedition , collective motion is driven both by internal interactions between the carrying ants and a response to newly arrived informed ants that orient the cargo towards the nest . During the transport process , the carrying group must overcome obstacles that block their path to the nest . Here , we investigate the dynamics of cooperative transport , when the motion of the ants is frustrated by a linear obstacle that obstructs the motion of the cargo . The obstacle contains a narrow opening that serves as the only available passage to the nest , and through which single ants can pass but not with the cargo . We provide an analytical model for the ant-cargo system in the constrained environment that predicts a bi-stable dynamic behavior between an oscillatory mode of motion along the obstacle and a convergent mode of motion near the opening . Using both experiments and simulations , we show how for small cargo sizes , the system exhibits spontaneous transitions between these two modes of motion due to fluctuations in the applied force on the cargo . The bi-stability provides two possible problem solving strategies for overcoming the obstacle , either by attempting to pass through the opening , or take large excursions to circumvent the obstacle .
Many living groups exhibit collective modes of motion [1] . Among these , groups such as cell clusters [2 , 3] , locust [4] and fish [5] , have been found to display spontaneous transitions between co-existing collective dynamical phases . Among these collective phases are disordered modes of motion , in which the group swarms whilst remaining cohesive , and ordered modes of motion , where the individuals orient along a single polarized direction , or rotate around the group center of mass . The spontaneous transitions between these collective modes of motion have been attributed to noise [2 , 4] , or interactions with external constraints [3 , 5] . However , there is currently no theoretical description that defines the necessary conditions for the emergence of co-existing dynamical phases , or a precise mechanism that explains the transitions between the different modes of motion . In this study , we investigate dynamical bi-stability , and its theoretical underpinnings during cooperative transport by a group of ants . Cooperative transport by ants , also known as group retrieval , is the process by which individual ants join efforts to retrieve large items of food [6–10] . Cooperative transport is known to exist in at least forty different ant species [11–20] . Among these species , the longhorn crazy ants Paratrechina longicornis are well known for displaying highly coordinated retrieval , of items , that can reach orders of magnitude larger than their own size and weight [13 , 21 , 22] . After a recruitment phase [22] , the longhorn crazy ants lift the load above the surface to reduce friction , and pull towards chemical depositions they leave [22] , which mark the pheromone scent trail that leads to the nest [23] . Cooperative transport by P . longicornis ants was shown to exhibit a rich variety of collective modes which range from random motions to ballistic movement [21] , that is either directed towards the nest [21] or exhibits oscillatory modes [24] and direction changes when the motion was externally constrained [22 , 25] by semi-natural obstacles ( Fig 1A ) . In addition , cooperative transport by this species has allowed for a comprehensive theoretical description [21 , 24] . The existence of multiple modes of motion , along with the theoretical understanding , suggest the potential of this system for investigating the origins and dynamics of co-existing collective modes . To test this possibility , we have used a simple experimental system , in which the ants interact with a rigid obstacle . Under this constraint , the paths to the nest are blocked from all directions , except for a single narrow opening , that allows passage for single ants , but not for the cargo ( Fig 1B and Methods section ) . In the presence of such an obstacle , ants that accompany the transport of a large cargo were observed to locally mark pheromone trails that lead from the cargo to the narrow opening [22] . Even though these trails lead to a dead end ( the cargo can not fit through the opening ) , the markings serve as the only available source of information , as new ants that join the carrying effort pull and direct the cargo towards the narrow opening [22] . Furthermore , the simple geometry of the obstacle makes the motion of the ant group amenable to analytic theoretical description , which allows us to expose the conditions for bi-stability , and the mechanisms governing the transitions between the co-existing dynamical modes of motion . Here , we present a theory of cooperative transport by ants near rigid obstacles , which predicts bi-stability between two dynamical modes of motion: a convergent mode of motion that keeps the cargo near the opening , and large quasi periodic excursions that can circumvent an obstacle . We demonstrate how noise in the discrete decision making process of the ants induces mechanical fluctuations that can stochastically switch the group between these two modes . The predictions of the model are then verified in experiments , where ants carrying cargoes with the size of natural prey [21] exhibited spontaneous transitions between the bi-stable collective dynamical modes . In addition to the agreement between observations and simulations , our analytical model allows us to explain the conditions for bi-stability , and propose a physical mechanism for how groups of ants overcome obstacles when carrying food to the nest .
To describe the motion of a rigid cargo along an obstacle , we use the model of force driven coupling between ants [21 , 24] . In this theory , the carrying ants can either pull the cargo along their body axis , or lift the cargo to reduce its friction with the surface . Based on previous observations [21] , we divide the carrying ants in our model into two groups: • Uninformed ants: Carrying ants with no recollection regarding the location of the pheromone trail . These ants switch between pulling and lifting roles according to the forces that they sense , and can detach at a constant average rate [21] . • Informed ants: Newly attached ants that arrive with spatial information about the direction of the pheromone trail . These ants transfer the information to the group by pulling the cargo towards the trail , until they lose their directionality at a constant average rate and become uninformed [21] . In this model , the uninformed carrying ants can not sense the pheromone trail , and the navigation relies on the pulling forces of the informed ants that steer the group towards the pheromone trail . The uninformed ants role switch between pulling and lifting at rates given by r l → p = k c e x p ( + f → t o t · p → i F i n d ) ( 1 ) r p → l = k c e x p ( - f → t o t · p → i F i n d ) ( 2 ) where p/l note the pullers/lifters , kc is the basal role switching rate , f → t o t is the total pulling force applied by the puller ants and p → i is the body axis unit vector of an ant labeled by index i along the cargo . The scalar multiplication f → t o t · p → i in Eqs ( 1 ) and ( 2 ) imply that each individual ant instantaneously senses the mechanical forces applied by all other ants . When the direction in which an ant can pull , p → i , is aligned with f → t o t , Eqs ( 1 ) and ( 2 ) provide a low ( high ) rate to switch from lifter to puller ( puller to lifter ) . This implies that ants in the front ( back ) of the cargo will tend to be pullers ( lifters ) , and gives rise to coordinated pulling and cooperative transport . When the cargo changes direction of motion , the ants switch their roles according to Eqs ( 1 ) and ( 2 ) , such that lifters that used to be in the back become predominantly pullers in the new front . The individuality parameter Find in Eqs ( 1 ) and ( 2 ) plays the role of the temperature in the Ising model [26] , where the pullers and lifters are analogue to the binary spin states . The individuality parameter is an ant-level trait , which defines the threshold of forces above which single ants respond to the group [21] . For low Find the ants conform and align their forces , while for high Find the ants ignore the forces applied by the rest of the group . We treat Find as a constant average value among all ants , while it may vary between individuals ( previous calculations show that such a variability does not lead to any qualitative change in the collective behavior [21] ) . Due to the roughly one-dimensional nature of the motion along the obstacle ( Fig 1A ) , we consider a cargo that is confined to move on an infinite line along the x axis . The cargo has ntot binding sites that are equally distributed between the front and the back sides of the cargo , that face the ±x direction respectively . We neglect the processes of attachment/detachment from the cargo , and consider the cargo to be fully occupied by a fixed population of uninformed ants that decide on their role at rates given by Eqs ( 1 ) and ( 2 ) . Note that the cargo has no dimension of length ( point particle ) . In the presence of an opening located at x = 0 , the cargo force balance equation accounts the sum of all the pulling and lifting forces applied to the cargo f t o t = f 0 ( n p f r o n t - n p b a c k ︸ uninformed pullers - G · s i g n ( x ) ︸ informed pullers - h ( n l ) / ︸ uninformed lifters ) ( 3 ) where f0 is the magnitude applied by a single ant , and is equal to all ants . The first term on the right hand side of Eq ( 3 ) describes a tug of war between the uninformed pullers np on both sides of the cargo , while the second term describes the pulling force of the informed ants , where G is a fixed number of informed pullers that direct the cargo towards the opening at x = 0 . The function h ( nl ) describes the role of the nl lifter ants , that reduce the friction of the cargo with the surface ( S1 Appendix ) . Throughout the model , we consider the cargo to be fully lifted above the surface , such that the friction force is neglected . The total force ( 3 ) can be converted to velocity by using the linear relation f t o t = γ v ( 4 ) where γ is the mechanical cargo response coefficient [21] . We assume a linear relation since the cargo is held steady throughout the motion by the ants , which have very high friction with the surface , thus keeping the motion in a highly damped non-inertial regime . The acceleration of the cargo is therefore obtained by plugging Eq ( 4 ) into Eq ( 3 ) and taking the time derivative of v v ˙ = f 0 γ ( d d t ( n p f r o n t - n p b a c k ) - G d d x s i g n ( x ) d x d t ) ( 5 ) By considering the discrete stochastic role switching dynamics of the uninformed ants ( S1 Appendix ) , we obtain the mean drift velocity of the cargo , as a result of the tug of war d d t ( n p f r o n t - n p b a c k ) = k c f 0 γ [ ( n l f r o n t + n p b a c k ) e x p ( f t o t F i n d ) - ( n p f r o n t + n l b a c k ) e x p ( - f t o t F i n d ) ] ( 6 ) Under our assumption of a fully occupied cargo , Eq ( 6 ) can be written in terms of the total number of ants ntot , the mean velocity v , and the magnitude of the restoring force G ( Eqs . S22-S26 in S1 Appendix ) . The final form of the equations of motion , that represent the deterministic evolution of the average position and average velocity of the cargo are given by x ˙ = v ( 7 ) v ˙ = q ( x , v ) ( 8 ) q ( x , v ) = k c [ n · s i n h ( v f i n d ) - 2 ( v + g · t a n h ( x ϵ ) ) c o s h ( v f i n d ) ] - v g ϵ s e c h 2 ( x ϵ ) ( 9 ) where n = f 0 n t o t γ , f i n d = F i n d γ and g = f 0 G γ . The approximation sign ( x ) ≈ tanh ( x/ϵ ) is used to avoid discontinuities when passing over x = 0 . The parameter ϵ can be related to the cargo radius r , as by considering the motion of the cargo along the obstacle in a two dimensional plane , the projection of the restoring force on the x axis yields a similar results to our approximation when ϵ ∼ r ( Fig 1C ) . Stability analysis of ( 7–9 ) shows that the system has a single fixed point at ( x* , v* ) = ( 0 , 0 ) ( for full derivation see S1 Appendix ) . This fixed point undergoes a subcritical Hopf bifurcation for a critical value of find given by f c ( 1 ) = n 2 ( 1 1 + g 2 k c ϵ ) ( 10 ) where for f i n d > f c ( 1 ) the fixed point is stable , and in the limit of ϵ → 0 ( i . e , tanh ( x/ϵ ) → sign ( x ) ) , the fixed point is always stable ( f c ( 1 ) → 0 ) . The find − g phase diagram for the limit of ϵ → 0 ( Fig 2A ) displays three dynamical phases: ( i ) The low find phase: In this phase the system displays bi-stability between spiral convergent flow to the origin , and free motion to infinity at terminal velocity along the nullclines of q ( x , v ) ( Fig 2B ) . These two modes of motion are separated by an unstable limit cycle , the seperatrix . As find increases two of the q ( x , v ) nullclines approach each other in each half space of x ( S1 Fig ) . At a critical value of f i n d = f c ( 2 ) the system undergoes a homoclinic bifurcation , where the two nullclines merge and confine the free motion to a stable limit cycle . We find that the velocity at the transition is given by v ± = ± g ∓ n 2 ( n 2 - f i n d ) ( 11 ) where ± note the two halfspaces of x . By plugging ( 11 ) into the nullcline solution of ( 9 ) we obtain a transcendental form for the critical point of the homoclinic bifurcation ( for full derivation see S1 Appendix ) f c ( 2 ) = n 2 s e c h 2 ( - g + n 2 ( n 2 - f c ( 2 ) ) f c ( 2 ) ) ( 12 ) ( ii ) The intermediate find phase: For f i n d > f c ( 2 ) we find bi-stability between relaxation oscillations [27] and convergent flow to the origin ( Fig 2C ) . The velocity is bounded by the nullclines of ( 9 ) in each half space of x . As individuality find increases , the limit cycle shrinks in size , and the seperatrix region expands ( S2 Fig ) . At a critical value of f i n d = f c ( 3 ) the system undergoes a saddle node bifurcation where the stable limit cycle coalesces with the separatrix and both collapse . The saddle node bifurcation critical line was evaluated using numerical continuation [28] . ( iii ) The high find phase: For f i n d > f c ( 3 ) the system spirals to the fixed point for every initial condition ( no bi-stability ) . The phase space trajectories are shown in Fig 2D . For finite and increasing values of ϵ , the sub-critical Hopf bifurcation line ( Eq ( 10 ) ) shifts to larger values of find and g on the phase diagram , revealing larger regions where the fixed point is unstable with no bi-stability ( Fig 2E–2H ) . Increasing ϵ also shifts the critical line of the saddle node bifurcation , yet the transition between phases ( i ) and ( ii ) remains unaffected . For large values of ϵ , where the pulling force of the informed ants , G , increases linearly when the cargo moves away from the opening , the system ( 7–9 ) displays a behavior similar to the tethered cargo [24] , and is further discussed in S1 Appendix . The mean field description of our system ( Eqs ( 7 ) – ( 9 ) ) ignores the stochastic nature of the carrying ants . During the transport , the participating ants randomly attach/detach to/from the cargo , role switch between pulling and lifting , and switch from informed to uninformed ants [21] . Each of these processes introduces a new source of noise to the dynamics , and modifies the trajectories with respect to the deterministic solution . As a result , the applied force to the cargo fluctuates , which drives spontaneous transitions between the two modes of motion in the bi-stable regions of the phase diagram ( Fig 2A ) . To understand the effects of the different sources of force fluctuations ( noise ) on the dynamics in the bi-stable region ( phase ( ii ) , Fig 2C ) , we simulated the motion of the cargo using a Gillespie algorithm [29] , for three different kinetic schemes ( see Methods for algorithm ) : ( i ) Role switching: In this scheme , the cargo binding sites are fully occupied by ntot uninformed ants at all time . Therefore , the motion of the cargo results from role switchings between pullers and lifters with respect to Eqs ( 1 ) and ( 2 ) . The number of informed ants G is constant , and they act as a restoring force pulling towards the origin ( Fig 3A ) . ( ii ) Attachments/detachments: This scheme allows attachments/detachments of uninformed ants to/from the cargo binding sites with constant rates kon/koff respectively , such that the number of uninformed ants that role switch with respect to Eqs ( 1 ) and ( 2 ) fluctuate around ntot . The number of informed ants , G , is constant , and they act as a restoring force towards the origin ( Fig 3B ) . ( iii ) Informed pullers: In the final scheme , newly attached ants act as informed pullers that apply their force towards the origin . The informed ants convert to uninformed ants at a constant rate kforget [21] . The number of informed ants fluctuate around a mean value G , and the number of uninformed ants , that can attach/detach and role switch , fluctuate around a mean value of ntot ( Fig 3C ) . All three processes initiate spontaneous transitions between the two modes of motion ( Fig 3D–3F ) . Furthermore , as the number of noise sources increase , the oscillatory motion becomes irregular , with larger amplitude excursions that deviate from the stable limit cycle ( Fig 3G–3I ) . During these excursions the mean velocity is almost unchanged , and is approximately on the nullcline values of the deterministic model ( Fig 2C ) . The deviations from the predicted stable limit cycle can be attributed to the fluctuations in ntot and G . When the number of ants fluctuate , the force applied to the cargo fluctuate as well: for example , a transient increase in the number of uninformed ants and/or a transient decrease in the number of informed ants leads to an increase in group persistence . When the motion is more persistent , this leads to larger amplitude excursions away from the opening ( Fig 3G–3I ) . Analysis of the motion , made over many realizations , reveals several features of the bi-stable behavior: ( 1 ) The distributions of the position ( Fig 3J ) display a large peak centered around x = 0 , which corresponds to the convergent mode of motion . ( 2 ) The distributions of the velocity ( Fig 3K ) show peaks that correspond to the oscillatory motion along the nullclines , and to ±g when the motion is convergent in the proximity of x = 0 . ( 3 ) The distributions of the turning point and turning time ( Fig 3L and 3M ) are found to have an exponential tail , indicating that random , statistically independent fluctuations , limit the motion when it extends beyond the predicted stable limit cycle amplitude . These distributions display longer tails as the number of stochastic processes increase and correspond to larger amplitude excursions . These results provide a mechanistic explanation for the exponential relation between the first arrival time at each position along the obstacle and the distance of that position from the opening [30] , that was observed in previous experiments [22] . The convergent mode of motion was identified by applying the following thresholds over the time series: ( i ) Position: −0 . 1 < x < 0 . 1 [cm] , as indicated by the sharp peak near the origin ( Fig 3J ) . ( ii ) Velocity: −0 . 1 < v < 0 . 1 [cm/s] , which is the magnitude of ±g ( Fig 3K ) . ( iii ) Duration: t > 5 [sec] , a sufficient period time to distinguish between a convergent flow and oscillations that pass near the origin . The physical process that prompts the bi-stability in phases ( i ) and ( ii ) is the following: Consider a system initially at rest ( v|t = 0 = 0 ) at a location near the origin that is bound by the seperatrix ( Fig 2B and 2C ) . The system will be pulled by a force with magnitude g towards the origin and begin to order the internal pulling forces of the uninformed ants by switching their roles ( Eqs ( 1 ) and ( 2 ) ) . However , when passing the origin , the external force changes its direction abruptly , which disrupts the ordering process . If by the time the group passes the origin the pulling forces of the uninformed ants are insufficient for crossing the separatrix , the abrupt change in direction of g will cause the group to lose its order by switching roles in the opposite direction , slow down and spiral to the origin . For a sufficiently large initial distance outside the seperatrix , the system is able to order the pulling forces sufficiently , and overcome the restoring force to continue in a ballistic motion ( phase ( i ) ) or perform relaxation oscillations ( phase ( ii ) ) . The phase space trajectories from the simulations show that the transitions between the modes of motion occur near the origin , due to the fluctuations in the force applied to the cargo ( Fig 4A–4E ) : • Convergence → oscillations ( Fig 4A–4C ) : These transitions occur when the system accelerates and reaches its maximal velocity near the origin . When passing the origin , the restoring force changes sign and the system starts to slow down . However , at this point large enough fluctuations can kick the system out of the separatrix convergent domain , before it has slowed down significantly due to g . • Oscillations → Convergence ( Fig 4D–4E ) : The system approaches the opening at x = 0 with maximal velocities close to the nullcline solutions of ( 9 ) in either half spaces of x . When the system passes the origin it experiences a drop in velocity by ∼2g . This abrupt change in velocity , with an addition of a fluctuation , can drop the velocity low enough to transit the system into the stable region of convergence . The distributions of the duration spent in each mode of motion has an exponential form ( Fig 4G–4L ) , showing that the transitions between the two modes of motion are stochastic , driven by fluctuations of the applied force to the cargo . We show that within phase ( ii ) , as find increases , the mean duration spent in convergence increases ( Fig 4M ) , while the mean duration spent in oscillations decreases ( Fig 4N ) . These results demonstrate that an increase in find increases the probability to transit from oscillations to convergence when the motion is persistent . Since an effective increase in find is achieved experimentally by decreasing the system size [21] , these results indicate that smaller carrying groups have a higher probability to display co-existence between the two modes of motion in the presence of an obstacle . To test our predictions of dynamical bi-stability during cooperative transport in the presence of an obstacle , we have conducted experiments using ring-like cargoes of different sizes [21] . These cargoes were similarly treated with cat food , which made them equally attractive ( see Methods ) . In addition , the number of binding sites were equally distributed along the perimeter of the cargo rings , which made the number of carrying ants ntot proportional to the radius of the cargo r , i . e , ntot ∝ r . The motivation for using different cargo sizes arises from its effect on the level of cooperativity between the carrying ants [21 , 24] , which in our model is set by the individuality parameter Find , and the total applied force ftot ( Eqs ( 1 ) and ( 2 ) ) . While Find is independent of object size , the total applied force ftot is proportional to the number of carrying ants ntot , which make ftot ∝ r . Moreover , the mechanical response coefficient γ ∝ r as well ( see S1 Appendix for scaling arguments ) . Consequently , the dynamics of the cargo ( Eq ( 9 ) ) depend on the rescaled parameter , find , as find = Find/γ ∝ 1/r . Therefore , changing the cargo size allows us to effectively probe different levels of rescaled individuality find , and use cargo size as a means for manipulating the cooperativity level between the ants . In addition , changing the cargo size effectively controls the parameter ϵ in our model ( Fig 1C ) , as a decrease in r effectively decreases ϵ , and exposes a larger region of bi-stability in the predicted phase diagram ( Fig 2E–2H ) . We recorded ants carrying cargoes of three sizes in the experimental setup shown in Fig 1A ( see Methods for full set-up ) . The experiments were compared to two dimensional simulations that reproduced the same experimental features ( see S1 Appendix for 2D mathematical model ) . The results show similar trends as function of cargo size in both simulations and experiments ( Fig 5 ) : • Large cargo ( r = 2 [cm] ) : For this cargo size the motion is the most persistent , and the phase space trajectories show clear non-linear relaxation oscillations . The step-like changes in the velocity are observed to occur when passing the opening ( Fig 5A–5D ) . • Medium cargo ( r = 1 [cm] ) : Here , the phase space trajectories demonstrate non linear relaxation oscillations as well , however with a smaller amplitude compared to the large cargo ( Fig 5E–5H ) . • Small cargo ( r = 0 . 5 [cm] ) : This size is approximately the size of common natural prey [21] . For this cargo the phase space trajectories demonstrate noisy oscillations , as well as dense trajectories near the origin . These results suggest that the system spends a significant time near the opening ( Fig 5A–5C and 5G–5I ) . The turning time distributions of the medium and small cargoes have an exponential tail ( Fig 6B and 6F ) , similar to the one dimensional simulations ( Fig 3M ) , and in agreement with the first passage time observed in [22] . The large cargo however , has a rather well defined period , indicating that for a large system size the oscillatory mode of motion dominates . For the smallest cargo , we find that the velocity distribution peaks at vx = 0 in both simulations and experiments ( Fig 6A and 6E ) . This peak appears in addition to peaks that represent the oscillatory motion , and are observed for all cargo sizes . This result suggests that for small cargoes , the system exhibits two co-existing modes of motion with different characteristic velocities , similar to the 1D simulations ( Fig 3K ) . To detect the time the small cargo spends in the convergent mode near the opening ( Fig 6C–6L ) , we applied the following thresholds over both the experimental and simulation data: ( i ) Position threshold: Over the x and y time series , the thresholds were set to −r < xconv < r and yconv < 2r . These thresholds consider the cargo size . ( ii ) Velocity threshold: The threshold over the velocity vx time series considers the theoretical velocity given by the pull of a single ant in the absence of friction , and in a direction parallel to the obstacle . In such a case vx = f0/γ ≈ 0 . 14 [cm/sec] ( Table 1 ) . Considering that fluctuations are dominant for this system size , the threshold was set to vxconv < 0 . 1 ( Fig 6A and 6C ) . ( iii ) Duration threshold: Convergent events were counted only if surpassed the x , y , vx thresholds and lasted more than 4 seconds . The threshold tconv > 4 [sec] assured a sufficient period of time dwelling near the opening , that can be distinguished from an oscillatory motion at low velocity . Counting the durations spent in each mode of motion suggests an exponential trend in the simulations ( Fig 6C and 6D ) . The limited experimental data did not allow us to extract the type of distribution ( Fig 6G and 6H ) , however , the trend seems exponential , and both experiments and simulations provide similar durations of convergent events . These results show , as predicted by our model , that a small group may exhibit co-existing dynamical bi-stability , and that a decrease in the cargo size leads to a decrease in group persistence , along with an increase in the duration of convergent motion . In S1 Video we show a typical dynamical bi-stability in the two dimensional simulations , with parameters that correspond to phase ( ii ) ( Fig 2 ) . In S2 and S3 Videos we show the typical oscillatory motion of the large and medium size cargoes ( corresponding to the data shown in Fig 5G and 5H ) . Finally , in S4 Video we provide examples of transitions between oscillations and convergent motion when the ants carry the small cargo ( corresponding to the data shown in Fig 5I ) .
During cooperative transport of food , ants often encounter obstacles , such as barriers with small openings that allow single ants to pass , but are too narrow for the food to be retrieved [21 , 22] . These encounters raise a conflict within the carrying group: While the group attempt to maintain a persistent motion that will allow them to bypass the obstacle , informed individuals attempt to direct the load towards the opening , which is identified as the direct route to the nest . In this work , we explain the behavior of ant groups near obstacles , using a physical mechanism that originates from the mechanical interactions between the individuals in the group . We show that when a group of ants encounters such an obstacle during cooperative transport ( Fig 7A ) , two modes of motion spontaneously emerge . These two modes of motion can assist the group in overcoming the obstacle: Either by dwelling near an opening , which may allow flexible loads to be squeezed through the direct route to the nest ( Fig 7B ) , or , when carrying large items of food , perform persistent excursions ( Fig 7C ) , which may lead to obstacle circumvention . ( Fig 7D ) . With the use of experiments and simulations , we demonstrate how intrinsic noise , in the form of mechanical fluctuations , allows stochastic transitions between the two modes of motion , and keeps the system out of detrimental behaviors , such as remaining stuck near an opening with large loads that cannot be squeezed through , or oscillating for long durations , when carrying loads that can be passed through the opening . Previous studies of cooperative transport by ants in the presence of constraints have investigated the behavior of the carrying ant group near obstacles with open boundaries [21 , 22 , 25] , a fully confining obstacle that trap the group [25] , and by confining the motion of the cargo by a tether [24] . These studies have related the decision making process of the individual ants to problem solving behaviors during obstacle navigation [21 , 22 , 24] , and proposed a strategy in which the ants’ behavior changes over the course of time when facing obstacles [25] . Here , we further examine the interaction of ant groups with obstacles , using a simple experimental setup that allows a detailed analysis of the cargo’s motion . Our results provide direct evidence for the emergence of dynamical bi-stability in the presence of rigid obstacles , and elaborates further the current physical understanding of cooperative transport by ants [21 , 22 , 24] . In addition , the analytical framework displayed in this work provides a detailed explanation of the origin and conditions for maintaining co-existence between the two collective dynamical modes . These findings could have implications for other biological ensembles [2–5] and theoretical models [31 , 32] , that have reported co-existence between several collective dynamical modes .
Experiments were carried out in a single colony of P . longicornis ants in Rehovot , Israel during the summer months ( June—September ) at day time ( between 9:00-14:00 ) when the foraging activity is high [13] . Conducting these experiments on a single large colony was sufficient , as it was previously found that cooperative transport behavior in these ants is similar among different colonies [21 , 22 , 24] . Cooperative transport was tested with three ring shaped cargoes with radii r = 0 . 5 , 1 , 2 [cm] and width 0 . 1 [mm] ( Fig 8A ) . In each experiment the ants were filmed from a top vertical view , using a Sony FDR-AX100 4K Ultra HD Camcorder camera . An enclosed 80 × 60 [cm] PMMA frame with a opening was placed on the flat surface with a graph paper on top , such that the opening faced the direction of the nest ( Fig 8B ) . The frame was sufficiently large to mimic an infinite length obstacle , as the carrying groups never reached the frame edges with all cargo sizes . Each experiment started with a recruitment phase , where ants were lured from the nest with several fresh pieces of cat food placed at the proximity of the opening . Once a recognizable trail could be identified , and a significant amount of ants were seen milling through the camera , the food was replaced by a circular cargo smeared with cat food . Each experiment lasted approximately an hour . A total of 15 experiments were conducted , 5 experiments for each cargo size . The opening has a width of 0 . 5 [cm] . For the experiments of the small cargo where r = 0 . 5 [cm] , the hole was shaped as a comb such that the cargo cannot get physically stuck at the opening ( Fig 8C ) . Tracking was carried over the cargo center of mass using a designated software developed on Matlab . Two of the free parameters were obtained directly from the experiments: The detachment rate koff [21] , and the average number of carrying ants occupying the cargo . The rest of the parameters were estimated by considering the steady state populations of the informed and uninformed ants . For the second stochastic scheme ( Fig 3C ) , the steady state population of the uninformed ants Pu is given by d P u d t = k o n ( 1 - P u ) - k o f f P u ⟶ P u = k o n k o n + k o f f ( 13 ) Therefore the total number of binding sites can be estimated by: P u = n t o t n m a x ⟶ n m a x = n t o t ( 1 + k o f f k o n ) ( 14 ) For the third stochastic scheme ( Fig 3C ) , and the two dimensional simulations , the steady state populations of the informed/uninformed ants , Pinf/Pu are given by d P i n f d t = k o n ( 1 - P i n f - P u ) - k f o r g e t P i n f ⟶ P i n f = 1 1 + κ 1 + κ 2 ( 15 ) d P u d t = k f o r g e t P i n f - k o f f P u ⟶ P u = κ 1 1 + κ 1 + κ 2 ( 16 ) where kforget is the rate at which informed ants convert to uninformed , κ 1 = k f o r g e t k o f f and κ 2 = k f o r g e t k o n . Therefore the number of binding sites can be estimated by: n t o t + G n m a x = P u + P i n f ⟶ n m a x = ( n t o t + G ) ( 1 + κ 2 1 + κ 1 ) ( 17 ) and the forget rate was estimated by k f o r g e t = ( n m a x G + ⟨ n t o t ⟩ - 1 ) k o n 1 - k o n k o f f ( n m a x G + ⟨ n t o t ⟩ - 1 ) ( 18 ) The full parameter list is given in Table 1 . The simulation of the dynamics is based on a Gillespie algorithm [29] , where in each iteration of the simulation , one of the following events can occur: Attachment of an informed ant to an empty site . Detachment of an uninformed puller/lifter from an occupied site . Role switching between the uninformed pullers/lifters . Conversion between an informed puller to an uninformed puller/lifter . Re-orientation of an un-informed puller ant towards the direction of motion of the cargo . The first step: The time step of the next event is calculated by: d t = 1 R t o t l o g ( 1 r 1 ) ( 19 ) where r1 is drawn from a uniform distribution and Rtot is the sum of all possible rates given by R t o t = R a t t + R d e t + R d e c + R f o r g e t + R o r i e n t ( 20 ) The rates in Eq ( 20 ) are given by R a t t = k o n · n e m p t y ( 21 ) R d e t = k o f f · ( n p + n l ) ( 22 ) R d e c = k c ∑ i = 0 n m a x | α i k | e x p ( α i k f → t o t · p → i F i n d ) ( 23 ) R f o r g e t = k f o r g e t · G ( 24 ) R o r i e n t = k o r i e n t · n p ( 25 ) where kon , koff , kc , kforget , korient are the constant basal rate constants of attachment , detachment , role switching , forgetting and reorientation respectively . G , np , nl , nempty are the average numbers of informed ants , pullers , lifters and empty sites respectively . The second step: The type of event at time t+dt is determined by the following conditional statement: If ( r 2 < R a t t R t o t ) then an informed ant attaches to the cargo else if ( R a t t R t o t < r 2 < R a t t + R d e t R t o t ) then an uninformed ant detaches from the cargo else if ( R a t t + R d e t R t o t < r 2 < R a t t + R d e t + R d e c R t o t ) then an uninformed ant role switch else if ( R a t t + R d e t + R d e c R t o t < r 2 < R a t t + R d e t + R d e c + R f o r g e t R t o t ) then an informed ant turns into an uninformed ant else an uninformed ant orients its position with respect to the total force vector . The numbers r2 is drawn at each step from a uniform distribution . The third and final step: The position and velocity are updated by v → t + d t = v → t + f → t o t γ ( 26 ) x → t + d t = x → t + v → t + d t d t ( 27 ) Note that in the cases where an informed ants “forgets” or an uninformed ant detaches , the type of uniformed ant ( puller or lifter ) should be determined as well . For such cases , another number r3 is drawn from a uniform distribution and the following conditional statement takes place: if ( r 3 < 1 1 + e x p ( - 2 f → t o t · p → i F i n d ) ) then an un-informed puller will participate else an un-informed lifter will participate The last conditional statement , results from the assumption of role switching equilibrium ( at each site i ) n p i e x p ( f → t o t · p → i F i n d ) = n l i e x p ( - f → t o t · p → i F i n d ) ( 28 ) and by denoting the average number of pullers and lifters N p = n p i n p + n l and N l = n l i n p + n l , we get N p e x p ( f → t o t · p → i F i n d ) = ( 1 - N p ) e x p ( - f → t o t · p → i F i n d ) ( 29 ) which results in the required condition N p = 1 1 + e x p ( - 2 f → t o t · p → i F i n d ) ( 30 ) Therefore , if r3 < Np , the ant will switch to a puller , and if r3 > Np , the ant will switch to a lifter . | Among animal groups , ants hold what may perhaps be the richest repertoire of collective behavior such as trail formation , nest excavation and food dissemination . Of the most intriguing of these behaviors is cooperative food transport , where many ants carry items that individuals cannot move . Using experiments and theory , we study cooperative transport when the motion is frustrated by an obstacle which contains a single narrow opening that leads to the nest . We find that the group exhibits two co-existing modes of motion that allow exploration of possible routes to overcome the obstacle: Either dwelling near the opening and attempting to pass the cargo through , or performing large excursions that can lead to obstacle circumvention . Previous studies have found that co-existing collective dynamic modes emerge when animal groups interact with constraints , however the origin of the phenomena remains unknown . Here , we provide a detailed theoretical explanation of the source of bi-stability and show how stochastic processes drive the transitions between the two dynamical modes . |
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In recent years , the field of network science has enabled researchers to represent the highly complex interactions in the brain in an approachable yet quantitative manner . One exciting finding since the advent of brain network research was that the brain network can withstand extensive damage , even to highly connected regions . However , these highly connected nodes may not be the most critical regions of the brain network , and it is unclear how the network dynamics are impacted by removal of these key nodes . This work seeks to further investigate the resilience of the human functional brain network . Network attack experiments were conducted on voxel-wise functional brain networks and region-of-interest ( ROI ) networks of 5 healthy volunteers . Networks were attacked at key nodes using several criteria for assessing node importance , and the impact on network structure and dynamics was evaluated . The findings presented here echo previous findings that the functional human brain network is highly resilient to targeted attacks , both in terms of network structure and dynamics .
Complex systems may be represented as networks by modeling the system components as nodes and the interactions between components as links , and graph theory methods and dynamical simulations may then be applied to these networks in order to understand their structure and dynamics . The human brain is an example of such a system that can be described as a network . The functional relationships between brain regions , typically measured using imaging techniques such as functional magnetic resonance imaging ( fMRI ) , can be described as a brain network; in particular , nodes represent various brain regions and edges represent strong coherence among the nodes . For a review of the construction and analysis of functional brain networks , we refer the reader to [1] and [2] . An exciting finding since the advent of brain network research was that the functional brain network can withstand extensive damage , even to highly connected regions [3] . In this prior work , regions of the brain network were systematically attacked based on their degree , the number of links to which each region was functionally connected . Regions having the highest degree were eliminated and the associated changes on network topology were evaluated . Then the next highest degree nodes were identified and eliminated and the changes in the network topology were recorded . This process was repeated until all nodes of the network had been removed . This type of systematic removal is referred to as targeted attack , where the most critical hubs are targeted for removal . Additionally , the effect of random failure was studied by selecting nodes for removal with uniform probability . Achard et al . compared the resilience of brain networks to that of two null models , random networks and scale-free networks , since the level of robustness of these networks had been studied previously [4] . Random networks , where the majority of nodes have a similar number of connections ( or degrees ) , proved to be highly resilient to both targeted attack and random failure . Scale-free networks , on the other hand , fragmented rapidly . This may be because a scale-free network is highly vulnerable at a very small number of high-degree nodes , or mega-hubs , which mediate connections among low degree nodes constituting the majority of the network [4] . Functional brain networks , while not as resilient as random networks , were shown to be far more robust than scale-free networks . It is well known that brain networks have characteristics of small-world architecture , that is a combination of high clustering for local specialization and low path length to enable distributed processing [5] , [6] , [7] . Achard et al . proposed that the resilience of the brain network was due to this small-world architecture . Furthermore , Achard et al . observed that the functional brain network degree distribution followed an exponentially truncated power law , meaning that there are fewer mega-hubs and a greater number of mid-degree nodes than would be expected in a scale-free distribution . This exponentially truncated power law distribution also likely contributed to the resilience against targeted attacks of hubs . However , it is possible that the highest degree nodes are not the most critical nodes of the brain network [8] . There are many measures of node importance , or centrality . Each centrality metric has a different consideration for the topological properties that make a node central , and therefore different centrality metrics may be more appropriate for different networks and their specific information flow processes [9] . Furthermore , it is unclear how the removal of these nodes may impact network dynamics in addition to topology . Alstott et al . have taken significant strides towards studying how failure of nodes in the brain network may impact network dynamics [10] . Their study involved simulating neural dynamics on structural brain networks constructed from diffusion spectrum imaging data . These simulated neural dynamics were used to create functional connectivity networks . Network nodes were eliminated based on degree , strength ( weighted degree ) , and betweenness centrality to study the effect on topology . The impact was evaluated by calculating changes in global efficiency and the size of the largest connected component . In dynamical simulations , lesions were simulated by targeting groups of nodes centered on anatomical locations . The impact of a particular lesion was evaluated by simulating neural dynamics on the lesioned networks , and noting changes in the resulting functional networks . They found that betweenness centrality had a considerable impact on network topology , and that the effect on network dynamics is highly dependent on the anatomical location of the lesion . Another study evaluated the effect of brain lesions due to stroke , traumatic brain injury , and brain tumors on functional brain network structure [11] . Specifically , Gratton et al . were concerned with the impact of lesions on brain network community structure , the topological property where network nodes tend to associate into well connected groups . Images from healthy participants and patients with lesions were used to create networks with approximately 90 nodes , in which corresponding nodes in each population were mapped to the same anatomical space . Each network was partitioned into modules ( communities ) using Newman's modularity [12] . Each node was evaluated for its within-module degree , or the number of links connecting nodes in the same module , as well as its participation coefficient , a summary metric of how diversely the node is connected to multiple modules . Gratton et al . discovered that the networks of lesioned patients had lower modularity scores when the lesions were in areas that exhibited higher participation coefficients in normal subjects . There was no statistical relationship between the within-module degree of lesioned nodes and the effect on modularity . They concluded that damage to brain regions linking multiple modules leads to a reorganization of the network that is detrimental to the entire network topology . A large body of previous work on dynamics in complex networks has been focused on artificial networks . Watts studied global cascades in random networks due to small perturbations in the signals embedded in the network [13] . In these networks , each node has a state ( either 1 or 0 ) , and it may choose to change its state based on the states of its neighboring nodes according to a threshold rule . A cascade occurs when a few nodes switch states , causing a large scale propagation of state-switching throughout a large portion of the network . Watts found that as the distribution of threshold values for state-switching was made to be more heterogenous , the system became more prone to producing large cascades . In a similar experiment studying cascades in coupled map lattices , Wang and Xu noted that the size of the cascade is highly dependent on the network structure [14] . They showed that coupled map lattices with small-world architecture or scale-free degree distributions are much more likely to exhibit large cascades due to local shocks than globally coupled ( fully connected ) lattices . Rubinov et al . designed a neurobiologically relevant dynamic model consisting of a computerized network of spiking neurons [15] . They investigated the topological factors necessary for the emergence of self-organized criticality , marked by system dynamics that are self-similar on multiple spatial and temporal scales . They found that the presence of community structure ( groups of nodes that are tightly interconnected ) , low wiring cost ( an estimation of the average distance each wire traverses across the network ) , and synaptic plasticity were all necessary components for producing self-organized criticality . Tanaka et al . studied targeted attack on networks of coupled oscillators [16] . They discovered that the removal of low degree nodes has a large effect on the dynamics of these networks while the removal of high degree nodes does not . They speculate that this is due to the fact that low degree nodes do not interact with a large number of other nodes and therefore have the ability to sustain high levels of activity . As such , the removal of low degree nodes has the potential to alter the overall activity in the system to a great extent . Despite all of the important work on the topological resilience of functional brain networks to targeted attack , and impact on the dynamics of artificial networks , it is still not clear how targeted attacks impact the dynamics in functional brain networks . In this work , we sought to expand our understanding of the resilience of the human functional brain network , both in terms of topology and dynamics . We conducted targeted attack experiments on voxel-based functional brain networks and region-of-interest ( ROI ) networks of 5 healthy volunteers . Networks were selectively attacked using several node centrality metrics to determine which centrality metric best identifies critical nodes . We measured the resulting impact on network topology using three criteria , and utilized two frameworks for assessing the dynamical impact .
All experiments were conducted in accordance with the ethical standards of the Wake Forest University institutional review board and with the Helsinki Declaration of 1975 . Functional brain networks of 5 healthy volunteers were constructed according to [8] . For each subject , 120 fMRI full-brain volumes were acquired over approximately 5 minutes . Images were corrected for motion , normalized to the MNI ( Montreal Neurological Institute ) space , and re-sliced to 4×4×5 mm voxel size using SPM99 ( Wellcome Trust Centre for Neuroimaging , Longdon , UK ) . From these volumes , one time series was extracted for each of the 15 , 996 voxels encompassing all of the gray matter of the cerebrum . Images were corrected for physiological noise by band-pass filtering to eliminate signal outside of the range of 0 . 009–0 . 08 Hz [17] , [18] , and mean time courses from the entire brain , the deep white matter , and the ventricles were regressed from the filtered time series . In the past , the practice of global mean regression has been under scrutiny due to the propensity to produce artificial deactivations , particularly in the white matter and cerebrospinal fluid ( CSF ) [19] . It is important to note , however , that failure to regress the mean signal will prevent detection of true deactivations that are known to occur in the brain . Additionally , the regions that are highly sensitive to these artifacts ( white matter and CSF ) are not considered in the present work . A full discussion on this topic can be found in [20] . The time series in each voxel was correlated with every other voxel using the Pearson's correlation coefficient . These correlation values were then represented in a correlation matrix summarizing the functional relationships between every pair of voxels . A threshold was applied to the correlation matrix , above which voxel pairs were said to be connected . This resulted in a binary adjacency matrix where 1 indicated the presence of a link and 0 indicated the absence . The threshold was defined such that the relationship between the number of nodes N and average number of connections between nodes k was consistent across subjects . Specifically , the ratio of log ( N ) to log ( k ) was the same across subjects [21] . This threshold resulted in a link density of approximately 0 . 0015 , where density is the ratio of the number of links present in the network to the number of possible links . This density is consistent with the size-density relationship of many self-organized networks described in [22] . Moreover , links defined by this threshold represented correlations that are approximately 3 standard deviations above the mean . Figure 1 depicts the process of generating the functional brain networks . Each functional brain network was selectively attacked at the nodes with the highest centrality . In particular , the top 5% highest centrality nodes were removed from the network , along with any links directly connected to those regions . After the removal of the nodes , the respective centrality measure was recalculated and another set of top 5% nodes were identified . This process was repeated until all nodes in the network were removed . Four centrality metrics were utilized , namely , degree centrality , leverage centrality , eigenvector centrality , and betweenness centrality . Degree centrality defines highly central nodes to be those having a high number of links connected to that node . Leverage centrality relates the degree of a node to that of its immediate neighbors . In particular , nodes with higher degrees than their neighbors are considered highly central to their local neighborhood [8] . Eigenvector centrality evaluates centrality based on the centrality of immediately connected neighbors , and therefore a node connected to nodes with high degree is highly central by association [23] . Betweenness centrality defines the importance of a node by the number of shortest paths between pairs of nodes on which the node lies . In this way , high betweenness nodes facilitate the exchange of information along the most efficient trajectories [24] . Formulations for these metrics are provided in [8] . In addition to targeted attacks , we also conducted random attacks by iteratively removing 5% of nodes randomly at each step . After attacking the networks , changes in the network structure were evaluated by assessing three network characteristics: local efficiency ( Eloc ) , global efficiency ( Eglob ) , and the size of the giant component ( S ) . Local and global efficiency are used to infer the efficacy of information exchange through a network by studying its topology [25] . Local efficiency quantifies the extent to which nodes communicate with immediate neighbors and can be thought of as an indication of regional specificity . Global efficiency quantifies the extent to which nodes communicate with distant nodes , and indicates the efficacy of information exchange throughout the entire network . As nodes are removed , the network may fragment into isolated subgraphs . The size of the giant component is defined to be the largest connected subgraph , and may be used to indicate the extent of fragmentation . The impact on dynamics was evaluated using two models . The first is an equation-based spreading activation model described in [26] . This model injects signal into a network , and allows the signal to spread through links and decay according to model parameters . The equation governing the spread of activation is given in Equation 1 below . ( 1 ) If N is the number of nodes in the network , St is an N×1 vector describing the signal at time t , Et is an N×1 vector containing the external signal injected at time t , γ is the relaxation rate of the signal ( 0≤γ≤1 ) , α is the relative amount of activity that flows from a node to its neighbors per unit time ( α>0 ) , and R is the N×N connectivity matrix . R was constructed by eliminating all negative connections in the correlation matrix , setting the diagonal of the matrix to 0 , and normalizing the matrix such that each column sums to 1 . Therefore R contained only weighted ( normalized ) positive connections from the original correlation matrix . External signal , E , was only present at time t = 0 , where the 50 seed nodes were set to 1 , and all other nodes were 0 . The seed nodes for the external signal were randomly selected from the population of nodes that were not deleted . The equation was iterated for 100 time steps . This spreading activation model was tested on the original network and the networks with nodes removed , where 5% through 80% of the nodes were removed in increments of 5% . By examining the total activation in the system over the course of the simulation , we evaluated the impact of removal of highly central nodes on the ability of information to spread through the network . Here , total activation is defined to be the sum of activity values across all nodes in the network at a given time during the simulation . This procedure was performed on 5 subjects . Additionally , the impact of targeting low degree nodes was examined in a single subject in order to further investigate the findings in [16] , where the targeted removal of low degree nodes had a greater impact on the dynamics of a network containing coupled oscillators than high degree nodes . For this experiment , we removed nodes that were the top 5% through 30% highest centrality nodes as well as the 5% through 30% lowest centrality nodes , in increments of 5% . Seed nodes were again randomly selected from the pool of remaining nodes in the networks . Varying the ratio α/γ results in a phase change in the spreading activation model . When α/γ is small , the total activation in the system decays to zero over time ( referred to as Phase I ) , but as α/γ increases , the system enters a regime where the activation builds exponentially in a small component of the system , referred to as Phase II [26] . We chose α = 1 and tuned gamma until the original networks exhibited Phase II behavior , resulting in α/γ = 0 . 96 . Changes in dynamics were also evaluated by embedding a coarser form of each network into an agent-based model called the agent-based brain-inspired model ( ABBM ) described in [27] . An agent-based model is a collection of agents that interact with one another by following simple rules . The rules used here were inspired by the work of Stephen Wolfram [28] , who has been a major contributor to the study of cellular automata . In this case , agents are represented by the nodes of the functional brain network , and links in the network represent communication pathways between agents . Each agent possesses a state , which can be either on or off , and may update its state based on the states of all connected neighbors by following one of Wolfram's Rules . Due to the computational demand of this model , these networks were constructed by parcellating the brain volume of each subject into 90 anatomical regions using the AAL ( automated anatomic labeling ) atlas [29] . The time series of all voxels belonging to a particular ROI were averaged in order to create 90 ROI time series . These time series were cross-correlated to construct a 90×90 ROI correlation matrix containing positive and negative connection weights . A threshold was applied to these networks to preserve only strong positive or negative connections while preventing fragmentation . Therefore , positive and negative weighted links were present in the ROI networks . The process of creating the ROI networks and the mechanisms underlying the ABBM are described in full in a prior publication [27] . These ROI networks were selectively attacked by removing 10% of the nodes ( 9 regions ) with the highest centrality , at random , or with the lowest centrality . Slight modifications to the centrality metrics were necessary in order to calculate these metrics in the weighted , signed correlation matrix . Degree was calculated as the sum of the absolute value of the weights of all links belonging to a node . Leverage and eigenvector centrality , which depend only on the degree of the node and its connected neighbors , were calculated using this definition of degree . The weighted form of betweenness was calculated on the absolute value of the correlation matrix using the MATLAB BGL package ( http://dgleich . github . com/matlab-bgl/ ) . The impact on dynamics was evaluated by testing the ability of the attacked agent-based model to solve the density classification problem , a problem originally utilized to evaluate whether a one-dimensional cellular automaton ( CA ) could support computation [30] . A CA can be thought of as belonging to a class of agent-based models , where agents are spatially embedded as adjacent cells . The goal of the density classification problem is to find a rule that can determine whether greater than half of the cells in a CA are initially in the on state . If the majority of cells are on ( i . e . density >50% ) , then by the final iteration of the CA , all cells should be in the on state . Otherwise , all cells should be turned off . The system should be able to do this from any random initial configuration of node states . The key is that each node receives input from only a few other nodes in the network . Each node must decide based on this limited information whether to turn on or off in the next time step , resulting in network-wide cooperation without the luxury of network-wide communication . The rule and model parameters that must be used in order to perform this task are identified using a search optimization technique known as genetic algorithms . We have demonstrated that the ABBM is able to perform the density classification task with a high level of accuracy across a range of densities , while null models with randomized connectivity are not successful , indicating that the topology of the brain network is amenable to computation . Here we wished to determine how targeted removal of high centrality nodes would impact performance on this task . Table 1 contains a summary of treatments of the functional brain networks used in each procedure for evaluating network structure and dynamics .
The quantities used to analyze the topological changes to the functional brain networks were the size of the giant component , global efficiency , and local efficiency . Each time the highest centrality nodes were identified and eliminated from the network , these three measures were recalculated and plotted along a curve . Figure 2 contains these curves , averaged across the networks of 5 subjects . The size of the giant component , S , was normalized to the size of the giant component of the original network ( S0 ) . As the network nodes were selectively removed , the size of the giant component decreased , but did not show a dramatic reduction until nearly 40% of the nodes were eliminated from the network , regardless of the type of centrality used to identify hubs ( Figure 2 ) . Eliminating these hubs steadily decreased the global and local efficiency of the network as well . When comparing the removal of nodes based on different centrality metrics , removing nodes with high eigenvector centrality had the least effect on the networks . Network metrics declined visibly less for the removal of high eigenvector centrality nodes compared to degree or leverage , when evaluating all three of the network metrics . Targeted attack on high betweenness nodes was not highly different from degree or leverage , but it is notable that betweenness was also not highly different from eigenvector centrality when assessing local efficiency . Table S1 , Table S2 , and Table S3 in Text S1 show the ranges where there was a statistically significant difference in the size of the giant component , global efficiency , or local efficiency depending on the type of attack . Targeted attack and random failure were also evaluated on a network with randomized connectivity . This network was generated using the method described in [31] , where a functional brain network was rewired such that the degree distribution was preserved . The size of the giant component , local efficiency , and global efficiency underwent noticeably steeper declines after targeted attack than the original brain networks . Results from this experiment can be found in Text S2 . Simulations using a spreading activation model were employed to demonstrate changes in network dynamics after targeted attack or random failure . Figure 3 contains the results of the spreading activation model using the original ( intact ) network of one subject , as well as after attacking 20% of the highest degree centrality nodes . The activity within each node was computed at each time step , and during the simulation both the intact network and the attacked network were exhibiting Phase II activity . Recall that the Phase II activity pattern is characterized by a few nodes having activation that is exponentially increasing over time , while all other nodes in the network have activation that decays rapidly to zero . In the case of the original network , 9 nodes were exhibiting exponentially increasing activity . Panel A contains the two-dimensional color map of the time-series for the 9 nodes with exponentially increasing activity . The total activity in the network over the course of the simulation , defined to be the sum of activation across nodes at a given time step , is plotted in panel B . After the network was attacked , the number of nodes with exponential activity increased to 14 , as pictured in panel C . The increase in nodes with building activity caused the total activity in the network , shown in panel D , to increase relative to the intact network . Figure 4 shows the total activity achieved at the end of the simulation ( t = 100 ) , depending on the percentage of nodes removed using the four centrality metrics and for random failure . The original total activity is included , shown at 0% removed . These curves illustrate that the total activity achieved in the network increased depending on the extent of attack when high centrality nodes are targeted . Total activity was maximal when high degree and betweenness nodes were removed . Total activity actually decreased after random failure but removing further nodes had little effect beyond 5% . All targeted attack curves in Figure 4 show a peak in total activity at a certain percentage of nodes removed . The peak in the curves corresponding to removal of high degree nodes occurs at 35% , and the peak corresponding to removal of high betweenness and eigenvector centrality nodes occurs at 40% . However , the curve corresponding to removal of high leverage nodes occurs much sooner , after 20% of nodes have been removed . According to Equation 1 , the signal in a node is the sum of any external signal , previous activity that has not yet decayed , and new activity received from neighbors . The first two factors are not directly impacted by network attacks , as the seed nodes used for initial external signal are held constant as attacks are performed , and the decay factor is not dependent on the connectivity matrix . However , the changes in network connectivity will impact the spread of activity from neighboring nodes . Leverage centrality is designed to identify nodes that are connected to more nodes than their neighbors , and therefore control the content and quality of the information received by their neighbors . Therefore , leverage centrality tends to identify hubs whose directly connected neighbors would be negatively impacted by the loss of those hubs . As high leverage centrality nodes are removed , the remaining nodes that are highly dependent on high leverage nodes are not receiving as much signal . Therefore , leverage has the largest effect on hindering the spread of activation as measured via the peak in the total activation curves . Despite the change in total activation , full activation curves demonstrated that , in the majority of cases , the networks remained in Phase II after targeted attack or random failure ( Figure 5 ) . The exceptions were networks where 70–80% of high degree centrality nodes were removed . These networks exhibited Phase I behavior , in which the total activity in the network decayed to zero . Activation curves are shown for the original network and after removing 20% , 40% , 60% , and 80% of nodes . While targeted attack of high centrality nodes generally increased the total activity in the network ( to a point ) , random failure decreased the total activity for all levels of node removal . One-sample t-tests were performed to compare the final total activity across the 5 subjects after removing 20% , 40% , 60% , and 80% of the nodes . Text S1 contains the resulting statistics . Total activity curves after removing high centrality hubs and low centrality antihubs are shown in Figure 6 for a single subject . Recall that the seeds selected for this experiment were different from those used in previous experiments . Here , seed nodes were randomly chosen from the population of nodes not selected for removal as high centrality hubs or low centrality antihubs . It was necessary that seed nodes not be removed throughout the simulations in order keep the initial external signal , which originated at seed nodes , constant . As the seed nodes for this simulation were unique from the ones used previously ( Figures 3–5 ) , these networks achieved higher total activity values . As hubs were attacked , the total activation increased as in the previous simulation . In this case , the peak and subsequent decrease in final total activity are not captured , as in Figure 4 , although the leverage curve peaked at 20% of nodes removed in the previous experiment . The profiles of the degree , leverage , betweenness , eigenvector , and random curves are similar to Figure 4 . On the other hand , as antihubs were attacked , the activation decreased to a slightly greater extent than random . Spreading activation experiments were also performed with seed nodes selected from the auditory cortex . However , the choice of seed nodes does not appear to change the observed dynamics in the spreading activation model . The results of this experiment can be found in Text S3 . In addition to the spreading activation model , simulations using an agent-based brain-inspired model ( ABBM ) were used to evaluate the impact of targeted attack and random failure on the ability of the ABBM to support global computation . A genetic algorithm was used to identify model parameters that enabled the ABBM to solve the density classification task using the original ( intact ) network ( see Materials and Methods for details ) . The ABBM was then asked to solve the density classification task using the same parameters while operating on the networks with nodes removed . Accuracy curves were generated for each subject in order to evaluate the impact of targeted attack of hubs or antihubs and random failure on the ability of the model to solve this task . Mean accuracy curves , averaged across all subjects , are shown in Figure 7 . On the left half of the density axis , where density <0 . 5 , fewer than half of the nodes were on at the first time step . To the right , where density >0 . 5 , greater than half of the nodes were initially on . All curves have a pronounced decrease in accuracy around density = 0 . 5 , where the classification becomes more difficult . These accuracy curves show that , despite loss of highly central nodes , the ABBM maintains a high level of accuracy in solving the density classification task . This would suggest that the nodes that would be considered to be the most structurally integral components of the network have only marginal importance in information flow . On the other hand , the impact of random failure is greater than any type of targeted attack , specifically in higher density ranges . An ANOVA comparing mean accuracy across attack types revealed that targeting low centrality antihubs resulted in significantly decreased accuracy when compared to targeting hubs in only a select number of cases . Differences were found between leverage antihubs and eigenvector antihubs at density = 0 . 46 ( mean difference 0 . 028 , p = 0 . 019 ) , leverage hubs and eigenvector antihubs at density = 0 . 53 ( mean difference 0 . 064 , p = 0 . 040 ) , degree hubs and eigenvector antihubs at density = 0 . 61 ( mean difference 0 . 046 , p = 0 . 013 ) , and leverage hubs and betweenness antihubs at density = 0 . 61 ( mean difference = 0 . 058 , p = 0 . 019 ) . There were no significant differences in accuracy using the intact network versus any of the attacked networks .
We have presented a study on the topological and dynamical effects of targeted attack and random failure in human functional brain networks . Structural analyses employing local and global efficiency as well as the size of the giant component corroborate the findings presented in [3] in which the authors measured changes in the largest cluster size and the path length in functional brain networks , and further demonstrate that the choice of hub does not change the results appreciably . For any given centrality metric , nearly 40% of the nodes were removed before the size of the giant component qualitatively diverged from the random failure curve , which underwent a steady decrease as nodes were removed ( although statistically significant differences exist much earlier ) . The reduction in local and global efficiency due to targeted attack followed curves only slightly steeper than random failure , with the effect on local efficiency somewhat greater than global efficiency . Global and local efficiency capture characteristics of the network structure that lend themselves to efficiency of information transfer . High local efficiency indicates topology that is conducive to local processing specificity , and topology with high global efficiency is amenable to long range information sharing . The topological characteristics that give the brain networks good local efficiency and reasonably high global efficiency are preserved , even when highly central nodes are targeted . Seemingly , whether high degree , betweenness , leverage , and eigenvector nodes are targeted , the result is the same: the topology of the functional brain network is relatively resilient to targeted attack . Dynamical simulations using the spreading activation model revealed similar findings for information spreading across functional brain networks . Although targeted attack modified the total activity in the system at the end of the simulation , there was no phase transition in the overall behavior . The intact networks displayed Phase II activity , characterized by a limited number of nodes exhibiting exponentially increasing activity , while the activity in all other nodes decayed to zero . Random failure had very little impact on the total activity in the system . In contrast to random failure , the total activity in the network increased initially as high centrality nodes were targeted for removal , indicating that the signal was pooling in a number of nodes to a greater extent than before the attack , driven by the α parameter in the spreading activation model . Subsequently , as an increasing number of nodes were removed from the network , the final total activity decreased . Despite these quantitative changes , there was very little qualitative change in the system across all levels of targeted attack . It is important to note that the overall qualities of the system dynamics did not change . Despite initial expectations based on the work by Tanaka et al . mentioned previously , targeted removal of low centrality antihubs , while decreasing the final total activity , did not have a greater effect than targeted removal of highly central hubs . As Tanaka et al . note , low centrality nodes can maintain higher levels of activity because they do not spread their activity to many other nodes , while high centrality nodes tend to disperse activity to many other nodes . In the spreading activation model , when low centrality nodes are removed , less activity is allowed to pool , and the decaying term ( γ ) in the spreading activation model drives the behavior . On the other hand , removing high centrality nodes and their accompanying links decreases the dispersion of activity , and furthermore allows for increased pooling by simultaneously lowering the centrality of their former neighbors; therefore the total activity in the system increases . The density classification task , rather than modeling the diffusion of information , tests whether a system can support computation . The agent-based brain-inspired model is constructed using the structure of the functional brain network . The agents in the model must make a collective decision ( turn on or turn off ) in order to solve the density classification task . As the network structure changes due to targeted attack or random failure , the information shared between nodes changes . Previously , we demonstrated that randomized connectivity patterns are not well suited to the density classification task , but that the functional brain network is . Therefore , we tested whether changes in network topology would impact the ability of the ABBM to make decisions . While targeted attack of hubs or antihubs impacted the accuracy to some degree , the average accuracy over a range of densities was still high , much higher than the accuracy of null models with randomized connectivity shown in [27] . Random failure resulted in a greater decrease in accuracy than targeted attack . The density classification task is not a trivial problem . Each agent is supplied with a limited amount of local information , and must infer the state of the entire system . Furthermore , simply using the majority rule , where an agent chooses to take the state that the majority of its neighbors have taken , is not effective at solving this task [30] . Rather , the system must evolve a complex , yet simple , rule that can solve this task over just a few time steps , and moreover can accomplish this for any initial configuration . Simply solving this problem alone is notable , but solving it after 10% of the most central hubs and their accompanying links have been removed is an even more impressive feat . The fact that random networks with the same degree distribution as the brain network cannot solve this task would indicate that the network topology that enables the brain network to solve the density classification task remains intact after removal of central nodes . Since the global efficiency of the network remains high after targeted attack , one might be tempted to conclude that the efficient long range communication in the network lends it the ability to support computation . However , random networks , which cannot solve the density-classification task , are also characterized by high global efficiency . On the other hand , elementary cellular automata and other lattice-like networks with high local efficiency have been shown to be able to solve the density classification task with high accuracy [27] , [30] . In these networks , nodes are clustered into well-connected groups and can share information readily , and therefore may be able to synchronize more easily . As high centrality nodes are targeted for removal from the voxel-wise functional brain networks , the networks maintain their high local efficiency to a much greater extent than randomized networks . In the agent-based model simulations , functional brain networks with 10% of the highest centrality nodes removed were still able to perform the density-classification task . These two findings together suggest that functional brain networks are able to perform computational tasks after targeted attack because the networks maintain their efficient local connectivity . The two models we chose to employ for modeling dynamics on functional brain networks are the spreading activation ( SA ) model designed by Shrager et al . [26] , and an agent-based brain-inspired model ( ABBM ) , originally introduced in a prior publication [27] . The SA model and the ABBM simulate the flow of information in two disparate ways . We chose the SA model , a type of diffusion model , due to its application in physiologically relevant settings over the past several decades . Spreading activation has been used in artificial intelligence applications such as studying semantic networks , natural language processing , and information retrieval , as it was designed to be a model for memory associations and recall [32] , [33] , [34] , [35] . While diffusion models are prevalent , agent-based modeling takes a somewhat different approach to simulating information flow . The ABBM is used to examine information sharing dynamics that can produce a collective behavior in the system . While the ABBM does not replicate the exact mechanisms of the brain , the method of agent-based modeling is well suited to producing emergent behaviors , which is almost certainly necessary to produce the most complex human behaviors . In the ABBM , each agent collects and integrates the information received from each of its neighbors , distills the information to a binary signal , and makes a decision on whether to fire based on that signal . Although the ABBM operates on a far coarser scale , this process mirrors action potential generation in a neuron . Other widely used models include artificial neural networks , which consist of a set of nodes which take an input , operate on the input using mathematical functions , and produce an output . The networks are then trained to perform a particular task by allowing connections and mathematical operations to change . Neural networks are used in many pattern recognition applications , such as detecting seizures in EEG data [36] , [37] . The distinction between the ABBM and neural network approaches to modeling brain functions is that the ABBM uses the network architecture determined from human functional brain imaging data , whereas the structure of neural networks is often determined by a set of features and desired outputs . By using functional brain network connectivity , the ABBM is generalized to solve different tasks without the need to re-train the network structure . Alternatively , some researchers model cognitive functions using physical microcircuits . Neural microcircuits are used in applications such as the Blue Brain Project [38] , where brain-like neural structures are modeled using a supercomputer dubbed Blue Gene . The computer consists of a network of 4 , 096 interconnected integrated circuits . The enormous computational power of Blue Gene enables the machine to solve cognitive problems using a brute force approach ( e . g . analyzing the result of any possible move in a game of chess ) . Although the computational capability of Blue Gene is impressive , the advantage of using a combination of genetic algorithms and agent-based modeling is the elimination of the need to evaluate all possible outcomes , but instead search the solution space is a systematic way . The field of network science provides a multitude of measures to capture the characteristics of complex systems , but , paradoxically , the complexity of these systems makes the task of understanding their underlying mechanisms quite challenging . The brain is intrinsically difficult to study . The measures and simulations presented here are surrogates for understanding the structural and dynamic changes that can occur in the brain . One limitation of these simulations is that they do not account for functional specialization of the various brain regions , where specific brain regions are thought to play key roles in specific functions . Certainly , many case studies in history have shown that damage to certain locations in the brain have unique effects due to functional specialization ( e . g . , the famous Phineas Gage [39] ) . The simulations presented here also do not account for neuroplasticity , which enables the brain to remap cortical functionalities in response to sustained injuries . One study by Rubinov et al . examined the impact of random failure and targeted attack of high betweenness nodes in a synthetic neuronal network with neuroplasticity . They showed that allowing for the addition of new nodes through synaptogenesis , even at rates much slower than simulated neuronal death , was able to combat the impact on global and local efficiency [40] . Perhaps incorporating similar components of neuroplasticity into the models used in this work would enable an even greater demonstration of resilience . Despite these limitations , this work progresses our understanding of the resilience of the human functional brain network . Based on the topological and dynamical simulations presented here , we conclude that the functional human brain network is highly resilient to targeted attack , both in terms of network structure and dynamics . | Why can the brain endure numerous micro-strokes with seemingly no detrimental impact , until one cataclysmal stroke hinders the ability to perform essential functions such as speech and mobility ? Perhaps various small regions or foci of the brain are highly important to information transfer , and the loss of such highly central foci would be severely injurious to brain function . Identification of such foci , via modeling of the functional brain using network theory , could lead to important advances with regard to brain disease and stroke . In this work , we utilized functional brain networks constructed from human volunteers to study how removing particular regions of the brain impacts brain network structure and information transfer properties . We sought to determine whether a particular measure of region importance may be able to identify highly critical regions , and whether targeting highly critical regions would have a more detrimental impact than removing regions at random . We found that , while in general targeted removal has a larger impact on network structure and dynamics , the human brain network is comparatively resilient against both targeted and random removal . |
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Hepatitis B virus ( HBV ) core protein ( HBc ) contains an N-terminal domain ( NTD , assembly domain ) and a C-terminal domain ( CTD ) , which are linked by a flexible linker region . HBc plays multiple essential roles in viral replication , including capsid assembly , packaging of the viral pregenomic RNA ( pgRNA ) into nucleocapsids , viral reverse transcription that converts pgRNA to the genomic DNA , and secretion of DNA-containing ( complete ) virions or genome-free ( empty ) virions . The HBc linker is generally assumed to act merely as a spacer between NTD and CTD but some results suggest that the linker may affect NTD assembly . To determine its role in viral replication , we have made a number of deletion and substitution mutants in the linker region , in either the presence or absence of CTD , and tested their abilities to support capsid assembly and viral replication in human cells . Our results indicate that the linker could indeed impede NTD assembly in the absence of CTD , which could be partially relieved by partial linker deletion . In contrast , when CTD was present , the linker deletions or substitutions did not affect capsid assembly . Deletion of the entire linker or its C-terminal part resulted in a partial defect in pgRNA packaging and severely impaired viral DNA synthesis . In contrast , deletion of the N-terminal part of the linker , or substitutions of the linker sequence , had little to no effect on RNA packaging or first-strand DNA synthesis . However , the N-terminal linker deletion and two linker substitution mutants were defective in the production of mature double-stranded viral DNA . Secretion of empty virions was blocked by all the linker deletions and substitutions tested . In particular , a conservative linker substitution that allowed mature viral DNA synthesis and secretion of complete virions severely impaired the secretion of empty virions , thus increasing the ratio of complete to empty virions that were secreted . Together , these results demonstrate that the HBc linker region plays critical and complex roles at multiple stages of HBV replication .
Hepatitis B virus ( HBV ) , a major cause of viral hepatitis , liver cirrhosis , and hepatocellular carcinoma [1] , replicates a small ( ca . 3 . 2 kb ) , partially double-stranded ( DS ) , relaxed circular ( RC ) DNA via reverse transcription of an RNA intermediate , the pregenomic RNA ( pgRNA ) [2 , 3] . Virus assembly begins with the formation of an immature nucleocapsid ( NC ) incorporating the pgRNA and the viral reverse transcriptase ( RT ) , which then undergoes a process of maturation defined as the conversion of the pgRNA first to a single-stranded ( SS ) DNA and subsequently to the RC DNA , catalyzed by the RT protein [4] . The RC DNA-containing NC is defined as the mature NC , which can be enveloped by the viral envelope proteins and secreted extracellularly as complete virion . HBc is a small ( 183 or 185 amino acids depending on the strains , ca . 21 kd ) protein that forms the shell of the NC and also plays a critical role at multiple other stages of HBV replication [2 , 5 , 6] . It is composed of three regions , an N-terminal domain ( NTD ) , a C-terminal domain ( CTD ) , and a linker that connects the NTD and CTD . NTD encompasses amino acid residues 1–140 and forms the classical assembly domain , generally thought to be necessary and sufficient for capsid assembly [7–9] . CTD encompasses residues from 150 to the C-terminal end , is highly basic ( enriched in R , protamine-like ) , displays non-specific nucleic acid-binding activity [7 , 10] , and is functionally important in pgRNA packaging and reverse transcription but generally thought to be dispensable for capsid assembly [11–14] . Furthermore , CTD is known to undergo dynamic phosphorylation and dephosphorylation , which regulate HBc functions in pgRNA packaging and reverse transcription [15–22] . In between the NTD and CTD is a “linker” peptide with a conserved sequence , 141STLPETTVV149 ( Fig 1 ) [23] . The linker is routinely included together with NTD ( as in HBc149; Fig 1 ) for recombinant expression and capsid assembly in bacterial systems and in vitro assembly reactions using HBc proteins purified from bacteria under high protein and/or salt concentration conditions [5] . Under these assembly conditions , the linker clearly does not interfere with NTD assembly . Indeed , deletion of most of the linker ( from 143–149 ) in the context of the full-length HBc , resulting in the fusion of CTD directly to NTD , abolished capsid assembly when expressed in E . coli , suggesting a positive role for the linker in capsid assembly by the full-length HBc [24] . Furthermore , permutation of the last 7 residues of the linker in the context of HBc149 also prevented capsid assembly but replacement of these same seven residues by the seven N-terminal residues of HBc ( MDIDPYK ) maintained assembly [24] . These results thus further indicated that the specific sequence of the linker can modulate capsid assembly by the full-length HBc under those conditions . On the other hand , the linker can be removed entirely and NTD alone is able to assemble into capsids under those conditions , in the absence of both the CTD and the linker [8 , 24 , 25] . Interestingly , truncation of the linker , in the complete absence of the CTD , affected the ratio of the T = 3 ( with 90 HBc dimers ) or T = 4 ( with 120 dimers ) capsids assembled under these conditions [24 , 25] . Thus , whereas most capsids formed when the linker is present belong to the T = 4 class , most capsids formed when the linker is removed belong to the T = 3 class . Other than this apparent effect on the dimorphism of capsid assembly , the mechanism of which remains elusive , the linker is not known to have any other specific functions in HBV replication . As a para-retrovirus , HBV is selective in virion morphogenesis in that only mature NCs containing the DS , RC DNA , but not immature NCs containing either pgRNA or the SS DNA , are selected for envelopment and secretion as complete virions [26 , 27] . Situated between the genome and the envelope , the capsid plays an integral role in this selective virion formation process . Within the NTD and spatially located on the surface of the capsid shell , a so-called matrix binding domain ( MBD ) has been defined , through elegant genetic analysis , that is thought to interact with a short segment in the preS1 region of the viral large envelope protein ( L ) , the so-called matrix domain ( MD ) , for complete virion formation [28–30] . L is one of three HBV surface or envelope proteins ( HBs ) ( the other two being the middle or M and small or S surface protein ) , which are also secreted as the classical HBsAg particles ( the Australian antigen ) that contain no capsid or genome , in huge excess over complete virions ( by up to 100 , 000-fold ) [27] . Surprisingly , recent studies have revealed that HBV also secretes very high levels ( ca . 1011/ml ) of genome-free ( empty ) virions , which contain the envelope and capsid but no DNA or RNA and are found at ca . 100-fold excess over complete ( i . e . , RC DNA-containing ) virions in cell culture supernatant and in the blood of experimentally infected chimpanzees and naturally infected humans [27 , 31 , 32] . Neither the capsid nor the envelope requirements for empty virion formation are clear at present . Naked ( non-enveloped ) capsids are also released in cell cultures via an unknown mechanism that appears to be different from that for the secretion of virions [33] . However , the release of naked capsids seems to be a phenomenon in transformed cell lines , and has not been observed in vivo during HBV infection [31 , 32 , 34] . We have recently demonstrated that contrary to expectation , the HBc CTD is apparently needed for capsid assembly in living human cells and in the rabbit reticulocyte lysate ( RRL ) , where both the protein concentration and salt conditions mimic more closely the conditions in authentic human host cells than the previous assembly systems using bacterial expression and purified HBc proteins [35] . An HBc construct containing both the NTD and the linker ( i . e . , HBc149 ) but no CTD was unable to assemble under these ( near ) physiological conditions . On the other hand , other CTD-lacking HBc constructs that also lack part of the linker attached to the NTD ( truncated at position 147 , 145 , or 144 ) accumulated and assembled to varying but detectable levels [11 , 36–38] , indicating that the exact truncation point within the linker region affects the capacity of NTD to assemble in the absence of CTD . These results raise the possibility that the linker sequence can somehow interfere with the assembly by NTD in the absence of CTD under ( near ) physiological conditions in RRL and in human cells , and this inhibitory effect of the linker on the NTD assembly function is somehow overcome by CTD in the full-length HBc . Given the unexpected role of CTD , and potentially of the linker , in capsid assembly in RRL and in human cells , it is now important to further assess the role of the CTD , the linker and the interplay between the CTD and the linker , in capsid assembly under physiological conditions . Also , these results bring about the possibility that the linker may have potential roles in the other functions of HBc beyond capsid assembly , which has never been tested so far . Therefore , we have carried out a genetic analysis to test the role of the linker in capsid assembly , both in the presence and absence of CTD , under near physiological conditions in vitro and in cells . Furthermore , the effects of a panel of linker deletion and substitution mutants on pgRNA packaging , DNA synthesis , and virion secretion were assessed . Our results have revealed that the linker indeed can affect capsid assembly in a manner that is dependent on CTD , and furthermore , it plays a critical role in multiple stages of HBV replication beyond capsid assembly .
Since previous reports have found that HBc mutants with truncation of the linker region , in addition to CTD removal ( i . e . , C-terminal truncation beyond 149 ) , could be expressed and assemble at appreciable levels [11 , 36–38] whereas NTD plus an intact linker ( also without CTD ) ( i . e . , HBc149 ) failed to assemble and accumulate [35 , 38] , we reasoned that deletion of the linker may restore HBc expression and/or assembly , when the CTD was absent . Thus , we deleted the entire linker ( from 141 to 149 ) or only part of the linker ( from 144–149 ) , both in the absence of CTD , to make HBc140 and HBc143 ( Fig 1 ) , respectively , and determined their expression and assembly in human hepatoma cells ( HepG2 and Huh7 ) . A second plasmid expressing the HBV pgRNA and all viral proteins except HBc ( HBV-C- ) was co-transfected in a trans-complementation assay to assess the ability of the mutant HBc proteins to carry out the other functions of HBc including pgRNA packaging , DNA synthesis , and virion secretion . We selected the plasmid pSVHBV1 . 5 to derive the HBc-defective genomic construct , as our pilot experiments showed that this plasmid secreted significantly higher levels of HBsAg than another genomic construct pCMVHBV ( S1 Fig ) . Since the secreted HBsAg is known to be in great excess over virions during natural HBV infection [27 , 31] , the higher levels of HBsAg produced from pSVHBV1 . 5 helped to ensure that the complementation experiment mimicked better the natural infection in terms of HBsAg expression and to avoid the potential situation where the expression of the envelope proteins might become limiting for virion secretion . Even though the HBc sequence used here was from HBV genotype D , and the complementing ( HBV-C- ) construct was from HBV genotype A , they complemented each other efficiently in all aspects of viral replication assayed here , as shown below . In support of a negative effect of the linker on NTD expression/assembly as hypothesized above , the expression levels of HBc140 and HBc143 , as assessed by SDS-PAGE and western blot analysis , approached those of the WT HBc in both HepG2 and Huh7 cells ( Fig 2A and 2C , 3rd and bottom panels ) , much better than that of HBc149 , which retains the entire linker [35 , 38] . As shown in Figs 2 and S2 , the mAb T2221 , recognizing an epitope towards the end of the HBc NTD [39] , detected the WT and CTD- ( and linker- ) deleted HBc proteins very well , in comparison with two other mAbs targeted to the beginning of NTD , 10E11 [40] ( commercially available ) and the anti-WHc made against the very N-terminal sequences of the woodchuck hepatitis virus ( WHV ) core protein ( WHc ) , which are identical to those in HBc [32 , 41] ( see Materials and Methods ) . The levels of intracellular capsids ( Fig 2A and 2C , 2nd panels ) , and the naked capsids released into culture medium ( Fig 2B , top right ) , as assessed by native agarose gel electrophoresis and western blot analysis , were also higher than those of HBc149 although still lower than those of the WT HBc . For HepG2 cells , the naked capsids released into the culture supernatant by HBc140 and HBc143 ( and even HBc149 ) were relatively abundant ( though still less than the WT HBc ) ( Fig 2B , top right ) although the levels of intracellular capsids from these mutants were very low ( Fig 2A , 2nd panel ) . Thus , the release of naked capsids into the culture supernatant might be enhanced by the linker ( and CTD ) deletions in HBc140 and HBc143 . An enhanced release of capsids plus a partial defect in capsid assembly could explain the relative abundance of HBc140 and HBc143 proteins detected by SDS-PAGE western blot analysis ( Fig 2A , 3rd panel ) but very low levels of intracellular capsids ( Fig 2A , 2nd panel ) . For Huh7 cells , a similar phenomenon could have occurred but the released naked capsids from HBc140 ( and to a lesser degree , HBc143 ) could have been rapidly disrupted/degraded in the supernatant ( see also Fig 3 below ) . This could explain the relative abundance of these mutant proteins detected by the SDS-PAGE western blot analysis ( Fig 2C , 3rd panel ) but very low levels of intracellular and extracellular capsids ( esp . for HBc140 ) ( Fig 2C , 2nd panel; Fig 2D , top right ) . To assess the potential role of the linker in the context of the full-length HBc ( i . e . , with both NTD and CTD ) , we constructed HBc/Δ141–149 ( with the entire linker deleted ) and HBc/Δ145–149 ( deleting the C-terminal portion of the linker ) , which share the similar linker deletions as HBc140 and HBc143 but retain the CTD ( Fig 1 ) . Both of these linker deletion constructs were expressed and assembled into capsids like the WT HBc in both HepG2 and Huh7 cells ( Fig 3A and 3C , middle and bottom panels ) . These results were rather surprising in light of the previous report showing that deletion of the linker , thus fusing the CTD directly to NTD , abolished capsid assembly in the bacterial expression system , which was taken as evidence to indicate a need for a flexible linker between the NTD and CTD to prevent the CTD from interfering with NTD assembly [24] . In light of this surprising result , we constructed another partial linker deletion construct , in the context of the full-length HBc , by deleting HBc residues 141–144 ( i . e . , the N-terminal portion of the linker ) to make HBc/Δ141–144 ( Fig 1 ) . In addition , we made the same partial N-terminal deletion of the linker , in the absence of CTD , to construct HBc149/Δ141–144 ( Fig 1 ) . HBc/Δ141–144 was expressed and assembled just like the WT HBc in both HepG2 and Huh7 cells ( Fig 4A and 4C , lane 5 , middle and bottom panels ) . On the other hand , the same partial linker deletion , in the absence of the CTD , in HBc149/Δ141–144 did not rescue NTD expression or assembly ( Fig 4A and 4C , lane 6 , middle and bottom panels ) , unlike the deletion of the entire linker ( in HBc140 ) or its C-terminal portion ( in HBc143 ) described above . It was previously shown that the sequence of the linker between the NTD and CTD could affect capsid assembly in the bacterial expression system [24] . We thus tested two different linker substitution mutations that were shown to be either compatible or not with assembly ( Fig 1 ) . The substitution that disrupted assembly was a randomized WT linker sequence ( STETVPVLT , dubbed LR for “linker random” here ) , whereas the substitution that retained assembly was the replacement of the last seven residues of the linker by the first seven residues from the N-terminal end of HBc with the first two residues unchanged ( STMDIDPYK , dubbed LN for “linker N-terminal” here ) . Interestingly , we found both of these linker substitutions were similar to the WT HBc in expression and assembly in human hepatoma cells ( Fig 4A and 4C , lanes 2–3 , middle and bottom panels ) , in contrast to their severe defect in assembly in bacteria [24] , further attesting to the drastic effects of the expression host on the assembly behavior of the different HBc constructs . We also made a third linker substitution with a nine-residue segment ( TTLPETTII ) from a cellular protein ( dubbed LC for “linker cellular” here ) that is very similar to the WT HBc in sequence ( the middle six residues being the same as the WT linker and the other three residues representing conserved substitution: S141T , V148I , and V149I ) and in predicted secondary structure [24] . This substitution was also compatible with capsid assembly in hepatoma cells ( Fig 4A and 4C , lane 4 , middle and bottom panels ) . We next tested the potential effect of the linker deletions and substitutions on the HBc function in pgRNA packaging into NCs . Given the known critical role of CTD in mediating pgRNA packaging , it was no surprise that none of the CTD deletion mutants with or without linker deletions ( HBc140 , HBc143 , HBc149 , HBc149/Δ141–144 ) were able to support packaging of viral RNA ( Fig 2A and 2C , lanes 2–4 , top panels; Fig 4A and 4C , lane 6 , top panels ) . On the other hand , it was interesting that some of the linker mutations , in the presence of an intact CTD , also impaired pgRNA packaging . The complete linker deletion , HBc/Δ141–149 , showed a decrease in pgRNA packaging by ca . 5–10 fold after normalizing to the amount of capsids ( Fig 3A and 3C , lane 2 , top panels ) , whereas partial deletion of the C-terminal portion of the linker , HBc/Δ145–149 , decreased pgRNA packaging less severely , by ca . 3–4 fold ( Fig 3A and 3C , lane 3 , top panels ) . Partial deletion of the N-terminal portion of the linker , HBc/Δ141–144 had the weakest effect , decreasing pgRNA packaging by ca . 2 fold ( Fig 4A and 4C , lane 5 , top panels ) . In contrast to the linker deletions , none of the linker substitutions affected pgRNA packaging ( Fig 4A and 4C , lanes 2–4 , top panels ) , indicating that the specific sequence of the linker was not critical for this HBc function . As expected from the essential role of CTD in pgRNA packaging as well as in facilitating viral reverse transcription , none of the CTD deletion mutants ( HBc140 , HBc143 , HBc149 , HBc149/Δ141–144 ) showed any viral DNA in NCs ( Figs 1 , 2B and 2D , top left , lanes 2–4 ) . Intriguingly , even in the presence of the CTD , the complete linker deletion ( HBc/Δ141–149 ) and the C-terminal partial linker deletion ( HBc/Δ145–149 ) showed no viral DNA in NCs ( Fig 3B and 3D , lanes 5 , 6 , top panels ) , indicating a critical role of the linker , particularly its C-terminal portion ( 145–149 ) , in viral reverse transcription beyond its role in facilitating pgRNA packaging described above . On the other hand , the N-terminal partial linker deletion , HBc/Δ141–144 , contained some viral DNA in NCs , although at reduced levels compared to the WT HBc ( Figs 1 and Fig 4B , lane 5 , top panel ) . The three linker substitutions apparently contained viral DNA in their capsids at levels similar to the WT ( Fig 4B , lanes 2–4 , top panel; Fig 4D , lanes 3–5 , top panel ) . To assess the species of DNA synthesized in mutant capsids , we extracted viral DNA from the WT and mutant capsids and analyzed their DNA content by Southern blot analysis . Previous results from us and others indicated that certain capsid mutants allow viral DNA synthesis but are unable to protect their DNA content from exogenous nuclease digestion , which is routinely used to remove plasmid DNA during core DNA extraction [19 , 42 , 43] . To avoid this potential issue so as to obtain a more accurate assessment of viral DNA synthesized in the mutant capsids , we extracted capsid-associated DNA ( or core DNA ) without nuclease digestion but then degraded the contaminating plasmid DNA in the resulting core DNA preparation with DpnI , which digests plasmid DNA ( methylated in bacteria ) but not viral DNA synthesized in hepatoma cells [42] . All capsids that contained viral DNA ( the three linker substitutions and the partial N-terminal linker deletion , HBc/Δ141–144 ) based on the particle gel analysis ( Fig 4B and 4D ) had SS DNA ( i . e . , minus strand ) , although the SS DNA levels were reduced in HBc/Δ141–144 by ca . 2-fold compared to the WT HBc ( Fig 5 ) . As the SS DNA is reverse transcribed from pgRNA , this modest reduction of SS DNA in HBc/Δ141–144 was at least partly due to the moderately reduced levels of pgRNA packaging in this mutant described above . In contrast , HBc/Δ141–149 showed no DNA and HBc/Δ145–149 showed barely detectable levels SS DNA ( Fig 5 ) , consistent with the particle gel results ( Fig 3B and 3D ) . Again , this DNA synthesis defect could be partly the consequence of the defect in pgRNA packaging by these two mutants . These results thus indicated that the specific sequence of the linker was not critical for the first step of reverse transcription to generate the minus strand DNA , and a linker that was only five ( instead of the nine in WT ) residues long was sufficient for SS DNA synthesis . Intriguingly , the partial N-terminal linker deletion ( HBc/Δ141–144 ) , as well as two linker substitutions ( LR and LN ) , showed no RC DNA in their capsids in contrast to the WT HBc ( Fig 5 , lanes 5–7 ) . These three mutants did make immature DS DNA intermediates ( running as a smear between the SS DNA and RC DNA in Fig 5 ) , indicating they were able to initiate plus strand DNA synthesis and elongate the plus strand to a limited extent . However , only the conservative linker substitution ( LC ) was competent in RC DNA synthesis ( Fig 5 , lane 8 ) , thus implicating a critical role of the linker , in a sequence-specific manner , in the second step of reverse transcription ( extensive plus strand DNA synthesis to generate RC DNA ) . We next assessed the capacity of the linker mutants to be enveloped and secreted into the culture supernatant as virions . Viral particles ( including both virions and naked capsids ) released into the culture supernatant of transfected HepG2 or Huh7 cells were analyzed by native agarose gel electrophoresis , whereby naked ( non-enveloped ) capsids released into the culture supernatant were well separated from virions ( enveloped ) as the former migrated much faster than the latter on the gel ( Figs 2–4 , panels B and D ) . Complete virions were detected by Southern blot analysis of HBV DNA . Empty virions were detected by western blot analysis of the HBc protein in virions , assuming that the vast majority of HBc signal ( 99% or more ) from virions was from empty virions , as shown in previous studies [18 , 31 , 32] . As expected , HBV DNA in ( complete ) virions ( or naked capsids ) , readily detectable in WT virions , was not detected from HBc140 , HBc143 or HBc149 in HepG2 cells ( Fig 2B , top left ) ( true also for Huh7 cells; see below Fig 2D , top left ) , due to their lack of CTD , which is known to be essential for pgRNA packaging or DNA synthesis . On the other hand , the HBc protein signal detected in the WT virions ( i . e . , empty virions ) was also undetectable from these mutants when tested in HepG2 cells ( Fig 2B , top right ) . These results thus indicated that the linker , and/or CTD ( see below also ) , was important for secretion of empty virions . This suggestion was then confirmed by results obtained using Huh7 cells , when decreasing amounts of culture supernatant from the WT HBc transfection were analyzed along with that from the HBc140 and HBc143 transfection ( Fig 2D ) . When the amount of supernatant from the WT HBc transfection was decreased by 10-fold , the levels of naked capsids released into the medium were similar to those from the HBc143 transfection ( Fig 2D , top right , lanes 3 and 5 ) ; virion capsids were clearly detectable from the WT HBc even with this reduced loading whereas no virion capsids from either HBc143 or HBc140 were detected ( Fig 2D , top right , lanes 3–5 ) . As expected , the HBs signals were only detected with virions but not naked capsids ( Fig 2B and 2D , bottom ) . As HBsAg particles ( with no capsids or genome ) are not separated from virions ( either empty or complete ) on the agarose gels under these conditions [31 , 32] , the abundant HBsAg signals , in the absence of HBc signals at the top of the gel in the case of HBc140 , HBc143 and HBc149 represented just HBsAg particles ( no virions ) ( Fig 2B , lanes 2–4 , bottom; Fig 2D , lanes 4 , 5 , bottom ) , as verified by the detection of HBsAg at the same position on the gel in the complete absence of HBc expression ( Fig 2D , lane 6 , bottom ) . These results thus indicated that capsids formed by NTD , in the absence of CTD and the linker , could not be enveloped for secretion as empty virions . It was noticeable that the complete linker deletion ( HBc/Δ141–149 ) showed little to no naked capsids in the culture supernatant either ( Fig 3B and 3D , lanes 2 and 5 ) , suggesting that the complete linker deletion might also have blocked the release of naked capsids into the culture medium , or if released , was rapidly degraded in the supernatant . On the other hand , we detected a smeary HBc signal migrating just below the virions and much slower than naked capsids , detectable only from this mutant ( in Huh7 but not HepG2 cells ) , in a manner that was independent of the viral envelope proteins ( Fig 3B and 3D , lanes 2 and 5 , bottom ) . This result suggested that some naked mutant capsid might be disrupted once released extracellularly under certain conditions . The exact nature of the slowly-migrating HBc smear ( in a non-capsid form ) from this mutant , and its apparent cell line dependence , remained unclear . If the HBc/Δ141–149 capsid was indeed blocked from release from the cell , the excess mutant capsid might be degraded intracellularly such that its level in the cell did not exceed that of the WT HBc ( Fig 3A and 3C ) . The role of the linker in virion secretion , both complete and empty , was assessed in the context of HBc linker mutants which retain an intact CTD . Both the complete linker deletion ( HBc/Δ141–149 ) and the two partial linker deletions ( HBc/Δ141–144 and HBc/Δ145–149 ) , despite being competent for capsid assembly intracellularly , did not show any virion secretion ( Fig 3B and 3D , lanes 5 , 6; Fig 4B , lane 5; Fig 4D , lane 2 ) . As two of these three mutants ( HBc/Δ141–149 and HBc/Δ145–149 ) failed to synthesize any viral DNA and the third linker deletion mutant ( HBc/Δ141–144 ) failed to make RC DNA ( which is a prerequisite for complete virion secretion ) ( Figs 3–5 ) , the specific effect of these mutants on secretion of complete virions could not be ascertained from these experiments . However , these results clearly indicated that both parts of the linker were required for secretion of empty virions . The critical role of the linker in virion secretion was further confirmed with the linker substitution mutants . All three linker substitution mutants were defective in secreting empty virions ( Fig 4B , lanes 2–4 , bottom; Fig 4D , lanes 3–5 , bottom ) , although the conservative substitution ( LC ) showed a low level of empty virions ( ca . 10% of WT ) ( Fig 4B , lane 4 , bottom; Fig 4D , lane 5 , bottom ) . Again , since the LR and LN substitution mutants failed to make RC DNA ( Fig 5 ) , the specific effects of these mutations on DNA virion secretion could not be determined from these experiments . Interestingly , the conservative substitution ( LC ) allowed secretion of complete virions ( virion DNA ) , despite severely blocking the secretion of empty virions ( virion HBc ) ( Fig 4B , lane 4; Fig 4D , lane 5 ) . The results presented above indicated that the linker was required for virion secretion ( Figs 3B and 3D and 4B and 4D ) , but a role for CTD could not be excluded , since when the CTD alone was deleted and the linker was retained ( as in HBc149 ) , there was little to no accumulation of intracellular capsids ( Fig 2A and 2C , 2nd panel ) , precluding an assessment of its virion secretion capacity in the absence of the CTD . To overcome this limitation , we appended four positive R residues ( 4R ) to HBc149 , reasoning that the supply of the positive charges might rescue assembly of HBc149 , in the absence of CTD , by either interacting with non-specific RNA or with NTD of HBc [35 , 44] . Indeed , HBc149-4R , in contrast to HBc149 , accumulated substantial , though still lower than WT , levels of intracellular capsids that were released in the culture medium ( Fig 6B and 6D ) . As expected , the HBc149-4R mutant capsids failed to package pgRNA or synthesize viral DNA due to the lack of a complete CTD ( Fig 6A and 6C ) . Since the capsid levels of HBc149-4R were still lower than those of the WT HBc , we titrated the amount of WT HBc and HBc149-4R plasmids used for transfection , relative to the HBc-defective genomic construct , and measured levels of capsids and virions across the titration to facilitate a direct comparison of virion secretion efficiency of HBc149-4R relative to the WT HBc . Importantly , HBc149-4R , was secreted as virions ( empty ) as efficiently as the WT , when normalized to the capsid levels ( Fig 6B and 6D ) . Thus , the linker was able to support efficient secretion of ( empty ) virions in the absence of a complete CTD . Since the state of CTD phosphorylation is known to play a critical role in capsid assembly , pgRNA packaging , and reverse transcription , which were affected by the linker mutants studied here , we decided to test if the various linker mutants could affect the CTD phosphorylation state . Since HBc assembles into capsid particles rapidly in hepatoma cells , which can affect CTD phosphorylation state indirectly by influencing the accessibility of the CTD phosphorylation sites to host kinases and phosphatases , and CTD also undergoes dynamic phosphorylation and dephosphorylation associated with pgRNA packaging and reverse transcription , we decided to use the RRL in vitro translation system for HBc expression and assembly that we developed recently [35] . In this cell-free system , HBc is phosphorylated during translation by endogenous cellular kinases , at ( at least ) some of the same CTD sites as in vivo , which is independent of capsid assembly , pgRNA packaging or DNA synthesis [35] , and HBc assembly does not occur until triggered by exogenous phosphatase treatment . We therefore examined the CTD state of phosphorylation of the WT HBc and various linker mutants immediately after translation , before triggering capsid assembly , to determine HBc phosphorylation state in the absence of capsid assembly . Following resolution of HBc by SDS-PAGE , we used an NTD-specific mAb ( T2221 ) to measure the total HBc levels , irrespective of CTD state phosphorylation ( Fig 7 , top panel ) and two CTD-specific mAbs , B701 that is selective for the phosphorylated CTD with an epitope between 155–164 ( Fig 7 , middle panel ) , and 25–7 that is selective for the non-phosphorylated CTD with an epitope between 164–182 ( Fig 7 , bottom panel ) [18 , 35] , for western blot analysis . The specificity of the mAbs was verified by using the non-phosphorylated HBc protein purified from E . coli ( Fig 7 , lane 1 ) . The complete linker deletion mutant ( HBc/Δ141–149 ) , as well as the two partial deletion mutants ( HBc/Δ141–144 and HBc/Δ145–149 ) , showed strongly increased ( by ca . 5- to 7- fold ) B701 signal relative to the WT HBc after normalization of the total HBc signal ( as detected by mAb T2221 ) ( Fig 7 , lanes 4–6 ) , indicative of enhanced CTD phosphorylation at the B701 epitope . The linker substitution mutant , HBc-LN , also showed a similar effect on CTD phosphorylation to the linker deletion mutants , albeit to a lesser degree ( by ca . 3-fold ) ( Fig 7 , lane 8 ) . On the other hand , the 25–7 signal for the complete linker deletion ( HBc/Δ141–149 ) , the C-terminal partial linker deletion ( HBc/Δ145–149 ) , and the LN substitution mutants was modestly ( by ca . 2-fold ) increased relative to the WT HBc ( Fig 7 , lanes 4 , 5 , 8 ) , suggesting the 25–7 epitope was less phosphorylated in these mutants as compared to the WT HBc .
We have demonstrated here that mutations of the HBc linker affected multiple steps in HBV replication , including modulation of capsid assembly , pgRNA packaging , DNA synthesis , and virion secretion , implicating a critical role for the linker in multiple stages of HBV replication ( Fig 1 ) . The mechanisms of action for these linker functions remain to be elucidated . As the nine-residue long linker peptide is not known to have any enzymatic function or biochemical activity ( such as nucleic acid binding ) , we consider it plausible that the effects of the linker on the HBc functions in capsid assembly , pgRNA packaging , and reverse transcription , are exerted through its effects on the NTD or CTD ( Fig 8 ) . This is supported by our findings that the linker affected NTD assembly and CTD state of phosphorylation . On the other hand , the linker may function in a more direct manner ( independent of its effects on the NTD or CTD ) to facilitate virion secretion by interacting with the viral envelope proteins ( Fig 8 ) . As introduced earlier , in the absence of CTD , the linker is not required for capsid assembly in bacteria or under in vitro assembly conditions with high HBc and/or salt concentration [24 , 25] . However , deletion of the linker , thus fusing the CTD to NTD , or substitution of the linker sequence , interfered with NTD assembly in bacteria ( Fig 1 ) [24] . In sharp contrast , we have shown here that in human hepatoma cells , the linker interfered with NTD assembly if the CTD was absent , but in the presence of the CTD , linker deletions or substitutions did not interfere with capsid assembly . Consistent with the inhibitory effect of the linker on assembly by NTD reported here , a recent study also found that HBc149 failed to accumulate in a mouse hepatocyte cell line but HBc144 did ( similar to HBc143 here ) [38] . It thus appears that the NTD alone is sufficient , at least to a limited extent , for capsid assembly in human cells , but the presence of the linker , in the absence of the CTD , interferes with NTD assembly specifically in human cells but not in bacteria . Furthermore , we have shown here that in the absence of CTD , deletion of the linker sequence 141–144 ( HBc149/Δ141–144 ) was less effective in restoring capsid assembly , compared to deletion of the entire linker ( in HBc140 ) or 144–149 ( in HBc143 ) . This suggests that N- and C-terminal sequences of the linker are not equivalent in modulating capsid assembly and the C-terminal part of the linker ( 144–149 ) may have a more detrimental effect on NTD assembly than the N-terminal part of the linker ( 141–144 ) when the CTD is absent . How the linker may influence capsid assembly , in a host cell- and CTD-dependent manner , is one of the intriguing questions brought up by our studies here that warrants further studies . The linker may interfere with NTD assembly in human cells , in the absence of CTD , by affecting the conformation of NTD , or by interacting with a host factor ( s ) to inhibit assembly ( Fig 8 ) . When expressed in bacteria , the high protein concentration achieved may somehow overcome the inhibitory effects of the linker on NTD assembly , or host cell-specific factors may alleviate the linker effect . As HBc149-4R , in contrast to HBc149 , assembled efficiently in human cells , a role for electrostatic interactions between the highly basic CTD and a negatively charged ligand ( e . g . , RNA , or acidic residues in the HBc NTD ) can be implicated in alleviating the inhibitory effect of the linker on NTD assembly in human cells by the CTD . Moreover , capsid stability , instead of or in addition to assembly , could be affected by the linker , as suggested by the apparent disruption of the HBc/Δ141–149 ( with the complete linker deletion , fusing the CTD directly to the NTD ) capsid once it was released extracellularly . The linker , and its specific sequences , are important for capsid assembly in bacteria when both the NTD and CTD are present ( i . e . , in the context of the full-length HBc ) [24] , but not in human cells as we have shown here . Other than the differences in HBc subunit concentration and salt/pH conditions , phosphorylation of the HBc CTD , which occurs in human cells but not in bacteria and is furthermore modulated by the linker as we have shown here , is known to modulate capsid assembly [35] . This host cell-dependent and linker-modulated CTD phosphorylation ( Fig 8 ) may be part of the reason why deleting the linker or substituting its sequences interferes with capsid assembly in bacteria but not in human cells . In addition , CTD is known to interact with host factors in mammalian cells , such as I2PP2A and B23 [45] , and SRPK [46] , which may also contribute to the host cell-dependent effects of the linker mutations . Indeed , we have shown previously that the binding of I2PP2A and B23 to the CTD is modulated by the linker in the case of the duck hepatitis B virus core protein [45] , which is thought to be much longer than the HBc linker and located between position 186–230 [47] . It remains possible that deletion of the CTD impaired the production and/or stability of the mutant protein in human cells ( but not in bacteria ) , accounting for the very low expression level of HBc149 in hepatoma cells . However , we believe that the lower expression level of this mutant was mostly due to its defect in efficient assembly in mammalian cells ( and consequently , more rapid degradation ) . First , we have shown recently that this same mutant is expressed at levels equal to or higher than the WT HBc in a mammalian cell extract , the rabbit reticulocyte lysate in vitro translation system; yet , it still fails to assemble , unlike the WT HBc in the same system that assembles efficiently [35] . Second , HBc149 expression and assembly in human cells can both be rescued by co-expression of the WT HBc [35] . Deletion of the entire linker severely impaired pgRNA packaging , and partial deletion of sequences from 145–149 had a more deleterious effect than that of 141–144 , suggesting that the linker sequences from 145–149 had a more important role than 141–144 in pgRNA packaging in the presence of the CTD , similar to the non-equivalent role of the two parts of the linker on capsid assembly in the absence of the CTD . On the other hand , none of the linker residues individually was absolutely required for pgRNA packaging as they could be substituted without affecting pgRNA packaging . One potential mechanism for the linker to modulate pgRNA packaging may be via its influence on CTD phosphorylation ( Fig 8 ) , which we could demonstrate here . As we proposed recently [35] , hyper- or hypo-phosphorylation of HBc CTD can both impair specific pgRNA packaging , by decreasing overall RNA ( including the specific pgRNA ) binding affinity or failing to block non-specific RNA binding , respectively . Details of the effects of the linker on CTD phosphorylation , in a phosphorylation site- and maturation stage-specific manner will require comprehensive studies in the future . How the linker may affect CTD phosphorylation state also remains to be elucidated . One possibility is that the linker modulates CTD conformation , which in turn affects the accessibility of the CTD phosphorylation sites to host kinases and/or phosphatases . Alternatively , the linker may affect the recruitment of these CTD-modifying host enzymes , either directly by serving as binding sites for these factors , or through an indirect means ( Fig 8 ) . Additional effects of the linker , beyond affecting CTD phosphorylation , including its influence on NTD assembly , may also play a role in modulating pgRNA packaging . Deletion of the entire linker , or partial deletion of the linker sequences from 145–149 abolished viral reverse transcription , whereas deletion of 141–144 had only a modest effect . This result again suggests that the linker sequences from 145–149 had a more important role than 141–144 in reverse transcription , as in pgRNA packaging . However , since the linker is nine-residues long , it was impossible to construct a deletion mutant removing precisely half of the linker ( i . e . , 4 . 5 residues ) . So , it remains possible that HBc/Δ141–144 was more effective than HBc/Δ145–149 in making immature DNA ( and packaging pgRNA ) simply because it is one residue longer than HBc/Δ145–149 . On the other hand , as with pgRNA packaging , it is clear that none of the linker residues individually was required for SS DNA synthesis as they could be substituted with little effect on SS DNA levels . Furthermore , our results here have shown that a linker that is five ( instead of nine as in the case of the WT ) -residues long is still capable of supporting pgRNA packaging and SS DNA synthesis , at least partially . This is consistent with the observation that the linker is disordered in recombinant capsids assembled in bacteria from HBc149 ( i . e . , missing the entire CTD ) [9] and the notion that the linker may form a flexible , mobile array on the inner surface [24] of the maturing NC to facilitate this stage of viral DNA synthesis . On the other hand , RC DNA synthesis was clearly impaired by two of the three linker substitutions as well as the partial deletion from 141–144 , which had little effect on SS DNA synthesis . The remaining linker substitution that was competent for RC DNA synthesis is a very conservative one with almost identical sequence and predicted structure to the WT linker . Thus , for RC DNA production , the linker did not merely function as a flexible spacer but played a specific role . How the linker might facilitate RC DNA synthesis in a sequence-dependent manner is not yet known . As the CTD state of phosphorylation is known to be important for RC DNA synthesis [17 , 19 , 20] , and the linker sequences could affect CTD phosphorylation , the specific linker sequences could modulate RC DNA synthesis through their effect on CTD phosphorylation ( Fig 8 ) , as proposed above for their effects on pgRNA packaging . In addition , the linker may be involved in the conformational changes of the maturing NC that accompany , and may be required for , RC DNA synthesis [48] . In addition , the linker itself may undergo conformational changes , in a sequence-dependent manner , during the viral replication cycle that are modulated by the NTD or CTD . We note also that although no exogenous nuclease digestion was used during viral DNA extraction , our results here can’t exclude the possibility that in those mutants where no RC DNA was detectable , some RC DNA might actually have been made but degraded as soon as it was made in the cell . Perhaps the most intriguing result we have obtained here regarding the linker functions is its essential role in the secretion of empty virions . Those linker deletion and substitution mutants that impaired RC DNA synthesis were also defective in the secretion of complete ( RC DNA-containing ) virions . The conservative linker substitution ( LC ) that remained competent for RC DNA synthesis was also capable of secreting complete virions . This is expected as RC DNA synthesis is required for complete virion formation . A specific effect of these mutants on the secretion of complete virions , however , could not be ascertained from these results ( Fig 8 ) . On the other hand , it is clear from our results here that the specific linker sequence is critical for empty virion secretion . All linker mutations , either complete or partial deletions or substitutions , impaired secretion of empty virions . Even the conservative linker substitution ( LC ) , which was fully competent in all other aspects of the viral life cycle tested here including the secretion of complete virions , showed a severe defect ( though not as severe as the other linker substitution or deletion mutants ) in the secretion of empty virions . The LC linker substitution increased the ratio of complete to empty virions , by ca . 10-fold from ca . 1% to 10% [32] , by decreasing the secretion of empty virions without affecting that of complete virions . Thus , we have not only uncovered an essential role of the linker in the secretion of empty virions , but also revealed that the requirements for the secretion of complete vs . empty virions can be separated genetically . The efficient secretion of HBc149-4R capsids in empty virions further suggests that the linker is not only necessary but may be sufficient to support empty virion formation , although it remains formally possible that both the linker and several R residues from the CTD are required for the secretion of empty virions . As the beginning of the HBc CTD has the sequence 150RRRGR154… , it may be argued that HBc149-4R actually retains a severely truncated “CTD , ” i . e . , the first three ( or five without G153 ) residues of the CTD . Thus , further studies will be needed to clarify the contribution of the CTD , if any , in the secretion of empty HBV virions . It was recently reported that HBc147 capsids ( missing the entire CTD and two C-terminal residues of the linker ) failed to be secreted in empty virions [36] . Whereas the authors hypothesized that their result implicated a critical role for the CTD in empty virion secretion , our findings here suggest an alternative interpretation of the same result , i . e . , the last two residues of the linker ( 148 and 149 ) plays a critical role in supporting empty virions secretion . The CTD state of phosphorylation , which appeared to be affected by the linker mutations , is unlikely to account for the effect of the linker mutations on virion secretion as CTD phosphorylation state , per se , does not play a critical role in virion formation [18] . These results , combined , suggest the intriguing possibility that linker residues interact , directly , with the envelope proteins during virion formation ( Fig 8 ) . Whereas the linker is generally thought to be located inside the capsid [24 , 49] and thus unlikely to interact with the envelope proteins on the capsid surface , it may nevertheless be exposed , at least transiently , on the capsid surface . Some evidence in support of an exterior localization of the linker has indeed been presented; for example , epitopes attached to the linker are accessible to antibody binding in empty HBV capsids [50 , 51] . Additionally or alternatively , the linker sequence may be involved , perhaps via interactions with host factors , in trafficking of the capsids to the site of budding for their envelopment . Future studies , including high-resolution structural analysis , will be required to further elucidate the mechanisms of action of the linker functions uncovered here and to determine any structural changes in the capsid , at the various stages of viral replication , that may be modulated by the linker . For example , whereas the linker is known to affect the dichotomy of T = 4 or T = 3 capsids in bacteria , whether this is also the case in human cells remains to be determined . Furthermore , it remains unknown if both size classes of capsids are competent in pgRNA packaging or reverse transcription . On the other hand , both T = 3 and T = 4 capsids are found in extracellular virions [52] , indicating that they are both competent for virion formation and so the capsid size is unlikely to be a determinant of virion formation . As uncovered here , the critical roles that the HBc linker plays at multiple stages of HBV replication , which have been thought to involve only the HBc NTD and/or CTD , emphasize the close and dynamic interactions among all three regions of HBc that together carry out the multiple essential functions of HBc in viral replication . As conformational changes are likely to be associated with NC maturation and envelopment [6 , 30 , 32 , 48 , 53] , further structural studies of the HBc linker mutants that affect various stages of viral replication should provide important insights into the effects of the linker on the conformations of the HBc NTD , CTD , and the NC as a whole and how the conformational effects translate to functional effects on NC maturation and envelopment . The multiple roles of the HBc linker in HBV replication that we uncovered here provide an explanation for the high degree of sequence conservation in this region of HBc . In addition , as the same DNA sequence coding for the HBc linker also codes for the very N-terminal part ( residues 5–14 ) of the viral RT protein , the need to preserve polymerase sequence and functions possibly also has contributed to the conservation of the DNA sequence in this region of the HBV genome . However , as we highlighted recently [54] , the N-terminal sequences of the polymerase are actually not highly conserved and mutagenesis work so far indicates that this region of the polymerase is not essential for any known functions of the polymerase although it does contribute , to some degree , to the polymerase functions in pgRNA packaging and protein-primed initiation of reverse transcription . Thus , it is likely that the preservation of the HBc linker sequence and functions has played a more important role in the DNA sequence conservation of this region of the viral genome . On the other hand , some variations of the linker sequence have been observed [23] . In light of our findings here , future studies to examine the functional effects of the naturally occurring linker variations are warranted . HBc has emerged recently as the primary target , after the HBV RT protein , for developing effective antiviral strategies to clear HBV infection . Almost all agents in development so far are targeted to the NTD [5 , 55 , 56] . Our results here indicate that sequences outside the NTD , including the CTD as well as the linker , could represent important targets for HBc-directed antiviral development . In fact , a small molecule compound has been reported recently that inhibits HBc assembly and functions in a manner that is dependent on sequences in the CTD [57] . Similarly , it may be possible to identify compounds that target the conserved HBc linker region to inhibit multiple steps of HBV replication . Our discovery here of the multiple critical functions of the HBc linker in HBV replication also has broad implications . Thus , linkers connecting protein domains are common occurrences including those in other viral capsid proteins [58] . For the human immunodeficiency virus type 1 ( HIV-1 ) , the linker in its capsid protein has been shown to regulate capsid stability and reverse transcription [59] .
pCI-HBc and -HBc149 expressing the full-length and CTD-deleted HBc have been described before [35] . pCI-HBc149-4R is identical to pCI-HBc149 , except four R residues are added after HBc position 149 ( Fig 1 ) . pCI-HBc140 , -HBc143 , -HBc149/Δ141–144 , -HBc/Δ141–149 , -HBc/Δ141–144 , -HBc/Δ145–149 were derived from pCI-HBc through PCR-mediated mutagenesis for the expression of CTD and/or linker deletion mutants ( Fig 1 ) . Three linker substitution mutants of HBc were also constructed via PCR mutagenesis . The C-terminal seven residues of the linker were randomized in sequence in the mutant LR , or replaced with the seven N-terminal residues of HBc in LN as described before [24] . In the third substitution mutant , LC , the entire linker was replaced with a nine-residue segment from a cellular protein ( cellobiose dehydrogenase ) similar in sequence and predicted structure to the linker [24] ( Fig 1 ) . pSV-HBV1 . 5/C- expresses a HBc-defective HBV genome [30] , which is capable of supporting viral replication upon complementation with HBc . pCMV-HBV expresses the HBV pgRNA from the heterologous cytomegalovirus ( CMV ) immediate early promoter and the HBV surface mRNAs from the endogenous HBV promoter , leading the production of all viral RNAs and proteins required for replication and virion secretion [60 , 61] . A mouse monoclonal antibody ( mAb ) , clone T2221 , against the HBc NTD [39] was purchased from Tokyo Future Style ( Cat no . 2AHC24 ) . The mAb 10E11 against HBc NTD ( residues 2–10 ) [40] was purchased from Abcam ( Cat no . ab8639 ) . The mAb , anti-WHc , specific for the WHc NTD ( likely the first 8 residues ) , is cross-reactive with HBc due to the identity of the very N-terminal HBc and WHc sequences , as reported before [32 , 41] . The HBc CTD-specific mAbs , 25–7 and B701 , have been described recently [18 , 35] . The rabbit polyclonal antibody against HBc were purchased from Dako . The rabbit anti-HBs polyclonal antibody was purchased from Virostat [18] . The anti-preS2 mAb ( Arigo Biolaboratories ) detect the preS2 region that is shared by both the L and M ( but absent from the S ) HBV envelope proteins . HBc expression constructs and/or HBV genomic constructs were transfected into the human hepatoma cell line HepG2 or Huh7 cells ( kindly provided by Christoph Seeger , Fox Chase Cancer Center ) as previously described [42 , 62 , 63] . Briefly , HepG2 cells in 60-mm dishes were transfected with 4 μg of plasmid using FuGENE6 ( Roche ) . Huh7 cells seeded in 60-mm dishes were transfected with 10 μg of plasmid using CalPhos Mammalian Transfection Kit ( Clontech ) . Cells and culture supernatant were harvested on day 7 post-transfection . Cells were lysed with NP40 and HBc proteins in the cytoplasmic lysate were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) , transferred to polyvinylidene difluoride ( PVDF ) membrane , and detected by the indicated antibodies as described previously [19 , 32] . Core DNA from NCs was isolated from the cytoplasmic lysate without nuclease digestion and analyzed by Southern blot analysis as described previously [42] . A genome-length , 32P-labeled HBV DNA probe was used to detect the viral DNA replicative intermediates by Southern blot analysis . Native agarose gel electrophoresis of intact NCs from the cytoplasmic lysate , or extracellular viral particles obtained after DNase I digestion of polyethylene glycol ( PEG ) precipitated cell culture supernatant [32] were carried out by using previously reported procedures [18 , 19 , 31 , 32] . Briefly , following transfer to nitrocellulose membrane , viral DNA associated with the particles was detected using a full-length HBV DNA probe , or pgRNA packaged into NCs by using a minus-sense riboprobe . The same membrane was subsequently probed with the indicated HBc or surface specific antibody to detect HBc or surface proteins . The signals from the 32P-labeled RNA probe were quantified using a phosphor imaging system ( GE Healthcare ) . The chemiluminescent signals representing the capsid protein were quantified using the ChemiDoc MP system and BioLab software , as previously described [64] . Densitometry using appropriately exposed films was also used in some cases to quantify the RNA and protein signals . All quantifications were repeated with at least three separate transfection experiments . A TnT-coupled rabbit reticulocyte lysate ( RRL ) in vitro translation system ( Promega ) was used to express the WT HBc or linker deletion/substitution mutants , as described previously [35] . In vitro-translated proteins were analyzed by SDS-PAGE and western blot using the indicated anti-HBc antibodies . | The hepatitis B virus ( HBV ) is a major human pathogen that infects hundreds of millions of people worldwide and represents a major cause of viral hepatitis , liver cirrhosis , and liver cancer . The HBV capsid protein ( HBc ) plays multiple roles in the viral life cycle and has emerged recently as a major target for developing antiviral therapies against HBV infection . HBc is divided into three separate regions , an N-terminal domain ( NTD ) responsible for capsid assembly , a C-terminal domain ( CTD ) that plays critical roles in the specific packaging of the viral pregenomic RNA ( pgRNA ) into replication-competent nucleocapsids and the subsequent reverse transcription of the pgRNA into the viral genomic DNA , and a linker region between the NTD and CTD . In contrast to the prevailing assumption that the linker merely serves to connect the NTD and CTD , we have discovered here that it plays a critical role in almost every stage of HBV replication . The linker likely exerted its pleiotropic effects via affecting the NTD and CTD as well as via direct interactions with other viral factors independent of the NTD or CTD . Our results thus not only deepen understanding of HBc structure and functions but also implicate the linker as a potential novel target for antiviral development against HBV infection . |
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Integrative and Conjugative Elements ( ICEs ) of the SXT/R391 family disseminate multidrug resistance among pathogenic Gammaproteobacteria such as Vibrio cholerae . SXT/R391 ICEs are mobile genetic elements that reside in the chromosome of their host and eventually self-transfer to other bacteria by conjugation . Conjugative transfer of SXT/R391 ICEs involves a transient extrachromosomal circular plasmid-like form that is thought to be the substrate for single-stranded DNA translocation to the recipient cell through the mating pore . This plasmid-like form is thought to be non-replicative and is consequently expected to be highly unstable . We report here that the ICE R391 of Providencia rettgeri is impervious to loss upon cell division . We have investigated the genetic determinants contributing to R391 stability . First , we found that a hipAB-like toxin/antitoxin system improves R391 stability as its deletion resulted in a tenfold increase of R391 loss . Because hipAB is not a conserved feature of SXT/R391 ICEs , we sought for alternative and conserved stabilization mechanisms . We found that conjugation itself does not stabilize R391 as deletion of traG , which abolishes conjugative transfer , did not influence the frequency of loss . However , deletion of either the relaxase-encoding gene traI or the origin of transfer ( oriT ) led to a dramatic increase of R391 loss correlated with a copy number decrease of its plasmid-like form . This observation suggests that replication initiated at oriT by TraI is essential not only for conjugative transfer but also for stabilization of SXT/R391 ICEs . Finally , we uncovered srpMRC , a conserved locus coding for two proteins distantly related to the type II ( actin-type ATPase ) parMRC partitioning system of plasmid R1 . R391 and plasmid stabilization assays demonstrate that srpMRC is active and contributes to reducing R391 loss . While partitioning systems usually stabilizes low-copy plasmids , srpMRC is the first to be reported that stabilizes a family of ICEs .
Integrative and conjugative elements ( ICEs ) are highly prevalent and widely distributed in bacterial genomes [1–3] . Their ability to self-transfer by conjugation between genetically unrelated bacteria contributes to the emergence of multidrug resistant pathogens in diverse taxonomic groups [4–6] . ICEs usually reside within and replicate with the host cell’s chromosome to be vertically inherited . ICEs eventually excise from the chromosome and form circular covalently closed molecules that serve as the substrate for the conjugative machinery that translocates the ICE DNA to recipient cells [6 , 7] . With a few exceptions reported only for ICEs of Actinobacteria , this conjugative machinery usually consists of a relaxase , a type IV coupling protein and a type IV secretion system [1–3 , 8] . The SXT/R391 family of ICEs encompasses one of the largest and most diverse set of ICEs studied , including elements that have been found over the past 40 years in clinical and environmental isolates of diverse species of Gammaproteobacteria [9 , 10] . ICEs of the SXT/R391 family largely contribute to the spread of antibiotic resistance genes in the seventh-pandemic lineage of Vibrio cholerae , the etiologic agent of cholera , which remains a major cause of mortality and morbidity on a global scale [11] . The ICE SXT is a prototypical member of the SXT/R391 family originally isolated from a 1992 Indian multidrug resistant clinical isolate of V . cholerae O139 [12] . SXT and several variants detected in V . cholerae O139 , O1 and non-O1 non-O139 isolates confer resistance to sulfamethoxazole , trimethoprim , streptomycin and chloramphenicol [9] . The second prototypical member of this family is R391 , which was originally detected in a 1967 South African isolate of Providencia rettgeri [13] . R391 confers resistance to kanamycin and mercury . Members of the SXT/R391 family all share a common integration site , the 5’ end of prfC , and a highly conserved core of genes and sequences that mediate their regulation , integration/excision and conjugative transfer [10] . Expression of the conjugative function of SXT/R391 ICEs is tightly regulated by SetR , which represses the expression of the master activator genes setC and setD . Their products activate transcription of int , xis and conjugation-associated operons [14] . Repression of setC and setD is alleviated by induction of the bacterial response to DNA damage , which promotes autoproteolysis of SetR [15] . SXT and R391 can exist co-integrated in a tandem fashion in prfC in the same host cell [16 , 17] . Such tandem arrays are suitable substrates for frequent homologous recombination events yielding hybrid ICEs that can be easily segregated in exconjugant cells [16 , 18] . Interestingly , R391 was reported to be found as a circular extrachromosomal replicative form in a recA recipient strain bearing an integrated copy of R997 , another SXT/R391 ICE found in Proteus mirabilis [19] . A similar behavior was also reported for R997 entering a recA recipient bearing an integrated R391 . However , no extrachromosomal form of R391 or R997 could be recovered from recA+ hosts . These observations suggest that , at least in specific circumstances , SXT/R391 ICEs are capable of autonomous replication . Autonomous replication was previously suspected for several ICEs and recently well characterized for ICEBs1 , an ICE of the Gram-positive bacterium Bacillus subtilis [20–25] . Plasmid-like replication was also shown to be essential for the stability of ICEBs1 [24] . However , whether autonomous replication is relevant to the biology and stability of SXT/R391 ICEs remains to be established . Breaking with old paradigms about ICEs , we report here that replication is a key step of the lifecycle of SXT/R391 ICEs by using R391 as a model . By monitoring the frequency of excision , the ICE copy number as well as the frequency of loss of a set of mutants , we show that the putative relaxase TraI and the origin of transfer ( oriT ) are essential for R391 replication and its stability in the progeny of host cells . Furthermore , we demonstrate that , besides diverse non-conserved toxin-antitoxin systems , all SXT/R391 ICEs also encode a conserved plasmid-like type II partitioning system that enhances their stability . Together , these results unravel an unforeseen similarity between the biology of ICEs and conjugative plasmids .
To have a better understanding of SXT/R391 ICEs biology , we evaluated five key factors of R391 lifecycle in Escherichia coli: ( i ) the dynamics of excision/integration , which will be reported as the frequency of excision in the rest of the manuscript , ( ii ) the frequency of transfer , ( iii ) the average copy number per cell in the whole cell population , ( iv ) the average number of extrachromosomal circular copies per cell , and ( v ) the ICE stability in the cell population . The frequency of R391 excision was assessed by quantifying by real-time quantitative PCR ( qPCR ) the relative amount of free integration site ( attB ) resulting from R391 excision per chromosome as measured by the amount of chromosomal prfC target ( Fig 1A ) . R391 excised at a frequency of 1 . 90×10-3 ± 0 . 38×10-3 ( Fig 2A ) , which is about tenfold lower than the excision frequency of SXT ( 1 . 76×10-2 ± 0 . 65×10-2 , P = 0 . 0140 , two-tailed Student t-test ) in similar conditions . Mating assays showed that R391 transfers at about 5 . 01×10-4 ± 0 . 31×10-4 exconjugant/donor ( Fig 2B ) , which is about 20 fold higher than SXT ( 2 . 71×10-5 ± 0 . 55×10-5 exconjugant/donor ) . Hence there is no correlation between the frequency of excision of these elements measured in the whole cell population and their respective frequency of transfer . This observation indicates that excision is not a factor that limits the rate of dissemination of these two ICEs . Using the same approach , we then measured the mean copy number per cell of R391 in the whole cell population as the ratio between the amount of R391-borne int target and the amount of chromosomal prfC target ( Fig 1A ) . This ratio was found to be 0 . 96 ± 0 . 04 as expect for a single copy of R391 integrated in the chromosome . We also measured the mean copy number of the extrachromosomal circular form of R391 per cell by establishing the ratio between the amount of attP recombination site resulting from R391 excision and the amount of unoccupied chromosomal attB sites . In theory , each event of R391 excision is expected to yield one unoccupied attB site on the chromosome and one attP site on the circular excised R391 ( attP/attB = 1 ) . We observed that this ratio reached 21 ± 2 ( Fig 2C ) , suggesting that R391 is capable of replicating in a small subset of the cell population in which it is excised from the chromosome . This observation is consistent with results previously reported for SXT , for which 4 attP sites on average exist for each unoccupied attB site [16 , 26] . New measurements carried out in this study to confirm these reports revealed 3 . 6 ± 0 . 2 attP sites per unoccupied attB site for SXT . We then assessed the stability of R391 by monitoring the number of cells lacking R391K in the cell population after 16 hours of growth ( about 20 generations ) in LB medium with or without selective pressure . R391K is tagged with the galK reporter gene under the control of the Plac promoter to enable high-level galactokinase activity in a lacI mutant strain such as E . coli VB38 [18] , a ΔgalK derivative of E . coli CAG18439 ( lacI42::Tn10 ) ( Fig 1B ) . The frequency of loss was determined as the percentage of white colonies ( galK- , devoid of R391K ) on MacConkey indicator agar supplemented with 1% galactose ( Fig 1C ) . R391K was found to be inherently stable because it was lost in only 0 . 0037% of the cell population in the absence of selective pressure ( Fig 2D , WT ) , whereas no detectable loss was observed when cells were grown with kanamycin in liquid culture ( detection limit of 0 . 0001% ) . Stability of many mobile genetic elements relies on a post-segregational killing mechanism , which induces a strong selective disadvantage or even death to cells that have lost them [27–31] . While previous studies have shown that two functional toxin-antitoxin ( TA ) systems , mosAT and s045-s044 , enhance the stability of SXT [32 , 33] , neither of these TA systems was found in R391 . Nevertheless , in silico analysis of the R391 sequence revealed that the two overlapping open reading frames ( ORFs ) orf02 and orf03 , which belong to the variable region I located upstream of xis , encode a putative hipAB-like TA system ( Fig 1B ) [34 , 35] . Indeed , orf03 ( hipA ) is predicted to encode a HipA-like toxin , while orf02 ( hipB ) likely codes for the HipB cognate antitoxin , which carries a DNA-binding HTH-XRE ( HTH_19 ) domain . To measure the impact of this putative hipAB-like TA system on R391 stability , we constructed a ΔhipA mutant of R391K . This mutation did not impair the transfer of R391K and had no effect on the excision or extrachromosomal copy number of the element ( Fig 2A , 2B and 2C , compare WT and ΔhipA ) . However , ICE stability was affected as R391K loss increased by 12 fold for the ΔhipA mutant compared to wild-type ( Fig 2D ) . No loss of R391K ΔhipA was detectable in the presence of kanamycin . These results revealed the functionality of the hipAB TA system and its involvement in the stability of R391 , as previously demonstrated for mosAT of SXT [32] . However , like the mosAT and s045-s044 loci of SXT , hipAB of R391 is not a conserved feature of SXT/R391 ICEs; therefore hipAB is likely not an inherent and ancestral mechanism used by SXT/R391 ICEs to enhance their stability in their respective hosts . Cell death or growth reduction associated with hipAB after R391 loss was likely to hinder our investigations on R391 stability . To circumvent this issue , the ΔhipA mutant provided us with a useful tool for additional investigations aimed at unraveling other stabilization mechanisms conserved among ICEs of the SXT/R391 family . Conjugation has been shown to be a powerful stabilization mechanism for conjugative plasmids that can reenter in cells having lost them by infectiously spreading in the cell population [28 , 36] . While Wozniak and Waldor [32] have shown that conjugation does not promote SXT loss , their assay does not allow to conclude whether conjugation is an efficient mechanism of stabilization of SXT/R391 ICEs . To answer this question , we looked at the frequency of loss of a ΔtraG mutant of R391K . traG codes for an inner-membrane component of the donor cell mating pair formation apparatus that is essential for SXT transfer [37] . As previously reported for SXT , deletion of traG abolished R391K transfer ( Fig 2B ) . However , the inability to transfer did not reduce the stability of R391K , as the frequencies of loss of wild-type R391K and its ΔtraG mutant were nearly identical ( Fig 2D ) . No detectable loss of R391K ΔtraG was observed when kanamycin was added during liquid culture . The frequencies of excision of the ΔtraG and ΔhipA ΔtraG mutants were reduced by ~2 . 3 fold , while their extrachromosomal copy numbers were more than twice as high as wild-type R391K and its ΔhipA mutant , reaching up to ~54 copies per cells ( Fig 2A and 2C ) . This observation suggests that , once excised from the chromosome , the circular form of R391K accumulates in the cell possibly because a defective mating apparatus caused by the ΔtraG mutation cannot mediate its transfer to a recipient cell . The plasmid-like replication of ICEBs1 was shown to be essential for its stability [24] . Rolling-circle replication of ICEBs1 requires the relaxase NicK and oriT , a cis-acting locus initiating the translocation of DNA through the mating pore . In a subset of a cell population , R391 seems to be in a multicopy plasmid-like state that may be important for preserving ICE stability when it is excised from the chromosome in actively dividing cells . To test this hypothesis , oriT and traI deletion mutants were constructed in R391K ( Fig 1B ) . traI codes for the putative relaxase of SXT/R391 ICEs that recognize the origin of transfer ( oriT ) [38] . As expected , the ΔtraI mutation abolished R391K conjugative transfer ( Fig 3B ) . We also observed a ~5-fold reduction of the extrachromosomal copy number of R391K when either traI or oriT were missing compared to wild-type ( Fig 3C ) . The ΔtraI mutation also led to a ~20-fold increase of the frequency of excision and to an 11-fold increase of R391K loss ( Fig 3A , 3B and 3D ) . Combined ΔtraI and ΔhipA mutations led to a 34-fold increase of R391K loss , thereby confirming that traI is important for R391 stability ( Fig 3D ) . Expression of traI in trans from the arabinose-inducible PBAD promoter in pTraI restored and even enhanced the transfer and the stability of both ΔtraI and ΔtraI ΔhipA mutants compared to wild-type ( Fig 3B and 3D ) . Although the copy number of the plasmid-like form of R391K ΔtraI doubled upon complementation with pTraI , it failed to reach the wild-type level ( Fig 3C ) . Interestingly , ΔtraI mutants were so unstable that selective pressure exerted by kanamycin in liquid culture did not , or only slightly , improved R391K stability ( Fig 3D ) . The high instability affecting ΔtraI mutants also led to the formation of sectored colonies on agar plates likely resulting from loss of R391K during colony development ( Fig 1C ) . Since conjugative transfer of SXT/R391 ICEs is known to be stimulated by DNA-damaging agents , we tested the effect of mitomycin C on the stability of the ΔhipA , ΔtraG and ΔtraI mutants of R391K . We observed that the drug did not induce high-frequency loss of the ΔhipA , ΔhipA ΔtraG or ΔtraI mutants of R391K ( Fig 3E ) . In striking contrast , deletion of both ΔhipA and ΔtraI led to a hypersensitivity of R391K to mitomycin C treatment as the ICE was lost in more than 90% of the cell population ( Fig 3E ) . We suspect that ΔtraI mutants are highly unstable; yet in the presence of hipAB , cells that have lost R391K ΔtraI likely have no progeny or strong growth reduction due to the persistence of the HipA toxin , thereby masking this high instability in conditions that strongly induce R391 excision . In silico analysis of R391 sequence using CD-search on the Conserved Domain Database v3 . 11 [39 , 40] and protein fold recognition server Phyre2 [41] revealed that orf07 , the first gene of an operon containing int , codes for a predicted actin-like NTPase structurally related to the ParM plasmid segregation proteins of plasmids R1 and pSK41 ( Fig 1B ) . ParM proteins are a key component of type II ParMRC partitioning systems that mediate plasmid DNA segregation during cell division via a pushing mechanism [42 , 43] . ParR adaptor protein connects parC , a cis-acting centromere-like locus , to the ParM filament . ParR proteins have low conservation and their genes are found downstream of the parM gene . The open reading frame orf06 , located downstream of orf07 , is predicted to code for a small basic protein ( pI 9 . 3 ) with no recognizable domain ( Fig 1B ) . Hence , orf06 may encode a ParR DNA-binding protein that binds the centromere-like region in partitioning systems . Based on these observations and results described below , orf06 and orf07 were renamed srpR and srpM for SXT/R391 ICEs partitioning proteins R and M , respectively ( Fig 1B ) . By functional analogy with the parMRC partitioning systems carried by the plasmid R1 of E . coli and the staphylococcal plasmid pSK41 [42] , the DNA fragment located upstream of srpM likely corresponds to the centromere-like region bound by SrpR and was annotated srpC ( Fig 1B ) . The srpMRC locus is strictly conserved in all SXT/R391 ICEs , suggesting that it may somehow play an important role in their biology . Deletion of srpR , srpM or both had no measurable effect on the frequency of excision or the extrachromosomal copy number of R391K ΔhipA , and had a slight inhibitory effect ( about 4- to 14-fold reduction ) on the frequency of transfer ( Fig 4A , 4B and 4C ) . The lack of impact on the excision frequency confirmed that neither deletion has a polar effect on the expression of the int gene located immediately downstream of srpR ( Figs 1B and 4A ) . Since the deletion of srpM had very little effect on R391 transfer , we used this mutation to further study the phenotype associated with a non-functional srpMRC locus . Cumulative mutations of traG , hipA and srpM did not affect the frequencies of excision or loss of R391K compared to the ΔtraG ΔhipA mutant ( Fig 4A and 4D ) . However , the extrachromosomal copy number of R391K dropped nearly 4 fold when comparing the same mutants ( Fig 4C ) . Furthermore , a ΔtraI ΔhipA ΔsrpM triple mutant exhibited a visible but statistically non-significant 35% reduction of stability compared to the ΔtraI ΔhipA mutant ( Fig 4D ) . However , deletion of srpM led to a 3-fold increase of the stability of R391K ΔtraI ΔhipA after 40 generations ( Fig 4D , dark grey bars ) . Finally , overexpression of traI from pTraI did not completely prevent the loss of R391 ΔtraI ΔhipA ΔsrpM , which was lost about 4 times more frequently than the ΔtraI ΔhipA mutant ( Fig 4D ) . These data revealed that srpM is important for R391 stability when the number of copy of the ICE is low and thus could be a functional active partition system . To test further whether srpMRC is a functional DNA partitioning system , plasmid stabilization assays were carried out using pBeloBAC11Δsop , an unstable derivative of the single-copy plasmid pBeloBAC11 that lacks its native sopABC partitioning system . In SXT/R391 ICEs , expression of srpM , srpR and int was shown to be driven from Ps003 , a promoter exclusively dependent upon activation by the transcriptional activator SetCD ( Fig 1B ) [14] . To bypass the need for SetCD , the srpMRC loci of R391 and SXT were cloned into pBeloBAC11Δsop downstream of the IPTG-inducible Plac promoter ( Fig 4E ) . Expression of srpMRC loci of SXT ( pSrpSXT ) or R391 ( pSrpR391 ) upon IPTG induction led to a respective ~1 . 8 and ~2 . 1-fold increase of pBeloBAC11Δsop stability , thereby confirming that srpMRC is a functional plasmid stabilization system ( Fig 4E and S1 Fig ) . The absence of srpR , srpM or srpC prevented plasmid stabilization , which was then comparable to the empty vector ( Fig 4E and S1 Fig ) . SrpR lacks homologies with known ParR proteins that have been shown to bind parC-centromere-like sequences upstream of parMR genes in plasmids such as R1 . To test whether SrpR is capable of binding the srpC locus , we carried out electrophoretic mobility shift essays ( EMSA ) experiments using purified C-terminally 6xHis-tagged SrpR protein ( predicted molecular weight of 10 . 4 kDa ) . EMSA assays revealed a specific binding of SrpR to the 615-bp fragment ig ( srpM-mobI ) which corresponds to the intergenic region between srpM and mobI and likely contains the srpC region ( Fig 5A and 5BI ) . Addition of high concentrations of sonicated salmon sperm DNA ( non-specific competitor ) did not destabilize SrpR binding to this probe . Further investigation confirmed that SrpR specifically binds the 251-bp srpC region as SrpR binding to srpC was resilient to the addition of the non-specific competitor DNA ( Fig 5A and 5BII ) . The presence of multiple specific shifts suggests that SrpR binds multiple sites or binds as different multimeric forms ( Fig 5BII ) . While SrpR was able to bind to the 298-bp fragment containing oriT , addition of the non-specific competitor DNA destabilized SrpR binding ( Fig 5BIII ) . Non-specific SrpR binding to oriT indicates that SrpR exhibits a significant non-specific affinity for DNA molecules . In silico analysis of the centromere-like srpC region of R391 and SXT using the Multiple Em for Motif Elicitation tool ( MEME ) [44] revealed four conserved 13-bp direct and inverted repeats that might be recognized by SrpR ( Fig 5C and 5D ) . EMSA results showed that a 40-bp fragment containing either R1-R3 or R2-R4 was bound by SrpR ( Fig 5E ) . Addition of competitor DNA strongly decreased SrpR binding but did not completely alleviate the interaction . Binding of SrpR to the sequences R2 or R4 used as probes was abolished by the addition of the competitor suggesting that half-sites do not produce stable complexes with SrpR ( Fig 5E ) . Since ParR-like DNA binding proteins have been shown to form multimeric complexes [45] , SrpR multimerization assays were carried out using glutaralehyde cross-linking . These assays suggests that , even without any DNA substrate , SrpR seems to be able to assemble as dimeric and tetrameric complexes in solution as shown by the apparition of large bands migrating at compatible molecular weights in a SDS page gel ( Fig 5F ) . Copious amounts of SrpR were also trapped in the well , thereby suggesting that SrpR could be able to assemble in complexes of higher order . To assess the relationship of SrpMRC with other type II partitioning systems , we carried out a phylogenetic analysis based on the ParM actin-like homologs found by BlastP . Since the ParR adaptor proteins and parC sequence usually retain low conservation , they were not included in the analysis . Our analyses revealed that as expected , SrpM of R391 clusters with close orthologs encoded by all SXT/R391 ( Fig 6A , green branch ) . SrpM is also closely related to ParM orthologs encoded by a putative type II partitioning system carried by conjugative plasmids of the IncA/C ( Fig 6A , red branch ) and pAQU groups [46–48] . SrpM and all of these orthologs cluster with more distantly related plasmids such as Rts1 ( IncT ) [49] and the catabolic plasmids pCAR1/pDK1 ( IncP7 ) [50 , 51] . Interestingly , with the exception of Rts1 , which seems to lack a gene coding for a ParR protein , the genetic contexts of the orthologous parMRC loci in all these mobile elements are strikingly similar , located between traG- and mobI-like genes , thereby supporting their common ancestry and divergent evolutionary pathways ( Fig 6B ) . This large group of related par loci is distantly related those carried by diverse plasmids broadly distributed among bacterial species of Delta- and Gammaproteobacteria , including the most distantly related parMRC systems carried by the conjugative plasmids R1 and R27 ( Fig 6A ) [52–54] .
In our modern world , antibiotics are widespread in most environments , subjecting microorganisms to a strong and constant selective pressure [57 , 58] . ICEs circulating among environmental and pathogenic bacteria can take advantage of this selective pressure by collecting and accumulating antibiotic resistance-conferring genes . The selective advantage conferred by antibiotic resistance enhances the stability of ICEs in their hosts as well as their odds to eventually spread into and invade a new bacterial population . However , ICEs likely predate the antibiotic era and have evolved other means to prevent their loss . Indeed , several ICEs are stably maintained despite the lack of genes coding for any obvious selective advantage for their host [59 , 60] . One strategy of stabilization consists in a tight control of the excision of the ICE from the chromosome . However , too tight a regulation could prevent its efficient dissemination . For ICEs of the SXT/R391 family , excision and transfer were shown to be coupled with the activation of the host’s SOS response [15] . In bacteria such as E . coli , spontaneous induction of the SOS response in the absence of DNA damaging agents has been shown to occur in 0 . 3 to 3% of the cell population [61] , thereby inherently leading to unscheduled excision that is detrimental to ICE stability . Indeed , cell division occurring after ICE excision can generate ICE-free cell lineages , which likely have a competitive advantage in the absence of selective pressure . Between attL and xis , R391 bears genes coding for a HipAB-like TA system that enhances the stability to the ICE as inactivation of hipA increased R391K loss by 12-fold ( Fig 2D ) . hipAB is also found at the same position in ICEVchMex1 , another member of the SXT/R391 family , which does not seem to confer any heavy metal or antibiotic resistance to its original host [10 , 60] . The toxin/antitoxin system mosAT has been shown to strongly improve the stability of SXT [32] . Interestingly , mosAT expression was found to be correlated with activation of SXT excision and conjugative transfer [32] . However , coupling of mosAT expression with SXT excision was later shown to be circumstantial to the activation by SetCD of the expression of the upstream traVA genes [14] . Furthermore , since neither mosAT nor hipAB are conserved in all SXT/R391 ICEs [10] , element-specific TA systems located in variable regions should only be considered as auxiliary determinants of stabilization for this family of ICEs . The same holds true for the tad-ata-type TA system s044-s045 carried by SXT in the variable region located between traIDJ and traLEKB [10 , 33] . In addition to diverse TA systems encoded by variable DNA , we have shown here that SXT/R391 ICEs rely on specific and conserved strategies to enhance their stability within their host genome . Besides the most obvious one , which is their integration within the host chromosome , our data support the notion that SXT/R391 ICEs are not only capable of replication , but they can also actively segregate the resulting plasmid-like forms . Conjugation has been shown to be a possible stabilization mechanism for IncP-1 conjugative plasmids in cell populations , allowing the recolonization of plasmid-free cells [36 , 62] . However , our results show that conjugation is not a key factor for the stability of SXT/R391 ICEs as a traG mutant that is unable to transfer was no less stable than wild-type R391K ( Fig 2D ) . Interestingly , we found that deletion of traG , which prevents translocation of the ICE DNA to the recipient cell , had unexpected side effects . In such mutants , the frequency of excision decreased while the extrachromosomal copy number increased ( Fig 2A and 2C ) . A plausible explanation for this phenotype is that plasmid-like molecules of R391K can somehow accumulate due to the blocked mating pore . This accumulation of R391K circles would then tend to displace the site-specific recombination reaction from excision toward reintegration , hence the lower excision frequency ( Figs 2A and 7 ) . Consistent with this hypothesis , deletion of traI produced the exact opposite effect . We observed that while deletion of traI drastically reduced the number of R391K circles , the frequency of excision was very much increased ( Fig 3A and 3C ) . Impaired replication of R391K seems to displace the site-specific recombination reaction equilibrium toward excision instead of integration ( Fig 7 ) . We observed that the frequency of excision does not correlate with the frequency of transfer when comparing SXT and R391 . R391 excises at a frequency that is ~10 fold lower than SXT whereas it transfers at a frequency that is ~20 fold higher . We previously reported for SXT that neither its excision from the chromosome of donor cells nor its integration in the chromosome of recipient cells was a step limiting the rate of transfer [14 , 26] . This suggests that assembly of the mating apparatus , initiation of transfer or DNA translocation across the cell membranes through the mating pore was the limiting factor . In fact , our data revealed that availability of TraI is a key regulatory element since ovexpression of traI in cells bearing R391K ΔtraI increased the frequency of transfer by 2 logs over wild-type R391K . This observation is supported by RNA-seq data that revealed the relatively low level of expression of traI compared to other tra genes in SXT , R391 and ICEVflInd1 , another member of the SXT/R391 family [14] . Therefore , initiation of transfer and/or replication , both depending on TraI and oriT , seem to determine the rate of transfer of SXT/R391 ICEs . Deletion of traI or oriT drastically reduced the copy number of R391 circles ( Fig 3C ) , which is consistent with a form of replication initiated at oriT by the relaxase TraI . The process of conjugation usually relies on an intercellular rolling-circle replication of conjugative elements , making their intracellular replication also virtually possible [8 , 63] . Although ICEs were initially defined as non-replicative elements [6] , several recent reports strongly support that single-stranded DNA transferring ICEs can replicate as extrachromosomal plasmid-like molecules , in both Gram-positive and Gram-negative bacteria [20–25] . This replication is initiated at oriT by the relaxase together with other ICE- and host-encoded auxiliary factors [22 , 24] . Notably , the transient replication associated with the conjugative transfer of ICEBs1 of B . subtilis , while not required for transfer , plays an important role in the stability [24] . It relies on oriT used as an origin of replication ( oriV ) and on the conjugative relaxase NicK used as the replication initiator protein . Therefore , the rolling-circle replication module being an intrinsic part of the conjugation module , many ICEs , if not all , might be able to transiently replicate as plasmid-like molecules . Our work revealed yet another intriguing feature of ICEs of the SXT/R391 family besides replication , which seems to blur the frontier between ICEs and plasmids even more . All SXT/R391 ICEs carry srpMRC , a locus coding for a functional active partition system . Contrary to low-copy plasmids such as F , which must actively segregate in the daughter cells following cell division , SXT/R391 ICEs usually remain quiescent , integrated into the chromosome of their host , and are passively passed on from one generation to another . Active partition of these ICEs would only be required in their transient excised state , even more so if their copy number per cell is low , such as in the traI mutant ( Fig 4D ) . In agreement with this observation , srpMRC is part of the same operon coding for the integrase that catalyzes both the integration and excision of SXT/R391 ICEs , all directly under control of the SetCD activator [14] . Therefore , srpMRC is expressed only prior to excision , replication and transfer of the ICE . We observed that a ΔhipA ΔtraG ΔsrpM R391K mutant has an extrachromosomal copy number similar to the wild-type . The apparent suppression of the effect of the ΔtraG mutation on the extrachromosomal copy number by the loss of srpM suggests a link between conjugation and partition that remains to be elucidated . Active partition of ICEs could be an overlooked feature that is in fact rather common among ICEs . The ICE PAPI-1 of Pseudomonas aeruginosa encodes the putative active partition system Soj . Deletion of soj leads to high-frequency loss of PAPI-1 [64] . Although the exact mechanism of action of Soj is not well understood , its expression was shown to be stimulated when PAPI-1 excises . ICEs of the pKLC102-ICEclc group , including PAPI-1 and ICEHin1056 , were shown to be able to replicate and code for putative partitioning systems [21 , 23 , 65–67] . Moreover , the core region of Tn4371-like ICEs and the ICE pNOB8 from Sulfolobus codes for ParA and ParB proteins , whose homologs are known to play a role in plasmid partition [68–72] . Finally , ICEA of Mycoplasma agalactiae encodes a ParA homolog that could be part of a partitioning system [73] . All these putative partitioning systems could also be involved in incompatibility with other ICEs and/or plasmids , as well as in transcriptional regulation of ICE- and/or host-borne loci [74–76] . Classification of mobile genetic element is extremely laborious mostly because of their modular structure . Our increasingly precise comprehension of their biology unravels some unexpected features that make them even harder to label [77] . On the one hand , ICEs exhibit phage-like behaviors , such as integration by site-specific recombination and , for some ICEs , regulation controlled by CI- or ImmR-like regulators [15 , 37 , 59 , 78 , 79] . On the other hand , ICEs also share several characteristics with plasmids , such as a single-strand DNA intermediate during transfer , their conjugative apparatus and entry exclusion systems ( traG/eex ) [80 , 81] . For instance , the conjugation modules and master activators SetCD and AcaCD of SXT/R391 ICEs and conjugative plasmids of the IncA/C group share a common ancestry [10 , 14 , 82] . As such SXT/R391 ICEs and IncA/C plasmids offer a dramatic example of divergent evolution from a common ancestor into two different lifestyles . Although SXT/R391 ICEs are capable of transient replication using the relaxase TraI and oriT , this lifestyle does not seem to be sustainable over multiple generations [14] . IncA/C plasmids lack the int and xis genes required for integration and excision , and instead carry a dedicated RepA/C replicon , allowing autonomous , stable and efficient replication in the cell . IncA/C plasmids code for a putative ParMRC-like partitioning system closely related to SrpMRC ( vcrx152/vcrx151 in pVCR94 ) ( Fig 6A and 6B ) . Interestingly , expression of parMRC-like locus of SXT/R391 ICEs and IncA/C plasmids is directly under the control of similar yet distantly related class II transcriptional activator complexes: SetCD for SXT/R391 ICEs and AcaCD for IncA/C plasmids [14 , 82 , 83] ( Fig 6B ) . Given the pleitropic role of these activators , this mode of regulation directly pairs the expression of DNA segregation functions to expression of conjugative transfer functions . However , although IncA/C plasmids retain a type II parMRC-like partitioning system ( actin-type ATPase ) , they also carry a type I parABC-like partitioning system ( Walker-type ATPase ) ( vcrx031/vcrx032 in pVCR94 ) , which does not seem to be regulated by AcaCD [48 , 82 , 84 , 85] . The exact function and eventual redundancy of each par locus remains to be investigated for IncA/C plasmids . The IncHI1 conjugative plasmid R27 also contains two independent partitioning loci , a type I partitioning system , and a type II partitioning system [54] . The type I partitioning system was shown to be the major stability determinant of R27 whereas type II is the minor stability determinant . Finally , our results put an end to a long standing question: Do SXT/R391 ICEs behave like plasmids and replicate ? R391 and related elements such as R705 , R748 , R997 , and pMERPH were initially reported as R factors belonging to the same J incompatibility group ( IncJ ) [9 , 86] . R391 and R997 were even isolated as circular molecules and physically mapped by restriction analysis [19] . Subsequent identification of SXT as an integrative element , and reports of the site-specific integration of R391 and R997 into the same chromosomal site as SXT highlighted seeming incongruities between otherwise extremely similar mobile genetic elements as revealed by sequence comparison [10 , 12 , 87–90] . In fact , our results indicate that replication , coupled with partition , is a normal yet transitory step of the lifecycle of SXT/R391 ICEs . The transitory nature of this replication does not allow stable maintenance and inheritance as a plasmid-like form . Therefore , integration into the chromosome remains the main mechanism ensuring stable vertical transmission of SXT/R391 ICEs over multiple generations . In the end , despite using similar strategies for their maintenance in the cell population and transfer between cell populations , ICEs and conjugative plasmids remain distinct entities regarding their respective maintenance by integration or replication .
The bacterial strains and plasmids used in this study are described in Table 1 . The strains were routinely grown in lysogeny broth ( LB-Miller , EMD ) at 37°C in an orbital shaker/incubator and were preserved at -80°C in LB broth containing 15% ( vol/vol ) glycerol . Antibiotics were used at the following concentrations: ampicillin ( Ap ) , 100 μg/ml; chloramphenicol ( Cm ) , 20 μg/ml; kanamycin ( Kn ) , 50 μg/ml; mitomycin C ( MC ) , 50 ng/ml; nalidixic acid ( Nx ) , 40 μg/ml; rifampicin ( Rf ) , 50 μg/ml; spectinomycin ( Sp ) , 50 μg/ml; sulfamethoxazole ( Su ) , 160 μg/ml; tetracycline ( Tc ) , 12 μg/ml; trimethoprim ( Tm ) , 32 μg/ml . When required , bacterial cultures were supplemented with 0 . 02 mM of isopropyl β-D-1-thiogalactopyranoside ( IPTG ) or 0 . 02% L-arabinose . Conjugation assays were performed by mixing equal volumes of each donor and recipient strains that were grown overnight at 37°C . The cells were harvested by centrifugation for 3 min at 1200g , washed in 1 volume of LB broth and resuspended in 1/20 volume of LB broth . Mating mixtures were then deposited on LB agar plates and incubated at 37°C for 6 hours . The cells were recovered from the plates in 1 ml of LB broth and serially diluted before plating . Donors , recipients and exconjugants were selected on LB agar plates containing appropriate antibiotics . Plasmid DNA was prepared using the EZ-10 Spin Column Plasmid DNA Minipreps Kit ( Biobasic ) according to manufacturer’s instructions . All the enzymes used in this study were purchased from New England BioLabs . PCR assays were performed with the primers described in S1 Table . The PCR conditions were as follows: ( i ) 3 min at 94°C; ( ii ) 30 cycles of 30 sec at 94°C , 30 sec at the appropriate annealing temperature , and 1 minute/kb at 68°C; and ( iii ) 5 min at 68°C . When necessary , PCR products were purified using an EZ-10 Spin Column PCR Products Purification Kit ( Biobasic ) according to manufacturer’s instructions . E . coli was transformed by electroporation as described by Dower et al . [94] in a BioRad GenePulser Xcell apparatus set at 25 μF , 200 V and 1 . 8 kV using 1-mm gap electroporation cuvettes . Sequencing reactions were performed by the Plateforme de Séquençage et de Génotypage du Centre de Recherche du CHUL ( Québec , QC , Canada ) . Plasmids and oligonucleotides used in this study are listed in Table 1 and S1 Table . pTraI and pTraG were constructed by cloning traI of SXT and traG of R391 into the TA cloning expression vector pBAD-TOPO ( Invitrogen ) according to the manufacturer’s instructions . traI was amplified by PCR with its native Shine-Dalgarno sequence using primers pBad-traI_Fw and pBad-traI_Rev and genomic DNA of E . coli HW220 as the template . traG was amplified using the primer pair traGEcoRI . for / traGEcoRI . for and genomic DNA of E . coli GG13 as the template . pVB15 was constructed by amplifying the origin of replication of pUC19 ( oriVpMB1 ) using the primer pair pUC_oriF/pUC_oriR and subsequent cloning into the 5 838-bp fragment of NheI/NotI-digested pAH56 to replace oriVR6K-attPλ and generate the high-copy number expression vector pVB15 . ps002-his was then obtained by cloning s002 ( srpR ) from SXT amplified with the primer pair s002F/s002-hisR into the 4 319-bp fragment of NdeI/BamHI-digested pVB15 . Plasmids used for plasmid stabilization assays were derived from pBeloBAC11Δsop , a pBeloBAC11 vector derivative from which the partitioning system sopABC was deleted by NdeI digestion and re-ligation . The srpMRC locus of SXT ( srpMRCSXT ) and R391 ( srpMRCR391 ) were amplified by PCR using genomic DNA of strains containing either SXT or R391 as the templates and primers pairs SXTpartHindIIIstop . for/SXTR391partHindIII . rev and R391partHindIIIstop . for/SXTR391partHindIII . rev , respectively . Amplicons were digested by HindIII and cloned into HindIII-digested pBeloBAC11Δsop to generate pSrpSXT and pSrpR391 . Subsequent deletions of segments of srpMRCR391 were obtained by high fidelity PCR amplification of the pSrpR391 vector using primer pairs pBeloDelSO02 . for/pBeloDelSO02 . rev , pBeloDelSO03 . for/pBeloDelSO03 . rev or pBeloDelparC . for/pBeloDelparC . rev , digestion by NheI and ligation using the T4 DNA ligase . The resulting plasmids were verified by restriction profiling and DNA sequencing . Deletion mutants of R391::galK ( R391K ) [18] were constructed using the one-step chromosomal gene inactivation [92] and P1vir transduction [95] techniques . Deletion of hipA , srpR , srpM , srpRM , traI and traG were constructed using primer pairs R391DhipAnoFRT . for/R391DhipAnoFRT . rev , 2SXTR391DSO02 . for/2SXTR391DSO02 . rev , R391DSO03 . for/2SXTR391DSO03 . rev , R391DSO03 . for/2SXTR391DSO02 . rev , R391DtraInoFRT . for/R391DtraInoFRT . rev , R391DtraGnoFRT . for/R391DtraGnoFRT . rev , respectively . Gene resistance cassettes were amplified using the pVI36 , pKD3 and pKD4 vectors . The λRed recombination system was expressed using pSIM5 or pSIM6 as described by Datta et al . [91] . If possible , the antibiotic resistance cassette was removed from the resulting construction by Flp-catalyzed excision using the pCP20 vector [96] . All deletions were verified by PCR and antibiotic resistance profiling . The stability of R391::galK and derivative mutants was monitored based on the methodology described by Wozniak and Waldor [32] . Cells were grown for 16 hours in 4 ml of LB medium supplemented or not with kanamycin . Serial dilutions were plated on MacConkey agar plates supplemented with 1% D-galactose . Loss of R391 resulted in the formation of white clones ( Fig 1C ) . For each experiment , at least 16 white clones were purified and tested on agar plate for their susceptibility to kanamycin . These clones were also tested by PCR amplification of an internal fragment of R391 using primer pair R391HipBM1 . for/R391HipB . rev . The stability of pBeloBAC11Δsop and derivatives containing the srpMRC locus of SXT or R391 was tested for 16 hours in M9 or LB medium using the approach described by Sanchez et al . [97] . Expression of the srp locus from Plac was induced by addition of 0 . 02 mM IPTG . Relative stability was calculated as the ratio of chloramphenicol resistant colonies in the population in the induced compared to the non-induced conditions . For both ICE and plasmid stability assays , each experiment was carried out at least in biological triplicate . The frequency of excision as well as total copy number in the population and copy number of the excised circular form of the ICE were assessed by real-time quantitative PCR as described elsewhere [20 , 26] . Genomic DNA was obtained from cell cultures of E . coli CAG18439 bearing SXT , R391K or its mutants grown for 16 h in LB medium . prfC , attB , attP and int were quantified using primer pairs prfC . qec . F1/prfC . qec . R1 , attB . qec . F2/attB . qec . R2 , attP . qec . F2/attP . qec . R2 and int . qec . F1/int . qec . R1 , respectively ( S1 Table ) . For frequency of excision and copy number determination , E . coli VI61 , which contains one chromosomal copy of attB , attP and prfC , was used to simulate 100% of excision and normalize the results . qPCR experiments were performed in triplicate on the RNomics platform of the Laboratoire de Geénomique Fonctionnelle de l’Universiteé de Sherbrooke ( http://lgfus . ca ) ( Sherbrooke , QC , Canada ) . Macroscopic observations were done using a SZX7 zoom stereomicroscope with a DF PLAPO1X-4 objective coupled to a SC30 digital camera via a U-TV1X-2 & U-CMAD3 adaptor ( Olympus ) . To express and purify SrpR tagged with a 6×His C-terminal epitope ( SrpR6×His ) , cultures of E . coli BL21 bearing ps002-his were grown overnight , diluted 1:500 in fresh 2×YTA broth and incubated at 37°C with agitation . At mid-exponential phase ( OD600 of 0 . 6 ) , protein expression was induced with 0 . 1 mM IPTG and cultures were incubated for 3 hours . Cells were then harvested by centrifugation at 1500×g for 10 min at 4°C and stored at -20°C . The cell pellet was weighted and re-suspended in Native Purification Buffer ( NPB ) ( 50 mM NaH2PO4 pH 8 . 0 , 2 . 5 M NaCl ) containing 0 . 1% Triton X-100 , 1 mM phenylmethanesulfonylfluoride ( PMSF ) , and protease inhibitors at 1 ml / 20g of cell pellet ( Protease Inhibitor Cocktail , Sigma ) . Purification of SrpR6×His was done by Ni-NTA affinity chromatography following the manufacturer’s instructions ( Qiagen ) . Cells were lysed by sonication , cell debris was pelleted by centrifugation , and the supernatant was incubated for 1 h at 4°C with 750 μl of Ni-NTA Agarose resin ( QIAGEN ) with agitation . The Ni-NTA Agarose resin was then transferred into a column and washed 4 times with 1 . 25 ml of native wash buffer ( NPB with 20 mM imidazole , pH 8 . 0 ) . SrpR6×His was eluted with native elution buffer ( NPB with 250 mM imidazole , pH 8 . 0 ) and stored at -20°C . Protein concentration was estimated using a Bradford protein assay ( BioRad ) and purity was determined by SDS-PAGE analysis . The linear double-stranded DNA probes srpC ( 251 bp ) , oriT ( 298 bp ) and ig ( srpM-mobI ) ( 615 bp ) used in the EMSA experiments were amplified by PCR using primer pairs RRintF/RRintR , oriT2F/oriT2R and MELR1/oriT1R , respectively , and E . coli HW220 as the template ( Table 1 and S1 Table ) . Probes were purified using an EZ-10 Spin Column PCR Products Purification Kit ( Bio Basic ) according to the manufacturer's instructions and their concentration was determined using a NanoDrop ND-1000 . Probes R3-R1 , R4-R2 , R2 and R4 were obtained by mixing equimolar concentrations ( 50 μM ) of primers , srpCSXTR3R1F and srpCSXTR3R1R , srpCSXTR4R2F and srpCSXTR4R2R , srpCSXTR2F and srpCSXTR2R , or srpCSXTR4F and srpCSXTR4R , respectively . The primer mixtures were heated at 95°C for 3 min , then annealed by slow cool down overnight . EMSA assays were carried out using the Electrophoretic Mobility-Shift Assay Kit with SYBR Green & SYPRO Ruby EMSA stains ( Life Technologies ) according to the manufacturer's instructions . Briefly , a total of 40 ng of DNA probe was used in each reaction . Quantities of SrpR6×His and of the non-specific competitor DNA ( sonicated salmon sperm DNA ) varied from 10 to 100 ng , and 10 to 200 pg , respectively . The non-specific competitor DNA was mixed with the probe before adding SrpR6×His to maximize competition . All binding reactions were done in a total volume of 10 μl for 15 min at room temperature followed by 10 min incubation on ice . Samples were then loaded on a pre-run ( 25 min at 100 V ) non-denaturing 4% acrylamide gel containing 1× TBE buffer and migration was carried out at 4°C during electrophoresis . SYBR Green staining was done according to the manufacturer's instructions and gel pictures were scanned using a Typhoon FLA 9500 ( GE Healthcare Life Sciences ) with a LPB filter for SYBR Green I at a 100 μm resolution . Dimerization assays were carried out using 2 μg of purified SrpR6×His . Samples containing srpC were carried out using 1 μg of DNA probe and were incubated prior to the dimerization assay in the same conditions as for the EMSA assays . Samples were incubated with or without 0 . 6% glutaraldehyde for 30 min at room temperature and 3% of β-mercaptoethanol was added to the samples prior to denaturation at 95°C for 3 min . Samples and ladder ( Precision Plus Protein Kaleidoscope Standards , BioRad ) were separated by electrophoresis on a 12% SDS-PAGE gel , later stained using Coomassie Brilliant Blue R-250 . The molecular phylogenetic analysis of SrpM was conducted in MEGA6 [98] The primary sequence of SrpM encoded by R391 was used to search for homologous sequences in the Genbank Non-redundant protein sequence ( nr ) database using blastP [99] . Phylogenetic analyses were computed using a protein alignment generated by MUSCLE [100] and poorly aligned regions were removed with the trimAl v1 . 3 software using the automated heuristic approach [101] prior to phylogenetic analyses . The evolutionary history was inferred by using the Maximum Likelihood method . Initial tree ( s ) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model , and then selecting the topology with superior log likelihood value . A discrete Gamma distribution was used to model evolutionary rate differences among sites ( 5 categories ( +G , parameter = 4 . 1321 ) ) . The rate variation model allowed for some sites to be evolutionarily invariable ( [+I] , 2 . 6007% sites ) . The tree is drawn to scale in iTOL v2 [102] , with branch lengths measured by the number of substitutions per site . The analysis involved 60 amino acid sequences with a total of 261 positions in the final dataset . | Integrative and conjugative elements ( ICEs ) constitute a class of mobile genetic elements defined by their ability to integrate into the chromosome of their host cell and to transfer by conjugation . Some of the most studied ICEs belong to the SXT/R391 family , which are major drivers of multidrug resistance dissemination among various pathogenic Gammaproteobacteria . Transfer of SXT/R391 ICEs to a new host first requires its excision from the chromosome as a circular molecule , which may be lost if the cell divides . In silico analyses revealed several putative stabilization systems carried by R391 , a prototypical member of the SXT/R391 ICEs family originally isolated from Providencia rettgeri . We discovered that , besides stabilization by integration into the chromosome , stability of SXT/R391 ICEs also depends on toxin/antitoxin systems and plasmid-like features including intracellular replication and active partition . Thus , although it has been known for a long time that ICEs and conjugative plasmids use similar strategies to transfer between bacterial populations , our work reveals additional unforeseen similarities in their mechanisms of maintenance in the host cell . |
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Amebiasis , a global intestinal parasitic disease , is due to Entamoeba histolytica . This parasite , which feeds on bacteria in the large intestine of its human host , can trigger a strong inflammatory response upon invasion of the colonic mucosa . Whereas information about the mechanisms which are used by the parasite to cope with oxidative and nitrosative stresses during infection is available , knowledge about the contribution of bacteria to these mechanisms is lacking . In a recent study , we demonstrated that enteropathogenic Escherichia coli O55 protects E . histolytica against oxidative stress . Resin-assisted capture ( RAC ) of oxidized ( OX ) proteins coupled to mass spectrometry ( OX-RAC ) was used to investigate the oxidation status of cysteine residues in proteins present in E . histolytica trophozoites incubated with live or heat-killed E . coli O55 and then exposed to H2O2-mediated oxidative stress . We found that the redox proteome of E . histolytica exposed to heat-killed E . coli O55 is enriched with proteins involved in redox homeostasis , lipid metabolism , small molecule metabolism , carbohydrate derivative metabolism , and organonitrogen compound biosynthesis . In contrast , we found that proteins associated with redox homeostasis were the only OX-proteins that were enriched in E . histolytica trophozoites which were incubated with live E . coli O55 . These data indicate that E . coli has a profound impact on the redox proteome of E . histolytica . Unexpectedly , some E . coli proteins were also co-identified with E . histolytica proteins by OX-RAC . We demonstrated that one of these proteins , E . coli malate dehydrogenase ( EcMDH ) and its product , oxaloacetate , are key elements of E . coli-mediated resistance of E . histolytica to oxidative stress and that oxaloacetate helps the parasite survive in the large intestine . We also provide evidence that the protective effect of oxaloacetate against oxidative stress extends to Caenorhabditis elegans .
Entamoeba histolytica is a protozoan parasite , which inhabits the gastrointestinal tract , and an E . histolytica infection is a substantial health risk in almost all countries where the barrier between food and water and human feces is inadequate . The major clinical manifestations of an E . histolytica infection are amebic colitis , amebic liver abscess , and extraintestinal amebiasis . It is estimated that amebiasis accounted for 55500 deaths and 2 . 237 million disability-adjusted life years ( the sum of years of life lost and years lived with disability ) in 2010[1] . This mortality rate makes an E . histolytica infection the second leading cause of death due to a parasitic infection . E . histolytica is a dimorphic organism whose life cycle has two stages: a trophozoite , a cell-invasive form which can be found in the human intestine , and a cyst , an infective form which is found in the external environment . The conversion between the two stages is usually reversible[2] . Infection of the host occurs upon ingestion of water or food contaminated with cysts . After ingestion , the cysts pass through the stomach , excyst in the small intestine where they produce ameboid trophozoites , which then colonize the large intestine . In the colon , the trophozoites can either asymptomatically colonize the gut , re-encyst , and be expelled in the feces or cause invasive disease[3] . Although the exact conditions , which trigger the onset of invasive disease , are still unknown , the interaction between the parasite’s virulence factors and the host’s response contribute to the development of disease[4] . The human gastrointestinal tract is a nutrient-rich environment which harbors a complex and dynamic population of 38 trillion microbes [5] . About 500–1000 bacterial species colonize the adult intestine , with 30–40 species comprising up to 97% of the total population[6] , [7] . The majority reside in the colon where densities approach 1011 - 1012 cells/ml[8] . Following colonization of the gut , the parasite is constantly interacting with the gut microbiota whose contribution to the manifestation of disease is poorly understood . The trophozoites are quite selective in their interactions with different bacterial species and only those bacteria which have the appropriate recognition molecules get attached to the trophozoites and are ingested[9] . The relationship between E . histolytica and the gut microbiota was the subject of many studies which concluded that the gut microbiota affects greatly several aspects of E . histolytica’s physiology[10 , 11] , [12 , 13] , [14] . In areas where amebiasis is endemic , mixed intestinal infections of E . histolytica and enteropathogenic Escherichia coli ( EPEC ) are common[15] . Bacteria in the intestinal flora including EPEC have been proposed as inducers of amebic virulence , but the causes or mechanisms which are responsible for the induction are still undetermined [16 , 17] . The presence of enteropathogenic bacteria[15] or the presence of Prevotella copri[18] , a normal component of the gut microbiota , has been correlated to a symptomatic outcome of E . histolytica infection in young children . In contrast , mice which were inoculated with commensal Clostridia spp . and segmented filamentous bacteria are protected against an E . histolytica infection[19] . Amebiasis is marked by acute inflammation with the release of cytokines ( tumor necrosis factor alpha ( TNFα ) , interleukin 8 ( IL-8 ) , IL-1β , interferon gamma ( IFN-γ ) , reactive oxygen species ( ROS ) , and nitric oxide ( NO ) from activated cells of the immune system . Depending on their concentration , ROS and NO have been reported to ( a ) trigger stress responses , ( b ) control the activity of E . histolytica virulence factors , and ( c ) be cytotoxic [20] , [21–24] . Recent evidence suggests that the gut microbiome can control oxidative stress and inflammation in the gut [25–29] . Recently , we demonstrated that enteropathogenic Escherichia coli O55 protects E . histolytica against oxidative stress and that this bacterium exerts a strong influence on the transcriptome of oxidatively stressed parasites [30] . In this report , we inform on the mechanism of E . coli-mediated resistance of E . histolytica to oxidative stress by examining the redox proteome of the parasite exposed to E . coli and oxidative stress . We found that live E . coli trigger significant changes in the redox proteome profile of oxidatively stressed E . histolytica . We also found that E . coli malate dehydrogenase ( MDH ) and its product , oxaloacetate , are essential for protecting the parasite against oxidative stress .
The results of our recent investigation indicate that preincubation of E . histolytica trophozoites with live E . coli O55 , but not with heat-killed E . coli O55 , confers resistance to H2O2-induced oxidative stress to the parasite [30] . In order to obtain insights into the mechanisms of survival of oxidatively stressed E . histolytica trophozoites , we did an OX-RAC analysis of the proteins in E . histolytica trophozoites which were exposed to live or heat-killed E . coli O55 for 30 minutes and then exposed to 2 . 5 mM H2O2 for 60 minutes at 37°C ( Fig 1A ) [22] . The purification of OX-proteins by OX-RAC analysis , which has been previously described in detail [22] , has three steps: ( i ) blocking by N-ethylmaleimide ( NEM ) of non-oxidized cysteine residues present in E . histolytica proteins; ( ii ) reduction of oxidized cysteine residues with dithiothreitol ( DTT ) ; and ( iii ) binding of the cysteine residues reduced by DTT to a thiopropyl sepharose resin . The OX-proteins are then eluted from the thiopropyl sepharose resin and identified by mass spectrometry . We identified 329 OX-proteins in those trophozoites which were exposed to heat-killed E . coli O55 and H2O2 ( THK ) and 300 OX-proteins in those trophozoites which were exposed to live E . coli O55 and H2O2 ( TL ) ( S1 & S2 Tables ) . Using the PANTHER sequence classification tool[31] , we found that a third of the OX-proteins are shared by THK and TL ( ( Fig 1B and S3 & S4 Tables ) . We also found that OX-proteins in the following biological processes were significantly enriched in THK ( Fig 1C and S5 Table ) : redox homeostasis ( GO:0045454 ) ( exemplified by thioredoxin EHI_004490 or EHI_006700 ) , lipid metabolism ( GO:0044255 ) ( exemplified by 3-ketoacyl-CoA synthase EHI_111000 or geranylgeranyl pyrophosphate synthase EHI_105060 ) , small molecule metabolism ( GO:0044281 ) ( exemplified by glyceraldehyde-3-phosphate dehydrogenase EHI_008200 and threonine dehydratase EhTD1 EHI_092390 ) , carbohydrate derivative metabolism ( GO:1901135 ) ( exemplified by mannosyltransferase EHI_103330 and glucosidase 2 subunit beta EHI_135420 ) , and organonitrogen compound biosynthesis ( GO:1901566 ) ( exemplified by alpha-1 , 3-mannosyltransferase ALG2 , EHI_162230 and dolichyl-diphosphooligosaccharide—protein glycosyltransferase subunit 1 EHI_029540 ) . In contrast , we found that OX-proteins in the following biological process were significantly enriched in TL: redox homeostasis ( GO:0045454 ) ( exemplified by thioredoxin EHI_004490 or EHI_006700 ) ( Fig 1C and S5 Table ) . The small number of shared OX-proteins and biological processes in THK and TL suggests that exposure of the parasite to live E . coli influences the parasite’s redox proteome . We also found 70 E . coli proteins that were co-purified with E . histolytica OX-proteins ( S6 Table ) . It has been previously reported that a catalase-deficient strain of Salmonella dysenteriae , which cannot decompose H2O2 to water and oxygen , cannot boost the virulence of oxidatively stressed trophozoites whereas a wild-type S . dysenteriae does [32] . Accordingly , we posited that those E . coli proteins which are involved in the bacterium’s resistance to oxidative stress also participate in the parasite’s protective mechanism against oxidative stress . Five of the 70 E . coli proteins were selected because of their participation to the resistance of E . coli to oxidative stress , namely , 60 kDa chaperonin ( it protects proteins which are denatured by oxidative stress from misfolding and aggregating [33 , 34] ) ; superoxide dismutase ( it catalyzes the decomposition of the superoxide free radical [35] ) ; MDH ( it catalyzes the formation of oxaloacetate from malate and it protects E . coli against oxidative stress [36] ) ; aspartate ammonia-lyase ( it catalyzes the formation of ammonia and promotes the formation of antioxidant polyamines in E . coli [37] [38] ) , and alkyl hydroperoxide reductase subunit C ( it catalyzes the NADH-dependent reduction of H2O2 to H2O [39] ) . We then investigated the ability of E . coli K12 and the following E . coli mutants: 60 kDa chaperonin ( JW 4103- Δ gro ) , superoxide dismutase ( JW1648- Δsod ) , MDH ( JW 3205- Δ mdh ) , aspartate ammonia-lyase ( JW4099- ΔaspA ) , and alkyl hydroperoxide reductase subunit C ( JW0598- Δ ahpc ) to protect E . histolytica against H2O2-induced oxidative stress . We found that the levels of protection that are conferred by E . coli O55 [30] and E . coli K12 on oxidatively stressed E . histolytica trophozoites are similar ( Table 1 ) . Among the E . coli K12 mutants , we found that the MDH mutant E . coli JW 3205 was the only one that did not protect E . histolytica against H2O2-induced oxidative stress ( Table 1 ) . In order to demonstrate whether MDH is essential for protecting E . histolytica against H2O2-induced oxidative stress , E . coli JW 3205 was complemented with a plasmid which harbored wild-type E . coli mdh . MDH activity was determined in the whole lysate of E . coli K12 , E . coli JW 3205 , and E . coli JW 3205 complemented with mdh ( Table 2 ) . We found MDH activity in E . coli K12 and in E . coli JW 3205 complemented with mdh but not in E . coli JW 3205 ( Table 2 ) . We also found that E . coli JW 3205 complemented with mdh , but not E . coli JW 3205 , protects E . histolytica against H2O2-induced oxidative stress ( Table 1 ) . MDH catalyzes the reversible transformation of malate into oxaloacetate and it has been reported that oxaloacetate in millimolar concentrations protects E . coli against H2O2-induced oxidative stress[36] . In order to test the hypothesis that oxaloacetate is essential for protecting E . histolytica against H2O2-induced oxidative stress , we determined the viability of E . histolytica trophozoites first exposed to different concentrations of oxaloacetate ( 0–2 mM for 15 minutes ) and then to H2O2 ( 2 . 5 mM for one hour ) . We found that oxaloacetate at concentrations higher than 0 . 25 mM protects the parasite against H2O2-induced oxidative stress ( Table 1 and S1 Fig ) . We also found that malate ( 0 . 5 mM ) has no effect on the resistance of the parasite to H2O2-induced oxidative stress ( Table 1 ) . Paraquat has been previously used to trigger oxidative stress in E . histolytica[40] . In order to demonstrate whether oxaloacetate protects the parasite against paraquat , trophozoites were incubated with oxaloacetate ( 2 mM ) and then exposed to paraquat ( 2 . 5 mM ) . We found that oxaloacetate does not protect the parasite against paraquat ( S2 Fig ) . Next , we tested whether E . coli O55 also protects E . histolytica against nitrosative stress . For this purpose , we exposed E . histolytica trophozoites to the nitric oxide ( NO ) donor , S-nitrosoglutathione ( 350 μM for 120 minutes ) [41] . We found that E . histolytica trophozoites exposed to live or heat-killed E . coli O55 are not protected against S-nitrosoglutathione-induced nitrosative stress ( S3 Fig ) . We also found that oxaloacetate ( 2 mM ) does not protect the parasite against S-nitrosoglutathione-induced nitrosative stress ( S3 Fig ) . It has been reported that E . coli can boost the virulence of E . histolytica and that this boosting is contact dependent[42] . However , it is not known whether contact between E . coli and the parasite is necessary for protecting the parasite against H2O2-induced oxidative stress . This question was addressed by physically separating the trophozoites from E . coli O55 or E . coli K12 with a polycarbonate insert ( 0 . 4 μm ) prior to the exposure of the parasite to H2O2 . We found that E . coli O55 and E . coli K12 inside the polycarbonate insert protect the parasite against H2O2-induced oxidative stress ( S4 Fig ) . Since E . coli MDH ( EcMDH ) , which is secreted by E . coli[43] , is essential for protecting E . histolytica against H2O2-induced oxidative stress ( Table 2 ) and this protection is contact independent ( S4 Fig ) , we posited that EcMDH contributes to the resistance of E . histolytica to H2O2-induced oxidative stress . This hypothesis was tested by first incubating E . histolytica with commercial His-tagged EcMDH and then exposing the parasite to H2O2-induced oxidative stress . As a prerequisite to this experiment , we checked the activity of the commercial His-tagged EcMDH and found that the recombinant protein is catalytically active ( Table 2 ) . We found that the presence of His-tagged EcMDH did not protect the parasite against H2O2-induced oxidative stress ( Fig 2 ) . However , we found that the parasite is protected against H2O2-induced oxidative stress when the parasite is incubated with both His-tagged EcMDH ( 1 . 5 μg ) and L-malate ( 50 mM ) prior to its exposure to H2O2-induced oxidative stress ( Fig 2 ) . No MDH activity was detected in a whole lysate and the secretory products of control E . histolytica trophozoites [21] ( Table 2 ) despite the presence of six putative MDH genes in the genome of the parasite [44] ( EHI_152670 , EHI_067860 , EHI_165350 , EHI_030810 , EHI_092450 and EHI_014410 ) and one MDH in the secretome of E . histolytica [45] ( EHI_092450 ) . When the E . histolytica MDH ( EhMDH , EHI_067860 ) is overexpressed in E . coli , its activity is less than 40% of its E . coli homolog ( Table 2 ) . These results raise a question on whether the parasite can express an active MDH . To answer this question , E . histolytica trophozoites were complemented with HA-tagged EcMDH . The expression of HA-tagged EcMDH in the trophozoites was confirmed by western blotting ( Fig 3A ) and detecting MDH activity in their whole lysates ( Table 2 ) . When we compared the resistance of HA-tagged EcMDH trophozoites to H2O2-induced oxidative stress ( Fig 3B ) and trophozoites transformed with the pcontrol plasmid[41] , we found that the sensitivity of the HA-tagged EcMDH trophozoites and the pcontrol trophozoites to H2O2-induced oxidative stress was identical . Incubation of the HA-tagged EcMDH trophozoites in presence of malate ( 2 mM ) prior to their exposure to H2O2-induced oxidative stress did not increase their resistance to H2O2-induced oxidative stress ( Fig 3B ) . The intracellular concentration of oxaloacetate was determined in pcontrol trophozoites , HA-tagged EcMDH trophozoites , E . coli K12 , and E . coli JW 3205 . We were able to detect 12 . 7 nmol oxaloacetate/mg proteins in E . coli K12 , but no oxaloacetate was detected in the pcontrol trophozoites , the HA-tagged EcMDH trophozoites , and E . coli JW 3205 . No oxaloacetate was also detected in the supernatant of the growth medium of E . coli or E . histolytica . Ketoacids act as non-enzymatic antioxidants due to their ability to scavenge H2O2 [46 , 47] . We determined the amount of H2O2 in presence or absence of oxaloacetate and confirmed that the amount of H2O2 dropped in presence of oxaloacetate ( S5 Fig ) . The antioxidant property of oxaloacetate was also evaluated by determining the formation of OX-proteins in trophozoites exposed to H2O2 and trophozoites exposed to H2O2 and oxaloacetate using OX-RAC ( Fig 4; left panel ) . The amount of OX-proteins in those trophozoites exposed to H2O2 was four times bigger than that in those trophozoites exposed to H2O2 and oxaloacetate . Gal/GalNac lectin is a surface protein which is essential for the binding of the parasite to target cells [48] and its binding activity is inhibited by the oxidation of the cysteine residues present in the carbohydrate recognition domain of the heavy subunit Gal/GalNAc lectin ( Hgl ) ( EHI_012270 ) [22] . We decided to determine the redox status of Hgl in trophozoites exposed to H2O2 and trophozoites exposed to H2O2 and oxaloacetate by western blotting of the OX-proteins ( Fig 4; right panel ) . We detected a strong Hgl signal in those trophozoites exposed to H2O2 whereas Hgl is barely detectable in those trophozoites exposed to H2O2 and oxaloacetate . Collectively , these results indicate that oxaloacetate reduces the formation of OX-proteins in trophozoites exposed to H2O2 . It has been previously reported that H2O2-induced oxidative stress impairs the cytopathic activity of E . histolytica [16] . We found that the cytopathic activity of E . histolytica trophozoites incubated with E . coli K12 or E . coli JW 3205 complemented with mdh was not impaired when they were exposed to H2O2-induced oxidative stress and was impaired when they were exposed to E . coli JW 3205 ( Fig 5A ) . In order to test whether oxaloacetate can protect the cytopathic activity of oxidatively stressed E . histolytica trophozoites , E . histolytica trophozoites were exposed or not exposed to oxaloacetate prior to their exposure to H2O2 ( Fig 5B ) . We found that the cytopathic activity of the oxidatively stressed E . histolytica trophozoites is substantially impaired , but this impairment does not occur when the parasite was preincubated with oxaloacetate before being exposed to H2O2 ( Fig 5B ) . The amebicidal activity of murine macrophages depends on the formation of reactive oxygen and nitrogen species and the addition of catalase to the culture medium of activated murine macrophages reduces their amebicidal activity [49] . Based on these data , we determined the effect of oxaloacetate on the amebicidal activity of activated murine macrophages ( Fig 6 ) . We found that the amebicidal activity of activated murine macrophages is substantially impaired when these macrophages were incubated with oxaloacetate ( Fig 6 ) . The effect of oxaloacetate on the survival of E . histolytica in the large intestine was tested in a mouse strain resistant to intestinal amebiasis C57BL/6 ( B6 ) [50] and in a mouse strain susceptible to intestinal amebiasis CBA/J [51] . The survival of the parasite was determined by counting the number of trophozoites in the stool after cultivation and by amplification of E . histolytica 18S rRNA in DNA extracted from the stool by polymerase chain reaction ( PCR ) . We found that intracecal injection of the parasite with oxaloacetate ( 2 mM ) helps the parasite to survive in the intestine . ( Fig 7A & 7B ) . It has been reported that the glycolytic activities of oxidatively stressed E . histolytica trophozoites are impaired and this impairment results in a redirection of the metabolic flux toward glycerol production[40] . Therefore , the amount of glycerol in the parasite could be used as an indicator of the level of oxidative stress sensed by the parasite . We found that the amount of glycerol in those parasites exposed to H2O2 was two-fold greater than that in the unexposed parasites ( Fig 8 ) . We also did not detect any differences in the amount of glycerol in those parasites exposed to oxaloacetate and in those trophozoites exposed to oxaloacetate and H2O2 . Husain et al . [40] reported that the amount of isocitrate in control ( unstressed ) and oxidatively stressed trophozoites is similar . This finding suggests that the intracellular amount of isocitrate could be used as an internal standard for our metabolomic data in the control and oxidatively stressed trophozoites . We found no difference in the amounts of isocitrate in the oxidatively stressed and control parasites which confirmed the finding of Husain et al . [40] and concluded that the quality of our metabolomic data is good . ( Fig 8 ) . The environment in which C . elegans lives contains bacteria and C . elegans uses these bacteria as its food source . Based on our findings in E . histolytica , we tested the hypothesis that bacteria can also influence the ability of C . elegans to resist H2O2-induced oxidative stress . It has been previously reported that oxaloacetate increases the life span of C . elegans [52] . Additionally , our knowledge on the effect of oxaloacetate on the resistance of the nematode to H2O2-induced oxidative stress is lacking . In order to fill these knowledge gaps , C . elegans at the L1 developmental stage were incubated with different concentrations of oxaloacetate and then exposed to H2O2 . We found that the survival rate of those worms which were treated with oxaloacetate ( 2 . 5 mM or higher ) is higher than those worms which were treated with oxaloacetate ( 1 mM or less ) prior to their exposure to H2O2 ( Fig 9A ) . C . elegans were also incubated in M9 medium without bacteria or with E . coli K12 or E . coli JW 3205 prior to their exposure to H2O2 . We found that both E . coli K12 and E . coli JW 3205 protect C . elegans against H2O2 ( Fig 9B ) whereas only E . coli K12 protected E . histolytica against H2O2 ( Table 1 ) . These findings suggest that the mechanism of protection of C . elegans against H2O2 is more complex than that in E . histolytica and that it does not solely depend on the activity of EcMDH .
In a previous report , we informed on the results of our redox proteomics analysis of oxidatively stressed E . histolytica trophozoites [22] . In order to understand the mechanisms of survival of E . histolytica trophozoites that were incubated with E . coli prior to their exposure to H2O2 , we did a redox proteomics analysis of the parasite which was exposed first to heat-killed or live E . coli and then exposed to H2O2 . The proteins involved in redox homeostasis , mainly , thioredoxin ( TRX ) and protein disulfide isomerase ( PDI ) were identified as OX-proteins in oxidatively stressed E . histolytica trophozoites which were exposed to heat-killed or live E . coli . This result indicates that the presence of heat-killed or live E . coli has no detectable effect on the oxidation status of these proteins . In previous investigations , we reported that TRX and PDI are oxidized and nitrosylated when E . histolytica trophozoites are exposed to oxidative and nitrosative stress [22 , 53] . PDIs are oxidoreductases and isomerases which are involved in the unfolded protein response[54] . In the oxidation-reduction reaction to reduce peroxyredoxin or decompose H2O2 into H2O , TRX is the first substrate to be transformed by TRX reductase ( TrxR ) [55] . Since TRX is susceptible to oxidation[56] and is reduced as part of its antioxidant activity[57 , 58] , these properties may be reasons why TRX is oxidized in the oxidatively stressed parasite after its exposure to heat-killed or live E . coli . We detected OX-proteins in E . histolytica trophozoites which were incubated with heat-killed E . coli and exposed to H2O2 . Since these OX-proteins were not detected in E . histolytica trophozoites which were incubated with live E . coli and exposed to H2O2 , their absence suggests that their oxidation in E . histolytica trophozoites depends on the presence of live E . coli . One of these OX-proteins is the 60S acidic ribosomal protein L9 which belongs to the “organonitrogen compound biosynthesis” class of proteins . In our previous investigation , we also identified the 60S acidic ribosomal protein L9 ( EHI_193080 ) as an OX-protein[22] . We have previously discussed that the inhibition of protein synthesis in oxidatively stressed E . histolytica trophozoites results from the oxidation of different components of the parasite’s translational machinery which includes the 60S acidic ribosomal proteins[22] . The absence of oxidized ribosomal proteins in trophozoites which were incubated with live E . coli prior to their exposure to H2O2 suggests that the presence of live E . coli protects these proteins from oxidation . We can also deduce that this presence facilitates the survival of the oxidatively stressed parasite . The results of previous studies have shown that a short preincubation of E . histolytica trophozoites strain HM-1:IMSS with E . coli O55 can boost the parasite’s ability to destroy monolayers of cultured cells[10] . Our findings indicate that the impaired cytopathic activity of oxidatively stressed E . histolytica trophozoites can be regained by pre-incubating the parasite with E . coli or oxaloacetate . These results and those of others[20] suggest that the parasite’s virulence and ability to resist oxidative stress are linked . This boosting of E . histolytica’s virulence by E . coli O55 is contact dependent[10] and relies on the presence of galactose lectin on the parasite’s surface . We found that the protective effect of E . coli on oxidatively stressed E . histolytica trophozoites does not rely on the binding of the bacteria to the parasite but on the formation of oxaloacetate by E . coli . Pyruvate , oxalo-ketoglutarate , and other ketoacids function as non-enzymatic antioxidants due to their ability to scavenge H2O2[46 , 47] . It has also been reported that ketoacids can protect E . coli[36] , several eukaryotic cell types[59] , [60] , [61–63] and even whole organs , such as the heart and kidney[64–66] against oxidative stress . It is also interesting to note that the increase in the life span of C . elegans after exposure to oxaloacetate[52] may be due to the ability of oxaloacetate to scavenge H2O2 . The ability of oxaloacetate to scavenge H2O2 is supported by our findings of a reduction in ( i ) the amount of OX-proteins in trophozoites exposed to H2O2 and oxaloacetate; ( ii ) the intracellular amount of glycerol in trophozoites exposed to H2O2 and oxaloacetate and ( iii ) the concentration of H2O2 is reduced in presence of oxaloacetate . The mechanism of H2O2 scavenging by oxaloacetate is not compatible with the detoxification of s-nitrosothiol groups which are formed during nitrosative stress[67] . Since it is also not compatible with the intracellular formation of superoxide by paraquat[68] , this finding may explain why oxaloacetate cannot protect E . histolytica against nitrosative stress and oxidative stress induced by S-nitrosoglutathione and paraquat , respectively . We found that oxaloacetate produced by E . coli protects E . histolytica against H2O2induced oxidative stress . It is possible that H2O2 is detoxified by oxaloacetate inside the bacteria . The need of an active EcMDH for protecting E . histolytica against H2O2 is supported by the results of our experiment in which we found that heat-killed E . coli are unable to protect the parasite against H2O2 . It is also possible that H2O2 is detoxified by oxaloacetate which is secreted into the culture medium . To our knowledge , such secretion of oxaloacetate by E . coli and other microorganisms is not supported by the literature . Our attempts to detect oxaloacetate in the extracellular medium using metabolomics or an enzymatic-based kit failed because it is a very unstable metabolic intermediate[69] . However , it is possible that oxaloacetate is formed extracellularly by secreted EcMDH because ( i ) EcMDH is part of E . coli’s secretome [43] and ( ii ) the addition of recombinant EcMDH to the medium in presence of malate confers resistance to the parasite against H2O2-induced oxidative stress ( this work ) . Despite the presence of six putative MDH genes in the genome of the parasite [44] , we and others [21] were unable to detect any MDH activity in a total lysate of the parasite . Several different explanations may account for this experimental observation: ( i ) EhMDHs are very sensitive to environmental conditions and are denatured once the parasite is lysed , ( ii ) the parasite has lost an active MDH during the course of evolution because it can relies on malate and oxaloacetate produced by the gut microbiota and ( iii ) EhMDHs may have a different enzymatic activity unrelated to their metabolic function as reported for a number of E . histolytica moonlighting enzymes [41 , 70] . This third hypothesis is supported by the unexpected presence of two MDHs ( EHI_030810 and EHI_165350 ) on the parasite’s surface [71] . Since E . histolytica is able to express a functional EcMDH ( based on the activity measured in a total lysate of the EcMDH trophozoites ) , its sensitivity to H2O2-induced oxidative stress was comparable to that of pcontrol trophozoites . Since oxaloacetate was not being detected in the EcMDH-overexpressing trophozoites even when malate was present in the medium , we concluded that ( i ) the concentration of intracellular malate in the parasite is limiting , ( ii ) the parasite cannot transport external malate , and ( iii ) EcMDH is not functional inside the parasite . Another possibility is that the newly formed oxaloacetate is quickly converted to a non-protective metabolite in the parasite . Pyruvate:ferredoxin oxidoreductase ( PFOR ) is an enzyme which uses oxaloacetate as an alternative substrate to pyruvate may be responsible for this conversion[72] . Alternatively , it is possible that EcMDH is active inside the parasite , but the production of oxaloacetate is insufficient to neutralize the cytotoxic effect of H2O2 . Accordingly , any neutralization or antioxidant mechanism must be operative extracellularly in order to be effective . We found that the survival of oxaloacetate-treated parasites is better than that of parasites which were not exposed to oxaloacetate in a mouse model of amebic colitis . It has been previously shown that a strong inflammatory response occurs after injecting the parasite into the large intestine of mice [73] . This inflammatory response is essential for killing the parasite because neutrophil-depleted or dexamethasone-treated C3H or CBA mice are more susceptible than untreated mice [73] . It is possible that oxaloacetate neutralizes H2O2 which is produced by neutrophils inside the colon of the trophozoite-infected mice and consequently promotes the parasite’s survival . This hypothesis is supported by our in vitro data which demonstrated that the amebicidal activity of activated murine macrophages which depends on the formation of ROS and NO [74] is reduced in presence of oxaloacetate . The susceptibility of mice to an E . histolytica infection depends in part on the content of sialic acids in intestinal mucins and the binding of the parasite to these mucins is mediated by Hgl [75] . The better colonization of oxaloacetate-treated parasites than untreated parasites in the large intestine of mice may be explained by the presence of a functional Hgl in the oxaloacetate-treated parasite . Specifically , Hgl in the untreated parasite is inactivated or becomes non-functional when it is oxidized [22] . Although we don’t know the exact concentration of oxaloacetate in the human large intestine due to the instability of oxaloacetate , the concentration of malate , its precursor , is in the millimolar range [76] . Based on this information , it is tempting to speculate that enough oxaloacetate is produced in the human large intestine to protect the parasite against H2O2-induced oxidative stress . To conclude , we have done the first redoxomics of E . histolytica incubated with E . coli and exposed to H2O2-induced oxidative stress . Although it is difficult to deduce from our data whether changes in the redox status of E . histolytica proteins actually occur when the parasite resides in its host , this investigation highlights that the interaction between the parasite and the gut flora is more complex than the predator-prey relationship . A complex interaction has also been recently described in C . elegans . This bacteria-feeding nematode can avoid pathogenic bacteria , such as Pseudomonas aeruginosas , by sensing some of their secondary metabolites [77] . Other parasitic protozoa and helminths which are also in a tight relationship with the host’s intestinal microbiota may benefit from the antioxidant properties of oxaloacetate which is produced by the gut microbiota [78] . This proposition is supported by our data about the protection of C . elegans by oxaloacetate against H2O2-induced oxidative stress . Strategies that counteract the protective effect of oxaloacetate against oxidative stress may be valuable in the treatment of amebiasis .
E . histolytica trophozoites HM-1:IMSS strain were grown under axenic condition at 37°C in Trypticase Yeast Extract Iron Serum ( TYI-S-33 ) medium prepared according to a previously reported protocol[79] . The trophozoites were harvested during the logarithmic phase of growth by chilling the culture tubes at 4°C and pelleted by centrifugation at 500 g for five minutes . The pellet was washed twice with ice-cold phosphate-buffered saline . The bacterial strains used in this study are listed in S7 Table . E . coli was grown at 37°C in Luria-Bertani ( LB ) medium[80] . C . elegans strain Bristol N2 were grown and maintained at 16°C on nematode growth media ( NGM ) agar using a previously reported protocol[81] . E . coli OP50 was used as their food source . For synchronizing the worms , gravid adults were treated with a freshly prepared 20% sodium hypochlorite solution to isolate embryos . Embryos were then incubated overnight in M9 solution in a nutator at 20°C without food to allow hatching to the L1 developmental stage and to prevent further development . The number of worms was estimated using a hemocytometer . HeLa cells ( a kind gift from Dr . Kleinberger , Faculty of Medicine , Technion ) were maintained in continuous culture using a previously described protocol [41] . The transfection of E . histolytica trophozoites was done using a previously described protocol[82] . E . histolytica trophozoites ( 1×106 ) in TYI-S-33 medium ( without serum ) were exposed to different concentrations of H2O2 ( 0-5mM ) for one hour at 37°C . The viability of the trophozoites was determined by the eosin dye exclusion method[41] . E . histolytica trophozoites were first cultivated in standard TYI-S-33 medium in 7 ml culture tubes for 12 hours at 37°C . The culture medium was then replaced with fresh and warm culture medium and paraquat ( 2 . 5 mM final concentration ) was added and the culture was continued for 12 hours . The viability of the trophozoites was determined by the eosin dye exclusion method[41] . C . elegans ( 100 L1 larvae ) after synchronization in M9 medium were placed into each well of a 24-well plate . The worms were first exposed to different concentrations of oxaloacetate ( 0–5 mM ) for five minutes , exposed to 2 . 5 mM H2O2 , and then incubated in an orbital shaker at 20°C for 2 , 4 , 6 , and 24 hours . To determine their viability after each exposure time , the worms were seeded on one side of a NGM agar plate and E . coli OP50 was seeded on the opposite side . After a 1-hour incubation at room temperature , the number of viable worms was assessed by measuring their mobility[83] . At least three biological replicates were performed for each experiment . The viability of C . elegans was also measured after preincubation of the worms with E . coli K12 or E . coli JW3205 for five minutes . Subsequently , the worms were exposed to 2 . 5 mM H2O2 for 2 , 4 , 6 , and 24 hours . The viability of the worms was assessed by measuring their mobility using the previously described method . E . histolytica trophozoites ( 1×106 ) in TYI-S-33 medium ( without serum ) were exposed to 350μM S-nitrosoglutathione ( Sigma-Aldrich , St . Louis , MO , USA ) for two hours at 37°C . The viability of the trophozoites was determined by the eosin dye exclusion method[41] . E . histolytica trophozoites ( 1×106 ) in 500 μl TYI-S-33 medium ( without serum ) were seeded into each well of a 24-well plate ( Nunclon delta surface , Thermo Scientific , Israel ) . A polycarbonate SPL insert ( 0 . 4 μm ) ( SPL Biosciences , Israel ) was introduced into each well and E . coli O55 or E . coli K12 ( 1×109 bacteria in 500 μl TYI-S-33 medium ( without serum ) ) were introduced into the SPL insert . No bacteria were introduced in the SPL insert of the control trophozoite culture . Oxidative stress was generated in some wells by adding 2 . 5 mM H2O2 directly into the SPL insert . The viability of the trophozoites was determined by the eosin dye exclusion method[41] . The cytopathic activity of E . histolytica trophozoites was determined using a previously described protocol [84] . The amebicidal activity of activated murine macrophages was determined using previously described protocol [49] . Briefly , RAW 267 . 7 macrophages ( a kind gift from Dr . Moran Benhar , Faculty of Medicine , Technion ) were activated by a 22-hour incubation with lipopolysaccharides ( LPS ) ( 1 μg/μl ) and interferon γ ( INF-γ ) ( 100 U/ml ) in absence of presence of oxaloacetate ( 2mM ) . Activated macrophages ( 2 × 106/ml ) and E . histolytica trophozoites ( 2 × 104/ml ) were co-incubated at 37°C for six hours . The viability of trophozoites was determined by the eosin dye exclusion method [41] . C57BL/6 and CBA/J mice were purchased from the Jackson Laboratory ( Japan ) . The mice were maintained under specific pathogen-free conditions . Trophozoites for intracecal injections were originally derived from laboratory strain HM1:IMSS ( American Type Culture Collection ) that were sequentially passaged in vivo through the mouse cecum [50] . For all intracecal injections , axenic trophozoites were grown to the log phase and counted with a hemacytometer , and 1 × 106 trophozoites in 200 μl TYI-S-33 medium were injected in the presence or absence of oxaloacetate ( 2 mM ) into the proximal , middle , and apical regions of the cecum [85] of mice anesthetized with Domitor ( medetomidine hydrochloride , 0 . 1 mg/kg ) and Dormicum ( midazolam , 0 . 1 mg/kg ) . At the end of the experiment , mice were sacrificed by barbiturate overdose . We anesthetized mice with Domitor ( medetomidine hydrochloride , 0 . 1 mg/kg ) and Dormicum ( midazolam , 0 . 1 mg/kg ) . Mice were sacrified by Barbiturate overdose . All experiments that involved mice were reviewed and approved by the Committee for Ethics on Animal Experiments in the Graduate School of Gunma University , and were conducted under the control of the Guidelines for Animal Experiments in the Graduate School of Medicine , Gunma University , and the Law ( No . 105 ) and Notification ( No . 6 ) of the Japanese Government . The protocol number 16–041 has been assigned by the Committee for Ethics on Animal Experiments in the Graduate School of Gunma University after approval of the animal experiments described in this study . QIAamp DNA stool kits ( Qiagen , Valencia , CA ) was used for DNA extraction from the mice feces according to the manufacturer’s instructions . To quantify the presence of E . histolytica trophozoites in stool , real-time quantitative PCR was performed by using using SYBR Green Supermix ( Life Technologies , TA , CA , USA ) in the Quant Studio 7 Flex Real-Time PCR System ( Applied Biosystems® , Life Technologies , CA , USA ) . Primer sets specific to E . histolytica 18S rRNA were EntaF and EhR ( S8 Table ) . To make a standard curve , DNA extracted from E . histolytica trophozoites was serially diluted from 105 to 100 . Based on the standard curve and the stool weight , the number of trophozoite/mg stool was calculated . The presence of live trophozoites in the stool was confirmed by cultivation of stool in TYI-S-33 medium in presence of 103 unit/ml penicillin G , 1 mg/ml streptomycin , and 2 . 5 μg/ml amphotericin B . Overnight cultures of E . coli were diluted 100-fold in 10 ml of fresh LB medium and incubated at 37°C until the OD600 reached 0 . 5 . The cells were then harvested by centrifugation , washed once with phosphate-buffered saline , and finally re-suspended in 500 μl buffer which contained 0 . 1M Tris ( pH 7 . 4 ) , 2 mM EDTA , 0 . 2M DTT , and 0 . 5mM PMSF . The cells were lysed by ultrasonic disintegration using an ultrasonic disintegrator ( Topas GmbH ) which was operated five times for ten seconds at 50% output power at 4°C . The resultant homogenates were centrifuged at 15 , 000×g for 15 minutes at 4°C , and the supernatants were used for measuring the MDH activity . Proteins secreted by E . histolytica trophozoites were isolated using a previously described protocol [45] . Enzyme assays were performed in a 1-cm cuvette which contained 890 μl MDH assay buffer ( 50 mM Tris buffer and 2 mM NAD+ ) and 10 μl of test sample . The reaction was initiated by the addition of 100 μl L-malate ( 500mM ) and the rate of formation of the reduced form of nicotinamide adenine dinucleotide ( NADH ) was monitored at 340 nm using spectrophotometer ( Pharmacia Biotech Ultrospec 2000 ) . One unit of MDH activity is defined as 1 μmol of NAD+ converted to its reduced form/min/mg protein . The protein concentration was determined by the Bradford method [86] . The commercial E . coli His-tagged MDH was purchased from Abcam ( Recombinant E . coli mdh protein ab124594 ) . The recombinant E . coli MDH protein was diluted with MDH assay buffer to a concentration of 60 μg/ml . Viability of E . histolytica trophozoites preincubated with His-tagged EcMDH ( 1 . 5 μg ) with or without malate ( 50 mM ) was performed as described above . For the cloning of E . coli MDH , the E . coli MDH gene was amplified from E . coli genomic DNA using the E . coli MDH 5’ and E . coli MDH 3’ primers ( S8 Table ) . The PCR product was sub-cloned using the pGEM-T easy vector system ( Promega , Madison , Wisconsin , USA ) . For construction of the pJST4-E . coli MDH expression vector that was used to express HA-tagged EcMDH in the parasite , a synthetic E . coli MDH gene was ordered ( Synthezza , Israel ) . The synthetic gene was digested with the restriction enzymes KpnI and BamHI . The released MDH gene was cloned into the pJST4 vector that has been previously linearized with KpnI and BamHI restriction enzymes . The construction of the pcontrol plasmid , which was used in this investigation , has been previously described [41] . For complementation of the mutated E . coli strain JW3205 with E . coli MDH , E . coli MDH gene was amplified from E . coli genomic DNA using primers 5’ BamHI MDH and 3’ EcoRI MDH ( S8 Table ) and the PCR product was cloned in the pGEM-T easy vector . E . coli MDH was digested with BamHI and EcoRI and then cloned in the vector pGFP ( Genbank accession No: U17997 ) which had been previously linearized with BamHI and EcoRI . For complementation of the mutated E . coli strain JW3205 with E . histolytica MDH , the E . histolytica MDH gene was amplified from a PGEX-EhMDH vector using the primers EhMDH BamHI 5’and EhMDH EcoRI 3’ ( S8 Table ) . The PCR product was cloned in the pGEM-T easy vector . Eh MDH was digested with BamHI and EcoRI and then cloned in the pGFP vector ( Genbank accession number: U17997 ) which had been previously linearized with BamHI and EcoRI . Primers used in this study are displayed in S8 Table . The detection of OXs by OX-RAC was performed using a previously described protocol[22] . Captured proteins were eluted with 30 μl elution buffer which contained 10 mM HEPES , 0 . 1 mM EDTA , 0 . 01 mM neocuproine , 0 . 1% SDS and 100 mM 2-mercaptoethanol for 20 minutes at room temperature . Proteins in a 10-μl aliquot of each eluent were resolved on a 12 . 5% SDS-PAGE gel . Each gel was then stained with silver ( Pierce Silver Stain ) and each gel slice was independently analyzed by mass spectrometry ( MS ) . A protein was considered to be oxidized when its relative amount in the dithiothreitol ( DTT ) -treated lysates was significantly less than that in the DTT-untreated lysates ( p <0 . 05 according to the results of a unpaired t-test ) . In gel proteolysis by trypsin and analysis by LC-MS/MS on Q Exactive plus ( Thermo ) and data analysis with MaxQuant 1 . 5 . 2 . 8 [87] and the Uniprot database as the reference were done using a previously described protocol[22] . The data was quantified by LF analysis using the same software . The identifications are filtered for proteins identified with a false discovery rate of <0 . 01 and at least two identified peptides in the project . The intensities are presented as raw intensities without normalization and as LFQ with normalization , both presented as log2 intensities . The OX-proteins were classified according to their protein class using PANTHER software ( Protein ANalysis THrough Evolutionary Relationships ) Classification System ( http://www . pantherdb . org/ ) [31] . Following the OX-RAC procedure , proteins in a 10 μl aliquot of each eluent were resolved on an 8% SDS-PAGE gel and stained with silver or transferred onto a nitrocellulose membrane ( Whatman , Protran BA83 ) . The blots were first blocked using 3% skim milk , and then probed with 1:500 rabbit polyclonal Gal/GalNAc lectin antibody ( a kind gift from Dr . N . Guillen , Pasteur Institute , Paris , France ) for 16 hours at 4°C . After incubation with the Gal/GalNac lectin antibody , the blots were incubated with 1:5000 secondary rabbit antibody for one hour at room temperature ( Jackson ImmunoResearch ) , and then developed by enhanced chemiluminescence . The detection of oxaloacetate in E . coli and in E . histolytica was done using a commercial enzymatic-based kit ( Abcam , Zotal , Israel ) . Oxaloacetate ( 0 . 25 mM ) and H2O2 ( 3 . 5 mM ) in 10 mM phosphate buffer , pH 7 . 4 , were mixed together at 25°C and the concentration of H2O2 was determined by UV spectrophotometric analysis according to a previously described protocol [47] on a UV spectrophotometer NanoDrop 2000c ( Thermo Fisher Scientific , USA ) . E . histolytica trophozoites were suspended in 2 ml ice cold methanol and transferred into a 4-ml tube which contained glass beads ( 0 . 10–0 . 11-mm diameter ) ( Sartorius AG ) . Cell disruption was performed by using a FastPrep-24 instrument ( MP Biomedicals , LLC ) twice for 40 seconds each at 6 . 0 m/s . Methanolic cell extract was transferred to 15-ml tubes after centrifugation for five minutes at 4°C . Cell debris and glass beads were washed twice with 1 ml ultrapure water as a second extraction step . The aqueous and methanolic cell extracts were combined . An aliquot ( 0 . 4 ml ) of chloroform was added to the cell extracts and the suspension was vortexed and shaken five times for ten seconds . For separation of the aqueous and organic layers , the samples were stored at -20°C for ten minutes . After centrifugation for five minutes at 4°C and 10 , 015g , the upper layer was transferred to a 50-ml tube , diluted with water , and stored at -80°C for lyophilization . Derivatization of lyophilized samples was done using a previously described protocol[88] . Analysis was performed by using an Agilent 7890B GC system with an autosampler ( model G4513A ) , and a coupled mass selective detector ( model 5977B MSD ) ( Agilent ) . The 2-μl injection volume of the sample was split 1:10 at 250°C with an inlet split flow of 10 ml/min . Helium was used as the carrier gas at a pressure of 8 . 8 lb/in2 . Chromatographic separation on a 30-m HP 5-ms column ( Agilent Technologies ) with a 0 . 25-mm inner diameter and a 2 . 5-μm film thickness was performed at a constant gas flow of 1 ml/min . The oven program started with an initial temperature which was held at 70°C for one minute , continued at a heating rate of 1 . 5°C/minute up to 76°C , followed by heating at 5°C/minute up to 220°C , and 20°C/minute up to 325°C , with a hold time of eight minutes . The analytes were transferred to the mass selective detector via the transfer line at 325°C and ionized by electron impact ionization at 230°C . After a solvent delay of six minutes , mass spectra were acquired using a quadrupole temperature of 150°C and SIM acquisition mode . , The selected quantifier ion for glycerol was m/z 205 . 1 and for isocitrate was m/z 245 . 1 . Data analysis was done by using MassHunter Workstation software Quantitative Analysis 8 . 0 . The area of the quantifier ion of each metabolite was integrated and normalized to the area of the quantifier ion of one internal standard ( glycerol and isocitrate were normalized to p-chlorophenylalanine ) . This ratio represents the relative metabolite amount and was normalized to the protein content of the sample . | Entamoeba histolytica is a unicellular parasite which infects millions of humans worldwide via contaminated food and water . It resides in the colon and most infected individuals are asymptomatic . In some people , the parasite can spread into both intestinal and extraintestinal tissues , and results in amebiasis . E . histolytica feeds on bacteria in the colonic microflora . Since changes in the composition of the colonic microflora coincide with the onset of symptomatic amebiasis in affected individuals , a potential role of this microflora in disease manifestation has been suggested . Some modulating effects of the intestinal bacteria and the nature of the native intestinal flora on amebic virulence have been reported , but the exact mechanisms have not been described . Here , we report that E . coli confers increased resistance against oxidative stress to the parasite via the production of oxaloacetate . This antioxidant metabolite is the result of the oxidation of malate by malate dehydrogenase . Our results indicate that E . histolytica can use oxaloacetate of bacterial origin to increase its resistance against oxidative stress and that this oxaloacetate promotes its survival in the large intestine of mice with experimentally-induced amebiasis . We also present evidence that oxaloacetate can protect Caenorhabditis elegans , another bacteria-feeding organism , against oxidative stress . |
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The tradeoff between the need to suppress drug-resistant viruses and the problem of treatment toxicity has led to the development of various drug-sparing HIV-1 treatment strategies . Here we use a stochastic simulation model for viral dynamics to investigate how the timing and duration of the induction phase of induction–maintenance therapies might be optimized . Our model suggests that under a variety of biologically plausible conditions , 6–10 mo of induction therapy are needed to achieve durable suppression and maximize the probability of eradicating viruses resistant to the maintenance regimen . For induction regimens of more limited duration , a delayed-induction or -intensification period initiated sometime after the start of maintenance therapy appears to be optimal . The optimal delay length depends on the fitness of resistant viruses and the rate at which target-cell populations recover after therapy is initiated . These observations have implications for both the timing and the kinds of drugs selected for induction–maintenance and therapy-intensification strategies .
The failure of antiretroviral therapies to completely suppress viral replication in some patients represents a major difficulty in the management of HIV infection . In therapy-naive patients without clinically apparent resistance mutations , triple-drug therapy with two nucleoside–analog reverse transcriptase inhibitors and a protease inhibitor or a non-nucleoside reverse transcriptase inhibitor is standard [1] . In these patients , treatment success rates , defined as viral load <50 copies/ml at 48 wk , range from 70% to 80%–85% ( reviewed in [2] ) . However , in patients with previous regimen failure requiring salvage therapy , response rates are usually considerably lower [3–5] , and it is frequently not possible to assemble a three-drug regimen with uncompromised activity against all viral strains present . In these individuals , treatment failure often occurs after an initial period of response to a new regimen , and is usually associated with the appearance of multiply drug-resistant viral strains . This has led to attempts to treat highly experienced patients with various deep salvage regimens consisting of four , five , or six individual drugs [6–11] . These patients are particularly vulnerable to the many drug interactions [12] ( also reviewed in [13] ) and adverse metabolic , hematologic , neurologic , cardiovascular , and gastrointestinal side effects that complicate HIV therapy and seriously undermine the success of clinical management [14–20] ( also reviewed in [21] ) . The need to minimize drug resistance while reducing treatment-related toxicities has engendered an interest in induction–maintenance ( IM ) strategies , in which a period of intensified antiretroviral therapy ( induction phase ) is followed by a simplified long-term regimen ( maintenance phase ) [22–25] . Most such trials have yielded higher failure rates in the treatment group than in controls receiving conventional therapy . Failure typically occurs during maintenance therapy , and has been attributed to poor regimen adherence [25] and recrudescence of resistance mutations present before institution of induction therapy [23] . One weakness of existing studies has been that induction therapy consisted of standard three-drug antiretroviral therapy ( ART ) regimens in common clinical use at the time of the study , under conditions now recognized to permit subclinical viral replication [26 , 27] . Moreover , in these early studies , the induction phase only lasted between 3 to 6 mo , which may be insufficient . However , two recent studies have shown the apparent effectiveness of induction therapy for 48 wk followed by maintenance therapy with atazanavir [28] or lopinvir/ritonavir [29 , 30] , and this has led to new optimism concerning IM approaches . We have hypothesized that a longer period of a highly suppressive induction therapy that is appropriately timed relative to the start of maintenance therapy may allow minority resistant variants to decay below a stochastic extinction threshold , allowing for successful long-term treatment with simpler and better-tolerated regimens . To explore this hypothesis quantitatively , we constructed a detailed computer simulation model of the dynamics of sensitive and resistant viruses during a hypothetical IM regimen . We show that the timing and duration of induction therapy relative to maintenance therapy can affect the probability that viruses resistant to the maintenance regimen will be eradicated in ways that are somewhat counterintuitive . Under biologically plausible conditions , we find that 6–10 mo of induction therapy are required to maximize the probability of eradicating these resistant viruses . For shorter induction periods , we find that it is optimal to use a “delayed-induction” regimen administered several days to weeks after the start of the intended long-term maintenance therapy .
The model consists of CD4+ target cells , free viruses , and three types of infected cells: short-lived infected cells with t1/2 of ∼1 d , moderately long-lived infected cells with t1/2 of ∼2 . 5 wk , and long-lived infected cells or “latently” infected cells with t1/2 of ∼3 . 5 y ( Figure 1A ) . The model includes four possible mutations that confer resistance to three antiretroviral drugs; mutations 1 and 2 each confer partial resistance to drug I , whereas mutations 3 and 4 confer a high level of resistance to drugs II and III , respectively ( Figure 1B ) . Our model allows viral recombination , and includes the effects of partial drug efficacy , incomplete viral resistance , and cross-resistance between drugs II and III . Drug-resistant viruses can infect moderately long-lived and latently infected cells , allowing for the formation of latent drug-resistant viral reservoirs . Because the model assumes finite population sizes , the various viral genotypes may fall below a threshold for extinction . Since extinction is a chance event , we used random , stochastic modeling terms to model the rate of change of free viruses and infected cell populations that are near the extinction threshold . In the absence of therapy , viral load rises to a peak of approximately 106 virions/ml by day 25 , then falls to an equilibrium of ∼105 virions/ml by day 100 . Target-cell populations decrease during acute viremia , then recover somewhat as viral load falls to its steady state . ( Analytical formulas for the steady-state concentrations of infected cells and free virus under a model very similar to the one here can be found in [31–37] . ) As observed in [31–37] , our model assumes that resistant viruses have lower fitness in the absence of drug . With our conservative parameter choices , viruses with one , two , and three drug-resistance mutations are generally present at frequencies of 10−3 , 10−6 , and 10−9 , respectively , during the period of acute primary infection , whereas viruses with four drug-resistance mutations are generally absent ( Figure 2A ) . Thereafter , the frequency of mutants and latently infected cells ( unpublished data ) increase slowly to equilibrium . To account for this increase in our simulations , we allowed viral populations to equilibrate over a 4 , 000-d period ( >10 y ) before initiating therapy . With less conservative parameter choices , viruses with three resistance mutations will not generally preexist . In this case , the qualitative results described below can be duplicated with less intensive drug therapies . After initiation of conventional triple-drug therapy , the viral load decays at a rate of 0 . 6/d ( first phase decay ) for ∼10 d , then at 0 . 04/d ( second phase decay ) , until HIV-1 RNA falls below the detection limit of 50 RNA copies ( 25 virions ) per ml of plasma around day 120 ( Figure 2B ) . A population of latently infected cells is assumed to contribute a third phase of decay beginning around day 200 , during which virus decays at a rate of 0 . 00052/d . Viral loads during the third phase are on the order of 1 . 0/ml [40] . Model behavior during primary infection , chronic disease , and ART has been designed to match experimental viral dynamics [38–40] . The minority populations of resistant mutants form a reservoir of drug-resistant viruses that can fuel viral rebound if therapy is prematurely reduced or withdrawn . As expected , at low population densities under conditions prevailing during induction therapy , the appearance and loss of drug-resistant populations behave as random , stochastic processes . We have used this model to investigate two questions about IM therapies . ( 1 ) How long should the induction phase be in order to eradicate viruses resistant to the drugs in the maintenance regimen ? ( 2 ) What is the optimal timing of induction therapy relative to maintenance therapy ? Could IM therapies be improved , for example , if the agents that were unique to the induction regimen were started before starting the maintenance drugs ? In the simulations below , the maintenance regimen consists of drugs I and II , while drug III is applied only during induction therapy ( Figure 3 ) . We define “success” as achieving and maintaining a fully suppressed circulating free virus population for a period of at least 3 y after the end of induction therapy . Figure 3A–3B gives typical results; Figure 3A shows how the probability of success varies with the length of the induction phase . In this simulation , the percentage of success increased dramatically as the length of the induction therapy was increased to ∼120 d , and increased more gradually between 120 and 180 d . Further increases in the length of the induction phase beyond 180 d had little effect with these parameters . Figure 3B shows a typical simulation in which the timing of induction therapy was altered . In these simulations , a 30-d course of therapy intensification was started before maintenance therapy ( start days −30 to −10 ) , at the same time as maintenance therapy ( start day 0 ) , or after drugs unique to the maintenance therapy were started ( start days 10 and higher ) . In the latter case , we refer to the period of intensified therapy as a “delayed-induction” therapy . Interestingly , we note that for induction therapies of limited duration , the highest success rates occurred with delayed-induction therapy initiated ∼40 d after the start of maintenance therapy . Delayed-induction therapy ( also referred to as delayed-intensification or booster therapy ) results in higher eradication rates because drug-resistant viral populations are predicted to decline transiently after the start of maintenance therapy [41–43] . This decline occurs because resistant viruses , which are assumed to be less fit than sensitive viruses [31–37] , are no longer created via mutation once drug therapy interrupts viral replication within the drug-sensitive population . Drug-resistant populations do not increase until target-cell populations increase enough to offset their intrinsic growth rate disadvantage . Specifically suppressing replication of resistant viruses with additional drugs when this population is reduced in size maximizes the net impact of induction therapy . This result can be shown analytically using a simple one-infected cell , one-resistant virus , deterministic version of this model in which wild-type ( WT ) virus is completely sensitive to drug , and resistant virus is completely resistant to drug ( Figure 4A and 4B ) . With these simplifications , Nowak et al . [41] have shown that the dynamics of resistant virus after therapy is approximately where V1 ( 0 ) is the density of the resistant virus at the time that therapy is initiated , m is the turnover rate of target cells at steady state , δ is the death rate of infected cells , R0 = psk/cδm , and R1 = psk1/cδm . R0 and R1 are the basic reproductive numbers ( i . e . , the mean number of new cells infected from a single infected cell in a newly infected host who is not being treated ) for WT and resistant viruses [41] . For t ≪ 1 / m and 0 < R1 < R0 , the second term inside the curly brackets is large compared with the first , leading to transient declines in V1 . As t becomes large compared with 1/m , this second term approaches R1 ( 1 − R0 ) / m , whereas the first term continues to increase linearly with t , allowing for eventual increases in V1 . Setting the derivative of V1 ( t ) equal to zero , it is straightforward to show that V1 reaches a nadir at This indicates that the turnover rate of target cells is of major importance in determining the optimal timing of induction therapy relative to the maintenance therapy ( as illustrated in Figure 4B ) , though the replicative fitness of resistant viruses ( as quantified by values of R1 and R0 ) also plays a role . Although we have focused on reductions in the infection rate constant as the most logical way of modeling fitness reductions , the dependence of tmin on R0 and R1 indicates that we will observe nearly identical results if the resistant viruses have lower fitness due to a lower burst size or a higher clearance rate . The results above suggest that induction therapy should be at least 180 d if started at the same time as the maintenance therapy . It also suggests that the optimal time to initiate short-term induction therapy may be several weeks after the start of maintenance therapy . To explore these results in more detail , and to verify that the results are not overly specific to our parameter choices , we systematically varied the key parameters in the full , stochastic model . We first explored the effect of altering the fitness costs associated with resistance to antiviral drugs ( Figure 5A and 5B ) . As expected , the probability of success decreased with increasing viral fitness under both treatment strategies . Consistent with the equation for tmin above , the optimal time to intensify therapy increased as the fitness of the resistant virus decreased . Interestingly , we found that changing the fitness of viruses resistant to the induction regimen ( drug III ) had little or no effect on the optimal time to intensify therapy: the effects depicted in Figure 5B can be ascribed almost entirely to decreased fitness of viruses resistant to the maintenance regimen . As predicted from the equation for tmin above , we obtained nearly identical results if fitness costs were due to resistant viruses having low burst sizes ( unpublished data ) . Under simple population genetic models , the frequencies of singly and doubly resistant viruses prior to therapy are proportional to μ/s , and μ2/s2 , respectively , where s is the selective disadvantage of a drug-resistance mutation [43] . When viruses resistant to the maintenance therapy suffer large fitness costs ( e . g . , w1 = w2 < 0 . 65 ) , they rarely , if ever , contribute to the pool of long-lived infected cells . However , when these mutations have very small fitness costs ( e . g . , w1 = w2 > 0 . 96 ) , these viruses frequently infect cells destined for latency . ( We note that if the cost of resistance to the maintenance therapy is very low , simultaneous triple therapy will fail as well . ) We conclude , therefore , that the success of maintenance therapy will depend greatly on resistance mutations having measurable fitness costs . We next explored the effects of altering the turnover rate ( m ) of the target-cell population , which we accomplished by simultaneously increasing m and k . From the approximate equation for steady-state viral load: obtained from the simple one-infected cell model , we predict that varying m and k proportionally will change the dynamics of target-cell renewal without affecting pre-therapy viral load ( which is a potentially important confounding factor ) . In the full model , we found that both the optimal time to intensify therapy and the probability that standard IM therapy is successful increased as target-cell turnover rates decreased ( Figure 5C and 5D ) . Success rates are influenced by m because the target-cell populations needed for the growth of resistant viruses recover more slowly when m is small . In the simple one-infected cell model , recovery of target cells after therapy is given by where t is time since the initiation of therapy . From this equation , we see that the rate at which target cells return to their pre-therapy steady state is strongly affected by their death rate , m . To examine the role of the latent viral reservoir , we varied the rate at which latently infected cells are created ( fL ) in the full , stochastic model . ( Unless otherwise specified , all subsequent results are derived from this stochastic model . ) With our canonical simulation parameters ( with its conservative estimate for the number of latently infected cells ) , latently infected cells affected outcomes in only a small percentage of cases . The probability of IM therapy failure changed little within the range of fL = 10−8 – 10−6 , but decreased significantly for fL ≥ 6 . 4 × 10−6 ( Figure 6A and 6B , and unpublished data ) . These results indicate that both IM and conventional triple-drug therapy may fail if the number of latently infected cells is pushed too far above 106 , a value near the upper end of experimentally derived estimates ( Table 1 ) . As expected from the analytical equations above , altering the number of latently infected cells did not change our previous conclusions concerning optimal timing of IM therapy ( Figure 6B ) . Finally , we varied the death rate of the moderately long-lived infected cells . In contrast to our conservative estimate for δL , our canonical value for the death rate of moderately long-lived cells , δM = 0 . 04/d , is at the upper end of what might be inferred from second-phase decay rates [38 , 44–55] . We believe δM = 0 . 04/d is appropriate because imperfect efficacy and/or poor adherence will cause the second-phase decay rate to be less than δM . Second-phase decay rates , furthermore , have been shown to be higher in patients with higher viral loads [55] ( the situation modeled here ) . When we repeated our simulations with lower values for δM , we found , as expected , that the duration of induction therapy needed for successful IM therapy increased ( Figure 6C ) . ( In these simulations , we simultaneously changed δM and fM in order to study the effect of altering δM without affecting the pre-therapy density of moderately long-lived infected cells . ) For the case δM = 0 . 02/d , we observed that induction therapy needed to be at least 300 d to have a high probability of driving viruses resistant to the maintenance therapy to extinction . As expected , changing δM had little effect on the optimal time to intensify therapy ( Figure 6D ) . Our canonical simulation includes somewhat arbitrary choices for IC50 values for both drug I ( for which high-level resistance is assumed to require two mutations ) and drugs II and III ( for which a single mutation confers high-level resistance ) . To explore the effects of varying IC50 values , we conducted simulations under a range of IC50 values for drugs II and III ( Figure 7A and 7B ) and for drug I ( Figure 7C and 7D ) . As expected , we found that the probability of success in eliminating drug-resistant viruses decreased with increasing IC50 values and decreasing drug concentration . As in our previous simulations , the marginal benefit of increasing the length of an induction regimen reached a plateau between 150 d and 270 d . We explored the effect of adding a cross-resistance term wherein resistance to drug II confers partial ( or full ) resistance to drug III , and vice versa . Success rates decreased with increasing degree of cross-resistance , particularly when induction therapy preceded the start of maintenance ( Figure 8A and 8B ) . However , the qualitative results of our previous simulations remained unchanged . All of the delayed-induction therapy simulations above assume a delayed-induction phase of 30 d . To explore the effect of varying the duration and start time of delayed-induction therapy , we repeated our simulations over a range of induction treatment lengths and start times relative to maintenance therapy ( Figure 9 ) . For induction therapies of 40 d or less , the optimal time to initiate induction therapy continued to be 30–50 d , as in previous simulations . When the length of induction therapy was increased to 160 d , however , the curve flattened out considerably , indicating that the benefit of delaying induction is diminished at longer treatment durations . This is intuitively reasonable , since longer induction therapies will cover the critical time when resistant viruses are predicted to hit their nadir , even though they might be started well before the optimal therapy intensification times . The benefit of an optimally timed induction therapy , therefore , is most acute when the length of therapy intensification is short . To explore the effects of viral recombination on these strategies , we extended the model further to account for the effect of recombination between genotypes V12 and V34 . At realistic recombination rates ( i . e . , with r ≤ 0 . 01 ) , we observed virtually no effect on the success rate of IM therapy ( unpublished data ) . This is in part because terms of the form μkTV123 , which approximate the rate of production of V1234 by mutation , are at least an order of magnitude greater than terms of the form rkI12V34 , which approximate the rate of input into the V1234 population by recombination in our model . To achieve a higher-order resistance genotype by recombination , two or more dissimilar resistant virions must coinfect a cell , establish productive infection , and copackage two nonidentical templates to produce a heterozygous virus during virus production . After infection of a new target cell , an odd number of recombination events must occur between templates during reverse transcription , within a locus between the relevant resistance mutations . In the case of drugs targeting protease and reverse transcriptase ( the two most common drugs ) , recombination must occur within a span of ∼900 bp , or roughly one-tenth of the viral genome . Only a fraction of resistant viruses will overcome all of these hurdles . Given published estimates of approximately three recombination events per replication cycle [56] , r = 0 . 01 is reasonable , and perhaps somewhat high . To illustrate the ultimate consequences of very high recombination rates , we also performed simulations with unrealistically high recombination rates ( i . e . , with r ≥ 1 ) . At these extreme values , success rates declined in a manner similar to other perturbations that make therapy less likely to be effective ( unpublished data ) . Thus , biologically plausible recombination rates had little qualitative or quantitative effect on the outcomes observed in our four-mutation model . The fact that effective population sizes are so much lower than census sizes is one of the major riddles of HIV-1 evolution . This controversy arises from the observation that the viral effective population size , as measured using standard tools of population genetics , is orders of magnitude lower than the census size ( physical count of the number of viruses ) . In the simulations shown so far , we have conservatively assumed that the dynamics of viral resistance can be described using a model in which the number of viruses in the body equals a liberal estimate of census size . The controversy over viral effective population size has led to suggestions that the use of viral census size is too conservative [57 , 58] . Unfortunately , it is not clear how to model the effective population size since there is a lack of agreement on why effective population sizes are so low . However , it is possible to explore the effects of some of the more commonly proposed explanations using the modeling framework developed here . One explanation for low viral effective population size is that most of the infected cells and virions assayed by PCR are noninfectious . If this were the entire explanation for extremely low effective population sizes , use of current estimates of census size would be inappropriate . To explore what occurs if very few virions and integrated proviruses are replication-competent , we repeated our simulations with a census size 10 , 000-fold lower than the one used previously . Under this assumption , we obtained qualitatively similar results under a treatment regimen in which both the induction and the maintenance therapies consist of one drug . While a reduced therapy burden would be a welcome finding , two-drug therapies have not been generally successful , suggesting that these conditions are a less accurate approximation of biological conditions . Another possibility is that the effects of a genetic bottleneck during primary infection and rapid turnover of viral populations due to strong immune selection periodically purge HIV-1 populations of genetic variation . Because the effective population size is proportional to the amount of genetic variation , these factors would have a large negative effect on the measured effective population size during primary infection . To examine the impact of these processes on the dynamics of resistant virus , we set viral load to a very low value at the beginning of primary infection , and simulated immune selection for a character unrelated to resistance mutations , starting near day 200 . We found that neither mechanism for low effective population size had a significant long-term impact on the frequencies of drug-resistant viruses ( unpublished data ) . Although these simulations cover only some of the possible mechanisms for low effective population size [59–61] , they indicate that it is possible to appropriately model drug therapy using population sizes similar to the census size , regardless of the calculated effective population size . The results above are all based on a “standard” model that assumes that HIV is limited in vivo by the supply of CD4+ target cells [38 , 45–48 , 62 , 63] . We have chosen to use this standard model because it is supported by independent lines of evidence [64] and is well-studied mathematically , and because there is no clear consensus on appropriate methods to model immune responses . However , some modelers have argued that viral load is determined primarily by the dynamics of the immune response ( reviewed in [65] ) . To verify that our results are not specific to this target-cell limited model , we have performed analogous simulations under a model in which viral populations are limited instead by the immune effectors ( ones that act by preventing virus from infecting cells ) . In Figure 10A , we show using this model that drug-resistant viruses transiently drop in density following drug therapy in a manner very similar to that which occurred under the one-drug , one-cell , one-mutation , target-cell limited model in Figure 4A . When this model was extended to account for moderately long and very long-lived infected cells and varying turnover rates for immune effectors , we obtained results analogous to those for the stochastic target-cell limited model ( Figure 10B ) . In both models , the essential feature is that the environment for resistant viruses improves as viral load decreases , and in both models the length of the dip depends on how rapidly “the environment” improves . In the target-cell limited model , drug-resistant viruses showed a larger transient reduction if target cells regenerated slowly after therapy . In the immune-control model , drug-resistant viruses underwent larger transient declines if the HIV-specific effector cells decayed slowly during drug therapy .
In this study , we have used a detailed differential equation model to investigate induction–maintenance ( IM ) strategies for treating HIV-1 infections . In these strategies , an induction regimen is used to drive viral load to low levels before switching patients to a simpler and potentially better tolerated long-term maintenance regimen . We find that an appropriately deisgned IM regimen is likely to result in long-term suppression of viremia , and may also result in the eradication of minority virus populations resistant to the maintenance regimen . The marginal benefit of increasing the induction phase starts to level off between 4 and 10 mo , depending on the parameter choices . Interestingly , we find that in cases where target-cell populations recover slowly after ART , the optimal time to initiate a short-term induction regimen may be optimally started several days to weeks after the start of maintenance drugs . ( This delayed-induction therapy may also be referred to as delayed-intensification or booster therapy . ) These delays are advantageous because viruses resistant to the maintenance regimen briefly decline after exposure to the maintenance drug , due to reduced mutational input from the majority sensitive population . These resistant viruses do not increase again until the environment for the virus improves ( modeled here as a recovery in target-cell populations ) . Intensifying therapy when the resistant virus population is close to its nadir maximizes the effectiveness of the additional therapy . These results therefore illustrate the importance of considering dynamic feedback mechanisms such as those that occur under classical predator–prey models in ecology [66 , 67] when implementing IM regimens . Although our exploration of this model has caused us to view IM therapy in an optimistic light , our model predicts that IM therapies can fail under a variety of conditions , including situations in which drug resistance imposes little or no fitness costs ( Figure 5A and 5B ) , situations in which latently infected cells are formed at high rates ( Figure 6A and 6B ) , and situations in which the primary mutations responsible for drug resistance have large effects on the IC50 values , either directly ( Figure 7 ) or indirectly via cross-resistance ( Figure 8A and 8B ) . Our specific predictions about the optimal length for the induction period , likewise , depend on the size of the overall viral reservoir and the rate of the decay of moderately long-lived infected cells ( the primary determinant of optimal induction length ) . Finally , as discussed above , our finding that the best time to intensify therapy is often several days to weeks after the start of regular therapy depends critically on two parameters: the fitness of the resistant virus and the rate at which target-cell populations recover after initiation of therapy . The lower the fitness of resistant viruses and the slower the rate of recovery of target cells ( or other factors regulating viral density ) , the later the optimal time to maximize therapy . In cases where target-cell populations increase rapidly , or when other factors that limit viral replication decay quickly during therapy , delaying the induction phase may not be beneficial . These findings may be important in several clinical scenarios . IM therapy may be useful in resource-poor settings where patients have limited access to antiretroviral drugs . In these settings , it is particularly important to minimize the chance of selecting for drug-resistant viruses during the initial attempt to administer antiretroviral drugs . In addition , an intensification–maintenance approach could provide protection against the development of drug resistance in antiretroviral-naive patients , particularly in patients infected by a donor with known poor adherence to medications ( in which case it would be advisable to consider a maintenance phase consisting of three or more drugs , as opposed to the two-drug maintenance regimens modeled here ) . Recent estimates suggest that up to 10%–15% of treatment-naive patients harbor one or more drug-resistance mutations [68–70] , and this problem is likely to increase with increasing availability of ART . Finally , the principle of IM approaches could also be applied to the difficult problem of salvage therapy . The latter two scenarios have not been specifically modeled here . The results presented here must be weighed against several practical considerations: a two-drug maintenance regimen may incur a higher failure risk among patients prone to subtherapeutic drug levels for any reason , since there will be a reduced level of concurrent coverage by other agents in the regimen . It is also essential that the maintenance regimen not include drugs for which the patient previously developed drug resistance , a requirement that is complicated by the problem of cross-resistance . In addition , it would be highly desirable that agents used in maintenance therapy be simple and well-tolerated , with favorable pharmacokinetics , and have a high barrier to the development of resistance—both in terms of the number of mutations required for resistance and the fitness of the resulting mutants . By contrast , the requirements for induction regimens are considerably less stringent: induction therapy must be able to suppress replication of viruses resistant to the maintenance regimen and be free of intolerable adverse effects during short-term use . Although we have gone to considerable lengths to make the model realistic , we still make a number of simplifying assumptions . First , we ignore drug redistribution , and assume that drug levels immediately reach the therapeutic window at the time of initiation , remain constant during therapy , and fall to zero at discontinuation . There will clearly be some deviation from these ideal conditions in vivo because of pharmacokinetic “loading effects , ” individual failure to adhere to treatments , antagonistic drug interactions , and other factors . Although we believe that four mutations are sufficient to capture the basic behavior of drug resistance , this is clearly a simplification , as are some of our assumptions about IC50 values and cross-resistance . Our point is to make a reasonable model that captures key features , not to make a complete model of drug resistance . We have also neglected reversion of drug-resistant variants to WT virus . However , this effect is likely to be small under drug therapy , and would result in lower failure rates than modeled here . In building our model , we assumed that double therapy usually fails and that triple therapy usually succeeds , as has been observed in clinical practice . There are , of course , wide regions of parameter space where double therapy always succeeds and , conversely , where triple therapy always fails , and it is possible that many real patients could fall into one of these two categories . Although the specific simulations presented here would not be relevant to these patients , the same concept ( but with a different number of drugs ) can be applied to these patients . The key to applying IM strategies to such patients would be develop methods for distinguishing among patients whose maintenance therapies would require one , two , three , or more drugs . Finally , our model assumes a degree of fitness cost of resistance to drugs . Several studies have linked the presence of resistance mutations with decreased RT processivity [71] , reduced replicative capacity in vitro [72–75] , a competitive disadvantage against WT viruses in competition assays [75] , lower viral loads , and lower rates of CD4 T cell loss in vivo [72 , 73 , 75] , and have shown a tendency for overgrowth by WT viruses after discontinuation of therapy in cases of mixed infection [76 , 77] . As shown in Figure 5A and 5B , the probability of treatment success drops dramatically as the cost of resistance decreases . An essential feature of any two-drug maintenance regimen , therefore , is that the maintenance regimen includes drugs for which resistant mutations incur measurable fitness costs . In cases where fitness costs are small , it would be advisable to choose maintenance regimens in which four or more mutations are required for resistance ( something that can easily be implemented using a three-drug maintenance regimen ) . Key experiments needed to test the model's assumptions would focus on how the concentration of resistant viruses residing in short-lived , moderately long-lived , and latently infected cells changes during the first 90 d of therapy . Experiments designed to test the prediction that resistant viruses decrease transiently during therapy could be particularly informative . A better understanding of factors that allow for continued replication in the face of various therapies ( e . g . , identification of sanctuary sites in which drugs do not penetrate ) would also be very important . More generally , experiments designed to improve our understanding of viral effective populations size and factors that control viral load in the absence of therapy could lead to the construction of more realistic models for viral dynamics . Also , since our model shows that the probability of therapy success decreases as the number of latently infected cells increases , our study suggests that it would be useful to obtain additional quantitative estimates of the size of the latent viral reservoirs . Most studies of the latent reservoir have focused on blood . If less intensively studied sites such as the lung , brain , or gastrointestinal tract were found to have larger than expected numbers of latently infected cells , it might be necessary to choose more conservative treatment strategies . In addition to HIV-1 , IM approaches are being used for the treatment of a growing number of infectious illnesses , including active tuberculosis [78] , bacterial endocarditis [79] , and prosthetic joint infections [80] , and have widespread application in oncology . In these settings , induction therapy is usually timed to coincide with initiation of maintenance therapy , and maintained for an empirically determined period of time . Although the replication dynamics of the pathogenic elements in these cases ( i . e . , infecting microorganisms or aberrant host cells ) differ significantly from those of HIV , the chronic nature of these conditions , the requirement for long-term therapy , and the potential for developing resistance to drugs and immune responses pose similar challenges to the host . The counterintuitive results that have emerged from our analysis of HIV replication under therapy suggest that it may be beneficial to explore dynamic modeling approaches in these cases as well .
As with most biological models , certain parameters and assumptions are better supported than others . Parameters used in our model are given in Table 1 . These values resulted from a sequential process in which we first fixed parameters , such as viral load , δI , δM , and δ , which have been characterized experimentally . We then manipulated unknown/less-well–characterized parameters to match in vivo data on the viral kinetics during primary infection , during therapy , and after a treatment interruption . Most of these parameters were set to yield conservative ( i . e . , higher than average ) estimates for the number of infected cells . We then varied the drug concentrations and IC50 values ( within estimated ranges ) to match experimental observations that triple therapy is usually successful but double therapy usually fails . After completing these three steps , we performed our key exploratory simulations in which we examined the effects of varying the length and timing of induction therapy . Simulations were repeated across a wide range of reasonable values for parameters that remain poorly characterized by experimental methods ( e . g . , target-cell turnover rates ) . Dynamics of infection were simulated using an extension of a commonly used model for viral dynamics [38 , 41 , 45–48 , 62 , 81–85] that assumes that viral load is limited by the supply of CD4+ target cells . Our model consists of 65 differential equations accounting for target cells , free virions , three types of infected cells , and 16 viral genotypes ( Figure 1B ) . The dynamics of target cells and drug sensitive viruses are given by where I , M , and L represent short-lived , moderately long-lived , and latently infected cells , respectively; V represents free virions; T represents target cells; fM and fL are the fractions of target infected cells that become moderately long-lived and latently infected cells upon HIV-1 infection; fI = 1 − fM − fL; s is the input rate of target cells; m is the death rate of target cells; δI , δM , and δL are the death rates of short-lived , moderately long-lived , and latently infected cells , respectively; pI , pM , and pL are the rates at which short-lived , moderately long-lived , and latently infected cells produce virus; c is the clearance rate of free virus; t is time in days; K is the rate at which WT virus infects cells , and Ki is the rate at which virus with resistance mutation i infects target cells in the presence of therapy . To model the effects of drugs on these different viruses , we assume that infection rate constants K , K1 , K2 , . . . , K1234 decline in the presence of drug-using functions described below ( see Modeling of viral replication under drug therapy ) . The dynamics of mutants partially resistant to drug I , but sensitive to drugs II and III , are given by equations of the form: where μ is the probability that a cell infected with WT virus will acquire a resistance mutation to one of these drugs . The equations of other resistant mutants are straightforward extensions of these equations with sequential mutation accumulation . For example , the dynamics of mutants with high-level resistance to drug I , but sensitive to drugs II and III , are given by the equations . while the dynamics of mutants resistant to all four drugs is given by the equations We note that this model assumes that reverse mutations from resistance to sensitivity is negligible . Another cryptic assumption is that short-lived , long-lived , and latently infected cells are derived from a single population of CD4+ target cells , as modeled by Nowak et al . [41] . In preliminary simulations and/or calculations , we have determined under reasonable conditions that neither of these factors has much effect on our qualitative conclusions . The extinction threshold was set to 3 × 10−9 infected cells/μl , which is roughly equivalent to one infected cell per 2 × 1011 CD4 cells ( the approximate total body CD4 cell population ) . In preliminary work , we found that it is almost impossible to eliminate viruses resistant to any single drug during triple-drug therapy . IM therapy was therefore considered to be successful when the concentration of viruses and cells infected with viruses resistant to both of the drugs in a two-drug maintenance regimen fell to zero or if viral load failed to rebound for a period of 3 y after ending induction therapy . To allow for imperfect drug efficacy against WT virus , we assumed that the infection rate constant for genotype i in the presence of drug j can be modeled as: where k is the baseline infection rate constant for WT virus in the absence of drug , wi is the replicative fitness cost associated with mutation i ( expressed as a percentage of k ) , IC50i , j is the concentration of drug j at which infection rate constant for mutant i is 50% of its original value , and Dj is the concentration of drug j [49] . In our four-mutation system , mutations 1 and 2 confer partial resistance to drug I , while mutations 3 and 4 confer substantial ( though not 100% ) resistance to drugs II and III , respectively . For the “canonical case , ” we assumed that mutations 1 and 2 each confer a 5-fold increase in IC50 value against drug I , resulting in a 25-fold increase in resistance for the double mutant V12 as expected [86] , while mutations 3 and 4 confer 100-fold increases in IC50 values against drugs II and III , respectively . In the figures , we refer to the fold increase in resistance conferred by mutations 1 or 2 as “IC50INT” ( since these mutations confer an intermediate level of resistance ) , and the fold increase in resistance conferred by mutations 3 and 4 as “IC50MUT” ( since these mutations confer high-level resistance; i . e . , they are completely mutated ) . Under this model , resistance to drug I would be analogous to resistance to a protease inhibitor , while resistance to drugs II and III would resemble resistance to nucleoside reverse transcriptase inhibitors and first-generation nonnucleoside reverse transcriptase inhibitors . The resulting IC50 values are summarized in Table 2 . To calculate the infection constants in the presence of multiple drugs , we used generalizations of the IC50 formulas given above , wherein fitness effects and IC50 effects are multiplied together to give the composite infection rate constant . For example , the infection rate constant for the quadruple mutant V1234 in the presence of drugs is given by: where k is the baseline infection rate constant for WT virus in the absence of drug; w1 , w2 , w3 , and w4 are the negative fitness effects associated with each resistance mutation; IC501 , 1 , IC501 , 2 , and IC501 , 3 are the IC50 values for genotype V1 against drugs I , II , and III , respectively; IC501234 , 1 , IC501234 , 2 , and IC501234 , 3 are the IC50 values for genotype V1234 against drugs I , II , and III , respectively; and D1 , D2 , and D3 are the concentrations of drugs I , II , and III , respectively . In the presence of drug , we assumed drug concentration values of 20 ng/ml . In our model , drug concentrations immediately rise to therapeutic levels or fall to zero when therapy is changed . In preliminary calculations , we have determined that pharmacokinetic transients have relatively little effect on our qualitative results under reasonable conditions . Finally , we modeled ( reciprocal ) cross-resistance between mutations conferring resistance to drugs II and III by setting the IC50 value each of these drugs to IC50WT × ( IC50MUT / IC50WT ) α , where α is a coefficient giving the degree of cross-resistance . When α = 0 , the IC50 value equals that of the WT value; when α = 1 , the IC50 value of the mutant equals that of the mutant that is resistant to the other drug . These α values were then converted to percentages , where 0% indicates no cross-resistance , and 100% indicates that mutations conferring resistance to drug II are equally resistant to drug III and vice versa . In models with three mutations , recombination acts only on the same order as the mutation rate , since the triple mutant V123 can be created by either one mutation added to V12 or recombination between V12 and V3 . However , in models with four or more mutations , recombination between V12 and V34 reduces the number of mutation/recombination events needed to create a fully resistant virus . To account for recombination without adding a huge number of terms , we assumed that infection of I34 by V12 or infection of I12 by V34 results in the formation of the quadruple mutant with probability r , where 0 ≤ r ≤ 1 . For example , the equation for short-lived infected cells with virus with all four resistance mutations becomes: Modifications for M1234 and L1234 were similar . To account for random genetic drift occurring at low population densities , we used stochastic terms similar to those used in [46] to model populations near the cutoff for extinction . For each time-dependent variable x ( e . g . , I , V ) , we first determined if x < nsxmin , where ns is the number of copies below which x is subject to stochastic forces and xmin is the concentration at which there is only one virus or infected cell in the body . For x ≥ nsxmin , we set x ( t + h ) = x ( t ) + [B ( x ) − M ( x ) ]h , where h is the step size , B ( x ) is the sum of the “birth” terms , and M ( x ) is the sum of the “mortality” terms . For x < nsxmin , we set x ( t + h ) to x ( t ) − 1 , x ( t ) , or x ( t ) + 1 according to the probabilities hM ( x ) , 1 − [M ( x ) + B ( x ) ]h , and hB ( x ) . Use of deterministic equations for x > nsxmin strikes a balance between the need to account for stochastic effects at low population densities and the need to reduce computation times at higher densities where stochastic effects are negligible . Since preliminary runs with ns = 25 , 50 , 100 , and 200 gave similar results ( but clearly distinct from ns = 1 or ns = 5 ) , we reasoned that ns = 25 would be sufficient to capture most of the stochastic variation that occurs at low density . Probabilities were determined using the random number generator MT19937 [87] . Simulations performed using the random number generator ran2 [88] yielded indistinguishable results ( unpublished data ) . To create a realistic simulation of IM therapy , we adjusted the parameters to match the dynamics of viral decay during potent combination therapies [38 , 40 , 89 , 90] . Prior to the initiation of therapy , we assumed that there are ∼1010 viruses , ∼3 × 108 short-lived infected cells , ∼107 moderately long-lived infected cells , and ∼106 latently infected cells per body . Unless otherwise stated , other parameter values used were: s = 2 . 0 cells/d , m = 0 . 02 cells/d , k = 0 . 0008 cells × μl/d , w1 = 0 . 95 , w2 = 0 . 95 , w3 = 0 . 95 , w4 = 0 . 95 , δI = 0 . 6 cells/d , δM = 0 . 04 cells/d , δL = 0 . 00052 cells/d , f M = 0 . 07 , fL = 10−6 , p = 100 virions/d , pM = 6 virions/d , pL = 2 virions/d , c = 3 d−1 , and μ = 1 × 10−4 . All three drugs ( D1 , D2 , D3 ) are set at 20 ng/ml when these drugs are present . The input rate of target cells , s , was set so that the steady state concentration of target cells is 100 cells/μl , or approximately 10% of a typical peripheral blood CD4 T cell count , since not all CD4+ T cells are susceptible to HIV-1 infection . Units for target cells are based on a total estimate of 2 × 1011 CD4 cells per body , of which 2% are in blood . The stochastic cutoff threshold was set at one infected cell per body , or 3 × 10−9 cells/μl . The death rate of latently infected cells of δL = 0 . 00052/d ( t1/2 = ∼44 mo ) was conservatively set to one of the lower experimental estimates [50 , 89 , 91–93] . The mutation rate was deliberately set to approximately three times the estimated per-base rate to account for the fact that more than one nucleotide mutation may lead to an amino acid change that results in resistance . In all simulations , we assume that fitness effects are multiplicative: that is , that k12 = k1k2 / k , k13 = k1k3 / k , k23 = k2k3 / k , and k123 = k1k2k3 / k2 , as in [94] . The effects of changing less well-quantified parameters , such as m and k , are summarized in the results . Although we focus on the target-cell limited model described above , we also explored a simple immune-control model to determine how dependent our qualitative results are on the factors that regulate HIV-1 density . In our immune-control model , the virus population expands exponentially without limitation in the absence of immunity . Immune effectors , which increase at a rate proportional to the number of infected cells , interfere with the ability of virus to infect cells ( as might happen if immune cells release chemokines and/or neutralizing antibodies ) . We implemented this initially using the following model with one mutation and one type of infected cell: where X is the concentration of immune effectors , sX is the rate of appearance of immune effectors in the absence of immune stimulation , mX is the death rate of immune effectors , kX is the rate at which HIV-1–infected cells activate immune effectors , and Ks is a saturation constant describing the negative effect that the immune effectors have on the ability of HIV-1 to initiate infections . The symbols T , I , I1 , V , V1 , K1 , p , c , δ , and μ have the same meanings as in the target-cell limited model above , though when simulating dynamics under this model , we assume that T does not change over time . To extend this immune-control mechanism to the full , stochastic model , we made analogous extensions , setting dX / dt = sX − mXX + kX ( I + I1 + … + I1234 + M + … + M1234 ) X and multiplying the infection rate constants ( K , K1 , K2 , … , K1234 ) by Ks / ( Ks + X ) , while keeping T constant . To simulate drug treatment for different rates of turnover of immune effectors without also changing pretherapy viral loads , we increased sX , mX , and kX proportionately . ( The latter is needed since steady-state viral load is the sum of terms proportional to sX / kX and mX / kX . ) | Clinicians treating HIV infection must balance the need to suppress viral replication against the harmful side effects and significant cost of antiretroviral therapy . Inadequate therapy often results in the emergence of resistant viruses and treatment failure . These difficulties are especially acute in resource-poor settings , where antiretroviral agents are limited . This has prompted an interest in induction–maintenance ( IM ) treatment strategies , in which brief intensive therapy is used to reduce host viral levels . Induction is followed by a simplified and more easily tolerated maintenance regimen . IM approaches remain an unproven concept in HIV therapy . We have developed a mathematical model to simulate clinical responses to antiretroviral drug therapy . We account for latent infection , partial drug efficacy , cross-resistance , viral recombination , and other factors . This model accurately reflects expected outcomes under single , double , and standard three-drug antiretroviral therapy . When applied to IM therapy , we find that ( 1 ) IM is expected to be successful beyond 3 y under a variety of conditions; ( 2 ) short-term induction therapy is optimally started several days to weeks after the start of maintenance; and ( 3 ) IM therapy may eradicate some preexisting drug-resistant viral strains from the host . Our simulations may help develop new treatment strategies and optimize future clinical trials . |
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The genomic information of microbes is a major determinant of their phenotypic properties , yet it is largely unknown to what extent ecological associations between different species can be explained by their genome composition . To bridge this gap , this study introduces two new genome-wide pairwise measures of microbe-microbe interaction . The first ( genome content similarity index ) quantifies similarity in genome composition between two microbes , while the second ( microbe-microbe functional association index ) summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries . These new indices are then used to predict co-occurrence between reference genomes from two 16S-based ecological datasets , accounting for phylogenetic relatedness of the taxa . Phylogenetic relatedness was found to be a strong predictor of ecological associations between microbes which explains about 10% of variance in co-occurrence data , but genome composition was found to be a strong predictor as well , it explains up to 4% the variance in co-occurrence when all genomic-based indices are used in combination , even after accounting for evolutionary relationships between the species . On their own , the metrics proposed here explain a larger proportion of variance than previously reported more complex methods that rely on metabolic network comparisons . In summary , results of this study indicate that microbial genomes do indeed contain detectable signal of organismal ecology , and the methods described in the paper can be used to improve mechanistic understanding of microbe-microbe interactions .
Classical approaches for characterizing microbe-microbe interactions include environmental surveys where the presence or abundance of different species in the community is estimated from the presence or abundances of lineage specific 16S rRNA or other phylogenetic markers [7 , 8] . These types of data collected from a variety of different but related habitats [9–11] or from the same habitat across time or space [12 , 13] are used to understand microbe-microbe interactions . The interactions are inferred from concerted changes in organismal abundance or patterns of species co-occurrence . While 16S rRNA based approaches to the problem are informative , they do not provide a clear way to understand the molecular mechanisms of inferred dependencies between the species . While 16S rRNA based approaches do not lead mechanistic understanding of inferred patterns of microbe-microbe interactions , it is known that such interactions are driven by microbial metabolism and physiology: bacteria compete for essential nutrients [14 , 15] , form food chains [16] , and influence each other via secondary metabolites [17] and signaling molecules [18] . However , the extent to which global genome composition and structure influences organismal ecology remains undetermined , and only recently have researchers attempted to use genomics-based approaches to characterize microbial communities and their governing molecular principles . The most popular currently existing genomics-based approaches for predicting relationships between microbes were developed within “reverse ecology” framework [19 , 20] . This framework produces indices measuring metabolic complementarity ( the fraction of biochemical compounds predicted to be necessary for the metabolism of one microbe but synthesized by another ) and metabolic competition ( the fraction of biochemical compounds predicted to be necessary for the metabolism of both microbes ) , which can be used to evaluate how two given microorganisms might interact metabolically [21] . While these metrics are well regarded and have been used to study microbe-microbe interactions in human gut and other human associated habitats [21–23] , it is not known to what extent they are able to explain ecological associations between microbial species . Metabolic competition and complementarity indices are constructed upon the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) biochemical pathway annotations , which are available only for some proteins ( often a small fraction ) in any given genome . Additionally , level of KEGG pathway annotation depends heavily on how extensively a given microorganism has been studied . These are two potential limitations to using KEGG microbial comparative genomics in general and for understanding microbial ecology with metabolic indices in particular . An alternative to using KEGG pathways is to assess protein functional associations with genome content-based methods . These methods infer functional associations between proteins using measures derived from a number of distantly related genomes , summarizing information on their composition and structure [24] . Well-known genome content-based methods for predicting protein functional associations include phyletic profile , gene neighbor , and gene fusion [24] . The gene neighbor approach is built upon the observation that functionally related proteins tend to be encoded next to each other in various microbial genomes . Co-encoding is driven by the contribution of horizontal gene transfer to microbial genome evolution [25] as well as some aspects of transcriptional regulation in prokaryotes [26] . The gene neighbor approach has been shown to outperform other methods when evaluated using EcoCyc complexes and pathways data [27] . While composite methods incorporating information from several genome content-based prediction strategies have been proposed , they were not found to provide a significant advantage over the gene neighbor method alone [28] . While the gene neighbor method or other genome content-based methods do not pinpoint the exact molecular mechanisms of functional associations between the proteins , they have been successfully used to predict novel cellular systems [28 , 29] , biosynthetic gene clusters producing secondary metabolites [30 , 31] , CRISPR associated genes [32] , and novel genetic components of known metabolic pathways [33 , 34] . The success of genome content-based methods in understanding the biology of individual genomes suggests that these methods could be useful in evaluating functional relationships between the proteins within supra-genomes of microbial communities as well [35] . Additionally , genome content-based inference about functional associations between proteins should be less affected than KEGG pathways by how well an organism is understood , which mitigates at least one limitation of using KEGG annotation . In this study I aim to address two following points: ( 1 ) to develop new genomic indices for quantifying propensity of the microbes to interact with each other using gene neighbor method for predicting functional associations between proteins; ( 2 ) to understand to what extent microbe-microbe interactions , represented by microbial co-occurrence , can be explained using genomic information alone . I also evaluated how well newly developed genomics-based methods can predict microbial co-occurrence in comparison to already existing ones .
To better understand what fraction of ORFs ( Open Reading Frames ) across variety of microbial genomes is annotated with KEGG pathways information or using gene neighbor-based predictions I surveyed . 308 microbial genomes from ecological dataset 2 ( described later ) . Results indicate that between 10% and 65% of the ORFs are included in the KEGG pathways data from IMG JGI ( S1A Fig ) . On the other hand , using gene neighbor-based predictions allows for the incorporation of information from a much larger fraction of genes encoded in each genome . Putative pathways predicted using clustering of protein functional association networks ( also called “clusters of functionally linked genes” , or “gene sets” throughout the manuscript ) incorporate between 35% and 95% of all ORFs ( S1B Fig ) . Proportion varies across organisms and depending on minimal allowed gene set size . In order to evaluate the empirical properties of the indices described above , I calculate genome content similarity and microbe-microbe functional association metrics by comparing the genome of Escherichia coli str . K-12 substr . MG1655 , Clostridium tetani E88 , or Halobacterium sp . NRC-1 , to the other 759 microbial genomes in STRING ( S2A Fig ) . Only representative , distantly related , core genomes from STRING are included here . Accessory genomes , closely related to the three species in focus , are also included , but not ones related to core genomes other than E . coli , C . tetani or Halobacterium sp . Uneven representation of bacteria and archaea in STRING is evident from the bimodal distribution of phylogenetic distances between each of the three focal genomes and the rest of the included species ( Fig 2 histograms on top ) , and from differences between distributions of phylogenetic distances measured from Halobacterium ( archeae ) and E . coli and C . tetani ( bacteria ) . Across all pairs of species examined , the two indices show a unimodal distribution ranging from 0 . 5 to 1 ( Fig 2A ) and 0 to 0 . 4 ( Fig 2B ) for genome content similarity and microbe-microbe functional association , respectively . Both indices decay with growth of phylogenetic distance . In the case of the genome content similarity index for E . coli , the relationship appears exponential , while in case of other genomes , they seem linear ( Fig 2A ) . This can possibly be attributed to the presence of closely related strains of E . coli in STRING and the absence of a large number of closely related taxa for other genomes . Correlation analysis is performed to reveal if new indices developed here can be useful in predicting microbial co-occurrence ( S2B Fig ) . Pairwise genome content similarity , microbe-microbe functional association , and co-occurrence of the microbes are calculated for STRING genomes detected in following ecological datasets: The two ecological datasets described above should represent different conditions for the metrics to predict associations between microbes within different habitats ( dataset 1 ) or to capture concerted patterns of presence or absence of microbes within the same environment ( dataset 2 ) . Next , I calculate partial correlations between the indices and co-occurrence accounting for phylogenetic relationships between the species for both ecological datasets ( S2B Fig ) . Co-occurrence is correlated positively with the genome content similarity index in both ecological datasets ( Fig 3 ) , with Pearson correlations between the measures equal to 0 . 207 ( p-value = 0 . 0001 ) in dataset 1 and 0 . 1954 ( p-value = 0 . 0001 ) in dataset 2 ( Fig 3A and 3C ) . This result means that the more similar the genomes of two microbes are , the more likely they are to be found together in the environment , thus highlighting the importance of habitat filtering in microbial community assembly both across the environments ( dataset 1 ) and within similar ecological habitats ( dataset 2 ) . Co-occurrence is also correlated positively with the microbe-microbe functional association index in both ecological datasets ( Fig 3 ) . The Pearson correlations between the measures are 0 . 2437 ( p-value = 0 . 0001 ) and 0 . 06768 ( p-value = 0 . 0331 ) in datasets 1 and 2 , respectively ( Fig 3B and 3D ) . This result indicates that taxa , which tend to be found together , have higher potential for interaction at the molecular level as captured here through reconstructed protein-protein functional association network . Ecological dataset 2 is also used to compare metrics developed here to previously reported metabolic competition and complementarity indices , which are constructed using KEGG pathways . To compare predictive power of different metrics , I perform Mantel regression analysis between co-occurrence as the response variable , and ether phylogenetic distance alone ( regression model 1 ) , or one of the four available genomics-based indices , two generated in this study and two existing ones , and phylogenetic distance between organisms ( regression models 2 to 5 ) , or all five predictors as the independent variables ( regression model 6 ) , in ecological datasets 2 ( S2C Fig ) . All of the tested regression models are statistically supported ( Table 1 ) , p-values associated with F-statistics are less than 0 . 05 . Phylogenetic distances alone explain 9 . 84% of the variance in co-occurrence of microbes ( regression 1 ) . All of the genomic indices , when considered one at a time in a combination with phylogenetic distances between microbes also produce statistically supported models ( p-values for t-statistics associated with coefficients for genomic indices is less than 0 . 05 in regressions 2 to 5 ) and explain a significant amount of the variation in the co-occurrence data ( Table 1 ) . Genome content similarity explains the highest fraction of the variance in addition to the fraction explained by phylogeny alone ( 3 . 44% ) , followed by the metabolic competition index , which accounts for 2 . 11% of the variance . Metabolic complementarity explains 1 . 7% of the variance in co-occurrence data , and the microbe-microbe functional association index explains less than 1% of the variance . Regression model 5 , which combines all four genomics-based indices , does not seem to improve over the predictive ability of regression model 2 , which includes phylogenetic distance and genome content similarity . Microbe-microbe functional association index is the only other significant predictor in model 6 ( p-value 0 . 0257 ) . The regression model 6 explains 3 . 81% of variance ( in addition to phylogeny ) marking the current predictive power of genomics-based techniques in predicting ecological associations between microbes . To evaluate the potential use of genomics-based methods developed here for understanding mechanisms driving microbe-microbe interaction I conduct analysis of putative pathways in one set of four taxa from phylum Firmicutes , found in stool samples . The set of taxa under consideration includes: C . comes , E . rectale , R . intestinalis and E ventriosum ( Fig 4 ) . Phylogenetic relationships between these four species inferred using 16S rRNA support C . comes—E ventriosum; and E . rectale—R . intestinalis as pairs of sister taxa . In this case , however , C . comes , E . rectale co-occurred more frequently ( Jaccard index of 0 . 65 ) than other combinations of four taxa ( Fig 4B ) . Subsequently , I search for putative pathways exhibiting high enough level of genome content similarity between co-occurring taxa and sufficient overall pathway representation ( see Materials and Methods section for details ) , in comparison to their not co-occurring sister lineages . Four gene sets are identified ( Fig 4A ) . Three of those gene sets , according to the annotation , are related to riboflavin and cobalamin metabolism ( gene sets 105 and 589 ) and energy metabolism ( gene set 1290 ) . Additionally , this search identifies one gene set which includes gene families of unknown function ( gene set 694 ) .
In this study , I attempt to advance genomics-based methods for understanding ecological associations between microbes . I introduce two novel genome-wide measures of microbe-microbe interaction—genome content similarity and microbe-microbe functional association indices—and demonstrate how these measures predict associations between microbes in different environments . Specifically , I show that both metrics predict common environmental affiliations of bacterial species when ecological divergence between habitats is high ( Fig 3 , dataset 1 ) . The predictive power of both indices stays significant even when the surveyed environmental conditions become more similar and the expected ecological differentiation between habitats is reduced ( Fig 3 , dataset 2 ) . This indicates the presence of detectable genome-wide signal of co-occurrence of the microbes in both highly differentiated and similar environments . Regression analysis also allows me to compare indices proposed here to the previously proposed metrics . The results indicate that genome content similarity index explains patterns of microbial co-occurrence better than sophisticated metabolic competition index constructed upon KEGG pathway annotation ( 3 . 4% versus 2 . 1% of variance explained , Table 1 , regression models 2 and 4 respectively ) . While it is clear that genomic information is one of the major factors determining species ecology , it is still not known to what extent ecological interactions between the species , as measured here by co-occurrence , can be explained by genomic data . In this study I aim to address this question . Using regression analysis , I show that genomic summaries alone predict co-occurrence of microbes even when accounting for phylogenetic relationships between the organisms and explain up to 4% of the variance in co-occurrence data ( Table 1 , regression model 6 ) . This study also finds phylogenetic relatedness of the organisms to be the best predictor of their co-occurrence . On it’s own phylogenetic relatedness explains about 10% of the variance in co-occurrence data . This findings highlight the importance of the evolutionary process in the emergence of ecologically important traits in microbial genomes and in agreement with previous reports [9 , 10] . The observation , however , contradicts “limiting similarity hypothesis” in community ecology [42] . Empirical studies suggesting closely related species tend to exclude each other have been reported [43 , 44] but contradictory reports also have been published [45 , 46] . Existence of evidence pointing in different directions might be an indication that the effect of “limiting similarity” can only be detected for specific values of divergence , or specific time scales on which a community is surveyed , it might also vary depending on the rate of evolution of the traits important in particular ecosystem . Elucidating these possibilities would require in depth analysis and is not pursued in the study . It is important to note that the indices introduced here are informed only by the genome content and structure of various microbial species and not by biochemical annotation of proteins . While some biases in the resolution of the protein functional association networks predicted using genome content are expected , given that taxa are not sampled into genome sequencing studies at random [47] , they should nevertheless be small in comparison to biases in experimental biochemical annotation . Therefore , the metrics introduced here should be more reliably applicable to a wide range of microbes , not only the well-studied taxa with large number of annotated metabolic pathways . The positive correlation between co-occurrence as measured by 16S rRNA and genome content similarity detected here ( Table 1 ) highlights the importance of habitat filtering processes in community assembly [48 , 49] . This finding does not exclude other processes , such as species assortment , as important drivers of community assembly [50] . Perhaps processes that result in differences in genome composition , cooperation or cheating [15 , 51 , 52] , operate on the level of a small set of biological functions and go undetected at the level of the genome . For instance , the loss of siderophore biosynthesis genes , but not reuptake genes , by some strains of marine Vibrionaceae leads to differences in genome content composition in co-existing strains but only for one particular cluster of functionally linked genes , not genome-wide [14] . Additionally , similarities observed at the level of genome composition do not necessarily translate into similarities at the level of mRNA or protein expression . It has been shown that social cheating in Pseudomonas quorum sensing arises from changes in gene expression rather than complete loss of the genetic modules encoding quorum-controlled factors [51] . Quick loss of metabolic independence due to loss-of-function mutations in protein coding sequences , but not loss of detectable orthologs , are also known [53] . The methods introduced here assume that all the proteins present in the genomes of a microbial species are expressed and functional . This unrealistic assumption , in theory , could be relaxed , but such a development would require information on genome-wide patterns of gene expression for both species in question , grown under the same conditions . This kind of information is limited for reference genomes but should be accessible for wild strains from metatranscriptomics studies [54] . Avoiding the use of biochemical pathway annotation is advantageous , as it allows for the incorporation of signals from large numbers of proteins ( S1 Fig ) . On the other hand , using genomics-based predictions about protein functional associations makes it harder to interpret the results , especially for the microbe-microbe functional association index , as “functional association” is broadly defined here and encompasses an ensemble of interactions ranging form direct physical contact to genetic regulation to involvement in the same biochemical process [24] . It should be highlighted that both genome content similarity and the microbe-microbe functional association index are based on static gene family annotations from STRING , which assumes that all the genes from the same automatically predicted orthologous group have same functional associations . This is clearly a naïve assumption , given that proteins evolve new functions across phylogeny [55 , 56] . For instance , comparative genomics study on the SecA_DEAD domain protein in some Gram-negative microbes suggested several functional associations for some of the proteins from the SecA family ( COG0653 ) from STRING , but not all of them [57] . Therefore , investigating the role of lineage specific protein evolution on this type of inference could be of interest . Genome content similarity and microbe-microbe functional association indices summarize information genome-wide . In reality , however , only a fraction of the genome might be mediating ecological interactions . Therefore , one potential extension of the methods introduced here is to predict small set of putative pathways driving ecological interactions between microbes . Experimental and computational detection of protein-protein interactions in host-microbe systems [58 , 59] allowed to discover a number of microbial proteins potentially interacting with human proteins . Detailed analysis of protein-protein functional association networks in search of clusters of gene families contributing to the elevated microbe-microbe functional association index could lead to the discovery of some promising candidate molecular systems . In the case of genome content similarity , one approach is to search for gene sets exhibiting higher than expected compositional similarity . In this manuscript , such a survey was conducted for two co-occurring species from phylum Firmicutes , family Lahnospiracea . Several promising candidate gene sets were discovered ( gene sets 105 , 589 , 694 and 1290 ) . Three of the gene sets have assigned metabolic functions ( 105 , 589 and 1290 ) . For instance , gene set 1290 includes genes linked to butyrate metabolism . Sporadic phylogenetic distribution of butyric acid producing enzymes , potentially driven by HGT , has been reported in Lahnospiracea [60] . Here the evidence indicates that lahnospiracea species with similar set of butyric acid metabolism related genes , C . comes and E . rectale , also tend to co-occur in the environment . Gene set 694 containes genes of unknown function , which could not have been identified by KEGG pathways analysis . Gene set 105 includes genes related to vitamin B12 biosynthesis and while the overall gene family profile in this gene set is similar in co-occurring C . comes and E . rectale , several genes catalyzing initial steps in the pathways [61–63] are missing from E . rectale . These findings potentially suggest exchange of intermediates of vitamin B12 biosynthesis between co-occurring E . rectale and C . comes . Overall , the gene sets discovered here constitute a list of promising potential candidates for further functional studies but at this point inspire speculation . Evolutionary processes generating detected patterns are not evaluated here and would require more in-depth phylogenetic analysis . However , a parsimonious assessment of the observed gene presence and absence profiles in the examined four taxa ( Fig 4A ) suggests that identification of the gene sets might be attributed to gene loss in E . ventriosum , related to C . comes , for two gene sets ( 105 two 589 ) and concerted gene gain and loss by two co-occurring taxa ( gene set 694 and 1290 respectively ) . In summary , this study finds phylogenetic relatedness to be strongest predictor of microbial co-occurrence ( explains about 10% of the variance in microbial co-occurrence ) . Genome content similarity index is also identified as a strong predictor ( explains 3 . 5% of the variance ) , highlighting the importance of habitat filtering in microbial community assembly . Genome content similarity index provides an improvement over more sophisticated metabolic competition index which requires metabolic pathway annotation for each of the genomes and is highly limited for poorly studied microbes . Despite the fact that none-trivial fraction of variance in co-occurrence data is explained by genomic indices , detected explanatory power is rather modest . This highlights the need for the development of methods to improve current genomic techniques to help in understanding the inner workings of microbial communities .
The files species . v10 . txt , species . mappings . v10 . txt and COG . mappings . v10 . txt , which provide information on species and genome annotation , relationships between genomes , genes and gene families , were downloaded from the STRING version 10 . 0 website [66] . The file COG . links . detailed . v10 . txt , which provides information on functional associations between gene families , was also downloaded from the database website . Information was extracted from these files using custom Python scripts . The global network of all the gene families existing in the STRING database was defined as a collection of all nodes ( orthologous groups from STRING ) and all edges ( gene neighbor scores from STRING above critical value ) connecting the nodes . The network is available within the genomics2ecology R package under the reference_network table . Gene neighbor score values were not derived within this study but obtained from COG . links . detailed . v10 . txt file from STRING [66] . A critical score value of 275 was used to define if link between two gene families exists of not . The critical value of 275 was chosen because it corresponds to the best values of both specificity and sensitivity in the ROC curve [57] when the scores are evaluated on a set of known functionally related proteins . To compute microbe-microbe functional association index values edges of reference gene network were treated as unweighted . The genome content similarity index was calculated based on sets of functionally linked genes ( putative pathways and complexes ) . To identify such gene sets the global network of gene families , constructed as described above , was clustered with mcl-14-137 [67 , 68] . Edge weights ( gene neighbor score values above 275 ) in the global network of gene families were unit-based normalized by subtracting minimal weight ( 275 ) from each value and then dividing the result by the range ( 1000–275 ) . This weight adjustment scheme is similar to what is recommended in the literature in analysis of other networks with mcl [69] . The inflation value for mcl was set to 4 to obtain fine-grained clusters , and the program was run in --abc mode to accommodate the format of input data , for the rest of parameters default settings were employed . The obtained clusters of gene families were further treated as putative protein pathways and complexes . The gene sets are available within genomics2ecology R package under reference_gene_sets data structure . Given that I further used putative protein pathways and complexes to derive the genome content similarity index , it was of interest to understand how the fraction of genes contributing to putative protein pathways and complexes varies between genomes . I calculated this percentage for 308 genomes using clusters from mcl and protein . aliases . v10 . txt file from STRING . I also obtained information on the percent of ORFs in KEGG pathways for the same genomes from JGI . In this study , I used two ecological datasets to understand whether genomics-based indices can predict co-occurrence of microbes in the environment . The first dataset was from the Greengenes database files from May 2013 [70 , 71] , and the second one was from a previously published study [21] . To map STRING genomes onto the Greengenes OTUs , I first obtained the 16S rRNA sequences for 1780 reference genomes ( STRING_16S_tid . fa file in the GitHub repository [65] ) from the Ribosomal Database Project [72] by matching the NCBI taxonomy ID provided within the STRING database and in the files current_Archaea_unaligned . gb and current_Bacteria_unaligned . gb files downloaded from the RDP website in June 2015 . Sequences were extracted from current_Archaea_unaligned . fa and current_Bacteria_unaligned . fa . Taxa not found in RDP were found in IMG JGI [73] . The longest 16S rRNA sequence for each genome was selected . Data from the Greengens database were handled as follows: one representative sequence which had “isolation source” , “authors” and “title” annotation in its arb record was selected for each of the 97% OTUs from Greengens ( 97_otu_map . txt file from gg_13_5_otus . tar . gz archive ) . Sequences shorter than 900 and 1 , 200 nucleotides in the case of Archaea and Bacteria , respectively , were removed . The rest of the sequences were searched with 16S rRNAs from STRING genomes using blastn . Weak blast hits with less than 95% identity or covering less than 85% of the query sequence length were ignored . 16S rRNAs from STRING genomes and corresponding Greengenes sequence which produces strong blast hits were collected . Collected sequences were aligned to each other using the bacterial 16S model and clustered using complete linkage clustering based on sequence identity with tools available on the RDP website , 97% identity cut-off was used for clustering . One Greengenes OTU and one STRING genome were selected to represent each cluster that contained both STRING genome and Greengenes OTU . In total 1119 STRING genomes were assigned to Greengens OTU this way . R code is in get_data_ds1 . R . txt on GitHub [65] . In order to establish correspondence between 154 genomes from [21] ( file sd01 . xlsx from PNAS website , sheet A , row names ) and STRING genomes , I first modified the “official name” of the STRING genomes by replacing space , dot or dash characters with underscore characters , then replacing repeated underscores with just one , and the looked for exact match between modified STRING genome names and genome names from [21] . Using this method way 83 genomes from [21] were matched . The rest of the genomes were assigned manually , by strain if possible , otherwise by species name . If several strains of the same species were present in STRING , one strain was selected at random to represent the genome in subsequent analysis . If no species with the same name was present in STRING , the genome was excluded from the dataset . A total of 127 genomes were included into dataset 2 . The R code is in get_data_ds2 . R , the list of assigned genomes is in genomes_ds2 . txt file in GitHub [65] . Sample information was extracted from gg_13_5_arb_records files obtained from the Greengenes ftp site [71] . Record files link sequences to samples . Only sequences annotated with “isolation source” , “authors” and “title” were used . Sample IDs were created by concatenating “isolation source” , “authors” and “title” fields . Individual 16S rRNA sequences in Greengenes are grouped into OTUs . 97% identity OTUs were utilized here ( 97_otu_map . txt ) . OTUs which did not match STRING genomes , OTUs present in less than 3 samples and samples with less than 3 OTUs were removed . In total 308 OTUs ( out of 1119 initially matched to genome from STRING ) and 532 samples were retained for further analysis . A similar strategy for OTU/sample filtering was used before [10] . The co-occurrence between the OTUs was calculated as Jaccard similarity coefficients [74] between profiles of OTU presence/absence in samples as was previously done in [21] . R code is in get_data_ds2 . R; the generated matrix of species co-occurrence is in cooccurence_ds1 . txt , and a list of assigned genomes is in genomes_ds1 . txt on GitHub [65] . Metabolic complementarity and competition indices ( file sd01 . xlsx , sheet A ) as well as co-occurrence measures using the Jaccard similarity coefficient ( file sd01 . xlsx , sheet B ) for human gut microbiome data are provided as part of supporting information for [21] and were downloaded from the PNAS website , competition_ds2_full . txt and cooccurrence_ds2 . txt on GitHub [65] . Genome content similarity and microbe-microbe functional association indices were calculated for every relevant pair of genomes ( as described in Results section ) using similarity and functional_association functions from the genomics2ecology R package [64] . Code is provided in get_data_ds1 . R and get_data_ds2 . R files; generated genomics based indices are in similarities_ds1 . txt , similarities_ds2 . txt , similarities_F2 . txt , associations_ds1 . txt , associations_ds2 . txt , associations_F2 . txt files on GitHub [65] . To approximate relationships between species in two ecological datasets and for the collection of species used to create Fig 2 , I first aligned 16S rRNA sequences from relevant STRING genomes using the RDP web-server [72] . I then reconstructed 16S rRNA phylogeny using FastTree 2 . 1 . 9 [75] , files STRING_16S_ds1_FastTree , STRING_16S_ds2_FastTree , STRING_16S_fig2_FastTree on GitHub [65] . FastTree was compiled for double precision to improve length estimation of very short branches . It is necessary to note that the procedure adopted here is not intended to recover a precise species tree but rather to account for a strong signal of ancestry between closely related species . To address how genomics-based indices are related to co-occurrence of species in environmental samples I used partial Mantel test accounting for phylogenetic distance between species [76] . S2B Fig provides a graphical guide of the process . To calculate partial correlations raw species phylogeny , data on co-occurrence , genome content similarity and microbe-microbe functional association indices were used for ecological dataset 1 and 2 . For dataset 2 metabolic competition and complementarity indices were also used . Phylogenetic distances between the species in each of the trees was calculated from the phylogenetic tree from the corresponding dataset using the cophenetic function from the ape R package version 3 . 4 [77] . The tests were performed using the vegan R package version 2 . 3–5 [78] . Adjustment for phylogenetic distance was done because genomes cannot be considered as independent observations as they are related to each other through evolutionary processes . The code is in analysis . R on GitHub [65] . In addition to partial correlation , I performed Mantel regression analysis of co-occurrence of microbes in the environment and genomics-indices in ecological dataset 2 ( See S2C Fig for graphical guide ) . This analysis was performed using phytools R package version 0 . 5–20 [79] on the same set of raw data as used in the correlation analysis ( analysis . R on GitHub [65] ) . To identify gene sets potential driving co-occurrence of C . comes and E . rectale I first identified gene sets that included at least 8 gene families , showed overall similarity of at least 0 . 6 , and overall gene set representation of at least 0 . 6 when co-occurring C . comes and E . rectale were compared to each . Then I excluded from this list gene sets which were also identified when R . intestinalis to E ventriosum in the same way . Resulting data were visualized using gplots R package . | It is still unknown to what extent ecological associations between microbes , as measured by co-occurrence of different taxa in 16S rRNA surveys , can be explained , or predicted , using composition and structure of microbial genomes alone . Here I introduce two new genome-wide , pairwise indices for quantifying the propensity of microbial species to interact with each other . The first measure quantifies similarity in genome composition between two microbes . The second measure summarizes the topology of a protein functional association network built for a given pair of microbes and quantifies the fraction of network edges crossing organismal boundaries . I then study the ability of two newly proposed and two previously reported indices to explain variation in microbial co-occurrence . All four measures are significantly correlated with co-occurrence of microbes even when accounting for evolutionary relationships between the species . One of the newly developed indices outperforms previously proposed ones and explains up to 3 . 5% of the variance in co-occurrence . In summary , the indices described here are able to detect ecological associations between species using only their genomic information; however , additional methods are needed to provide more reliable genomic tools for microbial ecology . |
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Rasa3 is a GTPase activating protein of the GAP1 family which targets Ras and Rap1 . Ubiquitous Rasa3 catalytic inactivation in mouse results in early embryonic lethality . Here , we show that Rasa3 catalytic inactivation in mouse hematopoietic cells results in a lethal syndrome characterized by severe defects during megakaryopoiesis , thrombocytopenia and a predisposition to develop preleukemia . The main objective of this study was to define the cellular and the molecular mechanisms of terminal megakaryopoiesis alterations . We found that Rasa3 catalytic inactivation altered megakaryocyte development , adherence , migration , actin cytoskeleton organization and differentiation into proplatelet forming megakaryocytes . These megakaryocyte alterations were associated with an increased active Rap1 level and a constitutive integrin activation . Thus , these mice presented a severe thrombocytopenia , bleeding and anemia associated with an increased percentage of megakaryocytes in the bone marrow , bone marrow fibrosis , extramedular hematopoiesis , splenomegaly and premature death . Altogether , our results indicate that Rasa3 catalytic activity controls Rap1 activation and integrin signaling during megakaryocyte differentiation in mouse .
Ras families GTPase-activating proteins ( GAP ) , like Ras GAPs , Rho GAPs and Arf GAPs , are tumor suppressors as the loss of their GAP activity allows uncontrolled Ras , Rho and Arf activities and promotes cancer . Rasa3 ( or GAP1IP4BP , R-Ras GAP ) is a member of the Ras GAP1 subfamily with Rasa2 ( or GAP1m ) , Rasa4 ( or Capri ) and Rasal ( or Rasal1 ) [1]–[5] . This Ras GAP subfamily is known to function as dual GAP for Ras an Rap-GTPases [6] , [7] . Rasa3 protein structure is characterized by a conserved basic domain structure comprising two N-terminal tandem C2 domains , a central GAP domain and a C-terminal pleckstrin homology ( PH ) domain that is associated with a Bruton's tyrosine kinase ( Btk ) motif [8] . Binding of the latter domain to phosphoinositides determines Rasa3 targeting to the cytosolic leaflet of the plasma membrane where it inactivates Ras and Rap1 [9]–[11] . Down-regulation of Rasal and Rasa4 induces cellular transformation in vitro [12] , [13] , and Rasal is down-regulated in multiple human tumors by epigenetic silencing [14] . Rasa4 inactivation in mouse leads to impaired macrophages Fcγ receptor-mediated phagocytosis and oxidative burst , as well as to increased bacterial infection [15] . No clear definition of Rasa2 function in vivo is currently available . Mutant mice expressing a catalytically-inactive Rasa3 protein have been reported to die at mid embryonic life [16] . Indeed , removal of exons 11 and 12 of the mouse Rasa3 gene , 2 exons which are essential for the Ras GAP activity , leads to the expression of a 88 amino acids-truncated but catalytically inactive Rasa3 protein [16] . Phenotypically , Rasa3 mutant embryos present massive subcutaneous and intraparenchymal hemorrhages probably consecutive to abnormal adherens junctions between capillary endothelial cells [16] . Multiple roles for Ras and Rap1 , the Rasa3 targets , have been defined in hematopoietic cells: these proteins control cellular proliferation , differentiation , migration and adhesion . In particular , Rap1 has been implicated in the maturation of megakaryocytes and the pathogenesis of chronic myelogenous leukemia [17] . Here , we found that catalytic inactivation of Rasa3 specifically in the hematopoietic system results in a lethal syndrome characterized by major alterations during megakaryopoiesis . These alterations were associated with increased active Rap1 level and constitutive integrin activation in megakaryocytes , a phenotype quite different clinically , biologically and mechanistically from that of recently published mice with a spontaneous missense mutation between the two N-terminal tandem C2 domains of Rasa3 [18] .
In order to study the specific effects of a catalytically-inactive Rasa3 mutant protein on the hematopoietic system and to circumvent the early embryonic lethality reported in Rasa3−/− mice , we used irradiated Severe Combined Immune Deficient ( SCID ) mice reconstituted with E12 . 5 liver cells derived from Rasa3+/+ , Rasa3+/− or Rasa3−/− embryos . SCID mice were first analyzed 6 weeks after irradiation/reconstitution: all Rasa3 genotypes were able to reconstitute the lymphoid compartment in irradiated SCID mice since no significant difference was detected between SCID-Rasa3+/+ , SCID-Rasa3+/− and SCID-Rasa3−/− mice in total numbers of splenic T and B cells ( Table S1 ) . No significant difference was observed in red blood cell , blood platelet and bone marrow megakaryocyte counts as well as spleen weight between SCID-Rasa3+/+ and SCID-Rasa3−/− mice at this stage ( Table S1 ) . More than 80% of SCID mice reconstituted with Rasa3−/− cells died within 14 months after reconstitution while , at the same time , about 95% of SCID-Rasa3+/+ and SCID-Rasa3+/− mice were still alive ( Fig . 1a ) . Pathological analysis revealed that 85% of SCID-Rasa3−/− mice presented with thoracic and/or peritoneal hemorrhages ( data not shown ) and that more than 80% had a splenomegaly ( Fig . 1b ) . Our results below present the analysis of a total of 24 moribund SCID-Rasa3−/− mice . Among these 24 mice , 20 had a megakaryocytic dysplasia associated with a severe thrombocytopenia , and the remaining 4 developed a preleukemia . The main objective of this study was to define the cellular and the molecular mechanisms of the megakaryocytic dysplasia . In ∼80% ( 20/24 ) of SCID-Rasa3−/− mice , blood analysis revealed a thrombocytopenia ( Fig . 1c ) . Thrombocytopenia in these mice was associated with megakaryocyte alterations . A significant increase in the percentage of CD41+ megakaryocytes was observed in the bone marrow of SCID-Rasa3−/− mice ( Fig . 1d ) . These megakaryocytes were morphologically abnormal and presented a marked increase in the intensity of von Willebrand factor ( vWF ) staining , as compared with SCID-Rasa3+/+ megakaryocytes ( Fig . 1e ) . vWf+ deposits were also abnormally detected along the SCID-Rasa3−/− diaphysis ( Fig . 1e , arrowheads ) . In addition , more megakaryocytes were present in the osteoblastic niche in SCID-Rasa3−/− mice , as compared with SCID-Rasa3+/+ mice ( Fig . 1f , left panel ) . Inversely , the vascular niche hosted less megakaryocytes in mutant mice ( Fig . 1f , right panel ) . The stem and megakaryocyte progenitor cell compartments were characterized in the bone marrow of SCID-Rasa3+/+ and SCID-Rasa3−/− mice by flow cytometry . A similar percentage of live c-Kit+/Lin− cells was detected in the bone marrow of these mice , and the proportion of Sca-1+ cells within this population was also not significantly different in SCID-Rasa3+/+ and SCID-Rasa3−/− mice ( Table S2 ) . Staining of these c-Kit+ Lin− Sca-1+ ( KLS ) cells with CD34 and Flk-2 antibodies defined the KLS-CD34− Flk-2− hematopoietic stem cell compartment; but again , no difference was detected in the percentage of these cells between SCID-Rasa3+/+ and SCID-Rasa3−/− mice ( Table S2 ) . It has been recently shown that bone marrow progenitors with megakaryocyte potential reside in the Lin− c-Kit+ FcγRII/IIIlo Sca-1− CD150+ cell population [19] . This cell population was decreased in the bone marrow of SCID-Rasa3−/− mice , as compared with SCID-Rasa3+/+ , but the difference did not reach statistical significance ( Table S2 ) . Femur sections revealed the presence of a fibrosis characterized by numerous collagen trabeculae in the cavity of SCID-Rasa3−/− femurs , while the cavity of SCID-Rasa3+/+ femurs was totally free of collagen trabeculae ( Fig . S1a ) . Consequently , the number of nucleated cells recovered on average from one femur of SCID-Rasa3−/− mice was significantly reduced , as compared with SCID-Rasa3+/+ mice ( SCID-Rasa3+/+: 20 . 9±2 . 8×106 nucleated cells , n = 12; SCID-Rasa3−/−: 6 . 4±1 . 1×106 nucleated cells , n = 19; P<0 . 001 , unpaired t test ) . Bone marrow cell density was similar in the cavity of SCID-Rasa3+/+ and SCID-Rasa3−/− femurs ( Fig . S1b ) , and similar percentages of CD117+/c-Kit+ progenitor cells and Ter119+ CD71+ erythroblasts were detected in SCID-Rasa3+/+ and SCID-Rasa3−/− bone marrow cells ( Table S2 and data not shown ) . An increased splenic hematopoiesis - including megakaryopoiesis - , associated with a disorganized pulp architecture , and foci of liver hematopoiesis were also observed in SCID-Rasa3−/− mice ( Table S3 and Fig . S2 ) . Thrombocytopenia and hemorrhages in SCID-Rasa3−/− mice were associated with a regenerative anemia , whereas normal counts were maintained for total white blood cell and circulating neutrophil , lymphocyte , monocyte and eosinophil ( Fig . S3 and Table S4 ) . Thrombopoietin ( TPO ) levels were significantly decreased in SCID-Rasa3−/− mice , as compared with SCID-Rasa3+/+ mice , a probable consequence of the markedly increased Mpl+/CD150+ megakaryocyte number in the spleen of these mice ( TPO level in SCID-Rasa3+/+ mice: 1732±211 pg/ml , n = 11; TPO level in SCID-Rasa3−/− mice: 653±74 pg/ml , n = 16; mean ± SEM; P<0 . 001 ) . Collectively , our results indicate that the loss of Rasa3 catalytic activity in 20/24 SCID-Rasa3−/− mice leads to megakaryocyte alterations , to thrombocytopenia , hemorrages and a regenerative anemia . Bone marrow was isolated from SCID-Rasa3 mice 2 months after irradiation/reconstitution and cultured under a confocal microscope . Despite a ∼2-fold increased percentage of megakaryocytes in the SCID-Rasa3−/− bone marrow , there was a trend for a decreased number of megakaryocytes released from SCID-Rasa3−/− bone marrow explants , as compared with SCID-Rasa3+/+ explants ( Fig . 2a , left panel ) . Released SCID-Rasa3−/− megakaryocytes were able to spread on the culture plate but never fully differentiated in proplatelet forming megakaryocytes ( Fig . 2a , centre and right panels , and Fig . 2b ) . The linear distance covered by the released megakaryocytes and their velocity were significantly lower in SCID-Rasa3−/− than in SCID-Rasa3+/+ explants ( Fig . 2c ) . In order to further analyze the role of Rasa3 in megakaryocyte adhesion and differentiation , we used megakaryocytes obtained from Rasa3+/+ and Rasa3−/− fetal liver cells ( FLC ) cultured in the presence of TPO . This cellular model is simpler and faster than the model of bone marrow explants isolated from SCID-Rasa3 mice , and it recapitulated the megakaryocyte defects previously observed in the later model . Indeed , after 2 days of TPO treatment , flow cytometry analysis detected a significant 1 . 42-fold increase in the percentage of CD41+ megakaryocytes in the Rasa3−/− FLC culture , as compared with Rasa3+/+ culture ( Fig . 3a ) . CD41+ megakaryocytes with 16N and 32N ploidy were significantly increased in these Rasa3−/− FLC cultures , as compared with Rasa3+/+ FLC cultures ( Fig . 3b ) . Colony-forming unit-megakaryocyte ( CFU-Mk ) assay using FLC revealed a significant decrease in the number of small immature megakaryocyte colonies in Rasa3−/− cell culture , as compared with Rasa3+/+ cell culture ( Fig . 3c ) . However , numerous large mature megakaryocytes were detected in the Rasa3−/− CFU-Mk assay , while not in the Rasa3+/+ CFU-Mk assay ( Fig . 3c , arrowheads ) . After 6 days of TPO treatment , many proplatelets were observed in the Rasa3+/+ FLC culture whereas , in the Rasa3−/− FLC culture , no proplatelets were detected ( Fig . 3d ) . Instead of proplatelets , many abnormal adherent cells were detected in the Rasa3−/− FLC culture that were CD41 positive , indicating their megakaryocyte origin ( Fig . 3d , lower panels and Fig . 3e ) . Actin cytoskeleton staining of these abnormal adherent Rasa3−/− megakaryocytes revealed a unique dotted actin pattern without stress fiber at the contact with the culture plate , significantly different from the expected actin stress fiber pattern observed in the few adherent Rasa3+/+ megakaryocytes present in the FLC culture at day 6 of TPO treatment ( Fig . 4a , bottom , and 4b ) . The actin cytoskeletal organization was also altered at the top of the adherent Rasa3−/− megakaryocytes: actin was decreased at the periphery and much more concentrated at the center of the cell , as compared with adherent Rasa3+/+ megakaryocytes ( Fig . 4a , top ) . Collectively , these results indicate that Rasa3−/− FLC abnormally develop into mature megakaryocytes , and that Rasa3−/− megakaryocytes derived from FLC culture have an altered actin cytoskeleton organization associated with an abnormal adherent phenotype , a reduced motility and an absence of normal terminal differentiation in proplatelets . Interestingly , this Rasa3−/− megakaryocyte phenotype ( i . e . defect in proplatelet formation , dotted actin cytoskeletal pattern with reduced stress fibers and abnormal adherent megakaryocytes ) resembles that of rare thrombocytopenic patients with a constitutive αIIbβ3 integrin activity caused by specific mutations in ITGA2B or ITGB3 genes [20]–[22] . Soluble fibrinogen binding to αIIbβ3 integrin present at the megakaryocyte surface is regulated by inside-out signaling which determines the affinity/avidity of the integrin for its ligand . In the absence of megakaryocyte stimulation , only little amount of soluble FITC-fibrinogen bound to day 3 FLC culture-derived Rasa3+/+ mature megakaryocytes ( Fig . 5a ) . By contrast , in this resting condition , a larger amount of soluble FITC-fibrinogen bound to Rasa3−/− mature megakaryocytes , reaching the binding level of Rasa3+/+ megakaryocytes when stimulated by TPO for 30 min ( Fig . 5a ) . Stimulation of Rasa3−/− mature megakaryocytes by TPO did not further increase soluble FITC-fibrinogen binding . Importantly , no difference in αIIb/CD41 surface expression was detected by flow cytometry between day 3 FLC-derived Rasa3+/+ and Rasa3−/− mature megakaryocytes ( Rasa3+/+: 1234±70 arbitrary units ( A . U . ) , Rasa3−/−: 1084±244 A . U . , n = 3 independent experiments , P = 0 . 11 ) , suggesting that Rasa3−/− megakaryocytes have a constitutively activated inside-out signaling leading to a constitutive binding of soluble fibrinogen to αIIbβ3 integrin . Staining of day 3 FLC culture-derived Rasa3+/+ and Rasa3−/− mature megakaryocytes with the JON/A antibody , which selectively binds to the high affinity conformation of integrin αIIbβ3 , confirmed this hypothesis: a significant increase of JON/A+ megakaryocyte percentage was detected in Rasa3−/− megakaryocytes in resting and TPO-stimulated conditions , as compared with Rasa3+/+ megakaryocytes ( percentage of JON/A+ megakaryocytes , mean ± SEM: non-stimulated Rasa3+/+ megakaryocytes: 14 . 0±1 . 9%; non-stimulated Rasa3−/− megakaryocytes: 70 . 1±3 . 4% , P = 0 . 002; TPO-stimulated Rasa3+/+: 23 . 3±0 . 5%; TPO-stimulated Rasa3−/− megakaryocytes: 73 . 1±3 . 4% , P = 0 . 02 ) . Integrin activation triggers megakaryocyte adhesion to immobilized integrin ligands like collagen-I or fibrinogen and an outside-in signaling , resulting in the reorganization of the actin filaments and the modification of the cell shape [23] . Megakaryocytes from FLC-Rasa3+/+ cultured on day 3 adhered to collagen-I- and fibrinogen-coated plates , but nearly not to Poly-D-Lysine-coated plates , as expected ( Fig . 5b ) . Adherence to immobilized collagen-I and fibrinogen resulted in cell spreading reaching diameters over 50 µm in a limited number of Rasa3+/+ megakaryocytes , as described ( Fig . 5c , red dots , and Fig . 5d ) ( 24 ) . Adherence to Poly-D-Lysine- , collagen-I- and fibrinogen-coated plates was significantly higher in Rasa3−/− than in Rasa3+/+ megakaryocytes ( Fig . 5b ) . The percentage of megakaryocytes with a diameter over 50 µm was significantly increased in the Rasa3−/− culture , as compared with the Rasa3+/+ culture ( Fig . 5d ) . Outside-in integrin activation triggers the binding of the cytoskeletal protein talin to membrane integrins [24] . In association with their abnormal adhesion properties , Poly-D-Lysine adherent Rasa3−/− megakaryocyte recruited more talin to their membrane , as compared with Rasa3+/+ megakaryocytes ( Fig . 5e ) Collectively , our results indicate that Rasa3−/− megakaryocytes have a constitutively activated inside-out αIIbβ3 integrin signaling associated with major alterations in outside-in integrin signaling leading to cell adherence and spreading independently of integrin ligands . Since the small GTPase Rap1 , a Rasa3 substrate , controls inside-out and outside-in integrin signaling in megakaryocytes and platelets [25]– , the level of active , GTP-bound Rap1 was analyzed in mature Rasa3+/+ and Rasa3−/− megakaryocytes . A significant 2 fold-increase in active Rap1 was observed in Rasa3−/− megakaryocytes , as compared with Rasa3+/+ megakaryocytes , providing a molecular mechanism for the link between Rasa3 , talin and integrin activation ( Fig . 6a; P = 0 . 019 ) . By contrast , no significant difference was detected in the level of active GTP-bound Ras between Rasa3+/+ and Rasa3−/− megakaryocytes ( GTP-Ras mean fluorescence intensity: Rasa3+/+ megakaryocytes: 670 . 1±117 . 9 arbitrary units ( A . U . ) ; Rasa3−/− megakaryocytes: 706 . 2±56 . 7 A . U . ; P = 0 . 48; 3 independent experiments , 50 megakaryocytes analyzed per field , 2 fields per FLC culture ) . In order to confirm the important role of Rap1 in the abnormal adherent phenotype of Rasa3−/− megakaryocyte , outside-in experiments were performed in the presence of the Rap1 inhibitor GGTI-298 , which is not active on Ras . Addition of GGTI-298 to the culture medium completely abolished the abnormal adhesion phenotype of Rasa3−/− megakaryocyte , but had no effect on the proplatelet phenotype ( Fig . 6b and 6c , and data not shown ) . Altogether , these results indicate that the absence of Rasa3 increases Rap1 activation , and that Rap1 rather than Ras is probably responsible for the abnormal Rasa3−/− adherent megakaryocyte phenotype . Thus , increased Rap1 activation in the absence of Rasa3 leads to constitutive activation of integrins and increased outside-in signaling . Platelet adhesion and activation were analyzed on platelets isolated from adult Rasa3+/+ and Rasa3+/− mice ( Fig . S4 ) . Rasa3+/− platelets adhesion to BSA-coated plates was significantly increased compared with Rasa3+/+ platelets ( Fig . S4a ) . On fibrinogen-coated plates , a trend for an increase adhesion was detected in Rasa3+/− platelets , but the difference with Rasa3+/+ platelets did not reach statistical significance ( platelet counts per field of view ( FOV ) ( means ± SEM ) ; Rasa3+/+ platelets: 71±18 platelets/FOV; Rasa3+/− platelets: 114±39 platelets/FOV; P = 0 . 12 ) . In resting condition , two platelet activation markers were found altered in Rasa3+/− platelets: the JON/A antibody binding to Rasa3+/− platelets and the percentage of CD62P P-selectin positive Rasa3+/− platelets were significantly increased , as compared with Rasa3+/+ platelets ( Fig . S4b and S4c ) . No difference in the percentage of CD62P+ platelets was detected after stimulation with ADP or CRP ( Fig . S4c ) . In resting condition , we found no difference in CD61 expression on Rasa+/+ and Rasa3+/− platelets , whereas CD41 expression was significantly reduced on Rasa+/− platelets; this data indicates that the increased JON/A binding to Rasa3+/− platelets is not simply a consequence of an increased αIIβ3 integrin expression ( Fig . S4d ) . Finally , platelet aggregation after ADP stimulation was similar in Rasa3+/+ and Rasa3+/− platelets ( Fig . S4e ) . Altogether , these results indicate that Rasa3+/− platelets present adhesion and activation defects in resting conditions , suggesting that a similar pathological mechanism is present both in megakaryocytes and platelets . In the ∼20% ( 4/24 ) remaining SCID-Rasa3−/− mice , a very different phenotype was observed: a massive and homogeneous cellular infiltration was detected in the bone marrow and spleen , suggestive of a leukemia ( Fig . 7a and data not shown ) . Adult naïve SCID mice intraperitoneally injected with 107 splenocytes isolated from these SCID-Rasa3−/− mice did not develop a similar proliferative disorder within 4 months after injection , suggesting the presence of a preleukemia rather than a leukemia in these 4 SCID-Rasa3−/− mice ( data not shown ) . No fibrosis was detected in the bone marrow of these 4 SCID-Rasa3−/− mice . Flow cytometry analysis with a panel of antibodies revealed that cells massively infiltrating the bone marrow and the spleen were positive for CD117/c-Kit , CD38 and Sca-1 , and negative for all other cell surface markers tested , including B220 , CD3 , MAR-1 , Gr1 , Mac1 , Ter119 , CD71 , CD4 , CD34 and F4 . 80 ( Fig . 7b , 7c and data not shown ) . As expected , the percentage of B220+ , CD3+ , Gr1int Mac1+ , Ter119+ CD71+ , CD41+ and F4 . 80+ cells was significantly decreased in the bone marrow and the spleen of these 4 mice ( data not shown ) . These 4 mice had a reduced survival ( survival range: 6–11 months after SCID mice irradiation/reconstitution ) and a splenomegaly ( spleen weight range: 0 . 185–1 . 062 g ) . Collectively , these results indicate that about 20% of SCID-Rasa3−/− mice develops a preleukemia with a massive infiltration of bone marrow and spleen with CD117+ Sca-1+ CD38+ cells , probably leading to bone marrow failure and premature death . They also suggest that Rasa3 is a potential tumor suppressor gene , acting may be on Ras , as proposed by Blanc et al . [18] . However , the level of active , GTP-bound Ras was similar in CD117+/c-Kit+ hematopoietic stem cells derived from Rasa3+/+ and Rasa3−/− FLC cultures ( Fig . 7d ) .
Using a Rasa3 catalytic mutant in FLC and irradiated/reconstituted SCID models , we show here that Rasa3 catalytic activity controls megakaryocyte development and differentiation into proplatelet forming megakaryocytes . In the irradiated/reconstituted SCID model , these megakaryocyte alterations are associated with thrombocytopenia , bleeding , regenerative anemia and decreased survival , as well as with bone marrow fibrosis , extramedular hematopoiesis and splenomegaly . An increased percentage of mature megakaryocytes with an abnormal morphology was detected in bone marrow cells from irradiated/reconstituted SCID mice when Rasa3 catalytic activity was inactivated . This increased percentage was associated with a slightly decreased percentage of progenitors with megakaryocyte potential , suggestive of a megakaryopoisis alteration . An obvious megakaryopoiesis alteration was also detected in Rasa3−/− FLC culture , where the number of CFU for immature megakaryocyte was significantly decreased and associated with the presence of numerous mature megakaryocytes . Ploidy in these Rasa3−/− abnormal megakaryocytes was also slightly altered . On the contrary to active Ras level , level of active GTP-bound Rap1 was significantly increased in Rasa3−/− megakaryocytes . Interestingly , the small GTPase Rap1 is both a Rasa3 substrate and a well known regulator of integrin signaling in megakaryocytes and platelets [2] , [25]–[29] . Both inside-out and outside-in integrin signaling are controlled by Rap1 , including αIIbβ3 signaling . Thus , the increased active GTP-bound Rap1 level detected in Rasa3−/− megakaryocytes represents a plausible molecular mechanism linking Rasa3 to integrin signaling and the altered megakaryocyte development and differentiation . Indeed , altered inside-out and outside-in integrin signaling in Rasa3−/− megakaryocytes probably results in the adherence and motility defects that we observed in this study . These defects may secondarily lead to an abnormal distribution of megakaryocytes between osteoblastic and vascular niches and to altered megakaryopoiesis . Constitutive activation of integrin signaling in Rasa3−/− megakaryocytes is associated with alteration in actin cytoskeleton organization , including a lack of stress fiber assembly , in talin recruitment to the plasma membrane and in cell adherence and spreading that occurred independently of integrin ligands . These alterations probably prevent terminal differentiation of Rasa3−/− megakaryocytes since megakaryocyte αIIbβ3 and β1 integrins are known to control proplatelet production and platelet release [30]–[32] . Moreover , stress fiber assembly is known to require optimal β1 integrin activation , a process also regulated by αIIbβ3 integrin [33] , [34] . In future work , it will be important to analyze Rasa3−/− platelets , since integrins play also important roles in these cells . Our preliminary studies indicate that unstimulated Rasa3+/− platelets have altered adhesion to BSA-coated plates and activation , as compared with Rasa3+/+ platelets , thus mimicking defect of Rasa3−/− megakaryocytes . Interestingly , constitutive αIIbβ3 integrin activation in human megakaryocytes mimics most of the Rasa3−/− megakaryocyte phenotypical traits . Indeed , in rare thrombocytopenic patients with activating mutations in ITGA2B or ITGB3 genes , megakaryocyte spreading on fibrinogen is abnormal , with 50% of spread cells showing a disordered actin distribution where focal adhesion points are more evident than stress fibers [21] . Sustained and substrate-independent activation of the outside-in αIIbβ3 signaling was detected in megakaryocytes of these patients , leading to severely impaired proplatelet formation and congenital thrombocytopenia [20]–[22] . It is noteworthy here that these patients do not develop the entire Rasa3−/− phenotype , like megakaryocytosis and bone marrow fibrosis , and its consequences . This discrepancy suggests that Rasa3 has additional function beside the control of integrin signaling , and/or that the enzyme has slightly different roles in man and mouse . In a recent report , Peters and collaborators have described a new spontaneous mutant mouse with a missense mutation in the Rasa3 protein [18] . The G125V Rasa3Scat mutation causes mislocalization of the protein to the cytosol and phenotypical traits that are clinically and biologically most often different from SCID-Rasa3−/− and Rasa3−/− phenotypes ( Table S5 ) . Indeed , Rasa3Scat/Scat mice have a cyclic phenotype of crisis-remission with a first embryonic to P9 wave of lethality – which affect ∼60% of the mutant mice – followed by a second wave of lethality at P30 ( affecting 94% of the first crisis survivors ) . This unexplained cyclic phenotype is fully transferable via hematopoietic stem cells injection into SCID or RAG−/− mice , ruling out the possibility that expression of the mutant Rasa3Scat protein outside the hematopoietic system is responsible for the different phenotype [18] . Another notable difference between Rasa3Scat/Scat and SCID-Rasa3−/− mice is the presence of a delayed erythropoiesis in the former mice . By contrast , in SCID-Rasa3−/− mice , many hallmarks of regenerative anemia are present . It is noteworthy that no bone marrow fibrosis nor extramedullar hematopoiesis have been reported in the Rasa3Scat/Scat model , and no mechanism was presented to explain the severe Rasa3Scat/Scat thrombocytopenia . Finally , no evidence for predisposition to oncogenesis was observed in Rasa3Scat/Scat mice , but the very small numbers of homozygous mice that survive the second crisis period ( ∼6% of Scat/Scat newborns ) may explain this difference and preclude more extensive analysis . The cause of the major differences between the Rasa3Scat/Scat and SCID-Rasa3−/− phenotypes is currently not known , but may be due to the different mutation present in the Rasa3 protein - affecting protein localization and enzymatic activity , respectively - and/or to the different genetic background of the two models . Indeed , relocalization of the Rasa3Scat/Scat protein from the membrane to the cytosol may eventually create a new function in this cell compartment and lead to phenotypic alterations that are not present in mice expressing a catalytically-inactive and truncated Rasa3 protein . About 20% of SCID-Rasa3−/− mice develop a preleukemia characterized by a massive infiltration of bone marrow and spleen with CD117+ Sca-1+ CD38+ cells , a phenotype very similar to acute myeloid leukemia in man . The exact mechanism of this preleukemia was not defined in this work , but active GTP-bound Ras level was similar in Rasa3+/+ and Rasa3−/− fetal liver CD117+ hematopoietic stem cells . However , our studies in the human K562 leukemic cell line which overexpresses Rasa3 suggest that Rasa3 is a probable negative regulator of proliferation in these cells ( Fig . S5 ) . Alternatively , it has been reported that β1 and β3 integrin signaling regulates the balance among hematopoietic stem cell self-renewal , differentiation and quiescence in the osteoblastic niche [35] , [36] . Furthermore , β1 and β3 integrins can regulate stem cell functions via direct or indirect participation in cellular signaling [37] , providing a potential mechanism to explain the predisposition to preleukemia in a minor percentage of SCID-Rasa3−/− mice . In conclusion , our results demonstrate that mice with a catalytic inactivation of Rasa3 protein in the hematopoietic system develop a lethal syndrome characterized by defects during megakaryocyte development and differentiation , and leading to a severe thrombocytopenia . This syndrome is associated with Rap1 and integrin signaling alterations and a predisposition to develop preleukemia .
All animal studies were authorized by the Animal Care Use and Review Committee of the Université de Liège and of the Université Libre de Bruxelles . Rasa3−/− mice with Rasa3 exons 11 and 12 replaced by a neomycin resistance cassette express a catalytically-inactive Rasa3 truncated protein [16] . These mice were analyzed on a hybrid 129/SvJ×C57BL/6J genetic background . C . B . -17 SCID mice were purchased from Charles River , Belgium . All mice were bred in a specific pathogen free facility at the GIGA–Research Centre . The Rasa3 genotype was determined by PCR as previously described [16] . For reconstitution , 4–6 week-old C . B . -17 SCID mice were irradiated ( 200 rad ) and a total homogenate of E12 . 5 fetal liver cells ( FLC ) obtained from Rasa3 embryos was intravenously injected . SCID-Rasa3−/− mice were killed and analyzed either when moribund ( ie presenting a severely reduced mobility and/or feeding incompatible with a more than 2 days survival ) or 14 months after irradiation/reconstitution . Individual liver was recovered from E12 . 5 embryo and single cell suspension was prepared by passage through a 23-gauge needle . Recovered cells were cultured in DMEM ( Gibco ) supplemented with 10% heat-inactivated FBS , 2 mM L-Glutamine , 50 U/mL Penicillin , 50 ng/mL streptomycin , 0 . 1 mM nonessential amino acids and 50 ng/ml of recombinant mouse TPO for megakaryocyte differentiation ( PreProtech ) . Bone marrow from SCID-Rasa3+/+ and SCID-Rasa3−/− femurs were flushed with PBS . The marrow was cut in 1 mm transverse sections and placed in an incubation chamber containing complete DMEM medium . Chamber was maintained at 37°C for 6 h . Megakaryocytes at the periphery of the explant were observed under a confocal microscope ( Nikon A1R , 20× objective ) . Each experiment was performed in duplicates . One transversal section was used to determine by flow cytometry the number of CD41+ cells present in the explant . Images were acquired sequentially at 10 min intervals and processed with NIS-software and ImageJ . Three mice from each genotype were analyzed . FLC from Rasa3+/+ and Rasa3−/− embryos were cultured in the presence of TPO as described above . On day 3 , recovered cells were enriched for mature megakaryocytes on a 1 . 5–3% bovine serum albumin ( BSA ) gradient under gravity for 45 min at room temperature . The percentage of mature megakaryoctes in the enriched population was always over 70% . Cells were resuspended in Tyrode's buffer containing 1 mM CaCl2 and 1 mM MgCl2 for 3 h . For inside-out integrin signaling , cells were incubated for 30 min at room temperature with FITC-fibrinogen ( 250 µg/ml ) and 100 ng/ml TPO , 1 mM MnCl2 or nothing , in the presence or absence of 10 mM EDTA . After a 10-fold dilution with PBS containing 1 µg/ml propidium iodide , fibrinogen binding was quantified by flow cytometry [22] . Specific fibrinogen binding was defined as binding that was inhibited by 10 mM EDTA . To compare independent experiments , specific fibrinogen binding was expressed as a percent of maximal binding obtained in the presence of 1 mM MnCl2 , an activator of integrins . For outside-in integrin signaling , coverslides were coated with murine fibrinogen ( 100 µg/ml ) , collagen-I ( 35 µg/ml ) or Poly-D-Lysine ( PDL , 15 µg/ml ) for 1 h at room temperature , blocked with denatured BSA ( 5 mg/ml ) for 30 min and washed with PBS before use . Cells ( 25×103 ) were incubated for 18 h on the indicated substrate and non adherent cells were removed . Adherent cells were fixed in 10% formalin , permeabilized with 0 . 2% Triton X-100 in PBS and stained as described below . Cells were then analyzed by confocal microscopy and ImageJ Software . For Rap1 inhibitor studies , purified mature megakaryocytes were cultured over PDL coated-plates as in outside-in experiments in the presence of 3 µM GGTI-298 ( Sigma ) or DMSO as control . Adherent cells were fixed in 10% formalin , permeabilized with 0 . 2% Triton X-100 in PBS and stained as described above . Cells were then analyzed by confocal microscopy and ImageJ Software . A single-cell suspension of femur bone marrow was prepared by flushing the bones with PBS followed by gentle disaggregation through Pasteur pipette . Cells were released from spleen by gentle disruption with a piston of syringe . Spleen cells were treated with ACK buffer to lyse erythrocytes and washed once with PBS . Cells were incubated with 2 . 4G2 to saturate Fcγ receptors II and IIIa before staining with primary and secondary antibodies in PBS containing 0 . 1% FBS and 0 . 1% NaF for 20 min , and washed with the same solution before flow cytometric analysis on a FC 500 ( Beckman Coulter ) . Cell counts were determined by adding fluorospheres ( Flow-Count Fluorospheres , Beckman Coulter ) to the cell suspension , as described by the manufacturer . The following anti-mouse biotinylated or fluorochrome-conjugated antibodies were obtained from BD Pharmingen: anti-CD3ε , anti-CD71 , anti-CD41 and anti-CD117 . Anti-B220 , anti-F4/80 , anti-IgM , anti-Mac1 , anti-Sca-1 , anti-CD34 , anti-CD38 , anti-Ter119 , anti-CD41 and anti-Gr1 , as well as streptavidine-cychrome 5 were obtained from eBioscience . Anti-FcεRIa ( Mar-1 ) was obtained from O . Leo's laboratory ( Université Libre de Bruxelles , Belgium ) . JON/A antibody was obtained from Emfred Analytics . Fetal liver cell were analyzed on a FACS CantoII ( Beckman Coulter ) . For hematopoietic stem and megakaryocyte progenitor cells staining , anti-mouse biotinylated or fluorochrome-conjugated antibodies specific for Ter-119 , Gr1 , Mac1 , CD4 , CD8 , CD5 , IL7Rα , B220 and c-Kit ( CD117 ) were used to define the c-Kit+ Lin− cell population [19] , [38] . Then , anti-Sca-1 , anti-CD34 and anti-Flk2/Flt3 were used to define the hematopoietic stem cells , whereas anti-Sca-1 , anti-FcRγII/III and anti-CD150 were used to define the megakaryocytes progenitor cells ( all antibodies were from eBioscience , except anti-Flk2 , from BD Pharmingen and anti-CD150 , from BioLegend ) . Streptavidin phycoerythrin-Texas Red was from Invitrogen . Debris , aggregates and propidium iodide-positive dead cells were first excluded . Cells were analyzed using an LSRII flow cytometer ( Becton Dickinson ) . Data were analyzed with FlowJosoftware ( Tree Star , Ashland , OR ) . Fetal liver cells were stained for CD41 as described above and fixed with 5% formalin for 15 min . Cells were permeabilized in PBS containing 0 . 25% Tx-100 for 5 min at 4°C . DNA was stained with DAPI for 20 min and DNA content in CD41+ cells was determined by flow cytometry . Spleen and liver were fixed in paraformaldehyde 4% and embedded in paraffin following standard procedures . Femurs were fixed in paraformaldehyde 3 . 7% , decalcified in 0 . 5M EDTA pH 8 for one week and then processed as spleen and liver . Serially cut 5-µm-thick sections were stained with hematoxylin/eosin or Sirius Red ( for femur ) according to standard protocols . Spleen was processed as described and sections were stained with an anti-B220 antibody [39] . Femur sections were stained with a rabbit polyclonal anti-von Willebrand Factor ( vWF ) antibody from Dako . For quantification of megakaryocytes in osteoblastic and vascular niches , the whole diaphysis of three consecutive femur sections was scanned with a conventional microscope ( 20× objective ) for vWF+ cells , as described [40] . Megakaryocytes in the osteoblastic niche were calculated as the number of megakaryocytes in contact with the endosteal border . Megakaryocytes in the vascular niche were calculated as the number of megakaryocyte per vessel border . Osteoblastic and vascular borders were calculated with ImageJ software . Results are means ± SEM of 3 mice per genotype . Platelet counts were determined with Unopette ( Becton Dickinson ) . Red cells , total white cells , lymphocytes , neutrophils , eosinophils , basophils , hemoglobin , hematocrit and red cell volume were quantified with a Cell Dyn 3500 analyzer ( Abott Diagnostic ) . Serum erythropoietin and thrombopoietin levels were determined with ELISA mouse EPO and mouse TPO Quantikine kits ( R&D Systems ) . Blood smears were stained with Giemsa's , methylene blue and Romanowsky's solutions . Immunofluorescence studies using conventional and confocal microscopes were performed on total FLC cultured in the presence of TPO , on purified mature megakaryocytes and on FL hematopoietic stem cells . Cells were fixed in 5% formalin for 15 min , washed , permeabilized with 0 . 2% Tx-100 in PBS containing 2% of FBS for 15 min and incubated 1 h at room temperature with APC-conjugated anti-CD41 ( MW Reg30 , eBioscience ) for megakaryocyte or CD117 ( BD Pharmingen ) for HSC . Active , GTP-bound Rap1 or Ras immunofluorescence was detected using GST-RalGDS-RBD or GST-Raf1-RBD , respectively , and a FITC-conjugated mAb against GST ( Santa Cruz ) as described [41] Negative controls included the omission of GST-RalGDS-RBD/GST-Raf1-RBD , the substitution of GST-RalGDS-RBD/GST-Raf1-RBD with GST and the substitution of the anti-GST antibody with an irrelevant FITC-conjugated mouse IgG . After several washes , phalloidin-TRICT ( Sigma ) and DAPI ( Sigma ) were added for 20 min in PBS . After 3 washes in PBS , samples were mounted in ProLong ( Invitrogen ) for observation under a confocal microscope ( NikonA1R ) and/or an epifluorescence microscope ( Nikon Eclipse 90i ) . For active Rap1 or Ras images , z-sections of 0 . 150 microns were acquired from megakaryocytes or HSC . Pseudocolor scale was used to depicture the intensity of active Rap1 or Ras staining along the cell membrane . ImageJ was used to quantify the intensity of active Rap1 or Ras staining on each cell . All images were acquired and analyzed in the same conditions . For immunofluorescence studies of adherent megakaryocyte , cells were fixed with 10% formalin for 15 min , washed , permeabilized with 0 . 2% Tx-100 in PBS containing 2% of FBS for 15 min and incubated 1 h at room temperature with the indicated primary and secondary antibodies . After several washes , phalloidin-TRICT ( Sigma ) and DAPI ( Sigma ) were added for 20 min in PBS . After 3 washes in PBS , samples were mounted in ProLong ( Invitrogen ) for observation under a confocal microscope ( NikonA1R ) . The following antibodies were used: APC-conjugated anti-CD41 ( MW Reg30 , eBioscience ) , anti-Rap1 ( Millipore ) , anti-Talin-FITC and anti-rabbit-alexa 488 . A collagen-based system ( MegaCult-C , StemCell Technologies , Inc . ) was used for the colony assay . Briefly , 1 . 25×105 freshly isolated fetal liver cells were resuspended in IMDM completed with recombinant mouse TPO ( 50 ng/ml ) , IL-3 ( 20 ng/ml ) and IL-6 ( 10 ng/ml ) , followed by addition of cold collagen . Suspension was dispensed into 2 wells of a four chamber slide ( Millipore ) for duplicates . Cultures were kept at 37°C in a 5% CO2 atmosphere for 3 days . The collagen matrix was then fixed in a methanol–acetone solution ( 1∶3 ) , at room temperature for 20 min for colony fixation . Slides were then allowed to air dry for 15 min and stained for Acetylcholinesterase . For scoring , acetylcholinesterase-positive colonies with 3 or more immature megakaryocytes of about 10 µm of diameter were scored as CFU-Mk . Mature megakaryocytes averaged approximately 30 µm in diameter . Eight- to twelve-week old male mice were bled under sodium pentobarbital anesthesia from the retro-orbital plexus . Blood was collected on acid citrate dextrose ( ACD: 93 mM Na3-citrate , 7 mM citric acid , 14 mM dextrose , pH 6 . 0 ) containing 1 U/ml apyrase ( Grade I , Sigma ) in a volume ratio of ACD to blood of 1∶6 . Blood was centrifuged for 5 s at 800× g followed by 5 min at 100× g to obtain platelet rich plasma ( PRP ) . PRP was diluted 3 fold in ACD containing 1 U/ml apyrase and centrifuged at 1000× g . The platelet pellet was resuspended at a concentration of 3×108/ml in Tyrode's buffer ( 137 mM NaCl , 12 mM NaHCO3 , 2 mM KCl , 0 , 34 mM Na2HPO4 , 1 mM MgCl2 , 5 , 5 mM glucose , 5 mM Hepes , 0 . 35% BSA ) . In order to test the adhesion of unstimulated platelets to BSA-coated surface , 3 . 5×106 platelets in 300 µl of tyrode's buffer were added to each well of a 8 chambers slide ( Millipore ) and incubated for 45 min in a CO2 incubator at 37°C . Adherent platelets were washed twice with PBS , fixed with 10% formalin , and stained with phalloidin-TRICT . Washed platelets were stimulated or not with ADP ( 25 µM ) or collagen-related peptide ( CRP ) ( 1 µg/ml ) , under non-stirring conditions . After 15 minutes of activation , saturating concentrations of FITC-conjugated CD62 anti-P-selectin and PE-conjugated JON/A antibodies were added to the platelets , and incubations were continued for additional 15 minutes in the dark . Samples were fixed before the analysis with a FACS Calibur flow cytometer ( BD Biosciences ) . Light transmission was recorded during platelet aggregation induced by ADP ( 50 µM ) in the presence of 2 mM CaCl2 on a Chrono-Log Lumi-Aggregometer ( Havertown , PA ) . The Rasa3-tet-ON-inducible K562 cell line was generated by GEnTarget Inc . Briefly , Rasa3 expression and TetR repressor lentiviruses were generated and cotransduced in K562 cell by the company . K562 mutant cell line ( K562-Rasa3 ) was cultured in IMDM supplemented with 10% heat-inactivated FBS , 2 mM L-Glutamine , 50 U/mL Penicillin , 50 ng/mL streptomycin , 0 . 1 mM nonessential amino acids , 10 µg/ml blasticidin and 1 µg/ml puromycin . Treatment of K562-Rasa3 cells with tetracycline ( 2 µg/ml ) induced Rasa3 expression from the lentiviral constructs after 48 h . For the proliferation assay , 4×105 cells per ml were cultured in the absence or presence of tetracycline for 12 days . At the indicated days , number of alive cells was counted with a hemocytometer . Death cells were excluded by trypan blue staining . Rasa3 expression was confirmed by western blot . Two independent experiments were performed in duplicates-triplicates . Results are expressed as means ± SEM . Statistical analyses were performed with Graphpad Prism 3 . 0 . The test used for each experiment is described in the corresponding legend . For each test , a difference of P<0 . 05 was considered significant . | Megakaryocytes are the bone marrow cellular precursors of circulating blood platelets and give rise to nascent platelets by forming branching filaments called proplatelets . Terminal differentiation of round megakaryocytes into branched proplatelet forming megakaryocytes is a complex cytoskeletal-driven process which is affected in rare human familial thrombocytopenias . Interactions of megakaryocytes with extracellular matrix proteins are essential in this process since constitutive megakaryocyte integrin activity caused by specific mutations in ITGA2B or ITGB3 genes encoding for extracellular matrix protein receptors may result in abnormal adherent megakaryocytes , defect in proplatelet formation and thrombocytopenia . Here , we show that Rasa3 , a GTPase activating protein of the GAP1 family , controls Rap1 activation and integrin signaling during megakaryocyte differentiation . We found that Rasa3 catalytic inactivation in mice altered megakaryocyte development , adherence , migration , actin cytoskeleton organization and differentiation into proplatelet . Thus , these mice presented a severe thrombocytopenia , bleeding and anemia . |
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Expansion of a trinucleotide ( CGG ) repeat element within the 5′ untranslated region ( 5′UTR ) of the human FMR1 gene is responsible for a number of heritable disorders operating through distinct pathogenic mechanisms: gene silencing for fragile X syndrome ( >200 CGG ) and RNA toxic gain-of-function for FXTAS ( ∼55–200 CGG ) . Existing models have focused almost exclusively on post-transcriptional mechanisms , but co-transcriptional processes could also contribute to the molecular dysfunction of FMR1 . We have observed that transcription through the GC-rich FMR1 5′UTR region favors R-loop formation , with the nascent ( G-rich ) RNA forming a stable RNA:DNA hybrid with the template DNA strand , thereby displacing the non-template DNA strand . Using DNA:RNA ( hybrid ) immunoprecipitation ( DRIP ) of genomic DNA from cultured human dermal fibroblasts with both normal ( ∼30 CGG repeats ) and premutation ( 55<CGG<200 repeats ) alleles , we provide evidence for FMR1 R-loop formation in human genomic DNA . Using a doxycycline ( DOX ) -inducible episomal system in which both the CGG-repeat and transcription frequency can be varied , we further show that R-loop formation increases with higher expression levels . Finally , non-denaturing bisulfite mapping of the displaced single-stranded DNA confirmed R-loop formation at the endogenous FMR1 locus and further indicated that R-loops formed over CGG repeats may be prone to structural complexities , including hairpin formation , not commonly associated with other R-loops . These observations introduce a new molecular feature of the FMR1 gene that is directly affected by CGG-repeat expansion and is likely to be involved in the associated cellular dysfunction .
The human fragile X mental retardation 1 gene ( FMR1; HGNC:3775 ) contains a ( CGG ) n trinucleotide repeat that is responsible for a family of heritable disorders affecting both early neurodevelopment ( fragile X syndrome; FXS ) and late-onset neurodegeneration ( fragile X-associated tremor/ataxia syndrome; FXTAS ) [1]–[4] . The repeat element is located in the 5′ untranslated region ( 5′UTR ) of the gene , and is thus transcribed into mRNA but not translated into the amino acid sequence of the gene product , the FMR1 protein ( FMRP ) . Alleles in the ∼55–200 CGG-repeat range are historically referred to as “premutation” alleles in reference to increased instability and the tendency in maternal transmission to expand into the “full mutation” range of FXS ( >200 CGG repeats ) [3] , [5] , [6] . Premutation alleles are also variably associated with several clinical phenotypes; in addition to FXTAS , these phenotypes include primary ovarian insufficiency ( FXPOI ) [7] and neurodevelopmental involvement [8] , [9] . Contrary to the gene silencing observed in FXS alleles , premutation alleles are associated with increased transcriptional activity . Indeed , FMR1 mRNA levels are positively correlated with size of the repeat expansion in the premutation range [10] . The molecular pathogenesis of the premutation disorders is generally considered to be a toxic RNA gain-of-function resulting from the expanded CGG-repeat region in the mRNA , but a definitive mechanism for the RNA involvement has not yet emerged [1] , [11]–[15] . Stable RNA:DNA hybrids can form upon transcription of cytosine-rich template sequences because a guanine-rich RNA:cytosine-rich DNA heteroduplex is thermodynamically more stable than the corresponding DNA:DNA duplex [16] , [17] . Recent work has revealed that such structures form throughout the human genome , particularly at CpG island promoters [18] , [19] . Additionally , in vitro transcription experiments showed that CGG trinucleotide repeats alone are able to form R-loops [20] . R-loops at CpG island promoters serve a natural and important role in protecting CpG-rich regions from acquiring DNA methylation and becoming epigenetically silenced [18] . In addition , R-loop formation at the 3′ end of numerous human genes is thought to permit efficient transcription termination [19] , [21] . However , R-loop formation has also been linked to genomic instability in numerous systems [22]–[24] and is thought to trigger recombination at class-switch regions [25] , [26] . Recent results suggest that defects in mRNA processing can result in an R-loop-dependent activation of the DNA damage response , and to the accumulation of γH2AX , a histone variant associated with the repair of DNA breaks [27] , [28] . R-loops at the Prader-Willi syndrome Snord116 locus are responsible for chromatin decondensation and for regulating the transcription of nearby imprinted genes [29] . Thus , it appears that R-loop formation in the genome is a widespread , dynamic process that is sensitive to perturbation , and has both physiological roles and potential “toxic” consequences through activation of the DNA damage response . Herein we present evidence for R-loop formation at the endogenous human FMR1 locus , and explore the impact of CGG-repeat expansion and transcription induction on the extent of FMR1 R-loop formation .
We examined the sequence of the human FMR1 5′UTR to identify important features for R-loop formation , including proximity to transcription start sites ( TSSs ) , GC skew , and G-clusters [18] , [30] . We calculated the GC content ( GC% ) together with the density in CpG dinucleotides ( CpG observed/expected ratio; CpG O/E ) and GC skew ( G−C/G+C ) across the 5′ end of the FMR1 gene ( hg19 chrX:146 , 992 , 969–146 , 994 , 458; shown here for CGG = 100 ) ( Figure 1 ) . The multiple FMR1 TSSs are located upstream of the CGG repeats and constitute the upstream boundary of the UTR [31]–[33] , as depicted in Figure 1 . Overall , the promoter and 5′UTR are exceptionally GC-rich , with GC% peaking at 100% through the repeats , and staying above 60% through the entire UTR ( Figure 1 ) . Part of this region also shows an elevated frequency of CpG dinucleotides and can be classified as a CpG island ( CGI ) . The FMR1 CGI overlaps with the promoter sequences and the 5′UTR through the CGG repeats ( Figure 1 ) , and therefore belongs to a large class of promoter CGIs [18] . It is notable that CGG-repeat expansions characteristic of FXTAS and FXS directly stretch out the 3′ boundary of the CGI promoter element , as defined by its high GC content and CpG density [34] . In addition to elevated GC% and CpG O/E , the FMR1 CGI is also characterized by elevated GC skew downstream of the TSSs and through the CGG repeats ( Figure 1 ) . As noted for CGI promoters and other regions in the human genome , GC skew is highly predictive of R-loop formation [18] , [19] . As with GC skew , G-clusters ( ≥4 Gs in a row ) act as nucleation points for RNA:DNA hybridization [30] . Five such clusters are found in the 5′UTR , as indicated by red ticks on the schematic in Figure 1 , including one that is included/excluded in the transcript depending on TSS choice . In total , these features predict R-loop formation at FMR1 following transcription . We used DNA:RNA immunoprecipitation ( DRIP ) to directly test the existence of R-loops at the endogenous FMR1 locus in human genomic DNA , and compared the relative abundance of R-loops across the range of transcribed CGG-repeat expansion alleles . The S9 . 6 antibody recognizes RNA:DNA hybrids without any known sequence preference or sensitivity to DNA methylation ( [18] , [21] , [35]; unpublished data ) . After DRIP , we calculated the fold enrichment of FMR1 relative to input genomic DNA , and to a non-R-loop-forming genomic locus ( ZNF554; HGNC:26629 ) using qPCR , where DRIP enrichment is not expected . In genomic DNA from cultured human male dermal fibroblasts , we observed a 2 . 1- to 13 . 9-fold enrichment for FMR1 across the range of CGG-repeat alleles tested ( Figure 2A ) . Although there was substantial inter-subject variation in fold enrichment , both in control and premutation groups , the premutation group as a whole demonstrated greater enrichment ( mean 9 . 0 , SD 3 . 9 , range 2 . 9–13 . 4 ) than the control group ( mean 4 . 2 , SD 2 . 4 , range 1 . 6–8 . 6 ) ( P = 0 . 0008; linear mixed-effects model , see: Material and Methods ) . By contrast , a positive control for a strong R-loop-forming locus , MYADM ( HGNC:7544 ) , showed consistently high enrichment ( 25- to 50-fold ) , which was not influenced by FMR1 CGG-repeat size ( Figure S1 ) . As expected for R-loop formation , treatment with purified recombinant human RNases H1 and H2 eliminated DRIP pulldown . Hence , enrichment for FMR1 in 3 different fibroblast lines went from a mean of 6 . 36±2 . 31 ( SEM , n = 4 ) to 1 . 59±0 . 219 ( SEM , n = 4 ) upon RNase H treatment ( Figure 2B ) , a significant reduction ( unpaired t-test on log-transformed enrichment values , P = 0 . 0125 ) . Likewise , elimination of DRIP enrichment following RNase H treatment was also observed at the positive MYADM locus ( unpaired t-test on log-transformed enrichment values , P = 0 . 0002 ) ( Figure 2B ) . We used a doxycycline ( DOX ) -inducible episomal system in SK-N-MC neuroepithelioma cells [36] to investigate the relationship between the frequency of transcription initiation and R-loop formation . The TRE-Tight promoter allows for precise control of transcription through an FMR1 5′UTR sequence harboring either a 95 or 30 CGG-repeat element , or a non-FMR1 linker sequence ( [36]; Figure 3A ) . All three constructs include EGFP cDNA , which was used as a target for qPCR to avoid amplification from endogenous sequences . Treatment with DOX at 10 ng/mL and 100 ng/mL resulted in a clear induction of transcription with equal expression levels for all three constructs , relative to the no-DOX baseline ( Figure 3B ) . Using DRIP-qPCR , we observed increased R-loop formation through the FMR1 5′UTR , mirroring the transcriptional response to DOX induction ( Figure 3C; Figure S2A ) . Fold enrichment for the 30 CGG-repeat allele increased from 0 . 62±0 . 096 ( n = 3 ) without DOX , to 3 . 1±0 . 65 ( n = 3 ) at 10 ng/mL DOX , and 3 . 8±0 . 24 ( n = 3 ) at 100 ng/mL DOX . The 95 CGG-repeat allele increased from 0 . 41±0 . 044 ( SEM , n = 3 ) without DOX , to 2 . 9±0 . 22 ( n = 3 ) at 10 ng/mL DOX , and 3 . 45±0 . 50 ( n = 3 ) at 100 ng/mL DOX . By contrast , the non-FMR1 control locus showed little to no increase upon induction ( Fig . 3C ) . We note that the episome backbone also showed modestly increased pull-down efficiency with increasing expression ( Figure S2B ) , which could result from R-loop formation around the EGFP poly ( A ) sequence . Indeed , R-loops have the propensity to form broad peaks around poly ( A ) -dependent termination regions ( [19] , [22]; F . C and L . S . , unpublished observations ) . Given that R-loops inhibit the activity of restriction enzymes , this inhibition could prevent the cleavage required for separating the GFP restriction fragment from the background fragment and lead to apparent DOX-inducible R-loop formation over the episomal backbone . To account for this background , R-loop formation at the target GFP locus , as measured by DRIP-qPCR , was calculated relative to R-loop formation over the episome backbone and normalized to non-induced baseline ( Figure 3C ) . To assess the stability of R-loops once formed , we induced transcription for 6 hours with 100 ng/mL DOX , after which DOX was removed from the media and R-loop presence was measured by DRIP 1 , 2 , and 24 hours following the washout . Recovery of the 30-repeat allele decreased to 46 . 8%±15 . 3% ( n = 2 ) of maximum after a 1-hour washout . It persisted at 47 . 2%±2 . 3% ( n = 3 ) after a 2-hour washout , and dropped to 16 . 6%±1 . 2% ( n = 3 ) after 24 hours ( Figure 3D ) . Recovery of the 95-repeat allele decreased to 33 . 8%±6 . 7% ( n = 2 ) of maximum after 1 hour , then remained essentially unchanged to 24 hours ( 33 . 9%±6 . 6%; n = 3 ) ( Figure 3D ) . These data show that R-loop formation through the FMR1 5′UTR depends on active transcription initiation and that R-loops are dynamic structures , which are progressively formed and resolved . We used non-denaturing sodium bisulfite treatment to map the extent of the displaced single-stranded DNA ( ssDNA ) constituting the FMR1 R-loop in human male fibroblast genomic DNA . Sodium bisulfite deaminates unmethylated cytosines , but only with high efficiency in ssDNA . When applied in a non-denaturing manner , it can therefore be used as an efficient probe for R-loop formation and has been used extensively to footprint R-loop structures at single-nucleotide resolution [18] , [26] . As expected from DRIP data , non-denaturing bisulfite footprinting revealed extensive single-strandedness through the 5′UTR across the range of transcribed FMR1 alleles ( CGG = 29 , 30 , 55 , 79 ) ( Figure 4 ) . R-loop structures began at the first G-cluster downstream of the TSSs and continued through the repeat region for all four alleles examined . However , unlike any other region analyzed to date , stretches of unconverted cytosines indicative of double-stranded DNA were found inside the repeats . Furthermore , the extent of unconverted DNA was much greater for expanded alleles with higher CGG-repeat sizes ( Figure 4 ) . For normal CGG-repeat sizes , patterns of non-conversion were short and symmetrical and were centered on an AGG-repeat interruption not shown in the figure . The single converted CpG dinucleotide in the center of the unconverted CGG track of both 29- and 30-repeat samples was located immediately adjacent to the AGG polymorphism . Such a pattern would be expected if a short hairpin formed within the repeat region , with the stem of the structure being double-stranded and protected from conversion while a short loop is exposed . For longer CGG repeats , the footprinting data suggests that a large region of ssDNA exists upstream and downstream of the CGG repeat , but that most of the repeat region itself is in fact protected from conversion , save a few scattered points of conversion ( Figure 4 ) . Note that , for a certain number of molecules in the 30- , 55- , and 79-CGG samples , R-loops seemed to initiate at a G-cluster downstream of the repeats . Overall , these data show that R-loop formation at FMR1 can initiate from different G-cluster seeding points , both upstream and downstream of the CGGs , and that R-loop formation through expanded CGG repeats may result in hairpin formation or other structural conformations .
At the DNA sequence level , R-loop formation is best predicted by the combination of GC content and GC skew , which measures the density and strand asymmetry in the distribution of guanines and cytosines , and correlates with the stability of RNA:DNA hybrids . In comparison to other R-loop forming regions of the genome , the normal ( unexpanded ) FMR1 promoter matches “Class II” CGI promoters [18] , [19] . This category is typical of skewed promoters on the X-chromosome and associates with marginally weaker GC skew [19] . As far as GC sequence composition is concerned , the human FMR1 CGI is at the extreme end of the spectrum in the genome . A GC content of greater than ∼65% is generally viewed as “high GC , ” and only 22 CGIs are listed at >80% in the human reference genome [37] . Our analysis shows that GC% at the FMR1 promoter/5′UTR region peaks at 100% across the CGG-repeat , saturating this sequence characteristic . All three metrics of CGI composition and predictors of R-loop formation ( GC% , CpG O/E , GC skew ) peak at the repeat region . Importantly , CGG-repeat expansions associated with FXTAS and FXS will push FMR1 into the category of stronger skewed promoters by increasing the lengths of the GC content , GC skew , and CpG tracks . CGG expansions are also likely to increase R-loop formation efficiency in two additional ways . First , expanded ( premutation ) alleles trigger higher transcriptional rates [10] , which should favor the frequency of co-transcriptional R-loops . Second , expanded alleles are characterized by a shift in the usage of transcription initiation sites to upstream sites [31] , [32] . This shift is expected to allow the inclusion of additional G-clusters , which are de facto R-loop initiation points , in the transcript . The CGG-repeat expansions that associate with FXTAS and FXS are therefore unique in that they strongly enhance the propensity of the FMR1 CGI to form co-transcriptional R-loops . Here , we provide direct experimental evidence that R-loops form at the endogenous genomic FMR1 locus , which was first detected by the ability of the S9 . 6 anti-RNA:DNA hybrid antibody to specifically immunoprecipitate the FMR1 locus . As expected , enrichment was lost following enzymatic resolution of the RNA:DNA hybrids using purified RNases H ( Figure 2 ) . These experiments are consistent with the notion that repeat expansions cause a corresponding increase in R-loops at the FMR1 locus . Indeed , an upward trend in the FMR1 S9 . 6-pulldown efficiency was observed for individuals with increasing CGG-repeat sizes ( Figure 2A; Figure S3 ) , despite the variation that exists between individuals within both control and premutation allele classes . Based on three control CGG subjects ( 2–3 independent replications per subject; n = 8 experiments ) and four premutation subjects ( 2–4 independent replications per subject; n = 10 experiments ) , the fold enrichment in premutation ( mean 9 . 0 , SD 3 . 9 , range 2 . 9–13 . 4 ) was significantly greater than for controls ( mean 4 . 2 , SD 2 . 4 , range 1 . 6–8 . 6 ) ( P = 0 . 0008; linear mixed-effects model; see: Materials and Methods ) . Our DOX-inducible episomal FMR1 system provides a more controlled isogenic platform to directly parse out the impact of repeat expansion and transcription frequency on R-loop formation . As expected , R-loop formation increased in direct response to increasing transcription ( Figure 3C ) . CGG-repeat length at equivalent transcription levels , however , appeared to have little effect on R-loop frequency . Removing DOX resulted in a corresponding decrease in R-loops in the episomal FMR1 5′UTR ( Figure 3D ) , demonstrating the plasticity of R-loop formation at a given locus , with formation driven by active transcription and dissolution catalyzed by native enzymes such as RNases H , RNA:DNA helicases , or DNA topoisomerases [21] , [38] . R-loops at CGI promoters were recently implicated in mediating protection against DNA methylation and epigenetic silencing [18] . R-loops at FMR1 likely contribute to the same function for alleles in the normal and premutation ranges , in which the 5′UTR remains essentially unmethylated . Additionally , R-loop formation , by fostering a more open chromatin environment [29] , is possibly responsible for the increased transcription resulting from repeat expansion in FMR1 , although there is no direct evidence for this suggestion . The FMR1 CGI is unique among CGIs in that it undergoes hypermethylation and silencing for full mutation alleles ( ≥200 CGG repeats ) , which suggests that , above a certain genetically-encoded threshold , the protection force operating at FMR1 may be overcome by an as-yet-undefined silencing mechanism ( either at the DNA or histone level ) . This transition between protection and silencing regimes could in fact be mediated by the unusual nature of R-loops formed through expanded CGG repeats . We provide evidence here that the non-template strand of FMR1 R-loops presents stretches that are refractory to bisulfite footprinting ( Figure 4 ) , indicative of possible hairpin-like structures , which is consistent with the well-documented propensity of trinucleotide CGG repeats to fold into higher-order structures [39] , [40] . Such structural characteristics distinguish FMR1 from other non-repetitive R-loop-forming CGI promoters [18] , [19] and even from repetitive R-loop-forming sequences such as class-switch regions [26] , [41] . Our non-denaturing bisulfite footprinting data also show that regions of ssDNA often appear to be located downstream of the CGG repeats themselves , particularly for larger repeats ( Figure 4 ) . This pattern is unusual in that GC skew should favor R-loop initiation within the repeats . It is possible that hairpin formation on the displaced G-rich strand may cause collapse of the R-loop structure by imposing torsional stress on the RNA:DNA hybrid ( Figure 5 ) . Such hairpin-mediated interconversion between a “regular” R-loop and a “collapsed” R-loop would explain the patterns observed and would be compatible with the unique sequence characteristics of the region . Interestingly , the structural complexities observed at FMR1 may have relevance to the transition from an active to a silenced state characteristic of fully expanded repeats . For instance , DNMT1 , the most powerful DNA methyltransferase in human cell extracts , recognizes structured/hairpin DNA as a substrate for methylation [42] . This recognition could seed DNA methylation inside the repeats before spreading up- and down-stream over the rest of the UTR/promoter . Alternatively , collapse of the RNA:DNA hybrid inside the CGG repeats could potentially disrupt the protective effect of R-loops against DNA methylation , which has been observed at other CGI promoters [18] . Previous studies of FMR1 hypermethylation have not mapped high-resolution methylation patterns inside the repeats themselves [43] , [44] , and thus would easily overlook this repeat-centric model . In addition to changes in secondary structure , repeat expansion and increased transcription could result in R-loop-driven activation of the DNA damage response and genomic instability [22] , [27] , [28] , [45]–[49] . R-loops in the inappropriate context or timing result in DNA breaks , as indicated by recruitment of γH2AX . In this regard , we have previously reported activation of the double-stranded-break repair pathway in this same episomal system , but only in highly transcribed expanded CGG repeats [36] . Even though elevated exogenous expression likely exaggerates this effect in the model system , γH2AX is observed in the characteristic intranuclear protein inclusions of post-mortem neurons in FXTAS patients [36] . In addition to potential involvement in FXTAS pathology , R-loop formation and/or DNA damage are responsible for genomic instability generally [22] , [48] , and at FMR1 specifically [50] , [51] , including the eponymous fragile site [52] . In addition to toxicity and instability , DNA damage has been linked to aberrant DNA methylation [53]; increased R-loop formation and/or increased damage of a full mutation CGG R-loop could overcome the protective features of 5′UTR CGI R-loops and trigger the methylation and silencing characteristic of FXS . We introduce here a previously unrecognized molecular feature of the FMR1 gene that is influenced by expansion of the CGG-repeat element . R-loop formation is a normal and important feature of the FMR1 promoter , but expansion of the CGG repeats , and the associated increase in transcription , results in increased formation of longer R-loops that are more prone to folding into complex secondary structures , which could trigger instability and hypermethylation associated with FMR1-repeat expansion . This discovery provides a novel area of inquiry for understanding the aberrant cellular responses to CGG-repeat expansion at FMR1 , and at transcribed trinucleotide-repeat loci throughout the genome .
Human dermal fibroblasts were originally cultured from skin biopsies acquired under an IRB-approved protocol , as previously described [54] . Cells were grown at 37°C , 5% CO2 in a 50∶50 mix of RPMI-1640 , supplemented with 1× Amphotericin B ( JR Scientific , Woodland , CA ) , 1× Penicillin-Streptomycin-Glutamine ( Life Technologies , Carlsbad , CA ) , 1× MEM Non-Essential Amino Acids Solution ( Life Technologies ) , and 10% Fetal Bovine Serum ( JR Scientific ) and AmnioMAX C100 media ( Life Technologies ) . Fibroblasts were harvested at 80% confluency to avoid decreased transcription associated with contact inhibition . SK-N-MC-rtTA cell lines harboring expanded CGG-repeat episomes were created , as previously described [36] . These cells were grown in DMEM ( Life Technologies ) +10% Tet-system-approved fetal bovine serum ( Clontech , Mountain View , CA ) and 1× Penicillin-streptomycin ( Life Technologies ) at 37°C , 5% CO2 . DOX media was prepared from 1 mg/mL stock doxycycline hyclate ( Sigma-Aldrich , St . Louis , MO ) dissolved in sterile water . For the DOX washout , DOX media was aspirated and cells were washed once with DPBS ( Life Technologies ) before adding DOX-free media . Adherent cells were trypsinized ( 0 . 25% trypsin; Life Technologies ) for fibroblasts and 0 . 05% trypsin ( JR Scientific ) for SK-N-MC cells for 4 minutes at 37°C before quenching with an equal volume of media and pelleting at low speed ( 200 RCF ) . Cell pellets were washed with DPBS ( Life Technologies ) and divided for DRIP or RNA harvests . Cell pellets for RNA harvest were lysed in RLT buffer ( Qiagen , Hilden , Germany ) and frozen at −80°C before processing at a later date according to the RNeasy kit ( Qiagen ) . Cell pellets for DRIP were resuspended in 4 mL of 10 mM Tris-HCl , 10 mM EDTA , 100 mM NaCl pH 8 , lysed with 0 . 5% SDS , and digested with 400 units of Proteinase K ( Thermo Fisher Scientific , Waltham , MA ) at 37°C overnight . Cell lysates were then extracted once with 1 volume of equilibrated phenol pH 8 ( USB , Cleveland , OH ) and twice with 1 volume of chloroform ( Sigma-Aldrich ) . DNA was precipitated with 1 volume of isopropanol and 300 mM sodium acetate , and was swirled out of solution with a glass shepherd's hook . The DNA pellet was washed twice by rinsing the hook with 400 µL of 70% ethanol , and was rehydrated in 10 mM Tris-HCl pH 8 . Harvested nucleic acids ( ∼50 µg ) were digested using a restriction enzyme cocktail ( 20 units each of EcoRI , HindIII , BsrGI , XbaI ) ( New England Biolabs , Ipswich , MA; NEB ) overnight at 37°C in 1× NEBuffer 2 . Digests were cleaned by phenol and chloroform extraction followed by precipitation in isopropanol . The resulting fragmented DNA was pelleted at full speed ( 16 , 100× g ) at 4°C and washed twice with 70% ethanol . Air-dried pellets were rehydrated in 10 mM Tris-HCl pH 7 . 5 , 1 mM EDTA ( TE ) . We adapted the previously described DRIP protocol [18] . Six to eight µg of digested nucleic acids were diluted in 450 µL of TE , and 10 µL was reserved as input for qPCR . Fifty-two µL of 10× IP buffer was added for a final buffer concentration of 10 mM sodium phosphate , 140 mM sodium chloride , 0 . 05% Triton X-100 , and 20 µL of S9 . 6 antibody ( 1 mg/ml; prepared from ascites , as previously described [18] ) . The samples were incubated with the antibody at 4°C for 2 hours . This incubation and all wash steps were performed on a rotisserie mixer . Forty µL of Protein A/G Agarose slurry ( Pierce , Rockford , IL ) was washed twice with 800 µL of 1× IP buffer for 5 minutes at room temperature . After adding agarose slurry to each sample , they were incubated for 2 hours at 4°C . Each DRIP was then washed three times with 700 µL 1× IP buffer for 10 minutes per wash at room temperature . After the final wash , the agarose slurry was resuspended in 250 µL of 1× IP buffer and incubated with 60 units of Proteinase K for 30 minutes at 50°C . Digested DRIP samples were then cleaned with phenol/chloroform extraction and isopropanol precipitation . Air-dried DRIP pellets were resuspended in 80 µL of 10 mM Tris-HCl pH 8 . We used 12 µL reactions with Sensi-FAST Lo-Rox 2× qPCR mix ( Bioline , London , UK ) to assay for genomic loci: FMR1 ( 200 nM each ) ( F: TTGCCCCTTAGTTCCCTGAG; R:TCTTCCATCAGTGCAGACCA ) , MYADM ( 300 nM each ) ( F: CGTAGGTGCCCTAGTTGGAG; R: TCCATTCTCATTCCCAAACC ) , and ZNF554 ( 300 nM each ) ( F: CGGGGAAAAGCCCTATAAAT; R: TCCACATTCACTGCATTCGT ) . For the episomal DRIP experiments , we assayed for EGFP ( F: TCAAGATCCGCCACAACATC; R:TTCTCGTTGGGGTCTTTGCT ) and the pCEP4 backbone ( F:ATCCCCATCCCTACCGTCCA; R:CCCCATCCTCCGAACCATCC ) using 5 µL of 1∶500 diluted template or 5 µL undiluted DRIP output ( from 80 µL total ) . Reactions were incubated with the following program on a Viia 7 System ( Life Technologies ) : 50°C 2 minutes , 95°C 10 minutes , 40 cycles of 95°C 15 seconds , 64°C 1 minute , followed by a melt curve: 95°C 15 seconds , 60°C 1 minute , 0 . 05°C/second to 95°C 15 seconds . For each DRIP sample , 5 µL of the output and 5 µL of diluted input ( 1∶100 ) were assayed in triplicate . Fold enrichment for a given locus ( i . e . , FMR1 or EGFP ) was calculated using the comparative Ct method [55] , relative first to input and then to the appropriate reference ( i . e . , ZNF554 or pCEP4 backbone ) . Comparison of fold enrichment between premutation ( 4 subjects , 2–4 independent replications per subject; n = 10 experiments ) and control ( 3 subjects , 2–3 independent replications per subject; n = 8 experiments ) subjects ( Figure 2 ) was based on a linear mixed-effects model to account for correlation between repeated measurements on the same subjects . The analysis was done using SAS version 9 . 3 . Harvested nucleic acids ( 4–10 µg ) were digested with HindIII ( 20 units , ∼5 hours at 37°C; NEB ) and then treated with the sodium bisulfite conversion mix from the EZ-DNA Methylation Kit ( Zymo Research , Irvine , CA ) overnight at 37°C . Bisulfite-treated DNA was then desulphonated and cleaned according to kit protocol and was eluted in 10 µL 10 mM Tris-HCl pH 8 . Bisulfite-treated DNA was amplified using a method adapted for CGG-repeat amplification [56] . One to two µL of bisulfite-treated DNA was amplified in a 30 µL reaction with 0 . 5 mM dNTPs , 2 . 25 M betaine ( Sigma ) , 333 nM of each primer in 1× buffer and 0 . 2 µL of enzyme mix from the Expand Long Template Kit ( Roche , Basel , Switzerland ) . Enzyme and buffer were added after 8 minutes at 98°C , followed by an additional 2 minutes at 98°C , then 10 cycles at 97°C for 35 seconds , 64°C for 35 seconds , 68°C for 4 minutes , 25 cycles at 97°C for 35 seconds , 64°C for 35 seconds , 68°C for 4 minutes , plus a 20-second increment for each cycle , and a final extension at 68°C for 10 minutes . In order to successfully and cleanly amplify through the bisulfite-converted CGG repeats , we used two rounds of amplification with a nested primer set ( first round: F:GAGGGAACAGCGTTGATCACGTG R: CACTTAACACCAATTTCAACCCTTCCCACC; second round: F: GGAACAGCGTTGATCACGTGACGTGGTTTC R: CTTCCCTCCCAACAACATCCCACCAAAC ) . PCR-amplified DNA was sub-cloned using the Qiagen PCR Cloning Kit . Chemically competent E . coli Top10 cells ( Life Technologies ) were transformed by heat-shock with ligated plasmid , and were grown overnight at 37°C on LB agar plates with 100 mg/ml ampicillin selection . Picked colonies were grown in 4 mL LB broth with 100 mg/ml ampicillin at 30°C with 150 rpm shaking overnight; plasmid DNA was extracted using the Qiagen plasmid miniprep kit . Plasmid DNA PCR clones were sequenced ( Davis Sequencing , Davis , CA ) with M13R or SP6 primers , depending on orientation of the PCR insert . Clean sequence clones were then aligned to an unconverted reference sequence with Clustal W2 [57] to score cytosine conversion events . The full-length cDNA for human RNASEH1 ( ATCC , Manassas , VA ) was PCR-amplified , excluding the first 26 amino acids of the protein corresponding to the mitochondrial localization signal [58] . The amplified fragment was recloned in frame in a modified pMAL vector [59] to generate an MBP-RNASEH1 fusion protein . Protein expression was induced for 2 hours at 37°C in E . coli Rosetta cells grown in exponential phase in Terrific Broth . Cells were harvested and lysed with a microfluidizer in amylose buffer ( 10% glycerol , 25 mM Tris-HCl pH 7 . 5 , 250 mM NaCl , 1 mM DTT , 0 . 5 mM EDTA supplemented with complete protease inhibitor cocktail; Roche ) , and the lysate was spun for 1 hour at 30 , 000× g . The supernatant was then applied to a 20 ml amylose column ( NEB ) equilibrated in 1× amylose buffer , after which the column was washed with 10 column volumes of binding buffer . The MBP-RNASEH1 protein was eluted in batch in binding buffer supplemented with 20 mM maltose . The protein was then dialyzed against Q buffer ( 20 mM Tris-HCl pH 7 . 5 , 50 mM NaCl , 0 . 1 mM β-mercaptoethanol ) overnight at 4°C and was applied to a pre-equilibrated 5 ml FastFlow Q column ( GE Healthcare , Little Chalfont , UK ) using an Akta FPLC system . The protein mostly flowed through . The flow-through was then re-applied to a Mono-Q column ( GE Heathcare ) to separate the protein from any contaminating nucleic-acid species . The flow-through was again collected , concentrated , and dialyzed against storage buffer ( 20 mM Tris-HCl pH 7 . 5 , 50 mM NaCl , 1 mM DTT , 0 . 1 mM EDTA , 20% glycerol ) , then aliquoted and snap frozen in liquid nitrogen before storage at −80°C . The concentration of the preparation was calculated to be 17 . 7 µM ( 1 . 3 mg/ml ) using an extinction coefficient of 112 , 710 M−1cm−1 . The pMAR22 expression vector for the heterotrimeric RNASEH2 complex was a kind gift from Dr . Reijns; the complex was purified essentially as described [60] . The protein was stored as described for RNASEH1 . The concentration of the preparation was calculated to be 15 µM ( 1 . 34 mg/ml ) using an extinction coefficient of 81 , 050 M−1cm−1 . Both preparations were ∼98% pure , as judged from Coomassie-stained SDS-PAGE gels , and gave expected sizes of either one single band for MBP-RNASEH1 or three equimolar bands for the RNASEH2 complex . Both preparations were devoid of detectable endo- or exonuclease activity after incubating 1 µl of undiluted protein with double-stranded circular or linear DNA substrates for 4 hours at 37°C ( data not shown ) . Both preparations were highly active even under 10 , 000-fold diluted concentrations against artificial R-loop substrates prepared by in vitro transcription ( data not shown ) . | Expansion of a CGG-repeat element within the human FMR1 gene is responsible for multiple human diseases , including fragile X syndrome and fragile X-associated tremor/ataxia syndrome ( FXTAS ) . These diseases occur in separate ranges of repeat length and are characterized by profoundly different molecular mechanisms . Fragile X syndrome results from FMR1 gene silencing , whereas FXTAS is associated with an increase in transcription and toxicity of the CGG-repeat-containing mRNA . This study introduces a previously unknown molecular feature of the FMR1 locus , namely the co-transcriptional formation of three-stranded R-loop structures upon re-annealing of the nascent FMR1 transcript to the template DNA strand . R-loops are involved in the normal function of human CpG island promoters in that they contribute to protecting these sequences from DNA methylation . However , excessive R-loop formation can lead to activation of the DNA damage response and result in genomic instability . We used antibody recognition and chemical single-stranded DNA footprinting to show that R-loops form at the FMR1 locus with increasing frequency and greater structural complexity as the CGG-repeat length increases . This discovery provides a missing piece of both the complex FMR1 molecular puzzle and the diseases resulting from CGG-repeat expansion . |
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Developing an immunogen that elicits broadly neutralizing antibodies ( bNAbs ) is an elusive but important goal of HIV vaccine research , especially after the recent failure of the leading T cell based HIV vaccine in human efficacy trials . Even if such an immunogen can be developed , most animal model studies indicate that high serum neutralizing concentrations of bNAbs are required to provide significant benefit in typical protection experiments . One possible exception is provided by the anti-glycan bNAb 2G12 , which has been reported to protect macaques against CXCR4-using SHIV challenge at relatively low serum neutralizing titers . Here , we investigated the ability of 2G12 administered intravenously ( i . v . ) to protect against vaginal challenge of rhesus macaques with the CCR5-using SHIVSF162P3 . The results show that , at 2G12 serum neutralizing titers of the order of 1∶1 ( IC90 ) , 3/5 antibody-treated animals were protected with sterilizing immunity , i . e . no detectable virus replication following challenge; one animal showed a delayed and lowered primary viremia and the other animal showed a course of infection similar to 4 control animals . This result contrasts strongly with the typically high titers observed for protection by other neutralizing antibodies , including the bNAb b12 . We compared b12 and 2G12 for characteristics that might explain the differences in protective ability relative to neutralizing activity . We found no evidence to suggest that 2G12 transudation to the vaginal surface was significantly superior to b12 . We also observed that the ability of 2G12 to inhibit virus replication in target cells through antibody-mediated effector cell activity in vitro was equivalent or inferior to b12 . The results raise the possibility that some epitopes on HIV may be better vaccine targets than others and support targeting the glycan shield of the envelope .
There is widespread acceptance that eliciting neutralizing antibodies is likely to be an important goal of an effective HIV vaccine [1] , [2] , [3] . A good correlation is generally reported between the ability of an antibody to neutralize in vitro and to protect in vivo against HIV in animal models [4] , [5] , [6] , [7] , [8] , [9] . The most quantitative studies have titrated the ability of specific antibodies to protect and found that sterilizing immunity is achieved when the serum concentration of antibody in the challenged animals is many multiples of the in vitro neutralization titer [4] , [8] , [10] . For instance , Nishimura , et al . reported that 99% of macaques were protected against intravenous challenge with an R5 SHIVDH12 by a specific polyclonal antibody at a 100% neutralization titer of 1∶38 [10] . In another example , we have reported sterilizing immunity against R5 SHIVSF162P4 vaginal challenge in 4/4 macaques with a dose of the broadly neutralizing human antibody b12 yielding a serum neutralizing titer of about 1∶400 at challenge [8] . The titer corresponded to 90% neutralization in a PBMC assay . Nishimura et al [10] estimated that this titer corresponded to 1∶32 . 5 or greater in their assay system providing good correspondence between the two studies . At an antibody dose giving a serum neutralizing titer of about 1∶80 in the Parren , et al . study , 2/4 macaques showed sterilizing immunity and the other 2 were infected with a delayed and lower primary viremia as compared to controls . At an antibody dose giving a serum neutralizing titer of about 1∶16 , no animal was protected but there was a slight delay and some lowering in the magnitude of primary viremia . Most other studies have not titrated the ability of antibodies to protect but high serum concentrations of antibody relative to neutralizing titer were generally used and shown to provide protection against virus challenge [4] , [5] , [6] , [9] , [11] . The one notable exception is provided by studies of Mascola and colleagues [7] on protection by the broadly neutralizing human MAb 2G12 . In particular 2/4 macaques showed sterilizing immunity when challenged by an ×4 SHIV ( SHIV89 . 6P ) when the serum neutralizing titer , as measured at 90% neutralization in a PBMC assay , was less than 9 . In fact , the mean concentration of 2G12 in the sera of the animals at challenge was calculated to provide 90% neutralization only with neat serum ( i . e . 1∶1 neutralizing titer ) . The actual concentration of 2G12 in the protected animals at the time of challenge was relatively high , about 200 µg/ml following an i . v . administration of 15 mg/kg antibody , but 2G12 is relatively poor at neutralization of SHIV89 . 6P ( IC90∼200 µg/ml ) hence the low neutralizing titer . The authors also carried out protection experiments with mixtures of antibodies , including 2G12 . These experiments when taken together again suggested that 2G12 may provide protection that is unusually effective relative to its neutralizing titer . Monoclonal human IgG1 2G12 is a very interesting and unique antibody . It is broadly neutralizing , particularly against clade B HIV-1 isolates [12] , [13] , [14] . It has a domain-exchanged structure that leads to closely proximal antibody combining sites that are well suited to the recognition of a cluster of oligomannose residues on the glycan shield of HIV [12] , [15] , [16] , [17] , [18] . 2G12 belongs to a small set of human MAbs that are described as broadly neutralizing and that recognize distinct epitopes on the HIV envelope spike . The MAb b12 recognizes an epitope overlapping the CD4 binding site “on the side” of the spike and the MAbs 2F5 , 4E10 and Z13e1 recognize gp41 very close to the viral membrane , whilst 2G12 recognizes an epitope which is more on the “top” of the spike [19] , [20] , [21] . Given the suggestion that 2G12 may have unusual prophylactic activities and given the potential importance of this for HIV vaccine design , we decided to carry out a macaque protection study using a virus different from that of Mascola and colleagues and to pursue potential properties of 2G12 that might correlate with protection . Ideally , we would have had available a SHIV that was relatively neutralization sensitive to 2G12 to permit study of a maximum dynamic range of 2G12 concentrations with neutralizing activity . However , currently available SHIVs are relatively resistant to 2G12 and the R5 virus SHIVSF162P3 was chosen as the most sensitive to 2G12 neutralization . An R5 virus was thought to be more appropriate for modeling human infection than an ×4 virus . The challenge virus was used intravaginally following pre-administration of 2G12 intravenously . The results indicate that 2G12 can provide protection against an R5 virus challenge at a surprisingly low neutralization titer . Unusually efficient transport to the vaginal mucosal surface does not appear to explain the activity of 2G12 . The results support targeting the glycan shield through vaccine design .
The ability of 2G12 to neutralize a panel of SHIVs in PBMC and pseudovirus assays was first assessed . A comparison with b12 was included in the study . As shown in Table 1 , 2G12 was not particularly effective against any of the SHIVs tested . The activity of 2G12 against the R5 SHIVSF162P3 was comparable to that against the ×4 SHIV89 . 6P used in previous studies described above and was chosen for macaque studies . Figure 1 depicts the outcome of the protection study that was carried out with five 2G12-treated animals , two antibody isotype ( anti-Dengue NS1 IgG1 , DEN3 ) treated control animals and two antibody-untreated control animals . The Indian rhesus macaques were first treated with Depo-Provera to thin the vaginal epithelium and to synchronize menstrual cycles [8] , [22] . One day before vaginal challenge with 500 TCID50 ( 50% tissue culture infectious doses ) of SHIVSF162P3 , each animal was given an intravenous dose of 40 mg/kg of either 2G12 or the isotype control antibody . Prior to the protection experiment , two additional control animals were challenged with 500 TCID50 SHIVSF162P3 without administration of antibody to verify the infectivity of the viral stock . Blood was drawn from the animals at regular intervals following challenge to monitor viral infection , serum levels of passively administered antibody and serum neutralizing activity . The 4 control animals became infected with peak viremias of approximately 107 virus copies per ml between days 14 and 21 as is generally noted in this system [9] . Two of the five 2G12-treated animals also became infected . One was infected with viral kinetics closely similar to that of the control animals . The second had a notably delayed and lower peak viremia at day 35 . Three of the five 2G12-treated animals were protected and showed no detectable viremia at day 55 . In order to determine whether breakthrough infection may be associated with selection of antibody escape mutants , we attempted to sequence the env gene from plasma virus of the unprotected animals . Env from animal 95113 could not be amplified but interestingly sequence analysis of animal 90154 env revealed a T388A mutation disrupting the position 386 N-glycosylation and consistent with 2G12 escape . The characterization of a 2G12 escape mutant in one of the unprotected animals raises the possibility that such variants already exist in the inoculum . Alternatively , in the presence of Ab , in particular at the suboptimal concentration achieved in the study , a certain level of viral replication may take place following challenge , allowing in some cases for the generation and selection of an escape mutant . Such scenario would suggest that the mechanism of antibody protection is not only to prevent cell infection but also to abort an already ongoing infection , presumably though effector functions as discussed below . These two possible scenarios for escape are currently under investigation . The 2G12 antibody concentrations in the sera of the macaques at different time points were measured using three different ELISA formats . With a few exceptions , the determined serum concentrations derived from the three formats were generally in good agreement ( Table 2 ) . The first format used was an ELISA based on the ability of 2G12 to specifically recognize a relatively conserved cluster of oligomannose glycans . Serum was titrated against an immobilized synthesized oligomannose dendron conjugated to BSA [21] . The second ELISA format was based on a highly specific anti-idiotype-2G12 antibody ( MIgG1 L13 ) that does not block the binding of gp120 or inhibit the neutralizing ability of 2G12 [23] . For comparison , a third ELISA format using monomeric gp120 JR-FL was used to measure the transferred 2G12 contained in the macaque serum . In all formats , a dilution series of serum was compared to a 2G12 standard curve and the concentration determined using a non-linear regression curve fit analysis . The i . v . transfer of 2G12 at 40 mg/kg resulted in a high 2G12 serum concentration at the time of challenge that varied between 0 . 9 and 1 . 2 mg/ml . Table 3 summarizes the half-life of serum 2G12 that varied between 7 . 2 and 15 . 6 days in the 5 macaques . The average half-life of all animals as measured in the three ELISA formats is about 11 days ( Table 3 ) . The half-life of 2G12 in rhesus macaques has previously been noted as about 13 days [5] . While for b12 , 90% neutralization titers ( IC90 ) of approximately 1∶80 in a PBMC assay were associated with protection in 50% of SHIVSF162P4 [8] and 90% of SHIVSF162P3 [9] challenged animals , a titer of only 1∶1 was sufficient to protect 60% of all animals with 2G12 in the experiment described here . It should be noted the IC90 for 2G12 neutralization of PBMC is approximately 900 µg/ml and the serum concentration of 2G12 at challenge was approximately 900–1 , 200 µg/ml making it impractical to directly measure neutralization in the PBMC assay . Using the generally more sensitive pseudovirus assay , 90% neutralization was not reached at a 1∶50 serum dilution for any of the 2G12-treated animals . Therefore , it does appear that MAb 2G12 can offer substantial protection at relatively low serum neutralizing titers . We next compared properties of 2G12 and b12 that might help explain the observed differences in protective activity relative to serum neutralization . One possibility is a gross difference in transudation efficiency for the two MAbs . For b12 , it has been noted previously that the concentration of antibody at the vaginal surface following passive administration is only a small fraction of that in the serum [8] . If 2G12 was transudated to the vaginal surface much more efficiently than b12 then it is possible that it could achieve comparable neutralizing titers in vaginal fluids . This might lead to improved protection although it should be noted that no correlation between mucosal antibody levels and protection has been established . Earlier data suggests that 2G12 is not transudated unusually effectively although vaginal concentrations can vary widely [7] . We carried out a direct comparison of vaginal concentrations of b12 and 2G12 for a time period of 7 days after i . v . administration of 5 mg/kg MAb to 3 control macaques . As shown in Figure 2 , the concentrations of the two MAbs transudated to the vaginal mucosal surface are similar and thus transudation is unlikely to contribute to protection differences between the MAbs . Evidence has been obtained to suggest that interaction of b12 with Fc receptors can contribute to protection against SHIVSF162P3 challenge in macaques [9] . In particular , it was noted that wild-type b12 mediated antibody-dependent cell-mediated virus inhibition ( ADCVI ) and was effective at protection , whereas a b12 variant lacking FcR binding did not mediate ADCVI and was less effective than the wild-type antibody . If 2G12 were effective at ADCVI then this might contribute to enhanced protective activity . Figure 3 suggests that 2G12 is somewhat less effective than b12 at ADCVI on the basis of a strict concentration comparison . The significance of this result is discussed further below . Since two MHC class I alleles ( Mamu-B*08 and –B*17 ) have been associated with elite control of SIV replication , we evaluated all experimental animals by MHC genotyping ( Table 4 ) . We used PCR-SSP to test for a panel of 9 class Ι alleles previously shown to be important in SIV epitope presentation [24] , [25] , [26] , [27] , [28] . Protected animal 90140 was positive for Mamu-A*01 , an allele that appears with high frequency in many colonies and has been associated with moderate reduction of SIVmac239 replication [27] , [28] , [29] , [30] . Protected animal 95066 expresses the Mamu-B*01 allele . This allele remains on the panel based on early reports of SIV-derived epitopes [31] , [32] , but studies have shown that Mamu-B*01 does not bind SIV-derived epitopes and has no effect on SIV disease progression [26] . However , even with the presence of the Mamu-A*01 allele ( which is not associated with elite control of SIV replication ) in animal 90140 , there is no apparent correlation with the allelic profiles of the animals in this study that would account for any unusual ability to resist infection .
The results presented here lend strong support to the notion that passively administered bNAb 2G12 is able to offer substantial protection against mucosal SHIV challenge at low serum neutralizing titers . In particular , 3 of 5 macaques showed sterilizing immunity on vaginal challenge with a high dose of SHIVSF162P3 when serum-neutralizing titers of 2G12 were of the order of 1∶1 . This result contrasts strongly with protection observed with bNAb b12 when sterilizing immunity for the majority of animals is associated with neutralizing titers of very approximately 1∶100 and greater [8] , [9] , [33] . The result also contrasts with the quantitative studies of Martin and colleagues [10] , which show sterilizing immunity against challenge with the ×4 SHIVDH12 only at high specific anti-DH12 antibody neutralizing titers . The result is however consistent with studies of Mascola and colleagues who showed that low serum neutralizing titers of 2G12 provided sterilizing immunity for 2 of 4 macaques vaginally challenged with the ×4 virus SHIV89 . 6P [7] . We investigated factors that might help explain the protective efficacy of 2G12 , especially in relation to b12 . This efficacy might be explained if 2G12 was transported very effectively to the site of infection; the results presented suggest this is not the case . We next noted that , although the neutralizing titers of 2G12 in our experiments are low , the actual serum concentrations of 2G12 are high since the antibody neutralizes the challenge virus SHIVSF162P3 , and indeed other available SHIVs rather poorly . Therefore , it is possible that the protective efficacy of 2G12 derives from another anti-viral function of antibody that becomes important at high antibody concentration . One such function could be antibody-mediated host cell activity against SHIV-infected cells , which can be measured in the ADCVI assay . The results showed that 2G12 is somewhat less effective than b12 in the ADCVI assay . However , at the serum concentrations achieved in the passive transfer experiments , 2G12 should be able to promote infected cell killing in vivo . Therefore , one possible explanation for the differing relationship between neutralization and protection for b12 and 2G12 against SHIVSF162P challenge is that protection is determined not by neutralizing ability but solely by activity against infected cells . An argument against this explanation is provided by the observation that b12 can still provide substantial protection in the complete absence of Fc receptor function and ADCVI [9] . Furthermore , HIVIG , which tends to mediate effective ADCVI ( GL and DNF , unpublished observations ) is rather ineffective at protection against SHIV challenge [5] , [7] . An explanation more concordant with the totality of data is that both neutralizing and extra-neutralizing activities are important for protection . Fc-mediated extra-neutralizing activities include , not only host cell activities against infected cells , but also those against free virions such as phagocytosis . It may be that 2G12 is able to compensate for its weak neutralization of SHIVSF162P3 by effective extra-neutralizing activities such as ADCVI . Protection studies using a SHIV that is sensitive to neutralization at low 2G12 concentrations but only sensitive to extra-neutralizing activities at high concentrations may help to better separate the contributions of different mechanisms to protection here . Alternatively , a 2G12 mutant lacking effector activity analogous to that generated for b12 [9] may help towards this aim . One intriguing difference between b12 and 2G12 that might relate to differences in protection has been described in terms of neutralization kinetics ( PP , unpublished observations ) . Thus , it appears that following antibody-virus preincubation in vitro , neutralization by 2G12 occurs almost immediately while b12-mediated neutralization slowly progresses with time , reaching 2G12 neutralization levels only after hours . Only at high concentrations , corresponding to high neutralization titers , do b12 neutralization kinetics match those of 2G12 . In vivo such kinetic differences may give 2G12 a noticeable advantage over b12: immediate efficiency at preventing target cell infection may be of particular importance in vivo as delay in neutralization may lead to cell infection and viral spread spinning out of antibody control . Further studies looking at correlations between antibody neutralization kinetics and protection efficiency should help reveal whether kinetics are important for protection . A further consideration for 2G12 is that the antibody recognizes high mannose glycans on the envelope gp120 surface . These glycans are also recognized by a number of lectins including DC-SIGN , which has been proposed to have a critical role in transmission by facilitating the transport of virus by dendritic cells to lymphoid tissues [34] , [35] . Indeed , it has been shown that the addition of an N-glycan site to the V2 loop of SF162P leads to a gain of DC-SIGN binding and that this correlates with enhanced mucosal transmission of SHIVSF162P3 [36] . The gp120-DC-SIGN interaction can be perturbed by 2G12 but not b12 as shown in a number of assays including inhibition of whole virus binding to DC-SIGN-expressing cell lines [37] , [38] . If the HIV-DC-SIGN interaction is critically important for the establishment of infection , then it is possible that 2G12 protection is mediated by inhibiting this interaction . Intriguingly , although 2G12 requires relatively high concentrations to neutralize SHIVSF162P3 , presumably because of relatively low affinity for the envelope trimer [39] , [40] , [41] , it binds with high nM affinity to monomeric gp120 from SHIVSF162P3 . HIV is suggested to express both functional and other forms of envelope including monomeric envelope [42] , [43] . If DC-SIGN was exploiting nonfunctional as well as or instead of functional envelope on virions then 2G12 might be an efficient competitor for binding to nonfunctional envelope at the in vivo concentrations achieved in our passive experiments . Future studies on the potential role of inhibiting the HIV-DC-SIGN interaction in blocking transmission could make use of anti-DC-SIGN antibodies . In summary , the data presented here , together with earlier data , show conclusively that monoclonal antibody 2G12 can offer protection against mucosal SHIV challenge at low neutralization titers . An explanation based on unusual 2G12 transudation properties to the mucosal surface is ruled out . Viable explanations include: ( 1 ) rapid 2G12 neutralization kinetics , ( 2 ) a critical role for extra-neutralizing , e . g . Fc-mediated , 2G12 activities under the conditions of the experiment , and ( 3 ) a critical role for 2G12 inhibition of virus interaction with lectin , e . g . DC-SIGN , bearing cells . Further in vivo protection studies will be required to distinguish these possibilities . Nevertheless , the results are provocative in suggesting the glycan shield as a potentially favorable HIV vaccine target .
All protocols for female Indian rhesus macaques were reviewed and approved by the Institutional Animal Care and Use Committees . The animals were housed in accordance with the American Association for Accreditation of Laboratory Animal Care Standards . At the start of the experiments , all animals were experimentally naïve and were negative for antibodies against HIV-1 , SIV , and type D retrovirus . Virus challenge and i . v . antibody protocols are more fully described elsewhere [8] , [44] . The virus used in this study was SHIVSF162P passage 3 , which has been described elsewhere [45] , [46] , [47] . SHIVSF162P3 retains the R5 phenotype of HIV-1SF162 . SHIVSF162P3 , propagated in phytohemagglutin ( PHA ) -activated rhesus macaque peripheral blood mononuclear cells ( PBMC ) , was obtained through the NIH AIDS Research and Reference Reagent Program , Division of AIDS , NIAID , NIH ( Cat . No . 6526; Contributors: Drs . Janet Harouse , Cecilia Cheng-Mayer , and Ranajit Pal ) . Recombinant 2G12 was obtained from Polymun Scientific , Vienna , Austria . The isotype control antibody DEN3 , an anti-Dengue NS1 human IgG1 antibody was expressed in Chinese hamster ovary ( CHO-K1 ) cells in glutamine-free custom formulated Glasgow minimum essential medium ( GMEM Selection Media ) ( MediaTech Cellgro ) . For large-scale tissue culture , media was supplemented with 3 . 5% Ultra Low Bovine IgG Fetal Bovine Serum ( Invitrogen ) and grown in 10-layer Cellstacks and Cell Cubes ( Corning ) . The antibody was purified using Protein A affinity matrix ( GE Healthcare ) , and dialyzed against phosphate-buffered saline ( PBS ) . Care was taken to minimize endotoxin contamination , which was monitored using a quantitative chromagenic Limulus Amoebecyte Lysate assay ( Lonza ) performed according to the manufacturer's recommendations . Antibody used for the passive transfer experiments contained <1 IU of endotoxin/mg . The quantity of SIV viral RNA genomic copy equivalents ( vRNA copy Eq/ml ) in EDTA-anticoagulated plasma was determined using a quantitative reverse-transcription PCR ( QRT-PCR ) assay . Briefly , vRNA was isolated from plasma using a GuSCN-based procedure as previously described [48] . QRT-PCR was performed using the SuperScript III Platinum One-Step Quantitative RT-PR System ( Invitrogen , Carlsbad , CA ) . Reaction mixes did not contain bovine serum albumin ( BSA ) . Reactions were run on a Roche Lightcycler 2 . 0 instrument and software . vRNA copy number was determined using LightCycler 4 . 0 software ( Roche Molecular Diagnostics , Indianapolis , IN ) to interpolate sample crossing points onto an internal standard curve prepared from 10-fold serial dilutions of a synthetic RNA transcript representing a conserved region of SIV gag . 2G12 antibody concentrations in macaque sera were determined in ELISA by three different methods: ( 1 ) by binding to an immobilized synthetic oligomannose dendron [21] conjugated to BSA; ( 2 ) by binding to the anti-Idiotype 2G12 mouse IgG1 L13 kindly provided by Polymun Scientific , Vienna , Austria [49]; and ( 3 ) by binding to monomeric gp120 JR-FL kindly provided by Progenics [8] . The determination of antibody concentration in mucosal secretions was performed as described by Kozlowski , et al . [8] , [50] . Briefly , vaginal secretions from each animal were absorbed to cellulose wicks ( Solan Weck-Cel surgical spears; Xomed Surgical Products , Jacksonville , FL ) . A set of 3 samples per animal was taken at 6 hours , 12 hours , 24 hours , 4 days , and 7 days post i . v . antibody administration . Wicks were weighed before and after secretion absorption . Clarified supernatants extracted from the wicks were used to determine the concentration of antibody in mucosal secretions by ELISA . Resulting data was compared to the corresponding antibody standard curve using nonlinear regression . Arithmetic means and standard deviations were calculated for each set of triplicate samples per animal . The differences in the mean concentrations of b12 and 2G12 at each timepoint were evaluated in a student's t test . Analyses performed in GraphPad Prism Software for Mac , Version 5 . 0a . Neutralization of antibodies and sera was assessed by 2 different methods . Neutralization of the primary isolate SHIVSF162P3 , was performed using phytohemagglutinin ( PHA ) -activated peripheral blood mononuclear cells ( PBMC ) from a single rhesus macaque ( no . 355 ) as target cells . Cells from this animal replicate SHIVSF162P efficiently . Neutralization assessment was carried out as described previously [8] . Neutralization titers of animal sera were reported by Monogram Biosciences , South San Francisco , CA after preparation of an HIV-1 envelope pseudotyped luciferase SHIVSF162P3 capable of single-round replication . The pseudovirus-based neutralization assay was performed as previously described [51] . MHC genotyping by sequence-specific PCR was performed by the University of Wisconsin Genotyping Core with support of NIH grant 5R24RR16038-6 awarded to David I . Watkins and previously described [25] . Viral RNA was extracted from 140 µl of monkey serum using the QIAamp Viral RNA Mini Kit ( Qiagen ) according to the manufacturer's instructions . 8 µl of this viral RNA was then used for cDNA synthesis using Superscript III ( Invitrogen ) primed by primer sf162rtn ( 5′-TTATAGCAAAATCCTTTCC-3′ ) . 3 ul of the cDNA reaction was then used to amplify the gp160 open reading frame using primers sf162mf ( 5′–CACCATGAGAGTGAAGGGGATCAGGAAG-3′ ) and sf162rn ( 5′-TTATAGCAAAATCCTTTCCAAGCCCTGTC-3′ ) in combination with PfuUltra Hotstart DNA Polymerase from Stratagene . After an initial denaturation step at 95°C for 4 minutes , 35 cycles were performed with 95°C for 30 seconds , 52°C for 30 seconds and 72°C for 3 minutes , before a final elongation at 72°C for 10 minutes concluded the amplification . Sequences were determined after subcloning the PCR products into TOPO vectors . The experiment consisted of a total of 9 animals ( n = 9 ) divided into treatment groups as follows: 2 animals ( n = 2 ) in the isotope control group , 2 animals ( n = 2 ) in the non-antibody-treated controls , and 5 animals ( n = 5 ) in the 2G12-treated group . Statistical analyses were performed using Graph Pad Prism for Windows , version 5 ( Graph Pad Software Inc . , San Diego , CA , 2005 ) . GenBank accession locus for 2G12 is 10M3_H ( heavy chain , Fab 2G12 unliganded ) and 10M3_L ( light chain , Fab 2G12 unliganded ) . GenBank accession locus for IgG1 b12 is AAB26306 . | An effective HIV vaccine should elicit broadly neutralizing antibodies , i . e . antibodies that neutralize a wide spectrum of different HIVs in vitro . A number of human monoclonal antibodies have been isolated with broad neutralization and shown to protect macaques against vaginal HIV challenge . Protection is generally correlated with neutralization and requires relatively high antibody concentrations that may be difficult to achieve by vaccination . Here , we show that one monoclonal antibody ( 2G12 ) is unusually potent in protection relative to its neutralizing ability as hinted at by earlier data . Further studies eliminate an unusual ability of 2G12 to be transported to the vagina ( site of infection ) as a possible explanation for our observations . Although the precise mechanism is unclear , the studies have important implications for HIV vaccine design in general by suggesting that some vaccine targets on HIV may be better than others and , specifically , by suggesting that the sugar coat of HIV may be a particularly rewarding target if appropriate immunogens can be designed . |
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Malarial infection is associated with complex immune and erythropoietic responses in the host . A quantitative understanding of these processes is essential to help inform malaria therapy and for the design of effective vaccines . In this study , we use a statistical model-fitting approach to investigate the immune and erythropoietic responses in Plasmodium chabaudi infections of mice . Three mouse phenotypes ( wildtype , T-cell-deficient nude mice , and nude mice reconstituted with T-cells taken from wildtype mice ) were infected with one of two parasite clones ( AS or AJ ) . Under a Bayesian framework , we use an adaptive population-based Markov chain Monte Carlo method and fit a set of dynamical models to observed data on parasite and red blood cell ( RBC ) densities . Model fits are compared using Bayes' factors and parameter estimates obtained . We consider three independent immune mechanisms: clearance of parasitised RBCs ( pRBC ) , clearance of unparasitised RBCs ( uRBC ) , and clearance of parasites that burst from RBCs ( merozoites ) . Our results suggest that the immune response of wildtype mice is associated with less destruction of uRBCs , compared to the immune response of nude mice . There is a greater degree of synchronisation between pRBC and uRBC clearance than between either mechanism and merozoite clearance . In all three mouse phenotypes , control of the peak of parasite density is associated with pRBC clearance . In wildtype mice and AS-infected nude mice , control of the peak is also associated with uRBC clearance . Our results suggest that uRBC clearance , rather than RBC infection , is the major determinant of RBC dynamics from approximately day 12 post-innoculation . During the first 2–3 weeks of blood-stage infection , immune-mediated clearance of pRBCs and uRBCs appears to have a much stronger effect than immune-mediated merozoite clearance . Upregulation of erythropoiesis is dependent on mouse phenotype and is greater in wildtype and reconstitited mice . Our study highlights the informative power of statistically rigorous model-fitting techniques in elucidating biological systems .
Malarial infection of humans is a major cause of morbidity and mortality , continuing to cause around 250 million cases and close to a million deaths annually [1] . The vast majority of severe cases and deaths are due to Plasmodium falciparum , which is endemic in most of sub-Saharan Africa and other tropical areas [2] . Although there is no simple relationship between the pathogenic processes and clinical syndromes , disease only begins once the asexual parasite begins to multiply within the host's red blood cells ( RBCs ) [3] . The asexual dynamics depend on a complex interaction between the malaria parasite and the host's immune and erythropoetic responses [4] . Experimental methods have helped elucidate key aspects of this interaction . Such factors include the parasite's destruction of RBCs due to reproduction [5] , [6] , immune-mediated clearance of merozoites and parasitised RBCs ( pRBC ) [7] , [8] , and immune-mediated clearance of unparasitised RBCs ( uRBC ) . In particular , there is evidence that loss of uRBCs is responsible for the vast majority of the anaemia [9]–[11] . Suppression of RBC production ( dyserythropoiesis ) during the acute phase may also contribute to anaemia [12] , [13] , although recent modelling suggests that , overall , the level of erythropoiesis increases during malaria infection [8] , [9] , [14] . A full understanding of the infection dynamics requires quantitative analysis of the relative importance of the contributory factors [3] . Such an assessment is vital to help inform malaria treatment and intervention programmes [8] , [15] , [16] . In particular , the design of effective vaccines and immunotherapies depends largely on our understanding of the innate and adaptive immune responses [2] , [17] . In this context , rodent malaria models allow a highly replicable , highly controlled experiment . Although there are important differences between rodent and human malarias , a quantitative understanding of the rodent system , where we can control both host and parasite genetics , should help our understanding of the human case in which controlled experiments are unethical . As in other areas of science , mathematical models can be used to make inferences about complex dynamical systems by fitting them to data . This approach allows us to formally and quantitatively test and compare competing hypotheses , and to make quantitative predictions for future empirical testing . It is the most powerful and rapid way of culling possible , but incorrect , hypotheses . In the mathematical modelling literature on malaria , there are a number of studies that quantitatively fit models to data [6] , [8] , [9] , [14] , [15] , [18]–[22] . However , the poorly developed statistical , diagnostic and computational methodologies of fitting nonlinear dynamical models to noisy data ( see [23] and Discussion ) meant that these studies had to focus on particular aspects of the host-pathogen system in isolation . The method used was maximum likelihood . Its application to nonlinear systems is problematic because the nonlinearities create complex multi-dimensional likelihood surfaces . Search algorithms easily become trapped in local maxima , leading to false inferences [24] . Even if one is reasonably sure of having found the global maximum , evaluating parameter confidence intervals and covariances is computationally expensive and laborious , and computing predictive intervals practically impossible [25] . Recent developments in adaptive , population-based Markov chain Monte Carlo ( McMC ) methods overcome all of the problems associated with maximum likelihood [24] , [26]–[30] . The use of a Bayesian framework enables us to incorporate prior knowledge and uncertainty about the parameters . It allows us to quantify our relative belief in one model predicting the data over another , rather than accepting and rejecting models using conventional , but arbitrary , cut-offs . In order to use Bayesian statistics , we need to know the structure and variance of the measurement errors . Fortunately , these are known for our data sets . In this study we develop a set of models to test competing hypotheses describing the asexual stage of the malaria parasite . We fit the models to a set of data on Plasmodium chabaudi infections [31] using an adaptive McMC algorithm . We provide parameter estimates , examine differences between mouse and parasite strains , and make quantitative predictions about the immune and erythropoietic systems' dynamics , and their effects on the RBC population . In modelling the asexual dynamics , there are three general processes we need to consider: ( i ) the infection of RBCs , ( ii ) the immune response , and ( iii ) the response of the erythropoietic system to malaria-induced anaemia . The immune system's response to malaria is exceedingly complex and there is still much to learn about it qualitatively , let alone quantitatively [17] . Mathematical models have generally represented the immune response either as a single variable functionally linked to parasite density , or as separate innate and adaptive components [8] , [21] , [32]–[35] . The model of Recker et al . ( 2004 ) further discriminates , on the basis of human serologic data , between short-term , partially cross-reactive immune responses and long-term specific responses [36] . These models have given valuable insights into the immune dynamics , but it is important to acknowledge that the immune response consists of multiple arms , each targeting different aspects of the parasite [2] . Here we model the immune system as time-dependent immune-mediated clearance rates of merozoites , pRBCs and uRBCs . This allows us to bypass the debate about the highly interdependent innate and adaptive arms of the immune response , i . e . , when they are activated , what they target , and how they develop over time , and instead focus on the functional consequences in terms of the infection dynamics . We also draw attention to a key aspect of malaria asexual reproduction universally ignored in previous modelling studies . It is established that individual RBCs may be parasitised by more than one merozoite . Multiply-parasitised RBCs are often observed in experiments , but it is not known whether their subsequent behaviour is the same as that of singly-parasitised RBCs; previous models have generally assumed that their dynamics are identical . Here we test that assumption . In particular , we test whether multiply-parasitised RBCs have a greater death rate than other RBCs , and whether they produce a greater number of merozoites than singly-parasitised cells .
