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Development of high-throughput monitoring technologies enables interrogation of cancer samples at various levels of cellular activity . Capitalizing on these developments , various public efforts such as The Cancer Genome Atlas ( TCGA ) generate disparate omic data for large patient cohorts . As demonstrated by recent studies , these heterogeneous data sources provide the opportunity to gain insights into the molecular changes that drive cancer pathogenesis and progression . However , these insights are limited by the vast search space and as a result low statistical power to make new discoveries . In this paper , we propose methods for integrating disparate omic data using molecular interaction networks , with a view to gaining mechanistic insights into the relationship between molecular changes at different levels of cellular activity . Namely , we hypothesize that genes that play a role in cancer development and progression may be implicated by neither frequent mutation nor differential expression , and that network-based integration of mutation and differential expression data can reveal these “silent players” . For this purpose , we utilize network-propagation algorithms to simulate the information flow in the cell at a sample-specific resolution . We then use the propagated mutation and expression signals to identify genes that are not necessarily mutated or differentially expressed genes , but have an essential role in tumor development and patient outcome . We test the proposed method on breast cancer and glioblastoma multiforme data obtained from TCGA . Our results show that the proposed method can identify important proteins that are not readily revealed by molecular data , providing insights beyond what can be gleaned by analyzing different types of molecular data in isolation .
In recent years , there have been substantial efforts in integrating multiple omic data types that provide information on cancer pathogenesis and progression , with a view to predicting patient outcome , identifying drug targets , and understanding the functional relationships among key players in cancer . In the context of predicting patient outcome , Hofree et al . [5] used a network propagation based strategy to incorporate the functional relationships among mutated genes into the clustering of patients . They showed that the resulting clustering correlates with patient outcomes better than the clustering of patients according to mutation data alone . Similarly , several groups demonstrated that integration of transcriptomic data with protein-protein interaction networks leads to the identification of protein subnetworks that serve as reliable markers for the prediction of survival in such cancers as glioblastoma multiforme [6] and ovarian cancer [7] . In the context of understanding the functional relationships among key players in cancer , enrichment-based approaches aimed at identifying significantly mutated pathways provide insights into how different mutations influences similar biological processes [8] . Analysis of mutually exclusive mutations further elucidate the functional relationships among mutated genes by interpreting mutual exclusivity among mutations in the context of networks , thereby recovering key functional modules that provide systems-level insights into the mechanisms of pathogenesis [9] . Integration of sequence data with gene expression data based on eQTL analysis is also shown to be effective in the identification of cancer-related pathways [10] . These studies establish that the addition of network information can enhance predictive power in many applications , but most of these methods focus on a single data type in addition to network relationships . Though previous studies combine mutational or differential expression data with protein interaction networks , few use network information to integrate mutational and expression data . In particular , Nibbe et al . [11] propose a method that integrates protein expression data with mRNA expression data , with the purpose of extending the scale of of proteomic data that has limited coverage of the proteome . In Nibbe et al . ’s study proteomic and transcriptomic data from different patients is used to integrate mRNA-level gene expression and protein expression data . However , efforts like TCGA make it possible to extract multiple types of omic data ( mutation , mRNA expression , microRNA expression etc . ) . In this study , we aim to develop an algorithmic framework for the integration of these multi-omic data at the level of individual samples . We stipulate that during pathogenesis of cancer , mutations in up-stream proteins may lead to transcriptional dysregulation of down-stream genes . Similarly , transcriptional dysregulation of some processes may lead to conservation of certain mutations during neoplastic evolution . The dynamics of the interplay between genomic mutations and transcriptional dysregulation likely involves signaling proteins ( e . g . , kinases , phosphatases , transcription factors ) that mediate the relationship between mutated genes and dysregulated gene products . However , due to limitations in proteomic and phosphoproteomic screening [12] , the changes in those mediator proteins may not be readily detectable from genomic and transcriptomic data alone . We propose that such “silent” proteins can be detected by integrating mutation and differential expression data in a network context , since these proteins are likely to be in close proximity to both mutated and differentially expressed proteins in the network of protein-protein interactions ( PPIs ) . Based on our hypothesis , we develop an algorithmic workflow aimed at quantifying the proximity of all proteins in the human proteome to the products of mutated and differentially expressed genes in each sample . The proposed workflow is illustrated in Fig 1 . Here , our emphasis is on utilizing sample-specificity to be able to deal with molecular heterogeneity of pathogenesis at the population level . In order to utilize sample-specific data , we use network propagation to separately score proteins based on their network proximity to 1 ) mutated and 2 ) differentially expressed genes in each sample . This procedure provides us with two vectors in the space of samples for each protein: a “propagated mutation profile” indicating proximity to genes mutated in each sample and a “propagated differential expression profile” indicating proximity to genes differentially expressed in each sample . We then use these vectors to extract descriptive features for each protein , to be used for predicting its involvement in the disease being studied . We apply the proposed method to breast cancer ( BRCA ) and glioblastoma multiforme ( GBM ) data obtained from The Cancer Genome Atlas ( TCGA ) project . First , we assess the power of mutation data , expression data , and network-based integration of these two in unsupervised prediction of genes known to play a role in each cancer . We show that one can gain significant predictive power by propagating mutation or expression data over a PPI network , as compared to using raw mutation or differential expression data ( area under ROC curve ( AUC ) gains of 0 . 16–0 . 18 for BRCA and 0 . 17–0 . 27 for GBM ) . We then combine the two signals to derive several features and used these features to train a supervised predictor with further improved AUC of 0 . 836 for BRCA and 0 . 933 for GBM . Importantly , by using this predictor we are able to recover important proteins that are not readily revealed by molecular data . These genes are supported by the literature and by an independent cancer gene resource . This observation suggests that incorporation of network data can provide insights beyond what can be gleaned from sequence or expression data in isolation . Seven of those novel predictions are further found be significantly predictive of patient outcome . Our results also suggest important features that contribute significantly to the prediction of causal genes in breast cancer and glioblastoma multiforme , which provide insights into how the crosstalk among mutated and differentially expressed proteins contributes to pathogenesis .
The input to our method consists of BRCA ( breast cancer invasive carcinoma ) and GBM ( glioblastoma multiforme ) data obtained from TCGA [13] . We use two categories of data: somatic mutations obtained from whole-exome sequencing and microarray gene expression data . We also obtain differential expression status for TCGA samples from the COSMIC cancer gene census [2] . We collect this data into a binary mutation matrix M , and a binary differential gene expression matrix D , with samples as rows and genes as columns . We use C ( A ) to denote the set of column labels of matrix A , so that e . g . C ( M ) is the set of genes that appear in the TCGA somatic mutation data . Similarly , we define R ( A ) as the set of row labels of matrix A , corresponding to the distinct samples present in each data set . The mutation matrices M are defined as M [ i , j ] = 1 if gene j is mutated in sample i , 0 otherwise ( 1 ) The differential expression matrices D are defined similarly , using differential expression status instead of somatic mutation status for each gene . BRCA data includes somatic mutations in 15189 genes across 974 samples , and differential expression in 18018 for 973 samples . GBM data likewise includes 9507 genes and 591 samples , with differential expression measurements in 17660 genes across the same 591 samples . We use the HIPPIE protein-protein interaction network [14] ( version released 2014-09-05 ) , which contains confidence scores for 160215 interactions over 14680 proteins . All samples present in the gene expression data also appear in the mutation data . 12042 genes are contained in both the mutation and expression data , out of which 9303 are present in the HIPPIE network . We use the network propagation method described in Vanunu et al . [4] . Given a network G = ( V , E , w ) with V as the set of proteins , E as the set of their interactions , w ( u , v ) representing the reliability of an interaction uv ∈ E , and a prior knowledge vector Y: V → [0 , 1] , we seek to compute a function F ( v ) ∀v ∈ V that is both smooth over the network and accounts for the prior knowledge about each node . In the context of our problem , the prior knowledge about each node is the mutation or differential expression status of the respective gene in a sample . As described by Vanunu et al . [4] , we use Laplacian normalization to produce the normalized network edge weight w′ . Briefly , we construct a |V| × |V| matrix W from the edge weights w , and construct a diagonal matrix Δ with Δ[i , i] = ∑j W[i , j] . The normalized weight matrix is computed as W′ = Δ−1/2 WΔ−1/2 . Our W′ is a 14680 × 14680 sparse matrix with each row and column corresponding to a node in the HIPPIE network , and each nonzero entry signifying an interaction between two proteins . With the normalized weight matrix W′ , we use the iterative procedure described by Zhou et al . [15] to compute F . Namely , starting with F ( 0 ) = Y , we update F at iteration t as follows: F ( t ) = α W ′ F ( t - 1 ) + ( 1 - α ) Y ( 2 ) This procedure is repeated iteratively until convergence; namely we stop the iterations when ‖F ( t ) − F ( t−1 ) ‖2 < 10−6 . We use network propagation on a sample-specific basis to compute propagated mutation and differential expression vectors for each sample . Namely , we produce new “propagated” matrices MP and DP , by separately using each row of matrices M and D as the prior knowledge vector Y in Eq 2 . This is illustrated in Fig 1 . Given the data matrix A ( either M or D ) and each protein in the network v ∈ V , we construct the vector Y i ( A ) for sample i as follows: Y i ( A ) [ v ] = A [ i , v ] if v ∈ C ( A ) ∩ V , 0 otherwise ( 3 ) That is , the prior knowledge about a protein is 1 if and only if the protein is part of the HIPPIE network and the corresponding gene is mutated in sample i or differentially expressed in it . For each sample i ∈ R ( A ) , we denote the prior information vectors by Y i ( M ) and Y i ( D ) . Subsequently , using each of these prior information vectors , we use the iterative procedure described above to compute propagated mutation and expression vectors , denoted respectively as F i ( M ) and F i ( D ) for sample i . Next , we collect each propagated vector F i ( A ) into the rows of a “propagated” matrix AP , where R ( AP ) = R ( A ) and C ( AP ) = V . Intuitively , the propagated matrices MP and DP contain the per-sample binary vectors of M and D smoothed over the network . In biological terms , each row of these matrices represents the network proximity of each gene product to mutated and differentially expressed genes in that sample . Consequently , as illustrated in Fig 1 , the columns of these matrices provide propagated mutation and differential expression profiles for each gene product across all samples , indicating the proximity of the respective gene product to the products of mutated or differentially expressed genes in the respective sample . We seek to use the propagated mutation and differential gene expression matrices MP and DP ( with sample set S = R ( MP ) = R ( DP ) ) to predict causal genes based on network proximity to mutated and differentially expressed genes in BRCA . To this end , we define several features that express the mean , variance and cross-correlation of the columns of those matrices across the n = |S| samples: μ M [ g ] = 1 n ∑ i n M [ i , g ]: mutation frequency of gene g across samples . μ M P [ g ] = 1 n ∑ i n M P [ i , g ]: mean of propagated mutation scores across samples . μMP[g] quantifies the mean proximity of gene g to mutated genes across all samples . σ M P 2 [ g ] = Var M P [ · , g ]: variance of propagated mutation scores across samples . σ M P 2 [ g ] quantifies how inconsistently the gene products in the neighborhood of gene g are mutated across different samples . μ D [ g ] = 1 n ∑ i n D [ i , g ]: differential expression frequency across the n samples . μ D P [ g ] = 1 n ∑ i n D P [ i , g ]: mean of propagated differential expression scores across the n samples . μD[g] quantifies the mean proximity of gene g to differentially expressed genes across all samples . σ D P 2 [ g ] = Var D P [ · , g ]: variance of propagated differential expression scores across samples . σ D P 2 [ g ] quantifies how inconsistently the gene products in the neighborhood of gene g are differentially expressed across different samples . ρ[g] = Spearman correlation between MP[· , g] and DP [· , g] . ρ[g] quantifies whether samples that harbor mutations in the neighborhood of gene g also harbor differentially expressed genes in the neighborhood of gene g and vice versa . δ [ g ] = ∑ i n M P [ i , g ] · D P [ i , g ]: dot product between MP[⋅ , g] and DP[⋅ , g] . δ[g] can be interpreted similarly as ρ[g] . However , unlike correlation , this is a non-normalized measure of the consistency of proximity to mutated and differentially expressed genes . As such , δ[g] includes information about the magnitude of values in columns MP[⋅ , g] and DP[⋅ , g] as well as the agreement between those columns . χmax[g] and χmean[g]: For a gene g , high χ[g] scores denote a gene that is in close proximity to other genes that are frequently mutated or frequently differentially expressed . χmax[g] = maxi ∈ S ( max{MP[i , g] , DP[i , g]} ) . A high χmax[g] denotes a gene that is close to mutations or differential expression in any patient . χ mean [ g ] = 1 n ∑ i n max { M P [ i , g ] , D P [ i , g ] } . χmean[g] represents the gene’s mean distance to mutations or differential expression across all samples . νmax[g] , νmean[g]: A high ν[g] score denotes a gene that is in close proximity to other genes that are frequently mutated and frequently differentially expressed . νmax[g] = maxi ∈ S ( min{MP[i , g] , DP[i , g]} ) . A high νmax[g] denotes a gene that is close to mutations and differential expression in any sample . ν mean [ g ] = 1 n ∑ i n min { M P [ i , g ] , D P [ i , g ] } . νmean[g] quantifies the gene g’s mean distance to mutations and differential expression across all samples . γ[g]: Network centrality of gene g , as quantified using eigenvector centrality . Propagation of mutation and differential expression data across the network may bias results in favor of nodes that are central to the network or have high degree [3] . Our propagation method uses node degrees to normalize edge weights , offering some correction for nodecentrality [4] . However , to explicitly account for node centrality without unfairly penalizing hub nodes , and to gain insights into the effect of network centrality , we include network centrality as a feature in the model . An example of the νmean feature in a simulated data set is shown in Fig 2 . We see that genes which score highly via propagated mutation and differential expression frequency are scored highly with νmean , conversely , genes that are proximal to only mutations or differential expression may be scored highly in each individual data set but need not be scored highly in this combined feature . The features described above are used as input to a standard logistic regression model to predict the causal status of gene g . To train this model , we use prior knowledge of whether each gene is known to be associated with breast cancer based on the integrated breast cancer pathway ( Table A in S1 Text ) , or in glioblastoma based on the GBM KEGG pathway ( Table B in S1 Text ) . The logistic regression model represents the probability p that a gene is associated with the cancer of interest as log p 1 - p = β 0 + β 1 x 1 + … + β n x n . ( 4 ) Here , β0 represents the background probability that a gene is related to the disease , each xi represents one of the features described above , and each βi represents the magnitude to which xi influences p . In addition to estimating the magnitude of a feature’s effect on p , logistic regression models also allow for the investigation of whether a feature is statistically significant in the model fit . This framework therefore allows us to examine the relationship between the role of a gene in cancer and its mutational frequency , differential expression frequency , network distance to mutations or differential expression , and the relationship between these distances . Using the genes labeled based on prior knowledge of the molecular basis of each cancer , we fit this model using the features described above , perform step-down via AIC ( Akaike Information Criterion [16] ) , and use the probabilistic output of the stepped-down model as prediction scores for further analysis . We perform experiments to investigate whether this model can effectively recover cancer-related genes even though they are not frequently mutated or differentially expressed in available samples . We also evaluate the model’s performance on an independently curated set of genes known to be implicated in cancer . Finally , we investigate which features significantly contribute to the model fit , in order to gain insights into the factors that have important roles in pathogenesis .
We evaluate the predictive ability of our model using ROC curves , using the integrated breast cancer pathway from the NCBI BioSystems database [17] and the glioblastoma KEGG pathway [18] . We label a gene as positive if and only if it is contained in the respective pathway , and use these positive/negative labels to evaluate various prediction schemes . Better scoring systems naturally induce a higher area under the ROC curve ( AUC ) . We first examine the ability of naïve scoring methods in recovering known BRCA and GBM genes . Namely , we investigate how each of mutation frequency , differential expression frequency , and the network propagated mutation and differential expression , i . e . , respectively the column-wise means of matrices M , DG , MP and DP described in “Consolidation of Mutation and Expression Data” , can predict known BRCA and GBM genes . The results of this analysis are shown in Fig 3 and Tables 1 and 2 . We see that both mutation and differential expression frequency are slightly informative ( AUC 0 . 581 and 0 . 625 , respectively ) in choosing genes that are part of the integrated BRCA pathway . In other words , frequency of mutation or differential expression in TCGA breast cancer samples provides some information on whether a gene is involved in the BRCA pathway , but this information is quite modest . We see that the propagated signals ( with propagation parameter α = 0 . 8 ) show much more discriminative power: the mutational AUC increases to 0 . 757 after network propagation , and likewise the differential expression AUC increases to 0 . 781 . We see similar gains in predictive power in GBM: raw mutational and differential expression AUC are informative ( AUC 0 . 679 and 0 . 511 , respectively ) , and the application of network propagation to these signals boots the AUC values to 0 . 854 and 0 . 782 . Though the increase in predictive power through network propagation is considerable , we seek to improve the AUC values further through a more sophisticated integration of the propagated mutation and differential expression signals . For this purpose , we evaluate the regression model described in subsection “Consolidation of Mutation and Expression Data . ” We first fit the logistic regression model described in the aforementioned section to the full data sets , and perform a step-down procedure to remove features that do not significantly contribute to the model fit . We use the standard AIC ( Akaike information criterion ) measure [16] to determine whether a model term should be preserved . At each iteration of the step-down procedure , the AIC is computed for the full model and for reduced models with each single term removed . The term whose removal most improves AIC is removed from the model . The step-down procedure terminates when no term removal improves AIC . Fig 4 shows ROC curves resulting from this analysis; Fig 4a and 4c respectively show performance in recovering genes in the BRCA and GBM pathways . Fig 4b shows the accuracy in predicting genes’ membership in the COSMIC database using the BRCA model , and likewise Fig 4d shows performance in predicting COSMIC membership using the model trained from GBM data . We see that the stepped-down models improve ROC AUC when compared to the single features shown in Fig 3 , and perform well when selecting genes contained in the COSMIC set . The final model coefficients and P-values for each disease are shown in Tables 3 and 4 . For BRCA , we see that νmean and δ are highly significant predictors of a gene’s membership in the integrated BRCA pathway , with a positive coefficient for νmean and a negative coefficient for δ . We also see a large negative coefficient for feature χmean . We interpret this result by noting that for some sample i and gene j , the value max{MP[i , j] , DP[i , j]} is high if gene j is close to either mutations or differential expression , and genes that score highly in only one of these measures are likely to simply be frequently mutated or differentially expressed . Conversely , the ν signals measure the degree to which a gene is close to both mutations and differential expression . We indeed see that νmean is significant ( P < 2 × 10−16 ) with positive coefficient 91 . 7 . We see similar trends in GBM: again νmean is the most significant individual feature , with positive coefficient , and δ is also significant with a negative coefficient . Unlike BRCA , in GBM the χ features which select for proximity to mutations or differential expression are not preserved after AIC step-down . It is also notable that δ is preserved in both diseases but ρ is not . This result is not entirely surprising since ρ only represents agreement between propagated differential expression and mutation signals , and δ also quantifies a gene’s total proximity to mutations and differential expression . We also evaluate the predictive power added by our combined features in comparison to models fit with purely mutational and differential expression data . These results are shown in Fig 5 . These results show ROC AUC values for six models in each disease: one fit with all available mutational features , one fit with all available differential expression features , the full model with all features , and stepped-down versions of the three aforementioend models . We see that in both BRCA ( Fig 5a ) and GBM ( Fig 5b ) , the combined models improve on performance of those fit with only mutational or differential expression features . Additionally , we evaluate the distribution and univariate predictive power of each individual feature included in the predictive models shown above . Fig 6 shows the AUC values for each feature defined in “Consolidation of Mutation and Expression Data” in comparison with the AUC value of the fitted model that combines individual features . Fig 6a shows the AUC values for recovering BRCA genes; Fig 6b shows GBM . In both cases we see that νmean is the most informative individual feature , which favors genes that are close to both mutations and differential expression . In both BRCA and GBM we see that the predictive model improves upon the AUC values of each individual predictor . We observe that the mean propagated mutation feature ( μMP ) provides better predictive performance than mutation frequency ( μM ) for BRCA . However , this feature is dropped from the stepped down model while mutation frequency is preserved . This observation applies to several other features for both BRCA and GBM as well . This observation demonstrates the benefit of using logistic regression , in that features that are themselves significant may be almost colinear and not all of them need to be preserved if there is overlap in the information provided by multiple features . In particular , the specific observation stated above suggests that the smoothed mutational signal in μMP is subsumed by the combined features , whereas mutation frequency provides information in addition to the information provided by other selected features . It is also interesting to note that the coefficient of mutation frequency is negative in the stepped down model . It is likely that this reflects a correction for passenger mutations ( mutated genes that are not functionally related to tumorigenesis ) , since the information provided by driver mutations ( mutated genes that play a role in tumorigenesis ) is incorporated by another feature ( combined propagated mutation and differential expression signals ) in the model . Fig 7 shows the CDFs of each individual feature , with separate curves for genes that are contained in each respective pathway . Fig 7a showsBRCA; Fig 7b shows GBM . These figures indicate significant difference between cancer genes and other genes in terms of the distribution of some individual features , and reveal bimodality in δ ( dot product ) in GBM and νmax ( minimum between MP and DP , maximum across samples ) in both diseases . We also fit models with multiple values of the propagation parameter α , ranging from 0 . 01 to 0 . 99 . The results are shown in Fig 8 , and we see that the performance of stepped-down predictive models does not significantly depend on the propagation parameter α . In order to evaluate the utility of our method in predicting new causal genes , we investigate the high-scoring genes that are not already known to be implicated with breast cancer and glioblastoma . The cumulative distributions of genes’ prediction scores ( outputs of the stepped-down logistic regression models ) are shown in Fig 9 . We see that the distributions of scores are skewed toward 0 , and for demonstration purposes we consider a gene to be high-scoring if its prediction score is ≥ 0 . 2 . The highest-scoring such genes are shown along the horizontal axis of Fig 10; ( Fig 10a ) shows BRCA and ( Fig 10b ) shows GBM . Several interesting genes appear; PIK3R1 is known to be implicated in human immunodeficiency [19] and the PI3K kinase has been shown to regulate insulin-induced cell proliferation in the MCF-7 breast cancer cell line [20] . GRB2 interacts with BCAR1 as part of the CIN85 complex [21] , and CBL is a known oncogene in myeloid malignancies [22] . Since our goal is the identification of potential “silent players” that cannot be selected by each data set in isolation , we identify genes scored highly ( prediction score ≥ 0 . 2 ) by the combined model ( Tables 3 and 4 ) that are not scored highly by the models shown in Fig 5 . Genes for BRCA are shown in Table 5 and genes for GBM are shown in Table 6 . Many of these genes are known to be implicated in diseases , but few have been previously reported as associated with cancer . GATA3 controls differentiation of luminal cells in mammary glands [23] . HRAS mutations have been reported to cause altered glucose metabolism in mammary carcinogenesis [24] and to promote epithelial-mesenchymal transition in mammary epithelial cells [25] . NOTCH1 [26] has previously been associated with head and neck squamous cell carcinoma [27] , acute lymphoblastic leukemia [28] , and chronic lymphocytic leukemia [29] . SHC1 interacts with the atypical kinase PEAK1 , which is involved in a basal breast cancer signaling pathway [30] . Alterations in methylation of ANK1 are common in Alzheimer’s disease [31 , 32] . Overexpression of ERBB2 ( also known as HER2 ) has been shown in several cancers , including non-small cell lung [33] and endometrial cancers [34] . Mutations in the tyrosine phosphatase PTPN11 have been shown to cause a predisposition for leukemia and some solid tumors [35] . As an independent evaluation of our method , we also examine our scoring system’s ability to select genes that are included in the COSMIC cancer gene census [2] . As with our original set of BRCA interesting genes , we treat membership in the COSMIC data as a positive label for a gene , and evaluate our ability to rank these genes higher than others . Fig 4b and 4d show ROC curves for this gene selection using the models shown in Tables 3 and 4 , with AUC values of 0 . 7833 for BRCA and 0 . 7701 for GBM . We evaluate the statistical significance of selection of genes in the COSMIC database among those not contained in the respective pathways for BRCA and GBM using hypergeometric tests . In BRCA , 321 genes remain in the COSMIC set after removing those that are included in the integrated BRCA pathway . 8 of the 36 genes with prediction scores ≥ 0 . 2 overlap with the COSMIC dataset; choosing at least 8 of 321 in 36 trials from the remaining 14562 genes yields P = 5 . 09 × 10−8 . In GBM , 250 genes remain in the COSMIC set after removing those that are included in the respective KEGG pathway . 10 of the 40 genes with prediction scores ≥ 0 . 2 overlap with the COSMIC dataset; choosing at least 10 of 250 in 40 trials from the remaining 14562 genes yields P = 2 . 06 × 10−9 . We also examine our method’s ability to recover genes for which mutation or differential expression status is predictive of patient outcome ( survival ) . While the main objective of this study is not to identify markers for predicting patient outcome , these results are presented as an additional validation of the silent players we identify . As such , for both BRCA and GBM , we identify the 25 top-scoring genes that are not contained in the respective pathway , and use the mutational and differential expression status of these genes to repeatedly separate the sample set into two groups . We then use the logrank test to estimate the significance of the difference in survival between those groups; P-values are shown in Fig 11 . BRCA samples are shown in Fig 11a , and we see nominal statistical significance from somatic mutations in FLNB and SHC1 . FLNB is involved in vascular repair and has not been shown to be associated with cancer , but SHC1 interacts with a kinase signaling pathway that has been implicated in breast cancer [36 , 37] . Differential expression status in GRB2 , FYN , and HTT also show utility in predicting differences in survival between groups . In GBM , we see that differential expression status of ESR2 is also nominally significant in stratifying patient survival . Molecular data is a gold-mine for studying human disease , but current methods do not seem to exploit its full potential due to computational problems and lack of statistical power to examine all genomic markers or combinations of those . Network-based analyses provide an appealing bypass as they greatly narrow the search space . Here we have shown the power of network propagation in exploiting weak signals , from either sequence or expression studies , to predict disease causing genes . An application of our approach to breast cancer and GBM data revealed novel genes with literature support and significant association to disease outcome . Our preliminary results can be extended in several ways . While our analysis focused on breast cancer , the methodology is general and could be applied to any multi-factorial disease for which there are available gene expression and/or sequence data . Furthermore , the method is extensible to other types of omics data such as protein expression and DNA methylation . Finally , it is interesting to study how the method can benefit from prior knowledge on disease causing genes , potentially better guiding the propagation process . | Identification of cancer-related genes is an important task , made more difficult by heterogeneity between samples and even within individual patients . Methods for identifying disease-related genes typically focus on individual data sets such as mutational and differential expression data , and therefore are limited to genes that are implicated by each data set in isolation . In this work we propose a method that uses protein interaction network information to integrate mutational and differential expression data on a sample-specific level , and combine this information across samples in ways that respect the commonalities and differences between distinct mutation and differential expression profiles . We use this information to identify genes that are associated with cancer but not readily identifiable by mutations or differential expression alone . Our method highlights the features that significantly predict a gene’s association with cancer , shows improved predictive power in recovering cancer-related genes in known pathways , and identifies genes that are neither frequently mutated nor differentially expressed but show significant association with survival . |
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Enterohemorrhagic Escherichia coli ( EHEC ) , particularly serotype O157:H7 , causes hemorrhagic colitis , hemolytic uremic syndrome , and even death . In vitro studies showed that Shiga toxin 2 ( Stx2 ) , the primary virulence factor expressed by EDL933 ( an O157:H7 strain ) , is encoded by the 933W prophage . And the bacterial subpopulation in which the 933W prophage is induced is the producer of Stx2 . Using the germ-free mouse , we show the essential role 933W induction plays in the virulence of EDL933 infection . An EDL933 derivative with a single mutation in its 933W prophage , resulting specifically in that phage being uninducible , colonizes the intestines , but fails to cause any of the pathological changes seen with the parent strain . Hence , induction of the 933W prophage is the primary event leading to disease from EDL933 infection . We constructed a derivative of EDL933 , SIVET , with a biosensor that specifically measures induction of the 933W prophage . Using this biosensor to measure 933W induction in germ-free mice , we found an increase three logs greater than was expected from in vitro results . Since the induced population produces and releases Stx2 , this result indicates that an activity in the intestine increases Stx2 production .
Enterohemorrhagic E . coli ( EHEC ) has emerged as a serious health threat with numerous outbreaks most commonly due to contaminated beef , but also to contaminated vegetables and water [1] . Although EHEC strains [2] , and another recently identified pathogenic E . coli [3] , encode a number of virulence factors , the most serious sequelae of infection by these strains are due to the acquisition and expression of genes encoding Shiga toxins ( Stx ) . In many EHEC strains these toxins are encoded in the genomes of prophages of the λ family ( referred to as lambdoid phage ) [4] . Two major classes of Shiga toxins , Stx1 and Stx2 , have been identified in EHEC strains [5] . Although sharing the same activity , they differ somewhat in sequence and Stx2 is associated with the more severe sequelae in humans [6] and is the cause of disease in animal models [7] . These members of the AB5 class of toxins bind eukaryotic cells by attachment of the pentameric structure of the B subunit to a glycoprotein receptor on the eukaryotic cell [8] . Retrograde transit through the endosomic pathway to the cytosol results in the A subunit , a glycosidase , reaching the ribosomal RNA [9] . There , a specific adenine residue in the large ribosomal subunit is cleaved , resulting in arrested protein synthesis that leads to cellular intoxication [10] . EHEC strains commonly isolated in outbreaks are those of the O157:H7 serotype [6] . Members of the lambdoid family of temperate phages share a common genome organization with prototypical λ . Genes at the same relative position on their respective genomes may differ in sequence , but for the most part they share the same activity [11] . For example , the repressors and operators may differ in sequence and specificity , but the different lambdoid phages have a common structure and location for these genetic elements on their genomes [12] . Moreover , the lambdoid phages are mosaics with each phage sharing a number of different genes with different members of the family [11] , [13] . These conserved structure-function relationships allowed for the relatively rapid determination of the role of the phage in Stx expression [14] . When present , the stxA and B genes are located downstream of PR′ , the late phage promoter [15] , [16] , and upstream of the phage lysis genes ( Fig . 1 ) [14] , [17] . In vitro and in vivo studies with the O157:H7 strain 1∶361 and its resident stx2-phage , φ361 , showed that transcription from PR′ is required for Stx2 production [18] . In vitro studies with the E . coli strain K9675 ( a derivative of the nonpathogenic strain K37 lysogenized with the stx2-phage 933W ) showed that Stx2 expression requires prophage induction [19] . Hence , Stx2 expression , at least under these in vitro conditions , depends on the phage induction cascade . Prophage induction explains why patient treatment with antibiotics that can act as inducing agents , such as the quinolones , lead to higher Stx levels [20] and exacerbate the disease [21] . The lambdoid phage regulatory cascade which leads to phage production and cell lysis has been the subject of years of study with λ and to a lesser extent with other members of this family of phages [22] . Induction , which results in the initiation of the regulatory cascade , is set in motion when the bacterium containing the prophage ( lysogen ) sustains DNA damage and responds by activation of the LexA regulon , leading to a cellular change in gene expression termed the SOS response [23] , [24] . One member of this regulon , RecA , increases in quantity and assumes an activated form , RecA* , by interacting with single-stranded DNA generated by DNA damage [25] . RecA* , through its co-protease activity , facilitates the autocleavage of phage repressor [25] , allowing initiation of transcription from the early PL and PR promoters ( Fig . 1 ) . Transcription from PL results in expression of N protein , which acts to modify RNA polymerase initiating specifically at PL and PR to a form resistant to downstream terminators [26] . N-modified transcription from PR transcends downstream terminators resulting in Q expression . Q in turn modifies transcription initiating at the late PR′ promoter to a termination-resistant form allowing expression of downstream genes [27] , including stx A and B in stx-phages [14] , [17] , [18] , [28] . A λ prophage fails to induce if the repressor gene ( cI ) has a mutation that inhibits autocleavage [29] , [30] . These mutations , called ind , change amino acid residues within the repressor that participate in a serine protease activity that catalyzes autocleavage [25] . We have previously suggested that the induced subpopulation is responsible for Stx production and release [14] . Lysogens with most lambdoid prophages are stable with only an extremely small fraction of the population , in the absence of an external inducing agent , sustaining sufficient DNA damage to be induced , a stochastic process referred to as “spontaneous induction” [25] . It has been suggested that collapse of the replisome in normally growing bacteria caused by single-stranded breaks or noncoding lesions may be an internal event responsible for spontaneous induction [31] . DNA damage-inducing agents change induction from a stochastic to a deterministic process that activates RecA and , in turn , repressor cleavage [32] . Although recA mutants have been used to study conditions where the prophage fails to be induced and Stx is not expressed [33] , such an experimental approach suffers from the disadvantage of the pleiotropic effects on bacterial physiology due to loss of RecA activity [34] , [35] . Using a phage with an ind mutation avoids this problem by limiting the failure of SOS control only to the prophage with the ind mutation . Linkage of Stx expression to prophage induction raises the question as to whether the intestinal environment increases Stx levels by causing prophage induction . One way this could occur would be by increasing DNA damage in the bacterium . In vitro experiments showed a modest increase in Stx production by an O157:H7 strain when bacteria were cultured with neutrophils [36] , which produce H2O2 that can cause DNA damage leading to an SOS response and prophage induction . Here , we report experiments with the O157:H7 strain EDL933 and derivatives of EDL933 that carry a 933W prophage with a cI ind mutation . Using a germ-free mouse model of disease , we show that whereas the parent EDL933 with wild-type 933W prophage produces high levels of Stx in vivo and causes severe disease that can lead to death , a derivative isogenic except for a cI ind mutation in the 933W prophage produces extremely low levels of Stx2 and does not cause any observable disease . These results provide compelling evidence that induction of the 933W prophage is a major factor in pathogenesis of EDL933 and prophage induction may play a role in the severity of infection by other O157:H7 strains . Using an EDL933 SIVET reporter strain , which survives induction but undergoes a change in antibiotic resistances following induction , we show that the intestinal environment contributes to induction of the 933W prophage in EDL933 .