We used data obtained from a previous experiment [31] . Briefly , three different mice phenotypes were infected with either of two genetically distinct clones of Plasmodium chabaudi ( AS or AJ ) . Both clones were originally isolated from thicket rats ( Thamnomys rutilans ) in the Central African Republic [37] . The AS clone is associated with a lower peak density relative to AJ; it also has lower virulence , as measured by anaemia and weight loss [38] . Three different phenotypes of BALB/c mice were used: ( i ) wildtype mice; ( ii ) nu/nu mice ( “nude mice”; Harlan UK ) ; and ( iii ) nude mice reconstituted with T cells taken from wildtype mice . The mutation nu is a recessive mutation that blocks the development of the thymus . Nude mice therefore lack mature T cells , whereas heterozygotes ( nu/+ ) have a normal immune system [39] . Nude and reconstituted mice are smaller than the wildtype and are hairless . Mice of each phenotype ( wildtype , nude , reconstituted ) were innoculated with pRBCs of either AS or AJ . This gave six treatment groups . There were seven mice in both treatment groups for nude mice , and six mice in each treatment group for reconstituted and wildtype mice . Measurements of RBC and parasite density were taken on days 0 , 2 , 4 , and then daily until day 18 when the experiment was terminated . Parasite density was measured daily at 08:00 hrs using quantitative PCR , at which point the asexual merozoites have yet to replicate within pRBCs . Both RBC and parasite densities are expressed in terms of the number per microlitre ( ) of blood . Full details of the experimental methods are given in [31] . We removed a single data point from one of the reconstituted AJ-infected replicates . This mouse had much lower parasite density on day 14 than all the other mice . The data point was therefore considered to be an outlier . The averaged dynamics of each treatment are shown in Fig . 1 . The AJ clone ( solid line ) does not show the normal higher peak density compared to the AS clone ( dotted line ) ; however , the AJ clone exhibits a higher density during the exponential growth phase compared to AS . Parasite density tends to level off in reconstituted and nude mice from day 12 , but continues declining in the wildtype , presumably because of a stronger immune response in these mice . In reconstituted and wildtype mice , the AJ clone causes greater anaemia than the AS clone . All three mouse phenotypes show an earlier drop in RBC density from days 6–8 when infected with AJ compared to AS . The recovery of RBC density in nude mice is weaker than in reconstituted and wildtype mice . We discuss these observations below in relation to inferences from the model fitting . In Plasmodium chabaudi , pRBCs rupture synchronously every 24 hours , releasing on average 6–8 parasites ( merozoites ) into the bloodstream [40] . These newly released merozoites infect further RBCs and the cycle repeats . The rupture of pRBCS ( schizogony ) occurs at approximately midnight under normal lighting conditions [41] . We use a discrete-time formulation to model the dynamics , where each time step corresponds to a single day . The start of day is defined as the point immediately following rupture of pRBCs , before any infection has occurred ( i . e . , the point at which merozoites are released into the bloodstream ) . The densities of merozoites and uRBCs at the start of day are denoted and , respectively . We assume that the processes determining RBC density occur on two non-overlapping timescales . The first corresponds to the short infection phase during which merozoites infect RBCs , which occurs within a few minutes following schizogony . The second and subsequent timescale ( the remainder of the day ) corresponds to the RBC turnover phase: here the parasites replicate within pRBCs , new uRBCs are released into the bloodstream and , if active , the ( non-merozoite ) immune responses clear pRBCs and uRBCs . At the end of the RBC turnover phase , surviving pRBCs rupture and release new merozoites . Using the statistical algorithm described in the Supporting Information ( Text S1 , Figure S1 and Figure S2 ) , we fit to the observed data on RBC and parasite densities . The fitted parameters are given in Table 2 , along with their prior distributions . The prior distributions were based either on values taken from the literature , or approximate estimates obtained before the main model-fitting ( see Text S1 for details ) . Our aim is to find a set of minimal adequate models which explain the data well and contain as few parameters as possible . We take as our baseline the model described above . This is denoted and contains 20 fitted parameters . We developed a set of nested and non-nested models in which specific assumptions are made about the immune and erythropoietic responses ( outlined in Table 3 ) . All models were fitted to the data . Each model fit was then evaluated relative to that of the baseline . The model fits were compared using Bayes' factors , which naturally penalise overfitting [44] ( refer to Text S1 for further details on measurement error , the likelihood function , model fitting , assessment and comparison ) . We estimate parameters separately for each mouse , rather than across treatments . Even inbred mice are phenotypically different , and these differences result in variability in parasite and RBC dynamics . Immune responses are significant sources of variability in vivo; but we might also expect variation between mice in parameters such as infection rate , because of the multifactorial nature of such processes which involve the interaction of many host and parasite proteins . We therefore make no assumption about which parameters are invariant across mice and instead estimate parameters separately for each mouse . This method allows us to infer parameter ( and hence process ) variability within and between treatments from the posterior estimates .
We begin by analysing the baseline model , . The fits to all mice are shown in Figs . 2–4 . Note that the posterior predictive interval ( PPI ) of the dynamics widens from around day 15 for some mice because of the lack of data . The model fits appear to adequately explain the data . A more rigorous assessment of the model fits is attained by plotting the overlaid standardised residuals for parasite and RBC densities . Fig . 5 shows the standardised residuals ( the blue crosses ) for all mice across the six treatments . Poor fits are suggested by outlying and serially correlated residuals . The fits to parasite density are accurate but not perfect . There are no outliers , but the model tends to overestimate parasite density on day 4 . This is unexpected because we expect parasite density to be growing exponentially during this time , and indeed this is how the model behaves . This discrepancy suggests that parasite density may initially grow at a slower than exponential rate . Also , the model tends to underestimate parasite density on day 11 . Currently , we have no explanation for this . The fits to RBC density are accurate , except on day 8 where RBC density is marginally overestimated . Therefore , the model may not be correctly capturing the trough in RBC density . We calculated the Bayes' factors of models , for , relative to the baseline model . We adopt the scale of interpretation for Bayes' factors proposed by Jeffreys [45] and reproduced in Table 4 . For ease of interpretation , the Bayes' factors are converted to deciBans; i . e . , ( Bayes' factor ) . Bayes' factors were calculated for each mouse , giving a total of 38 values for each model comparison . The full list of Bayes' factors is given in Table S1 and Table S2 . For conciseness and clarity , we report for each model: ( i ) the sum of the deciBans for all mice within each treatment , and ( ii ) the sum of the deciBans for all mice across treatments ( Table 5 ) . We also report the standard error of the deciBans . Errors occur because deciBans are estimated from a finite sample of the posterior distribution . Our inferences are conservative; thus , we interpret a deciBan of as “barely worth mentioning” ( see Table 4 ) . Statistical comparison of parameter values between treatments was performed using Analysis of Variance ( ANOVA ) in the R statistical package [46] . The method was as follows . For a given parameter , we first took the mean of the posterior distribution , , for each individual mouse . The mean for a given treatment , , was then calculated as the average of the posterior mean values taken across all mice in the treatment .
The aim of this paper was to provide a quantitative assessment of the immune and erythropoietic responses in Plasmodium chabaudi infections . Hypotheses were drawn from experimental data and the mathematical modelling literature . These were fit to data on malaria infected mice using a Bayesian statistical framework . Crucially , by quantifying the experimental error , we were able to provide a rigorous assessment of the model fit . In particular , we were able to evaluate and compare the accuracy of different models in explaining the data . Models were compared using Bayes' factors , which impose a penalty for additional parameters . We interpreted our results with reference to the product of Bayes' factors ( sum of deciBans ) within and across treatments . Our results provide very strong evidence that the immune response to P . chabaudi involves clearance of both parasitised and unparasitised RBCs . Both effects were evident during the initial peak of parasite density , implying that control of the peak is regulated by the immune response . Previous modelling studies have suggested that innate or early specific immune responses regulate the initial dynamics of parasite density and anaemia [8] , [9] , [21] , [47] . Our study provides a statistically rigorous analysis in support of this hypothesis . Parasite-infected erythrocyte surface antigens ( PIESA ) may be an important immune target in both rodent and human malarias . In the case of P . falciparum , a longitudinal study of Kenyan children found that clinical malaria was caused by parasite isolates expressing PIESA variants that corresponded to gaps in the repertoire of antibodies carried by the children before they became ill [48] . We have shown that uRBC clearance by the immune system plays a key role in determining the infection dynamics of P . chabaudi . Experimental studies confirm that in the rodent malaria P . berghei [10] , and also in P . falciparum which infects humans [11] , the vast majority of anaemia is due to uRBC loss . Our results suggest that the level of anaemia following control of the initial peak ( from about day 12 ) , is mediated by the activity of the uRBC targeting response . This provides a mechanistic explanation for the variation in RBC dynamics between individual mice . If uRBC clearance decreases following control of the initial peak , then the increase in erythropoiesis that occurs from approximately day 10 allows the host to recover quickly from anaemia; in contrast , prolonged uRBC clearance is associated with a slow recovery from anaemia . Due to the lack of data to describe parasite density , the role of the pRBC targeting response during this later stage of infection is less certain . Our results suggest that the immune responses targeting pRBCs and uRBCs do not show a high degree of synchronisation . This implies they may be controlled by different effector mechanisms . Our model does not account for the antigenic variation that occurs in P . chabaudi infections [49] , [50] . Indeed , our model formulation only permits a single “switching-on” and “switching-off” of each immune component ( merozoite , pRBC , uRBC ) , and therefore does not distinguish between non-specific ( innate ) versus specific antibody responses to antigenically distinct variants . We modelled the infection up until day 18 , at which point antigenic variation may have a significant effect on the dynamics . In some mice , the data show a second drop in RBC density following the recovery from initial anaemia ( Fig . 2 ) . Although we only have data on parasite density up to day 14 , this second anaemia is commensurate with a recrudescent parasite density . However , it is significant that our model is able to explain the observed dynamics so well without including antigenic variation ( Fig . 5 ) . Extending the model up to , for example , day 30 post-infection would require explicit modelling of immune responses to the different antigenic variants . Such modelling would probably need to include both short-lived ( innate ) and long-lasting antibody responses , and may also need to consider cross-reactivity . The cascade of sequentially dominant antigenic variants seen in P . falciparum infections has recently been explained as the result of short-lived cross-reactive immune responses directed against shared epitopes [36] . Our results are consistent with the observation that T-cell-deficient ( “nude” ) mice have impaired immune responses , and are unable to resolve malaria infections [2] . T cells play an important role during the early stages of malarial infection , by amplifying the phagocytic and cell-mediated antiparasite responses; later in the infection , they help B cells to produce antibody , and assist in regulating the innate response [51]–[53] . Immune-mediated clearance of uRBCs was necessary to explain the dynamics in all three phenotypes , but nude mice had the higher maximum clearance rate of uRBCs . The infection rate of RBCs with merozoites , as reflected in the parameter , could also be higher in nude mice . As a simple proxy for the real biological system , these results indicate that nude mice are less able to limit the replication of the malaria parasite , and that their less specific immune response is associated with greater destruction of uninfected cells . At the individual mouse level , there was no evidence for immune-mediated clearance of merozoites . However , the cumulative evidence over all mice suggests that it is required to explain our data . Our interpretation of this result is that merozoite clearance is weak during the first few weeks of infection , and that pRBC and uRBC clearance are the major determinants of the dynamics during this time . Previous models have shown that , for a given level of immune activity , merozoite clearance is less effective at controlling parasite density compared to an equivalent response that clears pRBCs [7] , [8] . One explanation for this is that the duration of the merozoite infection phase ( estimated to be on the order of minutes ) is too short for the immune system to effectively target merozoites [7] . However , there is no a priori reason that one or several fast-acting , highly effective immune responses could not operate during this phase . There is considerable empirical evidence that merozoite surface protein one ( MSP1 ) is a target of immune mechanisms in malaria infections [54] . The presence of high levels of naturally acquired IgG antibodies to merozoite surface protein two ( MSP2 ) is also strongly associated with protection against clinical malaria [55] . Recent results suggest that this naturally acquired protection is not specific in relation to the major allelic dimorphisms of MSP2 [56] . We have shown that erythropoiesis upregulates during malarial infection , and that wildtype and reconstituted mice have higher upregulation than nude mice . This may reflect that the erythropoietic response only upregulates to the extent that the host is controlling the parasite . Recent theoretical results have shown that excessive upregulation of erythropoiesis facilitates the growth of the parasite , and may result in greater anaemia and a higher peak of parasite density [57] . We also investigated whether there is a time delay before the upregulation of erythropoiesis , and a lag in the feedback between RBC density and the level of erythropoiesis . The results for the AJ-infected wildtype mice suggest that upregulation of erythropoiesis occurs from day 10 . Both reconstituted and nude mice show no evidence of a time delay . Our results also suggest a time lag of 2–3 days in the feedback between RBC density and erythropoiesis in AJ-infected reconstituted mice; however there is no evidence for a time lag in the other treatments . The reasons for this are unclear . One possibility is that our putative time lag is compensating for another process not included in the model . Only analysis of other data sets may reveal what this may be . In summary , our results show that the immune system plays a key role in determining the RBC and parasite dynamics in malaria-infected mice . We have shown that immune-mediated clearance of both parasitised and unparasitised RBCs is necessary to explain the RBC and parasite dynamics . Previous models have examined the implications of RBC age structure for the infection dynamics [14] , [22] . In particular , recent work by Mideo et al . ( 2008 ) suggests that P . chabaudi may preferentially infect mature RBCs ( normocytes ) , but produce more merozoites in younger cells ( reticulocytes ) [14] . Future models may need to consider how RBC age structure and immune system dynamics can be combined to obtain a more complete picture of the asexual stage of malaria . | Malaria is a disease caused by a protozoan parasite of the genus Plasmodium . Every year there are around 250 million human cases of malaria , resulting in around a million deaths . Most of the severe cases and deaths are due to Plasmodium falciparum , which is endemic in much of sub-Saharan Africa and other tropical areas . The pathology of malaria is related to the asexual stage of the parasite . Understanding the infection dynamics during this stage is therefore essential to inform malaria treatment and vaccine design . Experimental infections of rodents represent an important first step towards understanding the more complicated human infections . We developed a series of models representing different hypotheses about the main processes regulating the infection dynamics during the asexual stage . Models were fit to data on Plasmodium chabaudi infections of mice , using a Bayesian statistical framework . The accuracy of different models in explaining the RBC and parasite densities was quantified . We identify the role of different types of immune-mediated mechanism , and show that RBC production ( erythropoiesis ) increases during infection . Differences between mouse phenotypes are explained . Our study highlights the informative power of model-fitting techniques in explaining biological systems . |
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An increasing number of biological machines have been revealed to have more than two macroscopic states . Quantifying the underlying multiple-basin functional landscape is essential for understanding their functions . However , the present models seem to be insufficient to describe such multiple-state systems . To meet this challenge , we have developed a coarse grained triple-basin structure-based model with implicit ligand . Based on our model , the constructed functional landscape is sufficiently sampled by the brute-force molecular dynamics simulation . We explored maltose-binding protein ( MBP ) which undergoes large-scale domain motion between open , apo-closed ( partially closed ) and holo-closed ( fully closed ) states responding to ligand binding . We revealed an underlying mechanism whereby major induced fit and minor population shift pathways co-exist by quantitative flux analysis . We found that the hinge regions play an important role in the functional dynamics as well as that increases in its flexibility promote population shifts . This finding provides a theoretical explanation of the mechanistic discrepancies in PBP protein family . We also found a functional “backtracking” behavior that favors conformational change . We further explored the underlying folding landscape in response to ligand binding . Consistent with earlier experimental findings , the presence of ligand increases the cooperativity and stability of MBP . This work provides the first study to explore the folding dynamics and functional dynamics under the same theoretical framework using our triple-basin functional model .
Biomolecular function is executed through conformational dynamics at physiological conditions , ranging from small fluctuations in atomic positions to large movements of parts of or even entire molecules [1] , [2] . It has been recognized that large-scale domain rearrangement is involved in processes like protein folding , molecular recognition , enzyme catalysis , signal transduction , transcriptional regulation and allostery , and plays an important role in biomolecular machines , such as the ribosome , transporter , molecular chaperones , enzyme , and molecular motors , carrying out their respective functions [3] . Though local conformational changes can be detected using experimental probes such as NMR , there are still a number of challenges for exploring large conformational changes in experiment . On the other hand , computational exploration of large conformational changes is made difficult by the fact that the time scale of the conformational changes are often on the order of sub-seconds to minutes , while the molecular dynamics simulations for moderately sized proteins are on the order of microseconds , thus presenting a challenge for modeling conformational changes . Advancing the modeling in this would not only provide us the local information and connect the modeling directly to experiments , but would also predict and give guidance for the ongoing and future experimental explorations of global conformational changes . In recent years , theoretical models have been developed to explore functional dynamics near the bottom of the energy landscape through integrating native structural information from two reference states . These models have been used to investigate the conformational transitions of typical allosteric proteins such as adenylate kinase ( ADK ) [4]–[8] , Rop dimer [9] , GFP [10] , glutamine-binding protein [11] , Arc repressor [12] , calmodulin [13] , [14] , Src kinase [15] , NtrC [16] and protein kinase A [17] etc . With advanced experimental approaches , more and more systems have been revealed to have more than two states . While it is well established that the inactive ligand-free state and the active ligand-bound state are both critical to protein function , it is worth emphasizing that other metastable conformational states are also of importance . However , the present models with two basins seem to be insufficient to describe such multiple-state systems . Recently , a multiple-basin landscape was explored from microscopic models [5] and macroscopic models [18] , [19] . In these models either the realization of macroscopic states is hard to control or the implementation is not readily available in common molecular modeling packages [19] . In this work , we developed a coarse grained triple-well structure-based model with the consideration of ligand binding , extending the two-state approach [7] for recapitulating multiple state large amplitude conformational changes . We explored maltose binding protein ( MBP ) which has three observed states . MBP is a member of the large family of periplasmic binding proteins ( PBPs ) [20] with a similar ( two-domain ) folding structure and size , linked by a rather flexible -strand region . Proteins in PBPs family display a common functional conformational switch between the apo-open state and the holo-closed state upon ligand binding . This is proposed to be a consequence of natural selection for fitting their function for efficient uptake in the periplasm and for directed chemotaxis [21] . A database of the PBP superfamily with hundreds of X-ray structures from E . coli , thermophilic bacteria and eukaryotes etc . , is a treasure trove for studying the relationship between ligand binding and conformational coupling as well as for applications in protein engineering [20] . MBP is found in the periplasmic space of gram-negative bacteria and serves as a receptor for osmotic shock and chemotaxis in response to the maltose and other maltodextrins from the environment [22] , [23] . It has been extensively studied by X-ray crystallography [24]–[27] , NMR spectroscopy [28]–[30] , atomic force microscope [31] and other biophysical techniques [32] , [33] . Recently , NMR paramagnetic relaxation enhancement ( PRE ) studies [33] have revealed that a minor species of apo MBP which represents a partially closed state in solution without ligands . This is in addition to the major species and is consistent with the structure of open MBP resolved by X-ray . Presently , we face several mechanistic questions: What are the processes by which conformations change ? Is the system better described by an induced-fit [3] or population shift mechanism [34] ? How many reaction pathways are there ? What are their importance and the relative weights ? Which part ( s ) of the protein plays the most important role ? We addressed these questions using molecular dynamics simulations based on our structure based model . So far , there are relatively few computational studies on the conformational dynamics of MBP [35]–[38] . Stockner et al . used short-time ( 30 ns ) atomic MD simulation to investigate the functionally important transition of MBP [35] . They observed an open-to-closed transition starting from the open structure with ligand and a closed-to-open transition from close structure without ligand . However , the minor partially closed state was not found during the process , reflecting that the simulation time scale is insufficient . Most recently , the dynamical equilibrium between open and apo-closed states in free MBP was validated in MD simulations carried out by Bucher et al . [36] , [37] and Kondo et al . [38] . They explored the conformational dynamics by an enhanced sampling algorithm called accelerated MD [39] , or by umbrella sampling to overcome the high free energy barriers . These methods both require one to modify the true potential by introducing a biasing potential into the system , making it difficult to obtain kinetic information . It is still challenging to draw definitive conclusions due to the computational bottleneck in the number of transition events . In this work , we explored the multi-state dynamics from a global perspective by building a coarse-grained functional landscape based on available structural information ( Figure 1 ) , which was sampled sufficiently by molecular dynamics simulation . The present work allows us to perform a complete dynamical simulation of conformational transitions between all three observed states of MBP , and obtain thousands of transitions giving statistical reliability . In addition , this enables us to extract the thermodynamics and kinetic information directly from the equilibrium MD trajectories . Furthermore , these trajectories can be projected onto any arbitrary coordinates , such as in Cartesian , dihedral , or contact space as suggested in Ref . [40] . This feature is lack in the umbrella sampling approach or similar methods which require prior knowledge of reaction coordinates [39] . Overall , coarse grained models can provide a useful first order approximation and global landscape while the atomistic simulations explore the system in greater detail . The combination of both methods may provide a fuller picture of functional conformational transition . This combination strategy has been applied by us on binding-folding dynamics [41] . Based on our model , we approximated various perturbations including ligand concentration and hinge flexibility . We revealed the dependence of shape of the functional landscape and the robustness of the underlying mechanism of conformational transition in MBP . We established a link between the underlying energy landscape and thermodynamic stability , kinetic paths/speed , and structure function correlation .
Based on dihedral angles parameterized from open state ( model , see section of Superior Angle Models in Text S1 for details ) , we calibrated our triple-well model so that the relative population of open and apo-closed states is comparable to experimental measurements in the absence of ligand binding ( ) . The free energy surfaces is shown in the two-dimensional space of and for the ligand-unbound case ( Figure S4 in Text S1 ) . Here , and ( X can be O , A and H ) are the order parameters which are employed to monitor the closeness to their respective states including open , apo-closed and holo-closed states ( See the definitions in Text S1 ) . ( ) = ( 0 . 9 , 0 . 3 ) for the apo-closed conformation and ( 0 . 1 , 0 . 9 ) for the open conformation . The free energy barrier from apo-closed to open state is , and from open to apo-closed state is . Free energies are correlated with the equilibrium probabilities of the specific states which can be probed in biophysical tools such as spectroscopy , single molecule fluorescence [43] , and atomic force microscopy ( AFM ) [44] . Equilibrium denaturation experiments [30] revealed a linear correlation between free energy of unfolding and the rotation angle between the two domains of MBP . The stability of the ( apo ) protein decreases with domain closure by about per degree of rotation . By measuring the difference in mechanical work between the ligand-free and ligand-bound states using AFM , the open-closed transition energy for MBPs was determined to be [44] . From our simulation , we can conclude that , for free MBP , the free energy barrier from open state to holo-closed state should be larger than 10 , which is consistent with the experiment and provides the physical basis of the underlying energy landscape . We quantify the population of open and apo-closed state to be and , respectively , which agrees well with the paramagnetic relaxation enhancement data . Note that there is about misfolded conformation located outside the native basins from the simulation . Next , we investigate the effects of ligand binding by increasing from 0 . 0 to 1 . 5 . In Figure 2 , the free energy surfaces for different ligand-bound ratios are shown in the order parameter space of and . In Figure 2A , there are two free-energy minima: one corresponds to the open state at ( ) = ( 0 . 1 , 0 . 6 ) , the other corresponds to the apo-closed state state at ( ) = ( 0 . 9 , 0 . 6 ) . It indicates that conformational transitions occur between the open state and the apo-closed state in the absence of ligand ( ) . In Figure 2B–E , a free-energy minimum corresponding to the holo-closed state at ( ) = ( 0 . 5 , 0 . 9 ) connects the open basin and the apo-closed basin . This minimum does not exist in the absence of a ligand as shown in Figure 2A and the population of conformations at this minimum is small when binding to ligand with low concentration . In Figure 2F–H , we can see that the two basins for the open and the apo-closed states gradually disappear as the concentration of the ligands becomes high enough , making the holo-closed basin the unique one meaning that the protein stabilizes at the holo-closed state when saturated with bound ligand . We further examine the underlying mechanism of how ligand binding and conformational change of MBP from the open state to the ligand-bound state are coupled . Two distinct mechanisms have been suggested to describe the conformational transition in biomolecular recognition . The first is called induced fit [3] . In this mechanism , the relationship between ligand binding and conformational change is accounted for as ligand binding drives a ligand-free , or apo ( usually open ) enzyme towards activated conformation . The second proposed mechanism is called conformational selection or population shift [34] . Within this paradigm , the unbound protein takes on multiple native conformations , subsequently , binding to ligand stabilizes the pre-existing higher energy conformation . For the conformational change of MBP responding to ligand binding , the population shift mechanism indicates that ligand binding stabilizes the pre-existing partially closed state of MBP ( A basin ) , and the protein changes its conformation into fully closed state ( H basin ) passing through A basin . The induced fit mechanism indicates the direct transitions from O basin to H basin without the need of passing through the minor populated state A . Therefore , the mechanism can be simply described by two possible kinetic routes: ( i ) the “induced fit route” ( IF route ) , where predominantly the open form of MBP changes into H basin directly , that is , ; and ( ii ) the “population shift route” ( PS route ) , where MBP arrives at the H basin through the A basin , that is , . Therefore , we can infer the mechanism of conformational change of MBP based on the state-transition routes under the free energy landscape . Rigorously speaking , the PS route represents that MBP first shifts the pre-existing distribution of conformations upon ligand binding followed by local conformational adjustment . The conformational energy landscape of MBP shows that the landscape has three major basins with one basin biased towards the open and the other two toward the apo-closed and holo-closed conformations of the protein . This indicates that the experimentally determined structures are perfectly integrated into our model . The entire conformational dynamics of the system is characterized by the inter-basin transitions , which depends on the barriers between and relative energies of basins [5] . The conformational energy landscapes are reshaped by the change of ligand concentration which can modulate the height of free energy barriers or the depth of native state basins . In Figure 3A–B , the typical time trajectories as a function of RMSDs ( ) and Q fractions ( ) are shown . It indicates transitions between open and holo-closed states , and between holo-closed and apo-closed states . In addition , Figure 3C–D show the free energy profile as a function of and the two-dimensional free energy profile as a function of and . We found a direct route from open basin to holo-closed basin clearly implying an induced fit pathway . The apo-closed basin is not on the way to holo-closed basin , but is , in fact , circumvented en route . By analysing the kinetic trajectories , we found the evidence that PS routes exist which are hidden in thermodynamic free energy profiles ( Figure S5 in Text S1 ) . A typical kinetic PS route in MBP is shown in Figure S6 in Text S1 . A thermodynamically invisible path may actually exist . Under certain conditions related to reactant concentrations and rate constants , the flux through a given pathway can quantitatively reflect a reaction proceeding along the respective path [45] . From the energy landscape perspectives , the induced fit is a limiting case of conformational selection when the interaction partner selectively binds to the lowest energy conformation [3] . Hence , the actual mechanism in each allosteric protein may reside between the two limits . In many cases , the allosteric pathway may be modulated by the ligand and protein concentrations , so that both mechanisms may occur at certain conditions [46] . To precisely measure how much a transition proceeds through a given route , we calculate the reactive flux through that route . From our calculation , only OH transition flux ( ) and OAH transition flux ( ) are computed to analyse the relative importance of two limiting mechanisms when a ligand binds to MBP . Furthermore , we investigated the dependence of fractional flux of IF route ( ) and PS route ( ) on the ligand concentration and hinge flexibility . Note that a comparison of the effects with different parameters should be performed under the same conditions . The fractional flux and can be calculated as follows:where is the number of direct transition from the open basin to the holo-closed basin without passing through any other basins . is the number of sequential transitions from the open basin to the apo-closed basin and then arriving at the holo-closed basin . The basin dynamics of the system was investigated by applying the method analogous to the one described in [47] that explored the folding free energy landscape . We construct a reduced kinetic scheme by considering the transitions just between the native basins . The transition rate constant for this model , for example , is estimated as the ratio of transitions number to total residence time in open basin . Generally , , where is the the number of transitions from basin A to B , is the probability of protein in basin A , is the duration of the trajectory . Note that is in unit of . We next considered the kinetic response of MBP to changes in ligand concentration . The relationships of the fractional flux and transition rate constant with are shown in Figure 4 . From Figure 4A , it is clear that the IF transition flux increases with increasing , especially for slightly higher values ( ) . The correlation between and indicates that the increasing ligand binding interactions encourage MBP to follow an induced fit pathway . This finding is consistent with the conclusions by Hammes et al . [45] that increasing ligand concentration favors the induced fit pathway . Intriguingly , is always kept at relatively high values ( ) for all ligand concentrations . This strongly supports that the induced fit pathway is the predominant activation route of the system . The contribution of PS routes to the total activation flux does not exceed ( Figure S7 in Text S1 ) . As pointed out by Zhou [46] , the conformational transition rates are also a key factor in controlling the population of pathways . Figure 4B indicates that the transition rate constants and increase as ligand concentration increases . However , increases more sharply than and . This can be easily understood considering that while the conformational transitions between the open and the holo-closed basin are fast , MBP can quickly switch to the active state directly from the open state . Our simulation results indicate that if the transition rate between the ligand-free major state and the ligand-binding active state is sufficiently high , then the protein will mostly follow the direct conformational transition route that results in a predominant induced fit mechanism . The relationships between kinetic behaviors and ligand concentration were studied using model ( Figure 4 ) . To alleviate the model dependence caused by the effects of the dihedral barrier of the hinge regions in different dihedral models , we employed parameter . Note that is introduced to rescale the energetic contribution of the angle term for the hinge regions . Thus , this parameter can be used to tune the hinge flexibility . The effects of the hinge flexibility on kinetics are discussed in detail in the following section . In particular , we analyze the detailed kinetic trajectories generated by the specific model with potential using a parameter set with moderate values ( ) . Note that the corresponding free energy profiles under the same model are shown in Figure 3 . Figure 5A shows the schematic representation of the basins dynamics . The transitions between the basins are represented by arrows along with the transition numbers . Transitions that occur clock-wise are represented as blue arrows . The fractional flux of IF route is 0 . 796 . It means that of open to holo-close transitions proceed through induced fit pathway , the rest follow population shift pathway . The basin probabilities , and are , and respectively . The percentages of basin probability do not necessarily add up to exactly 100% because of minor misfolding conformations outside these native basins . Moreover , for the timescale of the transitions between the open state and the apo-closed state , the experiments suggest an upper limit of [48] and a lower limit of [33] . In addition , the OH transition rate can be estimated around ns [36] by experimental association rate constants that fall within a very narrow range of × [49] , despite the differences in the size and nature ( linear and cyclic ) of the maltodextrin ligands . In our simulations , the activation transitions from ligand-free MBP is illustrated in Figure 5B in which , and are 2 . 9 , 0 . 9 , 13 . 0 , respectively . It should be noted that , in our coarse grained structure-based model , transitions between native basins are significantly faster than the realistic time scale . The absolute time scales cannot be obtained due to the coarse grained nature of our model and the lack of explicit solvent molecules . It is , however , possible to make an order of magnitude estimate based on the gap of time scale between simulations and experiments . Accordingly , it may be reasonable to scale the simulation time by two or three order of magnitude . This transformation should be taken as a very crude order of magnitude estimate . Clearly , the rate of AH transition is about 4 times the OH transition rate , and 15 times the OA transition rate . The finding that is larger than in our model is supported by the fact that the open to holo-closed transition can be observed in MD simulations that are at least 30 ns long [35] , and the pre-existing equilibrium in apo MBP between an open and a partially closed conformer was only observed after 300 ns of simulation time [36] . The true transition rates should be dependent on a number of factors , such as temperature , pH , and ligand concentration . Here , they are calculated under a condition corresponding to modest ligand concentration ( ) . The results indicate that the relatively less contribution of PS routes is due to the fact that OA transition is the rate-limiting step , although the protein transitions into the H basin from the A basin more quickly than from the O basin . It is widely accepted that enzyme activity is closely related to the fast , local fluctuations reflecting the flexibility of mostly the hinge regions . These hinges were computationally identified from differences in both pseudo-angles and pseudo-dihedral angles between native states ( see details in section Model and Methods in Text S1 ) . In the following , we will investigate the influence of hinge flexibility on the thermodynamics and kinetics of the system . First , the influences of hinge flexibility on thermodynamics are summarized in Figure S8 in Text S1 . Considering the different native angle biases of and model , we compared the results to assess the model dependence . With model , the hinge flexibility ( the decrease of ) is found to mainly decrease the depth of the open basin , by destabilizing the open-closed basin and increasing the stability of the apo and holo-closed basins , but has little influence on either or transition barrier . With model , it shows that hinge flexibility mostly decreases the stability of holo-close basin and increases the stability of apo-closed basin . However , there is little impact on free energy barrier and stability of open basin . Although the models may have different native biases , they all imply that increasing hinge flexibility can decrease the free energy barriers from their native basin to other basins . Furthermore , we investigate the effects of hinge flexibility on kinetics ( Figure S7 in Text S1 ) . From the correlations between fractional flux of IF routes and hinge flexibility , we can see that the increase of hinge rigidity in both models facilitates the conformational transition of MBP along the induced fit pathway . The PS routes are more favored for high hinge flexibility as the transition between the open and the apo-closed conformations is not only accompanied by domain rotation , but also by significant domain twist , both of which depend on hinge flexibility . From the relationships between transition rate constants and hinge flexibility , we can see that in model , increase of hinge flexibility can greatly accelerate the OH transition . However the corresponding fractional flux decreases as hinge flexibility increases . This can be explained as the competition between IF transitions and PS transitions as and also increase . To be more exact , increases 17 . 3 times and increases 10 . 6 times from to . In contrast , in model , the OH transition process is slightly slowed down when increasing the hinge flexibility , and the corresponding fractional flux also decreases . Overall , the flexibility of hinge regions play an important role in both basin stability and basin dynamics . The above thermodynamics and kinetics results reveal a mixed mechanism for the conformational change in MBP upon ligand binding . It consists of a major induced fit route and a minor population shift route . We located the transition state by finding the extremum between the minima of the free energy profile as the saddle point of the free energy landscape . From the thermodynamic free energy profiles in Figure 3D , we can identify two transition states between the O , A , H basins which are located at ( ) = ( 0 . 3 , 0 . 8 ) and ( 0 . 7 , 0 . 65 ) . And from Figure S4 in Text S1 , another transition state between the O and A basin can be identified and located at ( ) = ( 0 . 6 , 0 . 5 ) . First , we describe the structural information in the transition state ensembles . In Figure 6 and 7 , we show the contact probability maps for transition state ensembles of and . We map these contacts onto the three-dimensional structures of MBP . Note that only the state-specific contacts are illustrated . This is due to the fact that most of the contacts are shared by the two-ending transition basins . Their probabilities are shown by points with different colors according to side color bar in which dark colors mean high probability and light colors mean low probability . Figure 6 shows the structural characterization of the transition state between the open and holo-closed states . Clearly , we can see that the contacts between the tip of balancing loop and NTD ( C region ) are broken and the interface between NTD and CTD at the tip of ligand binding cleft ( D region ) forms 7 native H-specific contacts ( formation ) . Note that H-specific contacts represent the contacts that are specificial for the holo-closed state . A-specific denotes contacts for the apo-closed state and O-specific denotes contacts for the open state . By contrast , the interface at the tail of cleft ( E region ) only forms 2 contacts ( formation ) , and the long-range H-specific contacts whose optimal distances in open state are larger than 20 Å are not formed . However , the contacts between balancing the linker and NTD ( A region ) and part of contacts between balancing the loop and NTD ( B region ) are not broken . We propose that the balancing loop movement switches the closure of the tip of ligand binding cleft and the balancing linker motion triggers the final closure of the cleft in open to holo-closed transition . Figure 7 , shows the structural characterization of . We can see that contacts ( C region ) between the balancing loop and NTD are broken and a number of contacts ( D region ) between the balancing linker ( mostly located at residues K322 to I329 ) and NTD are also broken . The interface at the tip of cleft ( E region ) forms all A-specific native contacts ( formation ) . A part of A-specific contacts at tail interface ( F region ) is formed and the rest ( G region ) is not formed . In addition , the O-specific contacts between helix part of balancing linker and two domains ( region A and B ) are not broken . Comparing with , we found that more contacts between the balancing interface ( containing the balancing loop and balancing linker ) and NTD are broken in , and more contacts are formed between the interface at the ligand binding cleft . It seems that not only the balancing loop acts as a switch that initiates the domain closure , but also the balancing linker plays an important role in the conformational change whose role was not investigated in the earlier simulation [36] . Note that A-specific contacts in H-region contain pairs D10-E107 , K11-E107 , V106-Q321 , V257-Q321 which represent the interactions between linker1 and NTD and between linker1 and linker2 in apo-closed state . These contacts are not formed in TS . The green squares in contact probability map and green dashed lines in mapped structure are used to label the O-specific contacts ( F88-V255 and V106-A299 ) between linker1 and NTD at the bottom of the ligand bind cleft , which are not broken in TS . It indicates that the domain twist does not occur in because that the twist has to disrupt the O-specific contacts F88-V255 and V106-A299 and may form A-specific contacts ( in H region ) between linker1 and the loop1 ( N8-K11 ) in NTD ( such as , ion pair K11-E107 ) . From this point , we propose that domain partial closure occurs before domain twist in the transition between the open and apo-closed states . Characterization of the transition state properties , especially the inhomogeneous distribution of contacts between residues , will help us to understand the microscopic structural mechanism of conformational change through locating the sites or seeds for the nucleation . The values provide important characterization for particular residues at the transition state ensemble . By experimental value analysis , one can identify the critical residues with high values that cluster together in the transition state to form the nucleus for conformational change from the unfolded state to the native folded state . In our model , we use functional values to characterize the transition state ensemble for conformational changes between multiple basins [6] . See details of functional values calculation in Text S1 . We calculate the functional -values and for particular residues with a sensible difference in the thermal mean number of interaction contacts in the transition states of transition and transition . The results are summarized in Figure S9 in Text S1 . It shows that certain residues in NTD ( D10 , K11 , K293 , P294 , L295 ) and in CTD ( P225 , W226 , S229 ) play a key role in stabilizing the holo-closed state ( is larger than ) and contribute to the closure of MBP ( ) . These residues are clustered together in the holo-closed state to form a tightly interacting network corresponding to the high contact probability region in Figure 7 . Our model also predicts that the values of residues F88 , D91 , R94 , N96 , Y167 , N169 , Y172 and Q321 in are especially close to their in open state ( ) . This can also be explained by the contact probability map where O-specific contacts ( A region ) between balancing linker and NTD and partial O-specific contacts ( B region ) between balancing loop and NTD are intact . In addition , the values for residues Y95 , N96 , G170 , K171 ( white small spheres ) are about 0 . 5 and values for them are larger than , indicating that the interactions between the balancing loop and NTD are partially broken in . This is consistent with the result reported from contact probability maps . The analysis indicates that the clustered residues are the same as in except that K38 also plays an important role in stabilizing the apo-closed state . In addition , the value for E107 is 0 . 7 , which indicates that E107 also plays a favorable role in stabilizing the apo-closed conformation in . These interactions may include the salt bridge between K11 and E107 as a member of the “hook-and-eye” motif , which can be important to lock the protein-ligand complex in a semiclosed conformation [35] . E107 is located at the base of the binding cleft , in the middle of linker1 . Compared to the open state , the side-chain carboxylate shifts 3 . 4 Å in ligand-bound closed state and 6 . 2 Å in apo-closed state thus moving up into the cleft . Furthermore , for helix A15 in C2 domain ( see secondary structural definition in Text S1 ) , there are not only high residues ( W336 , Y337 , R340 ) , but also low a residue ( M332 ) . This is consist with the contact probability map in Figure 7 that shows part of native contacts in the apo-closed state is formed and part of them is absent in the interface at the tail of the ligand binding cleft . The high values for residues in the balancing loop ( N169 and G170 ) indicates that the interactions between NTD and the balancing loop are completely broken as are close to 0 for F88 , D91 , N96 , G97 , N169 and G170 . Interestingly , we found that values of K252 , K322 and G323 are larger than 1 ( yellow spheres ) as is out of the range of and . For K252 , , and are 3 . 8 , 2 . 8 , 3 . 3 respectively . For K322 , they are 3 . 9 , 2 . 6 , 3 . 1 , and 2 . 7 , 1 . 6 , 2 . 2 for G323 . K252 is located at the loop region of linker1 , and K322 and G323 are located at the loop region of linker2 ( balancing linker ) . Their abnormal values indicate that the movements of linker1 and linker2 are not completely coupled during the open to apo-closed transition . Especially , for the loop region in linker2 , the large structural fluctuation may cause it to move away from linker1 , resulting in loss of contacts between K252 and K322 and G323 . On the other hand , the loss of contacts in linkers brings about more flexibility for rigid-body domain movement . Such behaviour of breaking and reforming native contacts is known as “backtracking” . It is somewhat surprising that such a “backtracking” phenomenon is observed during the exchanging process of predominantly open form and minor partially closed form in MBP . To our knowledge , it is the first reported case in protein allostery , although such behavior has been been observed in protein folding [50]–[52] and binding [41] . Such functional “backtracking” behavior is in favor of conformational change . It is well known that large structural arrangement of protein can be realized by changes of several dihedral angles at the hinge regions . In some cases the high strain energy may accumulate in various localized regions . It can be relaxed by local unfolding or cracking if the energy exceeds a threshold [7] . See the detailed calculation of local unfolding in Text S1 . The results of local cracking calculation are summarized in Figure S10 in Text S1 . Our model indicates that the local unfolding points contain V106 , L117 , P119 , E149 , E168 and N328 in . And for , these unfolding regions include H60 , I104 , P150 , Q148 , T204 , P244 , K322 , G323 , M326 . These residues are located at the loop regions with high flexibility with the exception of I104 and V106 approaching to the known hinge residue E107 . They are located at linker1 . The high value ( see definition in section of Local Cracking in Text S1 ) for E168 which is located at the balancing loop may be caused by the high flexibility of balancing loop that are not in contact with NTD . In addition , N328 in and K322 , G323 , M326 in are located at the loop region of the balancing linker . In MBP , the hydrophibic residues have been found to be important to the domain closure [30] , [32] , [36] , which makes the protein more compact by reducing the solvent accessibility . When the balancing interface loses contacts with NTD , the side-chain of non-polar residues become more solvent-exposed , which in turn drives a distant conformational change in active site cleft . More specifically , our simulation reveals that the solvent exposure of the balancing loop drives the tip of active site cleft to close . We also found that the charged residues are important , however , compared to , the electrostatic interactions seem to be more important for in which four charged residues ( D37 , K38 , E41 , R340 ) take part in the contact networks formed at the tail of active site cleft . Among these contacts , there is a salt bridge D37-R340 formed between NTD and CTD . Another inter-domain salt bridge K38-E149 can be found specifically in the holo-closed state , but it is not formed at . In addition , the functional value analysis reveals that E107 may help stabilizing the through the formation of salt bridge between K11 and E107 . Taken together , we propose that the population shift pathway is more electrostatically driven than the induced fit pathway for MBP . We further explored the relationship between unfolding characteristics of MBP and ligand concentration through simulation . The thermodynamics of unfolding in MBP has been previously explored by a combination of differential scanning and titration calorimetry and fluorescence spectroscopy under different solvent conditions [53] . In this work , we simulated the folding dynamics using our functional model . The results are summarized in Figure S11 in Text S1 . It indicates that the specific heat curves shift to higher temperatures with increasing ligand concentration , suggesting an increase of protein stability . In addition , the folding cooperativity increases as well , which is illustrated by the narrowing of the specific heat profile . Structurally , MBP contains two globular domains . Between the two domains , it forms a deep groove whose base locates the ligand-binding site . The X-ray structure of MBP in complex with maltotriose [24] , [26] , [54] shows that the binding pocket is lined with a number of polar and aromatic groups from both domains that participate in hydrogen-bonding and van der Waals interactions with the ligand . These interactions make the two interlaced domains more tightly packed against each other . Overall , our simulations support that the presence of a ligand increases the cooperativity and stability of MBP because the ligand binding bridges the two domains more tightly . Higher ligand concentration or binding with ligands with higher affinity , makes the protein not only more stable , but more cooperative . This conclusion is in agreement with earlier experimental findings [53] .
Conformational transitions are central to a multitude of physiological processes , such as enzyme catalysis , and also essential for nonenzymatic binding events . Due to the limit of current experimental techniques , the molecular details for the microscopic structural mechanism of conformational change are often lacking . Therefore we developed a theoretical model for describing the structure-function relationship , and extended the double-well model [5]–[10] , [12]–[17] , into a triple-well model , which we used to account for conformational switching between the open , apo-closed and holo-closed states of MBP . It is worthwhile noting that the microscopic mixed double-well models with two reference conformations may construct additional basins in the free energy landscape , such as in Lu and Wang's microscopic double-well model [5] and in Whitford's mixed contact map model [6] . However , these models don't necessarily yield the required free energy basins . For example , Lu and Wang's microscopic double-well model can generate four free energy basins for ADK [5] , but no additional basins beside the two reference basins for glutamine-binding protein ( GlnBP ) [55] . In fact , we have attempted to build a dual-basin model , which only integrated the open and the holo-closed conformations of MBP . However , this model failed to generate the apo-closed basin , demonstrating the limitations of these double-well models in simulating complex multi-state systems . Such limitations are what led us to develop the present triple-basin model . Constant temperature molecular dynamics simulations were performed to obtain the free energy landscape and kinetics for conformational changes . It clearly reveals an induced fit pathway on the free energy surface . In addition , a minor population shift pathway is found through the analysis of kinetic trajectories . By introducing the flux analysis as suggested by Hammes et al . [45] , we measured the relative weight of the two parallel activation paths in a quantitative way . The results support a mixed mechanism of MBP in the presence of moderate concentration of ligand whereby major induced fit pathways and minor population shift pathways coexist . The thermodynamic and kinetic manifestations of function transitions are closely related due to the correlation between transition rates and free energy barriers of basin hopping . Rigorously speaking , the population shift route ( ) represents the population shift followed by local conformational adjustment ( induced fit ) similar to some other systems [16] , [56] , [57] . The mixed mechanism is consistent with the free energy landscape sampled by atomic simulation [37] , [38] , where the ligand binding induced conformational change is proposed to be dominant for MBP . In our work , we quantified the relative weights of both induced fit and population shift from kinetic pathway perspectives . These results support the view that the underlying mechanism of conformational transition does not necessarily follow the population shift scenario , and that even free protein populates a wide range of conformations . In nature , conformational change of protein upon ligand binding can be explained by either one of the two mechanisms or their combinations . It is dependent on the systems under different conditions . The relative weights of both pathways are dependent on a number of factors , including the ligand and protein concentrations , the conformational transition rates , and the properties of ligands etc . [11] , [45] , [46] , [58] . Other methods also have been developed , such as Markov state models ( MSM ) [57] . Our work provides a good example of quantifying the relative contributions of multiple possible mechanisms . A “Venus Fly-trap” model was employed to explain the conformational change of PBPs family [59] . The hinge regions are believed to play a key role in control of the rapid response of PBPs upon ligand binding resembling that the carnivorous plant traps its prey [60] . In the present work , we investigate the role of the hinge in conformational change , especially for MBP . We found the flexibility of hinge regions plays an important role not only in the basin stability and but also in basin dynamics . The results also support that increasing the flexibility of the hinge regions promotes the population shift route . It is still a matter of debate whether it is a general rule that dynamical equilibrium between the open and the minor closed conformation exists for all the free PBPs though our current and past work certainly supports this assertion [33] , [37] , [38] , [57] , [61] , although Bermejo et al . claimed that there was no such apo-closed state for GlnBP [61] . The findings in our simulation support the explanation that the population shift mechanism is unfavourable for conformational change of GlnBP due to the fact that its hinge relatively rigidified by strong hydrogen bond interactions . Protein folding is critical for three dimensional structural formation , and the conformational switching between multiple well defined states is vital for interactions and functions at the molecular level . They are often studied separately . So far , there have been abundant studies to explore the structure-folding relationship [9] , [62] . Recently , more and more attention has been turning to the functional transitions and native dynamics of proteins [5] , [6] , [17] , [34] , [38] . However , there are very few studies to investigate the interplay between protein structure , folding , and function as a whole . In this work , we explored the folding landscape and functional landscape of MBP under the same theoretical framework using our triple-well structure-based model . To our knowledge , this work is the first study to provide such a description . In addition , the effects of ligand concentration in protein melting were also investigated . The conclusion that the presence of ligand increasing the cooperativity and stability of MBP agrees well with earlier experimental findings . The success of reproducing experimental folding properties also implies that the coarse-grained implicit ligand model has the ability to capture the essential effects of ligand binding not only on functionally conformational change but also on the global folding landscape . From the perspective of energy landscape theory , protein folding and protein function share a common funneled energy landscape [63] ( Figure 8A ) . Proteins fold their conformation into low energy ensembles from the top of the funnel to the bottom , and carry out the biological function by changing their conformations between a modest number of folded states in response to ligand binding or environmental changes . Compared with the whole folding phase space , the number of conformations is much smaller in the functional landscape . It makes the bottom of the funnel more easily sampled using current computational resources , especially with the aid of coarse grained models . We can consider protein folding as the large-scale ( inter-basin between native and non-native ) conformational changes between unfolding state and folded states , and functional transitions as small-scale ( intra-basin , native ) conformational changes between multiple folded basins . Although the folding dynamics were usually sampled by structure-based models with a single native basin , in principle , these multi-basin models were specially developed for functional dynamics should have the ability to capture the folding dynamics , as done in the present work . Furthermore , the physical reason why unfolding/melting occurs in the functional study can be answered using the the thermal energy scale ( Figure 8B ) . When the temperature is high ( in fact melting temperature is much higher than the functional transition temperature ) , the energy scale is high . At this scale , the differences between global energy landscape of double well and triple well for function and single well funnel for folding are blurry and even indistinguishable . Conformational dynamics at this temperature in these models all look like large-scale folding/unfolding transitions between the top of the energy funnel and the bottom ( native basins ) . At the low temperatures , corresponding to the low energy scale near native states , the conformational dynamics occur at the bottom of landscape ( functional landscape ) , the difference between folding funnel model and functional multi-basin model will be explicit and distinct . In other words , on the coarsed grained level , we have a funneled energy landscape for folding . If we dive into the smaller scales of the energy landscape near the native states , we see the more detailed structure and functional energy landscape emerging ( Figure 8C ) . There have been several theoretical studies about conformational dynamics of MBP , most recently [36]–[38] , [64] stimulated by the experimental finding of hidden partially closed conformation [33] . There are substantial differences between these studies and the present work . Our model is based on energy landscape theory in which proteins minimize the conflicting interactions so as to be minimally frustrated and have globally funneled energy landscapes biasing towards their native structures as a consequence of evolution [34] . It is well established that the folding mechanism and binding mechanism are encoded in the topology of proteins in the native folded state [9] . Therefore , our model is developed based on the assumption that the functional transitions are mostly governed by protein topology . Our model is structure based , which can give both statistically reliable thermodynamic and kinetic information at the coarse grained level , while others that are more detailed atomic based giving statistically sampling limited thermodynamic information but no kinetic information . Although , electrostatic interactions are taken into account in our model , it may be true that other energetic frustrations also contributing to the roughness of energy landscape . There are several limitations in our models . The water solvent molecules are not explicitly considered here . Furthermore , the ligand is also implicitly introduced by ligand-mediated contacts . The implicit modeling of the ligands has an advantage in simplification of simulation and interpretation of the results , however it is unable to precisely account for the local nature of the interactions between ligand and protein . For example , the beta-cyclodextrin does not elicit domain closure for MBP , except for interactions with only the CTD [65] . In our simulation , we introduced the ligand-mediated interactions between NTD and CTD to reflect the ligand binding . So , our simulation is only able to represent the binding effect for these physiological ligands ( such as maltose , maltotriose ) with the exception of beta-cyclodextrin which interacts with CTD only . This important feature is also missing in the other models [5]–[10] , [12]–[15] . Indeed , we have further refined this model by introduction of explicit ligands . The improved model has been applied in this protein and another allosteric system and the related works will be submitted to be published soon . Taken together , we propose that the conformational dynamics of MBP can be unraveled by assuming a triple-basin energy landscape ( whose depths are modulated by ligand ) that corresponds to distinct but related topological states . This work demonstrates that a multiple native basin biased landscape which follows the principle of minimal frustration is sufficient to fold and function with complex topologies . From this perspective , we extend the application of the funneled energy landscape that explains how most proteins fold efficiently and robustly to their structures in functional transitions . Furthermore , we expect that our model can be extended to address more complex allosteric systems .
To account for the side-chain dynamics , especially for active site , we developed a mixed coarse-grained model in which part of amino acids are represented by two beads . The coarse-graining process is similar to SMOG tool [68] . Based on the fact that the interface between the NTD and CTD is rich in charged residues , we introduced the electrostatic interactions into the model . In addition , we introduced a pseudo-ligand into the bound simulation by adding selected ligand-mediated interactions to the potential , an approach that has been used before in the double-well coarse-grained simulation [6] , [11] , [40] . The total Hamiltonian for the MBP system is given by the expression:The total energy is divided into backbone , non-bonded and electrostatic interactions . The backbone interaction maintains the geometry and local bias . The non-bonded interaction can be partitioned into two components , an attraction term to provide the triple-basin bias by a mixed contact map and a repulsive term to provide the excluded volume . For the last term , we used the Debye-Huckel potential to introduce the electrostatic interactions [69] . Water and ions were incorporated implicitly into the interaction model as the dielectric constant and the Debye screening length . We have tested the DH model using an extensive set of dielectric constants ranging between 40 and 160 and ion concentrations ranging from 0 to 0 . 20 M . The results of DH model with different parameters are summaried in Figure S12 in Text S1 . Changing the dielectric constant or the salt concentration did not significantly affect the functional dynamics of MBP . Finally , we employed a salt concentration 0 . 10 M and a dielectric constant 80 . All the analysis in manuscript was based on this values except where specified . Of course , the DH model is valid for low salt concentration and mainly for dilute solution . See Text S1 for the detailed descriptions of these terms and the accompanying parameters of the force field . Simulations were performed with Gromacs 4 . 0 . 5 [70] . Reduced units were used for all calculations . A time step of 0 . 0005 time units ( or ps ) was used and the simulation was coupled to a temperature bath via Langevin dynamics with a coupling time of 1 . 0 . For each individual trajectory , the total simulation time was time units . To ensure that the simulation is converged and the statistical errors are small enough , we simultaneously ran several independent simulations using the same parameters set . Simulations were performed at T = 0 . 5 , and this temperature is used throughout the article , except where specified . Systems were initialized arbitrarily in one of the three native states . Q ( and also RMSD ) has been suggested to be useful when studying the folding landscape . Unfortunately , for functional dynamics which occurs at the bottom of the energy funnel , the traditional folding Q is unable to monitor the process due to the fact that most of the contacts shared by the native states are not needed to be broken . Fortunately , the reaction coordinates from protein folding can be borrowed to describe the functional landscape with minor modification . That is , to measure the formation of state-specific contacts instead of all native contacts . We can denote this quantity as the state-specific Q fraction or functional Q . The state-specific Q fraction can distinguish the conformational transitions between the native basins . This reaction coordinate has also been successfully used in other allosteric studies [18] , [40] . See more details in Text S1 . | A central goal of biology is to understand the function of the organism and its constituent parts at each of its scales of complexity . Function at the molecular level is often realized by changes in conformation . Unfortunately , experimental explorations of global motions critical for functional conformational changes are still challenging . In the present work , we developed a coarse grained triple-well structure-based model to explore the underlying functional landscape of maltose-binding protein ( MBP ) . By quantitative flux analysis , we uncover the underlying mechanism by which the major induced fit and minor population shift pathways co-exist . Though we have previously lent credence to the assertion that dynamical equilibrium between open and minor closed conformations exist for all the free PBPs , the generality of this rule is still a matter of open debate . We found that the hinge flexibility is favorable to population shift mechanism . This finding provides a theoretical explanation of the mechanism discrepancies in PBP protein family . We also simulated the folding dynamics using this functional multi-basin model which successfully reproduced earlier protein melting experiment . This represents an exciting opportunity to characterize the interplay between folding and function , which is a long-standing question in the community . The theoretical approach employed in this study is general and can be applied to other systems . |
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Interplay between apicobasal cell polarity modules and the cytoskeleton is critical for differentiation and integrity of epithelia . However , this coordination is poorly understood at the level of gene regulation by transcription factors . Here , we establish the Drosophila activating transcription factor 3 ( atf3 ) as a cell polarity response gene acting downstream of the membrane-associated Scribble polarity complex . Loss of the tumor suppressors Scribble or Dlg1 induces atf3 expression via aPKC but independent of Jun-N-terminal kinase ( JNK ) signaling . Strikingly , removal of Atf3 from Dlg1 deficient cells restores polarized cytoarchitecture , levels and distribution of endosomal trafficking machinery , and differentiation . Conversely , excess Atf3 alters microtubule network , vesicular trafficking and the partition of polarity proteins along the apicobasal axis . Genomic and genetic approaches implicate Atf3 as a regulator of cytoskeleton organization and function , and identify Lamin C as one of its bona fide target genes . By affecting structural features and cell morphology , Atf3 functions in a manner distinct from other transcription factors operating downstream of disrupted cell polarity .
Epithelia are sheets of highly polarized cells that represent the defining tissue type of metazoans . With their capacity to achieve various shapes and serve as selective barriers , epithelial tissues play vital roles in morphogenesis , tissue differentiation and compartmentalization , and intercellular signaling . The integrity and function of epithelia rely on the interplay between key polarity determinants and a highly ordered yet dynamic cytoskeleton , which ensures tissue plasticity and the asymmetric distribution of cellular components along the apicobasal polarity axis [1] . Genetic studies have established a network of evolutionarily conserved signaling pathways and effector molecules that govern the organization of epithelial cellular architecture . In particular , the basic leucine zipper ( bZIP ) transcription factors , including Jun , Fos , and Atf3 , are important regulators of epithelial function from the fruit fly Drosophila to mammals [2–6] . During Drosophila development , expression of atf3 is dynamic and under tight temporal constraints . Ectopic Atf3 activity in larval epidermal cells ( LECs ) disturbs epithelial morphogenesis of the adult abdomen , stemming from perturbed cytoskeleton dynamics and increased cell adhesion which prevent normal LEC extrusion [5] . The importance of finely tuned Atf3 expression during Drosophila development corresponds with the role of mammalian ATF3 as a stress-response gene regulated at the level of mRNA expression by various stimuli , including genotoxic radiation , wounding , cytokines , nutrient deprivation , Toll signaling , oncogenes and inhibition of calcineurin-NFAT signaling [7–9] . Independent transcriptome analyses of Drosophila epithelia have shown deregulated atf3 expression in wing imaginal discs lacking the conserved neoplastic tumor suppressor genes scribble ( scrib ) or discs large 1 ( dlg1 ) encoding components of the Scribble polarity module [10] , and in rasV12scrib− tumors in the eye/antennal imaginal disc ( EAD ) [11–13] . These results point to loss of epithelial integrity as a novel trigger of atf3 expression and are congruent with studies linking Atf3 to processes involving transient controlled epithelial depolarization during morphogenesis and wound healing [5 , 14 , 15] as well as to pathological disturbances in polarity that characterize chronic wounds and tumorigenesis [6 , 9 , 16–20] . However , which polarity cues induce atf3 expression and how Atf3 activity contributes to phenotypes associated with loss of polarity have yet to be determined . In this study , we establish that loss of the Scrib polarity module is sufficient to increase the levels and activity of Atf3 via aPKC signaling . Increased Atf3 activity drives major phenotypic attributes of the Dlg1 deficiency as abnormal distribution of polarity proteins and differentiation defects in dlg1 mutant epithelial clones can be alleviated by removal of Atf3 . Chromatin immunoprecipitation followed by high-throughput sequencing further revealed that Atf3 target genes are enriched for roles in cytoskeletal organization and dynamics . Thus , Atf3 links defects in the Scrib polarity module with gene dysregulation and subsequent perturbations in cellular morphology and differentiation .
Previous transcriptome profiling by our group and others [11–13] has suggested that disturbed cell polarity leads to upregulation of atf3 expression . Consistently , qRT-PCR from scrib1 homozygous mutant larvae and adult heads bearing dlg1G0342 homozygous mutant clones showed increased levels of atf3 mRNA ( Fig 1A ) , thus confirming induction of atf3 transcription upon depletion of the Scrib polarity module . To extend this evidence , we tested whether Scrib or Dlg1 deficiency impacts the levels of an Atf3::GFP fusion protein expressed from a recombineered BAC ( atf3gBAC ) that is sufficient to rescue the lethality of the atf376 null mutants [21] . In control eye/antennal imaginal disc ( EAD ) , Atf3 was enriched in differentiated photoreceptors of the eye primordium and a subset of peripodial cells of the antenna ( Fig 1B ) . In wing imaginal discs , Atf3::GFP protein labeled the squamous cells of the peripodial epithelium and columnar cells of the outer ring that encircles the wing pouch ( Fig 1C ) . Knockdown of dlg1 ( en>dlg1RNAi ) or scrib ( en>scribRNAi ) in the posterior compartment of the wing disc resulted in a marked increase of the Atf3::GFP signal in the columnar epithelium of the wing pouch ( Fig 1D and 1E ) . Importantly , Atf3 was also upregulated in dlg1G0342 loss-of-function mutant clones induced in the larval EAD ( Fig 1F and 1G and S1A–S1C Fig ) . Previous studies have shown that disruption of the Scrib module leads to activation of Jun-N-terminal kinase ( JNK ) signaling and to deregulation of the atypical protein kinase C ( aPKC ) [22–25] . To determine if either one of these two pathways is sufficient to induce Atf3 expression , we assessed both Atf3::GFP protein levels and the activity of a synthetic Atf3-responsive element ( ATRE ) reporter . The ATRE construct expresses GFP or RFP under the control of four concatenated genomic segments , each of which is 22 bp long and contains an Atf3 binding site identified through ChIP-seq in this study ( S1 Table , Materials and Methods ) . Both in vivo and in cultured Drosophila S2 cells , Atf3 activated the ATRE reporter ( Fig 1M’ and S2A , S2B and S2E Fig ) but not its mutated version ( mATRE ) bearing three base pair substitutions in the Atf3 recognition sequence ( S2C and S2D Fig and S1 Table ) . While activation of JNK signaling by expressing a wild type form of Drosophila JNKK ( Hemipterous; Hep ) induced a JNK-responsive TRE-DsRed reporter [26] ( Fig 1J and S2G Fig ) , it failed to upregulate Atf3 expression when targeted to the posterior compartment of the wing imaginal disc ( en>hepwt ) ( Fig 1J” ) . Similarly , the ATRE-GFP reporter was insensitive to or only minimally activated by JNK signaling in the wing imaginal disc ( dpp>hepwt ) and S2 cells , respectively ( Fig 1N and S2E Fig ) . Moreover , JNK signaling appeared dispensable for Atf3 induction caused by loss of polarity , as elevated Atf3::GFP signal persisted in dlg1 deficient EAD clones expressing a dominant negative form of the Drosophila JNK Basket ( dlg1G0342 bskDN ) ( Fig 1H and 1I ) . In contrast , expression of a membrane tethered aPKC ( aPKCCAAX ) that causes mild overgrowth was sufficient to induce both Atf3::GFP and the ATRE reporter in specified wing disc compartments ( en>aPKCCAAX and dpp>aPKCCAAX ) ( Fig 1K and 1O ) . Although inhibiting JNK signaling suppressed the aPKCCAAX–mediated overgrowth , it did not prevent Atf3 induction ( en>aPKCCAAX bskDN ) ( Fig 1L ) . Signaling via aPKC has been shown to engage Yorkie ( Yki ) , a transcriptional co-activator in the Hippo pathway [10 , 24 , 27 , 28] . However , expression of a constitutively active form of Yorkie ( dpp>ykiact ) did not induce the ATRE reporter ( Fig 1P ) . Together , these findings highlight a role for Atf3 as a polarity response gene that is activated by loss of key polarity determinants downstream of aPKC but not JNK . Imaginal disc clones lacking either Scrib or Dlg1 surrounded by normal epithelium are severely disorganized , suffer from disturbed vesicular transport , lose the ability to terminally differentiate and are eliminated through cell competition involving JNK signaling [22 , 25 , 29] . While JNK and its downstream transcription factor Fos are required for apoptosis and suppression of Yki-mediated hyperproliferation of scrib and dlg1 mutant cells [23 , 25 , 30–33] , aPKC is responsible for the aberrant morphology and differentiation of the clonal cells [22] . Because Atf3 upregulation results from depletion of the Scrib complex components as well as from ectopic aPKC activation ( Fig 1D , 1E and 1K ) , we investigated whether this excess Atf3 might contribute to the differentiation and morphological defects of EAD clones lacking Dlg1 . While EADs bearing dlg1 deficient clones ( S1B and S1C Fig ) produced significantly smaller , malformed adult eyes with patches of undifferentiated tissue ( Fig 2A and 2C and S3A , S3F and S3G Fig ) , simultaneous removal of atf3 ( atf376dlg1G0342 ) almost completely restored normal eye size and morphology ( Fig 2D and S3B , S3F and S3G Fig ) , with only minor irregularities to the orderly hexagonal lattice ( Fig 2D’ ) . An equivalent genetic interaction was observed between atf3 and the dlg1m52 allele [34 , 35] ( S3D–S3G Fig ) . Importantly , adding a single copy of the atf3gBAC transgene to animals with the double mutant mosaic EADs ( atf376dlg1G0342;; atf3gBAC/+ ) was sufficient to reinstate the aberrations to the adult eye morphology ( S3C Fig ) , thus clearly showing that Atf3 is required for this dlg1 deficiency phenotype to develop . Abnormalities were further exacerbated in adult eyes bearing dlg1 deficient clones in which Atf3 was overexpressed ( dlg1G0342 atf3wt ) ( Fig 2E ) . It is important to note that mosaic overexpression of Atf3 alone disturbed the normal ommatidial arrangement in the adult eye ( Fig 2F ) . However , immunostaining of third instar EADs against a pan-neuronal marker Elav showed that , unlike cells lacking Dlg1 , Atf3-expressing clones ( atf3wt ) differentiated ( S4 Fig ) . In contrast , clonal loss of atf3 alone ( atf376 ) did not markedly impact adult eye morphology ( Fig 2B ) . These data provide causal evidence for the role of Atf3 downstream of disturbed epithelial polarity . They also indicate that while Atf3 is required for phenotypes caused by loss of dlg1 , the gain of Atf3 alone does not fully recapitulate these defects . Closer examination of third instar larval EADs revealed the presence of fewer dlg1G0342 clones relative to control and atf376 mosaic EADs ( Fig 2G–2I ) . Many of these mutant cells showed increased activity of the JNK-responsive TRE-DsRed reporter ( S5B Fig ) . Interestingly , simultaneous loss of atf3 neither restored the size or abundance of dlg1G0342 mutant cells nor prevented JNK activation ( Fig 2J and S5A and S5C Fig ) . The exacerbated phenotype of dlg1G0342atf3wt mosaic adult eyes correlated with the presence of contiguous clonal patches in third instar EAD that were markedly rounder compared to clones lacking dlg1 alone ( Fig 2I and 2K ) . Although the individual clones appeared larger , the overall amount of GFP positive tissue was not increased when compared to dlg1 mutant clones ( S5A Fig ) . The cross sections of the eye primordia further revealed that in contrast to dlg1 deficient cells which accumulated basally , the majority of dlg1G0342atf376 clones remained in the disc proper similar to control and atf3 mutant cells ( Fig 2M–2P ) . Strikingly , overexpression of Atf3 in cells lacking dlg1 promoted their apical extrusion ( Fig 2Q ) . To rule out that clone elimination contributed to the observed rescue phenotype in the atf376dlg1G0342 adult eyes , we took two alternative approaches . To inhibit death of clonal cells , we expressed the baculoviral caspase inhibitor p35 in dlg1G0342 and atf376dlg1G0342 EAD clones . To reduce competition elicited by neighboring cells , we utilized the EGUF/hid technique [36] , which facilitates expression of a pro-apoptotic protein Hid in the non-clonal cells within the Glass multiple reporter ( GMR ) domain causing their elimination during pupal stages [37] . As expected , blocking apoptosis by p35 increased the abundance of mutant clonal cells to a similar extent in both genotypes ( S5A Fig ) . Importantly , nearly four times more adults bearing atf376dlg1G0342p35 mosaic EADs emerged compared to dlg1G0342p35 animals ( S5D Fig ) . Adult eyes derived from dlg1G0342p35 EAD contained only small patches of differentiated photoreceptors compared to controls ( S5E and S5F Fig ) , whereas atf376dlg1G0342p35 eyes were either similar to those of atf376dlg1G0342 animals ( Fig 2D and S5G Fig ) or exhibited modest differentiation defects ( S5H Fig ) . Consistently , the overall morphology and differentiation pattern was markedly improved in adult eyes composed entirely of atf376dlg1G0342 mutant tissue generated by the EGUF/hid method . In contrast to only remnants of differentiated tissue in dlg1G0342 mutant eyes , clear ommatidial arrays were observed in control and atf376dlg1G0342 adult eyes , ( S5I–S5K Fig ) . Taken together , these data strongly argue against apoptosis being the basis of the genetic rescue conferred by atf3 deficiency in dlg1 mutant clones , and instead suggest that Atf3 contributes to cell extrusion and differentiation defects caused by loss of Dlg1 . To better characterize how loss of atf3 impacts dlg1 mutant phenotypes , we stained third instar larval EADs for markers of differentiation and cell adhesion . Unlike control clones , dlg1G0342 mutant cells located posterior to the morphogenetic furrow of the eye primordium frequently lacked the pan-neuronal marker Elav . Many of them flattened and delaminated , pushing some of the non-clonal Elav-positive cells to the basal side of the epithelium ( Fig 3A and 3B ) . In contrast , atf376dlg1G0342 clones remained columnar , contributing to photoreceptor and interommatidial cell differentiation ( Fig 3C ) . Importantly , immunostaining for the apical determinant Crumbs , the adherens junction protein DE-cadherin ( DE-cad ) and the lateral membrane marker Fasciclin III ( FasIII ) revealed a clear rescue of the apicobasal organization of the ommatidia in atf376dlg1G0342 clones compared to the disturbed architecture in clones mutant for dlg1G0342 alone ( Fig 3A–3F ) . These findings uncover novel genetic interaction between Atf3 and the central Scrib polarity module component Dlg1 and establish Atf3 as a driver of the structural and differentiation defects stemming from the loss of dlg1 . Besides altered differentiation and mislocalization of polarity proteins , disturbed cellular trafficking [29 , 38–40] is a hallmark feature of epithelial cells lacking the Scrib module components . Therefore , we tested if the distress of the trafficking machinery upon dlg1 loss was Atf3-dependent . Interestingly , neither dlg1G0342 nor atf376dlg1G0342 EAD clones enlarged by p35 expression showed changes in the uptake of fluorescently labeled dextran compared to surrounding tissue ( S6A–S6C Fig ) , indicating that endocytic activity in cells lacking dlg1 was not affected . However , the staining of intracellular vesicle components revealed marked changes to the amount and distribution of early and recycling endosomes . While levels of the Rab5-positive vesicles were reduced in dlg1G0342 and dlg1G0342p35 mutant cells located in front or behind the morphogenetic furrow ( Fig 4A’ and 4B’ and S7B and S7E Fig ) , the pool of Rab11 recycling endosomes increased ( Fig 4A” and 4B” ) . In line with these findings , Dlg1-deficient cells also displayed an abnormal distribution of the Syntaxin7/Avalanche ( Avl ) -positive vesicles ( S6E Fig ) . In contrast , the Rab5 , Avl and Rab11 staining patterns in atf376dlg1G0342 double mutant EAD clones were comparable to those seen in control clones or surrounding normal tissue ( Fig 4C and 4D and S6D , S6F , S7A , S7C , S7D and S7F Figs ) . Strikingly , the changes to the endosomal compartment observed in dlg1 mutant tissue could be recapitulated by the Atf3 overexpression . While apical levels of Rab5 were decreased , Rab11-marked vesicles were enriched on the basal side of the wing imaginal discs in cells expressing Atf3 when compared to surrounding tissue ( S8A and S8B Fig ) . In addition , immunostaining of wing discs bearing Atf3-expressing clones revealed changes in levels and localization of polarity proteins whose proper membrane placement requires a functional trafficking machinery [41 , 42] . While the apical levels of Crumbs were markedly lower , the integrin subunit Myospheroid ( Mys ) , which normally is restricted to the basal cell surface , was detected along the entire lateral membrane in Atf3-expressing clones relative to surrounding tissue ( S8C and S8D Fig ) . These data demonstrate that Atf3 contributes to the alterations of the endosomal machinery upon loss of polarity and its overexpression is sufficient to mimic some of the hallmark features of dlg1-deficient epithelial cells . Interestingly , despite the shift in apicobasal polarity markers Atf3-expressing cells , unlike those lacking dlg1 , maintained their columnar shape and were not extruded from the epithelium ( Fig 2L and 2R and S8 Fig ) . As a bZIP transcription factor , Atf3 is expected to regulate gene expression through binding to specific DNA sequences . To capture a snapshot of genomic regions bound by Atf3 , we employed the Atf3::GFP fusion protein expressed from the recombineered atf3gBAC to perform chromatin immunoprecipitation followed by high throughput sequencing ( ChIP-seq ) . The ChIP-seq using an anti-GFP antibody identified 152 genomic locations significantly enriched in samples prepared from atf376;; atf3gBAC/+ mated adult males compared to yw controls ( Fig 5A and S1 Dataset ) . Using the Multiple Em for Motif Elicitation ( MEME ) tool [43] , we derived a 12-column position weight matrix ( PWM ) yielding a palindromic binding site which we call the Drosophila Atf3 motif ( Fig 5B and S2 Dataset ) . This motif occurred in 112 of the 152 significantly enriched peaks in Atf3 samples , which correspond to 121 genes whose cis-regulatory regions contained an Atf3 motif ( Fig 5A and S1 Dataset ) . Submission of this PWM to Tool for Motif to Motif comparison ( TOMTOM ) [44] determined that Atf3 recognizes DNA that is most similar to the Atf2 binding motif in Drosophila and ATF3/JDP2 motif in humans ( Fig 5B ) . Closer examination of the Atf3-response elements revealed an 8-nucleotide core that closely resembles the cyclic AMP response element ( CRE ) ( TGACGTCA ) ( Fig 5B ) . We have previously reported that the bZIP domain of the Drosophila Atf3 protein indeed directly binds this DNA element [5] . Our present experiments using the ATRE and mATRE reporters further confirm that Atf3 can control expression from this site in cultured cells and in vivo ( Fig 1M’ and S2 Fig ) . The gene ontology ( GO ) analysis with FlyMine [45] revealed that structural and regulatory components of the cytoskeleton were significantly enriched among Atf3-bound regions . In total , 40 of the identified 121 genes containing Atf3 motifs belong to at least one out of five GO terms corresponding to cytoskeleton regulation ( Fig 5A and 5C and S1 Dataset ) . Fig 5D depicts a summary of putative Atf3 targets engaged in their corresponding functions in a model epithelial cell . Importantly , an independent Atf3 ChIP experiment from third instar larval EADs determined that several of these targets including microtubule subunits ( αTub84B and βTub56D ) and nuclear Lamin C ( LamC ) were also bound by the Atf3 in imaginal epithelia ( S9A and S9B Fig ) suggesting a potential mechanistic basis for the Atf3 driven phenotypes in epithelial cells with compromised apicobasal axis . To complement the ChIP-seq approach and characterize the transcriptional response to excess Atf3 , we compared the transcriptomes of mosaic EADs overexpressing Atf3 with control expressing only GFP . Strikingly , RNA-seq revealed a widespread shift in the expression of 4957 genes ( S3 Dataset ) , with nearly three times as many transcripts being downregulated ( n = 3666 ) than enriched ( n = 1291 ) by fold change ≥1 . 5 relative to control ( Fig 6A and S3 Dataset ) . Of the 121 genes identified as Atf3 targets by ChIP-seq in the adults , 32 were differentially expressed at the mRNA level ( Fig 6B and 6C ) in atf3wt mosaic EADs , most of which ( 23 ) showed transcriptional upregulation including six genes ( Arp1 , Chd64 , DCTN6-p27 , Ilk , LamC , Rep ) that belonged to the cytoskeleton GO cluster ( Fig 6B ) . In accordance with the mRNA-seq , an independent qRT-PCR of selected Atf3 ChIP-seq targets detected enrichment of LamC , ude , DCTN6-p27 and Arp1 transcripts while αTub84B and βTub56D remained unchanged ( S9A Fig ) despite being occupied by Atf3 in both the adults and EADs ( S1 Dataset and S9B Fig ) . To identify additional putative Atf3 targets among genes misregulated in the RNA-seq dataset , we scanned the Drosophila genome with our experimentally derived Atf3 PWM using the Find Individual Motif Occurrences ( FIMO ) [46] and Peak Annotation and Visualization ( PAVIS ) [47] utilities . The Atf3 motif ( Fig 5B ) was found within 5 kb upstream and 1 kb downstream of 1252 genes differentially regulated in atf3wt mosaic EADs ( S3 Dataset ) . Importantly , a significant proportion of transcripts altered by Atf3 expression in the mosaic EADs overlapped with genes that had been found deregulated in either scrib or dlg1 mutant wing imaginal discs [10] ( S10 Fig and S3 Dataset ) . This intersection shows that the gain of Atf3 and the loss of the Scrib polarity module elicited a partly overlapping genetic response . The requirement of Atf3 for the manifestation of cytoarchitecture and polarity defects in dlg1 deficient cells prompted us to further focus on its regulation of microtubules and LamC , the primary building blocks of the cytoskeleton and nucleoskeleton , respectively . Microtubules are classically associated with controlling cell shape and division , but they are also central to polarity by serving as railroad tracks for directed vesicular trafficking [48] . Nuclear lamins on the other hand provide structure and stiffness to the nuclear envelope [49 , 50] . The coupling between the cytoskeleton and the nuclear lamina via the LINC complex is essential for maintaining the mechanical properties of the cell and signal transduction [51] . Immunostaining for LamC showed that the increase in LamC transcription in response to excess Atf3 ( Fig 6B and S9A Fig and S3 Dataset ) translated into a dramatic enrichment of the LamC protein in the nuclear envelope of EAD or wing disc cells overexpressing Atf3 ( Fig 7A and 7D ) . Importantly , LamC protein levels also increased in the wing disc epithelium deficient for Scrib ( en>scribRNAi ) and in dlg1 mutant clones of the EAD ( Fig 7B and 7E ) , whereas they were markedly reduced in atf376dlg1G0342 double mutant EAD clones compared to the surrounding tissue and control clones ( Fig 7C and 7F ) . In contrast to LamC enrichment , expression of at least two microtubule genes ( βTub85D and γTub37C ) was downregulated in atf3wt mosaic EADs ( Fig 6D , S3 Dataset ) . Interestingly , immunostaining for α- and β-Tubulin revealed a reduced microtubule network and markedly lowered levels of non-centrosomal γ-Tubulin in Atf3-overexpressing clones of the wing imaginal disc relative to surrounding epithelial tissue ( Fig 8A–8C ) . In addition , eye primordia bearing dlg1 mutant clones showed abnormal microtubule organization that was partially normalized by simultaneous clonal loss of atf3 ( Fig 8D–8F ) . Taken together , our molecular and genetic approaches identified microtubule encoding genes and LamC as new targets of Atf3 . We demonstrate that the upregulation of LamC and disturbances to microtubule network are the common hallmarks of epithelial cells in which Atf3 activity is enhanced either by transgenic overexpression or as a result of disturbed polarity . While LamC is a bona fide target of Atf3 , changes to microtubule network likely arise indirectly as a part of the secondary response to ectopic Atf3 activity .
A growing body of evidence points to striking similarities in the cellular and molecular events underlying loss of polarity and wounding [10 , 66] . In this context , the induction of Atf3 upon loss of polarity is in line with the early discovery of ATF3 as a gene rapidly induced in the regenerating rat liver [67] and recent studies showing Atf3 induction during epithelial wounding [14 , 15] . Here we demonstrate that Atf3 levels remain low as long as epithelial polarity is intact , whereas loss of polarity due to deficiency in tumor suppressors of the Scrib complex triggers Atf3 expression . Future investigations into the link between Drosophila Atf3 and cell polarity are likely to unravel the impact of ATF3 expression on epithelial homeostasis and on human pathologies arising from polarity breakdown .
The following fly strains were used: ( a ) y w1118 , ( b ) w1118 , ( c ) y atf376 w/FM7i , P{ActGFP}JMR3 [5] , ( d ) atf3gBAC [21] , ( e ) en-GAL4 ( RRID: BDSC_30564 ) , ( f ) dpp-GAL4 ( RRID: BDSC_7007 ) , ( g ) UAS-atf3V ( this study as atf3wt ) [5] , ( h ) hsFLP; act>y+>GAL4 , UAS-GFP , ( i ) hsFLP; act>y+>GAL4 , UAS-GFP , UAS-atf3wt ( this study ) , ( j ) FRT19A ( RRID: BDSC_1744 ) , ( k ) FRT82B ( RRID: BDSC_5619 ) , ( l ) UAS-p35/CyO ( RRID: BDSC_5072 ) , ( m ) GMR-hid y w FRT19; ey-Gal4 , UAS-FLP ( RRID: BDSC_5248 ) , ( n ) FRT19A dlg1G0342 ( 111872 , DGRC ) , ( o ) dlg1m52 ( a generous gift from F . Papagiannouli ) , ( p ) FRT82B scrib1 [25] , ( q ) eyFLP FRT19A tubGAL80; act>y+>GAL4 , UAS-GFP ( this study ) , ( r ) eyFLP FRT19A , tubGAL80; act>y+>GAL4 , UAS-myrRFP ( this study ) , ( s ) eyFLP; act>y+>GAL4 , UAS-GFP; FRT82B , tubGAL80 [68] , ( t ) UAS-dlg1RNAi ( 41134 , VDRC ) , ( u ) UAS-scribRNAi ( 105412 , VDRC ) , ( v ) TRE-DsRed ( attP16 ) [26] , ( w ) UAS-hepwt , ( x ) UAS-ykiS111A . S168A . S250A . V5 ( RRID: BDSC_28817 , this study as ykiact ) , ( y ) UAS-mCD8-ChRFP ( RRID:BDSC_27391 , this study as UAS-mCherry ) , ( z ) UAS-aPKCCAAX [69] , ( aa ) UAS-LamC [70] , ( bb ) UAS-bskDN . A transgenic ATRE-GFP line was obtained using the attP16 landing site [71] . The atf3gBAC fly line was used for immunostaining of Atf3::GFP . For detailed descriptions of fly crosses , see S2 Table . All crosses were carried out at 25°C unless stated otherwise . For each biological replicate , total RNA was isolated from 10 larvae or 15 adult heads with Isol-RNA Lysis Reagent ( 5 Prime ) . After DNase I treatment ( Ambion , Foster City , CA ) , cDNA was synthesized from 1μg of RNA using oligo ( dT ) primers and Superscript III ( Life Technologies ) . PCR was performed in triplicate with BioRad 2x SYBR Green mix in the CFX96 real-time PCR system ( Bio-Rad , Hercules , CA ) . All primers were designed to anneal at 62°C ( see S1 Table for oligonucleotide sequences ) . All data were normalized to rp49 transcript levels , and fold changes in gene expression were calculated using the ∆∆CT method [72] . Clones expressing atf3 in the wing imaginal disc were generated using a hsFLPout method described in [5] . Crosses were kept at 22°C . Four days after egg lay , progeny were heat shocked for 30 min in a 37°C water bath . Imaginal discs were dissected from wandering third instar larvae . Generation of mosaics in EADs using Mosaic Analysis with a Repressible Cell Marker method ( MARCM ) [73] was carried out as described in [31] . For each genotype ( see S2 Table ) , three biological ChIP replicates and one input replicate were generated . For each ChIP replicate , 1 . 2 g of adult males were collected and processed using a modified version of the protocol described in [74] . Anesthetized males were split into two batches of 600 mg and flash frozen using liquid nitrogen , pulverized with a mortar and pestle , and disrupted further via 20 strokes of a loose fitting pestle in a Dounce homogenizer containing 10 ml of Crosslinking Solution ( 1 mM EDTA , 0 . 5 mM EGTA , 100 mM NaCl , 50 mM HEPES , pH 8 . 0 ) supplemented with proteases inhibitors ( 11873580001 , Roche ) . The remainder of the protocol describes the processing of a single batch . The suspension was centrifuged for 1 min at 400 g in a swing bucket rotor and then passed through two layers of 64 μm Nitex membrane into a 15 ml Falcon tube . To crosslink Atf3 to chromatin , formaldehyde was added to a final concentration of 1 . 8% to the Falcon tube containing 10 ml of cleared fly homogenate . The Falcon tube was then incubated on an orbital shaker at room temperature for 10 min . Crosslinking was stopped by adding glycine to a final concentration of 225 mM and shaking for a further 5 min . After centrifugation for 10 min at 1 , 100 g in a swing bucket rotor , cells were resuspended in 10 ml of Cell Lysis Buffer ( 85 mM KCl , 0 . 5% IGEPAL CA-630 ( v/v ) , 5 mM HEPES , pH 8 . 0 ) supplemented with protease inhibitors and lysed in a Dounce homogenizer via twenty strokes with a tight fitting pestle . Nuclei were pelleted at 2000 g for 4 min and washed in 5 ml of Cell Lysis Buffer three times . Nuclei were resuspended in 2 ml of TBS Lysis Buffer ( 50 mM Tris-Cl pH 7 . 8 , 150 mM NaCl , 1 mM EDTA pH 8 . 0 , 1% Triton-X100 , 0 . 01% IGEPAL CA-630 ) supplemented with protease inhibitors and incubated for 10 min on an orbital shaker . While submerged in an ice water bath , chromatin was sheared to 300–500 bp fragments by applying fifty 30 seconds on/30 seconds off cycles of a Branson 250 microtip sonifier with Output set to 3 . 5 and Duty Cycle set to 50% . The 2 ml of chromatin was split equally into two 1 . 5 ml low binding microcentrifuge tubes ( 710176 , Biozym ) . Debris was cleared from the chromatin by centrifugation at 20 , 000 g for 10 min . Chromatin was cleared overnight in two tubes at 4°C by the addition of 40 μl of sepharose-IgG beads ( 17-0969-01 , GE ) equilibrated in TBS Lysis Buffer . 100 μl of cleared chromatin was retained as an INPUT sample for sequencing . Remaining cleared chromatin was transferred to two new low binding tubes and precipitated overnight at 4°C with 40 μl GFP Trap beads ( gta-20 , Chromotek ) per tube . Beads were washed 5 times with cold TBS Lysis Buffer supplemented with protease inhibitors . From this point on , the beads and INPUT sample retained earlier were processed in the same fashion . Samples were resuspended in 100 μl TE buffer ( 1 mM EDTA , 10 mM Tris-HCl , pH 8 . 0 ) supplemented with 50 μg/ml RNase A , transferred to PCR tubes , and incubated in a thermal cycler with heated lid at 37°C for 30 min . SDS was added to a final concentration of 0 . 5% ( w/v ) from a 10% stock , proteinase K to a final concentration of 0 . 5 mg/ml . Tubes were then incubated at 37°C for 12 hours followed by 65°C for 6 h in a thermal cycler with heated lid to partially reverse the crosslinks . H2O was added to a final volume of 200 μl , and DNA was purified by phenol:chloroform extraction . DNA was ethanol precipitated in a low binding 1 . 5 ml microcentrifuge tube with sodium acetate and glycogen . DNA was washed once with 70% ethanol . DNA pellets derived from the original 1 . 2 g of starting adult fly material ( 4 microcentrifuge tubes in the end ) were resuspended in 15 μl of TE . Sequencing libraries were generated from input and ChIP samples according to the Illumina protocol for total ChIP-seq library preparation . All samples were loaded onto a single Illumina flow cell ( Illumina , San Diego , CA ) . Using the Illumina HiSeq 2000 instrument , 50 bp of sequence were read from one DNA end . Image analysis and base calling were done with the Illumina RTA software ( RRID: SCR_014332 ) at run time . Sequences were mapped to the Drosophila genome assembly ( version BDGP R5/dm3 , April 2006 ) and delivered as BAM files . To identify regions bound by Atf3 , BEDTools [75] ( RRID: SCR_006646 ) was used to convert BAM files from Atf3 and control samples ( 3 biological ChIP replicates and one input sample each ) into BED files , which were subsequently submitted to a Comparative Profile Analyzer ( CoPrA ) workflow to call and test the statistical significance of differentially enriched genomic locations in Atf3 ChIP-seq samples relative to control . The differential analysis depends on base-resolution coverage profiles of the Atf3 and control ChIP samples as well as the input data from both samples . They were generated from uniquely mapped reads that were extended to the original fragment length of 200 bp resulting in 6 ChIP and 2 input coverage profiles . All profiles are normalized through division of the base-wise coverage values by the total number of respective sample reads multiplied by one Million . Thereby normalized input profiles from Atf3 and control samples are subtracted from normalized Atf3 and control ChIP-profile replicates , respectively . Every processed ChIP profile replicate is then scaled separately to transform its values into the range [0 , 1] . They were then discretized by calculating the coverage mean of a defined sliding window . Since transcription factor binding peaks cover a small region on the genome , its size was adjusted to 50 bp . The shift is set by default to half of the window size . In the next step the discretized replicate profiles of the same sample type are combined to one resulting discretized profile by taking the mean of the window values at the same genomic position respectively . The thus processed sample profiles of Atf3 and control are finally utilized to generate a difference profile by taking the Euclidean difference between Atf3 and control values at the same genomic position . The obtained values of the difference profile are normally distributed and are located between -1 and 1 with a mean very close to 0 . To filter out non-informative difference regions the profile is cleaned by removing all values within the range of 4 times the standard deviation of the mean . Remaining difference regions which are adjacent to each other and that possess the same sign are merged . They either show enrichment for Atf3 or control . They are size filtered by taking the discretization window size as a minimal threshold . The resulting difference regions are finally tested for significance by a two-sided Kolmogorov-Smirnov test of the discretized region values of Atf3 and control and multiple testing corrected by Benjamini-Hochberg . We accepted differential regions with a q-value < 0 . 05 that are enriched in the Atf3 sample and extracted their DNA sequences with BEDTools . Peaks were assigned to genes when located within a gene or 1 kb upstream of the transcriptional start site . Multiple genes were manually associated with a peak in the case of nested genes or dense gene regions . Enriched regions mapping to positions within the CH321-51N24 BAC ( source of the atf3gBAC ) were discarded ( n = 59 ) , except for two peaks corresponding to the genes atf3 and CG11403 , which contained the Drosophila Atf3 motif . Sequences of regions bound by Atf3 were uploaded to MEME ( http://meme-suite . org/ ) for motif identification [43] . The positional weight matrix returned by MEME was submitted to TOMTOM ( http://meme-suite . org/tools/tomtom ) for interspecies motif comparison [44] . FlyBase gene ontology ( GO ) terms identified with FlyMine ( http://www . flymine . org , RRID: SCR_002694 ) were used for functional annotation [45] . A custom protocol combining the methods described in [74 , 76] was used to perform ChIP against Atf3 in the EADs . For each replicate , 100 EADs with mouth hooks attached were dissected from third instar larvae in ice-cold PBS . Discs were fixed at room temperature by gently mixing in 1 ml cross-linking solution ( 1 . 8% formaldehyde , 50 mM Hepes pH 8 . 0 , 1 mM EDTA , 0 . 5 mM EGTA , 100 mM NaCl ) , which was changed 3–4 times during fixation . Fixation was stopped by washing for 3 min in 1 ml PBS/0 . 01% Triton X-100/125 mM glycine 3–4 times . Fixed discs were washed for 10 min in 1 ml wash A ( 10 mM Hepes pH 7 . 6 , 10 mM EDTA , 0 . 5 mM EGTA , 0 . 25% Triton X-100 ) and subsequently for 10 min in 1 ml wash B ( 10 mM Hepes pH 7 . 6 , 200 mM NaCl , 1 mM EDTA , 0 . 5 mM EGTA , 0 . 01% Triton X-100 ) , with the wash solutions being changed 3–4 times over the 10 min . Eye discs were separated from the brain and mouth hooks and transferred into 550 μl of TBS Lysis Buffer ( see ChIP-seq protocol ) . Chromatin was sheared using an Active Motif EpiShear sonicator ( Active Motif , 53052 ) equipped with a 1 . 5 ml EpiShear Cooled Sonication Platform ( Active Motif , 53080 ) and 1 . 5 ml Benchtop Cooler ( Active Motif , 53076 ) . With Amplitude set to 50% , sonication was performed in cycles of 20 seconds on/30 seconds off for 20 minutes . Chromatin was centrifuged at 14 , 000 RPM for 10 min to clear debris , transferred into clean 1 . 5 ml tubes , and cleared overnight at 4°C with 20 μl sepharose-IgG beads equilibrated in TBS Lysis Buffer . Cleared chromatin was transferred to new 1 . 5 ml tubes and precipitated overnight at 4°C with 25 μl GFP Trap beads per tube . Beads were washed the indicated number of times with 1 ml of each of the following buffers at 4°C on a rotating wheel for 10 min: 1 × TBS Lysis Buffer , 4 × TBS 500 Lysis Buffer ( 50 mM Tris-Cl pH 7 . 8 , 500 mM NaCl , 1 mM EDTA pH 8 . 0 , 1% Triton-X100 , 0 . 01% IGEPAL CA-630 ) , 1 × LiCl buffer ( 250 mM LiCl , 1 mM EDTA , 0 . 5% IGEPAL CA-630 ( v/v ) , 10 mM Tris-HCl , pH 8 . 0 ) . Beads were transferred into clean 1 . 5 ml tubes and washed two times with TE for 10 min at 4°C on a rotating wheel . Beads were subsequently transferred into PCR tubes . Chromatin was decrosslinked and precipitated as described in the ChIP-seq protocol . Purified DNA was raised in 500 μl water . qPCR was performed in triplicate with BioRad 2x SYBR Green mix in the CFX96 real-time PCR system ( Bio-Rad , Hercules , CA ) . All primers were designed to anneal at 62°C ( see S1 Table 1 for oligonucleotide sequences ) . Data were normalized to amplification values for ecd . Fold enrichment was calculated using the ∆∆CT method [72] . RNA was isolated from FRT82B atf3wt mosaic EADs of third instar larvae as described [77] . Total RNA libraries were generated according to the Illumina protocol and single-end sequenced on an Illumina NextSeq 500 instrument at 75 bp read length . Image analysis and base calling were done with the Illumina RTA software at run time . Published sequence data from FRT82B mosaic EAD samples [11] were used as control . Data were processed using a high-throughput Next-Generation Sequencing analysis pipeline [78] . Basic read quality check was performed with FastQC ( v0 . 10 . 1 ) ( RRID: SCR_014583 ) and read statistics were acquired with SAMtools v0 . 1 . 19 ( RRID: SCR_002105 ) [79] . Reads were mapped to the Drosophila reference assembly ( version BDGP R5/dm3 , April 2006 ) using Tophat v2 . 0 . 10 ( RRID: SCR_013035 ) [80] , and gene quantification was carried out using a combination of Cufflinks v2 . 1 . 1 ( RRID: SCR_014597 ) [81] , and the DESeq2 package v1 . 10 . 1 ( RRID: SCR_000154 ) [82] , with genomic annotation from the Ensembl database ( RRID: SCR_002344 ) , version 84 . In all samples , the number of total reads exceeded 50 million , from which an average 83 . 6 percent could be mapped , and on average 97 . 5% of these mapped reads fulfilled the MAPQ≥30 criterion . The results were uploaded into an in-house MySQL database and joined with BiomaRt ( RRID: SCR_002987 ) v2 . 26 . 1 [83] annotations from Ensembl , version 84 . Lists of differentially expressed genes were defined by a final database export using 5 and 0 . 01 as cutoffs for DESeq2-based FCs and p-values , respectively . To identify genes differentially expressed under the respective conditions , the average of at least three biological replicates was calculated . The S3 Dataset shows all transcripts whose expression differed ≥ 1 . 5-fold in FRT82B atf3wt compared to FRT82B control . The experimentally derived Drosophila Atf3 PWM was submitted to the online FIMO utility ( http://meme-suite . org/tools/fimo ) ( RRID: SCR_001783 ) to identify Atf3 motifs in Drosophila melanogaster genome ( UCSC , dm3 ) , with a p-value threshold set at 0 . 0001 . FIMO results , in the form of a BED file , were subsequently submitted to the online PAVIS tool ( https://manticore . niehs . nih . gov/pavis2 ) , using default settings . To create the Atf3 expression reporter , oligonucleotides containing four intact or mutated Atf3 sites ( see S1 Table for sequences ) were cloned via Mlu1 and Not1 sites into the pRedRabbit and pGreenRabbit vectors [84] . To express N-terminally tagged GFP- or FLAG-Atf3 proteins from the UAST promoter , atf3 cDNA ( see S1 Table for oligonucleotide sequences ) was cloned into pENTR4 and subsequently recombined using LR Clonase II ( 11791–020 , Life Technologies ) into pTGW and pTFW vectors , respectively ( T . Murphy , Drosophila Genomic Resource Center ) . Schneider 2 ( S2 ) cells were cultured at 25°C in Shields and Sang M3 insect medium ( S8398-1L , Sigma-Aldrich ) containing 8% fetal bovine serum ( Gibco , Life Technologies ) without antibiotics . Cells were transfected using X-tremeGENE ( Roche Applied Science ) . Expression of UAS-driven genes was induced by co-transfection with a pWA-GAL4 plasmid expressing GAL4 under an actin5C promoter . Cells were fed 24 hours after transfection . Cells were imaged or lysed 72 hours after transfection . Tissues from third instar larvae were processed as described previously [5] . The following primary and secondary antibodies were used at the indicated dilutions: anti-Dlg1 ( RRID: AB_528203; 1:200 ) , anti-Fasciclin III ( RRID: AB_528238; 1:200 ) , anti-Elav ( RRID: AB_528217 and AB_528217; 1:200 ) , anti-DE-cad ( RRID: AB_528120; 1:200 ) , anti-Crumbs ( RRID: AB_528181; 1:200 ) , anti-p120-catenin ( RRID: AB_2088073; 1:200 ) , anti-α-Tubulin ( RRID: AB_579793; 1:200 ) , anti-β-Tubulin ( RRID: AB_2315513; 1:200 ) , anti-LaminC ( RRID: AB_528339; 1:500 ) and anti-βPS integrin ( RRID: AB_528310; 1:200 ) from the Developmental Studies Hybridoma Bank ( DSHB ) ( Iowa City , Iowa ) , anti-γ-Tubulin ( T6557 , Sigma Aldrich; 1:50 ) , anti-GFP ( G10362 , Invitrogen; 1:500 ) , anti-Rab5 ( ab31261 , Abcam; 1:200 ) , anti-Rab11 ( BD610656 , BD Biosciences; 1:200 ) , chicken-anti-Avl ( a gift from D . Bilder , 1:500 ) and Cy2 , Cy3- and Cy5-conjugated secondary antibodies ( Jackson Immunoresearch ) . Tissues were counterstained with DAPI ( 1 μg/ml ) and mounted in DABCO-Mowiol medium ( Sigma-Aldrich ) . Third instar imaginal discs were incubated in M3 media containing 0 . 5 mM MW3000 Texas Red dextran ( D3328; ThermoFisher Scientifc ) for 15 minutes ( pulse ) at 25°C . Discs were briefly washed 2 times in M3 media followed by incubation in M3 media for 60 minutes ( chase ) . Discs were subsequently fixed in 1% formaldehyde in PBS for 20 min , counterstained with DAPI , and mounted in DABCO-Mowiol medium ( Sigma-Aldrich ) . S2 cells were imaged using a DP72 camera mounted on an Olympus CK4X41 miscroscope , in conjunction with cellSens 1 . 1 Software ( RRID: SCR_014551 ) . Confocal images were acquired at room temperature using an Olympus FV1000 confocal microscope equipped with 20x UPlan S-Apo ( NA 0 . 85 ) , 40x UPlan FL ( NA 1 . 30 ) and 60x UPlanApo ( NA 1 . 35 ) objectives . Transversal sections were generated using Imaris 7 . 0 . 0 ( Bitplane ) ( RRID: SCR_007370 ) . Figure assembly and image brightness and contrast adjustments were done in Photoshop CS5 . 1 ( Adobe Systems , Inc . ) ( RRID: SCR_014199 ) . Z-stacks of adult eyes were taken using a motorized Leica M165 FC fluorescent stereomicroscope equipped with the DFC490 CCD camera . Images were processed using the Multifocus module of LAS 3 . 7 . 0 software ( Leica ) . Third instar larvae were collected and washed two times with PBS . EADs were dissected in PBS ( on average sixty EADs /replicate/genotype ) and transferred to 1 . 5 ml low binding microcentrifuge tubes ( no more than one hour before dissociation ) . PBS was removed from EADs and was replaced with 100 μl dissociation solution containing 1 mg/ml collagenase I ( Sigma , C2674 ) , and 1 mg/ml papain ( Sigma P4762 ) . Samples were incubated for 60 min at room temperature and gently swirled every 15 min . After the dissociation solution was removed , discs were carefully rinsed with 500 μl PBS , which was then replaced with 100 μl of PBS . The final dissociation was performed by passing the discs through a 27G insulin syringe ( Terumo ) five times . Additional 200 μl of PBS was added for a final volume of 300 μl ( 5 μl PBS per disc ) . Following sample filtration through a Filcone filter , propidium iodide was added to measure cell viability and samples were stored on ice until sorting . For each sample ( n≥3 per genotype ) , 30 , 000 events were counted on a BD LSRFortessa Cell Analyzer in combination with BD FACSDiva software v8 . 0 ( both BD Bioscience ) using gates set to distinguish GFP+ , GFP- , and PI- cells . Adult heads were fixed in 80% ethanol , postfixed with 1% osmium tetroxide , dehydrated in ethanol , critical point dried , gold coated , and observed under a JEOL JSM-7401F ( Tokyo , Japan ) 6300 scanning electron microscope . S2 cells were lysed in 50 mM Tris-HCl ( pH 7 . 8 ) , 150 mM NaCl , 1 mM EDTA ( pH 8 . 0 ) , 1% Triton X-100 , 0 . 01% Igepal , and protease inhibitors ( Roche Applied Science ) . Protein concentration was quantified using Pierce 660 reagent ( 22660 , Thermo Scientific ) according to manufacturer’s instructions . Following SDS-PAGE , proteins were detected by immunoblotting with anti-Flag M2 ( 1:1000 , Sigma Aldrich ) , anti-GFP ( 1:5000 , TP401 , Acris ) and anti-α-Spectrin ( 1:1000 , RRID: AB_528473 ) antibodies , followed by incubation with corresponding HRP-conjugated secondary antibodies ( Jackson Immuno Research ) . Chemiluminescent signal was captured using ImageQuant LAS4000 reader ( GE Healthcare ) . For sample size criteria , post hoc analysis of results presented in Figs 1 , S1 , S3 , S5 and S9 using G*Power 3 . 1 ( RRID: SCR_013726 ) [85] determined that the statistical power of all statistically significant differences ( 1-β ) exceeded 0 . 94 , given sample size , mean , and standard deviation for each condition . Raw data are available in S4 Dataset . All raw next generation sequencing data from FRT82B and FRT82B atf3wt mosaic EAD samples and the Atf3 ChIP-seq data are available from the Gene Expression Omnibus ( GEO ) database ( RRID: SCR_007303 ) under accession numbers GSE65261 , GSE89845 , GSE71206 . | Epithelial cells form sheets and line both the outside and inside of our body . Their proper development and function require the asymmetric distribution of cellular components from the top to the bottom , known as apicobasal polarization . As loss of polarity hallmarks a majority of cancers in humans , understanding how epithelia respond to a collapse of the apicobasal axis is of great interest . Here , we show that in the fruit fly Drosophila melanogaster the breakdown of epithelial polarity engages Activating transcription factor 3 ( Atf3 ) , a protein that directly binds the DNA and regulates gene expression . We demonstrate that many of the pathological consequences of disturbed polarity require Atf3 , as its loss in this context results in normalization of cellular architecture , vesicle trafficking and differentiation . Using unbiased genome-wide approaches we identify the genetic program controlled by Atf3 and experimentally verify select candidates . Given the evolutionary conservation of Atf3 between flies and man , we believe that our findings in the Drosophila model will contribute to a better understanding of diseases stemming from compromised epithelial polarity . |
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Genome Wide Association Studies ( GWAS ) and expression quantitative trait locus ( eQTL ) analyses have identified genetic associations with a wide range of human phenotypes . However , many of these variants have weak effects and understanding their combined effect remains a challenge . One hypothesis is that multiple SNPs interact in complex networks to influence functional processes that ultimately lead to complex phenotypes , including disease states . Here we present CONDOR , a method that represents both cis- and trans-acting SNPs and the genes with which they are associated as a bipartite graph and then uses the modular structure of that graph to place SNPs into a functional context . In applying CONDOR to eQTLs in chronic obstructive pulmonary disease ( COPD ) , we found the global network “hub” SNPs were devoid of disease associations through GWAS . However , the network was organized into 52 communities of SNPs and genes , many of which were enriched for genes in specific functional classes . We identified local hubs within each community ( “core SNPs” ) and these were enriched for GWAS SNPs for COPD and many other diseases . These results speak to our intuition: rather than single SNPs influencing single genes , we see groups of SNPs associated with the expression of families of functionally related genes and that disease SNPs are associated with the perturbation of those functions . These methods are not limited in their application to COPD and can be used in the analysis of a wide variety of disease processes and other phenotypic traits .
Genome Wide Association Studies ( GWAS ) have created new opportunities to understand the genetic factors that influence complex traits . Excepting highly-penetrant Mendelian disorders , the majority of genetic associations seem to be driven by many factors , each of which has a relatively small effect . In a recent study [1] , 697 SNPs were associated with height in humans at genome-wide significance , yet these SNPs were able to explain only ∼20% of height variability; ∼9 , 500 SNPs were needed to raise that to ∼29% . In addition , ∼95% of GWAS variants map to non-coding regions [2] , complicating biological interpretation of their functional impact . To bridge the functional gap between genetic variant and complex trait , expression Quantitative Trait Locus ( eQTL ) analysis associates SNP genotype with gene expression levels . The first empirical , genome-wide linkage study with gene expression in yeast was published in 2002 , linking expression levels of 570 genes to genetic loci [3] . In humans , loci have been associated with the expression of thousands of genes [2 , 4] , and eQTLs are enriched for phenotype associations and vice versa [5–7] . Most eQTL analyses have focused on cis-SNPs—those near the Transcriptional Start Site ( TSS ) of the gene in the association test . Recent computational developments [8] and work demonstrating the impact and replicability of trans-eQTLs [9 , 10] have increased interest in identifying and understanding the role played by trans-acting SNPs . However , new methods are needed to elucidate the potential functional impact of the thousands of GWAS SNPs and tens to hundreds of thousands of eQTL SNPs that can be detected in a single study . Here we present CONDOR , COmplex Network Description Of Regulators , ( Fig 1 ) a method that incorporates both cis- and trans- associations to identify groups of SNPs that are linked to groups of genes and systematically interrogate their biological functions . The method has been implemented as an R package and is publicly available at https://github . com/jplatig/condor . We then validate this approach using genotyping and gene expression data from 163 lung tissue samples in a study of Chronic Obstructive Pulmonary Disease ( COPD ) by the Lung Genomics Research Consortium ( LGRC ) .
We used the MatrixEQTL package in R to calculate cis- and trans-eQTLs , considering only autosomal SNPs , using age , sex , and pack-years as covariates ( see Methods ) . The cis- and trans- associations were run separately , with an FDR threshold of 10% . This analysis identified 40 , 183 cis-eQTLs and 32 , 813 trans-eQTLs . Quantile-quantile plots for both cis- and trans- are shown in Fig 2 . In total , 72 , 996 statistically significant associations were detected between 57 , 062 SNPs and 7 , 051 genes . We represented these associations as a bipartite network consisting of two classes of nodes—SNPs and genes—with edges from SNPs to the genes with which they are significantly associated based on the eQTL FDR cut-off . The network had a Giant Connected Component ( GCC ) with 41 , 813 links , 28 , 593 SNPs , and 3 , 091 genes . As a network diagnostic , we estimated whether or not we could reject the hypothesis that the SNP and gene degree distributions were power-law distributed . To test this , we fit each degree distribution to a power law , and determined the goodness of fit using the method described in [11] ( see Methods ) . If the edges from all connected components are considered , the p-value for the SNP degree is very low , Ppl ≈ 0 , suggesting that we can rule out a power law distribution . However , if very small connected components ( fewer than 5 SNPs and 5 genes ) are excluded , the SNP degree may follow a power-law ( Ppl < 0 . 8 ) as shown in Fig 3a . The gene degree distribution ( Fig 3b ) may be power-law distributed when considering all connected components or only those with more that 5 SNPs and 5 genes ( Ppl < 0 . 4 in both cases ) and there are multiple network hubs , shown in the tail of the distribution in Fig 3b . For our further analysis we considered all connected components with more than 5 SNPs and 5 genes . It is often cited in complex networks literature that the hubs , those nodes in the network that are most highly connected , represent critical elements whose removal can disrupt the entire network [12 , 13] . As a result , one widely-held belief about biological networks is that disease-related elements should be over-represented among the network hubs [14] . To test the hypothesis that disease-associated SNPs are concentrated in the hubs , we projected GWAS-identified SNPs associated with a wide range of diseases and phenotypes onto the SNP degree distribution ( Fig 4 ) . We used the gwascat package [15] in R to download GWAS SNPs annotated in the NHGRI GWAS catalog; 274 of those SNPs mapped to the eQTL network ( S1 Table ) . To our surprise , the network hubs—the right tail of Fig 4—were devoid of disease-associated SNPs which were instead scattered through the upper left half of the degree distribution . The difference in degree distributions did not appear to be driven by linkage disequilibrium or distance to nearest gene ( see Methods and S1 , S2 , S3 and S4 Figs ) . While the SNPs associated with a single gene are easier to interpret , the concentration of disease-associated SNPs in the middle of the distribution prompted us to look at other features of the network and its structure . Given the low phenotypic variance explained by any single GWAS SNP , we expected groups of SNPs to cluster with groups of functionally-related genes in our eQTL network . Unlike previous work [16–18] which imposes “known” pathway annotations and other data to posit the function of GWAS SNPs or identifies modules with only a handful of SNPs [19] , we used the structure of the eQTL network to identify densely connected groups of SNPs and genes and then tested those groups for biological enrichment . Our goal is the identification of those densely connected communities in the bipartite network . Methods for finding bicliques ( subgraphs with all-to-all connections within the larger bipartite network ) have been described for bipartite networks with a small number ( ∼102 ) of nodes in each connected component [20] . However , these methods do not scale to networks with connected components containing thousands of nodes [20 , 21] . Further , we do not expect biologically meaningful eQTL clusters to contain only all-to-all connections . To cluster our eQTL network , we adapted a well-established strategy [22] , community structure detection , which has been shown to scale well to large networks [23] . Many real-world networks have a complex structure consisting of “communities” of nodes [24] . These communities are often defined as a group of network nodes that are more likely to be connected to other nodes within their community than they are to those outside of the community . A widely used measure of community structure is the modularity , which can be interpreted as an enrichment for links within communities minus an expected enrichment given the network degree distribution [22] . To partition the nodes from the eQTL network into communities—which contain both SNPs and genes—we maximized the bipartite modularity [25] . As recursive cluster identification and optimization can be computationally slow , we calculated an initial community structure assignment on the weighted , gene-space projection , using a fast uni-partite modularity maximization algorithm [23] available in the R igraph package [26] , then iteratively converged ( ΔQ < 10−4 ) on a community structure corresponding to a maximum bipartite modularity . The bipartite modularity is defined in Eq ( 1 ) , where m is the number of links in the network , A ˜ i j is the upper right block of the network adjacency matrix ( a binary matrix where a 1 represents a connection between a SNP and a gene and 0 otherwise ) , ki is the degree of SNP i , dj is the degree of gene j , and Ci , Cj the community indices of SNP i and gene j , respectively ( see [25] for further details ) . Q = 1 m ∑ i , j A ˜ i j - k i d j m δ ( C i , C j ) ( 1 ) This analysis identified 52 communities across 10 connected components in the LGRC data , with 34 of those communities mapping to the GCC ( Qgcc = 0 . 79; Fig 5 ) . The density of these communities can be seen in Fig 5 . In Fig 5b , there is visible enrichment for links within each community ( colored links ) compared to links between different communities ( black links ) . These communities represent groups of SNPs and genes that are highly connected to each other and span multiple chromosomes ( see Fig 6 ) , suggesting that groups of genes may be jointly moderated by groups of SNPs that together represent specific biological processes . To investigate this hypothesis , we tested each community for GO term enrichment using Fisher’s Exact Test ( available in the R package GOstats [27] ) and found 11 of the 52 communities contained genes enriched for specific Gene Ontology terms ( see S2 Table ) ( P < 5e − 4; overlap >4 ) , encompassing a broad collection of cellular functions that are not generally associated with COPD . Indeed , this is what one might expect as the genetic background of an individual should have an effect not only on disease-specific processes , but more globally on the physiology of his or her individual cells . A number of communities do , however , show enrichment for biological processes that are known to be involved in COPD , including genes previously associated with the disease . For example , Community 29 ( see Fig 5 and S2 Table ) was enriched for chromatin and nucleosome assembly/organization and includes members of the HIST1H gene superfamily . Community 33 ( see Fig 5 and S2 Table ) included GO term enrichment for functions related to the HLA gene family , including T cell function and immune response; autoimmunity has been suggested as a potential contributor to COPD pathogenesis [28] . This community also contains PSORS1C1 , which has been previously implicated in COPD [29] . Another of the genes in Community 33 , AGER , has been implicated in COPD [30] and encodes sRAGE , a biomarker for emphysema . Its expression is negatively associated via eQTL analysis ( β = −0 . 3 ) with rs6924102 . This SNP has been observed to be an eQTL in a large blood eQTL dataset for a number of neighboring genes [9] , but it has not previously been described as an eQTL for AGER . This SNP lies in a region containing a DNase peak in cell lines analyzed by ENCODE [31] ( indicating it sits in a region of open chromatin ) and there is evidence of POLR2A binding from ChIP-Seq data in the GM12878 cell line as reported by ENCODE ( http://regulomedb . org/snp/chr6/32811382 ) . This suggests that rs6924102 may inhibit the expression of AGER through disruption of RNA Polymerase II binding and subsequent mRNA synthesis . This SNP is located ∼700KB from the well-studied non-synonymous AGER SNP , rs2070600 . Examining Fig 5a , it is evident that within each community there are local hubs that are highly connected to the genes within that community . While a wide array of network node metrics exist ( for example , [32 , 33] and references in [33] ) , most of these metrics are global measures that do not consider a node’s role in its local cluster/community and so may miss SNPs that are central to their communities and therefore likely to alter gene expression of functionally associated genes . Such within-community hubs have been observed in protein-protein interaction networks [34] and metabolic networks [35] . We defined a core score that estimates importance of a SNP in the structure of its community . For SNP i in community h , its core score , Qih , Eq ( 2 ) , is the fraction of the modularity of community h , Qh , Eq ( 3 ) , contributed by SNP i . This allows for comparison of SNPs from different communities , as each community does not have the same modularity , Qh . Q i h = 1 m ∑ j ( A ˜ i j − k i d j m ) δ ( C i , h ) δ ( C j , h ) Q h ( 2 ) Q h = 1 m ∑ i , j ( A ˜ i j − k i d j m ) δ ( C i , h ) δ ( C j , h ) ( 3 ) If one views disease as the disruption of a process leading to cellular or organismal dysfunction , one natural hypothesis is that SNPs with the greatest potential to disrupt cellular processes might be enriched for disease association . To test this we used both the Wilcoxon rank-sum and Kolmogorov-Smirnov ( KS ) tests to assay whether the 274 NHGRI GWAS-annotated SNPs in the network were more likely to have high Qih scores . For both tests , the distribution of Qih scores for GWAS-associated SNPs were compared to the distribution of non-GWAS SNP scores . To obtain an empirical p-value for these tests , we permuted the GWAS/non-GWAS labels and recalculated the KS and Wilcoxon tests 105 times . Histograms of the test statistics are shown in Figs 7 and 8 . The red dot in the histogram represents the test score with the true labeling . Both tests had highly significant permutation p-values , with P < 10−5 for the KS and Wilcoxon tests , indicating that GWAS SNPs were over-represented among SNPs with high core scores . Furthermore , the median core score for the GWAS SNPs was 1 . 74 times higher than the median core score for the non-GWAS SNPs . To test this result for dependence on Linkage Disequilibrium ( LD ) and gene distance , we reran the KS and Wilcoxon permutation tests with a subset of SNPs matching the LD structure and distance to nearest gene of the 274 GWAS SNPs ( see Methods for details ) . Neither the LD structure ( P < 0 . 001 for KS and Wilcoxon tests , S5 and S6 Figs ) nor distance from the nearest gene ( P < 0 . 001 for KS and Wilcoxon tests , S7 and S8 Figs ) of the GWAS SNPs was signficantly associated with the core score . Thus , while global hubs are devoid of GWAS associations with disease , local hubs within communities are significantly enriched for disease associations . As a way of further assessing the link between GWAS significance and functional perturbation in COPD , we calculated a GWAS-FDR for all SNPs clustered in our network that had a reported p-value from a recent GWAS and meta-analysis of COPD [36] ( see Methods ) . There were 30 SNPs with an FDR < 0 . 05 , and 28 of the 30 had evidence of functional impact according to RegulomeDB [37] , with 15 SNPs identified as likely to affect transcription factor binding and linked to expression ( See Fig 9 and S3 Table ) . These 30 SNPs mapped to 3 different communities ( see S3 Table ) including Community 33 , which contains other COPD-associated SNPs and genes , and is enriched for GO terms describing T cell function and immune response . One of the SNPs in this community likely to affect binding ( S3 Table ) is rs9268528 , which is linked by our network to HLA-DRA , HLA-DRB4 , and HLA-DRB5; the cis-eQTL associations between rs9268528 and both HLA-DRA and HLA-DRB5 have been previously observed in lymphoblastoid cells [38] . All three HLA genes lie in Community 33 and contribute to the community’s enrichment for T cell receptor signaling pathway ( GO:0050852 ) [39] . To determine the network influence of these 30 SNPs , we compared their core score , Qih , to the core scores of SNPs with a GWAS-FDR ≥ 0 . 05 ( See Fig 10 ) . The median Qih value for the 30 GWAS-FDR significant SNPs was 20 . 3 times higher than the median for SNPs with an FDR ≥ 0 . 05 . Using the KS and Wilcoxon tests described in the Methods , these core scores were not significantly associated with LD structure ( P < 0 . 001 , S9 and S10 Figs ) or distance to nearest GSS ( P < 0 . 001 , S11 and S12 Figs ) . Genome-wide association studies have searched for genomic variants that influence complex traits , including the development and progression of disease . However , the number of highly-penetrant Mendelian variants that have been found is surprisingly small , with most disease-associated SNPs having a weak phenotypic effect . GWAS studies have also identified many SNPs that do not alter protein coding and have found significant loci that are shared in common across multiple diseases . This body of evidence suggests that in most instances it is not a single genetic variant that leads to disease , but many variants of smaller effect that together can disrupt cellular processes that lead to disease phenotypes . The challenge has been to find these variants of small effect and to place them into a coherent biological context . We chose to address this problem by analyzing the link between genetic variants and the most immediate phenotypic measure , gene expression . In doing so , we chose not to focus solely on cis-acting SNPs , but also to consider trans-acting variants . Our motivation was , in part , to try to understand SNPs found through GWAS studies to be associated with phenotypes , but that could not be immediately placed into a functional context . After performing a genome-wide cis- and trans-eQTL analysis , we identified a large number of many-to-many associations: single SNPs associated with many genes as well as single genes that were significantly associated with many SNPs . To represent those associations , we constructed a bipartite network , one that contains two types of nodes—SNPs and genes—with edges connecting SNPs to the genes with which they were significantly associated . Our analysis of that network led to a number of observations that independently speak to our intuition about disease and the genetic factors that control it . First is the observation that the highly connected SNPs , the global hubs in the network , are devoid of variants that have been identified as being disease-associated in the hundreds of studies collected in the NHGRI GWAS catalog . While initially surprising , further consideration suggests that this may be the result of negative selection . Since a true hub SNP influences genes across the genome that are involved in many biological processes , highly disruptive variants that are hubs are likely to significantly affect cellular function . In fact , this is the expected impact of a hub—its disruption should lead to the catastrophic collapse of the network . And so , disruptive SNPs that would be network hubs are likely to be lethal or highly debilitating and therefore strongly selected against and quickly swept from the genome . Second , we found that SNPs and their target genes form highly connected communities that are enriched for specific biological functions . This too speaks to our inituition and to the evidence about polygenic traits that has accumulated over time . They are not the result of a single SNP that regulates a single gene , but a family of SNPs that together help mediate a group of functionally-related genes . Third , the enrichment for GWAS disease associations among the high core score SNPs has a very simple and intuitive interpretation . The SNPs that are most significantly connected within a particular functionally-related group are those most likely to disrupt that process and therefore be discovered in GWAS analysis . After all , diseases do not develop because the cell’s entire functionality collapses , but because specific processes within the cell are disrupted . What our analysis provides is a new way of exploring cis- and trans-eQTL analysis and GWAS . What one must do is to consider not only the local effects of genetic variants , but also the complex network of genetic interactions that help regulate phenotypes , including gene expression . This method also suggests a new way of filtering genes for inclusion in GWAS analysis . Since many disease-associated SNPs appear to be either cis-acting or those which are central to functionally-defined communities , one could focus on those SNPs most likely perturb specific biological processes rather than considering the entirety of SNPs in the genome . One might note that this analysis was carried out using data on genetic variation and gene expression from the LGRC representing COPD and control lung tissue and question both the generalizability of the results and the use of GWAS-associated disease SNPs from many diseases in the analysis . While these are potentially legitimate concerns , many of the community-based processes we find are not specific to COPD or to the lung but instead are active in nearly all human cell types . Although one might expect some processes to change in different disease states , the impact of common variants and the structure of the network is likely to be highly similar . Consequently , although there may be some SNPs whose impact is disease- and tissue-specific , many are likely to be independent of disease state . This suggests that it may be useful to develop eQTL networks across disease states and tissue types and to explore changes in the overall network and community structure across and between phenotypes due to rare variants and tissue-specific expression . Validating individual associations in the eQTL network is a difficult challenge . Most eQTL studies limit their validation efforts to downstream effects of high-confidence cis-acting eQTLs . The bipartite network presented here captures not only these strong cis-eQTLs but also the weak effects of many more cis- and trans-acting SNPs . So the likelihood that any individual association can be easily validated may not be that great , as it is likely to be of small phenotypic effect and important in only a subset of individuals . However , this is not the point . What is important for the phenotype is not any single SNP-gene association , but the “mesoscale” organization of genes and SNPs represented by the communities in the network . We believe this intermediate structure better reflects the aggregation of weak genetic effects that contribute to late-onset complex diseases . What we hope to have demonstrated in this manuscript is that the higher order structure , which was not an input to the network model , provides insight into a number of aspects of the genetics and manifestation of polygenic traits .
For each empirical degree distribution , we fit the two parameters for a power-law: the minimum degree at which the power-law behavior starts , dmin , and the exponent , α . A Kolmogorov-Smirnov test was then used to estimate the goodness of fit between 5 , 000 randomly generated power-law distributed synthetic data sets given dmin and α and their corresponding power-law fit . The p-value , Ppl , used to reject the power-law hypothesis is then the fraction of times a synthetic data set has a KS statistic larger than that of the true test . For both the SNP and gene degree distributions , Ppl was calculated using the 5 , 000 goodness of fit values ( code for the parameter estimation , goodness of fit and probability estimation was obtained from the website associated with [11] ) . To test the effect of LD and distance from Gene Start Site ( GSS ) on the degree distribution and core score ( Qih ) distribution of a set of GWAS SNPs , we created equivalently sized sets of SNPs that matched on a given characteristic of interest ( LD or GSS ) and compared that subset to all other SNPs . We repeated this process for each GWAS SNP set 1000 times . For the LD testing , we calculated LD blocks using the PLINK [41] “blocks” flag , estimating blocks using all SNPs that passed quality control . To achieve adequate sample sizes in the resampling , we binned LD blocks in 5kb windows , grouped all blocks >100kb into one bin and grouped all SNPs not in a block into one bin . For each resampling , the random set matched the GWAS set for both the LD bin and the number of SNPs in LD together within a block . As a proxy for the gene density of a region , we used each SNP’s distance from the nearest GSS . Distances were grouped into 1kb bins , with all distances >400kb grouped into one bin . The resampled sets were then matched on the GWAS SNP sets such that the number of SNPs in each bin was the same . | Large-scale studies have identified thousands of genetic variants associated with different phenotypes without explaining their function . Expression quantitative trait locus analysis associates the compendium of genetic variants with expression levels of individual genes , providing the opportunity to link those variants to functions . But the complexity of those associations has caused most analyses to focus solely on genetic variants immediately adjacent to the genes they may influence . We describe a method that embraces the complexity , representing all variant-gene associations as a bipartite graph . The graph contains highly modular , functional communities in which disease-associated variants emerge as those likely to perturb the structure of the network and the function of the genes in these communities . |
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Individual rapid tests for serodiagnosis ( RDT ) of human African trypanosomiasis ( HAT ) are particularly suited for passive screening and surveillance . However , so far , no large scale evaluation of RDTs has been performed for diagnosis of Trypanosoma brucei gambiense HAT in West Africa . The objective of this study was to assess the diagnostic accuracy of 2 commercial HAT-RDTs on stored plasma samples from West Africa . SD Bioline HAT and HAT Sero-K-Set were performed on 722 plasma samples originating from Guinea and Côte d’Ivoire , including 231 parasitologically confirmed HAT patients , 257 healthy controls , and 234 unconfirmed individuals whose blood tested antibody positive in the card agglutination test but negative by parasitological tests . Immune trypanolysis was performed as a reference test for trypanosome specific antibody presence . Sensitivities in HAT patients were respectively 99 . 6% for SD Bioline HAT , and 99 . 1% for HAT Sero-K-Set , specificities in healthy controls were respectively 87 . 9% and 88 . 3% . Considering combined positivity in both RDTs , increased the specificity significantly ( p≤0 . 0003 ) to 93 . 4% , while 98 . 7% sensitivity was maintained . Specificities in controls were 98 . 7–99 . 6% for the combination of one or two RDTs with trypanolysis , maintaining a sensitivity of at least 98 . 1% . The observed specificity of the single RDTs was relatively low . Serial application of SD Bioline HAT and HAT Sero-K-Set might offer superior specificity compared to a single RDT , maintaining high sensitivity . The combination of one or two RDTs with trypanolysis seems promising for HAT surveillance .
Human African trypanosomiasis ( HAT ) or sleeping sickness is a fatal parasitic infection affecting rural populations in sub-Saharan Africa . During the last decade , active case finding by specialized mobile teams has considerably contributed to the reduction of the prevalence of HAT caused by Trypanosoma brucei ( T . b . ) gambiense . Since 2009 , the number of cases reported annually has dropped below ten thousand . At low prevalence , cost-effectiveness of active screening decreases and passive case finding becomes increasingly important [1] . This shift from the mobile team to the fixed health system for HAT detection requires an adapted diagnostic approach . Detection of trypanosome specific antibodies in blood with the card agglutination test for trypanosomiasis ( CATT ) , [2] is routinely applied for large scale active population screening . CATT is however ill-adapted to the conditions encountered in health-care centers . The limited shelf-life of the reconstituted CATT reagent at ambient temperature leads to considerable reagent loss when only few tests are performed per day . Another limitation of the CATT is the need of an agitator and a cold chain and therefore electric power , which are not always available in rural health-care centers . The venue of individual rapid tests for serodiagnosis of HAT that are stable at ambient temperature and can be performed without additional material [3–5] , is a key event in the development of an effective passive screening and HAT surveillance system [6] . Two rapid diagnostic tests ( RDT ) have been evaluated in phase 2 diagnostic trials [5 , 7] , show sufficient diagnostic accuracy and have been commercialized . So far , all RDT diagnostic evaluations have been performed on samples originating from Central-Africa , and no large scale evaluation has been performed for diagnosis of gambiense HAT in West Africa , while geographic variation in the accuracy of HAT serodiagnostic tests may occur [8] . The objective of this study was therefore to assess the diagnostic accuracy of 2 RDTs on stored plasma samples collected from HAT cases , negative controls , and serological suspects originating from Guinea and Côte d’Ivoire , two countries where HAT transmission is still active [9 , 10] .
Samples were collected during medical surveys conducted by the national HAT control programs . All participants were informed about the study objectives in their own language and gave written informed consent . Children less than 12 years old were excluded . For participants between 12 and 18 years old , informed consent was obtained from the parents . Approval for this study was obtained from the consultative committee for deontology and ethics ( Comité Consultatif de Déontologie et d’Ethique ) of the Institut de Recherche pour le Développement . In Côte d’Ivoire , the protocol was approved by the national ethical committee ( N°0308/MSLS/CNER-P ) . Plasma samples originated from subjects identified during active screening campaigns in the Dubreka , Boffa and Forecariah coastal mangrove HAT foci , situated north of Conakry in the Republic of Guinea and in the HAT foci of Oumé , Bouaflé , Sinfra , and Bonon in western central Côte d’Ivoire . All subjects underwent CATT/T . b . gambiense performed on whole blood ( CATT-WB ) . Blood was collected in heparinised tubes and for CATT WB-positive persons , the plasma end titre was determined . All CATT-pl ≥1/4 positive persons underwent parasitological examination by direct microscopic examination of the lymph node aspirate if swollen lymph nodes were present and/or mini-anion exchange centrifugation technique on buffy coat ( mAECT-BC ) [11] . Based on the CATT and parasitological result , four categories of study participants ( n = 722 ) were defined: 1° HAT: Parasitologically confirmed HAT patients with positive CATT-WB and CATT-pl end titer ≥1/4 ( n = 229 from Guinea , n = 2 from Côte d’Ivoire ) ; 2°Control: CATT-WB negative individuals for whom there was no suspicion for sleeping sickness infection ( n = 101 from Guinea and n = 156 from Côte d’Ivoire ) ; 3° SERO: Individuals with positive CATT-WB and CATT-pl end titer ≥1/4 ( Seropositives ) but no parasites detected ( n = 123 from Guinea , n = 42 from Côte d’Ivoire ) ; 4° SUSP: Individuals with positive CATT-WB but CATT-pl <1/4 ( Suspects ) in whom parasitological examinations were not performed ( n = 69 from Côte d’Ivoire ) . Samples were retrospectively tested in two commercial RDTs for serodiagnosis of gambiense HAT: SD Bioline HAT ( SD Diagnostics , Korea ) and HAT Sero-K-Set ( Coris BioConcept , Belgium ) . Both tests use purified native variant surface glycoproteins of T . b . gambiense variable antigen types LiTat 1 . 3 and 1 . 5 as antigens: SD Bioline HAT in two separate test lines ( line 1 and 2 respectively ) , HAT Sero-K-Set in a single test line consisting of a mix of both glycoproteins . The methodology applied was previously described for evaluation of RDTs for malaria diagnosis [12] . Tests were performed according to the indications of the manufacturers . Briefly , for SD Bioline HAT , 10 μls of test plasma were applied in the sample well , followed by 4 drops of assay diluent . For HAT Sero-K-Set , 15 μls of plasma were applied in the sample well , followed by 2 drops of BL-A buffer , after which the test device was re-inserted into its pouch . Tests were performed in batches of 10 . Reading was done in day light , 15 minutes after application of the buffer . In case the control line did not appear , the test result was considered invalid . A scoring system was used for estimating the individual test line intensity: negative ( no visible test line ) , faint ( barely visible test line ) , weak ( test line weaker than the control line ) , medium ( test line equivalent to the control line ) or strong ( test line more intense than the control line ) [13] . Reading was performed by 3 independent readers that were blind to other results ( 2 experienced and 1 less experienced that had been trained ) . The consensus test line intensity was based on consensus between two readers . In absence of consensus ( 3 different scores ) , the median score was taken . The test line was interpreted positive if the consensus test line intensity was faint or stronger . The HAT Sero-K-Set was positive if the test line was positive , the SD Bioline HAT was considered as positive if at least 1 test line was read as positive . For immune trypanolysis [14 , 15] , 25 μl of plasma were mixed with 25 μl of guinea pig serum and incubated for 30 minutes at room temperature . Blood of mice infected with T . b . gambiense was diluted in guinea pig serum to a final concentration of 107 trypanosomes/ml . 50 μl of this trypanosome suspension were added . After 90 min of incubation at room temperature , the suspension was examined microscopically at 400x magnification . Trypanolysis was considered positive when 50–100% of the trypanosomes were lysed , otherwise it was considered negative . Two trypanolysis series were run , one with T . b . gambiense variable antigen type LiTat 1 . 3 and one with LiTat 1 . 5 . A sample was considered positive in trypanolysis if it was positive with at least 1 variable antigen type . Diagnostic sensitivity and specificity with binomial exact 95% confidence intervals ( CI ) were calculated for the results obtained in respectively the HAT and control group . Specificities and sensitivities were compared using the McNemar chi-square test . Differences between independent groups were assessed using a Chi squared test . Taking into account that the SERO and SUSP group are heterogeneous and might contain individuals that ( i ) are or have been in contact with T . b . gambiense but did not have detectable parasitemia , or ( ii ) are CATT false positives [16] , immune trypanolysis was used as a reference test for presence of T . b . gambiense specific antibodies [15] .
For both RDTs , not a single invalid RDT result was observed . The line intensities scored by the 3 readers as well as the consensus intensity are shown in Table 1 . In HAT Sero-K-Set , the consensus test line intensity was negative for 370 persons , and faint to strong for 352 persons whom were considered positive . Absence of a consensus intensity or differences between individual scores larger than one grade occurred in 1 . 4% of readings ( 10/722 ) . The kappa values for a positive or negative test result , when comparing each of the readers , were between 94 . 2 and 95 . 8% . The consensus test line intensity in SD Bioline HAT line 1 and 2 were respectively 362 and 363 times negative and 360 and 359 times positive . In respectively 3 . 8% ( 28/722 ) and 1 . 7% ( 12/722 ) of readings of line 1 and 2 , at least one reader scored more than 1 grade different than another reader . At least 1 of both test lines scored positive in SD Bioline HAT for 396 persons . Kappas between readers were 91 . 7–95 . 0% for line 1 , and 92 . 8–94 . 5% for line 2 . The number and proportion of positive test results by study participant category are summarized in Table 2 . Sensitivities observed in HAT patients were respectively 99 . 6% ( CI 97 . 6–100 ) for SD Bioline HAT , and 99 . 1% ( CI 96 . 9–99 . 9 ) for HAT Sero-K-Set . There was no difference in sensitivity ( p = 0 . 6 ) between the 2 RDTs . Specificities in healthy controls were respectively 87 . 9% ( CI 83 . 3–91 . 7 ) for SD Bioline HAT and 88 . 3% ( CI 83 . 8–92 . 0 ) for HAT Sero-K-Set . There was no difference in specificity between the lines 1 and 2 in SD Bioline HAT ( p = 0 . 2 ) , nor was there any difference in specificity between the 2 RDTs ( p = 0 . 8 ) . HAT Sero-K-Set was slightly more specific ( p = 0 . 04 ) on samples from Côte d’Ivoire ( 91 . 7% , CI 86 . 2–95 . 5 ) than those from Guinea ( 83 . 2 , CI 74 . 4–89 . 9 ) , while no difference was observed with SD Bioline HAT ( p = 0 . 7 ) . Considering combined positivity in both SD Bioline HAT and HAT Sero-K-Set , increased the specificity significantly to 93 . 4% ( CI 89 . 6–96 . 1 ) compared to the single RDTs ( p≤0 . 0003 ) , while high sensitivity was maintained ( p>0 . 16 ) . Sensitivity and specificity of immune trypanolysis were respectively 100% ( CI 98 . 4–100 ) and 95 . 7% ( CI 92 . 5–97 . 8 ) . Immune trypanolysis was significantly more specific than SD Bioline HAT and HAT Sero-K-Set ( p<0 . 0009 ) . However , no significant difference in specificity could be observed between immune trypanolysis and the combination of SD Bioline HAT with HAT Sero-K-Set ( p = 0 . 2 ) . In SD Bioline HAT , respectively 64 . 6% of SERO and 40 . 6% of SUSP tested positive . These percentages were respectively 50 . 9 and 13 . 0% for HAT Sero-K-Set , and respectively 47 . 9 and 10 . 1% for the combination of the 2 RDTs ( Table 2 ) . In immune trypanolysis , respectively 46 . 7 and 4 . 3% of SERO and SUSP were positive . Thus , significantly more SERO tested RDT or trypanolysis positive than SUSP ( p≤0 . 001 ) . With HAT Sero-K-Set or trypanolysis a similar proportion of SUSP and controls were positive ( p≥0 . 1 ) , while significantly more SUSP than controls tested positive in SD Bioline HAT ( p<0 . 001 ) . Immune trypanolysis is considered to be the reference test for presence of trypanosome specific antibodies and T . b . gambiense contact . SD Bioline HAT and HAT Sero-K-Set were positive in respectively 93 . 5% ( 301/322 , CI 90 . 2–95 . 9 ) and 94 . 4% ( 304/322 , CI 91 . 3–96 . 7 ) of immune trypanolysis positive persons . In immune trypanolysis positives , there was no significant difference between both RDTs in number of positives ( p = 0 . 4 ) . In immune trypanolysis negative persons , respectively 76 . 3% ( 305/400 , CI 71 . 8–80 . 3 ) and 88 . 0% ( 352/400 , CI 84 . 4–91 . 0 ) were negative in SD Bioline HAT and HAT Sero-K-Set . In this group , HAT Sero-K-Set was significantly more negative than SD Bioline HAT ( p<0 . 0001 ) . Table 3 shows test line 1 and test line 2 results for SD Bioline HAT compared to trypanolysis with the corresponding variable antigen type , respectively LiTat 1 . 3 and LiTat 1 . 5 . In SD Bioline HAT , line 1 and 2 were significantly more positive than the corresponding variable antigen type in immune trypanolysis ( p≤0 . 0001 ) . Among the samples that were trypanolysis negative for both LiTat 1 . 3 and LiTat 1 . 5 , there was no significant difference in test line 1 or 2 positivity , nor was there in trypanolysis positive samples ( p values of 0 . 2 ) . Table 4 shows the number of positives in one or both RDTs combined with trypanolysis , considering only those subjects positive that are positive in all individual tests . Sensitivities in HAT patients were respectively 99 . 6% ( CI 97 . 6–100 ) for the combination SD Bioline HAT and trypanolysis , 99 . 1% ( CI 96 . 9–99 . 9 ) for HAT Sero-K-Set combined with trypanolysis , and 98 . 7% ( CI 96 . 3–99 . 7 ) for the combination of the 2 RDTs with trypanolysis . There was no difference in sensitivity between the 3 different test combinations ( p>0 . 2 ) . Specificities in controls were respectively 98 . 8% ( CI 96 . 6–99 . 8 ) for the combination SD Bioline HAT and trypanolysis , 98 . 1% ( CI 95 . 5–99 . 4 ) for HAT Sero-K-Set combined with trypanolysis , and 99 . 2% ( CI 97 . 2–99 . 9 ) for the combination of the 2 RDTs with trypanolysis . No significant differences were observed between the specificities of the different test combinations ( p>0 . 08 ) . However , the combination of one or 2 RDTs with immune trypanolysis was more specific than one or 2 RDTs without immune trypanolysis ( p<0 . 005 ) . The combination of one or two RDTs with trypanolysis was positive in 38 . 2–41 . 2% of SERO , 2 . 9% of SUSP ( Table 4 ) . Again , significantly more SERO tested positive than SUSP ( p≤0 . 001 ) , while a similar proportion of SUSP and controls were positive ( p≥0 . 6 ) .
This is the first study to report on HAT diagnostic accuracy on a large number of samples originating from West Africa , and also the first to perform both commercially available RDTs for serodiagnosis of HAT on the same sample set . Although sensitivity of the two tested RDTs for serodiagnosis of HAT in West Africa was high , specificity remained limited to 88% . Specificity significantly increased to 93% considering combined seropositivity in both RDTs . Using a combination of one or two RDTs with trypanolysis further improved specificity to 99% while maintaining sensitivity at 99% . For interpretation of the results , a selection bias caused by routine screening of the population at risk using the CATT test should be taken into account . This could result in an overestimation of test sensitivity and specificity , as CATT consists of whole fixed and stained trypanosomes of the LiTat 1 . 3 variable antigen type and the corresponding purified native VSG is one of the two antigens used in both RDTs as well . Furthermore the evaluation was done on stored plasma samples and not on fresh whole blood . We cannot exclude that this could influence the test results , although antibodies are well conserved after freezing . Subjectivity of scoring of the RDT test result was largely eliminated by the use of 3 independent readers . Absence of a consensus intensity or the occurrence of large differences between scores , were not frequent but can be explained by a non-uniform coloration of the test line . The RDT specificities around 88% observed in this study are close to the 87% specificity mentioned in the SD Bioline HAT test instructions ( version 53FK10–04-En-0 ) but below the previously observed specificities of 98 . 6% for HAT-Sero-K-Set [7] and of 94 . 6% for a SD Bioline HAT prototype [5] . Specificity of both RDTs was also below the 98 . 7% specificity of CATT on whole blood previously reported in West Africa [15] . Possible explanations could be regional differences [8] , cross reaction with other infections or superior challenge by animal trypanosomes to cause false positive reactions [15] , or other . Although immune trypanolysis has been considered 100% specific for HAT [15] , 4 . 3% of controls tested positive . It is not clear if this is due to false positivity , if previously treated HAT cases who did not declare themselves were included as controls , or if they were trypanotolerant individuals who became negative in CATT but remained immune trypanolysis positive [17] . The phenomenon of immune trypanolysis positive , CATT negative healthy controls requires further examination . Taking into account the high number of false positive test results observed , we examined the possible performance of combined positivity in both RDTs for diagnosis of HAT , taking the example of the strategy of serial testing applied with RDTs for diagnosis of HIV [18] . Although both RDTs actually available for serodiagnosis of HAT are based on identical antigens , considering combined positivity significantly increased specificity and reduced the number of false positives by almost half . Serial application of SD Bioline HAT and HAT Sero-K-Set could therefore be considered as an option for passive case finding , as long as no second generation RDTs for serodiagnosis of HAT are available based on different antigens . However , as the combined specificity of 93 . 4% is still suboptimal , the local context , on-site availability of parasitological confirmation tests and the relative cost should be taken into account when deciding on test algorithms . For surveillance of HAT , RDTs are actually being implemented in fixed health centres . In case of clinical suspicion and a positive RDT , and depending on the experience of the health centres in HAT diagnosis , local prevalence , and availability of sensitive confirmation diagnostic tests , blood on filter paper is sampled and sent to a reference centre for immune trypanolysis , either directly , or after unsuccessful parasitological examination . Those persons with a trypanolysis positive result are considered at high suspicion for infection , should be ( re- ) examined parasitologically and followed-up closely . Although in this study stored plasma samples were used for immune trypanolysis instead of filter paper , our results show the potential high diagnostic accuracy of a combined RDT-trypanolysis approach . In the final result no difference in accuracy occurred when combining one or two RDTs followed by trypanolysis . However , the serial application of two RDTs may present considerable advantages . The number of unnecessary parasitological examinations may be significantly reduced as well as the number of filter papers to be dispatched and tested in trypanolysis . Use of filter paper instead of plasma for immune trypanolysis , may further decrease of the number of trypanolysis positive SERO and SUSP individuals [19] thus further decrease the number of people to be followed up . Our data suggest that the specificity of actual RDTs for serodiagnosis of HAT might be lower than expected . Care should therefore be taken in interpretation of the result , especially since the future use of RDTs alone , without parasitological confirmation , for patient management has already been suggested [20] . Serological screening using serial application of SD Bioline HAT and HAT Sero-K-Set might offer superior specificity compared to a single RDT , maintaining high sensitivity . The combination of one or two RDTs with trypanolysis seems promising for HAT surveillance . However , the diagnostic accuracy and especially the specificity of applying a combination of RDTs on fresh blood for HAT diagnosis , without prior CATT selection , remains to be determined as well as their combination with trypanolysis on filter paper , not only in West Africa but also in Central Africa . | Screening for gambiense human African trypanosomiasis ( HAT ) or sleeping sickness is traditionally based on detection of trypanosome specific antibodies in blood . Whereas the card agglutination test is particularly suited for mass screening , individual rapid serodiagnostic tests ( RDTs ) are rather adapted for use in peripheral health-care centres . Two RDTs have been commercialized recently , and we assessed their diagnostic accuracy on stored plasma samples from West Africa . Immune trypanolysis was performed as a laboratory reference test for antibody presence . Although sensitivity for serodiagnosis of HAT in West Africa was high for both RDTs , their specificity was only 88% . Taking into account the high number of false positive test results , combined seropositivity in both RDTs was considered , raising specificity to 93% . Serial application of two RDTs should therefore be considered as an option for passive case finding , especially in settings with low HAT prevalence . A combination of one or two RDTs with immune trypanolysis further improved specificity for HAT to 99% , while maintaining sensitivity at 99% and seems promising for HAT surveillance . |
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Individual synapses vary significantly in their neurotransmitter release properties , which underlie complex information processing in neural circuits . Presynaptic Ca2+ homeostasis plays a critical role in specifying neurotransmitter release properties , but the mechanisms regulating synapse-specific Ca2+ homeostasis in the mammalian brain are still poorly understood . Using electrophysiology and genetically encoded Ca2+ sensors targeted to the mitochondrial matrix or to presynaptic boutons of cortical pyramidal neurons , we demonstrate that the presence or absence of mitochondria at presynaptic boutons dictates neurotransmitter release properties through Mitochondrial Calcium Uniporter ( MCU ) -dependent Ca2+ clearance . We demonstrate that the serine/threonine kinase LKB1 regulates MCU expression , mitochondria-dependent Ca2+ clearance , and thereby , presynaptic release properties . Re-establishment of MCU-dependent mitochondrial Ca2+ uptake at glutamatergic synapses rescues the altered neurotransmitter release properties characterizing LKB1-null cortical axons . Our results provide novel insights into the cellular and molecular mechanisms whereby mitochondria control neurotransmitter release properties in a bouton-specific way through presynaptic Ca2+ clearance .
Neurotransmitter release properties vary greatly between presynaptic terminals of different neurons , but also between presynaptic release sites of the same neuron . At nerve terminals , rapid calcium ( Ca2+ ) influx through voltage-gated Ca2+ channels ( VGCC ) triggers exocytosis of neurotransmitter vesicles on a sub-millisecond timescale . Over the past two decades , significant progress has been made in understanding how calcium sensors , synaptotagmins , drive vesicle exocytosis by binding to phospholipids and SNARE machinery [1–4] . Interestingly , several studies revealed that action potential ( AP ) -evoked presynaptic Ca2+ signals can also vary drastically between different boutons along the same axons [5–8] . For example , in cortical pyramidal neurons , individual presynaptic release sites distributed along a single axon have different patterns of Ca2+ dynamics and neurotransmitter release probability depending on the postsynaptic target cells [5 , 9–13] . However , the cellular and molecular pathways regulating Ca2+ dynamics in a synapse-specific way are poorly understood . In various cell types , mitochondria perform critical biological functions , including ATP production through oxidative phosphorylation , Ca2+ clearance , and lipid biogenesis [14 , 15] . These pathways have been intensely studied in non-neuronal cells and also in the context of neurodegeneration [16] , but the roles of mitochondria during neuronal development and physiological synaptic function in adult axons are still poorly understood . It has been suggested that mitochondria are involved in presynaptic Ca2+ clearance , but the impact on modulation of neurotransmitter release varies in different species and neuron subtypes [17–24] . Mitochondria can also regulate presynaptic release properties through their metabolic functions [25] . Nevertheless , the signaling pathways regulating presynaptic mitochondrial function in this context are largely unknown . We and others identified that the serine/threonine kinase LKB1 ( Liver Kinase B1 also called STK11 or Par4 ) is a master regulator of axon morphogenesis in the mammalian central nervous system ( CNS ) . LKB1 is necessary and sufficient for axon formation in long-range projecting , cortical pyramidal neurons [26 , 27] . More recently , we also found that at later stages of axon development , LKB1 plays an essential role in terminal axon branching in vivo by regulating presynaptic mitochondria capture at nascent boutons [28] . These results raised a central unresolved question regarding the relevance of presynaptic mitochondria in axon morphogenesis . Here , we report that the presence or absence of presynaptic mitochondria represent a key component of presynaptic Ca2+ homeostasis and neurotransmitter release properties in a synapse-specific manner . In addition , we identified that the LKB1 kinase controls presynaptic Ca2+ homeostasis through regulation of the abundance of the mitochondrial calcium uniporter ( MCU ) . Disruption of this signaling pathway leads to increased presynaptic Ca2+ accumulation and drastic changes in neurotransmitter release properties , including ( 1 ) increased rate of spontaneous vesicle fusion , ( 2 ) augmentation of asynchronous mode of evoked neurotransmitter release , ( 3 ) abrogation of short-term synaptic depression during trains of action potentials ( APs ) , and ( 4 ) an increase in the frequency of action potential burst firing . Our results identify a new LKB1-dependent signaling pathway regulating neurotransmitter release properties in neurons through the control of mitochondria-dependent presynaptic Ca2+ clearance .
Mitochondria are associated with about half of presynaptic sites in axons of mature pyramidal cortical neurons ( S3D Fig ) [21 , 29] . In order to study the function of mitochondria in Ca2+ homeostasis at individual presynaptic boutons , we used a genetically encoded calcium sensor , GCaMP5G [30] , fused with vesicular glutamate transporter 1 ( vGlut1; SLC17A7 ) , a transmembrane protein enriched at the membrane of presynaptic vesicles in cortical pyramidal neurons ( S1A and S1C Fig ) . This vGlut1-GCaMP5G fusion protein has improved sensitivity for monitoring presynaptic cytoplasmic Ca2+ levels compared to non-targeted , cytosolic GCaMP5G ( S2 Fig ) . This plasmid was introduced with mitochondrial-targeted blue fluorescent protein mTagBFP ( mito-mTagBFP ) and vGlut1-mCherry via ex utero cortical electroporation ( EUCE ) at E15 . 5 . Following dissociation and plating , cortical neurons were imaged at 15–18 d in vitro ( DIV ) under evoked APs using a concentric bipolar electrode . This system allowed us to measure presynaptic , cytoplasmic Ca2+ ( [Ca2+]c ) dynamics at individual presynaptic boutons associated or not with mitochondria along the same axon segments ( Fig 1A ) . Interestingly , using this approach , we found that during moderate trains of AP stimulation ( 20 AP at 10 Hz ) , the peak intensity of vGlut1-GCaMP5G signals and the total charge transfer ( area under curve ) were significantly increased at presynaptic sites not associated with mitochondria ( Fig 1B–1E ) compared to presynaptic boutons associated with mitochondria along the same axon segments . Similar results were observed following 100 AP stimulation ( Fig 1F–1H ) , as well as a whole range of stimulation conditions ( 10 AP , 20 AP , 50 AP; S3 Fig ) . These data suggest that differential presynaptic Ca2+ dynamics characterizing individual synapses along the same axon could be influenced by the presence or absence of presynaptic mitochondria . Also , these results suggest that Ca2+-dependent presynaptic release properties such as asynchronous release and short-term synaptic plasticity could be regulated by the presence or absence of mitochondria at specific presynaptic boutons along cortical axons . Presynaptic Ca2+ triggers neurotransmitter release through Ca2+-dependent vesicle exocytosis . Therefore , one prediction of the elevated presynaptic Ca2+ accumulation at mitochondria-free boutons is that it could lead to increased neurotransmitter release properties . In order to monitor synaptic vesicle release at individual presynaptic boutons , we employed a recently developed fusion protein made with the pHluorin tag fused to luminal domain of Synaptophysin ( syp-pHluorin [31] ) . pHluorin is a pH-sensitive green fluorescent protein ( GFP ) whose fluorescence is quenched inside the lumen of a synaptic vesicle due to the acidic pH , and fluorescence is induced when it is exposed to the extracellular pH following exocytosis [32–35] . This probe allowed us to measure neurotransmitter release at individual presynaptic boutons along axons of layer 2/3 cortical neurons . We expressed syp-pHluorin , synaptophysin-mCherry , and mito-mTagBFP in cortical neurons by ex utero electroporation , and at 15-18DIV , and imaged syp-pHluorin signals using the same stimulation protocol as described above ( 20 AP or 100 AP at 10 Hz ) . Then , the maximum syp-pHluorin signal was measured for normalization following application of 50 mM NH4Cl application ( Fig 2A and 2B ) . This normalization is required because individual presynaptic responses vary greatly along the axons , and de-acidification by NH4Cl normalizes not only for potential difference in syp-pHluorin expression levels but also total pool size of individual boutons ( [36]; and see S4 Fig ) . Therefore , using this standard normalization approach , one can reliably estimate the amount of neurotransmitter vesicle exocytosis as a fraction of the total pool size at individual boutons ( S4 Fig ) . Strikingly , mitochondria-free presynaptic boutons showed significantly increased synaptic vesicle fusion compared to presynaptic boutons associated with mitochondria following both types of repetitive stimulation ( both 20 AP or 100 AP at 10 Hz; Fig 2C–2F ) . The peak of normalized syp-pHluorin intensity was 50%–80% higher at mitochondria-free presynaptic boutons compared to presynaptic boutons associated with mitochondria . These results are in agreement with the higher cytoplasmic Ca2+ levels measured with vGlut1-GCaMP5G ( see Fig 1 ) observed at mitochondria-free presynaptic boutons compared to mitochondria-associated presynaptic boutons . Overall , these data suggest a model whereby the presence of mitochondria is associated with significant differences in neurotransmitter release properties in a bouton-specific manner possibly through mitochondria-dependent Ca2+ clearance . Mitochondria uptake of cytoplasmic calcium is mediated through the mitochondrial calcium uniporter ( MCU; also called ccdc109a ) , an ion channel selective for Ca2+ and present in the inner mitochondrial membrane [37–39] . In order to test more directly if the presence of mitochondria at specific presynaptic bouton and in particular if MCU-dependent mitochondrial Ca2+ import is required acutely for proper presynaptic Ca2+ homeostasis in cortical axons , we first took a pharmacological approach . We tested if acute inhibition of MCU function leads to increased [Ca2+]c accumulation during synaptic transmission . To do this , we examined the effects of Ru360 , the membrane permeable MCU inhibitor [40] , on mitochondrial [Ca2+]m and presynaptic [Ca2+]c dynamics . In order to monitor Ca2+ dynamics inside the mitochondrial matrix ( [Ca2+]m ) , we fused GCaMP5G to a short mitochondrial targeting peptide isolated from cytochrome c subunit VIII ( mito-GCaMP5G ) ( S1B and S1D Fig ) . Together with vGlut1-GCaMP5G , we observed that acute application of Ru360 ( 10 μM , 3 min pre-incubation before imaging ) significantly reduced mito-GCaMP5G signals and increased vGlut1-GCaMP5G signals during 100 AP stimulation ( Fig 3A–3E ) , suggesting that MCU function is required for proper presynaptic [Ca2+]c clearance under sustained forms of evoked neurotransmitter release . The serine-threonine kinase LKB1 plays an important role in axon morphogenesis through regulation of presynaptic mitochondria capture [28] . Therefore , we tested if LKB1 also regulates mitochondrial function in more mature cortical neurons . First , we examined the expression level of MCU in the brain of LKB1-deficient mice . We used a pyramidal neuron-specific NexCre mouse line [41] which induces recombination throughout the dorsal telencephalon including the hippocampus and neocortex during development in vivo [28] . In order to probe a region of the forebrain with reduced cell type diversity , we collected hippocampi from P17–21 NexCre;LKB1F/F cKO . Subunit III of cytochrome c oxidase ( COXIII ) , a component of the mitochondrial electron transport chain , and the voltage-dependent anion channel ( VDAC ) on the outer mitochondrial membrane did not show altered expression by quantitative western blot ( Fig 3F–3J ) and COXIII was therefore used to normalize MCU expression levels ( Fig 3G ) . Interestingly , MCU expression level was significantly reduced in NexCre;LKB1F/F cKO compared to control littermates ( Fig 3G and 3K ) . These results suggest that LKB1 might regulate presynaptic Ca2+ homeostasis through mitochondrial Ca2+ import during synaptic transmission . We recently showed that LKB1 regulates axon branching by controlling presynaptic mitochondrial capture during early stages of axon morphogenesis in vitro ( 5–7 DIV ) and in vivo ( P7–P15 ) . However , we found that at later stages of axon development i . e . after the peak of axon branching and synaptogenesis , mitochondria are being successfully captured at presynaptic sites in LKB1-null axons ( S5 Fig ) . Along mature cortical axons ( 15–18 DIV ) , the same proportion of mitochondria are located at presynaptic boutons in both control and LKB1-deficient axons ( 39% +/- 2 . 9 in control; 44% +/- 4 . 5 in LKB1-null axons ) , and ~50% of vGlut1+ presynaptic boutons are occupied by mitochondria in layer 2/3 cortical neurons of both genotypes ( S5 Fig ) [42] . In order to compare the function of presynaptic mitochondria in the regulation of [Ca2+]c homeostasis in control and LKB1-deficient cortical neurons , we restricted our analysis to presynaptic sites occupied by mitochondria . We next tested if conditional deletion of LKB1 in postmitotic cortical neurons using NexCre affected presynaptic development using an in vitro heterologous assay where presynaptic boutons are induced by Neuroligin-1 expression in COS7 cells [43] ( S6A and S6B Fig ) or in vivo using presynaptic reporter expression in layer 2/3 pyramidal neurons ( S6E–S6H Fig ) . We found that LKB1-deficient axons form vGlut1-positive presynaptic boutons at the same density and similar size onto Neuroligin-1-expressing COS7 cells compared to control axons ( S6C and S6D Fig ) . As previously reported [28] , LKB1 is required for terminal axon branching thereby affecting the total number of presynaptic boutons made by axons of layer 2/3 pyramidal neurons in vivo ( S6E Fig ) . However , LKB1 deletion does not affect the linear density of presynaptic boutons along the remaining axon branches ( S6F–S6H Fig ) . We conclude that LKB1 is required for terminal axon branching but is not required for presynaptic development at early [28] or late stages of cortical axon maturation ( S6 Fig ) . Then , we tested if the decreased MCU level observed in LKB1 mutants leads to defective mitochondrial calcium import ( [Ca2+]m ) . To this end , three vectors encoding mito-GCaMP5G ( [Ca2+]m ) , a presynapse-targeted mCherry ( vGlut1-mCherry ) and Cre recombinase were introduced into the cortex of LKB1F/F mouse embryos [26 , 44] via ex utero cortical electroporation at E15 . 5 . At 15–18 DIV we monitored intra-mitochondrial calcium dynamics ( [Ca2+]m ) by time-lapse microscopy when presynaptic release was induced using the stimulation protocol described above ( 100 AP at 10 Hz ) . Interestingly , LKB1-null axons showed a significant reduction in peak intensity of mito-GCaMP5G signal ( Fig 4A–4C ) and the total amount of Ca2+ imported inside the mitochondrial matrix ( total [Ca2+]m calculated as the area under the curve in Fig 4D ) compared to control . Next , we tested whether the decrease of MCU expression observed in LKB1-null neurons causes altered presynaptic [Ca2+]c homeostasis . To this aim , we validated that a plasmid expressing MCU-HA is properly targeted to mitochondria in cortical axons ( S7 Fig ) . We identified a low level of MCU expression ( 0 . 2 mg/ml ) in LKB1-null cortical neurons that restored the peak intensity of [Ca2+]m following 100 AP stimulation back to control levels ( Fig 4B and 4C ) and also caused significant increase of total [Ca2+]m compared to control ( Fig 4D ) . We next tested if mitochondria-dependent presynaptic calcium dynamics is regulated by LKB1 . Using co-expression of vGlut1-GCaMP5G ( [Ca2+]c ) and a mitochondria-targeted mCherry ( mito-mCherry ) allowed us to monitor presynaptic cytoplasmic Ca2+ ( [Ca2+]c ) dynamics during neurotransmission . We augmented excitatory synaptic network activity in these cortical cultures by applying a GABAA receptor antagonist , picrotoxin . Interestingly , we found that LKB1-null axons show a significant increase in the maximal intensity of [Ca2+]c during spontaneous bursts of presynaptic release compared to presynaptic sites in control axons ( S8 Fig ) . Furthermore , [Ca2+]c decay kinetics ( t1/2 ) was more than two times slower in presynaptic sites of LKB1-null axons compared to control ( S8D Fig ) . In these conditions , when cultured neurons are subject to spontaneous activity , the observed increased decay time of presynaptic [Ca2+]c might be due to prolonged burst width observed during spontaneous network activity using electrophysiological recordings ( S11 Fig ) . These results suggest a defect in presynaptic Ca2+ clearance in LKB1-null axons compared to control . Since picrotoxin-induced bursts of network activity are triggered by variable and unknown numbers of APs , we further examined presynaptic [Ca2+]c dynamics during induction of repetitive APs using electrical stimulation . In this case , network responses were blocked with glutamate receptor antagonists , CNQX and APV , and presynaptic Ca2+ levels were measured during trains of 100APs as shown in S1 Fig . Again , under these conditions , presynaptic [Ca2+]c elevated to significantly higher levels in LKB1-null axons compared to control axons ( Fig 4E–4G ) . In addition , although wild-type LKB1 rescued the impaired presynaptic [Ca2+]c , but two catalytically inactive forms of LKB1 ( LKB1D194A and LKB1K78A ) did not restore the phenotype ( S9 Fig ) . These results demonstrate that LKB1 signaling is required for proper presynaptic [Ca2+]c clearance . More interestingly , MCU overexpression rescued [Ca2+]c elevation in LKB1-deficient cortical neurons back to control levels ( Fig 4E–4G ) . In addition , LKB1-null neurons showed no difference between mitochondria-free and mitochondria-associated presynaptic [Ca2+]c , which are significantly higher than mitochondria-associated [Ca2+]c of wild type ( WT ) neurons ( Fig 5 ) . These results suggest that , unlike in control WT neurons , in LKB1-null neurons , presynaptic [Ca2+]c dynamics are not different between boutons associated with mitochondria or not . Therefore , we conclude that LKB1 regulate presynaptic [Ca2+]c homeostasis through mitochondria-dependent calcium buffering and that MCU is a central effector for presynaptic [Ca2+]c homeostasis downstream of LKB1 in axons of cortical pyramidal neurons . Since presynaptic Ca2+ influx through VGCC plays a critical role in vesicle exocytosis and recycling [1–4] , we reasoned that the aberrant presynaptic Ca2+ homeostasis observed in LKB1-null axons should lead to strong defects in neurotransmitter release . Based on published evidence , elevation of resting and residual presynaptic [Ca2+]c levels should lead to ( 1 ) increased frequency of spontaneous miniature Excitatory Post-Synaptic Currents ( mEPSCs ) , ( 2 ) augmentation of “slow” asynchronous release , and ( 3 ) altered facilitation/depression of release during high-frequency stimulation [12 , 45–49] . We tested all three predictions using electrophysiological approaches . Dissociated LKB1F/F cortical neuron cultures were infected with ΔCre cDNA lacking the enzymatic domain of Cre recombinase or full-length Cre-encoding lentiviruses at 3–5 DIV . Spontaneous synaptic currents were monitored in whole-cell voltage-clamp mode at 15–18 DIV in dissociated cortical pyramidal neurons . First , we measured mEPSC rates ( Fig 6A–6D ) . Loss of LKB1 significantly increased mEPSC frequency , reflected by 25% decrease in inter-event interval compared to control ( Fig 6A , 6B and 6D ) . This effect was reversed following brief incubation with a low-affinity , membrane-permeable , calcium chelator EGTA-AM ( 100 μM for 3–5 min ) ( Fig 6C and 6D ) , suggesting that the increased frequency of spontaneous release observed in LKB1-deficient axons is linked to accumulation of [Ca2+]c rather than an increase in synapse numbers . Next , we examined the properties of evoked EPSCs that were elicited by single AP . LKB1-deficient neurons show a 60% reduction in EPSCs amplitude ( Fig 6F ) compared to control ( Fig 6E ) , suggesting reduced efficacy of fast , synchronous mode of neurotransmitter release ( Fig 6G ) . In addition , LKB1-null neurons displayed increased spontaneous burst firing frequency ( S11 Fig ) , and this defect can also cause decreased amplitude by reduction of the size of synaptic vesicle pools . Furthermore , the average amplitude of mEPSC was not altered in LKB1-deficient condition ( S10 Fig ) , suggesting that neurotransmitter content of presynaptic vesicle does not cause this defect . Detailed analysis of isolated EPSC traces revealed that LKB1-null cortical neurons show prolonged form of delayed exocytosis ( Fig 6E and 6F ) . This mode of asynchronous neurotransmitter release is mediated by a slow calcium sensor , and only becomes pronounced during presynaptic [Ca2+]c accumulation [48 , 50–53] . Loss of LKB1 also leads to a significant increase of total charge transfer ( Fig 6H ) and more than 3-fold increase of slow tau constant ( Fig 6I ) , indicating that asynchronous release was strongly potentiated in LKB1-null compared to control cortical neurons . During repetitive high-frequency stimulation , central synapses undergo short-term synaptic plasticity expressed as facilitation/depression of synchronous EPSCs , which reflects progressive depletion of the readily releasable pool and recruitment of synaptic vesicles from the reserve pool . In addition , strong presynaptic calcium buildup [50 , 51] augments asynchronous exocytosis [49] . To test if the defect in presynaptic Ca2+ homeostasis observed in LKB1-deficient neurons leads to a change in short-term presynaptic plasticity , we applied 10 APs at 10 Hz . Consistent with single AP results , LKB1-null neurons show increased delayed synaptic release following 10 APs with significantly longer decay time , which is reflected by an increased delayed charge to total charge transfer ratio compared to control ( Fig 7A , 7B and 7E ) . This increased delayed synaptic release was eliminated by acute application of EGTA-AM ( Fig 7C , 7D and 7F ) demonstrating that it is largely the result of increased [Ca2+]c [47] . Also , this increased [Ca2+]c can induce desynchronization of release [48] , and loss of LKB1 leads to increased variability of neurotransmitter release properties during stimulus trains ( S12 Fig ) . As previously reported [54] , cortical pyramidal neurons show presynaptic depression following 10 Hz stimulation ( Fig 7A , 7B and 7G ) . In contrast , LKB1-deficient cortical neurons did not display presynaptic depression during repetitive stimulations , and this was completely reverted by EGTA-AM application ( Fig 7C , 7D and 7G ) . These results suggest that loss of LKB1 increased asynchronous release and alters short-term presynaptic plasticity by regulating presynaptic calcium homeostasis . We tested if there is a causal relationship between the defect in MCU-dependent [Ca2+]c clearance observed in LKB1-null cortical axons and the abnormal neurotransmission properties described above ( Figs 6 and 7 ) . Remarkably , co-infection of LKB1F/F dissociated cortical neuron cultures with MCU- and Cre-encoding lentiviruses rescued the increased frequency of spontaneous mEPSC to control levels ( Fig 8A–8D ) . In addition , from the Ca2+ imaging , we observed significantly elevated basal level of presynaptic [Ca2+]c compared to control , and this is also restored to control level following MCU overexpression ( S13 Fig ) . Expression of MCU in LKB1-deficient neurons also reduced the delayed charge/total charge transfer ratio to control levels following the end of the 10 Hz stimulation ( Fig 8E–8H ) . Decreased amplitude of the first evoked AP in LKB1-null neurons was not rescued by MCU overexpression ( Fig 8I ) suggesting that this change in amplitude of EPSC might involve a postsynaptic change or reduced synaptic vesicle pool size independent of MCU-dependent presynaptic Ca2+ clearance . Finally , following 10 Hz stimulation , MCU overexpression re-established short-term depression of neurotransmitter release in LKB1-deficient neurons back to control levels ( Fig 8J ) .
As mentioned above , a single axon has spatial heterogeneity of presynaptic Ca2+ levels during AP-evoked synaptic transmission [6–8] . In addition , the same pyramidal neurons in the neocortex have target cell-specific neurotransmitter release properties [9 , 11 , 12] . Layer 2/3 pyramidal cells in the neocortex show distinct short-term synaptic plasticity at synapses made onto two types of GABAergic interneurons . Excitatory synapses made by axons of pyramidal neurons onto parvalbumin-positive interneurons ( basket cells ) , display high level of Ca2+ influx upon single AP stimulation and high release probability , and short-term depression ( STD ) . However , synapses made by the same axon of pyramidal cells onto somatostatin-positive ( bitufted or Martinotti cells ) display low Ca2+ influx upon single AP stimulation , low release probability and short-term facilitation ( STF ) . Interestingly , this facilitation phenotype is abolished by EGTA injection to presynaptic cells , therefore the residual Ca2+ level is important to modulate the STF on these synapses [12] . However , some of other findings showed that the synapses of pyramidal-to-bitufted cells have lower single-AP evoked presynaptic Ca2+ levels , higher failure rates of synaptic transmission , and smaller EPSP amplitudes [9 , 12] . Our data using syp-pHluorin imaging of neurotransmitter release demonstrate that presynaptic boutons associated with mitochondria display lower level Ca2+ influx during evoked neurotransmission and lower neurotransmitter release compared to presynaptic boutons not associated with mitochondria . Our data are normalized for the size of the SV pool ( using de-acidification by NH4Cl; see for 32 , 34 , 36 ) , therefore , we cannot infer if some of these differences reflect a change in the size of the presynaptic vesicle pool ( sum of the readily releasable pool [RRP] and the reserve pool [RP] ) . In fact , non-normalized signals showed no difference in evoked syp-pHluorin signals between mitochondria-free boutons and boutons associated with mitochondria . This is highly reminiscent of a previous report , which did not use total pool normalization although the study elucidated that mitochondria contribute to the variability of presynaptic strength bouton-specifically [55] . Total vesicle pool size of mitochondria-free presynapses measured by syp-pHluorin de-acidification is smaller than mitochondria-associated ones ( S4 Fig ) . In addition , a recent electron microscopy ( EM ) study described that presynaptic sites associated with mitochondria have a significantly larger number of SV compared to presynaptic boutons not associated with mitochondria [56] . Therefore , synaptic transmission could be larger at presynaptic boutons associated with mitochondria because they possess larger RRP . However , several reports showed that independent of RRP size , presynaptic Ca2+ level affects Pv ( fusion probability of individual vesicle upon single AP ) [57 , 58] . Overall , our study implies that a single neuron displays significantly different release properties through bouton-specific association with mitochondria and therefore that mitochondria represent a key effector of synapse-specific neurotransmitter release properties , including short-term facilitation versus depression , through their ability to regulate Ca2+ clearance . Multiple molecular mechanisms involved in Ca2+ clearance have been postulated to operate at presynaptic sites such as plasma membrane Ca2+-ATPase ( PMCA ) , Na+/Ca2+ exchanger ( NCX ) , smooth endoplasmic reticulum Ca2+-ATPase ( SERCA ) , and MCU [17 , 19 , 22 , 59] . Conflicting evidence has been reported regarding the importance of mitochondria-dependent presynaptic Ca2+ clearance in the regulation of presynaptic release properties in various types of axons such as the neuromuscular junctions ( NMJ ) and Calyx of Held [17 , 19] . Blockade of mitochondrial Ca2+ sequestration by mitochondrial depolarization or inhibition of MCU increased presynaptic [Ca2+] or decay time , but induced depression of presynaptic release or slowed recovery from depression [17 , 19] . In addition , recent report supports mitochondria Ca2+ level in resting state modulates spontaneous mEPSC frequency [60] . In retinal bipolar neurons and Drosophila NMJ , mitochondria have limited effects in presynaptic Ca2+ handling during PMCA inhibition or long stimulation [20 , 61] . In rat hippocampal CA3 region , PMCA isoform 2a is localized mainly at excitatory presynaptic sites , and pharmacological blockage of PMCA increased mEPSC frequency and paired-pulse facilitation ( PPF ) of evoked EPSCs [59] . SERCA pump also contributes presynaptic Ca2+ clearance and inhibition of this pump induces PPF at the frog NMJ and rat hippocampal CA1 region [62 , 63] . These divergent results might reflect that specific types of synapses might utilize different molecular effectors to clear presynaptic Ca2+ because of vast differences in the cytoplasmic volume at these different presynaptic terminals and also because these different type of Ca2+ clearance mechanisms vary greatly in their spatial and temporal dynamics . Nevertheless , our results reveal that mitochondria represents a critical component of presynaptic Ca2+ homeostasis in cortical axons and as such play a critical role in regulating neurotransmitter release properties in a synapse-specific way . Several studies have suggested that mitochondrial Ca2+ import leads to increased ATP production [15 , 64] . Several mitochondrial matrix dehydrogenases are directly or indirectly activated by Ca2+ present in the mitochondrial matrix . Activation of these proteins increases NADH levels , which drives electron transfer , ultimately resulting in increased ATP production by the ATP synthase . In addition , mitochondrial Ca2+ can stimulate ATP production by inducing metabolite transport through the aspartate/glutamate exchanger that contains an EF-hand , Ca2+-binding domain . In Drosophila motor neurons , presynaptic terminals have increased mitochondrial matrix pH ( pHm ) , membrane potential ( Δψm ) , and NADH level , which can lead to ATP production following AP triggering [65] . More recent evidence demonstrated that in rat hippocampal neurons , the main driver of ATP consumption is actually presynaptic release and synaptic vesicle recycling [66] . Therefore , the LKB1 signaling pathway may also coordinate ATP production by regulating mitochondrial Ca2+ influx which is directly triggered by cytoplasmic Ca2+ increase during action potential invasion of the presynaptic terminal and opening of voltage-gated Ca2+ channels . However , our results also suggest that the presence or absence of mitochondria at individual presynaptic sites cannot simply explain differences in ATP demand upon neurotransmitter release . In fact , our results suggest that the presynaptic boutons associated with mitochondria show lower levels of neurotransmitter release than boutons not associated with mitochondria . Our data demonstrate that the main function of presynaptic mitochondria in axons of pyramidal neurons is to regulate presynaptic Ca2+ dynamics and neurotransmitter release properties in a synapse-specific manner . Very little is currently known about signaling pathways regulating MCU expression and function in mitochondria , but recent reports suggested that in cardiomyocytes , MCU can be regulated by CAMK2-dependent phosphorylation , which is activated during ischemic injury [67] . Using a model of neuronal excitotoxicity where neurons are exposed to high doses of NMDA , a recent study has shown that MCU is down-regulated at the transcriptional level in a CAMK2-Npas4-dependent manner , leading the authors to hypothesize that MCU expression is activity-dependent and participates in a homeostatic mechanism underlying neuroprotection [68] . However , our preliminary results suggest that MCU mRNA abundance is unchanged in LKB1-deficient neurons compared to control ( S14 Fig ) . Another hypothesis could be that LKB1 regulates the translation , protein stability and/or the import of MCU protein into mitochondria . MCU , like >99% of mitochondrial proteins , is genomically encoded , and previous work shows that mitochondrial protein import can be regulated through phosphorylation by cytosolic kinases [69] . Future work will need to address these possibilities . Overall , our results identify a new function for the serine/threonine kinase LKB1 in the regulation of presynaptic Ca2+ homeostasis through MCU-dependent presynaptic Ca2+ uptake into mitochondria . This study uncovers the critical importance of presynaptic mitochondrial capture for the regulation of neurotransmitter release properties . Our finding also has important implications for microcircuit properties . As described above , it is possible that target cell type-specific plasticity could be influenced by the presence or absence of mitochondria presynaptically . Recent studies showed that , in hippocampus , Elfn1 , a synaptic cell adhesion molecule containing extracellular leucine-rich repeat domain , is selectively expressed at somatostatin-positive interneurons , and induces STF in retrograde [70 , 71] . Therefore , these synaptic cell adhesion molecules could be potential determinants of mitochondria capture in a postsynaptic target cell type-dependent way . Future experiments will be needed to explore this hypothesis .
All animal experiments employed the protocols approved by Institutional Animal Care and Use Committee ( IACUC ) as well as NIH guidelines at The Scripps Research Institute , CA , and Columbia University , NY ( Protocol#AC-AAAH4600 ) . All mice were maintained in a 12 h light/dark cycle . C57BI/6J , Balb/C were used for dissociated cortical culture , and floxed LKB1 ( Stk11tm1 . 1Rdp ) and NexCre mice were described previously [28] . We inserted the cDNA encoding Cre-recombinase to pCAG-IRES-GFP ( pCIG2 [72] ) by replacing IRES-GFP , and we used pCAX as a control to Cre-expressing vector . We conjugated mitochondrial targeting sequences from human cytochrome c oxidase subunit VIII to mTagBFP , mCherry and GCaMP5G ( from pCMV-GCaMP5G; Douglas Kim and Loren Looger , Addgene #31788 ) by swapping DsRED1 in pCAG-mitoDsRED1 [28] . pCAG:vGlut1-mCherry and pCAG:vGlut1-GCaMP5G were created by fusion of rat vGlut1 and mCherry or GCaMP5G . Human MCU ( gift from Mark Anderson [67] ) cDNA was amplified with C-terminal HA- epitope tag and subcloned into pCAG plasmid . Synaptophysin-pHluorin [31] was subcloned into pCAG vector . Synapsin promoter driven lentiviral shuttle vectors , pSyn-Cre and pSyn-ΔCre , were provided by Anton Maximov laboratory . Embryonic mouse cortices ( E15 . 5 ) were dissected in Hank’s Buffered Salt Solution ( HBSS ) supplemented with HEPES ( 10 mM , pH 7 . 4 ) , and incubated in HBSS containing papain ( Worthington; 14 U/ml ) and DNase I ( 100 μg/ml ) for 20 min at 37°C . Then , samples were washed with HBSS , and dissociated by pipetting . Cell suspension was plated on poly-D-lysine ( 1 mg/ml , Sigma ) -coated glass bottom dishes ( MatTek ) or coverslips ( BD bioscience ) in Neurobasal media ( Invitrogen ) containing B27 , Glutamax , FBS ( 2 . 5% ) and penicillin/streptomycin ( all supplements were from Invitrogen ) . After 5 to 7 d , media were changed with FBS-free supplemented Neurobasal media . We injected plasmids and 0 . 5% Fast Green ( Sigma , 1:20 dilution ) mixture using a micro-injector ( Picospritzer III , Parker ) into the lateral ventricles of isolated head of E15 . 5 mouse embryo , and electoporated using an electroporator ( ECM 830 , BTX ) with five pulses of 20 V at 100 ms interval . Then , we followed the dissociated neuronal culture protocol as previously described . Hippocampal regions were dissected from 3-wk-old wild-type ( NEXCre;LKB1F/+ or NEXCre;LKB1+/+ ) or NexCre;LKB1F/F conditional knockout ( KO ) mice and lysed with RIPA buffer containing 1% NP-40 , 0 . 5% sodium deoxycholate , 0 . 1% SDS , 150 mM NaCl in 50 mM Tris buffer ( pH 8 . 0 ) supplemented with the cocktail of protease inhibitors ( Roche ) . Samples were loaded onto SDS-PAGE gels and transferred to polyvinylidene difluoride ( PVDF ) membrane ( Amersham ) . After transfer , membranes were blocked for 30 min with blocking buffer containing 5% skim milk in TBS-T ( 20 mM Tris-HCl [pH7 . 4] , 150 mM NaCl , 0 . 1% Tween 20 ) . Then , primary antibodies were incubated overnight at 4°C , and washed three times by TBS-T . HRP-coupled secondary antibodies ( Invitrogen ) were incubated for 1 h at room temperature followed by three times washes with TBS-T . Chemiluminescence images were taken by Fluorochem Q imager ( ProteinSimple ) and quantified using AlphaView software ( ProteinSimple ) . Primary antibodies are rabbit anti-MCU ( 1:2000 , Sigma ) , mouse anti-OxPhos complex kit ( 1:2000 , Invitrogen ) , rabbit anti-LKB1 ( 1:1000 , Cell Signaling ) , and mouse anti-actin ( 1:5000 , Millipore ) . RNA was isolated from hippocampi of P19 Control and Nex-Cre;LKB1F/F mice with NucleoSpin RNA ( MACHEREY-NAGEL ) according to the instructions of the manufacturer . cDNA was reverse transcribed using SuperScript III First-Strand Synthesis SuperMix for qRT-PCR ( Life Technologies ) according to the directions of the manufacturer . The resulting cDNA was analyzed using quantitative PCR with Power SYBR Green PCR Master Mix ( Life Technologies ) in an Eppendorf Realplex using the following primers: Actin ( 5′- GGCTGTATTCCCCTCCATCG -3′ , 5′- CCAGTTGGTAACAATGCCATGT-3′ ) MCU ( 5′-GAGCCGCATATTGCAGTACG-3′ , 5′-CGAGAGGGTAGCCTCACAGAT-3′ ) Transfected cortical neurons were imaged at 15-18DIV with EMCCD camera ( Andor , iXon3-897 ) on an inverted Nikon Ti-E microscope ( 40x objective NA0 . 6 with 1 . 5x digital zoom or 60x objective NA1 . 4 ) ; 488 nm and 561 nm lasers shuttered by Acousto-Optic Tunable Filters ( AOTF ) or 470 nm , 555 nm , and 360 nm Spectra X LED lights ( Lumencor ) were used for the light source , and a custom quad-band excitation/mirror/emission cube ( based off Chroma , 89400 ) followed by clean up filters ( Chroma , ET525/50 , ET600/50 , ET435/26 ) were applied for excitation and emission . We used the modified normal tyrode solution as a bath solution at 37°C , which contained ( in mM ) : 145 NaCl , 2 . 5 KCl , 10 HEPES pH 7 . 4 , 1 . 25 NaH2PO4 , 4 CaCl2 , 10 glucose . For presynaptic calcium imaging during spontaneous release , we added picrotoxin ( 50 μM , Tocris ) and images were captured by 1 s interval for 10 min . For calcium imaging on evoked release , we added APV ( 50 μM , Tocris ) and CNQX ( 20 μM , Tocris ) in bath solution . For testing MCU inhibitor effect , Ru360 ( 10 μM , EMD Millipore ) was incubated for 3 min before imaging . Evoked releases were triggered by 1 ms current injections with a concentric bipolar electrode ( FHC ) placed 20 μm away from transfected axons . We applied 20 AP or 100 AP at 10 Hz with 20 V using the stimulator ( Model 2100 , A-M systems ) and imaged with 350 ms interval ( 2 . 86 Hz ) during 90 s for vGlut1-GCaMP5G signals and 1 s interval ( 1 Hz ) for 210 s for mito-GCaMP5G signals . At the end of experiments , we added the calcium ionophore ionomycin ( 5 μM , EMD Millipore ) and continued imaging with 1 s interval to obtain Fmax value for each axon segment imaged . For syp-pHluorin imaging , 20 AP or 100 AP were applied at 10 Hz and neurons were imaged with 500 ms interval ( 2 Hz ) during 90 s , then the bath solution was changed with tyrode solution containing 55 mM NH4Cl for Fmax value . Images were analyzed in Fiji ( Image J ) using a Time Series Analyzer ( v3 . 0 ) plugin . Single presynaptic bouton imaged from multiple experiments were analyzed individually and pooled for statistical analysis . Each vGlut1-GCaMP5G , mito-GCaMP5G or syp-pHluorin puncta and nearby backgrounds were selected by circular ROIs and intensities were measured by plug-in . After intensities were corrected for background subtraction , ΔF values were calculated from ( F-F0 ) . F0 values were defined by averaging 10 frames before stimulation , and Fmax values were determined by averaging 10 frames of maximum plateau values following ionomycin or NH4Cl application , then , used for normalization . Mito-free presynapses for Ca2+ and pHluorin imaging were defined by 3 . 5 μm distance from axonal mitochondria . All graphs were drawn using Prism ( GraphPad Software ) and statistical analyses were performed with the same software . We used non-parametric tests ( Mann-Whitney test ) when at least one of the group tested displayed a distribution that deviated significantly from normality . For comparing before and after drug treatment , we employed paired t test ( Fig 3A–3C ) . Statistical tests and significance levels are indicated in figure legends . Recombinant lentiviruses were produced from human embryonic kidney 293T ( HEK293T ) cells by co-transfection with shuttle vectors , LP1 , LP2 , and VSV-G . FuGENE transfection reagent ( Roche ) was used and 24 h after transfection , media were exchanged with Neurobasal media , and 48hrs later , supernatants were harvested and centrifuged for removing cellular debris . Then , 200 μl of viral supernatants was added to each 24-well containing dissociated cortical neurons at 3–5 DIV and recorded at DIV 15–18 [73] . For MCU rescue experiments , because of apoptotic effect by mitochondrial calcium increase , we only added 10 μl supernatants . Analysis of synaptic transmission in cultured neurons was performed as described [74 , 75] . Evoked synaptic responses were triggered by 1 ms current injections with a local extracellular stimulating electrode ( FHC , Inc . ) , and were monitored from randomly selected nearby neurons by whole-cell patch clamp recordings using a Multiclamp 700B amplifier in voltage clamp mode ( Axon Instruments , Inc . ) . Short-term presynaptic plasticity was assessed by stimulating at 10 Hz for 1 s . The frequency , duration , and magnitude of extracellular stimuli were controlled with Model 2100 Isolated Pulse Stimulator ( A-M Systems , Inc . ) . The whole-cell pipette solution contained 135 mM CsCl2 , 10 mM HEPES-NaOH pH 7 . 4 , 1 mM EGTA , 1 mM Na-ATP , 0 . 4 mM Na-GTP , and 1 mM QX-314 . The resistance of filled pipettes varied between 3–5 mOhm . The bath solution contained 140 mM NaCl , 5 mM KCl , 2 mM CaCl2 , 0 . 8 mM MgCl2 , 10 mM HEPES-NaOH pH 7 . 4 , and 10 mM glucose . EPSCs were isolated pharmacologically by addition of 100 μM picrotoxin to the bath solution . For calcium chelation , 100 μM EGTA-AM was added 10 min before recording . The currents were sampled at 10 kHz and analyzed offline using pClamp10 ( Axon Instruments , Inc . ) software . For illustration purposes only , traces of evoked synaptic currents were filtered at 50 Hz . | Mitochondria perform many important biological functions , ranging from ATP production through oxidative phosphorylation to calcium clearance and lipid biosynthesis . In neurons of the mammalian cerebral cortex , we discovered that only ~50% of presynaptic boutons ( the site where neurotransmission occurs ) are associated with mitochondria while ~50% are not . Our results demonstrate that the presence of a presynaptic mitochondria plays a key role in calcium homeostasis and , thereby , regulates the properties of neurotransmitter release . We identify a protein kinase called LKB1 , which regulates the ability of presynaptic mitochondria to clear calcium , by controlling the abundance of the mitochondrial calcium uniporter—a Ca2+-selective ion channel located in the mitochondria’s inner membrane . Our results suggest that mitochondrial capture at individual presynaptic boutons along cortical axon plays a synapse-specific role in regulating neurotransmission through the ability to regulate presynaptic calcium homeostasis . |
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Mapping the polymorphisms responsible for variation in gene expression , known as Expression Quantitative Trait Loci ( eQTL ) , is a common strategy for investigating the molecular basis of disease . Despite numerous eQTL studies , the relationship between the explanatory power of variants on gene expression versus their power to explain ultimate phenotypes remains to be clarified . We addressed this question using four naturally occurring Quantitative Trait Nucleotides ( QTN ) in three transcription factors that affect sporulation efficiency in wild strains of the yeast , Saccharomyces cerevisiae . We compared the ability of these QTN to explain the variation in both gene expression and sporulation efficiency . We find that the amount of gene expression variation explained by the sporulation QTN is not predictive of the amount of phenotypic variation explained . The QTN are responsible for 98% of the phenotypic variation in our strains but the median gene expression variation explained is only 49% . The alleles that are responsible for most of the variation in sporulation efficiency do not explain most of the variation in gene expression . The balance between the main effects and gene-gene interactions on gene expression variation is not the same as on sporulation efficiency . Finally , we show that nucleotide variants in the same transcription factor explain the expression variation of different sets of target genes depending on whether the variant alters the level or activity of the transcription factor . Our results suggest that a subset of gene expression changes may be more predictive of ultimate phenotypes than the number of genes affected or the total fraction of variation in gene expression variation explained by causative variants , and that the downstream phenotype is buffered against variation in the gene expression network .
Mapping the loci that control quantitative variation is a crucial step towards understanding complex disease [1]–[3] . Genome-wide association studies ( GWAS ) have shown that a large proportion of human disease-risk alleles consist of non-coding variants [4] . Since alterations in transcriptional regulation can drive disease states , there have been extensive studies to map eQTL , the genetic variants responsible for variation in gene expression [5]–[10] ( for reviews , see [11]–[14] ) . Finding eQTL is now a widely accepted strategy for identifying new variants that potentially affect phenotype [15] , for screening GWAS alleles to find those that affect disease risk by altering transcription [16] , and for uncovering the molecular pathways underlying disease [17] . These studies make a distinction between cis-eQTL ( genetic variants that affect the expression of physically linked genes ) and trans-eQTL ( variants that are physically unlinked from their target gene ) [18] . cis-eQTLs also have effects in trans on unlinked genes that are downstream targets of the gene linked to the cis-eQTL . A large amount of effort is now directed towards the identification and analysis of eQTL . However , it remains extremely difficult to identify the precise nucleotide variant/s responsible for the changes in gene expression or phenotype , even in model organisms . eQTL studies rely on an assumption that an unknown subset of the transcriptional changes in the target genes of the eQTL are responsible for the downstream disease phenotype . cis-eQTL that affect transcription factors are considered particularly interesting as they may identify the transcriptional program involved in the disease . However , despite numerous studies linking GWAS and eQTL results [16] , [17] , [19] , fundamental questions remain about how a variant's effect on gene expression relates to its effect on phenotype . It is unclear if the amount of gene expression variation explained by an eQTL correlates with the amount of phenotypic variation it explains . In addition , it remains to be established if cis-eQTL play a more significant role in controlling gene expression variation compared to trans-eQTLs . The best way to address these questions would be to compare the effects of a set of variants that are responsible for changes in both gene expression and the ultimate phenotype . Our lab has been studying the genetic variation responsible for the differences in sporulation efficiency in natural populations of Saccharomyces cerevisiae ( S . cerevisiae ) [20] . In the presence of nitrogen and non-fermentable carbon sources , diploid S . cerevisiae cells face a cell fate decision that involves a switch from fermentation to aerobic respiration and the cessation of mitosis followed by the initiation of meiosis [21]–[23] . Sporulation efficiency is defined as the percentage of cells in a culture that form meiotic spores , and is a highly heritable , complex trait [20] , [24]–[26] . We have identified the exact nucleotide variants responsible for most of the variation in sporulation efficiency between a natural oak tree isolate ( YPS606 ) and a vineyard strain ( BC187 ) [27] . The oak tree isolate sporulates at 100% efficiency while the vineyard strain sporulates at 3 . 5% under sporulating conditions [27] , [28] . By swapping the causative nucleotides in the vineyard background for the oak nucleotide variants , we generated an isogenic panel of vineyard strains that have completely identical genomes except at the causative variants [27] . Here , we describe the use of this allele replacement strain panel to study the primary question posed above: What is the relationship between the effect of causative nucleotides on the variation in gene expression and in phenotype ? There are four quantitative trait nucleotides ( QTNs ) in three genes ( IME1 , RME1 and RSF1 ) that are responsible for most of the differences in sporulation efficiency between the oak and vineyard strains [27] . The four QTN consist of two non-coding and two coding variants . The non-coding regions of RME1 ( RME1nc - RME1 ( indel-308A ) ) and IME1 ( IME1nc - IME1 ( A-548G ) ) contain one causative variant each , implying that changes in RME1 and IME1 expression may be responsible for the differences in sporulation efficiencies between the parent strains . The remaining two QTN are coding variants in IME1 ( IME1c - IME1 ( L325M ) ) and RSF1 ( RSF1c - RSF1 ( D181G ) ) . Strikingly , the three QTN-containing genes are either known ( IME1 [29] , [30] and RME1 [31] ) or putative ( RSF1 [32] , [33] ) transcription factors . Given their role in transcriptional regulation , it is reasonable to assume that the four sporulation QTN affect phenotype through changes in gene expression . The allele replacement panel is isogenic at all loci , except for the causative variants . Since the sporulation QTN are the only genetic variants in the panel , they must be responsible for all reproducibly observed gene expression variation among the panel strains . Consequently , the sporulation alleles are nucleotide variants responsible for the variation in phenotype ( QTN ) as well as variation in gene expression ( eQTN ) . We present the results of a study in which we measured the effects of individual single-nucleotide variants on both gene expression and sporulation efficiency in a controlled setting . Since the QTN underlying variation in sporulation efficiency reside in transcription factors , and have been swapped individually and in all combinations into a clean background , our experiment represents a rigorous test of the relationship between the effect of a variant on gene expression and on the ultimate phenotype . Our analysis reveals that 1 ) the amount of variation in gene expression explained by a polymorphism is not always correlated with the amount of phenotypic variation explained by that same polymorphism , 2 ) genetic interactions between variants are responsible for a larger proportion of gene expression variability than phenotypic variability , and 3 ) that alleles that change either the level or activity of a transcription factor affect expression variation of the same genes to different extents . We also find that while the allele replacement panel displays extensive variation in gene expression , the downstream phenotype is largely buffered from the variation in the upstream transcriptional network .
To explore the relationship between genetic variation , gene expression and phenotype , we utilized a panel of sixteen isogenic strains in the vineyard background . The panel was generated by swapping causative vineyard nucleotides with their oak allele counterparts [27] . This panel includes the vineyard parent , the “vineyard converted” strain that has all four oak QTN in place of the vineyard alleles , as well as strains with all possible combinations of oak and vineyard alleles at the four QTN . Using conditions which differed slightly from those in Gerke et al [27] ( see Materials and Methods ) , we first measured the sporulation efficiencies of the allele replacement strains to quantify the effects of the QTN on sporulation efficiency under these conditions ( Table S1 ) . We assessed the effect of genotype on sporulation efficiency by building a linear model of the effects of the four QTN on sporulation efficiency ( Table S2 ) . The analysis of variance shows that the allelic status of the QTN explains 98% of the differences in sporulation efficiencies between the strains in the panel ( Table 1 ) . 93% of the variance in sporulation efficiency is due to a simple linear combination of the individual ( main or additive ) effects of the four vineyard QTN alleles ( Table 1 ) . The variation in sporulation efficiency explained by the main effects of the vineyard alleles of RME1nc , RSF1c and IME1c is almost equal while the vineyard allele of IME1nc explains a smaller but significant amount . An additional small but significant amount of variance ( 5% ) can be explained by the genetic interactions between the vineyard alleles . The small number of significant interaction parameters indicates that a simple additive model of the main effects between the four QTN explains almost all the variation in the phenotype under these conditions . We next measured the effect of each QTN on global-expression profiles during the cell fate decision phase when all three genes are active . RSF1 is required for transcription of mitochondrial genes [32] and respiration is known to be required for Ime1 expression and meiosis [34] . In addition , RME1 [31] and IME1 [30] , [35] control some of the critical transcriptional changes during this phase . IME1 expression is induced rapidly after the switch to sporulation medium [35] . We showed previously that differences between the oak and vineyard strains in making the decision to sporulate occur very early after the switch to non-fermentable carbon , before meiotic DNA synthesis [20] . We , therefore , used RNA-Seq [36] to measure global mRNA expression-profiles in all sixteen strains in the panel after two hours in sporulation medium , before meiotic DNA replication begins . We surmised that the causative QTN would be active during this period and that the differences in gene expression between the strains at this time point would be linked to the differences in sporulation efficiencies . We obtained good reproducibility between the biological replicates ( the range of mean Pearson's correlation coefficients for pair-wise comparisons between replicates of each strain was 0 . 86–0 . 93 ) . The coefficient of variance , CV , ( standard deviation/mean ) , for the biological replicates is a measure of the variance in our measurements . The CV for gene expression ( median CV = 0 . 15 ) is slightly greater than the CV for sporulation efficiency ( median CV = 0 . 076 ) but is consistent with reports from previous RNA-Seq experiments [37] , [38] . We assessed the effects of the QTN on the expression of each gene in the genome by regressing genotype on gene expression patterns across the sixteen strains in the panel . After removing the effect of day-to-day experimental variation ( see Materials and Methods ) , we applied a linear model framework to assess how much of the variation in the expression of each gene could be explained by the allelic status of the sporulation QTN ( Table S3 ) . After correcting for multiple hypothesis testing , we obtained 289 significant gene-specific models ( ∼5% of the genome ) in which gene expression was significantly affected by the allele status of the QTN ( Figures 1 & S1 ) . Within these 289 genes , the genetic status of the QTNs explains 45–88% of the observed variation in expression ( median 49% ) ( Figure 1 ( inset ) , Table S4 ) . The best model of gene expression ( for URC2 , a putative Zn ( II ) Cys6-containing transcription factor [39] ) explains 88% of the variance in this gene's expression . These results stand in stark contrast to the model of sporulation efficiency , which explains 98% of the variation in this phenotype . The median variance explained by the polymorphisms depends on the exact FDR we chose in our analysis ( a lower FDR would yield a higher median variance explained ) . However , for any FDR threshold , the gene expression models are always less predictive than the sporulation model . Applying a similar linear model framework to log-transformed expression counts did not increase the gene expression variance explained by the QTN ( Figure S2 ) and , therefore , we analyzed the models using untransformed gene expression counts . These results suggest that the statistical relationship between QTN and phenotype is simpler than the link between eQTN and gene expression . Genetic interactions between the QTN account for a large fraction of the variation in gene expression . We found that all four QTN play a role in the expression of most of the 289 significantly affected genes , either through main or interaction effects ( Tables 2 & S3 ) . As RME1 , IME1 and RSF1 act at similar points in the sporulation network [23] , [33] , [34] , it is not surprising that interactions between the alleles explain a major portion of the variation in gene expression ( Figure 2 ) . Main and interaction effects explain almost equal amounts of the variation in gene expression , which stands in contrast to the model for sporulation efficiency , in which main effects explain the vast majority of the variation in phenotype . The median variance in gene expression explained by main effects of the QTN across all 289 genes is 20% and by the interaction effects is 29 . 7% . Only a small fraction of the genes ( 26/289 ) show the additive-interaction balance observed in the sporulation model where main effects account for over 90% of the explained expression variance . These genes include RIM4 ( a known target of Ime1 [40] ) , RME1 itself , and PRD1 ( a zinc metalloendopeptidase that is involved in the degradation of mitochondrial proteins [41] ) . Our results show that , while complex interactions between the QTN drive most of the variation in gene expression patterns , additive effects of the QTN account for most of the variation in sporulation efficiency under the conditions tested here . Given the significant differences between the explanatory power of the gene expression models and the sporulation efficiency model , our results suggest that the downstream phenotype is robust to expression variation in the network . We also found that the balance between main and interaction effects on the variation in gene expression was different for different QTN ( Figure 3 ) . RSF1c's role in controlling expression variation was primarily through its main effects while RME1nc and both IME1 alleles exerted their influence on expression variation primarily through interactions with the other alleles . These results are not surprising as RME1 and IME1 act at the same point in the sporulation transcriptional network [23] with Rme1 binding directly to the promoter of IME1 [31] . We next asked whether the fraction of variation in gene expression explained by sporulation QTN was similar to that explained for sporulation efficiency . We found that the proportion of gene expression variation explained by the QTN was not predictive of the explanatory power in the sporulation efficiency model . RSF1c controls the variation in expression of a large number of genes . It affects the expression of almost all of the 289 genes with significant expression models and explains a significant proportion of the variation of 71% of the target genes ( 205/287 genes ) ( Table 2 ) . The main effect of RSF1c also explains the largest proportion of the variation in gene expression compared to the other three QTN ( median variance explained by RSF1c main effect = 8 . 5% , Figure 3 ) . However , it is surprising that , despite its significant role in gene expression , RSF1c does not have the largest role in explaining the variation in sporulation efficiency . The RSF1c allele explains 23% of the variation in sporulation efficiency as compared to RME1nc ( 38% ) and IME1c ( 35% ) ( Table 1B , Figure 4 ) . Little is known about RSF1 except that it may be a transcriptional modulator of respiration [32] which is known to be required for sporulation in S . cerevisiae [34] . These results suggest that RSF1 plays a significant role in the transcriptional cascade that initiates sporulation along with the known sporulation transcriptional regulators , RME1 and IME1 . However , it is also possible that , despite being responsible for a large fraction of the variation in gene expression , only a subset of RSF1c's target genes affect sporulation efficiency . In contrast , RME1nc or IME1c may account for a greater proportion of the variation in the phenotype as more of their target genes may be directly involved in sporulation . RME1nc and IME1c both explain a comparatively modest fraction of the variation in gene expression ( Figure 4 ) . The main effects of both alleles account for the expression variation of 35% of their targets ( Table 2 ) but exert their influence primarily through interactions with the other QTN ( Figure 3 ) . As stated before , this is not surprising as Rme1 and Ime1 act at the same point of the transcriptional cascade [23] and RME1 is a known repressor of IME1 expression [31] . The expression of RME1 itself is a notable exception . The main effect of RME1nc explains 75% of the variation in RME1 expression ( Table 3 ) . The expression of RME1 is almost bimodal with increased expression in strains containing the RME1nc oak allele and reduced expression in the presence of the vineyard allele . These results are striking given the role of the two QTN on the variation in sporulation efficiency . The main effects of RME1nc and IME1c explain a large proportion of the variation in sporulation efficiency ( Table 1 , Figure 4 ) . However , their role in controlling gene expression variation is not as significant as RSF1c and occurs primarily through interactions with the other alleles ( Figure 3 ) . These results , again , highlight the differences between the QTN in their control of gene expression and sporulation efficiency variation . IME1 is considered the primary regulator of the sporulation transcriptional cascade [30] , [42] . However , the IME1nc allele does not explain as much of the variation in gene expression as RSF1 ( Table 2 ) possibly because RSF1 acts earlier than IME1 and affects both respiration and sporulation genes . Accordingly , RSF1 is responsible for a significant proportion of the variation in IME1 gene expression ( Table 3 ) though it is unclear if it directly affects the transcription of IME1 . Similar to RME1 and the coding allele of IME1 , IME1nc affects gene expression through genetic interactions with the other three alleles ( Figure 3 ) . The main effects of IME1nc explain the variation of a slightly larger number of genes than IME1c ( 134/273 genes ) ( Table 2 ) . It is striking , therefore , that IME1nc , is responsible for the smallest proportion of the variation in sporulation efficiency ( Figure 4 ) showing that a genetic variant such as IME1nc can cause significant changes in the variability of gene expression upstream in the network but play a modest role in the variation of the ultimate phenotype . Our results thus indicate that the proportion of variation in gene expression explained by a QTN is not predictive of the amount of phenotypic variation that it explains . The number of eQTL targets has also been used to identify “hot spots” of regulatory activity that may be important for the disease phenotype [5] , [10] , [43] , [44] . In addition , there has been some discussion that trans-eQTL are more likely to be eQTL “hot spots” than cis-eQTL as their effects may be more pleiotropic [45] . The oak and vineyard parental strains used in these studies also exhibit some pleiotropy as they differ in the size of the cells entering meiosis , the relative numbers of dyads , triads , and tetrads in fully sporulated cultures , and growth on non-fermentable carbon sources [20] . While we know that the RSF1c is not responsible for the growth differences of the parental strains on glycerol [27] , it is possible that some of the sporulation QTN-dependent genes may influence these other phenotypes . All four of the eQTN studied here affect the expression variation of a large number of overlapping genes ( Table 2 ) , thereby , behaving as expression “hot spots” . As expected , the cis-eQTL affect the variation in gene expression of the linked genes ( RME1 and IME1 ) but also affect the variability of many genes in trans . We do not observe any consistent differences in the number of genes whose expression variation is affected by either the cis-eQTL ( RME1nc and IME1nc ) or the trans-eQTL ( RSF1c and IME1c ) . We also do not find significant enrichment for any particular gene ontology ( GO ) category ( P . S & B . A . C , unpublished data ) . More importantly , as described above , even though all four eQTN behave as “hot spots” for transcriptional changes , there are significant differences in the amount of downstream phenotypic variation that they control . The comparisons indicate that the number of genes affected , the balance between the additive-interaction effects in their control of expression variation and the fraction of gene expression variance explained are not predictive of the effect of the QTN on sporulation efficiency . One striking result is the difference between the effects of the two IME1 QTN on the variation in sporulation efficiency . The non-coding allele of IME1 , IME1nc , affects the expression level of IME1 and consequently , the amount of Ime1 protein . The coding allele of IME1 , IME1c , probably affects the activity of Ime1 protein as it lies in a domain of Ime1 that is responsible for protein-protein interactions with Rim11 and Ume6 [30] , two factors that are required for the initiation of sporulation . Given that both alleles occur in the same transcription factor , we investigated if their effects on the variation in gene expression matched their roles in controlling variation in sporulation efficiency . While the distributions of the effects on the variation in gene expression for the two alleles look very similar and they affect similar sets of genes ( Figure 4 ) , the IME1c allele explains a larger proportion of the variation in sporulation efficiency than IME1nc ( Table 1 ) . Closer inspection of the expression data revealed that while both alleles explained the expression variation of the same set of genes , the rank order of the amount of variance explained by each of the alleles is quite different ( p-value<0 . 005 , Wilcoxon rank sum test ) . In other words , the two IME1 alleles both affect the same set of genes , but expression variation of specific genes is more or less sensitive to either the coding or non-coding allele . These differences can be seen by comparing the fraction of variance explained by the two IME1 alleles in individual gene expression models . While the expression variation of most IME1-dependent genes is affected by both alleles when the full model is applied , the proportion of variance explained varies between the alleles ( Figure 5a , correlation coefficient , r = 0 . 43 ) . This difference between the alleles is magnified when only the variance explained by main effects is considered ( Figure 5b ) . While there are a few genes where the main effects from both alleles affect a significant proportion of the variation , the expression variation of most of the dependent genes is affected primarily by only one or the other allele . The difference between subsets of genes in their sensitivity to either the level ( IME1nc ) or the activity ( IME1c ) of Ime1 manifests itself as a dramatic difference in the effects of the two IME1 alleles on sporulation efficiency .
We have used a set of individual single nucleotide variants in known or putative transcriptional regulators that are causative for variation in sporulation efficiency to explore the relationship between genetic variants and their effects on gene expression and phenotype . The allele status of the QTNs explains almost all of the variation in sporulation efficiency but the median variation in gene expression explained is only 49% . In addition , variation in gene expression results from many interactions between the alleles while simple additive effects of the QTN explain most of the variation in sporulation efficiency . It is intriguing that gene expression varies more than the phenotype as the four QTN represent the sole genetic changes in the panel . Why might the QTN show a stronger correlation with sporulation efficiency than with expression variation , even though the QTN reside in transcriptional regulators ? It is possible that our gene expression measurements are “noisier” than those of sporulation efficiency as RNA-Seq may be more sensitive in measuring variation in gene expression than the fluorescence measurements used to assess sporulation efficiency . It is also possible that experimental variation was introduced during sample preparation . We know that day-to-day variation in media conditions , oxygen levels , etc . can affect sporulation efficiency and expect that they would affect gene expression as well . We accounted for this variation by including the day of growth as a covariate in our gene expression models . However , it is possible that there is some additional unexplained gene expression variation even among strains grown on the same day . The fact that genotype better explains sporulation efficiency than the “endo-phenotypes” of gene expression suggest that sporulation efficiency is buffered from changes in the transcriptional network . Developmental biologists have invoked the concept of “phenotypic robustness” to explain how body patterns remain invariant despite perturbations in the upstream gene regulatory network [46] , . QTL mapping studies in Arabadopsis lines have also suggested that genetic variation in gene expression does not always manifest itself as phenotypic variation [48] . Phenotypic changes often require gene expression changes beyond certain thresholds . As long as transcriptional fluctuations do not cross the threshold , the phenotype does not vary . When transcription is tuned to be close to the threshold , variability in gene expression has been shown to be responsible for incomplete penetrance [49] . Conversely , surplus gene expression i . e . gene expression levels that are considerably higher than the threshold needed to cause phenotypic change , can result in “wild-type” phenotypes [50] . The fact that , in our conditions , main effects account for most of the variation in sporulation efficiency whereas allele interactions account for a significant , but much smaller amount of the phenotypic variation , suggests that the sporulation efficiency phenotype is buffered from the variation in the transcriptional network . The sporulation transcriptional cascade contains multiple points for feedback control [51] which probably impose several thresholds on gene expression levels . One obvious possibility is that cells only sporulate when the levels of the sporulation transcriptional activators are above a certain level . This also implies that , in properly powered studies , genotype will be more strongly associated with phenotype than with gene expression . Our analyses of the relationship between gene expression variation and sporulation efficiency variation are based on expression measurements taken at a single time point . We chose to analyze the gene expression changes at this early stage of sporulation as the transcription factors containing the sporulation QTN exert their effects soon after the switch into sporulation medium . In addition , Gerke et al . [20] showed that the critical differences between the oak and vineyard parental strains also occur early in sporulation . Gene expression changes at later time points are likely to correlate better with sporulation efficiency , but this correlation will be driven by gene expression changes due to differences in the numbers of actively sporulating cells . Our expression measurements reflect the early gene expression changes in the decision to sporulate during the period when the QTN are active , not the downstream effectors of sporulation . The main effects of the two IME1 alleles , IME1nc and IME1c , play distinct roles in controlling the variation in gene expression , despite residing in the same transcription factor . Our results suggest that individual target genes are more dependent on either the level ( IME1nc ) or activity ( IME1c ) of Ime1 . Ime1 binds its target promoters through Ume6 , which encodes a DNA-binding protein [52] . Binding of Ime1 for Ume6 activates transcription of early-meiosis genes by displacing the repressive activities associated with Ume6 [30] . The IME1c allele probably affects the affinity of Ime1 for Ume6 or other co-factors as it lies in a domain of Ime1 that is responsible for protein-protein interactions with Rim11 and Ume6 [30] . Given this mode of action , the differences between the two IME1 alleles suggest that changing the affinity of Ime1 to Ume6 or other co-factors has a different effect on IME1-dependent promoters compared to changing the concentration of Ime1 . It is possible that Ime1 exhibits cooperativity at IME1nc-dependent genes but not at IME1c-dependent genes , rendering these particular targets more sensitive to changes in Ime1 levels but insensitive to changes in the affinity of Ime1 binding . An initial search for transcription factor motifs uncovered the Ume6 binding site in both sets of genes , but did not reveal any notable differences in the motif content of the two sets of target promoters ( P . S & B . A . C , unpublished data ) . However , it remains possible that each set of promoters contains a unique combination of motifs and co-factors that control the allele-dependent response . Finding consistent patterns among the hundreds of eQTL is a major challenge in the study of quantitative variation in gene expression [13] . Investigators have focused on cis-eQTL , the number of targets , or the effect size of a given eQTL as ways to screen eQTL for the variants most likely to be important . We find that that the fraction of variation in gene expression explained by the sporulation QTN is not predictive of the fraction of variation in phenotype that they explain . The results are surprising since all four QTN lie in known or putative transcriptional regulators and , therefore , must exert their phenotypic effects through changes in gene expression . It remains to be determined if this same trend will hold for causal genes that are not TFs . Perhaps the indirect effects of non-TFs on gene expression will better correlate with downstream phenotypes than the direct effects of TFs . However , early studies on laboratory-derived mutations showed that there were no significant differences between TFs and non-TFs in terms of their effects on gene expression [53] . Therefore , we suspect that our results will be applicable to naturally occurring polymorphisms in non-TFs as well . We have also not found any distinction between cis- and trans-QTN . While all four QTN act like eQTL “hot spots” , either cis- or trans-eQTL can can explain large proportions of the variation in gene expression ( RSF1c and IME1nc ) or in phenotype ( RME1nc and IME1c ) . These results suggest that , along with the amount of gene expression variation explained by a given QTN , the identity and function of the particular genes affected may be important in identifying the eQTL that has the most significant role in controlling phenotypic variation .
The culture conditions for sporulation efficiency were modified from Gerke et al . [27] to accommodate larger samples for RNA-Seq preparations . Two replicates each of the 16 strains in the vineyard background allele replacement panel were grown for 14 hours at 30C in 96-well blocks containing 500 ul of Yeast Peptone Dextrose ( YPD ) medium with 2% dextrose . The replicates were pooled and diluted 1∶50 into 250 ml conical flasks containing 50 ml of 1% potassium acetate to induce sporulation . Cultures were grown for 30 hours and sporulation efficiencies were measured as described in Gerke et al . [27] . The entire procedure was repeated on different days until we had four biological replicates for each strain . For RNA-Seq , cultures were grown as described above but growth was stopped after 2 hours in potassium acetate by spinning cells down and freezing the cell pellets at −80°C . Cells were harvested at this stage and total RNA was extracted [20] . The entire procedure including total RNA extraction was repeated on different days until we had four biological replicates for each strain . mRNA was extracted with the DynaI mRNA DIRECT kit ( Life Technologies ) and fragmented with a Covaris Focused ultrasonicator . mRNA extraction and fragmentation , random hexamer priming of cDNA and Illumina library preparations were done by the Genome Technology Access Center ( GTAC ) at Washington University in St . Louis ( https://gtac . wustl . edu ) using standard procedures [54] . The liquid handling steps from the mRNA extraction stage onwards were performed on all 64 samples simultaneously using the Caliper Sciclone Automated Liquid Handling Workstation ( PerkinElmer ) . Illumina libraries were prepared from the cDNA of each of the 64 samples . We obtained libraries from all the samples except the strain with vineyard alleles of RME1nc , RSF1c , IME1nc and oak allele of IME1c which had only 3 replicates for the subsequent analyses . The libraries were indexed separately and pooled into one sequencing reaction . The pool was run on multiple lanes until we obtained a minimum of 4 million reads per sample . The sequencing reads for each sample were combined across all sequencing runs . If present , adapter dimers were removed and the sequencing reads were aligned to the Verified and Uncharacterized open reading frames ( ORFs ) in the S . cerevisiae reference genome ( S288C , genome release R63-1-1 , Saccharomyces Genome Database ( SGD , http://www . yeastgenome . org/ ) ) using Bowtie , version 0 . 12 . 7 [55] . Only unique alignments with maximum 2 mismatches in the –best alignment mode were accepted . The counts for all the reads aligned to a given ORF were summed to give the raw counts per ORF . The raw counts were scaled to account for differences in sequencing depths per sample by calculating the normalized count values across all samples as described in DESeq , version 1 . 9 . 11 [56] . To normalize samples , the ratio of a gene's counts to its geometric mean across all the samples was calculated for each gene . Assuming that most genes are not differentially expressed , the scaling factor for each sample was the median of the ratios of all the genes in the sample . For each gene in a given sample , the counts were then normalized by the scaling factor for that sample . The normalized gene counts were used for all further analyses . The lowest 20th percentile of ORFs , based on the sum of the normalized counts across all samples for the given ORF , was removed to reduce the number of tested hypotheses and false positives . 4633 ORFs out of the initial 5792 ORFs remained after the filtering stage . The normalized gene counts and the raw expression data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus [57] and are accessible through GEO Series accession number GSE55409 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? acc=GSE55409 ) . All statistical analyses were performed in R [58] . Linear regression was performed using the lm function in R . The genes whose expression is best explained by the genotype of the sporulation QTN were found in a two-step process . To eliminate any variation due to growing the allele replacement panel on different days , for a given ORF , i , we computed the residual gene expression ( εi ) for all 4633 ORFs after removing the additive effect of the day of growth ( DAY ) on the normalized counts ( Ni ) of each ORF . Thus , the DAY model was applied on a gene-by-gene basis resulting in 4633 gene-specific DAY models . The residual gene expression from the DAY models was used in subsequent analyses . For each gene , the effect of the sporulation QTN on gene expression was computed in a second linear model by regressing the genotype of each of the four QTN ( RME1nc , RSF1c , IME1c and IME1nc ) on the residual gene expression from the previous modeling step . Again , 4633 gene-specific expression models were run . The in the model below indicates that both additive and interaction effects were considered . The effect of the sporulation QTN on gene expression was also compared to results from alternative model where the effect of DAY as well as the genotype of each of the four QTN ( RME1nc , RSF1c , IME1c and IME1nc ) was regressed on the log-transformed normalized expression counts of each gene . We used the Benjamini-Hochberg procedure [59] on the model p-values to control the False Discovery Rate ( FDR ) to 10% and obtained 289 significant models . The unadjusted p-value of the significant models was 0 . 006 or lower . We also assessed significance of the gene expression models by permuting the genotype designations on all 63 samples and regressing the effect of the permuted genotype on the residual expression from the DAY model for all ORFs . The p-value corresponding to the lowest 5th percentile was obtained from the distribution of model p-values across the genome . The permutations and genotype modeling were repeated 1000 times to determine the distribution of the 5th percentile of model p-values . We found the unadjusted p-value threshold from the FDR control to be almost 2 standard deviations below the average of the distribution of 5th percentile model p-values obtained from the permutations . Since the FDR p-value threshold was more stringent than that obtained from the permutations above , we performed the remaining analyses on the 289 significant models . The effect of individual QTN on gene expression was found by comparing nested models using ANOVA and calculating the fraction of variance explained by all significant factors of the given allele . In the ANOVA analysis , individual factors were considered to be statistically significant with a fairly permissive threshold ( f-statistic p-value<0 . 1 ) . We chose to report the effect of each variant as the computed variance explained by each variant , rather than the magnitude of the regression coefficients . We chose this metric because genes are expressed on very different scales which makes it difficult to interpret effect sizes across genes . The coefficient of variation ( CV = σ/μ ) of the expression of each ORF across all four biological replicates was calculated for all 5792 ORFs in the genome . For a given ORF , σ represents the standard deviation of gene expression counts across the four biological replicates and μ represents the mean of gene expression counts across the biological replicates . To remove the effect of day of growth and to perform this particular analysis on the original expression scale , the normalized expression counts ( using the DESeq normalization procedure ) for each gene were further normalized for day-to-day variation as follows . A given day was arbitrarily chosen as Day A . For each ORF , the fitted values from the DAY model for all samples grown on a given day represent the mean expression of the ORF across all 16 strains in the panel for the given day . Variation due to the growing the allele replacement panel on different days was removed by dividing Ni , the normalized gene expression counts for the ORF by the ratio of the mean expression of the particular ORF on a given day to the mean expression of that ORF in the 16 strains grown on day A . These “day-corrected” expression values were used for the CV calculations as well as for the heat map ( Figure S1 ) . The wilcoxon rank sum test was applied using the standard wilcox . test function in R [58] . Enrichment analysis for gene-ontology ( GO ) categories was performed using the functional category analysis tools at DAVID Bioinformatics Resource 6 . 7 [60] , [61] . | There have been major efforts in the study of human disease to identify genetic polymorphisms that cause changes in gene expression . The assumption underlying these studies is that gene expression changes will be responsible for the disease . However , it is unclear if we can predict how a polymorphism affects the variation in disease based on the extent to which it explains variation in gene expression . We have taken advantage of four genetic polymorphisms that affect the ability of budding yeast cells to form spores . The variants were identified in naturally occurring strains , subject to natural selection pressures in the wild , and not from lab strains . These variants lie in factors that control gene expression , which gives us power to compare how the polymorphisms affect variation in both gene expression and the downstream phenotype . We find that the amount of variation in gene expression explained by the variants does not correlate with the amount of variation observed in spore formation , which has implications for studies that attempt to infer the effect of a polymorphism on phenotypic variation by studying its effect on gene expression variation . |
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After oral exposure , prions are thought to enter Peyer's patches via M cells and accumulate first upon follicular dendritic cells ( FDCs ) before spreading to the nervous system . How prions are actually initially acquired from the gut lumen is not known . Using high-resolution immunofluorescence and cryo-immunogold electron microscopy , we report the trafficking of the prion protein ( PrP ) toward Peyer's patches of wild-type and PrP-deficient mice . PrP was transiently detectable at 1 day post feeding ( dpf ) within large multivesicular LAMP1-positive endosomes of enterocytes in the follicle-associated epithelium ( FAE ) and at much lower levels within M cells . Subsequently , PrP was detected on vesicles in the late endosomal compartments of macrophages in the subepithelial dome . At 7–21 dpf , increased PrP labelling was observed on the plasma membranes of FDCs in germinal centres of Peyer's patches from wild-type mice only , identifying FDCs as the first sites of PrP conversion and replication . Detection of PrP on extracellular vesicles displaying FAE enterocyte-derived A33 protein implied transport towards FDCs in association with FAE-derived vesicles . By 21 dpf , PrP was observed on the plasma membranes of neurons within neighbouring myenteric plexi . Together , these data identify a novel potential M cell-independent mechanism for prion transport , mediated by FAE enterocytes , which acts to initiate conversion and replication upon FDCs and subsequent infection of enteric nerves .
Prions are infectious proteins composed of an abnormally folded isoform of the prion protein ( PrPSc ) , the accumulation of which causes variant Creutzfeldt–Jakob disease , scrapie , and bovine spongiform encephalopathy , among other diseases . Prions propagate by converting endogenous , cellular prion protein ( PrPC ) into PrPSc containing a β-sheet core . Isolated PrPSc can be found in a wide range of aggregation states , from small oligomers to amyloid , and at least in larger aggregates the C-terminal portion of PrPSc acquires resistance to protease treatment [1] . PrPC is a ubiquitously expressed protein that is most abundant in the nervous system . The accumulation of PrPSc causes morphological changes in the central nervous system including astrocytosis , neuronal cell loss and spongiform pathology and , in some types of prion disease , amyloid plaque formation . Pathology builds up during a long incubation period that ends in a short clinical phase and death . Expression of PrPC in the host is required for successful infection , since it provides the substrate for the conversion to PrPSc [2]–[5] . Prions are highly resistant to denaturation by chemical and physical means , making disposal and disinfection difficult . This resistance may also contribute to their ability to survive passage through the digestive tract [6] , allowing transmission of prion disease via prion-contaminated food . Many naturally occurring prion diseases are considered to be acquired orally , and are accompanied by accumulation of PrPSc in the lymphoreticular system long before invasion of the nervous system takes place [7]–[13] . Indeed , when specific components of the gut-associated lymphoid tissues ( GALT ) are absent , the transport of prions from the gut lumen to the nervous system is dramatically impaired [9] , [12] , [14] . The exact mechanisms by which infectious prions are transmitted from the gut lumen to the central nervous system remain elusive ( for reviews see [15]–[17] ) . The luminal surface of the intestine limits the access of pathogenic microorganisms to the underlying host tissues , and is protected by a single layer of epithelial cells bound by tight-junctions . Located within the villus epithelium and follicle-associated epithelium ( FAE ) of the Peyer's patch are microfold cells ( M cells ) , a unique epithelial cell type specialized for the transepithelial transport of macromolecules and particles ( for a review of M cells see [18] ) . M cells enable the host's immune system to sample the intestinal lumen and mount an appropriate immune response . However , some pathogenic microorganisms exploit M cells and use them to gain entry into mucosal tissues [18] . Using an in vitro system , M cell-like cells have been shown to actively transcytose the scrapie agent through to the basolateral side of the epithelium [19] , [20] , and studies in mice suggest prions might likewise be translocated across the FAE by M cells in vivo [21] . Together these data imply that M cells are plausible sites for the transepithelial transport of TSE agents across the intestinal epithelium . However , other data suggest such transport might also occur independently of M cell-mediated transcytosis via enterocytes [22] , [23] . Studies in which isolated sheep gut loops were injected with scrapie brain homogenate [22] suggested that disease-specific PrP was transported across the absorptive epithelium of villi into lacteals . Other studies have shown that in response to inflammatory stimuli , mononuclear phagocytes within the lamina propria , including macrophages and classical dendritic cells ( DC ) , can insert dendrites through the tight junctions between intestinal epithelial cells . These projections enable the cells to sample the luminal contents directly [24] implying another potential route of transepithelial TSE agent transport . Clearly , the way that orally introduced , partially proteinase-resistant prions survive the proteolytic conditions in the alimentary tract without losing all their infectivity and then cross the gut epithelium is still a matter of debate . After passing the epithelial barrier , prions are thought to be captured by underlying migratory classical dendritic cells [25] that transmit the infectious agents by an as-of-yet unknown mechanism to the germinal centres . Within the germinal centres , prions accumulate upon follicular dendritic cells ( FDCs ) , which are specialized stromal , mesenchymal cells of the immune system . FDCs are nonphagocytic , nonmigratory cells that trap native immune complexes on their surface through the expression of cellular complement receptors . The expression of high levels of PrPC in FDCs is regarded to be important for the accumulation and replication of prions upon their surfaces [26]–[30] . FDCs are considered to amplify the prions above the threshold level necessary to infect peripheral nerves [9] , [12] , [14] , [31] . Prions are then thought to gain access to the central nervous system via physical interaction with peripheral nerve fibers of the enteric nervous system [32] . Retrograde axonal transport would deliver prions to their main pathological target , the brain . Due to the lack of satisfactory experimental models , some of the observations in prion transmission are based solely on in vitro studies . Here , we examined the in vivo time-course of oral prion infection in the GALT of wild-type ( wt ) mice that are susceptible to prion infection and of PrP-deficient ( Prnp–/– ) mice that do not succumb to prion disease [33] . Both wt and Prnp–/– mice were orally infected with 3 different rodent-adapted scrapie strains ( ME7 , RML and Sc327 ) , and intestinal and lymphoid tissues collected at specific days post-feeding ( dpf ) . Samples were then analysed by immunofluorescence ( IF ) and cryo-immunogold electron microscopy ( cryo-immuno EM ) in order to identify , at the ultrastructural level , the cell types and subcellular organelles that are involved in prion trafficking and early pathogenesis . Our data show that prion uptake and transfer across the follicle-associated epithelium ( FAE ) of the gut occurred of the follicle-associated epithelium ( FAE ) independently of cellular PrPC expression . We show that PrP was first transiently detectable mainly within large LAMP1-positive endosomes of FAE enterocytes and at much lower levels within M cells . Proteins of FAE enterocytes were found on vesicles in the extracellular material adjacent to FAE enterocytes , and on the surface of FDCs , and these vesicles could act to transport prions towards FDCs . Furthermore , between 7–21 dpf , increased PrP labelling was only observed on the plasma membranes of FDCs from wild-type mice , identifying these membranes as the first site of PrP conversion and replication within Peyer's patches . Together , these data identify a novel potential M cell-independent route of prion uptake and transfer from the gut lumen mediated by FAE enterocytes that may have an important influence on susceptibility to oral prion infection .
In orally infected animals , prions are suspected to enter the lymphoid and nervous systems from the intestine through the FAE-overlying follicles of the mucosa-associated lymphoid tissue [15]–[17] . Thus , we examined the FAE and subepithelial dome ( SED ) region of Peyer's patches in wild-type ( wt ) uninfected mice and in wt mice infected with ME7 or RML prions . For comparison we also examined normal villi in prion-infected and uninfected mice . Late endocytic compartments were identified using antibodies against LAMP1 , a membrane protein specific for these compartments ( for an overview , see Figure 1A ) . By immunofluorescence ( IF ) analysis , we found significantly larger , LAMP1-positive endosomes with a typical multivesicular body phenotype in the FAE as compared to those in neighbouring villi ( Figure 1B , 1D and Figure S1 ) . Large LAMP1-positive endosomes were also observed in macrophages in the SED region , but had different more pleiomorphic morphology and stained less intensively with LAMP1-antibody ( Figure 2A , Figure S1 ) . Large LAMP1 endosomes of FAE enterocytes were abundant , had a more regular round shape ( appeared as ring-like structures in IF and histochemistry sections ) and had predominantly an apical location between the nucleus and brush border . Occasionally , when macrophages invade into the FAE their LAMP1 positive endosomes retain their pleiomorphic morphology and are mostly located basolaterally between the enterocyte nucleus and the basal membrane ( see Figure S1C ) . At EM level investigating the cellular contours of individual cells by following their plasma membranes it could be confirmed that the apical large LAMP1 positive endosomes were located in FAE enterocytes and not for example in FAE-invading macrophages ( Figure S1 ) . Similar observations were made in both prion-infected and uninfected wt mice ( data not shown ) . These data suggest that FAE enterocytes could play an important role in transcytosing partially digested , gut-derived proteins to macrophages within the SED . The apical large LAMP1 positive endosomes of FAE enterocytes were so distinctive that their presence could be used as a useful landmark for detecting the FAE in IF and EM sections . Epithelial M cells ( or microfold cells ) within FAE are considered as an entry point for many intestinal pathogens , both bacteria and viruses [18] and also proposed as portals of entry for prions [19]–[21] . We identified M cells in the FAE using Ulex europaeus agglutinin 1 ( UEA-1 ) as a marker . As anticipated , UEA-1 bound strongly to the brush borders of M cells and to a lesser extent to their basolateral cell membranes and the limiting membranes of intracellular vesicles ( Figure 1 C and D ) . The UEA-1 label was restricted to M cells in the FAE and not observed in the neighbouring villus ( Figure 1E , although especially the mucus containing granules of goblet cells elsewhere in the villi were often UEA-1 positive . Occasional goblet cells could also be seen in the FAE , but were easily distinguished from the UEA-1 positive M cells by their distinct morphology . The identification of M cells in the FAE was further confirmed by using antibodies specific to the M cell markers GP-2 [34] and annexin V [35] see Figure S2 . Cryo-immuno- EM revealed that UEA-1 -positive cells within the FAE had typical , short , irregular microvilli ( Figure 1F and 1G , Figure S2 and S3 ) and often harboured lymphocytes within an intracellular pocket . Each of these observations is an established characteristic of M cells . Our analysis revealed that M cells had smaller , LAMP1-positive late endosomes and completely lacked the large late endosomes found in FAE enterocytes . Few UEA-1 positive goblet cells were seen in the FAE by EM , but were easily distinguished from M cells by their apical mucus-containing granules and pronounced ER , typical for secretory cells ( see Figure S2 ) . Next , groups of PrP-expressing wt mice and PrP-deficient ( Prnp-/- mice ) were orally exposed to prions , by feeding brain homogenates from prion-infected mice . As controls , mice were fed uninfected brain homogenate . In order to trace the fate of the brain inoculum Peyer's patches were collected at intervals after exposure , and the early cellular and subcellular localization of brain homogenate-derived neurofilaments ( NF ) determined by IF . Peyer's patches were also examined from age-matched mice that had not been fed brain homogenate . Both wt and Prnp-/- mice fed either prion-infected or uninfected brain homogenate showed positive immunolabeling for NF , whereas untreated control mice were negative for NF immunolabeling ( data not shown ) . In Peyer's patches taken at 1 dpf , NFs were detected in FAE enterocytes within the large LAMP1-positive endosomes of mice exposed to prion and normal brain homogenate ( Figure 2A , 3A ) . Both wt and Prnp–/– mice showed similar levels of NF staining in FAE enterocytes , indicating that the uptake of NF was not dependent on PrPC expression ( Figure 3A ) . Much fewer NF-positive endosomes or other NF-containing organelles were found within M cells in the FAE of Peyer's patches from the prion-exposed or control animals ( Figure 3A ) . By immunogold-EM the prion inoculum was locally visible in the lumen of the gut ( Figure 2F and Figures S3 , S4 and S5 ) indicating that the transcytosis process of the orally introduced PrP was still going on at 1 dpf . In addition , throughout this study , no evidence was found to indicate direct uptake from the gut lumen via macrophages or dendritic cells , and few NF-positive endosomes were seen in neighbouring villi . In samples taken at later time-points ( >1 dpf ) , the NF signals decreased in the LAMP1-positive FAE enterocytes of all mice . Together , these data suggest that the initial uptake of the brain homogenate from the gut lumen occurred mainly via enterocytes within the FAE and that the active uptake of the inoculum was still ongoing one day after feeding . To determine whether the NF immunolabeling was related to prion uptake from the gut lumen , we analysed subcellular structures of the FAE for PrP accumulation by cryo-immuno EM . No PrP-specific gold particles were found in the FAE of Peyer's patches from uninfected wt and Prnp-/-mice ( day 0; Figure 3B ) . In tissue obtained at 1 dpf , PrP-specific immunolabelling was detected in FAE enterocytes from all mice , regardless of their PrPC expression levels . Animals fed prion-infected brain homogenate showed greater PrP labelling compared to mice fed normal brain homogenate ( Figure 3B ) . Although the immunodetection conditions used here do not allow discrimination between cellular PrPC and pathogenic PrPSc , the PrP labelling in prion-exposed mice is likely due to PrPSc . The PrP signal was occasionally found on electron-lucent , small early endosomal vacuoles ( Figure 2B and 2C ) , but was most abundant within large endosomal vacuoles similar to those labelled with LAMP1 ( Figure 2D–F , Figure S4 and S5 ) . As observed for the uptake of NF , PrP accumulated in FAE enterocytes . Comparatively much lower levels were found in M cells or in the enterocytes of the neighbouring villi at 1 dpf ( Figure 3B ) . Also similar to the NF signals , the PrP signal detected within FAE enterocytes was transient: PrP labelling was decreased in samples at 2 dpf , and undetectable by 14 dpf in both wt and Prnp–/– mice ( Figure 3B ) . In order to discriminate the “exogenous” PrPSc within the orally administered prion inoculum from “endogenous” PrPC or PrPC within the prion inoculum an additional set of experiments was performed . Groups of wt and Prnp-/- mice were fed PK-treated brain homogenate prepared from terminally Sc327 scrapie-infected hamsters . Immunoblot analysis confirmed that treatment of the inoculum with PK destroyed any PrPC present within the brain homogenate leaving proteinase-resistant PrPSc ( Figure S6 ) . Peyer's patches were collected at 6 and 24 hours post infection , processed for cryo-immuno EM and sections were immunolabelled with mAb 3F4 directly conjugated to 10 nm gold particles . Since the mAb 3F4 recognizes only hamster PrP and does not label mouse PrP [36] , the use of this mAb ensures any PrP detected is hamster-specific PrPSc from the Sc327-infected brain inoculum , and not PrPC expressed in the host mouse . Small clusters of gold labelling , indicative of PrPSc , were detected within small electron-lucent early endosomal vacuoles at 6 h postfeeding ( Figure S7A and B ) . By 24 h after infection similar clusters of PrPSc were detected in the lumen of multivesicular bodies ( Figure S7C and D ) . mAb 3F4-specific immunolabelling was observed predominantly in FAE enterocytes both in prion-infected wt and Prnp-/- mice , but was not detectable in control animals fed with PK-digested normal brain homogenate . Together , these data demonstrate that enterocytes within the FAE acquire PrPSc from the gut lumen . Furthermore , these data also confirm that PrP uptake from the gut lumen was independent of host PrPC expression , and also independent of the PrP sequence of the inoculum and therefore not affected by the species barrier between hamster and mouse . Together , these data suggest that both PrPC and PrPSc cross the FAE via the endosomal system of enterocytes within the bulk flow of transcytosed material from the gut lumen . Furthermore , our data clearly show that the majority of PrP uptake from the gut lumen occurs within FAE enterocytes when compared to M cells and independently of the expression of cellular PrPC , since no substantial differences were observed between tissues from prion-exposed PrP-deficient and wt mice . Following transcytosis of brain material across the FAE , NFs were detected within a cluster of large LAMP1-positive organelles ( Figure 2A ) . The SED region is known to harbour a large population of CD11c-positive cells that are often referred to as classical dendritic cells ( DC ) , although they most likely represent a heterogeneous mixture of subpopulations of classical dendritic cells and macrophages in their different maturation forms [37] , [38] . Unfortunately , CD11c-specific antibodies did not work on the aldehyde-fixed tissues used in the current study . However , most mucosal mononuclear phagocytes and all those within Peyer's patch germinal centres express CD11c indicating that this marker alone does not directly discriminate classical DC [38] . Thus , in order to discriminate between classical dendritic cells and macrophages , we used antibodies specific to major histocompability complex class II ( MHC II ) molecules to detect classical dendritic cells and antibodies against ferritin as a marker for macrophages ( Figure 4 , A and B , respectively ) . Macrophages express high levels of ferritin and harbour much lower levels of MHC II molecules on their surface ( See Figure S8 ) . In 1 dpf samples , the macrophages within the SED harboured large quantities of NF ( Figure 2A ) which co-localized with LAMP1-positive structures . Many of these cells also appeared strongly positive for ferritin . Ferritin-positive cells that contained phagocytosed remnants of apoptotic lymphocytes , a landmark for tingible body macrophages ( TBMs ) were found further away from SED in the germinal centers ( Figure 4C and D ) . In SED PrP-specific labelling was found in late endosomes of low MHC II –expressing macrophages by cryo-immuno EM ( see Figure S9 ) indicating transcytosed prion inoculum in the SED region . Some macrophages were observed with intensely labelled intracellular LAMP1 structures , and LAMP1-positive membrane structures were also found in the surrounding extracellular space ( Figure 4E ) . These probably secreted vesicles suggested intercellular exchange of membranous material by exocytosis and endocytosis ( Figure 4E ) . We conclude that within the SED , PrP mostly accumulates within ferritin-positive macrophages , but not in MHC II–positive classical dendritic cells . FDCs are typically binuclear with distinct chromatin pattern characteristics , electron-lucent cytoplasm and numerous convoluted extracellular extensions surrounding lymphocytes at different stages of differentiation . These characteristics allow FDCs to be readily identified by EM [39] , [40]; typical examples of FDCs engulfing B lymphocytes are shown in Figure 5A–C . In wt mice , oral exposure to either ME7 or RML prions resulted in a significant increase in PrP immunolabeling on the plasma membrane of FDCs beginning at 7 dpf ( Figure 6A and B ) , which increased and became more uniform in samples taken at 21 dpf ( Figure 5D–E ) . At 105 dpf , locally high levels of PrP label were found on FDC plasma membranes in germinal centers of PrP-infected wt ( Figure S10 ) . The increase in PrP signal ( Figure 6 ) was observed only in prion-infected wt mice and was restricted to the plasma membrane of FDCs These observations suggest that the increase in PrP immunolobelling on FDCs of prion-infected mice was PrPSc . Initially , the increased PrP immunolabelling was often found heterogeneously on long dendritic extensions of single FDCs . Increased PrP levels were simultaneously also observed in germinal centres of mesenteric lymph nodes draining the intestines of wt mice ( data not shown ) . Increased PrP labelling was not observed in wt mice fed normal brain homogenate , suggesting that the labelling we found on FDCs of prion-infected mice was disease-related PrPSc . No PrP immunolabelling was observed on FDC in prion-exposed Prnp–/– mice , indicating that the increased PrP signal was unlikely to be due to accumulation of PrPSc from the prion inoculum . No accumulation of PrP was observed in samples taken from wt spleens during the first 21 dpf ( data not shown ) . Thus , coincident with the disappearance of PrP from FAE enterocytes , PrP accumulated on the plasma membrane of FDCs within Peyer's patches and mesenteric lymph nodes in a PrPC-dependent fashion . Together , these data imply the first site of PrPSc conversion and replication following oral exposure . To characterise the PrP on FDCs , and to investigate whether protease-resistant PrPSc had formed , sections were additionally treated with trypsin before immunolabeling with R2 . This antibody recognises an epitope of PrP containing a trypsin cleavage site [41] , and as seen previously for sections of mouse brain infected with RML prions [42] , little R2 labelling of PrPC or PrPSc remained on FDC plasma membranes after the treatment , either at 21 days ( Figure 5F ) or 105 dpf ( Figure S10 ) . Further IF and cryo-immuno EM of cells in the vicinity of FDCs revealed PrP within LAMP1-positive , late endocytic compartments in cells that had all the morphological landmarks of tingible body macrophages ( TBMs ) ( Figure 4C and D; Figure 7; Figure S10 ) . TBMs are a subset of large mononuclear phagocytes that reside in germinal centers of secondary lymphoid tissues . TBM contain many phagocytised apoptotic cells in various states of degradation ( referred as tingible bodies ) . The detection of PrP within these compartments implied that these cells were most probably scavenging PrP . The number of TBM cell profiles positive for PrP increased during the course of prion infection , suggesting enhanced TBM activity in the Peyer's patches of infected wt mice ( Figure S11 ) . Compared to FDCs at the same time points , a higher portion of the PrP labelling of R2 antibody in the late endosomes / lysosomes of TBMs was unaffected after trypsin treatment ( Figure 7E and S10F ) , suggesting that the early PrP labelling on FDC plasma membranes may be protease-sensitive PrPSc , but that PrPSc targeted for degradation may have acquired a degree of protease resistance . Again , tissues from uninfected mice and prion-infected Prnp-/- mice did not show PrP in TBMs ( data not shown ) , which argues that this increase is specific to the propagation of PrPSc . In samples from prion-infected wt mice obtained at 21 dpf , the first signs of PrP accumulation within myenteric ( Auerbach's ) plexi were observed between the inner circular and outer longitudinal layers of the muscularis in regions close to Peyer's patches ( Figure 8A and B ) . In samples from prion-infected , wt mice obtained at 105 dpf , almost all the plexi closely associated with Peyer's patches had strong accumulations of PrP ( data not shown ) . Congruent with observations above , increased PrP was only detected within the enteric nervous systems of wt mice exposed to prion-infected brain homogenate . We have previously shown that caveosomes are involved in intracellular PrPC trafficking in cultured CHO cells [43] . We found no evidence for a role of caveosomes in PrP trafficking in vivo within the stroma of Peyer's patches . However , smooth muscle cells neighbouring infected plexi appeared rich in caveosomes ( Figure 8C ) , which often contained PrP ( Figure 8D ) , implying a potential mechanism through which PrP may be disseminated within the muscle layer of the intestine . After trypsin treatment of cryosections of prion-infected wt animals , approximately 9% of PrP label remained on plasma membranes of neurons at myenteric plexi of sections collected at 21 dpf , and increased to 15% for sections obtained at 105 dpf , indicating the presence of protease-resistant PrPSc on enteric nerves at relatively early phases of prion infection ( Figure 8E and F ) . The definitive epithelial marker A33 ( Gpa33; [44] ) is expressed at high levels on the basolateral plasma membrane of the FAE and villous enterocytes ( Figure 9A ) . Although Gpa33 is not expressed by macrophages and classical dendritic cells ( See Figure S12 ) , cryoimmuno EM revealed the presence of A33 protein within the endosomes of SED macrophages , indicating delivery of A33-positive membranes from enterocytes to macrophages ( Figure 9B ) . Careful analysis revealed A33-positive vesicles within the germinal centres of Peyer's patches ( Figure 9C ) from each mouse group . In contrast , no A33 immunostaining was detected in Peyer's patches from A33-deficient control mice , confirming the specificity of the A33-specific antibody ( Figure S13 ) . Weak A33 immunolabeling was also observed within the germinal centrers of mesenteric lymph nodes , whereas those in the spleen were negative ( data not shown ) . The presence of small amounts of A33 antigen within the germinal centres of mesenteric lymph nodes most likely represents membrane trafficking from the gut epithelium to the B cell follicles as described [45] . Within the germinal centres , A33 was present on the surface of FDCs and often in close proximity to TBMs . It is therefore tempting to speculate that the scavenging macrophages may be carrying epithelial membrane components ( including PrPSc on A33-positive membranes ) from the FAE to the germinal centre , where they are deposited on the surface of the FDCs . Our EM data ( Figure 9 ) suggest that LAMP1-positive , late endocytic multivesicular bodies of FAE enterocytes fuse with the plasma membrane [46] , releasing their intravesicular membrane contents as exosomes in the extracellular space , where they may be phagocytosed within the SED by macrophages and further spread in a “taste and spit” -manner of repetitive endo- and exocytosis events by macrophages . Consistant with this idea , exosome-like membrane structures were shown to be positive for A33 , PrP , and LAMP1 ( Figure 9D–F , respectively ) . Thus , together these data provide further evidence for the association of FAE enterocyte-derived antigens such as A33 upon the surface of FDCs within Peyer's patches and mesenteric lymph nodes .
Over the last few years , it has been noted that several amyloid or protein-misfolding diseases are characterized by prion-like propagation mechanisms [47] , [48] . The main difference that distinguishes the prion diseases from other amyloid or protein-misfolding diseases is the fact that prions can be transmitted from one animal to another as truly infectious diseases . In many cases , this transmission occurs via the oral route , emphasizing the relevance of the present study . Here , we analyzed the initial uptake of PrP from the intestinal lumen via enterocytes of the FAE . While this initial uptake proved to be independent of the expression of cellular PrPC , the subsequent propagation and disease progression required an endogenous supply of PrPC . The precise cellular mechanism through which prions are acquired from the gut lumen and transferred to FDCs within Peyer's patches is not known , although it has been shown that the accumulation of prions upon FDCs within Peyer's patches is critical for their efficient spread to the enteric nervous system [9] , [12] , [14] . Many other aspects of prion invasion have remained hypothetical , especially due to the lack of physiologically relevant in-vivo studies performed at the subcellular level . Prion detection during the early stages of oral infection has also been a problem . In previous immuno EM studies , PrPSc was first detected 70 days after oral exposure and only in denatured samples [11] , [22] . We were able to investigate earlier stages after oral inoculation , by using adaptations of high-resolution methods developed in previous studies [42] , [49] . The GALT are filled with endogenous immunoglobulins , which limit the use of mouse antibodies for immunodetection . However , many PrP-specific monoclonal antibodies ( e . g . , mAb 6H4 [50] and mAb R2 [51] ) were raised in Prnp–/– mice . To avoid interference of endogenous mouse immunoglobulins and to permit better penetration in the tissue analysed , we collaborated with Aurion ( Wageningen , The Netherlands ) to conjugate mAb 6H4 and Fab fragments mAb R2 to UltraSmall gold particles . The PrP binding sites were visualised by silver enhancement , which enabled us to detect directly by EM the binding sites of the primary PrP antibody . Unconjugated R2 Fab fragments and UltraSmall gold-labelled Fab fragments of another PrP antibody have previously been used for IF and cryo-immuno EM on normal and prion-infected mouse brain , and R2 has been shown to recognize both PrPC and PrPSc on undenatured tissue sections [42] , [51] . By applying these novel PrP antibody derivates , we gained higher PrP detection levels and were able to follow PrP trafficking in nondenatured GALT as early as 1 dpf , as inoculated PrP , and 7 dpf , as replicating PrP on the surface of FDCs . The results of our time-course studies , employing IF and cryo-immuno EM with two distinct in-vivo mouse models , revealed the cellular and intracellular sites of prion trafficking after oral prion infection . Figure 10 represents a proposed model for prion neuroinvasion from gut lumen via Peyer's patches to enteric nervous system based on the results of the present study . In all gut samples examined , we found PrP accumulation in large , late endocytic multivesicular bodies of FAE enterocytes . Although PrP was also observed within M cells , this was at much lower levels than that within FAE enterocytes . Furthermore , our data indicated that PrP transcytosis across the FAE was independent of cellular PrPC expression . In prion-infected wt mice , we subsequently found PrP in late endosomes of SED macrophages followed by a gradual increase upon the surface of FDCs within germinal centres . Relatively large amounts of PrP could also be found in late endosomes of TBMs in germinal centres . Subsequently , beginning at 21 dpf , and following accumulation upon FDCs , increased levels of PrP were observed on the surface of neurons at submucosal and myenteric plexi . At 105 dpf ( approximately one third through the incubation period for 129/Ola mice orally exposed to ME7 scrapie prions ) PrP-immunolabelling on neurons had increased . Our observations of increasing PrP on FDCs are consistent with previous reports . However , our data did not support the prevailing view concerning the initial uptake and trafficking of orally administered prions . In the gut , the FAE-overlying follicles of the mucosa-associated lymphoid tissue are key players in initiating mucosal immune responses and are strongly suspected to be the major entry site from the intestine into the lymphoid and nervous systems in animals orally infected with prions [22] . Within the FAE specifically , an in-vitro study suggested that M cells were the main target and gatekeepers for prion invasion [18] , [19] , [21] . In contrast , data from our in-vivo study suggest that M cells are not the primary target for prion entry through the FAE . Instead , most of the PrP was found in the endosomes of enterocytes within the FAE . These appear to be specialized enterocytes with enlarged late endosomal compartments compared to the “normal” enterocytes in the villi . Data in the current study appear to differ significantly between those data in the study by Foster et al . [21] where PrP was mainly detected in association with Peyer's patch and cecal patch M cells . The reasons for this apparent discrepancy are uncertain . In the current study mice were fed doses of scrapie prions that require amplification in the GALT prior to neuroinvasion [9] , [25] . The use of such physiologically-relevant doses is important as direct neuroinvasion can occur following exposure to higher doses . In the Foster study [21] a substantially higher dose of prions was delivered directly into the stomach of recipient mice than that used in the current study . Due to the high concentration of prions within the intestine it is plausible that other cell populations were principally involved in acquiring prions . We found indirect evidence that the FAE enterocytes may exocytose the intravacuolar contents ( including endocytosed PrP from the gut lumen ) of their late endosomes into the extracellular space of the SED . These exosome-like structures ( for a review , see [52] ) are LAMP1- and A33-positive and may be part of normal machinery [45] for antigen presentation to the immune system , since we found traces of the epithelial marker A33 within macrophages and upon FDCs . The concept of exosomes as membrane carriers was originally described in [53] . A33-related exosome secretion from intestinal epithelium has been described in general [54] and specifically related to prion transmission upon oral challenge [55] . However , it is difficult to find direct evidence for this highly dynamic process at the ultrastructural level in in-vivo models . Genetic ablation of PrP abrogates susceptibility of mice to prion diseases [4] . For PrP trancytosis through the FAE , PrPC is not required , since transcytosis also occurs in PrP-deficient animals . Therefore , we conclude that PrP is likely to be transcytosed across the FAE in a non-specific manner within the bulk flow of other lumen-derived , digested , and endocytosed material . After transcytosis through the FAE , the migrating CD11c-positive cells ( generally considered to be classical dendritic cells within the SED ) are thought to endocytose and transfer the prions to germinal centres where prion replication may occur [25] . From there , by as-of-yet unknown cellular mechanisms , the prions are able to infect the peripheral enteric nervous system and gain further access to the main pathological target of prion diseases , the central nervous system . In macrophages of the SED , we found PrP in LAMP1-positive compartments of ferritin-positive cells and to a lesser extent in MHC II-positive cells ( Figure S9 ) . These observations suggest that PrP trafficking through the SED occurs in endosomal compartments of more macrophage-like mononuclear phagocytic cells rather than classical dendritic cells . Furthermore , in the SED , PrP was only detected in endosomal compartments of these macrophages and not on the cell surface . Transient depletion of CD11c-positive cells before oral exposure to prions has been shown to markedly delay the course of neuroinvasion [25] . When we retrospectively analysed the Peyer's patches of these CD11c-positive-cell-depleted mice ( diphtheria toxin–treated , CD11c-diphtheria toxin receptor-transgenic mice ) , we found that the ferritin-positive macrophages within the SED were likewise transiently depleted ( unpublished data ) . Interestingly , protease-resistant PrP has been reported to be transcytosed in a complex together with ferritin through intestinal epithelial cells [23] , but the relevance of this finding for the ferritin-positive macrophages that are active in PrP endocytosis remains unclear . The subsequent accumulation of PrP upon the surface of FDCs was only observed in Peyer's patches from orally exposed wt mice , and increased throughout the duration of the experiment . These PrPC-expressing , nonmigratory cells have a limited endosomal/phagocytic apparatus and are specialized to present native immuno complexes on their surface . These observations strongly suggest conversion and replication of PrPSc de novo upon the surfaces of FDCs and not merely the accumulation of inoculated PrPSc . Taken together , these observations implicate two cellular compartments with distinct roles during the initial stages of orally-acquired prion disease: ( 1 ) endosomal compartments active in transient transcytosis and/or degradation/storage of PrPSc ( LAMP1-positive endosomes in FAE enterocytes , M cells , villous enterocytes , SED macrophages , and germinal centre TBMs , and caveosomes in submucosal smooth muscle cells ) and ( 2 ) the cell surface of FDCs and enteric neurons , where prion replication most likely occurs . Importantly , no increase of PrP surface labelling was observed on macrophages in the course of the early infection , even if they had high labelling densities in their endosomal compartments . Our data suggest uptake into the endosomal compartments occurs independently of cellular PrPC expression , whereas PrP accumulation upon FDCs and enteric neurons is critically dependent upon the expression of PrPC . Further , we were able to confirm the relevance of our results by using three different mouse infection models , with Prnp-/- mice resistant to disease as controls . In our previous cryoimmuno EM studies , on FVB mouse hippocampus infected with RML prions , the PrPC levels were also specifically examined and were not found to increase as a result of prion disease . Increases in R2 labelling could thus be attributed to the formation of PrPSc . This labelling was also mainly on plasma membranes and on early endocytic or recycling vesicles rather than late endosomal compartments [42] . A high proportion of the R2 labelling in RML prion-infected FVB hippocampus was found to be trypsin sensitive [42] , as was the case with the labelling on FDCs and enteric neurons in the present study . These results are consistent with reports that protease-sensitive forms of PrPSc predominate in some types of prion disease [56] , [57] . Prions in the oral inoculum probably included protease-resistant forms that were able to withstand the general proteolytic degradation that occurs in the intestinal lumen and be processed by the endosomal machinery of FAE enterocytes without losing their infectivity . We observed a spread of PrPSc to secondary lymphoid tissues . This may be due to several different mechanisms . First , PrPSc may spread by cell-cell contacts within the Peyer's patches and via lymphatics to the mesenteric lymph nodes . Additionally , the local concentration of the prion inoculum appears to have an impact on the speed of disease progression , which could explain why PrP accumulates first in Peyer's patches , where the enlarged endocytic capacity of FAE enterocytes sufficiently loads the locally dense population of SED macrophages . We cannot exclude the possibility that the intestinal routing of prions after oral exposure may vary depending on the combination of prion and host strain . To address this issue in the current study , we used two different mouse-passage prion isolates that have been shown to have distinct cellular requirements for replication in lymphoid tissues [2] , [27] and the hamster-passaged Sc237 scrapie strain . Our analysis showed consistent data from each of these distinct prion agents . However , there are examples of other prion strain and host combinations that do not appear to require accumulation and amplification in the GALT prior to neuroinvasion , such as BSE in cattle [58] , some strains of TME [59] , CWD in some cervid species [60] and atypical scrapie [61] . Of course , in each of these examples , the prions must still be transcytosed across the FAE prior to establishing infection within enteric nerves in the submucosa . At 105 dpf ( the latest time point we examined ) , the increased PrP labelling remained restricted to germinal centres and their neighbouring enteric plexi . For clarity , at 105 dpf , the animals infected with ME7 did not show any clinical symptoms of prion disease . The onset of clinical signs were observed at approximately 272 dpf at the dose we used in our studies . PrPSc has been reported to be found in the FAE of perorally challenged hamsters at 60 days [6] or 69 days [8] after intestinal infection with 263K scrapie , and it has been suggested that the FAE might serve as a site for prion release from the host some time after oral infection . In our study , we did not see a spread of PrPSc back to the FAE . 263K scrapie is considered to be a highly neurotropic prion strain , whereas ME7 is considered more lymphotropic . One could argue that during 263K scrapie infection , PrPSc returns to the gut epithelium via infected enteric nerves which presumably infiltrate the FAE . The same nerves may have been infected at the time of prion exposure . This does not appear to occur in mice , or if it does , it happens at later stages in the incubation period . Together , these data suggest that uptake via large late endosomal compartments of FAE enterocytes represents a novel potential M cell-independent mechanism through which prions are acquired from the gut lumen . While these data do not exclude a role for M cells or villous enterocytes in the initial uptake of prions from the gut lumen , much lower levels of PrP were detected within them when compared to FAE enterocytes . Our data show that the transcytosis of prions to the germinal centres of Peyer's patches is PrPC-independent as it occurs also in PrPC-deficient animals . In contrast , PrPC expression is required for the observed high labelling densities on plasma membranes of FDCs and enteric neurons . Indeed , our data suggest that FDCs within Peyer's patches are the first site of prion conversion and replication after oral exposure . These findings provide insight into the subcellular localisation and trafficking of prions , which might provide suitable targets to arrest oral prion infection . Furthermore , these data identify a novel , previously unrecognised , enterocyte-dependent route of prion uptake and transfer from the gut lumen that may have an important influence on susceptibility to oral prion infection .
Prnp-/- mice were bred and maintained on a 129/Ola background [33] . Age- and sex-matched 129/Ola mice were used as wt controls . For oral infection with ME7 prions , both wt and Prnp-/- ( n = 12 for each group ) were fed individual food pellets doused with 50 µl of a 1% ( wt/vol ) brain homogenate prepared from wt mice terminally affected with ME7 scrapie prions . Food pellets doused with 50 µl of a 1% ( wt/vol ) brain homogenate prepared from uninfected mice ( “normal brain homogenate” ) were used as a control . These experiments were approved by the Roslin Institute's Protocols and Ethics Committee and carried out according to the strict regulations of the UK Home Office ‘Animals ( scientific procedures ) Act 1986’ . For oral infection with RML prions , wt FVB and Prnp-/- mice ( n = 10 for each group ) were infected by gavage with 100 µl of 1% brain homogenate from wt mice infected with mouse-adapted RML scrapie prions . An additional group of 6 wt and 6 Prnp-/-mice was infected with 1% brain homogenate from Syrian hamsters infected with hamster-adapted Sc237 scrapie prions . As respective controls , 100 µl of 1% brain homogenates prepared from uninfected wt mice or Syrian hamsters was administered by gavage as well . The brain homogenates from infected and uninfected Syrian hamsters were treated with proteinase K before being administered to the mice ( See legend of Figure S6 for more details ) . The ME7 and RML samples were used without proteinase treatment . Use of these mice was according to the Public Health Services/National Institutes of Health Guide for the Care and Use of Laboratory animals . A33-deficient mice were created as described [62] . Mice were culled at 0 , 1 , 2 , 7 , 14 , 21 and 105 dpf , and Peyer's patches , mesenteric lymph nodes , and spleens were collected and prepared for IF and cryoimmuno EM . Briefly , tissue samples were dissected from animals and immersion-fixed in a solution containing 2% paraformaldeyde and 0 . 2% glutaraldehyde in PHEM-buffer ( 25 mM HEPES , 10 mM EGTA , 60 mM PIPES , 2 mM MgCl2; pH 7 . 2 ) . Fixed tissues were embedded in gelatin , infused in sucrose , and frozen in liquid nitrogen [63] . Frozen samples were cut on a cryo-ultramicrotome as semithin sections for IF ( 200 nm , -100°C ) or as ultrathin sections for cryoimmuno EM ( 70 nm , -120°C ) . Sections were picked up with sucrose for IF or with a mixture of methylcellulose/sucrose for cryo-immuno EM . M cells were identified with biotinylated Ulex europaeus ( UEA-1 ) lectin ( L8262; Sigma ) . Bound lectin was detected with polyclonal rabbit anti-biotin ( Rockland ) . The following primary antibodies were used for immunolocalisation by IF and/or cryo-immuno EM: monoclonal rat anti-LAMP1 ( 1D4B; BD Biosciences Pharmingen ) ; rabbit polyclonals MHCII ( JV2; generous gift from Dr . Hidde Ploegh ) ; anti-neurofilament 200 ( Sigma ) ; anti-ferritin ( F5012; Sigma ) ; goat polyclonal anti-A33 ( AF2756; R&D Systems ) ; rabbit polyclonal anti-annexin V ( Ab14196; Abcam ) ; rat monoclonal anti-GP2/glycoprotein 2 ( D277-3; MBL ) ; monoclonal anti-PrP 6H4 [49] R2 [50] and polyclonal 1B3 [64] . To detect hamster PrP biotinylated mAb 3F4 ( SIG-39640 Covance Signet Antibodies ) [36] directly conjugated to Streptavidin Gold Nanoparticles ( Nanocs ) was used . The following secondary bridging antibodies were used for cryo-immuno EM when rat or goat primary antibodies were applied: polyclonal rabbit anti-rat and rabbit anti-goat ( DAKO ) , respectively . For IF microscopy , semithin cryosections on glass slides were labelled with primary antibody , followed by species-specific secondary antibodies coupled to Alexa Fluor 488 ( green ) or Texas red dyes ( Invitrogen , Paisley , UK ) . Sections were mounted in fluorescent mounting medium ( DakoCytomation ) and examined using a Zeiss LSM5 confocal microscope ( Zeiss , Welwyn Garden City , UK ) . Simultaneous Dapi staining was applied to visualise the nuclei and the cellular organisation of the tissue . To prevent possible false-positive signals caused by autofluorescence , sections were treated with 1% sodium borate for 5 min . A TUNEL assay was performed using the fluorescein-conjugated in situ Cell death Detection kit ( Roche Applied Science ) according to the manufacturer's instructions . For cryoimmunogold EM , PrP-specific 6H4 [49] and Fab-fragments of R2 antibodies [50] were conjugated to UltraSmall gold particles ( 0 . 8 nm; Aurion , Wageningen , The Netherlands ) to allow increased penetration into the cryosections and circumvent labeling artefacts caused by cross-reaction with endogenous immunoglobulins in the tissue . Labeling reactions were performed under native conditions; no antigen retrieval method was applied . The R-GENT SE-EM silver enhancement kit from Aurion was used according to the manufacturer's instructions . For other immunogold-labelling experiments , the primary antibody , or the bridging antibody , was detected via the standard protein A–gold method [63] . For double labelling , conjugates of PrP-specific antibody and UltraSmall gold were applied and briefly silver enhanced , prior to incubation with the second primary antibody that was subsequently detected by protein A–gold . The immunolabeling of sections was done as described previously [63] . In brief , after blocking with 1% cold fish gelatin and 1% bovine serum albumin for 15 min , sections were incubated with primary antibody for 60 min , washed , and bridging rabbit antibodies were applied for 30 min when necessary . Sections were then incubated with protein A-gold ( 15 nm ) for 20 min . The specificities of the antibodies were controlled by omission of the primary antibody . Labeled sections were viewed with a Philips CM10 electron microscope ( FEI Company , Eindhoven , The Netherlands ) at 80 kV . The quantification of the distribution of gold particles was done according to routine stereological methods in double-blind fashion . Data are presented as means ± SD . Data were analysed using a T-test and differences were considered significant when p<0 . 05 . | Prion diseases are orally transmissible , but how the abnormally folded isoform of the prion protein ( PrPSc ) transits from the gastrointestinal tract to infect neural tissues is not known . Here we demonstrate that in contrast to the current literature , PrPSc enters Peyer's patches primarily through specialised enterocytes with much lower levels trafficking through M cells . Proteins from homogenized PrPSc infected brain tissue are transcytosed across the follicle-associated epithelium and delivered to macrophages and follicular dendritic cells , which appear to serve as the primary site of PrP conversion and replication following oral exposure to PrPSc before infecting the enteric nerves . |
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Regulatory T cells expressing the transcription factor Foxp3 play indispensable roles for the induction and maintenance of immunological self-tolerance and immune homeostasis . Genome-wide mRNA expression studies have defined canonical signatures of T cell subsets . Changes in steady-state mRNA levels , however , often do not reflect those of corresponding proteins due to post-transcriptional mechanisms including mRNA translation . Here , we unveil a unique translational signature , contrasting CD4+Foxp3+ regulatory T ( TFoxp3+ ) and CD4+Foxp3− non-regulatory T ( TFoxp3− ) cells , which imprints subset-specific protein expression . We further show that translation of eukaryotic translation initiation factor 4E ( eIF4E ) is induced during T cell activation and , in turn , regulates translation of cell cycle related mRNAs and proliferation in both TFoxp3− and TFoxp3+ cells . Unexpectedly , eIF4E also affects Foxp3 expression and thereby lineage identity . Thus , mRNA–specific translational control directs both common and distinct cellular processes in CD4+ T cell subsets .
Regulation of gene expression is a multi-step process involving transcriptional , post-transcriptional and post-translational mechanisms . Recent studies have revealed that only 30–40% of steady state protein levels correspond to steady-state mRNA levels and identified mRNA translation as the principal post-transcriptional mechanism [1] , [2] . Furthermore , several studies have documented that changes in steady-state mRNA expression-profiles frequently do not correspond to changes in the proteome [3]–[6] . Thus , studies of the translatome ( i . e . those mRNAs that are being translated ) can potentially help to explain biological processes beyond standard profiling of mRNA levels . CD4+ T helper ( Th ) -cell lineage differentiation is defined by expression of specific transcription factors required for subset identity [7] . Foxp3 is a master-switch transcription factor impacting lineage commitment by driving the intra-thymic differentiation of natural CD4+Foxp3+ regulatory T ( TFoxp3+ ) cells , a critical mediator of immune self-tolerance and prevention of excessive inflammatory responses [8] , [9] . In the absence of Foxp3 , CD4+ T cells can differentiate into a spectrum of inflammatory effector subsets . Furthermore , Foxp3 expression can be up-regulated in CD4+ T cells to generate induced TFoxp3+ ( iTFoxp3+ ) cells [10] in vitro and in vivo . Genome-wide expression profiles using steady-state mRNA samples have defined canonical “TFoxp3+ gene expression signatures” that distinguish primary resting or activated TFoxp3+ from CD4+Foxp3− non-regulatory T ( TFoxp3− ) cells [11]–[15] . In contrast , studies of mRNA translation in T cells are limited but suggest that T cells augment mRNA-translation and induce translation of specific mRNAs upon activation [16]–[18] . However , such studies compared non-activated to activated total CD4+ T cells , used non genome-wide approaches and/or immortalized cell lines and are thus limited in scope . Hence , the contribution of mRNA translation to establishment of the proteome in different T cell subsets is still largely unknown . We therefore asked whether translational control contributes to establishment of the proteomes in TFoxp3+ and/or TFoxp3− cells . Here , we report the first genome-wide study on translational control in primary CD4+ TFoxp3+ and TFoxp3− cell subsets directly ex vivo and post-activation in vitro . We reveal substantial mRNA specific quantitative and qualitative differences in the translatome between primary CD4+ T cell subsets . Remarkably , these translationally regulated genes were not previously identified in genome-wide studies of steady-state mRNA and therefore provide hereto unknown information on gene expression programs in T cell subsets . We further identified distinct translational control of the eIF4E-mRNA as a mechanism regulating proliferation in both TFoxp3+ and TFoxp3− cells . Surprisingly , modulation of eIF4E activity also affects T cell lineage identity . Thus , CD4+ T cell subsets exhibit common and specific translational programs that orchestrate expression of genes that direct fundamental cellular processes .
Translation is mainly regulated at the initiation step , during which ribosomes are recruited to the mRNA [19] . Efficiently translated mRNAs are therefore associated with a larger number of ribosomes than poorly translated mRNAs . Consequently , an approach to enrich for mRNAs being translated is based on poly ( ribo ) some preparations where mRNAs from cytoplasmic extracts are sedimented according to the number of ribosomes they bind ( Figure 1a ) . To determine whether CD4+ T cell subsets regulate gene expression at the level of mRNA translation , we prepared cytosolic and polysome-associated ( with ≥3 ribosomes ) mRNA from either primary TFoxp3+ or TFoxp3− CD4+ cells ( i . e . CD4+ and GFP+ or GFP− cells from Foxp3-GFP reporter knock-in mice ) directly ex vivo or 36 h post in vitro activation . Isolation of polysome-associated mRNA in sufficient quantities was technically challenging because TFoxp3+ cells represent a scarce population ( 5–10% of total CD4+ T cells ) , and TFoxp3+ and TFoxp3− cells are relatively inactive transcriptionally and translationally ex vivo . Consequently , the UV-absorption profiles of polysomes from TFoxp3+ cells and ex vivo TFoxp3− cells were below the detection limit except for the 80S ribosome peak ( Figure 1b ) . The 80S peak was therefore used to align all polysome RNA preparations to assure that fractions with mRNAs carrying the same number of ribosomes ( ≥3 ) were pooled for each sample . Affymetrix GeneChips were then used to quantify genome-wide cytoplasmic and polysome-associated mRNA levels . We assessed the reproducibility of the procedure by comparing gene expression data across all genes and samples using Pearson correlations ( Figure 1c ) . The replicates clustered according to activation state followed by cell type and RNA origin indicating that , despite low mRNA amounts , high quality , reproducible data were obtained . To assess whether studies of polysome-associated mRNAs provide new information regarding gene expression in CD4+ T cells , we compared polysome-associated to cytosolic mRNA levels in TFoxp3− cells directly ex vivo or post activation in vitro . While polysome-associated mRNA levels largely resembled those of cytosolic mRNAs in the ex vivo condition ( although many mRNAs showed moderate differences [2–3-fold] ) , abundant and dramatic differences ( >3-fold ) were observed in activated TFoxp3− cells ( Figure 2a ) . Similarly , in TFoxp3+ cells ( Figure 2b ) differences between levels of polysome-associated and cytosolic mRNAs occurred primarily in the activated condition . Thus , polysome-associated and cytosolic mRNA profiles differ indicating that steady-state mRNA signatures may not faithfully reflect corresponding protein levels for many genes . As TFoxp3+ and TFoxp3− cells were isolated and treated identically , we expected comparable levels of polysome-associated mRNAs after correcting for differences in cytosolic mRNA levels ( i . e . that translation would be regulated uniformly across T cell subsets ) . To examine this we first compared data from polysome-associated mRNA between TFoxp3+ and TFoxp3− cells ( Figure 2c ) . This analysis showed that TFoxp3+ and TFoxp3− cells vary substantially in terms of which mRNAs are more abundant in polysomes , particularly in activated cells . However , to identify those mRNAs that show differential translational activity , data from polysome-associated mRNAs must be corrected for cytosolic mRNA data to exclude a contribution from e . g . transcription or RNA-stability . We performed such correction using anota [20] , [21] and , unexpectedly , found large differences in translational activity of specific mRNAs , especially between activated T cell subsets ( Figure 2d ) . After adjusting the p-values for multiple testing , we found that while differences in translation were modest ex vivo ( ∼20 mRNAs with a Benjamini-Hochberg false discovery rate [FDR] <30% , corresponding to a nominal p-value<0 . 004 ) , there were substantial differences in translation between activated T cell subsets ( ∼200 and 500 mRNAs were translationally activated or suppressed , respectively , in activated TFoxp3+ as compared to TFoxp3− cells [FDR<15%] ) . These data strongly suggest that translational control plays an important role in regulating gene expression programs in TFoxp3+ and TFoxp3− cell lineages . Although we identified specific mRNAs that showed both qualitative and quantitative differences in translational activity between activated T cell subsets , this signature may overlap with previously described steady-state mRNA signatures and hence not shed light into unknown aspects of T cell gene expression . This possibility arises because while we used cytosolic mRNA levels to correct levels of polysome-associated mRNAs , previous studies measured whole cell steady-state mRNA levels ( which also reflect nuclear mRNA levels ) . We therefore compared the activated T cell translational signature to data from 5 independent studies of steady-state mRNA levels [11]–[15] , and focused our analysis on comparisons between TFoxp3+ and TFoxp3− cells isolated ex vivo or activated in vitro ( Figures S1 , S2 ) . Although iTFoxp3+ cells only partially recapitulate the TFoxp3+ steady-state mRNA signature , we also determined whether this signature overlapped with the translation signature ( Figure S2 ) [11] , [15] , [22] . To assess the overlap with steady-state mRNA signatures we calculated the percentage of mRNAs that were translationally regulated and exhibited differential mRNA levels in any of the studies of steady-state mRNA levels . Only 11% of the mRNAs were shared ( Figure S1 ) and only 5% were identified in at least two steady-state mRNA signatures ( 7 additional comparisons confirmed this pattern , Figure S2 ) . Although we expect that there will be differences between cytosolic ( present study ) and whole-cell ( previous studies ) steady-state mRNA signatures we wanted to validate that the observed distinct translational signature was not entirely driven by a very small overlap between these . We therefore performed the same analysis but compared our signature from cytosolic mRNA to previous datasets on steady-state mRNA . In contrast to the translational signature the signature from cytosolic mRNA showed a considerable overlap ( 47% or 32% were shared between the present cytosolic and at least one or two steady-state mRNA signatures , respectively ) – indicating that the lack of overlap between the translational signature and previous steady-state signatures is not due to that we studied cytoplasmic mRNA . Similar comparisons to the translational signature from ex vivo cells were hampered by that few genes were differentially translated ( Figure S3 ) . Thus , the newly identified translational signature discriminating activated CD4+ T cell subsets is unique . Gene expression programs are commonly viewed as being “modular” where each module consists of several co-regulated genes that control specific cellular functions and several studies indicate the existence of such modules at the post-transcriptional level [23]–[25] . We therefore determined the functional relationship between mRNAs that are translationally regulated in a T cell subset-specific manner . To assess whether there was an overlap of cellular functions targeted by differential translation or cytosolic mRNA levels , we also identified mRNAs that were differentially expressed using data obtained from cytosolic mRNA . As a control , we studied mRNAs that were differentially expressed using data obtained from polysome-associated mRNA ( functions regulated at the translational level [significant after anota analysis] should also be regulated using data from polysome-associated mRNA ) . We separated the resulting mRNAs into those that were activated or suppressed in TFoxp3+ cells as compared to TFoxp3− cells and sought for significantly enriched biological functions in each subset ( Figure 3 ) . Few functions were enriched among mRNAs that were translationally more active in activated TFoxp3+ cells , whereas translationally suppressed mRNAs were highly functionally related . When comparing to the enrichment analysis for cytosolic mRNA data , several functions were primarily regulated at the level of translation including ubiquitination , chromatin modification and cell cycle . Such functions were also identified as regulated by translation ( FDR<0 . 05 ) using an alternative gene set enrichment approach ( GAGE ) [26] . To further examine these functions , we collected all differentially translated mRNAs annotated to the identified cellular functions and compared their translational activity across all studied conditions ( Figure 4a–4c ) . For each function there was a strong signature regarding both the number of mRNAs involved and the magnitude of differential translation between activated TFoxp3+ and TFoxp3− cells . For the cell cycle cluster , the profile was uniform as most mRNAs were translationally suppressed in activated TFoxp3+ cells as compared to activated TFoxp3− cells . The chromatin modification and ubiquitination clusters contained both translationally activated and suppressed mRNAs in activated TFoxp3+ cells as compared to activated TFoxp3− cells , indicating complex regulation of these functions via translational control . Thus , the translational signature contrasting activated T cell subsets is enriched for mRNAs whose encoded proteins participate in distinct cellular processes . Surprisingly , we identified eIF4E as translationally suppressed in activated TFoxp3+ cells as compared to activated TFoxp3− cells . eIF4E is the rate-limiting translation initiation factor that binds to the mRNA 5′-cap structure to recruit mRNA to the ribosome [19] . Activated TFoxp3+ cells showed a 5-fold translational suppression of eIF4E as compared to activated TFoxp3− cells ( Figure 5a ) . Consistently , the levels of eIF4E protein were higher in activated TFoxp3− than in activated TFoxp3+ cells ( Figure 5b ) . eIF4E dramatically regulates translation of mRNAs which encode proteins participating in various cellular processes including cell cycle [27]–[29] , apoptosis [30] and innate immunity [31] but only modestly affects global protein synthesis . Thus , parts of the activated T cell translational signature could be mediated by an activation-induced disparity in eIF4E levels between TFoxp3+ and TFoxp3− cells . eIF4E activity is repressed by the eIF4E-binding proteins ( 4E-BPs ) which compete with eIF4G for binding to eIF4E . Kim et al . recently measured genome-wide translational activity in lungs from wild type ( WT ) and 4E-BP1/2 double-knockout mice ( 4E-BPdko ) [32] . 4E-BPdko mice would hence be expected to show increased eIF4E activity as compared to WT . Although the impact of increased eIF4E activity likely differs between T cells and lungs , the eIF4E translational signature could nonetheless be partly conserved , reflecting the central role of eIF4E in cellular function . We therefore compared the translational signature contrasting activated CD4+ T cell subsets to the signature contrasting 4E-BPdko and WT lung to determine if part of the activated translational signature could be accounted for by differences in eIF4E activity . Strikingly , more mRNAs showed similar regulation between the two studies than distinct regulation ( Figure 5c , binomial test p-value = 1 . 7e-13 ) . Notably , differential eIF4E levels , as observed between TFoxp3+ and TFoxp3− cells , had larger impact on translation as compared to presence or absence of 4E-BPs ( compare log2 fold changes in Figure 5c between T cells and lungs ) . Thus , a part of the translational signature from activated T cell subsets can be explained by differences in eIF4E levels . Whereas 87 of the similarly regulated mRNAs were translationally regulated in a manner that paralleled the activity of eIF4E ( eIF4E-sensitive ) , 19 mRNAs showed translational suppression following eIF4E activation . These 19 mRNAs could either represent the noise in the comparison or reflect a phenomenon observed in several studies of translational control downstream of eIF4E that is likely caused by secondary effects [27] , [28] . To assess the phenotypic consequence of increased eIF4E levels , we identified enriched functions among the 87 encoded proteins whose translation paralleled eIF4E activity , and identified 17 biological functions , including cell cycle and ubiquitination ( Table S1 ) . Further analysis revealed that 25% of the mRNAs in the eIF4E signature were related to the cell cycle and that ubiquitination partly overlapped with the cell cycle cluster as close to one fourth ( 22% ) of the cell cycle genes were annotated also to the ubiquitination system . Indeed , proteins translated from eIF4E-sensitive cell cycle related mRNAs were more highly expressed in activated TFoxp3− cells as compared to activated TFoxp3+ cells ( Figure 5b; cyclin E1 is part of the translational signature; translation of Anapc4 paralleled eIF4E activity also in mouse lungs; and cyclin-D3 is eIF4E sensitive [29] ) . Thus , the translational signature differentiating activated CD4+ T cell subsets exhibits functional and mechanistic modularity . Although TFoxp3+ cells are suppressive following in vitro activation they are , unlike TFoxp3− cells , anergic to T cell receptor ( TCR ) -induced proliferation . However , despite their anergy in vitro , TFoxp3+ cells can expand under homeostatic or inflammatory settings in vivo [33] . Because our translational signature from in vitro activated T cells compared suppressive and anergic TFoxp3+ cells to non-suppressive and proliferating TFoxp3− cells the signature will reflect both suppressive activity and anergy . Consistently , although we identified an enrichment of cell cycle related genes as translationally suppressed in TFoxp3+ cells activated in vitro these only represented ∼11% of the mRNAs that were translationally suppressed and only a minute fraction of the ∼200 genes that were translationally activated ( Figure 4c ) – indicating that most of the translational signature is related to other biological processes differentiating activated TFoxp3− and TFoxp3+ cells . Nevertheless , the correlation between eIF4E level , translational activation of proliferation-related genes ( Figure 5c , Table S1 ) and proliferation raises the possibility that eIF4E may control proliferation in both TFoxp3+ and TFoxp3− cells . To assess the relationship between eIF4E level and TFoxp3+ cell proliferation , we used a condition where the in vitro anergy of TFoxp3+ cells to TCR signals is rescued by relatively higher doses of exogenous IL-2 [34] . A higher concentration of IL-2 induced proliferation of TFoxp3+ cells to a level similar to that observed for TFoxp3− cells ( Figure 5d ) and strikingly also induced higher eIF4E protein levels ( Figure 5e ) . The increase in eIF4E level was accompanied by increased synthesis of cell cycle related proteins from eIF4E sensitive mRNAs ( Figure 5e ) . Thus , IL-2 abrogates the anergy in TFoxp3+ cells , which is associated with increased eIF4E levels and translation of eIF4E sensitive mRNAs . In aggregate , these data indicate that eIF4E may control expansion of both TFoxp3+ and TFoxp3− subsets . It was therefore important to examine whether increased eIF4E activity is necessary for induction of TFoxp3+ and/or TFoxp3− cell proliferation in vitro . To this end , we used an eIF4E inhibitor – the pro-nucleotide 4ei-1 ( Figure S4 ) , which inhibits binding of eIF4E to the mRNA cap structure and thereby selectively reduces eIF4E activity and eIF4E sensitive translation . 4ei-1 is a stable , non-toxic , pro-nucleotide that , when activated intracellularly by HINT , binds to eIF4E with a Kd of 0 . 80 µM [35] . Strikingly , 4ei-1 suppressed proliferation and accumulation of activated TFoxp3− and TFoxp3+ cells in a dose-dependent manner ( Figure 6a–6b ) without affecting viability or IL-2R expression ( Figure S5a–S5b ) . To assess the selectivity of the drug response , we treated cells with a structurally-related eIF4E inhibitor , 4ei-4 ( Figure S4 ) , that has a 10-fold lower affinity for eIF4E ( Kd = 7 . 5 µM ) as compared to 4ei-1 . The inhibitory effect of 4ei-4 on TFoxp3+ and TFoxp3− cell proliferation was substantially lower as compared to 4ei-1 ( Figure 6c–6d ) . The apparent increased anti-proliferative effects of eIF4E inhibition in TFoxp3+ cells compared to TFoxp3− cells ( Figure 6a–6b ) could be related to the differential eIF4E protein levels in these T cell-subsets ( Figure 5e ) and/or differential uptake of 4ei-1 . Thus induced eIF4E activity is necessary for proliferation in both TFoxp3+ and TFoxp3− cells in vitro . Although our data indicated that modulation eIF4E activity occurs through translational activation of the eIF4E mRNA leading to induced eIF4E protein levels ( Figure 5a–5b ) , regulation of eIF4E also occurs via 4E-BPs . The 4E-BPs are inhibitors of eIF4E downstream of mTORC1 and are inactivated by mTOR signalling [19] . To examine whether signalling through the 4E-BPs was also necessary for induction of proliferation , we TCR-activated TFoxp3+ or TFoxp3− cells from 4E-BPdko/Foxp3-GFP mice in the presence of low or high IL-2 concentrations . High IL-2 concentration augmented proliferation and correlated with increased expression of the eIF4E protein also in TFoxp3+ cells from 4E-BPdko mice ( Figure 6e ) . Moreover , the proliferative potential of TFoxp3+ and TFoxp3− cells of either genotype was comparable under similar conditions ( compare Figure 5d and Figure 6e ) . The lack of contribution from the 4E-BPs could be explained by their sustained inactive state following in vitro activation-induced signalling through the mTOR pathway . Consequently , 4E-BP deficiency will not further affect eIF4E activity . Thus , translational activation of the eIF4E-mRNA , independent of signalling via the 4E-BPs , is necessary for proliferation of TFoxp3+ or TFoxp3− cells . Next , we sought to validate that eIF4E levels are also associated with proliferation of TFoxp3+ and TFoxp3− cells in vivo . For this we first established a flow cytometric approach to quantify eIF4E levels during T cell subset proliferative responses and evaluated it in vitro . Such analysis confirmed that in vitro proliferation of both TFoxp3+ and TFoxp3− cells is associated with higher expression of eIF4E and indicated that TFoxp3+ and TFoxp3− cells proliferate and induce eIF4E expression with different kinetics ( Figure S6 ) . Flow cytometric analysis of cells isolated ex vivo confirmed that proliferating ( Ki-67+ ) CD4+ T cells exhibit higher eIF4E expression as compared to non-proliferating ( Ki-67− ) cells ( Figure 6f left panel ) , irrespective of T cell subset ( Figure 6f right panel ) . Finally we examined the relationship between eIF4E expression and proliferation in vivo . To this end , TFoxp3+ or TFoxp3− cells were adoptively transferred into TCRβ−/− recipient mice and isolated from mesenteric ( mes ) or peripheral ( per ) lymph nodes ( LN ) 4 days post-transfer . Both proliferating ( eFluor670low ) TFoxp3+ and TFoxp3− cells expressed higher eIF4E levels than non-proliferating ( eFluor670high ) cells ( Figure 6g ) . Intriguingly , and consistent with our findings in vitro ( Figure S6 ) , the eIF4E level was higher in proliferating ( eFluor670low ) TFoxp3− cells as compared to TFoxp3+ cells in mesLN but not in perLN ( Figure 6h ) . This suggests that microenvironmental factors such as the inflammation in mesenteric sites may selectively enhance eIF4E-induced T cell subset expansion . Thus eIF4E level correlates with TFoxp3+ and TFoxp3− cell proliferation in vivo . Whereas mTOR deficiency blocks differentiation into Th1 , Th2 or Th17 cells under respective polarizing conditions , in vitro activation of mTOR deficient TFoxp3− cells induces Foxp3 expression and a suppressive phenotype [36] . Furthermore , inhibition of mTOR in TFoxp3− cells induces Foxp3 expression accompanied by TFoxp3+-like steady-state mRNA and microRNA expression profiles [37] . Given that we identified eIF4E as a component responding to in vitro activation , we asked whether eIF4E activity affects subset identity . To this end , TCR-activated TFoxp3− cells from Foxp3-GFP mice were treated with 4ei-1 and Foxp3 expression was measured by GFP fluorescence . Strikingly , there was a dose-dependent induction of Foxp3 expression in activated TFoxp3− cells upon inhibition of eIF4E activity using 4ei-1 under undifferentiating conditions ( Figure 7a ) . A similar experiment using 4ei-4 resulted in substantially less Foxp3 induction suggesting that strong inhibition of eIF4E activity is required for TFoxp3+ cell differentiation ( Figure 7b ) . Collectively , these data pinpoint to the modulation of eIF4E activity as a key component that affects T cell subset identity .
A functional immune system relies on controlled and coordinated induction , and rapid termination of immune responses to avoid erroneous or excessive triggering of pro-inflammatory responses . In this regard , translational control of gene expression appears advantageous as compared to transcriptional control as it provides a fast mode of action that does not require de novo mRNA synthesis . Accordingly , a number of individual mRNAs encoding proteins involved in both innate and adaptive immunity are regulated at the translation step . Expression of IRF7 is normally translationally suppressed to avoid faulty activation of the interferon response [31]; T cell production of the chemokine RANTES/CCL5 is dependent on the transcription factor RFLAT-1 whose expression is translationally regulated [38]; translational suppression of cytokine production is a key mechanism by which self-reactive T cells are kept anergic [39] . Here we show that regulation of mRNA translation plays a central role in the orchestration of genetic programs in TFoxp3+ and TFoxp3− lineages . Activation of TFoxp3+ and TFoxp3− cells leads to selective qualitative and quantitative changes in translational activity of specific mRNAs . Notably , genes in the newly identified translational signature have not been uncovered in previous comparisons of TFoxp3+ and TFoxp3− cells using steady-state mRNA and therefore represent hereto unknown aspects of CD4+ T cell biology . Qualitative and quantitative changes in translation are more pronounced upon T cell activation , in agreement with the notion that translation is often regulated during cellular responses that require rapid and coordinated control of protein expression . However , this does not exclude that individual mRNAs are translationally regulated in the resting state , although the number of these mRNAs appears to be modest as compared to that observed upon activation . Coordinated regulation of groups of functionally related mRNAs has been postulated to be a common mechanism by which cellular functions are regulated [23] . Here we identified eIF4E as a target for post-transcriptional regulation which , in a modular fashion , activates translation of a set of cell cycle related genes - thereby further exemplifying the complexity of how post-transcriptional circuits affect cellular functions [23] . While the T cell activation-associated dramatic increase in translation was previously suggested to be linked to eIF4E activity [40] , we show that eIF4E induction is necessary for proliferation of both TFoxp3+ and TFoxp3− cells in vitro and that eIF4E level correlates with cell proliferation in vivo . We thereby provide important insights into regulation of proliferation of T cell subsets . The translational signature also involved individual genes which may be part of yet undefined post-transcriptional modules but with previously established functions in T cell biology . Foxo1 and Foxo3 [41] , [42] , both have essential roles in the stability of Foxp3-dependent TFoxp3+ cell lineage commitment [43] . Interestingly , we found translational activation of the Foxo3 mRNA ( 4-fold ) in TFoxp3+ as compared to TFoxp3− cells in the activated condition ( Figure S7 ) indicating that translational control of specific mRNAs may be important for expression of proteins regulating T cell lineage commitment . A developmental relationship exists between various Th cell effector lineages , suggesting a high degree of functional plasticity which enables cells to switch from one lineage to another [44] . Cytokines including IL-2 , TGF-β1 and IL-10 influence the induction or stability of Foxp3 expression in iTFoxp3+ or natural ( n ) TFoxp3+ cells thereby influencing TFoxp3+ cell fate and the type of immune response . Surprisingly but in agreement with previous studies on mTOR signalling , we identified eIF4E , a downstream target of mTOR signalling , as affecting T cell lineage identity . However , our data do not exclude that other downstream targets of mTOR also contribute to cell lineage identity or that the effects are indirect ( e . g . as a result of inhibition of proliferation during cell activation ) . Thus , further studies will be needed to address whether the effects of eIF4E on cell lineage identity are direct or indirect . Collectively our data favour a model whereby eIF4E levels could be dynamically regulated in response to changes in the local inflammatory environment thereby providing a direct link between the extracellular micro-environment , gene expression and biological responses .
GFP-Foxp3 knock-in ( ki ) mice have been described previously [13] and were kindly provided by A . Y . Rudensky ( now at Memorial Sloan-Kettering Cancer Centre , NY ) . For the experiments with 4E-BP deficient T cells , GFP-Foxp3ki mice were crossed to 4E-BPdko mice . TCRβ−/− mice are αβT cell deficient due to the absence of the gene encoding the TCR βchain . Mice were housed and bred under specific pathogen free conditions according to Canadian Council on Animal Care ( CCAC ) -approved institutional guidelines at the animal facility of the Department of Microbiology and Immunology; McGill University . Female mice 6–12 weeks old were used for the study . Cells isolated from lymph nodes and spleens were stained with PE conjugated CD4 antibody ( GK1 . 5 , eBioscience , San Diego , CA ) and MACS purified . Thereafter TFoxp3+ and TFoxp3− cells were sorted based on CD4 and GFP-Foxp3 expression using a FACSAria to obtain cell populations of high purity ( >97% ) . For the naïve cells all buffers and media were supplemented with cycloheximide ( Sigma , St . Louis , MO ) ( 100 µg/ml ) . Cycloheximide immobilizes ribosomes on the mRNA and enables separation of polysome-associated RNA . For the activated samples cells were activated for 36 h with plate bound CD3 and CD28 antibodies ( BD Bioscience ) ( 5 µg/ml ) in the presence of recombinant hIL-2 ( [100 U/ml]: a kind gift from the Surgery Brach , NCI/NIH ) . Cycloheximide ( 100 µg/ml ) was added to the medium at the end of the culture . Cytosolic and polysome-associated RNA were prepared directly ex vivo or post-activation in vitro as described previously [27] and labelled for probing with microarrays using the Ovation Pico WTA system ( NuGEN ) according to the manufacturer's instructions . All experiments were performed in biological duplicates . For cells isolated directly ex vivo , RNA from two experiments was pooled for each sample . Data were extracted and normalized using rma implemented in the R package “affy” ( www . r-project . org ) using updated probe set definitions ( ENTREZ_GENE ) [45] . Integrity of samples was assessed using 5′ to 3′ ratios and the comparability of the arrays by scaling factors . The reproducibility was assessed by correlation analysis using both Spearman and Pearson correlations in R and visualized using the hclust function in R . All these analyses confirmed good data quality . We used anota-RVM [20] to identify differential translation and applied the following stringent filtering for gene selection to assure correct linear models in anota: slopeP = 0 . 05; maxSlope = 1 . 5; minSlope = ( −0 . 5 ) ; deltaP = 1; deltaPT = 1 ( as defined in the anotaPlotSigGenes function in anota [21] ) . We used Benjamini-Hochberg multiple testing adjusted p-values ( false discovery rates [FDR] ) as a cut off for differential translation ( FDR<30% for ex vivo cells and FDR<15% for activated cells ) . RVM was also applied to identify differentially expressed mRNAs between TFoxp3+ and TFoxp3− cells using data from polysome-associated mRNAs and cytosolic mRNA data from the activated condition . The resulting p-values were corrected using the Benjamini-Hochberg multiple testing adjustment method and an FDR <15% was used as cut off . We used GO::Termfinder [46] to identify enriched cellular functions within subsets of differentially regulated mRNAs that were upregulated in TFoxp3+ cells ( 123 unique mRNAs from the cytosolic mRNA analysis; 226 unique mRNAs from the polysome-associated mRNA analysis; and 251 unique mRNAs that were translationally regulated from anota ) or upregulated in TFoxp3− cells ( 404 unique mRNAs from the cytosolic mRNA analysis; 666 unique mRNAs from the polysome-associated mRNA analysis; and 504 unique mRNAs that were translationally regulated from anota ) and collected those functions that showed: >2-fold enrichment; at least 10 annotated and regulated mRNAs; and a FDR <1% . For identification of the eIF4E signature we down-loaded the data set with the accession number GSE17406 from the Gene Expression Omnibus ( GEO ) and used anota to identify differential translation . mRNAs that were differentially translated in the TFoxp3+ vs . TFoxp3− comparison and showed >1 . 3-fold difference in the GSE17406 data set were collected and compared to identify an overlapping eIF4E translational signature ( the analysis was robust at more restrictive fold changes ) . The binomial p-value for rejecting the NULL hypothesis ( no eIF4E signature ) was calculated in R . This dataset has been deposited at the Gene Expression Omnibus ( GEO ) accession GSE45401 . TFoxp3+ and TFoxp3− cells were activated for 72 h with plate-bound anti-CD3 and -CD28 antibodies in the presence of 100 U/ml or 1000 U/ml of recombinant human IL-2 ( rhIL-2 ) . Cells were counted before plating and at the end of the culture to determine the fold-increase in cell number . Cell viability was assessed either using a trypan blue or eFluor780 Fixable Viability Dye ( eBioscience , San Diego , CA ) exclusion assays . For the inhibition of eIF4E activity in vitro , TFoxp3+ and TFoxp3− cells were stained with the Cell Proliferation Dye eFluor 670 ( eBioscience , San Diego , CA ) and activated as described above in the presence of rhIL-2 ( 1000 U/ml ) and in the presence or absence of selective inhibitors of mRNA cap structure-binding to eIF4E: 4ei-1 , a prodrug ( pronucleotide phosphoramidate ) of 7Bn-GMP ( Kd of 0 . 80 µM ) , or its control 4ei-4 , a prodrug of 7Me-GMP , which has a 10- fold lower affinity for eIF4E than 7-Bn-GMP ( Kd = 7 . 5 µM ) . When assaying T cell proliferation in vivo total CD4+ T cells , isolated from LNs of GFP-Foxp3 ki mice , were stained directly ex vivo with a V450-conjugated Ki-67 antibody ( B56 , BD Biosciences , Missisauga , ON ) and a primary eIF4E antibody or an isotype control ( Abcam , Cambridge , MA ) followed by staining with a PE-conjugated secondary antibody ( Abcam , Cambridge , MA ) . ΔMFI for eIF4E was calculated by subtracting the MFI value for the isotype control from that obtained with the eIF4E antibody . For the experiments involving TCRβ−/− mice , congenic ( Ly5 . 1+ ) TFoxp3− ( CD4+CD25− ) and TFoxp3+ ( CD4+CD25+ ) cells were MACS purified from GFP-Foxp3 ki mice based on CD4 and CD25 expression , subsequently stained with the eFluor 670 Cell Proliferation Dye and adoptively transferred into separate TCRβ−/− recipient mice . Four days post adoptive transfer donor T cells from perLN and mesLN were stained with eIF4E or isotype control antibodies as described above . TFoxp3− ( CD4+GFP− ) and TFoxp3+ ( CD4+GFP+ ) cells were analyzed for eIF4E expression and eFluor 670 levels by FACS . Cell lysates were prepared from activated TFoxp3+ and TFoxp3− cells , and western blotting was carried out as previously described [28] using 25 µg of protein per sample . Antibodies against eIF4E ( BD Biosciences , Mississauga , ON ) and β-actin ( AC-15 , Sigma , St . Louis , MO ) were used at a 1∶1000 and 1∶5000 dilutions , respectively . Antibodies against Anapc4 ( Bethyl Laboratories , Montgomery , TX ) , cyclin-E1 ( Abcam , Cambridge , MA ) , and cyclin-D3 ( Cell Signaling Technology , Danvers , MA ) were used at 1∶1000 dilution . | Regulatory T cells expressing the nuclear protein Foxp3 are essential for the control of immune responses towards self and foreign antigens . Genome-wide gene expression studies have defined canonical signatures of T cell subsets . However , changes in mRNA levels often do not reflect those of corresponding proteins due to post-transcriptional mechanisms including mRNA translation . In Bjur et al . , we discovered a unique translational signature , which distinguishes immunosuppressive Foxp3+ regulatory T from inflammatory Foxp3− T cells and establishes proteomes and functions in T cell subsets . We also show that cell activation or growth factors increase the translation of eukaryotic translation initiation factor 4E ( eIF4E ) , which induces proliferation in both T cell subsets . Unexpectedly , eIF4E also affects Foxp3 expression and can drive lineage identity . Thus , distinct translational control directs both common and distinct cellular processes in CD4+ T cell subsets . |
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Implementing precision medicine for complex diseases such as chronic obstructive lung disease ( COPD ) will require extensive use of biomarkers and an in-depth understanding of how genetic , epigenetic , and environmental variations contribute to phenotypic diversity and disease progression . A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS ( N = 750 ) ; COPDGene ( N = 590 ) ] was used to identify single nucleotide polymorphisms ( SNPs ) associated with measurement of 88 blood proteins ( protein quantitative trait loci; pQTLs ) . PQTLs consistently replicated between the two cohorts . Features of pQTLs were compared to previously reported expression QTLs ( eQTLs ) . Inference of causal relations of pQTL genotypes , biomarker measurements , and four clinical COPD phenotypes ( airflow obstruction , emphysema , exacerbation history , and chronic bronchitis ) were explored using conditional independence tests . We identified 527 highly significant ( p < 8 X 10−10 ) pQTLs in 38 ( 43% ) of blood proteins tested . Most pQTL SNPs were novel with low overlap to eQTL SNPs . The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers , with a single SNP ( rs7041; p = 10−392 ) explaining 71%-75% of the measured variation in vitamin D binding protein ( gene = GC ) . Some of these pQTLs [e . g . , pQTLs for VDBP , sRAGE ( gene = AGER ) , surfactant protein D ( gene = SFTPD ) , and TNFRSF10C] have been previously associated with COPD phenotypes . Most pQTLs were local ( cis ) , but distant ( trans ) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins . The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema , and the explanation of variance ( R2 ) for emphysema improved from 0 . 3 to 0 . 4 when the pQTL SNP was included in the model along with clinical covariates . Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema . In conclusion , given the frequency of highly significant local pQTLs , the large amount of variance potentially explained by pQTL , and the differences observed between pQTLs and eQTLs SNPs , we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms . Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group .
Implementing precision medicine will require extensive use of biomarkers and in-depth understanding of the contributions of genetic , epigenetic , and environmental variation to phenotypic diversity and disease progression . Genome-wide association studies ( GWAS ) linking disease phenotypes to single nucleotide polymorphic ( SNP ) markers have successfully identified genes and pathways involved in complex phenotypes [1 , 2] . GWAS are complemented by efforts of functional studies , such as the Genotype-Tissue Expression ( GTEx ) program [3] , which seek to identify expression quantitative trait loci ( eQTLs ) linking SNP markers with mRNA expression [4] . Such eQTLs can illuminate relationships between genetic variation and disease phenotypes . However , genetic variants can also affect protein levels by mechanisms not detectable by eQTL analyses by altering post-transcriptional processes involving stability , translation , secretion and/or detection of the gene product . Few studies have been focused on the impact of genetic variation on large numbers of protein biomarkers in chronic diseases . However , the recent work in Battle et al . , [5] suggests that variants affecting gene expression and protein level may be distinct , so identifying the genetic features that affect protein variation [protein quantitative trait loci ( pQTLs ) ] and gene expression for disease-relevant biomarkers will be important . To investigate the role of genetic variation on blood biomarkers and their relationship to a chronic disease , we examined genotyping-biomarker-clinical phenotype relationships in two independent , large , well-characterized cohorts of subjects at risk for chronic obstructive lung disease ( COPD ) : Sub-Populations and InteRmediate Outcome Measures in COPD Study ( SPIROMICS ) [6] and COPDGene [7] . COPD is the third most common cause of death in developed countries [8] and has strong demographic ( age , gender ) and behavioral ( e . g . , smoking ) risk factors , yet most smokers do not develop clinically important lung disease . Furthermore , COPD has several clinically important , but highly variable , phenotypes including extent and progression of airflow obstruction , loss of lung tissue ( emphysema ) , frequent cough and sputum production ( chronic bronchitis ) and exacerbations . There have been many publications that have examined the relationship between blood biomarkers and these COPD phenotypes [9] . These biomarkers include both non-specific markers of inflammation ( e . g . , fibrinogen , C reactive protein , interleukin 6 ) as well as lung specific proteins ( e . g . , surfactant protein D , club cell 16 ) and other proteins [e . g . , soluble receptor for advanced glycosylation endproducts ( sRAGE ) , chemokine ( C-C motif ) ligand 18 ( CCL18 ) , and adiponectin] . Many of these biomarker studies have been replicated in independent cohorts and nearly all studies used antibody-based assays . The SPIROMICS and COPDGene biomarker efforts included many of these biomarkers as well as additional novel understudied biomarkers ( S1 Table ) . Although some recent publications suggest that there may be important genetic associations for some blood protein measurements [10] , there have been no studies that use multiple independent populations for large scale blood biomarkers , nor are there extensive evaluations on how the SNP-biomarker relationship influences prediction of disease phenotype . Because both SPIROMICS and COPDGene have complete genotyping data , some transcriptomic data , an identical panel of a large number of blood biomarkers , and extensive well-phenotyped clinical data , there is a unique opportunity to identify novel pQTLs and explore their influence on biomarker-disease relationships for COPD and its disease phenotypes .
Written informed consent was received from all subjects . Collection and use of subject information and samples was approved at each clinical center ( see S1 File ) with the main approval from the IRB at National Jewish Health ( HS-1883a ) and the IRB at the University of North Carolina at Chapel Hill ( 10–0048 ) 114 candidate blood biomarkers ( S1 Table ) were initially evaluated using custom 13-panel multiplex assays ( Myriad-RBM , Austin , TX ) . The 13-panel multiplexes were primarily selected because they contained at least one biomarker with known or putative links to COPD pathophysiology [12 , 13] . Any analytes measured in addition to the pre-selected biomarkers were intended to be utilized for discovery purposes . Although reports of general assay performance are beyond the scope of the present work , details of a pilot study using the SPIROMICS samples on these assays is available that describes the coefficient of variation and reliability estimates for a majority of the analytes measured [12] . Details of the ability of the panels to detect the analyte above background [the lower limit of quantification ( LLOQ ) ] are provided for both studies ( S1 Table ) . Assay performance across the two cohorts was highly similar . Reproducibility of the platform was assessed for selected biomarkers ( S1 Fig ) using a subset of COPDGene subjects: for sRAGE using Quantikine human RAGE ELISA kit ( R&D Systems , Minneapolis , MN ) as previously described [14]; CRP ( Roche Diagnostics , Mannheim , Germany ) and fibrinogen ( K-ASSAY fibrinogen test , Kamiya Biomedical Co . , Seattle , WA , USA ) levels were measured using immunoturbidometric assays as previously described [15]; surfactant protein D using colorimetric sandwich immunoassay method ( BioVendor , Heidelberg , Germany ) as previously described [16] . Additionally , serum from 63 SPIROMICS subjects who were either GG ( N = 27 ) or TT ( N = 36 ) at rs7041 were analyzed using a monoclonal antibody assay from R&D ( Quanitkine ELISA kit ) at the Clinical Research Unit Core Laboratory at Johns Hopkins . Polyclonal vitamin D binding protein measurements ( ALPCO , Salem , NH ) were performed in the same SPIROMICS subjects . pQTL features were characterized by: ( 1 ) Ensembl Variant Effect Predictor ( VEP ) [30]; ( 2 ) GWAS catalog [31]; and ( 3 ) comparison with gene expression QTLs ( eQTLs ) using subset of COPDGene blood microarrays [20 , 32] . Details are provided below:
Demographic and clinical characteristics of subjects from the SPIROMICS ( n = 750 ) and COPDGene ( n = 590 ) cohorts , including disease phenotypes , are shown ( Table 1; S3 Fig ) . These NHW subjects were representative of NHWs in the parent cohorts ( S2 Table ) . At a significance level of 8 X 10−10 we identified 290 pQTLs in the SPIROMICS cohort and 182 pQTLs in the COPDGene cohort ( S3 Table ) . Many of the pQTLs SNPs were replicated between cohorts ( Fig 1; S3 Table ) . Because of the similarity of the two studies in terms of sample size and subject characteristics as well as good replication of pQTLs between these two studies , we used a meta-analysis to increase power for finding pQTLs . Weighted meta-analysis identified 527 pQTL SNPs in 38 ( 44% ) of the biomarkers ( S4 Table ) meeting genome-wide significance with Bonferroni correction for multiple testing of SNPs and biomarkers ( P <8 X 10−10; Fig 2 ) . The most significant independent pQTL SNP was rs7041 ( P = 10−392 ) in GC ( vitamin D binding protein—VDBP ) on chromosome 4 . Thirty-seven other biomarkers had significant pQTL SNPs ( Table 2 ) ; corresponding Manhattan plots , Q-Q plots , and LocusZoom plots are shown for each individual analyte that had an associated pQTL ( S4 Fig ) . Two or more independent pQTL SNPs were identified in 26 of 38 biomarkers using recursive conditioning ( S5 Table ) . To determine whether pQTLs SNPs were local ( cis ) or distant ( trans ) , we examined proximity of each SNP to its assigned biomarker gene . The majority ( 76% ) of pQTL SNPs were local ( S5 Fig; S4 Table ) . However , distant pQTLs were observed for eleven biomarkers , and nine biomarkers had a distant pQTL SNP as their most significant pQTL ( S2 Table ) . Five biomarkers had their most significant pQTL SNPs ( either rs687289 or rs507666 ) in the ABO blood group locus on chromosome 9 , which encodes alpha 1-3-N-acetylgalactosaminyltransferase , a major determinant of ABO blood type . This SNP is in the same genetic region as other QTLs and disease associations reported from a wide variety of a sources , including metabolites from the urine ( Fig 3 ) . An additional region on chromosome 19 contained distant pQTLs for more than one biomarker ( S4 Table ) . The pQTLs represented SNPs with a broad range of minor allele frequencies ( MAF ) with distributions of MAFs of pQTL SNPs similar to all SNPs studied ( S6 Fig ) . Using VEP , we found intronic SNPs to be the most represented pQTL SNP category ( 43% ) , followed by intergenic variants ( 22% ) ; however , missense variants showed the most significant enrichment ( P<10−12 ) compared to all SNPs on the genotyping platform ( Fig 4 ) . Importantly , pQTLs were robust and concordant across the two source cohorts ( S4 Table; S7 Fig ) . Nine biomarkers had at least 10% of their variance explained by a single pQTL SNP in both SPIROMICS and COPDGene ( Fig 5 ) . For example , a single local pQTL SNP ( rs8192284 SNP in IL6R ) explained 45% of variance of plasma IL6R in SPIROMICS and 50% of this variance in COPDGene , and a single distant pQTL SNP ( rs507666 SNP in ABO ) explained 25% of variance of blood E-selectin ( SELE ) in SPIROMICS and 27% of variance in COPDGene ( Fig 6 ) . In many cases , pQTL SNPs explained more variance in the quantitative biomarker than did clinical covariates . To assess the novelty of these pQTL SNPs , we cross-referenced the unique 478 pQTL SNPs we identified with SNPs associated with any published GWAS based on NHGRI GWAS catalog , including those related to COPD phenotypes or pulmonary function ( n = 242 ) . By these criteria , 90% of pQTL SNPs were novel ( P < 10−34; S4 Table ) , even after removing SNPs in linkage disequilibrium [280 significant pQTL SNPs remained and , of those , 29 ( 10 . 4% ) overlapped with at least one GWAS report ( P < 10−20 ) ] . We next evaluated whether pQTL SNPs were also eQTLs , by utilizing an overlapping dataset of peripheral blood mononuclear cell gene expression from COPDGene [32] . In this analysis , only COPDGene data were available , so results are limited to this dataset . Although there were more positive correlations between gene expression and protein levels than expected by chance ( sign test P = 0 . 0009 ) , the overall magnitudes of such correlations were low ( S8 Fig ) , and there was little overlap between pQTL and eQTL SNPs ( Fig 7; S6 Table ) . Furthermore , as previously shown , although both eQTL and pQTL SNPs were more likely to be intronic [20] , among those that were not , pQTL SNPs were more likely to be in 5′ or 3′ untranslated region or to be missense SNPs , compared to eQTL SNPs ( S9 Fig ) . Only one biomarker ( haptoglobin , corresponding to gene HP ) had pQTL SNPs that were also eQTL SNPs , and this is the only case where regression modeling suggested that measured biomarker levels are mediated by gene expression ( S6 Table ) . Given that QTLs may be dependent upon the cellular/tissue-specific expression [74] , we examined whether the pQTLs would be significantly affected by the cellular composition of the blood by repeating the pQTL analysis adding cell counts ( red blood cells , neutrophils , lymphocytes , basophils , monocytes , eosinophils , and platelets ) as covariates in the models . For either all possible SNPs or only significant pQTL SNPs , the correlation between the p-values of the pQTLs with and without blood cell counts added as covariates was > 0 . 985 , indicating that the pQTLs were not markedly dependent upon blood cell type composition ( S10 Fig ) . A recent report suggests that monoclonal antibodies for vitamin D binding protein may preferentially recognize a selected protein isoform [75] caused by the rs7041 pQTL , which is a missense mutation causing aspartic acid to glutamic acid change at position 432 ( D432E ) . Therefore we used a polyclonal antibody to compare to measurements to the monoclonal assay used on the RBM platform in a subset of SPIROMICS subjects . Indeed , the measurements using the monoclonal antibody were significantly lower for the TT genotype compared to the GG genotype ( P < 0 . 001 ) , suggesting that measurements using the monoclonal antibody assay detected the D432E protein isoform less well compared to the polyclonal assay ( S11 Fig ) . With SNPs , biomarker levels , and disease phenotypes all available for both cohorts , statistical modeling could be used to infer the relationships among these three data types employing methods previously applied to eQTL-gene expression-phenotype relationships [22–27] . We chose four clinically important COPD phenotypes [airflow obstruction ( FEV1% predicted ) , emphysema , chronic bronchitis , and a history of exacerbations] and applied regression models adjusted for covariates and PCs [22 , 26] . We categorized the relationships of all 2 , 108 trios of SNP , biomarker , and disease phenotype ( 527 pQTL SNP/biomarker pairs and four disease phenotypes ) into five categories , based on ( conditional ) dependence testing ( Fig 8 and full results supporting Fig 8 , including regression coefficients , are in S7 Table ) . Results for biomarker associations to disease phenotype for pQTL SNPs are also provided ( S8 Table ) . Significant evidence for inferred causal , complete , or collide relationships were found for emphysema and airflow obstruction for six biomarkers , with AGER represented by the same model in both phenotypes ( Fig 8 ) . In all of these cases , the direction of the regression coefficients were the same between SPIROMICS and COPDGene ( S7 Table ) . By contrast , no significant relationships were found for chronic bronchitis or exacerbations . In the case of the collide model , the association between pQTL SNP and disease phenotype is strengthened given the biomarker , and thus inclusion of pQTL SNP information in biomarker-disease association testing will add predictive value . An example is AGER , which is classified as the “collide” model for the phenotype of emphysema . Including both AGER levels and its top pQTL SNP improved the explanation of variance ( R2 ) for emphysema to 40% , compared to just 30% for the biomarker alone , and 22% when only clinical covariates were used .
In this study we identified hundreds of novel SNPs significantly associated with nearly 40% of blood biomarkers commonly used in both pulmonary and non-pulmonary clinical research . For many biomarkers , a single pQTL SNP accounted for a large percentage of measured variance . We demonstrated that pQTLs provide unique information compared to eQTLs and that inclusion of pQTL SNPs can improve explanation of variance when added to clinical covariates in statistical models , e . g . , sRAGE and emphysema . Although the subjects in this study were recruited for COPD phenotypes , many of the pQTLs identified and the biomarkers studied have been associated with other diseases or traits , suggesting that the pQTL-biomarker relationships reported here are broadly relevant to human pathophysiology . Furthermore , the pQTL-biomarker-disease phenotype relationship is frequently not a simple SNP → gene expression → biomarker → disease phenotype association . These findings suggest that modeling with inclusion of measurements from multiple omics technologies may be needed to optimize precision medicine predictions . A significant finding in this study is the number of distant pQTLs associated with the ABO locus ( commonly associated with ABO blood group ) . PQTLs at the ABO locus were the strongest genetic association among six proteins encoded by genes on six different chromosomes . This ABO region , along with the FUT2 gene ( galactoside 2-alpha-L-fucosyltransferase 2 ) , which contained pQTLs for CDH1 , was found to overlap with a growing number of previously reported QTLs for a variety of blood analytes , blood processes ( such as clotting time ) , metabolites , lipids , and even urinary metabolites ( Fig 3 ) . The most likely explanation is these two loci affect enzymes that post-translationally modify multiple proteins leading to impaired protein function , half-life , or detection . Interestingly , older literature , prior to extensive genotyping and biomarker analysis , has reported association between ABO blood group and COPD [76] and has been associated with other diseases such as goiter [77] and hepatitis [78] in the candidate gene era . The extensive number of associations now reported at the ABO blood group from a wide variety of studies suggests that greater attention should be paid to ABO status for blood biomarker studies . Much of the recent effort to identify genetic variants and genomic signatures associated with clinical disease has extensively used eQTLs to understand the function of loci identified in GWAS , including for COPD [4 , 79–81] . We demonstrate a clear distinction between known eQTLs and pQTLs , which is consistent with previous work that compared variants associated with three different levels of gene regulation ( transcription , translation and protein levels ) in a study of 62 HapMap Yoruba ( Ibadan , Nigeria ) lymphoblastoid cell lines ( LCLs ) [5] . The authors used SILAC mass spectrometry to quantitate proteins and showed that only 35% of the pQTL variants overlapped with eQTLs using RNAseq . Some of the variance in protein expression was due to ribosomal occupation ( ribosomal profiling ) ; however , there were many pQTLs in which there was little variation in the mRNA or ribosomal profiling , suggesting that post-translational events may be responsible for differences in protein abundance . Similar to what we report , this is supported by the observation that the pQTLs are significantly enriched in protein coding ( missense ) and potential translational regulation ( e . g . , 3’ UTR ) regions . They hypothesize this may be due to differences in protein degradation; however one cannot exclude that the peptide variants may be differentially measured with mass spectrometry , or that there may be altered biomarker stability , secretion rates , or processing/release from the cell surface . Another limitation of this study is that they only considered genetic variants within a 20-kb window around the corresponding gene; however , we found a significant number of pQTL SNPs mapped outside of this region . Another study of 441 transcription factors and signaling proteins in the Yoruban LCLs found that many pQTLs were not associated with gene expression and were also distant from the corresponding gene [82] . These studies highlight the general need to include protein expression in large-scale population variation studies such as GTEx to better understand the relationship between genome and protein in humans . Although such efforts are ongoing on a small scale ( e . g . Chromosome-Centric Human Proteome Project [83] ) , our results imply these efforts can also be incorporated cost-effectively into large existing clinical cohorts . These findings will be useful for GWAS and biomarker studies of other diseases . For instance , we identified novel pQTL SNPs explaining greater than 25% of variance in blood proteins such as interleukin 6 receptor , eotaxin-2 , and E-selectin , which could be useful in studies of asthma and of non-pulmonary diseases . The sRAGE-emphysema example demonstrates that the application of causal modeling can provide new insights to the relationship between SNP , measured biomarker levels , and disease phenotypes . Additionally , this example demonstrates how predictive models of disease phenotype can be improved by adding pQTL information . Furthermore , evaluating all possible statistical relationships among pQTL SNPs , biomarkers , and disease phenotypes suggests that many pQTL SNP effects may not be causally mediated directly through measured biomarkers . For instance , the minor allele rs2070600 SNP in AGER is associated with lower sRAGE in blood; COPD severity and emphysema extent have also been negatively associated with lower blood sRAGE concentrations in cross-sectional studies [13 , 14] . Paradoxically , however , in large GWAS studies , the minor allele of rs2070600 is associated with reduced COPD severity and reduced emphysema [80 , 81] suggesting potentially opposite effects of the SNP . Indeed , our evidence points to a “collide” relationship; however , given the previous published large scale genetic association studies have shown that rs2070600 is associated with COPD and emphysema , it is likely that this study is underpowered to distinguish between the “collide” and the “complete” model , which can be distinguished by a statistically significant association between the pQTL SNP and disease phenotype . Nevertheless , the association between pQTL SNP and disease phenotype becomes much stronger given the biomarker , which implies the collide relation . Regardless of whether rs2070600 is “collide” or “complete” , it is a missense SNP that causes a G82S amino acid change and thus illustrates the enrichment of coding SNPs in pQTL analysis . The mechanism by which rs2070600 causes disease is unknown , but the resultant amino acid substitution may block shedding of this cell surface receptor , reducing blood levels but at the same time improving sensing of damage-associated molecular pattern molecules , with a net protective effect [84] . However , once emphysema progresses , the source of sRAGE in the blood ( the alveolar cells ) is reduced , so that emphysema progression would be manifested by reduced sRAGE levels . Several other relationships identified are also worth considering . For example , we identified evidence for the “collide” relationship for rs926144 , an intergenic SNP in SERPINA1 ( alpha-1-antitrypsin; AAT ) , a protein whose normal function is linked directly to the development of emphysema . Although we find strong pQTL SNPs for SERPINA1 , and we see a relationship between COPD and SERPINA1 levels , we see no statistically significant evidence that pQTL SNPs associate directly with disease . This is similar to what authors of an GWAS of AAT serum levels have recently reported in this journal [85] , in which they identified strong serum AAT pQTLs , but their association with lung function was driven by the rare disease variants ( PiSZ and pZZ , who were excluded from SPIROMICS and COPDGene ) . Since SERPINA1 is produced by the liver and is well-known as marker of systemic inflammation , an established feature of COPD , this would support the finding that common SNPs may not be representative of the known disease-causing variants , which are rare , and that both non-disease causing variants and the disease itself may be associated with changes in biomarker levels . We found that a “complete” model was suggested for the Complement Factor 3 ( C3 ) pQTL SNP rs2230203 . In a study of 111 subjects with COPD and 111 matched controls , blood C3 was noted to be lower in COPD subjects [86] . Similarly in a more recent study of 15 COPD subjects and 15 matched controls serum C3 was lower in COPD subjects [87] . Our findings confirm the relationship between C3 and COPD and emphysema and further suggest that it is partly mediated through C3 genetic variants . Although the rs2230203 variant is in the coding region of C3 , it is a synonymous variant and was the only pQTL we identified for C3 . The variant might affect protein levels though siRNA binding or other pre-translational mechanisms , but mechanistic studies will be necessary to confirm this . As a final example , the “causal” relationship suggested for CDH1 ( E-cadherin ) for both emphysema and FEV1% predicted is also intriguing at a mechanistic level . The CDH1 pQTL SNPs are distant ( trans ) and are located in FUT2 , which codes for a fucosyltransferase that , along with ABO , determines the expression of distinct blood group antigens . Evidence for a role of CDH1 and COPD is growing [13 , 88 , 89] , yet the underlying mechanisms are not entirely clear . Our results suggest that future studies should focus on a direct role of CDH1 in the pathogenesis of disease . Strengths of this study include the large number of subjects and the inclusion of validation cohorts . However , there are some limitations . Although it is one of the largest biomarker-GWAS studies reported , 1 , 340 subjects is still small compared to clinical GWAS studies , thus we are likely underpowered to detect some of the SNP-disease phenotype associations . Thus , we cannot say for certain , for example , that a causal or collide model might not actually be a complete model ( e . g . for rs207060 in AGER with sRAGE ) . Second , because we identified distinct and independent pQTL SNPs for some biomarkers , there may be multiple mechanisms by which pQTL biomarkers mediate SNP-biomarker-disease phenotype interactions . Proving the validity of the causal inference models will require detailed mechanistic studies at both a genomic and proteomic level . Additionally , like nearly all biomarker assays , we used antibody based detection methods to measure biomarkers . Since antibodies recognize specific epitopes on proteins , it is possible that our pQTL may detect a specific isoforms of a protein rather than total protein . This has recently been suggested , but not proven , as an explanation for the strong genetic ( racial ) associations observed for vitamin D binding protein and the cis-SNP rs7041 ( Asp432Glu ) . As we have and others have shown for vitamin D binding protein [75] , assays that use polyclonal antibodies compared to the monoclonal sandwich immunoassay ( R&D Systems ) may overcome this limitation . Another example in the literature is a pQTL identified for TNF-alpha was not replicated when a different assay was applied to the same samples [10] . However , similar pQTLs for plasma proteins such as HP , SERPINA1 , C3 , APOE , and AHSG were identified using mass spectrometry [90] and for IL6R , F7 , and others using aptamer-based detection [91] , suggesting many pQTLs we identified were not platform specific . Thus , knowing that antibody used in biomarker measurement may preferentially detect a specific isoform of a protein does not discount its importance , particularly if the pQTL SNP has also been associated with the disease phenotype in genetic association studies , as is the case with vitamin D binding protein , sRAGE , and several other pQTL SNPs described in this study ( see Table 2 ) . Thus , investigators who conduct biomarker studies need to consider the possibility that genotype plays a role when measuring blood biomarkers . An additional limitation of the study is using a candidate panel of 114 biomarkers that are overrepresented for inflammation and lung proteins . At the time , this was state of the art for large scale human studies; however , in the future there will be high-throughput , 1000+ biomarker panels that may be used such as SomaScan ( Somalogic , Boulder , Colorado ) . Other limitations of this study include that it was limited to subjects over 45 years of age and only NHW subjects . Future studies should include other populations and the types of variants assessed , e . g . , rare variants . Finally , due to the nature of the available data , evaluating quantitative change in biomarkers with disease progression was not conducted , but would be expected to enhance understanding of disease mechanisms in future studies . In summary , this large scale , dual-cohort , combined GWAS and biomarker study represents a powerful approach to combine different omics data sets to better understand complex diseases such as COPD . We replicated some previously reported pQTL associations and discovered a large number of novel pQTLs , including distant pQTLs , which many studies are poorly powered to detect . Integration of pQTL genotypes with biomarker measurements will improve the precision of disease prediction for some clinically relevant phenotypes , and improve the mechanistic understanding of others , thus increasing the implementation of targeted clinical care . | Precision medicine is an emerging approach that takes into account variability in genes , gene and protein expression , environment and lifestyle . Recent advances in high-throughput genome-wide genotyping , genomics , and proteomics coupled with the creation of large , highly-phenotyped clinical cohorts now allows for integration of these molecular data sets at the individual level . Here we use genome-wide genotyping and blood measurements of 88 biomarkers in 1 , 340 subjects from two large NIH-supported clinical cohorts of smokers ( SPIROMICS and COPDGene ) to identify more than 300 novel DNA variants that influence measurement of blood protein levels ( pQTLs ) . We find that many DNA variants explain a large portion of the variability of measured protein expression in blood . Furthermore , we show that integration of DNA variants with blood biomarker levels can improve the ability of predictive models to reflect the relationship between biomarker and disease features ( e . g . , emphysema ) within chronic obstructive pulmonary disease ( COPD ) . |
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Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges . Sparse coding and independent component analysis ( ICA ) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli . However , a salient feature of image statistics , occlusions of image components , is not considered by these models . Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields . We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model . For both models we simultaneously estimate optimal receptive fields , sparsity and stimulus noise . The two models are identical except for their component superposition assumption . We find the image encoding and receptive fields predicted by the models to differ significantly . While both models predict many Gabor-like fields , the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields . This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies . While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding , no or only low proportions are reported in the vast majority of studies on linear models ( including all ICA models ) . Likewise , for the here investigated linear model and optimal sparsity , only low proportions of ‘globular’ fields are observed . In comparison , the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well . Our computational study , therefore , suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex .
Evolution and synaptic plasticity optimize the visual cortex for the processing of visual stimuli . The quantification of the degree of optimization has long been subject of theoretical and physiological studies . Among the most influential contributions are models such as independent component analysis [1]–[3] ( ICA ) and sparse coding [4] which became popular because they linked response properties of simple cells in primary visual cortex to the view of sensory systems as optimal information encoders [5]– . Since they were first introduced , many different versions of sparse coding and ICA have been investigated . While many technical studies focused on different ways to efficiently infer the model parameters [3] , [9] , many others investigated the assumptions used in the underlying stimulus model itself such as the sparsity prior or the assumed stimulus noise [10]–[13] . An assumption that has been investigated very little in the context of sparse coding models is the assumption of linear superposition of basis functions . For many types of data , linear superposition can be motivated by the actual combination of stimulus components ( e . g . , sound waveforms combine linearly ) . However , for image patches an assumption of linear superposition implies that component occlusions are not considered . But does neglecting or including occlusions have an impact on receptive fields predicted by sparse coding ? If so , what is the main difference if occlusions are considered and how do model predictions compare with experimental measurements ? A critical inspection of standard sparse coding as a model for simple cell responses has recently been motivated by increasingly detailed experimental studies of simple cell responses . Using reverse correlation , a broad variety of receptive field shapes has been recorded , e . g . , for macaque monkeys [14] , ferrets [15] or mice [16] . In general , the distribution of receptive field shapes was found to be more diverse than the distributions predicted , e . g . , by sparse coding or ICA [14] . The most significant qualitative difference from modeling predictions was the experimental finding of large numbers of simple cells with globular instead of Gabor-like receptive fields [14]–[16] . None of the seminal papers on simple cell coding [2] , [17] had predicted such fields . Experimentally , globular fields were presumably not prominently reported earlier because of previously used estimation and/or cell selection methods . If oriented stimuli ( often Gabors or light-bars ) with different orientations and positions are used , cells with globular or center-surround fields are difficult to detect . After the discrepancy of diverse receptive field shapes and standard encoding models was pointed out [14] , further studies did show that large numbers of globular fields can be obtained in computational models [11] , [18]–[20] . Notably , two of these models [11] , [20] are sparse coding versions based on a linear superposition assumption . One uses a specific sparse prior and a specific hand-set sparsity [11] . The other [20] reports large numbers of globular fields for specific combinations of overcompleteness and sparsity . For the very large number of other studies on models with linear superposition ( including all ICA models ) , no or only low proportions of globular fields were observed ( compare , e . g . , [9] , [10] , [21] ) . In this study we , for the first time , provide a systematic investigation of the impact of occlusion-like non-linearities on predicted simple cell responses . In order to quantify the differences to the neglection of occlusions , we study two sparse coding models: one assuming standard linear superposition [2] , [17] and the other approximating occlusions with strongly non-linear superpositions of components [22] , [23] . Fig . 1A , B illustrates the difference between the linear and the non-linear superposition used . By comparing the two combination rules with the actual combination of components in images , we can observe a better match of the non-linear superposition rule to the actual combination of components . If all components had the same intensity ( i . e . , the same color for the illustration in Fig . 1A , B ) , the -combination rule would represent the correct model for component occlusions [22] ( also see Fig . 2 ) . For components with different intensities , the non-linear combination is an approximation of the actual combination rule . However , the much weaker interferences resulting from the non-linear rule are a significantly closer match to occlusion non-linearities ( see Fig . 1B ) . Although the only difference between the two sparse coding models investigated is the rule for component combination , non-linear sparse coding versions have been investigated much less than linear versions because parameter optimization becomes more challenging . To model image patches for instance , large-scale applications of non-linear models with large numbers of observed and hidden variables have not yet been reported . By applying novel training methods [24] it is possible to overcome computational tractability limitations , e . g . , for the strongly non-linear model illustrated in Fig . 1 . Consequently , we can systematically study the effect of the combination rule on receptive fields predicted by sparse coding . The models' predictions will allow us to answer the question if and how occlusions can impact simple cell coding . Comparison of the model predictions to in vivo recordings then provides experimental evidence for the impact of occlusions on simple cell coding .
We compare two generative sparse coding models for the encoding of image patches by simple cells . Both models have the same set of parameters and both assume , like standard sparse coding , independent visual components and Gaussian noise in the data . The distinguishing feature of the non-linear model is the use of a point-wise maximum to describe the combination of visual components . The maximum combination is illustrated and contrasted with the standard linear combination in Fig . 2 . If we denote by an observed image patch and by the hidden units encoding presence or absence of components , the full generative formulation of the non-linear model is given by: ( 1 ) ( 2 ) This model is compared to one assuming the standard linear superposition: ( 3 ) ( 4 ) The parameters of both models are the basis functions ( which will later be related to receptive fields ) , the noise variance , and the sparsity parameterized by . We define to be the matrix containing all basis functions ( columns of ) and for brevity denote to be the set of all model parameters . The non-linear superposition in equation 2 is given by the function ( compare Fig . 2 ) . Instead of linearly summing basis function entries at pixel like in the linear model ( Eqn . 4 , ) , the mean value of the Gaussian , , is set by the ( active ) basis function entry with maximal magnitude: where . The function in ( 2 ) is the vector valued version defined by applying the maximum magnitude function for each entry . By using a point-wise maximum , the model is a variant of maximal causes analysis ( MCA ) [22] , [23] and will be referred to accordingly . For the generation of image patches , both models assume a basis function to be either part of the patch or not ( binary hidden variables ) . Such an assumption is consistent with objects or edges being either present or absent in a given patch . However , binary hidden units are different from conventional sparse coding in which continuous hidden variables are used . For later comparison , we therefore also study conventional sparse coding based on the generative model given by: ( 5 ) ( 6 ) where a Laplace prior is used to model continuous sparse values ( instead of the Bernoulli prior used in the other two considered models ) . This model is the generative analogue of the objective function formulation of sparse coding with regularization . We will refer to the model of Eqn . 5 and Eqn . 6 as standard sparse coding ( SC ) and to the linear model with Bernoulli prior ( Eqns . 3 and 4 ) as binary sparse coding ( BSC ) [25] , [26] . For each model above we now seek the parameters that optimally model the statistics of image patches . As a result , each model predicts a set of basis functions which can be compared to each other and to in vivo recordings of simple cell receptive fields . To find optimal parameters , we apply maximum likelihood learning on the same set of preprocessed image patches ( see Methods ) . For maximal causes analysis ( MCA ) and binary sparse coding ( BSC ) we applied a variational EM approach [24] , while parameter optimization for standard sparse coding ( SC ) applied a maximum a-posteriori approach [4] , [9] . All optimization approaches used allow for the inference of parameters for large numbers of input and hidden units . While large-scale applicability of linear sparse coding models has been demonstrated repeatedly in the past [9] , [17] , [27] , comparatively efficient optimization of strongly non-linear models has only been demonstrated very recently [23] , [24] . The optimization procedure applied to MCA and BSC furthermore allows the inference of all model parameters including stimulus noise and sparsity . The only remaining parameters are the size of image patches and the number of basis functions ( with the degree of over-completeness given by the ratio of the two ) . For the generative models above , we optimized the model parameters for a set of natural image patches . First , natural image patches were preprocessed using an array of linear center-surround filters to model preprocessing by the lateral geniculate nucleus ( LGN ) . Details are given in the Methods section . Given a fixed set of preprocessed stimuli , we optimized the parameters for the non-linear model ( MCA ) , for binary sparse coding ( BSC ) , and for standard sparse coding ( see Methods and Supporting Information ) . All models were applied to the same set of preprocessed patches ( no independent ON-/OFF-channels ) . After optimization , all models predicted a large number of Gabor-like receptive fields ( compare Fig . 3 A , B ) . However , we found significant quantitative differences in the statistics of receptive field shapes . Most saliently , the different models showed different fractions of globular fields , i . e . , fields that are not Gabor-like but are best described as center-surround ( difference-of-Gaussians ) fields [14] . In the primary visual cortices of different species , significant proportions of simple cells with such receptive fields have been reported [14]–[16] ( see Fig . 1 C for examples of such cells in macaque monkeys ) . However , globular fields are either not observed or only done so in relatively small numbers when standard sparse coding or ICA are applied to image patches . We observed globular fields for both linear and non-linear models . However , the predicted proportions of such fields were very different . Fig . 1 D shows the proportions of globular cells for hidden units for the different models and Fig . 3 C shows the proportions for each model for different numbers hidden units ( different degrees of overcompleteness ) . For standard sparse coding [9] , the percentage of globular fields tends to increase corresponding to an increase in overcompleteness [27] but stays low in relative comparison ( below ) . Sparse coding with binary latents as in BSC results in a consistently higher percentage of globular fields ranging from for units to about for . By far however , the highest percentages of globular fields were observed in applications of the non-linear model ( MCA ) . Relatively independent of the number of latents , fractions between and of globular receptive fields were obtained . For comparison , the fraction of globular fields in macaque monkeys [14] is estimated to be about ( see Methods and SI ) , in ferrets about of the fields were reported to be globular [15] , and in mice about globular fields were measured [16] . For ferrets and mice the percentages were reported in the corresponding studies [15] , [16] , and for macaque monkeys we used original receptive field recordings ( courtesy of D . Ringach ) and applied the same classification procedure as for the predictions computed by the models ( see Methods and Fig . S6 and S7 ) . The percentages of globular fields estimated on the grounds of the three experimental studies [14]–[16] are given as horizontal red lines in Fig . 3 C . Of all remaining non-globular fields predicted by the models , almost all have a Gabor-like shape ( with few fields having unspecific shapes; see Methods and compare Figs . S3 and S7 . ) . To analyze remaining differences between these Gabor-like fields , we followed an approach suggested by an earlier experimental study [14] , i . e . , we matched the fields with Gabor functions and plotted Gabor shape parameters ( Gaussian envelope parameters and frequency ) using dimensionless -plots ( see Methods and SI for details ) . is proportional to the width of the Gaussian envelope in wave-vector direction; is proportional to its width orthogonal to the wave-vector . The widths are measured in multiples of the spatial wavelength . As we have separated out the globular fields first , we avoided having to match center-surround fields with Gabor functions , which removes a problem of earlier applications of the analysis . Fig . 4 A shows the obtained distributions for the non-linear and the linear model ( for , ) , respectively . As can be observed , both distributions are relatively broadly shaped but differ . The distribution predicted by the non-linear model is shaped upwards starting from while the distribution predicted by the linear model is more elliptical . Furthermore , the receptive fields of the non-linear model tend to lie closer to the origin with a center-of-mass at about compared to a center-of-mass at for the linear model . For comparison , we applied the same analysis of receptive field shapes to in vivo recordings of macaque simple cells [14] ( data provided by D . Ringach , see Methods and Fig . S7 ) . The resulting shape distributions are overlaid with the model predictions in Fig . 3 A . The center-of-mass of the experimental recordings lies at and is much closer to the center-of-mass of the non-linear model . In general , the distributions predicted by both models show a large diversity of Gabor shapes and a relatively large overlap with macaque recordings , however . Other than investigating different models for image patch encoding , we explored different preprocessing methods prior to the application of the encoding models . We used a neurally plausible preprocessing by modeling LGN input to the cortex using center-surround ( difference-of-Gaussians ) filtered patches . Another ( and related ) method of preprocessing popular for functional modeling is zero-phase PCA whitening [28] . To control for the influence of the preprocessing method ( i . e . , the model for LGN input to the cortex ) , we applied the linear and non-linear models also to image patches preprocessed using zero-phase PCA whitening ( ZCA ) . We found that preprocessing has a significant influence on the shapes of predicted receptive fields . A change in preprocessing both changes the percentages of globular fields ( see Fig . 3 C , ZCA curves ) and the shape distribution of Gabor fields ( see Methods and Fig . S4 ) . The main difference between the linear and non-linear receptive fields remains the consistently much higher percentage of globular fields for the non-linear model , however . Similarly , the degree to which center-ON and center-OFF cells are assumed to convey input independently from one-another [29] has an impact on the shapes of receptive fields . Controls with ON- and OFF-cells treated independently of each other again reproduce the same qualitative results , with the non-linear model showing a much higher percentage of globular fields than the linear model ( see Fig . S5 ) . Finally , also controls with sparsity levels fixed to the same values for both models always resulted in a much higher percentage of globular fields for the non-linear model . This much higher percentage was , without exception , observed in all of the experiments and controls of this study . Unlike standard sparse coding [4] and most of its variants [9] , [11] , [30] , the non-linear MCA model and the linear BSC model both do not only infer parameters for the basis functions but also parameters for sparsity and stimulus noise . Consequently , these parameters do not have to be hand-set or inferred by cross-validation in numerical experiments . More importantly , however , we can directly ask if the degrees of inferred sparsity differ between the non-linear and linear model . Sparsity is of high interest for understanding neural coding [31]–[33] . Theoretical predictions of sparsity levels have , so far , only been studied for linear models . Here we can study sparsity for the non-linear and linear model very directly . Because of binary hidden variables described by a Bernoulli prior , we use the number as sparsity measure . This number is simply the average number of active units across all image patches . Or in other words , the average number of basis functions a model needs to combine for the generation or reconstruction of an image patch . Note that the value corresponds to an inverse sparsity ( however , we will refer to this value as sparsity measure or simply sparsity if the meaning is clear from the context ) . In analogy to Fig . 3 C , inferred degrees of sparsity are plotted in Fig . 4 B for different numbers of basis functions . For both models , MCA and BSC , the average number of active hidden units decreases ( sparsity increases ) with increasing number of basis functions ( i . e . , with increasing over-completeness ) . However , while both models converge to increasingly sparse solutions , the non-linear model was found to be consistently and very significantly sparser . On patches and hidden variables the non-linear model estimates a patch to consists of on average four to five components ( basis functions ) compared to seven to eight as estimated by the linear model . Fig . 5 illustrates the different encodings of the two models for different example patches . For the simple example patch showing an oriented ‘branch’ ( Fig . 5 , top ) , both models combine basis functions of similar orientation . However , MCA uses fewer ‘line segments’ to re-construct the patch while BSC uses more basis functions . For patches with more complex structures ( Fig . 5 , examples in the middle ) , the differences become still more salient . Again , MCA uses fewer basis functions and usually reconstructs a patch from components which correspond to actual components in a patch . The final example ( Fig . 5 , bottom ) illustrates inference with Gabor-like and globular components . The MCA model uses a globular field to reconstruct a two dimensional end-stopping structure . In the example , BSC reconstructs the patch by exclusively using Gabors . Some of them are very localized but clearly Gabor-like fields ( the two right-hand-side fields ) . Often the BSC fields are not closely aligned with true image components . Sometimes we also observed BSC to use a globular field for an end-stopping structure but it does so much more rarely than MCA . We have never observed standard sparse coding to use a globular field for the examples investigated . In general , BSC and ( much more so ) standard sparse coding use more basis functions ( reflecting the lower sparsity ) and usually combine components which do not directly correspond to actual image components . In control experiments using different preprocessing approaches , we found that concrete sparsity levels do depend on the type of preprocessing . However , as was the case for the percentage of globular fields , in all experiments sparsity levels were consistently much higher for the non-linear model than for the linear one ( see Methods and SI ) .
The non-linear model consistently predicted a high percentage of globular receptive fields ( Figs . 1 D and 3 C ) which was relatively independent of the degree of overcompleteness ( i . e . , number of fields ) . The linear model and standard sparse coding showed much lower percentages . For comparison with in vivo recordings of simple cells , we used data from macaques [14] , ferrets [15] and mice [16] . Notably , high percentages of globular fields were found in all these experimental studies . The percentage of globular fields in macaques was estimated here based on data provided by D . Ringach . By applying the same classification procedure as for the theoretical predictions , of the original receptive field recordings were classified as globular fields . For ferrets , globular or center-surround receptive fields were reported [15] . For mice , of recorded cells consisted of just one subfield [16] , which is a close match to globular fields as defined in this work . It should be pointed out that none of the experimental studies had a focus on globular fields . These fields have been observed while general properties of V1 receptive fields were investigated . For comparison , the experimentally measured percentages of globular fields ( , , and ) tend to be lower than the percentages predicted by the non-linear model ( to ) but they are much higher than the low percentages ( below ) of the linear models . Fig . 3 C visualizes the predictions of the models for different degrees of overcompleteness with experimental results shown as horizontal lines . For the measurements and for the models , the percentages of globular fields can depend on different experimental or model settings . On the experimental side , receptive field measurements can depend , e . g . , on the type of stimuli used for reverse correlation . On the modelling side , the percentage of globular fields can change , e . g . , by changing sparsity levels or overcompleteness . For our comparative study we removed the arbitrariness in sparsity levels by applying an optimization procedure which automatically infers the level of sparsity . To study the influence of overcompleteness , we screened through different values for the number of hidden units . Considering all numerical experiments , the type of component superposition emerged as having by far the most significant influence on percentages of globular fields , with the non-linear model showing robustly very high percentages . Neither standard sparse coding with the usual parameter settings nor a range of other standard models predict such high percentages: For sparse coding , globular fields only emerge with specific priors and/or specifically chosen sparsity levels [11] , [20] , [30] . For independent component analysis , k-means , sparse auto-encoders or restricted Boltzmann Machines no globular fields were observed [21] . The high percentages of globular fields for the occlusive model studied here and the high percentages observed in in vivo recordings suggest a strong impact of visual occlusions on simple cell encoding . Furthermore , the reported results suggest direct experiments to verify or falsify the models studied here: Suppose different simple cells with receptive fields at the same location in the visual field were identified , then the linear and non-linear models could be used to predict the responses if complex stimuli are presented at the same location . For a crossing of two edges the linear model would for instance predict responses less aligned with responses to the individual edges than the non-linear model ( compare Fig . 5 ) . This is because the linear model combines less specific components ( and more of them ) as they can be added and subtracted more freely than those of the non-linear model . The linear model would thus predict a higher difference between the response to overlapping line segments and the responses to the individual segments . Measuring the difference of a response to a crossing and to the individual lines would thus allow to verify or falsify the linear or non-linear model more directly . Also predictions of different sparsity levels could be verified or falsified but such experiments are more difficult because it is challenging to accurately measure sparsity levels in vivo . The consistently much sparser encoding predicted by a non-linear sparse coding model has , however , a significant potential impact on the ongoing debate on sparse encodings and recent experimental results [32] , [33] . In contrast to differences in sparsity and in the percentage of globular receptive fields , we found the differences of Gabor-shape distributions ( Fig . 4 ) less instructive for distinguishing image encoding based on linear or occlusion-like models . For both superposition assumptions we obtained a large diversity of Gabor shapes . Notably , both distributions are broader and have a larger overlap with macaque receptive fields than ICA and standard sparse coding [14] . As the non-linear and linear model studied here use binary hidden units , the higher overlap of both models with experimental results may , instead , be taken as evidence for a more discrete neural encoding of components than assumed , e . g . , by a standard continuous Laplace prior [17] , [27] . Since the diversity of receptive field shapes was suggested as a means for comparison of models to experimental data [14] , [34] , different modeling approaches have been shown to result in broad distributions of Gabor shapes . Consistent with our observation that more discrete priors result in a large diversity of shapes , recent studies [11] , [30] reported a large diversity based on more discrete values for the hidden units . Two studies [11] , [20] notably obtained high percentages of globular fields by simultaneously assuming a linear combination of components . However , parameter optimization of both studies focused on the basis functions themselves , sparsity was hand-set and not inferred from data . One of the studies [11] specifically chose the sparsity level which resulted in the highest similarity between model and experimental distribution of receptive fields . The hand-set sparsities of these two linear models are , consequently , unlikely to be the optimal sparsity values for the data . It therefore remains an open question what percentages the models would predict for ( approximately ) optimal values of sparsity and data noise . For sparse coding with standard parameter settings ( e . g . , SC in Fig . 3 C ) , for novel linear sparse coding models ( e . g . , [30] ) or for other models [21] no or only relatively few globular fields were observed . For the non-linear model investigated here , high percentages of globular fields robustly emerged in all experiments with sparsity levels ( and data noise ) always automatically estimated from the used set of image patches . In addition to functional and probabilistic approaches to model simple cell coding , other computational investigations are based on models of neural circuits . While many studies directly relate to linear sparse coding [11] , [30] , [35] , other contributions are not directly linked to an underlying functional model and , notably , often point out that non-linearly overlapping components can be learned well [19] , [36]–[39] . The non-linear generative model studied in this paper can be seen as a functional correlate to neural circuit models that do well in learning non-linearly combining components . Consequently , a neural model for non-linear component extraction [19] , [38] was among the first modelling approaches to report and discuss globular receptive fields [18] , [19] . Such microcircuit models suggest that , on the one hand , a neural implementation of the non-linear model may have some advantages over the linear model because the -superposition is closely related to a ( soft ) k-winner-take-all competition or rank-coding among computational units [19] . On the other hand , standard linear models with appropriate sparse priors can be shown to result in mono-modal posteriors [17] . Such modes can efficiently be found using gradient-based neural dynamics which may represent a computational advantage of such models . In the case of ICA , activities of hidden units can directly be computed via filter responses . In general there may , therefore , be relevant aspects other than the theoretical optimality of the generative model itself . To obtain as optimal as possible results , an encoding model has to fulfill two requirements: ( A ) it has to reflect the data generation process well and ( B ) it has to provide an efficient procedure to learn optimal parameters . A simpler model may in practice have the advantage of a more efficient learning procedure while learning based on a non-linear model may be harder . There may , for instance , be higher computational costs associated with a non-linear model or convergence to local optima may represent a problem . It has , therefore , been argued in the literature [40] that discussions about coding efficiency should contain learning efficiency as an integral part . In controls with our models using ground-truth stimuli , we indeed found a higher tendency of the non-linear model to converge to local optima compared to the linear model ( see Methods , Numerical experiments ) . Learning still frequently converged to a global optimum , though , and could easily be improved using annealing . For natural image patches , we did not observe differences between runs with and without annealing ( Methods ) . All experiments resulted in the same percentages of globular fields ( within the limits of the error bars in Fig . 3C ) , for instance . Based on the used learning approach , finding optimal parameters therefore does not seem much more challenging for the non-linear model than for the linear one . Also the computational cost is about the same ( compare Methods and [24] ) . Furthermore , both models face essentially the same challenges regarding neural implementability . Because of discrete hidden variables , a standard MAP estimation can not be applied and would be prohibitive for a direct inference of the optimal sparsity and stimulus noise . An implementation in neural microcircuits would consequently have to focus on how the posterior could be represented efficiently . This may be realized through population codes ( e . g . , [41] , [42] ) or through a sampling based representation ( e . g . , [32] , [43] ) . The latter can be related to the approximation used here [44] . Accuracy and response times would then depend on the concrete realization of such a neural implementation . Functionally , sensory coding efficiency is very task dependent ( see [40] for a discussion ) . Regarding metabolic coding efficiency , a sparser code is preferable over a less sparse code , which would favor the non-linear model . For image reconstruction , linear models may remain well suited ( compare , e . g . , reconstructions in Fig . 5 ) , and a reduced sparsity can help for this task . However , best results for general tasks and for further processing in the visual pathway are presumably achieved for the best stimulus model , i . e . , for a model which well approximates the actual stimulus generation process . Note , that the maximum non-linearity and standard linear superposition as studied here are only two possible models for the combination of components . In the literature , other non-linearities such as noisy-OR combinations [45]–[47] or non-linear ICA [48] have been investigated before . Neither these non-linearities nor the maximum non-linearity have , so far , been shown to predict simple cell receptive fields , however . The reason is that non-linear models could , so far , not be scaled-up to the problem size required to study optimal codes on image patches . This is , again , due to the requirement of learning approaches that go significantly beyond MAP-based approximations . Although sparse coding and its variants represent the standard model for simple cell coding , other computational models have been suggested . More recently , for instance , the suitability of mixture model approaches has been discussed [21] , [49] , [50] . While such models emphasize fitting model to data distributions , approaches such as ICA , sparse coding or MCA aim at learning a distributed encoding based on a combination of components . Still another functional approach to model visual stimuli is a line of research referred to as dead leaves approaches [50]–[53] . These statistical models of visual stimuli have long emphasized the importance of occlusions , and they were shown to reproduce many statistical properties of visual stimuli [52] , [53] . So far , this prominent line of statistical image models was incompatible with sparse coding and simple cell models , though . The incorporation of occlusion non-linearities into sparse coding offers a way to reconcile these lines of research . Again it should be noted , however , that the non-linear model studied here accounts for occlusions by assuming strongly non-linear superpositions of low-level image components . A more explicit encoding of occlusion would result in a more accurate functional model but involves a larger set of parameters and further increases computational requirements [54] . Furthermore , explicit occlusion models are presumably more relevant for mid- and high-level vision ( with objects and object parts as components ) than they are for low-level image statistics . While different recent models report that globular receptive fields do emerge in applications to image patches [11] , [18] , [19] , [30] , they offer no explanation why this is the case . In this context , our comparative study allows for an explanation that is closely linked to discrete hidden units and the superposition model . First consider the selection of typical DoG preprocessed image patches as displayed in Fig . 6 A . As can be observed , the patches contain Gabor-like components as well as globular components . Also note that the maximal intensities of Gabor and globular components are similar . Now suppose that a sparse coding model has already represented Gabor-like fields such as those shown in Fig . 6 B ( left-hand-side ) . If these two Gabor fields are linearly superimposed and then rescaled by a factor ( Fig . 6 B ) , an ( approximately ) globular field is generated . If the two Gabors are linearly superimposed but can not be rescaled ( Fig . 6 C ) , then the intensity of the globular field becomes higher than the intensity of typical globular structures in the data . For the non-linear superposition ( Fig . 6 D ) no globular structures can be generated by superimposing Gabors . Fig . 6 illustrates that globular structures in image patches can be explained by linearly superimposing Gabors . For linear sparse coding approaches with continuous values for hidden variables , globular structures do , consequently , not have to be represented explicitly . This may explain why almost all versions of sparse coding or ICA do not predict globular fields or only very low percentages thereof [2] , [3] , [9] , [10] . If hidden units are prevented from taking on continuous values [11] , [26] , a stronger incentive is generated to explicitly represent globular fields . This can explain the observation of larger numbers of globular fields for models with more discrete priors [11] , [26] , [30] . A strongly non-linear superposition of Gabors can not generate globular fields . Consequently , such components have to be represented explicitly . This may explain the high percentages of globular fields in the non-linear model and , presumably , the high percentages of globular fields in the experimental measurements . Also note that the generation of globular structures in the linear models requires more fields than in the non-linear model , which is consistent with the sparser encoding in the non-linear case . Both Gabor-like and globular fields are useful for image encoding . While Gabors are closely associated with edges , we observed globular fields to be more closely associated with two dimensional structures ( see Fig . 5 ) such as corners or ends of branches ( also compare [20] for a discussion ) . Furthermore , both component types may be useful for texture encoding . Both types are certainly observed in preprocessed stimuli ( Fig . 6 A ) and they are both measured in vivo . On the functional side , many tasks seem to work well with approaches not resulting in globular fields , as a large body of literature , e . g . , on image processing with linear models shows . Also inference examples , e . g . those of Fig . 5 , show that linear models ( with low percentages of globular fields ) can perform well , e . g . , in terms of image reconstruction ( mainly because they use a large number of components which they can add and subtract ) . For data with non-linearly combining components , non-linear models are naturally performing better if inference of the true components is the task [22] , [24] , [38] , [55] . The functional capabilities of non-linear models and globular fields will , therefore , be very task dependent . The observation that globular fields are observed in in vivo recordings may , however , be interpreted as evidence for them being functionally very useful for the typical tasks animals and humans have to accomplish . Our study answers whether occlusions can have an impact on theoretical predictions of simple cell models . Based on a direct comparison of superposition assumptions we have observed very significant differences between the receptive fields and sparsity levels predicted by the linear and the occlusive model . Both models represent approximations of the exact model for local visual component combinations . However , we have observed that a non-linear superposition results in both a closer match to the true combination rule of visual components and a closer match of predicted receptive fields to in vivo measurements . This higher consistency between predicted receptive fields and in vivo recordings suggests that stimulus encoding in V1 is optimized by taking visual occlusions into account . Most significantly , high quantities of a new type of simple cells with center-surround fields , reliably and robustly emerge if visual occlusions are considered .
In this study we compared the predictions of two sparse coding models , MCA and BSC , when trained on natural image patches . Given the generative models ( Eqns . 1 and 2 for MCA; Eqns . 3 and 4 for BSC ) and a set of preprocessed image patches to we sought for each model the parameter values that maximize the data likelihood . In its logarithmic form the likelihood function is given by: ( 7 ) For all models considered here ( MCA , BSC and conventional SC ) , the optimization of the likelihood function represents a computationally intractable problem for higher dimensional hidden spaces . We therefore require approaches that approximately but efficiently optimize the likelihood . For MCA and BSC we apply variational expectation maximization [56] ( variational EM ) . That is , instead of maximizing the likelihood directly , we maximize the so-called free-energy: ( 8 ) where the sum runs over all binary vectors and where is an entropy term . The free-energy function is a lower bound of the log-likelihood . By applying variational EM , the function is maximized alternately with respect to in the E-step ( while is kept fixed ) and with respect to in the M-step ( while is kept fixed ) . For the M-step , expectation values of functions with respect to distributions have to be computed . The optimal choice for these distributions in the E-step are the posterior probabilities given the stimulus , . Sparse coding models are computationally intractable because these exact posterior distributions and their expectation values are intractable . E-step . To efficiently optimize the models' parameters , we apply a variational EM approach by choosing distributions which are truncated approximations to the exact posteriors [24]: ( 9 ) where is an indicator function ( i . e . , if and zero otherwise ) and where is a data point dependent subset of the hidden space . By choosing the variational distributions as in Eqn . 9 , we obtain the following approximations for expectation values with respect to the exact posteriors: ( 10 ) The sums for the approximate expectation values now run over instead of the entire hidden space . If is chosen to be small but to contain the states with most posterior probability mass , the computation of the expectations in Eqn . 10 becomes tractable while a high accuracy of the approximations is maintained [24] . The set is , therefore , chosen to consider the subset of the most relevant hidden units for a patch . Furthermore , at most of these units are assumed to be active simultaneously . More formally we define: ( 11 ) where the index set contains those hidden units that are the most likely to have generated data point ( while the last term in Eqn . 11 assures that all states with just one non-zero entry are also considered ) . To determine the hidden variables for , we use those units with the largest values of a selection function given by: ( 12 ) Through the selection of states with high posterior mass , the function resulted in a high accuracy for parameter recovery on data with ground-truth ( see numerical experiments further below ) . Parameters of the approximation are the maximal number of components considered , , and the maximal number of simultaneously active components . They can be chosen such that a high approximation accuracy is achieved with simultaneously high efficiency ( see numerical experiments ) . M-step . If the variational distributions of the free-energy are chosen as in Eqn . 9 , then M-step equations for parameter updates follow from the optimization of a truncated free-energy [24] which is given by: ( 13 ) where is the set of all states with less than active hidden units . The set is a subset of those data points with less or equal components . Data points with more than components are not well approximated and are therefore not considered for learning . is defined to contain the data points with smallest values for , where is the expected number of well approximated data points [24] given by with defined as in Eqn . 20 below . To verify that the learning algorithms for MCA and BSC correctly recover data components at least approximately , we first applied them to artificial stimuli where ground-truth is available . For each model , a dataset of stimuli was generated . The generation followed the MCA and BSC model , respectively , using the same set of generating parameters for the basis functions , stimulus noise and sparsity . The used stimuli consisted of patches with pixels generated from ten basis functions in the form of horizontal and vertical bars ( five bars for each orientation ) . The parameter values of each bar were defined to be either or ( with small amounts of additive Gaussian noise ) . The generating sparsity was set to ( two bars on average ) , and the stimulus noise was set to . Examples of the generated patches are shown in Fig . S1 A for the MCA model , and in Fig . S2 A for the BSC model . The stimuli represent forms of a standard ground-truth stimulus set [36] . For MCA experiments the softening parameter in equation 14 was set to ( a large odd integer ) . The MCA and BSC algorithms were run on the respective data using EM iterations each . For both algorithms the first third of the iterations ( up to EM step ) were performed on the full dataset with . For iterations upto was linearly decreased to . After EM iterations , both models recovered the generating parameters of the data with high accuracy . The recovered generative fields after iterations and the time courses of data noise and sparsity are shown in Fig . S1 B–D for the MCA model , and in Fig . S2 B–D for the BSC model . Parameter optimization for both models is non-convex but , after convergence , we observed the parameters to represent the ground-truth basis functions for both models in most of the trials . MCA we observed to converge more frequently to local optima . By applying annealing , MCA and BSC both more efficiently avoided local optima . The bars stimuli have very pronounced local optima because the stimulus values are not continuously distributed . For stimuli with more continuous distributions of observed values such as images , we observed no significant differences between runs with and without annealing . In particular , no significant differences in the numbers of globular fields were observed . Both algorithms were , therefore , run without annealing for all the experiments on image patches . To optimize the model parameters on natural image stimuli , we extracted a set of patches of size pixels for one set of experiments , and patches of size for another set of experiments . Patches were extracted at random positions from the van Hateren natural image database [58] . In mammals , visual information is transferred to the visual cortex via center-ON and center-OFF cells in the lateral geniculus nucleus ( LGN ) . The sensitivity of these neurons can be modeled by a difference-of-Gaussians ( DoG ) filter . We therefore preprocessed all patches by convoluting them with a difference-of-Gaussians kernel . Following experimental results [59] , the ratio between the standard deviation of the positive and the negative Gaussian was chosen to be and the amplitudes were chosen to obtain a mean-free center-surround filter [19] , [23] . After DoG filtering , values were scaled to fill the interval [−10 , 10] which provides a form of divisive contrast normalization [60] . Control experiments with divisive variance normalization [28] , [60] ( which serves the same purpose ) produced closely matching results . To control for the influence of the DoG convolution filtering , we ran further experiments using zero-phase PCA whitening ( ZCA ) which represents a standard preprocessing procedure often used with functional models [28] . Furthermore , we controlled for the influence of separating positive and negative channels . For each experiment , the same set of stimuli was used to train the three models under consideration . Those experiments , where we screened through different degrees of overcompleteness ( overcomplete with to overcomplete with ) were performed on stimuli of size pixels ( Fig . 3 C and Fig . 4 B ) . Each experiment was repeated five times to obtain empirical error bars on the recovered sparseness and the predicted percentage of globular fields ( we show twice the standard deviations in Figs . 3 C and 4 B ) . All other experiments , including those investigating the shape statistics ( Fig . 4 A ) were performed on stimuli of size . In total , results of experiments were gathered to create Figs . 3 C and 4 B; additionally , about experiments were performed for various -plots and for additional controls on differently preprocessed sets of image patches ( see below ) . For each experiment on image patches we performed EM iterations . Analogously to the verification experiments on artificial data , the first of the EM steps ( 1 up to 33 ) were run on the full dataset . For iterations 34 to 66 , was again linearly decreased to and kept at for the last 34 EM steps . The smoothing parameter for the non-linearity of the MCA algorithm was set to as for the artificial data . The approximation parameters for the non-linear and the linear model were both set to and . Each experiment to find optimal parameters was typically run on CPU cores using a parallelized implementation . After parameter optimization we computed an estimate of the predicted receptive fields by convolving the learned basis functions with the same DoG filter as used for preprocessing . Subsequently , we matched both the predicted receptive fields and the in vivo data with Gabor-wavelets and difference-of-Gaussians to gather the statistics of shapes . The convolution with the DoG filter is an estimate of the receptive field assuming a linear mapping: If denotes a patch ( with pixel values as vector entries ) and if parameterizes the mapping , the linear response is given by . The original response of a unit to a patch consists of two steps: a linear preprocessing and a non-linear response to the preprocessed patch , where the non-linear response is described by the corresponding sparse coding model . We therefore rewrite the mapping as a two-step mapping . If denotes a preprocessed patch ( as in the main text ) , it is given by: ( 32 ) where is the DoG kernel for the convolution and where parameterizes a linear mapping from preprocessed patches to hidden units . The mapping can be estimated by reverse correlation [14] using the models' approximate posteriors as responses . If we denote such an estimate by , the total linear response is given by: ( 33 ) This means the receptive field estimate is given by convoluted with the same kernel as used for preprocessing . Fig . S6 ( top row ) shows examples of estimates obtained in this way . Alternatively , note that the basis functions are already similar to stimuli that best drive the hidden units . A direct estimate of the parameters is therefore given by the basis function parameters themselves ( ) , and the corresponding receptive field estimate is given by convoluted basis functions: . In numerical experiments , both estimates resulted in very similar receptive fields , and some representative examples are shown in Fig . S6 . Because of this high similarity we used the convoluted basis functions as receptive field estimates , which reduced the otherwise extensive computational costs of reverse correlation for the very large number of receptive fields that were analysed in this study . To analyse the shape statistics of the estimated receptive fields resulting from our numerical experiments and from experimental recordings [14] , receptive fields were matched against Gabor-wavelets and difference-of-Gaussians . Note that for notational purposes we replace the index denoting the input units by two-dimensional coordinates and denoting the actual planar position in the two-dimensional field . The in vivo data analysed for comparison was obtained in experiments on macaque monkeys in an earlier study [14] . These receptive fields were recorded from neurons in the primary visual cortex using reverse correlation , and were matched with Gabor and DoG functions in the same way as the receptive fields predicted by the models . Representative examples are shown in Fig . S7 A . For each receptive field , we sought the eight parameters which minimized the mean squared error between the field and the Gabor-wavelet . Where and are the center coordinates of the Gabor-wavelet , is its spatial rotation , and parameterize the shape of the Gaussian envelope , is a measure of the frequency of the planar wave component , is its phase shift and is the overall amplitude of the Gabor-wavelet: ( 34 ) where are the translated and rotated coordinates of the function . Similarly , again for each receptive field , we sought the eight parameters of the difference-of-Gaussians kernel which minimized the squared distance to each field . and are the center coordinates of the DoG kernel , its spatial rotation . and parameterize the shape of the inner Gaussian , parameterizes the size difference between the Gaussians and and specify the amplitudes of the Gaussians:We classified a receptive field as being globular if the reconstruction error of the best matching DoG function was smaller then the reconstruction error of the best matching Gabor wavelet and if the aspect ratio of the DoG was smaller than 2 . 0 ( , where is the parameter for the more elongated axis ) . A small difference between the errors of a match with DoG and a match with a Gabor function means that the receptive field is neither clearly center-surround nor clearly Gabor-like . In such cases we call the field ambiguous . Using a standard least-square optimization method [62] , we got robust result for fitting and classification for almost all receptive fields . We applied matching and classification to the results of each of our numerical experiments as well as to the experimental data [14] provided by D . Ringach . The experimental data consisted of fields of pixels , fields of pixels , and fields of pixels . Our procedure classified fields as clearly globular and as clearly Gabor-like ( see Fig . S7 A for some examples ) . As the experimental data is less smooth than the theoretical receptive field predictions , a relatively large number of ( out of 250 ) fields were ambiguous in this case ( see Fig . S7 B for some examples ) . By considering half of these fields as globular , we obtained globular fields ( a percentage of ) ; considering all of them as globular corresponds to globular fields; and considering all ambiguous fields as Gabor-like results in a percentage of globular fields . In Fig . 2 C we used as mean with the higher and the lower percentages defining the limits of the corresponding error bar . To analyse the shape distribution of receptive fields , the shape relevant parameters can be visualized as an -plot . That is , for each receptive field ( predicted or measured ) the dimensionless values given by and were computed , where is the spatial frequency of the fitted Gabor function , and where , are the standard deviations of its Gaussian envelope in wavevector direction and orthogonal to it [11] , [14] , [19] , [30] . For our analysis , we first removed the globular fields from the sets of experimentally measured fields as well as from the sets of predicted receptive fields before visualizing the corresponding distributions . This procedure removed the otherwise ill-posed problem of having to match center-surround fields with Gabor wavelets . | The statistics of our visual world is dominated by occlusions . Almost every image processed by our brain consists of mutually occluding objects , animals and plants . Our visual cortex is optimized through evolution and throughout our lifespan for such stimuli . Yet , the standard computational models of primary visual processing do not consider occlusions . In this study , we ask what effects visual occlusions may have on predicted response properties of simple cells which are the first cortical processing units for images . Our results suggest that recently observed differences between experiments and predictions of the standard simple cell models can be attributed to occlusions . The most significant consequence of occlusions is the prediction of many cells sensitive to center-surround stimuli . Experimentally , large quantities of such cells are observed since new techniques ( reverse correlation ) are used . Without occlusions , they are only obtained for specific settings and none of the seminal studies ( sparse coding , ICA ) predicted such fields . In contrast , the new type of response naturally emerges as soon as occlusions are considered . In comparison with recent in vivo experiments we find that occlusive models are consistent with the high percentages of center-surround simple cells observed in macaque monkeys , ferrets and mice . |
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Unique purine-rich mRNA sequences embedded in the coding sequences of a distinct group of gammaherpesvirus maintenance proteins underlie the ability of the latently infected cell to minimize immune recognition . The Epstein-Barr virus nuclear antigen , EBNA1 , a well characterized lymphocryptovirus maintenance protein has been shown to inhibit in cis antigen presentation , due in part to a large internal repeat domain encoding glycine and alanine residues ( GAr ) encoded by a purine-rich mRNA sequence . Recent studies have suggested that it is the purine-rich mRNA sequence of this repeat region rather than the encoded GAr polypeptide that directly inhibits EBNA1 self-synthesis and contributes to immune evasion . To test this hypothesis , we generated a series of EBNA1 internal repeat frameshift constructs and assessed their effects on cis-translation and endogenous antigen presentation . Diverse peptide sequences resulting from alternative repeat reading frames did not alleviate the translational inhibition characteristic of EBNA1 self-synthesis or the ensuing reduced surface presentation of EBNA1-specific peptide-MHC class I complexes . Human cells expressing the EBNA1 frameshift variants were also poorly recognized by antigen-specific T-cells . Furthermore , a comparative analysis of the mRNA sequences of the corresponding repeat regions of different viral maintenance homologues highlights the high degree of identity between the nucleotide sequences despite very little homology in the encoded amino acid sequences . Based on these combined observations , we propose that the cis-translational inhibitory effect of the EBNA1 internal repeat sequence operates mechanistically at the nucleotide level , potentially through RNA secondary structural elements , and is unlikely to be mediated through the GAr polypeptide . The demonstration that the EBNA1 repeat mRNA sequence and not the encoded protein sequence underlies immune evasion in this class of virus suggests a novel approach to therapeutic development through the use of anti-sense strategies or small molecules targeting EBNA1 mRNA structure .
Members of the viral family Herpesviridae , which are widely distributed throughout the animal kingdom , are characterized by their large double-stranded , linear DNA genomes . The gammaherpesviruses , one of three sub-families of Herpesviridae , predominantly replicate and persist in lymphoid cells with the distinguishing characteristic that they are able to establish lifelong latent infections of their hosts [1] . Gammaherpesviruses are of particular interest mainly due to the two human viruses , Epstein-Barr virus ( EBV ) and Kaposi's sarcoma-associated herpes virus , ( KSHV ) and the diseases they cause; Burkitts lymphoma , Nasopharyngeal carcinoma and Hodgkins lymphoma in the case of EBV and Kaposi's sarcoma , primary effusion lymphomas and AIDS-related lymphoproliferative disorders in the case of KSHV [2]–[4] . Latent infection of host cells by many gammaherpesviruses is dependent upon the expression of a viral maintenance protein , which ensures persistence of the viral episome within actively dividing cells , yet simultaneously evades immune surveillance [5]–[8] . Recent studies have investigated the unique properties of gammaherpesvirus maintenance proteins that allow the virus to restrict detection by host CD8+ cytotoxic T lymphocytes ( CTLs ) at crucial times during latency [1] , [9]–[16] . Expression of the EBV nuclear protein 1 ( EBNA1 ) is widespread in all forms of EBV infection , accentuating its central role in the maintenance of the viral DNA episome , a process essential for viral persistence and associated oncogenic potential [17] , [18] . A wide range of studies have demonstrated that EBV latently infected B cells are able to escape immune recognition , due in part to an internal glycine–alanine repeat ( GAr ) domain within EBNA1 , which significantly limits MHC class I-restricted presentation of EBNA1 epitopes linked in cis [9]–[15] , [19]–[22] . An earlier report suggested that the GAr polypeptide directly interfered with the translational machinery [10] . However , more recent studies including reports from the Hoeben group , have proposed that the EBNA1 purine-rich mRNA secondary structure encoding the GAr , rather than the protein sequence , is the critical component underlying the regulation of self-synthesis and evasion of immune recognition by cytotoxic T-cells [9] , [11] , [12] , [15] , [22] . Similar to EBNA1 , the latency-associated nuclear antigen 1 ( LANA1 ) maintenance protein of the closely related KSHV virus also acts to tether the viral episome to the host genome , thereby permitting the necessary segregation of viral DNA during cell division [9] , [15] . Studies have also demonstrated that LANA1 inhibits MHC class I peptide presentation in cis as a means of immune evasion [9] , [15] . Interestingly , studies of several other members of the gammaherpesvirus family have also reported similar immune evasive properties for the maintenance proteins of these viruses [23] , [24] . To define the underlying mechanism influencing the cis-translational inhibition responsible for minimizing the exposure of EBNA1 epitopes to immune surveillance , we have designed a series of EBNA1 expression constructs encoding alternative repeat reading frames to assess their impact on self-synthesis and antigen presentation . As well as these genetic experiments , we have also undertaken a detailed comparative analysis of the mRNA and protein sequences of the repeat regions of different gammaherpesvirus maintenance protein homologues . Based on these analyses , we conclude that the cis-inhibitory effect of the internal repeat sequences of gammaherpesviruses operates at the nucleotide level and is unlikely to be mediated through the direct action of the GAr polypeptide .
The internal GAr sequence within the EBV maintenance protein , EBNA1 , has been shown to inhibit self-synthesis , which in turn significantly restricts in cis antigen presentation [10]–[14] , [19]–[22] . To assess the functional importance of the mRNA sequence versus the protein sequence of the EBNA1 internal repeat in inhibiting self-synthesis , we compared both the mRNA and encoded protein sequences of similar internal repeat structures within the viral maintenance proteins of several gammaherpesviruses . Similar to EBNA1 , these maintenance proteins are critical for the persistence of the viral genome within latently infected cells . Gammaherpesviruses have been subdivided into four genera: Lymphocryptovirus , Rhadinovirus , Macavirus and Percavirus ( Table 1 ) [25] . Lymphocryptoviruses ( LCVs ) include the well-characterized EBV or Human herpesvirus 4 [26] , [27] , Lymphocryptovirus of rhesus monkeys , and Herpesvirus papio of baboons [26] , [28] , [29] . The Rhadinoviruses include the second human gammaherpesvirus KSHV or Human herpesvirus 8 [30] , [31] , Herpesvirus saimiri ( HVS ) [32] and Rhesus monkey rhadinovirus ( RRV ) [33] . The genera Macavirus includes the Alcelaphine herpesvirus 1 [34] and a newly defined species [25] the Ovine herpesvirus 2 ( Table 1 ) [35] . The coding mRNA sequence and deduced protein sequence of the viral maintenance proteins of these gammaherpesviruses were extracted from GenBank [36] . The overall homology between the EBNA1 coding mRNA sequence and coding mRNAs for different gammaherpesvirus maintenance proteins was investigated by performing mRNA dot-plot pair wise sequence alignments [37] to visualize local alignments of repeated regions between the maintenance protein homologues and EBNA1 ( Fig . 1 ) . The over-all homology between sequences is shown as a straight line on the diagonal , while regions of repeats are shown as lots of lines in the same region , allowing visualization of where the repeated regions are between sequences . In each panel the intensity of the dot plots indicate the level of homology between the sequences being compared . As illustrated in Figure 1 , the EBNA1 internal mRNA repeat sequence is highly identical to regions of similar repeat sequences , albeit in different positions within the coding sequences of the maintenance proteins from other gammaherpesviruses . The plot in Panel A highlights a highly repetitive homologous region between Human Herpes virus 4 EBNA1 ( 280–1180 bp ) and Human Herpes virus 8 LANA1 ( 1000–2800 bp ) , while Panel B highlights a highly repetitive homologous region towards the 5′ ends of both the Human Herpes virus 4 EBNA1 ( 280–1180 bp ) and Papiine Herpes virus 1 baboon EBNA1 ( 290–580 bp ) sequences . All six viral maintenance protein mRNAs showed varying sized repeated regions that have strong homology with the internal repeat present within the EBNA1 mRNA . In Table 1 it is apparent that the identity between these purine-rich mRNA repeat sequences of EBNA1 and other viral maintenance proteins is relatively high ( 50–75 . 6% ) , whilst strikingly the corresponding repeat amino acid sequences showed markedly reduced identity levels and in some cases the complete absence of any similar conservation . For example , there is less than 1% homology between Human Herpes virus 4 EBNA1 and Human Herpes virus 8 LANA1 repeat amino acid sequences and only 2 . 1% homology between Human Herpes virus 4 EBNA1 and Macacine herpesvirus 5 Rhesus rhadinovirus ORF73 repeat amino acid sequences , despite corresponding repeat mRNA identities of 76 . 2% and 66 . 5% , respectively . Three EBNA1 expression constructs were designed comprising identical mRNA sequences whilst encoding three , alternative repeat reading frames . The constructs were used to assess the impacts of the EBNA1 repeat region mRNA and protein sequence on self-synthesis and antigen presentation . Three DNA fragments were synthesized to generate the alternative EBNA1 repeat reading frames encoding either glycine/alanine residues , referred to as E1-GA ( wild-type ) ; glycine/glutamine/glutamic acid residues , referred to as E1-GQE ( frameshift 1 ) ; or glycine/arginine/serine , referred to as E1-GRS ( frameshift 2 ) . The synthesized DNA fragments were cloned into an EBNA1 expression construct lacking the internal GAr sequence ( E1ΔGA/pcDNA3 ) to generate the EBNA1 protein sequences outlined in Figure 2 . This strategy maintained the wild-type protein sequences in the regions flanking the internal repeat . As illustrated in Figure 2 , a single nucleotide deletion near the start of the EBNA1 repeat sequence generated a strongly acidic ( GQE ) repeat domain , whilst the deletion of two nucleotides at the same position resulted in a third repeat reading frame encoding a repetitive peptide with both basic and neutral residues ( GRS ) . The corresponding insertion of either one or two nucleotides at the end of the repeat sequence allowed the contiguous encoded C-terminal domains for these constructs to maintain wild-type EBNA1 protein sequence ( Figure S1 in Text S1 ) . Thus , the three proteins generated by these constructs were highly dissimilar in their repeat regions in terms of amino acid composition and charge . For intracellular localization studies , the EBNA1 frameshift expression sequences were also sub-cloned in-frame with a sequence coding for green fluorescent protein , generating fusion proteins with GFP at the C-terminus . In addition , the H-2Kb-restricted SIINFEKL epitope from ovalbumin , was inserted in-frame into the different EBNA1-GFP frameshift variants for endogenous processing studies thereby generating the following expression constructs E1ΔGA-SIIN-GFP , E1-GA ( wild-type ) -SIIN-GFP , E1-GQE ( frameshift 1 ) -SIIN-GFP and E1-GRS ( frameshift 2 ) -SIIN-GFP . A microscopic analysis presented in Figure 3 ( panels A and B ) demonstrated an unaltered nuclear staining pattern for all three EBNA1-SIIN-GFP frameshift variants as well as for E1ΔGA-SIIN-GFP , which lacks the internal repeat . Whilst constructs encoding the wild-type GA repeat or GQE repeat resulted in similar expression levels following transfection , there was reduced EBNA1-GFP expression for the construct encoding GRS repeat sequences ( Fig . 3 , panel A ) . The reduced expression of E1-GRS ( frameshift 2 ) -SIIN-GFP transfectants was not due to differential transfection efficiencies as all three alternative reading frame constructs contain virtually identical DNA sequences which differ by only one or two nucleotides . The lower expression of E1-GRS ( frameshift 2 ) -SIIN-GFP transfectants was consistent with decreased cell viability observed in the phase contrast microscopic analysis of the GRS transfectants ( Figure 3 , panel C ) , demonstrating a reduced percentage of GFP-expressing cells following transfection . Also of note , the GFP+ve GRS-transfectants are very low GFP expressers ( MFI of 705 ) compared to an MFI of 4692 for the EBNA1 wild-type transfectants and 11 , 385 for E1ΔGA transfectants , indicating less EBNA1-GFP is being synthesized in GRS transfectants ( Figure 3 panel D ) and possibly resulting in a percentage of the GRS GFP-expressing cells being below the threshold level for GFP detection . Expression levels of the EBNA1-SIIN-GFP frameshift variants were also confirmed by flow cytometry ( Fig . 3 , panel D ) . Thus , the three EBNA1-GFP frameshift variants are all expressed and demonstrate similar nuclear localization . To discount the possibility that altered peptide sequences due to alternative reading frames within the EBNA1 repeat domain may have changed protein stability , we determined the intracellular kinetics of degradation of the EBNA1-SIIN-GFP frameshift variants following cycloheximide treatment of 293KbC2 cells transiently transfected with E1ΔGA-SIIN-GFP , E1-GA ( wild-type ) -SIIN-GFP , E1-GQE ( frameshift 1 ) -SIIN-GFP , E1-GRS ( frameshift 2 ) -SIIN-GFP or E1ΔGA-SIIN-GFP expression constructs ( in the presence or absence of 10 µM of the proteasomal inhibitor MG132 ) over a 30 hour time course . Both the E1-GA ( wild-type ) -SIIN-GFP and E1-GQE ( frameshift 1 ) -SIIN-GFP transfectants displayed a similar pattern of degradation ( Fig . 4A ) which was slightly lower than that observed for E1ΔGA-SIIN-GFP transfectants . The E1-GRS ( frameshift 2 ) -SIIN-GFP transfectants displayed a less pronounced decrease in EBNA1-SIIN-GFP expression at 24 hours ( Fig . 4A ) . The degradation kinetics carried out in the presence of the proteasomal inhibitor MG132 demonstrate that the observed loss of EBNA1-GFP fluorescence following cycloheximide treatment is due to turnover ( Figure 4A ) . In vitro translation assays of the EBNA1/pcDNA3 frameshift variants demonstrated similarly low translational efficiencies for EBNA1 sequences encoding either GQE or GRS repeat domains as observed for the wild-type GA repeat domain ( Fig . 4B ) . In contrast , the translational efficiency of the EBNA1 sequence lacking the internal repeat domain , E1ΔGA , is 10-fold higher ( p<0 . 05 ) ( Fig . 4B ) . The different migration rates observed for the EBNA1 frameshift variants is due to the nature of the highly repeated residues within the frameshift repeat domains leading to varying amounts of bound SDS per unit mass of protein . The strongly acidic protein , E1-GA ( GQE ) , binds less SDS and hence migrates slower than expected . The E1-GA ( GRS ) protein is strongly basic and binds excess SDS causing the protein to migrate faster than expected . All of the expression constructs containing the purine-rich repeat also display premature termination products arising from the difficulty in ribosome transit through this sequence . In summary , neither of the two frameshift variants E1-GA ( GQE ) or E1-GA ( GRS ) were able to override the translational inhibition observed for the EBNA1 sequence encoding the wild-type GA repeat domain . Several experiments were undertaken to examine the impact of alternative EBNA1 repeat peptide sequences on the endogenous processing of MHC class I-restricted epitopes within EBNA1 . In the first set of experiments , the endogenous loading of MHC class I molecules with a H-2Kb-restricted epitope from ovalbumin ( SIINFEKL residues 257–264 ) inserted at the C-terminus of the EBNA1 sequence was assessed [11] . H-2Kb expressing HEK293 cells were transiently transfected with either E1-GA ( wild-type ) -SIIN-GFP , E1ΔGA-SIIN-GFP , E1-GQE ( frameshift 1 ) -SIIN-GFP or E1-GRS ( frameshift 2 ) -SIIN-GFP expression constructs with transfection efficiencies of 47% , 50% , 42 . 8% and 8% respectively . Following transfection ( 24 h ) , cells were assessed by flow cytometry for GFP expression and surface expression of H-2Kb-SIINFEKL complexes using a monoclonal antibody ( 25-D1 . 16 ) that recognizes the SIINFEKL epitope bound to H-2Kb molecules [38] . Flow cytometry results shown in Figure 5 demonstrate that the percentage of cells expressing surface H-2Kb-SIINFEKL complexes was similar for all three EBNA1 repeat reading frames and ranged from 1 . 9%–2 . 0% . In contrast , a 4–4 . 3-fold increase in the surface expression of H-2Kb-SIINFEKL complexes was observed for transfectants expressing EBNA1-GFP lacking the GAr domain ( E1ΔGA-SIIN-GFP ) , indicating that all three repeat reading frames inhibited the endogenous processing of MHC class I-restricted epitopes within EBNA1 to a similar extent ( Fig . 5 ) . Transfection of a control parent plasmid without SIINFEKL provided a baseline ( 0 . 67% cells expressing surface H-2Kb-SIINFEKL complexes ) above which an increase in fluorescence would indicate specific surface expression of H-2Kb-SIINFEKL complexes . In the next set of experiments , the influence of EBNA1 repeat frameshifts on the T-cell recognition of a H-2Kb-restricted SIINFEKL epitope encoded within EBNA1 was evaluated . H-2Kb expressing HEK293 cells were transiently transfected with either E1-GA ( wild-type ) -SIIN-GFP , E1ΔGA-SIIN-GFP , E1-GQE ( frameshift 1 ) -SIIN-GFP or E1-GRS ( frameshift 2 ) -SIIN-GFP expression constructs with transfection efficiencies of 45 . 5% , 49% , 43% and 9 . 8% , respectively . Twenty-four hours post-transfection , cells were incubated with CD8+ T-cells specific for the SIINFEKL epitope and stimulation was assessed by intracellular cytokine staining assays ( ICS ) . Data presented in Figure 6 ( panels A and B ) show that both E1-GA ( wild-type ) -SIIN-GFP and E1-GQE ( frameshift 1 ) -SIIN-GFP transfectants stimulated a similar number of IFN-γ producing SIINFEKL-specific T-cells ( 4 . 2% and 4 . 4% , respectively ) . Transfectants expressing E1-GRS ( frameshift 2 ) -SIIN-GFP stimulated 4-fold less IFN-γ producing SIINFEKL-specific T-cells ( 1 . 1% ) than transfectants expressing GA ( wild-type ) -SIIN-GFP or GQE ( frameshift 1 ) -SIIN-GFP , consistent with reduced EBNA1 expression levels following transfection of EBNA1-GFP constructs expressing repeat sequences encoding GRS residues . Cells expressing EBNA1-GFP lacking the GAr domain ( E1ΔGA-SIIN-GFP ) stimulated 2–2 . 1-fold more IFN-γ producing SIINFEKL-specific T-cells ( 8 . 9% ) compared to GA ( wild-type ) ( 4 . 2% ) or GQE ( frameshift 1 ) ( 4 . 4% ) repeat domains ( Fig . 6 , panels A and B ) . The endogenous processing of a second CD8+ T-cell epitope , this time encoded within EBNA1 ( HLA B*3508-restricted , HPVGEADYFEY residues 407–417 ) was similarly assessed . EBV-negative DG75 B-cells were transiently co-transfected with E1ΔGA-SIIN-GFP or EBNA1-GFP expression vectors encoding alternative EBNA1 repeat peptide sequences; E1-GA ( wild-type ) -SIIN-GFP , E1-GQE ( frameshift 1 ) -SIIN-GFP , E1-GRS ( frameshift 2 ) -SIIN-GFP and a HLA B*3508-GFP expression construct . Transfection efficiencies were similar for all constructs ranging from 63 . 2%–68 . 8% . Co-transfection with the HLA B*3508-GFP expression construct allowed evaluation of endogenous processing of EBNA1 using HPV-specific T-cell clones . At twenty-four hours post-transfection , cells were incubated with CD8+ T-cells specific for HPV epitopes and stimulation assessed using ICS . Data presented in Figure 6 ( panels C and D ) demonstrate that cells expressing E1-GQE ( frameshift 1 ) -SIIN-GFP or E1-GRS ( frameshift 2 ) -SIIN-GFP showed no increased stimulation of IFN-γ producing HPV-specific T-cells compared to cells expressing E1-GA ( wild-type ) -SIIN-GFP . This result was identical to our earlier endogenous processing and presentation data with the SIINFEKL epitope . Collectively , the endogenous processing results demonstrate that the poor immunogenicity of EBNA1 is unlikely to be due to the alanine/glycine repetitive peptide sequence within the EBNA1 repeat since alternative repeat peptide sequences also failed to enhance the presentation of MHC class I-restricted epitopes within EBNA1 to the level observed for cells expressing E1ΔGA-SIIN-GFP , where the GAr had been deleted . To assess the impact of a purine-rich repeat sequence from another gammaherpesvirus maintenance protein on both EBNA1 expression and T-cell recognition of a SIINFEKL epitope fused to EBNA1 , a 519 nucleotide repeat sequence from the Ateline herpesvirus 3 ORF73 was synthesized and cloned into the E1ΔGA-SIIN-GFP expression vector to generate ( E1-Ateline-SIIN-GFP ) . The resulting EBNA1-Ateline expression vectors encoded a 173 amino acid acidic repeat domain of predominantly glycine and aspartic acid ( GD ) residues ( Figure S2 in Text S1 ) . A microscopic analysis presented in Figure 7A demonstrated the expected nuclear staining pattern for the E1-Ateline-SIIN-GFP variant , similar to the EBNA1 wild-type and frameshift variants . Following transfection of the E1-Ateline-SIIN-GFP expression construct , we observed a notably reduced EBNA1-SIIN-GFP expression level , as evidenced by an MFI of 1224 ( Fig . 7A ) , which was 9 fold lower than the MFI for E1ΔGA-SIIN-GFP and 3 . 5 fold lower than the MFI observed for E1-GA ( wild-type ) -SIIN-GFP ( Fig . 3 panel D ) . Next , the influence of the Ateline herpesvirus 3 repeat sequence in the context of EBNA1 on T-cell recognition of the H-2Kb-restricted SIINFEKL epitope fused to EBNA1 was also evaluated . H-2Kb expressing HEK293 cells were transiently transfected with either E1-Ateline-SIIN-GFP , E1ΔGA-SIIN-GFP or E1-GA ( wild-type ) -SIIN-GFP expression constructs . Twenty-four hours post-transfection , cells were incubated with CD8+ T-cells specific for the SIINFEKL epitope and stimulation assessed by intracellular cytokine staining assays . Data presented in Figure 7B demonstrate that E1-Ateline-SIIN-GFP transfectants stimulated only 1 . 2% of IFN-γ producing SIINFEKL-specific T-cells compared to 4 . 04% for E1-GA ( wild-type ) -SIIN-GFP transfectants and 7 . 59% for E1ΔGA-SIIN-GFP transfectants , at a responder∶stimulator ratio of 2 . 5∶1 . This result is consistent with the reduced EBNA1-SIIN-GFP expression levels observed for E1-Ateline-SIIN-GFP transfectants in Figure 7A and demonstrates that the purine-rich mRNA repeat of the Ateline herpesvirus 3 ORF73 is able to inhibit both protein expression and T-cell recognition .
Viruses that establish chronic latent infections of host cells have evolved numerous mechanisms to evade the host immune system . One such example is the Epstein-Barr virus nuclear antigen 1 , EBNA1 , which is responsible for maintenance of the viral episome within latently infected B cells . The synthesis of EBNA1 is tightly regulated to achieve levels sufficient to maintain viral infection , but low enough so as to minimize EBNA1's exposure to EBNA1-specific T-cells . The regulated inhibition of EBNA1 synthesis has been shown to occur in cis as a result of an internal purine-rich repetitive mRNA sequence that dramatically reduces the rate of EBNA1 protein synthesis [12] . Removal of the repeat sequence leads to increased EBNA1 synthesis and enhanced recognition of MHC class I-restricted epitopes within EBNA1 . The current studies demonstrate that this regulated inhibition of EBNA1 synthesis and the resultant restriction of antigen presentation and host immune recognition is independent of alternative repeat protein sequences embedded in EBNA1 mRNA . Combined microscopic analyses , in vitro translation assays and intracellular cytokine presentation experiments , investigating frameshift changes within the EBNA1 internal repeat demonstrate that altered peptide sequences within the repeat do not override the repeat's cis-inhibitory effect on EBNA1 translation and antigen presentation . The results show that the repetitive purine-rich mRNA sequence itself is responsible for the inhibition of EBNA1 protein synthesis and subsequent poor immunogenicity . When taken together with other studies [12] , [22] , these results suggest that an unusual RNA secondary structure within the repeat region may interfere with translation of the EBNA1 mRNA by inhibiting ribosome transit through the purine-rich sequence , thereby leading to a reduction in the levels of EBNA1 such that the infected cell evades the normal host immune surveillance mechanisms . Comparison of the mRNA sequences of related viruses encoding corresponding proteins responsible for maintenance of latent infections reveals the presence of highly homologous purine-rich repetitive sequences interspersed within the functional coding regions of these proteins . Although highly conserved in mRNA sequence , these repeat regions encode very different peptide sequences in the different viruses . Moreover , substituting the native EBNA1 mRNA repeat sequence with the purine-rich mRNA repeat sequence from the related viral maintenance protein Ateline herpesvirus 3 ORF73 demonstrated that the mRNA repeat of the Ateline herpesvirus 3 ORF73 is able to inhibit both EBNA1-GFP expression and T-cell recognition . These observations strongly support the conclusion that the purine-rich mRNA sequence , rather than its encoded protein sequence , is responsible for the reduced expression of these viral mRNAs . The immune suppressive effects of these mRNA repeat sequences on antigenic epitope generation may represent a more general immune evasive strategy as hundreds of eukaryotic viral mRNAs have evolved with a purine bias [39] . The loss of conservation of protein sequence in the face of evolutionary conservation of the purine-rich mRNA sequence needed for translational repression and avoidance of immune surveillance may be the result of the tendency for “translational recoding” or frame-shifting that has been shown to be induced by G-rich mRNA sequences [40] . Although the overall purine-rich mRNA repeat sequence regions are strongly conserved , such an evolutionary mechanism would lead to random frame shifting and different repeat protein sequences . This suggests that the repeat sequences are subject to strong purifying selection acting at the level of the nucleotide sequence and not the protein sequence . Separate from the repeat region in EBNA1 , there is a nuclear localization signal , two short domains flanking the internal GAr involved in binding to host cell chromosomes and also an overlapping DNA-binding and dimerization domain required for EBNA1 dimerization and binding to the OriP region of the viral genome , [8] [41] . Therefore , it is likely that the design of this protein serves two primary functions – viral genome maintenance and immune evasion , with the latter involving translational repression mediated by the repeat region mRNA sequence . The identification of mRNA repeats which inhibit EBNA1 translational efficiency and endogenous antigen presentation suggests a novel approach to potential new therapeutic interventions involving the use of specific “antisense” therapeutics aimed at the putative structures in the purine-rich mRNA sequence . Such strategies would increase the amount of EBNA1 protein in latently infected cells , thus facilitating normal immune recognition and thereby elimination of the virus by the immune system .
The Queensland Institute of Medical Research Ethics Committee approved all experiments ( P353 ) . All patients provided written informed consent for the collection of blood samples and subsequent analysis . The EBV negative cell line , DG75 was maintained in RPMI 1640 supplemented with 2 mM L-glutamine , 100 IU/ml penicillin , and 100 µg/ml streptomycin plus 10% FCS ( referred to as Growth Medium ) and used as targets for T-cell assays . HEK293 cells stably expressing the mouse class I allele H-2Kb ( 293KbC2 ) were maintained in DMEM supplemented with 5 . 56 mM D-glucose , 4 mM L-glutamine , 1 mM sodium pyruvate , 100 IU/ml penicillin and 100 µg/ml streptomycin plus 10% foetal calf serum ( referred to as DMEM/10FCS ) and were used for EBNA1 localization studies , intracellular degradation analysis and CTL assays . Comparisons between the coding sequence mRNA of EBNA1 and a number of other gammaherpesvirus maintenance proteins were performed using a pair wise sequence alignment visualized as dot plots . The alignments were performed using zPicture , which is a dynamic alignment and visualization tool based on the BLASTZ alignment program [37] . The Genbank accession numbers for the viral mRNAs were: ( HHV-4 ) EBNA1 ( NC_007605 ) ; ( HHV-8 ) Lana1 ( U75698 . 1 ) ; ( Papiine HV-1 ) baboon EBNA1 ( HPU23857 ) ; ( Macacine HV-4 ) rhesus EBNA1 ( NC_006146 . 1 ) ; ( Alcelaphine HV-1 ) ORF73 ( AF005370 . 1 ) ; ( Ovine HV-2 ) ORF73 ( AY839756 . 1 ) and ( Saimirine HV-2 ) ORF73 ( NC_001350 . 1 ) . An EBNA1 expression construct encoding native GAr sequence was generated by synthesizing a 615 nucleotide DNA fragment corresponding to EBNA1 nucleotides 209–814 and incorporating a 3′ Cla1 site ( DNA 2 . 0 , Menlo Park CA ) . This DNA fragment was cloned into the Bspe1 and a mutagenized Cla1 site ( position 250 ) of a previously generated E1ΔGA/pcDNA3 expression vector [12] to generate the expression construct E1-GA ( wild-type ) corresponding to native EBNA1 sequence encoding a 175 amino acid ( aa ) glycine/alanine repeated peptide sequence . An alternative EBNA1 frameshift expression construct was generated by altering the reading frame of the internal EBNA1 repeat sequence to encode a glycine/glutamic acid/glutamine ( GQE ) repeated peptide sequence ( 175 aa ) . This was achieved by synthesizing a second DNA fragment similar to that described above but with a single ( A ) nucleotide deletion at position 56 within the synthesized DNA fragment ( corresponding to EBNA1 nucleotide position 264 ) to generate the expression construct E1-GQE ( frameshift 1 ) . Likewise , a second EBNA1 frameshift expression construct was generated to encode a glycine-arginine-serine ( GRS ) repeated peptide sequence ( 175 aa ) by synthesizing a third DNA fragment ( again similar to the first DNA fragment described above ) but with two ( A ) nucleotides deleted at positions 56–57 within the synthesized DNA fragment ( corresponding to EBNA1 nucleotide positions 264–265 ) to generate the expression construct E1-GRS ( frameshift 2 ) . To maintain the wildtype EBNA1 reading frame immediately following the internal repeat , either a single ( G ) nucleotide was inserted at EBNA1 nucleotide position 809 by mutagenesis in the E1-GQE ( frameshift 1 ) construct or two nucleotides ( AG ) were inserted at EBNA1 nucleotide positions 813–814 in the E1-GRS ( frameshift 2 ) construct . The DNA sequences of all three frameshift expression constructs did not encode stop codons as verified by DNA sequencing . The three EBNA1 frameshift DNA sequences , E1-GA ( wildtype ) , E1-GQE ( frameshift 1 ) and E1-GRS ( frameshift 2 ) in addition to E1ΔGA were also sub-cloned in-frame with a sequence coding for green fluorescent protein ( pEGFP-N1 , CLONTECH , Palo Alto , CA ) to generate E1-GA ( wild-type ) -GFP , E1-GQE ( frameshift 1 ) -GFP , E1-GRS ( frameshift 2 ) -GFP and E1ΔGA-GFP . For the assessment of endogenous loading of MHC class I molecules , a H-2Kb-restricted epitope from ovalbumin , ( Ser–Ile–Ile–Asn–Phe–Glu–Lys–Leu , residues 257–264 ) , referred to as SIINFEKL [38] was inserted in-frame into all three EBNA1-GFP frameshift expression constructs as well as into the E1ΔGA-GFP expression construct between the 3′ end of the EBNA1 sequence and the start of the GFP sequence to generate E1-GA ( wild-type ) -SIIN-GFP , E1-GQE ( frameshift 1 ) -SIIN-GFP , E1-GRS ( frameshift 2 ) -SIIN-GFP and E1ΔGA-SIIN-GFP . Endogenous processing and surface presentation of EBNA1 was also assessed using a second epitope encoded within the EBNA1 sequence and restricted through HLA B*3508 , HPVGEADYFEY ( His–Pro–Val–Gly–Glu–Ala–Asp–Tyr–Phe–Glu–Tyr , residues 407–417 ) and referred to as HPV . HEK293KbC2 cells ( 2×105 ) were transiently transfected with 0 . 4 µg of the expression constructs E1ΔGA-SIIN-GFP , E1-GA ( wild-type ) -SIIN-GFP , E1-GQE ( frameshift 1 ) -SIIN-GFP or E1-GRS ( frameshift 2 ) -SIIN-GFP in the presence or absence of the proteasomal inhibitor MG132 ( 10 µM; Merck Biosciences ) using Effectene ( QIAGEN , Hilden , Germany ) according to the manufacturer's instructions . At 24 hours post-transfection , cycloheximide ( 50 µg/ml ) was added to each sample well . Equal aliquots of cells were trypsinized , washed and processed to measure EBNA1-GFP expression by flow cytometry at time points 0 h , 3 h , 6 h , 24 h and 30 h . EBNA1-pcDNA3 frameshift expression constructs E1-GA ( wild-type ) , E1-GQE ( frameshift 1 ) and E1-GRS ( frameshift 2 ) ; E1ΔGA and E1-Ateline were transcribed and translated in vitro with T7 RNA polymerase using a coupled transcription/translation reticulocyte lysate system ( Promega , Madison WI ) supplemented with 10 µCi 35[S]-methionine ( Perkin-Elmer Pty Ltd . , Boston , MA . ) . Lysates were subjected to SDS-PAGE followed by autoradiography and band intensities were quantified by densitometric analysis using Imagequant software ( Molecular Dynamics ) . 293KbC2 cells ( 2×105 ) , which stably express H-2Kb [38] , were transfected with 0 . 4 µg of the EBNA1-SIIN-GFP frameshift expression constructs using Effectene . A separate transfection of the parent construct without SIINFEKL was also performed to provide a negative control . Cells were harvested after an overnight transfection and stained with mAb 25D1 . 16 [38] conjugated to Allophycocyanin ( Molecular Probes , Invitrogen ) for 30 min at 4°C . Cells were washed and analyzed by flow cytometry on a FACSCanto II ( BD Biosciences ) for GFP expression and 25D1 . 16 binding . HEK293KbC2 cells ( 2×105 ) transiently transfected with EBNA1-SIIN-GFP frameshift expression constructs ( 24 h ) were incubated with ovalbumin-specific T-cells ( OT-1 ) for 3 hours at 37°C at responder to stimulator ratios of 2 . 5∶1 , 5∶1 , 10∶1 and 20∶1 and 40∶1 in DMEM/10FCS medium supplemented with Brefeldin A ( BD Pharmingen , San Diego , USA ) . Cells were washed and incubated with Allophycocyanin ( APC ) -conjugated anti-CD3 and PerCP-conjugated anti-CD8 for 30 min , rewashed , then fixed and permeabilized with cytofix/cytoperm ( BD Pharmingen ) at 4°C for 20 minutes . Cells were washed in perm/wash ( BD Pharmingen ) , incubated with PE-conjugated anti-IFN-γ ( BD Pharmingen ) at 4°C for 30 mins , rewashed and analyzed for IFN-γ production by OT-1 T-cells by flow cytometry on a FACSCanto II . DG75 cells ( 5×106 ) co-transfected ( 24 h ) with 1 . 2 µg of EBNA1-SIIN-GFP frameshift expression constructs E1-GA ( wild-type ) -SIIN-GFP , E1-GQE ( frameshift 1 ) -SIIN-GFP , E1-GRS ( frameshift 2 ) -SIIN-GFP or E1ΔGA-SIIN-GFP and 0 . 8 µg of a HLA B*3508-GFP expression construct using the Amaxa Cell Line Nucleofector Kit V ( Lonza , Cologne , Germany ) were incubated with HPV-specific T-cells overnight ( 37°C ) at responder to stimulator ratios of 2 . 5∶1 , 5∶1 , 10∶1 and 20∶1 in Growth Medium supplemented with Brefeldin A . IFN-γ production by HPV-specific T-cells was determined by intracellular cytokine staining as described above with FITC-conjugated anti-CD4 , PerCP-conjugated anti-CD8 , APC labeled B*3508 HPV Pentamer ( ProImmune , Oxford , UK ) and PE-conjugated anti- IFN-γ . HEK293KbC2 cells seeded on glass coverslips were transfected with the EBNA1-SIIN-GFP expression constructs as described above . At twenty-four hours post-transfection the cells were fixed in 4% paraformaldehyde for 20 mins , washed , permeabilized in 1% Triton-X100 in PBS for 20 mins , washed and then mounted in Pro Long Gold antifade reagent with DAPI ( Molecular Probes , Invitrogen ) . GFP fluorescence in cells was detected using a laser-scanning Bio-Rad ( Hercules , CA ) MRC600 confocal microscope with original magnification ×63 . | Viruses establishing persistent latent infections have evolved various mechanisms to avoid immune surveillance . The Epstein-Barr virus-encoded nuclear antigen , EBNA1 , expressed in all EBV-associated malignancies , modulates its own protein levels at quantities sufficient to maintain viral infection but low enough so as to minimize an immune response by the infected host cell . This evasion mechanism is regulated through an internal purine-rich mRNA repeat sequence encoding glycine and alanine residues . In this study we assess the impact of the repeat's nucleotide versus peptide sequence on inhibiting EBNA1 self-synthesis and antigen presentation . We demonstrate that altered peptide sequences resulting from frameshift mutations within the repeat do not alleviate the immune-evasive function of EBNA1 , suggesting that the repetitive purine-rich mRNA sequence itself is responsible for inhibiting EBNA1 synthesis and subsequent poor immunogenicity . Our comparative analysis of the mRNA sequences of the corresponding repeat regions of different gammaherpesvirus maintenance homologues to EBNA1 highlights the high degree of identity between the nucleotide sequences despite very little homology in the encoded amino acid sequences . These studies demonstrate the importance of gammaherpesvirus purine-rich mRNA repeat sequences on antigenic epitope generation and evasion from T-cell mediated immune control , suggesting novel approaches to prevention and treatment of latent infection by this class of virus . |
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Network robustness is a crucial property of the plant immune signaling network because pathogens are under a strong selection pressure to perturb plant network components to dampen plant immune responses . Nevertheless , modulation of network robustness is an area of network biology that has rarely been explored . While two modes of plant immunity , Effector-Triggered Immunity ( ETI ) and Pattern-Triggered Immunity ( PTI ) , extensively share signaling machinery , the network output is much more robust against perturbations during ETI than PTI , suggesting modulation of network robustness . Here , we report a molecular mechanism underlying the modulation of the network robustness in Arabidopsis thaliana . The salicylic acid ( SA ) signaling sector regulates a major portion of the plant immune response and is important in immunity against biotrophic and hemibiotrophic pathogens . In Arabidopsis , SA signaling was required for the proper regulation of the vast majority of SA-responsive genes during PTI . However , during ETI , regulation of most SA-responsive genes , including the canonical SA marker gene PR1 , could be controlled by SA-independent mechanisms as well as by SA . The activation of the two immune-related MAPKs , MPK3 and MPK6 , persisted for several hours during ETI but less than one hour during PTI . Sustained MAPK activation was sufficient to confer SA-independent regulation of most SA-responsive genes . Furthermore , the MPK3 and SA signaling sectors were compensatory to each other for inhibition of bacterial growth as well as for PR1 expression during ETI . These results indicate that the duration of the MAPK activation is a critical determinant for modulation of robustness of the immune signaling network . Our findings with the plant immune signaling network imply that the robustness level of a biological network can be modulated by the activities of network components .
How network properties , such as robustness against network perturbations , emerge from biological networks has been a central question in systems biology [1] , [2] . Possible modulation of network robustness in a biologically relevant context and mechanisms underlying the modulation are areas of study that have rarely been explored . Innate immunity , in which defense responses are induced through signaling events initiated by recognition of pathogen attack , composes a major part of plant immunity [3] . PAMP/Pattern-Triggered Immunity ( PTI ) and Effector-Triggered Immunity ( ETI ) are modes of plant innate immunity defined by the way pathogens are detected [4] , [5] . PTI is triggered by recognition of microbe/pathogen-associated molecular patterns ( MAMPs/PAMPs ) by the cognate pattern-recognition receptors ( PRRs ) , which are typically receptor-like kinases or receptor-like proteins [6] . For example , Arabidopsis thaliana FLS2 is the PRR for flg22 , an elicitor-active epitope of flagellin from Gram-negative bacteria [7] . While most non-adapted pathogens cannot overcome PTI , adapted pathogens deliver effectors into the plant cell that manipulate plant cell functions to facilitate their infection by , for instance , interfering with PTI signaling [8] , [9] . ETI is triggered by specific recognition of effectors by resistance ( R ) proteins , which are often nucleotide-binding leucine-rich repeat ( NB-LRR ) proteins [10] . For example , the Arabidopsis intracellular NB-LRR R proteins RPS2 and RPM1 indirectly recognize perturbations of the PTI signaling component RIN4 by the effectors AvrRpt2 and AvrRpm1/AvrB , respectively , of a Gram-negative bacterial pathogen , Pseudomonas syringae [3] . In addition to proteinaceous effectors , some P . syringae strains deliver coronatine , which is a jasmonic isoleucine mimic , in order to suppress plant immunity [11] . Recently , it was shown that coronatine suppresses immune responses dependent on salicylic acid ( SA ) as well as independent of SA [12] , [13] . Thus , there are evolutionary arms races between hosts and pathogens . Pathogens evolve much faster than hosts , rapidly changing effector repertoires , thereby changing points of attack in host immune networks . As hosts cannot match the speed of pathogen evolution , it is important that hosts develop robust immune networks that remain functional in the face of effector attack . Mechanisms underlying network robustness are thus a critical aspect of immunity . SA is a signal molecule controlling a major portion of immunity against biotrophic and hemibiotrophic pathogens , including P . syringae [14] . SID2 encodes a key enzyme for SA biosynthesis in response to pathogen infection [15] . In Arabidopsis sid2 mutants , pathogen-induced SA accumulation is almost undetectable [14] . Hundreds of genes are transcriptionally regulated by SA signaling , mediated mainly by a positive regulator of SA signaling , NPR1 [14] . PR1 is one SA-inducible gene used as a canonical SA marker [14] . Arabidopsis has 20 mitogen-activated protein kinases ( MAPKs ) [16] , and four of them , MPK3 , MPK4 , MPK6 and MPK11 , have been described as immune signaling components [17] . MPK3 and MPK6 are associated with immune responses , such as reactive oxygen species ( ROS ) production , ET production/signaling , phytoalexin production and cell death [17] . For instance , ethylene production is positively controlled by dual regulation of enzymes ( ACS ) synthesizing the ethylene precursor 1-amino-cyclopropane-1-carboxylic acid . MPK6 stabilizes ACS2 and ACS6 by their phosphorylation , and MPK3 and MPK6 control gene expression through a transcription factor , WRKY33 , which is activated by the MAPKs [18] , [19] . The same cascade is required for production of a phytoalexin , camalexin , by controlling expression of a biosynthetic gene , PAD3 [20] . A double mutant deficient in MPK3 and MPK6 is embryonic lethal but the single mutants are viable , suggesting functional redundancy between them in development [21] . MPK3 phosphorylates the bZIP type transcription factor VIP1 whose phosphorylation is required for its nuclear translocation [22] . Transient over-expression of VIP1 led to weak induction of PR1 in Arabidopsis protoplasts although involvement of SA in this PR1 induction is not known [23] . The overall spectra of induced defense responses are overlapping between PTI and ETI whereas the kinetics and intensity of the responses seem different [4] , [24] . In Arabidopsis , knocking out the hub genes of four major signaling sectors abolished 80% of flg22-triggered PTI ( flg22-PTI ) and AvrRpt2-triggered ETI ( AvrRpt2-ETI ) , indicating extensively shared signaling network machinery between PTI and ETI [25] . Relationships among these signaling sectors are part compensatory and part synergistic in flg22-PTI but are predominantly compensatory in AvrRpt2-ETI , which explains a high level of robustness in the ETI level against network perturbations [25] . Single mutations ( dde2 , ein2 , pad4 and sid2 ) weakly but significantly compromised flg22-PTI but not AvrRpt2-ETI while the quadruple mutation largely abolished both . These observations demonstrated differences in the robustness of the highly overlapping signaling networks during the two modes of plant immunity . However , the molecular mechanism controlling modulation of the network robustness is not known . Here we report a molecular mechanism that affects the robustness of the plant immune signaling network . Although Arabidopsis MPK3 and MPK6 are activated during both PTI and ETI , the duration of the activation was much longer during ETI than PTI . Only sustained activation of the MAPKs supported expression of a majority of SA-responsive genes in the absence of SA . The roles of MPK3 and SA signaling during AvrRpt2-ETI were compensatory , contributing to network robustness against perturbations during ETI . Our findings demonstrate that a biologically important differential network property , robustness , can emerge from duration of the activity of a network component .
We previously reported that ETI is more robust against network perturbations than PTI due to a higher level of network compensation [25] . We hypothesized that this compensation occurred at the level of gene regulation . To test this hypothesis , we examined expression of a canonical SA marker gene , PR1 , during ETI . Transcriptional induction of PR1 was completely dependent on SID2 , which is a key SA biosynthetic enzyme , and hence completely dependent on SA signaling during PTI [26] . We found that PR1 induction was only partially dependent on SID2 and NPR1 at a late time point of 24 hours post inoculation ( hpi ) with ETI-triggering P . syringae pv . tomato DC3000 ( Pto ) strains expressing the effectors AvrRpt2 ( Pto AvrRpt2 ) or AvrRpm1 ( Pto AvrRpm1 ) ( Figure 1A and Figure S1 ) . While AvrRpt2 and AvrRpm1 are recognized by the CC-type NB-LRR proteins RPS2 and RPM1 , AvrRps4 is recognized by the TIR-type NB-LRR protein RPS4 [3] . We also observed SID2- and NPR1-independent PR1 induction during AvrRps4-triggered ETI although induction levels were lower compared to AvrRpt2- and AvrRpm1-ETI ( Figure S1 ) . In contrast , PR1 induction was completely dependent on SID2 in the case of the non-ETI triggering Pto strain carrying an empty vector ( Pto EV ) . Inoculation of the ETI-triggering strains at a high dose can trigger a form of programmed cell death called a hypersensitive response ( HR ) [3] . The inoculation dose used in this experiment was relatively low ( OD600 = 0 . 001 ) , and we did not observe a macroscopic HR within 24 hpi . To test the possibility that the SA level increased independently of SID2 during ETI , we measured the SA level in these tissues . The increased SA accumulation was completely dependent on SID2 in all conditions ( Figure 1B ) . These results indicate that some SA-independent mechanism ( s ) can activate PR1 during ETI . At an earlier time point of 6 hpi , only SA-dependent PR1 induction was observed with all three strains ( Figure 1A ) , suggesting that this SA-independent mechanism ( s ) during ETI requires more than 6 hours to be effective . SA-independent mechanism ( s ) for PR1 induction during ETI prompted us to investigate the possibility that other SA-responsive genes can also be transcriptionally regulated in an SA-independent manner during ETI . For this purpose , mRNA profiles were analyzed using a whole genome DNA microarray . Leaves of wild type ( Col ) or sid2 plants were inoculated with water ( mock ) , Pto hrcC , Pto EV , or Pto AvrRpt2 , and were collected at 24 hpi for mRNA profiling . The Pto hrcC strain is deficient in the type III secretion system used to transport effectors into plant cells . It elicits the PTI response by presenting various MAMPs [11] . Among 2828 genes that were significantly up- or down-regulated ( with q values<0 . 01 and more than 2-fold changes ) in both Pto EV and Pto AvrRpt2 infection in Col , regulation of 187 genes showed strong SID2-dependence in Pto EV infection ( Figure 2A and Table S1 ) . These genes are designated SA-responsive genes hereafter . Remarkably , regulation of most SA-responsive genes , including PR1 , at 24 hpi with Pto AvrRpt2 is largely SID2-independent although SA contributes to their full expression , indicating that SA-independent signaling mechanism ( s ) can regulate most SA-responsive genes during AvrRpt2-ETI . The SID2-dependency of gene regulation after Pto hrcC inoculation was similar to that after Pto EV inoculation , although the overall extent of up- or down-regulation was lower , and distinct from that after Pto AvrRpt2 inoculation ( Figure S2 and Table S2 ) . Thus , initiation of ETI appears to be the key for activation of this SA-independent mechanism ( s ) . We hypothesized that a kinetic difference in activation of network components is responsible for activation of SA-independent mechanism ( s ) . A prior study suggested that the duration of MPK3 and MPK6 activation is longer during ETI than non-ETI [27] . We compared the duration of MAPK activation in ETI and PTI . When wild-type seedlings in a liquid medium were treated with the PTI inducer flg22 , activation of the MAPKs was observed after 10 min and returned to the basal level within one hour ( Figure 3A ) , confirming previous observations [28] . The possibility that flg22 was rapidly degraded in the liquid culture was excluded since the MAPKs were activated similarly when fresh seedlings were placed in the liquid medium containing flg22 that had been incubated with other seedlings for 3 hours ( Figure 3A ) . Thus , MAPK activation is truly transient after flg22 treatment . We employed transgenic seedlings carrying an estradiol-inducible AvrRpt2 transgene ( XVE-AvrRpt2 ) to measure MAPK activation during ETI in the absence of PTI . The MAPKs were activated by three hours and remained active for at least 7 hours after estradiol treatment ( Figure 3B ) . This sustained MAPK activation was ETI-specific as no such activation was observed in the rps2 mutant background , which lacks the corresponding receptor ( Figure 3C ) . PR1 induction during AvrRpt2-ETI was independent of SA in XVE-AvrRpt2 transgenic seedlings ( Figure S3 ) , which is consistent with the results obtained using adult leaves inoculated with a Pto strain expressing AvrRpt2 ( Figure 1 ) . Similar trends in MAPK activation duration were observed when adult leaves were inoculated with Pto strains: sustained activation of the MAPKs was observed with Pto AvrRpt2 in a manner dependent on the R gene RPS2 , but not with the strains that do not trigger ETI ( Figure 4 ) . While the amounts of activated MPK3 and MPK6 were similar during AvrRpt2-ETI triggered in XVE-AvrRpt2 transgenic plants ( Figure 3 ) , there was more activated MPK3 than activated MPK6 during AvrRpt2-ETI triggered by Pto AvrRpt2 ( Figures 4 , S4 and S5 ) , suggesting that MPK3 plays a major role during AvrRpt2-ETI in bacterial infection . We also observed sustained MAPK activation during AvrRps4-ETI although levels of activation were weaker compared to AvrRpt2-ETI ( Figure S4 ) . Since there are 20 MAPKs in Arabidopsis [16] , we determined the identities of the activated MAPKs . Indeed , the activated MAPKs during AvrRpt2- and AvrRps4-ETI were MPK3 and MPK6 ( Figure S4 ) . Previously , Beckers et al ( 2009 ) reported that an SA analog , benzo ( 1 , 2 , 3 , ) thiadiazole-7-carbothioic acid S-methyl ester ( BTH ) , induced priming of MPK3 activation by inducing expression of MPK3 [29] . In contrast , sustained activation of MPK3 during AvrRpt2-ETI was independent of SA ( Figure S5 ) . The sustained activation was not due to an increased amount of MPK3 as we did not observe obvious changes in the MPK3 protein level during AvrRpt2-ETI ( Figure 3 ) . Taken together , our data show that sustained activation of the MAPKs is SA-independent and occurs during ETI but not during non-ETI responses . To test if sustained activation of MPK3 and MPK6 can induce PR1 in an SA-independent manner , transgenic plants expressing constitutively active forms of MKK4 ( MKK4DD ) or MKK5 ( MKK5DD ) under the control of a dexamethasone ( DEX ) -inducible promoter were employed ( DEX-MKK4DD and DEX-MKK5DD ) . MKK4 and MKK5 are MAP kinase kinases , whose activated forms phosphorylate and activate MPK3 and MPK6 [17] . DEX-induced expression of MKK4DD or MKK5DD leads to sustained activation of MPK3 and MPK6 ( Figure S6 ) [30] . Induction of PR1 was observed 9 hours after DEX treatment ( Figure 5A ) , suggesting that sustained activation of MPK3 and MPK6 is sufficient for induction of PR1 . Induction of FRK1 is thought to be a good marker for activation of MPK3 and MPK6 [31] and was observed 3 hours after DEX treatment while PR1 was not ( Figure 5B ) . FRK1 was strongly induced 30 minutes after flg22 treatment [32] , and the induction did not require SA accumulation ( Figure S7 ) . Thus , although transient MAPK activation of MPK3 and MPK6 is sufficient for FRK1 induction , sustained MAPK activation is necessary and sufficient for SA-independent PR1 induction . The sustained activation of MPK3 and MPK6 by DEX-induced MKK4DD or MKK5DD did not increase the level of SA ( Figure 6A ) . Furthermore , a wild-type-like PR1 induction 24 hours after DEX treatment was observed in plants deficient in SID2 or NPR1 ( Figure 6B ) . Since PR1 induction was not observed in a DEX-inducible ß-glucuronidase ( GUS , an arbitrary reporter gene ) line after DEX treatment , PR1 induction was not caused by the DEX-inducible system or DEX but by induced expression of MKK4DD or MKK5DD . Although MPK4 was activated as well as MPK3 and MPK6 during PTI and ETI ( Figure 4; [17] ) , expression of MKK4DD or MKK5DD does not lead to strong activation of MPK4 [30] . Therefore , it is unlikely that MPK4 plays a role . We conclude that sustained activation of MPK3 and/or MPK6 causes PR1 induction in an SA-independent manner . We tested whether mpk3 and mpk6 single mutations had effects on PR1 induction by MKK4DD or MKK5DD expression . PR1 induction was unaffected in mpk6 but strongly reduced in mpk3 plants ( Figure S8A ) . MKK4DD induction was also strongly reduced in mpk3 plants ( Figure S8B ) , so the reduction of PR1 induction in DEX-MKK4DD/mpk3 may be due to reduction of MKK4DD expression . MKK5DD induction in DEX-MKK5DD/mpk3 was reduced compared to DEX-MKK5DD/Col yet 10 times higher than MKK4DD induction in DEX-MKK4DD/mpk3 while PR1 induction was similarly compromised in both plant lines . Thus , these results suggest that MPK3 is required for SA-independent PR1 induction conferred by forced MKK5 activation while MPK6 is dispensable . We tested whether sustained activation of MPK3 and/or MPK6 also regulates other SA-responsive genes . Leaves of the DEX-MKK4DD transgenic lines in wild type ( Col ) or sid2 backgrounds were treated with DEX or mock control and were collected for mRNA profiling at 24 hours after treatment . The transcriptomic changes caused by DEX treatment were very similar between Col and sid2 ( Figure S9 and Table S3 ) , indicating that gene regulation by sustained activation of the MAPKs is mostly independent of SA . Therefore , only the mRNA profile from the DEX-MKK4DD sid2 line was included in the following analysis . The heatmap in Figure 2A shows that a majority of the SA-responsive genes responded in the DEX-treated DEX-MKK4DD sid2 line similarly to sid2 plants during AvrRpt2-ETI: most up-regulated or down-regulated SA-responsive genes in sid2 during AvrRpt2-ETI were up-regulated or down-regulated , respectively , in the DEX-treated DEX-MKK4DD sid2 line . This suggests that sustained activation of the MAPKs regulates a majority of SA-responsive genes in an SA-independent manner during AvrRpt2-ETI . Three gene clusters were selected for further analysis ( Clusters I–III in Figure 2A ) . The expression level changes of genes in each cluster were averaged and shown in Figure 2B–D . Clusters I and III include genes up- or down-regulated , respectively , in a SID2-independent manner during AvrRpt2-ETI and by sustained activation of the MAPKs . Thus , these genes appear to be regulated by sustained activation of the MAPKs during ETI . Cluster II includes genes that were up-regulated in a largely SID2-independent manner during ETI but not up-regulated by sustained activation of the MAPKs . Thus , up-regulation of the Cluster II genes during ETI is supported by a mechanism ( s ) other than the mechanism mediated by the MAPKs . When the GO terms associated with the clusters were examined , Cluster I , but none of the other clusters , was enriched with genes related to biological stresses ( response to biotic stimulus , P = 2 . 8×10−5; response to other organism , P = 1 . 1×10−4; multi-organism process , P = 5 . 9×10−4 ) . The results imply that genes induced by both SA and the MAPKs are important for biological stress responses . The regulatory trends for the clusters were confirmed by qRT-PCR analysis of one gene from each cluster ( Figure S10 ) . We investigated if compensation between MPK3/MPK6 and SA signaling could be detected in the PR1 expression level during ETI . Leaves of wild type ( Col ) , mpk3 , mpk6 , sid2 , mpk3 sid2 and mpk6 sid2 plants were inoculated with Pto AvrRpt2 or Pto AvrRpm1 , and PR1 expression levels were determined 24 hpi ( Figure 7A ) . While PR1 expression was compromised in sid2 but not in mpk3 or mpk6 during AvrRpt2-ETI , it was compromised in mpk3 sid2 more than in sid2 ( blue bar ) , suggesting compensation between MPK3 and SID2 on PR1 expression during AvrRpt2-ETI . To quantify the level of compensation between MPK3 and SID2 on PR1 expression , a signaling allocation analysis was applied [25] . In this analysis , the effects of the genes and their interactions were estimated for contribution to the PR1 expression level after inoculation . We estimated the individual contribution of MPK3 on the PR1 expression level as the difference in expression levels between sid2 and mpk3 sid2 , that of SID2 as the difference in PR1 expression levels between mpk3 and mpk3 sid2 and their combined contribution as the difference in PR1 expression levels between the wild type and mpk3 sid2 . The value of the genetic interaction between MPK3 and SID2 was calculated by subtracting the sum of the individual contributions of MPK3 and SID2 from their combined contribution . Their combined contribution in the wild type was less than the sum of the individual contributions of SA and MPK3 , which is signified by the negative interaction between them . We previously defined this less-than-additive combined contribution as compensation [25] . Such compensation was observed for AvrRpt2-ETI ( Figure 7B , top ) . Thus , signaling mediated by MPK3 and SA is compensatory on PR1 expression during AvrRpt2-ETI . No significant effects of MPK6 or the interaction ( MPK6:SID2 ) on PR1 expression were detected during AvrRpt2-ETI ( Figure 7A and B ) . No significant effects of MPK3 , MPK6 or their interactions ( MPK3:SID2 and MPK6:SID2 ) on PR1 expression ( Figure 7A and B , red bar ) or resistance ( Figure S11 ) were detected during AvrRpm1-ETI , suggesting a divergence in the mechanisms that modulate network robustness between different cases of ETI . A similar trend was observed with the effects of MPK3 and MPK6 on bacterial resistance in AvrRpt2-ETI ( Figure 7C ) . AvrRpt2-ETI is defined as the difference in in planta growth of Pto EV and Pto AvrRpt2 on a log10-scale [25] . The compensation between MPK3 and SID2 was clear from the signaling allocation analysis , as both had positive effects and their interaction was negative ( Figure 7D , left ) . We did not detect significant effects of MPK6 or the interaction ( MPK6:SID2 ) , although we observed a similar pattern to the case of MPK3 ( Figure 7D , right ) . Thus , compensation of SA signaling by a signaling mechanism involving MPK3 exists in inhibition of bacterial growth , as well as in PR1 expression , during AvrRpt2-ETI . Lethality of the double mutants mpk3 mpk6 [21] does not allow us to determine combined contributions of MPK3 and MPK6 to compensation of SA signaling during ETI . It is possible that MPK6 is not a major factor in SA signaling compensation during ETI and that a signaling mechanism ( s ) other than that involving MPK3 or MPK6 is important during AvrRpm1-ETI . Nonetheless , these results clearly demonstrate that at least during AvrRpt2-ETI , SA signaling can be compensated by MPK3-mediated signaling in regulation of SA-responsive gene expression and that this compensation increases the robustness of the network output . This allows immunity to be maintained even if the major network sector , SA signaling , is compromised .
A prior study implied that the duration of MPK3 and MPK6 activation is longer during ETI compared to during non-ETI upon P . syringae infection [27] . However , it did not rule out the possibility that the effector AvrRpt2 caused sustained MAPK activation through a mechanism independent of recognition of AvrRpt2 via RPS2 . We clearly demonstrated that sustained MAPK activation occurs when ETI is triggered ( Figures 3 and 4 ) . The duration of MPK3 and/or MPK6 activation is the determinant for activation of the SA-independent alternative mechanism to regulate the SA-responsive genes: only sustained MAPK activation results in activation of the alternative mechanism . One potential cause of the differential activation duration is rapid turnover of PTI receptors , PRRs . FLS2 is rapidly degraded and disappears within one hour upon exposure to flg22 [33] , [34] . Although turnover rates of other PRRs are not known , if many PRRs turn over rapidly upon activation , this could explain transient activation of the MAPKs by Pto hrcC ( Figure 4 ) , which presents multiple MAMPs [11] . The turnover rates of R proteins , the ETI receptors , upon their activation are largely unknown . Whether turnover rate is involved or not , this hypothesis that the duration of MAPK activation and , consequently , the robustness of the network can be tuned to each receptor is attractive because it would enable network robustness to be evolutionarily adapted according to what pathogen-derived signals are recognized by the receptors . Another potential but not mutually exclusive cause of the differential activation duration is involvement of protein phosphatases that dephosphorylate and inactivate the MAPKs: activation of the MAPKs may be negatively regulated by a phosphatase ( s ) during non-ETI responses while the phosphatase may be inactivated during ETI , resulting in the sustained activation of the MAPKs . Multiple types of such phosphatases including MAPK phosphatases are known in Arabidopsis [35] . Differential regulation of these phosphatases during ETI and non-ETI responses may explain the differential duration of MAPK activation . Switching of downstream signaling by differential duration of MAPK activation is known in animals and yeast [36]–[38] . In one case , it is explained by nuclear translocation of a MAPK that occurs only after its sustained activation [36] . In this way , sets of substrates available to the MAPK are distinct between its transient and sustained activation , which could lead to distinct downstream signaling . In plants , it has also been reported that MAPKs are translocated to the nucleus upon stimulation [39] , [40] . Investigation of potential subcellular localization changes of Arabidopsis MPK3 and MPK6 during PTI and ETI will provide insight into this possibility . Another appealing explanation is involvement of a feed-forward network motif [41] . For example , activation of a transcription factor TF-X may mediate the alternative mechanism regulated by sustained MAPK activation . The activation of TF-X may require signal Y in addition to active MPK3 and/or MPK6 . Signal Y may be slowly generated as a consequence of the activation of the MAPKs ( e . g . , 5 hours ) . The MAPKs would need to be activated for a long time to simultaneously have both signal Y and the active MAPKs to activate TF-X and regulate the SA-responsive genes . In either scenario , discovery of the signaling components downstream of the sustained MAPK activation will be the key to elucidate the mechanism that decodes duration of MAPK activation . Multiple transcription factors , such as TGAs , WRKYs , TBF1 and VIP1 [14] , [22] , [23] , [42]–[44] , are involved in regulation of PR1 . These transcription factors may provide a good starting point for a search for the decoding mechanism . Pto produces the small molecule coronatine , which is a molecular mimic of the JA-Ile conjugate and promotes virulence by suppressing SA signaling [13] . Pto is highly virulent on Arabidopsis plants while ETI-triggering strains of Pto , such as Pto AvrRpt2 , are much less virulent . Nevertheless , coronatine could suppress SA signaling . Therefore , SA-independent alternative mechanism ( s ) to regulate expression of the SA-responsive genes , such as that mediated by the MAPKs , may have a substantial role against perturbation of the immune signaling network by coronatine . This hypothesis is consistent with our observation that loss of both MPK3 and SA led to increased susceptibility to Pto AvrRpt2 ( Figure 7 ) . Pto DC3000 possesses type III effectors which directly or indirectly suppress MAPK activation [45]–[48] . However , we observed sustained activation of MPK3 and MPK6 during AvrRpt2-ETI when AvrRpt2 was delivered from Pto DC3000 ( Figure 4 ) . We speculate that the amounts of such MAPK-inhibiting type III effectors delivered and/or the kinetics of their delivery are not optimal to effectively suppress MAPK activation when the type III effectors are delivered from Pto DC3000 , which represents a relatively natural context . The effector HopAI1 from Pto DC3000 can physically interact with and inactivate MPK3 and MPK6 by removing the phosphate group from phosphothreonine via a phosphothreonine lyase activity [45] . HopAI1 also targets MPK4 and decreases MPK4 activity [48] . Decreased MPK4 activity appears to be monitored by the NB-LRR protein SUMM2 , resulting in triggering ETI . Overexpression of HopAI1 in wild-type Col-0 plants but not summ2 mutant plants leads to dwarfism and constitutive activation of immune responses [48] . However , Pto DC3000 does not trigger SUMM2-mediated ETI . Consistently , HopAI1 of Pto DC3000 is disrupted by an insertion in its promoter region [49] . Thus , the amount of HopAI1 delivered from Pto DC3000 appears insufficient for effective inhibition of MPK3 and MPK6 activation during AvrRpt2-ETI . Another effector , HopF2 , from Pto DC3000 can also suppress activity of MPK3 , MPK4 and MPK6 by targeting the upstream MKK5 and likely other MKKs as well [46] , [47] . When overexpressed in plants , HopF2 interferes with AvrRpt2-ETI by inhibiting AvrRpt2-mediated RIN4 degradation [50] . Again , the reason that HopF2 cannot suppress sustained activation of MPK3 and MPK6 triggered by AvrRpt2 when it is delivered from Pto DC3000 ( Figure 4 ) is likely insufficient HopF2 or inappropriate timing of its delivery . Delivery of AvrRpt2 may precede that of HopF2 [50] . One enigma is why plants need to make the robustness of the immune signaling network lower during PTI when the network itself has the capacity to be highly robust . If the network output during PTI were as robust as during ETI , the chance that “true” pathogens will overcome PTI would be much lower . We speculate that the lower robustness during PTI is selected through evolution as trade-offs with other requirements . Many MAMPs are shared among pathogens and benign microbes and provide low quality information about pathogen attack . It is probably not adaptive for plants to respond to a MAMP with strong and sustained immune responses similar to those during ETI since in many cases , plants encounter benign microbes and ETI-type responses cost fitness . A strategy apparently selected is to respond weakly first and wait to intensify the response until further information increases the probability that a true pathogen is present [24] . In contrast , since effectors are a hallmark of true pathogens and provide high quality information , during ETI plants can induce rapid and strong immune responses with a very low chance of needless fitness costs . The signaling sector activated by sustained activation of the MAPKs during ETI and the SA signaling sector can regulate the common set of genes . This is one of the mechanisms underlying robustness of the immunity level against network perturbations during ETI . This modulation of the network robustness is controlled by signaling kinetics of a network component . Our findings imply that properties of biological networks can be modulated through network component activities .
Arabidopsis plants were grown in a controlled environment at 22°C with a 12 h photoperiod and 75% relative humidity . Arabidopsis thaliana accession Col-0 was the background of all mutants used in this study . Arabidopsis mpk3-1 ( SALK_151594 ) [21] , mpk6-2 ( SALK_073907 ) [18] , npr1-1 [51] , rps2 101C [52] and sid2-2 [15] were previously described . We generated the double mutants mpk3 sid2 and mpk6 sid2 by standard genetic crosses . Estradiol-inducible AvrRpt2 transgenic lines [53] and the DEX-MKK4DD and -MKK5DD transgenic lines [30] were previously described . We crossed DEX-MKK4DD and -MKK5DD into the mutant backgrounds mpk3 , mpk6 , npr1 , sid2 and vip1 . Primers and restriction enzymes used for screening of the mutants are listed in Table S4 . Flg22 peptide was purchased from EZBiolab Inc ( Westfield , IN , USA ) . Estradiol ( E8875 ) and DEX ( D1756 ) were purchased from Sigma ( Saint Louis , MO , USA ) . Pto DC3000 strains ( or water for mock ) or 2 µM DEX ( or 0 . 1% ethanol for mock ) were infiltrated into leaves of 4-week-old plants . Leaves were collected at the indicated time points . Total RNA isolation and qRT-PCR analysis were carried out as described previously [54] , [55] . The following models were fit to the relative Ct value data compared to Actin2 using the lme function in the nlme package in the R environment: Ctgytr = GYTgyt+Rr+εgytr , where GYT , genotype:treatment:time interaction , and random factors; R , biological replicate; ε , residual; Ctgyr = GYgy+Rr+εgytr , where GY , genotype:treatment interaction; Ctgtr = GTgt+Rr+εgtr , where GT , genotype:time interaction . The mean estimates of the fixed effects were used as the modeled relative Ct values and visualized as the relative log2 expression values and compared by two-tailed t-tests . For the t-tests , the standard errors were calculated using the variance and covariance values obtained from the model fitting . Primers used in the study are listed in Table S4 . Four-week-old Arabidopsis Col-0 and sid2 leaves were infiltrated with Pto hrcC , Pto pLAFR ( EV ) , Pto AvrRpt2 or water ( mock ) . Independently , leaves of four-week-old DEX-MKK4DD plants in Col-0 or a sid2 background were infiltrated with 2 µM DEX or 0 . 1% ethanol ( mock ) . Samples were collected at 24 hpi . Total RNA was extracted as described previously [26] and profiled using the NimbleGen DNA microarray ( A . thaliana Gene Expression 12×135K array TAIR9 . 0 ) following the manufacturer's protocol ( Roche Applied Science , Indianapolis , IN , USA ) . Three independent experiments ( biological replicates ) were performed . The microarray data were submitted to Gene Expression Omnibus ( Accession , GSE40555 ) . Probe signal values were subjected to the robust multi-array average ( RMA ) summarization algorithm [56] using the standard NimbleGen software to obtain the expression level values of the transcripts . Among transcripts of a single gene , those with higher expression values were selected as the representative transcripts of the genes . The following models were fit to log2 expression values using the lmFit function in the limma package in the R environment: Sgyr = GYgyt+Rr+εgyr , where S , log2 expression value , GY , genotype:treatment interaction , and random factors; R , biological replicate; ε , residual . The eBayes function in the limma package was used for variance shrinkage in calculation of the p-values and the Storey's q-values were calculated from the p-values using the qvalue function in the qvalue package . First , genes whose expression was up-regulated or down-regulated ( q values<0 . 01 and more than 2 fold change ) in both Pto EV and Pto AvrRpt2-infected Col compared to mock were selected ( 2828 genes ) . Second , SID2-dependent genes in Pto EV infection ( inductions/suppression in sid2 are less than 20% compared to Col ) were selected ( 187 “SA-responsive” genes ) for the clustering analysis . Heatmaps were generated by CLUSTER [57] using uncentered Pearson correlation and complete linkage , and visualized by TREEVIEW [57] . The accession numbers for the Arabidopsis genes discussed in this article are as follows: Actin2 ( At2g18780 ) , Chitinase ( At1g02360 ) , CHS ( At5g13930 ) , FRK1 ( At2g19190 ) , MKK4 ( At1g51660 ) , MKK5 ( At3g21220 ) , MPK3 ( At3g45640 ) , MPK4 ( At4g01370 ) , MPK6 ( At2g43790 ) , NPR1 ( At1g64280 ) , RPM1 ( At3g07040 ) , RPS2 ( At4g26090 ) and SID2 ( At1g74710 ) . | Robustness of a network is defined by how consistently it performs upon removal of some of its components . It is a common strategy for plant pathogens to attack components of the plant immune signaling network in an attempt to dampen plant immunity . Therefore , it is crucial for the plant immune signaling network to have a high level of robustness . We previously reported that the robustness level of the plant immune signaling network is higher during Effector-Triggered Immunity ( ETI ) than Pattern-Triggered Immunity ( PTI ) . Here we discovered a molecular switch that determines two robustness levels during ETI and PTI . Salicylic acid ( SA ) is a major plant immune signal molecule that regulates many immune-related genes . SA-independent alternative mechanisms also regulated the majority of SA-responsive genes during ETI but not PTI . One of the SA-independent mechanisms was mediated by prolonged activation of MAP kinases ( MAPKs ) . MAPK activation was prolonged during ETI but transient during PTI . Thus , the duration of MAPK activation switches the robustness level of the plant immune signaling network . Our findings imply that the robustness level of a biological network can be modulated by activities of its components . |
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During clathrin-mediated endocytosis in yeast cells , short actin filaments ( < 200nm ) and crosslinking protein fimbrin assemble to drive the internalization of the plasma membrane . However , the organization of the actin meshwork during endocytosis remains largely unknown . In addition , only a small fraction of the force necessary to elongate and pinch off vesicles can be accounted for by actin polymerization alone . In this paper , we used mathematical modeling to study the self-organization of rigid actin filaments in the presence of elastic crosslinkers in conditions relevant to endocytosis . We found that actin filaments condense into either a disordered meshwork or an ordered bundle depending on filament length and the mechanical and kinetic properties of the crosslinkers . Our simulations also demonstrated that these nanometer-scale actin structures can store a large amount of elastic energy within the crosslinkers ( up to 10kBT per crosslinker ) . This conversion of binding energy into elastic energy is the consequence of geometric constraints created by the helical pitch of the actin filaments , which results in frustrated configurations of crosslinkers attached to filaments . We propose that this stored elastic energy can be used at a later time in the endocytic process . As a proof of principle , we presented a simple mechanism for sustained torque production by ordered detachment of crosslinkers from a pair of parallel filaments .
The cytoskeleton protein actin assembles into three major structures in yeast cells , including endocytic actin patches , actin cables , and the contractile ring [1 , 2] . In actin cables and the contractile ring , formin-nucleated actin filaments are crosslinked into long bundles with a length on the order of microns [3–5] . Computational models of these actin structures typically treat actin filaments as semi-flexible polymers that are connected by rigid segments [6–9] . In contrast , the organization of the actin network in actin patches formed during clathrin-mediated endocytosis is drastically different from that in actin cables or the contractile ring . The length of filaments in actin patches is strongly limited by capping and severing proteins [10] , and mathematical modeling predicted that the average length of filaments is less than 200 nm [11] . Filaments of this length scale can be considered as straight rods , because the persistence length of actin filaments is on the order of 10μm [12–14] , which allows them to sustain forces larger than 10pN without buckling [15] . At the endocytic actin patch , a small area of the flat plasma membrane invaginates towards the cytoplasm upon assembly of actin . In budding yeast , the invagination elongates up to 140nm in depth , and then is pinched off , releasing a tear-shaped vesicle [16] . Actin is essential for many of these steps , from the initiation of invagination to vesicle scission [17–19] . Despite extensive experimental work that characterized the overall dynamics of assembly , disassembly and ensemble movements of proteins of the actin meshwork [19–26] , the precise structural organization of actin filaments within the endocytic patch remains unknown . Indeed , individual filaments are not resolvable even in electron micrographs , in which the actin network appears as a ribosome-exclusion zone , which is about 200nm in depth and 100nm in width [16] . Actin crosslinking proteins play a crucial role in determining the mechanical responses of the actin network to force perturbation [27–29] . Fimbrin ( Fim1p ) is the second most abundant protein recruited to the endocytic patch during clathrin-mediated endocytosis in fission yeast , after actin [20] . It has two actin binding domains that enable it to crosslink adjacent filaments . Deletion of fimbrin results in significant defects in endocytic internalization [24 , 30 , 31] . In vitro experiments have shown that fimbrin efficiently bundles long actin filaments , but bundling efficiency is reduced in the presence of capping protein as a result of decreased filament length [31] . It remains unclear how this length-dependent bundling activity arises and how this activity is related to the role of fimbrin during clathrin-mediated endocytosis . Internalization of the endocytic membrane is hindered by the high turgor pressure ( P ∼ 0 . 8 × 106 Pa [32 , 33] ) inside yeast cells [34] . Under such high pressure , theoretical studies suggest that the force needed to initiate membrane invagination is on the order of 3000pN [35 , 36] and actin polymerization is thought to provide the driving force . However , assuming no more than 150 filaments are simultaneously generating the force [11 , 20 , 21] , each of these filaments has to generate a force of at least 20pN , an order of magnitude larger than the maximum polymerization force of ∼1pN of actin filaments measured in vitro [37] . This number of 20pN is likely an underestimate since the calculation here uses a very generous estimate for the number of growing filaments , up to 20-fold of what mathematical modeling predicts [11] . Therefore , actin polymerization alone is not enough to provide the force necessary to elongate a clathrin-coated pit . Even though type-I myosins participate in endocytosis , their low power output over a narrow range of forces suggest that they are more likely force-sensing tethers rather than force generators [38–40] . In this paper , we present a computational model for dynamic crosslinking of rigid actin filaments in conditions relevant to clathrin-mediated endocytosis . We show that kinetic and mechanical properties of the crosslinkers finely tune the structural transition of actin network between bundles and meshworks . In addition , we show that the chemical binding energy is converted into elastic energy upon binding of crosslinkers . The elastic energy stored in individual crosslinkers is significantly higher than their thermal energy . This surprising property is a consequence of the helical pitch of actin filaments , which leads to torsional strains between crosslinkers attached to a common pair of filaments . We discuss the mechanical implications of these torsionally stressed crosslinkers and propose a possible mechanism to generate directed rotation of filaments by orderly detaching the crosslinkers .
We model actin filaments as rigid cylindrical rods with subunits that carry a helical pitch ( Fig 1A ) . The length of filaments is restricted to the range of actin filament size during endocytosis ( 81nm to 216nm ) , around 2 orders of magnitude shorter than their persistence length ∼10μm . Filaments of this length scale remain virtually straight under ∼10pN of force and untwisted under ∼100pN ⋅ nm of torque ( see the Methods section ) . Therefore we can neglect bending and twisting , and describe the motion of a filament by its translational velocity Vc of center of mass , and angular velocity Ω relative to the center of mass . Details of the model can be found in the Methods section . We model actin crosslinkers as elastic springs that connect two actin subunits in different filaments . Each spring has three elastic components: one in extension , which represents how much the spring is stretched , and two in torsion , each one representing how much the orientation of the axis of the crosslinker linking both actin subunits deviates from the vector normal to the binding interface of each actin subunit ( Fig 1C ) . This elasticity is the simplest model to account for: ( i ) the intra-molecular flexibility between both actin binding domains , and ( ii ) the flexibility in the binding interface between each actin binding domains and the actin subunits they are bound to . Specifically , the elastic energy E of a crosslinker is composed of an extensional part Eext and a torsional part Etor , E = Eext + Etor . The extensional energy accounts for ( i ) , E ext = 1 2 κ ext ( l c - l 0 ) 2 , ( 1 ) where κext denotes the extensional stiffness , lc denotes the length , and l0 denotes the rest length of the crosslinker ( Fig 1C ) . The torsional energy accounts for ( ii ) , E tor = 1 2 κ tor ( θ i 2 + θ j 2 ) , ( 2 ) where κtor denotes the torsional stiffness , θi and θj denote the angles between the actin subunits and the axis of the crosslinker ( Fig 1C ) . Note that our model does not take into account the stiffness of the rotation of filaments around the axis of the crosslinker . Doing so would force us to consider a preferred relative orientation of the filaments with each other , and strongly favor either bundles or meshworks . No experimental values for the extensional and torsional stiffnesses of fimbrin are available in the literature . For our simulations , we used values within a few orders of magnitude of stiffnesses measured for fascin and antibodies [41 , 42] . Crosslinker turnover is modeled as Poisson processes with a crosslinker formation rate constant kf and a breakage rate constant kb , which increases exponentially with the total elastic energy ( Fig 1B ) . A more detailed description is presented in the Methods section . The length of filaments is kept constant in a given simulation and the total number of actin subunits in the filaments is fixed to Nactin = 7000 for all simulations [20] . The maximum occupancy of crosslinkers on filaments is constrained to remain under 25% ( or 1 crosslinker for 4 subunits ) , which is equivalent to a maximum of 875 attached crosslinkers , close to the peak value ∼900 measured experimentally [20] . We initiate each simulation with uncrosslinked filaments that are randomly positioned and oriented . Reflecting boundary conditions are imposed to ensure filaments stay in a cubic box of 500nm in size . Simulations are performed using the reference values listed in Table 1 unless otherwise mentioned . To quantify the organisation of actin filaments , we introduced the global and local nematic order parameters Sglobal and Slocal . These quantities characterize the degree of alignment between filaments in the entire simulation space , or in a local neighborhood , respectively . Their values range from 0 to 1 , and a larger value indicates that filaments are more aligned with each other globally , for Sglobal , or locally , for Slocal . Note that , in practice , the minimum reachable value for Slocal is usually close to 0 . 4 ( S6 Fig ) . The detailed mathematical definition of these parameters can be found in the Methods section . Actin assembly and disassembly takes tens of seconds during endocytosis . We set the total simulation time to be 50s . Within this period , in most of our simulations , the metrics reach steady state . However , there are cases where the system is in a transient state , mostly due to slow convergence of the global nematic order parameter Sglobal ( S8 Fig ) . Therefore , we chose the local nematic order parameter Slocal averaged from 40s to 50s as the major metric to characterize the organisation of actin networks . Conclusions based on Slocal are more robust than based on Sglobal . We first studied how filament length influences the structure of actin clusters . For crosslinking rates kf below 0 . 1s−1 , the number of attached crosslinkers remained small , and actin filaments did not organize into higher order assemblies ( S1 Fig ) . When the crosslinking rate kf was high enough , initially disconnected short filaments ( 81nm ) quickly formed small clusters that eventually coalesced into three to four larger clusters ( Fig 2A ) . The number of attached crosslinkers rapidly saturated to a dynamic steady-state ( Fig 2C ) , as the crosslinkers underwent constant turnover . Filaments within the cluster are organized into a disordered meshwork , which is characterized by a small local nematic order parameter ( Slocal ∼ 0 . 4 , Fig 2B ) . Long filaments ( 216nm ) rapidly aligned with their neighbors into small bundles ( Fig 2D ) , as indicated by the fast convergence of the local nematic order parameter Slocal to its steady state value around 1 ( Fig 2E ) . Throughout the simulation , these locally aligned filaments remained connected with each other , and the number of clusters remained small ( Fig 2F ) . These connected bundles slowly adjusted their orientations to eventually coalesce into a few large bundles . When filament length was increased from 135nm to 162nm , the structure of the actin network transitioned from meshwork to bundle , indicated by the sharp increase of the local nematic order parameter Slocal from ∼0 . 5 to above 0 . 9 ( Fig 2G ) . The global nematic order parameter Sglobal showed similar trend as Slocal , but with a smaller magnitude , since filaments formed 2 to 4 independent clusters ( Fig 2H ) . Altogether these results show that crosslinked actin filaments with a size and crosslinker density comparable to what is measured during endocytosis can self-organize into either meshworks ( for short filaments ) or bundles ( for long filaments ) , and the phase transition is tightly controlled by filament length . Next , we explored the influence of the mechanical properties of actin crosslinking proteins on the organization of actin filaments . The organization of medium length filaments ( 135nm ) varied dramatically for different combinations of κext and κtor ( Fig 3A–3C ) . For low extensional stiffness κext , actin filaments organized into a meshwork ( Fig 3A ) , while they formed bundles for higher κext values ( Fig 3B ) . The transition between meshwork and bundle was tightly controlled , since the local nematic order parameter had a sharp increase around κext = 0 . 1pN/nm ( Fig 3E , red ) at given κtor = 10pN ⋅ nm ⋅ rad−1 . This sharp transition was even more pronounced for longer filaments ( 189nm ) , as reported in both local and global nematic order parameters ( Fig 3E and S2A Fig , orange ) . In contrast , when filaments were short ( 81nm ) , the transition was relatively smooth ( Fig 3E , blue ) . From the above results , we conclude that crosslinkers with large extensional stiffness favor bundle formation . This result can be intuitively explained by the following simplified but heuristic example involving only two filaments . If two filaments are initially aligned with each other , a slight change in orientation between both filaments results in the stretching of crosslinkers bound at different positions , which leads to a restoring torque to realign the filaments . The torque is proportional to the extensional stiffness and the distance between the positions of attached crosslinkers , thus stiffer crosslinkers create larger realignment torque than softer crosslinkers . Longer filaments not only have more crosslinkers , but also crosslinkers that are more distantly positioned , therefore more extended and more inclined to create a larger torque that will restore the parallel alignment of filaments . We next investigated the impact of torsional stiffness κtor on the organization of actin networks , keeping the extensional stiffness at a relatively small value ( κext = 0 . 1pN/nm ) . Torsional stiffness had virtually no influence on the organization of short filaments ( 81nm ) ( Fig 3F , blue ) . However , medium length filaments ( 135nm ) had a sharp transition from bundle to meshwork at κtor = 10pN ⋅ nm ⋅ rad−1 ( Fig 3F , red ) . A similar trend was also observed for long filaments ( 189nm ) ( Fig 3F , orange ) . At highest torsional stiffness tested ( κtor = 100pN ⋅ nm ⋅ rad−1 ) , crosslinker attachment lifetime was extremely short because their torsional energy often became much larger than the critical energy Ec that modulates the detachment rate ( see Eq 16 ) , therefore the number of attached linkers was significantly reduced ( S2H Fig ) . The uncrosslinked actin network formed at this regime was different from the connected meshwork formed at κtor = 10pN ⋅ nm ⋅ rad−1 , though their nematic order parameters Slocal were similarly low . The above results show that crosslinkers with high torsional stiffness disfavor bundle formation . This result can be explained by the fact that , when torsional stiffness is high , formation of several crosslinks between two aligned filaments results in very high torsional energies , due to the frustrated interactions between crosslinkers . This will become clear later in the paper . Therefore , it is energetically more favorable to form a few but over-stretched crosslinkers with many distant filaments than to form many but under-stretched crosslinkers with a few proximal filaments . In the former situation , filaments form a highly entangled actin meshwork . This explanation is supported by the decreasing number of clusters ( S2E Fig ) , as well as the increasing extensional strains with κtor in the neighbourhood of κtor = 10pN ⋅ nm ⋅ rad−1 ( S4C Fig ) . We have shown that high extensional stiffness of crosslinkers favors bundle formation . When the breakage rate k b 0 was reduced to 0 . 01s−1 , even for large extensional stiffness ( 1pN/nm ) , filaments formed a structure where small bundles were interconnected but did not align with each other ( Fig 3D ) , as indicated by the relatively low local nematic order parameter at k b 0 = 0 . 01 s - 1 compared with Slocal at k b 0 = 10 s - 1 ( Fig 3G ) . The difference in the global nematic order parameters Sglobal was even more pronounced for long filaments ( 189nm ) ( S2C Fig , orange ) . At very high breakage rate , the lifetime of bonds between filaments was so short that filaments formed an essentially random , uncrosslinked network . Altogether , we conclude that crosslinker turnover is essential for bundle formation , as alignment of bundles requires the breaking of the bonds that disfavor filament alignment . Parameter dependence of the actin network structure is summarized in Fig 4 where we plotted the local nematic order parameter Slocal and the number of attached crosslinkers Nattach as a function of crosslinking rate constant kf and filament length L . For a combination of low extensional and high torsional stiffnesses ( κext = 0 . 1pN/nm , κtor = 10pN ⋅ nm ⋅ rad−1 ) , values of Slocal are concentrated either close to 1 or close to 0 . 5 ( Fig 4A , yellow and blue regions , respectively ) . These two regions are separated by a narrow transition band around Slocal = 0 . 75 ( Fig 4A , green ) . Values of Nattach are clustered either close to the saturation number 875 , or less than 100 , divided by a transition band around Nattach = 300 ( Fig 4B , yellow , blue and green regions , respectively ) . Therefore we chose the lines Slocal = 0 . 75 and Nattach = 300 as the boundaries to define the phase diagram ( Fig 4C ) . These two lines divide the parameter space into three regions: ( 1 ) Above the line Slocal = 0 . 75 , actin filaments are locally aligned into a bundle; ( 2 ) Between the lines Slocal = 0 . 75 and Nattach = 300 , filaments form a crosslinked , disordered meshwork; ( 3 ) Below the line Nattach = 300 , filaments are essentially uncrosslinked over the entire simulation time . For larger extensional stiffness ( κext = 1pN/nm ) , the relative positions of the regions are similar , but most of the phase diagram corresponds to bundles ( Fig 4F ) , and only the shortest filaments ( 81nm ) form meshworks . In all cases , the existence of three regions in these phase diagrams requires moderate to high breakage rates . When the breakage rate is low ( k b 0 = 0 . 01 s - 1 ) , meshworks occupy the entire parameter space ( S3 Fig ) . Though crosslinkers in our model were not active elements , we found that crosslinkers rapidly became stretched in length , and twisted in angle . To quantify the crosslinkers’ deformations , we introduced the extensional strain ϵ = ( lc − l0 ) /l0 , which measures the relative change of a crosslinker’s length lc from its rest length l0 , and the torsional strain θ , which measures the angle between the crosslinker and its bound actin subunits . For all stiffness values , the distributions of the extensional strain ϵ and the torsional strain θ significantly deviated from the corresponding Boltzmann distribution for a single independent free spring ( Fig 5A–5C ) . Strikingly , the extensional strain ϵ peaked at ∼0 . 5 but not zero ( Fig 5A–5C , top ) , indicating that the crosslinkers were stretched on average . The distribution had a narrower width for higher extensional stiffnesses κext . The peak of torsional strain θ decreased with increasing torsional stiffness , while the widths of the distributions were essentially the same as in the Boltzmann distribution ( Fig 5A–5C , bottom ) . The deviation from the Boltzmann distribution can be primarily accounted by the coupling between crosslinkers that are attached to the same pair of filaments . Indeed , a simpler 1D example of the Brownian motion of two particles each subject to a spring follows similar properties ( S5 Fig ) . In this example , each spring generates a force of −κx if the particle is displaced from the equilibrium position 0 to x . If the movement of the two particles were independent ( S5A Fig ) , the joint distribution of their positions x1 and x2 would simply be the product of identical individual distribution p ( x1 , x2 ) = g ( x1 ) g ( x2 ) , where g ( x ) = κ 2 π k B T e - κ x 2 / 2 k B T denotes the Boltzmann distribution of an individual particle . However , if the movements of the two particles are coupled , for instance , subject to the constraint x1 − x2 = 2x0 ( S5B Fig ) , the position distribution of particle 1 becomes p ( x1 ) = Cg ( x1 ) g ( x1 − 2x0 ) , where C is the normalization constant . Therefore particle 1 is displaced by x0 due to the coupling with particle 2 . Similarly , if two rigid filaments are bound by several crosslinkers , the extensional and torsional strains of these crosslinkers are coupled , and this coupling gives rise to significant strains in the crosslinkers . We then determined the dependence of the elastic energy stored in the crosslinkers on the crosslinker stiffness . At fixed torsional stiffness κtor = 10pN ⋅ nm ⋅ rad−1 , the average magnitude of the extensional strain |ϵ| decreased with increasing extensional stiffness κext ( Fig 5D ) . However , the average extensional energy per crosslinker E ext = 1 2 κ ext l 0 2 ϵ 2 increased ( Fig 5E ) , from ∼0 . 3kBT for soft extensional springs up to ∼5kBT for stiff ones , with only a weak dependence on filament length ( Fig 5E ) . The average torsional energy per crosslinker reads E tor = 1 2 κ tor ( θ i 2 + θ j 2 ) , with θi and θj being the average torsional strains at the two actin subunits . They have the same magnitude θ . At fixed extensional stiffness κext = 0 . 1pN/nm , Etor increased with torsional stiffness κtor from ∼0 . 2kBT to up to ∼10kBT , with again a weak dependence on filament length ( Fig 5G ) . Both extensional and torsional energies vary relatively smoothly with the corresponding stiffnesses over three orders of magnitude , which is in stark contrast with the sharp structural transition between meshwork and bundle when stiffnesses are varied over the same range ( Fig 3E and 3F ) . Compared with the increase of the extensional energy Eext with κext ( ∼8 − 17 fold ) , the increase of torsional energy Etor with κtor ( ∼40 − 50 fold ) was more pronounced . This was likely due to stronger coupling between torsional strains of crosslinkers than between extensional strains . Elastic energy plateaued and then slightly decreased for very high stiffnesses ( κext = 10pN/nm or κtor = 100pN ⋅ nm ⋅ rad−1 ) , which was the consequence of higher detachment rate leading to a smaller number of attached linkers , thus reducing the frustrated interactions ( S2 Fig ) . We have shown that crosslinking of filaments leads to elastic energy stored in the crosslinkers . How can this energy be transformed into mechanical work ? Here , we propose a mechanism for torque generation through orchestrated detachment of crosslinkers by studying a simple model with only two filaments . Let us consider a pair of short filaments , where every other subunit of each filament is crosslinked ( Fig 6 ) . For simplicity , we assume the two filaments are parallel and consider only the rotation of filaments around their axes . We show that consecutive detachment of crosslinkers from the pointed end to the barbed end lets the filament rotate in the same direction by π/13 for each detachment ( Methods ) . Building on this simple proof of principle , we show that sustained directional rotation can be achieved with any filament length and crosslinker spacing and configurations such that ( i ) the angles between two consecutive crosslinkers along the pair of filaments have the same sign , and ( ii ) the sum of all these angles is smaller than 2π . Under these conditions , breakage of crosslinkers from one end to the other produces directional torque ( see Methods ) .
In this paper , we showed that highly crosslinked actin networks made of rigid filaments ( < 200nm ) can form either disordered meshworks or ordered bundles , depending on the filament length and the mechanical and kinetic properties of the crosslinkers . A recent in vitro study of short actin filaments ( 200nm ) showed that with increasing density of filamin , the initially sparsely distributed actin filaments condensed into a spindle-shaped aggregate , in which the organization of actin filaments displayed nematic order [50] . This observation is consistent with the phase diagram in Fig 4C of our study for filaments of 200nm with increasing crosslinking rate . We investigated the possible structures formed by actin filaments in the presence of elastic crosslinkers . However , our study does not take into account other actin regulating proteins involved in endocytosis , such as the Arp2/3 complex and capping protein , and thus does not completely resolve the question of the organization of filaments crosslinked with fimbrin at the site of endocytosis in yeast . Even with this limitation , these results provide valuable insights about possible actin filament architectures for endocytosis and other cellular processes that involve short actin filaments . Using the rate constants for fimbrin that have been measured in vitro [31 , 45] , and the fimbrin concentration in fission yeast cytoplasm ( 3 . 7 μM ) [20] , these values correspond to rates in our model kf = 0 . 2s−1 and k b 0 = 0 . 04 s - 1 . Our simulations suggest that the slow off-rate of fimbrin should favor an assembly of actin filaments into a meshwork ( S3 Fig ) . Further simulations with branched filaments , and with geometries and dynamics more representative of endocytosis will tell us which type of structure is present at endocytic sites . In addition , further experimental characterization of the mechanical properties of fimbrin and other crosslinkers will be key to understanding the self-organization of actin filaments in diffraction limited structures , and to test the predictions of our simulations . Our simulations demonstrate that individual actin crosslinkers are able to store up to 10kBT of elastic energy , which is one order of magnitude higher than the elastic energy stored in an uncoupled spring in a thermal bath ( 1 . 5kBT ) , and about half of the energy released by ATP hydrolysis ( ∼25kBT ) . To get a better sense of the amount of energy stored in the crosslinkers , one can make a comparison with the energy necessary to deform the plasma membrane into an endocytic vesicle . A back of the envelope calculation estimates the work needed to create a cylindrical invagination of Rt = 25nm in radius and Dt = 140nm in depth [16] against the turgor pressure P ∼ 0 . 8 × 106 Pa is P π R t 2 D t = 5 . 4 × 10 4 k B T . The results of our model suggest that crosslinking filaments once with ∼900 crosslinkers , around 104kBT energy could be stored , or about 1/6 of the total energy needed . One may wonder where this large elastic energy comes from . We can show in a simplified model that the chemical binding energy of crosslinkers is indeed the source of the elastic energy . Let us consider two filaments with fixed positions and orientations , each having N subunits . In the following , we will consider the chemical balance between configurations where there is either n = 0 or 1 crosslinker between the two filaments . The rate at which a crosslinker is formed is kfΓ ( 1 ) , where Γ ( n ) denotes the number of possible pairings to form n crosslinkers between subunits in the two filaments . The value of Γ ( 1 ) depends on the orientations and positions of the two filaments , and the mechanical properties of the crosslinkers , and could vary from 1 for very stiff crosslinkers and orthogonal filaments , to N2 for infinitely soft crosslinkers and parallel filaments . Here we assume Γ ( 1 ) = N , which implies that for each subunit in one filament , there is a unique subunit in the other filament that is within the reaction distance to allow a crosslinker to be formed . When one crosslink is formed , its detachment rate is k b 0 e E / E c , assuming the crosslinker bears an elastic energy E . If the attachment rate is greater than the detachment rate , the system is more likely to be crosslinked , even though the elastic energy E stored in the crosslinker tends to drive down the crosslinker occupancy . Noting μ eff ≡ E c ln ( N k f / k b 0 ) the effective chemical binding energy that tends to drive up the occupancy of crosslinkers , this condition can be expressed as comparison between the effective chemical binding energy and the elastic energy μeff > E . Using the parameters k b 0 = 10 s - 1 , kf = 1s−1 , Ec = 10kBT and N = 50 , we estimate μeff = 16kBT for the first crosslink formation , which is larger than the 10kBT of elastic energy per crosslinker computed in our simulations . If we now consider the case where there are n crosslinkers formed between the filaments , the effective energy for binding an extra crosslinkers becomes μ eff ( n ) = E c [ ln ( k f / k b 0 ) + ln ( Γ ( n + 1 ) / Γ ( n ) ) ] . The second term in the bracket represents an entropic contribution that comes from the different ways of building n or n + 1 crosslinks between subunits of both filaments . This simplified two filament system illustrates how in our simulations with multiple filaments crosslinker occupancy is driven up by a similar entropic contribution in the chemical binding energy . Our model considers “slip-bond” crosslinker detachements , i . e . crosslinkers are more likely to detach if force and torque are exerted on them ( Eq 16 ) . However , we could consider the case of “catch-bond” detachments , where crosslinkers are less likely to detach under force and torque , as it has been shown for some cytoskeleton proteins [40 , 51 , 52] . In this case , we expect that the elastic energy stored in crosslinkers would be larger than what we have observed in our simulations , and the conformational change required for the catch-bond behavior would increase the available energy limit . The main reason energy storage is possible is that short actin filaments are rigid , which creates geometrical constraints on bound crosslinkers , forcing virtually all of them to fluctuate around average lengths and angles that are different from their rest lengths and angles . This implies that the crosslinkers are rigid enough to store elastic energy , but not as rigid as the filaments , so that filaments cannot be twisted or bent when crosslinked , or the distance over which filaments are twisted and bent in order to reduce the frustration , noted Ltb , is much longer than the filament length L . In the opposite case , when filament length L ≫ Ltb , filaments could form bundles in which individual filaments are twisted . Experiments conducted at this regime suggest that the frustrated interaction serves as a mechanism to control the size of the bundle [53] and cooperative binding of actin crosslinkers [54] . The two regimes have been theoretically studied by C . Heussinger and G . Grason [55] . Only a small fraction of the force necessary to deform the plasma membrane during clathrin-mediated endocytosis in yeast can be accounted for by actin polymerization alone . We predict that at least some of the missing force can come from the conversion of the elastic energy stored in the crosslinkers into force and/or torque . In this paper , we proposed a specific mechanism for torque production by orchestrated detachment of crosslinkers . This mechanism is different from the Brownian ratchet mechanism of force production that is directly coupled to ATP hydrolysis [56 , 57] . However , the ordered detachment has to be coupled to a non-equilibrium process to provide the information necessary for the ordered detachment . Treadmilling of filaments coupled to ATP hydrolysis could play such a role . To estimate the order of magnitude of free energy necessary to provide this information , let us consider a pair of short actin filaments ( e . g . 50-subunit long ) that are crosslinked by 10 crosslinkers . The free energy cost of detaching the crosslinkers in a specific order among all the 10 ! possibilities is ∼kBT ln 10 ! = 15kBT , which is only a small fraction of the energy provided by the ATP hydrolysis of two actin filaments undergoing treadmilling ( 50 × 25 = 1250kBT ) . We stress that this ordered detachment is only one possible mechanism to use the energy and more mechanisms need to be discovered . Future work with more realistic models for endocytosis or other actin-based processes will likely uncover new orchestrated mechanisms for force production . In our system , crosslinkers are driven up to a mechanically pre-stressed state by chemical binding energy . In principle , in order to release the elastic energy stored in crosslinkers , change in energetics of crosslinker binding/unbinding is necessary to induce collective detachment of crosslinkers . When actin polymerization is considered , ATP-bound actin is incorporated at the barbed end and undergoes hydrolysis after incorporation . The nucleotide content change could alter the crosslinkers’ binding affinity , causing rapid detachment of crosslinkers , which is accelerated by the pre-stressed structure . Elastic energy released during this process could be converted into work by the reorganization of actin filaments . We developed a computational model to study the dynamic assembly of actin filaments mediated by elastic crosslinkers . The organization of actin filaments were classified into either a meshwork or a bundle , characterized by their nematic order parameter and the number of attached crosslinkers . We showed that the elastic energy stored in crosslinkers increased with their stiffness due to coupling between crosslinkers bound to rigid filaments . As a proof of principle , we showed that the elastic energy could be converted into mechanical work by orchestrated detachment of crosslinkers between two parallel filaments . Our work provides a new perspective to study the mechanisms of force and torque production by actin filaments , in addition to the traditional end polymerization . It also provides an alternative energy source to account for the insufficient force production by actin polymerization during clathrin-mediated endocytosis .
Actin filaments are modeled as rigid cylindrical rods with diameter b and length L . The position of a filament is represented by its center of mass C . A unit vector N pointing from the filament’s pointed end to the barbed end indicates the orientation of the filament . The i-th subunit ( counting from the pointed end ) carries a unit vector Oi , which is normal to the binding surface with a crosslinker ( Fig 1A ) . We assume all Oi-s are perpendicular to the filament’s orientation N . Based on the atomic structure of actin filaments [43] , two consecutive subunits Oi and Oi+1 span an angle of 14π/13 calculated counter-clockwise from Oi to Oi+1 . This means two consecutive subunits on different strands have their binding interface in almost opposite directions , and two consecutive subunits on the same strand have their binding interface at an angle of 2π/13 ( 28° ) . We arbitrarily choose the filament’s rotational vector M as the normal vector of the first subunit M = O1 . Thus the orientational degree of freedom of the filament is fully captured by three orthonormal vectors N , M and N × M . The motion of a filament is described by its translational velocity Vc and angular velocity Ω , which are defined by the following equations: d C d t = V c , ( 3 ) d N d t = Ω × N , ( 4 ) d M d t = Ω × M . ( 5 ) The velocities Vc and Ω are governed by the force-balance and torque-balance equations: Ξ t V c = F e + F s , ( 6 ) Ξ r Ω = T e + T s . ( 7 ) Here the 3 × 3 matrices Ξt and Ξr denote the frictional matrix associated with translational and rotational motion of the filament , respectively . The vectors Fe and Te denote the total deterministic force and torque generated by crosslinkers or induced by steric interactions between filaments . The vectors Fs and Ts denote the stochastic force and torque , which obey the fluctuation-dissipation relations: ⟨ F i s ( t ) F j s ( t ′ ) ⟩ = 2 k B T δ ( t - t ′ ) Ξ i j t , ( 8 ) ⟨ T i s ( t ) T j s ( t ′ ) ⟩ = 2 k B T δ ( t - t ′ ) Ξ i j r . ( 9 ) Here the subscript indicates the element of the vectors or matrices . The frictional matrices are anisotropic , and given by [58]: Ξ t = ξ ‖ t N ⊗ N + ξ ⊥ t ( I - N ⊗ N ) , ( 10 ) Ξ r = ξ ‖ r N ⊗ N + ξ ⊥ r ( I - N ⊗ N ) , ( 11 ) where ξ ‖ t and ξ ⊥ t are the frictional coefficients for translational movement parallel with and perpendicular to the filament’s central axis , and ξ ‖ r and ξ ⊥ r are the corresponding frictional coefficients for rotation . The 3 × 3 identity matrix is denoted by I , and ⊗ denotes the outer product of two vectors . The anisotropic frictional coefficients depend on filament length L and diameter b via the relations [43]: ξ ‖ t = 2 π η L ln ( L / b ) - 0 . 2 , ξ ⊥ t = 4 π η L ln ( L / b ) + 0 . 84 , ( 12 ) ξ ‖ r = π η b 2 L , ξ ⊥ r = π η L 3 3 ( ln ( L / b ) - 0 . 66 ) . ( 13 ) Here η denotes the viscosity of the medium . To account for the steric interaction between filaments , if the shortest distance rmin between two filaments is less than the diameter b of a filament , a constant repulsive force fst is applied along the lines connecting the two nearest points . In each time step , we calculate all the forces and torques acting on a filament and determine the translational velocity Vc and angular velocity Ω of the filament according to Eqs ( 6 ) and ( 7 ) . The center of mass of a filament is then updated as: C ( t + Δ t ) = C ( t ) + V c Δ t . ( 14 ) The updated orientations are: N ( t + Δ t ) = Rot ( Ω Δ t ) N ( t ) , ( 15 ) where Rot ( ΩΔt ) denotes the rotation matrix defined by the vector ΩΔt . The rotation vector M ( t ) is updated in the same way . Each actin crosslinking protein is modeled as an elastic spring that bridges two actin subunits in two separate filaments . The crosslinking of two unoccupied subunits proceeds with a rate constant of kf , as long as the subunits are less than rc apart . The breakage of an established crosslink is assumed to follow a “slip-bond” mechanism and occurs with an energy-dependent rate constant: k b = k b 0 e E / E c ( 16 ) where k b 0 denotes the strain-free breakage rate constant , E denotes the total elastic energy , and Ec denotes the critical energy that determines the sensitivity of the bond breakage on the forces and torques . The elastic energy E of a crosslinker that bridges actin subunits in filaments α and β is a function of the positions , orientations and rotations of both filaments , as well as its positions in the filament , E = E ( Cα/β , Nα/β , Mα/β ) . The force generated by the crosslinker on filament α reads: F α = - ∂ E ∂ C α . ( 17 ) To determine the torque generated by the crosslinker on filament α , we choose three orthnormal vectors e1 , e2 , e3 and virtually rotate filament α by an infinitesimal angle ϕi around the axis ei . These operations are equivalent to applying the following infinitesimal changes to the orientational vectors of filament: N α → N α + ϕ i e i × N α , ( 18 ) M α → M α + ϕ i e i × M α . ( 19 ) The elastic energy correspondingly has an infinitesimal change E → E + ΔE . The torque then reads: T α = - ∑ i = 1 3 ∂ Δ E ∂ ϕ i e i ( 20 ) Forces and torques acting on filament β are derived in a similar way . The total elastic force and torque are obtained by summing ( 17 ) and ( 20 ) over all the crosslinkers bound to the filament . At each time step Δt of the simulation , we perform the following operations: In our simulation , we always set the time step Δt at least 100 times smaller than the relaxation time of the spring τ = min ( ξ ‖ / ⊥ t / κ ext , ξ ‖ / ⊥ r / κ tor ) to ensure that we correctly capture the dynamics of the springs . For computational reasons , we used a high viscosity value η = 10Pa ⋅ s , such that the relaxation time τ ∼ 0 . 01s for κext = 0 . 1pN/nm and κtor = 10pN ⋅ nm ⋅ rad−1 . We tested values of lower viscosity down to η = 0 . 1Pa ⋅ s . There is no significant difference in the local nematic order parameter Slocal and the elastic energies Eext and Etor between η = 0 . 1Pa ⋅ s and η = 10Pa ⋅ s . However , the global nematic order parameter Sglobal for long filaments is increased to 1 at lower viscosity ( S7 Fig ) . This is because the enhanced diffusion increases the probability of filaments moving close to each other . As a result , the separated bundles observed at high viscosity merge into a single bundle when the viscosity is low , increasing the global nematic order parameter . We characterize the structure of actin clusters by introducing local and global nematic order parameter Slocal and Sglobal . We map the connections between filaments into an undirected graph , with filaments being the nodes , and the number of crosslinkers being the value of the edges connecting two nodes . Filaments in a connected component of the graph are said to form a cluster if the number of filaments in the component is more than 10 . The nematic order parameter S for a group of filaments is the maximum eigenvalue of the following matrix [59]: Q = 1 G ∑ α ( N α ⊗ N α - 1 3 I ) , ( 21 ) where G denotes the number of filaments in the group , Nα denotes the orientational vector of filament α . For global nematic order parameter Sglobal , the group in Eq ( 21 ) includes all the filaments . For a particular filament α* , S local α * is defined by grouping the filament α* and its connected nodes in Eq ( 21 ) . The local nematic order parameter Slocal is the average of S local α over all the filaments that have at least 2 connected nodes . Both Slocal and Sglobal are in the range of [0 , 1] . Values of Slocal close to 1 indicate that filaments are locally aligned with their connected neighbors . Values of Sglobal close to 1 indicate that all the filaments are aligned . In general Slocal is greater than Sglobal , and reaches steady state more rapidly , because filaments that are in close proximity can rapidly align , but it takes time for distant clusters of filaments to collide and reorient . Slocal is also more consistent over different simulations than Sglobal , as reported by smaller error bars for Slocal than for Sglobal ( e . g . Fig 2G ) . If the viscosity of the medium η is reduced to 1Pa ⋅ s , filaments form a single cluster and the error bars of Sglobal become comparable with Slocal ( S7B and S7C Fig ) . Note that in a sparsely connected network with filaments in random orientation , Slocal ∼ 0 . 5 ( S1B Fig ) is higher than one should expect ( ∼0 ) . This artifact is due to the fact that the sum in Eq ( 21 ) is done over a very small number of filaments ( ∼3 ) . We confirmed this property by numerically calculating the nematic order parameter for three unit vectors with random orientations . The resulting distribution of Slocal has a peak at 0 . 45 ( S6 Fig ) . This almost uncrosslinked network should be distinguished from the densely connected actin meshworks , which have local nematic order parameters Slocal in the same range ( ∼ 0 . 4 Fig 2B ) but possess a large number of attached crosslinkers . Therefore , the number of crosslinkers in the meshwork is required to distinguish these two structures . In our model , we assumed that filaments are rigid . This rigidity assumption implies that ( i ) thermal fluctuations , and ( ii ) forces and torques exerted by crosslinkers do not significantly bend or twist the filaments . We can verify a posteriori that these conditions are actually fullfiled in our simulations . Indeed , the maximum force produced by a crosslinker in our simulations is ∼10pN when the extensional stiffness κext reaches 10pN/nm , and the maximum torque is ∼100pN ⋅nm when the torsional stiffness κtor reaches 100pN ⋅ nm ⋅ rad−1 . Given the persistence length of actin filament for both bending and twisting is Lp ∼ 10μm [12 , 14 , 60 , 61] , for a filament of length L = 200nm which consists of N = L/δ = 74 subunits , the angular change between two consecutive subunits due to thermal fluctuation is arccos ( e - L / L p ) / N = 0 . 15 ° . The angular change due to bending caused by a force of f = 10pN in the middle of the filament when the two ends are fixed is arctan ( f L 2 48 L p k B T ) / N = 0 . 15 ° [62] . The twisting angle by a torque of T = 100pN ⋅ nm is ( T L L p k B T ) / N = 0 . 37 ° . Therefore , it is safe to consider filaments as stiff , and the energy stored in crosslinkers would not be dramatically different even if the finite stiffness of filaments was taken into account . We consider the rotation of two parallel filaments around their axes by consecutive detachment of crosslinkers from the pointed end to the barbed end . We assume that every other subunit of each filament is crosslinked , such that the i-th crosslinker has an angle of θ i = θ 1 + ( i - 1 ) 2 π / 13 ( 22 ) with its attached actin subunit . The torque generated by the i-th crosslinker on the filament thus is −κtorθi . Here the crosslinker label i is ordered according to their distance to the pointed end of filaments . At torque balanced state , i . e . , ∑ i = 1 n θ i = 0 , we have θ1 = − ( n − 1 ) π/13 . Upon detachment of crosslinker 1 , the total torque becomes imbalanced and the filament makes a rotation of angle Δϕ to reach a new torque balanced state , i . e . , ∑ i = 2 n ( θ i + Δ ϕ ) = 0 . This leads to Δϕ = −π/13 . Similarly we can show that attachment of a new crosslinker at the ( n + 1 ) -th position θn+1 = θ1 + n2π/13 will cause the filament to rotate the same angle in the same direction as caused by detachment of the first crosslinker . Note that even though the rotation angle Δϕ is independent of the number of attached crosslinkers , it is required that crosslinkers are present in large enough number or are stiff enough to ensure that rotation will be significantly larger than thermal fluctuations , i . e . 1 2 κ tor θ 1 2 ≫ k B T . For instance , if the number of crosslinkers n = 10 , this requires κtor ≫ 2pN ⋅ nm ⋅ rad−1 . The above calculation can be easily extended to situations with more relaxed conditions than Eq ( 22 ) . The angular rotation Δϕ ( i ) of the filament upon detachment of the i-th crosslinker satisfies the recursive relation: Δ ϕ ( i ) = θ i + ∑ j = 1 i - 1 Δ ϕ ( j ) n - i . ( 23 ) Directed rotation requires that Δϕ ( i ) have the same sign for all i . This condition is equivalent to considering that the angles between consecutive crosslinkers ( θi − θi−1 ) have the same sign for all i . We consider a simplified model in which , when there are n crosslinkers formed , each crosslinker stores an elastic energy of En . In fact , En is varied among different crosslinkers and dependent on their positions and orientations in the filaments . Here we assume En only depends on n , and the probability distribution P ( n , t ) for the number of crosslinkers is governed by d P ( n , t ) d t = k f Φ ( n | n - 1 ) P ( n - 1 ) + ( n + 1 ) k b 0 e E n + 1 / E c P ( n + 1 ) - [ k f Φ ( n + 1 | n ) + n k b 0 e E n / E c ] P ( n ) , ( 24 ) where Φ ( n|n − 1 ) denotes the number of ways to build the n-th crosslinker , given there are already n − 1 crosslinkers formed . The steady state distribution reads P ss ( n ) = P ( 0 ) ( k f k b 0 ) n Γ ( n ) e - 1 E c ∑ i = 1 n E i , ( 25 ) where Γ ( n ) = 1 n ! ∏ i = 1 n Φ ( i | i - 1 ) is the number of possible ways to build n crosslinkers . By comparing the distribution of Pss ( n + 1 ) with Pss ( n ) , we have P ss ( n + 1 ) P ss ( n ) = e - E n + 1 - μ eff ( n ) E c , ( 26 ) where μeff ( n ) is the effective binding energy defined in the text . If μeff ( n ) > En+1 , the system is driven up to the ( n + 1 ) -state , storing an elastic energy of En+1 . | In many cellular processes that involve the deformation of membranes or the movement of vesicles and organelles , the energy from biochemical reactions is converted into forces . The biological filaments called actin are one of the major force producing machineries of the cell . It is commonly believed that the elongation of these filaments at their tip is the only way actin filaments can exert force . However , the amount of force produced by this mechanism can only account for a small fraction of the force in key cellular processes , such as clathrin-mediated endocytosis . In this paper , we demonstrate that connecting actin filaments with each other with flexible proteins called crosslinkers is a new way to transform biochemical energy into mechanical energy , and that this stored mechanical energy can be used to rotate filaments in a sustained direction . This mechanism of chemical energy conversion into mechanical work is a new paradigm for understanding how the actin filaments can produce forces without considering polymerization or molecular motors . |
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Many host-adapted bacterial pathogens contain DNA methyltransferases ( mod genes ) that are subject to phase-variable expression ( high-frequency reversible ON/OFF switching of gene expression ) . In Haemophilus influenzae , the random switching of the modA gene controls expression of a phase-variable regulon of genes ( a “phasevarion” ) , via differential methylation of the genome in the modA ON and OFF states . Phase-variable mod genes are also present in Neisseria meningitidis and Neisseria gonorrhoeae , suggesting that phasevarions may occur in these important human pathogens . Phylogenetic studies on phase-variable mod genes associated with type III restriction modification ( R-M ) systems revealed that these organisms have two distinct mod genes—modA and modB . There are also distinct alleles of modA ( abundant: modA11 , 12 , 13; minor: modA4 , 15 , 18 ) and modB ( modB1 , 2 ) . These alleles differ only in their DNA recognition domain . ModA11 was only found in N . meningitidis and modA13 only in N . gonorrhoeae . The recognition site for the modA13 methyltransferase in N . gonorrhoeae strain FA1090 was identified as 5′-AGAAA-3′ . Mutant strains lacking the modA11 , 12 or 13 genes were made in N . meningitidis and N . gonorrhoeae and their phenotype analyzed in comparison to a corresponding mod ON wild-type strain . Microarray analysis revealed that in all three modA alleles multiple genes were either upregulated or downregulated , some of which were virulence-associated . For example , in N . meningitidis MC58 ( modA11 ) , differentially expressed genes included those encoding the candidate vaccine antigens lactoferrin binding proteins A and B . Functional studies using N . gonorrhoeae FA1090 and the clinical isolate O1G1370 confirmed that modA13 ON and OFF strains have distinct phenotypes in antimicrobial resistance , in a primary human cervical epithelial cell model of infection , and in biofilm formation . This study , in conjunction with our previous work in H . influenzae , indicates that phasevarions may be a common strategy used by host-adapted bacterial pathogens to randomly switch between “differentiated” cell types .
The pathogenic Neisseria are host-adapted human pathogens that pose a significant health problem worldwide . Neisseria meningitidis colonizes the upper respiratory tract and causes meningitis and septicemia . Neisseria gonorrhoeae colonizes the genitourinary tract and can cause a spectrum of disease ranging from uncomplicated mucosal infection to disseminated gonococcal infection . There is no N . gonorrhoeae vaccine , and no fully protective vaccine for N . meningitidis . Vaccine development has been hampered due to the high frequency of antigenic and phase variation of surface structures typical of these organisms . Phase variation is the high frequency reversible on/off switching of gene expression and is commonly mediated by mutations in simple tandem DNA repeats in the open reading frame or promoter region of genes encoding surface expressed virulence determinants [1] . The independent , random switching of these genes results in phenotypically diverse populations that enables rapid adaptation to host environments and evasion of immune responses [2] . While phase variation is typically associated with genes encoding surface structures , several host-adapted bacterial pathogens have methyltransferases ( mod genes ) associated with type III restriction modification ( R-M ) systems that contain simple tandem DNA repeats that have been proven to phase vary ( Pasteurella haemolytica [3] , Haemophilus influenzae [4] and Helicobacter pylori [5] ) or predicted to phase vary ( N . meningitidis , N . gonorrhoeae [6] , [7] , and Moraxella catarrhalis [7] ) , as reviewed in Fox et al [8] . R-M systems are ubiquitous in bacteria and confer protection to the bacterial host against invasion by foreign DNA [9] . R-M systems are classified into three groups; Types I , II or III on the basis of subunit composition , DNA cleavage position , sequence-specificity and co-factor requirements [10] . Type III systems are composed of a methyltransferase ( modification , mod ) gene and an endonuclease ( restriction , res ) gene , whose products form a two-subunit enzyme – Mod and Res [11] . Type III systems are unusual in that Res must form a complex with Mod to be functional [12] , however , Mod can function independently of Res [13] . The Mod subunit contains several conserved motifs in the N- and C-terminal regions and the central region contains the DNA-recognition domain that dictates sequence specificity [14] . In H . influenzae , the random switching of the modA gene controls expression of a phase variable regulon of genes ( a “phasevarion” ) , via differential methylation of the genome in the modA ON and OFF states [15] . This was the first report of the coordinated random switching of a “regulon” of genes and , considering the wide distribution of phase variable type III R-M systems , may represent a widely used mechanism in bacterial pathogens [8] . In this study we investigate the phase variable type III R-M systems of pathogenic Neisseria to determine whether they play a role in gene regulation and virulence .
To investigate whether the type III R-M systems of the pathogenic Neisseria behave as a phasevarion [15] , we first carried out a phylogenetic analysis of mod genes associated with type III R-M systems of N . meningitidis and N . gonorrhoeae . A comparison of the available genome sequences revealed that each strain contains two distinct phase variable mod genes , which we define as modA and modB , that share only 37% similarity to each other along the full length of the Mod deduced amino acid sequence . Both genes contain tracts of simple tandem repeats , 5′-AGCC-3′ ( modA ) and 5′-CCCAA-3′ ( modB ) , that mediate phase variation of mod gene expression ( Figure 1 ) . ModA is highly homologous ( >90% identity along the length of the Mod deduced amino acid sequence excluding the variable DNA recognition domain ) to the mod gene of H . influenzae strain Rd ( HI1058/56 ) [4] , [15] . Differences in the modA DNA recognition domain [14] ( Figure 1 ) have previously been observed in H . influenzae [16] with 17 distinct mod alleles defined in this organism ( modA1–17; [17] ) . The Neisseria modA alleles present in the genome strains surveyed have the designations modA11 , 12 and 13 and share >94% similarity to each other along the length of the Mod deduced amino acid sequence , excluding the variable DNA recognition domain . Our recent work also shows that the modA gene of H . influenzae and Neisseria are essentially the same gene with evidence of horizontal transfer of this gene in both directions between these organisms [17] . Unlike modA , the modB gene appears to be specific to Neisseria species . Two distinct modB alleles , modB1 and 2 , distinguished by differences in their DNA recognition domain , were also observed ( Figure 1 ) . ModB1 and 2 share >95% similarity to each other along the length of the Mod deduced amino acid sequence , excluding the variable DNA recognition domain ( which shares <33% identity ) . To investigate whether additional alleles of modA and modB are present in these organisms , and to look at the distribution of mod alleles and their repeat sequence type and number , sequence analysis of a large , genetically diverse set of N . meningitidis and N . gonorrhoeae isolates was performed . This analysis revealed that all strains examined contained both modA and modB genes . Sequencing of the repeat region of the mod alleles revealed that the repeat numbers vary in length between different strains , resulting in the mod genes being in-frame ( ON ) or out-of-frame ( OFF ) for expression , consistent with phase variation of the mod genes ( Table S1 , Table S2 ) . The N . gonorrhoeae strains contained either the modA12 or modA13 allele , and only the modB1 allele . One strain was found not to have a modB gene ( Figure 1 , Table S2 ) . A complete analysis of modA allele distribution was conducted in N . meningitidis , which has a well characterized population structure defined by multi locus sequence typing ( MLST; [18] ) . The complete 107 strain MLST modA survey revealed that the majority of N . meningitidis strains had either the modA11 or modA12 allele , with modA15 found in two strains and modA4 and modA18 found in one isolate each ( Figure 1 , Figure 2A and 2B , Table S1 ) . The most notable associations were in capsule type , where 100% of serogroup A strains and 92% of serogroup C strains contained the modA12 allele ( Table 1 , Figure 2B ) . Some association with clonal complex was also observed , with meningococci belonging to the ST-32 clonal complex predominantly harbouring the modA11 allele . Further clustering could be seen among ST-41/44 and ST-8 clonal complexes . Unlike N . gonorrhoeae , which contained only one type of modB allele , modB1 , N . meningitidis strains contained either modB1 or modB2 . There were seven strains , all from the ST-32 group , which contained point mutations in modB1 suggesting the gene is inactive in these strains ( Figure 1 , Table S1 ) . The modA genes of N . meningitidis and N . gonorrhoeae have two alternate initiation codons ( Distal ATG and Proximal ATG ) that are predicted to code for proteins of either 589 aa/640 aa or 707 aa/758 aa , depending on the number of tetranucleotide repeats that are present ( Figure 3A ) . As this study is focused on modA phase variation and expression , a clear understanding of the relationship between tetranucleotide repeat number and modA expression was established . ModA expression was examined in the three frames; Distal , Proximal and OFF ( which has no candidate ATG and a stop codon immediately after the 5′-AGCC-3′ repeats ) , by constructing a modA::lacZ reporter fusion in the N . meningitidis strain MC58 chromosome ( Figure 3A and Figure S1 ) . Maximal expression was found to be from the Distal ATG only and unlike H . influenzae strain Rd [15] , minimal expression was observed from the Proximal ATG and the OFF frame ( Figure 3B and 3C ) . Natural modA ON and OFF colonies of N . meningitidis strain MC58 and N . gonorrhoeae strain FA1090 were required for microarray analysis and biological characterization experiments . N . meningitidis strain MC58 has 21 5′-AGCC-3′ repeats resulting in the modA11 gene being out-of frame ( OFF ) from the Distal ATG . Single colonies of MC58 were picked and screened by PCR and sequencing to find modA11 in-frame ( ON ) with the Distal ATG ( see Figure 3 ) . Similarly , single colonies of N . gonorrhoeae strain FA1090 were picked and screened by PCR and sequencing to find modA13 in-frame and also out-of-frame with the Distal ATG . During this process , ModA13 repeat tracts ranging from 13 ( ON ) to 26 ( OFF ) and also 37 ( ON ) were observed , demonstrating phase variation of N . gonorrhoeae strain FA1090 modA13 ( results not shown ) . Having established the relationship between modA repeats and modA expression ( see Figure 3 ) , we were in a position to conduct studies to determine whether phase variation of the various modA alleles in pathogenic Neisseria resulted in changes in gene expression . These studies were initiated with N . meningitidis strain MC58 modA11 gene , where the modA11 gene was inactivated by insertion of a kanr cassette to make the mutant strain MC58 modA11::kan ( Figure S2 ) . Wild-type MC58 modA11 ON and MC58 modA11::kan were compared by microarray analysis using N . gonorrhoeae/meningitidis genome arrays ( Materials and Methods ) . Initially , microarray analysis was performed using RNA isolated from wild-type MC58 modA11 ON and MC58 modA11::kan strains grown under standard culture conditions . However , these studies revealed no statistically significant difference in gene expression . Experiments were then performed in which N . meningitidis were cultured under iron-limiting conditions to more closely reflect in vivo conditions . Using this more physiologically relevant culture condition , many genes were found to have an expression ratio of 1 . 5-fold and over , with 162 genes up-regulated in MC58 modA11::kan relative to wild-type and 123 genes down-regulated , confirming modA11 phase variation has an influence on gene expression ( Table 2 , Table S3 ) . Five of these genes encode surface exposed proteins , including NMB1540 ( lbpA ) and NMB1541 ( lbpB ) , encoding LbpA and LbpB respectively , which are part of the lactoferrin receptor that allows acquisition and binding of iron from lactoferrin containing compounds . LbpA is the TonB-dependent integral outer membrane lactoferrin receptor and iron transport channel . LbpB is an accessory lipoprotein anchored to the outer membrane that contributes to lactoferrin binding/use [19] , [20] . The lactoferrin receptor is a potential vaccine candidate in N . meningitidis [21] . Quantitative real time PCR ( RT-PCR ) was performed to confirm that the lbpA and lbpB genes were expressed at a higher level in MC58 modA11::kan compared to the MC58 modA11 ON parent strain ( see Figure 4A ) . Altered expression was further confirmed by an lbpB::lacZ fusion ( Figure S3 ) located on the chromosome of each strain , which showed ∼2-fold higher expression in the modA11::kan mutant strain compared to the modA11 ON parent strain ( see Figure 4B ) . Consistent with this , western blot analysis confirmed the effect of modA11 phase variation on expression of LbpA , with an apparent reduction in expression when modA11 is ON ( Figure 4C ) . The same effect is seen when comparing a wild-type modA11 ON strain to either a modA11::kan mutant or a natural phase variant in which the modA11 gene had switched OFF due to an alteration in the 5′-AGCC-3′ repeat tract ( from 22 to 21 AGCC repeats; see Figure 4C ) , confirming the regulation of LbpA by ModA11 is not related to the use of a kanr inactivated modA11 gene . A similar microarray study was conducted using a N . meningitidis modA12 clinical isolate , B6116/77 . To determine whether phase variable expression of the N . meningitidis strain B6116/77 modA12 gene also led to alteration in global gene expression , the modA12 gene was inactivated by insertion of a kanr cassette to make the mutant strain B6116/77 modA12::kan ( Figure S2 ) . Wild-type B6116/77 modA12 ON and B6116/77 modA12::kan were compared by microarray analysis using N . gonorrhoeae/meningitidis genome arrays . Experiments were performed under the same conditions as described above . Twenty six genes were found to have an expression ratio of 1 . 5-fold and over , with 14 genes up-regulated in B6116/77modA12::kan relative to wild-type and 12 genes down-regulated , confirming modA12 phase variation has an influence on gene expression ( Table 2 , Table S4 ) . The set of genes differentially expressed in the modA12 mutant were different to the modA11 set of genes . This is consistent with the differences in the DNA recognition domain between modA11 and modA12 , and confirms that these distinct alleles control different phasevarions in N . meningitidis . An additional phasevarion study was conducted in N . gonorrhoeae . In this case , a modA13 knockout mutant was constructed by interrupting the modA13 gene with a kanr cassette ( Figure S2 ) . Comparison of the phenotype of the FA1090 modA13::kan mutant strain with wild-type FA1090 modA13 ON formed the basis of expression and phenotypic studies . Global gene expression was compared between wild-type FA1090 modA13 ON and FA1090 modA13::kan under iron-limiting conditions . 34 genes were up-regulated in FA1090 modA13::kan relative to wild-type , and 20 genes were down-regulated ( Table 2 , Table S5 ) . Five of the differentially regulated genes have obvious roles in virulence; four in oxidative stress and one in antimicrobial resistance . NGO0929 ( metF ) and NGO0928 ( metE ) are part of the MetFE operon , which plays a role in the methylation of homocysteine , the final step of methionine biosynthesis , and is involved in defence against oxidative stress [22] . NGO0554 encodes a gonococcal-specific hypothetical protein that is shown to protect against damage caused by high levels of H2O2 [23] . NGO0650 ( recN ) encodes the DNA repair protein RecN . The gonococcal RecN protein is demonstrated to be involved in DNA repair and DNA transformation [24] and plays an important role in H2O2 damage protection as well as resistance to killing by polymorphonuclear leukocytes [25] . NGO1368 ( mtrF ) encodes the inner membrane protein , MtrF , and has been shown to have a role in antimicrobial resistance [26] . Our phylogenetic analysis revealed that N . gonorrhoeae strains have one of two distinct modA alleles ( modA12 or 13; see Figure 1 ) , indicating that different phasevarions may exist within N . gonorrhoeae and that strains with the same mod allele may regulate similar sets of genes . To determine if a strain with the same DNA recognition domain as FA1090 ( modA13 allele ) would result in the same set of genes being regulated , we chose a N . gonorrhoeae clinical isolate , strain O1G1370 , from a representative set of N . gonorrhoeae strains ( Table S2 ) that also contains a modA13 allele . A modA13::kan knockout mutant was made using the same approach as described for FA1090 ( Materials and Methods ) . Quantitative RT-PCR on the metF , mtrF and NGO1581 genes confirmed that metF and mtrF , which are up-regulated in expression in the FA1090 modA13::kan mutant , are also up-regulated in the O1G1370 modA13::kan mutant ( Table 2 ) . NGO1581 , which was down-regulated in the FA1090 modA13::kan mutant , is also down-regulated in the O1G1370 modA13::kan mutant ( Table 2 ) . Furthermore , similar results were seen with quantitative RT-PCR on the same set of genes when comparing a wild-type FA1090 modA13 ON strain to a natural phase variant of FA1090 in which the FA1090 modA13 gene had switched OFF due to an alteration in the 5′-AGCC-3′ repeat tract ( from 37 to 26 AGCC repeats ) ( Table 2 ) , confirming the regulation of this set of genes by ModA13 is not related to the use of a kanr inactivated modA13 allele . When we conducted microarray analysis of a N . gonorrhoeae modA12 strain 96D551 , comparison of 96D551 modA12 ON and 96D551 modA12::kan ( OFF ) strains revealed a distinct set of genes being regulated compared to the modA13 data above ( Table 1 , Table S6 ) . These results are consistent with the differences in the DNA recognition domain between modA13 and modA12 ( see below ) , and supports the idea that these distinct alleles control different phasevarions in N . gonorrhoeae . However , unlike the modA11 , modA13 and modA12 ( N . meningitidis ) expression studies described above , the 96D551 modA12 ON and 96D551 modA12::kan ( OFF ) strains showed a significant difference in growth rate for the cultures used to make RNA ( Figure S4D ) . We cannot rule out the possibility that these differential growth rates may have influenced the gene expression data in Table 1 and Table S6 . In all cases , phase variation or mutagenesis of modA of pathogenic Neisseria results in altered gene expression , defining these systems as functional phasevarions . In order to determine whether the observed changes in modA expression correspond to global changes in DNA methylation , thereby indicating this as the likely mechanism of gene control , it was necessary to identify one or more of the modA target sites . In addition to confirming global changes in methylation , target site identification would also facilitate future studies on the molecular mechanisms operating at individual promoters within the phasevarion . In order to identify methylation target sites , a strategy based on inhibition of DNA restriction was used . In initial studies , plasmid pCmGFP was isolated from N . gonorrhoeae or N . meningitidis modA11 , modA12 or modA13 ON strains and their corresponding modA::kan mutants , and digested with a range of restriction enzymes known to be inhibited by methylation of an adenine within their recognition sequence ( see Materials and Methods ) . Differences in digestion patterns between plasmid extracted from modA ON cells ( ModA methylated DNA ) and modA::kan cells ( DNA not methylated by ModA ) would indicate an overlap of the respective ModA methylated target and the restriction enzyme used . No such fortuitous inhibition pairs were seen with modA11 or modA12 strains , but were with modA13 . Figure 5A shows an obvious difference in the restriction pattern of plasmid extracted from modA13 ON and modA13::kan cells , indicating overlap between the ModA13 site and ApoI . The recognition sequence of ApoI is 5′-RAATTY-3′ . The specific ApoI site displaying inhibition ( 5′-AAATTC-3′ ) was not unique on the plasmid . Comparison between the inhibited ApoI site and other ApoI sites whose sequence was also 5′-AAATTC-3′ revealed that the overlap with ModA13 must be 5′ to the ApoI site . As seen in Figure 5B , methylation of the different adenines in the ApoI recognition site inhibits digestion to varying degree . Since over-digestion did not result in the 722 bp band being cleaved into the two smaller fragments , it can be assumed that the first adenine of the ApoI sequence was not the methylation target , as methylation of this adenine would result in only 10% inhibition of restriction [27] , an effect which could potentially be overcome by over-digestion . Therefore , depending on which of the other two adenines was methylated , the ModA13 recognition sequence must be found within 5′-CAGAAA-3′ . To confirm which adenine is the ModA13 methylation target , and to further specify the ModA13 recognition sequence , overlaps of the putative ModA13 recognition sequence and ApoI restriction sites were identified on the FA1090 chromosome . Chromosomal DNA was extracted from FA1090 modA13 ON and FA1090 modA13::kan cells , digested with ApoI and examined by Southern blot . Inhibition of ApoI restriction at the internal ApoI/ModA13 overlap results in the presence of one large band at 3408 bp , while the unmethylated DNA ( modA13::kan ) is cleaved at this site by ApoI into two smaller bands of 2500 and 908 bp ( Figure 5C ) . This confirms that ApoI restriction can be inhibited in DNA methylated by ModA13 , as shown by the plasmid digest ( Figure 5A ) . Similar studies were done with three other ApoI restriction sites in the genome that overlap ModA13 with 5′-TAGAAA-3′ , 5′-GAGAAA-3′ or 5′-AAGAAA-3′ . In each of these cases , ApoI restriction of ModA13 methylated DNA was inhibited compared with DNA extracted from modA13::kan cells ( data not shown ) . This confirms the ModA13 recognition sequence to be 5′-AGAAA-3′ . To identify which of the two potential adenines in 5′-AGAAA-3′ is methylated , ModA13 recognition sequences in the FA1090 chromosome were identified which overlapped with restriction enzymes other than ApoI and were known to be inhibited by methylation of adenines . Two of these enzymes were HindIII ( 5′-AAGCTT-3′ ) and AluI ( 5′-AGCT-3′ ) . Although the recognition sequence of HindIII contains two adenines ( Figure 5E ) , both these adenines are part of the ModA13 recognition sequence . Methylation of the adenine in the AluI recognition sequence is known to result in complete inhibition [28] . When chromosomal DNA digested with AluI is probed using a PCR product containing an AluI/ModA13 overlap ( Figure 5D ) no difference in restriction is seen between the modA13 ON and modA13::kan lanes , indicating that the common adenine in this overlap is not methylated by ModA13 . This suggests that ModA13 methylates AGAAA on the second most 3′ adenine of recognition site 5′-AGAAA-3 . Information on the sensitivity of HindIII to hemimethylation is only known for the 5′ adenine of the HindIII recognition site . Hemimethylation of this adenine results in a 95% inhibition of restriction [27] . Using a random site in the FA1090 genome where the overlap between the ModA13 target site and HindIII resulted in the 5′ adenine of HindIII site corresponding to the second last adenine of the AGAAA ( see Figure 5E ) we were able to determine whether this was the residue methylated by ModA13 . The results shown in Figure 5E confirm the expected restriction inhibition phenotype [27] allowing us to conclude that the ModA13 methylation site is AGAAmA , with the methyl group being added to the third adenine in the sequence . Having established the ModA13 target sequence , we tested DNA derived from a modA12 strain and confirmed that the ModA12 target site was distinct as there is no difference between modA12 ON and modA12::kan ( OFF ) DNA in a ModA13/ApoI inhibition assay ( Figure 5F ) . Analysis of the FA1090 genome has revealed a total of 5135 ModA13 target sites . To determine whether the phasevarion mediated changes in gene expression correspond to altered phenotypes in model systems , we chose to focus on the modA13 allele of N . gonorrhoeae . Several model systems were available from our previous studies on oxidative stress , biofilm formation and bacterial - host cell interactions [29]–[32] . Furthermore , strains FA1090 and O1G1370 provided an opportunity to test the reproducibility of key phenotypes in two independent modA13 strains . Previous studies using N . gonorrhoeae strain FA19 demonstrate that mtrF is required for induction of high-level antimicrobial resistance to Triton X-100 by gonococci [33] . Our data show that MtrF expression is up-regulated in the modA13 mutant relative to the wild-type under iron-limiting conditions . To test whether differences in antimicrobial-resistance could be observed between wild-type FA1090 modA13 ON and the FA1090 modA13::kan mutant , an antimicrobial-resistance assay was performed using increasing Triton X-100 concentrations ( Figure 6A ) . The FA1090 modA13::kan mutant was found to be more resistant than wild-type FA1090 modA13 ON , consistent with the higher level of expression of MtrF in this modA13 OFF strain . As modA13 ON is free to phase vary to OFF , and OFF cells appear to be fitter in this assay , the status of modA13 expression was monitored by PCR with fluorescent primers across the repeat region to determine whether ON to OFF phase variants had been selected in the survivor colonies at various Triton X-100 concentrations . This analysis revealed that the FA1090 modA13 ON culture plated on zero Triton X-100 remained ON , with only 11 . 21% OFF cells . However , cells plated on increasing Triton X-100 concentrations changed to 46 . 99% OFF , 80 . 29% OFF and 80 . 15% OFF over the course of the assay for 40 , 50 and 60 µg/ml Triton X-100 , respectively ( Figure 6B ) . A number of studies have shown that N . gonorrhoeae can form a biofilm in a continuous-flow chamber and over primary human genital tract epithelial cells in culture [30] , [31] . Biofilms provide a number of advantages in survival of the bacteria . It is suggested that biofilm formation by N . gonorrhoeae may contribute to its ability to persist in an asymptomatic state in the female genital tract [34] . In addition , bacteria within biofilms show increased resistance to antimicrobial agents [35] , [36] and links between biofilm formation and oxidative stress defenses have been observed in N . gonorrhoeae [30] . The ability of N . gonorrhoeae O1G1370 modA13 ON , O1G1370 modA13 OFF and O1G1370 modA13::kan ( OFF ) to form a biofilm was evaluated after two days of growth under continuous flow conditions . Three-dimensional images of these biofilms were created in Volocity ( Materials and Methods ) . These images show that O1G1370 modA13::kan and modA13 OFF form a thick and dense biofilm , while O1G1370 modA13 ON forms an extremely weak biofilm with a few sparse patches of cells scattered across the surface of attachment ( Figure 7A ) . The O1G1370 modA13 ON strain also formed biofilms with lower maximum thicknesses than the O1G1370 modA13::kan and O1G1370 modA13 OFF strains . ( Figure 7D ) . Scanning electron microscopy of the surface of the biofilm taken at 5 , 000× magnification shows that there are gaps between clusters of biofilm in the O1G1370 modA13 ON strain , unlike the O1G1370 modA13 OFF and O1G1370 modA13::kan strain biofilms , where there are no areas where the glass surface of attachment is visible . There are also large areas where no biofilm is present in the O1G1370 modA13 ON samples ( Figure 7B ) . Scanning electron microscopy taken at 15 , 000× magnification shows that O1G1370 modA13::kan and O1G1370 modA13 OFF form biofilms that are tightly enmeshed in an extracellular material that obscures the structure of individual cells , while cells in the modA13 ON biofilm are clearly distinguishable ( Figure 7C ) . Transmission electron microscopy shows that O1G1370 modA13::kan forms a biofilm where individual cells are shedding copious amounts of membrane , as seen in the numerous enclosed membrane blebs on the surface of the cells , while there is no evidence of blebbing in the O1G1370 modA13 ON biofilm . Cells in the O1G1370 modA13 OFF biofilm also appear to be blebbing , like those in O1G1370 modA13::kan biofilm , as numerous blebs can be seen forming on the surface of the O1G1370 modA13 OFF strain . These electron micrographs suggest that the extracellular matrices of the O1G1370 modA13::kan and O1G1370 modA13 OFF biofilms may be at least partially composed of fused membrane blebs ( Figure 7C ) . COMSTAT [37] was used to quantitatively assess the biomass , and average and maximum thickness of confocal z-series photomicrographs taken for each flow chamber . COMSTAT analysis showed that the O1G1370 modA13::kan and O1G1370 modA13 OFF strains form significantly thicker biofilms with significantly more biomass than the O1G1370 modA13 ON strain . Specifically , O1G1370 modA13 ON had 3 . 5% of the biomass and 4 . 2% of the thickness of the O1G1370 modA13::kan mutant on average and 4 . 7% of the biomass and 5 . 2% of the thickness of the O1G1370 modA13 OFF ( Figure 7E ) . Similar results were observed using N . gonorrhoeae strains FA1090 modA13 ON and FA1090 modA13::kan ( Figure S5 ) . The use of primary human cervical epithelial ( pex ) cells as a model system of gonococcal cervicitis is well established and has been used in a number of studies , such as the examination of the role of oxidative stress regulators in host-pathogen interactions [29] , [32] . To determine the biological significance of O1G1370 modA13 expression using this pex cell culture model , we performed quantitative association , invasion , and survival assays using O1G1370 modA13 ON , O1G1370 modA13 OFF , and O1G1370 modA13::kan mutant gonococci ( Figure 8 ) . These data revealed that there was no significant ( P≥0 . 2338 ) difference in the ability of the O1G1370 modA13 OFF and O1G1370 modA13::kan strains to adhere to , invade , or survive within pex cells . This confirmed that the modA13::kan knockout allele behaves in the same way as a natural phase variant modA13 OFF strain . In contrast , behavior of the O1G1370 modA13 ON strain was significantly ( P≤0 . 001 ) different from that obtained with the use of either the O1G1370 modA13 OFF or O1G1370 modA13::kan strains in parallel assays . In this regard , a modA13 ON phenotype resulted in the increased ability of gonococci to associate with pex cells , whereas a modA13 OFF configuration augmented the ability of gonococci to invade ( Figure 8A , invasion index ) and survive within pex cells following invasion ( Figure 8A , survival index ) . These data suggest a possible role for Mod-dependent phase variation in promoting the adaptive changes required for gonococci to switch from an extracellular to an intracellular existence . This idea is supported by our observation of selection for a switch from ON to OFF in the O1G1370 modA13 ON strain . Fragment analysis confirmed that the O1G1370 modA13 ON inoculum , which contains only 5 . 86% OFF , changes to ∼49 . 84% OFF by the time the 3 hour intracellular survival sample was taken ( Figure 8B , Table S9 ) . An independent N . gonorrhoeae modA13 strain , 1291 , displayed the same intracellular survival and modA13 switching phenotype ( Figure S6 ) .
The pathogenic Neisseria are the archetypal organisms for the study of phase variation . Simple tandem repeats are typically associated with individual genes involved in biosynthesis of a surface component , such as an outer membrane protein , or a poly- or oligosaccharide . The consequence of hyper-mutation of these simple tandem repeats is phase variable expression of these genes , i . e . , the presence or absence of a single component on the surface of the cell . Independent , random switching of many different phase variable genes encoding these surface structures leads to a combinatorial effect generating a huge number of alternate combinations of surface components . Phase variation , in conjunction with antigenic variation of the major antigen pili [38] , leads to evasion of host immune responses . The distinction between the phasevarion and typical phase variation of genes encoding surface factors is that the ON/OFF switching of the phasevarion methyltransferase mediates expression changes in multiple genes in a coordinated manner [15] . Our phylogenetic studies on the mod genes of a collection of pathogenic Neisseria strains reveal that differences in the DNA recognition domain within the mod gene results in distinct mod alleles . R-M systems show extreme diversity in their DNA sequence recognition specificities . DNA sequence specificity in type III R-M systems is conferred by the Mod subunit [14] . Based on differences in the DNA recognition domain , three major modA alleles were found - modA11 , modA12 , modA13 , and two distinct modB alleles were found - modB1 and modB2 . This suggests the possibility that multiple phasevarions exist within the pathogenic Neisseria , each regulating a different set of genes . Furthermore , as each strain has both modA and modB , and these genes switch independently , there are four potential combinations of mod gene expression ( ON/ON;ON/OFF;OFF/ON;OFF/OFF ) . We confirmed that two strains with the same DNA recognition domain ( modA13 allele ) regulated the same set of genes , while , N . meningitidis modA11 and modA12 were found to regulate the expression of different sets of genes , consistent with differences in their DNA recognition domain . In this study we also identified the recognition sequence for ModA13 as 5′-AGAAmA-3′ . In all , five randomly selected sites were tested for ModA13 inhibition of digestion in genomic DNA from modA13 ON and OFF strains . All five sites tested displayed the expected inhibition of digestion phenotype ( with either ApoI or HindIII ) , supporting the hypothesis that when expressed , ModA13 methylates all AGAAA sites in the genome , and thereby indicating this as the likely mechanism of gene control . Identification of the ModA13 target site is facilitating current studies on the molecular mechanism of regulation operating in the promoters of genes controlled by the modA13 phasevarion ( see Figure S7 ) . Gene regulation through the methylation of specific DNA sequences by methyltransferases has been reviewed [39] , [40] , but has focused on the role of Dam methylation . Dam methylation has been reported to be essential for bacterial virulence . In Salmonella species , dam mutants are highly attenuated for virulence and have been proposed as live vaccine candidates [41]–[43] . In addition , mutations in Dam attenuate the virulence of several other pathogens [44]–[46] . In these studies the mechanisms of attenuation ( genes regulated ) are unknown . In contrast , there are a few well-established examples of Dam mediated phase variation of genes encoding individual virulence factors[47]–[49] , for example the pyelonephritis-associated pilus ( pap ) operon in uropathogenic Escherichia coli [47] , [50] , [51] . The fundamental characteristic of these DNA-methylation-dependent phase variable systems is that the target site's methylation state affects the DNA binding of a regulatory protein , which directly regulates transcription . The key point to note is that the Dam methyltransferase itself does not phase vary , nor are there any examples of Dam itself being regulated by an environmental signal . These systems are not analogous to phasevarions , but do provide examples of how DNA methylation may alter gene expression at a bacterial promoter . In the case of N . meningitidis , most strains have been found to be dam negative [52] , as are all of the N . meningitidis and N . gonorrhoeae strains used in expression profile analysis and functional assay in this study ( result not shown ) . The question of whether the phase variable mod genes are associated with a functional type III restriction system remains to be fully resolved . In H . influenzae we have reported inactivating mutations in the res gene that is required for restriction function in strains containing phase variable mod genes [17] . We propose that in these cases the R-M system function has been lost and that the modA gene is dedicated to a gene regulation function . We have observed a similar inactivating mutation in the res gene associated with modA11 of N . meningitidis ( Table S2 ) , and in N . gonorrhoeae , a 250 aa in-frame deletion has been observed in the res gene associated with modA11 and modA12 ( see Table S3 ) , supporting a dedicated gene regulation function for mod genes in pathogenic Neisseria . Several of the genes regulated by the modA11 phasevarion of N . meningitidis strain MC58 are outer membrane proteins , including the vaccine candidates LbpA and LbpB . These are typical of the class of gene that have evolved phase variation mechanisms under immune selection . It is clear that phasevarion mediated phase variation of candidate vaccine antigens has the potential to mediate escape from a vaccine primed immune response . In contrast , none of the typical genes encoding outer membrane structures were influenced by the modA13 phasevarion in strain FA1090 . Instead , the genes under phasevarion control were involved in functions such as oxidative stress , antibiotic resistance , and transport of nutrients . For example , the MetFE operon , which plays a role in the methylation of homocysteine , the final step of methionine biosynthesis . MetE catalyses the methylation of homocysteine using a methyl group that is donated by the metF gene product , 5-methyltetrahydrofolate [53] . In E . coli , a correlation is shown between oxidative stress , methionine availability , and MetE , where MetE is inactivated under conditions of oxidative stress [22] . In addition , MtrF , which is required for high-level , hydrophobic agent-resistance that is mediated by the MtrC-MtrD-MtrE efflux pump [26] , [33] , is controlled by the modA13 phasevarion . The active efflux of antimicrobial agents from the cell by this systems is recognized as a major contributor to bacterial resistance to antibiotics [33] , [54] . Altered expression of this group of genes is more consistent with a switch between cell types that are more suited to alternate physical environments , rather than switching to enable evasion of a particular primed immune response . Phenotypic analyses of modA13 ON , modA13::kan mutant or modA13 OFF revealed distinct behavior in the model systems tested: modA13::kan and modA13 OFF mutant cells were superior at formation of a biofilm . Bacteria within biofilms display increased resistance to antimicrobial agents [35] , [36] . In addition , links between biofilm formation and oxidative stress defenses have been observed in N . gonorrhoeae [29] . Consistent with this observation , genes involved in oxidative stress and antimicrobial susceptibility were found to be up-regulated in expression in a modA13::kan mutant . Furthermore , a modA13::kan mutant was also found to be more resistant to antimicrobial agents in a Triton X-100 killing assay . Finally , the modA13::kan mutant and modA13 OFF strains were more fit in an intracellular survival assay in the pex model system , and this assay selected for a switch from ON to OFF during the course of the 3 hour assay . These observations are consistent with random generation of two populations containing different cell types with distinct niche specialization . The observation of common biofilm and intracellular survival phenotype in independent modA13 strains suggests that these can be attributed directly to modA13 phase variation , rather than an independent phase variation event in an unrelated gene , and that these may be key aspects of gonococcal - host interactions . This study shows that all modA alleles regulate gene expression of many genes , and that we observe distinct behavior of , and switching between , modA ON and OFF states in model systems . It is clear that any future study of pathogenic Neisseria that investigates gene expression or behavior of strains in model systems should take modA phase variation into account . Our initial microarray studies resulted in data with no statistically significant difference in the regulation of any gene . Changing the culture conditions to iron limitation resulted in the differential expression of 54 genes ( Table S5 ) . This change in expression was not due to differential growth rates ( Figure S4 ) or a direct effect of iron on expression of the Mod methyltransferase ( Figure S8 ) . Clearly , a difference in gene expression can only be detected if the genes in question are being expressed . It is well established that many genes are expressed under iron-limiting conditions in pathogenic Neisseria , via the Fur regulon , either directly , or due to cascade effects resulting from activation of the Fur regulon [55] , [56] . One limitation of the data presented in this study is that the gene expression profile analysis of the phasevarions was only done under one culture condition . Using different physiologically relevant conditions may enable other virulence-associated genes under the control of the phasevarion to be discovered . For example , it is established that the interaction of N . meningitidis with epithelial cells induces changes in the expression of 347 genes [57] . The results presented in this paper , in conjunction with our recent studies in H . influenzae [15] , [17] , provide confirmation of a role for phase variable mod genes associated with type III R-M systems in gene regulation . The widespread distribution of phase variable R-M systems in host-adapted pathogenic bacteria suggests that this novel mechanism of coordinated random switching of multiple genes may be a commonly used strategy for generation of distinct , “differentiated” , cell types with distinct niche specialization in host adapted bacterial pathogens .
N . meningitidis and N . gonorrhoeae strains were grown at 37°C with 5% CO2 in either GC broth or GC agar with IsoVitaleX ( Becton Dickinson ) . E . coli strains DH5α and JM109 ( Promega ) were used to propagate plasmids and were grown at 37°C in Luria-Bertani ( LB ) broth supplemented with either ampicillin ( 100 µg/ml ) or kanamycin ( 100 µg/ml ) . All enzymes were sourced from New England Biolabs . Sequencing was performed on PCR products using QiaQuick PCR purification kit ( Qiagen ) and Big-Dye ( Perkin Elmer ) sequencing kits . Data was analysed using MacVector v9 . 0 ( Accelrys ) . PCR products specific for the DNA recognition domain and repeat regions of modA and modB were generated using the primers listed in Table S7 . N . meningitidis isolates [58] and N . gonorrhoeae DGI and MI clinical isolates [59] were used as templates . The reaction was performed in 50 µl using 1× Taq buffer , 1 . 5 mM MgCl2 , and 1 unit of Taq DNA polymerase ( Promega ) with the following cycling conditions for the DNA recognition domain: 30 cycles of 94°C for 30 sec , 57°C for 30 sec , 72°C for 2 min and 1 cycle of 72°C for 7 min with 5 µM of the primer pair ModADRDF and ModADRDR for modA or ModBDRDF and ModBDRDR for modB . For modA , a 597 bp region containing the DNA recognition domain ( 393 bp downstream of ModADRDF and 101 bp upstream from ModADRDR ) was compared to the genome strains to determine the modA allele group . For modB a 537 bp region containing the DNA recognition domain ( 461 bp downstream from ModBDRDF and 285 bp upstream from ModBDRDR ) was compared to the genome strains to determine the modB allele group . The following cycling conditions were used for the repeat region: 30 cycles of 94°C for 30 sec , 57°C for 30 sec , 72°C for 30 sec and 1 cycle of 72°C for 7 min with 5 µM of the primer pair ModAF and ModAREPEATR or ModBREPEATF and ModBREPEATR . PCR products were cleaned using the QIAquick PCR Purification Kit ( Qiagen ) . PCR products specific for the res gene were generated using the primers listed in Table S7 . N . meningitidis isolates [58] and N . gonorrhoeae DGI and MI clinical isolates [59] were used as templates . The reaction was performed in 50 µl using 1× Taq buffer , 1 . 5 mM MgCl2 , and 1 unit of Taq DNA polymerase ( Promega ) with the following cycling conditions for the DRD: 30 cycles of 94°C for 30 sec , 55°C for 30 sec , 72°C for 3 min and 1 cycle of 72°C for 7 min with 5 µM of the primer pair ResF and ResEDR2 . PCR products were sequenced using ResF , ResR , ResEDF2 and ResEDR2 . The modA nucleotide sequences were assembled using the Staden sequence analysis package [60] and all sequences aligned manually in the Seqlab alignment program ( Genetics Computer Group , Madison , Wis . ) . Phylogenetic analysis was undertaken using the software package ClonalFrame version 1 . 1 , which is a statistical model-based method initially described for inferring bacterial clonal relationships using multilocus sequence data [61] . Inference is performed in a Bayesian framework and a neutral coalescent model is assumed based on the hypothesis that the bacteria in the sample come from a constant-sized population in which each bacterium is equally likely to reproduce , irrespective of its previous history . The key assumption of ClonalFrame is that recombination events introduce a constant rate of substitutions to a contiguous region of sequence with the end result that a clonal frame can be inferred . In the present study , over 50 , 000 iterations were performed with every hundredth tree sampled after which , a 95% majority-rule consensus tree was derived . ClonalFrame is available at available at http://bacteria . stats . ox . ac . uk . The modA gene is composed of relatively conserved N and C-terminal regions with the DNA recognition domain in between . Consequently , sequence input into ClonalFrame was undertaken by firstly adding the N-terminal region starting at bp 359 in the modA gene belonging to the reference N . meningitidis isolate MC58 , followed by the DNA recognition domain occurring from 416 to 795 bp and ending with the C-terminal region 796 to 1242 bp . Annotation was then undertaken by importing the tree into the Molecular Evolutionary Genetics Analysis software package ( MEGA ver 4 . 0 ) [62] . Plasmid pCmGFP ( Source M A Apicella ) was extracted from N . meningitidis strain C311 . Primers used to sequence this plasmid are listed in Table S7 . Sequencing reactions were prepared using the plasmid as template and Big-Dye sequencing kit ( Perkin-Elmer ) . Samples were analysed using a 3130×l Capillary Electrophoresis Genetic Analyser ( Applied Biosystems International ) . Data were analysed and plasmid map constructed using MacVector ( version 9 . 0 ) . The plasmid sequence is deposited in GeneBank under accession number FJ172221 ) . A modA::lacZ fusion was constructed in N . meningitidis MC58 . The gene fusion was initially constructed in E . coli with subsequent transformation into the N . meningitidis chromosome . In the fusion construct , the codons for LacZ are in the same translational frame as ModA resulting in an in-frame Mod-LacZ fusion protein . A 4 kb fragment of a promoterless lacZ::kan fragment was amplified by PCR using the primer pair LacZStyI1 and KanStyI . The plasmid pBluescriptlacZ::kan was used as template . Following digestion with StyI , the 4 . 0 kb lacZ::kan fragment was then ligated into the XbaI site of pGEMmodA . The ligation mixture was transformed into E . coli JM109 and transformants were selected on LB agar plates supplemented with kanamycin ( 50 µg/ml; Sigma ) . The orientation and sequence of the insert were checked and found to be correct . The resulting construct was named pGEMmodA::lacZ::kan . This plasmid was linearized with SacII and used to transform competent N . meningitidis . The MC58modA::lacZ::kan transformants were streaked on BHI plates containing Levinthal supplement and X-gal ( 40 µg/ml ) . The modA open reading frame ( ORF ) was amplified using PCR with primers ModAF and ModAR ( see Figure S1 ) . N . meningitidis strain MC58 was used as template . The PCR product was cloned into vector pGEM-Teasy ( Promega ) and named pGEMmodA . The pGEMmodA construct was digested with XbaI and blunt ended using Klenow Polymerase ( New England Biolabs ) . The Tn903 kan resistance gene from the pUC4K vector ( Pharmacia ) was excised using HincII and inserted into the blunt XbaI site . Previous work has demonstrated that the pUC4Kan kanamycin cassette has no promoter or terminator that is active in Neisseria and will neither affect transcription nor have a polar effect on expression of adjacent genes [29] . The resulting plasmid , pGEMmodA::kan was linearized by digestion with SphI and used to transform competent N . meningitidis strains MC58 and B6116/77 or N . gonorrhoeae strains FA1090 , O1G1370 or 96D551 . MC58 modA11::kan , B6116/77 modA12::kan , FA1090 modA13::kan O1G1370 modA13::kan and 96D551 modA12::kan transformants were selected on BHI plates containing Levinthal supplement and 100 µg/ml kanamycin . Transformants were confirmed by PCR and sequence analysis using primers ModAF2 and kanamycin specific primers . RNA midi-preps of both the wild-type ( MC58 modA11 ON , B6116/77 modA12 ON , FA1090 modA13 ON and O1G1370 modA13 ON ) and mutant ( MC58 modA11::kan , B6116/77 modA12::kan , FA1090 modA13::kan and O1G1370 modA13::kan ) were made using the RNeasy Midiprep kit ( Qiagen ) . Wild-type colonies , from which RNA was isolated for microarray analysis , were sequenced using primers ModAF and ModAREPEATR to check that the mod repeat region was in-frame . Triplicate cultures of N . meningitidis strain MC58 modA11 ON and the MC58 modA11::kan mutant , N . meningitidis strain B6116/77 modA12 ON and the B6116/77 modA12::kan mutant or N . gonorrhoeae strain FA1090 modA13 ON and the FA1090 modA13::kan mutant , O1G1370 modA13 ON and the O1G1370 modA13::kan and 96D551 modA12 ON and 96D551 modA12::kan mutant were grown to exponential phase ( optical density at 600 nm = 0 . 5 to 0 . 6 ) with 30 µM desferal ( Sigma ) in GC broth prior to RNA extraction . Growth rates of strain pairs used to make RNA for microarray comparison were determined ( Figure S4 ) and were found to be equivalent ensuring that the samples taken were in the same growth phase . Only 96D551 modA12 ON and 96D551 modA12::kan ( OFF ) strains showed a significant difference in growth rate ( see Figure S4 ) . Culture media for RNA preps was free of antibiotics as once the modA::kan mutation is transferred to the chromosome by double crossover we observed that it is stable without selection . Approximately 100 µg of total RNA was prepared from each sample using the RNeasy Maxi Kit according to the manufacturer's instructions ( Qiagen ) . The triplicate samples were pooled and the integrity and concentration of RNA was determined via micro-fluidic analysis on a bio-analyser ( Agilent Technologies ) . All microarray analysis was performed on N . gonorrhoeae/meningitidis genome arrays ( TIGR; http://pfgrc . tigr . org/ ) . Each microarray consists of 6 , 389 70mer oligonucleotides representing open reading frames ( ORFs ) from N . gonorrhoeae strains FA1090 and ATCC 700825 ( reference strain ) , as well as N . meningitidis strains Z2491 ( serogroup A ) and MC58 ( serogroup B ) . Methods and analysis were as previously described [29] . All primary data was imported into an in-house installation of the comprehensive microarray relational database , BASE ( accessible at: http://kidney . scgap . org/base login: Nmmod , password: Nmmod , login: NmmodA12 , password: NmmodA12 , login: NgmodA12 , password: NgmodA12 or login: Ngmod , password: Ngmod ) . Oligonucleotides ( Table S7 ) were designed using Primer Express 1 . 0 software ( ABI Prism; PE Biosystems ) and are named according to the ORF being amplified . All real-time PCR reactions were performed in a 25 µl mixture containing 1/5 volume of cDNA preparation ( 5 µl ) , 10XSYBR Green buffer ( PE Applied Biosystems ) and 2 µM of each primer . We used 16S RNA as the standard control in each quantitative PCR . Amplification and detection of specific products were performed with the ABI Prism 7700 sequence-detection system ( PE Applied Biosystems ) with the following cycle profile: 95°C for 10 min , followed by 45 cycles of 95°C for 15 sec and 60°C for 1 min . Data was analysed with ABI prism 7700 ( version 1 . 7 ) analysis software . Relative gene expression between the MC58 modA11::kan mutant and wild-type MC58 modA11 ON , N . meningitidis strain B6116/77 modA12 ON and the B6116/77modA12::kan mutant or the FA1090 modA13::kan mutant and wild-type FA1090 modA13 ON was determined using the 2ΔΔCT relative quantification method . Total RNA was isolated using the RNeasy kit ( Qiagen ) . The equivalent of 1 µg of the total RNA preparation was treated with RQ1 RNase-free DNase ( Promega ) . RT-PCR was performed using the TaqMan RT-PCR kit ( PE Applied Biosystems ) as recommended by the manufacturer . PCR was carried out in 50 µl using 1× Taq buffer , 1 . 5 mM MgCl2 , and 1 unit of Taq DNA polymerase ( Promega ) and cDNA amplified using gene specific primers designed for Real-time PCR ( Table S7 ) with the following cycling conditions: 30 cycles of 94°C for 30 s , 50°C for 30 s , 72°C for 30 s and 1 cycle of 72°C for 7 min . 16S rRNA internal standards for comparison were used with amplification resulting in a 200 bp RT-PCR product . PCR products ( 20 µl ) were run on a 3% agarose gel . Growth experiments were carried out in GC medium supplemented with IsoVitaleX , at 37°C with 5% CO2 , under iron-limiting conditions ( 30 µM desferal ) . Triplicate cultures of the strain pairs being compared were adjusted to an identical initial OD600 . One milliliter of culture was removed at fixed times to measure the OD600 . Wild-type MC58 modA11 ON , wild-type MC58 modA11 OFF and MC58 modA11::kan mutant bacterial cells were grown under iron-limiting conditions to an optical density at 600 nm = 0 . 55–0 . 6 . Cells were spun down at 5000 rpm for 5 min and then washed once in PBS , pH 7 . 2 . Cells were then re-suspended in PBS to an optical density at 600 nm = 2 . 5 and separation was carried out on a 4–12% Nu-PAGE Novex Bis-Tris gel ( Invitrogen ) according to the manufacturer's instructions . The Nu-PAGE semi-dry system was used to transfer protein from gel to nitrocellulose membrane ( 0 . 22 µM pore , Bio-Rad ) , as recommended by Invitrogen . Immunoblotting of membranes was carried out in a 1∶2000 dilution of LbpA specific monoclonal 269-H1 [19] in 5% skimmed milk powder in TBS . Bands were visualized following incubation in 1∶5000 dilution of alkaline phosphatase-conjugated anti-mouse IgG secondary antibody ( Sigma ) . An lbpB::lacZ fusion was constructed in N . meningitidis strain MC58 . The gene fusion was initially constructed in E . coli with subsequent transformation into the N . meningitidis chromosome . In the fusion construct , the codons for LacZ are in the same translational frame as lbpB resulting in an in-frame LbpB-LacZ fusion protein . A 1 . 7 kb DNA fragment was amplified by PCR using the primer pair LbpBF and LbpBR . MC58 was used as the template . The reaction was performed in 50 µl using 1× Taq buffer , 1 . 5 mM MgCl2 , and 1 unit of Taq DNA polymerase ( Promega ) with the following cycling conditions: 30 cycles of 94°C for 30 sec , 57°C for 30 sec , 72°C for 1 min and 1 cycle of 72°C for 7 min . The fragment was then cloned into vector pGEM-Teasy ( Promega ) . A 4 kb fragment of a promoterless lacZ::kan fragment was amplified by PCR using the primer pair LacZStyI+1 and KanStyI . The plasmid pBluescriptlacZ::kan ( M . Dieckelman , personal communication ) was used as template . Following digestion with StyI , the 4 . 0 kb lacZ::kan fragment was blunted using Klenow Polymerase and then inserted into the EcoRV site of the lbpB construct . The ligation mixture was transformed into E . coli JM109 and transformants were selected on LB agar plates supplemented with kanamycin ( 50 µg/ml ) . The orientation and sequence of the insert were checked and found to be correct . The resulting construct was named pGEMlbpB::lacZ::kan . This plasmid was linearized with NcoI and used to transform competent N . meningitidis strain MC58 with a naturally derived number of mod ON and OFF repeats . The MC58lbpB::lacZ::kan mod ON and MC58lbpB::lacZ::kan mod OFF transformants were streaked on BHI plates containing Levinthal supplement and X-gal ( 5-bromo-4-chloro-3-indolyl-D-galactopyranoside; 40 µg/ml ) . MC58lbpB::lacZ::kan modA11 OFF and MC58lbpB::lacZ::kan modA11 ON strains were grown on GC plates with 15 µM desferal at 37°C over night . The next day triplicate cultures of iron-starved strains were grown to exponential phase ( optical density at 600 nm = 0 . 55 to 0 . 6 ) with 30 µM desferal in GC broth . Cells were spun down at 15 , 000×g for 10 min , resuspended in PBS and lysed by repeated freeze-thaw cycles . The cells debris was spun down at 15 , 000×g for 5 min . The amount of protein was calculated by using the BCA protein assay reagent kit ( Pierce ) . The amount of β-galactosidase in the cell extracts was measured in Miller units , in triplicate , as described [64] . Miller units were calculated as follows: Units ( 1000×A420 ) / ( t×v×C ) , where t is the time of the assay ( in mins ) , v is the volume of cell extract used in the assay , and C is the total protein concentration ( in µg/ml ) . The antimicrobial resistance assay was adapted from a method described by Dougherty et al . [65] . In brief , N . gonorrhoeae FA1090 wild-type modA13 ON and FA1090 modA13::kan mutant colonies were re-suspended in PBS to a density of 106 colony forming units ( CFUs ) , and 5 µl of serial ten-fold dilutions were spotted in triplicate onto GC agar plates containing 15 µM desferal , supplemented with IsoVitaleX and increasing concentrations of Triton X-100 ( 40 , 50 , 60 , and 80 µg/ml ) . The plates were then incubated at 37°C under 5% CO2 for 24 h . Colony counts were used to compare wild-type FA1090 modA13 ON to the FA1090 modA13::kan mutant by plating each dilution in triplicate . The experiment was repeated on three separate occasions . The ratio of FA1090 modA13 ON to FA1090 modA13 OFF at the following concentrations of Triton X-100 ( 40 , 50 and 60 ug/ml ) was calculated as follows . Colonies were taken from the triplicate samples of the original inoculum and each of the increasing concentrations of Triton X-100 from FA1090 modA13 ON and used as PCR template . The percentage of modA13 ON and modA13 OFF from the starting inoculum and the three different Triton X-100 concentrations was verified via fragment analysis [17] using primers ModAF6Fam and ModAREPEATR ( Table S7 ) . A Student's t-test was used to determine the statistical significance between the percentage of modA13 ON and modA13 OFF from the original inoculum of modA13 ON and the percentage of modA13 ON and modA13 OFF from the three different Triton X-100 of modA13 ON . For examination of biofilm formation via confocal microscopy , the N . gonorrhoeae FA1090 modA13::kan and wild-type FA1090 modA13 ON strains and N . gonorrhoeae strains O1G1370 modA13::kan , modA13 OFF and modA13 ON were transformed with a plasmid encoding a green fluorescent protein , pCmGFP . Formation and analysis of biofilms was as described previously , except the cells were grown under the same iron-limiting conditions as for the microarray analysis [29] . Colonies used to inoculate cultures for biofilm assays were assessed for morphology to ensure equivalent level of piliation . Biofilms images are three-dimensional reconstructions of stacked z-series taken at 200× magnification , which were rendered by Volocity . Biofilms of N . gonorrhoeae strain FA1090 modA13::kan and modA13 ON and N . gonorrhoeae strains O1G1370 modA13::kan , modA13 OFF and modA13 ON were grown at in glass flow chambers at 37°C and a flow rate of 180 µl/min in 1∶10 GC broth diluted in PBS with 10 ml/L IsoVitaleX , 3 µM desferal , and 100 µM sodium nitrite . The modA13 status of the starting inoculum was verified via fragment analysis [17] using primers ModAF6Fam and ModAREPEATR . After 48 hours of growth , biofilms were prepared for scanning electron microscopy ( SEM ) and transmission electron microscopy ( TEM ) as follows . Glass coverslips , which served as the surface of attachment for biofilm , were removed from the chambers and fixed in 1% osmium perfluorocarbon for 1 h . The coverslips were then gently rinsed for 15 min with pure perfluorocarbon three times . To avoid destruction of the biofilm , rinse solution was gently added to coverslips in a 100 mm Petri dish , allowed to incubate at room temperature for 15 min , then the rinse was aspirated and another rinse was applied . The samples were then dehydrated with 100% ethanol by performing another three 15 min rinses . At this point , the coverslips were cut in half and one half was processed for SEM , while the other half was processed for TEM . SEM samples were transitioned into HMDS for two 15 min washes and then allowed to air dry . SEM samples were then sputter-coated and viewed with the Hitachi S-4800 SEM . TEM samples were infiltrated with a 50% Eponate-12 resin ( epon ) in ethanol for 1 h . The coverslips were then inverted and imbedded in 100% epon at 42°C overnight . Thin-sections ( 70 nm ) were prepared on an ultramicrotome , mounted on a grid , and then stained with uranyl acetate and lead citrate . TEM samples were viewed with the JEOL 1230 TEM . Surgical cervical biopsies were used to seed primary cervical epithelial ( pex ) cell cultures and were procured and maintained as described previously [32] . Quantitative association , invasion , and survival assays were performed as previously described using a multiplicity of infection of 100 [32] with modification as follows . Our previous studies demonstrate that pex cells produce a full alternative pathway of complement , and that iC3b serves as a critical opsonin for CR3-mediated gonococcus adherence to and invasion of these cells . Thereby , antibiotic-free medium was harvested from uninfected pex cell monolayers and treated overnight with 30 µM desferal ( Sigma ) . Our previous ( unpublished ) studies have revealed that N . gonorrhoeae strain FA1090 uniquely becomes cytotoxic to human , primary cervical and male urethral epithelial cells within 2 to 3 hours post-challenge , which prohibits their confident use in gentamicin survival assays for time periods totaling greater than 90 min . Therefore , N . gonorrhoeae strains O1G1370 modA13 ON , O1G1370 modA13 OFF , and the O1G1370 modA13::kan mutant , and 1291 modA13 ON , 1291 modA13 OFF , and 1291modA13::kan were selected to elucidate the role of mod-dependent phase variation during pex cell challenge . Complement-containing , iron-depleted , primed medium was inoculated with 5×106 gonococci per ml . Colonies used to inoculate cultures for these assays were assessed for morphology to ensure equivalent level of piliation . Bacterial cultures were incubated ( 37°C , with shaking ) for 2 h , after which the optical density of the gonococcal cultures was adjusted to 107 gonococci per ml and directly used to challenge ( new ) pex cell monolayers . Pex cell infections were then allowed to progress at 37°C , 5% CO2 . Association ( 90 min infection ) , invasion ( 90 min infection plus a 30 min incubation in 100 µg/ml gentamicin ) , and survival ( 90 min infection , 30 min gentamicin treatment , plus a 3 h incubation in antibiotic-free medium ) assays were performed using a modified gentamicin-resistance assay as described previously [32] . Serial dilutions of the cervical cell lysates were plated to determine CFUs . The percent association , invasion , and survival were determined as functions of the original inoculum . From these data the invasion and survival indices were determine as follows: Invasion index , percent invasion/percent association; Survival index , percent survival/percent invasion . P-values were determined for the actual data points using a Kruskal-Wallis non-parametric analysis of variance . A Student's t-test was used to determine the statistical significance of the invasion and survival indices . The ratio of modA13 ON and modA13 OFF within the O1G1370 modA13 ON and O1G1370 modA13 OFF original inoculum , association , invasion , and survival time points were determined as follows . Samples were taken from the original inoculum , association , invasion , and survival time points from three independent assays and chromosomal DNA extracted . The percentage of modA13 ON and modA13 OFF from the starting inoculum was verified via fragment analysis [17] using primers ModAF6Fam and ModAREPEATR ( Table S7 ) . A Student's t-test was used to determine the statistical significance between the percentage of modA13 ON and modA13 OFF from the original inoculum of O1G1370 modA13 ON and the percentage of modA13 ON and modA13 OFF from the invasion time point and survival time point of O1G1370 modA13 ON . Similarly , a Student's t-test was used to determine statistical significance between the percentage of modA13 ON and modA13 OFF from the original inoculum of O1G1370 modA13 OFF and the percentage of modA13 ON and modA13 OFF from the invasion time point and survival time point of O1G1370 modA13 OFF . The mod gene was amplified from H . influenzae strain Rd chromosomal DNA using primers listed in Table S7 . The NcoI restriction site at the 5′ end and the BamHI site at the 3′ end of the mod gene were introduced . The resulting PCR fragment was subsequently digested with NcoI and BamHI and cloned into the digested pET16b expression vector ( Novagen & EMD , San Diego , CA , USA ) carrying the same enzyme cutting sites , leading to the construct , pET16b::mod . The sequence of the insert was confirmed and then used for generating the recombinant Mod protein with the ( His ) 10-tag ( MGHHHHHHHHHH ) attached at the N-terminal end . For generating the recombinant Mod protein , the construct , pET16b::mod , was transformed into E . coli strain BL21 ( DE3 ) and the cells were grown in LB broth at 20°C . Induction of the expression was initiated by adding IPTG to the final concentration of 0 . 1 mM and then incubated at 10°C for 3 days . After harvesting the bacteria by centrifuging at 6 , 000 rpm for 30 min at 4°C , the bacterial pellet was lysed with the lysis buffer ( 25 mM Tris-HCl , 300 mM KCl , 5 mM imidazole , pH 7 . 5 ) plus protease inhibitor , Complete cocktail EDTA-free ( Roche , Switzerland ) . Soluble proteins were obtained from the supernatant by centrifuging at 20 , 000 rpm for 20 min at 4°C to remove the cell debris and precipitates . The Mod protein was purified using the Ni2+-nitilotriacetic acid ( Ni-NTA ) column ( Amersham Biosciences , Piscataway , NJ , USA ) with an elution gradient from 25–500 mM imidazole in the buffer solution ( 25 mM Tris-HCl , 300 mM KCl , pH 7 . 5 ) . The purity of the eluted protein was examined by SDS–PAGE analysis and the concentration determined by Bio-Rad Protein Assay ( Bio-Rad , Hercules , CA , USA ) . The pure fractions were collected and transferred to 25 mM Tris , pH 7 . 5 by the HiPrep 26/10 Desalting column ( Amersham Biosciences , USA ) and store at −80°C . Rabbits ( New Zealand White strain , weighing 3–3 . 5 kg , were immunized by intrasplenic injection with the soluble recombinant Mod protein at 300 µg per immunization . The antigen was administered together with an equal amount of Gold TiterMax adjuvant ( CytRx , Norcross , GA , USA ) . The rabbit antisera were collected from weeks 4∼9 and the titers of rabbit sera from weeks 4∼6 were analyzed using Western blot assays . Antiserum of week 6 had a high titer of 5 , 000 , 000 against 1 µg of the Mod protein . The antisera recognized a single band in wild type modA ON H . influenzae strain RD and N . meningitidis strain MC58 , but not in their corresponding modA::kan mutants ( not shown ) . For the subsequent Western blot experiments , 1/1 , 000 dilution of the antiserum of week 6 was used . | The pathogenic Neisseria are bacterial pathogens that cause meningitis and gonorrhoea . They have adapted to life exclusively in humans and have developed unique strategies to colonize the host and to evade the immune response . Central among these strategies are genetic switches that randomly turn genes on and off . In most cases , the genes controlled by these switches , contingency genes , are required for making bacterial surface structures . Recently we described a new class of contingency gene that methylates DNA . Rather than affecting the synthesis of a single surface structure , on/off switching of this DNA-methyltransferase gene leads to random switching of multiple genes . In this study , we have shown that this mechanism exists in all pathogenic Neisseria , and alters expression of multiple genes in all cases we examined . The two distinct populations of bacteria generated by this process had different behavior in model systems of colonization and infection . Understanding this process is key to understanding these human pathogens , and to developing strategies for treatment and prevention of the diseases they cause . |
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Microorganisms exist almost exclusively in interactive multispecies communities , but genetic determinants of the fitness of interacting bacteria , and accessible adaptive pathways , remain uncharacterized . Here , using a two-species system , we studied the antagonism of Pseudomonas aeruginosa against Escherichia coli . Our unbiased genome-scale approach enabled us to identify multiple factors that explained the entire antagonism observed . We discovered both forms of ecological competition–sequestration of iron led to exploitative competition , while phenazine exposure engendered interference competition . We used laboratory evolution to discover adaptive evolutionary trajectories in our system . In the presence of P . aeruginosa toxins , E . coli populations showed parallel molecular evolution and adaptive convergence at the gene-level . The multiple resistance pathways discovered provide novel insights into mechanisms of toxin entry and activity . Our study reveals the molecular complexity of a simple two-species interaction , an important first-step in the application of systems biology to detailed molecular dissection of interactions within native microbiomes .
Microorganisms are typically found in complex communities such as those in the soil , aquatic environments , and the microbiome [1] , and interactions between microbial species can critically impact their survival and evolutionary trajectories [1 , 2] . Current knowledge suggests that competition plays an important role in interspecies microbial interactions [3 , 4] . This includes both exploitative competition , where species compete for limited nutrients , as well as interference competition , where species directly antagonize each other [5] . However , such ecological processes are understudied and poorly characterized in microbial systems [2] . Previous studies have identified molecules produced by bacteria that may affect the behavior or fitness of other species . Such molecules could be beneficial to the target species [6] , but a wide variety of them have been shown to be antagonistic in nature [7–9] . In most cases , such studies have looked at a single molecule or class of molecules , and the potential effects these could have on exogenous bacteria . However , the entire breadth of interactions that actually determines fitness in a specific multispecies system has rarely been identified and characterized at the molecular level . As communities are established , bacteria evolve in response to the biotic and abiotic challenges present . Although adaptation to various physicochemical stresses has been widely studied ( for example [10] ) , the mechanisms that underlie adaptation to interspecies competition remain largely unknown . The immediate cellular effect of toxic exoproducts on target bacteria has been described for some interactions , but how target populations can evolve upon such exposure to combat the antimicrobials has not been studied . Here we systematically dissect interactions in a two-species bacterial system containing P . aeruginosa and E . coli . P . aeruginosa , an opportunistic pathogen , is frequently found in multi-species infections [11 , 12] and is capable of interacting with other microorganisms via a variety of antimicrobial molecules [8] . The other interacting partner , E . coli , is a commensal and the best studied bacterial species , which we utilized as a model target organism . Our genome-scale analyses of this two-species bacterial system identified both interference and exploitative competition , mediated by multiple molecules in the antagonistic species , which explained all of the observed competition . We also discovered several diverse genetic determinants of resistance in the target species , gaining insights into the properties of adaptive trajectories in the face of interspecies competition .
We studied a bi-species system containing P . aeruginosa and E . coli in planktonic culture ( using media conditions in which they have very similar growth rates ) . We tested whether any interactions are seen between these species , by measuring the relative fitness of wild-type ( WT ) E . coli and P . aeruginosa in direct competition with each other . E . coli cells were found to have a relative fitness of 0 . 2 ( ± 0 . 09 ) , which is significantly less than 1 , revealing that P . aeruginosa was inhibiting E . coli growth . Further , E . coli cells showed substantial reduction in growth upon exposure to P . aeruginosa spent media for a few hours ( Fig 1 ) , indicating that , at least part of the P . aeruginosa antagonism was mediated by secreted molecules . Each of the conditions in this experiment had the same volume of fresh media ( 50% ) to enable comparison across conditions , and the remainder was made up of the appropriate volume of spent media added to the media salts base . E . coli spent media did not have a significant effect on P . aeruginosa growth ( S1 Fig ) . We determined the global transcriptional response of E . coli to P . aeruginosa spent media , and identified the Gene Ontology ( GO ) annotations enriched ( and depleted ) across the full range of change in gene expression , using iPAGE , a mutual-information based pathway analysis tool [13] . One of the most strongly induced pathways was iron transport ( Fig 2 ) . P . aeruginosa is known to secrete two siderophores , pyoverdine and pyochelin , that chelate iron and transport it inside the cell [14] . Although these molecules are thought to have evolved primarily for iron acquisition by the producer , they may also limit iron-availability for other microbial species within the community . P . aeruginosa spent media also induced several amino acid biosynthesis pathways , while repressing genes involved in core cellular processes such as ribosomal translation , nucleotide biosynthesis , ATP synthesis , and the electron transport chain ( Fig 2 ) . These changes are similar to those produced during the stringent response , which is known to be induced by iron starvation [15 , 16] . Additionally , genes involved in ciliary or flagellar motility are also upregulated , which may represent an adaptive response to enable migration away from competition or to niches with higher iron availability . Mass spectrometry ( both MALDI and ESI ) on whole spent media from WT P . aeruginosa revealed a major component that had an m/z of 1335 Daltons ( S2 Fig ) , which matches the molecular mass of pyoverdine [17 , 18] . We also fractionated the spent media by reverse-phase HPLC using an acetonitrile-water gradient , and tested the fractions for growth-inhibitory activity against E . coli . While some bioactive fractions showed a complex mass-spectrometry profile making it difficult to identify the active component , the main component in one of the active fractions also had an m/z of 1335 Daltons ( S3 Fig ) . Consistent with the iron-sequestration mechanism of competition , the addition of surplus iron partially alleviated the growth inhibition of E . coli by P . aeruginosa spent media in a concentration-dependent manner ( Fig 3A ) . Iron supplementation also caused a marginal 1 . 4-fold increase in growth under control conditions ( S4 Fig ) , with the effect saturating at 10μM ferric citrate supplementation . This mild iron limitation in the growth media does not account for the up to 27-fold increase in growth caused by iron supplementation in the presence of P . aeruginosa spent media , indicating that the spent media was causing the significant iron limitation seen . Further , deletion of genes encoding key enzymes in the pyoverdine ( pvdJ ) and pyochelin ( pchE ) biosynthetic pathways , singly and in combination , caused significantly lower growth inhibition of E . coli than the WT ( Fig 3B ) . E . coli also had higher relative fitness in competition with a P . aeruginosa siderophore double mutant , compared to WT ( Fig 4 ) , confirming that iron-limitation by the P . aeruginosa siderophores pyoverdine and pyochelin engenders exploitative competition and inhibits the growth of E . coli , in our system . One of the major P . aeruginosa quorum sensing molecules , PQS ( Pseudomonas Quinolone Signal ) , is also known to chelate ferric ions [19 , 20] . We tested a deletion mutant for a gene encoding a key enzyme in the PQS biosynthesis pathway ( pqsA ) . Spent media from the ΔpqsA mutant as well as a ΔpvdJ ΔpchE ΔpqsA mutant caused significantly lower growth inhibition of E . coli compared to the WT ( Fig 5 ) , and E . coli had higher relative fitness in competition with these mutants as compared to WT P . aeruginosa ( Fig 4 ) demonstrating that the PQS pathway is also involved in the antagonism . PQS not only chelates iron , but also induces a wide range of virulence factors such as hydrogen cyanide , rhamnolipids , lectin , and phenazines [21] , via the PQS-response protein PqsE . A ΔpqsE mutant was deficient in inhibiting E . coli growth ( Fig 5 ) , indicating that the PQS pathway molecules inhibited E . coli growth indirectly , likely through the expression of one or more virulence factors . To identify these factor ( s ) , we determined the transcriptional response of E . coli to WT P . aeruginosa spent media supplemented with ferric citrate ( to eliminate the effect of iron-limitation ) , and analyzed the results using iPAGE [13] . The ‘transcription factor regulon’ module in iPAGE [22] identified the SoxRS regulon as being enriched in the upregulated genes ( Fig 6 ) . Interestingly , soxS has recently been shown to be upregulated in E . coli in response to several biotic stresses including a Vibrio cholera strain known to kill E . coli , the P1vir bacteriophage , and the antimicrobial peptide Polymyxin B , likely to protect against reactive oxygen species generated due to these stresses [23] . The P . aeruginosa secondary metabolite pyocyanin ( the terminal phenazine molecule ) is known to upregulate the soxS-response in P . aeruginosa [24] . Further , phenazines are known to be PQS-induced , and to have antimicrobial properties , likely due to the production of reactive oxygen species or inhibition of bacterial respiration [25–27] . We thus hypothesized that the phenazine pathway was responsible for the PQS-mediated growth inhibition of E . coli . Spent media from a mutant lacking both copies of the phenazine biosynthesis operon ( Δphz1/2 ) , as well as a ΔpvdJ ΔpchE Δphz1/2 mutant , showed significantly reduced E . coli growth inhibition ( Fig 7A ) , and E . coli had higher relative fitness in competition with these mutants ( Fig 4 ) , compared to the WT . Further , pyocyanin , the terminal phenazine molecule , caused concentration-dependent growth inhibition of E . coli ( Fig 7B ) , confirming that phenazine molecules directly inhibit E . coli growth via interference competition . The concentration of pyocyanin used in our experiments ( 25–100μM ) covers the range of measured pyocyanin concentrations in the growth media of PA14 ( 30–60μM ) in both minimal media as well as LB [24] . Importantly , E . coli had a relative fitness of almost 1 in competition with P . aeruginosa strains deficient for both siderophores and phenazines ( Fig 4 ) , demonstrating that these molecules account for the entirety of the measurable P . aeruginosa antagonism against E . coli . Thus , using unbiased genomic level approaches , we have identified pathways by which P . aeruginosa inhibits the growth of E . coli in the conditions under study , via both exploitative and interference competition . P . aeruginosa limits iron availability in the environment , thereby shutting down most core cellular processes in E . coli cells . The phenazine molecules further limit E . coli growth possibly by inhibiting cellular respiration [25 , 26] , and inducing the production of reactive oxygen radicals in the E . coli cells that are still able to respire aerobically [27] . Despite identification of individual molecules that can mediate competitive interspecies interactions , the mechanisms of adaptation to such competition and the attributes of the adaptive solutions have remained largely unstudied . To identify pathways by which E . coli can resist P . aeruginosa antimicrobials , we carried out laboratory evolution of E . coli in the presence of either WT spent media , ΔpvdJ spent media , or pyocyanin . We exposed WT E . coli to increasing concentrations of the spent media or pyocyanin , and performed 7–19 daily transfers into the selective media ( the transfers were stopped when the cultures did not show significantly improved growth under the selective condition for two consecutive days ) . We then carried out whole genome sequencing on 2 or 3 clones each from 2 or 3 populations evolved under each condition . All the evolved clones had between 1–9 mutations , with recurring mutations in mprA in the spent-media selected clones , and in fpr and ompC in the pyocyanin-selected clones ( S1 Table ) . The transcriptional repressor mprA negatively regulates the expression of the multidrug resistance ( MDR ) pump EmrAB [28] , and is also predicted to regulate the MDR pump AcrAB [29] . We identified mutations in mprA in all sequenced clones from 5 independent populations selected against either WT P . aeruginosa or ΔpvdJ spent media ( S1 Table ) . While the mutations were identical within a population , only 2 populations showed a common mutation–a single base-pair deletion at position 446 of the gene ( henceforth referred to as mprA* ) . Thus , while there is little parallelism at the level of individual mutations , adaptive convergence is extensive at the gene level , a phenomenon seen previously in E . coli [10 , 30] . All the mutations identified in mprA were either non-synonymous or resulted in a frameshift mutation ( S1 Table ) in different parts of the gene , indicative of a hypomorphic ( as opposed to a hyper- or neo-morphic ) phenotype . We transferred the mprA* allele to the WT parental background and tested it along with an mprA deletion mutant against WT P . aeruginosa spent media . Surprisingly , although the mprA* substituted strain showed significant resistance to WT P . aeruginosa spent media , the ΔmprA mutant did not show either increased resistance or sensitivity ( Fig 8A ) . This suggested that either there is some compensatory regulation in the ΔmprA mutant , or the adaptive alleles are neomorphic , and not simple hypomorphs . The mprA mutants ( as well as the fpr and ompC mutants described below ) did not show a significant difference compared to the WT under control conditions ( S5 Fig ) . Interestingly , mprA was repressed 2 . 5-fold in E . coli exposed to P . aeruginosa spent media supplemented with ferric citrate ( in the transcriptional response measurements described above ) , which might be an adaptive response to exposure to antimicrobials , resulting in the upregulation of efflux pumps . Parallel evolution at the gene level was also seen in the two independent populations selected against pyocyanin–both had mutations in the fpr and ompC genes ( S1 Table ) . The fpr gene codes for the flavodoxin NADP+ reductase enzyme that transfers electrons between flavodoxin and NADPH , and is required for the activation of anaerobic ribonucleoside reductase , pyruvate-formate lyase and methionine synthase [31] . We identified multiple alleles of fpr in our strains ( S1 Table ) , and further studied both the synonymous mutation common in population pyo1 ( henceforth called fpr1* ) as well as the non-synonymous mutation common in population pyo2 ( henceforth called fpr2* ) . We transferred both alleles to the parental background , and tested them along with an fpr deletion strain . Interestingly , both fpr1* and fpr2* , but not Δfpr , showed increased resistance to pyocyanin ( Fig 8B ) , confirming that even the fpr1* synonymous mutation had a significant phenotypic effect . Further , the Δfpr mutant showed increased sensitivity to pyocyanin at lower concentrations , compared to the WT parental strain ( Fig 8C ) . Thus , it is likely that both the synonymous fpr1* and the non-synonymous fpr2* are hypermorphic alleles , and an increase in Fpr activity can lead to pyocyanin resistance . Pyocyanin inhibits respiration in target cells [26] , which could lead to the induction of metabolic pathways that normally function under anaerobiosis , and our results suggest that Fpr activity is a limiting step for growth under these conditions . The expression of fpr is induced 30-fold in E . coli exposed to P . aeruginosa spent media supplemented with ferric citrate ( in the transcriptional response measurements described above ) , which is likely a response by E . coli cells to the perceived anaerobic conditions created by pyocyanin exposure . The ompC gene codes for one of the two main porins in E . coli which allow for the influx of mostly hydrophilic small molecules across the outer membrane [32] . We identified 2 different alleles of ompC in the 2 independent populations evolved in the presence of pyocyanin . The allele in the pyo1 population ( henceforth referred to as ompC* ) results in an early stop at position 54 , while the allele in the pyo2 population has a frameshift that also leads to an early stop after 6 additional amino acids . Both the ompC* allele and an ompC deletion , in the parental background , provided significant resistance against pyocyanin , with the deletion showing approximately 2-fold higher resistance ( Fig 8D ) , suggesting that the ompC* allele is a hypomorph . Thus , it is likely that pyocyanin enters target E . coli cells via the OmpC porin , and modulation of this protein can lead to pyocyanin resistance . Additionally , a double mutant carrying both the ompC* and fpr2* alleles had significantly higher resistance to pyocyanin than either of the single mutants or the ΔompC mutant ( Fig 8D ) . This indicates that some pyocyanin can enter the cell even in the absence of OmpC , and increased Fpr activity can provide further resistance .
Our study of a P . aeruginosa–E . coli two-species system utilized genome-scale methods to identify the pathways and molecules that underlie all the various components of the observed P . aeruginosa antagonism against E . coli . Specific molecules that could have an effect on exogenous species under certain conditions have been identified previously , and these include siderophores [33] and phenazine molecules [25] , among various others . However , here we used unbiased , agnostic methods such as HPLC and mass-spectrometry based identification of bioactive molecules , as well as measurement and computational analyses of transcriptional responses , to comprehensively characterize the specific competitive interactions present in our bi-species system under the particular conditions of our study . Such approaches can be easily carried out in less well-characterized bacterial species , thus accelerating research into the study of other basic and biomedically relevant bacterial interactions . Furthermore , the use of E . coli as a model “target” organism can also aid in the discovery of molecules underlying interspecies interactions , the immediate molecular responses elicited in target bacteria , as well as potential pathways of adaptation to such interactions . P . aeruginosa is known to produce a wide variety of small antimicrobial molecules [8] , and our results underscore the multi-pronged mode of its microbial antagonism . The combination of both interference and exploitative competition seen in a single interaction suggests that P . aeruginosa encounters other microbial species frequently in its natural habitats , and has evolved a variety of strategies to compete with this microbial diversity . In our system , sequestration of iron limits the ability of other species to carry out basic cellular processes including respiration and DNA synthesis . The target cells that are still able to grow are further exposed to phenazine molecules , which are thought to target the electron transport chain and inhibit respiration [25 , 26 , 34] . Lastly , the subset of target cells that are still able to carry out aerobic respiration under these conditions are then likely to be subjected to cellular toxicity due to the production of reactive oxygen radicals by the phenazine molecules [27] . Iron is a scarce resource in many environments , and competition for iron is likely to be crucial in most communities . The pyoverdine biosynthesis locus is the most divergent alignable locus in the P . aeruginosa genome [35] , likely due to evolutionary pressure to counter siderophore piracy by “cheater” strains , as well as for protection against the pyocin S3 [36] . Our results suggest that it plays a role in interspecies competition as well . Regulation of pyoverdine production is dependent on iron levels , although other factors also modulate this regulation [37 , 38] . Since P . aeruginosa is known to detect and respond to the presence of other species [39] , and competition is thought to have shaped bacterial regulatory networks [4] , it is an intriguing possibility that the induction of siderophore production may be dependent on sensing foreign species , to inhibit niche invasion . Quorum-sensing pathways and molecules were originally thought to regulate population behavior within a species , but more recently , these molecules have been shown to have other functions , including the modulation of behaviors of exogenous species by quorum-sensing interference [40] , or the regulation of antimicrobial production [41] . P . aeruginosa has multiple quorum-sensing pathways , and our results show that the PQS-pathway is important for interference competition by inducing the production of antimicrobial molecules such as the phenazines . Interestingly , while mutations in the lasR quorum-sensing pathway are frequently found in P . aeruginosa isolates from chronic infections [42] , PQS pathway mutants have not been seen , and isolates from cystic fibrosis infections may even over-produce PQS-pathway molecules [43–45] . Thus , despite the complex interconnected quorum-sensing pathways in P . aeruginosa , there might be a separation of functions between the PQS and the classical homoserine lactone pathways , based on their roles in virulence and microbial antagonism . While antagonistic molecules produced by various species have been identified in competitive interactions , we have limited knowledge about adaptation to such competition . Our laboratory evolution experiments identified adaptive mutations in mprA , fpr and ompC , demonstrating that there are multiple pathways to combat interspecies competition . Additionally , even though we evolved only a few populations in each specific condition , we still observed parallel evolution and adaptive convergence at the gene level , with multiple independent mutations in each of the above three genes , supporting the idea that these are critical genetic determinants of resistance to P . aeruginosa antimicrobials . Laboratory evolution also revealed the OmpC porin to be a major means of pyocyanin entry into target cells , suggesting that this antimicrobial pathway takes advantage of endogenous membrane permeability routes to enter the cell . The Fpr protein is known to be important for activation of the anaerobic ribonucleoside reductase , pyruvate-formate lyase , and methionine synthase [31] , and ribonucleoside reductase is the rate-limiting step for DNA synthesis [46] . The modulation of pyocyanin resistance by Fpr suggests that by inhibiting respiration [26] , pyocyanin likely causes cellular metabolism to shift to anaerobic pathways , and the activation of anaerobic ribonucleoside reductase underlies the role of Fpr in acquisition of pyocyanin resistance . Mutants lacking Fpr have also been shown to have increased sensitivity to paraquat , which is a redox-cycling drug , and mutants that overproduce this protein are resistant to this drug [31] , further supporting the notion that Fpr activity is rate limiting for anaerobic growth . The MprA transcriptional repressor is known to negatively regulate the transcription of the MDR pumps [28 , 29] , and non-synonymous mutations in this gene have been shown to confer resistance against compounds such as thiolactomycin and CCCP [28] . Thus a loss-of-function mutation in mprA could likely result in resistance to the P . aeruginosa antimicrobials . However , the lack of a phenotype seen in the ΔmprA mutant implies that the mutations we identified in mprA may be neomorphic despite the fact that there are 4 different mutations found in different parts of the protein . Two independent populations in our selections showed the same identical mprA* mutation–a deletion of a single base pair at position 446 which causes a frameshift mutation resulting in a novel 26 amino-acid C-terminus . Interestingly , an almost identical C-terminus ( with a difference of only 2 amino acids between the two proteins , both of which are positive matches ) was identified previously in a pathogenic isolate ECA-0157 from clinical bovine mastitis [47] , raising the possibility that the adaptive pathways we identified may be relevant for interspecies interactions seen in natural niches . Our results clearly show that even bi-species microbial interactions can be complex , including both exploitative and interference competition , and involving multiple genetic determinants and mechanisms . We provide a framework for identifying the actual fitness-determining interactions under any condition , and demonstrate the utility of applying systems-biology approaches to such problems . This framework can be expanded and applied to the study of bacterial interactions in diverse settings , including competitive and cooperative interactions within healthy and diseased states of the human microbiome , as well as polymicrobial infections . Approaches similar to those presented here can also help elucidate how stable microbial communities are formed and maintained , and how community structure can be manipulated .
All strains used in this work are described in S2 Table . For all experiments in liquid media , bacterial strains were grown in modified M63 media [48] ( 13 . 6g/L KH2PO4 , 2g/L ( NH4 ) 2SO4 , 2μM ferric citrate , 1mM MgSO4; pH adjusted to 7 . 0 with KOH ) supplemented with 0 . 3% glucose and 5g/L casamino acids , at 37°C , and shaken at 250rpm . For the P . aeruginosa–E . coli competition assays , the bacterial mixtures were plated on M9 + 0 . 5% lactose plates to select for E . coli , and on M9 + 10mM sodium citrate plates to select for P . aeruginosa . M9 media [49] contained 12 . 8g/L Na2HPO4 . 7H2O , 3g/L KH2PO4 , 1g/L NH4Cl , 0 . 5g/L NaCl , 0 . 1mM CaCl2 and 2mM MgSO4 . For the spent media resistance assays , cells were plated on LB plates . Strains were grown in LB liquid media ( 10g/L Bacto-tryptone , 5g/L yeast extract , 10g/L NaCl ) or on LB plates for routine cloning and strain construction . Salt-free LB + sucrose plates contained 10g/L Bacto-tryptone , 5g/L yeast extract and 10% v/v sucrose . All plates contained 15g/L agar . The antibiotic concentrations used are listed in S3 Table . For all bi-parental conjugations , the donor and recipient cells were grown overnight shaking at 250rpm at 37°C in LB ( with the appropriate antibiotic , if required ) ; 0 . 5ml of each overnight culture was used per conjugation . The overnight cultures were washed twice with PBS , and resuspended in 1/10th the original volume of 100mM MgSO4 . Multiple mating spots from a 1:1 mixture of the two parental strains were placed on LB plates , and incubated at 37°C for 3–4 hours . Cells were scraped off , collected in PBS , and plated on the appropriate selection plates . We generated all single and multiple in-frame gene deletion mutants except for the phenazine deletion mutants ( in P . aeruginosa strain PA14 ) using the Gateway-compatible vector pEX18ApGW [50] , similar to that described in [50] . We amplified a FRT-site flanked Gentamycin resistance cassette ( GmR ) by PCR from a pPS856 plasmid template [50] . We also amplified ~600bp fragments flanking the gene of interest by PCR ( all primer sequences for the deletion constructs are listed in S4 Table ) , and then carried out PCR overlap extension between these 3 fragments to generate the mutant cassette . This cassette was cloned into the Gateway Entry vector PCR8/GW/TOPO ( Invitrogen ) by TA cloning , and transferred to the pEX18ApGW plasmid via an LR reaction using the LR Clonase II Enzyme mix ( Invitrogen ) . The cloned fragments were verified at each stage by sequencing . The final knockout plasmid was transformed into the conjugative S17-1 λ-pir E . coli strain , and then transferred to the parental P . aeruginosa strain using bi-parental conjugation , followed by selection on LB + irgasan + gentamicin plates . Individual conjugant colonies were streaked on salt-free LB + sucrose plates , and sucrose-resistant colonies were streaked out on LB + gentamicin and LB + carbenicillin plates . Gentamicin-resistant carbenicillin-sensitive clones were verified for the gene knockout by sequencing the target locus . To remove the gentamicin-resistance cassette , the pFLP2 plasmid expressing the Flp recombinase [51] was transferred to these knockout strains via a bi-parental conjugation with a pFLP2 carrying E . coli S17-1 λ-pir strain , followed by selection on LB + irgasan + carbenicillin plates . Individual conjugant colonies were streaked on salt-free LB + sucrose plates , and sucrose-resistant colonies were streaked out on LB , LB + gentamicin , and LB + carbenicillin plates . Gentamicin- and carbenicillin-sensitive clones were verified for proper recombination by sequencing the target locus . The phenazine mutants were generated in strain PA14 using the pΔphzA1-G1 and pΔphzA2-G2 knockout plasmids [24] . These plasmids were transformed individually into E . coli S17-1 λ-pir , and pΔphzA1-G1 was mobilized into the parental strain using bi-parental conjugation , followed by selection on LB + irgasan + gentamycin plates . Individual conjugant colonies were streaked on salt free LB + sucrose plates , to resolve merodiploids , and sucrose-resistant clones were verified for the phzA1-G1 knockout by sequencing the target locus . Subsequently , a phzA2-G2 deletion was similarly generated in the phzA1-G1 mutants to obtain a phenazine deletion mutant . All single mutants ( as well as the ΔphzA1-G1 ΔphzA2-G2 mutant ) were generated in the PA14 strain . For the multiple gene knockouts , the above protocol was followed multiple times in succession for each gene deletion . Overnight cultures of P . aeruginosa or E . coli strains were diluted 1:100 in fresh media , shaken at 37°C at 250rpm for 22h , and then centrifuged at 5000g for 20 minutes . The supernatant was passed through a 0 . 22μm filter , aliquoted if required , and stored at -20°C . To determine the time-course of the response of E . coli to WT P . aeruginosa spent media , an overnight culture of WT E . coli was diluted 1:250 in fresh media , and grown for 1 . 5h shaking at 250rpm at 37°C . 500μl of this culture was added to either 500μl of spent media , 200μl spent media + 300μl of 1X M63 salts , 100μl spent media + 400μl of 1X M63 salts , or 500μl 1X M63 salts ( for the control ) . Appropriate dilutions of the starting culture in 1X PBS were plated on LB plates , and the cultures were grown shaking at 250rpm at 37°C . Aliquots were removed from these cultures at the appropriate time-points , diluted appropriately in 1X PBS , and plated on LB plates . Samples were diluted and plated in triplicate , and the plate counts were averaged across the replicates . The time-course of the response of P . aeruginosa to WT E . coli spent media was determined similarly–an overnight culture of WT P . aeruginosa was diluted 1:250 in fresh media , and grown for 1 . 5h shaking at 250rpm at 37°C . 500μl of this culture was added to either 500μl of spent media , 200μl spent media + 300μl of 1X M63 salts , or 500μl 1X M63 salts ( for the control ) . Appropriate dilutions of the starting culture in 1X PBS were plated on LB plates , and the cultures were grown shaking at 250rpm at 37°C . Aliquots were removed from these cultures at the appropriate time-points , diluted appropriately in 1X PBS , and plated on LB plates . To measure the resistance of E . coli to P . aeruginosa spent media , an overnight culture of E . coli was diluted 1:250 in fresh media , and grown for 1 . 5h shaking at 250rpm at 37°C . 500μl of this culture was added to either 500μl of spent media , 200μl spent media + 300μl of 1X M63 salts , 500μl 1X M63 salts + appropriate volumes of 20mM pyocyanin , or 500μl 1X M63 salts ( for the control ) , and the cultures were grown for 16h shaking at 250rpm at 37°C . Thus , all samples had only 50% of fresh media with the rest being made up of spent media + 1X M63 salts ( without glucose or casamino acids ) , to enable comparison between the samples . The cultures were diluted in 1X PBS and plated on LB plates before and after growth in the presence of spent media or pyocyanin to obtain the fold change in cell-density . For the E . coli–P . aeruginosa competitions , overnight cultures of the competing strains were diluted 1:250 in fresh media , shaken at 250rpm at 37°C for 90 minutes , and then mixed at a 1:1 ratio . Appropriate dilutions of the strains in PBS were plated on M9 + lac and M9 + citrate plates as selective conditions for E . coli and P . aeruginosa respectively . Appropriate dilutions were also plated after 20 hours of shaking at 250rpm at 37°C . Samples were diluted and plated in triplicate , and the plate counts were averaged across the replicates . The mean cell densities for each competitor were used to calculate the effective growth rate m ( the realized Malthusian parameter ) as the number of doublings over the duration of the competition [52 , 53]: mStrain=log2 ( Nf/Ni ) /t where Ni and Nf are the initial and final cell densities , and t is the duration of the competition . The relative fitness of strain A to its competitor strain B was then calculated as the ratio of their effective growth rates ( mA / mB ) . To measure the transcriptional response of E . coli to P . aeruginosa spent media , we diluted an overnight culture of the E . coli MG1655 into 40ml of media , to a final A600 of 0 . 05 ( ~130-fold dilution ) . The cultures were incubated shaking at 250rpm at 37°C for 2 hours . We added 10ml of WT P . aeruginosa spent media to the flask , and immediately removed 2 . 5ml of the mixture for the 0 minute time-point . Subsequently , we removed a similar aliquot after 20 minutes of shaking at 250rpm at 37°C . Two replicates were performed for this experiment . We added each aliquot immediately to 5ml of the RNAprotect Bacteria Reagent ( Qiagen ) , incubated at room temperature for 5 minutes , and then centrifuged at 5000g for 10 minutes . We discarded the supernatant , and stored the pellets at -80°C . We isolated RNA from these samples using the Total RNA Purification Kit ( Norgen ) , as per the manufacturer’s protocol for bacteria . To label the RNA , we first polyadenlyated it , by combining 25μl of the undiluted RNA with 5μl 10X Poly ( A ) Polymerase Reaction Buffer ( New England Biolabs ) , 5μl 10 mM ATP , and 1μl ( 5 U ) E . coli Poly ( A ) polymerase ( New England Biolabs ) in a total volume of 50μl , and incubating at 37°C for 30 minutes , followed by a 20 minute incubation at 65°C to inactivate the enzyme . We cleaned the samples using the RNeasy Mini Kit from Qiagen , and then labeled 300ng of the 0 minute RNA with cyanine 3-CTP , and 300ng of the 20 minute sample with cyanine 5-CTP using the Low Input Quick Amp Labeling Kit ( Agilent ) . We hybridized the two samples on custom tiling arrays from Agilent ( Design ID 024568 ) [52] , according to the manufacturer’s protocol . To measure the transcriptional response of E . coli to P . aeruginosa spent media in the presence of iron , we diluted overnight cultures of the E . coli MG1655 into 24 . 75ml of media , to a final A600 of 0 . 05 ( ~130-fold dilution ) . The cultures were incubated shaking at 250rpm at 37°C for 105 minutes . We added 20 . 25ml of WT P . aeruginosa spent media and 100μM ferric citrate to the flask , and immediately removed 2ml of the mixture for the 0 minute time-point . Subsequently , we removed a similar aliquot after 20 minutes of shaking at 250rpm at 37°C , and processed the aliquots as above . Two replicates were performed for the experiment . The fluorescence intensities were extracted using the Agilent Feature Extraction Software Version 9 . 5 , using the protocol GE2-v5_95_Feb07 without spike-in controls . The probes were filtered using the IsFound , IsFeatNonUnif , IsBGNonUnif , ISFeatPopnOL , and IsBGPopnOL flags , and discarded if the first flag had a value of 0 , or any of the others had a value of 1 . We used the ‘LogRatio’ value for each probe , and all probes which were on the sense strand of the coding region of a gene were assigned to the gene . The values were averaged across all probes for a gene , and across the two biological replicates for each experiment . We ran iPAGE [13] in continuous mode with various numbers of bins , which did not significantly change the categories identified . The ‘GO annotation’ module was used for the data shown in Fig 2 , and the ‘Transcription factor regulon’ module was used for the data shown in Fig 6 . Whole spent media from WT P . aeruginosa was analyzed using both MALDI-TOF and ESI static nanospray mass spectrometry . The main component seen in the spent media was the same in both spectra , and had a mass of 1335 Daltons . The mass spectrometry analysis was performed at the Protein Core Facility at Columbia University . For the analysis of active fractions of P . aeruginosa spent media , spent media from WT P . aeruginosa was fractionated by HPLC-MS using a C18 reverse-phase column in a linear 5%–95% acetonitrile-water gradient , with a flow-rate of 1 ml/minute for 90 minutes . Fractions were collected every 2 minutes for a total of 45 fractions . The fractions were dried using a Savant DNA120 concentrator and resuspended in 200μl water . The growth of E . coli cells was then tested against 20% ( v/v ) of the resuspended fractions in 100μl media in a 96-well plate , starting from a 1:100 dilution of an overnight culture of E . coli . A sample with no spent media fractions was used as the control . The media was covered with 100μl mineral oil to prevent evaporation . The plate was shaken continuously without the lid at the ‘medium’ setting at 37°C for 22 hours in a Biotek Synergy MX plate reader . The absorbance at 600nm was read , and fractions which inhibited the fold-change in absorbance more than 10-fold compared to the control were identified . Three consecutive active fractions had a mass-spectrometry profile with the same single peak ( shown in S2 Fig ) . The HPLC-MS was performed at the Princeton Proteomics and Mass Spectrometry Core Facility . WT E . coli cells were grown shaking at 250 rpm at 37°C , in the presence of increasing concentrations of the selective agent ( WT P . aeruginosa spent media , ΔpvdJ spent media , or pyocyanin ) , with a daily 50–100 fold dilution into 1 ml fresh media in snap-cap tubes containing the selective agent . 7 daily transfers were carried out for the WT P . aeruginosa spent media ( concentration increasing from 12 . 5–35% ( v/v ) ) , 15 for the ΔpvdJ spent media ( concentration increasing from 30–70% ( v/v ) ) , and 19 for pyocyanin ( concentration increasing from 75–800 μM ) . Following the selections , 2 populations each evolved in the presence of WT spent media and pyocyanin , and 3 populations evolved in the presence of ΔpvdJ spent media were streaked out to obtain individual clones and 2–3 individual clones were analyzed by whole-genome sequencing . We prepared genomic DNA from the evolved clones using the Qiagen DNeasy Blood and Tissue kit , and prepared indexed paired-end libraries from the DNA using the Illumina Nextera XT DNA Library Preparation kit . The samples were pooled and sequenced on a NextSeq 500 ( Illumina ) for 150 cycles . The bcl2fastq package from Illumina was used to demultiplex the data and obtain FASTQ files for each sample . The Illumina adapters were removed using cutadapt [54] and the sequences were trimmed to remove poor quality bases at the ends using trimmomatic [55] . The sequences from each sample were then analyzed using the default settings of breseq-0 . 26 [56] , to identify the mutations in the evolved strain compared to the parental MG1655 background . The samples had an average of 20–45x coverage over the genome . The breseq-0 . 26 pipeline identifies any variants between the given sequence and a reference genome ( in this case Genbank Accession NC_000913 . 2 ) . We only focused on the high-confidence mutations , and do not report the marginal predictions . The parental strain used also has mutations compared to the reference genome ( listed in S5 Table ) , some of which have been previously reported [57] . The mutations identified in the evolved clones compared to the ancestral genome are listed in S1 Table . We generated all single and multiple allele-replacements ( in the parental E . coli MG1655 strain ) using the pKOV plasmid [58 , 59] . We amplified the evolved allele from the appropriate strain , including ~500bp flanking the mutation on both sides , using primers that had 20–25bp overlap with the ends of the pKOV plasmid linearized with BamHI/NotI ( all primer sequences for the constructs are listed in S6 Table ) . The pKOV plasmid was digested with BamHI and NotI ( New England Biolabs ) and the 5 . 6kb fragment was purified using the Zymoclean Gel DNA Recovery Kit . The mutant allele was then cloned into pKOV using Gibson Assembly , and the cloned fragment verified by sequencing . The allele replacement was carried out similar to the original protocol [59] . The allele-replacement plasmid was transformed into the appropriate strain , followed by selection on LB + chloramphenicol plates at 42°C to obtain integrants . Individual colonies were re-streaked out on LB + chloramphenicol plates at 42°C to reduce the background of non-integrants . Chloramphenicol-resistant clones were streaked out on salt-free LB + sucrose plates at 30°C to resolve the integration and individual sucrose-resistant colonies were tested for the allele-replacement by PCR with mismatched primers [60] . Strains with the appropriate allele replacement were streaked out on LB + chloramphenicol plates at 30°C and chloramphenicol sensitive clones were verified for the allele replacement by sequencing the target locus . All single mutants were generated in the WT E . coli strain . For the multiple allele replacements , the above protocol was followed multiple times in succession for each allele . The single gene deletions were obtained from the Keio collection [61] and transferred to the WT E . coli MG1655 background using P1 vir transduction [49] , followed by selection on LB + Kanamycin plates . Kanamycin-resistant clones were tested for the appropriate mutation by PCR , and then cured of the kanamycin resistance cassette by transforming with the plasmid pcp20 [62] , and selecting on LB + Ampicillin plates at 30°C . Ampicillin resistant clones were streaked out on LB plates and incubated at 42°C for 24 hours , and then streaked out on LB , LB + Ampicillin and LB + Kanamycin plates . Ampicillin- and kanamycin-sensitive clones were verified for the deletion by sequencing the target locus . The microarray data have been deposited in the Gene Expression Omnibus ( GEO ) with the accession number GSE72283 . The whole genome sequencing data have been deposited in the Sequence Read Archive , associated with the BioProject PRJNA292975 . | Bacteria commonly exist in nature as part of large multispecies communities , and their behavior is affected by the surrounding species via secreted molecules or physical contact . Such interactions are poorly understood , and the pathways that actually affect bacterial growth and behavior in any multispecies system have rarely been studied . In this study , we show that the opportunistic pathogen Pseudomonas aeruginosa inhibits the growth of the commensal Escherichia coli , and we use unbiased genome-scale methods to identify the mediators . We find that P . aeruginosa iron-chelating molecules and redox-active phenazines account for all of the E . coli growth inhibition seen in our system . We also evolve E . coli in the presence of the P . aeruginosa antimicrobials and identify multiple pathways that lead to resistance , gaining novel insights into the mechanism of action of these antimicrobial molecules . Thus , our study demonstrates the complexity of even simple two-species bacterial systems and lays down a framework for studying such interactions at the molecular level . |
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Previous work in Arabidopsis showed that after an ancient tetraploidy event , genes were preferentially removed from one of the two homeologs , a process known as fractionation . The mechanism of fractionation is unknown . We sought to determine whether such preferential , or biased , fractionation exists in maize and , if so , whether a specific mechanism could be implicated in this process . We studied the process of fractionation using two recently sequenced grass species: sorghum and maize . The maize lineage has experienced a tetraploidy since its divergence from sorghum approximately 12 million years ago , and fragments of many knocked-out genes retain enough sequence similarity to be easily identifiable . Using sorghum exons as the query sequence , we studied the fate of both orthologous genes in maize following the maize tetraploidy . We show that genes are predominantly lost , not relocated , and that single-gene loss by deletion is the rule . Based on comparisons with orthologous sorghum and rice genes , we also infer that the sequences present before the deletion events were flanked by short direct repeats , a signature of intra-chromosomal recombination . Evidence of this deletion mechanism is found 2 . 3 times more frequently on one of the maize homeologs , consistent with earlier observations of biased fractionation . The over-fractionated homeolog is also a greater than 3-fold better target for transposon removal , but does not have an observably higher synonymous base substitution rate , nor could we find differentially placed methylation domains . We conclude that fractionation is indeed biased in maize and that intra-chromosomal or possibly a similar illegitimate recombination is the primary mechanism by which fractionation occurs . The mechanism of intra-chromosomal recombination explains the observed bias in both gene and transposon loss in the maize lineage . The existence of fractionation bias demonstrates that the frequency of deletion is modulated . Among the evolutionary benefits of this deletion/fractionation mechanism is bulk DNA removal and the generation of novel combinations of regulatory sequences and coding regions .
Decades ago it was proposed that whole-genome duplication provides raw material for evolutionary innovation , as reviewed [1] . The angiosperm phylogenetic tree of organisms with complete genome sequence has provided evidence for repeated ancient tetraploidies in all lineages ( Figure 1 ) . However , tetraploidies occurring before approximately 150 million years ago ( MYA ) in plants and 500 MYA in animals are difficult to detect [2] . Genomes that have experienced tetraploidy events tend to reduce their genome structure toward their ancestral chromosome number and gene content , though not gene order . The mutational process accomplishing this reduction in gene content is called fractionation , and its mechanism is unknown . Theoretically , the expected fate of the average gene following tetraploidy is loss from one or the other , but not both , homeologous chromosomes [3] , [4] , . Previous studies on fractionation of the most recent tetraploidy in the Arabidopsis lineage ( known as the alpha tetraploidy event ) found significantly more gene loss on one homeolog than the other [7] . However , some genes are retained as homeologous pairs . This same study found that genes retained as pairs were significantly clustered and that any mechanism of fractionation causes clustering of retained genes , especially on the over-fractionated homeolog , as retained genes will inevitably be physically closer to each other once the intervening genes have been removed . Figure 2 illustrates expectations of biased and unbiased fractionation and shows how fractionation by any mechanism tends to cluster retained genes . Any of the following gene loss mechanisms could contribute to fractionation after a tetraploidy event: ( 1 ) single gene loss via inactivation and sequence randomization ( i . e . the pseudogene pathway ) as observed in mammals , including primates [8]; ( 2 ) single gene “loss” from orthologous sites by gene transposition , as was observed in the Brassicales [9]; ( 3 ) single gene loss by a short deletion mechanism; ( 4 ) multiple gene loss events of any type , like long ( multi-gene ) deletions or segmental transpositions . Lai and coworkers [10] compared five orthologous panels of bacterial artificial chromosome sequence for rice , sorghum , maize homeolog 1 , and maize homeolog 2 . Each set of panels was anchored on a gene shared by all four genomes . They found examples of genes that moved out of the syntenic position in maize but were conserved syntenically between rice and sorghum . Another likely mechanism for fractionation is short deletions via illegitimate , or intra-chromosomal , recombination , as introduced in point 3 above . Devos and coworkers [11] implicated recombination , both homologous and illegitimate , as the mechanism used by plants , including maize , to remove retrotransposons . This suggestion was based on finding short direct repeats from 2–13 bp , sometimes imperfect , flanking small deletions in the inferred target chromosome . This was the same conclusion derived previously from data implicating short deletions in nonfunctional transposons in Drosophila [12] . Citing bacterial illegitimate recombination studies , these researchers implicated recombination mechanisms as the transposon loss mechanism . Using sorghum as our primary outgroup and rice as a secondary outgroup , we examine in detail the gene and chromosome fragments identifiable at the current stage of fractionation in the maize inbred B73 , a genome sequenced recently [13] . We also examine such fragments in the recently sequenced soybean genome that result from a tetraploidy estimated to have occurred approximately 13 MYA [14] . We conclude that the most likely mechanism of fractionation is single gene loss by short deletions , predominantly in sizes ranging from 5 bp to 178 bp , with deletions being found 2 . 3 times more often on one homeolog than the other; infrequent longer deletions are possible . The fractionation mechanism , like the mechanism of transposon removal , is likely to be intra-chromosomal recombination , and this has general implications for bulk DNA removal and the wholesale generation of new sequence combinations .
To study the post-tetraploidy fractionation process in detail , both a sequenced genome that is undergoing fractionation and an outgroup with a sequenced genome that diverged before the tetraploidy event is required . For the most recent tetraploidy in maize , which happened from 5 to 12 MYA [15] , [16] , sorghum is such an outgroup . Sorghum diverged from the maize lineage just before the tetraploidy event ( Figure 1 ) [17] . Sorghum has been sequenced [16] , and the first assembly of a maize genome has recently been published [13] . In addition to its phylogenetic position , the maize lineage tetraploidy possesses another characteristic that recommends it for the study of fractionation: it is relatively recent . The alpha tetraploidy in Arabidopsis , at 23–50 MYA , is older than the most recent ( alpha ) tetraploidy in maize . Even so , the maize alpha tetraploidy is known to be highly fractionated [10] , [18] . Our primary research aim was to detail what happened to those orthologous ( syntenic ) genes shared by sorghum and maize following the maize tetraploidy . Using the procedures described in Methods , we identified 37 orthologous regions between sorghum and the corresponding maize homeologs retained after the maize alpha tetraploidy event . From these regions , we found that of the 2 , 943 sorghum-maize ( Sb-Zm ) syntenic shared genes that we studied , 43% of them were retained as homeologous pairs in maize . Note that we count as present any significant fragment of gene . If the maize tetraploidy behaved as other known tetraploidies in plants and microbes , retained genes should be enriched in those encoding transcription factors , as reviewed [19] . Indeed , the frequency of genes encoding transcription factors was 4 . 3 times greater among the retained genes as compared to the fractionated genes . Figure 3A is a cartoon of a GEvo output screenshot of a 13-gene segment of one of the 37 orthologous regions ( region Sb2 ) between sorghum and its two maize homeologs , as described in Methods . The GEvo comparative sequence alignment tool output generated the original blastn output detailed in Methods . Supplemental Information 1 ( Dataset S1 ) gives our primary data as inferred from analyses like that shown in Figure 3A . Dataset S2 shows how any one sorghum chromosomal region is orthologous to two maize regions , generating information essential to construct our sorghum-maize1-maize2 regions . One way to measure fractionation bias is to first assume that gene loss involves one gene independently from any other gene , and then count the number of gene losses ( deletions ) on one homeolog as compared to the number of gene losses on the other homeolog . If fractionation were unbiased , this ratio is expected to be 1∶1 . Other measures of fractionation include total number of genes or base pairs in an orthologous stretch , but counting deletions of shared genes is most direct , so we present this first . Figure 3B shows two representative diagrams of our data for shorter regions ( Dataset S3 contains all 37 such diagrams with bias statistics ) and indicates that fractionation has been significantly biased in 68% of our regions . Using data from nine representative longer sorghum regions ( Table 1 ) , we conclude that the over-fractionated chromosomes have 2 . 3 times as many deleted genes as do the under-fractionated chromosomes . We next asked , what was the average extent of gene loss ? Most importantly , are deletion events longer than one gene ? Figure 4A , B , C , and D monitors runs , or the sequential series of deleted genes , and Figure 4E monitors runs of retained genes . The experimental runs data plotted in Figure 4A , B , C , and E were compared to the 95% confidence interval around the median of Monte Carlo simulated data ( Methods ) based on the assumption that one gene is deleted at a time , and the chromosome was ligated before the next deletion , as such a mechanism would be predicted to work in nature . The distributions of Figures 4A , B , and C are all very similar: they differ only as to whether or not the over-fractionated or under-fractionated chromosome is evaluated or as to whether or not gene losses of 10 genes or greater were included . The most frequent run length in distributions Figure 4A , B , or C is one gene , followed by two genes , and so forth . If we recalculate expectations for distribution of Figure 4C using an evolutionary method that permits varying percentages of deletions of genes , the best fit is one-gene deletions 80% , two-gene deletions 15% , and three-gene deletions 5% ( Figure 4D ) . The possibility existed that longer deletion runs were not authentic deletions but were segmental translocations . Deletion runs consisting of 12 genes or more were found somewhere else in the genome . Deletions of between 11 and 6 genes were found elsewhere about 10% of the time , but identification was made more difficult because fractionation is expected to remove genes from any position in the genome , and sometimes is expected to leave behind fewer than the three syntenic genes needed for a positive identification . That is why deletions of 10 or more genes were removed from all distributions of Figure 4 except Figure 4A . There is a possibility , a possibility we evaluate , that the smaller deletions are also undetectable segmental translocations and not authentic deletions , but removal is essentially one gene at a time . We next asked if it were possible that , rather than being deleted , single genes observed as lost between orthologous maize and sorghum regions were instead transposed or translocated elsewhere in the genome , as we had observed for longer runs of genes . Large-scale single gene transposition has been documented in the eudicot order Brassicales [9] , and cases have also been reported in the maize lineage [11] . To address the possibility that the majority of the fractionating gene loss we were observing was actually a result of whole-gene transposition , we attempted to identify potentially orthologous maize genes in a position-independent manner . Sorghum genes with known orthologs in rice were blasted against sorghum , rice , and maize genomes , as described in Methods . From the resulting data we found that genes identified as retained had a mean of 1 . 57 copies in the genome , with a median of 2 genes . It is expected that this number would be less than 2 , as the manual annotators considered a gene to be retained if a significant fragment of it was still present , which included genes in the process of being removed by small deletions ( as will be discussed ) . Genes identified as being fractionated in Dataset S1 were present at a mean of 1 . 17 copies in the genome , with a median of 1 gene . A few of these extra copies are likely maize-specific duplications , but others no doubt represent apparently deleted homeologs that have transposed to other locations in the genome . Nevertheless , these data provide strong evidence that while some apparently fractionated genes may have been lost via translocation ( transposition to a new site ) , translocation is not the prevailing mechanism explaining our fractionation data . This conclusion does not imply that fragments of genes are not transposed around the genome , as is known to occur frequently via transposon-mediated gene capture [20] . Indeed , when we re-calibrated our search to find shorter stretches of high-identity sequence , we found many pieces of genes present at higher copy numbers elsewhere in the genome . Examination of a sample of these hits identified gene fragments , but no intact genes were found . As shown in Figure 2 , fractionation itself clusters retained genes . Figure 4E identifies runs of retained genes ( Bs ) and distributes them by run length and compares this to expectations based on deletions one gene at a time . Is this distribution more highly clustered than expected from fractionation alone ? The mode is clearly one retained pair , as expected . Expectation intervals were generated assuming that deletions occurred one gene at a time . Although clustering of retained genes is not dramatic , runs of retained genes greater than 9 gene pairs are not expected at all; in total , there are 62 genes ( out of 1 , 203 , or 5% ) in such longer runs ranging from 9 through 12 genes in length . When expectations are changed to be 80% single-gene deletions , 15% two-gene deletions , and 5% three-gene deletions ( Figure 4F ) , the actual and expected are similar . Now there are only four unexpected runs greater than 9 genes in length . With the exception of these few longer runs , genes are retained approximately as expected based on 80%/15%/5% 1/2/3 gene deletion predictions . Table 1 focuses on nine longer representative homeologous regions of maize representing different sorghum chromosomes . The under-fractionated and over-fractionated homeologs in maize are identified in this table ( Column 2 ) . This over/under designation derives from the deletion bias data quantified in Table 1 and evaluated for significance in Column C . Table 2 shows these data for each of the nine representative regions individually ( Column H ) . In this case , the numbers are less than 1 because the ratio is under-fractionated/over-fractionated , and the under-fractionated homeolog has fewer deletions . The homeolog with the fewest deletions contains the most genes , so another measure of fractionation is the number of genes on the under-fractionated/over-fractionated , where bias will now be indicated by ratios greater than 1 . The fractionation bias ratios , using total gene data , for each of the nine representative regions are listed in Column L of Table 1 . To extrapolate bias in our manually annotated regions to more of the genome , we used the slope of syntenic lines in Zm-Zm dot plots ( Dataset S4 ) . A slope of 1 implies unbiased fractionation . A significant difference in the number of genes or base pairs between the two homeologous maize chromosomes alters that slope from 1 , and this is what we observed . If the unit of Zm-under/Zm-over measurement is total number of genes annotated by maizegnome . org , the average slope value corresponds to a mean fractionation bias value of 1 . 5 ( Table 1 , Column M ) . If the units are in total base-pairs , the fractionation bias is 2 . 3 ( Table 1 , Column N ) . Again , the under/over direction of fractionation in both cases remains greater than 1 , as expected , but the dot-plot analysis made it possible to examine considerably longer regions of paired homeologs , each anchored on the indicated sorghum region . Most important here is that the three measures of bias based on gene number ( Columns L and M ) or base pair length ( N ) are concordant with expectations based on the rigorous deletion bias data generated manually for our representative regions . Based on the concordance between bias in orthologous gene loss and base pair length , and given that 85% of the maize genome is composed of transposable elements [13] , we conclude that homeologous regions that preferentially lose genes also lose intergenic , primarily transposon , DNA more frequently . Table 1 , Column O , reports our measured ratio of Ks [Zm-under/Sb] to Ks [Zm-over/Sb] for a total of 1 , 772 Sb-Zm-Zm gene units in the five sorghum homeologous regions for which highly significant under/over-fractionation expectations existed ( Table 1 ) . We removed 16% of pairs with the most extreme Ks ratios , many of which represent misalignments or alignments to pseudogenes . Using the remaining data , we found no difference between the Ks values between sorghum and either of the two maize homeologous regions . We conclude that mutation by base substitution and mutation by short deletion are mechanically distinct and are targeted differently . Three of our representative regions are within pairs of homeologous chromosomal arms: Sb1 = Zm1S/9L ( sorghum chromosome 1 = maize chromosome 1S and maize chromosome 9L ) , Sb3 = Zm3L/8L , and Sb6 = Zm2S/10L . The under-fractionated ( longer ) homeolog is the numerator . These are the only arm-arm exact homeologies in the maize genome; examination of syntenic Sb-Zm dot plots ( like that in Dataset S2 ) made clear that segments of these arms are not present syntenically on any other chromosomes . The total map unit's length of these maize chromosomal arms is known , making it possible to directly compare the degree of fractionation within any given arm to the overall recombination frequency within that arm . Mapping data for maize inbred T232×x inbred CM37 generated the following data [21]: the proportion of map units for under-fractionated arm/over-fractionated arms are Zm1S/9L = 0 . 9 , Zm3L/8L = 1 . 1 , and Zm2S/10L = 1 . 9 ( Table 1 , Column N ) . Note that although Zm2S/10L has the largest difference in recombination frequency , it has the lowest fractionation bias of these three paired arms ( Table 1 , Column C ) . We conclude that there is no obvious correlation between biased fractionation and overall frequencies of reciprocal recombination during meiosis . Even before BAC sequencing was complete , one group [22] identified methylation domains of maize chromosome in shoot or root nuclei using McrBC restriction endonuclease , a treatment that degrades DNA between methylated half sites of the form m5C-N40–500-m5C . McrBC is non-specific for different types of methylation patterns . Using this crude measure of methylated regions ( BAC start-stop ) in maize shoot nuclei , we overlifted ( translated the start-stop nucleotide designations ) the data from BACs to pseudomolecules and found no correlation at all between the over-fractionated and under-fractionated homeologs ( Table 1 , Column M ) . Two representative regions were concordant , two were not concordant , and one region was vastly over-methylated on the under-fractionated homeolog . Our methods for deciding whether or not a maize gene was retained ( “B” ) did not require that the entire coding sequence be present , but only a significant fragment . Because of this , our calculation of the number of whole sorghum-maize genes retained post-tetraploidy , about 40% , is surely an overestimate . If we were to assume that the process of fractionation is ongoing , we reasoned that some of our retained genes might have internal deletions whose flanking sequences might give us a clue as to the mechanism behind gene fractionation . By visual examination , we identified cases where a maize gene seemed to have a gap within an exon . To verify each fully flanked deletion , we extracted the sorghum exon sequence and used it as query for a blastn to rice , a grass that diverged from sorghum about 50 MYA [23] , to sorghum itself , and to the two homeologous maize regions . We then studied each Os-Sb-Zm1-Zm2 blastn result using GEvo , our synteny visualization platform ( in CoGe , Methods ) . We verified that eight genes , containing a total of 16 deletions , were fully flanked by conserved , known sequence . Table 2 gives the data for these fully flanked deletions . Figure 5A and B shows an exemplary GEvo graphic and the pertinent orthologous sequences of rice , sorghum , and the two maize homeologs . In two cases ( Sb01g039030 and Sb09g023840 , Table 2 ) , the apparent gap was actually several short gaps within the homeologous flanking sequence . The gap size within these 16 deleted regions ranged from 5 bp to 178 bp , with a mean gap size of 25 . 9 bp . Bias for gaps is consistent with the fractionation bias found locally: in other words , when a gap is present , it is in the maize homeologous gene located on over-fractionated chromosome 93% ( 15/16 ) of the time ( Table 2 ) . As mentioned in the Introduction , deletions due to illegitimate recombination are often flanked by a short stretch of sequence that , before the deletion , had been a direct repeat [11] . In theory , such repeats facilitate ectopic , intra-chromosomal , reciprocal recombination ( as drawn in Figure 5C ) generating a circle and a solo copy of the original repeat sequence in place of the sequence deleted ( the circle ) . Using ClustalW , we found such direct repeats flanking 10 of the 16 gaps in our study ( Table 2 ) . These repeats were between 3 and 24 bp in length; an example is given in Figure 5B . Notice how the repeats surrounding the gap in the fractionated homeolog are truncated in comparison to the repeat sequence within the whole homeolog: this is a typical footprint of intra-chromosomal recombination [11] . In an attempt to generalize our results from monocots ( e . g . grasses ) to eudicots ( e . g . legumes ) , we found several such small deletions where the inferred precursor sequence was flanked by direct repeats , within retained duplicate genes of Glycine max ( Gm: soybean , unpublished data ) from the more recent of the two easily observable tetraploid events in the sorghum genome . Soybean has had two recent genome duplication events , the most recent one ( alpha ) having taken place between 14 and 3 MYA [14] . The close relative , Medicago trunculata , was used as the outgroup in order to infer the precursor gene sequence before deletion . We conclude that small deletions are involved in the fractionation of genes following ancient ( successful ) plant tetraploidies .
Comparison of the sorghum outgroup to the newly released maize sequence permitted a detailed description of the consequences of tetraploidy and the ensuing fractionation process on grass genes shared orthologously between sorghum and maize . We used graphic displays of blast results , both as pairwise dot-plots ( SynMap ) and multiple ortholog line drawings ( GEvo ) , to facilitate large-scale genome analyses at the level where 100 bp deletions from genes were observed visually . The maize tetraploidy is much more recent than the previously studied alpha tetraploidy of Arabidopsis . Combining the power of the sorghum outgroup and the recent and potentially ongoing fractionation of the maize genome permitted a definitive description of the sequences left after fractionation . We observed: ( 1 ) If we define a gene stringently , then it appears that fractionation generally involves gene deletion , not gene repositioning . However , if we define a gene as a 150 bp fragment of exon , significantly more transposition/duplication is evident . Any transposon-capture [20] or fragment transposition mechanism could help explain these results . ( 2 ) If the unit of deletion is “genes , ” then the deletion mechanism of fractionation most frequently removes one gene ( Figure 4 ) . Indeed , our best-fit evolutionary model for predicting the actual gene loss on the over-fractionated chromosome was the loss of one gene 80% of the time , the loss of two genes 15% of the time , and the loss of three genes 5% of the time . The genes that resist fractionation are naturally clustered by fractionation , as predicted , though a few runs of retained genes are unexpectedly long ( Figure 4E and F ) . ( 3 ) The lower limit of gene loss was estimated from those infrequent deletions that were completely contained within an exon; these ranged from 5–178 bp in length . We think it likely that these intra-exon deletions are the consequence of a single event rather than the summation of an ongoing series of events . Because single genes were found with deletions in more than one exon , it is clear that smaller deletions ( less than 200 bp ) are common , but larger deletions also sometimes happen . We also found evidence that illegitimate recombination acts in soybean as it does in maize ( unpublished data ) , so this mechanism is not maize-specific . ( 4 ) By adding the orthologous rice genes to the Sb-Zm-Zm panel , we inferred the sequence of the maize ancestral chromosome before the small deletions described above took place . The ancestral to-be-deleted sequence was flanked by a direct repeat of between 3 and 24 bp in length . Such flanking repeats have been interpreted as signatures of illegitimate recombination . One such mechanism is intra-chromosomal recombination , which pairs on the direct repeat and generates a circle and a deletion [12] . ( 5 ) Overall , one homeolog is , on average , 2 . 3 times more likely to have a gene removed by deletion than the other homeolog , demonstrating biased fractionation . Biased fractionation was also seen by the team of researchers who collaborated to first describe the maize genome [13] . That the DNA between genes on the over-fractionated chromosome are even more over-fractionated than the genes themselves—DNA composed primarily or entirely of transposons thought to be without function—makes it unlikely that fractionation bias is the result of any sort of selection bias . ( 6 ) We found no correlation between Sb-Zm Ks values with over/under-fractionation . Divergence by point mutation and fractionation by short deletion are independent and independently regulated . ( 7 ) Preliminary identification of methylation domains in maize [22] permitted an attempt to correlate the number of such domains with over- or under-fractionation . We found no such correlation , but this does not rule out other types of epigenetic marks ( e . g . histone modification ) as possible tags for biased fractionation . ( 8 ) Although we implicate some sort of recombination mechanism to facilitate short deletion , there is no correlation between maize chromosome arms that are over/under-fractionated and the number of total map units ( % reciprocal recombination ) in those arms . Our detailed analysis evaluates one outcome of the maize alpha tetraploid fractionation , based on the B73 inbred line . Since gene fragments remain , we have no reason to believe that fractionation is complete , and if not complete , then it is probable that different accessions of the species Zea mays , and perhaps different inbred lines of the Zea mays mays subspecies , have different fractionation outcomes . We do not know how many individual deletions , on average , it takes to completely remove a gene . However , the observation that 93% of the deletions we found within exons were on the over-fractionated homeolog probably reflects the general scenario: one of the two homeologs is inactivated by deletion , at which point deletions of the other homeolog are selected against ( since this second deletion would result in the loss of the function encoded by the gene pair ) . Additional deletions would then accumulate only on the homeologous gene that suffered the original loss as fractionation of this now-inactivated gene progressed . Even so , it took little effort to find a case in soybean where a flanking repeat signature implied that an entire gene was removed in one deletion event ( Dataset S5 ) from a region where there were few exon deletions . We do not know unequivocally the relative frequency of this sort of larger deletion compared to genes being deleted away in smaller increments . Perhaps the nature and distribution of direct repeats , the length of the circle to be deleted , and the epigenetic receptivity of the target chromosome all contribute to the details of fractionation . Sometimes genes that resist fractionation , the retained genes , are significantly clustered ( [24] and Figure 2 ) beyond expectations derived from any mechanism of gene deletion . One explanation for this could be that genes that would be otherwise fractionated are protected by their position next to a fractionation-resistant gene . Alternatively , fractionation-resistant genes might exist as clusters in the pre-tetraploid ancestor . There are two occurrences of particularly large genomic consequence that happened along the maize lineage only after the divergence of maize and sorghum . First was the maize alpha tetraploidy event that is thought to have occurred roughly 12 MYA . Second , and later , was a massive bloom of transposable element activity , resulting in a modern maize genome 3 . 4 times as large as that of sorghum . About 85% of maize's 2 , 300 Mb genome is thought to be composed of transposons [13] , many of which inserted within the last 3 million years [25] . Illegitimate recombination has been proposed as a mechanism for genome-size reduction—transposon removal—independently in maize and Drosophila [11] , [12] . On a similar theme , some indels within genes in Arabidopsis appear to be due to illegitimate recombination [26] . Our evidence for ancestral flanking direct repeats , and our evocation of intra-chromosomal recombination , are therefore consistent with these previous studies . Unlike previous work , we have focused on typical genes that are targets of fractionation in order to address the mechanism of gene loss following tetraploidy . We now propose that illegitimate recombination is the primary means by which excess DNA in the form of redundant genes and transposons are removed from genomes . Intra-chromosomal recombination is one way to envision this sort of recombination , but any chromosomal complex that deletes between tandem repeat sequences would fit our data . This mechanism is a check against what has been called a “one-way ticket to genomic obesity” [11] . That is not to say that this mechanism evolved in any sort of purpose-oriented ( teleological ) way . The sort of purifying selection via deletion we observe in maize is very different from that described for primates , where genes are removed via the pseudogene pathway . For instance , the components of a pheromone signal transduction pathway lost in old-world monkeys , including humans , are still present in the form of identifiable pseudogenes [27] , and recent work indicates that 100% of human-specific gene losses among the primates studied are present in the genome as pseudogenes without deletions [8] . It is possible that mammals and plants evolved different mechanisms for genome purification , adapted to fit differences in their capacities to cope with high frequencies of individuals carrying DNA deletions without going extinct . Unlike transposons , coding regions , such as exons , do not have built-in long direct repeats and do not present obvious targets for illegitimate recombination . Nevertheless they do have randomly situated , shorter direct repeats , and we now know that some of these short repeats facilitate small deletions . An accumulation of such deletions could eventually lead to the disappearance of entire genes . Additionally , deletions in the cis-acting regulatory regions near genes could hypothetically give rise to a new regulatory binding activity . The same can be said for cis-acting regulatory sequences that affect a local chromosomal region rather than a single gene . Following deletion of intervening genes on fractionated chromosomes , new clusters of genes would be expected to respond in new ways to their local regulatory environment . Thus , in large and small ways , the fractionation mechanism we describe has the potential to create huge regulatory variation around genes as a by-product ( or “spandrel” ) of purifying selection . Whether or not the fractionation mechanism is induced by “excess” is not yet known . This discussion is not complete without considering the origin and utility of fractionation bias itself . The alpha-syntenic genome of maize is actually two genomes , the over-fractionated and the under-fractionated , and the total DNA and gene count differences between them are diagnostic for any longer stretch of chromosome . We show that Ks data neither support nor refute allotetraploidy . Allotetraploidy—for example , a tetraploidy following a very wide cross—could explain the origin of over- and under-fractionated genomes , where one of the genomes acquired an “invader” epigenetic tag in the new polyploid . Alternatively , the tetraploidy might have been autotetraploidy , and the mode of sexual transmission generated a genome-wide epigenetic tag . Either way , logic alone dictates that some sort of heritable genomic mark precedes the bias in fractionation since biased fractionation is ongoing . One immediate benefit of having such a tag could be to prevent homeologous pairing and consequent dysfunctional pollen and eggs . We do not have any direct data at the level of DNA or histone modification . We also do not know anything about the relationship between chromosome pairing/mispairing and the inferred epigenetic mark . In summary , we suggest that direct repeats throughout the genome facilitate frequent and continuous sequence deletion via illegitimate recombination . Repeats abound , so targets are not limiting . Among the evolutionary benefits of this selectively neutral deletion/fractionation mechanism is bulk DNA removal and the wholesale generation of new combinations of regulatory and coding sequences . Both tetraploidy and transposon blooms confront the genome with a great deal of potentially dispensable DNA , and both cases of genomic excess probably share the same purification mechanism: intra-chromosomal recombination . Fractionation bias demonstrates that the frequency of this mechanism can be modulated . The inducibility , target specificity , and rate modulation of purifying selection via illegitimate chromosomal recombination is a particularly important subject for further research .
The sorghum sequence was Sbi1 assembly and Sbi1 . 4 annotation ( Paterson et al . 2009 [23] ) downloaded from Phytosome V4 . 0 http://www . phytozome . net/sorghum , last modified 3-25-08 . The B73 maize genome sequence was obtained in the form of pseudomolecules in 3-09 ( ftp://ftp . genome . arizona . edu/pub/fpc/maize/ ) and stored in our CoGe platform as database 8082: http://synteny . cnr . berkeley . edu/CoGe/OrganismView . pl ? dsgid=8082; and with draft models annotations in 10-09 from maizegenome . org ( http://ftp . maizesequence . org/release-4a . 53/ ) . The sequence of these two releases is identical . The draft annotated maize sequence will be called “4a . 53” in the few instances where we use the official CDS models . The TIGR 5 Nipponbare rice assembly and annotation was downloaded onto our CoGe platform ( http://synteny . cnr . berkeley . edu/CoGe/ ) before the MSU6 update file://localhost/ ( http://rice . plantbiology . msu . edu/data_download . shtml ) , and was used in 2008 to generate the sorghum gene list used here; the differences between rice TIGR5 and MSU6 annotations are of no consequence to this project . The soybean and Medicago trunculata genomes were downloaded from Phytosome 4 . 0 http://www . phytozome . net/soybean . php and http://www . phytozome . net/medicago . php , respectively , in early 2009 . Sorghum genes were Sbi1 . 4 to which we added many genes on the basis of orthology to rice Nipponbare , TIGR 5; the added genes included many with corresponding RNAs since these are absent in Sbi1 . 4 . Dataset S1 uses the format Sbxgxxxxxx for Sbi1 . 4 genes and sorghum_chrmosomex_startx_stopx for genes we added based on Sb-Os orthology . The detailed syntenic alignment of the entire genomes of sorghum and rice was automated and frozen in September 2009 as the rice-sorghum CNS discovery Pipeline 1 . 0 . Dataset S6 diagrams this pipeline and details each step . What is most important is that any sorghum gene we use in this analysis is shared syntenically between sorghum and least one of the two possible homeologous maize positions . Some of our added sorghum genes are shared with maize as orthologs; those that are not shared with maize were not studied . After adding 10 , 585 new putative genes to the 34 , 003 official JGI ( Joint Genome Institute ) sorghum genes , the augmented sorghum genome was masked for any sequence repeated over 50 times , and then everything but exons or RNA-encoding sequence was additionally masked . This heavily masked sorghum genome was then used to query the maize genome . We found a total of 37 orthologous regions between sorghum and the corresponding maize homeologs retained after the maize alpha tetraploidy event ( Sb-Zm1-Zm2 ) in two ways , and both ways used applications available online in the CoGe comparative genomics platform ( http://synteny . cnr . berkeley . edu/CoGe/ ) . Central to our success was our ability to clearly visualize the locations of the many translocations and inversions that happened in both the sorghum and maize lineages . Knowing all breakpoints makes it clear that any single sorghum chromosome is orthologous to exactly two maize chromosome regions , even though many smaller segments are often involved ( Dataset S2 ) . To this end all of the 37 regions begin and end with at least one gene retained by both maize homeologs . In this way ( Methods ) , a total of 4 , 461 sorghum genes ( 10% of the sorghum genome ) were set up for manual evaluation . In order to define those genes that had an ortholog in maize , we condensed all members of locally duplicated arrays into one gene and discarded those 492 duplicates ( 11% ) , leaving one parent gene for each array . We also invalidated 74 genes that had annotation incongruencies , and then disregarded another 953 genes for which we failed to find maize orthologs . Each sorghum gene is given an evaluation code of “1” ( has an ortholog in the first Zm homeologous region ) , “2” ( has an ortholog in the second Zm homeologous region ) , or “B” ( has an ortholog in both Zm homeologous regions ) . The designations “O , ” “N , ” and “D”: D = local duplicate; N = invalid data; O = no ortholog in Zm . In each of the 37 regions within Dataset S1 , every sorghum gene has been annotated with one of these six symbols . A link ( tinyurl . com , a URL abbreviation service , or genomevolution . org ) is provided for each Sb-Zm1-Zm2 panel to facilitate the repetition of our research in the GEvo alignment graphic tool we used for research ( Dataset S1 ) . We were left with 2 , 943 orthologous Sb-Zm1-Zm2 genes spread over 37 Sb-Zm1-Zm2 regions . CoGe is an integrated collection of maintained databases , algorithms and applications useful to compare complete genomes on demand [28] , [29] and without which it would be difficult to perform our analyses in a reasonable amount of time . SynMap , within CoGe , is a dot plot application that implements the DAGchainer algorithm [30] to identify syntenic lines in two-dimensional arrays of blastn hits between two identical ( to find homeologies ) or different ( to find orthologies ) genomes . Each “dot” is a gene pair . The color of this dot can be portrayed to reflect Ks ( synonymous base-pair substitution frequency ) , so syntenic lines of different ages have different colors ( see Dataset S2 ) . Clicking on any dot in SynMap anchors the GEvo sequence comparison tool and automatically generates a blastn alignment output . Each output ( like a BLAST or LAGAN output ) includes a graph , a link , and can be repeated on demand with different settings . CoGeBlast takes sequence from any other CoGe applications or text as query to any number of genomes; the blastn or tblastx results may be downloaded into GEvo panels . GEvo panels may be combined via links to create experiments . Ks values may be calculated for each data point in SynMap if the genomes being compared are repeat-masked and have annotated CDS sequences . The 4a . 53 maize sequence was used . Several genomic comparisons in SynMap have Ks values pre-calculated , including sorghum/maize . Syntenic gene pairs were identified by using blastn with SynMaps's default settings [−W ( word size ) = 11 , −G ( gap open ) = −1 , −E ( gap extend ) = −1 , −q ( mismatch ) = −3 , −r ( match ) = 1] and an e-value cutoff 0 . 05 . These pairs were used to identify any putative homeologs between coding sequences using DAGchainer to identify collinear sets of putative genes with the following parameters: −D = 20 , −g = 10 , −A = 5 . Ks values for syntenic gene pairs were calculated by first performing a global alignment of virtual protein sequences using the Needleman-Wunsch algorithm [31] implemented in python ( http://python . org/pypi/nwalign/ ) . The BLOSOM62 scoring matrix was used for the alignments [32] . From these protein alignments , the codon DNA alignment was generated through back-translation . Ks values were calculated using codeml of the PAML software package [33] on the codon alignment with the following parameters: outfile = mlc , aaDist = 0 , verbose = 0 , noisy = 0 , RateAncestor = 1 , kappa = 2 , model = 0 , ndata = 1 , aaRatefile = wag . dat , Small_Diff = . 5e-6 , CodonFreq = 2 , runmode = −2 , alpha = 0 , omega = 0 . 4 , fix_kappa = 0 , Mgene = 0 , method = 0 , fix_omega = 0 , getSE = 0 , NSsites = 0 , seqtype = 1 , cleandata = 0 , icode = 0 , fix_alpha = 1 , clock = 0 , ncatG = 1 , Malpha = 0 , fix_blength = 0 . This pipeline is part of the SynMap application in the CoGe suite of comparative genomics software , and its dotplot visualization tool was used to generate the Ks color-coded lines of Dataset S2 , and its text output was used to supply the Ks values for the “sorghum/maize Ks differences” Methods section to follow . Since this Ks pipeline will calculate Ks values for erroneously aligned pairs , values far off from an expected normal distribution for any experiment were discarded . The entire sorghum genome was subjected to a 50× repeat mask , where every base pair that was covered more than 50 times by a blast hit from a whole-genome self-self blast was masked , using parameters of blastn at word size 16 and e-value cutoff of 0 . 001 . Repeats over 50× genome-wide were masked by changing their sequence to “x . ” We needed to use a step-wise approach to accomplish the same 50× mask for maize because a direct self-blast was too memory-intensive for our computers . First we self-blasted pseudomolecules 1–3 as if they were the whole maize genome and masked their 50× repeats . Then we added these 3 larger masked chromosomes to the other 7 unmasked and performed self-BLAST—as with sorghum above . Repeated sequences are color-coded pink in Figure 3 , panels B and C . The sorghum 50× masked genome was further masked for every sequence that is not either an Sbi1 . 4 exon or other sequence shared orthologously with rice , as derived from the rice/sorghum Pipeline 1 . 0 . The non-exon , non-conserved sorghum sequences masked by this method are colored orange in our GEvo graphics ( e . g . Figure 3 , panel A ) . The nine Sb-Zm1-Zm2 regions were derived from SynMap blastn [34] dotplots using the DAGchainer settings −g = 10 genes , −D = 20 genes , −A = 5 genes , and a Ks color code that clearly distinguishes syntenic lines reflecting sorghum/maize orthologs to lines reflecting more ancient syntenies . When a single stretch of sorghum clearly hits two longer stretches of maize , the center of the overlapped region was used as an anchor to create Sb-Zm1 and Sb-Zm2 GEvo panels , which are then combined into a single view . The sorghum/maize Ks-colorized dotplot can be seen in Dataset S2 , where the identification of Sb1 is illustrated . It is possible to regenerate a near-identical graphic in CoGe by visiting http://tinyurl . com/ygx2apu . The 28 additional Sb-Zm1-Zm2 regions were discovered by choosing as query exons from sorghum genes that encode transcription factors . Each query found , using CoGeBlast , two orthologs in maize about one-third of the time . From CoGeBlast output , it is easy to create Sb-Zm1-Zm2 GEvo panels . Lengths of these three chromosomes were adjusted so that a chosen segment of sorghum begins and ends with a retained gene , was entirely represented syntenically within the two maize segments , and syntenic coverage did not improve by adding 500 kb on both sides of the maize chromosomes . Inversions do not cloud our analyses because all inversions we include begin and end within each region . Our primary data of Dataset S1 required that every gene on the sorghum gene list receive one among several possible annotations . Genes in local arrays were marked as parent , duplicate ( D or DUP ) , or interrupter ( a gene located within a tandem repeat ) using published methods [7] and duplicates were marked and ignored subsequently; up to three interrupter genes were permitted . If a remaining gene occurred syntenically ( blastn bitscore >50 ) on a maize homeolog , then it was coded “1” or “2” if it occurred on only one of the homeologs or “B” if it occurred on both . A few genes were invalidated for technical reasons , and some genes were not found in the syntenic position in either maize homeolog ( encoded as “0” ) . Genes represented by fragments were counted as “present” even though they were almost certainly in the process of removal . In this manner , each of our 37 Sb-Zm1-Zm2 regions were reduced to a code of shared genes , like B1122BBB12BB2B21BBB11B21B , and trimmed to begin and end with a B ( present in both maize homeologs ) where the terminal Bs were not within inversions . For the diagrams of Figure 3 B , C , and D and Dataset S2 , and for all analyses of runs , as discussed in the text we removed runs of 1's or 2's that extended beyond nine genes . This is because our analyses suggested that a run of 10 or more 1's indicates that the 10 genes that would be the corresponding 2's had jumped elsewhere in the genome . The unmodified data are in Dataset S1 . At this time , accurate fractionation annotations would be difficult or impossible to achieve automatically largely because of biological complications involving inversions and also by contig misalignments during sequence assembly . The binomial test was used to evaluate the probability that the ratio of deletions on the maize homeologs could occur by chance given an expectation that a single deletion is equally likely to occur on either homeolog . The distribution of all observed deletion lengths is plotted in Figure 4 as the blue bars for the over- and under-fractionated homeologs . Using the initial hypothesis of a deletion mechanism that independently eliminates one gene at a time , a simulation of gene loss was carried out . Starting with a length equal to all genes , both deleted and still present , genes were deleted at random until the simulated number of deletions was equal to the true observed number . The distribution of apparent deletion lengths for the run was then saved and the preceding steps were repeated 1 , 000 times . This gives a distribution of frequencies of all deletion lengths . The median number of apparent deletion runs from these simulations is shown by the white circles in the grey lines of Figure 5 , with grey line itself marking the values between which the results from 95% of the simulations fall . For Figure 5E , which plots runs of genes conserved on both maize homeologs , the above model was modified by generating two lengths each equal to the total number of sorghum genes within the dataset , and then deleting genes from either one or the other sequence ( with an bias for deleting genes from one or the other dataset equal to that observed in the overall fractionation dataset ) until the number of retrain genes ( Bs ) was equal to the true number observed , with the constraint that once a simulated gene was deleted from one dataset , the orthologous gene in the other dataset would never be deleted . As the simulated distribution did not perfectly match the observed results , a genetic algorithm using 20 ( genetic ) character states , each representing a 5% ( 1/20 ) chance that a deletion would be some length between 1 and 5 genes long was used to determine , given the region length and the distribution of observed deletion lengths , the ratio between different deletion lengths to use in the simulation described above to achieve the best match between simulated and observed data . The fitness of solutions in the evolutionary algorithm were scored using the Monte Carlo method described in the proceeding paragraph ( with the modification that rather than fixing the deletion length at 1 gene , deletion lengths were selected using the weighted averages generated by the evolutionary algorithm ) with the most fit solutions being those where the median simulated number of deletion runs was least different from the observed number of runs . The genetic algorithm was allowed to run for 100 , 000 generations . These new weighted average deletion lengths can then be used to generate new sets of expectations for data , as seen in Figure 4D . The script used to run the genetic algorithm is available at http://code . google . com/p/bpbio/source/browse/trunk/scripts/fractionation/fractionation_ga . py and in Dataset S7 . Sorghum genes with known orthologs in rice were blasted against the sorghum ( JGI 1 . 4 gene models ) , rice ( TIGR 6 . 0 ) , and maize ( 4a . 53 filtered gene set; maizegenome . org ) datasets . We used the score of the best sorghum-rice alignment as a cut-off to avoid hits from genes that diverged before the rice sorghum split , and removed genes with more than one hit above that threshold in the sorghum-sorghum blast to avoid the inclusion of genes that duplicated in the sorghum-maize lineage since the divergence from rice . These criteria left us with a set of approximately 10 , 000 genes with a single hit in sorghum that had a greater bit score than any hit in rice , and one or more hits satisfying the same conditions in maize . 406 genes from this dataset overlapped with genes identified as retained ( noted as “B” in Dataset S1 ) by manual annotators , and 771 overlapped with genes identified as fractionated . Stretches of 10 or more genes deleted from the same chromosome were identified on Dataset S1 and the missing region was identified by a discontinuity in the appropriate sorghum/maize dot plot . We built a string of exons that identified each gene in the deleted region and used it as query to the subject maize genome . The maize genome was 50× repeat-masked , as described , and blastn used settings of word size 7 , and e-value <0 . 001 . Hits were achieved in CoGeBlast and evaluated in GEvo . Any three of the expected genes , arranged syntenically , in unexpected regions of genome were taken as evidence for a segmental translocation even though a gene might have been represented by a fragment rather than an entire gene . The coding sequences of the subset of genes from the JGI sorghum 1 . 4 gene set that had been identified as orthologous to a single rice gene were blasted against the MSU6 rice gene set and the maize 4 . a53 filtered gene sets as well as against the same sorghum gene set . For each sorghum gene , the bit score of the highest-scoring alignment against a rice gene was used as a cutoff to exclude hits from genes that had diverged from the gene being tested before the rice/sorghum split . Sorghum genes that hit one or more additional sorghum genes with bit scores higher than that cutoff were excluded from the analysis to exclude genes duplicated in the maize/sorghum lineage since the rice/sorghum split . The number of hits to genes in the maize filtered gene set for the remaining sorghum genes ( with scores higher than the best hit in rice ) was recorded . After the accuracy of a sample of the results were manually checked using CoGe , the final data were generated by looking at the average number of maize genes found using this process for genes assigned to the fractionated and unfractionated categories by manual annotation . Ks values for shared open reading frames in sorghum and maize ( 4 . a53 ) were precalculated and loaded into SynMap , in CoGe as described previously in METHODS . The sorghum/maize orthologs that also fell into the 37 regions that were hand-annotated for the primary fractionation data ( Dataset S1 ) were identified . Next , sorghum genes that hit to genes in both maize homeologs ( encoded “B” ) were paired and their Ks values compared . Data were reported in the format Sb-Zm1-under-fractionated/Sb-Zm2-over-fractionated . Visual examination of the Ks data showed a minority portion of very extreme ratios , likely the result of misalignments , alignments to pseudogenes , or alignments to non-orthologous genes . Such misalignments were expected due to the fragmented nature of many B73 genes and contig assembly error . The 16% of pairs with the most extreme ratios as compared to the median were removed from the dataset and not used to calculate results . We overlaid McrBC methylation data from [22] onto the annotated maize pseudomolecule sequence ( dataset Zm 4a . 53 ) and uploaded the modified database into the genome viewer we use with CoGe: GenomeView . We were able to visualize on GenomeView the locations of methylated sites on maize chromosome regions . After anchoring both maize homeologs to their orthologous sorghum sequence with the stop-start sites used in our fractionation analyses , we manually counted the number of methylation peaks in each maize homeologous region in question . Using GEvo , with parameters set for blastn with a spike-length of 15 bp , we visually scanned all retained maize genes from our Sb/Zm1/Zm2 dataset to look for gaps within exons of one or the other maize homeolog . This level of resolution did not permit us to identify single gaps less than approximately 15 bp long . However , we did not intend to be exhaustive . Once a gap was identified , we extracted the sorghum exon sequence and used it as query in a blastn comparison to rice; this use of the rice as a secondary outgroup often confirmed the sorghum full-length exon annotation , and when it did , we re-blastn'd this sequence against the multiple subjects rice ( Oryza sativa v5 masked repeats 50×X ) , sorghum ( vSbi1 . 4 exons , 50×X mask+syntenic thread with Os ) , and maize v4a . 53 to produce GEvo images like that shown in Figure 5A . We then took the corresponding exon sequence data from rice , sorghum , and both maize homeologs and used ClustalW ( http://www . ch . embnet . org/software/ClustalW . html ) to visualize the sequence alignment surrounding the gap , as well as the sequence on the homeolog without the deletion ( as in Figure 5B ) . | All genomes can accumulate dispensable DNA in the form of duplications of individual genes or even partial or whole genome duplications . Genomes also can accumulate selfish DNA elements . Duplication events specifically are often followed by extensive gene loss . The maize genome is particularly extreme , having become tetraploid 10 million years ago and played host to massive transposon amplifications . We compared the genome of sorghum ( which is homologous to the pre-tetraploid maize genome ) with the two identifiable parental genomes retained in maize . The two maize genomes differ greatly: one of the parental genomes has lost 2 . 3 times more genes than the other , and the selfish DNA regions between genes were even more frequently lost , suggesting maize can distinguish between the parental genomes present in the original tetraploid . We show that genes are actually lost , not simply relocated . Deletions were rarely longer than a single gene , and occurred between repeated DNA sequences , suggesting mis-recombination as a mechanism of gene removal . We hypothesize an epigenetic mechanism of genome distinction to account for the selective loss . To the extent that the rate of base substitutions tracks time , we neither support nor refute claims of maize allotetraploidy . Finally , we explain why it makes sense that purifying selection in mammals does not operate at all like the gene and genome deletion program we describe here . |
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The ability to induce a defense response after pathogen attack is a critical feature of the immune system of any organism . Nucleotide-binding leucine-rich repeat receptors ( NLRs ) are key players in this process and perceive the occurrence of nonself-activities or foreign molecules . In plants , coevolution with a variety of pests and pathogens has resulted in repertoires of several hundred diverse NLRs in single individuals and many more in populations as a whole . However , the mechanism by which defense signaling is triggered by these NLRs in plants is poorly understood . Here , we show that upon pathogen perception , NLRs use their N-terminal domains to transactivate other receptors . Their N-terminal domains homo- and heterodimerize , suggesting that plant NLRs oligomerize upon activation , similar to the vertebrate NLRs; however , consistent with their large number in plants , the complexes are highly heterometric . Also , in contrast to metazoan NLRs , the N-terminus , rather than their centrally located nucleotide-binding ( NB ) domain , can mediate initial partner selection . The highly redundant network of NLR interactions in plants is proposed to provide resilience to perturbation by pathogens .
Signal Transduction ATPases ( STAND proteins ) comprise an ancient group of modular proteins sharing a conserved nucleotide-binding ( NB ) domain [1] . STAND proteins are present in Archaea , Bacteria , and Eukaryota , implying a common , ancient evolutionary origin [1 , 2] . Duplications and associations of the NB domain with other functional domains have driven their divergent evolution , allowing them to participate in multiple signaling processes . Typically , STAND proteins act as intracellular receptors triggering cellular signaling responses upon elicitation . In animals , members of two major groups of STAND proteins , the nucleotide-binding oligomerization domain ( NOD ) -like receptors ( also referred to as NACHT [1] or animal nucleotide-binding leucine-rich repeat receptors [NLR] ) and the nucleotide-binding ARC [1] domain ( NB–ARC or simply NB ) -containing apoptotic proteins , include some of the key players involved in the induction of immune responses or programmed cell death ( pcd ) , respectively [1 , 3] . The NB domain controls the transition from a resting to an activated state through its involvement in differential adenosine diphosphate ( ADP ) /ATP ( or guanosine triphosphate [GTP] ) binding and nucleotide hydrolysis [1 , 4] . The best studied STAND protein , the Apoptotic Protease Activating Factor 1 ( APAF1 ) , induces pcd in human cells upon perception of cytochrome C released from mitochondria [5] . APAF1 activation triggers a conformational change that frees its C-terminal caspase-recruitment domain ( CARD ) and exposes its NB domain , enabling interactions with other APAF1 monomers [5] . Subsequently , intermolecular interactions are formed between the NB domains of adjacent monomers allowing formation of a circular heptamer called the apoptosome . The apoptosome is the active form of the protein and can initiate a caspase-signaling cascade resulting in pcd [6 , 7] . Similarly , APAF1 orthologs in Drosophila and Caenorhabditis elegans ( DARK1 and CED-4 , respectively ) form multimeric assemblies upon their activation and trigger pcd [8 , 9] . The NOD domains of metazoan NOD-like receptors also interact to form oligomeric assemblies of nine or more subunits [10 , 11] . The bacterial transcription factor MalT , which is evolutionarily related to ancestral STAND proteins , similarly oligomerizes to form a curved homopolymer upon its activation [12] . In all of these cases , oligomerization of the central NB or NOD domain serves to bring the N-terminal domains in close proximity , allowing their partners to interact and induce downstream signaling [13] . Hence , formation of apoptosome-like complexes facilitating the induced proximity of N-terminal domains may represent a common feature of STAND proteins [13] . In plants , numerous STAND receptors are present , and those that have been functionally characterized are mostly involved in innate immunity , conferring protection against diverse pests and pathogens [14 , 15] . At least part of their recognition specificity can be attributed to highly variable leucine-rich repeats ( LRRs ) , defining the C-terminal portion of plant NB-LRR receptors or plant NLRs . The majority of hundreds of genetically characterized disease resistance traits in plants map to genes encoding NLRs; the large numbers of such sequences in the genome and their high diversity reflect dynamic interactions between hosts and rapidly evolving pathogens [16] . NLRs are integral to effector-triggered immunity ( ETI ) [14] through direct or indirect recognition of effectors ( virulence-enhancing proteins secreted by pathogens during infection ) . ETI complements the less specific microbe-associated molecular pattern ( MAMP ) -triggered immunity ( MTI ) mediated by extracellular receptor-like kinases ( RLKs ) [14] . The NB–ARC domains of APAF1 ( a NB–ARC-WD40 type of receptor ) and its orthologs are the most similar at the sequence level of the NLRs outside the plant kingdom . Instead of a CARD , the N-termini of plant NLRs contain ( with some exceptions ) sequences similar to either Toll/interleukin-1 receptor ( TIR ) or a coiled–coil ( CC ) domains , allowing subclassification into TIR–NLRs ( TNLs ) and CC–NLRs ( CNLs ) [17] . In dicotyledonous plants , TNLs are sometimes more abundant than CNLs; however , in monocots , CNLs provide the core repertoire of receptors mediating ETI [18] . NLR activation most likely releases the N-terminal CC or TIR domain ( similarly to the release of CARD upon APAF1 activation ) to induce defense responses , which are often concomitant with pcd [19] . This type of ETI response is often referred to as the hypersensitive response ( HR ) [20] . Even though plant NLRs trigger pcd , universal signaling mediators ( such as caspases in case of APAF1 ) activated by plant NLRs have not been identified to date [3] . Unlike in vertebrates , which typically have fewer than two dozen NB–ARC or NOD proteins encoded in their genomes , the number of NLRs expressed in a single plant may exceed several hundred [21] . This large number provides potential for homo/heteromerization , but formation of multimeric complexes in plants following NLR activation has so far not been conclusively demonstrated [22 , 23] . Hence , it was currently an open question whether plant STAND proteins oligomerize upon activation . Consistent with their involvement in downstream signaling , in planta expression of TIR or CC domains alone can induce HR [24–27] , but their potential role in NLR oligomerization is unclear . Some TIR domains form dimers or even homotypic heteromultimers ( implying the existence of higher-order complexes ) when expressed without the adjacent NB and LRR domains , suggesting their involvement in NLR multimerization [23 , 28–30] . The extended CC domain of the barley powdery mildew resistance 10 ( Mla10 ) receptor ( Mla10-CC ) forms a helix-loop-helix rod-shaped homodimer , and mutations affecting dimerization compromise Mla-mediated resistance against powdery mildew [26 , 31] . Subsequent structural studies proposed that only the extended Mla10-CC dimerizes and folds into a monomeric four-helix bundle structure , a structure similar to that reported for CCs of wheat stem rust resistance 33 ( Sr33 ) and potato virus X resistance ( Rx ) receptors [32 , 33] . Full-length CC domains of Mla10 and Sr33 form homomeric and heteromeric associations , disruption of which compromises induction of cell death [32 , 34] . The observed monomeric and dimeric CC structures may reflect different states in a receptor’s activation [35] . Similarly , self-association of CC corresponding to Arabidopsis resistance to Pseudomonas syringae ( avrRpm1 ) ( RPM1 ) receptor appeared to be required for its activity [36] . Furthermore , heterodimer formation between the CC domains of two rice CNLs , RGA4 and RGA5 , is required to respond to the Avr-Pia effector from the fungus Magnaporthe oryzae [22] . So although homo- and heterodimerization of N-terminal domains had been shown for some CNL proteins , it was unknown whether the full-length proteins form higher-order complexes in plants and , if so , what the role of the CC was in this process [37] . NLRs in plants can be categorized into two functional groups , the sensors and the actors ( also referred to as helpers or activators ) , in which sensor NLRs are proposed to detect the pathogen-derived effectors , and an evolutionary conserved downstream-signaling partner NLR triggers defense [38–40] . The sensor/actor concept emerged after the discovery of a conserved class of CNLs referred to as CCR–CNLs , RPW8- , or NRG-like CNLs that share a distinct consensus sequence of their CC ( CCR ) domains [24 , 38] . CCR–CNLs are required for the activity of some canonical CNLs and TNLs , and expression of the CCR domain alone triggers extensive pcd , consistent with their proposed actor role in downstream immune–signaling [24 , 39] . Physical association between putative sensors and CCR–CNLs had not been demonstrated and how the phylogenetic diversity of CC domains reflects their roles in CNL cross-activation and signaling remains unknown [37] . To investigate the network of CNLs mediating ETI in plants and to elucidate the role ( s ) of CC domains in induction defense , we performed extensive genome-wide functional and in silico analyses of N-termini containing a predicted CC domain of nearly all of the CNLs in Arabidopsis thaliana ecotype Columbia-0 ( At-Col-0 ) . By combining data on their sequence variation with their ability to homo-/heterodimerize and induce cell death and/or disease resistance in three different plant species , we identified regions required for their function . Subsequent genetic mapping and reverse complementation confirmed the involvement of canonical receptors in CNL signaling in other plant species , implying that the role of CC domains in downstream signaling involves transactivation of other CNLs . Surprisingly , a highly variable part of the CC domain is required for this transactivation . Accordingly , we present two lines of evidence that NLR receptors form a network mediated by physical and functional associations .
CC domains are defined by heptad repeats of hydrophobic residues ( L , I , or V ) [41]; these form a binding interface of α-helical secondary structure that is involved in helix-to-helix binding [42] . We aligned the sequences of N-terminal fragments representing all CNL receptors predicted in At-Col-0 , including sequences encoding truncated receptor proteins referred to as CC–NBs ( CNs ) [17] . The alignment was refined using structure-based words ( patterns such as hydrophobic heptad repeats along with their predicted accessibility ) devised from crystallographic/NMR data of Mla10-CC , Sr33-CC , and Rx-CC [26 , 32 , 33] . The alignment ( S1 Fig ) revealed four major Groups that we designated Group A , B , C , and D and an E outgroup ( S2 Fig ) . The naming of these Groups is based on a previous study in which At-Col-0 CNLs were clustered based on the topology of their NB–ARC domains and the intron/exon features of the encoding genes [17] . A classical sequence alignment ( Clustal Omega ) [43] using the CC and NB–ARC sequences resulted in a very similar cladogram as compared to the NB–ARC domain sequence alone ( S3 Fig ) , implying that the CC domains follow a similar pattern of diversification as the remaining part of the receptor . However , the obtained trees were not identical; whereas , for instance , Groups B and D are well-defined in both studies , three members of Group C ( AT4G19060 , AT5G45440 , and AT5G45490 ) were placed with Group A in the CC–NB–ARC-based cladogram ( S3 Fig ) . These differences can be attributed to high diversity and rather ambiguous alignments of the CC-containing fragment . However , for consistency with the existing literature , we applied the same letter designations as before [17] . Group E gathered N-terminal fragments of the CN homologs lacking a clearly defined CC domain precluding their structure-based alignment ( S1 Fig , S2 Fig ) . Group D had the highest average sequence identity ( id . ) /similarity ( sim . ) of the Groups to sequences of Mla10 or Sr33 ( Mla10: id . 18 . 0% , sim . 44 . 3%; Sr33: id . 17 . 5% , sim . 39 . 2%; Rx: id . 11 . 1% , sim . 30 . 2% ) . Groups C and D , comprising eight and 14 members , respectively , were the most similar and shared the four predicted α helices of the CC domains of Mla10 , Sr33 , and Rx: H1a , H1b , H2a , and H2b [26 , 32] ( Fig 1 , S2 Fig ) . Even though all members of Group B also contain four predicted α helices , these helices did not align with helices predicted for Groups C and D ( S2 Fig ) . Detailed secondary structural analysis of Groups C and D revealed a profile that resembled Mla10–CC and Sr33–CC more than Rx–CC ( S1 File ) . This is seen especially in the H1a–H1b turn region; for these Groups , the separation of the first two α helices ( H1a and H1b ) was not consistently predicted , which is in agreement with crystallographic data for Mla10–CC , in which helices H1a and H1b form one single helix H1 [26] . However , the molecules may adopt different secondary structures in the presence of an interacting partner . In contrast , to the H1a–H1b region , the predicted helices H1b , H2a , and H2b were clearly separated from each other by areas of flexibility , which corresponds to turns in the three resolved CC structures [26 , 32 , 33] ( Fig 1 , S1 Fig , S2 Fig ) . The strongest sequence identity within Groups C and D occurs around the five-amino acid–long EDVID motif in helix H2a; in addition , Group D contains a conserved stretch of 10 polar amino acids immediately preceding this motif ( S2 Fig ) . The CC–NB–ARC-based phylogeny ( Fig 2A , S3 Fig ) places Group D within Group C; thus , both can be considered a merged C/D Group . In accordance with earlier reports , we did not find a clearly distinguishable EDVID motif in Group A and Group B , yet in Group B the corresponding area of eight amino acids showed some conservation , including a hydrophobicity distribution similar to the EDVID motif ( Fig 1 , S1 Fig ) . In Group B , representing more than a third of all analyzed CNLs , structure-based alignments predicted the existence of two short β-strands immediately preceding the first H1a helix and immediately following the H2b helix ( Fig 1 , S2 Fig ) . Also , only CNLs in Group B carry the previously described [44] putative myristoylation ( Gly-2 , Gly-3 ) and palmitoylation motifs ( e . g . , Cys-4 , Ser-4 , and others ) in their N-termini . In contrast to the N-terminal domain of Mla10 , Sr33 , and Sr50 , in which all four α helices appear to form CC structures , available algorithms [45] only predict formation of CC structures among N-termini of At-Col-0 CNLs for helices H1a and H1b . The smallest group , Group A ( comprising CCR–CNLs ) include the three previously described activated disease resistance 1 ( ADR1 ) homologs: ADR1 , ADR1-L1 , and ADR1-L2 [47 , 48]; two NRG1 homologs ( AT5G66900 and AT5G66910 ) [24]; and a third NRG1-like protein annotated as DAR5 ( AT5G66630 ) ( Fig 1 ) . Each of these six homologs contain RPW8-defined consensus sequences and four predicted α helices , yet these did not align with those of Groups B , C , and D ( Fig 1 , S1 Fig ) . In contrast to canonical CNLs , formation of CCs by N-termini of CCR–CNLs was predicted for three C-terminal α-helices but not the first α-helix . Heteromeric interaction between CC domains of the rice RGA4 and RGA5 CNLs and homomeric association of Mla10-CC have been previously detected using yeast 2 hybrid ( Y2H ) assays [22 , 26] . Therefore , we used Y2H to assess homo- and heteromeric interactions of the N-terminal CNL fragments . We cloned 56 DNA fragments encoding the N-terminal regions of CNLs , referred to hereafter as extended CC domains ( ECCs ) ( Fig 1 , S2 Fig , S2 File ) . Each ECC contains the entire N-terminus up to the predicted P-loop in the NB–ARC domain . This region includes the amino acids immediately preceding the predicted α-helices and the linker encompassing the pre-P–loop motif ( Fig 1 ) . Next , we tested their interactions in Y2H assays using initially two and later one pair of vectors , creating fusions to binding and activation domains ( see Materials and methods ) . Five ECC fragments showed homomeric associations ( Fig 2B ) , while 123 heteromeric interactions were observed involving 39 ECCs . Interactions occurred at similar frequencies between sequence-related and sequence-unrelated ECCs . There seems to have been no evolutionary selection toward homomeric associations because homomerization was observed only for ECCs that also interacted frequently with other partners . If ECCs had evolved to facilitate CNL homodimerization ( like previous studies have implied [22 , 26 , 32] ) , homomeric interactions should be prevalent , and heteromeric interactions between ECCs corresponding to close paralogs should be infrequent . However , 34 ECCs showed higher affinity toward at least one partner other than themselves . The large number of heteromeric interactions suggests that N-termini–mediated heteromerization is a common feature of CNLs . To better understand the molecular features within ECC domains that may be required or involved in the interaction , we correlated their sequence variation and the ability to interact in Y2H with their predicted CC monomeric and dimeric structures . First , all possible models of monomeric and dimeric structures that could be derived from available crystal structures were built . Based on knowledge and physical binding free energy calculations , the most probable models were retained ( S1 File ) . The monomeric structural model ( mono4α ) resembles the four-helix bundle observed in Sr33–CC [32] , whereas the dimer model resembles the intertwined CC structure ( 2α ) observed in Mla10–CC [26] . Binding free energy calculations ( S1 File ) suggests that dimer configurations based on two mono4α domains have a higher binding free energy than the 2α monomers and are less likely . Second , we identified sequence variants that could be correlated with the ability to interact . For this , we focused on members of Group D , as these share overall high-sequence similarity but differ in their ability to interact ( Fig 2B ) . As compared to rarely interacting ECCs in Group D , four amino acids were found to be conserved and unique to frequently interacting ECC domains of AT1G58390 , AT1G58848 , AT1G59218 , AT1G58807 , and AT1G59124: C21 , S42 , V57 , and R107 ( S1 Fig ) . To resolve their putative location on the protein surface , the positions of these residues were mapped on both the monomeric and dimeric 3D models ( Fig 3 ) . In the dimer , C21 , S42 , and R107 add up to six amino acids located on the H1 helices . All six residues are surface exposed and on the same face of the protein , while V57 has a more lateral location . In the monomer structure , C21 resides in a highly flexible region and may reach the proximity of the EDVID motif ( Fig 3 ) , whereas in the 2α dimer configuration , it resides in a rigid region , and the EDVID motif is no longer within reach . The genetic variation in Group D enabled correlating oligomerization potential to four amino acids residues that likely form a patch on the CC surface . However , the exact role of these residues in this process remains to be resolved in future studies and awaits elucidation of the protein structure . Transient expression of a CC domain can induce necrosis and activate defense-related genes , thereby recapitulating induction of HR and defenses triggered by activated full-length CNLs [24 , 26 , 34] . To obtain a comprehensive assessment of abilities to induce cell death across CNLs , we transiently expressed all ECCs in At-Col-0 plants using the Tobacco Rattle Virus ( TRV ) system [49 , 50] . Arabidopsis is a good host for TRV supporting its systemic spread [51 , 52]; therefore , utilization of TRV allowed us to monitor not only necrosis but also disease resistance by assessing viral spread using reverse transcription PCR ( RT-PCR ) ( Fig 2 ) . Expression of ECCs in At-Col-0 plants induced various phenotypes . These responses varied from very mild leaf deformations to more severe deformations of the entire plant and , in extreme cases , necrosis or even death of infected plants ( Fig 2C ) . In a separate category of response , full resistance was observed in which viral movement was blocked without any development of symptoms , and accordingly , TRV was not detected in leaves after inoculation . Different colors and numbers ( zero through six; legend in Fig 2C ) were assigned to these distinct categories for visualization and correlation analysis . None of the members of Group D elicited a macroscopically visible response ( Fig 2B ) . Expression of ECCs corresponding to the CN genes [17] belonging to Group E also did not induce any visible response in At-Col-0 plants . However , within Group C , two ECCs induced necrosis ( AT3G46710 and AT3G46530 ) , and one triggered mild plant deformations ( AT3G14470 ) ( Fig 2 ) . In Group B , 14 of the 23 ECCs induced either necrosis or resistance in infected plants . Notably , all ECCs corresponding to CCR–CNLs ( Group A ) induced either necrosis or resistance . This Group includes AT5G66630 , which our functional analysis classified as a third functional NRG1 homolog , raising the total number of CCR–CNLs in At-Col-0 to six . Besides CCR–CNLs , the ability to induce cell death was more prevalent ( but not exclusive ) for ECCs in Group B lacking the conserved EDVID motif ( Fig 1 ) . In conclusion , the ability of ECCs to trigger immunity varied over the different Groups from none ( Groups D and E ) to all members ( Group A ) . Notably , whereas some ECCs triggered necrosis to various extents , others had the ability to fully block viral movement , showing that expression of an ECC alone can be sufficient to trigger disease resistance . CC-mediated oligomerization may be involved in communication between sensor and actor CNLs at either activation or downstream-signaling stages . To assess the requirement of these interactions for immune/HR signaling , we investigated whether these properties were correlated within and between NLR Groups . Therefore , we mapped all known resistance specificities onto the ECC interactome in order to link known functions to the ( in ) ability of the respective ECCs to interact and/or induce necrosis/resistance . The number of interactions , including self-association , and the ability to induce necrosis or resistance between ECCs were not significantly correlated ( r = −0 . 035; p ≤ 0 . 1 ) . Apparently , the ability of ECCs to interact is independent of their ability to trigger immune responses . Accordingly , we found that many noninteracting ECC fragments triggered necrosis ( e . g . , ECC corresponding to AT1G12290 , AT1G61180 , and AT4G27190 or ECCs corresponding to CCR–CNLs: AT5G66900 or AT5G04720 ) , while several ECCs showing extensive interactions lacked the ability to induce cell death ( e . g . , ECC corresponding to AT5G63020 or ECCs corresponding to the bottom clade within Group D; Fig 2C ) . Four out of five ECC fragments capable of homomerization triggered only mild or moderate plant responses . Notably , the same five fragments were also among the most frequent interactors with other partners , implying common features required for both associations and promiscuity in these interactions . CCR–CNLs have been proposed to function as actors [24] for sensor CNLs to induce defenses following pathogen perception . This hypothesis corresponds with the ability of their N-termini to induce cell death [38 , 53] . In our screens , the frequency of interactions between CCR–ECCs ( putative actors ) and ECCs corresponding to canonical CNLs ( putative sensors ) were not different ( p ≤ 0 . 05 ) from frequencies of interactions between ECCs of canonical CNLs . Because ECCs of several canonical CNLs induced cell death similarly to CCR–ECCs ( implying that they also function as actors ) , we examined whether necrosis-inducing ECCs interacted more frequently than those that did not . Again , the frequencies of such interactions were not higher than expected from random distribution . Thus , despite the fact that in our assays , ECCs of several putative sensors , such as RPS5 , ZAR1 , or RPP13 homolog ( note that the ECCs of RPS5 and ZAR1 did not induce cell death ) , interacted with several CCR–ECCs , we did not find evidence for higher prevalence of such interactions as compared to associations of ECCs corresponding to putative sensors . Moreover , the ECC of RPS2 , a receptor whose function depends on three CCR–CNL homologs ( ADRs ) [38] , did not interact with any of the ADR ECCs in our screens . Accordingly , based on the ECC interactome , we did not find evidence for preferential CC-mediated communication between putative sensors and actors; however , this does not preclude the possibility that sensors and actors may interact transiently in planta or via other domains , as shown for the RGA4/RGA5 pair in rice [22] . The extensive heteromeric interactions between ECC members from different classes is suggestive for such a signaling network . Genetic support for a CNL-signaling network comes from mutating hubs that compromise its activity . Despite mutating many putative interacting NLRs , no loss of necrosis was obtained upon ECC expression ( Fig 2B , S3 File ) . This result is consistent with the resilience of a network in that other hubs can take over the function of the mutated node . To obtain evidence for a CNL-signaling network activated by At-Col-0 ECCs , we assessed the activity of these fragments in heterologous species . The rationale was that in a heterologous species , redundancy might be lower , as the node ( representing a CNL ) did not coevolve with the ECC that triggers the response . Occurrence of an ECC-induced response in evolutionarily distant species would imply conservation and compatibility of the immune-signaling network in these species . Furthermore , if a polymorphic response is obtained in a heterologous species , it might allow identification of the ECC-interacting partner ( s ) . To evaluate the ability of CNLs to induce defenses in other species , we expressed all At-Col-0 ECCs in Nicotiana benthamiana ( Nb ) and in lettuce , Lactuca sativa ( Ls ) cultivar ( cv . ) Ninja ( Fig 4 ) . ECCs corresponding to CCR–CNLs induced strong responses not only in Nb , as reported previously [24] , but also in lettuce and the source species Arabidopsis . Out of the 16 Group B ECCs that triggered necrosis or resistance in At-Col-0 , 13 induced necrosis in Nb and five did in lettuce , implying greater compatibility with downstream-signaling components in the source species and in Nb than in lettuce . This response is unlikely to be triggered by general toxicity because for some ECCs induction of necrosis was clone-specific and sometimes limited to one or two of the three species tested . The finding that many ( and often the same ) CC fragments triggered responses in distantly related species indicates conservation of compatibility with specific signaling partners . Induction of cell death by ECCs in the three plant species allowed us to screen for an ECC/species combination that showed a polymorphic response upon At-Col-0 ECC expression . Interestingly , transient expression of ECC of AT4G14610 in lettuce cv . Ninja triggered clear cell death , whereas it did not elicit necrosis in cv . Valmaine ( Fig 4 , Fig 5A ) . This differential response allowed genetic mapping to determine the genomic location of the potential AT1G14610-signaling partner in lettuce . In F1 hybrids between cv . Ninja and Valmaine and derived F2 plants , we observed intermediate phenotypes , indicating that either the ability to induce necrosis in cv . Ninja or the lack of a response in cv . Valmaine is due to incomplete dominance . Variation in plant response to the ECC of AT4G14610 expression was assessed in 75 individual F2 plants on a scale from 1 to 5 . All plants were genotyped , and subsequent QTL mapping linked the variation to a single locus on linkage group 3 ( LG3 ) of lettuce ( Fig 5A ) . This locus contains candidate disease resistance genes , including sequences encoding TNLs and a large CNL family previously described as the Resistance Gene Candidate 21 family ( RGC21 ) [55] . In a previous project , transgenic lettuce plants of cv . Cobham Green ( CG ) , referred thereafter as CGsil-RGC21 , were generated expressing an interfering hairpin RNA ( ihpRNA ) designated to trigger posttranscriptional gene silencing ( PTGS ) of RGC21 family members [55] . The ECC of ATG14610 did not trigger necrosis in cv . CG; consequently , cell death assays could not be performed in CGsil-RGC21 . Therefore , these silenced CGsil-RGC21 plants were outcrossed to cv . Ninja and to cv . Valmaine . Among 30 F1 hybrids derived from the cross between cv . Ninja and CGRNAi-RGC21 , 14 were identified as silenced and 16 as not silenced for RGC21 , which is as expected due to the hemizygous state of the transgene in CGRNAi-RGC21 ( Fig 5B ) . All 16 nonsilenced hybrids showed a similar weak necrosis following expression of ATG14610–ECC as F1 cv . Ninja x cv . Valmaine F1 hybrids and many F2 plants derived from the same cross ( Fig 5B ) . In contrast , similar to wild-type cv . CG plants , none of the 14 RGC21-silenced hybrids showed necrosis , indicating that one or more RGC21 member ( s ) are required for induction of ATG14610-mediated cell death . Furthermore , none of the cv . Valmaine x CGRNAi-RGC21 hybrids showed necrosis despite many being identified as silenced . Notably , all accessions and hybrids described above responded with moderate necrosis to transient expression of bacterial effector AvrPto [58] , indicating that the activity of RGC21 member ( s ) in induction of cell death is specific to the ECC of ATG14610 . The sequences and repertoire of RGC21 paralogs in cv . Ninja are unknown , and the exact identity of the RGC21 member ( s ) acting downstream of ATG14610 remains undetermined . However , reverse BLAST of RGC21 sequences ( Gene Bank accession number EU889315 . 1 ) to At-Col-0 sequences specifically identifies multiple members of Group C/D as closest homologs , indicating that ATG14610 requires member ( s ) of this Group for cell death induction in lettuce . From this experiment , we concluded that an At-Col-0 ECC requires CNL partner ( s ) to induce cell death and that despite the large diversity among NLR receptors , compatibility between CNLs can be retained across distantly related plant species . To identify the region/motifs within ECCs required for their activity , we searched for correlations between all 56 sequence variants and their ability to trigger cell death . Following the predicted four α helices , each ECC fragment contains a variable linker that separates the last α helix ( H2b ) from the pre-P–loop at the beginning of the NB–ARC domain . Alignments of all ECCs refined the consensus of a pre-P–loop motif among CNLs in Arabidopsis to V/IG x ( 8 ) L/I x ( 3 ) L and disclosed a cluster of charged amino acids within the linker region . This cluster , which we refer to as the “charged motif , ” maps to positions −11 to −3 relative to the highly conserved VG residues of the pre-P–loop motif ( S1 Fig ) . To determine exactly which regions in the ECC are required for At CNLs to trigger signaling , we generated a series of deletions , swaps , and point mutants involving cell death–inducing and non-cell death–inducing ECCs to delineate the region responsible for induction of cell death ( Fig 6 , S4 File ) . We focused on four ECCs in Group B because their high homology and accurate alignment allowed precise swaps , yet they had different phenotypes to differentiate the output . The following amino acids residues were used as break points to create chimaeras: the last conserved hydrophobic residue of heptad repeat of H2b ( referred thereafter to as CC-END ) , the start ( VG residues ) of the pre-P–loop , and the charged motif ( Fig 6 ) . Because the selected ECCs induced necrosis in more than one plant species ( Fig 4 ) , we examined the plant response in At-Col-0 , Nb , and in lettuce cv . Ninja . Four wild-type clones and all chimeras were fused to C-terminal hemagglutinin ( HA ) tag to evaluate their expression in Nb ( Fig 6A ) . Reciprocal swaps at CC-END between ECCs ( corresponding to two pairs of CNLs: AT1G63360 and AT1G15890 , and AT1G62630 and AT4G14610 ) surprisingly resulted in chimaeras that lost their ability to induce cell death in any species ( A1 and A2 , B1 and B2; Fig 6A ) . Accordingly , clones that previously did not induce necrosis ( like ECC of AT1G63360 in Nb and in lettuce or ECC of AT1G62630 in lettuce ) did not gain this ability after introducing the CC-END-linker-pre-P–loop fragment from a necrosis-inducing clone ( A1 and B1 , respectively; Fig 6 ) . This indicated that regions upstream and downstream of CC-END were required but alone were insufficient for induction of cell death . Furthermore , these results showed that compatibility between both regions was essential for induction of cell death . A reciprocal swap at the VG residues ( pre-P–loop ) between the same two pairs of ECCs ( A3 , A4 , B3 , and B4; Fig 6A ) did not affect their ability to induce cell death nor their patterns across the three plant species , implying that the pre-P–loop itself is not required for elicitation of cell death . Deletions in ECCs at the charged motif did also not affect the ability or patterns of necrosis induction in three clones ( clones A5 , A6 , and B5 ) but eliminated growth deformations triggered by the ECC of AT1G14610 in At-Col-0 ( clone B6; Fig 6A ) . This implied requirement of the fragment between CC-END and the charged motif for cell death induction . The charged motif itself was not essential for cell death induction but clearly modulated the response . Indeed , substitutions G158A at the beginning of pre-P–loop and E/E148/149A/A within the charged motif in the ECC of AT1G12290 did not compromise cell death induction ( M1–M3; Fig 6B ) in At-Col-0 plants , but substituting the same EE residues with positively charged KK potentiated the immune response and made At-Col-0 fully resistant to viral infection ( clone M4; Fig 6B ) . Reciprocal swaps at the charged motif between ECCs corresponding to AT1G63360 and to AT1G15690 ( the exact position of the swaps is shown in the left panel in Fig 6A ) resulted in the elimination of plant deformations induced by the former in At-Col-0 and weakened the response induced in all three plant species by the latter ( A7 through A10; Fig 6 ) . A similar swap between ECCs corresponding to AT1G62630 and AT4G14610 eliminated the response induced by the latter in At-Col-0 ( B8 and B10 , Fig 6A ) . Swapping the entire charged motif between the ECC of AT4G14610 with the ECC of AT1G62630 compromised cell death induced in lettuce and At-Col-0 but not in Nb ( B10 , Fig 6A ) . This implied that the charged motif , besides modulating the strength of the response , may also determine host specificity . To exclude the possibility that a lack of responses was due to a lack of stability of the chimaeras , protein accumulation of wild-type ECCs and their derived variants was assessed in Nb leaves using immunoblotting ( Fig 6C ) . Full-length ECCs migrated at an apparent size of 25 to 35 kDa , which is slightly larger than the predicted sizes of 22–23 kDa ( including HA tag ) and might be attributed to the high content of hydrophobic residues ( V , I , and L ) . Protein accumulation levels varied between constructs and appeared to be slightly reduced for four clones swapped at CC-END ( A1 , A2 , B1 , and B2 ) . Notably , A3 or A10 triggered plant responses despite comparably low accumulation levels; this implied that the amount of protein in the aforementioned swaps should have sufficed to trigger a response in the three plant species tested . Interestingly , ECC At1G63360 and the A4 , A8 , and A9 proteins ran at an apparent increased molecular weight , suggesting an unknown posttranslational modification of these fragments . The reduced accumulation of A1 , A2 , and A5 through A7 , A10 , B1 , and B2 , as compared to the WT protein , may indicate a stabilizing role for this region and necessity for compatibility between this motif and the N-terminal portion of the fragment . To conclude , the lack of responses following expression of the chimaeras was likely due to a loss of activity rather than a reduced accumulation or stability of the produced protein . From these experiments , we concluded that the integrity of a variable stretch of 16 to 18 amino acids ( referred hereafter to as CC-variable amino acids residues [CCVX] ) following the last hydrophobic residue in helix H2b is required ( but insufficient ) for induction of cell death ( Fig 6 and Fig 7 ) . The potency of the CCVX fragment in induction of cell death can be modulated by its charge , implying the involvement in electrostatic intra- or intermolecular interactions . Because of its involvement in cell death elicitation , the charged motif may be analogous to the hydrophilic motif identified in three homologous CNLs in monocots [34] despite being localized in a slightly different position relative to H2b ( Fig 7 ) .
Among STAND receptors , the presence of an N-terminal CC domain is exclusive to CNL pathogen receptors in plants [2 , 37] . Our interactome analyses revealed a high tendency of ECCs to form heteromeric interactions as compared to homomerization , suggesting functional importance of associations involving different CNLs . Approximately two-thirds of the ECCs associated with more than one ECC , which implies functional redundancy among the CNL interactome . Consistently , knockouts of multiple NLR interacting partners in Arabidopsis did not compromise the ability of a specific At-Col-0 ECC to trigger an immune response ( S3 File ) . In lettuce , however , a single dominant CNL locus ( RGC21 locus ) could be associated with the ability of AT4G14610–ECC to trigger HR . Existence of a CNL network in Arabidopsis is consistent with the availability of the many putative sensor- and actor-type receptors [39] . In tomato , an NLR network is defined [39] in which sensors signal via downstream actors . Whether the tomato sensor NLRs form similar heteromeric interactions as the At-Col-0 ECCs ( Fig 2A ) needs to be resolved . Unlike in tomato , involving CCR–CNLs as actors ( or helpers ) [39] , the partnering receptor of AT4G14610–ECC in lettuce appeared to be an EDVID-type CNL ( Fig 2B ) . Nevertheless , the ability of NLRs to transduce their signal via various partners seems a recurring theme in plant immune signaling because not only NLRs involved in ETI form networks but also At-Col-0 LRR receptor kinases involved in MTI show an extensive network of interactions and functional dependencies [59] . A network provides resilience to perturbation and manipulation by pathogens and facilitates compatibility to adjust to new recognition specificities [40] . Identifying a lettuce CNL as a downstream-signaling partner for AT4G14610–ECC also has implications for CNL evolution . Canonical NLRs evolve rapidly , being highly diverse across different taxa , yet despite approximately 100 million years following the separation of superrosid ( Arabidopsis ) and superasterid ( lettuce ) lineages [60] , the identified NLR pair retained a functional relationship . Interactions between Group B and Group C/D members were frequent ( Fig 2B ) . Because compatibility between the AT4G14610–ECC and RGC21 CNL were retained , specific molecular features must be conserved within the N-terminal fragment . For Mla10 homologs , both the predicted α-helices and the hydrophilic motif was proposed to be involved in CC–CC interactions [26 , 32]; hence , the evolutionarily conserved feature retaining compatibility between ECCs may simply be the ability to oligomerize . Adaptation of the TRV-based expression system enabled functional analysis of the ECCs in the source plant , Arabidopsis , in N . benthamiana , and in lettuce . Induction of cell death by ECCs was not unique to CCR–CNLs ( the number of which we increased to six by the identification of AT5G66630 ) but could also be triggered by approximately half of the ECCs present in the repertoire of CNLs in At-Col-0 . Notably , five ECC fragments were able to trigger full immunity in At-Col-0 to TRV infection . It is unclear whether these fragments themselves function as actors or represent sensors signaling via an actor . Overexpression of some confirmed sensor ECCs , such as the Group B members RPS2 and RPS5 , triggered a mild reaction but no resistance . Overexpression of ECC domains from putative sensors from Group C ( RPM1 and ZAR1 ) did not trigger any response like most other members of this Group and from Group D . Together , these findings show that an unambiguous distinction between actors and sensors based on the ability of their ECC to trigger immune responses may not be possible . Nevertheless , these observations together demonstrate that the ECC represent the “effector” domain of a CNL that is sufficient to trigger full resistance , supporting recent conclusions of Jacobs and coworkers [27] . Furthermore , our findings are in agreement with loosening the association between the CC domain and the remainder of the CNL following conformational rearrangements upon activation [15] . Such a mechanism is analogous to release of the CARD of APAF1 or metazoan NLR receptors following their activation [5 , 10 , 61] ( S3 Fig ) . However , in contrast to the CARD in metazoan receptors , the CC is capable to signal and interact autonomously and apparently does not require an NB–ARC to trigger defense responses when heterologously expressed in planta . A recent study revealed an increased binding affinity of the CC domain of RPM1 toward CC–NB–ARC domain variants that harbored autoactivating mutations [36] . Notably , this increased affinity was only observed using extended CC domains containing the NB–ARC linker encompassing the charged motif identified in our study . This finding implies that ECCs may transactivate full-length downstream CNLs in at least two distinct ways . First , ( as indicated by our Y2H data ) , the CC of one receptor may interact with the CC of the downstream partner , thereby relieving the negative regulation of its NB , allowing it to adopt an activated ATP-bound conformation . Second , the ECC linker may directly interact with the NB domain of a partner CNL , thereby activating the receptor . In either scenario , a cascade of transactivation may be initiated because the released ECCs could potentially activate additional CNL receptors . The presence of two interaction surfaces ( CC–CC and CC–NB ) would stabilize the complex formed , facilitating the formation of multimeric ( yet self-terminating ) complexes similar to heteromeric inflammasomes described for mammalian NLR family apoptosis inhibitory protein 2 ( NAIP2 ) and NLRC4 [7 , 10] ( S4 Fig ) . In contrast to the metazoan NLRs in which the NB triggers transactivation and multimerization , CNLs appear to be able to also employ the CC domain to transactivate other CNLs and possibly nucleate NLR multimerization ( S4 Fig ) . After activation and reaching initial proximity , further association of monomers could involve the NB–ARC domain of the full-length receptor . Such a mode of association is consistent with oligomerization of other STAND receptors , such as APAF1 , in which the interface between monomeric subunits is complex and involves different parts of the receptor [6] . We demonstrated that variable area ( CCVX ) ( Fig 6 , S2 Fig ) located in CC–NB linker is involved but not sufficient for induction of cell death . Chimeras swapped at the last hydrophobic residue of the CC lose their ability to induce necrosis , implying that compatibility between the C-terminal part of the H2b helix and the CCVX with the remainder of the CC is required to induce cell death . This requirement implies that certain structural features are needed to trigger cell death , which is consistent with the hypothesis that the CC may share its origin with metazoan death folds [37] . Accordingly , the hydrophilic motif [34] and the CCVX may be structural motifs required for CC folding and function . The observation that substitution of EE residues within the charged motif to positively charged lysines enhanced the plant immune response opens up an interesting possibility to engineer CNL receptors to enhance their performance after activation in a more subtle manner than mutations in the NB–ARC domain that typically trigger autoactivity or loss of function [62 , 63] . Possibly , a potentiated ECC might confer a stronger defense against pathogens . The transfer of resistance genes ( encoding NLRs ) from one species to another is believed to require compatibility of upstream ( such as decoys ) and downstream-signaling partners [64] . If endogenous CNLs are immediate signaling partners of receptors , then the observed interspecies compatibility is surprisingly high , as suggested by the similar patterns of HR induction in unrelated plant species ( Fig 4 ) . Notably , CCs of Mla10 homologs from a monocot induce cell death in the dicot Nb [26 , 32 , 34] . Therefore , it is surprising that their structurally closest homologs in At-Col-0 ( Groups C and D ) ( Fig 2B , S1 Fig ) rarely induced necrosis . Whether this lack of functional homology can be attributed to convergent evolution or represents diversification in the ability of the ECCs to trigger defenses among domains of a common origin remains a question for future study . It will also be interesting to test whether compatibility of ECC signaling in heterologous species solely involves compatibility to the corresponding CNL , or whether there are other requirements . If not , this would suggest that CC-mediated oligomerization is the main factor in CNL immune signaling , as proposed in our model in S4 Fig . ECC-driven formation of multimeric CNL complexes ( similar to apoptosomes or inflammosomes in metazoa ) [65] results in the formation of the active resistasome ( or NLRsome ) , thereby providing a platform for communication between sensor and actor NLRs . The ability of ECCs to recruit other receptors and the self-terminating nature of a circular complex may enable precise control of defense induction , which is consistent with the quantitative nature of plant immune responses .
Protein sequences were profiled for their predicted physicochemical profiles , as previously described [66–68] . Profiles were raised for linker , coil–coil , intrinsic disorder , secondary structure , contact , and turn-forming propensities [69–73] . For each profile , several methods were used , and the consensus was built to increase the prediction reliability , as described in the references above . The alignment was refined using Mla10–CC , Sr33 , and Rx–CC structural data [26 , 32 , 33] . Sequence-clustering/phylogenetic tree–building was carried out using structural words weighting and variability analysis . Remote homology 3D models of CC domain structures were built , as described [26 , 74] . In essence , the alignments of target sequences were optimized , incorporating predicted secondary structure profile data as well as other predicted physicochemical profiles , followed by threading with SLIDE [75] . Molecular modeling was performed using Discovery Studio software ( Accelrys-Dassault Systèmes ) and Modeler v9 . 18 [76] . Along the conserved regions of the proteins , coordinates were assigned using standard Modeler coordinate transfer functions , while insertion loops were generated randomly and chosen by energy- and steric-based procedures . Generated loops were brought to local minima using a divide and conquer strategy , including recursive rounds of energy minimization and/or simulated annealing . The global model generated was further subjected to energy minimization , followed by global and local quality check using MetaMQAP [77] , MolProbity [78] , and PROCHECK V . 3 . 4 . 4 [79] for crystallographic standards compliance . The overall structural optimization was performed with NAMD [80] . Molecular dynamics simulation experiments were then performed with Amber16 [81] using ff14SB force field at 300 K and 1 bar . The standard protonation state at physiological pH ( 7 . 4 ) was assigned to the ionizable residues using H++ server [82] . Dimer structures were solvated with TIP3P waters in an octahedral box . Periodic boundary conditions and Ewald sums ( grid spacing of 1 Å ) were used to treat long-range electrostatic interactions . The nonbonded cut-off distance was maintained at 12 Å , and the temperature and pressure were controlled by Langevin thermostat and Berendsen barostat with coupling constant of 1 ps . The quality of the simulations was assessed by analyzing the potential energy , root-mean-square deviations , and root-mean-square fluctuations profiles from molecular dynamics simulations . Binding free energies were calculated using molecular mechanics-based MM/PB ( GB ) SA methods , as implemented in Amber16 package specialized scripts [81 , 83] and knowledge-based Prodigy method [84] . Visual inspection and protein structure graphics were performed with PyMol ( PyMOL Molecular Graphics System , v1 . 8 Schrödinger , LLC ) and VMD [85] . Hydrophobicity profiles ( Fig 1 ) were calculated using the Protscale server ( web . expasy . org ) using hydropathicity scale [86] and sliding window size of three residues . ECCs were expressed in planta plants using the TRV system [89] , in which pTRV2-attR2-attR1 was modified to enable expression of recombinant sequences by adding a coat protein promoter from Pea Early-Browning Virus ( PEBV ) [50 , 90] . The PEBV CP promoter [91] was amplified using primers 5′ATATGGTTACCGCACACAAGGTTAAAAACGCTG and 5′ATCTCGAGTTAGCTAGTTAGGCCTCTCGTTAACTCGGGTAAGTGA ( restriction sites are underlined ) and after digestion with BstEII and XhoI ligated to the pTRV2-attR2-attR1 vector digested with the same enzymes . Subsequently , the vector was converted into a Gateway compatible vector by introduction of a ccdB Frame B cassette ( Gateway Conversion System; Cat . No . 11828029 , ThermoFisher Scientific , https://www . thermofisher . com/ ) into the StuI site . To facilitate protein detection , a derived vector was created that contained the sequences encoding a human influenza HA tag introduced between StuI and XhoI sites . The vector contains a PEBV CP promoter , followed by a Gateway conversion site , as described above . Clones containing swap- and substitution-mutations were custom synthesized as gBlocks ( IDT; https://www . idtdna . com/ ) flanked by attB1 and attB2 recombination sites , and cloned to pENTR207 vector via a BP reaction . All ECCs and their mutants were recombined into this modified pTRV2-attR2-attR1 vector and transferred to Agrobacterium tumefaciens C58 ( RifR ) [92] . Inoculation assays were performed as described for At-Col-0 [93] , Nb , and lettuce [94] . Prior to infiltrations , suspensions of A . tumefaciens ( OD600 = 0 . 5 ) harboring plasmids encoding TRV RNA1 [89] or TRV RNA2 were mixed in a 1:1 ratio . Col-0 plants were evaluated 4 , 6 , and 8 days post inoculation ( dpi ) , whereas the reactions in Nb and lettuce were scored 2 and 3 dpi . Phenotypes reported in Fig 1C , Fig 4 , and Fig 6 reflect the final score at the last day of observation . The assays in Col-0 were replicated at least twice for each clone using at least three plants in each replicate , the assays in Nb and lettuce were replicated at least twice and involved at least two leaves , each inoculated in two places . The presence of TRV , indicative of systemic infection , in At-Col-0 plants was detected by standard RT-PCR using primers design to amplify a fragment of the At-Col-0 actin-2 gene ( control; AT3G18780 ) and the sequences encoding the coat protein of TRV: TAACCCAAAGGCCAACAGAG and GGGCATCTGAATCTCTCAGC for actin-2 and ACGATTCTTGGGTGGAATCA and CGGTGCAGATGAACTAGCAG for TRV CP ( AF406991 ) . Total RNA was extracted from At-Col-0 leaves using RNeasy Plant Mini Kit ( Cat . No 74904 , Qiagen; https://www . qiagen . com/ ) , and cDNA used for PCR was synthesized using SuperScript II Reverse Transcriptase ( Cat . No 18064014 ) and random hexamers . Detection of HA-labelled proteins was performed as described [95] . Standard statistical tests involving Pearson Correlation and Chi-Square tests were applied to data analysis . For segregation analysis , a population of 75 F2 individuals derived from the cross between cvs . Ninja and Valmaine was tested for the response to the ECC of AT4G14610 . This population was genotyped using next-generation sequencing ( GBS ) [96] . In brief , DNA was extracted from each individual , digested with AvaII to reduce the genomic complexity , and ligated to unique barcoded adapters ( Truco and colleagues , in preparation ) . All samples were pooled and sequenced using Illumina HiSeq 4000 . After sequencing , TASSEL [97] was used for demultiplexing , read mapping against the lettuce reference assembly , and SNP calling . Custom scripts ( https://github . com/alex-kozik/atgc-xyz . ) were used to obtain single haplotypes per scaffold . Scaffold-based haplotypes were used to construct a genetic map using MSTmap [98] . QTL analysis was conducted using WinQTL Cartographer and Composite Interval Mapping [99] . Significance threshold at p ≤ 0 . 05 was calculated by permutation analysis ( 1 , 000 permutations ) . The graph in Fig 7A was created using CIRCOS ( http://circos . ca ) . Cv . CG was transformed with transgene LserNBS02_NB_RNAi ( chr 3 ) to producing ihpRNA corresponding to fragments of a RGC21 family member and the uidA gene [55] . Seedlings derived from two independent transgenic plants were tested for silencing by assessing a decreased transient GUS expression , as described previously [54] . Six individuals exhibiting silencing were crossed to cvs . Ninja and Valmaine . Hybrids were identified based on distinct morphology as compared to cv . Ninja used as a maternal parent and repeatedly tested for silencing using transient GUS expression . Progenies of two transgenic CG plants showed 1:1 segregation for the silencing phenotype and were tested for the response to ECC of AT4G14610 , as shown in Fig 7B . | The ability to induce defenses in response to pathogen attack is a critical feature of immunity in any organism . Nucleotide-binding leucine-rich repeat receptors ( NLRs ) are key players in this process and have evolved to perceive the occurrence of nonself-activities or foreign molecules . In plants , coevolution with a variety of pests and pathogens has resulted in repertoires of several hundred diverse NLRs in single individuals and many more in populations as a whole . The mechanism by which defense signaling is triggered by these NLRs is poorly understood . Here , we show that upon pathogen perception , NLRs use their N-terminal domains to transactivate other receptors . Their N-terminal domains homo- and heterodimerize , suggesting that plant NLRs oligomerize upon activation , similar to the vertebrate NLRs; however , consistent with their large number in plants , their complexes are highly heterometric . Also , in contrast to metazoan NLRs , their N-terminus , rather than their centrally located nucleotide-binding ( NB ) domain , can mediate initial partner selection . The highly redundant network of NLR interactions is proposed to provide resilience to perturbation by pathogens . |
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Gap junctions are present in both vertebrates and invertebrates from nematodes to mammals . Although the importance of gap junctions has been documented in many biological processes , the molecular mechanisms underlying gap junction dynamics remain unclear . Here , using the C . elegans PLM neurons as a model , we show that UNC-44/ankyrin acts upstream of UNC-33/CRMP in regulation of a potential kinesin VAB-8 to control gap junction dynamics , and loss-of-function in the UNC-44/UNC-33/VAB-8 pathway suppresses the turnover of gap junction channels . Therefore , we first show a signal pathway including ankyrin , CRMP , and kinesin in regulating gap junctions .
Gap junctions were first discovered in the myocardium and nerves for their properties of electrical transmission between two adjacent cells [1 , 2] , and they are clusters of channels connecting two cells to allow direct transfer of ions and small molecules [3–5] . Gap junctions play essential roles in many biological processes , such as embryo development , cell differentiation , cell growth , metabolic coordination of avascular organs , and neural development [6] . In excitable cells , the presence of gap junctions provides them with abilities to generate synchronized electrical and mechanical outputs [3–5] . Gap junction channels form polymorphic maculae or plaques with a few to thousands of units [4] and are composed of connexins in chordates and innexins in prechordates [7 , 8] . Although connexins and innexins are not homologs in terms of their primary sequences , they share similar structures with four transmembrane domains , two extracellular and one intracellular loop , and intracellular amino- and carboxy- termini [5] . Vertebrates have innexin-related proteins , called pannexins , however , their roles in forming gap junctions are still under debate [9 , 10] . Regulation of gap junctions is observed at two levels: fast regulation involving change of channel conduction and open probability , and slow regulation including alternation of composition and turnover of channels [4] . Studies show that voltage changes and phosphorylation of channel proteins are important for fast regulation of gap junctions [11–14] . In terms of slow regulation , the turnover of gap junctions plays an important role [15] . Gap junctions have remarkably rapid turnover rate , for example connexin/Cx 43 has a half-life of only 1–3 hours [16–20] . The rapid turnover rate allows cells to quickly eliminate and rebuild their gap junctions to adapt to environmental conditions [4] . Gap junction channel proteins can form gap junctions in homomeric or heteromeric manners , and different combinations have distinct permselectivity [4] . The rapid turnover rate of gap junctions provides cells with the ability to change the composition of gap junctions in a timely manner . During gap junction turnover , the addition of new channels is at the edge of gap junction plaques , and the removal of channels happens at the center of the plaques [21 , 22] . Although recent studies show that the phosphorylation of gap junction channels and channel binding proteins is involved in regulating gap junction turnover , the molecular mechanisms orchestrating the removal of gap junctions are still largely unknown [4] . Transient gap junctions are important for mammalian brain development [3 , 23–25] . In invertebrates , transient gap junctions can regulate the formation of chemical synapses in leeches [26] and are required for asymmetry development of sensory neurons in C . elegans [27] . To avoid interruption of neuronal functions , those transient gap junctions need to be eliminated during development , but it remains unknown what regulates their elimination . Understanding the molecular mechanisms underlying gap junction dynamics may answer this question . As scaffolding proteins , ankyrins can organize membrane proteins into discrete domains and integrate them with the cytoskeleton [28] . In neurons , ankyrin-G is essential for the assembly of axon initial segment ( AIS ) and nodes of Ranvier [29 , 30] and is important for synapse formation [31 , 32] . Collapsin response mediator proteins ( CRMP ) are conserved microtubule interaction proteins that regulate neuronal polarity and axon growth [33–35] . C . elegans has only one ankyrin homolog unc-44 and one CRMP homolog unc-33 . Loss of function of unc-44 and unc-33 generate similar defects in locomotion , axon growth and axon-dendrite differentiation , suggesting they may function in the same pathway in regulating neuronal development [36–41] . Indeed , a recent study by Maniar et al . has shown that unc-44 acts upstream and regulates the localization of unc-33 in organization of microtubules in C . elegans neurons [41] . Although the important roles of ankyrin and CRMP in neuronal development have been documented in many organisms , their functions in regulating gap junctions have not been explored . Here , we show that in gap junction turnover , UNC-44/ankyrin acts upstream of UNC-33/CRMP and VAB-8/ kinesin to regulate the removal of UNC-9/innexin from gap junctions .
To study molecular mechanisms underlying gap junction regulation , we used C . elegans PLM neurons as a model . PLM neurons are a pair of mechanosensory neurons with simple morphology , that cell bodies are located at the tail region with a long axon growing to the middle part of body and a short posterior process toward the end of tail ( Fig 1A ) [42] . Electron microscope studies showed that PLM formed gap junctions at two regions along the axon: at zone 1 , PLM neurons form gap junctions with PVC , LUA and PVR neurons [43]; at zone 2 , PLM neurons form gap junctions with BDU neurons [44] ( Fig 1A ) . Three innexins , unc-9 , inx-7 and inx-3 , are expressed in PLM neurons [45] . To visualize PLM gap junctions in vivo , we used GFP labeled UNC-9 as marker . UNC-9 has four transmembrane domains and cytoplasmic N-terminus and C-terminus ( S1A Fig ) . Tagging GFP to the UNC-9 N-terminus affects its interaction with the gap junction regulator UNC-1 , but UNC-9::GFP retains its endogenous localization and forms functional gap junctions [46] . Since UNC-9 has a rather short intracellular N-terminus ( 28 amino acids ) , we tested whether tagging GFP to its N-terminus will affect its function . We found that expression of GFP::UNC-9 under its own promoter ( a 2 . 5kb fragment upstream of the start codon ) rescued the uncoordinated phenotypes of unc-9 mutants to a similar level as untagged UNC-9 ( S1 Movie ( unc-9 ) , S2 Movie ( Punc-9::GFP::unc-9 ( 5ng/ul ) ;unc-9 ) , S3 Movie ( Punc-9:: unc-9 ( 5ng/ul ) ;unc-9 ) ) , supporting the idea that GFP::UNC-9 fusion protein may function in the same manner as untagged UNC-9 . Therefore , we examined UNC-9 localization using transgenes expressing GFP::UNC-9 in PLM neurons . We found that GFP::UNC-9 formed stereotypical patterns at two gap junction zones with 2–3 GFP puncta at zone 1 and one GFP puncta at zone 2 ( Fig 1A ) , and the same expression pattern was also observed in UNC-9::GFP transgene ( Fig 1A ) . Using an UNC-9 specific antibody [46] , we confirmed that endogenous UNC-9 formed similar punctate structures as those observed transgenes ( Fig 1B ) . We further confirmed those puncta localized at the region where PLM neurons meet their gap junction partners . As shown in S1B Fig , we expressed mCherry in PVC neurons ( Pglr-1::mCherry ) and consistently observed that one GFP::UNC-9 punctum localized to the region where PVC axons crossed PLM axons . C . elegans stomatin protein UNC-1 is co-localized with and functionally important for UNC-9 containing gap junctions in muscle cells [46] . Consistent with this observation , we found that UNC-1 formed similar punctate patterns in PLM neurons ( Fig 1A ) . In addition to forming gap junctions , UNC-9 and UNC-7 could also function as hemichannels in C . elegans motor neurons [47] . The conserved cysteines ( Cys ) at extracellular loops are essential for formation of UNC-7/UNC-9 gap junctions but not hemichannels [47] . We found that mutating these cysteines ( Cys ) to alanines ( Ala ) blocked the formation of GFP::UNC-9 puncta , supporting the conclusion that those puncta were gap junctions but not hemichannels ( S1C Fig ) . All together , we believe that the GFP puncta in GFP::UNC-9 transgenes represent the localization of PLM gap junctions . Since UNC-1 is co-localized and functionally important for UNC-9 containing gap junctions [46] , it might be involved in gap junction assembly . To verify this possibility , we tested the formation of GFP::UNC-9 puncta in unc-1 ( lf ) background and found that loss of function of unc-1 did not affect UNC-9 puncta , suggesting UNC-1 was not required for the formation of UNC-9 puncta in PLM neurons ( Fig 1C ) . This observation is consistent with previous findings in muscle cells [46] . We also tested whether UNC-9 localization depended on inx-7 , an innexin co-expressed in PLM neurons with unc-9 . As shown in Fig 1C , we did not observe any defects of UNC-9 distribution in inx-7 mutants , suggesting that the assembly of UNC-9 puncta did not rely on other gap junction proteins . Previous studies showed that Netrin and its receptor Frazzled could regulate the formation of gap junctions between Drosophila interneurons and motor neurons [48] . We tested whether the Netrin signaling pathway was also involved in formation of PLM gap junctions . We found that loss of function of unc-6 , the only Netrin homolog in C . elegans , did not affect the formation of UNC-9 puncta . There are two Netrin receptors in C . elegans , UNC-5 and UNC-40/DCC . Loss of function of unc-5 did not affect UNC-9 puncta , but about 3–5% unc-40/DCC ( e271 ) mutant animals lost UNC-9 puncta ( S1D Fig ) . Further studies will be necessary to confirm the function of UNC-40/DCC in gap junction regulation . Gap junctions are required for the formation of chemical synapses in leeches [26] . The conserved Neurobeachin is involved in development of both chemical synapses and gap junctions in zebrafish [49] . It seems that the formation of gap junctions and chemical synapses could share some common mechanisms . To test this idea in C . elegans , we examined GFP::UNC-9 puncta in loss-of—function mutants of rpm-1 and syd-2 , two genes playing important roles in C . elegans chemical synapse formation [50–53] and found that neither of them was required for the organization of UNC-9 puncta ( Fig 1D ) . Double mutants of rpm-1 and syd-2 suppress the formation of chemical synapses [54] , but we did not observe any defects of UNC-9 puncta in double mutant animals ( Fig 1D ) . These results are in support of different mechanisms regulating gap junction and chemical synapse formation in C . elegans neurons . To uncover the molecular mechanisms underlying gap junction regulation , we carried out an unbiased genetic screen using yadIs12 ( Pmec-4::GFP::unc-9 ) as a starting strain and isolated mutants with three types of phenotypes: 1 , type one mutants lost UNC-9 puncta; 2 , type two mutants had more UNC-9 puncta close to the original gap junctions; 3 , type three mutants had more UNC-9 puncta along the axon ( Fig 1E ) . In this study , we focused on two mutants with type two phenotypes . As shown in Fig 2A and 2B , about 65% of yad21 and 35% of yad26 animals had more UNC-9 puncta at both zone 1 and zone 2 , and the length of gap junction zone 1 in those animals was enlarged from 2–3 μm to about 25 μm ( Fig 2C ) . Neither yad21 nor yad26 changed the overall expression level of GFP::UNC-9 ( S1F Fig ) . Both yad21 and yad26 were linked to yadIs12 marker on Chromosome IV and had strong uncoordinated ( unc ) phenotypes . After testing some unc genes on chromosome IV , we found yad21 failed to complement the loss-of-function allele of unc-44 ( e362 ) , and yad26 failed to complement the loss-of-function allele of unc-33 ( mn407 ) . unc-44 ( e362 ) had the same UNC-9 punctate defects as that seen in yad21 , and unc-33 ( mn407 ) and unc-33 ( e204 ) had identical phenotypes with yad26 ( Fig 2A and 2B ) . Sequencing results showed that yad21 introduced a point mutation ( P4813S ) and a premature stop codon ( Q6827* ) in the neuronal specific long isoform of unc-44 , and yad26 had two point mutations ( E168K and G328D ) in unc-33 ( S2A and S2B Fig ) . These evidences supported the conclusion that yad21 was a loss-of-function allele of unc-44 , and yad26 was a loss-of-function allele of unc-33 . unc-44 and unc-33 are homologs of ankyrins and CRMPs , respectively , and loss of function of them causes defects in axon development [36 , 39] , raising a concern that the UNC-9 distribution defects could be an indirect result of misregulation of axon development . In majority of yad21 ( 83% n = 220 animals ) and yad26 ( 94% , n = 175 animals ) animals , PLM axons grew straight from cell bodies to the middle section of the body similar to wild type animals , but about 70% of yad21 ( lf ) and yad26 ( lf ) animals had either shorter or longer PLM axons ( S3A Fig ) . However , we did not notice any correlation between the UNC-9 distribution defects and axon phenotypes . We quantified UNC-9 distribution defects in animals with normal axon length and found that the percentage of animals with UNC-9 distribution defects ( yad21: 62% n = 71 , yad26: 32 . 3% n = 62 ) was same as those seen in all animals . Loss of function of unc-34/Enabled/VASP affected PLM development . Loss-of-function mutation in rpm-1 caused overextension of PLM axons , but we did not observe any defects of UNC-9 distribution in these mutants ( Fig 1D and S1E Fig ) [41] . These results showed that UNC-9 distribution defects in unc-44 ( lf ) and unc-33 ( lf ) were not results of axon growth defects . The UNC-44/UNC-33 pathway regulates neuronal polarity without changing the overall morphology of neurons [41] . There are three isoforms of unc-33 in C . elegans , named L ( long ) , M ( middle ) and S ( short ) isoforms based on the length of cDNA , and only the long isoform could rescue unc-33 ( lf ) polarity defects [41] . We found that expression of any of these three isoforms in PLM neurons rescued UNC-9 distribution phenotypes in unc-33 ( lf ) animals , supporting the phenotypes we observed were not results of mis-regulation of neuronal polarity ( Fig 2A and 2B ) . The successful rescue of unc-33 ( lf ) phenotypes by expressing unc-33 cDNA in PLM neurons also showed that unc-33 cell autonomously regulated UNC-9 puncta ( Fig 2A and 2B ) . Using the UNC-9 specific antibody , we observed similar mis-accumulation of endogenous UNC-9 in unc-44 and unc-33 mutants ( S3B Fig ) . We also noticed that UNC-1 was mis-localized ( Fig 2D ) and lost its co-localization with UNC-9 in unc-33 ( lf ) and unc-44 ( lf ) mutants ( Fig 2E ) . Since the interaction between UNC-1 and UNC-9 was important for gap junction function [46] , these results suggested that unc-44 and unc-33 might regulate gap junction functions . During C . elegans development , sensory neurons form NSY-5 ( INX-19 ) contained transient gap junctions , and most of these gap junctions are eliminated in adults [27] . In day one adults , about 13% of control animals did not have any NSY-5 puncta , and 87% of control animals had 30–40 NSY-5 puncta ( Fig 2F ) . In unc-33 ( lf ) and unc-44 ( lf ) mutants , we observed significantly more NSY-5 puncta ( 50–90 punca/animal ) in all examined animals ( Fig 2F ) . These results support that UNC-44 and UNC-33 are involved in regulating multiple gap junction channels in different neuronal types . To determine whether unc-44 and unc-33 could regulate each other , we carried out immunostaining experiments . As shown in Fig 3A , UNC-33 ( S ) accumulated at the nerve ring of control animals , and unc-44 mutants induced more diffuse distribution of UNC-33 ( S ) . Using an antibody specifically recognizing the neuronal specific long isoform of UNC-44 [39] , we found that UNC-44 ( L ) was present in the nerve ring and neuronal processes , and unc-33 loss-of-function mutants did not affect UNC-44 distribution ( Fig 3B ) . These results were consistent with a previous report [41] and supported the hypothesis that UNC-44 acted upstream and regulated UNC-33 localization . We further confirmed this conclusion by testing the suppression ability of overexpressing unc-33 ( S ) on unc-44 ( lf ) phenotypes . As shown in Fig 3C , expressing unc-33 ( S ) at high level suppressed unc-44 ( lf ) phenotypes . In conclusion , by analyzing mutants with abnormal accumulation of UNC-9 in PLM neurons , we uncovered an important role of the UNC-44/UNC-33 pathway in the regulation of gap junctions . The striking phenotypes of unc-44 and unc-33 suggested that this pathway might regulate gap junction dynamics . To test this possibility , we analyzed GFP::UNC-9 movement at the gap junction zone one . In control animals , we observed bidirectional movement of UNC-9 both anterior and posterior to gap junctions , and each animal had almost equivalent numbers of UNC-9 particles moving toward and away from gap junctions ( Fig 4A and 4B and S4 Movie ) , implicating the stable number of UNC-9 puncta at zone 1 was due to the balance of bidirectional movement . Loss-of-function mutation in unc-33 or unc-44 decreased the number of UNC-9 particles moving away from gap junctions and induced an imbalance of gap junction dynamics ( Fig 4A and 4B , S5 and S6 Movies ) . These results suggested that unc-44 ( lf ) and unc-33 ( lf ) phenotypes might be due to suppression of gap junction turnover . To further examine this hypothesis , we used a transgene expressing photoactivatable GFP ( PAGFP ) tagged UNC-9 [55] . In this experiment , we first photoactivated PAGFP::UNC-9 in both cell bodies and axons , and we found that the fluorescence intensity at cell bodies decreased 35% at 3 hours after photoactivation , but the fluorescence intensity at gap junction zone 1 did not change in the same time period ( Fig 4C and 4D ) . The decrease of fluorescence intensity at cell bodies was likely due to continuous transport of PAGFP::UNC-9 out of cell bodies . At gap junction zone 1 , the stable fluorescence intensity indicated rapid turnover and replacement of PAGFP::UNC-9 . In support of this conclusion , in animals that we locally photoactivated PAGFP::UNC-9 only at zone 1 , we found that the fluorescence intensity decreased 43% after 3 hours in control ( Fig 4E and 4H ) , and loss of function of unc-44 and unc-33 suppressed the decrease of fluorescence intensity ( Fig 4F , 4G and 4H ) . We also tested whether unc-44 and unc-33 were involved in the assembly of UNC-9 puncta using the Fluorescence Recovery After Photobleaching ( FRAP ) assay . Briefly , we photobleached the GFP signal in zone 1 and measured the recovery of GFP signal after 3 hours . As shown in S4 Fig , loss of function of neither unc-44 nor unc-33 affected the recovery of GFP signal , supporting the UNC-44/UNC-33 pathway did not regulate the assembly of UNC-9 puncta . We believed that the UNC-44/UNC-33 pathway regulated gap junction turnover through suppression of transport of UNC-9 out of gap junctions . UNC-33 homolog CRMP-2 has been shown to bind to the kinesin light chain subunit kinesin-1 in transport of neurotrophin receptors into growth cones [56 , 57] . The effect of mutating unc-44/unc-33 on UNC-9 dynamics suggested that they might regulate some motor proteins . We first tested three classic motor proteins , UNC-104 , UNC-116 and DHC-1 , that are known to be important for neuronal development . UNC-104 is the kinesin transporting synaptic vesicles from cell bodies to chemical synapses [58] . UNC-116 is a kinesin that is required for organization of presynaptic buttons and axonal mitochondria [59 , 60] . DHC-1 is the major dynein mediating retrograde transportation in C . elegans neurons [61] . However , we did not observe any UNC-9 defects caused by mutations in these three genes ( S5 Fig ) . In testing the function of other motor proteins in UNC-9 regulation , we found that loss of function of a potential kinesin vab-8 induced similar phenotypes as those seen in unc-44 ( lf ) and unc-33 ( lf ) ( Fig 5A , 5B and 5C ) . Further genetic analysis showed that double mutants of vab-8 unc-33 enhanced single mutant phenotypes to a degree similar to that seen in unc-44 mutants , indicating that VAB-8 might work together with UNC-33 downstream of UNC-44 in regulating gap junction turnover ( Fig 5B and 5C ) . Indeed , using the local photoactivation assay , we confirmed that loss of function of vab-8 suppressed UNC-9 turnover ( Fig 5D ) . vab-8 has two isoforms , vab-8 ( L ) and vab-8 ( S ) , and the major difference between these two isoforms is that vab-8 ( S ) lacks the kinesin domain present in vab-8 ( L ) [62] . We found that only expression of vab-8 ( L ) in PLM neurons was able to rescue vab-8 ( lf ) phenotypes , suggesting the potential kinesin function of VAB-8 was essential for gap junction regulation ( Fig 5B ) . Since UNC-33 homolog CRMP-2 has been shown to directly bind to kinesin , we examined whether UNC-33 ( S ) could bind to VAB-8 ( L ) . As shown in Fig 5E , using a transgene pan-neurally expressing FLAG::VAB-8 ( L ) and HA::UNC-33 ( S ) , we detected co-immunoprecipitation of UNC-33 ( S ) and VAB-8 ( L ) , supporting that UNC-33 ( S ) could bind to VAB-8 ( L ) in vivo . This observation raised the possibility that UNC-33 might regulate the activity of VAB-8 in control of UNC-9 dynamics , such that , adding additional VAB-8L would restore UNC-9 distribution in unc-33 and unc-44 mutants . Indeed , we found that overexpression of VAB-8L in PLM neurons was able to suppress both unc-33 ( lf ) and unc-44 ( lf ) phenotypes ( Fig 5F ) . These results showed that VAB-8L might function together with UNC-33 at the downstream of UNC-44 to regulate UNC-9 dynamics .
Using C . elegans PLM neurons as a model , we investigated the molecular mechanisms underlying gap junction regulation . In adult animals , PLM neurons form gap junctions at two regions , at zone 1 with PVC , LUA and PVR neurons and at zone 2 with BDU neurons [42–44] . Using transgenes expressing GFP tagged UNC-9 , we were able to observe punctate structures that represented the localization of gap junctions in PLM neurons . In a genetic screen targeting isolation of mutants affecting gap junctions , we uncovered the important roles of C . elegans ankyrin/unc-44 and CRMP/unc-33 in regulating gap junctions . By imaging GFP::UNC-9 dynamics and PAGFP::UNC-9 decay , we further demonstrated that the UNC-44/UNC-33 pathway regulated UNC-9 dynamics and turnover . In searching for motor proteins contributing to unc-33/unc-44 phenotypes , we uncovered a role for a potential kinesin VAB-8 in UNC-9 turnover . VAB-8 ( L ) directly bind to UNC-33 ( S ) , and overexpression of VAB-8 ( L ) suppressed both unc-33 ( lf ) and unc-44 ( lf ) phenotypes , supporting the hypothesis that VAB-8 is the downstream target of the UNC-44 pathway in gap junction regulation . Double mutants of vab-8 and unc-33 had stronger phenotypes than each of single mutant and showed similar phenotypes as unc-44 ( lf ) , suggesting that UNC-33 and VAB-8L function in parallel downstream of UNC-44 . The suppression of unc-33 ( lf ) phenotypes by VAB-8L overexpression was likely due to the enhanced contribution from VAB-8L ( Fig 6A ) . The other possibility is that VAB-8 is regulated by both UNC-33 and an unknown factor that is in parallel with UNC-33 and downstream of UNC-44 ( Fig 6B ) . CRMPs bind to tubulin heterodimers and microtubules to promote the assembly of microtubules during neuronal polarity and to regulate Numb-mediated endocytosis at growth cones [33–35 , 63 , 64] . In all these cases , the phosphorylation of CRMPs by Rho kinase or GSK-3β is important for its regulation [33 , 34] . However , UNC-33 ( S ) , the minimal rescue fragment of UNC-33 , does not have those phosphorylation sites , suggesting other mechanisms may be involved in regulating UNC-33/CRMP . Besides its roles in regulating microtubules , CRMP-2 has been shown to bind with the kinesin light chain subunit , kinesin-1 , in transport of neurotrophin receptors into growth cones [56 , 57] . In cultured neurons , CRMP-2 forms a complex with Slp1 and Rad27B to directly link TrkB to kinesin-1 , and to mediate the antrograde transportation of TrKB receptor upon stimulation of BDNF [56] . CRMP-2 can also mediate interactions between kinesin-1 and Sra-1/WAVE1 complex to control axon growth [57] . In our study , we found UNC-33/CRMP could bind to VAB-8L/Kinesin to regulate the dynamic and turnover of gap junction channels . These results suggest that the interactions between CRMPs and kinesins may play multiple roles in control of membrane proteins . In our experiments , we found that the localization of UNC-33 ( S ) was regulated by unc-44 . Together with the suppression of unc-44 ( lf ) phenotypes by overexpressing unc-33 ( s ) , we concluded that unc-44 acted upstream of unc-33 in gap junction regulation . However , the localization of UNC-33 ( S ) and UNC-44 appeared to be different , that UNC-33 ( S ) was concentrated at the nerve ring , but UNC-44 had a broader distribution in neurons . One concern is that the transgene used to label UNC-33 ( S ) ( Prgef-1::FLAG:: unc-33 ( S ) ) may not reflect its in vivo localization . We compared our results of UNC-33 ( S ) with the previous description of UNC-33 ( L ) localization [41] and found that their localization were largely overlap , suggesting our transgene likely showed the in vivo localization of UNC-33 ( S ) . Since UNC-44 plays important roles in many aspects of neuronal development , the broad distribution of UNC-44 seems consistent with its multiple functions in neurons . The UNC-44 homolog ankyrins have the ability to bind to channels and to integrate them to cytoskeleton , and the interactions between ankyrins and channels are dynamically regulated by intracellular signals [65] . It is possible that UNC-44 may play a role in holding UNC-9 at gap junctions . During gap junction turnover , the conformational change of UNC-44 could recruit UNC-33/CRMP and VAB-8L to UNC-9 , and the activated UNC-33 could promote the transport of UNC-9 away from gap junctions through VAB-8L or an unknown factor .
We maintained C . elegans strains on NGM plates at 20–22 . 5°C . All transgenes and strains are described in the S1 Table . We use yadIs12 ( Pmec-4::GFP::UNC-9 ) to visualize gap junctions in PLM neurons . Using yadIs12 as a starting strain , we performed a clonal recessive screen following standard ethyl methane sulfonate ( EMS ) mutagenesis protocol . In 1000 mutagenized haploid genomes we examined , 12 mutants were isolated with three type phenotypes as mentioned in the manuscript . All DNA expression constructs were made using Gateway cloning technology ( Invitrogen ) . Sequences of the final clones were confirmed . The S1 Table lists the genotypes and DNA constructs for the transgenes . unc-33 ( L ) cDNA was obtained from Dr . Cori . Bargmann , and unc-33 ( M ) and unc-33 ( S ) was amplified from unc-33 ( L ) cDNA . vab-8 ( L ) and vab-8 ( S ) cDNA was amplified from yk clones from Dr . Yuji Kohara lab . Photoactivatable GFP ( PAGFP ) plasmid was obtained from Addgene . PAGFP coding sequence was inserted in an ASCI enzyme site before the start codon of UNC-9 . All primer sequences are available upon request . Transgenic animals were generated following standard procedures . In general , plasmid DNAs of interest were used at 1–50 ng/ml with the co-injection marker Pttx-3::rfp/Pttx-3::gfp at 50 ng/μl . Representative images and immunostaining results were acquired with a Zeiss LSM700 confocal microscope using a Plan-Apochroma 40x/1 . 4 objective . Worms were immobilized in 1% 1-phenoxy-2-propanol ( TCI America , Potland , OR ) in M9 buffer . For quantification of the percentage of animals with gap junction defects in PLM neurons , we used a Zeiss Axion Imager 2 microscope equipped with Chroma HQ filters . Each analyzed data performed takes at least three independent experiments and total 200–300 1-day old adults . For quantification of the length of gap junction zone , Images were acquired with LSM700 confocal microscope using a Plan-Apochroma 40x/1 . 4 objective , and the length of gap junction zone was measured using Zeiss Zen Black software . The photoactive experiments were carried out with a Zeiss LSM700 confocal microscope using a Plan-Apochroma 40x/1 . 4 objective . 1-day young adult transgenes expressing mCherry and PAGFP::UNC-9 in PLM neurons were immobilized in 1% 1-phenoxy-2-propanol . We first use mCherry signal to localize PLM neurons and use 405 laser to globally or locally photoactivate PAGFP::UNC-9 . After photoactive animals were recovered on the NGM plates for 3 hours before the second images were taken . The fluorescent intensity was analyzed using Image J software . GFP::UNC-9 dynamic experiments were preformed using an Andor revolution microscopy with a 60 x /1 . 46 Plan-Apochromat objective controlled by MetaMorph software . All videos were acquired by an Andor EM-CCD camera ( DU897 ) . 1-day adult animals were immobilized in 5mM levamisole and on 5% agar pads for imaging . Videos for GFP::UNC-9 dynamic analysis were roughly 30–40s with 8 frames per second . Kymographs were generated using ImageJ , and the direction of GFP::UNC-9 movement was judged by the direction of the black lines in the kymograph pictures . In general , dynamic puncta were defined as their velocities >0 . 1 μm/s for last at least 5s . For puncta that change directions during experiments , we trace them for the overall direction . 1-day adult animals were immobilized in 5mM levamisole and on 5% agar pads . FRAP was performed using Zeiss LSM700 confocal microscope using a Plan-Apochromat 40x/1 . 4 objective . A high-powered laser ( at 100% energy , 488 nm ) was used to photobleach the region of interest . Worms were recovered on NGM plates with food for 3 hours before taking the second image . Quantification of GFP::UNC-9 was carried out using Image J . Percentage Recovery = ( I3h-Ibleach ) / ( Ipreblach -Ibleach ) x100 . I3h: the intensity at the region of interest ( ROI ) three hours after photobleaching; Ibleach: the intensity at the ROI after photobleaching; Iprebleach: the intensity in the ROI before photobleaching . Background ( the intensity in the non-bleached part of ROI ) was subtracted , respectively . All immunostaining experiments were carried out following the standard protocol using 1-day young adults . The Rabbit anti-UNC-9 antibody was a gift from Dr . Zhao-Wen Wang . The Rabbit anti-UNC-44 ( L ) antibody was a gift from Dr . Anthony Otsuka . Mouse anti-FLAG M2 antibody ( Cat# F1804 ) , mouse anti- acetylated tubulin antibody ( Cat# T7451 ) and rabbit anti-GFP ( Cat# G1544 ) were purchased from Sigma . The dilutions for each antibodies are: anti-UNC-9 ( 1:100 ) , anti-UNC-44 ( 1:100 ) ; anti-FLAG ( 1:300 ) , anti-acetylated tubulin ( 1:300 ) and anti-GFP ( 1:150 ) . Alexa Fluor 488 Donkey-anti-rabbit IgG ( H+L ) antibody ( Cat# A-11008 ) and Alexa Fluor 594 Goat Anti-Mouse IgG ( H+L ) Antibody ( Cat# A-11005 ) from Molecular Probes were used as secondary antibody in 1:500 dilution . For the immunoprecipitation experiment , we generated an transgene ( yadEx421 ) expressing FLAG-VAB-8 ( L ) and HA-UNC-33 ( S ) under the control of Pan-neuronal promoter Prgef-1 . Proteins from mixed stages animals were first extracted using RIPA buffer by frozen-throw about 50 times in ethanol with dry ice , and protein lysis was incubated with mouse anti-FLAG M2 antibody ( Cat# F1804 ) in room temperature for 5 hours and then precipitated using Protein A/G PLUS-Agarose ( Santa Cruz Bio . esc-2003 ) . Heated Protein samples were separated using SDS-PAGE Gradient Gels ( 4–20% ) , and then transferred to nitrocellulose . Blots were probed with mouse anti-Flag antibodies ( sigma , F1804 ) and rabbit anti-HA ( Sigma H6908 ) , and then visualized with Amerisham HRP-conjugated anti-rabbit secondary antibodied at 1:5000 using the SuperSignal West Femto kit ( Pierce , Rockford , 1L ) . We analyzed our data using one-tailed Student’s t test , one way ANOVA or Fisher exact test in Graphpad Prism ( GraphPad Software , La Jolla , CA ) . | The nervous system is made up of individual neurons connected by junction structures called synapses . There are two fundamentally different types of synapses: chemical synapses and electrical synapses ( also called gap junctions ) . Through studies in different model organisms , we have gained rich knowledge about the development and dynamics of chemical synapses . However , we still know little about how gap junctions are formed during development and what regulates the dynamic of gap junctions in functional circuits . Using C . elegans PLM neurons as a model , we carried out an unbiased genetic screen and isolated mutants altering gap junctions . In this study , we focus on two of them , unc-44/ankyrin and unc-33/CRMP . Through genetic analysis in combination with live imaging , we find that UNC-44/ankyrin and UNC-33/CRMP play important roles in gap junction turnover and demonstrate that UNC-44/ankyrin acts upstream of UNC-33/CRMP and VAB-8/ kinesin to regulate the removal of gap junction channels from gap junctions . |
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Synapses on dendritic spines of pyramidal neurons show a remarkable ability to induce phosphorylation of transcription factors at the nuclear level with a short latency , incompatible with a diffusion process from the dendritic spines to the nucleus . To account for these findings , we formulated a novel extension of the classical cable theory by considering the fact that the endoplasmic reticulum ( ER ) is an effective charge separator , forming an intrinsic compartment that extends from the spine to the nuclear membrane . We use realistic parameters to show that an electrotonic signal may be transmitted along the ER from the dendritic spines to the nucleus . We found that this type of signal transduction can additionally account for the remarkable ability of the cell nucleus to differentiate between depolarizing synaptic signals that originate from the dendritic spines and back-propagating action potentials . This study considers a novel computational role for dendritic spines , and sheds new light on how spines and ER may jointly create an additional level of processing within the single neuron .
Glutamatergic synaptic inputs onto dendritic spines of pyramidal neurons induce phosphorylation of the transcription factor CREB ( cAMP-Responsive-Element Binding protein ) in the nucleus [1]–[3] . CREB phosphorylation is important for converting specific synaptic inputs into long-term memory in several animal species [4] , [5] . Interestingly , action potential ( AP ) trains induced post-synaptically by direct intracellular current injection fail to initiate CREB phosphorylation [1] , [6] . Several studies [1]–[3] , [7] , [8] have aimed at finding the spine-to-nucleus signaling involved in CREB phosphorylation that enables the nucleus to discriminate between orthodromic and antidromic signals . The nature of this signal transduction , however , remained unclear . It has been shown that bulk elevation in cytosolic Ca2+ is neither necessary nor sufficient for activity-dependent CREB phosphorylation [1] , [2] , [7] . It was further shown that regenerative Ca2+ waves along the dendritic endoplasmic-reticulum ( ER ) are not necessary for mediating this synapse-to-nucleus signaling [1] . The means by which signals travel from spines to nucleus has therefore been suggested to involve diffusion of a second messenger . Since the Ca2+-Calmodulin complex ( Ca2+/CaM ) is readily generated in the spine during synaptic activity and since activity-dependent CREB phosphorylation follows translocation of Ca2+/CaM from cytosol to nucleus , Ca2+/CaM diffusion was suggested to carry the spine-to-nucleus signal [3] , [8] . However , CREB phosphorylation appears 15 seconds after the beginning of the stimulus , which is substantially faster than expected from diffusion of CaM [8] . During a 15 second period , the mean-square displacement of CaM is 5 µm , whereas the diameter of pyramidal somata ranges between 15–20 µm and the most proximal spines do not appear within 10–15 µm from the soma ( spine density approaches zero at the first 25 µm , [9] , [10] and the first spine was reported to appear 39 . 7±12 . 1 µm from the soma [9] ) . Mermelstein et al . have therefore suggested that CaM diffuses in a phosphorylated form , which can reach 20 µm during 15 seconds due to an increased cytoplasmic diffusion rate . This suggestion , which provides the best , up-to-date , estimate for synapse-to-nucleus signaling , disregards the fact that the spine neck acts as a diffusion barrier for second messengers as small as cAMP , cGMP , and IP3 [11] , [12] ( molecular weights 300–1000 D; Compared with 16 . 8 kD [13] for CaM ) . We hereby suggest an alternative means of signal transduction that readily complies with the described time frame of spine-to-nucleus signaling , namely , an electrotonic signal along internal membranes ( Table 1 [8] , [14]–[16] ) . By the end of the 90's it was acknowledged that the endoplasmic reticulum ( ER ) forms a continuous network of tubes and sacs that extends from the nuclear envelope out to the cell periphery [17]–[20] . This view followed studies which employed EM reconstruction [18] and diffusion of dye along internal membranes [20] to show the ER continuity across the axon , soma , dendrites and the spine apparatus at the dendritic spines' heads . Accordingly , the ER has been suggested to act as a ‘neuron-within-neuron’ , as originally suggested by Michael Berridge [21] . However , until now signal propagation and integration along the ER have been described to take place via regenerative Ca2+ wave [21] . Here , we propose a passive electrotonic potential along the ER lumen and across the ER membrane ( Figure 1A ) . This hypothesis is supported by reconstruction studies of spiny dendrites describing the ER as a continuous network of anastomosing tubes running parallel to the longitudinal axis of the dendrite [22] and extends , virtually , into all mature dendritic spines [18] , [20] . This hypothesis is further supported by direct recordings from ER in skinned myocytes having an input resistance of ∼850 MΩ and a resting membrane potential around 0 mV between the ER lumen and the cytosol [23] ( values of ∼7 . 5 kΩ/cm2 and 15–20 mV were estimated earlier for ER membrane specific resistance [24] and membrane potential [25] , respectively ) . Those studies provide the experimental grounds for suggesting that ER membrane can separate charges and that it exhibits a specific resistance that is similar in magnitude to the plasma membrane ( e . g . a typical input resistance for L2/3 pyramidal cells is around 100 MΩ [26] with 20 kΩ/cm2 specific resistance for plasma membrane [27] ) . In order to test the suggested hypothesis against realistic parameters , provide realistic predictions and enable analytic study of the theory , we developed a mathematical model of a cable-in-cable , thereby generalizing the classical cable theory developed by Rall [28] ( Figure 1B and 1C ) . The model shows that current flow along a system of a cable-in-cable ( CIC ) would , essentially , follow the predictions of the classical cable theory along the external cable ( i . e . the plasma membrane ) , but at the same time , would exhibit counter-intuitive properties over the internal cable , which cannot be predicted by the classical cable theory . Using the CIC model we show that under realistic parameters the excitatory synaptic activity can give rise to an EPSP-like depolarization across the nuclear envelope , whereas a depolarizing signal initiating at the soma ( e . g . action potential ) would result in hyperpolarization of the nuclear envelope . This study provides a novel electrotonic explanation for the ability of the neuronal nucleus to discriminate between orthodromic and antidromic sources of membrane depolarizations . The study further predicts a novel role for compartmentalization of Ca2+ within dendritic spines and proposes an additional dimension for synaptic plasticity .
The cable-in-cable model principally follows the conventional cable theory and represents the internal membrane system as one passive internal cable that lies within another passive cable of plasma membrane . The key assumptions of the model , ER continuity [17]–[20] and its ability to separate charge similar to the external membrane [23]–[25] , rely on reports employing different experimental approaches . To simplify the qualitative description of the CIC theory , the analytical description of the cable-in-cable is reduced into 4 non-dimensional parameters: the ratio between ER diameter and the PM diameter ( E ) , the fraction of the non-conductive cross-section ( e . g . mitochondria , nucleus ) from the PM cross-section ( N ) , the ratio between membrane resistivity of ER and PM ( m ) and the ratio between the current actively injected into the ER and the current injected into the PM ( I ) . The CIC model can be viewed as an extension to the conventional cable theory , as it collapses to the traditional equations when the internal cable is reduced to zero ( E = 0 ) and no axial obstacles are allowed ( N = 0; for details see ‘Non-dimensional representation’ in the Methods section ) . The CIC system demonstrates a few noteworthy , qualitative properties: ( 1 ) The CIC system is governed by two space constants , where both space-constants affect each of the membranes; ( 2 ) As the transmembrane potential along the internal cable ( VmE ) is given by the difference between two decaying exponents ( the potential in the cytosol , Vi , and the potential inside the ER , VER ) , it is capable of generating an intriguing non-monotonic pattern as shown in Figure 2A ( inset ) . Namely , localized injection of current into the CIC system would induce depolarization at the external cable and hyperpolarization at the internal cable . However , while in both cases the transmembrane potential would approach zero with distance , the transmembrane potential across the internal cable would continue rising with distance , beyond zero , to form a region of depolarization and thereafter it would decay again to zero . We term the area , where a locally-distinct region of depolarization emerges along the internal cable after a segment of hyperpolarization , ‘virtual electrode’ ( VE; Dashed area in Figure 2A; see Discussion for details ) . Using the realistic parameters described in Table 2 , we plotted the steady-state potential inside the cytosolic compartment ( Vi ) and inside the ER lumen ( VER ) following local current injection into an infinite CIC system . Figure 2A shows that under these parameters the potential along both compartments ( i . e . cytosol and ER-lumen ) decays gradually towards zero . Nevertheless , the transmembrane potential along the internal membrane , given by the difference between these two compartments ( VmE≡VER−Vi ) , forms a virtual electrode ( inset of Figure 2A ) . The VE starts and reaches its peak depolarization after a distance of ∼0 . 6 and ∼1 . 3 electrotonic units , respectively . The electrotonic unit followed the space constant definition used by the conventional cable theory ( defined by Eq . G2 . 1 in the Methods section ) , which is equivalent to ∼1 mm using the parameters in Table 2 . The conventional cable theory is well supported by numerous transmembrane recordings . It is therefore interesting to compare the transmembrane potential along the plasma membrane predicted by the CIC model and the transmembrane potential predicted by the conventional model . Figure 2B shows the steady-state change across the external cable ( plasma membrane; VmP ) and compares it to the prediction of the conventional cable theory ( see ‘Space constant considerations’ in Methods for details ) . Both models predict exponential decay of transmembrane potential along the plasma membrane , whereas the difference between these two predictions for VmP is negligible ( ∼1–2%; Figure 2B ) and therefore , would be difficult to detect empirically . In order to test the significance of the VE amplitude , its peak was compared to the EPSP amplitude . We , therefore , normalized the VE amplitude to the EPSP ( Normalized VE Amplitude; nVE ) by dividing VmE by the amplitude of the VmP at the position where the VE-peak occurred ( Figure 2C ) . Thus , the peak amplitude of nVE represents , in percents , the ratio between VE-peak and EPSP at the same position and time . Depending on the specific set of CIC parameters the amplitudes of the VE-peak exhibit EPSP-like levels ( nVE range 150%–10%; Figure 2D ) . Moreover , representing the VE amplitude by nVE is underestimating the relation between VE amplitude and EPSP amplitude , since the ratio between the depolarization across the internal cable ( the VE amplitude ) and the potential along the external cable ( the EPSP amplitude ) , gets bigger with distance ( Figure 2C and 2D , dashed lines ) . Thus , the fraction of VmE amplitude relative to the local EPSP amplitude is substantially larger at positions beyond the VE peak . Moreover , when the initial EPSP amplitude ( i . e . at the synapse ) is altered , the proportion between transmembrane potentials of the internal and external cables is maintained , indicating that the relation between EPSP and VE amplitudes is determined by the specific cable parameters and not affected by changes in synaptic efficacy ( see inset for Figure 3B ) . An analytic rationale for relating the VE amplitude to VmP arrives from Equations H13 . 1 and H13 . 2 , which show that at any point in space and time , the amplitudes of both transmembrane potentials are linearly dependent on VmP ( x = 0 , t ) , the transmembrane potentials across the plasma membrane at the synapse ( or dependent on Ii ( x = 0 , t ) , the axial current entering the cytosolic compartment at the synapse; since VmP ( x = 0 , t ) = Ii ( x = 0 , t ) ·ri ) . Namely , the ratio between VmE ( x , t ) and VmP ( x , t ) along a given CIC system ( i . e . the pattern over space and time ) is fixed and not affected by the magnitude of the synaptic signal . Thus , introducing a realistic set of parameters to the CIC model predicts that excitatory synaptic activity can give rise to depolarization across the internal membrane , with an EPSP-like amplitude at a realistic distance from the synapse . Moreover , the unique VE-shape of transmembrane potential along the internal cable can explain the ability of the cell's nucleus to differentiate between dendritic origin and somatic origin of a depolarizing signal . Namely , a depolarizing signal that is proximal to the target ( e . g . antidromic signal originating from the soma ) would hyperpolarize the internal membrane at the target ( i . e . around the nucleus ) , whereas depolarizing signals with remote origin would depolarize the internal membrane at the target . The effect of the signal along the internal cable ( i . e . the VE ) would be further subjected to modulations of synaptic efficacy ( e . g . LTP or LTD ) . Evidently , different synaptic inputs originate from a wide range of distances from the cell nucleus . Yet , the VE predicted by the CIC model is essentially a spatial phenomenon that reaches its peak at a fixed distance from the synapse namely , the distance between the VE-peak and the synapse is fixed for any given set of passive cable properties , regardless of the initial potential at the synapse . It is , therefore , interesting to examine its range limits and its significance for distal synapses . For that purpose let us define imER and imPL , as the currents that are actively injected across the ER membrane and across the plasma membrane at the synapse ( x = 0 ) , respectively ( see Discussion for an actual mechanism which may generate imER in synchrony with imPL ) . As illustrated by the circuit in Figure 1C ( and implemented in Eq . D1–D5 formulating the Kirchhoff's law ) , Ii ( x = 0 ) is given by the difference between imPL , the current actively entering the cytosol through plasma membrane , and imER , the current actively leaving the cytosol into the ER lumen at the synapse; whereas , IER ( x = 0 ) is given by imER , the current actively entering the ER lumen through the ER membrane at the synapse . Along distance , Ii ( x = 0 ) leaks out to the external compartment and also into the ER lumen , thereby feeding the axial currents Ie and IER , respectively . The rest of Ii ( x = 0 ) travels axially along the cytosol as Ii ( as illustrated in the inset to Figure 1A and 1C and formulated in the ‘Methods’ ) . Accordingly , imER represents a supplementary current that is generated by active processes at the ER membrane simultaneously with the specific synaptic activity . Positive imER would , therefore , augment IER and diminish Ii whereas negative imER would diminish IER and augment Ii . Alternatively , the effect of imER may be simplistically illustrated as an increase ( for imER>0 ) or a decrease ( for imER<0 ) of the VmE at the synapse ( VmE ( x = 0 ) ; red curve in Figure 2A ) The analytical description of the transmembrane potential along the internal cable ( Eq . H13 . 2 in the ‘Methods’ section ) suggests that the ratio ( I ) between the initial axial currents at the synapse ( ; Eq . G1 . 4 , G1 . 5 ) can modify the pattern of transmembrane potential along distance . Since this ratio is determined at the synapse , we found it interesting to single out the qualitative features of the VE that are governed by I . As a first step in answering this question , we used the realistic set of parameters in Table 2 , for plotting the effect of I on the amplitude and location of VE's peak . Figure 3 shows that VE location can be greatly modified by I ( Figure 3A and 3B ) with no effect on nVE amplitude ( Figure 3B , inset ) . The ability of the CIC system to generate the VE pattern along the internal cable without active current injection into the ER-lumen indicates that the principle VE pattern can be induced passively following synaptic activity , whereas VE location can be further tuned by active processes at the synapse-ER complex . Examples for actual processes which may involve negative and positive imER are activation of Ryanodine receptors and/or activity of the electrogenic SERCA pumps . Both are localized at the ER in the spine heads and both processes can be triggered by excitatory synaptic activity ( via calcium influx through activation of glutamate receptors; see Discussion for details ) . This ability of an individual synapse-ER complex to determine the VE location may play two roles: First , it may serve as a mechanism for compensating for the wide range of synapse-to-nucleus distances and second , it can introduce a parallel level of synaptic plasticity , which is specifically modulating the synapse-to-nucleus signal in a manner that is largely independent of the efficacy of the specific synapse ( i . e . the EPSP ) . This second level of synaptic plasticity is demonstrated in Figure 3C , where VmE level at a wide range of arbitrary distances from the synapse , can be modulated or inversed , solely , by properties of the individual synapse ( range: −200% to +100% of EPSP ) . Thus , local modulations of the internal compartment at the synapse are capable of introducing a second level of synaptic plasticity , which would modulate the effect of the VE signal on the nucleus . Such modulations could be facilitated by passive properties ( e . g . local changes in membrane permeability or surface area of the ER at the synapse ) as well as by active properties ( e . g . electrogenic pumps and ion channels; see Discussion for details ) . The majority of the synaptic activity in the cortex is mediated by glutamatergic synapses onto pyramidal neurons . These synapses terminate on mushroom-like structures , dendritic spines . Principally , the above description of VE along the ER can be generated by a synapse located directly on the dendritic shafts . Does the CIC hypothesis predict an advantage of introducing synaptic input via secluded compartments such as dendritic spines ? A plausible answer to this question may be linked to compartmentalization of Ca2+ dynamics , which is commonly conceived as one of the main roles of dendritic spines . Excitatory synaptic activation on a dendritic spine initiates Ca2+ influx into the spine mainly through glutamatergic receptors ( i . e . N-methyl D-aspartate receptors; NMDAR ) . Approximately 30% of the Ca2+ entering the spine is carried into the ER lumen by the electrogenic [29] Ca2+ pump , SERCA ( Sarcoplasmic Endoplasmic Reticulum Calcium-ATPase ) [11] . Additionally , Ca2+ influx into the spine has been suggested to induce Ca2+-induced Ca2+ release ( CICR ) from the ER within the spine [30] , [31] . Thus , excitatory synaptic activity onto the spine is coupled with positive and/or negative Ca2+-mediated currents flowing into the ER lumen at the spine head . In the context of the CIC system , these two processes actively govern the I ratio presented above . Under the assumption that VEs play a role in synaptic signaling , one would expect that the synaptic parameter governing their properties ( i . e . the ratio I ) would be synapse-specific . Namely , different synapses would maintain different I ratios . In order to achieve this , ( 1 ) the ER segment , which directly responds to the increase in cytosolic Ca2+ , should employ different levels of CICR and SERCA; and ( 2 ) the cytosolic Ca2+ elevation evoked by synaptic activity should be confined to that specific segment of ER ( i . e . confined to the ER segment which binds a particular I ratio to a specific synapse ) . Figure 3D presents the predicted spatial decay of Ca2+ level along the distance from a point source of Ca2+ in the cytoplasm with endogenous Ca2+-buffer . ( The endogenous Ca2+-buffer parameters were taken from Naraghi et al . [32] and the calculations employed conventional models [33] , [34] ) . This estimate demonstrates that , under realistic spine density of 20–30 spines per 10 µm [35] , [36] ( up to 60 spines per 10 µm where reported [10] at the distal dendritic branches ) , activation of a single glutamatergic synapse is expected to trigger calcium-induced currents at multiple surrounding synapses . Namely , in order to enable a synapse-specific I ratio and comply with the realistic spine density , the spatial expansion of Ca2+ elevation should be significantly restricted by at least one order of magnitude . Therefore , we suggest that compartmentalization of free calcium by the dendritic spines is essential for maintaining synapse-specific tuning of signaling via VE along the internal membrane . This assumption is further supported by experimental evidence indicating that each dendritic spine usually accommodates a single synapse [37] , [38] . A pivotal stage in processing synaptic inputs is their convergence and integration at the soma , which leads to initiation of action potentials ( APs ) and activation of transcription factors at the nucleus . The soma is typically characterized by two anatomical features: ( 1 ) it is the widest region of the neuron and ( 2 ) it contains the cell nucleus . If VE participates in synapse-to-nucleus signaling it is useful to examine the CIC system at the transition from the dendrite to the soma . In order to model the effect of dendrite-to-soma transition of the VE signal , a second CIC compartment ( somatic-CIC ) was connected to the CIC system described above ( for details regarding multiple CIC systems please find ‘Finite CIC system with arbitrary boundary conditions’ in ‘Methods’ ) . The somatic-CIC construct was aimed at representing the soma at the peri-nuclear region . The peri-nuclear region is characterized by two nuclear envelopes ( NE ) , where the outer envelope is continuous with the ER membrane [39] . These two envelopes enclose the nucleus and form between them a space that is continuous with the ER lumen [39] . The NE allows continuity between the nucleoplasm and the cytosol through nuclear pores ( P; about 9 nm in diameter , see Figure 4A ) . The nuclear pores allow non-selective flux of ions and therefore enable electrical continuity between cytoplasm and nucleoplasm . For illustrating the VE at the peri-nuclear region the somatic-CIC had wider diameter ( 16 µm ) and included an initial part with narrow cytosolic cross-section ( representing the perinuclear area ) followed by a part with larger non-conductive cross-secession , representing the nucleus ( Figure 4B ) . Except for these parameters the others parameters were kept as described above ( Table 2 ) . Figure 4C shows a simulation of two VE signals arriving at the soma from two electrotonically-dispersed synapses . It illustrates ( in Figure 4C , right ) that the transition from a dendritic-CIC into somatic-CIC may act on disperse VEs as a “converging lens” . Namely , amplifying the VEs amplitude and converging them to the soma . This converging effect of the soma preserves the ability of current ratio at the dendritic spine ( I ) to modulate the VE amplitude and actual manifestation at the nucleus . The VE pattern displayed above represents the steady-state difference between potentials in the ER-lumen and in the cytosol . However , since actual synaptic currents are confined in time , the validity of a steady-state description of VE needs to be evaluated at the time scale of synaptic input duration . To address this question , we simulated the ER-membrane potential at several time points after the beginning of synaptic activity . We described CIC dynamics by units of the conventional membrane time constant ( τm; τm≡RmCm ) , which is equivalent to 48 ms under our specific parameters ( Table 2 ) . Indeed , Figure 5A shows that VE pattern is not a unique steady-state phenomenon , as it is already established within 0 . 005 time constants ( equivalent to 0 . 2 . 5 ms ) whereas the amplitude of its peak develops over time similarly to the development of an EPSP over time ( Figure 5B ) . Figure 3A ( dashed line ) shows that while the VE travels ( electrochemically ) along the internal cable , the ratio between the amplitudes of the VE-peak and EPSP ( nVE ) is higher than the steady-state ratio at the final position of the VE-peak . In order to get a better estimate of the VE kinetics over time , we compared ( Figure 5C ) the change over time of the two transmembrane potentials ( VmE and VmP ) at the position of the VE peak ( X = 1 . 29; the peak of the blue trace , T = 100 , in Figure 5A ) . We therefore used the conventional cable theory [40] for simulating the development of plasma-membrane potential , VmP , over-time ( as described explicitly in Eq . I1 in the Methods ) . Using the conventional spatio-temporal solution described by Jack et al . [40] ( Eq . I1 ) and the conventional definition for traveling speed of electrotonic signals , Figure 5D , reveals that VmE amplitude rises , to its steady-state level , slightly faster than EPSP amplitude . Moreover , despite the fact that the electrotonic time constants are the same for both the inner and outer cables , the VE pattern and its amplitude are established dramatically faster than the EPSP , at the segment around VE-peak ( Figure 5C and 5D ) . The fact that at the position of VE-peak , VmE amplitude reaches its steady-state level within ∼0 . 6 time constants ( 29 ms ) and thereafter overshoots its steady-state peak by 20% shows higher efficacy for VE as electrotonic signal for signal durations around 1 time constant . This is in line with the typical duration of the synaptic current induced by glutamatergic synapse ( EPSC's time to 50% decay: ∼10 ms [41] ) and the expected time for it to spread out to that position . In conclusion , time domain analysis shows that electrotonic signaling by means of VE along the internal cable has kinetics that are similar and slightly faster than electrotonic kinetics of the EPSP along the external cable . The comparable kinetics indicates that VE can , similarly , convey synaptic signals induced by realistic synaptic current duration . Altogether , time domain analysis demonstrates that the steady-state analysis provides plausible representation of VE .
ER is conventionally regarded as an unstructured network of tubes and sacs . Thus , an equivalent cylinder with a cross-section similar to the axial cross-section of the ER network may misrepresent the effective axial resistance along the ER lumen . For example , a cross-section measurement of a single tube tangled in a bigger volume will appear misleadingly higher than the actual cross-section available for an axial current traveling along that tube , generating an underestimation of the actual axial resistance along that tube . Thus , lumen cross-section in an unstructured network can not faithfully represent the effective axial cross-section . However , a detailed structural study of the neuronal ER architecture by Martone et al . [22] , reveals that the ER in the dendrites of a spiny neuron forms a network of tubules running in parallel to the longitudinal axis of the dendrite . Thus , dendritic ER architecture appears to support axial conductance , whereby axial ER cross-section provides a more realistic estimation for its axial resistance . A key question that lies beyond the focus of our electrotonic model is: how would a depolarization at the ER pass a signal into the cell nucleus ? One plausible route may be electrotonic signals across the nuclear envelopes ( NEs ) . Since the ER lumen is continuous with the lumen between the inner and outer nuclear envelopes and the nucleoplasm is continuous with the cytosolic compartment via holes ( i . e . the nuclear pores ) through the NEs , the transmembrane potential across the NEs follows the transmembrane potential changes across the ER ( namely , VE ) . Thus , voltage-sensitive properties across the ER membrane forming the outer nuclear envelope , may mediate the signal into the nucleus . This proposal for nuclear signaling is in line with reports about several types of voltage sensitive ER channels [23] , [42]–[46] provide partial support this possibility . A second option is an initiation of locally-distinct perinuclear , Ca2+ signals , which may have a bearing on nuclear moieties . A single VE or sequence of multiple VEs may initiate and modulate theses signals through activation of voltage-sensitive properties cross the ER membrane . The fact that Ca2+-elevation in the nucleus is necessary for numerous nuclear activities and specifically activity-dependent CREB phosphorylation [3] , [47] , implies that voltage-activated Ca2+-channels may initiate a local Ca2+ signal that will be amplified and modulated by Ca2+-activated Ca2+-channels . This possibility is supported by ( 1 ) experimental reports from the CNS [43] and from non-nerve tissue [44] describing ER Ca2+ channels which increase their opening probability sharply by depolarization; and ( 2 ) studies showing that the inner NE expresses intracellular Ca-activated Ca release channel , i . e . inositol 1 , 4 , 5-trisphosphate receptors ( IP3Rs ) and ryanodine receptors ( RyRs ) [48] , [49] . Moreover , this explanation is consistent with various experimental observations showing that: ( 1 ) locally-distinct cytoplasmic events of ER-Ca2+-release ( e . g . Ca2+ puffs ) , originating within a 2–3 micron perinuclear zone , appear to initiate Ca2+ elevation in the nucleus , [50] ( 2 ) The NE is a functional calcium store [51] , [52] and Ca2+ signals within the nucleus can be evoked in the absence of elevation in cytosolic Ca2+ [48] , [51] , [52] . Thus , co-localization of VE with voltage-sensitive channels [23] , [42] , [45] , [46] , voltage-sensitive calcium channels and calcium-sensitive calcium channels [48] , [49] along the ER at the nuclear envelopes , is one possibility for instantly coupling VE with perinuclear and nuclear calcium signals . Such machinery introduces a new layer of Ca2+-mediated control of nuclear function in neurons and , possibly , in non-neuronal cells . Modification of the effect that a specific synaptic activity has on the postsynaptic cell is conventionally termed synaptic plasticity . This theoretical study shows that a synapse-specific property , the I ratio ( defined in Eq . G1 . 4 ) , can modulate the effect a specific synaptic activity has on the transmembrane potential across the nuclear envelopes of the postsynaptic cell . Modification of the synapse-specific I ratio may , therefore , represent a second level of synaptic plasticity . Figure 3C shows that regardless of the strength of the signal across the plasma membrane ( e . g . EPSP ) , the signal across the internal membrane ( VE ) at an arbitrary distance from the synapse can be set exclusively by the I ratio to be positive , negative or zero . Nevertheless , the magnitude the VE signal will be subjected to the conventional synaptic plasticity as well ( i . e . potentiation or depression of EPSP ) . This suggests that the synapse-to-nucleus signal bares the capability for independent synaptic plasticity at various ranges of electrotonic distances between synapse and nucleus . One plausible physiological mechanism , which may sustain a synapse-specific I ratio modulation , may be electrogenic calcium fluxes across the ER membrane at the synapse . This suggestion is inline with the fact that a typical EPSP in the cortex , which is generated by glutamatergic synaptic activity onto dendritic spines , involves calcium influx from the extracellular compartment into the specific spine head . The extension of the ER into the spine head ( spine apparatus ) exhibits capabilities to link elevation in spine-calcium into inward or outward currents across the ER at the spine . Inward calcium-dependent current across the ER may be mediated by the electrogenic activity of SERCA pumps [29] , whereas outward currents across the ER in the spine head may be mediated by Ca2+-sensitive channels [31] . Immunocytochmical studies shows that RyR labeling is notable in dendritic spines of cortical pyramidal cells , whereas their dendritic shafts are mostly unlabeled [53] . A large body of theoretical studies supported by experimental data [11] , [54]–[57] shows that the interaction between SERCA , RyR , IP3R , endogenic buffers and intracellular calcium stores can generate a wide variety of Ca2+ dynamics , which are fundamentally dependent on the temporal pattern of Ca2+-inputs . Taken together , the spine head seems to contain the hardware necessary for generating synapse-specific modulation in VE , which may be further modified by the pattern of the specific synaptic input . This assumption may be further supported by the facts that: ( 1 ) the majority of excitatory communication in the cortex is mediated via dendritic spines which are structures that can compartmentalized Ca2+ [11]; ( 2 ) each spine head receives a single glutamatergic synaptic input [37] , [38]; and that ( 3 ) pyramidal neurons , the main source for glutamatergic synaptic inputs in the cortex , respond to their preferred sensory input by burst patterns of action potentials . Thus , the CIC prediction for the I ratio combined with the current knowledge on excitatory synaptic signaling in the cortex , provide circumstantial support to the existence of synaptic plasticity of the spine-to-nucleus signaling , which may be further modified by the pattern of the specific synaptic input . Evidently , the VE pattern and amplitude is parameter-dependent . For ruling out the possibility that the model's predictions are specific to a narrow range of parameters ( as described in Table 2 ) , we evaluated the robustness of its predictions over a wide range of parameters . Using one-dimensional parameter-mapping , we show ( Figure 6 ) that VE along the internal cable is a reliable phenomenon and its amplitude has an EPSP-like magnitude . For simplicity , we focused our study on analytical description of the CIC theory and therefore we have neglected the role of ER curvatures . Likewise , for simplifying the time-domain analysis , we have assumed that the inner and outer the cables have identical time-constants ( τm ) . Nevertheless , we allowed different membrane-specific resistivities for ER and PM ( ; Eq . G1 . 2 ) , and kept the similar time constant for both PM and ER membranes by constraining the relation of the two membrane-specific capacitances ( ; Eq . H8 ) . This constraint over the relation between the two membrane-specific capacitances does not affect the analytical analysis of different membrane resistivities at steady-state . One of the major features of the ER , which was neglected in our study , is the network structure of the ER . One may ask whether this simplification can breach the prediction of the CIC model ? Apparently , the principal prediction of the CIC model , the VE , is in line with a model specifically developed for describing a network of passive cable elements , [58] the unequal anisotropic bidomain model ( for review see [59] ) . Moreover , virtual electrodes predicted by the bidomain model have been demonstrated empirically over cardiac myocardium [60] , [61] . Thus , the ability of a network of passive cable elements to generate VE , is well supported . The CIC theory provides several experimentally testable predictions . Interestingly , we found that each prediction can be supported ( at least partially ) by recent experimental reports . The first prediction resolves the question presented above , regarding the traveling speed of the ‘synapse-to-CREB’ signal . The CIC model predicts that ( 1st prediction ) synapse-to-nucleus signaling would exhibit an electrotonically-fast propagation velocity that is 2 or 3 orders of magnitudes higher than expected from a regenerative Ca2+-wave or diffusion of a second messenger ( i . e . kinase-bound CaM , proposed previously ) , respectively ( see Table 1 ) . This prediction is in line with the ‘synapse-to-CREB’ time ( ∼15 seconds ) reported by Mermelstein et al . [8] . Moreover , this prediction expands our ability to comprehend the way synapses , as myriad sources of fast electrical signals , communicate their information stream to the distant nucleus . The CIC model suggests that active properties within the spine heads ( e . g . Ca2+-mediated currents across the spine apparatus , via SERCA pumps , ryanodine receptors and IP3 receptors ) encode an additional level of synaptic plasticity by determining the efficacy of the VE at the nucleus . This suggests that ( 2nd prediction ) as an information-encoding parameter , spine Ca2+-dynamics would exhibit high variability between spines in the same cell and spines in the same cell group . Namely , measurements of the fraction of Ca2+ , which enters the ER at the spine head following excitatory synaptic activity , would show a wide range of values between spines of similar neurons at similar location . This prediction is supported by Sabatini et al . [11] who measured the fraction of Ca2+ entering the ER at the spine head of CA1 pyramidal neurons . The CIC model shows that the ability of a dendritic spine to induce effective VE at the nucleus is impaired if the spine is too close to the cell nucleus . Therefore , some properties of dendritic spines are predicted to undergo gradual change in relation to their distance from the nucleus . For example ( 3rd prediction ) spines , which otherwise exhibit high density along the dendrites , should be absent from the soma and proximal part of the dendrites , which has been observed in several studies [9] , [10] , [35] , [37] , [62] , [63] . Likewise , it is expected ( 4th prediction ) that on average , a proximal spine would exhibit a lower activity of CICR and/or higher activity of SERCA , compared to a dendritic spine located at remote dendritic regions . Similarly , for facilitating the passive conductance of synaptic currents into the ER , ( 5th prediction ) the ER branch at the spine head ( the spine apparatus ) should exhibit varying degrees of laminar organization and increased surface area compared to the spine head enclosing it [64] . For example , the ratio between the surface area of the spine apparatus and spine head would be ( on average ) greater for spines which are distal from the nucleus . These last two predictions should be testable by appropriate physiological , immunocytochemical and morphological experiments . Finally , while activation of glutamatergic synapses at the dendrite induces robust CREB phosphorylation at the nucleus , ( 6th prediction ) a concomitant activation of extra-synaptic glutamatergic receptors at the soma would suppress the electrotonic induction of VE at the nucleus and therefore suppress CREB activation . This prediction is supported by Hardingham et al . [65] who showed that , while synaptic activation of glutamatergic synapses induces CREB phosphorylation , bath application of glutamate suppresses it . One way of obtaining direct experimental evidence is to apply the patch clamp technique for recording and manipulating the transmembrane potential simultaneously across both the ER and the PL , during synaptic activity . This can be achieved by employing the pipette-within-pipette patching technique described by Jonas et al [66] . Although this approach would be technically challenging , its successful application would enable a simultaneous recording of the two transmembrane potentials . Such an experiment would directly address the question of whether a direct interaction is present or not . In summary , the significant contribution of the current study is proposing a VE along the ER membrane as a means of ultra-fast intracellular signal transduction and demonstrating its feasibility under realistic parameters using a cable-in-cable model . The CIC hypothesis presented here contributes also by introducing the possibility of an additional level of synaptic plasticity and a new perspective for the role of dendritic spines , which densely populates the dendrites of spiny neurons . Since ER is continuous also in non-neuronal cells , electrotonic signaling along internal membranes may act as a general means of fast signaling between cell periphery and nucleus and other sub-cellular compartments . This study shows that intracellular level biophysical theory may introduce concepts and principles that appear counter-intuitive with views originating from conventional cellular level electrophysiology , suggesting that the phenomenological richness of intracellular architecture and the associated electrophysiology may still offer surprises .
The model follows the classic cable theory [40] , [67] , [68] and introduces a model of a cable in cable . We used Mathematica5 ( Wolfram Research ) for numerical calculation and for checking the analytical derivations . Model assumptions are: The equations below follow the circuit in Figure 1C . The parameters and their definitions are provided in Table 2 . A . Ohmic Axial current: ( A1 ) ( A2 ) ( A3 ) B . Total axial current ( IT ) is constant: ( B1 ) C . Radial ( trans-membranal ) currents in a cable with no additional current source: ( C1 ) ( C2 ) D . Kirchhoff's law: ( Inward current is defined , negative ) ( D1 ) ( D2 ) Therefore: ( D3 ) ( D4 ) ( D5 ) A system of ODE is obtained from combining all the above: ( E1 ) ( E2 ) ( E3 ) The system can be represented as: where ( E4 ) The general steady-state solution is characterized by two space constants ( ) given as the sum of two decaying exponents: ( F1 ) ( F2 ) For the explicit solution , see ‘Time Domain’ below . We found it advantageous to describe the solution ( Eq . F1 and Eq . F2 ) by four non-dimensional and independent parameters . For that purpose we defined the following parameters ( Eq . G1 . 1–G1 . 4 ) : Note that ( Eq . G1 . 5; see text for details ) The conventional cable theory defines the space constant ( λ ) as: . Under the assumption that re→0 , λ is often represented as . In order to avoid non-specific parameters ( ri , rm ) we followed the second representation and defined: ( G2 . 1 ) Non-dimensional scaling is obtained by defining: ( G2 . 2 - G2 . 5 ) For non-steady state conditions the trans-membranal current of cylindrical cable includes transient capacitance currents and is given by: ( H1 ) where: Accordingly , ImP and ImE can be described as ( H2 ) ( H3 ) The ODE system becomeswhere: M1 is as described above ( Eq . E4 ) ( H4 ) For enabling the analytical solution we assumed similar time constant ( τm ) for both PM and ER membranes . We , therefore , defined specific membrane parameters ( Cm , Rm ) : τm≡Rm·Cm , Cm≡CmP and Rm≡RmP ( Eq . H5–H7 ) ( Units: sec , F/cm2 , Ω·cm2 , respectively ) . We allowed different membrane-specific resistivities for ER and PM ( ; Eq . G1 . 2 ) , and forced a similar time constant RmE· CmE = τm ( = RmP· CmP ) for both PM and ER membranes by assuming: ( H8 ) This assumption ( Eq . H8 ) , which is taken for enabling an analytical solution for the time-domain ( see below ) , do not affect the steady-state solution . By incorporating the specific membrane parameters ( Eq . H5–H8 ) , matrices M1 and M2 become: ( H9 ) ( H10 ) where: The system becomes: where:The non-dimensional representation of the system is: where: Eigenvalues of : ( H12 ) where: κe→0 The explicit solution ( obtained analytically using Laplace transform ) [40] is: ( H13 . 1 ) ( H13 . 2 ) where VmP ( X , T ) ≡Vi ( X , T ) −Ve ( X , T ) and VmE ( X , T ) ≡VER ( X , T ) −Vi ( X , T ) . ( Note that VmP ( X , T ) = Vi ( X , T ) under the assumption that Ve ( X , T ) →0 ) where ƒ is a non-dimensional function of X and T: ( H14 . 1 ) where erfc is the complementary error-function: erfc ( x ) ≡1−erf ( x ) Note that at steady-stat ( T→∞ ) : fi ( X , T ) →1 where λ1 , λ2 are non-dimensional space constants: ( H14 . 2 ) ( H14 . 3 ) Note that for any realistic set of parameters: {0≤N<1 , 0≤E<1 , 0<m , 0< ( 1−N−E2 ) } As a result: λ1 and λ2 has real solution where C1–C5 are constants defined as followed: ( H14 . 4 ) ( H14 . 5 ) ( Note that ( 1−N−E2 ) describes the fraction of the cytosolic cross-section and therefore: ( 1−N−E2 ) >0 ) ( H14 . 6 ) ( H14 . 7 ) ( H14 . 8 ) Note that when the internal cable collapses to zero ( E = 0 ) and no axial obstacles are allowed ( N = 0 ) , the system collapses to the conventional cable equation . Namely , , C1→1 , C2→0 , C3→1 , ( C4 , C5 are not defined ) and make Eq . H13 . 1 collapses into the traditional solution: . It can be shown that when E = 0 , the CIC system collapses to the conventional cable equation , for any realistic N: {0≤N<1} ( see ‘Space constant considerations’ below , for details ) . Where the electrotonic kinetics predicted by CIC model are compared with those predicted by the conventional cable theory , we followed the conventional spatio-temporal solution described by Jack et al . [40]: ( 11 ) where erfc is the complementary error-function: erfc ( x ) ≡1−erf ( x ) . The classic cable theory assumes no obstacles for the axial current . It practically defines an effective axial intracellular resistivity , Ri , which is already adjusted ( empirically ) to the actual non-conductive cross-sections ( e . g . mitochondria ) along the specific cable . In contrast , the CIC model incorporates an independent , axial non-conductive cross-section . Therefore , the CIC model assumes that the axial intracellular resistivity represents a cytoplasm without non-conductive cross-sections . Evidently , this deviation from the convention is inevitable if axial obstacles should not be omitted from the CIC model . This difference in terminology can be rectified as described blow . The relation between Ri , RC and non-conductive cross-sections ( N ) along the cable becomes:where RC is the axial intracellular resistivity , specific for a cytoplasm without non-conductive cross-sections . Accordingly , N is incorporated in the conventional space-constant ( λN ) as:Thus , when the internal cable collapses to zero ( E = 0 ) and axial obstacles are allowed {0≤N<1} , the system collapses to the conventional cable equation with space-constant λN . Namely , , C1→1 , C2→0 , C3→1 , {C4 , C5 are not defined} which makes Eq . H13 . 1 collapses into the traditional solution [40] , formulated in Eq . I1:orwhere . In Figure 2B we compared the potential along the external cable ( VmP ( X ) ) predicted by the classic theory and the VmP ( X ) predicted by the CIC model . In that calculation we followed an empirical definition and defined the classic-model's space constant by fitting a single exponent to two points along the CIC prediction for VmP ( X ) . The first point was X = 0 ( VmP ( X ) = VmP ( 0 ) ) and the second point was arbitrarily chosen as the point where VmE ( X ) = 0 . Nevertheless , we also tested a second approach for defining a single space constant to the CIC system using λN ( as described above ) . Under both approaches the difference between the two predictions is too small to be detected experimentally ( within range of few percentages of the initial potential , VmP ( 0 ) ) . Explicit solution for finite CIC at steady-state with arbitrary boundary conditions . Boundary conditions: VmP ( 0 ) , VmER ( 0 ) Initial potentials at: X = 0 VmP ( L ) , VmER ( L ) Ending potentials at: X = L The explicit solution:where a is an index that takes values 0 or L in the expressions aboveWhen {L→∞} , the above explicit solution for finite CIC gives the CIC solution for semi-finite CIC ( provided in Eq . H13 . 1–H14 . 8 ) at steady state: where Within the classic cable-theory the conventional definition for input resistance ( namely the ratio between potential and current at the point where X = 0 ) provides a constant parameter , which is solely determined by structural cable properties . Applying that definition for input resistance to the semi-finite CIC system , produces an expression which , in addition to structural cable properties , also includes the ratio between the initial potentials ( or currents ) at the ER lumen and the cytosolic compartment . Accordingly , at identical position and CIC structure , different synaptic signals ( i . e . different I parameters ) are subjected to different input resistance:where all the definitions follow the definition given in the main text ( see Eq . H13 . 1–H14 . 8 ) . Similarly , the conventional definition of resistance at the finite CIC , depends on the ratio between the potentials of ER lumen and the cytosolic compartment at the initial point and at the ending point , as well as the electrotonic length of the specific finite CIC . For simplicity , the calculation of successive CIC systems , in Figure 4 , approximated the input resistance at each finite CIC system to be determined only by the ratio of the initial potentials . In the interest of completeness we also provide a more detailed expression of the input resistance in a finite CIC system without employing the approximation of the input resistance at each finite CIC system by being determined only by the ratio of the initial potentials . The explicit solution for the steady-state input resistance of finite CIC with arbitrary boundary condition: | Our study incorporates the fact that the endoplasmic reticulum ( ER ) forms a complete continuum from the spine head to the nuclear envelope and suggests that electrical current flow in a neuron may be better described by a cable-within-a-cable system , where synaptic current flows simultaneously in the medium between the cell membrane and the ER , and within the ER ( the internal cable ) . Our paper provides a novel extension to the classical cable theory ( namely , cable-within-cable theory ) and presents several interesting predictions . We show that some of these predictions are supported by recent experiments , whereas the principal hypothesis may shed new light on some puzzling observations related to signaling from synapse-to-nucleus . Overall , we show that intracellular-level electrophysiology may introduce principles that appear counter-intuitive with views originating from conventional cellular-level electrophysiology . |
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Ivermectin has emerged as very promising pediculicide , particularly in cases of resistance to commonly used pediculicides . Recently , however , the first field-evolved ivermectin-resistance in lice was reported . To gain insight into the mechanisms underlying ivermectin-resistance , we both looked for mutations in the ivermectin-target site ( GluCl ) and searched the entire proteome for potential new loci involved in resistance from laboratory susceptible and ivermectin-selected resistant body lice . Polymorphism analysis of cDNA GluCl showed no non-silent mutations . Proteomic analysis identified 22 differentially regulated proteins , of which 13 were upregulated and 9 were downregulated in the resistant strain . We evaluated the correlation between mRNA and protein levels by qRT-PCR and found that the trend in transcriptional variation was consistent with the proteomic changes . Among differentially expressed proteins , a complexin i . e . a neuronal protein which plays a key role in regulating neurotransmitter release , was shown to be the most significantly down-expressed in the ivermectin-resistant lice . Moreover , DNA-mutation analysis revealed that some complexin transcripts from resistant lice gained a premature stop codon , suggesting that this down-expression might be due , in part , to secondary effects of a nonsense mutation inside the gene . We further confirmed the association between complexin and ivermectin-resistance by RNA-interfering and found that knocking down the complexin expression induces resistance to ivermectin in susceptible lice . Our results provide evidence that complexin plays a significant role in regulating ivermectin resistance in body lice and represents the first evidence that links complexin to insecticide resistance .
Sucking lice ( Anoplura ) are obligate blood-feeding ectoparasites of eutherian mammals [1] . Humans are the preferred host for two species: Pthirus pubis and Pediculus humanus [2 , 3] . The latter has significant relevance to public health and includes two ecotypes: head lice ( P . h . capitis ) , which live in the hair , and body lice ( P . h . humanus ) , which live in clothing [1 , 3 , 4] . Head lice are common and can be found worldwide [1] , with children being at increased risk [2] . Conversely , body lice are associated with poor socio-economic conditions [1 , 4] and homeless people and refugee-camp populations are predominantly affected [4 , 5] . Body lice are the main vectors of at least three dangerous pathogenic bacteria , namely: Rickettsia prowazekii , Bartonella quintana and Borrelia recurrentis [1 , 4] . The prevalence of the body louse is underestimated in many developed countries and , as the number of homeless people increases , louse-borne infectious diseases are also on the rise [1 , 5] . Recently , more emphasis has been placed on the ability of head lice to transmit bacterial diseases . Indeed , the DNA of several pathogenic bacteria is being increasingly detected in head lice , such as: B . quintana , B . recurrentis and Yersinia pestis [6–8] . In addition , studies have shown that experimentally infected head lice are capable of acquiring , maintaining and transmitting R . prowazekii and B . quintana , demonstrating that these lice have the potential to be a vector of pathogen under optimal epidemiological conditions [9 , 10] . This fact may pose a very substantial threat to humanity , as such infestations are not controlled in any country , including developed countries , despite repeated efforts to eradicate them [11] . This is mainly due to the resistance developed by lice to widely-used insecticides such as malathion and pyrethroid [1 , 12] . The use of new effective products with different modes of action , such as ivermectin ( IVM ) , have proven to be a promising alternative to combating the problem of resistance [1 , 13] IVM belongs to the macrocyclic lactone complex [14 , 15] and blocks synaptic transmission in invertebrates by binding to glutamate-gated chlorine channels ( GluCls ) in nerves and muscles , which are its primary target , leading to hyperpolarization , paralysis and death [14] . GluCls are not present in vertebrates and , as such , are thought to confer the broad safety margin of IVM [15] . IVM was the world’s first endectocide , capable of killing a wide variety of parasites and vectors , including lice [16] . IVM is already used to treat human lice and several reports indicated that both orally and topically formulations were highly effective in controlling lice infestations [13 , 17–20] . Moreover , the study conducted by Sangaré et al , showed that combination therapy with doxycycline plus IVM was highly effective compared to IVM alone in treating and preventing body lice under laboratory conditions and could be used to completely eradicate lice and potentially delay the emergence of IVM resistance [21] . Currently , resistance to IVM has been widely demonstrated in many arthropods and is an increasing problem for their control [22 , 23] . Recently , field evolved resistance to oral IVM treatment in head lice was documented in Senegal , for the first time , and was reported to cause reduced field control efficacy [24] . Understanding the mechanisms of IVM resistance is , therefore , a key step in delaying and tackling this phenomenon . IVM resistance in arthropods has been associated with several mechanisms , including reduced cuticular penetration [22] , mutation in the target site [25] and metabolic resistance due to the overexpression of xenobiotic pumps from the ABC family [22 , 23 , 26] . Although mechanisms of IVM-resistance in lice have not yet been elucidated , in an attempt to identify inducible metabolic factors involved in IVM-tolerance , Yoon et al . , showed that IVM induced detoxification genes , including ATP binding cassette and cytochrome P450 , suggesting their association with its xenobiotic metabolism , thereby resulting in tolerance [27] . To gain a deeper understanding of mechanisms underlying IVM resistance in lice , we analyzed the IVM-target site from laboratory susceptible and IVM-selected resistant strains . Additionally , we used functional proteomics and performed a global proteomic analysis between the two strains . In addition , we assessed the correlation between mRNA and protein levels for differentially expressed proteins using quantitative real-time PCR , and further verified the functionality of a key candidate gene by RNA interference ( RNAi ) .
From the body lice susceptible strain ( Orlando strain; Lab-IVS ) , IVM resistant selection ( Lab-IVR ) was successfully achieved by continuous exposure to IVM for ten generations in the laboratory . The median lethal time ( LT50 ) value for Lab-IVS strain was 28 . 83 hours ( 24 . 47–32 . 78 hours ) , and for the Lab-IVR strain , 157 hours ( 144 . 91–172 . 37 ) ( Table 1 , S4 Table ) . The Lab-IVR strain exhibited 5 . 4-fold greater resistance against IVM when compared with the reference Lab-IVS strain , suggesting that the Lab-IVR strain had developed low and moderate resistance . The open reading frame ( ORF ) of the body louse GluCl was composed of 1 , 110 nucleotides encoding 369 amino acids . Analysis of the polymorphism patterns of the cDNA sequences from the Lab-IVR ( 48 sequences ) and Lab-IVS ( 48 sequences ) strains showed the presence of six-point synonymous mutations thymine to cytosine ( T363C ) , thymine to cytosine ( T385C ) , guanine to adenine ( G417A ) , guanine to adenine ( G447A ) , adenine to guanine ( A594G ) and cytosine to thymine ( C897T ) ( numbering based on the reference sequence XM002429761; S5 Table ) . The G417A and C897T mutations were found in both Lab-IVR and Lab-IVS strains , while the remaining four mutations were specific to the Lab-IVR strain . Differentially expressed proteins from the Lab-IVS and Lab-IVR strains were identified and quantified by label-free Nano-LC-MS/MS analysis . In total , 407 proteins were identified , including 22 which were differentially expressed , of which 13 were up-regulated and 9 were down-regulated in the resistant Lab-IVR strain ( Table 2 ) . Gene ontology annotation , including molecular function , biological process and cellular component , was conducted to categorize these proteins ( Table 3 and S1 Fig ) . The main molecular functions were catalytic activity and binding for both down- and up-regulated proteins . The cellular components of down- and up-regulated proteins were mainly cell , membrane , organelle and macromolecular complex . According on biological process , the proteins were mainly classified in cellular process , single-organism process and metabolic process ( S1 Fig and S2 Fig ) . To evaluate the proteomic data and the correlation between mRNA and protein levels , we randomly selected 15 differentially expressed genes to quantify their relative expression levels by qRT-PCR . As shown in Fig 1 and S6 Table , the trend in transcriptional variation for all the selected proteins was consistent with the proteomic changes determined in the proteomic analysis , suggesting that this method is a reliable way of identifying and quantifying differentially expressed proteins in lice . Of all the differentially expressed proteins , complexin ( Cpx ) showed the most dramatically altered expression at proteomic level ( 10-fold down-regulated in Lab-IVR strain ) , which was correlated at the mRNA level with a slight difference ( 3 . 4-fold down-regulated in Lab-IVR strain ) . Furthermore , because of its impact on neuronal functions as the key regulators of neurotransmitter release [28] we suggest that this gene may play a significant role in regulating the IVM resistance mechanism . Thus , a Cpx was selected as our candidate gene for the subsequent functional verification . The complete cDNA sequence of the Cpx was obtained by RT-PCR and RACE methods based on the partial sequence annotated from the body lice genome sequencing project . Its cDNA contains 426-bps open reading frame encoding 141 amino acids residues . A multiple sequence alignment of lice Cpx with insect and worms Cpxs demonstrates that homology is particularly high in the central predicted SNARE-binding domain ( boxed ) and in adjacent regions ( Fig 2B , S7 Table ) . Homology analysis of amino acid sequence indicated that lice Cpx shared 91 . 5% identity with the ortholog in Cyphomyrmex costatus , 87 . 9% identity with Anoplophora glabripennis , 73 . 4% identity with Drosophila melanogaster and 44 . 7% identity with Caenorhabditis elegans . Phylogenetic relationships showed that lice Cpx clustered with insect Cpx and had the highest homology with C . costatus Cpx ( Fig 2A ) . To investigate whether mutations in the Cpx gene are involved in the mRNA downregulation leading to a decrease in protein expression observed in Lab-IVR , full-length ORF Cpx cDNA sequences were compared between the Lab-IVR and Lab-IVS strains . Cloning and sequencing ( 48 clones from each of the two independent cDNA batches ) followed by multiple sequence comparison revealed the presence of a one nucleotide base pair insertion ( A ) at position 292-bps . The insertion was found on 17 clones out of 48 analyzed only from the Lab-IVR . This insertion causes a frameshift starting at amino acid 100 within the C-terminal domain of Cpx and results in a premature stop codon at amino acid 111 compared to normal Cpx ( S3 Fig ) . To further investigate the function of Cpx , we used RNAi technology to knockdown the expression of this gene in the susceptible Lab-IVS strain . The results showed that the Cpx mRNA levels reduced significantly , by 16 . 34% at 24 hours post-injection of dsRNA-Cpx and reached maximal reduction by 75 . 52% at 48 hours post-injection compared to the control injected with dsRNA-plasmid ( pQE-30 ) ( Fig 3A , S8 Table ) , indicating that this gene was mostly silenced by RNAi . No apparent physiological alterations were noticed in lice injected either with dsRNA Cpx or pQE-30 compared to the control non-injected lice . These findings demonstrate that dsRNA injection-based RNAi resulted in the knockdown of the Cpx gene in body lice . To determine whether knocking down the expression of Cpx decrease the susceptibility to IVM in the susceptible Lab-IVS strain , we performed bioassays to compare IVM resistance levels among Lab-IVS lice at 48 hours post-injection with either dsRNA Cpx or pQE-30 . The results showed that the mortality rates in lice down-expressing Cpx were significantly reduced compared to the control from 24 hours post exposure to a lethal dose of IVM ( 150 μg/kg ) ( P < 0 . 05 , Fig 3B , S9 Table ) . Based on LT50 , the IVM susceptibility of dsRNA-Cpx lice decreased by 3 . 2-fold ( LT50 = 92 . 54 hours; Table 4 ) compared to the reference susceptible Lab-IVS ( LT50 = 28 . 8 hours; Table 1 ) and it was only 2 . 2-fold higher than that in the Lab-IVR strain ( LT50 = 157 hours , RR = 5 . 4; Table 1 ) . Taken together , the RNAi-mediated knockdown of Cpx decreased the lice’s susceptibility to IVM ( increased their resistance ) , providing relatively convincing evidence that this gene contributes to IVM resistance in this resistant strain .
IVM is a very promising tool to fight different infestations such as , for instance , pediculosis , especially in cases of resistance to commonly used pediculicides . However , as with other insecticides , IVM is subject to selection pressures that have led to the development of resistance in many arthropods . To gain insight into the mechanisms underlying IVM resistance in body lice , we both looked for mutations in the GluCl and searched the entire proteome for potential new loci involved in resistance . Firstly , the comparison of the cDNA GluCl between the Lab-IVR and Lab-IVS strains yielded no non-silent SNPs . This fact is most likely due to the lower level of resistance of our Lab-IVR strain . Indeed , in most published studies that have identified non-synonymous mutations in GluCl gene that affect IVM sensitivity , the level of resistance of the organism was considerably high . This is the case of Plutella xylostella where the A309V mutation was associated with 11 000-fold resistance [29] , in Tetranychus urticae the G326E mutation was associated with 2000-fold resistance [30] and D . melanogaster where the P299S mutation was associated with more than 10-fold IVM resistance [25] . Although , target site insensitivity resulting from non-synonymous mutations within GluCl gene of the arthropod nervous system is known to be of primary importance in the development of resistance to IVM [25 , 30 , 31] , the potential contribution of other types of mutations mediated by mechanisms other than changing protein sequences should not be ruled out . Indeed , recent research suggests that a variety of mechanisms involving changes to upstream regulators ( change in trans ) , mutations of the noncoding regulatory DNA sequences ( change in cis ) , as well as certain types of synonymous mutations of the coding sequences of a gene , may play a significant role in altering gene functions , including gene expression , formation of secondary structures of proteins , protein folding and substrate protein interaction [31–34] . In this study , six synonymous mutations were identified in the cDNA GluCl , of which four mutations were found only in the resistant Lab-IVR strain . Further studies are needed to examine the potential roles of these mutations in terms of protein folding and function . Secondly , in our efforts to shed light on other possible mechanisms of resistance , we compared the overall protein expression pattern of the IVM-resistant and susceptible strains . In total , 22 proteins were differentially regulated , of which 13 were up-regulated and 9 were down-regulated in the resistant strain . Among the differentially expressed proteins found in the resistant strain , the most interesting observation was the activation of energy metabolism through the up-regulation of several key enzymes in the metabolic pathways ( i . e . the isocitrate dehydrogenase [NAD] subunit , adenylate kinase , ATP synthase delta chain , tubulin alpha chain , tubulin beta chain , and ornithine aminotransferase ) , affecting the Krebs cycle , phosphorylation oxidative , purine , vitamins and amino acid metabolisms . Such nutrient availability may be necessary to overcome the elevated demands for energy and metabolism in the ‘toxic’ environment of the resistant lice , and consequently to maintain normal metabolism and energy balance at the cellular level . This is consistent with the fact that insecticide resistance is usually associated with higher demands for energy observed in other insect species [35 , 36] . A Cpx was the most significantly ( P<0 . 0001 ) and highly downexpressed ( 10-fold ) protein in the IVM-resistant lice . Indeed , this protein plays a key role in regulating synaptic exocytosis and neurotransmitter release . It was , therefore , selected to investigate its possible involvement in IVM-resistance . The Cpx cDNA from the body louse was identified and characterized . The deduced amino acid sequence presented very high similarity with the Cpx of other insects , suggesting that the basic mechanisms of its functions are similar to those described for D . melanogaster , the model organism , from which Cpx has been extensively studied . The decrease of Cpx in protein expression was found to be associated with its mRNA downregulation , suggesting that the factors influencing Cpx expression occur at a pretranscriptional level . To gain insight into the mechanisms underlying this downexpression , we performed a DNA mutation analysis of Cpx transcripts and found that some mRNAs Cpx from the Lab-IVR strain had gained a premature stop codon . Therefore , the reduction of Cpx expression might be due , in part , to a secondary effect of a nonsense mutation inside the Cpx gene . Such a mechanism , known as nonsense-mediated mRNA decay , has been reported in insects [37 , 38] whereby mutations inside a gene cause premature termination codons and quickly degrade mRNA , inhibiting the accumulation of nonsense ( inactive ) proteins . RNAi techniques for the suppression of specific transcripts is proving to be a powerful tool in several insect species [39] . Body louse genome analysis has been shown to contain the genes necessary for RNAi [40] . Subsequent studies have reported that the injection of dsRNA can effectively suppress target genes in lice , and this ability has been widely used in gene function research [27 , 41] . Thus , we evaluated the resistance function of Cpx in susceptible lice via RNAi . Our findings showed that the injection of dsRNA-Cpx resulted in an effective suppression of Cpx expression and significantly decreased susceptibility to IVM compared with the control . This result provides convincing evidence that Cpx plays an important role in conferring IVM resistance in the body louse studied and represents the first evidence linking Cpx to insecticide resistance . Cpx is small neuron-specific cytosolic protein that interact with the assembled SNARE ( soluble N ethylmaleimide-sensitive factor [NSF] attachment protein receptor ) complex to modulate the vesicle fusion process and neurotransmitter release [28 , 42] . Despite intensive research , the precise functions of Cpx remain controversial and continue to present a conundrum [28 , 42] . For instance , genetic studies conducted in mammalians , worms and fruit flies have all shown that a Cpx has a dual function and can act either as inhibitory or facilitatory for neurotransmitter release depending on the species , type of synapse ( at CNS or at neuromuscular junction ( NMJ ) ) and whether or not the vesicles are activated by Ca2+ ( spontaneous or evoked release ) [28] . Although it is not known how this downregulated protein is associated with IVM resistance , as IVM acts as a ligand for the inhibitory ligand-gated ion channel , activated by its natural agonist neurotransmitter glutamate [15 , 43] , we hypothesized that the effect of Cpx on IVM resistance may be through regulating the glutamate release machinery at glutamatergic synapses . Indeed , studies conducted on worms have shown that the affinity of IVM is enhanced dramatically in the presence of glutamate , suggesting that the natural ligand , by binding to a distinct site , can allosterically enhance the activity of IVM and exert complementary , and possibly additive , effects on the conformational changes needed for the channels to open [43–45] . Moreover , it was thought that the extraordinary potency of the IVM killing parasites at much lower concentrations than those needed to activate recombinant channels expressed in Xenopus oocytes [44] is due to the interaction between endogenous glutamate and IVM [45] . Taken together , one could speculate that the downexpression of Cpx constitutes a primary mechanism by which lice protect their CNS , given that the GluCls in insects are mostly expressed in CNS [46] and the function of Cpx in that synapses is speculated to be mostly facilitatory for neurotransmitter release [28] . Although many issues remain to be investigated , the result of our study is exciting and provides the first insights into the mechanism underlaying IVM resistance in body lice at proteomic level , and links Cpx to insecticide resistance for the first time .
Adult New Zealand white rabbits were obtained from Charles River laboratories , were handled according to the rules of N° 2013–118 , February 7 , 2013 , France and the experimental protocols ( references APAFIS # 01077 . 02 & 2015050417122619 ) , were approved by the Ethics Committee “C2EA-14” of Aix-Marseilles University , France and the French Ministry of National Education , Higher Education and Research . Rapid amplification of cDNA ends ( RACE ) with SMARTer RACE cDNA Amplification kits ( Clontech , PaloAlto , CA , USA ) was used to obtain the full-length cDNA of Cpx following the manufacturer’s protocol , using universal primers supplied in the kits and gene-specific primers ( GSPs ) designed based on the partial cDNA sequence annotated from the body lice genome sequencing project ( GenBank accession XM002426374 ) . Subsequently , the full-length cDNA of Cpx was generated using a specific primer pair designed based on the 5’and 3’end sequences of the putative Cpx mRNA . Full-length cDNA was subjected to bioinformatic analysis using an ORF ( open reading frame ) finder tool ( http://www . ncbi . nlm . nih . gov/gorf/gorf . html ) . Subsequently , the complete ORF of Cpx was amplified using the same cDNA synthesized for qRT-PCR from both resistant and susceptible strains to perform DNA polymorphism analysis . Amplification of the ORF cDNA GluCl was also conducted , as described for the Cpx gene , using a set of primers designed based on the cDNA gene sequence available in the NCBI database ( GenBank accession XM002429761 ) . All primers used are listed in S3 Table . PCRs amplifications were performed using a Peltier PTC-200 thermal cycler ( MJ Research Inc . , Watertown , MA , USA ) with the Hotstar Taq-polymerase ( Qiagen ) . The purified PCR products were ligated into a pGEMT-easy vector ( Promega ) and transformed into JM109 Competent Cells . The plasmid inserts were PCR amplified using a vector-specific primer ( M13 forward and reverse primers ) and subjected to sequencing using the Big Dye Terminator Cycle Sequencing Kit ( Perkin Elmer Applied Biosystems , Foster City , CA ) with an ABI automated sequencer ( Applied Biosystems ) . The electropherograms were assembled using ChromasPro ( ChromasPro 1 . 7 , Technelysium Pty Ltd . , Tewantin , Australia ) . Alignment of the nucleotide and amino-acid sequences was conducted using the ClustalW2 computer program ( http://www . ebi . ac . uk/Tools/clustalw2/index . html ) and phylogenetic trees were constructed with MEGA7 . 1 . | Through its unique mode of action , ivermectin represents a relatively new and very promising tool to combat multiple helminthic diseases and other infestations in humans and animals . However , ivermectin resistance in the field began to be reported . Therefore , understanding the mechanisms involved is a key step in delaying and tackling this phenomenon . In this study , through proteomic analysis of laboratory susceptible and ivermectin-selected resistant body lice , a complexin , a neuronal protein that plays a key role in regulating neurotransmitter release , was shown to be the most significantly down-expressed protein in ivermectin-resistant lice . Its down-expression by RNA-interference in susceptible lice induced resistance to ivermectin , providing evidence that complexin plays a significant role in regulating ivermectin resistance . This is the first evidence linking complexin to insecticide resistance . |
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The nuclear lamina is the structural scaffold of the nuclear envelope and is well known for its central role in nuclear organization and maintaining nuclear stability and shape . In the past , a number of severe human disorders have been identified to be associated with mutations in lamins . Extensive research on this topic has provided novel important clues about nuclear lamina function . These studies have contributed to the knowledge that the lamina constitutes a complex multifunctional platform combining both structural and regulatory functions . Here , we report that , in addition to the previously demonstrated significance for somatic cell differentiation and maintenance , the nuclear lamina is also an essential determinant for germ cell development . Both male and female mice lacking the short meiosis-specific A-type lamin C2 have a severely defective meiosis , which at least in the male results in infertility . Detailed analysis revealed that lamin C2 is required for telomere-driven dynamic repositioning of meiotic chromosomes . Loss of lamin C2 affects precise synapsis of the homologs and interferes with meiotic double-strand break repair . Taken together , our data explain how the nuclear lamina contributes to meiotic chromosome behaviour and accurate genome haploidization on a mechanistic level .
Correct segregation of the chromosomes during meiosis depends on accurate prearrangement of the homologs that culminates in their precise and unambiguous pairing . Recent studies established that the nuclear envelope ( NE ) plays an important role during these processes . It functions as a platform for telomere driven chromosome rearrangement , which is essential for chromosome pairing and synapsis [1] . This special role requires a general reorganization of the NE which also involves the nuclear lamina , a structural protein network underlying the inner nuclear membrane ( INM ) . Through its multiple interactions with a variety of proteins , the lamina functions in nuclear organization and maintenance as well as regulation of transcription [2] . Because of the many regulatory and structural roles , impaired lamina function is responsible for numerous severe human diseases , collectively termed laminopathies , which are often caused by mutations within the LMNA gene that codes for the A-type lamin proteins [3] , [4] . Mammalian meiotic cells are distinguished by the absence of three of the four lamin isoforms that are typically expressed in differentiated somatic cells [5] . Instead , they express , together with lamin B1 , a unique lamin , lamin C2 , which is a short meiosis-specific A-type lamin isoform encoded by the LMNA gene [6] , [7] . Compared to its somatic counterparts , meiotic lamin C2 is an N-terminally truncated version which lacks the complete N-terminal head including a substantial part of the rod domain . As a consequence the structure differs from that typically observed in somatic lamins [8] , [9] . This N-terminal truncation is quite remarkable , as it concerns protein domains , which have been shown to be crucial for the assembly into higher order structures [10] , [11] . Thus , lamin C2 resembles a “natural deletion mutant” that features unique properties and , consistent with this , shows altered polymerization and higher mobility compared to other A-type lamin isoforms [12] . Strikingly , the distribution of lamin C2 in meiotic cells differs significantly from the typical patterns shown for lamins in somatic cells . While somatic lamins usually distribute evenly along the NE , lamin C2 forms distinct domains within the nuclear lamina of meiocytes [13] . In early prophase I , telomeres attach to and subsequently move along the inner nuclear membrane [14] . During these movements , attached telomeres are permanently embedded within the lamin C2 enriched domains . Therefore , it was suggested that lamin C2 locally modulates the NE to allow proper telomere attachment and/or movement [13] . A first indication for such a role came from a previous study investigating spermatogenesis in Lmna−/− mice that were supposed to lack all A-type lamin isoforms [15] , [16] . In that study , A-type lamins emerged to be essential for male fertility . Furthermore , the obtained results indicated that the general integrity of A-type lamin expression is critical for correct synapsis of homologous chromosomes in male meiocytes [16] . However , Lmna−/− mice actually lack expression of both meiosis-specific lamin C2 and somatic A-type lamins A and C and , as a consequence , show a strong somatic disease phenotype . This matter sets obvious limitations to the interpretation of the results obtained in the Lmna−/− genetic background . For example , in the given genetic background one cannot exclude possible side effects that might arise from defective somatic cells of the male gonad . Therefore , it is not clear whether the observed meiotic phenotype is caused by the absence of meiotic lamin C2 or is rather a result of a more general lamin A/C dependent somatic cell dysfunction [16] . It should be noted , that we recently demonstrated that the Lmna−/− mice aberrantly express an , as yet unrecognized , short progerin-like A-type lamin , a matter which further complicates the interpretation of the former results obtained in the Lmna−/− background [17] . So far , no additional , detailed analysis of the role of A-type lamins for meiotic homologous pairing and recombination in females has been carried out . Hence , the molecular mechanism by which lamins could contribute to homologous pairing and promote normal meiotic progression has remained elusive . To address these issues , we for the present study decided to generate a lamin C2 isoform specific knockout model that , hence in a clear-cut genetic background , allows analysing the direct impact of lamin C2 on meiotic events .