Induction of lambdoid prophages , including that of 933W , occurs when the repressor protein autocleaves in the presence of activated RecA protein [25] . Mutations , ind , in the cI gene result in a noncleavable repressor and thus an uninducible prophage [29] . The strategy used to obtain the ind mutation in the cI gene of the 933W prophage in EDL933 , a change of Lys codon 178 [suggested by John Little [37]] , was based in part on the procedure previously employed by our laboratory to construct an identical point mutation in the cI gene of the 933W prophage in strain K9675 [19] . Sequencing confirmed that the cI gene in EDL933 with the mutant 933W had only the designed nucleotide substitution at codon 178 . The mutation , named ind1 , is a change of the Lys codon to an Asn codon ( K178N ) . This change interferes with the autocatalytic serine protease activity of the CI repressor [38] , rendering the prophage uninducible . We will refer to the derivative of EDL933 carrying the 933W prophage with the cIind1 mutation as EDL933cIind1 ( Table 1 ) . This strain carries the stx2 genes and differs from EDL933 only by the 933W cI mutation . To assess the effectiveness of the ind1 mutation on prophage induction , we treated EDL933 and EDL933cIind1 with mitomycin C [39] . At an appropriate concentration , this DNA damaging agent activates the SOS response of most of the population sufficiently to induce the prophage [40] . Treatment of the EDL933 parent with 2 µg/ml of mitomycin C led to full induction of the culture; i . e . , lysis was nearly complete ( Fig . 2 ) . Identical treatment of EDL933cIind1 failed to cause lysis ( Fig . 2 ) . This result confirms that the ind1 mutation blocks induction of 933W . Additionally , it shows that the inducing agent does not cause any of the large number of defective prophage in EDL933 [41] to express lytic activity . This finding provides direct evidence that induction of 933W is not only responsible for Stx2 production , as shown below , but also for the lysis that releases Stx2 from the bacterium . We used an ELISA to assess Stx2A levels; comparing levels in EDL933 with those in the EDL933cIind1 derivative and the nonpathogenic 933W lysogen K9675 . In the absence of an inducing agent , the parent EDL933 expresses ∼40 times the level of Stx2 expressed by EDL933 cIind1 mutation ( Fig . 3 ) . This result provides compelling evidence that in culture a significant fraction of Stx2 production derives from the subpopulation of EDL933 in which the 933W prophage is induced . These results are only partially consistent with our previous findings with strain K9675 [19] . In that study we found that in the absence of an external inducing agent , the level of Stx2A produced by K9675 was ∼10 fold lower than the level produced under these conditions by EDL933 with its wild-type 933W prophage . In the current study , we confirmed these findings , showing that in the absence of an external inducer ( spontaneous induction ) EDL933 produces ∼10 times more Stx2A than K9675 ( Fig . 3 ) . To rule out the possibility that the low Stx2A levels in the nonpathogenic strain resulted from alteration of the prophage or the host , we measured Stx2A production from another nonpathogenic K12 related strain , MC1000 [42] , with a 933W prophage . As above , we observed the lower level of Stx2A expression in the non-pathogenic strain compared to EDL933 ( data not shown ) . Although comparison of spontaneous induction shows that EDL933 produces ∼10 fold higher levels of Stx2A than K9675 , the source of Stx2A for each is primarily that fraction of the population in which the prophage is induced ( this study and Tyler et al . [19] . To specifically assess the role of induction of the 933W prophage in Stx2 production , we determined Stx2 levels following treatment with mitomycin C ( 2 µg/ml ) . As shown in Fig . 3 , mitomycin C treatment resulted in a 100 to 200-fold increase in Stx2 production by EDL933 . Although K9675 produced significantly less Stx2 than EDL933 in the absence of an inducing agent , it produced about the same levels of Stx2 following treatment with mitomycin C as similarly treated EDL933 . The EDL933cIind1culture treated with mitomycin C produced 5- to 10-fold more Stx2 than the untreated culture . Although two orders of magnitude lower than the Stx2 production reached by EDL933 treated with mitomycin C , the increased levels we observed with the treated EDL933cIind1 were reproducible . The increase in Stx2 following mitomycin C treatment is consistent with the observation of measurable levels of Stx2 produced by EDL933cIind in the absence of an inducing agent . Either the mutant repressor retains some ability to autocleave ( leaky mutant ) in the EDL933 environment or there is an alternative route to Stx2 expression . However , in either event the production of Stx2 is extremely low in the presence of the cIind1 mutation . Results of clinical studies of children with EHEC infection show that phage induction likely plays an important role in the disease; e . g . , those treated with antibiotics that elicit an SOS response may experience more severe outcomes [21] . In mice , treatment with ciprofloxacin ( an antibiotic that elicits the SOS response ) also results in greater in vivo expression of Stx , likely via prophage induction [43] . Although suggestive , these findings are far from definitive . As discussed , the SOS response has pleiotropic effects on bacterial gene expression and does far more to affect cell physiology than induce prophage [34] , [35] . Relevant to our studies , treatment of EDL933 with the DNA damaging agent norfloxacin results in changes in the expression of a number of prophage and non-prophage genes in EDL933 [44] . Because the only effect of the ind1 mutation is to interfere with induction of the 933W prophage , experiments with EDL933cIind1 allowed us to ask specifically how significant induction of the 933W prophage is in causing the pathology associated with EDL933 infection . The germ-free mouse has proven an effective and practical animal model for studying the pathology of EHEC infection [7] . We found that germ free mice infected with O157:H7 strains such as EDL933 develop acute renal tubular necrosis and renal glomerular thrombosis leading to renal failure and death . In the same study , we also reported that a similar infection with a derivative of EDL933 isogenic except for a deletion of the stx2 genes does not result in any of the pathogical changes seen with the wild-type parent strain . Hence , in this animal model , all of the described pathological changes result from the action of Stx2 . For these reasons , we chose the germ-free mouse to assess the role in the disease process of induction specifically of the 933W prophage carried by EDL933 . Groups of 6 ( 3 female and 3 male ) germ-free Swiss-Webster mice were used in the experiments . They were infected with one of three bacteria , EDL933 or either of two isogenic strains that differed by having the Δstx::cat deletion substitution or the cIind1 point mutation . For all strains tested , each animal was challenged with 106 cfu administered orally . All three groups of mice were equally colonized over the seven days of the experiment in which bacteria in the feces were measured ( ∼1010 cfu/g ) . As expected from our previous work , all 6 mice infected with the wild-type EDL933 parent strain became moribund prior to the scheduled time mice were euthanized at three weeks . All mice infected with the Δstx::cat deletion derivative showed no signs of disease . Like the latter group , mice infected with EDL933cIind1 showed no signs of disease ( Fig . 4 ) . Figure 4A shows a Kaplan-Meier survival curve of mice inoculated with the three strains . All 6 mice given EDL933 became moribund or died prior to 21 days after inoculation . At necropsy , these mice were dehydrated and thin , and their ceca were distended with fluid contents . Mice in this group had moderate-severe acute renal tubular necrosis ( Fig . 4B ) , failed to gain weight as indicated by significantly lower body weights at necropsy ( Fig . 4C ) , and all and dilute urine ( Fig . 4D ) , indicating renal failure . Histologically , renal disease was characterized by necrosis of renal tubules and occasional glomerular fibrin thrombi ( Fig . 4E ) . Mice in the other two groups did not show any signs of disease , and had normal renal morphology Fig . 4F ) . As noted above , cecal colonization was similar in all three groups of mice ruling out poor colonization as an explanation for the failure of EDL933cIind1 to cause disease . As discussed , in vitro EDL933cIind1produces measurable levels of Stx2 , raising the question of whether it produces measurable levels of Stx2 in the infected mouse . Although there was wide variation , we found low but measurable levels of Stx2 in the feces of some of the mice infected with EDL933cIind1 , 0–300 ng/ml of feces . Much higher levels of Stx2 , with considerable variation , were found in the feces of mice infected with EDL933 , 6529±4432 ng/ml of feces ( P = 0 . 0039 ) . Based on the RIVET ( recombinase based in vivo expression technology ) [45] , [46] , we developed SIVET ( selectable in vivo expression technology ) , with the aim of determining if there is any effect on prophage induction when bacteria are in the intestine . Studies with the first generation SIVET , constructed in the nonpathogenic E . coli strain MC1000 , established this reporter system as a valid method for measuring prophage induction [47] . Here we report construction of a second generation SIVET through modification of EDL933 ( see Materials and Methods for details ) . Figure 1-II outlines the essential features of the SIVET system . Briefly , the 933W and 933V prophages in EDL933 were genetically altered so that functions lethal to the bacterial host [48] are not expressed upon induction and the bacterium therefore survives challenge with an inducing agent . The tnpR gene from the γδ transposon [49] was cloned downstream of the 933W early PR promoter distal to the cro gene . Thus , following induction of the 933W prophage transcription initiating at the phage promoter PR results in production of the TnpR resolvase that , in turn , acts at another site on the bacterial chromosome to excise a kanR cassette that interrupts a cat gene . This recombination serves two purposes , establishes a functional cat gene and removes the kanR cassette , conferring CamR . Hence , upon induction of the altered 933W prophage there is an irreversible and inheritable change of the host bacterium from KanR/CamS to KanS/CamR . The fraction of the total bacterial count that is CamR provides a measurement of the number of bacteria in which the prophage was induced . That this change is due to prophage induction is shown by the results of the following experiments . First , treatment of the SIVET strain with mitomycin C , known to cause prophage induction [39] , results in an increase of ∼1000 fold in CamR colonies and a reduction of ∼1000 fold of KanR colonies ( Fig . 5 ) . Second , treatment of a cIind1 mutant derivative of the SIVET strain ( K11607 ) under exactly the same conditions used with the SIVET parent failed to cause any measurable change in the levels of KanR or CamR bacteria ( Fig . 5 ) . In the following in vitro and in vivo experiments , the ratio of CamR/KanR SIVET was standardized to simplify the presentation using what will be referred to as the “Induction Index” . This function is calculated as the log10 of ( CamR/KanR output ) / ( CamR/KanR input ) ( for details see Materials and Methods ) . Because of the way the Induction Index is calculated , the starting point in the graphs , the input , is equal to log10 ( 1 ) or 0 . This allows changes in induction to be monitored by observing movement of the Index away from 0 . The only way we see the ratio deviate , beyond expected scatter , from 0 on the Induction Index , is if one of the two populations increases more than the other either by a growth advantage or by addition of newly generated derivatives . To rule out alteration in the induction index due to a growth advantage of one or the other marked strain , we used two SIVET derivatives; one , K11607 , locked in the KanR form by virtue of the cIind1mutation and the other , K11608 , a derivative of K11607 which is isogenic except for the excision of the KanR cassette and thus is locked in the CamR form . The CamR/KanR ratio ( calculated employing the formula used to generate the Induction Index ) following coinfection with the locked in CamR and KanR derivatives hovers around 0 ( Fig . 6A ) . Since there is no growth advantage to either form , any positive increase in the CamR/KanR Induction Index of the parental SIVET would have to be explained as addition by conversion from the KanR population to the CamR population , a direct consequence of induction of the 933W prophage in the KanR bacteria . As discussed above , a small fraction of a population of lysogens growing in the absence of an added inducing agent undergo induction , a process called spontaneous induction [25] . To determine whether spontaneous induction of the SIVET prophage adds to the population of CamR bacteria , we measured the CamR/KanR ratio , determined as the Induction Index , over the course of a large number of doublings in vitro in two different ways ( Fig . 6 ) . In both approaches , the SIVET strain was serially passaged in vitro for a number of generations in LB medium and the CamR and KanR populations periodically measured by viable counts . In one set of experiments , the SIVET bacteria were grown to stationary phase and diluted 10-fold for the next passage ( Fig . 6B ) while in the other , the bacteria were kept in log phase and diluted from an OD600 of ∼1 . 0 to an OD600 of 0 . 1 for the next passage ( Fig . 6C ) . Both protocols yielded similar experimental results; the Induction Index remained relatively constant over many doublings , hovering around 0 . These results lead us to conclude that spontaneous induction does not significantly affect the CamR/KanR ratio . We consider these results further in the Discussion . To determine if the intestine environment contributes to prophage induction and thus Stx production , we employed the ELD933 SIVET strain using the infection protocol as described above . Each mouse was orally infected with ∼106 SIVET bacterium . Because the 933W prophage was mutationally disarmed ( see Materials and Methods for details ) and thus does not produce Stx2 , as expected , mice infected with EDL933 SIVET did not show signs of disease . Feces were isolated each day for seven days and bacterial counts were determined by plating on LB agar plates containing kanamycin or chloramphenicol . The total EDL933 SIVET count remained relatively constant over the course of the experiment , ∼108 CFU/g of feces , although slightly decreasing by the seventh day ( data not shown ) . The Induction Indexes over the 7 days presented in Fig . 7 were compiled from results of three independent experiments , each comprised of five mice . By day seven the Induction Index has increased by over three logs . The study was terminated at day 7 , when the onset of severe disease caused by EDL933 usually occurs [7] . To determine if the change in the CamR/KanR Induction Index during in vivo growth of SIVET reflects a difference in viability of the two forms of the SIVET , we employed the SIVET pair K11607 and K11608 . These derivatives , as discussed above , are locked in either the KanR or CamR form . Mice were co-infected with K11607 and K11608 and followed essentially as described for the in vivo SIVET study outlined above . Examination of fecal samples showed that the ratio of CamR/KanR ( calculated using Induction Index formula ) did not significantly change over the course of 7 days ( Fig . 7 ) ; i . e . , neither form of SIVET has a growth advantage during in vivo growth . Hence , the null hypothesis stands and we conclude that the increase of the CamR/KanR Induction Index observed during growth in the mouse intestine results from prophage induction . Based on this collection of data , we conclude that there is significant induction of the 933W prophage in the germ free mouse intestine . Since Stx2 production is directly linked to 933W induction , it follows that the intestine , through action of a yet to be identified factor ( s ) , stimulates Stx2 production through induction of the 933W prophage .
With the information gained from sequencing numerous bacterial genomes , it has become apparent that virulence factors are commonly located in genomes of prophage [50] , [51] . Introduction of a new function , such as a virulence factor , to a bacterium by a prophage is referred to as lysogenic conversion . Although Stx2 is an example of a phage-encoded toxin whose expression is controlled by the phage regulatory cascade , many other phage-encoded toxins are expressed independently of prophage regulatory functions . This is true for the classic toxin of Corynebacterium diphtheriae [52] and of cholerae toxin ( CTX ) , which is encoded in the genome of the CTXΦ prophage [53] . Expression of CTX is controlled by a complex circuitry of proteins encoded by regulatory genes located outside of the prophage genome [54] . Observations like these led to the idea that phage , like other mobile elements , serve as agents that can transfer genetic information from one bacterium to another [55] . However , at least in the case of stx-phages , the phage serves a wider role , not only being the source of transfer , but also the regulator of expression from the transferred virulence gene [16] . The construction of a derivative of EDL933 with the ind1 mutation in the 933W prophage coupled with an animal model that mimics , to a large degree , the human disease , has allowed us to specifically assess the contribution of induction of the 933W prophage to the disease process . Like its EDL933 parent , EDL933cIind1 effectively colonizes the host intestines . However , unlike the parental strain , the ind1 strain fails to elicit any of the hallmarks of an EHEC infection; e . g . , physical signs of illness , renal disease , and death . That EDL933cIind1 colonizes the host intestine is consistent with our previously reported findings showing that a derivative of EDL933 with a deletion-substitution of the stx2 genes colonized as well as the parent strain with a functional stx2 gene [7] . This observation is contrary to the findings of Robinson et al . [56] , who reported that colonization was reduced if the O157:H7 strain did not express Stx2 . As we have suggested previously [7] , this difference may reflect our use of germ-free mice , while Robinson et al . used mice with normal microbiota . Our results provide evidence that the major pathogenic effect of EDL933 results from induction of the 933W prophage . Hence , the phage regulatory cascade plays a central role in the pathogenesis of this O157:H7 strain and likely many others . Since repressor auto-cleavage requires activated RecA protein , which , in turn , is a product of the SOS response , it is primarily that subpopulation of bacteria , with a sufficiently vigorous SOS response that induces the 933W prophage and results in the production and release of Stx2 . Our observation that Stx2 production and disease in the mouse are directly related to induction of the 933W prophage raises the question as to whether there is a factor ( s ) in the intestines that increases the SOS response resulting in increased prophage induction beyond that expected from results of in vitro experiments . Such a role was found for a factor in human pharyngeal cells that induces a group A Streptococcus prophage [57] . And a small but significant level of induction of Stx was observed when an EHEC strain was co-cultured with human neutrophils [36] . In a similar manner , a factor ( s ) in the intestines that induces an SOS response might increase the levels of Stx produced by a population of infecting EHEC . Such a factor ( s ) could be a product of the host ( e . g . , neutrophils ) . Not considered here is the possible importance of the interaction between the microbiota and the mammalian intestine in the SOS response and resulting Stx2 production [58] . Our studies with germ free mice show that even in the absence of the normal microbiota there is sufficient prophage induction to produce and release levels of Stx capable of causing renal disease and death . Constructed regulatory networks as biosensors have wide biological applications [59] . The studies reported here demonstrate the utility of the comparatively simple SIVET regulatory network as a tool for identifying conditions where prophage induction is enhanced . First , treatment in vitro of SIVET with the inducing agent mitomycin C results in overwhelming conversion of KanR to CamR ( Fig . 5 ) , confirming that SIVET responds to inducing agents as designed . Second , the experiments with the 933W cIind SIVET derivatives showed that the increase in CamR relative to KanR colonies observed during in vitro and in vivo growth is not due to a growth advantage of the CamR variants ( Figs . 6 and 7 ) . Third , no significant change in the ratio of CamR to KanR was observed over a large number of doublings during continuous in vitro growth of SIVET in the absence of an inducing agent ( Fig . 6 ) . This observation held true whether cultures prior to dilution were allowed to grow to stationary phase or were maintained in log phase . In each case dilutions were at a sufficiently high level to ensure that CamR bacteria were carried over during each dilution . It might be expected that CamR bacteria contributed de novo by induction should add to the growing population , resulting in an increase in the CamR/KanR ratio . However , the in vitro experiments failed to show an increase in the Induction Index over a large number of doublings ( we discuss this apparent paradoxical finding in detail below ) . Based on these results , we conclude that spontaneous induction ( induction in the absence of a known inducing agent ) of the 933W prophage fails to lead to a measurable increase in conversion of SIVET from KanR to CamR . Hence , SIVET is not sufficiently sensitive to distinguish no induction from low levels of induction . By eliminating obvious alternative explanations and showing that mitomycin C treatment results in an increase in the SIVET CamR/KanR ratio , these results confirm that SIVET can be used to identify the presence of inducing agents . Moreover , the failure to observe changes in the Induction Index over many rounds of doubling during in vitro growth , in the absence of an extrinsic inducing agent , indicates that even small measurable increases in the Induction Index should provide evidence of an extrinsic inducing agent . In the light of this background information , the >3 log increase in the Induction Index observed in SIVET isolated from feces ( Fig . 7 ) over the seven days following the initial infection provides evidence for action of an inducing factor in the mouse intestinal tract . We suggest three alternative , but not mutually exclusive , scenarios to explain this increase in the rate of induction: 1 ) a substantial portion of the bacteria reach a section of the intestinal tract that contains resident inducing activity; 2 ) the infection causes an increase in the amount and/or activity of a resident inducing activity; or 3 ) infection attracts an inducing activity or a cell ( e . g . , neutrophils ) producing an activity . Since Stx2 production , in large measure , is directly related to phage induction ( Fig . 3 ) , the intestinal environment likely contributes to the severity of the EHEC infection . Although we failed to observe any significant change in the Induction Index over many generations of in vitro growth , an increase in the Induction Index over time might be expected because spontaneous prophage induction [25] should result in TnpR expression and , at some level , conversion of KanR to CamR bacteria . This , in turn , would add to the total of CamR population over the number produced by replication of preexisting CamR population resulting in an increase in the CamR/KanR ratio . We used mathematical modeling to gain a quantitative understanding of what the expected Induction Index over time would look like if all of the spontaneously induced KanR bacteria were able to contribute immediately to the CamR bacterial population . Based on a starting Induction Index of 0 , the model adds the newly produced CamR bacterium at each division to the growing preexisting CamR population , predicting an increase in the Induction Index over time as shown in Fig . S1 . If we assume a doubling every hour over the seven days of in vivo growth , the model predicts the Induction Index would increase a little over one log and , even assuming a doubling time of 20 minutes , the Index would increase by slightly over two logs , both substantially less than the over three logs observed in the in vivo SIVET experiment . The counterbalancing actions that we see as potentially reducing the contribution of spontaneous induction might make to the CamR population , include: 1 ) as discussed above , SIVET may not be sufficiently sensitive to distinguish no induction from low induction; 2 ) there may be a delay in initiation of growth following recovery from the consequences of DNA damage that caused the induction [60] , [61]; i . e . , a phenotypic lag ( graphed in Fig . S1 ) ; 3 ) removal of the KanR cassette may occur in only one of the multiple bacterial chromosomes [62] resulting in segregation of both CamR and KanR derivatives from a single induced KanR bacterium and thus resulting in no change in the CamR/KanR ratio ; and 4 ) there may be sufficient DNA damage in some of the bacteria to block further growth , compromising survival of those bacteria . This subpopulation would be part of the induced pool that although theoretically adding to the CamR population would not be alive to do so . Although collectively these actions could explain our results , we are far from having a definitive answer as to how the Induction Index maintains this steady state . Nor can we explain how the ratio of CamR/KanR colonies reaches a steady state that is maintained for many generations . However , failure of SIVET to identify low level induction ( spontaneous ) , but identify high level induction , as with mitomycin C , indicates measurements by SIVET are likely to be an under representation .
All animal protocols were approved by the University Committee on Use and Care of Animals at the University of Michigan Medical School . The University of Michigan is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care , International ( AAALAC , Intl ) and the animal care and use program conforms to the standards of “The Guide for the Care and Use of Laboratory Animals” ( published by the NRC ) . See Table 1 . See Table 2 . LB , 10 g tryptone , 5 g yeast extract , 5 g NaCl/liter of H2O . For LB plates 10 g of agar was included . LB sucrose plates are LB plates without NaCl and made 10% in sucrose . Antibiotics were added at the following concentrations; spectinomycin 80 µg/ml , ampicillin 100 µg/ml ( plasmids ) 25 µg/ml ( chromosomal ) , kanamycin 30 µg/ml , hygromycin 200 µg/ml , and chloramphenicol 9–10 µg/ml . TB plates , 10 g tryptone , 2 . 5 g NaCl , and 10 g agar/liter of H2O . All of our constructs were engineered using the λ Red recombination system , colloquially referred to as recombineering [63] . The λ Red functions were supplied in either of two ways: transiently by a heat pulse freeing a λ promoter on a truncated λ prophage from control by a Ts repressor ( cI857 ) so that the downstream red genes could be transcribed , using DY378 [64] or from pKD46 and derivatives of that plasmid carrying cloned λ red genes by adding arabinose to the growth medium to activate an Ara-regulated promoter [65] . Single-stranded oligonucleotides or double-stranded PCR products of varying lengths having ∼40 nucleotides of flanking sequences with homologies to the target regions were introduced by electroporation into bacteria expressing λ Red functions . The expressed Red functions recombine the introduced DNAs with the target site . In the absence of a selectable marker , a two-step procedure was used: a cat-sacB ( CSB ) cassette [64] was inserted by recombineering and the recombinant selected by resistance to chloramphenicol . This cassette was then exchanged by recombineering with the designed DNA product using as selection resistance to sucrose and confirming by screening for CamS . DNA sequencing by the University of Michigan Sequencing Core Facility confirmed structure of constructs . Recombineering was used to cross the designed mutation from a single stranded oligonucleotide to the chromosome of strain K10985 . The oligonucleotide contained a single nucleotide change that resulted in a replacement of Lys codon 178 ( AAG ) with an Asn codon ( AAC ) . The following is the sequence of the DNA oligonucleotide ( oligo #2 ) with the mutant nucleotide capitalized: 5′-ccgggtgatgaggtgtttgtcagaaccgttgaaggacacaacatgattaaCgttcttggctatgacagagatggagaataccaatttacaagcattaacca-3′ . The pairing of the oligonucleotide with its complementary chromosomal DNA strand forms a C-C mismatch at the position of the nucleotide change . This mispairing is not repaired by the mismatch repair system [66] . In the absence of mismatch repair there is a significant increase in isolation of bacteria with the designed nucleotide change [67] . K10985 , an EDL933 derivative with the pKD46 plasmid [65] , was prepared for electroporation essentially as described by Murphy and Campellone [68] . Following electroporation , bacteria were resuspended in 10 ml of LB broth and grown at 37° . After ∼5 hrs of growth , dilutions of the bacteria were placed on LB plates and incubated overnight at 37° . The following day colonies were picked and stabbed to an LB plate and a TB plate that was layered with a lawn of K37 , a strain that supports growth of 933W . Plates were incubated at 37° for two hours and the seeded plate was UV irradiated ( 1 . 6 Joules/M2/S for 30 seconds ) . Following overnight incubation at 37° , a zone of lysis in the lawn showed phage had been synthesized by an induced prophage . Two clones out of 160 tested showed no zones of lysis . These derivatives failed to lyse following treatment with mitomycin C and subsequent DNA sequencing showed that although they both had the cIind mutation , only one , EDL933cIind1 , had no other changes and was selected for further study . A similar strategy was used to construct an EDL933 SIVET ind mutant , K11607 , that was KanR . A CamR derivative , K11608 , isogenic except for the loss of the KanR cassette and thus converted to CamR , was constructed from K11607 using a plasmid , pJLTnpRhygro , which supplied the TnpR resolvase . Overnight cultures were diluted and grown to early log phase in LB . The cultures were divided into two aliquots; one grown untreated and the other treated with 2 µg/ml mitomycin . Cultures were grown for 3–4 hours , based on time of lysis for the mitomycin C treated culture . Uninduced cultures were diluted every 30 minutes to maintain them in logarithmic growth . Cultures were sonicated 3× for 10 seconds at amplitude of 30% to obtain total cell lysis . Lysates were passed through 0 . 22 µm filter and concentrated using Amicon Ultra-4 ( Millipore ) . Stx2A levels in supernatants were measured using an enzyme-linked immunoabsorbent assay ( ELISA ) following a previously published procedure [69] using anti-Stx2A monoclonal and anti-Stx2 polyclonal antisera . Results were determined as ng Stx2A/µg total protein . Germ-free Swiss-Webster mice of both sexes were raised in the University of Michigan Laboratory of Animal Medicine germ free colony , housed in soft-sided bubble isolators , and fed autoclaved water and laboratory chow ad libitum . Inoculations , monitoring of animals , and sample collections were performed as previously described [7] . In brief , mice were inoculated orally with ∼106 cfu of LB-cultured bacteria . Each group of inoculated animals contained 3 male and 3 female mice between 5 and 6 weeks of age . Throughout the experiment and at necropsy , feces or cecal contents were collected for quantitative EHEC culture . Gram stain and aerobic and anaerobic culture were used to demonstrate the absence of microorganisms other than EHEC . Mice remained sterile ( except for the infecting EHEC strain ) throughout the course of the experiment . Mice inoculated with EDL933Δstx:cat or EDL933cIind1 showed no signs of disease and were euthanized 3 weeks after inoculation . All of the mice inoculated with EDL933 became moribund prior to the scheduled necropsy date , and these mice were necropsied when they became moribund , between 10 and 18 days after inoculation ( see Results ) . All animal experiments were conducted with the approval of the University of Michigan Animal Care and Use Committee . At necropsy , cecal contents were cultured to determine bacterial colonization density . Quantitative counts were determined using LB agar plates containing appropriate antibiotics . Stx concentration in cecal contents was measured using a commercial kit ( Premier ) as previously described [7] . For histologic examination , right and left kidney were immersion-fixed in formalin , embedded in paraffin , cut in 5 micron sections , and stained with hematoxylin and eosin ( Fig . 4 ) . Kidney sections were scored by a single pathologist without knowledge of the source of the section . For quantitation , a midline section of the right renal cortex was examined in its entirety , and the number of 200× fields with tubular or glomerular lesions was recorded . Acute tubular necrosis was subjectively scored as mild , moderate , or severe . For the SIVET experiment , animals were similarly infected with ∼106 cfu of LB-cultured bacteria . Because of the deletion-substitutions in the 933W prophage , the SIVET strain does not express significant levels of Stx2 . Details of the experiment procedure have been discussed above . In these experiments colony counts were obtained using LB plates containing either kanamycin ( 30 µg/ml ) or cloramphenicol ( 9 µg/ml ) . Statistics: Quantitative data were analyzed by Mann-Whitney U test . Multiple groups were compared by ANOVA and Fisher's Least Significant Difference . The design of SIVET [47] is based on Camilli and colleague's “Recombinase-based Reporter of Transcription ( RIVET ) system” [45] , [46] . However , SIVET differs from RIVET in providing a selection for cells in which the assayed transcription occurred ( Fig . 1-II ) . The first generation of EDL933 SIVET , was constructed similarly to the original K12 SIVET strain [47] , [70] using recombineering [63] , with a SpcR ( this laboratory ) derivative of pKD46 [65] supplying the λ Red functions . The 933W prophage was inactivated by elimination of genes controlling two critical components of phage growth , transcription and replication . The N gene , encoding a transcription regulator , was deleted and replaced with a KanR cassette . The O and P genes , encoding proteins involved in initiation of DNA replication [48] , were replaced with the tnpR gene and ampR cassette . This was accomplished using a PCR product containing the ampR cassette and the sequence encoding the 168 variation of the γδ resolvase , tnpR-168 , [46] with flanking sequences having homology to the 933W cro and ren genes ( Fig . 1-I ) . These changes generated strain K11084 that , even though having a defective 933W prophage , is unable to survive treatment with an inducing concentration of mitomycin C . The cryptic prophage CP933V in EDL933 , although defective , has nearly a complete lambdoid phage genome [41] , leading us to suspect that its induction might be responsible for this sensitivity to mitomycin C . Therefore , we deleted the control region of CP933V rendering that prophage uninducible; the deletion included the putative repressor ( cI ) gene with immediate surrounding putative promoters , operators , genes , and relevant associated genetic material in a two-step process . A cat-sacB ( CSB ) cassette [71] with flanking ends having appropriate homologies to CP933V ( primers 3 and 4 , template K9685 ) was recombined into the targeted region , extending from N to cII ( Fig . 1-I ) in CP933V , generating strain K11114 . The CSB inserted in CP933V was then replaced with a single-stranded DNA oligomer ( oligo #1 ) [71] , generating strain K11115 . This strain survives the inducing levels of mitomycin C used in our studies . Addition of the reporter cassette in a two-step procedure completed the construction of the EDL933 SIVET strain . First , K11161 was constructed by crossing a cat cassette ( primers: 9 and 10 , K10373 template ) into the lacZ gene of K11115 providing homology for the next step . Second , K11173 was constructed by crossing the cat::resC-tetR-resC::cat cassette ( primers7 and 8 , K10449 template ) into the inserted cat gene in K11161 with selection for tetracycline resistance . This first EDL933 SIVET construct had to be modified because its constitutive expression of TetR made the bacteria sensitive to the in vivo environment . We therefore made the following changes using recombineering , λ Red functions were supplied by a hygromycin resistant derivative of pKD46 ( pKD46hygR ) . The KanR cassette in the N gene was replaced by a spcR cassette and the selective tetR cassette in the cat::resC::tet::resC::cat reporter was replaced by a kanR cassette yielding the cat::resC-kan-resC::cat reporter . To complete the process , the strain was cured of pKD46hygR yielding K11604 , the SIVET strain used in the experiments reported here . The method used to obtain the results shown in figure 5 was essentially those outlined in Livny and Friedman [47] . Briefly , SIVET strain was grown ∼108/ml in LB , made 2 µg/ml in mitomycin C , grown for 2 hrs , washed and resuspended in LB , grown for 4 hours , and dilutions of bacteria were plated on selective media . This metric provides a log10 scale readout that allows for a simplified comparison of results of different SIVET experiments . The calculations compare the ratio of CamR/KanR colonies at any given time relative to the starting ratio of CamR/KanR colonies . It is calculated as log10 [ ( CamR titer/KanR titer at any time after start of experiment ) / ( CamR titer/KanR titer at start of experiment ) ] . It follows that the Induction Index at the start would obviously be 0; i . e . , log10 1 ( starting ratio/starting ratio ) . Overnight cultures of O157:H7 and the cIind1 derivative grown in LB broth were diluted 1∶100 in LB and grown to early log phase . Each were divided into two aliquots , one untreated and the other treated with 2 µg/ml of mitomycin C . Samples , 200 µl , were placed in a 96 well plate and grown at 37° with OD600 read at 30 minute intervals in the SpectraMax 250 ( Micro Devices ) . | Infection with Enterohemorrhagic E . coli ( EHEC ) , and more recently with the Enteroaggregative E . coli strain O104:H4 , is a significant health risk , causing bloody diarrhea , kidney failure , and even death . The virulence factor in these bacteria responsible for the severe outcomes is Shiga toxin ( Stx ) . Genes encoding Stx are in the genome of bacterial viruses ( prophages ) on the pathogenic E . coli chromosomes . The prophage remains quiescent until damage to the bacterial chromosome occurs causing prophage gene expression ( called induction ) , which leads to production of bacteriophages that are released into the environment . Because stx expression is controlled by the phage regulatory system , prophage induction leads additionally to production and release of Stx . This study provides conclusive evidence that in a mouse model of EHEC infection , induction of the prophage carrying the stx genes is specifically required for EHEC to cause disease and that the intestinal environment adds to the induction and therefore to the production of Stx . Similar events likely regulate Stx production and release by the Stx encoding phage in the O104:H4 strain . Controlling prophage induction offers a means to control EHEC infection . |
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Coronary heart disease ( CHD ) is the leading cause of mortality in both developed and developing countries worldwide . Genome-wide association studies ( GWAS ) have now identified 46 independent susceptibility loci for CHD , however , the biological and disease-relevant mechanisms for these associations remain elusive . The large-scale meta-analysis of GWAS recently identified in Caucasians a CHD-associated locus at chromosome 6q23 . 2 , a region containing the transcription factor TCF21 gene . TCF21 ( Capsulin/Pod1/Epicardin ) is a member of the basic-helix-loop-helix ( bHLH ) transcription factor family , and regulates cell fate decisions and differentiation in the developing coronary vasculature . Herein , we characterize a cis-regulatory mechanism by which the lead polymorphism rs12190287 disrupts an atypical activator protein 1 ( AP-1 ) element , as demonstrated by allele-specific transcriptional regulation , transcription factor binding , and chromatin organization , leading to altered TCF21 expression . Further , this element is shown to mediate signaling through platelet-derived growth factor receptor beta ( PDGFR-β ) and Wilms tumor 1 ( WT1 ) pathways . A second disease allele identified in East Asians also appears to disrupt an AP-1-like element . Thus , both disease-related growth factor and embryonic signaling pathways may regulate CHD risk through two independent alleles at TCF21 .