We created a targeting construct to selectively eliminate the lamin C2 specific exon 1a , while all other regions of the Lmna gene were left intact to ensure regular expression of the somatic A-type lamin isoforms ( Figure 1; see also [18] ) . Successful targeting was verified through various approaches ( Figure 1 ) . Correct deletion of the lamin C2 specific exon 1a in heterozygous and homozygous animals was confirmed by Southern blot analysis ( Figure 1B ) . As expected , subsequent RT-PCR and Western blot analysis demonstrated that both lamin C2 mRNA and protein are present in the testes of wildtype males , but are clearly absent from the testes of lamin C2−/− mice . Further immunohistochemical approaches using anti-A-type lamin antibodies confirmed that lamin C2−/− male meiocytes fail to express any A-type lamin . Co-testing for somatic A-type lamin isoforms revealed that somatic lamin A/C expression is virtually not affected by the deletion of the lamin C2 specific exon 1a as in wildtype , heterozygous and lamin C2−/− mice comparable amounts of lamins A and C could be detected in liver cells as well as in somatic cells of the testes ( Figure 1C ) . These results are fully consistent with previous reports demonstrating that lamin C2 is the only A-type lamin expressed in meiocytes [7] . More importantly , they also confirm that the applied exon specific targeting strategy selectively disrupted the expression of meiosis specific lamin C2 , but not of somatic lamins A and C . Detailed analysis of the phenotype revealed that , in clear contrast to the previously described Lmna−/− mice which show severe laminopathy-associated somatic tissue dysfunction [15] , [16] , mice deficient for lamin C2 were fully viable and of normal size and weight . Repeated mating attempts of lamin C2−/− males with wildtype females never produced offspring implying that lamin C2−/− males were completely infertile . Lamin C2-deficient females , however , produced offspring when mated with wildtype males , indicating a sexual dimorphic impact of lamin C2 on fertility ( see below ) . These results are consistent with the histological appearance of the gonads . As in the wildtype , ovaries from 11 days postpartum ( dpp ) and 28 dpp lamin C2-deficient females contained growing diplotene oocytes ( data not shown ) . Lamin C2−/− males , however , had significantly smaller testes compared to wildtype controls ( Figure 1D ) . Detailed histological analyses of testes from adult lamin C2−/− mice revealed that post-meiotic stages were completely absent from the seminiferous tubules ( Figure 1D ) . Consequently , also no sperm were found within the epididymis ( Figure S1 ) . TUNEL assay on lamin C2−/− testes sections revealed a high frequency of cell death in regions of the seminiferous tubules , where prophase I stages predominated ( Figure S1 ) , indicating that mutant spermatocytes are unable to complete meiosis and are removed by apoptosis . To investigate the actual function of lamin C2 during gametogenesis in closer detail , we analysed the consequences of its absence on meiotic progression . A key feature of meiosis and an indispensable requirement for correct genome haploidization is the precise and unambiguous pairing of the homologs and their subsequent physical linkage ( synapsis ) mediated by the synaptonemal complex . Chromosome spread preparations of lamin C2−/− pachytene meiocytes revealed frequent defects in synaptic pairing of the homologs in both sexes , although sex-specific differences were observed ( Figure 2A–2C ) . In males , quantification of pairing defects ( Figure 2D ) showed that virtually none ( <1% ) of the lamin C2−/− spermatocytes showed normal meiotic progression , while in wildtype controls >98% of pachytene spermatocytes appeared normal . Univalent chromosomes were clearly the most frequent defect observed , as 96% of mutant spermatocytes showed varying numbers of unsynapsed chromosomes in cells , where fully synapsed homologs were also present ( Figure 2A″ ) . Prominently , the sex chromosomes , where synapsis is reduced to the pseudoautosomal region , remained univalent in 86% of lamin C2−/− spermatocytes ( Figure 2A′″ ) . Further frequently observed phenomena in knockout spermatocytes were heterologous associations between non-homologous chromosomes and associations between telomeres ( in 45% and 52% , respectively; Figure 2A′ , 2A″″ ) . Analysis of lamin C2-deficient females disclosed that synapsis of the homologs was also affected in oocytes , though in a less dramatic manner . While most wildtype mid-pachytene oocytes isolated from 17 . 5 days post fertilisation ( dpf ) embryos achieved full synapsis ( 91% ) , a significant portion ( 32% ) of lamin C2-deficient oocytes showed defective synapsis that , in contrast to the situation in males , most frequently manifested as pairs of homologs that initiated , but did not complete synapsis ( Figure 2B′ ) . In order to exclude a simple delay in synapsis formation at mid-pachytene stage , we also analysed synapsis defects in late-pachytene oocytes ( 19 . 5 dpf ) . In the absence of lamin C2 we found a significantly increased number of late-pachytene oocytes having overt synaptic defects ( wt: 13 . 33% , n = 75; lamin C2−/−: 27 . 38% , n = 84; Pearson's Chi2 test p-value 0 . 033 ) which is similar to the situation found in mid-pachynema . However , consistent with the pronounced differences in phenotypes of males and females , we found an increased average number of chromosomes affected by synaptic defects per cell in males compared to females ( Figure S2 ) . Overall , these results demonstrate that loss of lamin C2 significantly interferes with chromosome synapsis in mammalian meiocytes of both genders with sex-specific differences regarding the severity of meiotic complications . Telomere driven formation and release of the meiotic bouquet at the leptotene/zygotene stage is a well-conserved phenomenon that has been shown to be essential for preparing later events of meiosis [1] , [14] , [19]–[23] . Current models suggest that bouquet formation enhances homologous pairing by increasing proximity of homologous chromosomes . Furthermore , the release of the bouquet conformation may be a means of preventing incorrect associations of non-homologous chromosomes [19] . In recent years , it has been established that meiotic tethering and moving of telomeres within the nuclear envelope ( NE ) depend on SUN- and KASH-proteins [24] , [25] and that this is broadly conserved . These form LINC-complexes , thereby creating a connection between nuclear and cytoskeletal components [26] . For mammals , an involvement of SUN1 and SUN2 in NE attachment of meiotic telomeres has been reported [27] , [28] . Moreover , impairment of telomere attachment has repeatedly been shown to cause chromosome synapsis defects and thus interferes with correct progression of mammalian meiosis [28]–[30] . Nonetheless , the mechanisms by which meiotic telomeres are attached and repositioned have remained largely unclear . Particularly , direct functional evidence for an involvement of nuclear lamins in these processes is missing . Since lamin C2 is enriched at the sites of telomere attachment [13] , an obvious reason for synaptic defects and meiotic disruption as seen in lamin C2−/− males could be impairment of telomere attachment . To address this issue , we used SUN1 , an NE protein known to tether meiotic telomeres [28] , [29] , in co-localisation experiments with fluorescently labelled telomeres to quantify telomere attachment in lamin C2−/− spermatocytes [30] ( Figure 3A ) . Quantifying co-localised and non-co-localised telomere and SUN1 signals revealed no statistically significant difference between wildtype and knockout spermatocytes ( Figure 3B , p-value: 0 . 799 using Pearson's Chi2 test ) . Consistent with this , virtually all telomeres were located at the nuclear periphery in 3D reconstructed nuclei of mutant spermatocytes ( Video S1 ) . Furthermore , chromosome spreads of pachytene-like lamin C2−/− spermatocytes demonstrated that , in fact , all telomeres were connected to SUN1 as all chromosome axes had SUN1 foci on both ends ( Figure 3C , 3D ) . This clearly shows that , even though telomeres are embedded within lamin C2 enriched domains , lamin C2 is dispensable for telomere attachment to the NE . As telomere-driven meiotic chromosome rearrangement , which lead to bouquet conformation and its subsequent release , are prerequisites for intact synapsis formation [31] , we asked whether lamin C2 has a role in movement rather than in attachment of telomeres . In order to address this question , we analysed the temporal behaviour of telomere movements during bouquet formation and release over the first wave of spermatogenesis . Quantification of 3D reconstructed spermatocytes from testes tissue of sequential ages ( 10 to 14 dpp ) with regard to their state of telomere clustering ( Figure 4 , Video S2 ) revealed a distinct bouquet resolution phenotype caused by lamin C2 deficiency . In the wildtype , at 10 dpp , when most cells synchronously reach leptotene/zygotene transition [32] , 74 . 2% of spermatocytes were in the bouquet stage showing a typically clustered telomere pattern ( Figure 4A , 4C ) . Compared to the wildtype situation , at 10 dpp lamin C2−/− mice showed no significant alterations in bouquet frequency ( 77 . 6% ) . Similarly , at 11 dpp there was no difference in bouquet frequency between wildtype and knockout siblings , both showing 57 . 8% of spermatocytes with clustered telomere patterns . Hence , spermatocytes lacking lamin C2 appear to have no problem in attaining bouquet stage . With progression of spermatogenesis bouquet configuration is resolved . Accordingly , in wildtype testes we found gradually decreasing numbers of bouquet stages with 45 . 4% , 28 . 7% and 20 . 5% at 12 , 13 and 14 dpp , respectively . In corresponding lamin C2−/− littermates , however , numbers of spermatocytes in bouquet stage remained significantly elevated . In particular , knockout animals showed 51 . 8% , 45 . 4% and 43 . 9% of spermatocytes with clustered telomeres at ages of 12 , 13 and 14 dpp , respectively . Compared to their wildtype siblings at 13 and 14 dpp , knockout mice roughly showed the 1 . 5 fold and 2 . 5 fold amount of bouquet stages , resulting in statistically highly significant differences at these ages ( mean p-values for 13 and 14 dpp<0 . 01 using Pearson's Chi2 test; Figure 4C ) . Nonetheless , wildtype and knockout animals reached comparable sub-stages of prophase I at 14 dpp as judged by progression of synaptonemal complex assembly ( Figure 4D ) . Thus , our results demonstrate that while NE attachment per se is not affected , the movement of telomeres during bouquet release is significantly delayed in male mice lacking lamin C2 . Since release of the bouquet is thought to promote resolution of incorrect chromosomal associations [31] , impairment of timely bouquet resolution appears to be the basic mechanism responsible for the meiotic defects observed in lamin C2-deficient mice , i . e . defective synapsis formation . Consistent with the findings reported by other groups , our observations suggest a similar dependency of efficient homologous pairing on telomere clustering and movement in mammals as has been previously described for yeast . In fission yeast it has been shown , that telomere clustering and repositioning is required for efficient chromosome alignment and subsequent association [33] , [34] . Within this line of argument , studies in budding yeast have discussed the roles of Ndj1 and Csm4 in meiotic telomere dynamics [35] , [36] . In csm4 mutants , where meiotic telomeres are still associated with the NE , the absence of telomere-led chromosome movements , rather than altered DSB repair , serves as an explanation for the observed homolog non-disjunction . Because of the previously determined molecular properties of lamin C2 and its ability to alter NE integrity [12] , we conclude that lamin C2 locally modulates NE properties at the sites of telomere attachment to allow efficient directed telomere movement and thus promotes homologous chromosome synapsis . Lack of lamin C2 in turn could reduce local NE flexibility and , by this means , interfere with regular movement of attached telomeres as found here in the lamin C2-deficient background . Since chromosome synapsis , homologous recombination and bouquet formation and release are closely interdependent processes during mammalian meiosis [37] , [38] , we then asked whether loss of lamin C2 has a direct effect on recombination as well . To assess lamin C2 function in recombination we next examined selected markers of DSB repair . In early meiotic prophase , sites of DSBs , introduced by SPO11 , become strongly labelled by γH2AX , a phosphorylated H2A histone variant associated with unrepaired DSBs . During leptonema and zygonema it is found in large domains around the DSBs . As meiotic prophase I progresses , γH2AX labelling successively disappears from the autosomes and , in the male , becomes restricted to the sex chromosomes [39] ( Figure 5A ) . In pachytene-like lamin C2−/− spermatocytes , however , γH2AX remained associated with most of the chromosomes in a cloud-like manner , indicating that meiotic DSBs are formed , but are not efficiently repaired . Interestingly , sex chromosomes , although they often failed to synapse in knockout spermatocytes , showed strongly γH2AX labelled chromatin ( Figure 5A′ ) . Further analysis of later stages of DSB processing revealed that in lamin C2-deficient pachytene-like spermatocytes numerous RAD51 and RPA signals , which mark early and intermediate stages of DSB repair [37] , aberrantly persist along both paired and unpaired axes ( Figure 5B′ , 5C′ ) . In wildtype late pachytene spermatocytes MLH1 , a component of late recombination nodules and a marker of presumed crossing overs [40] , appeared with at least one distinct MLH1 focus per pair of synapsed homologs . These foci were consistently absent from paired and unpaired chromosome axes of lamin C2−/− spermatocytes ( Figure 5D , 5D′ ) . Overall , this indicates that recombination events are initiated in lamin C2-deficient spermatocytes , but repair is not efficient or complete and functional crossing overs do not form . Nonetheless , the formation of sex body specific chromatin does occur even if synapsis between sex chromosomes is defective . This affirms that lamin C2−/− spermatocytes initiate , but fail to complete , pachynema , a phenomenon observed in numerous knockout models of meiosis-specific proteins [41] . Inducing apoptosis during mid-pachynema of spermatocytes carrying defects , such as incomplete synapsis , points to an activation of a pachytene-checkpoint mechanism preventing defective germ cells from further maturation [42] . Aberrant persistence of RAD51 and RPA on chromosome axes and the observed lack of MLH1 in lamin C2-deficient spermatocytes might be a consequence of the activation of the male pachytene-checkpoint and the subsequent elimination by apoptosis in mid-pachynema rather than a direct effect of lamin C2-deficiency on maturation and completion of homologous recombination per se . Mammalian oogenesis apparently lacks a stringent checkpoint operating at mid-pachytene stage and , thus , allows for analysis of recombination in late stages of meiotic prophase I [43] . Hence , we performed a detailed examination of recombination in lamin C2−/− oocytes at 19 . 5 dpf . Consistent with the above described findings , lamin C2-deficient oocytes of 19 . 5 dpf embryos reached late pachynema despite the persistence of synaptic defects ( Figure 5F″ ) . Additionally , temporal progression through meiotic prophase I stages per se appeared normal as , when compared to the wildtype , the ratio of pachytene to diplotene oocytes was not significantly altered in lamin C2−/− ovaries ( n>50 each; Pearson's Chi2 test p = 0 . 5478 ) . As in the males , γH2AX staining persisted until mid-pachytene stage , thereby surrounding incompletely synapsed chromosomes in lamin C2−/− oocytes from 17 . 5 dpf embryos ( Figure 5E , 5G ) . In contrast to males however , lamin C2-deficient oocytes from 19 . 5 dpf embryos were able to recruit MLH1 onto chromosome axes at late pachynema ( Figure 5F ) . Strikingly , quantification of MLH1 , revealed a significant reduction of MLH1 foci in lamin C2−/− females , indicating a reduced rate of meiotic recombination ( Figure 5H ) . Moreover , while 80% of wildtype late pachytene oocytes had at least one obligate MLH1 focus on each bivalent , 46% of lamin C2-deficient oocytes completely failed to recruit MLH1 at least on one pair of homologs , regardless whether or not they exhibited synaptic defects ( Figure 5I ) . Absence of MLH1-marked recombination nodules indicates the lack of cross over recombination on the affected chromosomes that , in the female , may account for increased chromosome segregation defects at later stages of meiosis [40] , [44] , [45] . Notably , as shown here , lamin C2−/− females are fertile despite the fact that lamin C2−/− oocytes revealed overt defects in homologous recombination and chiasmata formation . To some extent this matter resembles the situation described for Sycp3−/− mice that show a severely disrupted synapsis and reduced chiasmata formation . Similar to the lamin C2−/− mice presented here , males deficient for SYCP3 are completely infertile . In female Sycp3−/− mice , by contrast , oocytes form chiasmata , but at a lower level than in the wildtype , which results in a reduction , but not a complete loss , of fertility [46] . Regardless of the sexual dimorphic impact on fertility , our analyses clearly demonstrated that the meiotic nuclear lamina has a central role in regulating chromosome bouquet dynamics and is therefore essential for correct progression of meiotic homologous recombination in both male and female mice . Such interdependencies between bouquet stage resolution and DSB repair and homologous recombination have been described earlier . Mice that are unable to induce meiotic DSBs due to the absence of SPO11 and those showing defects in late recombination events , as is the case in the Mlh1−/− background , also show elevated bouquet frequencies . However , in mice with altered early recombination phenotypes , caused by the lack of recombination proteins DMC1 or HOP2 , no significant increase in bouquet stages could be observed [38] . This suggests , that whilst defects in the formation of DSBs during leptonema or alterations of late recombination events during late pachynema may influence bouquet duration , the intermediate steps of DSB repair and early recombination do not . In lamin C2−/− spermatocytes DSBs are definitely induced but the stages when late recombination events normally occur are not reached due to checkpoint induced apoptosis during mid-pachynema . Therefore , neither altered early DSB repair nor defective late recombination is able to explain the observed delay in bouquet stage release in lamin C2−/− spermatocytes . Since defects in earlier DSB repair and recombination do not influence bouquet stage frequencies , inefficient DSB repair in lamin C2−/− meiocytes is likely to be a consequence of the delay in bouquet stage release , rather than the converse . Consistent with this , loss of telomere-led dynamics also affects recombination and crossing over in budding yeast meiosis [35] . Though to some respect the effects of altered meiotic chromosome movements on crossing over events differ between the budding yeast mutants and our lamin C2-deficient mouse , the significance of directed telomere-led chromosome dynamics per se for homolog recombination and disjunction seem to be as widely conserved as the bouquet formation itself .
All animal care and experiments were conducted in accordance with the guidelines provided by the German Animal Welfare Act ( German Ministry of Agriculture , Health and Economic Cooperation ) . Mouse generation , housing , breeding and experimental protocols at the CNIO , Madrid , were performed in accordance with protocols revised and approved by the Institutional Ethics Committees of the CNIO and following the European Regulation ( 2010/63/UE of September 22 , 2010 ) . Animal housing and breeding at the University of Wuerzburg was approved by the regulatory agency of the city of Würzburg ( Reference ABD/OA/Tr; according to §11/1 No . 1 of the German Animal Welfare Act ) . All aspects of the mouse work were carried out following strict guidelines to insure careful , consistent and ethical handling of mice . To generate a lamin C2 specific knockout mouse line , in which the expression of other A-type lamins is left intact , a replacement vector was constructed to selectively eliminate lamin C2 specific exon 1a and the flanking putative upstream promoter elements . Therefore , a 4 kb genomic region including exon 1a was replaced by a neomycin cassette in reverse orientation using a modified pKSloxPNT vector [47] , [48] . The vector for homologous recombination was designed as follows ( see also Figure 1A ) : a 1 . 9 kb genomic fragment located 1 . 5 kb upstream of exon 1a ( amplified from mouse genomic DNA by PCR; oligonucleotides Table S1 ) was cloned into the SalI/KpnI restriction sites downstream of the neomycin cassette and a corresponding 5 kb fragment 2 . 5 kb downstream of exon 1a ( oligonucleotides Table S1 ) was ligated into the EcoRI restriction site between the thymidine kinase and neomycin cassette . The replacement vector , linearized with KpnI , was electroporated into mouse R1 ES cells and recombinant clones were selected in the presence of G418 and gancyclovir as previously described [47] . Positively targeted ES clones were identified by PCR using external primers ( oligonucleotide sequence for genotyping: Table S1 ) and correct targeting was confirmed by Southern blot . For Southern blot analysis 15 µg NsiI ( not shown ) or SmaI digested DNA derived from ES cells ( or tail tips of lamin C2+/+ , lamin C2+/− and lamin C2−/− mice ) was separated on a 0 . 8% agarose gel , subsequently transferred to a nylon membrane and correct targeting was tested with both external ( see Figure 1A ) and neomycin probes . Blastocyst injection of one of the positive ES clones gave rise to germline transmitting chimeras that were mated to produce heterozygous founder mice . Intercrossing of lamin C2+/− founder mice produced offspring with all genotypes in mendelian ratio . To confirm the genotypes we performed RT-PCR , Southern blot and immunofluorescence analysis as described below . Gonads from wildtype , heterozygous and lamin C2−/− mice were either fixed and embedded in paraffin wax for sectioning , frozen in 2-methylbutane for swab preparations or freshly used for chromosome spreads . Gonads for paraffin embedding were fixed in either 1% PBS-buffered formaldehyde ( pH 7 . 4 ) for 3 hours or in 4% overnight . Tissues were then dehydrated in an increasing ethanol series and infiltrated with paraffin at 58°C overnight . Tissue samples for swab preparations [49] were placed in 2-methylbutane at −70°C immediately after dissection . Procedures for chromosome spreads were adapted from de Boer et al . [50] . For this , fresh tissue samples were incubated in hypotonic buffer ( 30 mM TrisHCl , 17 mM Na-citrate , 5 mM EDTA , 50 mM sucrose , 5 mM DTT; pH 8 . 2 ) . For spermatocyte spreads , testes tubules taken from the hypotonic buffer were resuspended in 20 µl of sucrose solution ( 100 mM ) and transferred to a slide covered with 1% formaldehyde solution ( 1% formaldehyde , 0 . 15% Triton X-100; adjusted with NaOH to pH 9 . 2 ) . Slides were incubated in closed moisture chambers for 2 h , followed by 30 min incubation with the lid left ajar; finally slides were dried in the opened chambers . For oocyte spreads , ovaries were transferred from hypotonic buffer to a small droplet of sucrose solution ( 50 µl ) placed on a slide . Ovaries were then decapsulated , fragmented with forceps and incubated with gentle shaking for 10 min to elute oocytes . Debris was removed from the slides and an equal amount ( 50 µl ) of 2% formaldehyde solution ( 2% formaldehyde , 0 . 15% Triton X-100; adjusted with NaOH to pH 9 . 0 ) was added to the droplet of sucrose containing the oocytes . Slides were then incubated in closed moisture chambers for 1 h , followed by a 30 min incubation with opened chambers at room temperature . Finally , slides were dried at 37°C for approximately 2 h . Standard histology was performed on 5 µm sections of paraffin-embedded tissues fixed overnight in 4% formaldehyde as described previously [16] . To visualize and identify apoptotic cells , TUNEL assays were carried out on 10 µm sections of paraffin embedded testes using the ApopTag Fluorescein In Situ Apoptosis Detection Kit ( Millipore , Schwalbach , Germany ) according to the manufacturer's protocol . To show absence of lamin C2 expression in lamin C2−/− mice and to verify that expression of somatic lamins A/C is not affected by lamin C2 isoform specific targeting , we performed RT-PCR analysis on wildtype , heterozygous and knockout mice . Total RNA was isolated from testes suspensions or liver tissues of six week old littermates . RNA isolation was performed using TriFAST™ ( Peqlab , Erlangen , Germany ) according to the manufacturer's manual . 1 µg of total RNA was used for reverse transcription using oligo ( dT ) primer and M-MLV reverse transcriptase ( Promega , Mannheim , Germany ) . Using 1 µl of RT-reaction we performed PCRs specifically amplifying either lamins A/C or lamin C2 transcripts . In case of lamins A/C , transcripts were amplified using a 5′ primer corresponding to the ATG region of the lamin A/C specific exon 1 , whereas for lamin C2 we used a 5′ primer selectively binding to the ATG region of the lamin C2 specific exon 1a . For both transcripts the same 3′ primer was chosen from a region shared by both the lamins A/C and the lamin C2 transcripts ( see Table S1 for oligonucleotide sequences ) . Primary antibodies used in this study were: rabbit anti-lamin A/C ( H-110; Santa Cruz , Heidelberg , Germany ) , rabbit anti-SYCP3 ( anti-Scp3; Novus biologicals , Littleton , CO ) , guinea pig anti-SYCP3 , rabbit anti-SYCP1 , guinea pig anti-SYCP1 [51] , rabbit anti-TRF1 ( TRF12-A; Alpha diagnostics , San Antonio , TX ) , mouse anti-γH2AX ( Millipore ) , mouse anti-RPA ( clone RPA34-20; Calbiochem , Darmstadt , Germany ) , mouse anti-MLH1 ( clone G168-15; BD Biosciences , Heidelberg , Germany ) , rabbit anti-RAD51 ( Calbiochem , Darmstadt , Germany ) and guinea pig anti-SUN1 [52] . The corresponding secondary antibodies conjugated to Cy2 , Texas red , Alexa647 ( immunofluorescence ) or peroxidase ( immunoblot ) were obtained from Dianova ( Hamburg , Germany ) . Additionally , for FISH analysis a monoclonal mouse anti-digoxigenin and an anti-digoxigenin fluorescein conjugated fab fragment ( Roche , Mannheim , Germany ) were used according to the manufacturer's protocol . To assess the efficient disruption of lamin C2 and unaffected expression of lamins A/C in lamin C2−/− mice on the protein level , western blots using cell suspensions from whole testes preparations and from liver tissue ( as a somatic control ) of six week old wildtype , heterozygous and lamin C2−/− littermates were performed . Tissues or cells were resuspended in 2× SDS sample buffer ( 120 mM Tris/HCl , 10% SDS , 20% glycerine , 20% 2-mercaptoethanol; pH 6 . 8 ) and denatured at 95°C for 15 min before applying 5×105 cells for each tissue onto a 12% SDS PAGE . After separation of the proteins with SDS-PAGE , proteins were transferred to a nitrocellulose membrane . Membranes were blocked overnight in TBST ( 10 mM Tris/HCl , 150 mM NaCl , 0 . 1%Tween 20 ) containing 10% milk powder . Anti-lamin A/C primary antibodies were diluted ( 1∶2000 ) in blocking solution and membranes were incubated for 60 min at room temperature with subsequent washing in TBST . Peroxidase-conjugated secondary antibodies were applied as specified by the manufacturer . Bound antibodies were detected using the Western Lightning Plus-ECL Enhanced Chemiluminescence Substrate ( Perkin Elmer , Rodgau , Germany ) . Immunofluorescence analysis was carried out on chromosome spreads , swab preparations from frozen tissue or paraffin sections . Immunofluorescence staining of chromosome spreads were performed according to procedures adapted from de Boer et al . [50] . Blocking of cell spreads was performed with the supernatant of centrifuged ( 16 . 000 g , 30 min ) blocking solution ( 5% milk , 5% FCS , 1 mM PMSF in DMSO; pH 7 . 4 in PBS ) . For double-label immunofluorescence , spreads were then incubated with the first primary antibody followed by washing in PBS before blocking again in blocking solution and incubating with the first secondary antibody . After another blocking step , slides were incubated with the second primary antibody followed by washing , reblocking and incubation with the second secondary antibody . For immunofluorescences on swab preparations , cells were fixed in PBS containing 1% formaldehyde for 10 min followed by permeabilisation in PBS/0 . 05% Triton X-100 for another 10 minutes . After washing and blocking in blocking solution , slides were incubated with both primary antibodies . Following this , slides were washed and blocked again and subjected to both secondary antibodies . To prepare paraffin sections for immunofluorescence , antigen retrieval and removal of paraffin was conducted as described before [53] . After washing the sections in PBS , sections were blocked with PBT ( 0 . 15% BSA , 0 . 1% Tween 20 in PBS , pH 7 . 4 ) prior to the incubation with both primary antibodies . After washing in PBS , sections were subjected to corresponding secondary antibodies . For all preparations DNA was counterstained using Hoechst 33258 ( Sigma-Aldrich , Munich , Germany ) . To directly label telomeres , we performed fluorescence in situ hybridisation using digoxigenin-labelled ( TTAGGG ) 7/ ( CCCTAA ) 7 oligomeres . After initial immunofluorescence staining ( see above ) , spreads were subsequently briefly refixed for 20 min using 4% formaldehyde in PBS and washed in PBS . After rinsing slides in 2× SSC ( 0 . 3 M NaCl , 0 . 03 Na-citrate; pH 7 . 4 ) for 5 min , spreads were incubated in RNase A ( 100 µg/ml in 2× SSC ) at 37°C for 1 hour . After rinsing in 2× SSC , cells were denatured for 20 min at 95°C in the presence of 10 pmol of each labelled probe in 40 µl of hybridisation solution ( 30% formamid , 10% dextrane sulphate , 250 µg/ml E . coli DNA in 2× SSC ) . Hybridisation was performed overnight at 37°C . After washing twice in 2× SSC at 37°C for 10 min , samples were blocked using 0 . 5% blocking-reagent ( Roche ) in TBS ( 150 mM NaCl , 10 mM Tris/HCl; pH 7 . 4 ) . Probes were incubated for 1 h with anti-digoxigenin antibodies ( Roche ) . After washing slides in TBST ( TBS , 0 . 05% Tween 20; pH 7 . 4 ) primary antibodies were detected using Cy2-conjugated anti-mouse secondary antibodies ( Dianova ) . Fluorescence images were recorded using a Leica TCS-SP2 AOBS confocal laser scanning microscope ( Leica Microsystems , Mannheim , Germany ) , equipped with a 63×/1 . 40 HCX PL APO oil-immersion objective , or an iMIC microscope with 100×/1 . 40 NA oil-immersion objective ( Till Photonics , Munich , Germany ) . Confocal images shown are calculated maximum projections of sequential single sections processed in Adobe Photoshop ( Adobe Systems ) . Images for the quantification of telomere clustering were taken using the iMIC and the Live Acquisition software package . 3D reconstruction , analysis and quantification of telomere attachment and clustering were conducted using the three-dimensional reconstruction tool of ImageJ ( version 1 . 42q; http://rsbweb . nih . gov/ij ) . All statistics shown were calculated using R ( version 2 . 10 . 1; http://www . r-project . org ) , Microsoft Office Excel 2007 or StatSoft STATISTICA 10 . P-values were generated using Pearson's Chi2 test or Mann-Whitney U-test with the significance level set <0 . 05 . | Diploid germ line cells have to undergo meiosis to produce haploid gametes . Haploidization involves pairing and recombination of homologous chromosomes as a prerequisite for their proper segregation . Pairing of homologous chromosomes requires their active repositioning within meiotic nuclei , which depends on the interaction of telomeres with the nuclear envelope . This dynamic association is vital for a faithful meiosis and thus crucial for fertility . However , very little is known about the relationship between telomeres and nuclear envelope components . Here , we have investigated the role of the nuclear lamina , a structural scaffold that is intimately associated with the inner nuclear membrane . In somatic cells , the lamina is a key player in chromatin organization and fulfils various functions such as nuclear structure maintenance and regulation of transcription . In order to understand its role in meiosis , we investigated lamin C2 , the only A-type lamin isoform expressed in mammalian meiotic cells . We demonstrate that lamin C2 is essential for timely repositioning of meiotic telomeres . In its absence , synapsis of homologous chromosomes and double-strand break repair are severely affected . These multiple meiotic defects lead to infertility in males . We conclude that the nuclear lamina contributes directly to fertility through facilitating meiotic chromosome movements . |
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The role of the basement membrane is vital in maintaining the integrity and structure of an epithelial layer , acting as both a mechanical support and forming the physical interface between epithelial cells and the surrounding connective tissue . The function of this membrane is explored here in the context of the epithelial monolayer that lines the colonic crypt , test-tube shaped invaginations that punctuate the lining of the intestine and coordinate a regular turnover of cells to replenish the epithelial layer every few days . To investigate the consequence of genetic mutations that perturb the system dynamics and can lead to colorectal cancer , it must be possible to track the emerging tissue level changes that arise in the crypt . To that end , a theoretical crypt model with a realistic , deformable geometry is required . A new discrete crypt model is presented , which focuses on the interaction between cell- and tissue-level behaviour , while incorporating key subcellular components . The model contains a novel description of the role of the surrounding tissue and musculature , based upon experimental observations of the tissue structure of the crypt , which are also reported . A two-dimensional ( 2D ) cross-sectional geometry is considered , and the shape of the crypt is allowed to evolve and deform . Simulation results reveal how the shape of the crypt may contribute mechanically to the asymmetric division events typically associated with the stem cells at the base . The model predicts that epithelial cell migration may arise due to feedback between cell loss at the crypt collar and density-dependent cell division , an hypothesis which can be investigated in a wet lab . This work forms the basis for investigation of the deformation of the crypt structure that can occur due to proliferation of cells exhibiting mutant phenotypes , experiments that would not be possible in vivo or in vitro .
Colorectal cancer ( CRC ) is one of the leading causes of cancer-related death worldwide , demanding a response from scientists and clinicians to understand its aetiology and develop effective treatment . CRC is thought to originate via genetic alterations that cause disruption to the cellular dynamics of the crypts of Lieberkühn , test-tube shaped glands located in the small and large intestine , which are lined with a monolayer of epithelial cells ( see Fig . 1 ) . A delicate balance of cell division , migration and death is coordinated in the crypts to renew the epithelial layer every few days [1] , [2] . The regular upward migration and removal of cells from the crypt provides a frontline defense mechanism against potential damage from mutated cells , which are prevented from remaining in the crypt long enough to do significant damage . However , if cells accumulate genetic mutations that alter migration velocity or provide resistance to apoptosis cues , then such cells acquire the ability to persist and multiply in the crypts . This alone can increase stress on the walls of the crypts , but the problem will be aggravated if such cells acquire additional mutations that increase proliferation , or alter cell-cell adhesion . In turn , the increased stress can cause the walls of the crypt to buckle . Dysplastic crypts allow the formation of a benign adenoma if mutated cells do not leave the crypt as they should , but rather persist and proliferate in a localised area . Over time and via accumulated mutations , these growths can progress to a malignant lesion that can break through to the underlying tissue stroma , and so aid metastasis . The dynamic cell properties that are required to initiate crypt buckling are poorly understood , as it is difficult for biologists to observe experimentally , either in vivo or in vitro , the initial changes in this sequence of events . For example , the organoids grown in culture by Sato et al . [3] , while recapitulating the crypt geometry , have not yet been compared in detail with crypts in situ . The organoids lack some of the forces that are present in vivo , and the cells themselves do not migrate . However , performing in silico experiments using a computational model of the crypt in situ could highlight the conditions required for buckling to occur , and so provide crucial insight into the tissue-level effects of genetic mutations that lead to CRC . To achieve reliable predictions of the breakdown of the crypt structure that occurs at the onset of carcinogenesis , such a theoretical model of the crypt must link processes occurring at the subcellular , cellular and tissue levels . The model must also take into account the tissue structure and geometry . While a fully comprehensive model is not yet realised , this work concerns a key step in the development of a predictive , computational model of the crypt which defines structural components in accordance with the tissue architecture that is observed experimentally , and reported here . These elements are incorporated into a crypt model which also addresses the coordination of cell division , polarity , differentiation and apoptosis . As depicted in Fig . 1 , individual crypts are closely packed , surrounded and separated by connective tissue . Each crypt is lined with an epithelial monolayer that consists of contiguous cells separated from the connective tissue and musculature by the basement membrane , the primary contact site for epithelial cells to the extracellular matrix . Below the basement membrane are myofibroblasts that provide chemical and mechanical factors for normal crypt structure . There is an established proliferative hierarchy of cells within the epithelial layer: stem cells reside at the base and divide to produce transit amplifying cells , which migrate up the crypt and perform several symmetric divisions before terminally differentiating . The polarised epithelial cells are oriented with the apical membrane facing the crypt lumen and , during symmetric division , mitotic spindles align parallel to the tissue layer [4] , [5] . Consequently a cell places its daughter cell next to it within the plane and the monolayer is maintained throughout growth . Asymmetric division occurs as a consequence of the perpendicular alignment of the mitotic spindle . Differentiated epithelial cells , upon having reached the crypt collar , undergo apoptosis and/or are shed into the lumen [6] , [7] . This process permits the renewal of the epithelial layer every few days . In addition to this , a form of programmed cell death , anoikis , is triggered when there is inadequate adhesion of the epithelial cells to the extracellular matrix [8] , with detachment inducing apoptosis [9] . Functioning correctly , this maintains tissue homeostasis by restricting proliferation to the monolayer , thereby averting dysplasia , and by preventing cells from reattaching in another location and resuming growth . The Wingless/Int ( Wnt ) signalling pathway is involved in the control of cell proliferation , migration , differentiation and adhesion in the crypts [10] , [11] . The Wnt signalling pathway is required to maintain the stem cell compartment in the crypt , and so is crucial to stem cell renewal and differentiation [12] . Moreover , it has been observed that there is a spatial gradient of extracellular Wnt signalling factors along the vertical crypt axis , which suggests a localised source of diffusible Wnt factors in the stroma that surrounds the crypt base , and leads to the hypothesis that a Wnt gradient may be responsible for the observed proliferative hierarchy [13] . As described in Van Leeuwen et al . [10] , cells in the presence of high concentrations of Wnt cycle for longer than those exposed to low Wnt and hence cells at the base of the crypt are expected to remain proliferative . A number of mathematical models exist that aim to describe specific aspects of crypt behaviour , from Wnt dependent ordinary differential equation ( ODE ) cell cycle models that govern mitosis of individual cells [14] , [15] , to cellular automata and lattice-free mechanical models of cell proliferation and migration [16]–[19] . These ideas have been combined in a multiscale model that has been used to investigate clonal expansion and the disruption of crypt homeostasis that forms the first step in colorectal carcinogenesis [15] . However , these models restrict the domain of investigation by prescribing a rigid , cylindrical geometry to the crypt , and are limited by simplifying the tissue structure without considering the basement membrane and surrounding stroma . This prevents such models from realistically examining the tissue-level effects of abnormal cell behaviour . There also exist models that seek to describe crypt buckling . Edwards and Chapman ( 2007 ) [20] present a continuum representation of the crypt , modelled as a growing beam , while Drasdo and Loeffler ( 2001 ) [21] apply an off-lattice overlapping spheres model to describe a two-dimensional ( 2D ) chain of deformable circles such as occurs during blastula formation , and then restrict this to a U-shape for modelling the crypt . These models commonly assign a bending stiffness to the layer , and predict that buckling will occur if growth by cell division is not adequately matched by this force . Edwards and Chapman generalise cell division events and so do not implement a specific cell cycle model to govern mitosis , though possible in this framework , and none of these examples take into account the deformation of the surrounding tissue stroma . More recently , Nelson et al . ( 2010 ) [22] extended the continuum model due to Edwards and Chapman [20] to investigate how growth of an epithelial monolayer constrained to a flexible substrate can recapitulate the geometry of the crypt , and Hannezo et al . ( 2011 ) [23] present a model of the intestinal crypt-villus architecture arising from a buckling instability in a proliferating epithelial monolayer lying on an elastic substrate . A three-dimensional ( 3D ) agent-based crypt model was proposed by Buske et al . ( 2011 ) [24] , which defines lineage specification and differentiation according to threshold-dependent rules that correspond to the effects of Wnt- and Notch- signalling . This model addresses the pedigree concept of cell stemness , and reproduces the spatio-temporal organisation experimentally observed in the crypt without assuming an explicit stem cell population . For this purpose , the authors model the basement membrane as a fiber network with a defined local radius for each cell position , which thereby defines a fixed crypt geometry . Consequently , it is not possible to follow any deformation of the crypt structure , and the authors do not include more sophisticated subcellular pathways that determine cell division or fate . Also relevant to the work presented here are those cell-based models which consider , for example , generic epithelial monolayers . In particular , Galle et al . ( 2005 ) [25] present a 3D overlapping spheres model to examine growth regulation in epithelial layers , where deformation of the cells is calculated using the Hertz force law . This model considers the role of anoikis and density-dependent inhibition of cell division , and how failure of the former can be prevented from corrupting the monolayer if contact-mediated growth inhibition is applied and there is sufficiently strong cell-substrate anchorage . Schaller and Meyer-Hermann ( 2005 ) [26] propose a 3D model to investigate the growth of tumour spheroids , and while cell shapes are again defined as deformable spheres , the neighbour interactions are instead determined by a weighted Delaunay triangulation between cell centres . The dual Voronoi tessellation is applied to provide a more realistic definition of the contact surface between neighbouring cells , which is subsequently used throughout the calculations instead of the sphere contact surface . Drasdo et al . ( 2007 ) [27] also consider the growth of monolayers on a substrate and multi-cellular spheroids , and revisit single-layered tissues such as the blastula during development ( considered in [21] ) to examine the mechanical influence of contact inhibition on the growth of the cell population . Such examples demonstrate the usefulness of individual-based models to investigate the growth dynamics of epithelial cell populations . Dunn et al . ( 2011 ) [28] define a discrete off-lattice cell centre multiscale model that focuses on the role of the basement membrane beneath a growing epithelial monolayer in a simplified 2D geometry: a single layer of proliferating epithelial cells constrained to lie on a rectangular bed of stromal cells , which approximate the connective tissue . Spatial connectivity is determined by a Delaunay triangulation of cell centres , and interactive forces are modelled as springs that act along the edges of this triangulation . An additional force is applied to model the role of the basement membrane , which acts in proportion to the local curvature of the epithelial layer , and to maintain a zero spontaneous curvature . Results from this simple geometry show that a large enough basement membrane force successfully maintains a stable , flat monolayer throughout successive division events , and that increasing the strength of this force favours horizontal migration along the layer , reducing the incidence of epithelial cell detachment from the layer ( whereupon cells are removed by anoikis ) . This work presents the foundation of a realistic representation of epithelial cell growth and migration in a deformable environment , and is extended here to model a specific case in a realistic 2D geometry – the cross section of the crypt . Given the coupling that exists between events at the genetic level and the tissue level , it is necessary to extend the scope of theoretical modelling to address both the role of the crypt geometry and subcellular events . In addition , such a multiscale model should include a mechanical description of migration , cell-cell and cell-matrix adhesion; in so doing , the model can more fully describe all of the processes inherent in crypt dynamics and homeostasis . The remainder of this paper is composed as follows . Firstly , experimental results are discussed that examine the tissue structure of the crypt . These results identify the composition of the connective tissue and surrounding musculature , and how the components relate to crypt shape and function . These findings are incorporated into a new crypt model which assumes the basement membrane force proposed by Dunn et al . ( 2011 ) [28] , and investigations are conducted firstly using a simple rectangular geometry , to determine appropriate parameter balances and investigate the migration of epithelial cells out from the crypt base region . Conclusions from this modelling step inform parameter choices for a complete 2D cross-sectional geometry which is subsequently defined , and the behaviour of the extended model is demonstrated . The results and future work are discussed , where the advantages as well as the restrictions of the model are highlighted , and experiments to investigate model hypotheses are suggested . The direction for future work is outlined , centred on an extension of the 2D cross-sectional model to a realistic 3D geometry .
Immediately beneath crypts lies a thin layer of smooth muscle , known as the muscularis mucosae ( MM ) that forms the boundary between the mucosa and submucosa , as shown in Fig . 2 . In the small intestine of the mouse , the MM is one or two cells thick and forms a network that follows the contours of the crypt bases ( Figs . 1 and 2 ) . By examining intact mouse tissue in three dimensions , we found that , contrary to the reported structure of human gut tissue , the smooth muscle cells of the small intestinal MM are oriented mostly parallel to the longitudinal muscle layer of the muscularis externa ( ME ) ( Fig . 2 ( A ) , ( B ) , ( C ) ) . In the small intestine , the smooth muscle fibres of the MM extend up into the villi . It is thought that the role of the MM is to constantly agitate the epithelium gently to help expel secretions from crypts and enhance contact between epithelium and luminal contents [29] . When viewed in transverse section ( Fig . 2 ( A ) ) , the MM appears to follow closely the outline of the base of each crypt . When viewed in longitudinal section ( Fig . 2 ( B ) ) the MM appears to form individual baskets beneath each crypt , analogous to an eggbox that contains each crypt base as a single egg . The MM of the colon is composed of two distinct layers of smooth muscle fibres , the outer orientated parallel with the longitudinal ME , the inner layer more disorganized , but generally oriented parallel with the circular ME ( Fig . 2 ( D ) , ( E ) ) . Other components of the mucosa are a laminin-rich basement membrane that is directly attached to the basal surface of gut epithelial cells ( Fig . 2 ( A ) ) and , just below , surrounding each crypt , a pericryptal fibroblast sheath ( PCFS ) , comprising a highly organized system of fibroblasts , collagen and mucopolysaccharide ground substance [30] . There are 38 PCFS cells per mouse small intestinal crypt and 124 per colonic crypt [31] . PCFS cells produce signaling factors involved in the growth and maintenance of the crypt . Beneath the MM lies the submucosa ( SM ) , which consists of loose connective tissue rich in collagen and elastic fibres . Embedded in this material are larger blood vessels , lymphatics and nerves . The SM is enclosed by the muscular wall of the gut , called the muscularis . It consists of outer longitudinal and inner circular layers of smooth muscle . The muscularis is responsible for peristalsis , the contractile movements involved in advancing intestinal contents . A discrete off-lattice cell centre model is defined , in which spatial connectivity is determined by a Delaunay triangulation of cell centres , and cell shapes are prescribed by the Voronoi tessellation of these centres . Interactive forces are modelled as springs that act along the edges of this triangulation , as described in the Materials and Methods section . Individual model components are now summarised . All parameters are given in Table 1 . Firstly , in silico experiments were run to demonstrate the effect of increasing the spontaneous curvature in the central region , , and the strength of the basement membrane force as governed by the parameter , which characterises the strength of adhesion of the epithelial layer to the basement membrane and the stiffness of the membrane itself . Figs . 4 ( a ) and ( b ) illustrate the change in behaviour of the monolayer by plotting the -coordinates of all epithelial cells at the final timestep for typical simulations . In ( a ) , the arrows on these plots indicate the direction of increasing , while in ( b ) the arrows indicate increasing . Also marked are the boundaries between the non-zero and zero target curvature regions . As these plots are generated from typical simulations , the curves are not symmetric due to recent division events . Simulations reveal that as the spontaneous curvature increases , the epithelial monolayer is pushed further down into the tissue stroma as the central portion of the monolayer bends , behaviour that is demonstrated clearly in Fig . 4 ( a ) , where . It is also observed that increasing decreases the radius of the circle that can be extrapolated from the arc length of the layer – this is as expected . Simulation snapshots are shown in Fig . 5 , taken after 60 hours , to illustrate the difference in deformation of the layer for and . As the basement membrane force increases , a stronger force acts on the outer edges to maintain a zero curvature , preventing these regions from bending to compensate the deformation of the region of non-zero curvature . This is emphasised in Fig . 4 ( b ) , which fixes . This plot shows that as increases , the outer edges flatten and are pushed further down into the stroma . Accordingly , there is less distinction with the crypt base region , and the central portion of monolayer is not pushed down as much . Fig . 4 ( c ) plots the total number of epithelial cells in the layer at the final timestep for and increasing , averaged over fifty simulations . This reveals the trend that the number of epithelial cells in the layer decreases as increases . As seen in Fig . 4 ( b ) , as increases the deformation of the epithelial layer decreases . Correspondingly , the arc length of the layer decreases and fewer cells are held within the monolayer . To relate this to the biology of the layer , it is necessary to know more about variability in the rigidity of , and adhesion of epithelial cells to , the basement membrane . The simple geometric framework employed for the investigations thus far is a limiting factor preventing realistic modelling of the colonic epithelium , and so the next step is to incorporate the crypt geometry . As shown in Fig . 12 , it is possible to deform an initially flat epithelial monolayer to adopt a test-tube crypt shape by suitable application of the basement membrane force ( see supplementary video S2 ) . However , in order to do so it is necessary to define the initial rectangular geometry to be sufficiently wide , which in turn increases the width of the tissue stroma surrounding the crypt once the layer has fully deformed . This is unrealistic , as the stroma between neighbouring crypts is only 2–3 cells thick . The starting point for the following simulations is instead an initial geometry that approximates the shape of the crypt , described below . Distinct proliferative compartments can be defined as dependent on an imposed Wnt gradient , and this also has the advantage of eliminating the time required to fully deform the flat layer . From the approximate geometry , the basement membrane force acts to maintain the test-tube shape within the tissue through local calculation of the discrete curvature . This is a key feature of the model , as the test-tube geometry emerges due to the action of the forces , rather than being fixed and imposed as in most earlier models . The results found for the simple rectangular geometry are now translated to the cross-sectional geometry . The conditions required for homeostasis are sought , which present a balance between the basement membrane force and the adhesion and repulsion between neighbouring cells , to allow constant upward migration that is matched by cell removal at the collar . Thus , the number of epithelial cells in the crypt should fluctuate only slightly around a constant value , the cells should not be overly compressed , and the structure should not buckle .
The work described in this paper thus far constitutes the foundation of a realistic , theoretical representation of growth in a deformable environment within the colonic crypt . The usefulness and the need for mathematical modelling as a tool to guide and inform biological experimentation is becoming increasingly recognised [40] . In particular , the field of oncology lacks a comprehensive model to which existing data can be applied , nonetheless such a model is required to identify key system parameters [41] . The collaboration that resulted in this paper is intended to produce qualitative results that identify parameter balances and mechanisms that describe the behaviour of the system and which cannot be obtained by alternative methods for ethical , financial or viability reasons . Such results will inform experimental work , and identify areas for future investigation . A simple approach is adopted to describe the evolution of the tissue structure while linking subcellular processes ( Wnt signalling , cell cycle control , cell adhesion , cell differentiation ) with cellular mechanics that control division , migration and apoptosis . The role of the basement membrane is defined by an additional force which takes into account the structural support provided by the surrounding connective tissue , including the PCFS . Evidence for this is based on experimental observation of the tissue structure of the crypt in situ , reported here , which identifies key components that contribute towards crypt shape and function . Although only the colonic crypt is considered here , extension to consider the small intestinal crypt would be possible based on this work , but would require the definition of the villus and the incorporation of paneth cells . This is a possible direction for future work . In addition , one could examine the effect of possible gradients of adhesion along the crypt axis , via a spatially-dependent basement membrane force parameter , and monitor any subsequent change in cell migration or cell death . Simulations of the cross-sectional crypt model demonstrate that dynamic homeostasis can be achieved , in which repeated mitotic events evolve to force consistent epithelial cell migration towards the crypt collar , without compromising the overall structure and architecture . This is characterised by a steady , constant turnover of cells , achieved in the presence of known constraints on the number of dividing cells , and applying the two known mechanisms of cell death . It is by the application of the basement membrane force that the shape of the crypt evolves , and will allow the structure to deform , rather than imposing a fixed geometry . This is a key aspect of this work , given that all previous crypt models , with the exception of Drasdo and Loeffler ( 2001 ) [21] , have assumed a fixed geometry [15] , [16] , [18] , [24] , and permits investigation of the destabilisation of the crypt structure that occurs at the onset of carcinogenesis , and indeed which can aggravate the growth of a pre-cancerous adenoma . Moreover , two insights are proposed regarding mechanisms of cell dynamics within the crypts . Firstly , in the absence of sufficient apoptosis at the crypt collar and intercrypt table , epithelial cells reach a state of confluence , do not divide and migration is inhibited . This suggests that cell migration in the crypt may not be due solely to proliferative pressure from below , but that a feedback mechanism exists between cell birth and cell death , such that the epithelial cells move into the space created by cell death at the collar . Subsequently , cells below are able to grow , divide and migrate upwards , which has the secondary effect of maintaining barrier function . This is a theory that is in line with the extrusion process that occurs for apoptotic cells in epithelial layers [42] , [43] and is known as the negative pressure hypothesis [37] . In the model , apoptosis has been defined to occur randomly , given that the cause of cell removal at the crypt collar is currently unknown . It is likely that programmed cell death acts in combination with anoikis events that may be induced by the crowding of cells at the crypt collar , shown in Fig . 16 ( a ) , where the layer has a negative curvature , to remove cells and ultimately enable cell migration . To test this hypothesis in a wet lab , it is suggested that apoptosis could be induced uniformly along the crypt-villus axis , and any alteration to the typical migratory pattern subsequently monitored . Secondly , the model demonstrates a high incidence of anoikis events at the crypt base . This is not commonly observed by experimentalists [34] . However , when these results are considered in light of the process of asymmetric division in stem cells , whereby one nuclei is positioned apically ( towards the lumen ) before reinserting basally by an as yet unknown mechanism , it is suggested that the crypt shape may play a role in forcing the alignment of the mitotic spindle for the compressed cells at the base . In the model , the compression of cells at the base forces one of the daughter cell centres to lose contact with the basement membrane , whereupon it is removed and this registers as an anoikis event . That a high incidence of anoikis events happen following division at the base of the crypt therefore indicates a mechanosensory cause for asymmetric division in the stem cells at the base of the crypt . This hypothesis is supported by experimental results which demonstrate that cells do respond to their mechanical environment , and moreover that cytokinesis is a mechanical process [44] . It has also been suggested that the shape of cells and tissues can influence cell division via cortical tension heterogeneity which guides spindle orientation [45] . The simulation results also show that anoikis events occur at the curve of the crypt collar . As cells in this region are now differentiated , it is hypothesised that such events arise due to the negative curvature of the layer at this point , rendering cells vulnerable to extrusion . To test this , one could grow epithelial cells on curved substrates to examine the incidence of extrusion on negatively curved regions . Alternatively , cells could initially be grown on a flat substrate , which is bent once the cells reach confluence . The experiments suggested above identify ways in which model development and wet lab experiments can enter a feedback loop to advance understanding of the system . Support or invalidation of the hypotheses proposed will guide future model iterations , generally advancing the understanding of the system . Ideally , the most useful experiments would be those imaging live tissue using appropriate markers , so as to measure individual cell behaviours over time . This would be another way to parametrise the model out of imaging data ( in addition to that used to generate the data shown in Fig . 14 ) . It has been demonstrated that crypt-like organoids can be grown in matrigel in the presence of growth factors distributed uniformly throughout the gel , which appear similar to crypts in tissue . That additional forces do play a significant role in vivo is illustrated by the fact that crypts which are mutant in Apc form apparently normal shapes in whole tissue , but do not in matrigel [3] . Therefore , at this stage , it is not yet possible to state decisively how crypt organoids could be used to test hypotheses generated by theoretical models . When constructing a mathematical model of a biological system , it is wise to keep the model as simple as possible , focussing accurately on the key components and understanding the outcome of crucial interactions without over-complicating the description and analysis required [46] . However , the computational framework within which the crypt model has been developed ( Chaste ) makes it extensible and amenable to additional complexity , should it be required . For example , more detailed cell cycle models can be applied , and many currently exist within the Chaste framework . At present , however , there are limiting factors that prevent full examination of the destabilisation of the crypt structure that occurs in the progression from a healthy system to the growth of a malignant tumour . As the cross-sectional model consists of a 1D chain of cells , it is only possible for epithelial cells to move vertically or to displace the surrounding stroma . Consequently , the introduction of a mutant cell that migrates aberrantly , e . g . more slowly , will always affect those cells directly beneath it in the chain . In reality , these cells would be able to move around a blockage by moving laterally across the inner surface of the crypt . To correct this , it would not be sufficient to simply apply radial symmetry to the model , a method that would produce unrealistic results due to the imposed symmetry , causing mutations to spread uniformly upwards as cells migrate . In contrast , a full deformable 3D model which permits lateral movement could eliminate false positives observed in the 2D model . For example , a detailed response of the system to the introduction of mutant cell populations could be obtained , and meaningful experiments to investigate the “top-down” [47] and “bottom-up” [48] , [49] theories of mutant cell invasion could be conducted . In addition , a full 3D model makes it possible to define a localised stem cell compartment within the crypt that is distinguished from the transit-amplifying cell compartment by specific proliferative properties . ( In 2D , the cell chain would force stem cells out of the base of the crypt . ) This would enable investigation of stem cell number in the crypt , an open question within the field , and how this affects cell dynamics . The additional degrees of freedom associated with cell movement introduced by a deformable 3D model will increase the scope for accurately modelling the response of the system to different cellular events . For example , the merging or rearrangement of cell columns , causing the lumen to narrow or widen should the number of cells decrease or increase . This will have bearing on the persistence of mutations in the crypt , as well as on the incidence of anoikis events , which are likely to decrease as cells can exploit movement and growth in more directions . However , it is necessary to re-evaluate the structure of forces in 3D , firstly in light of the additional complexity of the Delaunay triangulation , and also to consider the effect of shear forces that may contribute to the stability of the structure . This work is underway .
Mouse gut tissue was prepared for imaging following the methods described in Appleton et al . ( 2009 ) [50] . Sectioned gut was imaged on a Leica DMIRB fluorescent microscope and the wholemounts imaged on a Zeiss 710 confocal microscope . 3D images of fixed whole-mount tissue stained with DAPI and rhodamine-phalloidin were acquired using multiphoton fluorescence microscopy [50] . The outer surface of the crypt is defined by the basement membrane on which the epithelial cells sit , and the outer crypt area and crypt lumen area were measured in a cross section half way along the crypt length using Volocity image analysis software ( Perkin Elmer ) . The cell area was estimated by subtracting the lumen area from the outer crypt area , followed by dividing by the number of DAPI-stained nuclei present in the center cross section . A total of 150 crypts were examined , taken from the colons of three wild type C57BL/6J mice . A discrete multiscale model is considered , where cell centres are defined as nodes which evolve spatially according to an off-lattice definition of cell-cell mechanics [15] , [16] . As such , spatial connectivity is determined by a Delaunay triangulation of cell centres , and the corresponding cell shapes are subsequently defined and visualised by the dual Voronoi tessellation , which has been shown to produce realistic polygonal cell shapes [51] . An example of this triangulation and tessellation is illustrated in Fig . 3 .
Please note , the authors would like to refer the reader to the article by Eisenhoffer et al . ( doi:10 . 1038/nature10999 ) , which was published during the final proof stages of this publication . This paper examines cell extrusion in the crypt , and deduces that the overcrowding of cells at the crypt collar and intercrypt table leads to anoikis events , which is in agreement with the results found using the cross-sectional model . | At the onset of colorectal carcinogenesis , marked changes can be observed in the structure and dynamics of the crypts of Lieberkühn . These test tube shaped glands regularly punctuate the surface of the gut and are lined with a monolayer of epithelial cells which divide and migrate upwards to renew the intestinal surface every few days . The process by which the crypt structures breakdown , and the compliant environment that can be subsequently provided to mutated cells to allow the formation of adenomatous growths , is not yet well characterised . A limiting factor in the understanding of this process is the ability to observe easily the initial changes that occur , and which are necessary to disrupt the normal behaviour of the system . However , a predictive , theoretical model of the crypt that mimics the geometry and the tissue architecture can be used to perform in silico experiments and further such understanding . A model is introduced here that addresses the tissue structure of the crypt , and the stability it provides to the epithelial layer , while remaining deformable and without imposing a fixed geometry . |
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Stigma plays in an important role in the lives of persons affected by neglected tropical diseases , and assessment of stigma is important to document this . The aim of this study is to test the cross-cultural validity of the Community Stigma Scale ( EMIC-CSS ) and the Social Distance Scale ( SDS ) in the field of leprosy in Cirebon District , Indonesia . Cultural equivalence was tested by assessing the conceptual , item , semantic , operational and measurement equivalence of these instruments . A qualitative exploratory study was conducted to increase our understanding of the concept of stigma in Cirebon District . A process of translation , discussions , trainings and a pilot study followed . A sample of 259 community members was selected through convenience sampling and 67 repeated measures were obtained to assess the psychometric measurement properties . The aspects and items in the SDS and EMIC-CSS seem equally relevant and important in the target culture . The response scales were adapted to ensure that meaning is transferred accurately and no changes to the scale format ( e . g . lay out , statements or questions ) of both scales were made . A positive correlation was found between the EMIC-CSS and the SDS total scores ( r = 0 . 41 ) . Cronbach's alphas of 0 . 83 and 0 . 87 were found for the EMIC-CSS and SDS . The exploratory factor analysis indicated for both scales an adequate fit as unidimensional scale . A standard error of measurement of 2 . 38 was found in the EMIC-CSS and of 1 . 78 in the SDS . The test-retest reliability coefficient was respectively , 0 . 84 and 0 . 75 . No floor or ceiling effects were found . According to current international standards , our findings indicate that the EMIC-CSS and the SDS have adequate cultural validity to assess social stigma in leprosy in the Bahasa Indonesia-speaking population of Cirebon District . We believe the scales can be further improved , for instance , by adding , changing and rephrasing certain items . Finally , we provide suggestions for use with other neglected tropical diseases .
The framework for cross-cultural equivalence testing used in this study , draws entirely on the work of Herdman et al [25] , [30] , Terwee et al [26] and Stevelink & van Brakel [29] . Five equivalences and the universalist approach are important for this study . Herdman et al note that a universalist approach: ‘Conceptual equivalence’ looks at how the concept of social stigma is conceptualized , which domains are important and at the significance accorded to these domains . ‘Item equivalence’ similarly explores how domains are conceptualized and whether items are equally relevant and acceptable in the original and the new culture . ‘Semantic equivalence’ deals with language and how meaning is transferred , for instance , whether the level of language is appropriate . ‘Operational equivalence’ concerned the suitability of the questionnaire format , instructions and mode of administration . Finally , ‘measurement equivalence’ refers to the psychometric properties ( internal consistency , construct validity , agreement , reliability , floor and ceiling effects and interpretability ) of the scale ( for a more detailed description of each equivalence type we refer to Herdman et al [30] , [35] ) . Table 1 describes when each of these equivalences is attained .
The study area of the SARI project is Cirebon District , located on the North Coast of West Java near the provincial border with Central Java . Cirebon District has a multi-cultural population of about 2 . 3 million . Different languages are spoken , such as , Bahasa Indonesia ( the national language ) , Sundanese , Javanese and Cirebonese . Annually , about 300 new leprosy cases are detected in the district and , according to key informants , there was a high level of leprosy-related stigma and limited activities to reduce this . The stigma-reduction interventions of the SARI project are implemented in 30 kecamatan ( sub-districts ) . The SARI project team is interdisciplinary , including staff from public health , medicine , disability studies , psychology and development studies from universities in the global North and South . This validation study was executed by one postdoc researcher , three PhD students and ten research assistants from Cirebon or neighbouring districts who spoke the local languages . Four of the research assistants who interviewed community members were disabled or affected by leprosy themselves . The study described in this paper is part of a larger validation study that included persons affected by leprosy and community members from the 30 kecamatan described above . The latter group is the study population for this paper . To achieve adequate power for the various statistical calculations we estimated a sample size of at least 100 community members , with at least 50 repeated assessments to assess reproducibility [26] . The selection was done as follows; first , people affected by leprosy were invited to the puskesmas ( Health Care Centre ) for an interview . At each puskesmas , three persons affected by leprosy were randomly selected . For each respondent a small paper with a number was created , three papers were drawn , if the respondent came from the same village a new paper was drawn . Their Rukun Tetangga ( RT , smallest administrative level in Indonesia approximately 10–20 households ) was visited by a small team of research assistants ( 2–3 ) of the SARI project . First they visited the head of the RT to introduce themselves and explained the purpose of the project . Using convenience sampling , they then selected three community members from this RT or a neighbouring RT for the interviews . Two key persons , such as , the head of the RT , a teacher , religious leader , women's leader and one general community member about the same age and sex of the person affected interviewed that morning were selected . Data was collected during three phases: i ) first validation study in August 2011 , ii ) baseline study from September – October 2011 and the iii ) second validation study in July 2012 . The EMIC-CSS was selected based on its prior cross-cultural and cross-condition use [5] , [33]–[35] , [38] . The scale has 15 items and covers areas of life that are often affected by stigma , such as concealment , avoidance , perceptions of self-worthy , shame , marriage ( prospects ) and work . The scale has four response options; yes ( 2 points ) , possibly ( 1 ) , no ( 0 ) and do not know ( 0 ) . Item 15 is scored differently; yes ( 0 points ) , possibly ( 1 ) , no ( 2 ) and do not know ( 0 ) . There was no qualitative component used as part of the scale , as in some previous studies [22] , [38] . The SDS was selected because it measures attitudes more directly than the EMIC-CSS and had been used widely in mental health research in different countries [24] , [42] , [43] , [47] . The scale is also short , simple and easy to contextualise , because of the use of vignettes . The SDS interview started with reading out a vignette describing a male named Rahman or female named Rahmi , depending on the sex of the interviewee . The content of the two vignettes is similar . The vignettes were developed by one of the co-authors ( WvB ) based on vignettes used in the field of mental health used by Angermeyer et al [42] , [44] , [48] . The scale has 7 items representing different degrees of social distance . The items have four response options; definitely willing ( 0 points ) , probably willing ( 1 ) , probably not willing ( 2 ) and definitely not willing ( 3 ) . Both scales assess aspects of the same construct ‘social stigma’ , but take a different approach; the EMIC-CSS asks how leprosy is considered in the community of the interviewee , while the SDS assesses the personal perception of the interviewee . The sum score of the individual items that all have the same weight is used as the overall score and higher scores reflect greater levels of social stigma . The scales are interviewer administrated . Each respondent was first asked to provide demographic information , such as , age , sex , profession and income , next the EMIC-CSS was administrated followed by SDS with vignette . This order was chosen because this sequence allows questions to go from general community perspectives to specific and personal choices and avoids ‘contamination’ of the EMIC-CSS with the vignette . When a respondent did not speak Bahasa Indonesia with sufficient fluency , the questions were translated on the spot into the first language of the respondent often Sundanese or Javanese . To determine the conceptual , item , semantic and operational equivalence different steps were taken . First , an exploratory study took place in which 53 in-depth interviews and 20 focus group discussions ( FGDs ) were conducted to understand the cultural background and situation in which people lived ( see for more details on the methods [1] ) . Second , the versions of the EMIC-CSS and the SDS that were selected for this study were translated from English to Bahasa Indonesia by someone knowledgeable regarding stigma and later back–translated to English by someone not involved in stigma research . Third , a discussion on the content of the instruments , the vignettes , the phrasing of items , and the response scales took place within the team and with experts knowledgeable on Bahasa Indonesia , leprosy , stigma and quantitative instruments . Fourth , two half-day pre-test sessions were organised with 20 participants ( people affected by leprosy and with a disability ) . The questions of the instruments and the vignette were checked with the participants for coherence , understanding and terminology . Fifth , the research assistants of the SARI project received a full week of training in the use of the scales , with practice sessions in the office . Finally , a two-week pilot study was conducted in the study area , with daily meetings in the office . Once all scales were optimized and the interviewers felt confident , the data collection for testing measurement equivalence started followed by the baseline study . During the validation and baseline study , weekly meetings were held to discuss issues that had arisen during the interviews . To determine the measurement equivalence , the data was entered using Epi Info for Windows , version 3 . 5 . 3 , and analysed using Stata 12 . 1 and SPSS 21 . Records were deleted from the raw database if the demographic information or a full scale was missing . Outliers were explored with descriptive statistics and box plots . Interviews conducted in a language other than Bahasa Indonesia were left out the analyses . To provide an overview of the socio-demographic characteristics of the sample , basic descriptive statistics were calculated . The respondent was asked for either income per day or income per month; the latter was converted into one variable ‘household income per day’ by dividing the income per month by 30 . 5 . A mean and SD were used to describe each item of the scales . Psychometric properties were tested using appropriate statistical methods based on predefined quality criteria . The term ‘assessing’ stigma is used throughout this paper , instead of for example ‘measuring’ , ‘evaluating’ , ‘quantifying’ or ‘rating’ stigma as this reflects best the aim of the applying the scales . The study was approved by the relevant offices; Ethics Committee of Atma Jaya University; Sub-Directorate for Leprosy and Yaws , Ministry of Health , Public Health Office , West Java and District Health Office , Cirebon District . Written consent was obtained from individual study subjects . The study guarantees the confidentiality of the information provided by the participants . No incentives were offered to interviewees other a small token of appreciation such as a drinking mug or t-shirt . The study abided by the CIOMS Guidelines for Research on Human Subjects [50] .
Based on the opinion of experts and the responses of participants of the pre-test sessions and pilot study , the domain ‘social distance’ employed in the SDS seems equally relevant and important in the target culture . Because a Likert scale is used ( instead of the original Guttman scale ) , the fact that the type of relationship might represent different degrees of social distance is not relevant . The EMIC-CSS assesses different aspects of a broader phenomenon that can be described as ‘perceived stigma against persons affected by leprosy’ . The aspects that can be recognized in the scale applied by van Brakel et al [36] are: i ) concealment ( 2 items ) , ii ) process of discrediting ( 3 items ) , iii ) shame and embarrassment ( 1 items ) , iv ) avoidance/taking distance/isolation ( 2 items ) , v ) problems with getting married or on-going marriage ( 2 items ) , vi ) problems for family or other people ( 3 items ) and vii ) problems with work ( 2 items ) . The exploratory study of the SARI project described in Peters et al [1] already indicated that the aspect ‘shame and embarrassment’ and ‘avoidance/taking distance/isolation’ are relevant in the target culture . In the interviews and FGDs we found evidence for the relevance of all the other aspects and there were no indications that led us to change the emphasis placed on the aspects . In the following quote ‘concealment’ comes to the front , and at the same time reveals shame and avoidance: The following three quotes support the relevance of the aspect problems with getting married and on-going marriage: The following quotes confirms the relevance of the aspects problems for family or other people . The first comes from a FGD among mothers of children affected by leprosy , the second from discussion among community leaders: The next quote illustrates the significance of the aspect process of discrediting: One relatively new aspect in the EMIC-CSS is the aspect ‘problems with work’ . Several studies [7] , [36] have shown that this aspect is relevant . Also the data from the interviews and FGD strongly support this as shown by several quotes each highlighting a different element or perspective of this aspect . The first three quotes are from persons affected by leprosy the latter three from community members: Based on the opinion of experts and the response during the pre-test sessions and the pilot study there was no indication for a need to change any of the items in the SDS or in the EMIC-CSS . The target population speaks different languages , but the scales are translated in Bahasa Indonesia only , because this is the national language and is most commonly spoken by the target population . Some minor changes were made in the first version of the translation to make sure that the words in the scales fitted the day-to-day language used in the people in the rural areas of Cirebon . The response options ‘possibly’ of the EMIC-CSS and ‘probably’ in SDS were difficult to translate into Bahasa Indonesia and therefore changed into ‘maybe’ translated as ‘mungkin’ . Sometimes , interviewees requested to fill the forms by themselves , which was often accepted . The interviewer would be there to answer any questions . Therefore , a mixture of interviewer-administrated and self-administered form filling was used , using the same questionnaires for both . No other changes were made to the administration , formats of scales and their scoring . A total of 326 observations were in the initial database . Of these , 29 ( 8 . 9% ) were omitted due to missing values and 38 ( 11 . 7% ) were omitted due a language used other than Bahasa Indonesia . The remaining 259 community members were included in this validation study . The observations omitted differed from the main sample . The former were less frequently male ( 58 . 8% versus 62 . 2% ) , were older ( mean 46 . 9 versus 42 . 1 years ) , more frequently married ( 98 . 0% versus 91 . 1% ) and had fewer years of education ( 6 . 1 versus 9 . 1 years ) . Of the 259 observations , 72 were collected during the first validation study , 142 during the baseline and finally 46 during the second validation study . Their socio-demographic characteristics are described in Table 2 . The key persons in this sample were more frequently men ( 75% compared to 42% ) , had higher age ( mean 44 . 3 versus 39 . 4 years ) , were more frequently married ( 96% versus 85% ) and had a higher level of education ( mean 10 . 2 versus 7 . 5 years ) than the respondents in the ‘general’ community sample ( data not shown ) . The mean total score of the items of the EMIC-CSS was 15 . 38 ( SD 6 . 46 ) and ranged from 0 representing the minimum stigma score to 30 representing the maximum total score . These figures for the SDS are , respectively , 9 . 05 ( SD 4 . 01 ) and 0 to 21 . Table 3 and 4 provide the mean score per item . We found a moderately positive correlation between the EMIC-CSS total score and the SDS total score ( r = 0 . 41 ) . We identified one outlier with contradicting total scores; EMIC-CSS total score of 0 and a SDS total score of 19 . This respondent frequently answered ‘do not know’ at the items of the EMIC-CSS . Without this outlier , the correlation increased somewhat ( r = 0 . 45 ) . This correlation confirmed the a priori hypothesis . Cronbach's alphas of 0 . 83 and 0 . 87 were found for the EMIC-CSS and SDS , respectively . Item E15 of the EMIC-CSS has a low item-test correlation ( 0 . 16 ) and item-rest correlation ( 0 . 04 ) ; if left out Cronbach's alpha of the EMIC-CSS increases slightly to 0 . 84 . The exploratory factor analysis for both scales showed an adequate fit as a one-dimensional scale , with a first factor explaining 77% of the score variability for EMIC-CSS and 94% for SDS . However , additional factor analysis of the EMIC-CSS also supports two factors as shown in Table 5 . The first factor with 9 or 10 items and a second with 4 or 5 items . The two factors were strongly correlated ( r = 0 . 63 ) , supporting the presence of a single higher-order factor . Item E15 did not fit well in either scale and was therefore omitted . Cronbach's alphas for the subscales were sufficient and are provided in Table 6 . While exploring the data with frequencies and a box plot several outliers where identified and these were checked visually . Three observations seems to be errors and were therefore deleted from the database leaving in 67 repeated observations . Community members were revisited on average after 12 days , but at least after 3 and before 29 days . The mean difference between interviewers is in the EMIC-SDD −0 . 52 ( SD 3 . 37 ) . This led to a SEMagreement of 2 . 38 , which represents 7 . 9% of the score range . The limits of agreement are −7 . 12 and 6 . 08 . The SDCindividual is 6 . 60 and SDCgroup is 0 . 81 . In the SDS , the mean difference between interviewers is −0 . 06 ( SD 2 . 54 ) . The SEMagreement 1 . 78 , this is 8 . 6% of the total score range . The limits of agreement are −5 . 04 and 4 . 91 . The SDCindividual is 4 . 94 and SDCgroup is 0 . 60 . For the EMIC-CSS and the SDS the test-retest reliability was above 0 . 70 , the ICCagreement is respectively 0 . 84 ( Confidence Interval ( CI ) 0 . 75–0 . 90 ) and 0 . 75 ( CI 0 . 62–0 . 84 ) . No floor or ceiling effects were identified for the EMIC-CSS of SDS . Only 2 ( 0 . 8% ) of the respondents scored the lowest possible score of 0 and also 2 ( 0 . 8% ) scored the highest possible score of 30 points on the EMIC-CSS . For the SDS , only 4 respondents ( 1 . 5% ) had the lowest possible score of 0 and 1 ( 0 . 4 ) had the highest possible score of 21 . The means and SD of the different subgroups of the baseline data ( n = 142 ) show varied results as illustrated in Table 7 . The mean total score of EMIC-CSS and SDS is higher in females , but the differences are very small . Among age groups , the EMIC-CSS steadily increases with age , but for SDS it slightly drops at first before increasing again . EMIC-CSS and SDS total scores follow a similar fluctuating pattern across education groups . Finally , EMIC-CSS and SDS total scores are lower fore key persons compared to the ‘general’ community . A summary of the key findings for the two scales can be found in Table 8 .
The EMIC-CSS assesses the perceptions towards people affected by leprosy from a general community perspective ( E4 , E6 , E7 , E11 , E12 , E14 , E15 ) . It also addresses perceptions towards family members of a person affected by leprosy ( E2 , E8 , E9 , E13 ) , towards other persons near a person affected by leprosy ( E5 ) and the disease in general ( E3 ) . The EMIC-CSS assesses different aspects related to the social stigma of leprosy . This study showed that all aspects and items assessed in the EMIC-CSS are relevant in the target culture . The question whether all aspects together comprehend the concept of social stigma is more difficult and also a more theoretical/fundamental question . Experiences , such as , mocking and gossiping are real and very important experiences of people affected by leprosy in the target community [1] . These aspects are , for instance , not yet assessed and could be added to improve the content validity of the scale . Suggestions for items are: “Would other people in your community gossip about a person affected by leprosy ? ” or “Would other people in your community mock a person affected by leprosy ? ” Two relatively new items related to ‘problems with work’ were shown to be highly relevant based on the qualitative and quantitative data . The items have high total means scores . ‘Would having leprosy cause difficulty for a person to find work ? ’ has a mean score of 1 . 53 ( highest ) and ‘Would people buy food from a person affected by leprosy ? ’ has a mean score of 1 . 28 ( reverse coded; fourth highest ) . The psychometric property results , however , show that the last item does not fit in the scale . This item might be an early sign of stigma , it may be scored positive while items are scored negative . We have considered ‘Would people dislike buying food from a person affected by leprosy ? , but the translations of this question caused some confusing in Bahasa Indonesia . An option to pilot in the future would be: ‘Would having leprosy cause difficulty for a person to sell food ? ’ For now , we recommend researchers who wish to validate or apply the EMIC-CSS in their study to include this item if it is considered culturally relevant . Investigators who are particularly interested in this item may want to develop and test a set of questions that assess the same activity in different ways . In our opinion , question 5 ‘Would knowing that someone has leprosy have an adverse effect on others ? ’ is a rather abstract question in particular the ‘others’ part could have multiple interpretations . Although this did not cause challenges in our study , we would recommend caution when piloting this question to make sure the question is phrased in clearly understandable terms . The construct validity of the EMIC-CSS ( and automatically also of the SDS ) was supported by the moderately positive correlation between the EMIC-CSS total score and the SDS total score . Cronbach's alpha found in this study ( 0 . 83 ) is comparable to the values found in other studies [37] , [38] . The exploratory factor analysis indicates an adequate fit for a one-dimensional scale , with a first factor explaining 77% of the score variability . We conclude , therefore , that the internal consistency is good . The factor analysis and internal consistency analysis illustrate that two shorter versions of the EMIC-CSS may also be of value; one with 9–10 items that could be conceptualised to assess ‘perceived attitudes towards persons affected by leprosy’ and another with 4–5 items that would assess ‘perceived behaviour towards persons affected by leprosy’ . The responsiveness of the EMIC-CSS was sufficient in this study . For evaluation purposes a small measurement error is required , as one wants to be able to distinguish clinically important change from measurement error [51] . The SDC of the EMIC-CSS was small at the group level ( 0 . 81 out of a score range of 30 ) , but larger at the individual level , which means that at individual level , large score differences are required to demonstrate changes , while at group level , small score differences will already be sufficient . With an ICCagreement of 0 . 84 the reliability is good . The absence of floor and ceiling effects was akin to other studies [37] . For interpretability , the SDC should ideally be compared with the score difference representing Minimally Important Change ( MIC ) . However , this figure is not yet available for the measures under study . We agree with de Vet et al ( 22 ) and prefer not to calculate a MIC based on statistical tests but to use an anchor method . Hence , we underline the importance of future research to investigate the MIC for this scale . The EMIC-CSS as used in the SARI project did not include a qualitative component as described by Weiss [22] . We would like to underscore that we do value qualitative methods very much , especially in the context of the concept of stigma . In the SARI project we use separate methods for qualitative data collection . This study has shown that the EMIC-CSS is adequately culturally valid in the field of leprosy in Cirebon District . The scale could easily be adapted to other NTDs . This has indeed been done already in the case of onchocerciasis and Buruli Ulcer [5] , [38] . Certain items can be more or less relevant in different conditions . For instance , the item related to food might be less relevant when there are no feelings of disgust or fear for infection , as in vector-borne diseases . The SDS assesses the perceptions of the interviewee towards people affected by leprosy by asking how they feel regarding different types of social relationships ( e . g . neighbours , caretakers , colleagues ) . The concept of social distance has been studied in several countries around the globe , including Argentina [52] , Japan and China [53] , Nigeria [54] , Germany [42] , and Egypt [55] . This confirms its cross-cultural value . The cross-cultural validation of the SDS has to our knowledge not received any attention and , therefore , this is the first study of its kind . The current study has shown that the concept of social distance and the different types of relations used in the items of the scale are relevant and understood in Cirebon District . In retrospect , the item ‘renting a room to an affected person’ is less appropriate in the context of Cirebon District . Difficulties did not emerge with this item during the pilot , validation or baseline study . However , it is not a common practice for community members in the primarily rural study area to rent a room to somebody . Respondents could envision the situation , but were by and large not acquainted with the practice themselves . To make the scale more appropriate for Cirebon District or similar contexts , a replacement item is suggested . A first exploration with the SARI research assistants resulted in the following suggestion: ‘How would you feel visiting the house of someone like Rahman/Rahmi ? ’ It important to note that the item renting a room seems appropriate in other contexts in Indonesia . For instance , in Yogjakarta , a university city , many households offer kost ( rooms for rent ) . The internal consistency of the SDS in this study was good . Cronbach's alpha ( 0 . 87 ) was equivalent to the alphas found in other studies [24] , [42] , [43] . Factor analysis suggested one factor , which accounted from 94% of the variance . The SDC of the SDS was small at the group level ( 0 . 61 out of score range of 21 ) , but larger at the individual level ( 4 . 98 ) resulting in the implications as described for the EMIC-CSS . The ICCagreement was 0 . 75 , indicating good inter-interviewer agreement . For interpretability a MIC is needed , which is unfortunately not yet available . This study demonstrated that also the SDS is adequately culturally valid in the context of leprosy in Cirebon District . The scale can be easily adapted to other NTDs , by changing the vignette . The items need to be checked for relevance and appropriateness in the new target culture . First , the convenience sampling used , the difference between observations omitted and the main sample and the high proportion of key persons are weaknesses in this study . Ameliorating circumstances are the size and diversity of the sample and the fact that the key persons are likely to know and represent well the views of their community regarding leprosy . The influence of the sampling bias on the results presented in this paper are in our point of view minor . We do not expect any influence on the conceptual , item , semantic and operational equivalences , due to the fact that the exploratory study included ‘general’ community members , the pre-test sessions were with people affected by leprosy and disabled people and the still relatively large group ( 99 ) of ‘general’ community members in the sample . We expect an influence on the measurement equivalence , but only on the interpretability as illustrated in the results; a higher total score of the EMIC–CSS and SDS for the ‘general’ community compared to key persons . Second , the effect of the mixture of interviewer-administrated and self-administrated is difficult to ascertain , because not all research assistants did this and those who did , did not do it with all respondents . Given that the same questionnaires were used and that the interviewer was present while the respondent filled in the questionnaire , we do not expect an important influence . Third , the fact that we were able to only validate Bahasa Indonesia versions of the EMIC-CSS and SDS is another weakness . A substantial group of people in Cirebon District do not speak Bahasa Indonesia sufficiently and to assess the level of social stigma in these groups , scales in different languages will still be needed . Some reflections on the process follow . First , as mentioned in the introduction , the validation of scales is a crucial process as unreliable and invalid scales can lead to wrong conclusions . This places a responsibility on researchers who intend to use an instrument in a new cultural setting or with a different target group to test the validity of the instrument using state-of-the-art qualitative and quantitative methods . Tools like the Herdman-Stevelink framework are helpful in conducting such a validation study [29] , [30] . Second , a valid scale does not mean a perfect scale that we should set in stone and leave untouched . This paper shows that although the scales are valid , there remain several points for improvements . Several of these suggestions came from observations and reflections after the testing , training and piloting phases . We would like to recommend other researchers also to continue reflecting on valid scales as this might generate valuable insights and lessons in the future . Third , can the construct of stigma be assessed and , if so , how can this best be done ? Opinions differ within our own team and these can be linked to our different scientific disciplines and epistemologies . However , we all agree that a combination of qualitative and quantitative methods offer the richest perspective . In relation to stigma-reduction interventions , the data that comes from quantitative assessments have particular value in determining the effectiveness of such interventions in groups and when generalizability is important . Qualitative assessment is particularly valuable when we look at individuals and want to understand the changes ( and underlying reasons for these ) interventions brought in their lives . Fourth , we consider it important to reflect on the impact assessing stigma has on the interviewee . It goes without saying that an instrument or the way data is collected should not create concern or discomfort regarding people affected by leprosy . This is why , in this type of research , the attitudes , understanding and skills of the interviewers and hence their training is crucial . Also the code of conduct of the team , for instance , on how to deal with questions about leprosy from the interviewee is vital . Phrasing questions in scales more positively , e . g . , ‘Do people in your community support people affected by leprosy ? ’ Or assessing different constructs , such as social closeness , inclusion and care would be interesting topics for future research . Fifth , Parker and Aggleton noted that the way one conceptualizes and investigates the construct of stigma influences forms of intervening [13] . To assess social stigma , the concept of stigma needs to be conceptualized , but this simplification of a complex construct should then not dictate other activities in the field of stigma . Simplification for the purpose of quantitatively assessing the effect of interventions is valid . However , when designing or implementing stigma reduction interventions , we believe that we need to step back , appreciate and take into account the complexity of the concept of stigma . According to current international standards , our findings indicate that the EMIC-CSS and the SDS have adequate validity to assess social stigma of leprosy in the Bahasa Indonesia-speaking population in Cirebon District . However , these findings cannot be generalized to other NTDs , countries or even other provinces in Indonesia that are culturally different , such as Papua , Sulawesi , and Nusa Tenggara , where they would need to be re-validated . We believe the scales can be further improved and we have provided several suggestions in the discussion . With some adaptations the scales can be validated for other NTDs . | Persons affected by neglected tropical diseases , such as , Buruli ulcer , lymphatic filariasis , onchocerciasis , leishmaniasis and leprosy , can experience stigma . One important source of stigma are members in the community . Neighbours , religious leaders , and community leaders can exclude , reject , blame or devalue a person affected by one of these diseases . It is important to be able to assess this type of stigma for the prevention and management of these diseases . Assessing stigma is not an easy task . There are several instruments available , but these were developed with different aims or tested in different settings . We can use these instruments , but we need to be sure that they assess what we want them to assess and whether the instrument produces consistent results . In this paper the authors report a study that investigated the validity of two scales that assess stigma in the community towards people affected by leprosy in Indonesia . The names of the scales are Explanatory Model Interview Catalogue Community Stigma Scale ( EMIC-CSS ) and Social Distance Scale ( SDS ) . The results show the two scales to be adequately valid and reliable in the target culture . There are , however , also several improvements possible and the authors provide suggestions how to incorporate these . In addition , the authors provide recommendations for the use of these scales among people affected by other neglected tropical diseases . |
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One Health addresses complex challenges to promote the health of all species and the environment by integrating relevant sciences at systems level . Its application to zoonotic diseases is recommended , but few coherent frameworks exist that combine approaches from multiple disciplines . Rabies requires an interdisciplinary approach for effective and efficient management . A framework is proposed to assess the value of rabies interventions holistically . The economic assessment compares additional monetary and non-monetary costs and benefits of an intervention taking into account epidemiological , animal welfare , societal impact and cost data . It is complemented by an ethical assessment . The framework is applied to Colombo City , Sri Lanka , where modified dog rabies intervention measures were implemented in 2007 . The two options included for analysis were the control measures in place until 2006 ( “baseline scenario” ) and the new comprehensive intervention measures ( “intervention” ) for a four-year duration . Differences in control cost; monetary human health costs after exposure; Disability-Adjusted Life Years ( DALYs ) lost due to human rabies deaths and the psychological burden following a bite; negative impact on animal welfare; epidemiological indicators; social acceptance of dogs; and ethical considerations were estimated using a mixed method approach including primary and secondary data . Over the four years analysed , the intervention cost US $1 . 03 million more than the baseline scenario in 2011 prices ( adjusted for inflation ) and caused a reduction in dog rabies cases; 738 DALYs averted; an increase in acceptability among non-dog owners; a perception of positive changes in society including a decrease in the number of roaming dogs; and a net reduction in the impact on animal welfare from intermediate-high to low-intermediate . The findings illustrate the multiple outcomes relevant to stakeholders and allow greater understanding of the value of the implemented rabies control measures , thereby providing a solid foundation for informed decision-making and sustainable control .
The One Health paradigm aims to effectively manage complex risks affecting human , animal , and environmental health by forging new interdisciplinary partnerships and collaborations . Rabies , an acute progressive encephalomyelitis with almost 100% case fatality rate caused by viruses in the genus Lyssavirus , is a zoonotic disease that is responsible for an estimated 55 , 000 human deaths , tens of millions of human exposures , and substantial animal losses annually [1] . It requires a generalised approach if it is to be managed effectively and efficiently [2] . While One Health thinking has come into vogue , systematic integration of various disciplines such as biological , environmental , social , and health sciences to manage health more holistically is often complicated by interdisciplinary and intersectoral barriers to effective collaboration [3] . One major challenge is the paradigm debate caused by the philosophical assumptions that guide the collection and analysis of quantitative ( post-positivist ) and qualitative ( constructivist ) data which may be viewed differently by disciplines . It has been suggested that using both approaches in the same study provides , in combination , a superior understanding of research problems than either approach alone [4] . Another important barrier is the current institutional architecture in which public funds are allocated to specific ministries thereby hindering development of joint public health programmes , which in the case of zoonotic diseases can result in a fragmented approach to control . The most important vector for maintenance of rabies virus and transmission to humans is the domestic dog , with over 90% of human cases attributable to dog bites . The tools to eliminate rabies from animal populations exist , yet relatively few countries are currently rabies-free placing a major strain on public health budgets . Nearly all human rabies deaths occur in developing countries because they are lacking the resources and capacity to provide both adequate pre-exposure prophylaxis and post-exposure prophylaxis ( PEP ) in humans and effective management of the virus in animal populations . The World Health Organisation estimates that the annual cost of rabies may be in excess of US $6 billion per year including an estimated US $1 . 6 billion for PEP [5] . Where rabies control has been successful , efforts have been based on quarantine in an advantageous geographical location ( e . g . United Kingdom ) or the systematic mass vaccination of domestic and wild host populations ( e . g . mainland Europe ) . In the long term , controlling rabies in the dog population through mass dog vaccination has been shown to be more cost-effective than human PEP alone [6] . The World Health Organisation , the World Organisation for Animal Health , and the Food and Agriculture Organisation of the United Nations acknowledge the need for intersectoral collaboration to manage rabies [5] . However , the systematic control of rabies in animal populations requires financial resources , and the technical capacity to plan , implement and evaluate the vaccination campaign; aspects that are often lacking in affected countries . Sustaining control demands political , societal and financial backing to maintain the campaign as well as the logistic and human resource capacity to deliver vaccine , and knowledge of , and access to , target populations . On-going collection of data through surveillance systems to monitor and evaluate the economic and technical efficiency of campaigns is necessary to ensure objectives are being achieved , and surveillance must be continuous following eradication to detect re-emergence of the virus promptly . Many of these components need the active support of the public in affected areas . In many countries where rabies is endemic these requisite criteria are not met , and interventions against other diseases are given a higher priority . As a result rabies is considered a neglected disease . Modern science tends to abstract phenomena and reduce reality into smaller portions that can be easily understood and , as much as possible , be expressed in mathematical terms . While these mathematical abstractions are critical in modelling the dynamics of disease in a population and to assess the effectiveness of interventions , they do not provide an understanding of the support for rabies control measures in society nor do they shed any light on wider-reaching issues such as ethical concerns or animal welfare , in short , they oversimplify reality . For example , anecdotal evidence suggests that some people are not supportive of rabies control measures such as dog culling and actually jeopardise the process by hiding or moving their dogs . Thus , both reductionist in-depth studies , as well as collaboration with other disciplines are needed to understand and plan sustainable and publicly acceptable control programmes . Many projects have focused on individual components of rabies impact , for example the use of pre-exposure prophylaxis and PEP in humans [7]–[10] , the effectiveness of different strategies for dog vaccination [11] , [12] , willingness-to-pay for dog vaccination [13] and the indirect costs of rabies exposure [14] . However , they have all been assessed independently . Assessed in conjunction , they provide important insights into the positive and negative consequences of rabies management and build a robust basis for informed decision-making . This paper proposes a generic framework for the assessment of rabies interventions encompassing a wide range of positive and negative consequences and local conditions in order to assess economic efficiency and illustrates its use by applying it to the rabies control programme in Colombo City , Sri Lanka .
In Colombo City , canine rabies has been endemic for several decades . The national anti-rabies strategy aims to protect people who are exposed and those at risk of contracting the disease , establish dog population immunity and to control the dog population . A well regulated system of PEP is in place , limiting the average number of human rabies cases between 1995 and 2011 to 0 . 65 per year in a city of 650 , 000 ( unpublished data , Veterinary Department of Colombo Municipal Council ) . The Veterinary Department of Colombo Municipal Council used to combat rabies through culling of roaming dogs via carbon monoxide and carbon dioxide poisoning in a gas chamber and vaccination of owned dogs , but canine rabies cases continued to persist in the city . From 2007 to 2012 , following cessation of culling by Presidential decree in 2006 , a modified comprehensive intervention to control rabies was implemented , which included mass vaccination of dogs , targeted sterilisation of both owned and unowned dogs , education of children and adults in bite prevention and rabies awareness , and development of dog managed zones in public areas . The stakeholders involved in the intervention hypothesised that the new measures would lead to a decrease in the number of dog rabies cases , an associated reduction in the administration of PEP to people , an increased acceptance of dogs in society , and overall a positive net value of the intervention in Colombo City . The aim of this case study was to assess the economic value of the intervention explicitly taking into account monetary and non-monetary consequences resulting from the change in rabies prevalence , animal welfare and social acceptance .
The survey in 2007 found 23 dog bites in 1 , 063 household members or an annual incidence rate of 0 . 0216 . The survey in 2010 found 8 dog bites in 559 household members or an annual incidence rate of 0 . 0143 . The difference in incidence rate in 2007 and 2010 was not significant ( p = 0 . 3105 , significance level set at 5% ) . Extrapolating these dog bite incidence rates to the total population of Colombo City of 642 , 163 in 2007 and 644 , 450 in 2010 , respectively , resulted in the following inputs for the economic assessment: 13 , 871 annual dog bites for the baseline scenario and 9 , 216 annual dog bites for the intervention . These figures were multiplied by four to estimate the total number of dog bites for a four year period , which resulted in 55 , 484 and 36 , 864 dog bites for the baseline scenario and the intervention , respectively . The average number of human deaths for the four year duration of the intervention and the baseline scenario , respectively , was three human deaths each for the four year period . The national hospital reported that in May 2006 , 131 people sought care following dog bites and in May 2011 , 160 people were recorded . These monthly figures were multiplied by 48 to estimate proxies for the number of people seeking medical attention for dog bites in Colombo City for the baseline scenario ( n = 6 , 288 ) and the intervention ( n = 7 , 680 ) , respectively . The estimated rate of reporting was 0 . 11 for the baseline scenario and 0 . 21 for the intervention , respectively . The number of dog rabies cases was 19 for 2007 ( proportionally estimated from annual figure for the period June to December ) , 17 in 2008 , 20 in 2009 , 10 in 2010 , and 2 in 2011 ( until June ) . For the baseline scenario , the estimated average number of dog rabies cases per year was 43 , i . e . 172 for the four year duration . The number of dogs culled with a mixture of carbon monoxide and dioxide in the exhaust fumes produced by a freestanding combustion engine was zero in the intervention due to the presidential decree in 2006 that stopped the elimination of dogs and an estimated 9 , 384 in the baseline scenario for the four years . Field data from Colombo City collected by the BPT from 5 July to 13 August 2011 during 24 vaccination sessions in 12 different wards ( total dogs vaccinated = 658 ) showed that a mean 28% ( SD = 21 . 9% ) of the total dogs vaccinated were held by people from the community ( owner or other people ) and the remaining dogs were caught in a net for vaccination . Using this proportion to estimate the number of dogs in the situation ‘dogs held by owner and vaccinated’ resulted in 36 , 300 dogs for the intervention and 25 , 013 dogs for the baseline scenario for the four years . The number of dogs in the situation ‘catch in net and vaccinate’ was estimated at 10 , 740 for the four years of intervention . The number of dogs sterilised in the intervention during the four years was 5 , 323 in total based on records from the Blue Paw Trust . Table 4 summarises the additional investment and the additional outcomes in monetary and non-monetary terms resulting from the intervention when compared with the baseline scenario over a time period of four years . The overall costs of the intervention were US $1 . 03 million , which was the sum of the additional investment of US $818 , 851 for the control measures in the animal health sector and the additional US $215 , 064 spent on monetary human health costs . The net benefits from the intervention were 738 DALYs averted resulting from the reduction in dog bites , increased acceptance of roaming dogs in society and improved animal welfare . The detailed findings are presented below . Table 5 illustrates the total costs incurred for dog rabies control activities for the intervention from different organisations involved ( Sri Lankan government , Blue Paw Trust ) . Table 6 lists the total costs incurred by the Sri Lankan government for dog rabies control in the years 2002 to 2006 which reflect the control costs in the baseline scenario . In the intervention , the largest proportion of the total costs was staff costs ( 33% ) , followed by implementation costs ( 21% ) , other costs ( 19% ) , and planning and preparation costs ( 11% ) . In the baseline scenario , the costs for implementation activities contributed most ( about 92% ) to the total annual costs in all years . The difference in costs between the baseline scenario and the intervention over a time period of four years was US $818 , 851 . The total human health cost per dog bite was estimated at US $159 without using immunoglobulin , US $163 with equine immunoglobulin and US $39 for the people who only needed medical care , but not vaccination . The total human health costs for the four years of intervention and the baseline scenario were US $1 , 179 , 925 and US $964 , 861 , respectively ( Table 4 ) . The difference between the two was US $215 , 064 . The total DALYs lost for the four years related to psychological distress were 1 , 461 for a total 36 , 864 dog bites in the intervention and 2 , 199 for a total 55 , 484 dog bites in the baseline scenario , respectively . The total DALYs lost for a four year period related to human deaths were 83 . 97 for both the intervention and the baseline scenario with three human deaths each . The total number of DALYs averted in the intervention period as compared to the baseline scenario for the four year period was 738 . The sensitivity analyses on the input variables that determined the outcomes “difference in monetary human health costs” and “DALYs averted” over the four years are illustrated in Figures 2 and 3 . For the outcome “difference in monetary human health costs” the most influential variables were the number of people bitten and seeking treatment in the intervention ( outcome changed by 82% ) and the baseline scenario ( outcome changed by 67% ) , respectively , followed by the overhead cost per hospital visit ( outcome changed by 13% ) and the proportion of people presented with dog bites receiving PEP ( outcome changed by 11% ) . All other input variables caused changes in outcome of 1% or less ( Figure 2 ) . The difference in monetary human health costs when varying the two most influential inputs number of people bitten and seeking treatment in the intervention and baseline scenario , respectively , between −30% and +30% from the base is shown in Table 7 . The results demonstrate by how much the inputs need to change for the intervention to create a benefit in terms of monetary human health costs . When keeping the base value for the baseline scenario constant , a reduction of the intervention input by at least 20% would lead to a monetary benefit in the human health sector . The additional expenditures for the intervention spent by the animal health sector could be recovered by monetary human health benefits if , ceteris paribus , the input people seeking treatment in the intervention was 950 ( 12% of the base value ) or the input people seeking treatment in the baseline scenario was 13 , 026 ( 207% of the base value ) . For the outcome “DALYs averted” the most influential variables were the number of dog bites in the baseline scenario ( outcome changed by 45% ) and in the intervention ( outcome changed by 30% ) , respectively , followed by the DALYs lost per dog bite due to psychological distress ( outcome changed by 15% ) . The DALYs lost per human rabies death did not influence the outcome ( Figure 3 ) . Table 8 and Table 9 illustrate the score per situation without taking into account dog numbers and the score per situation taking into account dog numbers . For the intervention , the qualitative estimates ranged between very low and high . For the baseline scenario , the estimates ranged between very low and very high . The overall score was estimated as low-intermediate for the intervention and intermediate-high for the baseline scenario . Table 10 summarises the overall acceptance scores for the baseline scenario and the intervention among dog owners and non-dog owners derived from the two surveys . The Kruskal-Wallis rank test to compare different groups showed that the differences between the four groups of dog owners and non-dog owners were statistically significant ( p = 0 . 001 ) . The post-hoc Wilcoxon rank-sum tests yielded a significant difference between dog owners and non-dog owners in 2007 ( z = 8 . 22 , p<0 . 0001 ) , dog owners and non-dog owners in 2010 ( z = 3 . 836 , p = 0 . 0001 ) , and non-dog owners in 2007 and 2010 ( z = −2 . 71 , p = 0 . 0068 ) . There was no significant difference between all participants in the baseline scenario and the intervention ( z = −0 . 938 , p = 0 . 35 ) . Of the 61 focus group participants , 53 were women and 8 were men . There were 17 housewives and 28 who did not indicate their professions . The rest of the occupations included salesmen , students , nursery teachers , garment makers , an architect and business people . When asked about dog-related issues in the past , the groups described significantly more problems for the past than the present , specifically past problems 7 . 8±1 . 5 and present problems 3 . 3±1 . 2 ( Wilcoxon test , p<0 . 01 ) . Figure 4 illustrates the number of dog related problems reported by the nine focus groups . Significantly fewer groups mentioned rabies and breeding or puppies as problems at present than in the past ( Mc Nemar's test , p<0 . 05 ) . The stark decrease in the perception of rabies as a problem was explained by workshop participants as being due to possession of knowledge about the disease and knowing what to do when bitten by a dog . The population control measures mentioned by participants were sterilisation , vaccination , shelter , re-homing , treatments , birth control injection , dumping , education , and awareness campaigns . The highest preference across all groups was given to sterilisation , vaccination and education . None of the groups mentioned culling as a means of population control . All focus groups indicated that their behaviour following a dog bite had changed . Many groups reported the application of Murunga ( a local plant ) in the past , but would nowadays wash the wound with soap and running water and go to a hospital to seek treatment . The mean acceptable total number of roaming dogs reported in the vicinity ( i . e . street ) was 2 ( SD 2 , range 0 to 10 ) . There was a significant difference in levels of roaming dogs reported for the past and the present across all focus groups ( p<0 . 001 ) ( Table 11 ) . There was no significant difference in the total number of roaming dogs reported by income levels ( p = 0 . 184 ) , whether the household reared dogs ( p = 0 . 708 ) , gender ( p = 0 . 535 ) , and occupation of participants ( p = 0 . 696 ) . The economic analysis showed that the use of an additional US $818 , 851 in the animal health sector to combat rabies and manage the dog population in Colombo City had both negative and positive consequences in society when contrasting the intervention and the baseline scenario . Non-monetary benefits included an increase in the acceptance of roaming dogs among non-dog owners and dog owners , a reduction in animal suffering , and 738 DALYs averted . The increased acceptance of roaming dogs and the DALYs averted increased well-being of society . While reducing animal suffering overall , the intervention strategy at the same time compromised animal welfare ( e . g . due to sterilisation or catching in a net ) . Negative consequences included an increase of US $215 , 064 in human health costs related to seeking health care following dog bites . Hence , there was a net cost to society in monetary terms of US $1 . 03 m and a net benefit in non-monetary terms . The lower number of estimated dog bites and the improvement in reporting of bites and treatment of people indicated that the risk to people of contracting rabies was decreasing . The intervention was shown to be effective , as the official number of dog rabies cases decreased from an average of 43 cases per year ( 2001 to 2005 ) to just two cases in the first six months of 2011 . Ethical aspects relating to the rights and fairness approach in dogs and humans as well as the virtue approach in people included the following: In people: In dogs: The judgement if the good of the intervention outweighed the harm ( the utilitarian approach ) and if it best served the community as whole and not just some members ( the common good approach ) depends on how decision-makers prioritise ethical issues . It might be argued that the avoidance of animal suffering and the increased well-being of people justified the net monetary cost of the strategy . Others might attribute more weight to monetary values resulting from the control activities .
The article proposes a comprehensive framework for assessing multiple aspects of rabies control and combining them in an economic analysis . It is composed of five components ( epidemiological , economic , social , animal welfare and ethical assessments ) that are all interlinked to guide decision-making and the allocation of resources . While almost all parts were covered individually in previous studies , to the authors' knowledge there are no publications on rabies control that cover all these aspects in the spirit of One Health and link them in an economic analysis . The advantage of the framework is its comprehensive nature that provides decision-makers with a wide array of information that they need to be able to take informed decisions on disease management . However , it requires capacity in multiple disciplines , extensive data collection and an acknowledgment of the multi-factorial processes of decision-making . Similar elements essential for One Health decision making have also been identified by others . For example , a framework published after this study was conducted for the estimation of the economic costs of zoonoses [31] conceptually linked epidemiological and economic models and placed them in the context of wider risk management strategies including assessment of the context , hazard identification , risk assessment , capacity building and communication . The approach proposed here can be considered as an expansion of the risk assessment and risk management steps described in the other framework , whilst providing more detail on a specific disease ( i . e . rabies ) and the associated effects . The comparison of additional costs with both monetary and non-monetary outcomes required presenting the results in an unconventional way . On the one hand , this presentation allowed reflecting the complexity of the real world and the various economic consequences related to a decision . On the other hand , the combination of negative monetary and positive non-monetary outcomes made the interpretation more challenging than a conventional net present value or cost-benefit ratio . Cost-benefit analysis is an approach that is intuitively appealing , because it assesses the positive and negative consequences of a strategy in a common unit , generally money . Cost-effectiveness analysis uses the same basic approach , but presents the outcome of a strategy in non-monetary units . The selection of an appropriate measure of effectiveness is critical , and must be in accordance with the control objective . A “CEA is only as valid as its underlying measures of effectiveness and cost” [32] , but unlike in health economics , where attempts have been made to harmonise CEA methodologies and encourage comparability of studies [33] , there are no specific guidelines available yet for its application in animal health . Currently , due to variability of interests , approaches , designs , capacity and resource availability of organisations involved in rabies control , any incremental cost-effectiveness analyses going beyond human health will vary depending on the outcome measures defined . If the scientific community was to find an agreement on a standardised approach to measure outcomes of rabies control in an integrated way , the economic efficiency of such control measures could be compared internationally and the best approach chosen . As long as there is no standardisation of effectiveness measures for rabies or disease control in general , the variety in outcomes will make a meta-analysis difficult or even impossible . The presented framework is a starting point that may help to create awareness and stimulate discussion . A range of approaches were used in the case study to cover the multifaceted control measures implemented which were expected to decrease the number of dog rabies cases , to reduce the number of PEP applied to people , to increase acceptance of dogs in society , and to generate a positive net value overall . The case study illustrates the various components of the proposed framework in a developing country context . Because of the limited availability of resources for the case study , secondary data were used whenever possible and where primary data collection was necessary , low-cost approaches were considered for data collection . While the case study is subject to various limitations as described below , it provides information for Sri Lankan stakeholders involved in rabies control on the profitability and cost-effectiveness of the implemented intervention and demonstrates the advantages and challenges of the proposed framework . Importantly , the number of dog rabies cases was drastically reduced during the time of the intervention to only two in the last six months of the study period compared to a previous high number of dog rabies cases ( an average of 43 per year in the period of 2001 to 2005 ) . This indicated that high enough vaccination coverage was achieved and that good progress was being made towards the elimination of rabies in the years 2014–2015 , the specified long term target . Given that rabies is still prevalent in other parts of the island , it is important to continue intervention and surveillance efforts in Colombo City to maintain the favourable situation until rabies can be eliminated island-wide . One critical variable in the estimation of monetary and non-monetary human health consequences was the number of dog bites . While the number of people seeking health care following a dog bite derived from data from the national hospital showed an increase from 2006 to 2011 , the numbers derived from the two surveys in 2007 and 2010 showed a decrease in the number of dog bites . There are four possible explanations for this increase: 1 ) people were more aware of rabies prophylaxis and went to the hospitals more often , 2 ) there was a better system in place to record dog bites in hospitals , 3 ) there were effectively more dog bites , and 4 ) unknown factors related to the two months of data provided caused a fluctuation in numbers ( a comprehensive data set for the entire period of 2006 to 2011 was not available ) . Given the fact that the intervention substantially decreased the number of dog rabies cases in the population , an increase in the number of dog bites seems highly unlikely . This hypothesis is corroborated by the survey and focus group data . Because the survey data showed a decrease in the number of dog bites and the focus groups an increase in disease awareness , it is most likely that the increase in the number of registered dog bites was due to a higher number of people seeking medical advice in case of dog bites . The analysis of the focus groups demonstrated that people's reaction following a dog bite had changed . All focus groups reported that they would now wash the wound with soap and water and go to the hospital to receive PEP . Also , the development of a better system to record bites in hospitals in recent years was expected to have had a positive impact on the number of registered cases ( personal communication Dr Obeyesekere ) . The difference between the number of dog bites collected from the national hospital and the number estimated from the surveys provided an indication of the rate of under-reporting . The estimated reporting rates indicated an improvement in dog bite reporting in the intervention compared to the baseline scenario . This observation further confirmed the increased rabies awareness of people in the community . However , it also showed that a considerable part of the population did not seek medical attention after being bitten by a dog . As long as rabies is not eradicated from the dog population , people should constantly be informed about the appropriate behaviour in case of a dog bite . The increase of registered dog bite cases in health centres caused an increase in human health costs . For the savings in monetary human health costs to cover the additional investment made in the animal health sector , the number of people seeking treatment following dog bites would have to be reduced drastically as shown in the sensitivity analysis . It is expected that the number of people seeking medical advice will remain high or increase despite a reduction in dog bites , because the on-going intervention activities constantly promote disease awareness . Only elimination of rabies from the dog population will allow reducing the provision of PEP after dog bites . As long as rabies is endemic in the dog population , people bitten by rabies-suspect animals should get a thorough assessment by health professionals and PEP , as recommended by World Health Organisation guidelines . The only way to reduce public health costs in a rabies endemic situation is to find cheaper and equally effective methods of PEP . The public health sector has already initiated such cost savings by using intradermal vaccines and only administering immunoglobulin in priority cases following a sound history taking and assessment . Remarkably , there was a considerable reduction in the number of problems listed in all focus groups . Nearly all groups reported that there had been a reduction in rabies , barking , puppies and breeding behaviour and dog fights since the implementation of the intervention . Thus , dogs were perceived more favourably by people , because they looked healthier and showed reduced breeding and nuisance behaviour . Moreover , some focus group participants indicated that their fear of rabies had decreased drastically , because of their improved knowledge of the disease . The selection of participants was performed independently by the community liaison officers in collaboration with community leaders and therefore not influenced by the staff of the BPT . Because the community liaison officers did not receive fixed criteria about socio-economic status of participants , it is likely that ‘high’ socioeconomic groups represented more the middle level , as those at the truly high end did not have the time or interest to participate and were not known well to the community leaders . To promote open sharing of thoughts and concerns , the facilitator made sure to create a comfortable atmosphere and assured participants that the data would be handled anonymously and that their answers did not have any negative consequences for them . However , it is still possible that a few participants may have felt that a less than positive evaluation would result in discontinuation of the project . While such behaviour introduces bias into the results , it also reflects the social desirability of the project , i . e . a community wanting the project to continue is in itself an indication of the degree of perceived success . A source of bias that could not be controlled was the imbalance in gender representation in the focus groups . Only a few men were able to join the focus groups , which was due to the fact that all groups met during the day when the men were at work . While a variety of approaches are available to assess animal welfare ( e . g . welfare assessment protocols for commercial livestock ) , there are no guidelines in place for the systematic assessment of the impact of rabies and its control on animal welfare . Therefore , we developed a qualitative approach to assess defined situations related to rabies and its control that may negatively affect animal welfare . The assessment was a combination of field data , scientific literature , logical reasoning and professional judgment . Importantly , the scores attributed to the different situations were relative and not absolute . The development of an absolute scoring system would require systematic measurement of physiological and behavioural parameters , which was not within the scope of this project . Taking into account the numbers of dogs in the situation , the highest score ( ‘very high’ ) was attributed to the situation culling dogs via carbon monoxide and carbon dioxide poisoning using the exhaust fumes of a combustion engine , and the lowest scores to the situation of holding dogs by the owner or people from the community , and vaccination . Thus , replacing the culling of dogs by other intervention strategies reduced animal suffering . Because none of the focus groups mentioned culling of dogs as an intervention strategy for rabies or population control , it is most likely that the avoidance of culling dogs not only promotes animal welfare , but also the well-being of people in society who care for the dogs . The ethical assessment helped guide the interpretation of the results . However , it did not attribute weights to the different criteria analysed . Such weights were expected to differ among decision-makers depending on the political agenda , local norms and customs , available resources , experience and personal preferences . Further benefits that were not quantified in the analysis and remain open to further research include a potential reduction of rabies cases in other animals , promotion of responsible dog ownership and thus better animal welfare , and the decrease of fear in the human population . This case study explicitly took into account a range of factors that impact on the value of rabies control measures . By combining different monetary and non-monetary aspects , it not only provided information about the impact of rabies control on monetary public health costs , but also important insights about non-monetary effects , particularly animal welfare and social acceptability that were not only valuable outcomes in themselves , but also helped to explain and support some of the other findings . For example , the epidemiological data on the number of dog rabies cases as well as the information from the surveys on dog bites and the focus groups on disease awareness provide an explanation for the increase in human health costs . Linkages between the individual components could be more formalised by for example making social assessments an integral part of epidemiological analysis . The proposed framework provides a first proposal for looking at rabies control in a holistic way and covers multiple facets that inform decision-making . The framework is expected to help planning impact evaluations of rabies control so that future data collection protocols can take into account not only the health costs , but also consider factors like social acceptance and animal welfare . It thereby helps to conduct integrated assessments for zoonotic disease control and can be further developed to address more complex One Health challenges . | Successful rabies control generates benefits in terms of improved human and animal health and well-being and safer environments . A key requirement of successful and sustainable rabies control is empowering policy makers to make decisions in an efficient manner; essential to this is the availability of evidence supporting the design and implementation of the most cost-effective strategies . Because there are many , at times differing , stakeholder interests and priorities in the control of zoonotic diseases , it is important to assess intervention strategies in a holistic way . This paper describes how different methods and data from multiple disciplines can be integrated in a One Health framework to provide decision-makers with relevant information , and applies it to a case study of rabies control in Colombo City , Sri Lanka . In Colombo City , a new comprehensive intervention was initiated in 2007 based on vaccination , sterilisation , education , and dog managed zones . Results showed that for the four year time period considered , the new measures overall cost approximately US $ 1 million more than the previous programme , but achieved a reduction in dog rabies cases and human distress due to dog bites , reduced animal suffering and stimulated a perception of positive changes in society . All these achievements have a value that can be compared against the monetary cost of the programme to judge its overall worth . |
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Isogenic bacterial populations can consist of cells displaying heterogeneous physiological traits . Small regulatory RNAs ( sRNAs ) could affect this heterogeneity since they act by fine-tuning mRNA or protein levels to coordinate the appropriate cellular behavior . Here we show that the sRNA RnaC/S1022 from the Gram-positive bacterium Bacillus subtilis can suppress exponential growth by modulation of the transcriptional regulator AbrB . Specifically , the post-transcriptional abrB-RnaC/S1022 interaction allows B . subtilis to increase the cell-to-cell variation in AbrB protein levels , despite strong negative autoregulation of the abrB promoter . This behavior is consistent with existing mathematical models of sRNA action , thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation . Importantly , we show that the sRNA-induced diversity in AbrB levels generates heterogeneity in growth rates during the exponential growth phase . Based on these findings , we hypothesize that the resulting subpopulations of fast- and slow-growing B . subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions .
In their natural habitats , bacteria constantly adapt to changing environmental conditions while simultaneously anticipating further disturbances . To efficiently cope with these changes , intricate interlinked metabolic and genetic regulation has evolved [1] . This complex regulatory network includes the action of small regulatory RNAs ( sRNAs ) [2] . sRNAs are a widespread means for bacterial cells to coordinate ( stress ) responses by fine-tuning levels of mRNAs or proteins , and they have been studied in great detail in Gram-negative bacteria [3] . Regulation by some sRNAs takes place by short complementary base pairing to their target mRNA molecules , for instance in the region of the ribosome-binding site ( RBS ) to inhibit translation or trigger mRNA degradation . In Gram-negative bacteria many of these sRNA-mRNA interactions are mediated by the RNA chaperone Hfq [4] . However , the Hfq homologue in the Gram-positive model bacterium Bacillus subtilis has no effect on the regulation of the eight sRNA targets reported in this species so far [5–7] . Owing to the complexity of sRNA regulation , only a relatively small number of studies have focused specifically on the physiological necessity of sRNA-target interactions . This is again particularly true for Gram-positive bacteria , such as B . subtilis , despite the fact that many potential sRNAs have been identified [8 , 9] . Within a bacterial population , genes and proteins can be expressed with a large variability , with high expression levels in some cells and low expression levels in others [10] . Examples of expression heterogeneity in B . subtilis are the extensively studied development of natural competence for DNA binding and uptake and the differentiation into spores [11–13] . In both cases , expression heterogeneity is generated by positive feedback loops , and results in bistable or ON-OFF expression of crucial regulators [14] . Distinctly from bistability , proteins can also be expressed with large cell-to-cell variability . This variation in expression levels , or noise , can originate from intrinsic or extrinsic sources [15 , 16] . Extrinsic noise is related to cell-to-cell fluctuations in numbers of RNA polymerase , numbers of genome copies , or numbers of free ribosomes . Conversely , intrinsic noise is caused by factors directly involved in the transcription or translation of the respective gene or protein . Interestingly , particularly noisy genes are often found to be regulators of development and bacterial persistence [12 , 17 , 18] . Because of the importance of noise in protein expression , cells have evolved mechanisms to regulate the noise levels of at least some proteins [10] . Reducing noise levels has been suggested as an important explanation why many transcriptional regulators in bacteria ( 40% in E . coli [19] ) autorepress the transcription of their own promoter ( i . e . negative autoregulation ( NAR ) ) . AbrB is a global transcriptional regulator in Gram-positive bacteria , including the important human pathogens Bacillus anthracis and Listeria monocytogenes [20 , 21] . B . subtilis AbrB positively regulates some genes when carbon catabolite repression ( CCR ) is relieved [22] , and negatively regulates the expression of over two hundred genes in the exponential growth phase [23] . Transcription of abrB is negatively autoregulated by binding of AbrB tetramers to the abrB promoter [24 , 25] . Upon entry into stationary phase , abrB transcription is repressed via increasing levels of Spo0A-P and AbrB is inactivated by AbbA [26 , 27] . The resulting AbrB depletion is consequently followed by activation of AbrB repressed genes , which are often important for stationary phase processes . Notably , because of its role in the elaborate sporulation and competence decision making network [26 , 27] , AbrB has mainly been studied in the context of entry into stationary phase while much less is known about its exact role in the exponential growth phase . We selected putative sRNAs from a rich tiling array dataset of 1583 potentially regulatory RNAs [9] . This selection was made for evolutionary conserved putative sRNAs with a high expression level on defined minimal medium . Deletion strains of these putative B . subtilis sRNAs were subsequently tested for growth phenotypes . One sRNA—RnaC/S1022—stood out since the mutant strain displayed a strongly increased final optical density on minimal medium with sucrose as the sole carbon source . The present study was therefore aimed at determining how RnaC/S1022 influences the growth of B . subtilis . Inspection of consistently observed predicted RnaC/S1022 targets indicated that the aberrant growth phenotype could relate to elevated AbrB levels . Here we show that , under certain conditions , B . subtilis employs RnaC/S1022 to post-transcriptionally modulate AbrB protein expression noise . The observed noise in AbrB protein levels is remarkable , because the abrB gene displays low transcriptional noise consistent with its NAR . Importantly , the sRNA-induced noise in the AbrB protein levels generates growth rate heterogeneity in the exponential phase .
RnaC/S1022 was first identified in a systematic screening of B . subtilis intergenic regions with an oligonucleotide microarray [28] . RnaC/S1022 is located in between yrhK , a gene of unknown function , and cypB , encoding cytochrome P450 NADPH-cytochrome P450 reductase ( also known as yrhJ ) . We tested the conservation of the B . subtilis RnaC/S1022 sequence with BLAST analysis against a set of 62 Bacillus genomes , and found evolutionary conservation in a clade of the phylogenetic tree including 19 B . subtilis , Bacillus atrophaeus , and Bacillus amyloliquefaciens genomes ( Fig . 1A and S1 Fig . for extensive alignments ) . Within these 19 genomes , the 5’ and 3’ ends of the RnaC/S1022 sequence are conserved , but the core sequence is disrupted in all 9 B . amyloliquefaciens genomes ( S1 Fig . ) . Notably , the RnaC/S1022 from B . atropheus 1942 seems to represent an in-between form of RnaC/S1022 that mostly resembles the RnaC/S1022 sequences from the B . amyloliquefaciens sp . genomes . Therefore , an alignment of only the RnaC/S1022 sequences from the 9 remaining B . subtilis genomes was used to predict the RnaC/S1022 secondary structure using the LocARNA tool [29] ( Fig . 1B , S1 Fig . ) . These analyses predict RnaC/S1022 to fold into a stable structure with a Gibbs free energy for the sequence shown in Fig . 1B of −38 . 5 kcal/mol , as calculated with RNAfold [30] . RnaC/S1022 was recently included in a screen for possible functions of conserved putative sRNAs identified by Nicolas et al . [9] that are highly expressed on M9 minimal medium supplemented with different carbon sources . Here , the RnaC/S1022 mutant stood out , because it consistently grew to a higher optical density ( OD ) in M9 minimal medium supplemented with sucrose ( M9S ) than the parental strain ( Fig . 1C ) . To distinguish effects on the growth rate and growth yield , lin-log plots of these growth profiles are presented in S2 Fig . , which show that the growth rate was only slightly influenced by the RnaC/S1022 deletion while the growth yield was strongly increased . Compared to M9S , the growth phenotype was less pronounced in M9 with glucose ( M9G ) . Since transcription of RnaC/S1022 is exclusively regulated by SigD [28] , we also tested a sigD mutant for growth under these conditions . Interestingly , the ΔsigD mutant displayed similar growth characteristics as the ΔRnaC/S1022 mutant ( Fig . 1C ) . Differential growth and increased competitiveness were previously reported for a sigD mutant [31] , and our observations suggest that in some conditions the increased final OD of the ΔsigD strain is partly due to deregulation of RnaC/S1022 . We wondered whether deregulation of an sRNA target was responsible for the remarkable growth phenotype observed for the ΔRnaC/S1022 mutant and decided to perform exploratory target predictions using TargetRNA [32] . Predicting sRNA targets can be successful , but target verification is complicated by the large number of false-positively predicted targets . We argued that additional information about the likelihood of a true target could be obtained by determining whether the predicted interaction is conserved over evolutionary time . To identify predicted RnaC/S1022-target interactions that are conserved , a bioinformatics pipeline was established that predicts sRNA targets in genomes in which the RnaC/S1022 sequence is conserved . Since we were interested in finding true B . subtilis sRNA targets , we only considered targets also predicted in B . subtilis , and these are listed in S1 Table . This analysis reduced the number of considered RnaC/S1022 targets to 47 ( from 147 predicted targets for TargetRNA_v1 predictions with P value ≤ 0 . 01 on the B . subtilis 168 genome ) . These 47 predicted targets included seven sporulation-related genes ( phrA , spoVAD , spoIIM , spoIIIAG , cotO , sspG , spsI ) . The sigma factor sigM was also consistently predicted but , since a sigM mutant strain only displays a growth phenotype under conditions of high salinity [33] , this seemed unrelated to the observed growth phenotype of the ΔRnaC/S1022 mutant on M9 medium . In addition , two consistently predicted targets are involved in cell division ( racA and ftsW ) , but we observed no specific cell-division abnormalities of the ΔRnaC/S1022 strain by live-imaging microscopy . Furthermore , the TCA cycle genes citB and citZ were predicted targets and tested by Western blot analysis , but no deregulation was observed . The last consistently predicted target of initial interest was the gene for the transition state regulator AbrB ( Fig . 1D ) . Reviewing the literature on abrB pointed us to an interesting observation where a spo0A mutant was reported to display increased growth rates on media similar to our M9 medium [22] . Furthermore , it had been reported that AbrB has an additional role in modulating the expression of some genes during slow growth in suboptimal environments [34] , which we argued could also be relevant to the M9S growth condition . Since abrB is a consistently predicted target of RnaC/S1022 ( Fig . 1D ) , we checked whether the presence of this sRNA coincides with the presence of the abrB gene . Indeed , abrB is conserved in 53 out of 62 available Bacillus genomes , and RnaC/S1022 is present in 19 of these 53 genomes ( Fig . 1A ) . In addition , we identified no genomes that contain RnaC/1022 but lack the abrB gene ( Fig . 1A ) . Accordingly , we hypothesized that RnaC/S1022 might be a regulator of AbrB . The combined clues from bioinformatics analyses and literature suggested that the growth phenotype of the ΔRnaC/S1022 mutant could relate to elevated AbrB levels . To test whether AbrB levels are indeed altered in this mutant , we performed Western blot and Northern blot analyses . This indeed revealed a strong trend towards higher AbrB protein and mRNA levels in the RnaC/S1022 mutant and for cells grown in M9G or M9S this effect was statistically significant ( Fig . 2 ) . Importantly , the growth phenotype as well as AbrB protein and mRNA levels returned to wild-type ( wt ) by ectopic expression of RnaC/S1022 under control of its native promoter from the amyE locus ( Fig . 1C and 2 ) . We also tested the effects of a Δspo0A mutation by Western and Northern blot analyses . Interestingly , the combined deletion of RnaC/S1022 and spoOA seemed to lead to a further increase in the AbrB protein and mRNA levels compared to the already elevated levels in the spo0A mutant background . Lastly , we observed a three-fold reduced natural competence of the ΔRnaC/S1022 mutant , which is expected when the AbrB levels are elevated [35] ( S3 Fig . ) . To test whether the AbrB levels were directly dependent on RnaC/S1022 levels , we placed the RnaC/S1022 complementation cassette in the amyE locus of the parental strain and used Western and Northern blotting to measure AbrB protein and mRNA levels . These analyses showed a trend towards reduction of both the AbrB protein and mRNA levels in cells grown on M9G and M9S , which would be consistent with elevated RnaC/S1022 expression and increased abrB regulation ( Fig . 2 ) . Since the amount of AbrB was apparently correlated to the amount of RnaC/S1022 , this suggested a stoichiometric relationship between these two molecules . Before testing whether there could be a direct interaction between RnaC/S1022 and the abrB mRNA , we decided to investigate the fate of the abrB mRNA in the presence or absence of RnaC/S1022 . For this purpose , we assayed the levels of the abrB mRNA at different time points after blocking transcription initiation with rifampicin in the RnaC/S1022 mutant strain and in the strain with two chromosomal copies of RnaC/S1022 . This analysis showed that the abrB mRNA level decreased significantly faster in the presence of RnaC/S1022 than in its absence ( S4 Fig . ) . In case of a direct interaction between RnaC/S1022 and the abrB mRNA , the observed difference could relate to an RnaC/S1022-triggered degradation of the abrB mRNA . Alternatively , this difference could be due to an RnaC/S1022-precluded protection of the abrB mRNA by elongating ribosomes [36] . The apparently stoichiometric relationship between AbrB and the sRNA RnaC/S1022 is suggestive of a direct sRNA—target interaction . The predicted interaction region in B . subtilis 168 spans a region from the RBS of abrB ( -10 ) until 19 bp after the start of the abrB ORF of which the strongest consecutive stretch of predicted base-pair interactions are present from +7 bp till +19 bp ( left top panel in Fig . 3 ) . In addition , only this region within the abrB-encoding sequence is part of the conserved predicted interaction region in B . atrophaeus and B . subtilis spizinenzii ( Fig . 1D ) . It has been reported that loop-exposed bases of sRNAs are more often responsible for regulation than bases in stems [37] . Two predicted loop regions of RnaC/S1022 are complementary with the predicted abrB interaction region ( one of two basepairs and one of seven basepairs; bases 51–52 and 57–63 in Fig . 1B and 3 ) . We therefore decided to introduce a point-mutation by a U to A substitution in the predicted 7-bp loop of RnaC/S1022 encoded by plasmid pRM3 and a compensatory mutation in a plasmid pRM15-borne truncated abrB-gfp reporter construct ( abrBtrunc-gfp ) . Strains containing different combinations of the respective plasmids were grown on M9G and assayed by Flow Cytometry ( FC ) in the exponential growth phase . Cells containing one of the abrBtrunc-gfp constructs in combination with the empty pRM3 plasmid displayed a unimodal distribution in GFP levels ( Fig . 3 , lower panels ) . However , when the wt abrBtrunc-gfp was assayed in combination with the wt RnaC/S1022 , a bimodal distribution in AbrBtrunc-GFP levels was observed , including a new peak of lowered fluorescence intensity ( Fig . 3 , top left ) . Interestingly , a unimodal fluorescence distribution was found when the wt abrBtrunc-gfp construct was combined with point-mutated RnaC/S1022* ( Fig . 3 , middle left ) or the mutated abrB*trunc-gfp with the wt RnaC/S1022 ( Fig . 3 , top right ) . In the case of the point-mutated abrB*-gfp construct , however , a bimodal fluorescence distribution was only observed when this construct was combined with the mutated RnaC/S1022* ( Fig . 3 , middle right ) . This implies that a direct mRNA-sRNA interaction takes place between abrB and RnaC/S1022 . Studying the condition-dependency of sRNA expression can give clues to its function and targets . To obtain high-resolution expression profiles , we constructed an integrative RnaC/S1022 promoter-gfp fusion [38] . As expected , the presence of this PRnaC/S1022-gfp fusion caused GFP fluorescence in wild-type cells , but not in cells with a sigD mutation ( Fig . 4D ) . Next , a live cell array approach was used to compare the PRnaC/S1022-gfp activity with that of another SigD-dependent promoter , Phag , which drives flagellin expression . These promoter fusion strains revealed that the expression of hag was consistently ∼4 fold higher than that of RnaC/S1022 ( Fig . 4 ) , which is in agreement with previously published expression data [9] . On LB medium , the expression of both RnaC/S1022 and hag peaked in the late exponential and transition phase , while on both tested minimal media the peak in expression occurred in early exponential phase ( Fig . 4 ) . This higher RnaC/S1022 expression level in the exponential phase on M9 relative to that in LB is in concordance with the stronger effect of ΔRnaC/S1022 on AbrB levels , as indicated by the Northern and Western blot analyses . Experimental methods that measure average protein levels in a population obscure possible cell-to-cell variation . To further study the cell-to-cell variation of AbrB-GFP in the exponential growth phase ( as observed in Fig . 3 ) , we therefore employed a full-length translational abrB-gfpmut3 fusion that was integrated into the chromosome via single cross-over ( Campbell-type ) recombination . Specifically , this integration resulted in a duplication of abrB where one full-length copy of abrB was expressed from its own promoter and fused in-frame to gfp , while the downstream abrB copy was truncated lacking the start codon required for translation [39] . In this AbrB-GFP strain all AbrB monomers have a C-terminally attached GFP molecule . While AbrB-GFP still localized to the nucleoid ( S5 Fig . ) , this AbrB-GFP strain displayed a somewhat reduced growth rate on media where AbrB is required for rapid growth . Since the translational abrB-gfp fusion is chromosomally integrated at the abrB locus , this system is insensitive to fluctuations in noise levels by plasmid copy number variation and its chromosomal location in the division cycle . We first used the AbrB-GFP fusion to test whether B . subtilis Hfq might have an effect of the RnaC/1022-abrB interaction . Consistent with previous studies on other sRNA targets of B . subtilis [5–7] , the direct RnaC/S1022-abrB regulation was found to be independent of Hfq since comparable FC profiles for AbrB-GFP expression were obtained for the parental strain and the hfq deletion mutant ( S6 Fig . ) . Next , we analyzed all AbrB-GFP strains by FC in the exponential phase on both LB and M9G . Noise measurements were not performed on M9S because of the strong growth difference between the parental and ΔRnaC/S1022 strains on this medium ( Fig . 1C ) . We observed that the difference between cells expressing AbrB-GFP at the highest level and those at the lowest level was large ( Fig . 5A ) . This means that AbrB-GFP is expressed with high noise ( quantified as the coefficient of variation; CV% ) . Interestingly , we observed lower AbrB-GFP noise in strains lacking RnaC/S1022 and , crucially , the presence of an additional genomic RnaC/S1022 copy further increased AbrB-GFP noise . Remarkably , increased RnaC/S1022 levels only reduced the minimal expression level of the distribution while not affecting the maximum AbrB-GFP expression level ( Fig . 5A ) , which is consistent with the data presented in Fig . 3 . There was a statistically significant positive linear correlation between RnaC/S1022 levels ( 0 , 1 or 2 genomic copies ) and AbrB-GFP noise ( on LB for pooled data points from Δspo0A and parental backgrounds R2 0 . 48 , P-value <0 . 001 , and M9G R2 0 . 43 , P-value <0 . 001 ) ( Fig . 5B ) . Direct statistical comparisons between AbrB-GFP noise levels at different sRNA levels also revealed significant changes ( Fig . 5 ) . The noise increase therefore seems correlated to the level of RnaC/S1022 . Notably , this relation was also observed for noise measurements in a spo0A deletion background , even though the mean AbrB-GFP expression was between 1 . 37 and 2 . 32 fold ( for LB and M9G respectively , μ = 1 . 79 ) higher in Δspo0A strains . This suggests that RnaC/S1022 has a specific role in noise modulation of AbrB-GFP . After observing that RnaC/S1022 specifically increases AbrB-GFP expression noise , we aimed to elucidate the origin of this AbrB-GFP noise . Three possibilities for noise generation by an sRNA are conceivable . Firstly RnaC/S1022 could have an additional indirect effect on abrB expression , leading to noisy expression from the abrB promoter and subsequent propagation of this noise to the AbrB protein level . Secondly , RnaC/S1022 may itself be expressed either in bimodal fashion or with high noise . The third possibility would be an AbrB-dependent repression of the RnaC/S1022 promoter and subsequent repression of AbrB protein levels by RnaC/S1022 . This double negative repression would correspond to positive feedback on the AbrB protein level , and positive feedback is a known source of expression heterogeneity [40] . To study the distribution of the abrB promoter , we integrated the pBaSysBioII plasmid [38] directly behind the Spo0A binding site in the promoter region of abrB [41] , resulting in a single-copy promoter fusion at the native genomic locus ( PabrB; -41bp of the abrB start codon ) . This location was selected to include the effect of AbrB autorepression and Spo0A ( -P ) repression , while excluding RnaC/S1022 regulation . We observed no bimodal or particularly noisy expression of this abrB promoter fusion , showing that transcription from the abrB promoter is homogeneous in the exponential phase ( Fig . 5C ) . Of note , bimodal or noisy expression of PabrB would have been surprising since transcription of abrB is autorepressed and it is generally found that this NAR reduces the noise of promoter expression [42 , 43] . Interestingly , the expression from the abrB promoter rises with increasing levels of RnaC/S1022 . This observation can be explained by AbrB autorepression and noise . There are more cells with low AbrB levels when the levels of RnaC/S1022 are increased . On average , this will lead to lowered repression of the abrB promoter , leading to a higher level of expression ( but not more noise ) from the abrB promoter ( Fig . 5C ) . This higher expression from the abrB promoter is apparently compensated for at the protein level by the elevated regulation of RnaC/S1022 ( Fig . 2 and 5B ) . Since we observed only a slight increase in abrB promoter noise specific to RnaC/S1022 ( Fig . 5C ) , the hypothesis that AbrB-GFP noise promotion originates from an additional effect of RnaC/S1022 on the abrB promoter can be rejected . A second possibility of noise promotion by RnaC/S1022 is that it is itself expressed with large noise similar to the SigD-dependent hag gene [44] . In this case , large cell-to-cell variation in sRNA levels would only lead to regulation in cells that have above-threshold sRNA levels , and this could generate the variation in AbrB-GFP levels . We tested this at the promoter level by FC analysis of the integrative RnaC/S1022 promoter-gfp fusion ( PRnaC/S1022; Fig . 4 ) and found this promoter fusion to be homogenously expressed with a tight distribution of GFP levels ( CV% of 64% for the M9G condition; Fig . 4D ) . Furthermore , we argued that the relatively low expression of PRnaC/S1022 could result in threshold-level regulation where the sRNA is only involved in regulating abrB in cells with above-threshold levels of RnaC/S1022 . However , this is not consistent with the observation of further increased noise levels in cells with two genomic copies of RnaC/S1022 ( Fig . 5B ) . We therefore consider the possibility of AbrB noise promotion via heterogeneous expression of RnaC/S1022 unlikely . It cannot be excluded , however , that variation in the levels of RnaC/S1022 might be introduced further downstream , for instance via mRNA degradation , or via regulation by a dedicated RNA chaperone . The third option would be a double-negative feedback loop consisting of sRNA repression of AbrB levels and AbrB repression of sRNA levels , which would ultimately lead to an increase in AbrB protein expression noise . This would thus depend on repression of the RnaC/S1022 promoter by AbrB , in addition to the confirmed negative regulation of abrB by RnaC/S1022 . Together this would lead to a decrease in AbrB protein levels in cells that start with below-threshold AbrB levels . First of all , we found no indication for AbrB binding sites in the region upstream of RnaC/S1022 in the dataset of Chumsakul et al . , where the binding sites of AbrB were mapped genome-wide [24] . To test whether the RnaC/S1022 promoter is indeed not directly controlled by AbrB , or possibly under indirect control of AbrB , we deleted the abrB gene from the above-mentioned PRnaC/S1022 promoter fusion strain . As expected , the RnaC/S1022 promoter activity levels were not detectably affected by the abrB deletion ( S7 Fig . ) , showing that it is unlikely that there is a negative feedback loop consisting of AbrB-dependent RnaC/S1022 repression and RnaC/S1022-dependent abrB repression . Since the experimental data presented above pointed to a direct role of the RnaC/S1022 sRNA in AbrB protein noise promotion , we wondered whether this possibility is consistent with mathematical models of sRNA regulation . To verify this , we considered a simple model of RNA regulation with two independently transcribed RNA species ( sRNA and mRNA ) [45–47] . In this model , these molecules are synthesized with constant transcription rates αs and αm , respectively . Translation of mRNA into protein Q , and the degradation of sRNA , mRNA , and protein molecules were modeled as linear processes that occur with rates δ . βs , βm , and γ , respectively . The sRNA-mRNA duplex formation was assumed to be an irreversible second-order process that occurs with a rate κ . In the model , molecules in the sRNA-mRNA duplex were removed from the dynamical system . A summary of all reactions and the master equation used in the model can be found in Fig . 6 . We first implemented model parameters used in an earlier sRNA modeling study by Jia et al . [47] ( Set I; Fig . 6A and D ) . Of note , these parameters were essentially the same as those of Levine et al . [45] . In all cases the sRNA transcription rate ( αs ) was a free variable to capture the effect of 0 , 1 , or 2 genomic copies of RnaC/S1022 . In addition , for each set of parameters we included two possible αm values to model the effect in the Spo0A deletion strain where the abrB transcription rate ( αm ) is approximately two-fold higher than in the parental strain ( as determined with PabrB-gfp ) . Varying extrinsic noise in the abrB transcription rate had no effect on the general modeling outcome ( S8 Fig . ) and the intermediate αm CV% level of 40% was selected for plots in the main text . After running the model with parameters from Set I , we observed that model-predicted protein noise strongly increased with increasing αs . This trend of increasing protein noise with increasing sRNA transcription rates was similar to what we observed for the genomic AbrB-GFP fusion ( Fig . 5A and B ) . Importantly , doubling αm ( two-fold higher mRNA transcription rate ) resulted in a more gradual noise increase with increasing αs , just as was observed in the Δspo0A mutant with the AbrB-GFP fusion ( Fig . 5 ) . We next sought to determine the effect of changing modeling parameters on the modeling outcome , because the selected mRNA half-life of ∼35 min ( βm 0 . 02 ) in parameter Set I would only be relevant for a subset of mRNA molecules as shown experimentally by Hambraeus et al . [48] ( with a relation between these of mean lifetime from Fig . 6A * ln 2 = half-life ) . We therefore constructed a second set of modeling parameters ( Set II ) , which gave the mRNA and sRNA species a half-life of ∼3 . 5 min ( βm and βs 0 . 20 ) while keeping protein half-life at ∼35 min . In addition , αm was increased from 2 transcripts per minute to 4 per minute , and δ was doubled to 2 synthesized proteins per minute . Although the maximum noise level from these Set II simulations was markedly different , it again clearly showed the trend of increasing protein noise with increasing sRNA transcription rates . We can therefore conclude that the modeling results robustly support the idea that sRNA regulation can generate noise at the protein level . This noise would be induced locally at the level of mRNA degradation or translation initiation , and the corresponding fluctuations would subsequently be propagated to the protein level . Recently , the theoretical background of this concept was also reported by Jost et al . , who stated that such behavior is especially expected when the levels of the srRNA and the mRNA are approximately equal [49] . Altogether , our experimental data and the modeling approach are consistent with the view that RnaC/S1022 is an intrinsic noise generator for AbrB-GFP at the post-transcriptional level . After defining the experimental and theoretical framework for noise promotion by the RnaC/S1022 sRNA , we wondered what the physiological relevance of this regulation might be . Since we and others ( Fig . 1A; [22] ) have reported an effect of AbrB levels on the growth of B . subtilis , a growth-related function seemed obvious . We therefore tested whether AbrB levels are a direct determinant of growth rate and yield under the relevant conditions . To do this , we placed the abrB gene under control of an isopropyl ß-D-1-thiogalactopyranoside ( IPTG ) -inducible promoter in the amyE locus using plasmid pDR111 [50] , and subsequently deleted the abrB gene from its native locus in this strain . We first verified the IPTG-dependent expression of AbrB from this construct by growing the parental strain , the ΔabrB strain , and the ΔabrB amyE::abrB strain on LB medium and , in the case of the ΔabrB amyE::abrB strain , the medium was supplemented with increasing IPTG concentrations . Subsequently , AbrB production was assessed by Western blot analysis ( Fig . 7A ) , which showed that AbrB production in the ΔabrB amyE::abrB strain was indeed IPTG-dependent . Notably , the abrB mutant strain displays a growth phenotype on LB medium , but this is only apparent in the late exponential growth phase [51] . To analyze the effect of differing AbrB levels on growth under conditions that are more relevant for the RnaC/S1022—abrB interaction , we grew the same strains on M9G , which was supplemented with differing amounts of IPTG for the ΔabrB amyE::abrB strain . As shown in Fig . 7B , the abrB deletion mutant did not grow in this medium . Importantly however , IPTG-induced expression of abrB in this mutant repaired the growth phenotype in a dose-dependent manner . This shows that the AbrB levels determine the growth rate and yield when cells are cultured on M9G . We next aimed to unravel the effect of sRNA-induced AbrB heterogeneity on growth . This requires the tracking of cells with low and high AbrB-GFP levels over time . To do this , we performed a live imaging experiment with the Δspo0A AbrB-GFP strain either containing zero sRNA copies due to the ΔRnaC/S1022 mutation , or two genomic copies due to the insertion of an additional RnaC/S1022 copy in amyE . The Δspo0A background was used to elevate AbrB-GFP levels and thereby to facilitate fluorescence measurements . Cells were pre-cultured in M9G as was done for the FC measurements and applied to agarose pads ( at OD600 ∼0 . 15 ) essentially as was described by Piersma et al . [52] . From these experiments , and consistent with FC data in Fig . 5 , it was apparent that there was a larger variation in AbrB-GFP levels in the strain with two genomic RnaC/S1022 copies , compared to the strain lacking RnaC/S1022 ( Fig . 8B; S1 Movie ) . In addition , this variation in AbrB-GFP levels was correlated to the variation in growth rates ( quantified as the specific cell length increase ) observed during the first 20 min of each live imaging run ( Fig . 8A ) . We excluded the possibility that this growth rate difference was dependent on the position on , or quality of the slide . Instead , it was solely linked to the cellular level of AbrB-GFP ( Fig . 8C; S1 Movie ) . Notably , in the two example cells from Fig . 8C ( S1 Movie ) AbrB-GFP levels gradually increase in the cell with a low start level ( i . e . high level of sRNA repression ) , which would be consistent with a gradual reduction in RnaC/S1022 expression on this solid agarose medium ( S9 Fig . ) . However , our experimental setting determines the effect of AbrB-GFP on growth before this reduction in RnaC/S1022 becomes relevant ( e . g . the first 5 pictures , or 20 min ) ( Fig . 8A ) . Beyond this , the increase in AbrB-GFP levels observed later ( >150 min ) in the live imaging experiment seems coupled to a concomitant increase in growth rate ( S9 Fig . ) . This is again consistent with the positive correlation of AbrB-GFP levels with growth rate . Interestingly , while we observed a few cells switching their AbrB-GFP expression state from high to low , the AbrB-GFP levels were generally stable throughout a cell’s lineage . Combined , these analyses show that the RnaC/S1022-induced heterogeneity in the AbrB-GFP expression levels generates diversity in growth rates within the exponential phase of growth .
In this study we show that B . subtilis employs the RnaC/S1022 sRNA to post-transcriptionally regulate AbrB and that this regulation results in increased heterogeneity in growth rates during the exponential phase of growth . RnaC/S1022 is the third sRNA in B . subtilis for which a direct target has been reported and this study reveals the value of evolutionary target predictions to identify true sRNA targets for this species . The observed growth rate heterogeneity induced by RnaC/S1022 is conceivably of physiological relevance since slowly growing bacterial cells are generally less susceptible to antibiotics and other environmental insults than fast growing cells [53–55] . Specifically , it was noted for hip strains of E . coli that slowly growing cells within a population will develop into persister cells when challenged with ampicillin [17] . Notably , in this system , the initial heterogeneity in growth rates was reported to be dependent on the HipAB toxin-antitoxin module [56] . Analogously , it is conceivable that a B . subtilis toxin-antitoxin module under negative AbrB control could be responsible for the heterogeneity observed in the present study . Another perhaps more likely possibility is that low AbrB levels cause the premature activation of transition- or stationary phase genes , thereby slowing down growth and causing premature stationary phase entry . AbrB has also been implicated in the activation of some genes when CCR is relieved [22 , 23] , and this could be related to the stronger growth phenotype observed on M9S compared to M9G . However , the AbrB level also determines growth rates on M9G ( this study; [22] ) , when CCR is active and AbrB is not known to have an activating role [22] . The initially observed growth phenotype of the ΔRnaC/S1022 mutant can be explained by the present observation that AbrB is an important determinant for growth on M9 medium , and that RnaC/S1022 regulation of AbrB is specifically linked to increasing AbrB noise . Specifically , the absence of RnaC/S1022 will reduce the number of cells expressing AbrB at a low level . Growth of the ΔRnaC/S1022 population will therefore be more homogeneous and , when inspected as an average , the population will enter stationary phase later than the parental strain . Beyond the mechanism of AbrB-mediated growth regulation , we show that noisy regulation of a growth regulator can also cause heterogeneity in growth rates . This suggests that the AbrB noise level has been fine-tuned in evolution , possibly as a bet-hedging strategy to deal with environmental insults . Two other questions addressed by this study are the origin of AbrB expression noise , and the likely reason why this noise is generated at the post-transcriptional level . The origin of AbrB expression noise via triggering of abrB mRNA degradation and/or inhibition of abrB translation fits the definition of an intrinsic noise source where the absence of RnaC/S1022 reduces the number of sources for intrinsic noise by one , and therefore results in lower protein expression noise . This specific noise-generating capacity of sRNA regulation might be due to the specific kinetics of the RnaC/S1022- abrB mRNA interaction . It is currently unclear whether this feature of sRNA-mediated regulation can be extended to other sRNA-mRNA pairs . Specifically , subtle consequences of sRNA regulation , such as noise generation , may have been overlooked in previous studies due to the use of plasmid-encoded translational fusions with fluorescent proteins expressed from strong non-native promoters as reporters . We therefore expressed all RnaC/S1022 and AbrB-GFP constructs from their native genomic location , from their native promoters , and assayed the effects in the relevant growth phase . NAR of AbrB seems to be the answer to the second question why noise is generated post-transcriptionally and not at the promoter level . AbrB’s NAR is important for its functioning in the stationary phase sporulation network [26 , 27] and is therefore likely a constraint for evolutionary optimization of AbrB expression in the exponential phase , which is the growth phase addressed in this study . In turn , NAR is a clear constraint on noise generation since it is generally believed to dampen noise [42 , 43] . Consistent with this view , we observed only a slight increase in PabrB promoter noise upon increasing AbrB protein noise , suggesting that AbrB NAR is responsible for minimizing promoter noise . Besides reducing noise , NAR has been implicated in decreasing the response time of a genetic circuit , linearizing the dose response of an inducer , and increasing the input dynamic range of a transcriptional circuit [19] . Individually , and in combination , these mechanistic aspects of NAR could explain why NAR is such a widespread phenomenon in transcriptional regulation . Besides this , the idea that AbrB and AbrB NAR are more widely conserved than RnaC/S1022 would be in line with the idea that AbrB expression in B . subtilis 168 has become fine-tuned by an additional regulator , which has evolved later in time . Lastly , on a more general note , the inconsistency between the abrB promoter and AbrB protein noise measurements make it clear that it is premature to draw conclusions about homogeneity or heterogeneity of protein expression when only data is gathered at the promoter level , especially for genes under a NAR regime . In conclusion , we have identified a novel direct sRNA target in the important B . subtilis transcriptional regulator abrB . Specifically , we provide functionally and physiologically relevant explanations for the evolution of the noise-generation aspects of this regulation in generating heterogeneity in growth rates . This noise is induced at the post-transcriptional level due to AbrB NAR . Based on our present observations , we hypothesize that the resulting subpopulations of fast- and slow-growing B . subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions .
E . coli and B . subtilis strains and plasmids used in this study are listed in S2 Table and oligonucleotides in S3 Table . E . coli TG1 was used for all cloning procedures . All B . subtilis strains were based on the trpC2-proficient parental strain 168 [1] . B . subtilis transformations were performed as described previously [57] . The isogenic RnaC/S1022 mutant was constructed according to the method described by Tanaka et al . [58] . pRMC was derived from pXTC [59] by Circular Polymerase Extension Cloning ( CPEC ) [60] with primers ORM0054 and ORM0055 using pXTC as PCR template and ORM0056 to circularize this PCR fragment in the final CPEC reaction . In this manner , the xylose-inducible promoter of pXTC was replaced with the AscI Ligation Independent Cloning ( LIC; [61] ) site from pMUTIN-GFP [39] . As a consequence , pRMC carries a cassette that can be integrated into the amyE locus via double cross-over recombination , allowing ectopic expression of genes in single copy from their native promoter . RnaC/S1022 was cloned in pRMC under control of its native promoter as identified by Schmalisch et al . [28] , and the subsequent integration of RnaC/S1022 into the amyE locus via double cross-over recombination was confirmed by verifying the absence of α-amylase activity on starch plates . The LIC plasmid pRM3+Pwt RnaC/S1022 , which is a derivative of plasmid pHB201 [51] , was used to express RnaC/S1022 under control of its native promoter . For IPTG-inducible expression of abrB , the abrB gene was cloned into pDR111 [50] , and subsequently placed in the amyE locus via homologous recombination . Deletion alleles were introduced into this and other strains by transformation with chromosomal DNA containing the respective mutations . The RnaC/S1022 , hag and abrB promoter gfp fusions were constructed at the native chromosomal locus by single cross-over integration of the pBaSysBioII plasmid [38] . A minimum of three clones were checked to exclude possible multi-copy integration of the plasmid . Lysogeny Broth ( LB ) consisted of 1% tryptone , 0 . 5% yeast extract and 1% NaCl , pH 7 . 4 . M9 medium supplemented with either 0 . 3% glucose ( M9G ) or 0 . 3% sucrose ( M9S ) was freshly prepared from separate stock solutions on the day of the experiment as previously described [9] . For live cell imaging experiments , the M9 medium was filtered through a 0 . 2 μm Whatman filter ( GE Healthcare ) . Strains were grown with vigorous agitation at 37°C in either Luria LB or M9 medium using an orbital shaker or a Biotek Synergy 2 plate reader at maximal shaking . Growth was recorded by optical density readings at 600 nm ( OD600 ) . For all growth experiments , overnight B . subtilis cultures in LB with antibiotics were diluted >1:50 in fresh prewarmed LB medium and grown for approximately 2 . 5 hours . This served as the pre-culture for all experiments with cells grown on LB medium . For experiments with cells grown on M9 medium , the LB pre-culture was subsequently diluted 1:20 in pre-warmed M9 medium and incubated for approximately 2 . 5 hours , which corresponds to mid- or early exponential growth . This culture then served as the pre-culture for experiments with cells grown on M9 medium . When required , media for E . coli were supplemented with ampicillin ( 100 μg ml−1 ) or chloramphenicol ( 10 μg ml−1 ) ; media for B . subtilis were supplemented with phleomycin ( 4 μg ml−1 ) , kanamycin ( 20 μg ml−1 ) , tetracyclin ( 5 μg ml−1 ) , chloramphenicol ( 10 μg ml−1 ) , erythromycin ( 2 μg ml−1 ) , and spectinomycin ( 100 μg ml−1 ) or combinations thereof . In order to find predicted targets co-conserved with RnaC/S1022 , we used the 62 Bacillus genomes available in Genbank ( as of January 31 , 2013 ) . On each of these genomes a BLAST search ( Blastn v2 . 2 . 26 with default parameters ) was conducted with the B . subtilis 168 RnaC/S1022 sequence as identified in Nicolas et al . [9] . Genomes where a homologue of RnaC/S1022 ( E-value < 0 . 001 ) was found were then subjected to TargetRNA_v1 search with extended settings around the 5’UTR ( −75 bp; +50 bp around the start codon and additional command line arguments “-z 250 -y 2 -l 6” ) using as query the sequence of the first high-scoring-pair of the first BLAST hit in that particular genome . A bidirectional best hit criterion ( based on Blastp v2 . 2 . 26 with default parameters and E-value cut-off 0 . 001 ) was used to compare the predicted targets in each genome with the predicted targets in the reference B . subtilis 168 genome ( Genbank: AL009126-3 ) . The data was tabulated and subsetted for B . subtilis 168 genes predicted for RnaC/S1022 in 8 or more genomes . The Bacillaceae phylogenetic tree was computed based on an alignment of the rpoB gene BLAST result from the same set of genomes mentioned above . RpoB was reported to be a better determinant of evolutionary relatedness for Bacillus species than 16S rRNA [62] . Cultures grown on LB , M9G , or M9S were sampled in mid-exponential growth phase ( OD600 0 . 4–0 . 6 ) and were directly harvested in killing buffer and processed as previously described [9] . Northern blot analysis was carried out as described previously [63] . The digoxigenin-labeled RNA probe was synthesized by in vitro transcription with T7 RNA polymerase and an abrB specific PCR product as template . 5 μg of total RNA per lane was separated on 1 . 2% agarose gels . Chemiluminescence signals were detected using a ChemoCam Imager ( Intas Science Image Instruments GmbH , Göttingen , Germany ) . Western blot analysis was performed as described [64] using crude whole cell lysates . To prepare lysates , cell pellets were resuspended in LDS-sample buffer with reducing agent ( Life technologies ) , and disrupted with glass beads in a bead beater ( 3 x 30 sec at 6500 rpm with 30 sec intermittences ) . Before loading on Novex nuPAGE 10% Bis-Tris gels ( Life technologies ) , samples were boiled for 10 min and centrifuged to pellet the glass beads and cell debris . Equal OD units were loaded on gel and the intensity of the AbrB band was corrected with the intensity for the unrelated BdbD control . Data from Northern blots and Western blots were quantified ImageJ software ( available via http://rsbweb . nih . gov/ij/ ) . Rifampicin ( Sigma Aldrich ) was added to 100 ml of exponentially growing M9G culture to a final concentration of 150 μg/ml from a 100x stock solution in methanol stored at −20°C . Just before the rifampicin addition and at 1 , 2 , 4 , 6 , 8 and 10 min after rifampicin addition , 10 ml of cells were harvested in killing buffer as described previously [9] . Cell pellets were washed once with 1 ml killing buffer and frozen in liquid nitrogen . RNA was extracted according to the hot phenol method as described previously [63] . Quantitative PCR was performed as described by Reilman et al . [51] . The Ct value corresponds to the PCR cycle at which the signal came above background . We analyzed the four mRNA decay time-series ( two strains and two replicates ) with a non-linear model of mRNA concentration described in [65] that aims at capturing initial exponential decay followed by a plateau . The rate of the initial decay is supposed to correspond to the physiological degradation of the mRNA . In contrast , the final plateau can be contributed by several factors , such as background noise in measurement , a stable subpopulation of molecules , or a higher stability of the mRNAs at the end of the dynamic . In our context , we assumed that the mRNA concentration is proportional to 2-Ct and thus we fitted ( with the nls function of the R package stats ) the model-Ct ( ti ) = log2 ( A* ( α1exp ( -γ1ti ) +α2 ) ) + εi , for i = 1…7 ( ti = 0 , 1 , 2 , 4 , 6 , 8 , 10 min ) with A>0 , α1>0 , α2>0 , γ1>0 , α1+α2 = 0 and εi a Gaussian white noise . The estimates of the γ1 parameters of the first model were compared between the two genetic backgrounds ( 0 genomic copies vs . 2 copies of RnaC/S1022 ) with a student t-test after a log-transformation to stabilize the variance . For the 2-copy background , we also examined a second model that involves two exponential decay terms as would for instance arise when two sub-populations of mRNAs with distinct degradation rates coexist . It writes-Ct ( ti ) = log2 ( A* ( α1exp ( -γ1ti ) +α2exp ( -γ2ti ) +α3 ) ) + εi with A>0 , α1>0 , α2>0 , α3>0 , γ1>γ2>0 , and α1+α2+α3 = 0 . For each pair of background and model , we plotted a “consensus” line whose parameters were obtained from the geometric mean between the two replicate experiments . Promoter activity was monitored every 10 min from cells grown in 96-well plates in a Biotek® Synergy 2 plate reader . Promoter activity was computed by subtracting the fluorescence of the previous time-point from that of the measured time-point ( as in Botella et al . [38] ) . Moving average filtering ( filter function in R with filter = rep ( 1/5 , 5 ) was applied for smoothing of the promoter activity plots . Cultures grown on LB , M9G , or M9S were sampled in mid-exponential growth phase OD600 0 . 4–0 . 5 and were directly analyzed in an Accuri C6 flow cytometer . The number of recorded events within a gate set with growth medium was 15 , 000 . The coefficient of variation ( i . e . relative standard deviation ) ( CV%; standard deviation / mean * 100% ) was used as a measure of the width of the distribution , or protein/promoter expression noise . To inspect co-localization of AbrB-GFP with the nucleoid , cells were cultured until the exponential growth phase , pelleted by centrifugation , resuspended in 400μl phosphate-buffered saline ( PBS ) containing 1μl 500 ng/μl 4' , 6-diamidino-2-phenylindole ( DAPI ) , and incubated for 10 min on ice . After this , the cells were washed once with PBS and slides were prepared for microscopy . Live imaging analysis was conducted on aerated agarose cover slips as described previously [52] . Segmentation , calculation of Feret diameter , and auto-fluorescence correction for every microcolony were performed with ImageJ also as described by Piersma et al . [52] . Subsequent computations and plotting was done with R . The specific cell length ( Feret diameter ) increase per hour was computed as follows: ( ( cumulative Feret diameter at t20 min / number of cells at t0 min ) – ( cumulative Feret diameter at t0 min / number of cells at t0 min ) ) / ( ( t20 min—t0 min ) / 60 min ) . Noise promoting dynamics by sRNA regulation was modeled in a stochastic simulation model [45–47] . The considered reactions , employed parameters , and the master equation are listed in Fig . 6 . The master equation was numerically integrated by employing an in-house developed implementation of the Gillespie algorithm [66] for each combination of model parameters . The stochastic simulations were started without any molecules and were run until a quasi-stationary state was reached . To capture the inherent stochasticity of the model we performed , for each set of model parameters , 50 x 10 , 000 simulation replicates ( i . e . 500 , 000 in total ) . This can be interpreted as 50 experiments involving 10 , 000 cells each . Mean , standard deviation , and the median was computed for every molecular species in the population of 10 , 000 cells . | Bacterial cells that share the same genetic information can display very different phenotypes , even if they grow under identical conditions . Despite the relevance of this population heterogeneity for processes like drug resistance and development , the molecular players that induce heterogenic phenotypes are often not known . Here we report that in the Gram-positive model bacterium Bacillus subtilis a small regulatory RNA ( sRNA ) can induce heterogeneity in growth rates by increasing cell-to-cell variation in the levels of the transcriptional regulator AbrB , which is important for rapid growth . Remarkably , the observed variation in AbrB levels is induced post-transcriptionally because of AbrB’s negative autoregulation , and is not observed at the abrB promoter level . We show that our observations are consistent with mathematical models of sRNA action , thus suggesting that induction of protein expression noise could be a new general aspect of sRNA regulation . Since a low growth rate can be beneficial for cellular survival , we propose that the observed subpopulations of fast- and slow-growing B . subtilis cells reflect a bet-hedging strategy for enhanced survival of unfavorable conditions . |
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Due to the omnipresent risk of epidemics , insect societies have evolved sophisticated disease defences at the individual and colony level . An intriguing yet little understood phenomenon is that social contact to pathogen-exposed individuals reduces susceptibility of previously naive nestmates to this pathogen . We tested whether such social immunisation in Lasius ants against the entomopathogenic fungus Metarhizium anisopliae is based on active upregulation of the immune system of nestmates following contact to an infectious individual or passive protection via transfer of immune effectors among group members—that is , active versus passive immunisation . We found no evidence for involvement of passive immunisation via transfer of antimicrobials among colony members . Instead , intensive allogrooming behaviour between naive and pathogen-exposed ants before fungal conidia firmly attached to their cuticle suggested passage of the pathogen from the exposed individuals to their nestmates . By tracing fluorescence-labelled conidia we indeed detected frequent pathogen transfer to the nestmates , where they caused low-level infections as revealed by growth of small numbers of fungal colony forming units from their dissected body content . These infections rarely led to death , but instead promoted an enhanced ability to inhibit fungal growth and an active upregulation of immune genes involved in antifungal defences ( defensin and prophenoloxidase , PPO ) . Contrarily , there was no upregulation of the gene cathepsin L , which is associated with antibacterial and antiviral defences , and we found no increased antibacterial activity of nestmates of fungus-exposed ants . This indicates that social immunisation after fungal exposure is specific , similar to recent findings for individual-level immune priming in invertebrates . Epidemiological modeling further suggests that active social immunisation is adaptive , as it leads to faster elimination of the disease and lower death rates than passive immunisation . Interestingly , humans have also utilised the protective effect of low-level infections to fight smallpox by intentional transfer of low pathogen doses ( “variolation” or “inoculation” ) .
The first encounter of a host with a particular pathogen often leads to the outbreak of the disease , yet a secondary exposure rarely causes illness , due to the immunological memory of the host . Whereas immune memory in vertebrates is well appreciated [1] , the phenomenon of an individual developing specific immunity against a subsequent pathogen exposure—referred to as immune priming—has only recently been described in invertebrates , both within the lifetime of an individual [2]–[8] and in transgenerational protection of offspring ( [8]–[12] , but see [13] ) . In contrast to vertebrates , the underlying mechanisms are not yet understood in invertebrates [14] , [15] . In addition to this immunological memory at the level of individuals , a similar phenomenon occurs at the colony level in insect societies [16]–[18] . Society members act collectively , similar to cells in a body , and work as a superorganism [19] , [20] in multiple aspects , including anti-pathogen defence [21] . For instance , an initial pathogen contact of a colony due to the presence of exposed individuals has been shown to lower the susceptibility of their nestmates to infection when they are later exposed to the same pathogen [16]–[18] . In addition to this physiological “social immunisation , ” the collectively performed hygiene behaviour that complements individual defences in social insects [22]–[24] is also affected . Allogrooming of exposed individuals by their nestmates occurs more frequently in colonies with previous experience with this pathogen than in naive colonies [25] , [26] . In contrast to individual immune priming , social immunisation thus refers to a protection of naive individuals of a colony after social contact to exposed individuals . The phenomenon of social immunisation occurs broadly in insect societies—in unrelated social host species ( ants and termites ) and against divergent pathogen taxa ( fungi [17] , [18] and bacteria [16] ) —yet the mechanisms underlying this effect are largely elusive ( but see [16] ) and have only been hypothesised upon for fungal pathogens [3] , [17] , [18] , [27] . In this study , we therefore aimed to determine the underlying causes of social immunisation in colonies of the ant Lasius neglectus after exposure of single individuals to the entomopathogenic fungus Metarhizium anisopliae , a common natural pathogen of ants [28] , [29] . In this system , we have previously described that 5 d of social contact to an individual exposed to fungal conidia ( conidiospores; [30] ) led to a lower susceptibility of nestmate ants when challenged with a high fungal dose after this period [18] . It remained open , however , which social interactions may trigger this effect and how they elicit changes in nestmate immunity . The observed protection in nestmates of exposed ants may be caused by the active upregulation of their own immune systems following social contact to the fungus-exposed individual . Alternatively , social transfer of immune mediators produced by colony members may lead to passive protection of nestmates without requiring the activation of their own immune systems ( as outlined by [3] , [17] , [27] ) . The active and passive route to social immunisation may also act in concert . Active upregulation of the nestmates' immune system may be caused by perception of a trigger signal elicited from the exposed individual , possibly of behavioural or chemical nature . In humans , mere visual perception of sick individuals was recently shown to cause preventive stimulation of the immune system [31] . Similarly , in plants , herbivory defence was promoted by perception of volatile chemical cues elicited by an attacked neighbouring plant [32] . Active stimulation of the immune system can also be caused by low-level infections [3] , [8] , [33] , [34] , which may result from social transfer of the pathogen from the exposed individual to its nestmates ( as suggested by [3] ) , occurring during “normal” social interactions , or as a byproduct of collective sanitary behaviour such as allogrooming of the exposed individual by its nestmates [22] , [35] . Passive immunisation may result from a social exchange of antimicrobials produced by the exposed individuals and transferred to their nestmates . Possible transfer pathways include the “external route” over the body surface or the “internal route” by exchange of body fluids [16] . The external body surface ( cuticle ) of ants is covered with antimicrobial substances produced in an ant-specific gland ( metapleural gland [36] , [37] ) and nestmates could easily pick up these substances and apply them on their own bodies by allo- and self-grooming . Immune effectors produced inside the body of infected individuals may be exchanged during the common social feeding behaviour of regurgitation and feeding of trophallactic droplets [16] , [38] , as has recently been suggested as a mechanism for social immunisation of ant colonies after bacterial exposure [16] . Whereas bacterial infections are typically orally transmitted [39] , entomopathogenic fungi are externally transmitted , making distinct disease dynamics of these pathogen taxa likely . In this study , we applied a multi-level approach to determine the functional mechanism of social immunisation of ant colonies against a fungal pathogen . We analysed the behavioural interaction rates between group members and determined whether social contact may lead to exchange of the pathogen or immune effectors , or whether social immunisation may be triggered by social signals . We determined both the physiological immunity of fungus-exposed individuals and their nestmates , as well as their immune gene expression . Lastly , we developed an epidemiological model to explore long-term colony-level effects of social immunisation depending on the underlying mechanisms .
We have previously shown that social contact to a Lasius worker exposed to conidia ( dispersal form , conidiospores; [30] ) of the entomopathogenic fungus M . anisopliae , but not to control-treated ants , increased the survival of previously naive nestmates when challenged with the same M . anisopliae strain 5 d later [18] . We now directly assessed the immune function of nestmates with a novel and sensitive “antifungal activity assay . ” We incubated ant tissue with blastospores ( within-host infection form; [30] ) of the fungus to measure the ability of ants to inhibit fungal growth . We found a significantly higher antifungal activity in nestmates of fungus-exposed as compared to nestmates of control-treated individuals ( Figure 1 ) . This was true not only after 5 d of social contact to an exposed individual , but already after 3 d ( GLM , F = 3 . 859 , df = 3 , p = 0 . 017; treatment type [fungus treatment versus sham control]: F = 10 . 634 , df = 1 , p = 0 . 002; time [3 versus 5 d post-treatment]: F = 0 . 001 , df = 1 , p = 0 . 973; interaction [Treatment Type×Time]: F = 0 . 942 , df = 1 , p = 0 . 338 ) . To understand the mechanism behind increased antifungal defence in nestmates of exposed ants , it is important to study the behaviour of group members . First , behavioural changes of individuals after fungal exposure may be a signal to their nestmates to upregulate their immune system . Second , the social interactions define the routes and opportunities for potential exchange of immune effectors [40] , [41] or the pathogen itself [42] . Compared to control-treated ants , which did not elicit social immunisation in their nestmates , fungus-exposed ants did not show significantly changed rates of either brood care behaviour [18] or self-grooming activity ( LVU , unpublished data ) . Similarly , other studies found that pathogen exposure had no effect on self-grooming [26] or only when doses present in the colony were very high [25] . This makes it unlikely that nestmates may have perceived a trigger signal by social interaction or potential observation of the individual behaviour of exposed ants . To obtain information on possible pathways for transfer of the pathogen or immune mediators , we analysed the social interactions between colony members in more detail . As in our original experimental setup we grouped five naive nestmates with a single treated Lasius worker that had either received infectious M . anisopliae conidia ( fungus treatment ) or the same treatment without the pathogen ( sham control ) . We observed three types of social interactions between group members . Antennation behaviour—that is , nestmate recognition behaviour by antennal contact [43]—occurred extremely rarely ( 6 . 6% of all interactions ) . Moreover , rates did not differ between treated and nestmate ants or among nestmates , for both fungus treatment and sham control ( Generalised Linear Model [GLM] with negative binomial errors , LR χ2 = 1 . 969 , df = 3 , p = 0 . 579; data not shown ) . All other social interactions observed between group members consisted of ( a ) allogrooming ( i . e . , cleaning the body surface of another ant ) and ( b ) trophallaxis behaviour ( i . e . , exchange of regurgitated liquid food droplets ) [43] . Both may be important pathways for social immunisation [3] , [16] , [17] , [27] . It is well known that nestmates actively contact exposed individuals and remove infectious material with their mouth by allogrooming , which is a very efficient social sanitary behaviour [43] , [44] increasing survival of pathogen-exposed individuals , but typically not compromising the survival of the nestmates [25] , [35] , [45] , [46] . Still , the grooming ant may contract the pathogen if it is not able to kill all infectious material in its mouth ( infrabuccal pockets; [47] , [48] ) or gut [49] , or if it unintentionally rubs off conidia with other body parts than the mouth during this intimate social interaction . In addition , allogrooming may lead to uptake of antimicrobial substances from the body surface of an exposed individual similar to exchanges of cuticular waxes important for nestmate recognition [50] . In our experiment , allogrooming rates between treated individuals and their nestmates were higher than among nestmates , but independent of the treatment type ( fungus versus sham control; Figure 2A; GLM with negative binomial errors , LR χ2 = 15 . 134 , df = 3 , p = 0 . 002; ant pairing [treated-nestmate versus nestmate-nestmate]: Wald χ2 = 14 . 501 , df = 1 , p<0 . 001; treatment type [fungus versus sham control]: Wald χ2 = 0 . 006 , df = 1 , p = 0 . 939 ) . Upregulation of grooming frequency not only against individuals treated with infectious material but also with sham control solutions is known from previous studies [29] , [51] and indicates that ants are very sensitive to applications on the bodies of their group members . Despite the lack of difference between the two treatment types , intensive grooming towards treated individuals provides a potential route for transfer of either the pathogen itself or external immune effectors . One important factor is the timing of allogrooming expression during the infection course of M . anisopliae . Entomopathogenic fungi like M . anisopliae infect their hosts by external adhesion onto and active penetration of the cuticle [52] . After contact to the insect cuticle , the conidia first adhere loosely to the body surface within several hours and then germinate and form a penetration plug to actively enter the host body within approximately 24 to 48 h [46] , [53] . Infection of the host and onset of an active immune response therefore occurs with a time delay of 2 to 3 d after exposure [54] , [55] . Allogrooming in the first 1 to 2 d would therefore allow for pathogen transfer , whereas after this time exposed ants lose their infectiousness [26] . Intensified allogrooming 3 or 4 d after exposure would instead indicate exchange of external antimicrobial substances . We analysed the time course of allogrooming frequency between treated individuals and their nestmates and found no change over time in the control treatment ( GLM with repeated measures , time: F = 0 . 973 , dfHuynh-Feldt = 3 . 648 , p = 0 . 416 ) . Allogrooming between nestmates and fungus-exposed individuals , however , was significantly higher in the first 2 d compared to later phases of the experiment ( Figure 2B; time: F = 4 . 006 , dfHuynh-Feldt = 3 . 306 , p = 0 . 006 [day1 versus day2: p = 0 . 178; day1 versus day3: p = 0 . 041; day1 versus day4: p = 0 . 001; day1 versus day5: p = 0 . 014] ) . Based on these data we suggest that if a transfer between group members occurs via allogrooming , it more likely involves a transfer of conidia , detachable early after exposure , than immune effectors , which can only be upregulated and transferred to the cuticle after infection of the individual 24–48 h after exposure . Social feeding via regurgitation and transfer of a trophallactic droplet may promote transfer of internal antimicrobial substances [16] . However , we found no differences in the rates of trophallaxis among all four groups , that is , neither between treated ants and their nestmates nor among the nestmates in either the fungus treatment or the control group ( Figure 2C; GLM with negative binomial errors , LR χ2 = 2 . 555 , df = 3 , p = 0 . 465 ) . Our data show that fungal exposure does not alter trophallaxis rates between exposed individuals and their nestmates , making passive immunisation by transfer of internally produced antimicrobial substances rather unlikely in our model system . Our findings after fungal exposure contrast with observations that trophallaxis rates between individuals injected with dead bacteria or bacterial cell wall components ( but also wounding controls ) were increased compared to trophallaxis rates among untreated individuals ( [16] , [56] , but see [57] ) . Taken together , our behavioural observations strongly suggest exchange of the fungal pathogen between the fungus-exposed ant and its nestmates during intensified , early grooming as the most likely mechanism for the observed anti-fungal protection in the nestmates . We therefore determined if fungal conidia indeed were transferred from the exposed individual to its untreated nestmates by direct tracing of fluorescently labelled conidia . We applied conidia of M . anisopliae labelled with red fluorescent protein ( RFP ) onto the exposed ant and determined their presence or absence on the cuticle of all group members after 2 d of social contact . We expected maximum pathogen transfer to have occurred at this time as ( a ) grooming activity between exposed ants and their nestmates is most intense in the first 30 h ( Figure 2B ) and ( b ) conidia are no longer transferable after this time [26] , [53] . As expected we found high amounts of conidia on all directly exposed individuals ( 15/15 ) and furthermore detected low numbers of conidia on the cuticles of 37% ( 17/45 ) of nestmates ( Figure S1; for negative controls see Materials and Methods ) . Interestingly , not only the quantity but also the location of conidia differed: whereas directly exposed individuals carried them mostly in areas likely difficult to reach by grooming such as joints and the antennal grooves , conidia on nestmates were rather attached to antennae and legs ( Figure S1 ) , suggesting that nestmates pick up the pathogen from the fungus-exposed individual during grooming . We can thus confirm pathogen transfer to the nestmates . In a next step we determined if the transferred conidia successfully established an infection in the nestmates . To quantitatively determine the infection load of directly fungus-exposed individuals and their nestmates over the course of the experiment , we sterilised their body surface to destroy all remaining conidia , dissected the ants , and plated their body contents on agar plates to count emerging fungal colony forming units ( CFUs ) . We used morphological determination , as well as PCR [58] , to confirm that outgrowing CFUs were indeed M . anisopliae , which was the case for all CFUs ( see Figure S2 as an example ) . None of the 30 negative controls ( see Materials and Methods ) and none of the individuals measured within 24 h after exposure ( 0/10 fungus-treated , 0/14 nestmates; Figure S3 ) showed fungal growth , confirming that we effectively sterilised the ants and measured only live fungus from inside the body . Three as well as five days after exposure , CFUs grew from the body content of nearly all directly exposed ants ( 80% [8/10] and 90% [9/10] ) and a similarly high number of nestmates ( 64% and 64% [each 9/14]; Figures 3 , S3; Fisher's exact test; day 3 , p = 0 . 653; day 5 , p = 0 . 341 ) . These data show that fungal infections in nestmates were more common than estimated from external pathogen transfer using labelled conidia . This may either indicate that we did not detect all conidia or that an additional infection route via the infrabuccal pocket in the mouth or the gut system occurred , for instance if groomed-off conidia were not completely prevented from germinating [47]–[49] . Fungal infection load in nestmates revealed that their infections were “low-level infections . ” The number of CFUs growing out of their bodies when infected was significantly lower than those growing from directly exposed ants at both day 3 ( Figures 3A , S3; Mann-Whitney U-test: n1 = 8 , n2 = 9 , U = 4 . 0 , p = 0 . 002 ) and day 5 ( Figures 3B , S3: n1 = 9 , n2 = 9 , U = 0 . 0 , p<0 . 001 ) . On average , the infection load of infected nestmates was 8 ( 4 . 4 versus 36 . 0 ) and 12 ( 8 . 1 versus 102 . 4 ) times smaller than that of directly exposed individuals on days 3 or 5 , respectively . Even if low-level infections occurred in the majority of nestmates , only 2% ( 3/150 ) died from a M . anisopliae infection after 5 d of social contact with the exposed individuals ( who showed death rates of approximately 50% due to application of an LD50 ) . This confirms that the effects of M . anisopliae infections are highly dosage dependent ( [35] and MKo and STr , unpublished data ) . To determine if the observed increase in antifungal activity of nestmates was a direct cause of these low-level infections , we established low-level infections in individuals in the absence of social interactions . To this end , we exposed isolated ants with a conidia dose that led to the same death rate ( LD2 ) and infection level as observed in the socially exposed nestmates . We found that low-dose , directly exposed ants had a significantly increased antifungal activity 3 d after exposure compared to control-treated ants ( Figure 4 ) . Interestingly , directly exposed individuals with a high dose ( LD50; as used for exposure of the single ants in our experiment above ) showed a significantly decreased capacity to inhibit fungal growth ( Figure 4; ANOVA: F = 10 . 361 , df = 2 , p<0 . 001; post hoc Protected Fisher's LSD tests all pairwise: sham control versus LD2: p = 0 . 046 , sham control versus LD50: p = 0 . 021; LD2 versus LD50: p<0 . 001 ) . This immune-suppressive effect of a high-dose infection is likely caused by the immune-interference and toxicity of M . anisopliae or by the fact that the immune responses had been depleted [41] , [59]–[61] . Immune stimulation of low-level infections has previously been described for both vertebrates and invertebrates [3] , [8] , [33] , [34] , and its protective effect yielded clinical application in humans [62] , [63] and poultry health management [64] . We have established that low-level infections , caused by social contact or direct low-dose exposure , lead to increased antifungal activity . Yet this does not exclude that nestmates with social contact to an exposed individual may also obtain signals that could actively trigger their antifungal immunity ( similar to [31] , [32] ) . To test this , we performed a “spatial-separation experiment” in which body contact and pathogen transfer to the exposed individual were prevented , whereas exchange of visual signals or volatile chemicals was still possible . The antifungal activity of nestmates of fungus-exposed individuals did not differ from that of nestmates of control-treated ants after 3 d of this constrained contact ( t test: t = −0 . 376 , df = 18 , p = 0 . 711 ) . These data suggest that a visual or volatile signal alone—at least one that acts over distance—is not sufficient to promote antifungal activity in the nestmates . Non-volatile chemical signals , such as cuticular hydrocarbons [65] that are part of the ants' cuticle , may in theory still play an additional role . However , their perception would always require body contact , which promotes pathogen transfer at the same time . We conclude that low-level infections alone provide a sufficient explanation for an active social immunisation of nestmates . We then tested if it may be complemented by a passive transfer of antimicrobial substances among nestmates . We performed a “temporal-separation experiment” and allowed the exposed ant to interact with its nestmates for 48 h . In this period , the pathogen ( a ) lost its ability to be transferred ( for confirmation see Materials and Methods ) and ( b ) established an infection in the ants , likely triggering an immune response [53]–[55] . After this time , we separated the treated individual and its “early nestmates” and added five “new nestmates” to both ( see Figure 5A , B ) . Three days later , we measured the antifungal activity of the new nestmates . We found no difference between new nestmates of control-treated versus fungus-exposed ants ( Figure 5A; t test: t = −0 . 159 , df = 18 , p = 0 . 876 ) or between new nestmates of early nestmates to a control-treated versus exposed individual ( Figure 5B; t test: t = −1 . 273 , df = 18 , p = 0 . 219 ) . This reveals that nestmates do not show an increase in antifungal activity if pathogen transfer is excluded . Passive transfer of antimicrobials among the group members thus seems very unlikely as an explanation for social immunisation . However , such transferable substances might be upregulated in infected individuals and simply failed to elicit immunisation of nestmates in our experiment . We therefore also analysed both the fungus-exposed ant and its nestmates directly for the presence of potentially transferable antimicrobials 3 d after treatment . Although allogrooming rates among nestmates were low in both sham control and fungus-treated groups ( Figure 2A ) , and trophallaxis rates were completely independent of treatment ( Figure 2C ) , infected nestmates may be important in transferring antimicrobial substances , as their antifungal activity is higher than that of directly exposed ants , which suffer a much higher infection level ( Figure 4 ) . We tested whether transferable substances of fungus-exposed individuals or their nestmates had higher antifungal activity than those of control-treated individuals and their respective nestmates . For externally transferable substances via allogrooming , we measured the antifungal activity of ( a ) the cuticle and ( b ) the thorax containing the metapleural gland content , which is known to have antimicrobial function and to be secreted onto the cuticle [36] . We also measured the antifungal activity of ( c ) the trophallactic droplet that is produced in the ant's body and is transferred via social feeding . We found that neither the cuticles nor the thoraxes containing the metapleural gland nor the trophallactic droplets of fungus-exposed individuals showed a different antifungal activity than the respective body parts of control-treated individuals ( Figure 5C; t tests; cuticle: t = 1 . 064 , df = 10 , p = 0 . 312; thorax: t = 0 . 224 , df = 10 , p = 0 . 828; trophallactic droplets: t = −0 . 594 , df = 18 , p = 0 . 560 ) . The same was true for the nestmates ( Figure 5D; t tests; cuticle: t = 0 . 107 , df = 18 , p = 0 . 916; thorax: t = 0 . 894 , df = 18 , p = 0 . 383; trophallactic droplets: t = −0 . 717 , df = 18 , p = 0 . 482 ) . This result was not an artifact caused by a potential effect of the control treatment , as the antifungal activity in these individuals was not different from completely untreated ants ( Materials and Methods ) . Taken together , we found no evidence for ( a ) a potential protective effect of nestmates in the absence of pathogen transfer and ( b ) potential upregulation of socially transferable antimicrobials in exposed colonies . This contrasts observations that trophallactic droplets obtained from bacteria-exposed ants had higher antibacterial activity than that of controls [16] , making passive immunisation a likely mechanism involved in social immunisation of ant colonies after bacterial exposure [16] , but not after fungal exposure . Instead , we documented that social interaction , most likely allogrooming , leads to pathogen transfer and sublethal low-level infections in the majority of nestmates of fungus-exposed individuals and that low-level infections are necessary and sufficient to induce an increased antifungal activity . To directly assess the effect of low-level infections on the immune response , we measured immune gene expression in nestmates using quantitative real-time PCR . We chose three immune genes known to be involved in the humoral and cellular defences of ants: ( 1 ) the antimicrobial peptide ( AMP ) defensin [66] , [67] , a soluble mediator that most closely resembles termicin , an antifungal peptide in termites [68] , [69]; ( 2 ) prophenoloxidase ( PPO ) , a key mediator of immune function in ants [70] , [71] that is essential for the process of melanization upon infection by a variety of pathogens , including entomopathogenic fungi [72] , [73]; and ( 3 ) cathepsin L , a lysosomal protease expressed in hemocytes [74] , which has both antibacterial [75] and antiviral activity [76] , but has not been implicated in antifungal responses . In Camponotus pennsylvanicus , another cathepsin ( cathepsin D ) was found to occur in higher amounts in the trophallactic droplets of ants after injection of heat-killed bacteria or LPS [16] , suggesting the involvement of cathepsins in antibacterial responses in ants . We confirmed that our host ant , L . neglectus , also responds to bacterial infection with cathepsin upregulation . Septic injury with Bacillus thuringiensis led to upregulation of cathepsin L gene expression , but not PPO , or defensin expression , compared to pricked controls ( Figure S4; defensin: t test; t = 0 . 186 , df = 4 , p = 0 . 862; PPO: t test; t = −1 . 448 , df = 4 , p = 0 . 221; cathepsin L: t test; t = −3 . 695 , df = 4 , p = 0 . 021; gene expression standardised to the housekeeping gene 18s rRNA ) . The choice of these three immune genes in this study therefore allowed us to examine the specific effects of social immunisation against the fungus M . anisopliae on immune pathways involved in insect defences . We compared mRNA levels of the three genes in nestmates of fungus-exposed individuals versus nestmates of control-treated individuals on day 3—that is , the first day that we observed an increase in their antifungal activity ( Figure 1 ) . After normalising to a housekeeping gene ( 18s rRNA ) , elevated expression was observed in nestmates of fungus-exposed individuals relative to nestmates of control-treated individuals for both defensin and PPO ( Figure 6; defensin: Welch's t test; Welch t = −2 . 348 , df = 26 , p = 0 . 032; PPO: t test; t = −2 . 923 , df = 26 , p = 0 . 007 ) , whereas cathepsin L showed no difference ( t test; t = −0 . 094 , df = 26 , p = 0 . 926 ) . This reveals an active upregulation of immune gene expression in nestmates of fungus-exposed ants and suggests the induction of a specific immune response distinct from immune responses to bacteria ( Figure S4; [16] ) . Similar specific immune upregulation after fungal infection is known to occur in Drosophila [77] . To determine if the observed specificity in our candidate gene approach , which is limited to a small set of genes , reflects specificity at the functional level , we tested the nestmates' capacity to inhibit growth of the bacterium Arthrobacter globiformis in an “antibacterial activity assay . ” We found that nestmates exhibited similar antibacterial activity for fungus and control treatment ( Figure 7; t test: t = −0 . 644 , df = 18 , p = 0 . 528 ) , revealing that social immunisation after fungal exposure of the colony is specific and does not lead to a protective effect against bacteria . We developed an epidemiological model to explore the adaptive value and colony-level long-term effects of social immunisation . We compared the effect of active versus passive immunisation in our ant-fungus system by extending classical SIS and SIR ( Susceptible-Infectious-Recovered/Removed ) models , which describe the progress of epidemics over time using the simplification that the diversity in the population can be reduced to a few states . Possible states in SIR models include individuals susceptible to the disease outbreak ( S ) , infectious individuals ( I ) , and recovered or dead individuals ( R; [78] , [79] ) . We included an active or passive immunisation mechanism by constructing a SIRM ( Susceptible-Infectious-Removed-iMmune ) model , in which ants can take five different states . Healthy nestmates are defined as susceptible ( S ) individuals , pathogen-exposed individuals as infectious ( I ) ones , and individuals dying from the disease are removed ( R ) from the model . Successful immunisation ( by active or passive immunisation ) leads to initially immune ( Mi ) individuals that may persist to create late-stage immune individuals ( Ml; Figure 8 ) . We describe the mean number of ants in each state by ordinary differential equations ( ODEs; for details , see Text S2 ) . We have thereby chosen a simple approach focusing on the comparison of active versus passive immunisation , but not taking into account spatial effects on epidemiology in societies that have been modelled elsewhere by cellular automata [27] , [80] , [81] or pair-wise approximations models [82] . Ants can change their state by social interactions with each other and depending on their infection state ( Figure 8A , B ) . Allogrooming reduces the fungus load of infectious ( I ) , changing them to susceptible ( S ) , but at the same time can increase the fungus load of the susceptible individuals ( S ) , changing them to infectious ( I ) . Active immunisation can occur when individuals receive a low-level infection and actively build up immunity , changing from infectious ( I ) to immune ( Mi ) with a given active immunisation rate . Under passive immunisation , susceptible ( S ) individuals change directly to the immune state ( Mi ) with a passive immunisation rate when receiving antimicrobial substances from infectious ( I ) individuals . Under the active immunisation scenario , initially immune ants ( Mi ) may then either die ( R ) if infection levels are too high and lead to the disease or enter into the later stage of immunity ( Ml ) . Under passive immunisation , all initially immunised individuals become late-stage immune . Late-stage immune ants ( Ml ) can then lose their immunisation and become susceptible individuals ( S; see Figure 8A , B and Text S2 ) . Each transition is governed by a transition rate , which in total were fixed to similar ranges in order to allow easy model comparison . The following qualitative results did not depend on the precise rate values , so that we report only representative outcomes of our simulations in Figure 8C , D . We found that more individuals typically reach the immune state ( Mi , and turn into Ml ) after passive immunisation ( Figure 8C ) , as a single infectious individual may immunise multiple susceptible nestmates , whereas actively immunised ants need to first be in the infectious state themselves . Yet we found that infections die out ( I becomes 0 ) more quickly under active immunisation ( Figure 8D ) , leaving only a very small reservoir for individuals to become immunised . Moreover , active immunisation leads to a lower number of dead individuals ( R ) . This is despite the fact that contraction of disease through pathogen transfer can only occur in the active route ( with a risk of dying similar to our experimental outcome ) . Increasing this risk leads to higher death rates and lower immunisation in a linear relationship ( simulations not shown ) . Taken together , active immunisation via pathogen transfer seems beneficial , as it allows more rapid disease elimination and produces lower death rates in colonies , except if the pathogen requires only a very low exposure dose to establish lethal infections in its host . In this study , we identified active immunisation as the underlying mode of social group-level immunisation in ant societies after fungal exposure of single individuals . Social contact to a fungus-exposed individual led to low-level infections in the majority of previously naive nestmates ( Figures 3 , S1 , S3 ) and to a higher capacity to inhibit fungal growth ( Figure 1 ) . We found that these low-level infections per se , even in the absence of social contact , are necessary and sufficient to explain the increased antifungal activity of nestmates ( Figure 4 ) . We found no evidence for visual or volatile chemical cues acting as additional trigger signals for the immune stimulation of the nestmates . Furthermore , neither ant behaviour ( Figure 2 ) nor physiology ( Figure 5C , D ) gave an indication for passive nestmate immunisation via transfer of antimicrobials from either exposed ants or their nestmates to the other group members . Finally , experimental elimination of the active route resulted in the absence of protective antifungal activity in nestmates ( Figure 5A , B ) . The increased immune activity of nestmates of fungus-exposed individuals correlates with an increased expression of immune genes such as the antimicrobial peptide defensin and the enzyme , prophenoloxidase ( PPO , Figure 6A , B ) , which both have known antifungal properties [55] , [83] . Cathepsin L , a lysosomal protease rather involved in antibacterial and antiviral responses ( [75] , [76]; Figure S4 ) , was not expressed at higher levels in nestmates of fungus-exposed compared to control-treated ants ( Figure 6C ) . In addition to the specific immune gene upregulation revealed by our candidate gene approach , we also found in a functional assay that nestmate immunity is not generally increased , but acts against Metarhizium fungus ( Figure 1 ) and not Arthrobacter bacteria ( Figure 7 ) . Precisely how specific social immunisation is at both the functional and gene expression levels remains to be addressed , and will be facilitated by the emerging genomic information on ants and other social insects [84]–[87] . To our knowledge , our study provides the first mechanistic explanation for the phenomenon of reduced susceptibility of nestmates after social contact to a fungus-exposed individual , that is , social immunisation , described for both ants [18] and termites [17] . Whether group-level immunisation in termite societies follows the same principle as in Lasius ants remains to be shown . Interestingly , our study on fungal exposure contrasts with findings of the suggested mechanisms of social immunisation of ants after bacterial exposure , where transfer of antimicrobial substances from the exposed individual via social feeding seems to elicit protection of nestmates [16] . We suggest that distinct infection modes of bacterial and fungal pathogens underlie these differences . Bacterial infections typically occur via oral uptake [39] , so that bacteria-exposed individuals do not carry socially transferable spores on their cuticle , as is the case with entomopathogenic fungi . Moreover , the long delay between exposure and infection is not common in bacterial infections , allowing for faster production of immune effectors in the exposed individuals and an earlier potential onset of immunisation . Social immunisation may not be limited to the highly eusocial insect societies but could similarly occur in other societies or at the family level . If also detected in vertebrates , the underlying mechanisms may be very different , as vertebrates have the additional adaptive/acquired immune component and do not rely solely on the innate immune system that characterises invertebrate immunity [1] , [21] . Humans have used the intentional transfer of low-level infections—referred to as “variolation” or “inoculation”—in an attempt to fight smallpox and frequently succeeded in creating long-term protection against this otherwise often deadly disease [62] , [63] . In humans , the technique was later replaced by less risky immunisation with attenuated strains as soon as these became available [88] , but variolation is still used for , for example , poultry disease management [64] . It is still unclear whether acquiring the protective low-level infections in ants is also an active strategy or , rather , an unintentional byproduct of social contact similar to “contact immunity” occurring in human societies , for example , after live strain polio or smallpox vaccination , where vaccinated individuals became spreaders and vaccinated their family members [89] , [90] . It is interesting that allogrooming by the ants is not restricted to single individuals , which would be a good strategy to avoid infecting the whole colony , but is rather performed by many colony members , all of which pick up a low-level infection . This may hint at social immunisation by low-level infections being an adaptive evolutionary strategy . Our epidemiological modeling indeed suggests that active immunisation is a beneficial strategy for ant colonies , as it allows for faster disease elimination and therefore leads to lower death rates than passive immunisation would . This is particularly true if exposure to low pathogen levels confers a low risk of mortality , as is the case with Metarhizium fungus , which requires relatively large doses to elicit a deadly course of disease . We therefore predict that social transfer of pathogens with higher infectivity [91] would not be an advantageous strategy for societies . A comparative analysis of mechanisms employed by social insects against pathogen types differing in their virulence and transmission would thus be highly interesting . Moreover , it seems likely that active immune stimulation following low-level infections may induce individual immune priming and , thereby , a longer lasting protection of colony members than if they simply received immune effectors . The long-lived societies of social insects [43] are at especially high risk of re-encountering the same pathogens multiple times during their lifespans [21] , and could greatly benefit from a persistent , rather than transient , social immunisation , particularly against common pathogens such as the fungus Metarhizium . To fully understand long-term epidemiological dynamics at the society level it will be indispensable to learn more about the mechanisms involved at the individual level—for example , to better understand if immune priming plays a role in social immunisation .
The unicolonial ant species Lasius neglectus [92] , [93] was sampled from four populations ( Jena , Germany; Volterra , Italy; Seva and Bellaterra , both Spain; for details on sample locations , see [94] ) and reared in the laboratory as described in Ugelvig and Cremer ( 2007 ) [18] . Behavioural observations were performed on workers collected in 2006 from all four populations , whereas all further experiments used L . neglectus workers collected in 2008 from Jena , Germany . Ants were kept at a constant temperature of 23°C with 75% humidity and a day/night cycle of 14 h light/10 h dark during the experiments . Experiments were performed in petri dishes with a plastered floor and 10% sucrose solution as food . We used the entomopathogenic fungus Metarhizium anisopliae var . anisopliae ( strain Ma 275 , KVL 03-143; obtained from Prof . J . Eilenberg , Faculty of Life Sciences , University of Copenhagen , Denmark ) to expose the ants in our experiments . To determine inhibition of fungal growth by ant material ( antifungal activity assay , see below ) and the transfer of conidia to the cuticle of nestmates traced by fluorescence microscopy , we used the RFP ( Red Fluorescent Protein ) labelled strain 2575 ( [95]; obtained from Prof . M . Bidochka , Brock University , Canada ) . For exposure of ants , we applied the fungal conidia ( conidiospores ) —that is , the dispersal form that is produced in a natural infection cycle from dead insect cadavers [30]—on the ants , whereas we used blastospores—that is , a single cell spore stage produced inside the body of the infected host [30] , [52]—for measuring the antifungal activity . Multiple aliquots of conidia of each strain were kept at −80°C and were grown on malt extract agar at 23°C for 2–4 wk prior to each experiment . Conidia were harvested by suspending them in 0 . 05% Triton X-100 ( Sigma ) and stored at 4°C for a maximum of 3–4 wk . All conidia suspensions had a germination rate of >98% as determined directly before each experiment . We produced liquid cultures of blastospores following an adjusted protocol by Kleespies and Zimmermann ( 1994 ) [96] , though growing the spores at 23°C . Blastospores were harvested by sieving them through a sterile 41 µm nylon net filter ( Merck Millipore ) . We exposed individual ant workers by applying a 0 . 3 µl droplet of a suspension of 109 conidia/ml in 0 . 05% Triton X solution ( fungus treatment ) , which corresponds to the lethal dose ( LD ) 50 for isolated ants . To obtain low-level infections in the same order as those picked up by the nestmates during social contact ( as confirmed by comparison of internal infection load of the socially transferred and directly applied group ) , we exposed the ants to 0 . 3 µl of a 105 conidia/ml suspension ( LD2 ) and kept them isolated . For the sham control , we treated the ants with a 0 . 3 µl droplet of a 0 . 05% Triton X solution only . Subsequently , the ants were dried on a piece of filter paper for several minutes . We grouped six workers ( 1 treated individual and 5 naive nestmates , to be distinguished by colour marking [Edding 780] ) and three larvae of L . neglectus in a petri dish ( Ø = 5 . 5 cm ) with a dampened plaster floor and a piece of filter paper ( 1×1 cm ) moistened with 10% sucrose solution as food supply . The treated individual received either a sham control or a fungus treatment as described above . Our experimental setup is equivalent to the experiment described in more detail in Ugelvig and Cremer ( 2007 ) [18] , which either led to a social immunisation of nestmates ( fungus treatment ) or not ( sham control ) after 5 d of social contact . We used this setup for observations of ant-ant interactions , obtaining physiological immune measures and conidia transmission analysis , yet made some measurements already after 1 , 2 , or 3 d of social contact . We changed this general setup for two experiments . First , to determine if signal transfer alone may be sufficient to elicit social immunisation in nestmates , we prevented direct social contact between the treated ant ( n = 10 for sham control and fungus treatment , respectively ) and its nestmates . This was done by keeping the treated individual in a plastic tube ( 200 µl , Ø of opening = 0 . 7 cm , containing cotton wool moistened with 10% sucrose solution ) , attached to the main petri dish , but separated by a double-layered nylon mesh ( mesh size 20 µm ) . The setup prevented direct physical contact yet allowed exchange of visual or volatile chemical signals . After 3 d , nestmates were frozen and subjected to the antifungal activity assay as described below . In a second setup , we excluded both signal and pathogen transfer from the exposed individual to its nestmates occurring in the first 2 experimental days , only allowing for potential later exchange of antimicrobial substances . To this end , we removed the exposed individual 2 d after fungal exposure from its “early nestmates” and placed it with “new nestmates” ( Figure 5A ) , the latter being tested for their antifungal activity after 3 d with the treated individual ( n = 10 replicates for sham control and fungus treatment , respectively ) . The new nestmates therefore only had contact to an exposed nestmate after conidia had firmly attached to the host's cuticle , and no longer could be transferred to nestmates ( as experimentally confirmed by absence of colony forming units [CFUs] in the new nestmates , see below ) . When removing the treated individual , we added five new nestmates to the five early nestmates ( Figure 5B ) to test if early nestmates may transfer immunity to the new nestmates in the form of antimicrobial substances . New nestmates were frozen after 3 d of social contact to the early nestmates of either the control-treated or fungus-exposed individual , and their antifungal activity measured as described below . All workers in the observed ant groups were individually colour marked . We then conducted 10 daily behavioural scan samples for each individual in each of six ant nests ( replicates ) from each of the four study populations ( total n = 24 ant groups per treatment , i . e . 288 ants ) over the 5 d of the experiment ( as described in [18] ) . We were interested in the behavioural interactions between different individuals , which we analysed separately for interactions between the treated individual ( total interactions n = 240 per treatment ) and its nestmates and among nestmates only ( total interactions n = 480 per treatment ) . The following types of interactive behaviours could be recorded: antennation ( recognition behaviour ) , allogrooming ( mutual cleaning of the body surface ) , and trophallaxis ( exchange of regurgitated liquid food; [38] ) . For statistical analysis of the behavioural data , see the statistics section below . We developed a sensitive antifungal and antibacterial assay ( MS , unpublished ) that reveals the antimicrobial activity of ant tissue via the growth inhibition of a pathogen culture ( as reduced absorbance in a spectrophotometer ) compared to a pathogen growth control without an ant sample . For each assay , we first determined the required ratio of pathogen , ant sample , and buffer to be in the linear range of the growth curve in which antimicrobial activity could be detected . We measured growth inhibition against blastospores of M . anisopliae by using either complete ants ( n = 10 replicate samples for each group ) , specific ant body parts ( gaster cuticle and thorax; n = 6 replicate samples for each group ) , or the trophallactic droplet ( n = 10 replicate samples for each group ) of treated ants ( sham control and fungus treatment ) and their respective nestmates . Most measurements were taken 3 d ( i . e . , 72 h ) after treatment of the single individual . Nestmates of control and exposed ants were also analysed on day 5 ( i . e . , 120 h ) after treatment . Bacterial growth inhibition against vegetative cells of A . globiformis was determined for the nestmates of fungus-exposed and control-treated individuals ( n = 10 replicates each ) . In all cases , the body parts or exudates from five individuals were pooled to obtain a single replicate sample . Both antifungal and antibacterial activity was determined as the reduction of either M . anisopliae fungal blastospore or A . globiformis bacterial vegetative cell growth , measured as absorbance in a spectrophotometer ( SpectraMax M2e , Molecular Devices , similar to [97] , [98] ) , after incubation of ant samples with the fungal or bacterial suspension . For detailed information , see Text S1 , and for statistical analyses , see below . We set up 15 experimental groups each consisting of five nestmates and one individual exposed to RFP-labelled conidia . After 2 d of social contact all ants were removed and frozen at −20°C . The cuticles of three random nestmates per group—that is , 45 nestmates in total—and cuticles from the 15 directly exposed individuals were examined for the presence of RFP-labelled conidia using a fluorescence microscope ( Leica MZ16 FA; Software: Leica Application Suite Advanced Fluorescence 2 . 3 . 0; Filter Cube: ET DsRed ) . Each ant was screened for the presence of conidia for a maximum duration of 30 min . In addition we checked the cuticle of 15 naive ants as negative control using the same method . We did not detect any structures resembling RFP-labelled conidia on any of the naive ants . We exposed 30 ants , kept them in individual petri dishes , and randomly assigned them to either of the three groups ( n = 10 ants each ) : ants that were frozen ( −20°C ) after 1 , 3 , or 5 d post-exposure . On day 1 post-exposure 10 of 10 ants were alive , 3 d post-exposure 8 of 10 ants survived , and 5 d post-exposure 4 of 10 ants survived . In addition , we set up 21 experimental groups , each consisting of five nestmates and one fungus-exposed individual , which were also frozen ( in equal numbers ) 1 , 3 , or 5 d post-exposure . None of the nestmates had died at this time point . All individually kept , directly exposed ants ( i . e . , 10 per day ) and two randomly chosen nestmates per experimental group ( i . e . , 14 per day ) were surface-sterilised in ethanol and sodium hypochlorite ( as described in [18] ) to destroy all fungal material on the cuticle prior to dissection under a stereomicroscope ( Leica S6E ) . For each ant , all contents of the gaster ( abdomen ) without the cuticle were removed and dissolved in 30 µl of Triton X . The body contents were then plated on selective medium agar plates ( containing: chloramphenicol 100 mg/l , streptomycin 50 mg/l , dodin 110 mg/l ) and kept at 23°C . After 2 wk of cultivation , the number of colony forming units ( CFUs ) per plate was determined . We identified CFUs as pure M . anisopliae cultures by morphological fungal determination and amplification of specific M . anisopliae genes by PCR ( see Text S1 ) . For statistical analysis , we used both presence/absence of CFUs for each individual and the number of CFUs growing out of infected ants ( for details , see statistical analysis section below ) . For method development , we performed the following negative controls: ( a ) 15 completely untreated ants and ( b ) 15 ants that were exposed to conidia but were surface-sterilised after 3 h ( i . e . , before the fungus could penetrate the cuticle and reach the inside of the ant ) . We did not detect any fungal growth from these 30 ants . Moreover , we could confirm that pathogen transfer did not occur towards the new nestmates of either directly exposed ants or early nestmates ( n = 14 replicates each ) . We set up 30 experimental groups consisting of five nestmates and one fungus-exposed individual each . After the 5 d of social contact to the exposed individuals , each nestmate was isolated in a single petri dish for another 12 d . During the whole experimental period of 17 d , the survival of nestmates was checked daily . Dead nestmates were surface-sterilised as above and put on moist filter paper in a petri dish at constant temperature , 23°C . Cadavers were checked for a period of 3 wk for the growth of M . anisopliae . The bacterium Bacillus thuringiensis ( strain NRRL B-18765 , obtained from the permanent strain collection of the Northern Research Laboratory , U . S . Department of Agriculture , Peoria , Illinois , USA ) was precultured in LB medium and grown to an OD600 of 0 . 1 . We centrifuged 1 ml of the suspension at a speed of 3 , 000× g for 5 min and discarded the supernatant to obtain a concentrated bacterial pellet as in [99] . Ants were immobilized and pricked ventrally between the 2nd and 3rd gaster sternite with a sterilized needle ( minutien needles , Sphinx V2A 0 . 1×12 mm , bioform ) dipped in either LB medium ( sham control ) or the concentrated bacterial pellet ( n = 10 ants per treatment , replicated three times; i . e . , total n = 30 ants per treatment ) . The ants were frozen for gene expression analysis 12 h after pricking . Ants were analysed either individually ( nestmates of Metarhizium-exposed ants ) or in pools of 10 ants ( bacterial septic injury ) by qPCR for gene expression of three immune genes and the housekeeping gene , 18s rRNA . For immune genes , we chose the antimicrobial peptide defensin [68] , [69] , the enzyme prophenoloxidase ( PPO [72] , [73] ) , and the lysosomal protease cathepsin L [74] , [76] . For details of the procedures on RNA extraction , cDNA preparation , and qPCR , please see Text S1 and the statistical analysis section below . We always tested the distributions underlying our data and chose the corresponding tests . If data were not normally distributed even after transformation , we applied models with specified error structures or non-parametric tests . Reported p values are two-sided . All statistical analyses were carried out in IBM SPSS Statistics version 19 . 0 or Sigma Stat 3 . 5 ( Systat Software Inc . ) . All figures are based on raw data . For the behavioural observations , we first analysed all behaviours overall over the 5 experimental days . Due to the nature of the data ( overdispersed count data ) , generalised linear models ( GLM ) with negative binomial errors and a log link function were employed using the following factors: treatment type ( fungus treatment versus sham control ) , ant pairing ( treated-nestmate versus nestmate-nestmate ) , and the interaction between them . As neither nests within populations nor populations behaved differently , they were not included in the final models . We give the likelihood ratio ( LR ) χ2 to test if our overall model explains the data better than a model with only the intercept . As we detected significant differences for allogrooming , we performed a second test to analyse the effect of time in the interactions between treated individuals and their nestmates for the two treatment types separately ( n = 240 ) using a GLM with repeated measures . Simple contrasts with day 1 as reference were employed to test the differences between day 1 and the succeeding days ( Figure 2B ) . For statistical analysis of the antifungal and antibacterial activity , the absorbance values ( optical density ) of the different treatment groups were compared by one-way ANOVAs or t tests as data were normally distributed or could be transformed to obtain normality . For the antifungal activity of nestmates of exposed versus control nestmates , we applied a GLM to analyse the effects of treatment type ( fungus treatment versus sham control ) and time ( day 3 versus day 5 post-treatment ) , as well as their interaction ( Figure 1 ) . For analysis of pathogen load , we compared directly exposed and nestmate ants for ( a ) the proportion of individuals that were infected ( i . e . , showed at least a single CFU; Fisher exact test ) and ( b ) the number of CFUs in the individuals that showed an infection ( Mann Whitney U test; Figure 3 ) . As the experimental grouping did not influence the number of CFUs found in nestmates from the same ant group , this factor could be excluded from statistical analysis comparing treated individuals and nestmates ( GLM with negative binomial errors , LR χ2 = 112 . 362 , df = 34 , p = 0 . 000; Replicate , Wald: χ2 = 21 . 273 , df = 17 , p = 0 . 214 ) . Gene expression analyses were run in two to three technical replicates . Normalised gene expression values ( the average of technical replicates , standardised to the housekeeping gene ) were either a priori normally distributed or could be normalised by transformation and were analysed using t test or—in the case of unequal variances between groups ( defensin , Figure 6A ) —Welch's t test for unequal variances [100] . We applied ordinary differential equations ( ODE ) to extend classical SIR modeling ( Susceptible-Infectious-Removed ) with an immunised state to a SIRM model ( Susceptible-Infectious-Removed-iMmune ) , in which the immune individuals were further separated into an initial and a late phase of immunity . See Figure 8A , B for the model and how we calculated state changes and Text S2 for model construction and simulations . | Close social contact facilitates pathogen transmission in societies , often causing epidemics . In contrast to this , we show that limited transmission of a fungal pathogen in ant colonies can be beneficial for the host , because it promotes “social immunisation” of healthy group members . We found that ants exposed to the fungus are heavily groomed by their healthy nestmates . Grooming removes a significant number of fungal conidiospores from the body surface of exposed ants and reduces their risk of falling sick . At the same time , previously healthy nestmates are themselves exposed to a small number of conidiospores , triggering low-level infections . These micro-infections are not deadly , but result in upregulated expression of a specific set of immune genes and pathogen-specific protective immune stimulation . Pathogen transfer by social interactions is therefore the underlying mechanism of social immunisation against fungal infections in ant societies . There is a similarity between such natural social immunisation and human efforts to induce immunity against deadly diseases , such as smallpox . Before vaccination with dead or attenuated strains was invented , immunity in human societies was induced by actively transferring low-level infections ( “variolation” ) , just like in ants . |
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Accurate means to detect mild traumatic brain injury ( mTBI ) using objective and quantitative measures remain elusive . Conventional imaging typically detects no abnormalities despite post-concussive symptoms . In the present study , we recorded resting state magnetoencephalograms ( MEG ) from adults with mTBI and controls . Atlas-guided reconstruction of resting state activity was performed for 90 cortical and subcortical regions , and calculation of inter-regional oscillatory phase synchrony at various frequencies was performed . We demonstrate that mTBI is associated with reduced network connectivity in the delta and gamma frequency range ( >30 Hz ) , together with increased connectivity in the slower alpha band ( 8–12 Hz ) . A similar temporal pattern was associated with correlations between network connectivity and the length of time between the injury and the MEG scan . Using such resting state MEG network synchrony we were able to detect mTBI with 88% accuracy . Classification confidence was also correlated with clinical symptom severity scores . These results provide the first evidence that imaging of MEG network connectivity , in combination with machine learning , has the potential to accurately detect and determine the severity of mTBI .
Detection of mild traumatic brain injury ( mTBI ) using neuroimaging remains a challenge , as no abnormalities are typically apparent using routine MRI [1 , 2] . Accordingly , diagnosis is usually a clinical judgement based on self-report measures and behavioural assessments . Despite the lack of apparent injury on conventional clinical scans , many patients with mTBI suffer post-concussive symptoms ( PCS ) . Although such symptoms typically resolve within a few months , a subset of individuals continue to experience long-term cognitive and behavioural impairments [3–5] , underscoring the need for quantitative and objective methods for detecting and determining the severity of mTBI . The presence of lingering PCS indicates the presence of subtle brain injuries , with significant functional consequences that cannot be detected using current clinical techniques; there is a need to develop new imaging approaches for the detection of mTBI using quantitative and objective evidence . Recent advances in magnetoencephalographic ( MEG ) imaging indicate that identification of mTBI is possible through detection of excessive slow-wave activity [6] and that this approach can localize the foci of the damage [7] . MTBI is associated with altered white matter microstructure as indicated by diffusion tensor imaging ( DTI ) , in agreement with the view that mTBI results in axonal injury [8] . The focal excessive MEG slow wave activity has been shown to be related to the location of white matter injury , consistent with the supposition that oscillatory slowing can occur from deafferentation [9] . Disruption of inter-regional oscillatory synchrony in mTBI has been reported using EEG [10] . Oscillatory synchrony among brain areas is understood to play a vital role in network connectivity supporting cognition and behaviour [11 , 12] , and the expression of such neurophysiological network connectivity at rest relates to the intrinsic organization of brain activity pertinent for brain function and its dysfunction in clinical populations [13] . Converging evidence now indicates that traumatic brain injury is associated with diffuse axonal injury , which disrupts intrinsic functional network connectivity , thereby contributing to associated cognitive sequelae [14] . Machine learning approaches have been successfully combined with imaging of intrinsic functional brain connectivity during resting state to accurately classify single individuals [15 , 16] . Moreover , electrophysiological recordings from subjects have also been shown to be effective for accurately determining group membership of individuals [17 , 18] . We used MEG to investigate alterations in resting state oscillatory network synchrony in adults with mTBI , and investigated the hypothesis that machine learning algorithms could accurately detect mTBI in individual subjects .
Frequency and source resolved imaging of resting MEG network synchrony was able to accurately detect whether individual participants had been diagnosed with mTBI or not . Fig 1 shows the predictive power of phase synchrony measured at 30 specific frequencies points covering the range between 1Hz and 75Hz . Specificity remains stable around 80% and fluctuates slightly ( 76–83% ) within a relatively wide range of frequencies: from 3Hz to 50Hz . Conversely , sensitivity and hence total accuracy have a local maximum around 8-13Hz ( α rhythms ) , reaching 80% . Fig 2A provides a more aggregated view of the results shown in Fig 1 . Specifically , prediction accuracy is given as functions of frequency bands , each including the features from several frequency points ( wavelets ) . Fig 2B illustrates the same prediction accuracies with respect to the random chance prediction , wherein the distributions of accuracies were generated by shuffling the labels ( mTBI or not ) across subjects , and repeating the same procedure 500 times . With an accuracy of 80% ( p < 0 . 01 ) , inter-regional resting state phase synchrony in the α band carries the most discriminative information for inferring the presence or absence of mTBI within a single individual . Accordingly , the remainder of the results presented in this section pertain to phase synchrony in the α band , using features that individually provided the highest separability between mTBI and controls under the ROC criterion . The list of ranked features reflects an estimate of how valuable a given feature was found to be for classification . We can choose the best number of features , i . e . the number that maximizes prediction accuracy . In this case , the dimensionality of the feature space will correspond to the number of source-pairings within the alpha range . Note that in this study the features were ranked using training data at each round of leave-one-out cross-validation . While the number of features to keep was set a priori , the best features themselves were determined within each round of cross-validation . Fig 3 shows accuracy values as functions of the number of best features selected for classification analysis with cross-validation . As can be seen from Fig 3C , classification accuracy can be improved with a proper threshold on the number of variables k with two peaks around k = 8−15 and k = 30−35 . For example , for k = 33 accuracy is 88% , with 90% specificity and 85% sensitivity ( all p < 0 . 01 ) . To investigate potential relations between SVM classification and symptom severity obtained from the concussion assessment tool ( SCAT2 ) , we quantified the distance to the decision boundary for each subject , and correlated these values with clinical scores for participants within the mTBI group . Note that the larger the distance that an individual is from the decision boundary , the higher our confidence that a subject with mTBI is classified correctly as mTBI . Similar to the procedure shown in Fig 4 , for each subset of features k = 1 , … , 100 , we computed the distances to the decision boundary for mTBI patients , and correlated these distances with the severity and symptom scores , shown in Fig 4A and 4B , respectively . Two scatter plots with superimposed least-squares regression lines illustrate relations between these variables at two peaks , k = 11 for severity ( Fig 4C ) , and k = 33 for symptoms ( Fig 4D ) . Note that negative distances at the scatter plots reflect cases of misclassification , when the learning function F ( x ) projects the feature vectors x of mTBI subjects to other side of the optimal hyperplane , corresponding to controls . Moreover , the confidence of classifying a subject as mTBI positively correlated with the self-reported severity scores ( Fig 4A ) , reaching a local maximum ( r = 0 . 54 , p < 0 . 05 ) at k = 11 . It also correlated positively with the symptoms scores with a peak of r = 0 . 34 ( p-value< 0 . 10 ) around k = 33 . Finally , Fig 5 illustrates a distribution of the connections extracted from the pool of the best k = 33 features in the α band . It plots connections within a transparent template of the brain in the MNI space , using the BrainNet Viewer [19] . The width of the connections represents the weights are between −1 and 0 , where being close to −1 implies a robust contribution of a specific connection to classification , and zero means no contribution . Specifically , for each wavelet frequency within the α range and each round of cross-validation ( m = 1 , … , 41 ) , we assigned −1 to a connection if this feature survived the threshold and participated in classification , otherwise it was 0 , subsequently averaging across subjects and wavelets frequencies . The ability to predict evidence of injury of a subject is largely based on synchrony between frontal and parietal/temporal sites , located mainly in the left hemisphere . We also employed PLS to characterize and test the statistical reliability of differences in resting state network synchrony between adults with and without mTBI . This analysis revealed the existence of one significant latent variable ( p = 0 . 002 ) which indicated alterations of resting MEG network synchrony in mTBI ( Fig 6A ) . The overall distribution of all the bootstrap ratio values , each associated with a pair-wise connection between the sources and frequencies , is shown in Fig 6B . As can be seen , there are relatively large positive and negative bootstrap ratio values , which reflect phase-locking and phase scattering effects , respectively , in controls with respect to mTBI . The difference between increased and decreased phase locking is broken down further in Fig 6C and 6D , which shows how the strength of these effects varies across frequencies . Specifically , we identified the 1% tails , cut off by the 0 . 01− and 0 . 99-quantiles of the overall distribution of the bootstrap ratio values in Fig 6B . At each frequency , the number of connections with the bootstrap ratio values larger than the 0 . 99-quantile ( right tail ) was computed and plotted in Fig 6C . The strongest effects are robustly expressed at δ and lower γ frequencies , directly supporting higher phase locking in controls compared to mTBI at these frequencies . Similarly , the number of connections in the left tail defined by the 0 . 01-quantile is plotted in Fig 6D , as a function of frequency . These connections also support the contrast in Fig 6A , but in a reverse way , representing hyper-connectivity in the mTBI which were strongest at α frequencies . Pair-wise connections that show decreased phase synchrony in the δ and lower γ bands in mTBI are depicted in Fig 7 . The bootstrap ratio values were averaged across wavelet frequencies within corresponding frequency bands . A threshold of >1 was used for the figures to emphasize the spatial distribution . Reduced δ and γ resting phase synchrony in mTBI was most pronounced between occipital areas and other brain regions , and also preferentially involved temporal lobe connections . Similar to Figs 7 and 8A was created with a threshold of <−1 , and shows the distribution of pair-wise connections associated with increased phase synchrony in mTBI at α frequencies . It is interesting to note that the distribution of connections which carry discriminative information between mTBI and controls , as illustrated on the transparent brain in the MNI space ( Fig 5 ) and its matrix version ( Fig 8B ) , is part of the spatial pattern representing hyper-connectivity of α rhythms in mTBI ( Fig 8A ) , which involved numerous temporal and parietal connections . To quantitatively compare the contribution of individual frequency bands to the contrast depicted in Fig 6A , we performed a series of steps testing difference in proportions . First , we identified the wavelets closest to the central frequencies of five canonical frequency bands: 2 Hz ( delta ) , 6 Hz ( theta ) , 11 Hz ( alpha ) , 23 Hz ( beta ) , 48 Hz ( lower gamma ) . Then , for a given z-sore threshold ( 1% tails ) , at each central wavelet frequency , we counted connections ( out of the total 90*89/2 = 4005 ) within the positive and negative tails of the overall distribution of z-scores ( see Fig 6C and 6D ) . For the effects defined by the tail with negative z-scores , where we observed a peak around 8 Hz ( Fig 6D ) , we ran two-sample proportion z-tests between the alpha and other frequencies . Specifically , we tested if the numbers of connections within the negative tail at two frequency points were statistically different . We found that the number of connections was significantly higher at alpha relative to delta ( p = 0 . 0013 ) , beta ( p = 0 . 0238 ) , and lower gamma ( p<0 . 0001 ) , but not theta ( p = 0 . 757 ) . For the positive tail of z-scores ( Fig 6D ) , where we identified two peaks around 2 Hz and 75 Hz , we performed a series of similar two-sample proportion z-tests . We found that the number of connections from the positive tail was statistically higher at delta relative to theta ( p = 0 . 0035 ) and alpha ( 0 . 0013 ) , whereas the number of connections at gamma was higher than theta ( p<0 . 001 ) , alpha ( p<0 . 001 ) , and beta ( 0 . 0015 ) , but not delta ( p = 0 . 1211 ) . In addition , Fig 9 provides an example of the effects shown in Fig 6C and 6D , indicating the range of absolute values of PLV for specific connections at the characteristic frequencies . Specifically , Fig 6 depicts a spatiotemporal interplay between synchronizations and de-synchronizations in the delta , gamma , and alpha frequency bands , and we chose three connections with the largest z-scores to illustrate the effects: i ) between the left middle occipital gyrus ( Occipital Mid L ) and the left median cingulate and paracingulate gyri ( Cingulum Mid L ) at 2 Hz; ii ) between the temporal pole of the left middle temporal gyrus ( Temporal Pole Mid L ) and the left gyrus rectus ( Rectus L ) at 8 Hz; and iii ) between the left inferior temporal gyrus ( Temporal Inf R ) and the right calcarine fissure and surrounding cortex ( Calcarine R ) at 75 Hz . Finally , we explored the effect of the length of time between injury and scan acquisition on resting MEG connectivity . We applied the behavioural PLS analysis to correlate the phase locking value with the time between brain injury and scanning . PLS analysis revealed a significant latent variable ( LV ) with p = 0 . 016 , which is plotted in Fig 10A as an overall correlation ( first component of LV ) and a distribution of all the bootstrap ratio values ( second component of LV ) , each associated with a unique combination of frequency and source pairing . The right ( red ) and left ( blue ) tails of the histogram in Fig 10B represent robust positive and negative correlations , respectively , between the length of time between injury and scan and phase synchronization . Frequency-specific number of connectins in these tails are shown in Fig 10C and 10D , respectively . As can be seen from Fig 10D , the effect for negative correlations between the connectivity at alpha frequencies and time of scanning is strongest at alpha frequencies . In other words , the more time that has passed since injury , the less connectivity we observed in the alpha frequency band . It is worth noting that mTBI patients , when compared to controls , were characterized by increased connectivity at alpha frequencies ( Fig 6D ) .
The present study provides the first evidence for altered resting state neuromagnetic phase synchrony in a group of patients with mTBI , and showed that these alterations were associated with the amount of time elapsed between injury and scan acquisition . More importantly , we demonstrate that atypical MEG network connectivity , in combination with SVM learning , can accurately detect mTBI . This is an important step forward as mTBI is typically not detectible using conventional imaging . Our findings indicate that neurophysiological network imaging using MEG may provide an objective method for detection of mTBI . Moreover , we show that the distance of individual participants from the classification decision boundary was correlated with clinical symptom severity . These results demonstrate that MEG imaging of resting state functional connectivity may offer new approaches for assessing and tracking injury severity in mTBI . Using a data-driven approach , we showed that group differences can be characterized in terms of interplay between synchronizations and desynchronizations at different frequencies . Specifically , we observed more increases in connectivity around theta/alpha frequencies in mTBI , whereas more decreases in connectivity in mTBI were detected for delta rhythms . This fits the hypothesis that processing of information in the brain requires both phase synchrony and phase scattering . Speculatively , phase synchronization can be viewed as a mechanism for long-range integration , whereas phase scattering can be a strategy to allow different local neural ensembles to share the same frequency channel by assigning specific neural signals to their own timeslots . Furthermore , we also found that the length of time elapsed between injury and scan tended to be negatively correlated with alpha synchronization and positively correlated with delta connectivity . These results may indicate that brain plasticity , a fundamental property for functional recovery from brain injury [20] , may potentially be described in terms of redistribution of phase synchronyzation and phase scattering at different rhythms . A similar pattern of the interplay between increases and decreases in functional connectivity was reported in an MEG study of TBI patients in two conditions: following an injury and after a rehabilitation treatment [21] . Noticeably , the study reported an opposite pattern , as increases in connectivity at higher frequencies such as alpha and beta , and conversely decreases in connectivity for delta and theta rhythms were associated with recovery from TBI . One of the key differences between the two studies was the time since injury . In our study , MEG data were recorded from mTBI patients , who were all within 3 months of injury ( on average , one month ) . In [21] , the mean time since injury was almost 4 months , and the rehabilitation program lasted for about 9 months . Prior studies have indicated that resting state MEG can be used to detect mild and moderate TBI at the level of single individuals , but rather than focusing on inter-regional oscillatory synchrony , such research focused on the regional expression of excessive slow-wave activity [6 , 7] . It has been proposed that axonal sheering caused by rapid deceleration and rotational forces plays a critical role in the pathology of TBI as well as its impact on functional networks and cognition [14] . Interestingly , regional expression of increased slow-wave activity has been shown to be either proximal to white matter abnormalities revealed by DTI , or in some cases , remote if micro-structural abnormalities occur in a major tract innervating that region [8] . Furthermore , this implies that excessive slow-wave activity reported in prior studies may be related to alterations in functional connectivity reported in the present investigation . Recent evidence indicates that regional concentrations of oscillatory slowing also correspond to particular symptoms expressed [7] , raising the question of whether region-specific differences in functional connectivity may relate to specific patterns in post-concussive symptoms . Research using EEG has also reported that electrophysiological interactions among brain regions are atypical in mTBI . Reduced inter-hemispheric phase synchrony among EEG scalp electrodes has been reported , and it was shown that such connectivity reductions in the beta and gamma frequency ranges were associated with alterations in white matter microstructure [10] . The network organization of resting state EEG connectivity has also been shown to be altered in mTBI [22] . An MEG investigation of patients with mild , moderate and severe TBI reported functional network disconnection in this group [23] . Using the data set employed in the present study , we previously showed that resting state correlations in the amplitude envelope of MEG activity is elevated in the delta , theta and alpha bands in mTBI , and that these alterations are associated with cognitive and affective sequelae in this group [24] . Interestingly , this pattern of alteration is different from MEG network alterations associated with PTSD ( which is often a co-morbidity of mTBI ) which was associated with high-frequency increases in resting phase synchrony [25] . Neural oscillations and their synchronization among brain areas are thought to play a critical role in cognition [11 , 21] , and resting neuromagnetic synchrony and amplitude correlations are presently thought to reflect intrinsic functional networks underpinning cognition , perception and their disturbance in clinical populations [13] . EEG research has also indicated that reduced electrophysiological interactions among brain areas may contribute to cognitive and behavioural problems associated with PCS . Reduced EEG coherence , for example , has been observed during visuospatial working memory in mTBI [26] and disrupted organization of network synchronization during episodic memory processing has also been reported [27] . Such reports of altered task dependent connectivity are congruent with reports of atypical electrophysiological and hemodynamic responses during cognitive processing following mTBI [28] . MRI studies have indicated altered functional network connectivity in mTBI [29–31] , in the very low hemodynamic frequency oscillations measured by fMRI , which have been related to cognitive problems and recovery in this group [32] . During resting state , fMRI abnormalities have been reported which encompass visual , limbic motor and cognitive networks [29] . Altered default mode network connectivity [32] and regulation have been reported in mTBI . Spontaneous BOLD correlations have also been shown to be atypical in thalamocortical networks in mTBI patients , and these alterations are correlated with both clinical symptomatology and cognitive performance [30] . That altered connectivity is prominent in both neurophysiological and hemodynamic imaging studies is not surprising , as damage to white matter tracts in the form of diffuse axonal injury is common in severe brain injury [32–35] . Investigations of brain microstructure in such populations indicate altered axonal structure in both gray and white matter [36–38] . The present study capitalizes on rapidly emerging methods combining analysis of brain network connectivity with machine learning approaches supporting classification at the level of individual participants . This provides new insights into complex spatiotemporal shifts in intrinsic coupling in neurophysiological brain networks following mTBI . More importantly , the present work provides potentially clinically translatable methods that will permit the detection of mTBI in single individuals where conventional radiological imaging approaches are inconclusive . The finding that classification confidence is associated with self-reported symptom severity indicates that these methods may provide quantitative and objective measurements of brain changes underlying PCS . This could have significant impact on current clinical practice . An objective , quantitative method for diagnosing brain dysfunction after mTBI would allow identification of patients at risk for a subsequent injury , be invaluable for developing parameters around return to play / work / duty , and assist in developing guidelines for providing care , monitoring treatment efficacy and tracking recovery .
MEG data were recorded from 20 men with mTBI ( 21–44 years of age , mean = 31±7 years , 2 left-handed ) , all within three months of injury ( days since injury = 32 ± 18 days ) . Participants with mTBI were recruited through the Emergency Department of Sunnybrook Health Science Centre in Toronto . The inclusion criteria were: concussion symptoms while in emergency; Glasgow Coma Scale ≥13 ( within 24 hours of injury ) ; if loss of consciousness occurred , then less than 30min; if post-traumatic amnesia occurred , then less than 24 hours; causes of head injury were clear ( e . g . , sustaining a force to the head ) ; no skull fracture; no abnormalities on Computer Tomography ( CT ) scan and no previous incidences of concussion . Participants in the mTBI group completed the Sports Concussion Assessment Tool 2 ( SCAT2 ) Symptom Checklist and Symptom Severity Score; were able to tolerate the enclosed space of the MRI; were English speaking and able to complete tasks during MEG and MR scans and able to give informed consent . The mean Severity score of mTBI patients was 20 ± 19 , whereas the Symptom score was 9 ± 6 . The MEG and MRI scans were obtained , on average , on 32nd day since injury: 32 ± 18 days . Potential participants were screened prior to recruitment and none of the mTBI participants reported any post-traumatic stress disorder , neurological or psychiatric symptoms , and psychoactive medication use . All of the MRI scans were read by a neuroradiologist , and there were no abnormalities noted . An age- and sex-matched control group without any history of TBI included 21 participants ( 20–39 years of age , mean = 27±5 years , 1 left-handed ) . The control group had no history of TBI ( mild , moderate or severe ) , no neurological or psychiatric disorders , and were not on psychoactive medications . None of the participants had MRI contraindications such as metallic implants or metal dental work . Data acquisition was performed with the informed consent of each individual and with the approval of the Research Ethics Board at the Hospital for Sick Children ( SickKids ) . MEG data were acquired in a magnetically shielded room at SickKids using a whole-head CTF system ( MISL Ltd . , Coquitlam , BC , Canada ) with 151 axial gradiometers as well as reference sensors for gradient correction . For each subject , 5 minutes of MEG data were continuously recorded at 600Hz using third-order spatial gradient noise cancellation . 60Hz and 120Hz notch filters were applied to MEG recordings . Data were also band-pass filtered between 1Hz and 150 Hz with a fourth-order Butterworth digital filter applied first in a forward , and then in a reverse direction so as to produce zero phase distortion . Head position during testing was monitored via three localization coils , positioned at the nasion , and the left and right pre-auricular points . Anatomical MRI was performed on the same day at SickKids on a 3T MR scanner ( MAGNETOM Tim Trio , Siemens AG , Erlangen , Germany ) with a 12-channel head coil . The three fiducial coils used in the MEG were replaced with radio-opaque markers for all participants . These markers can be seen on their T1-weighted images for co-registration of the MEG source locations to the MRI images . Anatomical images were collected by whole-brain T1-weighted MRI scans ( 3D SAG MPRAGE: GRAPPA = 2 , TR/TE/TI/FA = 2300/2 . 96/900/9 , FOV/Res = 192x240x256 , 1mm isotropic voxels ) . Individual MRI scans were normalized into Montreal Neurological Institute ( MNI ) space based on the ICBM 2009c Nonlinear Symmetric 1 × 1 × 1mm template [39] . We applied a nonlinear diffeomorphic registration , as implemented in the ANTS toolbox [40 , 41] . This transformation to MNI space was additionally used to warp a manually segmented inner skull surface from the MNI ICBM template to subject space . Using this inner skull surface , a multi-sphere head model was fit for each subject [42] . MEG data were co-registered to each participant’s individual anatomical MRI to constrain neuromagnetic sources to subject-specific head shape and structural anatomy . To reconstruct neuromagnetic source activity , we first selected 90 seed locations in MNI space , which represented all cortical and subcortical brain regions in the Automated Anatomical Labeling ( AAL ) atlas [43] . Regions specified by the AAL atlas and located in the cerebellum were excluded from the further analysis . For visualization purposes , the regions were re-ordered according to which lobe each region belongs to . The new order of the regions is given in Table 1 ( the left region goes first , followed by the right one ) . Specifically , for each region from the AAL parcellation , the seed location was defined as a voxel within the region , which was closest , in the mean-square sense , to the means of x- , y- , and z-coordinates , averaged across all the voxels in this brain region [44] . Source estimation was performed at these 90 locations , using an adaptive spatial filter ( vector beamformer ) [45] . For each subject , 27 non-overlapping epochs of 10 seconds duration were extracted such that head motion within each epoch did not exceed 3mm in any direction for any of three head location coils . The time-frequency representation of the original time series for each reconstructed source was derived from the wavelet decomposition , using a time-frequency toolbox [46] . Thirty frequency points equally spaced on a logarithmic scale were selected to cover the range between 1Hz and 75Hz . The analysis of phase synchronization between the neuromagnetic sources was performed on spectrally decomposed data . We computed phase-locking values [47] , which are known in the literature under different names such as mean phase coherence [48] or phase synchronization index [49] . Phase synchronization emerged from studying coupled nonlinear systems [50] , and is based on an idea that the existence of correlations between the phases of coupled systems does not imply correlation between their amplitudes . A common method for obtaining phase dynamics for analyzing phase synchronization between brain signals is based on wavelet transformation [51] . A signal can be decomposed into a set of brief oscillatory patterns called wavelets . Specifically , wavelet coefficients Wx ( τ , f ) at time τ and frequency f are obtained by convolving a given signal x ( t ) with a zero-mean special function or wavelet ψτ , f ( t ) : Wx ( τ , f ) =∫−∞+∞x ( t ) ψτ , f ( t ) dt ( 1 ) where ψτ , f ( t ) is a short segment of a oscillatory signal ( wavelet ) obtained from an elementary function called the mother wavelet by dilutions and translations . Often , a specific form of the mother wavelet is used , known as the Richer wavelet or Mexican hat function , which is defined as the negative normalized second derivative of a Gaussian function . To decompose a signal at a specific frequency f and time τ , the mother wavelet is compressed or dilated , and then translated such that ψτ , f ( t ) is centered at time τ . To maintain a consistent frequency resolution , the bandwidth of the envelope is set to be inversely proportional to f , such that each wavelet contains the same number of cycles . In general , the coefficients Wx ( τ , f ) are complex numbers . The transformation Eq ( 1 ) thus defines both the amplitude of signal x ( t ) and the phase over a range of times τ and frequencies f . The instantaneous phase ϕx ( τ , f ) is the angular component ( phase angle ) of Wx ( τ , f ) . The relative phase Δϕx ( τ , f ) of two signals , x ( t ) and y ( t ) , is defined as a time series of the difference between the instantaneous phase of each signal , namely Δϕx , y ( τ , f ) =ϕx ( τ , f ) −ϕy ( τ , f ) ( 2 ) which can be computed from the wavelet coefficients at time τ and frequency f from eiΔϕx , y ( τ , f ) =Wx ( τ , f ) Wy* ( τ , f ) |Wx ( τ , f ) ||Wy ( τ , f ) | ( 3 ) where Wy* ( τ , f ) is the complex conjugate of Wy ( τ , f ) . The phase differences can be projected as a series of two-dimensional vectors onto the unit circle , one per time point τ = τ1 , … , τN . The phase-locking value PLVx , y ( f ) , which reflects the amount of phase-synchrony between two signals across time , is computed as the length of the resultant ( mean ) vector: PLVx , y ( f ) =〈eiΔϕx , y ( τ , f ) 〉τ=|1N∑k=1NeiΔϕx , y ( τk , f ) | ( 4 ) By construction , PLVx , y ( f ) is limited between 0 and 1 . When the relative phase distribution is concentrated around the mean , the PLV is close to one , whereas phase scattering will result in a random distribution of phases and PLV close to zero . For each epoch , for all pairs of 90 regions of interest ( ROIs ) , frequency-specific phase differences were computed as functions of time . The phase-locking value , PLVx , y ( f ) , was calculated as relative stability of the phase differences between two signals at a given frequency , subsequently averaging across epochs . Thus , 30 90-by-90 matrices were produced for each subject , representing functional connectivity in terms of phase-locking between 90 neuromagnetic sources at 30 frequency points . In the present study , Support Vector Machine ( SVM ) learning was used to predict the clinical status Y of a subject ( mTBI or control ) from a set of features X obtained from the subject’s MEG data [52] . These features are represented by frequency-specific phase-locking values ( PLV ) between the neuromagnetic activity reconstructed for 90 regions of interest ( ROIs ) . Each of the samples ( subjects ) i = 1 , … , m , where m = 41 , can be treated as a point xi in a n-dimensional feature space , where n is the total number of features—unique combinations of all the connections and frequencies of interest . A learning machine can be seen as a function F , which determines a learning model: F:X→Y ( 5 ) The function F transforms vectors xi from the feature domain X to the set Y of possible outcome values . When Y is a set of only two symbols ( mTBI and control ) , the learning problem Eq ( 5 ) is called a binary classification , and Y is called the set of class labels . Learning machines encompass many computational approaches . For classification problems , they can produce models with various types of decision borders . In this study , we applied a linear version of a SVM to determine a linear border between the classes [52] . Depending on which side of the border the sample xi is located , it can be assigned to one of two classes:Y = {1 , −1} coding mTBI and control groups , respectively . Samples used to define the border are called training data . The clinical status of new cases ( test data ) can be predicted based on their locations with respect to the decision border . If we know the true status of the test data , we can estimate the accuracy of that prediction . In practise , the entire data with known labels are typically divided into two sets: training data to learn the function ( 5 ) and test data to validate it . Mathematically , learning the model ( 5 ) with a linear SVM is equivalent to finding the optimal hyperplane ωTx + b = 0 in the feature space , where ω is an n-dimensional weight vector , and–b defines the threshold . Optimal here means separating the two classes Y = {1 , −1} with maximal margin . Mathematically , training the model ( 5 ) is reduced to an optimization problem , maximizing the minimum distance between vectors xi and the hyperplane: maxw , b min{ ‖x−xi‖ such that wTx+b=0 , i=1 , … , m } ( 6 ) If a vector x satisfies F ( x ) = ωTx + b > 0 , then the model ( 5 ) will assign the label 1 ( class mTBI ) to it , otherwise the label −1 ( class Controls ) is assigned . The distance from the decision boundary F ( x ) = ωTx + b can serve as a measure of confidence in the classification . Leave-one-out cross-validation was used in this study to estimate prediction accuracies of the classification . During this procedure , all samples xi , i = 1 , … , j − 1j + 1 , … , m except one xj were designated as the training data to determine the optimal model ( 5 ) for separating the classes , whereas ability of this model ( 5 ) to correctly predict the outcome was tested with the remaining sample xi . This procedure was repeated m times such that each subject served as the test sample only once . The prediction accuracies such as sensitivity and specificity were then computed by comparing the predicted and true statuses of m subjects . Until now , we assume that all features , i . e . all frequencies and connections , were used for classification . However , predictive accuracy could be improved by selecting the most relevant and informative features . In general , feature construction and selection is a critical step in classification . In practise , it is essentially heuristic . Fig 11 schematically illustrates one round of cross-validation used to learn a model from the training data , and then predict the group status of the test data . Feature selection was based on supervised learning , wherein the features were the phase synchrony estimates with three feature selection schemes: i ) individual wavelet frequencies , ii ) canonical frequency bands , and iii ) best representative features within the α band . As a first-pass analysis , contribution of individual wavelets to the classification was estimated . Then , five frequency bands , namely δ ( 1-4Hz ) , θ ( 4-8Hz ) , α ( 8-14Hz ) , β ( 14-28Hz ) , and lower γ ( 28-75Hz ) were a priori selected , and all the wavelets representing more fine grained frequency bins were assigned to one of these canonical bandwidths . Further , for the frequency band that carried the most discriminative information ( namely , α ) , the features were ranked in a univariate manner . Specifically , for each feature ( PLV for a given frequency and connection ) , overlapping probability distribution functions for two classes were compared , and the area under the resulting receiver operating characteristic ( ROC ) was computed [53] . The area under the ROC ( AUR ) provides an estimate of how valuable a feature can be for separating the two classes . Accordingly , the feature selection can be summarized as follows . First , the features that were computed to quantify the brain state were separated into 30 sub-sets , each associated with a wavelet frequency . Classification analysis with leave-one-out cross-validation was applied separately for each subset , using linear SVM [54] as implemented in a Matlab statistics toolbox ( MATLAB and Statistics Toolbox Release 2012a , The MathWorks , Inc . , Natick , Massachusetts , USA ) . Next , total accuracy as well as specificity and sensitivity were computed . The features were then regrouped in an alternate way , and the classification process was repeated . Specifically , the features were separated into 5 subsets , each associated with a frequency band ( δ , θ , α , β , lower γ ) , containing phase-locking values calculated for a set of wavelets within a specific frequency range . Further , the features representing phase synchrony in the α band were ranked , computing the area under the ROC curve ( AUR ) . In the next step , leave-one-out cross-validation was applied for every number k = 1 , … , 100 of features with the highest AUR , and classification accuracy was estimated as a function of the number of selected features . Significance of accuracy values was tested with respect to the distribution created by shuffling 500 times the labels ( mTBI and Control ) among the subjects . Partial Least Squares ( PLS ) analysis was used to further explore possible group differences in connectivity ( PLV ) between the neuromagnetic sources across groups , as well as how these differences are expressed across frequencies and specific connections [55] . In contrast to the prediction analysis with linear SVM , wherein the learning model F was estimated for a subset of subjects ( training data ) , PLS analysis was performed on the entire data in a single analysis . PLS is a multivariate technique , which decomposes the covariance between the neurophysiological data and a discrete variable coding a contrast ( between groups , for example ) or a continuous variable ( such as the time since injury ) into mutually orthogonal factors ( latent variables ) , similar to the principal component analysis [55] . In practice , PLS analysis can identify data-driven contrasts between groups or test specific a priori contrasts , and finds optimal relations among these contrasts and features ( combinations of individual connections and frequencies in our case ) . Significance of the contrast can be tested with permutation tests , whereas the robustness of the contribution of specific connections and frequencies to the identified contrast can be tested with bootstrap procedures . Here we give a brief description of the technique [55–58] , which was previously applied in a number of EEG and MEG studies to characterize changes in the brain signals [59–61] . PLS operates on the whole data matrix at once . Typically the rows of the data matrix correspond to participants within groups , whereas the columns correspond to voxels in functional MRI , electrodes in EEG , sensors or sources in MEG . These features ( voxels , electrodes , sensors ) can be called the elements . In our case , the elements were represented by all the possible combinations of a pair of neuromagnetic sources and a frequency point . Specifically , to prepare for the PLS analysis , the data matrices were organized in the form of subjects within groups by elements , each associated with a connection and a frequency point ( 30 × 90 × 89/2 = 120 , 150 elements in total ) . Thus , the neuroimaging data were organized as a matrix: subjects within groups by all the possible combinations of connections and frequencies . Then , the covariances were computed between the data matrix and the vectors representing either the contrast between groups or the length of time elapsed between injury and scan . Next , singular value decomposition ( SVD ) was used to project the covariances to a set of orthogonal latent variables ( LVs ) , mathematically described as a products of three vectors: the left-singular vectors , the non-zero singular values , and the right-singular vectors . Each latent variable ( LV ) thus had three components: ( a ) a singular value , representing how much variance can be explained by this LV , similar to principal component analysis; ( b ) weights within the left singular vector , representing an underlying contrast between groups or an overall correlation between imaging and clinical data; ( c ) weights within the right singular vector ( element loadings ) , representing the robustness of contribution of all the elements to the group contrast or overall correlations . The overall significance of each LV and the importance of the individual elements within a specific LV was assessed using resampling procedures . First , we randomly reassigned subjects between groups , performing a permutation test . This global permutation test assessed the overall significance of a given LV , measuring how it is different from random noise . Specifically , we computed a measure of significance as the number of times the singular values from permuted data were higher than the observed singular value ( 500 permutations ) . In the second step , we tested the element loadings for stability across subjects by bootstrap resampling of subjects within groups ( 500 bootstrap samples ) . A measure of stability ( bootstrap ratio value ) was calculated as the ratio of the original element loading to the standard error of the distribution of the element loadings generated from bootstrapping . This is approximately equivalent to a z-score: a bootstrap ratio value of 3 or -3 corresponds to 95%-confidence under the assumption of a Gaussian distribution . Elements ( all the combinations of connections and frequencies ) with positive bootstrap ratio values directly support the contrast or overall correlation associated with the left-singular vector of a given LV . Negative bootstrap ratio values also indicate the robustness of the effects , but in the reverse direction . In other words , to correctly interpret the output , the bootstrap ratio values ( or z-scores ) need to be reported with respect to the contrast or overall correlation in order to correctly understand the direction of the loadings . We distinguish two types of PLS analysis: so called “contrast” and “behavioural” PLS [55 , 56] . In the contrast PLS , there are groups of subjects ( in our case , mTBI and healthy controls ) , and the PLV data are projected to an a priori defined contrast . In this case , the weights within the left singular vector are equivalent to the group contrast . The “behavioural” PLS , which is typically based only on one group of subjects , explores the covariance between the brain data and some continuous , subject-specific variables , such as time of scanning since injury . In this case , the weights within the left singular vector represent the overall correlations ( one correlation per variable ) between the PLV and time-of-scanning matrices . Both in the contrast and behavioural PLS , the right singular vector reflects the contribution of the individual elements to the tested effects . One assumption of our study is that the volume conduction effects do not represent a significant confounding factor . It is not entirely true that MEG is not sensitive to effects of volume conduction . It has been shown , however , that secondary currents resulting from volume conduction do not contribute to the radial component of the magnetic field under the assumption of a dipolar source in a spherical homogeneous conductor [62] . For our study , we used a first order axial gradiometer system , which is mainly sensitive to the radial component of the magnetic field ( that is , the field of a source dipole with tangential orientation ) . In this setting , estimating PLV , which may capture the couplings with a phase shift close to zero , seems reasonable . Using a more conservative measure such as weighted phase lag index ( PLI ) would further minimize the volume conduction effects , but it would also remove some physiologically meaningful couplings , which may reduce both the sensitivity and specificity . Another point is related to the segmentation of MEG recording . The original data were epoched into 10s segments . We choose 10s as a compromise between our intent to estimate phase synchrony at the lowest frequencies and to increase the robustness of the estimation by averaging across different epochs . Specifically , we believe that 10s is , on the one hand , long enough to robustly estimate the phase locking effects at the frequencies close to 1Hz , and on the other hand , is short enough to allow us to extract relatively large number of segments . The latter helps to increase the robustness of the results by averaging the phase synchrony across segments . Furthermore , the segments should be relatively short to not introduce large movement artefacts . Please note that the segments were extracted from 5 minutes of recordings using a rather conservative threshold of less than 3mm of movement . | Detecting concussion is typically not possible using currently clinically used brain imaging , such as MRI and CT scans . Magnetoencephalographic ( MEG ) imaging is able to directly measure brain activity at fast time scales , and this can be used to map how various areas of the brain interact . We recorded MEG from individuals who had suffered a concussion , as well as control subjects who had not . We found characteristic alterations of inter-regional interactions associated with concussion . Moreover , using a machine learning approach , we were able to detect concussion with 88% accuracy from MEG connectivity , and confidence of classification correlated with symptom severity . This potentially provides new quantitative and objective methods for detecting and assessing the severity of concussion using neuroimaging . |
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Transmissible spongiform encephalopathies ( TSEs ) are caused by the prion , which consists essentially of PrPSc , an aggregated , conformationally modified form of the cellular prion protein ( PrPC ) . Although TSEs can be experimentally transmitted by intracerebral inoculation , most instances of infection in the field occur through extracerebral routes . The epidemics of kuru and variant Creutzfeldt-Jakob disease were caused by dietary exposure to prions , and parenteral administration of prion-contaminated hormones has caused hundreds of iatrogenic TSEs . In all these instances , the development of postexposure prophylaxis relies on understanding of how prions propagate from the site of entry to the brain . While much evidence points to lymphoreticular invasion followed by retrograde transfer through peripheral nerves , prions are present in the blood and may conceivably cross the blood-brain barrier directly . Here we have addressed the role of the blood-brain barrier ( BBB ) in prion disease propagation using Pdgfbret/ret mice which possess a highly permeable BBB . We found that Pdgfbret/ret mice have a similar prion disease incubation time as their littermate controls regardless of the route of prion transmission . These surprising results indicate that BBB permeability is irrelevant to the initiation of prion disease , even when prions are administered parenterally .
Transmissible spongiform encephalopathies ( TSEs ) are progressive , invariably lethal neurodegenerative diseases which include Creutzfeldt–Jakob disease , kuru , fatal familial insomnia and Gerstmann–Sträussler–Scheinker syndrome in humans , scrapie in sheep , and bovine spongiform encephalopathy ( BSE ) in cattle [1] . The infectious agent , termed prion , consists primarily of PrPSc , a conformationally modified form of PrPC , a protein encoded by the gene PRNP [2] . Conversion of PrPC into PrPSc leads to accumulation of insoluble , partially protease-resistant prion protein deposits in the brain parenchyma around neurons and neuronal loss which is accompanied by gliosis and spongiform changes . Deletion of PrPC renders mice resistant to prion infections , indicating that its conversion into PrPC is necessary for the development of disease [1] . Although there have been instances of patients intracerebrally infected by prion-contaminated medical equipment or by dura mater grafts of cadaveric origin [3] , transmission of prion infections occurs more frequently through peripheral routes . The oral route of transmission has caused epidemics of kuru and variant CJD in humans , as well as BSE in cows [3] . Likewise , the parenteral route of prion transmission is highly effective in laboratory mice and hamsters . But how do prions reach the central nervous system ( CNS ) upon entering the body from peripheral sites ? After extraneural inoculation , prions accumulate and replicate in lymphoid tissues [4] . Follicular dendritic cells ( FDC ) and their precursors may constitute the first site of prion amplification [1] . Several studies indicate that prions travel to the CNS along peripheral sympathetic nerves , and the distance between FDC and sympathetic nerve endings specifies the speed of neuroinvasion [5 , 6] . However , none of these findings exclude the possibility that prions , in addition to following the lymph invasive route , may directly colonize the CNS through hematogenic spread followed by direct crossing of the brain vasculature . Prions are present in the blood of hamsters , mice and humans , and it was recently shown that both , PrPc and PrPSc , can cross the blood-brain barrier ( BBB ) [7–9] . Whether this contributes to the initial spread of the disease into the CNS is largely unknown . Here we have addressed the role of the BBB in prion pathogenesis using a genetically modified mouse strain ( Pdgfbret/ret ) which possesses a highly-permeable BBB as the result of the expression of a platelet-derived growth factor B ( PDGF-B ) lacking the PDGF-B retention motif [10 , 11] . We show that Pdgfbret/ret mice succumb to prion disease similarly to their littermate controls regardless of the route of prion transmission . In addition , there are no differences in histopathological characteristics of the disease nor in the resistance to the protease K of PrPSc in the brains of terminally sick Pdgfbret/ret mice compared to the controls . Our study indicates that although PrPSc can cross the BBB [7 , 8] , this route of entry into the CNS is negligible as regards the initiation of the disease when prions are administered intravenously , and highlights the importance of peripheral replication in prion disease pathogenesis in the case of blood-borne transmission .
B6 . 129-Pdgfb<tm3Cbet> [12] heterozygous mice ( Pdgfbwt/ret ) in the C57BL/6J genetic background were crossed to obtain Pdgfbwt/wt , Pdgfbwt/ret and Pdgfbret/ret littermates that were used for prion infection studies . Pdgfbret/ret animals possess an open BBB [10] . The BBB defect occurs at the level of endothelial transcytosis and tracers with a wide range in the molecular weight ( 1 kDa– 200 kDa ) or different chemical composition enter the brain parenchyma in Pdgfbret/ret animals . Animal care and experimental protocols were in accordance with the “Swiss Ethical Principles and Guidelines for Experiments on Animals” , and approved by the Veterinary office of the Canton of Zurich ( permits ZH130/2008 , ZH14/2012 , ZH90/2013 and ZH196/2014 ) . Pdgfbwt/ret and Pdgfbret/ret mice received 2 . 5mg/20g 70 kDa-dextran conjugated to Texas Red ( Invitrogen , Cat # D1864 ) via the tail vein . The tracer was allowed to circulate for 5 hours . Mouse brain tissue was prepared for whole-brain clearing according to published protocols [13 , 14] . Mice were deeply anaesthetized and transcardially perfused with ice cold PBS followed by a fixative mixture of 4% acrylamide , 1% paraformaldehyde , 0 . 05% Bis , 0 . 25% VA-044 in PBS . Mouse brains were removed and post-fixed in the same fixative for 24 hours at 4 °C . The brains were de-gassed , exposed to gaseous nitrogen , and polymerized for 2 . 5 hours at 37 °C . Brains were extracted from the hydrogel and placed in 8% sodium dodecyl sulfate ( SDS ) , 200 mM boric acid , pH 8 . 5 ( clearing solution ) . Brains underwent clearing by electrophoresis ( 4–8 hours ) . Cleared brains were then washed in PBS . The refractive index was equilibrated with refractive-index matching solution prepared according to published protocols [14] . Brains were imaged using a custom mesoscale selective plane illumination microscope ( mesospim . org ) that will be described in detail elsewhere . Images were processed using Image J and Imaris ( Bitplane ) software . Mice were infected with the Rocky Mountain Laboratory ( RML ) scrapie strain ( passage 6 , RML6 ) . Three different inoculation routes were used: intracerebral , intravenous and intraperitoneal . For inoculations , we used 30 μl of RML6 brain homogenate prepared in a solution of 0 . 32 M sucrose containing 5% BSA . Control groups of mice received intracerebrally 30 μl of non-infectious brain homogenate ( NBH , 10% w/v ) prepared from healthy CD-1 mice . Clinical assessment and scoring of mice based on the presence of neurological signs ( including ataxia , kyphosis , priapism , leg paresis , lack of grooming ) was performed as previously described [15] . Mice were euthanized on the day of onset of clinical signs of scrapie according to the approved protocols . One group of mice was inoculated intracerebrally with 30 μl of RML6 brain homogenate containing 1 . 5 log LD50 of infectious agent . Two groups of mice received RML6 intravenously 100 μl of RML6 brain homogenate containing 6 log LD50 and 100 μl of RML6 brain homogenate containing 3 log LD50 of infectious agent . One group of mice received 30 μl of RML6 brain homogenate containing 4 . 5 log LD50 intraperitoneally . Prism software ( www . graphpad . com ) was used to perform statistical analysis . The log-rank test was used to compare the survival curves between Pdgfbwt/wt , Pdgfbret/wt and Pdgfbret/ret littermates . Brains were homogenized in 0 . 32 M sucrose in PBS . Total protein concentration was determined using the bicinchoninic acid assay ( Pierce ) according to manufacturer’s instructions . Samples were adjusted to 1 μg/μl and digested with proteinase K ( PK ) ( 20 μg/μl ) in PBS , 0 . 5% SDS and 0 . 5% NP-40 for 30 minutes at 37 °C . Proteinase K reaction was stopped by adding loading buffer ( Invitrogen ) followed by boiling samples for 5 minutes at 95 °C . PK-treated and untreated samples were separated on a 12% Bis-Tris polyacrylamide gel ( NuPAGE , Invitrogen ) and blotted onto a nitrocellulose membrane . Anti-PrP antibody ( POM1 , 200 ng ml−1 ) [16] was used as a primary antibody which was detected using rabbit anti-mouse IgG1 conjugated to horseradish peroxidase ( HRP ) . Western blots were developed using Luminata Crescendo Western HRP substrate ( Millipore ) and visualized using the FUJI-FILM LAS-3000 system . The glycoform profiles of PrPSc after PK treatment were quantified using the Quantify One software ( BioRad ) . The relative intensity of each PrPSc glycoform ( i . e . di- , mono- , ungylcosylated ) was measured which was expressed then as a percentage of the total signal . Statistical analysis ( two-way ANOVA ) was performed using the Prism software ( www . Graphpad . com ) . Formalin-fixed tissues were treated with concentrated formic acid for 60 minutes at room temperature to inactivate prion infectivity . Tissue was embedded in paraffin and cut into 2 μm sections . After deparaffinization through graded alcohols sections were stained with hematoxylin/eosin . Antibody SAF-84 ( A03208 , 1:200 , SPI-Bio , Waterloo , Australia ) was used to detect partially protease-resistant prion protein deposition on a NEXES immunohistochemistry robot ( Ventana Instruments , Basel Switzerland ) using an IVIEW DAB Detection Kit ( Ventana ) , after incubation with protease 1 ( Ventana ) . Microglia was detected using anti-Iba 1 antibody ( WAKO ) . Sections were deparaffinized through graded alcohols and heat-induced antigen retrieval was performed in citrate buffer ( 0 . 01 M; pH 6 ) . Sections were incubated with anti-Iba1 Ab ( 1∶2500 ) . Stainings were visualized using DAB ( Sigma-Aldrich ) and H2O2 ( Sigma-Aldrich ) , after incubation with a biotinylated secondary antibody ( Vector Laboratories ) followed by the ABC complex solution ( Vector laboratories ) . Sections were counterstained with Hematoxylin . Images of HE and DAB stained sections were acquired using a NanoZoomer scanner ( Hamamatsu Photonics ) and NDPview digital pathology software ( Hamamatsu Photonics ) . The quantification of Iba1 positive cells was performed after DAB immunohistochemistry ( n = 4 fields/mouse , n = 3–6 mice/group ) using Qupath software ( manual quantification function ) [17] . Data were presented as number of Iba1 positive cells/mm2 . Quantification of vacuoles was performed on HE images ( ( n = 8 fields/mouse , n = 3–5 mice/group ) . The algorithm to count vacuoles was developed in MATLAB R2016B using the Image Processing toolbox . Image segmentation was performed using Otsu’s thresholding method ( T = 0 . 7 ) . Only round vacuoles with an area in the range of [200 pixels , 2000 pixels] and with a shape values > 0 . 9 ( where shape was calculated as shape = ( 4*π*Area ) / ( Perimeter^2 ) ) were quantified . The code is available at: https://github . com/AndraCh/Vacuoles_segmentation . Statistical analysis ( one-way ANOVA ) was performed using the Prism software ( www . Graphpad . com ) . PK-resistant PrPSc was quantified in brain tissue homogenate by FRET using monoclonal antibodies POM1 and POM19 [16] . Protein concentration of samples were determined with a bicinchoninic acid assay performed according to the manufacturer’s instructions ( Thermo Fisher Scientific ) . A total amount of 10 μg protein per well was diluted to the correct volume in PBS . To determine PK-resistant PrPSc levels , samples were PK digested using 50 μg/ml PK ( Roche ) at 37°C for 30 min under constant agitation . Digestion was stopped by adding PMSF to a final concentration of 2 . 24 mM and samples were incubated for 10 min at room temperature ( RT ) . Denaturation of remaining PrPSc in samples was achieved by the addition of NaOH to a final concentration of 56 mM and samples were incubated for 10 min at RT under constant agitation . To neutralize the samples , NaH2PO4 was added to a final concentration of 66 mM and incubated for 10 min at RT . Samples were pipetted in triplicates to a 384-well OptiPlate ( Perkin Elmer ) . For FRET assay we used two in-house produced monoclonal antibodies recognizing different epitopes of PrP ( POM19 and POM1 ) [16] . POM19 was coupled to Europium ( EU , FRET donor ) and POM1 was coupled to allophycocyanin ( APC , FRET acceptor ) . The antibody pair was diluted in 1X Lance buffer ( Perkin Elmer ) to a final concentration of EU-POM19 of 2 . 5 nM and APC-POM1 of 5 nM . After adding the antibody pair to samples , the plate was centrifuged at 2000 g for 1 minute and incubated overnight at 4°C . The following day , FRET was measured using a multilabel plate reader ( EnVision , Perkin Elmer ) . The excitation wavelength was 337 nm and emission wavelength for EU was 615 nm and for APC 665 nm . Following measurement , the net-FRET signal was used in accordance with formula published in [18] . For subsequent analysis , the triplicates were averaged and the signal for non-infectious brain homogenate was subtracted to remove the background . PrPSc levels are presented as a ratio of PrPSc in each individual animal to the average of wild-type littermates . Statistical analysis ( one-way ANOVA ) was performed using the Prism software ( www . Graphpad . com ) .
In order to detect any possible role of the BBB in prion pathogenesis we used Pdgfbret/ret mice which express a hypomorphic variant of PDGFB [10] . The cerebrovascular tree in these animals is highly permeable to blood-borne macromolecules , as has been demonstrated in several studies using many orthogonal experimental approaches including immunohistochemistry , spectrophotometry , MRI [10 , 11 , 19] . Surprisingly , these mice are viable and enjoy an almost-normal life span despite their deeply dysfunctional BBB . These findings made it possible to perform prion infection experiments and study the development of disease over many months . Certain areas of the brain are differentially susceptible to neurodegeneration induced by different prion strains [20] . It is conceivable that the BBB defect of Pdgfbret/ret mice may not coincide with the areas of selective vulnerability to prions . Therefore , we investigated the regional characteristics of BBB permeability in Pdgfbret/ret mice using electrophoretic clearing based on the CLARITY method [13] in custom designed clearing chambers . After receiving an intravenous injection of 70 kDa dextran conjugated to TexasRed , mice were perfused , brains were cleared and then imaged using a mesoscale selective-plane illumination microscope . The entire brain of Pdgfbret/ret animals showed passage of 70 kDa-dextran Texas Red into the brain parenchyma ( S1B Fig and S1 Movie ) . The strongest leakage of dextran was detected in the cortex compared to other brain regions , which is in agreement with previously published data [19] . In control animals ( Pdgfbwt/ret ) , no fluorescent signal can be detected in the brain parenchyma ( S1A Fig ) . Thus , these data further demonstrate an enhanced permeability of the BBB in the entire brain of Pdgfbret/ret mice . To test whether Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) are competent for prion replication and succumb into prion disease similarly , mice were inoculated intracerebrally with RML6 . There was no difference in disease incubation between Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) when infected intracerebrally ( Fig 1A ) . Inoculation of brain homogenate can induce autoimmune encephalitis , and it is not known whether such pathology may be exacerbated by a leaky BBB . Therefore , Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) were inoculated with non-infectious brain homogenate prepared from CD-1 strain as a control . None of these mice developed clinical signs of disease . They were sacrificed 350 days post-inoculation and showed no signs of encephalitis or prion pathology ( Figs 2 and 3 and S2 and S5 Figs ) . When prions are administered peripherally , neuroinvasion is dependent on peripheral replication in lymphoid tissues [4] . Therefore we assessed the course of prion disease in Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) after intraperitoneal administration of prions . Similarly to intracerebral inoculation , Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) showed no differences in prion disease pathogenesis after intraperitoneal inoculation ( Fig 1B ) . Thus , we conclude that the altered BBB does not affect prion neuroinvasion when prions are administered peripherally nor lead to accelerated prion pathogenesis when administered intracerebrally . It has been previously claimed that intravenously injected prions reach the CNS within minutes [7] , and the authors of that study concluded that the quantity of PrPSc that reaches the brain via the BBB is sufficient to induce the disease in mice possessing a normal BBB . However , another study with Syrian hamsters found that although prions were found in the CNS few days after peripheral administration , the levels of prions were sub-infectious [21] . The mode of PrPSc transport into the CNS via brain endothelium is not known . The brain vasculature of Pdgfbret/ret mice is permeable to plasma proteins such as albumin , IgG , and the BBB permeability occurs at the level of endothelial transcytosis [10] . Normal brain endothelial cells show a paucity of transcytotic vesicles , however , the increased transcytosis is seen as a first sign of BBB defect in several brain insults ( e . g . ischemic stroke ) [22] . To directly assess the role of the open BBB in prion pathogenesis , Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) received RML6 intravenously , either a high dose ( 6 log LD50 ) or a low dose ( 3 log LD50 ) . We reasoned that Pdgfbret/ret animals should show an earlier onset of prion disease compared to the control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) since their BBB is permeable to plasma proteins . However , this was not the case , Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) animals did not show any statistically significant differences in the speed of prion pathogenesis and in their attack rate , either at high dose ( 6 log LD50 , p = 0 . 37 ) or at low dose ( 3 log LD50 , p = 0 . 86 ) of intravenously administered prions ( Fig 1C and 1D ) . The minimal trend towards earlier lethality of Pdgfbret/ret mice was well within the biological variability expected in this kind of experiments . Our results using a mouse model with a compromised BBB due to increased transcytosis show that the permeability of CNS vasculature has a negligible effect on prion disease transmission into the CNS . This observation is in agreement with two decades of studies on the role of lymphoid organs in prion spread . If prions could enter the brain directly after peripheral inoculation , it would be difficult to understand why mice lacking B-cells [23] or complement components [24] experienced delayed neuroinvasion , and why the distance between follicular dendritic cells and peripheral nerve endings controls the speed of neuroinvasion [6] . Instead , the current results validate a model by which peripherally administered prions first colonize the lymphoid organs , then undergo a phase of clinically silent peripheral replication , and finally achieve neuroinvasion by exploiting peripheral nerves belonging to the sympathetic nervous system [5] . Therefore , prions resemble neurotropic viruses such as rhabdoviruses and herpesviruses that utilize retrograde axonal transport to gain access to the central nervous system , thereby bypassing the need for breaching the BBB . However , the limitation of our study is that the amount of PrPSc in the brain parenchyma shorty after intravenous injection with RML6 homogenate has not been quantified since currently available methods for detecting infectious prions in the brain parenchyma shortly after inoculations are not sufficient for this task . Although Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) did not show differences in the prion disease incubation , they may conceivably differ with respect to the histological and biochemical hallmarks of prion infection . We therefore assessed the extent of spongiosis , the most characteristic feature of TSEs , on hematoxylin-eosin stained brain sections . All prion-inoculated animals , but none of the mice injected with NBH , showed the presence of spongiosis regardless of their genotype ( Fig 2A ) . Quantification of vacuolation showed no difference in number of vacuoles and area covered by vacuoles between Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) ( Fig 2B and 2C ) . Microglia , whose activation in prion disease is considered neuroprotective partly by prion clearance [25 , 26] , express Pdgfb [27] . Although it is unlikely that the lack of Pdgfb retention motif will have a cell-autonomous effect on microglia—since microglia do not express the receptor of Pdgfb—Pdgfrb , we nevertheless assessed microgliosis in terminal stage of the disease . No differences in number of microglia in between Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) was detected ( S3 Fig ) . Finally , we investigated the levels of PrPSc accumulation using immunohistochemistry on formalin-fixed paraffin-embedded tissue sections , Western blotting and fluorescence-resonance energy transfer ( FRET ) . Immunohistochemical analysis showed the presence of partially protease-resistant PrP ( PrPSc ) in the brains of all prion-inoculated mice , regardless of the genotype and of the inoculation route , whereas NBH-injected mice never exhibited any PrPSc deposits ( Fig 3 ) . Interestingly , Pdgfbret/ret mice showed more prominent accumulation of PrPSc along blood vessels than wild-type mice ( S4 Fig ) . All prion-inoculated animals showed PrPSc accumulation in spleen ( S5 Fig ) . Western blotting of partially proteinase K-resistant PrPSc showed similar levels of disease-associated PrP among the three genotypes ( Pdgfbwt/wt , Pdgfbwt/ret , Pdgfbret/ret ) for all inoculation routes and prion doses ( Fig 4A–4D ) . Quantification of PrPSc levels using a FRET assay after PK digestion [18] did not show a difference in PrPSc levels between Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) ( Fig 4E ) . It is conceivable that BBB opening would result in unorthodox prion replication and possibly in a strain shift [28] . Thus , we investigated whether differences in the BBB permeability in Pdgfbret/ret mice lead to alteration in prion strain . As a proxy for strain identification we quantified the ratio of the mono- , di- , and unglycosylated forms of PrPSc . No significant difference between glycoform ratios were found among genotypes ( Fig 4F and 4G ) , suggesting that no shift in prion strains had taken place in Pdgfbret/ret animals . The altered BBB in Pdgfbret/ret mice in chronic inflammatory condition could lead to infiltration of peripheral leukocytes which could modify disease cause . However , other studies have shown that during prion disease there is a minimal recruitment of inflammatory monocytes and mice showing various T cell deficiencies develop clinical signs of prion disease with a comparable temporal dynamic/pattern to that seen in wild-type mice [23 , 29] . The development of prion disease does not differ between Pdgfbret/ret and control mice ( Pdgfbwt/wt , Pdgfbwt/ret ) despite of the breached BBB of Pdgfbret/ret animals after all tested inoculation routes , including intracerebral inoculation . Thus , if there is a component in pathology caused by peripheral leukocytes then the effect on the disease has no detectable influence . In conclusion , these data indicate that when prions are inoculated directly into the blood , the intactness of the BBB has a negligible effect on the incubation time of the disease . This suggests that extracerebrally administered prions do not need to trespass the BBB in order to enter the CNS . Alternatively , one could construe that prions can trespass the neurovascular barriers and colonize the brain through a mechanism that is fundamentally independent of the BBB . There is no factual evidence supporting the latter scenario . A wealth of data accrued in multiple model systems supports the idea that prions , after entering the body from extraneural sites , undergo an early phase of replication in lymphoid organs , which is then followed by the colonization of peripheral nerve endings . It is then through the latter , according to this hypothesis , that prions eventually gain access to the CNS—akin to neurotropic viruses such as rhabdo- and herpesviruses . If prion spread to the CNS were directly hematogenic , one might expect the first site of CNS replication to be solely determined by the differential prion replication competence of select brain areas , rather than by the site of inoculation . In reality , however , the first site of CNS invasion after intraperitoneal inoculation with prions corresponds to the segmental projections of the peripheral nerves to the spinal cord [5 , 6] . Moreover , chemical or immunological sympathectomy suffices to prevent neuroinvasion after intraperitoneal prion inoculation [5] . In the framework of the studies enumerated above , the data reported here add to the conjecture that prion spread from the periphery to the brain does not occur by direct transition across the BBB . Besides their significance for the basic understanding of prion neuroinvasion , these results may be of relevance to the possibility of developing effective post-exposure prophylaxis of prion diseases , which may prevent neurodegeneration even after extraneural infection has already taken place . | Prion diseases or transmissible spongiform encephalopathies ( TSEs ) are incurable brain diseases caused by conformational changes in the endogenous prion protein . Prions can be transmitted through contaminated food , surgical instruments and blood . Transmission of prions has caused the kuru epidemic in humans and bovine spongiform encephalopathy in cattle , which in turn caused variant Creutzfeldt-Jakob disease ( CJD ) in humans . Furthermore , injection of prion-contaminated hormones has caused hundreds of TSE cases . In order to develop drugs to prevent the spread of prions into the brain after exposure via food or medical procedures , it is necessary to gain an understanding of how prions propagate from the site of entry to the brain . Prions were shown to reach the spinal cord by traveling along peripheral nerves . However , prions are also found in blood . Although normal brain vessels act as a barrier between the blood and brain , some studies suggested that prions in blood may enter the brain via blood vessels . Here we have tested the latter hypothesis using mice with increased brain blood vessel permeability . We found that these mice are similar to wild-type mice in their susceptibility to prion disease and incubation times after peripheral inoculation . These results suggest that passage of prions through the blood-brain barrier may not be relevant to the development of disease , and imply that any effective post-exposure treatment should rather aim at other rate-limiting steps of prion propagation . |
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One of the most puzzling features of the prehistoric record of hominid stone tools is its apparent punctuation: it consists of abrupt bursts of dramatic change that separate long periods of largely unchanging technology . Within each such period , small punctuated cultural modifications take place . Punctuation on multiple timescales and magnitudes is also found in cultural trajectories from historical times . To explain these sharp cultural bursts , researchers invoke such external factors as sudden environmental change , rapid cognitive or morphological change in the hominids that created the tools , or replacement of one species or population by another . Here we propose a dynamic model of cultural evolution that accommodates empirical observations: without invoking external factors , it gives rise to a pattern of rare , dramatic cultural bursts , interspersed by more frequent , smaller , punctuated cultural modifications . Our model includes interdependent innovation processes that occur at different rates . It also incorporates a realistic aspect of cultural evolution: cultural innovations , such as those that increase food availability or that affect cultural transmission , can change the parameters that affect cultural evolution , thereby altering the population’s cultural dynamics and steady state . This steady state can be regarded as a cultural carrying capacity . These parameter-changing cultural innovations occur very rarely , but whenever one occurs , it triggers a dramatic shift towards a new cultural steady state . The smaller and more frequent punctuated cultural changes , on the other hand , are brought about by innovations that spur the invention of further , related , technology , and which occur regardless of whether the population is near its cultural steady state . Our model suggests that common interpretations of cultural shifts as evidence of biological change , for example the appearance of behaviorally modern humans , may be unwarranted .
We investigate not only the effects of innovation processes on the cultural repertoire , but also ways in which these processes can fundamentally change the dynamics of cultural accumulation . Here we provide a description of the dynamic model and , under simplifying assumptions , some analytical derivations of the expected number of tools arising from the three interacting processes ( see also SI section 2 of [33] ) . We implement this model as an agent-based stochastic simulation . In the model of Kolodny et al . [33] , which we reframe and expand in the current study , tool innovation consists of three interacting stochastic processes . The first process consists of invention of large-scale innovations . These are also called lucky leaps , and are stochastically added to a population of size N with probability Plucky per individual per time step with an expected rate of change of ΔnluckyΔt=Plucky⋅N . In a population that starts out with a cultural repertoire of size zero , the expected number of these lucky leaps at time t can thus be written as nlucky=Plucky∙N∙t . ( 1 ) These large-scale innovations facilitate two other innovation processes , allowing the accumulation of two additional types of tools: toolkit innovations , and innovative combinations . First , each lucky leap can be associated with L toolkit innovations , which are tools that are made useful by the existence of the lucky leap , where L is sampled from a uniform distribution U ( 1 , Lmax ) . The expected rate of change of toolkit innovations is ΔntoolkitΔt=Plucky⋅N⋅⟨L⟩ , where ⟨L⟩ denotes the mean value of toolkit sizes , i . e . 1+Lmax2 . With both lucky leaps and toolkit innovations , the expected potential size of this tool repertoire at time t is given as: nlucky+ntoolkit=Plucky∙N∙t+Plucky∙⟨L⟩∙N∙t . ( 2 ) In the stochastic simulations , this potential number of toolkit innovations often accumulates over multiple time steps . Each individual has a probability Ptoolkit of producing a toolkit innovation per time step; if Ptoolkit or N is large , the full potential size of a lucky leap’s toolkit is quickly reached once there is a lucky leap . Second , a lucky leap can be combined with another tool to produce an innovative combination , which is useful to the population with probability PcombUseful . With this type of innovation included , and considering for simplicity only combinations of lucky leap tools , the expected rate of change of the number of combination tools per time step is ΔncombΔt=Plucky⋅N⋅nlucky⋅PcombUseful . Summing the expected number of lucky leaps , toolkit innovations , and combinations at time t gives: nlucky+ntoolkit+ncomb=Plucky∙N∙t+Plucky∙L∙N∙t+½ ( Plucky∙N ) 2∙PcombUseful∙t2 . ( 3 ) As mentioned above , the analytical derivations presented in Eqs 1–3 allow us to calculate the potential number of tools expected at a given time step under the simplifying assumption that all toolkit and combination innovations are immediately tested when a new lucky leap is invented . In the stochastic simulations , each individual has a probability Pcombine in each time step of combining two tools to check whether this gives rise to a useful tool . At each time step , tools can also be randomly lost , which occurs with probability PSpontLoss/N , since we expect the rate of cultural loss to decrease as population size increases . Toolkit innovations and combination tools are lost in our model if the lucky leap tool with which they are associated is lost . With probability nluckynlucky+ntoolkit+ncomb , the tool that is lost is a lucky leap , and thus its associated toolkit and combinations are lost with it , so when a lucky leap is lost , the total number of tools lost is Lt + Ct + 1 , where Lt and Ct are , respectively , the mean number of toolkit innovations and combination innovations associated with a lucky leap innovation at the time of its loss , t , and the 1 accounts for the lucky leap itself . With probability 1−nluckynlucky+ntoolkit+ncomb , the tool lost is a toolkit or combination innovation , and the number of tools lost is 1 . Thus , at each time step , there is an expected loss term that is subtracted from the number of tools gained at that step: −PSpontLossN⋅ntotal⋅ ( nluckyntotal⋅ ( Lt+Ct+1 ) + ( 1−nluckyntotal ) ) , ( 4a ) where ntotal = nlucky + ntoolkit + ncomb . Making this substitution gives the loss term: −PSpontLoss/N· ( nlucky+ntoolkit+ncomb ) −PSpontLoss/N·nlucky·Lt−PSpontLoss/N·nlucky·Ct ( 4b ) Simply by accounting for multiple interacting innovation processes , this model produces punctuated bursts of cultural innovations after periods of stasis , since the stochastic addition of a new lucky leap can facilitate the addition of numerous combinations and toolkit innovations on a relatively short timescale [33] . When stochastic innovation and spontaneous loss both occur in the model , the number of tools in the population’s repertoire eventually approaches a steady state . To describe the number of tools at steady state , Kolodny et al . derive the following equations for each type of tool under some simplifying assumptions . These describe the asymptote of the curve , and represent the expected number of tools from each type in a scenario that considers spontaneous loss ( see also SI of [33] ) : nlucky*=N2⋅PluckyPSpontLoss ( 5 ) ntoolkit*=N2⋅Plucky⋅<L>2⋅PSpontLoss ( 6 ) ncomb*=N4⋅Plucky2⋅PCombUseful2⋅PSpontLoss2 ( 7 ) In the model described so far , periods of stasis in the cultural record represent the waiting times between large-scale innovations , which can then facilitate the accumulation of many other innovations through other pathways . In other words , some human innovations require large leaps of insight , but other innovations can be created by drawing parallels with existing technologies or by combining existing technologies to make a new tool . These different processes of innovation occur at different rates , and the relationships between them and their rates determine whether the accumulation of tools occurs in a punctuated pattern . However , with given rates of innovation and loss , a population’s number of tools eventually reaches a steady state in which there is a stochastic balance between the loss and accumulation of tools in the population . The idea of this cultural steady state suggests an alternative explanation for the dramatic bursts observed in the cultural record: periods of stasis could be stretches of time in which the population is at steady state in its cultural evolution , and extensive cultural change could occur following a change in one or more of the parameters that determine the cultural carrying capacity ( the size of the tool repertoire at a steady state ) . In other words , if processes change one or more of the parameters in Eqs ( 5 ) – ( 7 ) , we would expect a major punctuated shift to a new steady state . Changes in these parameters could be a result of changes in extrinsic factors , as has been suggested in the literature to explain punctuated cultural shifts . For example , the increase in cognitive capacity that Klein [16] suggested as an explanation for the cultural burst ~50kya could be represented in our model’s terms as an increase in Plucky . We suggest a parsimonious and realistic alternative to the explanation of cultural bursts as responses to cognitive or environmental changes: a punctuated shift in the steady state could also result from factors intrinsic to cultural evolution itself , such as the spread of a game-changing innovation that alters the parameters of cultural evolution . Here , we explore two particular processes that could give rise to such parameter changes . First , we account for the possibility that a rare cultural trait might foster an increase in the biological carrying capacity of the habitat and hence of the population size . For example , the invention of certain agricultural techniques might lead to increased crop yields and thus produce an increase in population size [39] . According to Eqs ( 5 ) – ( 7 ) , such an increase is expected to result in a larger cultural repertoire at steady state . Population size and cultural repertoire have been linked in both empirical ( e . g . [40–47] but see [48 , 49] ) and theoretical studies ( e . g . [5 , 33 , 34 , 50–52] , but see the debate in [53 , 54] ) . At each time step in our model , a lucky leap occurs with probability Plucky per individual . Each such lucky leap is assumed to have a probability PToolChangesCarryingCapacity/N of turning out to be an innovation that alters the biological carrying capacity of the system . If this occurs at time t , we assume that the increase in the population size is carried out by multiplying the current population size , N , by a number T sampled from a uniform distribution: Nt+1=Nt∙T ( 8 ) In the results presented here , we chose a range for T of U ( 1 . 2 , 1 . 6 ) for visual clarity of the figures: with the parameters of cultural evolution used in our simulations , population size changes by factors of 1 . 2 to 1 . 6 lead to visually clear changes in the cultural steady state , but do so without suddenly changing the steady state’s order of magnitude . Choosing a different range for T does not qualitatively change the results . After this population size increase , with no change in other parameters of the model , the population can accumulate more tools , and the number of tools in the cultural repertoire increases . Analytically , the steady state is linearly dependent on the rate of lucky leap innovation ( Plucky ) , quadratically dependent on the population size ( ~N2 ) , and inversely proportional to rate of loss ( ~1/PSpontLoss ) ; see Eq 6 . These relationships are sensitive to the details of the model; for example , if innovative combinations are included , Plucky varies as ~N4 ( Eq 7 ) . If the lucky leap innovation associated with the increased biological carrying capacity is lost in the population , the population size can revert to its previous level , and the tool repertoire will subsequently drop as well . In realistic terms , this would be manifested as a change back to the population’s original subsistence strategy . As noted above , the probability that a lucky leap innovation leads to a change in biological carrying capacity is dependent on PToolChangesCarryingCapacity , but is also assumed to be inversely proportional to the population size at the time of its invention . The reasoning behind this choice is that as the population is larger , it is more likely to already be making efficient use of a larger proportion of the habitat’s resources , making it harder to invent a means of increasing the resources available that would translate to an increased biological carrying capacity . In reality , the relationship between population size and the likelihood of increasing biological carrying capacity may be nonlinear and possibly involve additional factors; we choose this inverse relationship for simplicity ( but see discussion for an alternative ) . Because the rate of lucky leap innovations is dependent linearly on population size , there is a constant expected rate of occurrence of innovations that increase biological carrying capacity , independent of population size . Second , we modified our model [33] to account for the possibility that a rare cultural trait might cause a decrease in the rate at which culture is lost . For example , the invention of a writing system [55] or other ways of preserving cultural information might lead to a lower probability that a trait is forgotten . A decrease in the rate at which innovations are lost effectively increases trait retention and thus results in an increased cultural repertoire size without altering the population size . With probability PLossRateReduction , a lucky leap innovation reduces the loss rate . When this occurs , the loss rate , PSpontLoss , at time t+1 is that at time t multiplied by a number S sampled from a uniform distribution: PSpontLoss ( t+1 ) =PSpontLoss ( t ) ∙S . ( 9 ) As with the factor of population size increase , T , a range for S of U ( 0 . 5 , 0 . 9 ) was chosen for visual clarity of the presented results . Choice of a different range does not qualitatively change the results . With probability Preverse per time step , an innovation that decreased PSpontLoss is itself lost , and PSpontLoss reverts to its previous value and tools are lost with higher probability in the population .
Here , we analyze a model that encompasses two realistic ways in which innovations can affect the trajectory of cultural evolution: one in which a large-scale innovation , for example a more effective subsistence strategy , can alter the population size and thus the cultural steady state of the population , and one in which a large-scale innovation , such as writing or other mechanisms of preserving cultural information , can affect the rate of cultural loss . With all external factors remaining constant in our previous model [33] , a population’s cultural gains and losses reach an eventual cultural steady state , such that the rates of cultural innovation and loss balance one another and the population maintains a relatively stable number of tools . By altering the parameters that affect cultural gains and losses , game-changing innovations in human culture can perturb the stochastic steady state , resulting in large-scale cultural bursts . In the simulations presented here , we restrict the possible innovation processes to lucky leaps and toolkit innovations for simplicity . The results of simulations that include innovative combinations are qualitatively similar , but the dynamics are more difficult to visualize because tools accumulate much more rapidly . Fig 1 is an example of a time trajectory of a cultural repertoire under the assumption that innovations do not change the parameters of cultural evolution . The punctuation in this trajectory ( see inset ) is thus driven exclusively by lucky leap innovations , which trigger further invention of related toolkit innovations , corresponding to the minor cultural punctuations discussed above . The repertoire initially grows in size , and stabilizes on a stochastic equilibrium near a repertoire size of 480 tools around which large fluctuation can be seen , driven by losses and inventions of tools . A decrease of a single tool in the cultural repertoire is typically a result of stochastic loss of a toolkit innovation , while instantaneous loss of multiple tools is the result of stochastic loss of a lucky leap innovation , which in our model results in the loss of its associated toolkit innovations as well . Fig 2 shows the effects of an innovation that alters the stochastic steady state of the cultural repertoire . At several time steps ( indicated by blue dots on the x-axis ) , an innovation occurs that changes the biological carrying capacity and thus increases the population size . This is implemented by multiplying the population size , N , by a number drawn from a uniform distribution between 1 . 2 and 1 . 6 . At each time step , there is a small probability , PToolChangesCarryingCapacity/N , where PToolChangesCarryingCapacity = 0 . 002 , that a lucky leap innovation alters the biological carrying capacity in this way . In some simulations , the number of tools in the population plateaus at the cultural steady state between each major punctuation event , and the pattern of cultural accumulation is punctuated and stepwise ( Fig 2A ) . However , if by chance these changes in biological carrying capacity occur more frequently , the population does not have the opportunity to approach the steady states , and the increase in tool repertoire is more gradual and less punctuated ( Fig 2B ) . Thus , even for the same underlying probability of this type of culturally induced change in the biological carrying capacity , a range of qualitative results is possible , from continuous to very punctuated changes . Although the two cultural trajectories in Fig 2 were simulated with the same set of parameters , a similar pattern can be produced by altering the probability that an innovation changes the biological carrying capacity . For example , for very low values of PToolChangesCarryingCapacity , the biological carrying capacity changes rarely enough that the cultural repertoire nearly always reaches a steady state between these changes , whereas for higher values , these changes occur often enough that plateaus are very rare . The rates at which tools of different types are invented , as well as the rate of tool loss , affect the overall rate of tool accumulation . Thus , whether the cultural repertoire reaches a steady state between changes in the biological carrying capacity depends on the relations between all of the model parameters , and not only on PToolChangesCarryingCapacity . In Fig 2 , we chose a single intermediate value of PToolChangesCarryingCapacity for both panels to demonstrate that the inherent stochasticity in the system can produce a spectrum of different qualitative patterns with the same underlying parameters . In the simulations presented in Fig 2 , we did not allow the possibility of loss of an innovation that had led to an increase in biological carrying capacity , which is discussed in greater detail below . Fig 3 shows the effects of innovations that alter the rate of cultural loss . In this scenario , there is a small probability that a lucky leap innovation reduces the rate of stochastic cultural loss; for example , a writing technology or techniques for accurate transmission of oral tradition . With probability PLossRateReduction , the stochastic rate of cultural loss , PSpontLoss , is multiplied by a number drawn from a uniform distribution between 0 . 5 and 0 . 9 , as shown by the green dots in Fig 3 . This alteration in the loss rate changes the stochastic cultural steady state of the population , and the cultural repertoire increases . Because of the stochastic nature of these innovations , multiple changes can occur within a short time period , resulting in an even more dramatic burst , such as in Fig 3B near time step 142 , 000 . With probability Preverse per time step this loss-reducing technology is itself lost , as shown by the yellow dots in Fig 3B , leading to a corresponding decrease in the number of tools accumulated .
The trajectory of accumulation of cultural innovations , such as tools or knowledge , often follows a punctuated pattern [1–4 , 8 , 16] . This seems to be particularly true–and puzzling–with regard to the archaeological record of stone tools during the evolution of hominids . Moreover , there appear to be at least two types of punctuated changes apparent in the archaeological record: large and rare punctuation events that encompass major cultural upheavals , and minor , more frequent punctuations , that are smaller in scope and occur between relatively short periods of stasis . Previous accounts have attributed both punctuated patterns to such external factors as environmental or cognitive changes [16 , 17 , 22–25] , suggesting that these changes affect the parameters that determine the cultural steady state of the hominid population . In previous work , we suggested an alternative explanation that does not invoke external factors , and suggested that such punctuations may be an intrinsic characteristic of cultural evolution , deriving from the complex cognitive and behavioral mechanisms underlying the innovation process . We suggested that punctuations in the cultural record could represent waiting times between novel innovations that trigger and provide cultural niches for further , related , innovations [33] . This process may underlie the minor , more frequent , cultural changes described above , but is less likely to fully explain the major cultural upheavals occasionally observed in the archaeological record , such as between the Lower , Middle , and Upper Paleolithic . Thus , the question remains: how can we understand the most dramatic shifts in cultural repertoires ? Can these major large-scale punctuations be explained as a feature of cultural evolution , as minor , smaller shifts can , or must we invoke external factors to explain them ? Here , we propose a framework in which the largest cultural shifts can result from the process of cultural evolution itself: innovations can themselves alter the underlying rates of cultural gain and loss or change the population size by bringing about changes in food availability , thereby spurring large-scale changes in the cultural steady state . Our model is somewhat unusual in that the parameters that characterize a population are not held constant throughout a simulation . Instead , we suggest a mechanism for these parameters to be changed through the cultural processes that are being modeled . The two drivers of punctuation–technological leaps that facilitate further related innovations , and changes to parameters that alter the cultural steady state , triggering major cultural upheavals–can potentially occur on similar timescales . However , in many cases , such as those demonstrated here , they occur on different timescales , with changes in the cultural steady state occurring rarely , but leading to explosions of cultural change that are typically much larger than changes that are driven by innovations that do not affect the system’s parameters . Notably , one study [56] has previously proposed a model in which a population is drawn towards one of two stable states of cultural complexity and population size , and shifts between the two may occur , driven by the combined dynamics of culture and demography . However , the model in [56] primarily focuses on recurring shifts between two particular states , and also does not account for the two scales of magnitude of punctuated cultural changes that are observed in the archaeological record . Here we have considered game-changing innovations of two types—those that increase the biological carrying capacity and those that decrease the rate at which tools are lost . To demonstrate the scope of possibilities , loss of a game-changing innovation was implemented only in the latter section of the results , with regard to innovations that change the rate of tool loss . The qualitative result of the loss of an innovation that increased biological carrying capacity is similar: the cultural repertoire shrinks towards its new steady state . Although loss of both types of game-changing innovations is conceivable , their likelihoods may differ: an innovation that affects the biological carrying capacity is likely to be one that significantly influences the population’s subsistence strategy , and to be known by many individuals . Its loss is thus unlikely , and , moreover , the results of such loss ( for example , a smaller food supply ) are likely to be evident within a short time period , perhaps less than a generation , while some notion of the innovation still exists in the population , possibly facilitating its re-invention if it is lost . On the other hand , knowledge that reduces the rate of cultural loss , such as writing , may be concentrated in small segments of the population , and thus may be more readily lost . The outcome of such a loss–the subsequent loss of other innovations at a somewhat increased rate–would play out quite slowly , over many generations , and the cost of the loss would be more likely to go unnoticed by the population as a whole than the loss of a subsistence strategy , thus decreasing the likelihood of a response such as re-invention of a similar trait while its notion still exists . For the human cultural record , it is important to consider punctuation in the context of its magnitude and the timescale on which it occurred , since different timescales can lead to inferences of punctuation that may derive from quite different processes . A note of caution is due in this regard: some interpretations of cultural records as being punctuated may derive from the fact that cultural records are in many cases incomplete . Thus , a process of gradual continuous cultural change may leave an archaeological trace that is sparse , creating a false impression of punctuation . We are agnostic with regard to the interpretation of any particular cultural record , but point out that , depending on parameter values , our model can give rise both to punctuated cultural trajectories and to continuous gradual change as can be seen in panel B of Fig 2 ( see also [33] ) . Our model demonstrates that interpreting the existence of a large-scale punctuated cultural change as evidence for a biological change in the human population might be unwarranted , given that an exclusively cultural framework such as ours is capable of explaining such changes in the cultural repertoire through realistic cultural modification of the parameters that affect cultural evolution . As noted above , different processes can underlie a change in steady state that is driven by cultural dynamics . For example , in our model and in others , an increase in population size is often assumed to increase the rate of innovation and to decrease the rate at which culture is lost . Such an increase in rates of innovation may induce further inventions that alter the parameters of cultural evolution . For example , one could imagine a ratchet-like accelerating process in which the changes to the steady state become more frequent as the population grows and culture accumulates . A similar result would be achieved if the likelihood of a steady-state-changing innovation were dependent on cultural complexity in itself . Such a case may be represented by archaeological epochs , whose boundaries are defined by the lithic technologies that were practiced during each of them: these epochs decreased in duration as cultural complexity and population sizes increased , from a very long Lower Paleolithic ( ~3 . 3 Mya–300 kya ) , to a shorter Middle Paleolithic ( 300–45 kya ) , to an even shorter Upper Paleolithic ( 50–10 kya ) . Previous models have demonstrated that a process of tool invention via combination of existing tools may lead to accumulation of tools at a polynomial or even exponential rate [9 , 33 , 38 , 51] . The results presented here suggest an alternative mechanism that would produce such dynamics: a rapid , nonlinear increase in the cultural repertoire may be a result of a ‘moving target’ of the cultural steady state and/or related parameters , if these parameters change relatively often before a steady state is achieved . Thus , for example , if cultural innovations bring about changes in population size and occur fairly frequently , and if the rate of cultural accumulation is dependent on population size , the resulting trajectory would increase nonlinearly in time . Many intuitions about human cultural change and hypotheses that emerge from the archaeological record can be tested using a framework such as the present one . In addition to the advent of agriculture and writing suggested above , our model could accommodate such game-changing innovations as the printing press [57] , techniques for an accurately transmitted oral tradition [58] , modern medicine [59] , and the Green Revolution in agriculture , including synthetic fertilizers and new cereal strains [60] . An aspect of cultural evolution that is beyond the scope of the current study , but whose study in a framework such as ours could prove insightful , is the way in which the cultural repertoire is affected by the functional relationships between novel technologies and existing ones . Thus , for example , some inventions seem to take on the role of earlier technologies and lead to their complete or near-complete replacement , as has occurred with recent changes in technologies of digital communication and data storage . Other technologies , despite providing an alternative to traditional methods , continue to exist side by side with them for millennia , as , for example , in the technologies of production of ceramics , olive oil , cheese , and wool . Future empirical and theoretical research could investigate the persistence of multiple technologies associated with the production of these and similar goods . For example , earlier ceramic production methods might persist because knowledge of or access to the new technology is confined to a subset of the population; alternatively , the earlier technology might persist because it is more efficient for small-scale home production even if the new technology is more effective for larger-scale production . A particularly interesting avenue of exploration along this path could incorporate the functional role of new technology with the historical fate of its bearers: some technologies , particularly subsistence technologies , may be superior to previous ones to an extent that they either replace them directly or replace them via replacement of the groups that fail to adopt the new technology . Some would argue that this was the fate of Neanderthals at the end of the Middle Paleolithic ( see , e . g . , [61 , 62] ) and of hunter-gatherers in Europe following the spread of agriculture ( e . g . [63–65] ) . Although our model demonstrates that extrinsic changes , such as environmental and cognitive changes , are not necessary to explain large-scale bursts of cultural accumulation , it is likely that cultural , environmental , and genetic changes all play a role in large-scale changes in human history . Moreover , their dynamics are likely to be intertwined in many cases ( e . g . , [66] ) ; a promising avenue of future exploration would be the study of dynamics in which biology , environment , cultural norms , and innovative processes co-evolve and feed back on one another . | The ability to accumulate culture is unique to hominids . Yet , understanding of the processes that drive cultural evolution is limited . We develop a computational model that incorporates characteristics of human innovation and offers an explanation for one of the puzzling observations in the archaeological record of stone tools: its extreme punctuation . The record shows long periods of little technological change , interspersed with abrupt increases in cultural complexity . Within these periods of relative stasis smaller punctuations occur , with sudden gain or loss of suites of tools . We suggest that the two types of punctuation may result from two processes: small punctuations are driven by innovations that trigger invention of related tools , and large punctuations reflect changes in the steady state of the size of the population’s tool repertoire . In our model , these changes of steady state occur when cultural processes can change the parameters of their own evolution . This occurs , for example , through innovations that change the availability of food and lead to an increased population size or innovations that increase the effectiveness of cultural transmission . Our results suggest that the common attribution of sudden cultural shifts to external processes such as cognitive or environmental change may be unwarranted . |
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Experiments have generated a plethora of data about the genes , molecules , and cells involved in thymocyte development . Here , we use a computer-driven simulation that uses data about thymocyte development to generate an integrated dynamic representation—a novel technology we have termed reactive animation ( RA ) . RA reveals emergent properties in complex dynamic biological systems . We apply RA to thymocyte development by reproducing and extending the effects of known gene knockouts: CXCR4 and CCR9 . RA simulation revealed a previously unidentified role of thymocyte competition for major histocompatability complex presentation . We now report that such competition is required for normal anatomical compartmentalization , can influence the rate of thymocyte velocities within chemokine gradients , and can account for the disproportion between single-positive CD4 and CD8 lineages developing from double-positive precursors .
The mammalian thymus receives stem cells from the bone marrow . These cells—thymocytes—go through a series of anatomical subcompartments in a process termed T cell education [1 , 2] . About 97% of candidate T cells die , while the remaining 3% are essential to the continuing development of the adaptive immune system [3] . For a recent review of thymic architecture and cell traffic , see [4] . For a brief schematic animation of thymic maturation , see Video S1 . Extensive research in disparate disciplines has uncovered a mass of data regarding thymocyte development . Subfields of thymus research include genes , gene expression and differentiation; molecules ( integrins , chemokines , cytokines , receptors , antigens , and other ligands ) ; cells ( stem cells , thymocytes , epithelial cells , dendritic cells , and macrophages ) ; cell behavior ( adhesion , migration , and anatomic localization ) ; cell states ( differentiation states , cell cycle , proliferation , and apoptosis ) ; and physiology ( antigen expression , positive and negative selection , lineage choice , and antigen-receptor repertoires ) . The technologies used to study thymopoeisis include genetics , transgenes and gene knockouts , protein chemistry , microscopy and immunohistochemistry , in vitro cell cultures and interactions , in vivo phenotypes , cell and organ transfers , immunizations , and more . A systematic integration of these data into an accurate and comprehensive representation is much needed . We address this need using reactive animation ( RA ) to reveal multiscalar emergent properties and to guide experimentation in thymocyte development . RA is a computational approach to simulating complex dynamic systems . The technology of RA has been described elsewhere [5–7] . Briefly , the RA simulation is built in two tiers . The first tier is built , bottom-up , from the actual cellular and molecular data , and incorporates the program , the logic , and the dynamics of the simulation . The second tier is a front-end visualization of the simulation capable of real-time interactive manipulation of the action . RA allows the experimenter to extract statistical and local information from the running simulation . Moreover , the experimenter can intervene in the simulation and observe in silico the effects of thought experiments . Our simulation was built primarily using for the first tier the language of Statecharts [8] , with its enhanced legibility and organizational structure . We added to Statecharts a second tier of animation . Between the two tiers , we built a set of tools to facilitate data-mining options , such as tracking cells , manipulation of surface receptors , inducement of apoptosis , tracking cell ancestry , data displays , visualization of chemokine gradients , zooming in and out across scales , streaming reports , statistical data , and more ( see Figure 1B ) . Demonstrations of these tools are in Videos S2–S4 and online . The references to papers we used for the database are listed in Text S1 . RA differs fundamentally from other approaches directed to network modeling [9–11] or to simulating the microarchitecture of the immunological synapse or membrane [12–14] . We did not simulate membrane data in the present work due to the lack of quantitative data . Another spatially based system has been developed [15] , and thus far it has been applied to molecular signaling [16] .
Data at the level of single cells and their microenvironment culled from hundreds of papers were coded to the simulation . Anatomic localization is critical to thymus development; thymocytes at different developmental stages migrate to specific thymic compartments [2] . Validation of a bottom-up simulation such as RA ( and of the database itself ) requires that the microscale molecular data put into the model suffice to generate realistic macroscale thymocyte migration and anatomical location . Cell migration depends on thymocyte receptor profiles , chemokine gradients , epithelial cells , cell proliferation , cell survival , cell velocity , and other factors that enter the simulation . For example , a thymocyte at the double-negative ( DN ) 1 stage expresses a profile of surface markers CD4−CD8−CD25−CD44+LselectinlowCD69− [17] . Experimentally , we know that thymocytes at the DN1 stage migrate towards the chemokine CCL25 [18] . The thymus stroma , too , influences migratory decisions; a thymocyte's path to a chemokine may be blocked by another cell . Furthermore , the chemokine itself is involved in two dynamic processes: first , specific regions of the thymus continuously secrete the chemokine , and second , the chemokine diffuses over time and space . Thus , the migration of a thymocyte continuously changes as a function of secretion and diffusion of chemokines and the current locations of other migrating thymocytes and stationary stromal cells . This and much additional information is included in the simulation . Figure 1 demonstrates that the simulated thymic lobule faithfully produces the fine anatomical relationships of real thymic structure; thousands of thymocytes , individually computed , localize , as seen in histological sections , to particular anatomical sites according to twelve distinct developmental stages—color-coded in the legend box . Figure 2 shows the migratory path of a single thymocyte . Both the emerging anatomy and the emerging path are faithful to experimental results [2] . Video S1 and Video S3 show dynamic versions of RA figures . These results demonstrate that the molecular data in hand suffice to generate the macroscale thymus , and that RA can reveal this cross-scalar emergence . The effects on thymus fine anatomy of chemokine receptors CXCR4 [19] and CCR9 [20] have been studied experimentally , so we could compare an RA simulation of knocking out these receptors to the experimental results . Targeted gene deletion of CXCR4 [19] resulted in failed cortical localization and developmental arrest . Figure 3A ( left ) shows the thymic lobule as it was captured under the microscope [19] and as it is captured during simulation ( right ) . In both cases , the thymocytes do not respond to CXCR4 stimulation; both Figure 3 panels show that thymocyte development gets hung up close to the cortico–medulary junction ( CMJ ) in the DN1 stage ( labeled red ) . Figure 3B shows the same time frame and anatomical section in a wild-type thymus . Note that double-positive ( DP ) cells ( blue cells in the simulation ) have spread into the cortex . Unlike the static histology of the experimental model , RA provides a dynamic representation . In contrast to the CXCR4 knockout , deleting CCR9 had no major effect experimentally on intrathymic T cell development [20] . However , competitive transplantation experiments revealed that bone marrow cells from CCR9−/− mice were less efficient in repopulating the thymus of lethally irradiated Rag-1−/− mice than were bone marrow cells from littermate CCR9+/+ mice [18 , 20] . The RA simulation results , presented in Figure 4 and in Videos S1–S4 , show both the influence of the lack of response to CCL25 , the chemokine ligand of CCR9−/− , and the outcome of competition between CCR9−/− and wild-type cells . Figure 4A shows the normal thymus at the same time point as the altered thymus that appears in Figure 4B; the abnormal cells are coded gray here . Figure 4 is animated in Video S3 , where the upper panel shows the wild-type phenotype and the lower panel shows the CCR9−/− phenotype: the CCR9−/− cells congregate around the subcortical zone ( SCZ ) . Thymocytes at the transition from DN to DP would normally migrate towards the chemokine CCL25 and enter the cortex , but the CCR9−/− cells cannot respond to this chemokine and are blocked . The blocked thymocytes , however , can still move randomly , and population pressure pushes them away from the SCZ , so that some of them reach their next developmental checkpoint—the cortical epithelial cells—passively . These fortunate cells can then mature into their next developmental stage and migrate towards the medulla ( via a different chemokine ) , where they can further mature ( depending on further selection events ) into fully functional single-positive ( SP ) cells . RA discloses these dynamics , surmised from static experimental histology alone . RA also made it possible to observe the dynamics in silico of a competitive experiment , in which equal numbers of CCR9−/− and wild-type cells are seeded into the thymus: Video S2 shows that the wild-type cells survive and mature in much higher numbers . RA makes it possible to quantify the ratios between mature wild-type cells and mature CCR9−/− cells ( Figure 5 ) : we can see an initial peak of maturing wild-type cells , followed by a decrease and an eventual asymptotic ratio , as the buildup of random pressure of CCR9−/− cells eventually generates homeostasis . This competition has not yet been performed experimentally , but RA simulation predicts the outcome shown in Figure 5 . Figure 5 shows an asymptotic value of four wild-type thymocytes to every CCR9−/− thymocyte . The dynamics of the asymptote and the final value are our predictions if such an experiment was to be performed . A critical point evident from Figure 5 is the overwhelming advantage that wild-type cells have immediately after seeding the thymus . Such a marked effect should be easy to witness experimentally . Thymocytes need to traverse developmental niches; thus , when the number of thymocytes exceeds the space available for antigen presentation sites on epithelial cells , the thymocytes pile up and those waiting their turn for stimulation may undergo apoptosis from the lack of interaction [21 , 22] . RA makes it possible to study the function of competition by modifying interaction time constants , as shown in Video S3 . The results indicate that competition is essential to normal thymic development . Figure 6A shows the normal pattern of apoptosis that occurs in the cortex in the competition-enabled thymus . Figure 6B , in contrast , shows that an abnormal pattern of apoptosis develops in a thymus free of cell competition; here , most thymocytes die of negative selection in the medulla , rather than in the cortex . The RA simulation suggests that the waiting times for interactions with cortical epithelial cells constitute a bottleneck that is a factor in normal thymus development . RA in silico experimentation suggests that competition also selects for differential speeds of trafficking in response to chemokine gradients . Figure 7 shows that faster thymocytes enjoy greater chances of survival , at least up to a point . Nevertheless , some thymocytes that are relatively slower may avoid the negative selection suffered by some of their more speedy brothers . Thus , competition selects for a range of cell velocities , and not only for a uniformly high velocity . How selection for a range of T cell velocities might enhance defense against invaders [23] as well as for body maintenance [24] needs to be investigated . Another prediction emerging from cell competition relates to lineage commitment . A developing thymocyte must choose whether to become an SP CD4 T cell ( helper ) or an SP CD8 T cell ( cytotoxic ) . The decision-making process is obscure because mature SP CD4 and CD8 T cells evolve from precursors that are DP for both CD4 and CD8 , yet CD4 cells predominate at a 2:1 ratio . Current theories of lineage commitment deal with the molecular details of the choice . The two most significant themes in the theories distinguish between an “instruction” approach and a “stochastic” approach [25] . The “instruction” approach proposes that the productive interaction of a T cell receptor with a particular major histocompatability complex ( MHC ) molecule , class I ( for CD8 ) or class II ( for CD4 ) as the case may be , rewards the thymocyte and induces a genetic choice to differentiate to the CD8 or CD4 phenotype . The more fitting T cell receptor–MHC interaction instructs the T cell . The “stochastic” approach proposes that SP CD4 or CD8 thymocytes are “randomly” generated , and are later selected according to their functional performance with the MHC . These theories attempt to found lineage choice on its molecular components aimed at showing where exactly , during development , the cell chooses its lineage [25–27] . However , the emergence of competition between thymocytes for interaction space provides a novel solution to the CD4:CD8 2:1 paradox . If the dissociation rates of CD8 cells from epithelial cells are lower than those of CD4 cells , then the CD8 cells will remain longer at their epithelial-cell interaction stations ( peptide-MHC I sites ) . As long as a CD8 thymocyte lingers at a peptide-MHC 1 niche , this niche is unavailable for other , competing CD8 thymocytes . CD8 thymocytes , we propose , do not compete with the CD4 thymocytes , because CD4 thymocytes compete among themselves for stimulation by interacting with peptide–MHC II stations on epithelial cells . We tested the outcome on lineage frequency of simulating different dissociation rates for interactions between epithelial cells and CD4 and CD8 thymocytes . The results are shown in Figure 8 . It can be seen that about two-thirds of thymocytes will mature into CD4 T cells and one-third into CD8 T cells ( the de facto ratio ) when the dissociation rate of CD8 thymocytes is 1 . 7 to 3 . 3 times slower than the dissociation rate of CD4 thymocytes . A relatively greater avidity of CD8 cells for epithelial cell niches ( by 1 . 7–3 . 3 ) would generate the observed lineage predominance of CD4 T cells .
RA analysis of thymocyte development sheds new light on the dynamic relationship between molecules and cells in generating the structure and function of the thymus organ . First , we can see that the existing body of data , however discrete and piecemeal , can be integrated by RA simulation into a representation of the functional anatomy of the thymus seen in histologic sections . What we know about cells and molecules can indeed account for what we see; the macroscale organ emerges from the microscale mass of data in hand . In this regard , RA can be said to validate the database . Note , however , that classical histologic sections are two-dimensional slices of a three-dimensional organ frozen in time; RA simulation adds the dimension of time—dynamics—and so shows us the formative power of the dynamic flux of cells , molecules , and interactions that give rise to the higher-scale organ . In another project involving a different organ , we are currently extending RA simulations to accommodate the third dimension in space; hopefully , the added complexity of the representation will enhance our understanding of the biology . Second , RA simulation offers novel explanations for the observed outcomes of experimental intervention . In our case , for example , RA simulation suggested that the lack of phenotype observed in mice with CCR9 knocked out ( CCR9−/− ) might be explained by dynamic compensation through population pressure . RA simulation also explains the competitive growth advantage enjoyed by wild-type cells over CCR9−/− cells . Indeed , overexpression of CCR9 on thymocytes leads to an in vivo phenotype that can be explained by RA as an untimely attraction of the thymocytes by cortical epithelial cells . RA simulation also suggests that the absence of thymic output resulting from CXCR4 inhibition can be attributed to the nonmigratory behavior of cells entering the thymus . Third , the visualization of cell dynamics through RA provides a view of emergent physiology . Although the thymus is packed full of cells , the existence of competition among thymocytes for space and stimulation has not been a subject for experimentation or even discussion; competition is simply not seen in static histologic sections . Since competition was not recorded in the database , we did not explicitly program competition into our model . Nevertheless , cell competition emerged before our very eyes as we witnessed , via RA , the animated struggle between individual thymocytes for productive interactions with thymic epithelial cells . In silico manipulation of various parameters suggested that thymocyte competition might function as an important factor in three emergent properties of T cell maturation: the functional anatomy of the thymus , the selection of thymocytes with a range of migratory velocities , and the relative preponderance of SP CD4 T cells . Obviously , these suggestions require experimental validation . Irrespective of the outcome , however , the animation arm of RA , in providing a higher-scale view of complex emergent properties [28] , can alert us to new questions for experimentation . Ultimately , we would like to model a complete biological system—an entire cell , organ , or organism—in a way that is sufficiently realistic so as to be able to test the role of any known fact about the system . This goal has been formulated as the ability of a model to pass a sort of Turing test , and can be viewed as taking to the utmost limit the notion of prediction , confirmation , and verification of emergent properties; see [29] .
The RA simulation was written in C++ using the Rhapsody tool , and so RA code was generated by Rhapsody's code-generation engine , initiated by the language of Statecharts . To this automatically generated code , manually encoded objects and function were added . RA is the bridge made between the running simulation and the animation . Communication is made over a TCP/IP connection between a server implementing the Statecharts simulation and built-in animation functions in Flash . We used Matlab to analyze populations and population-level behavior . See [6] for further details . | Biological systems are the embodiment of complexity that defies intuitive understanding . Biologists have accumulated masses of data about the molecules , cells , and discrete interactions that compose living systems , but the list of facts alone cannot explain how such systems work dynamically . We have developed a hybrid , computational approach to the simulation of complex systems called reactive animation ( RA ) . RA uses a bottom-up integration of diverse experimental data to create an integrated and dynamic representation of the system's interacting cells and molecules . RA is faithful to experimental fact , while it plays out the action in animated formats directly accessible to the eye and mind . Most importantly , RA is analytical , interactive , and allows experimentation in silico . Here , we use RA to reveal unexpected emergent properties of thymocyte development . In particular , we now report that competition between thymocytes for sites of stimulation could be important in generating the fine anatomy of the thymus , in selecting for thymocytes with a range of migration velocities , and in explaining the paradox of CD4 to CD8 T cell lineage ratios . This study highlights the explanatory power and the potential aid to experimentation offered by an animated , interactive simulation of complex sets of data . |
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Pseudomonas aeruginosa is an opportunistic pathogen causing severe infections often characterized by robust neutrophilic infiltration . Neutrophils provide the first line of defense against P . aeruginosa . Aside from their defense conferred by phagocytic activity , neutrophils also release neutrophil extracellular traps ( NETs ) to immobilize bacteria . Although NET formation is an important antimicrobial process , the details of its mechanism are largely unknown . The identity of the main components of P . aeruginosa responsible for triggering NET formation is unclear . In this study , our focus was to identify the main bacterial factors mediating NET formation and to gain insight into the underlying mechanism . We found that P . aeruginosa in its exponential growth phase promoted strong NET formation in human neutrophils while its NET-inducing ability dramatically decreased at later stages of bacterial growth . We identified the flagellum as the primary component of P . aeruginosa responsible for inducing NET extrusion as flagellum-deficient bacteria remained seriously impaired in triggering NET formation . Purified P . aeruginosa flagellin , the monomeric component of the flagellum , does not stimulate NET formation in human neutrophils . P . aeruginosa-induced NET formation is independent of the flagellum-sensing receptors TLR5 and NLRC4 in both human and mouse neutrophils . Interestingly , we found that flagellar motility , not flagellum binding to neutrophils per se , mediates NET release induced by flagellated bacteria . Immotile , flagellar motor-deficient bacterial strains producing paralyzed flagella did not induce NET formation . Forced contact between immotile P . aeruginosa and neutrophils restored their NET-inducing ability . Both the motAB and motCD genetic loci encoding flagellar motor genes contribute to maximal NET release; however the motCD genes play a more important role . Phagocytosis of P . aeruginosa and superoxide production by neutrophils were also largely dependent upon a functional flagellum . Taken together , the flagellum is herein presented for the first time as the main organelle of planktonic bacteria responsible for mediating NET release . Furthermore , flagellar motility , rather than binding of the flagellum to flagellum-sensing receptors on host cells , is required for P . aeruginosa to induce NET release .
Pseudomonas aeruginosa is a ubiquitous opportunistic Gram-negative pathogen found in the environment . P . aeruginosa rarely infects healthy individuals and mainly causes lung infections in patients with compromised immune defenses [cystic fibrosis ( CF ) , chronic obstructive pulmonary disease ( COPD ) , HIV , non-CF bronchiectasis and hospital-acquired pneumonia] [1–6] . P . aeruginosa colonizes up to 80% of CF patients , 4–15% of COPD patients , 8–25% of HIV patients with pneumonia , 28% of non-CF bronchiectasis patients and 18–20% of patients with hospital-acquired pneumonia [4 , 7–9] . The high incidence of P . aeruginosa infections among these patients demonstrates that this bacterium represents a serious clinical problem . Polymorphonuclear neutrophilic granulocytes ( PMN ) play a critical role in fighting P . aeruginosa . Mammalian species lacking phagocytic cells or innate immune defense molecules are highly susceptible to infection with P . aeruginosa [10–12] . Humans deficient in key neutrophil-mediated antimicrobial mechanisms , such as specific granule deficiency or leukocyte adhesion deficiency ( LAD ) , are prone to P . aeruginosa infection [10] . Neutropenia , caused by chemotherapy , HIV infection or autoimmune disorders , predisposes patients to P . aeruginosa pneumonia [13–15] . Only patients with the full defensive arsenal of PMNs are able to defeat P . aeruginosa infections . An adequate immune response to P . aeruginosa requires the full spectrum of neutrophilic defenses . PMNs are the first to arrive at the site of infection where they fight pathogens via various mechanisms . In addition to phagocytic killing [16] , PMNs also trap and kill microbes via an alternative mechanism known as Neutrophil Extracellular Trap ( NET ) formation [17] . NETs are composed of a DNA scaffold associated with histones and neutrophil granule components , such as myeloperoxidase ( MPO ) and neutrophil elastase ( NE ) [17–19] . Only NET-forming PMNs and not apoptotic or necrotic PMNs release protein-DNA complexes ( MPO-DNA , NE-DNA or histone-DNA ) [17 , 20–22] . Signaling pathways leading to NET formation are largely unknown . The few known players are: NADPH oxidase , MPO , HNE ( human neutrophil elastase ) and histone citrullination mediated by peptidylarginine deiminase 4 ( PAD4 ) [23 , 24] . Both MPO and HNE are required for NET release [23] . The neutrophil respiratory burst produced by the NADPH oxidase is also essential for induction of NET formation by most bacterial stimuli studied [25 , 26] . PAD4-mediated citrullination of histones is required for NET formation [27 , 28] . These citrullinated histones are only present in NETs , not in resting PMNs [29] and PAD4-deficient murine PMNs do not form NETs [28 , 30] . PAD4-deficient mice have impaired NET-mediated antibacterial defenses [27] . Robust neutrophil infiltration and NETs have been detected in most of the diseases associated with P . aeruginosa lung infection [31–39] . This suggests that P . aeruginosa-triggered NET formation takes place in vivo under those disease conditions . Several independent studies performed by us and other groups confirmed that P . aeruginosa induces robust NET release in human PMNs [19 , 33 , 40–45] . We found that P . aeruginosa-induced NET formation requires the NADPH oxidase that leads to the release of citrullinated histones [19 , 41] . However , the mechanism by which P . aeruginosa initiates NET extrusion from PMNs remains unknown . Here , we aimed to identify components of planktonic bacteria and their associated mechanism ( s ) responsible for inducing NET release in PMNs . We identified the flagellum as the main bacterial component required to trigger maximal NET release . Interestingly , flagellum-mediated swimming motility , and not flagellum production itself , proved to be the main inciting mechanism . Our studies provide novel insight into P . aeruginosa-induced NET formation , a host-microbe interaction clinically relevant in several airway diseases .
Although bacteria have been shown to trigger NET release , it is unknown which microbial components mediate this process . To gain insight into this question , we monitored P . aeruginosa’s ability to trigger NET release at various phases of growth as P . aeruginosa expresses different phenotypic features depending on its growth phase [46] . Early exponential phase cultures are characterized by motility and expression of virulence factors , while in later growth phases these features are lost and quorum-sensing molecules and extracellular polysaccharides become expressed to a greater extent [47] . We used early exponential phase ( OD = 0 . 4 at 600nm , ~6hrs incubation ) ( Fig 1A ) , early stationary phase ( ~24 hrs ) and late stationary phase ( ~48 hrs ) cultures of two laboratory strains of P . aeruginosa , type A flagellin-producing PAK and type B flagellin-producing PAO1 [48] . PMA ( phorbol 12-myristate 13-acetate , a potent activator of PKC ) is capable of inducing robust NET release by PMNs [19] and was utilized as a positive control . At the indicated times , bacteria were washed and exposed to human PMNs to measure NET release . As Fig 1B and 1C show , early exponential phase cultures of both strains induced the greatest NET release with decreasing induction of NET release by bacteria at each subsequent time . The PAK strain reproducibly induced larger amounts of NETs than PAO1 ( Fig 1B and 1C ) . These data show that P . aeruginosa in its early exponential growth phase triggers the most robust NET release . This finding suggested that bacterial components expressed at this early growth stage but lost at later stages are the main inducers of NET release . Flagellum-promoted swimming motility is often a hallmark characteristic of planktonic bacteria in their exponential growth phase [49 , 50] . To study the role of the flagellum in NET formation we used flagellum-deficient PAO1 and PAK strains ( PAO1 fliC and PAK flgC ) . The fliC gene encodes the flagellin monomer that polymerizes to form the flagellar filament , and the flgC gene encodes the flagellar hook to which the flagellar filament attaches [51] . As expected , both flagellum-deficient strains were immotile whereas their parental , flagellated counterparts displayed strong swimming motility ( S1 Fig ) . There are currently no commercially available methods to quantitate flagellin production in P . aeruginosa . Immunoblotting performed on bacterial lysates using an anti-P . aeruginosa flagellin antibody is described in the literature , but this method provides only semi-quantitative results [52] . Therefore , we developed an ELISA assay using a commercially available antibody capable of accurate quantitation of P . aeruginosa flagellin levels in bacterial lysates . Briefly , bacterial lysates are immobilized to the bottom of high-binding ELISA plates , blocked and exposed to anti-P . aeruginosa flagellin antibody , followed by repeated washes and addition of a secondary , peroxidase-labeled , anti-murine IgG antibody ( S2A Fig ) . Reliable and highly reproducible standard curves can be established using commercially available , purified P . aeruginosa flagellin resulting in a tight correlation between flagellin levels and optical density ( S2B Fig ) . To show the specificity of the assay for flagellin obtained from P . aeruginosa , we tested the ELISA assay with identical concentrations of P . aeruginosa and Shigella flexneri flagellin . The assay detected flagellin derived only from P . aeruginosa , not from S . flexneri ( S2C Fig ) . With this new tool , we observed no flagellin expression by the PAK flgC strain , as opposed to confirmed flagellin expression by its parental strain ( PAK WT ) ( S2D Fig ) . Previously , we demonstrated that human PMNs release active MPO and HNE in the presence of P . aeruginosa PA14 [19] . Fig 2A shows that PAO1 and PAK strains also induce MPO and HNE release in human PMNs . Flagellum-deficiency significantly reduced P . aeruginosa-triggered MPO release [PAO1: 53 . 5+/-12 . 3% reduction ( p = 0 . 0495 ) , PAK: 44 . 3+/-13 . 1% reduction ( p = 0 . 0296 ) ] ( Fig 2A ) . HNE release was also reduced in the case of both strains [PAO1: 56 . 4+/-11 . 5% reduction ( p = 0 . 0482 ) , PAK: 32 . 0+/-6 . 0% ( p = 0 . 0467 ) ] ( Fig 2A ) . Our previous data also show that MPO remains enzymatically active after being released from PA14-exposed PMNs [19] . This was also true using both PAO1 and PAK strains ( Fig 2B ) . Flagellum-deficient strains induced significantly less release of active MPO than their corresponding wild-type strains [PAO1: 68 . 0+/-11 . 8% reduction ( p = 0 . 020 ) , PAK: 80 . 9+/-6 . 2% reduction ( p = 0 . 004 ) ] ( Fig 2B ) . Thus , the bacterial flagellum is required to maximal release of HNE and active MPO from PMNs upon P . aeruginosa exposure , allowing for a more impactful immune response . Our previously published data suggest that NET formation provides the primary mechanism of MPO and HNE release from PMNs in the presence of P . aeruginosa [19] . We next tested how flagellum deficiency affects P . aeruginosa-initiated NET release . Non-flagellated P . aeruginosa strains induced only minimal extracellular DNA release ( ecDNA ) while their flagellated counterparts triggered a signal closer to that induced by PMA in human PMNs ( Fig 2C ) . Lack of flagellum resulted in a 74 . 1+/-6 . 3% ( PAO1 , n = 12 ) or 81 . 8+/-3 . 6% ( PAK , mean+/-S . E . M . , n = 5 ) reduction in ecDNA release ( Fig 2C ) . To specifically quantitate NETs , we used established ELISA assays detecting NET-specific MPO-DNA and HNE-DNA complexes developed in our laboratory [41 , 53] . These assays do not detect NET components alone ( DNA , MPO , HNE or nucleosomes ) ( S3 Fig ) . We observed robust NET release triggered by the wild-type flagellated P . aeruginosa ( PAO1 and PAK ) but not by isogenic non-flagellated bacteria ( Fig 2D ) . Lack of flagellum resulted in a reduction in P . aeruginosa-induced MPO-DNA release of 77 . 2+/-20 . 7% ( PAK , n = 3 ) and 61 . 4+/-17 . 1% ( PAO1 , n = 3 ) , as well as , a HNE-DNA release reduction of 88 . 0+/-12 . 5% ( PAO1 , n = 3 ) and 109 . 9+/-11 . 5% ( PAK , mean+/-S . E . M . , n = 3 ) ( Fig 2D ) . Since NETs have a distinctive morphology [17] and we had previously shown that MPO and citrullinated histone H4 co-localize with DNA in P . aeruginosa-induced NETs [19 , 41] , we compared immunofluorescence staining of human neutrophils exposed to flagellum-deficient bacteria to those exposed to wild-type strains . The absence of flagellum greatly reduced NET release , as assessed by the amount of characteristic DNA structures expelled from PMNs ( Fig 3A and 3B ) . MPO and citrullinated histones co-localized with DNA in NETs triggered by flagellated P . aeruginosa ( Fig 3A and 3B ) , further confirming that NET formation is the main neutrophil mechanism responding to P . aeruginosa [19 , 41] . P . aeruginosa flagellum is required for phagocytosis by macrophages [50] . Previously , we reported the requirement of a functional cytoskeleton for human PMNs to release NETs triggered by P . aeruginosa [19] . Based on this , we tested whether the bacterial flagellum is also essential for P . aeruginosa engulfment by PMNs . Our results in Fig 4A demonstrate that phagocytosis of flagellum-deficient PAO1 is greatly diminished in comparison to its flagellated counterpart . In addition to phagocytosis , the NADPH oxidase has also been described as a mediator of NET formation induced by different stimuli [26] . Although recently emerging data indicate the existence of NADPH oxidase-dependent and NADPH oxidase-independent mechanisms of NET release , we have previously shown that NET formation stimulated by P . aeruginosa requires the NADPH oxidase [19 , 41] . Therefore , to determine if the flagellum is required for NET formation upstream or downstream of the NADPH oxidase , we measured the neutrophil respiratory burst upon exposure to P . aeruginosa . Absence of the flagellum results in markedly reduced superoxide production triggered by PAO1 and PAK ( Fig 4B and 4C ) . Taken together , these results identified that the flagellum plays a key role in P . aeruginosa-induced NET release via both phagocytosis and NADPH oxidase-mediated superoxide production . Our data herein established that the flagellum is the main bacterial component of P . aeruginosa mediating induction of NET release in human PMNs ( Figs 2 and 3 ) . To determine the mechanism of this finding , we asked whether purified flagellin , the monomer constituent of flagella , is capable of triggering NET release . As flagellin of other bacterial species including Listeria monocytogenes has been shown to stimulate superoxide production in PMNs [54] , we first assessed the PMN respiratory burst in the presence of commercially available , recombinant P . aeruginosa flagellin . Flagellin stimulated PMN superoxide production in the micromolar range in a dose-dependent manner ( Fig 5A and 5B ) . However , same concentrations of flagellin failed to trigger NET release ( Fig 5C and 5D ) indicating that flagellin alone is not sufficient to induce NET extrusion in human PMNs . We also tested P . aeruginosa type a and type b flagellins purified from P . aeruginosa as described [55] and observed no NET release induced by them ( Fig 5E ) . Similarly , purified flagellin of Shigella flexneri also failed to induce NET formation in human PMNs ( S4 Fig ) . The main surface receptor for extracellular bacterial flagellin is Toll-like receptor 5 ( TLR5 ) that is expressed in PMNs [56 , 57] . It is unknown whether TLR5 has any role in NET formation . To assess the potential contribution of the TLR5-flagellin interaction to P . aeruginosa-induced NET release , we stimulated human PMNs with P . aeruginosa in the absence or presence of a neutralizing antibody against human TLR5 . This antibody inhibits P . aeruginosa flagellin-stimulated superoxide production in human PMNs in a dose-dependent manner ( Fig 6A ) . The same concentrations of an isotype control antibody had no effect ( Fig 6B ) . Blockade of TLR5 on PMNs with the neutralizing antibody had no effect on NETs expelled in response to P . aeruginosa PAO1 ( Fig 6C ) . These data suggest that the flagellum mediates P . aeruginosa-induced NET formation in human PMNs in a TLR5-independent manner . Inhibitors and blocking antibodies are currently the only options to experimentally manipulate human PMNs since these cells cannot be genetically modified in vitro . Due to the limited experimental repertoire of human PMNs , we also used primary murine PMNs allowing us to test cells obtained from genetically engineered animals . Murine PMNs isolated from bone marrow are capable of releasing NETs both in vitro [58] and in vivo [59] . We isolated viable , highly pure PMNs from murine bone marrow ( S5 Fig ) . Using fluorescence microscopy , we observed NETs expelled by murine PMNs after exposure to P . aeruginosa with MPO and DNA co-localization ( Fig 6D ) . Flagellum-deficient P . aeruginosa did not trigger significant NET release in murine PMNs ( Fig 6D–6F ) . These results confirm similar human PMN data and demonstrate that murine PMNs serve as an excellent model to study the role of flagellum in P . aeruginosa-induced NET extrusion . In addition to TLR5 sensing extracellular flagellum , flagellin in the cytosol is sensed by NOD-like receptor CARD domain containing 4 ( NLRC4 ) [60] that is expressed in PMNs [61] . To further assess whether TLR5/NLRC4-mediated flagellin recognition has any role in NET formation , we subjected murine PMNs expressing ( wild-type , WT ) or deficient in both TLR5 and NLRC4 ( TLR5-/- NLRC4-/- DKO ) to flagellated P . aeruginosa . Flagellated PAK and PAO1 induced NET release in murine PMNs ( Fig 6F and 6G ) . Interestingly , lack of ability to sense flagellin by murine PMNs ( TLR5-/- NLRC4-/- DKO ) did not affect P . aeruginosa-induced NET formation ( Fig 6F and 6G ) . This confirms our previous data with human PMNs showing that flagellin recognition pathways are dispensable for neutrophilic deployment of NETs against P . aeruginosa . P . aeruginosa flagellar motility has been shown to activate the PI3K/Akt pathway to induce phagocytic engulfment [62] . To study whether this pathway is required for NET formation mediated by P . aeruginosa swimming motility , we used wortmannin to inhibit the PI3K pathway . Inhibiting PI3K had no significant effect on P . aeruginosa-induced NET formation in neither human , nor murine PMNs ( Fig 6H ) . In our previous study , the same dose of wortmannin significantly inhibited NET formation stimulated by pseudogout-causing calcium pyrophosphate microcrystals suggesting that PI3K involvement in NET release is stimulus-dependent [29] . The finding of P . aeruginosa-initiated NET formation being flagellum-dependent but TRL5- and NLRC4-independent ( Figs 2–6 ) could be explained by the fact that the bacterial flagellum not only binds to its host receptors but also confers the ability to swim . Flagellar motility is characteristic during the early exponential phase and is lost at later stages of bacterial growth . Motility is an underappreciated feature of bacterial interactions with the host immune system that is recently gaining recognition [50 , 62] . Flagellar motility , not simply flagellum production , has been shown to be a key player in initiating immune responses in macrophages [63 , 64] . No study , however , has investigated the role of flagellar motility in PMN activation and NET release . To separate these two functions of the flagellum from each other we took advantage of P . aeruginosa mutants deficient in genes responsible for propulsion of the flagellum [51] . The P . aeruginosa flagellum is powered by a complex bacterial motor consisting of multiple proteins encoded by two sets of homologous motor genes: 1 ) motA , motB and 2 ) motC , motD [51 , 65] . Disruption of both loci is required to completely abolish swimming motility; deletion of either set of operons is not sufficient to eliminate swimming [51 , 65] . We characterized motility and flagellin production in 2 mutant PAK strains deficient in the following mot genes: strain LMP16 ( ΔmotCD motB ) and strain LMP50 ( ΔmotAB motD ) [51] . As expected , swimming motilities of the flagellar motor-deficient strains ( LMP16 and LMP50 ) and the flagellum-deficient ( flgC ) mutant were abolished ( Fig 7A ) . On the other hand , flagellin production ( measured by western blotting and ELISA in bacterial lysates ) was only missing in the flgC strain ( Fig 7B ) . Flagellar motor-deficient mutants produced flagellin to an extent similar to that of the wild-type ( WT ) strain ( Fig 7B ) . Together , these data demonstrate that the flagellar motor-deficient strains produce a flagellum that is paralyzed for rotation; they have impairment in swimming motility but express normal levels of flagellin . To assess the potential role of flagellar motility in NET release induced by bacteria , we stimulated human PMNs with wild-type , flagellum-deficient and flagellar motor-deficient P . aeruginosa PAK strains to measure NADPH oxidase activity and NET release . Superoxide production was abolished in the absence of both flagellar motility and flagellum production ( Fig 7C ) : nonstimulated ( 6 . 08+/-4 . 24 ) , PMA ( 57 . 25+/-7 . 08 ) , PAK WT ( 21 . 44+/-3 . 10 ) , LMP16 ( 8 . 21+/-2 . 89 ) , LMP50 ( 8 . 03+/-2 . 81 ) and flgC ( 7 . 68+/-3 . 43 ) ( *109 RLU , mean+/-S . E . M . , n = 4 ) . Similarly , P . aeruginosa-induced ecDNA release was significantly reduced when motility was abolished ( Fig 7D ) : 81 . 8+/-3 . 6% reduction with LMP16 , 85 . 8+/-4 . 6% with LMP50 and 88 . 6+/-4 . 1% with flgC ( mean+/-S . E . M . , n = 5 ) . PAK-induced MPO release showed a similar pattern ( Fig 7E ) . Wild-type PAK triggered 767 . 6+/-101 . 0 ng/ml MPO release , whereas flagellum-deficient P . aeruginosa induced considerably less MPO release of 405 . 8+/-37 . 7 ng/ml ( mean+/-S . E . M . , n = 10 ) ( Fig 7E ) . Immotile , flagellum-expressing PAK mutants induced PMN behavior similar to that of the flagellum-deficient strain: 387 . 9+/-47 . 9 ng/ml MPO release by LMP16 and 360 . 0+/-47 . 9 ng/ml by LMP50 strains ( Fig 7E , mean+/-S . E . M . , n = 10 ) . Of note , a significant portion of P . aeruginosa-stimulated MPO release , unlike other measures , is independent of the flagellum ( Fig 7E ) . These data confirm our previous observations that NET formation is the main , but not the only , mechanism to mediate MPO release from human PMNs stimulated by P . aeruginosa [19] . The fact that a motile flagellum provides the main mechanism of P . aeruginosa-triggered NET extrusion was further confirmed by our MPO-DNA ELISA data , showing greater amounts of MPO-DNA complexes released in response to motile P . aeruginosa ( Fig 7F ) . NET inductions by P . aeruginosa were reduced: by 84 . 5+/- 7 . 4% ( LMP16 ) , by 73 . 3+/-3 . 5% ( LMP50 ) and by 73 . 2+/-3 . 7% ( flgC ) ( mean+/-S . E . M . , n = 3 ) ( Fig 7F ) . Immunofluorescence staining of NETs following stimulation with wild-type and PAK mutants confirmed these data qualitatively ( Fig 7G and 7H ) . As reported previously , MPO co-localized with extracellular DNA in PAK-induced NETs ( Fig 7G ) . Human PMNs exposed to flagellum-deficient or flagellar motor-deficient P . aeruginosa strains released only minimal amount of NETs ( Fig 7G ) . Murine PMNs exposed to the same PAK mutants exhibited an identical pattern of NET release ( Fig 7I ) . Taken together , the results presented in Fig 7 indicate that flagellar motility , and not flagellum production alone , is the main factor of NET release , in the presence of flagellated P . aeruginosa . To support our previous findings we centrifuged wild-type , flagellum- and flagellar motor-deficient strains of P . aeruginosa on human PMNs and measured NET release . Centrifugation of bacteria on PMNs bypasses the need for motility to establish cell-cell contact . All four immotile bacterial strains were capable of inducing close to maximal DNA release in human PMNs upon centrifugation on PMNs ( Fig 8A and 8B ) . NET release by the wild-type bacterium was not affected by centrifugation ( Fig 8A and 8B ) . To confirm that live bacteria are required to induce NET formation , human PMNs were stimulated with heat-killed P . aeruginosa and superoxide production and DNA release were measured . Both readouts were inhibited by the heat-treatment indicating that live bacteria are needed to induce maximal NET formation ( S6 Fig ) . Thus , bypassing the requirement for motility to enable live P . aeruginosa-PMN contact restores the ability of immotile bacterial strains to trigger NETs . To assess which motility genes are required for P . aeruginosa to induce NET formation in human PMNs , we tested PAK strains deficient in either one of the motAB or motCD loci [51] . As shown in Fig 9 , motCD-deficient P . aeruginosa ( LMP9 , LMP84 and PAO1014 ) exhibited significant impairment in inducing NET formation while motAB-deficient bacteria ( LMP13 , LMP09 and PAO1020 ) remained largely unaffected . These data suggest that the motCD genes are more crucial for inducing NETs . However , NET release triggered by ΔmotCD PAK was still higher than that induced by the completely immotile bacteria that have lesions in both loci , suggesting that a fully functional motor complex is needed to trigger maximal NET formation ( Fig 9 ) . Next we aimed at restoring the impaired NET-inducing ability of motAB-deficient bacteria by cloning and reintroducing functional motAB genes in both PAK and PAO1 backgrounds . The following PAK strains were created: wild-type PAK transformed with empty vector ( LMP 80 ) , motCD-deficient PAK with the vector ( LMP84 ) and motCD-deficient PAK complemented with functional motCD ( LMP85 ) ( Table 1 ) . Similar PAO1 strains were generated: PAO1 wild-type containing empty vector ( PAO1010 ) , motCD-deficient PAO1 containing vector ( PAO1014 ) and motCD-deficient PAO1 complemented with motCD genes ( PAO1015 ) ( Table 1 ) . MotCD-deficiency lead to significantly impaired induction of NET release; whereas introduction of functional motCD genes resulted in increased NET release in both PAK and PAO1 backgrounds ( Fig 9A and 9B ) . Similar pattern was observed with superoxide production ( S7 Fig ) . Complemented strains were also created for the motAB mutants: PAK wild-type transformed with empty vector ( LMP82 ) , motAB-deficient PAK ( LMP90 ) and motAB-deficient PAK complemented with functional motAB genes ( LMP91 ) ; PAO1 wild-type transformed with empty vector ( PAO1012 ) , motAB-deficient mutant ( PAO1020 ) and motAB-deficient strain complemented with functional motAB genes ( PAO1021 ) ( Table 1 ) . Consistent with previous data , motAB mutants did not result in impairment in NET-induction and there were no significant differences between the motAB-deficient strain and its complemented derivatives in each background ( Fig 9A and 9B ) . Swimming motility of the strains was characterized in semisolid motility agar ( Fig 9C ) . Both , motAB- and motCD-deficiencies led to significant losses of swimming motility in semisolid agar while complemented derivatives regained swimming motility ( Fig 9C ) . Thus , our experiments suggest that the motCD genes play a primary role in determining the extent of NET induction by planktonic P . aeruginosa but both sets of motor genes are required for maximal NET-induction .
The purpose of this study was to identify the main bacterial component of P . aeruginosa triggering NET formation and to gain insight into its mechanism . P . aeruginosa is an opportunistic pathogen representing a serious medical problem . PMNs , the primary immune cells fighting this bacterium , release large amounts of NETs when challenged with P . aeruginosa [19 , 33 , 41 , 45] . These NETs then kill and trap bacteria [17] . NETs , however , also cause tissue damage in airway diseases characterized by P . aeruginosa infections ( e . g . , CF and COPD ) [31 , 32 , 70] . Therefore , it is important to illuminate the cellular-molecular details of P . aeruginosa-induced NET formation to better understand its clinical relevance in various disorders . Our data show that early growth-phase bacteria are the strongest NET-inducers . Our data identify flagellum as the main component of bacteria triggering NETs , thereby filling in a major gap in our understanding of the molecular details of bacterium-triggered NET formation . Until now , only two bacterial components ( LPS and pyocyanin ) were described as weak NET-inducers [17 , 40] . Our detailed characterization documents the major contribution of the flagellum to P . aeruginosa-induced NET extrusion in both human and murine PMNs , thus adding a new mechanism to the proinflammatory repertoire of flagellum [71] . Detection of P . aeruginosa flagellin was traditionally performed by immunoblotting or electron microscopy [72] . Neither of these methods is , however , suitable for accurate absolute quantitation . The new ELISA assay established here enables easier detection of varying P . aeruginosa flagellin levels in a large number of samples . The highly reproducible standard ensures accurate absolute quantitation of flagellin levels that is vital to study P . aeruginosa flagellum interactions within host cells including PMNs . TLR5 is expressed on airway epithelial cells and several innate immune cell types including PMNs [56 , 73] . TLR5 is the main receptor mediating activation of airway epithelial cells of P . aeruginosa via flagellin recognition [73] and the TLR5-flagellin interaction is a major mediator of airway inflammation in CF [74] . TLR5 also acts as a modifier gene in CF [74] . Therefore , it was very surprising to observe that P . aeruginosa-induced NET release is independent of TLR5 in both human and murine PMNs . In further support of this finding , we also show that recombinant P . aeruginosa flagellin monomers are capable of stimulating NADPH oxidase activity without inducing NET release . Flagellin monomers bind to and activate TLR5 [57] . Thus , our data suggest that the flagellum mediates P . aeruginosa-induced NET release by a novel mechanism independent of the flagellum-sensing machinery of PMNs ( TLR5/NLRC4 ) . Moreover , we found that flagellum-mediated swimming motility is the key mediator of NET release . These data add to currently published reports emphasizing the importance of swimming motility in P . aeruginosa virulence in macrophages [50 , 62] and imply that flagellum also contributes to bacterial virulence by mechanisms other than activation of the TLR5 signaling pathway . Motility-mediated bacterial virulence mechanisms represent a large gap in the scientific literature and require more detailed studies . It cannot be automatically assumed that motile bacterial pathogenesis is entirely the result of direct flagellum-receptor interactions . Instead , we must consider that other motility-based mechanisms can also take place . The presence of a rotating flagellum enables P . aeruginosa to swim which significantly increases the chances for bacterium-neutrophil encounters . It is very likely that pattern recognition receptors other than TLR5 and NLRC4 are responsible for direct binding of P . aeruginosa to PMNs during initiation of NET extrusion . The identities of these receptors remain to be elucidated . The polar flagellum of P . aeruginosa is powered by a complex motor containing dual stator units , MotAB and MotCD [reviewed in [75]] . The stators generate the torque used to turn the flagellar rotor . In liquid medium , either stator is sufficient to power the flagellum as deletion of either the motAB or motCD locus has little effect on swimming speed and only the deletion of both loci renders the bacterium immotile [51 , 75] . However , under conditions requiring higher torque , for example in semi-solid motility agar as ( shown in Fig 9C ) or on a swarming motility plate , the MotCD stator plays the dominant role [51 , 69] . Results presented here reveal that the motCD mutants strongly impair P . aeruginosa-induced NET formation , motAB mutants have little effect , and mutants with defects in both motAB and motCD , are most severely impaired for NET release induced in human PMNs . These findings suggest that motility is key to trigger NET formation . Moreover , they suggest that the stator that can provide the highest torque to the rotor is more important in NET formation . In line with previous studies , our results also suggest that flagellar motility genes could provide novel targets of pharmaceutical intervention to intervene with P . aeruginosa motility as a virulence mechanism currently gaining recognition . A flagellum is typically expressed in environmental isolates of P . aeruginosa and early clinical isolates of CF patients [76 , 77] . Loss of the flagellum is one of the characteristic changes accompanying the adaptation of P . aeruginosa in CF airways [76–80] . In chronic CF patients , P . aeruginosa mainly exists in biofilms [81] . However , biofilms are dynamic structures , and motile , flagellated bacteria likely break free from biofilms , possibly interacting with PMNs and shedding flagella . This is supported by recent data showing that P . aeruginosa flagellin is detected in sputa of chronic CF patients [82] . Thus , we speculate that the mechanism described here not only can have clinical relevance in early but also in chronic CF airway disease . Our studies provide a potential , novel explanation as to why it is advantageous for P . aeruginosa to lose its flagellar motility early on in colonization of the airways in CF . PMNs and NETs could provide a significant external pressure for P . aeruginosa’s down-regulation of flagellin expression in CF airways . This is supported by published data showing that NE cleaves flagellin and down-regulates flagellum expression in P . aeruginosa [83 , 84] . Loss of flagella and swimming motility could be the primary mechanism by which late-phase and mucoid CF isolates of P . aeruginosa acquire resistance against NET-mediated killing [33 , 41 , 45 , 85] . It is important to note that while P . aeruginosa-induced NET release is almost entirely flagellum- and flagellar motility-dependent , total MPO and HNE release is only partially dependent on these factors . This is in line with our previous observations stating that NET formation is the main , but not the only , mechanism of MPO and HNE release from PMNs challenged with P . aeruginosa [19 , 41] . Excessive degranulation could potentially be responsible for the NET-independent release of these primary granule components [86 , 87] . Our results demonstrating that murine PMNs also expel NETs in response to P . aeruginosa in a flagellum-dependent but TLR5-independent manner confirm the usefulness of murine PMNs as a model to study the mechanism of NET release stimulated by planktonic forms of bacteria . Having established murine NET measurements enables us to test ex vivo NET release in genetically modified murine PMNs . This genetic approach complements results obtained with human PMNs that are not suitable for genetic modifications . Our data show that early growth-phase planktonic bacteria are the strongest NET-inducers in human PMNs . Although the idea that planktonic bacteria can induce NET release has previously been challenged [88] , our observation is in line with numerous articles published by several independent groups all reporting robust NET release induced by ( planktonic or individual ) bacteria of a wide variety of different species [19 , 33 , 41 , 45 , 89–91] . That said , it is very likely that different mechanisms are responsible for NET release induced by bacteria or large microbes ( fungal hyphae ) [88] . Although it does not form the focus of the current study , but our data showing that flagellum is required for both phagocytosis and NET release and bacterium-PMN contact is essential to induce NET formation , indicate that phagocytosis of P . aeruginosa is necessary for NET release . This idea suggests that the same PMN can engulf bacteria and undergo subsequent NET release , as well . Previously , it has been proposed that a single PMN either performs phagocytosis or undergoes NET formation but not two functions in one cell [88] . Likely , the primary response of PMNs to planktonic bacteria is phagocytosis that is followed by NET release once uptake of more microbes is not feasible . This mechanism has already been proposed earlier [26] and is also supported by our data . Future focused studies need to be performed to understand the very exciting question what factors determine PMN effector mechanisms and cell fate in response to bacteria . It is highly important to address this problem to learn about the unanswered questions of NET formation [92] , to understand what leads to unnecessary PMN activation in several diseases [93] and to be able to develop novel PMN-based therapies [94] . Overall , the results presented here reveal a novel proinflammatory mechanism of the bacterial flagellum and identify it as the main factor of flagellated bacteria triggering NET formation . We also identified flagellar motility as its primary mechanism to mediate P . aeruginosa-induced activation of PMNs that likely occurs in CF airways , contributes to disease pathogenesis and possibly points to a new , future therapeutic target .
The Institutional Review Board of the University of Georgia approved the human subject study to collect peripheral blood from volunteers anonymously ( UGA# 2012-10769-06 ) . Enrolled healthy volunteers were non-pregnant and heavier than 110 pounds without any infectious disease complication . All adult subjects provided informed consent , and no child participants were enrolled into the study . The studies were performed following the guidelines of the World Medical Association's Declaration of Helsinki . The Institutional Animal Care and Use Committees ( IACUC ) of the University of Georgia and the Georgia State University reviewed and approved the mouse protocols used in this study: UGA IACUC protocols: A2012 11-003-Y3-A3 , A2014 08-019-Y2-A0 and GSU IACUC protocol: A14033 . All animal experiments were performed in accordance with NIH guidelines , the Animal Welfare Act and US federal law . Animals were housed in centralized research facilities accredited by the Association of Assessment and Accreditation of Laboratory Animal Care International . Human PMNs were isolated as described previously [19 , 41] . Briefly , whole blood was drawn at the Health Center of the University of Georgia from volunteers . Coagulation was prevented with heparin . Red blood cells were removed by Dextran sedimentation ( GE Healthcare ) , and PMNs were separated using Percoll gradient centrifugation . Cell viability was determined by Trypan blue dye extrusion ( >98% ) . Neutrophil purity was assessed by cytospin preparations and flow cytometry . Autologous serum was prepared from coagulated blood by centrifugation and sterile filtration . Calcium- and magnesium-containing HBSS ( Mediatech , Manassas , VA , USA ) supplemented with 1% autologous serum , 5 mmol/l glucose and 10 mmol/l HEPES was used as the assay buffer . Wild-type ( WT ) C57BL/6 mice were purchased from Jackson Laboratories and maintained in the animal facility of the College of Veterinary Medicine , University of Georgia , Athens . 10-15-week-old mice were used throughout the study . TLR5/NLRC4 double gene-deficient mice on a C57BL/6 background were kept in the Georgia State University animal facility . TLR5KO mice used here were originally generated by Dr . Shizuo Akira ( Osaka University , Osaka , Japan ) [95] and backcrossed/maintained as previously described [96] . NLRC4 KO mice generated on a pure C57BL/6j background were kindly provided by Genentech ( Genentech , Inc . South San Francisco , CA ) [97] . Age- and sex-matched healthy C57BL/6 mice were used as controls . Mice were euthanized on the day of the experiment by CO2 asphyxiation and cervical dislocation according to University of Georgia and Georgia State University IACUC guidelines . Murine bone marrow-derived PMNs were collected from femur and tibia . Bones were flushed with RPMI-1640 medium ( Corning , Manassas , VA ) and washed in sterile PBS . 1 ml AKC buffer ( Lonza , Walkersville , MD ) was used to lyse red blood cells . Cells were passed through a 40 μm pore size Nylon Mesh strainer ( Fisherbrand , Fisher Scientific , Pittsburgh , PA , USA ) and subsequently washed twice with and re-suspended in sterile PBS before being layered on top of a two-step Percoll ( Sigma-Aldrich , St . Louis , MO , USA ) gradient ( 62% and 81% ) , as described previously [98] . After centrifugation ( 1600 g , 30 min ) , PMNs accumulated at the interface of 81% and 62% Percoll layers were collected and washed twice in sterile PBS . Cell numbers were determined with a hemocytometer . Cell viability determined by Trypan Blue exclusion was always higher than 98% . Neutrophil purity was also periodically confirmed with hematoxylin and eosin ( H/E ) staining ( Sigma , St . Louis , MO , USA ) of cytospins prepared using CytopsinTM Cytocentrifuge ( ThermoScientific , Waltham , MA , USA ) ( S5C Fig ) . The purity of the murine neutrophil preparations was routinely confirmed via flow cytometry ( LSRII , BD Technologies ) . Anti-Gr-1 antibody ( Miltenyi Biotec , San Diego , CA , USA ) was used against Gr-1-expressing granulocytes following the manufacturer’s recommendations . Cells were analyzed in BD LSRII flow cytometer ( BD Biosciences , San Jose , CA , USA ) using BD FACSDiva 6 . 0 software ( BD Biosciences , San Jose , CA USA ) at the Imaging Core Facility of the Department of Infectious Diseases at UGA . The described protocol resulted in more than 95% PMNs ( S5 Fig ) . The following Pseudomonas aeruginosa strains were used in this study . The PAO1 parental strain ( wild-type , WT ) was MPAO1 and its flagellum-deficient PAO1 mutant ( strain ID: 245 , genotype: PA1092-G03::lacZbp01q1 , referred to as “fliC” ) were obtained from the Pseudomonas aeruginosa PAO1 transposon mutant two-allele library ( University of Washington , Seattle , WA; Manoil laboratory ) established using NIH funds ( grant#: P30 DK089507 ) [66] . The PAK wild-type ( WT ) and flagellum-deficient mutant flgC1::Tn5 were obtained from Pathogenesis Corporation ( referred to as “flgC” ) . The flagellar motor mutant strains were described elsewhere ( 51 ) . Strains are listed in Table 1 that also includes the complementation plasmids [67 , 68] . The gene designations were revised to be consistent within the Pseudomonas aeruginosa field and refer to PA1460-61 ( motCD ) and PA4954-53 ( motAB ) [51] . P . aeruginosa strains were cultured in Luria-Bertani broth for the indicated periods of time . Bacteria were washed twice in PBS and resuspended in calcium- and magnesium-containing HBSS . Bacterial cultures were set to an optical density ( OD ) = 0 . 6 at 600 nm in 96-well microplates measured using a Varioskan Flash combined microplate reader ( ThermoScientific , Waltham , MA USA ) . This corresponds to a bacterial density of 109/ml , as determined by serial dilutions and colony forming unit ( CFU ) assays [40 , 99] . In some experiments , optical density of bacterial cultures was followed over time in an Eon Microplate Spectrophotometer ( BioTek , Winooski , VT ) to record kinetic growth curves ( Fig 1A ) . The genotype of the PAO1 fliC mutant was confirmed by PCR using the primers and conditions suggested on the web site of the PAO1 two-allele library . PAO1 WT and fliC bacteria were cultured overnight , washed twice in HBSS and set to an OD = 0 . 6 as described above . 100 , 000 bacteria in 2 μL distilled water were added to the PCR reaction mix and served as a DNA template . The PCR mix ( 20 μl reaction volume ) contained 10 mM dNTP ( Life Technologies , Carlsbad , CA ) , 50 mM MgCl2 , 10 μM forward and reverse primers and Taq DNA Polymerase ( Life Technologies , Carlsbad , CA ) . The following gene-specific primers were used: fliC ( F: 5’- TGCAGCAGTCCACCAATATC-3’; R: 5’- GTTGGTAGCGTTTTCCGAGA -3’ , product size: 1081 bp ) , pilA ( F: 5’- GGAATCAACGAGGGCACC -3’; R: 5’- ACCCAGTTTCCTTGATCGTG -3’ , product size: 865 bp ) . PCR reaction parameters were: 94°C for 0 . 5 min , followed by 35 cycles of 94°C for 30 sec , 60°C for 1 min and 68°C for 90 sec . The PCR reaction was carried out in a Biometra PCR thermocycler ( Biometra , Göttingen , Germany ) . PilA was used as loading control . The PCR products were resolved on 2% agarose gel and stained with Gelstar DNA stain ( Lonza , Walkersville , MD , USA ) . The genotype of the flagellum-deficient PAO1 fliC strain was confirmed by PCR ( S1C Fig ) . Lack of contaminating DNA was confirmed by PCR without template ( no bacteria ) ( S1C Fig ) . For the swimming motility assay , bacteria were grown overnight , washed twice in HBSS and set to an optical density ( OD ) = 0 . 6 as described previously . 10 μL of bacterial cultures were spotted on the center of freshly prepared LB+0 . 3% agar plates and incubated at room temperature . After 48 hours , diameters of colonies were measured and expressed in millimeters ( mm ) [100] . For complementation experiments , the strains were streaked for single colonies on LB medium with 300ug/ml carbenicillin . Three single colonies were toothpicked into tryptone motility agar containing 10 g/L tryptone 5 g/L NaCl and 3 g/L agar . Plates were incubated overnight and photographed . Flagellin of P . aeruginosa was obtained from two independent sources . First , purified P . aeruginosa flagellin was purchased from Invivogen ( San Diego , CA , USA ) . Flagellin obtained from this commercial source is extracted by acid hydrolysis and is purified by ultrafiltration and chromatography . The identity of the P . aeruginosa strain and the type of the flagellin were not revealed by the company . Second , P . aeruginosa flagellin was also obtained as a kind gift from Dr . Gerald Pier ( Massachusetts General Hospital , Boston , MA ) . Recombinant P . aeruginosa flagellins were purified from E . coli expressing His-tagged type a or b fliC genes as described previously [55 , 101] . DNA release from human PMNs was quantitated as described [19 , 29] . Briefly , 250 , 000 PMNs were seeded on 96-well black transparent bottom plates in the presence of 0 . 2% Sytox Orange ( Life Technologies , Grand Island , NY , USA ) membrane-impermeable DNA-binding dye . PMNs were infected with 10:1 multiplicity of infection ( MOI ) P . aeruginosa as indicated . Fluorescence ( excitation: 530 nm , emission: 590 nm ) was recorded for up to 8 hrs in a fluorescence microplate reader ( Varioskan Flash , ThermoScientific , Waltham , MA , USA ) at 37°C . DNA release is expressed as % of the maximum obtained by saponin-mediated ( 1 mg/ml; Sigma-Aldrich , St . Louis , MO , USA ) neutrophil lysis and DNA exposure . Immunofluorescence staining of human MPO and citrullinated H4 was performed as previously described [19 , 29 , 40 , 41] . Briefly , adherent murine or human PMNs were exposed to different strains of P . aeruginosa ( 10 MOI , 3 hrs , 37°C ) . After incubation , fixed and permeabilized samples [4% paraformaldehyde ( Affymetrix , Celeveland , OH ) ] were blocked with 5% Normal Donkey serum ( Sigma-Aldrich , St . Louis , MO , USA , in PBS ) for 30 min at room temperature . Fixed human NETs were incubated with monoclonal mouse anti-human myeloperoxidase/FITC antibody ( 1:500 , Dako , Clone MPO-7 ) and polyclonal rabbit anti-histone H4 ( citrulline 3 ) ( 1:1000 , Millipore , Billerica , MA ) overnight at 4°C . The citrullinated histone staining requires the use of a secondary antibody after three washes: Alexa Fluor 594-labelled donkey anti-rabbit secondary antibody for 1 hr ( 1:2000 , Molecular Probes , Grand Island , NY ) . Murine PMNs were first stained with goat anti-mouse MPO antibody ( R&D Systems , Minneapolis , MN , 1:1 , 000 ) overnight at 4°C , followed by staining with FITC-labelled , donkey anti-goat IgG ( Jackson ImmunoResearch , West Grove , PA , USA , 1:800 , 1 hr , dark ) . DNA was stained with DAPI ( 2 min , room temperature , 1:20 , 000 , Molecular Probes , Grand Island , NY ) . Specimens were washed three times with PBS containing 0 . 1% Tween-20 ( Sigma-Aldrich , St . Louis , MO , USA ) between each step . Mounted specimens were analyzed with Zeiss AxioCam HRM fluorescence microscope Axioplan2 imaging software . NET formation was quantitated by counting at least 200 PMNs per sample and by determining the proportion of NET-forming cells compared to the total population . Concentration of human MPO in PMN supernatants was quantitated by commercial ELISA kit ( R&D Systems , Minneapolis , MN , USA ) as previously described [19 , 41] . Human neutrophil elastase release was assessed by sandwich ELISA: diluted supernatants were applied to 96-well high binding microloan ELISA plates ( Greiner bio-one , Germany ) pre-coated overnight with anti-human neutrophil elastase rabbit polyclonal antibody ( 1:500 in PBS , Calbiochem , 481001 , EMD Millipore , MA , USA ) . After blocking with 1% BSA for 1 hr , a secondary anti-human neutrophil elastase antibody was applied ( 1:2000 in PBS , IgG1 , cat #: MA1-10608 , ThermoScientific , Hudson , NH ) followed by the addition of a horse radish peroxidase-linked ( donkey ) anti-mouse IgG antibody ( 1:2000 in PBS , NA934V , GE Healthcare , UK ) for 1 hr at room temperature . Blue coloration developed in the presence of the Pierce TMB Substrate Kit ( ThermoFisher Scientific , Waltham , MA , USA ) , and results were quantitated using a human neutrophil elastase standard . Flagellin concentrations in P . aeruginosa lysates were quantitated by ELISA established in this manuscript . Bacterial cultures were sonicated after repeated washes and their density was adjusted as described above . Bacterial lysates were centrifuged twice ( 10 , 000 g , 15 min ) , and supernatants were used in the ELISA . Supernatants of bacterial lysates and P . aeruginosa flagellin standards ( Invivogen , San Diego , CA ) were immobilized to 96-well high-binding capacity ELISA plates ( Greiner Bio-one , Frickenhausen , Germany ) by mixing them with an equal volume of 100 mM carbonate/bicarbonate buffer and incubating the samples overnight at 4°C . Plates were washed three times with PBS containing 0 . 1% Tween-20 ( Sigma , St . Louis , MO , USA ) and blocked by 5% bovine serum albumin ( Hyclone , Logan , Utah ) for 3 hrs at room temperature . After three washes with Tween-20/PBS , anti-P . aeruginosa flagellin antibody was added ( 1:250 dilution in PBS , 250 ng/ml , mouse IgG1 , hybridoma clone: 18D7 , Invivogen , San Diego , CA ) and incubated overnight at 4°C . Samples were washed again three times , followed by addition of a secondary horse radish peroxidase-labelled , sheep anti-mouse IgG antibody ( 1:1000 dilution in PBS , GE Healthcare Bio-Sciences , Pittsburgh , PA , USA ) for 30 min ( room temperature , dark ) . After four repeated washes with PBS/Tween-20 , color reaction was developed with 3 , 3’ , 5 , 5’-tetremthylbenzidine ( TMB , 0 . 16 mg/mL , Sigma , St . Louis , MO ) peroxidase solution and the reaction was stopped by adding 1M HCl . Absorbance was read at 450 nm with either Eon ( BioTek , Winooski , VT ) or Varioskan Flash ( ThermoScientific , Hudson , NH ) microplate photometers . Absolute quantitation of P . aeruginosa flagellin concentrations were calculated using the standard curve and expressed as micrograms per milliliter ( μg/ml ) or “ng/6x108 bacteria . ” NETs ( MPO-DNA and HNE-DNA complexes ) in human PMN supernatants were quantitated by specific ELISA assays as described [41 , 53] . Briefly , supernatants of attached human PMNs were treated with 1 μg/ml DNAseI to achieve limited DNA digestion [41 , 53] . Diluted samples were added to and incubated overnight on ELISA plates pre-coated with anti-MPO or anti-HNE capture antibodies , followed by addition of horseradish peroxidase-labelled anti-DNA detection antibody [41 , 53] . Coloration of added TMB substrate solution ( Thermo Scientific , Hudson , NH ) was stopped by 1N HCl and absorbance ( 450 nm ) was read either with Eon ( BioTek , Winooski , VT ) or Varioskan Flash ( ThermoScientific , Hudson , NH ) microplate photometers . “NET concentrations” were expressed as percentage of the “NET-standard , ” consisting of pooled supernatants ( 5 donors ) of PMA-stimulated human PMNs after limited DNAseI-digestion , and were handled parallel with unknown samples [53] . Phagocytosis of PAO1 strains by human PMNs was assessed by measuring the decrease in the number of extracellular ( non-phagocytosed ) bacteria over time . Human PMNs were mixed with 10 MOI of PAO1 WT or fliC and incubated for 60 min . At different time points ( 0 , 2 , 20 , 40 and 60 min ) , aliquots were taken , added to ice-cold PBS and centrifuged ( 300 g , 3 min , 4°C ) to pellet PMNs but leave extracellular bacteria in the supernatant . The centrifugation step was repeated once . 100 μL volume of the supernatant was added to 900 LB growth medium , and bacterial concentration was determined using a microplate-based assay [102] . Superoxide production was measured by two different assays: Lucigenin-based or Diogenes-based superoxide chemiluminescence kits ( National Diagnostics , Atlanta , GA ) [19 , 29 , 40 , 41] . 100 , 000 PMNs adhered to 96-well white plates for 15 min at 37°C in HBSS containing 1% serum . Cells were stimulated by Pseudomonas aeruginosa strains ( 10 MOI ) , PMA ( 100 nM ) or left unstimulated . Chemiluminescence was measured by a Varioskan Flash microplate luminometer ( Thermo Scientific , Waltham , MO , USA ) for 90 min . Data are shown as kinetics of representative curves ( relative luminescence units , RLU ) or integral superoxide production by analyzing accumulated luminescence for the entire duration of the measurement . Myeloperoxidase activity was measured by hydrogen peroxide-dependent oxidation of Amplex Red as described [19] . Undiluted neutrophil supernatants were mixed with assay solution containing 100 μM Amplex Red ( Sigma , St . Louis , MO ) and 100 μM hydrogen peroxide ( Sigma , St . Louis , MO ) . Production of the fluorescent product was measured in 96-well black plates using a fluorescence microplate reader ( Varioskan Flash , ThermoScientific , Waltham , MO , USA ) for 30 min at 560 nm excitation and 590 nm emission wavelengths . Calibration was achieved using an MPO standard [19] . P . aeruginosa PAK strains were grown overnight in LB liquid medium , washed and resuspended in RIPA Lysis and Extraction buffer ( ThermoFisher Scientific , Waltham , MA USA ) before sonication . Bacterial lysates were collected as supernatants after centrifugation ( 14 , 000 g , 20 min , 4°C ) . Protein concentrations were determined with a Pierce BCA Protein Assay Kit ( ThermoFisher Scientific , Waltham , MA USA ) . Equal amounts of proteins from bacterial lysates were loaded onto Novex 8–16% Tris-Glycine Gel together with molecular weight standards and run for 120 min at 110 V . Samples were blotted onto nitrocellulose membrane using the iBlot dry blotting system ( Life Technologies , Carlsbad , CA ) . Membranes were blocked in 5% milk for 1 hr and probed with the primary antibody ( anti-P . aeruginosa flagellin antibody , 1:250 , 250 ng/ml , mouse IgG1 , hybridoma clone: 18D7 , Invivogen , San Diego , CA ) overnight at 4°C . After three washes , the secondary antibody was added ( HRP-labelled goat anti-mouse IgG , 1:2000 , ThermoFisher Scientific , Waltham , MA , USA ) for 1 hr at room temperature . Following three repeated washes , blots were probed with the Amersham ECL Western Blotting Detection Kit ( GE Healthcare Life Sciences , Pittsburgh , PA , USA ) , and chemiluminescence was recorded with Konica Minolta SRX-101A developer using HyBlot CL Autoradiography films ( Denville Scientific , Holliston , MA ) . Results were analyzed by Student's t-test or one-way ANOVA . Each experiment was independently performed at least three times with PMNs isolated from different donors . Statistically significant differences were considered as * , p<0 . 05; ** , p<0 . 01; *** , p<0 . 001 . | Pseudomonas aeruginosa leaves a large footprint in human disease because it causes infections in immunocompromised patients . Its ability to quickly adapt to diverse environments and to form biofilms poses a significant challenge to the medical community . Neutrophil granulocytes , professional phagocytes found cruising through the body’s circulatory system and tissues , provide the most efficient immune response against P . aeruginosa . Neutrophils utilize multiple strategies to eliminate bacteria . Formation of neutrophil extracellular traps ( NETs ) , a DNA-based scaffold with attached antimicrobial proteins , provides an efficient mechanism to trap P . aeruginosa . The detailed mechanism of NET release induced by bacteria remains unclear . Our data show that the flagellum , the organelle that provides swimming motility to P . aeruginosa , is the main factor required to induce NET release . Our novel findings indicate that the flagellum , and in particular swimming motility , mediates P . aeruginosa-induced NET extrusion independently of the well-characterized flagellin receptors . The novel data presented here also suggest that down-regulation of flagellar motility characteristically seen in P . aeruginosa lung infections in cystic fibrosis is relevant for P . aeruginosa to avoid neutrophil attacks . |
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Cooperation is key for the evolution of biological systems ranging from bacteria communities to human societies . Evolutionary processes can dramatically alter the cooperation level . Evolutionary processes are typically of two classes: comparison based and self-evaluation based . The fate of cooperation is extremely sensitive to the details of comparison based processes . For self-evaluation processes , however , it is still unclear whether the sensitivity remains . We concentrate on a class of self-evaluation processes based on aspiration , where all the individuals adjust behaviors based on their own aspirations . We prove that the evolutionary outcome with heterogeneous aspirations is the same as that of the homogeneous one for regular networks under weak selection limit . Simulation results further suggest that it is also valid for general networks across various distributions of personalised aspirations . Our result clearly indicates that self-evaluation processes are robust in contrast with comparison based rules . In addition , our result greatly simplifies the calculation of the aspiration dynamics , which is computationally expensive .
Cooperation is forgoing the focal individual’s own interest to benefit others . It is ubiquitous across every level of biological systems . Yet cooperation seemingly contradicts the evolution theory , since free riders , who contribute nothing , are better off than cooperators in fitness . The gap has attracted considerable attention to tackle how and why cooperation evolves [1] . A pairwise game , in which there are only two players , is widely employed to model the conflicts between cooperation and defection . The well-known Prisoners’ Dilemma is a pairwise game [2] . While the dyadic games capture the conflicts between cooperators and defectors , they neglect that the interactions in the real world usually involve more than two individuals . For example , all the nations are involved to reduce the emission of greenhouse gases , leading to a multiplayer collective risk dilemma [3–5] . More than two yeast cells interact to form a multicellularity when the resource is rare , resulting in a multiplayer game [6] . Thus , it is natural to adopt multiplayer games as a paradigm to study cooperation . The last decade has seen an intensive study on the mechanisms promoting cooperation including group selection , direct reciprocity and spatial reciprocity [7–14] . In particular , spatial reciprocity attracts considerable attention with the development of network science . Spatial structure is typically described as networks , where nodes represent individuals and links social ties . Individuals on networks play games only with their neighbours . In this case , cooperators form clusters not only to invade but also to resist a population of defectors [13] . Consequently , the population structure promotes both the emergence and the stabilization of cooperation [15] . Yet the evolutionary outcome can be dramatically altered on networks , even if some detail of the evolutionary process changes: in degree-homogeneous networks , Death-birth process can promote cooperation for the Prisoners’ Dilemma whereas Birth-death process cannot [13]; In degree-heterogeneous networks , the fate of cooperation relies on the rate at which interactions occur and how interactions transform into the fitness of strategies [16] . In fact , the evolutionary outcome is not robust for different evolutionary processes even in the well-mixed population where all the individuals interact with everyone else [17 , 18] . Yet , all of the above works assume that all the individuals adopt the same strategy updating rule . In reality , it is a personal trait to adjust one’s own behavior . In other words , individuals are equipped with their own updating rules , resulting in a great heterogeneity in strategy updating rule across the population . Considering that the details of evolutionary processes would greatly alter the evolutionary outcome , the fate of strategies is expected to be extremely sensitive to the heterogeneity in updating rules . Here , we study aspiration dynamics of multiplayer games on regular networks and investigate how the heterogeneity in aspiration levels alters the evolutionary outcome . The idea of aspiration is self-evaluation: an individual is likely to keep its strategy if its aspiration is met by the payoff , and to switch otherwise . Aspiration dynamics are widely present in behavioural ecology . For example , experienced ants go back to their nest based on their own chemical trails [19] or by counting their own previous steps [20] . In our manuscript , every individual has its personal aspiration . We show that cooperation is more abundant over defection for all possible personal aspiration levels provided cooperation is advantageous for a homogeneous aspiration level . Furthermore , we analytically figure out the condition under which cooperation prevails , which is also true for all two-strategy multiplayer games . On the one hand , our result clearly shows that heterogeneity in aspiration does not alter the fate of strategies , in contrast with previous results that evolutionary outcome is sensitive to the details of the evolutionary processes [18 , 21] . On the other hand , the result also provides computational savings when identifying the conditions under which one strategy is more abundant than the other .
We show that the average abundance of strategy A is one half when the selection intensity vanishes . And this remains true for all possible individualised aspirations ei and decision making functions provided gi ( 0 ) > 0 for all i = 1 , 2… , N ( see Section The average abundance is 1/2 for vanishing selection intensity in S1 File . for the proof ) . In particular , it is true for our decision making functions , since our decision making function gi is positive for the real line . Intuitively , no individual has any bias towards any strategy for the vanishing selection intensity , although they update strategies in their own ways . Thus strategy A is more abundant than strategy B if and only if the abundance of strategy A is more than that of its neutral case , which holds for all possible individualised aspirations , aspiration-based decision making functions and selection intensities . In the following , we introduce two lemmas , which are crucial to the main result . Lemma 1 For the decision making function gi with 0 < gi < 1 and positive derivative on the real line g i ′ > 0 ( i = 1 , 2 , … , N ) , under weak selection limit , there exist parameters αk , ωk ( k = 0 , 1 , … , d − 1 ) and ϕi ( i = 1 , 2 , … , N ) , which are neither dependent on the payoff entries nor the aspiration levels , such that if ∑ k = 0 d - 1 α k a k + ∑ k = 0 d - 1 ω k b k + ∑ i = 1 N ϕ i e i > 0 , ( 1 ) strategy A is more abundant than strategy B . We outline the proof: The differentiability of the decision making function ensures that the average abundance of strategy A , i . e . , ∑ s ∈ Sκ s ( ∑ i = 1 N s i ) / N , is differentiable with respect to the selection intensity [24] . Thus the average abundance of strategy A under weak selection limit is given by ∑ s ∈ S κ s ( 0 ) ( ∑ i = 1 N s i ) / N + ( ∑ s ∈ S κ s ′ ( 0 ) ( ∑ i = 1 N s i ) / N ) β + o ( β ) . ( 2 ) The first term is the average abundance of strategy A when the selection intensity vanishes , it is one half based on Section The average abundance is 1/2 for vanishing selection intensity in S1 File . Therefore , the average abundance of strategy A is more than that in the neutral case if ∑ s ∈ S κ s ′ ( 0 ) ( ∑ i = 1 N s i ) is positive . Thus ∑ s ∈ S κ s ′ ( 0 ) ( ∑ i = 1 N s i ) > 0 is the condition under which strategy A is more abundant than strategy B . We show that ∑ s ∈ S κ s ′ ( 0 ) ( ∑ i = 1 N s i ) is linear in payoff and aspiration level with no constant term , i . e . , a term does not contain payoff entries or aspirations ( Section The criterion is a linear inequality of payoffs and aspirations in S1 File ) . Lemma 2 If all the payoff entries are the same , i . e . , there exists a constant h such that ak = bk = h for all k = 0 , 1… , d − 1 , then the average abundance of strategy A is one half for all possible individualised aspirations and all aspiration-based decision making functions fulfilling gi ( 0 ) > 0 , i = 1 , 2 , … , N and all selection intensities . ( See Section The average abundance is 1/2 for neutral mutants for any selection intensity in S1 File for the proof . ) Lemma 2 suggests that if a neutral mutant arises , the heterogeneity in the decision updating rule and aspiration level does not alter the evolutionary outcome , compared with the homogeneous case . In particular , Lemma 2 is true for any selection intensity . Intuitively , both strategies are identical in payoffs when all the payoff entries are the same . It resembles a neutral mutant in population genetics [25] . Furthermore , each individual shows no bias towards any of the two strategies , although everyone has its own way to update strategies . By symmetry , the two strategies should be equally abundant .
Theorem 1 Consider a regular network with degree d − 1 , and the decision making function with 0 < gi < 1 and positive derivative on the real line g i ′ > 0 ( i = 1 , 2 , … , N ) , in the limit of weak selection , there exist d parameters σks ( k = 0 , 1… , d − 1 ) which are neither dependent on payoff entries nor aspiration levels such that if ∑ k = 0 d - 1 σ k ( a k - b d - 1 - k ) > 0 , ( 3 ) then strategy A is more abundant than strategy B . Proof 1 For the payoff table with all the entries being zero , i . e . , ak = bk = 0 for 0 ≤ k ≤ d − 1 , strategy A and B are equally abundant for any selection intensity by Lemma 2 . Thus the two strategies are equally abundant under weak selection limit . On the one hand , strategy A is not more abundant than strategy B . Thus there exist N parameters ϕis ( i = 1 , 2… , N ) which are neither dependent on the aspiration levels nor payoff entries such that ∑ i = 1 N ϕ i e i ≤ 0 holds by Lemma 1 . On the other hand , strategy B is not more abundant than strategy A . Thus ∑ i = 1 N ϕ i e i ≥ 0 holds with the same argument by Lemma 1 . Therefore , it yields that ∑ i = 1 N ϕ i e i = 0 . ( 4 ) Note that the parameters ϕis , i = 1 , 2 , … , N , are independent of the aspirations levels . And Eq ( 4 ) is valid for any eis ( i = 1 , 2 , … , N ) . In particular , for any j ( 1 ≤ j ≤ N ) , let ei = δij , where δij is the Kronecker Delta . We have that ϕj = 0 by Eq ( 4 ) . Therefore , based on Lemma 1 , the condition under which strategy A is more abundant than strategy B is ∑ k = 0 d - 1 α k a k + ∑ k = 0 d - 1 ω k b k > 0 . ( 5 ) Switching the name of A and B , by Lemma 1 , strategy B is more abundant than strategy A if ∑ k = 0 d - 1 α k b d - 1 - k + ∑ k = 0 d - 1 ω k a d - 1 - k > 0 ( 6 ) holds . On the one hand , the linear combination of Inequality ( 6 ) is proportional to the first-order derivative of the abundance of strategy B; On the other hand , the sum of the abundance of strategy A and B is always one for any selection intensity . Thus the first-order derivative of the sum of the two abundances is zero . In other words , the first-order derivative of the abundance of strategy A is always different from that of strategy B in sign , provided neither of them is zero . Thus , strategy A is more abundant than strategy B if and only if Inequality ( 6 ) does not hold . Therefore , strategy A is more abundant than strategy B if ∑ k = 0 d - 1 α k b d - 1 - k + ∑ k = 0 d - 1 ω k a d - 1 - k < 0 ( 7 ) or ∑ k = 0 d - 1 ( - ω d - 1 - k ) a k + ∑ k = 0 d - 1 ( - α d - 1 - k ) b k > 0 . ( 8 ) Both Inequality ( 5 ) and ( 8 ) are the condition under which strategy A is more abundant than strategy B . Thus there exists a positive rescaling factor λ > 0 such that they are mapped to each other , i . e . , αk = −λωd−1−k and ωk = −λαd−1−k hold for 0 ≤ k ≤ d − 1 . In particular , we have α0 = −λωd−1 and ωd−1 = −λα0 . Thus λ2 = 1 . We have that λ = 1 since the constraint λ > 0 . This yields that αk = −ωd−1−k for k = 0 , 1 , … , d − 1 . In this way , Inequality ( 5 ) becomes ∑ k = 0 d - 1 α k ︸ σ k ( a k - b d - 1 - k ) > 0 . ( 9 ) By Lemma 1 , αk is not dependent on the aspirations or the payoff entries . Therefore σk is neither dependent on the aspirations nor the payoff entries . With the aid of Lemma ( 1 ) and Lemma ( 2 ) , we obtain the condition under which strategy A is more abundant than strategy B . Herein , Lemma 1 states that the criterion under which a strategy is more abundant than the other is determined by both payoff entries and individualised aspirations . Yet Lemma 2 implies that the criterion is not dependent on the aspiration levels at all , and it is the cornerstone of the proof . The theorem indicates that the criterion under which strategy A is favored is a simple linear inequality ( 3 ) . The coefficients of the inequality are neither dependent on the payoff entries nor the aspirations , but are determined by the decision making functions , the way of payoff collecting , population size and the population structure . The coefficients of the inequality do not depend on the payoff entries , which is similar to the σ-rule of the mutation-selection dynamics [24 , 26] . The independence ensures that the coefficient σk is obtained by adopting the payoff table with ak = 1 and all the rest being zero . Thus it opens up an avenue to compute the evolutionary dynamics under weak selection limit [27] . The theorem ensures that the coefficients do not depend on the aspiration levels at all . Simulations also show that the abundance of strategy A almost keeps invariant for a diverse class of distributions of individual aspirations ( Figs ( 1 ) and ( 2 ) ) . It is counter-intuitive that the coefficients are independent of the aspiration levels . In fact , it is well-known for games on networks that the heterogeneity of personal traits alters the evolutionary outcome [28–32] . For example , cooperation can be greatly promoted if individuals have heterogeneous neighborhood sizes [33] . Aspirations are part of the individual’s updating rule . Thus the heterogeneity in updating rule should have changed the evolutionary outcome for some games . The intuitive understanding resorts to weak selection limit . On the one hand , the evolutionary outcome is not determined by the aspiration under weak selection limit when the aspiration is homogeneous in the population [34] . On the other hand , the driving force of the strategy updating is stochastic neutral drift under weak selection . Therefore , the effect of heterogeneity in aspiration is weakened , resulting in a similar dynamics with the case of homogeneous aspiration . In fact , individual i uses strategy B more often if the switching probability from A to B is greater than the other way around . In other words , strategy B is more abundant for individual i if g ( β ( ei − πA ) ) > g ( β ( ei − πB ) ) , i . e . , πB > πA under weak selection . This suggests that the criterion should be independent of the individualised aspiration , consistent with our theorem . The coefficients are only dependent on individuals’ decision making functions , population size , the way of payoff collecting and the underlying population structure . In particular , they are determined by all the decision making functions gi , i = 1 , 2⋯ , N . Thus personalised updating rules might alter the evolutionary outcome , which is not the focus of our manuscript . To sum up , the theorem shows that personalised updating rules might alter the evolutionary outcome , but not via heterogeneous aspiration levels . So far , we have obtained the condition under which a strategy is more abundant than the other , when everyone is equipped with its own aspiration . The condition is a linear inequality with as many coefficients as the game size . Further investigations show that those coefficients are always non-negative , and at least one of them is positive ( see Section Non-negativity of the coefficients in S1 File ) . It mirrors the non-negativity of the structure coefficients of the σ-rule for mutation-selection dynamics [35] . In the following , we estimate the d coefficients , i . e . , σk k = 0 , 1 , 2… , d − 1 . For simplicity , we assume that all the individuals adopt the Fermi function as the decision making function , i . e . gi ( x ) = [1 + exp ( −x ) ]−1 for i = 1 , 2⋅⋅⋅N . By our theorem , the coefficients for a population with individualised aspirations are exactly the same as the coefficients for a population with the homogeneous aspiration . Therefore , the coefficients for the population with heterogeneous aspirations can be calculated via those with a homogeneous aspiration ( Section Calculation of coefficients in S1 File ) . And we obtain that σk= ( d−1k ) , k=0 , 1… , d−1 . ( 10 ) Eq ( 10 ) implies that the condition under which strategy A is more abundant on regular networks is exactly the same as that when the population is well-mixed . Generally , Eq ( 10 ) holds , provided all the individuals have the same decision making function with non-vanishing derivative at zero . ( see Section Estimating coefficients S1 File ) . Numerically , we estimate the coefficients for a three-player game on both well-mixed populations and rings . In well-mixed populations , the focal individual gets its payoff by playing games with two coplayers randomly selected from the rest of the population while on rings , its payoff is derived from interacting with its two nearest neighbours . We set the aspirations of individuals based on a uniform distribution . In this case , the aspiration is so personal that it is of great possibility that no one shares the aspiration with anyone else . The numerical results imply that the coefficients are i ) exactly the same for both well-mixed populations and rings , ii ) σ0 = 1 , σ1 = 2 and σ2 = 1 ( ( Table 3 ) ) . Therefore they are in perfect agreement with the theorem and Eq ( 10 ) . Simulation results , i . e . , Fig ( 2b ) and ( 2c ) show that the criterion in the theorem to favor strategy A is also valid for random and scale-free networks , beyond regular networks , i . e . , Fig ( 2a ) . In particular , simulations also show that the criterion is independent of individual aspirations . It is noteworthy that the number of neighbors each individual has should be no less than d − 1 for non-regular networks , such that everyone has enough neighbours to play the d-player game with . Up till now , we assume that an individual organizes a d−player game with its d − 1 neighbours , and it gets its final payoff via this single game . We now allow this individual participates in the d−player games organized by thy neighbours besides the one organized by itself . In other words , the focal individual takes part in d such d−player games , one of which is organized by itself and all the rest d − 1 games are organized by its d − 1 neighbors . The final payoff of the focal individual is the average payoff over those d games . In this case , the focal individual’s final payoff is not only determined by its nearest neighbors , but also is up to the second nearest neighbours . We investigate whether this alternative way of payoff collection alters the criterion under which strategy A is more abundant . We perform simulations on rings ( ( Fig 3 ) ) . We find that heterogeneity in aspiration does not change the evolutionary outcome either . Thus , the simulation suggests that the way of payoff accumulation does not change our result that the evolutionary outcome is insensitive to the individualised aspirations . Note that the theorem is obtained under the assumption of weak selection limit . It implies that the selection intensity can be sufficiently small , given the payoff entries and individual aspirations . In other words , given the payoff table and the individualised aspirations , the criterion ∑k=0d−1 ( d−1k ) ( ak−bd−1−k ) >0 is valid for ensuring that strategy A is more abundant than strategy B , provided the selection intensity can be tuned as small as possible . This does not mean that given a small yet fixed selection intensity and individualised aspiration , the criterion is valid . In fact , fixed selection intensity violates the assumption of the weak selection limit in our theorem . However , it brings us into a question in simulation: how weak the weak selection is to ensure that the criterion in the theorem is valid for a given individualised aspiration . The key idea is to ensure that perturbation analysis is correct . In other words , all the individuals have to be mainly driven by blind switching . And the switching probability from A to B differs little from the other way around , or the difference should be of order β . In other words , g ( β ( ei − πA ) ) − g ( β ( ei − πB ) ) , i . e . , g′ ( 0 ) ( πB−πA ) β+12g″ ( 0 ) ( πB−πA ) ( 2ei−πA−πB ) β2+o ( β2 ) =g′ ( 0 ) ( πB−πA ) β ( 1+g″ ( 0 ) 2g′ ( 0 ) ( 2ei−πA−πB ) β ) +o ( β2 ) ( 11 ) should be of order β . It is required that | g ′ ′ ( 0 ) 2 g ′ ( 0 ) ( 2 e i - π A - π B ) β | ⪡ 1 makes the criterion valid . In other words , if g′′ ( 0 ) ≠ 0 , a sufficient condition to ensure that the criterion is valid is that β ≪ g ′ ( 0 ) | g ′ ′ ( 0 ) | [ max { | e i | }+max { | a k | , | b k | } ] . It suggests that the larger the maximum value of the aspiration , the weaker the selection intensity is to ensure that the criterion is valid . This is consistent with Fig 1 that aspirations generated via uniform distribution on the interval [0 , 5] need weaker selection intensity to make the criterion valid than those generated via the uniform distribution on the interval [0 , 1] . If g′′ ( 0 ) = 0 , then | g ′ ′ ( 0 ) 2 g ′ ( 0 ) ( 2 e i - π A - π B ) β | = 0 ⪡ 1 holds . Higher order expansion of the difference has to be performed to figure out how weak the weak selection is .
Evolutionary processes are typically categorised into two classes , one is comparison based and the other is self-evaluation based . Comparison means that strategies are adjusted via payoff comparison . For example , imitation is comparison based . Therein , an individual compares its payoff with another one’s , and is likely to adopt the strategy of the more successful . Thus the comparison happens between a pair of individuals . Fitness-based process is also comparison based , including Moran process [36–39] ( Death-birth and Birth-death ) and Wright-Fisher process [40] . Therein , individuals reproduce with a probability , which is based on the payoff comparison among all the competing individuals . Individuals with higher payoffs are more likely to reproduce . In this case , the comparison occurs among all the individuals in the population . Self-evaluation indicates that an individual is likely to alter its strategy if the payoff does not meet its aspiration . It is similar to the ‘Win-stay-lose-shift’ strategy [41] . For the self-evaluation process , we do not take the cost of evaluation into account for the sake of simplicity following the convention in evolutionary game theory [42–45] . Compared with the comparison-based individuals , individuals with self-evaluation based updating rules only make use of their own information to update the strategy . In addition it introduces ‘exploration’ [46] , since an entire population with all the same strategy can have a new mutant , provided an individual is not satisfied with its current payoff . It , however , is impossible for comparison-based individuals unless exploration is additionally assumed . Differences between the two classes of evolutionary processes suggest that they can lead to different evolutionary outcomes . In fact , differences arise even when the population is well-mixed and selection intensity is weak [34] , where evolutionary outcomes are almost identical for all the comparison-based processes [23 , 47] . Strategy updating rule , either comparison-based or self-evaluation-based , has been typically assumed to be homogeneous for all the individuals . All the individuals make decisions in the same way . Yet strategy updating rule is a personal trait and differs among individuals . We tackle how the heterogeneity in strategy updating rules alters the evolutionary outcome . Previous studies have shown that this heterogeneity could dramatically alter the evolutionary outcome , if individuals are equipped with personalised comparison-based rules [28 , 29 , 31] . In particular , the heterogeneity can promote cooperation in spatially structured populations . This is in line with the expectation , since the evolutionary outcome is sensitive to the details of the comparison-based processes , even if the updating rule is homogeneous [18] . However , it is still unclear what happens if the evolutionary processes are chosen in the class of self-evaluation based processes . Or how this heterogeneity affects the evolutionary outcome within the class of self-evaluation processes . Intuitively , it is natural to expect that alternative outcomes emerge if individuals are using their own self-evaluation rules . Our theorem , however , shows the opposite: the outcome based on heterogeneous aspirations is the same as that for homogeneous ones under weak selection limit . In fact , it results from the fact that the evolutionary outcome is robust for different aspiration levels if they are homogeneous [34] . The theorem ensures that the criterion to favor strategy A under weak selection limit is a linear inequality , which is independent of all the aspirations . Based on the theorem , the coefficients of the inequality with heterogeneous aspirations are calculated via that with homogeneous aspirations instead . It paves the way to obtain the criterion analytically based on the mean-field approximation [48] ( see section Calculation of coefficients S1 File ) . Note that the calculation of the coefficients itself has shown that the criterion to favor strategy A does not depend on the aspiration for homogeneous aspiration ( see Eq . ( 12 ) in S1 File ) . Yet the calculation for the homogeneous case cannot be extrapolated to the heterogeneous case . The obstacle is overcome by the theorem , which ensures the equivalence of the criterions between homogeneous and heterogeneous aspirations . The proof of our theorem is similar to the proof in [24 , 27 , 49] in the sense that they both are based on weak selection limit and symmetric evolutionary rule . Yet a transition probability for the aspiration dynamics is a function of the products of payoff entry and selection intensity and the products of the aspiration and selection intensity , whereas a transition probability for [24 , 27 , 49] is a function of the products of payoff entry and selection intensity only . This mathematical difference prevents us from directly applying their results [24 , 49] , and we overcome the obstacle by introducing the two lemmas in the Method section . We concentrate on a regular network with degree d − 1 . It is to ensure that every individual has exactly d − 1 neighbors such that every individual is able to play the d-player game . In fact , the proof of the theorem applies to a set structured population , in which all the individuals are in club-like sets . Therein , each sets consists of d individuals , but the number of sets one belongs to varies from individual to individual . In particular , a regular network with degree d − 1 is a set structured population , whose set size is d and the number of the sets one belongs to is d for all the individuals [50] . Furthermore , Fig 2 shows that the criterion in the theorem also holds for random and scale-free networks across a wide range of distributions of individual aspirations . It suggests that the theorem can be true for a wide range of population structures , which requires investigation in the future . ( Fig 1 ) indicates that there are games such that the heterogeneity in aspiration does not alter the rank of the abundances for non-weak selection , although our theorem is based on the weak selection limit . It seemingly suggests that the criterion to favor a strategy A under strong selection is the same as that under weak selection . Yet , it is not true . Let us consider a multiplayer game whose payoff entries are of order 1 . In this case , the payoff is of order 1 for both strategies . Let us assume that there are two types of aspirations in the population , one is positive and the other is negative , which are both of higher order than 1 . Under strong selection , individuals with high aspiration are never satisfied with their payoffs , and they switch their strategies all the time . By the symmetry of the updating rule ( similar to the proof in Section The average abundance is 1/2 for vanishing selection intensity in S1 File ) , the two strategies are equally abundant among individuals with high-aspiration . Yet individuals with low aspiration are always satisfied and they act as zealous individuals who never alter strategies [51 , 52] . Thus individuals using strategy A are more abundant eventually if individuals using strategy A with low aspiration are more abundant than those using strategy B with low aspiration in the beginning . It clearly indicates that the criterion under which a strategy is more abundant under strong selection differs from that under weak selection . Therefore there are games such that the ranking of strategies is sensitive to the selection intensity in a population with heterogeneous aspirations . In other words , the criterion in the theorem to favor a strategy in abundance is sensitive to the selection intensity , in line with the mutation-selection dynamics [18 , 21] . The criterion under which a strategy is more abundant than the other is robust under aspiration dynamics for weak selection limit . In fact , the criterion holds true for various networks , individualised aspirations , and different ways of payoff collection . A potential way to violate the present criterion is to allow individuals to use different updating functions when using strategy A and B . In this case , updating of the two strategies is not symmetric anymore . And the two strategies can be different in abundance even when all of the payoff entries in the table are the same , violating Lemma 2 , which is crucial for the theorem . A study along this way is undergoing . To sum up , in the limit of weak selection , we show that the heterogeneity in aspiration does not alter the evolutionary outcome compared with the homogeneous case . Our result not only shows a striking difference between comparison based and self-evaluation based rules , but also provides a novel way to compute the aspiration dynamics . | Cooperation is the cornerstone to understand how biological systems evolve . Previous studies have shown that cooperation is sensitive to the details of evolutionary processes , even if all the individuals update strategies in the same way . Here we propose a class of updating rules driven by self-evaluation , where each individual has its personal aspiration . The evolutionary outcome is the same as if all the individuals adopt the same aspiration for regular networks , provided the selection intensity is weak enough . In addition , we provide a simple numerical method to identify the favored strategy . Our result shows a very robust class of strategy updating rules . And it implies that complexity in updating rules does not necessarily lead to the sensitivity of evolutionary outcomes . |
You are an expert at summarizing long articles. Proceed to summarize the following text:
X chromosome inactivation in female mammals results in dosage compensation of X-linked gene products between the sexes . In humans there is evidence that a substantial proportion of genes escape from silencing . We have carried out a large-scale analysis of gene expression in lymphoblastoid cell lines from four human populations to determine the extent to which escape from X chromosome inactivation disrupts dosage compensation . We conclude that dosage compensation is virtually complete . Overall expression from the X chromosome is only slightly higher in females and can largely be accounted for by elevated female expression of approximately 5% of X-linked genes . We suggest that the potential contribution of escape from X chromosome inactivation to phenotypic differences between the sexes is more limited than previously believed .
Dosage compensation is a regulatory process that alters gene expression along entire X chromosomes resulting in equivalent levels of X-linked gene products in males and females . Dosage compensation has evolved independently several times and is achieved in various ways . In Drosophila melanogaster , for example , the male X chromosome is hypertranscribed , doubling the output of X-linked genes ( reviewed in [1] and [2] ) . This balances gene expression between the sexes and also satisfies the second requirement of a dosage compensation system , which is to balance X chromosome gene expression with that of the autosomes . The situation in mammals is more complex . Inactivation of one of the X chromosomes in female mammals [3] balances X-linked gene expression between males and females . However , if this were the only component of the mammalian system , the single active X chromosome of females and males would effectively make both sexes aneuploid with respect to autosome gene expression . Ohno hypothesized therefore that balance would be achieved by doubling the output from the male X ( and active female X ) [4] . The hypothesis was confirmed recently when 2-fold upregulation of the X chromosome was demonstrated for both human [5] and mouse [6] . The molecular mechanism for this X chromosome upregulation in mammals is unknown . By contrast X chromosome inactivation ( XCI ) has been known about for almost fifty years and is extensively characterized . XCI is a complex and tightly regulated process unique to mammals that results in heterochromatization and transcriptional silencing of one of the female X chromosomes ( for a review see reference [7] ) . In eutherian mammals , the maternal or paternal X chromosome is inactivated randomly early in embryogenesis , and once established the pattern is mitotically stable . XCI was first suggested in 1961 to explain mosaic phenotypes seen in female mice heterozygous for sex-linked mutations in coat colour genes [3] . The theory was supported by the observation that cloned fibroblasts from human females heterozygous for an electrophoretic protein variant from the X-linked gene G6PD expressed either the paternal or the maternal allele , but not both [8] . Similar observations were reported for HPRT [9] , PGK [10 , 11] and a number of other X-linked genes . This pattern of complete silencing of one allele in females is seen for the majority of X-linked genes tested . However , the finding that the steroid sulphatase gene ( STS ) was always expressed in female fibroblast clones with one STS-deficient allele , regardless of which X was inactivated , suggested that some genes are not subject to XCI [12] . This “escape” from XCI results in differential expression of STS loci on the active X ( Xa ) and inactive X ( Xi ) chromosomes: clones expressing STS from the Xi have approximately half the level of STS enzyme activity of clones expressing from the Xa [13] . The use of rodent-human hybrid cell lines retaining an inactive human X chromosome has contributed greatly to our knowledge of escape from X inactivation [14–16] . This approach has the advantage of being able to assay gene expression from the Xi without the interference of the active copy . The largest study of this type has estimated that minimally 16% of human genes escape from XCI [16] . In a complementary approach , the same authors compared expression from maternal and paternal alleles of 94 genes in a panel of cell lines with skewed XCI and found that 15% of these were consistently expressed from both X chromosomes [16] . These approaches can detect low levels of expression from Xi . However , measuring the effect on dosage compensation requires a method to compare gene expression between the sexes . Expression microarrays , designed using the annotated X chromosome sequence [17] , are suitable for such comparisons . Microarrays have previously been used to identify human genes escaping XCI by comparing gene expression in cell lines with supernumerary X chromosomes [18] , in male and female lymphocytes [19] , and in a range of male and female tissues [20] . A genome-wide survey of sex-differences in gene expression in lymphoblastoid cells also yielded several examples of X chromosome genes with elevated female expression levels [21] . In the light of this reported widespread escape from X inactivation , we sought to determine its effect on dosage compensation in the largest comparison to date of X chromosome gene expression in females and males . Here we report analysis of a microarray expression dataset obtained using lymphoblastoid cell lines from 210 individuals in four populations . We show that the proportion of X-linked genes with significantly higher expression in females is around 5% , and that dosage compensation in these cell lines is virtually complete .
Microarray data from the Gene Expression Variation project ( GENEVAR [22–24] ) were analysed for 210 unrelated individuals from the four HapMap populations [25] , designated CEU , CHB , YRI and JPT ( see Methods ) . First , we identified 371 genes on the X chromosome and 11 , 952 genes on autosomes that are expressed in the cell lines ( see Methods ) . We then compared gene expression from autosomes and the male X chromosome . The median expression value of the 11 , 952 autosomal genes was plotted against the median of the 371 X chromosome genes for 105 unrelated male individuals from four populations ( Figure 1A ) . There is a clear linear relationship between autosomal and X gene expression in all populations . The majority of data points fall along a diagonal close to that where the autosomal median is equal to the X median . We compared mean expression of the 371 X genes with randomly selected sets of 371 autosome genes ( n = 100 ) for 30 YRI males using Student's t-test and saw no significant difference ( data not shown ) . Average expression from the single X chromosome in males is , therefore , similar to expression from an autosome pair . Next , we compared the male X chromosome with each autosome pair separately . Results for the YRI population are shown in Figure 1B , and very similar results were obtained for the other populations ( data not shown ) . Median expression from the single X chromosome falls within the normal range seen for autosome pairs and is slightly above average . The latter accounts for the observation that most data points in Figure 1A lie above the diagonal . We conclude that expression from the single X in male cell lines is upregulated 2-fold relative to autosomes , thus achieving dosage parity between the X and autosomes . Upregulation occurs precisely and consistently in 105 individual male samples . This supports and extends the findings of Nguyen and Disteche [5] . The two X chromosomes in females are not equivalent as the majority of genes on one are subject to X inactivation; however , it is well established that a number of genes escape the silencing process . Therefore , we compared expression of X chromosome genes in females and males , reasoning that escape from XCI should produce a substantially higher level of expression in females . First we compared expression of the 11 , 952 expressed autosome genes and 371 expressed X genes in 30 males and 30 females from the YRI population . Figure 2A shows median male expression plotted against female expression for each gene . The autosome genes lie on a diagonal with the vast majority showing very similar expression in males and females . Most X genes lie on the same diagonal , indicating that X chromosome gene expression too is similar in males and females and is not proportional to the number of X chromosomes . These data suggest that for most X chromosome genes , dosage compensation is achieved between males and females . We then normalised expression of the 371 X chromosome genes to the median of the 11 , 952 autosome genes for each individual , and calculated the mean of the normalised X chromosome genes for 105 males and 105 females . In each population mean expression of X chromosome genes was higher in females than males ( Figure 2B ) . However , these differences are small , representing increased expression in females of just 2 . 6% , 3 . 4% , 1 . 5% , and 2 . 2% for CEU , CHB , JPT and YRI , respectively . Genes escaping X inactivation do not , therefore , have a great effect on the overall level of X-linked gene expression in the female cell lines . This indicates that dosage compensation is occurring effectively . Although the difference is small , there is a measurable and consistent increase in X chromosome gene expression in females compared to males . In order to identify the genes that contribute to this difference , we looked for X chromosome genes with significantly higher expression in females . We used a Student's t-test to assess differences in expression levels between females and males within each population separately for the 371 X chromosome genes and 11 , 952 autosome genes ( Table S1 ) . Figure 3 compares the proportion of X chromosome and autosome genes expressed more highly in females or males at three different levels of significance: p < 0 . 05 , p < 0 . 01 and p < 0 . 001 . The proportion of X chromosome genes expressed more highly in females is greater than that from autosomes in each population . The difference between X and autosomes is consistent across the three levels of significance and at p < 0 . 001 ranges from 3 . 5% to 4 . 9% of genes in different populations . In contrast , the proportion of X chromosome genes with higher expression in males remains similar to that of autosome genes at all levels of significance ( Figure 3 ) . The most likely explanation for the observations above is that a proportion of X chromosome genes escape from X inactivation to a measurable level in these female cell lines . We supposed that X-linked genes able to escape the silencing process would be expressed more highly in females in all populations . We also reasoned that other X-linked or autosomal genes reaching a significance threshold may not do so in all populations . We therefore assessed the population commonality of autosomal and X chromosome genes with higher female expression at different levels of significance ( Table 1 ) . At p < 0 . 05 and p < 0 . 01 , the proportion of genes with significantly higher female expression is almost identical for autosomes and the X chromosome . However , the distribution of genes among populations is strikingly different , with a far greater percentage of X chromosome genes achieving significance in all four populations . When the significance threshold is raised to p < 0 . 001 , the proportion of genes retained is now lower for autosomes than for the X chromosome , and no autosome gene is common to three or four populations ( Table 1 ) . By contrast , approximately 3% of X chromosome genes are significantly elevated in the females of all populations at p < 0 . 001 ( Table 1 ) . A single gene ( CD99 ) is expressed more highly in the males of all four populations ( p < 0 . 001 ) . This is the only notable difference between X and autosomes in respect of higher male expression ( Table 1 ) . Using the combination of significance values and population commonality as a filter ( Table 1 ) , we identified a group of 20 X chromosome genes that are remarkable in the consistency of their elevation across females ( Table 2 ) : ALG13 , CA5B , DDX3X , EIFIAX , EIF2S3 , FUNDC1 , HDHD1A , JARID1C , MSL3L1 , PCTK1 , PNPLA4 , PRKX , RPS4X , SMC1L1 , STS , UBE1 , USP9X , UTX , ZFX and ZRSR2 . Eleven of these were expressed more highly in females at p < 0 . 001 in all four populations ( Table 2 ) . This situation was not observed for any of 11 , 952 autosomal genes tested . Figure 4A illustrates the female to male ratio of expression for each of the genes in the four populations . Taking the mean of the four populations , there is a subset of six genes ( JARID1C , UTX , HDHD1A , PNPLA4 , DDX3X and EIF1AX ) for which expression in females is around 1 . 5-fold greater than in males . Most genes have a much smaller difference: EIF2S3 , USP9X , CA5B and PCTK1 , ZFX and SMC1L1 all have less than 1 . 2-fold higher expression in females compared to males . We interpret these ratios as expression from the active X that is equivalent to expression from the single X in males , combined with a lower level of expression from the inactive X that is more variable between genes . Some genes ( e . g . , DDX3X , STS ) have a much higher ratio in some populations than others , which may represent a biological difference in the extent of escape from X inactivation in different human populations . We observe higher female expression for 5 . 4% of the X chromosome genes expressed in the cell lines ( 20/371 ) . The possibility remains that other X-linked genes may escape from XCI in lymphoblastoid cells . However , we have determined that these 20 genes account for almost all of the difference in gene expression between males and females seen in Figure 2B ( data not shown ) . Therefore , any expression of additional genes from the inactive X chromosome must be very low and/or must occur in only a small fraction of female cell lines . In either case , the impact on dosage compensation at the population level would be minimal . Therefore , we conclude that 94 . 6% of X-linked genes are effectively dosage compensated in human lymphoblastoid cell lines . We hypothesized that escape from XCI may be partly stochastic , and that this might lead to greater variation in expression levels among females than males for some genes . Figure 4B shows box and whisker plots for four of the genes that can be considered to escape from XCI on the basis of their higher female expression . The other 16 genes follow a similar pattern ( data not shown ) . The size of the box ( interquartile range ) is a good indicator of the similarity of distribution between females and males . In the majority of cases there is little difference in the distribution or range of values between the sexes , although the female values have a higher median and therefore the entire plot is shifted upwards . There is no correlation between the size of the interquartile range and median expression level . We also calculated the variance for each gene and found no significant difference between males and females within each population ( unpublished data ) . We conclude that genes escaping XCI in these cell lines are not expressed more variably in females than males , which suggests that escape may be a tightly regulated rather than a stochastic event . While the medians and interquartile ranges are clearly different between males and females , there is considerable overlap between the distributions of the expression levels of the two datasets . Individual data points from males and females of the YRI population are shown for four genes escaping XCI as a scatter graph ( Figure 4C ) . JARID1C is unique in that all data points for females are higher than all data points for males . For the other 19 genes , the female and male datasets overlap to varying extents ( contrast EIF2S3 with RPS4X in Figure 4C ) . This can also be seen as overlap of whiskers in Figure 4B . This observation illustrates the extent of inter-individual variability of gene expression and highlights the importance of comparing large samples of males and females to identify differences in gene expression that are a consequence of escape from XCI . The genes from the pseudoautosomal regions ( PARs ) are a special case as they lie within regions of XY recombination and are essentially equivalent on the X and Y chromosomes . For genes in PAR1 , escape from XCI is generally believed to be a prerequisite for dosage compensation between the two female X chromosomes and the male X and Y . We found that twelve PAR1 genes show no significant difference between females and males across populations and are therefore dosage compensated ( Table S1 ) . The only exception is CD99 , which is expressed significantly more highly in males in all four populations ( p < 0 . 001 ) . We identified single nucleotide polymorphisms ( SNPs ) in PAR1 genes SLC25A6 , CXYorf3 , ZBED1 and CD99 and used a quantitative assay to measure relative expression from the X and Y alleles in heterozygous males . As shown in Table 3 , the relative contribution from the X and Y chromosomes is very similar for each of the four genes . We conclude that the majority of PAR1 genes escape from XCI and are dosage compensated , consistent with the expectation above . The majority of genes that have a functional Y chromosome homologue were found to be expressed in hybrid cells containing the Xi [16] . Therefore , we decided to test the possibility that genes with higher female expression might , like the genes in PAR1 , be compensated by functionally equivalent Y-linked copies . Eight of the 20 genes with higher female expression have functional Y-linked gametologues . We excluded PRKY and EIF1AY from the analysis . The PRKY probe has 94% sequence identity to PRKX and gives a strong signal in females . Expression of EIF1AY is approximately 13-fold greater than EIF1AX in males , suggesting that EIF1AY is not involved in a compensation mechanism . Gene expression for the remaining six X-Y gene pairs is shown in Figure 5 . USP9X expression is significantly higher in females at p < 0 . 001 in all populations . Interestingly , the sum of USP9X and USP9Y expression in males is not significantly different from the level of USP9X in females ( p = 0 . 822 [CEU] , p = 0 . 024 [CHB] , p = 0 . 245 [JPT] and p = 0 . 610 [YRI] ) . USP9Y expression therefore completely restores the USP9X dosage imbalance between males and females . Figure 5B–5D shows similar results for gene pairs RPS4X/RPS4Y1 , UTX/UTY and DDX3X/DDX3Y . In each case dosage from the X and Y copies in males is similar to that of the X copies in females . Expression of the Y copy is always much lower than expression of the X copy and appears to reflect the expression from the Xi . For these four genes , dosage compensation appears to be achieved by expression of the Y copy . In contrast , expression levels of JARID1C ( X ) and JARID1D ( Y ) ( Figure 5E ) are approximately equal and their combined expression in males is significantly greater than JARID1C expression in females . A similar picture is obtained with ZFX and ZFY in males ( Figure 5F ) . We have identified an X-Y gene pair ( TMSB4X/TMSB4Y ) whose X copy is dosage compensated according to our data . TMSB4Y is expressed at less than 1% of the level of TMSB4X and therefore does not affect dosage compensation between males and females . Genes that escape from XCI in hybrid cell lines are non-randomly distributed on the X chromosome [16 , 17] . In light of identifying a smaller proportion of genes with higher female expression , we assessed the relationship between gene expression and chromosomal location . We observed that the distribution of genes with elevated female expression is also non-random , with most lying on the short arm ( Figure 6 ) . The chromosome can be divided into strata that ceased to recombine with the Y chromosome at different times in evolutionary history [17 , 26] . The most ancient parts of the chromosome ( strata S1 and S2 ) , covering the long arm and proximal short arm , contain 287 of the 371 expressed genes , but only six that have higher female expression . By contrast , a larger fraction of genes have elevated female expression levels in regions that stopped recombining with the Y chromosome more recently , either in early eutherian mammals ( S3 ) or in primates ( S4 , S5 ) . Ten out of 66 S3 genes fit this picture , while all three expressed genes in S4 , together with the single example in S5 , are more highly expressed in females . These findings support the model that X-linked genes are recruited into the XCI system following the Y chromosome degeneration that occurs when regions cease to recombine [27] .
On the basis of these data , we suggest that dosage compensation in human lymphoblastoid cells is virtually complete . Gene expression from the single X chromosome in males is upregulated 2-fold compared to the autosomes . Expression from the female X chromosome pair is almost the same as from the male X , suggesting that few genes escape the silencing process to any great extent . Twenty genes in this study ( 5 . 4% ) have significantly higher female expression , and four of these could have dosage balance maintained through expression of a Y-linked homologue . Ohno predicted 40 years ago that the evolution of a dosage compensation mechanism in mammals must have involved a doubling of expression of each X-linked gene as the Y chromosome degenerated [4] . Two-fold upregulation of the X chromosome has now been demonstrated for both humans and mice [5 , 6] and in a range of tissues [5] . In D . melanogaster , a slight but significant overexpression of the X chromosome compared to the autosomes in all XX;AA samples has been reported [6] which may be due to inherent hypertranscription of the X chromosome . We have determined that human X chromosome expression is not significantly elevated above the autosome average , suggesting that the X chromosome is not hypertranscribed in the cell lines over and above the 2-fold upregulation . Our data show that upregulation occurs precisely and consistently in 105 individual male samples , also leading us to conclude that in lymphoblastoid cell lines gene expression is appropriately regulated . Upregulation of the X is not seen in mouse germ cells suggesting that it takes place in the developing embryo [5] . Two-fold upregulation may be a general feature of X chromosomes , affecting genes on Xa and those on Xi that escape inactivation . Alternatively , upregulation and silencing could be mutually exclusive choices for X chromosomes in embryogenesis , simultaneously achieving correct X gene expression and dosage compensation . Previously , genes have been classified as partially escaping XCI if their female to male expression ratio is below two . However , under the second model described , genes fully expressed from both Xi and an upregulated Xa in females would have a theoretical maximum expression that is 1 . 5-fold greater in females than males . We favour this model as we have identified six genes expressed approximately 1 . 5-fold more highly in females and the greatest ratio we observe for any gene is 1 . 56 , averaged across four populations . Is 2-fold upregulation a chromosome-wide phenomenon ? The PAR1 region is the only surviving remnant of a large autosomal addition to both sex chromosomes that still undergoes recombination in male meiosis . The X and Y chromosomes are equivalent in PAR1 and genes here are predicted to escape XCI . Accordingly , all PAR1 genes tested were expressed from Xi in hybrid cell lines [16] . The majority of PAR1 genes included in our study showed no significant difference in expression between females and males . This gives rise to two models for PAR1 gene expression . In the first , PAR1 genes have equal expression from Xa , Xi and Y copies and so no dosage compensation is necessary . Under this model , PAR1 is excluded from both the upregulation and the silencing components of dosage compensation . In the second model , the PAR1 is 2-fold upregulated on the Xa only and dosage compensation is achieved through equal expression from the Xi and Y copies . A corollary of this model is unequal expression of PAR1 alleles within a cell , but equal expression between males and females . We have tested three dosage compensated PAR1 genes in males and find that expression levels are similar from the X and Y alleles . Therefore , we favour the first model and propose that PAR1 is protected from both upregulation and silencing . CD99 is the only PAR1 gene found to be more highly expressed in males , yet has equivalent expression from the X and Y copies . CD99 is the gene that lies closest to the boundary between the PAR1 and X-linked material , and we suggest that spreading of the XCI signal across the pseudoautosomal boundary results in partial silencing of CD99 . Consistent with this hypothesis , the protein product of the CD99 gene was found to be present at lower levels in hybrids containing Xi than those containing Xa [28] . Outside the pseudoautosomal regions , twenty genes are expressed at significantly higher levels in female cell lines . We hypothesized that these genes would escape from XCI . Formally , the alternative explanation for the elevated female expression could be that these genes are hypertranscribed from the female active X chromosome . However , since all of these genes are included in previous reports of escape from XCI [12 , 15 , 16 , 29–33] , we prefer the former explanation for higher female expression . An intriguing observation is that the dosage of some of these genes could be effectively compensated by expression of a Y copy . An underlying assumption of this analysis is that the X and Y gametologues are functionally equivalent , despite their evolutionary divergence . The DEAD box RNA helicase proteins DDX3X and DDX3Y appear to be interchangeable , as both rescue a temperature-sensitive mutant hamster cell line incapable of growth at a non-permissive temperature [34] . The ribosomal proteins RPS4Y1 and RPS4X show functional equivalence in a similar rescue assay and can function interchangeably in ribosomes [35] . RPS4Y1 and RPS4X are among a very small number of genes from the ancient sex chromosomes that have functional Y copies [17] . Despite their considerable divergence time and very high synonymous substitution rate , their protein products share 93% identity and are the same length , consistent with their being functionally equivalent . Previous microarray studies have assessed escape from XCI by looking for increased expression in cell lines with supernumerary X chromosomes [18] or by comparing female and male expression [19 , 20] . More recently , a larger study assessed genome-wide sex differences in gene expression using lymphoblastoid cell lines from monozygotic twin pairs [21] . Each study reports some genes that are considered to be well established as escaping from XCI , but the four vary considerably in the number and identity of genes documented . These differences might be explained in part by variation in escape from X inactivation in different tissues , for which there is evidence [20] . However , a further possibility is that inter-individual variability in expression that is unrelated to XCI could increase the risk of false positives or negatives where the sample size is small . The scale of our study , which measured gene expression in 210 individuals for 81% of protein coding genes on the X chromosome , means that we can confidently detect significantly higher expression in females at the population level in spite of this factor . Notably the study by McRae et al . [21] , which assayed 38 lymphoblastoid cell lines , agrees most closely with our study in the identity and proportion of genes with higher female expression . We find that 5 . 4% of X-linked genes have increased female expression in the cell lines . Analysis of somatic-cell hybrids that retain Xi and of fibroblast cells with non-random XCI has put the proportion of genes escaping XCI at 15%–25% [16] . This difference could be explained by a lower proportion of genes escaping XCI in lymphoblastoid compared with fibroblast ( or hybrid ) cells . However , perhaps more important are the differences of approach between the two studies . Our study used a population based analysis of dosage compensation , whereas Carrel and Willard [16] detected expression in hybrid cell lines , or compared expression levels from Xa and Xi alleles in female cell lines , using methods capable of detecting very low levels of expression from Xi . Some genes , therefore , could be expressed from Xi but at a level that is insufficient to cause a dosage imbalance . Other genes could escape to a larger extent in a small number of females , as suggested by Carrel and Willard [16] . Neither of these , though , is substantial enough to generate a significant sex-difference at the population level . We conclude that dosage compensation in human lymphoblastoid cell lines is highly effective and tightly controlled . It will be interesting to extend these studies to other tissues , but it seems unlikely that this level of regulation would be restricted to this single cell type . Therefore , we propose that the contribution of escape from XCI to male-female phenotypic differences may be small and furthermore , we suggest that the number of genes contributing to phenotype in X chromosome aneuploidies is lower than previously thought .
Gene expression was assayed in lymphoblastoid cell lines of all 210 unrelated HapMap individuals [25] from four populations ( CEU: 60 ( 30 Male/30 Female ) Utah residents with ancestry from northern and western Europe; CHB: 45 ( 22M/23F ) Han Chinese in Beijing; JPT: 45 ( 23M/22F ) Japanese in Tokyo and YRI: 60 ( 30M/30F ) Yoruba in Ibadan , Nigeria ) . RNA preparation , labeling , hybridization to Sentrix Human-6 BeadChip ( Illumina ) , gene expression quantification and normalization of raw data were described previously [23] . Briefly , each RNA sample was labelled in duplicate and each labelled sample was hybridised to two separate arrays . Data were subjected to quantile normalization then were median normalized across all individuals . Final data points for each gene are the mean of the four normalized hybridisation values . Log2 transformed mRNA expression values were used throughout except where otherwise stated . Data can be downloaded from http://www . sanger . ac . uk/humgen/genevar/ and the Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) entry GSE6536 . We established an appropriate cut-off point for evaluating gene expression by examination of four non-human probes ( lysA , pheA , thrB and trpF ) in 210 individuals: 836/840 data points had log2 expression values <6 . 4 . We also evaluated signals from Y-linked genes in female samples: 593/620 data points were found to have values <6 . 4 , excluding two probes that apparently cross-hybridised with X-linked genes . We conservatively chose to analyse genes with log2 median expression >6 . 4 in all four populations . We excluded redundant probes ( n = 21 ) for X chromosome genes and any X probes that matched autosomal exons . Data for 11 , 952 expressed autosome sequences and 371 expressed X chromosome genes were used in all downstream analyses . The complete set of X chromosome and autosomal genes represented in this study and median expression values are shown in Table S2 . Data from the four populations were considered separately throughout . We tested male and female datasets separately for skewness and kurtosis for 371 X chromosome probes and found no evidence for them , except for a very small number of genes in some populations where log2 expression was close to the expression cutoff of 6 . 4 . We therefore concluded that the gene expression data generally follow a normal distribution . We used median values to illustrate chromosome or population averages except where we have shown the standard deviation ( Figure 2B ) . To make comparisons between autosome genes and X genes , or between groups of individuals , we normalized all gene expression values to the median value of 11 , 952 autosomal probes for each individual . We compared the variances of male and female samples for X chromosome genes by placing the larger variance over the smaller to form an F statistic . We found that variances for females and males are not significantly different . We tested significance by calculating p-values associated with a Student's two sample homoscedastic t-test with a two-tailed distribution . The complete list of p-values is shown in Table S1 . SNaPshot was carried out on cDNA and genomic DNA from heterozygous males using the SNaPshot Multiplex Kit ( Applied Biosystems ) according to the manufacturer's instructions with the following modifications . Initial template generation was carried out using Platinum Taq polymerase ( Invitrogen ) in a standard reaction using touchdown polymerase chain reaction ( PCR ) : denaturation: 94°C 15 min; 20 cycles: 94°C 30 sec , 70°C , 30 sec reducing by 1°C per cycle , 72°C 45 sec; then 15 cycles: 94°C 30 sec , 50°C 30 sec , 72°C 45 sec; final extension 72°C for 7 min . PCR products were treated with 2 units of shrimp alkaline phosphatase ( USB ) and 1 . 5 units of Exonuclease I ( USB ) for 1 hour at 37°C to remove primers and nucleotides , then at 80°C for 15 mins . Primer extension products were analysed on an ABI 3730 DNA Analyzer with a POP-7 Polymer and a 36cm capillary array with the ABI standard run module . SNP data were analysed using ABI PRISM GeneMapper Software Version 3 . 0 . Primers used to generate template DNA for analysis and SNaPshot extension primers are shown in Table S3 . Peak heights from cDNA were normalized to genomic DNA values and expressed as an allelic ratio . | The males and females of many species are distinguished by their inheritance of different sets of sex chromosomes . This creates a significant imbalance in gene number between the sexes . Dosage compensation is the correction for this imbalance and is achieved by regulating gene activity across entire sex chromosomes . For example , human females have two X chromosomes and males have only one . Dosage compensation in humans involves X chromosome inactivation , which is the silencing of one X chromosome in female cells . Some genes are known to escape the silencing process and so are expressed at higher levels in females than males . We have investigated the extent to which such genes disrupt dosage compensation by comparing the activity of X chromosome genes in a large number of human male and female cell lines . We have shown that gene expression from the X chromosome pair in female cell lines is only slightly higher than from the single X in males . The small difference can be accounted for by increased female expression of approximately 5% of X chromosome genes . We conclude therefore that dosage compensation in these human cell lines is virtually complete , and we suggest that differences in X chromosome gene expression between males and females may be less extensive than previously thought . |
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Treponema pallidum subsp . pertenue ( TPE ) is the causative agent of yaws , a multi-stage disease , endemic in tropical regions of Africa , Asia , Oceania , and South America . To date , four TPE strains have been completely sequenced including three TPE strains of human origin ( Samoa D , CDC-2 , and Gauthier ) and one TPE strain ( Fribourg-Blanc ) isolated from a baboon . All TPE strains are highly similar to T . pallidum subsp . pallidum ( TPA ) strains . The mutation rate in syphilis and related treponemes has not been experimentally determined yet . Complete genomes of two TPE strains , CDC 2575 and Ghana-051 , that infected patients in Ghana and were isolated in 1980 and 1988 , respectively , were sequenced and analyzed . Both strains had identical consensus genome nucleotide sequences raising the question whether TPE CDC 2575 and Ghana-051 represent two different strains . Several lines of evidence support the fact that both strains represent independent samples including regions showing intrastrain heterogeneity ( 13 and 5 intrastrain heterogeneous sites in TPE Ghana-051 and TPE CDC 2575 , respectively ) . Four of these heterogeneous sites were found in both genomes but the frequency of alternative alleles differed . The identical consensus genome sequences were used to estimate the upper limit of the yaws treponeme evolution rate , which was 4 . 1 x 10−10 nucleotide changes per site per generation . The estimated upper limit for the mutation rate of TPE was slightly lower than the mutation rate of E . coli , which was determined during a long-term experiment . Given the known diversity between TPA and TPE genomes and the assumption that both TPA and TPE have a similar mutation rate , the most recent common ancestor of syphilis and yaws treponemes appears to be more than ten thousand years old and likely even older .
Treponema pallidum subsp . pertenue ( TPE ) is the causative agent of yaws , a multi-stage disease transmitted through direct skin contact between children or young adults; it is characterized by skin nodules and ulcerations and later accompanied by joint , soft tissue , and bone affections ( reviewed in [1] ) . To date , four TPE strains have been completely sequenced including three TPE strains of human origin ( Samoa D , CDC-2 , and Gauthier ) [2] and one TPE strain ( Fribourg-Blanc ) isolated from a baboon ( Papio papio ) in West Africa [3] . Compared to syphilis-causing strains of T . pallidum subsp . pallidum ( TPA ) , the observed genetic differences between TPA and TPE represent less than 0 . 2% of the total genome sequence length [2] . In addition , TPE strains have been shown to be closely related to the T . pallidum subsp . endemicum ( TEN ) strain Bosnia A [4] . The origin of syphilis and other treponematoses ( i . e . , diseases caused by TPE and TEN ) remain enigmatic and historically there have been several hypotheses about the origin of these diseases ( reviewed in [5] ) . One of the limitations was the fact that the mutation rate in syphilis and related treponemes was unknown , although estimates have been published based on either paleopathological findings [6] or on phylogenetic analyses of TPA strains/isolates with known isolation dates [7] . In this communication , we compared the complete genome sequences of two strains of TPE which came from infected patients living in Ghana in 1980 and 1988 . While there were more than 7 years between isolation dates of the two treponemal strains , the assembled consensus genome sequences of both TPE strains were identical , although both strains exhibited intrastrain heterogeneity at a limited number of nucleotide positions . These data were used to estimate the upper limit of the yaws treponeme evolution rate , as well as evaluate the possible evolutionary implications .
No vertebrate animals were used in the study . The study was approved by the ethics committee of the Faculty of Medicine , Masaryk University . The research was conducted in accordance with the Czech law standards . Two TPE strains ( TPE Ghana-051 and CDC 2575 ) were used in this study . In 1980 , TPE strain CDC 2575 was isolated in Akorabo , Ghana , from a 6-year-old female patient from a papillomatous lesion and inoculated into hamsters [8] , and later propagated in New Zealand White rabbits [9] . TPE strain Ghana-051 was isolated in 1988 from a papillomatous lesion of 9-year-old girl who was infected at an unspecified location in Ghana six months prior to admission to Sophia Children's Hospital in Rotterdam , The Netherlands [10] . An extract from a biopsy of this patient in secondary yaws stage was inoculated into New Zealand White rabbits in the Netherlands [10] . DNA from the TPE strains CDC 2575 was isolated from rabbit testes lysates containing 107 treponemes per ml . TPE Ghana-051 was provided as a DNA isolate that was previously isolated directly from rabbit testes containing 109 treponemes per ml . Both strains came from the laboratory strain collection of Dr . Gerda Noordhoek . The DNA of both TPE strains was isolated from treponemes obtained during experimental infection of New Zealand White rabbits . The genomic DNA was amplified using the multiple displacement amplification approach ( REPLI-g kit , QIAGEN , Valencia , CA , USA ) according to the manufacturer’s instructions and then used as a template ( 50x diluted ) for pooled segment genome sequencing ( PSGS ) as described previously [2–4 , 11] . Briefly , the TPE DNA was amplified with PrimeSTAR GXL DNA Polymerase ( Takara Bio Inc . , Otsu , Japan ) with 278 pairs of specific primers to obtain overlapping PCR products ( S1 Table ) covering the entire genome in both isolates . PCR products were amplified using touchdown PCR at the following cycling conditions: initial denaturation at 94°C for 1 min; 8 cycles: 98°C for 10 s , 68°C for 15 s ( annealing temperature gradually reduced by 1°C/every cycle ) , and 68°C for 6 min; 35 cycles: 98°C for 10 s , 61°C for 15 s , and 68°C for 6 min ( 43 cycles in total ) ; followed by the final extension at 68°C for 7 min . PCR products were subsequently purified using a QIAquick PCR Purification Kit ( QIAGEN , Valencia , CA , USA ) and mixed in equimolar amounts into four distinct pools ( S1 Table ) . Prior to Illumina sequencing , the PCR products from each pool were labeled with multiplex identifier ( MID ) adapters and sequenced as four different samples . Illumina Nextera XT library preparation and sequencing on a NextSeq 500 ( 2x150 bp ) was performed at CEITEC ( Brno , Czech Republic ) . The results were: ( 1 ) for CDC 2575 , 15 , 629 , 124 paired reads , 4 , 688 , 737 , 200 total bases , with an average coverage depth of 1028x , and ( 2 ) for Ghana-051 , 13 , 194 , 731 paired reads , 3 , 958 , 419 , 300 total bases , with an average coverage depth of 868x . The sequencing results are summarized in S2 Table . Resultant data were pre-processed with Trimmomatic ( 0 . 32 ) [12] . Low quality bases were removed with a sliding window having a length of 4 , with an average quality of at least Phred = 17 . After pre-processing , sequencing reads shorter than 50 bp were removed . The Illumina sequencing reads were handled separately with respect to the 4 distinct pools and were separately assembled de novo using SeqMan NGen v4 . 1 . 0 software ( DNASTAR , Madison , WI , USA ) . A total of 470 , 783 , 607 , and 667 contigs for Ghana-051 and 72 , 348 , 57 , and 215 contigs for TPE CDC 2575 were obtained for Pools 1–4 , respectively . Alternatively , all Illumina sequencing reads were mapped to the TPE Samoa D genome ( GenBank Acc . No . CP002374 . 1 ) . All genome gaps and discrepancies were resolved by location-specific PCR products that were Sanger sequenced . Altogether , 26 and 23 regions of the Ghana-051 and CDC 2575 genome were Sanger sequenced , respectively . The individual overlapping pool sequences were joined together , resulting in the complete genome sequence of both isolates . The individual Illumina reads were mapped to the final version of the corresponding complete genome sequences using SeqMan NGen v4 . 1 . 0 software ( DNASTAR , Madison , WI , USA ) with default parameters and requiring at least a 93% read identity relative to the reference genome . To determine the frequency of each nucleotide ( allele frequency ) in every single genome position , the haploid bayesian method was used for SNP calculation with default parameters using SeqMan NGen v4 . 1 . 0 software ( DNASTAR , Madison , WI , USA ) . Nucleotide positions located within homopolymeric tracts ( defined as a stretch of 6 or more identical nucleotides ) were omitted from the analysis . Chromosomal loci showing genetic heterogeneity within TPE genomes were defined as those containing more than 8% alternative reads in regions having coverage greater than 100x . The resulting candidate sites for heterogeneous nucleotide positions were subsequently visually inspected using SeqMan NGen v4 . 1 . 0 software ( DNASTAR , Madison , WI , USA ) . Geneious software v5 . 6 . 5 [13] was used for gene annotations , as described previously [4] . Genes were tagged with TPEGH051_ and TPECDC2575_ prefixes with the locus tag numbering corresponding to the tag numbering of the orthologous genes annotated in the TPE CDC-2 genome ( [2]; GenBank Acc . No . CP002375 . 1 ) . As in other TPE genomes , tprK showed intrastrain variability [14] and the corresponding nucleotide positions were denoted as “N” in the complete genome sequences . For proteins with unpredicted functions , a gene size limit of 150 bp was applied . In addition to the Ghana-051 and CDC 2575 genome sequences , whole genome nucleotide sequences of 4 TPE strains , i . e . , Samoa D ( CP002374 . 1 ) , CDC-2 ( CP002375 . 1 ) , Gauthier ( CP002376 . 1 ) , Fribourg-Blanc ( CP003902 . 1 ) , and TEN Bosnia A strain ( CP007548 . 1 ) were used for construction of a phylogenetic tree . Whole genome alignment was constructed using SeqMan software ( DNASTAR , Madison , WI , USA ) and the unrooted phylogenetic tree was constructed from the whole genome sequence alignment using the Maximum Likelihood method and MEGA7 software [15] . Trimmed sequencing reads that passed quality threshold for both TPE Ghana-051 and CDC 2575 were mapped to the rabbit reference chromosome 1 ( CM000790 . 1 ) using BWA-MEM ( version 0 . 7 . 5a; [16] ) software with default parameters . Altogether , sequencing of Ghana-051 and CDC 2575 resulted in 2 , 150 , 741 and 1 , 731 , 654 reads that aligned to the rabbit DNA , respectively . Single nucleotide variants in the rabbit chromosome-aligned sequences were detected using SAMtools ( version 0 . 1 . 19; [17] ) and VarScan ( version v2 . 4 . 2; [18] ) software by setting a minimum read depth of 8 , strand filter on , and a mutant frequency of 100% . Altogether , 21 positions in the rabbit DNA were found to be heterogeneous between the Ghana-051 and CDC 2575 samples . A region differing in 7 nucleotide positions and spanning approximately 1 kb was selected and analyzed in additional TPE samples containing rabbit DNA with one pair of primers including 5’-AAAGCCCCTTTGCCTAGTCC-3’ ( positions 53848016–53848035 in the genome of Oryctolagus cuniculus ( rabbit ) according to reference sequence CM000790 . 1 ) and 5’-CCTGCGGCCCCATTATTGTA-3’ ( positions 53849015–53848996 in CM000790 . 1 ) . The reaction mixture contained 2 . 5 μl of ThermoPol Reaction Buffer , 0 . 5 μl of 10 mM deoxynucleoside triphosphate ( dNTP ) mixture , 0 . 25 μl of each primer ( 100 pmol/μl ) , 2 μl of the DNA-containing sample and 0 . 1 μl of Taq DNA polymerase ( 5 , 000 U/ml; New England BioLabs , Ipswich , MA , USA ) . The reaction mixture was supplemented with PCR-grade water to a final volume of 25 μl . The DNA was amplified under the following cycling conditions: denaturation at 94°C for 2 min; 35 cycles: 94°C for 15 s , 57 . 5°C for 15 s , 68°C for 1 . 25 min; followed by the final extension at 68°C for 2 min . The PCR products were purified using a QIAquick PCR Purification Kit ( QIAGEN , Valencia , CA , USA ) and sequenced using the Sanger method ( GATC Biotech , Germany ) . The complete genome sequences of the TPE Ghana-051 and CDC 2575 strains were deposited in the GenBank under accession number CP020365 and CP020366 , respectively . The sequenced chromosomal rabbit DNA from TPE Ghana-051 , CDC 2575 , CDC-2 , and Sei Geringging samples were deposited in GenBank under accession numbers KY972336 , KY972337 , KY972338 , and KY972339 , respectively .
Both TPE strains ( CDC 2575 and Ghana-051 ) used in this study were isolated from papillomatous lesions of young patients of comparable age ( 6 and 9 years old , respectively ) , and both patients were infected in Ghana , West Africa . While TPE strain CDC 2575 was isolated in Akorabo , Ghana , in 1980 [8 , 9] , TPE strain Ghana-051 was isolated in 1988 after its introduction , via an infected patient , to the Netherlands [10] . The time and place of the isolations , clinical data , as well as the species of experimentally infected animals and the known number of passages , are shown in Fig 1 . While Liska et al . [8] is provided as an original reference for TPE CDC 2575 , this strain is not specifically mentioned in this study . According to the lab record from Dr . Gerda Noordhoek , CDC 2575 was provided by Dr . Peter Perine from CDC , one of the coauthors of Liska et al . [8] study , and strain details referred to Liska et al . [8] . Moreover , clinical data from Dr . Noordhoek´s notebook are in accordance with the description of the patient no . 1 in the Liska et al . [8] study ( see Table 1 in Liska et al . [8] ) . Therefore , CDC 2575 represents one of the three successfully hamster propagated samples in the Liska et al . [8] study . The other two TPE strains , named as CDC-1 and CDC-2 , were successfully transferred to rabbits and the third isolate ( likely CDC 2575 ) was lost [8] . It is not clear if CDC 2575 represents the lost and perhaps again found sample . Nevertheless , the mislabeling or swapping of CDC 2575 with CDC-2 or CDC-1 can be excluded since all these strains differ in the genomic sequence [2] . In the original article by Engelkens et al . [10] , strain Ghana-051 is also not specifically mentioned . However , according to the lab record of Dr . Noordhoek containing clinical data and reference , TPE Ghana-051 is the same strain as described by Engelkens et al . [10] . Both TPE Ghana-051 and TPE CDC 2575 genomes were sequenced using the previously described PSGS approach , originally described by Weinstock et al . [11] and later modified by others [2–4] . The average coverage of Illumina reads reached 868x and 1028x for TPE Ghana-051 and TPE CDC 2575 genome , respectively . The two sequencing projects were performed separately and by different people; MS analyzed TPE Ghana-051 , while LM analyzed the TPE CDC 2575 genome . Both sequencing projects ( i . e . , TPE Ghana-051 and TPE CDC 2575 ) revealed identical consensus genome sequences , except for regions showing intrastrain heterogeneity , including tprK variable regions . The basic characteristics of the TPE Ghana-051 and TPE CDC 2575 genomes are shown in Table 1 , and these are identical or highly similar to other completely sequenced TPE genomes [2–3] . The TPE Ghana-051 and CDC 2575 strains clustered with TPE strains isolated from Africa , especially with the TPE Gauthier strain ( Fig 2A ) , which differed from it in 129 nucleotide positions ( S3 Table ) . Both TPE Ghana-051 and TPE CDC 2575 genomes were assessed for the presence of intrastrain heterogeneity , as described previously [21] . The frequency of minor alleles was set to at least 8% and the list of heterogeneous sites is shown in Table 2 . While the genome of TPE Ghana-051 had 13 heterogeneous sites , the genome of TPE CDC 2575 had 5 heterogeneous sites . Four heterogeneous sites at positions 134948 , 522943 , 700634 , and 997894 , relative to strain Samoa D , were found at the same positions in both genomes , but differing in the frequency of both alleles ( Table 2 ) . All minor alleles were supported by at least 19 independent Illumina reads with a mean value of the number of minor allele Illumina reads of 278 . 5 . The average sequencing error rates for these positions , calculated from alternative reads ( i . e . , other than minor and major alleles ) for nucleotide positions ( presented in Table 2 ) was 0 . 094% . Therefore , a cutoff of 8% for alternative reads was more than two orders of magnitude higher than the average error rate of Illumina reads for these positions . One of the 14 polymorphic sites ( 235835 ) was in an intergenic region . Most of the alternative alleles ( 11 out of 13 ) resulted in non-synonymous mutations in genes encoding proteins involved in substrate transport and metabolism ( Table 3 ) . Since consensus genome sequences of both TPE samples were identical ( although both strains contained several intrastrain heterogeneous sites ) and since these samples were also contaminated by rabbit DNA derived from the experimentally inoculated rabbits , the rabbit chromosomal DNA in Ghana-051 and CDC 2575 samples was analyzed to verify the independent character of both samples . Altogether , sequencing of Ghana-051 and CDC 2575 resulted in 2 , 150 , 741 and 1 , 731 , 654 reads aligning to Chromosome 1 rabbit DNA , respectively . Altogether , 21 positions in rabbit DNA were different between Ghana-051 and CDC 2575 samples . From these reads , a region differing in 7 nucleotide positions and spanning less than 1 kb was selected , amplified , and analyzed in additional TPE samples from the same laboratory and time period . The sequenced region covered positions 53848036–53849014 in the genome of Oryctolagus cuniculus ( CM000790 . 1 ) ( Table 4 ) , i . e . , the region between the DMRT1 gene ( encoding Doublesex and mab-3 related transcription factor 1; coordinates 53687806–53804010 ) and the KANK1 gene ( encoding the KN motif and ankyrin repeat domain-containing protein 1; coordinates 53898320–53958377 ) . Within this region , 46 and 141 Illumina sequencing reads were mapped to the rabbit reference in the Ghana-051 and CDC 2575 samples , respectively . Another TPE sample that was propagated in rabbits in the same laboratory and at about the same time ( 1990; TPE Sei Geringging , [22] ) showed sequences in these nucleotide positions that were identical with the rabbit DNA from the TPE Ghana-051 sample ( 1988 ) . Similarly , the TPE CDC-2 sample ( isolated in 1980 , [8] ) showed identical rabbit sequences to the rabbit sequences from the CDC 2575 sample ( isolated in 1980 ) . There was a time span of 7 years and 3 months between isolation of the two strains and , during this time , the TPE strain Ghana-051 multiplied in infected humans . Using this time period ( 7 . 25 years ) and the number of detected mutations ( n = 0 ) , the upper limit of the mutation rate in yaws treponemes was estimated . The mutation rate ( μ ) per site per year can be calculated using the following formula: μ=ntxgl where n means the number of nucleotide differences , t means the time since the samples have diverged measured in years , and gl means the number of nucleotide positions ( genome length ) . For the number of nucleotide positions , a genome size of 1 , 139 , 577 nt was used . Since sites with intrastrain heterogeneity do not represent fixed mutations , they were not used for the estimation of the TPE mutation rate . No mutations during 7 . 25 years corresponds to a mutation rate of 1 . 21 x 10−7 per nucleotide site per year or lower . Estimation of a mutation rate ( μ ) per site per generation was calculated from this formula: μ=nngxgl where ng means the number of generations during the time the samples diverged . Considering the doubling time of yaws spirochetes , about 30 hours [23 , 24] , and the optimal proliferating conditions during the entire 7 . 25 year period , one can assume that 63 , 510 hours ( 7 . 25 years ) between the isolation of the two samples corresponded to 2 , 117 treponemal generations . This number corresponds to a TPE mutation rate of 4 . 1 x 10−10 per site per generation ( assuming 292 generations per year ) .
Two TPE strains isolated more than 7 years apart from each other , from young patients who were originally infected in Ghana , Africa , were analyzed and their genomes were completely sequenced with PSGS [2–4 , 11] . The consensus genome sequences were identical despite the fact that the isolation of DNA , genome amplifications , sequence assembly , and genome analyses were performed separately at different times and by different people . This finding indicates that ( 1 ) there is a very low error rate in PSGS sequencing , i . e . , on the order of 10−6 or lower and ( 2 ) the genomes of uncultivable pathogenic treponemes are quite stable over time . The identical consensus whole genome sequences raised the question as to whether the Ghana-051 and CDC 2575 samples really represented two different strains . There are several lines of evidence that indicate that the TPE Ghana-051 and TPE CDC 2575 samples are different and do not represent a single , duplicated sample . First , treponemes sequenced from the two samples showed differences in 14 intrastrain heterogeneous sites ( either in the positions of the heterogeneous sites or in the frequency of the detected nucleotide variants ) , indicating that the population of treponemes present in the two samples were different . At the same time , 4 common positions of intrastrain heterogeneous sites suggested that treponemes from both samples were highly similar and originated from a common ancestor population . Second , the contaminating chromosomal rabbit DNA in both samples ( between the DMRT1 and KANK1 genes ) differed , and the observed nucleotide sequences in Ghana-051 were similar to samples from the same time period and the same laboratory , i . e . , to TPE Sei Geringging , which was also propagated in the Netherlands . At the same time , the rabbit DNA in CDC 2575 was identical to the tested nucleotide positions in the CDC-2 sample , which was isolated in Ghana at the same time . These data clearly indicate that both Ghana-051 and CDC 2575 represented different samples with rabbit DNA sequences related to samples from a similar time period and place of isolation . Third , TPE CDC 2575 was provided to Dr . Noordhoek by Dr . Peter Perine from CDC and this strain was used in the study by Noordhoek et al . [9] , even before isolation of TPE Ghana-051 . A potential mislabeling or swapping of TPE CDC 2575 with other two TPE strains , CDC-1 and CDC-2 , isolated in Ghana in 1980 and described in Liska et al . [8] can be excluded since genome sequence of CDC 2575 differs from CDC-2 [2] as well as from CDC-1 according to an ongoing sequencing project in our laboratory . Fourth , TPE Ghana-051 was provided to Dr . Noordhoek from Sophia Children's Hospital in Rotterdam , The Netherlands , and according to the Dr . Noordhoek's lab record , this sample was described in the study by Engelkens et al . [10] . The sequenced sample of TPE Ghana-051 showed similar signatures of rabbit DNA as rabbits used in the Netherlands at the same time period ( 1988 ) and the original tube from which the TPE Ghana-051 was sequenced originated from 1989 . Both these facts support strain TPE Ghana-051 as the original sample isolated in the Netherlands . Taken together , the possible duplication of either CDC 2575 or Ghana-051 samples can be excluded . Several previous studies [25–27] described partial sequences of CDC 2575 ( n = 24 ) and Ghana-051 ( n = 22 ) comprising 8 , 733 bp and 7 , 583 bp , respectively . Altogether , 5 nucleotide discrepancies , located in genes TP0617 ( 1 nucleotide difference; GenBank Acc . No . EU101912 . 1 for CDC-2575 and EU101917 . 1 for Ghana-051 ) and TP0620 ( 4 nucleotide differences; GenBank Acc . No . JN582339 . 1 for CDC 2575 ) , were identified . However , the TPE CDC 2575 sequence of TP0620 ( JN582339 . 1 ) , published in a study by Pillay et al . [27] , was likely mislabeled since this sequence was also identical to the TP0620 of the TPA Nichols strain , which had also been used in the aforementioned study [27] . The remaining difference ( C vs . T ) at position 671367 corresponding to the complete genome sequence of CDC 2575 and Ghana-051 in the TP0617 gene was analyzed in individual sequencing reads and revealed a cytosine at this position , which was supported by 97 . 9% ( 1266x coverage ) and 96 . 9% ( 2080x coverage ) of the reads in CDC 2575 and Ghana-051 , respectively . A thymine at this position was detected in 2 . 1% and 2 . 9% of the reads in CDC 2575 and Ghana-051 , respectively , suggesting that the observed difference may represent an intrastrain heterogeneous site , a sequencing error , or both . Genetic diversity within individual treponemal strains , i . e . intrastrain genetic diversity , was detected in several previous studies and mostly affected tpr genes and sequences in their vicinity or sequences paralogous to tpr genes [28–34] . A previous genome-wide study [21] on genetic heterogeneity revealed 17 genes in 5 treponemal genomes with 23 intrastrain heterogeneous sites; the number of identified heterogeneous sites in individual genomes varied between 0 and 7 . However , the average sequence coverage of analyzed genomes in the previous study [21] ranged between 21x and 194x , while in the present study the coverage was well over 800x , which may explain the higher detection rate of intrastrain variable sites . Another recent genome-wide study [35] revealed 63 intrastrain heterogeneous sites in 25 TPA genomes . The median average depth of coverage of the genomes analyzed in this study [35] was also relatively high compared to the original study [21] and reached 131x ( ranging from 20x to 1 , 196x ) . While it is clear that the number of identified intrastrain heterogeneous sites somehow correlates with the average depth of coverage , it is not clear if the number of heterogeneous sites also reflects the T . pallidum subspecies , as suggested by Čejková et al . [21] , where the majority of heterogeneous sites were found among TPA strains and not among TPE strains . As with the previously identified heterogeneous sites , the alternative alleles primarily encoded non-synonymous replacements of amino acid residues in the corresponding protein sequences , suggesting an adaptive character for this genetic variability . Interestingly , TPE strain Ghana-051 showed more heterogeneous sites ( n = 13 ) compared to TPE strain CDC 2575 ( n = 5 ) . A previous study showed that when DNA preparations originated from two different rabbit passages , the relative proportions of alleles differed [21] . It is therefore not clear if the observed differences in occurrence and frequency of heterogeneous sites reflect differences associated with when ( i . e . , the year ) the patient’s sample was isolated , or if it was the differences in the number of hamster and rabbit passages , or both . Seven years and 3 months elapsed between the isolation of the two samples and this corresponds to a mutation rate of 1 . 21 x 10−7 per TPE nucleotide site per year or lower or 4 . 1 x 10−10 per site per generation ( assuming 292 generations per year ) . For our calculations , we omitted the time period over which the two TPE strains evolved separately , i . e . , the time between TPE infection and the appearance of clinical symptoms in the patient that originated the older sample ( CDC 2575 ) , and also the time that was needed for isolation of both strains during experimental hamster and rabbit infection . The reason for this was the lack of data indicating the exact time of infection and the exact number and duration of animal passages . However , both these factors would act to further lower the calculated mutation rate . In E . coli , an in vitro experiment revealed about 100 fixed genome mutations per 40 , 000 generations , i . e . , one mutation per approximately 400 generations [36] . As shown by Maughan [37] , differences in generation time between E . coli and TPE do not have to affect the evolutionary rates of other organisms with different generation times . The genome size of E . coli is 4 , 629 , 812 nt , i . e . , 4 . 06 times larger than the TPE genomes analyzed in this study and the corresponding mutation rate corresponds to 5 . 3 x 10−10 per site per generation . Interestingly , the mutation rates calculated for both organisms were surprisingly similar . However , in TPE , this estimate represents the upper limit of the mutation rate , which is likely to be lower . Based on paleo-pathological findings and phylogenetic analyses , de Melo et al . [6] estimated the evolutionary rate of syphilis treponemes to be 8 . 82 x 10−8 substitutions per site per year , i . e . , a number corresponding to 3 . 3 x 10−10 per site per generation . This estimate is close to the highest substitution rate revealed by the present study . Moreover , the mutation rates in microbes with DNA-based chromosomes were estimated to be close to 1/300 per genome per replication [38] , i . e . , 2 . 92 x 10−9 per site per generation , less than an order of magnitude higher than that estimated in this study . Altogether , the estimated upper limit of the TPE mutation rate appears to be close to that of other bacteria and to other mutation rate predictions in treponemes ( based on different data ) . Since the evolutionary rate differs between bacterial species [39] , it is not clear how the estimated upper limit of the TPE mutation rate is applicable to related treponemal subspecies , including syphilis treponemes ( TPA ) , TEN , and T . paraluisleporidarum [40] treponemes . Uncultivable pathogenic treponemes are monomorphic bacteria [5] that have extremely high sequence similarity; therefore , it is likely that other related treponemes show similar mutation rates . However , a study published by Arora et al . [7] estimated the mean evolutionary rate of TPA as 6 . 6 x 10−7 substitutions per site per year , which was based on sample isolation dates and a Birth-death serial skyline model [41] . This value is about 5 . 5 times higher than the upper limit estimated in this study ( 1 . 21 x 10−7 per nucleotide site per year ) . The differences in the estimated TPA error rate could be related to the model used and/or the differences between TPA and TPE mutation rates . Several studies showed that non-human primates can represent possible reservoirs of human yaws [42–46] . Since yaws treponemes appear to be genetically stable for years , molecular typing of yaws-causing strains will help in future to prove or exclude a transfer of yaws strains between non-human primates and humans . Moreover , epidemiological mapping of TPE strains could help to identify transmission networks also within humans which might be valuable especially in recent yaws eradication efforts . In 2012 , the WHO launched a plan for eradication of yaws using macrolide antibiotic azithromycin [47] . So far , azithromycin was found to be effective in treatment of yaws [48–51] , although in syphilis-causing strains , two mutation were associated with macrolide resistance [52–53] . Since resistance to azithromycin is caused by point mutation in the 23S rRNA gene and since both copies of the 23S rRNA genes need to be mutated , we have previously estimated the probability of emergence of macrolide-resistant yaws treponemes to be a rather rare event ( with probability 10−4–10−5 per infected patient ) [54] . Another implication of the yaws treponemes genome stability during human infections is the possible implication for syphilis treponemes typing studies [54–60] , where the stability of arp , tprEGJ , TP0136 , TP0548 , and 23S rRNA loci were repeatedly discussed . If the major finding of this study could be extrapolated to TPA strains , then all loci used , so far , for molecular typing of syphilis treponemes , should be sufficiently stable to infer epidemiological relationships . There are over 2 , 000 nucleotide differences between TPE and TPA strains [2] and the majority of them are scattered throughout the genomes with only a minority of loci positively selected or recombinant . Based on the known genetic diversity between TPA and TPE genomes , the observation-derived estimate of the upper limit of the mutation rate , and the assumption that the mutation rate in syphilis treponemes is similar to that of TPE , the most recent common ancestor of the syphilis and yaws treponemes is more than ten thousand years old . Assuming an example that uses a 10-times lower mutation rate compared to the upper limit revealed in this study , the most recent common ancestor of syphilis and yaws treponemes would have appeared at a time that would have fallen within the beginning of evolution of modern humans . Taken together , the data presented in this study are consistent with the relatively slow evolution rate of yaws treponemes and suggest that the appearance of the most recent common ancestor of syphilis and yaws treponemes took a very long time , perhaps even a length of time comparable to the evolution of modern humans . | The causative agent of yaws , Treponema pallidum subsp . pertenue ( TPE ) , belongs to a group of uncultivable treponemes causing several human and animal infections . Yaws is a multi-stage disease which is endemic in tropical regions of Africa , Asia , Oceania , and South America . In this study , whole genome sequences of two TPE strains ( CDC 2575 and Ghana-051 ) isolated from patients infected in Ghana , Africa , were determined . Despite being isolated more than 7 years apart ( 1980 and 1988 ) , both TPE strains had an identical consensus whole genome nucleotide sequence , although showing differences in 14 intrastrain heterogeneous sites . To support the independent character of both strains , re-analysis of available lab records , clinical data and sequencing of the contaminating rabbit chromosomal DNA was performed and revealed differences in TPE Ghana-051 and CDC 2575 samples . The identical genome sequence and the time between isolation of both TPE strains was used to estimate the upper limit of the yaws treponeme evolution rate , which was 4 . 1 x 10−10 per site per generation , a mutation rate that is slightly lower than the experimentally determined E . coli mutation rate . Given the obtained data , the most recent common ancestor of syphilis and yaws treponemes is likely to be more than ten thousand years old . However , since our estimation is an upper limit of the evolution rate , one can assume that the real evolutionary rate could have been even slower , and the time to the most recent common ancestor of syphilis and yaws treponemes even longer . |
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The architecture of dendritic arbors determines circuit connectivity , receptive fields , and computational properties of neurons , and dendritic structure is impaired in several psychiatric disorders . While apical and basal dendritic compartments of pyramidal neurons are functionally specialized and differentially regulated , little is known about mechanisms that selectively maintain basal dendrites . Here we identified a role for the Ras/Epac2 pathway in maintaining basal dendrite complexity of cortical neurons . Epac2 is a guanine nucleotide exchange factor ( GEF ) for the Ras-like small GTPase Rap , and it is highly enriched in the adult mouse brain . We found that in vivo Epac2 knockdown in layer 2/3 cortical neurons via in utero electroporation reduced basal dendritic architecture , and that Epac2 knockdown in mature cortical neurons in vitro mimicked this effect . Overexpression of an Epac2 rare coding variant , found in human subjects diagnosed with autism , also impaired basal dendritic morphology . This mutation disrupted Epac2's interaction with Ras , and inhibition of Ras selectively interfered with basal dendrite maintenance . Finally , we observed that components of the Ras/Epac2/Rap pathway exhibited differential abundance in the basal versus apical dendritic compartments . These findings define a role for Epac2 in enabling crosstalk between Ras and Rap signaling in maintaining basal dendrite complexity , and exemplify how rare coding variants , in addition to their disease relevance , can provide insight into cellular mechanisms relevant for brain connectivity .
Dendritic structure is critical for neuronal function , as the size and shape of the dendritic arbor defines the neuron's receptive field [1] . Generating and maintaining proper arborization is therefore crucial for neural circuit function [2] . The importance of maintaining dendritic arborization is illustrated by observations of loss of dendritic complexity in patients with neuropsychiatric disorders . Reduced dendritic arborization occurs in patients with psychiatric disorders with delayed onset , including schizophrenia [3] , as well as in autism spectrum disorders [4] , [5] , and disorders comorbid with autism , such as Rett [6] and Down syndromes [7]–[9] . Thus , the maintenance of dendritic arbor complexity for extended periods of time during development and into adulthood is likely to be crucial for the preservation of functional circuitry and connectivity relevant for learning and complex behaviors . Patterns of dendritic branching are integral to the computational ability of the neuron [10] , [11] . An increasing body of evidence suggests that the apical versus basal regions of the dendritic arbor are functionally specialized . These distinct dendritic compartments receive different inputs , integrate distinct signals , and are selectively regulated in physiological and pathological conditions [12] , [13] . Recent work probing the subcellular location of thalamocortical and intracortical connections has revealed tight spatial restriction of synapses to various somatic and dendritic compartments . Laminar positioning of target cells in the cortex and afferent cell type are critical determinants of synaptic positioning along the dendritic arbor . For example , ascending inputs target basal dendrites in layer 2/3 [13] . Basal dendrites are also the target of substantial inhibitory innervation by interneurons , allowing for the tight regulation of excitability [14] . From a computational perspective , even very small basal dendrites are capable of large effects on cell output [12] . Thus , subtle morphological alterations to the basal dendritic arbor may have large consequences for cellular and circuit function . Consistent with the selective function of dendritic compartments , there is evidence for the selective regulation and maintenance of apical versus basal dendritic compartments . Environmental enrichment appears to have region- and cell-specific effects on dendrites , but preferentially enhances basal arborization [15] , [16] , and sensory deprivation during a critical developmental period can prevent normal basal dendritic elaboration in the barrel cortex of rats [17] . While a few molecular alterations selectively affect distinct dendritic compartments , including PTEN or dopamine receptor D1 loss [18] , [19] , the molecular mechanisms that specifically govern basal dendrite maintenance in cortical neurons remain unclear . Regulators of Ras-like small GTPases have been extensively implicated in neuronal morphogenesis [20] . The EPAC2 gene encodes Epac2 ( exchange protein directly activated by cyclic AMP 2 ) , a guanine nucleotide exchange factor ( GEF ) for the Ras-like small GTPase Rap , which is highly enriched in the adult brain [21] and dendrites [22] . Previous studies utilizing an Epac-specific agonist have found that Epac activation can modulate synaptic plasticity [23] as well as memory retrieval in mice [24] , and EPAC null mice exhibit deficits in spatial reference memory and social interactions [25] , implicating Epac in brain function . Epac2 has been implicated in the outgrowth of neuronal processes in vitro [26] , [27] , but its role in dendritic morphogenesis within the cortex is not known . In the present study we observed that Epac2 knockdown robustly and selectively impaired basal dendrite maintenance in cortical pyramidal neurons in vivo and in culture . Recent genetic studies have detected numerous rare coding mutations in subjects with neurodevelopmental disorders [28] . While their significance for disease etiology remains to be elucidated , such mutations might provide insight into a protein's functional role in important cellular processes . Previously , four rare amino acid coding variants had been identified in EPAC2 in subjects with autism [29] . In the present study we observed that expressing one of these rare coding variants robustly and selectively reduced basal dendrite complexity in cortical pyramidal neurons and impaired Epac2's interaction with Ras . The use of a disease-associated point mutation as a method of probing molecular function revealed that Epac2 mediates crosstalk between Ras and Rap to specifically regulate basal dendritic complexity in cortical neurons . This approach exemplifies a more general “reverse translational” strategy for discovery of basic cellular mechanisms .
Epac2 , an upstream regulator of Rap activity , is involved in regulating synapse morphology [22] , but its role in regulating the architecture of the dendritic tree in cortical neurons is unknown . We first tested whether altered expression of Epac2 affected the maintenance of dendritic arbors in vivo , using in utero electroporation ( IUEP ) [30] to knock down protein expression ( Figure S1A ) . Using a previously characterized RNA interference ( RNAi ) construct selective for Epac2 [22] , we coinjected either Epac2-RNAi , or control ( pGSuper ) , with pCAG-eGFP into the subventricular zone of E16 . 5 mouse embryos to specifically target layer 2/3 neurons . Electroporated constructs were allowed to express until brain harvesting and sectioning on postnatal day 28 ( P28 ) . In vivo knockdown of the Epac2 protein via Epac2-RNAi was confirmed at P28 by immunohistochemistry of cortical sections . Quantification of GFP-positive cells revealed that Epac2 was knocked down by ∼75% compared to GFP-negative cells ( Figure S1B–C ) . Electroporated neurons were specifically found in layer 2/3 ( Figure 1A ) . When we analyzed dendritic morphology by measuring apical and basal dendritic number and length in GFP-positive cells , we observed that long-term knockdown of Epac2 expression specifically reduced basal dendrites . Examination of basal arbors of layer 2/3 neurons from P28 mice expressing GFP and Epac2-RNAi ( Figure 1B–F ) revealed a ∼42% reduction in basal dendritic branches and a ∼54% reduction in basal dendritic length ( p<0 . 001; Figure 1E–F ) compared to cells from P28 mice expressing GFP and control construct . This effect on basal dendritic morphology was mediated by a ∼30% reduction in secondary and a ∼65% reduction in tertiary order basal branch number ( p<0 . 05 , 0 . 001; Figure 1G , Table S1A ) . Additionally , basal dendritic length was reduced by ∼24% in secondary branches and ∼55% in tertiary branches ( p<0 . 05 , 0 . 001; Figure 1H , Table S1B ) . The effect of Epac2 knockdown from E16 . 5 to P28 was specific for basal dendritic arbors , as Epac2-RNAi had no effect on apical dendrite branch number or length in these cells ( Figure 1E–H , Table S1A–B ) . To eliminate the possibility of inter-individual and reporter expression variability , we took advantage of the sparse nature of IUEP gene transfer to directly compare dendritic morphology between neighboring non-Epac2-RNAi-expressing cells versus Epac2-RNAi-expressing cells within the same layer and cortical region from the same mice . Epac2-RNAi-expressing cells could be easily identified by the coexpression of pCAG-eGFP , while neighboring control cells not expressing Epac2-RNAi were identified by the lack of GFP expression . Pairs of GFP- and non-GFP-expressing neurons were filled with biocytin and stained with streptavidin-568 conjugated fluorescent probe to visualize dendritic morphology ( Figure 2A ) . Using 2-photon laser scanning microscopy , we imaged paired layer 2/3 cells in the anterior frontal cortex , an area previously described to display abnormal circuitry in disease-related animal models [31] . Cortical slices were cut at a thickness of 300 µm to allow reconstruction of the majority of the apical and basal dendritic fields of these cells . This approach further confirmed that loss of Epac2 resulted in a specific reduction of basal dendrite complexity: Epac2-RNAi-expressing cells displayed an overall decrease in basal dendritic branch number ( ∼47% ) and basal dendritic length ( ∼53% ) ( p<0 . 05 , 0 . 01; Figures 2B–C , S2A–B ) . Furthermore , Epac2-RNAi-expressing cells had ∼30% fewer basal secondary dendrites and ∼55% fewer basal tertiary dendrites ( p<0 . 05 , 0 . 001; Figure 2D–E , Table S2 ) . In addition , secondary basal length was reduced by ∼25% and tertiary basal length was reduced by ∼32% in Epac2-RNAi-expressing cells ( p<0 . 05 , 0 . 001; Figure 2D , F , Table S3 ) . This effect was driven by the absence of high order branches ( tertiary branches and beyond; Figure 2D–F ) . No effect on apical dendrite complexity ( Figure S2C , Table S2 ) or length ( Figure S2D , Table S3 ) was observed . Together , these data demonstrate that Epac2 signaling is required for maintaining higher order branching of basal dendrites in vivo . Interestingly , extended Epac2 knockdown in vivo also reduced dendritic spine density on both apical and basal dendrites , as compared to paired control electroporated cells ( Figure S2E–G ) . This effect contrasted with that of acute ( 5-d ) Epac2 knockdown in vitro [22] , which did not alter dendritic spine numbers , but suggests that prolonged reductions in Epac2 signaling can have pronounced effects on basal dendrites and more subtle effects on apical dendrites . Epac2 knockdown by in utero electroporation reduced Epac2 expression throughout development; however , it is not clear when Epac2 expression is required for normal dendritic morphology . To directly test the role of Epac2 in dendritic maintenance , we used an RNAi approach in mature cultured neurons , allowing perturbations in Epac2 expression levels after the dendritic arbor has already been established . This system has also been extensively used for mechanistic studies of structural plasticity [22] , [32]–[34] and allows examination of the potential molecular underpinnings of dendritic architecture . A number of studies have demonstrated that mature cultured pyramidal neurons develop pyramidal morphologies with primary ( classified as “apical” ) and non-primary ( classified as “basal” ) dendrites that resemble morphologies observed in vivo ( see Materials and Methods for description of criteria used for identifying apical and basal dendrites of cultured neurons ) [34]–[36] . We knocked down Epac2 expression in mature ( DIV 23–28 ) cultured cortical neurons ( Figure 3A ) , and used Sholl analysis [37] as well as dendritic length measurements to assess the complexity and morphology of basal or apical dendritic compartments ( Figure S3A–D ) . Consistent with our in vivo data , reduced Epac2 expression selectively decreased dendritic complexity in an asymmetric manner . Epac2 knockdown reduced basal dendritic intersections 25–175 µm from the soma , as well as basal dendrite length , without affecting apical dendrite length or complexity ( basal dendritic length: length ( µm ) , control: 956±164; Epac2-RNAi: 273±100; rescue: 647±124 , p<0 . 005; Figure 3B–C ) . Importantly , this deficit was rescued by overexpressing an RNAi-resistant mutant of Epac2 ( “Epac2-rescue” ) [22] . Epac2-rescue overexpression did not significantly alter basal or apical complexity compared to control , but significantly increased basal complexity 25–100 µm from the cell body and basal dendrite length compared to Epac2-RNAi ( Figure 3B–C ) . Similar to the effect of Epac2 knockdown in in utero electroporated neurons , Epac2 expression levels were reduced down by similar degrees ( ∼75% ) in both apical and basal dendritic compartments in cultured cells overexpressing Epac2-RNAi ( Figure S3E ) . Given that Epac2 knockdown occurred in mature neurons after their dendritic arbors had been established , these data suggest that Epac2 plays a role in maintenance of the basal dendritic arbor . Rare protein-coding variants of the EPAC2 gene have previously been identified in several subjects with autism [29] . One of these missense mutations ( Epac2-G706R ) , detected in four human subjects with autism from two families , is located within the Ras association ( RA ) domain of Epac2 ( Figure 4A ) , suggesting that it may affect one of Epac2's functional domains . To investigate the effect of this mutation on neuronal morphology , we expressed either Epac2-G706R or its wildtype counterpart in cultured cortical neurons . Expression of Epac2-G706R ( Figure 4B ) , followed by Sholl analysis , revealed a robust selective decrease in basal dendrite complexity 50–100 µm from the soma , compared to Epac2-WT , with no effect on apical dendrites ( Figure 4C ) . Furthermore , overexpression of Epac2-G706R reduced basal dendrite length relative to Epac2-WT ( p<0 . 05; Figure 4D ) , but did not affect apical dendritic length ( Figure 4D ) . We have previously shown that overexpression of Epac2-G706R in neurons does not affect basal Rap-GEF activity or dendritic spine morphology [22] . Comparison of the effects of Epac2-G706R overexpression to GFP alone revealed a decrease in basal complexity 25–50 µm from the soma , but no change in apical complexity , basal length , or apical length ( Figure S4A–C ) , suggesting that Epac2-G706R is a loss-of-function mutation . Taken together , these data suggest that Epac2-G706R , a variant that occurs in human patients , specifically alters basal dendrite maintenance , without affecting apical dendritic structure , synaptic morphology , or baseline Rap activation levels . We next reasoned that the location of the single amino-acid mutation in the Epac2 protein might offer insight into the mechanisms of asymmetric maintenance of dendritic compartments . The small GTPase Ras and its signaling partners have been implicated in neuronal morphogenesis [20] , [38]–[42] . Given that the G706R mutation is within Epac2's Ras-association ( RA ) domain ( Figure 4A ) , we hypothesized that this mutation might alter Epac2's interaction with Ras and that abnormal association with Ras could underlie the dendritic effects induced by Epac2-G706R . Epac2 has been shown to interact with Ras in non-neuronal cells [43] , but this interaction has not yet been established in cortical neurons . We therefore tested whether Epac2 interacted with Ras in rat cortical neurons by coimmunoprecipitation . We found that Ras coimmunoprecipitated with Epac2 in mature cortical neurons ( DIV 25 ) ( Figure 4E ) . This interaction was dependent on the activation state of Ras: Ras activation by incubation with GTPγS enhanced the interaction between Ras and Epac2 , whereas Ras inhibition by treatment with GDP reduced the interaction ( p<0 . 05 , 0 . 001; Figure 4F ) . This interaction was further confirmed by ectopic expression of HA-tagged Epac2-WT alone or with YFP-Ras in hEK293 cells ( Figure S4D ) . We then tested the ability of Epac2-G706R to interact with Ras by coexpressing YFP-Ras with HA-Epac2-WT or HA-Epac2-G706R in hEK293 cells , and immunoprecipitating with YFP-Ras ( Figure 4G ) . Indeed , quantitative analysis of coimmunoprecipitation revealed that Epac2-G706R displayed significantly impaired Ras interaction ( p<0 . 001; Figure 4H ) . These results demonstrate that a naturally occurring Epac2 variant specifically alters basal dendritic architecture , and that interaction with Ras may be a key feature of Epac2's role in regulating basal dendritic maintenance . The findings that Epac2 is required for the maintenance of basal ( non-primary ) dendrites , and that a rare coding variant that specifically disrupts Epac2's interaction with Ras mimics this selective morphological phenotype but has no affect on Epac2's basal Rap-GEF function [22] , suggest a role for Epac2 and Ras signaling in the maintenance of basal dendrite complexity . Ras is a small GTPase that has been strongly linked to structural plasticity in neurons [20] , [39]–[42] , [44] , [45] , but its specific role in the maintenance of basal dendrite complexity in mature neurons has not been directly tested . Thus , we tested whether disruption of endogenous Ras activity by the farnesyl transferase inhibitor II ( FTaseII ) could affect either apical or basal dendrite maintenance . We used time-lapse imaging of live mature ( DIV 25 ) cultured cortical pyramidal neurons , expressing GFP and treated with either vehicle or FTaseII ( 200 nM ) and measured dendritic complexity and length before and after treatment ( Figure 5A and S5A ) . Imaging of neurons for 2 h prior to treatment revealed a remarkable stability of the dendritic arbor ( Figure 5A–E ) with an almost equal gain and loss of apical and basal dendrites . Imaging of apical and basal dendrites for 6 h following vehicle treatment did not reveal any changes in basal or apical dendrite complexity or length ( Figure 5A–E ) . In contrast , incubation with FTaseII resulted in a robust retraction of basal dendrites over 6 h , as demonstrated by a reduction of basal complexity ( normalized basal dendrite intersections: 0 h: 1 . 04±0 . 03 versus 0 . 95±0 . 05; 6 h 1 . 02±0 . 5 versus 0 . 56±0 . 08; control versus FTaseII , p<0 . 001; Figure 5A–B ) , and a loss of dendritic length ( normalized basal dendrite length: 0 h: 1 . 06±0 . 02 versus 1 . 01±0 . 04; 6 h: 1 . 02±0 . 4 versus 0 . 54±0 . 06; control versus FTaseII , p<0 . 001; Figure 5A , C ) . This loss of length , driven by a progressive retraction of basal dendrites , was not seen in vehicle-treated neurons ( Figure 5D–E ) . There was no change in apical dendrites ( Figure 5B–C ) . We further confirmed these results in neurons fixed following treatment with FTaseII or vehicle for 6 h ( Figure S5B–D ) . Sholl analysis revealed that FTaseII treatment specifically reduced basal complexity 25–100 µm from the soma ( Figure S5C ) and basal dendritic length ( basal dendrite length ( µm ) ; control , 1 , 405±221; FTaseII , 602±155 , p<0 . 05; Figure S3D ) with no effect on apical dendrite complexity or length ( Figure S5C–D ) . The very specific effect of short-term FTaseII treatment on the dendritic tree is quantitatively similar to that of Epac2 knockdown or Epac2-G706R overexpression ( which displays impaired Ras binding ) , suggesting that interference with Ras signaling , but not other potential targets of FTaseII , in cortical neurons results in a selective reduction of basal complexity . To further investigate the impact of interference with Ras signaling on dendritic architecture , we mimicked Ras inhibition by expression of a dominant-negative Ras mutant ( Ras S17N; RasDN ) . Expression of RasDN alone ( Figure S5E–F ) , followed by Sholl analysis , revealed a robust selective decrease in basal dendrite complexity 50–100 µm from the soma , compared to GFP ( control ) , with no effect on apical arbors ( Figure S5F ) , paralleling the effects seen following short-term FTaseII treatment . Furthermore , overexpression of RasDN reduced basal dendrite length relative to control ( basal dendrite length ( µm ) ; GFP , 1 , 704±122; RasDN , 987±198 , RasDN+Epac2-WT 1 , 519±160 , p<0 . 05; Figure S5E–F ) , but did not affect apical dendritic length ( Figure S5G ) . Importantly , co-expression of Epac2-WT was sufficient to rescue RasDN-induced loss of basal dendrites and basal dendritic length ( Figure S5E–G ) . These data provide further support for the role of the Ras/Epac2 pathway in the maintenance of basal , but not apical , dendrites . The distribution of the Ras , Epac2 , and Rap proteins across the dendritic tree of cortical neurons has not yet been examined . We thus compared the relative amounts of Epac2 , Ras , and Rap immunostaining intensity in basal versus apical dendrites of cortical neurons in culture . All intensity measurements were limited to secondary apical or basal dendrites and were normalized to unit area ( µm2 ) to ensure that measurements of protein content between apical and basal dendrites of different thicknesses were comparable ( Figure 6 ) . We observed more intense labeling for each protein in apical dendrites than in basal dendrites ( p<0 . 05 , 0 . 01; Figure 6A–F ) . We also examined the distribution of phosphorylated ( active ) BRaf ( p-BRaf ) , a direct target of both Ras and Rap small GTPases [20] , [22] . As with Ras , Epac2 , and Rap , we found that p-BRaf was more abundant in apical dendrites than in basal dendrites ( p<0 . 001; Figure 6G–H ) . Interestingly the same subcellular distribution was observed for overexpressed Epac2-WT and Epac2-G706R ( p<0 . 01; Figure S6A–B ) . Epac2-G706R signal was reduced compared to that of Epac2-WT in basal dendrites , but not apical dendrites , in these cells ( p<0 . 05; Figure S6C ) . In order to determine whether this asymmetric pattern was specific for Ras/Epac2/Rap pathway , we also examined the distribution of kalirin-7 , a GEF for the small GTPase Rac , and the phosphorylated ( active ) form of p21-activated kinase ( p-PAK ) , a direct downstream effector of Rac [46] . In contrast with the above findings , kalirin-7 and p-PAK immunofluorescence was equally distributed across apical and basal arbors ( Figure S6D–E ) , indicating that the asymmetric distribution of Ras/Epac2/Rap was specific to this pathway . Because we observed asymmetry in the levels of Epac2/Ras/Rap across dendritic compartments of cortical neurons , we wondered if perturbations of this pathway might lead to differential signaling output in basal versus apical dendrites . To address this question , we specifically inhibited Ras signaling or reduced Epac2 expression by RNAi throughout the neurons , and examined p-BRaf immunofluorescence in individual dendritic compartments . Inhibition of Ras using FTaseII ( Figures 7A , S7A ) resulted in a ∼22% reduction in p-BRaf levels in apical dendrites compared to control ( Figures 7A–C , S7A ) , but produced a more profound reduction in p-BRaf levels in basal dendrites ( ∼45% compared to control basal levels ) , which was significantly different to both control and FTaseII-induced apical p-BRaf levels ( mean p-BRaf intensities relative to control levels: FTaseII; apical 0 . 76±0 . 04; basal 0 . 55±0 . 01; p<0 . 001; Figures 7B–C , S7B–C ) . Examination of endogenous Epac2 levels in control ( pGSuper ) and Epac2-RNAi cells revealed that Epac2 expression was less abundant in basal dendrites than apical dendrites after Epac2 knockdown ( reduced by ∼37% compared to apical levels; Figure S7D ) , suggesting that asymmetric localization of Epac2 is maintained under knockdown conditions . Moreover , comparison of Epac2 immunofluorescence in apical or basal compartments in control versus Epac2-RNAi-expressing neurons , relative to Epac2 levels in control apical dendrites , revealed that Epac2 expression in apical dendrites of Epac2-RNAi cells was not significantly different from Epac2 levels in control basal dendrites ( Figure S7E ) , suggesting that apical dendrites may contain enough Epac2 even in the knockdown condition to preserve apical dendrite morphology . Epac2 knockdown ( Figures 7D , S7F ) resulted in a greater reduction of p-BRaf levels in basal dendrites ( ∼47% ) versus apical dendrites ( ∼20% ) , when compared to control levels ( mean p-BRaf intensities relative to control levels: Epac2-RNAi; apical 0 . 80±0 . 07; basal 0 . 52±0 . 04; p<0 . 001; Figures 7D–F , S7F–H ) . Collectively , these data suggest that the signaling output of the Ras/Epac2/Rap pathway is asymmetric across dendritic compartments of cortical neurons .
Here we showed that Epac2 is important for the selective maintenance of basal dendrite complexity in cortical neurons . Utilizing a rare coding variant of Epac2 , found in human patients , to probe the molecular and cellular functions of Epac2 in the context of dendritic complexity of cortical pyramidal neurons , we identified Ras as a signaling partner of Epac2 in this pathway . Our findings support a model in which Epac2 , as a Rap-GEF , enables crosstalk between two morphogenic GTPase signaling pathways to maintain basal dendrites . The importance of maintaining dendritic architecture is illustrated by observations of dendritic complexity in pathological analysis of patients with neuropsychiatric and neurodevelopmental disorders . Reduced dendritic arborization occurs in patients with psychiatric disorders with delayed onset , including schizophrenia [3] , autism spectrum disorders [4] , [5] , and disorders comorbid with autism , such as Rett [6] and Down syndromes [7]–[9] . In Down syndrome , loss of dendritic arbors occurs in a progressive manner: prior to 2 y of age , dendritic hypertrophy was observed in the cortex of subjects with Down syndrome; thereafter , dendritic arbors were reduced in complexity relative to controls [7]–[9] , suggesting an initial period of dendritic overgrowth followed by a later stage in which maintenance mechanisms are potentially disrupted or lost . An early study of visual cortex from subjects with Down syndrome revealed a selective loss of basal dendrite complexity [47] , though subsequent studies have also detected apical deficits [8] . Rett syndrome , a monogenic disorder frequently accompanied by an autistic phenotype , features dendritic deficits , usually in basal dendrites and occasionally in apical dendrites [6] . Individuals with autism exhibited reduced dendrite numbers and dendritic cytochemical markers in the cortex [4] and hippocampus [5] . Thus , a more complete understanding of molecular mechanisms that contribute to the maintenance of specific aspects of the dendritic arbor may hasten the development of therapeutic strategies that aim to prevent the apparent progressive loss of dendritic complexity and to preserve functional cortical circuits in patients with neurodevelopmental and neuropsychiatric disorders in which dendritic structure is affected . Several lines of evidence support the selective regulation of apical versus basal dendritic compartments . Environmental enrichment has been shown to selectively increase basal dendrite length , while stress can reduce basal dendritic length , in layer 2/3 cells of the auditory cortex [15] . Sensory deprivation by whisker trimming during a critical developmental window , between P9 and P15 in rats , delayed normal basal dendritic elaboration of layer 2/3 pyramidal cells in the barrel cortex [17] . PTEN knockout results in selective outgrowth of apical dendrites , demonstrating that inhibition of mTOR blocks continued apical but not basal dendrite growth under stable conditions in mature animals [18] . Particularly relevant to Epac2 signaling is the recent report that knockout of dopamine receptor D1 in mouse cortex results in selective basal dendrite loss [19] , as Epac2 is also regulated by D1/cAMP signaling [22] . Given the ability of Epac2 to selectively regulate the basal arbor , signaling through Epac2 may be a key mechanism for control of select dendritic compartments . A current hypothesis is that the establishment and elaboration of apical and basal arbors occur at distinct time points , with basal dendrites developing after apical dendrites , and thus may involve distinct regulatory mechanisms [48] . Our knockdown data indicate that Epac2 is required for the maintenance of higher order basal dendrite branches . Elaboration of higher order basal dendrite branching , which increases basal dendrite complexity , occurs subsequent to the formation of the primary proximal basal arbor and the apical arbor [48] . Interestingly , this time point ( 3 wk postnatal ) coincides with a dramatic increase in Epac2 expression in cortical neurons [22] . Thus , our findings support a role for Epac2 in regulating the maintenance of basal dendritic complexity once these complex arbors have been initially established . Here we were guided by a rare coding mutation , naturally occurring in human subjects with autism , to identify the Ras/Epac2 interaction as important for the control of basal dendrite complexity in cortical neurons . Interestingly , a different point mutation in the RA domain of Epac2 , identified through sequence analysis rather than occurrence as a rare variant in human patients , has also been shown to disrupt the interaction with Ras in COS cells [49] , suggesting that single-residue sites in this domain of Epac2 may be crucial for its function in response to Ras , and that even single amino acid mutations occurring as rare variants may have functional or pathological consequences . Our strategy exemplifies how mutations identified in humans with neurodevelopmental or psychiatric disorders , beyond their relevance for disease , could provide functional insight into novel mechanisms underlying brain development and connectivity . A growing number of rare single amino acid mutations have been identified in neuropsychiatric disorders by recent genetic studies , and with the advent of whole exome or genome sequencing , their numbers are expected to increase dramatically [50] . While their significance for disease etiology remains to be elucidated , our approach taken in this study shows that such mutations might help identify cellular mechanisms that control crucial cellular processes , including dendrite arborization . About 20% of single amino acid mutations are thought to be damaging , with another 53% being mildly deleterious [51] . Given that rare mutations are thought to make up a significant fraction of the genetic architecture of complex diseases , functional characterization of such mutations may provide novel insight into both physiology and pathophysiology . In this study , we show that a pathway involving Ras/Epac2/Rap contributes to the maintenance of basal dendrite complexity . The G706R point mutation disrupted the Ras-Epac2 interaction and reduced basal complexity , and Ras inhibition experiments using FTaseII or RasDN overexpression specifically affected basal dendrite maintenance , suggesting that this pathway exerts specific control over basal dendritic complexity in pyramidal neurons . Our results establishing a role for Epac2 in linking Ras and Rap signaling to dendrite maintenance in mature cortical neurons are consistent with a number of previous findings . Dominant-negative Rap1-expressing layer 5 pyramidal neurons exhibit deficits in basal dendrite arborization during development [38] . In non-neuronal cells , activated Ras has been shown to recruit Epac2 to the plasma membrane , thereby activating membrane-associated pools of Rap [43] . In adrenally derived PC12 cells , Ras activation can recruit Epac2 to the plasma membrane , activate membrane-associated pools of Rap1 , and induce the outgrowth of neurite-like structures [49] . Recent characterizations of EPAC null mice reveal cognitive and behavioral phenotypes , illustrating the importance of Epac in complex behavior and brain function [25]; however , dendritic architecture of cortical neurons was not measured in this mouse model . Our data implicating Epac2 in the maintenance of basal arbors of cortical neurons provide a potential mechanism for the disruption of neuronal circuitry upon perturbations of this pathway . Our observation of asymmetric distribution of Epac2 , Ras , and Rap proteins is consistent with the selective effect of reduced Ras/Epac2 signaling on the maintenance of basal dendrites . Indeed , it is reasonable to expect that other redundant mechanisms are employed for the active maintenance of apical dendritic architecture , which may require more stability during the life of the neuron . Our data showing that disruption of a single pathway can alter basal maintenance are consistent with the intrinsic dynamism of basal dendrites , due to the higher demands for plasticity driven by sensory and inhibitory inputs to this compartment [11] , [13] , [14] . Taken together , our data support a model in which Epac2 couples with Ras signaling and actively maintains basal dendrites in cortical pyramidal neurons .
Farnesyl transferase inhibitor II was purchased from EMD Biosciences . We purchased the following antibodies: mouse anti-GFP monoclonal ( Millipore ) , chicken anti-GFP polyclonal ( Abcam ) , rabbit anti-Epac2 polyclonal and mouse anti-Epac2 monoclonal ( Santa Cruz ) , rabbit anti-HA polyclonal ( Enzo ) and mouse anti-HA monoclonal ( Santa Cruz ) , mouse anti-Ras monoclonal ( Upstate ) , mouse anti-Myc monoclonal ( Developmental Studies Hybridoma Bank , Iowa ) , mouse anti-MAP2 monoclonal ( Millipore ) , and rabbit anti-Rap polyclonal antibody ( Millipore ) . A rabbit anti-GFP polyclonal antibody was a gift from Dr . Richard Huganir ( Johns Hopkins University ) . The pCAG-EGFP construct was a kind gift from Atsushi Kamiya , Johns Hopkins University . The pEGFP-N2 construct was obtained from Clontech . Constructs encoding shRNA specific for Epac2 and a rescue construct ( an HA-Epac2 construct containing three silent point mutations in the RNAi target sequence ) were previously generated and validated [22] . Dissociated cultures of primary cortical neurons were prepared from E18 Sprague-Dawley rat embryos as previously described [22] . On DIV 21–23 , neurons were transiently transfected for 4 h with plasmids ( 1–3 µg DNA ) using Lipofectamine 2000 ( Invitrogen ) . For experiments utilizing soluble GFP , cultures were allowed to express the transfected constructs for 2 d . For RNAi and Epac2 mutant , constructs were expressed for 5 d . Rats were used in accordance with ACUC institutional and national guidelines under approved protocols . Treatment of live cultured neurons was performed in ACSF essentially as previously described [22] . Briefly , cultured neurons were transfected with GFP , and allowed to express for 2 d . Neurons were then pre-incubated in ACSF for 1 h , imaged 2 h and 1 h before beginning of treatment , and then imaged every hour for 6 h after beginning of treatment with either FTaseII ( 200 nM ) or vehicle . Coverslips were kept in culture plates throughout the experiment , and were returned to a 37°C incubator between imaging timepoints . Micrographs of healthy GFP-expressing neurons with pyramidal morphologies were acquired using a 10× objective ( NA = 0 . 17 ) and a Zeiss AxioCam MRm CCD camera . Dendrites were traced and binarized in ImageJ as described below . It is of note that whereas farnesyl transferase inhibitors were initially developed for their ability to inhibit Ras activity , a number of other proteins are also farnesylated , and therefore the potential contribution of other proteins to this specific loss of basal dendrites cannot be excluded . For dendritic spine morphologies in vivo ( Figure S2 ) , images of dendritic spines on biocytin-filled neurons were acquired with a Zeiss LD Lci Plan Apochromat 25×/0 . 8NA multi-immersion lens ( 440842-9870-000000 ) with a digital zoom of 4 . Volume imaging was acquired with 15–35 optical sections taken in 0 . 75 µm focal steps ( 2 . 13 µm axial resolution ) . For each condition , 5 neurons were imaged . Two dendrites between 50 and 100 µm in length per cell were measured: only spines on tertiary apical or secondary basal dendrites were imaged to reduce variability . Dendritic spine density ( number of spines per 10 µm ) was calculated using ImageJ . Cultured pyramidal neurons were fixed , immunostained , and imaged as previously described [22] . Protein clustering was imaged as z-series taken at 0 . 37 µm intervals using a Zeiss LSM5 Pascal confocal microscope and a 63× objective ( NA = 1 . 4 ) . Two-dimensional maximum projection images were reconstructed and analyzed using MetaMorph software ( Molecular Devices , Sunnyvale , CA , USA ) . Images were background-subtracted and thresholded equally to include clusters with intensity at least 2-fold above the adjacent dendrite . Regions along dendrites were manually outlined , and the linear density ( number per 100 µm of dendrite length ) and total gray value ( total immunofluorescence intensity ) of each cluster was measured automatically . Cultures that were directly compared were stained simultaneously and imaged with the same acquisition parameters . Experiments were carried out blind to condition and on sister cultures . Coimmunoprecipitations ( coIPs ) from hEK293 cells or rat cortical tissue were performed as previously described [22] , using RIPA buffer ( in mM: 150 NaCl , 10 Tris-HCl , pH 7 . 2 , 5 EDTA , 0 . 1% SDS , 1% Triton X-100 , 1% Deoxycholate , plus inhibitors ) . Precleared lysates were incubated with 2 . 5–5 µL of antibody for 3 h; 60 µL of protein-A Sepharose was added for 2 h at 4°C , after which samples were washed 3 times with 0 . 5 ml RIPA buffer , boiled for 5 min at 95°C in Laemmli buffer , and analyzed by SDS-PAGE and Western blotting . For treatment with GTPγS or GDP , cortical neurons were lysed in Mg2+ lysis buffer containing protease inhibitors . Cell lysates were then incubated with 100 µM GTPγS or 1 mM GDP at 30°C for 30 min . Reaction was stopped by the addition of 60 mM MgCl2 . Cell lysates were harvested as described above . C57BL6 female mice were checked for vaginal plugs ( E0 ) , and electroporation was performed at E16 . 5 . After proper sedation , both uterine horns were removed and placed on sterile , warm , and PBS-wetted pads . DNA solution was loaded into beveled glass micropipettes ( 100 µm oblique opening ) , and 0 . 26 µl was injected into the lateral ventricle through the uterus wall ( 4 injections; 65 nL/injection ) using a nanojector ( Drummond Nanoject II ) . DNA was electroporated into the neural precursor populations that reside on the ventricular zone by directed electroporation , by placing the ( + ) end of the electrode toward the developing neocortex . Unipolar electric pulse of 40 V was generated ( BTX ECM830 ) , and a total of five 50 ms pulses at an interval of 100 ms were applied to the cerebral wall . After electroporation , embryos were placed back into the abdominal cavity , and the rectus abdominis and abdominal oblique muscles were sutured with 5-0 coated vicryl suture for quick absorption and fast recovery . The skin was closed with LiquiVet tissue adhesive . Mice were allowed to recover and give natural birth . Injected animals were collected at P28 for further investigations . A preparation of DNA 3∶1 molar ratio was used to mix pCAG-EGFP construct [52] , which expresses EGFP under the chicken beta actin promoter , and Epac2-RNAi cloned into pGSuper expression vector [22] . 0 . 05% FastBlue was added to visualize DNA . In control experiments , pGSuper was mixed with pCAG-eGFP construct . Electroporated mice were anesthetized with sodium pentobarbital at 50 mg per gram of body weight , and fixed by transcardial perfusion of 4% paraformaldehyde in 0 . 1 M sodium phosphate buffer ( pH 7 . 4 ) at P28 . Brains were dissected out and sectioned into 50 µm coronal sections and immunostained with a chicken anti-GFP polyclonal antibody ( Abcam ) as floating sections , before being mounted onto glass slides and covered with glass coverslips . Cells exhibiting intact and healthy secondary and tertiary apical and basal dendritic arbors were imaged by taking 1 µm serial optical sections , 35–45 optical sections per cell , using a Zeiss LSM5 Pascal confocal microscope and a 40× objective ( NA = 1 . 3 ) . Following acquisition , images were projected as 2-D Z-projections using Fiji6/ImageJ ( http://imagej . nih . gov/ij/; NIH , Bethesda , MD , USA ) . Dendrites were analyzed using NeuronJ plugin [53] for Fiji6/ImageJ . Between 12 and 15 cells per condition were analyzed . At P28 , electroporated mice were deeply anesthetized with isoflurane and their brains were quickly removed . Brain sections were cut at a thickness of 300 µm using an off-sagittal slice angle to preserve apical and basal tufts of layer 2/3 cortical neurons of the anterior frontal cortex [31]; sections were cut in ice-cold carbogenated choline solution ( in mM: 110 choline chloride , 25 NaHCO3 , 2 . 5 KCl , 1 . 25 NaH2PO4 , and 0 . 5 CaCl2 , 7 MgSO4 , 25 D-glucose , 11 . 6 sodium ascorbate , 3 . 1 sodium pyruvate ) . Slices were transferred to carbogenated artificial cerebrospinal fluid ( ACSF , in mM: 126 NaCl , 2 . 5 KCl , 26 NaHCO3 , 2 CaCl2 , 1 MgCl2 , 1 . 25 NaH2PO4 , and 10 D-glucose ) and incubated for 30 min at 35°C . They were then maintained at room temperature for the remainder of the intracellular labeling procedure . To maximize the amount of dendritic arbor , we selected neighboring pairs of GFP-positive , Epac2-RNAi-expressing , and non-fluorescent control neurons with somata deeper than 60 µm from the surface of the brain slice ( average depth = 89±3 . 9 µm ) . Using a micropipette filled with biocytin intracellular solution ( in mM: 10 biocytin , 126 K-methylsulfate , 4 KCl , 10 HEPES , 4 ATP , 0 . 3 GTP , and 10 phosphocreatine ) , we dialyzed the neurons for at least 15 min and then allowed the cells to recover for at least 30 min before fixing the slices in 4% paraformaldehyde in 0 . 1 M sodium phosphate buffer ( pH 7 . 4 ) . Slices were then immunostained with a fluorescent streptavidin-568 conjugate ( Invitrogen ) and chicken anti-GFP polyclonal antibody ( Abcam ) as floating sections . Sections were mounted under a #1 . 5 coverslip with 2 #1 coverslips ( ∼150 µm thickness ) placed either side of the section to avoid damage to the tissue . Images were taken with a Prairie Ultima 2-photon in vivo microscope , using a Mira 900F laser at a wavelength of 795 nm ( 6 nm bandwidth ) to locally excite both Alexa-488 and -568 nm fluorescence , with a Zeiss LD Lci Plan Apochromat 25×/0 . 8NA multi-immersion lens ( 440842-9870-000000 ) . Volume imaging was acquired with 300–375 optical sections taken in 0 . 75 µm focal steps ( 2 . 13 µm axial resolution ) . The objective lens lateral resolution was defined to be 0 . 43 µm with 795 nm and NA = 0 . 8 and captured with pixels of 0 . 22 µm ( 2 , 048×2 , 048 , 440 µm field of view ) , 4 µs pixel dwell time . Best performance was achieved with Cargill Type FF immersion oil , an index of 1 . 479 , and using the glycerol with cover slip objective lens correction collar setting . Only pairs of cells exhibiting intact healthy secondary and tertiary apical and basal dendrites were imaged and used for quantification . Following acquisition , images were projected as 2-D Z-projections using Fiji6 . Dendrites were analyzed using NeuronJ plugin for Fiji6/ImageJ . 5 animals were analyzed . To quantify dendritic morphology in vitro , cultured neurons expressing GFP were imaged using a 10× objective ( NA = 0 . 17 ) , and micrographs were acquired using a Zeiss AxioCam MRm CCD camera . Dendrites were traced and binarized in ImageJ . The axon was identified by its distinct morphology and was eliminated from quantification . The following criteria for identifying apical and basal dendrites in cultured neurons were used . “Apical” dendrites were defined as the longest single protrusion , also referred to as the primary dendrite , which has the largest diameter proximal to the cell body [35] , [36] , whereas “basal” dendrites were identified as smaller and shorter protrusions , with a smaller diameter proximal to the cell body , compared to the primary dendrite ( Figure S3A–B ) . Examination of Golgi outposts in vitro and in vivo has demonstrated that the longest dendritic protrusions ( primary dendrite ) contain Golgi complexes in cultured neurons , and that in vivo , Golgi complexes are found in apical dendrites [34] , [35] . Indeed , we found that in the majority ( >90% ) of neurons in our cultures , only one dendrite , typically the longest one , was positive for giantin , a marker for the Golgi complex ( Figure S3B ) , which we have classified as the apical dendrite [34] . Dendritic length was measured in MetaMorph . For Sholl analysis , we used the Sholl analysis plugin for ImageJ ( http://biology . ucsd . edu/labs/ghosh/software ) to measure the number of dendritic processes that intersected with concentric circles spaced 25 µm apart starting at the center of the soma . For each parameter , 7–17 cells from 3–5 experiments were measured , and images were acquired and quantified by an experimenter blind to condition . For quantitative immunofluorescence experiments , coIPs , and dendrite length or number measurements , differences among condition means were identified by Student's unpaired t tests or ANOVAs performed in GraphPad Prism ( La Jolla , CA , USA ) or SPSS ( Armonk , NY , USA ) . Tukey-b or Bonferroni post hoc analyses were used for multiple comparisons . Error bars represent standard errors of the mean . For Sholl analysis , mixed model ANOVAs ( condition×distance from soma ) were conducted , with distance from soma as a repeated measure . Student's paired t tests were used to analyze paired cell morphology . | A fundamental feature of a neuron is the morphology of its dendrites , which are the processes that receive and integrate synaptic signals from other neurons . Neurons in the mammalian cortex exhibit two distinct dendritic arbors: apical dendrites , which extend far from the cell body , and basal dendrites , which elaborate locally around the cell body . After development , neurons must actively maintain each of these dendritic arbors to sustain their specific connectivity . Because several neurological and neurodevelopmental disorders are associated with disruptions in dendritic morphology , it is crucial to understand the molecular mechanisms that regulate the process of active maintenance of dendritic arbors . We find that disruption of a particular molecular pathway , the Ras-Epac2 pathway , can result in dramatic simplification of basal , but not apical , dendritic arbors in both cultured neurons and in the intact mouse brain . We show that a mutant form of Epac2 , identified in patients with autism , also impairs basal dendrite maintenance and disrupts its interaction with Ras . Our findings suggest that specific molecular pathways can regulate distinct dendritic regions , and that disease-related mutations can inform our understanding of the molecules that regulate important biological processes . |
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Tsetse flies are the notorious transmitters of African trypanosomiasis , a disease caused by the Trypanosoma parasite that affects humans and livestock on the African continent . Metacyclic infection rates in natural tsetse populations with Trypanosoma brucei , including the two human-pathogenic subspecies , are very low , even in epidemic situations . Therefore , the infected fly/host contact frequency is a key determinant of the transmission dynamics . As an obligate blood feeder , tsetse flies rely on their complex salivary potion to inhibit host haemostatic reactions ensuring an efficient feeding . The results of this experimental study suggest that the parasite might promote its transmission through manipulation of the tsetse feeding behavior by modifying the saliva composition . Indeed , salivary gland Trypanosoma brucei-infected flies display a significantly prolonged feeding time , thereby enhancing the likelihood of infecting multiple hosts during the process of a single blood meal cycle . Comparison of the two major anti-haemostatic activities i . e . anti-platelet aggregation and anti-coagulation activity in these flies versus non-infected tsetse flies demonstrates a significant suppression of these activities as a result of the trypanosome-infection status . This effect was mainly related to the parasite-induced reduction in salivary gland gene transcription , resulting in a strong decrease in protein content and related biological activities . Additionally , the anti-thrombin activity and inhibition of thrombin-induced coagulation was even more severely hampered as a result of the trypanosome infection . Indeed , while naive tsetse saliva strongly inhibited human thrombin activity and thrombin-induced blood coagulation , saliva from T . brucei-infected flies showed a significantly enhanced thrombinase activity resulting in a far less potent anti-coagulation activity . These data clearly provide evidence for a trypanosome-mediated modification of the tsetse salivary composition that results in a drastically reduced anti-haemostatic potential and a hampered feeding performance which could lead to an increase of the vector/host contact and parasite transmission in field conditions .
Tsetse flies ( Diptera: Glossinidae ) are obligate blood feeding insects that are important disease vectors given their involvement in the transmission of different pathogenic trypanosome species that cause human sleeping sickness and livestock trypanosomiasis in Africa . Trypanosomes of the Trypanosoma brucei group – including the two human-pathogenic subspecies T . b . gambiense and T . b . rhodesiense – have to go through a complex developmental cycle in the alimentary tract and salivary glands of the tsetse fly [1] . The salivary gland is the tissue in which T . brucei parasites undergo the final developmental phase , i . e . a continuous cycle of multiplication and cellular differentiation into the metacyclic form that is infective for the mammalian host [2] . Once this trypanosome population has been established in the salivary glands , it is continuously maintained at high density throughout the remaining life span of the tsetse fly . In the naive salivary gland micro-environment , saliva components are present that enhance the infection onset upon trypanosome inoculation in the host skin [3] . Other constituents are essential for the hematophagous behavior of the tsetse fly by counteracting host responses such as vasoconstriction , platelet aggregation and coagulation reactions involving serine proteases such as thrombin [4] . Several compounds have been implicated in facilitating blood feeding: a thrombin inhibitor [tsetse thrombin inhibitor ( TTI ) ] [5] , [6] and salivary apyrases [5′nucleotidase related protein , Glossina morsitans morsitans salivary gland protein 3 ( Sgp3 ) ] including at least one with fibrinogen receptor ( GPIIb/IIIa ) antagonistic properties ( 5′Nuc ) [7] . Other abundant salivary components include putative endonucleases [tsetse salivary gland proteins 1 and 2 ( Tsal1 and Tsal2 ) ] [8] , putative adenosine deaminases [tsetse salivary gland growth factors 1 and 2 ( TSGF-1 and TSGF-2 ) ] [9] and an antigen5-related allergen [tsetse Antigen5 ( TAg5 ) ] [10] . However , there is no information on the importance of these major tsetse saliva proteins in their interplay with the trypanosome life cycle . To date , a growing number of studies demonstrate the ability of vector-borne parasites to alter phenotypic traits of their insect vectors in a way that increases vector-host contact frequency and hence increases the probability of parasite transmission [11] , [12] . This type of parasite-induced modulation of the vector physiology and feeding behavior has already been documented for the Leishmania-sandfly model [13] , the Plasmodium-mosquito model [14]–[16] and other pathogen-vector models [reviewed in [17] , [18]] . A recurrent feature of infected vectors is a modified feeding behavior that results from the physical obstruction of the alimentary tract and interference with mechanoreceptors that are required to regulate the blood meal uptake . Indeed , Leishmania promastigotes produce a secretory gel , mainly composed of a filamentous proteophosphoglycan that blocks the foregut and impairs the phagoreceptors , thereby reducing the arthropod feeding efficiency [19] . Similarly , a proportion of plague-transmitting fleas display obstructed proventiculi as a result of Yersinia biofilm surrounded by an extracellular matrix [20] . In the tsetse fly-trypanosome interaction , mouthpart obstruction and interference with labral mechanoreceptors has been documented upon infection with Dutonella and Nannomonas subgenera of Trypanosoma ( T . congolense and T . vivax ) that form rosettes and colonize the tsetse fly labrum [21]–[26] . However , limited and contradictory data have been reported on the feeding behavior of tsetse flies infected with T . brucei parasites ( including the human pathogens ) which belong to the Trypanozoon subgenus and display a different developmental cycle in the vector than T . congolense and T . vivax [1] , [2] . Jenni et al . observed a more frequent probing behavior of T . brucei infected tsetse flies and hypothesized that this resulted from physical interference of trypanosomes with the function of the labral mechanoreceptors [27] . However , other experimental results suggested that T . brucei parasites in the salivary glands did not significantly alter the tsetse feeding [22] , [28] . In this study , we investigated whether T . brucei parasites alter the tsetse fly blood feeding behavior in a way that would favor parasite transmission within the mammalian host population . Next , we determined the impact of a T . brucei salivary gland infection on the saliva composition and the biological activities related to anti-haemostasis . The obtained data provide evidence that the trypanosome parasites drastically modulate the tsetse salivary composition and anti-haemostatic activity resulting in an alteration of the feeding behavior that favors parasite transmission .
The feeding efficiency of salivary gland infected ( SG+ ) tsetse flies ( n = 50 ) was compared to that of controls that did not develop a salivary gland infection ( SG- , n = 48 ) upon feeding on a Trypanosoma brucei brucei AnTAR1 parasitemic mouse . As a read-out , two variables were measured: ( i ) the time necessary to obtain a full blood meal including the probing behavior that precedes the actual blood ingestion and ( ii ) the size ( mass ) of the blood meal . Despite a considerable variability in both experimental groups , the blood meal acquisition was significantly slower ( p<0 . 05 , Table 1 ) for SG+ flies ( 267±23 s . ) than for SG- flies ( 210±16 s . ) , especially resulting from a prolonged probing behavior ( visual observation ) . No differences in ingested blood masses were observed ( p = 0 . 83 ) . The presence of a T . b . brucei infection in the salivary glands significantly compromised ( p<0 . 05 ) the expression of genes that encode the major G . m . morsitans saliva proteins ( Figure 1A ) . Expression levels were decreased by 63% ( tsal 2 ) up to 95% for the 5′nuc apyrase gene ( Figure 1 ) . In two independent experiments , threshold cycle values for actin and tubulin housekeeping genes did not significantly change as a result of the SG+ infection status . Concomitant to the reduced transcription of the major saliva genes , the saliva of SG+ flies contained 70% less protein ( p<0 . 01 ) as compared to the SG- flies ( 0 . 9±0 . 2 versus 3 . 0±0 . 5 µg per salivary gland , Figure 1B ) . A more detailed analysis of the SG- and SG+ saliva composition was performed using Tricine-SDS-PAGE ( Fig . 2A ) combined with either Coomassie ( Fig . 2A , lanes section 1 ) or Silver based staining methods ( Fig . 2A , lanes section 2 ) . Densitometry analysis of the Coomassie stained protein profiles revealed a generalized reduction of 70–97% in protein band intensities for SG+ saliva samples ( Fig . 2B ) . In addition , several protein and peptide bands that are visible in the SG- saliva profiles upon silver staining , are no longer detectable in SG+ saliva . Western blot analysis using anti-T . b . brucei infectome immune serum could not detect the appearance of trypanosome-derived components in SG+ saliva . Different biological activities ( apyrase , adenosine deaminase and anti-thrombinase ) that were previously described or suggested to be present in tsetse saliva , were quantified in SG- and SG+ samples . Based on the quantification of Pi-release from the individual substrates ATP and ADP as read-out for apyrase ( ATP diphosphohydrolase ) activity , an approximate 5-fold reduction ( p<0 . 01 ) in salivary apyrase was observed in trypanosome infected salivary glands ( Figure 3A ) . For the adenosine deaminase activity that was present in the SG- saliva at 6 . 0±1 . 0 mU/salivary gland , a similar reduction ( 82% , p<0 . 01 ) was observed in the SG+ flies exhibiting an activity of only 1 . 1±0 . 5 mU/salivary gland ( Figure 3B ) . The thrombinase-inhibitory properties of tsetse fly saliva were assayed with respectively 1/80 and 1/400 dilutions . The 1/80 SG- saliva dilutions almost completely inhibited the human thrombinase activity ( assayed by the release of pNA from thrombin-specific substrate ) at the concentration of 500 mU/ml ( Figure 3C ) . In contrast , a significant increase ( 83% , p<0 . 01 ) in thrombinase activity was observed for the same SG+ saliva dilution , suggesting a potentiation of the thrombin enzymatic activity in the used assay conditions . For the 1/400 SG+ dilution , an increase could still be detected although less pronounced ( 27% , p<0 . 05 ) . The enhancement of thrombinase activity by SG+ saliva did not depend on a trypanosome-derived enzyme with the same substrate-specificity , as saliva from SG+ flies by itself did not convert the chromogenic substrate ( data not shown ) . The salivary anti-thrombotic and anti-coagulant activities were monitored in human plasma using respective in vitro read-out assays . The aggregation of platelets in human platelet rich plasma ( PRP ) supplemented with ½ serial SG- and SG+ saliva dilutions ( 1/100–1/400 ) was analyzed in response to 10 µM ADP , revealing an approximate 3-fold reduction in anti-platelet aggregating capacity of SG+ saliva ( Figure 4 ) . Coagulation in human platelet poor plasma ( PPP ) , induced by 25 mU/ml thrombin in the presence or absence of ½ serial SG- ( 1/400–1/6400 ) and SG+ saliva dilutions ( 1/50–1/6400 ) , revealed a striking decrease of anti-coagulant activity in tsetse fly saliva upon trypanosome infection . Indeed , while all tested SG- saliva dilutions ( 1/400–1/6400 ) markedly increased the coagulation lag times ( Figure 5A ) , all SG+ saliva dilutions from 1/400 downwards ( 1/800–1/6400 ) exerted negligible anti-coagulant activity ( Figure 5B ) . Comparison of the coagulation lag times revealed a 16- to 32-fold reduction of anti-coagulant activity in SG+ as compared to SG- saliva ( Figure 5C ) . Moreover , thrombin did not induce maximal coagulation responses in PPP in the presence of the 1/400–1/6400 SG- saliva samples within a 3 hour reaction time ( Figure 5D ) , while endpoint O . D . values at 405 nm were even slightly higher when thrombin was incubated with 1/800–1/6400 SG+ saliva samples ( Figure 5D ) . These slightly increased endpoint O . D . values did not result from clotting of salivary components , as no thrombin-induced coagulation was observed in SG- and SG+ saliva ( data not shown ) .
African trypanosomes including the human-infectious Trypanosoma brucei subspecies , exploit the obligate blood feeding behavior of tsetse flies ( Glossina sp . ) for their transmission . These tsetse fly vectors rely on a pool feeding strategy which involves the laceration of the skin with their proboscis and blood ingestion from a superficial lesion . Once the skin is pierced , the proboscis is often partially withdrawn before being thrust again at a slightly different angle to probe for suitable blood vessels and to enhance the blood pool formation [29] . During these events , about 4 µg of salivary proteins are inoculated at the bite site in order to neutralize the complex anti-haemostatic host reactions that would lead to blood clotting and vasoconstriction [30] . In the case of feeding on a parasitemic host , tsetse flies can acquire a trypanosome infection which depends on a complex sequence of differentiation and migration that ends in the insect salivary glands [1] , [2] . Once the salivary glands are colonized by metacyclic T . brucei parasites ( SG+ ) , the tsetse fly can transmit parasites throughout its entire lifespan at each vector/host contact . Despite the epidemiological importance , information on the impact of the salivary gland infection on the tsetse feeding behavior and trypanosome transmission is scanty and contradictory . While Moloo et al . did not observe significant feeding behavioral differences as a result of the SG+ status [22] , Jenni et al . [27] reported that T . brucei-infected flies probed more frequently ( 2 to 3 fold increase ) before feeding and subsequently fed more voraciously as compared to uninfected ( SG- ) flies . The authors suggested that these effects resulted from the association of some trypanosomes with labral mechanoreceptors that play a role in the feeding and gorging response , analogous to what was reported for T . congolense infected flies . Indeed , the increased probing activity of T . congolense infected G . morsitans flies [31] may be caused by physical interference of the parasite with phagoreceptors in combination with a reduced diameter of the tsetse labrum due to the presence of parasite rosettes [22]–[23] , [32] . However , in contrast to T . congolense , T . brucei parasites never permanently colonize the tsetse fly mouthparts where the mechanoreceptors are localized [1] , [2] , which is not supportive for Jenni's hypothesis . In our study , we could confirm Jenni's observation that a T . brucei infection in tsetse fly salivary glands does significantly disturb the fly feeding behavior . Indeed , SG+ tsetse flies needed significantly longer times ( >25% longer ) to complete blood feeding due to a prolonged pre-feeding probing phase . Our experimental data clearly suggest that this altered feeding phenotype is the consequence of a changed protein content of the tsetse saliva due to the presence of a trypanosome infection , resulting in a much less potent anti-haemostatic activity . This reduced saliva production was confirmed using Tricine-SDS-PAGE , revealing a generalized suppression ( 70–97% ) of all protein bands in tsetse saliva which was found to be associated with a severely reduced ( 63%–95% ) transcription of the major tsetse fly salivary genes . Especially the 5′nuc gene that encodes an important tsetse fly salivary apyrase with GPIIb/IIIa ( fibrinogen receptor ) antagonistic properties [7] and another putative apyrase gene ( sgp3 ) were strongly suppressed ( >90% ) resulting in an overall 80% down regulation in the saliva apyrase [AT ( D ) Pase] activity . A similar phenomenon has been described for Plasmodium infected mosquitoes , where the salivary apyrase activity was reduced by three fold and which was also associated with prolonged probing times [14] , [33] . Salivary apyrase activity underlies one of the major anti-haemostatic strategies in a blood feeding insect [34] given that these enzymes inhibit purinergic thrombocyte triggering by hydrolyzing ATP and ADP , haemostatic triggers that are released from injured cells and activated platelets [35] . As such , the reduced apyrase activity in the SG+ tsetse saliva seriously affected the normally powerful capacity to inhibit the blood platelet aggregation demonstrated in an in vitro aggregation studies using human platelets . The significant suppressed adenosine deaminase activity in the trypanosome-infected saliva could also be a contributing factor in the decreased platelet aggregation inhibition . Indeed , adenosine deaminases convert adenosine into inosine , a nucleoside that was recently suggested to modulate platelet responses against various agonists including ADP and collagen [36] . The inhibition of the thrombin activity is another key anti-haemostatic activity of normal tsetse saliva . Indeed , a femtomolar affinity thrombin inhibitor ( TTI ) has been previously characterized in tsetse fly salivary gland extracts and shown to potently inhibit thrombinase activity and thrombin-induced haemostatic reactions [6] . In our study , we demonstrate that the presence of trypanosomes in the salivary glands severely impairs this ability of saliva to inhibit human thrombin and even modifies saliva to enhance the activity of this thrombinase in an in vitro pNA-release assay . This observed increase in thrombin activity was not related to the presence of a trypanosome-derived enzyme since SG+ saliva by itself did not hydrolyze the chromogenic thrombin substrate ( data not shown ) . Corroborating the observed effects of salivary gland infection on the measured enzymatic activities in the biochemical assays , the anti-coagulant potency of SG+ saliva was severely compromised in human plasma coagulation assays using human thrombin as a trigger . Indeed , while all tested SG- saliva dilutions significantly inhibited thrombin-induced coagulation , several SG+ saliva dilutions ( 1/800–1/6400 ) failed to inhibit this haemostatic reaction and even slightly increased the maximal coagulation response induced by thrombin . As such , both the biochemical and plasma coagulation assays suggested the presence of a parasite-derived or infection-induced procoagulant factor in the saliva of SG+ flies . Known thrombin activity enhancing cofactors include glycoprotein Ibα , fibrin and Na+ [37] . Given that experiments were performed under physiological salt conditions ( 150 mM ) with very low saliva concentrations , the influence of Na+ ions can be ruled out . Strikingly , tsetse fly transcriptome analyses revealed an abundant representation of a fibrinogen-domain-containing protein family that is enriched in the salivary gland tissue ( 197 ESTs ) as compared to other organs ( 16 ESTs in midgut , none in the fat body ) [38] . Possibly , these or other ( tissue or parasite-derived ) proteins might modulate thrombin activity through exosite binding and allosteric activation or even contribute as substrate in the coagulation reaction . The possibility that SG+ and SG- saliva by itself undergoes coagulation in response to thrombin was excluded experimentally . An experimental approach based on SG-/SG+ differential salivary proteome analyses and/or affinity purification using thrombin as bait could possibly unveil the identity of this thrombin enhancing factor . Collectively , we have demonstrated that upon colonization of the tsetse salivary glands with Trypanosoma brucei , the protein content and anti-haemostatic activity of the saliva change resulting in an altered insect vector feeding behavior . We assume that the reduced anti-haemostatic activity precludes the SG+ tsetse fly from efficiently generating and maintaining a primary blood pool as prerequisite in the feeding process . The observed prolonged probing/feeding time might result in an increased host contact as a result of interrupted feeding and partial blood acquisition and contribute to a higher probability of parasite transmission . To experimentally demonstrate the latter in a natural setting , i . e . to evidence the link between the behavioral modifications of tsetse flies and a more successful parasite transmission , is not obvious . However , field studies have indicated that tsetse flies are highly responsive to host defensive behavior and are prone to interrupted feeding [39] . Given that T . brucei salivary gland infected tsetse flies need longer times to feed successfully compared to non-infected ones , this high sensitivity to the host defensive behavior might result in a higher probability of interrupted blood feeding and of alternative host seeking . In other words , it might result in an increased biting rate of the infected tsetse within the available host population . As such , an infected tsetse fly is more likely to probe on multiple hosts during a single feeding cycle . Given that probing alone was proven to be sufficient to infect a mammalian host and that successive probing of the same fly on different hosts results in multiple infections [27] , the parasite-induced change in tsetse biting behavior might result in an enhanced trypanosome transmission . Here , it is clear that multiple transmission of the parasite in a single tsetse feeding cycle increases its survival and circulation within the natural mammalian host population . In the case of the human pathogenic T . brucei sp . , where the numbers of salivary gland infected tsetse flies in the natural population are extremely low [<0 . 1% , [40]–[42]] , the increased biting rate of the infected tsetse could be a major epidemiological factor . Currently , we do not know the molecular mechanism that underlies the trypanosome-induced modification of saliva composition and biological activities . Possibly , the high density of actively metabolizing parasites causes physiological stress to the salivary gland cells resulting in a suppression of salivary gene transcription and translation . In addition , the significant enhancement of the thrombin activity in the chromogenic thrombinase assay suggests that an activating factor is directly released or induced by the parasites in the saliva .
Animal ethics approval for the tsetse fly feeding on live animals and infection with T . brucei parasites was obtained from the Animal Ethical Committee of the Institute of Tropical Medicine , Antwerp ( Belgium ) ( Ethical clearance nrs . PAR013-MC-M-Tryp and PAR014-MC-K-Tryp ) . All tsetse fly infection studies were performed in compliance with the regulations for biosafety and under approval from the Environmental administration of the Flemish government ( licencenr . SBB 219 . 2007/1410 ) . Male Glossina morsitans morsitans ( Westwood ) from the colony at the Institute of Tropical Medicine ( Antwerp , Belgium ) were used in all experiments . This colony originated from pupae collected in Kariba ( Zimbabwe ) and Handeni ( Tanzania ) [43] . Flies were fed 4 days per week on rabbits and are maintained at 26°C and 65% relative humidity . Animal ethics approval for the tsetse fly feeding on live animals was obtained from the Animal Ethical Committee of the Institute of Tropical Medicine , Antwerp ( Belgium ) . The pleiomorphic Trypanosoma brucei brucei AnTAR1 strain , derived from the EATRO 1125 stock [44] , was used for the infection experiments . This strain was previously demonstrated to develop efficiently in the tsetse fly , resulting in >20% salivary gland infections [45] . Freshly emerged flies were offered their first blood meal on an anaesthetized mouse showing a pleiomorphic T . b . brucei parasitaemia of approximately 108 trypanosomes/ml blood with >80% intermediate/stumpy forms . Only fully engorged flies were further maintained at 26°C and 65% relative humidity and were fed 3 days per week on a naive rabbit . Thirty days after the infective blood meal , individual flies were evaluated for the presence of metacyclic trypanosomes in their salivary glands by salivation on pre-warmed ( 37°C ) glass slides [modification of the method of Burtt et al . [46]] . This allowed us to obtain two experimental fly groups of equal age and feeding history but with a different trypanosome infection status in the salivary glands ( SG+ and SG- ) . These flies were subsequently used for feeding efficiency analysis and for the dissection of salivary glands to assess salivary protein expression and associated biological activities . All tsetse fly infection studies were performed in compliance with the regulations for biosafety and under approval from the Environmental administration of the Flemish government . The feeding efficiencies of individualized SG+ and SG- flies of the same age and exactly the same feeding history were compared three days after the last blood meal on anaesthetized mice . Feeding efficiencies were monitored by direct observation by two observers ( JVDA and GC ) . Each observer contributed half of the observations for each experimental group , thereby excluding inter-group differences as a result of the different observers . For each fly , the total probing and feeding time was measured with a chronometer ( accuracy of 1 sec ) by direct observation . In order to determine the blood meal size , individual fly masses were measured to an accuracy of 0 . 1 mg before and immediately after blood feeding using an analytical balance ( Sartorius ) as described previously [3] . Three days after the last blood meal and following a 10 minute cold shock at 4°C , salivary glands of SG+ and SG- flies were dissected , pooled by 3 pairs in 30 µl sterile physiological H2O and incubated on ice for two hours before centrifugation ( 500 ×g , 2 min at 4°C ) . The supernatants were centrifuged an additional time to obtain saliva devoid of trypanosomes ( 2500 ×g , 2 min at 4°C ) . Saliva samples were stored at −80°C and only thawed once for analysing protein content and enzymatic activities . SG- and SG+ samples were always handled and tested in parallel in all subsequent analyses . Pellets ( salivary gland tissue ) were further processed to extract RNA for RT-qPCR purposes . The harvested salivary gland tissue was homogenised with a Teflon pestle in 1 ml Tripure reagent ( Roche ) followed by total RNA extraction according to the manufacturer's protocol . Six-hundred nanogram of each RNA sample was used for primary cDNA synthesis using 100 pmol oligo ( dT ) 15 primer ( Promega ) and 10 units Transcriptor Reverse Transcriptase ( Roche ) . For transcript-analysis , we made a selection of genes based on ( i ) the available literature data on identified genes that encode soluble saliva proteins , ( ii ) their relative contribution to tsetse fly proteome in terms of abundance and ( iii ) their predicted involvement in the blood feeding physiology . According these criteria we selected the identified thrombin inhibitor ( TTI ) , a highly abundant allergen ( TAg5 ) , two putative adenosine deaminases ( TSGF1&2 ) that might modulate adenosine-mediated platelet responses , two highly abundant putative endonucleases ( Tsal1&2 ) that might contribute to the blood feeding process by producing a defibrotide-like mixture of DNA haptamers and one predicted and one confirmed apyrase ( Sgp3 and 5′Nuc related protein ) . Relative transcript quantification was performed on an iCycler iQ detection system ( Bio-Rad ) and using the Bio-Rad software version 3 . 1 . RT-qPCR was performed on triplicate samples in a 25 µl reaction volume , containing 1 . 5 to 15 ng primary cDNA ( depending on the gene ) , 12 . 5 µl of iQ SYBR Green Supermix ( Bio-Rad ) and an optimized primer pair concentration for one of the respective saliva genes: tti [500 nM TTI_FW ( 5′- TTTATCTGATAGTTGCCGCAC -3′ ) and TTI_REV ( 5′- AAAGCCTTATGCCAGGAATC -3′ ) ] , tag5 [300 nM TAg5FW ( 5′-GTGGGTTGTGCCGCTTCTG-3′ ) and TAg5REV ( 5′-TTGACCTCGTATTTCTCGTTGG-3′ ) ] , tsal1 [700 nM Tsal1FW ( 5′-CTGATACCTCGATGATCACTC-3′ ) and Tsal1REV ( 5′-AGGCTCTTACATAATCCTTAAC-3′ ) ] , tsal2 [500 nM Tsal2FW ( 5′-CCAAGAACTGGCTGACCAA-3′ ) and Tsal2REV ( 5′-CTGCCAGCAGATTGTGTAAC-3′ ) ] , tsgf1 [300 nM TSGF1_FW ( 5′-CGGTTGTAAATCCGAATCTGT-3′ and TSGF1_REV ( 5′-GCGGCTGGCAAATAATGTAGA-3′ ) ] , tsgf2 [500 nM TSGF2_FW ( 5′-CAAACGCTCCGGTGTTGACGT-3′ ) and TSGF2_REV ( 5′-GCGGCTGGCAAATAATGTAGA-3′ ) ] , 5′nuc [300 nM 5NucFW ( 5′-CGGGTAATAAAGTTCTGGTCGTA-3′ ) and 5NucREV ( 5′-TTGGCAAGTCCACATTTGTTCTC-3′ ) ] and sgp3 [500 nM Sgp3_FW ( 5′- GCTATGGAACCATGGAAGGA -3′ ) and Sgp3_REV ( 5′- TTCTGATTCGCCTTCGTCTT -3′ ) ] . For normalization , G . m . morsitans actin and tubulin genes were amplified using respectively 700 nM and 300 nM of the following primer pairs: actinFW ( 5′-CGCTTCTGGTCGTACTACT-3′ ) and actinREV ( 5′-CCGGACATCACAATGTTGG-3′ ) , tubulinFW ( 5′-GATGGTCAAGTGCGATCCT-3′ ) and tubulinREV ( 5′-TGAGAACTCGCCTTCTTCC-3′ ) . The PCR conditions comprised an initial 10 min polymerase activation at 95°C followed by 35 cycles , each consisting of a denaturation step at 95°C for 15 s , 60 s annealing at 60°C and 60 s elongation at 72°C . In the data analysis , both actin and tubulin housekeeping genes were included to calculate an integrated normalization factor using the geNorm software v . 3 . 5 . Protein concentrations in the saliva extracts were determined using the BCA protein assay reagent kit ( Pierce Biotechnology ) . Saliva samples of SG- and SG+ flies were analyzed by Tricine-SDS-PAGE , using Novex tricine gels 10–20% ( 1 mm/10 well , Invitrogen ) and 100 mM Tris pH 8 . 3 100 mM Tricine 0 . 1% SDS as running buffer . Gels were run at 125 V in an XCell Surelock Mini-Cell ( Invitrogen ) . In parallel , the prestained PageRuler protein ladder and Spectra Multicolor Low Range Protein Ladder ( Fermentas ) were applied to the gels . Gels were either stained with 0 . 025% Coomassie dye R-250 in 10% acetic acid according to an established protocol [47] or Silverstained using the PageSilver kit ( Fermentas ) after a 30 minute fixation in 5% glutaraldehyde . The different Coomassie-stained protein profiles were digitalised as 300 dpi greyscale TIFF-files and analysed with the ImageMaster 1D Elite 3 . 01 programme ( Amersham Pharmacia Biotech ) . In this analysis , the size and intensity of each protein band was quantified by densitometry and expressed as integrated peak density values representing the amount of protein in the respective band . Different biological activities in tsetse saliva that were previously demonstrated or that could be predicted by EST-database analysis were assayed: thrombin-inhibitory ( TTI ) , apyrase ( 5′nucleotidase , gmmspg3 ) and adenosine deaminase activity ( TSGF1/2 ) . Salivary apyrase activity was quantified by assessing the dephosphorylation rate of 20 µM ATP and ADP at 27°C in a 25 mM Tris/HCl pH 7 . 8 buffer supplemented with 2 . 5 mM CaCl2 . ATP/ADP-conversion was monitored after 1 hour by quantifying the release of inorganic phosphate ( Pi ) using the Malachite green phosphate assay kit ( Gentaur ) and O . D . measurement ( λ = 650 nm ) in an Multiskan Ascent microplate reader ( ThermoScience ) . The ATPase and ADPase activities in tsetse saliva samples were expressed as pmole Pi release/min × salivary gland . Adenosine deaminase activity in tsetse saliva samples was measured spectrophotometrically by a direct kinetic assay , monitoring the change in O . D . ( λ = 265 nm ) upon conversion of adenosine into inosine . This ADA activity assay was performed in 10 mM HEPES 150 mM NaCl buffer ( pH 7 . 5 ) containing 100 µM adenosine and O . D . values were recorded at 15 s interval over a period of 5 min in a microplate reader ( ThermoScience ) . The ADA activity in the saliva samples was expressed as milliUnits ADA/salivary gland , where 1 Unit ADA will deaminate 1 µmole of adenosine to inosine per minute at pH 7 . 5 ( millimolar extinction coefficient of adenosine at 265 nm = 8 . 1 ) . The thrombin inhibitory potential of saliva ( 1/80–1/400 dilution ) was quantified in 96-well plates by a kinetic assay at 37°C that monitors the release of p-nitroanilide ( pNA ) from 750 nM of thrombin chromogenic substrate ( β-Ala-Gly-Arg-p-nitroanilide diacetate , Sigma ) by the proteolytic activity of human thrombin ( Roche , 500 mU/ml ) in PBS . pNA-release was measured for at least 1 hour at λ = 405 nm in a microplate reader ( ThermoScience ) . The thrombin inhibitory potential of the saliva samples was expressed relative to the pNA release obtained with thrombin ( 100% activity ) . The platelet aggregation was monitored in a 96-well flat-bottom microplate assay as described elsewhere [48] . Platelet-rich plasma ( PRP ) was prepared from venous human blood that was anticoagulated in Monovette coagulation tubes ( Sarstedt ) . Aggregation of platelets was induced at 37°C with 10 µM ADP ( in 150 mM NaCl ) in the presence or absence of serial saliva dilutions ( 1/100–1/400 , in 150 mM NaCl ) from SG+ and SG- flies . Reduction in optical density ( increase in transmission ) at 650 nm wavelength was monitored as a measure for platelet aggregation . Human platelet-poor plasma ( PPP ) , prepared by pelleting the platelets in PRP ( see above ) at 1500 ×g for 15 min , was used for thrombin-induced coagulation assays . Briefly , coagulation was triggered in a total volume of 180 µl by the 1/3 addition of PPP to 10 mM HEPES ( pH 7 . 4 ) 12 . 5 mM CaCl2 supplemented with thrombin at a 25 mU/ml final concentration in the presence or absence of 1/50–1/6400 dilutions of saliva from SG+ and SG- flies . Coagulation was measured as a steep increase in absorbance ( λ = 405 nm ) . The lag phase preceeding coagulation onset was determined as a measure for anti-coagulation activity in the respective saliva samples . tsal1 ( AF259958 ) , tsal2 ( EF409243 ) , tsgf1 ( AF140521 ) , tsgf2 ( AF140522 ) , 5nuc ( AF384674 ) , sgp3 ( EF398273 ) , tag5 ( AF259957 ) , tti ( AF054616 ) . | Human African Trypanosomiasis , or sleeping sickness , is a devastating parasitic disease that is fatal if left untreated . Infections are acquired via the bite of an obligate blood feeding fly , the tsetse fly , that is exclusively present on the African continent . In this insect vector , the trypanosome parasite has a complex development ending in the salivary glands . In this experimental study we demonstrate that the Trypanosoma brucei parasites change the composition of the tsetse fly saliva making it less efficient to keep the blood fluid at the biting site in the mammalian host . This results in a more difficult blood feeding process and favors the fly biting activity on multiple hosts , thereby promoting the survival and circulation of the parasite within the natural host population . These findings give us a better understanding of how trypanosome infections in the human population can be maintained given the fact that only very few tsetse flies are actually carrying the parasite . |
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A comprehensive analysis was done to evaluate the potential use of anti-parasitic macrocyclic lactones ( including avermectins and milbemycins ) for Buruli ulcer ( BU ) therapy . A panel containing nearly all macrocyclic lactones used in human or in veterinary medicine was analyzed for activity in vitro against clinical isolates of Mycobacterium ulcerans . Milbemycin oxime and selamectin were the most active drugs against M . ulcerans with MIC values from 2 to 8 μg/mL and 2 to 4 μg/mL , respectively . In contrast , ivermectin and moxidectin , which are both in clinical use , showed no significant activity ( MIC> 32 μg/mL ) . Time-kill kinetic assays showed bactericidal activity of selamectin and in vitro pharmacodynamic studies demonstrated exposure-dependent activity . These data together with analyses of published pharmacokinetic information strongly suggest that selamectin is the most promising macrocyclic lactone for BU treatment .
Buruli ulcer ( BU ) , caused by Mycobacterium ulcerans , is a chronic debilitating disease of the skin and soft tissue . Although mortality is low , permanent disfigurement and disability is high [1] . BU is mainly found in Africa , South America and the Western Pacific regions and is often linked to poverty . If detected early , BU can be cured in most cases with the standard treatment , a combination of rifampicin and the injectable antibiotic streptomycin [2] , without further adjunct surgical treatment required . However , new treatment regimens are needed to reduce the long median time to healing , treatment-related side effects , and the requirement for on-site health care workers to administer injections [3] . Furthermore , an alternative drug treatment regimen would be required in the event that rifampicin resistant M . ulcerans strains would emerge in the clinic [4] . Traditionally , the discovery of new antimicrobial drugs has focused on designing and screening for new compounds having novel targets , an approach that is costly in time and capital ( up to ~$800M and 15–20 years ) [5] . This is not a viable option for BU , since most large pharmaceutical and biotech companies are primarily interested in blockbuster , broad spectrum antibacterial drugs [6] rather than treatments for neglected tropical diseases . A faster and cheaper alternative to finding new BU treatments is drug repositioning , i . e . using approved drugs for alternative clinical indications [7] . These drugs with known pharmacokinetic and safety profiles could be more rapidly evaluated in clinical trials [8] . Such an approach would also allow for an easier drug introduction , since manufacturing and distribution infrastructures are already available . In the course of screening clinically approved drugs to find new drug combinations for tuberculosis ( TB ) therapy , we discovered anti-mycobacterial activities of the avermectins , a class of macrocyclic lactones [9] . Following up these findings , the in vitro activities of two clinically approved macrocyclic lactones ( ivermectin and moxidectin ) against M . ulcerans were recently reported [10] . The avermectins are a family of macrocyclic lactone derivatives with potent anthelmintic properties , produced by the soil actinomycete Streptomyces avermitilis . Since avermectins are inactive against all other bacterial species tested [9] , oral administration would not affect healthful intestinal microbiome balances . We undertook a comprehensive approach to evaluate additional macrocyclic lactones used in veterinary medicine . Based on our in vitro measurements of their activities and a literature review of their pharmacokinetic ( PK ) properties , we provide strong indications that selamectin ( used in veterinary medicine ) , and not ivermectin ( used in human medicine ) , is the avermectin with the highest potential for clinical efficacy to treat BU .
M . marinum isolates ( 1704 and 1705; kindly provided by Dr . Julian Davies , University of British Columbia ) were routinely propagated at 30°C in Middlebrook 7H9 broth ( Difco ) supplemented with 10% Middlebrook albumin-dextrose-catalase ( ADC ) ( Difco ) , 0 . 2% glycerol and 0 . 05% ( vol/vol ) Tyloxapol or on Middlebrook 7H10 agar plates ( Difco ) supplemented with 10% ( vol/vol ) oleic acid-albumin-dextrose-catalase ( OADC ) ( Difco ) . M . ulcerans strains S1012 , S1013 and S1047 ( isolated in 2010 and 2011 from Cameroonian BU patients ) were routinely grown in BacT/Alert culture bottles using enrichment medium ( bioMérieux ) or on Middlebrook 7H10 agar plates ( Difco ) supplemented with 10% ( vol/vol ) OADC . Macrocyclic lactones were purchased from the following providers: abamectin and doramectin ( Sigma ) , emamectin and eprinomectin ( LKT Labs ) , ivermectin ( Alpha Diagnostic ) , milbemycin oxime ( US Pharmacopeia ) , moxidectin and selamectin ( European Pharmacopoeia ) . Minimal Inhibitory Concentrations ( MIC ) were determined in 7H9 broth supplemented with 0 . 2% glycerol and 10% ADC ( M . marinum ) or 10% OADC ( M . ulcerans ) using two-fold serial dilutions of compounds in triplicate in polystyrene 96-well plates . MTT [3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyl tetrazolium bromide] and resazurin were used as the bacterial growth indicators [11] for M . marinum and M . ulcerans , respectively . For M . marinum , cultures were sampled ( 100 μL ) at a cell density of 105 cells/mL and incubated in the presence of the drug for 3 days before addition of 25 μL of MTT ( 5 mg/mL ) . After further overnight incubation , 100 μL of 10% Sodium Lauryl Sulfate ( SLS ) were added to solubilize the formazan precipitate that indicates bacterial growth and the optical density at 580 nm ( OD580 ) was then measured . In the case of M . ulcerans , 100 μL culture samples ( OD600 = 0 . 04 ) were incubated in the presence of the drug at 30°C for 8 days before addition of 20 μL of a resazurin solution ( 0 . 125 mg/mL ) , followed by overnight incubation at 37°C . Compound activity was determine by fluorescence measurements ( λ = 540/588 nm ) . The lowest concentration of drug that inhibited 90% of the MTT or resazurin color conversion ( IC90 ) was used to define MIC values . 96-well polystyrene plates containing 200 μL per well of 7H9 broth supplemented with 10% OADC were inoculated in duplicate with M . ulcerans S1013 to a final OD600 = 0 . 04 ( ca . 105 cells/mL ) . Cultures were grown at 30°C in the presence of 0 . 5 , 2 , 4 , 8 , 16 and 32 μg/mL of selamectin ( 0 . 5 , 1 , 2 , 4 , 8 and 16 fold the selamectin MIC value , respectively ) for 0 , 3 , 7 , 14 and 21 days . At every time point , 100 μL of undiluted and ten-fold serial dilutions were plated on 7H10 agar . Colony-forming units for all plates were determined after 8 weeks of incubation at 30°C .
Eight commercially available macrocyclic lactones used in human and veterinary medicine were tested in vitro against M . ulcerans and M . marinum . Milbemycin oxime and selamectin were the most potent drugs against the M . ulcerans isolates ( MIC in 2–4 μg/mL range ) . Emamectin and moxidectin had intermediate potency ( MIC = ca . 32 μg/mL ) . While it was not possible to determine minimal inhibitory concentrations for ivermectin ( IC90 >64 μg/mL ) , some inhibitory activity was observed in dose response studies . In contrast , most of the macrocyclic lactones showed activity against M . marinum , a faster growing phylogenetic progenitor of M . ulcerans , with milbemycin oxime being the most potent ( Fig 1 and Table 1 ) . The PK properties of selamectin ( described below ) , together with its high in vitro activity against M . ulcerans , strongly indicated it as the most suitable avermectin for further evaluation as a potential new anti-BU treatment . To further characterize this potential new application , the in vitro pharmacodynamic ( PD ) parameters of selamectin were evaluated using kill-kinetic assays ( Fig 2 ) . In vitro kill-kinetic curves for selamectin were obtained by plotting the number of CFU at every time point for every concentration of the drug ( Fig 2A ) . These experiments confirmed the MIC and dose-response data determined by reporters of metabolic activity ( resazurin and the MTT; Table 1 and Fig 1 ) and showed a sharp threshold of bactericidal activity above the MIC ( 2 μg/mL ) . We also used an alternative method to visualize kill kinetics: each selamectin concentration was multiplied by the time of exposure ( CSEL x Tdays ) and then divided by the MIC of selamectin to give the in vitro area under the concentration-time curve ( AUC/MIC ratio ) , a standard measure of drug exposure ( Fig 2B ) . These analyses showed that just seven days of exposure were needed to observe the bactericidal activity of selamectin . The AUC/ MIC needed to achieve a bactericidal effect ( 4-log10 CFU/ml reduction , 99 . 99% killing ) required AUC/MIC ratios between 10 and 15 . These ratios were comparable to those previously observed for M . tuberculosis [9] . In summary , these studies showed that the activity of selamectin against M . ulcerans is exposure-dependent; if a certain concentration is achieved , a bactericidal effect is observed by increasing the time of exposure but not by increasing dose concentrations . This information could have important implications when designing pre-clinical and clinical studies .
The family of anthelmintic macrocyclic lactone drugs is one cornerstone of modern parasite control with annual world sales of US $850 million , indicating a well-established production and distribution pipeline . These drugs share a poly-cyclic lactone chemical moiety and can be divided in two sub-families: avermectins and milbemycins [12] . Because members of this family of natural products have complex structures and specificity for parasites , only a few have been commercialized , mostly for veterinary medicine [13] . Ivermectin is used to treat the human parasitic diseases onchocerciasis and lymphatic filariasis [14] . Moxidectin was also recently evaluated for these indications in clinical trials [15] . The potential use of ivermectin for TB treatment is questionable due to its neurotoxicity at high doses and the low exposure levels achieved using clinically approved doses [16] . We analyzed available literature to compare the pharmacological properties of clinically approved drugs ( ivermectin , moxidectin ) to those with best in vitro activities against M . ulcerans ( milbemycin oxime and selamectin ) ( Table 2 ) . By integrating this information with in vitro data , we propose selamectin as the anthelmintic macrocyclic lactone with the highest potential for anti-BU therapy . In invertebrate nematodes , avermectins specifically bind to glutamate-gated chloride channels present in nerve and muscle cells , causing paralysis and reduced ability to reproduce . In general , macrocyclic lactones have a high margin of safety in mammals because P-glycoproteins ( P-gp ) or other types of efflux pumps , highly expressed at the blood–brain barrier , efficiently restrict their penetration into the central nervous system . In fact , dogs lacking the MDR1 efflux pump , such as collies , have much less tolerance for treatment with an array of avermectin compounds [29] . In contrast , milbemycin oxime , selamectin , and moxidectin can be safely administered at therapeutic doses to dogs having a homozygous MDR1 mutation without any signs of toxicosis [12 , 28] . PK and toxicological profiles of the clinically used macrocyclic lactones ( ivermectin and moxidectin ) have been studied extensively . Using standard dosages for onchocerciasis treatment in humans , ivermectin is extremely well tolerated , effective , orally active , and associated with long-term safety at the current clinical dose ( single dose of 12 mg ) [17] . Clinical studies have shown that it is safe in humans at doses up to 10-fold higher; however , further increased dosage provokes severe neurotoxicity [18] . To catalyze application of ivermectin’s therapeutic potential in needy areas throughout the world , Merck & Co . has donated it for over 20 years to treat patients with river blindness , human onchocerciasis , and lymphatic filariasis [14] . In the case of moxidectin , single doses of up to 36 mg were safe in humans , but not doses of 54 mg [23] . The extensive use of macrocyclic lactones in veterinary medicine has generated valuable pharmacological data that could guide selection of these drugs and facilitate their use in humans . Milbemycin oxime is a broad-spectrum intestinal anti-parasitic drug used to treat roundworm , hookworm and tapeworms in cats and dogs; it is also reported to be safer than ivermectin [12] . Administered routinely at a dose of 0 . 25 mg/kg , it showed no signs of toxicity [25] . Although LD50 values after oral administration in dogs are higher than 200 mg/kg , a single dose of 3 . 8 mg/kg was reported to cause reversible neurological signs ( trembling , ataxia ) in dogs [30] . In contrast , selamectin has fewer neurological side effects , and can be administered topically , subcutaneously , or orally to treat a variety of ecto- and endo-parasitic infections in cats and dogs . It is the drug of choice in avermectin-sensitive collies since it has no adverse effects [REVOLUTION—fact sheet] . A toxicity study in female CD1 mice found that selamectin was well tolerated at up to 300 mg/kg body weight ( bw ) , while similar doses of milbemycin oxime were toxic [31] . In the case of milbemycin , doses up to 24 mg/kg bw were safe in cats and dogs [27] and one study reported that doses up to 94 mg/kg bw were safe in dogs [28] . In addition , a 3-month repeated dose toxicity study in dogs found an oral dose of 40 mg/kg/day to be safe [28] . Extrapolated to humans , this corresponds to a dose of 2 , 800 mg/day ( for a 70 kg adult ) . Confirming this extrapolation , the LD50 in rats and mice could not be demonstrated and it was higher than 1 , 600 mg/kg bw [Stronghold ( selamectin ) —Product profile] . Based on established clinical experience in humans at low dosages , Omansen et al . [10] chose to study the anti-mycobacterial activities of ivermectin and moxidectin . They reported MIC values between 4 and 8 μg/mL against M . ulcerans and inactivity ( MIC ≥32 μg/mL ) against M . marinum . We confirmed the activities of macrocyclic lactones , but found different specificities against bacterial isolates representing these two species ( Table 1 and Fig 1 ) . In contrast to analyses reported by Omansen et al . [10] , we detected little or no activity of ivermectin and moxidectin against M . ulcerans isolates but they were active against M . marinum strains . Such discrepancies could reflect variations in methodology . While Omansen et al . used Mycobacteria Growth Indicator Tubes ( MGIT ) and bioluminescence assays for their inhibitions assays [10] , we performed metabolic-based activity assays in liquid cultures grown in 96 well plates . Subtle differences in methodology are known to play a critical role in quantification of the anti-mycobacterial activity of ivermectin [16] . Our in vitro results can be integrated with available PK data to predict which drug would be more suitable for anti-BU therapy . While no human data are available for milbemycin oxime and selamectin , extensive pharmacological data from animal studies provide valuable information to accelerate clinical testing . Standard oral doses ( in μg/kg bw range ) of ivermectin , moxidectin and milbemycin oxime used to treat helminths in humans only achieve low concentrations in the plasma ( ng/mL range ) . Area Under the Curve ( AUC ) values for moxidectin and milbemycin oxime are higher than those of ivermectin , mainly due to their extended residence times ( higher half-life ) . However , the much higher doses needed to achieve concentrations sufficient to kill mycobacteria might not be possible due to toxicity . In contrast , selamectin toxicity is negligible at comparable doses . Standard dose administration of selamectin is in the mg/kg bw range ( versus μg/kg bw ) and doses as high as 95 mg/kg bw have been administered without any side effects [28] . The ability to deliver such high doses without toxicity is also reflected in the elevated concentrations of selamectin that can be achieved in the plasma . These concentrations in the μg/mL range are several fold higher than MIC values against M . ulcerans [21 , 27] which , together with a long half-life ( in days ) , allows for high AUC values . In fact , AUC/MIC values are the most predictable PK/PD parameter for the anti-mycobacterial activity of the avermectins [9] . Similarly , AUC/MIC ratios between 10 and 15 are also needed for bactericidal activity against M . ulcerans ( Fig 2B ) . Thus , when theoretical AUC/MIC values were calculated by integrating data from available PK literature with those from our in vitro data , only selamectin was predicted to have therapeutic activity against M . ulcerans ( Table 2 ) ( nb , calculations based on the lower in vitro MIC measurement reported by Omamsen et al . [10] generated the same conclusion ) . We would also like to point out that when we made corresponding calculations based on in vitro MIC data for M . tuberculosis [9] , selamectin would also be the avermectin of choice for tuberculosis therapy . A synergistic interaction between rifampicin and ivermectin against M . ulcerans has also been reported [10] . Rifampicin is the cornerstone drug for BU treatment . Thus , co-administration of rifampicin with any synergistic , orally available drug would be ideal . Rifampicin is an inducer of the P-gp and other transporters . P-gp protects mammals not only by excluding macrocyclic lactones from the central nervous system , but also by limiting the uptake of compounds from the gastrointestinal tract and by promoting their excretion in the liver , kidney , and intestine . While ivermectin is a good P-gp substrate , thus further reducing available levels of this drug , selamectin is a poorer P-gp substrate [12 , 21] and its plasma levels would be affected to a lesser extent allowing for a potential co-administration with rifampicin . In summary , drug repositioning is an interesting avenue to provide new treatments for neglected diseases . We have tested the family of commercially available macrocyclic lactones against M . ulcerans and M . marinum and demonstrated that milbemycin oxime and selamectin are the most active drugs ( MIC = 2 μg/mL ) . Integrating these values with information gathered in a literature review of the pharmacological properties ( toxicity and PK/PD profiles ) of ivermectin , moxidectin , milbemycin oxime and selamectin , revealed selamectin as the most promising avermectin candidate for anti-BU treatment . Although selamectin is not approved for use in humans , extensive information is available on its pharmacological properties in animals , thus facilitating its progression into clinical trials . These would be warranted if its activity could be validated using in vivo models of M . ulcerans infection . Pre-clinical and clinical development of any drug is a task that one research group cannot achieve alone . Thus , we urge collaboration among the research communities , pharmaceutical companies , and non-governmental organizations to validate the potential of macrocyclic lactones , especially selamectin , as a new anti-BU treatment . | Buruli ulcer ( BU ) is a chronic debilitating mycobacterial disease of the skin and soft tissue caused by Mycobacterium ulcerans . It is mainly found in tropical regions and often linked to poverty . BU can be cured in most cases with the standard treatment , a combination of rifampicin and the injectable antibiotic streptomycin . However , new optimized treatment regimens are needed , especially to prepare for an eventual development of resistance to rifampicin , the most efficacious drug for BU therapy . Since traditional antibacterial drug discovery is not a practical option for BU , using approved drugs for alternative clinical indications would be a more economical and faster way to implement new anti-BU therapies . We reported previously that anti-parasitic avermectins are active against Mycobacterium tuberculosis . Here we show that some are also active in vitro against other mycobacterial species , including M . marinum and M . ulcerans . In this study , we undertook a comprehensive approach to evaluate additional macrocyclic lactones including compounds used in veterinary medicine . Based on our in vitro measurements of their activities and a literature review of their pharmacokinetic properties , we present strong arguments that selamectin is the avermectin with the highest potential for being repurposed for BU treatment . |
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The family Polydnaviridae is of interest because it provides the best example of viruses that have evolved a mutualistic association with their animal hosts . Polydnaviruses in the genus Bracovirus are strictly associated with parasitoid wasps in the family Braconidae , and evolved ∼100 million years ago from a nudivirus . Each wasp species relies on its associated bracovirus to parasitize hosts , while each bracovirus relies on its wasp for vertical transmission . Prior studies establish that bracovirus genomes consist of proviral segments and nudivirus-like replication genes , but how these components are organized in the genomes of wasps is unknown . Here , we sequenced the genome of the wasp Microplitis demolitor to characterize the proviral genome of M . demolitor bracovirus ( MdBV ) . Unlike nudiviruses , bracoviruses produce virions that package multiple circular , double-stranded DNAs . DNA segments packaged into MdBV virions resided in eight dispersed loci in the M . demolitor genome . Each proviral segment was bounded by homologous motifs that guide processing to form mature viral DNAs . Rapid evolution of proviral segments obscured homology between other bracovirus-carrying wasps and MdBV . However , some domains flanking MdBV proviral loci were shared with other species . All MdBV genes previously identified to encode proteins required for replication were identified . Some of these genes resided in a multigene cluster but others , including subunits of the RNA polymerase that transcribes structural genes and integrases that process proviral segments , were widely dispersed in the M . demolitor genome . Overall , our results indicate that genome dispersal is a key feature in the evolution of bracoviruses into mutualists .
Long-term associations between multicellular organisms and microbes are widespread . In the case of bacteria and fungi , several taxa contain species that have evolved into vertically transmitted , obligate mutualists or pathogens [1]–[3] . Traits inherited from ancestors and acquired by horizontal gene transfer have both contributed to the initiation and maintenance of these obligate associations [4]–[6] . In contrast , small effective population size associated with vertical transmission and increased levels of genetic drift appear to differentially affect genome size and architecture . Bacteria consistently exhibit size reductions due to mutational bias that causes deletions [7]–[9] , while fungi trend toward genome expansion due to gains in mobile elements , intronic sequences , and other types of non-coding DNA [5] , [10]–[13] . All viruses require another organism to persist and propagate with most species being horizontally transmitted by virions that are produced through replication . This lifestyle results in viruses often being pathogenic and having compact genomes with evolutionary rates that are usually much higher than the host organisms they infect [14] . Obligate associations occur when a viral genome integrates into the host germline to form a vertically transmitted endogenous viral element ( EVE ) [15] , [16] . EVEs deriving from many types of viruses have been identified including some of ancient origin that have reached fixation in host populations and can no longer remobilize . Most EVEs are subject to their host's neutral rate of evolution , which results in persistence as fragments of the ancestral viral genome rendered non-functional by mutation [15] , [17] . A few single genes or regulatory domains of viral origin have also been identified where natural selection has led to new , non-viral host functions [15] , [18] , [19] . The family Polydnaviridae is of interest because it is the only known example of viruses that have evolved into vertically transmitted agents that benefit their hosts yet do so by continuing to function in many respects like the viruses they evolved from [20]–[22] . As such , polydnaviruses ( PDVs ) have evolved into mutualists and provide a study system for understanding how their obligate associations with hosts affect viral genome architecture and function . All PDVs are associated with insects called parasitoid wasps ( Hymenoptera ) , which reproduce by laying eggs into other arthropods ( hosts ) their offspring consume [23] . The genus Bracovirus ( BV ) is associated with ∼50 , 000 wasp species in the family Braconidae: forming a monophyletic assemblage called the microgastroid complex that evolved ∼100 million years ago ( Mya ) [24]–[26] . Each wasp species in the complex carries a unique BV that persists in every cell of every individual as a provirus . However , BVs only replicate in calyx cells that are located at the base of the ovaries near the lateral oviducts [27]–[30] . BV virions package multiple circular , double-stranded ( ds ) DNAs of large aggregate size , which are released by lysis of calyx cells and stored in the oviducts [31] , [32] . Females inject eggs containing the integrated provirus plus a quantity of virions into host insects they parasitize . The DNAs delivered by these virions integrate into the genome of infected host cells , while expression of virulence genes on these DNAs generates products that alter the physiology of hosts in ways that wasp offspring depend upon for survival [32]–[35] . BVs differ from other known EVEs of ancient origin because they retain the ability to replicate in wasps and produce infectious virions . However , BVs also differ from most viruses because replication in wasps produces virions that are incapable of replicating in the host insects wasps parasitize . The net result is that each BV relies on its associated wasp for vertical transmission as a provirus while each wasp relies on its BV to produce replication-defective virions for delivery of virulence genes needed to successfully parasitize hosts . The monophyly of the microgastroid complex strongly suggests BVs evolved from an ancient virus that integrated into the germline of an ancestral braconid . Insights into the identity of this ancestor come from transcriptome studies of ovaries from three wasp species ( Cotesia congregata , Chelonus inanitus , Microplitis demolitor ) , which identify more than 30 homologs of genes with predicted functions in replication from another group of insect-infecting DNA viruses called nudiviruses [27] , [28] , [36] . The family Nudiviridae is also the sister taxon to the family Baculoviridae that likewise infects insects . Most nudiviruses and baculoviruses are virulent pathogens that package a single large , circular dsDNA ( >90 kb ) genome into virions containing all genes required for infection of hosts and replication [37]–[39] . This suggests the nudivirus ancestor of BVs initially integrated into the germline of a wasp as a linear proviral DNA [20] . In contrast , BV genomes have since changed in a manner that has resulted in: 1 ) all of the nudivirus-like genes being integrated in the genomes of wasps and transcribed in calyx cells but none residing on the DNAs that are packaged into virions [27] , [28] , [36] , 2 ) the DNAs packaged into virions encoding multiple virulence genes [40]–[44] , and 3 ) almost none of these virulence genes being transcribed in wasps but most being transcribed in the hosts that wasps parasitize [33] , [45] , [46] . The circular , dsDNAs in BV virions are referred to as the encapsidated form of the genome [20] , [33] . In turn , these DNAs are referred to as proviral segments when integrated in the genome of wasps , while the proviral segments and nudivirus-like genes together constitute the BV proviral genome [41] , [47] , [48] . Screening and sequencing of BAC genomic clones from four species in two genera ( Glyptapanteles indiensis , G . flavicoxis , Cotesia congregata , C . sesamiae ) have previously shown that BV proviral segments reside in multiple loci in the genomes of wasps [41] , [47] , [48] . Sequencing of BAC clones from C . congregata further show that 10 nudivirus-like genes reside in an 18 kb domain referred to as the nudivirus gene cluster [27] . These data combined with evidence that all BVs evolved from a common nudivirus ancestor have further led to the suggestion that proviral segment loci and the nudivirus cluster are physically linked in the genomes of wasps [22] , [27] , [47] . BAC clone sequence data , however , are too limited to provide direct evidence such linkages exist . In addition , many of the nudivirus-like genes identified in transcriptome studies [27] , [28] , [36] do not reside in the nudivirus cluster identified from C . congregata . As a result , the location of most nudivirus-like genes in the genomes of wasps , including several experimentally shown to be essential for replication [49] , is also unknown . Taken together then , prior studies clearly establish that BV proviral genomes consist of two components: proviral segments organized into loci and nudivirus-like genes [20] , [22] , [27] , [28] , [32] , [41]–[43] , [47]–[49] . In contrast , how these components are organized in relation to one another and where in the genomes of wasps most nudivirus-like genes identified from transcriptome studies reside is unknown for any species . Such information is important to issues ranging from understanding how BVs function to how genome content and architecture compares to nudiviruses and baculoviruses . The only means of addressing these questions though is through whole genome sequencing . In this study , we sequenced the microgastrine braconid Microplitis demolitor , which carries M . demolitor bracovirus ( MdBV ) and diverged from wasps in the genera Cotesia and Glyptapanteles ca . 53 Mya [24] . Assembly of the M . demolitor genome shows that MdBV proviral segments reside in multiple loci and that some nudivirus-like genes are clustered as found previously in Glyptapanteles and Cotesia wasps . However , we also determined that the MdBV nudivirus-like cluster is much larger than previously found in C . congregata and that a number of nudivirus-like genes that are functionally essential for replication are widely dispersed in the M . demolitor genome . Finally , our results provide direct evidence that MdBV proviral segment loci are not closely linked physically to one another or to the nudivirus-like cluster .
We generated a draft genome sequence for M . demolitor using Illumina technology . The haploid genome size was estimated to be 241±6 Mbp by flow cytometry using wasp cell nuclei normalized to nuclei of Drosophila virilis . Based on this estimate , the M . demolitor genome was sequenced to 26× using haploid male genomic DNA from a lab culture maintained for more than 20 years with no introduction of additional field material . Sequencing of multiple small and large insert paired-end libraries ( 180 bp , 1 . 5 kb , 5 kb , and 10 kb ) produced 1 . 04 billion raw reads . SOAPdenovo v2 . 04 ( 3 ) was employed with K = 49 to assemble the 180 bp-insert library reads into 357 , 737 contigs greater than 100 bp , totaling 195 , 839 , 919 bp with a contig N50 of 1 , 585 bp . Scaffolding with iteratively longer-insert mate-pair libraries followed by GapCloser v1 . 12 resulted in 5524 scaffolds consisting of two or more contigs in appropriate order and orientation separated by regions of approximately known lengths of unknown nucleotides . Remaining sequence data consisted of 47727 contigs greater than 100 bp . The scaffold N50 was 323 , 181 bp , the singleton contig N50 173 bp , and the total assembled genome sequence including intra-scaffold gaps was 258 , 751 , 082 bp . As discussed below , a total of 40 scaffolds with a cumulative size of 19 . 3 Mb contained elements of the MdBV proviral genome . Evidence supporting gene models ( see Methods ) identified 1 , 737 genes in the 40 scaffolds containing MdBV components of which 1 , 713 were predicted protein-coding sequences and 24 were tRNAs ( Table S1 ) . In addition , the estimated aggregate size of all components of the MdBV proviral genome accounted for less than 1% of the M . demolitor genome . Our next goal was to identify regions of the M . demolitor genome that contained MdBV proviral segments . This was accomplished using a combination of previously published and newly generated data . As background , each MdBV virion produced during replication contains only one circular dsDNA segment [50] . Thus , the total complement of DNAs in the encapsidated genome of MdBV are not present in each virion but instead are distributed among the total population of virions produced during replication . In addition , the circular , dsDNAs in MdBV virions are non-equimolar in abundance , which results in delivery of a higher copy number of some DNAs to parasitized hosts than others [50] . Non-equimolar abundance of DNAs in virions from other wasp species [summarized by 20]–[23] , [32] , [33] combined with data showing that Chelonus inanitus BV also packages a single DNA per virion [51] suggests these features apply to BVs generally . The encapsidated form of the MdBV genome was previously analyzed by isolating DNA from virions followed by construction of plasmid libraries that were Sanger sequenced . These data assembled into 15 circular dsDNAs ( named A through O ) , which had an aggregate size of 190 kb [43] . However , this approach can lead to misassembly or omission of segments due to their non-equimolar abundance and the presence of repetitive DNA [40] , [47] . Segments are also easily missed in the absence of having a reference proviral genome , which was a central goal of this study . Thus , we re-sequenced the circular , dsDNAs in MdBV virions by Illumina and then mapped these reads to the assembly of M . demolitor genome . This approach produced 50 million 100 bp read pairs . After quality filtering , 99% of 37 million read pairs were successfully mapped to M . demolitor genome scaffolds . The number of MdBV reads mapped to M . demolitor scaffolds containing proviral segments ranged from 281 , 000 to 28 million . This data set recovered the 15 segments identified previously with the exception of segment O , which was only partially recovered ( Figure 1 ) . Segment O contains large repetitive regions [43] , which likely prevented its successful assembly in this study . Read mapping identified the location of these DNAs as proviral segments in the M . demolitor genome ( Figure 1 ) . Segments A , E , G , I , and K were slightly larger than previously reported [43] , while segment D was split between two scaffolds and one contig totaling 13 , 691 bp compared to the previously published size of 7 , 823 bp [43] ( Figure 1 ) . We also identified 10 previously unknown segments named K1 , owing to sequence similarity with Segment K , and P through X . Thus a total of 25 proviral segments with an aggregate size of 278 kb are amplified and packaged into MdBV virions . In comparison , BVs from wasps in the genera Cotesia and Glyptapanteles have 30 to 35 proviral segments with aggregate sizes ranging from 517 to 731 kb [40] , [41] , [47] . Comparative analyses of baculovirus genomes suggest all species share 37 core genes of which approximately half are required for replication [38] , [59] . Nudiviruses , which diverged from baculoviruses ∼300 Mya [60] , share 20 baculovirus core genes [38] , [39] , including a DNA polymerase predicted to replicate the viral genome , a DNA dependent RNA polymerase comprised of four subunits ( lef-4 , lef-8 , lef-9 , p47 ) , and several structural genes with unique promoter features that are specifically recognized and transcribed by the viral RNA polymerase . Previous transcriptome sequencing by Illumina of M . demolitor ovaries during MdBV replication identified 41 genes with homology to nudivirus genes [28] . These include the four RNA polymerase subunits , several structural genes , and multiple tyrosine recombinases named integrases ( int ) , unknown from baculoviruses , but related to a baculovirus gene named vlf-1 . A nudivirus/baculovirus-like helicase with putative roles in DNA replication was identified but a nudivirus/baculovirus-like DNA polymerase was not , which suggested that amplification of proviral segments during replication requires a wasp DNA polymerase [28] . The remaining nudivirus-like genes included 11 proteins unknown from baculoviruses [27] , [28] . MdBV replication in calyx cells is extremely high with virion production exceeding replication levels for baculoviruses [28] . Experimental studies show the predicted MdBV RNA polymerase subunits form a functional holoenzyme that transcribes the nudivirus-like structural genes [49] . Nudivirus-like genes with predicted roles in capsid and envelope formation are also required for virion formation , while vlf-1 and int-1 are required for circularization of MdBV proviral segments [49] . Proteomic analysis further shows most predicted nudivirus-like structural proteins are present in MdBV virions [49] , while studies with C . congregata and C . inanitus indicate homologs of these structural proteins are present in CcBV and CiBV virions [27] , [36] . Thus , several lines of evidence strongly support that the BV RNA polymerase and several structural genes that are nudivirus/baculovirus homologs retain ancestral functions essential for replication and virion assembly . To identify the nudivirus-like genes in the M . demolitor genome , we searched our assembly using BLASTN and TBLASTN with previously identified transcript and protein sequences as queries [28] , [36] . This resulted in identification of all previously identified nudivirus-like genes plus a few unrecognized genes of potential nudivirus origin located on 29 scaffolds ( Table S1 ) . None of the new genes were homologs of a viral DNA polymerase . In addition , none of the nudivirus-like genes contained introns or were flanked by WIMs . Annotation indicated some of these genes resided in a multigene cluster , others were duplicated genes arrayed in tandem , and the balance were single genes separated by large stretches of intervening wasp DNA . The size and number of scaffolds together with large intervening regions of wasp genes collectively indicated MdBV nudivirus-like genes were widely dispersed in the M . demolitor genome . While proviral segment loci and nudivirus-like genes have been suggested to be physically linked in the genomes of wasps [21] , [22] , [27] , our assembly of the M . demolitor genome indicated most nudivirus-like genes , including those in the nudivirus-like gene cluster , reside in locations distant from proviral segments . The only direct physical linkage we identified was in Mdem_scaffold_0157 , which contained the nudivirus-like gene HzNVorf93 and 5 . 4 kb away MdBV proviral segment T ( Table S2 ) . We identified 18 wasp gene families shared between scaffolds containing nudivirus-like genes and proviral segments ( Table S2 ) . These included members of a gene family with an EB module present in five scaffolds containing nudivirus-like genes and also the scaffold containing proviral segment U . Several members of a protein tyrosine kinase family were also present on a scaffold ( Mdem_scaffold_0407 ) containing lef-4 , and the scaffolds containing proviral locus 3 ( Mdem_scaffold_0014 ) and proviral locus 8 ( Mdem_scaffold_0025 ) . A family of histone genes was shared among the scaffolds containing proviral loci 1 , 3 , 5 and 7 , which suggested these genes may link the genomic neighborhoods of these proviral segments ( Table S2 ) . Members of 13 other wasp gene families were detected on scaffolds containing nudivirus-like genes . Among these was a family of MFS sugar transporter domain-containing proteins present on two scaffolds ( Mdem_scaffold_0004 , Mdem_scaffold_0938 ) containing nudivirus-like genes ( odv-e66 , lef-4 ) . This finding is of potential interest because two MFS sugar transporter genes reside on homologous proviral segments of GiBV and GfBV , which provides an example of duplication and transfer of wasp genes into BV proviral segments [41] .
This study advances our understanding of BVs by providing the first overall picture of proviral genome organization . Unlike the compact , circularized genomes of nudiviruses and baculoviruses , our results show that proviral segment loci and nudivirus-like genes are highly dispersed in the M . demolitor genome . Our results also show that none of the MdBV proviral segment loci are physically closely linked to the nudivirus-like gene cluster . Parasitoid wasps are among the most species-rich animal groups on Earth with estimates suggesting more than 1 , 000 , 000 species worldwide [23] , [25] yet only one parasitoid wasp genome ( Nasonia vitripennis ) has been sequenced , assembled and annotated [63] . N . vitripennis belongs to a taxon of Hymenoptera that is distantly related to microgastroid braconids and has no association with polydnaviruses . Thus more broadly our results provide a genome for a second parasitoid wasp and the first species that is a polydnavirus carrier . Illumina sequencing the DNAs in MdBV virions and mapping these reads back to the M . demolitor genome identified several DNA segments an earlier study failed to detect [43] . The 25 proviral segments now identified in 8 loci likely represent most if not all of the DNAs packaged into MdBV virions . We also identified all nudivirus-like genes found previously by transcriptome analysis of M . demolitor ovaries plus several unrecognized variants of these genes . In our view , the most important new findings from these data are: a ) the nudivirus-like gene cluster of MdBV contains many more genes and overall is much larger than recognized from earlier data generated from C . congregata [27] , b ) most of these genes are structural components of BV virions , and c ) the four nudivirus-like RNA polymerase subunits previously shown to regulate expression of BV structural genes reside outside of the nudivirus-like gene cluster as single genes that are widely dispersed in the M . demolitor genome . Like other large DNA viruses , baculoviruses and nudiviruses exhibit high diversity in gene content outside of their core gene sets [37] , [38] . It is also well known that different lineages of baculoviruses and nudiviruses have acquired many genes from their arthropod hosts and other organisms by horizontal gene transfer and other mechanisms . Given this , it is fully possible some genes from the nudivirus ancestor of BVs remain unidentified given our reliance on sequence similarity for recognition of genes of nudivirus origin [37] , [38] . Primary structure together with proximity to conserved nudivirus-like genes identified four hypotheticals in the MdBV nudivirus-like gene cluster that potentially derive from the nudivirus ancestor . In contrast , identifying genes outside the nudivirus-like gene cluster derived from the nudivirus ancestor will be very difficult in the absence of data linking a given product to replication or other virus-related activities . The dispersed architecture of the MdBV proviral genome is remarkable in light of the very high levels of replication that occur in calyx cells following pupation of female wasps . Although viral genomes are typically viewed as a contiguous stretch of DNA or RNA , our results clearly show that dispersal of BV genomes does not functionally impede high-level amplification of a portion of the proviral genome or production of virions . We suggest the physical separation of nudivirus-like genes required for virion formation from the proviral segments containing virulence genes is selectively advantageous for wasps because it assures vertical transmission of the entire proviral genome but prevents any replication machinery from escaping , which could be deleterious to wasp offspring developing in a host . Our results also suggest two trans-acting factors play critical roles in linking the physically separated components of the MdBV proviral genome together . First , the two nudivirus-like integrases ( int-1 , vlf-1 ) , once transcribed and translated in calyx cells , likely use WIMs to recognize all proviral segments for processing regardless of their location in the wasp genome . Second , the MdBV RNA polymerase holoenzyme , once made , specifically transcribes the nudivirus-like structural genes through promoter recognition , which is similar to baculovirus RNA polymerases that also specifically transcribe structural and other late viral genes [49] . Our analysis of upstream sequence indicates some MdBV structural genes have baculovirus-like late gene promoter motifs but others do not , which suggests the promoter sequences BV RNA polymerases recognize differ somewhat from those of their nudivirus/baculovirus ancestors . Identification of these recognition sequences is not amenable to computational analysis and will require experimental studies . Based on the baculovirus literature [38] , we hypothesize wasp RNA polymerase II transcribes the MdBV integrase and RNA polymerase genes , but the factors responsible for restricting transcription to only calyx cells are unknown . The absence of any baculovirus/nudivirus-like DNA polymerase in the M . demolitor genome further strengthens earlier conclusions that a wasp DNA polymerase ( s ) amplifies MdBV proviral DNAs prior to their excision , circularization , and packaging [28] , [49] . However , the specific polymerase responsible also remains unidentified . In contrast , it has long been known the DNA segments in BV virions are non-equimolar in abundance [40]–[42] , [50] , which our read mapping data indicate is due to proviral segments in different loci being differentially amplified . Similar levels of MdBV proviral segment amplification in loci 1 and 2 are also broadly similar with recent findings for CcBV where multiple adjoining proviral segments are co-amplified before processing into circularized DNAs [64] . Although most nudiviruses and baculoviruses establish systemic lytic infections that are fatal to hosts , one nudivirus has been identified that in vitro establishes long-term persistent infections associated with integration into the host genome [65] , [66] . Such latent infections can also be reactivated . This suggests the nudivirus ancestor of BVs may have established a latent infection following integration of one or more copies of its genome into the germline of the braconid ancestor of microgastroids . This integration event was then followed by a series of modifications and rearrangements to arrive at the current dispersed architecture shown here for MdBV in M . demolitor . Reconstruction of these events for BVs generally is theoretically possible through comparative data of wasp species in the microgastroid complex with different divergence times . However , with near complete data on proviral genome architecture limited to M . demolitor and partial data available for just 4 other species [41] , [47] , only a few suggestive patterns are currently possible . First , experimental studies in M . demolitor combined with conservation of these nudivirus-like genes in two other microgastroid wasps ( C . congregata , C . inanitus ) [27] , [36] , [49] strongly suggest natural selection has maintained the ancestral functions of these factors in virion formation despite their dispersal in the genomes of wasps . Second , the conserved synteny of predominantly structural genes in the nudivirus-like cluster of M . demolitor and C . congregata suggests this domain represents an initial integration site for the nudivirus ancestor , and that maintenance of these genes in a cluster is functionally important for virion formation . These data also indicate the MdBV and CcBV nudivirus-like clusters have remained stable since divergence 53 Mya , which suggests dispersal of the other nudivirus-like genes occurred relatively early in BV evolution . Comparative sequence data from additional BV-carrying wasps will reveal whether dispersal is highly variable or dispersed genes reside in similar locations in the genomes of different wasps . Third , the distribution of MdBV proviral segment loci indicates these domains are also not clustered in the M . demolitor genome , while the presence of only one nudivirus-like gene near a proviral locus indicates that proviral loci and nudivirus-like replication genes reside distantly with respect to one another . We do identify a few wasp gene families shared between scaffolds containing nudivirus-like genes and/or proviral segments but without additional comparative data it remains unclear whether these genes are indicative of physical linkages that are conserved among microgastroid wasps generally . Future assemblies of the M . demolitor genome will eventually identify linkages between at least some of the MdBV proviral segment loci and nudivirus-like genes . However , based on the assembly used for this study , we conclude these linkages will not be in near proximity to each other . The finding that all BV proviral segments are flanked by WIM sequences together with evidence that nudivirus-like tyrosine recombinases recognize these motifs to produce circularized segments suggest both of these elements derive from the ancestral nudivirus genome [35] , [41] , [49] , [52] , [53] , [64] . Despite rapid evolution obscuring proviral segment homology [20]–[22] , the similarities in architecture of proviral segment loci between MdBV and GfBV , GiBV and CcBV also suggest shared ancestry . No data currently exist regarding motifs associated with integration of nudiviruses into the genomes of insects . On the other hand , if integration motifs with homology to WIMs or HIMs were identified from nudiviruses , it could provide important insights into the relationship between BV proviral segments and the nudivirus-like genes required for virion formation , proviral segment excision from the wasp genome , or segment integration into the genome of parasitized host insects . Duplication of genes into families is a recurring theme that serves as a key source of novelty in the molecular arms races that occur between parasites and hosts [51] , [67] . For MdBV and other BVs , previous studies show the virulence genes on proviral segments have diverse origins . Previously conducted evolutionary analyses show that some gene families on proviral segments , such as the sugar transporter genes identified from BVs associated with Glyptapanteles wasps and EGF gene family in MdBV are relatively recent acquisitions from wasps [41] , [58] . In contrast , other families show evidence of acquisition from other organisms or in the case of the PTP and Ank genes are of uncertain ancestry including possibly deriving from the nudivirus ancestor [54] , [55] , [68] . Prior studies also indicate that BV virulence gene family diversification has occurred through duplications plus rearrangements within and between segments , while also showing that some gene family members exhibit signatures of positive selection in response to arms race interactions with hosts [20]–[22] , [54] , [55] , [68]–[70] . In contrast , it currently is not possible to analyse how BV proviral segments have evolved among microgastroid braconids as a group because data are available for only five species in three genera including M . demolitor . Addressing this issue will require data from far more taxa . For M . demolitor , venom glands and teratocytes secrete large amounts of proteins that females introduce together with MdBV to parasitize hosts [58] . These products exhibit almost no overlap with MdBV genes , but notably many derive from gene families that have also diversified by duplication for selective expression in venom glands or teratocytes . In the current study , we find that some nudivirus-like replication genes have also duplicated more extensively than previously recognized with odv-e66 and 35a in particular being potentially significant in parasitism of hosts because products from both families are present in virions [49] . Variation in the size and contents of bacterial and eukaryotic genomes is thought to result from differences in effective population size and degree of genetic drift [7] , [11] . Genome evolution for vertically transmitted entities like BVs is largely unexplored , but is subject to different evolutionary processes compared to bacterial and eukaryotic symbionts , which retain their own cellular architecture and whose genomes are physically separated from that of their host . What we can conclude is that first , persistence as EVEs results in BV proviral genomes being inherited like other alleles in the wasp genome , which in turn subjects each BV to the effective population size of its associated wasp species . Second , on a broad scale BV proviral genomes show clear evidence of expansion relative to baculoviruses and nudiviruses . This is especially the case for proviral segments where decreases in gene density , increases in intron frequency , and gene acquisition from different sources followed by duplication are clearly apparent [40]–[44] . Interestingly , the direct repeat boundary regions recognized by viral integrases and tRNA loci , often associated with integration events , are features of proviral segments that are shared with pathogenicity islands in the genomes of disease-causing prokaryotes [71] . Pathogenicity islands initially evolve by horizontal gene transfer followed by site-specific recombination . Such processes could in part underlie the evolution of BV proviral segments as wasps adapt to parasitism of particular host species or guilds of closely related host species , and hosts reciprocally evolve to resist parasitism . Other studies have noted similarities between duplication of BV virulence genes and amplification of genes in insects associated with resistance to insecticides [47] . Many nudivirus-like genes in contrast show extreme dispersal throughout the M . demolitor genome but only a subset of these genes , also with potential roles in parasitism of hosts by wasps , have duplicated . Dispersal itself could occur through random processes of genome flux in wasps , similar to the rapid loss of microsynteny found by comparison of 12 species genomes in the genus Drosophila . Little is known currently about rates of genome flux in hymenopterans , although sufficient comparative genome data should eventually become available for BV-carrying species to perform a microsynteny analysis to see if proviral segments and nudivirus-like genes are similarly or more dispersed in the genome than wasp genes . Besides BVs , the Polydnaviridae currently contains a second genus , the Ichnovirus ( IVs ) , associated with parasitoid wasps in the family Ichneumonidae [20] , [21] . Recent evidence strongly suggests that IVs evolved independently of BVs from a still unknown virus ancestor ( s ) [72] . Nonetheless , while IV proviral segments encode largely different virulence genes , they exhibit many of the same organizational features as BV proviral segments , which suggests convergent evolution driven by the similar roles BVs and IVs play in parasitism [20] , [21] . Thus , the Polydnaviridae was originally recognized as a family because of the similarities in how the encapsidated form of BV and IV genomes are organized and their similar functions in parasitism of hosts by their associated wasps [20]–[22] , [33] . However , current data also now indicate this is a non-natural taxon that will be revised in the future . Most EVEs in animal genomes are non-functional fragments but a few cases are known of single viral genes or regulatory elements that have been exapted by hosts for new beneficial functions . Among these are mammalian syncytins derived from endogenized retrovirus env genes which function in placental development , and the Fv4 and Fv1 genes , also of retroviral origin , which function in antiviral defense [reviewed in 15] , [73] . Such EVEs are appropriately viewed as no longer being viruses because they no longer function as such [15] , [16] , [18] . BVs have also evolved to benefit wasps yet differ from the previous examples because their beneficial roles in parasitism depend on many genes whose functions remain the same as those of their ancestor [49] . BVs also retain much of the ancestral replication machinery and produce infectious particles that are quite similar to nudiviruses and baculoviruses [20] . BVs are thus ancient EVEs that benefit wasps but do so by continuing to function in many respects like a virus . As such , BVs also share features with other microbes that are viewed as obligate mutualists in which neither the symbiont nor host can survive without the other . BV genes required for replication are clearly of nudivirus origin . Genes on proviral segments on the other hand have a mixture of origins that include acquisition by horizontal transfer from wasps or other eukaryotes plus genes and motifs like WIMs that are ancient and exhibit features at least suggestive they too originated from the nudivirus ancestor [20]–[22] . Nudivirus and baculovirus genomes likewise consist of genes that are ‘viral’ in the sense they produce products required for replication , yet also contain genes and motifs of ancient origin and uncertain ancestry , plus genes acquired from arthropod hosts or other eukaryotes that function as virulence factors [38] . So what differs between BVs and their ancestors is not so much the types of genes they encode and their origins , but rather how their genomes are organized . Nudivirus and baculovirus genes , like those of most viruses , reside on a contiguous stretch of nucleic acid that is packaged into virions , whereas BV genes are organized in a manner that prevents all of them from being packaged into virions , which in turn also prevents BVs from existing independently of wasps . Other non-viral microbes that have evolved into vertically transmitted obligate mutualists do not persist by integrating into the genome of their host , but they too often exhibit profound alterations in genome organization and function that result in them no longer being genetically independent entities . The bacterium Buchnera in aphids , for example , was acquired in a single event that occurred more than 100 Mya , yet strict co-speciation thereafter has resulted in the phylogenies of these symbionts and aphids mirroring one another [74] . Thus , similar to discussions regarding whether entities like Buchnera are organelles or bacteria [9] , [75] , BVs will be viewed by some as EVEs that have been exapted by wasps to produce a novel organelle and others , including ourselves , as viruses that have evolved into wasp mutualists . Future studies will undoubtedly further advance our understanding of these fascinating associations .
All studies were approved by the Biological Safety and Animal Care and Use Committee of the University of Georgia and were performed in compliance with relevant institutional policies , National Institutes of Health regulations , Association for the Accreditation of Laboratory Animal care guidelines , and local , state , and federal laws . M . demolitor genomic DNA was isolated from single and pooled male wasps stored at −80 C . Briefly , 50 frozen males were pooled and ground in liquid nitrogen with mortar and pestle before lysing in a SDS solution overnight with Proteinase K . The homogenate was treated with RNaseA , and proteins/debris were collected after high-salt precipitation and centrifugation . After ethanol precipitation , the DNA was resuspended in 10 mM Tris and evaluated on an agarose gel and by Qubit quantification . The W . M . Keck Center for Comparative and Functional Genomics at the University of Illinois at Urbana-Champaign generated the following libraries for sequencing: a 180 bp-insert library from a single wasp , and 1 . 5 kb , 5 kb , and 10 kb-insert mate-pair libraries from pooled wasp DNA . The 180 bp and 1 . 5 kb-insert shotgun libraries were prepared with Illumina's TruSeq DNAseq Sample Prep Kit . The 5 kb and 10 kb mate-pair libraries were prepared similarly except a custom linker was ligated between the read-ends to facilitate mate-pair recovery . All libraries were sequenced for 100 cycles on a HiSeq2000 using the TruSeq SBS Sequencing Kit v . 3 . Data were analyzed with pipeline versions 1 . 8 and 1 . 9 . An additional 5 kb mate-pair library was constructed and sequenced by the Beijing Genome Institute using pooled wasp genomic DNA with all reads trimmed to 36 bp before assembly . The custom 5 kb and 10 kb mate-pair libraries were filtered for reads containing properly-oriented reads of the appropriate insert size and uniqueness using in-house custom pipeline scripts . Raw Illumina reads were 5′- and 3′-trimmed for nucleotide-bias and low-quality bases using the FASTX Toolkit ( http://hannonlab . cshl . edu/fastx_tookit/ ) . Trimmed reads were error-corrected by library with Quake [76] counting 19-mers . SOAPdenovo v2 . 04 [76] was employed with K = 49 to assemble the 180 bp-insert library reads followed by scaffolding with iteratively longer-insert mate-pair libraries and use of GapCloser v1 . 12 to close gaps generated in the scaffolding process with short paired read data [77] . MdBV DNA was isolated from virions as described previously [28] . The DNA pellet was resuspended in 10 µl of H2O and 1 ul was used as template in four phi29 amplification reactions as performed previously [78] . To resolve amplified DNA , the amplified product was incubated with S1 nuclease ( NEB ) at 37°C for 20 min . Precipitation and re-suspension in H2O yielded a total of 5 µg for Illumina sequencing . The sequencing library was prepared by the University of Georgia Genomics Facility using the Illumina TruSeq DNA sample preparation kit and the standard low-throughput protocol , and sequenced with the Illumina HiSeq system housed at the HudsonAlpha Institute for Biotechnology ( Huntsville , AL ) . Illumina sequenced reads from MdBV virions were filtered to retain read pairs with PHRED score equivalents >30 for >90% of nucleotides . Paired reads were mapped to the M . demolitor scaffolds using the bwa sampe algorithm and samtools [79] , [80] . Tablet was used to view the mapped reads relative to the reference genome , and scaffolds with clear read coverage boundaries indicating the presence of segments were selected [81] . These scaffolds had >280 , 000 mapped reads and were all >1 kb in size . The remaining scaffolds were identified by BLAST , indicating the presence of nudivirus-like genes . The longest translated ORFs from transcripts previously identified as nudivirus-like genes expressed in M . demolitor ovaries [28] were queried against the whole genome scaffolds and contigs BLAST database . BLAST results were manually filtered to retain real hits , which were filtered to have minimum 30% identity and at least 200 amino acids in alignment length with queries . These scaffolds were selected for annotation along with those identified by MdBV read mapping . Wasp transcriptome reads were generated and assembled previously from ovaries , teratocytes , venom glands , and wasp larvae [58] , and also from Pseudoplusia includens cells infected with MdBV . The wasp derived reads were assembled de novo using Trinity with the jaccard clip option , resulting in a total of 216 , 988 transcripts from 173 , 925 loci [82] . Reads were re-mapped to the assembled transcripts using bwa bwasw , successfully mapping 89–97% of reads for each tissue type [80] . The overall reads per kilobase per million reads mapped ( RPKM ) values were used to filter out low abundance transcripts ( <5 RPKM ) , in addition to the length of transcripts ( <500 bp removed ) [83] . The resulting filtered transcripts were used as evidence in gene model predictions described below . Forty eight million P . includens read pairs were generated and also used as described below . DNA was prepared from virus extracted from ovaries as above with DNAse treatment or with whole ovaries or animals without DNAse treatment . PCR was performed in a 10 µl reaction containing segment specific primers specific for amplifying across the WIM domain in a circularized segment ( 2 . 5 pmol ) ( Table S3 ) , 0 . 25 units of Hotmaster Taq polymerase ( 5 Prime ) and 1 µl of DNA as template . Reactions were run in a Bio-Rad thermocycler for 35 cycles with the following cycling conditions: initial denaturation at 94° for 2 min , followed by 35 cycles of denaturation at 94°C for 20 s , annealing for 20 s at 58°C , and extension at 65°C for 30 s with a final extension at 65°C for 7 min . Several forms of evidence were used as input into the MAKER annotation pipeline , including previous annotation of MdBV genes on proviral segments [43] , assembled transcripts from M . demolitor and P . includens ( see above ) [28] , [51] , unassembled read mapping information , and protein sequences from other insect species [84] . The assembled transcripts were generated from M . demolitor ovaries , whole larvae , teratocytes and venom gland transcriptomes as described above . These data sets generated 113 , 106 , 107 , and 147 million 100 bp paired reads respectively , which assembled into 36 , 891 transcripts with lengths greater than 500 bp and overall abundance greater than 5 Reads Per Kilobase of exon model per Million reads mapped ( RPKM ) . Of 51 million quality filtered Illumina read pairs from ovaries , 11% were successfully mapped to the 40 scaffolds containing MdBV proviral genome elements . Unassembled read mapping information was generated via mapping wasp ovary and C . includens hemocyte transcriptome reads against the scaffolds of interest using tophat and cufflinks [85] . The tophat read junctions file ( mapping splice sites ) and cufflinks transcripts file were converted into gff3 format using scripts bundled with MAKER , tophat2gff3 and cufflinks2gff3 . Protein sequences combined all coding sequences from the Nasonia vitripennis and Apis mellifera genomes , all known MdBV coding sequences , BV nudivirus-like coding sequences from M . demolitor , Cotesia congregata , and Chelonus inanitus , as well as all predicted ORFs from the genome scaffolds ( without introns ) >500 bp in size [27] , [28] , [36] , [63] , [86] . The de novo predictors GeneMark-ES , Augustus , and SNAP were also used within MAKER [87]–[89] . A GeneMark-ES model file was made using gm_es . pl and the scaffolds of interest as input . For Augustus prediction , a set of training genes was made by running Augustus on the scaffolds of interest with the species model “Nasonia” in addition to an intron position “hints” file generated with bam2hints from ovary transcriptome reads mapped by tophat . A “Microplitis” species file was made using the Augustus training gene set and the set of assembled transcripts described above with the autoAug . pl script . SNAP training was performed by iteratively running MAKER according to the “Training ab initio Gene Predictors” section of the MAKER tutorial , using assembled transcripts in the first iteration as evidence for gene models . The final MAKER run used the GeneMark-ES model file , the Augustus species file “Microplitis” and a SNAP model file generated by two rounds of training . Repeat masking was performed with default options . The resulting MAKER gff3 files were loaded into Apollo genome browser and gene models were manually edited if necessary [90] . All gene models were exported into Genbank table format . The coding sequences were assigned putative functional roles based upon BLAST results from the NCBI nr database , the Drosophila melanogaster set of protein-coding genes , and HMMER hmmsearch results from the PFAM and TIGRFAM databases [91] , [92] . tRNAs were identified using Aragorn [93] . These results were combined into GenBank format using custom perl scripts and tbl2asn from NCBI . The resulting dataset was submitted as BioProject 195937 and assigned GenBank accession number AZMT000000000 . Altogether , we identified 1 , 737 genes in the 40 scaffolds containing MdBV components of which 1 , 713 were predicted protein-coding sequences and 24 were tRNAs ( Table S1 ) . Wasp Integration Motifs ( WIMs ) share the common sequence AGCT and identify the site at which MdBV proviral segments excise from the M . demolitor genome [35] . WIMs were previously mapped for three segments by inverse PCR [35] . The locations of WIM sites for all proviral segments in the M . demolitor genome were obvious from read mapping data , and segment beginning and end coordinates were given to AGCT sites where circularization occurs . Host Integration Motifs ( HIMs ) were also identified previously by PCR methods and sequencing as the site where MdBV DNAs in virions integrate into the genome of infected host cells [35] . The twelve HIM sequences identified by these approaches were aligned and made into a Hidden Markov Model ( hmm ) using HMMER hmmbuild ( http://hmmer . janelia . org ) . This hmm was used as a query in a HMMER hmmsearch against the scaffolds of interest to identify HIM motifs in all correctly assembled old and new segments . Sequence motifs were aligned using MUSCLE and maximum likelihood phylogenetic trees were built using phylogeny . fr with the HKY+I+G model and 100 bootstrap replicates [94] . Sets of orthologous genes were identified using orthomcl and four datasets: 1 ) all protein-coding sequences from M . demolitor , 2 ) all protein sequences from G . flavicoxis BV segments and flanking regions of the wasp genome , 3 ) same information from 2 for G . indiensis , and 4 ) protein coding sequences from nudivirus-gene containing regions of C . congregata [27] , [41] , [95] . Groups of orthologous genes ( syntenic ) in the genome were identified using the orthomcl output with orthocluster [96] . Syntenic regions were viewed with Gbrowse syn . Assembly of the M . demolitor genome initially suggested the nudivirus-like gene cluster contained two identical duplications absent from the nudivirus-like gene cluster identified in the wasp C . congregata [27]: Cc50C22 . 3/HzNVorf94-like/38K and 27b-like/Cc50C22 . 6 . Given the identical nature of these predicted duplications , we assessed whether they were correct by primer walking and Sanger sequencing these domains . Resequencing showed that these genes were not duplicated . The above information was then used to make figures that included modifications of pictorial representations of M . demolitor genes on scaffolds generated by Gbrowse [97] , [98] . | Microorganisms form obligate associations with multicellular organisms that range from antagonistic ( parasitic ) to beneficial ( mutualists ) . Although numerous examples of obligate , mutualistic bacteria , fungi , and protozoans exist , viruses are thought to usually form parasitic associations . An exception is the family Polydnaviridae , which consists of large DNA viruses that have evolved into mutualists of insects called parasitoid wasps . Each wasp species relies on its associated polydnavirus to parasitize hosts while each polydnavirus relies on its wasp for transmission . Polydnaviruses in the genus Bracovirus evolved approximately 100 million years ago from a group of viruses called nudiviruses , which are closely related to another large family of viruses called baculoviruses that are virulent pathogens of insects . Bracoviruses are of interest , therefore , because they provide a study system for examining how evolution into mutualists affects the structure and function of viral genomes . In this study , we sequenced the genome of the wasp Microplitis demolitor to characterize the proviral genome of M . demolitor bracovirus ( MdBV ) . While the viral ancestor of bracoviruses possessed an independent circular genome , the proviral genome of MdBV is widely dispersed in the genome of M . demolitor . Our results also provide new insights into how the MdBV genome functions to produce virus particles that wasps rely upon to parasitize host insects . |
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The SUPPRESSOR OF rps4-RLD1 ( SRFR1 ) gene was identified based on enhanced AvrRps4-triggered resistance in the naturally susceptible Arabidopsis accession RLD . No other phenotypic effects were recorded , and the extent of SRFR1 involvement in regulating effector-triggered immunity was unknown . Here we show that mutations in SRFR1 in the accession Columbia-0 ( Col-0 ) lead to severe stunting and constitutive expression of the defense gene PR1 . These phenotypes were temperature-dependent . A cross between srfr1-1 ( RLD background ) and srfr1-4 ( Col-0 ) showed that stunting was caused by a recessive locus in Col-0 . Mapping and targeted crosses identified the Col-0-specific resistance gene SNC1 as the locus that causes stunting . SRFR1 was proposed to function as a transcriptional repressor , and SNC1 is indeed overexpressed in srfr1-4 . Interestingly , co-regulated genes in the SNC1 cluster are also upregulated in the srfr1-4 snc1-11 double mutant , indicating that the overexpression of SNC1 is not a secondary effect of constitutive defense activation . In addition , a Col-0 RPS4 mutant showed full susceptibility to bacteria expressing avrRps4 at 24°C but not at 22°C , while RLD susceptibility was not temperature-dependent . The rps4-2 snc1-11 double mutant showed increased , but not full , susceptibility at 22°C , indicating that additional cross-talk between resistance pathways may exist . Intriguingly , when transiently expressed in Nicotiana benthamiana , SRFR1 , RPS4 and SNC1 are in a common protein complex in a cytoplasmic microsomal compartment . Our results highlight SRFR1 as a convergence point in at least a subset of TIR-NBS-LRR protein-mediated immunity in Arabidopsis . Based on the cross-talk evident from our results , they also suggest that reports of constitutive resistance phenotypes in Col-0 need to consider the possible involvement of SNC1 .
Plants possess a highly effective immune system that responds to conserved non-self molecular patterns , or to specific pathogen-derived molecules deployed to alter host defenses [1]–[3] . The latter response , called effector-triggered immunity ( ETI ) , is largely mediated by resistance ( R ) proteins that directly or indirectly detect the presence of pathogen effectors [3] , [4] , although mechanistically overlap between ETI and the response to molecular patterns can be observed [5] , [6] . ETI can lead to programmed cell death termed the hypersensitive response ( HR ) [7] , [8] . In the case of resistance to some viral and hemi-biotrophic bacterial pathogens , it has been shown that the HR is not causally related to resistance [9]–[13] . Nevertheless , the plant immune response is deleterious to plant growth , normal development , and seed set even in the absence of HR , and therefore needs to be tightly controlled [14] . In order to explore the molecular mechanisms that negatively regulate ETI , we performed a suppressor screen for reactivated AvrRps4-triggered resistance in the naturally susceptible Arabidopsis ( Arabidopsis thaliana ) accession RLD [15] . This screen yielded two mutant alleles in SUPPRESSOR OF rps4-RLD1 ( SRFR1 ) . Mutations in srfr1 enhanced resistance of RLD specifically to Pseudomonas syringae pv . tomato strain DC3000 ( DC3000 ) expressing avrRps4 , while susceptibility to the virulent strain DC3000 was unchanged [15] . Apart from re-establishing a certain level of resistance to avrRps4 , no other marked phenotype was noted . RPS4 encodes an R protein of the Toll/Interleukin-1 receptor ( TIR ) - nucleotide binding site ( NBS ) - leucine-rich repeat ( LRR ) ( TNL ) class [16] , and was found to require the defense regulator EDS1 to trigger immunity [17] . This is in contrast to the coiled-coil ( CC ) -NBS-LRR ( CNL ) R proteins RPS2 , RPM1 and RPS5 , which require the defense gene NDR1 [17] . Combining mutations in SRFR1 and the CNL pathway genes RPM1 , RPS2 or NDR1 did not measurably alter the susceptibility to the cognate effector genes . The partial resistance to avrRps4 in srfr1 mutants required EDS1 [15] , [18] . In addition , mutations in RPS6 , another TNL gene that requires EDS1 [12] , led to susceptibility to DC3000 ( hopA1 ) that was diminished in srfr1-1 rps6-1 double mutants [19] . Taken together , these data indicated that SRFR1 function is closely associated with the EDS1 resistance pathway . Here we show that a mutation in SRFR1 in the accession Columbia-0 ( Col-0 ) , srfr1-4 , activates the Col-0 specific and EDS1-dependent R-like gene SNC1 , consistent with the genetic function of SRFR1 as a negative regulator of R gene-mediated resistance . Activation of constitutive defenses in srfr1-4 was temperature-dependent . In addition , RPS4 and SNC1 contributed redundantly to susceptibility to DC3000 ( avrRps4 ) in Col-0 at 22°C , whereas at 24°C RPS4 activity was the sole determinant of resistance . Interestingly , SRFR1 interacted with both RPS4 and SNC1 . Our data thus provide evidence for cross-talk between these TNL pathways that converge on SRFR1 , suggesting that SRFR1 may have a general function in regulating TNL protein signal output .
We previously had isolated the mutant alleles srfr1-1 and srfr1-2 from the Arabidopsis accession RLD [15] . Apart from enhanced resistance to DC3000 ( avrRps4 ) , they did not display marked phenotypes . To further investigate the function of SRFR1 , we aimed at isolating T-DNA tagged lines of SRFR1 in the accession Col-0 [20] , [21] . Out of four lines , one did not germinate ( SALK_106212 ) , and one was untagged ( SALK_095440 ) . We could verify a T-DNA insertion far upstream of the open reading frame in SALK_039199 , without causing an apparent phenotype . Interestingly , the fourth line , SAIL_412_E08 with a T-DNA insertion in the second intron of SRFR1 ( Figure 1A ) , showed pronounced stunting ( Figure 1B ) in one-fourth of plants ( 22 out of 97 plants; χ2 = 0 . 28 ) . Genotyping showed that the T-DNA insertion in SRFR1 segregated in the original seed stock , and that stunted plants were invariably homozygous for the T-DNA insertion . Reverse transcription ( RT ) PCR showed that no srfr1 mRNA was detected with primers on either side of the insertion ( Figure S1 ) . A low level of srfr1 mRNA could be detected with primers located 3′ of the T-DNA insertion , but this mRNA contained the T-DNA ( Figure S1 ) , indicating that srfr1-4 mRNA does not encode functional protein . Consistent with this , Li and co-workers recently showed that no SRFR1 protein can be detected in this knock-out line [22] . We named this line srfr1-4 . Subsequently , we back-crossed srfr1-4 to Col-0 . The stunted phenotype co-segregated with homozygosity of the srfr1-4 T-DNA tagged allele in F2 plants ( Table 1 ) . To prove that the phenotype originated from the srfr1-4 allele , we transformed healthy heterozygous srfr1-4 plants with pSHK102 containing a genomic clone of SRFR1 [18] , and by scoring for antibiotic resistance selected 5 single-locus homozygous transgenic SRFR1 T3 lines that contained at least one copy of the srfr1-4 T-DNA allele based on genotyping . Because the transgenic copy of SRFR1 prevented us from determining whether these T3 lines were homozygous or heterozygous for the srfr1-4 allele , we tested whether srfr1-4 segregated in the next generation by genotyping 15 progeny for each line . Three of the 5 lines were shown in this way to be homozygous for the srfr1-4 allele , and the transgenic copy of SRFR1 reversed the stunted phenotype in each case ( Figure 1B ) . We concluded that the stunted growth phenotype is caused by the T-DNA insertion in SRFR1 . The stunted srfr1-4 phenotype was in marked contrast to the normal phenotype of srfr1-1 and srfr1-2 plants . To determine whether the specific allele of SRFR1 or the Col-0 genetic background causes the severe phenotype of srfr1-4 , we first reexamined more closely F3 families of important break-point plants retained from the SRFR1 mapping populations . Plants in these F3 families were generated by crossing srfr1-1 or srfr1-2 ( RLD background ) to the SAIL RPS4 T-DNA knockout line rps4-1 ( Col-0 background ) [15] , [18] and were progeny of F2 plants selected for resistance to DC3000 ( avrRps4 ) . They were therefore homozygous for srfr1-1 or srfr1-2 , with varying degrees of Col-0 background . Two out of 4 srfr1-1 and 2 out of 6 srfr1-2 F3 families contained no individuals with abnormal growth phenotypes . However , the remaining F3 families gave rise to plants with phenotypes similar to srfr1-4 . The combined total number of stunted plants in these families was 20 out of 107 plants , consistent with the segregation of a single recessive gene in these populations ( χ2 = 2 . 43 , P>0 . 1 ) . We concluded that most likely the mutant alleles srfr1-1 and srfr1-2 also induce stunting in the Col-0 background and that Col-0 possesses a recessive genetic modifier that alters the srfr1 phenotype . We tested these predictions directly by out-crossing srfr1-4 to RLD and srfr1-1 . In the cross to srfr1-1 , 14 out of 46 plants were stunted , consistent with both srfr1-1 and srfr1-4 causing stunting and the segregation of a recessive gene ( χ2 = 0 . 45 , P>0 . 5 ) . In the cross to RLD , segregation of the stunted phenotype in the F2 generation was explained by two recessive genes , and genotyping showed that while all stunted plants were homozygous srfr1-4 , not all srfr1-4/srfr1-4 plants were automatically stunted ( Table 2 ) . In this cross , stunted F2 plants were also selected to determine a rough map position for the presumptive Col-0 modifier gene . This mapping placed the Col-0 modifier gene onto chromosome 4 ( Table 3 ) . Interestingly , in addition to the bottom of chromosome 4 where SRFR1 is located , individual break-point plants identified a map position towards the top of chromosome 4 between markers ciw6 and CH42 for the Col-0 modifier gene . The map position for the modifier gene contained the Col-0-specific TNL R gene homolog SNC1 , which was originally identified through a point mutation that autoactivates the SNC1 protein and constitutively induces PR genes even in the npr1 mutant line [23] . Additional work showed that wild-type SNC1 is easily autoactivated when expression of SNC1 is misregulated [24] . For example , mutations in BON1 , a member of the copine gene family encoding a plasma membrane-localized putative calcium-dependent phospholipid-binding protein [25] , [26] , lead to higher SNC1 expression levels , constitutive defense responses and reduced plant growth [27] . When the Col bon1-1 mutant was outcrossed to other Arabidopsis accessions , it was found that the wild-type SNC1 gene from Col-0 behaved as a recessive locus that causes stunting [27] . Our segregation data also indicated that the Col-0 modifier was recessive ( Table 2 ) . We therefore tested additional phenotypes displayed by bon1-1 plants , such as temperature dependence of constitutive defense activation and growth phenotypes . The stunted phenotype in srfr1-4 was severe at 22°C , but was intermediate at 24°C and absent at 28°C ( Figure 2A ) , reminiscent of the Arabidopsis bon1-1 mutant phenotype . In srfr1-1 and srfr1-2 plants , resistance to DC3000 ( avrRps4 ) was enhanced , but remained unchanged to virulent DC3000 , and plant growth was normal [15] , [18] . Interestingly , the srfr1-4 mutants were resistant not only to avirulent DC3000 ( avrRps4 ) , but also to virulent DC3000 and non-pathogenic DC3000 hrcC− ( Figure 2B ) . The srfr1-4 line showed approximately 50-fold lower DC3000 and DC3000 ( avrRps4 ) growth than wild type Col-0 , whereas the growth of DC3000 hrcC− in srfr1-4 was about 10-fold less than in Col-0 , suggesting that mutations in SRFR1 in Col-0 increased basal defenses at 24°C that were additive to AvrRps4-triggered immunity ( Figure 2B ) . Complemented srfr1-4 lines did not show either enhanced resistance phenotype ( Figure 2B ) . We could not test bacterial growth at 22°C because srfr1-4 plants were severely stunted at this temperature . However , consistent with an upregulation of salicylic acid ( SA ) -based defenses , PR1 and PR2 mRNA levels were upregulated and PDF1 . 2 levels down-regulated in srfr1-4 at 22°C ( Figure 2C ) . Characterization of the srfr1-4 phenotype and mapping therefore strongly suggested that the Col-0 modifier is SNC1 . To test this directly , we crossed srfr1-4 to snc1-11 , a T-DNA insertion allele in the first exon of SNC1 [27] . In the F2 population , the number of stunted plants was consistent with the segregation of two recessive loci ( srfr1-4 and wild-type SNC1 ) ( Table 4 ) . All of the stunted plants were homozygous for the srfr1-4 allele and the wild-type SNC1 allele . In contrast , all plants of normal stature that were homozygous for the srfr1-4 T-DNA allele possessed at least one copy of the snc1-11 T-DNA allele ( Table 4 ) . Therefore , the stunted phenotype of srfr1-4 plants requires two copies of SNC1 in Col-0 , analogous to the phenotype of bon1-1 plants [27] . We quantified the effect of mutations in SRFR1 on plant growth by measuring the shoot weight of srfr1 mutants in Col-0 and RLD ( Figure 3 ) . Shoot weights were close to normal in the original srfr1-1 and srfr1-2 plants compared to wild-type RLD . Mutations in srfr1 caused severe reductions in shoot weight in the Col-0 background that were completely reversed by introgressing snc1-11 . Interestingly , the shoot weight of srfr1 SNC1 plants was more strongly reduced than in bon1-1 plants ( Figure 3 ) , indicating that perhaps SRFR1 functions downstream of additional R genes apart from regulating SNC1 . Together with the negative regulation in AvrRps4- and HopA1-triggered immunity , these results show that SRFR1 is a negative regulator of plant immune responses of broader specificity than originally described . Previous studies had suggested that the readily autoactivatable SNC1 is limited to the Col-0 accession , but these studies had not included RLD [27] . We therefore sequenced the likely RLD ortholog of SNC1 in RLD to determine the molecular basis for the very different phenotypes of Col-0 and RLD srfr1 mutants . At the 5′-end , SNC1-specific primers consistently amplified a sequence with high overall similarity to SNC1-Col ( Figure 4A and 4B ) . SNC1-specific primers designed to amplify the complete SNC1 gene or the 3′-half of SNC1 failed to result in a unique RLD product . This reflected the very duplicated nature of the 3′-half of SNC1 in Col-0 . Whole sections of the gene are not only duplicated within SNC1 with 100% sequence identity , but are also found in linked family members [28] . We were not able to experimentally determine unequivocally which genomic PCR product from the 3′-end was physically linked to the 5′-end of SNC1-RLD . We therefore determined the SNC1 mRNA sequence from RLD using a combination of 3′-Rapid Amplification of cDNA Ends ( 3′-RACE ) and RT-PCR . As shown in Figure 4A , the open reading frame of SNC1-RLD predicted a protein of 619 amino acids , including a TIR and NBS domain but only a partial LRR domain . The predicted amino acid sequence identity between SNC1-Col and SNC1-RLD within the first three exons was 87% . However , our SNC1-RLD cDNA sequence was missing the fourth and fifth exons , leading to an in-frame stop codon at position 620 ( Figure 4B ) . Interestingly , in the SNC1-RLD cDNA the very 3′-end of the open-reading frame and the 3′-untranslated region showed high nucleotide sequence identity with the corresponding region in SNC1-Col . Because we only obtained cDNA sequence of SNC1-RLD at the 3′-end , we could not determine whether the 3′-end of the SNC1-RLD coding sequence is interrupted by introns . We also obtained RT-PCR products from Col-0 . These indicated that in contrast to the annotation of SNC1 in TAIR , we did not find evidence for the splicing of intron 5 , which does not contain in-frame stop codons ( Figure 4B ) . This alternative SNC1 transcript encoded a SNC1 protein of 1404 amino acids rather than the annotated 1301 amino acids . Taken together , sequencing of the RLD SNC1 ortholog provided evidence for polymorphisms at the 5′-end and major alterations in the 3′-half of the gene compared to Col-0 , consistent with the fact that RLD does not have a SNC1 ortholog that triggers stunted growth in the absence of SRFR1 . Activation of SNC1 , either by intragenic autoactivating mutations [23] or by mutations in negative regulators of SNC1 such as BON1 [27] , leads to constitutively enhanced resistance . Consistent with this and the constitutive expression of PR genes in srfr1-4 ( Figure 2C ) , we observed with in planta bacterial growth assays increased resistance of srfr1-4 to DC3000 ( avrRps4 ) and to virulent DC3000 ( Figure 2B ) . The latter shows that srfr1-4 plants possess elevated basal resistance that is independent of particular avirulence genes . To test if enhanced basal resistance in srfr1-4 , like stunted growth , is fully dependent on SNC1 , we performed in planta bacterial growth assays at varying temperatures . As noted before , we were not able to infiltrate srfr1-4 plants at 22°C because of the severe growth phenotype . At both 22°C and 24°C , the growth of DC3000 and DC3000 ( avrRps4 ) was reduced in srfr1-4 snc1-11 compared to growth in wild type Col-0 , even though the growth of DC3000 ( avrRps4 ) in srfr1-4 snc1-11 was slightly higher than that in srfr1-4 at 24°C ( Figure 5A and 5B ) . This remnant enhanced basal resistance in srfr1-4 snc1-11 plants may be related to the induced defense gene mRNA levels observed in RLD srfr1-1 and srfr1-2 plants , although the latter plants do not show enhanced basal resistance [18] , [29] . These results demonstrate that although the stunted phenotype of srfr1-4 at 22°C and 24°C is fully mediated by SNC1 , enhanced basal resistance at these temperatures in srfr1-4 is not entirely mediated by SNC1 . At 28°C , both basal and AvrRps4-triggered resistance were abolished in srfr1-4 and srfr1-4 snc1-11 plants ( Figure S2A ) . In addition , AvrRps4-triggered resistance was also abolished in wild-type Col-0 , confirming previous results [30] , and in snc1-11 plants ( Figure S2A ) . Consistent with normal growth and absence of resistance at 28°C , SNC1 and PR1 expression were not elevated in srfr1-4 or srfr1-4 snc1-11 plants ( Figure S2B ) . Previously , we showed that several defense-related genes were up-regulated in RLD srfr1 mutants , supporting our hypothesis that SRFR1 may function as a repressor in plant innate immunity by negatively regulating defense gene expression levels [29] . The growth and constitutive defense phenotypes of srfr1-4 at 22°C and 24°C prompted us to quantify defense-related gene mRNA levels in srfr1-4 at these temperatures using quantitative reverse transcription real-time PCR ( qPCR ) , and to determine whether all changes in expression in srfr1-4 can be attributed to SNC1 . As expected , SNC1 transcript levels were higher in srfr1-4 than in Col-0 at 22°C and 24°C , as were those of RPP4 and At4g16950 ( Figure 6A ) , two TNL genes in the SNC1 cluster that are co-regulated with SNC1 [31] . Interestingly , RPP4 and At4g16950 expression levels were higher also in the srfr1-4 snc1-11 double mutant ( Figure 6A ) , showing that higher mRNA levels of these genes is not an indirect effect of SNC1 activation . Similarly , we observed increased mRNA levels of the CNL R gene RPS2 , and to a lesser extent of RPM1 , in srfr1-4 and srfr1-4 snc1-11 plants at both 22°C ( Figure S3A ) and 24°C ( Figure S3B ) , indicating that upregulation of R genes by mutations in SRFR1 is not limited to TNL genes in Col-0 . In contrast to SNC1-RLD , upregulation of RPM1 and RPS2 was not observed in the RLD mutant srfr1-1 ( Figure S3C ) , possibly reflecting the presence of additional accession-specific SNC1-like genes in Col-0 [32] that may lead to enhanced expression of CNL genes . SA-dependent defense related gene mRNA levels were also higher in srfr1-4 than in wild-type at 22°C and 24°C ( Figure 6B ) . Unlike for TNL and CNL genes , these expression levels were reduced in srfr1-4 snc1-11 compared to srfr1-4 to varying degrees , although they were still higher than in wild-type ( Figure 6B ) . Interestingly , NPR1 and EDS1 mRNA levels in the double srfr1-4 snc1-11 mutant showed additive increases compared to the wild-type and single mutants at 22°C ( Figure 6B ) . In contrast , mRNA levels of PDF1 . 2 , a defensin gene whose expression is under negative regulation by the JA-responsive transcription factor JIN1 [33] , was strongly repressed at 22°C in srfr1-4 but induced in srfr1-4 snc1-11 plants compared to wild-type . PDF1 . 2 expression levels were not significantly different among the genotypes at 24°C ( Figure 6C ) . These results point towards complex modular control of defense gene expression that is influenced by a combination of SRFR1 , SNC1 and temperature to varying proportions . The Arabidopsis accession RLD carries a natural mutation in RPS4 and is fully susceptible to DC3000 ( avrRps4 ) [34] , [35] . In addition , introduction of RPS4 from Col-0 or Ler into RLD is sufficient to provide full resistance to DC3000 ( avrRps4 ) when compared to Col-0 and Ler [16] , [35] . We also observed susceptibility of rps4-1 , an RPS4 T-DNA allele in the Col-0 background , under our conditions that were used to map SRFR1 [15] . However , it was reported that rps4-2 , a second RPS4 T-DNA allele in the Col-0 background , was only slightly more susceptible to DC3000 ( avrRps4 ) [36] . Based on the accession-specific presence of SNC1 in Col-0 , the temperature-dependent srfr1-4 phenotype and the fact that SRFR1 was identified in a screen for enhanced DC3000 ( avrRps4 ) resistance in RLD , we speculated that the rps4-2 phenotype might be temperature-dependent . Indeed , when directly comparing plants grown in identical growth chambers at 22°C or 24°C , we observed a strong temperature dependence: rps4-2 plants were as resistant to DC3000 ( avrRps4 ) as Col-0 at 22°C , while at 24°C they were as susceptible as Col-0 treated with virulent DC3000 and as susceptible as RLD treated with either strain ( Figure 7 ) . Given the effect of temperature , we next tested whether SNC1 interferes with the susceptible phenotype at 22°C . Interestingly , rps4-2 snc1-11 double mutants displayed approximately 30-fold increased bacterial growth of DC3000 ( avrRps4 ) compared to Col-0 or rps4-2 at 22°C ( Figure 7A ) , suggesting that SNC1 in the absence of RPS4 contributes to AvrRps4-triggered immunity at 22°C in Col-0 . However , susceptibility of rps4-2 snc1-11 to DC3000 ( avrRps4 ) was not complete compared to Col-0 treated with virulent DC3000 or to RLD treated with either strain , indicating that additional factors interfere with rps4-caused susceptibility ( Figure 7A ) . No significant difference of DC3000 ( avrRps4 ) growth in rps4-2 and rps4-2 snc1-11 was observed at 24°C , reflecting full susceptibility of rps4-2 to DC3000 ( avrRps4 ) at this temperature ( Figure 7B ) . Recently , RRS1 was shown to be involved in DC3000 ( avrRps4 ) -mediated resistance [37] , [38] . However , we observed no temperature-dependent resistance to DC3000 ( avrRps4 ) in the Ws-0 mutants rps4-21 and rrs1-1 ( Figure S4 ) . As was observed before , mutations in either RPS4 or RRS1 had equal effects on DC3000 ( avrRps4 ) susceptibility , which was qualitatively different from the redundancy between SNC1 and RPS4 ( Figure 7 ) . Interestingly , as reported before [38] , we reproducibly observed approximately 10-fold higher growth of DC3000 compared to DC3000 ( avrRps4 ) in the single rps4-21 and rrs1-1 mutants and the double mutant , indicating that additional layers of resistance exist . The redundancy between RPS4 and SNC1 suggests that they function in parallel to provide resistance to DC3000 ( avrRps4 ) at 22°C . We speculated that this cross-talk between two R proteins might occur if both interact with proteins in a common complex . Perturbation of this complex by an effector could trigger one or the other R protein , and both need to be absent to observe susceptibility . Based on the results presented here , we reasoned that SRFR1 might be a common interaction partner of RPS4 and SNC1 . In the past , transient expression of SRFR1 in Nicotiana benthamiana led to variable protein expression levels and required a silencing inhibitor for detectable expression [18] . We therefore generated stable transgenic N . benthamiana plants expressing HA-SRFR1 encoded by a genomic clone driven by the native Arabidopsis SRFR1 promoter . We first determined the functionality of this genomic HA-SRFR1 construct in Arabidopsis by testing for complementation of the stunted srfr1-4 phenotype . Transgenic plants expressing HA-SRFR1 in the srfr1-4 background showed normal growth and development ( Figure S5A ) . Immunoblot analysis detected the expression of the transgene product in these transgenic plants ( Figure S5A ) . HA-SRFR1 in these plants localized to microsomal and nuclear fractions ( Figure S5B ) . This localization was consistent with the nuclear and punctate cytoplasmic localization of GFP-SRFR1 transiently expressed in N . benthamiana [18] . We observed improved and reproducible HA-SRFR1 expression in the stable transgenic N . benthamiana lines . As in Arabidopsis , HA-SRFR1 localized to the microsomal and nuclear fractions in N . benthamiana ( Figure 8A ) . A previous study showed that RPS4 was predominantly localized to microsomes [36] . Immunoblot assays of Myc-SNC1 transiently expressed in N . benthamiana suggested that SNC1 was mainly a soluble cytoplasmic protein , although a sizeable portion accumulated in the microsomal fractions ( Figure 8B ) . We also detected some SNC1 in the nuclear fraction ( Figure 8B ) . We tested for SRFR1 interaction with SNC1 and RPS4 by transiently expressing Myc-SNC1 , Myc-RPS4 or Myc-eGFP as a negative control in transgenic HA-SRFR1 N . benthamiana plants . Co-immunoprecipitation analysis on protein isolated 48 h after infiltration of Agrobacterium tumefaciens strains showed that SRFR1 interacted with both SNC1 and RPS4 in the microsomal fraction ( Figure 9 ) . No significant interaction between SRFR1 and SNC1 was detected in the soluble fraction , even though SNC1 was detected in this fraction . No interaction with eGFP was detected in either fraction ( Figure 9 ) . As an additional control , we probed SRFR1 co-immunoprecipitated samples for the presence of GAPDH and V-ATPase . Neither protein was co-immunoprecipitated with SRFR1 ( Figure S6A and S6B ) , indicating that the interactions of SRFR1 with SNC1 and RPS4 are specific .
Here we extend our analysis to the Col-0 specific TNL R-like gene SNC1 and show that mutations in SRFR1 activate SNC1 . SNC1 was originally identified based on an autoactivated allele that led to constitutive expression of PR1 [23] . Subsequently , it was shown that perturbation of wild-type SNC1 expression readily leads to autoactivation [24] , [27] , [40] . Our finding that SNC1 is activated in srfr1 mutants is reminiscent of the bon1/cpn1 phenotype [25]–[27] . How the absence of BON1 leads to SNC1 activation is not known . In particular , it is not known if sub-pools of BON1 and SNC1 reside in the same protein complex . Together , our data show that mutations in SRFR1 impact three resistance specificities , namely AvrRps4- , HopA1- and SNC1-triggered immunity . The impact of srfr1 mutations on SNC1 is novel , given that previously we observed effects of SRFR1 mutations only in the absence of the R genes RPS4 or RPS6 . SNC1 is therefore the first TNL gene for which a genetically direct negative regulation by SRFR1 could be shown . Whether this is also mechanistically direct remains to be determined . Consistent with the proposed function of SRFR1 as a transcriptional repressor , we found increased mRNA levels for SNC1 , RPP4 and At4g16950 in srfr1-4 plants . This altered expression level was not an indirect effect of SNC1 activation , since RPP4 and At4g18950 were also upregulated in the srfr1-4 snc1-11 double mutant . Because these members of the SNC1 locus were previously shown to be co-regulated with SNC1 [31] and because changes in SNC1 expression levels have been shown to cause autoactivation of SNC1 [24] , we propose that mutations in SRFR1 lead to misregulated expression of SNC1 , which in turn activates constitutive expression of an enhanced defense phenotype . The genetic connection of SNC1 and RPS4 via SRFR1 was measurable as cross-talk between these resistance pathways in disease assays under specific environmental conditions . Because it had been convincingly shown that the Col-0 rps4-2 mutant was not fully susceptible to DC3000 ( avrRps4 ) [36] , while we observed complete susceptibility , we tested whether environmental conditions had an influence on the Col-0 phenotypic response to DC3000 ( avrRps4 ) . Surprisingly , we found that a mere 2°C difference in temperature changed the phenotype of rps4-2 from almost completely resistant to DC3000 ( avrRps4 ) to fully susceptible . Other environmental factors that are likely to impact this response are humidity [26] , with drier conditions favoring resistance , and light intensity . Because cis or second-site mutants with activated SNC1 have a well-described conditional phenotype influenced by temperature and humidity , we tested whether the partial phenotype of rps4-2 is influenced by SNC1 . Indeed , we were able to measure a synergistic effect of mutations in RPS4 and SNC1 on susceptibility to DC3000 ( avrRps4 ) at 22°C . In addition , in the accessions RLD and Ws-0 that do not have SNC1 , mutations in RPS4 result in susceptibility to DC3000 ( avrRps4 ) that is not influenced by changes in temperature in the range investigated here . SNC1 was originally identified in a screen for mutants with constitutively activated defenses , and to date no cognate avirulence gene has been identified . Nevertheless , some suppressor mutants of the constitutive snc1-1 phenotype such as mos7 also impact effector-triggered immunity [41] . Our finding that SNC1 contributes to AvrRps4-triggered immunity further indicates that SNC1 can be considered a bona fide R gene . Conceptually , cross-talk between resistance pathways can occur if an effector protein has more than one target , or if R proteins guard a common target . The former seems to be the case for RPM1 and TAO1 , which additively contribute to full resistance to DC3000 ( avrB ) [42] . In contrast , AvrRpm1 induced measurable defenses in rpm1 plants that were dependent on RPS2 , presumably because both RPM1 and RPS2 guard RIN4 , a protein that is the target for both AvrRpm1 and AvrRpt2 [19] . As a first step to distinguish between these models , we tested whether SNC1 and RPS4 co-localize with a common protein . Given the regulatory function of SRFR1 on SNC1 and on AvrRps4-triggered resistance , we speculated that SRFR1 might be such a common protein . Interestingly , the microsomal pool of SRFR1 was found to be in a complex with SNC1 . Transiently expressed GFP-SRFR1 in N . benthamiana localized to the nucleus and cytoplasm [18] . The cytoplasmic localization was punctate . Here , further analysis of the cytoplasmic pool showed that most SRFR1 localized to the microsomal cytoplasmic fraction , and very little was soluble . Because the majority of SNC1 was in the soluble cytoplasmic pool , it was not possible to determine whether the microsomal pool of SNC1 diminishes in the absence of SRFR1 . In addition , the native N . benthamiana pool of SRFR1 may be sufficient to localize some proportion of SNC1 to microsomes . Most likely , SNC1 is in a higher-order complex with SRFR1 in a microsomal compartment of unknown identity . Interestingly , we found that RPS4 also interacted with SRFR1 in the same cell fraction . This suggests that perhaps additional R proteins localize to a common complex . The localization of SRFR1 and interactions with RPS4 and SNC1 are reminiscent of CRT1 [43] . However , the functions of CRT1 and SRFR1 likely differ , because mutations in CRT1 compromise , not enhance , effector-triggered immunity . Because mutations in SRFR1 lead to increased , not decreased resistance , we do not propose that SRFR1 is analogous to RIN4 as the guardee of RPS4 or SNC1 , since deletion of a guardee should prevent recognition of the specific effector that targets the guardee . The function of guardee for SNC1 may be fulfilled by BON1 [44] , although BON1 is localized to the plasma membrane [25] and to our knowledge it has not been determined whether BON1 interacts with SNC1 . Also , because no cognate effector is known for SNC1 and because deletion of BON1 leads to autoactivation of SNC1 , it is difficult to quantify the effects of BON1 mutations on disease resistance and susceptibility . Interestingly , we consistently observed a more severe growth phenotype of srfr1-4 plants compared to bon1-1 plants , yet the srfr1-4 growth phenotype is completely reversed by snc1-11 . Apart from negatively regulating the activation of SNC1 , SRFR1 most likely regulates additional R proteins . Because of positive feed-back , all these pathways may be turned on once SNC1 is activated . While in bon1-1 plants SRFR1 is still present to downregulate these other R proteins , this is not the case in srfr1-4 plants . Therefore , this observation is suggestive of a broad and central function of SRFR1 in downregulating R protein output . It is currently unknown where in the cell the recognition of AvrRps4 by RPS4 occurs . Several plant R proteins , including RPS4 , have been shown to function in the nucleus to trigger immunity [36] , [45] . Because the cytoplasmic pool of these R proteins predominates over the nuclear pool , it is difficult to establish whether R proteins translocate to the nucleus upon effector perception , or continuously cycle between the cytoplasmic and nuclear compartment . We also detected a low amount of SNC1 in the nucleus , whereas the autoactivated mutant snc1-1 protein appears to accumulate to higher levels in the nucleus [41] . It was also found that snc1-1 needed to be in the nucleus to cause a stunted phenotype [41] , and that temperature modulated the localization of snc1-1 [46] . Interestingly , a balanced partitioning of EDS1 between the cytoplasm and nucleus was recently shown to be required for full EDS1-mediated resistance [47] , indicating that immune regulatory proteins may have coordinated cytoplasmic and nuclear functions during the immune response . Here we found that SRFR1 interacts with RPS4 and SNC1 in the cytoplasm , and also that mutations in SRFR1 alter the expression of defense genes independent of a snc1 phenotype . Because of the low amount of RPS4 [36] , SRFR1 and SNC1 protein in the nucleus , so far we have not been able to ascertain whether they also interact in the nucleus . However , our results seem to suggest that at resting state , the majority of SRFR1 , RPS4 and SNC1 protein is extra-nuclear localized and forms a complex in the microsomal fraction . SRFR1 may therefore negatively regulate RPS4 and SNC1 translocation to the nucleus . We propose that a second point of regulation is in the nucleus , where SRFR1 may negatively regulate the transcriptional reprogramming upon pathogen perception . More detailed analyses before and during a defense response are required to substantiate these hypotheses . The genetics of enhanced resistance in RLD srfr1 mutants were originally interpreted to signify that an additional specific R gene is required for resistance [15] . In the mapping crosses rps4-1×srfr1-1 and rps4-1×srfr1-2 , resistant F2 plants were identified in the ratio 13 susceptible to 3 resistant , consistent with segregation of a recessive locus ( srfr1 ) and a dominant locus that was proposed to be a second specific R gene with weak recognition of AvrRps4 [15] . In light of the results presented here , we needed to reinterpret these results . Retesting our mapping population provided evidence for severely stunted plants at the expected ratio of one in 16 stunted plants . These would be double recessives ( srfr1 and wild-type SNC1 ) and would have been lost from our usual phenotypic analysis because of preferential retention of vigorously growing seedlings after planting for disease assays . Upon reinspection , the segregation of resistant plants in the two mapping populations was indeed statistically consistent with the segregation of a single recessive locus ( srfr1 ) in a population where 1/16th of the population ( genotype srfr1/srfr1 SNC1/SNC1 ) that would have been expected to be resistant was eliminated from consideration . In addition , in both mapping populations we had noticed an apparent suppression of recombination along chromosome 4 in retained plants [15] , which is consistent with the fact that both SRFR1 and SNC1 are located on chromosome 4 . At the same time , we show here that the original model for resistance in srfr1 mutants mediated by other R genes with weaker recognition of AvrRps4 is still valid because cross-talk between R genes exists in response to AvrRps4 . However , we now consider it unlikely that one single additional R gene is responsible for resistance in srfr1 mutants . In conclusion , our data contribute to evidence for extensive cross-talk between at least three TNL pathways that converge on SRFR1 , indicating that SRFR1 perhaps has a central function in regulating the output of additional TNL proteins . The present data also allow us to propose more directly that SRFR1 negatively regulates R proteins or R gene expression . While models for SRFR1 so far have focused on a nuclear-localized transcriptional repressor function [18] , the data here suggest that SRFR1 also has a function in the cytoplasm . Consistent with this , Li and co-workers recently showed that SRFR1 interacts with SGT1 in the cytoplasm [22] . Whether SRFR1 is merely an accessory protein in a cytoplasmic “resistasome” or has regulatory functions and migrates to the nucleus remains to be established . Nevertheless , our data highlight molecular architecture aspects of a subset of TNL-mediated resistance pathways that will allow further mechanistic insight into the function of TNL R proteins . The cross-talk evident from our results also means that any reports of constitutive resistance phenotypes in Col-0 need to consider the possible involvement of SNC1 .
The srfr1-4 line ( SAIL_412_E08 ) from the Syngenta Arabidopsis Insertion Library [21] was obtained from the Arabidopsis Biological Resource Center . The T-DNA insertion site in srfr1-4 in the second intron was determined by sequencing and was found to be upstream of the insertion site suggested by raw flanking sequence from the T-DNA Express website ( http://signal . salk . edu/cgi-bin/tdnaexpress ) . rps4-2 ( SALK_057697 ) was isolated from the Salk T-DNA knockout lines [20] . snc1-11 ( SALK_047058 ) and bon1-1 were a kind gift from Jian Hua ( Cornell University ) . Using snc1-11 as a recipient , srfr1-4 snc1-11 and rps4-2 snc1-11 double homozygous mutants were generated . The mutant lines rps4-21 , rrs1-1 and rps4-2 rrs1-1 in the Ws-0 background were kindly provided by Yoshihiro Narusaka ( Research Institute for Biological Sciences , Japan ) . The mapping populations generated by crossing srfr1-1 or srfr1-2 to rps4-1 ( SAIL_519_B09 ) were described previously [15] . Complemented srfr1-4 transgenic lines were generated by transforming srfr1-4 with pSHK102 , a genomic SRFR1 clone in vector pCAMBIA2300 [18] , using the floral dip method [48] . Single locus transgenic lines homozygous for the transgenic copy of wild-type SRFR1 were selected by scoring for kanamycin resistance , the selectable marker of pCAMBIA2300 ( the selectable marker for SAIL lines is BASTA ) . Among these homozygous lines , those with at least one copy of the srfr1-4 allele were selected by genotyping and propagated to the next generation . Lines homozygous for both the SRFR1 transgene and the srfr1-4 allele were identified as those where srfr1-4 did not segregate in the next generation . SNC1 was mapped by genotyping stunted plants in the F2 generation from the cross RLD×srfr1-4 using SSLP and CAPS markers [49] , [50] . Unless otherwise noted , Arabidopsis plants used in this study were grown in E-7/2 reach-in growth chambers ( Controlled Environments Ltd . , Winnipeg , Manitoba , Canada ) under an 8 h light/16 h dark cycle at 24°C and 22°C , with 70% relative humidity and a light intensity of 90–140 µmol photons m−2 s−1 . Virulent Pseudomonas syringae pv . tomato strain DC3000 containing the empty vector ( ev ) pVSP61 or DC3000 expressing avrRps4 from plasmid pVSP61 was grown as described previously [16] . To generate DC3000 hrcC− ( ev ) , pVSP61 was mobilized into the recipient DC3000 hrcC− mutant by triparental mating using the helper plasmid pRK2013 . In planta bacterial growth assays were performed by syringe infiltration . Leaves of 4-week old plants were infiltrated with bacterial suspensions of 5×104 cfu/mL . Leaf discs with a total area of 0 . 5 cm2 per sample were ground in 10 mM MgCl2 , and solutions were plated in serial dilutions on selective medium in triplicate at the indicated time points . Statistical comparison of bacterial growth was tested using a two-tailed Student's t-test . Quantitative reverse transcription PCR was performed as described previously [18] . Briefly , total RNA was extracted from the indicated plant lines using TRIZOL ( Invitrogen , Carlsbad , CA , USA ) . For RT-PCR experiments , cDNA was synthesized from 2 µg of total RNA using an oligo ( dT ) 15 primer and Moloney murine leukemia virus ( MMLV ) reverse transcriptase ( Promega , Madison , WI , USA ) following the manufacturer's protocol . Quantitative real-time reverse transcription PCR ( qPCR ) was performed with SYBR GREEN PCR Master Mix and an ABI 7500 system ( Applied Biosystems , Warrington , UK ) according to the manufacturer's instructions . The levels of transcripts were normalized using SAND gene ( At2g28390 ) mRNA levels as an internal standard . These experiments were performed at least twice with similar results . Semi-quantitative RT-PCR was performed from total RNA extracted from Col-0 and srfr1-4 . Equivalent amounts of cDNA from both samples were used to detect PR1 , PR2 and PDF1 . 2 . ACTIN2 ( At3g18780 ) was used as an internal control . Table S1 lists the oligonucleotide primer sequences used in qPCR and semi-quantitative RT-PCR . To determine the SNC1 cDNA sequence from RLD and Col-0 , the 3′-RACE procedure ( Invitrogen , Carlsbad , CA , USA ) and RT-PCR ( see above ) were performed as described previously [19] . PCR products were ligated into the pGEM-T Easy vector ( Promega ) for sequencing . See Table S1 for oligonucleotide primer sequences used in these experiments . All clones were verified by sequencing . To generate epitope-tagged SNC1 constructs , genomic SNC1 DNA including introns was amplified by PCR from Col-0 using SNC1 GATE primers listed in Table S1 . In vitro BP Clonase recombination reactions were carried out to insert the PCR product into the pDONR201 entry vector according to the manufacturer's instructions ( Invitrogen ) . LR reactions were performed to recombine the entry clones into GATEWAY-compatible destination vectors . Using BP and LR reactions , we constructed Myc-gSNC1 with six Myc tags under the control of the cauliflower mosaic virus 35S promoter . Similarly , Myc-gRPS4 was generated by amplifying the genomic fragment of RPS4 from the FLAG-gRPS4 construct [51] using the primers RPS4 FOR and RPS4 REV ( Table S1 ) . To construct the binary vector expressing genomic HA-tagged SRFR1 from its native promoter ( HA-gSRFR1 ) , independent PCR reactions were performed with the primer combinations HA-SRFR1 FOR/gSRFR1 XbaI REV and pCAMBIA PmeI FOR/HA-SRFR1 REV using the template pSHK102 [18] . The PCR products were mixed and used for overlap PCR with the pCAMBIA Pme I FOR/gSRFR1 XbaI REV primers . The 2 . 2 kb PCR product was digested with PmeI and XbaI and used for replacing the PmeI-XbaI fragment of pSHK102 . The resulting binary vector was electroporated into Agrobacterium tumefaciens strain C58C1 . Transgenic N . benthamiana plants expressing HA-gSRFR1 from the Arabidopsis native promoter were generated by stable Agrobacterium-mediated transformation as previously described [52] . Transgenic plants were selected on media containing 100 µg/ml kanamycin . Microsomal and soluble fractions were prepared according to published procedures [53] . Briefly , plant materials were ground in buffer H ( 50 mM HEPES , pH 7 . 5 , 250 mM sucrose , 15 mM EDTA , 5% glycerol , 0 . 5% polyvinylpyrrolidone ) containing 3 mM DTT and 1×protease cocktail inhibitors ( Sigma , St . Louis , MO ) . The extracts were filtered through two layers of miracloth pre-wetted with buffer H and centrifuged at 2000×g for 15 min at 4°C . The supernatant consisting of the cytoplasmic fraction was further subjected to ultracentrifugation at 100 , 000×g to separate the soluble and microsomal ( pellet ) fractions . The pellet was resuspended in buffer H . Nuclear extracts were prepared using the CelLytic™ PN Isolation/Extraction Kit ( Sigma ) following the manufacturer's instructions . Total protein concentrations of fractions were determined by Bradford assays with BSA as standard . Extracts were normalized to 1 µg/ml with buffer H . For co-immunoprecipitation assays , the nonionic detergent Igepal CA-630 ( Sigma ) was added to 0 . 2% and 1% final concentration to the soluble and microsomal fractions , respectively . The extracts were incubated overnight with 20 µl of anti-HA or anti-Myc agarose beads ( Sigma ) . The beads were washed three times with buffer H containing 0 . 2% Igepal CA-630 . The immunoprecipitates were analyzed by immunoblot assays with anti-Myc-HRP ( Santa Cruz Biotechnology ) or anti-HA-HRP ( Roche ) antibodies . The degree of enrichment in cellular fractionation was determined by immunoblot analyses with anti-GAPDH ( Genscript , Piscataway , NJ ) , anti-V-ATPase ( Agrisera , Vännäs , Sweden ) , anti-histone H3 ( Abcam , Cambridge , MA ) and anti-RNA pol I ( Agrisera ) antibodies . SNC1: At4g16890; SRFR1: At4g37460; RPS4: At5g45250; RPP4: At4g16860; NPR1: At1g64280; EDS1: At3g48090; PAD4: At3g52430; SID2: At1g74710; PR1: At2g14610; PR2: At3g57260; PDF1 . 2: At5g44420; SAND: At2g28390; ACTIN2: At3g18780 . | Plants , like humans , have an immune system to defend against disease . This immune system seeks out the presence of disease-causing microbes and other invaders by detecting non-plant molecules and proteins . Plants rely on this surveillance to activate an antimicrobial response of appropriate strength at the right time; as with humans , an overactive immune system can be harmful to plants . We study how plants achieve an appropriate balance , using genetics and the interaction between the reference plant Arabidopsis thaliana and the bacterial plant pathogen Pseudomonas syringae . So-called plant resistance proteins are important activators of immunity that directly or indirectly intercept foreign proteins deployed by pathogens . Resistance proteins are generally thought to be highly specific detectors that only respond to a single pathogen protein . However , while working with a negative regulator of plant immunity called SRFR1 , we discovered a surprising level of cross-talk between different resistance proteins that becomes evident only under certain environmental conditions such as low temperature . We also show that SRFR1 and these resistance proteins bind to each other , possibly explaining the observed cross-talk . Our work thus highlights linkages between resistance pathways and provides insight into the molecular architecture of the plant innate immune response . |
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This work examined the mechanisms underlying auditory motion processing in the auditory cortex of awake monkeys using functional magnetic resonance imaging ( fMRI ) . We tested to what extent auditory motion analysis can be explained by the linear combination of static spatial mechanisms , spectrotemporal processes , and their interaction . We found that the posterior auditory cortex , including A1 and the surrounding caudal belt and parabelt , is involved in auditory motion analysis . Static spatial and spectrotemporal processes were able to fully explain motion-induced activation in most parts of the auditory cortex , including A1 , but not in circumscribed regions of the posterior belt and parabelt cortex . We show that in these regions motion-specific processes contribute to the activation , providing the first demonstration that auditory motion is not simply deduced from changes in static spatial location . These results demonstrate that parallel mechanisms for motion and static spatial analysis coexist within the auditory dorsal stream .
Motion is a fundamental dimension of acoustic and visual stimuli that is critical for animals to interact with their environment . Human psychoacoustic studies have addressed whether auditory motion analysis depends on sequential perception of stationary sources or whether specific motion detection mechanisms exist , but the results so far have remained inconclusive [1–9] . Studies of neuronal activity in various mammalian species , including macaques , have shown that cues supporting auditory motion perception can induce pronounced asymmetry of neuronal responses to opposite motion directions in the inferior colliculus and primary auditory cortex A1 [10 , 11] . However , it has been argued that this apparent direction sensitivity does not represent genuine motion selectivity but results from “adaptation of excitation , ” defined as the reduced capacity of a neuron to respond to excitatory stimuli following the presentation of a prior excitatory stimulus , a mechanism also called “spatial masking” [12–14] . Motion processing per se has not been investigated beyond A1 in animal models . However , sensitivity to static spatial information has been shown to increase from A1 to the caudomedial ( CM ) and caudolateral ( CL ) belt areas in macaques [15–19] , opening the possibility that these regions might be more sensitive to dynamic spatial information than A1 . Human functional magnetic resonance imaging ( fMRI ) studies indicate that the planum temporale , the region of the auditory cortex that contains areas homologous to monkey areas CM and CL , is involved in auditory motion processing [20 , 21] . However , these studies did not directly address the question of the underlying mechanisms and did not allow for any conclusion about the existence of specific motion-detection processes for sound movement analysis as opposed to sequential processing of stationary sources . Similarly , lesion studies did not allow for distinguishing between the two hypotheses [22–25] . In this study , we measured the fMRI blood-oxygen-level-dependent ( BOLD ) response to auditory motion in the whole auditory cortex of awake macaques . We performed a series of experiments designed to elucidate the mechanisms supporting auditory motion processing . Data revealed that auditory motion perception relies on specific computational mechanisms beyond the simple representation of successive snapshots of location .
The two first experiments involved motion and stationary stimuli . Four moving and five stationary stimuli were presented in a random order to subjects involved in a visual fixation task , and a sparse-sampling paradigm was used to measure the BOLD response induced by each stimulus ( Fig 1B ) . Auditory stimuli were based on individual intra-auricular recordings of an amplitude-modulated broadband noise . Virtual moving stimuli were perceived as moving back and forth between the positions 0° and 80° within one hemispace , with half of the stimuli starting from the midline position ( 0° ) , while the other half started from the most lateral position ( 80° ) , resulting in four motion stimulus exemplars per subject . The five stationary stimuli corresponded to sounds perceived as coming from the locations −80° , −40° , 0 , +40° , and +80° , respectively ( the minus sign referring to the left hemispace and the plus sign to the right one ) . Because moving stimuli starting from the central position induced similar BOLD responses as those starting to move from the periphery , data were pooled , resulting in two motion conditions corresponding to sounds moving within the left hemispace ( Motion Left ) and those moving within the right hemispace ( Motion Right ) . The goal of this experiment was to identify the neural substrates of motion perception that were not explained by the processing of stationary sounds . We thus compared the BOLD response induced by moving sounds within each hemispace with the average BOLD response induced by the stationary stimuli corresponding to the spatial positions through which the moving sounds passed: the BOLD responses induced by stationary stimuli −80° , −40° , and 0° were thus averaged and compared to those induced by sounds moving in the left hemispace ( contrast Motion Left minus Stationary Left ) , while the BOLD responses induced by stationary stimuli +80° , +40° , and 0° were averaged and compared to those induced by sounds moving in the right hemispace ( contrast Motion Right minus Stationary Right ) . These comparisons allowed us to control for the laterality of the motion stimuli ( nondynamic spatial information , corresponding to the encoding of space within which the motion stimulus was moving ) , as well as for their intrinsic spectrotemporal content ( amplitude-modulated broadband noise filtered through the pinna ) . In each subject ( monkey 1 , M1; monkey 2 , M2 ) , the contrasts Motion Left minus Stationary Left and Motion Right minus Stationary Right revealed widespread activation of the posterior part of the auditory cortex contralateral to the stimuli , on the superior temporal gyrus ( STG ) ( Fig 2A and 2B ) . The activation included the three stages of the hierarchically organized auditory cortex , namely the core ( in A1 ) , the belt ( in the middle lateral [ML] and CL areas , surrounding A1 ) , and the parabelt ( the caudal part of the parabelt , lateral to CL and ML , on the STG convexity ) , and extended into the inferior bank of the STG ( also known as the superior bank of the superior temporal sulcus ) . Activation in the ipsilateral hemisphere was much more limited ( M1 ) or absent altogether ( M2 ) . These results indicate that motion-induced activity in the contralateral posterior auditory cortex cannot simply be explained by a spatial laterality process or by encoding of the intrinsic spectrotemporal content of the moving stimuli since these processes were controlled for by the stationary stimuli . While in Experiment 1 the BOLD response induced by each stimulus was measured 5 s after the stimulus onset ( targeting the peak of the hemodynamic response function [28] ) , in a second experiment we measured the time course of the hemodynamic response induced by each stimulus , recording the BOLD response 2 , 3 , 4 , and 5 s after the stimulus onset ( Fig 2C ) . This was to eliminate the possibility that the greater activation triggered by motion stimuli in Experiment 1 was due to adaptation of the BOLD response to the stationary stimulus while the motion-induced response was still sustained at its maximal level . This experiment revealed no interaction between the two stimulus conditions and the four time points tested , except in a small cluster in the right inferior bank of the STG , where stationary sounds did not induce any significant activation . This confirms that the contrast between moving and stationary stimuli revealed in Experiment 1 was not due to a different time course of the BOLD response to the two types of stimuli . The change of spatial location inherent to moving stimuli induces some temporal variations of the sound spectral envelope due to filtering of the sounds through the pinna . This spectrotemporal effect of motion could theoretically be the source of auditory motion selectivity as described so far . To control for this , we generated a new stimulus by averaging at each time point the signal coming from each channel and presenting the stimulus diotically ( i . e . , the same signal was sent to each ear ) . This stimulus had a spectrotemporal structure similar to the motion stimulus , allowing us to control for the combined effect of the intrinsic spectrotemporal content of the stimulus and the spectrotemporal effect of motion , but did not contain any static spatial cue ( Fig 1A ) . In our third experiment , we compared the BOLD response induced by motion with the one induced by the spectrotemporal control stimulus . The contrasts Motion Left minus Spectrotemporal control and Motion Right minus Spectrotemporal control revealed activation in ML , CL , the caudal parabelt , and the inferior bank of the STG in the hemisphere contralateral to the stimuli ( Fig 3 ) and no activation in the ipsilateral hemisphere . The results thus indicate that motion-induced activation in these contralateral regions cannot be explained by spectrotemporal processes . We then tested the hypothesis that a linear combination of spectrotemporal and stationary spatial processes could fully explain motion-related activity . To do so , we gathered in a single experiment the three different types of stimuli: motion , stationary , and spectrotemporal control stimuli . As a control , we first computed the same contrasts as in Experiments 1 and 3: the contrasts Motion minus Stationary and Motion minus Spectrotemporal controls revealed patterns of activation similar to those observed in Experiments 1 and 3 ( Fig 4 ) . Brain regions commonly activated by both contrasts were ML , CL , the caudal parabelt , and parts of the inferior bank of the STG ( green region in Fig 4 ) . This result indicates that in these regions , motion-related activation cannot be explained by static spatial processing ( controlled in the contrast Motion minus Stationary ) , spectrotemporal effect of motion ( controlled in the contrast Motion minus Spectrotemporal control ) , or the intrinsic spectrotemporal content of the stimulus ( controlled in both contrasts ) alone . We then tested the hypothesis that the addition of these processes could explain motion-induced activation . More specifically , we tested the following model: Motion = Static central sound + Spatial laterality + Spectrotemporal effect of motion , where spatial laterality was defined with the contrast Stationary sounds minus Stationary central sound and the spectrotemporal effect of motion with the contrast Spectrotemporal controls minus Stationary central sound ( S2 Fig ) . Motion-induced activation was not found to be significantly different from the sum of the three components in most parts of brain regions activated by motion stimuli , including A1 ( Fig 5 , transparent regions within the black boundary ) , indicating that the simple additive model provides a good estimation of the activation induced by motion stimuli in these regions . However , in parts of ML , CL , the caudal parabelt , and the inferior bank of the STG , the BOLD response induced by motion stimuli was significantly greater than the sum of the BOLD responses induced by stationary central sound , spatial laterality , and spectrotemporal effect of motion ( Fig 5 , green cluster ) . The excess signal ( i . e . , the part not explained by the above components ) that was present in these areas could arise if the three processes made different contributions ( i . e . , different weighting coefficients attached to each process ) , if the processes interacted , or if an additional , independent ( and thus genuine motion-specific ) process would trigger the response . To distinguish between these explanations , we performed a multiple linear regression analysis across those voxels where the BOLD response to auditory motion was not fully explained by the simple additive model ( green cluster in Fig 5 ) . This analysis revealed that the motion-induced BOLD signal was best estimated by a model including differential weighting factors ( with a slightly smaller contribution of spectrotemporal effect of motion , compared to the two other components in both subjects; see Table 1 ) , weak or no interactions between the components ( R2 change between models with and without interactions: M1: 0 . 004 , F = 0 . 13 , p = 0 . 969; M2: 0 . 019 , F = 4 . 09 , p = 0 . 004; R2 for best model: M1: 0 . 82; M2: 0 . 86; p-values below 1 x 10−6 for each subject ) , and , importantly , a term that assumes the presence of genuine motion-selective responses to occur in these areas ( the added constant term in the linear regression , Table 1 , p-values below 1 x 10−5 for each subject ) . This additional component accounted for about 42% of the mean signal intensity induced by motion stimuli ( M1: 42 . 5%; M2: 42 . 2% ) . A control analysis across voxels taken within the region where the full signal could be explained by the simple additive model ( transparent patch in Fig 5 ) revealed similar regression coefficients but no significant constant term ( Table 1 ) . S3 Fig illustrates the goodness of fit of the best-adjusted model with experimental data in each subject . To further characterize these auditory motion-selective areas , we tested whether they were also selective for visual motion . Visual-motion areas were identified by contrasting the BOLD signal induced by slowly moving horizontally oriented gratings with that induced by stationary gratings . The auditory and visual motion regions were found to partially overlap on the inferior bank of the STG ( Fig 6 ) .
Movement selectivity has been classically investigated by comparing moving and stationary stimuli . In humans , the contrast Motion minus Stationary has consistently revealed activation of the planum temporale but not Heschl’s gyrus [20 , 21 , 29–32] . While human A1 has traditionally been considered to be located on the Heschl’s gyrus , recent tonotopy data suggest that A1 is rather found on the posterior half of the Heschl’s gyrus and slightly extends posteriorly into a small part of the planum temporale [33 , 34] . The planum temporale could thus encompass human homologues of macaque auditory areas CM , CL , and ML but also of the posterolateral part of A1 . According to this model , our results fit well with human data since the contrast Motion minus Stationary induced the recruitment of the caudolateral parts of A1 and extended into CL and ML ( Experiments 1 and 4 ) . The contrast Motion minus Stationary controls for the intrinsic spectrotemporal content of the motion stimuli and , when the stationary control sounds cover the whole spatial range spanned by the motion stimulus , its nondynamic spatial component as well . However , it does not control for the spectrotemporal effects of motion: filtering of sound through the pinna differs for each spatial position , inducing slow modulations of the sound spectral envelope . This dynamic nonspatial component aspect has only been controlled for in one previous study [21] . In this human fMRI study , spectrotemporal effects of motion were found to be processed in parts of the planum temporale overlapping with those involved in motion processing ( revealed by the contrast Motion minus Stationary ) . Our study provides similar results in macaques ( Fig 4 ) . Compared to the previous human study [21] , our study in macaques went several steps further . First , we tested for the first time the possibility that the addition of the different components of auditory stimuli could explain the activation that they induced . Our results demonstrate that the additive combination of ( 1 ) the intrinsic spectrotemporal component ( “Stationary central sound” processing ) , ( 2 ) the nondynamic spatial component ( spatial laterality processing ) , and ( 3 ) the dynamic nonspatial component ( spectrotemporal effect of motion ) allows a full characterization of motion processing in large parts of the STG , including A1 . However , in a circumscribed region overlapping parts of ML , CL , the caudal parabelt , and the inferior bank of the STG , the additive model did not explain a significant fraction of the signal induced by motion stimuli . Second , we controlled for potential interactions between components . This was done by introducing in the linear models all possible interactions and testing whether the percentage of the variance explained by the model including the interactions was significantly higher than in the model without the interaction terms . This analysis revealed weak or no interactions between the components . Third , we controlled for mechanisms that can influence the gain of the three different components ( i . e . , different forms of adaptation or amplification ) . It has been argued that greater activation induced by moving sounds compared to stationary sounds could represent adaptation of responses induced by stationary sounds [35–38] . By measuring the time course of the hemodynamic response induced by motion and stationary sounds , here we demonstrate that the differential activation cannot be explained by ( slow ) adaptation of the BOLD response triggered by stationary sounds ( Experiment 2 ) . Rapid adaptation of neuronal responses could also , in principle , explain greater activation induced by moving sounds . In the present study , the use of an amplitude-modulated noise as a stimulus should have limited such an effect . Moreover , adaptation or amplification mechanisms were controlled in the final linear regression analysis by the coefficients weighting the different components and their interactions . The regression analysis revealed that the magnitude of these mechanisms was moderate ( coefficients close to 1 ) and similar between the region where part of the motion processing signal was left unexplained and the region where the signal could be fully explained by the simple additive model . Thus , adaptation or amplification fails to explain the unaccounted signal . The linear regression analysis revealed that after controlling for the different processes not specific to motion , their potential interactions , and their potential adaptation or amplification , on average 42% of the signal variance remained unexplained . Since processes directly or indirectly linked to processing of several stationary sounds were controlled for in this analysis , we conclude that this remaining part of the signal comes from a motion-specific process . The nature of the mechanisms underlying auditory motion perception has been debated for more than 30 y . The “snapshot hypothesis” postulates that motion is inferred from snapshots of object successive positions , without direct appreciation of motion . According to this hypothesis , auditory motion perception is based on the same mechanisms as those involved in the localization of static sound sources . The alternative hypothesis , usually referred as the “motion detector hypothesis” or “velocity detector hypothesis” , considers that motion perception is based on specific mechanisms . On the one hand , the fact that in humans , the minimum audible movement angle ( MAMA; defined as the smallest movement angle allowing a subject to determine whether a sound is moving or not ) differs from the minimum audible angle ( MAA; defined as the smallest location difference between two static sources that subjects could discriminate ) and was found to increase with speed has been interpreted as suggesting that motion detectors exist [3 , 4] . It has also been argued that if moving sounds are processed via a snapshot process , comparing the location of the starting and ending points should be sufficient to perceive movement , and information about intermediary locations should be redundant . However , the MAMA for moving sounds was found to be smaller than the MAA for tone bursts marking the starting and ending positions of the moving sound [5 , 39] , and subjects could discriminate between accelerating and decelerating 90 ms-long stimuli starting and ending at identical spatial locations [6] , indicating that the human brain extracts other information than the location of the starting and end points of a moving sound . On the other hand , movement detection and discrimination performances of human subjects have been explained by estimation of the distance traversed by the source rather than appreciation of the motion per se [1] . The MAMA and the MAA were also found to show similar dependency on sound frequency , spectral bandwidth , and source azimuth , suggesting that static spatial cue perception and dynamic spatial cue perception are dependent on the same underlying mechanisms [2 , 40] . Altogether , these data have failed to provide clear evidence about the mechanisms underlying motion perception . Our results indicate that the parts of the auditory cortex , including A1 , analyze auditory sources in movement by processing their spatial location and the consequence of location change ( spectrotemporal effect of motion ) , consistent with a “snapshot” strategy . However , we demonstrate that caudal belt and parabelt regions of auditory cortex extract the motion component of moving stimuli ( motion-specific process ) in addition to the non-motion specific components . The coexistence of the two mechanisms might explain why it has been so difficult to distinguish between the snapshot and the motion detectors hypotheses in the past . It is also relevant to psychophysical data illustrating facilitation of motion perception by static spatial information [7 , 8] . The BOLD response measured by fMRI only provides an indirect measure of neuronal activity . The choice of macaques as subjects of this study paves the way for a detailed investigation of the motion-specific mechanism at the cellular level . Our study indicates that motion-specific and snapshot processes coexist in the caudal belt and parabelt regions . Electrophysiological studies will be useful to determine whether the two types of processes are encoded by different populations of neurons or not . This study investigated the mechanisms underlying auditory motion along the azimuth axis restricted to each hemispace . It is possible that the relative contribution of motion-specific and snapshot mechanisms depends on the nature of the movement ( in elevation , in depth , or across hemispaces ) . Primates are particularly accurate at discriminating the spatial location of sounds coming from the regions near the midline [41] , and based on the main opponent-channel hypothesis [42] , the firing rate of auditory neurons contains more information about the precise location of a sound source when it is near the midline , by opposition to the peripheral space . It is thus possible that the contribution of motion-specific mechanism for sounds moving across the midline is less important than when sounds move within one hemispace . Looming sounds are particularly relevant from a behavioral point of view , often indicating a threat . In such sounds , information about the successive static position of the sound is limited to monaural cues and the distance or time to arrival is systematically underestimated [43] . Thus , one might expect the relative contribution of snapshot processes to be less important . These will be interesting hypotheses to test in the future . Auditory information has been proposed to be processed along two main streams: a ventral stream connecting the rostral belt and parabelt areas to the ventral prefrontal cortex , involved in the identification of sounds , and a dorsal stream connecting the caudal belt and parabelt to the posterior parietal cortex and the dorsolateral prefrontal cortex , involved in spatial processing [18 , 44] . The exact number and the respective role of each stream are still a matter of debate . For instance , several authors have suggested that the auditory dorsal stream could be divided into distinct substreams [15 , 45–47] . Anatomical tract-tracing studies suggest at least two substreams originating from the caudal belt areas: a “dorsodorsal substream” involving CM , projecting to Tpt posterior to CM and thence to the parietal and prefrontal cortex; and a “dorsocaudal substream” connecting ML and CL to the caudal parabelt and the inferior bank of the STG , which itself projects to the parietal cortex [48] . Evidence for a role of the dorsal auditory pathway in spatial processing in nonhuman primates comes from electrophysiological data indicating that sensitivity to static spatial information is higher in CL compared to A1 and the rostral belt areas [15 , 18 , 19] . Our study revealed a similar refinement of static spatial processing between A1 and CL ( see S4 Fig ) . Some single-unit electrophysiological studies also highlighted the role of CM in spatial processing [17 , 19] . However , spatial selectivity seems to be weaker compared to CL [19] , and modelling of neural data suggests that the firing rates of CL neurons , but not of CM neurons , carry enough information to account for sound localization performance in azimuth [16] . Using fMRI , we did find strong and consistent BOLD activation induced by static and moving sounds in CL but not in CM . Since the BOLD response indirectly reflects the activity of very large populations of neurons , it is possible that at this scale , the spatial sensitivity of CM neurons cannot be detected . Together , these results suggest that spatial sensitivity differs to some extent between CL and CM and that the specialization for static auditory spatial processing mainly occurs along the dorsocaudal substream . In addition to this static spatial processing , our data demonstrate a particular specialization for motion analysis within the dorsal-caudal substream , indicating that this stream carries out higher-level spatial computation rather than just representing fixed space . This result indicates the existence of parallel pathways for fixed and dynamic auditory spatial analysis within the dorsocaudal stream that likely feed into distinct downstream mechanisms as in the visual system . The exact number of substreams within the visual dorsal pathway and their respective roles are still debated [49–51] . Subdivision of the human visual dorsal pathway into at least two substreams has been proposed , with the dorsodorsal pathway involving the superior parietal lobule , while the ventral-dorsal pathway involves the visual motion areas in the temporal sulcus ( including MT ) and the inferior parietal region [52 , 53] . Auditory motion-specific areas described in the present study have been found to extend on the inferior bank of the STG ( equivalent of the superior bank of the temporal sulcus ) , in the vicinity of visual motion areas , and our visual experiment revealed a small overlap between auditory and visual motion-selective areas in this region . These data suggest that the auditory and visual motion substreams share some neural substrates in the inferior bank of the STG , which might potentially support the numerous behavioral interactions that have been reported between auditory and visual motion perception [54 , 55] . It will be interesting to determine whether this region represents the point where the auditory and visual motion substreams merge by testing whether the same population of neurons respond to auditory and visual motion stimuli and where these neurons project . Because the posterior auditory cortex in humans is involved not only in spatial analysis but also in speech and music perception , recent models of the dorsal auditory stream incorporate the idea that there may be a transformation of auditory information into a motor signal coding for the action necessary to produce the sound [47 , 56 , 57] . While this auditory-motor function could coexist with perceptual spatial processing in distinct substreams [47] , it has also been argued that spatial processing could be interpreted as a preparation for eye movement or grasping [58 , 59] . In this case , the whole dorsal pathway could be characterized as a mechanism for auditory-motor integration , with different substreams supporting different auditory-motor processes . We suggest that the ability to compute motion allows a substream of the auditory dorsal pathway to predict the trajectory of sources in a way that helps visual tracking and grasping .
All procedures were approved by the Animal Welfare and Ethical Review Body at Newcastle University and by the United Kingdom Home Office ( PPL 60/4095 , 60/4037 and 70/7976 ) . Experiments complied with the Animal Scientific Procedures Act ( 1986 ) , the European Directive on the protection of animals used for scientific purposes ( 2010/63/EU ) , and the United States National Institutes of Health Guidelines for the Care and Use of Animals for Experimental Procedures and were performed with great care to ensure the well-being of the animals . Two awake male rhesus monkeys ( Macaca mulatta ) M1 and M2 , respectively 7 and 11 y old ( weighing 7 and 17 kg ) , participated in the experiments . The monkeys were initially implanted with a head holder . All surgical procedures were performed under general anesthesia and sterile conditions . Details regarding surgical procedures , postoperative care , and the cleaning of the implant are published elsewhere [60] . The animals were first habituated to the scanner environment over the course of several days and then enrolled in the experiments . Sound stimuli were created in MATLAB 7 . 1 ( MathWorks , Natick , Massachusetts , US ) with a sample rate of 44 . 1 kHz and 16-bit resolution . All stimuli for the auditory motion experiments ( Experiments 1 to 4 ) were based on a random-phase noise carrier ( 1–20 kHz ) . The noise was amplitude modulated by a sinusoidal envelope of 80% depth at 80 Hz in order to produce an additional localization cue [61] and to prevent adaptation . Prior to the scanning experiments , the amplitude-modulated noise was delivered in free field in an echo-suppressed room from 17 different positions separated by 10° , along the azimuthal axis ( from −80° to +80° ) and recorded with a omnidirectional miniature electret microphone ( Knowles Corporation , Itasca , Illinois , US ) placed within each ear canal of the subject , resulting in the recording of the sound convolved by the head-related transfer function of each monkey . The microphone output was amplified and recorded digitally at a sampling rate of 44 . 1 kHz . In addition to interaural level and time differences , this whole procedure preserves spectral cues specific to each individual and has been shown in humans to induce stimuli to be perceived as localized in the external space when delivered through headphones [62] . While providing stimuli tailored to each subject , this approach is time consuming , and when applied to macaques , it is limited by the fact that the subject’s pinnae are mobile such that movements can distort spectral cues . By gently holding the subject’s pinnae from the back and keeping the stimulus recording sessions short ( ~1 h ) , we could prevent any movement . To accommodate this time limitation , we did not attempt to record sounds separated by less than the MAA ( around 3° in macaques; see [17 , 26 , 63] ) . Instead , we took advantage of the fact that a motion percept can be induced by sequentially presenting sounds coming from spatial positions separated by more than the MAA as long as these spatial positions are not too far apart and as long as the duration of each sound is short enough ( in other words , as long as the apparent speed is high enough ) . Motion stimuli moving three times back and forth between the central space ( 0° position ) and the most lateral position ( + or −80° position ) within each hemispace were created by concatenating 100 ms-long segments of recordings from adjacent locations ( Fig 1 ) ; half of the stimuli started from the central position and the other half started from the most lateral position . Any abrupt change of power between segments was avoided by concatenating on-phase segments , starting and finishing when the power of the amplitude-modulated signal was minimum . Since these spatial locations were separated by 10° , it resulted in stimuli virtually moving at a speed of 100°/s . A similar approach was used to create stationary stimuli: 100 ms-long recorded segments coming from the same location were concatenated to form long examplars of stationary sounds . Five different stationary stimuli were created , corresponding to spatial positions −80° , −40° , 0° , +40° , and +80° ( Fig 1 ) . Finally , spectrotemporal controls of each motion stimulus were created by averaging at each time point the signal coming from each channel and presenting the stimulus diotically ( Fig 1 ) . The resulting stimuli were sounds with intensity and spectral content varying with time like the motion stimuli but with no spatial information ( stimuli sounding as coming from inside the head , without spatial laterality ) . All auditory motion and control stimuli were 4 . 9 s long . To validate the percept induced by the virtual motion stimuli in our monkey subjects , we characterized our stimuli psychophysically in humans . This approach takes advantage of the similarity of spatial perception between humans and monkeys and of the fact that the MAA is actually smaller in humans [17 , 26 , 27 , 63] , allowing us to establish a more exacting test in which differences between concatenated and moving stimuli are more likely to be detected . Briefly , we built an apparatus capable of delivering static or moving sound stimuli in free field in our soundproof chamber . This used an electric motor with adjustable speed ( controlled by a potentiometer ) with an attached rotor arm to which a small speaker was attached , to achieve sound-source rotatory movement in the azimuthal plane through the subject’s ear canal . We have replicated in three human participants ( two males and one female with no hearing disorder , age range: 20–35 y , having given their informed consent ) the intra-auricular recording approach used in macaques , using exactly the same sounds and the same recording equipment . For each human participant , we recorded from the ear canal when static sounds were delivered from azimuthal positions recorded in 10° intervals from zero ( midline , front ) . Additionally , we recorded motion stimuli from the ear canal when a speaker moved around the head with an angular motion of 100°/s or 50°/s , clockwise and anticlockwise . The two types of recordings were based on the same amplitude-modulated noise stimulus used in the macaque work . The recording session lasted between 2 and 3 h , requiring the human participant to remain still during this period . Static recordings from adjacent positions were then concatenated to create stimuli virtually moving at speeds of 100°/s or 50°/s , the duration of each recorded segment being 100 and 200 ms , respectively ( concatenated stimuli ) . This procedure was used to create four concatenated stimuli per participant , while recordings of real moving sounds were used to create moving stimuli matching the travelled path and the direction of the concatenated stimuli: two stimuli of each type moved clockwise ( one moving from +90° to +180° and the other one moving from −90° to 0° ) , and two stimuli moved anticlockwise ( one moving from −90° to +180° and the other one moving from 90° to 0° ) . We tested each participant’s perception of these stimuli using criterion-free psychophysics . We used an AXB psychophysical paradigm , where X was always a moving stimulus and A and B were either a moving stimulus or a concatenated stimulus ( whether A or B was the moving stimulus was randomized across trials ) and each stimulus moved at 100°/s along the same path . Participants were asked to identify which of stimuli A or B was different from X . The results confirmed that no participant was able to distinguish concatenated stimuli from motion stimuli at 100°/s ( performance was at chance level in each participant: Chi-square tests , n = 240 , degrees of freedom [df] = 1 , X2/p = 0 . 6/0 . 44 [participant 1] , 0 . 42/0 . 52 [participant 2] , 0 . 6/0 . 44 [participant 3]; S5 Fig ) . Because spatial acuity in azimuth is better in humans than macaques [17 , 26 , 27 , 63] , this result supports our claim that concatenated stimuli used in the scanner were perceived by macaques as smoothly moving . In a control experiment , we replicated the AXB psychophysical paradigm using the concatenated and motion stimuli moving at a speed of 50°/s . This second experiment confirmed that the percept is speed dependent , as two participants were then able to discriminate concatenated stimuli from motion stimuli ( Chi-square tests , n = 240 , df = 1 , X2/p = 138/<1 x 10−19 [participant 1] , 86 . 4/<1 x 10−19 [participant 3] ) , while the third participant was still at chance level ( Chi-square test , n = 240 , df = 1 , X2/p = 0 . 42/0 . 52 , S5 Fig ) . Stimuli for the tonotopy experiment were based on a random-phase noise carrier with three different passbands , 0 . 5–1 kHz , 2–4 kHz , and 8–16 kHz , resulting in three different stimuli that encompassed different spectral ranges . The carriers were amplitude modulated with a sinusoidal envelope of 90% depth at 10 Hz to achieve robust responses . Stimuli for the visual motion localizer experiment ( Experiment 5 ) were horizontally oriented gratings ( spatial frequency: 0 . 5 cycles/° ) of 6° diameter , displayed at 7° to the right or to the left of the vertical meridian . Half of the stimuli were moving at a frequency of 8 Hz , while the remaining stimuli were stationary . Auditory stimuli were delivered in the scanner at an RMS sound pressure level of 74 dB using custom adapted electrostatic headphones based on a Nordic NeuroLab system ( Nordic NeuroLab , Bergen , Norway ) . These headphones feature a flat frequency transfer function up to 16 kHz and are free from harmonic distortion at the applied sound pressure level . We recorded the spontaneous eye movements of one monkey when exposed to the stationary lateralized stimuli ( −80° , −40° , +40° , and +80° ) through the headphones . Perception of the stimuli induced systematic eye movements in the direction of the sound ( S6 Fig ) , indicating that the spatial information of the stimuli was preserved through the headphones and that the monkey could perceive it . Subjects were scanned in a sitting position , head-fixed , while engaged in a visual fixation task ( fixation window: 2° ) . Eye position was monitored at 60 Hz with a camera-based system ( SensoriMotoric Instruments , Teltow , Germany ) , and correct fixation was rewarded by drops of fruit juice . To avoid any contamination of the stimulus-induced BOLD responses by the response evoked by the acoustic noise of the scanner , a sparse-sampling paradigm was used for all auditory experiments . Images were acquired every 10 s ( acquisition time: 1 . 6 s ) , stimuli being presented during the 8 . 4 s silent gap . Based on a previous time course characterization of the BOLD response in the auditory system of macaques [28] , the plateau phase of the BOLD response was targeted in experiments 1 , 3 , and 4 by starting acquisition of the images 5 s after the stimulus onset . In Experiment 2 , image acquisition started 2 , 3 , 4 , and 5 s after the stimulus onset . To obtain baseline data , stimuli were omitted in 25% of the trials prior to image acquisition . The visual localizer experiment was acquired with a continuous paradigm . Stimuli were delivered in a pseudorandomized way , ensuring that each stimulus was presented the same number of times within each daily session . Because we aimed to only analyze trials in which the monkey was fixating , we interrupted the session when the monkey stopped fixation for more than 5 min . The number of trials per stimulus therefore varied from one session to the next , according to the monkey’s willingness to participate in the visual fixation task . Between 28 and 50 images per stimulus were acquired in each daily session . For the tonotopy experiment , 3 sessions were acquired in each subject . For the first , third , and fourth auditory motion experiments , 5 , 6 , and 17 sessions were acquired in M1 , and 7 , 6 , and 14 sessions were acquired in M2 . For the hemodynamic response function experiment ( Experiment 2 ) , 11 sessions were acquired in M1 . For the visual localizer experiment ( Experiment 5 ) , 7 sessions were acquired in M2 . Data were recorded in a 4 . 7 T actively shielded vertical MRI scanner ( Bruker Biospec 47/60 VAS ) equipped with an actively shielded gradient system ( Bruker GA-38S ) of 38 cm innerbore diameter ( Bruker BioSpin , Ettlingen , Germany ) . A transmit/receive volume RF coil with an active decoupler ( Bruker ) was used to acquire functional and nonisotropic structural data . The volume coil in the transmit-only mode and an 8-channel receiving surface phased-array coil ( H . Kolster , Windmiller Kolster Scientific , Fresno , California , US ) were used to acquire isotropic structural data in order to generate three-dimensional surfaces . Nonisotropic structural T1-weighted images ( resolution: 0 . 5 mm x 0 . 5 mm x 2 mm ) were acquired at the end of each session using a modified driven equilibrium Fourier transform ( MDEFT ) sequence with the same slice geometry as the functional scans to simplify coregistration . The imaging parameters were as follows: FOV: 12 . 8 cm x 9 . 6 cm; FA: 30° , TI: 800 ms; TE: 6 ms , TR: 2 s . Isotropic structural T1- and T2-weighted images ( FOV: 10 cm x 10 cm; resolution: 0 . 6 mm x 0 . 6 mm x 0 . 6 mm ) were acquired during a separate session using a magnetization-prepared rapid gradient-echo ( MP-RAGE ) sequence ( FA: 27° , TI: 800 ms , TE: 7 ms , TR: 2 . 1 s ) , and a rapid acquisition with relaxation enhancement ( RARE ) sequence ( TE: 14 ms , RARE factor: 8 , TR: 5 . 5 s ) , respectively . No parallel acceleration was used . Functional data covering the whole STG were acquired with a single-shot gradient-echo echo-planar imaging ( EPI ) sequence optimized for each monkey . Typical parameters were as follows: FOV: 12 . 8 cm x 9 . 6 cm; FA: 90° , TE: 21 ms , TA: 1 . 6 s , axial orientation , slice thickness: 2 mm , interleaved slice acquisition . The inplane resolution was 1 mm x 1 mm for M1 and 1 mm x 1 . 5 mm for M2 . Functional MRI data were analyzed with SPM8 ( http://www . fil . ion . ucl . ac . uk/spm/ ) . Data acquired from each animal were processed separately in their native space . Images from each session were first realigned to the mean EPI image . No attempt was made to coregister EPI and structural scans . Instead , a pair consisting of a mean EPI image and a nonisotropic structural scan acquired during the same session was chosen as a reference , based on the quality of their alignment to each other . All functional images were coregistered to this reference EPI image , and all the nonisotropic structural scans were coregistered to the corresponding structural scan . The isotropic structural scans were coregistered to the mean of all nonisotropic structural scans . Functional data were smoothed with a kernel of 2 mm fullwidth at half maximum , high-pass filtered with a cut-off of 300 s to account for slow signal drifts , and adjusted for global signal fluctuations ( global scaling ) . In a general linear model analysis for the combined sessions of each experiment , the voxel-wise response estimate coefficients ( beta-values ) and t-values ( one sided t-test ) for the different contrasts of interest were calculated ( head movement parameters were regressed out ) . Associated p-values were corrected for multiple comparisons using the FWE correction on the bilateral STG ( Experiments 1 , 2 , and 3 ) and for the STG contralateral to the stimuli ( Experiments 4 and 5 ) . For auditory and visual motion experiments , data acquired while subjects did not fixate were discarded . For tonotopy experiments , all data were used . In Experiment 4 , a multiple linear regression analysis was performed across voxels in each subject using SPSS ( IBM SPSS Statistics 21 . 0 ) . The analysis was first performed across the set of voxels where the contrasts Motion Left minus ( Left Stationary central sound + Left Spatial laterality + Left Spectrotemporal effect of motion ) and Motion Right minus ( Right Stationary central sound + Right Spatial laterality + Right Spectrotemporal effect of motion ) performed at the voxel level were significant ( t-values > 4 . 1 , corrected p < 0 . 05 ) . In order to determine the source of the signal that was not explained by the linear addition of components , we tested the following model across those voxels: First- and second-order interactions between the three explanatory factors were incorporated only if this more complex model significantly increased the percentage of variance explained by the model . As a control , we performed the same analysis across a subset of voxels taken from the region where the voxel-based contrasts did not induce any significant difference ( t-values < 4 . 1 , corrected p > 0 . 05 ) . To match the statistical power of both analyses , the size of this second voxel set was matched to the first one by selecting voxels with the smallest t-values . Since the probability of false-negative results decreases with the t value , this procedure reduced the risk of selecting false-negative voxels . Performing these analyses on smoothed and unsmoothed data provided similar results . Only results based on unsmoothed data are described in the Results section of the manuscript . Isotropic structural images were used to generate the rendered surfaces . The ratio between T1-weighted and T2-weighted images was computed , and the resulting image was used to manually segment the gray matter of the STG . The binary image was used to generate a tri-dimensional triangulated mesh using BrainVisa suite ( http://brainvisa . info ) . The functional results ( contrast maps and t maps ) were then projected with BrainVisa onto the rendered surface . Tonotopy maps ( S1 Fig ) were calculated by subtracting the response estimate coefficient ( beta-values ) of the low-frequency condition ( 0 . 5–1 kHz ) from the high-frequency condition ( 8–16 kHz ) . The contrast High frequency minus Low frequency was inclusively masked by the contrast High frequency minus Silence , while the contrast Low frequency minus High frequency was masked by the contrast Low frequency minus Silence ( p < 0 . 05 , uncorrected for multiple comparisons for both masks ) . The low- and high-frequency reversals of tonotopic gradients were identified on the surfaces and used to define the position of the core and belt areas on the superior bank of the STG: the low-frequency gradient reversal defined the rostral border of A1 and ML , and the high-frequency reversal defined the caudal border of A1 and ML and the rostral border of CL ( see Fig 2A ) . The caudal parabelt was defined as the region lateral to ML and CL on the STG convexity . | Motion is a fundamental dimension of acoustic and visual stimuli that is critical for animals to interact with their environment . Yet , surprisingly , we still do not understand the basic mechanisms in the brain that underlie perception of auditory motion . For the last 30 y , this research field has been hampered by unsuccessful attempts to answer a simple but fundamental question: is auditory motion perception deduced from processing individual static sounds , or are there mechanisms in the auditory domain dedicated to detecting motion ? Here we report the discovery of specific motion detectors located in the auditory cortex of primates . We demonstrate that these auditory motion detectors are close to the well-known visual motion detectors . Both types of detectors are likely to be crucial for the planning of limb and eye movement . This study addresses a fundamental issue in neuroscience and sheds new light on the brain mechanisms underlying the essential aspects of our ability to navigate the world . |
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Mathematical models are powerful tools for epidemiology and can be used to compare control actions . However , different models and model parameterizations may provide different prediction of outcomes . In other fields of research , ensemble modeling has been used to combine multiple projections . We explore the possibility of applying such methods to epidemiology by adapting Bayesian techniques developed for climate forecasting . We exemplify the implementation with single model ensembles based on different parameterizations of the Warwick model run for the 2001 United Kingdom foot and mouth disease outbreak and compare the efficacy of different control actions . This allows us to investigate the effect that discrepancy among projections based on different modeling assumptions has on the ensemble prediction . A sensitivity analysis showed that the choice of prior can have a pronounced effect on the posterior estimates of quantities of interest , in particular for ensembles with large discrepancy among projections . However , by using a hierarchical extension of the method we show that prior sensitivity can be circumvented . We further extend the method to include a priori beliefs about different modeling assumptions and demonstrate that the effect of this can have different consequences depending on the discrepancy among projections . We propose that the method is a promising analytical tool for ensemble modeling of disease outbreaks .
Epidemiological forecasting is inherently challenging because the outcome often depends on largely unpredictable characteristics of hosts and pathogens as well as contact structure and pathways that mediate transmission [1] . Faced with such uncertainty , policy makers must still make decisions with high stakes , both in terms of health and economics . For instance , global annual malaria mortality was recently estimated at around 1 . 1 million [2] and to optimize control efforts , policy makers must make seasonal predictions about spatiotemporal patterns [3] . The prospect of an emergent pandemic influenza outbreak remains a global threat and emergency preparedness must evaluate the costs and benefits of control measures such as border control , closing of workplaces and/or schools as well as different vaccination strategies [4] . Livestock diseases are major concerns for both animal welfare and economics . As an example , the United Kingdom ( UK ) 2001 outbreak of foot and mouth disease ( FMD ) involved culling of approximately 7 million animals , either in an effort to control the disease or for welfare reasons , and the total cost has been estimated at £8 billion [5] . To minimize the size and duration of future outbreaks , various strategies for culling and vaccination must be compared [6–8] . As a tool to address these challenging tasks , mathematical models offer the possibility to explore different scenarios , thereby informing emergency preparedness and response to epidemics [1 , 9–12] . The predictive focus of epidemiological models can either be classified as forecasting or projecting [13] . Forecasting aims at estimating what will happen and can be used for example to predict seasonal peaks of outbreaks [3 , 14] or to identify geographical areas of particular concern [15] . Projecting , which is the main focus of this study , instead aims at comparing different scenarios and exploring what would happen under various assumptions of transmission , e . g . comparing the effectiveness of different control actions [7 , 16–19] . Whilst analytical models clearly provide important insight into observed dynamics and a theoretical understanding of epidemiology [20–22] , there has been a shift in recent years towards stochastic simulation models for predictive purposes [1] . Typically , dynamic models are constructed and outbreaks are simulated repeatedly , thus generating predictive distributions of outcomes [1 , 17 , 18 , 23] . This variability in outcomes caused by the mere stochasticity of the transmission process includes one level of uncertainty , but still only relies on a single set of assumptions about the underlying disease transmission process . However , multiple assumptions can often be justified , leading to further uncertainty in the predictions . For instance , different models may have different projections because of different assumptions about transmission or because they incorporate different levels of detail . It may also be informative to explore different projections in terms of different parameterizations of a single model , for example corresponding to worst or best case scenarios . Faced with a set of projections , an important issue is how to combine these in a manner such that they can be used to inform policy . The issue of multiple projections is not unique to the field of epidemiology , and various techniques of ensemble modeling have been used to merge projections based on different modeling assumptions . The key concept is that rather than relying on a single set of assumptions , a range of projections is used for predictive purposes . For instance , climate forecasting has employed ensemble techniques to account for uncertainty about initial conditions , parameter values and structure of the model design when predicting climate change [24 , 25] . Weather forecasting has been improved by combining the results of multiple models [26 , 27] . Similarly , hydrological model ensembles have been demonstrated to increase reliability of catchment forecasting [28] and have been used to assess the risk of flooding events [29] . Ensemble methods have also proven to be a powerful decision tool for medical diagnostics [30 , 31] and ecological considerations including management [32] and prediction of future species distribution [33] . Ensemble modeling has not yet been extensively used in epidemiology . However a few implementations exist , commonly by feeding climate or weather ensembles into disease models . Daszak et al . [34] coupled a set of climate projections to an environmental niche model of Nipah virus to predict future range distribution of the virus under climate change . Similarly , Guis et al . [35] investigated the effect of climate change on Bluetongue emergence in Europe by simulating outbreaks under different climate change scenarios . Focusing on a shorter time scale , Thomson et al . [3] used an ensemble of seasonal forecasts to predict the spatiotemporal pattern of within seasonal variation in malaria incidence . These studies all used a single disease model projection , coupled to an ensemble of climate or weather forecasts and the use of structurally different epidemiological models are to our knowledge still rare . However , Smith et al . [36] compared different malaria vaccination strategies by implementing an ensemble approach with different alterations of a base model . Also , in order to estimate global malaria mortality , Murray et al . [2] used weighted averages of different predictive models . Given the success of ensemble methods in other fields , we expect that epidemiological implementations will increase . For that purpose however , there is a need for methods that combine multiple projections . A central issue in ensemble modeling is how to weight different projections , and we envisage four main procedures for this . Firstly , all models can be given equal weights . For instance , the IPCC 2001 report on climate change [37] used a set of climate models and gave the range of probable scenarios by averaging over different models and uncertainty by envelopes that included all scenarios . Gårdmark et al . [32] used seven ecological models for cod stock and argued that in order to prevent underestimation of uncertainty , weighted model averages are not to be used and when communicating with policy makers , it is preferable to present all included projections as well as the underlying assumptions behind them . A similar approach was also used by Smith et al . [36] , who presented the prevalence of malaria under different vaccination strategies by medians of individual models and the range of the whole ensemble . Secondly , expert opinions can be used to weight models . To our knowledge , no ensemble study has implemented weights based exclusively on expert opinion , but Bayesian model averaging can incorporate expert opinion as a subjective prior on model probabilities [38] . This approach relies on engaging stakeholders and communicating the underlying assumptions of the projections . Thirdly , models can be weighted by agreement with other models . This approach was implemented by Räisänen and Palmer [39] , who used cross-validation to weight climate models . As a more informal approach to the use of model consensus , the third IPCC report excluded two models because these predicted much higher global warming than the rest of the ensemble , thus acting as outliers [24] . Fourthly , weights can be determined by the models’ ability to replicate data . If all models are fitted to the same data using likelihood based methods , weights can be given directly by Akaike or Bayesian Information Criterion ( AIC or BIC ) [40 , 41] . In the FMD context , this may be a suitable approach if all included models are data driven kernel models that estimate parameters from outbreak data , such as those proposed by Jewell et al . [42] or Tildesley et al . [43] . However , such weighting schemes would be unfeasible when including detailed simulation models that rely on a large number of parameters , that are determined by expert opinion or lab experiment , such as AusSpread [44] , NAADSM [45] and InterSpread Plus [46] . We propose that the future of ensemble modeling for epidemiology will benefit from combining structurally different model types , and methods of weighting need to handle both kernel type models as well as detailed simulation models . Thus , bias assessment is often confined to the ability of models to replicate observed summary statistics of interest , in particular when the resolution of data observation is on a courser scale than the model prediction [47] . Such methods have been developed within the field of climate forecasting . Giorgi and Mearns [48] introduced a formal framework in which model weights were assessed based on model bias compared to observed data as well as convergence , i . e . agreement with the model consensus . Tebaldi et al . [47] extended the approach to a Bayesian framework . This approach is appealing because it provides probability distributions of quantities of interest , hence uncertainty about the projected outcomes may be provided to policy makers . As such , it would be a suitable approach also for epidemiological predictions . However , methods developed in one field might not be directly transferable to another . Tebaldi et al . [47] points out that lack of data at fine scale resolution is a limiting factor for climate forecasting . Yet , at courser resolution climate researchers have access to long time series of climate variables to assess model bias . Comparable data may be available for endemic diseases , such as malaria [36] or tuberculosis [49] , or seasonally recurrent outbreaks , such as influenza [14] or measles [50] . However , for emerging diseases , long time series would rarely be available , making the lack of data an even bigger issue for epidemiology . In this methodological paper we aim to explore the potential of using ensemble methods based on the approach presented by Tebaldi et al . [47] for epidemiological projections . The Tebaldi et al . methodology focus on ensembles where projections are made with different models and our aim is to provide a corresponding framework for disease outbreak projections . To investigate the potential of the framework for epidemiology , we here use variations of a single model as a proxy for different models , thus allowing us to investigate how the methodology performs under different levels of discrepancy among projections in the ensemble . We exemplify the implementation by using the UK 2001 FMD outbreak and projections modelled by different parameterizations of the Warwick model [7 , 9] . In the 2001 UK FMD outbreak , livestock on all infected premises ( IPs ) were culled . In addition , livestock on farms that were identified to be at high risk of infection were culled as either traditional dangerous contacts ( DCs ) or contiguous premises ( CPs ) . CPs were defined as “a category of dangerous contact where animals may have been exposed to infection on neighboring infected premises” [5 , 8] . We start by focusing on ensemble prediction of epidemic duration under the control action employed during the 2001 outbreak compared with an alternative action that excludes CP culling . We investigate sensitivity to priors and explore a hierarchical Bayesian extension of the method to circumvent potential problems with prior sensitivity . We also explore the potential of including subjective a priori trust in the different modeling assumptions and extend the methodology further to allow incorporation of multiple epidemic quantities , here exemplified by adding number of infected and culled farms to the analysis . Through a simulation study , we finally explore the capacity and limitations of the proposed ensemble method , pinpointing some important features of ensemble modeling
We focus on projections of FMD made by the Warwick model [7 , 9] . This model was developed in the early stages of the 2001 FMD outbreak by Keeling and coworkers to determine the potential for disease spread and the impact of intervention strategies [9] . Here , we utilized a modified version of the model used in 2001 , and we briefly describe relevant aspects of the Warwick model with regard to ensemble modeling . Full details of the model can be found in [7 , 9] . The rate at which an infectious farm I infects a susceptible farm J is given by: RateIJ=SusJ×TransI×K ( dIJ ) ( 1 ) where SusJ= ( [Zsheep , J]ps , SSsheep+[Zcow , J]pc , SScow ) ( 2 ) is the susceptibility of farm J and TransI= ( [Zsheep , I]ps , TTsheep+[Zcow , I]pc , TTcow ) ( 3 ) is the transmissibility of farm I and K ( dIJ ) is the distance-dependent transmission kernel , estimated from contact tracing [9] . In this model Zs , I is the number of livestock species s ( sheep or cow ) recorded as being on farm I , Ss and Ts measure the region and species-specific susceptibility and transmissibility , dIJ is the distance between farms I and J and K ( dIJ ) is the distance dependent transmission kernel . The parameters , ps , S , pc , S , ps , T and pc , T , are power law parameters that account for a non-linear increase in susceptibility and transmissibility as animal numbers on a farm increase . Previous work has indicated that a model with power laws provides a closer fit to the 2001 data than when these powers are set to unity [43 , 51 , 52] . This version of the model has previously been parameterized to fit to the 2001 FMD outbreak [43] . Region-specific transmissibility and susceptibility parameters ( and associated power laws ) capture specific epidemiological characteristics and policy measures used in the main hot spots of Cumbria , Devon and the Welsh and Scottish borders . The model is therefore fitted to five regions: Cumbria , Devon , Wales , Scotland and the rest of England ( excluding Cumbria and Devon ) . A table listing all the parameter values used in this model is given in Tildesley et al . [43] . In order to obtain multiple modeling assumptions for ensemble modeling , we specified different transmission rates , i as RateIJi=SusJ×k1iTransI×K ( k2idIJ ) ( 4 ) where k1i and k2i are constants , specific for each modeling assumption , that scale the transmissibility and the spatial kernel , respectively . k1i = k2i = 1 , follow the parameterizations of Tildesley et al . [43] and by decreasing or increasing these constants , we obtain parameterizations that correspond to best or worst case expectations about the transmissibility and spatial range of transmission . We are interested in how the level of discrepancy among modeling assumptions influences the performance of the ensemble method and we therefore created two different ensembles with different k1i and k2i , as listed in Table 1 . We refer to these as the large and small discrepancy ensemble , corresponding to large and small differences , respectively , in the underlying modeling assumptions used for projections . DCs in our model were determined based upon both prior infection by an IP and future risk of infection in the same way as in previous work [8] . CPs were defined as farms that share a common boundary and were determined on an individual basis . The model was seeded with the farms that were predicted to be infected prior to the introduction of movement restrictions on the 23rd February . For each modeling assumption i and control action , 200 replicates were simulated and each simulation progressed until the epidemic died out . To demonstrate concepts and explore the potential of using the Tebaldi et al . [47] approach for epidemiological considerations we initially focus on outbreak duration . This is often considered to be the most costly aspect of FMD outbreaks due to its implication for trade [53] . In section 2 . 7 we extend the methodology to multiple epidemic quantities . However , the outbreak duration example offers transparent transition from the original climate analysis of Tebaldi et al . [47] that considers the ensemble estimated difference between current and future mean temperatures . In order to introduce the framework to epidemiology , we consider the difference between the implemented and an alternative control action , attempting to show whether the inclusion of CP culling was an appropriate choice given the conditions at the start of the outbreak . As this is a post outbreak analysis , we know the final outbreak duration of the observed outbreak , but that is just a single realization and due to the stochastic nature of disease transmission , the exact outcome may be quite variable . We also have no observed outbreak under the alternative control action to compare with the implemented control . Under these conditions , the most appropriate quantities to compare are the mean duration of a large number of outbreaks under the two control actions , something that can only be achieved through epidemic modeling . We are interested in comparing projections under the implemented control action to the observed data in order to estimate model weights . Using the Bayesian method of Tebaldi et al . [47] , weights as well as statistics of the outbreak , like duration , are considered unknown random variables , and we denote the mean outbreak duration under the implemented and the alternative control action as μ and v , respectively , corresponding to the mean current and future temperature , respectively , for the climate application . In order to fit with the normal assumptions of the method , we consider log-duration in the analysis . Weights are expressed through precision , λ = λ1 , λ2 , … , λn , with λi denoting the precision of modeling assumption i . The projection specific parameter xi indicates the mean of all replicates under the implemented control action ( analog of current climate ) for modeling assumption i . For the UK 2001 outbreak this included culling of IPs , DCs and CPs . The corresponding projection for the alternative control action ( analog of future climate ) , that included culling of IPs and DCs is denoted yi . The relationship between projections and ensemble parameters is expressed as xi~Normal ( μ , λi-1 ) ( 5 ) yi~Normal ( ν+β ( xi-μ ) , ( θλi ) -1 ) , ( 6 ) with Normal ( μ , λi-1 ) denoting the normal distribution with mean μ and variance λi-1 . Parameter θ is included to allow for difference in overall precision of the modeling assumptions under implemented and alternative control actions . However , since projections xi and yi are based on simulations , it is fair to assume that modeling assumption i that has a high precision for the observed control action also will do well for the unobserved action . This is incorporated by the λi term in both Eqs 5 and 6 . For the same reason , we may expect that a projection of a large xi also corresponds to a large value for yi and thus β is included to allow for correlation between corresponding projections for the two control actions; a projection that e . g . over-predicts duration of the outbreak for the observed control action can be expected to also over-predict the alternative control action . The analysis of Tebaldi et al . [47] also assessed bias of projections by their ability to reproduce observed current climate by incorporating the relationship between observed current climate x0 , an unobserved true mean climate variable ( μ ) and the precision of natural variability τ0 through x0~Normal ( μ , τ0-1 ) . ( 7 ) In climate modeling , it is a fair assumption that τ0 is a known , fixed parameter because it can be assessed through historical data . That would rarely be the case for the corresponding epidemic considerations , at least for emerging diseases . Using a single outbreak to evaluate bias , we clearly have no way of assessing variability in outcomes . We therefore include τ0 as an unknown , random variable that is estimated in the analysis as described in the following section . To aid the interpretation and transfer from the climate to the epidemiological interpretation , we have included Table 2 that lists the variables used in the analysis . Our main interest in terms of outcome under the implemented control action is μ rather than x0 . However , it is clear that in addition to the mean duration of the outbreak , the uncertainty about the process also results in some variability in the outcomes that we need to consider . The stochastic simulations used to generate projections provide not only a mean simulated outbreak quantity , but also a range of outcomes that projects the variability . In the absence of repeated outbreaks to evaluate variability of outcomes , an obvious choice would be to use this information to inform the variability τ0 . Defining the variability τi as the precision of projections under the implemented action for modeling assumptions i = 1 , 2 , … , n we include a hierarchical structure in the analysis so that for i = 0 , 1 , 2 , … , n τi~Gamma ( aτ , bτ ) , ( 8 ) where Gamma ( aτ , bτ ) indicates the gamma distribution with shape parameter aτ and rate bτ both of which are unknown parameters and are estimated in the analysis . Thus , as it would be cumbersome to elicit a fixed prior for τ0 based on our prior expectations about variability , we instead assume that τ0 comes from some , unknown distribution , and make use of τ1 , τ2 , … , τn to inform what this distribution should be . Similarly , we need to model the variability of projections under the alternative control action , and denoting this φi we specify The parameters aφ and bφ are conditionally independent from all other parameters in the analysis and can be modelled separately in the Bayesian analysis . As xi , yi , τi and φi are calculated from a finite number of realization with each modeling assumption and control action , there is some uncertainty related to this . Tebaldi et al . [47] however points out that while it is certainly possible to construct a Bayesian model that takes this uncertainty into account , the effect is minimal if the number of replicates is large . With the R = 200 replicates preformed here , the uncertainty of the mean will in practice have very little effect , and we have included xi , yi , τi and φi as fixed observations . Priors for aτ and bτ were specified as a gamma distribution with shapes Aaτ and Abτ , respectively , and rates Baτ and Bbτ , respectively . Similarly , the priors for aφ and bφ , were specified as a gamma distribution with shapes Aaφ and Abφ , respectively , and rates Baφ and Bbφ , respectively . We explored different parameter choices for the hyperpriors and found that the results were insensitive to the choice of prior for a wide range of values . In the analysis presented , we used Aaτ = Abτ = Baτ = Bbτ = Aaϕ Abϕ = Baϕ = Bbϕ = 0 . 001 . This corresponds to prior distributions with a mean of one and a variance of 1000 , thus allowing for a wide range of plausible values . Bayesian analysis requires the specification of prior parameters . We follow Tebaldi et al . [47] with priors specified as uniform on the real line for μ , ν , and β , and λi~Gamma ( aλ , bλ ) for i = 1 , 2 , … , n and θ~Gamma ( aθ , bθ ) . We also need to specify hyperpriors for aτ and bτ , and we implement aτ~Gamma ( Aaτ , Baτ ) and bτ~Gamma ( Abτ , Bbτ ) . Denoting x = x1 , x2 , … , xn and y = y1 , y2 , … , yn , the full posterior distribution under these priors is given by P ( μ , ν , β , λ , θ , τ0|x0 , x , y , τ1 , τ2 , … ) ∝∏i=1n ( λiaλ−1e−bλλiλiθ1/2exp{ −λi2[ ( xi−μ ) 2+θ ( yi−ν−β ( xi−μ ) ) 2 ] } ) θaθ−1e−bθθτ01/2exp{ τ02 ( x0−μ ) 2 }∏i=0n ( τiaτ−1e−bττi ) aτAaτ−1e−BaτaτbτAbτ−1e−Bbτbτ ( 10 ) This posterior only differs from the one defined by Tebaldi et al . in that we include τ0 as an unknown variable and use a hierarchical structure for its prior . Using Markov Chain Monte Carlo ( MCMC ) techniques as described in 2 . 9 , we first performed the analysis with priors as specified by Tebaldi et al . [47] where applicable , i . e . aλ = bλ = aθ = bθ = 0 . 001 , because they argue that this ensures that the prior contributes little to the posteriors . However , we propose that this argument is not necessarily always valid . In particular λi could be expected to be sensitive to priors because it is essentially only fitted to two data points , xi and yi . Yet , based on approximations of conditional distributions , Tebaldi et al . argued that the gamma distribution with aλ = bλ = 0 . 001 is appropriately vague for the analysis . For transparency we here follow their approach and investigate the effect of the prior for the simplified model where β = 0 . The mean of the conditional distribution of λi is then approximated by E ( λi|X0 , X , Y ) ≅aλ+1bλ+12[ ( xi−μ˜ ) 2+θ ( yi−ν˜ ) 2 ] , ( 11 ) where μ~ is the conditional mean of the distribution of μ , given by μ~= ( ∑i=1nλiXi+τ0x0 ) / ( ∑i=1nλi+τ0 ) ( 12 ) and ν~ the corresponding value for v , given by ν~= ( ∑i=1nλiyi ) / ( ∑i=1nλi ) . ( 13 ) We stress that Σλi need not sum to one , as might be intuitive when using weights . As given by Eqs 11 and 12 , the mean of μ and ν only depends on the relative values of λi , but the absolute values influence the width of the distribution , with the variance of the conditional distributions increasing with lower absolute values of λi ( Table 3 ) . While a low value of aλ certainly ensures little contribution to the numerator in Eq ( 11 ) , it is less evident that a low value for bλ contributes little to the denominator because if xi→μ~ and y→ν~ , the denominator actually approaches bλ . Hence , to ensure that a low value of bλ can be considered vague such that our posterior is informed primarily by x0 , x and y , we must conclude that |xi-μ~| or |yi-ν~| is clearly separated from zero . However , if λi≫λj for all i ≠ j and λi≫τ0 , then μ~≈xi and ν~≈yi and nothing in the model prevents this relationship . In fact , if we consider the gamma prior with shape and scale parameters set to 0 . 001 , the distribution has most of its density near zero , however with a fat tail ( yet exponentially bounded ) that allows for high values . In the current analysis , this corresponds to the prior belief that the majority of modeling assumptions will have very low precision while a few will have very high . Under this prior belief , it is expected that for some model i , λi≫λj for all i ≠ j . In the instance where instead τ0≫λi for all i , then μ~≈x0 and the approximation would hold , but we cannot expect that relationship . As we cannot a priori be sure that the choice of aλ and bλ does not influence our posterior as long as they are arbitrarily small , we performed a prior sensitivity analysis and re-ran the analysis with aλ = bλ = 0 . 01 and aλ = bλ = 0 . 0001 . We could expect that the sensitivity to priors depends on the difference among modeling assumptions , and we investigate this by analyzing ensembles with little and large discrepancy between assumptions in the ensemble as given by Table 1 . We refer to this as the Non Hierarchical Weighting ( NHW ) method . If we cannot ensure that the analysis is insensitive to the choice of prior , it implies that our prior beliefs will influence how much different projections contribute to ensemble predictions with the current method . Using prior beliefs is sometimes desirable , and in section 2 . 6 we consider the situation where we trust some modeling assumptions more than others . However , it would rarely be the case that we would have substantial expectations that could inform the shape , aλ , and scale , bλ , of the prior for λ . A potential solution might be to extend the model to include hierarchical priors such that the prior for λi is estimated in the model rather than a priori fixed . We make a slight change to the parameterization of the prior distribution such that λi~Gamma ( aλ , aλ/mλ ) , ( 14 ) i . e . specifying the distribution by its mean mλ and shape aλ , which are estimated in the model . In that way , we move our uncertainty up a level and express our beliefs about the distribution of mλ and aλ , rather than λ . Using mλ rather than bλ facilitates the specification of a prior for the mean precision parameter that corresponds to the priors previously specified on individual λi . This parameterization further aids the use of prior beliefs about weights in section 2 . 6 . While we can never be strictly uninformative in Bayesian analysis , the hierarchical prior can allow for a wide range of plausible mλ and aλ whereas the model presented in section 2 . 4 requires these to be specified explicitly . This also allows for the concept of “borrowing strength” [54] , such that the distribution of each λi can be indirectly informed by all other precisions via the hierarchical distribution . This is often beneficial in situations where individual parameters are fitted to a small amount of data [55 , 56] , which clearly is the case for λi here . To extend Eq ( 10 ) to a hierarchical model , we include hyperpriors such that aλ~Gamma ( Aaλ , Baλ ) ( 15 ) and mλ~Gamma ( Amλ , Bmλ ) . ( 16 ) We performed the corresponding sensitivity analysis for the hierarchical ensemble prediction by applying hyperpriors Aaλ = Baλ = Amλ = Bmλ set to 0 . 01 , 0 . 001 and 0 . 0001 . We refer to this as hierarchical sensitivity set-up one . Secondly , we performed a sensitivity analysis , hierarchical sensitivity set-up two , where we fixed the shape parameters Aaλ = Amλ = 0 . 001 and only varied Baλ = Bmλ , again set to either 0 . 01 , 0 . 001 or 0 . 0001 . We refer to this as model as the Standard Hierarchical Weighting ( SHW ) method . Using expert opinions may substantially improve predictions [57] , and there are several instances where incorporating prior beliefs that reflect the “trust” in different modeling assumptions could be useful . For instance , a policymaker might have more trust in one model type over another , and rather than excluding the models that are considered less reliable ( i . e . giving them a priori zero weigh ) , it could be useful to include them , yet with less contribution to the ensemble . In the case considered here , where modeling assumptions represent most likely , best and worst case in terms of parameterization , we might want to give the “most likely” modeling assumption higher weight . For the analysis with fixed aλ and bλ , described in section 2 . 4 , we could merely elicit a different scale parameter bλ for each λi , such that modeling assumptions with high trust are given a low value . However , with the shape parameter aλ set to a low value ( “vague” shape ) , the prior may have little effect on the posterior λi . Eliciting a high value of aλ would instead result in a posterior that is merely the results of our prior beliefs and we have no foundation for which to elicit some intermediate value . In order to combine the hierarchical approach with informative priors , we propose a modification of the analysis presented in section 2 . 5 , where the assumption of exchangeability is relaxed in the hierarchical structure with λi~Gamma ( aλ , aλ/m^λi ) , ( 17 ) where m^λi=wimλ and wi indicates the a priori trust in modeling assumption i . With wi = kwj , the prior distribution of λi will have a mean that is k times that of λj and from Eqs ( 12 ) and ( 13 ) the relationship also implies that before λ is estimated ( i . e . involving the data x0 , x and y ) , the outputs of modeling assumption i will contribute k times as much to μ and v as does assumption j . To demonstrate the effect that a priori trust in different modeling assumptions can have on the posterior estimates , we consider the case where the best , most likely and worst case scenarios for each of the two varied parameters corresponds to percentile 2 . 5 , 50 and 97 . 5 , respectively , of a normal distribution . Given that the density at percentiles 2 . 5 and 97 . 5 then is 0 . 15 of that at the mode , we specify wi = 0 . 15 for i = 2 , 3 , 4 and 7 , i . e . for modeling assumptions where one of the varied parameters follows the most likely scenario , whereas the other one is set to either worst or best case . With the same rationality , we specify wi = 0 . 021 for i = 5 , 6 , 8 and 9 , i . e . modeling assumptions where both parameters follow either best or worst case expectations . We also investigate the case where a high weight is given to a projection xi further away from the observed data x0 . Consistently , modeling assumptions i = 5 predicted the shortest duration for all actions and ensembles . We therefore also performed the analysis with w5 = 1 and w1 = 0 . 021 , and all other weights are as above . This allows us to investigate the performance of the informative weighting method when an outlier is up-weighted . We refer to this method as the Informative Hierarchical Weighting ( IHW ) method . In the above examples , we focused on a single epidemic quantity , allowing for transparent transition from the original Tebaldi et al . work [47] that focused on temperature . For epidemiology , it may however be useful to consider multiple epidemic quantities . This could be done in different ways , but here we offer a straightforward multi-quantity extension of the Bayesian model for the single epidemic quantity , based on the supposition that the relative weights are equal for all quantities . As such , we implement a single weighting parameter λ , shared among all quantities . For other parameters , we use a similar notation as for the single quantity analysis , but give many of the parameters an additional index q , indicating that the parameter is quantity specific . We expand the Bayesian model by defining xi , q~Normal ( μq , θμ , qλi-1 ) , ( 18 ) yi , q~Normal ( νq+βq ( xi , q-μq ) , ( θν , qλi ) -1 ) , ( 19 ) x0 , q~Normal ( μq , τ0 , q-1 ) , ( 20 ) where xi , q and yi , q are the mean projections of modeling assumption i for epidemic quantity q for the implemented and alternative control action , respectively , and x0 , q is the corresponding observed value . As for the single epidemic quantity example , μqand Vqare the expected values of quantity q , and because we cannot expect to have the same correlation between control actions for all quantities , βq is included as unique for each q . Parameters θμ , q and θv , q scales the precision of models between actions and quantities and the parameters of the Bayesian model are identifiable by defining θμ , 1 = 1 . Similarly , we specify quantity specific parameters τi , q~Gamma ( aτ , q , bτ , q ) , i=0 , 1 , 2 , … , n , φi , q~Gamma ( aφ , q , bφ , q ) , i=1 , 2 , … , n . ( 21 ) The conditional distributions for the multi-quantity extension are provided in Table 4 . We denote the total number of quantities in an analysis as Q . The above examples focus on the UK 2001 FMD outbreak and show how the introduced framework can be applied to actual outbreak data . However , a limitation to this approach is that we are confined to investigating the behavior of the ensemble methodology for that particular outbreak . To further investigate the potential and limitations of the proposed methods , we also performed analysis of simulated outbreak data . With simulated data , we have “true” estimates of μ and v , and we want to explore the ability of the ensemble to predict these under two different conditions; when the true values lies within the range of X and Y predicted by the individual models of the ensemble and when it does not . For multi-model ensembles , this corresponds to the situation where the true behavior of the outbreak is encapsulated within the range of underlying assumptions of the individual models and when it is not . Here we explore the outcome of these conditions by first simulating outbreaks with the parameterizations of modeling assumption 1 ( k1 = k2 = 1 ) , i . e . located in the center of both the small and large discrepancy ensemble . This simulates outbreaks where the true behavior of the outbreak is encapsulated within the range of underlying assumptions of the individual projections for both ensembles . We also simulate outbreaks with a parameterization where both k1 and k2 are set to 0 . 9 . This produces outbreaks where the true behavior is outside of the assumptions of the projections for the small discrepancy ensemble , yet inside the range of the large discrepancy ensemble . The exact behavior of the ensemble depends on the actual realization of the individual outbreak , because the observed values x0 are different due to the stochastic disease transmission process . We therefore apply both the small and large discrepancy ensembles to ten realizations of each of the simulation parameterizations . We implement both the single and multiple epidemic quantity analysis , thus further highlighting the effect of using multiple quantities . We use Markov Chain Monte Carlo ( MCMC ) techniques to obtain samples from the full posterior distribution of the proposed Bayesian models ( NHW , SHW and IHW ) . For many parameters , the conditional distribution belongs to a standard parametric family , thus allowing for Gibbs sampling . We list these conditional distributions in Table 3 for single quantity analysis and Table 4 for multiple quantities . We also rely on Metropolis-Hastings ( M-H ) updates , and with the computation used for multi-quantity analysis being a straightforward extension of that used for the single quantity , we start by describing the update scheme for the single quantity analysis . The conditional distribution of bτ has a known form , P ( bτ|… ) =Gamma ( Abτ+ ( N+1 ) aτ , Bbτ+∑i=0Nτi ) , that would allow for Gibbs sampling of bτ , whereas M-H updates need to be implemented for aτ . We however found strong correlation between the marginal posterior estimates of aτ and bτ , and mixing was improved by performing joint M-H updates of these parameters by multivariate Random Walk ( RW ) proposals . Mixing can be further improved by updating parameters on a transform that resembles a Gaussian distribution , and we therefore performed updates on the log-transform , i . e . based on current values of aτ and bτ We proposed candidate parameters [log ( aτ* ) , log ( bτ* ) ] from MVN ( [log ( aτ ) , log ( bτ ) ] , Στ ) . Here MVN indicates the multivariate normal distribution and Στ the covariance matrix . Candidate values are accepted with the probability min ( 1 , Gamma ( aτ*|Aaτ , Baτ ) Gamma ( bτ*|Abτ , Bbτ ) ∏i=0NGamma ( τi|aτ* , bτ* ) Gamma ( aτ|Aaτ , Baτ ) Gamma ( bτ|Abτ , Bbτ ) ∏i=0NGamma ( τi|aτ , bτ ) |Jτ| ) , ( 22 ) where |Jτ|=aτ*bτ* ( aτbτ ) -1 indicates the Jacobian determinant of the log-transform . Mixing can be improved if the covariance matrix Στ is proportional to the covariance of the marginal posterior of [log ( aτ ) , log ( bτ ) ] , here indicated as Φ . However , this is not known prior to the analysis . We therefore implement an optimized method of the Robbins-Monroe search process as presented by Garthwaite et al . [58] . This estimates the covariance during the MCMC and finds the scaling parameter ρ such that Στ = ρΦ provides a chosen long term acceptance rate , here set to 0 . 234 based on suggestions by Roberts et al . [59] . The method has been demonstrated to be appropriate also for RW on transformed scales of the parameters [60] . The corresponding updates of aφ and bφ were also performed with M-H updates and we proposed candidate parameters [log ( aφ* ) , log ( bφ* ) ] from MVN ( [log ( aϕ ) , log ( bϕ ) ] , Σϕ ) . and accepted them with probability min ( 1 , Gamma ( aφ*|Aaφ , Baφ ) Gamma ( bφ*|Abφ , Bbφ ) ∏i=1NGamma ( φi|aφ* , bφ* ) Gamma ( aφ|Aaφ , Baφ ) Gamma ( bφ|Abφ , Bbφ ) ∏i=1NGamma ( φi|aφ , bφ ) |Jφ| ) . ( 23 ) We used a similar approach for updates of aλ and mλ in the hierarchical methods ( SHW and IHW ) and proposed [log ( aλ* ) , log ( mλ* ) ] from MVN ( [log ( aλ ) , log ( bλ ) ] , Σλ ) . Candidate parameters were accepted with probability min ( 1 , Gamma ( aλ*|Aaλ , Baλ ) Gamma ( mλ*|Abλ , Bbλ ) ∏i=1NGamma ( λi|aλ* , b^λi* ) Gamma ( aλ|Aaλ , Baλ ) Gamma ( mλ|Abλ , Bbλ ) ∏i=1NGamma ( λi|aλ , b^λi ) |Jλ| ) , ( 24 ) where b^λi=aλ/mλ for all modeling assumptions i in the SHW method and b^λi=aλ/m^λi with m^λi=wimλ in the IHW method . As above , we used the method of Garthwaite et al . [58] to determine Σλ to obtain a long term acceptance rate of 0 . 234 . We also found strong correlation between μ and ν . In order to improve mixing , we repeated the updates of these parameters five times for each iteration of the MCMC . The same update scheme was used for the multi-quantity consideration , yet with a separate Στ , q , Σϕ , q and Σλ , q adaptively estimated for each quantity q . The algorithm was implemented in MATLAB ( The MathWorks , Inc . , Natick , Massachusetts , United States ) and code is available as supplementary information ( S1 File ) .
We start by presenting the results for the single quantity analysis , highlighting the behavior of the method for the NHW , SHW and IHW schemes . Fig 2 , panels A and B show the estimates of outbreak duration for the two control actions for the large discrepancy ensembles using the NHW method and reveals rather large prior sensitivity . Note that we plot marginal posteriors of M = eμ and N = ev , respectively . As such , the posteriors represent the geometric mean outbreak duration . The corresponding arithmetic mean can be calculated as eμ+1/ ( 2τ0 ) and ev+1/ ( 2ϕ0 ) , respectively , yet we use the geometric mean as it more clearly shows the relationship with individual projections , here presented by xi = exi and Yi = eyi , respectively . For aλ = bλ = 0 . 0001 ( solid gray lines ) , the distributions are multimodal with peaks at individual model predictions , whereas a more smooth shape is obtained for aλ = bλ = 0 . 01 ( dashed black lines ) and aλ = bλ = 0 . 001 ( solid black line ) yields an intermediate result . With the SHW method , we instead obtain posteriors that are largely insensitive to the choice of hyperprior . Fig 2 , panels E and F present the result of sensitivity set-up one , showing near identical posterior estimates when hyperparameters Aaλ , Baλ , Amλ and Bmλ are set to 0 . 01 , 0 . 001 or 0 . 0001 . Sensitivity set-up two produced results that were visually indistinguishable from panels E and F and are not presented . This further indicates that the hierarchical method is robust to the choice of hyperpriors . Within epidemiology , there is clearly an interest in not just the expected outbreak duration , but also other statistics such as the probability of large outbreaks occurring . We therefore consider the posterior predictive distributions of individual outbreak durations under the two control actions . For the non-hierarchical model ( Fig 2 , panels C and D ) , there is an obvious effect of the choice of prior with higher probability of long outbreaks for lower values of aλ and bλ . For the hierarchical model ( Fig 2 , panels G and H ) , there is again little difference among posteriors corresponding to different priors . When evaluating the efficiency of control actions , the difference N-M is of particular interest . In the example presented here , this estimates how much longer the outbreak would have been if culling of CPs had been excluded from the control . As shown in Fig 3 , the estimates are again sensitive to the choice of prior with the NHW method , yet insensitive with the SHW method . The range of the posterior under the NHW method is less sensitive to the prior for the low discrepancy ensemble ( panel B ) than for the large discrepancy ensemble ( panel A ) , where higher probability of less difference is estimated with aλ = bλ = 0 . 01 than for aλ = bλ = 0 . 0001 . However , the multimodal behavior of the NHW method with low values of aλ and bλ is obtained also for the low discrepancy ensemble . Fig 4 demonstrates the effect that a priori beliefs about the weights have on the predicted outbreak duration under large and small discrepancy ensembles . When using a priori higher weights for the most likely scenarios ( modeling assumption one; black dotted lines ) , the posterior estimates are shifted and become more centered on projections of that particular modeling assumption compared to the case where a priori weights are equal ( black solid line ) . The outcome of up-weighting the outlier ( modeling assumption five; solid gray lines ) is however different between the two ensembles . For the small discrepancy ensemble ( panels A and B ) , similar results are found as for the up-weighting of the most likely scenarios; posteriors are shifted towards the projection with a priori high weight . For the high discrepancy ensemble ( panels C and D ) , the posterior estimates of outbreak duration instead become wider for both control actions , indicating larger uncertainty about the expected duration of outbreaks . Fig 5 shows the marginal posterior estimates of individual weights λi under different discrepancy among projections and informative weighting schemes . When using a priori equal weights , there is little difference in the estimates for the small discrepancy ensemble ( top left panel ) whereas moderate differences are obtained for large discrepancy ( bottom left panel ) . Note that while the error bars are overlapping , the mean estimate of the most likely scenarios ( modeling assumption one ) is approximately 1 . 7 times as large as that of the outlier ( modeling assumption five ) , meaning that the former will contribute approximately 1 . 7 times as much to the posterior means of μ and v than the latter ( Eqs ( 12 ) and ( 13 ) ) . When giving a priori highest weight to the most likely scenario ( modeling assumption one; middle column panels ) , the posterior estimate of λ1 is consistently shifted upwards , meaning that the most likely scenarios ( modeling assumption one ) will contribute more to the posteriors of μ and ν than other projections . For the up-weighting of the outlier , projections corresponding to modeling assumption five , the same is found when there is little discrepancy among projections ( lower right panel ) . This is however not found for the high discrepancy ensemble ( top right panel ) , where the main effect is that compared to equal a priori weights ( top left panel ) , the error bars are wider; this indicates larger uncertainty about weights and consequently about the contribution of individual projections to the posterior estimates of outbreak durations . The proposed multi-quantity method can be implemented with either NHW , SHW or IHW schemes . Here we aim to illustrate the effect of using multiple quantities and focus on the SHW scheme . Fig 6 plots the marginal posterior density of mean outbreak duration under the two control actions as estimated for the multiple quantity analysis ( solid ) together with the corresponding estimates for the single quantity analysis ( dashed ) . The figure illustrates how inclusion of multiple quantities in the analysis leads to tighter distributions , centered on projections for i = 1 . The multi-quantity analysis produces a probability distribution of all considered quantities , and Fig 7 further illustrates how the marginal posterior densities are located above zero for all three considered quantities . To illustrate the performance of the method under different conditions , we also analyzed simulated outbreaks . Fig 8 shows the posteriors of mean duration for outbreaks simulated with the k1 = k2 = 1 parameterization and applying the small ( triangles ) and large ( circles ) discrepancy ensembles , represented by the median values and error bars indicating the 95% central credibility interval . Note that individual realizations , indicated by stars , are expected to frequently be outside of the credibility envelopes . Error bars are inclusive of the true mean outbreak duration ( dashed lines ) for all ten analyzed realizations for both the implemented and alternative control actions . However , the credibility envelopes are tighter and medians closer to the true value for the multi-quantity analysis . This indicates that the ensemble prediction is improved by including multiple quantities . When applying the analysis to outbreaks simulated with the k1 = k2 = 0 . 9 parameterization ( Fig 9 ) , the large discrepancy ensemble error bars are still consistently inclusive of the true value . As with Fig 8 , credibility envelopes are tighter for the multi-quantity analysis . The error bars of the small discrepancy ensemble that all rely on simulations with parameterizations with higher k1 and k2 than the true value , are not inclusive of the true value , indicating that the small discrepancy ensemble fails in predicting the true values of the outbreak .
Ensemble modeling is appealing because it offers the possibility to combine multiple projections . Within weather forecasting , the approach has given more robust predictions , and we could expect that to be the case for epidemiology as well . However , there is a need for the development of methods describing how to combine several epidemiological projections . The aim of this study was to investigate the possibility of using the Bayesian framework introduced by Tebaldi et al . [47] . We find that it is a promising approach , for primarily three reasons . Firstly , when the methodology is implemented in a hierarchical Bayesian framework , it provides an appealing interpretation of model exchangeability . Essentially , projections and their underlying modeling assumptions are treated as random draws from a population of possible projections . By estimating the hierarchical parameters aλ and mλ jointly with individual precisions ( weights ) λi , the characteristics of this hypothetical population are estimated . Smith et al . [61] used a similar approach for climate ensembles and pointed out that this reduces the impact of which models are included or excluded in the ensemble . That is , we should expect to get similar results when using a different set of model assumptions if they are chosen independently . We stress that this interpretation is more valid for multi-model ensembles , however . Also , the term “random draws” should not be interpreted as arbitrary . Rather , the interpretation is that models should come from a population of well-informed , reasonable models . The analysis treats the outputs of the performed simulations under different assumptions as data ( Eq 1 , Table 1 ) , and as such they are used to inform the quantities of interest ( μ and v ) . This may seem counterintuitive , yet it only serves as a formal means to combine the results of multiple projections , and by Eqs ( 7 ) and ( 20 ) , these are combined with available outbreak data . Secondly , the framework can handle several different weighting schemes simultaneously . The original methods introduced by Tebaldi et al . [47] used convergence and bias to assess weights . Here , we further extend the framework such that informative priors can be included to inform the weights , thus relaxing the supposition that all modeling assumptions are a priori exchangeable . Epidemiological predictions suffer from lack of available data to assess model bias , and we propose that expert opinions will play a larger role than in other fields of research . With the analytical tool proposed here , a policymaker can choose to include a range of projections based on different modeling assumptions , yet give them different weights , rather than including one or a few ( given a weight of one ) and excluding others ( given a weight of zero ) . When using different mechanistic models , subjective trust in the different models can be incorporated by using methods of prior elicitation based on expert opinion [38] . Importantly , our methods can incorporate these subjective beliefs in the hierarchical framework , requiring only the specification of the a priori relative confidence in the underlying assumptions of the projections . Definition of an individual , fixed prior would undoubtedly be cumbersome to elicit from expert opinion; it would not be feasible to ask policymakers to define an individual gamma prior for each modeling assumption . Here we used ensembles based on projections of the same model with different parameterizations , demonstrating the possibility to explore parameter space , yet with unequal probabilities of different parameterizations . Uncertainty about parameters will be an issue for most epidemiological models , and we propose that multi-model ensembles should incorporate projections with different models and different parameterizations . Thus , different mechanistic assumptions as well as parameter uncertainty would be incorporated in the ensemble . Thirdly , the framework produces easily interpretable probability distributions . It is important that communication with policymakers include uncertainties about prediction rather than just the most likely outcome [16] . In the ensemble context , these uncertainties take into account different assumptions about the transmission process . Gårdmark et al . [32] suggested that uncertainty should be communicated with policymakers by presenting the full range of predicted outcomes . However , that would give equal weights to all included projections and would require that the results be communicated with a detailed description of all assumptions made , thus allowing the policymaker to decide how much to trust each modeling assumption . This would be a cumbersome task , particularly for detailed simulation models that rely on a large number of parameters . We therefore argue that it is beneficial to communicate the aggregated and weighted result as easily interpretable probability distributions . With further modifications of the methodology , we propose that the approach could also be used as a forecasting tool during an outbreak , e . g . by letting xi and yi denote current and future numbers of infected farms . In such a situation , there is a great need for rapid and clear communication of model results to aid policy decisions . The visual manner in which uncertainty is presented using probability distributions makes them easy to understand and communicate [62] . We here show that these distributions are sensitive to the choice of priors when using the NHW method ( Fig 2 , panels A-D , Fig 3 , panels A , B ) . However , the impact of the prior is heavily reduced when using the hierarchical framework ( Fig 2 , panels E-H , Fig 3 , panels C , D ) . Thus , our results demonstrate that the hierarchical approach is preferred for ensemble modeling and using the non-hierarchical approach can lead to spurious conclusions . We argue that this would also be the case for other fields , such as climate ensembles , but it is likely to be a larger concern for epidemiology where data to modify the prior are fewer . Considering Eq ( 11 ) , we could ensure that b has little contribution to the denominator if τ0≫λi for all i , ensuring that the prior has little contribution to the posterior . For climate considerations , we envisage that the precision of natural variability , τ0 , would be large relative to each λi if bias is assessed by comparing model simulations to long time series of climate data . For epidemiological considerations , this would however rarely be the case . In the proposed method , we instead inform τ0 largely by the simulation outputs , letting the projections of the ensemble determine how variable outcomes are . Climate modeling , from which the proposed method is adapted , is primarily concerned with differences between current and future mean climate variables [24] . Epidemiology is not only concerned with mean projections but also with other quantities such as the probability of very large or long outbreaks occurring . Fig 2 , panels G and H illustrates the probability of a given epidemic duration occurring for a single outbreak under the two control actions with the preferred SHW method . Comparing the posterior predictive distribution to the density of merely lumping the results of all simulations , as illustrated by the colored bars , the posterior predictive distribution of the ensemble method has a lower probability of both very long and short outbreaks . This is because projections of such outbreaks are down-weighted when their bias is assessed in the analysis; the observed outbreak duration would be unlikely under the modeling assumptions that produce these projections . Thus , ensemble methods that give equal weights to all projections can overestimate the uncertainty about outbreaks , preventing the models from informing appropriate policy decisions . We have further extended the methodology to allow for informative priors on the weights . Compared to climate models , epidemiology often has far less available data to assess model bias . As such , expert opinion will often play a larger role within this field . Fig 4 illustrates the behavior of the ensemble prediction under such informative priors . When up-weighting projections for i = 1 , which is also likely under the observed outbreak duration , the posteriors are shifted towards these projections and produce tighter distributions . This is also found when up-weighting the outlier , i = 5 , in the small discrepancy ensemble ( Fig 4 , panels A and B ) , in which no projection is particularly unlikely for the observed duration . Projection x5 is however unlikely in the large discrepancy ensemble . As a result , the effect of up-weighting the underlying modeling assumptions of this projection primarily makes the distribution wider , resulting from a larger uncertainty about individual weights ( Fig 5 ) . This is to be interpreted such that if expert opinions a priori determine that a modeling assumption that is unlikely to predict the observed data is better than other assumptions , the conclusions should be that there is less information in the ensemble as whole . However , when expert opinions are well informed and do not contradict with observed data , they can lead to more precise predictions . It should be stressed that discrepancy among projections in the ensembles should be viewed as relative to τ0 , the estimated variability in outbreak duration given the initial conditions . A crucial difference between the original method applied to climate change and the epidemiological consideration presented here is that τ0 is unknown for the latter and therefore needs to be estimated . We argue that in the absence of multiple outbreaks , it is sensible to inform this by the model simulations . Stochastic simulations are often used to estimate the range of outcomes for non-ensemble projections [1 , 17 , 18 , 23] , and we propose that when extending the use of models to the ensemble context , they can be used to estimate this feature as well . We have therefore chosen a Bayesian model structure where τ0 is informed largely by the within projection variability , τ1 , τ2 , … , τn , via the Gamma ( aτ , bτ ) distribution in Eq ( 8 ) . All projections of the implemented control action contribute equally to this distribution in the method presented here , thus we are giving equal weights to all modeling assumptions in terms of informing τ0 . Estimation of different weights in terms of informing τ0 based on a single outbreak , analogous to the estimation of λ , would not be conceivable . However , if policymakers believe that some modeling assumptions are more reliable in terms of capturing the variability of outcomes , we envisage that the Bayesian model structure can be altered to include this . If applied to endemic disease , τ0 could be informed similarly to the natural variability of temperature in climate application , and the algorithm we supply is set up to handle this situation . Also , data from multiple outbreaks could be used to inform τ0 when available . Yet , data quality will rarely be comparable to climate data , which highlights one of the major challenges for epidemiological modeling . We also provide a multi-quantity extension of the Bayesian ensemble framework . Fig 5 shows that when adding number of infected and culled farms to the analysis , the marginal posteriors of outbreak duration become narrower and centered on x1 and y1 , i . e . the projections based on the most likely scenario . This illustrates that predictions can be improved by incorporating multiple quantities when assessing the weights . The main scope of this study is to introduce ensemble methods to the field of epidemiology rather than to produce inference about the 2001 FMD outbreak . However , Fig 7 illustrates the types of conclusions the method can provide . The three quantities we include in the multi-quantity analysis are all of great concern to policy makers when assessing the impact of control actions . The probability distributions represent the ensemble predicted difference in the outcome of the outbreak if the control action had excluded culling of CPs . The distributions all have most of the density above zeros , indicating that excluding culling of CPs would most likely have resulted in a prolonged and larger outbreak . We should however point out that these results are based on a single model . To make more robust predictions , we propose that the same type of analysis be made with projections of different models . We also analyzed simulated data to provide a more general depiction of the performance of the method under different conditions . Fig 8 shows the result of analysis of ten simulated outbreaks with the parameterization in the center of the ensemble , i . e . k1 = k2 = 1 . As this is in agreement with both the small and large discrepancy ensemble , the true values ( dashed lines ) consistently lie within the 95% credibility intervals . However , when using the k1 = k2 = 0 . 9 parameterization , the assumptions of the model used to simulate the outbreak is only inclusive of the large discrepancy ensemble , and consequently only the large discrepancy ensemble error bars are inclusive of the true values . Noting that we primarily use the different parameterization as a proxy for different models , this simple simulation example illustrates some obvious but essential points . Ensemble modeling should not be interpreted as a remedy for models based on poor assumptions about the modeled process . It offers the ability to combine multiple assumptions , thus integrating uncertainty with regards to this in the predictions . However , if all models are based on similar but inaccurate assumptions , ensemble modeling will not improve predictions . Intentionally making models similar to each other increases this risk and should be avoided if the models are to be used for ensemble purposes . Accepting these limitations , we argue that the ensemble approach will be beneficial to epidemiological risk assessment because rather than choosing a single model for the purpose , it offers the possibility to combine projections from models that make mechanistically different assumptions about the transmission process . Thus , uncertainty with regard to this is incorporated in the predictions , which is important as projections of different models have been reported to deviate [63–65] . The use of multi-model ensembles would rely on collaboration of modeling teams , as well as overcoming confidentiality constraints in accessing outbreak data and population demographics . The current development in FMD modeling is seeing encouraging development in that area . The Quadrilateral Epiteam [19] has compared simulation of several outbreak scenarios in a subset of the UK demographics with five different models: NAADSM [45] , Netherlands CVI [66] , InterSpreadPlus [46] , AusSpread [44] and ExoDis [67] . This demonstrates that potential obstacles for multi-model ensembles can be overcome and we envisage that epidemiology will see a shift towards multi-model ensembles to inform policy decisions , as has been seen in climate research [24 , 25] and weather forecasting [26 , 27] . Combining the results of multiple models however requires means of weighting these . We conclude that the presented framework is a promising approach because it provides easily interpretable probability distributions of quantities of interest . It also offers an appealing interpretation of model exchangeability , while at the same time combining several different weighting schemes , including a priori beliefs when such are available . In this study , we introduced this framework by applying it to a simple question: how would exclusion of contiguous premises culling from the control action have affected the outcome of the UK 2001 outbreak ? The aim of the study has been to introduce the methodological framework to epidemiology and solve some key issues associated with this transfer , including prior sensitivity , informing weights by expert opinion , using models to inform the variability in the outcome of individual outbreaks and extension to consider multiple epidemic quantities . We have purposely chosen the simple example because it allows for a straightforward transfer from the original climate implementation , and at the same time lets us demonstrate essential concepts and the potential of the framework . Models are however used to answer a range of different questions in epidemiology , and combining multiple projections has the potential to improve the way models are used to inform policy . We argue that the framework we introduce here has great potential , and foresee that many of the questions addressed in epidemiological modeling would require further developments of the Bayesian model , structured to fit with the specific problem . To facilitate this , we have supplied the algorithm ( S1 File ) and hope that it will aid further development of ensemble methods for epidemiology . | Policy decisions in response to emergent disease outbreaks use simulation models to inform the efficiency of different control actions . However , different projections may be made , depending on the choice of models and parameterizations . Ensemble modeling offers the ability to combine multiple projections and has been used successfully within other fields of research . A central issue in ensemble modeling is how to weight the projections when they are combined . For this purpose , we here adapt and extend a weighting method used in climate forecasting such that it can be used for epidemiological considerations . We investigate how the method performs by applying it to ensembles of projections for the UK foot and mouth disease outbreak in UK , 2001 . We conclude that the method is a promising analytical tool for ensemble modeling of disease outbreaks . |
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Adaptation is at the heart of sensation and nowhere is it more salient than in early visual processing . Light adaptation in photoreceptors is doubly dynamical: it depends upon the temporal structure of the input and it affects the temporal structure of the response . We introduce a non-linear dynamical adaptation model of photoreceptors . It is simple enough that it can be solved exactly and simulated with ease; analytical and numerical approaches combined provide both intuition on the behavior of dynamical adaptation and quantitative results to be compared with data . Yet the model is rich enough to capture intricate phenomenology . First , we show that it reproduces the known phenomenology of light response and short-term adaptation . Second , we present new recordings and demonstrate that the model reproduces cone response with great precision . Third , we derive a number of predictions on the response of photoreceptors to sophisticated stimuli such as periodic inputs , various forms of flickering inputs , and natural inputs . In particular , we demonstrate that photoreceptors undergo rapid adaptation of response gain and time scale , over ∼ 300 ms—i . e . , over the time scale of the response itself—and we confirm this prediction with data . For natural inputs , this fast adaptation can modulate the response gain more than tenfold and is hence physiologically relevant .
The ability of neurons to modulate their response as a function of the environment or the task is at once a staple of neural information processing and an achievement of neural biophysics . Adaptation is at play throughout sensory systems . In peripheral sensory cells , one expects significant adaptation as these cells convert wide-ranging natural inputs into neural activity confined to a comparatively restricted range . This is true , in particular , of peripheral visual cells [1]–[11] and especially of photoreceptors [11]–[25] . A wealth of experimental data gathered over more than four decades , across species , allows the identification of universal trends in their response and adaptation properties , and renders photoreceptors an ideal testing ground for our quantitative understanding of neural adaptation . In a typical experiment , photoreceptors are probed with flashes or steps of light , presented either in the dark or against a light background [11] , [12] , [14] , [16] , [18]–[20] , [22] . In these simple protocols , ‘adaptation’ refers to the dependence of the flash or step response upon the background intensity . In photoreceptors , the response to a transient input depends strongly upon background light intensity: both the response amplitude and its dynamics are affected . In the dark , a photoreceptor responds to a small flash of light with a relatively large , slow hyperpolarization . Under bright background conditions , the response amplitude to the same flash decreases ( reflecting a smaller gain , or ‘gain suppression’ ) and the dynamics of the response speed up [11] , [12] , [14] , [16] , [19] , [20] , [22] . The situation is further complicated in more elaborate protocols , in which the ‘background’ light intensity—thought of as a ‘conditioning stimulus’—itself varies in time [8] , [19] , [21] . Then the amplitude and dynamics of the response to a light flash or step—the ‘probe stimulus’—depend not only on the intensity but also upon the time course of the conditioning stimulus that precedes the probe stimulus . Similarly , the non-trivial dependence of the neural response on the amplitude and frequency of a periodic stimulus reflects a form of adaptation [3] , [9] , [17] , [26] . Thus , the distinction between conditioning and probe stimuli , though useful within the contexts of some experimental protocols , may be misleading . Any given photoreceptor relies upon a single stream of absorbed photons , based upon which it produces a response at each instant in time . It is artificial—as a number of authors have noted in the past and as some of the above-referenced literature observes—to treat adaptation and response as distinct phenomena , especially if they occur on similar time scales , and a consistent model ought to address both on equal footing . Adaptation is dynamical in two respects . First , photoreceptor adaptation reflects a memory of the time course of the light intensity input—we can call this ‘the dynamics of adaptation’ . Second , adaptation affects the dynamics of the response itself: quite generally , gain suppression is accompanied by a speed-up of the dynamics—we can call this ‘the adaptation of dynamics’ . This phenomenon is often referred to as the ‘gain-bandwidth trade-off’ [27] . Furthermore , roughly speaking , photoreceptors in the dark or in dim backgrounds tend to respond in proportion to the incident light intensity , while in bright backgrounds they respond to the time derivative of light intensity , for instance responding transiently to steps in intensity [18] . This qualitative modulation of the response as a function of background intensity supplements the quantitative effect of response speed-up . ( For references on these phenomena in photoreceptors of different organisms , see Table 1 below . ) Any model of photoreceptor response to light intensity ought to capture this phenomenology of adaptation , namely ( i ) gain suppression , ( ii ) gain-bandwidth trade-off , and ( iii ) the transition from proportional response ( to light levels ) to differentiating response ( to temporal derivatives of light levels ) with increasing background intensity . In order to model such phenomena , it is natural to turn to the biochemical phototransduction cascade , which converts light into neural activity and which has been studied in great detail [22] , [28]–[31] . Yet some of its parameters have not been measured and some of its modules , such as those involved in calcium feedback , are still a matter of investigation and , possibly , controversy ( see , e . g . , Ref . [22] ) . Of greater concern is the difficulty to extract an intuitive understanding or derive qualitative predictions from the large set of coupled biochemical equations that represents phototransduction . Here , we instead introduce a simple , phenomenological model , in the spirit of pioneering models of photoreceptor response [12] , [15] , [32] but differing from these in important ways . Throughout , we refer to it as the dynamical adaptation ( DA ) model . It is characterized by a dynamical non-linearity without feedback , the interplay of two time scales , and no more than a few numerical parameters . The DA model has three merits . First , it is simple enough to be solved exactly , at least formally , for any input . Second , the DA model remains rich enough to capture the phenomenology of short-term adaptation on the scale of milliseconds to seconds . Indeed , we show that it reproduces precisely a wide array of light adaptation phenomena recorded in classic experiments on turtle cone photoreceptors with flash and step inputs . Third , the DA model allows one to make new qualitative predictions on the adaptive behavior of photoreceptors , for example in response to inputs more complicated than mere flashes and steps; this is much more difficult to achieve using complicated biochemical models with many equations and a great number of numerical parameters . As an example of the predictive power of the DA model , we apply it to fluctuating inputs such as periodic or randomly flickering inputs . Such inputs are often employed in modern experiments as a means to explore a greater range of stimulus variability . It is also generally assumed that these prevent photoreceptors from undergoing significant adaptation . Contrary to this assumption , the DA model predicts that fluctuating inputs induce fast adaptation that depends upon both the intensity and the time course of the input . We find that the DA model reproduces the response of a salamander cone exposed to flickering light with great precision , and we indeed uncover fast adaptation in an analysis of the data . Motivated by this result , we use the DA model to make predictions about the adaptive properties of photoreceptors when these are presented with either periodic or randomly fluctuating inputs . In the case of sinusoidal light intensity , the character of the frequency-dependence depends upon the contrast of the input—an essentially non-linear effect . In the case of natural time series of light intensity , the instantaneous gain can vary more than tenfold on a fast time scale of ∼ 300 ms .
We start by presenting the equations of the DA model . Subsequent sections apply these equations to various light inputs and compare the outcome to data . In formulating the DA model , we look for simple equations that capture the dynamics and adaptation of photoreceptor response . That is , we set the parameters in the DA model equations to be fixed once and for all for a given cell , so that they need not be re-fitted for different choices of conditioning and probe stimuli: any adaptive behavior is to follow entirely from the dynamics prescribed by the equations . Furthermore , we limit as much as possible the number of parameters . Saturation and adaptation effects derive from a non-linearity in the equations . Specifically , we construct this non-linearity so that it informs both gain control and temporal modulations in agreement with the ‘gain-bandwidth trade-off’: smaller gains are associated with faster responses . We present the DA model equations , then explain the intuition that lies behind them and their merit in an analytical approach . The DA model describes the photoreceptor membrane potential , , but it is more natural to write down equations in terms of the photoreceptor response , , defined as the difference between the instantaneous membrane potential and the resting membrane potential in the dark: ( 1 ) The main DA model equation reads ( 2 ) where , , and are constants . Vertebrate photoreceptors hyperpolarize in response to light , so that vanishes in the dark and is negative otherwise . By convention , we define to be negative , while all other quantities are positive . The time-dependent quantities and are filtered versions of the incident light intensity , , given by ( 3 ) ( 4 ) The kernels and are products of monomial and exponential functions; each integrates to unity and is fully specified by a few parameters . ( Explicit expressions for these functions are given in the Methods section . ) The essential feature of the kernels is that they extend over comparable time scales , but with broader than and somewhat delayed ( see Fig . 1 ) . We note that all the time scales that enter the DA model are of the same order of magnitude—several tens of milliseconds: we focus on modeling the ‘fast’ adaptation that occurs on time scales comparable to that of the photoreceptor response , and we ignore long-term adaptive phenomena which take place over seconds or even minutes [22] , [25] . Equations ( 1–4 ) define the DA model . The motivation for the form of Eq . ( 2 ) becomes apparent if we consider in turn its linear and non-linear components . If , the equation is linear and the response , , is a low-pass filtered version of the input , ( 5 ) Since the kernel integrates to unity , in this linear version the response gain is entirely represented by the value of the parameter . As for dynamics , the response is smoothed over the time scale of and the ‘relaxation time’ . If , the multiplicative -term modulates both gain and dynamics . One way of seeing this is to divide both sides of the equation by a factor , to obtain the equivalent equation , ( 6 ) The -term yields effective , time-varying gain , , and time scale , . These co-vary in a manner that satisfies the gain-bandwidth trade-off: large yields both small effective gain and small effective time scale , and vice versa . Actually , the -term has a more involved effect on the dynamics than the mere rescaling of the relaxation time , , as will become clear in subsequent sections . In brief , because the effective gain is time-varying the response is in fact governed by an ‘effective kernel’ that results from a combination of the kernels and ; its time scale and dynamics depends upon the input's recent history . We refer to Eq . ( 2 ) as ‘non-linear’ even though the variable enters it only linearly; even though , in other words , the model is purely feedforward . The model is non-linear in that the output is not a linear function of the input . Throughout , when we refer to the ‘non-linearity’ in the DA model , we mean the multiplicative term , , which is the only term responsible for the non-linearity of the input-output relation . A great merit of the DA model is that its feedforward form allows one to write down an exact solution for any input choice . Indeed , it is easy to verify that Eq . ( 2 ) is solved by ( 7 ) One can ‘plug in’ the stimulus into Eqs . ( 3 , 4 , 7 ) , analytically or numerically , and produce the model photoreceptor response . These equations provide an alternative definition of the model , and are illustrated schematically in Fig . 1 . All adaptive phenomena arise because of the stimulus-dependent term that appears in the argument of the exponential and which modulates the gain and dynamics of the response . This non-linearity couples ‘conditioning’ and ‘probe’ stimuli to create a response to the stimulus history as a whole . Our model bears similarities to various mathematical models that have been introduced in the context of molecular signal transduction and which also display interesting adaptive behaviors [33]–[38] . We return to these in the Discussion . Classic experiments on photoreceptors have characterized their response and adaptation properties with the use of light flash and step stimuli . The resulting phenomenology is shared by different species ( see Table 1 ) . In order to assess the ability of the DA model to capture this phenomenology , we compare its output to data on one of the best-characterized photoreceptors , the turtle red-sensitive cone cell . We focus on experiments performed by three sets of researchers ( Baylor , Hodgkin , and Lamb [13]–[15] , Daly and Normann [16] , and Burkhardt [18]—henceforth , we refer to these with the acronyms ‘BHL , ’ ‘DN , ’ and ‘B . ’ ) , which include five stimulus protocols: single and paired light flashes in the dark ( Figs . 2 and 3 ) , light steps in the dark ( Fig . 4 ) , bright and dark flashes against a fixed light background ( Fig . 5 ) , and bright steps against a fixed light background ( Fig . 5 ) . We emphasize that DA model parameters were fixed across all experiments for each of the three data sets ( see Methods , Table 2 ) . We used an optimization routine for the choice of parameters , but even parameters found by a coarse search by hand yield very similar results . In fact , it is possible to derive satisfactory curve-fitting to the three sets of data by varying only a small subset of parameters from one data set to the next . The robustness of results with respect to parameter variations is one of the strengths of the DA model . The simple protocols we considered in the previous section make use of a transient input ( the ‘probe’ stimulus ) superimposed upon a constant light intensity background ( the ‘conditioning’ stimulus ) . They provide a complete characterization of photoreceptor activity if adaptation is governed by time scales much longer than those that control the response to transient inputs . By contrast , the DA model suggests that response and adaptation occur over comparable time scales , because the quantities and follow similar dynamics . When inputs fluctuate in time , and in particular when the fluctuations take place on time scales comparable to the photoreceptor time scales , the distinction between ‘conditioning’ and ‘probe’ stimuli fades . In order to investigate adaptive properties more broadly than with flashes and steps of light , we presented cones of the salamander with a time-varying white noise , whole-field light stimulus , and we measured their responses with sharp intracellular electrodes ( Fig . 6A ) . The DA model output closely follows the experimental membrane potential traces ( Fig . 6B ) . As a benchmark for the DA model's performance , we compare its output to that of a model devoid of dynamical adaptation , namely the linear-non-linear ( LN ) model [43]–[45] . The LN model is made up of a linear filter , derived by reverse correlating the data trace with the filtered input trace , followed by a static non-linearity ( Fig . 6C ) . Operationally , the non-linear function is extracted from a scatter plot of the linearly filtered input against output data ( such as the one in Fig . 6D ) . The DA model trace follows data more faithfully than the LN model trace ( Fig . 6B ) . In particular , the LN model tends to miss the peaks and troughs of the activity . This discrepancy suggests that dynamical adaptation is at play in salamander photoreceptors even under conditions of rapid light flicker: the observed instantaneous gain appears to depend upon the recent input history , whereas in the LN model any gain control is fixed as it results from a static non-linearity . In the DA model , history-dependent adaptation is embodied by the non-linear -term , and its effects indeed are strongest at peaks and troughs of the response , which reflect an unusually high or low light intensity level in the recent input history . One way to examine variable gain is to divide the data set into groups that correspond to different mean light intensities in a sliding time window of fixed duration . Here , we compared the entire data set to the 10% brightest and 10% dimmest preceding 300 ms time windows ( Figs . 6D–F ) . The data corresponding to these two extreme regimes is not captured by a purely linear fit of the entire data set: ‘corrective gains’ have to be applied in each regime and these differ by a factor of 1 . 8 ( Fig . 6D ) . Though it deviates more modestly from the data , the LN model output is not satisfactory either as corrective gains are still required to reproduce the two extreme regimes and differ by a factor of 1 . 4 ( Fig . 6E ) . In contrast to the LN model's static non-linearity , the DA model accounts for moment-to-moment adaptation and captures the data without the need for corrective gains ( Fig . 6F ) . To be precise , if corrective gains are applied in the extreme regimes , they differ by only a factor of 1 . 04 from each other . A statistical analysis reveals that the two corrective gains for high and low of light intensity were significantly different for the linear and LN models ( both with ) , while the discrepancy was not significant in the case of the DA model ( with , see Methods ) . We emphasize that this close agreement is obtained by fitting a few numerical parameters in the DA model ( see Methods ) , while in principle the LN model requires fitting an entire non-linear function . We also mention that the comparison , here , is with the usual formulation of the LN model , which makes use of a single temporal filter . Generalizations of the LN model that make use of more than one temporal filter ( see , e . g . , [46] ) would naturally achieve a higher performance . However , in the absence of a general prescription on how to combine the various filters , an LN model with , e . g . , two temporal filters would require fitting an entire surface ( rather than a line ) to the data . The DA model actually suggests a specific prescription for the case of photoreceptors; namely , that the second , slower temporal filter should act as a divisive modulation of the first , faster temporal filter . Indeed , if is small with respect to intrinsic times scales of the input or in general at high background light levels , the DA model reduces to a two-filter LN model in which the non-linearity is a simple divisive one ( see , e . g . , Eq . ( 15 ) below ) . The temporal filters used in the DA model indicate that dynamical adaptation occurs over a time scale of ∼200 − 300 ms , and indeed we obtain a clear negative correlation between the ‘instantaneous gain’ of the salamander cone response and the mean light intensity over the preceding 300 ms ( Fig . 6G , see the figure caption and Methods for a definition of the instantaneous gain ) . These data also suggest that the response time scale varies in a correlative manner with the instantaneous gain ( Fig . 6H ) : the response of the salamander becomes faster at moments following periods of high light intensity ( Fig . 6I , see also Methods ) . As before , a careful analysis reveals that this trend is statistically significant ( with 0 . 05 , see Methods ) . Periodic stimuli represent a standard choice for probing the temporal aspects of a response function , and we use them here to illustrate the temporal properties of adaptation in the DA model . We present the model photoreceptor with a sinusoidal fluctuation superimposed upon a constant light intensity background . Here , we define the ‘stimulus contrast’ as the fractional maximum deviation from the mean light intensity . The DA model predicts that the frequency-dependence of the response itself depends upon stimulus contrast . Low-contrast inputs induce a linear , phase-shifted response at all frequencies ( Fig . 7A ) . High-contrast inputs generate qualitatively different output traces , with shapes that depend upon frequency . At intermediate frequencies ( 1 . 25 Hz and 2 . 5 Hz in Fig . 7A ) , the skewed output traces predicted by the DA model are reminiscent of measurements in the primate outer retina , for which a model akin to the DA model has been advanced [9] . The data presented by Lee et al . [9] corresponds to horizontal cell recordings , the retinal neurons postsynaptic to photoreceptors; while it reflects adaptive processing in photoreceptors , it also includes further steps of processing in photoreceptor terminals and horizontal cells . For this reason , we have not attempted a direct quantitative comparison with the output of the DA model . At low frequencies ( 0 . 1 Hz in Fig . 7A ) , the response follows the input closely , without appreciable delay , with only near-instantaneous gain modulation at play . The response plots discussed above yield an interesting prediction on gain control . When we consider the frequency-dependent gain — the ratio of the trough-to-peak amplitude of the ( periodic ) response to the through-to-peak amplitude of the periodic input — two qualitatively different behaviors emerge in bright backgrounds ( Fig . 7B ) . As expected from flash responses , for low-contrast input the gain is suppressed at low frequencies and has a maximum at a given frequency that reflects the time scales in the DA model , provided that the background light intensity is appreciable . ( In fact , if and are defined to each integrate to unity , as we have it here , the gain vanishes in the limit of low frequency . ) Such band-passing behavior was observed in turtle cone [17] and salamander cone [26] experiments . For high-contrast inputs , the gain remains appreciable at low frequencies . When the model photoreceptor is exposed to high-contrast , slow , periodic input , its response simply follows the input with saturation; in the limiting case of a very intense light background , the response oscillates between zero and its saturation value , . There remains a small gain suppression at low frequencies due to the non-linear , saturation property of the photoreceptor response . Thus , when exposed to high-contrast inputs the model photoreceptor responds in a ‘low-passing’ manner . While we expect experiments to confirm this behavior , quantitative comparisons remain to be carried out . We note that the frequency dependence of the gain in the high-contrast case obtained from the DA model is very similar to the experimental frequency dependence presented in Fig . 1A of Ref . [3] . As in the case of Ref . [9] , Ref . [3] reports on recordings of horizontal cells submitted to periodic light input . It proposes a model that makes use of a feedback , frequency-dependent , divisive non-linearity . The DA model offers an alternative explanation . Flicker stimuli are used quite commonly to measure receptive fields ( see , e . g . , [45] ) . These can change as a result of adaptation ( to different levels of light intensity , contrast , or other stimulus parameters ) , and studies of adaptation often use flicker stimuli to evaluate the receptive field under different conditions ( see , e . g . , [1] , [6]–[8] , [47] ) . Thus , the flicker stimulus is meant as a ‘probe’ to test performance of the system in different ‘states’ . Here , we show that this stimulus itself induces substantial adaptation in the system , so the system actually experiences a range of states even during the probe stimulus . The issue is not a purely academic one , since in many natural situations individual photoreceptors indeed receive a flickering input . The time scales in natural situations are a function of spatial modulations and motion in the visual scene , as well as observer and eye movements . In humans , saccades occur every ms [48] , thus producing flicker at individual photoreceptors with a time scale of the order of response time scales . Furthermore , in a natural visual scene , light intensity varies in space by about four orders of magnitude [48] , yielding large-amplitude flicker from eye movement . Endeman and Kamermans [49] recorded from a goldfish cone which was presented with a clip of the naturalistic light intensity time series measured by van Hateren [50] ( Fig . 8A , top panel ) . We digitized the goldfish cone voltage trace and fitted the DA model to it ( Fig . 8A , bottom panel , see also Methods ) . For this specific movie clip , the gain in the goldfish cone response varies in time by a factor of three; the quantitative agreement ( with 0 . 934 ) between the experimental and theoretical traces demonstrates that the DA model replicates the modulation in the reponse properties also in the case of natural inputs , in which fluctuations can be more severe than in laboratory conditions . In order to explore the properties of the DA model in response to natural stimuli further , we calculated its response to a different clip of the same naturalistic stimulus ( Fig . 8B , top panel ) , deeper in the non-linear regime ( see Methods for details on model parameters ) . The series varies over close to three orders of magnitude on scales ranging from tens of milliseconds to seconds . The DA model response to this input ( Fig . 8B , bottom panel ) exhibits overall gain compression: the output varies over less than a single order of magnitude . But how can we extract the rapid , moment-to-moment adaptation induced by the fluctuating input ? An intuitive way to uncover rapid adaptation is to superimpose a set of dim flashes upon the natural light intensity series . The ‘impulse response’ ( i . e . , the response to the complete input minus the response to the natural time series alone ) reveals the moment-to-moment adaptation that occurs in the model photoreceptor: its amplitude varies as a function of time . It can be either smaller or larger than the response ( Fig . 8C ) to an identical flash superimposed upon a fixed light background matched to the mean intensity in the natural series . Furthermore , moment-to-moment adaptation is significant: impulse response amplitudes vary by more than twentyfold ( Fig . 8C ) . From the non-linear structure of the DA model it further follows that the mean impulse response has a greater amplitude than the impulse response in the case of a matched constant light background ( Fig . 8C ) . In other words , on average the model photoreceptor is more sensitive in a fluctuating visual environment than in a static one . The variations of impulse response amplitude follow from the fact that the instantaneous gain depends upon light received during the preceding 300 ms . And the enhanced average impulse response follows from the model's property that , in a bright visual environment , moments of brighter light only suppress the gain by a little bit while moments of dimmer light boost the gain appreciably . Yet another manifestation of dynamical adaptation as captured by the DA model lies in the difference between the average response to a flickering input and the response to a constant light input with matched mean . In order to examine this effect , we constructed an input in which 1 s windows of natural intensity time series alternated with 1 s windows of constant light with matched mean ( Fig . 8D , top panel ) . We calculated the model response over instantiations of natural time series , and derived two conclusions from the average response trace ( Fig . 8D , bottom panel ) . First , on average a transient hyperpolarization follows the onset of the constant light input , while a transient depolarization follows the onset of flicker . Second , the ‘steady-state’ average response to flicker is depolarized as compared to the steady-state response to constant light . The transient hyperpolarizing and depolarizing responses arise because a light-adapted photoreceptor is more sensitive to negative ( i . e . , hyperpolarizing ) deflections in the input than to positive ( i . e . , depolarizing ) ones ( Fig . 5D ) . This asymmetry biases the average steady-state response to flickering input toward depolarization , as compared to the response to constant , mean-matched light . The above arguments appear to be quite general , and should apply to cases in which flickering inputs are drawn from distributions other than the van Hateren series used here . In Methods , we discuss the case of Gaussian flickering inputs , often used in experiments . For that choice of inputs , we can work out analytical results , which confirm the above arguments and agree with numerics . Furthermore , the analytical results illustrate the fact that adaptive effects depend not only upon that magnitude of the flicker but also upon its temporal structure ( see Methods ) . The DA model is a phenomenological model that makes no explicit reference to the mechanisms of phototransduction . A number of studies [22]–[24] , [28]–[31] have examined the mechanism by which light is converted into electrical activity in photoreceptors . They have revealed the beautiful intricacies of the biophysics of phototransduction at the molecular level , but the resulting set of equations is too complicated to be used , as a whole , for developing intuition or making qualitative predictions . Phenomenological and mechanistic approaches are complementary in the purpose they serve; nonetheless it is worthwhile to look for possible connections . The hyperpolarizing response of a photoreceptor to light results from the closing of channels , due to the transformation of cyclic GMP ( cGMP ) into GMP through the action of activated phosphodiesterase . The molecular steps of the phototransduction cascade are illustrated schematically in Fig . 9 . In order to explain the properties of cone response at the molecular level , it is necessary to understand the nature and relative relevance of the non-linearities at each stage of the feedforward cascade . Because the reduction of the cGMP concentration , , depends upon the concentration of activated phosphodiesterase , , and upon its own concentration—the concentration of cyclic GMP ( i . e . , the activated form of the substrate ) —the corresponding phototransduction step is non-linear even at relatively low light intensities ( see Eq . ( 9 ) below ) . This reaction is often presented as the dominant source of non-linearity in the cascade ( see , e . g . , Refs . [23] , [25] and references therein ) . At high light levels , other sources of non-linearity may come into play . Pigment bleaching becomes relevant over the three or four upper decades of illumination , up to 1010 photons/µm2/s [18] . That is , in this range of illumination the very first reaction in the cascade , between photons and rhodopsin , becomes non-linear due to the limited pool of rhodopsin molecules . Whether similar substrate-limited non-linearities occur at intermediate light levels in the case of transducin and phosphodiesterase is as yet unclear for cones . ( While there are indications that the transducin and phosphodiesterase steps in the cascade may be substrate-limited in rods [51] , experiments on cones suggest that phosphodiesterase does not become limiting until the photopigment is already completely bleached [52] . These experiments , though , were performed on membrane preparations from cones and hence do not take into account morphological effects of natural phototransduction . Cone morphology may have a significant influence on the activation of phosphodiesterase by diffusing activated transducin molecules . ) It thus seems that the site of adaptation moves from the back end of the feedforward cascade—namely , the GMP step—to the front end at very high light intensities . ( In an interesting analogy , within the retina as a whole , the main site of light adaptation also moves from downstream processing—namely , the transfer from bipolar to ganglion cells—to the front end—namely , cones—at high light intensities [11] . ) Here , we focus upon the GMP step of the phototransduction cascade , as its non-linearity appears to play a dominant role over a major part of the natural range of light intensities . The analysis of earlier non-linearities would be similar , as their mathematical form is similar . The inactivation of GMP by phosphodiesterase can be modeled as ( 9 ) where , , and are constants . The photoreceptor response grows in proportion with the deviation from the resting value of the cGMP concentration , ( 10 ) where and are the resting values of the concentrations of cGMP and phosphodiesterase , respectively . Inserting Eq . ( 10 ) into Eq . ( 9 ) , we obtain an equation for , as ( 11 ) where is the deviation of the phosphodiesterase concentration from its resting value . Equation ( 11 ) is similar to the central equation of the DA model , Eq . ( 2 ) : here plays the role of the response , , and is an intermediate , light-responsive quantity analogous to and in the DA model . The essential difference between Eqs . ( 2 ) and ( 11 ) is that , in the former , the quantities and vary on different time scales , whereas in the latter , the two phosphodiesterase-related quantities vary on the same time scale . In the DA model , the action of is somewhat slower and slightly delayed with respect to that of . Indeed , can be written as resulting from a convolution with the sum of two kernels , one corresponding to the dynamics of and the other to slower dynamics ( see Eq . ( 13 ) in Methods ) . The fast component of , which operates on the time scale of the response , i . e . , the time scale of , can be identified with the non-linearity inherent in the feedforward pathway of the phototransduction cascade , discussed above . The slow component of can be interpreted as mimicking the delayed effects of feedback loops in phototransduction , i . e . , biophysical reactions that occur beyond the main cascade ( discussed above ) . The quality of our fits to data suggests that , at least within the experimantal range we considered , the complicated feedback processes involved in phototransduction may be well approximated by a simple feedforward non-linearity .
We introduced a new phenomenological model that captures the response and adaptation properties of cone photoreceptors . The DA model is expressed as a first-order differential equation in time ( Eq . ( 2 ) ) and relies upon a single non-linearity . Because of the interplay of a few time scales , response properties depend upon recent history . Both response gain and dynamics are influenced by the input history . Thus , the DA model provides an example of truly dynamical adaptation . The simplicity of the model allows for an exact analytical solution for any input time course ( Eq . ( 7 ) ) and for straightforward numerical calculations . We evaluated DA model outputs for inputs that have been used historically to characterize adaptation , namely flashes and steps of light , and we found that the DA model captures the phenomenology of adaptation qualitatively , and in most cases also quantitatively . Specifically , it reproduces gain compression and dynamical modulation of the response to large ‘probe’ stimuli ( flashes and steps ) ( Figs . 2 and 4 ) , as well as gain control and dynamical modulation as a function of ‘conditioning’ stimuli ( Fig . 5 ) . What is more , we found that the transition from a monophasic flash response in dim backgrounds to a biphasic flash response in bright backgrounds emerges naturally from the DA model ( Fig . 5 ) . Interestingly , also , while we fitted the model to data that did not present these , it predicted double-peaked ( ‘camel hump’ ) responses to intense flashes ( Fig . 2A , bottom panel ) ; responses of this characted indeed have been recorded experimentally [39] . When we stimulated the DA model with randomly flickering inputs , we found that it can reproduce salamander cone data with great precision ( Fig . 6 ) . In particular , it corrects systematic errors that appear if the dynamical character of adaptation is ignored ( as in LN models ) . Furthermore , the DA model predicts fast , moment-to-moment adaptation , controlled by a time scale of about 300 ms , even in the presence of rapid flicker . A careful analysis of salamander cone data indeed uncovered this form of fast adaptation ( Fig . 6 ) . The fundamentally dynamical nature of adaptation in the DA model implies other non-trivial response behaviors when the model photoreceptor is exposed to fluctuating inputs , such as periodic inputs or flickering inputs . In the case of periodic inputs , it predicts a qualitative change of the frequency-dependence of the response when contrast in varied: At low contrast slow inputs are suppressed , while at high contrast slow inputs elicit maximum gain ( Fig . 7 ) . In the case of randomly flickering inputs , the gain in response to transient stimulation varies significantly on a fast time scale ( Fig . 8 ) . Furthermore , the mean photoreceptor output itself is modulated by the amplitude of fluctuations ( Fig . 8 ) . Such a coupling between the mean and fluctuations about the mean is a signature of non-linearity . The DA model is a worthwhile compact description of phototransduction , especially as several of the important numerical parameters involved in the molecular cascade have not been measured , the forms of some of the non-linearities have not been determined , and the feedback mechanisms—in particular the multiple calcium feedback mechanisms—are still a matter of investigation ( see , e . g . , Ref . [22] ) . We have given a heuristic interpretation of the DA model in terms of phototransduction biochemistry . In the light of this interpretation , adaptation is seen as the result of a fast process inherent to the feedforward branch of phototransduction , supplemented by a slower , presumably feedback , process still accurately mimicked in the DA model by an additional feedforward term . Here , ‘feedback’ refers to a process in which the output state of the photoreceptor would affect an ‘upstream’ biophysical interaction . But this does not mean that the DA model provides a complete description of ( feedback ) adaptation . In the feedforward DA model , gain and time scales co-vary . Some calcium-related processes in feedback adaptation may work differently . Experiments indicate that calcium concentration can modulate response gain while leaving time scales unchanged [53] . Furthermore , calcium dynamics seem to involve much longer time scales than those of concern here [25] , [54] , [55] . Historically , light adaptation was defined with experiments that used a ‘conditioning’ stimulus and a ‘probe’ stimulus . The neuron under study was exposed to a conditioning stimulus for some time , and then its response to a probe stimulus was measured; adaptation was defined in terms of the difference between the responses to the probe stimulus with and without conditioning stimulus . Typically , conditioning stimuli were chosen to vary slowly in time or not vary at all , as in the case of a constant light intensity background , and probe stimuli were devised as transient variations in light intensity , such as flashes or steps . Quite generally , neural activity saturates in response to large stimuli . One concern , in defining adaptation , was to disambiguate this simple gain compression from a more involved effect of the conditioning stimulus [13] , [14] . Clearly , for this one needed a model of the gain compression . The LN model was often used as such a model: instantaneous gain compression was ascribed to the shape of the non-linear transfer function ( the ‘N’ part of the LN model ) , while ‘true adaptation’ was inferred from conditioning stimulus-dependent changes in the amplitude and shape of the linear filter ( the ‘L’ part in the LN model ) [6] , [8] , [46] . Thus , light adaptation is often defined in a model-dependent manner that may lead to some amount of confusion . For example , if a system is invested with dynamical non-linearities—as is generally the case for neural systems—it is unnatural to disambiguate ‘gain compression’ and ‘true adaptation’ with the use of a static non-linearity . But even if one ignores this caveat , the definition of adaptation in terms of ‘conditioning’ and ‘probe’ stimuli may be problematic . The definition is suitable if the time scales of response and adaptation are very far apart . Then any stimulus can be divided into a slowly varying component , which ‘conditions’ the system , and a rapidly varying component , with which the system is ‘probed’ . But if the time scales of response and adaptation are comparable , as is the case for photoreceptors , then the distinction between ‘conditioning’ and ‘probe’ stimuli becomes artificial . This is especially true when the input itself varies over these time scales . Put differently , photoreceptors adapt and respond concomitantly . In experiments in which the response properties of a cell are modulated by the intensity of the input fluctuations , rather than by changes in its mean , it is customary to invoke ‘contrast adaptation’ . In our case , too , one can say that the photoreceptor undergoes a kind of contrast adaptation , as its sensitivity is modulated by the intensity of input fluctuation ( see Fig . 8 ) . But this terminology may be misleading because , again , all three time scales—that of response , that of adaptation , and that of flicker—are comparable . For this reason , we prefer to talk about dynamical adaptation . In a model such as the DA model , and in reality , adaptation is dynamical in at least two ways . There is ‘the dynamics of adaptation’: the way in which response properties adjust depends upon the structure of the history of the stimulus , not only upon a single number . There is also ‘the adaptation of dynamics’: not only does the gain change as a function of the experimental conditions , but the response kinetics also vary . Regardless of the specific form it takes , adaptation is often viewed as a change of model parameters—gains or time scales , for example . But a complete model should incorporate the apparent change of parameters , on several nested time scales , as a natural result of its ( possibly very complicated ) dynamics . A number of studies have addressed this issue , and in particular have proposed models with temporal properties that vary adaptively [6] , [10] , [46] , [47] , [56]–[59] . Similarly , the simple DA model can account for the phenomenology that can appear as a change of LN model parameters , namely fast adaptation over a few hundred milliseconds . We have argued that , in a case such as this , response and adaptation are inseparably intertwined concepts . In the case of longer-term adaptive phenomena ( for example , those that result from photopigment regeneration ) , one can invoke slow parameter changes in the DA model . Here , ‘adaptation’ can be defined more easily . But , again , ultimately one would like to construct a richer model that incorporates dynamics over the longer time scales of interest . In this upgraded description , there will be no formal distinction between ‘adaptation’ and ‘response dynamics’ . In this sense , ‘adaptation’ is an elusive notion: once understood in terms of a system’s dynamics , it no longer stands as an independent feature [10] , [57] . Instead of speaking of adaptation , it may be more natural to characterize a neural system or a set of response phenomena by the time scales and non-linearities that govern the dynamics . In the case of the DA model , photoreceptors are described by the interplay of three time scales and a single , multiplicative non-linearity according to which gain and dynamics are modulated by a signal , . The DA model is a phenomenological ( or functional ) model . It came about as we were searching for a simple model that could capture data , and in particular the dynamical aspects of adaptation , quantitatvely . Somewhat to our surprise , we found that it reproduced a large quantity of observations collected over the past four decades . It also corrected systematic errors made by LN models—which , incidentally , fail to describe the dynamical aspects of adaptation—when it was checked against photoreceptor reponse traces that we recorded . Now , the photoreceptor is likely the best understood neural cell and the biochemistry of phototransduction is identified in some detail; it can be modeled as a relatively large set of coupled non-linear equations . A question , then , follows: Have simple , phenomenological models , such as the LN model or the DA model , any reason of being ? We believe that the answer is affirmative , and we explain here why . Biochemical models involve not only many coupled non-linear equations , but also a large set of numerical parameters , many of which cannot be measured directly . Thus , it is very difficult to explore the parameter space of these models and to extract from them generic behaviors and testable predictions . By contrast , a phenomenological model of a cell’s response can be tractable enough that generic behaviors and robust predictions be established . Phenomenological models are thus useful to identify ‘computational modules’ , which can be sought after in more complicated mechanistic models . For example , the DA model displays the computational power of the interplay of two time scales through a feedforward non-linearity . Phenomenological ( or functional ) models have proved fruitful in neuroscience . Besides the reasons just mentioned , this is because they embody what a post-synaptic neuron or , more generally , a neural circuit ‘cares about’ . While the study of the intricacies of the phototransduction cascade is eminently interesting , ultimately the input-output relation of photoreceptors is relevant to downstream visual processing , irrespective of biochemical details . Moreover , phenomenological models are useful in establishing connections between systems that share functional commonalities but may differ greatly in their mechanistic aspects . For example , the DA model is akin mathematically to models of signaling in non-neural cells [33]–[38] . Phenomenological models also come with a generalization power: they can be modified to describe other systems . We indeed expect that variants of the DA model will be useful to study the computational properties of visual or sensory cells other than photoreceptors , which do not rely upon any kind of phototransduction but which do display a similar phenomenology in their input-output relations . Finally , phenomenological models can be of use in analyzing mechanistic models . Here , we have used insights gleaned from the DA model—namely , that adaptation may result from feedforward coupling and that a simple non-linearity involving two time scales may be responsible for it—to examine the phototransduction cascade and to suggest a putative key step in the latter . The DA model is similar in spirit to the pioneering phenomenological models of photoreceptors by Fuortes , Hodgkin , Baylor , and Lamb [12] , [15] and by Carpenter and Grossberg [32] . The oldest model , put forth by Fuortes and Hodgkin [12] , is made up of a succession of linear filters followed by a feedback non-linearity . The cascade of filters in their model plays the role of the filter in the DA model , and the non-linearity governs adaptive phenomena as in the DA model . In another class of models , advanced later by Baylor , Hodgkin , and Lamb [15] , again a succession of linear filters controls the variation of an intermediate quantity ( presumed to be the concentration of some chemical ) which then translates into the membrane potential of the cell . But this quantity induces its own removal , through a feedback process on decay rates . These earlier phenomenological models and the DA model are similar in that they rely upon an initial linear filtering of the input and a subsequent non-linear transformation . The major difference between the two , however , is that earlier models come with a feedback non-linearity while the DA model comes with a feedforward non-linearity . The former involves higher powers of the model output , so that a linear analysis ( such as fitting an LN model ) would reveal output-dependent effective parameters . By contrast , the latter remains linear in the output; as a result , in a linear analysis effective parameters are independent of the output state of the system . Roughly speaking , in a feedback system the output can affect the earlier stages , while in a feedforward system it does not—only the input does . In the Fuortes-Hodgkin model [12] , for example , the role of our delayed signal , , is played by the cell output which enters the equation non-linearly; thus , adaptive properties depend upon the value of the output , whereas in the DA model they are affected only by the value of the input . Biochemical feedback loops in phototransduction are well-documented—so is a feedforward model bound to be useless ? There are at least two reasons for which a feedforward model applies well to this system . First , adaptation may be carried out in both the ( complicated ) feedforward part of the phototransduction cascade and in its feedback loops simultaneously ( see section ‘Putative Connection of the DA Model to the Biochemistry of the Phototransduction Cascade’ above ) . Second , even if feedback loops are essential to adaptive phenomena in photoreceptors , it is conceivable that they are well-approximated by a feedforward process for a range of inputs . In this approximation , one trades mechanistic details for computational simplicity . One may wonder why phototransduction requires several feedback loops ( illustrated in Fig . 9 ) and which aspect of visual computation they each relate to . One way to approach the problem is to identify a computation that a feedforward system cannot carry out . Somewhat surprisingly , we found that a feedforward system , such as the DA model , can reproduce sophisticated data with high accuracy . It is thus plausible that a non-linear feedforward system with several time scales mimics feedback quite well . Alternatively , it is possible ( though improbable ) that some of the feedback is necessary , not for functional computation , but for internal molecular bookkeeping or as a safety net when photoreceptors face extreme conditions . Carpenter and Grossberg have proposed several variants of phototransduction models [32] . One among these can be re-written in a way that makes the similarity with the DA model apparent . The important difference between the two , though , is that the Carpenter and Grossberg model is devoid of a delayed process , as opposed to the DA model which captures delayed effects with its term . Away from the specific realm of phototransduction , a number of studies of cellular signal transduction have introduced models that share similarities with the DA model; see Refs . [33]–[38] and references therein . In most cases , however , the decay part of the model equation contains only a non-linear term , while in the DA model it has both linear and non-linear components . One such example is referred to as the ‘perfectly adapting’ model in the cell signaling literature . One model of phosphorylation-dephosphorylation contains both linear and non-linear decay . But it is governed by a single time scale in the signal . By contrast , the interplay of different time scales , which appear through the and terms in the DA model , is central to its behavior . To our knowledge , the present work provides a novel application of dynamical systems ideas popular in signaling studies to transduction cascades in neurons , and offers detailed results on adaptation to stimuli with complicated correlation structures . We applied the DA model to turtle cone and salamander cone data , but we anticipate that it can be used to describe photoreceptors in other taxa since these exhibit a very similar phenomenology ( Table 1 ) . The trends we discussed in the context of experiments on the turtle cone are consistent across species , from invertebrates such as fly and Limulus to vertebrates such as salamander , mouse , and primate . In particular , modulation of both gain and dynamics is observed across taxa . Every studied species exhibits non-linear compression of the flash response in the dark as well as speed-up with flash intensity . Gain compression as a function of background light intensity is also apparent in all species , although in some cases it is best fitted with a modified , non-linear Weber-Fechner rule ( according to which the response is proportional to , the ratio of the flash intensity to the background intensity , raised to some power ) . Biphasic ( ‘differentiating’ ) flash responses in the presence of a bright background are observed widely , and indeed the DA model predicts a transition from monophasic impulse responses to biphasic impulse responses for increasing background intensity . Yet , it appears that the second , overshoot lobe may be less pronounced or even absent in some species , such as primates , as compared to the turtle data examined in detail here . Also , while much of the insect data does not present biphasic flash responses in bright backgrounds , the literature cited in Table 1 notes a small but distinct second lobe . ( Interestingly , however , laminar recordings in insects display perfect biphasic impulse responses that integrate to zero [60] . ) The DA model can account for such variations in the shape of the impulse response . In particular , the parameter sets the background intensity at which an overshoot lobe appears and the parameter sets the shallowness of the overshoot lobe; for large , the overshoot becomes very shallow and can be difficult to detect in the presence of noise . ( See Fig . 10A and B and captions for a more detailed discussion of this point . ) Since invertebrate and vertebrate phototransduction cascades are evolutionarily distinct [61] , one is led to think that the adaptation phenomenology summarized in Table 1 represents an adequate solution to the problem of encoding natural visual inputs [4] . Downstream visual neurons [1] , [2] , [5]–[8] , [10] , [62]–[67] and , indeed , neurons in the other sensory systems [68] display adaptive properties similar to those recorded in photoreceptors . A model in the spirit of the DA model may be suitable for these . What refinements or elaborations of the DA model would then be required—more complicated temporal filters ? a broader range of time scales ? a more involved form of the non-linearity ? several non-linear stages ? —is itself an interesting question .
Since the pioneering work of Hodgkin and Baylor [13] , [69] , standard functional forms have been used to fit the impulse response of visual neurons , and we found that these forms indeed appropriately fit all the data we examined . By convention , we require that the filters and each integrate to unity . For , we adopted the form ( 12 ) where specifies the time scale of the linear response , specifies its ‘rise’ behavior , and is the Heavyside function with if and if . This filter corresponds to a sequence of simple relaxation equations in time , as may occur in the phototransduction cascade . While other , more involved choices may yield a closer quantitative agreement with data , we found that a similar form for , with the added twist that it combines two time scales , is satisfactory . Specifically , throughout we used the form ( 13 ) according to which involves a fast component that responds on the time scales of the linear response , , and a slow component that responds on a somewhat longer time scale , . The prefactors , and , weigh the relative importance of the two components and ensure normalization to unity . Throughout , we integrated the DA model with standard techniques in Matlab ( Mathworks , Natick , MA ) . We fit our model parameters separately to each of the three data sets we used , from the experiments of Baylor , Hodgkin , and Lamb [13]–[15] , the experiments of Burkhardt [18] , and the experiments of Daly and Normann [16] . We used a gradient descent method in Matlab to find the parameter sets that yielded the least squared error from the experimental results . Fits performed with different initial conditions yielded similar parameter minima . In the case of the Baylor , Hodgkin , and Lamb data , the fit was performed to the voltage traces in Figure 2A ( extracted from Ref . [14] ) . All eight parameters of the model , , , , , , , , and , were varied in the minimization procedure . For the Burkhardt data , the optimized parameters were determined from the family of curves in Fig . 5D ( extracted from Ref . [18] ) . The parameters , , were varied; because these data represented not traces but amplitudes , the remaining parameters had little effect on the fit and were set to typical values before the fitting routine was applied . For the Daly and Normann data , the fit was performed on the flash response traces in Fig . 5A ( extracted from Ref . [16] ) . All eight parameters of the model , , , , , , , , and , were again varied to find the least squared error between the DA model responses and experimental flash responses . The value of was adjusted from experiment to experiment within the same data source , to match the scale . Nonetheless , remained in the vicinity of 2 mV⋅µm2⋅ms/photon ( see Table 2 ) , where we have assumed a cone cross-section of 1 µm2 . This value of yields a peak dark hyperpolarization of ∼15 µV/photon in agreement with experimental observations . The optimized parameter sets are recorded in Table 2 . For Fig . 2F , where the experimental flash intensity was unspecified , the flash intensity , rather than , was adjusted so as to obtain response strengths comparable to the data . Figure 10C displays the flash response for different values of the background light intensity , for each of the three parameter sets used in fitting the data . Again , we emphasize that the results are robust with respect to parameter changes and fits by eye resulted in similar parameters and goodness of fit . The values of and determine the location of the crossover to a non-linear behavior , as well as the relative strength of the effect , but have relatively little effect upon the shape of saturated flash responses ( see Fig . 10A and B ) . For Fig . 8 , the light intensity time series was extracted from van Hateren's recordings of naturalistic stimuli [50] and the goldfish cone response was digitized from Ref . [49] . In calculating the DA model output , the parameters , , were fit to the goldfish cone traces for Fig . 8A , where we used a mean light intensity of 1 . 5·105 photons/ ( ) [49]; the BHL time scales were used in the model as the low temporal resolution of the digitized trace did not allow for a more precise temporal fit . For Figs . 8B , C , and D , parameter set B was used together with a mean light intensity of photons/ ( ) . The gain was probed by superimposing ms flashes containing 100 photons on top of this fluctuating light background . Flash responses were found by subtracting the response to the naturalistic time series from the response to the same time series with superimposed light flashes . Salamander retinæ were exposed to whole-field flicker of time-varying intensity while a sharp electrode voltage recording was made of cone cells , following the protocol of Ref . [8] . Flicker was presented with a CRT at 67 Hz , with a mean light intensity of ∼ 10mW/m2 . Intensities were updated every 2 frames and chosen from a Gaussian distribution with standard deviation equal to 35% of the mean . All data integration and analysis were performed with custom-written routines in Matlab . ( We provide some of these codes as online supplementary material . ) LN model analysis . Best-fit linear filters were found using cross-correlation methods , as described in Ref . [8] . A 500 ms filter was used to produce the linear portion of the LN model output , followed by a third degree polynomial fit of that output to the experimental response . In order to compute the quantities in Figs . 6G–I , windows of 300 ms were selected every 100 ms during stimulus presentation . We computed the mean light intensity over each window as well as the ‘instantaneous gain’ . The instantaneous gain at a given time was defined as the slope of the scatter of the experimental response when it was plotted against the linearly filtered signal , with scatter points extracted from the trace over the 150 ms windows flanking the time in question ( Fig . 6G ) . We averaged the instantaneous gain over each of the 300 ms time windows to obtain an effective gain associated with a time window as a whole ( Fig . 6H ) . These 300 ms ( zero-padded ) windows were used to find the peak cross-correlation times of stimulus with response ( Fig . 6H ) and the linear filters displayed in Fig . 6I . Peak correlation time , for each average of cross-correlations , was defined as the average of the 10 times of maximum correlation . In Fig . 6I , we selected the time points with lowest , middle , and highest instantaneous gains , and plotted the filters corresponding to each of the three subsets of data . DA model analysis . Model parameters were fit to the salamander data ( Fig . 6B ) with a least squares minimization routine in Matlab and Eq . ( 2 ) was integrated with the use of standard methods in Matlab , with the functional forms described above . All parameters ( , , , , , , , and ) were varied , but and were imposed to take integer values . The parameters used to fit salamander data are recorded in Table 2 . Analysis of statistical significance . To assess the significance of the difference in slopes , we applied a Monte Carlo shuffle analysis , in which we ran the slope fitting routine 3000 times , each time offsetting the stimulus and response by a random temporal delay , using circular boundary conditions . The -value was calculated as the frequency with which a difference in slopes occurred with absolute value greater than the one measured in the absence of temporal shift . This same random shuffle method was used to assess the timing differences measured in Fig . 6I , using a random shift in the instantaneous gain value , so as to randomly select stimulus-response snippets . These snippets were used to estimate the peak cross-correlation ( as in Fig . 6H ) , and establish an estimated p-value for the difference . The DA model ( defined by Eqs . ( 2 , 3 , 4 ) above ) is solved exactly for any input , by ( 14 ) ( ( Eq . ( 7 ) above ) , where and are defined in Eqs . ( 3 , 4 ) . In the case of deterministic inputs such as flashes or steps , this expression can be evaluated readily analytically or numerically . In the case of random inputs , we evaluate this expression for a given instantiation of the noise and then take an average over instantiations . ( It is possible to calculate higher moment , such as the variance of _ , or the distribution of the response as a whole , in a similar manner , but we do not present the corresponding calculations here . In some instances below , we compute model photoreceptor responses with the simplifying assumption of small . In this limit , the DA model becomes an algebraic equation which can be solved immediately: ( 15 ) Results derived in this limit hold also in the case of bright backgrounds . In order to establish the notation , we now write down the most general input we shall consider here . It is made up of a constant light background , , a fluctuating ( random ) background , , and a flash of intensity presented at : ( 16 ) We assume that ( 17 ) where is a Gaussian random variable with temporal correlation ( 18 ) normalized such that ( 19 ) and is a deterministic envelope . We shall consider three different cases for the deterministic envelope: Note that , with the stimulus in Eq . ( 16 ) , the filtered quantities read ( 20 ) ( 21 ) where the kernels and are defined in Eqs . ( 12 , 13 ) above . The general solution of the DA model , Eq . ( 7 ) , can be rewriten as ( 31 ) or as ( 32 ) with ( 33 ) The average response , over instantiations of the flicker , is then given by ( 34 ) ( 35 ) Since all random variables in the problem are linear sums of Gaussian variables , we have ( 36 ) After replacing the variables and by their expressions in terms of inputs and filters , Eqs . ( 20 , 21 ) , and performing the Gaussian averages , we obtain the average response ( 37 ) where ( 38 ) ( 39 ) ( 40 ) ( 41 ) This average response includes a tonic component , the baseline response to the constant background , and a phasic component , the flash response . Because of the non-linearity in the DA model , both components are modulated by the flicker , as compared to the deterministic case . Hereafter , we examine this solution in two cases: flicker with constant variance and flicker with periodically varying variance . We answer the following question: How does flicker affect the phasic and tonic components of the model photoreceptor response ? | Photoreceptors constitute the interface between the visual world and the cerebral world , as they convert light inputs into neural signals . This conversion is subject to continuous adaptation: response gain and time scale vary as a function of input history . This adaptation is ‘dynamical’ both because it depends upon the temporal structure of the stimulus and because it affects the kinetics of the response . Traditionally , theoretical studies of photoreceptors fall within one of two extreme approaches: either detailed modeling based upon the biophysics of phototransduction or functional modeling based upon phenomenological descriptions of photoreceptor response . While the former approach involves too many coupled equations and unknown parameters to allow for analytical treatments , building intuition , or predicting trends , instances of the latter approach , such as the simple linear-nonlinear model , fail to capture essential features of dynamical adaptation . Here , we develop understanding at an intermediate level . We define and discuss a phenomenological model which is simple enough to allow for full solutions and predictions , but embodies features of phototransduction well enough to capture a rich phenomenology . We demonstrate that our model reproduces data with high accuracy and can be used to make predictions on the response to sophisticated visual inputs such as natural stimuli . |
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According to the ‘ceRNA hypothesis’ , microRNAs ( miRNAs ) may act as mediators of an effective positive interaction between long coding or non-coding RNA molecules , carrying significant potential implications for a variety of biological processes . Here , inspired by recent work providing a quantitative description of small regulatory elements as information-conveying channels , we characterize the effectiveness of miRNA-mediated regulation in terms of the optimal information flow achievable between modulator ( transcription factors ) and target nodes ( long RNAs ) . Our findings show that , while a sufficiently large degree of target derepression is needed to activate miRNA-mediated transmission , ( a ) in case of differential mechanisms of complex processing and/or transcriptional capabilities , regulation by a post-transcriptional miRNA-channel can outperform that achieved through direct transcriptional control; moreover , ( b ) in the presence of large populations of weakly interacting miRNA molecules the extra noise coming from titration disappears , allowing the miRNA-channel to process information as effectively as the direct channel . These observations establish the limits of miRNA-mediated post-transcriptional cross-talk and suggest that , besides providing a degree of noise buffering , this type of control may be effectively employed in cells both as a failsafe mechanism and as a preferential fine tuner of gene expression , pointing to the specific situations in which each of these functionalities is maximized .
The problem of tuning protein expression levels is central for eukaryotic cell functionality . A variety of molecular mechanisms are implemented to guarantee , on one hand , that protein copy numbers stay within a range that is optimal in the given conditions and , on the other , that shifts in expression levels can be achieved efficiently whenever necessary [1–3] ( whereby ‘efficiency’ here encompasses both a dynamical characterization , in terms of the times required to shift , and a static one , in terms of moving as precisely as possible from one functional range to another ) . Quantifying and comparing their effectiveness in different conditions is an important step to both deepen our fundamental understanding of regulatory circuits and to get case-by-case functional insight about why a specific biochemical network has been selected over the others . As the major direct regulators of gene expression , transcription factors ( TFs ) are most immediately identified as the key potential modulators of protein levels [4] . In a somewhat simplified picture , one may imagine that a change in amount of a TF can induce a change in the expression level of the corresponding gene , and that the ability to regulate the latter ( the output node ) via the former ( the input node ) can be assessed by how strongly the two levels correlate . The effectiveness of a regulatory element is however limited by the stochasticity of intracellular processes , from the TF-DNA binding dynamics to translation [5] . A convenient framework to analyze how noise constrains regulation is provided by information theory [6 , 7] . In particular , the simplest situation in which a single TF modulates the expression of a single protein can be characterized analytically under the assumption that the noise affecting the input-output channel is sufficiently small . The mutual information between modulator and target –a convenient quantity through which regulatory effectiveness can be characterized– depends on the distribution of modulator levels and can be maximized over it . Remarkably , in at least one case this maximum has been found to be almost saturated by the actual information flow measured in a living system ( for more details see [8 , 9] ) . In other terms , for sufficiently small noise levels in the channel that links TFs to their functional products , one may quantify the optimal regulatory performance achievable in terms of the maximum number of bits of mutual information that can be exchanged between modulator and target . Several control mechanisms however act at the post-transcriptional level [10–12] . Among these , regulation by small regulatory RNAs like eukaryotic microRNAs ( miRNAs ) has attracted considerable attention over the past few years [13–15] . In short , miRNAs are small non-coding RNA molecules encoded by nuclear DNA , that can inhibit translation or catalyze degradation of mRNAs when bound to them via protein-mediated base-pairing . miRNAs appear to be crucial in an increasing number of situations ranging from development to disease [16–18] . Their function however can differ significantly from case to case . For instance , they have been well characterized as noise buffering agents in protein expression [19 , 20] or as key signaling molecules in stress response [21] . Recently , though , investigations of the miRNA-mediated post-transcriptional regulatory ( PTR ) network have hinted at a possibly more subtle and complex role . It is indeed now clear [22–27] that the miRNA-RNA network describing the potential couplings stretches across a major fraction of the transcriptome , with a large heterogeneity both in the number of miRNA targets and in the number of miRNA regulators for a given mRNA . The competition effects that may emerge in such conditions suggest that miRNAs may act as channels through which perturbations in the levels of one RNA could be transmitted to other RNA species sharing the same miRNA regulator ( s ) . Such a scenario has been termed the ‘ceRNA effect’ , whereby ceRNA stands for ‘competing endogenous RNA’ [28] . In view of its considerable regulatory and therapeutic implications , the ceRNA effect has been extensively analyzed both theoretically and experimentally [29–44] . The apparent ubiquity of potentially cross-talking ceRNAs however raises a number of fundamental questions about the effectiveness of “regulation via competition” per se . Although hundreds of targets are predicted for a single miRNA , observations show that only few of them are sensitive to changes in miRNA expression levels . Most targets are likely to provide a global buffering mechanism through which miRNA levels are overall stabilized [28 , 29] . Effective competition between miRNA targets requires that the ratio of miRNA molecules to the number of target sites lies in a specific range , so that the relative abundance of miRNA and RNA species must be tightly regulated for the ceRNA mechanism to operate [29–34] . On the other hand , the magnitude of the ceRNA effect is tunable by the miRNA binding and mRNA loss rates [33 , 34 , 42] . The performance of a regulatory element , however , does not only depend on kinetic parameters , but also on the range of variability ( and possibly on the distribution itself ) of modulator levels ( e . g . TFs ) [8 , 9] . The maximal regulatory effectiveness of a given genetic circuit –quantifying how precisely the output level can be determined by the input level– can therefore generically be obtained by solving an optimization problem over the distribution of inputs . This type of approach provides an upper bound to the effectiveness of a regulatory mechanism as well as indications concerning which parameters , noise sources and/or interactions most hamper its performance . It would be especially important to understand in which conditions the degree of control of the output variable ( i . e . the ceRNA/protein level ) that can be accomplished through post-transcriptional miRNA-mediated cross-talk may exceed that obtainable by different regulatory mechanisms . In this work we characterize the maximal regulatory power achievable by miRNA-mediated control and compare it with that of a direct , TF-based transcriptional unit [45] . In principle , since fluctuations can be reduced by increasing the number of molecules , an ( almost ) arbitrary amount of information can be transmitted through a biochemical network . However , cells have to face the burden of macromolecular synthesis [46–48] . Optimality is therefore the result of a trade-off between the benefits of reduced fluctuations and the drawbacks of the associated metabolic costs . For this reason , we start by fixing a maximal rate of transcription ( or , alternatively , the maximal number of output molecules ) so as to have a simple but reasonable framework to characterize and compare the capacities of the different regulatory channels . Next , we quantify how an input signal is processed by the transcriptional ( TF-based ) and post-transcriptional ( miRNA-mediated ) regulatory elements by characterizing the response in the output ceRNA’s expression levels . In such a setting , information flow is hampered by intrinsic noise if the target gene is weakly derepressed by the activation of its competitor . Otherwise , target derepression appears to have a strong impact on a regulatory element’s capacity . Upon varying the magnitude of derepression by tuning the kinetic parameters , we then show that in certain regimes miRNA-mediated regulation can indeed outperform direct control of gene expression . Finally , we argue that the presence of miRNA molecules in large copy numbers notably reduces the level of intrinsic noise on weakly targeted transcripts . In this case , the mutual regulation of ceRNA molecules by miRNA-mediated channels may become a primary mechanism to finely tune gene expression . Besides providing a quantitative characterization of the maximal regulatory power achievable through miRNA-based post-transcriptional control , these results provide important hints on the circumstances in which regulation by small RNAs may function as the main tuner of gene expression in cells .