A recent meta-analysis of 14 Genome-wide association studies ( GWAS ) for CHD , Coronary ARtery DIsease Genome-wide Replication And Meta-analysis ( CARDIoGRAM ) , including 22 , 233 cases and 64 , 762 controls in Europeans , elucidated 13 novel susceptibility loci [1] . One of these novel loci includes a variant , rs12190287 at 6q23 . 2 , located within the 3′ untranslated region ( 3′UTR ) of TCF21 [1] . This lead SNP at 6q23 . 2 had the lowest P value ( P<4 . 6×10−11 ) of the novel loci in the meta-analysis and was also highly associated in the combined meta-analysis ( P<1 . 1×10−12 ) . rs12190287 was also identified as an expression quantitative trait locus ( eQTL ) through correlation with increased TCF21 gene expression in both liver and adipose tissue [1] , [2] . Importantly , the TCF21 locus was recently replicated in another GWAS for CHD in a Han Chinese population ( 15 , 460 cases and 11 , 472 controls ) , however a second variant ( rs12524865 ) that is poorly correlated with rs12190287 and located 14 kb upstream of TCF21 was the lead SNP in this racial ethnic group [3] . TCF21 is a member of the basic helix-loop-helix ( bHLH ) transcription factor ( TF ) family and regulates cell differentiation and cell fate decisions during development of the coronary vasculature , lung , kidney , and spleen [4] , [5] . Tcf21 is expressed in mesodermal cells in the proepicardial organ ( PEO ) as early as E9 . 5 in mice , and later in mesenchymal cells forming the pericardial layer [4] . Recent elegant studies employing knockout mice have established a specific role for this factor in the origin of coronary artery smooth muscle cells and cardiac fibroblasts [6] , [7] . Loss of Tcf21 expression in mouse results in increased expression of SMC markers in cells on the heart surface consistent with premature SMC differentiation [7] , and a dramatic failure of cardiac fibroblast development [6] , [7] . These data are most consistent with a role for Tcf21 in a bipotential precursor cell for SMC and cardiac fibroblast lineages , with loss of Tcf21 expression being essential for SMC development , and persistent Tcf21 expression being required for cardiac fibroblast development [6] , [7] . In studies described here we examine the function of a regulatory element at the lead variant rs12190287 though allele-specific reporter assays , gel mobility shift assays , and haplotype specific chromatin immunoprecipitation ( haploChIP ) . We further demonstrate allele-specific regulation of TCF21 gene expression , though modulating this regulatory element , via platelet derived growth factor receptor beta ( PDGFR-β ) and Wilms tumor 1 ( WT1 ) mediated signaling . Lastly , we identify a conserved AP-1 dependent mechanism acting upstream of TCF21 at rs12524865 , which was recently associated with CHD in East Asians [3] . Taken together , these studies elucidate both disease-related and embryonic pathways upstream of TCF21 , at two independent risk alleles , thus providing further pathophysiological insight into the common heritable risk of CHD .
The CARDIoGRAM meta-analysis in Caucasians identified rs12190287 as the lead CHD association at 6q23 . 2 ( P<4 . 64×10−11 ) , which was 3 orders of magnitude more significant than other SNPs in this region ( Figure 1A ) [1] . We then set out to identify potential causal risk-associated mechanisms at 6q23 . 2 using an integrated workflow ( Figure 1B ) . We examined the overall linkage disequilibrium ( LD ) plot around the TCF21 locus at 6q23 . 2 using 1568 individuals of European descent genotyped on the fine-mapping Metabochip array [8] ( Illumina ) , which contains 196 , 726 polymorphisms [8] , [9] , of which approximately 280 markers were contained in the 170 kb block between Chr6: 134 , 171 , 000–134 , 341 , 000 . We identified regions of high LD surrounding the lead SNP , rs12190287 , which is located in the 3′UTR of the non-coding exon of the long variant 1 of TCF21 . Two haplotypes contained the high-risk allele at the lead SNP rs12190287 , with one containing 3 out of the 5 additional eSNPs , while the haplotype with frequency 0 . 366 contained all of the eSNPS in the TCF21 locus ( Figure 1C and 1D ) . We examined the LD plot containing the eSNPs for TCF21 ( Figure 1C ) and found that none of these variants had r2 values >0 . 8 with the lead SNP rs12190287 , suggesting that if a single variant is responsible for the association observed by CARDIoGRAM , it is most likely to be rs12190287 . This LD pattern is also consistent with the observation that rs12190287 was 3 orders of magnitude more significant than other SNPs in the region [1] . We then mapped regulatory chromatin regions surrounding rs12190287 using the ENCylopedia Of DNA Elements ( ENCODE ) Integrated Regulation data from 7 cell lines ( Figure 1E ) , which demonstrated enrichment of the enhancer mark H3K4me1 . Enrichment for histone modifications H3K4me3 ( marks promoters ) , and H3K27ac ( marks active regulatory elements ) were also observed to a lesser extent . We also found regions of DNaseI hypersensitivity for open chromatin and overlapping RNA-seq peaks for transcriptional activity in the region containing rs12190287 . We validated these histone modification and DNaseI ENCODE data with our own ChIP-seq and FAIRE-seq ( Formaldehyde-Assisted Isolation of Regulatory Elements ) experiments in HCASMC ( Figure S1 ) , demonstrating consistent regulation at rs12190287 and relevance to CHD . We also mapped the transcription factor binding sites ( TFBS ) using ENCODE data , which identified enrichment of an activator protein 1 ( AP-1 ) component , JUND in a human embryonic stem cell line ( Figure 1E ) . Given that rs12192087 appeared to be the most likely causal variant at 6q23 . 2 , we proceeded with in vitro functional studies to identify the risk-associated mechanisms through this variant . First , we mapped the putative TFBS in silico using various bioinformatics tools , including TRANSFAC , PROMO , MatInspector , and TFSearch ( Table 1 ) . Interestingly , multiple AP-1 TFs were predicted to preferentially bind to the major risk C allele , containing the binding motif , TGACTTCA ( Figure S2A ) . Luciferase reporters containing the putative binding site for the risk and protective alleles were then transfected into various cell lines , including HepG2 , HEK , and A7r5 , as well as primary human coronary artery smooth muscle cells ( HCASM ) , and rat aortic smooth muscle cells ( RASM ) . We observed approximately 150–200 fold increase in activity with the rs12192087-C ( C-Luc ) reporter relative to the empty vector reporter , and this relative activity of the C-luc reporter was ∼20-fold greater than the rs12190287-G reporter ( G-Luc ) ( Figure 2A ) . Similar results were observed in primary HCASM and RASM , suggesting that the G allele disrupts TF binding , leading to reduced TCF21 transcription . Given the ubiquitous expression of AP-1 factors in various cell types , it is not surprising that cell-specific activity was not observed . Interestingly , the allele-specific difference in transcription was lost when we mutated the 8-mer binding site to create a classical AP-1 7-mer ( closely resembling a TPA element ) , but not when we mutated to another atypical AP-1 8-mer ( Figure 2B ) . This is consistent with predicted binding of either c-Jun homodimers or c-Jun/ATF heterodimers , rather than classical c-Jun/c-Fos AP-1 complex [10] , to confer allele-specific transcriptional regulation . In order to measure relative TF protein binding we performed electrophoretic mobility shift assays ( EMSA ) . We observed binding to both radiolabeled alleles containing a single putative binding site in nuclear extracts from various cell types ( Figure 2C ) . Greater binding to the C risk allele was observed , while competition with excess cold probe was more effective at the G allele . These results are consistent with the reporter assays , suggesting the G allele has weaker transcriptional regulatory activity due to decreased TF binding . Given that the putative binding site closely resembles an AP-1 or CRE element , we measured the effects of activating protein kinase C ( PKC ) via phorbol-12-myristate-13-acetate ( PMA ) or adenylyl cyclase ( AC ) via forksolin ( fsk ) on the transcriptional activity at rs12190287 ( Figure S2B ) . Surprisingly , neither PMA nor forskolin altered C or G-Luc reporter activity , while both activated the consensus AP-1 and CRE reporters , respectively . This may indicate the element at rs12190287 can be activated in normal growth media , which contains growth factors upstream of AP-1 . Overexpression of constitutively active MEKK ( preferentially upstream of AP-1 elements ) , but not active protein kinase A ( PKA ) ( preferentially upstream of CRE elements ) led to greater transactivation of the C allele ( Figure 2D ) . We observed an increase in the bound TF complex upon PMA stimulation , which was greater at the C allele , suggesting binding of an AP-1-like element ( Figure 2E ) . This is further supported by the gel shift observation of a similar higher molecular weight complex bound to C and G alleles , compared to the consensus AP-1 probe ( Figure S2C ) . Interestingly , a second lower molecular weight complex bound to the consensus AP-1 probe was not observed with C and G probes , suggesting some differences in TFs binding to these distinct elements . We then sought to determine the specific identity of the TFs predicted to bind rs12192087 in vitro . Using allele specific reporters for rs12190287 , we measured the regulatory effects of overexpression of AP-1 related factors meeting a predicted in silico binding threshold of >0 . 85 , which included c-Jun , JunD , and ATF3 ( Figure 3A ) . c-JUN overexpression elicited robust activation of the C allele , similar to the activation of consensus AP-1-luc . Less overall activity was observed with the G allele and minor effects were observed upon JUND and ATF3 overexpression . Prior reports also demonstrate that c-Jun predominately activates AP-1 elements in vitro , whereas JunD and ATF3 alone often result in transrepression [11] . We also measured the transcriptional regulation at rs12192087 via loss-of-function experiments . The dominant negative mutant ΔJun ( TAM67 ) , which lacks the transactivation domain of c-Jun , resulted in blunted transcriptional activity at C and G alleles ( Figure 3B ) . Similar results were observed with ΔATF3 , whereas ΔCREB led to slightly increased activity . Transfection of siRNAs against c-JUN , JUND , and ATF3 also led to reduced transcriptional activity at the C and G alleles , specifically implicating these factors in mediating the activity at rs12190287 ( Figure 3C ) . siRNA-mediated protein knockdown for each AP-1 TF was confirmed by immunoblotting ( Figure S3 ) . Using EMSA we also observed super-shifted complexes upon incubation with antibodies against c-Jun and JunD ( Figure 3D ) . Together these results implicate the AP-1 factors c-Jun , JunD , as well as ATF3 in regulating putative enhancer activity at rs12190287 . To ascertain the functional implications of allelic variation at rs12190287 , we measured the effects of relevant upstream stimuli on TCF21 expression in HCASMC . Platelet-derived growth factor ( PDGF ) is a potent growth factor ligand responsible for activation of AP-1-dependent gene expression in SMCs , leading to synthetic phenotypic properties such as increased proliferation , survival , and migration [12] . Further , signaling through PDGFRβ is required for epithelial-mesenchymal transition ( EMT ) and epicardial fate determination in developing CASMC [13] . Transforming growth factor beta ( TGF-β1 ) is also critically involved in both EMT and adult SMC phenotypic modulation . Interestingly , PDGF-BB treatment resulted in a time-dependent increase in TCF21 , whereas transforming growth factor beta ( TGF-β1 ) and PMA led to slightly reduced or unaltered TCF21 levels ( Figure 4A ) . Western blots demonstrated changes in TCF21 protein levels were consistent with changes in TCF21 mRNA levels in response to PMA and PDGF-BB ( Figure S4 ) . Concordantly , c-JUN and ATF3 were upregulated by PDGF-BB , while JUND levels were unchanged ( Figure 4B ) . We then assessed the effects of PDGF-BB and TGF-β1 treatment on allele-specific expression ( ASE ) of TCF21 in HCASMC using TaqMan allelic discrimination ( Figure 4C , Figure S5A , B ) . Using heterozygous CG HCASMC , we observed that PDGF-BB treated samples had greater normalized C/G ratios , which peaked around 6 hours , while TGF-β1 treated samples had lower C/G ratios ( Figure 4C ) . The phasic allelic imbalance observed with PDGF-BB may be partially dependent on activation of AP-1 to regulate TCF21 gene expression . Transcriptional regulation of gene expression is tightly controlled by the native chromatin architecture in vivo . Therefore , we interrogated allele-specific AP-1 occupancy at rs12190827 using chromatin immunoprecipitation ( ChIP ) and haplotype specific ChIP ( haploChIP ) . In HCASMC treated with PDGF-BB we observed a significant increase in enrichment at rs12190287 by c-Jun and ATF3 ( Figure 5A ) . JunD enrichment , while significantly above IgG in control samples , was unchanged with PDGF-BB . We then measured allele-specific enrichment in heterozygous HCASMC treated with PDGF-BB , as done previously for haploChIP [14] . Interestingly , c-Jun was predominately enriched at the C allele under control treatment , indicated by greater C/G ratios ( Figure 5B ) . Both c-Jun and ATF3 were more enriched at the C allele upon PDGF-BB treatment , and JunD enrichment was unchanged . Similar observations were made using pyrosequencing-based allelic discrimination ( Figure S5C ) . ChIP products were also amplified at FOSB and MYOG promoters , as AP-1 positive and negative control regions , respectively ( Figure 5C , D ) . We then measured putative enhancer activity at rs12190287 via post-transcriptional histone modification . PDGF-BB treatment led to significantly increased enrichment of H3K4me1 ( marks active/poised promoters ) and H3K27ac ( marks active enhancers ) and H3K27me1 ( marks active/poised promoters ) ( Figure 5E ) . We also observed increased relative enrichment of active histone modifications at the C allele , which was further potentiated with PDGF-BB stimulation in HCASMC ( Figure 5F ) . These data indicate that the AP-1 complex positively regulates the rs12190287 risk allele in the native and active chromatin state . We also investigated the potential functional effects of non-AP-1 TFs predicted to bind rs12190287 ( Table 1 ) . Wilms tumor 1 ( WT1 ) was of particular interest given its known role in CASMC development [15] , and evidence in developmental models indicating wt1 directly regulates tcf21 [16] . The G allele resides in a WT1-like binding element [17] ( WTE; 5′-GCGTGGGAGT-3′ ) , which was previously implicated in the regulation of the human thromboxane A2 receptor [18] . WT1-D ( +KTS amino acid insertion ) binds DNA with reduced affinity compared to WT1-B ( −KTS ) [19] , and the ratio of the two alternatively spliced isoforms has been implicated in Frasier syndrome [20] . We observed that expression of WT1-B ( −KTS ) and WT1-D ( +KTS ) led to similar transrepression of both C and G alleles at rs12190287 ( Figure 6A ) , consistent with the role of WT1 as a transcriptional repressor . As a tumor suppressor gene , WT1 often represses AP-1 mediated transcription [21] , [22] and WT1-B and WT1-D also repressed c-Jun-mediated activation of rs12190287 in vitro ( Figure 6B ) . Similar regulation was observed at both C and G alleles suggesting WT1-mediated regulation may not be the rate-limiting step altered by rs12190287 . Consistently , WT1 siRNA mediated knockdown led to increased activity of C and G alleles ( Figure 6C ) , with protein knockdown verified by immunoblotting ( Figure S3D ) . Next , we assessed the expression changes of WT1 upon TGF-β1 or PDGF-BB treatment of HCASMC ( Figure 6D ) . Interestingly , PDGF-BB led to a rapid decline in WT1 , which recovered by 24 hours . TGF-β1 led to a slower yet persistent reduction of WT1 . We also observed decreased enrichment of WT1 at rs12190287 upon PDGF-BB stimulation , consistent with effects observed at the FOSB promoter ( AP-1 positive control region ) ( Figure 6E ) . Less reduction was observed at the MYOG promoter ( AP-1 negative control region ) . Surprisingly we observed WT1 preferentially enriched at the C allele , which was increased upon PDGF-BB stimulation ( Figure 6F ) . Given that WT1 negatively regulates transcription at rs12190287 and is downregulated by PDGF-BB , may suggest that the WT1 cofactor preferentially associates with the C risk allele to temporally fine-tune AP-1 mediated transcription upon growth factor stimulation . TCF21 was one of three Caucasian CAD associated loci that was recently replicated in a Han Chinese population [3] . While association at rs12190287 did not reach genome-wide significance at the TCF21 locus , rs12524865 ( Figure 1A ) represented the lead SNP in this racial ethnic group and showed consistent association in the discovery and replication stages [3] . We examined the haplotype structure at 6q23 . 2 in ∼2400 Han Chinese from the HALST study in Taiwan who were also genotyped with the Metabochip , and augmented genotype data in this region through imputation using data from the HapMap II and III Han Chinese ( CHB ) samples . We identified regions of high LD surrounding rs12190287 , however much less LD surrounding rs12524865 compared to the European samples , and we found that rs12524865 is located in a distinct haplotype block from the lead SNP in Europeans , rs12190287 ( Figure 7A ) . Further , the risk haplotype containing rs12524865 and four other TCF21 eSNPs occurs at similar frequency ( 0 . 361 ) in this population ( Figure S6A ) . rs12524865 is in perfect LD with other eSNPs for TCF21 in one haplotype block , including rs1967917 and rs7752775 . However , the haplotype block for rs12190287 does not contain other alleles in LD ( Figure 7A ) . We then mapped the putative TFBS at rs12524865 using multiple prediction tools ( Table 2 ) . Interestingly , rs12524865 is also located within an AP-1/CREB-like element , TAA[C/A]GTCA , which closely resembles the consensus ATF/CREB binding site , TGACGTCA ( Figure S6B ) . As expected , the C allele ( also major , risk allele ) is predicted to bind AP-1 and CREB family members , whereas predicted binding is disrupted by the minor , protective allele . Using luciferase reporters containing this putative enhancer we observed robust transcriptional activity with the risk allele , which was absent with the protective allele ( Figure 7B ) . Forskolin but not PMA stimulation potentiated this activity , suggestive of a cAMP-responsive ATF/CREB element ( Figure 7B ) . Dominant negative mutants of CREB , Jun , and ATF3 reduced this activity ( Figure S6C ) . We then measured occupancy of AP-1 and active chromatin histone modifications at rs12524865 . Interestingly , we observed increased c-Jun and ATF3 enrichment with PDGF-BB treatment , with much greater enrichment by ATF3 ( Figure 7C ) . Enrichment of active histone modifications , H3K4me1 and H3K27ac also suggest a functionally active chromatin state at rs12524865 ( Figure 7D ) . Together these results implicate both rs12190287 and rs12524865 risk alleles in a shared AP-1-dependent mechanism for regulating TCF21 in HCASMC , thus further defining the genetic risk mechanisms of CHD which have been conserved across racial ethnic groups during evolution ( Figure 8 ) .
Therapeutic targeting of traditional CHD risk factors has reduced overall mortality rates , however there are currently no therapies that directly target disease processes of the vessel wall . Recent GWAS have identified 46 independent risk-associated loci for CHD/MI , and 104 independent loci associated at a false discovery rate <5% [8] , [23] . Many of the genes identified are implicated in the regulation of SMC plasticity during atherosclerosis , including PDGFD , COL4A1/2 , CDKN2B and CDKN2A/p19ARF [24]–[26] . However , the molecular mechanisms and relevant pathways underlying these risk associations are relatively underexplored . Here , using an integrated beyond GWAS strategy ( Figure 1B ) , we reveal the interplay of both developmental and disease-related pathways , which coordinate the regulation of TCF21 at independent CHD susceptibility alleles in humans . Individuals carrying the risk haplotype for rs12190287 and rs12524865 are predicted to have greater TCF21 expression due to increased binding of AP-1 complexes to a cis-regulatory element . Our studies further reveal a potential PDGFRβ-dependent mechanism for the CHD risk association in human coronary artery smooth muscle cells both in vitro and in vivo . Of the 13 novel loci identified in the initial CARDIoGRAM meta-analysis , TCF21 was particularly attractive as a missing link to CHD . Tcf21 ( Pod-1/Capsulin/Epicardin ) was initially cloned in our laboratory and two others [4] , [27] , and is expressed in epicardial progenitor cells that give rise to developing CASMC [4] . Studies of TCF21 function in the adult have been hampered since Tcf21 null mice die postnatally due to pulmonary hypoplasia and respiratory failure [5] . TCF21 has been identified as a candidate tumor suppressor gene and is frequently epigenetically silenced in various human cancers [28] , [29] . These studies have implicated loss of TCF21 expression as an early-stage biomarker for increased cancer risk . Based on our findings , we can reason that aberrant upregulation of TCF21 in coronary SMC may increase CHD risk through alteration of the SMC response to injury in the vessel wall . Identification of cis-regulatory elements altered by disease related variants is critical for post-GWAS functional characterization studies [30]–[32] . Here , we observed that PDGF-BB mediates binding of an AP-1 complex , likely containing c-Jun , and JunD or ATF3 heterodimers to the risk allele at rs12190287 , which is preferentially in an active chromatin state . The activator protein-1 ( AP-1 ) family of TFs have been implicated in growth factor-dependent SMC activation following vessel injury [33] . The prototypical basic region-leucine zipper ( bZip ) protein , c-JUN is expressed in human atherosclerotic plaques and promotes SMC proliferation and neointima formation in vivo [34] . ATF3 is another stress-inducible gene upregulated in many cancers [35] and also in SMCs within injured mouse femoral arteries , to promote SMC migration and ECM synthesis [36] . It has been shown that c-Jun readily forms heterodimers with ATF2 and ATF3 , which have distinct DNA binding affinities to CRE and AP-1 elements [37] . Interestingly , genes encoding extracellular matrix ( ECM ) proteins are often the targets of c-Jun/ATF enhancer elements [10] . A challenge in prioritization of regulatory SNPs is elucidating the biologically relevant upstream pathways driving these associations [38] , [39] . Platelet-derived growth factor ( PDGF ) is a critical growth factor involved in vascular development . It has been shown in mice that PDGFR-β is required for development of mural cells , CASMC and pericytes , involving epithelial-to-mesenchymal transition ( EMT ) in the epicardium [13] , [40] . PDGF-BB is also a potent inducer of the synthetic SMC phenotype , increasing migration , lipid uptake , and ECM synthesis , both in vitro and in vivo during vascular injury and atherosclerosis [12] . A recent GWAS for CHD in Europeans and South Asians identified the PDGFD gene , and this PDGF family member has been shown to have many of the same disease-related actions as related PDGFs [41] . In contrast , TGF-β is a pleiotropic cytokine mostly responsible for maintaining SMC differentiation , through activation of Smad , SRF , and RhoA signaling pathways [42] . Indeed , VSMC differentiation during aortic development likely depends on TGF-β rather than PDGF-BB/PDGFR-β [43] . Our observations that TCF21 was selectively induced by PDGF-BB rather than TGF-β in HCASMC is consistent with the notion that TCF21 inhibits coronary artery SMC differentiation while inducing SMC phenotypic modulation . Similar to Tcf21 , Wilms tumor 1 ( Wt1 ) is expressed in the early proepicardium , epicardium and mesenchyme during development of the heart and other mesoderm-derived tissues [15] , [44] , [45] . In fact , previous studies in zebrafish have demonstrated that tcf21 expression in the proepicardial organ is dependent on wt1 [46] . Wt1 expression is also induced in the coronary vasculature in regions of ischemia and hypoxia after MI in mice [47] . As a tumor suppressor gene , WT1 was previously shown to repress PMA induced transcription [22] . WT1 binding to the thrombospondin-1 promoter leads to repression upon c-Jun overexpression in ECs [48] and fibroblasts [21] . Here we identify WT1 upstream of TCF21 to repress the enhancer at rs12190287 . While in silico analyses predicted preferential binding to the G allele , haploChIP data suggest that WT1 preferentially associates with the C allele . This is consistent with greater c-Jun enrichment at the C allele . The orthogonal regulation of WT1 expression by PDGF-BB compared to TCF21 , c-JUN , JUND , and ATF3 , also may imply that WT1 acts to spatially and temporally fine-tune TCF21 expression , rather than cause repression . CHD involving atherosclerosis continues to burden both developed and developing countries , largely due to urbanization and westernization of diet and lifestyle . Compared to developed countries , CHD related deaths are predicted to rise more than 3-fold in China and India , for instance [49] . While most GWAS for CHD have focused on individuals of European ancestry , large-scale studies of non-European populations may allow further understanding of the risk-associated mechanisms driving CHD . A recent meta-analysis of GWAS for CHD in a Han Chinese population ( 15 , 460 cases and 11 , 472 controls ) replicated the TCF21 association in Europeans [3] . The combined discovery and replication stages identified a near genome-wide significant association at rs12524865 , upstream of TCF21 at 6q23 . 2 ( P = 1 . 87×10−7 ) . The discovery stage identified primarily rs12524865 ( P = 3 . 40×10−3 ) , although rs12190287 showed a trend and directionality as a reporter for Caucasian cohorts ( P = 3 . 03×10−2 ) . The linkage disequilibrium r2 values between these two eQTLs for TCF21 is 0 . 62 in Europeans and only 0 . 18 in Han Chinese , and these variants are found in separate haplotype blocks in Han Chinese . Further , we demonstrated rs12524865 disrupts a binding site for CREB/ATF in silico and measured enrichment for c-Jun and ATF3 at rs12524865 in HCASMC . These studies highlight the value of multi-ethnic post-GWAS validation of causal variants to assess both the functional impact and heritable risk of common variants in complex diseases . The compelling promise of the new disease associated genes and pathways afforded by GWAS methodology is that they will provide biological insights and targets for the development of new therapeutic approaches , and this is particularly compelling for CHD where there are no therapies directed at the blood vessel wall . TCF21 and its downstream targets provide one such pathway . eQTL data have suggested that the disease-associated major allele shows increased TCF21 expression , and this is consistent with the functional studies described herein , where the major risk C alleles at rs12190287 and rs12524865 confer greater transcriptional activity compared to the minor protective G and A alleles , respectively . The embryonic function of TCF21 in the developing coronary circulation seems most consistent with a role aimed at inhibiting differentiation of SMC progenitors , and thus it is likely that TCF21 function might interfere with the SMC response to vascular injury in the disease setting . It is now generally accepted that vascular SMC provide a stabilization of the atherosclerotic plaque , and thus therapeutic inhibition of the TCF21 pathway would be expected to decrease the risk for coronary events . However , such an approach might also put the patient at increased risk for head and neck and lung cancer , as TCF21 is a potent tumor suppressor gene that is frequently mutated or silenced in cells of these tumors . This situation contrasts with the emerging information related to the risk mechanisms at 9p21 . 3 , where the function of one likely causal gene CDKN2B is associated with decreased risk for vascular disease [25] , [50] as well as a broad range of human cancers [51]–[53] . Therapeutic activation of expression of this gene would be expected to decrease risk for both types of disease . While it is still early days for such extrapolations , follow-up of vascular wall GWAS genes is expected to provide insights into disease-related pathways to better inform therapeutic manipulation .