We consider ( see Fig 1 ) a system formed by two ceRNA species ( whose levels are labeled m1 and m2 , respectively ) and one miRNA species ( with level labeled μ ) , whose transcriptions are activated by a single TF each ( with levels labeled , respectively , f1 , f2 and fμ ) . Both ceRNAs are in turn targeted by the miRNA . miRNA-ceRNA complexes ( levels labeled ci with i = 1 , 2 ) assemble and disassemble at rates k i ± , respectively , whereas complexes can be degraded both stoichiometrically ( i . e . without miRNA recycling ) at rates σi and catalytically ( i . e . with miRNA recycling ) at rates κi [49] . In addition , ceRNA and miRNA molecules degrade ( resp . synthesize ) at rates di ( resp . bi ) and δ ( resp . β ) , respectively . Steps leading to the formation of the RNA-induced silencing complex ( RISC ) , allowing for the miRNA-ceRNA binding , are neglected for simplicity . TF levels are treated as externally controlled parameters . Denoting the TF-DNA binding/unbinding rates by kin and kout , respectively ( for simplicity , these parameters are taken to be the same for all involved TFs ) , the TF binding sites’ fractional occupancies nℓ ( 0 ≤ nℓ ≤ 1 , ℓ ∈ {1 , 2 , μ} ) obey the dynamics d n ℓ d t = k in ( 1 - n ℓ ) f ℓ h - k out n ℓ , ( 1 ) according to which transcriptional activation requires the cooperative binding of h TF molecules for each RNA species involved . In general , the occupancies by different TFs equilibrate on different timescales [50] . However it is often assumed that the transcriptional on/off dynamics is much faster than transcription itself [51 , 52] . As a consequence , each nℓ can be fixed at its ‘equilibrium’ value . n ¯ ℓ = k in f ℓ h k in f ℓ h + k out . ( 2 ) Every process in the above scheme contributes to the overall level of noise . We represent the mass-action kinetics of the system through the set of coupled Langevin processes ( i = 1 , 2 ) [41 , 53] d m i d t = - d i m i + b i n ¯ i - k i + μ m i + k - c i + ξ i - ξ i + + ξ i - , d c i d t = k i + μ m i - ( k i - + κ i + σ i ) c i + ξ i σ + ξ i + - ξ i - - ξ i κ , d μ d t = - δ μ + β n ¯ μ - ∑ i k i + μ m i + ∑ i ( k i - + κ i ) c i + ξ μ - ∑ i ξ i + + ∑ i ξ i - + ∑ i ξ i κ , ( 3 ) where the mutually independent random ( Poisson ) ‘forces’ ξi , ξ i ± , ξμ , ξ i κ and ξ i σ denote , respectively , the intrinsic noise in ceRNA levels ( due to random synthesis and degradation events ) , in the association/dissociation processes of complexes , in the miRNA level , in the catalytic complex decay and in the stoichiometric complex decay . Each of the above noise terms has zero mean , while correlations are given by ⟨ ξ i ( t ) ξ i ( t ′ ) ⟩ = ( d i m ¯ i + b i n ¯ i ) δ ( t - t ′ ) , ( 4 ) ⟨ ξ i + ( t ) ξ i + ( t ′ ) ⟩ = k i + m ¯ i μ ¯ δ ( t - t ′ ) , ( 5 ) ⟨ ξ i - ( t ) ξ i - ( t ′ ) ⟩ = k i - c ¯ i δ ( t - t ′ ) , ( 6 ) ⟨ ξ μ ( t ) ξ μ ( t ′ ) ⟩ = ( δ μ ¯ + β n ¯ μ ) δ ( t - t ′ ) , ( 7 ) ⟨ ξ i κ ( t ) ξ i κ ( t ′ ) ⟩ = κ i c ¯ i δ ( t - t ′ ) , ( 8 ) ⟨ ξ i σ ( t ) ξ i σ ( t ′ ) ⟩ = σ i c ¯ i δ ( t - t ′ ) , ( 9 ) where we introduced the steady state molecule numbers m ¯ i = b i n ¯ i + k i - c ¯ i d i + k i + μ ¯ , ( 10 ) μ ¯ = β n ¯ μ + ∑ i ( k i - + κ i ) c ¯ i δ + ∑ i k i + m ¯ i , ( 11 ) c ¯ i = k i + μ ¯ m ¯ i σ i + k i - + κ i . ( 12 ) We shall be interested in the fluctuations of molecular levels around the steady state that arise due to intrinsic noise sources . The stochastic dynamics of the system can be simulated via the Gillespie algorithm ( GA , see Materials and Methods ) . Fig 2A shows typical GA results for m ¯ 1 , m ¯ 2 and μ ¯ , with the corresponding Fano factors ( FFs ) shown in Fig 2B , as functions of f1 . Numerical results are matched against analytical estimates obtained by the linear noise approximation ( see Materials and Methods ) . In particular , in Fig 2 one sees that an increase of m ¯ 1 is accompanied by a concomitant increase of m ¯ 2 and by a decrease of the average number of free miRNA molecules . This is an instance of miRNA-mediated ceRNA cross-talk . Indeed , upon up-regulating m1 by injecting the corresponding TF ( i . e . by increasing f1 ) , the level of free miRNAs will decrease as more and more molecules will be actively repressing ceRNA1 , causing in turn an up-regulation of ceRNA2 . f1 will thus positively correlate with m2 . One easily sees that steady-state ceRNA levels depend on μ ¯ through a sigmoidal function , namely [42] m ¯ i = b i n ¯ i d i F i [ μ ¯ ] , F i [ μ ¯ ] = μ 0 , i μ 0 , i + μ ¯ . ( 13 ) The constant μ 0 , i = d i k i + ( 1 + k i - σ i + κ i ) acts as a ‘soft’ threshold for the miRNA level , allowing to distinguish three situations: if μ ¯ ⪡ μ 0 , i , ceRNAi is free or unrepressed: spontaneous ceRNA degradation dominates over miRNA-mediated decay channels , so that , effectively , the ceRNA level is weakly sensitive to small changes in μ ¯; if μ ¯ ≫ μ 0 , i , ceRNAi is bound or repressed: miRNA-mediated ceRNA decay dominates over spontaneous ceRNA degradation but , again , the ceRNA level is weakly sensitive to small changes in μ ¯ as most ceRNAs are bound in complexes with the miRNA; if μ ¯ ≃ μ 0 , i , ceRNAi is susceptible to μ: spontaneous and miRNA-mediated decay channels have comparable weights and the ceRNA level is very sentitive to small changes in μ ¯ . The behaviour of the FFs ( see Fig 2B ) emphasizes how noise patterns change in the different regimes . In the displayed example , ceRNA1 and ceRNA2 become susceptible for ln ( f1 ) ≃ 2 . 5 and ln ( f1 ) ≃ 2 . 1 , respectively . Indeed , one observes that the corresponding FFs peak close to these values , in accordance with the observation that stochastic fluctuations are enhanced when the rates of substrate supply are adequately balanced in a stoichiometrically coupled system [34 , 54 , 55] . The FF for ceRNA2 appears to approach one for large values of f1 , as expected for the pure Poisson birth/death process that characterizes the free regime [56] . On the other hand , for very small but nonzero mean fractional occupancy n ¯ 1 ( corresponding to small values of f1 ) , m1 will with high probability only take on the values 0 or 1 , as a transcribed molecule will quickly undergo degradation or sequestration in a complex . In such a situation , the mean and variance of m1 will be calculated by summing up zeros and ones over time , leading to a FF equal to one . Target prediction algorithms suggest that miRNA binding affinities vary significantly among their targets [57] . Such heterogeneities indeed are mapped to the miRNA binding kinetics and are shown to influence the susceptibility of the targets to the miRNA molecules [36] . Moreover , the level of complementarity between the regulator and the target seems to be decisive for the selection of a decay channel ( catalytic or stoichiometric ) for the miRNA-ceRNA complex [15 , 27] . One may therefore expect that the effectiveness of miRNA-mediated post-transcriptional control depends strongly on the kinetic parameters characterizing the network . Here in particular , we are going to investigate how the capacities of these regulatory elements are affected by changes in ( i ) miRNA-ceRNA binding kinetics , ( ii ) miRNA recycling rates , and ( iii ) effective transcription rates of all RNA species involved . In order to contrast the performances of miRNA- and TF-channels we shall start by analyzing their respective responses to the same input signal . More precisely , the input variable fj will be varied from 0 to a value fmax defined by the condition nj ( fmax ) = 0 . 99 ( i . e . from a situation in which the promoter is always free to one in which it is essentially always occupied ) . The quality of miRNA-target interaction influences the binding kinetics and may be decisive for the activation of the target decay channel [15 , 27 , 63 , 64] . Estimations of the miRNA-ceRNA binding affinities are experimentally challenging . However , computational methods predict a considerable degree of heterogeneity across different miRNA-ceRNA pairs [57] . Remarkably , for the majority of cases reported in the literature , the predicted miRNA-binding energies of the RNAs on the ‘input side’ of the channel are lower than those of the RNAs on the ‘output side’ , in line with the optimal conditions highlighted by our model . For example , binding affinities between the long non-coding RNA linc-MD1 and its regulatory miRNAs ( playing a central role in skeletal muscle cell differentiation ) are significantly lower than those characterizing miRNA interactions with linc-MD1’s competitors , namely the MAML1 and MEF2C mRNAs , as predicted by miRanda algorithm [39 , 64] . Likewise , the circular RNA CDR1 has been found to contain around 70 binding sites with high complementarity for miR-7 , corresponding to a strong effective coupling through which it can regulate the expression of miR-7’s target genes [37 , 65 , 66] . Finally , the high sequence homology of pseudogenes ( long non-coding RNA genes developed from protein-coding genes but unable to produce proteins ) with their parental gene allows them to compete for a large number of shared miRNAs [28 , 38 , 67] . Our study also points to the potential relevance of miRNA-ceRNA complex decay channels . It is known that , in case of sufficient complementarity , miRNA molecules can function as siRNAs and cleave their targets after binding them [15 , 27] . Such targets decay catalytically and are therefore effectively degraded by the miRNAs . Our model predicts that miRNA-mediated control may be the preferred regulatory mechanism in presence of kinetic heterogeneities at the level of miRNA recycling rates . In particular , targets that undergo catalytic degradation may be efficiently derepressed by their competitors . This type of scenario has been observed in experiments concerning the bacterial small RNA Qrr [61] . Qrr represses its targets by distinct mechanisms . For instance , luxR is repressed catalytically , luxM stoichiometrically , while luxO is silenced through translational repression . luxM and luxO are however able to derepress LuxR in the presence of Qrr . In the light of our results , these observations may therefore point to a higher than expected role for the ceRNA effect in vivo , especially in cases in which heterogeneities in kinetic parameters are thought to be strong [34 , 59 , 62 , 68 , 69] .
Non-coding RNA molecules , and miRNAs specifically , are increasingly associated to regulatory functions . Besides making it mandatory to characterize the specific role of ncRNAs on a case-by-case basis , especially for situations like disease or differentiation , this fact also raises the question of what ingredients can make miRNAs a preferred tool to regulate the level of a target RNA over , for instance , the target’s TF . A possible answer lies in the noise-buffering role that miRNAs can play , which is especially evident in genetic circuitries like incoherent feed-forward loops [19 , 20] . By reducing relative fluctuations in the output level , miRNAs can confer robustness to gene expression profiles . However the so-called ‘ceRNA hypothesis’ opens the way to the possibility that their regulatory functions are carried out at a broader , though more subtle , level . In short , according to the ceRNA scenario miRNAs can mediate an effective positive interaction between their target RNAs driven by the targets’ competition to bind them . In this sense , miRNAs can be seen as a sort of ‘channel of communication’ between RNAs through which RNA levels can be altered and noise can be processed ( both buffered and amplified ) . Previous work [42] has shown that the ceRNA effect may generate both highly plastic and highly selective ceRNA-ceRNA couplings , thereby representing a potentially powerful mechanism to implement gene regulation at the post-transcriptional level . Although predicted theoretically , the extent and relevance of ceRNA effect in vivo is poorly understood . On one hand , considerable evidence points to the ceRNA effect playing a major role in certain dis-regulated or transient cellular states . For instance , it has been shown that the expression of the tumor-suppressor gene PTEN can be regulated by its miRNA-mediated competitors VAPA , CNOT6L , SERINC1 or ZNF460 [70] . Furthermore , many pseudogenes have been found to compete with their parental genes for a shared pool of common microRNAs , thus regulating their expression as competitive endogenous RNA [28 , 38 , 67 , 71] . Such mechanisms seem to be of particular relevance in cancer . For instance , murine models engineered to overexpress the pseudogenes of the proto-oncogene BRAF develop an aggressive malignancy resembling human B cell lymphoma since , by functioning as ceRNAs , they elevate BRAF expression both in vitro and in vivo [38] . Likewise , the long noncoding RNA linc-MD1 has been shown to regulate the skeletal muscle cell differentiation clock by sponging miRNAs from its competitors , thereby enacting a ceRNA mechanism . In particular , MAML1 and MEF2C ( coding for transcription factors that activate muscle-specific gene expression ) compete with linc-MD1 for miR-133 and miR-135 respectively [39] . Taken together , the available evidence indicates that miRNA activity depends on the miRNA:target ratio , on miRNA target site abundance and on miRNA binding affinities . Further analyses of high throughput datasets confirm this observation [30 , 36] . One may therefore question how said factors may influence miRNA-mediated post-transcriptional control . The problem however arises of quantifying the degree of control that can be exerted through miRNAs . Taking the ‘channel’ analogy more strictly ( as done before for simpler regulatory elements [9 , 45 , 52] ) , one may resort to information theoretic concepts and tools to characterize precisely how well miRNAs can process fluctuations coming from the modulator nodes and transfer them to the target nodes . This issue goes beyond noise buffering , specifically including the ability to respond to large changes in the mean levels as well as to changes in the structure of fluctuations . As the properties of a channel are conveniently encoded in the mutual information between the input and output nodes , asking how well a channel can function amounts to asking what is the channel’s capacity , i . e . the maximum value of the input-output ( or modulator-target ) mutual information achievable through that channel . This work aimed precisely at quantifying the effectiveness of microRNA-mediated post-transcriptional control of gene expression by computing the capacity of the corresponding regulatory channel and comparing it to that of direct , TF-driven transcriptional regulation . Evidently , multiple factors can influence the flow of information across nodes in a biochemical network , starting from the intrinsic noisiness of each reaction step . Our basic challenge was therefore understanding in which circumstances miRNA-mediated control can outperform the TF-based one , thereby obtaining insight on why the ceRNA effect appears to be so often employed by cells in situations where accurate tuning and/or shifts of expression levels are required . We have therefore considered , along the lines of [33 , 34 , 41–44] , a mathematical model of the ceRNA effect and characterized its steady state in terms of both mean molecular levels and regulatory capacities of the miRNA-mediated and TF-based channels via stochastic simulations . We have first considered how the two channels process inputs ( the TF levels f1 and f2 ) that vary in the same range . We have shown that , while the capacity of the TF-channel depends monotonously on each miRNA-ceRNA binding rate and is largest when the target is unrepressed by miRNAs ( as might have been expected ) , the capacity of the post-transcriptional channel is maximal in a specific range of values of the miRNA-ceRNA binding rates . In agreement with [33] , we found that miRNA-channel’s efficiency is tunable to optimality by the binding kinetics . Furthermore , our model suggests that both capacities decrease as the miRNA recycling rates increase , confirming previous indications obtained by different analytical techniques [42] . Consistently with the scenario observed experimentally for the bacterial small RNA Qrr [61] , our model finally suggests that catalytically regulated targets are weakly capable of competing for miRNAs but might be significantly derepressed by their competitors . In addition , post-transcriptional miRNA-mediated information processing was shown to be characterized by a threshold behaviour as a function of the AOV . In other terms , no information can be transmitted across the channel unless the target’s degree of derepression is sufficiently large . This implies that the regulatory effectiveness of the channel is well encoded by the degree of target derepression when the latter is sufficiently high , in which case it is possible to identify regimes in which post-transcriptional regulation is more accurate than transcriptional control . To get a deeper insight on the origin of the observed threshold behaviour one must however go beyond the AOV and consider more carefully how the miRNA-ceRNA binding noise affects the overall picture . After showing that miRNA-ceRNA binding noise is indeed at the origin of the threshold behaviour that limits the miRNA-channel capacity , we have uncovered the rather remarkable property that in presence of large but weakly interacting miRNA populations the ceRNA effect can regulate gene expression as effectively as the target’s modulator node itself . The present work has focused on a small genetic circuit made up of a single miRNA species and two target RNA species at steady state . Previous work has however shown that cross-talk is possible even during transients [44] . Going beyond stationarity is therefore likely to bring to light new scenarios where miRNA-mediated regulation plays possibly a yet more prominent role . On the other hand in a typical eukaryotic cell there are thousands ceRNAs , hundreds miRNAs and a rich structure of conserved targeting patterns [72] . Moreover , cells might be interested in tightly controlling not only each output individually but also particular combinations of output levels ( which might be required e . g . for the efficient operation of metabolic pathways ) . In such a scenario , miRNA-mediated control could represent a powerful mechanism to increase robustness and flexibility in specific directions of the output space . In view of this , it would be important to consider a more general multi-source network coding problem in which a large number of transcription processes are seen as mutually independent information sources , and each of the information sources is multicast to sets of output nodes through the effective network of miRNA-mediated cross-talk interactions . The information-theoretic scheme employed in this work is easily generalized to deal with more complex networked situations . Novel insight might finally shed light on the partly controversial picture unveiled by recent experiments addressing the relevance of the ceRNA effect in vivo [30 , 35–39] .
Numerical simulations have been carried out using the Gillespie algorithm ( GA ) , a standard stochastic method to analyze the time evolution of a system of chemical reactions which is exact for spatially homogeneous systems [73] . In short , based on the reaction rates , GA calculates when the next reaction will occur and what reaction it will be , and then modifies the amount of each molecular species in the system according to the process that took place . If we denote the probability of reaction r to occur in the time interval ( τ , τ + dτ ) by P ( r , τ ) dτ , the algorithm proceeds through the following steps: Initiate the number of reactants in the system and the termination time; Generate a random pair ( r , τ ) according to P ( r , τ ) ; Using the pair ( r , τ ) just generated , advance time by τ and change number of species involved in reaction r accordingly; Read out the molecular population values . If the termination time is reached , stop the simulations , otherwise return to Step 2 . After a long run , independently on the initial setup , the system of chemical reactants will come to the equilibrium state . The mathematical model of ceRNA competition can be solved numerically in the so-called linear noise approximation . Letting x = ( m1 , m2 , μ , c1 , c2 ) stand for the vector of molecular levels , the kinetic mass action Eq ( 3 ) can be re-cast in compact form as d x d t = g ( x ) + η , ( 25 ) where the vector g encodes for the deterministic part of the dynamics , while the vector η represents the aggregate noise terms . Each element of η has zero mean , and we shall denote its correlations by 〈ηa ( t ) ηb ( t′ ) 〉 = Γab δ ( t − t′ ) . Denoting by x ¯ the steady state , small deviations from it ( i . e . δ x = x ( t ) - x ¯ ) relax , in the linear regime , according to d d t δ x = A δ x + η , ( 26 ) where A = d g d x | x = x ¯ . In this approximation , the correlation matrix Cab = 〈δxa δxb〉 is given by [74] C a b = - ∑ p , q , r , s B a p B b r Γ q s λ p + λ r ( B - 1 ) p q ( B - 1 ) r s , ( 27 ) where λ’s and B’s are , respectively , eigenvalues and eigenvectors of the matrix A . Table 1 reports the values of the kinetic parameters ( or of their range of variability ) employed in the different figures . As k 1 + , k 2 + , κ 1 , κ 2 , b 1 , b 2 , b μ and dμ are varied in wide ranges in order to test how channel capacities depend on them , our choice was mainly guided by the need to focus the analysis on regimes where ceRNA cross-talk is established ( so that the miRNA-mediated channel can actually convey information ) . | The discovery of RNA interference has revolutionized the decades’ old view of RNAs as mere intermediaries between DNA and proteins in the gene expression workflow . MicroRNAs ( or miRNAs ) , in particular , have been shown to be able to both stabilize the protein output by buffering transcriptional noise and to create an effective positive interaction between the levels of their target RNAs through a simple competition mechanism known as ‘ceRNA effect’ . With miRNAs commonly targeting multiple species of RNAs , the potential implication is that RNAs could regulate each other through extended miRNA-mediated interaction networks . Such cross-talk is certainly active in many specific cases ( like cell differentiation ) , but it’s unclear whether the degree of regulation of gene expression achievable through post-transcriptional miRNA-mediated coupling can effectively overcome the one obtained through other mechanisms , e . g . by direct transcriptional control via DNA-binding factors . This work quantifies the maximal post-transcriptional regulatory power achievable by miRNA-mediated cross-talk , characterizing the circumstances in which indirect control outperforms direct one . The emerging scenario suggests that , in addition to its widely recognized noise-buffering role , miRNA-mediated control may indeed act as a master regulator of gene expression . |
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Fluctuations in the temporal durations of sensory signals constitute a major source of variability within natural stimulus ensembles . The neuronal mechanisms through which sensory systems can stabilize perception against such fluctuations are largely unknown . An intriguing instantiation of such robustness occurs in human speech perception , which relies critically on temporal acoustic cues that are embedded in signals with highly variable duration . Across different instances of natural speech , auditory cues can undergo temporal warping that ranges from 2-fold compression to 2-fold dilation without significant perceptual impairment . Here , we report that time-warp–invariant neuronal processing can be subserved by the shunting action of synaptic conductances that automatically rescales the effective integration time of postsynaptic neurons . We propose a novel spike-based learning rule for synaptic conductances that adjusts the degree of synaptic shunting to the temporal processing requirements of a given task . Applying this general biophysical mechanism to the example of speech processing , we propose a neuronal network model for time-warp–invariant word discrimination and demonstrate its excellent performance on a standard benchmark speech-recognition task . Our results demonstrate the important functional role of synaptic conductances in spike-based neuronal information processing and learning . The biophysics of temporal integration at neuronal membranes can endow sensory pathways with powerful time-warp–invariant computational capabilities .
Robustness of neuronal information processing to temporal warping of natural stimuli poses a difficult computational challenge to the brain [1]–[9] . This is particularly true for auditory stimuli , which often carry perceptually relevant information in fine differences between temporal cues [10] , [11] . For instance in speech , perceptual discriminations between consonants often rely on differences in voice onset times , burst durations , or durations of spectral transitions [12] , [13] . A striking feature of human performance on such tasks is that it is resilient to a large temporal variability in the absolute timing of these cues . Specifically , changes in speaking rate in ongoing natural speech introduce temporal warping of the acoustic signal on a scale of hundreds of milliseconds , encompassing temporal distortions of acoustic cues that range from 2-fold compression to 2-fold dilation [14] , [15] . Figure 1 shows examples of time warp in natural speech . The utterance of the word “one” in ( A ) is compressed by nearly a factor of one-half relative to the utterance shown in ( B ) , causing a concomitant compression in the duration of prominent spectral features , such as the transitions of the peaks in the frequency spectra . Notably , the pattern of temporal warping in speech can vary within a single utterance on a scale of hundreds of milliseconds . For example , the local time warp of the word “eight” in ( C ) relative to ( D ) , reverses from compression in the initial and final segments to strong dilation of the gap between them . Although it has long been demonstrated that speech perception in humans normalizes durations of temporal cues to the rate of speech [2] , [16]–[18] , the neural mechanisms underlying this perceptual constancy have remained mysterious . A general solution of the time-warp problem is to undo stimulus rate variations by comodulating the internal “perceptual” clock of a sensory processing system . This clock should run slowly when the rate of the incoming signal is low and embedded temporal cues are dilated , but accelerate when the rate is fast and the temporal cues are compressed . Here , we propose a neural implementation of this solution , exploiting a basic biophysical property of synaptic inputs , namely , that in addition to charging the postsynaptic neuronal membrane , synaptic conductances modulate its effective time constant . To utilize this mechanism for time-warp robust information processing in the context of a particular perceptual task , synaptic peak conductances at the site of temporal cue integration need to be adjusted to match the range of incoming spike rates . We show that such adjustments can be achieved by a novel conductance-based supervised learning rule . We first demonstrate the computational power of the proposed mechanism by testing our neuron model on a synthetic instantiation of a generic time-warp–invariant neuronal computation , namely , time-warp–invariant classification of random spike latency patterns . We then present a novel neuronal network model for word recognition and show that it yields excellent performance on a benchmark speech-recognition task , comparable to that achieved by highly elaborate , biologically implausible state-of-the-art speech-recognition algorithms .
Whereas the net current flow into a neuron is determined by the balance between excitatory and inhibitory synaptic inputs , both types of inputs increase the total synaptic conductance , which in turn modulates the effective integration time of the postsynaptic cell [19]–[21] ( an effect known as synaptic shunting ) . Specifically , when the total synaptic conductance of a neuron is large relative to the resting conductance ( leak ) and is generated by linear summation of incoming synaptic events , the neuron's effective integration time scales inversely to the rate of inputs spikes . Hence , the shunting action of synaptic conductances can counter variations in afferent spike rates by automatically rescaling the effective integration time of the postsynaptic neuron . We implement this mechanism in a leaky integrate-and-fire model neuron driven by N exponentially decaying synaptic conductances . Here , denotes the peak conductance of the ith synapse in units of sec−1 , and τs is the synaptic time constant . The total synaptic current , measured at rest , is given bywhere denotes the reversal potential of the ith synapse relative to resting potential and ti denote the arrival times of the spikes of the ith afferent . The factor β denotes a global scaling of all incoming spike times; β = 1 is the unwarped inputs . The total synaptic conductance , Gsyn ( t , β ) , is For fast synapses , the total synaptic current is essentially a train of pulses , each of which occurs at the time of an incoming spike and delivers a total charge of . Changing the rate of the incoming spikes will induce a corresponding change in the timing of these pulses but not their charge . Therefore , ignoring the effect of time warp on the time scale of τs , which is short relative to the time scale of voltage modulations , the total synaptic current obeys the following time-warp scaling relation , Isyn ( βt , β ) = β−1Isyn ( t , 1 ) . A similar scaling relation holds for the total synaptic conductance . The evolution in time of the subthreshold voltage is given by ( 1 ) Thus , V integrates the synaptic current with an effective time constant whose inverse is 1/τeff = gleak+Gsyn ( t , β ) . If the contribution of Gsyn is significantly larger than the leak conductance , then 1/τeff is rescaled by time-warp similar to Gsyn and Isyn , and , hence , the solution of Equation 1 is approximately time-warp invariant , namely , V ( βt , β ) = V ( t , 1 ) . This result is illustrated in Figure 2 , which compares the voltage traces induced by a random spike pattern for β = 1 and β = 0 . 5 . To perform time-warp–invariant tasks , peak synaptic conductances must be in the range of values appropriate for the statistics of the stimulus ensemble of the given task . To achieve this , we have devised a novel spike-based learning rule for synaptic conductances , the conductance-based tempotron . This model neuron learns to discriminate between two classes of spatiotemporal input spike patterns . The tempotron's classification rule requires it to fire at least one spike in response to each of its target stimuli but to remain silent when driven by a stimulus from the null class . Spike patterns from both classes are iteratively presented to the neuron , and peak synaptic conductances are modified after each error trial by an amount proportional to their contribution to the maximum value of the postsynaptic potential over time ( see Materials and Methods ) . This contribution is sensitive to the time courses of the total conductance and voltage of the postsynaptic neuron . Therefore , the conductance-based tempotron learns to adjust , not only the magnitude of the synaptic inputs , but also its effective integration time to the statistics of the task at hand . We first quantified the time-warp robustness of the conductance-based tempotron on a synthetic discrimination task . We randomly assigned 1 , 250 spike pattern templates to target and null classes . The templates consisted of 500 afferents , each firing once at a fixed time chosen randomly from a uniform distribution between 0 and 500 ms . Upon each presentation during training and testing , the templates underwent global temporal warping by a random factor β ranging from compression by 1/βmax to dilation by βmax ( see Materials and Methods ) . Consistent with the psychophysical range , βmax was varied between 1 and 2 . 5 . Remarkably , with physiologically plausible parameters , the error frequency remained almost zero up to βmax≈2 ( Figure 3A , blue curve ) . Importantly , the performance of the conductance-based tempotron showed little change when the temporal warping applied to the spike templates was dynamic ( see Materials and Methods ) ( Figure 3A ) . The time-warp robustness of the neural classification depends on the resting membrane time constant τm and the synaptic time constant τs . Increases in τm or decreases in τs both enhance the dominance of shunting in governing the cell's effective time constant . As a result , the performance for βmax = 2 . 5 improved with increasing τm ( Figure 3B , left ) and decreasing τs ( Figure 3B , right ) . The time-warp robustness of the conductance-based tempotron was also reflected in the shape of its subthreshold voltage traces ( Figure 3C , top row ) and generalized to novel spike templates with the same input statistics that were not used during training ( Figure 3C , second row ) . Synaptic conductances were crucial in generating the neuron's robustness to temporal warping . Athough an analogous neuron model with a fixed integration time , the current-based tempotron [22] ( see Materials and Methods ) also performed the task perfectly in the absence of time-warp ( βmax = 1 ) ; its error frequency was sensitive even to modest temporal warping and deteriorated further when the applied time warp was dynamic ( Figure 3A , red curve ) . Similarly , the voltage traces of this current-based neuron showed strong dependence on the degree of temporal warping applied to an input spike train ( Figure 3C , bottom trace pair ) . Finally , the error frequency of the current-based neuron at βmax = 2 . 5 showed only negligible improvement upon varying the values of the membrane and synaptic time constants ( Figure 3B ) , highlighting the limited capabilities of fixed neural kinetics to subserve time-warp–invariant spike-pattern classification . Note that in the present classification task , the degree of time-warp robustness depends also on the learning load , i . e . , number of patterns that have to classified by a neuron ( unpublished data ) . A given degree of time warp translates into a finite range of distortions of the intracellular voltage traces . If these distortions remain smaller than the margins separating the neuronal firing threshold and the intracellular peak voltages , a neuron's classification will be time-warp invariant . Since the maximal possible margins increase with decreasing learning load , time-warp invariance can be traded for storage capacity . This tradeoff is governed by the susceptibility of the voltage traces to time warp . If the susceptibility is high , as in the current-based tempotron , robustness to time warp comes at the expense of a substantial reduction in storage capacity . If it is low , as in the conductance-based tempotron , time-warp invariance can be achieved even when operating close to the neuron's maximal storage capacity for unwarped patterns . In the conductance-based tempotron , synaptic conductances controlled , not only the effective integration time of the neuron , but also the temporal selectivity of the synaptic update during learning . The tempotron learning rule modifies only the efficacies of the synapses that were activated in a temporal window prior to the peak in the postsynaptic voltage trace . However , the width of this temporal plasticity window is not fixed but depends on the effective integration time of the postsynaptic neuron at the time of each synaptic update trial , which in turn varies with the input firing rate at each trial and the strength of the peak synaptic conductances at this stage of learning ( Figure 4 ) . During epochs of high conductance ( warm colors ) , only synapses that fired shortly before the voltage maximum were appreciably modified . In contrast , when the membrane conductance was low ( cool colors ) , the plasticity window was broad . The ability of the plasticity window to adjust to the effective time constant of the postsynaptic voltage is crucial for the success of the learning . As is evident from Figure 4 , the membrane's effective time constant varies considerably during the learning epochs; hence , a plasticity rule that does not take this into account fails to credit appropriately the different synapses . The evolution of synaptic peak conductances during learning was driven by task requirements . When we replaced the temporal warping of the spike templates by random Gaussian jitter [22] ( see Materials and Methods ) , conductance-based tempotrons that had acquired high synaptic peak conductances during initial training on the time-warp task readjusted their synaptic peak conductances to low values ( Figure 5 , inset ) . The concomitant increase in their effective integration time constants from roughly 10 ms to 50 ms improved the neurons' ability to average out the temporal spike jitter and substantially enhanced their task performance ( Figure 5 ) . To address time-warp–invariant speech processing , we studied a neuronal module that learns to perform word-recognition tasks . Our model consists of two auditory processing stages . The first stage ( Figure 6 ) consists of an afferent population of neurons that convert incoming acoustic signals into spike patterns by encoding the occurrences of elementary spectrotemporal events . This layer forms a 2-dimensional tonotopy-intensity auditory map . Each of its afferents generates spikes by performing an onset or offset threshold operation on the power of the acoustic signal in a given frequency band . Whereas an onset afferent elicits a spike whenever the log signal power crosses its threshold level from below , offset afferents encode the occurrences of downward crossings ( see Materials and Methods ) ( cf . also [6] , [23] ) . Different on and off neurons coding for the same frequency band differ in their threshold value , reflecting a systematic variation in their intensity tuning . The second , downstream , layer consists of neurons with plastic synaptic peak conductances that are governed by the conductance-based tempotron plasticity rule . These neurons are trained to perform word discrimination tasks . We tested this model on a digit-recognition benchmark task with the TI46 database [24] . We trained each of the 20 conductance-based tempotrons of the second layer to perform a distinct gender-specific binary classification , requiring it to fire in response to utterances of one digit and speaker gender , and to remain quiescent for all other stimuli . After training , the majority of these digit detector neurons ( 70% ) achieved perfect classification of the test set , and the remaining ones performed their task with a low error ( Table 1 ) . Based on the spiking activity of this small population of digit detector neurons , a full digit classifier ( see Materials and Methods ) that weighted spikes according to each detector's individual performance , achieved an overall word error rate of 0 . 0017 . This performance matches the error rates of state-of-the-art artificial speech-recognition systems such as the Hidden Markov model–based Sphinx-4 and HTK , which yield error rates of 0 . 0017 [25] and 0 . 0012 [26] , respectively , on the same benchmark . To reveal qualitatively some of the mechanisms used by our digit detector neurons to selectively detect their target word , we compared the learned synaptic distributions ( Figure 7A ) of two digit detector neurons ( “one” and “four” ) to the average spectrograms of each neuron's target stimuli aligned to the times of its output spikes ( Figure 7B; see Materials and Methods ) . The spectrotemporal features that preceeded the output spikes ( time zero , grey vertical lines ) corresponded to the frequency-specific onset and offset selectivity of the excitatory afferents ( Figure 7A , warm colors ) . These examples demonstrate how the patterned excitatory and inhibitory inputs from both onset and offset neurons are tuned to features of the speech signal . For instance , a prominent feature in the averaged spectrogram of the word “one” ( male speakers ) was the increase in onset time of the power in the low-frequency channels with the frequency of the channel ( Figure 7B , left , channels 1–16 ) . This gradual onset was encoded by a diagonal band of excitatory onset afferents whose thresholds decreased with increasing frequency ( Figure 7A , left ) . By compensating for the temporal lag between the different lower-frequency channels , this arrangement ensured a strong excitatory drive when a target stimulus was presented to the neuron . The spectrotemporal feature learned by the word “four” ( male speakers ) detector neuron combined decreasing power in the low-frequency range with rising power in the mid-frequency range ( Figure 7B , right ) . This feature was encoded by synaptic efficacies through a combination of excitatory offset afferents in the low-frequency range ( Figure 7A , right , channels 1–11 ) and excitatory onset afferents in the mid-frequency range ( channels 12–19 ) . Excitatory synaptic populations were complemented by inhibitory inputs ( Figure 7A , blue patches ) that prevented spiking in response to null stimuli and also increased the total synaptic conductance . The substantial differences between the mean spike-triggered voltage traces for target stimuli ( Figure 7C , blue ) and the mean maximum-triggered voltage traces for null stimuli ( red ) underline the high target word selectivity of the learned synaptic distributions as well as the relatively short temporal extend of the learned target features . In the examples shown , the average position of the neural decision relative to the target stimuli varied from early to late ( Figure 7B , left vs . right ) . This important degree of freedom stems from the fact that the tempotron decision rule does not constrain the time of the neural decision . As a result , the learning process in each neuron can select the spectrotemporal target features from any time window within the word . The selection of the target feature by the learning takes into account both the requirement of triggering output spikes in response to target stimuli as well as the demand to remain silent during null stimuli . Thus , for each target neuron , the selected features reflect the statistics of both the target and the null stimuli . We have performed several tests designed to assess the ability of the model word detector neurons to perform well on new input sets , different in statistics from the trained database . First , we assessed the ability of the neurons to generalize to unfamiliar speakers and dialects . After training the model with the TI46 database , as described above , we measured its digit-recognition performance on utterances from another database , the TIDIGITS database [27] , which includes speech samples from a variety of English dialects ( see Materials and Methods ) . This test has been done without any retraining of the network synapses . The resulting word error rate of 0 . 0949 compares favorably to the performance of the HTK system , which resulted in an error rate of 0 . 2156 when subjected to the same generalization test ( see Materials and Methods ) . Across all dialects , our model performed perfectly for roughly one-quarter of all speakers and with at most one error for half of them . Within the best dialect group , an error of at most one word was achieved for as many as 80% of the speakers ( Table S1 ) . These results underline the ability of our neuronal word-recognition model to generalize to unfamiliar speakers across a wide range of different unfamiliar dialects . An interesting question is whether our model neurons are able to generalize their performance to novel time-warped versions of the trained inputs . To address this question , we have tested their performance on randomly generated time-warped versions of the input spikes corresponding to the trained word utterances , without retraining . As shown in Figure 8 , the neurons exhibited considerable time-warp–robust performance on the digit-recognition task . For instance , the errors for the “one” ( Figure 8A , black line ) and “four” ( blue line ) detector neurons ( cf . Figure 7 ) were insensitive to a 2-fold time warp of the input spike trains . The “seven” detector neuron ( male , red line ) showed higher sensitivity to such warping; nevertheless , its error rate remained low . Consistent with the proposed role of synaptic conductances , the degree of time-warp robustness was correlated with the total synaptic conductance , here quantified through the mean effective integration time τeff ( Figure 8B ) . Additionally , the mean voltage traces induced by the target stimuli ( Figure 8C , lower traces ) showed a substantially smaller sensitivity to temporal warping than their current-based analogs ( see Materials and Methods ) ( Figure 8C , upper traces ) . We also found that our model word detector neurons are robust to the introduction of spike failures in their input patterns . For each neuron , we have measured its performance on inputs which were corrupted by randomly deleting a fraction of the incoming spikes , again without retraining . For the majority of neurons , the error percentage increased by less than 0 . 01% for each percent increase in spike failures ( Figure 9 ) . This high robustness reflects the fact that each classification is based on integrating information from many presynaptic sources .
The proposed conductance-based time-rescaling mechanism is based on the biophysical property of neurons that their effective integration time is shaped by synaptic conductances and therefore can be modulated by the firing rate of its afferents . To utilize these modulations for time-warp–invariant processing , a central requirement is a large evoked total synaptic conductance that dominates the effective integration time constant of the postsynaptic cell through shunting . In our speech-processing model , large synaptic conductances with a median value of a 3-fold leak conductance across all digit detector neurons ( cf . Figure 8B ) result from a combination of excitatory and inhibitory inputs . This is consistent with high total synaptic conductances , comprising excitation and inhibition , that have been observed in several regions of cortex [28] including auditory [29] , [30] , visual [31] , [32] , and also prefrontal [33] , [34] ( but see ref . [35] ) . Our model predicts that in cortical sensory areas , the time-rescaled intracellular voltage traces ( cf . Figure 3C ) , and consequently , also the rescaled spiking responses of neurons that operate in the proposed fashion , remain invariant under temporal warping of the neurons' input spike patterns . These predictions can be tested by intra- and extracellular recordings of neuronal responses to temporally warped sensory stimuli . A large total synaptic conductance is associated with a substantial reduction in a neuron's effective integration time relative to its resting value . Therefore , the resting membrane time constant of a neuron that implements the automatic time-rescaling mechanism must substantially exceed the temporal resolution that is required by a given processing task . Because the word-recognition benchmark task used here comprises whole-word stimuli that favored effective time constants on the order of several tens of milliseconds , we used a resting membrane time constant of τm = 100 ms . Whereas values of this order have been reported in hippocampus [36] and cerebellum [21] , [37] , it exceeds current estimates for neocortical neurons , which range between 10 and 30 ms [35] , [38] , [39] . Note , however , that the correspondence of our passive membrane model and the experimental values that typically include contributions from various voltage-dependent conductances is not straightforward . Our model predicts that neurons specialized for time-warp–invariant processing at the whole-word level have relatively long resting membrane time constants . It is likely that the auditory system solves the problem of time-warp–invariant processing of the sound signal primarily at the level of shorter speech segments such as phonemes . This is supported by evidence that primary auditory cortex has a special role in speech processing at a resolution of milliseconds to tens of milliseconds [11]–[13] . Our mechanism would enable time-warp–invariant processing of phonetic segments with resting membrane time constants in the range of tens of milliseconds , and much shorter effective integration times . The proposed neuronal time-rescaling mechanism assumes linear summation of synaptic conductances . This assumption is challenged by the presence of voltage-dependent conductances in neuronal membranes . Since the potential implications for our model depend on the specific nonlinearity induced by a cell-type–specific composition of different ionic channels , it is hard to evaluate the overall effect on our model in general terms . Nevertheless , because of its immanence , we expect the conductance-based time-rescaling mechanism to cope gracefully with moderate levels of nonlinearity . As an example , we tested its behavior in the presence of an h-like conductance ( see Materials and Methods ) that opposes conductance changes induced by depolarizing excitatory synaptic inputs and is active at the resting potential . As expected , we found that physiological levels of h-conductances resulted in only moderate impairment of the automatic time-rescaling mechanism ( Figure S1 ) . For the sake of simplicity as well as numerical efficiency , we have assumed symmetric roles of excitation and inhibition in our model architecture . We have checked that this assumption is not crucial for the operation of the automatic time-rescaling mechanism and the learning of time-warped random latency patterns . Specifically , we have implemented the random latency classification task for a control architecture in which all synapses were confined to be excitatory except a single global inhibitory input that , mimicking a global inhibitory network , received a separate copy of all incoming spikes . In this architecture , all spike patterns have to be encoded by the excitatory synaptic population , and the role of inhibition is reduced to a global signal that has equal strength for all input patterns . Due to the limitations of this architecture , this model showed some reduction of storage capacity relative to the symmetric case , but the automatic time-rescaling mechanism remained intact . For a time-warp scale of βmax = 2 . 5 ( cf . Figure 3 ) , the global inhibition model roughly matched the performance of the symmetric model when the learning load was lowered to 1 . 5 spike patterns per synapse , with an error fraction of 0 . 18% . To utilize synaptic conductances as efficient controls of the neuron's clock , the peak synaptic conductances must be plastic so that they adjust to the range of integration times relevant for a given perceptual task . This was achieved in our model by our novel supervised spike-based learning rule . This plasticity posits that the temporal window during which pre- and postsynaptic activity interact continuously adapts to the effective integration time of the postsynaptic cell ( Figure 4 ) . The polarity of synaptic changes is determined by a supervisory signal that we hypothesize to be realized through neuromodulatory control [22] . Because present experimental measurements of spike-timing–dependent synaptic plasticity rules have assumed an unsupervised setting , i . e . , have not controlled for neuromodulatory signals ( but see [40] ) , existing results do not directly apply to our model . Nevertheless , recent data have revealed complex interactions between the statistics of pre- and postsynaptic spiking activity and the expression of synaptic changes [41]–[44] . Our model offers a novel computational rationale for such interactions , predicting that for fixed supervisory signaling , the temporal window of plasticity shrinks with growing levels of postsynaptic shunting . One challenge for the biological implementation of the tempotron learning rule is the need to compute the time of the maximum of the postsynaptic voltage . We have previously shown for a current-based neuron model that this temporally global operation can be approximated by temporally local computations that are based on the postsynaptic voltage traces following input spikes [22] . We have extended this approach to plastic synaptic conductances and checked that the resulting biologically plausible implementation of conductance-based tempotron learning can readily subserve time-warp–invariant classification of spike patterns . Specifically , in this implementation , the induction of synaptic plasticity is controled by the correlation of the postsynaptic voltage and a synaptic learning kernel ( see Materials and Methods ) whose temporal extend is controlled by the average conductance throughout a given error trial . A synaptic peak conductance is changed by a uniform amount whenever this correlation exceeds a fixed plasticity induction threshold . When tested on the time-warped latency patterns with βmax = 2 . 5 ( cf . Figure 3 ) , the correlation-based tempotron roughly matched the voltage maximum–based version at a reduced learning load of 1 . 5 patterns per synapse with an error fractions of 0 . 35% . In our model , dynamic time-warp–invariant capabilities become avaliable through a conductance-based learning rule that tunes the shunting action of synaptic conductances . This learning rule enables neurons to adjust the degree of synaptic shunting to the requirements of a given processing task . As a result , our model can naturally encompass a continuum of functional specializations ranging from neurons that are sensitive to absolute stimulus durations by employing low total synaptic conductances , to time-warp–invariant feature detectors that operate in a high-conductance regime . In the context of auditory processing , such a functional segregation into neurons with slower and faster effective integration times is reminiscent of reports suggesting that rapid temporal processing in time frames of tens of milliseconds is localized in left lateralized language areas , whereas processing of slower temporal features is attributed to right hemispheric areas [45]–[47] . Although anatomical and morphological asymmetries between left and right human auditory cortices are well documented [48] , it remains to be seen whether these differences form the physiological substrate for a left lateralized implementation of the proposed time-rescaling mechanism . Consistent with this picture , the general tradeoff between high temporal resolution and robustness to temporal jitter that is predicted by our model ( Figure 5 ) parallels reports of the vulnerability of the lateralizion of language processing with respect to background acoustic noise [49] as well as to abnormal timing of auditory brainstem responses [50] . The architecture of our speech-processing model encompasses two auditory processing stages . The first stage transforms acoustic signals into spatiotemporal patterns of spikes . To engage the proposed automatic time-rescaling mechanism , the population rate of spikes elicited in this afferent layer must track variations in the rate of incoming speech . Such behavior emerges naturally in a sparse coding scheme in which each neuron responds transiently to the occurrences of a specific acoustic event within the auditory input . As a result , variations in the rate of acoustic events are directly translated into concomitant variations in the population rate of elicited spikes . In our model , the elementary acoustic events correspond to onset and offset threshold crossings of signal power within specific frequency channels . Such frequency-tuned onset and offset responses featuring a wide range of dynamic thresholds have been observed in the inferior colliculus ( IC ) of the auditory midbrain [51] . This nucleus is the site of convergence of projections from the majority of lower auditory nuclei and is often referred to as the interface between the lower brain stem auditory pathways and the auditory cortex . Correspondingly , we hypothesize that the layer of time-warp–invariant feature detector neurons in our model implements neurons located downstream of the IC , most probably in primary auditory cortex . Current studies on the functional role of the auditory periphery in speech perception and its pathologies have been limited by the lack of biologically plausible neuronal readout architectures; a limitation overcome by our model , which allows evaluation of specific components of the auditory pathway in a functional context . Psychoacoustic studies have indicated that the neural mechanism underlying the perceptual normalization of temporal speech cues is involuntary , i . e . , it is cognitively impenetrable [16] , controlled by physical rather than perceived speaking rate [17] , confined to a temporally local context [2] , [18] , not specific to speech sounds [52] , and already operational in prearticulate infants [53] . The proposed conductance-based time-rescaling mechanism is consistent with these constraints . Moreover , our model posits a direct functional relation between high synaptic conductances and the time-warp robustness of human speech perception . This relation gives rise to a novel mechanistic hypothesis explaining the impaired capabilities of elderly listeners to process time-compressed speech [54] , [55] . We hypothesize that the down-regulation of inhibitory neurotransmitter systems in aging mammalian auditory pathways [56] , [57] limits the total synaptic conductance and therefore prevents the time-rescaling mechanism from generating short , effective time constants through synaptic shunting . Furthermore , our model implies that comprehension deficits in older adults should be linked specifically to the processing of phonetic segments that contain fast time-compressed temporal cues . Our hypothesis is consistent with two interrelated lines of evidence . First , comprehension difficulties of time-compressed speech in older adults are more likely a consequence of an age-related decline in central auditory processing than attributes of a general cognitive slowing [56] , [58] . Second , recent reports have indicated that recognition differences between young and elderly listeners originate mainly from the temporal compression of consonants , which often feature rapid spectral transitions , but not from steady-state segments [54] , [55] , [58] of speech . Finally , our hypothesis posits that speaking rate–induced shifts in perceptual category boundaries [2] , [16] , [17] should be age-dependent , i . e . , their magnitude should decrease with increasing listener age . This prediction is straightforwardly testable within established psychoacoustic paradigms . In a previous neuronal model of time-warp–invariant speech processing [5] , [6] , sequences of acoustic events are converted into patterns of transiently matching firing rates in subsets of neurons within a population , which trigger synchronous firing in a downstream readout circuit . The identity of neurons whose firing rates converge to an identical value during an input pattern , and hence also the pattern of synchrony emerging in the readout layer , depends only on the relative timing of the events , not on the absolute duration of the auditory signal . However , for this model to recognize multiple input patterns , the convergence of firing rates is only approximate . Therefore , the resulting time-warp robustness is limited and , as in our model , dependent on the learning load . Testing this model on our synthetic classification task ( cf . Figure 3 ) indicated a substantially smaller storage capacity than is realizable by the conductance-based tempotron ( Text S1 ) . An additional disadvantage of this approach is that it copes only with global ( uniform ) temporal warping . Invariant processing of dynamic time warp as is exhibited by natural speech ( cf . Figure 1C and 1D ) is more challenging since it requires robustness to local temporal distortions of a certain statistical character . Established algorithms that can cope with dynamically time-warped signals are typically based on minimizing the deviation between an observed signal and a stored reference template [59]–[61] . These algorithms are computationally expensive and lack biologically plausible neuronal implementations . By contrast , our conductance-based time-rescaling mechanism results naturally from the biophysical properties of input integration at the neuronal membrane and does not require dedicated computational resources . Importantly , our model does not rely on a comparison between the incoming signal and a stored reference template . Rather , after synaptic conductances have adjusted to the statistics of a given stimulus ensemble , the mechanism generalizes and automatically stabilizes neuronal voltage responses against dynamic time warp even when processing novel stimuli ( cf . Figure 3C ) . The architecture of our neuronal model also fundamentally departs from the decades-old layout of Hidden Markov Model–based artificial speech-recognition systems , which employ probabilistic models of state sequences . These systems are hard to reconcile with the biological reality of neuronal system architecture , dynamics , and plasticity . The similarity in performance between our model and such state-of-the-art systems on a small vocabulary task highlights the powerful processing capabilities of spike-based neural representations and computation . Although the present work focuses on the concrete and well-documented example of time-warp robustness in the context of neural speech processing , the proposed mechanism of automatic rescaling of integration time is general and applies also to other problems of neuronal temporal processing such as birdsong recognition [3] , insect communication [9] , and other ethologically important natural auditory signals . Moreover , robustness of neuronal processing to temporal distortions of spike patterns is not only important for the processing of stimulus time dependencies , but also in the context of spike-timing–based neuronal codes in which the precise temporal structure of spiking activity encodes information about nontemporal physical stimulus dimensions [62] . Evidence for such temporal neural codes have been reported in the visual [63]–[65] , auditory [66] , and somatosensory [67] , as well as the olfactory [68] pathways . As a result , we expect mechanisms of time-warp–invariant processing to also play a role in generating perceptual constancies along nontemporal stimulus dimensions such as contrast invariance in vision or concentration invariance in olfaction [4] . Finally , time warp has also been described in intrinsically generated brain signals . Specifically , the replay of hippocampal and cortical spiking activity at variable temporal warping [69] , [70] suggests that our model has applicability beyond sensory processing , possibly also encompassing memory storage and retrieval .
Numerical simulations of the conductance-based tempotron were based on exact integration [71] of the conductance-based voltage dynamics defined in Equation 1 . With the membrane capacitance set to 1 , the resting membrane time constant in this model is τm = 1/gleak . Implementing an integrate-and-fire neuron model , an output spike was elicited when V ( t ) crossed the firing threshold Vthr . After a spike at tspike , the voltage is smoothly reset to the resting value by shunting all synaptic inputs that arrive after tspike ( cf . [22] ) . We used Vthr = 1 , Vrest = 0 , and reversal potentials and for excitatory and inhibitory conductances , respectively . Unless stated otherwise , the resting membrane time constant was set to τm = 100 ms throughout our work [20] . For the synaptic time constant , we used τs = 1 ms for the random latency task , which minimized the error of the current-based neuron , and to τs = 5 ms in the speech-recognition tasks . To check the effect of nonsynaptic voltage-dependent conductances on the automatic time-rescaling mechanism , we implemented an h-like current Ih after [72] as a noninactivating current with HH-type dynamics of the form Here , is the maximal h-conductance , with reversal potential and m is its voltage-dependent activation variable with kineticswhereand The voltage dependence of the rate constants α and β were described by the formwith parameters aα = −39 . 015 s−1 , bα = −259 . 925 s−1 , kα = 1 . 77926 and aβ = 365 . 85 s−1 , bβ = −2853 . 25 s−1 , kβ = −1 . 28889 . In Figure S1 , we quantified the effect of the h-conductance on the fidelity of the time-rescaling mechanism by measuring the time-warp–induced distortions of neuronal voltage traces for different values of the maximal h-conductance . Specifically , for a given value of and a time warp β , we measure the voltage traces and and their standard deviations across time σ1 and σβ , respectively . We define the time-warp distortion index as the mean magnitude of the time-warp–induced voltage difference across time normalized by the mean standard deviation , , In Figure S1 , values of are normalized by Λ ( 0 , β ) . The voltage traces were generated by random latency patterns and uniform synaptic peak conductances as used in Figure 2 . As increasing values of depolarized the neuron's resting potential , excitatory and inhibitory synaptic conductances were balanced separately for each value of . In the current-based tempotron that was implemented as described in [22] , each input spike evoked an exponentially decaying synaptic current that gave rise to a postsynaptic potential with a fixed temporal profile . In Figure 8C ( upper row ) , voltage traces of a current-based analog of a conductance-based tempotron with learned synaptic conductances , reversal potentials , and effective membrane integration time τeff ( cf . Figure 8B ) were computed by setting the synaptic efficacies ωi of the current-based neuron to and its membrane time constant to τm = τeff . The resulting current-based voltage traces were scaled such that for each pair of models , the mean voltage maxima for unwarped stimuli ( β = 1 ) were equal . Following [22] , changes in the synaptic peak conductance of the ith synapse after an error trial were given by the gradient of the postsynaptic potential , , at the time of its maximal value tmax . To compute the synaptic update for a given error trial , the exact solution of Equation 1 was differentiated with respect to and evaluated at tmax , which was determined numerically for each error trial . Whenever a synaptic peak conductance attempted to cross to a negative value , its reversal potential was switched . A voltage correlation-based approximation of tempotron learning was implemented by extending the approach in [22] such that the change in the synaptic peak conductance of the ith synapse due to a spike at time ti was governed by the correlation of the postsynaptic potential V ( t ) with a synaptic learning kernel Klearn ( t ) = ( exp ( −t/τlearn ) −exp ( −t/τs ) ) / ( τlearn−τs ) . The two time constants of the synaptic learning kernel were the synaptic time constant τs and the learning time constant , which was determined by the time-averaged synaptic conductance of the current error trial and approximated the effective membrane time constant during that trial . The voltage maximum operation was approximated by thresholding νi , yieldingfor changes of excitatory conductances on target and null patterns , respectively , and changes with the reversed polarity , ±1 , for inhibitory conductances . The plasticity induction threshold was set to κ = 0 . 45 . As in [22] , we employed a momentum heuristic to accelerate learning in all learning rules . In this scheme , synaptic updates consisted , not only of the correction , which was given by the learning rule and the learning rate λ , but also incorporated a fraction μ of the previous synaptic change . Hence , . We used an adaptive learning rate that decreased from its initial value λini as the number of learning cycles l grew , λ = λini/ ( 1+10−4 ( l−1 ) ) . A learning cycle corresponded to one iteration through the batch of templates in the random latency task or the training set in the speech task . Global time warp was implemented by multiplying all firing times of a spike template by a constant scaling factor β . In Figure 3A , random global time warp between compression by 1/βmax and dilation by βmax was generated by setting β = exp ( qln ( βmax ) ) with q drawn from a uniform distribution between −1 and 1 for each presentation . Dynamic time warp was implemented by scaling successive interspike intervals tj−tj−1 of a given template with a time-dependent warping factor , such that warped spike times with and . The time-dependent factor resulted from an equilibrated Ornstein-Uhlenbeck process ξ ( t ) with a relaxation time of τ = 200 ms that was rescaled by the complementary error function erfc to transform the normal distribution of ξ ( t ) into a uniform distribution over [−1 1] at each t . To ensure that the symmetry of excitation and inhibition in our model architecture was not crucial for the time-warp–invariant processing of spike patterns , we implemented a control architecture in which all afferents were confined to be excitatory , except one additional inhibitory synapse , which mimicked the effect of a global inhibitory network . In the time-warped random latency task , spike patterns were fed into the excitatory population as before; however , in addition , the inhibitory synapse received a copy of each incoming spike . All synaptic peak conductances were plastic and controlled by the conductance-based tempotron rule . In this model , synaptic sign changes were prohibited . Spike time jitter [22] was implemented by adding independent Gaussian noise with zero mean and a standard deviation of 5 ms to each spike of a template before each presentation . Sound signals were normalized to unit peak amplitude and converted into spectrograms over NFTT = 129 linearly spaced frequencies fj = fmin+j ( fmax+fmin ) / ( NFTT+1 ) ( j = 1… NFTT ) between fmin = 130 Hz and fmax = 5 , 400 Hz by a sliding fast Fourier transform with a window size of 256 samples and a temporal step size of 1 ms . The resulting spectrograms were filtered into Nf = 32 logarithmically spaced Mel frequency channels by overlapping triangular frequency kernels . Specifically , Nf+2 linearly spaced frequencies given by hj = hmin+j ( hmax−hmin ) / ( Nf+1 ) with j = 0…Nf+1 and hmax , min = 2 , 595log ( 1+fmax , min/700 ) were transformed to a Mel frequency scale between fmin and fmax . Based on these , signals in Nf channels resulted from triangular frequency filters over intervals with center peaks at . After normalization of the resulting Mel spectrogram SMel to unit peak amplitude , the logarithm was taken through log ( SMel = ε ) −log ( ε ) with ε = 10−5 and the signal in each frequency channel smoothed in time by a Gaussian kernel with a time constant of 10 ms . Spikes were generated by thresholding of the resulting signals by a total of 31 onset and offset threshold-crossing detector units . Whereas each onset afferent emitted a spike whenever the signal crossed its threshold in the upward direction , offset afferents fired when the signal dropped below the threshold from above . For each frequency channel and each utterance , threshold levels for onset and offset afferents were set relative to the maximum signal over time to and . For , onset and offset afferents were reduced to a single afferent whose spikes encoded the time of the maximum signal for a given frequency channel . We used the digit subset of the TI46 Word speech database [24] . This clear speech dataset comprises 26 isolated utterances of each English digit from zero to nine spoken by 16 adult speakers ( eight male and eight female ) . The data is partitioned into a fixed training set , comprising 10 utterances per digit and speaker , and a fixed test set holding the remaining 16 utterances per digit and speaker . We also tested our neuronal word-recognition model on the adult speaker , isolated-digit subset of the TIDIGITS database [27] . This subset comprises two utterances per digit and speaker , i . e . , a total of 20 utterances from 225 adult speakers ( 111 male and 114 female ) , that are dialectically balanced across 21 dialectical regions ( tiling the continental United States ) . Because the TI46 database only provides utterances of the word “zero” for the digit 0 , we excluded the utterances of “oh” from our TIDIGITS sample . Based on the spiking activity of all binary digit detector neurons , a full digit classifier was implemented by ranking the digit detectors according to their individual task performances . As a result , a given stimulus was classified as the target digit of the most reliable of all responding digit detector neurons . If all neurons remained silent , a stimulus was classified as the target digit of the least reliable neuron . To preserve the timing relations between the learned spectrotemporal features and the target words , we refrained from correcting the spike-triggered stimuli for stimulus autocorrelations [73] . Test errors in the speech tasks were substantially reduced by training with a Gaussian spike jitter with a standard deviation of σ added to the input spikes as well a symmetric threshold margin v that required the maximum postsynaptic voltage on target stimuli to exceed Vthr+v and to remain below Vthr−v during null stimuli . Values of λini , μ , σ , and v were optimized on a 4-dimensional grid . Because for each grid point , only short runs over maximally 200 cycles were performed , we also varied the mean values of initial Gaussian distributions of the excitatory and inhibitory synaptic peak conductances , keeping their standard deviations fixed at 0 . 001 . The reported performances are based on the solutions that had the smallest errors fractions over the test set . If not unique , we selected the solution with the highest robustness to time warp ( cf . Figure 8B ) . Note that this naive optimization of the training parameters did not maintain a separate holdout test set for cross-validation and might therefore overestimate the true generalization performance . We adopted this optimization scheme from [25] , [26] to ensure comparability of the resulting performance measures . HTK generalization performance was tested with the HTK package version 3 . 4 . 1 [74] with front-end and HMM model parameters following [26] . Specifically , speech data from the TI46 and TIDIGITS databases were converted to 13 Mel-cepstral coefficients ( including the 0th order coefficient ) along with their first and second derivatives at a frame rate of 5 ms . Mel-coefficients were computed over 30 channels in 25-ms windows with zero mean normalization enabled ( TARGETKIND = MFCC_D_A_Z_0 ) . In addition , the following parameters were set: USEHAMMING = T; PREEMPCOEF = 0 . 97; and CEPLIFTER = 22 . Ten HMM models , one for each digit plus one HMM model for silence , were used . Each model consisted of five states ( including the the two terminal states ) with eight Gaussian mixtures per state and left-to-right ( no skip ) transition topology . | The brain has a robust ability to process sensory stimuli , even when those stimuli are warped in time . The most prominent example of such perceptual robustness occurs in speech communication . Rates of speech can be highly variable both within and across speakers , yet our perceptions of words remain stable . The neuronal mechanisms that subserve invariance to time warping without compromising our ability to discriminate between fine temporal cues have puzzled neuroscientists for several decades . Here , we describe a cellular process whereby auditory neurons recalibrate , on the fly , their perceptual clocks and allows them effectively to correct for temporal fluctuations in the rate of incoming sensory events . We demonstrate that this basic biophysical mechanism allows simple neural architectures to solve a standard benchmark speech-recognition task with near perfect performance . This proposed mechanism for time-warp–invariant neural processing leads to novel hypotheses about the origin of speech perception pathologies . |
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TANK-binding kinase-1 ( TBK1 ) is an integral component of Type I interferon induction by microbial infection . The importance of TBK1 and Type I interferon in antiviral immunity is well established , but the function of TBK1 in bacterial infection is unclear . Upon infection of murine embryonic fibroblasts with Salmonella enterica serovar Typhimurium ( Salmonella ) , more extensive bacterial proliferation was observed in tbk1−/− than tbk1+/+ cells . TBK1 kinase activity was required for restriction of bacterial infection , but interferon regulatory factor-3 or Type I interferon did not contribute to this TBK1-dependent function . In tbk1−/−cells , Salmonella , enteropathogenic Escherichia coli , and Streptococcus pyogenes escaped from vacuoles into the cytosol where increased replication occurred , which suggests that TBK1 regulates the integrity of pathogen-containing vacuoles . Knockdown of tbk1 in macrophages and epithelial cells also resulted in increased bacterial localization in the cytosol , indicating that the role of TBK1 in maintaining vacuolar integrity is relevant in different cell types . Taken together , these data demonstrate a requirement for TBK1 in control of bacterial infection distinct from its established role in antiviral immunity .
Host organisms employ a multitude of innate defense mechanisms against invading microbial pathogens . Functions of the innate immune system include control and destruction of pathogens and instruction of the developing adaptive immune response through expression of cytokines , chemokines , and other proinflammatory molecules [1 , 2] . Innate recognition of invading pathogens can occur through pattern recognition receptors , such as Toll-like receptors ( TLRs ) , which bind to molecules characteristic of microbial organisms like lipopolysaccharide ( LPS ) [3] . Two well-characterized signaling pathways associated with TLR stimulation are the MyD88-dependent pathway , which primarily results in NFκB activation , and the TANK-binding-kinase-1 ( TBK1 ) -dependent pathway that induces transcription of Type I interferon genes [4] . The importance of the MyD88-dependent pathway in antibacterial immunity is well established , but the functional contribution of TBK1-dependent signaling in protecting against bacterial infection is unknown [3] . TBK1 , also termed NFκB-activating kinase or TRAF2-associated kinase , is a ubiquitous member of the IκB kinase ( IKK ) family that is required for embryonic development [5–7] . Upon microbial infection or LPS treatment , TBK1 phosphorylates the transcription factor interferon regulatory factor-3 ( IRF3 ) , resulting in IRF3 translocation into the nucleus and transcription of target genes , such as ip10 ( IFN-gamma inducible protein 10 ) , ifnb ( interferon beta; a Type I interferon ) , and subsequent interferon response genes such as mx1 [8–10] . TBK1 is required for optimal induction of ifnb and Type I interferon-dependent antimicrobial effector mechanisms during viral infection [11] . Induction of TBK1-dependent signaling by viruses has been extensively studied and occurs upon viral recognition by TLRs or upon cytosolic binding of viral double-stranded RNA by the DEAD-box helicases , RIG-I and MDA5 [12–16] . Gram-negative bacterial infections can also trigger TBK1-dependent interferon induction by association of LPS with the TLR4 signaling complex [10 , 17] . In contrast , extracellular Gram-positive bacteria do not activate expression of interferon genes , although cytosolic Gram-positive bacteria can upregulate ifnb transcription in an IRF3-dependent manner [18 , 19] . To investigate the requirement for TBK1 in response to bacterial infection , we used Salmonella enterica serovar Typhimurium as a model Gram-negative bacterium . Salmonella is a facultative intracellular pathogen that replicates within macrophages and non-phagocytic cells [20 , 21] . Salmonella invades non-phagocytic cells by secreting effector proteins through a syringe-like Type III secretion system ( T3SS ) encoded on Salmonella pathogenicity island-1 ( SPI-1 ) to induce membrane ruffling and bacterial uptake [22] . After entry into the host cell , Salmonella resides in a membrane-bound compartment termed the Salmonella-containing vacuole ( SCV ) . Subsequent to invasion , the SCV progresses through early stages of endocytic maturation and acquires the late endosomal marker , lysosomal-associated membrane protein 1 ( LAMP-1 ) [23] . At this stage , the SCV can diverge from the endocytic pathway by avoiding lysosome fusion and can localize to the perinuclear region [24] . Many Salmonella remain in the SCV and replicate; however , a small percentage of bacteria escape into the host cytosol where they acquire host-derived ubiquitin [25] . Like viruses , intracellular bacterial pathogens exploit the host cell as a replicative niche . By analogy to known antiviral immune mechanisms , we hypothesized that TBK1 would also protect host cells from infection by intracellular bacterial pathogens . However , specific effector mechanisms of antiviral innate immunity , such as Type I interferon-dependent induction of 2–5′ oligoadenylate synthase , which facilitates viral RNA degradation , would not necessarily be effective against intracellular bacteria [26] . Since loss of TBK1 results in embryonic lethality , we used tbk1+/+ and tbk1−/− mouse embryonic fibroblasts ( MEFs ) or RNAi to test the requirement for TBK1 during bacterial infection . Here , we show that TBK1 mediates an early cellular response to infection by Salmonella and other bacteria by maintaining these pathogens in a restrictive vacuolar compartment .
Strains used in this study are described in Table S1 . Salmonella strains expressing green fluorescent protein ( GFP ) were constructed by electroporating the bacteria with pFPV25 . 1 obtained from C . Detweiler [27] . The Salmonella strain expressing red fluorescent protein ( RFP ) was constructed by amplifying the RFP gene from pGEM:RFP ( J . A . Bauer , University of Michigan Medical School ) , ligating the sequence into PCR-II-Topo ( Invitrogen , http://www . invitrogen . com ) , and then electroporation into Salmonella . MEFs were obtained from W . C . Yeh ( tbk1+/+ and tbk1−/− ) and K . Mossman and B . Williams ( irf3+/+ and irf3−/− ) . MEFs and RAW264 . 7 cells ( American Type Culture Collection ) were grown in RPMI medium supplemented with 10% fetal bovine serum and 1% L-glutamine at 37 °C in 5% CO2 . Experiments in tbk1+/+ and tbk1−/− MEFs were reproduced both with primary MEFs in early passage and with MEFs that were immortalized by continuous culture and clonally derived . HeLa cells ( ATCC ) were grown in MEMα medium supplemented with 10% fetal bovine serum , 1% L-glutamine , 1% nonessential amino acids , and 1% sodium pyruvate at 37 °C in 5% CO2 . All Salmonella strains were grown overnight in LB medium at 37 °C , shaking and back-diluted 1:100 . When the bacteria reached exponential phase , they were washed twice with Dulbecco's PBS ( D-PBS ) and used to infect MEFs at a multiplicity of infection ( m . o . i . ) of 10 for 1 h , whereupon infected cells were washed three times with D-PBS and incubated in medium containing 100 μg/ml gentamicin for 2 h . The cells were then washed three times with D-PBS and fresh medium containing 5 μg/ml gentamicin was added . For intracellular growth curves , at indicated times post-infection ( p . i . ) , three cover slips containing infected MEFs were removed and individually lysed in 5 ml sterile water; a fraction of the lysate was plated on LB agar to enumerate colony-forming units . St SPI-1− required bystander infection with wild-type Salmonella; coinfections were performed with Salmonella grown and infected under the same conditions as stated for monotypic Salmonella infection in MEFs , except that the St SPI-1− mutant was added at an m . o . i . of 100 . Enteropathogenic Escherichia coli ( EPEC ) was grown statically overnight in LB at 25 °C , then back-diluted 1:100 , and incubated shaking at 37 °C in serum-free DMEM for 1 h . EPEC were then used to infect MEF cells at an m . o . i . of 25 for 1 h; washing and gentamicin treatment were carried out as described for Salmonella . Streptococcus pyogenes were statically grown overnight at room temperature in BHI . The stationary phase bacteria were washed two times with PBS , vigorously vortexed , and used to infect MEFs at an m . o . i . of 25 . The remainder of the infection was done under the same conditions as described for Salmonella infection in MEFs . Bacterial infections in transfected HeLa cells were performed as described for MEFs , except that Salmonella and S . pyogenes were incubated with the host cells for 30 min at an m . o . i . of 100 and EPEC at an m . o . i . of 50 for 1 h; the HeLa cells were washed three times with D-PBS and incubated with fresh medium for 30 min before gentamicin was added . Infections of RAW264 . 7 macrophages were performed with Salmonella at an m . o . i . of 10 for 30 min , EPEC at an m . o . i . of 50 for 45 min , and S . pyogenes at an m . o . i . of 25 for 30 min . The macrophages were washed three times with D-PBS , and medium with gentamicin added as described for MEFs . 2-μm beads were incubated with anti-streptavidin antibody in D-PBS + 1% BSA for 30 min , then washed three times in D-PBS + 1% BSA , added to the macrophage culture , and spun down onto the macrophage monolayer . Beads were incubated with the macrophages for 30 min and then the cells were washed three times with D-PBS before adding fresh medium . HeLa cells or MEFs were re-suspended in the appropriate growth medium as described above and plated in a 6-well plate at a concentration of 5 × 105 cells/plate . Cells were transfected for 24 h with either 4 . 2 μg of plasmid DNA or 40 μM of the indicated short interfering RNA ( siRNA ) ( Ambion , http://www . ambion . com ) complexed with Lipofectamine 2000 ( Invitrogen , http://www . invitrogen . com ) . For siRNA experiments , transfected cells were trypsinized , split 1:2 , and subjected to a second round of siRNA transfection as above , until time of harvest or infection with Salmonella . For transfection of RAW264 . 7 macrophages , cells were re-suspended in growth medium as described above and plated in a 6-well plate at a concentration of 1 × 106cells/plate . Macrophages were transfected as described for HeLa and MEF cells . Cells were grown in appropriate medium in a 6-well plate at a concentration of 7 × 105 cells/plate and infected as described above . At indicated times p . i . , cover slips were removed and fixed in 3 . 7% paraformaldehyde in D-PBS . Cells were then washed three times with 0 . 1% Triton X-100 in PBS and blocked for 10 min with TBS-TX ( 25 mM Tris-HCl [pH 8 . 0] , 150 mM NaCl , 0 . 1% Triton X100 , 1% BSA ) . TBS-TX with primary antibody was added to the cells for 1 h . Cells were then rinsed three times with TBS-TX and incubated for 30 min with secondary antibody . Cover slips were rinsed with TBS-TX and mounted with Pro-Long Gold Antifade ( Invitrogen ) . Samples were analyzed with the Olympus Fluoview FV-500 ( http://www . olympus . com ) confocal microscope using a 100× objective , unless otherwise stated , and Fluoview software . Quantitation of bacteria colocalized with LAMP-1 or ubiquitin was performed by scoring 150 randomly chosen bacteria per experiment for the presence of LAMP-1 or ubiquitin; only bacteria completely surrounded by LAMP-1 or ubiquitin as indicated by antibody staining were scored as colocalized . Transmission electron microscopy was performed on a Philips CM-100 transmission electron microscope equipped with automated compustage and Kodak 1 . 6 Megaplus ( http://www . kodak . com ) high-resolution digital camera . Samples were prepared as previously described [28] . The LAMP-1 ( 1D4B ) rat monoclonal antibody was obtained from Santa Cruz Biotechnology ( http://www . scbt . com ) , the anti-ubiquitin monoclonal antibody ( FK2 ) from BIOMOL International ( http://www . biomol . com ) , the anti-TBK1 monoclonal antibody from Imgenex ( http://www . imgenex . com ) , and anti-streptavidin antibody from Molecular Probes ( http://probes . invitrogen . com ) . TRITC-phalloidin was purchased from Invitrogen and 4′ , 6-Diamidino-2 phenylindole dihydrochloride ( DAPI ) from BioChemika ( http://www . sigmaaldrich . com ) . Recombinant mouse interferon-β ( PBL Biomedical Laboratories , http://www . pblbio . com ) was used at 100 U/ml to treat cells overnight and throughout the course of infection . Lysotracker Red DND-99 ( Invitrogen ) was a gift from K . Collins ( University of Michigan Medical School ) . Alpha-amanitin ( BioChemika ) was used at 50 μg/ml to pre-treat cells for 1 h prior to and during the course of infection . All siRNA reagents were obtained from Ambion . The EGFP-LC3 was obtained from Addgene , Incorporated ( plasmid 11546 ) ( http://www . addgene . com ) and constructed in the laboratory of K . Kirkegaard . Beads and anti-streptavidin antibody were a gift from J . Swanson ( 2 . 01-μm streptavidin silicon oxide microspheres; Corpuscular , Cold Spring , New York , United States ) . The tbk1 cDNA amplified from tbk1+/+ MEF RNA was cloned into the expression vector pcDNA3 ( Invitrogen ) . After sequence validation , QuikChange mutagenesis [29] was performed to replace lysine-38 with an alanine , which was validated by sequencing . TBK1:pcDNA3 and TBK1-KD:pcDNA3 both encoded a full-length TBK1 protein as determined by TNT Quick Coupled Transcription/Translation Systems ( Promega , http://www . promega . com ) and Western blot probed with anti-TBK1 antibody . Total RNA was isolated from cells using the RNAeasy kit ( Qiagen , http://www1 . qiagen . com ) and cDNA synthesis carried out using 2 . 5 μg of total RNA ( M-MLV Reverse Transcriptase; Invitrogen ) . Real time RT-PCR analysis was performed with the MX3000p ( Stratagene , http://www . stratagene . com ) and Brilliant SYBR Green MasterMix ( Stratagene ) . Relative amounts of cDNA were normalized to actin cDNA levels in each sample . The following primers were used for amplification: ip10 ( F- 5′ATGAGGGCCATAGG GAAGCTTGAA; R- 5′ACCAAGGGCAATTAGGACTAGCCA ) , mx1 ( F-5′ TTGTCTA CTGCCAGGACCAGGTTT; R-5′ TTTCAGGTGCTGGGTCATCTCAGT ) , actin ( F-5′AGGTGTGATGGTGGGAATGG; R-5′GCCTCGTCACCCACATAGGA ) .
To investigate the role of TBK1 in the cellular response to bacterial infection , we infected tbk1+/+ and tbk1−/− MEFs with Salmonella expressing GFP . Salmonella invaded the wild-type and mutant MEFs similarly; however , at 8 h p . i . , the monolayer of tbk1−/− MEFs contained approximately 10-fold more bacteria than the wild-type MEFs ( Figure 1A and 1B ) . Robust bacterial proliferation was observed in 35%–40% of infected tbk1−/− MEFs , which was consistent with immunofluorescence analysis of individual cells showing a more pronounced phenotype than that observed by measuring net increase in bacterial numbers . By immunofluorescence analysis , the remainder of the tbk1−/− infected MEFs appeared similar to tbk1+/+ infected MEFs ( Figure 1A; 40× magnification ) . Infected tbk1−/− MEFs that did not exhibit greater bacterial proliferation might have undergone an unproductive infection , since at 1 h p . i . , 20%–30% of the bacteria in either wild-type or mutant MEFs were found in autophagosomes , and an additional 25%–30% in lysosomes , which are likely nonreplicative compartments ( Figure S1A and S1B ) . The increased bacterial growth observed in tbk1−/− MEFs was not suppressed by addition of exogenous Type 1 interferon , nor was a similar phenotype observed when Salmonella infection was compared in irf3+/+ and irf3−/− MEFs ( Figures 1B , 1C , and S2A ) . Inhibition of de novo transcription and translation also had no effect on the phenotype ( Figure S2B and S2C; unpublished data ) . However , the robust bacterial replication in tbk1−/− MEFs was substantially decreased by transient transfection with a plasmid expressing wild-type TBK1 , but not a kinase dead mutant protein ( TBK1 KD ) [6] ( Figure 1D ) . Together , these data indicate that TBK1 kinase activity limits intracellular infection of Salmonella independently of the IRF3-Type I interferon axis . Previous studies have shown that shortly after invasion , SCV colocalize with the late endosomal marker , LAMP-1 [24] . To determine the nature of the Salmonella-containing compartment in TBK1-deficient cells , we infected MEFs with Salmonella-GFP and analyzed the samples by confocal immunofluorescence microscopy using an anti-LAMP1 antibody ( Figures 2A and S3 ) . SCV in tbk1+/+ cells were colocalized with LAMP-1 throughout the entire course of infection , with 94 . 5% colocalization at 2 h p . i . In contrast , as early as 90 min p . i . in tbk1−/− cells , individual Salmonella lost association with LAMP-1 , and at 2 h p . i . , only 63 . 0% of bacteria exhibited colocalization . Loss of LAMP-1 colocalization by individual bacteria was commonly observed early in infection , suggesting that replication per se was not required for this abnormal phenotype . Thus , in the absence of TBK1 , many SCV lose the late endosomal marker , LAMP-1 , and deviate from the characterized Salmonella endocytic trafficking pathway . Because Salmonella in TBK1-deficient cells were found in a LAMP-1 negative compartment dispersed throughout infected cells , we reasoned that the bacteria might be in the host cytosol . It was recently reported that under circumstances when Salmonella was found in the cytosol , the bacteria associated with host ubiquitin [25] . Therefore , we infected MEFs with Salmonella-GFP and analyzed the cells by confocal immunofluorescence microscopy to determine whether bacteria were colocalized with ubiquitin ( Figure 2B ) . In tbk1+/+ cells , 0 . 7% of Salmonella colocalized with ubiquitin at 4 h p . i . ; almost all of the bacteria remained in LAMP1+ SCV during the course of infection . In contrast , substantial numbers of Salmonella in tbk1−/− MEFs associated with ubiquitin ( 37 . 1% by 4 h p . i . ) , suggesting that Salmonella was released from the SCV into the cytosol . However , the possibility remained that the SCV was perforated; allowing access to cytosolic ubiquitin , but the vacuolar membrane remained around each bacterium . We directly visualized Salmonella in infected MEFs by transmission electron microscopy and found that by 1 h p . i . in tbk1−/− cells , 66 . 7% of bacteria ( n = 30 ) were surrounded by vacuolar space , compared to 87 . 5% of bacteria ( n = 24 ) in tbk1+/+ cells ( Figure 2C ) . From these data , it was not clear whether TBK1 regulated general integrity of the endocytic pathway , or whether infection specifically triggered a TBK1-dependent process . To test the general function of the endocytic compartment , we measured internalization and degradation of I125-labeled epidermal growth factor ( EGF ) in the absence of infection ( Figure 2D ) . We would predict ( based on earlier studies ) that general loss of integrity of the endocytic pathway would affect lumenal pH and therefore the ability to degrade proteins taken up by endocytosis , such as EGF [30] . Endocytic uptake and processing of the radiolabeled EGF appeared similar in both tbk1+/+ and tbk1−/− MEFs , demonstrating that escape of Salmonella into the cytosol of TBK1-deficient cells was not the result of general destabilization of the endocytic compartment . Therefore , TBK1 controls an early response to Salmonella infection that maintains integrity of the pathogen-containing vacuole . Since known mediators of TBK1-dependent signaling were not required to suppress intracellular bacterial growth , and endocytic function was not generally compromised , we hypothesized that Salmonella might be triggering a cellular process that requires TBK1 . Salmonella contains two Type III secretion systems encoded on SPI-1 ( termed SPI-1 T3SS ) and SPI-2 ( SPI-2 T3SS ) that enable the bacterium to secrete proteins directly into the host cell cytosol [22 , 31] . The SPI-1 T3SS is required for entry into non-phagocytic cells and modulation of endosomal trafficking; later in infection , SPI-2 T3SS-dependent effectors act to regulate membrane dynamics . We tested the possibility that Type III secretion might contribute to triggering the phenotype observed in TBK1-deficient MEFs . Salmonella strains deficient in either the SPI-1 ( St SPI-1− ) or SPI-2 ( St SPI-2− ) encoded T3SS were assessed for their ability to replicate within MEFs and access the host cytosol ( Figure 3A and 3B ) . The SPI-2 deficient bacteria proliferated similarly to wild-type Salmonella in both tbk1+/+ and tbk1−/− MEFs . We also analyzed a Salmonella mutant lacking the SPI-2-dependent effector SifA , which exhibits defects in SCV integrity [32–34] . If SifA and TBK1 were acting in concert , we would expect StΔsifA to proliferate equally in tbk1+/+ and tbk1−/−cells; however , we still observed increased cytosolic localization and replication by the mutant bacteria in tbk1−/− MEFs ( Figure S4A and S4B ) . In contrast , the SPI-1-deficient bacteria ( tetR ) , induced to enter independent vacuoles through bystander infection with wild-type Salmonella ( tetS ) , as measured by tetR colony-forming units or immunofluorescence , were unable to replicate in host cells of either genotype and were never released in the cytosol ( Figures 3A , 3B , and S5 ) [35] . A double SPI-1− SPI-2− mutant behaved similarly to the SPI-1− single mutant ( unpublished data ) . These data suggest that a function associated with the Salmonella SPI-1 T3SS stimulates TBK1-dependent modulation of the pathogen-containing vacuole . To determine whether vacuolar escape in tbk1−/−cells required a Salmonella-specific process , we used an invasive strain of EPEC , another Gram-negative bacterium , to infect MEFs and determined subcellular localization by quantitating colocalization with LAMP-1 ( Figure 3C ) [36] . LAMP-1 colocalization of EPEC was substantially decreased in tbk1−/− cells , similar to the phenotype we previously observed in Salmonella infection , and increased bacterial replication was observed compared to tbk1+/+ MEFs ( Figures 3C and S4C ) . We also investigated subcellular localization of S . pyogenes , an invasive Gram-positive bacterium , and found reproducibly that the streptococci were found less often in a LAMP-1+ compartment in TBK1-deficient cells , although the difference between tbk1−/− and tbk1+/+ MEFs was not as striking as that observed for Gram-negative bacteria ( Figure 3C ) . In addition , transmission electron microscopy revealed that at 1 h p . i . , fewer S . pyogenes were contained in vacuoles in tbk1−/− MEFs ( 50%; n = 30 ) than in tbk1+/+ MEFs ( 91 . 5%; n = 47 ) ( Figure 3D ) . These results demonstrate that TBK1 is necessary for restricting Gram-negative bacteria to the endocytic compartment during infection and may also play a similar role during cellular invasion by Gram-positive bacteria . MEFs represent an amenable genetic system with which to test the role of TBK1 during bacterial infection in the absence of a gene-deficient live animal model; however , they are not a cell type that would be present during a physiological infection . To establish that loss of TBK1 caused a specific defect in response to bacterial infection , we used an RNAi approach to knock down TBK1 expression in HeLa cells , an epithelial cell line commonly used to study Salmonella pathogenesis , and used the treated cells to examine bacterial growth and compartmentalization . HeLa cells were transfected with tbk1 siRNA , gapdh siRNA , or a nonspecific siRNA control for 72 h prior to infection; knockdown of TBK1 and GAPDH was confirmed by immunoblot analysis ( Figure 4A; unpublished data ) . HeLa cells treated with tbk1 siRNA supported increased replication of Salmonella compared to control treated cells , as observed in tbk1−/− MEFs ( Figure 4A ) . Consistent with our previous observations in MEFs , 27 . 8% of bacteria in tbk1 siRNA-treated HeLa cells were associated with ubiquitin compared to 2 . 2% of bacteria in cells transfected with control siRNA ( Figure 4B and 4C ) . No significant increase in ubiquitin-associated bacteria was observed with knockdown of GAPDH . IRF3 was also knocked down by siRNA in HeLa cells; the validated knockdown had no effect on Salmonella growth or ubiquitin association ( unpublished data ) . At 4 h p . i . , HeLa cells transfected with tbk1 siRNA had significantly lower numbers of Salmonella , EPEC , or S . pyogenes associated with LAMP-1 compared to the control transfection ( Figure 4D ) . These data demonstrate that TBK1-dependent maintenance of vacuolar integrity is not a MEF-specific phenomenon , but also protects epithelial cells during bacterial infection . We additionally sought to determine if there was a requirement for TBK1 in immune effector cells such as macrophages . We used RNAi to knock down TBK1 expression in the RAW264 . 7 macrophage cell line ( Figure 5A ) . It was previously reported that Salmonella grow poorly in macrophage cytosol , so LAMP-1 colocalization was assessed to reflect escape of bacteria from the SCV [32] . At 4 h p . i . , in macrophages treated with tbk1 siRNA , only 62 . 0% of Salmonella colocalized with LAMP-1 compared to 94 . 7% colocalization in control siRNA-treated cells ( Figure 5B and 5C ) . Tbk1 siRNA treatment of RAW264 . 7 cells also resulted in decreased LAMP-1 colocalization with bacteria during infection by EPEC or S . pyogenes . In contrast , 2-μm beads taken up by phagocytosis remained completely associated with LAMP-1 in both tbk1 and control siRNA-treated cells . From these observations , we conclude that TBK1 regulates the integrity of pathogen-containing vacuoles in multiple cell types .
We have shown here that the IKK-like kinase , TBK1 , mediates an early cellular response to bacterial infection . In the absence of TBK1 , Salmonella replicated rapidly and to high levels in MEFs . The IRF3-Type I inteferon axis which contributes to antiviral immunity was not required for the growth restrictive function of TBK1 , nor was de novo transcription or translation . After entry into tbk1−/−cells , Salmonella escaped into the cytosol where proliferation occurred . Loss of vacuolar integrity in TBK1-deficient cells was not specific to Salmonella infection , but occurred during infection by Gram-negative and Gram-positive pathogenic bacteria . Thus , TBK1 protects cells during bacterial infection by confining invading pathogens to a membrane-bound compartment . The best studied TBK-dependent signaling pathway triggered by bacterial infection is LPS-mediated induction of Type I interferons through TLR4 , TBK1 , and IRF3 [9 , 11 , 37 , 38] . However , our data showed that TBK1 did not require IRF3 or Type I interferon to exert a protective effect on host cells during bacterial infection . It is yet unclear whether TLR4 activation contributes to TBK1-dependent maintenance of vacuolar integrity . We still observed a protective effect by TBK1 on pathogen-containing vacuoles in HeLa cells , which do not express surface TLR4 due to lack of the accessory protein , MD2 [39] . This observation suggests that TLR4 signaling is not absolutely required for the restrictive function of TBK1 in bacterial infection , but further studies will be necessary to definitively determine the role of TLR4 or other TLRs in modulation of vacuolar integrity by TBK1 . The vacuolar compartment is a restrictive antimicrobial environment because of its ability to decrease pH , produce degradative enzymes , and in some cell types , to generate harmful reactive oxygen species in a confined environment [40–42] . In contrast , there are few cytosolic antimicrobial mechanisms , possibly to minimize damage to cytosolic host machinery . Given the more permissive nature of the host cytosol , it is surprising that relatively few bacterial pathogens exploit this intracellular niche [43] . TBK1-dependent mechanisms may prevent many bacterial pathogens from accessing the cytosol by modulating integrity or function of the endocytic compartment during infection . Our data suggest two nonexclusive models by which TBK1 may contribute to the cellular response to bacterial invasion . First , TBK1 could function at the post-transcriptional level in response to an infection by phosphorylating target proteins . In the case of bacterial infection , there may be TBK1 kinase substrates whose function directly or indirectly modifies the pathogen-containing vacuole . Secondly , TBK1 may act prior to infection to establish a state of immune competence , perhaps by regulating expression of gene products that are important for the immediate response to bacterial invasion . This model requires that TBK1 have some constitutive activity prior to infection . Since there is a known requirement for TBK1 in embryonic development in the absence of infection , it is likely that TBK1 may function in normal adult animals in the absence of infection as well [5–7] . Indeed , two recent studies have identified TBK1 as a regulator of angiogenesis and oncogenesis [44 , 45] . Furthermore , microarray analysis of uninfected tbk1+/+ and tbk1−/− MEFs identified over 400 genes that were differentially expressed , some of which have known associations with innate immune function ( ALR and MXDO , unpublished data ) . At the molecular level , there are at least two mechanisms by which TBK1 may ultimately regulate vacuolar integrity . In response to bacterial invasion , TBK1 could control initiation of autophagy , which can capture bacteria in damaged vacuoles [46 , 47] . This possibility is less likely , as colocalization with LC3 , a marker of autophagosomes , was slightly higher in infected tbk−/− cells than wild-type cells . However , it is notable that all of the bacterial species , for which we showed cytosolic localization in tbk1−/− cells , have mechanisms by which vacuolar membranes are damaged during infection , i . e . , Salmonella and EPEC contain Type III secretion machinery , and S . pyogenes encodes the pore-forming toxin Streptolysin O [48–50] . Alternatively , a TBK1-dependent target could modulate influx/efflux of ions or water into the pathogen-containing vacuole to maintain its physical continuity or otherwise alter membrane dynamics in response to infection . Previous studies have demonstrated that host cells activate repair mechanisms in response to membrane damage [51 , 52] . Our findings are consistent with damage to the vacuolar membrane as a possible trigger for TBK1-dependent function during bacterial infection . The innate immune system is required both for controlling pathogen replication and for communicating with other cell types . In viral infections , TBK1 clearly acts as a regulator of innate immunity by communicating with other cells through Type I interferon , which contributes to control of viral replication . However , we have demonstrated that in bacterial infections , TBK1 plays an important role in limiting pathogen replication by protecting the integrity of the pathogen-containing vacuole , independently of Type I interferon . Our findings do not preclude an additional requirement for TBK1 in antibacterial immunity through stimulation of cytokine or chemokine expression , but suggest an early TBK1-dependent mechanism by which host cells can achieve innate control of bacterial infection .
The DNA sequences used for primer design in this study from the NCBI Entrez Nucleotide sequence database ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? db=Nucleotide ) are actin ( NM_007393 ) , ip10 ( NC_00071 ) , mx1 ( NM_010846 ) , and tbk1 ( NC_00076 . 4 ) . | Early control of invading microbial pathogens is an essential function of the host response to infection . Previous studies have shown that upon viral infection , a protein called TANK-binding kinase-1 ( TBK1 ) signals the induction of a program of protection that results in inhibition of viral replication . During infection of mammalian cells by bacteria , a different type of microbe than a virus , TBK1 also sends signals , but the functional contribution of TBK1 to controlling bacterial infection was unknown . Here , we show that TBK1 does protect host cells from bacterial infection; however , the TBK1-dependent mechanisms that inhibit viral infection were not effective against bacterial growth . Instead , TBK1 maintained the integrity of the vacuolar compartment , consisting of small membrane-bound vesicles , where the invading bacteria were trapped . In the absence of TBK1 , pathogens such as Salmonella , enteropathogenic Escherichia coli , and Group A Streptococcus were able to escape from the confining host vacuoles and grow to high levels within the host cytosol . Thus , TBK1 plays an important role in the cellular response to bacterial infection , distinct from its function in antiviral immunity . |
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Glossina fuscipes fuscipes is the main vector of human and animal trypanosomiasis in Africa , particularly in Uganda . Attempts to control/eradicate this species using biological methods require knowledge of its reproductive biology . An important aspect is the number of times a female mates in the wild as this influences the effective population size and may constitute a critical factor in determining the success of control methods . To date , polyandry in G . f . fuscipes has not been investigated in the laboratory or in the wild . Interest in assessing the presence of remating in Ugandan populations is driven by the fact that eradication of this species is at the planning stage in this country . Two well established populations , Kabukanga in the West and Buvuma Island in Lake Victoria , were sampled to assess the presence and frequency of female remating . Six informative microsatellite loci were used to estimate the number of matings per female by genotyping sperm preserved in the female spermathecae . The direct count of the minimum number of males that transferred sperm to the spermathecae was compared to Maximum Likelihood and Bayesian probability estimates . The three estimates provided evidence that remating is common in the populations but the frequency is substantially different: 57% in Kabukanga and 33% in Buvuma . The presence of remating , with females maintaining sperm from different mates , may constitute a critical factor in cases of re-infestation of cleared areas and/or of residual populations . Remating may enhance the reproductive potential of re-invading propagules in terms of their effective population size . We suggest that population age structure may influence remating frequency . Considering the seasonal demographic changes that this fly undergoes during the dry and wet seasons , control programmes based on SIT should release large numbers of sterile males , even in residual surviving target populations , in the dry season .
Tsetse flies ( Diptera: Glossinidae ) are the sole vectors of pathogenic trypanosomes in tropical Africa , where they cause Human African Trypanosomiasis ( HAT ) , or sleeping sickness , one of the most seriously neglected tropical diseases . HAT is a zoonosis caused by the flagellate protozoa Trypanosoma brucei rhodesiense in East and Southern Africa and by T . b . gambiense in West and Central Africa [1] . The only country with known infection foci of both parasites is Uganda [2] . The World Health Organization ( WHO ) has estimated that there are around 10 , 000 cases of HAT as the recent epidemics are beginning to decline , but 60 million people continue to live at risk in 37 countries covering about 40% of Africa [3] . In addition to HAT , trypanosomes transmitted by tsetse cause a fatal disease in livestock , called Nagana , which represents a major impediment to agricultural development in Africa . No vaccines exist to prevent the disease and drugs currently available to treat HAT are expensive , can cause severe side-effects , and are difficult to administer in remote villages . As a consequence , an effective alternative for controlling the disease is to target the tsetse vector [1] , [4] . In 2001 , the African Union launched the Pan African Tsetse and Eradication Campaign ( PATTEC ) to increase efforts to manage this plague , which is considered one of the root causes of hunger and poverty in most sub-Saharian African countries [5] . Glossina fuscipes fuscipes , a member of the palpalis complex , is one of the most important vectors of human and animal trypanosomiasis in Africa . It is a riverine species confined to forested patches along rivers and lacustrine environments [6] . Its range extends across the central part of the African continent from Sudan , Democratic Republic of Congo to Uganda . As a trypanosome vector , G . f . fuscipes is exposed to a large reservoir of parasites , as it feeds on both domestic and wild animals in addition to humans . Attempts to control/eradicate tsetse require in-depth information about their population characteristics such as dispersal rates , distribution , densities and reproductive biology . The riverine nature of G . f . fuscipes has resulted in a patchy distribution of its populations and as a consequence of drift , populations arising from historical colonization events show a considerable population structure [7] . Nevertheless , Beadell et al . [8] inferred a high dispersal capacity for G . f . fuscipes , demonstrating ongoing gene flow among apparently isolated populations , with an equilibrium between drift and gene flow in western and south-eastern Uganda . Since populations undergo seasonal contractions during the year due to changes in water availability , Krafsur [9] suggests that high levels of genetic drift during the dry season could be masking effects due to gene flow . The capacity of G . f . fuscipes to disperse and colonize may also depend on the number of times a female mates in the wild and whether the matings are with the same or different males . This specific mating behaviour influences the effective population size , and may constitute a critical factor in determining the success of control methods [10] , [11] . Some aspects of mating behaviour , such as the effect of age on mating competitiveness , have been studied in laboratory colonies [12] , but to date , the polyandrous behaviour of G . f . fuscipes has not investigated in the laboratory or in the wild . Data on the proportion of tsetse females that mate more than once can be obtained in two ways: through the number of fathers ( male genotype ) represented in her offspring [13] , [14] or through genotyping stored sperm in the spermatheca of the female . In the first case , the genotyping of offspring can reveal the minimum number of males that sire a brood , but not necessarily the number of males with which a female had mated , as females may bias paternity towards one or a few of their mates , resulting in an underestimation of the actual level of polyandry [15] . In the second case a more accurate estimate of the number of mates can be obtained , through the genotyping of the female's stored sperm supply [16] , [17] . Using microsatellite markers to genotype sperm , we ascertained the minimum number of males that were able to transfer sperm to a female's spermatheca in two Uganda populations . The interest in Uganda is based on the fact that eradication efforts by PATTEC are at the planning stages in this country . The results obtained in two sites , which are eco-geographically differentiated , are of particular interest , as in both populations a large proportion of females were found to have mated more than once . The remating frequencies , validated with probability values obtained with two inference statistical models , are relevant for interpreting the reproductive biology of the species but may also have an immediate impact on the strategy to be employed for eradication success .
Natural populations of G . f . fuscipes were sampled from two localities in Uganda: Kabunkanga ( KB , Western territory , 0°58′37 . 88″N , 30°32′47 . 40″E ) and Buvuma Island in Lake Victoria ( BV , Southern zone , 0°15′23 . 15″N , 33°12′22 . 86″E ) ( Figure 1 ) . Both sites are favourable for this riverine species and harbour well established populations . Males and females were collected using biconical traps located 500 m apart at both sites . The traps were checked daily and the average daily fly catch per trap was recorded . The collections from Kabunkanga were made in November 2008 , at the end of the dry season from four traps with an average of 15 flies/day/trap . The collections from Buvuma Island were made at the beginning of April 2008 , during the wet season , from five traps with an average of 58 flies/day/trap . Individuals of each sex were removed from the traps and placed in tubes containing 95% ethanol . The Kabunkanga ( KB ) sample was composed of 20 males and 29 females , while for Buvuma Island ( BV ) 20 males and 40 females were analyzed . The number of males and females in each sample mirrored the sex-ratio observed in the collections . The age and the reproductive history of the sampled flies were unknown , but all the 29 Kabukanga females and the 40 females collected in Buvuma had mated as their spermathecae contained sperm . More precise information about the age structure of the flies collected in each sample could have been obtained from ovarian inspection and/or wing fray analysis [18]; however the extent of damage observed in the wings due to trapping and EtOH preservation , did not permit wing fray analysis . Ovarian age was not assessed . For each site , all of the collected flies were considered to compute allele frequencies and variability estimates . For the remating analysis , the 29 females from KB and 30 females , randomly chosen from the BV collection , were examined . For sperm isolation , the ethanol preserved female body was rehydrated in physiological solution ( 0 . 9% NaCl ) for 24–48 h before dissection . The spermathecae were easily isolated from the abdomen , stored in 70% ethanol to permit the sperm to coagulate in a “sperm bundle” [19] and then dissected in a drop of 1× PBS ( Phosphate buffered saline ) . The sperm bundle was isolated and DNA extraction was performed using QIAamp DNA Micro Kit ( Qiagen , Valencia , CA ) . DNA extraction from the legs was performed using the protocol described in Baruffi et al . [20] . The DNA extracted from legs and sperm was used as PCR template for the amplification of microsatellite markers ( SSRs ) . Nineteen SSR loci were previously isolated from a G . f . fuscipes SSR enriched library [7] . For eight of these loci ( A06 , A09 , A112 , B05 , C7 , C107 , D06 , and D109 ) the described primer sequences were adopted [7] , [8] , [21] . For the remaining 11 loci ( A03 , B03 , B06 , B11 , B109 , C104 , D3 , D05 , D12 , D101 and D103 ) primer sequences and amplification conditions were determined using DNA extracted from Kabunkanga flies as PCR template . Amplification reactions were performed in 15 µl volumes containing 1 µl of genomic DNA , 1× reaction buffer , 1 . 5 mM MgCl2 , 25 µM dNTP , 1 U Taq polymerase ( Invitrogen , Carlsbad , CA ) and 10 pmol of each primer . Reactions were performed with an Eppendorf MasterCycler Gradient thermocycler . After an initial denaturing step of 10 min at 96°C , the PCR consisted of 40 cycles of 1 min at 96°C , 1 min at optimal annealing temperature , and 1 min at 72°C , followed by a final extension step of 15 min at 72°C . Microsatellite loci were analyzed using an ABI PRISM 310 Genetic Analyzer and the GeneScan program ( Applied Biosystems ) . An individual was declared null ( non-amplifying allele ) after at least two amplification failures . Mitotic chromosome spreads were obtained from freshly deposited larvae obtained from the Slovakia laboratory strain . Briefly , brain tissues were incubated in 1% sodium citrate for 10 min at room temperature and transferred to methanol-acetic acid 3∶1 solution for 4 min . The material was disrupted in 100 µl 60% acetic acid and dropped onto clean slides and dried . Pre-hybridization was performed according to Willhoeft [22] . In situ hybridization was performed using the following protocol: the probe DNA was labelled using the Biotin High Prime kit ( Roche , Basel , Switzerland ) and detection of hybridization signals was performed using the Vectastain ABC elite kit ( Vector Laboratories , Burlingame , CA , USA ) and Alexa Fluor 594 Tyramide ( Invitrogen ) . Chromosomes were DAPI stained and the slides were mounted using the VECTASHIELD mounting medium ( Vector Laboratories , Burlingame , CA , USA ) . Chromosomes were screened under an epiflorescence Zeiss Axyoplan microscope; images were captured using an Olympus DP70 digital camera . For the chromosomal location of SSRs on mitotic chromosomes the karyotype description in Willhoeft [22] has been adopted . The polymorphic information content ( PIC ) of each of the 19 SSR loci was determined using the program Cervus 3 . 0 [23] . For each locus and population , the number of alleles ( Na ) , frequency range , observed heterozygosity ( HO ) and expected heterozygosity ( HE ) were estimated using the program Genepop version 4 [24] . The same software was also used to test for linkage disequilibrium between pairs of loci in each population ( 100 batches , 1000 interactions per batch ) and for deviations from Hardy-Weinberg ( HW ) equilibrium , at each locus/population combination , using Fisher's exact test . The Bonferroni correction was used for all tests involving multiple comparisons [25] . The average exclusion probability ( Excl . ) , i . e . the probability of excluding a single unrelated candidate parent from the parentage of a given offspring , knowing the genotype of the second parent , was estimated using the program Cervus 3 . 0 . For each locus and population , the frequency of null alleles was calculated using the Brookfield estimation [26] in Micro-Checker 2 . 2 . 3 [27] . For the X-linked loci the number of alleles and the frequency range were evaluated using the data from both males and females , whereas heterozygosity , exclusion tests and frequency of null alleles , were calculated using the data obtained from only the females . Microsatellite Analyser ( MSA ) software , version 4 . 05 [28] was applied to determine the degree of genetic differentiation between Kabunkanga and Buvuma in terms of Fst [29] . There are three potential sources of errors associated with the genotyping of the sperm stored in the spermathecae [30] , [31]; Two different approaches were used to determine the minimum number of mates per female . The first is a simple descriptive method , based on direct count , which does not involve any probabilistic model . The second approach , which incorporates information derived from the allele frequency in each population using the Hardy-Weinberg principle , provides expected values of multiple matings . This information would be lost if one followed only the first approach . It is worth noting that the expected values of multiple matings also take into account cases in which both males and females , in the population , share the same alleles for each locus . These cases are not recognizable as rematings in the direct count . For the second approach two different viewpoints were adopted: ( a ) the maximum likelihood technique and ( b ) the Bayesian analysis . For elementary explanations of these methods see [35]–[37] .
The characteristics of the 19 identified SSR loci , in terms of primer sequence , amplification conditions and PIC values , are summarized in Table 1 . The characterization was performed on DNA from single flies ( 29 females and 20 males ) collected in KB . Eleven of these loci are X-linked while the remaining eight are spread along the L1 and L2 autosomes , as assessed by chromosomal in situ hybridization analyses ( Figure 2 ) . Out of these 19 loci , 4 autosomal ( A03 , B11 , C7 , D101 ) and 2 X-linked ( C107 and D3 ) loci appear to be good candidates for sperm genotyping in remating studies , as they display high PIC values and are easy to score . The variability estimates describing the suitability of the six loci: A03 , B11 , C7 , D101 , C107 and D3 , for remating analysis in KB and BV , are shown in Table 2 . The number of alleles per locus ranged from 6 to 12 with a mean of 8 . 83 in the KB population , and from 3 to 11 with a mean of 7 . 00 in the BV population . After Bonferroni correction [25] for multiple comparisons , Fisher's exact test revealed that the six loci are in Hardy-Weinberg equilibrium in both populations . No significant genotypic linkage was detected between the six loci ( Fisher's exact test , Genepop ) and therefore they can be considered as independent loci . Analyses performed with Micro-Checker [27] indicated that the average frequency of null alleles is low , 0 . 02 in KB and 0 . 01 in BV . The accuracy of these six loci for assessing remating is measured by their combined probability of excluding ( Excl ) an unrelated candidate parent from parentage when the genotype of the mother is known . The combined exclusion value is 0 . 99 in KB and 0 . 93 in BV . The different levels of variability between KB and BV populations is accompanied by a significant level of differentiation [38] , as the estimate of FST is equal to 0 . 174 between the two populations . The six microsatellite loci were successfully amplified from sperm DNA isolated from the spermathecae of 29 KB and 30 BV females .
Our deductions are based on molecular data , which provide information on the number of males that were able to transfer sperm in a PCR-detectable quantity to a female's spermathecae . Consequently , a conservative ( minimum ) estimate of the number of males with which a female had mated , was determined in the Kabunkanga and Buvuma wild populations . Although our conditions were able to detect the presence of a second male sperm at a ratio as low as 1∶10 , an undetected sperm contribution cannot be excluded . Furthermore cases of failure of sperm transfer , apparently after normal copulations , have been reported [39] , [40] . Our results provide the first direct evidence that remating is a common event in the wild and what is more , females of G . f . fuscipes may store sperm from different males . These are biologically relevant data for interpreting the reproductive biology of this tsetse species , as it appears that many females preserve sperm from different mates , that could potentially be used for insemination . It is also known that this fly is able to maintain the sperm alive for long time [41] . The simultaneous presence of sperm derived from each mating suggests that one of the potential mechanisms of cryptic female choice , such as sperm displacement , [42]–[45] is not operating in this species . On the other hand , the storage of sperm from more than one male generates the opportunity for sperm competition for fertilization . Whether post-copulatory specific events/mechanisms are operating in the female storage organs to control or drive sperm use , is an important open question , which may clarify how the copulations are translated into fertilization in this fly . It is noteworthy that in G . austeni twice mated females utilize sperm from both matings for fertilization of oocytes [10] . If this is the case also for G . f . fuscipes , considering the high frequency of remating , this sperm use by polyandrous females may have a strong impact on the effective population size of the population . Both direct count estimates of remating and probability estimates , obtained with the two inference methods , are significantly lower in Buvuma than in Kabunkanga: more than fifty per cent ( 57% ) of females mated more than once in Kabunkanga while a smaller proportion ( 33% ) remated in Buvuma . Various factors , which may be interrelated , could be responsible for the observed difference . First , the lower genetic variability in Buvuma , with respect to Kabunkanga , diminishes the discriminatory power of the six SSR loci in this island population , as revealed by the lower combined exclusion probability estimate ( Excl 0 . 93 versus 0 . 99 ) . Probably this observation is not related to the choice of loci , as Beadell et al . [8] demonstrated that in Uganda there is a significant decline of microsatellite allelic richness from West to East: Kabunkanga and Buvuma are located at a great geographic distance in the West and East , respectively , of the predicted range of the species ( Figure 1 ) . Thus , considering that the Excl estimate is related to the level of genetic variability , with an Excl value of 1 . 00 , we would have increased our remating estimates , obtaining an expected value of 0 . 58 for Kabukanga and 0 . 36 for Buvuma . Since there is still a difference in the remating frequencies between the two populations , other interrelated eco-geographic and demographic factors must account for the difference . The average age structure may have played a role . In Buvuma Island , flies were caught in April , at the beginning of the rainy season when the population was expanding as also confirmed by the high fly density in the traps , which is about four times greater than the density in Kabunkanga . The Kabunkanga flies were collected in November , at the end of the cooler dry season , when the population undergoes seasonal demographic contractions with a high level of mortality particularly among the young teneral flies while the remaining flies concentrate in moist refugia . In the absence of objective observations regarding the age , such as ovarian measurements and wing-fray analysis [18] , we can speculate that in an expanding population , such as Buvuma , the proportion of young flies may be greater than that in a residual population after a seasonal bottleneck , such is the case of the Kabunkanga sample [9] , [46]–[51] . It is a reasonable assumption that the surviving flies collected in Kabunkanga at the end of the dry season , had more time and opportunity to remate , than those from Buvuma . In addition , according to Abila et al . [12] , male mating competitiveness increases with age , i . e . older males copulate significantly more frequently than younger flies and the peak of female receptivity is between the 8th–13th day after emergence [52] . It has been also reported that Glossina females tend to mate more than once with no apparent difference in receptivity and the number of matings appears to be directly related to the amount of semen in the spermathecae: young females contain less semen than older ones [53] . On the basis of these observations , it can be speculated that a demographic parameter such as age could be the cause of the observed difference in remating frequency between Kabunkanga and Buvuma . However , this hypothesis must be confirmed by appropriate analyses . Finally , as the two study sites , Kabunkanga and Buvuma island harbour well established populations which show a significant level of genetic differentiation ( Fst = 0 . 174 ) , we cannot exclude that the distinct genetic backgrounds of the two populations had an effect on the extent of the observed remating estimates . Several considerations concern the applied aspects of the present findings . As the Sterile Insect Technique ( SIT ) is being entertained for tsetse population control , the presence of remating and the fact that females maintain sperm from different mates , potentially available for insemination , may constitute a critical factor for the success of eradication programmes . Although specific experiments would be necessary to assess the sperm use and the possible presence of paternity skew in populations , multiple mating may potentially help maintain genetic variability and increase the effective population size . Thus polyandry may affect the long-term stability and effective size of G . f . fuscipes populations . In cases of eradication programmes , re-infestation of cleared areas and/or in cases of residual populations , the occurrence of remating may , unfortunately , enhance the reproductive potential of the re-invading propagules in terms of their effective population size . The comparison of two populations highlights another important factor , which , if confirmed , influences the remating frequency , i . e . the population age structure . Consequently , any vector control programme for G . f . fuscipes , according to the present results , must address the greater dimension of the young expanding population in the early wet season , and the increased rate of remating of the fewer , remaining adults after the bottleneck in the dry season . For instance in the case of SIT , a large number of sterile males should be released , also in a population with a reduced number of individuals because of the high rate of remating . These considerations agree with the recommendation to release aged , more competitive , sterile males in all cases [12] . Finally , analyses have identified the presence of parasitic Wolbachia infections in some individuals of natural populations of G . f . fuscipes , including those from Uganda described here . As it has been suggested that Wolbachia-associated incompatibilities may promote polyandry [54] , future studies can now investigate the potential influence of Wolbachia in the remating phenomenon described here . As Wolbachia infections are entertained as a tool to drive genetically desirable phenotypes into natural populations [55] , female mate choice and remating may also have an impact on strategies of population replacement . | Glossina fuscipes fuscipes is the most common tsetse species in Uganda where it is responsible for transmitting Trypanosoma brucei rhodensiense and Trypanosoma brucei gambiense parasites causing sleeping sickness in humans in addition to related trypanosomes that cause Nagana in cattle . An understanding of the reproductive biology of this vector is essential for the application of sustainable control/eradication methods such as Sterile Insect Technique ( SIT ) . We have analysed the number of times a female mates in the wild as this aspect of the reproductive behaviour may affect the stability and size of populations . We provide evidence that remating is a common event in the wild and females store sperm from multiple males , which may potentially be used for insemination . In vector eradication programmes , re-infestation of cleared areas and/or in cases of residual populations , the occurrence of remating may unfortunately enhance the reproductive potential of the re-invading propagules . We suggest that population age structure may influence remating frequency . Considering the seasonal demographic changes that this fly undergoes during the dry and wet seasons , control programmes based on SIT should release large numbers of sterile males , even in residual surviving target populations , in the dry season . |
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Individuals vary extensively in the way they respond to disease but the genetic basis of this variation is not fully understood . We found substantial individual variation in resistance and tolerance to the fungal pathogen Metarhizium anisopliae Ma549 using the Drosophila melanogaster Genetic Reference Panel ( DGRP ) . In addition , we found that host defense to Ma549 was correlated with defense to the bacterium Pseudomonas aeruginosa Pa14 , and several previously published DGRP phenotypes including oxidative stress sensitivity , starvation stress resistance , hemolymph glucose levels , and sleep indices . We identified polymorphisms associated with differences between lines in both their mean survival times and microenvironmental plasticity , suggesting that lines differ in their ability to adapt to variable pathogen exposures . The majority of polymorphisms increasing resistance to Ma549 were sex biased , located in non-coding regions , had moderately large effect and were rare , suggesting that there is a general cost to defense . Nevertheless , host defense was not negatively correlated with overall longevity and fecundity . In contrast to Ma549 , minor alleles were concentrated in the most Pa14-susceptible as well as the most Pa14-resistant lines . A pathway based analysis revealed a network of Pa14 and Ma549-resistance genes that are functionally connected through processes that encompass phagocytosis and engulfment , cell mobility , intermediary metabolism , protein phosphorylation , axon guidance , response to DNA damage , and drug metabolism . Functional testing with insertional mutagenesis lines indicates that 12/13 candidate genes tested influence susceptibility to Ma549 . Many candidate genes have homologs identified in studies of human disease , suggesting that genes affecting variation in susceptibility are conserved across species .
Fungal pathogens of insects are major regulators of insect populations , and are being developed for biocontrol of insect pests [1] . Beyond insects , fungal pathogens have an enormous influence on plant and animal life , leading to species extinctions , food security issues , and ecosystem disturbances [2] . The increased prevalence of fungal infections has stimulated investigations into antifungal immune responses in humans . A defining moment was the discovery of innate-immune Toll-like receptors in antimicrobial host defense . These were originally identified in Drosophila as essential components for the development of resistance to infection with Aspergillus ( and later , other opportunistic insect pathogens ) [3] . Fungi , such as Metarhizium anisopliae cause the majority of insect disease and play a crucial role in natural ecosystems [4]; M . anisopliae is also being developed as a biocontrol agent against fruit fly pests [5] . As most M . anisopliae strains , including the one used in this study , have a broad host range , they are unlikely to be engaging in a strict coevolutionary arms race with a particular Drosophila population . Using M . anisopliae in infection experiments gives us the possibility to study how hosts respond to a generalist fungal pathogen and to assess if variability among host populations is present , possibly due to divergent life histories [6] . Unlike viruses and bacteria that normally infect through the oral route , M . anisopliae breaches the cuticle reaching directly into the hemocoel using a combination of mechanical pressure and an array of cuticle-degrading enzymes [7] . We previously screened 2 , 613 insertional mutant Drosophila lines for their effects on resistance to M . anisopliae ARSEF strain 549 ( Ma549 ) [8] . Overall , 9% of the lines had altered resistance to Ma549 indicating a large mutational target for disease resistance , and approximately 13% of these where in genes encoding immune responses including coagulation , phagocytosis , encapsulation , and melanization [8] . The non-immune genes impacted a wide variety of biological functions , including behavioral traits and nutrition . It is generally agreed that complex traits such as disease resistance are caused by interactions between multiple gene variations and environmental factors [6] . Natural selection would weed out many of the highly deleterious mutations in the insertional mutant lines that affected disease resistance . Thus , the genetic changes with the biggest impact on disease risk are likely to occur infrequently in natural populations . A complementary approach to mutagenesis is to identify loci at which alleles with subtler effects segregate in natural populations [9] . Here , we use a community resource , the Drosophila Genetic Reference Panel ( DGRP ) [10] , [11] , to identify mutations associated with natural variation in disease resistance . The DGRP is a panel of inbred lines with fully sequenced genomes that was created by mating full siblings of wild-caught isofemale lines for 20 generations [10] . As experimental surrogates for individual variation , DGRP lines collectively deliver much higher statistical power compared to outbred individuals , and the lack of heterozygotes means that more extreme phenotypes may be represented in the population because rare recessives of large effect are exposed [10] . Using the DGRP , we show that wild-derived populations of Drosophila have substantial differences in susceptibility to Ma549 , a natural fungal pathogen , and that this variation correlates with resistance to a clinical isolate of Pseudomonas aeruginosa ( Pa14 ) . P . aeruginosa is a quintessential opportunistic pathogen that infects a broad range of hosts , including plants and insects [12] , and causes the highest human case fatality rate of all Gram-negative infections [13] . We additionally found correlations between susceptibility to Ma549 or Pa14 and several previously published DGRP phenotypes [10] , [14]–[16] . We used single nucleotide polymorphisms ( SNPs ) and indels ( hereafter collectively called polymorphisms ) , associated with natural variation in resistance in the DGRP to identify candidate genes . In contrast to variation in resistance to viruses [17] , the majority of alleles associated with variation in susceptibility to Ma549 and Pa14 were rare . We used insertional mutagenesis lines to validate a subset of candidate genes at a high rate . Combining tagged genes from Ma549 and Pa14 GWA analyses revealed a statistically enriched network of genes involved in phagocytosis and engulfment , cell mobility , intermediary metabolism , protein phosphorylation , axon guidance , response to DNA damage , and drug metabolism .
To characterize natural variation , we quantified susceptibility to M . anisopliae ( Ma549 ) using ~71 , 974 flies from 188 lines of the DGRP Freeze 2 , which includes documentation of insertion—deletion polymorphisms and chromosomal inversions in addition to SNPs [11] . Age-matched flies from each line were infected topically with spores of Ma549 , and survival time was monitored using three replicates ( ~20 flies each ) , per sex per line . Each line was screened this way at least twice , and ~30 lines with similar survival times were screened >3 times to validate small differences . A list of the lines used , along with LT50 data and Wolbachia status , can be found in S1 Table . ANOVAs showed highly significant genetic variation in disease resistance ( P<0 . 0001 ) between lines , with a broad sense heritability of H2 = 0 . 23 ( H2 = 0 . 27 ) for males ( females ) from the pooled data ( Table 1 ) . This compares with H2 values of 0 . 47 ( 0 . 38 ) males ( females ) for resistance to P . aeruginosa Pa14 . Disease resistance by males ( females ) to Ma549 was significantly [P = 0 . 036 ( 0 . 003 ) ] associated with only one of the 5 major chromosomal inversions ( In_3R_K ) ( S2 Table ) , indicative of localized LD effects . The same inversion , In_3R_K , also impacts disease resistance by females to Pa14 ( P = 0 . 044 ) . Wolbachia pipientis is a natural intracellular symbiont of many arthropods [18] , and Wolbachia may confer protection against the fungus Beauveria bassiana in one D . melanogaster line [19] . Wolbachia status in the DGRP lines was without significant effect on the susceptibility of either males ( P = 0 . 7332 ) or females ( P = 0 . 8070 ) to Ma549 , but this does not preclude an impact by Wolbachia on a line-by-line basis , i . e . , to an individual D . melanogaster line . The average LT50 for males ( females ) with Ma549 was 5 . 3 ( 5 . 1 ) days with a range of 3 . 73 ( 3 . 55 ) to 7 . 05 ( 6 . 81 ) days i . e . a range of 3 . 32 ( 3 . 26 ) days ( Fig 1a ) . The mean natural lifespan in the DGRP is 55 days [20] . To identify sexual dimorphism , we measured disease resistance separately for males and females infected with Ma549 ( Fig 2 ) . Cross-sex genetic correlations were high ( r = 0 . 74 ) , indicating that many of the same variants affect disease resistance in males and females; but that some alleles will have sex-specific effects . As observed previously [8] , males were typically more resistant than females ( t = 7 . 026 , P < 0 . 0001 ) , however in 57 of the 188 lines ( 30 . 3% ) females were more resistant ( Fig 3 ) . Of the 57 female-resistant lines , 45 ( 78 . 9% ) were in the 94 most susceptible lines in the DGRP collection . Thus , females were more resistant than males in 47 . 9% of susceptible lines and only 12 . 8% of resistant lines . We asked to what extent disease resistance responses to fungi and bacteria were correlated by determining LT50 values for a subset of 81 randomly chosen Drosophila lines fed food contaminated with PA14 ( Fig 1b ) . The average LT50 for males ( females ) was 4 . 2 ( 3 . 8 ) days with a range of 0 . 97 ( 1 . 07 ) to 9 . 2 ( 6 . 99 ) days . This 8 . 23 ( 5 . 92 ) day range in variation in LT50 values for males ( females ) infected by PA14 was ~ 2 . 48 ( 1 . 82 ) -fold greater than the range for Ma549 . Nevertheless , LT50 values for Metarhizium Ma549 and Pa14 were moderately correlated for both males ( r = 0 . 45 ) and females ( r = 0 . 40 ) ( p<0 . 0001 ) consistent with Drosophila only partially discriminating between these pathogens ( Fig 2b ) . Phenotypic correlations between sexes were greater than correlations between the pathogens , with r = 0 . 74 for Ma549 ( p < 0 . 0001 ) ) and r = 0 . 77 for PA14 ( p<0 . 0001 ) . The average LT50’s of Pa14 infected male flies ( 4 . 2 days ) was significantly higher than females ( 3 . 8 days ) ( t = 2 . 96 , p = 0 . 004 ) . The distribution of sexual dimorphism to Pa14 was also similar to Ma549 infected lines , with females being more resistant than males to Pa14 in 30 of the 81 lines ( 37% ) with the majority ( 22 , 73 . 3% ) of these being in the 40 most susceptible lines . However , the correlation of the magnitude of divergence in LT50’s between male and female flies infected with Ma549 or Pa14 fell short of significance ( r = 0 . 19 , P = 0 . 1043 ) . To identify trade-offs associated with disease resistance , we measured correlations between our disease resistance phenotypes and several other traits that have been measured in the DGRP and for which the data are publicly available ( longevity , fecundity , courtship behavior , starvation stress resistance , nutritional stores , chill coma recovery , startle response , aggression , oxidative stress response , endoplasmic reticulum stress , sleep indices ) [9] , [10] , [14] , [16] , [21]–[25] . Table 2 contains the correlation coefficient for each trait combination . Correlations between disease resistance and broad ecological measures of health such as longevity or several measures of fecundity [21] , were not significantly different than zero , indicating that in a pathogen-free environment disease resistance would not be associated or traded off against general robustness or lifetime fitness . Some weak but significant associations did not pass a Holm-Bonferroni correction for multiple testing e . g . , male courtship behavior and the startle response ( Table 2 ) . Negative geotaxis ( a measure of innate escape response and general stress resistance ) as determined by Jordan et al . , [14] is positively correlated with resistance to Ma549 in both males ( r = 0 . 2 ) and females ( r = 0 . 2 ) ( P < 0 . 01 ) , but was only correlated with the resistance of female flies to Pa14 ( r = 0 . 26 , P < 0 . 05 ) . Negative geotaxis has been shown to be sensitive to oxidative stress [14] . Sensitivity to oxidative stress , induced by paraquat but not menadione sodium bisulfate ( MSB ) [25] , was positively correlated with the resistance of female flies to Ma549 ( r = 0 . 31 P < 0 . 001 ) and male flies to Pa14 ( r = 0 . 36 , P < 0 . 001 ) . Resistance to starvation [10] is positively correlated with resistance to PA14 in both males ( r = 0 . 27 ) and females ( r = 0 . 28 ) ( p< 0 . 05 ) , but was only correlated with the resistance of female flies to Ma549 ( r = 0 . 16 , P < 0 . 05 ) , indicating that Pa14 causes greater nutrient stress to Drosophila than Ma549 . However , disease resistance was not associated with wet weight of the fly lines so larger flies are not necessarily more resistant . Various measurements of energy reserves by Unckless et al . , [16] , such as glycogen stores , total triglycerides and soluble proteins in flies showed no correlation with disease resistance , suggesting that there is no straightforward association between these traits . Unckless et al . , [26] found that bacterial ( Providencia rettgeri ) loads were negatively correlated with blood glucose levels . Conversely , we found resistance to Pa14 in male flies ( Unckless et al . , [16] only tested males ) was positively correlated ( r = 0 . 31 , P < 0 . 05 ) with glucose levels in flies fed a low glucose diet . Resistance to Ma549 was negatively correlated with sleep duration , particularly at night in males ( r = -0 . 32 ) and females ( r = -0 . 28 ) ( P < 0 . 001 ) and to a lesser extent , and then only in males , during the day ( r = -0 . 2 , P < 0 . 05 ) . Conversely , there was a positive association between resistance and the number of sleep bouts in males ( females ) of 0 . 25 ( 0 . 24 ) ( P < 0 . 01 ) . Similarly , resistance of female flies to Pa14 was positively correlated with number of nocturnal sleep bouts ( r = 0 . 27 , P< 0 . 05 ) and negatively correlated with average bout length ( r = -0 . 29 , P< 0 . 05 ) . Hence there is a trend for resistant flies to have more sleep bouts than susceptible flies , but these bouts are shorter and total sleep time is less . This may be related to findings that the phagocytic activity of Drosophila immune cells is circadian-regulated and peaks at night during the night rest [27] . However , our data suggests that the number of sleep bouts has more effect than sleep duration on resisting infections with Ma549 . Our measurement of longevity in 20 lines was moderately ( r = 0 . 52 , P < 0 . 05 ) correlated with that of Durham et al . , [21] , indicating the genetic robustness of phenotypes across lab groups and different assay conditions ( we used batches of flies grown on cornmeal-molasses-yeast-agar medium with Tegosept and propionic acid , whereas Durham et al . , [21] used pairs of flies kept separately and grown on sucrose-yeast agar ) . However , resistance to Pa14 was not significantly correlated ( r = -0 . 21 , P = 0 . 12 , n = 58 ) with resistance of female flies to Pseudomonas entomophila , even when we expressed our data as % killed in 3 days ( r = 0 . 22 , p = 0 . 10 , n = 58 ) following Sleiman et al [28] . Using this metric ( % killed ) for our data we lost correlations with sleep indices . As the specialized entomopathogen P . entomophila relies on novel secondary metabolites and toxins to kill insects [29] , we speculate that the Sleiman et al . , [28] analysis may have included measuring variation in resistance to these . The absence of overall positive or negative correlations between resistance and most metabolic indices does not exclude trade-offs in individual fly lines , as all these parameters are complex traits and the product of pleiotropic genes . Thus , polymorphisms associated with increases ( decreases ) in disease resistance are not consistently associated with increases ( decreases ) of resistance to oxidative stress , starvation stress , nutrient levels , fecundity etc . S3 Table shows a subset of the 10 most Ma549 resistant and 10 most Ma549 susceptible DGRP lines ( hereafter called the “divergent subset” ) , and their life cycle parameters and rankings in correlated data from other groups . RAL-38 , the most resistant line to Ma549 , ranked 154 out of 167 for resistance to paraquat , whereas the 3rd and 5th most resistant lines ranked 33rd and third , respectively . Thus , resistance to oxidative stress may be a factor in resistance of some fly lines but not others . While there is no significant correlation between MSB resistance and resistance to Ma549 in female flies , a t-test comparison of the absolute rankings of the divergent subset for MSB resistance reveals significant differences in survival time to MSB . Similarly , there are lines with increased levels of resistance to Ma549 and starvation stress , sleep duration or nutrient levels , but there are also resistant lines with moderate or low rankings for these indices . Consequently , overall correlations could be non-significant for some indices if there are pleiotropic effects of polymorphisms affecting disease resistance on other traits , but the effects are not in the same direction . To further investigate the impact of natural host variation on Ma549 fitness we compared a subset of 20 divergent lines ( S3 Table ) , for differences in impact on four key Ma549 life history traits at different steps of the infection process; within-host growth ( fungal load , measured as CFU’s ) , host life span ( LT50 values ) , latent period ( the lag time between inoculation and sporulation ) , and sporulation capacity ( the total number of spores per Drosophila cadaver ) . A time course of CFU counts showed that resistant flies delayed fungal growth compared to susceptible flies ( Fig 4 ) . Absolute numbers of viable fungi recovered after infection from hemolymph differed substantially between different lines and did not necessarily correlate directly with lethality ( LT50 ) . However , in all lines , except for the susceptible line RAL_439 , fungal loads increased in the 36 hours preceding death . Consequentially , there was a strong association between LT50 values and the time points at which flies carried fungal loads of >10 CFUs ( r = 0 . 61 , P = 0 . 0086 ) or >100 CFUs/fly ( r = 0 . 82 , P = 0 . 002 ) . We also used a Ma549 transformant expressing GFP ( Ma549-GFP ) to track infections in whole insects and hemolymph in the 20 different lines ( Fig 5 for exemplar images ) . Ma549-GFP is sufficiently bright as to be clearly visible from outside the infected insect’s abdomen , which confirmed that blastospores and hyphal bodies accumulated in the body cavity in the day preceding death . Consistent with CFU counts , the timing of colonization and the fungal load in the hemocoel are affected by the fly’s genetic background ( Fig 5 ) , indicative of micro-environmental plasticity . Fluorescence showed blastospores ( yeast-like budded cells thought to be important for dissemination of the pathogen ) appearing in the hemolymph ~day three in most susceptible lines , as illustrated by RAL_321 ( Fig 5 ) . In contrast , proliferation of blastospores and subsequently elongated ellipsoid cells only occurred 4 to 4 . 5 days post-infection in resistant fly lines , demonstrating a longer time lag between penetration and proliferation than occurred in susceptible lines . However , we also noted differences in fungal behavior in different fly lines , even where these had very similar LT50’s . Ma549 produced very few ( <5 ) blastopores in the susceptible line RAL_439 three to 3 . 5 days post infection when flies were already dying . In contrast , the slightly less susceptible line RAL_321 contained abundant blastospore’s 3 . 5 days post infection ( average 8 , 600 CFU counts/fly ) , and at time of death , these had differentiated into hyphal filaments with simple branching ( Fig 5 ) . These filaments consisted of chains of budding cells marked by constrictions rather than septa at the junctions , and thus fit the definition of pseudohyphae [30] . The proliferation of hyphal chains before fly death would result in CFUs underestimating the number of fungal cells in hemolymph . In most lines , long hyphal lengths accumulated in the body at or after death . This probably reflects different environments in line RAL_321 and the other lines in the day preceding death but the nature of the environmental signals that control the ability of Ma549 to form blastospores or pseudohyphae is unknown . Spore production is a measure of pathogen transmission potential and therefore pathogen fitness [8] . Host genotype impacted the onset of Ma549 sporulation ( latent period ) which moderately correlated ( r = 0 . 51 , P < 0 . 01 ) with life span . This is readily explained by sporulation only commencing on cadavers within 60 hours’ post-mortem . However latent period was not associated with total spore production . Indeed , we found no significant difference ( P = 0 . 26 ) in spore production per cadaver between the 10 most resistant ( 1 . 86 x 107 ± 1 . 94 x 106 ) and 10 most susceptible ( 1 . 67 x 107 ± 1 . 48 x 106 ) lines in the divergent subset ( S3 Table ) , indicating that rapid kill of susceptible hosts will not be disfavored by natural selection because it is traded off against reduced time to exploit host nutrients for substantial pathogen reproduction . To quantify micro-environmental plasticity ( variation among individuals of the same genotype reared in a common environment ) , for mean times to death values we used the within-line standard deviation ( σE , and its natural log ln ( σE ) , and the within-line coefficient of environmental variation ( CVE , ln ( CVE ) ) ( Figs 6 and 7 ) . The number of segregating sites and standard deviation per fly line were not correlated ( r = 0 . 07 , p = 0 . 354 ) which suggests residual heterozygosity does not contribute to within line standard deviation . CVE is often used to remove any relationship between mean and variance , but ln ( σE ) has other advantages [31] , so we used both metrics . The correlations between ln ( σE ) and CVE are high in Ma549 infected flies ( r = 0 . 94 , p <0 . 001 ) ( Fig 6 ) . Likewise , LT50 values and mean survival times were highly correlated ( r = 0 . 99 , p <0 . 0001 ) ( Fig 6a ) . With either metric , disease resistance was highly variable among flies with identical genotypes ( Fig 7 ) , suggesting that some lines are relatively more canalized and others more phenotypically plastic in response to the same random environmental effects [32] . Genetic correlations show that the micro-environmental plasticity ( σE or CVE ) , was most variable in lines having the highest LT50 or mean survival times ( Fig 6B and 6C ) suggesting that some of the variants affecting the mean also affect the micro-environmental variance . As reported for stress responses [31] , the magnitude of the genetic variance affecting micro-environmental plasticity is high , with broad sense heritability’s ( H2 ) of ln ( σE ) of H2 = 0 . 5 ( Ma549 ) and H2 = 0 . 52 ( Pa14 ) . Thus , the broad sense heritability at the variance level for resistance to Pa14 is of the same magnitude as that at the level of the mean and , for Ma549 , the heritability of micro-environmental variance is twice as large as that of the mean . To identify genes that harbor alleles that confer altered susceptibility , Ma549 and Pa14 mean LT50s were plugged into the DGRP pipeline with a discovery P value <10−5 . Most polymorphisms associated with mean time to death were at the low range of the allele frequency spectrum , with frequencies below 0 . 2 for 44% ( 41% ) of Pa14 ( Ma549 ) alleles ( Fig 8 ) . These lower frequency alleles had larger effects on LT50 values than common alleles ( Fig 8 ) , consistent with GWA studies on some other complex traits in the DGRP population [25] , [33] . Negative effects ( where flies homozygous for the minor low frequency allele live longer following infection than do flies homozygous for the major allele ) , greatly outnumbered positive effects . A corollary of this is that the most Ma549 resistant DGRP lines had a preponderance of low-frequency alleles ( r = 0 . 23 , P < 0 . 0012 ) ( Fig 9A ) . The effect was more complicated for Pa14 [where there was no overall correlation ( r = 0 . 05 , P = 0 . 67 ) ] and the distribution of minor alleles traced a parabola ( Fig 9B ) . The heritability of Pa14-induced mortality was analyzed on 81 lines only , which means there may be a higher level of false associations [14 ( >20% ) of the polymorphisms have a minor allele count of 5 or less ( S4 Table ) ] . However , if susceptible lines ( LT50’s < 4 days ) and resistant lines ( LT50’s > 4 days ) were considered separately the associations were r = -0 . 65 ( P < 0 . 0001 ) and r = 0 . 45 ( P 0 . 0034 ) , respectively , consistent with minor alleles being concentrated in the most susceptible as well as the most resistant lines . Pa14 polymorphism effects were much larger than those observed in Ma549 ( Fig 8C ) , consistent with the much greater variation between lines observed in Pa14 LT50 values ( Fig 1 ) . Likewise , male polymorphism effects with Ma549 or Pa14 were larger than those observed in females ( Fig 8a and 8b ) . The majority ( 63% ) of Ma549 tagged polymorphisms had sex-specific effects , with the greater number ( 20 ) effecting survival of males as compared to 14 for females . This is consistent with the mean differences in male and female survival ( Fig 1 ) , but contrasts with alleles conferring genetic risk to oxidative stress where SNP effects were larger in females than males [25] . For Pa14 , 14 mutants had female-specific effects , and 17 mutants had male-specific effects . SNPs/indels that are significantly associated with variation in LT50’s to Ma549 and Pa14 ( P < 10−5 ) are presented in S4 Table . We found 50 SNPs and 4 indels associated with Ma549’s speed of kill with a discovery P<10−5 ( 45 total associated genes ) . With a more stringent cut off of P<10−6 , there were four top-candidate genes ( hig , Cyp4p2 , msn , and Rab26 ) . Overall , polymorphisms significantly associated with variation in disease resistance are disproportionately found in introns and UTRs , as opposed to synonymous substitutions or positions more than 1000 bp from known genes . For example , of the 54 candidate polymorphisms , five were indels ( three in introns , two within 1000 bps downstream of a gene ) and 49 were unique SNPs . Of these SNPs , four were synonymous , five mapped to within 1000 bps downstream of a gene , 9 were intergenic ( more than 1000 bp from known genes ) , three were non-synonymous , two were in a 5’UTR and the remaining 26 were intronic . Fourteen polymorphisms are located in overlapping genes that could affect either or both genes . Thus , for Ma549 resistance , 34 out of 54 ( 63% ) total significantly associated polymorphisms are found in introns , UTRs , or as nonsynonymous SNPs , and 45 ( 83 . 33% ) overall were genic . Given the percentage composition of the Drosophila genome ( 48 . 2% genic ( including 18 . 3% exonic and 30% intronic ) and 51 . 8% intergenic [34] , this enrichment for putatively functional polymorphisms is significant ( χ2 = 4 . 714 , df = 1 , P < 0 . 03 ) . Each polymorphism that associates significantly with variation in a measured phenotype is given in S4 Table , including significance level , estimated effect size , minor allele frequency and type of polymorphism . Of the 45 candidate genes , 34 ( 75 . 6% ) have human counterparts , and 22 ( 48 . 9% ) have human counterparts associated with disease ( S5 Table ) . hig , Cyp4p2 , jhamt , Mctp , tRNA:CR30229 , sickie , CG12344 , CG13229 , CG33172 , CG17209 had multiple significant polymorphisms affecting resistance to Ma549 . With the singular exception of one of the four polymorphisms in CG13229 , the effects of these polymorphisms in each gene were in the same direction implying LD in variable genes . All four synonymous and intron polymorphisms in hig had a positive effect ( lines homozygous for the major allele survived infection longer than flies homozygous for the minor allele ) , and the best-supported SNP had a nominal P-value of P = 3 . 18x 10−7 . Aside this synonymous SNP , the other three SNPs were in the same intron and between 411–442 base pairs upstream of the nested gene Cyp4p2 . None of the SNPs outside the hig gene localized to genes surrounding hig so this area of strong linkage disequilibrium only extends across the hig gene . Cyp4p2 is involved in resilience to sleep deprivation and wakefulness [35] . Being involved in the functioning of cholinergic synapses , hig is also required for wakefulness , and deficiency mutations show severely reduced activity and longevity [36][37] . Both polymorphisms in jhamt ( hormone secretion ) had a negative effect . Two polymorphisms in Mctp ( calcium ion binding ) had negative effects; the Drosophila gene has no reported function but its human homolog is implicated in oxidative stress and disorders in eating [38] . Most of the genes affecting response to Ma549 have not been previously implicated in interactions with Drosophila pathogens . Overall , they fall into 11 ontological categories with reported roles in defense , metabolism , morphogenesis and development , stress responses , cellular communication , behavior , and gene expression . Immune genes include sickie required for activation of Relish , an Imd signaling component involved in antibacterial and antifungal polypeptide induction [39] , and CG5794/puffeye ( puf ) , a ubiquitin-specific protease that is a negative regulator of Imd and Toll innate immune defenses; its human homolog also plays regulatory roles in immune signaling [40] . Many of the candidate genes are pleiotropic with functions in cell adhesion and epithelial wound repair plausibly linked with infection , and some may link the immune system , nervous system and nutrition e . g . , Neuroglian ( Nrg ) is involved in neuron cell-cell adhesion as well as melanotic encapsulation of parasites [41] . Likewise , Lar is involved in multiple processes involving cadherin and adhesive interactions [42] . The cadherin Dystroglycan ( Dg ) is associated with stress responses in Drosophila and humans [43] . forked ( f ) regulates the differentiation of epidermal cells and cuticle formation [44] . The kinase misshapen ( msn ) regulates cell migration and the epithelial response to wounding [45] , and Mks1 is also involved in epithelial repair [46] . Schnurri ( shn ) and Star ( S ) also have roles in regulating tissue differentiation , and both antagonize Notch signaling [47]–[49] . Notch signaling is highly conserved and plays critical roles in cell fate specification . In Drosophila it is key to differentiation of crystal cells as vehicles for the prophenoloxidase-activating cascade [50] , previously implicated as one of Drosophila’s most effective defenses against Ma549 [8] . We found a number of genes involved in hormonal regulation of development , including taiman ( tai ) , co-activator of the ecdysone regulator [51] , juvenile hormone acid methyltransferase ( jhmat ) , and Sik3 , a hormone dependent regulator of blood glucose metabolism and starvation responses [52] , [53] . Ecdysone mediates the development of immunity in the Drosophila embryo [54] , and promotes induction of antimicrobial peptides , whereas juvenile hormone is an immuno-suppressor [55] . Juvenile hormone stimulates reproduction at the expense of shorter life span in Drosophila [56] , opening up the possibility of hormones being regulators of trade-offs between disease resistance and other aspects of fitness at the evolutionary level . As LT50 values correlate with sleep cycles we would expect to find genes that are known to regulate Drosophila circadian rhythms . The GABA receptor Rdl is a key gene regulating sleep and wake transitions in Drosophila [57] , [58] . In addition , we found considerable overlap between Ma549 tagged genes and sleep tagged genes from a previous GWAS with the DGRP [9] , including , not surprisingly Rdl and hig , as well as CG12344 , CG17209 , CG32061 , CG33172 , CG9990 , f , gem3 , Nrg , S , tai ( day average bout length ) , Rbp6 ( night sleep ) and jhamt , msn ( waking activity ) . Sickie and Rbp6 were also tagged in a screen for parquet-induced oxidative stress [25] . Lastly , several genes had inferred activities ( i . e . , no experimental evidence ) in FlyBase including Rab26 , a GTPase of no reported function in Drosophila , but its mammalian homolog regulates secretion by highly active secretory cells [59] . Rab26 harbored an SNP with a nominal P-value of P = 1 . 66 x 10−7 . Other inferred activities included G-coupled protein receptors ( CG13229 , CG44153 ) , a regulator of cell proliferation ( CG33172 ) , an RNA-binding protein ( Rbp6 ) , an extracellular-glycine-gated chloride channel ( CG12344 ) , and an ABC transporter ( CG9990 ) . Several genes had no known function: CG13313 , CG33136 , CG5111 , CG8508 , and non-protein coding gene CR43259 . To identify genes that confer micro-environmental plasticity to susceptibility to Ma549 infection , we also performed GWA analyses to associate CVE values with allelic variation . We found 39 SNPs and 7 indels ( 26 candidate genes ) that were associated with micro-environmental plasticity in response to Ma549 at P<10−5 . A total of five SNPs remained significant when the significance threshold was P<10−6 . A single gene , f , was identified in both this analyses and the GWAS using LT50 values . The screen tagged several cell adhesion molecules involved in the axon guidance system and/or phagocytosis including Con [60] , DSCAM and DSCAM4 [61] , gukh [62] , and hdc [63] . Interestingly , DSCAM and Mhcl have been studied for their ability to express multiple isoforms suggesting molecular complexity of the systems they influence . Consistent with complexity , wmd ( muscle morphology ) , and Mhcl1 ( myosin ) are associated with multiple developmental defects in Drosophila and have human homologs linked with multisystem diseases [64]–[66] . Likewise , bab1 , Xpd and qless have human homologs linked to multiple syndromes [67] , [68] , [69] , [70] . Another notable feature was the number of tagged genes previously implicated in regulating circadian rhythms ( bab1 , CCKLR-17D3 , CG10953 , CG9705 , Con , Ddr , DSCAM , f , gukh , CG33687 , and CG8664 ) [71–73] . Most of these genes as well as CG13917 , CG13983 , hdc , and Mhcl were tagged in a screen of genes affecting CVE of DGRP lines to sleep [9] . As with the LT50 screen , some tagged genes had inferred activities ( i . e . , no experimental evidence ) in FlyBase including CG14204 ( acetyltransferase ) and CG4901 ( helicase ) , and other genes had no known function CG10953 , CG13917 , CG33687 . Because we terminated the Pa14 bioassay at 14 days , when some of the most resistant fly lines had residual survivors , only the Pa14 mean LT50s were plugged into the DGRP pipeline . Of 62 polymorphisms ( P<10−5 ) ( 51 total associated genes ) , 12 were indels ( 7 introns , 4 intergenic and one codon deletion ) and 50 were unique SNPs ( 2 non-synonymous , 7 synonymous , one within 1000 bps downstream , 10 intergenic , two in a 5’UTR , 1 in an exon , and the remaining 27 were intronic ) . CG42343 , a protein coding immunoglobulin-like gene with no known function had 9 significant intronic polymorphisms ( 4 SNPs , 5 deletions ) affecting resistance to Pa14 , and all had a negative effect . Each polymorphism that associates significantly with variation in resistance to PA14 is given in S4 Table , including significance level , estimated effect size , minor allele frequency and type of polymorphism . Surprisingly , given the correlation between Ma549 and Pa14 virulence to DGRP lines , only one tagged gene , CG44153 , was in common . A similar lack of overlap has been reported in other DGRP studies and this is often attributed to epistasis [14] . Notwithstanding this , Pa14 and Ma549 responsive genes effected many of the same pathways and functions , including Notch signaling and secretion e . g . , CrebA activates expression of every secretory pathway component gene [74] . Several genes are involved in developmental processes , morphogenesis and tissue maintenance including Osi1 [75] , Zasp52 [76] , G-protein coupled receptor Mthl1 [77] , and Usp10 ( CG32479 ) ; an ubiquitin specific protease that functions as a positive regulator of notch signaling [78] . Several genes may relate to Pa14’s mode of per os infection , including Mnt involved in gut cell differentiation and body size [79] , and cert , which is required for a normal oxidative stress response in the gut [80] . As with Ma549 tagged genes , many of the Pa14 candidates were highly pleiotropic . Pura ( CG33275 ) is a positive regulator of Rho protein signaling involved in circadian rhythms , perception of pain , and regulation of locomotion [81] . Ca-alpha1T is also involved in neural pathways and behavior [82] . Pde9 has no reported function in Drosophila , but its human homolog ( 63% sequence similarity ) is involved in cGMP signaling , hyperglycemia , diabetes , learning , differentiation of stem cells , and neurodegenerative disease [83] . We used the DAVID algorithm [84] , [85] to perform GO enrichment analysis to assess to what extent the entire suite of candidate genes associated with variation in response to Ma549 and PA14 were functionally related . Using a Benjamini-corrected P < 0 . 05; GO categories that were significantly enriched for Ma549 resistance ( S6 Table ) included biological process terms for metamorphosis , morphogenesis , and neuron differentiation indicating that early developmental processes effect subsequent responses to disease . Resistance to PA14 was not associated with significant GO enrichment . We also ran an exploratory GO analysis on genes tagged by polymorphisms using a relaxed p-value of 1x10-4 as described [16] , [86] . The top Ma549 categories were analogous to GO categories identified at 1x10-5 including developmental and morphogenesis genes , but also included cell motion , chemotaxis , cell recognition and cell adhesion , and signaling ( S6 Table ) . Protein domain analysis for either the stringent or the relaxed GO categories displayed an over-representation of immunoglobulin-like genes , fibronectins , and epidermal growth factor-like domains . GO analysis on Pa14 genes tagged by polymorphisms with a p-value of 1x10-4 also included morphogenesis , development , adhesion and signaling , with an over representation of immunoglobulin-like genes and fibronectins . These domain analyses suggest that candidate gene with polymorphisms associated with disease resistance include an over representation of extracellular matrix proteins associated with cell adhesion and immunity . We also used the relaxed p-value of 1x10-4 to examine whether these polymorphisms where enriched for true positive associations and cellular networks . To accomplish this , we used the R spider program [87] , which organizes gene products into cellular pathways based on the Reactome signaling network and the KEGG metabolic network to determine if interactions are over-represented more than expected by chance . Using Ma549 GWA alone did not produce a significantly enriched network . We therefore performed a network enrichment analysis by pooling all GWA candidate genes associated with resistance to Ma549 ( including those that confer micro-environmental plasticity ) and Pa14 . Using a model that allows for no more than one missing gene or compound , we found a network ( P<0 . 005 ) , comprising 55 candidate genes associated with variation in resistance to Pa14 and Ma549 ( Fig 10 ) . The network revealed that genes that harbor alternative natural variation ( alleles ) associated with susceptibility/resistance are functionally connected through processes that encompass phagocytosis and engulfment , cell mobility , intermediary metabolism ( arginine and proline , purine , ether lipids and glycerolipid ) , protein phosphorylation , axon guidance , response to DNA damage , and cyp450 drug metabolism , which may play a role in detoxification . Many of these genes are well connected in the network , but not all potential connections are included . Thus , transcription factor FOXO ( 4 SNPs tagged in the Ma549 CVE GWA screen ( 6 . 98 x 10−5 , 2 . 46 x 10−5 , 5 . 67 x 10−5 , 7 . 31 x 10−5 ) , included in the “Response to DNA damage” domain ( Fig 10 ) , is also involved in cross regulation of metabolism and innate immunity [88] , and transcriptional regulation for nutrient-stressed flies during resource allocation [89] . Functional validation of the FOXO mutant , showed significantly decreased resistance of the mutant when compared to isogenic control flies ( S7 Table ) . The implication of axon guidance shows that individual variation in susceptibility to pathogens may at least in part be determined by polymorphisms that affect subtle variation in neural function . Ten of the random insertional mutations screened previously [8] were in genes tagged in the current Ma549 GWAS screen . Of these six ( Lar , msn , CG14304 , CG44153 , CG14995 , Rbp6 ) had insertional mutations with significant effects on disease resistance , a greater proportion than the 9% expected from random insertional mutations [8] . We additionally used the publicly available toolkit of P-element mutants to confirm the influence of a subset of 13 candidate genes [S ( Notch signaling ) , msn ( response to wounding ) , shn and CG33172 ( cell proliferation ) , tai ( ecdysone regulation ) , Sik3 ( response to starvation ) , Rdl ( circadian rhythms ) , f ( cuticle formation ) , CG9990 ( ABC transporter ) , CG32066 ( unknown function ) , CG33111 ( unknown function ) , puf ( negative regulation of innate immune responses ) , and FOXO ( cross regulation of metabolism and innate immunity ) . These genes were chosen based on the significance level of their association with Ma549 , or in the case of FOXO , its detection in a network enrichment analysis . To exclude mutations with generally deleterious effects on fitness we excluded from study genes in which lethal mutations are known . All 13 genes and corresponding controls were tested for both sexes with Ma549 . Nine mutants had significant effects on resistance to Ma549 in both sexes , two mutants ( Sik3 and CG32066 ) had a female-specific effect , one mutant ( puf ) had a male-specific effect , and a mutation in shn had no significant effects ( S7 Table ) . In total , 21 out of 26 tests were significant , a much greater proportion than the 9% [8] expected from random insertional mutations ( Fisher’s exact test; P = 0 . 0001 ) , supporting the contention that the top polymorphisms were enriched for true positive associations .
We have utilized two complementary strategies for studies on fungal disease resistance in the fruit fly model system: a mutant screen approach aimed at the characterization of individual candidate genes [8] , and in this paper a systems genetic approach to identify natural variation associated with disease resistance . The DGRP has lines that harbor most common variants and a representative sample of rare variants that have survived natural selection , and are unlikely to be produced by mutagenesis screens [9] . Our current study aimed at both quantifying levels of host genetic variation for resistance against different diseases and identifying the specific physiological and genetic factors that influence these traits . If resistance is defined as an individual’s ability to limit infection by reducing pathogen replication , then disease tolerance can be defined as the ability to limit the impact of infection on a host [90] . To elucidate the underlying mechanisms of variation in LT50 values we determined the fungal loads in hemolymph after infection . Although CFU counts from Ma549-infected insects are clearly affected by the genetic background , flies that succumb to Ma549 only carry high fungal loads in the 36 hours preceding death , irrespective of when this is , suggesting that flies in the more susceptible lines are less able to restrain Metarhizium growth . RAL_439 was exceptional in that there was very little fungal proliferation preceding death , suggesting that this line has a physiology that makes it less able to tolerate fungal colonization . Many Drosophila mutants succumb to bacterial infections because of defects in tolerance rather than resistance [91] , whereas RAL_439 is evidence for genetic variation for tolerance in natural lines . Previously , we suggested based on work with Drosophila mutants , that it may be more difficult to evolve tolerance traits to a filamentous fungal pathogen because unlike bacteria they actively penetrate and colonize infected tissues [8] . The present study suggests that most Drosophila lines have high tolerance to Ma549 , but this is only put to the test in advanced infections when resistance breaks down . The implications of selection for resistance acting in concert with tolerance will need to be considered . Presumably , resistance could lead to selection pressure for higher virulence in the pathogen , whereas selection for tolerance could plausibly result in co-existence of pathogen and host [92] . Susceptibility to Ma549 within the DGRP is sexually dimorphic ( Table 1; Fig 1 ) , with males demonstrating higher resistance than females for most lines consistent with our previous mutant screen [8] . This finding is contrary to what is known from most pathogenicity studies in mammals , where females are the more resistant sex . However , exceptions include female mice being more susceptible to P . aeruginosa infection , showing higher bacterial loads in the lungs [93] , so our finding may be part of a broader biological phenomenon . In addition to sexual dimorphism in susceptibility to Ma549 averaged over all DGRP lines , there is also genetic variation in the magnitude and direction of the difference in disease resistance , with females being more resistant than males in about half of the 94 lines that overall are most susceptible to Ma549 . More work will be needed in order to understand the molecular mechanisms of these predispositions but they are presumably attributable to multifactorial sex-specific differences in genetics , immune processes , behavior and physiology . We previously demonstrated that virgin and mated females of laboratory Drosophila lines have similar susceptibility to Ma549 [8] , but that would not necessarily carry over to wild strains . By studying micro-environmental plasticity , we determined that even a single genotype allows for the production of flies with different susceptibilities to disease , and that this plasticity itself varies depending on genotype . That plasticity is to a large extent under genetic control is shown by its broad sense heritability being twice as high as that of the trait mean for Ma549-induced lethality using the same data set . A similar discordance in heritability values between trait plasticity and mean was obtained for chill-coma recovery time in Drosophila [31] . Genetic variation for plasticity will provide the genetic basis of evolution of phenotypic plasticity , making plasticity a heritable trait in its own right and subject to evolutionary mechanisms . Phenotypic plasticity is beneficial in predictably changing environments . The DGRP lines are all derived from an out-crossed population in Raleigh , North Carolina [10] , but Drosophila’s relatively high migration rate [94] , means they may not all have come from the same habitat and be locally adapted to the same pathogens . In these circumstances , phenotypic plasticity to variable pathogen exposures could allow a population to shift from one environment to another without genetic changes , buffering the strength of selection and preventing loss of genetic variation ( a “bet-hedging” strategy ) [95] . The trait mean and micro-environmental plasticity were highly correlated . Thus , directional selection for an increase in the mean disease resistance will result in more phenotypic variation . If applicable to breeding programs for disease resistance in domestic animals [96] these programs will result in high environmental variance rather than the desired uniformity . Our results suggest that few of the polymorphisms that contribute to natural variation in resistance to bacteria and fungi in Drosophila populations affect canonical immune genes , but rather they cause variation in genes affecting many different aspects of host physiology . These observations are in line with insertional mutagenesis techniques to document resistance genes [8] . Lu [8] reported that 87% of mutated genes in more susceptible lines are involved in a broad spectrum of biological functions not connected with canonical immune systems including basic cellular processes , early development , muscle and nervous system development and function , the senses , and metabolism . Those results are broadly recapitulated in the current GWAS analysis , although there was little overlap in the individual genes implicated by random mutagenesis and natural polymorphisms . The large number of candidate genes involved in development and function of the nervous system is potentially an artifact of the observation that neurological genes tend to be large and therefore provide a larger target for association studies [10] , but neurological terms were enriched in our GO analysis that controlled for gene size . The absence of many canonical immunity genes or immunity related gene ontology terms is of interest as it indicates that these have not been targets of pathogen-dependent selection in the DGRP . Of particular interest , neither Lu [8] , nor this study implicate the antifungal peptide genes , although Lu [8] report that drosomyocin is induced by Ma549 . One potential explanation is that there is little natural variation in canonical immune genes . However , the DGRP contains 838 variants in the Toll gene , 4 in the Drosomyocin gene , and 6 in the Metchnikowin gene . In contrast , Lu [8] found several indications for octopamine having an immune effect , but that was not replicated in our current GWAS analysis . Octopamine is the key hormone involved in the acute stress response and prepares the insect for flight or fight behaviors , as well as mediating a connection between the nervous system and the immune system [97] . There are several possible and nonmutally exclusive explanations for these observations [98] . In the context of our current study: 1 ) Naturally occurring polymorphisms may not result in individual variation in resistance responses . This could occur if these genes are under purifying selection and hence functionally invariant . 2 ) Our GWAS necessarily did not take into account natural selection directed by other pathogens , and specialist pathogens evolving under pairwise co-evolution with Drosophila may be more likely to produce signatures of positive selection in immune genes . 3 ) Effect sizes of causal polymorphisms at these loci are too small to be detected given the resolution of the infection assay and the sample size . 4 ) Rare alleles at these loci , not interrogated in our analyses , affect natural variation in responses . 5 ) Functional redundancy in disease resistance genes may obscure the effects of individual polymorphisms on phenotypic variation . Overall , our results suggest that natural lines differ in their ability to control replicating fungi during infection through the coordinated interplay of morphological restraints and different physiological and immune system effectors . Changes in physiological state likely alter immune-system function via neural/neuroendocrine/immune connections that adapt the immune system to changing needs [6] , in subtly different ways in different lines . These results are consistent with studies on domestic animals where the proportion of the total variation in survival explicable by immune variables is too low to be useful as a selection criterion [96] . This was explained by the complexity of the mechanisms involved in the immune response and the large number of factors that may be involved in disease resistance [96] . A majority of polymorphisms associated with disease resistance were intronic , suggesting that gene expression variation may play a major role in determining variability in disease phenotypes . Most were rare suggesting that mutations that increase resistance to Ma549 and PA14 may tend to be deleterious , so are either removed from the population or kept at a low frequency by purifying selection . Our results contrast with a GWAS study which showed that a small number of common polymorphisms have a major effect on resistance to viruses [17] . This may be because there are relatively few genetic changes that can cause viral resistance [17] . Major effect polymorphisms that protect hosts against infection have also been identified in humans [99]–[101] , although the majority of human GWAS studies on non-communicable diseases have identified many rare alleles often with small effects [102] . Our association study , like a similar study on sleep in Drosophila [9] , found that the lower frequency variants had the largest effects ( Fig 8 ) , supporting the rare variants hypothesis . Previous studies have suggested that there is no clear-cut relationship between genetic resistance of Drosophila to different bacteria , so a given host genotype does not have a universal effect on a range of bacterial pathogens [103] . Sleiman [28] in their GWAS study found little correlation between enteric infection with P . entomophila and stab inoculated Erwinia carotovora , and concluded that the determinants of gut immunocompetence are distinct from those that govern systemic immunity . Martins [104] also conclude that Drosophila adaptation is contingent upon the infection route taken by the pathogen [104] . It was not axiomatic therefore that resistance to a clinical isolate of P . aeruginosa and Ma549 would be correlated , particularly as bacteria and fungi evoke the IMD and Toll pathways , respectively . Furthermore , we previously found an apparent trade-off in genes affecting resistance to bacterial and fungal infection [8] , that was not supported by this finding . The correlation of resistance to PA14 and Ma549 is suggestive of general ( multipurpose ) defense mechanisms . Metarhizium species are abundant in the same soil and plant locations as Pseudomonas spp , [105] , so local adaptation to these environments will be associated with heightened risk of contracting pathogens . Depending on the way a pathogen interacts with its host , it may encounter specific or less specific defense barriers . Some of these may also be encountered by other pathogens depending on their routes of entry , host tissues infected and other factors . Fungi infect via the cuticle and bacteria through the gut so these components of the defense machinery will be specific to these pathogens , the unspecific generalized defense components are presumably in host tissues and hemolymph . The host responses triggered by P . aeruginosa remain poorly understood [106] . However , like Metarhizium [107] , P . aeruginosa has means of limiting or resisting antimicrobial peptide gene expression [108] . Our current study shows that resistance to both Ma549 and PA14 correlates with survival times on paraquat but not menadione sodium bisulfite ( MSB ) . Toxicity of paraquat is primarily due to production of superoxides whereas MSB toxicity is due to electrophilic attack [109] , suggesting that the ability to alleviate or tolerate superoxide stress is a feature of a generalized defense response to multiple pathogens . Drosophila shows circadian regulation of response to oxidative stress [110] , so between-line variation in these rhythms may influence how they respond to ROS produced during infection . Circadian control of the immune system is well established in humans [111] , and circadian mutants in mice and flies have immune phenotypes [112] . Our data suggests that naturally occurring variation in sleep patterns also impact bacterial and fungal pathogenesis . Given the conservation of both circadian and innate immune signaling between flies and vertebrates , this could have significant implications for vertebrate immunity . It has recently been established that insects can anticipate infections by up-regulating immune genes when they find themselves in scenarios associated with increased disease risk . Zhong et al . , [113] raise the interesting possibility that control of immune genes by circadian clock genes might reflect “anticipation” of predictable fluctuations of disease risk over the course of 24 hours . Thus , if frequent naps were associated with pre-emptive up-regulation of immune genes this might be representative of a general pattern of immune anticipation in insects . Resistance to multiple pathogens should have a selective advantage unless this general defense is traded off against other ( pathogen-independent ) fitness components [114] . In the absence of such a trade-off , directional selection should lead to fixation of genotypes showing general resistance [115] . However , the most resistant lines to Ma549 were enriched in minor ( rare alleles ) , suggesting that these alleles have negative correlations with other fitness related traits . A trade-off of the cumulative cost of defense could have been reflected by a negative association with longevity and fecundity , which we did not observe . We found some weak positive correlations with blood sugar levels and resistance to starvation stress , consistent with nutritional status altering the quality of immune defense [16] . However , measurements of energy reserves ( glycogen stores , total triglycerides and soluble proteins ) showed no correlation with disease resistance . This was surprising since Ma549 would compete with hosts for resources , and it makes intuitive sense that overall genotypes that store more nutrients would have better tolerance to disease . Our previous insertional mutagenesis screen showed that half of the mutant lines with altered disease resistance had significant effects on starvation resistance , but there was no simple association between disease and starvation resistance as networks of pleiotropic genes regulate complex traits [8] . Clearly being more or less tolerant to starvation does not by itself alter resistance to Ma549 . However , many of the polymorphisms associated with variation in susceptibility to Ma549 are in genes affecting cellular processes and metabolism , and it is plausible that alterations in these processes could specifically change expenditure of energy on immune responses [116] . We found no negative genetic correlations between resistance to Ma549 and several other physiological variables and metabolic indices . Likewise , there were no correlations with measures of ability to cope with important abiotic stresses such as chill-coma recovery time . Southern Drosophila populations tend to have higher starvation resistance whereas northern populations tend to have fast chill coma recovery time [117] , but our data suggest that this would not be traded off against resistance to Ma549 . We did however find examples of genetic variation in the magnitude and direction of associations , such as DGRP lines RAL_399 and RAL_440 that were both highly resistant to Ma549 but demonstrating low and high life time fecundity , respectively . The absence of overall positive or negative correlations between resistance and metabolic indices does not exclude trade-offs as all these parameters were taken by other researchers from uninfected flies , and are complex traits that may not obey simple , single factor models [118] . The lack of a common pattern of correlations among the most resistant or the most susceptible lines i . e . , some resistant lines were also particularly resistant to oxidative stress and some were not , suggests that there are multiple mechanisms by which the complex trait of disease resistance can be altered . Consequentially , a GWAS study will identify common trends in populations and not idiosyncratic differences between lines . With that proviso , our single polymorphism association analyses using the DGRP provided insight into the genetic architecture of susceptibility/resistance to Ma549 associated with variation in this complex trait , and identified novel candidate genes outside the conventional immune system that may be selected for in determining susceptibility to infection . We performed secondary screens using mutations to confirm the reliability of the GWAs in predicting genes that indeed affect disease resistance toward different pathogens . The high validation rate engenders confidence that functional tests of other candidate genes involved in metabolism , development , oxidative stress and function of the nervous system will identify new components of genetic networks affecting disease resistance . The GWA studies presented here are a hypothesis-generating paradigm that lays the foundation for a detailed dissection of allelic effects of candidate genes in future endeavors . The ecological features that might function as good predictors of host immune investment in Drosophila are unknown , but environmental variables , such us parasite species richness , could be informative . For example , fly populations coming from locations with a rich bacterial community have been found to be less susceptible to the bacteria Lactococcus lactis [119] . Tinsley [120] found regional differentiation in Drosophila susceptibility to the fungus Beauveria bassiana , although Paparazzo [121] suggested these differences in susceptibility could be due to general stress resistance . Clearly , much would be gained by being able to integrate our GWA data with studies of ecological genetics in wild Drosophila systems that evaluated the process of adaptation to different environments and pathogens [122] . Our current study assessing patterns of variation in host-pathogen interactions improves understanding of the relationship between genetic variation and phenotypic variation for disease resistance , which is necessary for predicting responses to selection . This could have implications for estimating disease risk in humans as several studies have shown the Drosophila DGRP can be used to identify functionally similar homologous human genes [9] . It also has implications for development of wild type and genetically engineered entomopathogenic fungi as biocontrol agents of agricultural pests and mosquito vectors of human disease [123]–[125] . Extensive genetic variation in individual resistance from the same geographical population could set the stage for the evolution of resistance with implications for their sustainability . Future studies should also take account of the time of day when applying pathogens to insects in experimental settings or as biocontrol agents , as circadian rhythms may introduce considerable variability .
The Drosophila Genetics Reference Panel ( DGRP ) [10] , [11] , and transposon ( P-element and Minos insertion ) lines were obtained from Bloomington Drosophila Stock Center , IN USA . Candidate genes were tested for resistance to fungal infections using insertional mutant fly lines ( Bloomington stock number in parenthesis ) : S ( 20272 ) , msn ( 22796 ) , shn ( 22518 ) , CG33172 ( 15945 ) , tai ( 13204 ) , Sik3 ( 20921 ) , Rdl ( 26404 ) , f ( 14224 ) , CG9990 ( 24814 ) , CG32066 ( 16746 ) , CG33111 ( 24046 ) , puf ( 15697 ) , and FOXO . We received permission to use the FOXO mutant from Linda Partridge ( University College of London ) and the mutant and its control ( wDAH ) were generously provided by Michael Marr ( Brandeis University ) [126] . Flies were reared under standard culture conditions ( cornmeal-molasses—yeast-agar-medium with Tegosept and propionic acid , 25°C , 12-hr light-dark cycle ) . M . anisopliae ( ARSEF 549 ) was obtained from the USDA Entomopathogenic Fungus Collection ( Ithaca , N . Y . ) . Ma549 is the active ingredient of Metabiol; a commercial product effective against hemipterans , lepidopterans and dipterans , and is a frequently used as a vehicle for genetic engineering projects [124] . Fungal cultures were moved from -80°C stock tubes 10 days before each bioassay and grown on potato dextrose agar at 27°C . Plasmid construction and transformation for the GFP fluorescent Ma549 strain was described previously [123] . P . aeruginosa ( Pa14 ) was obtained from Vincent Lee ( University of Maryland ) . Bacterial cultures were moved from -80°C stock tubes and plated on LB plates at 37°C two days before each bioassay . Single colonies were moved the next day for overnight growth into flasks containing 25 ml of brain heart infusion ( BHI ) broth at 37°C and placed on a shaker at 200 rpm . Ma549 was used in an infection bioassay as described previously [8] . Flies were maintained at 27°C , 85% humidity , on food made without Tegosept and propionic acid . We bioassayed 3 tubes of ~20 flies ( aged 2–4 days ) per DGRP line , per sex with a spore suspension ( 2 . 5x104 spores/ml of water ) produced from 10 day old Ma549 plates . Replicates were run on different days to randomize environmental variation . Control flies were treated with water alone as a control for the bioassay process . Fly mortality was monitored every 12 hrs . We found that in many vials one or two flies died in experimental and control tubes in the first day but subsequently we found that as before [8] , there were no differences in longevity between untreated and water treated flies . A total of 81 randomly chosen DGRP lines were orally infected with PA14 as described in Lutter [127] . Approximately twenty 2–4 day old flies per line , per sex were put into vials and starved without food or water for 5 hrs . During this time , bacterial cultures were normalized to 3 . 0 at OD600 , and 2 ml aliquots centrifuged at 7000 rpm for 5 minutes . The bacterial pellet was suspended in 175 ul of sterile 5% sucrose and then added to 2 . 3-cm Whatman filter disks placed inside vials containing 6 ml of 5% sucrose agar . Flies were then transferred into the vials and incubated at 27°C and ~85% humidity . Fly mortality was monitored every 12 hrs for 14 days . As Drosophila survive night-time infections with Pseudomonas significantly better than day-time ones [128] , all infections with Ma549 and Pseudomonas Pa14 took place within an hour of 6pm . Time to die was calculated for each replicate tube and expressed as LT50 . The standard deviation and coefficient of variation for each line were calculated using Kaplan-Meier standard errors . All calculations were done using SPSSv23 . To assess the effect of Wolbachia infection status on survival time to Ma549 and PA14 , we used a factorial , type III mixed model ANOVA . The model used was Y = μ+S+I+S×I+L ( I ) +S×L ( I ) +ε , where I denotes the effect of infection status , S is the fixed effect of sex , L is the random effect of the DGRP line , and ε is the error variance . We partitioned phenotypic variance with the ANOVA model Y = μ +S + L + S × L+ ɛ to partition variance among lines ( L , random ) , sex ( S , fixed ) , line by sex interaction ( L × S , random ) , and within-line variance ( ɛ ) . Broad-sense heritabilities for disease resistance were estimated from the variance components as H2 = ( σL2 + σSL2 ) / ( σL2 + σSL2 + σE2 ) . To assess the degree of sensitivity of disease resistance to the environment , we first tested the heterogeneity of within line variance among lines using Levene’s test . We then estimated the error mean square separately for each line and replicate by fitting a linear model which only included the intercept ( Y = μ + ɛ , where Y is the phenotypic value of the trait , μ is the overall mean and ɛ is the within-replicate random error ) . We estimated the micro-environmental standard deviation , σE as the square root of the mean square errors . We then assessed the genetic variance of ln ( σE ) using a mixed model factorial ANOVA model of form Y = u + L + ɛ , where Y is ln ( σE ) , μ is the overall mean , and L is the random effect of the line . Broad sense heritability’s for micro-environmental heterogeneity was calculated as H2 = ( σL2 ) / ( σL2 + σE2 ) . All calculations were done using SAS University edition . Twenty lines were used to survey the impact of fly genetics on Ma549 life history traits . For epifluorescence imaging , ~40 individuals of each line were infected with Ma549-GFP . Fly images were taken starting 12 hours preceding the estimated LT50 for each line , using a Zeiss Axioimager M1 . Intact flies were placed on microscope slides underneath a coverslip and viewed at 100x . To view the hemolymph , flies were squashed with the coverslip . Ten of the lines were selected for a time course bioassay of fungal growth in the hemolymph , using previously described protocols [8] . At each time point , 10 flies per sex were individually homogenized with 45 μl of 0 . 1% Tween 80 . The homogenate was spread onto Rose Bengal Agar plates supplemented with oxbile , CTAP , oxytetracycline , streptomycin , penicillin , chloramphenicol , and cycloheximide . Colony forming units ( CFUs ) were counted after 7–10 days’ incubation at 25°C . For sporulation analysis , ten flies per sex were harvested within 12 hours of death and individually transferred into tubes containing a damp cotton ball . The first appearance of spores ( latent period ) was recorded , and after 20 days , 500 μl of 0 . 1% Tween 80 was added to each tube and the tubes were vortexed ( 1 minute ) . Spore counts per individual fly were made using a hemocytometer , and results are the average of 10 flies per line . We examined correlations among our measured traits , and between our disease-related phenotypes and independent traits that have been measured in the DGRP lines by other research groups . Correlation analyses were performed in R ( R Core Team 2012 ) using rcorr and our line mean estimates , and we report both correlation coefficient and P value . We used the Holm-Bonferroni method for significant correlations to control for the familywise error rate [129] . For significantly correlated traits , we queried whether a single gene or a few genes might drive the correlation by determining whether the same SNPs were significantly associated with variation in both traits with a P value threshold of 10−5 . Associations were computed for Ma549 and PA14 separately using line LT50’s and coefficient of variation for phenotypic scores , using ~2 million polymorphic markers [10] . These GWA analyses adjust for the effects of Wolbachia infection and 5 major chromosomal inversions ( In ( 2L ) t , In ( 2R ) NS , In ( 3R ) P , In ( 3R ) K , In ( 3R ) Mo ) , and were implemented using the DGRP website ( dgrp2 . gnets . ncsu . edu/ ) . The same analysis was performed for each sex separately and for sex average and sex difference of the adjusted phenotypes . Annotation of SNPs was based on Flybase release 5 . 49 [10] . SNPs were considered in a gene if they were located in or within 1 kb upstream and downstream of a gene model . GO analyses were performed using the DAVID algorithm [84] , [85] , with the Benjamini correction for multiple tests . To identify ensembles of interacting gene products , we used the R-spider program in the BioProfiling . de web portal [87] ) . This analysis tool incorporates data for ∼2 , 000 genes and combines signaling and metabolic pathways from Reactome and Kyoto Encyclopedia of Genes and genomes ( KEGG ) databases to determine whether interactions between the input genes are greater than expected by chance using a permutation test . The network is built by connecting genes with known interactions in the two databases , allowing zero , one , or two missing nodes . P-element insertions in 13 candidate genes were selected for functional assessment , using the criteria that the corresponding polymorphisms had high statistical significance in the GWA analyses , and the mutant alleles were available from Drosophila stock collections with co-isogenic controls . We tested P-element insertions for their effects on resistance to Ma549 with three to five replicates of approximately 20 flies per line and sex . The puf mutant ( 15697 ) was originally created using the p-element construct P{EPgy2} which contains a Scer\UAS binding site , inserted into the gene ash2 [130] . We therefore crossed this line and its isogenic control with a fly line expressing GAL4 ( 4414 ) to validate the effect of the puf gene . Statistically significant differences in responses to Ma549 between mutants and their coisogenic controls were determined using the log-rank test . | We have shown that there is significant genetic variation for host defenses against the fungus M . anisopliae ( Ma549 ) in a set of 188 Drosophila lines derived from nature that have been completely sequenced . This manifested as differences between lines in mean survival times , how they balanced resistance and tolerance to disease , and their microenvironmental plasticity . Despite having very different modes of infection , resistance to Ma549 is positively correlated with resistance to Pseudomonas aeruginosa , and resistance to the two pathogens is correlated jointly with phenotypes ( sleep , oxidative stress resistance ) that have been measured by other researchers . We identify a host of candidate genes associated with variation in disease resistance , many of which are known to interact physically and/or genetically enabling us to place them in a biologically informative genetic network . Overall , our results suggest that natural lines differ in their ability to control and tolerate replicating fungi during infection , which is achieved through the coordinated interplay of morphological and physiological restraints , and different immune system effectors that function in subtly different ways in different lines . Generally speaking , the results presented here can provide a starting point for further research on these important traits . |
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Meiotic recombination is required for proper homologous chromosome segregation in plants and other eukaryotes . The eukaryotic RAD51 gene family has seven ancient paralogs with important roles in mitotic and meiotic recombination . Mutations in mammalian RAD51 homologs RAD51C and XRCC3 lead to embryonic lethality . In the model plant Arabidopsis thaliana , RAD51C and XRCC3 homologs are not essential for vegetative development but are each required for somatic and meiotic recombination , but the mechanism of RAD51C and XRCC3 in meiotic recombination is unclear . The non-lethal Arabidopsis rad51c and xrcc3 null mutants provide an opportunity to study their meiotic functions . Here , we show that AtRAD51C and AtXRCC3 are components of the RAD51-dependent meiotic recombination pathway and required for normal AtRAD51 localization on meiotic chromosomes . In addition , AtRAD51C interacts with both AtRAD51 and AtXRCC3 in vitro and in vivo , suggesting that these proteins form a complex ( es ) . Comparison of AtRAD51 foci in meiocytes from atrad51 , atrad51c , and atxrcc3 single , double and triple heterozygous mutants further supports an interaction between AtRAD51C and AtXRCC3 that enhances AtRAD51 localization . Moreover , atrad51c-/+ atxrcc3-/+ double and atrad51-/+ atrad51c-/+ atxrcc3-/+ triple heterozygous mutants have defects in meiotic recombination , suggesting the role of the AtRAD51C-AtXRCC3 complex in meiotic recombination is in part AtRAD51-dependent . Together , our results support a model in which direct interactions between the RAD51C-XRCC3 complex and RAD51 facilitate RAD51 localization on meiotic chromosomes and RAD51-dependent meiotic recombination . Finally , we hypothesize that maintenance of RAD51 function facilitated by the RAD51C-XRCC3 complex could be highly conserved in eukaryotes .
Homologous recombination ( HR ) is important for repairing DNA damage and maintaining genomic stability . Meiotic HR and sister chromatid cohesion are required for maintaining physical associations between homologous chromosomes ( homologs ) and ensuring their accurate segregation . Meiotic HR is initiated by programmed DNA double-strand breaks ( DSBs ) that are catalyzed by SPO11 , a topoisomerase-like protein [1] . The resulting DSB ends are processed by the MRE11- RAD50-NBS1 ( MRN ) protein complexes to generate 3′ single-stranded DNA ( ssDNA ) tails [2 , 3] , which are subsequently protected by replication protein A ( RPA ) [4] . Functional homologs of the E . coli RecA protein , RAD51 and DMC1 [5 , 6] bind to the 3ʹ tails to form nucleoprotein filaments with the help of several proteins identified in multiple species , including Saccharomyces cerevisiae ( Rad52 [7] , Rad54 [8] , Tid1/Rhd54 [9] , Rad55-Rad57 [10] , Swi5-Sfr1 [11] and PCSS complex [12] ) , Arabidopsis thaliana ( RAD51C [13] , XRCC3 [14] , MND1-HOP2[15] and ATR/ATRIP [16] ) , and mammals ( Mnd1-Hop2 [17] and Brca2-Dss1 [18] ) . The nucleoprotein filaments facilitate single-end invasion of a non-sister chromatid , resulting in the formation of a recombination intermediate called a D-loop , which is then processed to ultimately produce either crossovers ( COs ) or non-crossovers ( NCOs ) [19] . In vertebrate animals and plants , the RAD51 gene family is highly conserved with seven members: DMC1 , RAD51 , RAD51B , RAD51C , RAD51D , XRCC2 and XRCC3 [20–23] , which share Walker A and Walker B motifs with over 37 . 5% similarity [24] . In mice , mutations in any of the paralogs , except DMC1 , lead to embryonic lethality following spontaneous DNA damage or errors [25–29] . In the model plant Arabidopsis thaliana , all seven genes are dispensable for vegetative growth [13 , 14 , 24 , 30–33] . However , AtRAD51 , AtRAD51C and AtXRCC3 are required for somatic and meiotic recombination , as well as plant fertility . Mutations in any of these three genes result in a meiotic chromosome fragmentation phenotype [13 , 14 , 24 , 30–32] . Moreover , AtDMC1 is specifically required for meiotic homolog pairing and recombination [34 , 35] . In contrast to atrad51 , atrad51c and atxrcc3 mutants , atdmc1 mutants do not suffer meiotic chromosome fragmentation; instead their DSBs are thought to be repaired using sister chromatids as templates [34 , 35] . The three other paralogs , AtRAD51B , AtRAD51D and AtXRCC2 , seem to be unnecessary for meiotic DSB repair , because the triple mutant has normal chromosome morphology and fertility [33] . Except for slight differences in synapsis , the chromosome morphology using light microscopy for DAPI-stained chromosomes and fertility phenotypes of atrad51c and atxrcc3 mutants are similar to those of atrad51 , suggesting that their functions are related , but further analyses are needed to understand their mechanistic roles in meiotic DSB repair . Biochemical studies in human cells demonstrate that RAD51 paralogs associate with one another in two distinct complexes: RAD51B-RAD51C-RAD51D-XRCC2 ( BCDX2 ) and RAD51C-XRCC3 ( CX3 ) [36 , 37] . The CX3 complex stabilizes RAD51 binding to ssDNA [36–39] in vitro , thus promoting single-end invasion . Moreover , RAD51C and XRCC3 also help mediate Holliday junction ( HJ ) resolution in vitro [40] , suggesting a later role in meiotic recombination . A yeast two-hybrid assay demonstrated that the Arabidopsis RAD51 paralogs also interact with each other [41] , supporting the idea that RAD51 paralogs function by formation of distinct protein complexes in both animals and plants . However , whether the RAD51 paralogs associate with each other in planta has not been tested . In this study , we report that Arabidopsis homologs of RAD51 , RAD51C and XRCC3 show highly similar meiotic chromosome morphological defects using immune-localization for key markers . We also provide evidence that AtRAD51C and AtXRCC3 are required for AtRAD51 localization on chromosomes . Both in vitro and in vivo data demonstrate that AtRAD51C interacts with AtRAD51 and AtXRCC3 . Furthermore , observation of AtRAD51 foci in atrad51 , atrad51c and atxrcc3 single , double and triple heterozygotes reveals that AtRAD51C and AtXRCC3 both are involved in AtRAD51 loading . Triple heterozygotes also experience non-homolog chromosome associations and have reduced CO frequencies . Together , these results demonstrate that AtRAD51C , AtXRCC3 and AtRAD51 form a complex in planta and are required for AtRAD51 loading on chromosomes .
Previous studies have found that AtRAD51 , AtRAD51C and AtXRCC3 are required for meiotic DSB repair and plant fertility and mutation of individual genes cause indistinguishable chromosome entanglement and fragmentation phenotypes [13 , 14 , 31 , 32] . The similarity of the phenotypes suggests that these genes might function in the same genetic pathway or process . To test this hypothesis , we generated double mutants between atrad51-3 ( SAIL_873_C08 ) [42] , atrad51c ( SALK_021960 ) [13] , and atxrcc3 ( SALK_045564 ) [14] and found that the chromosome morphologies of atrad51 atrad51c ( 48 cells ) , atrad51 atxrcc3 ( 65 cells ) , and atrad51c atxrcc3 ( 54 cells ) double mutants showed no obvious differences compared with each of the single mutants ( S1 Fig ) . The lack of an additive phenotype in the double mutants further supports the hypothesis that they act together in the same biological process . To search for subtle chromosomal phenotypes that could discriminate between the three mutants , we used FISH with a centromere probe for atrad51 ( 82 cells ) ; atrad51c ( 96 cells ) and atxrcc3 ( 81cells ) and a bacterial artificial chromosome ( BAC-F19K16 ) probe that targets a telomere proximal region on chromosome 1 for atrad51 ( 31 cells ) ; atrad51c ( 45 cells ) and atxrcc3 ( 22 cells ) [43] . Wild-type ( WT ) meiocytes had three to five centromere signals at pachytene , indicative of paired homologous centromeres in a cluster ( Fig 1A ) . Although the three mutants had no typical pachytene chromosomes , they all displayed similar centromere clusters or numbers of signals at a stage similar to that of WT , suggesting that AtRAD51 , AtRAD51C and AtXRCC3 are not required for early centromere pairing or clustering ( Fig 1D , 1G and 1J ) . At diakinesis and metaphase I , WT meiocytes had five bivalents , each with two paired centromere signals corresponding to the associated homologs ( Fig 1B ) . In contrast , the three mutants each had 10 centromere signals located on abnormally associated chromosomes ( multivalents-with more than two chromosomes ) ( Fig 1E , 1H and 1K ) , indicating a failure to maintain homolog association , at least at the centromere regions . We next examined homolog pairing on the chromosome arms using the telomere-proximal BAC probe . Unlike the single focus observed on WT pachytene chromosomes , indicative of fully synapsed homologs , meiocytes from each of the three mutants showed two separate signals , indicating a failure to pair properly ( Fig 1M–1P ) . We also performed ASY1 and ZYP1 immuno-localization in WT and mutants . No obvious difference of ASY1 signals at zygotene was found between WT and mutants ( S2 Fig ) . However , unlike WT with linear ZYP1 distribution on pachytene chromosomes , ZYP1 was completely disappeared in rad51 , while some punctate or discontinuous ZYP1 signals were observed in xrcc3 and rad51c ( S2 Fig ) . Together , these results demonstrate that AtRAD51 , AtRAD51C and AtXRCC3 are not required for recombination-independent centromere clustering , but are necessary for homolog pairing , consistent with previous findings obtained using FISH experiment [44] . The similarities of the mutant phenotypes further support the idea that they act in the same process . Loading of RAD51 on ssDNA is aided by several proteins , including Rad52 [45] , Rad55-57 ( Rad51 paralogs ) [46] and Sfr1-Swi5 [11] in yeast , the Brca2-Dss1 complex in mammalian cells [47] , and also by AtBRCA2 in Arabidopsis [48] . The similarity of meiotic defects in Arabidopsis rad51 , rad51c and xrcc3 mutants suggests the RAD51 paralogs RAD51C and XRCC3 may function in meiotic recombination by affecting RAD51 function . To test this hypothesis , we performed an immunofluorescence assay using an AtRAD51 antibody [49] . In Arabidopsis , formation of DSBs is thought to occur at leptotene [50] . At a similar stage , we found that WT plants had 187 . 7±24 . 5 AtRAD51 foci per meiocyte ( n = 14 ) , but the number of foci was greatly reduced in atrad51c ( 36 . 1±9 . 7 , n = 17; P = 1 . 5E-13 ) and atxrcc3 ( 33 . 7±10 . 3 , n = 34; P = 5 . 7E-13 ) mutant meiocytes ( Fig 2A , 2C , 2D and 2Q ) . In contrast , a parallel experiment did not detect any AtRAD51 foci in atrad51 mutant meiocytes at zygotene ( Fig 2B ) . A similar pattern was also observed using pachytene meiocytes ( Fig 2E–2H ) . These results provide evidence that Arabidopsis RAD51C and XRCC3 are required for formation of wild type level of RAD51 foci on meiotic chromosomes . This is consistent with the previous findings for Rad51 paralogs in yeast [46] . Nevertheless , the reduction of AtRAD51 foci in atrad51c and atxrcc3 homozygous mutants does not preclude the possibility that normal level DSBs are formed in these mutants . To test whether DSB frequency is altered in atrad51c and atxrcc3 mutants , we examined the distribution of a DSB marker , phosphorylated histone H2AX ( γ-H2AX ) [51] . At zygotene , after DSBs have been formed , no significant differences in the number of γ-H2AX foci were detected between WT ( 189 . 3±26 . 5 , n = 39 ) , atrad51 ( 176 . 7±15 . 5 , n = 19; P = 0 . 062 ) , atrad51c ( 183 . 6±18 . 0 , n = 18; P = 0 . 41 ) and atxrcc3 ( 178 . 3±13 . 5 , n = 19; P = 0 . 097 ) mutants ( Fig 2I–2L and 2R ) . In Arabidopsis , most meiotic DSBs are thought to be repaired during zygotene-pachytene . We found that γ-H2AX foci were obviously reduced in WT ( 56 . 9±15 . 2 , n = 55 ) pachytene meiocytes compared those of atrad51 ( 132 . 1±15 . 4 , n = 13; P = 1 . 5E-11 ) , atrad51c ( 120 . 6±16 . 6 , n = 14; P = 7 . 2E-11 ) and atxrcc3 ( 122 . 2±18 . 8 , n = 18; P = 1 . 7E-11 ) mutants ( Fig 2M–2P and 2S ) . The presence of normal numbers of zygotene γ-H2AX foci and reduced AtRAD51 foci suggests that AtRAD51C and AtXRCC3 are not required for meiotic DSB formation , but are necessary for AtRAD51 loading . In yeast , the Rad51 paralogs Rad55 and Rad57 form a heterodimeric complex to stimulate RAD51 activity [10] . Vertebrate Rad51 paralogs interact with one another to form two distinct complexes: BCDX2 and CX3 [52] . Like vertebrates , Arabidopsis has seven RAD51 paralogs , and previous yeast two-hybrid assays have shown that XRCC3 interacts with both RAD51C and RAD51 [41] . However , whether these proteins interact in planta has not been investigated . As an initial test for potential interactions we used a yeast two-hybrid assay ( Y2H ) and found that AtXRCC3 interacts with both AtRAD51 and AtRAD51C ( Fig 3A ) , consistent with the previously identified interactions in Y2H system [41] . The interaction between AtRAD51C and AtXRCC3 was further supported by a pull-down assay using recombinant fusion protein of glutathione S-transferase ( GST ) with AtRAD51C and an AtXRCC3-His tag fusion protein ( Fig 3B ) . In addition to the previously identified interactions , we also found that GST-AtRAD51 interacts with AtRAD51C-His ( Fig 3B ) . To explore whether these associations also occurred in planta , we examined the interactions using bimolecular fluorescence complementation ( BiFC ) in tobacco ( Nicotiana benthamiana ) cells . Strong nuclear signals , indicating interaction , were observed for AtRAD51C with either AtRAD51 or AtXRCC3 ( Fig 3C ) . These results provide the first direct evidence that plant RAD51 paralogs RAD51C and XRCC3 interact directly with RAD51 in vitro and in planta . A recent study identified a weak atrad51 allele , atrad51-2 [42] , with a T-DNA insertion in the 3′-untranslated region ( UTR ) that results in reduced AtRAD51 protein levels . This mutant had mild chromosome fragmentation and partial synapsis , as well as some bivalent formation with homologs and non-homologs [42] . In contrast , the atrad51-1 null mutant had severe chromosome fragmentation and formed multivalents during meiotic prophase I [31] . These findings suggest that reducing AtRAD51 level might be a strategy for investigating its meiotic function . Alternatively , analysis of double heterozygous mutants in genes encoding components of a complex can reveal phenotypic defects , even though the corresponding single heterozygotes are phenotypically normal [53 , 54] . We hypothesized that double/triple heterozygotes of atrad51 , atrad51c and atxrcc3 might reduce , but not abolish , their interactions in a complex and reveal informative meiotic phenotypes To test this hypothesis , we generated atrad51-/+ , atrad51c-/+ and atxrcc3-/+ double and triple heterozygous mutants and compared their meiotic phenotypes with WT . Analysis of meiotic chromosome morphology after DAPI staining showed that atrad51-/+ , atrad51c-/+ and atxrcc3-/+ single heterozygote meiocytes and atrad51-/+ atrad51c-/+ and atrad51-/+ atxrcc3-/+ double heterozygotes had similar phenotypes compared to WT ( Fig 4A–4L; S3 Fig ) . In addition , meiocytes from atrad51c-/+ atxrcc3-/+ double heterozygotes had chromosome morphology similar to WT at pachytene ( Fig 4M ) , but at diakinesis , WT formed five bivalents , whereas 32 . 8% ( 20 of 61 , n = 61 ) of the atrad51-/+ atxrcc3-/+ double heterozygote meiocytes had non-homologous chromosome associations ( Fig 4N ) . The cell appears to be able to resolve these associations since equal division of chromosomes was observed at anaphase I and II ( Fig 4O and 4P ) . Meiocytes from atrad51-/+ atrad51c-/+ atxrcc3-/+ triple heterozygotes had a more severe non-homolog association phenotype ( 47 . 8% at diakinesis , 22 of 46 , n = 46 , Fig 4R ) and had unequal chromosome segregation at metaphase II ( 16 . 7% , 2 of 12 , n = 12 , Fig 4T ) . No chromosome fragments were observed in the triple heterozygote , suggesting it is still capable of DSB repair . The results also suggest that RAD51C and XRCC3 are functionally more related to each other than either is to RAD51 . Previous studies showed that T-DNA translocation can cause a similar pattern of chromosome association using light microscopy because the translocated chromosome can associated with two normal chromosomes [55 , 56] . We have verified the T-DNA insertion site by sequencing the junction with flanking genomic DNAs and the results indicated that these mutations are not associated with translocations . To test whether meiotic DSB repair is delayed in the heterozygotes , we performed immunostaining experiments using a γH2AX antibody . As mentioned above , WT meiocytes had 189 . 3±26 . 5 ( n = 39 ) and 56 . 9±15 . 2 ( n = 55 ) γH2AX foci at zygotene and pachytene , respectively ( Fig 5A , 5I , 5Q and 5R and Table 1 ) . All single , double and triple heterozygotes had no obvious differences in the number of γH2AX foci at zygotene , but had significantly more foci at pachytene ( Fig 5A–5R , S1 Table ) . Moreover , the double and triple heterozygotes had more foci at pachytene than the single heterozygotes . There are significantly fewer foci in atrad51-/+ ( 78 . 1±19 . 4 , n = 17 ) , atrad51c-/+ ( 83 . 3±10 . 8 , n = 12 ) and atxrcc3-/+ ( 82 . 0±25 . 9 , n = 24 ) ( S1 Table ) compared to the double mutants atrad51-/+ atrad51c-/+ ( 96 . 3±15 . 4 , n = 30 ) , atrad51-/+atxrcc3-/+ ( 100 . 4±14 . 8 , n = 21 ) and atrad51c-/+atxrcc3-/+ ( 105 . 2±24 . 1 , n = 15 ) , which in turn have significantly fewer foci ( S1 Table ) than the triple atrad51-/+ atrad51c-/+ atxrcc3-/+ ( 113 . 3±14 . 8 , n = 46 ) ( Fig 5J–5P and 5R and Table 1 ) . These data suggest that DSB formation is normal in the heterozygotes , but there is a defect in the progression of DSB repair , and that AtRAD51 , AtRAD51C and AtXRCC3 function in this process . Because AtRAD51C and AtXRCC3 are required for normal AtRAD51 localization , we next examined AtRAD51 localization in heterozygous mutant meiocytes . As described above , WT meiocytes have 187 . 7±24 . 5 ( n = 14 ) AtRAD51 foci at zygotene and 51 . 2±14 . 0 ( n = 65 ) foci at pachytene ( see Fig 6A and 6I for examples and Table 1 ) . In contrast , single , double and triple heterozygous mutant meiocytes have significantly fewer AtRAD51 foci at zygotene ( p<0 . 05; Fig 6A–6H and 6Q ) . At pachytene , the three single mutant heterozygotes show no obvious differences in the number of AtRAD51 foci compared with WT ( Fig 6I–6L and 6R ) , but the double and triple heterozygotes exhibited reduced AtRAD51 foci ( p<0 . 05; Fig 6M–6P and 6R ) . These findings are consistent with the earlier results , suggesting that AtRAD51C and AtXRCC3 play related roles in AtRAD51 loading on chromosomes , likely in a protein complex . As described earlier , the weak atrad51-2 allele is capable of forming bivalents and executing recombination [42] . Similarly , the heterozygous plants analyzed here also completed meiotic recombination to some extent and had partial fertility . To examine CO frequencies in comparison between the various genotypes , we counted the number of chiasmata , the physical manifestation of crossing-over , in WT and mutant meiocytes at both diplotene and metaphase I . On average , WT had 10 . 1±1 . 1 ( n = 52 ) chiasmata per meiocyte and no obvious significant differences were observed in the single heterozygotes: atrad51-/+ with 9 . 6±0 . 7 ( n = 20; P = 0 . 072 ) per meiocyte , atrad51c-/+ with 9 . 6±0 . 7 ( n = 21; P = 0 . 052 ) per meiocyte and atxrcc3-/+ with 9 . 6±0 . 8 ( n = 24; P = 0 . 066 ) per meiocyte . The atrad51-/+ atrad51c-/+ , atrad51-/+ atxrcc3-/+ and atrad51c-/+ atxrcc3-/+ double heterozygotes showed a slight , but statistically significant , reduction of chiasmata with 8 . 4±1 . 2 ( n = 14; P = 6 . 0E-05 ) , 8 . 0±0 . 8 ( n = 10; P = 2 . 9E-06 ) and 7 . 1±1 . 0 ( n = 34; P = 1 . 2E-20 ) per meiocyte , respectively ( Fig 7A–7D and 7K ) . The atrad51-/+ atrad51c-/+ atxrcc3-/+ triple heterozygous mutant also had a significant reduction , with only 6 . 9±1 . 0 ( n = 15; P = 2 . 6E-10 ) chiasmata per meiocyte formed ( Fig 7E and 7K ) . Furthermore , the chiasmata numbers per meiocyte of atrad51c-/+ atxrcc3-/+ double heterozygote ( 7 . 1; P values = 2 . 0E-03 and 1 . 1E-02 , respectively ) and the triple heterozygote ( 6 . 9 , P values = 1 . 7E-03 and 8 . 8E-03 , respectively ) were significantly lower than those of the other two double heterozygotes . Arabidopsis forms two types of COs: interference-sensitive Type I COs that require ZMM proteins like MSH4 and MLH1 [57–59] , and interference-insensitive class II COs that are MUS81-dependent [60 , 61] . To assess the impact of RAD51 and its paralogs on Type I COs , we used an AtMLH1 antibody to visualize AtMLH1 foci , in WT , atrad51-/+ atrad51c-/+ , atrad51-/+ atxrcc3-/+ , atrad51c-/+ atxrcc3-/+and atrad51-/+ atrad51c-/+ atxrcc3-/+ meiocytes at diakinesis [59] . On average , WT meiocytes had 9 . 0±1 . 2 foci ( n = 61 , Fig 7F ) , whereas at similar stages , atrad51-/+ atrad51c-/+ , atrad51-/+ atxrcc3-/+ , atrad51c-/+ atxrcc3-/+ and atrad51-/+ atrad51c-/+ atxrcc3-/+ mutants had 7 . 9±1 . 4 ( n = 40; P = 5 . 8E-05 ) , 7 . 7±1 . 6 ( n = 25; P = 5 . 4E-04 ) , 6 . 4±1 . 3 ( n = 39; P = 5 . 9E-16 ) and 5 . 9±1 . 0 ( n = 16; P = 5 . 5E-12 ) foci , respectively ( Fig 7G–7J and 7L ) . The reduction of AtMLH1 foci in the mutants is consistent with the observed reduction in chiasmata , and supports the idea that Type-I COs are reduced in the mutants . Although the CO number was obviously reduced by ~30% in the atrad51-/+ atrad51c-/+ atxrcc3-/+ triple heterozygote , no univalents were observed , consistent with a mechanism that ensures at least one CO per chromosome [62] . If the COs were distributed among the 5 Arabidopsis bivalents randomly , they would follow the Poisson function P ( k COs per bivalent ) = ( λke-λ ) /k ! where λ is the mean number of COs per bivalent . Using this function , from the analyses of 52 WT and 15 atrad51-/+ atrad51c-/+ atxrcc3-/+ triple heterozygote meiocytes , we would expect to find 36 and 19 univalents in WT and the triple mutant , respectively , but none were observed ( Table 2 ) . To further quantify the remaining COs in atrad51-/+ atrad51c-/+ atxrcc3-/+ , we used a flow cytometry-based assay that measures the segregation of transgenes encoding fluorescent marker proteins expressed using a pollen-specific LAT52 promoter ( FTL markers ) [63 , 64] . The number of viable pollen grains is dramatically reduced in atrad51-/+ atrad51c-/+ atxrcc3-/+ , but it was still feasible to measure CO frequencies using this assay . We crossed atrad51-/+ atrad51c-/+ atxrcc3-/+ with line I2b , which carries two FTL markers ( YFP and DsRed ) on chromosome 2 ( Fig 8A ) . Pollen grains which express both fluorescent proteins have not experienced a crossover between the markers , while those that express only one or the other have . The relative abundance of these two classes can be used to calculate the genetic distance between the two markers [65] . We scored 10 , 092 WT pollen grains and 15 , 460 pollen grains from the triple heterozygote ( Fig 8B and 8C ) . The I2b map distance was 5 . 28±0 . 58 cM in WT and 2 . 87±0 . 33 cM in the triple heterozygote ( Fig 8D–8G ) . The genetic distance between the two fluorescent markers was significantly reduced in atrad51-/+ atrad51c-/+ atxrcc3-/+ ( Z score = 185 . 4 , P value << 0 . 01 ) ( Fig 8G ) , consistent with the reduction in chiasmata described above .
RAD51 family members are conserved across species , from yeast to humans [20] . The budding yeast S . cerevisiae has four RAD51 paralogs ( Rad51 , Dmc1 , Rad55 and Rad57 ) [10] , whereas humans have seven paralogs ( RAD51 , DMC1 , RAD51B , RAD51C , RAD51D , XRCC2 , XRCC3 ) [20] . In yeast , Rad55 interacts with Rad57 to form a stable heterodimer [10] . Similarly , in humans , two complexes are formed by the RAD51 paralogs: the BCDX2 and the CX3 complexes [36 , 37 , 39] . Moreover , a recent study in Caenorhabditis elegans showed that the RAD51 paralogs , RFS-1 and RIP-1 , also exist as a heterodimer and interact with RAD51 [66] . Arabidopsis XRCC3 has been shown to interact with both RAD51 and RAD51C using a yeast two-hybrid assay [41] . We confirmed the yeast two-hybrid result ( Fig 3A ) and demonstrated that the AtRAD51C-AtXRCC3 interaction occurs in planta by using pull-down and BiFC assays ( Fig 3B and 3C ) . It is noteworthy that both pull-down and BiFC assays support an interaction between AtRAD51C with AtRAD51 and AtXRCC3 . Our results strongly support the idea that AtRAD51C is a central factor in complex formation , and is associated with AtRAD51 and AtXRCC3 . These findings are consistent with previous results in human cells that show AtRAD51C associates with two protein complexes [36 , 37 , 39] . Our study is also the first time to show that RAD51 paralogs form a protein complex with RAD51 in plants , supporting the hypothesis that formation of RAD51-paralogs associated protein complexes is highly conserved across eukaryotes , including yeast , humans and plants . Previous studies in yeast showed that RAD51 paralogs are unable to form filaments with ssDNA and do not have a direct role in homology search or single strand invasion [10 , 23] . Nevertheless , studies in different organisms have reported that RAD51 paralogs play important roles in promoting RAD51 function in both mitotic and meiotic HR [46 , 67–69] . For example , the yeast Rad55-Rad57 complex has a role in RAD51-dependent HR [10 , 46] . Similar roles have been found for the C . elegans heterodimer of RAD51 paralogs RFS-1/RIP-1 [66] and the human CX3 complex [36 , 37 , 39] . Due to the lack of direct biochemical data , the role of RAD51 paralogs in meiotic HR in planta is unclear . Studies in the monocot model plant , Oryza sativa ( rice ) , showed that the RAD51 paralogs OsRAD51C and OsXRCC3 are required for meiotic DSB repair and mutations in either result in sterility , chromosome entanglement and fragmentation [70 , 71] . These results are consistent with similar findings in Arabidopsis [13 , 14 , 32] . Immunostaining showed that OsXRCC3 is required for OsRAD51C localization on chromosomes [70] , suggesting the existence of a potential OsRAD51C-OsXRCC3 complex in rice . Additionally , the single-end processing proteins OsCOM1 and OsDMC1 no longer associate with DSB sites in rice osxrcc3 , which suggests that OsXRCC3 , and by extension OsRAD51C , might function upstream of OsRAD51 [70 , 72] . Nevertheless , the relationship between OsRAD51 and its paralogs OsRAD51C and OsXRCC3 remains unclear , because a RAD51 antibody is currently unavailable in rice . In the present study , we showed that Arabidopsis RAD51 foci were obviously reduced in atrad51c and atxrcc3 mutants , consistent to the discovery in rice . Together , these studies , in both rice and Arabidopsis , strengthen the idea that AtRAD51 depends on its paralogs for normal function and that this relationship is highly conserved in eukaryotes . Previous studies showed that RAD51 paralogs have a later role in processing meiotic recombination intermediates [40] . Direct evidence to support the RAD51C-XRCC3 complex having a role in the later meiotic recombination process come from the observation that the RAD51C-XRCC3 complex is associated with HJ resolvase activity . Moreover , RAD51C- and XRCC3-defective hamster cells have reduced resolvase activity and HJ progression [40 , 73] . Similarly , the Arabidopsis RAD51 paralogs AtRAD51B and AtXRCC2 were also reported to affect meiotic recombination in terms of CO number [74] . However , mutations in these paralogs show an increase in meiotic recombination frequency [74] , suggesting that they have roles in meiotic CO formation . In the present study , we found that atrad51c atxrcc3 double heterozygous mutant and the atrad51 atrad51c atxrcc3 triple heterozygous mutant have significantly fewer COs ( Fig 7D , 7E and 7I–7L ) , compared with WT . Given that the reduced number of AtRAD51 foci observed in the double and triple heterozygous mutants , we propose that a diminished capacity to form wild type level of RAD51 foci results in fewer COs in the mutants . The previous finding further supports this idea that a weaker atrad51 allele had fewer chromosome fragments and some univalents , and also formed bivalents between homologs and non-homologs [42] . Therefore , we speculate that AtRAD51 could function in two manners , both dependent on the AtRAD51 paralogs AtRAD51C and AtXRCC3 . Most AtRAD51 foci are required for DNA repair using either homologs or sister chromatids as templates without CO formation , while a small number of AtRAD51 foci might play a role in normal CO formation dependent also on AtDMC1 . Therefore , the AtRAD51C-AtXRCC3 is critical for ensuring wild type number of AtRAD51 foci and COs and facilitating proper homolog recombination and association . Based on our results and previous studies , we propose a model for how AtRAD51C and AtXRCC3 function in conjunction with AtRAD51 in meiotic HR ( Fig 9 ) . Meiotic recombination is initiated by programmed DSBs that are catalyzed by AtSPO11-1 and other proteins . The broken ends are further processed by the MRX protein complex to produce ssDNA tails [2 , 75–77] . In WT , interaction between the AtRAD51C-AtXRCC3 complex and AtRAD51 is proposed to alter the latter’s configuration and facilitates its binding with the ssDNA tails , thus resulting in single end invasion . Consequently , repair of the DSBs yields either COs or NCOs . In the heterozygous mutants , the reduced AtRAD51 level is likely insufficient for supporting the AtDMC1 function , consistent with previous studies in both Arabidopsis and yeast showing that normal DMC1 function in meiosis requires RAD51 [6 , 78] . Thus , with reduced amounts of RAD51 proteins , single end invasion is possibly more promiscuous and targets both homologous and non-homologous templates , resulting in multivalent formation . This aspect of the model is supported by the observation that the triple heterozygous mutant and the weak atrad51 mutant had non-homologous associations and reduced COs . In the homozygous mutants , when AtRAD51 is either completely absent or reduced below a threshold , most or all DSBs are unrepaired , leading to severe chromosome fragmentation and chromosome entanglements . Further investigations are needed to establish the precise AtRAD51 thresholds and how the AtRAD51 paralogs maintain the necessary level of AtRAD51 during the single-end invasion process . In summary , meiotic DSB repair is essential for sexual reproduction in eukaryotes including budding yeast , animals and flowering plants . RAD51 paralogs facilitate the establishment of RAD51 at DSBs and mediate and single end invasion . These functions are also highly conserved in eukaryotes . We propose that facilitation of normal RAD51 function by its paralogs , such as RAD51C and XRCC3 , may be a general mechanism for meiotic DSB repair .
The mutants atrad51-3 ( SAIL_873_C08 ) [42] , atrad51c ( SALK_021960 ) [13] , atxrcc3 ( SALK_045564 ) [14] used in this study were shown previously to be null mutants in the Columbia ( Col-0 ) background . atrad51-/+ atrad51c-/+ and atrad51-/- atrad51c-/- mutants were crossed by atrad51-/+ ( male parent ) and atrad51c-/+ ( female parent ) , atrad51-/+ xrcc3-/+ and atrad51-/- xrcc3-/- mutants were crossed by atrad51-/+ ( male parent ) and xrcc3-/+ ( female parent ) , atrad51c-/+ xrcc3-/+ and atrad51c-/- xrcc3-/- mutants were crossed by atrad51c-/+ ( male parent ) and xrcc3-/+ ( female parent ) . Triple heterozygous mutants were crossed by atrad51c-/+ ( male parent ) and atrad51-/+ atxrcc3-/+ ( female parent ) . Plants were grown at 21°C with 16 h light and 8 h dark . Mutant genotypes were confirmed by PCR using the primers described in S2 Table . A minimum of 10 plants were characterized for each mutant . Chromosome spreads were stained with DAPI and centromere FISH , and immuno-localization experiments were carried out as described previously [79] . Rabbit polyclonal AtRAD51 and γ-H2AX antibodies were used at 1:200 fold dilutions and Alexa Fluor 488 Goat Anti-Rat IgG ( H+L ) secondary antibody ( A-21428 , Invitrogen , Carlsbad , CA , USA ) was used at a 1:1000 fold dilution [80] . Chiasmata distribution statistics were performed following the protocol of Sanchez et al . [81] . BAC DNA extraction ( F19K16 ) and probe labeling were described previously [43] . Images of chromosome spreads were obtained using an Axio Imager A2 microscope ( Zeiss , Heidelberg , Germany ) equipped with a digital camera ( Canon , Tokyo , Japan ) , and processed using Photoshop CS ( Adobe Systems , Mountain View , CA ) . Images were initially captured in black & white and , if necessary , globally false-colored post-capture for visual contrast . AtRAD51 and γ-H2AX foci in WT and mutant lines were counted and statistically analyzed using ImageTool version 3 . 0 software ( University of Texas Health Science Center , San Antonio , USA ) . In mutants that lacked synapsis , we distinguished zygotene from pachytene chromosomes by their relative condensation , with pachytene being more condensed than zygotene chromosomes . To construct the vectors for yeast two-hybrid , pull-down and BiFC assays , full-length AtRAD51 , AtRAD51C and AtXRCC3 cDNA were PCR-amplified using Phanta Super-Fidelity DNA polymerase ( Vazyme Biotech Co . , Ltd , China ) and appropriate primers ( S2 Table ) . For the Y2H assay , full-length AtRAD51 and AtXRCC3 cDNA were purified and ligated into pGADT7 pGBKT7 by NdeI and BamHI double-enzyme digestion , and full-length AtRAD51C cDNA was purified and ligated into pGADT7 and pGBKT7 by NdeI and EcoRI double-enzyme digestion . For the BiFC assay , full-length AtRAD51 and AtXRCC3 cDNA was purified and ligated into pXY103 , pXY104 , pXY105 and pXY106 by BamHI and SalI double-enzyme digestion , and full-length AtRAD51C cDNA was purified and ligated into pXY103 , pXY104 , pXY105 and pXY106 by XbaI and SalI double-enzyme digestion . For the pull down assay , full-length AtRAD51 and AtXRCC3 cDNA was purified and ligated into pET32a and pGEX-6P-1 by BamHI and SalI double-enzyme digestion and full-length AtRAD51C cDNA was purified and ligated into pET32a and pGEX-6P-1 by EcoRI and SalI double-enzyme digestion . All constructs were verified by DNA sequencing . Plasmid vectors were transformed into the Y2H gold yeast strain ( pGBKT7 constructs ) or the Y187 yeast strain ( pGADT7 constructs ) using the LiAc/PEG method . Transformants were mated on YPDA medium for 48 h , and selected on SD/–Trp–Leu plates for 36 h . Transformants were then selected on SD/–His–Ade–Trp–Leu with X-α-Gal and AbA plates to test for positive interactions [82] . AtRAD51 , AtRAD51C and AtXRCC3 were expressed in E . coli using the pGEX6P-1 and pET32a plasmids . The tagged proteins were mixed and incubated for 2 h at 4°C , then pulled down by GST beads for 1 h at 4°C . The protein mixture was confirmed by western blotting with a GST antibody ( AG768 , Beyotime Co . Ltd , China ) or a His-tag antibody ( AH367 , Beyotime Co . Ltd , China ) at 1:100 dilutions , followed by application of an horseradish peroxidase ( HRP ) goat anti-mouse IgG ( H+L ) secondary antibody ( A0216 , Beyotime Co . Ltd , China ) at a 1:2000 dilution . BiFC plasmids ( pXY103/104/105/106-RAD51 , pXY103/104/105/106-RAD51C , pXY103/104/105/106-XRCC3 and pXY103/104/105/106 ) were transformed into Agrobacterium GV3101 cells . Transformants were harvested once the OD600 reached 2 . 0 , and resuspended in MES/MgCl2/acetosyringone solution to a final OD600 of 1 . 0 . Cell suspensions were mixed in 1:1 ratios of various combinations , and young Nicotiana benthamiana leaves were infiltrated . Leaves were excised and visualized using a LSM-710 confocal microscope ( Zeiss ) following 36 h incubation [83] . Open flowers from WT plants or atrad51-/+ atrad51c-/+ atxrcc3-/+plants that were hemizygous for the fluorescent-tagged line ( FTL ) interval I2b and either QRT+/+ or qrt-/+ were collected [64] . The flowers ( 50 or more ) were mixed with 1 mL PBS buffer ( 10 mM CaCl2 , 1 mM KCl , 2 mM MES , 5% w/v sucrose , pH 6 . 5 ) supplemented with 0 . 01% Triton X-100 in a 1 . 5-mL microcentrifuge tube . The mixture was vortexed at maximum speed for 2–3 min and the solution filtered through a 70-μm Falcon® cell strainer ( 352350 , Corning Life Sciences , Tewksbury , MA , USA ) at 450 ×g for 2 min at 4°C . The flow-through was resuspended in a fresh tube with 1 mL PBS buffer at 4°C . Flow cytometry analysis was performed using a Gallios flow cytometer ( Beckman Coulter , Inc . ) . Statistical analysis was performed using Kaluza Analysis 1 . 3 software ( Beckman Coulter , Inc . ) using the two-color analysis methods described previously [65 , 84] . Excel 2016 ( Microsoft , USA ) was used to calculate the mean and standard error of the AtRAD51 foci , γ-H2AX foci , MLH1 foci and the chiasmata numbers of WT and mutants . Data was compared using Student’s t-tests and P values were reported as either exact values or Gaussian approximations . | Meiotic recombination and sister chromatid cohesion are important for maintaining the association between homologous chromosomes and ensuring their accurate segregation . Meiotic recombination starts with a set of programmed DNA double-strand breaks ( DSBs ) , catalyzed by the SPO11 endonuclease . Processing of DSB ends produces 3′ single-stranded DNA tails , which form nucleoprotein filaments with RAD51 and DMC1 , homologs of the prokaryotic RecA protein . The eukaryotic RAD51 gene family has seven ancient paralogs , in addition to RAD51 and DMC1 , the other five members in mammals form two complexes: RAD51B-RAD51C-RAD51D- XRCC2 ( BCDX2 ) and RAD51C-XRCC3 ( CX3 ) . To date , the molecular mechanism of CX3 in animal meiosis remains largely unknown due to the essential roles of these two proteins in embryo development . In Arabidopsis , RAD51C and XRCC3 are required for meiosis and fertility , but their specific mechanisms are unclear . Here we present strong evidence that Arabidopsis RAD51 forms a protein complex with AtRAD51C-AtXRCC3 in vivo . Our data also support the previous hypothesis that CX3 promotes RAD51-denpendet meiotic recombination by affecting its localization on chromosomes . Given that the RAD51 , RAD51C and XRCC3 proteins are highly conserved in plants and vertebrates , the mechanism we present here could be important for the regulation of meiotic recombination in both plants and vertebrate animals . |
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In the past decade , over 50 genome-scale metabolic reconstructions have been built for a variety of single- and multi- cellular organisms . These reconstructions have enabled a host of computational methods to be leveraged for systems-analysis of metabolism , leading to greater understanding of observed phenotypes . These methods have been sparsely applied to comparisons between multiple organisms , however , due mainly to the existence of differences between reconstructions that are inherited from the respective reconstruction processes of the organisms to be compared . To circumvent this obstacle , we developed a novel process , termed metabolic network reconciliation , whereby non-biological differences are removed from genome-scale reconstructions while keeping the reconstructions as true as possible to the underlying biological data on which they are based . This process was applied to two organisms of great importance to disease and biotechnological applications , Pseudomonas aeruginosa and Pseudomonas putida , respectively . The result is a pair of revised genome-scale reconstructions for these organisms that can be analyzed at a systems level with confidence that differences are indicative of true biological differences ( to the degree that is currently known ) , rather than artifacts of the reconstruction process . The reconstructions were re-validated with various experimental data after reconciliation . With the reconciled and validated reconstructions , we performed a genome-wide comparison of metabolic flexibility between P . aeruginosa and P . putida that generated significant new insight into the underlying biology of these important organisms . Through this work , we provide a novel methodology for reconciling models , present new genome-scale reconstructions of P . aeruginosa and P . putida that can be directly compared at a network level , and perform a network-wide comparison of the two species . These reconstructions provide fresh insights into the metabolic similarities and differences between these important Pseudomonads , and pave the way towards full comparative analysis of genome-scale metabolic reconstructions of multiple species .
With the development of rapid genome sequencing methodologies and powerful , scalable computational tools , the past decade has seen the generation of an increasing number of genome-scale metabolic reconstructions ( metabolic GENREs ) [1] . These reconstructions generally account for the functions of hundreds to thousands of genes , and are intended to incorporate all known metabolic reactions for a particular organism into a standardized format , enabling the generation of a computational model that can be analyzed with a variety of emerging mathematical techniques [2] . Despite the reconstruction of over 50 metabolic GENREs to date , little effort has been put towards comparison of multiple species at a genome level with a network-centric approach . Such a comparison is bound to yield interesting insights into the relationships between the structure of a metabolic network and the resulting phenotype of an organism , as well as contribute to the explanation of various physiological features such as virulence pathways and unique metabolic capabilities . Yet in order to draw meaningful conclusions from such a comparison , it is necessary to ensure that the identified differences are representative of true differences between the organisms , rather than artifacts from the reconstruction processes . The metabolic reconstruction process integrates the genome sequence and annotation of an organism with a multitude of different sources , including biological databases ( e . g . Expasy , KEGG , BRENDA ) and primary literature , to construct the metabolic GENRE [3] . A key difficulty in the reconstruction process is that these sources can contain incomplete and contradictory information , including ‘putative’ and ‘probable’ gene annotations , descriptions of enzymatic functions that require interpretation in order to be linked to specific substrates ( e . g . , substrates such as ‘acceptor’ and ‘long-chain-acyl-CoA’ ) , vague or missing data about reaction reversibility , and varying or often unknown enzyme efficiencies . Due to these issues , building a metabolic GENRE involves hundreds of decisions as to which genes possess which function , which reactions should be included , and finally in which direction these reactions occur . As these decisions are often based on ambiguous and even conflicting data , there is a high risk that , when two independently created GENREs are compared , a considerable number of the differences observed would be caused by the ‘noise’ in the reconstruction process itself rather than representing actual biology . Therefore , making any informative conclusions from a comparison between metabolic GENREs necessitates a prior preprocessing that brings the reconstructions to a common standard pertaining to naming conventions and , more importantly , that identifies whether the observed differences between the reconstructions are upheld by the biological evidence . To address these concerns , we have performed the first metabolic GENRE reconciliation , a process of eliminating erroneous differences between two existing metabolic GENREs . Reconciliation is similar to but distinct from consensus building efforts such as metabolic reconstruction jamborees [4] , as the focus is specifically on aligning metabolic reconstructions of two organisms to eliminate unverifiable differences , rather than merging different data to improve the metabolic GENRE of a single organism . This process was performed for the related but phenotypically distinct species , Pseudomonas aeruginosa ( PAO ) and Pseudomonas putida ( PPU ) . These bacteria represent an ideal pair of organisms for a genome-scale metabolic comparison due to their tremendous scientific and medical importance . P . aeruginosa is an opportunistic pathogen , notorious for its chronic inhabitance of the lungs of cystic fibrosis patient and its role in causing acute and deadly nosocomial infections in immunocompromised patients [5] , [6] , [7] . Both P . aeruginosa and P . putida are ubiquitous environmental organisms , capable of living varied lifestyles in many habitats , and are of interest for biotechnological applications [8] , [9] , [10] , [11] , [12] . However , unlike P . aeruginosa , P . putida is not a human pathogen . This last feature makes reconciliation of these two important species particularly valuable , as it paves the way for comparative analyses that could lend insight into metabolic features contributing to pathogenicity . The metabolic GENRE of each of these species was previously published by our groups [13] , [14] , and each reconstruction accounts for the metabolic function of approximately 1000 genes , along with gene-protein-reaction associations and full stoichiometric representations of the majority of known metabolic reactions present in the genome of each bacterium . As a result of the reconciliation process we describe in this paper , we have developed new reconstructions of P . aeruginosa and P . putida , labeled iMO1086 and iJP962 respectively following standard conventions [15] . These reconciled GENREs , like the initial GENREs , have been validated with BIOLOG substrate utilization data and viability data for transposon-derived mutants . The reconstructions achieve similar degrees of accuracy as the original reconstructions they were based on , and as a result of the elimination of non-biological differences between the metabolic networks , are now amenable to functional comparison between species . We use these reconciled models to perform the first genome-scale comparison of metabolic flexibility of P . aeruginosa and P . putida , a comparison uniquely enabled by this reconciliation process , and to provide insight into metabolic factors that might help in characterizing the lifestyle capabilities among these bacteria .
As each metabolic reconstruction is a reflection of the genetic content of the respective organism , the comparison of two reconstructions requires identification of the overlap between genomic content of the compared organisms . As P . putida and P . aeruginosa are closely related genomically and phylogenetically , it was expected that many orthologs would exist with high homology , and thus shared functions . Therefore , as a first step in the reconciliation process , we identified highly homologous gene pairs , which we termed “reciprocal genes” . These reciprocal gene pairs were used in the reconciliation process to determine the similarity of gene associations for reactions in the two reconstructions , so identifying these pairs was a crucial first step for performing the reconciliation . In order to identify reciprocal genes , we performed a homology search using the BLAST algorithm against the P . putida genome sequence database with P . aeruginosa genes as queries and vice versa . The searches were performed both with nucleotide and protein sequences . Two genes were termed reciprocal only if , in every aforementioned BLAST search performed with one of these genes as the query sequence , the other gene was reported as the first hit ( e . g . , P . aeruginosa gene X best matches P . putida gene X in protein and nucleotide BLAST , and vice versa ) . The metric used for comparing BLAST matches was the score of the highest scoring pair [17] . With this approach , a single gene can have at most one reciprocal gene . Altogether , 3207 such pairs were identified ( see Tables 1 and 2 in Text S1 ) . This analysis provided a standard for comparing gene functions between P . aeruginosa and P . putida , as the reciprocal genes were assumed to have the same function in the absence of opposing evidence . After identifying reciprocal gene pairs , we sought to identify the number of differences between the two reconstructions , which would reveal the scope of the reconciliation task . This step required identification of common metabolites and reactions for both reconstructions , but as the two metabolic GENREs were built and are maintained by different groups and often incorporate different names for many chemical species and reactions , this step was not completely straightforward and could not be performed entirely automatically . Of the set of 1328 reactions present in both reconstructions , we were unable to find a match in the other reconstruction for 619 reactions; 277 reactions were initially determined to have a functional but not an exact equivalent in the other reconstruction ( e . g . two reactions differing only in cofactor usage , but performing de facto the same conversion ) . These numbers are shown in Table 4 in Text S1 . Of the Gene-Protein Relationships ( GPRs: see [18] for an in-depth explanation ) of the 432 remaining reactions ( all of which were present in both reconstructions ) , 223 had identical gene associations in both reconstructions ( i . e . the reciprocal matches of all genes associated with a given reaction in one reconstruction were associated with the reaction in the other reconstruction and the genes were connected by the same logical expression ) . These 233 reactions were left alone , since they fulfilled the null hypothesis that no difference exists between the reconstructions for the given reactions . Additionally , a set of 20 reactions were removed from the P . aeruginosa reconstruction because they were partially redundant with other reactions in the reconstruction . This left a total of 1074 reactions to be reconciled . These reactions contained differences in reaction stoichiometry , reaction participation , or GPRs between the two reconstructions , and during the reconciliation process each of these differences was investigated and resolved in turn . Having identified reciprocal gene pairs and outlined the general scope of the reconciliation process , we next developed a system for assessing reactions in the two reconstructions and determining how to best reconcile differences . The process of reconciliation is schematically outlined in Figure 1 . Each reaction was assessed individually in order to determine if differences between the two reconstructions relating to that reaction were substantiated by experimental , genomic , or other available evidence . First , all genes associated with the reaction were assessed in both reconstructions . This assessment included an evaluation of whether any of these genes were members of a reciprocal pair and , if so , whether the other pair member was also assigned to the reaction . If a discrepancy was found in assignment of the reciprocal pair members to a given reaction , the reason for the discrepancy was identified and this discrepancy was reconciled by either removing one pair member from or adding the other to the GPR of the reaction . In addition to these types of discrepancies , genes not possessing a reciprocal in the other organism were also thoroughly evaluated for their function , in order to avoid creating or retaining apparent differences ( e . g . , if functionally similar reactions with slightly different stoichiometry were present in iMO1056 and iJP815 , but only one had a gene association while the other was added for gap-filling purposes ) . The decision of how to treat each gene or reciprocal pair was based on gene annotations , functional information from biological databases , and annotations of homologous genes from other organisms . Any literature associated with the reaction during the original reconstruction process was also re-analyzed , and new literature evidence was sought particularly in cases where sequence-based comparisons yielded incomplete or contradictory results . Furthermore , experimentally confirmed physiological phenomena ( e . g . , from the validation of the original reconstructions ) were taken into account in this process . Once the GPR associations had been reconciled for a given reaction , the required changes to the reconstructions were made . These changes could include adding or removing the reaction from one or both reconstructions , or modifying the reaction GPRs . The main factors contributing to decisions about each of the reactions in the reconstructions were recorded in a set of reconciliation notes , which serve as annotations of the reconciliation process for future investigation . The reconciliation notes are provided in Table 5 in Text S1 . It became apparent in the early implementation of the reconciliation process that certain reactions required different types and layouts of data in order to be reconciled . Therefore , to streamline the reconciliation process , reactions were split into groups , each of which would be reconciled with a slightly different procedure . The reactions were split into main groups as listed in Table 1 , and the number of reactions and the general process used for each group are indicated . Initially , the groups of reactions to be reconciled were based on patterns of gene associations ( e . g . , reactions only in one reconstruction , reactions in both reconstructions but with differing GPRs ) , but it was quickly determined that the reconciliation could be optimized in cases where multiple similar reactions were associated with the same set of genes . Therefore , in addition to information directly relevant to the reconciliation of a reaction ( such as reaction stoichiometry and directionality , gene associations and annotated functions ) , all other reactions in iMO1056 and iJP815 associated with any of the same genes were also considered along with the reaction being reconciled . This consideration of multiple reactions utilizing the same genes assured that the decisions pertaining to one particular gene remain consistent and enabled us , for example , to identify reactions that might have been present in both reconstructions but with slightly different stoichiometries . Furthermore , we found that certain pathways would be easier to reconcile if split off into separate groups , regardless of their gene association patterns . This division pertained mainly to linear pathways with few cross-connections to other pathways as well as highly organism-specific processes , such as the pathways for beta-oxidation and lipopolysaccharide ( LPS ) synthesis . Many of the reactions in the LPS pathway , for example , were reconstructed originally from literature sources rather than from database information , so reconciling this pathway as a group made more sense than breaking the reactions up into groups based on the type of gene associations present . The panel on the bottom right of Figure 1 shows data laid out for a reconciliation of the reaction ‘GMP synthase ( glutamine hydrolyzing ) ’ , which was initially included in both reconstructions , but with different numbers of genes associated in each model . In iMO1056 , there were initially two genes associated ( PA1742 and PA3769 ) . In iJP815 , only one gene ( PP1032 ) was associated . In the center of the panel , reciprocal gene matches are shown for all genes associated with GMP synthase in either iMO1056 or iJP815 . In this case all the assigned genes possess a reciprocal , yet PP1842 , the reciprocal of PA1742 , was originally not associated with GMP synthase . To determine if the gene annotations corroborated the reciprocal gene pairings , primary gene functions from the PseudoCAP annotation [19] were listed next to each gene . Then , auxiliary information about the genes was collected . In this case , the EC numbers listed in the PseudoCAP annotation were consistent for PA1742 and PP1842 and were the same as that for GMP synthase , contributing to the evidence that these genes both should be associated with GMP synthase . In some cases , various other databases or literature sources were included in this auxiliary section . In this case , the P . putida gene PP1842 was added to the reconciled GENRE , since available evidence indicated that this gene , like its reciprocal in P . aeruginosa , encodes a protein with GMP synthase activity . The reconciliation process resulted in a large convergence between the P . aeruginosa and P . putida reconstructions . While the original reconstructions shared 432 reactions , with 451 and 445 reactions unique to iMO1056 and iJP815 respectively , the reconciled final reconstructions ( iMO1086 and iJP962 for P . aeruginosa and P . putida , respectively ) share 925 reactions , with only 103 and 48 reactions unique to the each reconstruction ( see Figure 2a ) . Although such large changes in the overall statistics suggest that the reconstructions underwent major alterations , a detailed analysis of the types of changes at the reaction level reveals that most of the changes relate to equilibration of similar but non-identical functions , and as a whole do not tremendously alter the biology of the networks . Reactions in the final reconstructions were assigned to classes that best describe their fate during the reconciliation process . These assignments are depicted for both reconstructions in Figure 2b . The pie charts , along with the legend ( lower left ) , show the types of changes that all reactions in the final reconstructions went through during reconciliation . These classes can be grouped into four meta-classes , as shown in the inset graph . These classes are: ( i ) no change , describing reactions preserved with unchanged stoichiometry , ( but including some reactions that had changes in their GPRs ) ; ( ii ) added , describing reactions added to one of the reconstructions during the reconciliation process , such as in cases where a secondary function of an enzyme had been added to one of the pre-reconciliation models but not to the other , and the reconciliation resulted in the function being included in both; ( iii ) removed , describing reactions removed from the one of the reconstructions during the reconciliation process; and ( iv ) minor change , describing reactions whose general functions were preserved despite modifications to the specific stoichiometries or pathway participation of the reactions involved in the process . This last category includes , for example , reactions in the lipopolysaccharide production pathway , which was preserved in both GENREs but was modified based on a publication that appeared after the original reconstruction processes had been completed [20] . Only reactions present in the final reconstructions are shown in the pie charts in Figure 2b , hence the removed class is not represented in these charts . The largest of these meta-groups is no change , followed by added , minor change , and then removed ( see Figure 2b , inset ) . A fuller description of the different types of changes made to the models is provided in Text S2 ( see section “Categories of changes made during reconciliation” ) . After completing the reconciliation process , it was important to re-validate the post-reconciliation GENREs with the same data we had used to validate the original metabolic GENREs of P . aeruginosa and P . putida . Both original reconstructions had been validated by comparison of in silico growth predictions versus BIOLOG substrate utilization data and growth yield data collected from literature , and the P . aeruginosa network had been further validated by genome-scale gene essentiality data and P . putida against a set of auxotrophic mutations . We compared the reconciled GENREs to the same data to determine how the reconciliation process affected accuracy of the models in predicting in vitro phenotypes .
In order to overcome this hurdle and to enable a high-quality constraint-based comparative analysis of two organisms at genome scale , we developed a novel process termed metabolic network reconciliation . Reconciliation assesses reactions present in two genome-scale reconstructions , and determines whether or not changes should be made to the reconstructions in order to uphold the null hypothesis that no difference exists between the reconstructions . Differences between the two reconstructions for a given reaction are only upheld if sufficient ( preferably experimental ) evidence for the differences is present in literature , genome annotations , or online databases such as KEGG and Expasy . In this way , the reconciliation allows for verification of two reconstructions against each other and leads to removal of non-verifiable differences between the metabolic reconstructions , while still staying within the bounds of available knowledge about the organisms . We performed the reconciliation between manually-curated metabolic GENREs of P . aeruginosa and P . putida . These bacteria are both important for biotechnological applications , in addition to the notorious role of P . aeruginosa as an opportunistic pathogen . The biotechnological usefulness of these organisms is based on their extremely varied and expansive metabolic capabilities [14] , [27] , [37] , [38] . The reconciliation process resulted in a significant alignment of the P . aeruginosa and P . putida reconstructions . While in the original reconstructions only around 50% of the reactions were in common , the reconciled GENREs shared around 90% of their reactions . Furthermore , although a significant portion of the reactions in the reconstructions were altered , added , or removed during the reconciliation process , it is clear from Figure 2b that most of these changes reflected either an aligning of reciprocal functions ( as represented by the minor change meta-class ) or an addition of pathways that had initially only been reconstructed in one model despite evidence for inclusion in both ( as represented by most of the subcategories in the added meta-class ) . Few of the changes altered the fundamental biology of the networks , but these changes were critical in aligning similar functions in the reconstructions so that they could be meaningfully compared . If a comparison between P . aeruginosa and P . putida had been attempted without first reconciling the metabolic reconstructions , any biological variance between the bacteria would likely have been obscured by differences in the reconstructions derived from the respective reconstruction processes rather than from true biological differences between the organisms . The similar performance of the pre- versus post- reconciliation reconstructions as compared to validating data suggests that the reconciliation did not alter the overall biology of the reconstructions . However , as all analyses performed in the revalidation process relied on FBA with a growth objective , these validating analyses inevitably focus on the portions of the metabolic networks related to production of biomass and utilization of particular substrates . The fact that the changes in network validation were minor suggests that few functional changes occurred in the central portions of the reconstructions , but more functional changes might have occurred in more peripheral portions of metabolism . This hypothesis is consistent with our analysis of reconciliation-derived changes in the essential portions of the reconciled GENREs , as shown in Figure 6 . Since the comparison of the metabolic networks will likely focus on differences in peripheral functions ( where P . aeruginosa and P . putida most strongly diverge phenotypically ) , the network reconciliation might have played a larger role in removing non-verifiable functional differences between P . aeruginosa and P . putida than is suggested by the phenotypic stability of the pre- and post- reconciliation reconstructions with regards to validating data . In addition to contributing to our understanding of Pseudomonas metabolism , the reconciliation process offers a unique opportunity to critique and examine the metabolic reconstruction process itself . Some of the changes in the metabolic GENREs through the reconciliation process rectify inevitable mistakes in the original reconstructions , yet the reconciled reconstructions for P . aeruginosa and P . putida were built with essentially the same data as the original reconstructions , with the additional constraint that decisions on inclusion of genes must be consistent between the two networks . Therefore , in many cases , the changes are more reflective of ambiguity as to what evidence should be considered sufficient for genes to be included in a reconstruction , or what exact form the reactions they catalyze should take . When a reconstruction is built , the weights given to different data sources and the determination of cutoffs for inclusion of genes in the GENRE inevitably involve a degree of subjectivity , since a large amount of available gene annotation data is too vague to enable definitive determinations of gene function . The lack of changes in the validation outcome indicates that the original and reconciled reconstructions reflect phenotypes of the respective organism equally well . Therefore , there seems to be a degree of ambiguity in the genetic makeup of a reconstruction that is not fully represented in the phenotypes . This ambiguity can be described in the terms of a space of comparably accurate reconstructions of a particular organism , where the pre- and post- reconciliation reconstructions represent points in the space . The size of this ‘reconstruction space’ for a given organism is related to the degree of vagueness in functional assignments of genes , a metric for uncertainty in the current state of knowledge of the organism . The plots in Figure 3 therefore give an estimate of the pathways contributing most strongly to the breadth of this ‘reconstruction space , ’ as pathways changing the most during reconciliation are often also the ones whose functions are the most ambiguous based on the current knowledge in databases/literature . Examination of the portions of the reconstructions that changed most during reconciliation may therefore provide a roadmap for future improvement of these metabolic reconstructions . As more data are amassed about the metabolism of an organism and integrated into a reconstruction , the space of possible reconstructions will shrink and become a more accurate representation of the actual metabolism of the organism . This is the aim of the iterative model building and validating process . It would be also interesting to reconcile the P . putida reconstruction with the other independently performed GENREs of this organism [38] , [39] . This analysis could allow for further assessment of the noise related to the reconstruction process and would surely contribute to the creation of an even more exact model of the bacterium , as well as further defining the shape of the reconstruction space for this organism . An issue that contributes greatly to the difficulty of building and comparing metabolic reconstructions is the lack of standardization of the methods used for the reconstruction process . This difficulty , which is the basis for the work presented here , has also catalyzed several efforts towards developing common standards for metabolic reconstructions . One approach is based on reconstruction ‘jamborees’ , in which communities of biologists gather over several days for an intensive session of effort to improve and standardize a given metabolic GENRE . Jamborees have been held thus far for Saccharomyces cerevisiae [40] , Homo Sapiens , and Salmonella , for each of which there are multiple independent reconstructions available ( with significant variance in size and metabolic capability ) , and these meetings have resulted in progress in the standardization and improvement of metabolic reconstructions [4] . Another attempt at standardization is represented by the SEED project ( www . theseed . org ) [41] , in which databases of various metabolic subsystems are maintained by dedicated scientists , thus assuring the coherence between reconstructions of each subsystem in the metabolic GENREs of different organisms . A further effort is that of Microme ( www . microme . eu ) , a large project that aims to extend the scope of microbial genome annotation from functional assignment at gene level to the systematic generation of pathways assemblies and genome-scale metabolic reconstructions , with an initial focus on bacteria . Even with the efforts from jamborees , SEED , and Microme , ambiguity in the evidence used to build metabolic GENREs is unavoidable . Therefore , some type of reconciliation will likely be necessary prior to any multi-reconstruction comparison . Even in an ideal case where the knowledge about an organism is complete , there still remains some ambiguous decisions in the reconstruction process resulting from the core approximations of constraint-based modeling , which confine the fundamentally analog nature of biology to digital categorizations ( e . g . a continuum of enzyme thermodynamics is categorized into ‘reversible’ and ‘non-reversible , ’ a continuum of substrate affinities is converted into ‘yes’ or ‘no’ decisions on which metabolites can be acted on by an enzyme , etc . ) . With regards to this characteristic , metabolic reconstructions are akin to other types of biological models . The reconciliation process reveals challenges that might arise if a high quality comparison is to be performed between more than two species . Reconciling more than two models might prove difficult , since the addition of more models beyond two adds more degrees of freedom to the task . The issues faced in these cases will be similar to those faced in standardization efforts such as SEED , in which some organism specificity is forfeited in favor of model standardization . Our reconciliation process can inform these efforts , and the reconciled models can serve as a gold standard against which these automated reconstruction platforms can be compared . Although we performed the reconciliation of P . aeruginosa and P . putida manually , much of the process could be automated for future efforts . The workflow developed for performing model reconciliation ( see Figure 1 and Text S2 , Figure 1 ) , coupled with knowledge of the specific information types that are important for making decisions about each given reaction ( see Table 1 ) and the specific database structures for performing the reconciliation ( see Figures 2–4 in Text S2 ) will help make much of the process automatable in the future . The reconciliation we performed was for two species that are closely related . However , the process we developed should be equally applicable to more distant species if a comparison is to be attempted . Model reconciliation enables true differences in two metabolic networks to be identified above the noise . This identification enables a more confident comparison of differences , which is crucial whether or not the species are closely related . In comparing species that are more distantly related , more pathways might fall into the ‘organism specific’ categories , and thus need to be reconciled as contiguous pathways as opposed to as collections of reactions ( see Table 1 ) . This category will include the pathways that most diverge between the species being compared , where whole blocks of reactions might be different between one organism and the other . However , the reconciliation process will be similar in structure , and the insights gained in this study should serve as a guide for such attempts . Rigorous genome-scale , multiple-species comparisons are crucial for the elucidation of the evolution of cellular networks and of the underlying genotype-phenotype relationships . The availability of pathway assemblies and metabolic models for a large variety of microbial species would pave the way for new types of comparative and phylogenomic studies . Furthermore , process-based comparisons would enable the identification of system-wide properties that cannot be detected by a simple comparison of gene annotations and will allow the connection of genotype with phenotypic properties at a phylogenetic level . These phenotypes might include habitat specificity , ecological niche information , and the structure of metabolic systems properties that might be ( re ) engineered . The results of the comparative analysis lent significant insight into differences between P . aeruginosa and P . putida that might relate to the marked difference in virulence . Virulence of P . aeruginosa is derived from many sources , including its ability to produce a host of specific virulence factors and toxins [42] and its possession of deadly pathogenicity islands [43] , [44] . In contrast , although there are rare cases of P . putida infections in humans [45] , P . putida is typically not virulent . It is of note that P . putida does not grow well at 37°C ( human body temperature ) , while P . aeruginosa thrives at this temperature [46] . P . putida also lacks many of the factors necessary for establishing a human infection , including many of the virulence factors possessed by P . aeruginosa . Metabolic factors might play a role in forming some of these differential phenotypes . One unexpected difference identified through our metabolic comparison that might contribute to the virulence of P . aeruginosa versus P . putida is the increased flexibility of P . aeruginosa in sulfur related pathways ( see Figure 8 ) . Uptake and conversion of sulfate esters or carbon bonded sulfate derivatives into inorganic sulfate—the form of sulfur used by plants for production of cysteine and other essential sulfur containing compounds—is an important process performed in the rhizosphere by bacteria , including Pseudomonads [47] . Sulfur metabolism is an important process for both P . aeruginosa and P . putida , based on its important role in this common habitat . However , it has also been shown that a significantly different but also sulfur rich environment can be found in the CF lung , where mucin ( the family of glycosylated proteins forming the basis of mucus ) forms a major source of nutrition for P . aeruginosa . Several findings in literature support the importance of sulfated mucin in defining the in vivo environment in the CF lung , including the enhancement of mucin sulfation in CF patients versus in the normal lung , the presence of multiple highly regulated sulfur uptake mechanisms in P . aeruginosa that respond differently to the presence of inorganic sulfate versus the presence of more complex organosulfur compounds , and most notably , the demonstrated ability of P . aeruginosa CF isolates to utilize mucin as a sole sulfur source [48] , [49] . While the specific reactions that extract sulfur from complex organic sources are not represented in the P . aeruginosa reconstruction , this set of pathways is possibly of great import for P . aeruginosa virulence , and is a possible important area of study for the future . Taken as a whole , the results of this study uphold the observation that the virulence of P . aeruginosa is highly multifactorial [50] . Flexibility of certain pathways is larger in P . aeruginosa than in P . putida as shown through the pairwise pathway flexibility study , but this result does not extend to virulence precursors . More puzzling is the observation that P . aeruginosa displays higher pathway flexibility in production of certain virulence factors than P . putida , even though the flexibility of production of the virulence precursors remains similar between the two ( compare Figure 7b–c versus the ‘Demand: virulence factor’ row/column in Figure 8a ) . This observation bolsters the ‘multifactorial’ hypothesis , even within a metabolically-focused analysis . With the reconciled models presented in this study , we have opened an opportunity to gain a deeper understanding of both the difference in virulence of these organisms , as well as the different metabolic features that they possess with relation to metabolic engineering applications . Reconciled genome-scale models of these two organisms will therefore show many uses beyond the analyses of virulence precursor and pathway flexibility presented here .
The identification of pairs of reciprocal genes from P . aeruginosa and P . putida was performed with WU BLAST software version 2 . 0 ( © Gish , W . , 1996–2003 , http://blast . wustl . edu ) . Nucleotide sequences for all P . aeruginosa and P . putida genes were downloaded from the Pseudomonas Genome Database V2 ( http://v2 . pseudomonas . com ) , and the identification was performed as described in the results section . The gene essentiality validation followed the same procedure as in the original validation [13] . Subsequently , the influence of the in silico rich medium composition on the outcome essentiality analysis was evaluated . The original in silico rich medium did not contain L-cysteine but rather it contained L-cystine , a dimeric amino acid composed of two cysteine residues linked by a disulfide bond . Since it is possible that this compound can be broken down into its L-cysteine form and utilized , the influence of the inclusion of L-cysteine into the medium was evaluated . Furthermore , the original in silico medium did not contain any sources of purines or pyrimidines . The decision to exclude these compounds from the medium was based on the listed chemical composition of LB medium , as listed in the supplementary materials from [51] . However , evidence from [52] indicates presence of these molecules in LB medium , so in silico growth was also assessed in LB medium containing purines/pyrimidines that could be taken up by the reconciled reconstructions ( cytosine , 5′-deoxyadenosine , and uracil ) . The revalidation was performed as in the original validation [13] , [14] . Briefly , FBA simulations were performed for both reconstructions in in silico minimal media , with various single carbon sources allowed into the models . If biomass could be produced with a nonzero flux on a given carbon source ( above a threshold that allows for precision/rounding error ) , then that carbon source was considered to enable growth in silico . The revalidation was performed as in the original publication [14] . Briefly , FBA was performed with acetate as a sole in silico carbon source , maximizing biomass production . This simulation was done with each of the set of genes causing acetate-auxotrophy knocked out in silico to determine biomass yield . If no biomass was produced , the mutation was considered lethal on acetate medium . These in silico results were compared against in vitro results collected previously by our group [14] . The yields were computed by performing FBA [53] with the biomass production as the objective , while setting the NGAM parameter ( specifically , the minimal flux of the respective ATP dissipation reaction that accounts for NGAM ) to zero and the GAM parameter ( by setting the appropriate stoichiometry of the reactions accounting for it ) to the value used in the original reconstruction . Subsequently the obtained biomass production rates were divided by the upper bound on the glucose uptake rate ( 10 mmol·gDW−1·h−1 ) in order to obtain yields ( in units of gDW· ( mmol glucose ) −1 ) . For more details , see section “Analysis of changes in yields” in Text S2 . Pareto optimum curves denote the outline of the solution space of a metabolic network along the plane defined by two reaction fluxes . To construct a pareto optimum curve for two reactions , the flux through one reaction was fixed at a series of different levels , and the flux through the other reaction was both maximized and minimized at each level . These upper and lower bounds corresponding to each flux value of the first reaction gave shape to two pareto curves , which together outline the edges of the solution space along the plane defined by the two reactions . The area held within a pareto curve was calculated as a metric of metabolic flexibility in trading off resources between the two cellular objectives represented by the two reactions plotted . An analysis was performed to examine the differences in flexibility for all unique pairings of pathways present in both iJP962 and iMO1086 . First , reactions in both models were assigned to pathways based on KEGG assignments with some manual assistance ( e . g . , for demand reactions ) . ‘Demand’ pathways encompass reactions enabling drainage of certain interesting cellular metabolites from the metabolic network , and these reactions were only enabled for the purpose of the specific simulations they participated in ( they are not generally considered parts of the models ) . Reactions were sampled from the set of reactions common to both models and able to carry flux in at least one of the models ( there were 656 such reactions ) . The numbers of reactions meeting these criteria for each pathway are listed after the pathway names in Figure 8 . To generate the plot shown in Figure 8 , pareto curves were generated for both iMO1086 and iJP962 for twenty randomly chosen reaction pairs belonging to each pathway pair ( or ten reaction pairs in the cases where pathways were paired with themselves ) , with glucose allowed as the sole carbon source . Areas were computed for each of the sampled pareto curves . Next , a multiple-testing significance test was used to determine whether a significant trend existed among the results for a given pathway pair . Namely , areas computed for the 20 pairs of reactions for a given pathway pair ( 10 reaction pairs computed for P . aeruginosa and ( the same ) 10 computed for P . putida ) were randomly assigned to two groups in 1000 permutations , and cases where the true groupings of pareto areas into P . aeruginosa and P . putida groups showed a mean difference of at least 3 standard deviations from the average of random permutations of groupings of the 40 reactions were considered ‘significant . ’ Simulations were done on a PC using the Cobra Toolbox [54] with either Matlab ( MathworksInc . , Natick , MA , USA ) or Octave ( http://www . gnu . org/software/octave/ ) . Linear optimizations were performed utilizing either the free GLPK ( http://www . gnu . org/software/glpk/ ) or CPLEX ( IBM , Armonk , NY , USA ) linear programming solvers , except for the pathway flexibility study , which was performed using Gurobi ( http://www . gurobi . com/ ) . The reconstructions were stored and maintained in Excel and MATLAB and the ToBiN platform [14] . The SBML files were generated using an appropriate Perl script . Analyses were done in Matlab , OpenOffice Calc , and MS Excel . | Over the past decade , the increasing availability of fully sequenced genomes , functional genomics databases , and a broad existing scientific literature on metabolism have been leveraged towards the generation of genome-scale metabolic reconstructions for a wide variety of organisms . A major hurdle in the field , however , is the lack of standardization in this ‘metabolic reconstruction’ process , which makes it difficult to compare reconstructions of multiple species with each other . Notably , it is difficult to determine if differences seen between the models are of biological origin , or if they reflect noise from the respective reconstruction processes . To address this problem , we have developed a novel method , termed metabolic network reconciliation , in which genome-scale models of two well-studied and phylogenetically related bacteria ( P . aeruginosa and P . putida ) were compared such that differences between the models not upheld by biological evidence were eliminated . These reconciled models were re-validated with experimental data , and used to explore differences in the biology of these organisms and to gain insight into the reconstruction process itself . |
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A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste . Pseudomonas putida is an archetype of such microbes due to its metabolic versatility , stress resistance , amenability to genetic modifications , and vast potential for environmental and industrial applications . To address both the elucidation of the metabolic wiring in P . putida and its uses in biocatalysis , in particular for the production of non-growth-related biochemicals , we developed and present here a genome-scale constraint-based model of the metabolism of P . putida KT2440 . Network reconstruction and flux balance analysis ( FBA ) enabled definition of the structure of the metabolic network , identification of knowledge gaps , and pin-pointing of essential metabolic functions , facilitating thereby the refinement of gene annotations . FBA and flux variability analysis were used to analyze the properties , potential , and limits of the model . These analyses allowed identification , under various conditions , of key features of metabolism such as growth yield , resource distribution , network robustness , and gene essentiality . The model was validated with data from continuous cell cultures , high-throughput phenotyping data , 13C-measurement of internal flux distributions , and specifically generated knock-out mutants . Auxotrophy was correctly predicted in 75% of the cases . These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions , whereas biomass composition has negligible influence . Finally , we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates , a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival . The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential .
Pseudomonas putida is one of the best studied species of the metabolically versatile and ubiquitous genus of the Pseudomonads [1]–[3] . As a species , it exhibits a wide biotechnological potential , with numerous strains ( some of which solvent-tolerant [4] , [5] ) able to efficiently produce a range of bulk and fine chemicals . These features , along with their renowned stress resistance , amenability for genetic manipulation and suitability as a host for heterologous expression , make Pseudomonas putida particularly attractive for biocatalysis . To date , strains of P . putida have been employed to produce phenol , cinnamic acid , cis-cis-muconate , p-hydroxybenzoate , p-cuomarate , and myxochromide [6]–[12] . Furthermore , enzymes from P . putida have been employed in a variety of other biocatalytic processes , including the resolution of d/l-phenylglycinamide into d-phenylglycinamide and l-phenylglycine , production of non-proteinogenic l-amino acids , and biochemical oxidation of methylated heteroaromatic compounds for formation of heteroaromatic monocarboxylic acids [13] . However , most Pseudomonas-based applications are still in infancy largely due to a lack of knowledge of the genotype-phenotype relationships in these bacteria under conditions relevant for industrial and environmental endeavors . In an effort towards the generation of critical knowledge , the genomes of several members of the Pseudomonads have been or are currently being sequenced ( http://www . genomesonline . org , http://www . pseudomonas . com ) , and a series of studies are underway to elucidate specific aspects of their genomic programs , physiology and behavior under various stresses ( e . g . , http://www . psysmo . org , http://www . probactys . org , http://www . kluyvercentre . nl ) . The sequencing of P . putida strain KT2440 , a workhorse of P . putida research worldwide and a microorganism Generally Recognized as Safe ( GRAS certified ) [1] , [14] , provided means to investigate the metabolic potential of the P . putida species , and opened avenues for the development of new biotechnological applications [2] , [14]–[16] . Whole genome analysis revealed , among other features , a wealth of genetic determinants that play a role in biocatalysis , such as those for the hyper-production of polymers ( such as polyhydroxyalkanoates [17] , [18] ) and industrially relevant enzymes , the production of epoxides , substituted catechols , enantiopure alcohols , and heterocyclic compounds 13 , 15 . However , despite the clear breakthrough in our understanding of P . putida through this sequencing effort , the relationship between the genotype and the phenotype cannot be predicted simply from cataloguing and assigning gene functions to the genes found in the genome , and considerable work is still needed before the genome can be translated into a fully functioning metabolic model of value for predicting cell phenotypes [2] , [14] . Constraint-based modeling is currently the only approach that enables the modeling of an organism's metabolic and transport network at genome-scale [19] . A genome-wide constraint-based model consists of a stoichiometric reconstruction of all reactions known to act in the metabolism of the organism , along with an accompanying set of constraints on the fluxes of each reaction in the system [19] , [20] . A major advantage of this approach is that the model does not require knowledge on the kinetics of the reactions . These models define the organism's global metabolic space , network structural properties , and flux distribution potential , and provide a framework with which to navigate through the metabolic wiring of the cell [19]–[21] . Through various analysis techniques , constraint-based models can help predict cellular phenotypes given particular environmental conditions . Flux balance analysis ( FBA ) is one such technique , which relies on the optimization for an objective flux while enforcing mass balance in all modeled reactions to achieve a set of fluxes consistent with a maximal output of the objective function . When a biomass sink is chosen as the objective in FBA , the output can be correlated with growth , and the model fluxes become predictive of growth phenotypes [22] , [23] . Constraint-based analysis techniques , including FBA , have been instrumental in elucidating metabolic features in a variety of organisms [20] , [24] , [25] and , in a few cases thus far , they have been used for concrete biotechnology endeavors [26]–[29] . However , in all previous applications in which a constraint-based approach was used to design the production of a biochemical , the studies addressed only the production of compounds that can be directly coupled to the objective function used in the underlying FBA problem . The major reason for this is that FBA-based methods predict a zero-valued flux for any reaction not directly contributing to the chosen objective . Since the production pathways of most high-added value and bulk compounds operate in parallel to growth-related metabolism , straightforward application of FBA to these biocatalytic processes fails to be a useful predictor of output . Other constraint-based analysis methods , such as Extreme Pathways and Elementary Modes analysis , are capable of analyzing non-growth related pathways in metabolism , but , due to combinatorial explosion inherent to numerical resolution of these methods , they could not be used so far to predict fluxes or phenotypes at genome-scale for guiding biocatalysis efforts [30] . To address both the elucidation of the metabolic wiring in P . putida and the use of P . putida for the production of non-growth-related biochemicals , we developed and present here a genome-scale reconstruction of the metabolic network of Pseudomonas putida KT2440 , the subsequent analysis of its network properties through constraint-based modeling and a thorough assessment of the potential and limits of the model . The reconstruction is based on up-to-date genomic , biochemical and physiological knowledge of the bacterium . The model accounts for the function of 877 reactions that connect 886 metabolites and builds upon a constraint-based modeling framework [19] , [20] . Only 6% of the reactions in the network are non gene-associated . The reconstruction process guided the refinement of the annotation of several genes . The model was validated with continuous culture experiments , substrate utilization assays ( BIOLOG ) [31] , 13C-measurement of internal fluxes [32] , and a specifically generated set of mutant strains . We evaluated the influence of biomass composition and maintenance values on the outcome of flux balance analysis ( FBA ) simulations , and utilized the metabolic reconstruction to predict internal reaction fluxes , to identify different mass-routing possibilities , and to determine necessary gene and reaction sets for growth on minimal medium . Finally , by means of a modified OptKnock approach , we utilized the model to generate hypotheses for possible improvements of the production by P . putida of polyhydroxyalkanoates , a class of compounds whose production consumes resources that would be otherwise used for growth . This reconstruction thus provides a modeling framework for the exploration of the metabolic capabilities of P . putida , which will aid in deciphering the complex genotype-phenotype relationships governing its metabolism and will help to broaden the applicability of P . putida strains for bioremediation and biotechnology .
We reconstructed the metabolism of P . putida at the genome-scale through a process summarized in Figure 1 . The reconstruction process involved: ( 1 ) an initial data collection stage leading to a first pass reconstruction ( iJP815pre1 ) ; ( 2 ) a model building stage in which simulations were performed with iJP815pre1 and reactions were added until the model was able to grow in silico on glucose minimal medium ( iJP815pre2 ) ; and ( 3 ) a model completion stage in which BIOLOG substrate utilization data was used to guide model expansion and in silico viability on varied substrates . The final reconstruction , named iJP815 following an often used convention [33] , consists of 824 intracellular and 62 extracellular metabolites connected by 877 reactions . Eight hundred twenty one ( 94% ) reactions have at least one assigned gene as delineated in the gene-protein-reaction ( GPR ) relationships . GPR relationships are composed of Boolean logic statements that link genes to protein complexes and protein complexes to reactions via combinations of AND and OR operators . An ‘AND’ operator denotes the required presence of two or more genes for a protein to function ( as in the case of multi-protein complexes ) , while an ‘OR’ operator denotes a redundant function that can be catalyzed by any of several genes ( as in the case of isozymes ) . Only 56 reactions , of which nine are non-enzymatic , lack associated genes . The remaining 47 non-gene-associated , enzymatic reactions were added in order to close metabolic network gaps identified during the successive steps of the reconstruction process . Most network gaps ( 27 ) were identified during the second round of the reconstruction and were resolved through detailed literature mining , thereby enabling iJP815 to grow in silico on glucose in minimal medium . The remaining gaps identified in the model completion step ( Figure 1 ) were mostly single missing steps in the pathway for which there is experimental evidence of operation ( e . g . , a compound is consumed but not produced , and no alternative pathways exist ) . It should be noted that for some gaps , there is more than one combination of reactions with which the gap could be closed [34] . In cases where more than one gap closure method was available , the decision of which to use was made based on similarity queries to related bacteria . The iJP815 model includes 289 reactions for which non-zero flux values cannot be obtained under any environmental condition while enforcing the pseudo steady-state assumption ( PSSA ) . We term these reactions “unconditionally blocked” meaning that they are unable to function because not all connections could be made with the information available . Three hundred sixty two metabolites that are only involved in these reactions are classified as “unbalanced metabolites” . Another important subset of model reactions is the “weakly annotated” set , which means that all the genes assigned to these 57 reactions are currently annotated as coding for “putative” or “family” proteins . The relationships between all the subsets are shown in Table 1 and Figures 2 and 3 . The final reconstruction accounts for the function of 815 genes , corresponding to 15% of all genes in the P . putida genome and to 65% ( 1253 ) of those currently assigned to the classes ‘Metabolism’ ( K01100 ) and ‘Membrane Transport’ ( K01310 ) in the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) orthology classification [35] . These figures are consistent with recently published metabolic reconstructions for other prokaryotes ( see Table S1 ) . A high-throughput BIOLOG phenotypic assay was performed on P . putida to validate and extend the model . In this assay , P . putida was tested for its ability to oxidize 95 carbon substrates in minimal medium . Of these 95 substrates , P . putida oxidized 45 . We added 2 other carbon sources to the positive-oxidation group ( l-phenylalanine and l-threonine ) despite a negative BIOLOG result , since these substrates had been previously shown to be growth substrates [16] and since we confirmed these results experimentally ( data not shown ) , giving altogether forty seven compounds utilized in vivo . Forty seven out of the 95 carbon sources tested were accounted for in iJP815pre2 , enabling a comparison of these BIOLOG data with FBA simulations of iJP815 grown on in silico minimal medium with the respective compound as sole carbon source ( see Table 2 and Table S2 ) . The initial working version of the model ( iJP815pre2 ) was able to simulate growth with 14 of the 47 BIOLOG-assayed compounds as sole carbon sources . This version of the reconstruction contained only a few transport reactions , prompting us to identify compounds that could not be utilized in silico simply due to the lack of a transporter . This was achieved by allowing the intracellular pool of each compound of interest to be exchanged with environment in silico , and by evaluating the production of biomass in each case through FBA simulations . This approach increased the number of utilizable substances to 34 but also produced six false-positives ( i . e . , substances that support in silico growth , but which gave a negative phenotype in the BIOLOG assay ) . These included three metabolites involved in central metabolic pathways ( d-glucose 1-phosphate , d-glucose 6-phosphate and glycerol-3-phosphate ) , an intermediate of the l-histidine metabolism pathway ( urocanate ) , an intermediate of branched amino acids biosynthesis ( 2-oxobutanoate ) , and the storage compound glycogen . This analysis suggests that the inability of P . putida to utilize these compounds in vivo is likely due to the lack of appropriate transport machinery . The final P . putida model ( iJP815 ) grew on 39 of the 51 compounds tested in the BIOLOG assay and that concurrently were accounted for in the model . Of these , 33 were true positives ( compounds utilized in vivo and allowing for growth in silico ) . The mode of utilization of the remaining fourteen in vivo oxidized compounds ( i . e . , false negatives ) could not be elucidated . The remaining forty two compounds posed true negatives , eight of which were accounted for in the reconstruction . Ten utilized compounds also lack transport reactions , as nothing is known about their translocation into the cell . Nevertheless , this comparison of in silico growth predictions with BIOLOG substrate utilization data indicates that the core metabolism of P . putida has been properly reconstructed . A note of caution when comparing the BIOLOG assays with growth predictions is that this assay evaluates whether an organism is able to oxidize the tested compound and yield energy from it , which is different from growth . However , as P . putida is able to grow on minimal medium supplemented with these compounds , we considered the assumption to be justified . The reconstruction process systematizes knowledge about the metabolism of an organism , allowing the identification of errors in , and discrepancies between , various sources of data . A major value of a manual model-building effort is the careful revision of the current genome annotation , based on literature evidence encountered during the model building process , BLAST searches , and gap closures . During the reconstruction of the P . putida metabolic network , we discovered a number of genes that appear to have been improperly annotated in biological databases ( Pseudomonas Genome Database , KEGG , NCBI ) . These mis-annotations arose due to a lack of information at the time of the original annotation or because knowledge that was available in the literature had been overlooked in the original annotation . In a number of other cases , the model building process has also generated new hypotheses for gene functions . For instance , our reconstruction process identified an unlikely gap in the l-lysine degradation pathway of P . putida . Extensive literature search and careful reannotation has provided considerable evidence that the genes PP0382 and PP5257 , currently annotated as ‘carbon-hydrogen hydrolase family protein’ and ‘oxidoreductase , FAD binding’ respectively , most probably code for a ‘5-aminopentamidase’ and ‘l-pipecolate oxidase’ , respectively [36] . Another example is the propanoate degradation pathway: In the iJP815pre2 version this pathway was complete except for one enzymatic activity , namely the 2-methylisocitrate dehydratase . Analysis of the enzymes flanking this reaction showed that all of the enzymes are encoded by genes immediately adjacent to the ORF PP2330 . Inspection of this region of the genome revealed that PP2336 is annotated as “aconitate hydratase , putative” , although the flanking genes are responsible for degradation of propanoate . Analysis of PP2330 via BLAST revealed a homology of more than 99% over the whole length of the protein with the 2-methylisocitrate dehydratase from other bacteria , such as other strains of P . putida ( GB-1 , W619 ) , Burkholderia prymatum STM 815 , Burkholderia multivorans ATCC 17616 , Pseudomonas aeruginosa PA7 , and Stenotrophomonas maltophilia R551-3 . Consequently the gene was reannotated to code for this function and the gap in propanoate degradation pathway was thus closed by addition of the corresponding GPR . In other cases , discrepancies exist between various databases , as in the case of PP5029 , which is annotated in KEGG as ‘formiminoglutamase’ but in NCBI as ‘N-formylglutamate deformylase’ . Analysis of network gaps , genomic context and sequence homology provided a strong indication that ‘N-formylglutamate deformylase’ is the correct annotation . In many other cases the reannotation meant changing the substrate specificity of the enzyme ( which corresponds to changing the last part of the EC number ) . These were mainly identified by BLASTing the protein against protein sequences of other microbes and , whenever available , cross-checking the BLAST results against primary research publications . The full list of reannotations suggested by the reconstruction process is shown in Table 3 . After completing the reconstruction , we assessed whether the model was capable of predicting the growth yield of P . putida , a basic property of the modeled organism . In silico growth yield on succinate was calculated by FBA and compared with in vivo growth yield measured in continuous culture [37] . If the in silico yield were lower than the experimental , it would indicate that the network may lack important reactions that influence the efficiency of conversion of carbon source into biomass constituents and/or energy . In fact , the calculated in silico yield ( 0 . 61 gDW⋅gC−1 ) was higher than the experimental yield ( 0 . 47 gDW⋅gC−1 ) , indicating that some of the processes reconstructed in the network might be unrealistically efficient and/or that P . putida may be diverting resources into other processes not accounted for in the model . This greater efficiency of the in silico model versus in vivo growth data is also consistent with recent studies that suggest optimal growth is not necessarily the sole objective ( function ) of biochemical networks [38] , [39] . The in silico growth yield is influenced not only by the structure of the metabolic network , but also by other factors including biomass composition and the growth-associated and non-growth-associated energy maintenance factors ( GAM and NGAM ) , the values of which represent energy costs to the cell of “living” and “growing” , respectively [22] . Therefore , since both the biomass composition and the GAM/NGAM values were taken from the E . coli model [22] , [33] due to a lack of organism-specific experimental information , we evaluated the influence of these factors on the predicted growth yield . First , we analyzed the effects of changes in the ratios of biomass components on the iJP815 growth yield . These analyses ( displayed in the Text S1 , section “Assessment of the influence of the biomass composition the growth yield” ) indicated that varying any single biomass constituent by 20% up or down has a less than 1% effect on the growth yield of P . putida ( Figure S1 ) . These results are consistent with results of a previous study on the sensitivity of growth yield to biomass composition [40] . Although it is still possible that some components of P . putida biomass are not present in E . coli or vice versa , we conclude that the use of E . coli biomass composition in the P . putida model is a justified assumption for the purpose of our application and is probably not a great contributor to the error in our predictions of growth yield . Subsequently , the effects of changes in the GAM on the in silico growth yield were tested ( Figure S2A and S2B ) . It was found that if GAM was of the same order of magnitude as the value used in the E . coli model ( 13 [mmolATP⋅gDW−1 ) , its influence is negligible , as increasing or decreasing it twofold alters the growth yield by merely 5% . A higher GAM value in P . putida than in E . coli could contribute to the discrepancy between the experimental measurements and in silico predictions , but it could not be the only factor unless the E . coli and P . putida values differ more than twofold , which is unlikely . Finally , we assessed the effects of changes in the value of NGAM on in silico growth yield . The NGAM growth dependency is influenced by the rate of carbon source supply , and thus indirectly by the growth rate . If the carbon intake flux is low ( as in the case of the experiments mentioned above , with a dilution rate of 0 . 05 h−1 ) , the fraction of energy utilized for maintenance purposes is high and therefore so is the influence of the NGAM value on growth yield ( Figure S2A ) . Under such low-carbon intake flux conditions , a twofold increase of the NGAM value can decrease the growth yield by about 30% . This indicates that the main cause for the discrepancy between in vivo and in silico growth yields is that the NGAM value is likely to be higher in P . putida than in E . coli . Figure S2A indicates that increasing the NGAM value from 7 . 6 of 12 [mmolATP⋅gDW−1⋅h−1] would reduce the in silico growth yield and lead to a better match with experimental values . Consequently this NGAM value was used in subsequent FBA and Flux Variability Analysis ( FVA ) [41] simulations . For a high influx of carbon source ( Figure S2B ) the influence of NGAM on the growth yield is low and the influence of the NGAM and GAM values on growth yield are comparable . It should be noted that , while FBA predicts the optimal growth yield , few cellular systems operate at full efficiency . Bacteria tend to “waste” or redirect energy if it is abundant [42] , leading to a lower-than-optimal in vivo growth yield . It is also worth mentioning that maintenance values may depend on the carbon source used [43] and on environmental conditions [44]–[46] . Additionally , we computed the growth yields of P . putida on sole sources of three other important elements—Nitrogen ( N ) , Phosphorous ( P ) , and Sulfur ( S ) —and compared these with published experimental data from continuous cultivations [37] , as shown in Table 4 . Since biomass composition can play a role in the efficiency of in silico usage of basic elements , this analysis can aid in assessing how well the biomass equation , which is equivalent to the E . coli biomass reaction , reproduces the true biomass composition of P . putida . The yield on nitrogen differs only by 10% between in silico and in vivo experiments , which suggests that the associated metabolic network for nitrogen metabolism is well characterized in the iJP815 reconstruction . The yields on phosphorous and sulfur , however , differ by more than a factor of two between the in vivo and in silico analyses , suggesting that there may be significant differences between the biomass requirements and the metabolic networks of P . putida and E . coli for these components . The differences in yields , however , may be also caused by the change of the in vivo biomass composition , which decreases the fraction of compounds containing the limited element , when compared to the biomass composition while the bacterium is grown under carbon-limitation . Such changes were observed experimentally in P . putida for nitrogen and phosphate limitations [47] . Thus , the biomass composition of P . putida needs to be determined precisely in the future . However , for the purpose of this work and since the global effect of the biomass composition on the outcome of the simulations is negligible ( as shown above ) , we considered the use of the original biomass equation to be justified . As described above , iJP815 contains 289 unconditionally ( i . e . , not dependent on external sources ) blocked reactions ( that is , reactions unable to function because not all connections are made ) , corresponding to 33% of the metabolic network . In previously published genome-scale metabolic reconstructions , the fraction of blocked reactions varies between 10 and 70 percent [48] . Blocked reactions occur in reconstructions mostly due to knowledge gaps in the metabolic pathways . Accordingly , the blocked-reactions set can be divided into two major groups; ( 1 ) reactions with no connection to the set of non-blocked reactions , and ( 2 ) reactions that are either directly or indirectly connected to the operating core of the P . putida model . The first group of reactions includes members of incomplete pathways that , with increasing knowledge and further model refinement , will gradually become connected to the core . This subset comprises 108 reactions ( 35% of blocked reaction set ) . The second group of reactions comprises also members of incomplete pathways , but many of them belong to pathways that are complete but that lack a transport reaction for the initial or final compound . Examples of pathways lacking a transporter are the degradation of fatty acids and of propanoate . In addition , there could exist compounds whose production is required only in certain environmental conditions , e . g . , under solvent stress , and as such are not included in generic biomass equation . Pathways synthesizing compounds that are not included in the biomass equation but that likely are conditionally required include the synthesis of thiamine , various porphyrins and terpenoids . In this case , reactions involved exclusively in the production of such compounds would be blocked if no alternative outlets exist for those pathways . Allowing a non-zero flux through these reactions would require inclusion into biomass of the conditional biomass constituents , which in turn would require having various biomass equations for various conditions . This level of detail , however , is beyond the scope of our initial metabolic reconstruction and investigation . The high number of blocked reactions in iJP815 clearly indicates that there are still vast knowledge deficits in the model and , thus , in the underlying biochemical and genomic information . Since a genome-scale metabolic model seeks to incorporate all current knowledge of an organism's metabolism , these reactions are integral elements of the metabolic reconstruction and of the modeling scaffold , even if they are not able to directly participate in steady state flux studies . Therefore , the inclusion of these reactions in the model provides a framework to pin-point knowledge gaps , to include novel information as it becomes available and to subsequently study their embedding and function in the metabolic wiring of the cell . The assessment performed as described above by means of high-throughput phenotyping assays , growth experiments and continuous cultivations , has shown that the model is coherent and that it captures the major metabolic features of P . putida . We subsequently used the model to probe the network and to ascertain the distribution of internal fluxes and properties such as network flexibility and redundancy of particular reactions . To this end , we predicted the distribution of reaction fluxes throughout the central pathways of carbon metabolism by flux variability analysis ( FVA ) , and compared the simulations to internal fluxes computed from experimentally obtained 13C data in P . putida [49] , [50] . Genome-scale metabolic networks are , in general , algebraically underdetermined [41] . As a consequence , the optimal growth rate can often be attained through flux distributions different than the single optimal solution predicted by FBA simulations . Therefore we used flux variability analysis ( FVA ) to explore the network , as this method provides the intervals inside which the flux can vary without influencing the value of the growth yield ( if the flux of the reaction cannot vary then the range is limited to a single value ) [41] . The results of the simulations are given in Figure 4 . As isotopic ( 13C ) measurements are not able to distinguish which glucose uptake route is being used by P . putida , all the fluxes in the 13C experiment and in the FVA simulations were computed assuming that glucose is taken up directly into the cell . For the precise description of the network models used in this comparison ( i . e . , FBA/FVA vs . 13C-Flux analysis ) see Text S1 and Text S2 ( sections “Comparison of FVA analyses with 13C flux measurement data” ) . Figure 4 shows that the predictions ( in red ) generally agree well with the measurements ( in green ) throughout the network , as most of the 13C values fall within the FVA intervals , where intervals were predicted , or both values are close to each other ( in absolute values ) , when a single value was predicted . As P . putida lacks phosphofructokinase , glucose can be converted to pyruvate ( the entry metabolite of TCA cycle ) via the pentose phosphate ( PP ) or the Entner-Doudoroff ( ED ) pathways . The ED pathway is energetically more efficient and the 13C measurements indicate that KT2440 uses it preferentially over the PP pathway . Therefore , the FVA yields locally single flux values rather than intervals , which reflects the relative rigidity of this part of the network . In contrast , the energy generating part of the central metabolic network ( the TCA cycle and its vicinity ) exhibits greater flexibility , as illustrated by the broad flux intervals . Firstly , the conversion of phosphoenylpyruvate into pyruvate can proceed either directly or via oxaloacetate , although the bacterium appears to use the direct route ( the 13C-model assumes , in fact , only the direct route; see Text S1 , section “Comparison of FVA analyses with 13C flux measurement data” ) . Secondly , the conversion of malate to oxaloacetate may also occur directly or via pyruvate . The 13C flux measurements indicate that the bacterium uses the indirect route in addition to the direct one although , according to the FVA , the indirect route is energetically less efficient . Interestingly , our model suggests also that the glyoxylate shunt could be used interchangeably with full TCA-cycle without any penalty on growth yield . However , as the glyoxylate shunt is inactivated in many bacterial species via catabolite repression upon glucose growth [51] , it is possible that this alternative is not used in P . putida . Despite the general agreement between in silico predictions and 13C measurements , there still exist a number of discrepancies . For instance , the 13C-experiments suggest that the bacterium utilizes the portion of glycolysis between triose-3-phosphate and d-fructose-6-phosphate in the gluconeogenic direction , which is not energetically optimal and as such is not captured in standard FBA ( or FVA ) simulations . This illustrates one of the possible pitfalls of FBA , which per definition assumes perfect optimality despite the fact that microorganisms might not necessarily allocate their resources towards the optimization function assumed in analysis , and in some cases may not operate optimally at all [52] , [53] . Another group of differences concentrates around the pentose phosphate pathway ( PPP ) , although these are relatively minor and are likely due to differences in the quantities of sugar diverted toward biomass in the 13C model vs . iJP815 . A third group of differences revolves around pyruvate and oxaloacetate , whereby the in vivo conversion of malate to oxaloacetate shuttles through a pyruvate intermediate rather than directly converting between the two . The last area where discrepancies exist between in silico and 13C data is in the TCA cycle , around which the flux is lower in FVA simulations than in the experiment . This suggests that the in silico energetic requirements for growth ( maintenance values ) are still too low when compared to in vivo ones , as the main purpose of the TCA cycle is energy production . To investigate further these differences , we carried out a suboptimal FVA ( Figure 4 , blue values ) , allowing the production of biomass to range between 90 and 100% of its maximum value . In this suboptimal FVA experiment , the 13C-derived fluxes fall between FVA intervals for every flux value in the 13C network . To filter out artifacts , we re-did all FVA computations using the structure of the network used in the 13C-experiment and found no major differences ( see Figure S3 ) . We also assessed the influence of the biomass composition on the distribution of internal fluxes and network structure and found that this was negligible on both accounts ( see Text S2 , section “Evaluation of biomass equation composition on the outcome of FBA/FVA simulations” and Figure S4 ) . The results show that , in principle , the bacterium can use all the alternatives described above and that the penalty on the growth yield is minimal . While this analysis validates the FVA simulation results , the wide breadth of the intervals ( i . e . , the mean ratio of interval width to mean interval value exceeds three ) , suggests that the ( mathematical ) under-determination of central metabolism can be quite high , and indicates that there exist multiple sub-optimal solutions across the network and that is thus difficult to predict exact internal flux and to “pin-point” a particular solution . These results reflect the essence of constraint-based modeling and FBA , which provide only a space of possible flux distributions and not exact values . Therefore , deductions from results of FBA simulations have to be made with great care . This underscores the notion that constraint based modeling should be seen more as navigation framework to probe and explore networks rather than as an exact predictive tool of cellular metabolism . Assessment of network models through comparison of in silico growth-phenotypes with the growth of knock-out strains is a powerful way to validate predictions . This has been done in a number of studies for which knock-out mutant libraries were available [59] , [60] . As there is currently no mutant library for P . putida , we tested gene knock-out predictions with a set of P . putida auxotrophic mutant strains created in our laboratory that are incapable of growth on minimal medium with acetate as the sole carbon source . First we compared whether the corresponding in silico mutants followed the same behavior ( lack of growth on minimal medium with acetate , where zero biomass flux during FBA corresponded to a no-growth phenotype ) . This comparison was performed only for strains whose knocked-out gene is included in iJP815 . Thirty-eight out of the 51 strains tested did not grow in silico ( Table S4 ) . Of the remaining 13 false positives ( i . e . , those growing in silico but not in vivo ) , four ( PP1470 , PP1471 , PP4679 , and PP4680 ) are mutated in genes considered non-essential in silico due to “weakly annotated” gene putatively encoding redundant isozymes . In the case of PP5185 ( coding for N-acetylglutamate synthase ) , its essentiality is removed by PP1346 ( coding for bifunctional ornithine acetyltransferase/N-acetylglutamate synthase protein ) , which is not only an isozyme of PP5185 ( the N-acetylglutamate synthase function ) but which also catalyses a reaction ( ornithine acetyltransferase ) that produces N-acetyl-l-glutamate ( the product of N-acetylglutamate synthase ) and thus renders the activity of PP5185 redundant . It appears either that this is a mis-annotation or that the enzyme is utilized only under different conditions . In addition , PP0897 ( fumC ) seems to have two paralogues ( PP0944 , PP1755 ) coding for isoenzymes of fumarate hydratase , but since the mutant in PP0897 does not grow auxotrophically , they are either non functional or mis-annotated . The enzyme complex that is composed of proteins expressed from the genes knocked-out in the two false positives PP4188 and PP 4189 catalyzes the decarboxylation of α-ketoglutarate to succinyl-CoA in the TCA cycle , concurrently producing succinyl-CoA for anabolic purposes . In the model , this functionality is not needed as this part of the TCA cycle can be circumvented by the glyoxylate shunt , whereas succinyl-CoA can be produced by reverse operation of succinate-CoA ligase . Restricting this reaction to be irreversible renders both genes essential . This altogether suggests that either the succinate-CoA ligase is irreversible or the glyoxylate shunt is inactive . The latter solution is , however , impossible , due to the essentiality of the glyoxylate shunt upon growth on acetate . The false positive PP4782 is involved in thiamine biosynthesis . This cofactor is not included in the biomass , which is why the gene is not in silico essential . This suggests thus that the in-silico P . putida biomass reaction should be enriched with this cofactor . The remaining false positives ( PP1768 , PP4909 , PP5155 ) are involved in the serine biosynthesis pathway . We found experimentally that mutants in these genes can grow on acetate if the medium also contains l-serine . These genes can be rendered in silico essential by setting glycine hydroxymethyltransferase to operate only unidirectionally from l-serine to glycine . The operation of this enzyme , however , is required for growth of the bacterium on glycine , which is possible; though very slow ( results not shown ) . One of these genes ( PP5155 ) has also a weakly annotated isozyme ( PP2335 ) . We found out as well that several of the mutants ( PP1612 , PP4188-9 , PP4191-4 ) grow in silico on glucose , which we confirmed experimentally ( results not shown ) . Altogether , these experimental results assisted us in improving the accuracy of the model . Albeit limited to a relatively small mutant set , this analysis shows that while constraint-based models are not always able to predict exact flux values , they are very useful in the identification of essential reactions and , through the GPRs , the genes responsible for their catalysis . This enables identification of vulnerable points in the metabolic network . To illustrate the utility of a genome-scale model for metabolic engineering , we used iJP815 to predict possible improvements to an industrially relevant process; namely , the production of polyhydroxyalkanoates ( PHAs ) from non-alkanoic substrates for biomedical purposes [61]–[63] . As the production of PHAs uses resources that would be otherwise funneled towards growth , increasing in silico PHA production would decrease the growth . Consequently , in classic optimization-based approaches ( e . g . , FBA ) , no PHA production would be predicted while optimizing for growth yield . The aim was thus to increase the available pool of the main precursor of PHAs—Acetyl Coenzyme A ( AcCoA ) . This approach was based on the observation that inactivation of isocitrate lyase ( ICL ) enhances the production of PHAs in P . putida due to increased availability of AcCoA that is not consumed by ICL [64] . We therefore searched for other possible intervention points ( mutations ) in the metabolic network that could lead to the accumulation of AcCoA . This analysis was performed through application of a modified OptKnock approach [28] , which allowed for parallel prediction of mutations and carbon source ( s ) that together provide the highest production of the compound of interest . Two main methods were employed to model a cellular pooling of AcCoA . The first was the maximization of AcCoA production by pyruvate dehydrogenase ( PDH ) . In the second , an auxiliary reaction was introduced that consumed AcCoA ( concurrently producing CoA , to avoid cofactor cycling artifacts ) and that would represent the pooling of AcCoA ( Figure 6A and 6B , insets ) . It is noteworthy that the value of ‘AcCoA production’ predicted by the first method includes AcCoA that is then consumed in other reactions ( some of which will lead towards biomass production for instance ) , whereas the value of ‘AcCoA pooling’ predicted by the second method includes only AcCoA that is taken completely out of the system , and therefore made available for PHA production but unusable for growth or other purposes . Therefore , only with the first method ( AcCoA production ) can AcCoA fluxes and growth rates be compared directly with the wild-type AcCoA flux and growth rate , as the second method ( AcCoA pooling ) will display lower values for AcCoA fluxes and growth rates but will avoid ‘double counting’ AcCoA flux that is shuttled towards growth , and therefore is not available for PHA production ( see plots in Figure 6A and 6B ) . To create the in silico mutants , we allowed the OptKnock procedure to block a maximum of two reactions , which corresponds , experimentally , to the creation of a double mutant . To avoid lethal in silico strains , the minimal growth yield was limited to a value ranging between 0 . 83 and 6 . 67 gDW⋅molC−1 , corresponding to about 5 and 40 percent of maximum growth yield , respectively . Six mutational strategies suggested by this approach are presented in Table 5 . The first three were generated by the AcCoA production method , and the last three were generated by the AcCoA pooling method . The results provide a range of options for possibly increasing AcCoA production , some of which constrain growth more than others ( see Figure 6A and 6B ) . One promising hypothesis ( strategy 2 ) generated by the AcCoA production method predicted that a double-mutant devoid of 6-phosphogluconolactonase ( pgl/PP1023 ) and periplasmatic glucose dehydrogenase ( gcd/PP1444 ) , would produce 29% more AcCoA than the wild type growing on glucose as a carbon source ( Figure 6A ) . As we are currently still in the process of generating this mutant , we were not yet able to test the prediction . Another promising hypothesis ( strategy 1 ) included knocking-out triose phosphate isomerase ( tpiA/PP4715 ) . As the mutant for tpiA was generated in this work , we tested whether it is able to grow on the predicted carbon source ( d-fructose ) , but the observed growth was very weak ( only very small colonies grew on agar plates after three days ) . This suggests that growth might be too inhibited by this strategy for it to be of great use . One strategy suggested by the AcCoA pooling method ( strategy 4 ) called for knocking out 2-methylcitrate dehydratase ( prpD/PP2338 ) and citrate synthase ( gltA/PP4194 ) , and supplying P . putida with valine . Using this strategy , AcCoA pooling could theoretically reach 21 . 9 mmol⋅gDW−1⋅h−1 , but at a severe expense in bacterial growth ( Figure 6B ) . The other strategies suggested by the AcCoA pooling method highlight a somewhat linear tradeoff between growth and AcCoA pooling , which could be investigated experimentally to determine how much growth disruption is acceptable in a bioengineered production strain of P . putida ( Figure 6B ) . These strategies illustrate the possible approaches to optimizing production of a non-growth associated compound , and highlight the need for further experimental work to assess the performance of this approach .
A primary value of genome-scale metabolic models is their ability to provide a holistic view of metabolism allowing , for instance , for quantitative investigation of dependencies between species existing far apart in the metabolic network [20] . Once experimentally validated , these models can be used to characterize metabolic resource allocation , to generate experimentally testable predictions of cell phenotype , to elucidate metabolic network evolution scenarios , and to design experiments that most effectively reveal genotype-phenotype relationships . Furthermore , owing to their genome-wide scale , these models enable systematic assessment of how perturbations in the metabolic network affect the organism as a whole , such as in determining lethality of mutations or predicting the effects of nutrient limitations . Since these multiple and intertwined relationships are not immediately obvious without genome-scale analysis , they would not be found during investigation of small , isolated circuits or genes as is typical in a traditional reductionist approach [65] , [66] . We present here a genome-scale reconstruction and constraint-based model of the P . putida strain KT2440 , accounting for 815 genes whose products correspond to 877 reactions and connect 886 metabolites . The manually curated reconstruction was based on the most up-to-date annotation of the bacterium , the content of various biological databases , primary research publications and specifically designed functional genomics experiments . New or refined annotations for many genes were suggested during the reconstruction process . The model was validated with a series of experimental sets , including continuous culture data , BIOLOG substrate utilization assays , 13C flux measurements and a set of specifically-generated mutant strains . FBA and FVA were used to ascertain the distribution of resources in KT2440 , to systematically assess gene and reaction essentiality and to gauge the robustness of the metabolic network . Hence , this work represents one of the most thorough sets of analyses thus far performed for an organism by means of constraint-based modeling , providing thereby a solid genome-scale framework for the exploration of the metabolism of this fascinating and versatile bacterium . However , since this modeling endeavor relies upon a number of approximations , the limits , potential and applicability of the analysis must be clearly identified and defined . We address these points below . Altogether , our results and analyses show that the model accurately captures a substantial fraction of the metabolic functions of P . putida KT2440 . Therefore , the model was used to generate hypotheses on constraining and redirecting fluxes towards the improvement of production of polyhydroxyalkanoates , which are precursors for industrially and medically important bioplastics . This is , to our knowledge , the first reported application of constraint-based modeling to direct and improve the yield of a compound of which the production is not directly coupled to the growth of the organism . This opens up novel areas of application for the constraint-based approach . Our approach , based on the OptKnock algorithm , allows for both prediction of mutants with desirable properties and identification of conditions that support the expression of these properties . Notwithstanding the generally good agreement between experimental results and simulations of our model , several of the discrepancies encountered reflect pitfalls inherent to constraint-based modeling that go beyond the scope of our study: Firstly , the high number of blocked reactions and the mismatches with the BIOLOG data show that there are still many areas of the metabolism that require thorough exploration . The genes encoding transport-related are particularly relevant , as for most of them , neither the translocated compound nor the mechanism of translocation is known . Furthermore , it should be highlighted that the genome still has 1635 genes annotated as “hypothetical” or “conserved hypothetical” , more than 800 genes annotated as putative , and over 800 for which the functional annotation gives no information beyond the protein family name . It is thus likely that a fraction of the hypothetical and non-specifically annotated genes in the current P . putida annotation are responsible for unknown metabolic or transport processes , or that some might code for proteins that add redundancy to known pathways . This observation is common to all genomes sequenced so far and illustrates a major hurdle in the model building process ( and hence , its usefulness ) that can be overcome only through extensive studies in functional genomics . Secondly , although we carefully constrained the in silico flux space through FBA and FVA and obtained distribution spaces roughly consistent with those experimentally determined via 13C- flux analysis , these approaches are inherently limited as they assume growth as a sole metabolic objective and ignore any effects not explicitly represented in a constraint-based metabolic model . It has been shown that FBA using objective functions other than growth can improve predictive accuracy under certain conditions [53] . Kinetic limitations also may play a very important role in determining the extent to which a particular reaction or pathway is used . Teusink et al . [52] showed that in the case of L . plantarum these factors may lead to false predictions . Thirdly , the reconstruction includes causal relationships between genes and reactions via gene-protein-relationships ( GPRs ) but it lacks explicit information regarding gene regulation . The regulation of gene expression causes that there are many genes in the cell that are expressed only under certain growth conditions . Therefore , the in silico flux space is generally larger than the true in vivo flux space of the metabolic network . This , in turn , may influence the robustness of the metabolic network and the essentiality of some reactions and genes . The lack of regulatory information and of the genetic interactions involved is likely to be one of the causes for faulty predictions of the viability of mutant strains . Adding this information will be an important step in the further development and improvement of the accuracy of the reconstruction . Fourthly , although our analyses indicated that growth yield is relatively insensitive to changes in biomass composition , these analyses also suggest that factors other than the structure of the metabolic network play an important role in defining the relationship between the growth yield and environmental conditions . The prediction of the exact growth yield requires the precise measurement of maintenance values , which may vary substantially from one condition to the other [44]–[46] . As the maintenance accounts for 10–30% of the total carbon source provided in unstressed conditions , this may set a limit to the accuracy of the growth yield predictions . To enhance the usefulness and predictiveness of the model , several avenues could be followed in the future . Firstly , additional constraints can be overlaid on the network to reduce the space of possibilities and increase the accuracy of predictions . In addition to specific knowledge of particular enzymatic or transport processes , such constraints are best based on high-throughput experimental evidence such as transcriptomic and proteomic data , which are instrumental in expanding genotype-phenotype relationships in the context of genome-scale metabolic models [67] . Microarray experiments have guided the discovery of metabolic regulons , and usage of microarray and proteomic data to constrain metabolic models has improved model accuracy for other systems [23] . Secondly , P . putida provides a good opportunity for incorporating kinetic information into a genome-scale model as there are various kinetic models available and under development for small circuits in P . putida [68]–[71] . Incorporating data from these models into the genome-scale reconstruction would provide insights into the relationships of isolated metabolic subsystems within the global metabolism . This synthesis would also improve the flux predictions of the global model , particularly in areas where current FBA-based predictions methods fail due to their inherent limitations . Experimental validation of a genome-scale model is an iterative process that is performed continuously as a model is refined and improved through novel information and validation rounds . In this work , we have globally validated iJP815 as well as specific parts thereof by using both up-to-date publicly available data and data generated in our lab , but there will be always parts of the model that include blocked reactions and pathways that will require further , specific validation . As more knowledge becomes available from the joint efforts of the large P . putida community ( e . g . , http://www . psysmo . org ) , focus will be put on these low-knowledge areas for future experimental endeavors . We anticipate that this model will be of valuable assistance to those efforts . The metabolic reconstruction , the subsequent mathematical computation and the experimental validation reported here provide a sound framework to explore the metabolic capabilities of this versatile bacterium , thereby yielding valuable insights into the genotype-phenotype relationships governing its metabolism and contributing to our ability to exploit the biotechnological potential of pseudomonads . By providing the means to examine all aspects of metabolism , an iterative modeling process can generate logical hypotheses and identify conditions ( such as regulatory events or conditional expression of cellular functions ) that would reconcile disagreements between experimental observations and simulation results . Through a detailed in silico analysis of polyhydroxyalkanoate production , we show how central metabolic precursors of a compound of interest not directly coupled to the organism's growth function might be increased via modification of global flux patterns . Furthermore , as the species Pseudomonas putida encompasses strains with a wide range of metabolic features and numerous isolates with unique phenotypes , the reconstruction presented provides a basic scaffold upon which future models of other P . putida strains can be built with the addition or subtraction of strain-specific metabolic pathways . Due to its applicability across the numerous P . putida strains iJP815 provides a sound basis for many future studies towards the elucidation of habitat-specific features , bioremediation applications and metabolic engineering strategies with members of this ubiquitous , metabolically versatile and fascinating genus .
The P . putida model we present was built using a constraint-based ( CB ) approach . A constraint-based model consists of a genome wide stoichiometric reconstruction of metabolism and a set of constraints on the fluxes of reactions in the system [19] , [20] , [24] . The reconstruction represents stoichiometry of the set of all reactions known to act in metabolism of the organism , which can be determined in large part from genomic data since most cellular reactions are catalyzed by enzymes . Thus the model does not require any knowledge regarding the kinetics of the reactions , and the requisite thermodynamic knowledge is limited to the directionality of reactions . In addition to the reactions , the model includes a set of genes tied via Boolean logic to reactions that their protein products catalyze , which allows for accurate discrimination of the effects of genetic perturbations such as knockouts [33] , [72] . These Boolean rules together form the gene-protein-reaction relationships ( GPRs ) of the metabolic reconstruction [33] . The second part of the CB-model , namely the constraints , constitutes a set of rules that narrow down the interval within which the flux of particular reaction must lie . These constraints rest upon physico-biological knowledge . One of them , the information regarding reaction directionality , has already been mentioned above . Another constraint that is widely applied in biological systems is the Pseudo-Steady-State Assumption ( PSSA ) [73] , which states that a concentration of a chemical compound stays constant over the simulated time frame . The reactants to which this constraint is applied are usually called internal compounds , and in biological models correspond to the chemical substances located inside the cell or its compartments . Remaining substances , external compounds , correspond to species that can be taken up or secreted and thus exchanged with the environment . Other types of constraints are top and bottom limits that correspond to catalytic capabilities of the enzymes . More detailed description of constraint based modeling approach can be found in [74] and the Text S1 , section “Constraint based models—mathematical explanation” . OptKnock is an approach for identification of mutations that selectively increase production of a certain compound of interest , assuming that the mutant would optimize for the same quantity as the wild type ( e . g . , growth yield ) [28] . OptKnock points out reactions ( and genes , through GPR logic ) that must be blocked in order to maximize a linear combination of target fluxes ( outer objective ) while simultaneously maximizing for the cell's assumed objective ( growth yield; inner objective ) . OptKnock poses a bi-level optimization approach that is solved via Mixed-Integer Linear Programming ( MILP ) . Further details can be found in Text S1 , section “OptKnock – mathematical formulation” and [28] . The minimal growing set was identified using a Mixed Integer Linear Programming ( MILP ) approach , by modifying original FBA LP problem . For every non-blocked and non-essential reaction a binary variable was added that reflects the activity of the reaction . When the binary variable takes value of 1 the corresponding reaction is virtually unlimited ( or limited by rules of original LP problem ) . When the variable is set to 0 the corresponding reaction is blocked ( non-zero flux is impossible ) . This was achieved by adding a following set of equations to the original LP problem:for reversible reactions , andfor irreversible reactions . In order to assure that growth was not overly restricted , a minimal flux value was established for the biomass reaction . We set the lower limit on biomass flux to 0 . 05 when the supply of carbon source was 60 mmolC·gDW−1h−1 , which corresponds to growth yield of 0 . 07 gDW·gC−1 , 16 times lower than the wild type . The objective of the problem was set to minimize the sum of all binary variables yi: This method searches for a minimal set that is able to sustain growth greater than or equal to to the minimal growth requirement . The main sources of information regarding the composition of the metabolic network of Pseudomonas putida KT2440 were various biological databases . Most of the information came from the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) [35] , [81] and Pseudomonas Genome Database ( PGD ) [82] . Information regarding P . putida contained in these two databases is mainly based on the published genome annotation of the bacterium [14] , so there is a large overlap between them . Additionally , substantial information was taken from the BRENDA database , which catalogs reaction and enzyme information [83] . This all was augmented with knowledge coming directly from primary research publications ( see Text S3 ) . The reconstruction process was performed in an iterative manner , i . e . , by adding or removing reactions from the model in between rounds of model testing . First , reaction information for P . putida was collected from KEGG and PGD . Reactions supported by sufficient evidence and with specific enough functional annotations were incorporated into the model . For every accepted reaction its reversibility was assessed basing on assignments in KEGG pathways as well as information from BRENDA database . For reactions with inconsistent assignments a decision about reversibility was made basing on analysis of the reaction as well as its reversibility in other organisms . Hereby , a first version of the metabolic model was created ( iJP815pre1 ) . The next step involved assessing whether the reconstructed metabolic network is able to produce energy from glucose . This was achieved by running FBA with ATP production set as the objective function . Subsequently , the ability of the model to grow in silico on glucose was tested . Successful in silico growth indicates that every chemical compound belonging to the biomass equation can be synthesized from present sources , using the reactions contained in the model . Since the exact cellular composition of P . putida is not known , the composition of E . coli biomass was used as an approximation . This test was performed by running FBA with production of each biomass constituent set as the objective . If a compound could not be synthesized , the gaps in the pathway leading to it were identified manually and a search was performed for reactions that could fill the gaps . If this approach was unsuccessful , gaps were filled with reactions from the E . coli model . This yielded the second version of the reconstruction ( iJP815pre2 ) . The third round of reconstruction consisted of two sub-steps . First , the compounds for which transport proteins exist were identified and appropriate reactions added . Second , the results of BIOLOG carbon-source utilization experiments were compared with in silico simulations for growth on those compounds . It was assumed that the ability to grow in silico on the particular compound as the sole carbon source approximates the in vivo utilization . For those compounds that did not show in silico growth , a literature search was performed in order to identify possible pathways of utilization . The results of this search , in the form of reactions and GPRs , were added to the model . The outcome was the final version of the model ( iJP815 ) . Growth yields on sources of basic elements ( C , N , P , S ) were compared with experimental values obtained by Duetz et al . [37] . The yields of the model were computed using FBA , by setting the growth rate to the value of the dilution rate used in experiments and subsequently minimizing for consumption of source of respective element ( succinate , ammonia , phosphate and sulfate ) . The model was created and maintained using ToBiN ( Toolbox for Biochemical Networks , http://www . lifewizz . com ) . The optimizations ( FBA , FVA , OptKnock ) were computed by free , open source , solvers from the COIN-OR family ( COmputational INfrastructure for Operations Research , http://www . coin-or . org ) or by the lp_solve ver . 5 . 5 ( http://lpsolve . sourceforge . net/5 . 5/ ) software package . All computations were performed on a Personal Computer with a Intel Core 2 2 . 40 GHz CPU and 2GB of RAM . | The pseudomonads include a diverse set of bacteria whose metabolic versatility and genetic plasticity have enabled their survival in a broad range of environments . Many members of this family are able to either degrade toxic compounds or to efficiently produce high value compounds and are therefore of interest for both bioremediation and bulk chemical production . To better understand the growth and metabolism of these bacteria , we developed a large-scale mathematical model of the metabolism of Pseudomonas putida , a representative of the industrially relevant pseudomonads . The model was initially expanded and validated with substrate utilization data and carbon-tracking data . Next , the model was used to identify key features of metabolism such as growth yield , internal distribution of resources , and network robustness . We then used the model to predict novel strategies for the production of precursors for bioplastics of medical and industrial relevance . Such an integrated computational and experimental approach can be used to study its metabolism and to explore the potential of other industrially and environmentally important microorganisms . |
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Resistance of viruses to mutagenic agents is an important problem for the development of lethal mutagenesis as an antiviral strategy . Previous studies with RNA viruses have documented that resistance to the mutagenic nucleoside analogue ribavirin ( 1-β-D-ribofuranosyl-1-H-1 , 2 , 4-triazole-3-carboxamide ) is mediated by amino acid substitutions in the viral polymerase that either increase the general template copying fidelity of the enzyme or decrease the incorporation of ribavirin into RNA . Here we describe experiments that show that replication of the important picornavirus pathogen foot-and-mouth disease virus ( FMDV ) in the presence of increasing concentrations of ribavirin results in the sequential incorporation of three amino acid substitutions ( M296I , P44S and P169S ) in the viral polymerase ( 3D ) . The main biological effect of these substitutions is to attenuate the consequences of the mutagenic activity of ribavirin —by avoiding the biased repertoire of transition mutations produced by this purine analogue—and to maintain the replicative fitness of the virus which is able to escape extinction by ribavirin . This is achieved through alteration of the pairing behavior of ribavirin-triphosphate ( RTP ) , as evidenced by in vitro polymerization assays with purified mutant 3Ds . Comparison of the three-dimensional structure of wild type and mutant polymerases suggests that the amino acid substitutions alter the position of the template RNA in the entry channel of the enzyme , thereby affecting nucleotide recognition . The results provide evidence of a new mechanism of resistance to a mutagenic nucleoside analogue which allows the virus to maintain a balance among mutation types introduced into progeny genomes during replication under strong mutagenic pressure .
The biology of RNA viruses is heavily marked by high mutation rates and quasispecies dynamics , relevant not only for virus evolution but also for viral pathogenesis ( review in [1] ) . The adaptive potential of viral populations as they replicate in the infected hosts represents a formidable problem for the control of viral disease by treatment with antiviral agents . Indeed , selection of viral mutants with decreased sensitivity to one or multiple antiviral inhibitors is an almost systematic occurrence , mainly for riboviruses and retroviruses [2]–[7] . The understanding of pathogenic RNA viruses as quasispecies opened the way to the exploration of a new antiviral approach termed virus entry into error catastrophe or lethal mutagenesis . This strategy was inspired in one of the corollaries of quasispecies theory that asserted that for any replicating system there must be a limit to the average error rate during template copying above which the information conveyed by the system cannot be maintained [8]–[13] . Applied to viruses , this concept implies that an increase of the viral mutation rate by mutagenic agents should result in virus extinction . This prediction has been amply confirmed experimentally with several virus-host systems in cell culture and in vivo , using different mutagens , notably nucleoside analogues [14]–[27] . One of the problems for a successful application of lethal mutagenesis to virus extinction is the selection of mutant viruses resistant to mutagenic agents . This problem has been manifested with the selection of picornavirus mutants with decreased sensitivity to the mutagenic base analogue ribavirin ( 1-β-D-ribofuranosyl-1-H-1 , 2 , 4-triazole-3-carboxamide ) ( R ) [27]–[31] . R is a licensed antiviral agent that has been used over several decades to treat some human viral infections , notably hepatitis C virus ( HCV ) infections , in combination with interferon ( IFN ) α or IFN α derivatives [32]–[35] . Since the important discovery that R is mutagenic for poliovirus ( PV ) [36] , R has been used as mutagenic agent in experimental studies of lethal mutagenesis of several RNA viruses [21] , [27] , [36]–[42] . However , R has several mechanisms of action [43] , [44] and whether R mutagenesis participates in the elimination of HCV during treatment of chronic HCV infections is still an open question [45]–[49] . Picornaviruses have contributed to the understanding of the molecular basis of resistance to R . A poliovirus ( PV ) mutant with decreased sensitivity to R included substitution G64S in its RNA-dependent RNA polymerase ( termed 3D ) . This substitution confers resistance to R through a general increase in template copying fidelity , at the cost of producing mutant spectra of lower complexity than wt PV . Limited mutant spectrum complexity resulted in PV populations which were less adaptable to a complex environment , a direct proof of the essential contribution of high mutation rates to RNA virus adaptability [29] , [31] . In the case of foot-and-mouth disease virus ( FMDV ) , resistance to R was associated with substitution M296I in 3D . Contrary to substitution G64S in PV , M296I did not result in increased template-copying fidelity of the 3D of FMDV . Rather , the mutant FMDV restricted the incorporation of RTP into RNA through an alteration of residues in the neighborhood of the active site of 3D that did not have a significant effect on the rate of misincorporation of the standard nucleotides [27] , [50] , [51] . Replacement M296I was sufficient to prevent extinction of FMDV by high concentrations of R , but the virus was extinguished by an alternative mutagenic treatment that included 5-fluorouracil ( FU ) [52] . Since R-resistance mutations can jeopardize viral extinction by lethal mutagenesis , it is of upmost importance to understand the molecular mechanisms of R-resistance , with the objective of designing adequate protocols for virus extinction . FMDV with replacement M296I was selected upon passage of the virus in the presence of increasing R concentrations in the range of 200 µM to 800 µM included in the cell culture medium [27] . Since R reduces the viability for BHK-21 cells in 40% after two days of treatment [42] , [52] , [53] ( see Materials and Methods ) , and allowed virus replication , we tested the response of FMDV to replication in the presence of high concentration of R . Here we report that FMDV populations replicated in the presence of concentrations of R in the range of 800 µM to 5000 µM , accumulated two additional amino acid substitutions in 3D in a step-wise fashion . The substituted polymerase displays a new molecular mechanism of R-resistance based on modulation of the types of R-induced misincorporations during RNA synthesis , based on an alteration of the pairing preference of R opposite C and U . In this manner , the mutant FMDV , but not the wild type FMDV , produces progeny RNA that shows a balanced distribution of transition types despite replicating in the presence of R . Studies of polymerization activity by the purified polymerases suggest that a single amino acid substitution in a loop of the fingers domain is the alteration chiefly responsible of the altered mutational pattern . The crystal structures of the substituted polymerases in complex with RNA show a conformational change in the template entry channel of the polymerase , that may affect the binding of the ssRNA template to 3D , mainly at the base of the template which is immediately downstream of the position that receives the incoming nucleotide . Alteration of the position of the template RNA at the active site of the enzyme may affect nucleotide recognition and modify the transition mutation pattern in the presence of R . The findings establish a new mechanism of lethal mutagenesis-escape in viruses which rests on regulation of the mutational spectrum in progeny viral genomes .
A biological clone of FMDV termed C-S8c1 is the standard virus used in our studies of molecular evolution and lethal mutagenesis of FMDV [54] . FMDV C-S8c1 was serially passaged in BHK-21 cells , and a monoclonal antibody ( MAb ) -escape mutant termed MARLS was isolated from the population at passage 213 [55] . FMDV MARLS was then subjected to passages in the presence of 200 µM to 800 µM R in the culture medium , resulting in selection of population R-Ap35 which included amino acid substitution M296I in 3D [27] ( Figure 1 ) . Population R-Ap35 displayed higher fitness than wild type FMDV in the presence of R but not in its absence [27] . Although M296I was the only replacement that became dominant in FMDV populations passaged in the presence of R ( the diagnostic nucleotide band in the consensus sequence did not indicate any detectable amount of an alternative nucleotide ) , other substitutions in 3D that did not reach dominance were also observed [56] . To study the response of FMDV to replication in the presence of higher concentrations of R , population R-Ap35 was subjected to 10 additional passages in the presence of 800 µM R , and then to 15 passages in the presence of increasing concentration of R ( from 1000 to 5000 µM ) , to obtain population R-Ap60 ( Figure 1 ) . Populations R-Ap60 and R-Ap35 displayed a similar mutation frequency in their mutant spectra ( Table 1 ) , but the specific infectivity [plaque-forming-units ( PFU ) /amount of viral RNA] of R-Ap60 was 10-fold lower than that of R-Ap35 ( Table 1 ) . These results suggest that virus replication under increased R concentrations led to loss of virus viability , not necessarily correlated with a significant increase of average mutation frequency . To test whether population of R-Ap60 was better adapted to R than population R-Ap35 , the relative fitness of the two populations was determined in growth-competition experiments in the presence and absence of R , using as reference the virus population Ap35 ( which is FMDV MARLS passaged 35 times in the absence of R , as described in Materials and Methods and in [27] ) . The results ( Table 2 ) indicate that R-Ap60 is better adapted than R-Ap35 to replicate in the presence of R . The adaptation of R-Ap60 resulted from a specific response of FMDV to R , since the fitness of R-Ap60 relative to R-Ap35 in the presence of FU and guanidine hydrochloride ( GuH ) ( an alternative mutagenic combination used in lethal mutagenesis of FMDV [52] ) was 0 . 7 ( Table 2 ) . Thus , FMDV underwent a progressive adaptation to replicate efficiently under high R concentration . To study whether adaptation of FMDV to increased concentrations of R was associated with additional substitutions in 3D , the consensus nucleotide sequence of the 3D-coding region of R-Ap35 and R-Ap60 was analyzed ( Table 3 ) . Two new mutations were found as dominant in R-Ap60: C6739U ( that gives rise to amino acid substitution P44S in 3D ) , and C7114U ( that gives rise to P169S in 3D ) . In addition , 3D maintained as dominant substitution M296I which was already dominant in R-Ap35 [27] , [50] . P44S but not P169S was detected in a 70% proportion in R-Ap35 , as evidenced by analysis of both the consensus sequences and their corresponding mutant spectra ( Table 3 ) . These results suggest that the three substitutions in 3D were selected sequentially during replication in the presence of increasing concentrations of R: first M296I , then P44S and finally P169S . To investigate the effect of the 3D substitutions in the sequence context of pMT28 ( the plasmid from which C-S8c1 is expressed [57] ) without possible confounding effects of other mutations in the viral genome , plasmids pMT28-3D ( M296I ) , pMT28-3D ( P44S ) , pMT28-3D ( P169S ) , pMT28-3D ( P44S , M296I ) and pMT28-3D ( SSI ) ( SSI means the presence of the triple replacement P44S , P169S and M296I in 3D ) were constructed as described in Materials and Methods . These plasmids encode the genome of C-S8c1 with the mutations that give rise to the indicated substitutions in 3D , as the only difference with respect to the wild type sequence ( pMT28 or C-S8c1 [57] ) . BHK-21 cells were transfected with the corresponding RNA transcripts and the rescued viruses [termed FMDV 3D ( M296I ) , FMDV 3D ( P44S ) , FMDV 3D ( P169S ) , FMDV 3D ( P44S , M296I ) and FMDV 3D ( SSI ) , respectively] were tested regarding infectious progeny production ( Figure 2 ) . FMDV 3D ( M296I ) , FMDV 3D ( P44S , M296I ) , and FMDV 3D ( SSI ) , but not FMDV 3D ( P44S ) and FMDV 3D ( P169S ) , showed lower progeny production in the absence of R . Fitness measurements in the absence and presence of R ( Table 4 ) indicate that the triple replacement P44S , P169S and M296I conferred on the virus a selective advantage in the presence of R . The addition of P169S to a virus harboring P44S and M296I provided an advantage during replication in the presence of 5000 µM but not 800 µM R . A direct competition showed a selective advantage of FMDV ( SSI ) over FMDV 3D ( M296I ) in the presence of 5000 µM R . P44S and P169S individually did not inflict a fitness cost upon the virus in the absence of R , whereas M296I and the triple combination did ( Table 4 ) . The three dominant substitutions in the polymerase of the clonal FMDV 3D ( SSI ) failed to reproduce the fitness difference between populations R-Ap60 and R-Ap35 in the presence of R ( compare Tables 2 and 4 ) . This means that factors other than the three dominant replacement in 3D must intervene to confer the growth advantage of R-Ap60 in the presence of R ( see Discussion ) . These additional factors are presently under investigation . The mutant spectra of FMDV populations passaged in the absence of drugs display a balance among the four types of transition mutations with a slight dominance of U→C and A→G versus C→U and G→A [27] , [58]–[60] . However , FMDV replication in the presence of R inverted this trend , and resulted in a clear dominance of C→U and G→A transitions [27] , [42] , as also observed with poliovirus ( PV ) replicating in the presence of R [21] , [36] . It was suggested that the bias in favor of C→U and G→A observed in FMDV could reflect a preference for ribavirin-5′-monophosphate ( RMP ) to be incorporated by 3D polymerase more efficiently opposite to C than U in the template , but this was not supported by the biochemical data on the incorporation of RMP by purified FMDV 3D using heteropolymeric template-primers [27] . The biased mutation types during intracellular viral replication could be influenced by the decrease in intracellular GTP levels due to the inhibition of inosine monophosphate dehydrogenase ( IMPDH ) by ribavirin-monophosphate ( RMP ) [42] , [43] , [61] , although previous studies suggested a minor effect of decreased intracellular concentration of GTP on the mutagenic activity of R on FMDV [42] . To explore possible variations in mutation frequency and in the types of mutations as a result of R treatment , FMDV wild type ( Wt ) ( rescued from plasmid pMT28 ) and FMDV 3D ( SSI ) were subjected either to five passages in the absence of R ( that gave rise to populations abbreviated as Wt-5 and SSI-5 , respectively ) or to four passages in the presence of 5000 µM R ( that gave rise to populations abbreviated as R-Wt-4 and R-SSI-4 , respectively ) . The comparison of mutation frequencies in the mutant spectrum of the different populations showed a 3 . 5-fold increase in both viruses after passage in the presence of R , as expected [27] , [42] , [52] , but no significant difference in mutation frequency between Wt-5 and SSI-5 ( t = 0 . 45 , P>0 . 1; t Student's test ) or between R-Wt-4 and R-SSI-4 ( t = 1 . 16 , P>0 . 1; t Student's test ) was seen ( Table 5 ) . The mutant spectra of Wt-5 and SSI-5 showed a similar distribution of mutation types ( χ2 = 0 . 02 , P>0 . 1; χ2 test ) , with a slight dominance of U→C and A→G , as previously found in FMDV populations that had replicated in the absence of R [27] , [58]–[60] . However , the mutation pattern of R-SSI-4 was unexpected for a virus passaged in the presence of a high concentration of R . While in the mutant spectrum of R-Wt-4 the bias in favor of C→U and G→A transitions reached 80% , in R-SSI-4 these transition types amounted to 34% of the total number of mutations . Thus , the repertoire of transition types remained balanced in FMDV 3D ( SSI ) despite replication in the presence of R , in sharp contrast with FMDV Wt which presented a gross imbalance in favor of C→U and G→A transitions . The ratio ( C→U ) + ( G→A ) / ( U→C ) + ( A→G ) in the mutant spectra of R-SSI-4 and SSI-5 was virtually identical ( χ2 = 0 . 49 , P>0 . 1 ) ( Table 5 ) . This result indicates a remarkable insensitivity to the presence of R regarding the mutations represented in progeny RNA when replacements P44S , P169S and M296I were present in 3D . The insensitivity to R could not be attributed to the absence of replication of FMDV 3D ( SSI ) in the presence of R since in fact this virus replicates more efficiently than wild type in the presence of R ( Table 4 ) . Thus , R-Wt-4 and R-SSI-4 displayed a highly significant difference regarding mutation types in their mutant spectra ( χ2 = 13 . 3 , P<0 . 001 ) . These results suggest that adaptation of FMDV to high R concentrations was related to modulation of the types of transitions imposed by the pairing behavior of RMP , preventing a highly biased mutation pattern in progeny genomes . To investigate whether FMDV 3D ( SSI ) was resistant to extinction by R , FMDV Wt and FMDV 3D ( SSI ) were subjected to serial cytolytic passages in BHK-21 cells in the presence or absence of 5000 µM R . The wild type population was extinguished by passage 7 , as expected [52] ( Figure 3A ) . In contrast , FMDV 3D ( SSI ) was not extinguished and , interestingly , the virus titer decreased until passage 6 , and then it increased ( Figure . 3B ) . While the specific infectivity of FMDV Wt decreased in the presence of R , the specific infectivity of FMDV 3D ( SSI ) was very similar in the R-treated and untreated populations ( Figure . 3C , D ) . The consensus nucleotide sequence of the genome of FMDV 3D ( SSI ) at passage 10 in the presence of R indicated that only one additional mutation ( in the non-structural protein 2C-coding region ) became dominant in the entire genome ( data not shown ) . The biological significance of this mutation in the 2C-coding region is under investigation . Substitutions P44S , P169S and M296I in 3D were maintained as dominant in the population that escaped extinction and gained replication capacity . To further substantiate the hypothesis that substitutions P44S , P169S and M296I confer a selective advantage in the presence of R but not in the presence of another mutagen that induces a different mutational repertoire , growth-competition experiments between FMDV Wt and FMDV 3D ( SSI ) were carried out in the presence of either R or FU ( a mutagen which induces mainly U→C and A→G transition in FMDV [58] , [60] ) or a mixture of R and FU . The results ( Table 6 ) show that a selective advantage of FMDV 3D ( SSI ) was manifested in the competitions carried out in the presence of R , but not in the presence of FU or of a mixture of R and FU . To investigate the effects of substitutions P44S , P169S and M296I on 3D activity , the wild type polymerase ( termed 3DWt ) , the polymerases that include the individual substitutions [termed 3D ( P44S ) , 3D ( P169S ) and 3D ( M296I ) ] and the polymerase with the three substitutions [termed 3D ( SSI ) ] were purified as detailed in Materials and Methods and compared in RNA polymerization , VPg-uridylylation and RNA-binding assays ( Table 7 ) . 3D ( P44S ) and 3D ( SSI ) showed lower activity than the other enzymes in poly ( rU ) synthesis and in binding to heteropolymeric RNA . In addition , 3D ( P44S ) displayed a modest decrease in VPg-uridylylation activity . The comparison of activity values in vitro suggests that amino acid P44S inflicted a cost upon 3D function . Previous studies documented that 3D ( M296I ) displayed a defect in the incorporation of RTP opposite either U and C , in comparison with 3D Wt [27] , [50] . The capacity of 3DWt , 3D ( P44S ) , 3D ( P169S ) , 3D ( M296I ) and 3D ( SSI ) to use RTP as substrate , and to incorporate RMP opposite U and C was investigated using two symmetrical/subtrate template-primer RNAs [62] , termed sym/sub-AC and sym/sub-AU ( AC and AU indicate the two template residues that direct the elongation of the primer RNA in two positions , and that allow quantification of the incorporation of R at position +2 , opposite C and U , respectively ) ( Figures 4 and 5 ) . No significant differences in the incorporation GTP of and ATP by 3DWt and 3D ( SSI ) were observed . Additional experiments were carried out using 1 µM GTP or ATP at 37°C or 33°C , with sym/sub-AC , sym/sub-AU , sym/sub-C and sym/sub-U; again , no differences in the incorporation by 3DWt and 3D ( SSI ) were observed ( Supplementary material , Figures S1 , S2 , S3 ) . In all cases , the mutant 3Ds were less efficient in RMP incorporation than 3DWt . Interestingly , the incorporation of RMP opposite C was 3-fold lower for 3D ( SSI ) than for 3D ( M296I ) , but no such difference was observed when the incorporation of RMP was measured opposite U ( Figure 5 ) . 3D ( P44S ) displayed undetectable incorporation of RMP opposite C in the template ( <0 . 5% of elongated sym/sub-AC ) , and only a modest incorporation opposite U ( 5±1% of elongated sym/sub-AU ) . Thus , the incorporation of RMP by 3D ( P44S ) is at least 10-fold more efficient opposite U than opposite C , suggesting that P44S is the substitution responsible for the biased repertoire of transition mutations during replication of FMDV 3D ( SSI ) in the presence of R . Comparison of the results of 3D activity ( Table 7 ) and of RMP incorporation ( Figures 4 and 5 ) suggests that substitutions P169S and M296I could exert some compensatory effect to confer 3D with P44S a sufficient polymerization activity while maintaining a limited and biased RMP incorporation . The specific bias displayed by 3D ( SSI ) against incorporation of RMP opposite C determined in vitro , is consistent with the proportion of transition types observed during replication of FMDV 3D ( SSI ) in the presence of R during infections of BHK-21 cells ( compare Figures 4 , 5 and Table 5 ) . FMDV 3D ( SSI ) populations did not display a significantly lower mutant spectrum complexity than FMDV Wt ( Table 5 ) , suggesting that the biased incorporation of R is not directly linked to a significant change in the average template copying fidelity as regards the misincorporation of standard nucleotides . However , this point is under further investigation . To identify possible structural modifications of the viral polymerase associated with the important alterations of the mutational spectrum in progeny RNA , and to investigate how these modifications can affect RNA binding and polymerase activity , the different mutant 3Ds were incubated with the heteropolymeric sym/sub-U RNA of sequence 5′GCAUGGGCCC3′ , crystallized , and analyzed by X-ray diffraction . Sym/sub- U indicates that U is the template residue which directs the incorporation of A to produce a +1 elongation product . This is the same RNA used in our previous structural studies with FMDV 3D [63]–[65] ( see Materials and Methods ) . For the structural comparisons RNA residues are numbered starting at the 5′ terminal nucleotide . Two different crystal forms were obtained ( Table 8 ) ; the single mutants 3D ( P44S ) and 3D ( P169S ) incubated with sym/sub-U RNA crystallized in the tetragonal P42212 space group . The RNA molecule appeared mostly disordered in the two structures . In contrast , 3D ( SSI ) crystallized in the trigonal P3221 space group with the sym/sub-U RNA incorporated in the structure . Since the biochemical results indicate that P44S plays a critical role in the misincorporation of RMP into RNA by 3D , and a 3D ( P44S ) -RNA complex was not obtained , we attempted the crystallization of the double mutant 3D ( P44S , M296I ) in complex with RNA . 3D ( P44S , M296I ) also crystallized in the space group P3221 space group , with the sym/sub-U RNA incorporated in the structure . Further attempts to obtain the structures of ternary complexes , using ATP or RTP were unsuccessful , despite using different substrate concentrations and incubation times . The X-ray structures were determined to 2 . 2 Å and 2 . 6 Å resolution for 3D ( P44S ) and 3D ( P169S ) , respectively , and to 2 . 6 Å and 2 . 5 Å for 3D ( P44S , M296I ) and 3D ( SSI ) , respectively ( Table 8 ) . The quality of the resulting difference electron density maps allowed the unequivocal tracing of the mutated and surrounding residues that were omitted from the initial models to eliminate model bias ( Figure 6 ) . The analysis of the electron density showed also the presence of the duplex portion of the template-primer RNA in the central channel of the polymerase of the trigonal 3D ( P44S , M296I ) and 3D ( SSI ) crystals . In addition , two of the four nucleotides of the 5′ overhang moiety ( A3 and U4 ) were reasonably well defined , occupying the template channel , in both structures . No major structural changes were observed in the polymerase active site when the structures of the different polymerases ( either unbound or bound to RNA ) were compared . The structural superimpositions of all 476 amino acids residues of 3D ( P44S ) and 3D ( P169S ) and of 3D ( P44S , M296I ) onto the 3D ( SSI ) showed root mean square deviation ( rmsd ) values of 0 . 46Å , 0 . 35Å and 0 . 22Å , respectively . Subtle domain movements , in particular a ∼1° rotation of the thumb domains relative to the fingers , were observed between the unbound , tetragonal , and the RNA-bound , trigonal structures when the individual domains were superimposed . When the unbound and RNA-bound structures were compared for 3DWt a similar small rotation ( ∼2° ) was also observed . As a consequence of this rotation , the active site appeared more closed in the unbound state . Thus , the changes observed seem to be a consequence of either RNA-binding , or of the different packing constraints in the tetragonal and trigonal space groups or both , but they do not seem to be related to the presence of substitutions P44S or P169S . The 3D ( P44S , M296I ) -RNA and 3D ( SSI ) -RNA structures are almost identical ( rmsd of the superimposition of all polymerase residues of 0 . 22Å ) . These structures are also similar to the structure of 3D ( M296I ) -RNA mutant complex determined previously ( PDB 3KOA; [51] ) , and to the wild type 3D-RNA complex ( PDB 1WNE; [63] ) , with rmsds of 0 . 33Å and 0 . 38Å , respectively . Compared to the wild type 3D , two significant changes are observed in the substituted 3Ds: a conformational change in loop β9-α11 ( where substitution M296I lies ) and a structural rearrangement of the N-terminus of the polymerase . The conformation and interactions of loop β9-α11 are identical in the 3D ( P44S , M296I ) and 3D ( SSI ) complexes , retaining the same structure that was previously observed in 3D ( M296I ) in complex with RNA [51] . All mutants that contain the substitution M296I show a rearreagement in the loop β9-α11 , consisting in a rotation of the peptide bonds Ser298-Gly299 and Cys300-Ser301 ( Figure 7 ) . These residues were found hydrogen bonded to the incoming RTP molecule in the structure of the ternary complex between the wild type 3D-RNA-RTP [65] , and also interacting with the template acceptor nucleotide in all structures analyzed [51] , [65] , [66] ( Figure 7 ) . Interestingly , the amino acid residues from M16 to K18 , at the N-terminus of the enzyme , appear totally re-organized ( Figure 8 ) . This region , together with residues T115 to A122 of motif G and amino acids Q160 , F162 and T181 of motif F , form the template channel that binds the 5′ overhang region of the template , driving the ssRNA to the active site cavity [64] . The structures of the wild type 3D-RNA elongation complexes as well as the structure of the mutant 3D ( M296I ) -RNA complex show that R17 interacts with the sugar-phosphate backbone of template nucleotide A3 that is oriented towards the active site cavity ( Figure 9; [64] , [65] ) . In 3D ( P44S , M296I ) -RNA and 3D ( SSI ) -RNA complexes the re-oriented residue R17 points to the polymerase interior , interacting with the side chain of residues N41 ( which lies in the same loop of the substituted amino acid S44 ) , and with Y285 . Nucleotide A3 appears also reoriented , flipped-out towards a pocket formed by amino acids M16 , P117 , G118 , Q160 , F162 , V181 and V183 ( Figure 8 ) . These structural results indicate that the small movements in the loop , that contains the substituted residue S44 , facilitate the large conformation changes of the 3D N-terminal residues M16-K18 , and the reorientation of the template nucleotide A3 . The rearrangements in M16-K18 were also observed in the uncomplexed 3D ( P44S ) , but with a weak electron density and higher temperature factors than the average , reflecting some degree of flexibility of this region in the absence of RNA . Finally , no significant structural changes were observed associated with substitution P169S . Thus , the structural results point at P44S as the key substitution related to reorientation of template residues that might be associated with altered RMP recognition and incorporation .
The great adaptive capacity of RNA viruses to adverse environmental conditions has been fully manifested in the present study with the selection of mutant polymerases capable of biasing the incorporation of RMP so as to modulate the overall mutation types in progeny genomes . The adaptation of FMDV to high R concentrations was mediated by the sequential selection of M296I , P44S and P169S in 3D , with P44S being the main responsible for maintaining a balance of transition types in progeny RNA synthesized in the presence of R . The three amino acid substitutions in 3D were the result of mutation types that are favored during replication of FMDV in the presence of R: a G→A transition in the case of M296I , and a C→U transition in the case of P44S and P169S . Except for P44S and P169S when present individually in 3D , the substitutions in 3D had as consequence a modest but consistent decrease in viral fitness when measured in the absence of R . None of the three replacements in 3D has been previously observed in FMDV C-S8c1 populations ( or their mutant spectra ) passaged in the absence of R or in the presence of FU or 5-azacytidine [16] , [27] , [42] , [53] , [58]–[60] , [67]–[71] . Thus , they were selected as a specific response to R and , as expected , the combination of the three substitutions increased FMDV fitness during virus replication in the presence of R ( Table 4 ) . Remarkably , the selective advantage of FMDV expressing 3D with the triple combination P44S , P169S , M296I over virus expressing 3D with P44S and M296I was manifested in growth-competition experiments carried out in the presence of 5000 µM R but not in the presence of 800 µM R . Thus , P169S appears to have been selected to contribute a fitness increase in the presence of high R concentrations to a virus that had already acquired the capacity to modulate the mutational spectrum through substitution P44S in 3D . Additional growth-competition experiments between wild type and the triple mutant FMDV indicated that the substituted polymerase conferred a selective advantage when the virus replicated in the presence of R but not of FU or a mixture of R and FU , supporting a specific adaptative response in front of ribavirin ( Table 6 ) . The result is consistent with the fitness advantage of R-Ap60 over Ap35 in the presence of R but not of FU+GuH ( Table 2 ) . Since FU tends to evoke the opposite transition types than R [58] , [60] , the outcome of the competitions reinforces modulation of transition types as a major factor for the survival of FMDV 3D ( SSI ) in the presence of ribavirin . It may be argued that selection of the multi-substituted polymerase occurred as a result of subjecting the virus to extremely large ribavirin concentrations , unlikely to be reached during any standard antiviral treatment with R . Certainly , the concentrations used were not intended to reproduce actual R concentrations in the course of treatments with R in clinical practice . In the case of direct aerosol application of R to the upper respiratory tract the drug may reach intracellular concentrations of around 800 µM [72] , [73] . Other modes of administration are unlikely to achieve such high concentrations . For example , intravenous administration of R results in peak concentrations in the range of 20 µM to 150 µM [74] , [75] while oral administration resulted in concentrations between 10 µM and 20 µM in serum and cerebrospinal fluid [74] , [76]–[78] . Thus , unless procedures for targetted delivery of R to specific cells or tissues are developed , it is unlikely that concentrations equivalent to those used in our experiments would be encountered in vivo . Do the high concentrations of R used in our experiments weaken the relevance of the conclusions ? We think not for two reasons: ( i ) the actual concentration of RTP in the replication complexes of viruses is unknown , and it cannot be excluded that methods of targetted delivery could be developed that result in high local RTP concentrations; ( ii ) extreme environmental conditions ( a prolonged plaque-to-plaque passage regime , passages in the presence of monoclonal antibodies , etc . ) have previously been used to unveil either evolutionary responses or the sensitivity of biochemical processes to subtle genetic change [1] , [69] , [79] . Thus , our model studies must be regarded as designs to disclose potential mutagen-resistance mechanisms that are informative of the potential of the polymerase to adapt its catalytic machinery to extraneous substrates , despite using conditions unlikely to be encountered in vivo . The virological and biochemical evidence presented here support the hypothesis that the polymerase substitutions , whose effect was to avoid a highly biased distribution of mutation types normally induced by a mutagenic agent , contributed to viral survival and escape from extinction ( Figure 3 ) , implying a new mechanism of virus resistance to lethal mutagenesis . This new mechanism does not require significant reductions of mutant spectrum complexity thereby preserving an amplitude of the mutant cloud adequate for virus adaptability to complex environments or following a bottleneck event [1] , [28]–[30] . The balanced mutational spectrum produced by FMDV 3D ( SSI ) was maintained in the absence or presence of 5000 µM R , while FMDV Wt produced a mutant spectrum with 80% ( C→U ) + ( G→A ) only after 4 passages in the presence of 5000 µM R ( Table 5 ) . A deleterious effect of biased substitution types is likely because they can affect codon usage and specific RNA structures needed during viral replication [80]–[85] . Examination of the repertoire of mutations ( and corresponding amino acid substitutions ) present in the mutant spectra of FMDV Wt and FMDV SSI passaged in the presence of 5000 µM R is highly illustrative of the deleterious effects of the incapacity of the polymerase to modulate transition types ( Table 9 ) . First , the proportion of non-synonymous mutations relative to the number of nucleotides sequenced is 1 . 5-fold higher for R-Wt-4 than for R-SSI-4 . Second , in the R-Wt-4 population a stop codon was generated as a result of a G→A transition at genomic position 7319 , and 74% of the 27 amino acid substitutions scored were the result of C→U or G→A transitions . In contrast , of the 15 amino acid substitutions in R-SSI-4 only 20% were the result of C→U or G→A transitions . The most salient amino acid substitutions found in population R-Wt-4 are G125R , C300Y and G435E , each originated from a G→A transition ( Table 9 ) . These residues are conserved among picornaviruses and the substitutions observed might have relevant structural effects . G125R is an infrequent substitution that introduces a bulky residue that was tolerated probably because it lies in an exposed region at the entrance of the template channel [63] , [64] . C300 is located in loop β9-α11 , and its main chain interacts with the rNTP and the acceptor base of the template RNA . In the complex with RTP , the G299-C300 peptide bond is rotated in a way that favors the interaction with the pseudobase [65] ( Figure 7 ) . Replacement of C by Y is likely to affect the flexibility of this region and , as a result , the interactions with RNA and the rNTP . G435 is located in a short turn between helices α14 and α15 in the thumb domain , a region which is rich in small and flexible amino acids [63] . The introduction of an E residue in this region is not expected , and it might affect the stability of this 3D region . In contrast to R-Wt-4 , among the amino acid replacements found in the R-SSI-4 populations , the most noticeable is K164E located in motif F of 3D . K164 is not among the basic amino acids that interact with the incoming rNTP , but it is hydrogen bonded to template base A3 [63] . An E residue could participate in the same interaction , as also observed between 3D residue D165 and base U4 in the same complex [63] . Thus , the comparison of the mutant repertoire in R-Wt-4 and R-SSI-4 reinforces the likely adverse effects of an abundance of C→U and G→A transitions for FMDV fitness . It is not clear whether the most detrimental factor is the imbalance of mutation types by itself , or the increased frequency of U and A residues in genomic RNA , or a combination of both factors . Whatever the mechanism , the results suggest that the maintenance of a suitable transition pattern during RNA synthesis in an environment of high mutational pressure can be beneficial for the virus under increased average mutation rates . Despite the clear virological and biochemical effects of substitutions P44S , P169S and M296I in 3D , the comparison of fitness values for clones and populations indicates that it is unlikely that the replacements in 3D are the only determinants of high level resistance to R . Indeed , the fitness of the uncloned FMDV population R-Ap60 was 15-fold higher than the fitness of control population Ap35 , when measured in the presence of 5000 µM R , while the fitness of the cloned FMDV 3D ( SSI ) was 2-fold higher than the fitness of the cloned FMDV Wt , measured under the same conditions ( Tables 2 and 4 ) . There are two main possibilities to account for the larger difference of fitness between FMDV R-Ap60 and Ap35 than between FMDV 3D ( SSI ) and FMDV Wt . One is that the complexity or composition of the mutant spectrum of R-Ap60 conferred a selective advantage to the mutant ensemble that could enhance R resistance , even in the absence of additional dominant mutations ( or mutations in their way to dominance ) . Recent observations on the selective value of mutant spectrum complexity and composition [1] , [29] , [53] , [71] , [86] , [87] do not permit excluding this possibility . An alternative , not mutually exclusive possibility , is that mutations in genomic regions of FMDV other than 3D contribute also to R resistance in RAp60 . Current evidence suggests that non-structural protein 2C may also contribute to FMDV escaping extinction ( Agudo et al . , manuscript in preparation ) . P44 is conserved among known picornaviral polymerases , and it lies in a loop that connects strand β2 and helix α2 in the fingers domain ( Figure 9 ) . This loop contains a number of residues that establish tight contacts with amino acids V173 to G176 of motif F and with the N-terminal residues M16 , R17 and K18 of 3D . Amino acids M16 and R17 form part of the template channel that drives the ssRNA template towards the active site . Thus , substitutions at the conserved amino acid P44 might disturb both the shape and interactions of the template channel , and the interactions with the incoming rNTP that are mediated by residues of motif F . The structures of the mutant polymerases determined in the present study do not show large domain movements . However , the crystal structures of 3D ( P44S , M296I ) and 3D ( SSI ) in complex with the RNA template-primer reveal a rearrangement in the template channel with important effects in template binding , in particular , at position n+1 ( nucleotide A3 ) . The conformational changes in the main and side chains of residues M16 and R17 allow the opening of a hydrophobic pocket formed by residues of motifs G and F and by M16 that facilitates the entrance of nucleotide A3 ( Figure 8 ) . The polymerase with substitution M296I that acquired substitution P44S maintained the alteration of loop β9-α11 previously described for 3D ( M296I ) [51] ( Figure 8 ) . Thus , the catalytic domain and template interactions may be affected by additive effects of substitutions M296I and P44S . The different interactions established between the modified template channel of the substituted polymerases and nucleotide A3 could facilitate a different alignment of the template strand , thus altering the nucleotide incorporation activity . However , this possibility has not been substantiated because of the inability of nucleotide substrates to be incorporated into the mutant 3D-RNA complexes . Finally , P169 is a non-conserved residue located in motif F of 3D ( Figure 9 ) that has been implicated in the recognition of the triphosphate moiety of the incoming nucleotide . P169 is close to 3D residues that directly contact with either the triphosphate or ribose moieties of the incoming nucleotide [63] , [88] . The structural comparisons do not reveal any conspicuous change in the polymerase induced by substitution P169S . However , we can not exclude that a change at this position could also affect the recognition of an incoming nucleotide , modulating its incorporation rate , and thus altering the replication fidelity or replicative fitness . Thus , subtle structural modifications that affect the template channel of 3D mediate alterations in substrate recognition that may modify recognition of RTP and the repertoire of R-mediated mutations .
The origin of BHK-21 cells , procedures for cell growth and for infection with FMDV in the presence or absence of ribavirin ( R; Sigma ) , 5-fluorouracil ( FU; Sigma ) , or guanidine hydrochloride ( GuH; Sigma ) have been previously described [27] , [53] , [54] , [59] . Briefly , for each infection the first passage was carried out at moi 0 . 3 PFU/cell . For the following passages , 2×106 BHK-21 cells were passaged with supernatant of virus from the previous passage ( 0 . 2 ml ) , and the infection allowed to proceed for about 24h . Values of PFU for each passage can be estimated from infectivities given in Figure 3 . FMDV C-S8c1 is a plaque-purified derivative of natural isolate C1 Santa-Pau Spain 70 [54] , a representative of European serotype C FMDV . FMDV MARLS is a monoclonal antibody-escape mutant selected from the C-S8c1 population passaged 213 times in BHK-21 cells [69] . Ap35 and R-Ap35 are FMDV MARLS passaged 35 times in the absence or in the presence , respectively , of increasing concentrations of R as previously described [27]; FMDV MARLS populations passaged 45 and 60 times in the presence of increasing concentrations of R have been termed R-Ap45 and R-Ap60 ( see Figure 1 ) . R exerted a cytostatic effect in BHK-21 cell monolayers ( measured as cell viability using trypan blue staining ) . The cytotoxicities as a result of treatment of BHK-21 cell monolayers with R , FU and GuH have been previously described [53] , [58] , [59] . The maximum reduction of cell viability of confluent BHK-21 cell monolayers in the presence of 5000 µM R was around 40% at 48 h post-treatment , in agreement with our previous results [53] . Evidence that cytotoxicity by R does not contribute significantly to FMDV extinction includes the observation that FMDV mutant with amino acid replacements in 3D that confer resistance to R can replicate and survive after multiple passages in the presence of 5000 µM R ( Figure 3 , and unpublished observations ) . RNA was extracted from the supernatants of infected cells using described procedures [27] , [67] . Reverse transcription ( RT ) was carried out using AMV reverse transcriptase ( Promega ) , and PCR amplification was performed using EHF DNA polymerase ( Roche ) as specified by the manufacturer . RT-PCR amplification intended for the cloning of individual cDNA molecules was carried out using Pfu ultra DNA polymerase ( Stratagene ) . Amplification protocols , nucleotide sequencing and primers used for amplification and sequencing have been previously described [27] , [42] , [67] . FMDV RNA was quantified by real-time RT-PCR amplification using the Light Cycler instrument ( Roche ) and the RNA Master SYBR green I kit ( Roche ) as previously described [27] . Plasmid pMT28 encodes an infectious transcript of FMDV C-S8c1 [57] , [70] . The construction of plasmid pMT28-3D ( M296I ) ( an infectious clone expressing 3D with substitution M296I in the context of the C-S8c1 genome ) has been previously reported [27] . The rest of chimeric plasmids encoding mutant 3Ds were constructed by replacing part of the 3D-coding region of pMT28 with the corresponding mutant 3D-coding region of interest . To construct pMT28-3D ( P169S ) ( an infectious clone encoding 3D with amino acid substitution P169S in the context of the C-S8c1 genome ) , two DNA amplifications were carried out using Pfu ultra DNA polymerase and pMT28 DNA as template . A first amplification with 3AR3 ( GATGACGTGAACTCTGAGCCCGC; sense , 5′ position 5710 ) and 3′3DP169S ( CTTTCTCCATGCTGCGAATTTCGTCCTTCAGGAAGG; antisense , 5′ position 7126 ) ; and a second amplification with 5′3DP169S ( CGAAATTCGCAGCATGGAGAAAGTACGTGCCGG; sense , 5′ position 7104 ) and 3D1 ( CTTGTTGCGGAACAGCCAGATG; antisense , 5′ position 7520 ) were performed ( bold-face letters indicate modifications of the genomic sequence introduced to express 3D with substitution P169S ) . ( Nucleotide positions correspond to the numbering of FMDV genomic residues described in [69] ) . The two amplicons were shuffled and digested with RsrII ( position 5839 ) and ClaI ( position 7004 ) ( New England Biolabs ) and ligated to pMT28 DNA linearized with the same enzymes . A similar procedure was used to construct pMT28-3D ( P44S ) and pMT28-3D ( P44S , M296I ) . To prepare pMT28-3D ( P44S , M296I ) ( an infectious clone expressing 3D harboring substitution P44S and M296I in the context of the C-S8c1 genome ) , pMT28-3D ( M296I ) was subjected to the same procedure described above for pMT28 , except that the two pairs of primers used for the PCR amplification were 3AR3 ( described above ) with 3′3DP44S ( CGTTCAGACGGCTGTCCTTGTTAGACAAGGCGG; antisense , 5′ position 6751 ) , and 5′3DP44S ( CTAACAAGGACAGCCGTCTGAACGAAGGTG; sense , 5′ position 6728 ) with A3 ( CGTCGACAATGCGAGTCTTGCCG; antisense , 5′ position 7156; bold-face letters indicate modifications of the genomic sequence introduced to express 3D with substitution P44S ) . The two amplicons were shuffled , digested with RsrII and ClaI , and ligated to pMT28 or pMT28-3D ( M296I ) DNAs linearized with the same enzymes , rendering pMT28-3D ( P44S ) and pMT28-3D ( P44S , M296I ) , respectively . Finally , to construct pMT28-3D ( P44S , P169S , M296I ) ( an infectious clone expressing 3D with amino acid substitution P44S , P169S and M296I in the context of the C-S8c1 genome ) , procedures were carried out as those described for pMT28-3D ( P169S ) except that the parental plasmid used both as template for DNA amplifications and for cloning was pMT28-3D ( P44S , M296I ) instead of pMT28 . For simplicity , the plasmid that includes the three amino acid substitutions in 3D has been termed pMT28-3D ( SSI ) and the rescued virus FMDV 3D ( SSI ) . Ligation , transformation of E . coli DH5α , colony screening , nucleotide sequencing , preparation of infectious RNA transcripts , and RNA transfections were carried out as previously described [27] , [67] . To determine the complexity of mutant spectra , FMDV RNA was extracted as described above and subjected to RT-PCR using primers PolC-KpnI ( GTTGGTACCCACTCTGCTGGAGGC; sense , 5′ position 6502 ) and Pol1-XbaI ( AATCTAGATGTTTGGGGGATTATGCG; antisense , 5′ position 8060; the letters underlined indicate the sequences recognized by restriction enzymes KpnI and XbaI , respectively ) . cDNA was digested by KpnI and XbaI enzymes ( New England Biolabs ) , and ligated to plasmid pGEM-3Z Vector ( Promega ) previously digested with the same enzymes . Transformation , colony screening and nucleotide sequencing were carried out as previously described [27] , [67] . The region sequenced spans residues 6508 to 8036 and includes the entire 3D-coding region ( residues 6610 to 8020 ) . The number of clones analyzed and the total number of nucleotides sequenced are given in the appropriate section of Results . The complexity of mutant spectra was expressed as the mutation frequency , calculated by dividing the number of different mutations by the total number of nucleotides sequenced . Relative fitness was measured by growth-competition experiments in the presence or absence of R . The logarithm of the ratio of the two competing viruses was plotted against passage number , and the fitness vector was adjusted to an exponential equation y = a×ebx . The antilogarithm of the vector slope is the fitness of the virus tested , relative to that of the reference virus [27] , [68] . The proportion of the two competing genomes at different passages was determined by real-time RT-PCR , employing primers specifically designed to discriminate accurately the two RNAs in the competition ( Table 10 ) . For each fitness determination , the R2 value of the corresponding linear regressions is also given ( Tables 2 and 4 ) . A solution of ribavirin ( R ) in PBS was prepared at a concentration of 100 mM , sterilized by filtration , and stored at −70°C . Prior to use , the stock solution was diluted in DMEM to reach the desired R concentration . To prepare culture medium containing 5-fluorouracil ( FU ) ( Sigma ) , the analogue was dissolved in DMEM to yield a 5 mg/ml solution , and diluted in DMEM , as needed for the experiments . For infections in the presence of R ( 5000 µM ) and FU ( 2000 µM ) , cell monolayers were treated during 7 h and 10 h , respectively , prior to infection . The relative fitness of FMDV SSI was determined by growth competition with the Wt virus in BHK-21 cells in the presence of R , FU or a mixture of both drugs . Briefly , the viral population to be assayed was mixed with the same number of PFU of FMDV Wt , used as reference . For each determination , four serial infections were carried out at moi 0 . 3 PFU/cell . The proportion of the two competing genomes at each passage was determined by measuring the area of the three peaks corresponding to the residues that distinguish 3D Wt from 3D ( SSI ) . Each mutation was confirmed by two independent sequencing assays using primers of different orientation . The average of triplicate measurements and standard deviations are given . FMDV 3D with substitutions M296I [termed 3D ( M296I ) ] , P169S [termed 3D ( P169S ) ] , P44S [termed 3D ( P44S ) ] , with P44S and M296I [termed 3D ( P44S , M296I ) ] , or with the three of them [3D ( P44S , P169S , M296I ) which is abbreviated as 3D ( SSI ) ] were obtained from plasmid pET-28a 3Dpol [expression vector pET-28a ( Novagen ) containing the FMDV polymerase 3D-coding region [67]] by site-directed mutagenesis with oligonucleotides containing the corresponding mutated nucleotides , using the QuickChange site-directed mutagenesis kit ( Stratagene ) . Mutagenesis , 3D expression , and 3D purification by affinity chromatography , were carried out as previously described [63] , [67] . The enzymes were >95% pure , as judged by SDS-PAGE electrophoresis and Coomassie brilliant blue staining . Incorporation of standard nucleoside-5′-triphosphates or ribavirin-5′-triphosphate ( RTP ) by wild type and mutant 3Ds was measured in self-complementary RNAs that form double stranded RNA in which each strand can act both as template and primer [62] . RNAs 5′-CGUAGGGCCC-3′ ( termed sym/sub-AU ) , 5′-UGCAGGGCCC-3′ ( termed sym/sub-AC ) , 5′-GUACGGGCCC-3′ ( termed sym/sub-C ) and 5′-GCAUGGGCCC-3′ ( termed sym/sub-U ) ( Dharmacon Research ) were used . The oligonucleotides were purified , end-labeled with [γ-32P] ATP and polynucleotide kinase ( New England biolabs ) , and annealed using standard protocols [27] , [89] . For the reaction with sym/sub-AU and sym/sub-U , 0 . 5 µM of RNA-duplex and 2 µM 3D were incubated in 30 mM MOPS ( pH 7 . 0 ) , 33 mM NaCl , 5 mM Mg ( CH3COO ) 2 , and 50 µM UTP ( Amersham ) in the case of sym/sub-AU , for either 10 min [for 3D wt , 3D ( P169S ) and 3D ( M296I ) ] or 30 min [for 3D ( P44S ) and 3D ( SSI ) ] at 37°C , or 33°C when mentioned; 3D ( SSI ) and 3D ( P44S ) were incubated for longer periods of time because they display a defect in RNA binding ( see Results ) . After formation of a binary complex of 3D-RNA , [elongated in one nucleotide in the case of sym/sub-AU ( 3D-sym/sub-AU , n+1 complexes ) ] , an excess of unlabeled sym/sub-AU ( 5 µM ) was added to trap the unbound 3D , and to avoid the recycling of labelled sym/sub-AU . The reaction was initiated by adding either 50 µM ATP ( Amersham ) or 50 µM ( RTP ) ( Moraveck ) , or 1µM ATP when mentioned . The reaction was stopped at different times by the addition of EDTA ( 83 mM final concentration ) . Identical procedure was followed with sym/sub-AC , except that GTP and RTP were used as substrates . Reaction products were analyzed by electrophoresis on a denaturing 23% polyacrylamide , 7 M urea gel in 90 mM Tris-base , 90 mM boric acid , 2 mM EDTA . The 11 mer ( sym/sub elongated in one nucleotide by addition of UMP ) and ≥12-mer ( sym/sub elongated in two or more nucleotides by addition of the required nucleotides ) were visualized and quantitated with a Phosphorimager ( BAS-1500; Fuji ) . Poly ( rU ) synthesis using poly ( A ) -oligo ( dT ) 15 as template-primer molecule , VPg uridylylation with poly ( A ) as template and Mn++ as ion , and RNA binding assays were carried out as previously described [67] , [89] . Purified FMDV mutant polymerases 3D ( P44S ) , 3D ( P169S ) , 3D ( P44 , M296I ) and 3D ( SSI ) were stored in a buffer containing Tris-HCl ( 40mM , pH 7 . 5 ) , NaCl ( 0 . 5M ) , DTT ( 0 . 8mM ) , EDTA ( 0 . 8mM ) , and glycerol ( 8% ) , at a concentration of ∼4 . 6 mg/ml . The oligonucleotide 5′GCAUGGGCCC 3′ ( NWG-Biotech ) ( sym/sub-U ) was annealed following the described procedure [62] . Then the 3D was added slowly to an equimolar proportion in the presence of 2mM MgCl2 . The mutant 3Ds and their complexes were crystallized as previously described [63] . Four different data sets were collected at 100 K: 3D ( P44S ) ( 2 . 2 Å ) , 3D ( P169S ) ( 2 . 6 Å ) , 3D ( P44 , M296I ) -RNA ( 2 . 6 Å ) and 3D ( SSI ) -RNA ( 2 . 5 Å ) , using synchrotron radiation at the ESRF beamlines ID14 EH1 and EH2 ( λ = 0 . 93 Å ) . All data were processed and reduced using DENZO/SCALEPACK package [90] ( Table 8 ) . The initial maps for the 3D ( P44S ) and 3D ( P169S ) ( tetragonal crystals ) were obtained after a rigid body fitting of the coordinates of isolated 3D protein that was crystallized in the tetragonal p42212 space group ( PDB:1U09 ) [63] to the new unit cells , using the program REFMAC ( CCP4 ) . Initial maps for the 3D ( P44 , M296I ) -RNA and 3D ( SSI ) -RNA complexes ( P3221 crystals ) were obtained following the same procedure but using the trigonal P3221 coordinates of 3D ( PDB:1WNE ) [63] as starting model ( Table 1 ) . In the four structures the 2|Fo|-|Fc| and |Fo|-|Fc| difference maps clearly allowed the re-positioning of the mutated residues and surrounding regions and , in the trigonal structures , these maps showed the presence of extra densities corresponding to the RNA template-primers . However , the tetragonal crystals , 3D ( P44S ) and 3D ( P169S ) , did not contain RNA despite using the same incubation and co-crystallization conditions as in 3D ( P44 , M296I ) -RNA and 3D ( SSI ) -RNA complexes that crystallized in the P3221 space group . Several cycles of automatic refinement , performed with program REFMAC , were alternated with manual model rebuilding using the graphic programs TURBO and Coot [91] . The statistics of the refinement for the four complexes are summarized in Table 7 . | Viruses that have RNA as genetic material include many important human , animal and plant pathogens . A new strategy against RNA viruses consists in using mutagenic nucleotides . The objective is to provoke an excessive number of mutations , to deteriorate the viral functions to the point that the virus can not survive . One of the mutagens used in research on lethal mutagenesis is ribavirin , extensively employed in clinical practice . Unfortunately , viral mutants that are resistant to ribavirin have been selected , thus facilitating escape from lethal mutagenesis . Here we describe a new mechanism by which foot-and-mouth disease virus ( FMDV ) can become resistant to ribavirin . Amino acid changes in the viral polymerase , selected by ribavirin , are able to modify the types of mutations produced in the presence of ribavirin . Biochemical data indicate that the alteration of the enzyme changes the pairing behavior of ribavirin , avoiding the production of an excess of some types of mutations , supporting the hypothesis that an unbalanced mutation repertoire is detrimental to the virus . Thus , this new mechanism of resistance to ribavirin is based not as much in limiting the number of mutations in the virus genetic material but in ensuring an equilibrium among different types of mutations that favors viral survival . |
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Poliovirus is an enteric virus that rarely invades the human central nervous system ( CNS ) . To identify barriers limiting poliovirus spread from the periphery to CNS , we monitored trafficking of 10 marked viruses . After oral inoculation of susceptible mice , poliovirus was present in peripheral neurons , including vagus and sciatic nerves . To model viral trafficking in peripheral neurons , we intramuscularly injected mice with poliovirus , which follows a muscle–sciatic nerve–spinal cord–brain route . Only 20% of the poliovirus population successfully moved from muscle to brain , and three barriers limiting viral trafficking were identified . First , using light-sensitive viruses , we found limited viral replication in peripheral neurons . Second , retrograde axonal transport of poliovirus in peripheral neurons was inefficient; however , the efficiency was increased upon muscle damage , which also increased the transport efficiency of a non-viral neural tracer , wheat germ agglutinin . Third , using susceptible interferon ( IFN ) α/β receptor knockout mice , we demonstrated that the IFN response limited viral movement from the periphery to the brain . Surprisingly , the retrograde axonal transport barrier was equivalent in strength to the IFN barrier . Illustrating the importance of barriers created by the IFN response and inefficient axonal transport , IFN α/β receptor knockout mice with muscle damage permitted 80% of the viral population to access the brain , and succumbed to disease three times faster than mice with intact barriers . These results suggest that multiple separate barriers limit poliovirus trafficking from peripheral neurons to the CNS , possibly explaining the rare incidence of paralytic poliomyelitis . This study identifies inefficient axonal transport as a substantial barrier to poliovirus trafficking in peripheral neurons , which may limit CNS access for other viruses .
Many viruses are neurotropic , including West Nile virus , rabies virus , alpha herpesviruses , and poliovirus . To gain access and sustain infection in neurons , viruses must be able to efficiently traffic in axons , which can be up to one meter long . Therefore , viral trafficking in neurons requires an active transport system [1] , [2] . Poliovirus is thought to enter neurons via receptor-mediated endocytosis at the neuromuscular junction , followed by endocytic transport from the nerve terminal to the cell body using the host retrograde axonal transport system . Poliovirus and some herpesviruses are thought to hijack the host transport machinery via Tctex-1 , a component of the dynein light chain involved in retrograde axonal transport [3] , [4] . Poliovirus is an enteric virus that rarely causes disease; however , in the pre-vaccine era , ∼1% of infected individuals developed paralytic poliomyelitis due to viral invasion of the central nervous system ( CNS ) and destruction of motor neurons . It is still unclear whether poliovirus accesses the CNS via blood or neural routes , but it has been shown that viremia is a prerequisite for CNS invasion of humans and non-human primates [5] , [6] . In the 1990s , mice expressing the human poliovirus receptor ( CD155/PVR ) facilitated studies on poliovirus trafficking , although early models were limited in scope due to resistance of the mice to oral infection [7] , [8] . Ohka et al . recently developed PVR mice lacking the interferon α/β receptor ( IFNAR−/− ) , an important component of innate immunity , yielding PVR-IFNAR−/− mice that are orally susceptible to poliovirus , and can be used to study viral dissemination following the natural route of infection [9] . While there is evidence for both blood and neural routes of poliovirus dissemination [10] , recent in vitro studies with cultured neurons , and in vivo studies with PVR mice provide evidence for neural trafficking to the CNS [7] , [11] , [12] , [13] , [14] . It is thought that viremic blood seeds peripheral tissues , virus enters neurons of the peripheral nervous system ( PNS ) that innervate peripheral tissues , and virus traffics to the CNS using retrograde axonal transport . Sciatic nerve models of poliovirus trafficking further support CNS access via a neural route following peripheral infection , because sciatic nerve transsection prevented disease in PVR mice intramuscularly injected with poliovirus [12] , [14] . Similarly , sciatic nerve transsection prevented retrograde axonal transport of Theiler's virus , a picornavirus related to poliovirus [15] . Therefore , intramuscularly inoculated poliovirus traffics to the CNS in neurons via the sciatic nerve . The sciatic nerve contains a bundle of axons , each of which are single long cells that innervate the leg muscle and relay information from the periphery to their cell bodies in the spinal cord . Therefore , viral trafficking by this route requires viral uptake at the neuromuscular junction , active transport within the long axons of the sciatic nerve , viral release in the cell body within the spinal cord , and transport to the brain . Here we use an artificial quasispecies to identify host barriers limiting viral trafficking from the periphery to the CNS . Previously , we uncovered a significant obstacle to viral trafficking between muscle and brain that severely bottlenecked the viral population [16] , [17] , and here we identify multiple barriers that contribute to this effect . By following viral population diversity , we discovered three distinct barriers the virus encounters between the periphery and the CNS: inefficient retrograde axonal transport in peripheral neurons , the type I interferon response , and limited viral replication in neurons of the PNS . To our knowledge , this is the first time that efficiency of viral retrograde axonal transport has been quantified , and identified as a major barrier limiting viral access to the CNS .
Previously , using 10 marked viruses , we identified host barriers that limit poliovirus trafficking from the gut to the CNS [16] . The marked viruses contain groups of 4–8 silent point mutations detectable by a hybridization-based assay , and constitute an artificial quasispecies that can be used to monitor viral population dynamics and identify host barriers that limit spread ( Figure S1 ) . Using this assay and another artificial quasispecies assay , a barrier was uncovered between a peripheral intramuscular injection site and the brain [16] , [17]; however , the specific nature of this barrier was unknown . The goal of this study was to legitimize viral trafficking in PNS neurons as a potential route to the CNS following oral inoculation , and to identify the specific host barriers limiting viral trafficking from peripheral tissues to the CNS . To determine whether poliovirus is present in peripheral neurons following oral inoculation , orally susceptible PVR-IFNAR−/− mice were orally inoculated with 2×107 plaque forming units ( PFU ) of the 10-virus mixture , tissues were harvested upon disease onset , and viruses were detected by RT-PCR and the viral diversity assay . We monitored poliovirus in two peripheral nerves: the vagus nerve , which innervates multiple organs and is part of the enteric nervous system , and the sciatic nerve , which innervates leg muscle . Importantly , orally inoculated virus was detected in the vagus nerve in 76% of mice and in the sciatic nerve in 71% of mice ( Figures 1A and S1 ) . Because poliovirus was detectable in sciatic nerve following oral inoculation , we used the sciatic nerve as a relevant model peripheral neuron to identify barriers contributing to the bottleneck effect encountered by the virus between peripheral organs and the CNS . PVR mice were intramuscularly inoculated with 2×107 PFU of the 10-virus mixture , tissues were harvested upon disease onset , and viral population diversity was measured with the viral diversity assay ( Figure 1B ) . Of the 10 original input viruses injected , we found an average of 9 . 5 viral pool members present in muscle , 9 . 0 in sciatic nerve , 4 . 2 in spinal cord , and 1 . 8 in brain ( Figure 1C ) . Similar results were obtained for tissues harvested at an early time point , 30 hours post-infection ( Figure S2 ) . A dramatic decrease in the number of viral population members occurred between sciatic nerve and spinal cord , suggesting that the viral population encountered a major barrier between these sites . Importantly , viral titers from tissues do not reflect the dramatic bottleneck encountered by the viral population , because viral titers in spinal cord were 10 , 000-fold higher than viral titers in sciatic nerve ( Figure 2 ) . Therefore , the viral population was limited by a host barrier between the PNS and CNS , but robust replication occurred post-barrier in the CNS . These results uncovered barriers to viral trafficking that would have been masked by analyzing titer alone , and suggest that a significant barrier to viral CNS access occurs between the sciatic nerve and spinal cord . We devised three hypotheses to explain the barrier between the PNS and CNS . First , the interferon response may limit peripheral replication , reducing the amount of virus in the periphery . Second , viral replication in peripheral neurons may be minimal , limiting the number of viruses entering the CNS . Third , retrograde axonal transport may be inefficient for poliovirus . We tested each of these hypotheses to dissect the mechanism of the PNS-to-CNS barrier . To determine whether the type I interferon response contributes to the sciatic-spinal cord barrier , we intramuscularly injected 2×107 PFU of the 10-marked viruses into PVR-IFNAR−/− mice , which lack the INFα/β receptor and are therefore deficient in generating a type I interferon response . Upon disease onset , tissues were harvested and viral population diversity was determined by the viral diversity assay . As shown in Figure 1D , 2 . 5-fold more viral pool members reached the brain in PVR-IFNAR−/− mice than in PVR mice . Not surprisingly , viral titers were 4–17-fold higher in PVR-IFNAR−/− mice than in PVR mice ( Figure 2 ) . Interestingly , the largest viral titer difference between PVR and PVR-IFNAR−/− mice was in the periphery , suggesting that the interferon response limited viral trafficking by reducing replication in peripheral tissue . In fact , the difference between PVR and PVR-IFNAR−/− viral titers in the brain was minimal ( less than 4-fold ) and not statistically significant . One interpretation of these results is that the type I interferon response exerts its effects in the periphery and may contribute to the viral bottleneck by limiting viral replication in peripheral tissues . Lack of replication in peripheral neurons could limit viral diversity and contribute to inefficient trafficking to the CNS . To quantify viral replication in vivo , we used light-sensitive polioviruses [16] . Poliovirus propagated in the presence of neutral red dye becomes light sensitive due to dye incorporation into the virion [18] , [19] , [20] . Exposure to light inactivates neutral red-containing virions , likely due to cross-linking of virion RNA; however , neutral red viruses maintain viability if not exposed to light . Upon uncoating , neutral red dye is diluted and viruses lose light sensitivity . Therefore , viral replication can be quantified by measuring the ratio of light-sensitive to light-insensitive virus . We have adapted this assay for in vivo studies by injecting mice with a pool of 10-marked neutral red viruses in the dark and comparing light-exposed versus non light-exposed tissue virus samples by viral titer analysis or the viral diversity assay . First , we performed neutral red viral titer analysis to measure the kinetics of viral replication in various tissues along the route to the CNS . PVR mice were intramuscularly injected with 2×107 PFU of the neutral red 10-virus pool , and muscle , sciatic nerve , spinal cord , and brain were harvested in the dark ( using a red safety light ) at 2 , 6 , 30 or 72 hours post infection ( hpi ) . Tissues were processed in the dark , and samples of light-exposed virus and non-light exposed virus from each tissue were quantified by viral titer assay . Figure 3 shows the total titer ( i . e . , dark titer; grey lines ) , and the percent of virus that was replicated ( i . e . , light titer/dark titer x100; black bars ) at each time point for different tissues , and the data indicate three key points . First , there was no evidence of viral replication in any tissue at 2 hpi , but there was evidence of viral replication in muscle , sciatic nerve and spinal cord at 6 hpi; therefore , viral replication is relatively fast in vivo . Second , virus was detectable in the spinal cord by 6 hpi , indicating that virus moves very quickly from the muscle injection site to the CNS , in agreement with previous work demonstrating viral movement by fast retrograde axonal transport [12] . The virus found in the spinal cord at 6 hpi was a mixture of replicated virus and non-replicated virus from the inoculum . Third , while titers in spinal cord increase over time by an average of 10 , 000-fold , titers in sciatic nerve remain relatively constant . Similarly , viral titers in muscle remain relatively constant despite viral replication at that site . Taken together these results suggest a model where virus in muscle is transported rapidly to the spinal cord via the sciatic nerve , but little or no replication occurs in the PNS ( sciatic nerve ) ; however , robust replication occurs in the CNS ( spinal cord and brain ) . To further test the idea that poliovirus does not replicate in the sciatic nerve , we examined the replication status of individual viral population members using neutral red-virus in conjunction with the viral population diversity assay . PVR mice were intramuscularly injected with 2×107 PFU of the 10-marked neutral red virus pool and tissues were harvested in the dark at 72 hpi , near the time of disease onset . After processing tissues in the dark , we divided the virus sample and exposed half to light and kept half in the dark . Light exposed and non-light exposed virus samples were amplified for a single cycle in HeLa cells to expand surviving viruses and minimize the prevalence of inactivated viruses . Replication status of tagged pool members was analyzed with the viral diversity assay by comparing the signal of every viral pool member on the “light” vs . “dark” blots ( see Figure 4A ) . Light-sensitive viruses were scored as ‘non-replicated’ , and light-insensitive viruses were scored as ‘replicated+non-replicated’ because they may contain a sub-population of non-replicated viruses , which would be masked by the signal from replicated viruses . ‘Non-replicated’ viruses consist exclusively of viruses that did not replicate while in the mouse , due to the absence of signal from the light-exposed sample . The results from 10 PVR mice are summarized in Figure 4B , which shows the overall number of viral pool members present in each tissue and the proportion of those pool members that were non-replicated viruses . In muscle , 30% of virus was non-replicated , in sciatic nerve , 64% of virus was non-replicated , in spinal cord , 13% of virus was non-replicated , and in brain , 0% of virus was non-replicated . These results reinforce the idea that robust replication occurs in CNS tissues , since 100% of brain viruses showed evidence of viral replication . Interestingly , even at 72 hpi , 30% of virus in muscle was non-replicated , indicating that this virus was stable and not cleared over a three day period , but remained viable since productive replication occurred in HeLa cells after tissue harvest . In sciatic nerve , the majority of viruses ( 64% ) were non-replicated . Importantly , every single potentially replicated virus in sciatic nerve was also replicated in muscle ( 28/28 ) , implying that “replicated” virus in sciatic nerve had undergone replication in muscle prior to sciatic nerve entry . Because the majority of viruses in sciatic nerve were non-replicated , and the minority of light-insensitive/replicated viruses had undergone replication in muscle , it is likely that poliovirus does not replicate in axons of the sciatic nerve . To test whether the interferon response limits viral replication , we repeated the experiment using PVR-IFNAR−/− mice . Not surprisingly , we observed high percentages of replicated virus in all tissues ( Figure 4C ) . These data , in conjunction with titer data from PVR-IFNAR−/− mice ( Figure 2B ) , imply that in the absence of the interferon response , viral replication in muscle was so robust that nearly all viruses replicated prior to entering the PNS . Taken together , these results suggest that poliovirus does not replicate in axons of peripheral neurons , rather , virus moves quickly from the peripheral injection site to the CNS , and once in the CNS , undergoes robust replication . Our data support previous work demonstrating that retrograde axonal transport of poliovirus is fast [4] , [12] , [21]; however , the efficiency of viral axonal transport has never been quantified . To determine whether inefficient retrograde axonal transport contributes to the barrier observed between sciatic nerve and spinal cord , we monitored viral population diversity during viral ascension of the sciatic nerve by harvesting segments of the nerve . PVR mice were intramuscularly injected with 2×107 PFU of the 10-marked virus pool and viral diversity was quantified in the peripheral injection site ( muscle ) , in three sections of the sciatic nerve ( lower , middle , upper ) , in spinal cord , and in brain . As shown in Figure 5A ( black bars ) , the lower section of the sciatic nerve contained an average of 8 . 3 pool members , middle sciatic contained 4 . 9 pool members , and upper sciatic contained 2 . 3 pool members . Therefore , the entire barrier between the sciatic nerve and spinal cord was due to loss of viral population members between the lower sciatic nerve and upper sciatic nerve . Poliovirus entry into the sciatic nerve at the neuromuscular junction was efficient , since 87% of pool members present in muscle were present in lower sciatic nerve; however , retrograde axonal transport was inefficient , since only 28% of pool members were successfully transported from lower sciatic nerve to upper sciatic nerve . While limited population diversity in the upper sciatic nerve suggests inefficient transport as a potential barrier to poliovirus trafficking , to formally demonstrate that transport inefficiency is the barrier , we determined whether increasing the efficiency of retrograde axonal transport would increase poliovirus population diversity in the CNS . Muscle damage via needle sticks is thought to enhance access to the CNS because Gromeier and Wimmer demonstrated enhanced poliovirus disease in mice subjected to needle sticks following intravenous inoculation [21] . To test whether needle sticks increase the efficiency of poliovirus retrograde axonal transport , PVR mice were intramuscularly injected with the 10-marked virus pool , and mice received needle sticks twice per day to induce muscle damage . Upon disease onset , tissues were harvested and the viral population diversity assay was performed ( Figure 5 ) . In mice that received needle sticks , the brain contained an average of 6 . 4 pool members , 3-fold more virus than untreated mice , suggesting that muscle damage increased poliovirus transport to the CNS ( p<0 . 01 , Students t test ) . To verify that the muscle damage-mediated enhancement of poliovirus trafficking was due to increased efficiency of retrograde axonal transport , we monitored trafficking of a non-viral protein , wheat germ agglutinin ( WGA ) , which is commonly used as a neural tracer [22] , [23] . PVR mice were intramuscularly injected with 5 µg WGA , and treated with or without needle sticks . Tissues were harvested at 6 hours post injection , and WGA was quantified by immunoblotting ( Figure 5B ) . In support of the idea that muscle damage increased retrograde axonal transport , WGA signal in middle and upper sciatic nerve was >3-fold higher in mice given needle sticks compared with untreated mice . Interestingly , the combined total of WGA signal for all sciatic nerve segments was nearly identical in both treatment groups , suggesting that WGA uptake at the neuromuscular junction was comparable; however , WGA was transported more efficiently in mice with muscle damage since more WGA was present in the middle and upper sections of the sciatic nerve . Therefore , both poliovirus and WGA have inefficient trafficking in the absence of muscle damage . Taken together , these data suggested that retrograde axonal transport of poliovirus is inefficient and constitutes a major barrier to viral access to the CNS , but that efficiency of transport to the CNS can be enhanced by muscle damage . Having identified inefficient retrograde axonal transport and the interferon response as major barriers to viral trafficking , we sought to determine whether eliminating both barriers would facilitate efficient poliovirus trafficking to the CNS . PVR or PVR-IFNAR−/− mice were intramuscularly injected with the 10-marked virus pool in the presence or absence of needle sticks , and population diversity was monitored . As expected , tissues from PVR-IFNAR−/− mice contained significantly more population members in brain ( 2 . 5-fold ) than PVR mice ( p<0 . 01 , Students t test ) ( Figure 5A ) . These numbers were comparable to the increased diversity in brain ( 3-fold ) observed in PVR mice given needle sticks ( p<0 . 001 , Students t test ) . However , in PVR-IFNAR−/− mice given needle sticks , sciatic nerve , spinal cord , and brain contained nearly all ten viruses ( average of 9 . 4 in upper sciatic , 9 . 0 in spinal cord , and 8 . 0 pool members in brain ) , with significantly more viral pool members trafficking to the brain than in PVR-IFNAR−/− mice or PVR mice given needle sticks ( p<0 . 001 , Students t test ) . Our results suggest that the type I interferon response and inefficient retrograde axonal transport may be separate barriers and that overcoming both barriers facilitated efficient viral trafficking to the CNS . This notion is further supported by the time of disease onset for each treatment group . As shown in Figure 6 , using paralysis onset for 50% of mice per cohort as a measure of pathogenesis , untreated PVR mice developed disease on day 4 . 5 post infection , PVR mice given needle sticks and untreated PVR-IFNAR−/− mice developed disease on day 3 post infection , and PVR-IFNAR−/− mice given needle sticks developed disease on day 1 . 5 post infection . Therefore , overcoming one of the two barriers increased pathogenesis , as disease onset was 1 . 5-fold faster than mice with both barriers intact . Furthermore , eliminating two barriers dramatically enhanced pathogenesis , as disease onset was 3-fold faster than in untreated mice . Taken together , our results indicated that the type I interferon response and inefficient retrograde axonal transport are barriers of equivalent strength , and that these barriers reduce pathogenicity by limiting viral trafficking to the CNS .
Paralytic poliomyelitis due to viral CNS invasion and motor neuron destruction is very rare , occurring in less than 1% of unvaccinated individuals . A variety of hypotheses have been proposed to explain the rare poliovirus CNS invasion , ranging from fatigue to recent injury [21] , [24] , [25] . In this work , we identified three major barriers that may contribute to the rare incidence of paralytic poliomyelitis by limiting poliovirus trafficking from the periphery to the CNS: inefficient retrograde axonal transport , limited viral replication in the PNS , and the interferon response . Type I interferon has been shown to reduce replication of many viruses , and to limit the pathogenicity of neurotropic viruses [9] , [26] , [27] , [28] , [29] , [30] . Perhaps predictably , we demonstrated that the type I interferon response can limit poliovirus dissemination by limiting replication in peripheral tissues , such as muscle . Surprisingly , we identified inefficient retrograde axonal transport as a major barrier limiting poliovirus trafficking in PNS neurons and viral access to the CNS . In peripheral neurons , retrograde axonal transport of poliovirus and other viruses is very fast [1] , [12] , [31] , and transport can be increased by muscle injury [14] , [21] . While retrograde axonal transport may be fast , we demonstrate here that it is very inefficient for poliovirus , with only 28% of viral pool members successfully trafficking from lower to upper sciatic . By analogy , retrograde axonal transport of poliovirus can be thought of as a fast roller coaster without seatbelts , resulting in loss of passengers during the ride . The sciatic nerve consists of cells up to 5 cm long; therefore , active transport is required for viral trafficking . Although transport is inefficient , uptake of poliovirus at the neuromuscular junction is efficient , because 87% of pool members present in muscle were present in lower sciatic nerve . The inverse has been observed for neurotrophins , a class of host proteins that are transported by retrograde axonal transport [32] , [33] , [34] , [35] . For neurotrophins , retrograde axonal transport is thought to be efficient and processive; however , neurotrophin cellular entry at the neuromuscular junction is quite inefficient [23] , [36] , [37] . Therefore , either viral transit mechanisms are not completely conserved with host transit mechanisms , or there are multiple host pathways that differ in retrograde axonal transport processivity [11] . It is also possible that poliovirus overwhelms the transport system or is degraded during retrograde axonal transport , thus explaining inefficient transport despite efficient uptake at the neuromuscular junction . Alternatively , retrograde axonal transport of host cargo may be as inefficient as poliovirus transport . While retrograde axonal transport of poliovirus in peripheral neurons was inefficient , the efficiency increased upon muscle damage . Gromeier and Wimmer suggested that muscle damage enhances poliovirus CNS access and contributes to some cases of paralytic poliomyelitis [14] , [21] , and nerve injury is known to increase retrograde axonal transport of neurotrophins [38] . Additionally , muscle injury induces inflammation , which may also impact viral trafficking . Provocation poliomyelitis occurs when physical trauma near the time of poliovirus infection coincides with increased incidence of paralytic poliomyelitis . This effect was observed during the Cutter incident , where batches of incompletely inactivated poliovirus vaccine caused paralysis preferentially in the inoculated limb [39] , [40] . Additional cases of paralytic poliomyelitis occurred when the attenuated oral polio vaccine was administered near the time of multiple unrelated intramuscular injections [41] . Our data demonstrating that muscle damage increases the efficiency of retrograde axonal transport may provide the mechanism for the increased incidence of paralytic poliomyelitis following muscle damage . Several viruses traffic in PNS neurons to reach the CNS . For example , reovirus can traffic to the CNS via the vagus and sciatic nerves , and alpha herpesviruses traffic to the CNS in PNS neurons [42] , [43] , [44] , [45] , [46] . Our results suggest that after oral infection , poliovirus may traffic through PNS neurons to the CNS because orally inoculated poliovirus was detected in peripheral neurons ( vagus and sciatic ) . We also found that the viral pool members present in sciatic and vagus nerves were highly bottlenecked , and matched brain virus 63% of the time ( Figure S1 and data not shown ) , suggesting that transport of virus from PNS neurons to the CNS may occur after natural oral infection . In addition to inefficient retrograde axonal transport in neurons , we found that poliovirus replication was limited in peripheral neurons . Using light sensitive viruses , we found no evidence of viral replication in the sciatic nerve despite robust replication in the CNS . These results are supported by data from Ohka et al . , showing intact 160S virions in sciatic nerve [12] . Perhaps it is not surprising that viral replication is limited in peripheral neurons , since substrates required for viral replication are likely to be limited in long axons , and viruses may reside in endosomes during the entire axon transport period . Nonetheless , if virions or virion-containing endosomes disassociate from the retrograde axonal transport machinery , viral replication may be impossible within the axon . Taken together , our results support the neural route as a major pathway of poliovirus trafficking to the CNS in mice; however , trafficking in neurons is difficult due to inefficient retrograde axonal transport . We propose that PNS barriers contribute to the low incidence of paralytic poliomyelitis in humans , and may contribute to inefficient trafficking of other neurotropic viruses .
All animals were handled in strict accordance with good animal practice as defined by the relevant national and/or local animal welfare bodies , and all animal work was approved by the UT Southwestern Medical Center IACUC . Virulent Mahoney type 1 poliovirus was propagated and titered in HeLa cells as previously described [16] , [47] . The ten marked viruses for the viral diversity assay each contain groups of 4–8 silent point mutations that allow specific probe annealing following RT-PCR and dot blotting ( see Figure S1 and [16] for more details ) . These viruses exhibit no detectable fitness differences [16] . Light sensitive poliovirus was prepared and analyzed as previously described [16] , [18] , [19] , [20] . Briefly , HeLa cells were infected with each marked virus in the presence of 10 µg/ml neutral red dye . Work with neutral red viruses was preformed in the dark , using a red photography light . Inactivation of neutral red viruses was achieved by exposure to a fluorescent light for 10 min . Samples were processed in the dark and supernatant from each tissue was divided in half ( half was then exposed to light and the other half was always kept in the dark ) . For titer analysis , the ratio of PFU in light exposed versus non-light exposed samples were compared to determine the percent replicated virus [16] . The ratio of light-insensitive to light-sensitive PFU in the neutral red poliovirus stock was 1 to 1 , 270 , 000 [16] . Based on previous work and this study , we routinely detect 100% of in vitro and in vivo replicated viruses using this assay ( data not shown ) [16] . C57/BL6 mice expressing the human poliovirus receptor ( CD155/PVR , called PVR ) and C57/BL6 PVR mice deficient in the interferon-α/β receptor ( called PVR-IFNAR−/− mice ) were a generous gift from S . Koike ( Tokyo , Japan ) [7] , [29] . Oral inoculations were performed by pipetting 2×107 total PFU of an equal mixture of all 10 viruses in 15 µl volume into the mouth [16] . Inoculum was prepared by mixing 2×106 PFU of each virus per mouse ( according to viral titer assay ) , and a large cocktail was prepared for each experiment such that all animals received the same mixture . For intramuscular injections , 2×106 PFU of each marked virus ( 2×107 total PFU in 50 µl ) was injected into the lower left gastrocnemius muscle [16] . Needle sticks were given by inserting a 28-gauge needle into the leg 4 or 5 times twice daily [21] . For all poliovirus experiments , mice were monitored twice a day ( at ∼10–14 hour intervals ) and euthanized at the first sign of disease , which is typically paralysis of one hind limb . Upon onset of symptoms , mice fail to recover and typically succumb to disease within 12 hours ( data not shown ) . Therefore , time of disease onset correlates with time of death , and can be used as a more humane alternative to death as an endpoint . Whole sciatic nerve was removed by lifting the biceps femoris and removing the nerve segment between the spine and ankle . The nerve was then sectioned into three equal pieces to generate upper , middle and lower sciatic sections . The vagus nerve was removed as a segment from the heart-lung junction to the diaphragm . Muscle included all non-bone tissue below the hip . Tissues ( whole spine , brain , stomach , small intestine , colon ) were weighed and resuspended in three volumes PBS+ ( 1× PBS with 100 µg/ml MgCl2 and CaCl2 ) , and homogenized in liquid nitrogen with a mortar and pestle [17] or with a Bullet Blender tissue homogenizer ( Next Advanced Inc , Averill Park , NY ) as per manufacturers instructions , followed by freeze-thawing three times to release virus and chloroform extraction of gut samples to inactivate bacteria [16] . Vagus and sciatic nerve tissue were dounce homogenized . All samples and tissues were stored at −80°C . Detection of the 10 marked polioviruses was performed as previously described [16] . Briefly , viruses from homogenized tissues with low viral titers ( stomach , colon , small intestine , vagus , and sciatic nerve ) were amplified in HeLa cells , followed by TRIZOL ( Invitrogen , Carlsbad , CA ) extraction of RNA . Tissues with high viral titers ( brain , spine , muscle ) were directly extracted with TRIZOL because there was no difference between results from amplifying viruses in HeLa cells and direct TRIZOL extraction of high titer tissues ( data not shown ) . RT-PCR for the tagged region of the virus was performed as previously described [16] . After blotting equivalent concentrations of PCR products on Hybond N+ membranes ( GE Healthcare , Buckinghamshire , UK ) individual membranes were pre-hybridized and hybridized at 59°C . Primers specific for each of the 10 viruses were kinased with [γ-32P]ATP to serve as probes [16] . Following hybridization , membranes were exposed to PhosphorImager screens and specific signal was determined by normalizing blots to perfectly matched and mismatched control PCR product dots and image intensity was uniformly adjusted until mismatched sample was no longer visible in order to eliminate low low-level cross reactive signal ( see [16] for more details ) . For the neutral red diversity assay in Figure 4 , signal from light exposed samples was compared to signal from non-light exposed samples from the same tissue . Any viral pool members present only in the dark sample were scored as ‘non-replicated’ virus , and viral pool members present in both the dark and the light sample were scored as ‘replicated+non-replicated’ ( see Figure 4 for more details ) . Tissues were collected from mice 6 hours after injecting 5 µg WGA into the lower gastrocnemius muscle . Muscle was weighed and resuspended in 2 volumes of RIPA buffer ( 10 mM Tris , 150 mM NaCl , 0 . 02% NaN3 , 1% Na-deoxycholate , 1% Triton X-100 , 0 . 1% SDS ) and sciatic nerve was resuspended in 200 µl of RIPA buffer . Samples were then homogenized with the Bullet Blender , and 10 µl/ml of a protease inhibitor cocktail ( Sigma , St Louis , MO ) and 10 µl/ml of a phosphatase inhibitor ( Calbiochem , San Diego , CA ) were added to the supernatants . Dot blot westerns were performed in place of typical gel-based westerns due to multimerization of WGA; therefore , five microliters of each sample was pipetted directly onto a nitrocellulose membrane ( GE Water & Process Technologies ) , which was probed with rabbit anti-lectin ( triticum vulgaris ) primary antibody ( Sigma , St Louis , MO ) and goat anti-rabbit HRP secondary antibody . Signal was visualized with ECL reagent ( GE Healthcare , Buckinghamshire , UK ) and quantified by densitometry [48] . Specific WGA signal was distinguished from background by normalizing to a sciatic nerve sample that was not exposed to WGA . WGA signal was within the linear range of detection based on loading and quantification of purified WGA dilutions ( data not shown ) . | Neurotropic viruses , including herpesviruses , rabies virus , and poliovirus , initiate infection in the periphery and can move through peripheral neurons to reach the central nervous system ( CNS ) . Since peripheral neurons can be up to one meter long , inefficient neural transport could dramatically affect pathogenesis of neurotropic viruses . In this study , we used a novel viral “bar-coding” assay to quantify the efficiency of poliovirus transport from the periphery to the CNS using a mouse model . Only 20% of the poliovirus population successfully moved from the periphery to the CNS . We discovered that transport of poliovirus in peripheral neurons was very inefficient , and the innate immune response also limited viral movement . Surprisingly , the neural transport barrier was as strong as the innate immune response barrier . Importantly , by overcoming both the neural transport and innate immune barriers , 80% of the poliovirus population successfully moved from the periphery to the CNS , and mice succumbed to disease three times faster than mice with intact barriers . This study identifies inefficient neural transport as a substantial barrier to viral movement in peripheral neurons , which may limit CNS access for many viruses . |
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The major environmental determinants of honeybee caste development come from larval nutrients: royal jelly stimulates the differentiation of larvae into queens , whereas beebread leads to worker bee fate . However , these determinants are not fully characterized . Here we report that plant RNAs , particularly miRNAs , which are more enriched in beebread than in royal jelly , delay development and decrease body and ovary size in honeybees , thereby preventing larval differentiation into queens and inducing development into worker bees . Mechanistic studies reveal that amTOR , a stimulatory gene in caste differentiation , is the direct target of miR162a . Interestingly , the same effect also exists in non-social Drosophila . When such plant RNAs and miRNAs are fed to Drosophila larvae , they cause extended developmental times and reductions in body weight and length , ovary size and fecundity . This study identifies an uncharacterized function of plant miRNAs that fine-tunes honeybee caste development , offering hints for understanding cross-kingdom interaction and co-evolution .
Caste development in social insects represents a major transition from one level of organization to another in evolution and is believed to be central to the ecological success of social insects [1] . How castes evolved is an enduring puzzle that has long fascinated scientists but currently has no satisfactory answers . Honeybees ( Apis mellifera ) represent a principal example of caste development . Female honeybees develop into two castes , queens and workers , which differ in morphology , physiology and social function [1 , 2] . The queens are reproductive , have a larger body size , develop faster and live longer , whereas workers are characterized by the opposite traits and are mostly sterile helpers that nourish larvae and collect food [3] . This dimorphism is not a consequence of genetic differences but is mainly determined by larval feeding: female larvae receiving a rich diet of royal jelly develop into queens , whereas a less sophisticated diet named “beebread” leads to the worker bee fate [4 , 5] . However , it is still not fully understood how different diets modify the developmental trajectory of honeybees to such a thorough extent . While several components of the larval diet , such as specific royal jelly proteins , sugars , p-coumaric acid and fatty acids , have been independently shown to influence caste development in honeybees [6–10] , they still cannot account for the full impact of larval food on honeybee development . In this study , we investigated a largely overlooked component of larval food , microRNA ( miRNA ) , and examined its effect on caste development . miRNAs are a class of 19–24-nucleotide-long non-coding RNAs that act as post-transcriptional regulators of gene expression in eukaryotes [11] . Recently , we reported an unexpected finding that plant miRNAs that are ingested from plant food sources can pass through the gastrointestinal tract , enter into the blood , accumulate in tissues and regulate endogenous gene expression in animals [12] . Other studies have also documented the importance of small RNAs that are transmitted from one species to another and facilitate cross-talk and interspecies communication [13–16] . Moreover , multiple studies have proven that dietary exogenous miRNAs are detectable in consumed animal blood and tissues [17–20] . These studies furnish an additional layer of gene regulation: cross-kingdom regulation mediated by exogenous miRNAs . It is very tempting to speculate that small RNAs in larval food may be an active component that influences honeybee development . Because beebread is a mixture of pollen and honey , while royal jelly is a glandular secretion of nurse bees [4] , the main food sources of worker- and queen-destined larvae are , in theory , plant- and animal-derived , respectively . Thus , we hypothesize that different miRNA contents from larval food of different origins may have distinct impacts on honeybee development . In agreement with this hypothesis , it has been well established in the literature that insects , including honeybees and fruit flies , can ingest small RNAs and that ingested small RNAs can regulate the expression of insect genes , thus reshaping the insects’ phenotypes [21–24] . In this study , we provide evidence for a previously uncharacterized regulatory mechanism of worker bee development , which can be partially attributed to the plant miRNAs enriched in beebread and pollen fed to young larvae .
First , we analysed the small RNA components in royal jelly , honey , beebread and pollen using Illumina deep-sequencing technology . To investigate pollen as a larval food source under natural conditions , we used bee pollen collected and packed by worker bees . The royal jelly , honey , beebread and pollen were collected during the cole ( Brassica campestris ) flowering stage . Consistent with previous reports [25 , 26] , the lengths of small RNAs in pollen were concentrated in a range from 19 to 24 nucleotides ( S1 Fig ) . However , the lengths of small RNAs in royal jelly , honey and beebread were distributed over a wider range , from 13 to 28 nucleotides , probably due to degradation products from longer RNAs during their processing within the beehive . Next , total small RNAs were mapped to the reference transcriptome assemblies of honeybee and cole and were further assigned to different classes of small RNAs . In agreement with the hypothesis that royal jelly RNA is mainly animal-derived and beebread RNA is plant-derived , honeybee small RNAs were present at a far higher level in royal jelly than in beebread and pollen , while the abundance of cole small RNAs gradually increased from royal jelly to honey to beebread and pollen ( Fig 1A ) . A large proportion of the small RNAs were annotated as miRNAs and as the degradation products of tRNAs , rRNAs and mRNAs . By aligning small RNA reads to known miRNAs in the miRBase database 21 . 0 , a total of 46 , 39 , 14 and 15 annotated bee miRNA types were detected in royal jelly , honey , beebread and pollen , respectively ( S1 Table ) . Most of the bee miRNAs had less than 10 sequence reads in the samples , but they had much higher reads in royal jelly than in honey , beebread and pollen ( Fig 1B ) . On the other hand , there were 41 , 71 , 58 and 53 annotated plant miRNA types in royal jelly , honey , beebread and pollen , respectively ( S1 Table ) . These plant miRNAs were present at far higher concentrations than animal miRNAs , and their concentration in beebread and pollen was invariably much higher than that in royal jelly and honey ( Fig 1B ) . The differential enrichment of plant miRNAs in beebread and animal miRNAs in royal jelly is clearly shown in S2A Fig . In contrast , the miRNA compositions of beebread and pollen showed high similarities to each other , with a Pearson’s correlation coefficient ( R ) close to 1 ( S2A Fig ) . The 16 representative plant miRNAs ( miR156a , miR157a , miR158a , miR160a , miR162a , miR166a , miR166g , miR167a , miR168a , miR172a , miR172c , miR390a , miR397a , miR403 , miR824 and miR845a ) with the highest concentrations in beebread and pollen of cole are listed in Fig 1C . Given the diversity of pollen that is collected by honeybees , plant miRNAs might not be uniformly present in pollen from different sources . Therefore , it is essential to analyze the small RNA components in beebread and pollen collected from different geographical and botanical sources . We performed deep sequencing on royal jelly , honey , beebread and pollen collected during the camellia ( Camellia japonica ) flowering stage . The results revealed again that the plant miRNAs were more abundant in pollen and beebread than in royal jelly and honey ( S2 Table ) . Likewise , the miRNA profiles were quite similar between beebread and pollen and widely different between beebread and royal jelly ( S2B Fig ) . Interestingly , the plant miRNA profiles of cole and camellia beebread showed similarity to each other , especially for many plant miRNAs that are evolutionarily conserved across the major lineages of plants . For example , 13 of the 16 plant miRNA species enriched in cole beebread were also present in camellia beebread ( Fig 1D ) . Thus , the global components of plant miRNAs in beebread and pollen may not be very diverse between different sources . However , because deep sequencing is inferior to the more commonly used qRT-PCR for miRNA quantification [27] , we performed qRT-PCR assays with a standard curve set using synthetic oligonucleotides of known concentrations to determine the actual concentrations of plant miRNAs in royal jelly , honey , beebread and pollen . All 16 representative plant miRNAs except miR166g ( whose qRT-PCR primer was not commercially available ) could be readily detected using qRT-PCR in beebread and pollen of cole but were nearly undetectable in royal jelly and honey ( generally < 0 . 1 fmol per μg total RNA ) ( Fig 1E ) . It should be noted that we used two normalization strategies for cross-sample comparisons of miRNAs in royal jelly and beebread , and both strategies showed that each plant miRNA was much more abundant in beebread than in royal jelly ( S2C Fig ) . Moreover , northern blotting , which can determine the sizes and concentrations of RNAs , produced the same differences described above for plant miRNA concentrations and showed that miR156a , miR162a and miR168a were detectable in beebread and pollen but not in royal jelly and honey ( Fig 1F ) . To investigate the effects of plant RNAs , and particularly miRNAs , on honeybee phenotypes , we removed the larvae from the colony setting and reared them on a laboratory diet with or without the addition of plant RNAs or miRNAs . To avoid overfeeding and generating supra-physiological effects , our pilot study first determined the amounts of the 16 representative plant miRNAs that were contained in natural beebread ( Fig 1E and S2C Fig ) . Since the plant miRNA composition enriched in natural beebread is very similar to that in pollen ( S2A Fig and S2B Fig ) , we added total RNA purified from cole pollen to the laboratory diet at the same level as determined based on miRNA levels to reconstitute a close mimic of natural beebread in terms of its miRNA components ( “beebread mimic” in S3 Fig ) . When developing larvae were fed with this beebread mimic , 2-fold of beebread mimic dramatically suppressed the growth of the developing larvae and even caused some larvae to die , whereas 0 . 5- or 1-fold of beebread mimic reduced larvae growth but had little effect on their survival ( S4 Fig ) . Next , the effects of plant RNA supplements were characterized based on the developmental time , weight , length and ovary size of adult bees immediately upon emerging from the pupal stage ( Fig 2A ) . Feeding larvae with beebread mimic increased the whole-body accumulation of the 16 representative plant miRNAs ( S5A Fig ) . We did not distinguish the particular tissues where the ingested plant miRNAs were located but instead investigated the effects of plant miRNAs on the whole body as the uptake of exogenous small RNAs from the insect gut has been frequently observed [21 , 22 , 28] . As a result of the plant RNA supplements , larvae grew relatively slowly during their development and emerged as adults with more of a worker morphology ( S6 Fig ) characterized by a prolonged developmental time ( on average 0 . 49 days longer , p = 0 . 0444 ) , reduced weight ( on average 14 . 81% lighter , p = 0 . 0008 ) and size ( on average 6 . 55% shorter , p = 0 . 0005 ) at adult emergence and a decreased ovary size ( on average 21 fewer ovarioles , p = 0 . 0358 ) ( Fig 2B–2E ) . To validate the contribution of plant miRNAs to the observed honeybee phenotypes , we synthesized the 16 plant miRNAs enriched in beebread and pollen , and then the synthetic miRNA pool was added to the larval diet at levels equivalent to those in natural beebread . Compared to the control group , honeybees that were fed a diet containing the miRNA pool showed an increased accumulation of corresponding plant miRNAs within their body ( S5B Fig ) and developed worker bee-like characteristics , i . e . , reduced sizes at adult emergence ( 10 . 27% lighter and 4 . 01% shorter , p = 0 . 0194 and p = 0 . 0264 , respectively ) , extended pre-adult developmental time ( 0 . 58 days longer , p = 0 . 0254 ) and decreased ovary sizes ( 38 fewer ovarioles , p = 0 . 0094 ) ( Fig 2F–2I ) . Next , we performed bioinformatics analysis to dissect the potential functions of the plant miRNAs in honeybee’s food . Two bioinformatic algorithms ( RNAhybrid and miRanda ) were used in combination to scan honeybee mRNA sequences for potential binding sites for the 16 representative plant miRNAs . A total of 96 honeybee genes were predicted by both RNAhybrid and miRanda algorithms as the target genes of the 16 plant miRNAs . Most of the 96 genes were predicted to be targeted by only one plant miRNA , whereas a few genes were common targets of 2–3 plant miRNAs . We then used Gene Ontology ( GO ) analysis to look for biological processes that might be associated with the 96 target genes of the 16 plant miRNAs based on the strategy of a previous study [29] . Significant enrichment of GO functional categories related to “development” was observed ( S7 Fig and S3 Table ) , suggesting again that the plant miRNAs specifically enriched in beebread and pollen may be involved in regulation of the development process of honeybees . Among the 96 target genes , some genes known to influence the developmental fate of honeybees were specially selected and listed in S4 Table . Subsequently , plant miRNAs targeting Apis mellifera TOR ( amTOR ) were analysed , as previous studies have demonstrated that amTOR plays a stimulatory role in caste development: the queen fate is associated with elevated amTOR activity , and the inhibition of amTOR causes developmental changes towards worker characteristics in queen-destined larvae [22 , 30 , 31] . To screen for plant miRNAs that could directly regulate amTOR expression , luciferase reporter assays were conducted . Each plant miRNA binding site in the amTOR gene was fused separately into a position downstream of the firefly luciferase gene in a reporter plasmid . The resulting plasmids were co-transfected into a cell line in combination with above-mentioned plant miRNAs . Among 9 plant miRNAs that could potentially target amTOR , miR162a resulted in a 72% decrease in luciferase activity ( Fig 2J ) , whereas miR156a showed a 14% reduction and other 7 plant miRNAs did not affect luciferase activity ( S8 Fig ) , suggesting that miR162a specifically recognizes amTOR and mediates the post-transcriptional inhibition of this gene . In addition , the amTOR/miR162a hybrid is illustrated in Fig 2K , and its free energy was -26 . 4 kcal/mol , which was well within the ranges of genuine miRNA-target pairs ( -17 kcal/mol is a cutoff value of free energy ) [32] . However , when point mutations were introduced into the predicted “seed site” in the amTOR gene , the fused luciferase reporters were no longer affected by miR162a ( Fig 2J ) . Subsequently , to determine the potential effects of miR162a alone on amTOR expression and the corresponding phenotypes , honeybee larvae were reared with a diet to which either synthetic miR162a or scrambled RNA was added . Notable increases in the amount of ingested miR162a ( S5C Fig ) and decreases in the level of amTOR mRNA ( Fig 2L ) were detected in honeybees that were reared with a diet containing miR162a . Similarly , amTOR mRNA was downregulated in honeybees reared on a diet containing either total pollen RNA or the synthetic miRNA pool ( S9A and S9B Fig ) . In contrast to the scrambled RNA , which had no effect on any of the tested morphological characteristics , miR162a supplied in the larval food significantly reduced the body weights ( 7 . 87% lighter , p = 0 . 0292 ) and lengths ( 4 . 49% shorter , p = 0 . 0103 ) and ovary sizes ( 29 fewer ovarioles , p = 0 . 0301 ) of newly emerged adults but did not significantly increase the developmental time ( 0 . 17 days longer , p = 0 . 1755 ) of the adult bees ( Fig 2M–2P ) . To further investigate the evolutionary dynamics of the molecular mechanisms underlying social development between solitary and eusocial species , we tested plant RNA and miRNAs on a non-social model insect , Drosophila melanogaster . Although there is no caste differentiation in Drosophila , there is evidence that molecular pathways involved in establishing caste dimorphism are also conserved in the individual development of Drosophila [6] . Thus , we investigated the mechanism underlying honeybee caste differentiation in Drosophila ( Fig 3A ) . First , we ruled out the possibility that the residual chemicals from RNA isolation might block larval development as the mock group of Drosophila larvae fed the same chemical residues developed normally ( S10 Fig ) . In accordance with the observation that the beebread mimic postpones queen differentiation in honeybees , Drosophila larvae reared with medium containing total pollen RNA had longer developmental times ( p<0 . 0001 ) , were smaller ( 7 . 83% lighter and 2 . 99% shorter in females , p = 0 . 0038 and p = 0 . 0014 , respectively; 7 . 32% lighter and 4 . 33% shorter in males , p = 0 . 0144 and p<0 . 0001 , respectively ) , had fewer ovarioles ( 1 . 6 fewer ovarioles , p = 0 . 0008 ) and showed reduced fecundity ( a total of 28 . 04% eggs fewer ) compared to those reared with the control medium ( Fig 3B–3F ) . Similarly , plant miRNAs also accumulated in Drosophila larvae ( S5D Fig ) . Subsequently , to narrow down the active components in plant RNA , small RNAs were enriched from total pollen RNA , and the effects of small RNAs on Drosophila phenotypes were examined in the same manner as described above . Small plant RNAs also delayed Drosophila development ( p<0 . 0001 ) and reduced the final adult size ( 6 . 84% lighter and 3 . 45% shorter in females , p = 0 . 0002 and p = 0 . 0005 , respectively; 7 . 50% lighter and 3 . 98% shorter in males , p = 0 . 0030 and p = 0 . 0010 , respectively ) , ovary size ( 0 . 9 fewer ovarioles , p = 0 . 0288 ) and fecundity ( 25 . 13% eggs fewer ) of Drosophila as effectively as total RNA ( S11 Fig ) . Similarly , when the miRNA pool was fed to developing Drosophila larvae , we observed an increase in plant miRNA levels ( S5E Fig ) and corresponding decreases in final adult size ( 5 . 88% lighter and 7 . 73% shorter in females , p = 0 . 0208 and p<0 . 0001 , respectively; 6 . 82% lighter and 3 . 39% shorter in males , p = 0 . 0111 and p = 0 . 0013 , respectively ) , ovary size ( 1 . 6 fewer ovarioles , p = 0 . 0004 ) and fecundity ( 19 . 96% eggs fewer ) ( Fig 3H–3K ) . However , developmental times did not change in Drosophila reared with medium containing the miRNA pool ( p = 0 . 768 ) ( Fig 3G ) . To determine the specificity of the inhibitory effects of plant miRNAs on Drosophila development and to exclude the possibility that the phenotypic changes were caused by components other than plant miRNAs , an miRNA antisense pool against the above-mentioned 16 miRNAs was synthesized and added to the Drosophila larval medium together with small pollen RNAs to abolish the function of these plant miRNAs . The inhibitory effects of plant RNAs on the adult size , ovary size and fecundity of Drosophila were completely reversed by the addition of the antisense pool to the larval diet ( Fig 3L–3O ) . Next , a similar miR162a binding site in the Drosophila melanogaster TOR ( dmTOR ) gene was identified ( Fig 4A ) . When this binding site was fused into the luciferase reporter plasmid , miR162a also reduced luciferase activity ( Fig 4B ) . However , when a point mutation was introduced into the miR162a binding site in the dmTOR gene , the mutated luciferase reporter was unaffected by miR162a ( Fig 4B ) . The correlation between miR162a and dmTOR was further examined by evaluating dmTOR protein expression in Drosophila Schneider 2 cells ( S2 cells ) after the induction of miR162a . The expression of the dmTOR protein was significantly inhibited by miR162a in S2 cells ( Fig 4C ) . We further performed a biotin-avidin pull-down assay to assess the direct binding of miR162a to dmTOR mRNA . miR162a was only enriched in the pull-down product precipitated by the anti-dmTOR probe and was undetectable in the products that were precipitated by a random probe or no probe ( Fig 4D ) , suggesting that miR162a directly binds to dmTOR mRNA in S2 cells . Moreover , Drosophila larvae reared with synthetic miR162a supplied in the medium showed increased whole-body accumulation of miR162a ( S5F Fig ) and reduced whole-body expression of dmTOR mRNA ( Fig 4E ) . A similar reduction in dmTOR mRNA levels was observed in Drosophila reared with medium containing total pollen RNA or the synthetic miRNA pool ( S9C and S9D Fig ) . Consequently , Drosophila reared with miR162a in the medium exhibited a decrease in body weight ( 8 . 82% and 8 . 75% lighter in females and males , p = 0 . 0013 and p = 0 . 0003 , respectively ) , length ( 4 . 45% and 6 . 04% shorter in females and males , p<0 . 0001 and p<0 . 0001 , respectively ) , ovary size ( 0 . 9 fewer ovarioles , p = 0 . 0050 ) and fecundity ( 21 . 79% eggs fewer ) but had equal developmental times compared to the control larvae ( p = 0 . 323 ) ( Fig 4F–4J ) . In contrast , Drosophila larvae reared with the scrambled RNA in the medium showed no such phenotypes . Finally , the correlation between miR162a and dmTOR was analysed using transgenic Drosophila expressing a GFP reporter transgene with an miR162a binding fragment of dmTOR inserted downstream . In association with the observed phenotype of reduced fecundity in Drosophila reared with miR162a in the medium ( Fig 4J ) , decreased GFP levels in both nurse cells and follicle cells in the egg chambers were observed when miR162a was added into the larval diets of the transgenic line , while the addition of scrambled or seed-mutant miR162a mimics had no effect on GFP levels ( Fig 4K ) . These results suggest that miR162a in larval food was sufficiently delivered to Drosophila ovaries and that it suppresses endogenous dmTOR expression .
Caste differentiation of honeybees is a complex developmental process influenced by genetic , epigenetic and environmental variations . The prevailing view is that the nutrients in royal jelly ( primarily proteins , sugars and fatty acids ) drive queen development [6 , 10 , 33 , 34] . However , the active components that determine the developmental fate of honeybees remain elusive and even controversial [35] . Recent studies provide new insights into the relationship between epigenetic regulation and caste differentiation in insects [36–38] . In this study , we identified that plant miRNAs are significantly enriched in beebread and pollen and not in royal jelly . This striking difference prompted us to hypothesise that miRNAs , acting as important epigenetic regulators , may be transferred from the food of worker-destined larvae to their bodies and negatively regulate larval development; in contrast , miRNAs in the royal jelly are not sufficient to reach a functional level and to have biological relevance , therefore queen-destined larvae consuming royal jelly evade miRNA regulation . To test this hypothesis , we elucidated the effects of the plant RNAs and miRNAs that were enriched in beebread and pollen on honeybee phenotypes , and uncovered a previously unrecognized role for RNA as an environmental determinant of honeybee caste development . Furthermore , we investigated phenotypic changes in Drosophila caused by food supplemented with plant RNAs and miRNAs and observed larvae developing into adults with phenotypes similar to those of worker bees . We verified that these effects on the development of honeybees and Drosophila were caused by plant RNAs and specific miRNAs and excluded the possibility of a general effect of RNAs , because a synthetic scrambled RNAs added to the larval diet did not cause any phenotypic changes in honeybees or Drosophila . We also ruled out the possibility that the potentially toxic effects of chemical residues from the RNA isolation process caused the observed phenotypic changes in Drosophila , because a mock diet ( H2O instead of pollen was processed for RNA isolation and added to the diet ) with similar chemical residues had no effects on larval development . As a next step , we investigated whether honeybee development was regulated by variations in specific genes that are targeted by specific plant miRNAs . Mechanistic studies revealed that the blocking of the queen fate was , at least in part , due to amTOR knockdown by miR162a . Overall , our study revealed that the development of worker bee caste may be , at least in part , attributed to a previously uncharacterized effect executed by the transfer of enriched plant miRNAs in beebread and pollen to the young larvae . The mobility of small RNA molecules ( e . g . , siRNA and miRNA ) from one species to another is a newly discovered mechanism for the spread of gene-silencing signals and for facilitating cross-talk between different organisms , even between species of different kingdoms [39] . The cross-species transfer of small RNAs has been frequently reported to occur between interacting organisms: from bacteria to nematodes [40] , from fungal pathogens to plants [14] , from plants to pathogenic and symbiotic microbes [41–44] , from plants to nematodes [45] , and from plants to insects [46] . For example , transgenic plants engineered to produce siRNAs against essential pest genes are more resistant to pest attack [46] . In this study , we sought to broaden the understanding of the existence of small RNA transfer between representative species in the natural world: honeybees and plants . Our evidence indicates that ingested plant miRNAs affect gene expression and can reshape honeybee phenotypes , and it may provide additional support for the concept of horizontal small RNA transfer . We focused on the phenomenon of plant miRNA uptake and function but did not uncover a clear molecular mechanism accounting for the entrance and transfer of miRNAs within honeybees . We propose that systemic RNAi , which allows small RNAs to be transported across cellular boundaries and to spread throughout the whole body of insects [21 , 22 , 28] , might be a possible transport mechanism . However , this mechanism , which is mediated through SID-1 transmembrane protein activity [47 , 48] , has only been intensively characterized in C . elegans . Whether SID-1 homologues are present in honeybees and play equivalent roles in small RNA transport requires further investigation . Another open question is how honeybees make use of the available dosage of plant miRNAs to control their development . In our experiments , the same amount of plant miRNAs as is found in natural beebread was used , and this dose produced similar effects to those seem in nature . It is largely unknown if honeybees possess an amplification pathway as is found in C . elegans [49] to allow a small amount of RNA taken up from the environment to generate abundant secondary RNAs and to trigger strong responses within the body . In addition , plant miRNAs tend to induce mRNA cleavage through perfect or near-perfect complementarity with their target sequences , while animal miRNAs generally cause translational repression through partial complementarity [11 , 50–52] . The observation that miR162a decreased amTOR mRNA levels in vivo indicates that it behaves , at least in some ways , similarly to a plant miRNA . However , miR162a shows non-perfect complementarity with its target sequence , even with a G:U wobble in the seed region , indicating a regulatory action of animal miRNA . It is also unclear how plant miRNAs are incorporated into the honeybee’s Argonaute complexes . Because the ingested plant miRNAs should be mature single-stranded RNAs , it is not clear how these single-stranded small RNAs are loaded into Argonaute proteins to produce a functional miRNA form . Nevertheless , because miRNAs and other small RNAs have been frequently detected to be transported between species and hijack the RNAi machinery of host cells to exert biological functions [14 , 40–44] , it would be interesting to analyse the mode of action of plant miRNAs in honeybee cells . However , these questions are beyond the scope of this study . Protocols have been developed for rearing honeybee since 1927 [53–55] . The diet of a mixture of fresh royal jelly , fructose , glucose , yeast extract and H2O has been proven to be the optimal food for honeybee larvae [56–58] . In this study , it should be noted that one direct test could be to feed honeybees with beebread in which plant miRNAs have been eliminated . In fact , we have attempted to rear honeybees with pollen or beebread supplemented with antisense miRNAs without royal jelly . Unfortunately , all of the larvae died during cultivation . This result is consistent with previous observations that royal jelly is indispensable for the rearing of honeybee larvae in vitro [54 , 55 , 59] . Alternatively , we added plant RNAs or miRNAs to the larval diet of honeybees , which can defer the queen bee fate even in the presence of royal jelly and therefore supports our arguments . In fact , queen development is not the default trajectory in honeybees and royal jelly is needed to act on the endocrine system to direct larvae differentiation into a queen fate . The pathways controlling body size , developmental duration and fertility are anyway downregulated in worker-destined larvae [22 , 30 , 31] . According to our study , we suggest that the negative effects of beebread and pollen on larval development may be a part of the causation . Additionally , lab-reared honeybees largely develop with intermediate characteristics between a worker and queen , i . e . , with more ovarioles than natural worker bees [60] . This phenomenon implies that an essential ingredient may be missing from the larval diet used for in vitro cultivation that impairs the differentiation of worker bees . We suggest that the plant RNA enriched in natural beebread is a very likely candidate , although we cannot rule out other possibilities . Caste development is a complex process that involves multiple regulatory factors . Although this study largely focused on how plant miRNAs negatively affect the development of honeybees , we do not claim that plant miRNAs are the sole factor regulating honeybee development , and thus , removing plant miRNAs alone is not sufficient to disrupt the development of all phenotypes related to caste differentiation . Likewise , we do not expect that plant miRNAs can completely reverse the developmental fate , i . e . , turn worker into queen or queen into worker . It is worth noting that the inhibitory effects of plant miRNAs on honeybee development were gradually reduced from treatments with total pollen RNA to the miRNA pool and to only miR162a . For example , total pollen RNA prolonged the developmental time in honeybees and Drosophila , while miR162a did not . This phenomenon indicates that miR162a is not the sole active component , and other miRNAs , even larger RNAs , may also contribute to developmental regulation . Indeed , miR162a is only one of the multiple plant miRNAs enriched in beebread , and these miRNAs are only a portion of all classes of small RNAs , which themselves account for only a small fraction of total RNAs . Therefore , we propose that a single miRNA ( i . e . , miR162a ) does not operate as an all-around regulator of caste development; instead , more plant RNA components likely function in a cooperative manner in the regulatory network leading to caste development . Similarly , the miR162a-amTOR pair is only one of the pathways that participate in this cross-kingdom regulation . The involvement of other regulatory pathways ( e . g . , those indicated in the bioinformatic analysis summarized in S7 Fig and S3 and S4 Tables ) in honeybee development requires further investigation . In summary , the development of queens and workers is not determined by a single compound but , instead , is driven by the cooperation of multiple components in the larval food , which may include proteins , sugars , fatty acids and plant RNAs . However , why honeybees use such a sophisticated and intricate mechanism to regulate the queen-worker dimorphism is a fascinating question . For larvae that are destined to become queens , royal jelly is fed in copious amounts to drive the development of royal phenotypes . For worker-destined larvae , substantial quantities of plant miRNAs are absorbed when consuming beebread and pollen , thereby negatively influencing the larval development and inducing sterile worker bees . Reliance upon beebread and pollen as the exclusive food for sterile workers may have evolved in concert with the exploitation of plant miRNAs for caste regulation via a form of “RNAi castration” . The positive effects of royal jelly and the negative effects of beebread may maintain the stability of the colony’s social order and contribute to the survival of the colony in a coordinated manner . However , an opening question is raised regarding whether the plant miRNAs that reduce the development and fertility in honeybees and Drosophila have similar influences on solitary bees and bumblebees that would be exposed to the same plant miRNAs . Another opening question is about the widespread apicultural use of artificial pollen substitutes ( commonly consist of protein sources derived from soy , wheat or lentils ) in agricultural systems . Although the supplemental protein diets offset the poor nutritional conditions in honeybee colonies , long-term consumption of protein as the sole nutrition may compromise the ability of plant miRNAs to fine-tune honeybee development . Indeed , previous studies had explore the influence of natural pollen and artificial pollen substitutes on the cellular immunity , survival and parasite infection in honeybees and shown that the change from a natural to an artificial high nutritious diet in terms of protein content is not sufficient to promote healthy bees [61 , 62] . If consumption of natural or artificial diets did produce varying levels of plant miRNAs in honeybees and impact the survival and breeding of honeybees deserves further investigation . Overall , our study uncovered a new layer of caste regulation in which plant RNAs are transmitted between species of different kingdoms , offering hints for understanding cross-kingdom interactions and co-evolution .
The pollen used for this study was bee pollen , which are pollen pellets compressed and packed into corbicula on the outer surfaces of the hind legs after collection by forager bees . The pollen was separated using a specific collection device when bees come back to the comb . The royal jelly , honey , beebread and pollen were obtained in the cole or camellia flowering stage . All of the samples were stored at -80°C immediately after collection . Total RNA was extracted from royal jelly , honey , beebread and pollen using TRIzol Reagent ( Invitrogen , Carlsbad , CA , USA ) . Small RNAs were extracted from royal jelly , honey , beebread and pollen using the MirVana Protein and RNA Isolation System ( Ambion , Austin , TX , USA ) . Synthetic plant miRNA mimics and inhibitors and scrambled negative control RNAs were purchased from Invitrogen . The diets ( V . S . diet , D-1 diet and D-2 diet ) for laboratory rearing of honeybee larvae have been described previously [22] . The V . S . diet for the first 3 days was as follows: 50% fresh royal jelly , 6% fructose , 6% glucose , 1% yeast extract and 37% dd-H2O . The D-1 diet for the next 2 days was as follows: 53% fresh royal jelly , 6% fructose , 6% glucose , 1% yeast extract and 34% dd-H2O . The D-2 diet for the following days and until pupation was as follows: 53% royal jelly , 7 . 8% fructose , 7 . 8% glucose , 1% yeast extract and 30 . 4% dd-H2O . A healthy colony was chosen for egg laying , and the queen was caged in an empty comb from 6:00–18:00 . After 72 h , the hatched larvae were moved to 48-well plates , and total pollen RNA , small pollen RNAs , synthetic miRNA pool and synthetic miR162a were added to the diets . The detailed experimental procedure for preparation of the diets with added plant RNA ( total pollen RNA , synthetic miRNA pool or synthetic miR162a ) is shown in S12A Fig . DEPC-H2O was added to the diet as a control . The larvae were transferred to new plates with fresh diets every 12 h . The plates were kept in a crisper with 15 . 5% glycerine ( 90% relative humidity ) , and the crisper was placed in an incubator ( 33°C ) during the larval period . Defecating larvae were transferred into new 24-well plates , and each well contained a piece of filter paper . The plates containing defecating larvae were kept in a crisper with a saturated sodium chloride solution ( 70% relative humidity ) , and the crisper was left in an incubator ( 33°C ) . Then , the newly emerged adults were collected , and their characteristics were measured . At the beginning , we moved 48 larvae into the plates for each group and generally got 25–30 emerged adults due to the mortality during in vitro rearing . The honeybee larvae cultivated in this laboratory conditions largely developed to intermediates with characteristics between a worker and queen . For example , they generally had ovarioles ( 30–80 ovarioles ) more than natural worker bees ( < 10 ovarioles ) but less than queens ( > 150 ovarioles ) . A total of 20–30 pairs of Drosophila were caged in a tube containing ~15 mL of medium from 10:00–16:00 for egg laying ( 10–15 tubes for each experimental group ) . Total pollen RNA , small pollen RNAs , the synthetic miRNA pool and synthetic miR162a were added to the medium . The detailed experimental procedure for preparation of the medium with added plant RNA ( total pollen RNA , small pollen RNAs , synthetic miRNA pool or synthetic miR162a ) is shown in S12B Fig . DEPC-H2O was added to the medium as a control . Approximately 8–9 days later , newly enclosed adults were collected , and their characteristics were measured . We generally got 25–35 enclosed adults at this stage . On day 5 after eclosion , 5 pairs of Drosophila were placed in a custom tube for fertility analyses ( 10–15 tubes for each experimental group ) . The eggs that were laid by the 5 pairs of Drosophila were counted every day for 5 days . The culture environments for each parallel test were carefully controlled , and we only compared results obtained in the same parallel test , which excludes confounding environmental factors that may otherwise affect experimental results . The sequencing procedure was conducted as previously described [15] . Briefly , fresh samples of royal jelly , honey , beebread and pollen were collected from colonies of Italian honeybees . Total RNA was extracted from 10 g of these samples using Trizol Reagent ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer’s instructions . Then , equal amounts of total RNA were analysed using Illumina deep-sequencing technology , and the sequencing procedure was performed by BGI ( Shenzhen , China ) . After masking the adaptor sequences from the raw data and removing short and low-quality reads , a total of 9 , 548 , 986 , 13 , 683 , 503 , 9 , 559 , 836 and 9 , 561 , 153 reads from royal jelly , honey , beebread and pollen of cole and 8 , 996 , 733 , 12 , 160 , 200 , 15 , 237 , 283 and 16 , 690 , 115 reads from royal jelly , honey , beebread and pollen of camellia were obtained , respectively . The clean reads were aligned to the transcript sequences using bowtie 1 . 1 . 2 ( http://bowtie-bio . sourceforge . net ) with perfect match . Transcript sequences of Apis mellifera ( assembly Amel_4 . 5 ) and Brassica napus ( assembly Brassica_napus_assembly_1 . 0 ) were downloaded from the NCBI genome database ( https://www . ncbi . nlm . nih . gov/genome ) . Clean reads were also compared to the known miRNA precursors in the miRBase database 21 . 0 based on the Smith-Waterman algorithm . Only candidates with no mismatches and no more than 2 shifts were counted as miRNA matches . For normalization , the total sequencing frequency of each sample was normalized to 10 , 000 , 000 . Data for Illumina deep-sequencing have been deposited at GEO with the accession code GSE76286 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=yrevmigijrynjsl&acc=GSE76286 ) . To determine the plant miRNA levels in honeybee larval food , total RNA was extracted from royal jelly , honey , beebread and pollen using Trizol Reagent ( Invitrogen ) according to the manufacturer’s instructions . To determine the amTOR , dmTOR and miR162a levels in honeybees or Drosophila , newly emergence adults were collected , and total RNA was extracted using Trizol Reagent ( Invitrogen ) . Assays to quantify mature miRNAs were performed using TaqMan miRNA probes ( Applied Biosystems , Foster City , CA ) according to the manufacturer’s instructions . Briefly , 1 μg of total RNA was reverse-transcribed to cDNA using AMV reverse transcriptase ( TaKaRa , Dalian , China ) and a stem-loop RT primer ( Applied Biosystems ) . The following reaction conditions were used: 16°C for 30 min , 42°C for 30 min , and 85°C for 5 min . Real-time PCR was performed using a TaqMan PCR kit on an Applied Biosystems 7500 Sequence Detection System ( Applied Biosystems ) . The reactions were incubated in a 96-well optical plate at 95°C for 5 min , followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min . All of the reactions were run in triplicate . After the reactions , cycle threshold ( CT ) values were determined using fixed threshold settings , and the mean CT of triplicate PCRs was determined . To calculate the absolute expression levels of the target miRNAs , a series of synthetic miRNA oligonucleotides at known concentrations were reverse transcribed and amplified . The absolute amount of each miRNA was then calculated in reference to the standard curve . For cross-sample comparisons of miRNAs in royal jelly , honey , beebread and pollen , miRNA levels were normalized to the total amounts of RNA or to the total mass of the samples . To quantify amTOR and dmTOR mRNA , 1 μg of total RNA was reverse-transcribed to cDNA using a specific reverse primer and AMV reverse transcriptase ( TaKaRa ) under the following conditions: 16°C for 15 min , 42°C for 60 min , and 85°C for 5 min . Subsequently , real-time PCR was performed using the RT product , SYBR Premix Ex Taq ( Takara , Dalian , China ) and specific primers for amTOR and dmTOR . The primers that were used in this study were as follows: amTOR-forward , 5’-TTGGTTGGGTACCGCATTGT-3’; amTOR-reverse , 5’-AACCTGGGGCCATTCTTAGC-3’; dmTOR-forward , 5’-CTCTTACATGAATCCGATCCTCA-3’; and dmTOR-reverse , 5’-CGGAGCCTCCATTAACCT-3’ . The reactions were incubated at 95°C for 5 min , followed by 40 cycles at 95°C for 15 sec , 55°C for 30 sec , and 72°C for 30 sec . After the reactions were complete , CT values were determined using fixed threshold settings . The relative amounts of amTOR and dmTOR were normalized to amActin and dmActin , respectively . The primers for amActin and dmActin were as follows: amActin-forward , 5’-TGCCAACACTGTCCTTTCTG-3’; amActin-reverse , 5’-AGAATTGACCCACCAATCCA-3’; dmActin-forward , 5’-CGCGATTTGACCGACTACCT-3’; and dmActin-reverse 5’-TTGATGTCACGGACGATTTCA-3’ . Small RNAs were extracted from royal jelly , honey , beebread and pollen using the MirVana Protein and RNA Isolation System ( Ambion , Austin , TX , USA ) . The northern blot analysis was carried out using miRCURY LNA microRNA Detection Probes with DIG-labelling ( Exiqon , Woburn , MA , USA ) and a DIG luminescence detection kit ( Roche , Indianapolis , IN , USA ) according to the manufacturer’s instructions . Briefly , samples of small RNAs ( 15 μg ) and synthesized size markers ( Invitrogen ) were added to Gel Loading Buffer II ( Ambion ) and denatured at 95°C for 5 min . A 15% TBE-urea gel was pre-run at 250 V for 60 min , and the samples and size markers were added to the gel and run at 250 V until the bromophenol blue ( BPB ) from the loading solution reached approximately 1 cm above the bottom of the gel . Generally , BPB and cyanol from the loading solution run at approximately 15 bases and 60 bases , respectively . RNA was then transferred onto a nylon membrane ( Hybond N+ , Amersham Biosciences ) via electroblotting at 250 mA in 0 . 5× TBE ( Tris-borate-EDTA ) buffer for 1 h . After UV-crosslinking at 1200 mJ , a prehybridization step was performed by incubating the membrane with 40 mL of ULTRAhyb-Oligo solution ( Ambion ) pre-heated to 50°C . Prehybridization was performed for 30 min at 50°C in a standard rotating hybridization oven . DIG-labelled LNA probes were hybridized to the membranes overnight at 50°C with slow rotation . The next day , the membrane was washed twice for 15 min each in NorthernMax Low-Stringency wash solution no . 1 ( Ambion ) at 50°C , briefly rinsed for 10 min with Washing Buffer from the DIG wash and Block Buffer Set ( Roche ) , blocked for 30 min in 1× Blocking Solution ( Roche ) , incubated for 30 min in antibody solution ( anti-DIG-AP 1:10 , 000 in 1× Blocking solution , Roche ) , washed twice for 15 min each with Washing Buffer and incubated for 2–5 min with 1× Detection Buffer ( Roche ) . Then , the membrane was incubated with CSPD , the chemiluminescent substrate for alkaline phosphatase ( Roche ) and exposed to Amersham Hyperfilm ECL ( GE Healthcare Life Sciences , Piscataway , NJ ) following the instructions of the DIG Luminescent Detection Kit ( Roche ) . Sequence information of honeybee mRNAs was collected from the NCBI database . Two bioinformatic algorithms , RNAhybrid and miRanda [63 , 64] , were used in combination to scan honeybee mRNAs for potential binding sites for plant miRNAs . The gene lists generated by miRNA target prediction were assigned to orthology groups with Drosophila melanogaster genes on the basis of BLAST match , and GO terms were assigned to bee genes based on annotation of Drosophila genes . GO functional terms and Drosophila gene GO annotations were downloaded from the GO database . Counts of genes in specific categories were performed by using PANTHER , a gene functional classification tool . χ2 tests were performed in R , and differences were considered statistically significant at p < 0 . 05 . Cytoscape was used to build the GO network associations . We utilized the processing machinery of pri-dme-mir-184 to express miR162 in Drosophila S2 cells . The S2 cell line was cultured at 28°C with Schneider’s Drosophila medium containing 10% heat-inactivated FBS . The miR162a sequence was substituted into a 300-bp pri-dme-mir-184 backbone with structurally conserved nucleotide changes to maintain pairing . The 300-bp pri-dme-mir-184 was GTTTTCTATTCACGCTTTAGTGCACTTATTTACTCGATTGTATGATCCAAAGCTCCTCTTTGACTCGCCGAATTCCTGTCGATTCAATGGGTATTGGTTTGGTTGGCCGGTGCATTCGTACCCTTATCATTCTCTCGCCCCGTGTGCACTTAAAGACAACTGGACGGAGAACTGATAAGGGCTCGTATCACCAATTCATCCTCGGGTCAGCCCAGTTAATCCACTGATTTGCACACTTTTCTTTATACATACGAGGATACTTACCCCACGTTTCGATTACGCGCATCAATCAATCAATCA , and the underlined parts were replaced with TCGATAAACCTCTGCATCCAG and AATGAATGAGAGGCTTTATCGA , respectively . The 300-bp fragment containing the miR162a sequence was synthesized directly and cloned into a pAc5 . 1 vector . Cultured cells were prepared for transfection by seeding 1×106 cells/mL in a 24-well plate . After culturing the cells for 12–18 h , transfection was performed with Effectene transfection reagent ( Qiagen , Valencia , CA , USA ) . The transfection mixture per well contained 6 μL of Effectene reagent only , 6 μL of Effectene reagent and 0 . 3 μg of miR162a expressing plasmids , or 6 μL of Effectene reagent and 0 . 3 μg of pAc5 . 1 vectors without any insert . The cells were collected 48 h after transfection and used for western blotting analysis . Plasmids expressing miR162a were transfected into S2 cells using Effectene ( Qiagen ) according to the manufacturer’s instructions . The cells were lysed in RIPA buffer ( 0 . 5% NP-40 , 0 . 1% sodium deoxycholate , 150 mM NaCl , 50 mM Tris-HCl ( pH 7 . 5 ) ) . The lysates were resolved via 6% SDS-PAGE ( for the dmTOR protein ) or 10% SDS-PAGE ( for internal control GAPDH protein ) , transferred to a PVDF membrane ( Millipore , Bedford , MA , USA ) and probed with anti-dmTOR or anti-GAPDH antibodies ( Santa Cruz Biotechnology , CA , USA ) . Anti-dmTOR antibodies were polyclonal antibodies that were custom-made by GenScript USA Inc . ( Nanjing , China ) . The epitope was predicted using the GenScript OptimumAntigen design tool , and the peptide antigen was then synthesized . After the coupling reaction and mixing with complete adjuvant , the coupled antigen was used once for a subcutaneous injection . The host strain was a New Zealand rabbit . Then , the coupled antigen was mixed with incomplete adjuvant and injected into the rabbit . Subsequently , serum was taken from the immunized rabbit , and the antibody was purified . A DNA probe complementary to dmTOR was synthesized with 5’ and 3’ terminal biotin labels . The probe was dissolved in a wash/binding buffer ( 0 . 5 M NaCl; 20 mM Tris-HCl , pH 7 . 5; 1 mM EDTA ) to a concentration of 8 pmol/μL . Then , the probe was incubated with streptavidin magnetic beads ( New England Biolabs ) at room temperature for 1 h with occasional agitation . After incubation , the probe-coated beads were washed twice and captured with a magnet to remove the supernatant . The total RNA that was extracted from miR162a-transfected S2 cells ( 50~100 μg ) was pretreated with DNaseI and then heated at 65°C for 5 min , followed immediately by an ice bath . Then , the RNA was incubated with the prepared probe-coated beads at 37°C for 3 h with occasional agitation , and the beads were washed twice with wash/binding buffer and once with a cold low-salt buffer ( 0 . 15 M NaCl; 20 mM Tris-HCl , pH 7 . 5; 1 mM EDTA ) . After each wash , a magnet was applied to the tube , and the supernatant was removed . Finally , the RNA was eluted from the probe-coated streptavidin beads with Elution Buffer ( 10 mM Tris-HCl , pH 7 . 5; 1 mM EDTA ) prewarmed to 90°C and then analysed via qRT-PCR . The following probe sequences were used: anti-dmTOR pull-down probe 5’-CTAGAGCCCAAGTCTGCATTGAA-3’ and random pull-down probe 5’-GGCAGCTAACCTATATGACATGC-3’ . Drosophila were cultured following standard procedures at 25°C except for the transgenic lines , which were cultured at 29°C . Strain w1118 was obtained from the Bloomington Drosophila Stock Center . To generate the transgenic line , the miR162a binding sequence in the dmTOR gene was cloned into a pUbi-GFP expression vector , and the pUbi-GFP-dmTOR transgenic line was obtained via embryo injection according to standard procedures . After miR162a or mutant miR162a was added to the larval diets of the transgenic Drosophila , the ovaries of transgenic Drosophila were dissected in PBS and then fixed in a devitellinizing buffer ( 100 μl , 7% formaldehyde ) and heptane ( 600 μl ) mixture for 10 minutes . After 3 washes in PBS for 10 min each , ovaries were incubated in blocking solution ( PBT , 10% goat serum ) for 30 min . GFP levels were observed and compared between different groups . The analyses were performed using IBM SPSS Statistics 19 . One-way ANOVAs and two-tailed Student’s t-tests were used for the analyses . The data are presented as the means ± SEM of at least three independent experiments , and differences were considered statistically significant at p < 0 . 05 . | How caste has formed in honeybees is an enduring puzzle . The prevailing view is that royal jelly stimulates the differentiation of larvae into queen . Here , we uncover a new mechanism that plant miRNAs in worker bee’s food postpone larval development , thereby inducing sterile worker bees . Thus , the theories about honeybee caste formation need to be re-examined from a new angle besides the traditional focus on royal jelly and its components . Furthermore , since miRNAs are transmitted between species of different kingdoms and can contribute to the phenotype regulation , this new model of horizontal miRNA transfer may open up a new avenue to further study the molecular mechanisms underlying cross-kingdom interaction and co-evolution . |
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Lipoic acid ( LA ) is an essential cofactor of α-keto acid dehydrogenase complexes ( KADHs ) and the glycine cleavage system . In Plasmodium , LA is attached to the KADHs by organelle-specific lipoylation pathways . Biosynthesis of LA exclusively occurs in the apicoplast , comprising octanoyl-[acyl carrier protein]: protein N-octanoyltransferase ( LipB ) and LA synthase . Salvage of LA is mitochondrial and scavenged LA is ligated to the KADHs by LA protein ligase 1 ( LplA1 ) . Both pathways are entirely independent , suggesting that both are likely to be essential for parasite survival . However , disruption of the LipB gene did not negatively affect parasite growth despite a drastic loss of LA ( >90% ) . Surprisingly , the sole , apicoplast-located pyruvate dehydrogenase still showed lipoylation , suggesting that an alternative lipoylation pathway exists in this organelle . We provide evidence that this residual lipoylation is attributable to the dual targeted , functional lipoate protein ligase 2 ( LplA2 ) . Localisation studies show that LplA2 is present in both mitochondrion and apicoplast suggesting redundancy between the lipoic acid protein ligases in the erythrocytic stages of P . falciparum .
Lipoic acid ( 6 , 8-thioctic acid; LA ) is an essential cofactor that is covalently attached to the transacylase subunit ( E2-subunit ) of α-keto acid dehydrogenase complexes ( KADHs ) , namely pyruvate dehydrogenase ( PDH ) , α-keto glutarate dehydrogenase ( KGDH ) , and branched chain α-keto acid dehydrogenase ( BCDH ) as well as the H-protein of the glycine cleavage system ( GCS ) [1 , 2] . In eukaryotes , these multienzyme complexes are generally found in the mitochondrion . Only plants and plastid-containing organisms possess organelle-specific PDH with the plastid PDH providing substrates for fatty acid biosynthesis [3] . Therefore , mitochondrion and plastid require the enzymatic machineries for the posttranslational lipoylation of KADHs or H-protein [2–5] . LA is provided and ligated to the respective target proteins by two distinct pathways . The cofactor can be synthesised by almost all organisms using the LA biosynthesis pathway . This requires octanoyl-acyl carrier protein ( ACP ) as a substrate ( a product of fatty acid biosynthesis ) which is ligated to the apo-E2-subunits or the apo-H-protein by octanoyl-[acyl carrier protein]: protein N-octanoyltransferase ( LipB ) [6] . Subsequently , two sulphurs are introduced into position 6 and 8 of the protein-bound octanoic acid , a reaction that is catalysed by lipoic acid synthase ( LipA ) [7 , 8] . LA can also be acquired through the salvage pathway . In mammals free , salvaged LA is transferred to the E2-subunits of KADHs through two enzymatic steps but in bacteria , fungi , and apicomplexan parasites this reaction is catalysed by a single enzyme [6 , 9–12] . Scavenged LA in mammals is first activated through an ATP-dependent reaction catalysed by LA activating enzyme before the activated form of LA is then attached to the E2-subunits or the H-protein by LA transferase [9 , 10] . In contrast , bacterial-type LA protein ligases ( LplA ) catalyse the activation and transfer of LA in a single enzymatic step [6] . LA metabolism in the malaria parasite Plasmodium falciparum and the related apicomplexan parasite Toxoplasma gondii display an organelle-specific distribution of biosynthetic and salvage pathways [11–15] . LA biosynthesis is exclusively found in their plastid-like organelle , the apicoplast , whereas LA salvage is confined to their mitochondrion . It was shown that both organelles contain members of the KADHs , which require posttranslational lipoylation [16–18] . It is assumed that these multienzyme complexes play pivotal roles in the parasite's metabolism and it is thought that both LA biosynthesis and salvage are essential for parasite survival . This is further supported by the findings of Crawford and colleagues [13] showing that newly synthesised LA does not exit the apicoplast and by Allary and colleagues [14] who showed that radiolabelled LA is not utilised to lipoylate apicoplast PDH , but only leads to lipoylation of the mitochondrial E2-subunits . These studies therefore provide evidence that the organelles' lipoylation machineries act independently and inhibition of either one should have deleterious effects for the parasites . Indeed proof of this concept is supported by the lethal effect of the LA analogue 8-bromo-octanoic acid on intraerythrocytic stages of P . falciparum and also T . gondii [13 , 14] . In this study we have further tested this hypothesis by disrupting the Plasmodium apicoplast targeted LipB gene , which is part of the LA de novo biosynthesis pathway . Surprisingly , the gene disruption is not deleterious for the parasites suggesting that Plasmodium possesses alternative routes for LA ligation in this organelle . Our results provide evidence that a second LA protein ligase-like protein , LplA2 [13 , 14] , can replace LipB function .
To verify that the protein bands recognised by an antibody directed against protein-bound LA ( anti-LA ) , which was used in this study to detect lipoylated proteins in P . falciparum ( Figure 1 , lane 4 ) , Western blots of parasite extracts were also analysed with antisera raised against P . falciparum H-protein , BCDH-E2 and KGDH-E2 ( Figure 1 , lanes 1–3 ) . The sizes of the proteins detected by the anti-LA antibody correlated well with those protein bands detected by the antibodies directed against the three mitochondrial proteins . These results corroborate the previously published suggestion that the protein bands detected by the anti-LA antibody in fact represent the mitochondrial KADHs . The 75 kDa band that was detected by anti-LA was previously shown to correspond to the PDH-E2-subunit [14] . The gene encoding LipB was targeted using two different constructs ( see Materials and Methods ) cloned into the P . falciparum transfection plasmid pHH1 . This plasmid confers single cross over recombination resulting in a disruption of the endogenous gene locus [19] . Both constructs lacked the last 100 amino acids including a catalytically essential , highly conserved cysteine residue at position 369 ( Plasmodium LipB numbering ) which was shown to form a catalytic dyad with a conserved lysine residue in position 307 ( Plasmodium LipB numbering ) in the Mycobacterium tuberculosum LipB protein [20] . Therefore it was assumed that the disruption of the endogenous LipB gene locus should result in the generation of a C-terminally truncated LipB protein unlikely to display any enzymatic activity . Independent transfections of both constructs were performed and the genotype of the transfected parasite lines was analysed by Southern blotting to verify the presence of the transfected plasmid . All analysed parasite genotypes revealed that the LipB gene locus had been targeted and the independent transfectants were cloned by limiting dilution . Two independent clones from separate transfections with each construct were used for further analyses ( Figure 2 ) . LipBKO1–1 and LipBKO1–2 describe the 3D7 derived mutants and LipBKO2–1 and LipBKO2–2 describe the D10 derived mutants . Upon NdeI digestion of genomic DNA of all four clones , the endogenous gene of 2 . 8 kb ( Figure 2 , lanes 1 and 2 ) is replaced by two bands of approximately 3 . 7 kb and 4 . 9 kb ( Figure 2 , lanes 3–6 ) diagnostic for the disruption of the LipB gene locus . Growth experiments performed according to Sanders et al . [21] showed that the LipB disruption with either construct , and regardless of parasite strain , resulted in a modestly increased growth rate of the LipB mutants compared to both parent lines ( 3D7 and D10 ) ( Figure 3 ) . Six days after the experiment was started with highly synchronised , ring-stage parasites , the parasitemia of the wild-type controls was determined to be between 1 . 8% and 2% whereas the LipB mutants consistently had a parasitemia between 4% and 5% . This increased growth rate could be explained in several ways; e . g . , an accelerated cell cycle , the generation of a greater number of merozoites , or a more successful invasion rate by the mutant parasites . The first point was addressed by following tightly synchronised parasites through the 48 h developmental cycle . Blood smears were taken every 8 h and the progression through the cell cycle of the different parasite lines was analysed microscopically ( Figure 4 ) . The experiment was started with tightly synchronised ring-stage parasites of all six parasite lines and this time point was set as 0 h . Eight hours later , between 50% and 80% of the LipB mutants had developed into trophozoites , whereas only 20% to 25% of either D10 or 3D7 Plasmodium had progressed to this developmental stage . Generally the 3D7 wild-type and the 3D7-based mutants showed a faster progression through their intraerythrocytic development than the D10 wild-type and mutant lines ( Figure 4 ) . Thirty two hours after the start of the experiment almost all of the LipB mutant parasites had re-infected fresh erythrocytes and had successfully progressed through their life cycle , whereas 3D7 needed another 8 h to reach this point and D10 needed even longer . In summary , the data clearly show that all four LipBKO mutants ( the two selected 3D7-based KO1 clones and the two D10-based KO2 clones ) progress through their intraerythrocytic cell cycle faster ( about 4 to 8 h ) than the two wild-type lines , which suggests that the disruption of the LipB gene locus in some way affects parasite cell cycle control . Determining parasite numbers at the beginning and the end of the parasites' life cycles revealed that the LipBKO schizonts generated , on average , the same number of successfully infective merozoites as wild-type parasites per life cycle . The LA content of wild-type and the LipB null mutants was quantified by gas chromatography-mass spectrometry ( GC-MS ) . This revealed that all of the LipB null mutants showed a drastic reduction in total LA content ( Figure 5A; Table 1 ) compared to the wild-type parasite lines . Both P . falciparum 3D7 and D10 contain approximately 40 nmol/108 cells of LA whereas the LipB mutants only contain between 0 . 6 nmol/108 cells and 2 . 2 nmol/108 cells; a reduction of more than 90% of total LA . In addition to the total LA in the cell , it was possible to distinguish between oxidised and reduced protein-bound LA; the ratio of which was only marginally affected by the LipB disruption , varying between 1 . 2 and 4 . 5 , with the oxidised form of LA being predominant ( Table 1 ) . Apart from changes in LA levels , the LipB disruption lead to a 5-fold increase in myristate ( C14:0 ) in the LipB mutants ( from 1 . 8 nmoles/108 cells to 9 nmoles/108 cells ) . This is presumably the result of the increased availability of octanoyl-ACP , which is no longer required for LA biosynthesis and so can be further extended to C14 ( Figure 5B ) . Thus , the decreased requirement for LA biosynthesis because of the lack of LipB function generates a surplus of longer chain fatty acids , which can be used for instance for protein modifications such as acylations and lipid biosynthesis . The major overall finding of this part of the study was that the LipB disruption leads to a drastic reduction of total LA in the parasites without affecting parasite viability . From these data it also can be deduced that the level of lipoylation of PDH-E2 should be greatly reduced if not ablated if LipB is the only protein that transfers the LA cofactor to the multienzyme complex in the apicoplast . This was qualitatively analysed by Western blotting using anti-LA antibodies which shows that PDH-E2 lipoylation indeed decreases significantly but does not totally disappear ( Figure 5C , compare lane 1 with lanes 2 and 3 ) , suggesting an alternative mechanism that allows for partial PDH-E2 lipoylation in the absence of LipB activity . The E2-subunits of the mitochondrial KADH also show a slight decrease of lipoylation , suggesting that the knockout of the apicoplast LipB might also affect the modification of the mitochondrial enzyme complexes . Apicoplast PDH is thought to be the source of acetyl-CoA for type II fatty acid biosynthesis in apicomplexan parasites [22] and the drastic reduction of LA in the parasites led to the conclusion that the activity of PDH-E2 might be negatively affected and as a consequence the provision of substrates for de novo fatty acid biosynthesis might be reduced . This hypothesis was tested by investigating the sensitivity of the mutants to triclosan , an inhibitor of FabI; one of the enzymes involved in fatty acid elongation in P . falciparum and previously validated as a drug target [23] . However , our data show that the LipB disruption does not have any effect on the susceptibility of mutant parasites towards triclosan , possibly suggesting that the residual lipoylation of PDH-E2 is sufficient to provide enough acetyl-CoA to sustain fatty acid biosynthesis at wild type level ( Figure 6A ) . Another hypothesis requiring investigation was the suggestion that LA might act as a principal antioxidant in the organelles of Plasmodium . In order to further substantiate this , the IC50 for two pro-oxidants were determined; wild-type and LipB null mutants showed no differential susceptibility towards tert-butylhydroperoxide and N-methylphenazonium methosulfate , respectively ( Figure 6B and 6C ) . It was shown previously that Plasmodium possess a second functional LplA-like protein , which compensated growth of a bacterial strain lacking both LipB and LplA , but which was unable to compensate growth of a LipB deficient bacterial line in a previous study [14] . It was suggested that LplA2 is not able to replace LipB function in the bacteria because of their distinct substrate specificities . In this study we expressed three different expression constructs in LipB and LipB/LplA deficient Escherichia coli lines , respectively [24 , 25] . The constructs used here were full length at their C-termini and included a C-terminal tag as opposed to the construct expressed in the previous study . The N-terminus of the three constructs differed because the deduced amino acid sequence has an N-terminal extension of 28 amino acids when compared to E . coli LplA which potentially encodes an N-terminal targeting sequence . It is possible that such N-terminal targeting sequences interfere with efficient expression or function of the heterologous parasite protein in the prokaryotic expression system and therefore it was decided to analyse three different expression constructs of LplA2 ( as outlined in the Materials and Methods section ) in the complementation assay . In our hands all three constructs complemented the growth of the LipB deficient bacteria ( Figure 7A ) , suggesting that the protein can replace LipB function . These data are in contrast to those obtained by [14] but might be explained by differences in expression plasmids and constructs used in the two different studies . This does not exclude the possibility that LplA1 and LplA2 proteins have differential , albeit somewhat overlapping , substrate specificities or activity profiles through the developmental cycle of P . falciparum . The LplA2 constructs also compensated for the growth defect of TM136 , a bacterial line deficient in LplA and LipB ( Figure 7B ) [25] . A clear prediction of LplA2′s localisation is not possible and therefore this was analysed by expressing a C-terminally green fluorescent protein ( GFP ) -tagged full-length LplA2 protein in the erythrocytic stages of P . falciparum . The results suggest that LplA2-GFP is targeted to two distinct organelles—one of which is clearly the mitochondrion as LplA2-GFP colocalises with Mitotracker ( Figure 8 ) . Given the close association of the second organelle to the mitochondrion we suggest that this is likely to be the apicoplast . This was corroborated by immunofluorescence studies using antibodies raised against LplA2 and apicoplast lipoamide dehydrogenase ( aLipDH; aE3; P . J . McMillan and S . Müller , unpublished data ) . The results show that in some parasites both proteins clearly colocalise , supporting its localisation in the apicoplast ( Figure 9 ) . However , the localisation of LplA2 is not that straightforward . In some parasites we clearly observe colocalisation with either Mitotracker or , in the immunofluorescence study , with aLipDH , whereas in others the staining is present in both organelles . Analysing the distribution in 46 distinct parasites from the immunofluorescent experiment resulted in the following distribution: 19 . 6% of LplA2 colocalised with aLipDH and thus is apicoplast located; 53% were found in an organelle distinct to the apicoplast but likely to be the mitochondrion , and 28 % showed staining of both organelles . Similarly , the LplA2-GFP expressing parasites were analysed ( 50 parasites ) and 15% of staining was likely to be apicoplast ( distinct from the mitochondrion stained with Mitotracker ) , 68% were found to have LplA2-GFP in the mitochondrion and 18% showed staining in both organelles . These data indicate that LplA2 is dually targeted within the parasites albeit the precise mechanisms governing this distribution need to be analysed in future studies . Overall , the functionality and localisation studies of LplA2 strongly support our hypothesis that LplA2 indeed compensates for the loss of LipB function , although it appears that its ability to utilise the octanoyl-ACP substrate provided by fatty acid biosynthesis might not be as efficient as it is by LipB given the extensive loss of protein-bound LA and the reduction of lipoylation of PDH-E2 .
Lipoic acid metabolism in apicomplexan parasites is distributed between mitochondrion and apicoplast [11 , 12] . Both organelles lipoylate their KADHs independently , with de novo biosynthesis confined to the apicoplast and salvage solely found in the mitochondrion of the parasite [13–15] . Given this background it was surprising that the LipB gene , which encodes the principal LA protein ligase in the apicoplast , can be disrupted without negatively affecting P . falciparum growth . This was entirely unexpected because it was thought that LipB activity is absolutely required for the parasites to lipoylate PDH-E2 , which in turn is necessary to provide acetyl-CoA for fatty acid biosynthesis operating in the organelle [22] . In fact , down regulation of ACP expression using a Tet-inducible conditional knockout system in T . gondii revealed that one major function of apicoplast located type II fatty acid biosynthesis is to provide octanoyl-ACP for the lipoylation of PDH-E2 in these related parasites [15] . These data indirectly suggest that salvage of LA and its subsequent ligation to the PDH-E2 is not an alternative for the apicoplast located LA de novo biosynthesis pathway in Toxoplasma . This is in agreement with data on bacterial LipB , which does not accept free LA as a substrate [6 , 26] . Thus , even if exogenous LA would be taken up into the apicoplast of Toxoplasma it would require the presence of a LplA-like protein to guarantee ligation of the cofactor to the apo-PDH-E2 . Analyses of a gene identified in the ToxoDB database potentially encoding LplA2 ( gene locus: 83 . m01296 ) showed that the identity between the deduced amino acid sequences of the Toxoplasma and Plasmodium deduced amino acid sequences was modest , with less than 10% identity . This is in contrast to potential LplA2 encoding genes in other Plasmodium species but also other apicomplexans , such as Theileria , which contain highly conserved orthologues of P . falciparum LplA2 ( similarities between 58% and 37% ) . In addition , highly conserved amino acid motifs necessary for substrate interaction and activation appear to be absent from the potential T . gondii LplA2 [27–29] . Therefore , it appears that the lipoylation pathways in Plasmodium and Toxoplasma differ to some extent . The LA content of the LipB mutant parasites was analysed by GC-MS which revealed that the LipB mutants had a drastic reduction ( ∼90% ) of LA compared to wild-type parasites . Concomitantly , the levels of myristate ( C14:0 ) increased about 5-fold in the mutant parasites compared to wild-type parasites , which could potentially have implications such as the levels of protein lipidations or specific lipid species in the mutant parasite lines [30] . Despite the severe loss of LA , the LipB mutant parasites showed a faster growth phenotype which was primarily attributable to accelerated progression through the intraerythrocytic cell cycle . It is conceivable that the increased availability of endogenously generated myristate changes the acylation state of regulatory proteins and affects their activity and/or distribution , which potentially contributes to the observed phenotype . However , these speculations have to be further substantiated in future analyses of the LipB mutants . Another aspect is that the loss of LA has an impact on the parasites' capacity to defend themselves against oxidative stress considering that one of the most discussed roles of LA is its redox activity and its potential as antioxidant [31 , 32] . However , the LipB mutants appear to be unaffected in their susceptibility to exogenous and endogenous oxidants despite the significant loss of LA . This potentially could be explained by a compensatory upregulation of alternative antioxidants in these mutant parasites—a hypothesis that also needs further investigation . The observed reduction of LA in the parasites also implies that PDH-E2 lipoylation should be greatly reduced; by using an anti-LA antibody this was confirmed by Western blotting . The reduced lipoylation appears to not negatively affect de novo fatty acid biosynthesis as shown by the fatty acid analyses data ( reduction of LA biosynthesis leads to increased levels of longer chain fatty acids ) . In addition , mutant and parent parasite lines showed similar susceptibilities to the supposed FabI inhibitor triclosan [21] . This shows that the reduction of PDH-E2 lipoylation does not affect PDH activity as severely as originally believed and that the multienzyme complex still provides sufficient acetyl-CoA to sustain fatty acid biosynthesis . Previous studies on E . coli PDH showed that the loss of one or two of the three lipoyl-domains of the bacterial PDH-E2 subunit does not cause significant changes in PDH activity demonstrating that under-lipoylation does not necessarily yield a catalytically incompetent PDH complex [33] . The fact that PDH-E2 is at all lipoylated is surprising given that LipB was thought to be the principal LA protein ligase present in the apicoplast . However , we have shown in this study that an alternative pathway that allows lipoylation of PDH-E2 is provided by LplA2 , a second LA protein ligase like protein , identified in the genome of several Plasmodium species [14] and a number of other apicomplexan parasites . However , Toxoplasma appears to lack a LplA2 orthologue—a gene potentially encoding LplA2 lacks amino acid motifs essential for a functional LplA protein . The functionality of Plasmodium LplA2 was corroborated in this study and it was shown that different expression constructs complement the growth defect of bacteria deficient in LipB and LipB/LplA , supporting that the protein compensates for both LplA and LipB in this bacterial expression system . This is in contrast to the findings of [14] who suggested that LplA2 can replace mitochondrial LplA1 only , and not LipB . However , the expression constructs used in the previous study differ considerably from the ones used in this study , which might explain these different results . Furthermore , it needs to be emphasised that LplA2 seems to be less efficient in using octanoyl-ACP as a substrate to lipoylate the PDH-E2 subunit as shown by the large loss of total LA and the under-lipoylation of PDH-E2 . This clearly suggests that the substrate specificities of LipB and LplA2 might differ and studies to fully characterise LplA2 biochemically and its precise role for parasite survival are currently underway . The difference in substrate specificity between LipB and LplAs is , however , not very surprising and has been previously shown for the bacterial enzymes [6] . In fact , overexpression of LipB in E . coli render them insensitive towards selenolipoic acid , suggesting that LipB cannot use exogenously supplied LA or its derivatives as substrates [6 , 26] . The localisation of LplA2 cannot be reliably predicted and therefore the full-length protein C-terminally tagged with GFP was expressed in the erythrocytic stages of P . falciparum . The results that we obtained were intriguing because the GFP fluorescence was observed in both the mitochondrion and a closely associated organelle likely to be the apicoplast . In order to exclude that these results were attributable to the over expression of the LplA2-GFP-fusion protein we also performed immunofluorescence studies on wild-type parasites . The anti-LplA2 antibody used detected a protein that either colocalised with an apicoplast marker ( aLipDH ) , was closely associated with the apicoplast marker , or was observed in both organelles . This implies that LplA2 is dually targeted to mitochondrion and apicoplast supporting the hypothesis that the loss of LipB functionality can be compensated by LplA2 . Dual targeting has been shown previously in P . falciparum for the metalloprotease falcilysin [34] and potential mechanisms by which this is achieved or governed have been discussed by Ralph [35] . In organisms that contain plastid and mitochondrion dual targeting is not unusual , particularly for those proteins involved in biological processes that are found in both organelles [36 , 37] . Most proteins targeted to the two organelles are nuclear-encoded and they possess certain targeting signals that are not always predictable using bioinformatics approaches [38] . It has been suggested that not only the primary amino acid sequence of a protein is involved in the control of dual targeting but that also untranslated regions of the mRNA play a role in the process [37 , 39] . In apicomplexan parasites the posttranslational trafficking of apicoplast and mitochondrial proteins differs considerably from those in plants—mitochondrial proteins are delivered via the cytosol to their destination whereas the apicoplast targeting is through the secretory pathway [40] . Therefore it has to be assumed that specific mechanisms allow for dual targeting of apicomplexan proteins to both organelles . Recently , a study by Pino and colleagues showed that the nature of the signal peptide affects targeting of a number of unrelated proteins to both mitochondrion and apicoplast in T . gondii [41] . It is well possible that similar mechanisms occur in Plasmodium and the fact that lipoylation is an essential process in both organelles might be the reason for the dual targeting of LplA2 that we observed in this study . Overall , this study shows that redundancies exist between LA protein ligases in the malaria parasite P . falciparum , which appear to be achievable through dual targeting of LplA2 . The accelerated progression through the cell cycle during intraerythrocytic growth of LipB mutants implies that the lack of LA and LipB is affecting cell cycle control mechanisms . Future studies will elucidate the underlying reasons for the rapid progression through the intraerythrocytic cycle and analyse whether there are growth and developmental impairments during other life cycle stages of the LipB mutant parasites .
Albumax II and RPMI 1640 were obtained from Invitrogen Corporation , UK . Irgasan ( triclosan ) , tert-butylhydroperoxide and N-methylphenazonium methosulfate were purchased from Sigma-Aldrich , UK . The ImmobilonTM Western Chemiluminescent HRP Substrate was obtained from Millipore , UK . Anti-LA rabbit polyclonal antibody was supplied by Calbiochem and the anti-rabbit IgG ( H+L ) , HRP conjugate was from Promega . [α-32P]-ATP ( Adenosine 5′-triphosphate [α-32P] , EasyTides , specific activity: 3 , 000 Ci/mmol ) was purchased from Perkin-Elmer . [8-3H]-hypoxanthine ( specific activity: 10–30 Ci/mmol ) was from GE Healthcare , UK . All restriction enzymes were obtained from New England Biolabs . WR99210 was generously provided by Dr Jacobus , Jacobus Pharmaceuticals , USA . The vector pASK-IBA3 was purchased from Institut für Bioanalytik , Germany . The LipB-deficient ( KER 184 ) and LipB/LplA deficient ( TM136 ) bacterial strains were a kind gift from Dr John Cronan ( University of Illinois at Urbana-Champaign , USA ) . Plasmids pHH1and pCDH3/4 , PfHSP86 5′-pDONR4/1 , and PfCRT 5′-pDONR4/1 were kind gifts from Professor A . F . Cowman ( The Walter and Eliza Hall Institute for Medical Research , Melbourne , Australia ) and Professor G . I . McFadden ( University of Melbourne , Australia ) , respectively . The expression and knockout fragments of the P . falciparum LipB gene were amplified from P . falciparum 3D7 and D10 genomic DNA using Pfx Supermix ( Invitrogen ) . The specific oligonucleotide primers 5′-GCGCAGATCTAATAAAATAAACCTGCTTGTAC-3′ ( sense ) and 5′-GCGCCTCGAG ( TTA ) TTTATCCTTATAAAAGATACC-3′ ( antisense ) with the BglII and XhoI restriction sites , respectively , in bold , and an artificial stop codon ( in brackets ) within the antisense oligonucleotide , were used to generate the 999 bp insert equivalent to nucleotides 4–1 , 002 of the PfLipB open reading frame for the PfLipBKO1 construct . The PCR product was subcloned into the TOPO-Blunt PCR cloning vector ( Invitrogen ) and its sequence was verified ( The Sequencing Service , University of Dundee , UK , http://www . dnaseq . co . uk/ ) before it was cloned into the P . falciparum transfection plasmid pHH1 [19] . The second construct comprises nucleotides 304–1 , 002 of the open reading frame missing the potential bipartite targeting sequence . The insert for PfLipBKO2 was amplified using the sense primer 5′-GCGCAGATCTATTATGAAAAATAAAAATGAAGTACAAATATCAAATCATTTAG-3′ and the same antisense primer as for PfLipBKO1 . The 699 bp product was subcloned into TOPO-Blunt as described above for sequence verification before being cloned into pHH1 . P . falciparum 3D7 ( The Netherlands ) and P . falciparum D10 ( Papua New Guinea ) were cultured according to Trager and Jensen [42] with modifications in human erythrocytes , RPMI 1640 containing 11 mM glucose , with the addition of 0 . 5% Albumax II . The parasites were maintained under an atmosphere of reduced oxygen ( 1% oxygen , 3% CO2 , and 96% nitrogen ) at 37 °C . Parasites were synchronised using sorbitol according to Lambros and Vanderberg [43] . Transfection of PfLipBKO1-pHH1 , PfLipBKO2-pHH1 , HSP86-LplA2-GFP-pCHDR , and CRT-LplA2-GFP-pCHDR into P . falciparum erythrocytic stages was performed as described previously [44 , 45] . WR99210 resistant parasites appeared between 40 and 60 days after transfection . Parasites were cloned by limiting dilution according to Kirkman et al . [46] . The effect of triclosan , N-methylphenazonium methosulfate , and tert-butylhydroperoxide on P . falciparum erythrocytic stages was determined by measuring the incorporation of [3H]-hypoxanthine in the presence of increasing drug concentrations ( 0 . 5 μM to 100 μM ) according to [47] . Relative parasite growth rates were determined using the method of Sanders et al . [21] . Parasite cultures containing mainly ring stages were synchronised twice within 4 h using sorbitol [43] . Parasite density was determined and the culture was diluted to 0 . 5% parasitaemia , 5% haematocrit . Cultures were maintained under an atmosphere of reduced oxygen at 37 °C and medium was refreshed every 24 h . Cultures were diluted 5-fold at 48 h intervals and growth was monitored by Giemsa-stained thin blood smears every 24 h . For each determination of percentage parasitaemia the number of infected erythrocytes per 1 , 000 erythrocytes was recorded . Cultured parasites were enriched for late-stage forms by using the VarioMACS separator and CS MACS columns ( Miltenyi Biotec ) . The columns were equilibrated with MACS buffer ( PBS supplemented with 0 . 5% ( w/v ) BSA , 2mM EDTA ) for 5 min and rinsed with 60 ml of MACS buffer . The equivalent of 100 ml cultured P . falciparum was resuspended in MACS buffer and applied to the column . Following flow-through of the cell-suspension , the column was washed with 50 ml MACS buffer . The column was then removed from the magnetic field of the VarioMACS separator and the late-stage parasitised erythrocytes were eluted from the column using 30 ml MACS buffer . The suspension was centrifuged and the cells were resuspended in complete medium and returned to culture conditions for 30 min prior to harvesting by saponin lysis . The typical yield from this procedure was 1–2 × 108 isolated late-stage parasites . The parasites were liberated from erythrocytes by saponin lysis [48] and genomic DNA was isolated using the QIAamp DNA Mini Kit ( Qiagen ) . Protein extracts were prepared from saponin-isolated parasites by resuspending the pellets in lysis buffer ( 100 mM HEPES ( pH 7 . 4 ) , 5 mM MgCl2 , 10 mM EDTA , 0 . 5% ( v/v ) TritonX-100 , 5 μg/ml RNAse , 1 mM phenylmethylsulphonyl fluoride , 1 mM benzamidine , 2 μg/ml leupeptin , 10 μM E-64 , 2 μM 1 , 10-phenanthroline , 4 μM pepstatin A ) followed by three cycles of freeze/thawing and sonication in a sonicating water-bath ( Fisherbrand ) . Protein concentrations were determined using the Bradford assay [49] . One μg of genomic DNA was digested with NdeI , separated on a 0 . 8% agarose gel , and blotted onto positively charged nylon membrane ( GE Healthcare ) using standard methods [50] . The blot was probed with the LipB coding sequence . Radioactive probes were made using the MegaPrime Labelling Kit from GE Biosciences and [α-32P]-ATP ( Adenosine 5′-triphosphate [α-32P] , EasyTides , Specific Activity: 3 , 000 Ci/mMole ) from Perkin-Elmer following the manufacturer's recommendations . The membrane was prehybridised ( 0 . 5% ( w/v ) SDS , 5 × Denhardt's solution , 100 μg/ml salmon sperm DNA , 0 . 1% ( w/v ) sodium pyrophosphate ) for 2 h at 60 °C before addition of the probe . Hybridisation was then allowed to proceed at 60 °C over night . Membranes were washed once in 6× SSC , 0 . 1% ( w/v ) SDS at 60 °C for 20 min , and then twice in 2× SSC , 0 . 1% ( w/v ) SDS at 60 °C for 10 min . Membranes were exposed to Kodak film for several days before development depending on the activity of the probe used . To determine lipoylation of KADH-E2 subunits in P . falciparum , protein extracts of parasites were subjected to Western blotting . Briefly , 15 μg of each sample was separated on a 4%-12% SDS-PAGE ( Invitrogen ) and then blotted onto nitrocellulose ( Schleicher and Schüll ) , using standard techniques [50] . The blot was incubated with a rabbit anti-LA antibody ( Calbiochem ) at a dilution of 1:500 and the secondary anti-rabbit IgG ( H+L ) , HRP conjugate ( Promega ) at a dilution of 1:10 , 000 before being developed using the ImmobilonTM Western Chemiluminescent HRP Substrate ( Millipore ) . Similarly , blots of wild-type parasite extracts were also probed with antibodies against BCDH-E2 ( raised in rabbit ) , KGDH-E2 ( raised in rat ) and H-protein ( raised in rabbit ) of P . falciparum ( all generated by Eurogentec , Belgium ) at dilutions of 1:5 , 000 , 1:100 and 1:2 , 000 , respectively . Samples for LA analyses were prepared from late trophozoites prepared using the MACS columns described above . The LA detection method was modified from that of Pratt and colleagues [51] . LA determinations were done in triplicate , along with a parallel control of standards , all containing an internal standard of heptadecanoic acid ( 10 nmol ) . Experimental details for the determination of LA will be published elsewhere ( T . K . Smith , in preparation ) . Briefly , total LA was determined by acid hydrolysis of freeze-dried parasite pellets to release protein bound fatty acids . Reduction with NaBH4 and methylation with methyl iodide of the sulphydryl groups under basic conditions was followed by organic extraction of all fatty acids , including the methylated LA . The dried fatty acids were converted to fatty acid methyl esters ( FAME ) with diazomethane and stored dried at −20 °C until subjected to GC-MS . Quantification of oxidised LA was as above except no reduction or methylation of the sulphydryl groups is required . Free LA was extracted from a freeze-dried cell pellet with organic solvents which were checked for protein by analysis of SDS-PAGE and staining , prior to treatment with methanolic HCl and conversion to fatty acid methyl esters as above . Analysis of the FAMEs was conducted on a Hewlett Packward 6890–5973 system equipped with a ZB-5 30M × 0 . 25 mm ( I . D . ) column . The electron impact ionization/quadrupole mass detector was programmed to monitor selected ions for all FAMEs m/z 123 , heptadecanoic acid m/z 284 , oxidised LA m/z 220 , reduced and methylated m/z 250 , with typical elution times of oxidised LA , 26 . 4 min; reduced and methylated LA , 27 . 2 min; and heptadeconic acid ( C17:0 ) , 28 . 5 min . Molar response factor for oxidised LA and methylated reduced LA versus heptadecanoic acid were determined to estimate the LA content . In addition , the total ion current ( TIC ) chromatograms were analysed for changes in other longer chain fatty acids . The functionality of three expression constructs of LplA2 , generated in pASK-IBA3 , was analysed . The following anti-sense primer 5′-GCGCGCGGTCTCAGCGCTTAGAAAATATGTTGGTATATCGTAATACC-3′ was used to amplify all three constructs . In combination with the sense primer 5′-GCGCGCGGTCTCGAATGAGAATTATAAAGTGCCTGGATC-3′ a 1 , 152 bp fragment was amplified corresponding to the full length gene . The sense primer 5′-GCGCGCGGTCTCGAATGAAAAAAATAAACATTCTTTATTTTATTGATGTCAGC-3′ generated a truncated fragment from nucleotide 79–1 , 152 ( S1 construct ) . The third fragment ( S2 ) amplified using 5′-GCGCGCGGTCTCGAATGAATGAGTCCAAAGGAAACGAATGC-3′ corresponds to nucleotide 235–1 , 152 bp . All primers contained a BsaI restriction site ( boldface ) to allow directional cloning into pASK-IBA3 . The constructs were amplified from P . falciparum 3D7 genomic DNA using Pfx Supermix ( Invitrogen ) and were initially cloned into TOPO-Blunt PCR cloning vector ( Invitrogen ) for sequence verification . Subsequently , they were subcloned into pASK-IBA3 and transformed into KER 184 and TM 136 [24 , 25] to assess whether they complement the growth defect of the bacterial lines when grown on minimal medium agar plates . To analyse the localisation of LplA2 , the full length LplA2 gene was amplified from P . falciparum 3D7 genomic DNA using the sense primer 5′-CACCATGAGAATTATAAAGTGCCTGG-3′ and antisense primer 5′-TAGAAAATATGTTGGTATATCGTAATACC-3′ . The PCR product was cloned directionally into the plasmid pENTR/D-TOPO ( Invitrogen ) and the sequence was verified by sequencing . The cloning of the constructs was performed as described by van Dooren et al . [52] . The destination plasmid used was pCHDR-3/4 which contains the human dihydrofolate reductase ( hDHFR ) as a selectable marker . Two entry plasmids were used that differed in their promoter regions . The PfHsp86 5′-pDONR4/1 contains the P . falciparum heat shock protein 86 5′ UTR and the PfCRT 5′-pDONR4/1 possesses the P . falciparum chloroquine resistance transporter 5′UTR . The C-terminal GFP-tag was provided by GFP-pENTR2/3 . Thus , four plasmids ( either PfHspP86-pENTR4/1 or PfCRT-pENTR4/1 , LplA2-pENTR/D-TOPO , GFP-pENTR2/3 , and pCHDR-3/4 ) were incubated in the LR MultiSite cloning reaction according to manufactures guidelines ( Invitrogen ) which resulted in the generation of two LplA2 constructs ( Hsp86-LplA2-GFP-pCHDR and CRT-LplA2-GFP-pCHDR ) . Constructs were transfected as described above and parasites resistant to WR99210 were analysed using an Axioskop-2 mot plus microscope ( Zeiss ) equipped with a Hamamatsu C4742–95 CCD camera . Fixations of wild-type 3D7 parasites for subsequent immunofluorescence analyses were carried out according to [53] . The primary antibodies raised against LplA2 ( in a rat , Eurogentec ) and against apicoplast lipoamide dehydrogenase ( aLipDH; aE3 ) ( in a rabbit , Eurogentec ) were diluted in 3% ( w/v ) BSA in PBS at 1:500 and 1:200 , respectively . Secondary antibodies ( anti-rat conjugated with Alexa fluor 488 and anti-rabbit conjugated with Alexa fluor 594 , Molecular Probes ) were applied at 1:500 dilution in 3% ( w/v ) BSA in PBS for 1 h at 4 °C . DAPI at 0 . 5 μg/ml ( Sigma ) was added to the secondary antibody for 1 min and was then washed off as before . The slides were mounted with 2 . 5% ( v/v ) DAPCO in 50% ( v/v ) glycerol ( Sigma ) and were analysed using an Axioskop-2 mot plus microscope ( Zeiss ) equipped with a Hamamatsu C4742–95 CCD camera .
The accession numbers and ID numbers of the genes ( obtained from the NCBI-protein database ) described in this study are as follows: Bos taurus lipoate-activating enzyme ( BAB40420 ) , Bos taurus lipoyltransferase ( BAA24354 ) , E . coli ( K12 ) LplA ( NP_418803 ) , E . coli ( K12 ) LipB ( NP_415163 ) , P . berghei LplA2 ( XP_679932; CAH95194 ) , P . chabaudi LplA2 ( XP_745010; CAH79244 ) , P . falciparum ACP ( XP_001349595; AAC71866 ) , P . falciparum apicoplast lipoamide dehydrogenase ( XP_001349365; CAD51214 ) , P . falciparum BCDH-E2 ( XP_001351112; CAB38991 ) , P . falciparum enoyl-ACP reductase ( FabI ) ( XP_966137; CAG25389 ) , P . falciparum H-protein ( XP_001348010; AAN35923 ) , P . falciparum KGDH-E2 ( XP_001349947; CAD52355 ) , P . falciparum LipA ( XP_001350160; CAD52569 ) , P . falciparum LipB ( XP_001349288; CAD51137 ) , P . falciparum LplA1 ( XP_001349882; CAD52290 ) , P . falciparum LplA2 ( XP_001352107; CAD51918 ) , P . falciparum PDH-E2 ( XP_001347486; AAN35399 ) , P . knowlesi LplA2 ( gene ID PlasmoDB: PKH_072080 ) , P . vivax LplA2 ( PlasmoDB gene ID: Pv099590 ) , P . yoelii LplA2 ( XP_730272 ; EAA21837 ) , Theileria parva strain Ankara LplA2 ( XP_954802 ) , T . gondii ACP ( AAC63956; AAC63953 ) , T . gondii LplA2 ( ToxoDB gene ID 83 . m01296 ) . | Plasmodium falciparum is the causative agent of severe malaria . The parasites possess two organelles that are integral to their metabolism—the mitochondrion and the apicoplast , a remnant plastid . Both organelles contain enzymes that depend on the attachment of the cofactor lipoic acid for their catalytic activity . These are the α-keto acid dehydrogenase complexes and the glycine cleavage system ( GCS ) . The pyruvate dehydrogenase ( PDH ) is solely found in the apicoplast of the parasites whereas α-keto glutarate and branched chain α-keto acid dehydrogenase as well as the GCS are mitochondrial . Both organelles possess specific and independent mechanisms that guarantee the posttranslational lipoylation of these enzyme complexes . In this study we show that the apicoplast located lipoic acid protein ligase , octanoyl-[acyl carrier protein]: protein N-octanoyltransferase ( LipB ) , is not essential for parasite survival by disrupting the LipB gene locus . Despite a drastic loss of total lipoic acid , the parasites progress through their intraerythrocytic development unperturbed although the apicoplast-located PDH shows a reduced level of lipoylation . This phenotype is attributable to the presence of the recently described lipoic acid protein ligase 2 , LplA2 , which we show to be dually targeted to mitochondrion and apicoplast . |
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Tomato yellow leaf curl virus ( TYLCV ) and its related begomoviruses cause fast-spreading diseases in tomato worldwide . How this virus induces diseases remains largely unclear . Here we report a noncoding RNA-mediated model to elucidate the molecular mechanisms of TYLCV-tomato interaction and disease development . The circular ssDNA genome of TYLCV contains a noncoding intergenic region ( IR ) , which is known to mediate viral DNA replication and transcription in host cells , but has not been reported to contribute directly to viral disease development . We demonstrate that the IR is transcribed in dual orientations during plant infection and confers abnormal phenotypes in tomato independently of protein-coding regions of the viral genome . We show that the IR sequence has a 25-nt segment that is almost perfectly complementary to a long noncoding RNA ( lncRNA , designated as SlLNR1 ) in TYLCV-susceptible tomato cultivars but not in resistant cultivars which contains a 14-nt deletion in the 25-nt region . Consequently , we show that viral small-interfering RNAs ( vsRNAs ) derived from the 25-nt IR sequence induces silencing of SlLNR1 in susceptible tomato plants but not resistant plants , and this SlLNR1 downregulation is associated with stunted and curled leaf phenotypes reminiscent of TYLCV symptoms . These results suggest that the lncRNA interacts with the IR-derived vsRNAs to control disease development during TYLCV infection . Consistent with its possible function in virus disease development , over-expression of SlLNR1 in tomato reduces the accumulation of TYLCV . Furthermore , gene silencing of the SlLNR1 in the tomato plants induced TYLCV-like leaf phenotypes without viral infection . Our results uncover a previously unknown interaction between vsRNAs and host lncRNA , and provide a plausible model for TYLCV-induced diseases and host antiviral immunity , which would help to develop effective strategies for the control of this important viral pathogen .
TYLCV belongs to DNA geminivirus and can cause severe damages and yield loss to many important crops . Its infection results in stunted leaf growth and abnormal leaf development , including curling of leaf margins , reduction of leaf size , and yellowing and abscission of leaves . The disease symptoms and the virus were first reported in Israel during the late 1920s and the early 1960s , respectively [1 , 2] . A great diversity of TYLCV has since been reported , including other five related species and numbers of strains . These viruses have been introduced in many areas due to international trade , which then adapted rapidly to the new environments and gave rise to new variants through recombination . The strong invasive capability of the viruses and absence of robust control practices contributed further to the worldwide spread and emergence of these viral diseases [3–5] . Understanding pathogenesis of TYLCV and its related species may help to develop innovative control strategies and answer some key questions regarding viral invasion and spread . TYLCV has a circular single-stranded DNA genome which comprises a short intergenic region ( IR ) and six overlapping open reading frames ( ORFs ) in two opposite transcriptional directions [2] . It is widely believed that viral proteins encoded by these ORFs are pathogenicity determinants , and their biological activities and molecular functions during virus-host interactions have therefore been intensely studied [1 , 6–8] . For instance , TYLCV AV2/V2 protein is found to be a multifunctional counter-defense factor , which suppresses host post-transcriptional gene silencing ( PTGS ) by targeting plant SGS3 protein , a dsRNA-binding protein , and then preventing SGS3 from accessing substrate RNAs [8 , 9] . TYLCV V2 protein may also suppress transcriptional gene silencing ( TGS ) by reducing host DNA methylation [10] . In addition , V2 is shown to affect host cell death by interacting with and inhibiting enzymatic activity of CYP1 , a plant programmed cell death machinery component [11] . A recent report showed that overexpression of V2 induced cell death symptoms in tomato , in contrast to no cell death caused by TYLCV infection [12] . But a healthy environment ensures the survival and multiplication of TYLCV , so the accumulation of TYLCV was hindered at the late stages of infection [12] . These results suggest that the individual viral proteins are insufficient to account for the TYLCV disease symptoms and other viral factor ( s ) may also contribute to the pathogenesis of the virus . RNA silencing induced by double-stranded RNA ( dsRNA ) is an ancient mechanism in many eukaryotes that regulates gene expression and defends cells against invasive nucleic acids including viruses . During infection , virus derived dsRNA is processed by host Dicer-like enzymes to 21 to 24 nucleotide ( nt ) virus-derived small interfering RNAs ( vsRNAs ) . These vsRNAs are loaded to argonaute protein to form RNA-induced silencing complex and guide the degradation of single-stranded viral RNA . As a counter-defense strategy , viruses encode RNA silencing suppressors to interfere with the vsRNA-directed silencing for successful infections [13–15] . These silencing suppressors are therefore major virulence determinants of viruses [16 , 17] . Some viruses have adapted another RNA silencing-based virulence strategy , using vsRNA-directed silencing of a host gene for symptom modification . Cucumber mosaic virus ( CMV ) is associated with a noncoding Y satellite RNA ( Y-sat ) that modifies yellowing symptoms in some hosts . There is a 22-nucleotide ( nt ) complementary sequence between Y-sat and tobacco magnesium protoporphyrin chelatase subunit I ( ChlI , the key gene involved in chlorophyll synthesis ) gene . During interactions , Y-sat- siRNAs derived from the 22-nt region target and cleave the host ChlI gene to impair the chlorophyll biosynthesis pathway and cause yellowing symptoms [18 , 19] . Small RNA derived from the virulence modulating region of two Potato spindle tuber viroid variants target the callose synthase genes of tomato plants , thus causing leaf curling and severe stunting [20] . In Botrytis cinerea , a fungal pathogen , small RNAs also act as effectors to inhibit host immunity by silencing host immune genes [21] . It is unknown whether TYLCV and other viral pathogens employ vsRNAs as virulence effectors . Plants have evolved additional layers of immune responses to combat viruses , and some of these immune processes intersect with RNA silencing . For instance , viral RNA silencing suppressors can be monitored by plant R proteins to initialize effector-triggered immunity ( ETI ) [16 , 22 , 23] . The P38 RNA silencing suppressor of Turnip crinkle virus and the 2b suppressor of Tomato aspermy virus have been shown to elicit hypersensitive response in tobacco [22] and in some specific A . thaliana ecotypes [23] , respectively . Furthermore , RNA silencing pathways play a role in fine tuning R gene expression , by promoting gene expression levels or minimizing fitness cost of over-active virus resistance [24] . For TYLCV , six resistance/tolerance loci have been identified in tomato , including Ty-1 , -3 , -4 , and -6 from Solanum chilense , Ty-2 from S . habrochaites , and Ty-5 from S . peruvianum . All these six ty genes confer a tolerance phenotype allowing low levels of virus replication [25] . Among them , Ty-2 gene was identified as an NBS-LRR gene , TYNBS1 [26]; Ty-1 and Ty-3 genes code for an RNA-dependent RNA polymerase , and provide resistance by increasing cytosine methylation of the viral genome causing transcriptional gene repression of viral genes [27] . Thus , small RNA-directed RNA silencing therefore plays an important role in the molecular arm race between host plants and viruses , including TYLCV . Besides small RNAs , lncRNAs have emerged as new RNA regulators of gene expression in eukaryotes through diverse molecular mechanisms [28 , 29] . Many lncRNAs were shown to be associated with defense responses against biotic stresses , although their roles in plant-virus interactions are not reported . A number of Puccinia striiformis- and Fusarium oxysporum- responsive lncRNAs were identified in wheat and Arabidopsis thaliana [30 , 31] , respectively , five of which have important roles in plant defense against F . oxysporum infection [30] . An elf18 induced lncRNA ELENA1 confers resistance to Pseudomonas syringe pv tomato DC3000 through interacting with Mediator subunit 19a ( MED19a ) and affecting its enrichment on the PR1 promoter [32] . Tomato lncRNA16397 appears to play a role in plant resistance to late blight disease by regulating the expression of SlGRX , a gene family in plant reactive oxygen species ( ROS ) scavenging systems [33] . Overexpression of lncRNA16397 reduces ROS accumulation and alleviates cell membrane injury [33] . On the contrary , some lncRNAs were reported facilitating the disease infection such as the GhlncNAT-ANX2 and GhlncNAT-RLP7 to Verticillium dahlia of cotton [34] . Our previous research has characterized tomato lncRNAs during its interactions with TYLCV and found that some lncRNAs might act as small RNA target mimics to participate in the regulatory process of tomato viral resistance [35] . Here we report a direct interaction between vsRNAs and host-encoded lncRNA and its involvement in TYLCV disease development in tomato . We discovered that a 25-nt short segment of the TYLCV intergenic region ( IR ) has near-perfect complementarity with a tomato lncRNA , termed as SlLNR1 , and IR-derived vsRNAs direct silencing of the lncRNA during viral infection . We showed that SlLNR1 plays a role in TYLCV resistance and leaf development in tomato , and downregulation of the lncRNA by IR-derived vsRNAs results in TYLCV-like symptoms . Intriguingly , its allele in a TYLCV-tolerant has a 14-nt sequence deletion , making it resistant to vsRNA-directed repression . Our findings suggest that TYLCV deploys IR-derived vsRNAs as virulence effector to interfere with SlLNR1 and induce the stunted and curled leaf phenotypes of TYLCV symptoms . Our study provides a viral disease model involving an arm race between viral small RNAs and a host lncRNA .
Considering that the IR in geminivirus is a major source of vsRNAs [36–38] , we examined whether the TYLCV IR could be transcribed . Indeed , tomato plants infected using a TYLCV infectious clone [39] , contained an increasing levels of the IR transcripts during infection , despite the relatively lower abundance than the V2 transcript ( Fig 1A ) . The IR transcripts ( nt 2616–147 ) can be amplified using both sense and antisense strand-specific primers in TYLCV infected plants while no visible amplicons were found in the negative controls ( Fig 1B ) , indicating that the IR sequence is transcribed bidirectionally . To examine if the IR transcript plays a role in viral virulence and symptom development , we inoculated a TYLCV-susceptible ( JS-CT-9210 ) or -resistant ( CLN2777A ) tomato line with a tobacco rattle virus vector containing the IR sequence ( nt 2616–147 ) ( pTRV2:IR ) . As shown in Fig 1C , expression of IR from the TRV vector resulted in stunted and curled leaves and stems in the susceptible tomato genotype , which resemble the symptoms caused by TYLCV infection [39] . In contrast , its expression in the resistant tomato line failed to induce TYLCV-like symptoms ( Fig 1D ) , although the transcriptional levels of IR were comparable in these two lines ( S1A Fig ) . Similar to IR RNA levels , the viral accumulation of TRV was similar between the two lines as indicated by the similar amounts of TRV-specific vsRNAs ( S1B Fig ) and expressional levels of replicase and 2b-encoding genes ( S1C Fig ) . This excluded the possibility that the different phenotypes between the susceptible and resistant tomato lines are caused by different TRV accumulation hence virulence ( S1 Fig ) . Thus , we conclude that the IR alone is sufficient to induce symptoms in susceptible but not resistant tomato background , suggesting that it not only mediates TYLCV genome replication but also plays a direct role in viral virulence . The IR does not encode proteins , but gives rise to vsRNAs during viral infection . We therefore investigated whether IR-derived vsRNAs might be responsible for IR-induced symptoms by directing host gene silencing as reported for Cucumber mosaic virus Y-satellite-derived siRNAs [19 , 40] . We firstly characterized vsRNAs in TYLCV-infected tomato plants using small RNA deep sequencing . Overall , vsRNAs were discontinuously and unequally distributed along the TYLCV genome ( S2A Fig ) , which is consistent with the previous reports on other geminivirus-derived siRNAs [36–38] . IR-derived vsRNAs had a total of 49 , 569 reads ( 2 . 4% of all vsRNA reads ) with 845 unique sequences . To identify potential host target genes of vsRNAs , we aligned the IR-derived vsRNAs to available tomato sequences using BLAST , and identified an EST sequence with 25-nt near-perfect complementarity with a 25-nt segment of IR ( nt 2730–2754 ) that is associated with relatively high abundance of vsRNAs ( Fig 2A ) . The tomato EST was predicted to encode no protein , and therefore named SlLNR1 ( Solanum lycopersicum lncRNA1 ) . The high-level sequence complementarity raised the possibility that vsRNAs derived from the 25-nt IR segment induce silencing of the complementary tomato target RNA to mediate the IR-induced symptoms . Consistent with this , a pTRV2 vector carrying 4 tandem repeats of the 25-nt segment ( pTRV2:4TR ) was capable of inducing stunted and curled leave phenotypes ( Fig 2B ) , similar to those caused by pTRV2:IR containing the IR sequence ( Fig 1C ) . At the same time , we inoculated the above constructs in tobacco ( a TYLCV host ) and cotton ( a nonhost of TYLCV ) , and found that both pTRV2:IR and pTRV2:4TR could not cause abnormal phenotypes ( S2B Fig ) although they were expressed properly ( S2C Fig ) . Sequence similarity searching in other plants failed to find homologues of the SlLNR1 . Taken collectively , the above results demonstrate that the IR-mediated virulent activities are unique in tomato . To confirm the accumulation of the vsRNAs from the 25-nt IR segment during TYLCV infection , we performed siRNA northern blot to detect a 21-nt antisense siRNA ( -2752-21 ) , which was chosen because of its relatively high abundance in the deep sequencing data ( Fig 2A ) . As shown in Fig 2C , this siRNA was detected in the plant inoculated by TYLCV infectious clone at 24 dpi . We also analyzed the tomato plants infected using viruliferous whiteflies in the green house , and this siRNA was also can be detected ( Fig 2C ) . In addition , the siRNA is also expressed in the plants inoculated by pTRV2:4TR and pTRV2:IR ( Fig 2C ) . To examine the potential biological function of siRNA ( -2752-21 ) , its overexpression construct driven by the 35S promoter was transformed to susceptible tomato plants , and 2 lines of overexpressed plants ( pCAMBIA2301:siRNA-1/2 ) were obtained . The plants were verified by accumulation of the object siRNA ( Fig 2D ) , and exhibited abnormal phenotypes as stunted and curled leaves ( Fig 2E ) . Taken together , the results from siRNA analyses and host target gene searching supported an involvement of vsRNA-directed host gene silencing in IR-induced symptom development , and suggested that vsRNAs from the 25-nt IR segment are the silencing inducer , and SlLNR1 is likely a host target . The full length sequence of SlLNR1 ( SlLNR1+ ) was further validated by 5’ RNA Ligase Mediated Rapid amplification of cDNA ends ( 5’ RLM-RACE ) and 3’ RACE-PCR ( S3 Fig ) , based on which an 1132-nt sense strand of SlLNR1 was obtained in the susceptible cultivar by RT-PCR . Meanwhile , a 955-nt antisense-strand of SlLNR1 ( SlLNR1- ) was also recovered using strand-specific RT-PCR ( S4 Fig ) , indicating that SlLNR1 is bi-directionally transcribed . The SlLNR1-specific sRNAs could be detected in TYLCV infected susceptible tomato plants by sRNA deep sequencing , while which can hardly be detected of the control plants despite the existence of both sense and antisense RNA that can potentially form double-stranded RNA ( dsRNA ) ( Fig 3A ) . Interestingly , the SlLNR1 sequence from the resistant tomato cultivar contained a 14-nt deletion in the region matching the 25-nt IR segment ( S4 Fig ) , which would prevent binding and targeting by the IR-derived vsRNAs . Thus , we surmised that vsRNAs may specially target antisense SlLNR1 from the susceptible cultivar , but not the sense SlLNR1 or the SlLNR1 transcripts from the resistant cultivar due to lack of sequence complementarity . Consistent with this , the antisense SlLNR1 , but not the sense SlLNR1 , was markedly downregulated in N . benthimiana when co-expressed with siRNA ( -2752-21 ) ( Fig 3B ) . In addition , SlLNR1-specific sRNA can also be detected in the tomato plants inoculated by pTRV2:IR while quite few associated reads were found in the EV-infiltrated plants ( S5 Fig ) . Next , we surmised that vsRNAs may specially target the SlLNR1 in the susceptible cultivar , but not in the resistant cultivar due to lack of sequence complementarity . Consistent with this , the transcripts of SlLNR1 were downregulated in the pTRV2:IR inoculated susceptible but not resistant tomato line ( Fig 3C ) . Furthermore , upon TYLCV infection , the SlLNR1 expression levels were downregulated at 12 and 24 dpi , whereas in the resistant tomato cultivar the level of the SlLNR1 transcripts either increased ( for the sense transcript ) or remained unchanged ( for the antisense transcript ) ( Fig 3D and 3E ) . Importantly , we observed that the expression of SlLNR1 was also downregulated in the siRNA ( -2752-21 ) - overexpressed plants ( Fig 2F ) . Then , the promoter of SlLNR1 ( for the sense transcript ) from the susceptible or resistant line was constructed to drive the GUS reporter gene expression in N . tabacum . The results of GUS staining indicated both the promoters can be induced by the TYLCV infection at 12 dpi ( Fig 3F ) . And the expression of GUS was also increased at the same time ( Fig 3G ) . These results confirmed that the promoters of both the cultivars can be induced by TYLCV , and the SlLNR1 from the susceptible line could be silenced during infection but not in the resistant cultivar . The downregulation of the sense SlLNR1 could be due to the existence of antisense vsRNAs from the 25-nt IR segment , as viral dsRNA is expected to be processed into both sense and antisense vsRNAs . The absence of these antisense vsRNAs in the deep sequencing data could be due to relatively low abundance or sequestration by long sense viral RNAs as reported previously [41 , 42] . Furthermore , we generated mutated TYLCV infectious clones to evaluate the importance of the 25-nt sequence complementarity between the IR and SlLNR1 in TYLCV-caused disease symptoms . Since the IR is also involved in replication and activation of gene expression , we generated 9 mutants that may disrupt complementarity between the IR and SlLNR1 ( S6A Fig ) . Among them , a mutant ( named as MU1 ) exhibits similar activities of replication ( TYLCV AC1 gene expression ) and promoter ( GUS reporter gene expression ) in N . benthamiana ( S6B and S6C Fig ) , in which 2 nucleotides within the 25-nt IR region were modified ( Fig 4A ) . The tomato plants inoculated by the wild isolate appeared yellow , curled and shrinking , while those with MU1 has only slight pathogenic phenotypes which is comparable to the uninfected control ( Fig 4B ) . The virus accumulation in the susceptible plant treated with MU1 was lower than those with wild isolates , but not in the resistant plant ( Fig 4C ) . Then we examined the expression of SlLNR1 in the infected tomato plants , which was dropped significantly in the susceptible plants treated by the wild isolate but increased in those with MU1 . However , expression of SlLNR1 in the resistant plant was induced by inoculation with both of the WT and MU1 ( Fig 4D ) . As the IR region serves as the promoter for the bi-directional promoter , the transcriptional activity of the virus genes was analyzed in N . benthamiana . The transcripts of AV2 and AC1 were analyzed by qRT-PCR on the samples of N . benthamiana agro-inoculated with the MU1 and wild type isolate . The transcripts amount of AV2 and AC1 were not changed in the MU1 compared with the wild type ( S6D Fig ) . These results together validated that the vsRNAs from the 25-nt IR segment targeted the SlLNR1 . To examine vsRNA-mediated cleavage of SlLNR1 , we performed a 5’-RACE analysis on mRNA isolated from the TYLCV-infected susceptible tomato plants and detected a dominant SlLNR1-derived product . Sequencing showed the cleavage may occur at the 9th nucleotide from the 5’ end of the highly abundant siRNA ( -2752-21 ) ( Fig 2A ) . It has been reported that siRNA-guided RNA cleavage usually occurs between nucleotides 10th and 11th from the 5’ end of this siRNA [43] . Thus , we speculate that other less abundant vsRNAs , e . g . siRNA ( -2753-21 ) , may participate in the cleavage of SlLNR1 or siRNA ( -2752-21 ) -mediated cleavage may undergo a distinct manner . To examine if this lncRNA had a potential biological function , we analyzed its accumulation in different plant tissues , which showed that it accumulated at the highest level in the apex of stem ( Fig 5A ) . This tissue specific expression patterns were further confirmed by in situ RNA hybridization ( Fig 5B ) . To further explore the biological role of SlLNR1 , we knocked down the expression of the gene using TRV-based virus-induced gene silencing in the TYLCV-resistant cultivar CLN2777A [44] . The repression of SlLNR1 by pTRV2:SlLNR1 was confirmed by qRT-PCR analysis showing approximately 50% downregulation compared to pTRV2:EV-infiltrated plants ( Fig 5C ) . SlLNR1-silenced tomato seedlings developed curled and stunted new leaves about 15 dpi ( Fig 5D ) . The TYLCV-resistant levels were also severely impaired in the SlLNR1-silenced tomato seedlings ( Fig 5E ) . Furthermore , stable transgenic SlLNR1-silenced tomato lines were generated by expression of a selected fragment in a susceptible cultivar . Two independent transgenic lines were identified , in which SlLNR1 expression was decreased about 49%-77% ( Fig 5F ) . Both of them showed abnormal phenotypes including the inward rolling up leaves and curl of blade edge . Additionally , the phenotype abnormality was seemed to correlate with the degree of repression of the SlLNR1 ( Fig 5G ) . To verify the TYLCV resistant function of SlLNR1 , we generated the transgenic tomato lines overexpressing SlLNR1 ( R ) or SlLNR1 ( S ) . In total , we obtained 5 independent transgenic lines for SlLNR1 ( R ) ( Fig 6A ) and 6 for SlLNR1 ( S ) ( Fig 6B ) , respectively . All the overexpressed lines exhibited normal phenotypes ( Fig 6C ) . Then , we assessed TYLCV accumulation in these transgenic tomato plants . Viral DNA accumulation was significantly reduced in the plants with SlLNR1 ( R ) -overexpressed lines compared to the EV trasngenic plants ( Fig 6D ) . However , the amounts of virus were not repressed in all the SlLNR1 ( S ) -transformed plants ( Fig 6E ) . It was supposed only the SlLNR1 ( R ) escaping the targeting of vsRNAs has the TYLCV resistant function . Taken together , these results suggest that SlLNR1 is not only required for normal plant development , but also negatively regulates the accumulation of TYLCV , both of which may contribute to disease development in tomato ( Fig 6F ) .
A zigzag model has been widely accepted to encompass the complicated arms race between host plants and bacterial/fungal pathogens [45] . In plant-virus interactions , RNA silencing pathway senses viral RNAs using Dicer-like enzyme to produce vsRNAs and restrict virus replication , which is reminiscent of pattern-triggered immunity ( PTI ) in the zigzag model [15–18] . Viruses produce RNA silencing suppressors to counter this host defense mechanism . This study provides another example to illustrate that TYLCV may generate vsRNAs to interfere with host gene expression to modulate symptoms and achieve high-level infection . To cope with the virus infection , the resistant plant has developed an adaptive genomic change to escape targeting by vsRNAs . Thus , RNA silencing-mediated plant-virus interaction may play an important role in the evolution of both the viral and host genomes . We performed sequence alignment between the TYLCV-derived vsRNAs and tomato transcripts to identify the pathogenic determinant vsRNAs . Consistent with the previous reports [36–38] , our deep sequencing analysis show that the vsRNAs were unequally distributed throughout the viral genome with many hotspots and relatively low number was originated from the IR . Interestingly , among them , a 25-nt IR segment ( nt 2730–2754 ) shows near-perfect complementarity sequence with tomato SlLNR1 gene . During the validation of its virulent functions , we found that expressing of the IR , this segment alone or the siRNA in tomato caused similar phenotypes to the whole virus while mutants of the complementary sequences abolish the virulence . Deep sequencing results revealed that many 21–24 nt vsRNAs derived from this segment , suggesting that it is a vsRNAs-generating hotspot . Although we still could not conclude which particular siRNA plays dominant roles or they act in synergic manner , a high abundance 21-nt antisense siRNA ( -2752-21 ) was selected as example for analysis . Indeed , it was detectable in the nature field diseased plants , TYLCV-infected samples or the IR/25-nt-segment-expressing plants . Its expressional levels were highly induced during infection and negatively related to expression of SlLNR1 . Thus , we speculate that the vsRNAs derived from this segment may target and regulate host gene expression to trigger susceptibility , leading to viral symptoms . vsRNAs-triggered susceptibility has been observed in several cases in RNA viruses and viroids . Peach latent mosaic viroid ( PLMVd ) and Potato spindle tuber viroid ( PSTVd ) compose of only a short non-protein-coding RNA genome and cause visible symptoms during infection . A PSTVd-sRNA that is essential for virulence targets tomato two callose synthase genes [20] and two PLMVd-sRNAs may cleavage the mRNA encoding the chloroplastic heat-shock protein 90 [46] . CMV deploys a 22 nt Y-sat-derived siRNA to silence host ChlI gene to cause yellowing symptoms [19 , 20] . Like CMV and these two viroids , TYLCV as a DNA virus is also an inducer and target of host RNA silencing . Here we showed that its IR is bidirectionally transcribed , suggesting that the region may form a dsRNA precursor of primary vsRNAs . In another DNA geminivirus Cabbage leaf curl virus , the vsRNAs biogenesis is likely independent of RNA-dependent RNA-polymerase ( RDR ) in Arabidopsis and the primary vsRNAs may trigger RDR-dependent generation of secondary siRNAs [38] . Interestingly , the known TYLCV resistance Ty-1 and Ty-3 genes encode for RDRs , which are similar to Arabidopsis RDR3 , -4 , and -5 and responsive for amplification of the siRNA signal . Tomato lines carrying Ty-1/3 genes confer resistance through enhanced gene silencing , and then exhibit high levels of TYLCV-derived siRNAs [27] . Thus , we speculate that TYLCV may reinvent the generated siRNAs as virulent factors to counter the host resistant pathway . The biogenesis of these pathogenic determinant vsRNAs and its interactions with host immune systems remain to be analyzed . Over recent decades , pathogen effector proteins ( effectors ) have received much attention because of their critical roles in plant-microbe interactions and the zigzag model . The effectors interfere with host defense pathways by directly interacting with host immunity components to benefit pathogen infection , which is called effector-triggered susceptibility ( ETS ) [45] . Analogy to these , siRNAs produced by different pathogens may translocate into the host cells to induce silencing of host immunity genes . Thus , the term of ‘siRNA effecttor’ was proposed [47 , 48] and siRNA effectors have been reported in several pathosystems , including interactions of B . cinerea and hosts tomato/Arabidopsis [21] , nematode parasites and mammalian cells [49] , Escherichia coli and Caenorhabditis elegans [50] , and Wolbachia and host insects [51] . The study here provides many lines of evidence to show that siRNA effectors are also shared by TYLCV , a kind of plant DNA virus , suggesting of the wide spread of siRNA effectors and a common pathogenic strategy in diverse parasites . Future identification of similar siRNAs in different pathogens will help identify host immunity genes and unravel molecular mechanisms of plant-parasite interactions . This IR segment ( nt 2730–2754 ) has only one mismatch with tomato SlLNR1 gene in a susceptible cultivar and is a vsRNAs-generating hotspot . Interestingly , tomato SlLNR1 allele gene in a resistant cultivar loses a 14-nt fragment of this complementarity region , indicative a possible escaping mechanism during molecular arms race . Expression of SlLNR1 gene was significantly downregulated during infection in the resistant cultivar , but upregulated in the susceptible cultivar . Its expression was silenced in plant expressing the IR or the 25-nt-segment and its cleavage site was also determined . By bioinformatical analysis , one EST ( GenBank ID: BF097137 . 1 ) was another potential target of the segment-derived vsRNAs with three mismatch , which was also a lncRNA . However , we noticed that its expression was not altered by in the above experiments and the gene is unique in tomato based on sequence similarity searching in other plants . At the same time , genetic evidence of function loss and gain show that SlLNR1 gene was critical for leave normal development and TYLCV resistance . We found that it has both sense and antisense transcripts , among which the antisense form was complementary to the 25-nt segment . Plant lncRNAs have been implicated in a wide of biological functions through various mechanisms , including acting as precursors and sponges of siRNA or directing histone modification of transcripts [52] . SlLNR1 gene does not belong to the six known Ty loci . It is likely involved in different networks related to TYLCV resistance . Its precise role and crosstalk with the Ty genes are now essential . It has been reported that many genes are involved in TYLCV resistance and RNA silencing is a major resistant strategy . Among the six known Ty loci , Ty-1 and Ty-3 encode RDRs which intensify RNA silencing pathway to boost disease resistance [25 , 27] . TYLCV have RNA silencing suppressors to counter the defense . Here , we show that interaction between TYLCV-derived vsRNAs and host lncRNA mediates disease symptoms and severity , indicative of a novel evolutionary arms race between the vsRNAs and SlLNR1 gene . It is noteworthy to address whether and how Ty genes and other defense regulators are involved through population genetics . TYLCV represents a group of DNA geminivirus that can cause severe damages and yield loss to many important crops [1 , 2] . The lack of understanding of the viral disease or the host resistance mechanisms has hampered the development of effective approaches to control the viral diseases . The DNA nature of the viral genome also makes this group of viruses difficult to eliminate using transgenic RNA silencing technologies . This study provides a plausible , vsRNA-lncRNA based disease mechanism that should open new avenues in developing new viral control strategies . For instance , viral disease resistance could potentially be achieved by silencing the expression of the specific vsRNAs , introducing a silencing-resistant version of the target lncRNA or genetic editing the lncRNA . It could also help to develop molecular markers for selecting TYLCV-resistant plant materials during breeding programs .
The TYLCV-resistant tomato cultivar CLN2777A ( Ty-2/Ty-2 ) and susceptible cultivar JS-CT-9210 were grown in a growth chamber under 23/19°C ( day/night ) and 16/8 h ( light/dark ) conditions [53] . Tobacco ( Nicotiana tabacum ) cultivar K326 and N . benthamiana were grown in a 25/22°C ( day/night ) while cotton plants ( Gossypium hirsutum cv . Junmian 1 ) were grown in a 27/25°C growth chamber under a 12 h photoperiod . Transgenic plants were grown in the same conditions with the recipient plants . A . tumefaciens strains GV3101 were grown at 28°C in LB supplemented with 50 μg/mL kanamycin and 50 μg/mL rifampicin . The wild TYLCV infectious clone TYLCV ( CN:SH2 ) and mutated clones were used to inoculate tomato plants with agro-inoculated method [37] . The phenotype was observed at 15 days post inoculation ( dpi ) and the accumulation of TYLCV was verified by qPCR , and at least 50 plants were inoculated for each infection clone . Whiteflies viruliferous of the TYLCV-IL strain were propagated and maintained with tomato plants in insect-proof greenhouse . Three- to four-true leaf stage tomato plants were inoculated with viruliferous whiteflies in insect-proof cages for 3 days , which were then treated with an insecticidal imidacloprid to kill the whiteflies . The phenotype was observed at 15 dpi and the accumulation of TYLCV was verified by qPCR . Tomato plants inoculated by TYLCV infectious clone at 15 dpi and pTRV2:IR and EV pTRV2 at 20 dpi were collected in liquid nitrogen . Total RNA was extracted using the RNA simple purification Kit ( Tiangen , China ) and used for library preparation by means of the TruSeq Small RNA library preparation kit from Illumina . Then sRNAs were sequentially ligated to a 3’ adapter and a 5’ adapter . After each ligation step , sRNAs were purified using 15% denaturing PAGE . The final purified ligation products were reverse transcribed into cDNA using Superscript III reverse transcriptase ( Invitrogen ) . The first strand DNA was PCR amplified using Taq polymerase ( Takara , Japan ) and DNA amplicons from each library were purified and separately submitted for high-throughput sequencing using the Hi-seq 2000 platform ( Illumina , San Diego , CA ) . The raw sequencing data were firstly filtered out the adapter sequences by in-house Perl scripts . Then , the filtered reads with 18–25 nt in length were aligned with TYLCV genome sequences ( GenBank ID: AM282874 ) , TRV1 ( GenBank ID: AF406990 ) or TRV2 ( GenBank ID: AF406991 ) and SlLNR1 by bowtie software [54] . Only reads with perfect match were selected for further analysis . The RNA-blot analyses for detecting the siRNA were performed as described previously [55] . Total RNA was extracted using TRIzol reagent ( Invitrogen™ ) following the manufacturer's instructions . The concentration of all the samples should be adjusted to 3μg/μL and the total loading quantity of RNA was 60 μg . The RNA was resolved on a 14% denaturing 8 M urea-PAGE gel and then transferred and chemically crosslinked onto a Hybond N+ membrane using N- ( 3-Dimethylaminopropyl ) -N'-ethylcarbodiimide hydrochloride . The probe for siRNA detection was 5'-AATTCAAAATTCAAAAATCAA-3' , the 5’ and 3’ end was labeled with biotin and the LNA modified at the 7th , 12nd and 14th site . The U6 probe is 5'-AGGGGCCATGCTAATCTTCTC-3' with the biotin labeled at the 5’ and 3’ end . The Northern blot was carried at 50°C for 16h . The detection of immobilized nucleic acids was carried with the Chemiluminescent Nucleic Acid Detection Module Kit ( Thermo , cat . 89880 ) . All the used oligonucleotides and generated constructs were listed and described in S1 and S2 Tables , respectively . There is a Sac I digestion site in the genome sequence of TYLCV . To construct the mutant infectious clones of TYLCV , the full length of TYLCV was amplified with the primers introducing the mutant sites in the IR region and EcoR I digestion sites , which then was digested by the EcoR I and Sac I to obtain the 2185 bp fragment and inserted to the pCAMBIA2301 vector . The full length of TYLCV was digested by the EcoR I and the 2781 bp enzyme-digested products were constructed on the first constructed vector , and the position of the fragment should be confirmed . For the IR , AV2 , AC1 and SlLNR1 expression analysis , the RNA of young leaves or other organs were extracted with PLANT simple RNA extraction kit ( TIANGEN ) and was reverse transcripted by the HiScript II Q RT SuperMix for qPCR ( Vazyme ) . For the transient expression analysis , total RNA was extracted from the injection site of leaves at 48 hours after agro-infiltration . The extracted total RNA samples were treated with DNase to avoid the potential DNA contamination . For the VIGS analysis , the new emerging leaves from the pTRV2:SlLNR1 plants at 15 dpi were used to extract RNA , which was subsequently used to determine the expression level of target gene by qRT-PCR . The VIGS-treated plants were inoculated with TYLCV infection clone , and the DNA was extracted at 15 days after infected for determining the TYLCV accumulation by qPCR . PCR thermal cycler qTOWER 2 . 0/2 . 2 ( Analytik Jena , Germany ) was used for qRT-PCR and qPCR analysis with PCR conditions consisting of denaturation for 10 s at 95°C , annealing for 15 s at 60°C , and extension for 20 s at 72°C for 35 cycles . The expression levels of selected genes were normalized to tomato actin gene ( AB199316 ) expression . All qRT-PCR expression assays were independently performed and analyzed three times under identical conditions . The sense or antisense transcripts of IR were confirmed by RT-PCR with the cDNA reverse-transcribed with the IR specific primers and the RNA template was also amplified with the same primers to validate no DNA contamination . The 5’ flanking region of the sense transcripts of SlLNR1 were obtained by RNA ligase-mediated rapid amplification of 5' cDNA ends First Choice ® RLM-RACE Kit ( Invitrogen , USA ) , according to the instructions of the manufacturer , and the 3’ end was verified by 3’ RACE PCR kit ( TAKARA ) . Full sense transcript of the SlLNR1 was amplified with the primers designed according to the joint sequence by RT-PCR . The antisense transcripts of SlLNR1 were detected by strand-specific RT-PCR with the cDNA reverse-transcribed with the specific sense primers [56] . All the above PCR products were cloned into pMD19-T ( Takara ) and validated by sequencing . To verify the cleavage site of SlLNR1 ( - ) by siRNA , total RNA was isolated from the new born leaves with TYLCV infection clone inoculated plants for 36 days and the control plants . And the 5’-RACE was performed using FirstChoice ® RLM-RACE Kit ( Invitrogen , USA ) , according to the instructions of the manufacturer . The RNA was reversed transcribed using random primers and then the 5’-end of cDNA was amplified using the 5’ outer primer and the SlLNR1 ( - ) specific reverse primer for the first round PCR . The amplified product was used for subsequent nested PCR with the inner primer and the SlLNR1 ( - ) specific nested reverse primer . The PCR products were detected by agarose gel electrophoresis . The target fragments were cloned into pGEM-T Easy vectors ( Promega , USA ) and sequenced . The specific cDNA fragment of SlLNR1 was amplified and inserted the into pGEM-T Easy ( Promega ) for sequencing . The probe was then generated by primers T7-F and R , which then was transcribed in vitro from the T7 promoter with T7 RNA polymerases using the digoxigenin RNA-labeling kit ( Roche ) . Tissues for in situ hybridization was fixed overnight in 4% ( wt/vol ) paraformaldehyde in phosphate buffer , pH 7 . 0 , and embedded in Paraplast Plus ( Sigma ) . Nonradioactive RNA in situ hybridization with digoxigenin-labeled sense and antisense probes was performed on 8-mm sections of different root parts as described [57] . SlLNR1 was silenced by tobacco rattle virus ( TRV ) -based gene silencing system . For agroinfiltration , an equal volume of Agrobacteria containing of pTRV1 or pTRV2:SlLNR1 was mixed and infiltrated into the cotyledons of tomato seedlings at the cotyledon stage with 1 mL syringe . The agroinfiltration of pTRV1 with pTRV2:PDS and pTRV1 with empty pTRV2 served as positive control and negative control respectively . The TRV based expression system was also employed to mediate the overexpression of IR and 4×25-nt-fragment ( 4TR ) in tomato plants . The plants were from 15-days after infiltration and the samples were collected for RNA extraction . For transient expression assays , Agrobacterium cells grown overnight were harvested and resuspended in infiltration media ( 10 mM MgCl2 , 10 mM MES , 200 mM acetosyringone ) to an O . D . value of 1 . 0 and cultured at room temperature for 4 h . Then the suspensions were infiltrated into the leaves of N . benthamiana using a needleless syringe . The samples were collected for RNA extraction or GUS activity analysis at 48 hours after agroinfiltration . Agrobacterium-mediated tomato and tobacco transformation was performed in accordance to the protocol with some modifications [57 , 58] . And the susceptible tomato line AC and tobacco cultivar K326 was used for the transformation work of construct for overexpression . | Tomato yellow leaf curl virus ( TYLCV ) is an important threat to tomato production worldwide . Six resistance/tolerance genes ( Ty ) have been introgressed from wild tomato species for breeding against the virus and some of them confer tolerance to TYLCV by enhancing gene silencing . Here , we propose a novel model for TYLCV-induced symptoms and host antiviral immunity , which is independent of the known Ty genes . We show that the virus-derived vsRNAs may cause abnormal phenotypes similar to virus infection by targeting and regulating a lncRNA ( designated as SlLNR1 ) in tomato . Furthermore , we find that SlLNR1 contributes to the normal development and TYLCV resistance in tomato . Interestingly , SlLNR1 exhibits a natural variant in a tomato cultivar that may evade vsRNAs-mediated cleavage . Thus , we suggest a novel evolutionary arms race between the vsRNAs and lncRNA , which could open new avenues in developing new viral control strategies . |
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While genome-wide association studies ( GWAS ) have primarily examined populations of European ancestry , more recent studies often involve additional populations , including admixed populations such as African Americans and Latinos . In admixed populations , linkage disequilibrium ( LD ) exists both at a fine scale in ancestral populations and at a coarse scale ( admixture-LD ) due to chromosomal segments of distinct ancestry . Disease association statistics in admixed populations have previously considered SNP association ( LD mapping ) or admixture association ( mapping by admixture-LD ) , but not both . Here , we introduce a new statistical framework for combining SNP and admixture association in case-control studies , as well as methods for local ancestry-aware imputation . We illustrate the gain in statistical power achieved by these methods by analyzing data of 6 , 209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6 . 0 chip , in conjunction with both simulated and real phenotypes , as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5 , 761 African-American women . We show that , at typed SNPs , our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies . At imputed SNPs , we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed . Finally , we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed . Our methods and our publicly available software are broadly applicable to GWAS in admixed populations .
Genome-wide association studies ( GWAS ) are the currently prevailing approach for identifying genetic variants with a modest effect on the risk of common disease , and have identified hundreds of common risk variants for a wide range of diseases and phenotypes [1] , [2] . Although GWAS have initially focused on populations of European ancestry , studies of other populations will capture additional genetic diversity that may be absent or present only at low frequency in Europeans . GWAS in non-Europeans will often involve admixed populations , such as African Americans and Latinos , with recent ancestry from two or more ancestral populations [3] , [4] . GWAS disease mapping in homogeneous populations relies on linkage disequilibrium ( LD ) between nearby markers to identify SNP association [5] . Admixed populations exhibit another form of LD at a coarse scale ( admixture-LD ) due to chromosomal segments of distinct ancestry [6] . This enables admixture mapping ( mapping by admixture-LD ) to be an effective approach for identifying disease genes in admixed populations [7]–[14] . As genotyping costs have decreased , however , GWAS have become an increasingly appealing alternative . Although GWAS and admixture mapping have historically been viewed as distinct approaches , GWAS in admixed populations can in theory capture both SNP and admixture association signals , which have been shown to contain independent information [15] . To date , GWAS in such populations have either considered SNP association only [3] , [16] , [17] , or SNP and admixture association separately [4] . We show below that combining these signals leads to increased statistical power because case-only admixture association statistics contain information independent from case-control SNP association statistics . It is important to complement theoretical methods development with empirical evaluation on large real data sets . To this end , we have evaluated our methods using 6 , 209 unrelated African Americans from the CARe cardiovascular consortium as well as 5761 unrelated African-American women from a GWAS for breast cancer . We ran comprehensive simulations based on real genotypes and phenotypes simulated under a variety of assumptions . Our main focus was on case-control phenotypes , in which case-only admixture association is particularly valuable . Our analysis of simulated and real ( coronary heart disease , type 2 diabetes and breast cancer ) case-control phenotypes shows that our combined SNP and admixture association approach attains significantly greater statistical power than can be achieved by applying either approach separately . Although our main focus is on case-control phenotypes , we also provide a detailed evaluation of association statistics for quantitative phenotypes , using simulated and real ( LDL and HDL cholesterol ) phenotypes . Since the general assumption in GWAS is that the causal SNP is not directly typed in the study , it is important to assess how the newly introduced scores perform in the context of genotype imputation . First , we show that imputation accuracy is marginally improved when local ancestry is taken into account in the imputation procedure . Second , our analysis in African Americans shows that for case-control studies our methods for combining SNP and admixture association outperform other approaches even in the presence of imputation . Finally , we show that when the causal SNP is not typed and cannot be reliably imputed our methods yield higher statistical power at finding the region harboring the causal variant when compared to previous approaches . Based on these findings we provide recommendations for the use of our combined approach in GWAS of admixed populations .
We analyzed data from 6 , 209 unrelated African Americans from the CARe consortium who were genotyped on the Affymetrix 6 . 0 chip , and merged in genotype data from the HapMap3 project ( see Methods ) [18] . We ran principal components analysis ( PCA ) on the merged data using the EIGENSOFT software , using only the CEU , YRI and CHB populations from HapMap3 to compute principal components [19] , [20] . The CARe samples generally occupy intermediate positions between CEU and YRI , consistent with previous work ( Figure S1 ) [21] , [22] . We ran the HAPMIX program for inferring local ancestry ( 0 , 1 or 2 European chromosomes ) at each location in the genome on the CARe samples , using phased CEU and YRI haplotypes from HapMap3 as reference [23] . HAPMIX was run in a mode that assigns European or African ancestry to each allele , thus resolving the local ancestry of each allele when both genotype and local ancestry were heterozygous ( see Methods ) . We defined genome-wide ancestry for each sample as the average of local ancestry estimates across the genome ( scaled to 0 . 0 , 0 . 5 or 1 . 0 ) . Genome-wide European ancestry estimates had a mean of 19 . 2% and standard deviation of 12 . 0% across samples ( consistent with previous work [21] , [22] ) , and were >99% correlated with the top eigenvector from PCA analysis . We defined average local ancestry at each location in the genome as the average of local ancestry values across samples . A plot of average local ancestry shows no unusual peaks in average local ancestry ( Figure S2 ) , consistent with the fact that the full set of CARe samples were not ascertained for a specific disease phenotype and thus would not be expected to produce an admixture peak , and confirming that HAPMIX does not produce artifactual deviations in average local ancestry . Importantly , we note that local ancestry can be estimated using any of the local ancestry inference methods that have been proposed ( e . g . [7] , [23] , [24] ) , as long as they are accurate and do not produce artifactual deviations in average local ancestry . We mention in passing that very strong selection since admixture for an allele differing in frequency between Europeans and West Africans could in theory produce a true local ancestry deviation , and our data could be used to provide an upper bound on the size of any such effect . We do not pursue this here . We used the Armitage trend test with correction for genome-wide ancestry as a baseline for the evaluation of other approaches , as this approach was used in previous association analyses using CARe data [25] ( see Methods ) . Next , we considered a SNP association score conditioned on local ancestry , as well as a case-only admixture score which evaluates the causal hypothesis that , restricting to disease cases , the proportion of European ancestry at the candidate locus differs from the genome-wide proportion [7] ( see Methods ) . Historically , an advantage of admixture association was that disease mapping could be performed using a coarse set of markers , due to the large size of ancestry segments and the resulting admixture linkage disequilibrium [22] . However , even when GWAS data are available , admixture scores that compare disease cases to the same disease cases elsewhere in the genome contain different information than SNP association scores that compare cases to controls; the additional information is particularly valuable when the causal SNP has very different allele frequencies in the ancestral populations . One possibility is to add the SNP association score conditioned on local ancestry to the admixture score to produce a χ2 ( 2dof ) score , but as we show below , the higher degrees of freedom leads to a reduction in statistical power . We instead propose a mixed χ2 ( 1dof ) score that jointly evaluates both SNP and admixture association using a single SNP odds ratio , by using the implied ancestry odds ratio ( see Methods ) . An important question is whether the odds ratio conditioned on African local ancestry differs from the odds ratio conditioned on European local ancestry , as this has implications for fine-mapping the causal SNP . This can be addressed by comparing the χ2 ( 1dof ) SNP association score conditioned on local ancestry to a χ2 ( 2dof ) SNP association score which allows different odds ratios for African versus European local ancestry ( see Methods ) . A final question , important in the context of localizing the causal SNP , is whether the ancestry odds ratio is fully explained by the SNP odds ratio . This can be addressed by comparing the χ2 ( 1dof ) MIX score that accounts for both admixture and case-control signal using a single SNP odds ratio and the χ2 ( 2dof ) SUM score that allows for independent SNP and ancestry odds ratios . We also explored whether it is necessary to assign African or European ancestry to each allele for a sample and SNP in which both local ancestry and genotype are heterozygous . Although the HAPMIX algorithm supports this functionality , it represents a significant complexity , particularly if representing local ancestry inference in terms of real-valued probabilities . We focus below on scores based on diploid local ancestry ( AA , AE or EE ) that do not require this extra information , and show that these scores perform nearly as well as scores that are based on haploid local ancestry ( A or E ) for each of two chromosomes with local ancestry inference and phasing performed jointly . We randomly selected 100 , 000 autosomal SNPs and , for each SNP , assigned simulated phenotypes based on either a null model or causal model for that SNP . Under the null model , we chose 1 , 000 cases and 1 , 000 controls at random . Under the causal model , we chose 1 , 000 cases and 1 , 000 controls corresponding to odds ratios R = 1 . 2 , 1 . 5 or 2 . 0 ( see Methods ) . Thus , our simulations use real genotypes , with simulated phenotypes that are different for each SNP being tested ( and different for each value of R ) . This framework automatically leads to admixture association signals as would exist with real phenotypes: for example , a causal SNP in which the risk allele has higher frequency in Europeans than in Africans will lead to the selection of 1 , 000 cases with higher than average European ancestry at the disease locus . We compared 5 scores: Armitage trend test with correction for genome-wide ancestry ( ATT ) , SNP association conditioned on local ancestry ( SNP1 ) , admixture association using cases only ( ADM ) , sum of SNP1 and ADM ( SUM ) , and our new mixed score ( MIX ) . All of these are χ2 ( 1dof ) scores , except for SUM which is χ2 ( 2dof ) . We note that the strength of the induced admixture signal at highly differentiated SNPs ( as measured by the ancestry odds ratio ) in the simulated data fits the model assumed in the MIX score . In Table 1 ( Typed Genotypes ) we display results obtained by all scores averaged across all SNPs , and averaged across SNPs with CEU versus YRI allele frequency difference of at least 0 . 4 , roughly the top decile of differentiation . We used a p-value cutoff of 5e-08 for all scores except ADM for which a threshold of 1e-05 was employed . The different ADM threshold is motivated by the smaller number of independent hypotheses tested across the genome in an admixture scan ( an effect of the large size of the ancestry segments ) [6] , [7] . The MIX score attains 8% higher power than the ATT score for random SNPs ( 24% higher power for SNPs in the top decile ) at R = 1 . 5 . The SNP1 score , which is conditioned on local ancestry , is analogous to disease mapping in Europeans or Africans ( see Text S1 ) . Thus , disease mapping in African Americans using the MIX achieves an increase in statistical power of 13% for random SNPs and of 67% for SNPs in the top decile of population differentiation over disease mapping in Europeans or Africans . This advantage is obtained both because MIX is a more powerful score than ATT , and because of the inherent advantage of disease mapping in admixed populations , which contain more polymorphic variation . As expected , the advantage of the MIX score is greatest for SNPs with large allele frequency differences between Africans and Europeans , for which admixture association produces a strong signal ( Table 1 ( Typed Genotypes ) and Figure 1 ) . We obtained similar results for a variant of the MIX score based on haploid local ancestry with joint local ancestry inference and phasing ( Text S1 ) . Thus , fully powered association statistics in admixed populations do not require joint local ancestry inference and phasing . We finally note that the heterogeneity score that tests for differences in effect size for African versus European local ancestry ( HET ) attained average values between 0 . 99–1 . 01 ( data not shown ) , exactly as expected since simulated phenotypes did not involve heterogeneity in effect size . We also assessed all scores at null simulated data ( R = 1 ) using the standard genomic control [26] statistic λGC which attained a value of 1 . 001 for MIX , 0 . 986 for SNP1 and 0 . 999 for the ATT score , respectively . We observed a λGC of 1 . 101 for the ADM score , which is suggestive of inflation , although we note that , for 1000 cases and a thousand independent genomic regions ( as expected in the ADM score ) , a λGC of 1 . 101 can arise by chance . However , since multiple factors ( e . g . deviations from random mating , correlation in errors of local ancestry estimates ) could potentially lead to inflation of the ADM statistic , we have also devised an admixture statistic , ADMGC that incorporates the empirical variance of the average local ancestry ( see Methods ) . It can be shown that ADMGC is equivalent to dividing the ADM statistics by λGC . Furthermore , we show how to incorporate ADMGC within the MIX framework to obtain a new version of our score ( MIXGC ) that incorporates the new admixture component . As expected , both ADMGC and MIXGC attain λGC of 1 . 000 ( data not shown ) in simulated null data . We note that MIXGC should be used when there is significant indication of inflation . As this was not the case here , we chose to use MIX for all results below . We also assessed the performance of our scores when the disease model assumptions are not met . We simulated causal SNPs under various disease models such as dominant and recessive or when two causal independent SNPs are present within an admixture block . To simulate two causal independent SNPs within same admixture block , we restricted to SNPs less than 5Mb apart and with LD less than . 1 ( as measured by r2 ) . Results in Table S3 confirm that for most scenarios studied the MIX score outperforms the standard ATT score with correction for genome-wide ancestry . Interestingly , when restricting to 2 causal SNP scenario in which one of the causal is in the top decile of differentiation ( which induces a strong admixture signal ) we observe that the SUM score outperforms all other scores in terms of power , showing the potential utility of this score at loci with multiple causal variants . We also looked at heterogeneous effects across Europeans and Africans by simulating 100 , 000 causal SNPs with R = 1 . 5 ( under no heterogeneity ) and assessing the scores at SNPs with different levels of LD with the simulated causal in the two populations . Different LD across populations will induce heterogeneous effects as a function of the allele frequencies and the population specific LD pattern . Results in Figure S4 show that under small heterogeneous effects ( difference in observed odds ratios <0 . 25 ) , the MIX score outperforms the other scores in terms of power while in the presence of larger heterogeneity all scores are underpowered in this simulation . Due to the limited number of markers present on the genotyping platforms , it is often the case that the causal SNP is not directly typed within the GWAS . However , genotypes typed in a study can be used as predictors , in conjunction with haplotypes over denser sets of SNPs from external repositories of human variation such as the HapMap [27] , to impute genotypes at SNPs untyped in the current study . Genotype imputation has been widely used as a method for boosting statistical power in association and fine-mapping studies as well as in meta-analysis that combines information across studies as a tool for increasing the number of markers interrogated for association with the phenotype [28]–[30] . Multiple methods [31] , [32] have been proposed for solving the imputation problem and have been shown to be very accurate when the haplotypes used as a reference panel provide a good match to the study population [28] , [30] , [33] . In admixed populations various imputation approaches have been proposed ranging from assigning global weights to the reference panels based on empirical estimates of ancestry [30] , to assigning coalescent-based weights to each of the reference haplotypes in every sample and every locus in the genome [34] . A standard approach for imputation in African Americans is to use a reference panel composed of European and African chromosomes [18] , [25] . Recent work has shown that imputation conditional on local ancestry estimates can boost the overall accuracy when compared to imputation based cosmopolitan reference panels that contain haplotypes from all the ancestral populations [24] , [35] . Here , through the use of real CARe genotypes , we show that imputation conditional on local ancestry yields a small improvement in imputation accuracy in African Americans . Our local ancestry aware imputation framework uses , at every locus in the genome , a reference a panel of haplotypes that is specified by the local ancestry ( see Methods ) . Following a standard masking approach , we masked 100 , 000 SNPs at random from the CARe data , imputed them and assessed imputation accuracy using a standard accuracy measure , the squared correlation between imputed and true ‘masked’ genotypes . We observe an average imputation r2 of 0 . 858 when our local ancestry aware framework is used , as opposed to 0 . 855 under the standard cosmopolitan approach , confirming that there is a small gain in accuracy by conditioning imputation on local ancestry . We observe a smaller improvement in imputation performance than the one reported in [24] , [35] which can be an effect of different imputation methods as well as of difference in size of reference HapMap panels used . We employed a much larger set of reference haplotypes ( HapMap phase 3 versus phase 2 ) in imputation that could potentially reduce the effect of incorporating local ancestry . Importantly , we note that the gain in accuracy is observed across all SNPs and leads to a small gain in statistical power for association ( see Figure 2 and Table S1 ) . We also point out that a large percentage of the imputed SNPs show a large difference in imputation performances between the European and African segments ( see Figure S3 ) . Roughly 40% of the imputed SNPs show accuracies differing by at least 0 . 1 in terms of squared correlation in European versus African segments with 26% being more accurately imputed in European segments versus 14% in African segments . A straightforward approach for extending association statistics at imputed SNPs is to use the maximum likelihood estimates for unobserved genotypes . Although this procedure does not fully account for the uncertainty in the imputed genotypes , it has been previously shown to perform well when there is considerable confidence in the imputed genotype calls . Throughout this paper we compute statistics over the maximum likelihood genotype calls . Although our novel scores could potentially be improved by fully incorporating the imputation uncertainty in the likelihood framework we note that the MIX score outperforms the standard ATT score , even when the ATT score accounts for the imputation uncertainty through the use of dosages instead of maximum likelihood genotype calls ( see Table 1 ( Imputed Genotypes ) ) . An important aspect of applying the case-control statistics to imputed data in African Americans is to properly account for the difference in imputation quality between African and European segments . We accomplish this by adjusting the observed allelic odds ratio as a function of imputation quality in the MIX and SNP1 score ( see Text S1 ) . We masked the 100 , 000 SNPs that were used for simulation of phenotypes and imputed genotypes at these SNPs using our local ancestry aware imputation framework ( see Methods ) . We computed the scores over the imputed genotype calls with the results displayed in Table 1 ( Imputed Genotypes ) . As expected , scores over imputed data show a reduction in statistical power because of the noise introduced by imputation errors . Importantly , we note that , similarly to typed data , the MIX score outperforms the other scores in terms of power , attaining 11% higher statistical power than the ATT score for random imputed SNPs ( 97% higher power for imputed SNPs in the top decile of allele frequency differentiation ) at R = 1 . 5 . Even when the ATT score allows for imputation uncertainty in the form of dosages , there is still a gain in statistical power of 6% at random SNPS ( R = 1 . 5 ) of MIX over ATT . We also note that adjusting the MIX score for different imputation qualities leads to a small improvement in statistical power at imputed SNPs ( see Table S1 ) . An important aspect in disease scoring statistics is to assess their performance when the causal SNP is untyped and , due to various reasons ( e . g . not present in the reference panel ) , cannot be imputed . To address this scenario we randomly picked 100 , 000 autosomal SNPs and simulated case-control phenotypes for R = 1 . 5 using the methodology described above . For all the SNPs we evaluated the statistics at 40 SNPs in the neighborhood of the simulated SNP and , for each score , computed the maximum statistic in this region by either masking the simulated causal SNP or by including it in the computation of the maximum . Results in Table 2 show that , both when the causal SNP is present in the data and when it is absent from the data , the MIX score outperforms all the other scores in terms of power . We again used the Armitage trend test with correction for genome-wide ancestry as the baseline for our analyses . We also considered a SNP association score conditioned on local ancestry , as well as an admixture score that associates the local ancestry to the continuous phenotype with genome-wide ancestry as a covariate . ( There is no analogue to a case-only admixture score for quantitative traits ) . As in the dichotomous case , we summed the SNP association score conditioned on local ancestry with the admixture score to produce a χ2 ( 2dof ) score , but show below that the higher degrees of freedom lead to a reduction in statistical power . Finally , we considered a χ2 ( 1dof ) heterogeneity score that tests for a difference in effect size conditional on African or European ancestry , by comparing a model that allows different effect sizes to a model with a uniform effect size ( see Methods ) . Analogous to simulations of dichotomous phenotypes , for 100 , 000 randomly chosen SNPs we used CARe genotypes and simulated phenotypes for 2 , 000 samples based on a null model or a causal model with effect sizes ε = 0 . 05 , 0 . 10 , 0 . 20 ( see Methods ) . We compared 4 scores: Armitage trend test with correction for genome-wide ancestry ( QATT ) , SNP association conditioned on local ancestry ( QSNP1 ) , local ancestry admixture association ( QADM ) , and sum of QSNP1 and QADM ( QSUM ) . All of these are χ2 ( 1dof ) scores , except for QSUM which is χ2 ( 2dof ) . Results are displayed in Table 5 ( Typed Genotypes ) . We display results averaged across all SNPs , and averaged across SNPs with CEU versus YRI allele frequency difference of at least 0 . 4 , roughly the top decile of differentiation . We see that the Armitage trend test ( QATT ) outperforms the other scores . Here , there is no advantage to incorporating admixture scores , since no case-only score is available and since summing SNP and admixture association scores ( QSUM ) loses statistical power due to increased degrees of freedom . We finally note that the heterogeneity score that tests for differences in effect size for African versus European local ancestry ( QHET ) attained average values between 0 . 99–1 . 01 ( data not shown ) , exactly as expected since simulated phenotypes did not involve heterogeneity in effect size . As in the case of the dichotomous phenotypes , we masked the 100 , 000 SNPs followed by imputation and we applied the above scores on the imputed genotypes ( see Table 5 ( Imputed Genotypes ) ) . Although the overall statistical power decreases for all scores because of imputation errors , we note that as before , QATT outperforms the other scores in terms of statistical power . We evaluated the above scores using data from two quantitative phenotypes from CARe , LDL and HDL cholesterol , for which associations at several loci have previously been reported . Results for genotyped and imputed SNPs in the region are displayed in Table S4 . As in our simulations , the QATT score yields the best performance the majority of the time . However , one aspect of the results is of particular interest . Multiple LDL and HDL SNPs on chromosome 2 produce strong admixture association ( QADM ) scores , with the result that the χ2 ( 2 dof ) QSUM score outperforms the χ2 ( 1 dof ) ATT score . We point out that the presence of multiple causal variants , or alternatively an untyped/unimputed variant with large allele frequency differentiation , may invalidate the assumptions made by the QATT score and lead to poor performance . This suggests that the QSUM score can be of value in a minority of instances where strong admixture associations exist . We caution that in such cases an additional multiple hypothesis testing correction may be needed and that the QSNP1 score conditioned on local ancestry will be needed for localization [38] .
Incorporating admixture association signals into GWAS of admixed populations is likely to be particularly informative for diseases for which risk differs depending on ancestry . Cardiovascular disease ( CVD ) is a prime example , as African ancestry is associated to higher CVD mortality and to CVD risk factors such as hypertension , serum lipid levels and left ventricular hypertrophy [39]–[41] . Other diseases for which African ancestry is a risk factor include prostate cancer , diabetic retinopathy , lupus and uterine fibroids [42]–[45] . Although we have focused here on African Americans , our methods are broadly applicable to other admixed populations . By analyzing real and simulated case-control phenotypes , we have shown that the MIX score , which incorporates both SNP and admixture association signals , yields a significant increase in statistical power over commonly used scores such as the Armitage trend test with correction for global ancestry . For randomly ascertained quantitative traits , in contrast to case-control phenotypes there is no case-only admixture score and thus no benefit from joint modeling of admixture and SNP association . Thus , for quantitative phenotypes , in general , the QATT score yields higher statistical power than other compared scores . Therefore , we recommend the use of MIX and QATT scores for dichotomous and quantitative traits , respectively , in future GWAS in admixed populations . However , we note that in various scenarios ( e . g . , multiple causal variants , heterogeneous effects , absence of the causal variant from the typed or imputed markers ) assumptions made by the MIX and QATT may be invalid and using them can lead to poor performance . To this extent , we recommend that special consideration be given to regions with high signals of admixture association , in which the SUM and QSUM scores may produce higher association signals than MIX and QATT . As a future direction , we note that an improved score for non-randomly ascertained quantitative traits could potentially be developed , which would generalize both the MIX score for dichotomous traits and the QATT score for randomly ascertained quantitative traits . As GWAS in European populations have demonstrated , association statistics need not be limited to SNPs that have been genotyped , because imputation algorithms that we and others have developed can be used to infer the genotypes of untyped SNPs by making use of haplotype information from HapMap . Our methods also perform well in the setting of imputation , when the causal SNP is not genotyped . As future work we consider the extension of our likelihood based scores to fully account for imputation uncertainty , where a promising direction is to define the likelihood as a full integration over the missing data given the observed data and the parameters of the model [46] , [47] . Our results using simulated phenotypes show that , although benefiting from a reduced multiple-hypothesis testing burden , the admixture scoring yields lower power for finding associations when compared to SNP association scoring . An explanation is the limited number of SNPs that show high allelic differentiation among the ancestral populations ( e . g . , in our simulations only 7 . 6% of the SNPs have an allelic differentiation greater than 0 . 4 between Europeans and Africans ) . However , we note that the question of whether there exists a combined SNP and admixture score that benefits from reduced multiple hypothesis testing for the admixture component of the score is an important open question that requires further consideration . While this paper focuses on frequentist approaches for disease scoring in admixed populations , we mention that joint modeling of admixture and SNP association signals could be developed in a Bayesian framework [48] . For example , SNPs that lie in regions of high admixture signals could be given a higher prior of association with phenotype . We expect this type of approach to provide added value especially in regions with multiple independent causal variants in which region-based scores could yield increased signal over marginal SNP scores . Although in this work we have focused on African Americans , in theory our approaches can be extended to other admixed populations such as Latino populations , which inherit ancestry from up to three continental ancestral ( European , Native American and African ) populations . The approaches presented in this work can be extended to three-way admixed populations either by considering one ancestry versus the rest strategy or by jointly modeling the three ancestry odds ratios so that a single SNP odds ratio would lead to implied ancestry odds ratios for each ancestry . However , we caution that in the context of Latino populations , more work is needed to assess the performance and possible biases of the local ancestry estimates and its potential effects on methods that incorporate admixture and case-control signals into disease scoring statistics . A final consideration is in fine-mapping causal loci . Here the availability of samples—or chromosomal segments—of distinct ancestry is valuable [38] for localization of the causal variant . We note that the HET score could be used in localizing the causal variant under the hypothesis of no heterogeneity across populations; recent studies have provided empirical support for this hypothesis [49] . Importantly , by comparing MIX and SUM score the question whether the admixture signal is fully explained by the SNP odds ratio can be assessed . An important open question and future research direction is designing optimal algorithms for cross-population fine mapping that leverage the different LD patterns among the chromosomal segments of distinct ancestry .
The CARe project has been approved by the Committee on the Use of Humans as Experimental Subjects ( COUHES ) of the Massachusetts Institute of Technology , and by the Institutional Review Boards of each of the nine parent cohorts . Affymetrix 6 . 0 genotyping and QC filtering of African-American samples from the CARe cardiovascular consortium was performed as described previously [25] . After QC filtering for each of ARIC , CARDIA , CFS , JHS and MESA cohorts and subsequent merging , 8 , 367 samples and 770 , 390 SNPs remained . To limit relatedness among samples we restricted all analyses to a subset of 6 , 209 samples in which all pairs have genome-wide relatedness of 0 . 10 or less ( inferred using the smartrel program in EIGENSOFT 3 . 0; see Web Resources ) . We merged CARe genotype data with genotype data from the HapMap3 project [18] . HapMap3 samples had been genotyped on both Affymetrix 6 . 0 and Illumina 1M chips . We excluded SNPs that did not pass QC in HapMap3 , as well as A/T and C/G SNPs to avoid allele complementarity issues , leaving 556 , 698 SNPs for further analysis . ( We note that HAPMIX accuracy is insensitive to the number of SNPs , if at least 250 , 000 SNPs are used [23] . ) When run in default mode , HAPMIX outputs local ancestry estimates as the expected probability of 0 , 1 or 2 copies of European ancestry at each SNP ( see ref . [23] and Web Resources ) . However , HAPMIX can also be run in a mode that outputs the inferred joint distribution of local ancestry and allele value , so as to resolve the “het-het” case ( both genotype and local ancestry heterozygous ) . In order to obtain integer estimates of local ancestry , one approach is to simply round the probabilities , which however can lead to biased estimates in regions with limited SNP coverage . We chose an alternative approach that does not produce these types of biases: sampling from the probabilities for 0 , 1 or 2 European chromosomes at each position . Results in this mode are highly concordant with the default mode , producing correlations of 100% in genome-wide ancestry and 98 . 8% in local ancestry . We selected a random subset of 100 , 000 autosomal SNPs . For each SNP , we simulated phenotypes for R = 1 . 0 ( null model ) and R = 1 . 2 , 1 . 5 , 2 . 0 ( causal models ) . For the null model , we chose random subsets of 1 , 000 cases and 1 , 000 controls . For causal models , we chose a random subset of 1 , 000 controls , and then chose 1 , 000 cases from the remaining samples so that samples with 0:1:2 reference alleles have relative probabilities 1:R:R2 of being chosen . We incorporate the observed variance of the average local ancestry across the genome assuming that the average local ancestry at each SNP is normally distributed with mean and standard deviation , where is the ancestry odds ratio . We estimate empirically and set , where is the empirical mean across the genome of the per SNP average local ancestry estimates . Then , the admixture likelihood becomes . We can then compute a χ2 ( 1dof ) statistic , ADMGC , that incorporates the empirical variance and in the ADM score as:In a similar manner we can replace with in the admixture component of the MIX likelihood to compute a new χ2 ( 1dof ) statistic MIXGC , that incorporates the empirical variance of the average local ancestry: Many of the likelihoods defined above require a multidimensional optimization . The number of parameters optimized in the likelihoods is 3 for the SNP1 score , 1 for the ADM score , 3 for the MIX score and 4 for the HET score . ( The HET score can be reduced to two independent 2-parameter optimizations by considering cases and controls separately . ) For the ADM score , Newton’s method was used . For the SNP1 , MIX and HET scores , Brent’s algorithm was used ( GSL software library implementation; see Web Resources ) . The maximization is performed in one dimension over each parameter in turn , repeating for each parameter until the algorithm converges . In rare instances , extreme variation in the slope of the log likelihood as a function of odds ratio can cause the algorithm to not converge; in this situations a simple binary search is used . We employed the widely used MACH [51] imputation method to infer genotypes at untyped SNPs in the CARe African American samples . As reference haplotypes we used either the cosmopolitan approach of providing all the CEU and YRI haplotypes from HapMap Phase 3 data [18] , or a local ancestry aware approach in which , for every locus in every sample , we provided either YRI , CEU+YRI , CEU reference haplotypes to MACH according to the number of copies of YRI ( 2/1/0 ) inferred by HAPMIX . We note that the local ancestry aware approach has been previously shown to boost imputation accuracy in admixed populations [24 . 35] . For both strategies we ran MACH in two steps , first by training the model parameters on a random sample of 200 individuals with the rounds parameter set to 50 followed by imputation of all the samples using the trained model from step 1 . Importantly , we note that the local ancestry aware approach can be applied as an add-on to any imputation method . Even when the true odds ratio is the same across populations , different imputation quality across the segments with different ancestries can lead to different estimates for the allelic odds ratios in European versus African segments . We account for this by adjusting the observed allelic odds ratios in the SNP1 and the MIX scores as follows . Following a derivation similar to [52] ( see Text S1 ) we show that the expected observed odds ratio at an imputed causal SNP with true odds ratio R , is a function of R , the imputation accuracy ( as measured by the correlation between true and imputed SNP ) , and the allele frequency: Unfortunately we do not know the true genotypes , and thus cannot compute the correlation between the true and imputed genotypes . However , reliable estimates for this correlation have been proposed; here we chose to use MACH estimates shown to produce robust estimates of imputation quality [53] . To estimate ancestry-specific imputation error rates , we restrict the computation to segments containing both alleles from that ancestry . Given that imputation accuracies are estimated directly from the data , depend on the term R and the allele frequencies . Then , the likelihood term from the MIX admixture association score becomes . As in the previous version of the score , the optimization is done over the three free terms and . SNP1 score is updated in a similar fashion . We randomly selected 100 , 000 autosomal SNPs and simulated phenotypes as described above using R = 1 . 5 . For all the compared scores , we computed the maximum statistic over all SNP across a region centered on the SNP of interest ( taking the 20 SNPs upstream and 20 SNPs downstream ) . We computed the maximum of the statistics either over 41 SNPs by including the simulated causal SNP or over 40 SNPs by ignoring the statistics at the simulated causal SNP . Case-control phenotypes for coronary heart disease ( CHD ) and type 2 diabetes ( T2D ) were ascertained as described previously [25] . In each case , phenotypes were available for only a subset of the five CARe cohorts . Restricting to 6 , 209 unrelated individuals as defined above , we analyzed 929 cases and 4 , 150 controls for T2D , and 179 cases and 3 , 328 controls for CHD . For every analyzed SNP we performed imputation within a region of 10Mb centered on the SNP of interest using the MACH imputation method under the local ancestry aware framework . We assessed the scoring statistics at all SNPs within 100Kb of the SNPs of interest . The FGFR2 locus has been associated with breast cancer in women of European and Asian descent [36] , and further fine mapping in African-American women has identified SNP rs2981578 as showing the highest signal of association [36] , [37] . We analyzed data from a GWAS including 5 , 761 unrelated African-American women from 11 epidemiological studies: The Multiethnic Cohort Study ( MEC ) [54] , The Los Angeles component of The Women’s Contraceptive and Reproductive Experiences ( CARE ) cohort [55] , The Women’s Circle of Health Study ( WCHS ) [56] , The San Francisco Bay Area Breast Cancer Study ( SFBC ) [57] , The Northern California Breast Cancer Family Registry ( NC-BCFR ) [58] , [59] , The Carolina Breast Cancer Study ( CBCS ) [60] , The Prostate , Lung , Colorectal , and Ovarian Cancer Screening Trial ( PLCO ) [61] , The Nashville Breast Health Study ( NBHS ) [62] , The Wake Forest University Breast Cancer Study ( WFBC ) [63] . Informed consent was obtained from all subjects . Detailed information about the design and organization of each study will be provided elsewhere ( C . Haiman and colleagues , unpublished data ) . Genotyping was conducted using the Illumina Human1M-Duo BeadChip . A total of 1 , 043 , 036 SNPs were kept after QC filtering . Imputation was performed using the MACH software , providing as reference all the haplotypes of CEU and YRI HapMap Phase 2 panels ) . We focused our analysis on all the typed or imputed SNPs , 251 in total , located 100Kb upstream and downstream of SNP rs2981578 . For each of 100 , 000 autosomal SNPs , we simulated phenotypes for ε = 0 ( null model ) and ε = 0 . 05 , 0 . 10 , 0 . 20 ( causal model ) , using a random subset of 2 , 000 samples . For the null model , phenotypes were sampled from a normal distribution with mean 0 and variance 1 . For the causal model , the mean was shifted to 0:ε:2ε for 0:1:2 reference alleles . In each case , we subtracted out the overall phenotypic mean . LDL and HDL cholesterol phenotypes in CARe samples were ascertained as described previously . We analyzed 5 , 801 samples for LDL and 5 , 946 samples for HDL for which phenotypic data were available , restricting to 6 , 209 unrelated individuals as defined above . For every analyzed SNP we performed imputation within a region of 10Mb centered on the SNP of interest using the MACH imputation method under the local ancestry aware framework . We assessed the scoring statistics at all SNPs within 100Kb of the SNPs of interest . http://www . hsph . harvard . edu/faculty/alkes-price/software/ ( MIXSCORE software ) http://www . hsph . harvard . edu/faculty/alkes-price/software/ ( EIGENSOFT software ) http://www . stats . ox . ac . uk/~myers/software . html ( HAPMIX software ) | This paper presents improved methodologies for the analysis of genome-wide association studies in admixed populations , which are populations that came about by the mixing of two or more distant continental populations over a few hundred years ( e . g . , African Americans or Latinos ) . Studies of admixed populations offer the promise of capturing additional genetic diversity compared to studies over homogeneous populations such as Europeans . In admixed populations , correlation between genetic variants exists both at a fine scale in the ancestral populations and at a coarse scale due to chromosomal segments of distinct ancestry . Disease association statistics in admixed populations have previously considered either one or the other type of correlation , but not both . In this work we develop novel statistical methods that account for both types of genetic correlation , and we show that the combined approach attains greater statistical power than that achieved by applying either approach separately . We provide analysis of simulated and real data from major studies performed in African-American men and women to show the improvement obtained by our methods over the standard methods for analyzing association studies in admixed populations . |
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Mutualisms between species play an important role in ecosystem function and stability . However , in some environments , the competitive aspects of an interaction may dominate the mutualistic aspects . Although these transitions could have far-reaching implications , it has been difficult to study the causes and consequences of this mutualistic–competitive transition in experimentally tractable systems . Here , we study a microbial cross-feeding mutualism in which each yeast strain supplies an essential amino acid for its partner strain . We find that , depending upon the amount of freely available amino acid in the environment , this pair of strains can exhibit an obligatory mutualism , facultative mutualism , competition , parasitism , competitive exclusion , or failed mutualism leading to extinction of the population . A simple model capturing the essential features of this interaction explains how resource availability modulates the interaction and predicts that changes in the dynamics of the mutualism in deteriorating environments can provide advance warning that collapse of the mutualism is imminent . We confirm this prediction experimentally by showing that , in the high nutrient competitive regime , the strains rapidly reach a common carrying capacity before slowly reaching the equilibrium ratio between the strains . However , in the low nutrient regime , before collapse of the obligate mutualism , we find that the ratio rapidly reaches its equilibrium and it is the total abundance that is slow to reach equilibrium . Our results provide a general framework for how mutualisms may transition between qualitatively different regimes of interaction in response to changes in nutrient availability in the environment .
Species in a community interact in a bewildering variety of ways , from parasitic to competitive to mutualistic . Mutualisms , in which two species engage in reciprocal cooperative behavior that benefits both partners , are thought to be particularly important for the stability of ecosystems [1 , 2] , although recent work questioned this role of cooperation in ecosystem stability [3] . Mutualisms in nature are common and diverse , including the pollination of crops and other plants by bees [4] , the cross-protection between clown-fish and anemone [5] , and the symbiosis between tubeworms and bacteria [6] . In the case of the tubeworm , the interaction is completely obligatory because it has no digestive system and acquisition of energy depends completely on bacterial symbionts . The mutualism between most plants and their pollinators , however , is typically facultative , as most plants have multiple pollinators and most pollinators feed from multiple plant species . Within the microbial realm , mutualisms can be due to cross-protection [7] or due to cross-feeding , in which each species supplies their partner with nutrients . Cross-feeding interactions can be present within a species [8] , between pairs of species [9–11] , or could represent a complicated network of dependencies [12] and possibly play a major role in driving the diversity of microbial communities in environments such as the soil [13 , 14] . In addition , cross-feeding could play an important role in determining the species composition and community-level functioning within the human gut microbiome [15] . Laboratory experiments are ideal for studying cross-feeding mutualisms , as they enable fine-grained control of microbial populations and the resources available in the environment . This provides the potential to integrate experiments and models in ways not possible in the field . For example , laboratory experiments have been used to show that cross-feeding can have a stabilizing effect on the relative abundance of two microbial species [9] , which can protect against invasion by cheater strains [16] . Although species in a mutualism generally benefit from interacting with each other , these benefits might decrease in different environments . A major focus of recent research on mutualisms has attempted to elucidate the conditions in which a mutualism can break down or switch to parasitism [17 , 18] . For example , the cross-protection mutualism between ants and the plants that house them can break down when grazing pressure on the plant is reduced [19] , and mycorrhizal mutualisms can become parasitic in the absence of abiotic stresses [20] . Theoretical work predicts that certain mutualisms can become competitive in high nutrient conditions [21] . Moreover , a global analysis of plant interactions concluded that interactions were often facilitative in the challenging environments present at high elevation , whereas the interactions became increasingly competitive in the more benign environments at low altitudes [22] . More generally , the mutualism–parasitism continuum hypothesis posits that a number of environments may cause a mutualism to degrade into a parasitic interaction [23] . Conversely , exposure to certain challenging environments that favor cooperation can stimulate establishment of novel mutualistic interactions [24 , 25] , and theoretical work predicted that almost any pair of species in a microbial ecosystem could establish cooperative interactions when grown in the right nutrient conditions [26] . Resource availability can also alter features other than the growth rate of cooperative strains . For example , resource availability can affect the spatial structure of cooperative species in a biofilm [27 , 28] , as well as the degree of intermixing of cooperative strains during a range expansion [10 , 29] . Although multiple studies have observed a shift in interaction because of varying environmental conditions , a detailed understanding of these changes is missing . It is currently unknown what the possible interaction shifts are and how the population dynamics of a mutualism are affected by these shifts . In our work , we use a synthetic cross-feeding yeast system in which we can modulate the relative strength of the mutualistic and competitive aspects of the interaction by supplementing the media with the amino acids that the strains cross-feed . By changing these two nutrient concentrations , we are able to switch between a surprisingly large number of different interaction types , including obligatory and facultative mutualism , competition , parasitism , competitive exclusion , and extinction of the population . Each of these regimes shows qualitatively different dynamics , which we can understand using a simple model . Our experiments shed light on the important question of how resource availability can modulate the types of interaction between species in a mutualism .
As a model system for mutualistic interactions , we used two non-mating Saccharomyces cerevisiae budding yeast strains that have been engineered to be deficient in the biosynthesis of an essential amino acid and also overproduce the amino acid required by its partner ( Fig 1A ) [10] . The red fluorescent protein ( RFP ) -tagged leucine auxotrophic strain ( Leu- ) overproduces tryptophan , whereas the yellow fluorescent protein ( YFP ) -tagged tryptophan auxotroph strain ( Trp- ) overproduces leucine . These strains have previously been demonstrated to form a cross-feeding mutualism when grown on solid agar , with each strain leaking out the amino acid needed by its partner [10] . To determine if we could establish a stable mutualism between these strains in well-mixed liquid batch culture , we inoculated monocultures and co-cultures at a range of leucine and tryptophan concentrations ( Fig 1B and 1C ) . Co-cultures were started with equal amounts of each strain at the same total density as monocultures . Each day we diluted by a factor of ten into fresh media containing the same defined concentrations of leucine and tryptophan ( Fig 1B ) . For a culture to survive , the growth of a population during the day should be at least as large as the decrease caused by dilution , and a population thus needs to divide at least log2 ( 10 ) = 3 . 3 times each day . In monoculture , Trp- cells required at least 2 μM tryptophan to avoid going extinct due to dilution , whereas Leu- cells required a minimum of 32 μM leucine . In contrast , co-cultures could survive on concentrations of leucine and tryptophan where the monocultures would each go extinct . Co-cultures survived eight of these growth-dilution cycles , indicating a stable mutualism . Even in concentrations where monocultures survived , we found that co-culture density was often much higher than the sum of monoculture densities ( Fig 1C ) , suggesting that in this regime the strains were interacting in a facultative mutualism . Understanding the relative benefits that each partner in the mutualism does or does not receive requires that we also determine the population abundance of each strain at different amino acid concentrations . We therefore co-cultured the strains and measured the population composition by flow cytometry at the end of each day . We tried to make both strains receive equal benefits from the amino acids being supplemented by adding leucine and tryptophan in a ratio of 8 to 1 , which is approximately the intracellular ratio of these amino acids [30] . We found that at low amino acid concentrations ( 1 μM tryptophan , 8 μM leucine; 1 and 8 μM ) , the strains indeed form an obligate mutualism with an apparently stable coexistence , because relative abundance changes little over time ( Fig 1D ) . At medium amino acid concentrations ( 8 and 64 μM ) , the strains form a facultative mutualism , with both strains benefiting from the presence of the other strain , yet also surviving when grown in monoculture . At high amino acid concentrations ( 32 and 256 μM ) , we observed coexistence of the two strains , but with the Trp- strain at an equilibrium abundance below what it would have reached in a monoculture . At this high amino acid concentration , we therefore found that the strains are forming an amensalism , in which the Leu- strain is relatively unaffected by the interaction but the Trp- strain performs worse in co-culture than in monoculture . This demonstrates that a simple microbial cross-feeding mutualism can transition into a qualitatively different interaction by a simple change in environmental conditions . Throughout our study , we compare the final population size of each strain in monoculture and co-culture to assess whether each strain is benefitted , harmed , or unaffected by the presence of its partner in each environmental condition . Once populations have reached an equilibrium size , all populations have the same mean growth rate over the course of the day , because reaching the same population size after a cycle of dilution and growth requires that each cell type undergo log2 ( 10 ) = 3 . 3 divisions over the course of the day . The division rate of a population is therefore not an appropriate measure of fitness or benefit/harm from a partner , as the division rate at equilibrium is always the same given the constant dilution rate present within the experiment . We also note that throughout each daily cycle of growth , the strains alter their habitat by consuming and producing amino acids . Therefore , the label for the different environments ( e . g . , 2 μM tryptophan and 16 μM leucine ) corresponds to the amino acid concentration of the media that we use to initialize growth at the beginning of each day . To gain insight into the transition between the different regimes of interaction in our cross-feeding strains , we implemented a simple phenomenological model designed to capture the essential elements of the interactions between the strains . We assumed that the two strains Trp- ( X ) and Leu- ( Y ) have a per capita growth rate that is modulated by the mutualistic partner as well as the supplemented amino acids: dXdt=rxX ( Y+aY+a+κ ) ( 1−X−Y ) −δX ( 1 ) dYdt=ryY ( βX+aβX+a+κ ) ( 1−X−Y ) −δY ( 2 ) Here rx and ry are the growth rates , a is the amount of supplemented amino acids , δ is the death rate imposed by dilution , κ is an effective Monod constant , and β quantifies the asymmetry of benefit that each strain receives from its partner . The growth rate of each strain increases with the abundance of the mutualist partner and the needed amino acid , but this benefit saturates via a Michaelis-Menten/Monod form as a function of both the concentration of the partner and the supplemented amino acid . This particular form for the interaction arises from a resource-explicit model in which the amino acid dilution/degradation is larger than consumption , but the qualitative predictions of the model are robust to this assumption ( S1 Information ) . We assume that the supplemented amino acids are always added at a fixed ratio , so we use a single variable “a” to capture the amount of supplemented amino acids ( despite the fact that the two strains are actually consuming different amino acids ) . Because the 1-to-8 ratio of tryptophan to leucine should give about equal “relative” amounts of amino acids , we used the same scaling constant ( κ = 0 . 12 ) for both equations . The two strains are also assumed to use other resources in the environment and hence saturate at a total population size , which is normalized to 1 . Additionally , we recapitulated our daily dilutions by introducing a fixed death rate , δ = 0 . 5 ( although our experiments are done in batch culture , for simplicity we model our mutualism in continuous culture ) . We incorporated only two aspects of the asymmetry between our two strains . First , based on competition experiments in saturating amino acid concentrations ( 200 and 1 , 600 μM ) , we calculated that Leu- has a fitness disadvantage of ~7 . 5% in optimal conditions ( S1 Fig ) , so we set the normalized growth rates to be rx = 1 and ry = 0 . 925 . Second , we assume that the Trp- strain contributes more nutrients to the mutualism than the Leu- strain ( β = 2 ) because the Leu- strain dominated co-cultures at non-saturating amino acid concentrations ( Fig 1D , also see below ) . This simple phenomenological model was able to explain the qualitative regimes of interactions that we observed previously ( Fig 1D ) and suggested that simply by varying the amino acid concentrations we may be able to observe an even larger number of qualitative outcomes between our two strains ( Figs 2 and S7 ) . Increasing amino acid concentrations from the region of obligatory mutualism ( Fig 2 , blue ) , the model predicts that the interaction should become a facultative mutualism ( green ) followed by a parasitism ( yellow ) , with the Leu- benefiting from the interaction and the Trp- being harmed . The model then predicts that the amensalism previously observed in Fig 1D corresponds to the boundary of the parasitism region and a competition region ( orange ) , in which the strains coexist but at an equilibrium density below what they would reach in monoculture . This outcome is achieved despite the fact that the force leading to coexistence of the strains is still the sharing of amino acids . Since these strains have complete niche overlap , coexistence is not possible without a stabilizing influence , which is provided by amino acid transfer [31] . At even higher amino acid concentrations the model predicts that the strain with a higher maximal growth rate ( Trp- ) should outcompete the slower dividing strain , because in this regime , amino acids are no longer limiting ( Competitive Exclusion , red ) . The model also predicts that due to the asymmetry in the strains , there will be a small region where the interaction is a facultative mutualism for one strain yet an obligatory mutualism for the other strain ( cyan ) . Finally , the model predicts that in the absence of supplemented amino acids , the mutualism will fail and both strains will go extinct ( dark blue ) . These results are not the result of a particular parameter setting , as the model predicts a shift through the same qualitative regimes over a large range of values for the death rate δ ( S8 Fig ) . This model , although exceedingly simple , therefore predicts the existence of a surprisingly wide range of different qualitative outcomes within a mutualist pair . To test these model predictions of many different interaction regimes , we experimentally measured the equilibrium abundances at a wide range of amino acid concentrations ( Figs 3 and S2 ) . As predicted by the model , we found that varying the amino acid concentration caused the mutualist pair to switch between seven different qualitative regimes , with the ordering of these regimes as predicted by the model . From low to high amino acid concentrations , we observed collapse of the mutualism , obligatory mutualism , obligatory/facultative mutualism ( different for the two strains ) , facultative mutualism , parasitism , competition , and competitive exclusion . Note that there are slight differences between the model and experiment in the behavior of the monocultures , as the Leu- strain is more abundant than the Trp- strain at high amino acid concentrations in our experiment . Nevertheless , it is remarkable that such a simple model provides such effective guidance in the outcomes that we observe in our experimental microbial cross-feeding system . In both the model ( Fig 2 ) and in the experimental system ( Fig 3 ) , the two strains coexist for intermediate values of supplemented amino acids , but one or both strains go extinct if the amount of supplemented amino acids is either too small or too large . This means that if the environment were to deteriorate ( for example , by decreasing nutrient availability ) , the system would go through a series of changes in the type of interaction ( e . g . , parasitism , facultative mutualism ) before becoming an obligatory mutualism and finally going extinct due to the environmental deterioration . Similarly , a rich environment would render the mutualism ineffective , so that the strain with lower fitness would eventually be outcompeted by the other . In principle , knowing the interaction type would indicate whether the system is approaching extinction , although this information requires knowledge of the equilibrium densities for both monocultures and co-cultures , which may not be easily available for many natural systems . An alternative way to detect an imminent population collapse consists of looking at early-warning signals , which are characteristic features exhibited by biological populations prior to an abrupt change of state [32] . To this end , we have analyzed the model behavior near the two onsets of extinction , namely in the obligatory mutualism and competition regimes . The equilibrium densities of the two strains in co-culture are given by the single non-zero equilibrium point of Eqs 1 and 2 ( Fig 2 ) . This equilibrium is stable , meaning that the system recovers from small perturbations in the way described by its eigenvalues and eigenvectors ( Fig 4 ) . The two eigenvalues , both negative , indicate how rapidly the equilibrium point is approached by the population trajectories along the directions given by the corresponding eigenvectors . A large negative eigenvalue indicates a rapid convergence ( i . e . , solid black line ) , whereas a small negative value indicates a slow convergence ( i . e . , solid magenta line ) . At nutrient concentrations near the onset of extinction , both in the obligatory mutualism or competition regimes , there is a separation of time scales: the slow eigenvalue goes to zero , indicating that the system takes a long time to reach the equilibrium point ( blue dot in insets ) along the slow eigenvector ( magenta arrow in insets ) . Simulations of the model confirm that near the onsets of extinction , the trajectories align parallel to the slow eigenvector before reaching the equilibrium point ( insets I , IV , and V ) —a phenomenon that does not occur when the eigenvalues assume similar values ( insets II and III ) . Finally , the orientation of the slow eigenvector indicates which quantity is slowly relaxing: close to collapse of the mutualism ( inset I ) , the ratio of the densities of each strain within the population ( i . e . , f = X/Y ) relaxes faster than the total population size ( i . e . , n = X+Y ) ; in contrast , before competitive exclusion occurs ( inset V ) , the population quickly converges to a fixed n , while slowly equilibrating f to the amount determined by the equilibrium point . In summary , our model predicts that the approach to equilibrium is very different when the cross-feeding strains interact in an obligatory mutualism as compared to when they interact competitively ( Fig 4 , see Materials and Methods section ) . Competitively interacting strains rapidly reach carrying capacity , and only later does the ratio of the strains reach equilibrium ( Fig 4 , inset V ) . In contrast , in the obligatory mutualism regime close to collapse , it is the ratio that first reaches equilibrium , and the total population size is the variable that is slow to reach equilibrium ( Fig 4 , inset I ) . In between these two interaction regimes there is no separation of timescales , and the approach to equilibrium is predicted to be approximately uniform from all directions ( Fig 4 , insets II and III ) . These changes in dynamics are expected very generally due to critical slowing down , in which the slow relaxation mode is associated with the direction of the eigenvector as the eigenvalue goes to zero ( Fig 4 ) . The model therefore predicts that simply measuring the dynamics of the partner strains allows for an estimate of the kind of interaction and , hence , how close the population is to collapse . In order to test these model predictions , we measured the dynamics of co-cultures initialized at a wide range of population sizes n and starting ratios f , spanning four and eight orders of magnitude , respectively ( Fig 5 ) . In accordance with the predictions of the model , in high amino acid concentrations ( 32 μM tryptophan and 256 μM leucine ) , we observed rapid convergence of n , whereas f did not equilibrate even after five days ( Fig 5C ) . In contrast , in low amino acid conditions ( 1 μM tryptophan and 8 μM leucine , Fig 5A ) , the interaction is an obligatory mutualism and the cross-feeding interaction resulted in a strong stabilizing effect on the relative abundances [9] , with the populations rapidly reaching a 1-to-1 ratio ( i . e . , f = 1 ) . As f equilibrated , the fate of the populations depended on the population size n: those that started at sufficiently high abundance slowly increased their total population size to the equilibrium point value , whereas populations that started too small or imbalanced were fated to extinction ( n = 0 ) . We were therefore able to experimentally observe the two different separations of timescale predicted by the model in the two different extreme regimes of interaction . Finally , we found that at intermediate amino acid concentrations ( 8 μM tryptophan and 64 μM leucine ) , there was a balance between the two relaxation timescales , thus causing the trajectories to converge to equilibrium from all directions ( Fig 5B ) as predicted by the model ( Fig 4 insets II and III ) . Therefore , the relaxation dynamics of the cross-feeding partners provide an early-warning indicator of population collapse .
We have established an experimental system that captures a multitude of interactions by simply varying the amount of nutrients freely available to two partners in a cross-feeding mutualism . Although it is tempting to conclude that this cross-feeding interaction should be an obligatory mutualism , we demonstrate experimentally that the interaction varies greatly with the environment . Depending upon the environment , we found that our cross-feeding strains could interact as an obligatory or facultative mutualism , parasitism , amensalism , or competition . A simple phenomenological model explained this range of outcomes , which we view as a significant success given that many models of mutualisms have difficulty shifting between such qualitatively different outcomes; indeed , the Lotka–Volterra model of interspecies interactions fails to even describe an obligatory mutualism without leading to ever-expanding populations [33] . Moreover , the model predicts different relaxation time scales on the brink of collapse that have been confirmed in our experimental system . Our experiments and modeling suggest that the interaction becomes increasingly cooperative as the environmental quality deteriorates via decreasing nutrient availability . This observation is consistent with work done on a range of other mutualisms and interspecies interactions [16 , 19 , 20 , 22 , 26] . However , our results show a much greater range of possible interactions than demonstrated previously and strengthen the idea of interactions between species being contingent upon the environmental conditions rather than being fixed . We also found that the population dynamics change drastically with changing nutrient availability . Low nutrient concentrations have a strong stabilizing effect on relative abundance , whereas high nutrient concentrations stabilize total population size . These dynamics provide a possible way to estimate the interaction and stability of a potential mutualism without having data regarding the viability of each species on its own . The strong stabilizing effect on either total population size or relative abundance suggests that variation will be predominantly on the variable that is not strongly stabilized . In particular , at low nutrient availability , fluctuations may lie primarily along the total population size , whereas in high nutrient availability , the fluctuations in relative abundance may be larger . These differences could provide a more accessible way of studying the stability of species with positive interactions , as it requires only studying the fluctuations of the populations around their equilibrium . Moreover , an experimentally tractable cross-feeding system such as ours could be used to explore counterintuitive effects predicted to occur as a result of noise , such as enhanced sensitivity to environmental fluctuations [34] and noise-induced oscillations [35] . In our study , we focused on the ecological dynamics of mutualisms ( changes in the number of individuals in a population ) rather than evolutionary dynamics ( changes in genetic structure ) . Rather than asking questions about how two strains would evolve cross-feeding , we simply assumed a priori that such an interaction had arisen evolutionarily . Given such an interaction as a starting point , we sought to understand the environmental conditions under which the mutualism would transition into competition . It would be fascinating to explore the evolutionary stability of the cross-feeding studied here , particularly because the evolutionary stability may depend strongly upon the environmental context [36] . In this paper , we have focused on the interactions between two auxotrophic strains , each of which produces the amino acid needed by its partner . However , in principle , this cross-feeding mutualism can be invaded by other strains , the most relevant of which would be the double-producer ( producing both tryptophan and leucine ) and the non-producer ( auxotroph for leucine and tryptophan ) . At least within the realm of our model , we predict that at intermediate amino acid concentrations the mutualism is non-invadable by either of these alternative strains ( S5 Fig , S1 Information ) . However , at higher amino acid concentrations the non-producer is predicted to invade and coexist with the single producers ( and , similarly , at lower amino acid concentrations the double producer is predicted to invade ) . It would be interesting to explore further the degree to which cross-feeding can stabilize the coexistence of multiple strains , particularly given the wide range of nutrients that can be shared in a microbial community . It is also worth noting that the two strains used in our study were able to form an effective cross-feeding mutualism without ever having previously grown together , i . e . , in the absence of coevolution . There is still considerable debate regarding whether mutualisms in natural microbial communities arise primarily from this sort of ecological fitting or via coevolution [19 , 25] . Laboratory experiments have demonstrated the stabilizing effects of coevolution on mutualism dynamics [11] . Regardless , we note that our mutualism dynamics are quite stable even in the absence of a period of coevolution . One important feature of our mutualism is that the two strains are almost genetically identical . This means they have near-perfect niche overlap , which results in very strong competition between the two strains when amino acid concentrations are high . In many other mutualisms , the partners will have less niche overlap and will therefore experience less competition . Incorporating this in our model predicts that the degree of niche overlap will have a strong influence on the outcome of the interaction and the degree to which different environmental conditions will switch the nature of the interaction ( S6 Fig ) . As perhaps expected , less niche overlap results in a larger range of parameters in which the species are mutualistic . Future studies in the field and in the laboratory will be needed to elucidate whether the wide range of interactions observed here is relevant for other mutualisms .
Both S . cerevisiae strains are from a W303 background and are genetically modified to cross-feed as described in [10] . The strains were adapted to growing with low amino acid supplementation through seven cycles of daily dilution ( 10X ) and growth in 2 μM tryptophan and 32 μM leucine . In these cycles , populations consisting of ~100 , 000 to 500 , 000 cells underwent bottlenecks in which as few as 10 , 000 cells survived . Monoclonal lines from adapted strain were derived through plating on 1 . 5% agarose plates and were used for all experiments except for comparison with unadapted strains . Strains were grown in batch culture in synthetic medium consisting of Yeast Nitrogen Base ( YNB , Sunrise Sciences ) , Complete Supplement Mixture lacking leucine and tryptophan ( CSM-leu-trp , Sunrise Sciences ) , and 2% glucose . Synthetic medium was supplemented with varying amounts of amino acids as indicated in experiments . All daily dilution experiments were performed in BD Falcon 96-well flat bottom plates . Cells were grown in 200 μl batch culture at 30°C and mixed by a shaker rotating at 900 rpm . Plates were sealed with Bemis Laboratory Parafilm to prevent evaporation . At the start of each co-culture experiment , single colonies were grown for 24 h until saturation in 3 ml synthetic medium containing 100 μM tryptophan and 1 , 000 μM Leucine . They were then diluted by a factor of ten and grown for 4 h to prevent cells from being in stationary phase at the start of the experiment . Cells were spun down and washed three times to remove any excess amino acids . Leu- and Trp- cells were then mixed in appropriate ratios and seeded in BD Falcon 96-well flat bottom plates in 200 μl medium . A daily dilution cycle consisted of 23 . 5 h of growth , after which density was measured by spectrophotometry ( Thermo Scientific VarioSkan Flash Multimode Reader ) and relative abundance was measured by flow cytometry ( Miltenyi MACSQuant VYB , minimum of 10 , 000 cells analyzed ) . Cultures were then diluted by a factor of ten into new 96-wells plates containing fresh medium Figs 2 and 4 have been obtained by computing analytical formulae for the equilibrium point , eigenvalues , and eigenvectors of Eqs 1 and 2 . Bifurcation analysis of the model is shown in S7 Fig . The analytical treatment has been carried out using a computer algebra system and can be found in the supplementary files ( S2 Information ) . Simulated trajectories in the insets in Fig 4 have been obtained by Gillespie simulations [37] of the corresponding stochastic model of Eqs 1 and 2 . The C code used for simulations is attached as supplementary material ( S2 Information ) . | Species often engage in mutualistic interactions that are beneficial for both partners . However , there is also a cost associated with cooperation , for example , in the form of energy required to make nutrients for a partner . When environments change , the costs and benefits of cooperating can change as well , and this can cause the mutualistic interaction to break down into other interaction types , such as parasitism . In this study , we varied nutrient availability to examine how changing environments can affect the interaction between two cross-feeding yeast strains . Lower nutrient concentrations made each strain more dependent on the nutrients provided by its partner strain and thus favored cooperation . Using both experiments and mathematic models , we found that in different environments , these yeast strains can interact in at least seven different qualitatively different ways , including obligate mutualism , facultative mutualism , parasitism , and competition . We also found that the dynamics of how the two strains influence each other change drastically in different nutrient concentrations . Examining the population dynamics could therefore potentially be used to predict the stability or collapse of a community . |
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Since the late 1980s , wild salmon catch and abundance have declined dramatically in the North Atlantic and in much of the northeastern Pacific south of Alaska . In these areas , there has been a concomitant increase in the production of farmed salmon . Previous studies have shown negative impacts on wild salmonids , but these results have been difficult to translate into predictions of change in wild population survival and abundance . We compared marine survival of salmonids in areas with salmon farming to adjacent areas without farms in Scotland , Ireland , Atlantic Canada , and Pacific Canada to estimate changes in marine survival concurrent with the growth of salmon aquaculture . Through a meta-analysis of existing data , we show a reduction in survival or abundance of Atlantic salmon; sea trout; and pink , chum , and coho salmon in association with increased production of farmed salmon . In many cases , these reductions in survival or abundance are greater than 50% . Meta-analytic estimates of the mean effect are significant and negative , suggesting that salmon farming has reduced survival of wild salmon and trout in many populations and countries .
Since the late 1970s , salmon aquaculture has grown into a global industry , producing over 1 million tonnes of salmon per year [1] . The majority of this biomass is held in open net pens in coastal areas through which wild salmon migrate on their way to and from the ocean . A number of studies have predicted or evaluated the impacts of salmon farming on wild salmon through a single mechanism , in a given area . It is clear that some salmonids are infected and killed by sea lice originating from salmon farms [2–5] , that other diseases have been spread to wild populations from salmonid farming activities [6 , 7] , and there is evidence that salmon parr are at lower density in areas of Scotland where there is salmon aquaculture [8] . In addition , farmed salmon escape in all areas where salmon aquaculture is practiced , and although their breeding success may be low on average , competition for mates and hybridization with wild salmon are likely to reduce survival of wild populations [9 , 10] . It is well established that wild salmonids can be negatively affected by salmon farming [11] , however , the importance of these interactions at the population level has rarely been determined [2] . To determine population level impacts , we examined temporal trends in the abundance and survival of wild salmonids ( Figure 1 and Figure S1 ) . Our study contrasted trends in wild populations exposed to potential aquaculture impacts with those of populations not exposed . Populations in which juvenile salmonids pass by salmon farms during their migration were considered to be exposed to impacts of salmon farming . Exposed populations were carefully paired with control populations in the same region whose migrations did not lead past farms , but which otherwise experienced similar climate and anthropogenic disturbances . Use of such paired comparisons allowed us to control for confounding factors such as climate to detect population level impacts . Using the Ricker stock recruit model [12] , we performed 11 comparisons , involving many stocks from both sides of the Atlantic and from British Columbia in the Pacific ( Table 1 , Data section of Materials and Methods ) .
All estimates of the effect of aquaculture on survival or returns were negative . Both random effects estimates of the mean effect were negative and highly significant ( Figure 2 ) , indicating a very large reduction in survival and returns in populations exposed to aquaculture . Under the dynamics of Equation 1 ( see Materials and Methods ) , percent change in survival or returns is represented by where γ is the coefficient of aquaculture production ( P ) for region k . For example , the estimated change in survival per tonne of salmon farming ( γk ) for Bay d'Espoir in Newfoundland was estimated to be 0 . 026 ( Figure 2 ) . In 2003 , the farmed salmon harvest from this area was 1 , 450 tonnes ( t ) , so the estimated decrease in survival is ( 95% CI: 44%–80% ) , relative to what it would be in the absence of farms . Survival and total returns of many stocks were found to be reduced by more than 50% ( Figure 2 ) , for each generation . If all exposed populations were passing by farms with a total annual harvest of 15 , 000 t , the mean estimated total reduction in survival would be 73% ( 95% CI: 29%–90% ) ( Figure 2 ) . Many regions now have farmed salmon production in excess of 20 , 000 t/y . Generally , Atlantic salmon populations were depressed more than Pacific salmon populations , particularly Atlantic salmon in Atlantic Canada . Irish sea trout were also estimated to have been very strongly reduced by impacts of salmon farming , whereas estimated impacts on Atlantic salmon in Scotland depended on the data used . In British Columbia ( Pacific Canada ) , only pink salmon showed significant declines correlated with salmon aquaculture . Results are reported for a model including autocorrelated errors and with λ set at 0 . 5 , rather than 1 or 2 , because this minimized the Akaike information criteria ( AIC ) for most regions [13] . The parameter λ allows for the impacts of salmon farming to change nonlinearly with the aquaculture production . A λ of 0 . 5 indicates that relatively small amounts of aquaculture will depress wild populations , but the effect does not increase proportionally to aquaculture production . See Tables S1 and S2 for results of alternative models . For the New Brunswick comparison , the outer Bay of Fundy rivers are located much closer to salmon farms than the other exposed rivers . If only these outer Bay of Fundy rivers are considered exposed to salmon farming , and other Bay of Fundy rivers ( inner Bay of Fundy and Saint John River ) are included among the controls , the overall estimates ( i . e . , meta-analytic means ) are still significant and negative in both versions of the analysis .
We have estimated a significant increase in mortality of wild salmonids exposed to salmon farming across many regions . However , estimates for individual regions are dependent on assumptions detailed in the Materials and Methods section , and the estimates often have large confidence intervals . Given that the data analysed are affected by considerable noise—including changes in fishing and environmental factors—the important result of this study is that we are nonetheless able to detect a large , statistically significant effect correlated with trends in farmed salmon production . The significant increase in mortality related to salmon farming that we have estimated in almost all cases is in addition to mortality that is also acting on the control populations . In most cases , control populations were also experiencing decreases in marine ( and sometimes freshwater ) survival , for reasons that are only partially understood . At the same time , fishing mortality has been reduced or eliminated in many areas , which may have partially masked high mortalities associated with aquaculture . A key assumption in this study is that exposed and control areas do not differ in a systematic way across regions . We have identified three possible ways that exposed and control sites could differ systematically: first , salmon farms could be established only in areas where wild stocks have already collapsed; second , salmon farms could be established in areas where habitat is more disturbed by human activities; or , third , climate factors could differ between the exposed areas and the controls in a systematic way . Declines in control and exposed salmonid populations preceded the growth of the salmon aquaculture industry in some regions , but inspection of the data used do not indicate that salmon populations in the majority of our regions had declined dramatically in the exposed areas only , before the start of salmon farming ( averaged returns data are shown in Figure 1 ) . In regions such as Scotland , where declines precede the start of salmon farming , the strong aquaculture effect estimated reflects a faster decline in exposed populations concurrent with the growth of salmon farming . Areas that we consider exposed do not seem to be more developed than control areas in general . In the Atlantic , most areas have been highly altered by human activities for hundreds of years , but there is no obvious difference between the control and exposed groups in this regard . In British Columbia , all areas considered are very remote , and the main type of anthropogenic disturbance in rivers would be forestry . Comprehensive forestry records at the watershed scale are not easily available , but logging in British Columbia's Central Coast is extensive , both historically and recently [14] . It should be noted that the comparisons in British Columbia include large numbers of rivers ( > 80 rivers in each case ) , so differences in anthropogenic effects would have to hold over many watersheds to explain the effects we estimate . Finally , it is also very unlikely that our results are due to a climate driven trend in which more southerly populations show stronger declines than populations to the north . Although our exposed populations are to the south of control populations in three of five regions , differences in latitude are small . In New Brunswick , the control populations are to the north of the exposed populations , but by less than 200 km , and the headwaters of some of the exposed populations are adjacent to those of the controls . In Newfoundland , the difference in latitude between exposed and control populations is similarly small . In British Columbia , the control populations are also to the north , but by less than 300 km . Also , Mueter et al . [15] found that pink and coho salmon from all of the British Columbia populations we have examined respond similarly to large-scale climate trends . Thus , the pattern we found in this study does not seem attributable to a systemic difference between the control and exposed areas . We estimated higher impacts on populations in the Atlantic than those in British Columbia , possibly because Atlantic salmon populations are conspecific with farmed salmon , and therefore susceptible to genetic effects from interbreeding with escaped farm salmon , in addition to disease or other impacts . Estimated impacts in British Columbia may also be lower because we aggregated over large numbers of populations for pink , chum , and coho salmon , because estimates of fishing mortality were only available at a very coarse scale . The individual populations may vary in their exposure to salmon farms . The large apparent impact of Atlantic salmon farming on Irish sea trout , in contrast , can not be explained by interbreeding . In the mid-western region of Ireland ( the exposed region ) , the total rod catch decreased from almost 19 , 000 sea trout in 1985 to 461 in 1990 [16] . In the few rivers where data were available , catch declines could not be explained by reduced effort [16] . Welsh sea trout catches ( the controls ) have remained relatively constant during the same time period , whereas fishing effort has decreased considerably [17] . Sea trout ( anadromous brown trout ) might be expected to experience higher mortalities , because they spend lengthy periods in coastal areas near salmon farms , relative to Atlantic salmon , thus being exposed to disease or parasites for a longer time [18] . The time period over which we are estimating impacts of aquaculture includes the establishment of the industry in each region . Improvements in management as industries mature may explain our finding that impacts of salmon farming on wild salmon do not increase linearly with the tonnage of farmed salmon . Better management should decrease the impact of salmon farming on a per tonne basis , although such improvements may not be able to keep pace with the growth of the salmon farming industry . The estimated reduction in survival of wild salmonids is large , and would be expected to increase if aquaculture production increases .
We analysed data for five species of wild salmonid in five regions: Ireland and Wales , Scotland , Newfoundland ( Canada ) , New Brunswick ( Canada ) , and British Columbia ( Canada ) . There are three further regions with both wild salmonids and salmon aquaculture for which we could not carry out analyses: Norway , the west coast of Vancouver Island ( Canada ) , and Maine ( United States ) . We were unable to carry out analyses for Norway for three reasons . First , salmon farming in Norway is so widespread [21] that it was difficult to establish controls . Second , the adult population in many rivers has been found to contain over 50% aquaculture escapees [22] , making trends in returns to rivers difficult to interpret . Third , there are confounding effects from acidification and disease [23 , 24] . For the west coast of Vancouver Island , it was not possible to obtain aquaculture production data by region over time , and Maine was not included because of a lack of nearby wild populations to serve as controls . Most populations that we considered to be exposed breed in rivers that discharge into bays or channels containing at least one salmon farm . Others breed in rivers flowing into bays without salmon farms very close to areas containing many farms . Salmon from control rivers are very unlikely to pass by salmon farms early in their life cycle , due to the direction of their migration . However , some controls may be relative , in the sense that salmon may pass by farms from a considerable distance , later during their migrations . This would tend to be conservative with respect to our study , since we would then have to detect local effects that are additional to any impacts from distant farms . Data from scientific surveys , e . g . , counting fences , were used if possible; for Scottish salmon and Irish and Welsh sea trout , only catch data were available , so results are given for only the impacts on returns ( not survival ) . We compared rod catches of sea trout in Ireland's Western Region to rod plus in-river fixed engine catches in Wales , from 1985 to 2001 ( there are no fixed engine fisheries directed at sea trout in Ireland ) . Salmon farming is concentrated in the Western Region ( Connemara area ) of Ireland , but does occur in other parts of the country [25] . Based on farm locations [25] , it was estimated that all rivers considered exposed are located less than 50 km from a salmon farm , but most will enter the ocean less than 30 km from a salmon farm . There is no salmon farming in Wales . There were 16 rivers in Western Ireland considered exposed: Athry , Bhinch ( Lower ) , Bhinch ( Middle ) , Bhinch ( Upper ) , Burrishoole , Costello , Crumlin , Delphi , Erriff , Gowla , Inagh , Inverbeg , Invermore , Kylemore , Newport , and Screebe [16] . The following 32 Welsh rivers served as controls: Aeron , Afan , Arto , Cleddau , Clwyd , Conwy , Dee , Dwyfawr , Dwyryd , Dyfi , Dysynni , Glaslyn , Gwendreath , Gwyrfai , Llyfni , Lougher , Mawddach , Neath , Nevern , Ogmore , Ogwen , Rheidol , Rhymney , Seiont , Taf , Taff , Tawe , Teifi , Tywi , Usk , Wye , and Ystwyth [26 , 27] . Trout caught and released are included in catch data from both countries . Only catch estimates were available for most of these rivers . Recruitment could not be derived , because anadromous brown trout interbreed with freshwater resident trout , about which very few data are available , so this stock was only included in the returns modeling ( not survival ) . Farmed salmon production for all of Ireland was used in modeling [28] , because the majority of farms are in the region where the exposed populations breed . This will tend to have a conservative effect , resulting in a lower estimate of the impact of aquaculture , per tonne of salmon farming . We compared marine plus rod catches of Atlantic salmon from the east coast of Scotland to catches from the west coast of Scotland for the years 1971 to 2004 . Salmon farms appear to be located in the majority of bays on the west coast of Scotland in well over 300 sites ( http://www . marlab . ac . uk/Uploads/Documents/fishprodv9 . pdf ) , so all salmon from rivers on this coast were considered exposed . There is no salmon farming on the east coast , so salmon from east coast rivers were controls . For each coast , a single time series of total catch was used in modeling . Marine catch records were from the International Council for the Exploration of the Sea ( ICES ) Working Group on North Atlantic Salmon [28] and rod catch records were from Fisheries Research Services of Scotland ( J . MacLean , personal communication ) . Rod catches included salmon caught and released . These data were only used in modeling returns . Farmed salmon production for all of Scotland was used in modeling [28] , because regional production data were not available . We also used counts of Atlantic salmon of all ages returning to rivers from 1960–2001 in Scotland from Thorley et al ( 2005 ) [29] . The fish counters are maintained by Fisheries Research Services or by Scottish and Southern Energy plc . There were two exposed populations . One is from the Awe Barrage , which empties into a bay with numerous salmon farms . The other is from the Morar River , which is less than 20 km from the nearest salmon farm , in an area of the coast with many farms [8] . Salmon from the control rivers ( on the east coast ) do not pass by salmon farms in Scotland because of the direction of their migration routes [30] , unless they approach the Norwegian coast . There were ten control populations from the following rivers: Aigas , Beanna , Torr Achilty , Dundreggan , Invergarry , Logie , Westwater , Cluni , Erich , and Pitlo . Farmed salmon production for all of Scotland was used in modeling [28] because regional production data were not available . Estimates of marine survival to one sea winter for hatchery ( and two wild ) Atlantic salmon populations from Ireland and Northern Ireland ( 1980–2004 ) were collected and reported by the ICES Working Group on North Atlantic Salmon [28] . Because only survival estimates are provided , these data were only used in the survival analysis . Salmon from hatcheries on the Screebe , Burrishoole , Delphi , and Bunowen Rivers were considered exposed . Populations from hatcheries on the Shannon , Erne , Lee , Bush , and Corrib Rivers , plus wild populations from the Bush and Corrib Rivers were used as controls . Production data were not available on a regional basis , so national values [28] were apportioned to bays into which exposed rivers empty by assuming that 30% of national production is in the Kilkieren Bay , 10% is in Clew Bay , 5% is in each of Killary Harbour and Ballinakill Bay . These proportions are based on maps of salmon farm locations from the Irish Marine Institute [25] , and they approximately match stock numbers collected by the Central Fisheries Board in the years for which stock numbers are available ( P . Gargan , personal communication ) . Years in which each bay was fallowed were obtained from the Central Fisheries Board ( P . Gargan , personal communication ) , and in these years , the fallowed bays are assigned a production of zero . All exposed rivers empty into bays with salmon farms [25] , while control rivers are at least 55 km away from the nearest farm . Two data sets from Newfoundland were examined—marine survival estimates of wild Atlantic salmon from four rivers from 1987 to 2004 were used in the survival analysis , and grilse returns to 21 rivers from 1986 to 2004 were used in the returns modeling [31] . Salmon farming in Newfoundland is confined to Bay d'Espoir on the south coast [32] ( http://www . fishaq . gov . nl . ca/aquaculture/pdf/aqua_sites . pdf ) . Only the Conne River ( in Bay d'Espoir ) was considered exposed; the Little River ( also in Bay d'Espoir ) was excluded because it has been regularly stocked [31] . The Exploits and Rocky Rivers were also removed from the analysis because of stocking [33] . This left three control rivers for the survival analysis: the Campbellton River , the Northeast Brook ( Trepassey ) , and Western Arm Brook . For the returns analysis , there were 18 control rivers: Campbellton , Crabbes , Fischells , Flat Bay Brook , Highlands , Humber , Lomond , Middle Brook , Middle Barachois , Northeast Brook ( Trepassey ) , Northeast ( Placentia ) , Northwest , Pinchgut Brook , Robinsons , Salmon , Terra Nova ( upper and lower ) , Torrent , and Western Arm Brook . Salmon from control rivers are very unlikely to pass salmon farms because of the direction of their migrations [34] . Farmed salmon production data are from Fisheries and Oceans Canada ( DFO ) Statistical Services [32] . We compared Atlantic salmon returns to six rivers in the Bay of Fundy ( New Brunswick and Nova Scotia , Canada ) to returns to four rivers from other areas of New Brunswick and Nova Scotia . We grouped the six exposed rivers into three groups and estimated the impact of aquaculture on each group separately , because salmon from these three groups have different degrees of exposure to salmon farming . The three groups of exposed rivers are the inner Bay of Fundy group ( Stewiacke and Big Salmon Rivers ) , the Saint John River group ( Saint John and Nashwaak Rivers ) , and the outer Bay of Fundy group ( St . Croix and Magaguadavic Rivers ) . Salmon farming in New Brunswick is highly concentrated in the Quoddy region of the outer Bay of Fundy ( http://www . gnb . ca/0177/10/Fundy . pdf ) , although some farms are also found along the Nova Scotia coast of the Bay of Fundy . Salmon from control rivers enter into the Atlantic directly ( LaHave River ) or into the Gulf of St . Lawrence ( Restigouche River , Miramichi River , Catamaran Brook ) and do not pass by farms during their migrations . The same controls are used for all comparisons in New Brunswick and Nova Scotia . The estimates of returns to the rivers are published by DFO [28 , 35–40] . Outer Bay of Fundy salmon must pass through an area containing many salmon farms early during their migrations [41] . Although Saint John River salmon enter the ocean in an area without salmon farms , they are known to pass through the region containing many farms early during their migrations [41] . Salmon from inner Bay of Fundy rivers are considered exposed to salmon farming despite being up to 260 km away because of historical information indicating that juvenile salmon from these populations are found during the summer and fall in the area where salmon farms are currently located [42] . However , the evidence that this region is important habitat for inner Bay of Fundy and Saint John River populations is mixed [43] . For this reason , we ran an alternative model with only outer Bay of Fundy populations considered exposed , and all other New Brunswick and Nova Scotia rivers as controls . For all New Brunswick rivers , an estimate of egg deposition was used as an index of spawners , to account for a significant increase in the age of spawners in many rivers over the study period . The number of grilse ( salmon maturing after one winter at sea ) and large spawners ( repeat spawners or salmon maturing after two or three winters at sea ) in each year was multiplied by a river-specific estimate of fecundity for a salmon of that size . Then , the index of spawners in a given year was derived by adding up all the eggs that could produce smolts in a year y , using river-specific ages at smolting from the literature . Returning hatchery-origin spawners are also added to the “spawners” but not to “returns . ” “Recruits” is the number of grilse that return to each river in year y + 1 , so that ( in Equation 1 ) is the number of grilse returning per egg that would have smolted in year y . Estimates of returns to rivers from traps and other surveys were used in the returns analysis . No corrections were made to account for marine fisheries , but marine exploitation has been quite limited since the late 1980s , when salmon farming became a substantial industry [44] . Farmed salmon production data are from DFO Statistical Services [32] . For coho salmon in British Columbia ( BC ) , spawner estimates are based on DFO's escapement database ( NuSEDS ) , which includes estimates of spawning salmon of all species for hundreds of rivers and streams on the BC coast since 1950 ( P . VanWill , DFO Pacific , unpublished data ) . We considered rivers on the east side of the Queen Charlotte and Johnstone Straits to be exposed ( all rivers from Wakeman Sound to Bute Inlet , DFO Statistical Areas [SAs] 12 and 13 ) . All rivers on the BC Central Coast from Finlayson Channel to Smith Inlet ( SAs 7 , 8 , 9 , and 10 ) were included as controls . In the regions considered exposed in BC , all salmon must pass by farms to get into the open ocean , although in some cases , the farms are at the end of long channels down which the salmon migrate ( as far as 90 km in the most extreme case ) . Control populations to the north do not pass by farms , because of the direction of their migration routes [45] . Coverage in the NuSEDS database varies considerably in time and space , as does the quality of the estimates . We changed all indicators of unknown values ( including “none observed” and “adults present” ) to a common missing value indicator . To reduce effects of inconsistent monitoring procedures , only data since 1970 were included in the analysis . All rivers known to be regularly stocked with hatchery salmon or to contain constructed spawning channels were also removed from exposed and control areas , leaving 49 exposed and 70 control rivers . Estimates were combined for each SA , the smallest areas for which catch rates are estimated . This was done by modeling returns to each SA and year , using a generalized linear model with negative binomial errors . The predicted returns for each SA were then used as spawner estimates ( Si , y in Equation 1 ) . To derive recruitment estimates , we followed Simpson et al . ( 2004 ) [46] , applying exploitation rate estimates from Toboggan Creek ( J . Sawada , DFO Pacific , personal communication ) to the controls , and the average of the exploitation rates for Quinsam Hatchery , Big Qualicum Hatchery , and the Black Creek wild indicator population to the exposed stocks . After 1998 , only the estimates from Black Creek were used for exposed stocks . Recruitment estimates for coho were based on the assumption that coho follow a fixed 3-y life cycle . For pink , chum , and coho salmon , aquaculture production estimates include all salmon species farmed in SAs 12 and 13 ( the Queen Charlotte and Johnstone Straits ) from 1990 to 2003 ( H . Russell , BC Ministry of Agriculture , Food , and Fisheries , unpublished data ) . In years when two or fewer companies were raising salmon in either area , estimates were not available . BC salmon farm locations are made available at http://www . al . gov . bc . ca/fisheries/licences/MFF_Sites_Current . htm . Estimates of pink salmon spawner abundance were derived in the same manner as described above for coho salmon . “Returns” are spawners plus catch for a given year , assuming a fixed two year life cycle . The same regions were considered exposed , but because enumeration varies by species , there were only 36 exposed rivers from SAs 12 and 13 ( from Wakeman Sound to Bute Inlet ) included . Wood et al . ( 1999 ) [47] consider the pink salmon catches in SAs 8 , 9 , and 10 to consist mainly of salmon returning to those areas ( respectively ) , so catch data from DFO [48] were used in each of these SAs . Area 7 was excluded from the survival analysis because catches for SA 7 are difficult to estimate due to the adjacent regions being much larger [47] , leaving 47 control rivers from Burke Channel to Smith Inlet . For Queen Charlotte and Johnstone Straits ( the exposed areas ) , DFO does not estimate catches at the level of individual SA . To obtain approximate returns to each exposed SA , we found the proportion of total escapement to the Straits that was in our dataset ( i . e . , regularly enumerated rivers on the east side of the Straits without a major hatchery or constructed spawning channel ) and assumed the same proportion of the total catch would be returning to those rivers ( i . e . , assumed equal catchability across stocks ) . For odd years , we used estimates from the Pacific Salmon Commission ( B . White , unpublished data ) of the catch of pink salmon in Johnstone and Georgia Straits that were not returning to the Fraser River . In even years , there is no pink salmon run on the Fraser River , so total returns to the Straits could be used . For chum salmon , we used estimates of returns ( i . e . , before exploitation ) and spawners to large coastal areas [49] . Chum from the east side of Queen Charlotte and Johnstone Straits , from Wakeman Sound to Bute Inlet ( SAs 12 and 13 ) were considered exposed to salmon farming , while chum from the Central Coast from Bute Channel to Seymour Inlet ( SAs 8–11 ) were considered controls . Estimates were available as a single time series for the exposed area , and a time series for each SA for the controls . An index of recruits per spawner was generated by lining up returns with spawners according to age distributions given in Ryall et al . ( 1999 ) [50] , to 1998 , and then the average values from 1988–1998 for the subsequent years , to 2003 . | The impact of salmon farming on wild salmon and trout is a hotly debated issue in all countries where salmon farms and wild salmon coexist . Studies have clearly shown that escaped farm salmon breed with wild populations to the detriment of the wild stocks , and that diseases and parasites are passed from farm to wild salmon . An understanding of the importance of these impacts at the population level , however , has been lacking . In this study , we used existing data on salmon populations to compare survival of salmon and trout that swim past salmon farms early in their life cycle with the survival of nearby populations that are not exposed to salmon farms . We have detected a significant decline in survival of populations that are exposed to salmon farms , correlated with the increase in farmed salmon production in five regions . Combining the regional estimates statistically , we find a reduction in survival or abundance of wild populations of more than 50% per generation on average , associated with salmon farming . Many of the salmon populations we investigated are at dramatically reduced abundance , and reducing threats to them is necessary for their survival . Reducing impacts of salmon farming on wild salmon should be a high priority . |
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The glaucomas comprise a genetically complex group of retinal neuropathies that typically occur late in life and are characterized by progressive pathology of the optic nerve head and degeneration of retinal ganglion cells . In addition to age and family history , other significant risk factors for glaucoma include elevated intraocular pressure ( IOP ) and myopia . The complexity of glaucoma has made it difficult to model in animals , but also challenging to identify responsible genes . We have used zebrafish to identify a genetically complex , recessive mutant that shows risk factors for glaucoma including adult onset severe myopia , elevated IOP , and progressive retinal ganglion cell pathology . Positional cloning and analysis of a non-complementing allele indicated that non-sense mutations in low density lipoprotein receptor-related protein 2 ( lrp2 ) underlie the mutant phenotype . Lrp2 , previously named Megalin , functions as an endocytic receptor for a wide-variety of bioactive molecules including Sonic hedgehog , Bone morphogenic protein 4 , retinol-binding protein , vitamin D-binding protein , and apolipoprotein E , among others . Detailed phenotype analyses indicated that as lrp2 mutant fish age , many individuals—but not all—develop high IOP and severe myopia with obviously enlarged eye globes . This results in retinal stretch and prolonged stress to retinal ganglion cells , which ultimately show signs of pathogenesis . Our studies implicate altered Lrp2-mediated homeostasis as important for myopia and other risk factors for glaucoma in humans and establish a new genetic model for further study of phenotypes associated with this disease .
The multi-factorial nature of many ocular diseases poses a major challenge in understanding their molecular etiology and in engineering animal models to study mechanisms of pathology . Macular degeneration , myopia , and glaucoma are examples of prevalent and disruptive complex ocular diseases . While characterization of complement factor genes has provided insight into most cases of macular degeneration [1] , no major genetic pathway has been found to underlie myopia or glaucoma . Myopia is the most common human ocular disorder worldwide and is caused by abnormal growth of the eye resulting in refractive error [2] , [3] . Myopia also increases risk for other visual impairing diseases including glaucoma [4] . The glaucomas are a heterogeneous group of progressive blinding disorders that result from damage to retinal ganglion cells and their axons [5] . Important risk factors for glaucoma include elevated intraocular pressure ( IOP ) , age , family history , and myopia [6] . Although traditional human genetic analysis has been limited in identifying causative genes for complex disorders , mutational screens in animals can provide insights into disease etiology . Recently , progress has been made on establishing the zebrafish model to study phenotypes associated with glaucoma . From a forward-genetic perspective , zebrafish offer a major advantage in studying complex disease , in that large pedigrees can be efficiently generated with moderate space and time requirements . Through a mutational screen for adult ocular defects , we identified a complex mutant , bugeye , that manifests multiple adult-onset phenotypes associated with glaucoma including enlarged eyes with myopia , elevated IOP , and damage to retinal ganglion cells . Using linkage analysis we discovered non-sense mutations in low density lipoprotein receptor-related protein 2 ( lrp2 ) for bugeye , as well as within a non-complementing allele . Lrp2 is a large transmembrane protein of the LDL-receptor related protein ( Lrp ) family [7] . Lrp2 participates in receptor-mediated endocytosis and has a host of identified ligands including signaling molecules like Sonic hedgehog and Bone morphogenetic protein 4 , vitamin and hormone binding proteins , apolipoproteins , among others [8] . Lrp2 is expressed on cells of the renal proximal tubule , choroid plexus , developing neural tube , intestine , thyroid , and inner ear . Within the eye , Lrp2 is expressed on retinal pigment epithelial cells as well as ciliary epithelial cells [7]–[9] . In humans , mutations in LRP2 result in Donnai-Barrow syndrome [10] , a rare disease characterized by a spectrum of phenotypes including agenesis of the corpus collosum , diaphragmatic hernia , sensonurial deafness , hypertelorism , buphthalmia ( enlarged eye globes ) and high myopia [11] , [12] . As the eyes of bugeye zebrafish are also highly myopic , Lrp2 may be critical in regulating emmetropic eye growth across species . The strong association of myopia with glaucoma [13] makes bugeye an attractive model to study the genetic and molecular pathways involved in these ocular diseases .
The bugeye zebrafish mutant was identified in a three-generation forward-genetic screen for adult ocular abnormalities . Mutants were easily identified by 6 months as their eyes were visibly enlarged ( Figure 1A , 1C ) . Interestingly , the degree of eye enlargement often varied between the two eyes of a single fish ( Figure S1A–S1G ) . Occasionally the phenotype presented only in one eye , and the other eye remained normal in size ( Figure S1B ) . To address whether ocular enlargement in mutants might represent a retinoblastoma phenotype , we analyzed eyes by histology . Instead of obvious cellular overgrowth we found that the retina was notably thinner in all layers ( Figure 1B , 1D ) . As buphthalmia is often associated with elevated IOP , we used servo-null electrophysiology to measure the eye pressures in mutants and wild-type siblings [14] . Compared to wild-type fish , bugeye mutants consistently showed elevated IOPs ( Figure 1I ) . In addition , the rare fish that presented the phenotype in a unilateral manner had normal pressure in the wild-type sized eye and elevated pressure in the enlarged eye ( Figure S1H ) . IOP is maintained by the balance of aqueous humor production and drainage . Like mammals , aqueous humor in zebrafish is produced in the ciliary epithelium and drained at the iridocorneal angle . However , unlike mammals where drainage occurs circumferentially throughout the angle region , aqueous outflow for zebrafish is facilitated through a discrete ventrally localized canalicular network [15] . Histology did not reveal obvious disorganization in either the dorsal ciliary epithelium ( Figure 1E , 1G ) or in the ventral canalicular outflow network ( Figure 1F , 1H ) . However , the ciliary epithelium occasionally appeared mildly hypertrophied ( Figure 1G , arrow ) and the angle region of mutants was more prone than wild-type specimens to separation between the iris and corneal tissues during histological preparation ( Figure 1H , asterisk ) . Additional characterization of these regions at the time of phenotype onset confirmed these observations ( Figure S2 ) . The original bugeye mutants presented in the third generation of a three-generation screen , suggesting the mutation was recessive . However , only 3 fish out of a family of 28 showed the phenotype and therefore the penetrance was lower than predicted for a simple recessive mutation ( ∼9% vs . 25% predicted ) . Moreover , incrossing 2 of those original mutant fish resulted in 25 progeny that showed large eyes and 18 that never developed the phenotype . Again , if the mutation was a simple recessive mutation , incrossing should have resulted in all progeny showing the phenotype . To better characterize inheritance and establish recombinant mapping panels to genetically position the mutant locus , we set up a series of test-crosses . Table 1 summarizes the results of incross , outcross and backcross matings over multiple generations and genetic backgrounds ( Table 1 ) . The data indicate that the bugeye phenotype is most likely caused by a single recessive mutation , but like many multi-factorial complex diseases , the penetrance was modified by common wild-type backgrounds and/or by non-genetic factors . To map the mutant locus , progeny from single pair backcross matings were used for whole-genome linkage analysis . Co-segregation for markers on chromosome 9 and the mutant phenotype was found ( Figure 2A ) . Informatively , no other linkage in the genome was noted , consistent with the single recessive causative mutation hypothesis . Public databases revealed that the lrp2 gene was within the critical recombinant interval . Given the similarity of the bugeye phenotype to those caused by LRP2 mutations in humans , we sequenced this candidate gene . Analysis of lrp2 cDNA from bugeyemw1 mutants revealed a T to A conversion that changes a cysteine to a stop codon at predicted amino acid position 23 ( C23X ) ( Figure 2B , 2C ) . Through an independent genetic screen we identified a second large eye mutant that like the bugeyemw1 allele , presented in adulthood and showed reduced penetrance . Intercrosses between this mutant ( allele p5bnc ) and bugeyemw1 were non-complementing and suggested that lrp2 may also be affected in the p5bnc mutant . Indeed , sequencing of p5bnc cDNA revealed a separate non-sense mutation , also very early in the coding region of lrp2 ( bugeyep5bnc , Q413X ) ( Figure 2B , 2C ) . To test whether somatic reversion or alternate splicing around the non-sense mutations might underlie the reduced penetrance or variability often observed between the left and right eyes , we sequenced ocular cDNA in affected and unaffected eyes . However , we did not find evidence of mosaicism or alternate splicing surrounding the mutations , suggesting the penetrance and phenotype variability is influenced by other genes , epigenetics , and/or unpredictable changes in physiology which affects the phenotypes . In mammalian eyes , the multi-ligand receptor Lrp2 is known to be expressed in the developing and adult retinal pigment epithelium ( RPE ) and ciliary epithelium . We therefore analyzed Lrp2 expression in wild-type , bugeye mutant larvae treated with phenyl-thio-urea ( PTU ) , which blocks pigmentation and allows visualization of potential RPE immunoreactivity . As predicted , strong immunoreactivity was found in wild-type RPE and ciliary epithelium . Other regions of expression noted in wild-type fish included forebrain ventricles , regions of the inner ear , proximal pronephros , and gut epithelium ( data not shown ) . All Lrp2 immunoreactivity was completely absent in mutant larvae for both bugeye alleles ( Figure 2D , 2E and data not shown ) . We next developed genotyping protocols for both mutant alleles and confirmed that large-eyed fish never showed wild-type lrp2 genotypes ( Figure 2F , 2G ) . We also used this assay to test whether the reduced penetrance of the ocular phenotype could be explained by increased larval lethality of lrp2 mutants . However , we found that all genotypes were represented in Mendelian ratios in the adult progeny of either heterozygous or backcross pairwise matings , despite the fact that some homozygous mutants never developed the enlarged eye phenotype . Cumulatively , these data indicate that lrp2 mutations are responsible for the large-eyed phenotype in bugeye and that the reduced penetrance and variability in eye enlargement are due to either common ( yet unknown ) genetic background differences and/or non-genetic factors such as physiological modifiers of the mutation . Having established the causative gene for bugeye , we next investigated the onset of the ocular phenotype and quantified the pathology . To characterize the development of enlarged eyes in bugeye/lrp2 mutants we performed longitudinal studies tracking wild-type and mutant fish from 1-12 months . The zebrafish eye reaches its final adult anatomy by approximately 1 month of age [16] . Because overall growth rates can vary between equally aged fish — even within the same tank — we used the ratio of eye size to body length ( E:B ) to determine the relative size of the eye . This ratio remained constant in wild-type fish , allowing comparison of relative eye size between individuals regardless of the overall growth of the fish . Although this ratio remained flat as wild-type fish grew , the E:B ratio increased over time for most lrp2 mutants ( Figure 3A ) . Despite individual variability , the average body length growth rates between wild-type and lrp2 mutant fish were indistinguishable ( Figure S3 ) . For the E:B ratio , no wild-type fish had a value greater than 0 . 05 ( most fell between 0 . 02 and 0 . 04 ) , and mutants with visibly enlarged eyes had an E:B ratio ≥0 . 07 . The onset of large eyes was variable both within shared tanks of siblings and between generations , but a statistically significant difference between mutant and wild-type fish was consistently found at 2 months ( Figure 3A and data not shown ) . In general , lrp2 mutant eyes become visibly enlarged in adults between 2–6 months and eye growth often plateaus between 8–12 months . Histological cross-sections of lrp2 mutant eyes revealed that the region with the greatest increase in size was the depth of the vitreous chamber ( Figure 3B ) . This suggests that in large-eyed mutants , the retina lies behind the point at which the lens focuses light and the eyes are therefore myopic . We calculated the relative refractive error ( RRE ) , an estimate for the degree of myopia , for lrp2 mutant eyes relative to wild-type eyes at 1 and 2 months using retina and lens radius measurements collected from histological sections . Using the RRE equation , a myopic eye has a negative value and a hyperopic eye is positive ( Figure 3C; Methods ) . We found that lrp2 mutant eyes are slightly myopic at 1 month , but become significantly more myopic by 2 months ( Figure 3D ) . Histology suggested retinal cell density was affected in lrp2 mutant eyes ( Figure 4 ) . At 1 month , before eyes of mutant fish were visibly enlarged , there was a small reduction in retinal cell density as compared to wild-type fish ( Figure 4A , 4B ) . By 2 months , when the onset of large eyes had occurred in some mutants but not in others , there was a significant difference in cell density in all layers of the retina ( Figure 4C , 4D ) . As expected , at 6 months when relative eye size was greater overall , there was a further decrease in cell density ( Figure 4E , 4F ) . When considering retinal cell density for each layer as a function of relative eye size ( as measured by the ratio of the retinal cross-section length to body length ) , we found that for mutants , the relation between neuron density and relative eye size decreased in a linear manner ( Figure 5A ) . The same was true when considering just the absolute size of eye ( as measured by retinal cross-section length , Figure 5B ) . Interestingly , there was an increase in photoreceptor density in larger eyes for wild-type fish ( Figure 5B ) . When considering cell density for wild-type and mutant eyes of the same absolute size , but of different ages in order to match size , density was still reduced in lrp2 mutant fish ( Figure 5C ) . For this comparison we evaluated retinal cell density of 6-month old wild-type fish and 2-month old lrp2 mutant fish , each that had retinal lengths that fell between 2–3 mm . Importantly , there was no significant change in cell density for the retinal ganglion cells layer between 2–6 months in wild-type fish . For the inner nuclear and photoreceptor layers , there was a small , but significant change ( ANOVA , p<0 . 001 ) , where the cellular densities increased with age . Together , these data suggest that the reduced neuron density seen in lrp2 mutant retinas is not simply due to an acceleration of normal ocular growth . Despite the reduced cell density in mutant eyes , total retinal cell number was estimated to be greater than wild-type , owing to the much larger eye size overall . We estimated total retinal cell numbers by considering the retina area as that of the surface area for half a sphere and extrapolated total cell numbers using density data . These calculations showed that mutant eyes with E:B ratios >0 . 07 had significantly increased numbers of total neurons . More directly , analysis of DNA content , which is proportional to total cell number , confirmed that large-eyed mutant fish ( EB ratio >0 . 07 ) had more cells , even though retinal cell density was much lower ( data not shown ) . The altered retinal cell density in lrp2 mutants could be due to either insufficient cell generation to match scleral growth and remodeling , or through increased cell death . To address these possibilities we analyzed by immunofluorescence the number of proliferating cells within the ciliary margin zone ( using Minichromosome maintenance homolog 5 , Mcm5 antibodies ) and the number of apoptotic cells across the retina ( using activated-Caspase3 antibodies ) . Mcm5 is required for DNA replication and is expressed throughout the cell cycle in all proliferating cells , but the protein is rapidly lost in post-mitotic cells . Proteolytic cleavage of Caspase3 , recognized by the activated-Caspase3 antibody , is one of the last steps in the apoptosis cascade and marks cells committed to die in a number of contexts , including glaucoma . At 1 month , proliferation in both wild-type and lrp2 mutant retinas was primarily confined to the ciliary marginal zone , a stem cell niche where ongoing proliferation from multipotent elongated neuroepithelial cells is known to occur in fish [17] ( Figure 6A–6C ) . For each genotype , occasional Mcm5-positive cells were also located in the inner nuclear layer , which have previously been shown to be rod progenitor cells in teleost fish [18]–[20] . At 2 months , cell counts indicated a reduction in Mcm5-positive cells per CMZ niche in bugeye fish , suggesting maintenance of stem cells was inadequate to match eye globe growth ( Figure 6D–6F ) . Consistent with this observation , a role for Lrp2 in maintaining neuronal stem cells of the adult mouse forebrain has been recently described [21] . Similar to analysis of proliferation , cryosections of wild-type and lrp2 mutant retinas were used to investigate cell death . However , very few dying cells were noted in sections of retina from either condition . Similar results were obtained using the TUNEL assay to characterize dying cells . We therefore used activated-Caspase3 immunoreactivity on control and lrp2 mutant flat-mounted retinas to observe all neurons from individual samples . Even by flat-mount analysis , there was little apoptosis up to 6 months of age ( Figure 6G ) , although at these times bugeye mutants showed trends towards increased numbers of activated-Caspase3-positive cells . By 12 months , apoptosis in bugeye retinas was significantly elevated . We also noted that activated-Caspase3 immunoreactivity from all ages was restricted to the retinal ganglion cell layer ( Figure 6H ) . It is possible , however , that some cells , including those outside of the ganglion cell layer die by Caspase3- and TUNEL-independent mechanisms . Overall , these data indicate that initially , as lrp2 mutant eyes expand , proliferation is not sufficient to maintain proper cell density and later , perhaps following mechanical stress imposed by retinal stretch , retinal ganglion cells begin to die . In the following studies we evaluated the onset of retinal ganglion cell stress and pathology . Relative expression levels of twelve genes known to be up-regulated in animal models of retinal ganglion cell injury was surveyed by quantitative RT-PCR . This panel of markers included three transcripts expressed in microglia ( aif1l , [22] , [23]; apoeb , [24] , [25]; arg1 , [26] , [27] ) , one expressed in Müller glia and astrocytes ( gfap , [28] , [29] ) , and eight expressed in retinal ganglion cells ( atf3 , [30] , [31]; c1q , [32]–[34]; c-jun , [31] , [35] , [36]; gap43 , [37] , [38]; klf6a , [31] , [39]; socs3a and socs3b , [31] , [40] , [41]; thy1 , [42] , [43] ) . Analysis was conducted on cDNA isolated from pooled 1-month-old retinas , a time just prior to when mutant eyes were measurably enlarged . We chose this early time-point to avoid measuring changes that might simply reflect significant alterations in cell proportions and density . With this assay , we found induction primarily of transcripts associated with retinal ganglion cells , but not for the glia-associated genes ( Figure 7A ) . To investigate whether the markers of retinal ganglion cell stress correlated with optic nerve pathology , we first compared sagittal sections of wild-type and lrp2 mutant optic nerve heads from 6-month-old fish by light microscopy . We then analyzed cross-sections of wild-type and lrp2 mutant optic nerves , just posterior to the optic nerve head from 7- and 12-month-old fish by transmission electron microscopy ( TEM ) . In zebrafish , like other teleost fish as well as some rodents , the optic nerve head is comprised of an astroglial lamina without obvious elastin-collagen rich laminar plates as observed in primates [44]–[46] . In addition , as the optic nerve exits the fish eye , it is initially unmyelinated , like that in humans and most mammals [47]–[50] . Histology of the optic nerve head did not reveal excavation or cupping in lrp2 mutants , but did indicate mutant nerves were larger , consistent with increased total numbers of retinal ganglion cells in the large-eyed fish ( Figure 7B ) . Optic nerve cross-sections for TEM were collected distal to the exit point from the eye within the myelinated region of the optic nerve , which is adjacent to the site of axonal injury in glaucoma [51]–[53] . Nerve damage was scored as 1 ) degenerating axons , as noted by electron-dense appearance , 2 ) axons having an unraveled myelinated sheath , or 3 ) space left behind by a shrunken and degenerating axon . At both ages , examples for each type of pathology were found in wild-type and lrp2 mutant optic nerves ( Figure 7C ) . Surprisingly , when total counts were normalized to area ( mm2 ) there were no differences between genotypes or ages ( Figure 7D ) . Because the ultrastructural signature of degenerating axons following a crush injury is relatively short-lived in the optic nerve tract of teleost fish as compared to mammals [54] , we utilized a genetic tool to label damaged and regenerating axons over a longer period of time [55] . We crossed Tg ( 3 . 6Frgap43:GFP ) mil1 transgenic fish with lrp2 homozygous mutants and then used the resulting progeny to backcross with non-transgenic lrp2 mutant fish . This breeding scheme resulted in families with equal proportions of lrp2 heterozygous and homozygous mutant fish carrying single insertions of the 3 . 6Frgap43:GFP transgene . This transgene contains 3 . 6 kb of regulatory sequence ( 5′ flanking region and first intron ) from theTakifugu rubripes gap43 locus driving GFP . Importantly , in these transgenic fish , GFP is expressed in axons following injury [55] . For our analysis , we compared large-eyed lrp2 homozygous mutant fish ( >0 . 07 E:B ratio ) to normal-eyed heterozygous siblings ( Figure 8K–8T ) . In all large-eyed mutant fish we observed strong activation of GFP in a sub-set of retinal ganglion cells . In the majority of mutant retinas examined ( 6 of 6 at 6 months , Figure 8P–8T; and 10 of 12 at 12 months , data not shown ) , there was a characteristic axon ‘wandering’ and ‘circling’ around the optic nerve head . This axon phenotype , where GFP-positive axons approached the optic nerve head in a disorganized and circuitous fashion , was never observed in retinas from age matched lrp2 heterozygotes ( Figure 8K–8O ) or from 12-month wild-type fish that carried the 3 . 6Frgap43:GFP transgene ( data not shown ) . The transgene was activated with variability at 2 months in both wild-type or lrp2 mutant fish ( Figure 8A–8J ) , but the wandering axon phenotype was only rarely observed in mutants at this early timepoint . Weak expression of the transgene was noted in the nerve fiber layer of non-mutant retinas , consistent with the ongoing neurogenesis of zebrafish . In addition , older wild-type fish occasionally showed stronger GFP-positive axons , suggesting sporadic age-related degeneration . In wild-type eyes , all of the low-GFP expressing axons , as well as the occasional high-GFP expressing axons , exited the eye directly without wandering or circling the optic nerve head like those of mutants . To address whether the chronic stress conditions of lrp2 mutants differ from acute injury , we performed optic nerve crushes on adult gap43:GFP fish . At 6 days post-crush there was significant up-regulation of GFP across the retina ( Figure S4 ) . By 5 weeks post-crush , when axons had regrown [56] , there was only an occasional wandering axon . Most samples following nerve crush , however , showed accurate and direct axon targeting through the optic nerve head . By 11 weeks post-crush , there was significant reduction in transgene activation and no axons showed wandering or circling at the optic nerve head like age-matched lrp2 mutants . This comparison highlights the differences between the chronic stresses caused by the lrp2 mutation versus the acute , crush injury model , in which the genetic model results in changes at the optic nerve head that are not evident in the post-nerve head crush paradigm .
A major challenge with research on either myopia or glaucoma is identifying genetic lesions that impact the diseases . Recently , genome wide association studies for both diseases revealed non-coding associated changes , but the predicted effects on phenotypes were small and the actual gene products affected by the intergenic alterations have yet to be identified [57]–[59] . In addition to defining genetic susceptibilities for glaucoma , there is a need to understand and model how other risk factors like age , raised IOP , and myopia itself affect the onset , severity , and progression of neuropathology . In our studies we identified non-sense mutations in zebrafish lrp2 that lead to phenotypes that are known risk factors for glaucoma . These phenotypes included increased IOP , enlarged eye globes with significant refractive errors , decreased retinal neuron density , activation of retinal ganglion cell stress genes , and distinct axon pathology at the optic nerve head . The zebrafish lrp2 mutants have similar heritable phenotypes to the black moor goldfish [60] , [61] and the RCS;rdy- rat [62] . It will be interesting to see if lrp2 or pathway genes are affected in either of those models . Similarly , it is possible that alterations to genes that control pathways affected by loss of Lrp2 might influence myopia or forms of glaucoma . To date , however , polymorphisms in Lrp2 have only been linked with urate and cholesterol levels in serum [63] , [64] and the molecular and cellular pathways affected by loss of Lrp2 that impact the ocular phenotypes remain uncharacterized . In general , Lrp2 functions in regulation and homeostasis of multiple bioactive molecules including vitamins , hormones , nutrients , and growth factors through localized tissue delivery or reuptake by epithelia . In knowing the affected gene , the zebrafish mutants hold promise in shedding light on how de-regulated signaling and homeostasis affect phenotypes such as elevated IOP or excessive eye growth . While it is tempting to speculate that the excessive eye growth in lrp2 mutants is due to the elevated IOP , our studies do not rule out the possibility that these two phenotypes are distinct . In fact , the only two Donnai-Barrow patients who have had their IOPs reported ( each with non-sense mutations in LRP2 ) , showed values in the normal range [65] . Despite normal IOPs , the eyes of the two young siblings were enlarged and showed high myopia . Furthermore , as an endocytic receptor found on the RPE , Lrp2 is an interesting candidate as a direct regulator of emmetropization [66] . Potentially , Lrp2 mediates the availability or transport of signaling molecules from the retina to affect remodeling within the sclera . In this context Lrp2 might be key in facilitating the matching of visual input with axial length of the eye . Nonetheless , relationships between eye pressure and size are established and the elevated IOP in zebrafish lrp2 mutants is likely to be at least contributory to the observed buphthalmia . Consistent with this possibility , in the few mutant fish where the eye phenotype presented in a unilateral manner , IOPs were normal in unaffected eyes , yet elevated in enlarged ones . Indeed , expression of Lrp2 on the ciliary epithelium suggests a direct role in IOP regulation , particularly considering the function of Lrp2 at other sites of fluid regulation . For example in mice , Lrp2 has been shown to regulate glomerular filtration in the proximal tubule of the kidney and in the choroid plexus the receptor modulates homeostasis of cerebrospinal fluid [67]–[71] . A significant characteristic of lrp2 mutant fish is the strong relationship between abnormal eye globe growth , retinal thinning , and activation of retinal ganglion cell stress markers . In this context , lrp2 mutants have value as a genetic model for studying the effects of protracted mechanical stress on retinal ganglion cells , their axons , and the associated glia . As this phenotype relates to glaucoma , it was surprising that mutant fish did not show significantly elevated optic nerve pathology with TEM analysis . It is possible that the stresses induced by lrp2 mutations simply do not reach a threshold to cause ultrastructural pathology . Alternatively , low-grade stress may actually “pre-condition” and promote protective mechanisms in the mutant neurons [72] , [73] . However , the lack of a difference in ultrastructure pathology between mutant and wild-type siblings could also be explained by the surprisingly high number of pathological events noted in the wild-type fish . This perhaps relates to the regenerative capacity of teleosts [56] , [74] and a relaxation of selective pressure to maintain nerve health with normal aging . Through evolution , fish may have lost highly-robust nerve protective mechanisms against age-related stresses , and instead rely on ongoing growth and regeneration to maintain vision , perhaps accounting for the unexpected pathology scored in wild-type optic nerves . In addition , because a higher proportion of the ganglion cell axons in lrp2 mutant fish are in fact younger than those of wild-type siblings ( due to the excessive ongoing generation of neurons in their eyes ) , many of the optic nerve profiles might be expected to in fact look healthier in a relative manner . The modest death of retinal ganglion cells in lrp2 mutants was less surprising . First , extended retinal ganglion cell soma survival , despite axonal damage and dysfunction , is known for the DBA/2 mouse glaucoma model . DBA/2 mice show a pigment dispersion-related glaucoma with elevated IOP [75] , [76] . In young DBA/2 mice , axons at the nerve head often show focal insults with many having dystrophic features [51] . In many aged animals , axons are clearly degenerative [51] . Most retinal ganglion cells , however , survive for extended periods of time and their disconnected proximal ( intra-retinal ) axons take on reactive and stressed characteristics [51] , [77] , [78] . Second , the resilient nature of retinal ganglion cells in teleosts has been well characterized . In fact for goldfish , experimental axotomy or optic nerve crush results in less than 10% death of retinal ganglion cells [79] , and in zebrafish only 20% of the lesioned neurons are reported to die [80] . In contrast , optic nerve axotomy in mammals results in apoptosis of nearly all retinal ganglion cells [81]–[83] . The regrowth of axons in teleosts occurs over a course of weeks and results in correct axon pathfinding and appropriate tectal innervation [84] , [85] . In contrast , in lrp2 mutants , retinal ganglion cells appear to be under prolonged mechanical stress from the stretching and growth of the eye globe . This was evident from the changes in retinal density with eye enlargement and the activation of retinal ganglion cell stress markers . Of interest , axon regrowth through the optic nerve was affected in lrp2 mutants . The wandering and circling phenotype of the gap43:GFP axons in large-eyed mutants is reminiscent of the EphB3-dependent ‘reactive plasticity’ following optic nerve injury in mice [86] , [87] . Regardless , of why bugeye/lrp2 mutants do not show dramatic retinal ganglion cell death , this fact emphasizes that while these fish model initiating risk factors for glaucoma , they do not model the end stages of the disease . Lrp2 mutations in humans and mice are often lethal , but always developmentally relevant , particularly within the nervous system [71] , [88] , [89] . Our analyses of both bugeye alleles indicate Lrp2 is dispensable for survival in zebrafish . Furthermore , we did not detect morphological phenotypes in mutant embryos , similar to the observations following oligonucleotide knock-down of zebrafish lrp2 [67] . The total lack of lethality in zebrafish lrp2 mutants may be due to species differences in respiration , as mice mutants often die from respiratory failure at birth . Alternatively , there may be compensation from other Lrp family members in zebrafish . Compensation from Lrp family members may also explain the lack of obvious developmental defects . More detailed studies of the zebrafish mutant embryos and larvae are warranted to assess whether subtle defects exist . In summary , we have identified mutations in lrp2 that cause adult-onset ocular pathogenesis in zebrafish . While mutants appear normal during larval stages of development , as young adults they develop enlarged eyes with elevated IOP . Over time , retinal cell density becomes significantly reduced due to insufficient proliferation of marginal zone stem cells and increased neuronal cell death . Markers of retinal ganglion cell stress become elevated and damaged and/or regenerating axons at the optic nerve head show a characteristic wandering and circling phenotype . These fish will be valuable for future studies on the signaling and cellular mechanism of myopia and other risk factors for glaucoma .
Wild-type and mutant zebrafish ( Dano rerio ) were maintained at 28°C with a 14 on/10 off light cycle and were feed a standard diet [90] . All animal husbandry and experiments were approved and conducted in accordance with the guidelines set forth by the Institutional Animal Care and Use Committee of the Medical College of Wisconsin . bugeye; lrp2mw1 ( this study ) bugeye; lrp2p5bnc ( this study ) Tg ( 3 . 6Frgap43:GFP ) mil1 [55] lrp2 , HM_754616 aif1l , NM_198870 apoeb , NM_131098 arg1 , XM_001922563 gfap , NM_131373 atf3 , NM_200964 c1q , NM_001005976 c-jun , NM_199987 gap43 , NM_131341 klf6a , NM_201461 socs3a , NM_199950 socs3b , NM_213304 thy1 , NM_198065 Servo-null electrophysiology was used to measure IOPs as described previously [14] . Mapping panels of 6 month adult mutant fish ( obviously enlarged eyes ) were collected from backcross pedigrees . Bulked segregant analysis , using pooled samples of mutant genomic DNA and individual parental DNA , was conducted with simple sequence repeat ( SSR ) markers to establish linkage to Chromosome 9 . For higher-resolution mapping , sequencing of parental genomic DNA in regions associated with the closest linked microsatellite markers was done to find additional SSRs . These new SSRs were then used to refine the critical interval by analyzing single mutant fish . PCR was performed on DNA isolated using the Puregene kit ( Qiagen , Germantown , MD ) from tailfin-clips , using primers designed to amplify the allele specific mutations in lrp2: bug mw1 F: CGTTATTTTCTGTCTAGGTTCAGGTTA , bug mw1 R: GAAAAGAAAAGATTGATACATACGG bug p5bnc F: GTGTGTTTTCTGAAAACTGTCAAGC , bug p5bnc R: CTTTGCAGCTGGTAATGAAAATCCACACCAACAGCGGCTCCTCTGTCCTA . Underlined letter in primer denotes mutant nucleotide , bolded letter denotes a single nucleotide change in the primer to generate a novel restriction site for each allele ( bug mw1: MseI; bug p5bnc AvrII ) . Fish were anesthetized with 0 . 05% Tricaine and body lengths were measured in side-view from the tip of the head to the end of the trunk ( before the caudal fin ) . To measure eye size , anesthetized fish were imaged at a fixed magnification from a dorsal perspective using a Nikon CoolPix995 camera attached to a Leica MZFLIII microscope . These images were imported into Metamorph software ( Universal Imaging Corp , Philadelphia , PA ) , and the area of each eye from the dorsal view was traced using the Region Measurements function . Lens radius ( L ) was measured from histological cross sections; retina radius ( R ) was back-calculated by assuming the retina to be a semi-circle , measuring the length of the retina , and taking that measurement as half the circumference of a circle ( so R = length of retina/π ) . Sections with minimal distortion from processing were used and no attempts to correct for distortions were made . A focal length ( F ) of 2 . 32 x L for the lens was used as in studies with goldfish [91] . RRE was calculated as 1- ( R/F ) . By this calculation , all wild type fish were predicted to be slightly hyperopic ( RRE >1 ) , likely due to fixation artifact . To adjust this , the ratio of ( R/F ) was multiplied by a constant factor for both genotypes at each age ( 1 month , 1 . 15; 2 months , 1 . 18 ) , so that on average , the wild type fish were emmetropic ( RRE = 0 ) . Heads were removed from terminally anesthetized fish and fixed overnight in gluteraldehyde/paraformaldehyde at 4°C , washed three times in PBS , and dehydrated in increasing ethanol solutions ( 50% , 70% , 80% , 90% , 95% , 100% , 100% , 100% ) for 10 minutes each , all at RT . The heads were then infiltrated with propylene oxide for 15 minutes twice , then a 1∶1 mix propylene oxide:epon for 2 hours at RT . An additional equal volume of epon was added to the samples and these were incubated overnight with culture tube caps off so that the propylene oxide would evaporate . Heads were bisected when necessary to fit in block-molds , embedded in epon , and baked for at least 24 hours at 65°C . Semi-thin sections were cut on a Leica RM2255 microtome and stained with 1% Toluidine , 1% Borax . For each eye , 5 non-consecutive sections were imaged from the central retina ( sections with the largest lens diameter ) with a 40X objective on a Nikon E600FN microscope with a Photometrics CoolSnap camera attached . Each image was printed and the nuclei in each layer of the retina were counted . The average of the 5 sections was calculated and represented 1 data point . For sample condition , between 6–12 eyes were scored in this manner . Zebrafish embryos or isolated eyes were fixed overnight at 4°C in 4% PFA ( pH 7 . 4 , in PBS ) , washed three times for 10 minutes in PBS , then infiltrated with increasing concentrations of sucrose ( 15% , 30% ) for 2 hours each at 4°C , followed by overnight incubation in HistoPrep freezing media ( Fischer Scientific , Pittsburgh , PA ) . Cryoprotected embryos were embedded in HistoPrep and flash frozen , sectioned at 10–12 µM and collected on Supercharge Plus slides ( Fischer Scientific ) . Cryosections were allowed to dry on the slide for 1hr at RT , and the edge of the slide was traced with a PAP pen . Slides were rinsed briefly with PBTD ( PBS +1% DMSO +1% Tween-20 ) to rehydrate the tissue , and then incubated in block ( 5% donkey serum in PBTD ) for 2 hours at RT . Primary antibody was diluted in block ( Sheep-anti-Lrp2 1∶1000 , gift from Dr . Thomas Willnow ( Max Delbruck Center , Berlin , Germany ) ) and incubated on slides overnight at 4°C . Antibody was removed and slides were washed three times with PBTD rinses , and secondary antibody diluted in block ( Cy3-Donkey anti-Sheep 1∶250 , Jackson ImmunoResearch , Westgrove , PA ) was incubated at RT for 1 . 5 hours . Secondary antibody was removed with three washes of PBTD , and slides were mounted in 1∶1 PBS to glycerol with 0 . 1% Hoechst nuclear stain ( cryosections ) . Images were collected using a Nikon C1 confocal microscope . The same procedure was followed for dissected whole adult retinas prior to flat-mount analysis , using anti-cleaved caspase-3 primary antibody ( 1∶500 , Cell Signaling Technology , Danvers , MA ) and DyLight 488 secondary ( 1∶1000 , Jackson ImmunoResearch ) . 1 month fish measuring between 10–12mm were anesthetized in Tricaine , and both eyes were removed and placed immediately in TRIzol ( Invitrogen ) . Each sample was a pool of 3 pairs of eyes ( 6 eyes per sample ) , and 4 samples were used for each genotype . RNA was isolated following the Invitrogen protocol . Reverse-transcription PCR was carried out following the protocol for SuperScript III First Strand Synthesis ( Invitrogen ) . Gene specific primers were used as follows to amplify the genes of interest: Aif1l ( F: CAACATGGACTTACAAGGCG , R: TCCTCTTCGTCTCTGTACTTCTG ) ; ApoEb ( F: GTGCAAAACATCAAGGGCTC , R: GGGTCATCTGGGTTTGGAG ) ; Arg1 ( F: TGGGCATCAAAACCTTCTCC , R: AAACTCAGATGGATCGGCTTC ) ; Atf3 ( F: AGCCTGCATGAACACTGAG , R: TTTTCCTTCGGTCGTTCTCC ) ; C1q ( F: CTCTGCTGACACCTGTCCTG , R: GGTGGTCCTTTCAGACCAAA ) ; c-Jun ( F: ACGTGGGACTTCTCAAACTG , R: TCTTGGGACACAGAAACTGG ) ; Gap43 ( F: GAAGGCAATGCACAGAAAGAG , R: TGCTGGTTTGGATTCCTCAG ) ; Gfap ( F: AAGCTCTGCAAGACGAGATC , R: GCTTAGACACATCCAGATCCAC ) ; Klf6 ( F: CACTTAAAAGCACATCAGCGG , R: GAAGTGTCGGGTTAGCTCATC ) ; Socs3a ( F: CATTCAACAAAAGAGACTCATAGGC , R: TGTGGGTTATCATGGCGATAC ) ; Socs3b ( F: CCCAAGATTGAGTCGGATAACG , R: ACCAACACAAAGCCCAGAG ) ; Thy-1 ( F: CCGGTGTCAATCATTCAAACTG , R: CAGTGGGAAAGTGAGGAAGG ) . Initially , PCR products were amplified with Accuprime Taq HighFidelity ( Invitrogen ) , and sequenced to verify specificity . Real-time analysis was performed on a Bio-Rad iCycler using iQ SYBR Green SuperMix ( Bio-Rad ) . 3-step PCR with a 57°C annealing temperature was used for all primer sets except Arg1 , Atf3 , and Thy1 , which used a 2-step PCR with a 54°C annealing temperature to eliminate a non-specific product . All samples were run in triplicate , and fold change was calculated using the ΔΔCt method , with Ef1α as the housekeeping gene for all primer sets . Heads were removed from terminally anesthetized fish . In a Petri dish filled with buffer , the optic nerves were dissected from the heads first by removing the skin , skeleton , and connective tissue , leaving the eyes and attached nerves and tectum intact . The tectum was cut from the nerves , leaving the nerves intertwined at the chiasm . The nerves were separated by gently pulling on the eye globes with forceps , and making a cut with an 8 mm Spring Scissors ( Fine Science Tools ) when necessary . Dissected nerves with attached eyes were then incubated overnight at 4°C in gluteraldehyde/paraformaldehyde fixative . Heads were washed three times in 0 . 1M PO4 buffer , and then most of the eye globe removed by using the 8 mm scissors to make a circumferential cut around the optic nerve head , leaving a small portion of the posterior eye attached to the dissected nerve . The nerves were post-fixed in gluteraldehyde/paraformaldehyde for 1 hr at room temperature , washed 3X in 0 . 1M PO4 buffer , fixed in 1% buffered Osmuium for 1 hr on ice , and washed 3X with ice cold water . The following steps were all done at room temperature: nerves were dehydrated in an increasing series of MeOH ( 30% , 50% , 70% , 95% , 100% , 100% , 100% ) , then infiltrated with acetonitrile , 2X for 15minutes each , followed by 2 hours in a 1:1 mix of acetonitrile and EM Epon , and finally incubated in 100% EM Epon overnight , embedded in molds , and baked for at least 24 hours at 65°C . The blocks were trimmed to between 100–200 microns past the optic nerve head on a Leica RM2255 microtome , and ultra-thin sections were cut and plated on a grid , and imaged using a Hitachi H600 transmission electron microscope . The entire nerve cross-section was canvassed at 8000X , and 10–16 representative images were collected from each nerve at this magnification . Quantitative assessment of nerve pathology was conducted in a double-blinded manner in which both the TEM microscopist and the individual scoring pathology for the samples was unaware of the sample genotype . Eyes were dissected from terminally anesthetized adult fish and fixed overnight at 4°C in 4% PFA ( pH 7 . 4 , in PBS ) , then washed three times in PBS . In a Petri dish filled with PBS , a circumferential cut was made at front of the eye with a scalpel , near the border of the anterior and posterior segments . The anterior segment was discarded , followed by removal of the sclera from the posterior segment . The remaining retina with RPE was post-fixed 1-2 hrs with 4% PFA ( pH 7 . 4 , in PBS ) , washed in PBS , and then laid flat on a slide by making incisions through the retina so that it would lay flat . Whole retinas were mounted on the slides with 20 µl of Vectashield Mounting Medium ( Vector Labs , Burlingame , CA ) , and coverslipped . For retinas used for anti-activated-caspase-3 immunofluorescence , antibody incubations were done after removal of the anterior segment and sclera , but prior to flat-mount analysis . | Complex genetic inheritance , including variable penetrance and severity , underlies many common eye diseases . In this study , we present analysis of a zebrafish mutant , bugeye , which shows complex inheritance of multiple ocular phenotypes that are known risk factors for glaucoma , including high myopia , elevated intraocular pressure , and up-regulation of stress-response genes in retinal ganglion cells . Molecular genetic analysis revealed that mutations in low density lipoprotein receptor-related protein 2 ( lrp2 ) underlie the mutant phenotypes . Lrp2 is a large transmembrane protein expressed in epithelia of the eye . It facilitates transport and clearance of multiple secreted bioactive factors through receptor-mediated endocytosis . Glaucoma , a progressive blinding disorder , usually presents in adulthood and is characterized by optic nerve damage followed by ganglion cell death . In bugeye/lrp2 mutants , ganglion cell death was significantly elevated , but surprisingly moderate , and therefore they do not model this endpoint of glaucoma . As such , bugeye/lrp2 mutants should be considered valuable as a genetic model ( A ) for buphthalmia , myopia , and regulated eye growth; ( B ) for identifying genes and pathways that modify the observed ocular phenotypes; and ( C ) for studying the initiation of retinal ganglion cell pathology in the context of high myopia and elevated intraocular pressure . |
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The importance of host-specialization to speciation processes in obligate host-associated bacteria is well known , as is also the ability of recombination to generate cohesion in bacterial populations . However , whether divergent strains of highly recombining intracellular bacteria , such as Wolbachia , can maintain their genetic distinctness when infecting the same host is not known . We first developed a protocol for the genome sequencing of uncultivable endosymbionts . Using this method , we have sequenced the complete genomes of the Wolbachia strains wHa and wNo , which occur as natural double infections in Drosophila simulans populations on the Seychelles and in New Caledonia . Taxonomically , wHa belong to supergroup A and wNo to supergroup B . A comparative genomics study including additional strains supported the supergroup classification scheme and revealed 24 and 33 group-specific genes , putatively involved in host-adaptation processes . Recombination frequencies were high for strains of the same supergroup despite different host-preference patterns , leading to genomic cohesion . The inferred recombination fragments for strains of different supergroups were of short sizes , and the genomes of the co-infecting Wolbachia strains wHa and wNo were not more similar to each other and did not share more genes than other A- and B-group strains that infect different hosts . We conclude that Wolbachia strains of supergroup A and B represent genetically distinct clades , and that strains of different supergroups can co-exist in the same arthropod host without converging into the same species . This suggests that the supergroups are irreversibly separated and that barriers other than host-specialization are able to maintain distinct clades in recombining endosymbiont populations . Acquiring a good knowledge of the barriers to genetic exchange in Wolbachia will advance our understanding of how endosymbiont communities are constructed from vertically and horizontally transmitted genes .
The increasing availability of genomic data for closely related strains and species enables bacterial population sizes and structures to be explored in far greater detail than was possible until now . A major question is whether asexually reproducing bacterial cells are organized into “clusters” that contain genetic diversity , yet are distinguishable from each other [1]–[4] . Such clusters can arise through geographic isolation or extreme habitat specialization [5] . Whether bacteria that are not separated by any physical or geographic barriers can evolve into distinct groups is less clear , but studies of free-living bacteria such as Vibrio , Synechococcus and Bacillus have suggested that the formation of sequence clusters correlate with ecological specialization [6]–[8] . Likewise , a recent study of thermophilic archaea indicated ongoing speciation and suggested that these species are maintained by ecological differentiation within hot springs [9] . Studying the mechanisms and selective forces that influence the organization of genetic diversity in unicellular organisms is important for our understanding of speciation processes . In bacteria , recombination between incipient species can potentially be an important factor affecting speciation . In a speciation model whereby populations diverge mainly through neutral processes alone , sequence divergence depends on the ratio of recombination to mutation [10]–[12] . In an ecological model of speciation , adaptive and ecological divergence of incipient species instead depends on the ratio of the selection intensity against recombined , niche-determining genes from the other population to the recombination rate between these populations [13]–[16] . If however , the populations are geographically isolated they may diverge regardless of their potential to recombine . In any of these models , the distinctness of sequences of incipient species can be enhanced by periodic selection , the success of which depends on the rate of recombination within populations . Finally , recombination can be a source of adaptation whereby one species can acquire an adaptive gene from another species . The rate at which substitutions are introduced into a genome by recombination relative to mutation events ( r/m ) varies by more than two orders of magnitude in bacteria [17] . The highest r/m ratios ( >50 ) have been observed for oceanic bacteria of the SAR11 clade [18] , which are the most abundant bacteria in the upper surface waters of the oceans and have been shown to lack the mismatch repair system [19] . The lowest r/m ratios ( <0 . 5 ) have been associated with obligate host-associated bacteria , such as Buchnera aphidicola [20] and other endosymbionts , that have co-evolved with their hosts for hundreds of millions of years . Such long-term co-evolution serves as a strong physical barrier to gene exchange between bacteria adapted to different hosts . In effect , these highly specialized endosymbiont populations are perhaps best described as distinct taxonomic units , or species . Wolbachia is an obligate intracellular symbiont infecting various species of arthropods and filarial nematodes , where it is maternally inherited through the germ line cells [21] . In arthropods , Wolbachia is most known for the ability to manipulate the reproduction of their hosts in various ways , which include induction of parthenogenesis , feminization , male killing , and cytoplasmic incompatibility ( CI ) [21] . In filarial nematodes Wolbachia is mutualistic and necessary for normal development and fertility [22] . In addition to these roles , several studies have emerged in recent years indicating that Wolbachia may also have other functions , such as providing ATP for the host [23] , improving longevity [23] or fecundity [24] , protection against viruses [25] , [26] and uptake of iron [27] . Unlike maternally inherited mutualistic endosymbionts that have been co-evolving with their hosts , arthropod Wolbachia can be lost and gained from the host population and they show high recombination frequencies [17] . Wolbachia is currently defined as a single species , which is further classified into a number of divergent supergroups ( A–N ) . The most well studied supergroups are A and B that infect arthropods and C and D that infect filarial nematodes [28] , [29] . The supergroup classification scheme was originally proposed based on single-gene phylogenies [30] , and more recently supported by multi-locus sequence typing [31] . Since these analyses suggested that Wolbachia supergroups represent genetically distinct clades , it is debated whether some or all of these groups should be designated different species [32] . However , due to high levels of recombination between super-groups in a few marker genes such as the surface protein wsp [33] and frequent exchange of phage DNA [34] , it is unclear whether the super-group classification scheme is representative of the genomes overall . Moreover , no phenotypic traits have been identified that correlate with the separation of arthropod-infecting strains into different supergroups . On the contrary , strains of different supergroup affiliation may display similar phenotypic traits and host ranges . For example , double infections with super-group A and B strains have been found in many insects , and the induction of cytoplasmic incompatibility is common in both supergroups . The distribution of other phenotypic traits is less well investigated [23]–[27] . In the absence of strong host-specialization patterns , niche partitioning within hosts provides an alternative mechanism of speciation . To evaluate the extent of recombination and identify the ecological and physiological features that may explain the separation into supergroups , genome data is required . However , the sequencing of obligate endosymbionts such as Wolbachia is not trivial , since these bacteria are often present in low abundance in their hosts and cannot be cultivated outside of their hosts . Some protocols specifically designed to extract DNA from Wolbachia have been developed in recent years [35] , [36] but the preparation of enough DNA for sequencing is still very time-consuming for obligate host-associated bacteria with low infection densities . Because of these challenges , genomic data is currently only available for a few Wolbachia strains . These are the two supergroup A strains , wMel infecting Drosophila melanogaster [37] and wRi infecting Drosophila simulans [38] and the genomes of one supergroup B strain , wPip , from the mosquito Culex quinquefasciatus [39] , and one supergroup D strain wBm isolated from the nematode Brugia malayi [40] . Early draft genomes have also been presented for two other supergroup B strains , namely wAlbB infecting the mosquito Aedes albopictus [41] and wVitB infecting the parasitic wasp Nasonia vitripennis [42] . Genome sizes are small , in the range of 1 . 5 Mb . Recombination has been shown to be prevalent between strains that belong to supergroup A , suggesting that Wolbachia is a highly recombining intracellular community [38] . Genomes in the A and B-supergroups contain between 20 to 60 ankyrin repeat genes . Although it is generally thought that these genes play a key role in host-interaction processes and may be involved in the reproductive phenotypes , it has been difficult to pinpoint the particular functions of these genes . Wolbachia strains wHa and wNo are especially interesting in the context of this discussion since they share several phenotypic traits , but belong to different supergroups . Importantly , both strains cause CI in their host Drosophila simulans , where they occur as natural double infections in populations on the Seychelles and in New Caledonia [43] . Strain wHa has also been found as a single infection on Hawaii and in French Polynesia , but natural populations of D . simulans infected only with wNo are very rare [44] . Several studies support the hypothesis that the double infection originated on the Seychelles and spread east to the Indo-Pacific islands , where after wNo was lost from some populations [45]–[47] . Notably , a double-infection very similar to the one found for D . simulans is also found in the sister species Drosophila sechellia , which is endemic to the Seychelles . Furthermore , D . simulans and D . sechellia have very similar mitochondrial genomes , despite significant divergence in the nuclear genome . It therefore seems likely that the Wolbachia double-infection preceded the speciation event between D . simulans and D . sechellia . A recent study has estimated the time to a common ancestor of the D . simulans subcomplex ( including D . simulans , D . sechellia and D . mauritiana ) to be ∼242 . 000 years ago [48] , suggesting that the co-infection originated at least a few hundred thousand years ago . In this study , we present a new method for the preparation of DNA from Wolbachia , based on multiple-displacement amplification that enables genome data to be collected for Wolbachia strains with low infection densities . We have applied this protocol to the sequencing of the genomes of Wolbachia strains wHa and wNo that are co-infecting D . simulans . By comparative analysis of these and previously sequenced Wolbachia genomes , we have analyzed whether genomic features such as recombination , genome rearrangements and gene acquisitions could explain the separation of Wolbachia strains into distinct supergroups . The findings are discussed in light of the species concept for bacteria .
We developed a novel procedure for the isolation and amplification of Wolbachia DNA present in low quantity in the insect hosts . In brief , Wolbachia cells were purified from embryos of Drosophila simulans and multiple-displacement amplification ( MDA ) was performed directly on the isolated bacterial cells ( see Materials and Methods ) . Single and 3 kb paired-end sequence reads were collected from the amplified DNA using the 454 sequencing technology and assembled de novo . The sequence coverage obtained from each data set was very large , and we therefore only used 10% to 30% of the data for assembly with Mira ( Table 1 ) . The proportion of single and paired-end reads that assembled was estimated to between 96–97% and 86–88% , respectively ( Table S1 ) . The 454 sequence reads in the assembly had mean and median sizes of 300 to 400 bp , whereas the median length of the 454 sequence reads that did not assemble was less than 100 bp ( Table S1 ) and of lower quality ( Figure S1 ) . Illumina paired-end reads were mapped onto the assembly to correct for frameshift errors generated by the 454 technology . The overall coverage of the wHa and wNo genomes in the final assemblies was about 40 to 80-fold for the 454 data and 100 to 200-fold for the Illumina data ( Table 1 ) . All gaps were closed by PCR on non-amplified DNA , confirming the reliability of the scaffolds obtained from the sequence data of the amplified DNA . In two positions in the wHa genome , located 20 kb apart and containing a long repeat of 7 . 5 kb with 5 genes , the PCR reactions failed from one side . However , single reads and read pairs supported the connection between the repeats and the unique sequences flanking each of the two copies . This is the first demonstration that the MDA method can be applied in order to generate complete genome sequences from a small number of starting cells of uncultivable bacterial endosymbionts . The MDA method is known to produce amplification bias and chimeric reads when applied to single cells , which prevents genome closure . We considered the risk that such artifacts could have influenced the final genome sequence , but found these artifacts to be less dominant when multiple endosymbiont cells were used to start the reaction . Importantly , the entire Wolbachia genomes were represented by the sequence data in the final assemblies although coverage was unevenly distributed across the genome ( Figure S2 ) . The same coverage pattern was observed irrespectively of the method used for sequencing ( Figure S2A–S2C ) , suggesting that the amplification bias is not random , but probably determined by the primer sets included in the amplification kit . The fraction of chimeric reads in the single-end 454 sequence library was about 1% , which is considered normal according to the Newbler manual . As expected , the percentage was higher for the 454 paired-end reads , about 13–14% ( Figure S3 ) , but part of these chimeric read pairs might have been generated during library preparation rather than during the MDA reaction . Even though some regions have a higher amount of chimeric reads , we do not believe that they have had a significant effect on the assembly , since the coverage of these putative chimeric reads closely follow the coverage distribution of non-chimeric reads and hence regions with high amounts of chimeric reads also have high amounts on non-chimeric reads ( Figure S3 ) . We conclude that the overall fraction of chimeric reads was too low to have an effect on the assembly . In retrospect , we mapped the individual sequence reads back to the Wolbachia genome and estimated that more than 97% of all reads represented Wolbachia DNA ( Table 1 ) . The remaining few percent was mostly derived from mitochondrial DNA from Drosophila simulans , with little or no nuclear DNA in the preparation . However , a manual search in the non-assembled sequences produced by the Mira assembly software of the wNo sample revealed the presence of wHa reads in low quantities ( Figure S2D ) . Since most of the wHa genome was covered but no nuclear DNA was detected , it is unlikely that these reads were derived from bacterial sequences integrated into the host nuclear genome . Rather , we believe that there may have been a slight contamination of wHa during sample preparation and sequencing , or that the double-infected line from which wNo was generated was not completely cured of wHa . No wNo reads were found in any of the amplified DNA samples for wHa . In conclusion , the large majority of sequence data generated by the MDA method was of good quality , not chimeric and covered the entire genome with little or no contamination of nuclear DNA . The wNo and wHa genomes are 1 . 3 Mb in size and contain circa 1 , 000 genes , which corresponds to a coding density of about 80% ( Table 2 ) . This is comparable to the fraction of coding DNA in the previously sequenced Wolbachia genomes with the exception of wMel , in which a larger fraction of short open reading frames were identified as genes , resulting in a higher estimated coding density of 94% ( Table 2 ) . As in all previously sequenced genomes of arthropod Wolbachia , several phage-derived fragments were identified . The wHa genome contains two such regions , one of which encodes a nearly complete WO-phage . The wNo genome contains four segments of putative phage origin , of which the two larger fragments together contain all conserved parts of the WO-phage . Pseudogenes were identified in all four phage segments in the wNo genome , making it unlikely that any of them could individually produce phage particles . We observed a similar number of putatively functional IS elements in the two genomes , 12 in wHa and 14 in wNo ( Table S2 ) . Additionally , we identified 58 defective IS elements in wHa , of which 17 were defective IS3 elements . No defective IS3 elements were present in the wNo genome , which only contained a total of 14 defective IS elements . Adding the wHa and wNo genomes to the previously produced draft and complete Wolbachia genome sequences , we tested the robustness of the supergroup classification scheme using three A-group ( wHa , wRi , wMel ) and three B-group ( wNo , wPip , wAlbB ) strains . We identified 660 orthologous core genes present in all six genomes . A phylogenetic analysis with the maximum likelihood method based on a concatenated alignment of the core genes supported the separation of the two supergroups with 100% bootstrap support , and further suggested that wRi and wHa are most closely related within the A-group , and that wPip and wAlbB are sister taxa within the B-group ( Figure 1 ) . Consistently , gene order structures were largely conserved within supergroups , but highly scrambled in all pair-wise comparisons of A- and B-group genomes ( Figure 2 ) . Thus , the classification of these strains into two supergroups is strongly supported by both the sequences and the architectures of the Wolbachia genomes . To test the hypothesis that recombination mediates cohesion within supergroups but is reduced between supergroups , we examined the topologies of single gene trees , studied the spread of sequence divergence estimates , and inferred the relative fraction of intragenic recombination events both within and across the supergroup boundaries . Novel gene acquisitions may confer the ability to inhabit new niches . In the case of endosymbionts , the acquisition of a new gene might potentially broaden the host range , but could also lead to ecological specialization within the existing host . For example , the uptake of a novel gene might contribute to the physical separation of strains with and without the new gene , leading to speciation . To investigate this hypothesis , we examined gene content differences between the two supergroups . In total , we identified 33 and 24 protein clusters that were specific to the A- and B-group genomes , respectively ( Tables S5 , S6 ) . A comparison of the number of protein clusters solely present in the A- or B-supergroup strains to the number of protein clusters found in any other combination of three strains showed that the supergroup specific protein clusters are largely over-represented ( Figure S8 ) . Functional categorization of these clusters identified a few particularly interesting acquisitions in the A-group strains of genes putatively involved in the regulation of arginine transport systems ( argR ) , stress response ( cydAB ) and modulation of host cellular functions ( fic ) . Phylogenetic analyses revealed sequence similarities to several other intracellular bacteria , such as Legionella , Rickettsia and Chlamydia , indicating that these genes may serve a role for the intracellular lifestyle ( Figure 7 , Figure S9 ) . As in Legionella pneumophila , the gene for the arginine repressor ArgR is co-located with three genes for an arginine ABC transporter and phylogenetic reconstruction confirmed the close affiliation between Wolbachia and Legionella of the entire cluster of four genes ( Figure 7 , data not shown ) . Previous studies of other pathogenic bacteria have shown that arginine may be associated with virulence . Additionally , arginine can be converted to nitric oxide by the host as part of the innate immune response . In Legionella , the expression of the genes for the arginine repressor and transporter is sensitive to the presence of L-arginine and derepression is observed during intracellular growth [50] . In analogy , we infer that the Wolbachia ABC-transporters are expressed when the concentration of arginine is low , stimulating uptake of arginine through the ABC-transporters . The Fic domain proteins solely present in the A-group strains are particularly interesting since their homologs in other bacteria have been shown to be secreted into the host cell cytoplasm to modify host regulatory GTPases [51] , thereby causing the disruption of the host actin cytoskeleton [52] , [53] or host cellular rearrangements [54] . The ability to manipulate host GTPases is most likely a general feature of all proteins containing this protein domain since it has also been reported in the distantly related Fic-domain containing human HYPE protein [53] . The comparison of gene contents also indicates possible differences in cell division and lipid II biosynthesis due to gene loss in the B-group strains . For example , the ftsWIBL genes , which are involved in these processes in E . coli are present in the A-group strains , but absent from the B-group strains and present only as pseudogenes in wBm . Additionally , the murC gene , which catalyses the attachment of the first amino acid to the glycan , has been split into two genes located distantly from each other in the genomes of all B-group strains , including wVitB . One of the two genes encodes the N-terminal domain and the other encodes the C-terminal domain fused to a recombinase zinc beta ribbon domain ( PFAM: PF13408 ) ( Figure S10 ) . Interestingly , experimental evidence has shown that a lipid-II-like molecule is synthesized in the supergroup B Wolbachia strain wAlbB [55] , suggesting that the murC gene function is present despite the separation of the sequences encoding the functional domains into two genes . Uniquely present in the B-group strains is a cluster of genes encoding outer membrane proteins which are found in two to three copies in each of the supergroup B genomes , including wVitB . Located at the corresponding genomic position in the A-group strains is a non-coding region of approximately 1 kb , which does not show any significant sequence similarity to genes in the B-group strains ( Figure S11A ) . Eight of the nine proteins in this cluster contain PFAM domains annotated as outer-surface proteins , including the family to which the Wolbachia surface protein ( wsp ) belongs ( PF01617 ) . A phylogenetic analysis revealed a clustering of genes between the strains , rather than within the genome of one strain , suggesting that gene duplication occurred before divergence of these B-group strains ( Figure S11B ) . Short sequence fragments with significant similarity to these surface proteins were identified in the wBm genome , indicating loss from supergroup A . However , since no homologs outside Wolbachia supergroup B could be identified , the origin and function of this outer membrane protein family remain to be determined .
High recombination frequencies were previously estimated for strains belonging to super-group A [38] , and confirmed in this study in both supergroup A and B . Single-gene phylogenies showed all possible divergence patterns for strains within each supergroup in nearly equal proportions , and the spread of the relative dS values for individual genes within supergroups was very high ( 0 . 3–0 . 4 ) , which is in the range of the naturally competent and highly recombinogenic bacterial pathogen Neisseria meningitidis ( Spread = 0 . 34 ) [38] . Thus , there is a very strong bias for substitutions caused by recombination within Wolbachia supergroups , which suggests that there is very little selection against recombination within Wolbachia supergroups , consistent with the species concept . For endosymbionts , co-evolution with hosts is thought to generate a physical barrier that leads to the evolution of ecologically distinct species . This is exemplified by a strong congruence of Wolbachia and host phylogenies for nematode-infecting strains [60] , [61] . However , for Wolbachia strains infecting insects , host and endosymbiont phylogenies are generally not congruent [30] , [31] , [62] . Strains of different supergroups can infect the same host species , as exemplified by wHa and wNo in this study , just as strains of the same supergroup , such as wHa and wMel , can infect different host species . Furthermore , there is no simple association between supergroup affiliation and reproductive disorders , since strains of both supergroups are capable of inducing for example cytoplasmic incompatibility . Yet , our analysis shows that there are differences in gene content between supergroup A and B , which are likely to influence the interactions with the host and the surrounding environment . Notable among the A-group specific functions are genes for uptake of arginine , tolerance to stress and secretion of proteins involved in the modulation of host cellular functions , whereas the B-group specific gene set included genes for outer surface structures . Thus , our data raise the possibility that the supergroups might have evolved into distinct ecotypes within the same host species , potentially avoiding competition through niche partitioning and thereby achieving a stable co-existence . Although niche partitioning has not yet been investigated for hosts infected with multiple Wolbachia strains , Veneti et al . [63] demonstrated that Wolbachia strains of different supergroups show distinct localization patterns within the host embryo . The A-group strains ( with the exception of wRi ) were localized to the posterior part of the embryo , whereas the B-group strains were observed in the anterior part during the syncytial blastoderm stage . However , only a few highly similar strains of each supergroup were included in the analysis , and it remains to be determined whether the observed patterns are characteristic of a broader selection of strains from the two supergroups . Physical separation of endosymbionts within hosts does not necessarily have to be absolute to allow for speciation , since quantitative differences in associations with different habitats might also generate ecologically distinct species [6] . In analogy , differences in abundances and/or compartmentalization within the host could potentially lead to ecologically distinct species . Indeed , distinct localization patterns of endosymbionts within hosts have for example been observed for different genera of whiteflies [64] . While the current overlapping host ranges of supergroup A and B and the occurrence of multiple infections with strains from both groups appears to contradict the possibility of host specialization , several studies have provided some evidence for specialization to hosts and/or habitats [65]–[68] . However , these studies were based small gene datasets , such as the wsp gene that code for a hypervariable surface protein and/or core genes used in multi locus sequence typing . If these genes are as recombinogenic in all Wolbachia strains as reported here , sequence similarity measures within supergroups will reflect gene recombination histories rather than strain relationships . Correlations between genotypes and host-association patterns within supergroups will thus mostly depend more on the gene sets selected for the analyses . In conclusion , both experimental evidence and additional genome data is needed in order to evaluate the ecological distinctness of Wolbachia strains both within and between supergroups . Now that a supergroup specific gene repertoire has been identified , it should be possible to investigate both the ecological roles of these genes , as well as the strain localization at various stages of host development . All evidence gathered in this study indicates that strains from different supergroups represent distinct clusters , but are they irreversibly separated or do they still exchange genetic material ? Importantly , our analyses have shown that recombination events between supergroup A and B have occurred , but that the fragments are of shorter sizes and have had a much lower impact on the genomes than recombination events within the groups . Recombination events that span over all or most of a gene are very rare since only 8 of the 660 gene trees did not provide support for the supergroup division . Consistently , we only identified a few long recombination tracts between the supergroups . These few transfers of co-located genes might thus exemplify how one organism can acquire another population's adaptation while the integrity of its own niche-defining characteristics is still preserved . The wHa and wNo genomes are thought to have co-infected D . simulans for at least 200 , 000 years , which is a relatively short time period compared to at least a few million years since the divergence of the A and B-groups ( as inferred from a few % difference in their 16S rRNA genes ) . Hence , even though we do not find more recombination between wHa and wNo than between other strains belonging to different supergroups , we cannot exclude the possibility that the exchange of genetic material between them would increase given longer time . Alternatively , there is some form of barrier to genetic exchange between strains of supergroup A and B . The simplest form of barrier to gene transfer is the presence of incompatible mobile elements . However , we do not think that this is the case in Wolbachia since the gene phylogenies indicated transfer of phage genes across the supergroup boundary . Moreover , transfer of a complete bacteriophage genome between strains of different supergroups was recently discovered in Nasonia vitripennis [42] . Even so , there is no concrete evidence that these phages regularly transfer genetic material other than their own genomes , and thus there could still be differences in the frequencies at which genetic material is transferred between the two supergroups . Another form of barrier is that the sequence divergence per se limits recombination . The mismatch repair system has been seen to prevent homologous recombination between divergent sequences and loss of the mutSL genes for the mismatch repair system is known to cause dramatic increases in both mutation and homologous recombination frequencies . However , even though we found that the mutS gene is full-length and probably functional in both the A and B-group Wolbachia strains , and that all genomes except the wRi genome have two copies of the mutL gene , we found no inverse correlation between sequence divergence levels and recombination frequencies for individual genes . In natural populations , the mutS gene recombines and is gained and lost in a cyclic manner in response to environmental changes , leading to altered mutation and recombination rates . The resulting mutator phenotypes are selected during periods of environmental fluctuations and then restored by recombination with a functional copy from another strain [69] . We saw that in Wolbachia , one of the mutL genes is associated with a prophage element in wPip and wHa and located near to a previously detected insertion in wMel that might stem from a phage , indicative of horizontal gene transfer . Additionally , we detected intra-genic recombination in both the mutS and mutL genes . Thus , the presence of a seemingly functional mismatch repair system all strains analyzed does not preclude that recombination frequencies could have fluctuated in the past due to gains and losses of these genes . A recent model suggests that almost identical sequences between the donor and recipient are required at one or both ends of a recombination fragment in order for recombination to occur and that the imported fragments are digested until a good enough match is obtained [70] . Consistent with our data , this model predicts that shorter recombination tracts will be found between more divergent sequences , since more cuts are required in order for the ends to match . Essentially , if true , this implies that when two genomes have diverged enough only short fragments can recombine between them . As a consequence , it is unlikely that recombination events are sufficient to invoke convergence between the supergroups even though they share the same habitat for a long period of time , as is the case with the Wolbachia strains wHa and wNo . Although we did not see a correlation between sequence diversity and intragenic recombination , this model cannot be ruled out since the end points of each recombination fragment were not investigated . Genome rearrangements present yet another barrier to recombination and is thereby an important factor in speciation processes in eukaryotic organisms , mainly because of suppressed recombination at rearranged sites during meiosis in heterozygous individuals [71] . Although bacteria do not evolve by sexual reproduction , homologous recombination could be suppressed in chromosomal regions that are not co-linear because of rearrangements or insertions of genes in one of the two genomes . Indeed , a recent study showed that recombination frequencies are suppressed close to lineage-specific genes , which might lead to higher divergence levels in their vicinity [72] . Furthermore , long recombination events can only occur if the target genome has a similar gene order . Since a single long recombination event can override several shorter intra-genic recombination fragments , extensive rearrangements could contribute to the separation of the lineages . The genomes of Wolbachia strains that belong to the same supergroup show much higher colinearity than strains of different supergroups , potentially contributing to the observed lower frequency of recombination events between the A and B supergroups . In summary , a number of different explanations could account for the observed reduced level of recombination between supergroup A and B . Although we do not know whether there has been selection against recombination between supergroups or if the reduced levels of recombination was driven by neutral processes alone , our results strongly suggest that the A and B supergroups have now become irreversibly separated . The acquisition of advantageous novel genes or mutations is hypothesized to trigger speciation events according to the ecotype model of speciation , which has so far only been evaluated for free-living bacteria [73] . In this context , it is notable that we have identified supergroup-specific genes sets that appear to be the result of horizontal gene transfers . Although it is too early to speculate about the functions of these group-specific genes , it is quite possible that their acquisitions induced significant phenotypic changes . Selective advantages associated with any of these phenotypes could have purged diversity within the groups , thereby contributing to the genetic separation of the two lineages . Another scenario could be that the loss or gain of genes in one strain of Wolbachia resulted in reproductive isolation between infected hosts , for example through CI [74] , [75] . However , it is difficult to evaluate the likelihood for such a scenario , since multiple infections and recent horizontal transmission of Wolbachia strains between different host-species have blurred the ancestral patterns of infections . Alternatively , the speciation event may have been triggered or enhanced by extensive rearrangements , due to a burst in the activity of IS-elements . All Wolbachia genomes from supergroup A and B sequenced to date contain an unusually high level of IS-elements . For example , 11% of the genome of Wolbachia strain wRi was estimated to consist of IS-elements , and 17 of the 35 identified breakpoints between the genomes of wMel and wRi are located at IS-elements [38] . Additionally , many of the IS elements in Wolbachia genomes carry mutations that are likely to have rendered these elements non-functional , which is an unusual feature of bacterial IS-elements since they are commonly believed to have a rapid turnover rate within genomes [76] . Making use of the presence of these degraded IS-elements , a recently published simulation study aiming to explain the distribution of IS copies in the modern Wolbachia genomes suggested two major periods of intense transpositional activity , a very recent burst and an ancient expansion of the most divergent IS copies [77] . Such an expansion could have induced major changes in gene order structures , leading to suppressed recombination close to the breakpoints . Since two rearranged genomes can never converge to the same gene orders again , an ancestral expansion of IS-elements followed by genome rearrangements could have irreversibly separated the two groups . However , since the age of the ancestral expansion is not known , it is difficult to test this hypothesis . The recent expansion of IS-elements in Wolbachia could potentially have lead to similar diversifications in more closely related strains , a hypothesis that could be tested by investigating diverse lineages within the same supergroups . It is obvious that no single speciation hypothesis will be applicable to all bacteria . Although Wolbachia is an obligate intracellular bacterium , it is atypical in that it is a generalist with a high prevalence and a broad host range in a diverse group of insects . The most remarkable aspect of its evolution is the expansion of the host range , which might have occurred independently in both supergroups after their separation . The acquisition of genes to manipulate the host combined with high recombination frequencies to shuffle beneficial alleles among all members in the group could help explain much of this ability . Many questions remain to be solved , such as for example if there is adaptive selection for ecological divergence within supergroups , and if strains from different supergroups inhabit different niches within their broad range of host species . To further investigate speciation processes in Wolbachia , we need to study the global distribution patterns and population structures of hosts and endosymbionts . The methods developed in this paper offer the possibility to perform such large-scale , whole-genome surveys of Wolbachia and other endosymbionts .
The wNo-infected fly line was generated by a series of backcrosses on a double-infected fly line collected on Noumea in 1989 [78] . The wHa-infected fly line was collected on Hawaii in 1990 , as a natural single-infection [79] . Both Wolbachia-infected fly lines have been kept at the laboratory of Prof . Kostas Bourtzis for over fifteen years and have extensively been used in Wolbachia-related experimental work . The purification of Wolbachia cells was carried out as in [39] , with some modifications . Flies were allowed to oviposit on apple-juice agar for two hours , and 15–30 embryos were collected for the purification . The embryos were dechorionated in bleach , rinsed with water , and homogenized in phosphate-buffered saline ( PBS ) buffer with a sterile micropestle . The homogenate was centrifuged at 400 x g for 5 min to pellet large debris , including host nuclei . The supernatant was centrifuged at 5 , 400 x g for 5 min to pellet Wolbachia cells . The pellet was re-suspended in PBS , and another slow centrifugation was carried out ( 400 x g for 5 min ) to remove remaining debris . The supernatant was passed first through a 5 µm pore size filter ( Millipore , Bedford , MA ) , and then through a 2 . 7 µm pore size filter ( Whatman , USA ) . The filtrate was centrifuged at 6 , 900 x g for 15 min to pellet the Wolbachia cells . Most of the supernatant was removed , leaving a bacterial pellet in approximately 3–5 µl PBS . A multiple-displacement amplification ( MDA ) was carried out directly on the bacterial pellet , using Repli-g midi kit ( Qiagen ) according to manufacturer's instructions ( protocol for Amplification of Genomic DNA from Blood or Cells ) . The amplified samples were cleaned prior to sequencing with QIAamp DNA mini kit , according to manufacturer's instructions ( Qiagen , supplementary protocol for Purification of REPLI-g amplified DNA ) . Since MDA is known to be extremely sensitive , precautions were taken to avoid contamination during the purification of Wolbachia cells , including sterile-filtering of all solutions , and autoclaving/UV-treatment of plastic utensils . Three independently amplified samples for each Wolbachia strain were used for library construction and sequencing , so that each genome was sequenced by ½ plate of single-end and 3 kb paired-end 454 and 1/12 lane paired-end Illumina . 454 sequencing was done at SciLifeLab Stockholm on a 454 Roche FLX machine using Titanium chemistry and standard preparations for single-end and 3 kb paired-end libraries . Illumina sequencing was done on a HiSeq2000 instrument at the Uppsala SNP & SEQ platform , using standard Illumina protocols for preparation of paired-end libraries , generating 2×100 bp sequences from each fragment . The 454 datasets were assembled de novo with both Newbler ( 454 Life Sciences Corp . , Roche , Branford , CT 06405 , US ) and Mira [80] . Assemblies were compared with Mauve [81] and ACT [82] and the discrepancies between the best assemblies and all sequence gaps were resolved with PCR amplification from total fly DNA extractions ( DNeasy Blood and Tissue kit , Qiagen ) and subsequent direct sequencing of the PCR products . Since the Newbler assembly proved to be generally more correct it was used as a reference to order the contig sequences from MIRA into scaffolds and close the remaining gaps , resulting in two circular Wolbachia genomes . In two positions on the wHa genome PCR-products could not be obtained , but read-pairs that go in and out of the repeat sequence associated with these genome positions support the current arrangement . Gap closure and manual sequence editing of PCR products was done using Consed [83] . Consed was also used to map the Illumina sequences onto the contigs generated using 454 data , in order to correct errors in homopolymer tracts . To evaluate the purity and quality of the DNA samples used for sequencing , the sequence reads were mapped onto the completed genomes . The Illumina reads were filtered using Trimmomatic [84] , and mapped using bwa [85] . The sam-formatted output file from bwa was converted to bam , sorted in coordinates and duplicated reads were marked using Picard tools ( http://picard . sourceforge . net ) . Proper and non-proper read pairs ( as set in the sam-file flag by bwa ) were extracted with samtools [86] . The single and paired-end 454 reads were mapped separately using the Newbler mapper . For the paired-end 454 reads , true and false pairs ( as defined in the output file 454PairStatus . txt by Newbler ) were extracted and mapped separately . Coverage was calculated from the bam-files using the depth command in samtools and subsequently plotted using R ( R development core team 2011 ) . . The mean quality of assembled and non-assembled 454 reads was plotted with Prinseq [87] . An annotation pipeline was developed using the Diya framework [88] . Prodigal was used for gene prediction [89] , GenePrimp for identifying suspicious start/stop codons and pseudogenes [90] , and hmmsearch as implemented in pfam_scan . pl was used for domain prediction with the PFAM database [91] . All annotations were manually edited using Artemis [92] . Overview figures of similarity between complete genomes and local genome regions were generated with GenoPlotR [93] . IS-elements were identified based on open-reading frames and a manual search of all repeats . All IS-elements were assigned to an IS family by TBlastX searches against IS-finder [94] . Functional IS-elements were defined as alignments that could be extended to contain the complete annotated IS-element . IS-elements that were truncated compared to their best hit in IS-finder or contained frameshifts were considered non-functional . Homologous genes between six Wolbachia strains ( wHa , wNo , wRi , wMel , wPip and wAlbB ) were determined using reciprocal protein blast searches between all the protein sequences from the genomes and subsequent clustering with the MCL algorithm [95] . In order for genes to be considered homologous , the shortest protein in a pair needed to be at least 60% of the length of the longer gene and be aligned over at least 80% of its length . Ortholog clusters containing a single gene from all 6 Wolbachia genomes were aligned on the protein level using mafft [96] and backtranslated to nucleotides . The alignments were pruned to remove gap sites present in 50% or more of the aligned sequences . A strain phylogeny was inferred on a concatenate alignment of the single gene orthologs in RAxML using the GTRGAMMA model and constructing 1 slow best maximum likelihood tree and 1000 rapid bootstrap replicates . Additionally , phylogenetic trees were inferred independently for each ortholog cluster by RAxML [97] using the GTRCAT model , and constructing 1 slow best maximum likelihood tree and 100 rapid bootstrap replicates . Pairwise Robinson-Fould ( R-F ) distances were calculated using RAxML by inputting a concatenated file with the 660 individual gene trees . The weighted R-F distances were used to cluster the trees with hclust ( method complete and height cutoff of 1 ) in R . Phylogenetic trees of clusters with members of all strains , but containing paralogous copies and located in prophage regions were inferred by the same method as the single gene orthologs . However , since the current assembly of the wAlbB genome does not contain complete genes for most of the prophage , this strain was excluded from the analysis . The same 660 single-gene ortholog clusters were used to calculate synonymous substitution rates ( dS ) between all pairs of genes in the alignment using codeml from the PAML package with the codon-based model of substitutions described in [98] and nucleotide distances with RAxML using the GTR model . The pair-wise dS-values obtained were used to quantify the amount of recombination within supergroup A and B by plotting relative dS- values in a ternary plot and calculating the spread of the values from the mean relative dS-values by using R , as described in [38] . The alignments of the 660 single-gene ortholog clusters were used for recombination detection within genes with PhiPack [99] ( which calculates the p-values for three individual methods , Neighbour similarity score ( NSS ) , Maxchi and Phi ) and GENECONV [100] . Recombination was inferred for p-values less than 0 . 01 . For counting recombination between vs . within supergroups with geneconv , only global inner fragments with a Bonferroni corrected KA p-value less than 0 . 05 was used . Additionally , to calculate the r/m parameter , two independent ClonalFrame [101] runs were performed on a concatenated alignment of all the single orthologs as individual blocks using 100 . 000 iterations , with a burn-in of 50 . 000 iterations and recording the parameters every 100th iteration . Convergence between the clonal-frame runs was tested using the ClonalFrame graphical user interface . r/m for each node of the tree was calculated from the output file of the two separate ClonalFrame runs . The probability of a substitution generated by mutation was calculated as ( 1-R ) *S and the probability of a substitution being generated by recombination was calculated as R*S , where R is the posterior probability of recombination and S is the posterior probability of substitution . Only positions where the probability of substitution via mutation or recombination was higher or equal to 0 . 95 were counted . The number of recombination events was calculated by looking at continuous stretches of sites were the posterior probability of recombination was never lower than 0 . 5 and contained at least one site with a probability of 0 . 95 . Geneconv was run using three different levels of mismatch penalty , in order to account for differences in divergence between the strains and differences in age of the transferred fragments . The mismatch penalty is inversely proportional to the total number of site differences between two sequences , and directly proportional to the gscale parameter ( except when no mismatches are allowed , gscale = 0 ) according to the formula; mismatch penalty = ( number of total polymorphisms in the alignment ) * gscale/ ( number of site differences between each pair of sequences ) . This means that sequences with a lower number of total differences , will get a higher penalty for a mismatch with the same gscale setting . MCL clusters that contained genes from only super-group A or B were further analyzed by taking the protein sequences from either wHa ( representing the A supergroup ) and wNo ( representing the B supergroup ) and blasting ( tblastn ) them against the complete genomes from the other supergroups , including the supergroup D genome of Wolbachia wBm . Clusters that did not have a match in any of the other genomes with either an e-vale less than e-5 and 60% of the protein aligned or an e-value less than e-20 and 30% of the protein aligned and identity of minimum 35% , were considered supergroup specific . Additionally , if the matches from tblastn contained stop codon or frame-shifts , the hit was called a pseudogene even if the above criteria were met . The protein sequences of fic domain proteins with known function ( FiDo family ) as listed in [102] were downloaded from Genbank . Additionally , the protein sequences for the 10 best non-overlapping blastp hits against the nr database when using the three Wolbachia fic genes were downloaded . Similarly , for cydA , cydB , argR and the arginine ABC transporter genes , the top 50 blastp hits against the nr database were downloaded . For the outer membrane proteins specifically found in the B-supergroup , no additional species were found in the database , but the homologous protein sequences from wVitB were included . In all cases , the protein sequences were aligned with mafft and pruned to remove gap sites that were present in 50% or more of the aligned sequences . The phylogenetic trees were inferred with RAxML using the PROTCATWAG model , and constructing 1 slow best maximum likelihood tree and 1000 rapid bootstrap replicates . The complete sequences of Wolbachia wNo and wHa genomes are deposited in Genbank under accession numbers CP003883 and CP003884 , respectively . | Speciation in sexual organisms is defined as the inability of two populations to get viable offspring . Speciation in asexual , obligate endosymbionts is thought to be an indirect consequence of host-specialization . An important question is if divergent endosymbionts would start blending if the host barrier isolating them were removed . Here , we have studied Wolbachia , an abundant group of bacteria in the insect world . Wolbachia is classified into supergroups based on multi-locus sequence typing . We have sequenced the genomes from the Wolbachia strains wNo and wHa . These are particularly interesting since they belong to different supergroups yet co-occur as a double-infection in natural populations of Drosophila simulans . A comparative genomics study showed that wHa and wNo contain no uniquely shared genes . Instead , each strain shares unique gene functions with members of the same supergroup that infect other hosts . This unexpected finding suggests an alternative means of ecological speciation , indicating that speciation is not restricted to host-specialization but rather that related endosymbionts can coexist as separate species in the same host . Our study sheds light on the genomic divergence between different partners inhabiting the intracellular niche of the same host organism . |
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This study aimed to investigate the spatiotemporal clustering and socio-environmental factors associated with dengue fever ( DF ) incidence rates at street level in Guangzhou city , China . Spatiotemporal scan technique was applied to identify the high risk region of DF . Multiple regression model was used to identify the socio-environmental factors associated with DF infection . A Poisson regression model was employed to examine the spatiotemporal patterns in the spread of DF . Spatial clusters of DF were primarily concentrated at the southwest part of Guangzhou city . Age group ( 65+ years ) ( Odd Ratio ( OR ) = 1 . 49 , 95% Confidence Interval ( CI ) = 1 . 13 to 2 . 03 ) , floating population ( OR = 1 . 09 , 95% CI = 1 . 05 to 1 . 15 ) , low-education ( OR = 1 . 08 , 95% CI = 1 . 01 to 1 . 16 ) and non-agriculture ( OR = 1 . 07 , 95% CI = 1 . 03 to 1 . 11 ) were associated with DF transmission . Poisson regression results indicated that changes in DF incidence rates were significantly associated with longitude ( β = -5 . 08 , P<0 . 01 ) and latitude ( β = -1 . 99 , P<0 . 01 ) . The study demonstrated that social-environmental factors may play an important role in DF transmission in Guangzhou . As geographic range of notified DF has significantly expanded over recent years , an early warning systems based on spatiotemporal model with socio-environmental is urgently needed to improve the effectiveness and efficiency of dengue control and prevention .
Dengue fever ( DF ) is a widespread vector-borne viral infectious disease which has a rapidly increase in infections , geographic distribution , and the severity cases[1] . The rapidly expanding global footprint of DF has evolved to a major public health problem due to increased geographical extension , climate changes , population growth and global travel in the last 50 years [2] . DF is endemic and has been reported in more than 100 countries including the southeast Asia , the Americas , the western Pacific , Africa [3] . 3 . 9 billion people are at the potential risk of DF in these endemic regions [4] . The high economic burden brought could not been neglected [5] . Historically , DF has re-emerged in China in 1978 , from its first appearance in Foshan city of Guangdong province and then subsequently it has been reported in other areas such as Guangdong , Guangxi province and Hainan island after 32 years [6] . Since then , DF outbreak and epidemics were reported every year affecting several thousands of people , predominantly in the southeast coastal regions including Hainan , Guangxi , Fujian , Zhejiang and Yunnan provinces [7] . It was assumed that the large-scale epidemics occurred before 1990s was due to the imported dengue virus [8] . During the period 1978–2008 , a total of 655 , 324 cases including 610 deaths were recorded by Guangdong province Health Department . Vector-borne scientists have predicted that DF could potentially become an endemic disease in China [9] . For example , in 2014 , a large outbreak with more than 37 , 000 cases has occurred in Guangzhou city [10] . Due to the lack of effective vaccine and antiviral treatment , vector control is considered as a useful measure towards prevention of dengue disease [11] . DF epidemics in the different districts appeared not homogenous , due to the change of the transmission pattern of spatial and time [11] . However , the spatial clusters , socio-environmental factors at the new and smallest administrative unit ( street level ) and the temporal cluster at daily level in Guangzhou have not been explored in this epidemic regions . To help decision-makers or policy-makers in targeting the prevention and control areas and reduce the economic burden , vector control techniques could be selectively applied at high-risk areas or clusters of DF . Hence , this study aimed to examine the spatiotemporal pattern of DF using spatiotemporal scan technique at street-level[10 , 12 , 13] , to identify the socio-environmental risk factors of DF and to explore the spread of DF over the study period for improving prevention and control of DF and guiding to future study .
Ethical approval for this project was approved by Sun Yat-Sen University Ethical Review Committee ( Approval No: 2015024 ) and all of the data analyzed were anonymized . Guangzhou , as the third-largest city in China and the world-famous trade port , located at the Pearl River Delta Region of Guangdong province and spanned from 112° 57' to 114° 03' E longitude and 22° 26' to 23° 56' N latitude [14] ( Fig 1 ) . The total area under the city's administration is 7 , 434 . 4 square kilometers and the permanent resident population is 12 , 700 , 800 ( 2010 ) [15] . Guangzhou city has 12 districts and 166 streets . The permanent resident population of each street ranged from 3397 to 391287 ( 2010 ) [16] . Monthly averages range from 13 . 6°C in January to 28 . 6°C in July , while the annual mean is 22 . 6°C [14] , the relative humidity is approximately 68% , whereas annual rainfall in the metropolitan area is over 1 , 700 mm [14] . Daily data on indigenous DF cases were collected from China Notifiable Disease Surveillance System and Guangzhou Center for Disease Control and Prevention ( CDC ) for the years 2006 to 2014 . There were 240 cases with unknown street-level address in 2014 . These cases were excluded in this study . DF cases were diagnosed according to the national diagnostic criteria of DF , including the epidemiological exposure history , clinical manifestations and laboratory confirmation [10] . The street-level geographic vector polygon map of Guangzhou city was obtained from Guangzhou CDC and the latitude and longitude of the centroid of each street were calculated directly in the ArcGIS 10 . 0 software . The counts number of the indigenous DF cases were aggregated to counts at the street-level . Street-wise socio-demographic data was retrieved from the demographic bulletin of the 6th National Population Census [17] . Data on the urban-rural structure of communities was collected from the National Bureau of Statistics of People's Republic of China [18] . The location of all cases were matched to the street-level vector map based on their home addresses . The annual occurrence of street-wise first indigenous DF cases were mapped along with the date of onset . A retrospective spatiotemporal scan test was implemented using SaTScan ( Version 9 . 4 . 1 ) software . Firstly , the spatiotemporal cluster analysis of DF in Guangzhou from 2006 to 2014 was conducted annually . In brief , DF case , population and coordinates data were used as inputs in SaTScan . Scanning window for the spatiotemporal scanning method is the spatial scan combining with temporal scan . The scan window is a cylinder . The base of the cylinder is circle which represents the spatial dimension , and the height of the cylinder represents the temporal dimension . The radius of the circle varied from zero to the maximum spatial cluster size of 50% of the population at risk which could avoid pre-selection bias . In this study , the heights of the cylinder were varied daily from zero to 1 year . The results with the statistical significance of p-value were reported by Monte Carlo simulation replication at 9999 . The maximum log likelihood ratio ( LLR ) calculated in Poisson distribution is considered as the most likely cluster . The secondary clusters are defined as the second maximum LLR estimated by poisson model [19] . In this study , a holistic purely spatial cluster analysis from 2006 to 2014 was implemented with the same upper limits in the spatial window . ArcGIS ( Version 10 . 3 . 1 ) were used to convert the outputs of scan analysis into maps and visualize the spatial and temporal clusters . Univariate logistic regression and a stepwise logistic regression model were conducted to explore the relationship between the socio-environmental risk factors and the street with DF cases at high risk and low risk . Dichotomous dependent variable was set based on relative risks ( RRs ) of each street from the purely spatial cluster analysis result . The streets with RRs ≥1 were assigned “1” and those with RRs <1 were assigned “0” . The potential socio-environmental risk factors included at street-level were as following: percentage of people in each age-group; floating population; non-agriculture population; percentage of people with lower education ( lower than undergraduate ) ; percentage of different type communities ( urban communities , urban-rural communities and rural communities ) in all of the communities in each street . The floating population is defined as the people living in the street currently whose census registers were recorded in other street of the district in Guangdong province . There are two type of the census registers including agriculture and non-agriculture in China . The non-agriculture population was defined as the people whose census registers were recorded in the urban , not in the rural . The variations in the distribution of DF along the latitude and longitude of streets centroids were detected using Poisson model during the study period [20] . To explore the difference of DF distribution in the last three years and the first six years , we divided the study period into two periods: period 1 is from 2006 to 2011 and period 2 was from 2012 to 2014 . The dependent variable in this modeling was the differences of DF annual mean incidence rates of the all the streets which occurred DF epidemic between the period 1 and period 2 in Guangzhou .
The epidemic pattern of daily indigenous DF cases fluctuated during 2006 to 2014 with three major outbreaks in 2006 , 2013 and 2014 ( Fig 2 ) . The number of DF cases ranged from 0 to 1 , 627 cases daily ( mean = 52 . 9 , SD = 182 . 48 ) . Interestingly , outbreaks showed an increasing trend after 2010 . Fig 2 also displayed the daily variability of the number of streets with infected cases from 2006 to 2014 . The peaks in DF cases generally coincided with streets of high DF cases . The spread of indigenous DF incidence rates in each high-risk street was displayed in Fig 3 . All streets in Yuexiu , Liwan and Haizhu district , several streets in Baiyun , Panyu and Tianhe districts and streets in Huangpu , Luogang and Nansha district had relatively high DF spread . The streets with highest increase in DF were located in Baiyun , Panyu and Huangpu district . Baiyun districts included the streets with highest spread . Fig 4A showed the spatial distribution of high-risk areas or clusters of DF at street-wise . There were 75 high risk streets ( RRs ≥ 1 ) in the southwest of Guangzhou city . These streets were located mostly in Yuexiu , Liwan and Haizhu district , the southern part of Baiyun , the northern part of Panyu , Tianhe and Huangpu district . Fig 4B depicts the sum of daily indigenous DF cases of the streets with RRs <1 and RRs ≥ 1 during the study period . Spatial and temporal clusters of indigenous DF cases were showed in Fig 5A and 5B , respectively . The most likely clusters ( n = 9 ) were detected each year during 2006 to 2014 ( P<0 . 01 ) and the secondary clusters ( n = 2 ) were identified in 2006 and 2013 ( P<0 . 01 ) ( Table 1 ) . The most likely clusters were concentrated in streets of Yuexiu , Liwan and Haizhu districts . In 2006 , the most likely cluster included the southern Panyu district and part of the southern Nansha district whereas the secondary cluster included the northern Conghua district . In 2014 , the most likely clusters included the farther northern Baiyun district with the secondary clusters in the northern Zengcheng district ( Table 1 ) . The significant temporal clusters were found in autumn season , i . e . , late August to early November during 2006 to 2014 , except in 2008 and 2009 . Fig 5C shows the streets with the occurrence of first indigenous DF cases each year . The first indigenous DF cases occurred within or close to the spatial cluster circles yearly , except in 2014 , where it occurred in the distant Nansha district . The results of univariate and step-wise logistic regression model analyses were presented in Table 2 . In the univariate analysis , the age-groups , the percentage of non-agricultural population and the urban-rural population per street had significant association with DF risk: 0–14 years ( OR = 0 . 84 , 95% CI = 0 . 75 to 0 . 94 ) , 15–64 years ( OR = 0 . 94 , 95% CI = 0 . 88 to 0 . 99 ) , urban-rural communities ( OR = 0 . 97 , 95%CI = 0 . 95 to 0 . 98 ) and rural communities ( OR = 0 . 95 , 95% CI = 0 . 93 to 0 . 97 ) had negative association with DF risk whereas 65+ years ( OR = 1 . 26 , 95% CI = 1 . 15 to 1 . 39 ) , nonagricultural population ( OR = 1 . 05 , 95%CI = 1 . 04 to 1 . 07 ) and urban communities ( OR = 1 . 03 , 95% CI = 1 . 02 to 1 . 05 ) had positive association with DF risk . After the stepwise variable selection , four variables were entered into the multivariate logistic regression model . The results demonstrated that DF was statistically significantly associated with population belonging to 65+ years ( OR = 1 . 49 , 95% CI = 1 . 13 to 2 . 03 ) , floating population ( OR = 1 . 09 , 95% CI = 1 . 05 to 1 . 15 ) , non-agricultural population ( OR = 1 . 07 , 95% CI = 1 . 03 to 1 . 11 ) and low-education population ( OR = 1 . 08 , 95% CI = 1 . 01 to 1 . 16 ) . A statistically significant and negative association was obtained between the spread of DF incidence rates and longitudes ( β = -5 . 08 , P < 0 . 01 ) and latitudes of the streets ( β = -1 . 99 , P < 0 . 01 ) ( Table 3 ) . The results indicated that DF incidence rates increased with the areas geographically variation which may provide with the information of target streets for DF prevention and control in the future .
The results of this study suggested that DF incidence rates in the different districts appeared to be heterogeneous which was due to the changes in the transmission pattern of DF spatially and temporarily . A previous study has indicated that the prevention and control strategies towards DF will depend on high-risk and low-risk clusters [21] . Understanding and identifying the potential spatial and temporal clusters of DF transmission is the fundamental measure for surveillance and control [22] . A couple of studies have conducted cluster analysis of DF in Guangdong [23 , 24] . Previous research identified six risk factors for DF infection in Pearl River Delta [25] based on 2013 dengue surveillance data , which may improve our comprehension of the differences and socio-environmental factors on DF incidence rates . But in addition , few other studies have demonstrated that socio-demographic factors , such as population growth , levels of education , demographic structure and urbanization could influence the DF spread [26–30] . However , our research used a dynamic spatial and temporal analysis based on long term data ( ie . , January 2006 and December 2014 ) to detect the spatial clusters of DF and identify associated socio-environmental factors at a street level in Guangzhou . Moreover , Guangzhou was struck by an exceptionally severe outbreak in 2014 , resulting in almost 40 , 000 laboratory-confirmed DF cases . This outbreak is the largest and most severe epidemic of dengue fever ever documented in China , with incidence rates exceeded the combined total of all previous years [31 , 32] . This study detected spatial clusters of DF high risk regions in Guangzhou city and suggested the geographic range of notified dengue cases has significantly expanded over recent years . Relative importance of risk factors may vary across space and time . This finding will provide useful information for developing dynamic early warning system for DF transmission . We have performed stepwise logistic regression model as this technique was applied in the vector-borne diseases research . Our results demonstrated that old aged population ( 65+ years ) , floating population , low-education people and non-agriculture people were the potential determinants for the spread of DF . DF transmission has been reported in both rural and urban areas , and the dengue viruses have fully adapted to a human-Aedes aegypti-human transmission cycle , previous studies showed that the urbanization was linked to the DF incidence rates [33 , 34] . Guangzhou , as a large urban center of the tropics , where crowded human populations , especially nonagricultural population , live in intimate association with equally large mosquito populations . This setting provides the ideal home for maintenance of the viruses and the periodic generation of epidemic strains . In this longitudinal study , the result indicated nonagricultural population was positively related with DF risk , the central urban area and the old city area were the high-risk areas , where most aged ( 65+ years ) Guangzhou residents lived . The streets with high nonagricultural population in Guangzhou normally have higher population density and poor housing conditions and less environmental management . Previous studies have suggested that the accumulation of a susceptible population was essential to trigger DF epidemics [35] . In this study , a large number of floating population may be more susceptible for DF transmission . Residents , especially the aged , have the habit of planting flowers or hydrophyte in flowerpots or in household courtyards in Guangzhou . Several studies have identified the vegetation and breeding mosquitoes to DF that “vegetation can provide resting or feeding sites for mosquitoes or can serve as a proxy for the presence of breeding sites . " Water storage , containers with an abundance of organic matter ( e . g . those used for striking plant cuttings ) or those amongst foliage or under trees ( e . g . discarded plastic ) . As such progeny have been linked to a greater risk [36] . These containers with water provide a suitable breeding condition for mosquitoes . The water landscape and afforest landscape around the houses were also a perfect breeding habitat for mosquitoes . In addition , the movement of aged population may be limited to house surroundings and nearby areas , thus , increasing the chances of exposing themselves to mosquitoes . People with low-education generally have lack of knowledge and practices on the prevention measures of DF . These people usually work as laborers and spend most of their time outside , this in turn , may have given the possibility of being bitten by the mosquitoes . Another possible reason could be that these people live in rented apartments where the sanitary conditions are sub-optimal , thus this may have increased the chances of mosquitoes breeding and exposure . The results from temporal cluster analysis indicated that the DF clusters occurred mainly in autumn , particularly , in late August to early November . Indigenous DF cases peaked seasonally despite limited intra-annual climatic variability and seasonal fluctuations . In addition , the availability of immature densities of Aedes albopictus ( primary vector in Guangzhou ) was consistent with the dengue seasonality [37 , 38] as the vector biology and viral replication are temperature and moisture dependent [39 , 40] . These results could be used in planning future prevention and control measures towards DF , particularly , during the high-risk season . The consistent occurrence of first indigenous DF case within or close to the spatiotemporal clusters during the study period , except in 2014 requires further investigation . Over all , in the high risk streets , there were more indigenous DF cases than in the low risk streets: The cases in high risk streets occurred earlier and accelerated faster than those in the low risk streets as well . Without considering the number of cases , similar waves and crests were found in 2 sorts of streets . This could be due to the daily movements of working people from their living areas to working areas , i . e . , the high-risk areas . We observed an interesting result in the epidemic patterns of DF incidence rates during the study period . If the first case occurred in early summer , i . e . , June or July , large outbreaks often occurred . For example , large epidemics in 2006 , 2012 , 2013 and 2014 were initiated with the occurrence of first case in June , July , July and June respectively . Although there were not many DF cases in 2012 , the longest cluster period of DF was observed . On the contrary , if the first case occurred too early and too late , the large outbreaks often could not be triggered . In 2007 and 2010 epidemics , the first case occurred in April whereas in 2008 , 2009 and 2011 epidemics , the first case occurred in November , August and September . If the first case occurs too early , the local department of health may plan to provide early warnings of DF outbreaks and implement prevention and control measures , whereas if it occurred too late , the reduced density of mosquito and the capacity of virus loading could help to decrease the risk of a large DF outbreak . Although imported cases was considered as an important trigger for the DF outbreak in Guangzhou , scientists could not confirm whether or not the dengue outbreaks in Guangzhou were initially triggered by the imported cases [39] . So other uncertainties of DF outbreak are still unknown and needs further studies . In recent years , the impact of climate change on the transmission of mosquito-borne diseases has been studied in China [40] . Our results showed significant variation in the spatial distribution of DF in Guangzhou and that the geographic range of notified cases has expanded in this city ( from south towards north and concentrate on the southwestern Guangzhou city ) over the study period . Previous study reveal the movement tracks of the centre of mass for annual incidence rate of DF at municipality level in China , showing that the geographic expansion of dengue epidemics , such as gradually shifting from southern China ( Guangdong , Guangxi , and Hainan ) to northeastern China ( Fujian and Zhejiang ) and southwestern China ( Yunnan ) [41] . The associations between the spread of DF incidence rates and longitude and latitude were observed in this work , also demonstrated that DF has spread towards the southwestern Guangzhou city during the study period . Dengue is a complex disease and the spatiotemporal distribution involves socio-environmental factors , such as climate change , population movement , mosquito density and urbanization . Hence , future studies should include the impact of climatic and entomological factors on the transmission of DF in Guangzhou city . To our knowledge , this is the first study to investigate the spatiotemporal clusters of DF and assess the socio-environmental factors in Guangzhou city using the spatial techniques at street-level . The study provides readily accessible information on DF spread and GIS maps on high-risk areas which can be used by the local Department of Health towards prevention and control of DF in Guangzhou . There are two limitations in this study: 1 ) Model included few variables on socio-environmental factors , as it was difficult to obtain all other street-level data . 2 ) As this study is an ecological study , measurement and information biases are possible . For example , the data on the socio-demographic factors were only obtained from the 6th Nation Population Census ( collected in 2010 ) as the national demographic census in China was only conducted once 10 years . The socio-demographic data varied by time in Guangzhou and may have little impact on our results . However , we believe that the relative changes by different street level is unlikely to change dramatically in Guangzhou . We obtained the floating population in Guangzhou between January 1st 2006 and December 31st 2014 by accessing the registers at the online Guangzhou Statistics Bureau website ( http://www . gzstats . gov . cn/ ) . In addition , under-reporting is most likely possible as people with sub-clinical symptoms usually do not seek medical attention . The biases and drawbacks of stepwise multiple regression are well established within the statistical literature , including bias in parameter estimation , inconsistencies among model selection algorithms , etc . Whittingham et , al . discussed these issue and showed that stepwise regression allows models containing significant predictors to be obtained from each year's data [42] . In this study , we conducted stepwise logistic regression model as this technique was applied in the vector-borne diseases research , so as to select the main risk factors and develop predictive model . The spatial-temporal analysis presented in this paper differs from the one by explaining the observed distribution and perhaps ultimately permitting prediction . In conclusion , this study has detected spatiotemporal clusters and variation of DF epidemics , and assessed socio-environmental risk factors for DF in Guangzhou city . These results could be implemented towards prevention and control measures of DF in high-risk areas in Guangzhou . | Dengue fever ( DF ) as a mosquito-borne viral disease remains a challenge for the prevention and control caused by the increased population , global development , human movement , and urbanization in the last five decades . The largest DF outbreak occurred with more than 40 , 000 cases in Guangdong in 2014 since DF re-emerged in China . The accurately spatiotemporal identification of DF transmission and the related socio-environmental factors are considered to be important for the strategy decision-making of the official government . This study first identified the spatiotemporal pattern and socio-environmental factors associated with DF occurrence at street and daily level in Guangzhou , China from 2006 to 2014 , using spatiotemporal scan statistical methods . The results suggested that DF control should be targeted in the southwest of Guangzhou during autumn , particularly 75 high risk streets . We found that the aged population , floating population , low-education population and the non-agricultural population significantly contributed to the DF clustering risk at street level . Finally , a spread trend of DF toward southwest part of Guangzhou was noticed . These results could be implemented towards prevention and control measures of DF in high-risk areas in Guangzhou . |
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Genetic variation at immunoglobulin ( Ig ) gene variable regions in B-cells is created through a multi-step process involving deamination of cytosine bases by activation-induced cytidine deaminase ( AID ) and their subsequent mutagenic repair . To protect the genome from dangerous , potentially oncogenic effects of off-target mutations , both AID activity and mutagenic repair are targeted specifically to the Ig genes . However , the mechanisms of targeting are unknown and recent data have highlighted the role of regulating mutagenic repair to limit the accumulation of somatic mutations resulting from the more widely distributed AID-induced lesions to the Ig genes . Here we investigated the role of the DNA damage sensor poly- ( ADPribose ) -polymerase-1 ( PARP-1 ) in the repair of AID-induced DNA lesions . We show through sequencing of the diversifying Ig genes in PARP-1−/− DT40 B-cells that PARP-1 deficiency results in a marked reduction in gene conversion events and enhanced high-fidelity repair of AID-induced lesions at both Ig heavy and light chains . To further characterize the role of PARP-1 in the mutagenic repair of AID-induced lesions , we performed functional analyses comparing the role of engineered PARP-1 variants in high-fidelity repair of DNA damage induced by methyl methane sulfonate ( MMS ) and the mutagenic repair of lesions at the Ig genes induced by AID . This revealed a requirement for the previously uncharacterized BRCT domain of PARP-1 to reconstitute both gene conversion and a normal rate of somatic mutation at Ig genes , while being dispensable for the high-fidelity base excision repair . From these data we conclude that the BRCT domain of PARP-1 is required to initiate a significant proportion of the mutagenic repair specific to diversifying antibody genes . This role is distinct from the known roles of PARP-1 in high-fidelity DNA repair , suggesting that the PARP-1 BRCT domain has a specialized role in assembling mutagenic DNA repair complexes involved in antibody diversification .
The generation of high affinity antibodies through affinity maturation in B cells relies on the introduction of mutations into expressed immunoglobulin ( Ig ) gene alleles by somatic hypermutation ( SHM ) or gene conversion ( GCV ) . These closely related processes are mediated through introduction of a DNA lesion by activation-induced cytidine deaminase ( AID ) , followed by fixation of a mutation at or nearby the damage site via a mutagenic , rather than the usual conservative , DNA repair mechanism [1] , [2] . Mutations must be restricted to the Ig genes to protect the rest of the genome from accumulating potentially dangerous mutations , although this protection is far from perfect . Analysis of the mechanisms that direct mutagenesis to Ig loci has revealed the existence of multiple layers of regulation . One level of control is temporal regulation of expression of AID to activated B-cells in germinal centers , where cells with non-beneficial mutations can be quickly eliminated [3] . Another level of control is targeting of AID-mediated deamination to expressed Ig loci and , less frequently , a subset of other expressed genes through an as yet undefined transcription-dependent mechanism [4] , [5] . A third level of control is the Ig-specific targeting of mutagenic repair . While identical lesions at non-Ig loci are usually repaired by a high-fidelity mechanism , at Ig loci , a mutagenic repair pathway predominates , either through translesion synthesis by error-prone polymerases or GCV [6] . While mutagenesis is necessary for high affinity antibody production , mistargeting of either the AID-mediated deamination events or the mutagenic repair of incidental mutations has been linked to the generation of B-cell lymphomas and leukemias through the introduction of mutations into tumor suppressors and proto-oncogenes such as Bcl6 , Myc , RhoH , Pim1 , and Pax5 [7] , [8] , [9] . Recent data suggest that mistargeting of mutations occurs more frequently than previously thought , highlighting the importance of understanding how the processes that induce these mutations are targeted to specific genetic loci [6] , [10] , [11] . However , insights into the biochemistry through which either DNA lesions or mutagenic repair are targeted have been difficult to achieve , and so far have been limited to the definition of cis-acting DNA elements required for active mutagenesis at Ig loci [12] , [13] . The enzyme PARP-1acts as a gatekeeper of DNA repair . It is one of the first proteins to respond to DNA damage , where it binds and recruits the appropriate DNA repair enzymes . There is a slower , background level of repair in PARP-1 deficient cells , but DNA repair is severely impaired and these cells are rendered hypersensitive to DNA damaging agents such as methyl methane sulfonate ( MMS ) , N-Methyl-N′-Nitro-N-Nitrosoguanidine ( MNNG ) , and ionizing radiation [14] , [15] , [16] , [17] . In addition to a well-established role in base excision repair ( BER ) , there is evidence that suggests that PARP-1 may also play a role in repairing double strand breaks , although whether by homologous recombination ( HR ) , non-homologous end joining ( NHEJ ) , or micro-homology mediated end joining ( MMEJ ) is still the subject of lively debate [14] , [18] , [19] , [20] , [21] . A potential clue to the mechanisms of mutation targeting has been suggested by a recent report that the enzyme PARP-1 is constitutively bound to a DNA sequence within the Bcl-6 gene [22]—a locus which is frequently the subject of off-target mutations in B-cells [9] , [23] . This observation prompted us to evaluate mutation targeting to the Ig loci in a PARP-deficient variant of chicken DT40 B-cell line , in which Ig loci are constitutively mutated via GCV . Remarkably , we observe a nearly complete loss of GCV at Ig loci in PARP-deficient cells that is independent of the rate of AID-induced DNA lesioning . Functional analysis of PARP-1 variants demonstrated that PARP-1 is necessary for repair of AID-mediated DNA lesions , and that the capacity of PARP-1 to support GCV requires its BRCT domain which , to our knowledge , has no previously characterized function .
PARP-1 is thought to be one of the first proteins to respond to DNA strand breaks , where it binds and recruits the appropriate DNA repair enzymes [14] , [18] , [19] , [20] , [21] . Consistent with this model , PARP-1 deficient DT40 cells are rendered hypersensitive to DNA damaging agents such as MMS , MNNG , and ionizing radiation [14] , [15] , [16] , [17] . To further define the parameters of PARP-1's role in these repair processes , we evaluated the sensitivity of PARP-1−/− DT40 cells to MMS exposure , and assessed the capacity of human WT PARP-1 ( hPARP ) , a DNA binding mutant of hPARP-1 ( dZF2 ) , and two enzymatically inactive variants of hPARP-1 ( DBD-CAT and E988K ) to restore survival upon MMS challenge ( Figure 1A and B ) . As expected , hPARP expression fully restores MMS resistance to the PARP-1−/− cells , while the PARP-1 mutant lacking a DNA binding domain due to mutations in the cysteines critical for zinc finger folding is similar in phenotype to the knockout . In contrast , a catalytic inactive PARP-1 mutant that contains only the DNA binding domain and the catalytic domain minus the WGR portion exhibited poorer survival than the PARP-1−/− cells ( Figure 1B ) . As previously shown , this is likely due to the aggregation of inactive PARP-1 molecules at the site of DNA damage which could block access of DNA repair enzymes to the damaged site and/or deplete free PARP-1 and prevent binding and recruitment of repair enzymes to other sites of damage [28] . Surprisingly , we found that even after repeated attempts in parallel with successful transfection of other PARP-1 variants , we could not reconstitute PARP-1−/− cells with a full length , inactive variant of PARP-1 ( E988K ) , and thus we could not analyze the phenotype of PARP-1−/− cells reconstituted with that variant . As this variant had been successfully expressed in mouse embryonic fibroblasts previously [28] , we reasoned that a major difference between the DT40 cell context and other cell lines is the presence of constitutive AID expression and diversification of the Ig loci in DT40s . We therefore attempted expression of PARP-1 ( E988K ) in both wild type and AID-deficient DT40 cells . Remarkably , we were unable to grow out any resistant clones in wild type DT40 cells , but clones stably expressing E988K readily grew from the parallel transfection of AID−/− DT40s , with the transfection yielding dozens of transformants of which 15 were subjected to further analysis to verify expression ( Figure 1C shows a representative clone ) . When we subsequently attempted to reconstitute E988K , AID−/− DT40 clones with AID in the 4/TO vector ( Invitrogen ) with zeocin selection , of the 4 clones which eventually grew in the selective media , all had downregulated expression of E988K PARP-1 to below our limits of detection ( as detected by Western blot , unpublished data ) . This dramatic selection against clones that coexpress AID and E988K suggested that PARP-1 plays an important role in the repair of lesions induced by AID . The sensitivity of DT40s expressing AID to expression of PARP-1 E988K suggested that PARP-1 has a requisite role in repair of AID-induced lesions , either at mutating Ig loci or off-target lesions genome-wide . To assess the function of PARP-1 at mutating Ig loci , we sequenced the variable regions of the Ig light and heavy chains in PARP-1−/− and WT cells . While mutations still detectably accumulated within Ig genes , although at a dramatically reduced rate , the PARP-1−/− cells had essentially no discernible GCV events ( p< . 0001 at IgL and p = . 0436 at IgH , Figure 2A and B ) . To confirm that the observed defect in GCV was due to the actions of PARP-1 , we examined a cell line reconstituted with human PARP-1 ( hPARP ) ( Figure 2C ) and found that hPARP restored the GCV frequencies to wild type levels or above at both IgL ( p = . 0001 ) and IgH ( p = . 0108 ) ( Figure 2A and B ) , suggesting that PARP-1 not only is required to repair AID-mediated deaminations but also influences the outcome of the resulting repair . In this analysis , all mutations that matched chicken Ig pseudogene sequences published in the NCBI public database were counted as GCV events . This includes a subset of “ambiguous” mutations , which match the pseudogenes but occur as a single nucleotide change such that we cannot exclude the possibility that it arose as a point mutation . While the work of Saberi et al . shows that these mutations are generally true GCV events [29] , to ensure that the process of classifying GCV events is not affecting our conclusions , we have also analyzed the data with the “ambiguous” mutations excluded from the analysis or scored as point mutations ( Figure S2 ) . In these additional analyses , the difference in GCV frequencies between WT and PARP−/− cells at the IgL locus remains highly significant ( p< . 0001 and p = . 0006 , respectively ) , although the number of mutations scored at the less well defined IgH locus was not sufficient to reveal a significant difference in these more conservative analyses ( p = . 1023 and p = . 1280 , respectively ) . Subsequent restoration PARP-1 expression restores GCV at both IgL ( p = . 0002 and p = . 0029 ) and IgH ( p = . 0108 and p = . 0108 ) . The individual mutations observed at IgL can be found in Figure S1 and a schematic of mutations observed at IgH is shown in Figure S2E . In the course of characterizing the GCV of PARP-1−/− cells , we observed that the PARP-1−/− cell line and its hPARP derivative express less AID than the WT DT40 cells , and that the PARP-1−/− and hPARP lines had a lower overall mutation rate . As low AID expression seemed likely to be the inadvertent result of selection during derivation of the parental PARP-1−/− clone that was subsequently carried over to the reconstituted hPARP cell line , we investigated whether the reduction in AID expression and corresponding overall mutation rate could be influencing the proportion of GCV events . For this purpose , we used a retroviral vector to overexpress gallus gallus AID in each cell line , and matched AID expression as well as IgL transcript levels in selected clones ( Figure 3B and C ) , as the rate of target gene transcription has also been shown to affect mutation rate [30] , [31] , [32] . Consistent with previous reports , overexpressing AID does not significantly increase the proportion of mutations which are GCV events [29] and does not restore GCV to the PARP-1−/− cells , in spite of increasing the AID expression to well above the original WT levels ( Figure 3A and D ) . Interestingly , overexpression of AID does not significantly increase the mutation rate in the PARP-1−/− cells as it does in hPARP , and may be revealing a dose-dependent effect of PARP-1's ability to mediate GCV repair of deamination events as increasing AID expression results in a WT GCV phenotype intermediate to PARP-1−/− and hPARP ( Figure 3A ) . Furthermore , our ability to generate high stable expression of AID in PARP-1−/− cells suggests that the reduced mutation rate in PARP-1−/− cells relative to hPARP reconstituted cells cannot be explained by loss of cells that sustain AID-mediated lesions , but rather may reflect a reduced rate of mutagenic repair of AID-mediated lesions . As an initial step towards defining the role of PARP-1 in repair of AID-induced lesions , we investigated the influence of chromatin accessibility on mutagenic repair at Ig loci . It has been proposed that PARP-1 modifies histones at the site of DNA damage to open chromatin and increase the accessibility of a damaged site to repair enzymes [33] . To determine whether altered chromatin accessibility could account for deficient GCV in PARP-1−/− cells , we cultured PARP-1−/− cells in trichostatin A , a histone deacetylase ( HDAC ) inhibitor , as this has previously been shown to increase GCV rates , presumably via increasing accessibility of the pseudogene repair templates to repair machinery [34] . While the percent and length of GCV events in WT cells went up dramatically , indicating that the treatment was effective ( Figure 4A ) , the PARP-1−/− cells are still unable to carry out GCV ( Figure 4B ) . Thus , the mechanism of PARP-1's role in promoting GCV at Ig loci is not through increasing chromatin accessibility . Ig GCV in DT40s proceeds through a pathway involving HR . Since PARP-1 has previously been implicated in HR repair , we assessed whether PARP-1's capacity to promote Ig GCV was a part of a general role for PARP-1 in promoting HR by evaluating the capacity of PARP-1−/− cells to mediate HR in response to a single DNA double strand break generated by the homing endonuclease I-SceI ( Figure 5A ) . In a single-copy , integrated assay of HR , we find that the expression of PARP-1 does not promote HR . Rather , in agreement with studies by Wang et al . , PARP-1 may suppress HR and promote alternate DNA repair pathways ( Figure 5B ) [19] . This result indicates that the activity of PARP-1 at Ig loci is not part of a global role promoting HR , and further supports the hypothesis that PARP-1 has a specific role within GCV as a mutagenic repair pathway operating at Ig loci . It also raises the question of whether it is best to consider GCV at Ig loci as an unusual , mutagenic instance of otherwise high-fidelity HR or if a better model would be to consider GCV one pathway for mutagenic repair at Ig loci that uses much , but not all , of the same repair machinery as HR . To further investigate the mechanism by which PARP-1 mediates GCV , we evaluated the role of specific PARP-1 subdomains in promoting mutagenic repair at Ig loci . Hypothesizing that the role of PARP-1 in mutagenic repair at Ig loci may be distinct from its established role in high-fidelity BER , we compared DNA BER and Ig GCV among PARP-1−/− clones reconstituted with PARP-1 variants containing deletions or inactivating mutations in each domain . Consistent with their lack of ability to reconstitute any detectable DNA BER ( see Figure 1 ) , sequence analysis of Ig loci in PARP-1−/− cells reconstituted with the dZF2 or DBD-CAT variants indicated that expression of either of these PARP-1 variants was unable to reconstitute Ig GCV ( Figure S3 ) . We then analyzed PARP-1 variants that retained significant capacity to reconstitute BER in the PARP-1−/− cells . Analysis of PARP-1−/− cells reconstituted with variants of PARP-1 containing either a deletion of the BRCT domain ( dBRCT lacks aa384–479 ) or the automodification domain sparing the BRCT domain ( dAMD lacks aa372–383 and 480–524 ) ( Figure 6A ) gave surprising results . In an MMS survival assay , the dBRCT cells survived as well as WT cells ( Figure 6C ) , consistent with previous data from our lab and others indicating that the BRCT portion of the automodification domain is not required for PARP activation via base-damaging agents or PARP-dependent base excision repair [16] , [35] . In the same assay , dAMD-expressing cells exhibited an intermediate survival phenotype when treated with MMS ( Figure 6C ) , consistent with the dAMD mutant exhibiting a significant but delayed capacity to catalyze NAD degradation and ADPR production in response to base damaging agents [16] . We then assayed the ability of these cell lines to undergo GCV at IgL and found that the capacity to mediate GCV does not correspond with the ability of the cells to carry out global high-fidelity DNA repair . In contrast , sequence analysis of Ig loci revealed that the dBRCT cells , like PARP-1−/− cells , were essentially unable to mediate GCV and had a correspondingly low overall mutation rate , whereas the dAMD cells were able to gene convert at IgL at or near WT levels ( Figure 6D and Figure S3 ) . This observation demonstrates that the role of PARP-1 in mutagenic GCV at Ig loci is distinct from its role in high-fidelity DNA BER . Furthermore , it implies a novel role for the BRCT domain of PARP-1 in mediating mutagenic DNA repair at Ig loci . When AID is overexpressed in cells overexpressing PARP-1 ( hPARP ) , we observe that the mutation rate increases accordingly . However , when AID is overexpressed in PARP-1−/− cells , we do not see a significant change in mutation rate . This suggests that while AID deamination is the initial rate-limiting step for SHM and GCV , expression of PARP-1 may further limit mutation rate during repair of these lesions ( Figure 7A and B ) . One potential explanation for these observations is that lack of PARP-1 selects against PARP-1−/− cells with high AID expression that would have accumulated mutations . To test this possibility , we evaluated the stability of AID overexpression over the 3 wk culture period used for accumulation of mutations , based on the rationale that selection pressure against mutations would lead to a decreased expression of AID . In this experiment , comparison of AID expression before and after 3 wk of culture revealed no detectable difference in AID expression , indicating that selection against the PARP-1−/− cells that would have accumulated mutations cannot account for the observed decrease in mutation rate in the PARP-1−/− cells ( Figure 7C ) . Thus , the observed lower mutation rate reflects either a decreased rate of DNA lesioning or an increased rate of faithful repair of AID-mediated DNA lesions . While the combined evidence of an established role for PARP-1 in DNA repair and the baseline level of mutations which continue to accumulate in the PARP-1−/− cells lead us to prefer the latter hypothesis , we sought to resolve this issue by expressing an UNG inhibitor UGI in the PARP-1−/− DT40 cells expressing endogenous levels of AID . UGI has been shown to reveal an “AID footprint” of activity by blocking BER of deaminated cytosines and increasing the relative frequency of mismatch repair mutations at A/T and “replication over” events , fixing C to T and G to A mutations at the site of deamination [36] . Correspondingly , we found that UGI expression results in decreased transversion mutations at G/C base pairs , and a relative increase in replication over errors at G/C and mismatch repair mediated mutations at A/T base pairs ( Figure 7E ) . Analyzing the rate of mutation by sequence analysis , we observed that UGI expressing PARP-1−/− cells exhibited an increased rate of mutation relative to their parent cell line , and now matched the mutation rate observed in the hPARP cells ( Figure 7D ) , chosen as a control because they express a similar level of AID ( see Figure 2 ) . These findings support our hypothesis that the low mutation rate of the parental cell line was not the result of decreased deamination events , but rather reflected an increased proportion of high-fidelity repair . This collection of evidence leads us to conclude that PARP-1 is promoting mutagenic repair at Ig loci and that AID-induced lesions are more likely to be repaired faithfully in the PARP-1−/− cells .
In this paper , we studied the role of PARP-1 in mediating repair of AID-induced lesions at diversifying Ig loci . By sequencing Ig light and heavy chain genes in PARP-1−/− DT40 B-lymphocytes , we found that the overall mutation rate is reduced and GCV is essentially eliminated in the absence of PARP-1 , and these defects can be fully reconstituted by expression of the hPARP-1 gene . Dissection of the biochemical mechanisms underlying PARP-1's involvement in Ig diversification demonstrated that PARP-1 DNA binding and BRCT protein-protein interaction domain are required , while the major site of automodification is not . Furthermore , while the overall mutation rate in PARP-deficient cells could be slightly increased by AID expression , restoration to WT levels required concomitant inhibition of uracil-DNA-glycosylase , suggesting that PARP-deficiency leads to an increased rate of high-fidelity repair at Ig loci through the UNG-dependent BER pathway . Taken together , our data suggest that PARP-1 is an important part of the biochemical processes that promote mutagenic repair over faithful repair at Ig loci , through a mechanism that requires an intact BRCT domain . The requirement for the BRCT domain of PARP-1 in promoting mutagenic repair at Ig loci revealed in our studies defines a novel role for the BRCT domain of PARP-1 . In spite of the extensive work which has lead to our current understanding of the subdomains of PARP-1 , the function of the BRCT portion of the automodification domain has remained a mystery , as it is not required for PARP-1 mediated repair of the other types of DNA damage which have been studied [16] , [35] . As BRCT domains are thought to function as protein-protein interaction domains , an intriguing possibility arising from our observations is that there may be a protein interaction partner of the PARP-1 BRCT domain which is involved in the mutagenic repair of AID-induced lesions but not involved in high-fidelity repair of other types of DNA damage . Identification of the hypothetical interaction partner ( s ) for the PARP-1 BRCT domain should further illuminate the mechanisms involved in PARP-1-dependent targeting and regulation of mutagenic DNA repair . PARP-1-dependent targeting of mutagenic repair may also account for the frequent mistargeting of mutations to the Bcl-6 gene in B-cells , as PARP is constitutively targeted to the Bcl-6 locus via sequence specific binding of its zinc fingers [37] , and it would be interesting to see if PARP-1 also binds specifically to other common sites of mistargeted mutations involved in malignant transformation such as Bcl-2 , Pim1 , Pax5 , Myc , or RhoH . Diverse lines of evidence have recently developed to support the concept that AID-mediated DNA lesions are not uniquely targeted to Ig loci [10] , [11] but rather that off-target deamination events in germinal center B-cells went undetected because those that occur outside the Ig loci primarily undergo high-fidelity repair [6] . This new information emphasizes the importance of understanding not just how AID is targeted to Ig loci but , equally as important , how mutagenic repair of deaminations is targeted to the Ig loci to protect B cells from the dangers associated with antibody diversification such as oncogenesis . Our data demonstrating a role for PARP-1 in both high-fidelity BER genomewide and mutagenic repair of deaminations at Ig loci present a mechanism for the targeting of mutagenic repair . PARP-1 , known to play a key role in directing repair of alkylated DNA bases towards BER through interactions with the PARylated AMD , also directs repair down a mutagenic pathway at Ig loci through interactions with the BRCT domain . Our data suggest that in the WT system , PARP may be acting at the site of a single strand break generated by AP lyase to either ( 1 ) directly promote GCV repair of breaks that would otherwise undergo high-fidelity repair or to ( 2 ) prevent high-fidelity repair at Ig loci , resulting in diversion to the less efficient error-prone repair pathways , including GCV and SHM ( Figure 8 ) . When normal GCV pathways are blocked , such as in XRCC2/3 knockouts , the lesions are diverted to other mutagenic repair pathways , such that the total mutation rate is unchanged [38] . In PARP-1−/− cells , the lesions enter a high-fidelity repair pathway rather than undergoing GCV , which is more consistent with the latter hypothesis . On the other hand , an early report from PARP-1−/− mice would favor the former hypothesis , as the authors failed to find a hypermutation defect in B cells from those mice [27] . However , in those experiments Jacobs et al . examined ex vivo germinal center B cells which had undergone intense selective pressure for the accumulation of mutations . As we have shown that SHM is not eliminated but reduced in the absence of PARP , and the reported number of mutations in the PARP-1−/− mice was reduced by more than one third ( although they did not report how many sequences were analyzed from each mouse ) , our results are not in contradiction to this early report , and PARP-1 could still play a role in inhibiting high-fidelity repair during SHM and GCV . Additionally , a subsequent report identified reduced T cell-dependent responses and reduced AID expression in PARP-1−/− mice [26] , which could be explained by increased cell death among hypermutating B cells consistent with the theory that PARP-1−/− B cells accumulate fewer mutations than WT B cells , and that the cells which fail to increase affinity by hypermutation are selected against in germinal centers . Our results establish a role for PARP-1 in the repair of the AID-induced lesions required for SHM and GCV , and further show that this role is mediated in part by the BRCT domain of PARP-1 , a domain with , to our knowledge , no previously known function . Without the BRCT domain of PARP-1 , the mutation rate is lower and GCV essentially absent in DT40 B cells . The requirement for the BRCT domain of PARP-1 may suggest that an interaction partner for PARP-1 is important for mediating this role , likely by inhibiting high-fidelity repair in the UNG-dependent BER pathway and allowing alternative , mutagenic repair pathways to predominate or , alternatively , by directly promoting GCV , thus allowing fewer lesions to enter a high-fidelity repair pathway .
The variable regions of IgL and IgH were amplified with Accuprime Pfx and blunt end Topo cloned . Single colonies were picked and sequenced using the M13 reverse primer . The primers used for PCR were IgL-F: CAGGAGCTCGCGGGGCCGTCACTGATTGCCG , IgL-R: GCGCAAGCTTCCCCAGCCTGCCGCCAAGTCCAAG , IgH-F: CGGGAGCTCCGTCAGCGCTCTCTGTCC , IgH-R: GGGGTACCCGGAGGAGACGATGACTTCGG . A baseline rate of mutation was determined by sequencing an irrelevant gene , the constant region of IgL , or the variable region of IgL in AID−/− cells . The polymerases Pfx ( Invitrogen ) and Pfu Ultra ( Stratagene ) were also compared ( Figure S4 ) . The baseline mutation rates were well below the observed mutation rates in this study , and we decide to proceed with our analyses using Pfx Accuprime polymerase . Sequences were aligned using Phred and Phrap and viewed in Consed . High quality base discrepancies were noted and subjected to further analysis . As the total mutation rate was much lower than 1 mutation/read , tracks of multiple mutations in a read were scored as GCV events . Single mutations for which no donor template could be identified were scored as point mutations . To categorize ambiguous mutations ( which match the pseudogene templates but occur in isolation ) , results were compared when these mutations were ( 1 ) excluded from the analysis , ( 2 ) always considered point mutations , and ( 3 ) always considered GCV events . These changes made little difference to the final analysis as the mutations in the PARP-1−/− and dBRCT cell lines very rarely matched the pseudogene sequences through either blast searches or direct comparison to a database of collected pseudogene sequences and so were able to be clearly scored as point mutations . While the ability of WT DT40s to undergo GCV is not in question , in order to avoid missing any GCV events which may occur in the PARP−/− and dBRCT cell lines , it was decided to use the most inclusive definition of a GCV event , which is every mutation that matches the pseudogene sequences ( annotated as “similar to immunoglobulin lambda chain” within gi 118098819 ) by blastn . At IgH , where the pseudogenes are not well characterized , a match resulting from a whole genome blast which was located adjacent to IgH was considered a suitable donor sequence for a GCV event and again , and all mutations with a donor template match were scored as GCV events . In spite of the poor assembly at IgH , the availability of donor templates as assessed is equivalent for all the cell lines used , so they may be compared . p values were generated using Fisher's exact test . Q-PCR was performed on a BioRad icycler using the BioRad SYBR green master mix . The annealing temperature was 58°C . The following primers were used: IgL-F: caggagctcgcggggccgtcactgattgccg , IgL-R: gcgcaagcttccccagcctgccgccaagtccaag , Beta Actin F: tgagagggaaatcgtgcgtgacatc , Beta Actin R: caggaaagagggttggaacagagcc . IgL transcript level was normalized to β-actin and hPARP expression using the ΔΔCt method . Data analysis was performed using Microsoft Excel and Graphpad Prism . Whole cell lysates were separated by SDS-PAGE , transferred to Millipore immobilon membrane , and probed with the following antibodies: PARP-1: ALX-210-302 ( Alexis Biochemicals ) , βActin: A2228 ( Sigma-Aldrich ) , and AID: LS-C34861 ( Lifespan Biosciences ) . Secondary antibodies were labeled with IRdye-700CW or IRdye-800CW and analysis and quantification was done on the LICOR Odyssey Infrared Imager . DT40 cells were cultured in RPMI with 10% FBS , 5% CS , Pen/Strep , and b-ME at 41° . PARP-1−/− cells and the WT parent cell line were a generous gift from S . Takeda . Cell lines reconstituted with hPARP-1 and the variant hPARP constructs were generated by electroporation of PARP-1 cDNA in the 5/TO vector ( Invitrogen ) at 550V , 25 uF in 4 mm cuvettes using the GenePulser from Bio-Rad . Colonies which grew in hygromycin were matched for PARP-1 expression by Western blot . AID−/− cells were a gift from JM Buerstedde . AID−/− cells transfected with E988K were then transfected with human AID cDNA in the 4/TO vector ( Invitrogen ) as above , with selection in zeocin . Cells treated with TSA were incubated in 2 ng/mL TSA ( Sigma T8552 ) , refreshed daily , for the entire culture period . Cell lines overexpressing AID used for mutation analysis were generated by retroviral transduction of a plasmid encoding chicken AID IRES GFP , another gift from S . Takeda . GFP high cells were subcloned and AID expression was measured by Western blot . All cells used for mutation analysis were subcloned by limiting dilution immediately before the culture period to ensure a homogenous starting population . Cells were then allowed to accumulate mutations for a period of 6 wk in initial experiments and 4 wk for experiments in which all the cell lines overexpress AID . There were no notable differences in generation time and all cells were split 1∶16 every other day . For calculations which include generation time , 12 h was used . A recombination substrate encoding BFP containing an intron with the recognition sequence for I-SceI was transduced into DT40s using a limiting titer of lentivirus to bias toward single integration events ( cultures with less than 5% transduction efficiencies were used ) . Transient transfection of a GFP repair template plus I-SceI expression plasmid was performed by electroporation at 250 V , 950 uF in 4 mm cuvettes . Parallel transfection of a GFP control plasmid was used to estimate transfection efficiency and frequency of HR was calculated as the percent of mCherry positive cells that were also GFP positive , divided by the percent that were positive for the GFP control . Cells were exposed to MMS at the indicated concentrations for 1 h at 37° . They were then washed 2× in fresh media and resuspended in 3 mL media . 450 ul of 3% agar was added and 1 mL was plated in triplicate . Plates were grown for 3–4 d at 41° before colonies were counted . | To produce a limitless diversity of antibodies within the constraints of a finite genome , activated B cells introduce random mutations into antibody genes through a process of targeted DNA damage and subsequent mutagenic repair . At the same time , the rest of the genome must be protected from mutagenesis to prevent off-target mutations which can lead to the development of lymphoma or leukemia . How antibody genes are specifically targeted is still largely unknown . A potential player in this process is the DNA-damage-sensing enzyme PARP-1 , which recruits DNA repair enzymes to sites of damage . Using a chicken B cell lymphoma cell line because it has only a single PARP isoform and constitutively mutates its antibody genes , we compared the types of mutations accumulated in PARP-1−/− cells to wild type . We found that in cells lacking PARP-1 , the major pathway of mutagenic repair was disrupted and fewer mutations than normal were introduced into their antibody genes . To identify what might be important for mutagenesis , we tested different factors for their ability to rescue this mutagenic deficiency and found a role for the BRCT ( BRCA1 C-terminal ) domain of PARP-1 , a consensus protein domain known to be involved in directing protein-protein interactions . Our evidence suggests that PARP-1 may be interacting with another hypothetical protein via its BRCT domain that is required for the mutagenic rather than faithful repair of DNA lesions in the antibody genes . |
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Administration of ivermectin ( IVM ) as part of mass drug administration ( MDA ) campaigns for onchocerciasis and/or lymphatic filariasis ( LF ) has been suspended in areas co-endemic for Loa loa due to severe post-treatment adverse events ( SAEs ) associated with high-burden of infection ( >30 , 000 mf/ml ) . One simple approach for preventing SAEs is to identify and exclude individuals at risk from MDA . Here , we describe a repurposed hand-held automated cell counter ( Scepter 2 . 0; HHAC ) as a rapid , point-of-care method for quantifying microfilariae ( mf ) in the blood of infected individuals . The quantification of microfilarial levels in blood of naturally infected humans , experimentally infected baboons , or mf-spiked human blood was tested using a microfluidic-based automated counter and compared to traditional calibrated thick-smears . We demonstrate that mf can be quantified in 20 µl of whole blood following lysis with 10% saponin within a minute of obtaining blood . There was a highly significant concordance between the counts obtained by the HHAC and those by microscopy for mf densities of >5 , 000 ( p<0 . 0001 , rc = 0 . 97 ) or >30 , 000 per ml ( p<0 . 0001 , rc = 0 . 90 ) . Preliminary proof of concept field studies in Cameroon with 20 µl of blood from L . loa infected humans ( n = 22 ) and baboons ( n = 4 ) also demonstrated a significantly high concordance ( p<0 . 0001 , rc = 0 . 89 ) with calibrated thick blood smears counts . A repurposed HHAC is a portable , sensitive , rapid , point-of-care and quantitative tool to identify individuals with high levels of L . loa mf that put them at risk for SAEs following MDA . In addition , it provides ease of data storage and accessibility .
Among the many parasitic helminth infections , the diseases caused by Loa loa , Wuchereria bancrofti and Onchocerca volvulus infections are major public health and socio-economic problems in many countries in West and Central Africa . Commonly , individuals harboring microfilariae ( mf ) of W . bancrofti and L . loa can have extremely high parasite burdens , but are clinically asymptomatic . This balanced host/parasite state is often perturbed when patients are treated with the antifilarial drugs ivermectin ( IVM ) or diethylcarbamazine ( DEC ) whose actions are primarily directed against the microfilarial stage of these parasites . While it is not of direct concern in this study , severe post-treatment adverse events ( SAEs ) have been attributed to the rapid killing of the mf that in turn is associated with inflammatory responses to the parasite and , in the case of W . bancrofti and O . volvulus , to its Wolbachia endosymbiont [1] . Ivermectin has been used since ∼1988 as the basis for mass treatment strategies in the control of onchocerciasis [2]–[4] and in combination with albendazole for elimination campaigns for lymphatic filariasis ( LF ) in Africa . Administration of IVM as part of mass drug administration ( MDA ) campaigns in areas co-endemic for L . loa resulted in SAEs with resultant encephalopathy and death in individuals harboring >30 , 000 Loa loa mf/ml of blood [5]–[8] , similar to that seen in treatment of loiasis with DEC [9] . Individuals with >8000 mf of Loa loa/ml are also at risk for SAEs ( though primarily non-neurological ) that have resulted in temporary functional impairment that is usually reversible . Individuals harboring fewer than 8 , 000 Loa loa mf/ml have been considered to be at little serious risk for serious post-ivermectin adverse events . It has been estimated that approximately 5% and 1% of the ∼13 million Loa-infected individuals harbor >8000 mf/ml and >30000 mf/ml respectively [10] . The Loa-associated SAEs have led to the suspension of the MDA programs for onchocerciasis and LF in areas highly endemic for loiasis [8] , [11] , and this has been a major setback for LF and onchocerciasis elimination campaigns in certain West and Central African countries . Simple and potential solutions suggested to address the prevention of Loa-associated SAEs have focused on “safer” treatment regimens and/or excluding those Loa-infected individuals at the highest risk for SAEs ( those with high levels of microfilaraemia ) from IVM-based MDA . Because Loa loa does not contain the Wolbachia endosymbiont , a six-week course of doxycycline , a drug that could target the Wuchereria bancrofti and Onchocerca volvulus ( in Africa ) , would be alternative therapeutic choice . Limited data from a community-based study in an O . volvulus-endemic area suggest that doxycycline would be a safe regimen to treat patients co-infected with low to moderate levels of Loa loa ( <8000 mf/ml ) microfilaremia [12] . Because new drug development may take decades to be available at the community level in Africa , the objective of the present study was to develop a point-of-care diagnostic tool to identify individuals with levels of microfilaremia ( >5 , 000 mf/ml and >30 , 000 mf/ml ) that put them at risk for SAEs with the ultimate goal of excluding them from MDA . The small proportion of excluded individuals ( ∼5% of the population ) could be considered for alternative treatment regimens such as 3 weeks of albendazole alone or 6 weeks of doxycycline would be safe but impractical on large-scale . This strategy termed “Test and ( Not ) Treat” ( TNT ) relies , however , on having the tools available to perform such tests at the community level in Africa . One approach to the identification of such a tool was to re-purpose existing ( on the market ) devices for the rapid and accurate quantitation of mf . One such device is the Scepter 2 . 0 ( EMD-Millipore ) that is a handheld automated cell counter ( HHAC ) that was developed as a rapid and convenient method for enumerating purified population of cells . Here we report the adaptation of the Scepter 2 . 0 for use in the quantitation of mf of L . loa as well as for other blood-borne filarial species .
Sources of mf for this study included: 1 ) Brugia malayi from peritoneal cavities of infected gerbils ( Meriones unguiculatus ) or infected blood from cats ( both obtained under contract with the University of Georgia ( Athens , GA ) ; 2 ) Dirofilaria immitis infected dog blood ( also from the University of Georgia , Athens , GA ) ; or 3 ) L . loa from naturally infected humans ( infected individuals from Cameroon and patients seen at the NIH ) or experimentally-infected baboons ( Mandrillus sp ) in Cameroon ( University of Buea ) . Uninfected blood from cats , dogs , or humans was used as controls . The human blood was obtained with informed consent from all participants under protocols approved by the IRB of the NIAID and the study was conducted on the registered protocol NCT00001230 ( for the United States ) and NCT01593722 ( in Cameroon ) . Oral consent was obtained from illiterate participants in the presence of village elders as approved by the ethical review committees . The animal procedures were conducted in accordance with the guidelines with animal care and use committee at the National Institutes of Health and University of Georgia . The use of non-human primates for research was approved by the Committee on the Ethical Use of Animals in Research ( CEUAR ) , Research Foundation for Tropical Diseases and Environment ( ReFoTDe ) , Cameroon . All relevant guidelines of the International Primatological Society ( IPS ) on the acquisition , care and breeding of non-human primates ( Second Ed , 2007 ) for research were followed . Baboons were housed in large custom built cages that extend from floor to ceiling allowing animals to take the maximum advantage of the space available . Individual baboons were allowed to display their normal repertoire of locomotor behavior ( walk , climb , run , jump and swing ) by providing them with vertical climbing surfaces and perches . Horizontal surfaces were also provided to allow them to rest comfortably and perform their social interactions such as sprawling during grooming . Baboon behavior were regularly monitored to identify indications of poor welfare . Baboons received regular food and water and were fed on a broad range of food to mimic their natural diet ( leaves , grass , roots , bark , flowers , fruit , lichens , tubers , seeds , mushrooms , corms , and rhizomes ) . They were also fed on complete commercially available diet . The health and well-being of the baboons were regularly assessed by an animal welfare officer who advised on matters such as disease prophylaxis , zoonoses , anesthesia , and methods of humane euthanasia and provision of health certificates . All measures were taken to minimize suffering during captivity and under experimentation . Health screening of workers was performed to prevent animal losses from diseases transmitted from humans to baboons as well as zoonotic transmission of disease from baboons to workers . Calibrated thick blood smears from L . loa infected humans and baboons were prepared and stained with Giemsa as previously described [10] . Each of 2 slides was counted twice by each of two independent readers . The geometric mean of the four readings was used . B . malayi mf were purified as described earlier [13] and enumerated microscopically . For infected blood samples , 20 µl of whole blood was lysed with 80 µl of 10% saponin in a 96-well flat-bottomed plate and counted using an inverted microscope; the average of 5 readings was taken and expressed as mf/ml . Lysing agents included: 1 M guanidine hydrochloride and saponin ( Sigma-Aldrich , St . Louis , Mo ) , Lysis Buffer AL ( Qiagen , Gaithersburg , MD ) , ACK lysing solution ( Invitrogen , Carlsbad , CA ) , Zap-OGLOBIN II ( Beckman Coulter ) , Sodium Dodecyl Sulfate ( SDS ) . All agents were prepared in 0 . 85% NaCl ( normal saline ) solution . 20 µl of blood was lysed with 80 µl of 10% saponin in a 96-well round-bottomed plate . The lysed samples were aspirated and mf were enumerated with the Scepter 2 . 0 ( Millipore ) using 60 µm sensors ( Cat No . PHCC60050 ) . Events falling in the specified mf gates were analyzed . Unless otherwise stated , geometric means of microfilarial counts were used as measures for central tendency ( Prism V 6 . 0 ( GraphPad , La Jolla , CA ) ) . Correlations and concordance between microscopic counts and those by the HHAC were assessed using the Lin's concordance correlation coefficients ( http://services . niwa . co . nz/services/statistical/concordance ) . Specificity , sensitivity , positive predictive value ( PPV ) and negative predictive value ( NPV ) for HHAC were calculated based on comparison with microscopy .
Preliminary studies demonstrated the need to lyse the blood sample before analyzing it using the 60 µm sensors ( Text S1 ) . Saponin-based lysis was found to be rapid and performed in small volumes ( 100 µl-final volume ) . Using a lysis procedure in which 20 µl of mf containing blood was lysed with 80 µl of 10% saponin resulted in an easily identifiable peak ( 12–20 µm ) using the 60 µm sensor . The corresponding peaks were absent in normal unspiked human blood ( Figure 1A , 1B ) . Lysis of the cat or dog blood ( experimentally infected with B . malayi ( in cats ) and D . immitis ( in dogs ) ) and analysis using a 60 µm sensor displayed distinct peaks ( 18–24 µm , D . immitis; 12–20 µm , B . malayi ) in the infected blood samples , suggesting that the events being counted were indeed mf ( Figure 1C-1E ) . To confirm that the “events” counted were truly mf , we used PBS spiked with purified B . malayi mf . As shown in figure 1F , there was no detectable peak ( 12–24 µm ) in control PBS alone whereas there was an easily identifiable peak in the mf-spiked PBS ( Figure 1G ) . Furthermore , examination of the sensor using an inverted microscope indicated that the mf could easily be seen passing through the 60 µm microfluidic system without clogging the aperture ( Figure S1 in Text S1 ) . Although there was no detectable peak visually in the normal human blood or PBS ( Figure 1A & 1F ) , an inherent electronic signal ( noise ) resulted in spurious low number of events in the gated area ( 12–36 µm ) that ultimately influenced the final “counts” . Analyses of 20 µl of whole blood from several healthy , uninfected donors showed a range of background electrical signals resulting in noise that ( when calculated ) would have provided counts that ranged between 40–840 mf/ml ( Geometric mean ( 95% CI ) : 192 . 2 ( 146 . 4–252 . 4 ) ) ( Figure 2A ) . Increasing the volume of blood ( 40–50 µl ) did not alter the background noise or the true mf counts ( Figure 2B ) suggesting that the “noise” in the system was emanating from the device rather than from the blood sample . Further testing with 5–50 µl of B . malayi infected cat blood resulted in similar counts after adjusting for dilution factor , suggesting that volume of blood is not a limiting factor ( Figure S2 in Text S1 ) . As expected there was an increase in size of histogram peaks generated proportional to the volume of sample . Because the off-the-shelf programming of the HHAC is based on a 50 µl sample containing purified human cells , an algorithm was created to take into account the noise , dilution factor and the initial blood volume for enumerating mf . Assuming uniform distribution of mf , to obtain the final mf count ( mf/ml ) , the background “noise” was subtracted and then multiplied by the dilution factor ( x 5 ) . A simplified one-step lysis procedure ( 20 µl of whole blood +80 µl of 10% saponin ) mixed in a well of a 96-well round bottomed plate was found to be convenient , rapid , and to give reproducible results . To evaluate the ability of this simplified procedure to enumerate mf , microscopically enumerated B . malayi mf were spiked in normal human blood at concentration of 100 , 000 mf/ml and tested in triplicate determinations at dilutions that varied from 500 mf/ml to 100 , 000 mf/ml . As shown in figure 3A , there was extremely good concordance correlation coefficients between the counts obtained by the HHAC ( p<0 . 0001; rc = 0 . 95 , CI ( 0 . 83–0 . 98 ) and those based on microscopy . Further , the HHAC was tested in a blinded fashion with B . malayi mf spiked samples . Four mf density ranges ( 1–5 , 000 mf/ml; 5001–10 , 000 mf/ml; 10 , 001–30 , 000 mf/ml and 30 , 001–100 , 000 mf/ml ) were tested , with each range consisting of 15 randomly generated numbers . As expected and in accordance with the need for identifying individuals harboring greater than 5 , 000 mf/ml , highly significant concordance correlation coefficients were observed ( p<0 . 0001 , rc = 0 . 97 , CI ( 0 . 96–0 . 98 ) ) . The concordance coefficient correlations for mf levels greater than 30 , 000 mf/ml were also observed to be highly significant ( p<0 . 0001; rc = 0 . 90 , CI ( 0 . 76–0 . 96 ) ) ( Figure 3B ) . However , the ability of the HHAC to reliably detect mf at densities below 5000 mf/ml is poor ( p = 0 . 02; rc = 0 . 23 , CI ( −0 . 002–0 . 44 ) ) that is partly dependent on the background noise in the system . Overall , under laboratory setting , the HHAC was observed to be highly efficient for enumerating microfilarial counts at densities >30 , 000 mf/ml and >5 , 000 mf/ml with positive predictive values of 93% and 91% respectively in 20 µl of whole blood ( Table 1 ) . Field-testing of the HHAC was carried out with baboons experimentally infected with L . loa ( n = 4 ) blood and L . loa infected individuals ( n = 22 ) from Cameroon as preliminary ‘proof of concept’ study; parallel calibrated thick blood smears were used for comparison . As shown in Figure 4 , there was significant concordance ( p<0 . 0001; rc = 0 . 89 , CI ( 0 . 78–0 . 94 ) ) between the counts obtained by the HHAC and those of the calibrated thick-blood smear . As shown in Table 1 , for L . loa microfilaremia detection , the positive predictive values were 95% for mf densities >5000 mf/ml and 80% for levels >30 , 000 mf/ml . It should be noted that while the data obtained from the HHAC occurred within minutes of obtaining the blood , the time to result for the calibrated thick smear ranged from 4 hours to several days . Although there was a highly significant correlation , it was apparent that the HHAC performed less well when mf counts were <5000/ml .
Access to rapid point-of-care diagnostic technologies in resource-limited settings imposes unique challenges [16] . While the criteria for an ideal point-of-care test can vary based on the disease setting , the function of the test or device , and also the circumstances under which test or device is implemented , the outcome should lead to expedited clinical decision making [17] . Fundamental criteria for a successful point-of-care test are its accuracy and reliability . In this context of Loa loa infection ( in regions where onchocerciasis and/or lymphatic filariasis is co-endemic ) we demonstrate that a repurposed HHAC can rapidly and reliably quantify microfilarial loads that put people “at risk” for post-ivermectin SAEs . Since the goal of point-of-care test is also to expedite the clinical decision for safe patient outcomes this on-the-spot determination of risk could help to mobilize the MDA campaigns to eliminate LF and onchocerciasis in West and Central Africa . Finally , although the advantages and efficacy of the HHAC make it a functional point-of-care diagnostic tool , a comprehensive cost analysis will be necessary to help establish whether the introduction of this tool would be worth the investment of resources for a better clinical outcome . | Mass drug administration ( MDA ) efforts with ivermectin-based regimens for onchocerciasis and for lymphatic filariasis in Africa have been suspended in certain areas that are co-endemic for Loa loa infection . This is due to the serious adverse events ( encephalopathy and death ) that can develop following ivermectin administration in individuals with very high circulating levels of Loa loa microfilariae ( mf ) ( >30 , 000 mf/ml ) . Currently thick-blood smears are performed to quantify the mf levels that are neither rapid nor high throughput . To develop a point of care ( POC ) strategy for rapid and high throughput mf quantification , a repurposed handheld automated counter ( HHAC ) was assessed and shown to be an efficient POC tool to identify individuals with very high Loa loa microfilaraemia in a manner sufficient to be used as part of a Test and ( not ) Treat ( TNT ) program in an effort to restart the suspended MDA programs while preventing severe adverse neurologic events . |
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The performance of information processing systems , from artificial neural networks to natural neuronal ensembles , depends heavily on the underlying system architecture . In this study , we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information , thereby identifying tradeoffs between learning and memory processes . During the task of supervised , sequential function approximation , networks produce and adapt representations of external information . Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state , the structure of the external information , and the time given to learn the information . We link performance to complexity in network architecture by characterizing local error landscape curvature . We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information . Parallel networks generate smooth error landscapes with deep , narrow minima , enabling them to find highly specific representations given sufficient time . While accurate , however , these representations are difficult to generalize . In contrast , layered networks generate rough error landscapes with a variety of local minima , allowing them to quickly find coarse representations . Although less accurate , these representations are easily adaptable . The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems .
Learning , the assimilation of new information , and memory , the retention of old information , are competing processes; the first requires flexibility and the second stability in the presence of external stimuli . Varying structural complexity could uncover tradeoffs between flexibility and stability , particularly when comparing the functional performance of structurally distinct learning systems . We use neural networks as model learning systems to explore these tradeoffs in system architectures inspired by both biology and computer science , considering layered structures like those found in cortical lamina [1] and parallel structures such as those used for clustering [2] , image processing [3] , and forecasting [4] . We find inherent tradeoffs in network performance , most notably between acquisition versus retention of information and between the ability of the network to maximize success versus minimize failure during sequential learning and memory tasks . Identifying tradeoffs in performance that arise from complexity in architecture is crucial for understanding the relationship between structure and function in both natural and artificial learning systems . Natural neuronal systems display a complex combination of serial and parallel [5] structural motifs which enable the performance of disparate functions [6]–[9] . For example , layered [1] and hierarchical [10] architectures theoretically important for sustained limited activity [11] have been consistently identified over a range of spatial scales in primate cortical systems [12] . Neurons themselves are organized into layers , or “lamina , ” and both intra-laminar [13] and inter-laminar [14] connectivity differentially impact function . Similarly , information processing systems developed by technological innovation rather than natural evolution have structures designed to match their functionality . For example , the topological complexity of very large integrated circuits scales with the function to be performed [15] . Likewise , the internal structure of artificial neural networks can be carefully constructed [16] to enable these systems to learn a variety of complex relationships . While parallel , rather than serial , structures are appealing in artificial neural networks because of their efficiency and speed , variations in structure may provide additional benefits or drawbacks during the performance of sequential tasks . The dependence of functional performance on structural architecture can be systematically examined within the framework of neural networks , where the complexity of both the network architecture and the external information can be precisely varied . In this study , we evaluate the representations of information produced by feedforward neural networks during supervised , sequential tasks that require both acquisition and retention of information . Our approach is quite different from studies in which large , dense networks are given an extended period of time to produce highly accurate representations of information ( e . g . [17] , [18] ) . Instead , we investigate the links between structure and function by performing a statistical analysis of the error in the representations produced by small networks during short training sessions , thereby identifying mechanisms that underlie tradeoffs in performance . Our work therefore has important implications for understanding the behavior of larger , more complicated systems in which statistical studies of performance would be impossible . In the remainder of the paper , we discuss the extent to which network architectures differ in their ability to both learn and retain information . We first describe the network model and architectures considered in this study . We then quantify the best , worst , and average performance achieved by each network during sequential tasks that vary in both their duration and complexity . We consider the adaptability of these networks to variable initial states , thereby probing the structure of functional error landscapes . Finally , we explore how landscape variations that arise from structural complexity lead to differences in performance .
Our approach differs from traditional machine learning studies in that our goal is not to design the optimal network system for performing a specific task . Rather , we identify tradeoffs in network performance across a range of architectures that share a common algorithmic framework . In this context , the term “architecture” refers specifically to the structural organization of network connections and not , as is found in engineering studies , to the broader set of constraints governing the interactions of network components . In evaluating network performance , we use techniques relevant to both artificial and biological systems . Artificial network systems often favor high accuracy and consistency during a single task , regardless of the time required to achieve such a solution . In biological systems , however , speed and generalizability are often more important that absolute accuracy when dynamically adapting to a variety of tasks . To probe features such as network accuracy , consistency , speed , and adaptability , we examine the representations of information produced by neural networks during competing learning and memory tasks . We choose to study learning and memory within the biologically-motivated framework of feedforward , backpropagation ( FFBP ) artificial neural networks that perform the task of supervised , one-dimensional function approximation . The training process , which consists of adjusting internal connection strengths to minimize the network error on a set of external data points , can be mapped to motion within a continuous error landscape . Within this context , “learning” refers to the ability of the network to successfully navigate this landscape and produce an accurate functional representation of a set of data points , while “memory” refers to the ability to store a representation of previously-learned information . Additional details of this framework are described in the following subsection . To simultaneously study learning and memory processes , information must be presented to the network sequentially . “Catastrophic forgetting , ” in which a network learns new information at the cost of forgetting old information , is a longstanding problem in sequential training of neural networks and has been addressed with several types of rehearsal methods [19]–[21] . Standard rehearsal involves training the network with both the original and new information during sequential training sessions . We use a more biologically motivated approach , the pseudorehearsal method [22] , in which the network trains with a representation of the original information . Pseudorehearsal has been shown to prevent catastrophic forgetting in both feedforward and recurrent networks and does not require extensive storage of examples [22] , [23] . In training FFBP networks , local minima and plateaus within the error landscape can prevent the network from finding a global optimum [24] , [25] . While considered disadvantageous in machine learning studies , the existence of local minima may provide benefits during the training process , particularly in biological systems for which highly accurate global optimums may be unnecessary or undesirable . Additionally , FFBP networks can suffer from overfitting , a problem in which the creation of highly specific representations of information hinders the ability of the network to generalize to new situations [26] . While also considered disadvantageous , failure to generalize has important biological consequences and has been linked to neurological development disorders such as Autism [27] . Instead of attempting to eliminate these sensitivities , we seek to understand the architectural basis for differences in landscape features and examine their impact on representational capabilities such as specificity and generalizability . The construction of our network model is consistent with standard FFBP neural network models [26] . We consider the five distinct architectures shown in Figure 1 ( a ) , all of which obey identical training rules . Each network has 12 hidden nodes arranged into layers of nodes per layer . Nodes in adjacent layers are connected via variable , unidirectional weights . The “fan” and “stacked” networks are both fully connected and have the same total number of connections . The connectivities of the “intermediate” networks , which have slightly greater numbers of connections , were chosen in order to roughly maintain the same total number of adjustable parameters per network , , noted in Figure 1 ( a ) . Each node has a sigmoid transfer function with a variable threshold . The output of each node is a function of the weighted sum of its inputs , given by , where gives the weight of the input connection . Representing the threshold as , where for all nodes , allows us to organize all adjustable parameters into a single , -dimensional weight vector . During training , each network is presented with a training pattern of pairs of input and target values , denoted . We restrict the input space to the range , and the sigmoid transfer function restricts the output space to the range . The set of variable weights is iteratively updated via the Polak-Ribiere conjugate gradient descent method with an adaptive step size [28]–[30] in order to minimize the output error . We use online training , for which is the sum of squared errors between the network output and target output calculated after all points are presented to the network: ( 1 ) Each network shown in Figure 1 ( a ) is trained over two sequential sessions . In describing parameter choices for each training session , we use to denote a continuous uniform probability distribution over the interval . The steps of the sequential training process are shown schematically in Figure 1 ( b ) and are described below:
We train the five networks shown in Figure 1 ( a ) , first considering the differences between the boundary fan ( parallel ) and stacked ( layered ) networks . Given the large number of adjustable parameters relative to the small number of training points , we expect all five networks to fit the points with high accuracy . Instead , the networks show significant differences in performance both within individual training sessions and measured statistically over many sessions . These results , discussed in detail below , show the same qualitative features for larger networks ( Figures S1 and S2 ) and for different sets of original points ( Figures S3 and S4 ) . Both natural and artificial systems can be found in a variety of states when presented with new information . The success in learning this information may depend both on the initial state of the system and on the learning conditions . We explore these possible dependencies by varying both the randomly initialized network state and the training conditions . Given unlimited training time , the distributions in Figure 4 ( a ) mark the error of local minima found within the error landscape of each network . Each minimum can be characterized by the degree of local landscape curvature , where directions of high curvature specify combinations of weight adjustments that produce large changes in error . We adopt the terminology used in previous studies and refer to directions with high and low curvature as stiff and sloppy , respectively [31] , [32] . Stiff and sloppy directions are found by diagonalizing the error Hessian evaluated at the set of weights that produces the local error minimum . For computational efficiency , we use the approximate Levenberg-Marquardt ( LM ) Hessian [33] , defined as: ( 2 ) where is the residual of the original point . The LM Hessian is a good approximation to when the error of local minima , and thus the residual , is small and the additional Hessian term can be neglected . For a given model and data set , the LM Hessian agrees well with the stiffest eigenvectors of and is equivalent to when the model perfectly fits the data . In addition , it has a known number of exactly zero eigenvalues equal to the difference in the number of model parameters and the number of data points [31] , [32] . We diagonalize the LM Hessian about each of the 500 minima with the error values shown in Figure 4 ( a ) . Each error minimum produces a set of eigenvalues and normalized eigenvectors , which give the degrees and directions of stiffness in weight space . As an illustrative example of landscape features observed along these relevant directions , Figures 5 ( a ) and 5 ( b ) show the projection of the error landscape onto the two stiffest eigenvector directions and centered on zero error minima produced by the fan and stacked networks , respectively . The fan landscape shows a single deep basin surrounded by smoothly varying peaks . In contrast , the stacked landscape is rugged , showing a deep valley with several minima separated by small barriers . While these minima appear to be distinct , they may be connected by higher dimensional pathways that cannot be seen in this reduced space .
Given the wealth of structural motifs present in real world systems , it is of interest to first isolate the tradeoffs in performance associated with small parallel and layered network structures which together form the complex architectural landscape of larger systems and thereby constrain their overall performance . Here we found that the deep , narrow basins within the error landscape enabled the fan network to produce very accurate solutions . However , the difficulty of simultaneously adjusting many network connections in order to escape deep basins may have hindered the ability of the fan network to adapt , a result that helps explain the susceptibility of parallel networks to the problems of overfitting and failure to generalize [26] . In contrast , higher variability in the width and depth of local minima enabled the stacked network to quickly find coarse but generalizable solutions through the adjustment of a smaller fraction of weights . In combination , these results support the hypothesis that the number and width of local landscape minima may increase with increasing number of hidden layers [4] , and we suggest that this variability helps explain why layered networks may require fewer computational units and may better generalize than parallel networks [49] , [50] . However , the impact of structural variations on functional tradeoffs , for example between specificity and generalizability , extends beyond artificial network studies and is crucial for understanding the interaction of learning processes in large scale models of the brain [51] . While parallel architectures are often preferred in artificial network studies due to their consistency and accuracy [48] , [50] , our results highlight the advantages of layered architectures when performance criteria favor generalizability and minimization of failure . Building on the intuition gained from the two benchmark extremes – fan and stacked – we further assessed the characteristics of intermediate networks , which can be used to more directly probe the expected behavior of structurally complex composite systems . In particular , our intermediate structures were composed of several adjacent stacked networks and therefore shared principal features of both parallel and layered systems . Additionally , these networks had slightly larger numbers of connections than the fan and stacked networks . Due to these structural differences , the depth of local minima within the intermediate landscapes displayed more variation than fan minima but more continuity than stacked minima . As landscape variability was linked to improved generalization capabilities , a continuous range of basin depths may have enabled the more successful balance between flexible learning and stable memory observed in the intermediate networks . This performance supports the hypothesis that short path lengths ( similar to the serialization [52] ) and low connection densities may facilitate simultaneous performance of information segregation ( memory retention ) and integration ( generalization ) within natural neuronal systems [53] . These competing processes are also maintained in natural neuronal systems and neural circuit models through homeostatic plasticity mechanisms such as synaptic scaling [54] , [55] and redistribution [56] , [57] , in addition to the rehearsal methods employed here [19]–[23] . Even in the absence of such homeostatic plasticity mechanisms , we found that the architectural combination of parallel and layered connectivity helped foster a balance between learning and memory . We extended our analysis from the case of unlimited training time , which revealed information about error landscape structure , to the biologically-motivated case of limited training time . Comparison of these two cases revealed a tradeoff in performance between training speed and solution accuracy . In the absence of temporal constraints , the production of highly accurate representations required longer training times . Similarly , temporal constraints led to larger solution errors . This tradeoff between speed and accuracy has been observed in cortical networks , where emphasis on performance speed during perceptual learning tasks increased the baseline activity but decreased the transient task-related activity of neurons within the decision-making regions of the human brain [58] , [59] . Here we found that network architecture played a significant role in the manifestation of this tradeoff , and the presence of additional hidden layers helped minimize network susceptibility to changes in training time . In particular , the fan network demonstrated the greatest change in performance under temporal constraints , showing a decrease in consistency coupled with occasional catastrophic error values . In contrast , the intermediate and stacked networks improved consistency and minimized inaccuracy once training time was limited . Upon closer inspection , we found that the intermediate networks produced solutions with increased speed given unlimited time and with increased potential for accuracy when time was limited as compared to the fan and stacked extremes . The presence of additional connections may have influenced the number of iterations required to find a solution , or similarly the minimum error found with a fixed number of iterations . While the graph measure of path length is known to influence network efficiency [52] , these results imply that the number of networks connections may additionally enable the network to quickly find an accurate solution . In addition to static variations in connectivity , dynamic structural changes such as synapse formation [60] can facilitate learning and memory processes . The converse case of network degradation , or disruptions to structural connectivity , is also known to have widespread consequences in functional properties of the brain [61]–[63] . A more detailed study of the relationfship between connection number and robustness could provide additional insight into the effects of synapse formation and degradation on functional performance . Our analysis of error landscape features revealed that different architectures showed variable localization properties in the eigenvectors associated with local error minima , and we therefore expect robustness to depend on both the architecture and the location of growth or damage within the network . We found that parallel networks suffered from the creation of excessively detailed representations of information , an “overfitting” problem that is often addressed through the use of cross-validation [64] and weight regularization [65] techniques . As one goal of this study was to uncover the structural basis for differences in representational capabilities , it was crucial to understand network behavior in the absence of task-specific cross-validation schemes . Additionally , as the number of parameters was roughly constant across all network structures ( and identical for the fan and stacked networks ) , we were able to draw comparisons across network architectures in the absence of additional weight regularization constraints . While parallel network models have commonly been used in machine learning studies , multi-layer “deep” networks have recently gained interest due to their potential ability to compactly represent ( using fewer computational units and parameters ) highly variable functions [49] , [50] . The “deep belief” framework has been successful for training large , multi-layered networks , and training methods often couple unsupervised , layer-wise ( greedy ) training with supervised fine-tuning [66] . Recent studies of deep belief networks found that classification performance improved with the addition of layers [48] . In addition , it was suggested that a reduction in the number of hidden layers would require an exponential increase in the number of hidden units in order to achieve similar network performance [50] . These results emphasize the capabilities of layered networks and provide an additional framework in which to explore structure-function tradeoffs . Although biologically-motivated , the FFBP framework includes several simplifying assumptions that could be modified to include additional , realistic complexity . First , we assumed that only the connection weights , analogous to synaptic strengths , were variable . Real neurons also exhibit changes in intrinsic dynamics [67] that interact with network architecture to constrain functionality in the brain [68] . Accounting for such relationships could be particularly relevant , for example , in the study of neuron response profiles within different cortical layers [13] . Second , we assumed that signals passed between nodes had no temporal structure , analogous to representing steady state neuron firing rates . Temporally varying signals could be included to study the dependence of dynamic properties , such as synchronization [68]–[70] and signal propagation [71] , on structural organization [72] . Lastly , we assumed feedforward connectivity . The addition of recurrent connections could be used to study the relationship between recurrent structure and oscillatory functions such as cortical sleep rhythms [73] and oscillation couplings relevant for associative learning and memory [74] . In each of these directions , we anticipate that underlying structural complexity will continue to impact performance through functional tradeoffs . In summary , different network architectures produce error landscapes with distinguishable characteristics , such as the height and width of local minima , which in turn determine performance features such as speed , accuracy , and adaptability . Inherent tradeoffs , observed across a range of architectures , arise as a consequence of the underlying error landscape structure . The presence of local landscape minima enable greater speed , more generalizable solutions , and minimization of catastrophic failure . However , these successes come at the cost of decreased accuracy . Understanding how both the landscape characteristics and the resulting performance features vary across a range of architectures is crucial for both understanding and guiding the design of more complex biological and technical systems . | Information processing systems , such as natural biological networks and artificial computational networks , exhibit a strong interdependence between structural organization and functional performance . However , the extent to which variations in structure impact performance is not well understood , particularly in systems whose functionality must be simultaneously flexible and stable . By statistically analyzing the behavior of network systems during flexible learning and stable memory processes , we quantify the impact of structural variations on the ability of the network to learn , modify , and retain representations of information . Across a range of architectures drawn from both natural and artificial systems , we show that these networks face tradeoffs between the ability to learn and retain information , and the observed behavior varies depending on the initial network state and the time given to process information . Furthermore , we analyze the difficulty with which different network architectures produce accurate versus generalizable representations of information , thereby identifying the structural mechanisms that give rise to functional tradeoffs between learning and memory . |
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Annual mass treatment with ivermectin and albendazole is used to treat lymphatic filariasis in many African countries , including Tanzania . In areas where both diseases occur , it is unclear whether HIV co-infection reduces treatment success . In a general population study in Southwest Tanzania , individuals were tested for HIV and circulating filarial antigen , an indicator of Wuchereria bancrofti adult worm burden , before the first and after 2 consecutive rounds of anti-filarial mass drug administration . Testing of 2104 individuals aged 0–94 years before anti-filarial treatment revealed a prevalence of 24 . 8% for lymphatic filariasis and an HIV-prevalence of 8 . 9% . Lymphatic filariasis was rare in children , but prevalence increased in individuals above 10 years , whereas a strong increase in HIV was only seen above 18 years of age . The prevalence of lymphatic filariasis in adults above 18 years was 42 . 6% and 41 . 7% ( p = 0 . 834 ) in HIV-negatives and–positives , respectively . Similarly , the HIV prevalence in the lymphatic filariasis infected ( 16 . 6% ) and uninfected adult population ( 17 . 1% ) was nearly the same . Of the above 2104 individuals 798 were re-tested after 2 rounds of antifilarial treatment . A significant reduction in the prevalence of circulating filarial antigen from 21 . 6% to 19 . 7% was found after treatment ( relative drop of 8 . 8% , McNemar´s exact p = 0 . 036 ) . Furthermore , the post-treatment reduction of CFA positivity was ( non-significantly ) larger in HIV-positives than in HIV-negatives ( univariable linear regression p = 0 . 154 ) . In an area with a high prevalence for both diseases , no difference was found between HIV-infected and uninfected individuals regarding the initial prevalence of lymphatic filariasis . A moderate but significant reduction in lymphatic filariasis prevalence and worm burden was demonstrated after two rounds of treatment with albendazole and ivermectin . Treatment effects were more pronounced in the HIV co-infected subgroup , indicating that the effectiveness of antifilarial treatment was not reduced by concomitant HIV-infection . Studies with longer follow-up time could validate the observed differences in treatment effectiveness .
Lymphatic Filariasis ( LF ) is a mosquito-borne disease caused either by Wuchereria bancrofti which is distributed throughout the tropics , or Brugia malayi and Brugia timori , both limited to Southeast-Asia . It is estimated that 120 million people world-wide are infected with one of these pathogens , and 1 billion are at risk to acquire LF during their lifetime [1] . Before larger treatment programmes started , LF was present in most of the 21 regions of Tanzania with up to 63 . 8% of individuals testing positive for circulating filarial antigen , a marker for LF infection [2] . Since the year 2000 the “Global Alliance to Eliminate Lymphatic Filariasis” uses annual mass drug administration ( MDA ) , with the aim to control and ultimately eliminate the disease [3] . The campaign of the Tanzanian National Lymphatic Filariasis Elimination Programme ( NLEFP ) commenced in 2001 in the coastal regions of Tanzania . In the Mbeya district in Southwest-Tanzania the treatment programme started in October 2009 with the annual distribution of albendazole ( 400mg ) and ivermectin ( 150–200μg/kg ) . Ivermectin is considered to be mainly microfilaricidal [4] , for albendazole an effect on the release of intrauterine antigen components of the adult worm was described [5] . Some studies report on the treatment effectiveness of the combination of albendazole and ivermectin after 12-month: in Ghana a significant reduction in circulating filarial antigen ( CFA ) levels but no measurable reduction of CFA prevalence was described in 370 individuals receiving both drugs [6 , 7] . A longitudinal study from Northern Tanzania showed only small reductions of CFA positivity after two annual drug distributions ( from 53 . 3% to 51 . 4% ) , but a significant drop to 44 . 9% and 19 . 6% after four and seven years of treatment , respectively [8] . In South Western Tanzania , both LF and HIV are public health concerns . The HIV prevalence in the country has been documented in several national surveys [1 , 9 , 10] . The third population based Tanzanian HIV/AIDS and Malaria Indicator Survey in 20011/2012 ( THMIS ) revealed a country-wide HIV prevalence of 5 . 1% in Tanzanian adults between the age of 15 and 49 years , and a prevalence of 9 . 0% for this age-group in Mbeya Region [10] . Large scale distribution of antiretroviral ( ART ) drugs was initiated in Tanzania in 2005 . At the time of our study , ART was not widely available in Southwest Tanzania . [10–12] . Local differences in initial prevalence , coverage of treatment programs , co-infection with other pathogens , etc . can all affect treatment success , thus careful surveillance of the programs is necessary to control the infection . [1 , 13–17] . Only few manuscripts focus specifically on the possible interaction of HIV with LF and most of these use cross-sectional data [18–21] . Only one recently published study investigates the treatment effectiveness of MDA drugs in HIV/LF co-infected individuals [22] , but focusses on changes in CD4 and HIV viral load after antifilarial treatment in selected HIV-positive individuals . No study concentrated on the antifilarial treatment effectiveness of MDA drugs in HIV/LF co-infected individuals . Our study assesses LF prevalence in the Mbeya Region , before and after the governmental eradication program reached the area and examines the potential impact of HIV co-infection on LF treatment .
Data were collected during the SOLF cohort-study ( Surveillance of Lymphatic Filariasis , http://www . mmrp . org/projects/basic-research/solf . html ) in the Kyela district/Mbeya region in Southwest Tanzania which was conducted at the National Institute for Medical Research ( NIMR ) —Mbeya Medical Research Centre ( MMRC ) between 2009 and 2011 . The study was embedded into the population based EMINI ( Evaluation and Monitoring of the Impact of New Interventions , http://www . mmrp . org/projects/cohort-studies/emini . html ) cohort study , which was carried out in 9 selected communities in the Mbeya region ( Fig 1 ) from 2006 to 2011 . More than 170 , 000 inhabitants from ~42 , 000 households of these communities were registered and 10% of households randomly selected to participate in the study . No additional households entered the surveillance , but some new participants entered through birth or marriage into included household . The SOLF study was approved by the Mbeya Medical Research and Ethics Committee and the Tanzanian National Institute for Medical Research—Medical Research Coordinating Committee as an amendment to the EMINI cohort study . Prior to enrolment , each EMINI participant had provided written informed consent regarding study participation . Parents consented for their children below 18 years of age . In addition , children above the age of 12 years signed their own assent form . Data and samples from participants in the Kyela site of the EMINI study were collected annually from 2007 until 2009 . During the last two surveys ( 2010 and 2011 ) only half of the study households were visited in each year . During each visit , which took place between 8 am and 2 pm , blood , urine and stool samples were collected from each participant . Samples from 2 , 165 participants from March 2009 were used to estimate the prevalence of LF directly before the government treatment program commenced in Kyela in October 2009 . In March 2011 , 18 month after the first and 6 month after the second delivery of antifilarial treatment , samples from 1 , 010 participants were used to evaluate treatment impact . From each study participant , 2 . 7 ml of blood was collected during morning hours in EDTA tubes and immediately stored at 4°C . Cells and plasma were separated within 24 hours and subsequently stored at -80°C . All laboratory tests were performed at NIMR-MMRC , Mbeya Tanzania . HIV testing was performed using the SD-Bioline HIV-1/2 3 . 0 ( Standard Diagnostics , Kyonggi-do , South Korea ) rapid diagnostic test ( RDT ) . Negative RDT results from one survey , followed by another negative RDT result in a subsequent survey , were regarded as confirmed negative and not further tested . All positive results were confirmed using an ELISA HIV test ( Enzygnost Anti HIV 1/2 Plus , DADE-Behring , Marburg , Germany ) , and tested by Western blot ( MPD HIV Blot 2 . 2 , MP Biomedicals , Geneva , Switzerland ) if discordant . For all HIV incident cases , the negative result of the previous year , as well as the new positive results was confirmed by the testing algorithm described above . For children below the age of two years , HIV testing was done by PCR . Further details are described elsewhere [23] . Because confidential disclosure of the HIV-status could not be ensured during household visits , we did not inform participants about their HIV status . Instead they were offered voluntary counseling and testing by an independent team , which was travelling with our study team , who provided referral to the local care and treatment center , to everyone who was tested positive . A commercially available ELISA ( TropBio Og4C3 serum ELISA , Townsville , Australia ) was used to detect circulating filarial antigen ( CFA ) using 100 μl of the collected sera . The Og4C3 antibody detects Wuchereria bancrofti antigen with high specificity ( 98 . 5% ) and no known cross-reaction to Onchocerca volvulus , Brugia spp . , Mansonella , Dracunculus medinensis , Ascaris lumbricoides or Strongyloides stercoralis [24] . Sensitivity varies between 73% [25] and 100% [26] , but was found 97 . 9% in individuals carrying microfilariae [24] . CFA is secreted by fully developed W . bancrofti adults and can be found at similar levels during day and night . Antigen levels thus reflect the W . bancrofti worm burden . The measurement of CFA with the Trop Bio ELISA is semi-quantitative; seven control tubes with standardized amounts of antigen are supplied and allow an estimation of the filarial antigen levels in the analysed plasma according to the measured optical density ( OD ) . LF test results were considered negative , indeterminate or positive if the OD was <0 . 2 , ≥0 . 2 and ≤0 . 3 , or >0 . 3 respectively . Statistical analyses were performed using Stata statistics software ( version 14; Stata Corp . , College Station , TX ) . Pearson´s chi-squared test was used to compare binominal outcomes between groups and to compare CFA positivity before and after treatment in all participants . McNemar´s exact test for paired data was used to compare CFA positivity before and after treatment in those individuals who participated in both surveys . The non-parametric Wilcoxon rank sum test was used to compare selected baseline characteristics of continuous variables , since none of these was normally distributed . In order to examine the association of LF infection with HIV status and other potentially important covariates we performed uni- and multi-variable log link binomial regression analyses with robust variance estimates .
In March 2009 , before the first national MDA commenced , valid CFA results were obtained from 2 , 104 individuals ( Table 1 ) . Indeterminate results were found for the 61 of the tested 2 , 165 samples ( 2 . 8% ) . Their median age was 16 . 6 years ( range 0–94 , IQR: 8 . 8 to 34 ) , and 51 . 0% were female . Only 4 ( 1 . 6% ) of the 245 children below the age of 5 years were CFA-positive; LF prevalence started to rise in participants above 10 years and was 42 . 3% in adults above 18 years of age ( Fig 2 ) . When including all age groups , 24 . 8% of the study population were CFA-positive with a trend to higher prevalence in males ( 26 . 5% ) than in females ( 23 . 1% , chi-squared p = 0 . 074 ) . In the adult population above 18 years the difference in CFA-positivity between males ( 47 . 3% ) and females ( 38 . 0% ) was significant ( chi-squared p = 0 . 003 ) . In March 2011 , 18 months after the first MDA and six months after the second , ~50% of the initially included households were revisited for interviews and blood sample collection . Some scheduled participants were not found in 2011 , and some new individuals had entered the visited households ( see study population and design ) . In addition to an analysis where the data of all participants form each Survey ( = open cohort ) are evaluated , which reflects more a cross sectional design , a second analysis included only the 798 individuals who actively participated in both years of the surveillance longitudinally ( = closed cohort ) . The numbers of participants is shown in Table 1 . Of the 974 valid test results in 2011 , 19 . 7% were CFA-positive , leading to a calculated prevalence reduction of 5 . 1% ( 24 . 8% vs . 19 . 7% , chi-squared p = 0 . 002 ) when including all subjects who participated in at least one survey ( Table 1 , open cohort ) . In the analysis of samples from 798 individuals who actively participated in both surveys ( Table 1 , closed cohort ) , a lower prevalence reduction ( 21 . 6 to 19 . 7% , McNemar´s exact p = 0 . 036 ) was measured ( Fig 3 ) . At baseline the overall HIV prevalence in our study cohort was 8 . 9% , with a prevalence of only 2 . 1% in children and adolescents below the age of 18 years , and a prevalence of 16 . 9% in individuals ≥18 years of age ( Fig 3 ) . HIV-infection was more prevalent in female ( 10 . 7% ) , compared to male participants ( 7 . 1% , chi-squared p = 0 . 003 ) . Sixty-eight of the 968 adult individuals ( 7 . 0% ) were infected with both pathogens and among the whole group of 2 , 104 individuals 69 co-infections ( = 3 . 3% ) were observed . The initial univariable analysis of the potential association of HIV with LF infection showed a higher prevalence of LF in HIV-positive ( 36 . 9% ) ; compared to HIV-negative individuals ( 23 . 6% ) ( RR = 1 . 56 , 95% CI = 1 . 26 to 1 . 94 , p<0 . 001 ) . But we already demonstrated that HIV and LF are both less common in children than in adults , which confounds this association . To further study the pattern of co-infection we analysed CFA positivity in HIV infected and uninfected individuals stratified by age ( Fig 4 ) ; in adults ( > = 18 years ) only; and in log-link binomial multivariable regression adjusted for age and gender . None of these analyses showed a significant association of LF infection with HIV , neither within the single age strata nor overall in the multivariable regression model where the influence of age and gender were confirmed , but where the adjusted RR for HIV was only 1 . 04 ( Table 2 ) . When only analysing data from adults above 18 , the CFA prevalence was 42 . 6% in the HIV-negative and 41 . 7% in the HIV-positive subgroup ( univariable log-link regression RR = 0 . 98 , 95% CI = 0 . 80 to 1 . 20; p = 0 . 84 ) . In order to compare antifilarial treatment success in the HIV-negative and positive subgroups we again performed two analysis: one for all tested individuals who participated in at least one survey ( open cohort using chi-squared testing ) , and one only for the individuals who participated in both surveys before and after treatment ( closed cohort , using McNemar´s exact test ) . For the open cohort a CFA prevalence reduction from 23 . 6% to 18 . 9% ( chi-squared p = 0 . 015 , relative drop = 19 . 7% ) was found in HIV-negative participants , and from 36 . 9% to 27 . 5% ( chi-squared p = 0 . 023 , relative drop = 25 . 4% ) in HIV-positives . For the closed cohort we observed a drop in CFA positivity from 20 . 9% to 19 . 4% ( McNemar´s exact p = 0 . 117 , relative drop = 7 . 3% ) in 723 HIV-negatives and from 27 . 5% to 21 . 7% ( McNemar´s exact p = 0 . 125 , relative drop = 21 . 1% ) in 69 HIV-positive participants . The reason for this pronounced difference ( 7 . 3% vs . 21 . 1% ) is a higher incidence of CFA positivity in the HIV negative participants where 15 ( 2 . 6% ) of the 572 initially CFA negative participants turned CFA-positive , whereas none of the 50 HIV-positive participants who were initially CFA-negative turned CFA-positive ( chi-squared p = 0 . 246 ) . The proportion of initially CFA positives who turned CFA negative was very similar in HIV-negative ( 26/151 = 17 . 2% ) and HIV-positive participants ( 4/19 = 21 . 1% , chi-squared p = 0 . 679 ) . When combining this information about change in LF status in one outcome variable ( -1 = turned CFA negative; 0 = no change in CFA status; 1 = turned CFA positive ) univariable linear regression modelling resulted in a coefficient β for the HIV infected subgroup of -0 . 043 ( 95%CI = -0 . 102 to 0 . 016 , p = 0 . 154 ) . Analysing the prevalence reduction in the closed cohort for adults > = 18 years only , a drop from 42 . 7% to 40 . 3% ( McNemar´s exact p = 0 . 248 , relative drop = 5 . 6% ) was noted for HIV-negatives , and from 32 . 7% to 25 . 5% ( McNemar´s exact p = 0 . 125 , relative drop = 22 . 0% ) in the HIV-positive subgroup . Summarizing our results , we found more pronounced drops in prevalence among the HIV positive subgroup , compared with the HIV negative , no matter , whether all participants or only adults are analysed and also with both possible ways of evaluating the data ( open cohort or closed cohort ) . The measurement of CFA with the Trop Bio ELISA is semi-quantitative; with the OD of the plasma samples reflecting the participant’s worm burden . Our findings for CFA intensities parallel those for CFA prevalence: geometric mean intensities before treatment were relatively similar between HIV-positives ( 157 units ) and HIV-negatives ( 179 units , Wilcoxon rank sum p = 0 . 34 ) , which is also true for the relative reduction of geometric mean intensity after treatment , which was 26% and 30% respectively ( Wilcoxon rank sum p = 0 . 50 )
In an area with high prevalence of and no previous treatment against LF we investigated the potential association of HIV and LF infection . When adjusting for age we found similar CFA prevalence and intensities in HIV-positive and negative participants . After two rounds of treatment a significant reduction in CFA prevalence and intensity was demonstrated , which was more pronounced in the HIV-positive compared to HIV-negative participants . Hence , HIV co-infection does not seem to negatively affect antifilarial treatment . | Parasite infections and HIV show large geographical overlap in sub-Saharan Africa and could hence potentially interact in co-infected individuals . In a general-population study conducted in Southwest Tanzania , we found high prevalence of both , lymphatic filariasis and HIV , with 42 . 5% of the adult population infected with Wuchereria bancrofti and 16 . 8% infected with HIV . Seven percent of the adults were infected with both pathogens . When adjusting for age , there was no statistically significant difference in initial prevalence or worm burden between HIV-positive and negative participants . For 798 individuals test results for both diseases were available in 2009 , before and in 2011 , after 2 rounds of treatment against lymphatic filariasis . Between 2009 and 2011 , a significant drop of prevalence and worm burden in infected individuals were observed , which was more pronounced in the HIV co-infected subgroup . Hence , HIV co-infection does not seem to negatively affect lymphatic filariasis treatment programmes . |
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Comparative genomics of multiple related species is a powerful methodology for the discovery of functional genomic elements , and its power should increase with the number of species compared . Here , we use 12 Drosophila genomes to study the power of comparative genomics metrics to distinguish between protein-coding and non-coding regions . First , we study the relative power of different comparative metrics and their relationship to single-species metrics . We find that even relatively simple multi-species metrics robustly outperform advanced single-species metrics , especially for shorter exons ( ≤240 nt ) , which are common in animal genomes . Moreover , the two capture largely independent features of protein-coding genes , with different sensitivity/specificity trade-offs , such that their combinations lead to even greater discriminatory power . In addition , we study how discovery power scales with the number and phylogenetic distance of the genomes compared . We find that species at a broad range of distances are comparably effective informants for pairwise comparative gene identification , but that these are surpassed by multi-species comparisons at similar evolutionary divergence . In particular , while pairwise discovery power plateaued at larger distances and never outperformed the most advanced single-species metrics , multi-species comparisons continued to benefit even from the most distant species with no apparent saturation . Last , we find that genes in functional categories typically considered fast-evolving can nonetheless be recovered at very high rates using comparative methods . Our results have implications for comparative genomics analyses in any species , including the human .