Primary human coronary artery smooth muscle cells ( HCASMC ) were purchased from three different manufacturers , Lonza , PromoCell and Cell Applications and were cultured in complete smooth muscle basal media ( Lonza ) according to the manufacturer's instructions . All experiments were performed on HCASMC between passages 4–7 , using lots identified as heterozygous at rs12190287 as indicated . Primary rat aortic smooth muscle cells ( RASM ) were obtained from Dr . Phil Tsao ( Stanford University ) and cultured in Dulbecco's' Modified Eagle Media ( DMEM ) low glucose with 10% fetal bovine serum ( FBS ) . The rat aortic smooth muscle cell line , A7r5 was purchased from ATCC and also obtained from Dr . Joe Miano ( University of Rochester ) and were maintained in DMEM low glucose with 10% FBS . HepG2 and HEK cells were purchased from ATCC and maintained in DMEM low glucose with 10% FBS . Pre-designed Silencer Select siRNA duplexes against human c-JUN , JUND , ATF3 , and WT1 were purchased from Ambion/Life Technologies . At least two individual siRNAs were tested for each . Briefly , HCASMC were plated in 12-well ( dual-luciferase assay ) or 6-well plates ( qPCR ) in complete media . Approximately 24 hours after plating , and between 40–60% confluence , cells were transiently transfected with negative control or TF specific siRNAs ( 50 nM ) using RNAiMAX reagent ( Life Technologies ) according to the manufacturer's instructions . Cells were incubated for 48 hours prior to performing dual-luciferase assays , harvesting total RNA using miRNeasy Mini kit ( Qiagen ) for TaqMan based qPCR assays , or nuclear protein extraction for Western blotting . Oligonucleotides containing the putative enhancer elements for rs12190287 C/G and rs12524865 C/A ( Table S1 ) were annealed at 95 degrees for 10 minutes in annealing buffer and allowed to cool to room temperature . Double-stranded DNA fragments were then subcloned into the MCS of the minimal promoter containing pLuc-MCS vector ( Agilent ) . Constructs were validated by Sanger sequencing . Empty vector ( pLuc-MCS ) , rs12190287-C or rs12190287-G and Renilla luciferase constructs were transfected into HCASMC , RASMC , A7r5 , HepG2 , and HEK using Lipofectamine 2000 . Media was changed after 6 hours , and dual luciferase activity was measured after 24 hours using a SpectraMax L luminometer ( Molecular Devices ) . Relative luciferase activity ( firefly/Renilla luciferase ratio ) is expressed as the fold change of the empty vector control ( pLuc-MCS ) . Double stranded oligonucleotides for rs12190287-C/G , AP-1 , CREB , rs12190287 mixed were obtained by annealing single stranded oligos ( Table S1 ) , as previously described [54] . Annealed oligos were then labeled with [γ32P]-ATP ( Perkin Elmer ) using T4 polynucleotide kinase ( NEB ) for 30 minutes at room temperature and then purified through Sephadex G-50 Quick Spin columns ( Roche ) . After measuring radioactivity , reactions were assembled with 1× EMSA binding buffer , 1 µg poly-dIdC , 10 µg nuclear extract , 100× unlabeled probe ( for competitions ) , 2 µg polyclonal antibody ( for super-shifts ) , [γ32P]-ATP labeled probe , and incubated at room temperature for 30 min prior to protein separation on a 4% TBE gel . Gels were dried on Whatman paper using a heated vacuum drier and proteins were detected on radiographic film . Primary human coronary artery smooth muscle cells ( HCASMC ) were cultured in normal growth media until approximately 75% confluent , then cultured in the absence of serum and supplements for 24 hours , prior to stimulation with 50 ng/ml human recombinant PDGF-BB ( R&D Systems ) , 5 ng/ml human recombinant TGF-β1 ( R&D Systems ) , 100 nM PMA ( Sigma ) or vehicle for indicated times . Total RNA was prepared using miRNeasy Mini kit ( Qiagen ) and total cDNA was prepared from 0 . 5 µg of RNA using the TaqMan High Capacity cDNA synthesis kit ( Life Technologies ) . TaqMan gene expression probes ( Table S1 ) were used to amplify human TCF21 , c-JUN , JUND , ATF3 , and WT1 , which were normalized to human 18S levels . Nuclear extracts were generated from HCASMC harvested at indicated time points . Protein concentrations were determined using a BCA assay ( Pierce ) and 50–100 µg nuclear protein for each condition was loaded on a pre-cast NuPAGE 4–12% Bis-Tris polyacrylamide gel ( Invitrogen/Life Technologies ) , with gel run at 150 v for 1 h using MES buffer ( Invitrogen/Life Technologies ) , and transferred to PVDF membrane at 35 v for 1 h . Membranes were blocked in 5% non-fat dry milk in 1× TBST for 1 h and incubated overnight with rabbit polyclonal antibodies against TCF21 ( Sigma; 0 . 25 µg/ml ) , cJUN ( Santa Cruz; 1 . 0 µg/ml ) , JUND ( Santa Cruz; 1 . 0 µg/ml ) , ATF3 ( Santa Cruz; 1 . 0 µg/ml ) , or WT1 ( Santa Cruz; 1 . 0 µg/ml ) , followed by incubation in a secondary anti-rabbit HRP-conjugated antibody ( Invitrogen/Life Technologies; 0 . 2 µg/ml ) and detection by standard ECL ( Pierce ) . Blots were reprobed with a mouse monoclonal antibody against GAPDH as a loading control . Chromatin immunoprecipitation ( ChIP ) was performed according to the Millipore EZ-ChIP protocol with slight modifications . HCASMC were cultured as described above and treated with PDGF-BB or vehicle . Cells were fixed in 1% formaldehyde to cross-link chromatin , followed by quenching with glycine . 2×107 cells per condition were collected , and nuclear lysates were prepared as previously described [55] . Cross-linked chromatin nuclear extracts were sheared into ∼500 bp fragments using a Bioruptor ( Diagenode ) and clarified via centrifugation . 1×106 nuclei per condition were precleared with 20 ul Protein G Dynabeads ( Invitrogen ) for 1 hour , followed by incubation with 2 ug Rabbit IgG or anti-c-Jun , JunB , JunD , ATF3 , WT1 ( Santa Cruz or Active Motif ) , H3K4Me1 , H3K4Me3 , H3K27Ac , H3K27Me1 ( Diagenode or Abcam ) overnight at 4C . Immunoprecipitated chromatin samples were incubated with 20 µl Protein G Dynabeads for 1 hour at 4C to capture the protein-DNA complexes . Complexes were washed and eluted as described . Protein-DNA cross-links were reversed , treated with RNase A and proteinase K and free DNA was purified using Qiagen PCR purification kits . Total enrichment was measured using rs12190287 or rs12524865 specific primers , or a known AP-1 regulatory region , or a negative control region using the primers listed ( Table S1 ) . Semi-quantitative PCR was used to verify ChIP products via gel electrophoresis . Quantitative real-time PCR ( ViiA 7 , Life Technologies ) was performed using SYBR Green ( Applied Biosystems ) assays and fold enrichment was calculated by measuring the delta Ct – delta Ct IgG . Melting curve analysis was also performed for each ChIP primer pair . 1×107 HCASMCs per condition were processed as previously described [56] , using anti-H3K4me1 ( pAb-037-050 , Diagenode ) , anti-H3K4me3 ( pAb-003-050 , Diagenode ) , anti-H3K27me3 ( pAb-069-050 , Diagenode ) , or anti-rabbit IgG ( X 0903 , DAKO ) . ChIP-seq library generation , cluster formation and next-generation sequencing was performed at the Stanford Functional Genomics Facility , Stanford University , Stanford CA , USA , on an Illumina MiSeq instrument . 36 bp single reads from next-generation sequencing of ChIP libraries were then mapped to the reference genome using Burrows-Wheeler Aligner ( BWA ) . BigWig files were created using the R/Bioconductor environment . 1×107 HCASMCs were mainly processed similar to the ChIP-seq samples . However , instead of preclearing and immunoprecipitation , protein-depleted DNA was extracted from cross-linked nuclear lysates by phenol-chloroform extraction . After DNA precipitation , purification and reverse cross-linking , samples were sequenced and further processed as described above . Genomic DNA was isolated from >106 HCASMC cultured in complete media for approximately 48 hours , using the Blood and Tissue DNA isolation Kit ( Qiagen ) . 50 ng of gDNA template was amplified using primers flanking rs12190287 to generate 250 bp fragments . Fragments were then sequenced via Sanger sequencing using an internal forward sequencing primer , and genotypes were determined from chromatograms using Sequence Analyzer ( Applied Biosystems ) . Heterozygous genotypes were determined by Sanger sequencing , and RNA and cDNA prepared as described above . Allele-specific expression of TCF21 at rs12190287 was determined using a pre-designed TaqMan SNP genotyping assay for rs12190287 ( Table S1 ) . Calibration of the SNP genotyping assay was determined by mixing 10 ng of HCASMC gDNA or cDNA , homozygous for each allele at the following ratios: 8∶1 , 4∶1 , 2∶1 1∶1 , 1∶2 , 1∶4 , 1∶8 . The Log2 ratio of the VIC/FAM intensity at cycle 40 was then plotted against the Log ratio of the two alleles to generate a linear regression standard curve . The Log ratio of the intensity of the two alleles from cDNA samples was fitted to the standard curve . These values were then normalized to the ratio of gDNA for each allele to obtain the normalized allelic ratio . Heterozygous genotypes were determined as described above . Briefly , heterozygous HCASMC were cultured for the indicated timepoints , and chromatin cross-linked , sheared and immunoprecipitated as described above . Purified DNA was then amplified using TaqMan SNP genotyping assay probes for rs12190287 . The Log2 ratio of VIC/FAM intensity at cycle 40 was then fitted to the standard curve and normalized to gDNA ratio , with the normalized allelic ratio of IgG control enrichment arbitrarily set to 1 . Pyrosequencing assay for rs12190287 was generated using PyroMark Assay Design software ( Qiagen ) . Forward rs12190287 PCR primer: 5′- and biotinylated reverse PCR primer , and forward pyrosequencing primers were synthesized by the Protein And Nucleic acid ( PAN ) facility ( Stanford ) . Approximately 20 ng ChIP DNA was amplified using forward and reverse pyrosequencing primers under the following conditions: 94 C 4 min , ( 94 C 30 s , 60 C 30 s , 72 C 45 s ) ×45 , 72 C 6 min . Pyrosequencing reaction was performed on a PyroMark Q24 according to manufacturer's instructions . Allelic quantitation was obtained automatically from the mean allele frequencies derived from the peak heights using PyroMark Q24 software . Transcription factor binding site ( TFBS ) prediction was determined using the following online bioinformatics tools: TRANSFAC ( BIOBASE ) , PROMO , MatInspector , JASPAR , and TFSearch . Sequences from dbSNP for each allele were scanned for TFBS in vertebrates meeting a minimum similarity score of 0 . 85 . Regional association plot was generated from CARDIoGRAM meta-analysis dataset at TCF21 using LocusZoom [57] . Linkage disequilibrium plots and haplotype frequencies were generated from Europeans in the ADVANCE cohort ( Stanford ) from the CARDIoGRAM consortium , and East Asians from the HALST cohort within the TAICHI consortium . Briefly , genotyping data was extracted for each region of interest using PLINK [58] and transposed files were imported into Haploview [59] . Experiments were performed using at least three independent preparations with individual treatments/conditions performed in triplicate . Data is presented as mean ± standard deviation ( SD ) of replicates . GraphPad Prism 6 . 0 was used for statistical analysis . Comparisons between two groups were performed using paired two-tailed t-test . P values <0 . 05 were considered statistically significant . For multiple comparison testing , two-way analysis of variance ( ANOVA ) accompanied by Tukey's post-hoc test were used as appropriate . All samples reported in this study were obtained under written informed consent for participation in the Atherosclerotic Disease , VAscular functioN , and genetiC Epidemiology ( ADVANCE ) and Healthy Aging Longitudinal Study in Taiwan ( HALST ) studies with the approval of the Institutional Review Boards of Stanford University and National Health Research Institutes , respectively . | As much as half of the risk of developing coronary heart disease is genetically predetermined . Genome-wide association studies in human populations have now uncovered multiple sites of common genetic variation associated with heart disease . However , the biological mechanisms responsible for linking the disease associations with changes in gene expression are still underexplored . One of these variants occurs within the vascular developmental factor , TCF21 , leading to dysregulated gene expression . Using various in silico and molecular approaches , we identify an intricate allele-specific regulatory mechanism underlying altered expression of TCF21 . Notably , we observe that two apparently independent risk alleles identified in distinct populations function through a similar regulatory mechanism . Together these data suggest that conserved upstream pathways may organize the complex genetic etiology of coronary heart disease and potentially lead to new treatment opportunities . |
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Members of the COE family of transcription factors are required for central nervous system ( CNS ) development . However , the function of COE in the post-embryonic CNS remains largely unknown . An excellent model for investigating gene function in the adult CNS is the freshwater planarian . This animal is capable of regenerating neurons from an adult pluripotent stem cell population and regaining normal function . We previously showed that planarian coe is expressed in differentiating and mature neurons and that its function is required for proper CNS regeneration . Here , we show that coe is essential to maintain nervous system architecture and patterning in intact ( uninjured ) planarians . We took advantage of the robust phenotype in intact animals to investigate the genetic programs coe regulates in the CNS . We compared the transcriptional profiles of control and coe RNAi planarians using RNA sequencing and identified approximately 900 differentially expressed genes in coe knockdown animals , including 397 downregulated genes that were enriched for nervous system functional annotations . Next , we validated a subset of the downregulated transcripts by analyzing their expression in coe-deficient planarians and testing if the mRNAs could be detected in coe+ cells . These experiments revealed novel candidate targets of coe in the CNS such as ion channel , neuropeptide , and neurotransmitter genes . Finally , to determine if loss of any of the validated transcripts underscores the coe knockdown phenotype , we knocked down their expression by RNAi and uncovered a set of coe-regulated genes implicated in CNS regeneration and patterning , including orthologs of sodium channel alpha-subunit and pou4 . Our study broadens the knowledge of gene expression programs regulated by COE that are required for maintenance of neural subtypes and nervous system architecture in adult animals .
The Collier/Olfactory-1/Early B-cell factor ( COE ) family of transcription factors is necessary for animal development . COE proteins possess an atypical HLH domain and a unique zinc finger DNA binding domain conserved across metazoans [1] . Invertebrates encode a single homolog of COE , with roles in mesoderm and ectoderm development [2] , [3] , whereas vertebrates have four COE paralogs with functions in diverse cell types including B-cells and adipocytes [4] . In the central nervous system ( CNS ) , COE regulates neuronal differentiation , migration , axon guidance , and dendritogenesis during development [2] , [3] , [5]–[13] and maintains neuronal identity throughout adulthood [14] , [15] . COE proteins have also been proposed to function as tumor suppressors [16] and are associated with cancers such as acute lymphoblastic leukemia and glioblastoma [17]–[20] . However , the specific genetic programs regulated by these genes in adult stem cells and mature neurons remain poorly understood . Stem cells can be studied to determine how transcriptional regulators orchestrate developmental processes or cause disease [21] . An excellent animal model to investigate stem cell regulation in vivo is the freshwater planarian Schmidtea mediterranea [22] . S . mediterranea has the ability to regenerate all tissue types from a population of adult stem cells ( called neoblasts ) . These cells constitute approximately 10–20% of all the cells in the animal and include pluripotent [23] and lineage-committed neoblasts [24]–[29] . The planarian CNS is composed of two cephalic ganglia and a pair of ventral nerve cords that run along the length of the animal , which are comprised of molecularly diverse neuronal subtypes that are regenerated within days after injury or amputation [30]–[32] . Functional analysis of transcription factors in planarians using RNA interference ( RNAi ) has begun to identify regulatory molecules required for the generation and maintenance of specific neuronal subpopulations in the CNS such as serotonergic and cholinergic neurons [24]–[27] , [33]–[35] . Thus , planarians are outstanding organisms to study basic mechanisms that underlie stem cell-based maintenance and regeneration of the adult CNS . A previous functional screen for transcription factors encoding a helix-loop-helix domain identified a planarian coe homolog that is expressed in a small population of neural-committed stem cells ( approximately 4–7% of the neoblast pool ) and in neurons [24] . We showed that animals fed dsRNA designed to silence coe expression ( coe ( RNAi ) animals ) regenerated abnormal brains; furthermore , uninjured coe ( RNAi ) planarians displayed behavioral defects and reduced expression of neural subtype-specific genes [24] . In this study , we sought to identify genes regulated by coe with roles in CNS renewal by comparing the transcriptome profiles of uninjured control and coe ( RNAi ) animals , uncovering differentially expressed genes with predicted roles in CNS function . We validated a subset of these genes by testing for loss of expression after coe knockdown and visualizing their expression in coe+ cells . These analyses revealed a set of nine candidate targets of coe in adult neurons , many of which are important for neuronal subtype identity ( e . g . , ion channels , neuropeptides , and neurotransmitters ) . In addition , our findings demonstrate that coe functions to drive gene expression in multiple neuronal classes , including excitatory and inhibitory neurons . To gain insights into the roles candidate COE targets play in CNS turnover and repair , we analyzed the function of downregulated transcripts using RNAi . Our functional screen identified several genes required for CNS regeneration , including homologs of a voltage-gated sodium channel α-subunit ( scna-2 ) and the transcription factor pou4l-1 . Our results suggest that COE is required for the expression of neural-specific genes in differentiating and mature neurons , a function that is essential to maintain CNS architecture and regulate neuronal regeneration .
Using an optimized whole-mount in situ hybridization protocol ( WISH ) ( see Materials and Methods ) , we found that coe mRNA was primarily restricted to neurons in S . mediterranea ( Fig . 1A ) . In agreement with our previous findings [24] , we also observed coe transcripts in a subset of cycling stem cells ( h2b+ ) ( Fig . 1B–C ) . We previously reported that coe ( RNAi ) animals regenerate cephalic ganglia that fail to connect at the anterior commissure and have significantly smaller brains with fewer cpp-1+ , npp-4+ , and npy-2+ neurons when compared to the controls [24] . This defect is not restricted to the anterior portion of the animal . Additional experiments showed coe ( RNAi ) animals do not properly regenerate their ventral nerve cords ( Fig . S1A–B ) . Moreover , analysis of the brain patterning defect using anti-VC-1 , a marker of the photoreceptor neurons and their axons , revealed that the optic chiasm failed to connect at the midline in coe ( RNAi ) animals ( Fig . S1C ) . These data demonstrate that coe is essential for neuronal regeneration at both anterior and posterior facing wounds and that coe regulates genes required for reestablishing midline patterning following brain amputation . In addition , we previously noted that silencing of coe in intact uninjured animals results in a reduction of ChAT+ and pc2+ neurons near the anterior commissure and a loss of cpp-1+ neurons . Following the 6th feeding of coe dsRNA , 100% of the animals exhibited impaired negative phototaxis [24] . To investigate the specificity of the coe knockdown phenotype on the CNS , we examined the effect of coe RNAi on the intestine and muscle as representative endodermal or mesodermal tissues , respectively . We hybridized uninjured control and coe ( RNAi ) animals with riboprobes specific to ChAT ( as a positive control ) , mat [36] , and collagen [37] . As expected , we observed a decrease in ChAT+ neurons in the head [24] and noted a decrease in ChAT expression throughout the animal ( Fig . 2A ) ; by contrast , we did not observe a change in the spatial distribution of mat or collagen following coe knockdown ( Fig . 2B–C ) . To quantify the effect of coe RNAi treatments on the expression of ChAT , mat and collagen , we measured relative mRNA levels by reverse transcription quantitative PCR ( RT-qPCR ) . First , we confirmed coe knockdown led to a significant decrease in the relative expression of coe mRNA ( down 60%±16% compared to the controls; Fig . 2D ) . Measurement of ChAT , mat and collagen from coe ( RNAi ) planarians revealed that ChAT mRNA levels were significantly down ( 45%±15% ) compared to control animals; in contrast to ChAT , the relative mRNA levels of mat or collagen were not affected by coe RNAi treatment ( Fig . 2D ) . Combined with our previous work [24] , these results strongly suggest that coe knockdown specifically affects gene transcription in the nervous system and does not cause obvious defects in other tissues such as the intestine or muscle . Furthermore , our results are consistent with reports demonstrating that COE is required to maintain cholinergic and peptidergic neuronal subtype-specific gene expression in Caenorhabditis elegans and Drosophila melanogaster [14] , [15] . To investigate if the inhibition of coe perturbs nervous system architecture downstream of gene expression changes , we labeled neuronal cell bodies and their projections using anti-CRMP-2 , which labels a subset of neuronal cell bodies and their axon projections , and anti-β-tubulin to visualize nerve projections ( Fig . 3A–C ) . In coe ( RNAi ) animals , we observed a striking decrease in axon projections labeled by anti-CRMP-2 and anti-β-tubulin compared to the controls; however , expression of CRMP-2 was retained in the cell bodies ( Fig . 3C ) . In addition , when we labeled sensory neurons using cintillo [38] , coe ( RNAi ) animals exhibited significantly fewer cintillo+ cells ( Fig . 3D ) . Our results strongly suggest that nervous system architecture is severely reduced or lost in the absence of coe . These structural defects likely underlie the behavioral abnormalities observed in coe-deficient planarians . Although COE has been shown to drive differentiation of several classes of neurons during development [39] , the transcriptional programs controlled by this transcription factor in adult nervous system function are poorly defined . We reasoned that the CNS-specific coe RNAi phenotype in intact planarians represents an excellent opportunity to identify gene expression programs controlled by COE in the post-embryonic nervous system . Thus , we used comparative mRNA sequencing ( RNA-seq; see Materials and Methods ) to sequence mRNAs isolated from uninjured controls and coe ( RNAi ) animals one week after the 6th RNAi treatment , which was the point in time we consistently observed behavioral defects and loss of neural-specific gene expression in 100% of coe-deficient animals and did not detect overt defects in other tissues ( Fig . 2 ) . RNA-seq analysis identified 909 differentially expressed genes; 397 were downregulated , and 512 were upregulated ( Table S1 ) . Functional annotation using DAVID software showed that the set of downregulated genes was significantly enriched for Gene Ontology ( GO ) terms associated with “ion channel , ” “neuronal activities , ” “nerve-nerve synaptic transmission , ” “voltage-gated ion channel , ” and “cell adhesion molecule”; by contrast , the upregulated genes were enriched for GO terms associated with “cytoskeletal protein” and “muscle development” ( Table 1 ) . coe mRNAs were not detected in a muscle pattern ( Fig . 1 ) , nor did we detect overt phenotypes associated with muscle differentiation ( Fig . 2 ) . However , the RNA-seq data raised the possibility that coe might negatively regulate mesoderm specification , which is required for muscle development [3] , [40] . It is possible upregulation of muscle genes is an indirect consequence of a loss of nervous system influence such as cholinergic transmission and/or neuropeptide regulation . Previous studies have demonstrated cholinergic neurotransmission is required for coordinated muscle contractions in planarians [41]–[43] . Thus , we speculate that loss of nervous system modulation disrupts muscle homeostasis and leads to changes in expression of muscle-related genes . Although our experiments do not definitively assign the role of COE in muscle differentiation or maintenance , our data do clearly indicate that coe is required for expression of nervous system-specific genes in adult planarians . Based on the annotation of differentially expressed genes , we hypothesized that genes predicted to play roles in nervous system functions in the downregulated category likely include direct COE targets . To test our hypothesis and validate genes found in our RNA-seq dataset , we selected 65 genes that were dramatically downregulated , associated with neural functions , or annotated as transcription factor homologs . First , we performed WISH to determine the tissue-specific pattern of expression of all 65 genes ( representative examples are shown in Fig . 4 ) . As we expected , the most prominent mRNA expression pattern was in the nervous system ( 26 of 65 genes; see Table S2 ) , similar to ChAT and cpp-1 , which we had previously found to be putative downstream targets of COE [24] . In addition , we observed genes that were expressed broadly in the nervous system ( such as neural cell adhesion molecule-2 ( ncam-2 ) , vesicle-associated membrane protein like-1 ( vamp ) , gamma-aminobutyric acid receptor subunit beta like-1 ( gbrb-1 ) , and voltage-gated sodium channel alpha-1 ( scna-1 ) ) or in discrete neuronal subpopulations ( such as secreted peptide prohormone-19 , -18 , -2 ( spp-19 , -18 , -2 ) , neuropeptide like ( npl ) , voltage-gated sodium channel alpha-2 ( scna-2 ) , and caveolin-1 ( cav-1 ) ) ( Fig . 4A–J ) . Our list also included transcripts that labeled subsets of neurons in the brain ( such as netrin-1 ) ( Fig . 4K ) [44] . In addition , we found that the transcription factors iroquios-1 ( irx-1 ) and pou class 4 transcription factor 4 like-1 ( pou4l-1 ) were expressed at or near the cephalic ganglia ( Fig . 4L–M ) , and their mRNA was detected in ChAT+ neurons by fluorescent in situ hybridization ( FISH ) ( Fig . S2 ) . Next , we tested the effect of coe RNAi on the expression of 33 genes that could be visualized in discrete cell populations by WISH . Knockdown of coe led to a marked reduction in the expression of 31 genes ( Table S2; representative results are shown in Fig . 4A′–H′ , K′–M′ ) ; for two genes , scna-2 and cav-1 , we observed a loss of expression at the midline ( Fig . 4I′–J′ ) . Furthermore , we quantified the number of cells labeled by spp-19 , spp-18 , and npl probes . As expected , we found there was a significant reduction in the number of spp-19+ , spp-18+ , and npl+ cells following coe RNAi ( Fig . 4N ) . As an additional test to validate the in situ hybridization results , we measured the relative expression levels of downregulated genes in control and coe RNAi-treated planarians using RT-qPCR ( Fig . S3A ) . All of the genes we tested showed a decrease in relative expression following coe RNAi ( 9 of 14 genes were significantly downregulated; P<0 . 05 , Student's t-test ) . By contrast , when we measured the relative expression of CNS-expressed genes that were not on our list of differentially expressed genes , none were significantly reduced ( 11 of 11 genes; Fig . S3B–C ) . Although some of the control genes we selected were reduced near levels comparable to some genes downregulated following coe RNAi ( e . g . , ncam2 , vamp , and gbrb1; Fig . S3A ) , we noted that isotig13897 and npp-2 [30] , which are transcripts detected in subsets of neurons or throughout the CNS , respectively , remained unchanged ( Fig . S3B–C ) . It is possible that some changes in gene expression associated with coe RNAi are consequence of a reduction in nervous system tissue . We proceeded to perform double-FISH to coe and validated genes to determine if any were potential genetic targets of COE . Of the 17 genes we were able to reliably detect by FISH ( 33 genes were tested; see Table S2 ) , 11 were expressed in coe+ cells ( representative results are shown in Fig . 5 and Fig . S4 ) , including ChAT and cpp-1 [24] . Together , these results identified nine novel candidate targets of COE in the nervous system , including genes important for maintaining neuronal subtype identity such as ion channels , ion channel receptors , and neuropeptide genes ( Table 2 ) . In addition , our data suggest that COE is essential to maintain genetic programs in multiple classes of adult neuronal subtypes including excitatory ( cholinergic ) and inhibitory ( GABAergic ) neurons . Our RNA-seq dataset revealed that coe is essential to maintain the expression of hundreds of genes in the adult animal . This change in the neuronal gene expression landscape led to abnormal CNS structure and behavior . To identify genes downstream of coe that contribute to CNS differentiation , we took advantage of the experimental ease in examination of gene function in planarian regeneration and analyzed the role of 11 downregulated genes that were expressed in neurons or predicted to encode transcription factors ( Table 3 ) . Following RNAi , animals were amputated pre- and post-pharyngeally and allowed to regenerate for 10 days . We found that 6 out of 11 genes resulted in defective brain regeneration ( see Table 3 ) ; scna-2 , pou4l-1 , and nkx2l caused the strongest phenotypes . Compared to the controls , scna-2 ( RNAi ) animals had less eye pigmentation or developed a single eyespot; nkx2l ( RNAi ) animals exhibited photoreceptor defects; and pou4l-1 ( RNAi ) animals had less photoreceptor pigment ( Fig . 6A–D ) . To examine CNS architecture , we stained scna-2 , nkx2l , and pou4l-1 RNAi treated planarians with anti-SYNAPSIN and the coe-regulated genes ChAT and npl . Although subtle , all three showed abnormalities in brain morphology ( Fig . 6A–D ) . However , when we measured the area of the brain stained by anti-SYNAPSIN , only scna-2 and pou4l-1 RNAi animals had a significant reduction in neuropil density ( Fig . 6E ) . Consistent with this observation , the ChAT+ brain areas were smaller in scna-2 ( RNAi ) and pou4l-1 ( RNAi ) animals ( Fig . 6F ) but not in nkx2l ( RNAi ) animals . The smaller brain phenotype was accompanied by fewer npl+ neurons in scna-2 ( RNAi ) animals; however , despite their smaller brains , pou4l-1 ( RNAi ) animals regenerated significantly more npl+ cells than controls ( Fig . 6G ) . These findings demonstrate that scna-2 is required for CNS regeneration and highlight the importance of ion channels in neurogenesis regulation during CNS development , maintenance , and repair [45]–[47] . Interestingly , these data suggest that pou4l-1 plays a role in the specification of certain neuronal lineages . It is possible that in the absence of pou4l-1 , planarians regenerate the incorrect proportion of neuronal subtypes and have disorganized brains , but this possibility will require further analysis with additional neuronal subtype-specific markers . By contrast , our results suggest nkx2l is not required for CNS regeneration per se . Following coe RNAi , nkx2l expression was reduced by in situ hybridization and RT-qPCR ( Table S2 and Fig . S3A ) , but nkx2l , which is primarily expressed in stem cells and in progeny [48] , was not detected in the nervous system ( Fig . S5A ) . We hypothesize nkx2l functions in early regeneration to establish patterning , which is consistent with the observation that nkx2l ( RNAi ) planarians fail to regenerate properly patterned head ( Fig . 6C ) and tail tissues ( Fig . S5B ) . It is noteworthy that several transcription factors that we identified in our screen are putative COE targets in Xenopus development , including irx-1 , tal , pou4l-1 , and nkx2l [39] . Of these genes , we found that expression of pou4l-1 was important for CNS regeneration and nkx2l was involved in patterning . NKX and POU orthologs play critical roles during CNS development of invertebrate and vertebrate organisms [49]–[51] . These data suggest that regulatory genes downstream of COE are conserved and have roles in CNS regeneration . However , it will be important to experimentally resolve whether these transcription factors are bona fide targets of COE in planarians or other animals such as Xenopus . COE proteins are known to function as terminal selectors of neuronal identity in adult organisms [14] , [15] , [52] , yet the neuronal subtypes and specific genetic programs regulated by COE in the adult CNS are not well understood . In this study , we exploited the high rate of tissue turnover and regenerative capacity of planarians to expand our understanding of how COE may function in the post-embryonic nervous system . We combined RNAi with RNA-seq analysis and identified a set of differentially expressed genes associated with nervous system biological roles . Expression analysis of a subset of these genes revealed novel candidate targets of coe in planarian neurons ( Fig . 7A ) , some of which underscored coe's essential role in maintaining expression of genes vital for neuronal subtype identity and function ( such as neurotransmitter receptors , ion channels , and neuropeptide encoding genes ) ( Fig . 7A–B ) . Decoding which transcriptional changes are direct or indirect consequences of coe loss in the planarian model will be vital to further elucidate how mutations in COE proteins cause or contribute to disease pathologies in the CNS . The next step will be to find direct COE binding sites genome-wide using in silico and chromatin immunoprecipitation ( ChIP ) approaches and combining these findings with our differential expression data . In addition , molecular profiling of coe+ cell populations ( such as stem cells , postmitotic progeny , and neurons ) will be essential to determine how coe function alters in cell type-specific contexts . In conclusion , our study demonstrates the importance of COE family proteins in neuronal turnover and repair of the adult CNS and broadens our understanding of the regulatory programs governed by these factors .
Asexual Schmidtea mediterranea ( CIW4 ) were reared in 1× Instant Ocean Salts ( 0 . 83 mM MgSO4 , 0 . 9 mM CaCl2 , 0 . 04 mM KHCO3 , 0 . 9 mM NaHCO3 , and 0 . 21 g/L Instant Ocean Aquarium Salt diluted in ultra-pure water ) at 20°C . Animals were starved for one week , and those ranging between 2–5 mm in length were used for experimentation . Animals were administered six feedings of bacterially expressed dsRNA complementary to the indicated gene over three weeks as previously described [53]; gfp dsRNA was fed as a control . Unless otherwise indicated , all intact RNAi animals were fixed seven days following the 6th dsRNA treatment . For regeneration experiments , planarians were amputated pre- and post-pharyngeally 24 hours following the 6th dsRNA feeding . Animals were processed for colorimetric whole-mount in situ hybridization using the protocol described in [54] . Fluorescent in situ hybridization experiments were performed as described in [24] , [54] and developed using Tyramide Signal Amplification ( TSA ) as described in [55] . Briefly , animals were incubated for 5 min . in borate buffer ( 100 mM borate pH 8 . 5 , 0 . 1% Tween-20 ) and then developed in TSA Reaction Buffer ( borate buffer , 2% dextran sulfate , 0 . 1% Tween-20 , 0 . 003% H2O2 ) , containing fluor-tyramide and 4-iodophenylboronic acid for 30 min . For double-FISH , animals were quenched in 1% H2O2 for 1 hour . For γ-irradiation experiments , animals were fixed 6 days following a 100 Gy treatment , a time point when both stem cells and postmitotic progenitors are ablated . Accession numbers for the sequences used in this study are listed in Table S3 . For immunostaining with anti-SYNORF1 ( 1∶400 , 3C11 , DSHB ) or anti-VC-1 ( 1∶10 , 000; kindly provided by Hidefumi Orii ) , animals were fixed with Carnoy's solution [56] . For anti-CRMP-2 ( 1∶50 , 9393S , Cell Signaling ) or anti-β-TUBULIN ( 1∶1000; E7 , DSHB ) labeling , animals were fixed with formaldehyde , processed without a reduction step , and labeled using TSA [54] . One week after the final dsRNA treatment , RNA was extracted from three independent control and coe ( RNAi ) animal groups using Trizol ( Life Technologies ) . RNA samples were treated with DNase using the Turbo DNA-free Kit ( Life Technologies ) and purified using the RNeasy MinElute Cleanup kit ( Qiagen ) . Sequencing libraries were synthesized using the TruSeq RNA Sample Prep Kit v2 and sequenced on a HiSeq 2000 System ( Illumina ) . More than 12 million 100-bp single-end reads were generated for each sample . Sequenced reads were submitted to the Sequence Read Archive ( NCBI ) under the accession number PRJNA235907 . Reads were mapped to the planarian genome using TopHat [57]; gene models were predicted using a published transcriptome [58] , [59] . Differentially expressed genes were identified using the R Bioconductor package edgeR [60] with cutoffs of logCPM score ≥0 and FDR≤0 . 05 . Changes in gene expression detected by RNA-seq were represented as linear fold changes over controls . For the differentially expressed Schmidtea mediterranea transcripts , we performed BLASTX against the human UniProt database ( cutoff<1×10−4 ) ; human accession numbers were then used to assign Gene Ontology terms and perform clustering analysis using DAVID software [61] , [62] with the “Panther_BP_all” and “Panther_MF_all” gene annotation settings and an Enrichment Score cutoff >1 . 3 . For validation studies , transcript sequences were analyzed by BLASTX against protein sequences from human , mouse , fly , and nematode and identified as the top BLAST hit ( Table S3 ) . Sequences were obtained from a cDNA collection [63] or cloned into pJC53 . 2 [30] or pPR244 [64] using gene specific primers . GenBank accession numbers and the primers used in this study are listed in Table S3 . Total RNA was extracted and purified as described above . cDNA was synthesized using the iScript cDNA Synthesis Kit ( BioRad ) . Reverse transcription quantitative PCR was performed on a Bio-Rad CFX Connect Real-Time System using SsoAdvanced SYBR Green Supermix ( Bio-Rad ) with a two-step cycling protocol and annealing/extension temperature of 58 . 5°C . At least three biological replicates and two technical replicates were performed for each experiment . The relative amount of each cDNA target was normalized to Smed-β-tubulin ( accession no . DN305397 ) . The normalized relative changes in gene expression , standard deviations , and t-tests were calculated in Bio-Rad CFX Manager Software v3 . 0 . Primers are listed in Table S3 . Images of live animals and whole mount in situ hybridization samples were acquired using a Leica DFC450 camera mounted on a Leica M205 stereomicroscope . Fluorescent images were acquired with a Zeiss Axio Observer . Z1 equipped with an Axiocam MRm camera and ApoTome; images are displayed as maximum image projections from ten 1-µm optical sections . For all experiments , we counted cells by hand using ImageJ Software [65] , and biological replicates ( n≥3 ) were averaged and shown as mean ± standard deviation . The number of cintillo+ , spp-19+ , spp-18+ , and npl+ cells ( Fig . 4N ) was normalized to animal length ( mm ) . We used anti-SYNAPSIN staining and ChAT expression to determine brain area ( Fig . 6E–F ) , normalized to animal length ( µm ) . To quantify npl+ brain-specific neurons following amputation , npl+ cells were counted in the cephalic ganglia and normalized to the average total brain area ( Fig . 6G ) . When comparing two groups , we used a Student's t-test and significance was accepted at P<0 . 05 . | COE transcription factors are conserved across widely divergent animals and are crucial for organismal development . COE genes also play roles in adult animals and have been implicated in central nervous system ( CNS ) diseases; however , the function of COE in the post-embryonic CNS remains poorly understood . Planarian regeneration provides an excellent model to study the function of transcription factors in cell differentiation and in terminally differentiated cells . In planarians , coe is expressed in differentiating and mature neurons , and its function is required for CNS regeneration . In this study , we show that coe is required to maintain structure and function of the CNS in uninjured planarians . We took advantage of this phenotype to identify genes regulated by coe by comparing global gene expression changes between control and coe mRNA-deficient planarians . This approach revealed downregulated genes downstream of coe with biological roles in CNS function . Expression analysis of downregulated genes uncovered previously unknown candidate targets of coe in the CNS . Furthermore , functional analysis of downstream targets identified coe-regulated genes required for CNS regeneration . These results demonstrate that the roles of COE in stem cell specification and neuronal function are active and indispensable during CNS renewal in adult animals . |
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Sequence variation can affect the physiological state of the immune system . Major experimental efforts targeted at understanding the genetic control of the abundance of immune cell subpopulations . However , these studies are typically focused on a limited number of immune cell types , mainly due to the use of relatively low throughput cell-sorting technologies . Here we present an algorithm that can reveal the genetic basis of inter-individual variation in the abundance of immune cell types using only gene expression and genotyping measurements as input . Our algorithm predicts the abundance of immune cell subpopulations based on the RNA levels of informative marker genes within a complex tissue , and then provides the genetic control on these predicted immune traits as output . A key feature of the approach is the integration of predictions from various sets of marker genes and refinement of these sets to avoid spurious signals . Our evaluation of both synthetic and real biological data shows the significant benefits of the new approach . Our method , VoCAL , is implemented in the freely available R package ComICS .