We evaluate both well-known methods for gene identification as well as several metrics that we have developed . These metrics are briefly summarized here and in Table 1 , while we provide full implementation details in the Methods section . Most initial efforts at comparative gene identification used a single informant genome to support the annotation of a target genome [15] , [23]–[29] . We selected several metrics that capture the essential properties of coding sequence evolution that they observe: the KA/KS ratio [30] , [31] and the Codon Substitution Frequencies ( CSF ) score [5] observe biases towards synonymous and other conservative codon substitutions; the Reading Frame Conservation ( RFC ) score observes the strong bias of indels within coding regions to be multiples of three in length [4] , [32]; TBLASTX measures the genome-wide significance of protein sequence similarity [33]; finally , a baseline sequence conservation metric simply measures the percent nucleotide identity between the target and informant sequences . We also selected several metrics that use multi-species alignments: the dN/dS test observes biases towards synonymous codon substitution using a statistical test based on maximum likelihood phylogenetic algorithms [34]–[36]; the multi-species CSF and RFC scores use ad hoc strategies to efficiently combine their respective pairwise scores; lastly , a baseline multi-species sequence conservation metric measures the largest fraction of species having the same nucleotide in each column ( plurality ) , averaged across the alignment . We also included several single-sequence metrics in our benchmarks to compare them to the comparative methods . Since previous studies have benchmarked many single-sequence metrics extensively [1]–[3] , we chose only a representative set here: the Fourier transform measures the strength of the three-base periodicity in coding sequences [37]; codon bias observes the unequal usage of synonymous codons , resulting in part from how different synonymous codons affect translation efficiency [38]; interpolated context models ( ICMs ) are generative probabilistic models that observe reading frame-dependent biases in the frequencies of k-mers in coding sequences , simultaneously for several different k-mer sizes [39]; lastly , Z curve observes reading frame-dependent biases in k-mer frequencies using a discriminative approach based on Fisher linear discriminant analysis [2] . To benchmark the discriminatory power of each of these metrics , we assembled a test set consisting of 10 , 722 known protein-coding exons ( from 2 , 734 genes ) in the fruit fly Drosophila melanogaster , and 39 , 181 random intergenic regions with the same length and strand distribution ( see Methods ) . These provide an ideal setting in which to evaluate genome-wide comparative genomics methods given the high quality of the FlyBase gene annotations [5] and the recent sequencing of ten Drosophila genomes [21] , [22] , in addition to D . melanogaster [40] and D . pseudoobscura [41] . We extracted each of these regions from two different sets of whole-genome sequence alignments of the twelve fly genomes [22] , one generated by MULTIZ [42] , which uses local alignments of high-similarity regions , and the second generated by the Mercator orthology mapper ( C . Dewey and L . Pachter ) and MAVID sequence aligner [43] , based on the identification of orthologous segments in each genome by conserved gene order ( synteny ) . For each metric , we scored all the 49 , 903 regions in our test set ( 10 , 722 exons and 39 , 181 non-coding regions ) and then measured its ability to correctly classify them as coding or non-coding . We used four-fold cross-validation to train and apply the metrics that require training data . We evaluated the performance of each metric by examining receiver-operator characteristic ( ROC ) curves showing its sensitivity and specificity at different score cutoffs . ( Here and throughout this paper , we use the term specificity as it is defined in binary classification problems: the fraction of true negatives that are correctly classified as negative . This differs from the common usage of the term in the gene prediction field to refer to the fraction of the examples classified as positive that are true positives . Additionally , we use the term false positive rate to mean 1-Specificity , or the fraction of true negatives incorrectly classified as positive . ) Based on the ROC curve for each metric , we also computed two different summary error measures , to facilitate comparing the performance of different metrics and methodological choices:
We first compared the overall performance of the metrics ( Figure 1 ) . All of the metrics we evaluated demonstrated high classification performance , but some general trends were apparent . The comparative metrics ( using the MULTIZ alignments of all twelve fly genomes ) generally outperformed the single-sequence metrics ( except for the baseline sequence conservation metric ) . For example , the best comparative metric resulted in 24% lower error than the best single-sequence metric ( 0 . 050 MAE for the dN/dS test vs . 0 . 065 for Z curve ) . Different metrics were preferable at different sensitivity/specificity tradeoffs . For example , the CSF and dN/dS metrics achieved the highest specificity ( 99 . 9% for CSF ) even at fairly high sensitivities ( 85 . 2% ) . RFC tended towards higher sensitivity and lower specificity than CSF and dN/dS . We also compared the pairwise metrics , using the best pairwise informant ( D . ananassae; we investigate different pairwise informants below ) , and found similar trends ( Figure S1 ) . For example , CSF and KA/KS performed comparably , showing the highest specificity , while RFC tended towards higher sensitivity and lower specificity . TBLASTX performed substantially worse than KA/KS , CSF , and RFC , but it was still better than our baseline conservation metric . Notably , none of the pairwise comparative metrics outperformed the best single-sequence metric ( Z curve ) according to MAE and AAC error , and they exhibited generally lower sensitivity . CSF and KA/KS were , however , able to achieve higher specificity at a moderate sensitivity tradeoff . For example , at 80% sensitivity , CSF had a nearly ten-fold lower false positive rate than Z curve ( 0 . 15% and 1 . 39% ) ; the specificity of CSF exceeded Z curve at less than 85% sensitivity , compared to 93% sensitivity at Z curve's MAE point . We next assessed each metric's discriminatory power for different sequence length categories ( Figure 1C ) . All of the metrics performed better on longer sequences than shorter sequences . Single-sequence metrics performed comparably or slightly better than comparative methods for long sequences ( >240 nt ) , but comparative methods strongly outperformed single-sequence metrics on shorter sequences . For example , in the length range of 181–240 nt ( which includes the median exon length ) the best comparative metric resulted in 51% lower error than the best single-sequence metric ( 0 . 027 MAE for the dN/dS test and 0 . 056 MAE for Z curve ) . In the shorter length range of 121–180 nt , the best comparative metric resulted in 60% lower error than the best single-sequence metric ( 0 . 029 MAE for CSF and 0 . 073 MAE for Z curve ) . Different comparative methods were also preferred at different lengths . For example , CSF strongly outperformed the dN/dS test on the shortest sequences ( ≤60 nt ) , while they performed comparably on longer sequences . While each of the metrics we studied exhibited unique performance characteristics , some measure similar fundamental lines of evidence , and thus may tend to err on the same examples . We investigated the independence of the metrics , indicated by how differently they rank the exons in our test set , using a dimensionality reduction technique called multidimensional scaling ( MDS; see Methods ) . This analysis led to a two-dimensional visualization shown in Figure 2A , in which each point represents one of the metrics and the distance between the points approximately represents their dissimilarity . We found that the dN/dS test and CSF behaved very similarly , while RFC was clearly distinct . The sequence conservation metric was separate from each of these , while TBLASTX clustered with CSF and dN/dS . The four single-sequence metrics formed two additional clusters distinct from the comparative metrics . These findings agree with intuition: CSF and the dN/dS test both observe the distinctive biases in codon substitutions in protein-coding sequences , while RFC observes patterns of insertions and deletions that are essentially orthogonal to codon substitutions , and the single-sequence metrics observe compositional biases and periodicities that are ignored by the comparative metrics . The relative independence of several of the metrics suggests that combining them could lead to higher performance . We selected five metrics representing each of the MDS clusters ( CSF , RFC , sequence conservation , Z curve , and codon bias ) and combined them using cross-validated linear discriminant analysis ( LDA ) . As expected , the hybrid metric outperformed any of its inputs: by MAE error , the LDA hybrid resulted in 27% lower error than its best input metric ( 0 . 040 MAE for LDA vs . 0 . 055 for CSF ) . The hybrid metric demonstrated much higher sensitivity than any of its input metrics ( Figure 2B ) , and higher specificity than all of the input metrics except CSF . We obtained almost identical results using a second hybrid metric based on a linear support vector machine instead of LDA . Thus , although CSF and the dN/dS test remain the methods of choice for the highest specificity , the hybrid metrics achieved higher overall performance . We next investigated how strongly the performance of the comparative methods depends on genome sequence alignments . We compared the above results , based on MULTIZ local similarity-based alignments , with the corresponding results based on the synteny-anchored Mercator/MAVID alignments . Overall , the two alignments led to highly concordant results , with similar trends in the performance of the metrics relative to each other and across different sequence lengths . There were , however , some notable differences in their absolute levels of performance . We expected the local alignment approach to give higher sensitivity than the synteny-anchored alignments , since it should be better able to align exons that have undergone rearrangements [45] . Indeed , we found that MULTIZ tended to align more species for each region ( Figure S2 ) and led to higher sensitivity than the Mercator/MAVID alignments ( e . g . 90% vs . 87% for CSF at 99% specificity , with 85% of exons detected in both alignments; Figure S3 ) . Conversely , we expected the synteny-anchoring approach used by Mercator/MAVID to give higher specificity than the local alignment approach of MULTIZ , since it may generate fewer spurious non-orthologous alignments [45] . However , we found that while the Mercator/MAVID alignment could lead to slightly higher specificity , it did so only at disproportionate sensitivity tradeoffs . For example , with the baseline sequence conservation metric , specificity using the Mercator/MAVID alignments exceeded that of the MULTIZ alignments only at lower than 58% sensitivity ( compared to 80% sensitivity at the MULTIZ-based MAE point ) . Similarly , with RFC , specificity resulting from the Mercator/MAVID alignments was greater only at lower than 63% sensitivity ( compared to 92% MAE sensitivity ) . Overall , the Mercator/MAVID alignments led to somewhat lower sensitivity without a clear specificity advantage , and this was reflected in worse MAE and AAC error statistics ( Figure S3 ) . We therefore focused on the MULTIZ alignments for the remainder of our analysis . We note , however , that the Mercator/MAVID alignments did allow detection of some exons not detected in the MULTIZ alignments ( ∼2% of all exons ) . More generally , these empirical observations could be highly dependent on parameter settings of the genome alignment programs , and further investigation of these strategies is required . To investigate which species are the most and least effective informants for gene identification , we evaluated each pairwise comparative metric using informant genomes at increasing evolutionary distance from D . melanogaster . We applied each metric to pairwise alignments of D . melanogaster with D . erecta , D . ananassae , D . pseudoobscura , D . willistoni , and D . grimshawi , each representing various clades within the genus Drosophila ( Figure 3 ) . We found that D . ananassae was overall the most effective informant , outperforming other species on most metrics . However , inspection of the corresponding ROC curves often revealed a more complex situation , with multiple species showing similar performance , and sometimes higher for certain sensitivity/specificity tradeoffs . For example , with KA/KS , D . ananassae and D . willistoni performed comparably , with D . ananassae leading to slightly higher sensitivity and D . willistoni leading to slightly higher specificity ( Figure 4A ) . Similarly , with RFC , closely related species led to slightly higher sensitivities , and more distant species led to slightly higher specificities ( Figure S4 ) . Hence , while D . ananassae was overall the most effective informant , it did not robustly outperform the other pairwise informants we studied . The only exception was D . erecta , the most closely related to D . melanogaster of the species we studied . D . erecta was consistently less informative than the others , leading to the lowest overall classification performance on most of the pairwise metrics . To investigate more distant species for which we lacked whole-genome alignments , we also applied TBLASTX to the genomes of the mosquito [46] and honeybee [47] . We found that these species led to much worse performance than the Drosophila species as informants for D . melanogaster ( Figure 4B ) . We conclude that a broad range of species within the genus Drosophila ( outside of the melanogaster subgroup ) make effective pairwise informants for gene identification in D . melanogaster , while the mosquito and honeybee , the next most closely related species with fully sequenced genomes , are likely to be too distant for this application . These findings are consistent with a previous smaller-scale study of comparative gene identification power in flies [14] , and previous theoretical and simulation studies suggesting that , while some mathematically optimal distance may exist , species at a broad range of phylogenetic distances should be comparably effective informants for identifying exons and other conserved elements [13] , [15] . We next investigated the effectiveness of increasing numbers of informant species on the metrics that can use multiple informants . We evaluated each metric using subsets of the available species corresponding to increasingly broad clades within the genus Drosophila ( see phylogeny in Figure 3 ) : the melanogaster subgroup ( 5 species including D . melanogaster ) , the melanogaster group ( 6 species ) , the melanogaster and obscura groups ( 8 species ) , the subgenus Sophophora ( 9 species ) , and finally all 12 species of the genus Drosophila . We found that for each of the metrics we benchmarked in this way , discriminatory power tended to increase as additional informant species were used ( Figure 5A ) . In contrast to our previous pairwise analysis , in which the most distant Drosophila informants led to similar or slightly worse performance than closer species , adding informants at increasing distances led to a clear trend in higher classification performance . The dN/dS test , RFC , and the sequence conservation metric each showed a smooth progression of increasing performance with each successively larger group of informant species . For example , starting from the four informants within the melanogaster subgroup , the dN/dS test achieved an MAE of 0 . 103 . With the addition of each successive group of informants , the MAE was reduced relatively by 35% , 43% , 48% , and finally by 52% . CSF showed a similar trend through the subgenus Sophophora , but did not clearly benefit from the subsequent addition of the final three informants of subgenus Drosophila . In all cases , the improvement with multiple species was most pronounced for short exons ( Figure 5B ) . With a sufficient number of informants , the multi-species metrics surpassed single-sequence metrics according to MAE ( Figure 5C ) . This also stands in contrast to our pairwise analysis , in which no informant enabled any comparative metric to outperform the best single-sequence metric ( Z curve ) . CSF exceeded the performance of Z curve once we used at least six species ( ≥1 . 3 sub/site ) , dN/dS with at least eight species ( ≥1 . 9 sub/site ) , and RFC , using its simplistic vote-tallying scheme , with all twelve species ( 4 . 1 sub/site ) . The baseline sequence conservation metric never outperformed Z curve , although its performance also increased with additional species . ( We note that while these results show that a certain number of informants is sufficient , they do not imply that they are all necessary to achieve some level of performance; removing informants that contribute very little independent branch length might not substantially reduce performance . ) In most cases , the four informants of the melanogaster subgroup together yielded worse performance than pairwise analysis with the best pairwise informant , D . ananassae . In contrast , all of the informant clades that combined D . ananassae with more distant species led to better performance than any pairwise analysis . This affirms our earlier conclusion , based on a pairwise analysis with D . erecta , that the species within the melanogaster subgroup are sub-optimal informants for the metrics we studied , presumably because they are too closely related to D . melanogaster . Indeed , the neutral distance of D . ananassae from D . melanogaster is 1 . 0 substitutions per neutral site , while the total independent branch length provided by the four melanogaster subgroup informants is only 0 . 4 sub/site . It is well-known that genes in certain categories of biological function tend to be faster-evolving [41] , [46]–[48] . We lastly investigated whether comparative metrics therefore systematically fail to distinguish such genes from non-coding regions . We obtained Gene Ontology ( GO ) annotations [49] , [50] for each of the 2 , 734 genes comprising our test set . For each of the 192 GO terms represented by at least thirty genes in our test set , we determined the fraction of those genes with at least one exon scoring above a stringent cutoff ( “detected genes” ) . We found that all of the functional categories we investigated had very high detection rates ( Table S1 ) . For example , with a CSF cutoff corresponding to 85% exon sensitivity and 99 . 9% specificity using all twelve fly genomes , the overall fraction of detected genes was 92% , and the detection rates surpassed 90% for all but two functional categories: serine-type endopeptidase activity ( 89% detected genes ) and its superset , serine-type peptidase activity ( 86% ) . Serine proteases play key roles in insect innate immunity , and some likely evolve under positive selection [46] , [51] , [52] . Several other categories that intuition suggests might relate to more rapidly evolving genes , however , were not problematic , including immune response ( 94% ) , gametogenesis ( 95% ) and G-protein coupled receptor activity ( 100% ) . Instead , comparative metrics had the most difficulty detecting genes of unknown function . Three GO terms indicating unknown function ( unknown cellular component , molecular function , and biological process ) had only 67% , 61% , and 60% detected genes . In fact , of the genes that were not detected at this cutoff , 85% were of unknown function or lacked any GO term , compared to 49% of all the genes in our dataset . These trends held for all of the comparative metrics and cutoffs we investigated ( Table S1 ) . Overall , these results indicate that comparative methods using the twelve fly genomes were able to detect the vast majority of genes in all of the functional categories we investigated ( which were represented by at least 30 genes in our dataset; a larger sample might reveal more specific functional categories that are , in fact , very difficult for comparative methods to detect ) . They had much greater difficulty detecting genes of unknown function , which may be under less selective constraint overall [14] , [21] but could also include a higher proportion of incorrect or spurious annotations [5] . Interestingly , Z curve , a single-sequence metric , also showed much lower sensitivity to genes of unknown function ( Table S1 ) , suggesting that these genes , if they are correctly annotated , tend to be unusual in several ways .
Using a variety of different methods , we found that species ranging from 1 . 0–1 . 4 substitutions per neutral site from D . melanogaster are comparably effective informants for pairwise gene identification , with slight preference given to the closer end of this range . This “optimal” range might extend both towards closer species ( between D . erecta and D . ananassae ) and towards more distant species ( between D . grimshawi and A . gambiae ) , but these distances were not explored in the currently sequenced genomes . This range is comparable to the distance from human of the opossum ( 0 . 8 sub/site ) , chicken ( 1 . 1 sub/site ) , and lizard ( 1 . 3 sub/site ) , suggesting that species more distant than the eutherian mammals ( the farthest of which are less than 0 . 5 sub/site; Figure 3 ) may prove to be excellent informants for human gene identification . Moreover , our study showed that comparative genomics power did not saturate with the number of species compared , as the multi-species metrics tended to show continued improvement from each progressively larger group of informants studied ( Figure 5 ) . The overall improvement did become more incremental as the number of informants grew , which could be interpreted either as diminishing returns from additional genomes , or simply as the expected asymptotic increase in performance towards an achievable optimum . Importantly , the improvement from more informants was far more pronounced among short exons than long exons ( Figure 5B ) ; this suggests that , while long exons are easy to discover even with few species , still more informants may significantly improve the discovery of short coding exons , and perhaps other classes of small elements . Thus , especially for small elements , we apparently have not yet reached a saturation point with twelve metazoan species spanning a total of 4 . 13 substitutions per neutral site . We chose to express discovery power as a function of the neutral substitution rate estimated for the species compared ( Figure 3 ) . While this rate provides a compelling measure of expected discovery power [13] , it is important to note that genetic distance between species ( whether measured by neutral substitution rate or other metrics [21] , [53] ) is far from the only consideration that should guide comparative informant selection . For example , population dynamics affect the strength of selection relative to neutral drift , and thus may skew the relationship between neutral divergence and the significance of observed conservation in some lineages [54] , [55] . Additionally , the genome size and the density and type of repetitive elements in an informant genome may affect the ability to sequence , assemble , and align it to a target genome , especially if low-coverage [18] or short-read [56] , [57] sequencing strategies are used . Accurate alignment is further complicated by variation in the rates of chromosomal rearrangement and segmental duplication and loss , which are likely to affect the proportion of the genome that can be accurately recognized as orthologous , even for species that show similar nucleotide divergence . Much more fundamentally , distant species share less in common biologically; indeed , the 12 Drosophila species were selected in part to represent the diverse ecological niches they occupy [58] and the neutral distance they span ( approximately corresponding to the distance between human and reptiles ) . Thus , while our results suggest that such distant species may nonetheless be highly informative given high-quality sequences and alignments , future empirical studies should compare them to the use of many species at closer distances , such as those represented by the eutherian mammals , for gene identification . One application of the metrics we have studied will be their integration into de novo gene structure predictors based on semi-Markov conditional random fields , which can combine multiple discriminative metrics in a manner not unlike our LDA hybrid . Our results suggest that these systems should be able to use multiple informant species and multiple metrics to identify protein-coding sequences with higher accuracy , especially on short exons . Still , it is not obvious that these trends in the metrics' performance necessarily imply higher-accuracy prediction of complete gene structures , since the latter also strongly depends on the detection of splice sites and other sequence signals [12] , [59] . Additionally , like the more advanced metrics we studied , such systems tend to be highly parameterized and thus dependent on high-quality training data , which may not be available in less well-studied species . More fundamentally , the probabilistic models used in gene predictors make simplifying assumptions about gene structures that lead to many incorrect predictions , and that cannot be relaxed just by using more powerful metrics . For example , they currently cannot predict nested and interleaved genes , which are fairly common in metazoan genomes [5] , [50] , [60]–[62] , since these structures violate Markov independence assumptions . A similar challenge is presented by alternative splice isoforms with mutually exclusive exons that do not splice to each other in-frame . The methods we have studied also have other important applications , such as assessing and refining existing annotations , and searching the genome for coding regions that are systematically missed or erroneously modeled by other methods . In particular , the effectiveness of comparative methods for detecting short coding regions may prove crucial in identifying short proteins , which are known to serve important biological roles but have probably been systematically under-represented in genome annotations [63]–[66] . They also provide a promising way to search for gene structures that violate traditional assumptions entirely , such as stop codon readthrough , translational frameshifts and polycistronic transcripts , which also might be more common in animal genomes than currently appreciated [5] .
We used “Comparative Analysis Freeze 1” assemblies of the twelve Drosophila genomes [21] available from the following web site: http://rana . lbl . gov/drosophila/assemblies . html . We used two different genome alignment sets [22] . One was derived from a synteny map generated by Mercator ( C . Dewey , http://www . biostat . wisc . edu/~cdewey/mercator/ ) and sequence alignments generated by MAVID [43] . The other genome alignments were generated by MULTIZ [42] . These alignments are available from the following web site: http://rana . lbl . gov/drosophila/wiki/index . php/Alignment . We obtained FlyBase release 4 . 3 annotations from the following web site: ftp://ftp . flybase . net/genomes/Drosophila_melanogaster/dmel_r4 . 3_20060303/gff/ . We estimated branch lengths in the phylogenetic tree for the flies ( shown in Figure 3 ) based on four-fold degenerate sites in alignments of orthologous protein-coding genes . We identified one-to-one orthologs based on FlyBase annotation release 4 . 3 for D . melanogaster and community annotations for the 11 other species [21] , yielding 12 , 861 four-fold sites . Then , to estimate branch lengths , we ran PHYML v2 . 4 . 4 [67] with an HKY model of sequence evolution , a fixed tree topology ( Figure 3A ) , and remaining parameters at default values . For comparison with vertebrates , we estimated the branch lengths for 28 vertebrates using 10 , 340 four-fold sites , based on alignments of genes with one-to-one orthologs in human , dog , and mouse [68] . We obtained the MULTIZ vertebrate alignments from the UCSC Genome Browser [69] . We randomly sampled 2 , 734 of the 13 , 733 euchromatic genes in FlyBase annotation release 4 . 3 , and then selected all 10 , 722 non-overlapping exons of all transcripts of those genes . We chose this strategy of randomly sampling genes and selecting all exons of those genes , rather than directly sampling exons , to facilitate studying how the power of each metric varies across different functional categories of genes . Although not by design , the length distribution of sequences in our test set ( median = 224 nt , mean = 404 nt , sd = 570 nt ) is very similar to the length distribution of exons in the genome ( median = 220 nt , mean = 408 nt , sd = 568 nt ) . Each known exon was evaluated in its annotated reading frame . For each known exon in our dataset , we selected four non-coding regions of the same length and strand . We selected each of these regions by randomly choosing a start coordinate in the BDGP Release 4 assembly of the D . melanogaster euchromatic chromosome arms , and ensuring that the resulting region did not overlap an annotated coding exon . We also chose only regions consisting of at least 50% nucleotide characters ( as opposed to Ns ) . The codon reading frame for the non-coding regions was always set arbitrarily to 0 ( that is , they were always considered to begin with a complete codon ) . We removed in-frame stop codons in D . melanogaster from the non-coding regions ( the length of each control region matched the corresponding exon after removing stop codons ) . All the regions in the dataset were selected without regard to how well they were aligned in either genome alignment set we used . The coordinates , sequences , and alignments of our dataset are available for download ( Text S1 ) . CSF and the single-sequence metrics ( except for Fourier transform ) require training to estimate parameters . To avoid overfitting , we trained and applied them using four-fold cross validation: we randomly partitioned the dataset into four subsets , and then generated scores for each subset by training on the other three subsets . We then combined the scores for the subsets to obtain scores for the entire dataset . We applied the other metrics directly to each sequence . We computed ROC curves for each metric by choosing 250 cutoffs representing quantiles of the score distribution over the entire dataset , and at each cutoff , evaluating sensitivity and specificity when sequences scoring above the cutoff are considered positively classified , and sequences scoring less than or equal to the cutoff are negatively classified . Some metrics failed to produce a score for some sequences; for example , comparative metrics produced no score for sequences in which no alignment was present . These sequences were regarded as negatively classified at all cutoffs , reflecting a non-coding default hypothesis . Our ROC curves may therefore underestimate the sensitivity or overestimate the specificity that each comparative method would exhibit if given perfect alignments of all orthologous elements . We computed the MAE as the highest average sensitivity and specificity among the 250 points on the ROC curve , and the AAC by trapezoidal integration over these points . We created hybrid metrics by combining the pre-computed scores of the input metrics using linear discriminant analysis ( LDA ) and a support vector machine ( SVM ) . In both cases , prior to combination , the scores of each input metric were normalized to have zero mean and unit variance across the entire dataset . The normalized scores from each input metric were then used as feature vectors representing each sequence in the dataset . We trained and applied the hybrid metrics using four-fold cross-validation . We applied LDA with default settings in MATLAB . For SVM , we used SVMlight 4 . 00 [73] with a linear kernel and default cost parameters . We used the prediction confidence computed by the svm_classify program as the SVM hybrid metric score for each sequence . Multidimensional scaling ( MDS ) takes a high-dimensional matrix of pairwise similarities between items ( in our case , metrics ) , and assigns each item to a point in a low-dimensional space ( in our case , two dimensions for visualization ) , such that the distance between any two points approximately represents the dissimilarity of the corresponding items . We applied MDS to generate the visualization in Figure 2A using the R function cmdscale with default parameters . We defined the similarity between two metrics as S ( i , j ) = cor ( Ri , Rj ) , where Ri is the vector of ranks of the known exons according to the scores computed by metric i . For example , if the known exons are ordered in some way E1 , E2 , E3 , and metric i assigns them scores Mi ( [E1 , E2 , E3] ) = [0 . 2 , 1 . 0 , −0 . 5] , then Ri = [3] , [1] , [2] . | Comparing the genomes of related species is a powerful approach to the discovery of functional elements such as protein-coding genes . Theoretically , using more species should lead to more discovery power . Many questions remain , however , surrounding the optimal choice of species to compare and how to best use multi-species alignments . It is even possible that practical limitations in the sequencing , assembly , and alignment of genomes could effectively negate the benefit of using more species . Here , we used 12 complete fly genomes to study a variety of metrics used to identify protein-coding genes , including methods that analyze only the genome of interest and comparative methods that examine evolutionary signatures in genome alignments . We found that species over a surprisingly broad range of phylogenetic distances were effective in comparative analyses , and that discovery power continued to scale with each additional species without apparent saturation . We also examined whether comparative methods systematically miss genes considered fast-evolving , and studied how performance is influenced by genome alignment strategies . Our results can help guide species selection for future comparative studies and provide methodological guidance for a variety of gene identification tasks , including the design of future de novo gene predictors and the search for unusual gene structures . |
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High-throughput sequencing has enabled genetic screens that can rapidly identify mutations that occur during experimental evolution . The presence of a mutation in an evolved lineage does not , however , constitute proof that the mutation is adaptive , given the well-known and widespread phenomenon of genetic hitchhiking , in which a non-adaptive or even detrimental mutation can co-occur in a genome with a beneficial mutation and the combined genotype is carried to high frequency by selection . We approximated the spectrum of possible beneficial mutations in Saccharomyces cerevisiae using sets of single-gene deletions and amplifications of almost all the genes in the S . cerevisiae genome . We determined the fitness effects of each mutation in three different nutrient-limited conditions using pooled competitions followed by barcode sequencing . Although most of the mutations were neutral or deleterious , ~500 of them increased fitness . We then compared those results to the mutations that actually occurred during experimental evolution in the same three nutrient-limited conditions . On average , ~35% of the mutations that occurred during experimental evolution were predicted by the systematic screen to be beneficial . We found that the distribution of fitness effects depended on the selective conditions . In the phosphate-limited and glucose-limited conditions , a large number of beneficial mutations of nearly equivalent , small effects drove the fitness increases . In the sulfate-limited condition , one type of mutation , the amplification of the high-affinity sulfate transporter , dominated . In the absence of that mutation , evolution in the sulfate-limited condition involved mutations in other genes that were not observed previously—but were predicted by the systematic screen . Thus , gross functional screens have the potential to predict and identify adaptive mutations that occur during experimental evolution .
There is a great need for rapid , high-throughput methods to identify adaptive mutations among the growing list of mutations identified in experimentally evolved populations . Several recent ‘Evolve and Resequence’ studies [1] , in which populations or clones were sequenced after adaptation to a specific condition , have dramatically increased the list of mutations associated with adaptation to different conditions [2–12] . Within that growing dataset , only a few mutations have actually been confirmed experimentally as adaptive . Some large-scale microbial studies have distinguished adaptive mutations from background neutral mutations on the basis of statistical approaches based on the frequency , enrichment , and recurrence of specific mutations [2 , 3 , 9 , 13–17] . Such statistical approaches entail substantial false-positive and false-negative rates . Dissecting the fitness effects of every mutation observed in an evolved population is tedious , although generally straightforward . For example , mutations can be reassorted via a genetic cross , and the fitness of segregants carrying individual mutations or combinations thereof can be assessed . That strategy has been used with a few laboratory-evolved Saccharomyces cerevisiae clones , demonstrating that evolved clones isolated after several hundred generations of propagation in nutrient-limited conditions often carry one or two adaptive mutations [18 , 19] . However , such methods are difficult to scale . An alternative approach is computational models that predict the effects of mutations . A recent study directly compared several popular scoring metrics and found them to be far inferior to experimental testing of fitness [20] . Given its amenability to high-throughput experiments , S . cerevisiae is particularly well suited for genome-wide assessments of the relationship between genetic variation and fitness . As an alternative , we turned to currently available systematic mutant collections . Researchers have created barcoded strain collections in which thousands of genes are systematically deleted or amplified to uncover gene functions ( review in [21] ) . These strain collections have been used to mimic important classes of mutations such as those resulting in loss-of-function ( LOF ) , gene knockdown , gene duplication , or changes in expression level [22–26] . Missing from these collections are mutations that are not mimicked by copy-number changes , such as mutations in coding regions that generate new protein activities or LOF effects more subtle than those of simple knockout or knockdown alleles . Despite the large number of studies that have used the barcoded collections to detect deleterious effects such as haploinsufficiency , dosage sensitivity , synthetic lethality , drug sensitivity , and various other phenotypes [24 , 27–35] , only a few studies have looked at beneficial mutations . One study quantified antagonistic pleiotropy in a variety of laboratory conditions and determined that whereas 32% of deletion strains were less fit than a wild-type reference , only 5 . 1% of the strains were more fit [36] . Another study identified a large number of heterozygous deletions as beneficial but also demonstrated that the haploproficiency was context-dependent [23] . The further application of systematic amplification and deletion collections to study adaptive mutations will expand our understanding of that unique and important class of mutations . Most previous studies used phenotypic data to investigate gene function . The adaptive phenotypes displayed by the systematic amplification and deletion collections can also be used to investigate questions from an evolutionary genetics perspective . The ability to identify beneficial mutations en masse allows us to survey one set of beneficial mutations that could drive adaptation . A greater understanding of adaptive mutations will allow us to begin to address a number of open questions . How does the distribution of fitness effects differ across conditions ? What determines which of the possible beneficial mutations actually reach high frequencies in evolving populations ? Does the hierarchy of fitness among mutations drive those patterns strictly , or do other factors play a role ? How can we better design selective conditions to achieve specific evolutionary outcomes ? We sought to address these questions using a system that combines high-throughput functional genomics and experimental evolution . We first measured the fitness of deletions and amplifications of almost all of the genes in the S . cerevisiae genome , which we refer to as the amplification and deletion ( AD ) set , using pooled competitions of thousands of mutants under selection in nutrient-limited continuous culture in chemostats followed by barcode sequencing . We found that while most of the AD mutations were neutral or decreased fitness , ~500 of them increased fitness in at least one condition and hence represented potential adaptive mutations . We next compared the fitness values from the AD set to a set of mutations identified in experimental evolution studies , which we refer to as the evolutionary ( E ) set . By comparing the E set with the results from the AD set , we recapitulated five of eight previously verified beneficial mutations and predicted that on average at least one third of the mutations present in the evolved strains were likely to positively affect fitness . In sulfate-limited conditions , mutations in one gene dominated the distribution of fitness effects in both the AD set and the E set . In glucose-limited and phosphate-limited conditions , the distributions of fitness effects were characterized by a large number of beneficial mutations of smaller effect . We found that the distribution of fitness effects in the sulfate-limited condition could be modified by precluding the dominant adaptive solution , which allowed the evolving populations to explore alternative beneficial mutations predicted based on the AD set . This study takes an initial step towards determining the fitness effects of candidate adaptive mutations , substantially improving on the throughput of other experimental approaches as well as on the accuracy of purely statistical or computational approaches .
We measured the fitness effects of single-gene changes in copy number for ~80% of the genes in the yeast genome using pooled competitions of five different collections of yeast strains in three different nutrient-limited conditions followed by Illumina-based barcode sequencing ( [22] Fig 1; S1 Table ) . Two of the collections , the deletion collections , consisted of haploid and heterozygous diploid strains , respectively; in each strain , one copy of a single gene was replaced by a selectable marker with a unique DNA barcode [31] . One ( control ) collection consisted of ~2 , 000 otherwise isogenic wild-type strains created by placing unique barcodes at a single , neutral genomic location [32] . The other two collections consisted of diploid strains bearing a low or high copy-number plasmid , respectively; each plasmid contained a single gene , the corresponding native promoter , and a unique barcode [29 , 30] . We conducted a total of 30 continuous-growth competition experiments with phosphate , glucose , and sulfate , respectively , as the limiting nutrient . We screened each yeast collection twice in each condition ( S1 Fig ) . In each screen , we mixed all of the strains from a single collection together at approximately equal proportions in a single culture vessel and measured the proportion of each strain at time points throughout the course of ~20 generations of propagation ( S2 Fig ) . We used large populations ( ~109 cells ) to overcome the stochastic effects of drift [23] . We measured the fitness over a relatively short period of time to limit the effects of de novo mutations , sampling the populations every three generations to maximize the accuracy of the fitness quantification . We measured the frequency of each strain at each time point using barcode sequencing ( barseq; S3 Fig ) [22] . We note that this experiment design does not allow us to control for mutations already present in the strains before the onset of the competition experiment . We made a total of 100 , 853 measurements of relative fitness , ranging from -36 . 5% to 42 . 8% , based on an average of 462 reads per gene per screen . We then created fitness distributions of the AD strains in each of the three selective conditions ( Fig 2; Table 1 and S2 Table ) . We were able to measure the fitness effects of copy-number changes of 2 , 133 genes in all 12 experiments and to measure the fitness effects of copy-number changes of an additional 2 , 953 genes in at least one experiment . To determine the inherent noise originating from the strain construction , pool generation , competition , and sequencing , we quantified the relative fitness of the strains in the control collection . The fitness distribution was tightly centered on 0; 98 . 2% of the control strains had fitness between -10% and +10% ( Fig 2; S3 Table ) . We therefore used fitness values of ±0 . 10 ( corresponding to a 10% change in fitness ) as the cutoffs to identify strains in the other four collections that had a significant fitness benefit or deficit compared with the control strains . Previous analyses showed that a beneficial mutation resulting in a 10% fitness increase will reach 5% of the population in ~200 generations and will fix in ~500 generations [37 , 38] , which suggests that mutations causing a fitness increase of less than 10% would rarely be identified as beneficial in our experimental evolution regime . Most of the deletion and amplification strains displayed wild-type or near wild-type fitness . The fitness distributions of the AD strains were broader than that of the control strains . Based on the 10% cutoff values , the AD collections were enriched for strains with decreased fitness ( n = 1693 ) or increased fitness ( n = 506 ) compared with the control collection ( n = 19 and 80 , respectively; Chi square , p<0 . 001 and p = 0 . 0033 , respectively; Fig 2 ) . Of the strains with increased fitness ( S5 Table ) , 223 had increased fitness in sulfate-limited conditions , 210 in glucose-limited conditions , and 73 in phosphate-limited conditions . Only a small fraction of strains had increased fitness in more than one condition ( n = 25 ) . The 506 strains with increased fitness represented copy-number changes in a total of 458 genes ( S5 Table ) . Seventy three percent of those strains were from the plasmid collections , which comprised just 47% of the total strains tested , suggesting that duplications of single genes are more likely than deletions to produce fitness gains . The AD set only recreates gross dosage changes and not mutations acting via different mechanisms; however , our screen identified five of eight genes in which beneficial mutations were previously identified in evolution experiments ( considering only those known beneficial mutations with matching strains in the AD set ) : the amplification of SUL1 and LOF mutations affecting SGF73 in sulfate-limited conditions and mutations affecting MTH1 , WHI2 , and GPB2 in glucose-limited conditions [18 , 19 , 39] . These results demonstrate that the AD collections were able to replicate the phenotypes caused by some beneficial mutations , although they failed to replicate those caused by others ( e . g . , mutations in PHO84 , IRA1 , and RIM15 ) . Among the genes associated with a fitness increase in the AD set , SUL1 was associated with the greatest fitness ( 42 . 8% in the sulfate-limited condition for a strain carrying the high-copy plasmid ) . In previous experiments , SUL1 amplification was recurrently selected during evolution in sulfate-limited conditions , and increasing the SUL1 copy number via expression on both low-copy and high-copy plasmids increased fitness [39 , 40] . Our screen also identified one gene that was previously identified as the cause of putative secondary adaptive effects: BSD2 , a gene involved in the downregulation of the metal transporter proteins Smf1 and Smf2 [41 , 42] and located 6kb upstream of SUL1 on chromosome 2 . The amplification of BSD2 on a low-copy plasmid increased fitness by 5% and 12 . 4% in the sulfate-limited and glucose-limited conditions , respectively . In previous studies of the SUL1 amplicon [39 , 40] , we detected only three independent clones where the SUL1 amplicon excluded BSD2 . The fitness of each of 13 strains harboring an amplification of both SUL1 and BSD2 was higher than the fitness of three strains harboring an amplification of SUL1 but not of BSD2 [40] , a result that was further supported by a fitness analysis of synthetic amplicons [19] . The reintroduction of BSD2 using a low-copy plasmid into one of the three strains harboring only SUL1 amplification increased the fitness in the sulfate-limited condition by 6 . 1% ( from 37 . 7% to 43 . 8% ) , suggesting that the fitness effects of the two mutations are additive . These results demonstrate that the AD screen is able to detect adaptive mutations even of small effect , although our control experiments suggest that the identification of such mutations is likely subject to a higher false-positive rate than the identification of beneficial mutations of larger effect . A decrease in the cutoff to ±5% resulted in the identification of increased or decreased fitness in 15% of the control strains and increased the number of beneficial mutations identified in the AD collections by six fold ( n = 3143 ) . Although the less stringent cutoff still identified significantly more beneficial mutations in the AD collections than in the control collection ( Chi square , p<0 . 001 ) , we decided to use the more stringent cutoff to focus on the mutations with the highest impact . Next , we sought to apply the knowledge gained from the screen of the AD set to the hundreds of de novo mutations identified in laboratory evolution experiments ( E set ) . Our goal was to determine which of the hundreds of possible adaptive mutations identified in the AD set were actually selected during experimental evolution . To compare the genes in the AD set that we identified as potential sites of adaptive mutations to the genes in which mutations actually occurred during experimental evolution , we first needed to create a comprehensive database of mutations identified in yeast evolution experiments . To do so , we identified and resequenced the mutations that occurred in yeast evolution experiments carried out by our lab [39 , 40] . The experiments involved the propagation of haploid or diploid prototrophic strains of S . cerevisiae for 122 to 328 generations in continuous-culture conditions identical to those in which our AD screens were performed ( six sulfate-limited , six phosphate-limited , and four glucose-limited populations . We detected 150 mutations by whole-genome sequencing of 16 populations and 34 clones ( See Materials and Methods ) . We then collected a large set of mutations from various Evolve and Resequence studies of yeast performed in a variety of conditions [2–4 , 8 , 40 , 43] . Thus , we compiled a total of 1 , 167 mutations in 1 , 088 genes from 106 long-term evolution experiments conducted in 11 different conditions in nine previous studies . We refer to this set of mutations as the E set ( S4 Table ) . The features of the previous studies and the resulting mutations are summarized in Table 2 . The complete list of mutations , their frequencies , and their predicted effects are given in S4 Table . The E set did not include chromosomal rearrangements , because those events were not always reported in the previous studies . Two recent studies showed that LOF mutations were frequently selected in populations of haploid yeast [2 , 3] . Based on a small number of mutations , another study concluded that mutations affecting cis-regulatory regions are co-dominant in heterozygous diploids [44] . Although those results are suggestive , too few Evolve and Resequence studies have been performed in diploid yeast to draw firm conclusions about the effects of ploidy on the distribution of fitness effects . We divided the E set into four groups based on SNPeff , an annotation program that predicts the functional impact of the mutation of a gene , as follows [45]: ( 1 ) high-impact mutations , such as frameshifts or the gain or loss of a start or stop codon; ( 2 ) moderate-impact mutations , such as non-synonymous substitutions or the deletion or insertion of a codon; ( 3 ) low-impact synonymous mutations; and ( 4 ) modifiers , corresponding to mutations upstream of a gene or within intergenic regions . We found that different types of mutations tended to be present in haploid and diploid strains , respectively ( Fisher’s exact test , p<0 . 001 , corrected for multiple tests ) . We confirmed previous findings showing that in haploids , the main category of mutation is LOF mutations involving the gain of a stop codon ( Chi square , p = 0 . 003; Table 3 ) . In contrast , LOF mutations were relatively rare in diploid strains , which were instead enriched for intergenic and upstream mutations ( Chi square , p<0 . 001; Table 3 ) , suggesting that amplifications and gain-of-function ( GOF ) mutations are more important in the diploid background . This result is consistent with our previous observations that evolved diploid strains contain more and larger variations in gene and chromosome copy numbers than evolved haploid strains [39] . Using only the mutations identified in glucose-limited conditions from the E set , we determined that the mutational signature was different between haploids and diploids in glucose-limited conditions ( Fisher’s exact test , p<0 . 001 ) , with an enrichment of LOF mutations among the haploids ( Chi-square , n = 224 , p<0 . 001 ) . Conversely , the mutations identified in phosphate-limited conditions in the E set displayed only marginal enrichment of LOF mutations ( Fisher exact test , n = 54 p = 0 . 053 ) , while those identified in sulfate-limited conditions displayed no enrichment of LOF mutations ( n = 100 ) . The different types of mutations observed between ploidies are likely explained by the tendency of LOF mutations to be recessive [46 , 47] compared with mutations that increase gene expression , which are more likely to have an effect in heterozygotes . Although loss of heterozygosity has been observed in diploid populations [39 , 46] , such cases are relatively rare . To test that directly , we examined the fitness effects of 55 beneficial deletions identified in both the haploid and the diploid AD collections and found that those deletions indeed tended to be recessive , causing on average a 9 . 0% ± 4 . 6 greater fitness increase in haploids than in diploids . Seven of the 55 deletions ( WSC3 , TIM12 , IPT1 , MMS22 , NDL1 , PBS2 , and YLR280C ) had the same fitness effect in haploids and diploids , indicating that a subset of LOF mutations can in fact be dominant . Overall , LOF mutations appeared to provide a greater adaptive benefit in haploid strains than in diploid strains , which is consistent with prior results . Recurrence-based models , which assume that oncogenes are recurrently mutated among independent samples , are one of the most widely used approaches to identify putative driver genes in cancer [48–50] . Recurrent adaptive trajectories have also been frequently observed in microbial evolution [2 , 3] , leading to the discovery of drivers of adaptation such as SUL1 , HXT6/7 , and RIM15 in S . cerevisiae and rpoS in Escherichia coli [3 , 13 , 14 , 39 , 51] . Of the 1 , 088 genes in the E set , 154 were mutated in more than one sample , and 19 were mutated in more than five samples ( Fig 3A , S4 Table ) . The recurrently mutated genes were highly enriched with high-impact mutations ( Fisher’s exact test , p<0 . 001; Fig 3B ) and tended to be longer than genes that were mutated in only one sample ( Wilcoxon rank-sum test , p<0 . 001; S4A Fig ) . There are several tools that correct for gene length to detect true adaptive mutations and discard false-positives [52] . We decided to use a different approach by inferring the fitness effects of mutations using the results from the AD screen . Convergent evolution has been widely used as a predictor of evolutionary outcomes . We decided to compare the list of recurrently mutated genes from the E set to the results of the AD screen , restricting our analysis to experiments performed in the same conditions . In the E set , 36 genes were mutated twice in at least one of the three conditions used in the AD screen . Ten of those genes were associated with a fitness increase of at least 10% in at least one collection in the AD set ( SUL1 and SGF73 in the sulfate-limited condition and GPB2 , PBS2 , AEP3 , MUK1 , HOG1 , ERG5 , SSK2 , and WHI2 in the glucose-limited condition ) . Eight more genes were associated with a fitness increase that did not meet our stringent cutoff of 10% but exceeded 5% . The remaining 18 genes were either absent from the collections ( n = 12 ) or associated with no fitness increase in the corresponding condition ( n = 6 ) . The six genes that showed no fitness effect in the AD set could have been recurrently mutated by chance . Alternatively , the mutations in the E set could have provided fitness increases that were not mimicked by the AD collections , which could be the case for mutations that caused partial LOF or that resulted in a novel function , or due to fitness-changing errors or secondary mutations in the relevant strains . Another possibility is that those mutations only provided a benefit in a specific genetic background or in concert with other mutations . Strains from the AD set could also have accumulated additional mutations that mask the true effect of the query mutation . A large number of genes identified in the E set were mutated only in a single population . Because the number of Evolve and Resequence experiments is relatively small , akin to a non-saturating genetic screen , some adaptive mutations are likely to be found as singletons and would therefore be missed by a recurrence-based detection method . The E set contained 155 genes that were mutated only once in glucose-limited , sulfate-limited , or phosphate-limited conditions . We used the data from the AD set to determine if those singletons might be associated with a fitness increase in the corresponding environment . Of the 155 singletons , only three had a fitness effect of at least 10% when amplified or deleted: amplifications of NMA111 in the sulfate-limited condition and CLN2 and YOR152C in the glucose-limited condition . Thirty-eight more genes had a fitness effect between 10% and 5% ( average fitness = 7 . 2±1 . 1 ) . Cln2 is one of the three G1 cyclins and promotes cell-cycle progression . The expression of G1 cyclins is regulated in response to nutrient limitations; in particular , it is repressed in the presence of glucose [53] . These results show that while convergent evolution is useful for identifying adaptive mutations , some singletons might also have fitness effects and should not be overlooked . Only a small portion of the singleton mutations were predicted by the AD screen to be beneficial , suggesting three possibilities , which are not mutually exclusive: the relevant data are missing from the AD screen ( only 52 of the 202 genes with singleton mutations were represented in all four collections and all three conditions used for the AD screen ) ; the AD screen does not accurately reflect the fitness of these point mutations; or the singletons were increasing in frequency in the evolved populations due to the presence of a beneficial mutation elsewhere in the genome , a phenomenon known as hitchhiking . If the first or second explanation were true , many of the evolved samples should lack mutations predicted to be adaptive by the AD screen , because the AD screen would have a high false-negative rate . If most of the singletons were the result of hitchhiking , all of the evolved samples should carry mutations predicted to be beneficial by the AD screen in addition to the neutral or weakly deleterious hitchhiker mutations . In order to determine the relative contributions of these explanations , we predicted the number of adaptive mutations each population and clone in the E set should carry based on the frequency of recurrence in the E set and the fitness data from the AD set . We determined that each clone or population in the E set carried at least one adaptive mutation predicted by the AD screen , which is consistent with the modest false-negative rate for the AD screen . Each sample in the E set contained on average 1 . 8 ( 2 . 2 per population and 1 . 4 per clone ) adaptive mutations predicted by the AD screen , representing 35% of the total mutations identified in the E set ( Fig 4A–S6 Table ) . There was no difference in the prevalence of predicted adaptive mutations among the three selective conditions ( S4B Fig ) . That result is consistent with previous reports of frequent hitchhiking by neutral or deleterious mutations [2 , 51 , 54 , 55] . Our estimate largely agrees with the results of detailed genetic analyses of mutations carried by evolved strains , which found that one third of the single-gene mutations among a total of five evolved clones were associated with a fitness increase [18 , 19 , 39] . Thus , by combining the data from the AD set and the E set , we were able to generate a more comprehensive list of adaptive mutations in evolved populations as well as estimate the genomic reservoir of beneficial mutations that were not detected . We conclude that evolution is partly predictable based on the repeatability of adaptive mutations among independent populations and reflects , at least in part , the fitness distribution of possible mutations , as mimicked by genome-wide screens of gene deletions and amplifications . The E set defined a set of 28 genes that were the sites of adaptive mutations with large effects ( based on the classification of mutations present in the AD set ) , which we consider to be candidate driver genes . Three of the candidate driver genes were mutated in only one sample , and 25 were mutated repeatedly among different samples . The AD screen identified a large number of potential sites of beneficial mutations that were a single mutational step away from the ancestral genotype [56] . To determine what differentiates the actual mutational spectrum from the pool of potential beneficial mutations , we excluded the genes in the E set that harbored mutations that were predicted to be beneficial based on the AD screen ( n = 28 ) and examined the remaining genes that were associated with fitness increases in the AD screen ( n = 430 ) . Given the population sizes ( 105 to 1010 cells ) and numbers of generations ( 50 to 1000 ) in the evolution experiments and the size of the yeast genome ( ~12 megabases ) , it is likely that every base in the genome was mutated at least once at some point among the ensemble of experiments in the E set . It therefore seems unlikely that mutations in the 430 genes identified in the AD screen as potential sites of adaptive mutations failed to occur at some point in the evolution experiments , although there was a greater likelihood that mutations mimicking the plasmid-based amplifications actually failed to occur , because point mutations that significantly increase gene expression might simply not exist in some promoter regions [57] . Furthermore , gene-amplification rates are generally biased by genomic-architecture constraints , such as proximity to repeat sequences , and the fitness effects of multigenic amplicons are complicated by the contributions of genes linked to the driver gene [19] . In order to better understand those issues , we compared the condition-specific fitness effects of the AD mutations that matched E-set mutations in the same condition with those of the AD mutations that did not match any E-set mutations in the same condition . In the glucose-limited condition , there was no difference on average between the fitness effects of the AD mutations with and without matching E-set mutations ( Fig 5A ) . In the sulfate-limited condition , the AD mutations with matching E-set mutations had greater fitness effects on average than those without matching E-set mutations ( Wilcoxon rank-sum test , p = 0 . 001; Fig 5A ) . Consistent with previous findings , SUL1 dominated the fitness distributions in sulfate-limited conditions in both the AD set and the E set ( Fig 5B ) . When the SUL1 amplifications were excluded from the comparison of AD mutations with and without matching E-set mutations , the AD mutations with matching E-set mutations still had greater fitness effects on average than those without matching E-set mutations ( Wilcoxon rank-sum test , p = 0 . 05 ) . Other highly beneficial mutations ( with >20% fitness increase ) such as amplifications of MAC1 and PHO3; encoding proteins implicated in copper and phosphate-sulfate metabolism , respectively; appear to be potential drivers of evolution but have not been identified in evolved populations ( Fig 5B; [2 , 58] ) . That suggests that , at least under sulfate-limited conditions , adaptation can be predicted based on the fitness effects of potential single-gene mutations , with the mutations providing the largest increase in fitness being the most likely to reach high frequencies . Although fewer clones and populations have been sequenced from phosphate-limited evolution experiments , all of the beneficial mutations in that condition in the E set could be predicted based on recurrence . Conversely , in glucose limitation , a variety of beneficial mutations with smaller fitness effects appear to be possible and were indeed observed in evolved populations . Condition-dependent or genome-wide variation in mutation rates could bias adaptive outcomes relative to the distribution of fitness effects seen in the AD screen [2] . The lack of observed mutations in the E set corresponding to many of the genes identified by the AD screen as potential sites of beneficial mutations likely reflects a combination of many factors , including random chance , epistatic interactions , strain background differences , or a failure of the AD set to adequately recapitulate the fitness of de novo mutations . Clonal interference is also likely to play a role . We asked which mutations would be selected in sulfate-limited conditions if SUL1 amplification were not possible . Alternative adaptive mutations might only rarely reach high frequencies in sulfate-limited conditions because of the strong fitness effects of SUL1 amplification . We hypothesized that in the absence of the SUL1 amplification , a variety of alternative mutations of smaller effect would be selected , an outcome more similar to the pattern observed in glucose limitation . We analyzed two populations that lacked SUL1 amplifications ( Fig 6A , population s611 and s612 S4 Table ) but showed fitness gains after 200 generations of evolution in sulfate-limited conditions . The fitness gains of those populations ( ~30%; Fig 6B ) were near the lower end of the range of fitness gains in previously studied clones harboring SUL1 amplifications ( 37–53% ) [40] . To establish which mutations were responsible for the fitness gains in the absence of SUL1 amplification , we performed whole-genome sequencing of the populations isolated at generation 200 . We detected two independent , non-synonymous mutations ( N263H and N250K ) in the coding region of SUL1 in both populations ( S4 Table ) . We inserted each of those mutations into wild-type strains and found that N250K increased fitness by 23 . 1% ( ±2 . 3% ) and N263H increased fitness by 17 . 7% ( ±1 . 22% ) . In addition , one population ( s611 ) harbored a nonsense mutation in SGF73 , a gene previously identified as the site of an adaptive mutation ( S4 Table ) , and the other population ( s612 ) , harbored a 5 . 1 kb deletion on chromosome IV ( 587839–592999 ) affecting four genes ( FMP16 , PAA1 , IPT1 , and SNF11; Fig 6C ) . In the AD screen , deletions of IPT1 and SNF11 were beneficial in glucose-limited and sulfate-limited conditions ( 10–20% fitness increase ) , but mutations in those genes were not included in the E set ( Fig 5B ) . Because IPT1 and SNF11 are adjacent to one another on the chromosome , we suspected that one of them might be a false positive , resulting from a known artifact called the neighboring gene effect [59] . By employing complementation testing using centromeric plasmids , we found that the deletion of either gene increased fitness ( Fig 6D ) . Snf11 is a subunit of the SWI/SNF chromatin remodeling complex , which is known to act as a tumor suppressor in humans [60] . Ipt1 is implicated in membrane-phospholipid metabolism and nutrient uptake [61] . Thus , our results showed that adaptive mutations predicted by the AD screen can be relevant , even when they are rarely identified in evolution experiments . We predict that additional evolution experiments that preclude the possibility of SUL1 amplification will reveal even more alternative fitness peaks .
The recurrence-based identification of adaptive mutations provides an incomplete picture of the impact of mutations on cellular fitness [62] . In agreement with previous reports [2 , 3 , 9 , 13 , 39 , 51] , we found that experimental evolution resulted in non-uniform selection of mutations across the genome ( Fig 3A ) . It is currently impossible to screen all possible mutations , so we used whole-gene amplifications and deletions as a first step in approximating the spectrum of potential mutations . We believe that this is a reasonable approach given the prevalence of gene copy-number changes and LOF mutations in experimentally evolved populations [2 , 3 , 39] , and our success in identifying genes with previously validated high fitness mutations . Our results can be used to prioritize the experimental validation of potentially adaptive mutations found in evolved strains . The AD screen allowed us to discriminate between adaptive mutations and neutral or passenger mutations in evolved populations . Based on the results of the AD screen combined with the information provided by the E set , we predict that ~35% of the mutations appearing in laboratory-evolved populations are likely beneficial . As expected , that number is higher than previous estimates of the baseline rate of beneficial mutations ( 6–13% ) based on mutation-accumulation experiments with yeast [63] . The frequencies of different categories of adaptive mutations ( e . g . , LOF or altered level of expression ) differed between haploids and diploids . In agreement with previous work [3] , we detected an excess of LOF mutations in haploids and an excess of mutations that likely modify gene expression in diploids . Our results agree with those of several studies showing that mutations have greater fitness effects in haploids than in heterozygous diploids [64] and that the frequency of fixation is higher in diploids [37] . Mutations affecting cis-regulatory regions have often been described as co-dominant , whereas most mutations in coding regions cause LOF and are recessive [44] . Large copy-number variations ( CNVs ) have been shown to be enriched in diploid backgrounds compared with haploid backgrounds [39] , suggesting that a diploid context might buffer the detrimental effects of aneuploidy and CNVs seen in haploids [65 , 66] . These results emphasize the point that evolutionary trajectories are constrained by ploidy and that patterns observed at a particular ploidy are unlikely to act universally . We also observed that the majority of the beneficial mutations from the AD set are from the plasmid collection , further illustrating the importance of gene amplifications in adaptation . Despite our promising results , functional screens using single-gene amplifications and deletions have several limitations . The available yeast collections are based on single-gene copy-number changes and do not allow the study of mutations in protein-coding regions that are not mimicked by dosage changes , mutations in non-genic functional elements , or combinations of mutations . To explore the importance of non-genic regions and small genes that are not present in the yeast collections , billions of individual and combined mutations need to be generated in a comprehensive way , similar to the deep mutational scanning of proteins [67] , the Million Mutation Project [68] , or newly created resources such as the tRNA deletion collection [69] and large telomeric amplicons [19] . Previous studies in microbial and viral systems have provided evidence for both antagonistic and synergistic epistasis among beneficial mutations [36 , 70–73] . Synthetic genetic arrays and similar approaches using the S . cerevisiae deletion collection have been used to characterize negative and positive epistatic relationships , and a nearly complete yeast genetic-interaction network has been generated using double mutants [74 , 75] . Further studies using those resources will allow us to move beyond single-gene effects and begin to understand how interactions among multiple genes in CNVs and combinations of mutations shape the distribution of fitness effects . By expanding and developing these techniques , the increase of studies combining long-term experimental evolution and whole-genome sequencing will likely reveal additional mutational effects .
The MoBY-ORF collection of centromeric ( CEN ) plasmids in E . coli was obtained from Open Biosystems and stored at -80°C as individual strains in 96-well plates . The plates were thawed and robotically replicated onto LB-Lennox ( Tryptone 10g , yeast extract 5g , NaCl 5g ) agar plates containing 5Δg/ml tetracycline , 12 . 5μg/ml chloramphenicol , and 100μg/ml kanamycin and grown at 37°C for 14 h . Colonies were harvested by addition of 5ml LB-Lennox to each plate and subsequently pooled . Glycerol ( 50% ) was added , and 1ml aliquots containing 2×109 cells were frozen at -80°C . Plasmid DNA was prepared from the E . coli pool and then used to transform the S . cerevisiae S288C derivative strain DBY10150 ( ura3-52/ura3-52 ) using a standard lithium acetate protocol . The yeast cells were selected on -URA and 200μg/ml G418 plates , resulting in 88 , 756 transformants , which were then pooled together , giving an average library coverage of ~20× . The MOBY-ORF v2 . 0 collection ( 2 micron plasmid ) was obtained from the Boone lab and crossed for 3 h with YMD1797 ( MATα , leu2Δ1 ) . Clones were selected twice on MSG/B and G418 ( 200μg/ml ) and then pooled together . The MATa/MATα SGA Marker ( MM2N ) collection was obtained already pooled from the Spencer lab . The MATa SGA Marker ( MM1N ) library was obtained frozen from the Caudy lab; the strains were selected on -LYS and -MET and then pooled together . The barcoder collection was obtained frozen from the Nislow lab . The plates were thawed at room temperature , replicated onto YPD and G418 ( 200μg/ml ) , and crossed with FY5 ( MATα , prototrophic strain ) . The strains were then selected twice on MSG/B+G418 ( 200μg/ml ) and pooled together . A list of the strains used in this study can be found in S1 Table . Previously described nutrient-limited media ( sulfate-limited , glucose-limited , and phosphate-limited [13 , 39 , 76] ) were complemented with uracil and histidine ( 20mg/l ) for the SGA Marker pools . For each competition , a 200ml culture was inoculated with 1ml of a single pool ( ~2×107 cells ) . Two competition experiments were performed for each pool . The cultures were grown in chemostat culture at 30°C with a dilution rate of 0 . 17±0 . 01 volumes/h . The cultures were grown in batch for 30h and then switched to continuous culture . The continuous cultures reached steady state after ~10 generations and were maintained for an additional 20 generations ( S2 Fig ) . A sample taken just after the switch to continuous culture was designated generation 0 ( G0 ) . Subsequent samples were harvested every three generations thereafter . Samples for cell counts and DNA extraction were passively collected twice daily . Genomic DNA was extracted from dry , frozen cell pellets using the Smash-and-Grab method [77] . Plasmids from the MoBY collections were extracted with a Qiagen miniprep protocol ( QIAprep Spin mini prep kit; Qiagen , Hilden , Germany ) with the following modification: 0 . 350mg of glass beads were added to a cell pellet with 250μl buffer P1 and vortexed for 5min . Then , 250μl buffer P2 was added to the mix of cells and beads , and 350μl buffer N3 was added to the solution before centrifuging for 10 min . The supernatant was then applied to the Qiagen column following the recommendation of the Qiagen miniprep kit . Plasmid DNA was then eluted in 50μl sterile water . Genomic DNA was extracted from dry cell pellets by the Smash-and-Grab method and used for barcode verification of single strains by PCR amplification and Sanger sequencing as previously described [40] . For each sample , the plasmid copy number was determined using the copy number of KanMX relative to the copy number of DNF2 , a gene located on chromosome 4 and absent from the two MoBY collections ( see S6 Fig ) . The primers used are listed in S8 Table . Microarray assays , whole-genome sequencing , SNP calling , and qPCR analysis were performed as previously described [40] . The microarray data have been deposited in the Gene Expression Omnibus repository under accession GSE58497 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=sjgtsgwmdhajdud&acc=GSE58497 ) . The fastq file for each library is available from the NCBI Short Read Archive with the accession number PRJNA248591 and BioProject accession PRJNA249086 . Amplifications of the barcodes were performed using a modified protocol [22] . Uptag barcodes were amplified using primers containing the sequence of the common barcode primers ( bold ) , a 6-mer tag for Illumina multiplexing ( in italics ) , and the sequence required for attachment to the Illumina flowcell ( underlined; S8 Table ) . PCR amplifications were performed in 100μl , using Roche FastStart DNA polymerase with the following conditions: 94°C for 3min; 25 cycles of 94°C for 30s , 55°C for 30s , and 72°C for 30s; followed by 72°C for 3min . PCR products were then purified using the Qiagen MinElute PCR Purification kit ( cat . No . 28004 ) , quantified using a Qubit fluorometer , and then adjusted to a concentration of 10μg/ml . Equal volumes of normalized DNA were then pooled and gel purified from 6% polyacrylamide TBE gels ( Invitrogen ) using a soak and crush method followed by purification and concentration using Qiagen Qiaquick PCR purification . After quantification using a Qubit fluorimeter , the libraries were sequenced using the standard Illumina protocol as multiplexed , single-read , 36-base cycles on several lanes of an Illumina Genome Analyser IIx ( GAII ) . Thirty multiplexed libraries ( UPTAGS only ) were sequenced on several lanes of an Illumina GAII . An average of 25 , 664 , 072 million reads per library that perfectly matched the molecular barcodes were obtained ( S9 Table ) . The fastq file for each library is available from the NCBI Short Read Archive with the accession number PRJNA248591 and BioProject accession PRJNA249086 ( S10 Table ) . The 6-mer multiplexing tags were reassigned to a particular sample using a custom Perl script ( S1 File ) . Then , each barcode was reassigned to a gene using a standard binary search program ( programmed in C , S2 File ) . Only reads that matched perfectly to the reannotated yeast deletion collection [22] or the MoBY-ORF collection [29] were used . For the barcoder collection , 1885 barcodes were recovered using a compiled list of all barcodes previously published ( 1624 barcodes from the barcode list of the deletion collection and 260 barcodes from the Yeast Barcoders collection; [28 , 32] ) . Multiple genes with the same barcodes were discarded . Strains with less than 20 counts across the different samples were discarded . The numbers of strains identified for the five collections in the three conditions are summarized in S9 Table . To avoid division by zero errors , each barcode count was increased by 10 before being normalized to the total number of reads for each sample . To quantify the relative fitness of each strain during growth in the various conditions , the analysis was restricted to the time during which the populations were in a steady-state phase , defined as generations 6 through 20 . Generation 0 was used as t0 . The linear regression of the log2 ratios of the normalized barcode counts at generations 6–20 to that at generation 0 was used to calculate the fitness of each strain . The two replicate measurements were then averaged . The source code is provided in the Supporting Information ( R script , S3 File ) . The correlation between each pair of replicates was displayed using the R package corrgram . The distribution of the averaged fitness was displayed using the R package beanplot [78] . To ensure that the pooled fitness measurements accurately reflected the fitness of each strain , the relative fitness of 51 strains from the deletion and plasmid collections that had deleterious , neutral , or beneficial changes was measured by pairwise competitions against a control strain marked with a fluorescent protein ( eGFP ) in the three conditions used in the pooled experiments . Fitness measurements of the individual clones were performed as previously described [40] using FY strains in which the HO locus was replaced with eGFP ( MATa: YMD1214 and MATa/MATα: YMD2196; S5 Fig , S7 Table ) . The fitness values were similar in both assays , and there was a strong positive correlation ( R2 = 0 . 83 ) between the fitness values from the large pool screen and the pairwise fitness assays ( S5 Fig and S7 Table ) . To limit artifacts due to preexisting mutations or copy-number changes in the genomes of the pooled strains , most of the barcoded pools were created either by fresh transformation ( in the case of the plasmid collections ) or from a fresh cross of the commercially available collection stocks with a wild-type strain ( see the Materials and Methods ) . To detect the extent of extraneous mutations in the validation panel , 51 strains were screened for the most common secondary mutation detected previously in the deletion collection: mutations in WHI2 , which is involved in the regulation of cell proliferation [79] . Mutations in WHI2 were screened in the 51 strains by PCR using oligo ( YOR043W-for and YPR043W-rev ) and Sanger sequencing ( S7 Table ) . Microarray analysis of the last sample of one of the competitions of the low-copy plasmid collection was used to verify that there were no copy-number changes , other than those due to the plasmids , at the population level; although that approach would only detect CNVs that achieved at least a ~10% frequency in the population . All sequencing data from this study have been submitted to the NCBI Sequence Read Archive ( SRA; http://www . ncbi . nlm . nih . gov/sra ) under accession number PRJNA248591 and BioProject accession PRJNA249086 . Microarray data from this article have been deposited in the Gene Expression Omnibus repository under accession GSE58497 ( http://www . ncbi . nlm . nih . gov/geo/query/acc . cgi ? token=sjgtsgwmdhajdud&acc=GSE58497 ) . | Experimental evolution allows us to observe evolution in real time . New advances in genome sequencing make it trivial to discover the mutations that have arisen in evolved cultures; however , linking those mutations to particular adaptive traits remains difficult . We evaluated the fitness impacts of thousands of single-gene losses and amplifications in yeast . We discovered that only a fraction of the hundreds of possible beneficial mutations were actually detected in evolution experiments performed previously . Our results provide evidence that 35% of the mutations identified in experimentally evolved populations are advantageous and that the distribution of beneficial fitness effects depends on the genetic background and the selective conditions . Furthermore , we show that it is possible to select for alternative mutations that improve fitness by blocking particularly high-fitness routes to adaptation . |
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In the presence of oxygen ( O2 ) the model bacterium Escherichia coli is able to conserve energy by aerobic respiration . Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons . When E . coli encounters environments with different O2 availabilities , the expression of the genes encoding the alternative terminal oxidases , the cydAB and cyoABCDE operons , are regulated by two O2-responsive transcription factors , ArcA ( an indirect O2 sensor ) and FNR ( a direct O2 sensor ) . It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid . In this study , an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E . coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures . The molecules of O2 , transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E . coli cell . The model implies that there are two barriers that dampen the response of FNR to O2 , i . e . consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR . Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression .
The bacterium Escherichia coli is a widely used model organism to study bacterial adaptation to environmental change . As an enteric bacterium , E . coli has to cope with an O2-starved niche in the host and an O2-rich environment when excreted . In order to exploit the energetic benefits that are conferred by aerobic respiration , E . coli has two major terminal oxidases: cytochrome bd-I ( Cyd ) and cytochrome bo′ ( Cyo ) that are encoded by the cydAB and cyoABCDE operons , respectively [1] , [2] . Cyd has a high affinity for O2 and is induced at low O2 concentrations ( micro-aerobic conditions ) , whereas Cyo has a relatively low affinity for O2 and is predominant at high O2 concentrations ( aerobic conditions ) [3] . These two terminal oxidases contribute differentially to energy conservation because Cyo is a proton pump , whereas Cyd is not [1] , [2]; however , the very high affinity of Cyd for O2 allows the bacterium to maintain aerobic respiration at nanomolar concentrations of O2 , thereby maintaining aerobic respiratory activity rather than other , less favorable , metabolic modes [4]–[6] . The transcription factors , ArcA and FNR , regulate cydAB and cyoABCDE expression in response to O2 supply [7] . FNR is an iron-sulfur protein that senses O2 in the cytoplasm [8] , [9] . In the absence of O2 the FNR iron-sulfur cluster is stable and the protein forms dimers that are competent for site-specific DNA-binding and regulation of gene expression [10] . The FNR iron-sulfur cluster reacts with O2 in such a way that the DNA-binding dimeric form of FNR is converted into a non-DNA-binding monomeric species [10] . Under anaerobic conditions , FNR acts as a global regulator in E . coli [11]–[13] , including the cydAB and cyoABCDE operons , which are repressed by FNR when the O2 supply is restricted [7] . Under aerobic conditions , repression of cydAB and cyoABCDE is relieved and Cyd and Cyo proteins are synthesized [3] . In contrast , ArcA responds to O2 availability indirectly via the membrane-bound sensor ArcB . In the absence of O2 ArcB responds to changes in the redox state of the electron transport chain and the presence of fermentation products by autophosphorylating [14]–[16] . Phosphorylated ArcB is then able to transfer phosphate to the cytoplasmic ArcA regulator ( ArcA∼P ) , which then undergoes oligomerization to form a tetra-phosphorylated octomer that is capable of binding at multiple sites in the E . coli genome [17] , [18] , including those in the promoter regions of cydAB and cyoABCDE to enhance synthesis of Cyd and inhibit production of Cyo [7] , [17] . Because the terminal oxidases ( Cyd and Cyo ) consume O2 at the cell membrane , a feedback loop is formed that links the activities of the oxidases to the regulatory activities of ArcA and FNR ( Figure 1 ) . These features of the system - combining direct and indirect O2 sensing with ArcA∼P and FNR repression of cyoABCDE , and ArcA∼P activation and FNR repression of cydAB - result in maximal Cyd production when the O2 supply is limited ( micro-aerobic conditions ) and maximal Cyo content when O2 is abundant ( aerobic conditions ) [3] . Although the cellular locations of the relevant genes ( cydAB and cyoABCDE ) , the regulators ( ArcBA and FNR ) and the oxidases ( Cyd and Cyo ) are likely to be fundamentally important in the regulation of this system , the potential significance of this spatial organization has not been investigated . Therefore , a detailed agent-based model was developed to simulate the interaction between O2 molecules and the electron transport chain components , Cyd and Cyo , and the regulators , FNR and ArcBA , to shed new light on individual events within local spatial regions that could prove to be important in regulating this core component of the E . coli respiratory process .
The dynamics of the system were investigated by running the simulation through two cycles of transitions from 0–217% AU . Figure 3a shows a top view of a 3-D E . coli cell at 0% AU ( steady-state anaerobic conditions ) . Under these conditions , the FNR molecules are present as dimers , all ArcB molecules are phosphorylated and the ArcA is octameric . The DNA binding sites for ArcA ( 120 in the model ) and FNR ( 350 in the model ) in the nucleoid are fully occupied . The number of ArcA sites was chosen from the data reported by Liu and De Wulf [18] . The model must include a mechanism for ArcA∼P to leave regulated promoters . Upon introduction of O2 into anaerobic steady-state chemostat cultures ∼5 min was required to inactivate ArcA-mediated transcription [15] . In the agent-based model presented here , each iteration represents 0 . 2 sec . Therefore , assuming that ArcA∼P leaving the 120 DNA sites is a first order process , then t½ is ∼45 sec , which is equivalent to ∼0 . 3% ArcA∼P leaving the DNA per iteration ( Table 3 ) . The number of FNR binding sites was based on ChIP-seq and ChIP-Chip measurements , which detected ∼220 FNR sites and a genome sequence analysis that predicted ∼450 FNR sites; thus a mid-range value of 350 was chosen [23]–[25] . Interaction with O2 causes FNR to dissociate from the DNA ( Table 3 ) . Under fully aerobic conditions ( 217% AU ) the FNR dimers are disassembled to monomers , and the different forms of ArcA coexist ( Figure 3b ) . The ArcA- and FNR- DNA binding sites in the nucleoid are mostly unoccupied due to the lower concentrations of FNR dimers and ArcA octamers . Examination of the system as it transits from 0% to 217% AU showed that the DNA-bound , transcriptionally active FNR was initially protected from inactivation by consumption of O2 at the cell membrane by the terminal oxidases and by reaction of O2 with the iron-sulfur clusters of FNR dimers in the bacterial cytoplasm - the progress of this simulation is shown in Video S1 . This new insight into the buffering of the FNR response could serve a useful biological purpose by preventing pre-mature switching off of anaerobic genes when the bacteria are exposed to low concentration O2 pulses in the environment . In the various niches occupied by E . coli , the bacterium can experience the full range of O2 concentrations from zero , in the anaerobic regions of a host alimentary tract , to full O2 saturation ( ∼200 µM , equivalent to ∼120 , 000 O2 molecules per cell ) , but fully aerobic metabolism is supported when the O2 supply exceeds 1 , 000 O2 molecules per cell . The profiles of five repetitive simulations for each agent in the model are presented in Figure 4 . From iteration 1 to 5000 and iteration 15000 to 20000 , O2 was supplied at a constant value of ∼6 , 500 molecules per cell such that the total number of O2 molecules entering the cell increased linearly; when the O2 supply was stopped ( 5000 to 15000 and 20000 to 30000 iterations ) no more O2 entered the cell and thus the number of O2 molecules that had entered the cell remained unchanged during these periods ( Figure 4a ) . When O2 became available to the cell ( from iteration 1 ) , the sensor ArcB was de-phosphorylated and started to de-phosphorylate ArcA . Consequently , the number of ArcA octamers bound at their cognate sites in the nucleoid decreased rapidly . The ArcA tetramers and dimers produced during de-phosphorylation of the ArcA octamer were transformed to inactive ( de-phosphorylated ) ArcA dimers , ( Figure 4d–f ) . Under aerobic conditions ( iteration 5000 ) all the ArcA was decomposed to inactive ArcA dimers . When the O2 supply was stopped ( from iteration 5001 ) , the number of inactive ArcA dimers decreased rapidly as shown in Figure 4f , being transformed into phosphorylated ArcA dimers , tetramers and octamers ( Figure 4c–e ) . Due to the phosphorylated ArcA dimers and tetramers combining to form ArcA octamers , their numbers dropped after initially increasing . The rate at which the ArcA octomers accumulated ( ArcA activation ) after O2 withdrawal was slower than the rate of ArcA inactivation ( Figures 4b and c ) . In this implementation of the modeled transition cycle , the numbers of ArcA octamers in the cytoplasm and bound to DNA did not reach that observed in the initial state before the second cycle of O2 supply began , indicating that a longer period is required to return to the fermentation state . The numbers of FNR dimer bound to binding sites and free FNR dimer ( cytoplasmic FNR dimer ) decreased when O2 was supplied to the system ( Figures 4g–h ) , but the rate was slower than that for ArcA inactivation , consistent with O2 consumption at the membrane , which can be sensed by ArcB to initiate inactivation of ArcA , but lowers the signal for inactivation of FNR . When O2 was removed from the system ( from iteration 5001 ) FNR was activated over a similar timeframe to ArcA ( Figures 4b and g ) , which was again consistent with previous observations [15] . As with ArcA , free FNR dimers and FNR monomers did not fully return to their initial states after O2 supply was withdrawn in the model , indicating that further iterations are required to reach steady-state ( Figure 4h–i ) . These results clearly indicate that the model is self-adaptive to the changes in O2 availability , and the reproducible responses prove the reliability and robustness of the model . The ArcBA system simulated in this model is based on a preliminary biological assumption , and the agent-based model presented here should prove a reliable and flexible platform for exploring the key components of the system and testing new experimental findings . In order to validate the model with biological measurements of FNR DNA-binding activity estimated using an FNR-dependent lacZ reporter , the ArcBA system agents were removed from the model by setting their agent numbers to zero . The ArcBA system is an indirect O2 sensor and does not consume O2 , hence the FNR system was not affected by withdrawing ArcBA from the model , but this simplification increased simulation speed . The O2 step length and other model parameters were estimated using the experimental data obtained at 31% AU . Using the estimated O2 step length at 31% AU and defining the step length of O2 molecule , , as 0 at 0% AU , a linear model , , was constructed to predict the step lengths of O2 at other AU levels , where k = 2 . 1 and represents the O2 concentration at different AU levels ( Table 4 ) . The O2 step lengths predicted by this model were used to validate the model at 85% , 115% and 217% AU , and the accuracy of the linear model was shown by the good correlation between the model and experimental data . Profiles of five repetitive simulations in which the simplified model was used to predict the numbers of active FNR dimers in steady-state cultures of bacteria grown at different AU values are presented in Figure 5 . At 31% AU , the model implied that FNR-mediated gene expression is unaffected compared to an anaerobic culture ( 0% AU ) , i . e . the number of FNR binding sites occupied in the nucleoid remained unchanged ( Figures 5a and e ) . Even at 85% AU , ∼80% of the FNR-binding sites remained occupied ( Figures 5b and f ) . It was only when the O2 supply was equivalent to >115% AU that occupation of the FNR-binding sites in the nucleoid decreased ( Figures 5 c , d , g and h ) . These outputs matched the FNR activities calculated from the measurements of an FNR-dependent reporter ( Table 5 ) and thus demonstrate the abilities of the model to simulate the general behavior of FNR dimers in steady-state cultures of E . coli . A second validation approach using two FNR variants that are compromised in their ability to undergo monomer-dimer transitions was adopted . The FNR variant FNR I151A can acquire an iron-sufur cluster in the absence of O2 , but subsequent dimerization is impaired [26] . The FNR D154A variant can also acquire an iron-sulfur cluster under anaerobic conditions , but does not form monomers in the presence of O2 [26] . To mimic the behavior of these two FNR variants the interaction radius for FNR dimer formation was changed in the model . Thus , the interaction distance for wild-type FNR monomers , which was initially set at 6 nm ( r3 , Table 3 ) was increased to 2000 nm for the FNR D154A variant , essentially fixing the protein as a dimer , or decreased to 2 . 5 nm for the FNR I151A variant , making this protein predominantly monomeric under anaerobic conditions . The results of simulations run under aerobic ( 217% aerobiosis ) and anaerobic conditions ( 0% aerobiosis ) suggested that under aerobic conditions wild-type FNR and FNR I151A should be unable to inhibit transcription from an FNR-repressed promoter ( i . e . the output from the reporter system is 100% ) , whereas FNR D154A should retain ∼50% activity ( Table 6 ) . Under anaerobic conditions , wild-type FNR was predicted to exhibit maximum repressive activity ( i . e . 0% reporter output ) , whereas FNR I151A and FNR D154A mediated slightly enhanced repression compared to the simulated aerobic conditions ( Table 6 ) . To test the accuracy of these predictions , the ability of wild-type FNR , FNR I151A and FNR D154A to repress transcription of a synthetic FNR-regulated promoter ( FFgalΔ4 ) under aerobic and anaerobic conditions was tested [27] . The choice of a synthetic FNR-repressed promoter was made to remove complications that might arise due to iron-sulfur cluster incorporation influencing the protein-protein interactions between FNR and RNA polymerase; in the reporter system chosen FNR simply occludes the promoter of the reporter gene and as such DNA-binding by FNR controls promoter activity . The experimental data obtained matched the general response of the FNR variants in the simulation , but not very precisely for FNR D154A , with the experimental data indicating more severe repression by FNR D154A under both aerobic and anaerobic conditions than predicted ( Table 6 ) . This suggested that the interaction radius ( r2 = 5 nm; Table 3 ) , which controls the binding of FNR to its DNA target required adjustment to enhance DNA-binding of the FNR D154A variant . Therefore , the simulations were rerun after adjusting r2 to 7 nm for all the FNR proteins considered here . The results of the simulations for both FNR variants now matched the experimental data well ( Table 6 ) . However , it was essential to ensure that the adjustment to r2 did not significantly influence the model output for wild-type FNR . Therefore , simulations of the behaviour of wild-type FNR at 31 , 85 , 115 and 217% aerobiosis were repeated using the adjusted r2 value of 7 nm . The model output was very similar to those obtained when r2 was at the initial value of 5 nm ( Table 7 ) . These analyses imply that for FNR D154A , which is essentially fixed in a dimeric state , the rate of binding to the target DNA governs transcriptional repression , but for wild-type FNR the upstream monomer-dimer transition is the primary determinant controlling the output from the reporter . The FNR switch has been the subject of several attempts to integrate extensive experimental data into coherent models that account for changes in FNR activity and target gene regulation in response to O2 availability [15] , [28]–[31] . These models have provided estimates of active and inactive FNR in E . coli cells exposed to different O2 concentrations and the dynamic behavior of the FNR switch . The ability of FNR to switch rapidly between active and inactive forms is essential for it to fulfill its physiological role as a global regulator and the models are able to capture this dynamic behavior . Thus , it is thought that the ‘futile’ cycling of FNR between inactive and active forms under aerobic conditions has evolved to facilitate rapid activation of FNR upon withdrawal of O2 and hence the physiological imperative for rapid activation has determined the structure of the FNR regulatory cycle [30] , [31] . However , it is less clear from these approaches how the system avoids undesirable switching between active and inactive states at low O2 availabilities ( micro-aerobic conditions , >0%–<100% AU ) . To achieve rapid FNR response times it has been suggested that minimizing the range of O2 concentrations that constitute a micro-aerobic environment , from the viewpoint of FNR , is advantageous [31] . Unlike previous models of the FNR switch , the agent-based model described here recognizes the importance of geometry and location in biology . This new approach reveals that spatial effects play a role in controlling the inactivation of FNR in low O2 environments . Consumption of O2 by terminal oxidases at the cytoplasmic membrane and reaction of O2 with the iron-sulfur clusters of FNR in the cytoplasm present two barriers to inactivation of FNR bound to DNA in the nucleoid , thereby minimizing exposure of FNR to micro-aerobic conditions by maintaining an essentially anaerobic cytoplasm for AU values up to ∼85% . It is suggested that this buffering of FNR response makes the regulatory system more robust by preventing large amplitude fluctuations in FNR activity when the bacteria are exposed to micro-aerobic conditions or experience environments in which they encounter short pulses of low O2 concentrations . Furthermore , investigation of FNR variants with altered oligomerization properties suggested that the monomer-dimer transition , mediated by iron-sulfur cluster acquisition , is the primary regulatory step in FNR-mediated repression of gene expression . It is expected that the current model will act as a foundation for future investigations , e . g . predicting the effects of adding or removing a class of agent to identify the significant regulatory components of the system .
Knowledge of the rate of O2 supply , , to the E . coli cells was required in order to simulate the response of the regulators of cydAB and cyoABCDE to different O2 availabilities . Therefore , un-inoculated chemostat vessels were used to measure dissolved O2 concentrations , , as a function of the percentage O2 in the input gas , Pi , in the absence of bacteria . This allowed the rate at which O2 dissolves in the culture medium to be calculated from the equation: , yielding = 5 . 898 µmol/L/min . The number of O2 molecules distributed to a single bacterial cell was then calculated from the following equation: ( where , NA is the Avogadro constant ( 6 . 022×1023 ) , Vcell is the volume of E . coli cell ( 0 . 3925 µm3 ) and as a constant for this equation , n ( 3 . 3×10−9 ) includes the unit transformations , min to sec ( 60−1 ) and µmol to mol ( 10−6 ) , and the time unit represented by an iteration ( 0 . 2 sec ) . In the model the individual agents ( Cyd , Cyo , ArcB , ArcA , FNR and O2 ) are able to move and interact within the confines of their respective locations in a 3-D-cylinder representing the E . coli cell . To control the velocity of agents , the maximal distances they can move in 3-D space during one iteration ( step length ) were pre-defined ( Table 4 ) . Thus , a step length is pre-defined in program header file ( . h ) and for each movement , this is multiplied by a randomly generated value within [0 , 1] to obtain a random moving distance , which in turn is directed towards a 3-D direction ( movement vector ) that was also randomly generated within defined spatial regions . An example is shown in Figure 6 to illustrate the movements of an O2 molecule when it enters the cell . Interactions between agents depend upon the biological rules governing their properties and being in close enough proximity to react . The interaction radius of an agent encapsulates the 3-D space within which reactions occur . As the interaction radii cannot be measured , they were first estimated on the basis of known biological properties . For the radii r1…4 , r12 and r13 ( Table 3 ) , arbitrary values were initially set at 31% AU , and the model was then trained to match the experimental result for the number of FNR dimers at 31% AU ( Table 5 ) . The modeled output of FNR dimer number at steady-state was compared with the experimental data , and the difference suggested re-adjustment of interaction radii . The adjusted radii were then tested against the FNR dimer numbers at 85% , 115% and 217% AU ( Table 5 ) during model validation , and the results indicate that the interaction radii values are capable of describing the behavior of the system . The interaction radii of Cyd and Cyo with O2 reflect their relative affinities for O2 ( i . e . Cyd has a high O2 affinity and thus reacts more readily , 7 nm interaction radius , than Cyo , which has a lower affinity for O2 , 3 nm interaction radius ) . As , thus far , no accurate biological data is available for ArcBA system , the radii r5…11 were arbitrarily defined and were refined by training the model to match current biological expectations . The rod-shaped E . coli cell was modeled as a cylinder ( 500 nm×2000 nm ) [32] with the nucleoid represented as a sphere with a diameter of 250 nm at the centre of the cell . The experimentally-based parameters and locations of the agents in their initial state are listed in Table 2 . As the number of ArcB molecules has not been determined experimentally , this value was arbitrarily assigned ( see above ) . The interaction rules for the agents are shown in Table 3 ( additional descriptions of an exemplar agent ( O2 ) and the rules for ArcBA and FNR are provided in , Table S1 and Text S1 ) . These rules , combined with the interaction radii , determine the final status of the system . The scale of the model is such that high performance computers are required to implement it , and the flexible agent-based supercomputing framework , FLAME ( http://www . flame . ac . uk ) acted as the framework to enable the simulation [33] , [34] . For more information on FLAME see Figure S2 and Text S2 . Plasmids encoding the FNR variants were constructed by site-directed mutagenesis ( Quikchange , Agilent ) of pGS196 , which contains a 5 . 65 kb fragment of wild-type fnr ligated into pBR322 [35] . The three isogenic plasmids pGS196 ( FNR ) , pGS2483 ( FNR I151A ) and pGS2405 ( FNR D154A ) were used to transform E . coli JRG4642 ( an fnr lac mutant strain ) containing a pRW50-based reporter plasmid carrying the lac-operon under the control of the FFgalΔ4 promoter [27] . β-Galactosidase assays were carried out as described previously on strains grown in LBK medium at pH 7 . 2 containing 20 mM glucose [36] , [37] . Cultures were grown either aerobically ( 25 ml culture in a 250 ml flask at 250 rpm agitation with 1∶100 inoculation ) or anaerobically ( statically in a fully sealed 17 ml tube with 1∶50 inoculation ) . Cultures ( three biological replicates ) were grown until mid-exponential phase ( OD600 = 0 . 35 ) before assaying for β-galactosidase activity . | The model bacterium Escherichia coli has a modular electron transport chain that allows it to successfully compete in environments with differing oxygen ( O2 ) availabilities . It has two well-characterized terminal oxidases , Cyd and Cyo . Cyd has a very high affinity for O2 , whereas Cyo has a lower affinity , but is energetically more efficient . Expression of the genes encoding Cyd and Cyo is controlled by two O2-responsive regulators , ArcBA and FNR . However , it is not clear how O2 molecules enter the E . coli cell and how the locations of the terminal oxidases and the regulators influence the system . An agent-based model is presented that simulates the interactions of O2 with the regulators and the oxidases in an E . coli cell . The model suggests that O2 consumption by the oxidases at the cytoplasmic membrane and by FNR in the cytoplasm protects FNR bound to DNA in the nucleoid from inactivation and that dimerization of FNR in response to O2 depletion is the key step in FNR-mediated repression . Thus , the focus of the agent-based model on spatial events provides information and new insight , allowing the effects of dysregulation of system components to be explored by facile addition or removal of agents . |
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Phleboviruses transmitted by sandflies are endemic in the Mediterranean area . The last decade has witnessed the description of an accumulating number of novel viruses . Although , the risk of exposure of vertebrates is globally assessed , detailed geographic knowledge is poor even in Greece and Cyprus where sandfly fever has been recognized for a long time and repeatedly . A total of 1 , 250 dogs from mainland Greece and Greek archipelago on one hand and 422 dogs from Cyprus on the other hand have been sampled and tested for neutralising antibodies against Toscana virus ( TOSV ) , Sandfly fever Sicilian virus ( SFSV ) , Arbia virus , and Adana virus i . e . four viruses belonging to the 3 sandfly-borne serocomplexes known to circulate actively in the Mediterranean area . Our results showed that ( i ) SFSV is highly prevalent with 71 . 9% ( 50 . 7–84 . 9% depending on the region ) in Greece and 60 . 2% ( 40 . 0–72 . 6% ) in Cyprus; ( ii ) TOSV ranked second with 4 . 4% ( 0–15 . 4% ) in Greece and 8 . 4% ( 0–11 . 4% ) in Cyprus; ( iii ) Salehabad viruses ( Arbia and Adana ) displayed also substantial prevalence rates in both countries with values ranging from 0–22 . 6% depending on the region and on the virus strain used in the test . These results demonstrate that circulation of viruses transmitted by sand flies can be estimated qualitatively using dog sera . As reported in other regions of the Mediterranean , these results indicate that it is time to shift these viruses from the "neglected" status to the "priority" status in order to stimulate studies aiming at defining and quantifying their medical and veterinary importance and possible public health impact . Specifically , viruses belonging to the Sandfly fever Sicilian complex should be given careful consideration . This calls for implementation of direct and indirect diagnosis in National reference centers and in hospital microbiology laboratories and systematic testing of unelucidated febrile illness and central and peripheral nervous system febrile manifestations .
In the Old world , phleboviruses ( Bunyaviridae family , Phlebovirus genus ) transmitted by phlebotomines consist of three species or antigenic groups , namely Sandfly fever Naples , Salehabad , and Sandfly fever Sicilian serocomplexes . Each species contains several viruses among which Naples , Sicilian and Toscana virus cause 3-day fever , commonly called sandfly fever in humans; Toscana virus ( TOSV ) causes of neuroinvasive human infections such as meningitis and encephalitis [1] . In Greece , outbreaks of sandfly fever were reported in Athens among the local population , and among American , British and German troops during World War II [2] . Sandfly fever has been described in Cyprus and Greece with both sporadic cases and epidemics [3–7] . In both countries , the high rates of antibodies observed in seroprevalence studies indicate that viruses belonging to Sandfly fever Naples and Sandfly fever Sicilian serocomplexes are transmitted by local sand flies to human populations [7–10] . Sandfly fever Cyprus virus ( SFCV ) , closely related to Sandfly fever Sicilian virus ( SFSV ) , was isolated during a large outbreak of sandfly fever in Swedish United Nations troops stationed in Cyprus; few cases were also caused by TOSV [5] . In Greece , in recent sporadic cases of meningitis , ( i ) TOSV RNA was detected in the CSF of a patient [11] , and ( ii ) viral RNA corresponding to Adria virus , a novel virus belonging to the Salehabad species , was also identified in the CSF [12 , 13] . To date , SFSV or another SFS-like virus have been neither isolated nor detected by molecular techniques in Greece . During the last decade , field-to-laboratory integrated studies associating virologists , parasitologists and entomologists have discovered several new phlebotomine-borne phleboviruses; thus there is an increased diversity in each of the three aforementioned species or serocomplexes [14] . Although the pathogenicity of most of these newly discovered viruses remains unknown , they are sympatric with recognized pathogenic phleboviruses [15–17] . Because several viruses of the same serocomplex co-circulate in various regions , interpretation of seroprevalence studies requires using techniques that hold the capacity to discriminate between these antigenically-related viruses . To the best of our knowledge , all studies performed in Greece and Cyprus used either ELISA or IFA tests , which are notoriously prone to cross-reactivity between viruses belonging to the same serocomplex [7–10] . To conduct our nation-wide ( mainland Greece , Greek islands , Cyprus ) seroprevalence study in dogs , we selected neutralisation tests which is the most discriminant assay as previously reported in Algeria , Tunisia , Turkey and Portugal [15 , 18–20] . Although virus exposure to viruses may be quantitatively different in humans and dogs , because of different feeding preferences of phlebotomines , recent studies suggest that virus circulation can be estimated using either human or dog sera since dogs live in close proximity to humans and are readily infected by these viruses [15 , 18 , 20 , 21] . In our study , dog sera were tested for the presence of neutralising antibodies against TOSV , SFSV , and two viruses belonging to the Salehabad complex ( Arbia virus isolated in Italy and Adana virus isolated in Turkey ) .
From 2005 to 2010 , a total of 422 and 1 , 250 dog sera were collected in Cyprus and Greece , respectively . These sera originated from the five districts of Cyprus and 32 prefectures belonging to 12 regions of Greece ( Table 1 ) . Veterinarians were asked to provide dog samples from animals visiting their clinic for any reason: vaccination , hair cut , nail cut , deworming , general check up , treatments , and other purposes , without discrimination . The animals were examined clinically and peripheral blood samples ( without EDTA ) was collected , after the written consent of the owner , and questionnaires with personal , epidemiological , and clinical data for each dog were completed . Only domestic dogs that were raised in the area were considered for the study . The domestic dogs were included after owners’ informed consent . Information regarding age , sex , was obtained after interviewing dog owners ( Table 1 ) . Each dog was examined clinically by the veterinarian and blood samples were collected . Whole blood samples were collected ( 1–2 mL ) by cephalic or jugular venipuncture and serum was separated by centrifugation and stored at −20°C . Data on the region , gender , and age ( distributed according to 3 classes: young 6–11 months , adult 12–83 months , senior ≥ 84 months ) were recorded . This study was ethically approved by the Institutional Animal Care and Use Committee of the University of Crete Medical School and conform with the European Union Directive 2010/63/EU regarding use of animals and biological specimens in research , as well as the relevant Hellenic legislation ( Presidential Decree 160/91 , under the Code Numbers 31 EE 05 , 31 EPR 04 and 31EP 020 ) . Written informed consent was obtained from the dog owners , according to the aforementioned national legislations . Sera were tested by the virus microneutralisation assay ( MN ) , described for phleboviruses [19] in parallel for 3 distinct sandfly-borne phleboviruses: ( i ) TOSV strain MRS2010-4319501 ( TOSV belongs to the Sandfly fever Naples virus species or complex ) [22] , ( ii ) SFSV strain Sabin [23] , ( iii ) Arbia-like virus strain T131 ( Salehabad species or complex ) , and ( iv ) Adana virus strain 195 ( Salehabad species or complex ) [15] . Briefly , two-fold serial dilutions from 1:10 to 1:80 were prepared for each serum and a volume of 50μL was pipeted into 96-well plate . Viruses were titrated in Vero cells ( ATCC CCL81 ) . A volume of 50 μL containing 1000 TCID50 was added into each well except for the controls that consisted of PBS . A volume of 50 μL of EMEM medium enriched with 5% fetal bovine serum , 1% Penicilin Streptomycin , 1% L-Glutamine 200 mM , 1% Kanamycin , 3% Fungizone , was added to each well of the controls . The plates were incubated at 37C° for one hour . Then , a 100μL suspension of Vero cells containing approximately 2 x105 cells/mL of EMEM medium ( as previously described ) was added to each well , and incubated at 37C° in presence of 5% CO2 . The first row of each plate contained control sera diluted 1:10 and Vero cells without virus . After 5 days ( Toscana and Arbia virus ) and 7 day ( Sicilian and Adana virus ) , the microplates were read under an inverted microscope , and the presence ( neutralization titer at 20 , 40 , 80 and 160 ) or absence ( no neutralization ) of cytopathic effect was noted . Cut-off value for positivity was set at titre ≥ 20 [15 , 18 , 20 , 21] . Due to insufficient volume in Greek samples , ADAV was used for testing Cyprus specimens , only . Dog seroprevalence for each virus was estimated for each prefecture and mapped using the geographical information system software ( GIS , Redlands , CA; ArcGIS 10 ) . The chi-square or Fisher’s exact tests were used to compare percentages of positivity among categories of the same independent variables and also the total prevalence of each virus . A p value < 0 . 05 was considered as statistically significant . Analyses were performed with StatLib and SPSS® 21 software for Windows .
In Greece , a total of 1 , 250 sera ( 540 male and 710 female , sex ratio 0 . 76 ) were collected . The median age was 36 months ( range: 3–216 ) . The sera were collected from 32 prefectures , but owing to the variability in the number of collected sera from each prefecture ( range: 1–410 ) , the sera were grouped into 12 regions . Of these 12 regions , Thessaly was not included in the analyses because it consisted of 1 serum only . For the other 11 regions , the number of sera ranged from 14 to 410 . In Cyprus , a total of 442 sera ( 202 male and 240 female , sex ratio 0 . 84 ) were collected . The median age was 36 ( range: 3–144 ) . They consisted of 67 , 27 , 97 , 74 , and 177 sera collected from the districts of Ammochostos , Larnaca , Limassol , Nicosia , and Paphos , respectively . The two dog populations had the same median age ( 36 months ) and a similar sex ratio ( 0 . 76 vs 0 . 84 ) . Characteristics of the dogs and their geographic origin are presented in Table 1 . Results for domesticated dogs living in Greece and in Cyprus are presented in Table 2 and Table 3 , respectively . Toxic activity in the serum was detected in 65 and 73 sera from Greece and Cyprus , respectively; therefore calculations were done on the basis of 1 , 185 and 369 sera of Greece and Cyprus , respectively . As previously shown [26 , 27] , a cut-off titre ≥ 20-when used for 1000TCID50 inoculum , is equivalent to a cut-off titre ≥ 40 when a 100 TCID50 is used [28] . In Greece , a much higher rate of SFSV-NT-Ab was observed ( 71 . 9% ) compared with 4 . 4% and 2 . 6% for TOSV and ARBV , respectively ( Table 1 and Fig 1 ) . Similar results were observed in Cyprus where SFSV-NT-Ab was present in 60 . 2% of the dog sera , whereas 16 . 3% , 8 . 4% and 5 . 4% of sera were positive for ADAV , TOSV and ARBV , respectively ( Table 2 and Fig 2 ) . The distribution of TOSV positive sera is quite homogenous within the studied regions ( p = 0 . 248 , p = 0 . 094 ) . There is no significant difference according to the sex of the dogs . In contrast , it appears that the prevalence increases with the age , although it is not statistically significant even when the results of dogs from Greece and Cyprus are merged ( 3 . 7% / 5 . 3% / 7 . 7% , p = 0 . 3 ) . In both countries , a statistic association was found between SFSV prevalence and geographic area . In Cyprus , none of the sera containing ARBV-NT-Abs were also positive for ADAV-NT-Abs and vice versa; this demonstrated that there is no cross-reactivity through MN assay between these two viruses despite the fact that they belong to the same serocomplex . Exposure to ARBV and ADAV is significantly associated with the age with higher rates observed in older dogs . In contrast , the high rates of SFSV-NT-Abs were observed in the 6-11-month age class in Cyprus and Greece .
At the outset of our study , the following data were available for Greece: ( i ) there was no serological data in domestic animals ( cattle , goats , sheep , dogs or cats ) for any phlebovirus transmitted by sand flies , ( ii ) in the 1970's , 13 . 1% of 38 adults living in Crete had NT-Abs against Naples virus , and 24 . 7% and 8 . 5% of 632 human sera from Athens inhabitants had NT-Abs against Naples virus and Sicilian virus , respectively [29]; ( iii ) more recent studies reported various rates of TOSV IgG using ELISA and/or IIF tests in continental Greece as well as in the Ionian and Aegean islands [8–10]; ( iv ) TOSV RNA ( belonging to the lineage C ) was detected in the CSF of a patient with meningitis [11] , and ( v ) viral RNA corresponding to Adria virus , a novel virus belonging to the Salehabad species , was also identified in the CSF of a patient with meningitis [12 , 13] . To date , SFSV or another SFS-like virus have been neither isolated nor detected by molecular techniques in Greece . At the outset of our study , the following data were available for Cyprus: ( i ) there was no serological data in domestic animals ( cattle , goats , sheep , dogs or cats ) for any phlebovirus transmitted by sand flies; ( ii ) first evidence of the presence of TOSV , Naples and Sicilian viruses were observed in Swedish soldiers of the United Nations force [3] through detection of NT-Abs and isolation of strains of Naples and Sicilian viruses [30]; ( iii ) NT-based seroprevalence results showed that Naples , Sicilian , and TOSV were endemic with respective rates of 57% , 32% and 20% [7]; ( iv ) investigation of a second outbreak in Greek troops stationed in Nicosia of which almost 50% developed febrile syndrome had resulted in isolation of Cyprus virus ( SFCV ) , closely related but distinct from Sicilian virus although belonging to the SFSV serocomplex [4 , 5] . The recent discovery of several new viruses belonging to the three species associated with phlebotomines in the Old World has raised questions about the viral strain currently circulating in the two regions . Since broadly cross-reactive techniques such as ELISA and IIF are not capable to distinguish between viruses belonging to the same serocomplex , we decided to use microneutralisation assay using viral strains or surrogates which presence had been assessed in Greece and Cyprus . Indeed , we consider it valid to use SFSV as a surrogate for SFCV ( isolated in Cyprus ) and other SFSV-related viruses because amino acid distances observed between the proteins that elicit neutralizing antibodies ( Gn and Gc ) are well within the acceptable range , ie <5% different for SFSV and SFSV-related viruses[25 , 31] . Thus , neutralising antibodies are unlikely to discriminate between closely-related SFSV isolates . Since collecting human sera displaying a large geographic distribution was challenging , we decided to use dog sera; indeed , dog sera can be good surrogates for the following reasons: ( i ) dogs are readily infected with phlebotomine-borne phleboviruses which are human pathogens [18 , 20 , 31 , 32]; ( ii ) domestic dogs live in close contact with humans and therefore are exposed to sandfly bites , although different feeding preferences of sand fly species have to be considered [31] . The largest amount of data available on dogs , at the outset of this study , concerned TOSV , which observed rates ( 4 . 4% in Greece and 8 . 4% in Cyprus ) are in the same order of magnitude as those recently reported in dogs in Tunisia ( 6 . 8% , [20] ) , in Algeria ( 4 . 3% , [18] ) , in France ( 3 . 9% , [21] ) and in Portugal ( 6 . 8% [31] . Because all these studies measured neutralising antibodies against TOSV , their results are comparable and they reflect local circulation of TOSV only , not other viruses belonging to the SFNV complex . Together these results demonstrate that TOSV can readily infect dogs . Exposure level of dogs and humans may be drastically different in the same area as previously shown in Tunisia where MN-based seroprevalence rates were respectively at 6 . 8% in dogs compared with 41% in humans [20] . In the present study , dogs living in the Ionian island of Corfu showed a much lower seroprevalence compared to the human population living in the same island ( 3 . 9% vs 51 . 7% ) [8]; however , in this case the techniques used were different; in the human study , ELISA/IIF detected not only TOSV IgG but also IgG raised after infection with other viruses belonging to the Sandfly fever Naples serocomplex in which 6 new viruses were described during the last decade ( Arrabida , Fermo , Granada , Massilia , Punique , Zerdali ) [16 , 33–37] in addition to Naples virus ( a proven human pathogen ) and Tehran virus . Although none of these viruses were detected or isolated in Greece or Cyprus , the presence of one of these 6 recently discovered viruses or of a yet to be discovered virus may account for these apparently discrepant results . Last , these techniques do not hold the same sensitivity [38] . The same explanation applies for discrepancies observed between high rates of ELISA/IFA TOSV IgG reported in Aegean islands ( 17 . 6% , 11 . 5% , 20% , 22% and 34 . 7% for Lesbos , Rodos , Siros , Crete and Evia , respectively ) [9] compared with our findings: 5 . 3% in north Aegean islands ( Chios and Lesbos ) , 0% in south Aegean islands ( Rodos , Siros and Santorini ) , 1% in Crete and 4 . 4% in Evia ( Stere Hellas ) ( Table 2 ) . In Central Macedonia , 7 . 3% of dog sera contained TOSV-NT-Abs , which is in agreement with reported cases of human infections [38 , 39] and a recent study showing that TOSV and/or antigenically related viruses are circulating extensively in the area [10] . It is worth underlining that , despite using the same technique , discrepant prevalence rates were also described between dogs and humans in Tunisia [19 , 20] . Therefore , it is difficult to compare results of serological studies performed with different techniques . When using the same technique , results observed in humans and in dogs consistently detected TOSV although they varied considerably quantitatively; therefore dogs can serve as sentinel for humans and vice versa for assessing the presence of TOSV although quantitative results must be interpreted carefully . The absence of cross-protection between ARBV-NT-Abs and ADAV-NT-Abs confirm previous data from Turkey [15] . Accordingly , cumulative percentage of viruses belonging to the Salehabad species is 21 . 7% . This is congruent with the results observed in Adana , southern Anatolia , Turkey where domestic animals were presenting high rates of NT-Ab against viruses belonging to the Salehabad serocomplex [15] . Tesh et al [29] did not detect NT-Abs against Salehabad virus ( SALV ) in human populations suggesting that SALV was not infecting humans . In contrast , NT-Abs against Medjerda Valley virus were described in 1 . 35% ( 14/1260 ) of human sera collected from the general population living in Northern Tunisia [24] . This suggests that at least some viruses belonging to the Salehabad complex can infect humans and other vertebrates . Interestingly , Adria virus RNA has been detected in the CSF of a Greek patient presenting with meningitis [13] but was never isolated precluding serological studies aiming at defining the possible impact of this virus in the region and beyond . However , molecular detection of Adria virus in Albania ( in sand flies ) and in Greece ( in human ) suggests that its distribution might cover a large geographic area . This constituted the first direct evidence supporting human pathogenicity of a virus belonging to the Salehabad virus complex . Isolation of Adria virus is now a priority in order to pursue the studies using neutralization-based serological studies in humans and animals . Very high rates of SFSV-NT-Abs were observed in Cyprus and Greece . In the latter , rates were consistently above 50% ( range 50 . 7–84 . 9% ) ; in Cyprus , rates were above 40% ( range 40 . 0–72 . 6% ) except in Ammochostos ( 26 . 3% ) . The extremely high prevalence rates observed with SFSV in young dogs show that this virus continues to circulate very actively in these regions , and beyond as recently described in dogs from Tunisia ( 50 . 8% , [20] ) and in Portugal ( 38 . 1% , [31] ) . In both countries , a statistic association was found between SFSV prevalence and geographic area . The differences of prevalence depending upon the region may be due to the geographic and climatic characteristics of these regions which affect the distribution , proliferation and abundance of phlebotomine vectors of SFSV . Analysis of the questionnaires did not identify any clinical manifestations such a fever and/or neurological signs during the past weeks and months in the SFSV-positive dogs . This tends to suggest that SFSV is not or mildly affecting dogs during the viremic period . Whether or not dogs can play a role as reservoir in the natural cycle remains to be studied . To do so , experimental studies to understand the virus kinetics are necessary . Also , studies aiming at the identification of viremic domestic dogs should be planned in high prevalence areas . The massive prevalence of SFSV-NT-Ab observed in our study is not unexpected and is congruent with entomological and human data in the literature: ( i ) isolation of Corfu virus on the eponymous island from Phlebotomus neglectus [17]; ( ii ) SFSV IgG detected by IFA in human sera in Northern Greece ( Macedonia ) , Central Greece ( Evritania and Larisa ) , North–Western Greece ( Epirus ) , and Corfu Island; ( iii ) detection of Chios virus , SFSV-like , in Chios island; ( iv ) sandfly fever epidemics were reported in Swedish UN soldiers and Greek soldiers in 1984 and 2002 , respectively [3–5]; ( v ) a high attack rate ( 63% ) in tourists hosted in Cyprus for a short period [6]; ( vi ) a 32% prevalence rate of SFSV IgG in Cyprus native population [7] . In contrast with the two other serocomplexes which display an important range of genetic distance between their respective members , Sicilian virus strains are genetically and antigenically much more closely related [14 , 16]; therefore , exposure to different SFSV strains ( Italy , Turkey , Cyprus , Greece , Ethiopia ) can be measured by using the prototypic Italian strain . Despite high rates of antibodies in humans and other vertebrates and successive outbreaks in Italy , Cyprus , Greece and Ethiopia [3 , 5 , 23 , 40] , SFSV remains a neglected pathogen , almost never included in diagnostic algorithms despite repeated and accumulating evidence of its involvement in febrile syndromes and in neuroinvasive infections . In conclusion , this study indicates that ( i ) sandfly-borne phleboviruses belonging to 3 distinct genetic and antigenic groups are widely spread and co-circulate; ( ii ) dogs represent excellent qualitative sentinels for virus transmitted by sandflies and further studies must be done to estimate the role of dogs in the dynamics of transmission , and whether they play a role as reservoir hosts in the natural cycle of these viruses . Since several of these viruses are proven human pathogens , our results plead for performing similar studies using human sera to identify geographic hot spots . The increasing number of sequence data for these phlebotomine-borne phleboviruses now enables to design and develop real-time molecular assays . The improved diagnostic toolbox will allow to investgate the medical impact of these viruses in patients presenting unexplained febrile illness and neuroinvasive infections . | Phleboviruses transmitted by sandflies are endemic in the Mediterranean basin . An increased number of new viruses was described during the last decade . However , levels of exposure of human and animal populations are poorly known . A total of 1 , 250 dogs from Greece and 422 dogs from Cyprus were tested for the presence of neutralising antibodies signing previous infection with selected phleboviruses representing the 3 serological complexes known to be present in the Old World: Toscana virus ( TOSV ) , Sandfly fever Sicilian virus ( SFSV ) and Salehabad viruses ( Arbia and Adana viruses ) . Our data showed that ( i ) SFSV is largely predominant with infection rates higher than 50% , ( ii ) TOSV is widely distributed with 4 . 4% and 8 . 4% in Greece and Cyprus , respectively , and ( iii ) that viruses belonging to the Salehabad serocomplex should be further studied for their capacity to cause human disease in view of prevalence rates in dogs up to 22 . 6% . These findings confirm that dogs can be considered as excellent sentinels for sandfly-borne phleboviruses . The results also underline the importance to study the role of SFSV in humans and may lead to the set-up diagnostic tests for patients presenting unexplained febrile illness and neuroinvasive infections . Further studies are also needed to define whether these viruses cause diseases in dogs . |
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Invasion of host cells by the malaria parasite involves recognition and interaction with cell-surface receptors . A wide variety of parasite surface proteins participate in this process , most of which are specific to the parasite's particular invasive form . Upon entry , the parasite has to dissociate itself from the host-cell receptors . One mechanism by which it does so is by shedding its surface ligands using specific enzymes . Rhomboid belongs to a family of serine proteases that cleave cell-surface proteins within their transmembrane domains . Here we identify and partially characterize a Plasmodium berghei rhomboid protease ( PbROM1 ) that plays distinct roles during parasite development . PbROM1 localizes to the surface of sporozoites after salivary gland invasion . In blood stage merozoites , PbROM1 localizes to the apical end where proteins involved in invasion are also present . Our genetic analysis suggests that PbROM1 functions in the invasive stages of parasite development . Whereas wild-type P . berghei is lethal to mice , animals infected with PbROM1 null mutants clear the parasites efficiently and develop long-lasting protective immunity . The results indicate that P . berghei Rhomboid 1 plays a nonessential but important role during parasite development and identify rhomboid proteases as potential targets for disease control .
For successful development and transmission , Plasmodium has to invade multiple cell types both in the mammalian host and in the mosquito vector . Much of our knowledge about the molecular mechanisms of invasion comes from the study of P . falciparum merozoite invasion of red blood cells ( RBCs ) . RBC invasion involves an initial attachment followed by re-orientation and entry of the parasite into the host cell [1] . There are two main classes of parasite surface molecules , the GPI-anchored proteins such as the merozoite surface protein family ( MSP ) [2] and transmembrane domain-containing proteins such as AMA1 [3] , [4] , erythrocyte binding-like family ( EBL ) [5] , [6] and reticulocyte binding-like family proteins ( RBL ) [7] , [8] . A few host-cell receptors to which these ligands bind have been identified [9]–[12] . In the mosquito , motility plays an important role in ookinete and sporozoite invasion . Motile ookinetes form within the mosquito blood meal and invade the midgut epithelium . After exiting on the basal side facing the hemocoel they differentiate into sessile oocysts [13] . Subsequently , sporozoites released from mature oocysts invade the salivary glands from where they are delivered to the vertebrate host by a mosquito bite . These sporozoites travel through the blood stream until they reach the liver , where they invade and infect hepatocytes . All three invasive forms ( ookinetes , sporozoites in the mosquito and sporozoites in the mammalian host ) utilize the same actin-based motor for entry into the host cell . Thrombospondin-related anonymous protein ( TRAP ) family homologues constitute one class of protein required for motility and host cell invasion [14]–[16] . The extracellular domains of TRAP interact with host-cell receptors , while the cytoplasmic tail links to the actin-myosin cytoskeleton [17] . As the parasite glides , the parasite surface ligand-receptor complexes translocate towards the posterior end . Dissociation of these interactions by proteolytic processing is thought to be important , as this enables the parasite to move forward [18]–[20] . In another Apicomplexan parasite-Toxoplasma-the TRAP homologue MIC2 is cleaved within its transmembrane domain releasing the receptor-binding domain from the parasite surface [18] and Plasmodium merozoite TRAP ( MTRP ) also appears to be cleaved in a similar manner [16] . Rhomboid-family ( ROM ) proteins are serine proteases that cleave their substrates within their membrane domain [21] , [22] . Multiple rhomboid-family proteins have been identified in the genomes of Plasmodium and Toxoplasma [23] . Cleavage requires the presence of helix-destabilizing residues within the membrane domain of substrates [24] . Indeed , Apicomplexan surface proteins such as EBL and RBL proteins , AMA1 , TRAP and their homologues contain such helix-destabilizing residues [23] . Assays in cultured mammalian cells identified possible substrates for both Toxoplasma and Plasmodium falciparum rhomboid proteins [25] , [26] . Toxoplasma ROM5 localizes to the posterior end of the parasite and can cleave MIC2 within its transmembrane domain [25] , [27] . Plasmodium does not have a ROM5 homologue but ROM4 is able to cleave EBA175 [28] , an EBL family protein involved in binding to erythrocytes [10] . Processing of EBA175 within its membrane domain appears to be essential for parasite invasion [28] . Here we report on experiments investigating the role of Plasmodium berghei rhomboid 1 ( PbROM1 ) during parasite development in the vertebrate host and the mosquito vector . Our data suggests a role for PbROM1 throughout Plasmodium development and indicate a role in invasion of host cells . We also find that a null PbROM1 mutant is efficiently cleared from mice and that these animals are protected from a subsequent lethal challenge of wild-type P . berghei . These findings identify a unique target for interfering with both disease causing and disease transmitting forms of the parasite .
Antibodies were raised in rabbit against the N-terminal 52 amino acids of PbROM1 expressed in bacteria as a fusion protein using the pBAD expression system ( Invitrogen ) . P . berghei schizonts , merozoites and sporozoites were fixed in ice-cold methanol and incubated for 1 h with the anti-PbROM1 antibody diluted 1∶500 . Midgut and salivary gland sporozoites were obtained by gently homogenizing the infected tissues and centrifuging to remove cell debris . A anti-AMA1 monoclonal antibody ( 28G2 ) that recognizes the highly conserved cytoplasmic tail [30] was also used to label schizonts and merozoites , while a anti-CSP monoclonal antibody ( 3D11 ) that recognizes the repeat region [31] was used to label midgut and salivary gland sporozoites . Slides were then incubated for 1 h with Alexa Fluor 488-conjugated anti-rabbit IgG and rhodamine-conjugated anti-mouse or anti-rat IgG secondary antibodies . After washing , images were visualized in a Leica upright fluorescent microscope with a 100× objective and images were captured with a SPOT camera . Sporozoite-infected salivary glands were fixed in 4% paraformaldehyde ( Electron Microscopy Sciences , PA ) in 0 . 25 M HEPES ( pH7 . 4 ) for 1 h at room temperature , followed by 8% paraformaldehyde in the same buffer overnight at 4°C . The fixed glands were permeabilized , frozen and sectioned as previously described [32] . Sections were immunolabeled with rabbit anti-PbROM1 antibodies ( 1∶20 in PBS/1% fish skin gelatin ) , then with anti-rabbit IgG , followed by 10 nm protein A-gold particles ( Department of Cell Biology , Medical School , Utrecht University , the Netherlands ) before examination with a Philips CM120 Electron Microscope ( Eindhoven , the Netherlands ) under 80 kV . For targeted disruption of the PbROM1 gene , a disruption plasmid was constructed by PCR amplification with primers , PbROM1 ( − ) F-5′CCATACATTAGCAGAGTATAGGGA3′ and PbROM1 ( − ) R-5′ACTTGCAC CCACTTTTATTGTAC3′ using P . berghei genomic DNA as template . Cloning into the P . berghei transfection vector [33] resulted in plasmid pROM1 . This plasmid was linearized at the unique NdeI site and transfected into P . berghei schizonts as described [34] . To confirm disruption of the PbROM1 gene , integration-specific PCR was performed using specific primer combinations , P1-5′CGAGCAACAATGTCTGAC3′ , P2-5′GAGTTCATTTTACACAATCC3′ and P3-5′TAATACGACTCACTATAGGGAGA3′ . Disruption was also confirmed by RT-PCR using primers PbROM1F-5′TTATTACGGAGTGTTTCTTC3′ and PbROM1R-5′CGGAGAAATACATAGATTA3′ P . berghei circumsporozoite gene primers CSF-5′GTACCATTTTAGTTGTAGCGTC3′ and CSR-5′CATCGGCAAGTAATCTGTTG3′ were used as positive control . The ability of the parasites to differentiate into gametocytes and form male gametes ( exflagellation ) was assessed as described previously [35] . An . stephensi mosquitoes were fed on infected mice and the ability of the disruptant parasites to form ookinetes ( 24 h ) and oocysts ( day 15 ) was examined microscopically . To assess ookinete numbers , individual midguts were dissected 24 h after feeding . Ookinete numbers were calculated after examining a Giemsa-stained smeared preparation of the midgut contents and counting both ookinetes and red blood cells . We assumed that each mosquito ingested 2 µl [36] and that mouse blood has 4×109 RBCs/ml [37] . Mature oocysts were counted on day 15 by direct light microscopic examination of dissected midguts . Sporozoites were isolated from midgut oocysts and salivary glands and counted on day 25–26 using a hemocytometer . Sporozoites isolated from salivary glands were incubated for 15 minutes at 37°C in chamber slides coated with BSA . The supernatant was gently aspirated and sproozoite trails were fixed with 4% paraformaldehyde . The trails were visualized by labeling them with anti-CSP ( mAb 3D11 ) antibody and rhodamine conjugated anti-mouse secondary antibody . Sporozoites were isolated from salivary glands on ice and partially purified by passing through glass wool to remove mosquito debris . Protease inhibitors N-tosyl-L-lysine chloromethyl ketone ( TLCK , 20 mM stock in water ) and phenylmethylsulfonyl fluoride ( PMSF 100 mM stock in ethanol ) were obtained from Sigma . 30000 sporozoites were incubated at 4°C or 37°C in the presence or absence of protease inhibitors for 1h . EDTA was used to rule out nonspecific processing by metalloproteases . Parasite lysates were run on a SDS-PAGE and transferred onto PVDF membrane . These were probed with anti-TRAP antibodies that recognize the repeat region of the protein , followed by peroxidase-conjugated secondary antibody . To determine TRAP processing in PbROM1 ( − ) parasites , 30000 wild-type and PbROM1 ( − ) sporozoites were analyzed by SDS-PAGE as mentioned above . Sporozoites isolated from salivary glands were counted using a hemocytometer and mice were injected intravenously with 150 , 1000 or 10000 sporozoites . Infection efficiency was assayed by monitoring the pre-patent period of blood stage infection after sporozoite injection . Prepatent period is the time elapsed between mouse infection and when the first infected red blood cell ( RBC ) was observed upon examination of at least 25 , 000 RBCs . For quantifying efficiency of liver infection , mice were injected intravenously with either 103 wild-type or 103 PbROM1 ( − ) sporozoites . Animals were sacrificed 36–40 h after sporozoite injection and total RNA was prepared using Trizol reagent . P . berghei 18S rRNA was quantified using primers ( PbrRNA1-5′TGGGAGATTGGTTTTGACGT TTATGT3′ and PbrRNA2-5′ AAGCATTAAATAAAGCGAATACATCCTTAC3′ ) as described [38] and the results were normalized using mouse GAPDH . Results from 4 mice per group are expressed as mean±s . d . of rRNA copy number . Mice were infected with either PbROM1 ( − ) sporozoites or infected RBCs as described above . Parasitemia was checked every day until at least 30 days after the last PbROM1 ( − ) parasites was detected . To confirm complete parasite clearance , 3×107 RBCs from these animals were injected into naïve mice and these animals were observed for 30 days to ensure that no infection resulted . After complete remission , the PbROM1 ( − ) infected mice were challenged by intravenous ( iv ) or intraperitoneal ( ip ) injection of 105 wild-type P . berghei iRBCs . A second challenge was performed either 33 days or 7 months after the first challenge and a third 9 months after the first . Parasitemia was followed as described above . Protection is defined as the number of animals that survive the challenge .
Plasmodium berghei ROM1 ( PbROM1 ) was initially identified in a subtractive hybridization screen for genes expressed during parasite development in the mosquito [29] . PbROM1 encodes a protein predicted to have seven transmembrane domains carrying a conserved , membrane-embedded Asparagine , Glycine-X-Serine and Histidine “rhomboid” motif ( Figure S1 ) . At least seven rhomboid genes were identified in the genome of various Plasmodium species [23] . Though PbROM1 homologues are highly conserved among rodent ( 92% identity ) and human malaria species ( 55% identity ) , sequence identity among rhomboid genes of a given species is very limited ( <20% , data not shown ) . This points to independent evolution of different rhomboid genes and suggests that each rhomboid protein plays distinct functions in the parasite life cycle . Microarray analysis indicates that PfROM1 is expressed in both mosquito and vertebrate forms of the parasite [39] . We have produced an antibody to the first 52-amino acids of PbROM1 and used it to investigate protein expression and subcellular localization . The protein is expressed in both blood- and mosquito-stage parasites . PbROM1 protein has a punctate distribution in segmented ( mature ) schizonts and localizes to the apical end of free merozoites ( Figure 1A ) . A number of organelles such as rhoptries and micronemes are found in the apical tip of the merozoite . These organelles secrete parasite proteins involved in host recognition and invasion . AMA1 ( apical membrane antigen 1 ) is a micronemal protein required for invasion of RBCs and is also found on the surface of merozoites ( Figure 1A ) . Immunoelectron microscopy confirmed the apical localization of PbROM1 and >85% of the gold label were found in micronemes ( Figure 2A and 2B ) . PbROM1 expression is limited to schizonts and free merozoites and is not detectable in ring or trophozoite stages ( data not shown ) . This is in agreement with the microarray analysis of P . berghei asexual stages in which PbROM1 is induced only in mature schizonts [40] . In mosquito stages , the PbROM1 transcript was initially identified among RNAs from mosquito midguts infected with mature oocysts [29] . Despite this , little or no protein was detected in sporozoites from these oocysts ( Figure 1B ) . In contrast , PbROM1 protein is detected in sporozoites after invasion of mosquito salivary glands ( Figure 1B ) . Immuno-electron microscopy more precisely localized PbROM1 in such sporozoites ( Figure 2C and 2D ) . The protein is present along the entire length of the sporozoite both on the surface as well as in micronemes . We examined 65 parasite cryosections to quantify the distribution of PbROM1 in different cellular locations . Most of the gold particles were present on the sporozoite plasma membrane ( 76 . 4% ) and in the micronemal membrane ( 17 . 7% ) while the remaining particles were located over other parasite organelles ( 3 . 3% ) and the mosquito salivary duct ( 2 . 6% ) . To gain insights on PbROM1 function we disrupted the gene by homologous recombination and investigated the effects of gene loss on parasite development . Gene disruption was achieved by inserting a DNA fragment encoding a drug resistance marker into the open reading frame of PbROM1 ( Figure 3A ) . Gene disruption was confirmed by insertion-specific PCR that identifies the disrupted locus from the wild-type locus ( Figure 3B ) . In addition , disruption was confirmed by the absence of the transcript in PbROM1 ( − ) sporozoites ( Figure 3C ) . We examined the possible function of PbROM1 in ookinetes by feeding PbROM1 ( − ) parasites to mosquitoes . Ookinete efficiency of midgut invasion was assessed by counting the resulting number of oocysts . Disruption of the PbROM1 gene did not affect ookinete formation ( Figure 4A and Table S1 ) . However , in 6/7 experiments we found strong reduction in oocyst numbers ( Figure 4B and Tables S2 and S3 ) . These results suggest that loss of PbROM1 function impairs the ability of ookinetes to form oocysts . Subsequent development of PbROM1 ( − ) parasites appears to be normal . The number of sporozoites formed by PbROM1 ( − ) oocysts was similar to wild-type oocysts and no differences of salivary gland invasion could be detected ( Figure 4C and Table S4 ) . This result is consistent with the apparent lack of ROM1 protein expression in midgut sporozoites ( Figure 1B ) . To investigate whether PbROM1 plays a role in liver infection , we injected mice intravenously with an equal number of WT and PbROM1 ( − ) sporozoites . The efficiency of infection was dose dependent and mice infected with PbROM1 ( − ) parasites showed a consistent delay in the pre-patent period by one day or more compared to mice infected with wild-type sporozoites ( Table 1 ) . Efficiency of infection was also assessed by quantifying parasite loads in livers infected with equal numbers of mutant or wild-type sporozoites . Livers of mice infected with the mutant sporozoite had a 68% lower parasite load compared with mice infected with wild-type sporozoites ( Figure 4D ) . This suggests that PbROM1 is required for efficient hepatocyte infection . To determine if the defect observed in hepatocyte infection is due to a defect in motility , we performed a sporozoite gliding assay . PbROM1 ( − ) sporozoites are motile as observed by circumsporozoite protein trails on glass slides ( Figure 5C ) . PbTRAP , the parasite adhesin essential for gliding motility [41] , is proteolytically processed by a serine protease ( Figure 5A [42] , [43] ) . This processing appears to occur independent of ROM1 ( Figure 5B ) . This suggests that the reduction in parasite numbers may not be due to impairment in motility but rather a defect in invasion and/or a subsequent defect in development . Parasitemia develops slower in animals infected with PbROM1 ( − ) parasites compared to WT infected animals ( Figure 6A and 6B ) . This phenotype is observed in animals infected by injection of sporozoites ( Figure 6A ) as well as when bypassing liver invasion by injecting infected RBCs ( iRBCs ) ( Figure 6B ) . This slow-growth phenotype is specific to PbROM1 disruptants as another rhomboid ( ROM3 ) disruptant and an oocyst capsule protein disruptant [44] have growth kinetics similar to wild-type parasites ( data not shown ) . Mice infected with PbROM1 ( − ) parasites survive better than those infected with WT parasites ( Figure 6C ) . Animals infected with PbROM1 ( − ) parasites reach peak parasitemia of >35% , similar to WT parasites . At such high parasitemia , animals infected with WT parasites succumb to the infection . On the other hand , more than 80% of animals infected with PbROM1 ( − ) parasites survive and eventually clear the parasites from their blood stream . Mice that had cleared PbROM1 ( − ) parasites from their bloodstream were challenged by intravenous injection of 105 WT iRBCs at least 30 d after the last circulating parasite was detected . Peak parasitemia in 12/14 mice after WT challenge ranged between 0 . 004%–2 . 6% ( Figure 6D ) . Importantly , all the animals were able to successfully clear the wild-type parasites ( Table 2 ) . This protective immunity lasts for at least 7–9 months after the initial PbROM1 ( − ) parasite exposure ( Table 2 ) . It is possible that the reduced RBC invasion efficiency of PbROM1 ( − ) merozoites may trigger this protective immune response .
Invasion requires the specific recognition and attachment of parasite surface ligands to host cell receptors and subsequent processing of the bound ligands to facilitate detachment and entry into the host cell . This can be achieved by proteolytic processing of protein ectodomains [19] or in some cases by processing within the protein's transmembrane domain [18] . Plasmodium AMA1 , EBL , RBL and TRAP proteins function in host-cell interaction and all have potential rhomboid cleavage sites within their predicted transmembrane domains . Recent studies using an in vitro mammalian cell-based assay indicate that Plasmodium ROM1 and ROM4 are able to cleave AMA1 , EBL , RBL and TRAP members within their membrane-spanning domains [26] , [28] . This suggests an important function for rhomboid proteins in invasion of host cells . In the present study we undertook a genetic approach to investigate the role of Plasmodium berghei rhomboid 1 ( PbROM1 ) during the parasite development in the mammalian host and the mosquito vector . Microarray analysis of P . falciparum genes identified PfROM1 as being expressed in both the mosquito and the asexual forms of the parasite [39] . Similarly , P . berghei ROM1 is also expressed in the mosquito and in its mammalian host [29] , [40] . In agreement with the mRNA expression data , we find PbROM1 protein to be expressed in schizonts , in free merozoites and in sporozoites after salivary gland invasion . Though PbROM1 transcripts can be found in ookinetes ( Figure S2 ) , we could not detect the protein by indirect immunofluorescence . This may be due to the low abundance of the protein in this parasite form . The difference in PbROM1 protein expression between midgut and salivary gland sporozoites suggests post-transcriptional gene regulation . Incompletely spliced PbROM1 transcripts can be found in mature oocysts and sporozoites isolated from these oocysts ( Figure S2 ) . Furthermore , the ROM1 mRNA may be translationally regulated . Post-transcriptional regulation has been observed for a number of genes , especially in the sexual stages and plays an important role in Plasmodium development [29] , [45] , [46] . Our genetic analysis indicates that PbROM1 functions in both the vertebrate and mosquito stages . This is based on the observation that PbROM1 ( − ) ookinetes form fewer oocysts , sporozoites isolated from infected mosquitoes infect the mouse liver less efficiently and the growth kinetics of the asexual forms is significantly delayed . Hence the phenotype of PbROM1 ( − ) parasites points to ROM1 roles during cell invasion . However , a role in intracellular development cannot be formally excluded . We believe this to be less likely for several reasons . First , the mutant parasites fully complete development after invasion of the mosquito midgut epithelium , mouse liver and mouse RBCs . Second , WT and ROM1 ( − ) ookinetes ( Table S1 ) , sporozoites ( Table S2 ) and blood-stage merozoites ( data not shown ) develop equally well . Third , the ROM1 protein localizes to merozoite and sporozoite micronemes ( an organelle that secrete proteins involved in invasion ) , in addition to the sporozoite surface . Together , these observations point to a role for ROM1 in host cell invasion . Mice infected with PbROM1 ( − ) parasites survive longer and are able to clear the infection efficiently . Those that clear the infection develop long-lasting immunity against a subsequent lethal wild-type P . berghei challenge . The immunity developed by PbROM1 ( − ) -infected mice could be a result of slower infection , which provides the animal with an opportunity to mount a better immune response . Another interesting possibility is that parasite proteins normally processed by PbROM1 during invasion modulate the immune response . The absence or reduced levels of these cleaved proteins would allow the animals to develop immunity against the parasite . Interestingly , the Toxoplasma gondii ROM1 orthologue has also been shown to be required for efficient growth and invasion of host cells [47] . In addition to its role in invasion , TgROM1 also appears to play a role in intracellular replication as they form fewer parasites within the parasitophorous vacuole [47] . However , PbROM1 does not appear to play a significant role in the development neither of sporozoites within oocysts ( Table S4 ) nor of merozoites within schizonts ( data not shown ) . However , a role for PbROM1 in parasite replication in the mouse liver cannot be excluded . The observed differences between Plasmodium and Toxoplasma could represent a species-specific difference of ROM1 function . Even though PbROM1 ( − ) parasites are defective in multiple invasive stages , they do complete their life cycle successfully in both the vertebrate and invertebrate hosts . It is possible that in PbROM1 ( − ) parasites , impairment of proteolytic processing only delays parasite invasion . Alternatively or in addition , other rhomboid proteins and/or proteases may take over the function of PbROM1 , albeit with lower efficiency . There is precedent for such redundant function from in vitro data suggesting that some substrates are cleaved well by either PfROM1 or PfROM4 , while other substrates are cleaved by both enzymes , albeit at different efficiencies [26] . A number of candidate substrates for PbROM1 such as AMA1 have been identified using mammalian cell-based assays [26] . However , these would have to be validated by in vivo experiments and factors such as spatial and temporal regulation of the protease and its substrate ( s ) are also expected to play a role . Our results suggest that PbTRAP , the parasite adhesin required for sporozoite motility , is cleaved by a serine protease . The protease inhibitors used does not necessarily inhibit only TRAP processing , but would be expected to inhibit several other serine proteases . However , the assay specifically measures only TRAP processing . TRAP is processed in the absence of ROM1 suggesting that it might not be a substrate . Alternatively , as discussed above , TRAP processing in ROM1 ( − ) parasites could be due to functional redundancy . Data from in vitro processing assays suggest that this is unlikely because ROM4 but not ROM1 was able to cleave TRAP [26] . In conclusion , this study points to distinct roles for Plasmodium berghei ROM1 throughout parasite development . The lack of an effective vaccine is attributed to the high degree of antigenic variation [48] and the ability of the parasite to switch invasion pathways [49]–[52] . On the other hand , a common phenomenon in the different invasion pathways could be the need for processing and release of the adhesins . For instance , processing of EBA175 within the membrane domain is essential for invasion [28] . As suggested by our genetic analysis , targeting rhomboid proteins offers an attractive new approach to the control of malaria . | Malaria is one of the major infectious diseases and is responsible for the death of more than a million people , mostly children under the age of five . Plasmodium , the causative agent of malaria , is transmitted by female Anopheles mosquitoes . Successful development of the parasite requires efficient recognition , attachment , and invasion of host cells . Several parasite cell-surface molecules have been implicated in these processes and may require proteolytic processing in order for the parasite to complete invasion . Rhomboid family proteins are serine proteases that cleave within the transmembrane region of their substrates . Here , we use a genetic approach to study the function of Plasmodium berghei rhomboid 1 ( PbROM1 ) . PbROM1 is expressed in both vertebrate and mosquito stages of parasite development , and the protein is present in secretory organelles that contain other parasite molecules required for invasion . We find that PbROM1 is required for efficient infection of both the mosquito and the vertebrate host . Interestingly , we also find that mice infected with ROM1 ( − ) parasites clear the infection efficiently and are protected upon subsequent wild-type parasite challenge . Our study suggests a role for PbROM1 throughout parasite development and identifies ROM1 as a target for disease intervention . |
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The GI tract is preferentially targeted during acute/early HIV-1 infection . Consequent damage to the gut plays a central role in HIV pathogenesis . The basis for preferential targeting of gut tissues is not well defined . Recombinant proteins and synthetic peptides derived from HIV and SIV gp120 bind directly to integrin α4β7 , a gut-homing receptor . Using both cell-surface expressed α4β7 and a soluble α4β7 heterodimer we demonstrate that its specific affinity for gp120 is similar to its affinity for MAdCAM ( its natural ligand ) . The gp120 V2 domain preferentially engages extended forms of α4β7 in a cation -sensitive manner and is inhibited by soluble MAdCAM . Thus , V2 mimics MAdCAM in the way that it binds to α4β7 , providing HIV a potential mechanism to discriminate between functionally distinct subsets of lymphocytes , including those with gut-homing potential . Furthermore , α4β7 antagonists developed for the treatment of inflammatory bowel diseases , block V2 binding to α4β7 . A 15-amino acid V2 -derived peptide is sufficient to mediate binding to α4β7 . It includes the canonical LDV/I α4β7 binding site , a cryptic epitope that lies 7–9 amino acids amino terminal to the LDV/I , and residues K169 and I181 . These two residues were identified in a sieve analysis of the RV144 vaccine trial as sites of vaccine -mediated immune pressure . HIV and SIV V2 mAbs elicited by both vaccination and infection that recognize this peptide block V2-α4β7 interactions . These mAbs recognize conformations absent from the β- barrel presented in a stabilized HIV SOSIP gp120/41 trimer . The mimicry of MAdCAM-α4β7 interactions by V2 may influence early events in HIV infection , particularly the rapid seeding of gut tissues , and supports the view that HIV replication in gut tissue is a central feature of HIV pathogenesis .
Gut associated lymphoid tissue ( GALT ) is a primary target for HIV and SIV , particularly in the early weeks of infection [1 , 2] . Within days after transmission , high levels of proviral DNA can be isolated from GALT [2–4] . Subsequently , gut CD4+ T cells are severely depleted , and the structural integrity of GALT is to a large extent irreversibly damaged in a way that is thought to contribute to chronic immune activation [5–7] . Administration of anti-retroviral therapy ( ART ) , even shortly after infection , fails to fully reverse this damage [8] . These early events in infection are believed to contribute in a significant way to the immune dysfunction that characterizes HIV disease [5] . Thus , the early seeding of gut tissues plays a central role in HIV pathogenesis . We and others have demonstrated that both HIV and SIV recombinant envelope proteins directly bind integrin α4β7 ( α4β7 ) , a gut homing receptor [9–12] , while notably , some studies have failed to detect this interaction [13–15] . α4β7 is not required for viral entry [15–17] . However , our findings raise the possibility that there exists a link between the gut-tropic aspect of HIV infection and this physical interaction . It is possible that α4β7 functions simply as an attachment factor [18] . However , gp120 binding to α4β7 , like mucosal addressin cellular adhesion molecule ( MAdCAM ) transduces signals to primary CD4+ T cells , suggesting that such signals may be relevant to infection in vivo [9 , 17 , 19] . In this regard we have recently reported that MAdCAM delivers a signal to CD4+ T cells that promotes cellular activation and viral replication [19] . α4β7 is expressed on the cell-surface membrane of a number of cellular subsets including most naive CD4+ T cells and a subset of memory CCR5+/CD4+ T cells [20] . Similar to each of the 24 human integrins , α4β7 is a heterodimer . It is comprised of a 180 kDa α4 chain [21] and a 130 kDa β7 chain [22] . α4β7 is structurally dynamic and can adopt at least three conformational states , two of which are extended , and competent to mediate lymphocyte adhesion [23] . Transition between conformations is tightly controlled intracellularly , which provides a regulatory mechanism for α4β7 activity [24] . The normal function of α4β7 involves binding to two adhesion receptors , MAdCAM and vascular addressin cellular adhesion molecule ( VCAM ) , along with the alternatively-spliced III connecting segment ( CS ) fragment of fibronectin [25] . Importantly , α4β7 is the only integrin capable of binding to MAdCAM [26] . In healthy adults MAdCAM is expressed on follicular dendritic cells in gut tissues [27 , 28] and on endothelial cells lining the lumen of high endothelial venules in GALT and the gut lamina propria [29–31] . The specificity of MAdCAM for α4β7 , along with its tissue specific expression are the two factors that define α4β7 as the gut homing integrin receptor . There is growing evidence that α4β7 plays a significant role in the pathogenesis of HIV disease . It has been shown that α4β7high CD4+ memory T cells are preferentially infected during both HIV and SIV acute infection [8 , 32] . Additionally , the frequency of α4β7high CD4+ memory T cells is correlated with risk of acquisition in both SIV and HIV [8 , 33] , and in HIV this association was shown to be independent of other markers of cellular activation [8] . Sexually transmitted diseases ( STDs ) , which are associated with increased risk of acquisition of HIV , increase the frequency of α4β7high CD4+ memory T cells in both genital mucosa and blood [34 , 35] . In one recent study of HIV infected women , pre-infection levels of peripheral blood α4β7high CD4+ memory T cells correlated with the rate of CD4+ T cell decline post-infection [8] . In an SIV rhesus macaque model , a substantial proportion of animals pretreated with an anti α4β7 monoclonal antibody ( mAb ) were protected from infection following repeated low-dose vaginal challenge [36] . The same mAb , when combined with ART , promoted durable immune-mediated control of viremia in SIV infected macaques after all forms of therapy were withdrawn [37] . Taken together , these findings demonstrate the importance of α4β7-expressing cells in HIV/SIV infection , and also in the ensuing host immune response , and underscore the need for a more complete understanding the role of α4β7 in the pathogenesis of HIV/SIV disease . Previous studies have demonstrated that the carboxy-terminal region of the V2 domain of gp120 interacts with α4β7 [9 , 10 , 12] . Using site directed mutagenesis of gp120 we reported that a tripeptide motif leucine-aspartic acid-valine or isoleucine at positions 179–181 ( L179D180V/I181 ) in the V2 domain plays a central role in this interaction . This tripeptide motif is similar to critical binding epitopes in the natural ligands of α4β7 . MAdCAM encodes a leucine-aspartic acid-threonine ( LDT ) , VCAM encodes an isoleucine-aspartic acid-valine ( IDV ) , and the IIICS fragment of fibronectin encodes leucine-aspartic acid-valine ( LDV ) . The key feature of each is a core aspartic acid flanked by an aliphatic residue . In each of these natural ligands the aspartic acid coordinates with a Mg++ ion that sits in the metal ion dependent adhesion site ( MIDAS ) of β7 . Mg++ ion coordination is a strict requirement for ligand binding [23 , 38] . Cardozo and colleagues identified nearby amino acids , glutamine-arginine-valine ( QRV ) ( 170–172 ) that also influence V2-α4β7 interactions , demonstrating that the binding site is not limited to the LDV/I tripeptide [12] . These two regions of V2 are flanked by potential N-linked glycosylation sites ( PNGs ) , and removal of these flanking PNGs can enhance binding of recombinant gp120 to α4β7 [16] . It is unknown whether this enhancement is due to relief from steric hindrance , or allosteric changes in the α4β7 binding epitope . High-resolution cryo-electron microscopy and X-ray diffraction analyses indicate that the three V2 domains appear at the apex of the trimeric envelope spike [39–41] . In these structures , as well as in structures derived from scaffolded V1/V2 proteins and monomeric gp120 subunits , the region of V2 from 170–181 appears in the context of a β strand or β barrel [40–45] . In high-resolution structures , the LDV/I appears partially or fully buried in a way that would seem to make it inaccessible to α4β7 . Thus , it is reasonable to conclude that the context in which α4β7 binds to V2 must involve either a rearrangement of these structures , or an alternative presentation of V2 . In this study , we characterized the physical interaction between the HIV envelope and α4β7 reasoning that such information could provide valuable insight regarding the role of α4β7-expressing cells in HIV pathogenesis . In this report , we demonstrate that a region near the carboxy-terminus of gp120 V2 appears to mimic , to a significant degree , the way in which MAdCAM engages α4β7 . In this regard , MAdCAM utilizes dynamic and tightly regulated changes in the conformation of α4β7 to regulate α4β7-expressing lymphocyte access to both gut inductive and effector sites . Thus , this mimicry may provide HIV a mechanism to access gut tissues in a relatively efficient way , and argues that viral replication in gut tissues is central to HIV pathogenesis . One consequence of this mimicry is that drugs developed to antagonize MAdCAM-α4β7 interactions could also disrupt V2-α4β7 interactions . In addition , we find that a subset of HIV and SIV V2 antibodies derived from both infected subjects and vaccine recipients can effectively block V2 α4β7 interactions . Several of the vaccine -elicited weakly neutralizing mAbs have been linked with protection from infection . Rather than binding to the closed trimeric spike that is the primary target of broadly neutralizing antibodies these mAbs recognize an alternative conformation of the V2 region . This suggests that α4β7 also recognizes an alternative form of V2 , that is conserved in both HIV and SIV .
To characterize the interaction between gp120 and α4β7 we employed two assays . We developed a novel surface-plasmon resonance ( SPR ) based assay that utilized dextran surfaces coated with recombinant envelope ( env ) proteins , V1/V2 scaffolds , or synthetic V2 cyclic peptides . The analyte that we reacted with these surfaces was a recombinant soluble α4β7 heterodimer in which the carboxy-terminal transmembrane and cytoplasmic tail domains of both chains were removed and replaced by short peptides that function as an “α4 chain acid-β7 chain base coiled-coil clasp” [46] . This acid-base clasp was joined by a disulfide bond that served to stabilize the heterodimer . In one iteration of this assay we employed short linear peptides derived from V2 as competitive inhibitors . The second assay we employed was a static adhesion assay based on the method developed by Peachman and colleagues , in which RPMI8866 cells , that express α4β7 on the cell surface , were allowed to adhere to the recombinant env proteins , V1/V2 scaffolds or synthetic V2 cyclic peptides ( S1A Fig ) . The α4β7-expressing RPMI8866 cell line was derived from a human B cell lymphoma , and expresses α4β7 , but no detectable CD4 or CCR5 . Cells were grown in media containing retinoic acid , which increased both levels of expression , and clustering of α4β7 ( S2B Fig ) . In some assays we included anti-integrin and anti-gp120 mAbs as adhesion inhibitors . The SPR assay allowed us to evaluate the kinetics of integrin-gp120 binding , while the cell-based assay measured adhesion between two multivalent surfaces . Previous studies describing the interaction between gp120 and α4β7 have demonstrated , in a qualitative way , the specific interaction between these two proteins , without establishing an estimate of affinity [9–12] . We reasoned that a quantitative comparison of the binding kinetics of gp120 vs . MAdCAM to α4β7 would help determine if α4β7-gp120 interactions mimic α4β7-MAdCAM interactions . We carried out a kinetic analysis using the SPR assay noted above . Soluble α4β7 ( analyte ) was passed over surfaces coupled with either recombinant gp120 or a MAdCAM-Ig fusion protein ( ligands ) . A recombinant A244 gp120 ( subtype A/E produced by Global Solutions for Infectious Diseases ( GSID ) ) was employed in these assays . We initially measured binding kinetics in the presence of a buffer containing 1mM MnCl2 in order to uniformly stabilize α4β7 in an extended/activated conformation ( discussed below ) . Under these conditions MAdCAM and A244 gp120 demonstrated comparable high affinities ( KD ( nM ) ) of 0 . 597 and 7 . 140 , respectively ( Fig 1A and 1B ) . These affinities are generally comparable to gp120-soluble CD4 binding kinetics ( e . g 22 nM KD ) [47] . When MnCl2 was removed , affinity for both MAdCAM and gp120 fell below the detection limit of this assay ( Fig 1C and 1D ) . This requirement for Mn++ is consistent with our previous report that gp120 , similar to MAdCAM , interacts with an extended conformation of α4β7 [9] . This observation suggests that gp120 is likely to engage α4β7 only on cells with an enhanced potential to traffic to the gut . We next replaced A244 gp120 with a synthetic cyclic 42 amino acid peptide fragment ( cV2 ) derived from the V2 domain ( AA 157–196 ( HXB2 numbering ) ) of 92TH023 gp120 ( subtype A/E ) , in which N and C termini were joined by a disulfide bond , and the C terminus was biotinylated to facilitate coupling to NeutrAvidin coated biosensor chips . The 92TH023 V2 sequence is nearly identical to that of A244 gp120 V2 ( S1A Fig ) . The affinity of this peptide ( cV2 92TH023 ) for α4β7 ( KD ( nM ) 1 . 150 ) was close to that of A244 gp120 ( Fig 1E ) , demonstrating that a cV2 is sufficient to mediate the high-affinity interaction shown in Fig 1B . Moreover , it indicates that this high-affinity interaction does not require the glycans that decorate the V2 of GSID A244 gp120 , or any bridging protein [13] . When we replaced α4β7 with α4β1 affinity for cV2 92TH023 was reduced by >8000 -fold ( ( KD ( nM ) 9710 ) ( Fig 1F ) , demonstrating binding specificity and the fact that V2 , like MAdCAM , preferentially binds to α4β7 . We extended this analysis by measuring binding kinetics for a cV2 peptide derived from C06980v0c22 ( subtype C ) and a recombinant gp120 BG505 protein ( subtype A ) ( Fig 1G and 1H ) . α4β7 bound to each with similar high affinities , consistent with the conserved nature of this interaction across HIV clades as we had originally reported [9] . The V2 domains of HIV and SIV diverge both in primary sequence , as well as in length and the typical number of disulfide bridges ( 1 vs 2 ) , suggesting significant structural differences between them . Using a recombinant gp120 derived from SIVmac766 we obtained an α4β7 affinity ( KD ( nM ) ) of 104 ( Fig 1I ) . This is ~10-fold lower than that observed for HIV A244 gp120 . It is possible that this reduction in affinity reflects differences in the primary amino acid sequences of human vs . rhesus macaque α4β7 ( Figs 2A and S1A ) , such that SIV gp120s might exhibit a higher affinity for a rhesus macaque versus a human derived α4β7 protein . Detailed α4β7 binding parameters for each of the analytes appears in S1 Table . Similarities between MAdCAM and HIV gp120 with respect to affinity , cation-dependence , and preference for binding α4β7 over α4β1 suggests that gp120 V2 mimics MAdCAM in the manner in which it engages α4β7 . A number of α4β7 antagonists , developed to treat inflammatory bowel disease ( IBD ) act by occupying MAdCAM binding sites on α4β7 . To the extent that gp120 uses these same sites , such antagonist should also interfere with gp120 adhesion . These antagonists include a class of small molecule mimetics that resemble the Leu-Asp binding motif present in the CC′ β strands of MAdCAM IgSF domain 1 [48] . They compete directly with MAdCAM by binding to key residues in the ligand binding groove formed by the interface between α4 and β7 ( Fig 2A ) . Vedolizumab is a mAb antagonist of α4β7 with a different and unique mechanism of action ( MOA ) . It binds exclusively to the specificity-determining loop ( SDL ) in β7 ( Fig 2A ) [23] . The SDL mediates secondary interactions with a negatively charged DE loop in MAdCAM IgSF domain 2 ( Fig 2B ) [23 , 49–52] . We evaluated the capacity of a small molecule LDV mimetic , ELN-475772 , and vedolizumab to interfere with gp120-α4β7 interactions using the RPMI8866-based adhesion assay described above . As expected , both ELN-475772 and vedolizumab blocked adhesion of α4β7 to immobilized MAdCAM ( Fig 2C and 2E ) . We then tested their ability to block α4β7-mediated adhesion to the cV2 92TH023 . The anti-α4 mAb 2B4 , which inhibits most α4-ligand interactions , along with human IgG were employed as specificity controls . Both ELN-475772 and vedolizumab inhibited α4β7 adhesion to V2 by >90% ( Fig 2C and 2E ) . Because the inhibitory MOAs of ELN-475772 and vedolizumab involve direct interactions with two discreet MAdCAM binding sites on α4β7 , these results suggest that soluble MAdCAM would compete with gp120 V2 in binding to α4β7 . Of note , soluble MAdCAM-Ig blocked V2-α4β7 adhesion ( Fig 2C and 2E ) . We conclude that , at least in a general way , gp120 V2 effectively mimics MAdCAM in the manner in which it engages α4β7 . α4β7 activity is regulated by its conformation . Intracellular signaling events modulate the ectodomain to reversibly transition between bent and extended conformations ( Fig 2D ) . In addition , the headpiece , which mediates ligand interactions , can assume closed , intermediate , and open conformations . Transitions between these conformations allows α4β7 to mediate both rolling and firm adhesion [23] . Rolling adhesion is associated with lower affinity binding to MAdCAM , while firm adhesion is associated with a higher affinity interaction . Manipulation of Ca++ , Mg++ , and Mn++ concentrations provides a way to manipulate the affinity of α4β7-MAdCAM interactions in vitro [38] . Strength of adhesion is highest in Mn++ , followed by Mg++ > Mg++/Ca++ , > Ca++ . We asked whether the pattern of V2 adhesion to α4β7 was similar to that mediated by MAdCAM in buffers containing Mn++ vs Mg++ , vs low cations ( Ca++ ) . Adhesion of both MAdCAM and cV2 92TH023 was strongest in the presence of MnCl2 and reduced in the presence of MgCl2 by ~5-fold and 2 . 5-fold respectively ( Figs 2F and S3 ) . In low-cation conditions MAdCAM adhesion appeared close to background levels; however , we were still able to detect residual cV2 92TH023 adhesion . Overall , V2 adhesion to α4β7 responded to divalent cations in a similar manner as did MAdCAM . This is particularly noteworthy insofar as the dynamic changes in affinity required to mediate both rolling and firm adhesion reflect the unique and highly specialized nature of α4β7-MAdCAM interactions [23] . The way in which both HIV and SIV gp120 mimic this highly specialized interaction argues that it provides them a selective advantage . The conformation of the V2 domain of gp120 is dynamic , and consequently it was deleted from the recombinant proteins used to generate the initial high-resolution gp120-mAb cocrystals [53 , 54] . Instead , V2 structures were obtained by grafting V1/V2 fragments onto scaffolds derived from unrelated proteins . These scaffolds stabilized V1/V2 in a way that , in complex with conformation-dependent V2 mAbs , allowed for the derivation of high resolution structures . The first V2 structure was obtained by presenting V1/V2 on a scaffold termed 1FD6 in complex with the broadly neutralizing , glycan dependent monoclonal antibody PG9 [43] . In this context , V2 adopted a Greek key β sheet structure ( Fig 2A ) . Another study in which the same 1FD6-V1/V2 protein was complexed with mAb 830A provided additional detail and revealed V2 in a related β- barrel conformation [42] , which is consistent with what has been observed in pre-fusion env trimers [40 , 41] . However , when a linear V2 peptide is left unconstrained it can adopt α helical structure [55] . This is the case for a crystal structure of mAb CH58 in complex with a linear V2 peptide derived from HIV isolate 92TH023 gp120 [56] . CH58 does not exhibit broad potent neutralizing activity and we refer to here as weakly- neutralizing . It recognizes a helix structure ( Fig 3A ) and is noteworthy insofar as it was generated from an uninfected immunized individual who participated in the RV144 vaccine trial . It recognizes an epitope that maps within a short region of V2 ( AA 168–181 ) , that includes two residues , K169 and I181 , identified by sieve analysis as sites of vaccine elicited immune pressure in the RV144 trial ( Fig 3B ) . Additional V2-specific mAbs that recognize helical structures in this same region have subsequently been described . We report here the cocrystal structure of a V2 peptide with one such mAb , Mk16C2 , that was generated from a gp120 immunized rabbit ( Fig 3A and S2 Table ) . It binds to the same helical structure as CH58 but approaches V2 from the opposite side . Of note , helix -preferring V2 antibodies are not limited to vaccine elicited immune responses . mAb CAP228-16H , which was derived from an HIV-infected subject , recognizes a V2 helix structure that is strikingly similar to that recognized by CH58 [57] . Thus , mAbs reacting with the region of V2 from AA 153–194 recognize at least two distinct types of epitopes: those like PG9 and 830A that recognize a constrained β- sheet , and those like CH58 that recognize a less constrained helical conformation . In an ELISA assay mAbs 830A , CH58 , CAP228-16H and Mk16C2 bind to cV2 92TH023 ( S1B Fig ) , indicating that this cyclic peptide is sufficiently long and flexible to present the epitopes recognized by each of these mAbs . Although the core epitopes of CH58 and 830A differ , the C-terminal end of the CH58 epitope overlaps the 830A epitope ( Fig 3B ) , which is consistent with our observation that CH58 can compete with 830A ( S1C Fig ) . We evaluated the ability of each of these mAbs to inhibit α4β7 interactions with V2 using the adhesion assay described above . mAbs 2B4 and VRC01 were employed as specificity controls . CH58 and CAP228-16H inhibited α4β7 adhesion to V2 by >90% ( Fig 3C ) . Mk16C2 inhibited adhesion less effectively . Of note , 830A failed to inhibit adhesion in a significant way . Thus , while a V2 mAb ( CH58 ) that recognizes a helical structure interfered with α4β7 -mediated adhesion , mAb 830A , that shows preference for the β strand , failed to show detectable interference . Rao and colleagues have recently reported that adhesion of recombinant gp120 to α4β7 requires the partial enzymatic removal of glycans [58] . This is consistent with our finding that removal of several potential N-glycosylation sites ( PNGs ) within V2 can enhance gp120 binding to α4β7 [16] . In agreement with these reports we find that GSID A244 gp120 required limited deglycosylation with PNGase , under nonreducing conditions , in order to mediate α4β7 adhesion ( Fig 4A ) . This adhesion , similar to the adhesion of α4β7 to cV2 92TH023 , was efficiently inhibited by ELN-475772 , CH58 , and CAP228-16H ( >90% ) ( Figs 4A and S4 ) . Again , Mk16C2 was less effective ( ~47% reduction ) . We also evaluated α4β7 adhesion to a BG505 SOSIP gp120/41 trimer and monomeric SIVmac766 gp120 . Unlike the two monomeric gp120s , the SOSIP stabilized protein failed to mediate adhesion after removal of glycans ( Figs 4B and S5 ) . Inability to adhere to α4β7 is not a consequence of the primary sequence of BG505 V2 since a BG505 cyclic V2 ( cV2 BG505 ) was able to mediate α4β7 adhesion ( Figs 4C and S6 ) . These finding are in agreement with the observations of Rao and colleagues . Of note , in an SPR -based assay , CH58 binds to the cV2 BG505 but not to the BG505 SOSIP ( Fig 4D ) . Given that CH58 , which recognizes a helix , efficiently blocks V2 adhesion to α4β7 , we hypothesized that the failure of the BG505 SOSIP trimer to engage α4β7 reflects an underlying conformational constraint on V2 mediated by the SOSIP stabilization strategy in which this constraint precludes the formation of a structure required for α4β7 reactivity . To address whether the β- barrel conformation of V2 is incompatible with α4β7 -reactivity , we inserted the V1/V2 sequences of 92TH023 into two scaffolds . The first scaffold , termed 1FD6 , has been previously shown to constrain V1/V2 in a way that increases its propensity to form a β- barrel [42] . The second scaffold , termed tag , consists of V1/V2 that is untethered at the C-terminus , allowing it to adopt an unconstrained , CH58 -reactive , helical conformation [42] [PMID: 27707920] . We found that the deglycosylated 92TH023 V1/V2 tag scaffold showed ~5x greater adhesion than did deglycosylated 92TH023 V1/V2 1FD6 , which mediated only minor levels of α4β7 adhesion ( Fig 4E ) . We conclude that α4β7 -reactivity requires a degree of V1/V2 flexibility that is not present in the recombinant BG505 SOSIP trimer . Our results suggest that this is due to constraints placed on V2 by other sequences encoded in the closed trimer . However , we cannot rule out interference by PNGase resistant glycans . The data presented above suggests that α4β7 recognizes a structure distinct from the closed spike on virions that is the target of many well characterized neutralizing mAbs . However , it is well established that env appearing on virions is conformationally heterogeneous [59] . We asked whether , among these various env conformations was one that is α4β7 -reactive . To address this question , we employed magnetic nanoparticles ( MNPs ) coated with either α4β7 or V2 mAbs CH58 , PG9 , and 830A to capture 92TH023 virions derived from primary CD4+ T cells ( Fig 5 ) . mAb 2G12 was employed as a positive control . After extensive washing , virion capture was measured by a Luminex -based p24 detection assay [60] . In three independent experiments CH58 , PG9 , and 2G12 each captured ~5X greater amounts of virus than non-specific IgG . Capture by mAb 830A was less efficient ( ~3X over IgG ) ( Fig 5A ) . The capacity of mAb CH58 , which recognizes a helical structure and inhibits V2 adhesion to α4β7 is able to capture virions suggesting that these virions present an α4β7 -reactive form . To test this directly we incubated virions with α4β7- MNPs in the absence or presence of increasing amounts of the α4β7 inhibitor ELN-475772 . In three independent experiments α4β7- MNPs captured virus , and this capture was inhibited by ELN-475772 in a dose -dependent manner ( Fig 5B ) . Thus , 92TH023 virions derived from primary CD4+ T cells present gp120 in an α4β7 -reactive form . Liao and colleagues demonstrated that a 15 AA peptide corresponding to residues 168–181 of V2 adopts a helical structure when complexed with mAb CH58 ( Fig 3A ) [56] . Using the SPR assay described above we asked whether a similar 15 AA peptide was sufficient to bind to α4β7 . Soluble α4β7 was passed over a cV2 92TH023 coated surface in the absence or presence of 8 overlapping linear 15 amino acid peptides . These peptides corresponded to sequences in an HIV-1 subtype B consensus V2 domain ( Fig 6A ) . Peptide H43 ( Q170KEYALFYKLDVVPI184 ) , which closely aligns with the peptide employed by Liao and colleagues , inhibited α4β7 -binding by >90% ( Fig 6B ) . This peptide includes both the canonical L179D180 α4β7 binding site and a Q170K171E172 that aligns with the QRV cryptic α4β7 epitope identified by Cardozo and colleagues [12] . We repeated this analysis but substituted cV2 92TH023 with A244 gp120 and obtained a similar result ( Fig 6C ) . We then competed mAbs CH58 and CAP228-16H with these same peptides ( Fig 6D and 6E ) . Peptides H42 ( R166DKVQKEYALFYKLD180 ) and H43 each partially inhibited mAb CH58 binding and strongly inhibited ( >90% ) CAP228-16H binding . Taken together these results suggest that inhibition by peptide H43 involves direct binding to α4β7 . To rule out allosteric inhibition ( i . e . peptide H43 binding directly to , and altering the conformation of V2 ) , we carried out a similar peptide inhibition assay with immobilized MAdCAM and determined that H43 inhibited α4β7 binding to MAdCAM by >90% ( Fig 6F ) . This result is best explained by direct competition between the Leu179-Asp180 in peptide H43 and the critical Leu41-Asp42 encoded within MAdCAM IgSF domain 1 ( Fig 6G ) . However , we believe it is very likely that other residues in H43 also engage α4β7 . We conclude that an epitope contained within a linear peptide corresponding to residues 170–181 of V2 binds directly to α4β7 . This same region of V2 overlaps the epitopes recognized by CH58 and CAP228-16H . The α4β7 binding epitope of V2 is conserved in SIV as exemplified by the specific affinity of SIVmac766 gp120 for human α4β7 ( Fig 1I ) . To determine whether this region in SIV V2 is also involved in α4β7 -binding , we carried out a similar competition binding experiment as described above in Fig 6 , but utilized SIVmac239 derived V2 overlapping peptides , and immobilized SIVmac766 gp120 , along with SIVsmE660-CR51 gp120 ( Fig 7A ) . Of the eight SIV peptides we employed only S46 showed strong inhibition ( >90% ) ( Fig 7B ) . S46 is the SIVmac239 V2 domain peptide that corresponds to HIV peptide H43 ( Fig 7C ) . Peptides H43 and S46 show limited sequence identity , but notably 5 residues: K171/183 , E172/184 , Y173/185 , Y177/190 , D180/193 , appear to be conserved ( Fig 7C ) . We next asked whether SIV V2 mAbs could inhibit α4β7 adhesion to V2 . Five V2 mAbs , ITS03 , ITS09 , ITS12 . 01 , ITS41 , and NCI09 were evaluated using the same strategy outlined above for HIV V2 mAbs ( see Fig 3C above ) . We also included ITS13 , a V1 mAb and VRC01 an HIV CD4 binding-site mAb as reagent controls . Mapping studies of these V2 mAbs have been described in detail elsewhere [61] and are summarized in Fig 8A . The epitope for mAb ITS12 . 01 spans SIV V2 residues 187–197 and falls within S46 . It includes the key Asp that is conserved in HIV , SIV and MAdCAM IgSF domain 1 . However , ITS12 . 01 did not block α4β7 adhesion to SIVmac766 gp120 ( Fig 8B ) . Instead , ITS03 and NCI09 , which recognize residues NH2-terminal to the ITS12 . 01 epitope inhibited adhesion most efficiently ( >90% ) . ITS09 and ITS41 also inhibited adhesion , but to a lesser extent , while NCI05 , which maps to a region overlapping the ITS09 epitope failed to inhibit adhesion in a detectable way . Of note , the epitopes for both ITS03 and NCI09 do not include the canonical Leu-Asp binding site ( Ala192-Asp193 in SIV ) that is conserved in HIV V2 and MAdCAM ( Fig 8A ) . Both of these mAbs do however overlap with the corresponding region of HIV V2 , that includes the epitopes for CH58 and CAP228-16H , both of which inhibit α4β7 adhesion to HIV V2 . In summary , a 15-amino acid linear peptide derived from SIV V2 ( AA 183–197 ) inhibits SIV gp120 binding to α4β7 , and SIV mAbs whose epitopes overlap this peptide also inhibit α4β7 adhesion . One potential explanation for the conservation of α4β7 -reactivity between HIV and SIV , despite the divergent V2 sequences represented by peptides H43 and S46 ( < 50% sequence similarity ) is that the secondary structures of these two peptides share common features [62] . To address this possibility , we screened SIV mAbs for cross-reactivity with HIV V2 and found that ITS03 , one of the SIV mAbs that blocked α4β7 binding most effectively , reacted with relatively high-affinity to HIV cV2 92TH023 ( KD ( nM ) ) 0 . 105 ) ( Fig 8C ) . This cross-reactivity is consistent with shared secondary structure between the α4β7 -binding epitopes localized within the V2 regions of HIV and SIV gp120 . SIV infected macaques treated with a combination of ART and a recombinant rhesus anti α4β7 -mAb ( Rh-anti-α4β7 ) were able to durably control viremia at relatively low levels following treatment interruption [37] . Although these animals failed to mount neutralizing antibody responses , we noted that following withdrawal of ART , 8/8 generated anti-V2 antibody responses , while only 3/7 control animals administered normal IgG and ART , generated similar responses . Mapping studies indicated that this response was strongly focused on the region of V2 corresponding to S43 , the region of V2 that includes the NCI09 and ITS03 epitopes . These V2 responses persisted for at least 50 weeks , despite low plasma viremia [37] . Because NCI09 and ITS03 block adhesion to gp120 , we asked whether these polyclonal antibody responses included antibodies that could also inhibit α4β7 adhesion to gp120 . Serum IgG was purified from three ART + anti- α4β7 mAb -treated animals , RLn12 , RDa15 , and RId14 , by protein G affinity chromatography , and along with normal rhesus macaque IgG was evaluated in an α4β7 adhesion assay . While normal rhesus IgG showed minimal inhibition of α4β7-adhesion to SIVmac766 gp120 , the sera from all three ART + α4β7 treated macaques inhibited adhesion in a dose dependent manner ( Fig 9A ) . We then asked whether these sera contained antibodies with specificities similar to the SIV V2 mAbs ( described above in Fig 8 ) that inhibited α4β7 adhesion to gp120 . Using an SPR -based assay , SIVmac766 gp120 coated surfaces were preincubated with SIV V2 mAbs ITS03 , ITS09 . 01 , ITS12 . 01 or NCI09 . A surface preincubated with RLn12 serum served as a positive control . Surfaces were then reacted with RLn12 serum . As expected , pre-bound RLn12 serum inhibited RLn12 binding ( ~70% reduction ) ( Fig 9B ) . ITS03 , ITS09 . 01 and NCI09 -mediated similar levels of inhibition . ITS12 . 01 , which showed minimal inhibition of α4β7 adhesion ( Fig 8B ) , failed to inhibit RLn12 binding ( Fig 9B ) . We conclude that antibody responses generated in SIV infected macaques treated with ART and Rh-anti-α4β7 included V2 antibodies that target the α4β7 binding epitope of SIV V2 .
In this study we report that the way in which α4β7 interacts with the V2 region of gp120 shares key features with the interaction between α4β7 and its natural ligand , MAdCAM . This apparent mimicry may have important implications in HIV pathogenesis , particularly in regard to the role of the gut in the development of HIV disease . It may also impact anti-V2 loop immune responses in both infected and vaccinated subjects . One consequence of this mimicry is that antagonists developed to treat IBD interfere with V2-α4β7 interactions . The V2 domains of HIV and SIV gp120 vary in both length and sequence identity . Yet we find that V2s from three subtypes of HIV , as well as a V2 from SIV retain the capacity to bind to α4β7 , suggesting that this interaction is a general property across HIV subtypes . Other studies failed to detect a specific interaction between α4β7 and gp120 [13 , 14] . An explanation for this discrepancy likely reflects two variables . First , as we and others have shown , the addition of glycans can reduce the interaction between recombinant gp120 with α4β7 [16 , 58] . It is likely that excess amounts of complex carbohydrate and sialic acid moieties that are added to gp120s expressed in cell lines contribute to this inhibitory effect . Of note , we demonstrated that highly purified ( >95% ) cyclic V2 loop peptides that lack glycans bind α4β7 with high affinity in both SPR -based assays and in a cell -based adhesion assay . Removal of glycans in order to observe α4β7 reactivity in not an absolute requirement insofar as we were able to capture virions derived from primary CD4+ T cells with α4β7 coated nanoparticles . The second variable that may influence the sensitivity of α4β7 binding assays involves the expression level , and state of α4β7 on cell surfaces . Many integrins rely on complex avidity effects and clustering in order to engage ligands . It is likely that the surface density of α4β7 plays a key role in its interaction with gp120 . The specific affinity of α4β7 for gp120 is comparable to that of MAdCAM . Among the 24 integrins expressed in humans , the α4β7 heterodimer is distinct in its ability to mediate both lymphocyte rolling , and firm adhesion , which reflects the highly specialized nature of MAdCAM-α4β7 interactions . These two functions are achieved by dynamic changes in the overall structure of the heterodimer . In this regard , it is notable that the V2 region of gp120 , despite its variable sequence , is able to mimic the binding of α4β7 to MAdCAM . The evidence for mimicry comes from two observations . First , the manner in which V2 depends upon divalent cations to engage α4β7 tracks closely with the way cations are used by MAdCAM . To bind α4β7 , MAdCAM utilizes divalent cations . A Mg++ ion sits in the MIDAS of β7 and coordinates with an Asp in the ligand ( Asp42 in MAdCAM ) . Mn++ occupies the MIDAS in a more stable way , so that replacing Mg++ with Mn++ results in an apparent increase in affinity . This pattern holds for gp120 V2 , indicating that V2 appears to interact with the different conformations of α4β7 in the same way that MAdCAM does . Importantly , the conformational state ( inactive , intermediate or active ) of α4β7 is responsive to both intracellular and external cues that are linked to cellular signals generated during inflammatory responses [63 , 64] . The ability of V2 to discriminate between different forms of α4β7 provides a mechanism to distinguish between different subsets of lymphocytes , including those with high potential to home to GALT . Given the propensity of HIV to replicate in GALT , it is tempting to link the preferential infection and depletion of α4β7high CD4+ T cells in the very early stages of infection [8 , 32 , 65] , with V2-α4β7 interactions . However , such a link has not yet been established . The second line of evidence that supports the proposition that V2 mimics the binding characteristics of MAdCAM comes from our demonstration that α4β7 antagonists that were developed to block binding to MAdCAM , also block binding to V2 . The incidence and prevalence of IBD is increasing throughout the world [66] , and consequently there has been a concerted effort to develop effective treatments , including drugs that target α4β7 . To this end , detailed structural characterizations of both MAdCAM and α4β7 have been employed in the rational design of small molecule LDV mimetics [67] . These mimetics bind with precision to the MAdCAM binding site on α4β7 , which lies within a ~ 40 Å long , 10 Å deep groove formed by the α4-β7 interface ( Fig 2A ) [23] . By showing that one of these mimetics competes with V2 we conclude that the aliphatic amino acid-Asp motifs conserved in both HIV and SIV fit into this groove and engage α4β7 in a way that , at least partially , mimics the Leu41-Asp42 encoded in the MAdCAM CC' loop of IgSF domain 1 ( Figs 2B and 6G ) [52 , 68] . These results suggest that the carboxy-terminus of V2 and this IgSF domain 1 CC' loop can adopt similar conformations . Evidence for the conserved nature of this structure comes from our observation that one SIV V2 mAb , ITS03 , whose epitope maps close to the α4β7 binding site , blocks α4β7 adhesion to V2 and also cross-reacts with an HIV subtype A/E V2 . This raises the intriguing possibility that , with additional screening , one might identify a V2 mAb that cross-reacts with MAdCAM . Indeed , other regions of the HIV envelope have shown to mimic “self” epitopes [69] . Vedolizumab is a unique α4β7 antagonist . It binds exclusively to the SDL of β7 in the context of α4β7; however , structural constraints preclude it from binding to αEβ7 [23] . It inhibits V2-α4β7 adhesion , which further supports the idea that V2 mimics MAdCAM . However , the mechanism by which vedolizumab , and its parent mAb , Act-1 [70] , interfere with MAdCAM binding is less well defined than that for LDV mimetics . The key to understanding this mechanism is understanding the role of MAdCAM IgSF domain 2 . Mutagenesis or deletion of the charged C'-E loop of MAdCAM IgSF domain 2 abrogates α4β7 binding [49 , 50] . Docking experiments carried out in silico indicate that this loop comes in close proximity to the β7 SDL and may contact α4β7 directly ( Fig 2A and 2B ) . By binding to the SDL , vedolizumab is likely to prevent direct interactions with the DE loop and/or sterically interfere with interactions between IgSF domain 1 and the binding groove at the interface between α4 and β7 . As such , the α4β7 binding footprint on MAdCAM spans two IgSF domains , and interactions with two loops that are separated by ~18-36Å that are both involved in binding to α4β7 . This raises a question regarding our demonstration that vedolizumab blocks adhesion between V2 and α4β7 . We show that a limited region within a ~40 AA cV2 peptide appears to engage the binding groove . The limited size of these cV2 peptides suggests that simultaneous interactions between these peptides and both the binding groove and the SDL are unlikely . Thus , the mechanism by which vedolizumab blocks V2 binding remains unclear and requires further investigation . Although the evidence for molecular mimicry outlined above is strong , the extent of this mimicry is likely to be limited . Because the interaction between MAdCAM and α4β7 facilitates both rolling and firm adhesion of lymphocytes along the endothelium , it encompasses a high degree of complexity that may not be entirely reflected in the interaction between V2 and α4β7 . Moreover , the region of V2 involved in binding to α4β7 is variable which makes it unlikely that it could retain the functional complexity inherent in the interactions that occur between MAdCAM and α4β7 . We found that certain weakly neutralizing V2 mAbs elicited from both infection ( CAP228-16H ) and vaccination ( CH58 ) could inhibit V2 -mediated adhesion to α4β7 . In this regard , there is a growing interest among HIV vaccine researchers regarding the potential utility of weakly neutralizing , or “functional” antibodies . This stems in large measure from the results of the RV144 Phase III vaccine trial in Thailand , where risk of acquisition was found to correlate inversely with weakly neutralizing V1/V2 antibodies [71–75] . Subsequently , a molecular sieve analysis of viral quasi-species in vaccinated individuals who became infected showed that residues at positions 169 and 181 within the V2 region were subject to immune pressure around the time of infection ( Fig 3B ) [76] . These residues fall around the binding epitopes for both CH58 and α4β7 . Follow-up studies suggested that antibody effector functions might contribute to the observed RV144 risk-reduction [77 , 78] . Our finding that mAb CH58 , which was derived from an RV144 vaccine recipient , block V2 -mediated adhesion to α4β7 raise the possibility that antibody activities distinct from both neutralization and Fc -mediated effector functions might contribute to the efficacy of HIV vaccines . Supporting this concept , we found that mAb NCI09 , which was derived from a macaque administered an SIVmac251-based vaccine designed to mimic the RV144 vaccine [79] , also blocked V2 -mediated adhesion to α4β7 . As in RV144 , reduced risk of infection in animals administered this vaccine was correlated with weakly neutralizing V2 antibodies [79] . A more complete description of this antibody and its activities are described elsewhere ( Franchini et al . , in preparation ) . How weakly neutralizing antibodies that block V2 -mediated adhesion to α4β7 might contribute to reduced risk of infection is unknown . We previously reported that the V2 region of gp120 can deliver cellular signals through α4β7 [9] . In this regard , integrins including α4β7 are key components in integrin associated complexes that are able to modulate biochemical pathways and reorganize both cell-surface receptors and the actin-cytoskeleton [63 , 80] . In addition , α4β7 can deliver costimulatory signals to CD4+ T cells that impact cell activation , proliferation and apoptosis [81 , 82] . We recently reported that MAdCAM -mediated costimulation supports HIV replication in α4β7high CD4+ T cells [19] . Whether V2 signaling through α4β7 can similarly facilitate HIV replication requires additional investigation . Such information will help determine whether this type of signaling could facilitate HIV transmission/replication and whether antibodies that interfere with this signal can reduce the risk of infection . The nature of the epitopes recognized by mAbs that inhibit the interaction between V2 and α4β7 should help us identify the context in which these interactions occur in vivo . It is noteworthy that these weakly neutralizing mAbs recognize an epitope that is structurally distinct from the β- strand that is presented on the closed BG505 SOSIP trimer . As such , broadly neutralizing mAbs that do recognize epitopes on the closed BG505 SOSIP trimer are unlikely to be effective inhibitors of V2-α4β7 interactions . Instead , we show that a unique α4β7 -reactive conformation of V2 is formed when it is relieved from constraints mediated by other domains of gp120/41 . Although the context in which V2 engages α4β7 remains to be determined , evidence is accumulating that α4β7 -expressing cells play an important role in the early stages of HIV/SIV infection . The recent demonstration by Sivro and colleagues that α4β7high CD4+ T cells are preferentially depleted from gut tissues as early as Fiebig I/II provides strong evidence that these cells serve as prime early targets for infection following transmission , an observation that is consistent with our demonstration that an α4β7 mAb protects macaques from vaginal challenge [36] . However , we cannot exclude the possibility that α4β7 plays a broader role in HIV pathogenesis . When we combined this same α4β7 mAb with ART , SIV infected animals were able to control viremia in a sustained way [37] . In trying to identify the underlying mechanism of control we reported , among other findings , that each of the controlling animals generated anti-V2 specific antibody responses that mapped to the region of SIV V2 that corresponds to the CH58 epitope in HIV V2 . In this report , we show that these serum V2 antibodies can block V2 -mediated adhesion to α4β7 . This finding underscores the need to further explore the role of V2 α4β7 interactions in HIV pathogenesis . In conclusion , the way that the V2 region of gp120 engages α4β7 shares key features with the way that MAdCAM , a receptor expressed primarily in gut tissues , engages α4β7 . One consequence of this apparent mimicry is that antagonists developed to treat IBD by interfering with the interaction between MAdCAM and α4β7 also interfere with V2-α4β7 interactions . The nature of the epitope in V2 that engages α4β7 appears to involve a structure that is not present in a recombinant SOSIP trimer designed to mimic the closed trimeric spike that is the target of most broadly neutralizing antibodies . Antibodies that target this epitope and block V2-α4β7 interactions are not themselves broadly neutralizing , although the structure that they recognize is conserved across clades and is present in SIV V2 . These findings suggest that mimicry of MAdCAM-α4β7 interactions by V2 may influence early events in HIV infection and replication in GALT .
Generation of mAb Mk16C2 was approved and carried out under animal use protocol A-1896 by the Institutional Animal Care Committee ( IACUC ) of the University of Massachusetts Medical School . The University of Massachusetts Medical School is fully accredited by Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) and has an Animal Welfare Assurance on file with the Office of Laboratory Animal Welfare ( OLAW ) . Assurance number: A-3306-01 . RPMI8866 cells , a human B lymphoma cell line that constitutively expresses α4β7 was purchased from Sigma-Aldrich . Cells were maintained in RPMI-1640 ( Lonza ) containing 10% heat inactivated fetal bovine serum ( Gibco ) , 2% penicillin/streptomycin/glutamine ( Gemini Bio-Products ) and 0 . 1% retinoic acid ( RA ) . Cells were cultured for a minimum of 7 days prior to use in adhesion assays . RA was obtained from Sigma Chemical . CH58 , human mAb isolated from RV144 vaccinated individuals , and VRC01 were provided by the NIAID AIDS reagent program . CAP228-16H mAb was generated in the laboratory of Dr . Lynn Morris ( CAPRISA ) [57] . The 830A mAb was provided by Dr . Susan Zolla-Pazner ( Mt . Sinai Medical School ) [42] . Rabbit Mk16C2 mAb ( provided by Dr . Shan Lu , University of Massachusetts Medical School ) was isolated from a rabbit that received a gp120-JRFL DNA prime- protein boost immunization , using V2-peptide specific single B cell sorting , and produced by transfection of cloned Ig genes in 293F cells at the University of Massachusetts Medical School [83] . The SIV mAbs ITS03 , ITS09 . 01 , ITS12 . 01 , ITS13 , and ITS41 were provided by Dr . Rosemarie Mason , NIAID VRC [61] . NCI09 was provided by Genoveffa Franchini and produced in the laboratory of Rosemarie Mason in a manner identical to mAbs mAbs ITS03 , ITS09 . 01 , ITS12 . 01 , ITS13 , and ITS41 [61] . Rhesus macaque serum from animals RLn12 , RDa15 , RId14 was provided by Dr . Aftab Ansari ( Emory University ) [37] , was obtained after 72 weeks post-infection , and more than 40 weeks after the last infusion of anti-α4β7 mAb . Serum was purified by protein G column chromatography and dialyzed into HBS . Cyclic V2 peptides with > 90% purity and having an amino-terminal biotin , derived from 92TH023 , BG505 and C06980v0c22 were supplied by JPT Peptide Technologies . Linear HIV and SIV 15 amino acid peptides were obtained from the NIAID AIDS Reagent Repository , or from Biopeptide Co . and supplied at >90% purity . Scaffolds V1/V2 92TH023-1FD6 and V1/V2 92TH023-Tag were constructed , expressed and purified as described elsewhere [42] . CHO cell derived A244 gp120 ( Lot 26539–1 ) was provided by Global Solutions for Infectious Diseases ( San Francisco , CA ) . Purification employed an anti-gD immunoaffinity resin , followed by both cation and anion exchange chromatography steps . Purity was estimated at 97 . 1% . CHO cell derived SIVmac766 gp120 was provided by Advanced Biotechnologies Laboratories . BG505 SOISP trimer was generously provided by Dr . Paolo Lusso , LIR/NIAID . Vedolizumab was obtained from the NIH Clinical Center Pharmacy Department . Human integrin α4 mAb 2B4 , MAdCAM-Ig , soluble α4β7 and α4β1 were obtained from R&D Systems . The LDV mimetic ELN-475772 was provided by ELAN Pharmaceuticals [84] . Conjugation of HRP to mAbs was carried out using a LYNX HRP conjugation kit obtained from Bio-Rad , using the manufacturer’s instructions . Prior to use purified gp120 and gp140 proteins utilized in the α4β7 adhesion assays were first treated with a deglycosylation protocol [58] . Purified proteins were treated with 500U of PNGase F ( NEB ) per 20 μg of protein under non-denaturing conditions ( 1X GlycoBuffer 2 ( NEB ) , 5mM DTT , and water ) at 37°C for 3 hours . Experiments were performed using a Biacore 3000 instrument ( GE Life Sciences ) using CM4 or CM5 sensor chips . The data were evaluated using BIAevaluation 4 . 1 software ( GE Life Sciences ) . The chip surface was activated by injecting 35 μl of a 1:1 mixture of 0 . 05 M N-hydroxysuccinimide and 0 . 2 M N-ethyl-N- ( dimethylaminopropyl ) carbodiimide at 5 μl/min . NeutrAvidin , HIV gp120 or Hu-MAdCAM-Ig ( R&D Systems ) at concentrations of 5 μg/ml in 10mM NaOAc , pH 4 . 5 , were immobilized to approximately 750 resonance units ( RU ) . After the proteins were immobilized to the desired densities , unreacted sites on each surface were blocked with 35 μl of 1 M Tris-HCl ( pH 8 . 0 ) . Biotinylated cyclic V2 peptides ( 1 μg/ml in 20 mM Tris-HCl , pH 8 . 0 ) were bound to the NeutrAvidin surfaces to densities of approximately 250–300 RU . One surface was activated and blocked without ligand to act as a control surface for non-specific binding of the soluble ligand . Any binding was subsequently subtracted from the remaining surfaces . Running buffer was HBS ( pH 7 . 4 ) , 0 . 01 mM CaCl2 , either 1 mM MgCl2 or MnCl2 , 0 . 005% Tween-20 , 0 . 05% soluble carboxymethyl-dextran . Binding experiments were carried out at a flow rate of 25 μl/min at 25°C . After a 2 min injection , the surface was washed for an additional 2 min in running buffer to follow dissociation of the bound ligand from the surface . The surfaces were regenerated by multiple injections of 4 . 5 M MgCl2 at a flow rate of 100 μl/min . Inhibition of α4β7 or anti-V2 loop antibodies by linear V2-loop peptides was carried out by pre-incubating the proteins with the peptides in running buffer at the indicated concentrations for 2 hours at room temperature prior to passing them over the prepared surfaces as described above . Antibodies were diluted to the indicated concentrations in running buffer prior to being sequentially passed over the surface bound cyclic peptides as described above . The resulting sensorgram series were analyzed using the BiaEvaluation 4 . 1 software ( GE Life Sciences ) and fitted using a 1:1 Langmuir binding model to determine the kinetic rate and affinity constants . The Fab fragment of rabbit mAb Mk16C2 ( provided by Dr . Shan Lu , Univ . of Massachusetts Medical School ) was prepared by papain digestion as described ( PMID: 19913488 and 20622876 ) . Briefly , the IgG molecule was mixed with papain ( Worthington , Lakewood , NJ ) at a 20:1 molar ratio in 100 mM Tris ( pH 6 . 8 ) with 1 mM cysteine hydrochloride and 4 mM EDTA . The mixture was incubated for 1 hour at 37°C and the reaction was stopped by 10 mM iodoacetamide . The Fab fragment was separated from the Fc fragment and the undigested IgG by a protein A column and further purified by size exclusion chromatography . The Fab fragment was then concentrated to about 10 mg/ml for crystallization . The 15mer V2ConB peptide ( RDKVQKEYALFYKLD ) was dissolved in water and mixed with Fab of rabbit mAb Mk16C2 in excess at a 10:1 molar ratio . Crystallizations conditions were screened and optimized using the vapor diffusion hanging drop method . Well-diffracted crystals of Mk16C2 Fab/V2ConB complex were obtained with a well solution of 23% polyethylene glycol 3350 , 0 . 2 M LiCL , 0 . 1 M 2-ethanesulfonic acid ( MES ) pH 6 . 5 , and soaked briefly in the crystallization solution with an additional 20% glycerol before being flash frozen in liquid nitrogen . X-ray diffraction data sets were collected at the synchrotron beamline 14–1 of Stanford Synchrotron Radiation Lightsource ( SSRL ) of Stanford Linear Accelerator Center ( SLAC ) National Accelerator Laboratory . All data sets were processed using the XDS ( PMID: 20124692 ) , and structures determined by molecular replacement using another rabbit mAb R56 Fab structure ( PDB ID 4JO1 ) as the initial model . Cycles of refinement for each model were carried out in COOT ( PMID: 15572765 ) and Phenix ( PMID: 20124702 ) . Final structural analyses were carried out using ICM and figures were generated using PyMOL ( pymol . org ) and ICM ( www . molsoft . com ) . The antigen-antibody interactions described in Fig 3A are calculated by PDBePISA ( EMBL-EBI ) . Coordinate and structure factor of the complex have been deposited in the Protein Data Bank under PDB ID 6CEZ . The binding of α4β7 expressed by the RPMI8866 cell line to MAdCAM , HIV A244 gp120 , HIV cyclic V2 peptides , and SIVmac766 gp120 gp120 in the absence or presence of vedolizumab or 2B4 or ELN-475772 to MAdCAM , HIV A244 gp120 were analyzed by an adhesion assay ( adapted from KK Peachman et al . , [11] ( S2A Fig ) . This assay was modified by culturing RPMI8866 cells in media containing 1μM RA for at least 7 days prior to use in adhesion assays . Inclusion of RA increases adhesion to cV2 peptides , gp120 and MAdCAM ( S2C Fig ) . Briefly , triplicate wells of a 96-well flat-bottom polypropylene plate ( Greiner Bio-One ) were coated overnight at 4°C with 100 μl of 2 μg/ml of MAdCAM-1 ( R & D Systems ) or 100 μl of 2 μg/ml NeutrAvidin or 100 μl of 0 . 5–2 . 0 μg of deglycosylated SIV and HIV gp120 diluted in 50mM bicarbonate buffer , pH 9 . 6 . The NeutrAvidin-coated plates were then incubated with biotinylated cyclic V2 peptides ( 5 μg/ml in bicarbonate buffer ) for 1 hour at 37°C . The solution from the plates was discarded and the plates were then blocked with blocking buffer ( 25mM Tris , 2 . 7mM potassium chloride , 150 mM sodium chloride , 0 . 5% BSA , 4mM manganese chloride , pH 7 . 2 ) for 1 hour at 37°C . The solution was discarded and plates were manually washed 4 times with blocking buffer . After blocking and washing the plate , RPMI8866 cells in a volume of 50 μl/well were pre-incubated for 40 min at 37°C with sample buffer in the absence or presence of 10 μg/ml of vedolizumab ( α4β7 mAb ) or 2B4 ( α4 mAb ) or ELN-475772 ( α4β7/α4β1 dual inhibitor ) . Plates were then incubated with 50 μl/well of 2x105 RPMI8866 cells at 37°C ( 5% CO2 ) for 1 hour , washed 5 times with PBS followed by the addition of 100 μl of RPMI-1640 containing 1% FBS , 1% pen/strep/glutamine , 25mM HEPES with 10 μl/well of AlamarBlue dye . Fluorescence ( excitation 560 nm and emission 590 nm ) was measured immediately after the addition of the AlamarBlue dye for 8 hours . 92TH023 virions were captured with 15 nm magnetic nanoparticles ( MNPs ) coupled to α4β7 or 2G12 , CH58 , PG9 , and 830A mAbs as previously described [85] . Briefly , carboxyl-terminated iron oxide nanoparticles ( Ocean Nanotech , San Diego ) via two step carbodiimide reaction were coated with 1mg of anti-gp120 mAbs or recombinant soluble α4β7 according to manufacturer’s protocol . Virus preparations were derived from primary CD4+ T cells infected with an infectious molecular clone derived from 92TH023 , using standard conditions . To capture virions , MNPs coated with mAbs ( 3 . 9 x1012 ) in 60μl were incubated with viral preparation ( 33 ng/ml based on p24 content ) for 1hour at 37°C . Captured virions were separated on MACS magnetic columns attached to an OctoMacs magnet ( Miltenyi Biotech ) washed 4 times with 600 μl wash buffer ( 0 . 5% bovine serum albumin , 2mM EDTA in PBS ) , eluted in 200 μl PBS and analyzed on Luminex X200 for p24 content using a dynamic immunofluorescent cytometric bead assay [60] . In experiments with α4β7-MNPs , virions were incubated in the absence or presence of increasing amounts ( 1 . 25nM , 12 . 5nM , 125nM ) of the α4β7 inhibitor ELN-475772 . Incubation and washing were performed in complete medium with 1mM MnCl2 . Triplicate wells of a 96-well flat-bottom polypropylene plate ( Greiner Bio-One ) were coated with biotinylated cyclic V2 peptides , deglycosylated SIV and HIV gp120 as described above . After blocking and washing , plates were incubated with 20–100 μg/ml of the designated anti-V2 mAbs or 2 . 5–10 . 0 μg of protein G purified IgG from sera drawn from SIV infected rhesus macaques ( RLn12 , RDa15 , RId14 ( provided by Dr . Aftab Ansari , Emory University School of Medicine ) ) in sample buffer ( 25 mM Tris , 2 . 7 mM KCl , 150 mM NaCl , 4 mM manganese chloride , 1% fetal bovine sera , pH 7 . 2 ) for 1 hour at 37°C . RPMI8866 cells were pre-incubated for 40 min at 37°C with sample buffer . Plates were then incubated with 50 μl/well of 2x105 cells at 37°C ( 5% CO2 ) for 1 hour , washed 5 times with PBS followed by the addition of 100 μl of RPMI-1640 containing 1% FBS , 1% pen/strep/glutamine , 25mM HEPES with 10 μl/well of AlamarBlue dye ( Invitrogen ) . Following the addition of AlamarBlue dye ( excitation 560 nm and emission 590 nm ) fluorescence was measured for a period of 8 hours . 92TH023 V1/V2 was cloned into both 1FD6 ( constrained ) and tag ( unconstrained ) scaffolds . Plates were then coated with 0 . 5 , 1 . 0 , and 2 . 0 μg of either V1/V2 92TH023 1FD6 or V1/V2 92TH023 tag scaffolds followed by addition of RPMI8866 cells . The plates were washed and 100 μl of media and 10 μl of AlamarBlue dye was added to each well as described above . Fluorescence ( excitation 560 nm and emission 590 nm ) was measured immediately after the addition of the AlamarBlue dye . Briefly , Corning Costar 96-Well plates were coated with NeutrAvidin at 4°C overnight . Wells were washed six times with wash buffer ( water , 1 mM MnCl2 , and 1X plate wash buffer ) using Microplate Washer ELx50 , ( Bio Tek Instruments ) , and then blocked with blocking buffer ( HBS , 5% bovine serum albumin ) for 1 hour at room temperature . Plates were coated with 1 μg/ml biotinylated cyclic V2 peptides in binding buffer ( HBS , 1 mM MnCl2 ) for 1 hour at room temperature . The plates were washed six times with wash buffer and HRP-conjugated anti-V2 mAb was added to wells for 1 hour at room temperature . After washing , 100 μl/well of substrate was added for 10 min to develop color at room temperature in the dark . Plates were read at OD450 nm using an EnSpire Multimode Plate Reader ( PerkinElmer ) . 10 , 20 , 50 , 80 and 160 ng of the CH58 mAbs in binding buffer ( HBS , 1 mM MnCl2 ) were incubated with 50 ng of HRP-conjugated 830A mAb and then added to a plate coated with 100 ng of cV2 92TH023 . Substrate was added using conditions specified by the manufacturer . Plates were read at OD450 nm using an EnSpire Multimode Plate Reader , PerkinElmer . RPMI8866 cell line cultured in the presence or absence of RA were seeded at 1×105/well in Poly-d-lysine coated glass bottom dishes ( MatTek ) with cover glasses and incubated overnight at 37°C . The cells were fixed with 2% paraformaldehyde ( PFA ) , blocked with 1% BSA , and stained with an anti-β7 PE mAb ( BD Biosciences ) or an IgG2a-PE isotype control mAb ( R & D Systems ) . Stained RPMI8866 cells were microscopically analyzed using a Leica SP8 confocal microscope ( Leica Microsystem , Inc . ) and images were processed with Leica LAS AF software ( Leica Microsystem , Inc . ) and Imaris software v . 9 . 0 . 1 64x ( Bitplane AG ) . | HIV is gut-tropic . Disruption of GALT plays an important role in HIV -mediated immune dysfunction . α4β7 is a receptor that facilitates homing of lymphocytes to GALT . α4β7high CD4+ T cells are early targets of HIV infection . The HIV envelope protein gp120 binds to α4β7; however , a link between this interaction and the preferential infection of α4β7high CD4+ T cells has not been established . Here we report an apparent gp120 mimicry of MAdCAM , a natural ligand of α4β7 . That the HIV envelope protein mimics an adhesion receptor expressed primarily in gut tissues supports the concept of a central role for GALT in HIV pathogenesis . Antibodies derived from vaccination and infection were evaluated for their capacity to inhibit gp120-α4β7 interactions . Antibodies that inhibit binding target a region of the V2 domain that has been linked with reduced risk of acquisition in the RV144 vaccine trial . These antibodies recognize epitopes that do not appear on the closed gp120/41 trimer . Instead , they recognize structures that form when V2 is allowed to fold in a less constrained way . We show that such epitopes can appear on virions . Thus , an alternative structure of the V2 domain may facilitate the trafficking of HIV to GALT . |
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In human cells , DNA double-strand breaks are repaired primarily by the non-homologous end joining ( NHEJ ) pathway . Given their critical nature , we expected NHEJ proteins to be evolutionarily conserved , with relatively little sequence change over time . Here , we report that while critical domains of these proteins are conserved as expected , the sequence of NHEJ proteins has also been shaped by recurrent positive selection , leading to rapid sequence evolution in other protein domains . In order to characterize the molecular evolution of the human NHEJ pathway , we generated large simian primate sequence datasets for NHEJ genes . Codon-based models of gene evolution yielded statistical support for the recurrent positive selection of five NHEJ genes during primate evolution: XRCC4 , NBS1 , Artemis , POLλ , and CtIP . Analysis of human polymorphism data using the composite of multiple signals ( CMS ) test revealed that XRCC4 has also been subjected to positive selection in modern humans . Crystal structures are available for XRCC4 , Nbs1 , and Polλ; and residues under positive selection fall exclusively on the surfaces of these proteins . Despite the positive selection of such residues , biochemical experiments with variants of one positively selected site in Nbs1 confirm that functions necessary for DNA repair and checkpoint signaling have been conserved . However , many viruses interact with the proteins of the NHEJ pathway as part of their infectious lifecycle . We propose that an ongoing evolutionary arms race between viruses and NHEJ genes may be driving the surprisingly rapid evolution of these critical genes .
DNA double-strand breaks are a particularly toxic form of DNA lesion . Such breaks are repaired through several pathways , the most well-studied being homologous recombination and non-homologous end joining ( NHEJ; reviewed in [1] ) . NHEJ is also required for V ( D ) J recombination , which generates immunoglobulin and T cell receptor diversity . Accordingly , mutations in NHEJ genes have been linked to both cancer and immune deficiencies . Given the central importance of these processes , NHEJ genes are expected to have a low tolerance for mutations . Such a hypothesis would be supported if sequences of NHEJ genes are stable and relatively unchanging over evolutionary time . In contrast to this expectation , a genome-wide analysis uncovered NHEJ as one of the two functional pathways most enriched for positive selection during Saccharomyces evolution [2] . Positive selection occurs when natural selection operates on an advantageous mutation , driving an increase in its prevalence over time , and sometimes leading to fixation of this mutation in the species in which it arose . Because advantageous mutations commonly involve a change in protein sequence , recurrent rounds of positive selection can lead to relatively rapid protein sequence evolution over time . Positive selection has been found to predominantly affect genes in three functional classes: reproduction , immunity , and environmental perception ( smell , taste , etc ) , presumably because these processes are under strong selection for constant adaptive change [3]–[10] . The intriguing observation of positive selection in the NHEJ genes of Saccharomyces remains unexplained , but could potentially be attributed to the fact that NHEJ is not the major pathway for the repair of double-strand breaks in yeast [11] . Relaxation of evolutionary constraints on NHEJ genes in yeast species , due to their reliance predominantly on the homologous recombination pathway , could have made NHEJ genes vulnerable to competing evolutionary forces . In this study , we have analyzed the molecular evolution of NHEJ genes in primates , including humans , where NHEJ is the major pathway for DNA double-strand break repair . NHEJ is activated upon detection of DNA double-strand breaks . After detection , NHEJ proteins enzymatically process broken DNA ends to allow for efficient end joining . Repair is then completed through the action of repair-specific DNA polymerases and the NHEJ ligation complex , which fill in and seal the break [1] . To analyze the selective pressures that have shaped the genes of the human NHEJ pathway , we generated sequence datasets of primate orthologs from twenty simian primate species . We find support for positive selection in five NHEJ genes: NBS1 , CtIP , Artemis , XRCC4 and POLλ . Analysis of human polymorphism data indicates that positive selection has also operated on XRCC4 in modern humans . Crystal structures are available for the Nbs1 , XRCC4 , and Polλ proteins , and in all cases we find that amino acid sites targeted by positive selection fall on protein surfaces . It is well-established that rapidly evolving amino acid residues tend to be found on the surfaces of proteins [12]–[14] . In previous studies where the significance of these residues has been structurally or functionally investigated , it has been shown that they modulate protein-protein , protein-ligand , or protein-DNA interactions [15]–[24] . However , we demonstrate biochemically that positive selection in Nbs1 at one of the three residues identified has not affected its physical interactions with other DNA repair components . In the discussion , we propose that the positive selection of NHEJ genes may be explained by the diverse viruses and genetic parasites that interact with these proteins to promote their own lifecycle .
We utilized primate sequence datasets to study the evolutionary history of human NHEJ genes . With human population genetic data , evolutionary pressures can usually only be summarized for chromosomal regions larger than a single gene . However , with inter-species divergence data , resolution of evolutionary signatures can be increased to the level of a single gene , and it is sometimes possible to see the serial fixation of mutations in particular gene regions or even codons . The limitation in these studies is the number of available primate sequences . We first performed a preliminary survey of the selective pressures that have shaped all of the major genes of the NHEJ pathway ( Figure 1A ) , so that we could generate appropriate primate datasets for candidate genes containing signatures suggestive of positive selection . Five nearly complete primate genome projects are publicly available: human , chimpanzee , orangutan , rhesus macaque , and marmoset . Ten possible pairwise gene comparisons can be made between these five species , but three pairwise comparisons ( human-orangutan , human-rhesus , and rhesus-marmoset ) were chosen that maximize divergence and minimize phylogenetic re-sampling ( Figure 1B ) . For each NHEJ gene , these three pairwise gene alignments were constructed and analyzed with a custom algorithm that calculates dN/dS in sliding windows along the length of each gene [25] . The dN/dS ratio captures the ratio of non-synonymous ( dN; changing the encoded amino acid ) to synonymous ( dS; silent ) DNA mutations that have accumulated since two genes last shared a common ancestor [26] . For most protein-encoding genes , the observed number of non-synonymous mutations is far less than the number of synonymous mutations observed ( dN/dS<1 ) [5] . This is because mutations which cause an alteration in amino acid sequence are more likely to be detrimental to proper protein folding and function , and are therefore typically selected against ( purifying selection ) . As expected in a typical gene , dN is less than dS ( dN/dS<1 ) for all windows along the length of the NHEJ gene KU70 ( Figure 1C ) . Under positive selection , non-synonymous mutations are swept through populations more quickly than neutral or nearly-neutral synonymous mutations due to a selectable advantage that they convey . After many such rounds , such a regime gives rise to the dN>dS signature that is indicative of positive selection ( dN/dS>1 ) . Sliding window analysis of dN/dS is useful when making pairwise gene comparisons , as positive selection may be limited to specific regions that are buried within a gene that is otherwise conserved . In the case of XRCC4 , sliding window analyses of human-rhesus and rhesus-marmoset pairwise alignments highlight the 3′ end of the gene as having signatures of dN/dS>1 ( p<0 . 001 and p<0 . 005 , respectively; Figure 1C ) . In this region , human and rhesus XRCC4 sequences differ by nine non-synonymous DNA mutations and zero synonymous mutations . In the human–orangutan comparison , a different region in the 5′ end of the gene shows a significant inflation of dN/dS above 1 ( p<0 . 05 ) . The different location of this signal may indicate a unique selective force that is operating specifically in the great apes . Sliding window analyses have an inherent multiple testing problem that is difficult to correct because of the non-independence of tests ( windows overlap ) [27] . Nevertheless , we have successfully utilized sliding window analysis as a pre-screening tool in several previous studies [2] , [28] . As an ad hoc method for eliminating some false positive signatures , we sought genes with regions of dN/dS significantly>1 in at least two out of three different pairwise primate comparisons made . All pairwise comparisons for each NHEJ gene are shown in Figure S1 , and the maximum dN/dS value found in each comparison is summarized in Figure 1D . We find that five out of thirteen NHEJ genes bear significant regions of dN/dS>1 in at least two out of the three primate comparisons made ( highlighted in gray in Figure 1D ) . Thus , we have identified preliminary signals of positive selection in five candidate NHEJ genes: NBS1 , Artemis , CtIP , POLλ , and XRCC4 . In order to verify positive selection with greater statistical rigor , larger sequence datasets are required . We sequenced all five candidate genes from 15 additional hominoid , old world monkey , and new world monkey species . Despite the fact that no significant windows of dN/dS>1 were observed in any of the pairwise comparisons of XLF ( Figure 1D ) , we also included this gene because positive selection was previously reported in an analysis of mammalian XLF sequences [29] . In total , 90 primate genes were sequenced ( 6 genes , each from 15 species ) . We also re-sequenced all genes that were incomplete in the available primate genome projects ( chimpanzee , orangutan , rhesus macaque , or marmoset ) . Details of primate cell lines , cell culture , mRNA extraction , cDNA library construction , and divergent-species PCR are given in the materials and methods section and in Tables S1 , S2 , S3 . The resulting dataset for each gene is comprised of orthologs from 20 primate species that represent approximately 35 million years of primate evolution [30] . The multiple sequence alignment generated for each gene was analyzed for positive selection with the “codeml” program in PAML [31] . The codeml program provides a maximum likelihood framework for estimating dN/dS rates over the entire history of primate evolution by integrating over all ancestral gene sequences in the context of a phylogeny [32] , [33] . This program offers several models for gene evolution , some where no codons are allowed to evolve with dN/dS>1 ( NSsites models M1a , M7 and M8a ) , and others where positive selection of some codons is allowed ( NSsites models M2a and M8 ) . A likelihood ratio test allows comparison of positive selection models to null models . Results of all model comparisons for each gene are provided in Tables S4 , S5 , S6 , S7 , S8 , S9 , and the results of the M8a vs . M8 comparisons , using the f61 model of codon usage , are summarized in Table 1 . The null model ( M8a ) is rejected ( p<0 . 05 ) in favor of the model of positive selection ( M8 ) in four of these six genes: CtIP , Artemis , XRCC4 , and POLλ . For NBS1 , the null model was very nearly rejected ( p = 0 . 056 ) . This analysis did not support a model of positive selection in primate XLF ( p = 0 . 59 ) . As mentioned above , sliding window analysis did not detect domains of positive selection in XLF . In conclusion , we find strong support for positive selection in four genes of the primate NHEJ pathway , a surprising finding given the critical role that these proteins play in DNA repair . Analysis of the 20-species NBS1 dataset yielded marginal support for positive selection ( p = 0 . 056; Table 1 ) . However , we noticed that several amino acid positions in the NBS1 protein alignment had changed multiple times exclusively in hominoid species ( humans , great apes , and gibbons ) . Based on this , we considered that positive selection of NBS1 may be specific to hominoids . Indeed , analysis of NBS1 from only the hominoid species resulted in improved statistical support for positive selection ( p = 0 . 048; Table 1 ) , despite the fact that the analysis of only eight sequences should greatly reduce statistical power . To formally test the hypothesis of hominoid-specific positive selection , we analyzed our datasets with “branch-site” models of evolution [34] . This test allowed us to determine whether there are codon positions evolving under positive selection specifically in the hominoid clade . NBS1 was the only one of the six NHEJ genes for which this hypothesis was supported ( p<0 . 005; Table S10 ) , and support is robust under all models of codon usage ( Table S11 ) . Because three total tests were performed on the NBS1 dataset , a Bonferroni-corrected p-value can be calculated for the rejection of the null hypothesis in the branch-sites test ( p<0 . 015 ) . Thus , hominoid-specific positive selection is supported in NBS1 . Interestingly , the yeast ortholog of NBS1 ( XRS2 ) was also identified as being under positive selection during Saccharomyces evolution [2] . Specific codon sites that have been the target of recurrent positive selection could be identified in the dataset for each NHEJ gene ( Table 1 ) . Posterior probabilities of codons included in the dN/dS>1 site class are commonly considered highly significant at cutoffs as low as P = 0 . 90 , and potentially even lower [35] . The positions of these amino acid sites are summarized in Figure 2 . Crystal structures have been solved for Polλ , XRCC4 , and Nbs1 , allowing us to further analyze the patterns of positive selection in these three proteins . Polλ is one of two DNA polymerases involved in the filling of gaps formed during NHEJ [36] . Approximately 5% of the codons in this gene were identified as evolving under positive selection , with an average dN/dS value of 3 . 2 ( Table 1 ) . Eight specific codons could be assigned to this class with high posterior probability ( P>0 . 90 ) , and these sites are scattered across the linear protein sequence ( Figure 2 ) . The crystal structure of the 39 kDa Polλ catalytic domain has been solved in complex with substrate DNA , and this catalytic core is comprised of the fingers , palm , thumb , and 8 kDa subdomains ( Figure 3 ) [37] . Four of the eight amino acid sites identified as being positively selected are part of this catalytic core domain . All four ( E330 , S381 , R441 , and R484 ) map to the outer surface of the three-dimensional structure ( red balls in Figure 3 ) , with none of the sites being found within the enzyme active site . Thus , residues under recurrent positive selection fall on the protein surface , and mutations at these sites are not predicted to directly affect catalytic activity . The NHEJ-specific ligase complex is composed of DNA ligase IV ( Lig4 ) along with the regulatory molecules XLF and XRCC4 [1] . The dN/dS>1 site class in XRCC4 is assigned a value of dN/dS = 15 , nearly double the value seen for any other NHEJ gene ( Table 1 ) . Given the extreme value , only one codon , L243 , can be supported as a member of this class with high posterior probability ( P>0 . 99 ) . To uncover more codons that may be evolving under positive selection , a secondary analysis was performed on the three XRCC4 structural domains: the N-terminal head domain , which is involved in DNA binding , the coiled-coil stalk domain , which includes the ligase binding domain , and the unstructured C-terminal domain ( residues 204–336 ) . Positive selection is supported only in the C-terminal domain ( p<0 . 001; Table 1 ) . Because four tests were performed on the XRCC4 dataset , the Bonferroni-corrected p-value for the observation of positive selection in the C-terminal domain is p<0 . 004 . In this domain , six codon sites , including L243 identified previously , were identified as evolving under positive selection ( P>0 . 90 ) , with support for five of these being P>0 . 95 . These codons were now collectively assigned a dN/dS value of 8 . 7 . All of these codons were also identified , albeit with lower confidence , in the full-length XRCC4 analysis ( Table S7 ) . The partial crystal structure of the XRCC4 dimer in complex with its binding partner , Lig4 , has been solved [38] . All six of the identified codons map just downstream of the Lig4-binding domain ( red dots in Figure 4A ) , in a region of the protein where the structure is predicted to transition from an alpha-helix to an unstructured domain . This unstructured domain is not included in the crystal structure , but has been represented in schematic form for illustration . Strikingly , of the five sites supported at the 95% confidence level , the first four ( R205 , Q211 , A216 , and C218 ) lie within a 14 amino acid stretch of the protein ( 4% of the length of the protein ) , and the fifth site ( L243 ) lies just 25 residues downstream of this cluster . We assessed the significance of this clustering on the linear protein sequence by determining how many times a random sampling of five sites fell in a cluster equal to or smaller than the 39 amino acid region that contains the sites under positive selection . Comparing this observed distance to a null distribution ( 100 , 000 permutations ) lends statistical support to the hypothesis that these positively selected sites are clustered ( p = 0 . 0005 ) . The functional significance of this “patch” of positive selection is unknown . A protein alignment of primate XRCC4 in this region is shown in Figure 4B . To the left , a cladogram shows the relationship of the twenty primate species used in this study . Amino acid positions evolving under positive selection are shown in the alignment in gray . This unstructured C-terminal domain has been shown to be dispensable for repair and V ( D ) J recombination [39] , [40] . However , this domain also contains a number of regulatory sites including a SUMOylation site and several DNA-PKcs phosphorylation sites [41] , [42] , as well as a known cancer-linked mutation [43] ( Figure 4B ) . We investigated whether the NHEJ genes that have been subject to ancient recurrent positive selection in simian primates are also under recent local adaptation in humans . We examined the five genes POLλ , XRCC4 , Artemis , NBS1 , and CtIP for signals of selection in the HapMap Phase II [44] data using a recently published method , the Composite of Multiple Signals ( CMS ) [45] . By combining multiple tests , CMS increases resolution for localizing signals of selection by up to 100-fold , and has a lower false-positive rate than the component individual tests . We examined SNPs within and surrounding each gene of interest , with a window size of 100kb upstream and 100kb downstream of each gene ( see Materials and Methods ) . In the European population , the CMS signal for XRCC4 is significant at a threshold that yields a 0 . 1% false positive rate in simulations , and is one of the top 60 strongest signals in the genome ( Table S12 ) . Applying CMS to fine-map the region , we localized the signal to 83kb entirely within the gene , suggesting that XRCC4 is a target of recent local adaptation ( Figure 5 ) . In the other four genes , we did not observe any signals significant at the same level as XRCC4 , but we do observe suggestive signals by the individual tests ( in the top 1–5% tail genome-wide ) in POLλ and XRCC4 in the West African population , and Artemis in the European population ( Table S12 ) . As CMS is optimized to detect recent local adaptation in a single population , these signals by individual tests may reflect selective events outside of this model ( e . g . , selection on standing variation , or selection of the same allele in multiple populations ) . Indeed , a single allele of POLλ has previously been reported to be under positive selection in both Asian and Sub-Sahara African populations [46] . Thus we find that several of the genes that have been evolving under positive selection during primate evolution also show evidence suggestive of recent positive selection in human populations , with an especially strong signature identified in XRCC4 . Nbs1 is part of the MRN complex , containing Mre11 , Rad50 , and Nbs1 . This complex is involved in DNA break detection , end processing , and cellular signaling [47] . Mutations in NBS1 lead to the autosomal recessive disease , Nijmegen breakage syndrome , which is characterized by chromosomal instability . Three amino acid positions were identified as evolving under positive selection ( Table 1 ) . G9 , Q185 , and I531 are identified with P>0 . 90 , with support for I531 being P>0 . 99 . A partial Nbs1 structure is available [48] , and two of the amino acid sites targeted by positive selection ( residues 9 and 185 ) fall on the protein surface ( Figure 6A ) . The third site , residue 531 , is not included in this partial structure . The positive selection of NHEJ genes suggests that certain mutations are providing a fitness advantage in an unknown context . While the essential DNA repair functions of these genes would be expected to remain conserved , there is a formal possibility that adaptive evolution of NHEJ genes could come at the cost of DNA repair . We wished to consider this hypothesis because a human SNP at a site of positive selection in NBS1 ( Q185E; SNP ID rs1805794 ) has been linked to increased risk of renal , skin , and lung cancer in multiple association studies [49]–[52] . This SNP is found at high frequencies in human populations ( Figure 6B ) . While Q185E has been linked to cancer , association studies are limited in that they may identify either a causal SNP , or a SNP that is linked to a causal SNP . We wished to test whether amino acid substitution in this codon changes the performance of Nbs1 in DNA repair , as the association with cancer might suggest . We constructed NBS1 alleles encoding either an E or a Q at position 185 , and expressed these proteins in insect cells using a baculovirus system . We then tested the effects of this mutation on several of the known activities of Nbs1 . The Nbs1 N-terminus , including the BRCT domain in which this SNP is located , is known to bind to the checkpoint protein Mdc1 [53]–[56] . We produced and purified MRN complexes containing both versions of Nbs1 and find that both interact equally well with purified Mdc1 in an in vitro binding assay ( Figure 6C ) . Thus the Nbs1 E/Q polymorphism is not expected to affect the association of MRN with Mdc1 at sites of DNA damage in vivo . The MRN complex is also required for the activation of the checkpoint protein ATM [57] , [58] . We find that MRN complexes containing both versions of Nbs1 are equally efficient in stimulating ATM-dependent phosphorylation of one of the downstream targets of ATM , p53 ( Figure 6D ) . Nbs1 is also known to bind XRCC4/Lig4 [59] and we find that both versions of Nbs1 interact equally well with this complex in vitro ( data not shown ) . Therefore , we conclude that positive selection of this codon , regardless of what is driving it , has not affected the repair-related physical interactions of Nbs1 . However , it should be noted that laboratory-based assays may not be sensitive enough to detect subtle defects that could cause a minor fitness effect in nature .
The NHEJ pathway is over 3 billion years old , and is found in bacteria , archaea , and eukaryotes . Despite the ancient conservation of the pathway , we have identified five NHEJ genes that have evolved under positive selection during the evolution of simian primates: NBS1 , CtIP , Artemis , XRCC4 , and POLλ . An analysis of polymorphism data supports positive selection of XRCC4 in modern humans as well . Interestingly , the yeast ortholog of NBS1 ( XRS2 ) was also identified as one of the two Saccharomyces NHEJ genes with the most extreme signatures of positive selection [2] . One hypothesis is that these signatures of positive selection are reflective of natural selection for more efficient DNA repair . As certain NHEJ components evolve , compensatory mutations may arise in other NHEJ components to re-optimize protein-protein interactions between the various components . We feel that this model is unlikely . In the absence of an antagonizing force , there is no reason that recurrent adaptive change should be required of any member of this pathway , which would then need to be followed by compensatory change . Four observations from our study additionally argue against this model . First , our biochemical experiments with Nbs1 suggest that positive selection of at least one of the three sites identified has not altered interactions with other repair proteins . Second , although there are several core complexes involved in NHEJ ( the MRN complex and the Lig4/XRCC4/XLF complex ) , only one component of each of these was identified as evolving under positive selection . Third , the clustered sites of positive selection in XRCC4 fall within the C-terminal protein domain that is not essential for DNA repair . Fourth , the positive selection of the NHEJ pathway is not a primate specific phenomenon , but is also found in Saccharomyces yeast [2] , arguing against a model where some novel role for DNA repair during primate evolution has driven this selection . The finding of multiple primate NHEJ components evolving under positive selection , supported by parallel findings in Saccharomyces yeast , indicates a systematic perturbation of the NHEJ pathway . With positive selection observed in two highly divergent eukaryotic clades , a model for the cause of this rapid evolution must span such diverse species groups . We propose that NHEJ genes may be antagonized by genetic parasites , which in primates are comprised of viruses and retrotransposons . Proteins of the NHEJ repair pathway have been shown to act as antiviral factors in the lifecycle of human adenovirus , a linear double-stranded DNA virus . Adenoviruses are a major cause of upper respiratory and other infections in humans . During infection , components of the NHEJ pathway join together viral genome ends , causing “dead-end” viral genome concatenation [60] . To counteract this antiviral tactic , adenovirus proteins ( encoded by the E4 genes ) sequester and target for degradation a number of components of the NHEJ pathway , including components of the Mre11/Rad50/Nbs1 and Lig4/XRCC4/XLF complexes [60]–[63] . CtIP has also been implicated in the adenovirus lifecycle through its interaction with the adenovirus early region 1A ( AdE1A ) protein [64] . If primate NHEJ genes are continually selected to encode variants that can evade interaction with these adenoviral antagonists , while the viral antagonists continually counter-evolve , this could drive positive selection of primate NHEJ genes . Adenovirus has been found in stool samples from great apes and macaques [65] , indicating a possible long-standing co-evolution between this virus and primates . Retroviruses like HIV may also provide the selective pressure that shapes the recurrent positive selection of NHEJ genes . There is abundant genetic evidence suggesting a role for NHEJ in the retroviral lifecycle [66]–[70] . Upon cellular entry , the retroviral RNA genome is reverse transcribed into double-stranded DNA . The ultimate destination for this retroviral cDNA is integration into the genome of the host , but it must first survive passage through the nucleus without being detected as broken DNA by the cell . NHEJ proteins have been found to physically associate with retroviral proteins , cDNA , and pre-integration complexes in vivo and in two-hybrid interactions [67] , [71]–[74] . There are several models which have been proposed to explain this . In one model , NHEJ proteins are recruited by the viral complex to protect free viral cDNA ends from degradation or from triggering apoptosis . In another model , the viral complex recruits host NHEJ proteins to promote the repair of breaks created at sites of retroviral cDNA integration into the host genome . In a third model , NHEJ proteins act as antivirals , joining the two long-terminal repeat ( LTR ) ends of the viral cDNA into dead-end “2-LTR circles . ” These 2-LTR circles are ubiquitously observed in the nuclei of infected cells [67] . Regardless of the model , allelic variants of NHEJ genes that result in lower infection rates would be selectively advantageous to the host . Should such alleles go to high frequency or fixation , retroviruses would be expected to counter-evolve , and the back-and-forth interplay would drive recurrent positive selection of NHEJ genes . Retroviruses and primates have co-evolved for tens of millions of years , as illustrated by the fact that all sequenced primate genomes contain the remnants of hundreds of thousands of integrated retroviruses [75] . It is unknown whether the positive selection observed in NHEJ genes represents a response to a single selective force , or whether multiple forces are shaping their evolution . At least eight additional viral families have been shown to evade or exploit the host DNA damage response [76] . Several NHEJ proteins include one or more “BRCT” domains , which have been linked to viral infection in multiple instances . The Epstein-Barr viral protein Zta has been shown to interact with the BRCT domains of 53BP1 , a component of the DNA damage response , to prevent apoptosis that is activated in response to viral replication [77] . HIV-1 Tat has also been shown to interact with the BRCT domain of the human replication protein FCP1 [78] . In both Polλ and Nbs1 , we find an amino acid position at the C-terminal end of the BRCT domain to be evolving under positive selection ( Q185 in Nbs1 and Q102 in Polλ ) . The single site found to be under positive selection in Saccharomyces Xrs2 also falls near the end of the BRCT domain ( site 298 ) [2] . BRCT domains could be a critical link in the interaction between viruses and the NHEJ pathway . Antagonism of host NHEJ proteins by genetic parasites may be a universal feature of cellular life , as yeast Ty retrotransposons also interact genetically and physically with NHEJ machinery [79] , [80] . LINE-1 retrotransposons are major drivers of primate genome evolution , and LINE-1 retrotransposition rates are reduced in the absence of NHEJ genes [81] . The Corndog and Omega bacteriophages of mycobacteria have even incorporated the first gene in the bacterial NHEJ pathway , Ku , into their own genome [82] . This viral Ku now evolves under the selective pressures of the virus in order to recruit the bacterial NHEJ ligase , LigD , to circularize phage DNA . In summary , we have documented abundant signatures of positive selection in genes of the NHEJ pathway , which is the major pathway for repairing double-strand chromosomal breaks in mammalian cells . We propose the hypothesis that these signatures result from the long-term co-evolution between NHEJ genes and genetic parasites . While it is well known that genetic parasites shape genome architecture through insertion and subsequent inter-element recombination , the present study may indicate that selective pressures imposed by genetic parasites can drive the evolution of protein sequence in critical human proteins .
Chimpanzee , orangutan , rhesus macaque , and marmoset gene sequences were obtained from the UCSC genome database ( http://genome . ucsc . edu/ ) using the BLAT alignment tool [83] . NBS1 , CtIP , Artemis , XRCC4 , POLλ , and XLF were sequenced from 15 additional primate species , and poor-quality regions of chimpanzee , orangutan , rhesus and marmoset genes were also re-sequenced . Primary and immortalized primate cell lines ( sources and individual primate identifiers are listed in Table S1 ) were grown in standard media supplemented with 15% fetal bovine serum at 37°C and in 5% CO2 . Total RNA was harvested from cell lines using the AllPrep DNA/RNA kit ( Qiagen ) . PCR was performed from total RNA and/or cDNA with OneStep RT-PCR kit ( Qiagen ) or PCR SuperMix High Fidelity ( Invitrogen ) , respectively . Details of the PCR and sequencing strategy , along with primer sequences , can be found in Tables S2 and S3 . Primate NHEJ gene sequences have been deposited in GenBank ( accession numbers HM486750–HM486849 ) . Alignments between orthologous gene pairs were performed using ClustalX2 . 0 [84] . Sliding-window dN/dS calculations for each alignment were performed with the SLIDERKK program [25] . Human-orangutan , human-rhesus and rhesus-marmoset alignments were analyzed with standard window sizes of 450bp , 306bp and 153bp , respectively , to reflect the increasing level of divergence in these species pairs ( window size must be a multiple of nine in this program ) [2] , [28] . In order to generate confidence values for windows with dN/dS>1 , the K-estimator program [85] was utilized to generate a null distribution through Monte Carlo simulation of randomly derived dN/dS values in the gene region of interest . Multiple alignments were created with ClustalX2 . 0 [84] . Maximum likelihood analysis was performed with codeml in the PAML 4 . 1 software package [31] . To detect selection , multiple alignments were fitted to the NSsites models M1a ( neutral model , codon values of dN/dS are fit into two site classes , one with value between 0 and 1 , and one fixed at dN/dS = 1 ) , M2a ( positive selection model , similar to M1a but with an extra class of dN/dS>1 allowed ) , M7 ( neutral model , codon values of dN/dS fit to a beta distribution , dN/dS>1 disallowed ) , M8a ( neutral model , similar to M7 except with a fixed codon class of at dN/dS = 1 ) and M8 ( positive selection model , similar to M7 but with an extra class of dN/dS>1 allowed ) . Simulations were run with multiple seed values for dN/dS ( ω ) and assuming either the f61 or f3x4 model of codon frequencies . Likelihood ratio tests were performed to assess whether permitting codons to evolve under positive selection gives a significantly better fit to the data ( model comparisons M1a vs . M2a , M7 vs . M8 , M8a vs . M8 ) . In situations where the null model could be rejected ( p<0 . 05 ) , posterior probabilities were assigned to individual codons belonging to the class of codons with dN/dS>1 . Residues under positive selection were mapped onto existing crystal structures using MacPyMol ( v . 0 . 99; http://pymol . sourceforge . net/ ) . The branch-site test allows identification of positive selection that might be limited to a subset of codons along only a subset of the branches being analyzed [34] . To implement this test , multiple alignments were fitted to the branch-sites Model A ( positive selection model , codon values of dN/dS along background branches are fit into two site classes , one ( ω0 ) between 0 and 1 and one ( ω1 ) equal to 1 , on the foreground branches a third site class is allowed ( ω2 ) with dN/dS>1 ) , and Model A with fixed ω2 = 1 ( null model , similar to Model A except the foreground ω2 value is fixed at 1 ) . Hominoids were defined as the “foreground” clade , with all other branches in the tree being defined as background branches . The likelihood of Model A is compared to the likelihood of the null model with a likelihood ratio test . Simulations were run with multiple seed values for dN/dS and assuming either the f61 or f3x4 models of codon frequencies . The “Fequal” codon model was also tested in the branch-site analysis of NBS1 . To test the significance of clustering of the codons under positive selection in XRCC4 , the statistical program R was utilized to perform a permutation test . The observed span of the positively selected codons on the primary sequence was compared with a null distribution created by calculating the span resulting from randomly generated sets of equivalent numbers of codons . We generated 100 , 000 random distances . To examine evidence for recent positive selection in humans , we implemented a previously published method that combines multiple tests for selection , the Composite of Multiple Signals ( CMS ) [45] . We have adapted the method to detect genomic regions under selection by examining the fraction of high scores in 100kb sliding windows . To determine the significance threshold , we used the cosi coalescent simulator to simulate 1 , 000 1MB autosomal regions , evolving neutrally under a previously validated demographic model [86] . We set thresholds that yielded a 0 . 1% false positive rate in simulations . Two long-haplotype tests , XP-EHH and iHS , were used to examine evidence for selection in or around the genes of interest . iHS was calculated as described in [10] for all SNPs with a minor allele frequency greater than 5% . iHS was analyzed independently in the European ( CEU ) , East Asian ( JPT and CHB ) , and West African ( Yoruban; YRI ) populations . XP-EHH was calculated as in [9] for the each of the three populations . For each SNP , we found the maximum score of the comparisons with the two other populations . In each 100kb window along the gene regions , the fraction of SNPs with |iHS|>2 or the maximum XP-EHH score was used as the test statistic . To calculate empirical P-values for each window w , we calculated the test statistics for each 100kb window across the genome and found the fraction of genomic windows with values of the test statistic greater than that found for window w . The ancestral state for each SNP was determined by comparison to the chimpanzee genome . We calculated Fst for each SNP in the regions using the Weir-Cockerham estimator [87] . Three pairwise comparisons were made between the African ( Yoruban ) , European , and East Asian populations . For each population , we compared the allele frequency in that population to the average frequency in the other two populations . For each 100kb window across the region , the maximum Fst was used as the test statistic . To generate the null distribution , we performed the same procedure on each 100kb window in the genome and derived an empirical p-value based on this distribution . A biotinylated human MRN ( E185 ) complex was expressed in a baculovirus system from the transfer vectors pTP11 ( Rad50 ) , pTP814 ( Mre11 ) , pTP1014 ( Nbs1 ) , and pTP1016 ( BirA ) as described earlier [88] . To make biotinylated human MRN ( Q185 ) complex , the E to Q point mutation at Nbs1 position 185 was introduced into pTP994 , whose bacmid form is pTP1014 , by primer-based mutagenesis ( QuikChange Kit , Invitrogen ) . Flag-tagged Mdc1 ( amino acids 1–740 ) was expressed using bacmid construct pTP1188 , which was made from the corresponding transfer vector pTP1187 . Expression constructs for Flag-tagged and HA-tagged ATM were gifts from M . Kastan and R . Abraham . The E . coli expression construct for GST-p53 was described earlier [89] . Purification procedures for the biotinylated MRN complex were the same as for the non-biotinylated MRN complex as described earlier [90] . Dimeric ATM was made by transient transfection of expression constructs into 293T cells using calcium phosphate and purified as described earlier [91] . Mdc1 ( aa 1–740 ) was expressed in Sf21 insect cells using the Bac-to-Bac system ( Invitrogen ) and was purified identically to 53BP1 as described earlier [88] . The GST-p53 was purified identically to the GST–Brca1 fragments as described earlier [92] and was further purified by separation on a Superdex 200 gel filtration column ( GE ) in buffer A ( 100 mM NaCl , 25 mM Tris pH8 , 10% glycerol , and 1 mM DTT ) . Protein concentrations were determined by quantification of protein preparations with standards on colloidal Coomassie-stained SDS–PAGE gels using the Odyssey system ( LiCor ) . 20 nM biotinylated MRN complex was incubated with 45 nM Mdc1 ( aa 1–740 ) in buffer A for 1 hour at 30°C in a final volume of 100 µl , then incubated with streptavidin-coated magnetic beads ( Dynal ) and 0 . 2% CHAPS ( Sigma ) while rotating at 4°C for 15 min . Beads with associated proteins were washed three times with buffer A containing 0 . 2% CHAPS , and bound proteins were eluted by boiling the beads in SDS loading buffer . Proteins were analyzed by SDS–PAGE and western blotting using antibodies directed against the Flag epitope ( Sigma , F3165 ) and Nbs1 ( Genetex , MSNBS10PX1 ) . ATM kinase assays were performed with 0 . 2 nM dimeric ATM , 50 nM GST–p53 substrate , and varying amounts of MRN complex ( concentrations of MRN = 1 . 25 , 2 . 5 , 5 , and 10 nM ) . Kinase assays were performed in kinase buffer ( 50 mM HEPES , pH 7 . 5 , 50 mM potassium chloride , 5 mM magnesium chloride , 10% glycerol , 1 mM ATP , 1 mM DTT , and 10 ng DNA ) for 90 min at 30°C in a volume of 40 microliters as described earlier [91] . Phosphorylated p53 ( ser15 ) was detected as described earlier [91] using phospho-specific antibody from Calbiochem ( PC461 ) . | Because all cells experience DNA damage , they must also have mechanisms for repairing DNA . When the proteins that repair DNA malfunction , mutation and disease often result . Based on their fundamental importance , DNA repair proteins would be expected to be well preserved over evolutionary time in order to ensure optimal DNA repair function . However , a previous genome-wide study of molecular evolution in Saccharomyces yeast identified the non-homologous end joining ( NHEJ ) DNA repair pathway as one of the two most rapidly evolving pathways in the yeast genome . In order to analyze the evolution of this pathway in humans , we have generated large evolutionary sequence sets of NHEJ genes from our primate relatives . Similar to the scenario in yeast , several genes in this pathway are evolving rapidly in primate genomes and in modern human populations . Thus , complex and seemingly opposite selective forces are shaping the evolution of these important DNA repair genes . The finding that NHEJ genes are rapidly evolving in species groups as diverse as yeasts and primates indicates a systematic perturbation of the NHEJ pathway , one that is potentially important to human health . |
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Francisella tularensis , the etiological agent of the inhalation tularemia , multiplies in a variety of cultured mammalian cells . Nevertheless , evidence for its in vivo intracellular residence is less conclusive . Dendritic cells ( DC ) that are adapted for engulfing bacteria and migration towards lymphatic organs could serve as potential targets for bacterial residence and trafficking . Here , we focus on the in vivo interactions of F . tularensis with DC following airway infection of mice . Lethal airway infection of mice with the live vaccine strain ( LVS ) results in trafficking of a CD11bhigh/CD11cmed/autofluorescencelow DC subset from the respiratory tract to the draining mediastinal lymph node ( MdLN ) . Simultaneously , a rapid , massive bacterial colonization of the MdLN occurs , characterized by large bacterial foci formation . Analysis of bacteria in the MdLN revealed a major population of extracellular bacteria , which co-exists with a substantial fraction of intracellular bacteria . The intracellular bacteria are viable and reside in cells sorted for DC marker expression . Moreover , in vivo vital staining experiments indicate that most of these intracellular bacteria ( ∼75% ) reside in cells that have migrated from the airways to the MdLN after infection . The correlation between DC and bacteria accumulation in the MdLN was further demonstrated by manipulating DC migration to the MdLN through two independent pathways . Impairment of DC migration to the MdLN , either by a sphingosine-1-phosphate receptor agonist ( FTY720 ) or by the D prostanoid receptor 1 agonist ( BW245C ) , resulted in reduced bacterial colonization of MdLN . Moreover , BW245C treatment delayed the onset of morbidity and the time to death of the infected mice . Taken together , these results suggest that DC can serve as an inhabitation niche for F . tularensis in the early stages of infection , and that DC trafficking plays a role in pathogen dissemination . This underscores the therapeutic potential of DC migration impairing drugs in tularemia treatment .
Dendritic cells ( DC ) are a heterogeneous group of antigen presenting cells ( APC ) , which reside in peripheral tissues , and serve as sentinels for invading microorganisms . Most often , contact of DC with microorganisms results in engulfment of the microorganism , and triggers a series of programmed events ( maturation ) . These include surface expression of MHC class II and of co-stimulatory molecules , induction of cytokine secretion , and degradation of the internalized pathogen , allowing for surface-presentation of its antigens to naïve T cells [1] . The encounter between DC and T cells is facilitated by the induced trafficking of activated DC from the periphery to the vicinal draining lymph nodes . The mechanisms that control DC trafficking appear to be complex . Modulation of the surface display of chemokine receptors ( up regulation of CCR7 expression and down regulation of other receptors ) plays a key role in this process [2] , [3] , [4] . In addition , other effectors including prostaglandins [5] and sphingosine-1-phosphate [6] also play a critical role in DC migration . F . tularensis , the etiological agent of tularemia , is a zoonotic pathogen with a broad host range . Disease manifestations depend upon the route of infection and include ulceroglandular infection , respiratory infection and a typhoidal disease [7] . The high infectivity of certain F . tularensis strains via inhalation led to their classification as a Category-A threat agent . Two subspecies of F . tularensis , F . tularensis subsp . tularensis ( type A ) and F . tularensis subsp . holoarctica ( type B ) are highly infectious to humans yet only the type A bacterium is life-threatening [7] , [8] . Most research on the pathogenesis of F . tularensis has been performed on mice using the live vaccine strain ( LVS ) derived from F . tularensis subsp . holoarctica . This organism shows attenuated virulence in humans but is lethal to mice following pulmonary infection [9] . F . tularensis strains are capable of infecting and multiplying in a variety of cultured cells ( reviewed in [10] ) . The mechanisms used by the pathogen to escape the phagocytic pathway and grow inside cells have been the subject of intense research [11] , [12] , and specific proteins required for intra-macrophage growth were identified . These include members of the iglABCD operon [13] , [14] and the pdpA and pdpD genes contained within a Francisella pathogenicity island ( reviewed in [15] , [16] ) . In many cases mutant strains that are impaired in intra-macrophage growth were also identified as attenuated in murine infection models [12] , [17] . All this led to the accepted notion that intracellular growth is a key mechanism used by F . tularensis to escape host defense . Nevertheless , the direct body of evidence for intracellular growth in vivo is rather meager . Intracellular LVS bacteria were identified by immunostaining in lungs of infected mice [18] and PCR analyses suggest that practically all F . tularensis LVS organisms of bacteremic mice are cell-associated [19] . Nevertheless , the association between LVS and blood cells was recently contested by showing that the majority of F . tularensis recovered from blood of infected mice reside in the plasma [20] . Thus , in spite of the increased efforts in this direction , the various manifestations of the interaction of F . tularensis with host cells , and the role of these interactions in tularemia pathogenesis are far from being resolved . In the present study we have focused on the interactions of F . tularensis LVS with DC in vivo . We show that airway infection by LVS is followed by bacterial colonization of the mediastinal lymph node ( MdLN ) , which occurs in parallel to recruitment of respiratory tract DC ( RTDC ) from the airways to this draining lymph node . Moreover , administration of two different DC migration inhibitors impairs bacteria accumulation in the MdLN , and not less importantly , affects the course of disease . Finally , we show that cells with characteristic DC phenotype that have immigrated to the MdLN from the respiratory tract carry viable intracellular F . tularensis .
Several aspects of bacteria/DC interactions can affect the dynamics of dissemination in the infected host . Among the most relevant ones are cell inhabitation by bacteria and the ability to trigger DC trafficking . These two features were examined in an in vitro LVS-infection system , using bone marrow DC ( BMDC ) as target cells . In accordance with a previous report [21] LVS was found to replicate in BMDC as efficiently as in the macrophage-like J774A . 1 cells ( Figure 1A ) . Pulsing of host cells with bacteria ( even at an MOI as high as 200 ) led to an inefficient cell uptake ( ∼5×104 bacteria were taken up by 106 cells ) . Yet , once bacteria are taken-up by J774A . 1 cells or BMDC , intracellular propagation appeared to be very efficient , resulting in increase of more than two orders of magnitude within 24 hrs . Induction of maturation responses by F . tularensis in APC was examined previously , revealing complex interactions , involving activating as well as inhibitory effects [21] , [22] , [23] . Acquisition of migratory properties is one of the aspects of bacteria-triggered DC maturation and could , in principal , be also subjected to inhibitory effects by LVS . As a first step in examining the effect of LVS/DC interactions on cell migration we have examined surface expression of CCR7 , a receptor involved in DC trafficking from the periphery to lymph nodes [4] , [24] . Flow cytometry analysis of pulsed BMDC revealed a notable increase of CCR7 display ( Figure 1B and 1C ) . As expected , LPS of E . coli , which served as positive control , led to an effective CCR7 induction . Killed LVS bacteria were also found to be effective , though to a lesser extent , in inducing surface display of CCR7 , attesting to the stimulatory potential of LVS pathogen associated molecular patterns ( PAMP ) . Most importantly , live bacteria were as effective as killed bacteria in promoting CCR7 display , arguing against potential abrogating effects exerted by the live pathogen . This is in contrast to the effect of LVS on other markers of DC activation such as TNF-α secretion , where induction occurs with killed bacteria and is abrogated by live bacteria ( not shown ) . It is also interesting to note that display of certain co-stimulatory surface molecules , such as CD83 and CD40 is affected by the viability of LVS ( Figure 1C ) . However , display of other molecules ( e . g . , CD86 ) appears to be efficient following pulsing by live as well as killed bacteria . As an additional step in evaluating DC migratory properties , we have used an in vitro transmembrane chemotaxis assay . BMDC were exposed to various stimulants for 24 hrs and then allowed to migrate through a nylon mesh towards the CCR7-ligand CCL19 ( Figure 1D ) . As controls , we have used non-treated cells and cells exposed to E . coli LPS . Pulsing DC with E . coli LPS led to effective induction of migration towards CCL19 ( 6 folds increase over background ) . Pulsing with LVS bacteria resulted also in induction of cell migration ( about 4 . 5 folds over background ) . Migration appears to be , at least partly , CCR7-dependent since migration in presence of CCL19 is higher than in its absence ( Figure 1D ) . Again , no differences were found between the effects of killed and live bacteria , suggesting that LVS does not exert adverse effects on DC movement induction . Taken together , the in vitro analyses suggest that DC have the potential to serve as replication niches , as well as transport vehicles for LVS . This prompted us to examine these functions in vivo . The interrelationship between F . tularensis and DC in vivo was examined by using a mouse model for respiratory tularemia . As a first step , we have examined the kinetics of LVS dissemination from the respiratory tract to host organs . Mice were infected by intranasal administration of 105 CFU of LVS , which is equivalent to ∼100 LD50 ( LD50 = ∼1000 CFU , our results as well as those of others [9] ) leading to death within 5–6 days . Intranasal administration of LVS resulted in robust proliferation of the bacterium in the airways ( Figure 2A ) , confirming earlier reports [25] , [26] . Bacterial counts in lungs increased by at least three orders of magnitude within 2 days and remained constant as long as mice were alive ( Figure 2A ) . As expected , immediately after infection , bacteria were localized in the broncholaveolar lavage fluid ( BALF ) . In the following days bacterial counts in the lung were divided equally between BALF and lung tissue , and during the late days of infection the relative amount of BALF bacteria was somewhat lower ( Figure 2A ) . Monitoring mediastinal lymph node ( MdLN ) colonization , 24 hrs pot infection revealed viable bacteria , at levels as high as 104 CFU/MdLN ( Figure 2A ) . This number increased to about 106 CFU/MdLN on the second day and then remained unchanged . The accumulation of bacteria in the spleen and liver lagged behind their accumulation in the MdLN . Levels comparable to those found in MdLN at 24 hrs were reached in these organs 48 hrs post infection ( Figure 2A ) . Taken together , these results underline the role of the draining lymph nodes in respiratory LVS infection . MdLN colonization begins very early post airway infection . It occurs immediately after colonization in the primary infection site ( lung ) , and precedes the spreading of the pathogen to distant organs such as spleen and liver ( Figure 2A ) . The rate of LVS accumulation in the lymph node is very rapid , close to two orders of magnitude within 24 hours , resembling LVS propagation in tissue cultures at optimized conditions ( compare Figure 1A and Figure 2A ) . It is interesting to note that the maximal number of organ-associated bacteria in MdLN is 106 CFU/organ , compared to 107CFU/organ in spleens and livers , which are about 100 folds larger in size . To better characterize the robust colonization of the draining lymph nodes by LVS , we searched by light microscopy for bacterial localization in cryo-sections of MdLNs 72 hrs post airway infection . This led to identification of distinct bacteria-containing infection foci in LVS infected MdLNs . Most notable are rare ( 1–2 per cross section ) , yet very expanded foci carrying a large number of bacteria ( Figure 2B ) . The average number of LVS per cross section of such a focus was found to be 100–200 ( such large clusters could not be revealed 24 hrs post infection ) . Simple geometrical calculations suggest the presence of more than 103 bacteria per one global focus . This morphological feature implies an in-situ clonal expansion of bacteria that have entered the lymph node at an early stage . Thus , bacterial accumulation in the MdLN is not a mere reflection of bacterial influx from other organs . It should be noted that at the same time point ( 72 hrs post infection ) , one could find also much smaller foci ( not shown ) that could have been formed by bacteria entering the MdLN at a later stage . Taken together , our observations suggest that colonization of the draining lymph node is a major step in bacterial dissemination , following airway infection with LVS . Once the kinetics of bacterial spreading was defined , we have examined the cellular events related to respiratory infection of mice by LVS . We have focused on phagocytic cells of the monocytic lineage , and restricted the study to the early infection sites: the respiratory tract and the MdLN . In general , the respiratory tract monocytic phagocytes can be defined by surface display of two markers , CD11c and CD11b [4] , [27] and can be differentiated into alveolar macrophages and pulmonary DC by virtue of their autofluorescence ( AF ) level . Macrophages are defined by high AF , whereas pulmonary DC exhibit low AF [28] . Analysis of single cell suspensions derived from the MdLN , two days post intranasal infection revealed the presence of cells that are CD11cmed/CD11bhigh ( Figure 3A , top ) , of cells that are CD11cmed/AFlow ( Figure 3A , center ) , as well as of cells that are CD11bhigh/AFlow ( Figure 3A , bottom ) . These three populations are represented at comparable levels ( ∼7% ) , attesting to the presence of a CD11cmed/CD11bhigh/AFlow cell population in the MdLN . The appearance of such cells in the MdLN is infection-dependent , since background levels of this population are ∼10 times lower in non-infected animals ( Figure 3A , left panels ) , as well as in mock infected mice ( PBS instillation , not shown ) . The phenotype of the CD11cmed/CD11bhigh/AFlow cell identified in the MdLN matches accurately the phenotype of an identical population , identified in lung tissue ( plotting of CD11b vs . AF is shown in Figure 3B ) . The appearance of this population in the lung is not dependent on infection . Analysis of cells derived from the BALF revealed this same population , but also revealed a distinct population of CD11bmed/AFhigh cells ( Figure 3C ) which happens to be also CD11chigh ( not shown ) . The phenotype of these CD11chigh/CD11bmed/AFhigh cells and their presence in the BALF prompted us to define them as alveolar macrophages ( AMΦ ) . The CD11cmed/CD11bhigh/AFlow population , on the other hand , was defined as a subset of respiratory tract DC ( RTDC ) , relying on previous characterizations of such populations [4] , [28] . The definition of these cells as DC is further supported by surface expression of MHC class II ( not shown ) . The kinetics of RTDC recruitment to the MdLN following infection with 105 CFU of LVS is marked by a burst in cell influx on day 2 ( Figure 4 ) reaching levels close to 105 RTDC per lymph node . This is followed by a decline at later days . The representation of RTDC cells on the first day post infection with 105 CFU was not higher than background levels measured in non-infected mice ( Figure 4 ) . Nevertheless , when infection dose was increased from 105 to 107 CFU , one could clearly detect recruitments of RTDC , as early as 24 hours post infection ( not shown ) . In order to define the origin of the DC imported to the MdLN , we have labeled the respiratory tract cells by vital staining [29] . The orange cell tracer CMTMR was administered to mice by intranasal instillation , five hours prior to intranasal infection by LVS . MdLN cells were isolated on the next day , and examined for presence of CMTMR and for DC-marker expression . In this protocol we have used an infection dose of 107 CFU , which allowed early monitoring of cell trafficking . Flow cytometry analysis revealed MdLN cells carying CMTMR , which also express CD11c ( Figure 5A and 5B ) . One such cell population which is CMTMR+/CD11cmed exhibits a ∼4 fold increase following infection , indicating migration of CD11cmed cells from the respiratory tract to the draining lymph node ( Figure 5A and 5B ) . This migration is substantiated by another set of experiments indicating that the CD11cmed cells accumulating in the infected MdLN display on their surface CCR7 ( not shown ) . To further characterize the immigrating cells we have fractionated MdLN cells by magnetic sorting ( MACS ) , using beads coated with anti-CD11b antibodies . Bound and non-bound cell fractions were then analyzed for presence of CMTMR and for surface display of CD11b or CD11c ( Figure 5C–5F ) . Sorting by anti CD11b-coated beads resulted in the expected enrichment for cells expressing CD11b but at the same time in enrichment for cells stained by CMTMR ( Figure 5C ) . Actually 27% of the cells in the CD11b-sorted population were highly positive for both CMTMR and CD11b , as opposed to ∼2% in the non-bound fraction ( Compare Figure 5C and 5E ) . Sorting by CD11b also resulted in a comparable enrichment for cells that were CD11c positive ( Figure 5D and 5F ) , leading to 23% representation of CMTMR+/CD11c+ cells ( Figure 5D ) . Moreover , closer examination of the sorted cells reveals a correlation between high uptake of the viable dye and intermediate expression of CD11c . The viable staining experiments provide indication to the trafficking of cells expressing both CD11c and CD11b from the respiratory tract to the draining lymph node in the infected animal . This result together with the observed accumulation of RTDC in the infected MdLNs attests to an LVS-induced trafficking of DC in vivo . The in vivo trafficking of both viable LVS ( Figure 2 ) and of DC to the MdLN ( Figure 5 ) as well as the in vitro infection results ( Figure 1 ) , led us to search for association between host cells and LVS in the infected lymph node . As a first step in evaluating the localization of bacteria in the MdLN cells , we have subjected single-cell suspensions , obtained 48 hours post intranasal infection , to a gentamicin protection test . About 10% of the bacteria found in the cell suspensions survived gentamicin treatment ( Figure 6A ) , suggesting that this bacterial population is localized within cells and is therefore not accessible to the antibiotic . The other approach used to identify cell-associated bacteria was based on differential centrifugation and provided similar results . Mild spinning ( 200 g , 10 min ) resulted in a supernatant containing ∼90% of the MdLN bacteria and a cell pellet containing ∼10% of the bacterial content ( Figure 6A ) . To characterize the cells that carry LVS , we have subjected washed MdLN cell suspensions to magnetic cell sorting by beads conjugated to anti-CD11b antibodies ( Figure 6B , top panel ) . Fractionated cells were then analyzed for association with LVS . The amount of viable bacteria , per a given number of anti-CD11b bound cells was found to be 1000 folds higher than that in the unbound fraction ( Figure 6B , top ) . Moreover , the actual amount of bacteria in the unbound fraction is in good agreement with the residual amount of ∼0 . 5% CD11b+ cells found in this fraction ( data not shown ) . Altogether this suggests a specific association between LVS and CD11b+ cells in the infected lymph node . Flow cytometry analysis of the CD11b-MACS sorted cells revealed that , as expected ( Figure 3 and Figure 5 ) , bound cells display on their surface CD11c in addition to CD11b . This led us to sort infected MdLN cells by anti-CD11c microbeads as well ( Figure 6B , bottom panel ) . Results of this fractionation were very similar to those obtained by CD11b MACS sorting ( compare top and bottom panels of Figure 6B ) . The amount of viable bacteria , per a given number of anti-CD11c bound cells was found to be ∼500 folds higher than that in the unbound fraction ( Figure 6B , bottom ) . Thus , LVS appears to associate with CD11b+ as well as CD11c+ cells . In order to determine if the observed bacteria/cell association reflects intracellular residence of LVS , CD11b sorted cells from infected MdLNs were subjected to gentamicin protection tests ( Figure 6C ) . The number of viable bacteria was not affected by pretreatment with gentamicin , indicating that LVS indeed resides inside CD11b+ cells . Moreover , when gentamicin-treated cells were lysed with saponin , the number of measurable viable counts was higher by ∼4 folds , indicating presence of several bacteria within one cell . As expected , lysis of cells prior to antibiotic treatment , relieved cell protection , and rendered all bacteria sensitive to gentamicin . To substantiate the intracellular localization of bacteria in DC of infected lymph nodes , the CD11b sorted cells , as well as CDb11c sorted cells were stained with antibodies specific to LVS . Microscopic analysis revealed cells carrying LVS ( Figure 6B , insets ) . These cells were not very abundant; nevertheless , in almost all cases more than one bacterium was associated with a single cell . Taken together , the results presented in Figure 6 reveal that viable LVS bacteria reside within MdLN cells that exhibit an RTDC phenotype . To examine the origin of the CD11b-sorted , LVS-carrying cells , we have resorted to in vivo vital staining . Cells lining the respiratory tract were stained with CMTMR , as described above ( Figure 5 ) , animals were sacrificed 2 days later , and single cell suspensions derived from the infected MdLNs were first subjected to flow cytometric analysis ( Figure 7A ) . The bound fraction contained , as expected , a substantial amount of fluorescent cells , about 20% of which exhibited fluorescence levels higher then background autofluorescence . These and were therefore defined as CMTMR stained cells ( Figure 7A ) . Only trace amounts of this population were present in the non-bound fraction . Sorted cells were mounted on chamber slides , and were screened by fluorescence microscopy for the presence of the intracellular orange CMTMR staining , as well as presence of cell-associated bacteria . This resulted in identification of CMTMR+/LVS+ cells , CMTMR+/LVS− cells , and CMTMR−/LVS+ cells ( representative cells are shown in Figure 7B ) . The relative distribution of these cell types ( Figure 7C ) revealed a strong linkage between CMTMR staining and presence of intracellular bacteria . Screening of 1000 cells which were not bound to CD11b , failed to reveal any CMTMR+ cells , and revealed only two CMTMR− LVS-carrying cells . Screening of 2200 CD11b-bound cells revealed 220 cells that carried CMTMR at levels sufficient to allow microscopic detection . Of these CMTMR+ cells , as much as 38% were found to carry bacteria . In contrast , only 2 . 5% of the CD11b-bound cells , which were not stained by CMTMR were found to carry bacteria ( Figure 7C ) . To further define the intra-cellular residence of LVS , we counted the number of bacteria in the individual cells . The average number of bacteria in the CMTMR+ cells was found to be 5 . 2±3 . 5 , and that in the CMTMR− cells 2 . 8±1 . 9 . When the total number of bacteria residing in these two cell populations is calculated ( 440 in CMTMR+ cells and 140 in CMTMR− cells ) , it becomes evident that as much as 75% of the bacteria associated with CD11b-sorted cells reside within newly imported cells . While the actual number of bacteria per cell in the CMTMR− population does not seem to be very different from that in the CMTMR+ population ( 2 . 8 vs 5 . 2 ) , the statistical significance of the difference is very impressive ( P<10−5 , Figure 7C ) . This implies that the events leading to bacterial residence in these two cell populations are different . Taken together , these results attest to preferential bacterial residence in cells that have recently immigrated from the airways to the MdLN . These results also imply that two cell-infection mechanisms take place during airway infection; one occurs in the airway at the early stages of infection , and the other occurring later on , either in the airways or in the lymph node . The residence of intracellular bacteria in RTDC , and more specifically in newly-immigrated MdLN cells suggests that DC play a role in F . tularensis trafficking . To further examine this point , the effect of RTDC migration-impairment on bacterial dissemination was tested using FTY720 ( fingolimod ) , a sphingosine analogue . FTY720 counteracts the functions of the sphingosine-1-phosphate ( S1P ) , and thereby impairs cell migration [30] . FTY720 inhibits lymphocyte egress from lymphoid tissue , and was also found to inhibit migration of lung DC to the MdLN [6] . FTY720 in its carrier solution , or carrier solution alone were administered intranasally to mice , concomitantly to infection . MdLNs were analyzed two days later by flow cytometry . The MdLN of infected , mock-treated , animals carried the distinct CD11bhigh/autofluorescencelow cell population ( Figure 8A ) , which defines RTDC recruitment ( Figure 3 ) . In mice treated with FTY720 , this population was brought down to background levels ( Figure 8B and 8C ) , indicating that FTY720 impairs very effectively the LVS-triggered RTDC migration to the MdLN . The FTY720 treatment resulted also in a substantial reduction in the LVS counts in the MdLN ( Figure 8D ) . Mock-treated mice carried 1 . 3×106±0 . 9×106 live LVS in the MdLN . This number was reduced by two orders of magnitude in FTY720 treated mice , reaching values of 1 . 1×104±0 . 5×104 . Thus , administration of a S1P analogue to the airways of LVS infected mice impairs effectively recruitment of RTDC to the MdLN , as well as colonization of this organ , implying that these two events are linked . To substantiate the observed correlation between DC and LVS trafficking , we examined the effects of an additional inhibitor of DC-migration , which is known to act through a different mechanism . We have chosen BW245C , a D prostanoid receptor 1 ( DP1 ) agonist , known to inhibit in vivo migration of Langerhans cells [31] , [32] as well as airway DC [5] . BW245C was administered by two intranasal instillations , one immediately prior to LVS inoculation , and the other 24 hours later . The effect of agonist administration on recruitment of DC and bacteria to the MdLN was examined 48 hours post infection . The BW245C treatment resulted in a marked reduction in the representation of RTDC in the infected lymph node ( Figure 9A ) . While ∼7×104 of the MdLN cells exhibited the RTDC phenotype in LVS infected mice , DC representation in infected mice , treated with BW245C was reduced to ∼2×104 . It should be noted that background levels in non-infected mice , either treated or not treated with the agonist , remained ∼0 . 6×104 . The effect of BW245C on the infection-dependent DC recruitment was accompanied by a significant impairment of MdLN colonization by LVS ( Figure 9B ) . The average number of viable bacteria in the draining lymph nodes 48 hours post airway infection was 480 . 000±180 . 000 . This number was reduced to 75 . 000±21 . 000 upon treatment with BW245C . It should be noted that treatment had no effect on the number of bacteria or of DC in the lung , excluding possible bacteriostatic/bacteriocidic or cytotoxic effects of the drug in vivo . Taken together , these results provide additional evidence to the linkage between the accumulation of RTDC and of viable bacteria in the draining lymph nodes following infection . To examine the effect of the DP1 agonist on the course of experimental respiratory tularemia , infected mice were subjected to a short treatment by intranasal instillation of BW245C as described above . Animals were monitored for survival in comparison to infected , sham-treated ( instillation of carrier only ) animals . Results presented in Figure 9C indicate that the short treatment ( day 0 and day 1 ) resulted in a delay of one day in the time to death of mice . Mean time to death without treatment was 6 . 4 , as compared to 7 . 5 following treatment . This difference appears to result from a treatment-dependent delay in the onset of the disease manifestations . Monitoring of animal morbidity revealed significant differences ( Figure 9D ) in the kinetics of weight loss in treated vs . non-treated mice . The first day is marked by a moderate weight loss in all animals , which stems from the instillation procedure itself , as the same loss is also observed in treated non-infected mice . Nevertheless the disease-induced weight loss starts after day 1 in infected non-treated animals and after day 2–3 in treated animal ( Figure 9D ) . Similar experiments , conducted with FTY720 were marred by the intrinsic long-term pleotropic effects of this agonist on lymphocyte mobilization [30] . A single FTY720 instillation into infected animals resulted in a somewhat prolonged mean time to death , but did not alleviate weight loss ( not shown ) . In summary , treatment with a DP 1 agonist , like treatment with an S1P analogue has a marked effect on DC recruitment to the draining lymph node and on bacterial colonization of the lymph node . This treatment also delays the time point at which disease becomes apparent , and the time of death of the infected animals .
DC play a key role in surveillance for pathogen invasion through skin and mucosal epithelium . DC are highly adapted for sampling , killing and processing microbes , and for trafficking the processed material to regional lymph nodes for antigen presentation . Impairment of certain DC functions by pathogens has been addressed by numerous studies during the last years ( reviewed in [33] ) , yet much less is known about the specific interplay between DC migration and microbial infection . One can envision two ways by which manipulation of DC migration could enhance microbial invasiveness . DC/pathogen interaction could impair DC migration to the draining lymph node [34] , [35] , and thereby interfere with the onset of immune responses . Alternatively , hijacking of the DC migration process by intracellular pathogens and its adaptation for dissemination of live microbes [36] , [37] could provide an advantageous mechanism for subverting the immune system , and at the same time facilitate the progress of infection . In the present study we examined the dynamics of DC trafficking in mice infected with F . tularensis LVS . It is widely accepted that bacterial PAMP trigger migration of DC through TLR-mediated induction of surface display of the chemokine receptor CCR7 . The ability of live LVS to trigger this process is not self-evident . LVS carries on its surface an atypical LPS which is limited in its capacity to interact with cellular TLRs [38] , [39] , [40] , and could therefore be handicapped in triggering signal transudation involved in cell trafficking . Moreover , LVS can actively abrogate activation processes within immune cells , as indicated by the observed effect of live LVS on expression of certain co-stimulatory DC markers ( Figure 1C ) , as well as on expression of pro-inflammatory cytokines ( our unpublished results and those of others [22] , [23] ) . In spite of all this , pulsing of BMDC in vitro with LVS resulted in induction of notable , surface expression of functional CCR7 ( Figure 1B and 1D ) . To examine manifestations of these migratory properties in vivo , a mouse model based on airway infection of mice by LVS was employed , and migration of DC from the infection site to the draining lymph node was analysed . Identification of such DC is complicated by the unique features of respiratory tract APC . Several populations of monocytic phagocytes were identified in the different anatomical compartments of lung [27] , [41] , and moreover , APC in the pulmonary tract exhibit unusual phenotypes . On one hand , CD11b which is considered to be a typical marker for macrophages , is low in alveolar macrophages yet appears on certain pulmonary DC . On the other hand , CD11c which is routinely used as marker to identify DC in peripheral lymphoid organs is also present on pulmonary macrophages ( reviewed in [42] , [43] , [44] ) . In an attempt to define RTDC populations affected by LVS we have examined lungs of infected mice for expression of CD11c and CD11b and identified two major integrin-expressing populations . One population that is present only in the BALF is characterized by high expression of CD11c , low expression of CD11b and high autofluorescence ( Figure 3B ) and thus exhibits the characteristics of alveolar macrophages . The other major population can be defined as a RTDC subset ( Figure 3A and 3B ) , as it exhibits low autofluorescence [28] , [45] , high CD11b expression [4] , [46] , [47] , intermediate level of CD11c [48] , [49] and also high surface expression of MHC class II ( not shown ) [50] . The phenotypic definition of the relevant cell populations ( Figure 3 ) , prompted us to search for cell trafficking to the MdLN . This led to the identification ( Figure 4 ) of infection-dependent recruitment of RTDC to the draining lymph node , with no apparent recruitment of AMΦ . Moreover , our experiments suggest that cells have been imported from the infected respiratory tract . The phenotype of the cells matches accurately the phenotype of DC identified in lung tissue and BALF ( Figure 3 ) , and cells were shown to carry a vital dye ( CMTMR ) , instilled into the respiratory tract prior to infection ( Figure 5 ) . Taken together our findings indicate that in vivo interactions between F . tularensis and DC can induce the signaling process leading to cell migration from the infection site to the draining lymph node . This observation substantiates the prevailing notion that DC but not macrophages are the primary migratory APC following invasion of the airways . We show that such a migration is induced not only by soluble antigens [50] and inert particles and spores [4] , [51] , but also by viable respiratory pathogens . Moreover , the DC that take part in this process are identified as a specific subset of CD11cmed/CD11bhigh RTDC . The CD11bhigh DC-subset was recently distinguished functionally from the CD11blow subset , and was associated with directing leukocyte trafficking during lung inflammation [37] , [46] , [52] . Another part of this study was dedicated to bacterial spreading following airway infection with LVS ( Figure 2A ) . We show that infiltration of MdLN by bacteria is a very early event ( occurring during the first day of infection ) , resulting in a massive load of bacteria accumulating in the lymph node , at a very high rate ( Figure 2A ) . These results , which characterize infection by LVS as well as by the Schu4 strain [22] suggest that colonization of draining lymph nodes is an important stage in bacterial spreading . Moreover , microscopic analysis of MdLN from LVS infected mice at the late stages of infection reveal large foci of infection carrying tens of bacteria , suggesting in situ replication of a clonal nature , i . e . single bacteria infiltrating the tissue at early stages have replicated in the lymph node to generate a large condensed focus . All this underlines the major role played by tropism for lymphatic organs in F . tularensis pathogenesis . It is tempting to speculate that the invading bacteria are carried into the MdLN by immigrating RTDC . The rate of bacterial spreading from the infected airways actually supports such a model . Bacteria can be detected in MdLNs at substantial amounts , as early as 24 hrs post infection , and this correlates well with short time frames reported for transport of soluble or particulate antigen from the airways to the draining lymph nodes in mice [4] , [50] , [51] , [53] . Nevertheless , comparison of the kinetics of bacterial inhabitation of the MdLN to those of DC recruitment reveals that bacteria are detected in the lymph node at least one day prior to a notable increase in DC numbers ( compare results of infection with 105CFU in Figure 2A and Figure 4 ) . One should note , however , that the sensitivity of detecting bacterial import is much higher than that for detecting DC import . One can easily detect the presence of several CFU in the MdLN , whereas detection of newly immigrating DC over a background of ∼1% representation is difficult ( Figure 3A and Figure 4 ) . Actually , one cannot preclude the possibility that LVS infection triggered the immigration of ∼103 DC ( ∼10% of the observed background ) on the first day of infection and that these cells are instrumental in spreading the infection . Indeed when intranasal infection dose was increased from 105 to 107 CFU newly immigrating DC were identified 24 hrs post infection ( Figure 5 ) , indicating that early recruitment does occur , and suggesting that the extent of recruitment is dependent on the bacterial load in the respiratory tract . As a first step in defining the correlation between LVS and DC accumulation in the draining lymph node , we have identified intracellular inhabitation of LVS in MdLN cells . LVS-carrying cells could be enriched by magnetic sorting for expression of CD11c as well as CD11b . The similar efficiency of the two sorting processes ( Figure 6B and 6C ) in retrieving cell-associated bacteria suggests that LVS-containing cells express CD11c as well as CD11b on the same cell . CD11c and CD11b expression , which characterizes monocytic phagocytes is not exclusive to DC , yet presence of such cells in MdLNs following airway infection would argue for their definition as RTDC [4] . The cell-associated bacteria were found to be viable , as indicated by their ability to form colonies and are located within the cells , as indicated by their resistance to antibiotics ( Figure 6C ) . These observations provide one of the more solid indications to the localization of live F . tularensis in cells of the infected host , and identify the RTDC as an in vivo niche for pathogen residence . The fact that most cells carry several intracellular bacteria probably reflects intracellular replication of LVS , rather than simultaneous uptake of several bacteria by an individual cell . While MdLN DC appear to carry LVS , one cannot overlook the presence of a large extracellular bacterial load in the infected MdLN ( Figure 6A ) . This finding underlines the long and unresolved debate on the interrelationship between the intracellular and the extracellular life styles of F . tularensis , and their relevance to pathogenesis . While extracellular bacterial trafficking per se cannot be excluded , one can envision an infection mechanism that involves uptake of LVS by DC in the airways [18] , [21] and import of bacteria-harboring DC into the MdLN followed by apoptosis-mediated ( our unpublished results and those of others [14] ) release of bacteria from the cells . Such a model would imply an initial stage of intra-DC residence required to overcome early host defense mechanisms , which is then followed by an extracellular colonization process , and possibly re-infection of more cells A major support for the function of RTDC as transporters of LVS comes from the in vivo viable staining experiments ( Figure 7 ) . Staining of infected airway cells with CMTMR allowed us to determine that a major fraction of the CD11b-sorted , LVS-carrying cells are newly-immigrating cells , recruited from the respiratory tract to the MdLN after infection ( Figure 7 ) . Moreover enumeration of intracellular bacteria revealed that the about 75% of the bacteria carried by CD11b-sorted cells actually reside in the newly-immigrated cells . The preferential presence of intracellular LVS in newly-immigrating RTDC ( Figure 7C ) provides very strong support to DC-mediated , LVS trafficking . In order to address the less-likely possibility of preference in LVS uptake by the newly-immigrating within the MdLN , we have treated mice with inhibitors of DC migration . Trafficking of DC from the infection site is governed by a variety of mechanisms . The most characterized mechanisms relate to controlled change of the surface-displayed chemokine receptors repertoires . Other mechanisms involve sphingosine-1-phosphate [6] , [54] , scavenger receptor A [55] , osteopontin [56] and prostaglandins [5] , [42] . It should be noted however that these mechanisms do not act exclusively on DC migration , and often exhibit pleotropic effects . This led us to examine two independent experimental systems . One system is based on FTY720 , which desensitizes the S1P receptor , and thereby inhibits lymphocyte migration [30] . The other system is based on BW254C , a prostaglandin D1 analogue which interacts with the cognate DP1 receptor to inhibition of DC migration [5] , [42] . Choosing these two effectors allowed us to use a drug that hampers positive migration signals , as well as an agonist that activates a physiological migration-impairing signal . In spite of the major difference in their mode of action , treatment with either FTY720 or BW245C at the early stages of infection resulted in similar effects , impairment of both RTDC recruitment and LVS colonization of the MdLN ( Figure 8 and Figure 9 ) . Interestingly , the effect of FTY720 treatment on RTDC migration-impairment was more pronounced than that of BW245C ( Compare Figure 8A and 8B and Figure 9A ) . This was translated into a more effective inhibition of bacterial dissemination by FTY720 than by BW245C ( Compare Figure 8D and Figure 9B ) , providing additional support to the linkage between RTDC and LVS trafficking . Instillation of each one of the migration-impairing agents to airways of infected mice prolonged disease progression . We chose , however , to concentrate on BW254C which is a drug of short-residence , and not on FTY720 which has long term effects , and therefore could influence later stages of disease . Instillation of the BW254C on days 0 and 1 of infection delayed the onset of morbidity by two days , and delayed the time of death by one day ( Figure 9C and 9D ) . This again argues for a DC-driven bacterial spreading . Taken together , all the observations presented here suggest that F . tularensis can inhabit DC in vivo , and DC migration can play a role in enhancing the invasiveness of the pathogen . In addition , this study provides guidelines to the development of a novel potential therapeutic strategy against respiratory tularemia . A treatment , based on impairment of DC trafficking could delay the onset of disease and provide an adequate window for the identification and the application of a suitable antibiotic treatment .
Francisella tularensis live vaccine strain ( ATCC 29684 ) stocks were plated on GCHI agar ( GC Medium base , Difco , supplemented with 1% hemoglobin and 1% Iso-Vitalex BD , France ) . Working stocks were prepared from single individual colonies exhibiting the large-light phenotype [57] . For cell and animal infection experiments , bacteria were grown at 37°C to mid log phase ( optical density of 0 . 1–0 . 2 at 660 nm ) in TSBC ( TSB Difco , supplemented with 0 . 1% cysteine ) in a gyrostatory shaker . Bacteria were washed and then re-suspended at the desired concentration in either PBS for animal infection experiments , or RPMI 1640 medium ( see below ) , devoid of antibiotics , for cell infection experiments . Killed bacterial suspension were generated by incubating growth cultures , overnight , at room temperature in the presence of 0 . 4% formaldehyde , followed by extensive washing with PBS . BMDC were generated essentially according to the method developed by Lutz et al . [58] as described in detail previously [34] . More than 95% of the cells were CD11c positive as assessed by flow cytometry , indicating effective differentiation into DC . BMDC as well as the J774A . 1 macrophage–like cells were grown in RPMI 1640 medium supplemented with 10% FCS , 2 mM L-glutamine , 1 mM sodium pyruvate , 1% v/v MEM-EAGLE non-essential amino acid solution , 100 U/ml penicillin , 100 µg/ml streptomycin and 50 µM β-mercaptoethanol; all these components were supplied by Biological Industries ( Beit Haemek , Israel ) . BMDC ( 106 cells/well in 24 well plates in supplemented RPMI devoid of antibiotics ) were infected by pulsing with bacteria at an MOI ( multiplicity of infection ) of 200 . Infection was initiated by spinning ( 140 g , 5 min ) bacteria onto plated cells ( this is referred to as time point 0 of infection ) . Pulsed cells were incubated at 37°C , 5%CO2 for 1 h before adding gentamicin ( Sigma ) at a concentration of 2 µg/ml . Incubation was then continued for additional 23 hours prior to analysis . In an alternative protocol , gentamicin-containing medium was replaced by fresh gentamicin-free medium after 2 hrs . This had no effect on bacteria counting . It should be pointed out that extracellular LVS did not propagate in the eukaryotic-cell growth medium , as noted by others as well [13] , [59] . Infected cells were resuspended in RPMI containing 1% FCS to a concentration of 107 cells/ml and were placed ( 106 cells/well ) in the upper compartments of Transwell migration chambers ( Costar 3421 , Corning , NY ) . CCL19 ( R&D Systems ) diluted to concentrations of 200 ng/ml in RPMI+1% FCS was placed in the lower compartments to induce CCR7-dependent chemotaxis . Medium alone served to evaluate CCR7-independent migration . DC were allowed to migrate through a polycarbonate ( 5 µm pore size mesh ) at 37°C for 2 h , and cells migrating to the lower compartment were counted under light microscope . The experiments were performed in triplicates and migration was determined by calculating the percentage of migrating cells relative to input . Lipopolysaccharide ( LPS ) pulsing was conducted by application of 1 µg/ml of Escherichia coli LPS ( Sigma ) . C57BL female mice ( 8–10 weeks old ) were used in most infection experiments . Some experiments were repeated using BALB/c mice as indicated . Mice anesthetized with ketamine/xylazine were infected intranasally by careful application of 25 µl LVS suspension at the desired concentration . Bronchoalveolar lavages were performed on anesthetized mice through a 23-gauge catheter inserted into the trachea . Doses of 1 ml cold PBS were infused into each mouse and then aspirated from the airways . Animals were sacrificed by cervical dislocation and the desired organs were collected . Isolated organs were minced and treated with Liberase Blendzyme 3 ( Roche ) at final concentration of 2 µg/ml for 30–60 minutes at 37°C , followed by five minute treatment with 100 U/ml of Dnase I ( Boehringer Mannheim ) . This was followed by passage through a cell strainer to obtain single cell suspension . All experiments reported here were conducted in compliance with the guidelines of the animal use committee at the Israel Institute for Biological Research and are in accordance with the Animal Welfare Act . FTY720 and BW245C were both purchased from Cayman chemicals ( Ann Arbor , MI ) . FTY720 was diluted to a concentration of 40 µg/ml in PBS containing 10% ethanol . Twenty-five µl of the FTY720 solution ( final amount of 1 µg ) were administered to anesthetized mice by intranasal instillation at day zero ( 15 min prior to bacterial infection ) BW245C was diluted to a concentration of 0 . 4 mM in 0 . 3% DMSO in water , immediately prior to use . Twenty-five µl of the agonist solution ( final amount of 10 nmol ) were administered to anesthetized mice by intranasal instillation on day zero and on day 1 post infection . Carriers alone were distillated into mock treated animals Infected cell suspensions were submitted to centrifugation ( 140 g , 10 min ) , followed by two washes with PBS , in order to remove non-associated bacteria . When indicated , cells were incubated at 37°C for 30 min in the presence of 20 µg/ml gentamicin , followed by centrifugation and washes for gentamicin removal . Cells were lysed with either 0 . 1% deoxycholate ( DOC ) for 2 minutes or alternatively with 0 . 1% saponin for 30 minutes at 37°C . Bacterial suspensions were submitted to intensive mixing prior to serial dilution and plating on GCHI agar . Washed DC were incubated with PE or APC conjugated antibodies against CD11c ( Clone N418 ) , CD11b ( clone M1/70 ) , CD40 ( clone 1C10 ) , CD83 ( clone Mitchel-17 ) and CD86 ( clone GL1 ) , as well as with the appropriate isotype-matched control antibodies . Reagents were purchased from eBioscience ( San Diego CA ) . Staining was performed using standard incubation protocols ( 30 min at 4°C ) except for CCR7 staining which was conducted at 37°C as indicated by the manufacturer . In all staining protocols , FcR was blocked prior to staining with FCR Block Reagent ( Miltenyi Biotech , Germany ) , according to the manufacturer's protocol . Cells were collected on a FACSCalibur cytometry ( Becton & Dickinson ) , excluding events smaller than 200 on the FSC . Target cells were separated from the lymph node single cell suspension by magnetic microbeads carrying either anti-CD11c or anti CD11b antibodies ( Miltenyi Biotech , Germany ) , using the manufacturer's instructions . Prior to separation , cell suspensions were treated with the FcR Block Reagent ( see above ) . About 108 cells ( pool of 8–10 lymph nodes ) were then incubated with 100 µl of the microbead suspension for 15 min on ice , and washed to remove excess of free beads . Cells were passed through a column attached to a magnet and washed to separate bound and unbound cells . Out of 108 cells submitted to sorting , about 106 cells were bound to either anti-CD11c or anti-CD11b microbeads Migration of cells from the airways to the peripheral lymph node was evaluated by tracking cells labeled by viable staining [29] , [34] . Briefly , the orange cell tracer CMTMR ( Molecular Probes , OR ) was diluted in RPMI to a concentration of 8 mM and 30 µl/mouse were administered intranasally to mice anesthetized with ketamine/xylazine . Five hours later mice were instilled with 5×106 bacteria per mouse ( 25 µl of bacterial suspension in PBS ) . At the indicated time post addition of bacteria , MdLNs were isolated , shredded and treated with 2 µg/ml Collagenase-D ( Roche ) for 30 min at 37°C . Cells were then passed through a 70 µm nylon mesh to yield a single-cell suspension , and analyzed by FACS . Tissues or cells infected by LVS were examined for the presence of bacteria by fluorescent staining . J774A . 1 cells were cultivated and infected in chamber slides and fixed with 3 . 7% formaldehyde . DC suspensions derived from bone marrow or from lymph nodes were placed in LabTekII chambered glass slides which are capable of binding semi-adherent cells ( Nunc , Roskilde , Denmark ) . Cells were incubated for 20 minute at 37°C to allow binding . The bound cells were fixed with 3 . 7% formaldehyde in PBS for 30 min , followed by washing with PBS . Dissected MdLNs were infiltrated with OCT ( Sakura , The Netherlands ) , snap frozen in liquid nitrogen and transferred to storage at −70°C . Eight-micrometer cryostatic sections were cut and mounted on SuperFrost Plus slides ( Menzel-Galaser , Germany ) . Slides were then fixed by dipping in cold acetone ( −20°C ) for 1 min . Fixed slides were blocked with either 0 . 5% BSA in PBS in the case of infected cell preparations , or with Rodent Block M ( Biocare Medical , CA ) , for infected tissue . Hyper-immune antiserum of rabbits immunized with formalin killed LVS diluted 1/300 served as primary antibody , while normal rabbit serum served as negative control . Texas-Red or FITC-conjugated goat anti rabbit antibodies ( Molecular Probes , OR ) served as second antibody . Stained specimen were examined with an Axiovert 200 inverted microscope ( Zeiss ) equipped with an AxioCAM MRc5 digital camera ( Zeiss ) . | The high infectivity of Francisella tularensis via inhalation led to its classification as a Category-A bio-threat agent and renewed the interest in this pathogen . Here , we characterize early events in respiratory tularemia , which could be instrumental in designing new therapeutic approaches . We focus on the interaction of F . tularensis with dendritic cells , which serve as first-line sentinels for invading bacteria and are expected to be pivotal in initiation of host protective response . In this study , we show that lethal airway infection of mice with F . tularensis results in accumulation of both bacteria and dendritic cells in the draining lymph node , and that viable bacteria can be detected in dendritic cells that have been recently imported from the airways . The correlation between trafficking of dendritic cell and bacteria is further substantiated by demonstrating that impairment of dendritic cell migration to the draining lymph node through two independent pathways results in decreased bacterial accumulation in the lymph node . Taken together , our observations suggest that F . tularensis actually harnesses dendritic cells to facilitate bacterial dissemination and to enhance host invasion . These findings call for examination of the therapeutic potential of drugs that impair dendritic cell migration as countermeasures for tularemia . |
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TNF-alpha plays an important role in trypanocidal mechanisms and is related to tissue injury . This cytokine has been detected in the heart of human chagasic patients where it is associated with tissue damage . This study investigated whether TNF-alpha levels and the presence of genetic polymorphisms are associated with the presence of T . cruzi infection and/or with the development of the cardiac form in chronic chagasic patients . Genomic DNA of 300 subjects from an endemic area was extracted and analyzed by PCR using specific primers . TNF-alpha was assayed in culture supernatants by ELISA . An association was observed between the absence of the TNF-238A allele and negative serology . Furthermore , seropositive individuals carrying the TNF-238A allele produced significantly higher TNF-alpha levels without stimulation ( p = 0 . 04 ) and after stimulation with LPS ( p = 0 . 007 ) and T . cruzi antigens ( p = 0 . 004 ) . The present results suggest that the polymorphism at position -238 influences susceptibility to infection and that this allele is associated with higher TNF-alpha production in seropositive individuals .
Chagas disease is an important chronic infection caused by the protozoan Trypanosoma cruzi . The disease continues to be a major public health problem in most Latin America countries , affecting around 9 million people [1] . In addition , Chagas disease is an emerging health problem in non-endemic areas because of the increasing migration of individuals [2] . Chagas disease has two successive phases . The acute phase is usually asymptomatic or characterized by the presence of fever , discomfort , tachycardia and high parasitemia [3] , [4] . Manifestations of the acute disease resolve spontaneously in about 90% of infected individuals even if the infection is not treated with trypanocidal drugs . About 60–70% of these patients will never develop clinically apparent disease . These patients have the indeterminate form of chronic Chagas disease , which is characterized by positivity for antibodies against T . cruzi in serum , a normal 12-lead electrocardiogram ( ECG ) , and normal chest , esophagus and colon exams . The remaining 30–40% of patients will subsequently develop a determinate form of chronic disease: cardiac , digestive ( megaesophagus and megacolon ) , or cardiodigestive [2] . Geographic variations exist in the severity and prevalence of the clinical forms of Chagas disease , but the reasons for this clinical and epidemiological heterogeneity are unknown [5] . Possible causes include variations in the genetic constitution of the host , especially genes related to the immune system since the latter is involved in the control of parasitism and in tissue injury . In Chagas disease , like in other parasitic diseases , the causal factor ( i . e . , the infectious agent ) is necessary but often insufficient for clinical manifestation of the disease [6] . The complexity of the host-parasite relationship in Chagas disease suggests the involvement of different components of the immune system . The genes for tumor necrosis factor-alpha ( TNF-alpha ) and lymphotoxin-alpha , which are located in the MHC III region on chromosome 6 , are closely linked to the HLA class I and class II genes [7] . TNF-alpha , which is mainly produced by monocytes and activated T cells , plays an important immunoregulatory role [8] . This cytokine contributes to the pathogenesis of Chagas disease due to its role in both trypanocidal mechanisms and tissue injury . Production of this cytokine at high levels has been demonstrated in experimental models during the acute phase [9] and its function as an inducer of iNOS is important for the control of parasite growth [10] , [11] . TNF-alpha has been detected in the heart tissue of experimentally infected animals [12] , as well as in inflammatory exudate cells of human chagasic myocarditis [13] . These findings suggest that individual differences in TNF-alpha production may be responsible for the variation among individuals . These differences are the result of polymorphisms present in the general population . Polymorphisms in the promoter region of the TNF-alpha gene have been known for a long time and might be involved in the control of the expression of this gene [14] , [15] . The presence of these polymorphisms has been associated with susceptibility to certain inflammatory and infectious diseases [16]–[18] . Recent studies have investigated the role of TNF-alpha gene polymorphisms in Chagas disease [19]–[22] , but none of those studies evaluated a population from an endemic area or used the clinical assessment criteria established here . The present study investigated TNF-alpha production by peripheral blood cells and whether the presence of G substitutions at positions -238 and -308 are associated with the presence of T . cruzi infection and/or the development of the cardiac clinical form in chronic chagasic patients .
The study was performed in the municipality of Água Comprida ( 20°3′23″S , 48°6′31″W at 540 m above sea level ) , situated in the Vale do Rio Grande , southern region of Triângulo Mineiro , Minas Gerais State , Brazil . The town was endemic for Chagas disease and was included in the first National Campaign against T . cruzi that started in 1975 . Epidemiological and entomological data demonstrated the interruption of vector transmission of the parasite to humans in 1999 , and an international commission certified Brazil to be free of transmission in 2005 [23] , [24] . A total of 300 unrelated individuals agreed to participate in the study . Only subjects who were 25 years or older were included ( mean age: 51 . 2±14 . 4; range: 25–91 years ) , since they corresponded to the youngest seropositive individuals of the sample studied . Serological screening for anti-T . cruzi antibodies showed that 25 . 6% of these subjects were positive and 74 . 5% were negative [25] . All HIV-seropositive individuals were excluded from the study . The treatment criteria did not exclude any patient . The study was approved by the Research Ethics Committee of Universidade Federal do Triângulo Mineiro , Brazil ( protocol 343 ) . All individuals provided written informed consent . The presence or absence of T . cruzi infection was evaluated by passive hemagglutination ( Salck Laboratory , São Paulo , Brazil ) , enzyme-linked immunosorbent assay ( Abbott , Brazil ) and indirect immunofluorescence using fluorescein isothiocyanate ( FITC ) -conjugated to rabbit anti-human IgG ( Sigma ) . The assays were performed according to manufacturer instructions and the results are expressed quantitatively . A subject who presented at least two positive tests was defined as positive [25] . Patients infected with T . cruzi were submitted to clinical examination , electrocardiography and chest , esophagus and colon contrast X-ray exams for classification into the cardiac , digestive , mixed , or indeterminate form [26] , [27] . Patients with the cardiac form were classified according to the Criteria Committee of the New York Heart Association [28] . Appropriate statistical analysis was not possible because of the small sample size . Peripheral blood samples ( 20 mL ) were collected with a vacuum system using heparin as anticoagulant . Peripheral blood mononuclear cells ( PBMC ) were separated by density centrifugation on a Ficoll-Hypaque gradient ( Pharmacia ) according to manufacturer recommendations . After separation , the cells were washed by centrifugation in RPMI medium ( Gibco ) and resuspended in RPMI medium supplemented with 5% fetal bovine serum ( Gibco ) , 2 mM L-glutamine ( Gibco ) , 50 mM 2-mercaptoethanol ( Merck ) , and 40 µg/mL gentamicin . PBMC ( 2×106 cells/well ) were cultured in the presence of 5 µg/mL T . cruzi ( strain Y ) antigens , 2 µg/mL Salmonella typhimurium lipopolysaccharide ( LPS ) ( Sigma ) , and 5 µg/mL phytohemagglutinin ( PHA ) ( Sigma ) . The plates were incubated at 37°C in a 5% CO2 atmosphere for 48 h . The culture supernatants were collected and stored at −70°C . For TNF-alpha titration , microplates ( Nunc ) were sensitized overnight with anti-TNF-alpha mAb ( Pharmingen ) . Nonspecific binding was prevented by incubating the plates with 2% BSA ( Sigma ) in PBS . The plates were incubated overnight with 100 µL of the culture supernatants in PBS diluted 1∶2 , 2% BSA , and recombinant human TNF-alpha ( Pharmingen ) . The plates were then washed four times with PBS and 0 . 05% Tween 20 and incubated with biotinylated anti-TNF-alpha mAb ( Pharmingen ) for 2 h , followed by washing and incubation for 2 h with streptavidin-conjugated alkaline phosphatase . Finally , the plates were washed four times and enzymatic activity was developed by incubating the plates with p-nitrophenyl phosphate ( Sigma ) . Absorbance was read at 405 nm in a microplate reader ( BioRad ) . The sensitivity limit of the test was 10 pg/mL . The mass of red and white blood cells was submitted to osmotic lysis using Tris-EDTA lysis buffer ( 20∶5 ) consisting of 1 M Tris-HCl and 0 . 5 M EDTA , pH 8 . The samples were centrifuged at least three times at 1334×g for 15 min at controlled room temperature ( approximately 27°C ) . For DNA extraction , the samples were treated by two different techniques depending on the amount of red blood cells remaining in the leukocyte pellet to guarantee the best quality DNA . A commercially available kit ( DNAzol , Gibco ) was used for samples containing few red blood cells and phenol-chloroform extraction was used for samples containing higher amounts of red blood cells . The samples were resuspended in water and DNA was analyzed by 1% agarose gel electrophoresis . Polymorphisms in the promoter region of the TNF-alpha gene were determined by PCR amplification , followed by digestion with appropriate restriction enzymes . For TNF-238G/A [15] , PCR was carried out in a volume of 30 µL containing 11 . 70 µL sterile and filtered Milli-Q water , 3 . 0 µL 10× buffer without MgCl2 ( Gibco ) , 1 . 0 µL 50 mM MgCl2 ( Gibco ) , 3 . 0 µL 2 mM dNTPs ( Invitrogen ) , 3 . 0 µL of each primer ( 40 nmol 238F: 5′ GGT CCT ACA CAC AAA TCA GT 3′ , and 43 . 2 nmol 238R: 5′ CAC TCC CCA TCC TCC TCC CTG GTC 3′ ) ( Gibco ) , 0 . 30 µL ( 500 units , 5 U/µL ) Taq DNA polymerase ( Gibco ) , and 5 . 0 µL genomic DNA at a concentration of 20 µg/mL . The following PCR conditions were used: 5 min at 95°C for initial denaturation and 35 cycles at 95°C for 1 min ( denaturation ) , 55°C for 45 s ( primer annealing ) , and 72°C for 45 s ( extension ) , followed by a final extension step at 72°C for 3 min . Next , RFLP was performed using 0 . 1 µL AvaII ( 10 , 000 U/mL ) ( New England Biolabs ) , 1 . 0 µL NE 4 buffer provided with the restriction enzyme , 0 . 4 µL sterile and filtered Milli-Q water , and 8 . 5 µL of the PCR product in a final volume of 10 µL . The samples were incubated for approximately 18 h at controlled room temperature ( approximately 27°C ) . The digestion products were analyzed on 12% polyacrylamide gel ( 37∶5∶1 ) stained with 1% silver nitrate . Digestion of the PCR products from patients homozygous for the TNF-238A allele ( -238A/A ) generated only one 71-base pairs ( bp ) fragment , whereas those from patients homozygous for the TNF-238G allele ( -238G/G ) were completely digested ( 51 and 20 bp ) . All three fragments ( 71 , 51 and 20 bp ) were present in heterozygous patients . For TNF-308G/A [14] , the PCR mixture contained 11 . 70 µL sterile and filtered Milli-Q water , 3 . 0 µL 10× buffer without MgCl2 ( Gibco ) , 1 . 0 µL 50 mM MgCl2 ( Gibco ) , 3 . 0 µL 2 mM dNTPs ( Invitrogen ) , and 3 . 0 µL of each primer ( 38 nmol -308F: 5′ AGG CAA TAG GTT TTG AGG GCC AT 3′ , and 45 nmol -308R: TCC TCC CTG CTC CGA TTC CG 3′ ) ( Gibco ) , 0 . 30 µL ( 500 units , 5 U/µL ) Taq DNA polymerase ( Gibco ) , and 5 . 0 µL genomic DNA at a concentration of 20 µg/mL in a final volume of 30 µL . The following PCR conditions were used: 5 min at 95°C for initial denaturation , followed by 35 cycles at 95°C for 1 min ( denaturation ) , 52°C for 45 s ( primer annealing ) , and 72°C for 45 s ( extension ) , followed by a final extension step at 72°C for 3 min . For RFLP , the mixture contained 0 . 1 µL NcoI ( 10 U/µL ) ( Gibco ) , 1 . 5 µL REACT 3 buffer provided with the restriction enzyme , 10 . 5 µL sterile and filtered Milli-Q water , and 3 . 0 µL of the PCR product in a final volume of 12 . 1 µL . The samples were incubated for approximately 18 h at 37°C . The digestion products were analyzed on 10% polyacrylamide gel ( 37∶5∶1 ) stained with 1% silver nitrate . Digestion of the PCR products from patients homozygous for the TNF-308A allele generated only one 107-bp fragment , whereas those from patients homozygous for the TNF-308G allele were completely digested ( 87 and 20 bp ) . All three fragments ( 107 , 87 and 20 bp ) were present in heterozygous patients . The following antibodies purchased from BD Pharmingen were used: FITC anti-CD8 , FITC anti-CD14 , and PE-Cy5 anti-CD4 . PE anti-TNF-alpha and appropriate isotype controls were used . All of these antibodies were used according to manufacturer instructions . PBMC were recovered from 48 h cell cultures in the presence of T . cruzi antigens or medium alone . The cells were transferred ( 2×105 cells/tube ) to 5 mL polystyrene tubes ( Falcon® ) and washed once with cold buffer ( PBS-5% BSA ) by centrifugation at 400×g for 10 min at 20°C . Cell pellets were resuspended in 100 µL PBS-BSA buffer and reacted with FITC anti-CD8 plus PE-Cy5 anti-CD4 mAb or FITC anti-CD14 for surface labeling . After 30 min of incubation in the dark at 4°C , the samples were washed three times in buffer by centrifugation at 300×g for 5 min at 20°C and fixed and permeabilized with freshly prepared Fixation/Permeabilization Working Solution ( Pharmingen ) for 30 min at 4°C , followed by two washed in 2 mL buffer . After the last wash , the cell pellets were resuspended in 100 µL 1X Permeabilization Buffer and reacted with PE anti-TNF-alpha at 4°C in the dark . After 30 min of incubation , the cells were washed twice with 2 mL 1X Permeabilization Buffer and then resuspended in 1% paraformaldehyde in Dulbecco's PBS ( Sigma ) for analysis . A total of 20 , 000 events/tube were acquired using a FACScalibur® flow cytometer ( Becton Dickson ) . The Cell Quest™ software provided by the manufacturer was used for data acquisition and analysis . Genotype and allele frequencies were analyzed statistically by the chi-square test . The strength of association was estimated using odds ratios and 95% confidence intervals ( CI ) . The Mann-Whitney or Kruskal-Wallis test was used to analyze the association of TNF-alpha levels with genotypes , allele frequency , serology , and clinical forms . The unpaired t-test was used to analyze the association of intracellular TNF-alpha expression with the cardiac or indeterminate form . Analysis was performed using the StatView software , version 4 . 57 ( Abacus Concepts , USA ) . The level of significance was set at 5% ( p<0 . 05 ) .
A total of 300 individuals were included in the study , 168 ( 56% ) with positive serology for T . cruzi and 132 ( 44% ) with negative serology . All subjects were from the same endemic area . A total of 214 subjects were genotyped to position -238 . Of these , 100 individuals were seropositive for T . cruzi and 114 were seronegative . All 300 individuals were genotyped to position -308 . Of these , 168 individuals were seropositive for T . cruzi and 132 were seronegative . Clinical classification was possible in 119 of the 168 subjects infected with T . cruzi . Sixty-six ( 55 . 46% ) had the cardiac form and 53 ( 44 . 54% ) had the indeterminate form . Only 39 and 66 patients with the cardiac form were genotyped to positions -238 and -308 , respectively . Among the indeterminate patients , 36 were genotyped to position -238 and 53 to position -308 . Clinical manifestations were only compared between patients with the cardiac and indeterminate forms . Seronegative individuals produced higher levels of TNF-alpha than seropositive subjects without stimulation ( Mann-Whitney , p = 0 . 0003 ) and seropositive individuals produced more TNF-alpha than seronegative individuals after PHA stimulation ( Mann-Whitney , p = 0 . 01 ) ( Figure 1A ) . TNF-alpha levels were significantly higher in patients with the indeterminate form than in cardiac patients without stimulation ( Mann-Whitney , p = 0 . 01 ) , after LPS stimulation ( Mann-Whitney , p = 0 . 005 ) , and after stimulation with T . cruzi antigen ( Mann-Whitney , p = 0 . 01 ) ( Figure 1B ) . Comparison of seronegative individuals with cardiac and indeterminate patients showed that cardiac patients produced higher levels of TNF-alpha than seronegative individuals without stimulation ( Mann-Whitney , p = 0 . 0002 ) and after LPS stimulation ( Mann-Whitney , p = 0 . 005 ) ( Figure 1B ) . Moreover , indeterminate patients produced higher levels of TNF-alpha than seronegative individuals after PHA stimulation ( Mann-Whitney , p = 0 . 03 ) ( Figure 1B ) . Analysis was performed between genotypes and infection determined by serology . Table 1 shows the distribution of the TNF-238G/A and TNF-308G/A genotypes . No association was observed between genotypes and serology ( X2 , TNF-238G/A p = 0 . 08; TNF-308G/A p = 0 . 41 ) . When the TNF-238AA and TNF-238GA genotypes were grouped as allele A presence and the genotype TNF-238GG was considered to be allele A absence , the data verified that allele A absence was more frequent among seronegative individuals ( X2 , p = 0 . 03 ) ( Table 2 ) . An odds ratio of 1 . 846 was observed ( CI = 1 . 057 to 3 . 223 ) . Table 3 shows the distribution of the TNF-238G/A and TNF-308G/A genotypes only among individuals with positive serology divided into the cardiac and indeterminate clinical forms . No significant associations were observed ( X2 , TNF-238G/A p = 0 . 28; TNF-308G/A p = 0 . 64 ) . There were no differences in TNF-alpha levels produced by seropositive and seronegative individuals carrying different genotypes of the TNF-alpha polymorphisms position -238 ( Figure 2 ) and position -308 ( data not shown ) . On the other hand , higher levels of TNF-alpha were observed in individuals carrying the TNF-238A allele after LPS stimulation ( Mann-Whitney , p = 0 . 045 ) when all subjects were analyzed together ( Figure 3A ) . No differences were observed when the individuals were grouped according to negative serology ( data not shown ) . Interestingly , when seropositive individuals were analyzed alone , higher TNF-alpha levels were observed in those carrying the TNF-238A allele without stimulation ( Mann-Whitney , p = 0 . 04 ) , after stimulation with T . cruzi antigen ( Mann-Whitney , p = 0 . 004 ) , and LPS stimulation ( Mann-Whitney , p = 0 . 007 ) ( Figure 3B ) . The presence of intracellular TNF-alpha was analyzed by flow cytometry in 48-h cultured cells . In the absence of stimulation , 1 . 18% and 1 . 83% of CD8+ cells were positive for TNF-alpha in patients with the cardiac and indeterminate forms , respectively . In CD4+ cells , the rate of TNF-alpha production was 2 . 60% in cardiac patients and 1 . 713% in indeterminate patients . No association was observed between the cell type and clinical form presented by the patient ( data not shown ) . After stimulation with T . cruzi antigens , 28 . 47% and 38 . 80% of CD8+ cells were positive for TNF-alpha in patients with the cardiac and indeterminate forms , respectively . In CD4+ cells , positivity for TNF-alpha was 2 . 78% for cardiac patients and 6 . 54% for indeterminate patients ( Figure 4 ) . This difference was statistically significant ( unpaired t-test; p = 0 . 044 ) . Two levels of TNF-alpha production were observed in CD14+ cells , low and high levels . In cardiac patients , 63 . 36% and 3 . 76% of CD14+ cells were classified as low and high producers , respectively . These percentages were 29 . 96% and 0 . 70% in patients with the indeterminate form ( Figure 4 ) .
The immune response plays an important role in the control of T . cruzi infection . TNF-alpha is an important cytokine involved in parasite control during the acute phase [9]–[11] . However , there are few studies in the literature discussing its role during the chronic phase . Some studies have suggested the involvement of TNF-alpha in the development of the cardiac form [13] , [29] , [30] , but its role in the control of parasite growth in humans and the consequent development of specific antibodies has been little studied . The present study conducted on individuals ( control group and infected individuals ) from an endemic area in the central region of Brazil ( Água Comprida ) provided important results . This area was included in the first national campaign against Chagas disease that started in 1950 [31] . These characteristics result in homogeneous population exposition and similar environmental and social conditions , thus reducing possible confounding factors . Higher TNF-alpha levels were produced by seronegative individuals without stimulation , indicating that individuals who did not acquire the infection are able to produce TNF-alpha spontaneously . High basal levels of TNF-alpha may improve the host defense against T . cruzi , possibly by modulating the expression of iNOS and adhesion molecules involved in rolling , adhesion and extravasation during inflammatory events in response to T . cruzi invasion [10]–[12] . Higher levels of TNF-alpha were observed in seropositive individuals compared to seronegative subjects . This finding might be due to polyclonal expansion in response to nonspecific stimulation . Analyzing the development of the clinical form , TNF-alpha levels were significantly higher in patients with the indeterminate form than in those with the cardiac form . Comparison of cardiac patients with the control group showed higher levels of TNF-α in the cardiac group without stimulation and after LPS stimulation . Furthermore , indeterminate patients produced higher level of the cytokine after PHA stimulation . This fact suggests that TNF-alpha production by PBMC might be more important for the ability of the cytokine to control parasite growth than for promoting tissue damage or the development of heart lesions . It is important to point out that in the present study most patients classified as having the cardiac form did not exhibit severe heart involvement such as heart failure . Severe and end-stage Chagas disease has been associated with high levels of TNF-alpha [29] , [30] , [32] . Our results differ from those reported by Ferreira et al . [29] and Talvani et al . [30] . However , a previous study from our group analyzing plasma TNF-alpha levels was unable to show differences between the clinical forms of chronic Chagas disease [33] . TNF-alpha plays a potential dual role , controlling parasite growth or promoting tissue damage . Moreover , TNF-alpha is able to stimulate IL-10 synthesis [34] , a regulatory cytokine that contributes to the control of inflammation . A recent experimental study demonstrated that the blockage of TNF-alpha with Etanercept enhances left ventricular dysfunction in T . cruzi-induced chronic cardiomyopathy [35] . In this respect , the delicate balance between the ability of TNF-alpha to control parasite growth and to promote tissue damage may be responsible for human resistance/susceptibility to Chagas disease . Not only the level of this cytokine , but the cells involved in its production and the elapsed time after interaction with the parasite should be investigated to improve the current understanding of the role of TNF-alpha in Chagas disease . Regarding TNF-alpha gene polymorphisms , this study provided evidence of an association between the absence of allele A at position -238 and seronegativity . We observed that individuals carrying the TNF-238A allele produce higher levels of TNF-α than those without the allele . This result suggests that the TNF-alpha polymorphism affects gene expression and that this effect depends on the cell population and on the strength of the stimulus since no differences were observed after PHA stimulation . Moreover , infected individuals carrying the TNF-238A allele produced higher levels of TNF-alpha than those without allele A . This result suggests that the -238 polymorphism exerts its potential function in infected subjects , probably as a result of clonal expansion and immunoregulatory mechanisms established during infection . The effect of the polymorphism might be more pronounced under these conditions . No significant association was established for the -308 polymorphism . A higher proportion of the TNF-238A allele among patients when compared to controls was demonstrated for various other infectious diseases such as infection with Chlamydia trachomatis , although these findings were not statistically significant [36] . Another study reported a higher bacteriological index in patients with leprosy carrying the TNF-238A allele [37] . Homozygous carriers of the TNF-238A allele were more frequent among patients with psoriasis compared to controls [17] . In chronic and active hepatitis C , the TNF-238A allele was more frequent in the group of patients than in controls [18] . Other studies on Chagas disease were conducted in Peru , Mexico and Brazil . The first study analyzed 87 healthy controls and 85 individuals seropositive for anti-T . cruzi antibodies from an endemic area in Peru . No significant differences were observed between patients and controls or between asymptomatic individuals and patients with cardiomyopathy for either polymorphic region [19] . The second study compared 54 individuals seropositive for Chagas disease and 169 controls from the Mexican population . A higher frequency of the TNF-308A allele was observed in chagasic patients compared to the control group . Furthermore , the TNF-308A allele was more frequent among patients with cardiopathy compared to asymptomatic individuals [20] . However , these differences in the results might be attributed to ethnic variations among different populations and to differences in the study design and patient selection criteria . The third study analyzed 42 patients with severe ventricular dysfunction . Patients carrying the TNF-308A allele presented a significantly shorter survival time than those carrying other alleles [21] . The same group compared 166 chronic Chagas disease patients with cardiomyopathy to 80 asymptomatic patients , but observed no significant association with TNF polymorphisms [22] . However , none of the studies conducted in Brazil investigated the -238 gene polymorphism or included subjects from endemic areas . The present study differs from previous investigations because all individuals were from a region of high endemicity , with a greater probability of a more homogenous condition of exposure and accurate clinical analysis after some years of infection , permitting a sufficient time interval for the development of the clinical forms of the disease . This is advantageous in relation to the study's characteristics , though it represents a disadvantage due to the possible death of patients with more severe forms of the disease . Furthermore , in the present study , functional analysis showed that PBMC from seropositive individuals carrying the TNF-238A allele produced significantly higher levels of TNF-alpha when stimulated with LPS and T . cruzi antigens and in the absence of any stimulus . Other investigators observed higher TNF-alpha production by LPS-stimulated PBMC in individuals homozygous for the TNF-308A allele [38] . Studies using gene construction strategies in which the promoter region of TNF was ligated to the luciferase gene have also shown an increased production of TNF-alpha levels among individuals with the TNF-308A allele [39] , [40] . A repressor-binding site was identified at position -238 [41] . Thus , it is possible that in certain cell types , the presence of the polymorphism reduces affinity for the repressor , with a consequent increase in transcription [42] . These data indicate that the polymorphism at this position may influence gene expression and may help explain the association between the presence of allele TNF-238A and higher TNF-alpha production observed in this study . Flow cytometry analysis indicated that CD14+ and CD8+ cells are the major source of TNF-alpha after antigen stimulation . The number of CD14+ T lymphocytes expressing TNF-alpha was significantly higher in cardiac patients , whereas the number of CD4+ lymphocytes producing this cytokine was high in patients with the indeterminate form . The stimulation of CD8+ T lymphocytes with exogenous antigens , including T cruzi lysate antigens , under similar culture conditions has been demonstrated previously [43] , [44] . The importance of TNF-alpha as an activator of the mechanisms involved in parasite elimination [12] and in tissue injury [10] , [11] has been clearly demonstrated in experimental models by biological blockage of the cytokine or in genetically modified animals . Studies on humans have associated TNF-alpha with phenomena of tissue injury during the chronic phase of infection [13] . The present results demonstrate an association between gene polymorphisms and infection . In conclusion , the present results suggest that the TNF-238A allele exerts a significant effect on human susceptibility to infection . Furthermore , seropositive individuals carrying the TNF-238A allele produce higher levels of TNF-alpha after antigen and polyclonal stimulation , suggesting that the presence of the allele is associated with higher TNF-alphaproductionIn addition to its trypanocidal activity , TNF-alpha seems to trigger regulatory networks , such as the induction of IL-10 [34] , that permit parasite escape . The development of clinical forms of Chagas disease may implicate other genes involved in the delicate balance of immune response . | Chagas disease is an important parasitic disease that has no cure . The pathogenesis of the disease is still not completely understood . Studies using candidate genes are important to better understand the differences between individuals that lead to such heterogenous disease . TNF-alpha is a cytokine involved in the control of parasitemia during the acute phase and in cardiac injury during the chronic phase . The TNF-alpha gene is located in an important region of the MHC and its polymorphisms are associated with many parasitic and infectious diseases such as cerebral malaria and leishmaniasis . These studies are important since they were conducted in the same regions and involved populations leaving in the same conditions . The present study shows that patients with the indeterminate form produce higher levels of TNF-alpha than cardiac patients . The data suggest a control mechanism between inflammatory and regulatory cytokines . In addition , the presence of the TNF-238G allele contributes to the development of negative serology . We show that CD8+ T lymphocytes and macrophages are the main cells producing TNF-alpha . This study is an important contribution to explain the pathogenesis of Chagas disease . |
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Due to worldwide increased human mobility , air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens . However , the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated . We constructed a modelling framework , integrating air travel , epidemiological , demographical , entomological and meteorological data , to measure the seasonal probability of dengue introduction from endemic countries . This framework has been applied retrospectively to elucidate spatiotemporal patterns and increasing seasonal risk of dengue importation from South-East Asia into China via air travel in multiple populations , Chinese travelers and local residents , over a decade of 2005–15 . We found that the volume of airline travelers from South-East Asia into China has quadrupled from 2005 to 2015 with Chinese travelers increased rapidly . Following the growth of air traffic , the probability of dengue importation from South-East Asia into China has increased dramatically from 2005 to 2015 . This study also revealed seasonal asymmetries of transmission routes: Sri Lanka and Maldives have emerged as origins; neglected cities at central and coastal China have been increasingly vulnerable to dengue importation and onward transmission . Compared to the monthly occurrence of dengue reported in China , our model performed robustly for importation and onward transmission risk estimates . The approach and evidence could facilitate to understand and mitigate the changing seasonal threat of arbovirus from endemic regions .
The substantial growth and reach of human travel in recent decades has contributed to the global spread of infectious diseases [1–4] . In particular , air travel has allowed human hosts or carriers of pathogens to move long distances within the incubation period of infections [5] , such as the viruses that cause severe acute respiratory syndrome ( SARS ) , H1N1 , Ebola , Zika , and yellow fever [6–11] , or the parasites that cause malaria [12–14] . Regarding to the continual growth of international tourist arrivals , from 25 million in 1950 to 1 . 2 billion in 2015 [15] , understanding the global dynamics of infectious disease has become a major 21st-century challenge , and mechanistic or mathematical models built with air-transportation data been widely used to measure risks of arriving infected humans , growth rate of an introduced epidemic and the impact of specific surveillance and control strategies [2 , 6 , 16 , 17] . Some relevant factors for assessing the risk of disease importation from endemic regions into a country are: 1 ) the risk of a person acquiring the disease in the origin country; 2 ) the risk of a person traveling to the destination country of interest while infectious; and 3 ) the likelihood of subsequent local transmission in the destination country [18] . However , most previous modelling studies have only focused on some of these components , and the seasonal and inter-annual risks of international spread of infectious diseases have rarely been quantified [6 , 16 , 19–22] . Moreover , the relative exposure risk and importation probability in travelers are likely to differ between local residents in endemic regions and residents of non-endemic areas traveling to endemic countries [21 , 23 , 24] . Given the global expanding distribution of Aedes mosquitoes [1] , dengue has established itself throughout the world’s tropical and subtropical regions in both endemic and epidemic transmission cycles , causing significant morbidity and mortality , particularly highly endemic in South-East Asia ( SEA ) [24–26] . However , dengue remains a seasonal disease in China , with epidemics occasionally triggered by imported dengue viruses ( DENV ) [27] . More than 90% of imported cases between 2005 and 2014 originated from SEA [27–31] . Following China’s economic boom in the last two decades , the number of Chinese citizens travelling abroad has increased from 5 million in 1996 to 128 million in 2015 [32] . Recent government led initiatives to further foster international trade may contribute to increased flows between SEA and China [33] , which could also increase the number of importations of pathogens including DENV . While the risk of dengue in China is apparent and growing [27] , the seasonal pattern and changing risk of importation and subsequent transmission are unclear , a challenge amplified by a dearth of models for assessing seasonal risk for pathogen spread globally [18 , 34] . As international travel between SEA and China by airplane is fast and common , based on the assumption that human mobility via commercial air travel is an important conduit for the spread of infectious diseases internationally , we constructed a branching process model by focusing on the seasonal and multiannual movement of DENV from the endemic countries in SEA into China via air travelers of Chinese and SEA residents between 2005 and 2015 . We then retrospectively quantified and validated the seasonal risks , ranging from zero to certain ( 1 ) , of DENV importation from nine SEA countries and leading to autochthonous transmission ( introduced transmission ) in China , identified geographic and seasonal patterns of emerging origin-destination routes , and estimated the number of imported infections in Chinese travelers and SEA residents into China . With rising concerns about global pathogen dispersal , this study provides approaches and evidence that can inform efforts to mitigate the spread of DENV and other arboviral pathogens including Zika , chikungunya , and yellow fever viruses from endemic regions .
Ethical clearance for collecting and using secondary data in this study was granted by the institutional review board of the University of Southampton , England ( No . 18152 ) . All data were supplied and analyzed in an anonymous format , without access to personal identifying information .
The volume of airline travelers from 17 SEA countries into China nearly quadrupled from 3 . 6 million in 2005 to 13 . 8 million in 2015 , with the most ( 69 . 3% of all 73 . 9 million passengers ) departing from Thailand , Singapore , and Malaysia ( Fig A in S1 Appendix ) . Nine SEA countries with available monthly dengue incidence data for risk analysis had a total of 63 . 4 million airline travelers ( 85 . 8% passengers from 17 SEA countries ) into 165 cities in China between 2005 and 2015 , including 38 . 7 million ( 61 . 1% ) Chinese travelers and 24 . 7 million residents ( 38 . 9% ) from nine SEA countries with Chinese increased rapidly from 1 . 4 million ( 44 . 8% ) in 2005 to 9 . 5 million ( 79 . 0% ) in 2015 ( Figs B and C in S1 Appendix ) . Fig 1 shows the volume of travelers from SEA and the number of corresponding imported dengue cases into China have positive correlations by year and by origin ( Spearman’s rank correlation , both p<0 . 001 ) . Seasonal patterns of dengue transmission in nine countries were also seen with annual amplitude positively correlated to the latitude of each country . Furthermore , there was a significant synchrony between dengue incidence in SEA and importation to China , and the seasonal epidemics in China were also highly coherent with dengue transmission in SEA and importation into China ( Fig 2 and Figs D-F in S1 Appendix ) . The monthly DENV importation risk from nine countries of SEA into a province of China have increased from a median of 0 . 18 ( IQR 0 . 03–0 . 57 ) in 2005 to 0 . 98 ( 0 . 72–1 . 0 ) in 2015 ( Fig 3 , panel A ) . Both Chinese travelers and SEA residents contributed to increasing risk over that decade , but Chinese travelers ( median 0 . 26 , IQR 0 . 03–0 . 88 ) were more likely to introduce dengue into China than SEA residents ( 0 . 14 , 0 . 02–0 . 56 ) , particularly since 2011 ( Figs G-I in S1 Appendix ) . Across all years , the lowest risk ( median 0 . 22 , IQR 0 . 03–0 . 82 ) was in March , and the highest ( 0 . 65 , 0 . 12–1 . 0 ) was in August when 23 cities ( 13 . 9% of 165 cities ) had an average risk greater than 0 . 5 between 2005 and 2015 ( Fig 3 , panel B and Figs J and K in S1 Appendix ) . The percentage of cities with a median importation risk higher than 0 . 5 increased from 4 . 8% ( 8/165 ) in 2005 to 21 . 8% ( 36/165 ) in 2015 with most emerging destinations in central and western China ( Fig 4 ) , and cities with a median probability of risk greater than 0 . 5 due to Chinese travelers increased from 7 to 35 , versus 5 to 18 for SEA residents ( Fig L in S1 Appendix ) . Thailand , Malaysia and Singapore were consistently amongst the locations with the highest risk for DENV importation into China; while Sri Lanka and Maldives were emerging as important origins due to the increasing travel , particularly in Chinese ( Fig 5 and Figs M and N in S1 Appendix ) . Meanwhile , among the 1485 routes from nine SEA countries to 165 cities of China , those with a median risk higher than 0 . 5 rose from 15 ( 1 . 0% ) in 2005 to 84 ( 5 . 7% ) in 2015 . A total of 11 , 901 infections ( 95% UI 6923–16 , 917 ) via air travel was estimated to import from nine SEA countries into China between 2005 and 2015 , which was 13 . 5 times ( 7 . 8–19 . 2 ) of the 879 imported cases reported in dengue surveillance system of China . The estimates had positive correlations with the reported numbers by month and country , and by nationality ( Fig 6 and Figs O and P in S1 Appendix ) . Furthermore , the estimated time series with a one-month lag could significantly predict the numbers of cases reported in surveillance ( F = 203 . 7 , p<0 . 001 ) ( Fig Q in S1 Appendix ) . The probability of DENV introduced local transmission from nine SEA countries into China also rose , with a median risk increasing from 0 . 10 ( IQR 0 . 01–0 . 30 ) in August 2005 to 0 . 56 ( IQR 0 . 21–0 . 91 ) in August 2015 at provincial level ( Fig 3 , panel C ) . Significant seasonal variation was evident , with high risk during the warm months between May-October , but very low risk in other months ( Fig 3 , panel D ) . Compared to cities with intensive importation in cold regions of northern China , e . g . Beijing and Shenyang , the introduced risks in the lower latitude cities , e . g . Guangzhou , Shenzhen and Haikou , were much higher and extended over longer time periods ( Fig 4 and Fig K in S1 Appendix ) . The countrywide change in the probability of introduced transmission between 2005 and 2015 led to a much larger population being at risk: Guangzhou , Shanghai and Xiamen with 32 million people were the only three cities with a risk greater than 0 . 5 in August 2005 , while there are 102 million people in 10 cities ( Guangzhou , Shanghai , Fuzhou , Xiamen , Shenzhen , Hangzhou , Haikou , Nanning , Wuhan , Changsha and Chongqing ) with the same risk in August 2015 ( Fig R in S1 Appendix ) . The dynamics of dengue in SEA , the volume , demography and immunity level of airline travelers , and the environmental suitability of DENV local transmission in China have been changing the high-risk routes for importation and introduced transmission ( Figs S and T in S1 Appendix ) . For instance , the Maldives-Guangzhou , Philippines-Fuzhou and Malaysia-Hangzhou routes have had increasing risks since 2005 , and cities in central China and middle coastal regions , e . g . Hangzhou , Chengdu and Wuhan , are emerging as destinations with an increasing risk of introduced transmission . Additionally , compared to the monthly occurrence of cases reported at the provincial level in China , the ROC curves showed our model performed robustly with an AUC of 0 . 86 for importation risk estimates and 0 . 92 for introduced transmission estimates ( Fig U in S1 Appendix ) . Moreover , the importation risk estimates for SEA residents had a slight better performance than for Chinese travelers ( AUC 0 . 91 vs 0 . 86 ) .
Being able to identify areas at risk for introduction and spread of pathogens in a timely manner is critical for situational awareness and for tailoring strategies for preparedness and response , e . g . allocation of finite health and human resources [27] . In this study , we constructed a branching process modelling framework to elucidate seasonal probability of international spread of mosquito-borne viral disease from endemic countries in SEA via air travel . We have identified the emerging origins in SEA and locations in China that are most susceptible to dengue importation and onward transmission , and we also revealed the seasonal patterns and increasing risks in routes of DENV spread by air travel over a decade . The spatiotemporal heterogeneities of DENV importation risk have also been seen in the travelers of Chinese and SEA residents . The risk of introduced transmission from particular routes identified can be used to inform efforts to prevent and control the spread of DENV , and are particularly important for currently neglected , high-risk locations , i . e . Chengdu , Wuhan and Hangzhou . Moreover , with the increasing risk of dengue importation and transmission from SEA , China can also be the source of exportation , and this was amply shown in the introduction of dengue from Guangzhou into Japan resulting in a small outbreak in Tokyo [43] . The geographic , historical , and cultural ties between SEA countries and China , as well as increasing economic and tourism links , has contributed to the growing travel volume . We demonstrate here the epidemiological significance of this travel in the context of DENV importation from these countries into China over a decade . Compared to SEA residents travelling into China , the accelerated growth in the volume of international Chinese travelers over time has also facilitated increased DENV importation from SEA . For instance , the growth in risk of dengue importation from Sri Lanka since 2010 can likely be attributed to the increasing investment and workers from China [44] , while the rising risk from the Maldives is probably related to increasing numbers of Chinese tourists [32] . The megacities in China , e . g . Beijing , Shanghai and Guangzhou , each regional aviation hubs , have consistently received large volumes of international air passengers , leading to high risks of dengue importation from SEA . However , the rapid growth of travel abroad for tourism , business and migrant workers from cities in central and southwest China is also sufficient to cause substantial risk of importation . A similar pattern has been described for malaria importation from Africa and SEA into these areas [45] . The increase in imported DENV from SEA has also increased subsequent transmission risk in China , with Guangdong , Yunnan and Fujian provinces frequently reporting outbreaks following dengue importation throughout the last decade . Meanwhile , other provinces ( e . g . Henan , Shandong and Shanghai ) have reported autochthonous cases of dengue for the first time [27 , 46] . The increasing importation risk , together with increasing temperatures and the spatial spread of Ae . aegypti [47] , are all contribute to increased risk of introduced transmission and the potential for year-round autochthonous transmission of DENV and other flaviviruses in several tropical and subtropical regions of China ( e . g . Hainan , Guangdong and Yunnan ) . The variation in DENV serotypes introduced from different origins over time is especially relevant considering the potential for adverse effects from dengue haemorrhagic fever after infection with a different serotype of DENV [27] . The number of imported cases reported in surveillance systems could be predicted by the estimated time series with one-month lag , which might be due to the longer period of travel , and the delay identification and reporting of imported infections by the routine surveillance . The gaps between the estimates and reported numbers found in this study also highlight the needs to improve the capacity of surveillance systems and formulate strategies to mitigate these imported contagions , and public health authorities and partners in areas with huge volume of imported infections and high risk of autochthonous transmission should consider implementing appropriate actions at an early stage of potential seasonal transmission . These could include health education in Chinese travelers and early identifying the infections in entry points , and improve the capacity of surveillance , vector control , laboratory diagnosis , and clinical management . However , the risk of introduction is a more complex function that reflects more than travel volumes [48 , 49] , e . g . the incidence of the disease in the country of disembarkation , the probability of being infected/viremic at the time of travel and arrival in the destination country , the duration of viremia , the presence of favorable conditions ( vectors and seasonality ) in the destination . Therefore , our findings must be considered in the context of several assumptions and data limitations . First , the quality of incidence data on dengue incidence in SEA and China likely varies due to differences in surveillance systems including case definitions , reporting methods , availability of healthcare and laboratory diagnosis , under reporting , and the completeness and accuracy of data reported . Second , the risk of dengue infection in SEA was assumed to be identical across each country , without considering the immunity of different serotypes in Chinese travelers and SEA residents . Third , we only estimated the risk of the Chinese and SEA residents , but the actual nationalities in travelers might be much complex , not only the Chinese and SEA residents , but also the residents from other countries passing through SEA on their way to China . Fourth , we regarded Ae . albopictus as an equally competent vector to Ae . aegypti for DENV , with similar temperature dependency and extrinsic incubation period . Fifth , our estimates did not address variability in the public health and health-care capacity and resources for different years and locations in China and SEA countries in response to dengue . Furthermore , due to the availability of monthly disease incidence in SEA countries and the absence of monthly travel data by land and water from SEA into China and within China , we only estimate the seasonal risk of introduction through air travel for nine SEA countries . Therefore , the total risk of dengue introductions from all SEA countries into China must be underestimated in this study . To solve these problems in future studies , the monthly dengue incidence for all countries could be estimated by mathematical models based on epidemiological and entomological parameters and climate data , and the seasonal and multiannual cross-border population movements could be further estimated by gravity-type spatial interaction models or using novel sources of data , e . g . mobile phone data or social media data [50–52] . Nonetheless , the models and findings presented here leverage previous work suggesting that a probabilistic model of pathogen spread over a heterogeneous network by multiple populations could capture most of the information in much complex stochastic simulation models [18 , 34] . Moreover , our retrospective validation showed that the predicted seasonal risk of DENV into China coincided with a surge in the number of imported cases and volume of airline travelers arriving into China from SEA countries with reported dengue virus activity . Our model framework is sufficiently flexible to incorporate new forms of data and adapt to different vector-borne diseases . Moreover , it may be used to project into the future given different scenarios and to quantify the effects of different control methods . It also highlights the need for high-quality , accessible travel and surveillance data at national , regional , and global levels . As shown here , travel dynamics have a direct and drastic impact on regional and global infectious disease dynamics and having accessible data to assess those risks in real time can support appropriate risk assessment and prevention , and control activities . | Given the global expanding distribution of Aedes mosquitoes , dengue has established itself throughout the world’s tropical and subtropical regions in both endemic and epidemic transmission cycles , causing significant morbidity and mortality . Moreover , the rise of air travel over the past century has resulted in a highly inter-connected world , where geographical distance is becoming less of a barrier to pathogen dispersal . However , few studies have quantified and validated changes in seasonal and long-term risks of international spread for infectious diseases . In China , dengue remains a seasonal disease occasionally triggered by imported dengue viruses in travellers , with more than 90% of imported cases between 2005 and 2014 originated from South-East Asia . Therefore , taking dengue importation from South-East Asia into China as an example , we constructed a branching process modelling framework to integrate three components for assessing the risk of dengue introduction into a country: 1 ) the risk of a person acquiring the disease in the origin country; 2 ) the probability of a person traveling to the destination country of interest while infectious; and 3 ) the likelihood of subsequent local transmission in the destination country . This model has revealed the seasonal patterns and increasing risks in routes of dengue spread by air travel over a decade . The spatiotemporal heterogeneities of dengue importation risk have also been seen in the travelers of Chinese and SEA residents . The risk of introduced transmission from particular routes highlighted could be used to inform efforts to dengue prevention and control , particularly in currently neglected , high-risk locations . |
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Plants , as sessile organisms , survive environmental changes by prioritizing their responses to the most life-threatening stress by allocating limited resources . Previous studies showed that pathogen resistance was suppressed under abiotic stresses . Here , we show the mechanism underlying this phenomenon . Phosphorylation of WRKY45 , the central transcription factor in salicylic-acid ( SA ) -signalling-dependent pathogen defence in rice , via the OsMKK10-2–OsMPK6 cascade , was required to fully activate WRKY45 . The activation of WRKY45 by benzothiadiazole ( BTH ) was reduced under low temperature and high salinity , probably through abscisic acid ( ABA ) signalling . An ABA treatment dephosphorylated/inactivated OsMPK6 via protein tyrosine phosphatases , OsPTP1/2 , leading to the impaired activation of WRKY45 and a reduction in Magnaporthe oryzae resistance , even after BTH treatment . BTH induced a strong M . oryzae resistance in OsPTP1/2 knockdown rice , even under cold and high salinity , indicating that OsPTP1/2 is the node of SA-ABA signalling crosstalk and its down-regulation makes rice disease resistant , even under abiotic stresses . These results points to one of the directions to further improve crops by managing the tradeoffs between different stress responses of plants .
Plants , as sessile organisms , are continuously exposed to various environmental stresses in nature . To cope with such conditions using limited resources , plants have evolved various mechanisms that enable resource allocation to the most life-threatening stress [1] [2] . Such tradeoffs between the responses to different stresses are often regulated by crosstalk between signalling pathways [3] [4] [5] . A number of studies have reported various signalling components that appear to influence signalling crosstalk . However , the precise molecular mechanisms that regulate the crosstalk remain poorly understood in most cases [6] [4] [7] . The salicylic acid ( SA ) signalling pathway plays a crucial role in pathogen defence . In Arabidopsis , NPR1 , the transcriptional cofactor , plays a major role in the SA defence signalling pathway [8] . In rice ( Oryzae sativa ) , in addition to OsNPR1/NH1 , the rice ortholog of NPR1 , the transcription factor ( TF ) WRKY45 plays a crucial role in the branched SA pathway [9] [10] [11] [12] . Up-regulation of WRKY45 by chemical defence inducers , such as benzothiadiazole ( BTH ) , or its overexpression , renders rice plants resistant against several pathogens , including fungus , such as Magnaporthe oryzae causing blast disease , and bacterium , such as Xanthomonas oryzae pv . oryzae causing leaf blight disease [9] [13] [14] , without major negative effects on plant growth . WRKY45 auto-regulates the transcription of its own gene [12] and is regulated by the ubiquitin-proteasome system [15] . Abscisic acid ( ABA ) signalling is mainly involved in plant responses to abiotic stresses , such as the cold , drought , and high salinity [16] [17] . However , ABA also acts as a modulator of defence responses against pathogens , both positively and negatively , with its negative role being more prevalent [18] [3] [19] [20] [4] [21] [5] [22] . Recent studies have shown that ABA antagonizes SA-signalling , thereby interfering with defence responses in tomato , Arabidopsis , and rice [23] [24] [25] . The WRKY TFs can be phosphorylated and activated by MAP kinases , as is the case with Arabidopsis WRKY33 [26] and Nicotiana benthamiana WRKY8 [27] . The negative regulation of MAP kinases through dephosphorylation by protein phosphatases , including Ser/Thr-specific phosphatases , dual-specificity phosphatases , and Tyr-specific phosphatases ( PTPases ) , has been reported [28] [29] . We have previously reported that activated MAP kinases can phosphorylate WRKY45 in vitro , and MAP kinases can be activated in response to SA [30] . However , details of WRKY45 phosphorylation , and the biological significance of the phosphorylation have remained unknown . Rice blast is one of the most serious crop diseases in the world . It has been reported that rice plants are more blast susceptible under abiotic stresses , such as low temperature and drought [31] [32] [33] . In rice , ABA treatment severely compromised M . oryzae resistance [34] , which is mediated by suppression of WRKY45 and OsNPR1/NH1 genes via ABA signalling [25] [35] . These authors suggest that ABA signalling plays a role in increased blast susceptibility under low temperature . In this report , we show the mechanism underlying blast resistance through the activation of WRKY45 by MAP kinase ( MAPK ) -dependent phosphorylation in the SA pathway . Moreover , we showed that the tyrosine dephosphorylation of the MAPK by PTPases , OsPTP1/2 , is responsible for the MAPK inactivation under abiotic stresses or in the presence of exogenous ABA . Additionally , the knockdown of OsPTP1/2 uncoupled the induced blast resistance from the abiotic stresses . These findings should enable the development of technologies to protect rice from diseases even under the influence of environmental factors .
WRKY45 was phosphorylated by OsMPK6 in vitro in the presence of a constitutively active form of OsMKK10-2 ( MKK10-2D , Os03g0225100 , LOC_Os03g12390 . 1 ) , which is a rice MAPK kinase that phosphorylates and activates OsMPK6 in vitro [30]; however , its biological significance was unknown . To further analyse the WRKY45 phosphorylation , we therefore determined the sites in WRKY45 that are phosphorylated by OsMPK6 . Incubation in vitro of fragmented WRKY45 polypeptides fused to a maltose binding protein ( MBP ) with OsMPK6 revealed that only two regions ( amino acids 239–292 and 281–326 ) of WRKY45 were phosphorylated ( S1 Fig ) . By a consequent substitution study on candidate phosphorylation sites ( Ser or Thr ) , we found that Thr266 , Ser294 , and Ser299 were required for phosphorylation of WRKY45 by OsMPK6 in vitro ( S2 Fig ) . To assess the effects of the phosphorylation on transcriptional activity , we generated mutant forms of WRKY45 in which all the three amino acids were replaced by Asn ( NNN ) or Asp ( DDD ) to mimic dephosphorylation and phosphorylation of all three sites , respectively , and tested them by transient reporter assays ( Fig 1A ) . The transactivation activity of DDD was 2–4-fold higher than that of NNN ( Fig 1B ) , indicating that the phosphomimetic mutation elevated the transcriptional activity of WRKY45 . These results suggest that WRKY45 phosphorylation results in its activation in vivo . The level of wild-type ( WT ) WRKY45 activity was between those of NNN and DDD ( Fig 1B ) , consistent with its partial phosphorylation state . We attempted to generate transgenic plants overexpressing NNN and DDD mutants tagged with the myc sequence at their N-termini , but we obtained neither of them . Then , we focused on the carboxyl-terminal region that contained two of the three phosphorylation sites , the closely located Ser residues , Ser294 and Ser299 , because the carboxyl-terminal region of WRKY45 is critical for transcriptional activity [15] . We generated mutants in which the two serines were substituted with Asp ( TDD ) or Asn ( TNN ) , and attempted to overexpress the mutant cDNAs in rice transformants ( Fig 1A ) . While we were unable to obtain the transformants for TDD , we obtained several for TNN . Whereas the TNN transformants accumulated the transgene-derived proteins to levels comparable , or even slightly higher , than those in WT WRKY45-overexpressing ( ox ) transformants , they showed no significant enhancement of blast resistance ( Fig 1C ) , indicating that the substitution of these two serines compromised the function of WRKY45 . To examine the contribution of the phosphorylation at these two Ser to transcriptional activity , we generated new mutants , DNN and NDD , and analysed them in the transient system ( Fig 1A ) . The relative activity of NDD was as high as that of DDD , while that of DNN was as low as that of the NNN mutant ( Fig 1D ) . These results suggest that the phosphorylation of Ser294 and/or Ser299 , but not that of Thr266 , is important for the transcriptional activity of WRKY45 , consistent with the results of blast resistance test ( Fig 1C ) . To examine whether these two serines in WRKY45 are phosphorylated in vivo , we treated the extracts from WT and TNN WRKY45-ox plants with lambda protein phosphatase ( PPase ) . The electrophoretic mobility of the TNN form treated with the PPase was indistinguishable from that of WT WRKY45 ( Fig 1E ) , indicating that the effect of Ser-to-Asn substitutions at the two sites on the electrophoretic mobility is negligible . Without the PPase treatment , two bands were seen in both WT and TNN extracts ( Fig 1E ) , suggesting that both WT and the mutant WRKY45 proteins were phosphorylated in vivo . The slower mobility ( upper ) bands in WT extracts were broad and much more intense than the faster mobility ( lower ) band for the unphosphorylated WRKY45 protein . By contrast , the upper band in the TNN extract was less intense than the lower band and thinner . These reproducible results suggest that Ser294 and/or Ser299 are/is actually phosphorylated in plant cells . This idea was also supported by the results of Phos-Tag polyacrylamide gel electrophoresis [36] ( S3 Fig ) . Taken together , the phosphorylation of Ser294 and/or Ser299 of WRKY45 and consequent activation of its transcriptional activity are required for the full functioning of WRKY45-dependent blast resistance . We have previously shown that OsMPK6 becomes active in response to SA [30] . Here , we monitored the activation state of OsMPK6 by examining the dual phosphorylation of MAPK at Thr and Tyr in the TEY-signature ( position 225–227 in OsMPK6 ) [37] in calli . An immunoblot using anti-pTEpY antibody showed that a band , which is missing in osmpk6 mutant and corresponds to that for OsMPK6 in gel mobility , was intensified by the SA-treatment in WT calli ( Fig 2 ) . The increase of the band intensity in response to SA was rather weak , which we interpret to be due to sporadically elevated basal OsMPK6 phosphorylation level because of high SA levels in rice . In the osmpk6 mutant calli , a faster migrating band appeared , consistent with our previous observations in an in-gel kinase assay [30] . In leaves , OsMPK6 was also dually phosphorylated and another band appeared ( Fig 2 ) . These results suggest that OsMPK6 is a major MAPK activated by SA . To mimic the activation of the SA pathway that leads to the activation of OsMPK6 and then WRKY45 , we performed in vitro kinase assays using MKK10-2D and mutant forms of OsMPK6 as substrates ( Figs 3A and S4 ) . A kinase-dead form of OsMPK6 , K96R , was phosphorylated by MKK10-2D in vitro; however , another kinase-dead form , in which , in addition to the K96R mutation , the Thr and Tyr in the TEY-signature were replaced by Asp ( K96R/T225D/Y227D ) , was not ( Fig 3A ) . In addition , OsMPK6 mutants in which the Tyr and Thr were independently substituted , T225D and Y227A , were less phosphorylated than the WT OsMPK6 ( S4 Fig ) . These results indicate that MKK10-2D phosphorylates the TEY-signature of OsMPK6 specifically and suggest that OsMPK6 is activated by OsMKK10-2 through the specific phosphorylation . Then , we expressed MKK10-2D in rice plants using the dexamethasone ( Dex ) -inducible system ( GVG-MKK10-2D ) [38] and monitored the activation of OsMPK6 by pTEpY antibody . The dual phosphorylation was induced after the Dex-treatment in two independent GVG-MKK10-2D lines ( Fig 3B , α-pTEpY ) . In these plants , WRKY45 expression ( Fig 3C ) and blast resistance ( Fig 3D ) were also induced after the Dex treatment . These results demonstrate that the activation of OsMPK6 by MKK10-2D is sufficient for the induction of WRKY45 expression and blast resistance without exogenous SA or BTH . OsMKK4 can also phosphorylate and activate OsMPK6 in vitro and in vivo [39] [30] . However , the induced expression of the OsMKK4 constitutively active form using the Dex induction system failed to induce WRKY45 expression , while phenylalanine ammonia lyase gene , as a positive control , was induced ( S5 Fig ) . These results suggest that OsMKK10-2 , but not OsMKK4 , is involved in the MAPK cascade in the SA signalling pathway leading to WRKY45 up-regulation and blast resistance . Then , we investigated the effects of ABA on the OsMPK6–WRKY45 pathway in GVG-MKK10-2D lines ( Fig 4A and S6 Fig ) . We induced OsMKK10-2D in the GVG-MKK10-2D transformants by Dex treatment in the presence of ABA . Interestingly , the accumulation of WRKY45 transcripts was severely reduced by the ABA treatment , although the induction of the MKK10-2D transgene was unaffected by ABA ( Fig 4A ) . These results imply a possibility that ABA-signalling interfered with the SA pathway by affecting some molecular event ( s ) between OsMKK10-2 activation and WRKY45 transcription . We examined whether ABA-signalling affects the phosphorylation status of OsMPK6 . Strikingly , the ABA treatment significantly lowered the dual phosphorylation level of OsMPK6 in parallel with the repression of WRKY45 transcription ( Fig 4A and S6 Fig ) . These results suggest that ABA-signalling dephosphorylates OsMPK6 or inhibits its phosphorylation by MKK10-2D . In Arabidopsis , MAPK can be dephosphorylated by Ser/Thr-phosphatases , dual-specificity phosphatases , and PTPases [28] [29] . This information , taken together with our results , led us to presume that OsMPK6 could be dephosphorylated in response to ABA , rather than ABA inhibiting the MAPK kinase . To further investigate the phosphorylation state of OsMPK6 , we used anti-phospho-Tyr and anti-phospho-Thr specific antibodies in an immunoblot analysis . While the phospho-Thr signal was unaffected , the phospho-Tyr signal became faint in parallel with that of dual phosphorylation ( Fig 4A and S6 Fig ) , suggesting that the Tyr , but not Thr , of OsMPK6 was dephosphorylated in response to ABA . Based on these results , we predicted the involvement of dephosphorylation of OsMPK6 by PTPases in the action of ABA . To assess the possible involvement of PTPases , we treated rice leaf segments with SA and ABA in the presence or absence of the PTPase inhibitors , Bay11-7082 [40] and vanadate . In the absence of these inhibitors , WRKY45 transcripts dramatically increased after the SA treatment; however , the co-treatment with ABA largely compromised the induction ( S7 Fig ) [25] . Meanwhile , the reduction of WRKY45 transcript levels by ABA was significantly less in the presence of these inhibitors ( Fig 4B and S7 Fig ) . Taken together with the results of immunodetection described above , we postulated that ABA-responsive PTPase ( s ) dephosphorylated/inactivated OsMPK6 , which in turn deactivated WRKY45 by under-phosphorylation , leading to the reduction of WRKY45 transcripts . The rice genome has two genes encoding putative Tyr-specific PTPases , OsPTP1 ( Os12g0174800 , LOC_Os12g07590 ) and OsPTP2 ( Os11g0180200 , LOC_Os11g07850 . 1 ) ( S8 Fig ) , which have high homology to AtPTP1 , a Tyr phosphatase that dephosphorylates MPK6 [41 , 42] . Active site signature of Tyrosine phosphatase ( S8 Fig ) , which is conserved in PTPs from all the organisms [43] , is also present in OsPTP1 and -2 , further lending support for these proteins being Tyr-specific phosphatases . To test this hypothesis , we generated rice transformants in which both PTPase genes were knocked down using the construct shown in Fig 5A ( PTP-wkd ) . Then , we investigated the dual phosphorylation of OsMPK6 in WT [Nipponbare ( NB ) ] and PTP-wkd rice plants after SA treatment in the absence or presence of ABA ( Fig 5B ) . In NB , the dual phosphorylation level increased after the SA treatment , but the increase was completely cancelled in the presence of ABA . In PTP-wkd rice , ABA did not suppress the level of dual phosphorylation , which further increased after SA treatment . These results strongly suggest that OsPTP1/2 is involved in ABA-dependent dephosphorylation of OsMPK6 . In the absence of ABA in PTP-wkd rice , the dual phosphorylation level was relatively high even without SA treatment; thus , OsPTP1/2 could also play a role in reducing the basal level of OsMPK6 phosphorylation . To test whether OsPTP1/2 directly dephosphorylate OsMPK6 , we performed in vitro dephosphorylation assays ( Fig 6 ) . Phospho-Tyr , but not phosphor-Thr , of OsMPK6 , due to phosphorylation by OsMKK10-2 , decreased when incubated with WT OsPTP1 fused with MBP ( Fig 6 , upper panels ) . However , the decrease of signals was not significant when incubated with a mutant OsPTP1 in which catalytically essential cysteine [44] ( positions 258 , S8 Fig ) was replaced with serine ( Fig 6 ) . We also monitored the ability of OsMPK6 to phosphorylate WRKY45 by the addition of recombinant WRKY45 and [γ-32P] ATP to the system preincubated with unlabelled ATP to activate OsMPK6 . The WRKY45 phosphorylation was completely abolished in the presence of WT OsPTP1 , whereas the phosphorylation level remained unchanged in the presence of the mutant PTPase ( Fig 6 , lower panels ) . These results indicate that OsPTP1 directly dephosphorylates OsMPK6 and inactivates its WRKY45 phosphorylation activity . These results support the notion that OsPTP1 mediates the suppression of the SA-OsMPK6-WRKY45 pathway via ABA-signalling . We did not detect evident activities for OsPTP2 using the same reaction mixtures , possibly because of suboptimal conditions . BTH , a chemical defence inducer , enhances disease resistance by acting on the SA signalling pathway in various plants , including rice [45] [46] . In Arabidopsis , the effect of BTH on defence responses is compromised by high salt conditions acting through ABA signalling [24] . To test the effects of abiotic stresses on BTH-induced blast resistance in rice , we pretreated WT rice plants , NB , with 10 μM ABA , low temperature ( 15°C/9°C , day/night ) , or high salinity ( 250 mM NaCl ) in the presence or absence of 10 μM BTH , inoculated them with M . oryzae and monitored for fungal growth ( Fig 7 ) . BTH confers a strong resistance against blast disease in rice [9] . Co-treatment with ABA compromised the resistance as reported previously [25] . Interestingly , BTH-treated plants under the cold and high salinity conditions were more highly susceptible to blast than the control plants , similar to the ABA/BTH co-treated plants ( Fig 7 ) . These results imply a possibility that the abiotic stresses mediated by ABA signalling negatively affected the BTH-induced blast resistance in rice . Activation of OsMPK6 by MKK10-2D , which mimics the activation of the SA pathway by SA/BTH , conferred rice plants with blast resistance . OsPTP1/2 dephosphorylated and inactivated OsMPK6 probably in response to ABA . These results led us to examine whether PTP-wkd plants were less sensitive to the ABA-mediated abiotic stresses , in regard to BTH-induced disease resistance ( Fig 7 ) . Under normal conditions , BTH induced as strong a blast resistance in PTP-wkd plants as in NB ( Fig 7 ) . Unlike NB; however , PTP-wkd plants exhibited strong blast resistance even in the presence of ABA ( Fig 7 ) . Moreover , PTP-wkd plants showed strong blast resistance under abiotic stress conditions , low temperature ( 15/9°C , day/night ) , and high salinity ( 250 mM NaCl ) ( Fig 7 ) . These results indicate the involvement of OsPTP1/2 in the suppression of SA/BTH-dependent blast resistance by ABA . Visible morphological and growth phenotypes were not observed in PTP-wkd under normal or stress conditions . SA/BTH-dependent defence system involves two independent sub-pathways , WRKY45 and OsNPR1 sub-pathways [9–12] . To investigate whether OsPTP1/2 act ( s ) on either sub-pathway specifically , we examined the gene expression patterns in PTP-wkd lines after the treatment with SA in the absence or presence of ABA ( Fig 8 and S10 Fig ) . In the absence of ABA , WRKY45 transcript levels in NB and PTP-wkd lines were within a similar range ( Fig 8A ) . In the presence of ABA , the transcript levels were significantly decreased in NB . By contrast , the transcript levels in PTP-wkd plants were increased to the levels without ABA ( Fig 8A ) . WRKY62 is a direct target gene of WRKY45 , and we have previously observed that this gene behaves similarly to WRKY45 in response to SA/BTH [9] [14] . In this experiment ( Fig 8B ) , the expression pattern of WRKY62 paralleled that of WRKY45 . We have previously reported that the expression of OsNPR1 , as well as that of WRKY45 , were suppressed by ABA [25] . Interestingly; however , no effect of the OsPTP1/2 double knockdown was observed on the suppression of OsNPR1 expression by ABA ( Fig 8C ) . The SalT gene is an ABA-inducible gene [47] [25]; however , the effect of the OsPTP1/2 double knockdown was not observed on the expression of this gene either ( Fig 8D ) . These results suggest that OsPTP1/2 acts specifically on the WRKY45 sub-pathway of SA-signalling under abiotic stress conditions ( Fig 9 ) . The expression of OsPTP1/2 genes was not positively affected by ABA ( Fig 8E and 8F ) , high salinity , or low temperature condition ( S9 Fig ) , suggesting that these genes are regulated post-transcriptionally by ABA and abiotic stresses .
Tyr-specific PTPases are also encoded in dicot genomes . Of these , only Arabidopsis AtPTP1 has been functionally characterized , and the tyrosine dephosphorylating activity of AtMPK6 ( MPK6 ) has been reported [42] [41] . Elevated levels of SA , accompanied by PR-gene expression , have been reported in a double mutant of the AtPTP1 and MKP1 phosphatase genes [41] . However , no phenotype has been found in a single mutant of the AtPTP1 gene . Our finding for OsPTP1/2 is the first to report a function that is specific to plant PTPs; therefore , whether or not PTPs in other plant species have similar functions remains to be determined . In addition , this function is probably largely dependent on the pathosystem ( combination of host and pathogen ) . Why have ( rice ) plants developed this antagonistic signalling crosstalk ? Presumably , this is the mechanism behind the trade-off between the responses to biotic and abiotic stresses , which prioritizes the most life-threatening stress through the allocation of limited resources under various situations . In nature , such a system is likely to help plants to survive changing environments in a cost-efficient manner . Serious rice losses due to blast disease occurred during the cold summers of 1993 and 2003 in Japan . Other research reported that low temperatures and drought render rice plants more susceptible to blast disease [32] [33] . Moreover , high salinity conditions can breakdown the disease resistance to Fusarium and Phytophthora in tomato [48] . In our experiments , the rice plants prioritized the responses to the cold or high salinity over disease resistance , which eventually compromised the BTH-induced blast resistance . Owing to this rice response under multiple stresses , most of the rice plants died of blast disease . Considering this consequence , this trade-off mechanism does not appear to have been beneficial to plants in our experiments . Crop cultivation is often conducted under resource-rich fertile conditions , and so were our experiments . Under such conditions , the trade-off seems to be unnecessary and even harmful for plants , as well as for farmers . Therefore , down-regulating such crosstalk seems to be favourable for agriculture as long as there is no unexpected adverse side effect . Theoretically , provided that the PTPases act specifically on the SA–ABA crosstalk , there is unlikely to be any such side effects . Indeed , we have not observed any significant adverse effects on the growth of PTP-wkd plants so far . The same could also hold for the trade-offs between particular stress responses and/or between a particular stress and plant growth/yield . On the basis of this speculation , down-regulating a particular signalling crosstalk could be an important goal of crop breeding . So far , little is known about the molecules , besides OsPTP1/2 , that directly mediate the crosstalk . To date , several signaling components have been reported to mediate signalling crosstalk; however , most of them play indirect roles in the crosstalk . Modifications of such molecules would change the balance of responses to different stresses , but they are unlikely to eliminate the crosstalk , which would maximize the defence or tolerance to both stresses [22] . Currently , only a few molecules , such as BZR1 , which mediates the crosstalk between brassinosteroid- and gibberellin-signaling , as well as innate immunity [49] [50] , are known to play direct roles in the signalling crosstalk . Once researchers identify more of these molecules , it should become possible to develop multi-stress-tolerant crops without penalties on yields .
Rice plants ( Oryza sativa subsp . japonica cv . Nipponbare ) were grown in a greenhouse in soil ( Bonsol No . 2; Sumitomo Chemical Co . , Tokyo , Japan ) at 30°C/26°C ( day/night ) with a relative humidity ( RH ) of approximately 60% . Culturing of the blast fungus M . oryzae ( race 003 ) and fungal inoculations of rice plants were carried out essentially as described previously [25] , with slight modifications . Briefly , M . oryzae conidia suspended in 0 . 02% Tween20 at a density of 150 , 000/ml were sprayed onto rice leaves , which had been pretreated with chemicals in 0 . 1 × MS in 50-ml tubes sealed with surgical tape ( 3M , St . Paul , MN , USA; cat# 3530–1 ) . Detailed condition for each experiment is described below . Normal-temperature conditions: after chemical pretreatments for 1 d in a growth chamber ( 30°C , 14-h day and 26°C , 10-h night; 60% RH ) , the solutions were removed , and the leaves were spray-inoculated with fungal conidia . The leaves were incubated in a dew chamber at 24°C for 24 h , and then for a further 5 d period in a growth chamber . Low-temperature conditions: rice leaves were pretreated with chemicals for 2 d in a growth chamber ( 15°C , 14-h days and 9°C , 10-h nights; 60% RH ) . After fungal inoculations followed by incubation for 3 d under low-temperature conditions , solutions were removed , and leaves were further incubated for 5 d under normal-temperature conditions . High-salinity conditions: before detaching leaves , whole plants were soaked in 250 mM NaCl for 4 d . Other procedures are the same as those of the normal condition . Disease development was evaluated by quantifying M . oryzae 28S rDNA by qPCR [51] . Three or more biological repeats were performed for each disease resistance assay . Site-directed mutagenesis was performed with a QuickChange Multi Site-directed mutagenesis kit ( Stratagene , La Jolla , CA , USA ) according to the manufacturer’s instructions . For constructs using NanoLuc , cDNA encoding NanoLuc was subcloned from the pNL vector ( Promega , Madison , WI , USA ) . For overexpression of WT and mutant WRKY45 , cDNA amino-terminally fused with 3X myc tag was cloned into the pZH vector under the control of the maize ubiquitin promoter . For Dex-inducible MKK10-2D , OsMKK10-2D cDNA [30] was subcloned into the pINDEX vector [52] . For PTP-wkd , the 3′-untranslated regions of OsPTP1 and -2 genes were amplified using u7 ( 5′- CACCCGGGTATCCCTAAGGCAGGA-3′ ) and u8 ( 5′- AAATGATTCAGTTTAAACCTACTAACTCTCTTTAATTCCGT-3′ ) , and u9 ( 5′- TTAGTAGGTTTAAACTGAATCATTTCTATGGAACAATCAGT-3′ ) and u10 ( 5′- AGGCCTGGGTGGGCAGGAGAAGCG-3′ ) primers , respectively . Then , we performed an overlapping second PCR using the u7 and u10 primers , and the products of the first reactions . Each amplified fusion gene was cloned into pENTR/D-TOPO ( Invitrogen , Carlsbad , CA , USA ) and subsequently transferred into the pANDA vector [53] . A reporter assay in the rice leaf sheath was essentially performed as described previously [14] . The mixture of plasmids for the expression of effectors consisted of the plasmids encoding wild-type or mutant proteins amino-terminally fused with and without NanoLuc . The effectors with or without the N-terminal NanoLuc were mixed at the ratio of 1:10 . A total of 3 μg of the LUC reporter plasmid and 0 . 5 μg of the effecter plasmid mixture were used per assay . LUC and Nanoluc activities were determined using luciferin and furimazine as substrates , respectively , and their ratios ( LUC/Nanoluc ) were compared . Agrobacterium-mediated transformations of rice calli were performed as described previously [54] [55] . Plants were regenerated from transformed calli by selecting for hygromycin resistance . Chemical treatments of leaves were conducted essentially as described previously [25] . Leaf blades from rice plants at the four-leaf stage were cut into segments approximately 0 . 5 cm long and submerged in a solution containing the chemicals prepared in 0 . 002% Silwet L-77 . The leaf segments were incubated in the light at 30°C for the periods indicated . We applied ABA 1 h before SA or Dex treatments . The PTPase inhibitors were applied 1 h before the ABA treatment . Immunoblot assays were carried out essentially as described previously [15] . Proteins were extracted with 50 mM Hepes-KOH , pH 7 . 5 , containing Complete Protease Inhibitor Cocktail ( Roche Diagnostics , Mannheim , Germany ) , 1 mM PMSF , and protein phosphatase inhibitor cocktails ( phosphatase inhibitor cocktail 1 and 2; Sigma , St Louis , MO , USA ) or alternatively PhosTop ( Roche Diagnostics ) . For the treatment with PPase , the protein phosphatase inhibitor cocktails were excluded . After centrifugation , supernatants ( containing 6–20 μg protein ) were subjected to sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE; 10% 29:1acrylamide:bis-acrylamide ) , followed by electroblotting . In the case of the Phos-Tag SDS-PAGE , 10 μM Phos-Tag acrylamide ( Wako , Tokyo , Japan ) and 20 μM Zn ( NO3 ) 2 were included in the 7 . 5% polyacrylamide gel ( 29:1 acrylamide:bis-acrylamide ) . Other procedures were performed as described previously [36] . Immunodetection was performed using SNAPid ( Millipore , Billerica , MA , USA ) according to the manufacturer’s instructions . The antibodies used were as follows: anti-pTEpY ( Promega , cat# V8031 ) at a 1/1 , 500 dilution; anti-OsMPK6 [39] at a 1/1 , 500 dilution; anti-phosphoenolpyruvate carboxylase [56] at a 1/30 , 000 dilution; anti-pY ( Millipore , clone 4G10 platinum ) at a 1/1 , 500 dilution; anti-pT [Promega , anti-pT183 MAPK pAb ( rabbit ) ] at a 1/4 , 000 dilution; and anti-WRKY45 [15] at a 1/300 dilution . Total RNA was isolated from rice leaves treated with chemicals as described above using Trizol reagent ( Invitrogen ) . cDNA was synthesized using ReverTraAce ( Toyobo , Tokyo , Japan ) . Quantitative PCR was run on a Thermal Cycler Dice TP800 system ( Takara Bio , Tokyo , Japan ) using the SYBR premix ExTaq mixture ( Takara Bio ) as described previously [9] . Sequences of primers used for RT-qPCR are listed in S1 Table . Phosphorylation and dephosphorylation assays were carried out as described previously [57] [39] , with modifications . GST-MKK10-2D and MBP-MPK6 ( WT or mutant ) were incubated in reaction buffer ( 10 mM Hepes-KOH , pH 7 . 5 , 5 mM EGTA , 20 mM MgCl2 , 1 mM DTT ) containing 0 . 5 mM ATP at 25°C for 20 min . For dephosphorylation assays , MBP-PTP1 or -2 was added and the reaction mixture was incubated for an additional 20 min . For WRKY45 phosphorylation activity assays , the same reaction mixtures were pre-incubated for 20 min , and reactions were initiated by adding MBP-WRKY45 and 37 kBq [γ-32P]ATP . The mixtures were incubated for an additional 20 min , and then terminated by adding Laemmle’s sample buffer and boiling . Labelled proteins were analysed by SDS-PAGE . Coomassie brilliant blue staining was performed as loading controls . WRKY45: Os05g0322900 , LOC_Os05g25770 OsMKK10-2: Os03g0225100 , LOC_Os03g12390 . 1 OsMPK6: Os06g0154500 , LOC_Os06g06090 OsPTP1: Os12g0174800 , LOC_Os12g07590 OsPTP2: Os11g0180200 , LOC_Os11g07850 . 1 | Chemical defence inducers make plants resistant to diseases such as rice blast . However , plants sometimes become more pathogen susceptible under abiotic stresses even in their presence . Because such regulation prioritizes the responses to the most life-threatening stress , it could be necessary for plants to survive in nature . However , it seems dispensable or even disadvantageous for crops cultivated under fertile conditions . Here , we show the molecular mechanism underlying one of such phenomena in rice . WRKY45 is a central transcription factor that regulates strong defence signalling mediated by salicylic acid . We found that WRKY45 is activated through phosphorylation by a protein kinase , OsMPK6 , which is activated by dual phosphorylation in response to the defence signalling . We also found that OsMPK6 can be inactivated by tyrosine dephosphorylation in response to abiotic stresses such as low temperature and high salinity probably mediated by abscisic acid , leading to reduction of WRKY45-dependent disease resistance . Moreover , we found that specific tyrosine protein phosphatases dephosphorylate/inactivate OsMPK6 in response to abiotic stresses . Knockdown of their genes rendered rice plants resistant against blast disease even under the abiotic stresses , pointing to the way to further improve rice . |
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Retrograde signaling is essential for neuronal growth , function and survival; however , we know little about how signaling endosomes might be directed from synaptic terminals onto retrograde axonal pathways . We have identified Khc-73 , a plus-end directed microtubule motor protein , as a regulator of sorting of endosomes in Drosophila larval motor neurons . The number of synaptic boutons and the amount of neurotransmitter release at the Khc-73 mutant larval neuromuscular junction ( NMJ ) are normal , but we find a significant decrease in the number of presynaptic release sites . This defect in Khc-73 mutant larvae can be genetically enhanced by a partial genetic loss of Bone Morphogenic Protein ( BMP ) signaling or suppressed by activation of BMP signaling in motoneurons . Consistently , activation of BMP signaling that normally enhances the accumulation of phosphorylated form of BMP transcription factor Mad in the nuclei , can be suppressed by genetic removal of Khc-73 . Using a number of assays including live imaging in larval motor neurons , we show that loss of Khc-73 curbs the ability of retrograde-bound endosomes to leave the synaptic area and join the retrograde axonal pathway . Our findings identify Khc-73 as a regulator of endosomal traffic at the synapse and modulator of retrograde BMP signaling in motoneurons .
Bidirectional communication between the neuronal cell body and distant synaptic terminals is essential for synapse formation , plasticity and neuronal survival [1 , 2] . This is achieved primarily through highly regulated axonal transport . Anterograde transport is mediated by plus-end directed kinesin motor proteins that deliver synaptic vesicles and newly synthesized proteins to the synapse , while retrograde transport of cargo destined for the cell body , such as activated receptor complexes , is accomplished by dynein protein complexes [1 , 3–5] . Kinesin and dynein motors are also required for endosomal traffic within the cell . The coordinated action of anterograde and retrograde motors ensures the proper sorting and delivery of signaling complexes , proteins and organelles [6] . Although defects in endosomal traffic and axonal transport have been associated with a number of nervous system diseases including Charcot-Marie-Tooth disease , Amyotrophic Lateral Sclerosis , Huntington’s disease and Parkinson’s disease , we know little about how signaling endosomes are routed from the synapse to the retrograde pathway [7–13] . Retrograde signaling has been extensively studied at the Drosophila larval neuromuscular junction ( NMJ ) . In particular the Bone Morphogenic Protein signaling pathway ( BMP ) has been identified as a major regulator of synaptic growth and function . As such , many regulators of synaptic endosomal sorting have been identified in the regulation of BMP signals at synaptic terminals . Nevertheless , how activated receptors are preferentially sorted to travel to the nucleus is currently unknown . The movement of endosomes within the cytoplasm is directed through the actions of microtubule binding proteins such as minus end dynein motors , and plus end directed kinesins . Co-ordination and competition between these opposing motors for endosome cargoes regulates the transport of proteins to their correct targets [1 , 4 , 14–18] . In this study , we have discovered a surprising role for the plus-end directed microtubule motor protein Khc-73 in retrograde sorting of signaling vesicles at the Drosophila larval NMJ . Khc-73 and its vertebrate homolog KIF13B/GAKIN are kinesin 3 motor protein family members with multiple protein domains and diverse roles in both vertebrates and invertebrates [15 , 19–32] . At its N-terminal , Khc-73 contains a kinesin motor necessary for its association with microtubules and plus-end directed transport to synaptic terminals , and at its C-terminal , a Cytoskeletal Associated Protein GLYcine rich ( CAP-GLY ) domain that provides microtubule association properties [30 , 32] . In the nervous system , through microtubule cytoskeleton interactions , both KIF13B and Khc-73 have been shown to participate in mechanisms that control neuronal polarity: Khc-73 has a role in spindle orientation in neuroblasts [30] , and KIF13B is involved in the establishment of axonal structures in post-mitotic neurons [20] . Interestingly , KIF13B/Khc-73 has been implicated in the regulation of endosomal dynamics [33 , 34] and axonal transport [15] through interaction with Rab5-GTPases . Our previous findings suggested that Khc-73 , under strong inhibitory control of the microRNA miR-310-313 cluster in motoneurons at the Drosophila NMJ , plays an important role in the regulation of synaptic function by influencing presynaptic neurotransmitter release [35] . In order to investigate the mechanism of action of Khc-73 , we have generated loss of function deletions in Khc-73 gene in D . melanogaster and examined the motoneurons of third instar larvae . While the number of synaptic boutons at the NMJ and the amount of neurotransmitter release per action potential are unaffected in Khc-73 mutant larvae , we find a small but significant decrease in the number of presynaptic release sites . Our experiments indicate the presence of Khc-73 function in BMP signaling by demonstrating a strong genetic interaction between Khc-73 and members of the BMP signaling pathway . We further show that activation of retrograde BMP signaling that normally leads to accumulation of pMad in the nuclei of motoneurons is significantly suppressed when Khc-73 is genetically removed . Our findings suggest that Khc-73 exerts its function by influencing the sorting of endosomes at the NMJ and promoting retrograde routing of endosomes . Our findings identify , for the first time , a plus-end directed microtubule motor protein as a regulator of retrograde signaling in motoneurons .
Khc-73 is a member of the KIF superfamily of kinesin motor proteins and the homologue of the vertebrate KIF13B/GAKIN ( S1A Fig ) [30 , 35] . We have previously shown that Khc-73 is a target of the micro RNA miR-310-313 cluster in motoneurons . We found that loss of the miR-310-313 cluster led to abnormally enhanced neurotransmitter release at the NMJ; this enhancement was fully reversed to wild type levels as a result of neuronal knock down of Khc-73 [35] . In order to investigate the role of Khc-73 in more detail , we generated deletions in the Khc-73 gene by imprecise excision of a P-element transposon insert in the vicinity of the 5’ UTR of Khc-73 ( S1B Fig ) . We isolated two deletion flies Khc-73149 and Khc-73193 missing portions of the Khc-73 5’UTR and the ATG start ( S1B Fig ) ; we also isolated a fly where the P-element was excised precisely leaving the entire genetic region of Khc-73 intact ( Khc-73100 ) ( S1B Fig ) . Our western blot analysis with an antibody against the C-terminal end of Khc-73 indicates that both Khc-73149 and Khc-73193 are protein null alleles ( S1C Fig ) . Khc-73 is maternally expressed and is expressed in the embryo [32] . We examined the expression pattern of Khc-73 protein in motoneurons with transgenic overexpression , since we were not able to detect a specific signal using our antibody against Khc-73 in larval preparations . We overexpressed HA-tagged Khc-73 transgene in motor neurons and detected punctate accumulation of HA-Khc-73 both in axons and in synaptic boutons at the NMJ ( S1D and S1E Fig ) . In addition , we tested transcriptional activity of Khc-73 by generating a Khc-73-Gal4 fly ( containing 4kb of Khc-73 genomic sequence driving Gal4 expression , see methods ) . Crossing this fly to UAS-mCD8-GFP transgene led to the expression of GFP in nearly all neurons including motor neurons , suggesting that Khc-73 transcription is active in all motor neurons in third instar larvae ( S1F and S1G Fig ) . We also found Khc-73 transcription widely expressed in the brain of adult flies ( S1H Fig ) . Based on the previously published roles for Khc-73 in neuroblasts , endosomal sorting , axon morphology and synaptic function [21 , 29 , 30 , 34–36] , we expected loss of Khc-73 to cause significant defects in the normal synaptic function and/or structure . To our surprise , we found only mild defects ( S2E and S2F Fig ) in Khc-73 mutant larvae in our assessment of gross synaptic structure at the larval NMJ . The number of synaptic boutons and the muscle surface area ( MSA ) at NMJs were not significantly different comparing Khc-73 mutant and control larvae; this was true for muscle 4 NMJs ( Fig 1A–1C ) as well as muscle 6/7 NMJs ( S1I and S1J Fig ) . To test whether loss of Khc-73 might affect synaptic function , we examined the baseline electrophysiological properties including miniature excitatory postsynaptic currents ( mEPSCs ) , evoked excitatory postsynaptic currents ( EPSCs ) and quantal content ( QC ) and found no differences between Khc-73 mutants and wild type larvae ( Fig 1D and 1E ) . Similarly , we tested synaptic vesicle recycling dynamics in Khc-73 mutant NMJs with high frequency stimulation and found no significant difference in the decay of the synaptic response compared to controls ( S1K and S1L Fig ) . Consistent with the lack of defects in baseline synaptic function , we found no significant changes in the fluorescent intensity of the synaptic vesicle calcium sensor synaptotagmin ( SYT ) , synaptic vesicle marker cysteine string protein ( CSP ) or synaptic vesicle recycling protein Epidermal growth factor receptor pathway substrate clone 15 ( EPS15 ) in Khc-73 mutant larvae ( S2A–S2D Fig ) . We found a mild reduction in the staining intensity for the postsynaptic marker Discs large ( Dlg ) ( S2E and S2F Fig ) but no differences in the expression level of postsynaptic glutamate receptor subunit A ( GluRIIA ) ( S2G Fig ) . Altogether these findings indicate that synaptic growth and function are largely normal in Khc-73 mutant . We have previously reported an increase in the accumulation of the active zone protein Bruchpilot ( Brp ) in miR-310-313 cluster mutant larvae that could be reduced by transgenic knockdown of Khc-73 [35] . Therefore , we set out to conduct a deeper examination of Khc-73 mutants to understand the mechanism of action of Khc-73 in motoneurons . As our previous data would predict , we found a significant decrease in the number of presynaptic release sites per NMJ in Khc-73 mutant larvae , as indicated by a reduction of the number of Brp Puncta ( Fig 2A and 2B ) . Inclusion of a genomic fragment containing the entire genetic region of Khc-73 gene restored synaptic defects in Khc-73 mutant larvae , indicating that this defect is related to loss of Khc-73 ( Fig 2B ) . Previously we showed that Khc-73 is under control of the microRNA cluster miR-310-313 [35] . To maintain this relationship in our tissue specific rescue , we used a Khc-73 transgene Khc-73-3’UTR ( K014 ) [35] that retains this negative regulatory control . We found that transgenic expression of Khc-73 in presynaptic motoneurons , but not in postsynaptic muscles was sufficient to establish a normal number of presynaptic release sites ( Fig 2C and 2D ) . This result indicates that Khc-73 function in motoneurons is required for normal maturation of synaptic release sites . During larval development , both the coordinated growth of synaptic boutons and the establishment of synaptic strength at the NMJ are largely dependent on a retrograde signaling cascade that is initiated by the release of the Bone morphogenic protein Glass bottom boat ( Gbb ) in postsynaptic muscles . Gbb signals through type I and type II BMP receptors , leading to phosphorylation of and subsequent accumulation of the BMP transcription factor Mad ( Mothers against decapentaplegic ) in the nuclei of motor neurons [37–41] . Through this signaling cascade , genes that control synaptic growth and function are transcriptionally regulated [42–44] . The decrease in the number of presynaptic release sites in Khc-73 mutant larvae , therefore , prompted us to examine the state of BMP signaling in these mutants . The first indication of Khc-73 involvement with BMP signaling came from genetic interaction experiments between Khc-73 and the Drosophila homolog of vertebrate SMAD4 , Medea . Medea is a transcriptional co-factor that is required for normal BMP signaling in motor neurons [45] . We found that a combination of previously published alleles MedeaG112 and MedeaC246 resulted in a very small reduction in the number of boutons at the NMJ compared to MedeaC246 homozygous loss of function mutant [45] , suggesting that G112 is a hypomorphic allele ( Fig 3A and 3E ) . Interestingly , in transheterozygous combinations of Khc-73 and Medea , we found a significant reduction in the number of presynaptic release sites per NMJ ( Fig 3A and 3B ) , no change in Brp puncta per bouton ( Fig 3C ) , a significant reduction in synaptic area ( Fig 3D ) and a significant reduction in bouton number with MedC246 allele but not MedG112 ( Fig 3E ) , as compared to heterozygous MedeaG112 controls . This transheterozygous genetic interaction suggested that Khc-73 , while having a mild influence on baseline BMP signaling , becomes critical when BMP signaling is compromised . In support of these results , we also found a strong genetic interaction between Khc-73 mutants and a mutation in the BMP type II receptor wishful thinking ( wit ) : transheterozygous combination between Khc-73 and wit A12 mutants led to a significant reduction in the number of presynaptic release sites ( Fig 3F and 3G ) , synaptic area ( Fig 3I ) and bouton number ( Fig 3J ) but no change in Brp puncta per bouton ( Fig 3H ) . To further explore the functional link between Khc-73 and BMP signaling , we generated double mutant combinations of Khc-73 and Medea . We assessed these double mutant combinations for defects in Brp puncta number at the NMJ and for accumulation of Brp in axons ( as previously reported [46] ) . We found that defects in active zone number and abnormal Brp accumulation in axons in the transallelic combination of MedeaC246/MedeaG112 were not further enhanced upon removal of Khc-73 ( S3A–S3F Fig ) , indicating that Khc-73 and Med likely function in the same and not parallel pathways with respect to these phenotypes . We then tested whether defects in Brp puncta number in Khc-73 mutants can be restored by overexpressing BMP signaling in motoneurons . Indeed , overexpression of a constitutively active form of BMP type I receptor Thick veins ( TKVACT ) in motoneurons was capable of reversing the reduction in Brp puncta defect in Khc-73 mutant larvae ( Fig 4A–4C ) . These results prompted us to compare the degree of axonal accumulation of Brp and another synaptic marker , synaptotagmin ( SYT ) between Khc-73 and Mad mutants . For this we used a Mad mutant allele ( MadK00237 ) that is known to show a strong reduction of synaptic growth and function at the NMJ and exhibit defects in axonal transport of synaptic markers [41] . Both endogenous Brp and SYT accumulated in large aggregates in axons of Mad mutant larvae compared to wild type or Khc-73 mutant larvae , highlighting the fact that Khc-73 related axonal defects would be comparable to a hypomorphic loss of function of BMP signaling ( Fig 5A–5D ) . In support of this interpretation , the increase in Brp accumulation in axons of Khc-73 mutant larvae was fully reversed as a result of overexpression of TKVACT ( Fig 5E and 5F ) . We also tested whether abnormal axonal accumulation of Brp in Khc-73 mutant larvae could be due to changes in microtubule structures; however , we found no significant changes in the expression of acetylated tubulin in axons or terminals in Khc-73 mutant larvae when compared to wild type counterparts ( S3G and S3H Fig ) . These results further support that the defects associated with active zone numbers in Khc-73 mutant larvae are most likely related to defects in BMP signaling . While we did not detect measurable changes in the accumulation of pMad in response to loss of Khc-73 ( S4A and S4B Fig ) , or overexpression of Khc-73 transgene ( S4C and S4D Fig ) , the genetic interactions described above provide strong evidence for a functional link between Khc-73 and BMP signaling . In order to strengthen this link and extend it to the regulation of synaptic function , we conducted a number of electrophysiological examinations . Mild to moderate overexpression of TKVACT in motor neurons can lead to an enhancement in synaptic release without significantly affecting the number of synaptic boutons [44 , 47] . We found that loss of Khc-73 could significantly block the ability of TKVACT to enhance synaptic strength ( Fig 6A and 6B ) . Similarly , we found that overexpression of the BMP ligand Gbb in postsynaptic muscles led to a significant enhancement in quantal content ( Fig 7A and 7B ) . As in the case of TKVACT induced enhancement in neurotransmitter release , loss of Khc-73 led to a significant suppression of Gbb-induced enhancement in neurotransmitter release ( Fig 7A and 7B ) . In addition , we quantified the accumulation of pMad in motoneuron nuclei in the ventral nerve cord ( VNC ) as a result of postsynaptic overexpression of Gbb . It is well accepted that the accumulation of pMad in the nuclei of motor neurons is a reliable readout of the strength and efficiency of retrograde BMP signaling in motor neurons and is essential for BMP-dependent transcriptional regulation as well as regulation of synaptic function [38 , 40 , 44 , 48 , 49] . Muscle overexpression of Gbb led to a statistically significant increase in pMad in the nuclei of motoneurons , which was fully reversed as a result of loss of Khc-73 ( Fig 7C and 7D ) . Finally , we tested whether Khc-73 gain-of-function would be dependent on normal BMP signaling in motoneurons . We have previously shown that Khc-73 overexpression in motoneurons leads to an enhancement of neurotransmitter release [35] . We found that heterozygosity for the BMP type II receptor wishful thinking ( wit ) was sufficient to suppress this enhancement to a large extent ( Fig 8A and 8B ) , further supporting the presence of a functional link between Khc-73 and BMP signaling . From these results a picture emerges , indicating a strong functional link between Khc-73 and BMP signaling in motor neurons . But how does Khc-73 interact with BMP signaling ? BMP signaling in motoneurons depends on tightly regulated endosomal traffic . For example , pMad accumulation in motoneuron nuclei in response to activation of BMP signaling at the synapse is dependent on retrograde routing of signaling endosomes containing BMP receptor complexes from the nerve terminal along axons to the cell body [5] . Conversely , routing of BMP receptor complexes to lysosomal pathways appears as one of the mechanisms that attenuates BMP signaling in motor neurons [50–52] . Therefore , we considered a role for Khc-73 in both retrograde routing as well as lysosomal sorting of BMP receptor complexes . To test these possibilities , we assessed the level of BMP receptors Wit and TKV using a combination of Western blot analysis and immunohistochemistry . Western blot analysis of CNS and body wall muscle tissue ( containing NMJ terminals ) revealed no change in the level of endogenous Wit protein as a result of genetic removal of Khc-73 ( Fig 9A–9D ) . The available antibody to Wit does not detect endogenous Wit in immunohistochemistry . Thus we turned to transgenic Wit and Tkv to visualize their localization at the synapse . Static images of the boutons in live preps of WIT-GFP revealed punctate accumulations at the NMJ and an increase of Wit receptor intensity in Khc-73 mutants at muscle 4 and muscles 6/7 ( Fig 9E–9H ) . Similarly , TKV:YFP transgene expression appeared more punctate at muscle 4 ( S5A Fig ) and muscles 6/7 ( S5C Fig ) , trending towards increased intensity at muscle 4 ( S5B Fig ) , while significantly increasing in intensity at muscles 6/7 ( S5D Fig ) in Khc-73 mutants . We ruled out changes in TKV:YFP transgene transcription by quantitative PCR ( S5E Fig ) and did not observe obvious changes in axonal traffic of TKV:YFP in motoneurons ( S5F Fig ) . We next tested our model that Khc-73 loss can suppress BMP signaling by examining pMAD levels in larvae overexpressing the Wit receptor in presynaptic neurons and in larvae overexpressing the Gbb ligand from postsynaptic muscle . Overexpression of Wit enhanced presynaptic pMad levels ( Fig 10A and 10B ) . In Khc-73 mutants , this enhancement was significantly suppressed ( Fig 10A and 10B ) . Similarly , muscle overexpression of Gbb enhanced pMAD levels in presynaptic boutons . Khc-73 loss also suppressed this increase ( Fig 10C and 10D ) . It has been demonstrated that BMP receptor activity can be dampened when trapped inside the lumen of multivesicular bodies ( MVBs ) at the NMJ [53] . Generally , MVBs are intracellular vesicles that contain one or more smaller vesicles within their lumen and play an important role in signal transduction and endosomal sorting [54 , 55] . Current evidence suggests that MVBs may be at the crossroads for endosomal cargo joining the lysosomal pathway , the retrograde pathway or the exosomal secretory pathway [55 , 56] . We find that fluorescence intensity of the MVB localized protein Hrs ( hepatocyte growth factor related tyrosine kinase substrate ) is increased by 20% at the NMJ in Khc-73 mutant larvae overexpressing the BMP receptor Wit ( S6A and S6B Fig ) . Suggesting that there are more MVBs in Khc-73 mutants in this Wit overexpressing background . Therefore , a scenario can be considered in which retrograde bound BMP receptors are encapsulated in multivesicular bodies and may be stalled at the NMJ in Khc-73 mutants . Together , these results suggest that degradation of BMP receptors is not a likely explanation for the inhibition of BMP signaling in Khc-73 mutant larvae . Secondly , our findings suggest that while BMP receptors appear to accumulate at the NMJs in Khc-73 mutants , they are in an endosomal state that prevents these receptors from signaling . Previous studies on Khc-73/KIF13B have identified endosomal sorting roles for this protein [15 , 21 , 22 , 27 , 28 , 34 , 57] . In order to gain additional insight into the role of Khc-73 in the regulation of endosomal traffic , we conducted an ultrastructural analysis of NMJ synapses in Khc-73 mutant larva . Our analysis revealed no gross abnormalities in presynaptic boutons ( Fig 11A–11F ) : different morphometric measures of active zones and synaptic vesicles appeared normal in Khc-73 mutant larvae ( Fig 11B–11E ) ; however , we did detect a small increase in the depth of the subsynaptic reticulum ( SSR ) ( Fig 11F ) . Interestingly , although we find no statistical difference in the mean MVBs per bouton ( 1 . 28±0 . 29 control and 1 . 68±0 . 41 Khc-73 ) , we found a proportion of boutons with an abnormally higher number of MVBs ( 7–9 MVBs per bouton ) in Khc-73 mutant larvae ( Fig 11G and 11H ) . The trend towards more MVBs in Khc-73 mutant boutons suggested a role for Khc-73 in endosomal sorting . Therefore , we turned to exploring a possible role for Khc-73 in the regulation of endosomal dynamics by examining the expression of transgenic Rab-GTPases at the synapse . Rab-GTPases are small GTPases that associate with endocytic vesicles and are known to mediate many aspects of endosomal traffic in all eukaryotes [58] . Based on previous reports on interaction between Khc-73 with the early endosome associated Rab5 in vitro [34] , we tested the expression pattern of Rab5 at the NMJ in Khc-73 mutant larvae with a UAS-Rab5:YFP transgene . However , we found that in Khc-73 mutants the punctate appearance of Rab5:YFP was unaffected in terms of fluorescence intensity or localization ( Fig 12A and 12B ) . Similarly , we did not detect any effect on the expression level of the recycling endosomal marker Rab11 ( Fig 12C and 12D ) . In most eukaryotic cells Rab5 positive internalized vesicles become associated with Rab7 along their path of maturation [59–62]; Rab7 containing late endosomes are then either routed to the lysosomal pathway or the recycling pathway [58 , 63] . In neurons , the transition from Rab5 to Rab7 is also necessary for routing late endosomes onto the retrograde pathway [64] . The retrograde pathway is necessary for transporting signaling complexes , neurotrophic factors and other cellular proteins from nerve endings to the cell body [2] . Interestingly unlike the case of Rab5 , we found an abnormal increase in Rab7 accumulation at synaptic boutons in Khc-73 mutants ( Fig 12E and 12F ) . These results suggested to us that the normal dynamics of Rab7 positive vesicles and by extension those of BMP receptors are disrupted in Khc-73 mutant larvae . In order to examine the dynamics of late endosomal traffic in more detail , we set out to conduct live imaging in dissected larvae expressing Rab7:GFP . To see if our observations of Rab7:GFP would be relevant to the dynamics of Wit/Tkv complexes , we confirmed in fixed samples that Wit and Rab7:GFP colocalized when expressed simultaneously ( S7A Fig , Pearson’s r coefficient 0 . 68 ) . We also confirmed that Tkv and Wit colocalized at the NMJ ( S7B Fig , Pearson’s r coefficient 0 . 60 ) . In live dissected larval preparations , Rab7:GFP showed dynamic movement within synaptic boutons in both wild type and Khc-73 mutants ( Fig 13A–13C and S1 Movie and S2 Movie ) . We noticed that occasionally a Rab7 marked vesicle left the synaptic area and moved retrograde towards the shaft of the axon . Vesicles entering the axon moved , paused and continued moving out of the NMJ . We measured the velocity of these vesicles when in motion and calculated the mean velocity in the anterograde and retrograde directions ( Fig 13B–13E , S8A–S8D Fig and S3 Movie and S4 Movie ) and found no statistical difference in their velocities . We also recorded the time spent paused in a single spot ( Fig 13F ) , the number of pauses for each spot ( Fig 13G ) and summed the total time paused in the axon ( Fig 13H ) . Here , our assessment of Rab7 dynamics revealed a significant difference between control and Khc-73 mutant larvae . We recorded long periods of pausing or stalling of Rab7 positive vesicles in Khc-73 mutants , which showed statistical difference compared to our recordings in control larvae ( Fig 13F and 13H , S1 Movie and S2 Movie ) . This pausing phenotype provides one explanation for the increase in Rab7:GFP in Khc-73 NMJs , however alternative explanations related to Rab7:GFP protein turnover are also possible . We next performed time lapse imaging on TKV-YFP expressing Khc-73 mutant larvae focusing on the axon shaft near the synapse . Here we observed a similar stalling phenotype of TKV-YFP puncta in Khc-73 mutants whereas in control larvae the axonal shaft was devoid of stalled puncta ( Fig 13I and 13J , S5 Movie and S6 Movie ) ) . As an additional test for axonal retrograde transport , we used a peripheral axon injury model developed by Collins and colleagues for activating Jun-N-terminal kinase ( JNK ) signaling in motor neurons [65] . In this model , crushing peripheral axons in larvae leads to a strong transcriptional upregulation of the JNK phosphatase puckered ( puc ) in the injured motoneurons [65] . The puc transcriptional response to axon injury is dependent on axonal retrograde transport [65] . Using a puc-LacZ transcriptional reporter line , we assessed JNK activation in motoneurons in response to nerve crush . In Khc-73 larvae , we found that puc transcriptional upregulation as a result of axonal injury was indistinguishable from that of control larvae ( S8E–S8H Fig ) . Thus we can rule out a defect in retrograde axonal transport in Khc-73 mutants . Similarly , we did not find any significant changes in axonal transport of mitochondria in Khc-73 mutant larvae ( S7 Movie and S8 Movie ) . These results provided strong evidence for a model in which Khc-73 is required primarily in synaptic terminals for efficient routing of retrograde vesicles onto the retrograde path with little influence on bidirectional axonal transport .
Khc-73 function plays a supporting role in retrograde BMP signaling under basal conditions . However under conditions of enhanced BMP signaling , this endosomal coordination by Khc-73 becomes critical to transmit the retrograde signal from the synapse to the neuronal cell body . Efficient retrograde signaling from synaptic terminals back to the neuronal soma is critical for appropriate neuronal function and survival [2 , 7–11] . Nevertheless , we know very little about the molecular steps that facilitate the routing of synaptic endosomes destined for retrograde axonal pathways . Here we describe several lines of evidence for a potential role for Khc-73 in this process . Khc-73 mutant larvae develop grossly normal synaptic structure and function at the Drosophila larval neuromuscular junction ( NMJ ) , but we find a reduction in the number of presynaptic release sites . Through genetic interaction experiments , we show that this defect is most likely the result of abnormal BMP signaling in motoneurons: transheterozygous combinations of Khc-73 and Medea or wit mutants show a significant loss of presynaptic release sites compared to control . Khc-73 becomes even more critical , when higher demand is put on the motoneuron by activating BMP signaling: loss of Khc-73 largely blocks the retrograde enhancement in synaptic release in response to activation of BMP pathway in motor neurons . Consistently we have previously shown that transgenic knock down of Khc-73 in motoneurons blocks the ability of the NMJ to undergo retrograde synaptic homeostatic compensation [35] . Our findings show that when BMP signaling is activated , loss of Khc-73 reduces the accumulation of pMad in motoneuron nuclei , suggesting a role for Khc-73 in the regulation of retrograde signaling . Our immunohistochemical assessment and live imaging analysis of Khc-73 mutant larvae provide evidence for involvement of Khc-73 in at least two steps in endosomal dynamics in motoneurons . On the one hand , Khc-73 is required for normal dynamics of internalized endosomes through late endosomal and multivesicular stages , and on the other Khc-73 plays a role in facilitating the routing of endosomes onto the retrograde pathway ( see Fig 14A for model ) . These defects have two main consequences: first , we find an accumulation of BMP receptors at the NMJ ( possibly in multivesicular bodies ) without increased local signaling , suggesting that these receptor containing endosomes might be trapped in a state between late endosomal and lysosomal stage ( see Fig 14B for model ) . Second , we see a dampening of the ability of retrograde bound Rab7:GFP tagged endosomes to join the retrograde pathway , illustrating a defect in retrograde movement of vesicles and possibly providing an underlying explanation for the reduction in pMAD when retrograde BMP signaling is activated in Khc-73 mutants . These results together present Khc-73 , a plus-end microtubule motor , in the unexpected role of regulation of endosomal traffic from synapse to the soma in motoneurons with a role for ensuring the efficiency of retrograde BMP signaling . While our findings provide compelling evidence for the proposed model above , we cannot , at this time , rule out the possibility that the abnormal accumulation of BMP receptors at the NMJ and the slowing of retrograde movement of Rab7 positive endosome in Khc-73 mutant larvae could be due to a defect in an intermediate molecule , whose anterograde transport is dependent on Khc-73 . In support of such model , we do report an abnormal accumulation of Brp and SYT ( two synaptic proteins ) in axons . While our data suggests that this abnormal accumulation can be remedied by transgenic activation of BMP signaling in Khc-73 mutants , we cannot rule out the possibility that an anterograde transport defect might exist for other proteins independent of the interaction between Khc-73 and BMP signaling . Our findings point to a model in which Khc-73 facilitates the routing of retrograde bound vesicles onto the retrograde axonal pathway . This model predicts coordination between endosomes , dynein motors and kinesin Khc-73 . The coordinated involvement of dynein and kinesin motor proteins in the transport and sorting of endosomes has been previously proposed and examples supporting this model are mounting [14 , 66 , 67] . Previously published data for Khc-73 and KIF13B have provided evidence that interaction between early endosomes , dynein motors and microtubules are possible . Khc-73/KIF13B is capable of binding to the GTPase Rab5 ( found on early endosomes ) , thus allowing Khc-73 to localize directly to Rab5 endosomes [15 , 34 , 68] . As a kinesin motor protein , Khc-73 could then transport these endosomes to the retrograde pathway by moving along the microtubule network in the synapse . Compelling evidence for a dynein interaction with Khc-73 has been previously demonstrated during mitotic spindle formation [24] . The Khc-73/KIF13B stalk domain is phosphorylated by Par1b and this creates a 14-3-3 adapter protein binding motif [29] . It has been proposed that physical interaction between Khc-73 stalk domain and the dynein interacting protein NudE via 14-3-3 ε/ζ might underlie the interaction between Khc-73 and dynein that is necessary for appropriate mitotic spindle formation [24] . Interestingly , transgenic knock down of NudE in Drosophila larval motoneurons leads to a reduction in the number of presynaptic release sites , a phenotype reminiscent of Khc-73 loss of function [69] . Thus , Khc-73 contains domains and protein-protein interactions that are capable of coordinating endosomes , microtubules and dynein . We propose that Khc-73 is necessary for the normal endosomal sorting and exit of endosomes from the NMJ to support efficient retrograde BMP signaling .
Flies were cultured at 25°C on standard medium except for Gene Switch experiments where RU486 was added to the media ( 50μM ) . The following stocks were used: MedC246 ( Y324term mutation ) [45] and MedG112 ( mutation in splice donor site of exon 4 ) [45] from Herman Aberle [45] . witA12 [37 , 39 , 70] . witHA4 [37 , 45 , 49] . UAS-TKVACT and UAS-Gbb99 [40] provided by M . B . O’Connor ( University of Minnesota , Minneapolis , MN ) , UAS-Wit [39] , UAS-HA-Khc-73 and UAS-HA-Khc-73-3’UTR ( K014 ) [35] , UAS-Wit-GFP , UAS-TKV-YFP [5] , BG380-Gal4 [71] , Elav-Gal4 [72] , OK371-Gal4[73] , MHC-Gal4 [74] . Bloomington stocks used were P{y[+m8] = Mae-UAS . 6 . 11}Khc-73[DP00530] ( RRID:BDSC_22058 ) , UAS-Rab5:YFP ( RRID:BDSC_9775 ) , UAS-Rab7:GFP ( RRID:BDSC_42706 ) , UAS-Rab11:GFP ( RRID:BDSC_50782 ) , VGlut-Gal4 ( RRID:BDSC_24635 ) , MadK00237 ( RRID:BDSC_10474 ) , UAS-Mito-HA-GFP ( RRID:BDSC_8442 ) , nSyb-Gal4 ( RRID:BDSC_51635 ) . UAS-luciferase ( RRID:BDSC_35788 ) . puckered LacZ insertion pucE69 [75] . Wild type stock used was w1118 . Khc-73 deletions were created by mobilizing the P-element from y[1] w[67c23]; P{y[+m8] = Mae-UAS . 6 . 11}Khc-73[DP00530] . Virgin y[1] w[67c23]; P{y[+m8] = Mae-UAS . 6 . 11}Khc-73[DP00530] female flies were mated to Cyo/+; Δ2–3 , Sb/TM6b males . Male progenies of y[1] w[67c23]; P{y[+m8] = Mae-UAS . 6 . 11}Khc-73[DP00530]/Cyo; Δ2–3 , Sb/+ were mated to virgin y , w; CyoGFP/Adv females . Yellow , non Sb , yellow eyed progeny were singly mated to y , w; Adv/CyoGFP virgins and individual stocks were established . P-element excisions were screened with the following primers: OED91: CTGACGGCGCTGTTGCTTG and OED96: GATCTAGAGATGATTCTGCATCACTAG TAAAAATT . Khc-73 promoter GAL4 construct was generated by cloning a 4kb fragment upstream of the translational start site with primers OED453: CAG GTA CCG CCG AGG AAC CGC TAA CG and OED452:CAG GTA CCC GCG GAT GTG GAT GCA GC . Vector pW+SN attB was modified with a GAL4 sequence cloned as a KpnI/NotI fragment . Khc-73 promoter was subsequently inserted into the unique KpnI site . Genomic Khc-73 is from BACPAC clone CH321-36I16 ( BACPAC Resources Center ) . Transgenic fly CH321-36I16 was made by standard embryo injection of BACPAC clone CH321-36I16 ( BACPAC Resources Center ) with ΦC31 –mediated integration into attP site at position 86F of chromosome III . Wandering third instar larvae were dissected , prepared and embedded as described in [76] . Ultra-thin serial sections of 50 nm thickness were cut from muscle 6 , 7 and 12 of hemisegment A3 . Electron micrographs were taken at a magnification of 25 , 000x for measurements , 25 , 000x and 40 , 000x for figures . Serial Reconstruction and analysis was conducted on FIJI ( Fiji is Just ImageJ ) ( NIH ) [77] and Reconstruct v . 1 . 1 . 0 . 0 Software [78] . Wandering third instar larvae were dissected as previously described [74] . Third Instar larvae were dissected in cold HL3 and fixed with 4% Paraformaldehyde for 10 min or 5min ice cold Methanol for GluRIIA staining . Larvae were washed with PBS ( Phosphate buffered saline ) , permeabilized with PBT ( PBS with 0 . 1% Triton X-100 ) , blocked with 5% Normal Goat Serum ( NGS ) in PBT and placed in primary antibody overnight at 4°C . The larvae were then washed three times for 15min in PBT , placed in secondary antibody for 2 hrs , washed three times for 15min with PBT and mounted in Vectashield ( Vector labs ) . Antibodies used are as follows: anti-GluRIII ( 1:500 ) ( gift from A . DiAntonio , Washington Univ . St . Louis , MO ) , anti-Hrs ( 1:200 ) , anti-SYT ( 1:1000 ) ( gift from H . Bellen , Baylor College of Medicine , Houston , TX ) , anti-pMAD ( PS1 ) ( 1:200 ) ( gift from M . B . O’Connor , University of Minnesota , Minneapolis , MN ) . anti-Dlg ( 1:500 ) , anti-nc82 ( 1:500 ) , anti-GluRIIA ( 1:500 ) , anti-CSP ( 1:500 ) , anti-EPS15 ( 1:50 ) , anti-LacZ ( 1:100 ) and anti-Wit ( 1:10 ) ( Developmental Studies Hybridoma Bank ( DSHB ) ) , anti-HA ( 1:500 ) ( HA . 11 clone 16B12 ) ( Covance Research Products ) , anti-GFP ( 1:500 ) ( A6455 ) ( Molecular Probes ) , anti-GFP ( 1:500 ) ( Rat IgG2a , GF090R ) ( Nacalai Tesque Inc . ) , anti-HRP conjugated Alexa 647 ( 1:250 ) ( Jackson ImmunoResearch ) , anti-acetylated tubulin ( 1:500 ) ( T7451 , clone 6-11B-1 Sigma-Aldrich ) and anti-pSmad3 ( EP823Y ) ( Epitomics ) . Western blots were performed as previously described [41] . Muscle tissue ( without the nervous system and motor axons or imaginal discs ) or Brain tissue ( VNC and axons ) were isolated from wandering third instar larvae dissected in cold HL3 . Western blot analysis was performed according to manufacturer’s protocols . Antibodies used: anti-Khc-73 ( 1:2000 ) [35] , anti-Wit ( 1:10 ) ( DSHB ) , anti-actin ( Millipore , MAB1501 ) . Gel images were scanned and band intensities were quantified using FIJI ( Fiji is just ImageJ software ) ( NIH ) [77] . Synapses were imaged using a ConfoCor LSM710 and Zeiss LSM 780 on an Axiovert 200M inverted microscope ( Carl Zeiss , Inc . ) with 63x/1 . 4 oil objective . Image analysis was performed with ImageJ 1 . 46j ( NIH ) [79] , Imaris ( Bitplane Scientific Software ) , Image Analyst MKII ( Image Analyst Software , Novato , CA ) and Metamorph ( Molecular Devices ) . Wandering third instar larvae were dissected in room temperature HL3 to remove the guts and fat bodies . The larval filet was then inverted and stretched onto a coverslip using magnetic dissection pins inside a chamber consisting of a coverslip surrounded by magnet strips . Larval prep was maintained at room temperature in an HL3 bath during imaging . NMJs at hemisegment A3 and A4 , muscles 6/7 and 4 were imaged . Axons were imaged at hemisegment A3 to A4 . Larvae were imaged for a maximum of 30 minutes after dissection . Axons and NMJs were imaged with 63x 1 . 4NA oil objective on Axiovert 200 inverted microscope with Zeiss LSM780 confocal ( Carl Zeiss , Inc . ) . The nerve crush assay was performed as previously described [65] . Briefly , third instar larvae were anaesthetized with carbon dioxide . The segmental nerves at the midbody were then pinched with Dumostar number 5 forceps for five seconds . The larvae were then recovered on standard media for 25 hours at 25°C after which time they were dissected and stained for LacZ . Wandering third instar larvae were dissected in cold HL3 solution following standard protocol [80] . The spontaneous ( mEJC ) and evoked ( EJC ) membrane currents were recorded from muscle 6 in abdominal segment A3 with standard two-electrode voltage-clamp technique [41] . All the recordings were performed at room temperature in HL3 solution containing 0 . 5mM Ca2+ unless otherwise indicated . The current recordings were collected with AxoClamp2B amplifier ( Molecular Devices Inc . ) using Clampex 9 . 2 software ( Molecular Devices Inc . ) . The nerve stimulation was delivered through a suction electrode which held the cut nerve terminal cord . In all voltage clamp recordings , muscles were held at -80 mV . The holding current was less than 5 nA for 90% of the recordings and we rejected any recording that required more than 10 nA current to maintain the holding potential . The amplitudes of mEJC and EJC were measured using Mini Analysis 6 . 0 . 3 software ( Synaptosoft ) and verified by eye . QC was calculated by dividing the mean EJC amplitude by mean mEJC amplitude . The recording traces were generated with Origin 7 . 5 software ( Origin Lab ) . Spontaneous and evoked potentials were measured as previously described [49] . Standard two-electrode voltage-clamp technique was used as described in [44] . | Retrograde axonal transport is essential for normal synaptic function and neuronal survival . How endosomes are specifically sorted from the synaptic terminal for retrograde axonal transport is currently not known . At the Drosophila neuromuscular junction , receptors for the Bone Morphogenic Protein signaling pathway are transported from the synapse to the neuron cell body for the proper establishment of synaptic growth and function of motoneurons . Using this system we demonstrate that a kinesin motor protein , Khc-73 , is necessary for the efficient sorting of retrograde bound vesicles to the retrograde transport route . |
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