The immune system consists of a remarkable collection of immune cell subpopulations with complex interconnections . To gain a better understanding of immune processes at the cellular level , such as cell proliferation , differentiation , activation and migration , researchers have systematically quantified the abundance of particular immune cell types in health and disease . This approach has provided insights into the role of immune cells during both homeostasis and disease progression; for example , recruitment and accumulation of macrophages in adipose tissue are associated with obesity [1]; the presence of eosinophils in the airway lumen and lung tissues is considered a defining feature of asthmatic disease [2]; recruitment of monocytes to arterial vessel walls is an early step in the development of atherosclerosis [3]; and an increase in CD4+CD28null T cells is detectable in patients with complications of rheumatoid arthritis [4] . There is a strong need for workable methodological approaches that can identify the underlying molecular mechanisms determining the physiological state of the immune system . A major goal in this endeavor is to identify genetic variants that lead to inter-individual variation in the abundance of particular immune cell types . In studying the genetic basis of immune physiology , both genotyping and immune-cell quantification must be performed and analyzed in concert . Direct measurement of the abundance of a large number of immune cell types remains a challenge because of the relatively low throughput of cell-sorting technologies . Such direct quantification is particularly laborious when a large number of individuals is studied , and as a result , most association studies are restricted to only a few immune-cell types [5–17] , with few exceptions [18–20] . Thus , a simplified approach is required . With the advent of immune deconvolution methods , it is now possible to infer the relative abundance of immune cell subpopulations without the need for experimental cell sorting . Specifically , deconvolution methods take as input expression profiles of isolated immune cell types ( in short , a 'reference data'; e . g . , [21–24] ) and an expression profile from a complex tissue . The expression of each gene in a tissue is modeled as a linear combination of its expression in each cell type , where the weights stand for the unknown abundance of each immune cell type . This abundance can be resolved by solving a set of linear equations , one for each gene . Previous studies have shown that using only a subset of carefully selected genes ( rather than the whole expression signature ) typically reduces the signal-to-noise ratio and stabilizes the solution ( e . g . , [24 , 25] ) . For example , 360 , 61 , 240 and 547 genes were selected for immune cell type deconvolution in [24–27] , respectively . The selected genes , which are used as observations during the deconvolution process , are referred to as 'markers' . Deconvolution techniques have been successfully applied to predict the composition of immune cell types , but have not yet been applied in the context of genetic studies . We describe here a method for revealing the genetic basis of inter-individual variation in the abundance of immune cell types . Our method relies on a deconvolution algorithm that receives as input expression profiles from a complex tissue across a population of individuals , and uses this data to calculate relative cell type abundance values in each individual . The underlying genetic variants are then identified on the basis of the predicted cell type abundance levels , without the need for experimental cell quantification . In our framework , the predicted abundance of a particular cell type across a certain cohort of individuals is termed an 'immune trait'; the associated DNA variant is referred to as an 'immune quantitative trait locus' ( iQTL ) ; and the genetic association is termed an 'immune trait association' . Since we use predicted traits ( rather than direct measurements ) , special care has to be taken to ensure the reliability of the identified immune trait associations . To that end , we resample several disjoint sets of marker genes and then repeat the pipeline using the different marker sets . An association is considered reliable if it attains high significance , on average , based on several different sets of marker genes . We also realized that part of the reason for false positive iQTLs is the presence of genetic control on the expression levels of marker genes ( such genomic loci are typically termed 'expression QTLs' [eQTLs] ) . To overcome this difficulty , we filter out marker genes that are associated with potentially misleading eQTLs . Our rationale is to discriminate between true iQTLs and spurious ones: a false positive iQTL ( due to an eQTL ) can be eliminated by removing the relevant eQTL targets from the marker set; true iQTLs , in contrast , are generally robust to such alterations in the set of marker genes . We refer to this approach as the VoCAL ( Variation in Cell Abundance Loci ) algorithm . We used synthetic data to assess the performance of the VoCAL algorithm in a controlled setting . Using these data , we start by demonstrating the increasing complexity of the iQTL-identification problem with increasing numbers of eQTLs in a tissue . We next show the utility of VoCAL over a large range of data parameters , and demonstrate the benefits of discarding potentially misleading eQTLs while combining evidence from multiple sets of markers . As a proof of principle , we applied VoCAL to genotyping and lung expression profiles from recombinant inbred BXD mouse strains , thereby demonstrating the ability of VoCAL to identify significant iQTLs while removing spurious associations .
In the following we consider how to find , in the absence of direct cell-sorting measurements , the genetic basis of immune traits . To address this we rely on a computational inference of cell type abundance levels from gene expression data . We begin with an illustrated example to explain the basic rationale of this approach and follow with the actual pipeline of the VoCAL methodology . Consider a simplified reference data consisting of transcriptional profiles from three immune cell types ( c1-c3 ) , assuming that each cell type contains only five genes ( g1-g5; Fig 1A ) . In this reference data , each plot describes the RNA levels of each gene in each cell type . For example , the plots indicate the cell type-specific expression of gene g1 in cell type c3 . The scenario in Fig 1B ( left ) considers a certain genomic locus v that has an effect on the abundance of cell type c3 within a given tissue . This locus is therefore an iQTL . In accordance , Fig 1B ( left ) demonstrates the higher level of cell type c3 in TT-carrying compared to GG-carrying individuals . The plots of total RNA levels in the tissue are shown in the middle panel of Fig 1B; these RNA levels reflect the composition of cell types in the tissue and the RNA levels within each cell type . As can be seen in the figure , if a TT-carrying individual has an increased abundance of cell type c3 , then its level of the c3-specific gene g1 is also elevated ( Fig 1B , middle ) . Mathematical deconvolution methods take as input a certain list of marker genes , and then use the total RNA levels of these markers in the complex tissue to calculate the abundance of each cell type . In our example , the inferred ( deconvolved ) cell type quantities are shown in the right panel of Fig 1B , for each genotype and for two potential sets of marker genes ( marker sets g1-g4 [top] and g2-g4 [bottom] ) ; this prediction relies on the total RNA levels in the tissue from Fig 1B ( middle panel ) and the reference data from Fig 1A . It can be clearly seen that each of the two marker sets can be utilized to correctly predict ( i ) a higher abundance of cell type c3 in TT-carrying individuals , and ( ii ) a similar amount of cell types c1 and c2 in different genetic backgrounds ( Fig 1B , right ) . Thus , by repeating the same deconvolution process in multiple individuals , it is possible to identify true immune trait associations ( e . g , v-c3 ) and to reject false ones ( e . g . , v-c1 and v-c2 ) ; furthermore , we expect that the identification of true associations will be generally robust to the selection of marker genes . This observation is key to the success of the VoCAL algorithm . While studying the system , we discovered a potential pitfall of this approach—the existence of eQTLs acting on the intracellular RNA levels of genes . To gain some intuition about why this is the case , consider the presence of an eQTL acting in locus v ( instead of an iQTL in this genomic position ) . For instance , Fig 1C shows an effect of eQTL acting on the expression of gene g1 . We see that the TT- and GG-carrying individuals differ only in their RNA level of gene g1 ( Fig 1C , middle ) but not in their composition of cell types ( Fig 1C , left ) . Yet , when a marker set g1-g4 is used , a deconvolution algorithm may output an erroneous increased abundance of cell type c3 in TT-carrying individuals , which might be interpreted as an association between locus v and cell type c3 ( a false positive iQTL; Fig 1C , top right ) . We note that the spurious association stems from g1 ( a c3-specific marker and an eQTL target ) and can be eliminated by excluding g1 from the marker set ( e . g . , marker set g2-g4 , Fig 1C , bottom right ) . Thus , the inclusion of eQTL targets in the set of marker genes may interfere with our algorithm and lead to spurious iQTLs at the same genomic positions . Following this rationale , construction of marker sets that do not include eQTL targets can , in principle , be used to avoid spurious predictions . The VoCAL algorithm relies on this idea , as discussed below . We devised the VoCAL method with the specific object of using deconvolution to identify significant associations between cell type abundance traits and polymorphic DNA loci . The input of the VoCAL algorithm is the gene expression profiles of a given complex tissue across a population of genetically distinct ( genotyped ) individuals , as well as a large 'reference data' of transcriptional profiles from isolated immune cell subsets ( Fig 2A , top ) . The output is a collection of significant iQTLs ( Fig 2A , bottom ) . As we discussed , VoCAL relies on two observations . First , we expect noisy predictions to be weakly reproducible between marker sets , but true iQTLs to be consistently identified by multiple different marker sets . Based on this rationale , VoCAL combines iQTL predictions from multiple marker sets to produce a reliable model . Second , eQTL targets may lead to spurious iQTL associations . Naively , VoCAL could filter all eQTL targets in a pre-processing step . However , a potential caveat of this strategy is that the removal of many informative markers might reduce the ability to detect iQTLs . To address this , VoCAL leverages the observation that the expression of misleading markers likely associates with eQTLs located within the inferred iQTLs ( e . g . , locus v in Fig 1C ) . The problem is a challenging one , as the identification of iQTLs requires the selection of markers , and the selection of markers requires knowledge about the genomic positions of iQTLs . This necessitates the identification of both the iQTLs and the gene markers simultaneously . To address this , VoCAL applies an iterative approach . In each iteration , VoCAL uses the sets of selected markers to identify iQTLs and then uses the identified iQTLs to filter out confounding markers . In particular , VoCAL consists of five steps ( Fig 2B ) . In step 1—initialization—VoCAL constructs an initial collection of k marker sets . In this stage we do not yet have the inferred iQTLs . Thus , each set of markers is selected based on the ability of the genes to discriminate well between immune cell types in the reference data . This strategy has been proven useful in deconvolution of immune cell types [24–27] . Steps 2 and 3 are repeated k times , each time with a different set of markers . In step 2—Deconvolution—VoCAL relies on a mathematical deconvolution algorithm to predict cell type abundance levels . The input to this procedure is ( i ) the expression data of a complex tissue across individuals , ( ii ) the reference data , and ( iii ) a single set of marker genes . The output is a collection of immune traits , each consisting of inferred cell abundance values for a single cell type across the individual samples . In step 3—genome-wide association testing ( GWAS ) —VoCAL applies a statistical association test on each immune trait , producing association scores between each genomic locus and each immune trait . We term such a collection of association scores as an association map . Altogether , steps 2 and 3 provide a collection of k association maps ( a single map for each marker set ) . In step 4—aggregation—VoCAL combines the k association maps to produce a reliable model . In particular , for each given locus and each given immune trait , VoCAL calculates a single association P-value based on the relevant scores in the collection of k maps . Significantly associated loci are referred to as iQTLs . In step 5—filtration—VoCAL refines the k sets of marker genes by filtering out eQTL targets . Specifically , the filtration step tests whether any of the current marker genes is associated with an eQTL that coincides with an inferred iQTL . If such markers are found , VoCAL filters them out and returns to step 2 . In summary , the VoCAL procedure starts with an initial selection of k marker sets ( step 1 ) and then iterates between two tasks: a reliable identification of significant iQTLs relatively to a given collection of k marker sets ( steps 2 , 3 , and 4 ) , and the filtration of marker sets relatively to the collection of significant iQTLs ( step 5 ) . The algorithm terminates when there are no more changes to the marker genes . A detailed description of the VoCAL algorithm appears in the Methods section . The associated R package ComICS is available at https://cran . r-project . org/web/packages/ComICS/index . html and csgi . tau . ac . il/VoCAL/ . To evaluate the performance of VoCAL , it was necessary to simulate iQTLs and eQTLs in synthetic complex tissues . To do this over a population of individuals , we used genotyping of the recombinant inbred BXD mouse strains ( 102 individuals ) and a reference data containing expression profiles of isolated immune cell types ( taken from the ImmGen project [23] ) . First we randomly selected one or a few cell types from this reference data and a polymorphic locus ( an iQTL ) for each of these cell types; groups of co-expressed genes sharing the same eQTL hotspots were selected in a similar manner . Next , assuming an initial equal abundance of cell types for each individual , we altered the fractions of the chosen cell types according to the DNA allele of the selected iQTL . The magnitude of the change in cell type fractions is termed the iQTL effect size . Lastly , we generated the final expression values of each tissue sample by ( i ) mixing the signatures from the reference data according to those fractions , and ( ii ) introducing the effect of the selected eQTLs on their target groups of genes ( the magnitude of this effect is termed the eQTL effect size ) . To account for the common scenario in which the cell types that are used during the deconvolution process are not exactly the same as the cell types in the complex biological tissue , we used two disjoint sets of cell types: one set is used for synthetic data generation ( the 'data-generation cell types' ) , while the VoCAL algorithm—particularly the deconvolution process—was applied based on another set of cell types ( the 'deconvolution cell types'; Fig 3A ) . Each cell type in one set is closely related to a cell type in the other set ( for example , the same cell type isolated from different tissues; S1 Table ) , allowing us to use the ground truth immune-trait associations to evaluate the predictions of the VoCAL algorithm . Although the simulation may not perfectly mirror a real tissue , it can still provide a model for a tissue that is ( i ) affected by iQTLs and by eQTL hotspots leading to variation in specific cell types and genes , and ( ii ) characterized by cell types that are similar but not identical to the cell types given as input to the VoCAL algorithm ( see Methods and S1 Fig ) . Here we examine and demonstrate four different initialization methods ( used in step 1 of the VoCAL algorithm ) : ( i ) choosing sets of gene markers carrying the highest variability in expression between cell types ( top varying ) ; ( ii ) choosing representative marker genes that can discriminate well between cell types ( cell tagging; 24 ) ; ( iii ) using the cell-tagging strategy but adding a predefined set of cell surface markers that were used in the cell-isolation process ( cell tagging with FACS; 36 ) ; and ( iv ) using an unbiased selection of gene markers ( random sampling ) . We note that the three former methods ( but not random sampling ) are based on the cell type signatures in the reference data . The ability to identify the correct iQTLs was evaluated as the area under the receiver operating curve ( AUC score ) . Notably , the results are robust to variation in the parameters of the VoCAL algorithm ( S2 Fig ) . For example , different significance cutoff of the identified iQTLs ( varying between 0 . 05 and 10−12 ) had a little effect on the eventual AUC scores . Motivated by these results , we analyzed the effect of different data parameters and the benefits of the VoCAL algorithm , as discussed below . We first investigated how the complexity of the problem is affected by the presence of eQTLs and iQTLs in a tissue . To assess this , we generated synthetic datasets with varying numbers of iQTLS and eQTLs , each of which acts through a fixed size of its genetic effect . Overall , we tested a total of 15 such combinations of different numbers of QTLs . First , we applied VoCAL without the 'filtration step' ( using steps 1–4 only ) , allowing us to trace the effect of eQTL targets within the marker sets . As expected , predictions in datasets with smaller numbers of iQTLs were more accurate ( Fig 3B ) . Furthermore , consistent with our expectation ( Fig 1C ) , the ability to identify iQTLs depended not only on the number of iQTLs , but also on the number of eQTLs: the AUC scores were lower in datasets with higher numbers of eQTLs . For example , AUCs were significantly higher for datasets with no eQTL hotspots than for those with 2 eQTL hotspots for the same number of iQTLs ( average AUC = 0 . 7 vs . 0 . 39; P < 2∙2−16 ( t-test ) , in the presence of 4 iQTLs , effect sizes = 0 . 05 , cell-tagging and k = 1; Fig 3B ) . These results were quantitatively similar when using a larger number of association maps ( k = 10; Fig 3C ) and for different initialization methods and data parameters ( S3 Fig , left and middle panels ) . We conclude that the iQTL-identification problem becomes more complex with increasing amounts of different genetic effects; without applying the filtration step , the presence of eQTLs results in relatively low performance values . Next we were interested in the effect of applying the filtration step ( step 5 , Fig 2B ) . We found that the iterative filtration of marker sets improved the prediction of iQTLs . In particular , without filtration of eQTL targets , the presence of eQTLs in a tissue resulted in a drastic reduction in AUC scores ( Fig 3B and 3C ) ; in contrast , in the presence of the filtration procedure , there was little or no reduction in AUC scores when more eQTLs were added ( Fig 3D ) . The same was true when different initialization methods and data types were used ( e . g . , Figs 3E and S3 ) . Notably , marker filtration brought no improvement when the complexity was increased by multiple iQTLs ( Fig 3D and 3E ) ; this is consistent with the primary goal of the filtration procedure , which is to tackle the problem of confounding eQTLs ( rather than the problem of interactions among multiple iQTLs ) . To gain additional insights into the filtration step , we analyzed 2-dimensional plots of AUC scores for the same synthetic datasets with ( x-axis ) and without ( y-axis ) this step ( S4A Fig ) . In the case of 2 eQTL hotspots , all datasets appear above the diagonal line , indicating that the filtration step resulted in improved performance ( e . g . , using the top-varying initialization method , P < 2∙10−34 ( t-test ) ; S4A Fig , left panel ) . In contrast , the AUC scores remained nearly unchanged when eQTLs were not introduced into the simulation ( e . g . , using cell-tagging with FACS and 10 iQTLs , P > 0 . 15 ( t-test ) ; S4A Fig , right panel ) . The patterns were similar when we used false positive rate ( FPR ) and true positive rate ( TPR ) metrics instead of the AUC ( e . g . , S4B and S4C Fig ) . Taken together , these results indicated that the filtration procedure successfully reduces the amount of spurious associations derived from the effects of eQTLs in a tissue . We next investigated the added value of generating k association maps rather than a single map . To that end , we compared the performance of VoCAL with 10 association maps to its performance with a single map . We found that the power to detect iQTLs increased drastically when using 10 association maps ( Figs 3B and 3C , S3 and S5A ) . For example , using 1 eQTL and 6 iQTLs , the usage of 10 association maps is significantly better than using one selected map ( P <2∙10−16 , paired t-test; assuming effect size of 0 . 05 , the cell-tagging method , without filtration ) . In fact , the AUC scores were quantitatively correlated with the number of association maps ( Fig 3F ) . These results were qualitatively similar when using different initialization methods and different numbers of iQTLs ( Figs 3F and S5B–S5D ) . We further tested the possibility of pooling the k marker sets into a single large set . As a proof of principle , we focus on two alternative strategies . In the first strategy , VoCAL was applied using k association maps , where each map relies on a marker set consisting of Ψ markers . Alternatively , VoCAL was applied with a single marker set that was generated by pooling the k disjoint marker sets of the former method . Since we use the cell-tagging initialization method , the resulting pooled set is the same as direct selection of Ψ∙k cell-tagging markers . This way , both strategies were initialized with exactly the same list of markers . We find better performance with multiple marker sets as compared to a single pooled set ( S6 Fig ) . For example , when we use k = 6 and 2 iQTLs , P < 5∙10−11 ( t-test ) for multiple sets over the pooled set . Taken together , our results demonstrate the benefit of testing reproducibility in association signals when relying on multiple non-overlapping marker sets . We also compared the reference-based initialization methods—the top-varying and two tagging-based methods—with random sampling of marker sets . The reference-based selection of marker genes showed a striking improvement in performance over the random sampling of markers , especially when the number of iQTLs was large ( e . g . , S7 Fig ) . For example , when we used 8 iQTLs and 1 eQTL hotspot , P < 6∙10−91 ( t-test ) for cell-tagging over the random sampling approach . The results for different parameter settings were similar ( e . g . , S3 Fig ) . Thus , the current study clearly supports a rationalized initialization of marker sets . Notably , since one method of reference-based initialization did not seem to consistently outperform the others , we could not find a convincing reason to prefer one method over another . We applied the VoCAL algorithm to identify iQTLs in the lung gene expression dataset of Alberts et al . [28] , which was measured across a collection of ( genotyped ) naive BXD mouse strains ( a cross of C57BL/6J [B6] and DBA/2J [D2] strains ) . The analysis was conducted using the 'cell-tagging with FACS' initialization method on the basis of the ImmGen reference data [23] , which carries 207 immune cell types . VoCAL converged after three iterations , with removal of 13 and 3 markers in the first two iterations , respectively , and no additional filtration in the third . In the absence of marker filtration , 7 significant iQTLs were apparent , associated with the abundance of murine cytomegalovirus ( MCMV ) -stimulated natural killer ( NK ) cells , lung macrophages , mucosal Langerhans cells , non-classical MHC class IIint monocytes , effector T cells , transitional type 2 B cells , and B1a cells ( permutation FDR < 0 . 05; see Fig 4A and full details in S2 and S3 Tables ) . However , only the Langerhans cells exhibited significant association when we applied VoCAL with the iterative filtration of marker genes ( Fig 4A ) . On the assumption that our study with synthetic data was realistic , the six remaining associations probably indicate false positives , since they appeared only in the presence of a few eQTLs that could have stemmed from any of the cell populations in the tissue . The subpopulation of MCMV-stimulated NK cells demonstrates VoCAL's ability to address the eQTL-confounding problem . In the absence of marker filtration step , these cells were found to be significantly associated with a 25 . 6-Mb region on chromosome 6 , with a peak between 129 . 56–133 . 8 Mbp ( Fig 4A , top left ) . The RNA levels of three marker genes—Klrc3 , Klrk1 and Klra8—were associated with an eQTL residing in the same iQTL region . In accordance , the three markers were removed during the filtration step and the association completely vanished ( Fig 4A , top left ) . Consistent with our predictions , all three markers have a known role as NK-specific receptors , with a specific role of Klra8 in MCMV infection [29–31] . Brown et al . [30] reported that ( i ) splenic NK cells are abundant in both the B6 and D2 strains ( the parental strains of BXD lines ) ; and ( ii ) in NK cells , the Klra8 gene is expressed in B6 but not D2 mice . Furthermore , Lee et al . [31] showed that , using spleen and liver tissues , the Klra8 gene could be amplified from the B6 strain but not from the D2 strain . Thus , our predictions in NK cells agree well with previous studies . Additional experiments are required to test the NK hypothesis in the lung tissue . The mucosal Langerhans cells provide a clear example of a predicted iQTL ( Fig 4B ) . In lung tissue , mucosal Langerhans cells act as the first line of defense against invading pathogens . Using VoCAL , the Langerhans cells were significantly associated with a 1 . 2-Mb region iQTL on chromosome 12 ( from 59 . 05 to 60 . 24 Mbp ) with permutation-based FDR < 0 . 025 . The predicted iQTL interval consisted of 9 genes ( Fig 4B , left ) , none of these genes had any cis-association . Notably , 2 of these 9 genes located at the peak of this interval—somatostatin receptor 1 ( Sstr1 ) and C-type lectin domain receptor ( Clec14a ) —have documented roles in Langerhans cells ( e . g . , [32–34] ) . The association with Langerhans cells also demonstrates the advantages of aggregating k association maps , as the results consisted primarily of consistent association pattern ( 8 out of 10 independently derived maps; Fig 4B , left ) . These maps were in agreement with the overall prediction of the VoCAL algorithm ( Fig 4B , left , black line ) . Furthermore , in all of these cases strains carrying the D2 allele showed higher predicted quantities of Langerhans cells than strains carrying the B6 allele ( Fig 4B , right and S4 Table; based on the rs3705833 locus located at chromosome no . 12 at 59 . 05 Mbp ) . This highlights a major advantage of our approach: true iQTLs are expected to be revealed on the basis of distinct subsets of markers . The independent support of the iQTL interval from different marker sets and the lack of eQTLs in this region are in agreement with our hypothesis of an iQTL acting on Langerhans cells in chromosome no . 12 .
In this work we developed a novel method , which we call VoCAL , to reveal the genetic basis of variation in immune cell traits based on gene expression data . Whereas existing methods for genetic mapping require direct measurement of immune traits across a large population of individuals , VoCAL avoids cell quantification by inferring these immune traits indirectly . To address this , VoCAL utilizes a mathematical deconvolution technique , which relies on a set of marker genes , to calculate the abundance of a variety of immune traits; it then applies genome-wide association methods to uncover the causal loci for these traits ( iQTLs ) . By consolidating hypotheses from different marker sets we avoid errors from noisy predictions ( Fig 2B ) . This technique relies on the observation that true signals are generally robust to the choice of a marker set , as demonstrated in Fig 1B . Our analysis indeed demonstrates the improved performance of this approach ( Figs 3F , S5 and S6 ) and the consistency between predictions derived from distinct marker sets in the murine lung-tissue dataset ( Fig 4B ) . Suspecting that the existence of eQTL targets may lead to spurious iQTL associations ( as demonstrated in Fig 1C ) , the VoCAL pipeline refines the selected sets of markers by filtering out potentially confounding eQTL targets ( Fig 2B ) . Our analysis in synthetic data confirms the increased complexity of the problem with increasing number of eQTLs ( Figs 3B–3D and S3 ) and the improved performance when using the filtration step ( Figs 3D and 3E and S4 ) . Analysis of a biological dataset from the lung complex tissue further underscores the utility of the filtration step: of the seven putative associations found , only one still holds after filtration of eQTL targets ( Fig 4A ) , suggesting that the remaining associations are purely due to confounding eQTLs . For example , we discovered that the association of a subpopulation of NK cells was eliminated when the Klra8 gene was removed from the set of marker genes . This prediction is in agreement with previous in vitro measurements [30 , 31] . Taken together , our results emphasize VoCAL's ability to eliminate spurious associations that do not reflect an actual change in quantity of an entire cell subpopulation , but rather an inter-individual variation in expression of particular genes . Our findings point the way to several avenues of research . First , additional methods capable of dealing with biological tissues of high genetic complexity will have to be developed; for example , joint analysis of several iQTLs and eQTLs may enhance predictive power and make it possible to distinguish immune cell-cell and gene-cell interactions . Second , it will be important to extend VoCAL to human data . For example , we should use a human reference data ( such as [35] ) ; account for different confounding effects such as population structure and gender; and extend the association tests ( Eq 2 ) to handle heterozygous populations . Third , taking into account the correlations between the different marker genes might enhance our predictive power . Fourth , manipulation of the reference data ( as in [36] ) would allow us to explore genetic loci that lead to a shift of an immune cell to its inflammatory state . Fifth , it would be important to incorporate environmental effects , thereby highlighting the role of non-heritable factors in physiological immune responses . Finally , the ability to predict iQTLs provides plausible hypotheses for future experimental investigations . For example , this study suggests an iQTL acting on immune Langerhans cells located at the lung mucosa . The association holds when using multiple different marker sets and after applying the marker filtration step ( Fig 4B ) . Langerhans cells play a key role in innate defense against pathogens , suggesting a framework for understanding the genetic and immune cell interactions underlying susceptibility to respiratory infections . Additional investigations are needed to explore the functionality of changes in the abundance of these cells in the lung tissue . Overall , the methodology is general and can be applied with other deconvolution tools ( e . g . , [27] ) , and for other applications in the mammalian immune system .
VoCAL takes as input the following information: The VoCAL pipeline involves five steps: initialization , deconvolution , GWAS , aggregation and filtration . In the following we first describe the details of the different steps and then provide the overall VoCAL algorithm . A brief summary of the VoCAL framework is provided in S8 Fig . We analyzed the gene-expression profiling of whole lung tissue samples obtained from 47 BXD recombinant inbred mouse strains ( E-MTAB-848 [28] ) . These strains were originally generated by crossing the B6 and D2 inbred strains , which are also included in this dataset . Using log-transformed data , we normalized each strain by subtracting the expression profile of the B6 strain . We used the 207 cell type profiles that are part of the ImmGen reference data ( log-transformed; [23] ) . Genotyping data were reported and released in the GeneNetwork website ( http://www . genenetwork . org ) . The genome annotations were based on UCSC Mouse Genome Browser NCBI37/mm9 assembly ( RefSeq mm9 ) . We applied VoCAL using the 'cell-tagging with FACS' initialization method with k = 10 , Ti = 5 and Te = 10 . We used 100 permutations of the labeling of strains in the lung expression data to assess the empirical FDR , defined as the ratio of the average number of associations found in the permuted data to the number of associations in the real lung data ( denoted 'permutation FDR' ) . We note that VoCAL utilizes permutations tests in addition to the resampling of markers ( as detailed in step 1 ) . The two procedures were designed to address two distinct challenges: whereas the selection of markers addresses the problem of noisy associations due to confounding eQTLs , the permutations aims to account for the multiple testing problem . | Quantitative trait locus ( QTL ) studies have identified a plethora of genetic variants that lead to inter-individual variation in the abundance of immune cell subpopulations , both in normal and disease states . Cell sorting is an effective method of monitoring immune cell type quantities; however , owing to the large number of possible immune cell subsets , it can be difficult to apply this method to each cell type over multiple individuals . Recent QTL studies dealt with this difficulty by focusing on an a priori selection of one or a few cell subsets . Here we introduce VoCAL , a deconvolution-based method that utilizes transcriptome data to infer the quantities of immune cell types , and then uses these quantitative traits to uncover the underlying DNA loci . Our results in synthetic data and lung cohorts show that the VoCAL method outperforms other alternatives in revealing the genetic basis of immune physiology . |
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Across organisms , manipulation of biosynthetic capacity arrests development early in life , but can increase health- and lifespan post-developmentally . Here we demonstrate that this developmental arrest is not sickness but rather a regulated survival program responding to reduced cellular performance . We inhibited protein synthesis by reducing ribosome biogenesis ( rps-11/RPS11 RNAi ) , translation initiation ( ifg-1/EIF3G mutation and egl-45/EIF3A RNAi ) , or ribosome progression ( cycloheximide treatment ) , all of which result in a specific arrest at larval stage 2 of C . elegans development . This quiescent state can last for weeks—beyond the normal C . elegans adult lifespan—and is reversible , as animals can resume reproduction and live a normal lifespan once released from the source of protein synthesis inhibition . The arrest state affords resistance to thermal , oxidative , and heavy metal stress exposure . In addition to cell-autonomous responses , reducing biosynthetic capacity only in the hypodermis was sufficient to drive organism-level developmental arrest and stress resistance phenotypes . Among the cell non-autonomous responses to protein synthesis inhibition is reduced pharyngeal pumping that is dependent upon AMPK-mediated signaling . The reduced pharyngeal pumping in response to protein synthesis inhibition is recapitulated by exposure to microbes that generate protein synthesis-inhibiting xenobiotics , which may mechanistically reduce ingestion of pathogen and toxin . These data define the existence of a transient arrest-survival state in response to protein synthesis inhibition and provide an evolutionary foundation for the conserved enhancement of healthy aging observed in post-developmental animals with reduced biosynthetic capacity .
The differing phenotypes stemming from the loss of essential cellular functions , such as protein synthesis , are specific to the time in life ( development or adulthood ) when the deficit occurs . Under such deficits , arresting development is an established strategy at the disposal of animals to ensure future reproductive success . During its four larval stages , the nematode C . elegans has several possible arrested states that trigger in response to different stressors , including dauer [1 , 2] , starvation-induced arrest [3] , and adult reproductive diapause [4 , 5] , among others . Dauer diapause occurs under lack of food , high temperature , or high population density , inducing an alternative larval stage 3 [2]; this dauer state carries both metabolic and behavioral changes , including increased stress resistance [6 , 7] . This stress resistant and pre-reproductive arrest state is thought to have evolved to allow the worm to conserve its resources , and it affords protection from the environment until a more favorable environment is encountered . Starvation-induced arrest can occur at larval stage 1 ( L1 ) , induced from starvation occurring immediately after hatching , and this state similarly results in stress resistance[3] . Two other arrest states are adult reproductive diapause , which is induced by L4 starvation and results in an early-adult arrest state capable of surviving long periods of nutrient deprivation with the ability to later resume reproduction , and impaired mitochondria arrest , induced by deficiency in mitochondrial respiration and resulting in L3 arrest [4 , 8]; however , these two states have not yet been directly shown to have stress resistance phenotypes . These examples suggest the existence of cellular programs that function as checkpoints throughout development that stall reproduction to promote fitness [9] . Intriguingly , the same triggers that induce these genetically regulated arrest states during development , when initiated post-developmentally , lead to increased life and healthspan ( e . g . daf-2/Insulin IGFI signaling mutants [10–12] , mitochondrial deficiency [13 , 14] ) . Moreover , the loss of essential cellular functions was shown to alter animal behavior [15] , presumably to avoid further exposure to the environment causal for the perceived loss of cellular homeostasis [16–18] . Protein synthesis inhibition is another trigger of developmental arrest early in life and increased lifespan in adults [16 , 19–21] , although the underlying mechanisms are not well understood . Similar to inhibiting the insulin-signaling pathway in adults , inhibiting protein synthesis provides several resistances from stress—starvation , thermal , and oxidative [20 , 22] . Activation of the energy sensor AMP-activated protein kinase ( AMPK ) is linked to a reduction in protein synthesis [23–25] , and AMPK can be activated by reducing growth via starvation in C . elegans [26] or via inhibiting S6 kinase in isolated mouse cells [27 , 28]; this activation includes increased lifespan that is dependent on activation of AMPK in C . elegans [28] . Here we provide new characterization of a C . elegans survival arrest state brought on by reducing protein synthesis , which confers stress resistance and is reversible . Enacting protein synthesis inhibition in the hypodermis alone was partially sufficient for both the arrest and stress resistance phenotypes . Arrested animals had very high expression of a metallothionein and were found to have higher levels of calcium , which may be linked to an observed reduction in pharyngeal pumping . All of these survival phenotypes , save the arrest , were dependent on functional AMPK . Finally , these phenotypes could be recapitulated from exposure to xenobiotics , implying a potential evolutionary context for this fitness-promoting arrest state .
To elucidate the possible connection between the developmental arrest and longevity-promoting effects of protein synthesis inhibition [16 , 19–21 , 29] , we first defined the nature of the developmental arrest in C . elegans . We analyzed the effects of protein synthesis inhibition by targeting distinct and conserved aspects of the protein biosynthesis machinery ( S1A Fig ) . We measured the synthesis of two GFP reporters; a heat shock inducible promoter ( S1B Fig ) and a mlt-10p driven construct ( S1C Fig ) that is only expressed between developmental molts as a surrogate assessment for general protein biosynthesis [30] . Because GFP from these reporters is limited to temporally distinct periods , we can robustly measure differences in GFP levels between protein synthesis inhibition conditions . We targeted the translation initiation factor , egl-45/EIF3A , or the small ribosomal protein , rps-11/RPS11 , by RNA interference ( RNAi ) , so that we could control the strength and duration of inhibition , thereby avoiding the constitutive arrest that can occur when protein synthesis is inhibited by genetic mutation [31] . While there are many genes involved in protein synthesis that can induce arrest when inhibited [16 , 19 , 20] , egl-45 and rps-11 were selected as RNAi of these genes results in a fully penetrant larval arrest phenotype ( S1D Fig ) . There is a threshold effect to this arrest , as diluting the RNAi to 10% of total food allowed more escaping animals ( S1E Fig ) , while still impairing development . In all RNAi conditions tested at 100% of total food , we observed a potent developmental arrest that could persist beyond 10 days ( S1D Fig ) . To define the developmental arrest state more precisely , we made use of the molting reporter ( mlt-10p::gfp-pest ) that marks each of the four developmental molts in C . elegans [32] . This revealed a potent arrest after the first molt at larval stage 2 ( L2 ) ( Fig 1A–1C ) . In addition , these animals are morphologically different than other arrest states like dauer and L1 arrested animals ( S1F Fig ) and are smaller in length than wild type L2s; unlike arrested L2d animals [33] ( S1G Fig ) . Together , these data support the existence of a potent developmental arrest point in response to diminished biosynthetic capacity . To address the hypothesis that the induced developmental arrest in response to protein synthesis inhibition is beneficial , we challenged L2 arrested animals and non-arrested L2 control animals to oxidative ( 20mM H2O2 , Fig 1D and S2A Fig ) or thermal ( 36°C , Fig 1E and S2B Fig ) stress and found the arrested animals were more resistant to all tested environmental insults . Animals that remained in the arrested state for longer periods of time ( 2 or 10 days ) were markedly more protected against oxidative stress and extended exposures to thermal stress ( S2C–S2F Fig ) . Thus , the durability of the response and the capacity to further enhance resistance to perceived deficiencies is enhanced so long as it is needed . Collectively , these data show that loss of protein biosynthetic capacity during development does not induce a decrepit state , but rather a beneficial health-promoting state of impeded development . The amplification of stress resistance that correlated with time in the arrested state predicted that arrested animals could persist in the L2 stage for much longer than wild type animals . Given this , we examined the lifespan of animals in the arrested state and discovered that egl-45 RNAi and rps-11 RNAi animals had a mean survival in the arrested state of 24 and 12 days , respectively ( Fig 1F ) , compared to a normal eight hour L2 stage ( Fig 1A ) . As such , the developmental arrest resulting from reduction of protein biosynthetic capacity results in health-promoting state of extended diapause . One hypothesis is that pausing development in the L2 stage alone confers survival benefits . To test this , we screened all annotated RNAi clones that induce early and fully penetrant L2 arrest ( S2G and S2H Fig ) and measured their ability to resist the same exposure to stress . Despite sharing an L2 arrest phenotype , none of these RNAi treatments resulted in the same decrease in protein synthesis ( S2I Fig ) or afforded increased survival during stress ( S2J and S2K Fig ) . As such , arrest at the L2 stage does not require a loss in biosynthetic capacity and is not inherently stress resistance-promoting . In addition , the phenotypes observed are not tied to RNAi responses , as ifg-1 ( ok1211 ) mutant animals that arrest at the L2 state [31] are more resistant to oxidative stress as compared to wild type controls ( S2L Fig ) . We also tested the long-term survival of acn-1 , let-767 , and pan-1; while only acn-1 maintained long-term L2 arrest ( S2H Fig ) , the survival of acn-1 RNAi treated animals was significantly shorter than rps-11 and egl-45 RNAi treated animals ( S2M Fig ) . Finally , we tested the necessity of daf-16/FOXO , a transcription factor that is required for dauer arrest [9] , in these survival phenotypes . Reducing protein synthesis in daf-16 ( mgDf47 ) mutants still causes developmental arrest ( S3A Fig ) and results in increased resistance to oxidative ( S3B Fig ) and thermal ( S3C Fig ) stress . We further note that these animals are not dauers , morphologically ( S1F Fig ) and are not resistant to treatment with 1% SDS—a phenotype of animals that successfully enter dauer diapause . Moreover , reducing protein synthesis in daf-2 ( e1368 ) mutants , which form constitutive dauers at the restrictive temperature of 25C , enter this L2 arrest stage instead of developing into dauers . These findings support the protein synthesis inhibition arrest state at the L2 larval stage and prior to dauer formation , which is an alternative L3 stage ( S3D Fig ) . Considering the need for every cell to sense and respond to changes in biosynthetic capacity , but also the benefit of coordinating a systemic physiological response to a perceived organism-level deficit in any tissue , we hypothesized that the response to protein synthesis inhibition would be both cell autonomous and non-autonomous . The germline is a facile model for cell division in early larval development in C . elegans [34] . Similar to the developmental arrest observed at the organism level , tissue-general protein synthesis inhibition resulted in the clear arrest of the reproductive tissue at a stage typical for L2 animal development ( Fig 2A ) . We next sought to determine which tissues were capable of initiating the L2 arrest . Using tissue-specific RNAi , we systematically reduced the expression of egl-45/EIF3 or rps-11/RPS11 in the intestine , germline , or hypodermis ( S4A Fig ) . Similar to tissue-general RNAi , hypodermal-specific protein synthesis inhibition induced potent developmental arrest ( Fig 2B–2D ) and halted germline proliferation ( Fig 2E ) . In contrast , while still slowing development , intestinal or germline-specific RNAi was unable to induce developmental arrest ( S4B–S4J Fig ) . Germline-specific protein synthesis inhibition results in sterility ( S4H–S4K Fig ) , which differentiates the cell autonomous effects of protein synthesis inhibition from the cell non-autonomous impact on the entire organism when diminished biosynthetic capacity is restricted to the hypodermis . Hypodermal-specific protein synthesis inhibition was the most effective at enhancing resistance to oxidative ( Fig 2F ) and thermal ( Fig 2G ) stress , as compared to germline- and intestine-specific RNAi ( S4M–S4P Fig ) , which had modest or no effect on stress resistance . Moreover , hypodermal-specific protein synthesis inhibition initiated post-developmentally was capable of increasing lifespan and , in the case of egl-45 RNAi , was at least equally potent as tissue-general protein synthesis inhibition ( S4Q Fig ) . As predicted by their essential roles in protein synthesis , egl-45/EIF3 and rps-11/RPS11 expression is detectable in several tissues ( S4R–S4U Fig ) , but the differences in the expression level and location could explain the variance in the strengths of phenotypes observed in egl-45 RNAi versus rps-11 RNAi . Nevertheless , these data identify the hypodermis as an important mediator of organismal regulation of growth and development in response to diminished biosynthetic capacity . We examined the transcript levels of a panel of genes with established roles in stress adaptation ( see Methods ) under both 24 hours and 120 hours exposure to protein synthesis inhibition ( collected after 24 and 120 hour exposure to RNAi ) [35] . Despite the enhanced stress resistance observed in protein synthesis inhibition-induced L2 arrested animals , the expression of most genes tested—including several heat shock proteins , redox homeostasis pathway components , and isoforms of superoxide dismutase—was significantly repressed ( S5A–S5J Fig ) . The notable exception in this panel was the expression of mtl-1 , a metallothionine involved in metal homeostasis , which after 24 hours of either egl-45/EIF3 or rps-11/RPS11 RNAi was increased >10-fold ( Fig 3A and S5E Fig ) ; in animals arrested for 5 days , mtl-1 was increased >100-fold ( Fig 3B and S5F Fig ) . This temporal enhancement was not observed for other genes involved in stress adaptation ( S5G–S5J Fig ) . Moreover , hypodermal-specific protein synthesis inhibition also induced mtl-1 expression ( Fig 3A and S5E Fig ) , consistent with the notion that the hypodermis is a potent sensor for organismal biosynthetic capacity . As mtl-1 is activated in response to heavy metals , we challenged protein synthesis inhibition-arrested animals to toxic levels of Cd2+ ( 50mM ) and discovered this arrest state also enhanced resistance to heavy metal stress ( Fig 3C ) . Because heavy metal resistance was not previously annotated in adults with protein synthesis inhibition [19–21] , we initiated protein synthesis inhibition post-developmentally by egl-45/EIF3A or rps-11/RPS11 RNAi , which also resulted in resistance to Cd2+ exposure ( S5K Fig ) . Similar to oxidative and thermal stress , hypodermal-specific RNAi of egl-45/EIF3 or rps-11/RPS11 could recapitulate the whole animal RNAi phenotype ( S5L Fig ) . We next tested whether the increase in mtl-1 was causative for the resistance , so we created a double mutant of mtl-1 ( tm1770 ) and mtl-2 ( gk125 ) ( mtl-2 is a related metallothionine also activated in response to heavy metals ) , which greatly attenuated the ability to survive Cd2+ exposure when protein synthesis is inhibited ( S5M Fig ) . Based on these heavy metal responses , we wanted to further test if hypodermal RNAi could increase mtl-1 to the same degree as observed in wild type animals exposed to protein synthesis inhibition for extended periods . Correlating with the rate of developmental arrest , mtl-1 levels increase out to 48 and 120hrs of exposure to hypodermal specific RNAi of egl-45 or rps-11 ( S5N Fig ) . However , animals with longer exposure to rps-11 RNAi have mtl-1 transcript levels that return to near wild type levels , which correlates with the escape from developmental arrest under hypodermal specific rps-11 RNAi ( Fig 2D ) . Although heavy metals are not abundant in standard growth media , these findings led us to examine the total metal content of animals in protein synthesis inhibition arrest by inductively coupled plasma-atomic emission spectroscopy ( ICP-AES ) . The metal profiles revealed a significant reduction in Mg2+ and Mn2+ and a marked increase in Ca2+ ( Fig 3D and S6A Fig ) . These steady-state concentrations of metals were maintained in animals trapped in the arrested state for 5 days ( Fig 3D and S6A Fig ) . mtl-1;mtl-2 double mutant animals reduced multiple metal species by 10–20% , but did not affect Ca2+ levels ( S6B–S6D Fig ) ; protein synthesis inhibition treatment in this mutant was still able to induce many of the same Mg2+ , Mn2+ , and Ca2+ changes as seen in wild type , consistent with the transcriptional induction of mtl-1 acting as a stress response rather than as the upstream effector . Moreover , animals acutely exposed to CaCl2 treatment as larvae have an mtl-1 transcriptional profile that mirrors animals with protein synthesis inhibition ( Fig 3E ) , suggesting that the increase in Ca2+ could be physiologically significant and promote the increased mtl-1 expression . Animals have adopted several strategies , ranging from molecular adaptation to changes in behavior , in order to cope with less than ideal growth conditions [36] , and calcium plays several critical functions in these physiological responses . As such , we examined the behaviors of animals arrested from protein synthesis inhibition and noted a marked decrease in pharyngeal pumping ( Fig 4A and S7A Fig ) , a rhythmic behavior influenced by calcium transients [37 , 38] . The reduction in pharyngeal pumping was significant after 24-hours of protein synthesis inhibition and was more pronounced the more time animals were in the arrested state ( S7B Fig ) ; despite this reduction , a basal level of pumping continues even after 15 days in the arrested state ( S7B Fig ) . Similar to the developmental arrest and enhanced stress resistance observed in daf-16 ( mgDf47 ) animals , daf-16 is not required for the reduction in pharyngeal pumping rates when protein synthesis is inhibited ( S7C Fig ) . In line with previous cell non-autonomous effects , hypodermal-specific protein synthesis inhibition effectively reduced pharyngeal pumping ( Fig 4B ) , while protein synthesis inhibition in other somatic tissues could not evoke the same magnitude of responses ( S7D and S7E Fig ) . This reduction of pharyngeal pumping is intriguing as this behavior is correlated with food intake [39] , and caloric-restriction ( CR ) is an established means of enhancing organismal health- and lifespan [40 , 41] . With this in mind , we measured pharyngeal pumping in adult worms fed egl-45 or rps-11 RNAi to induce protein synthesis inhibition , which are long-lived [16] , and also discovered a significant reduction in pharyngeal pumping ( S7F Fig ) . Taken together , these data define reduced pharyngeal pumping as a physiological response of protein synthesis inhibition during development and adulthood . Protein synthesis is energetically expensive , and it is possible that protein synthesis inhibition leads to a state of excess ATP , which could be redirected to other cytoprotective pathways that drive stress resistance [42] . However , we found that animals exposed to protein synthesis inhibition during development have 50% less cellular ATP ( Fig 4C ) . AAK-2/AMPK is a conserved sensor of energy homeostasis that responds to changes in cellular AMP/ATP levels [43] . Indeed , animals with protein synthesis inhibition have significantly higher AMP/ATP and ADP/ATP ratios ( Fig 4D ) . As such , we tested aak-2 mutants for the protein synthesis inhibition survival and arrest phenotypes . aak-2 ( ok524 ) mutants exposed to protein synthesis inhibition were still arrested as L2 animals with reduced germ cell counts ( S8A–S8C Fig ) , but failed to dampen pharyngeal pumping rates ( Fig 4E and S8D Fig ) , which importantly uncouples these two protein synthesis inhibition responses and suggests that the developmental phenotypes are not a result of diminished food intake . Additionally , aak-2 mutant animals failed to evoke protein synthesis inhibition responses observed in wild type animals ( Fig 4F ) . Specifically , aak-2 mutants have minimal , often undetectable , changes in the expression of mtl-1 during protein synthesis inhibition ( S8F Fig ) —a phenotype similar to daf-16 mutant animals ( S8G Fig ) , which is a known regulator of the mtl-1 locus ( S8H Fig ) . aak-2 mutants are also as sensitive to Cd2+ as wild type animals ( S8I Fig ) , which further supports the connection between mtl-1 expression with resistance to environmental metal exposure . Furthermore , aak-2 mutants with protein synthesis inhibition are as sensitive to oxidative and thermal stress as wild type animals ( S8J , S8K , S8M and S8N Fig ) , indicating the essentiality of AMPK signaling in protein synthesis inhibition-induced stress resistance . We then tested mutant animals harboring a truncated and constitutively active ( CA ) form of AAK-2 [44] , which slowed development [44] and afforded resistance to oxidative stress while restoring thermal stress resistance under reduced protein synthesis , relative to aak-2 mutants ( S8A , S8C , S8J , S8L , S8M and S8O Fig ) . Intriguingly , expression of a constitutively activated version of AMPK ( CA-AMPK [44] ) restored the reduction of pharyngeal pumping phenotype when protein synthesis was reduced ( S8D and S8E Fig ) . Taken together with the AMP/ATP and ADP/ATP levels ( Fig 4G ) , these data define an AAK-2/AMPK molecular pathway that initiates organismal-level physiological responses to cellular deficiencies in protein synthesis . Importantly , our studies reveal a clear role for AMPK signaling in mediating the survival responses to protein synthesis inhibition beyond developmental arrest . In the context of a worm’s natural environment , we postulated that the ability to pause development in response to a perceived cellular deficiency would be advantageous—and perhaps evolved—as a response mechanism to deal with environmental hazards . In the wild , C . elegans consume diets that are far more complex than the simple and homogenous E . coli lawn provided to them in the laboratory [1] . These wild diets include heterogeneous populations of microorganisms , some of which can produce xenobiotic compounds that can target and disable essential biological pathways . Recently , the soil and intestinal microbiome of C . elegans has been characterized [45–47] . While only appearing at rates ranging from 0 . 001–0 . 1% in soil samples found in these studies , we chose to focus on the genus Streptomyces , as it is soil-dwelling , readily accessible with the lowest biosafety level , and has several members that produce commonly utilized molecules that can potently inhibit eukaryotic protein synthesis [48] . If wild C . elegans came upon a microcosm of Streptomyces species , or any other organism capable of producing xenobiotics that reduce protein synthesis , it would be important to have defenses available against these molecules . We exposed worms to S . griseus , S . griseolus , or S . alboniger , that produce cycloheximide ( CHX ) , anisomycin , and puromycin , respectively ( S9A Fig ) . Exposure to these Streptomyces species grown under stationary conditions for five days , in order to initiate secondary metabolism and the creation of these protein synthesis inhibition molecules [49] , resulted in delayed reproduction ( S9B Fig ) and significant reduction of their pharyngeal pumping in two species ( Fig 5A ) . This is in contrast to exposure with microbes in exponential phase growth which attenuates secondary metabolism [49] ( Fig 5A ) . Exposure to pathogens can alter several physiological parameters in the host , and of all the pathogens tested , exposure to S . griseus exerted the strongest influence on pharyngeal pumping . The remarkably similar impact that exposure to S . griseus had on C . elegans development and physiology , as compared to RNAi-induced protein synthesis inhibition , drove a further examination of how exposure to cycloheximide ( CHX ) , the bioactive secondary metabolite produced by S . griseus , affected C . elegans survival during development . CHX is a potent inhibitor of ribosome processivity and has recently been shown to exert health-promoting effects in adult C . elegans by an unknown mechanism [50] . Satisfyingly , CHX exposure upon hatching , which inhibits new protein synthesis ( S9C Fig ) , also resulted in arrested animal development ( S9D Fig and Fig 5B ) , and can arrest in a dose-dependent manner ( S9D Fig ) . Although animals arrested by RNAi-mediated protein synthesis inhibition can continue development upon removal from the RNAi state , not all animals in the population mature into fertile adults ( S9E–S9G Fig ) —likely a result of the persistence of RNAi [51–54] . However , initiating protein synthesis inhibition via exposure to 0 . 05mg/ml CHX rather than RNAi of essential protein synthesis factors ( S1A Fig ) enabled studies of recovery from the arrest state without the complications of RNAi . Once removed from the xenobiotic , developmentally arrested animals resume development—indicating the arrest state is truly transient ( Fig 5B , S2 Table ) . The CHX-induced arrest state caused reduced pharyngeal pumping ( Fig 5C ) , arrested germ cell proliferation ( Fig 5D ) , increased organismal [AMP]/[ATP] ratio ( Fig 5E ) . Importantly , this arrest state phenocopied all RNAi-based protein synthesis inhibition survival responses ( Fig 5F ) including: enhanced resistance to oxidative ( S9H Fig ) and thermal stress ( S9I Fig ) , induced the expression of mtl-1 ( S9J Fig ) decreased cellular ATP ( S9K Fig ) , and resulted in metal profiles similar to animals fed RNAi targeting egl-45/EIF3 and rps-11/RPS11 ( S9L and S9M Fig ) . 0 . 05mg/ml CHX exposure may not fully arrest all animals , as some daf-2 ( e1368 ) animals at the restrictive temperature did become dauers ( S2Q Fig ) . Animals that are released from CHX arrest have minimal ( if any ) changes in reproductive output ( S9N Fig ) , have a small but significant increase in resistance of oxidative stress ( S9O Fig ) , are delayed ~16-20hrs to reproduction ( S9P Fig ) , and have normal pumping rates at physiological day 3 of adulthood ( S9Q Fig ) . Thus , this transient arrest state is survival promoting when the deficiency in protein synthesis is present and is not afforded once homeostasis is reestablished , similar to animals released from dauer [36] . Intriguingly , the ability of Streptomyces griseus to reduce pharyngeal muscle pumping required the presence of live bacteria co-culture ( S9R Fig ) . In addition , increasing doses of CHX , similar to the threshold effects seen with RNAi targeting genes involved in protein synthesis ( S1E Fig ) , could further reduce the pumping rate of the arrested animal ( S9S Fig ) . Thus , the complexity of the environment and drug dosage are important for balancing the induction of this survival state .
In response to impaired organismal protein synthesis , animals are capable of entering an arrest state , reaping survival benefits , and exiting to become reproductive adults ( Fig 5B ) . In our studies , we are forcing continual exposure of animals to protein synthesis inhibiting RNAi or xenobiotics , which is likely "unnatural" , as previous studies of lethal RNAi treatment and xenobiotic treatments leads to aversion behaviors [15 , 17] . With this in mind , we predict that in the wild the perceived loss of translation would evoke a similar aversion response—allowing animals to escape to new pathogen-free environments . This model is supported by our studies with cycloheximide exposure , which drives a rapid induction of arrest and stress resistance , from which animals can quickly recover . In this regard , we believe that the use of cycloheximide as a transient inducer of protein synthesis inhibition in the worm will be of great use in studying protein synthesis inhibition going forward in order to circumvent the complications of RNAi expansion over the worm lifespan and subsequent generations . Given that there is a dose response to CHX exposure , higher doses can be utilized to prolong the arrest state and enhance arrest phenotypes although prolonged exposure to higher concentration reduces the rate of escape ( S2 Table ) . The lack of necessity of DAF-16 for the developmental arrest in response to protein synthesis inhibition indicates that the reduced protein synthesis pathway functions independently from the dauer development pathway . Yet , while most dauer constitutive daf-2 mutants that are arrested from CHX do not form dauers , intriguingly ~20–25% will develop into dauers instead of undergoing protein synthesis arrest ( S3D Fig ) . This finding suggests that animals can either alternatively arrest in the L2d stage [33 , 55 , 56] , or that the CHX dose requires a higher threshold for complete arrest of animals ( especially given the 100% non-dauer RNAi-treated animals ) . Of note , reduced protein synthesis arrested animals are distinct from the L2d stage as they are of smaller length than wild type L2s ( S1G Fig ) ( unlike 50% longer L2d animals [33] ) , functional AMPK is not necessary for the reduction of germ cell numbers ( S8B Fig ) as it is in L2d/dauer animals [57] , and we have never observed them becoming dauers after exiting the arrest state . Future characterization of any phenotypic parallels between L2d and reduced protein synthesis arrest , especially in the context of the differing role of AMPK in controlling germ cell proliferation , will be of interest for future studies . A persistent question in biology asks how cellular status is communicated across the organism and , more importantly , how an appropriate homeostatic response is engaged . Protein synthesis inhibition in the hypodermis alone was sufficient for all arrest and healthspan phenotypes . In addition to its important role in the molting process during larval development , the hypodermis has recently been implicated as being important in dietary checkpoints in larval arrest [9 , 58] . Although it is known that C . elegans tissues have differential capacity for RNAi , our work bolsters the hypodermis as a key tissue in larval development , and identifies a new cell non-autonomous communication pathway to initiate systemic responses . Given that the hypodermis is the first barrier to its external environment that covers the entire organism , it is reasonable that C . elegans might evolve sensing mechanisms for hypodermal cellular changes to influence whole-body cellular signaling . It is also possible that the high demand for protein synthesis during growth of the developing hypodermis amplifies the tissue-general effects of protein synthesis inhibition in this tissue , with or without specifically evolved signaling pathways . However , proliferation alone is not the only factor that influences responses to protein synthesis inhibition . The germline is a highly proliferative tissue in C . elegans , and while protein synthesis inhibition in the germline did not result in the same L2 arrest state as tissue-general or hypodermis-specific reduction , it did result in pre-reproductive adult animals with mild stress resistance ( S4 Fig ) . It remains to be seen if this germline arrest is also reversible , similar to starvation-induced adult reproductive diapause [4] . It is important to note the differences in stress resistance when protein synthesis is reduced in specific tissues . While hypodermis-specific RNAi of protein synthesis components results in increased stress resistance that is consistent when RNAi is initiated in all tissues , intestine-specific RNAi resulted in no change to stress resistance capacity except for a few instances of increased resistance only observed for rps-11 RNAi . The more tissue-general expression of rps-11/RPS11 ( S4 Fig ) , may explain these minor phenotypic differences as compared to egl-45/EIF3 RNAi . Taken together , these data support the idea that the systemic stress responses that stem from the loss of rps-11 are mediated by effects across multiple tissues . In contrast to the hypodermis and intestine , germline-specific loss of protein synthesis resulted in modest or no changes in oxidative stress resistance and surprisingly lead to reduced thermal tolerance . This suggests that the oxidative and thermal stress resistance responses , at least in the germline , may be uncoupled or , alternatively , that reducing protein synthesis in the germline activates a separate pathway that negatively affects thermal stress resistance . Finally , it is also worth noting that there is considerable variation in stress resistance among these tissue-specific RNAi strains . We attribute much of this both to the use of RNAi variance , as well as the ever-present "leakiness" of these tissue specific strains that can sometimes spread RNAi effects to other tissues [59 , 60] . The metallothionein , mtl-1 , is highly ( >100-fold ) upregulated under reduced protein synthesis . The increased expression of mtl-1 was required for heavy metal resistance in animals with protein synthesis inhibition , which is notable since hypersensitivity to cadmium has not been reported in adult C . elegans lacking MTL-1 or MTL-2 [61] . This finding further advocates for the importance of uncoupling developmental and adult specific responses . Transcription of MT1 , the mammalian homolog of mtl-1 , is also upregulated by oxidative stress agents in cell lines and mice [62 , 63] , so it is possible that protein synthesis inhibition causes an increase in ROS that triggers mtl-1 transcription; however , then we would also expect to see increased transcription of SKN-1 target genes ( e . g . gst-4 ) , which we do not observe . Moreover , mtl-1 expression was not necessary for the arrest , oxidative or thermal stress resistance , or reduced pumping , as daf-16 mutants ( which lack mtl-1 expression , S8 Fig ) still display both phenotypes . Thus , given the very specific transcription of mtl-1 , the changes in expression are likely due to the presence of its most well-defined binding partners , metal cations . Traditional targets of MTL-1 are Zn2+ , Cd2+ , and Cu2+ , but mammalian homologs can bind to Mg2+ , Mn2+ , and Ca2+ [64–66] . The increase in Ca2+ ions could be the cause of this high transcriptional response , especially given that Ca2+ treatment could induce mtl-1 in worms ( Fig 3E ) . However , it is also possible that higher levels of other heavy metals , such as Cd2+ , which never reached our detection limits , are responsible . Given that mtl-1 expression was disposable for the arrest , stress resistance , and reduced pumping rate , the increased expression change is a "biomarker" for the reduction of protein synthesis , rather than a central player in this developmental state . Given the ability for calcium to upregulate this mtl-1 response ( Fig 3 ) , we expect the protein synthesis loss triggers calcium abundance and daf-16 activation [16] , that both go on to increase mtl-1 levels . It is possible that the reduction of cellular ATP we observe reflects the use of ATP to “power” survival processes [42] . However , a ~50% reduction in ATP after 24hrs of protein synthesis inhibition is a remarkable loss , and it would not explain how this energy usage would be sustained to continue stress resistance over extended time periods , especially when accompanied by a reduction in pharyngeal pumping ( thereby reducing food/energy intake even further ) . Our data support an alternative model where increases in the [AMP]/[ATP] and [ADP]/[ATP] ratios activate AMPK pathways that signal for downstream survival pathways ( Fig 4C and 4F ) . The underlying mechanism driving the imbalance to cellular adenylate pools will be of future interest . We found that AMPK was necessary for all of our protein synthesis inhibition survival phenotypes , except for arrest . AMPK activation has been implicated in survival phenotypes before , including glucose restriction pathways [67] and oxidative stress resistance [68] in C . elegans . Juxtaposed to our work , activating AMPK ( such as via AICA ribonucleotide ) causes a decrease in protein synthesis [23–25] . While our work focuses directly on protein synthesis alone , AMPK is also increased in rsks-1/S6K mutants [27 , 28] and under starvation conditions [26] . This suggests that AMPK and protein synthesis may work together in a circular pathway or that they affect each other by cell non-autonomous signaling . In addition , an upstream activator of AMPK , ARGK-1 , is both important for rsks-1/S6K mutant longevity , and its overexpression caused reduced pumping rates in worms [69]; further study into the role of ARGK-1 in this protein synthesis inhibition survival state will be of interest in future studies . As a final note , C . elegans lacking the elongation factor efk-1 , which is activated by AMPK , fare worse under nutrient starvation conditions [70]; thus , there are multiple connections between starvation , protein synthesis , and energy homeostasis , and understanding them in context of survival states is important to consider . Previous studies suggest that the effects of protein synthesis inhibition on adult lifespan are distinct from caloric restriction ( CR ) [19] and that the CR state can drive a reduction in protein synthesis[20] . Our data suggest that during development the opposite is also true: that protein synthesis inhibition can reduce pharyngeal pumping leading to a CR-like state . CR across most organisms has both life- and healthspan promoting effects; however , the evolutionary basis of the CR response is unknown . One hypothesis generated from this study is that the physiological response to CR might stem from an ancient program to promote stress resistance when the presence of diminished biosynthetic capacity is perceived . Microorganisms such as Streptomyces provide a potential evolutionary explanation to a mechanism of a pathogen-derived CR pathway by engaging behavioral avoidance phenotypes toward toxin-producing pathogens [15] . It is important to note that Streptomyces was found at very low levels in recent studies looking at C . elegans soil samples [45–47] . Our xenobiotic experiments are not meant to emulate the wild environment , but to capture the interaction between the worm and a harmful species in the environment . It is altogether possible that there are areas ( or times in history ) where Streptomyces , or other species capable of inhibiting host protein synthesis , are a more common occurrence , demanding the need for such an arrest survival response documented here . There are connections between immune function and the regulation of protein synthesis—both to exposure to protein synthesis-impairing xenobiotics ( ExoA , Hygromycin ) as well as potential surveillance mechanisms for reduced protein translation as a surrogate for infection [71–73] . Pathogen response pathways can also be closely linked to promoting proteostasis [74] . In addition , a recent study found that C . elegans can enter a diapause to avoid pathogens ( unlike our study , this is reliant on the formation of dauers [75] ) . Nevertheless , our findings support the idea that the loss of protein synthesis might be perceived as "an attack" by a pathogen , which initiates a reduction in pharyngeal pumping , that could minimize ingestion of toxin-producing microbes . Given the remarkable overlap in phenotypes resulting from protein synthesis inhibition by pathogen-derived xenobiotics and our genetic and RNAi-mediated protein synthesis inhibition , it is suggestive that this survival-arrest state could have evolved as a stress response to the presence of pathogens ( Fig 6 ) . This idea parallels models of adult longevity pathways , which may have connections to xenobiotics targeting other essential pathways besides protein synthesis [35] . Unlike previous models that suggest the developmental arrest resulting from early loss of protein synthesis is a detrimental state [42] , these studies provide an alternative way of thinking about these developmental responses . The induction of protective responses to reduced protein synthesis is survival-promoting , and we predict that the capacity to engage these pathways would enable future opportunities for reproduction once the inhibition is alleviated . Lastly , our results provide an example of how the evolution and selection of developmental pro-fitness pathways may be utilized effectively later in life under the right conditions . Just as dauer diapause from reduced insulin/IGF-1 signaling ( IIS ) has mechanistic similarities with adult longevity responses when IIS is reduced post-developmentally , our studies establish a similar fitness-driven developmental program as the underlying mechanism of the enhanced healthy aging observed in adults with compromised protein biosynthetic capacity . The exceptional degree of conservation of these cellular pathways across organisms is suggestive that the pre- and post-developmental responses to protein synthesis inhibition observed in C . elegans could be similarly shared , even among humans .
Worm strains were grown at 20°C for all experiments except dauer studies that were conducted at 25°C . All strains were unstarved for at least 3 generations ( except for L1 synchronization ) before being used in any experiments . List of strains used: N2 Bristol ( wild type ) , DR1572 daf-2 ( e1368 ) , GR1329 daf-16 ( mgDf47 ) , MGH171 ( sid-1 ( qt9 ) ; Is[vha-6::sid-1::SL2::gfp] , JM43 ( rde-1 ( ne219 ) ; Is[wrt-2p::rde-1] , myo-2p::rfp] ) , NL2098 ( rrf-1 ( pk1417 ) ) , GR1395 ( mgIs49[mlt-10p::gfp-pest , ttx-3::gfp]IV] ) , SPC365 mtl-1 ( tm1770 ) ; mtl-2 ( gk125 ) , RB754 ( aak-2 ( ok524 ) ) , SPC366 ( aak-2 ( ok524 ) ; uthIs248[aak-2p::aak-2 ( genomic aa1-321 ) ::GFP::unc-54 3'UTR ( gain of function allele ) ; myo-2p::tdTOMATO] ) , SPC363 ( Ex[egl-45p::rfp; rol-6 ( su1006 ) ] ) , SPC364 ( Ex[rps-11p::gfp; rol-6 ( su1006 ) ] ) , CL2070 ( dvIs70[hsp-16 . 2p::GFP; rol-6 ( su1006 ) ] ) , KX38 ( ifg-1 ( ok1211 ) /mIn1 [mIs14 dpy-10 ( e128 ) ] ) . Some strains were provided by the CGC , which is funded by NIH Office of Research Infrastructure Programs ( P40 OD010440 ) . E . coli strain HT115 ( DE3 ) containing empty vector L4440 ( hereafter referred to as Control RNAi ) , or plasmid against a gene of interest , was grown overnight ( 16-18hrs ) at 37°C and seeded on NGM plates containing 5mM isopropyl-β-D-thiogalactoside ( IPTG ) and 50ug/ml carbenicillin . The bacteria were allowed to generated dsRNA overnight before being used within the next 1–3 days ( stored at 20°C for this period if not used immediately ) . Dose response curves were established by feeding HT115 bacteria expressing the indicated RNAi clone diluted with HT115 bacteria harboring the control RNAi plasmid L4440 . 0 . 05mg/ml Cycloheximide ( CHX ) or water ( vehicle control ) was added on top of bacteria and allowed to dry and rest for at least 1 hour before placing worms on treated bacterial lawns; this was the concentration of CHX throughout this paper , unless otherwise noted . Loss of protein synthesis was determined via measurements of de novo synthesis of GFP through both an internal ( via natural development ) and external ( via high temperature ) induction method . External: plated animals expressing hsp-16 . 2p::GFP were maintained at 20°C and fed RNAi since hatching . After 24hrs , one set of worms was shifted to 36°C for 3hr , while the other was mounted for the baseline 0hr time point . The baseline plate was also checked after 3 hours at room temperature as a control for any room temperature-induced GFP expression . Internal: plated animals expressing mlt-10p::gfp-pest , treated with RNAi or drug since hatching , were imaged via the same methods for GFP expression at 12 , 14 , and 16 hours post-feeding . Worms were imaged at 20x magnification with bright field and GFP filter ( Zeiss Axio Imager ) . Plated animals , treated with drug or RNAi since hatching , were counted in 24 hours intervals via a compound microscope as larval stage 1–3 ( size ) , larval stage 4 ( vulval invagination ) , adult ( size ) , or reproductive ( presence of internal eggs ) . In food switching assays , worms were moved to rde-1 RNAi after 24hrs on the listed RNAi . rde-1 RNAi was used to inhibit the RNAi machinery because RNAi effects can persist even after moving animals off of food containing double stranded RNA for multiple generations . Plated animals , treated for 24 or 48 hours on drug or RNAi since hatching , were placed at 36°C for up to 12 hours . Every 3 hours , one set of plates was removed to room temperature . Worms were allowed to recover for at least 10 minutes , and then counted for survival immediately by checking for touch response to prodding with a platinum wire . Plated animals , treated for 24 or 48 hours on drug or RNAi since hatching , were washed with M9 buffer twice in microcentrifuge tubes , then treated with 20mM H2O2 for up to 1 hour while rocking at room temperature . Every 20 minutes , one set of worms was removed from rocking , washed 3 times in M9 buffer , and plated back onto new plates containing their previous treatment ( drug or RNAi ) . Worms were checked 1 hour after plating to count any acute deaths ( "straight line" bodies or ruptured vulvas ) only by eye , and 24 hours after plating to count final survival as done in thermotolerance assay . Plated animals , treated for 24 hours on RNAi since hatching or at L4/YA stage , were washed with K-medium ( 32mM KCl , 51mM NaCl in dH2O ) twice in microcentrifuge tubes , then treated with 5 or 50mM CdCl2 in K-medium ( hatched or YAs , respectively ) for 30 minutes while rocking at room temperature . After 30 minutes , worms were washed 3 times in K-medium , and plated back onto new plates containing their previous treatment ( RNAi ) . Worms were checked 1 hour after plating to count any acute deaths ( "straight line" bodies or ruptured vulvas ) only by eye , and 24 hours after plating to count final survival as done in thermotolerance assay . Wild type and daf-2 ( e1368 ) were placed as synchronized L1s onto the listed RNAi clone or drug at 25°C for 48hrs . Worms were then washed in M9 , pelleted , and treated with 1% for 30min while rocked at room temperature . Treated animals were then plated onto plates with HT115 bacteria and counted for survival . Drug- or RNAi-treated animals were washed with M9 buffer twice in microcentrifuge tubes , then frozen at -80°C in TRI-Reagent® ( Zymo Research , R2050-1-200 ) . After at least 24 hours at -80°C , RNA was extracted from samples using the Direct-zol™ RNA MiniPrep kit ( R2052 ) . Quantitative reverse transcription PCR ( qRT-PCR ) was performed on the RNA samples with gene specific primers ( Table 1 ) . For evaluation of mtl-1 induced by calcium , wild type animals , grown for 24 hours on Control RNAi , were washed with K-medium twice in microcentrifuge tubes and then treated with 500mM CaCl2 ( in K-medium ) for 30 minutes at room temperature . Animals were then washed three times with K-medium , frozen at -80°C in TRI-Reagent® as above , and the same protocol as above was utilized . Two 24-well plates , each containing a single GR1395 worm on RNAi or Control RNAi , were visualized by fluorescence microscopy every hour for 72 hours . Worms were marked as green or non-green to indicate molting or non-molting , respectively . Worms that crawled off the side of the plate or burst were censored . Plated animals , treated for 24 hours on RNAi or drug since hatching , were imaged at 20x magnification ( Zeiss Axio Imager ) , and individual germ cells were counted with the Cell Counter plugin on Fiji software [76] . Plated animals , treated for the indicated time on drug or RNAi since hatching , were imaged via the Movie Recorder at 8ms exposure using the ZEN 2 software at 10x magnification ( Zeiss Axio Imager ) . Animals with zero pumping were excluded . 1000 or 500 plated animals , treated for 24 or 48 hours on drug or RNAi since hatching respectively , were washed 3 times in M9 buffer ( keeping ~100μl of supernatant at final wash ) , snap frozen in a dry ice/ethanol bath , and placed at -80°C until use . Frozen pellets were boiled for 15 minutes and spun down at 14 , 800g at 4°C . The supernatant was then diluted in dH2O ( 1/10 ) ( Adapted from[77] ) . Samples were tested for protein content via Bradford analysis ( Amresco M173-KIT ) , and ATP was assessed via the ENLITEN® ATP Assay System ( Promega ) . To determine relative levels of ATP/ADP/AMP , we followed the same method as above , but did not dilute the supernatant . Protein supernatant was directly assayed via the ATP/ADP/AMP Assay Kit ( University at Buffalo , Cat . # A-125 ) to determine total ATP/ADP/AMP in each sample; these values were then directly compared to determine relative ratios . 8 , 000–10 , 000 ( L4 stage ) or 20 , 000–25 , 000 ( L2 stage ) animals , treated for the listed time on the listed RNAi clone , were collected into microcentrifuge tubes ( tubes weighed beforehand ) using isotonic buffer ( 150mM Choline Chloride , 1mM HEPES , pH 7 . 4 with NaOH , filter sterilized ) . Worms were washed 3 times over 30 minutes ( pelleting at 1 , 000g/30s each time ) to clear gut content and then finally pelleted at 12 , 000g/2min . Worm pellets were then dried at 60°C for 48 hours using a heat block . Worm pellets were weighed after drying , and ICP analysis of the samples was conducted by Dr . David Kililea , Children's Hospital Oakland Research Institute . Before ICP analysis , dried pellets were acid digested with Omnitrace 70% HNO3 at 60°C overnight . Samples were diluted with Omnitrace water for a final concentration of 5% HNO3 . Derived metal content was normalized to dried worm pellet weights . Each animal is compared back to 24hr Control RNAi treated animals . 48hr Control RNAi animals are given as a reference for what the metal content of a chronologically matched animal would be; albeit animals that are L4-YA stage and thus 2–3 larger with higher food intake . Streptomyces Alboniger ( ATCC 12461 ) , Griseus ( ATCC 23345 ) , or Griseolus ( ATCC 3325 ) were grown at 26°C , shaking , in Tryptone-Yeast Extract Broth ( 5g Tryptone , 3g Yeast Extract in 1L dH2O , pH 7; taken from ATCC® Medium 1877: ISP Medium 1 ) for 5 days before plating unless otherwise noted . Strains were plated on Yeast Malt Agar plates ( HiMedia Laboratories , M424 ) , and mixed 1 part to 3 parts 25x HT115 when used with worms . For the egg laying comparisons , 100ul Saccharomyces cerevisiae was also added to induce competition; to compare total number of eggs , worms were mounted at ~52hrs after dropping to food source , and imaged at 20x magnification with DIC ( Zeiss Axio Imager ) . For testing dead HT115 , 75ml/L of 2 . 5% Streptomycin was added to 25x HT115 and the mix was rocked for 24hrs at room temperature . This mixture was then used in place of the 25x HT115 above . For survival in the arrested state , worms were dropped on the listed RNAi and counted each day ( for the majority ) for survival . Survival was assessed by touch response to prodding with a platinum wire . The Control RNAi wild type control strain used in this experiment was moved each day starting at adult day 1 as necessary until reproduction ceased . For tissue-specific lifespan analysis , worms were grown on Control RNAi until L4/young adult age , and then transferred to the listed RNAi plates treated with 50μM FUdR . Survival was assessed every other day as above . For all assays , animals were only censored ( bursting , vulval protrusion , etc . ) after the first counted death . Worm morphological comparisons were imaged at 20x magnification with DIC filter ( Zeiss Axio Imager ) . Worm length comparisons were made in ImageJ using the segmented line tool down the midline of each animal from head to tail . For GFP and RFP reporter strains , worms were mounted in M9 with 10mM Sodium Azide , and imaged at 40x magnification with DIC and GFP/RFP filters ( Zeiss Axio Imager ) . Fluorescence is measured via corrected total cell fluorescence ( CTCF ) via ImageJ and Microsoft Excel . CTCF = Integrated Density– ( Area of selected cell X Mean fluorescence of background readings ) . For imaging of heat-induced GFP expression via strain CL2070 , plated animals were maintained at 20°C and fed RNAi since hatching . After 24hrs , one set of worms was shifted to 36°C for 3hr , while the other was mounted ( as above ) for the baseline 0hr time point . The baseline plate was also checked after 3 hours at room temperature as a control for any room temperature-induced GFP expression . Worms were imaged at 20x magnification with bright field and GFP filter ( Zeiss Axio Imager ) . Thermotolerance , oxidative stress , and heavy metal stress were all compared using Fisher's Exact Test using the statistical software R [78]; specifically , the bars in each graph represent a unique set of biological replicates ( 2–6 independent biological replicates , see S1 Table ) relative to its own independent control cohort ( and the significance level relative to this control is indicated by the # of stars above each bar ) ; this test is employed as we are comparing the categorical variables of Alive vs Dead , and data is presented as changes in survival . Comparison of all RNAi clones and CHX for protein synthesis rates under the mlt-10p::GFP promoter was performed using one-way ANOVA . Lifespan curves were compared and analyzed via Log-Rank using JMP Pro 12 . qPCR , worm fluorescence , metal content , ATP/ADP/AMP levels , and pharyngeal pumping comparisons were made with Student's t test using Microsoft Excel . When comparing groups of three or more , Bonferroni multiple comparison post-correction was employed on Fisher's test , ANOVA , and t tests . | Protein synthesis is an essential cellular process , but post-developmental reduction of protein synthesis across multiple species leads to improved health- and lifespan . To better understand the physiological responses to impaired protein synthesis , we characterize a novel developmental arrest state that occurs when reducing protein synthesis during C . elegans development . Arrested animals have multiple survival-promoting phenotypes that are all dependent on the cellular energy sensor , AMP kinase . This survival response acts through the hypodermis and causes a reduction in pharyngeal pumping , indicating that the animal is responding to a perceived external threat , even in adults . Furthermore , exposing animals to pathogens , or xenobiotics they produce , can recapitulate these phenotypes , providing a potential evolutionary explanation for how a beneficial response in adults could evolve through the inhibition of an essential biological process such as protein synthesis . |
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Tubulin genes encode a series of homologous proteins used to construct microtubules which are essential for multiple cellular processes . Neural development is particularly reliant on functional microtubule structures . Tubulin genes comprise a large family of genes with very high sequence similarity between multiple family members . Human genetics has demonstrated that a large spectrum of cortical malformations are associated with de novo heterozygous mutations in tubulin genes . However , the absolute requirement for many of these genes in development and disease has not been previously tested in genetic loss of function models . Here we directly test the requirement for Tuba1a , Tubb2a and Tubb2b in the mouse by deleting each gene individually using CRISPR-Cas9 genome editing . We show that loss of Tubb2a or Tubb2b does not impair survival but does lead to relatively mild cortical malformation phenotypes . In contrast , loss of Tuba1a is perinatal lethal and leads to significant forebrain dysmorphology . We also present a novel mouse ENU allele of Tuba1a with phenotypes similar to the null allele . This demonstrates the requirements for each of the tubulin genes and levels of functional redundancy are quite different throughout the gene family . The ability of the mouse to survive in the absence of some tubulin genes known to cause disease in humans suggests future intervention strategies for these devastating tubulinopathy diseases .
Tubulin proteins are fundamental building blocks of the cell and assemble into dynamic microtubules . Microtubules are especially crucial for cortical development where they are used in multiple cellular contexts such as the mitotic spindle , axons , dendrites , and cilia formation [1] . Mutations in tubulin genes are now known to cause multiple human cortical malformations including lissencephaly , polymicrogyria , microcephaly , dysmorphic basal ganglia , and congenital fibrosis of extraocular muscles [2] . These are collectively discussed as “tubulinopathies . ” Many variants leading to malformations of cortical development have now been identified in TUBULIN , ALPHA-1A ( TUBA1A ) [2–18]; TUBULIN , BETA-2A ( TUBB2A ) [19–22]; TUBB2B [4 , 5 , 15 , 23–29]; TUBB3 [2 , 15 , 30 , 31]; TUBB4A [32]; and TUBB/TUBB5 [33] . In fact , for TUBA1A and TUBB2B alone , there are now 71 identified variants in cortical malformation patients [7–29 , 34] . Mutations of TUBA1A and TUBB2B account for ~5% and ~1 . 2% of lissencephaly and related malformations of cortical development , respectively [34] . Both , but especially TUBA1A , are associated with a wide spectrum of phenotypic severity [34] . With the exception of one TUBA1A variant inherited from a mosaic parent [6] and one inherited TUBB2B variant [24] , all of these variants are heterozygous , de novo changes in the identified proband . TUBB3 variants have been found to segregate as dominant traits in large pedigrees [31] . Previous work has shown that both α- and β- tubulins can be separated into different classes ( isotypes ) and some general characteristics about their spatiotemporal expression domains have been established [35 , 36] . However , the completed genome sequences of many species , including mouse and human , have revealed the presence of more tubulin proteins than those indicated by the initial six classes . The requirements and roles in cortical development for some of the individual tubulin genes are still unknown . One possible mechanism of disease in the tubulinopathies is the production of altered tubulin monomers from the variant alleles which alter normal tubulin function ( s ) in any number of cellular contexts . Experiments overexpressing these pathogenic variants and/or loss of function models in mouse and cells have indeed shown defects in microtubule polymerization and heterodimer assembly , neurite extension , growth cone dynamics , neuronal migration , vesicular axon transport , and peripheral nerve regeneration , among other processes [7 , 11 , 15 , 24 , 26 , 30 , 31 , 37–40] . Most tubulin genes exhibit high sequence homology and many are clustered in the genome . Complete genome assemblies now highlight that TUBB2A and TUBB2B are immediately adjacent to each other in both the human and mouse genome and are virtually identical ( 443 identical amino acids of 445 total ) . Similarly , TUBA1A , TUBA1B , and TUBA1C genes are also adjacent in the genome and contain high sequence homology in both human and mouse . These characteristics suggest these genes may be the product of genome duplications and the functions of each individual gene may be shared across the cluster ( s ) . Despite the central importance of these genes in the cytoskeleton and their relevance for human cortical malformation , relatively few genetic models of tubulin variants exist . No null alleles are published for Tuba1a , Tuba1b , Tuba1c , Tubb2a , or Tubb2b . ENU mutagenesis efforts have identified alleles in Tuba1a [7 , 40 , 41] and Tubb2b [42] , and a mouse model of CFEOM for Tubb3 has been made [31] . More recently a null allele of Tubb3 has been produced and shown to have effects on growth cone function and axonal regeneration after induced sciatic nerve injury [37] . Here we test the requirements for Tuba1a , Tubb2a , and Tubb2b with specific genetic deletions .
We used CRISPR-Cas9 genome editing to create individual deletions of Tuba1a , Tubb2a , and Tubb2b . Guide RNAs were generated to target the endonuclease to two intergenic regions flanking each tubulin gene ( Fig 1A , 1D and 1G; S1 Table ) . Tubb2a and Tubb2b are only 49kb apart ( 0 . 02cM ) in the mouse genome , suggesting that meiotic recombination between the loci will be an extremely rare event . With this consideration in mind , we first attempted to multiplex CRISPR-Cas9 editing for Tubb2a and Tubb2b with the goal of creating a single deletion of each gene as well as a simultaneous deletion of both . A mix of all eight Tubb2a and Tubb2b guides were constructed and used for blastocyst microinjection . The resulting founders were analyzed by PCR and Sanger sequencing for alterations at the Tubb2a and/or Tubb2b loci . We recovered two alleles of Tubb2a and one of Tubb2b . Tubb2aem1Rstot ( hereafter referred to as Tubb2ad3963 ) is a 3 , 963bp deletion ( chr13: 34 , 074 , 222–34 , 078 , 184; GRCm38/mm10 ) and Tubb2aem2Rstot ( Tubb2ad4222 ) is a 4 , 222bp deletion ( chr13: 34 , 074 , 225–34 , 078 , 446 ) . Both independent deletions excise the entire Tubb2a open reading frame ( Fig 1A–1C , S1 and S2 Figs ) . We also obtained one complete deletion allele of Tubb2b . Tubb2bem1Rstot ( Tubb2bd4185 ) is a 4 , 185bp deletion ( chr13: 34 , 126 , 374–34 , 130 , 558; Fig 1D–1F , S2 Fig ) . In a parallel manner , four CRISPR guides were generated to delete Tuba1a ( Fig 1G ) and we recovered two independent deletions ( Fig 1G–1I; S1 and S2 Figs ) . Tuba1aem1Rstot ( Tuba1ad4304 ) is a 4 , 304 bp deletion ( chr15:98 , 949 , 728–98 , 954 , 031 ) and Tuba1aem2Rstot ( Tuba1ad4262 ) is a 4 , 262 bp deletion ( chr15: 98 , 949 , 770–98 , 954 , 031 ) . For all guides used in this study , we predicted the top ten off-target sites ( S2 Table ) . Only one is even on the same chromosome as the desired target sequence . This potential target is 24 cM away from Tuba1a . At this genetic distance , there is virtually no chance this genomic sequence is segregating with the Tuba1a deletion allele we generated . Mice heterozygous for all the deletion alleles were viable and fertile . As we hypothesized the deletion alleles would yield recessive phenotypes , we intercrossed heterozygous carriers for each . We found no reduction from Mendelian expectations in the number of live animals at weaning for any allele of Tubb2a or Tubb2b , and animals homozygous for these deletions did not appear grossly different than littermates ( Table 1 ) . Total body weight at postnatal day ( P ) 28 did not show any evidence of a failure to thrive upon loss of Tubb2a or Tubb2b ( Fig 2A–2C , S3 Table ) . We collected brains for both whole mount analysis and histological examination from P28-31 animals to determine if loss of these tubulin genes caused any discernable malformations of cortical development . Total brain weight at P0 did not differ in Tubb2a or Tubb2b mutants ( Fig 2D–2F , S3 Table ) . Whole mount imaging and initial histological analysis of the brains did not reveal any gross phenotypes ( Fig 2G–2DD , S3 and S4 Figs ) . This included more detailed analyses of the corpus callosum ( Fig 2O–2R ) , hippocampus ( Fig 2S–2V ) , basal ganglia ( Fig 2W–2Z ) and cerebellum ( Fig 2AA–2DD ) . We performed a more comprehensive histological and immunohistochemical analysis which did reveal a number of more subtle anomalies in animals lacking tubulin genes ( Figs 2–4 , S3–S5 Figs ) . Quantification of the number of cells in the motor and somatosensory cortices as identified through Nissl staining did reveal some subtle differences ( Fig 3 , S3 Fig , S3 Table ) . The Tubb2ad3963/d3963 , Tubb2ad4222/d4222 and Tubb2bd4185/d4185 homozygous mice all do appear to have slightly decreased cell numbers in both the motor ( Fig 3A–3G ) and somatosensory ( Fig 3H–3N ) cortical tissues , albeit with differing levels of statistical certainty . We complemented this histological analysis with a series of immunohistochemical studies to further examine the effects of homozygous loss of Tubb2a or Tubb2b . The overall laminar organization was not disrupted in any deletion allele but we did note some subtle disruptions . NeuN is a marker of differentiated neurons and we note subtle differences in the staining as the NeuN cells seem to be more dispersed in the mutant cortex ( Fig 4A–4E ) . CTIP2 marks layer V neurons and appears relatively unaltered in the deletion alleles ( Fig 4F–4J , S5A–S5C Fig ) . We quantified the distribution of CTIP2-postive cells and did see subtle increases in the proportion of cells in the middle third of the cortex ( Bin 2 ) at the expense of the dorsal third ( Bin 3 ) consistent with a very mild migration defect of the CTIP2-positive neurons . This appears to be much less pronounced in the Tubb2b deletion mutant ( S5 Fig , S4 Table ) . TBR1 is a marker of layer IV-VI neurons and , as with CTIP2 , we observed a similar but more marked reduction in the number of cells in the middle third of the cortex ( Bin 2 ) of the dorsomedial cortex . In the immediately adjacent parietal cortex , this migrational anomaly is again seen in the Tubb2ad3963/d3963 homozygotes but not other mutants ( Fig 4K–4T , S5D–S5I Fig ) . Future studies will be needed to determine how significant and/or widespread these subtle anatomical differences are between the Tubb2a alleles . Tubb2bd4185/d4185 mutants also show decreased TBR1-positive cells in the middle third of the dorsomedial cortex but not the parietal cortex ( Fig 4N , 4O , 4S and 4T , S5 Fig ) We also used Myelin Basic Protein ( MBP ) to highlight axon tracts and found reduced staining in the corpus callosum and pyramidal fibers of the Tubb2a homozygotes but not in Tubb2b homozygotes ( Fig 4U-II ) . We conclude from these data that neither Tubb2a , nor Tubb2b , are uniquely required for survival in the mouse but do result in subtle cortical malformations . However , neither loss of Tubb2a nor Tubb2b results in the severe cortical malformations seen in the most common tubulinopathy patients . Tubulin expression is well known to be auto-regulated in mammalian cells [43 , 44] . The production of the initial portion of the tubulin polypeptide chain serves as a signal to the cell to reduce the stability of remaining tubulin mRNA [45–51] . This mechanism has implications for the ability of a cell to compensate for deletion of a single tubulin locus . We therefore considered if the amount of tubulin protein in the P0 brain was altered upon deletion of Tubb2a or Tubb2b . We are unable to definitively demonstrate the loss of TUBB2A or TUBB2B protein in our mutants as the amino acid sequences are identical at all but two amino acids precluding any antibody-based analyses such as Western immunoblotting ( S6 and S7 Figs ) . RNA analysis of gene expression is similarly challenging due to sequence similarity ( S8 and S9 Figs ) . Some RT-PCR primers have been used previously , but our analysis suggests these might amplify other genomic elements such as poorly annotated pseudogenes ( S10 Fig ) . Expression of Tubb2b mRNA in mouse has been reported [26] , but a further analysis of the published in situ probe shows high sequence similarity to the Tubb2a sequence , suggesting that this reported expression may , perhaps , be a combination of Tubb2a and Tubb2b ( S11 Fig ) . However , we were able to address the question more broadly with an antibody that recognizes multiple β-tubulins ( Fig 5 , S12 Fig , S5 Table ) . Immunoblotting with this antibody did not indicate any biologically significant reductions in the deletion mutants . ( We did note an apparent increase in total β-tubulin levels in Tubb2ad4222/wt animals , which become magnified in comparison to a very small decrease in Tubb2ad4222/d4222 total β-tubulin when each is compared to wild-type , Fig 5B ) . We note that the high levels of tubulin in comparison to other specific protein in the cell makes precise quantification difficult with the low levels of total protein analyzed in these experiments ( see below discussion in Tuba1a mutants ) . This suggests that loss of TUBB2A or TUBB2B is compensated by other β-tubulins expressed in the developing brain . This may explain the relatively mild phenotypes we report here in these homozygous deletion mutants . In stark contrast to our findings with Tubb2a and Tubb2b , Tuba1a is absolutely required for survival to weaning as we did not recover any homozygotes for either Tuba1a deletion allele ( n = 177 total; Table 2 ) . We do note a small reduction in heterozygote survival among the surviving animals . To begin to understand the reason for the lethality and to assess brain development in the homozygotes , we collected embryos at late organogenesis stages . A combined analysis of both Tuba1a deletion alleles in embryos from embryonic day ( E ) 14 . 5-E18 . 5 did not reveal a significant loss of homozygous embryos during embryonic stages ( Table 2 ) . Tuba1ad4304/d4304 and Tuba1ad4262/d4262 animals had very obvious and consistent phenotypes . We noticed thoracic curvature and thoracic edema with significant hemorrhaging in the cervical region extending towards the forelimbs ( Fig 6A–6C ) . Histological analysis of the developing forebrain revealed a striking series of forebrain malformation phenotypes . Sections from both posterior and anterior regions of the forebrain highlight enlarged lateral and third ventricles , reduced basal ganglia , as well as a widened base of the third ventricle in posterior brain regions ( Fig 6D–6O ) . Examination of cortical morphology showed a 115–179% increase in the width of the ventricular zone at E16 . 5 , a 50% reduction in the intermediate zone , and an approximate 30% reduction in width of the cortical plate as compared to wild-type ( Fig 6P–6U; S6 Table ) . We also noted a number of the homozygous mutants had cleft palate ( Fig 6W ) . This was incompletely penetrant and seen in 3/11 Tuba1ad4304/d4304 embryos ( 27% ) and 2/16 Tuba1ad4262/d4262 ( 12 . 5% ) embryos . We noted cleft palate in only one out of 52 heterozygous embryos analyzed , but we suspect this was likely due to delayed development in that particular embryo based on morphological staging . We conclude that Tuba1a is required for survival and that loss of Tuba1a leads to major cortical malformations with similarities to what is seen in the human tubulinopathy patients . We have performed an initial molecular analysis of neurogenesis in the Tuba1ad4304/d4304 embryos at E14 . 5 and E16 . 5 as compared to unaffected wild-type or heterozygous littermates ( Fig 7 , S7 Table ) . We first measured proliferation rates with immunohistochemistry for phosphorylated histone H3 ( pHH3 ) to mark actively dividing cells in M-phase and found no significant difference between wild-type and controls at E14 . 5 ( Fig 7A–7C ) but a slight 15 . 7% increase at E16 . 5 ( Fig 7D–7F ) . We further examined neurogenesis with an EdU-Ki67 pulse chase assay in which we treated pregnant dams with EdU at E13 . 5 to label dividing cells , sacrificed at E14 . 5 and used an antibody against Ki67 to mark cells in G1 , S , and G2 phases of the cell cycle at the time of sacrifice ( Fig 7G–7N ) . This analysis indicated a 15 . 6% decrease in total EdU-positive , Ki67-negative cells in the mutant cortex at E14 . 5 ( Fig 7K ) suggesting that fewer overall cells were produced in the 24-hour period between EdU administration and sacrifice . Cells which are Ki67-positive and EdU-negative are actively dividing at E14 . 5 and were increased 56 . 7% ( Fig 7L ) . Note that Ki67 marks a broader sample of the cell cycle than pHH3 which may explain why this is increased while the number of pHH3-positive cells did not appear different . Cells positive for both EdU and Ki67 were progenitors dividing at E13 . 5 which re-entered cell cycle and these are increased in mutants by 84 . 3% ( Fig 7M ) . The quit fraction is the proportion of cells leaving the cell cycle over total labeled cells ( EdU+Ki67-/EdU+ ) and is 2% reduced in mutants ( Fig 7N ) . We conclude from these data that the enlarged ventricular zone we see in the homozygous Tuba1a deletion mutants is due to disrupted neuroprogenitor cell division kinetics in which some cells at E14 . 5 are preferentially re-entering cell cycle rather than terminally dividing . EdU treatment also allowed us to assess the fate of cells 24 and 72 hours after division . We divided the cortex in three equal parts and quantified the percentage of EdU cells in each third of the cortex along the dorsal-ventral axis to query their developmental trajectory as they migrate into the cortical plate . We did not see a significant change in these parameters at E14 . 5 ( Fig 7O ) . However , when we administered EdU at E13 . 5 and sacrificed at E16 . 5 , we noted dramatic increases in cells in the middle third of the cortex at the expense of the outer third , consistent with a failure of neurons born at E13 . 5 to fully survive , differentiate and/or migrate towards the pial surface ( Fig 7P–7R ) . We also measured the levels of TBR2-positive intermediate progenitors and observed no changes at E14 . 5 ( Fig 7S–7U ) but a slight increase at E16 . 5 ( Fig 7V–7X; 13 . 5% increase ) . TuJI immunoreactivity of maturing neurons indicated a slight reduction in levels of differentiation and a broader dispersal of TuJI-positive cells throughout the cortical tissue in homozygous mutants at both E14 . 5 and E16 . 5 ( Fig 7Y-BB ) . We also examined levels of apoptotic cell death with immunohistochemistry for cleaved caspase 3 and found increases at both E14 . 5 and E16 . 5 ( elevated 75 . 9% and 866% respectively , Fig 7CC-HH ) . Taken together , all of these data lead us to conclude changes in neurogenesis , migration and cell death contribute to the cortical phenotypes . The ultimate cause of the lethality of these cells , and the mouse mutant embryos as a whole , remains to be determined . In a separate experiment utilizing an ENU mutagenesis forward genetic screen to ascertain new alleles important for cortical development , we identified an additional mutant with similar brain phenotypes to those seen in the Tuba1a deletion mice . The quasimodo mutants were first identified by an abnormal curvature of the thoracic region seen in many of these mutants ( Fig 8B ) . Histological analysis of mutants showed a significant brain malformation with lateral ventriculomegaly , reduced cortical tissue , and enlarged third ventricle ( Fig 8C–8F ) . Whole exome sequencing of three homozygous mutants identified a large number of homozygous variants as predicted . Filtering for SNPs which were shared by all three mutants , not present in dbSNP , predicted to have a high/moderate effect on the protein ( e . g . , missense variants at conserved residues , premature stop codons , etc . ) , and having only one variant in the gene ( i . e . , not a highly polymorphic sequence ) left a total of ten variants . A review of literature to identify known consequences for loss of function in these genes or known roles of these genes did not yield any compelling candidates ( Table 3 ) . We then performed a parallel analysis for all heterozygous missense variants shared by the three sequenced mutants with the alternative hypothesis that the quasimodo phenotype may be an incompletely penetrant heterozygous variant . One variant from this analysis was a nonsense mutation in Tuba1a creating a premature stop codon at amino acid 215 ( p . R215* ) . We noted that the sequence in this region of Tuba1a , Tuba1b and Tuba1c is highly similar and carefully analyzed the exome . bam files . Virtually all of the reads for this region of genomic sequence were aligned to the Tuba1a gene and virtually no reads were mapped to Tuba1b or Tuba1c . Given the phenotypic similarity to the Tuba1a deletion , we hypothesized the similarity among these genes may have confounded the automated sequence alignment and pursued Sanger sequencing with intronic primers that would specifically amplify Tuba1a . This analysis showed that the quasimodo mutants were all in fact homozygous for the Tuba1a variant creating the nonsense mutation ( Fig 8G–8I ) . This mutation is predicted to produce a protein missing approximately half of the amino acid sequence , if that protein is even stable in the cellular context ( Fig 8J ) . The mutant protein would be missing significant portions of the polypeptide which would normally interface with the adjacent β-tubulin monomer . In order to confirm that the Tuba1a R215* variant is actually the causative variant in the quasimodo mutants , we performed a complementation test with the Tuba1ad4304 deletion allele . We crossed Tuba1aquas/wt with Tuba1ad4304/wt animals and in three litters ( 22 live pups ) , we recovered no Tuba1ad4304/quas live animals at weaning ( Table 4 ) . We further explored this hypothesis with embryonic dissections and recovered two Tuba1ad4304/quas embryos at E17 . 5 . These mutants exhibited the exaggerated curvature of the thoracic region , as previously seen in Tuba1aquas/quas mice and Tuba1ad4304/d4304 mice ( Figs 6B and 8L ) . Histological analysis showed ventriculomegaly with disrupted cortical architecture including a loss of the intermediate zone , hypoplastic basal ganglia , and cleft palate in both mutants ( Fig 8M–8P ) . The similarity of these phenotypes to the Tuba1ad4304/d4304 and Tuba1aquas/quas mutants suggests that the Tuba1a R215* variant found in quasimodo mutant is indeed the causal variant . Moreover , this would support the above conclusion that loss of Tuba1a function results in significant cortical malformations . We performed a molecular analysis of neurogenesis in the Tuba1aquas mutants as we did for the Tuba1a deletion mutant ( Fig 9 , S7 Table ) . Surprisingly , although the terminal phenotypes are similar , the data suggest different underlying mechanisms . Proliferation in Tuba1aquas mutants was increased by 18 . 9% as measured by pHH3 immunoreactivity at E14 . 5 ( Fig 9A–9C ) but decreased 7 . 2% at E16 . 5 ( Fig 9D–9F ) . In contrast to the Tuba1a deletions however , the EdU-Ki67 pulse chase experiments did not reveal any robust differences in patterns of neurogenesis as we saw in the Tuba1a deletion mutant ( Fig 9G–9N ) . The EdU labeling to analyze cell migration highlighted a significant reduction in cells labeled at E13 . 5 which migrated to the upper third of the cortex at E14 . 5 and a corresponding increase in number of cells in the middle third ( Fig 9O ) . The same patterns are seen at E16 . 5 with more marked changes in cellular distributions ( Fig 9P–9R ) . This data is most consistent with a decreased radial migration by cells born on E13 . 5 , similar to what we observed in the Tuba1a deletion mutants . We also saw no marked changes in the numbers of TBR2-positive intermediate progenitors in the Tuba1aquas mutants at E14 . 5 ( Fig 9S–9U ) but a slight 21 . 5% increase at E16 . 5 ( Fig 9V–9X ) . TuJI immunoreactivity again showed an increased range of differentiated cells similar to the Tuba1a deletion mutants at both E14 . 5 and E16 . 5 ( Fig 9Y-BB ) . Similar to the analysis of the Tuba1a deletion mutants , Tuba1aquas mutants show elevated levels of apoptotic cell death which is readily apparent at E14 . 5 and E16 . 5 ( elevated 185% and 1721% respectively , Fig 9CC-HH ) . We again conclude that the Tuba1aquas phenotype is the result of disrupted neurogenesis , disrupted migration and a marked increase in cell death . Similar to the analysis we performed on the levels of β-tubulin protein in Tubb2a and Tubb2b mutants ( Fig 5 ) , we also analyzed total α-tubulin levels in the Tuba1a mutants described here . We used an antibody that shares similarity with multiple α-tubulin proteins at the N-terminal portion of the reported epitope ( Fig S13 ) . We find that total α-tubulin levels do not appear to be reduced in Tuba1ad4304/d4304 homozygous mutants when normalized to GAPDH levels within each litter matched set ( Fig 10A ) . Even after five independent experiments , we could not convincingly demonstrate loss of α-tubulin even though matched samples ( e . g . , asterisks in Fig 10A are a set of littermates ) often indicated a subtle decrease . We reasoned the very low level of protein input to our experiments may have challenged the limits of accurate and robust detection of the imaging system in embryonic brain lysate . We note GAPDH levels are extremely low at this stage of development . We ran a parallel analysis for total protein , rather than a single protein , as a loading control . REVERT total protein stain is not based on any particular protein epitope . When we analyzed REVERT staining of the transfer membrane we saw a striking decrease in total protein around the molecular weight of tubulin in mutant samples ( Fig 10B ) . We reason this species is likely mostly tubulin protein . When we normalize the levels of total α-tubulin to total protein as measured by REVERT total protein stain , we do see a significant reduction in total α-tubulin in mutants as compared to both wild-type and Tuba1ad4304 heterozygous littermates ( Fig 10B , S8 Table ) . We performed a similar analysis of total α-tubulin in Tuba1aquas mutants and regardless of which tubulin normalization method used , we see a dramatic loss of more than half the α-tubulin in mutant tissue ( Fig 10C and 10D ) . The dramatic reduction in overall tubulin on the membranes stained for total protein ( e . g . , Fig 10B ) led us to hypothesize that decreased levels of α-tubulin might have an effect on β-protein as well . This may be through an unknown mechanism to keep massive amounts of unpolymerized β-tubulin monomers from flooding the cell . To our surprise , we do indeed see a decrease in total β-tubulin upon loss of Tuba1a ( Fig 10E and 10F ) and in homozygous Tuba1aquas mutants ( Fig 10G and 10H ) . This regulation of tubulin monomers is a very intriguing question to be addressed further in future research . This will likely require work in vitro with tagged versions of the tubulins of interest .
We have for the first time demonstrated the consequences of the loss of three tubulin genes known to be important for normal mammalian cortical development: Tuba1a , Tubb2a , and Tubb2b . We also identified a new Tuba1a mouse allele with severe CNS phenotypes through an ENU mutagenesis experiment . These alleles contribute to a larger body of work indicating the importance of tubulin genes in cortical development and disease . These deletion alleles allow us to further explore the model that the human variants identified in TUBA1A , TUBB2A and TUBB2B are acting as dominant negative heterozygous mutations ( e . g . [52] ) , but may not be truly essential for development . We find that Tubb2a and Tubb2b can be deleted from the mouse genome with no obvious effect on gross cortical development , animal survival , or overall health . Tuba1a , however , is quite different , as loss of Tuba1a is lethal in the mouse and leads to severe malformations of cortical development . Thus , other tubulin genes with high sequence similarity cannot compensate for Tuba1a but may be doing so for Tubb2a and Tubb2b ( e . g . , Tubb3 ) . Together , these data show that some tubulin genes are absolutely critical for mouse CNS development and organism survival , while others are less critical . Tubb2a and Tubb2b are highly similar and immediately adjacent in the genomes of both mice and humans , suggesting that a recent duplication event in a common ancestor may have rendered these genes redundant for each other . This may explain why a single deletion of each does not lead to major CNS phenotypes . However , we do note more subtle cortical phenotypes including changes in cell number in the motor and somatosensory cortices and in disrupted cortical lamination consistent with more mild radial migration defects . We also note a striking reduction in MBP immuno-staining in the Tubb2a deletion homozygous mice . We suspect this is a result of reduced axon tracts rather than defective myelination . This phenotype and the possible behavioral consequences of the cortical disruptions should be interrogated rigorously in future studies . We acknowledge that mouse models of human cortical malformations may not be perfect due to species differences . However , we do not believe any such differences can explain the normal presentation of these mice . Indeed , we have previously shown structural and behavioral phenotypes in a mouse Tubb2b missense allele [42 , 53] . While the Tubb2a and Tubb2b deletion mutants appear to have relatively subtle phenotypes , they each may be rescued from more global malformations through functional redundancy . This model can be tested with a double mutant analysis . The genes are sufficiently close to each other within the genome such that this experiment will require creation of an independent deletion or conditional allele in cis to one of the existing mutations . Given these findings that homozygous deletions of Tubb2a or Tubb2b do not result in catastrophic malformations of cortical development , it is perhaps surprising that there are only two homozygous coding missense variants and no homozygous loss of function variants in both TUBB2A and TUBB2B combined in the gnomAD collection of over 140 , 000 healthy individuals as of this writing [54] . Given the phenotypes we now report , it may be that the homozygous missense mutations lead to neurological disease which has not yet been attributed to tubulin genes , but would prevent inclusion of the affected individuals in the gnomAD and ExAC databases . The sequence similarities may also confound mapping of genomic information . The mice heterozygous for the Tubb2a or Tubb2b deletions do not show any biologically significant loss of total beta-protein ( Fig 5 , S5 Table ) . We hypothesize other tubulins are produced at higher levels to compensate for the loss of each single deletion ( e . g . , Tubb3 ) . These findings are consistent with the known data on tubulin autoregulation ( see above ) . The reduced mRNA levels from the deletions should lead to reduced polypeptide production and , thereby , less de-stabilization of the tubulin mRNA , ultimately keeping monomer levels stable . The TUBA1A polypeptide sequence is highly similar to TUBA1B ( 99 . 6% identical ) and TUBA1C ( 98 . 2% ) and three genes coding for these similar proteins are also clustered in the genome . These features suggest they may also be able to compensate for each other similar to our hypothesis regarding Tubb2a and Tubb2b . In direct contrast to our findings with Tubb2a and Tubb2b , however , loss of Tuba1a is catastrophic for the mouse embryo . The individual requirements for Tuba1b and Tuba1c remain unexplored and we will address this hypothesis with future deletion alleles . Loss of Tuba1a leads to an increased ventricular zone at E16 . 5 and our initial data from E14 . 5 and E16 . 5 suggests this is from a dysregulation of neural progenitor proliferation and cell cycle exit . Ultimately , failures in radial migration and/or apoptosis contribute to the phenotypes we observed . We also report an independently derived ENU-induced mutation in Tuba1a which encodes a transcript with a premature stop codon approximately halfway through the protein coding sequence . These homozygous Tuba1aquas mutants have similar cortical phenotypes to the deletion mutants , but apparently develop through different mechanisms as the cell cycle kinetics do not appear to be disrupted ( Figs 7 vs . 9 ) . However , we do also see evidence for abnormal radial migration and elevated cell death in these Tuba1aquas mutants . The significant difference in molecular phenotypes is likely to be the effect of a deletion of the gene as compared to production of a truncated polypeptide that may perturb microtubule dynamics . The analysis of total α-tubulin protein levels in these mutants raises some other interesting points . Deletion of Tuba1a leads to decreased overall α-tubulin levels but not as dramatically as the reduction in the Tuba1aquas mutants . This is consistent with the autoregulation of tubulin and suggests that other isoforms of Tuba1a are upregulated in the deletion mutant . However , given the significant phenotypes in the Tuba1a mutant , these other isoforms are likely unable to carry out some currently unknown critical function of the TUBA1A protein . Note that the ENU variant protein in the Tuba1aquas allele is likely to create a protein lacking the amino acids needed for the anti-α-tubulin antibody we used . However , if the first several amino acids are translated from this locus as a missense mutation rather than completely missing in a deletion , this is consistent with the model of tubulin autoregulation where this initial translation leads to destabilizing existing tubulin mRNA transcripts . Given the extreme phenotype of the Tuba1aquas homozygous embryos , we hypothesize the truncated TUBA1A protein produced in these mutants is not sufficient for normal embryonic development and in fact may interfere with multiple processes of tubulin physiology . It is not known why the paradigm of genetic compensation is so different for these two gene clusters ( Tuba1a/b/c as compared to Tubb2a/b ) . Why does loss of one result in severe malformations of cortical development while loss of others does not compromise survival or gross brain development ? Do cells express a mix of α and β tubulin monomers ? Are there changes in specific gene expression over time , perhaps between neurogenesis stages and terminal differentiation ? These questions remain largely unanswered although a Tubb2b-eGFP transgenic line suggests Tubb2b is expressed in both progenitors and postmitotic neurons [55] . A detailed expression analysis would be useful in generating models to explain these genetic findings . Unfortunately , this is currently quite challenging . No antibodies will distinguish between these proteins in an immunohistochemical analysis ( S6 and S7 Figs ) . RNA expression studies are complicated by this homology as well ( S8 and S9 Figs ) . RNA in situ hybridization probes against the untranslated regions may be able to distinguish between genes but some hypotheses about co-expression at the cellular level would be challenging with in situ hybridization-based approaches . RNA-Seq , both bulk sequencing and single cell sequencing , would potentially be useful in addressing these models at least in a preliminary way . However , our experience with the quasimodo mouse mutant exome sequencing suggests the current library preparations and/or sequence alignment algorithms are confounded by the sequence similarities between these genes , and these data sets should to be interpreted with extreme caution . We propose that a series of epitope-fusion protein alleles in the mouse would be a useful tool set to explore both expression of discrete tubulin genes and will serve as a platform for biochemical approaches to identify microtubule associated protein and other cellular interaction partners . While the phenotypes we present here are striking malformations of cortical development , we do not yet completely know the cellular and molecular basis for these phenotypes . The histological changes in the ventricular zone , intermediate zone and cortical plate all suggest fundamental perturbations of neurogenesis and/or cellular migration . Detailed mechanistic studies are needed to determine how these processes are altered in the face of tubulin mutations . The human missense mutations are not extensively modeled in mice , with the exception of the Tubb3R262C model of CFEOM [31] . A mouse model of some of the most common mutations in Tuba1a would be helpful in both understanding the underlying mechanism ( s ) , but also as a platform for testing potential therapeutic intervention . Human genetics studies have identified a series of tubulin genes as crucial building blocks of the brain and tubulins have long been appreciated as important components for neuronal cell biology . As our genomic tools and mouse modeling capabilities have advanced , we now have the exciting opportunity to return to some fundamental questions about the requirement ( s ) and role ( s ) of tubulin genes in mammalian development and disease .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Animals were housed at or below IACUC determined densities with AALAC-approved veterinary care and fed Autoclavable Mouse Breeder Diet 5021 ( LabDiet , St . Louis , MO ) . The protocol was approved by the Institutional Animal Care and Use Committee of the Cincinnati Children’s Hospital Medical Center ( protocol number IACUC2016-0068 ) . All euthanasia ( cervical dislocation followed by thoracotomy ) and embryo harvests were performed after isoflurane sedation to minimize animal suffering and discomfort . CRISPR guides flanking the tubulin genes of interest were evaluated using the Fusi ( Benchling . com ) and Moreno-Mateos ( Crisprscan . org ) algorithms . Potential guide RNA ( gRNA ) sequences were selected and ordered as complementary oligonucleotide pairs with BbsI over-hangs ( S1 Table; IDT , Coralville , IA ) . These were ligated into the pSpCas9 ( BB ) -2A-GFP ( px458 ) vector and transfected into MK4 cells at low confluence using the Lipofectamine 2000 transfection reagent ( Thermo Fisher Scientific , Massachusetts ) . Cells were harvested 48 h after transfection and genomic DNA was isolated and used with the Surveyor mutation detection kit ( IDT ) in order to test gRNA cutting efficiency . As a control , cutting efficiencies of potential guides were compared with that of a previously-published mTet2 gRNA . Cas9 and gRNAs were injected into C57BL/6N zygotes ( Taconic ) by the CCHMC Transgenic Core . Potential founders were validated with Sanger sequencing of tail DNA and subsequently maintained on a C57BL/6J ( Jackson Labs ) background . pSpCas9 ( BB ) -2 A-GFP ( PX458 ) was a gift from Feng Zhang ( Addgene plasmid #48138 ) . For embryo collections , noon of the day of vaginal plug detection was designated as E0 . 5 . Embryos and brains were removed and imaged using a Zeiss Discovery . V8 stereo-microscope . ENU mutagenesis was performed as previously described [56 , 57] on C57BL/6J males . For adult histology , littermate animals underwent transcardial perfusion using cold heparinized phosphate buffered saline ( PBS ) and formalin ( SIGMA ) solution . Brains were dissected and fixed for 72 h in formalin at room temperature followed by immersion in 70% ethanol ( for histology ) . Embryo samples were fixed in Bouin’s fixative solution , formalin or 4% paraformaldehyde ( PFA ) . Samples were then paraffin embedded or sucrose-dehydrated and cryo-embedded , sectioned at 6μm for adult tissue and 10μm for embryonic tissue , and processed through hematoxylin and eosin ( H&E ) or Nissl staining . Sections were sealed using Cytoseal Mounting Medium ( Thermo-Scientific ) . All adult histological samples shown are representative examples from at least 3 animals of each genotype with at least 2 slides analyzed per animal . Dot plots are used to precisely show how many measurements are made . Immunohistochemistry was done accorded to standard protocols . In brief , E14 . 5 and E16 . 5 embryos and P0 brains were fixed overnight in 4% paraformaldehyde , dehydrated in 30% sucrose , cryo-embedded and sectioned at a thickness of 10 μm . P0 sections were collected at a thickness of 12 μm . Images were collected from a sample set of at least 2 slides of at least 3–5 sections for each animal and 3 control and 3 mutant embryos for each genotype at E14 . 5 and E16 . 5 . P0 immunohistochemistry done on at least two animals per genotype , with 4 slides from each animal with 3–5 sections per slide . Sections for pHH3 , TuJ1 , NeuN , CTIP2 , MBP , TBR1 were placed in boiling citrate buffer ( sodium citrate 1M , citric acid 1M pH 6 . 0 ) and allowed to cool on bench top for 40 minutes . Sections for TBR2 were boiled for 4 minutes in antigen unmasking solution ( Vector Labs , Burlingame , CA ) . Slides were blocked in 4% Normal Goat Serum in PBST for 30 minutes and then incubated overnight at 4 oC in primary antibody . Secondary antibodies were added for 1 hour at room temperature . Slides were then washed in 1X PBS and DAPI was added for 15 minutes , after which slides were sealed with ProLong Gold ( Invitrogen ) and imaged on a Nikon Eclipse Ti or Nikon C2 confocal microscope . Primary antibodies used were pHH3 ( 1:500 , SIGMA ) , TuJ1 ( 1:500 , SIGMA ) , TBR2 ( 1:200 , Abcam ab23345 ) , TBR1 ( 1:500 , Abcam ab31940 ) , CTIP2 ( 1:1000 , Abcam ab28448 ) , and NeuN ( 1:1000 , Millipore clone A60 ) . Secondary antibodies were goat anti-rabbit ( 1:500 Invitrogen Alexa Fluor 488 ) for pHH3 , TBR2 , TBR1 , and CTIP2 , and goat anti-mouse ( 1:500 Invitrogen Alex Fluor 488 ) for TuJ1 and NeuN . MBP immunohistochemistry staining was done with 3 , 3′-Diaminobenzidine ( DAB ) labeling . 5μm paraffin sections were de-paraffinized , rehydrated in graded ethanol , and incubated with chicken anti-MBP ( 1:500 , Aves MBP ) for 2 hours at room temperature , rinsed , and incubated in biotinylated goat-anti chicken secondary antibody ( 1:500; Vector Laboratories , Burlingame , CA , USA ) for 2 hours at room temperature . Sections were then washed in PBS and incubated in avidin-biotin-complex ( ABC ) solution ( Vector Laboratories ) for 1 hour at room temperature . Sections were then washed and visualized using DAB for 5 min followed by a PBS wash , ethanol dehydration , xylene clearance , and mounted with Cytoseal ( Thermo ) . Images were obtained with a Zeiss Discovery . V8 Stereoscope . From each genotype , 2 animals were included . From each of these animals , 2 slides each with 3–6 sections were stained . Sample sizes for quantifications are indicated in each relevant data plot . For P0 and adult histological analysis , areas of higher magnification analysis are shown ( S3 Fig ) . Established anatomical landmarks were used to define cortical areas . Quantification of pHH3-positive cells was done on Imaris Image Analysis software ( Bitplane , South Windsor , CT ) by manually drawing a surface around the Ventricular Zone and calculating the number of positive cells . Quantification of TBR2 and CC3 was done using Nikon Elements software by manually drawing a Region of Interest ( ROI ) around the entire cortex and calculating immuno-positive cells with a bright spot detection function . “Binning” data for CTIP2 and TBR1-positive cells were acquired with Nikon Elements software after drawing an ROI that extended from the VZ to the pial surface . The length of this ROI is divided by three . The three identical squares were drawn to represent the three bins . Bright spot detection tool was employed to determine the amount of immune-positive cells in each bin . GraphPad Prism was used to plot the data and perform statistical analysis . EdU ( Invitrogen ) was injected at a concentration of 20mg/kg interperitoneally into E13 . 5 pregnant females . Embryos were harvested 24 or 72 hours later and stained as above . Ki67 primary antibody ( 1:200 Abcam ab15580 ) was added overnight at 4°C . A goat anti-rabbit secondary antibody ( 1:500 Invitrogen Alexa-Fluor 594 ) was added for 1 hour at room temperature . Slides were washed twice with 1X PBS and once with 3% BSA in PBS . EdU was labeled using the Click-iT EdU Alexa Fluor 488 Imaging Kit ( Invitrogen ) . Quantification was done using Imaris software package . A surface was manually created that contained the cortex from the VZ to the CP and the enclosed area was quantified . EdU positive cells and Ki67 positive cells were then identified using the Imaris “Spots” function . Cells both EdU and Ki67 positive were quantified using the “Colocalize Spots” function . Each positive cell count was normalized to the area of the section . Binning data for EdU positive cells was performed with Nikon Elements . Three identical rectangular Region of Interest bins were made for each section by dividing the width of the cortex into three equal parts . The number of cells in each bin was calculated using bright spot detection . The percentage of cells in each bin was used to determine any differences . Whole cell lysate from P0/P1 brains were extracted with RIPA buffer ( Thermo Scientific ) with a protease and phosphatase inhibitor cocktail ( Calbiochem ) added immediately before cell lysis . A standard fluorescent western blotting protocol was followed . 5 μg of protein lysates were electrophoresed in 4–12% gradient Tris glycine PAGE gel ( Thermo Fisher Scientific ) and 1X Tris -Glycine SDS running buffer ( Bio-Rad ) and then transferred into PVDF membrane using Tris-Glycine transfer buffer with 20% methanol . Proteins were blocked in Odyssey blocking buffer ( Licor Bioscience ) . Antibodies used were mouse anti TUBB3 ( Tuj1 , 1:400 , SIGMA T8660 ) , mouse anti-pan-β-tubulin ( 1:1000 , Millipore 05–661 ) , anti-pan-α-tubulin ( 1:500 , Sigma T6199 ) and rabbit anti-actin ( 1:3000 , SIGMA A2066 ) and were incubated with membrane overnight . Species specific secondary antibodies were applied to the membrane after stringent PBS washes of the primary antibodies . Odyssey CLx Imaging System were used to detect the bands of expression and Image Studio Lite software was used to analyze the band intensity . In each western experiment , GAPDH served as loading control and intensity of tubulin was normalized against GAPDH band intensity . For lysate from embryonic brains , we used a second loading/ normalization method by normalizing against total protein stain ( REVERT from Licor Bioscience using manufacturer protocols ) . Analyses and data plots were generated with Prism8 ( GraphPad ) and plots show the mean +/- the 95% confidence interval . An ANOVA was performed for experiments with more than two comparisons . If an ANOVA p-value was ≤ 0 . 05 , a Tukey’s multiple comparison test was performed to analyze which group ( s ) within the experiment were different from each other . Experiments with two sets of data were analyzed with a student’s t-test . The p-values for these experiments are shown in the relevant figure and/or supplemental data set . We report the statistical test values directly to facilitate readership and largely refrain from assigning labels of “significance” on our own . All data presented here are available to the community in accordance with best practices for data sharing . | Tubulin proteins are assembled into microtubules to provide essential cellular cytoskeletal elements . Microtubules are especially crucial for neuronal development . Multiple studies demonstrate that human malformations of cortical development are caused by genetic variants in several tubulin genes . Interestingly , most of these developmental phenotypes are the result of de novo , heterozygous variants likely to act as dominant negatives interfering with normal tubulin function ( s ) . The roles of individual tubulin genes and the mechanism ( s ) leading to these malformations remain unclear . Genome sequencing efforts revealed high sequence similarity between many tubulin genes , raising the possibility of functional compensation . The requirements for many of these tubulin genes have not been previously addressed in loss of function experiments . We have generated novel deletions of several tubulin genes known to cause human disease to assess if they are required for brain development , or if the human variants act to alter function of the tubulin proteins leading to the pathogenesis . Surprisingly , our results show that some individual tubulin genes are absolutely required for survival while others are not and have much more benign cortical malformations . |
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FGF signaling is a potent inducer of lacrimal gland development in the eye , capable of transforming the corneal epithelium into glandular tissues . Here , we show that genetic ablation of the Pea3 family of transcription factors not only disrupted the ductal elongation and branching of the lacrimal gland , but also biased the lacrimal gland epithelium toward an epidermal cell fate . Analysis of high-throughput gene expression and chromatin immunoprecipitation data revealed that the Pea3 genes directly control both the positive and negative feedback loops of FGF signaling . Importantly , Pea3 genes are also required to suppress aberrant Notch signaling which , if gone unchecked , can compromise lacrimal gland development by preventing the expression of both Sox and Six family genes . These results demonstrate that Pea3 genes are key FGF early response transcriptional factors , programing the genetic landscape for cell fate determination .
During development of a complex multicellular organism , organ identity is determined by the combination of lineage-specific and signal-induced transcription factors . In mammalian lacrimal gland development , the extracellular signals include Fibroblast Growth Factor ( FGF ) , Bone Morphogenetic Protein ( BMP ) , Notch and Wnt that either cooperate or antagonize each other during budding , elongation and branching morphogenesis [1] . In particular , genetic evidence has revealed that FGF signaling initiated by the binding of FGF10/Fgf10 , sent from the periocular mesenchyme , to FGFR2B/Fgfr2b on the conjunctival epithelium is indispensable for lacrimal gland development in both human and mouse [2–5] . Demonstrating the striking potency of FGF signaling in driving the lacrimal gland fate , ectopic expression of either rat Fgf10 or human FGF7 in the lens led to the formation of lacrimal gland-like cells in an area that under normal physiological conditions develops into the planar corneal epithelium [6 , 7] . This is at least partly mediated by both the FGF-induced Sox9 expression required for lacrimal gland induction and the Sox10 expression for acini formation [8] . However , unlike BMP , Notch and Wnt which have well established downstream transcription effectors Smad , NICD and β-catenin , respectively , how FGF signaling triggers its transcriptional responses in lacrimal gland cell fate determination is not known . The Pea3 family of transcription factors , composed of Pea3 ( Etv4 ) , Erm ( Etv5 ) and Er81 ( Etv1 ) , are E26 transformation-specific ( ETS ) -domain proteins that can be phosphorylated by Mitogen-Activated Protein Kinase ( MAPK ) to control their subcellular localization , DNA binding and transactivation [9] . They have been shown to act as oncogenes in melanoma , breast , lung and prostate cancer , mimicking the aberrant activation of RAS-MAPK pathways commonly present in a multitude of malignancies [10] . During embryonic development , expression of the Pea3 genes closely correlates with the activities of FGFs , making these genes suitable candidates for being the downstream effectors of FGF-Ras-MAPK signaling [11 , 12] . Indeed , conditional inactivation of Pea3/Erm in the lung epithelium disrupted the Fgf10-Shh feedback loop , resulting in smaller lung sizes , but mice were grossly healthy and exhibited normal life-span [13 , 14] . In the limb buds , Pea3 and Erm mediate FGF signaling in the proximal-distal ( P-D ) and anterior-posterior ( A-P ) patterning , which was evident by the growth retardation and mild polydactyly in the Pea3/Erm mutants [15 , 16] . Nevertheless , these Pea3/Erm mutant phenotypes were relatively modest compared to the FGF signaling mutants in the same tissues . In this study , we show that the MAPK-regulated Pea3 family of transcription factors are critical for lacrimal gland duct elongation and branching . Deletion of all three Pea3 genes from the lacrimal gland epithelium resulted in ectopic expression of epidermal markers , shifting the lacrimal gland progenitor cells toward a cutaneous cell fate . In addition to previously reported FGF signaling response genes , we also identify Six1 and Six2 as being novel targets of the FGF-Pea3 axis , showing that these two genes cooperate in regulating lacrimal gland branching . Loss of Pea3 results in aberrant upregulation of Notch signaling in the lacrimal gland primordia driven by Jag1-mediated lateral activation and concurrent downregulation of the Notch modulator , lunatic fringe . Aberrant Notch signaling sustains this auto-stimulatory loop by upregulating Jag1 expression , leading to the downregulation of FGF signaling effector genes and failure of lacrimal gland induction . The shift of cellular identity and discordance of FGF-Notch crosstalk in the absence of Pea3 transcription factors establishes Pea3 genes as cell fate determinants in lacrimal gland development .
Mouse lacrimal gland development commences at E13 . 5 with the thickening of the conjunctival epithelium , which subsequently forms a bud , entering the surrounding periocular mesenchyme by E14 . 5 . This process is triggered by the mesenchymal release of Fgf10 which activates FGF signaling in the epithelium . This signaling leads to the activation of the Pea3 family of ETS transcription factors , Pea3/Etv4 , Erm/Etv5 , Er81/Etv1 ( Fig 1A–1C , dotted lines ) [5 , 17 , 18] . We conditionally deleted Mek and Erk using an Le-Cre transgenic mouse line , in which Cre-recombinase linked to an IRES-GFP reporter was expressed in the conjunctival epithelium and the lacrimal gland [19] . In both Le-cre; Mek1fl/fl; Mek2-/- ( Mek KO ) and Le-cre; Erk1-/-; Erk2fl/fl ( Erk KO ) lacrimal gland epithelia , expressions of Pea3 transcription factors were abolished ( Fig 1D–1I , dotted lines ) . To study the function of these transcription factors , we conditionally deleted Erm and Er81 , two members of the Pea3 family of transcription factors , in a Pea3-null background using Le-Cre . Indicated by the lacrimal gland progenitor cell marker Pax6 , the lacrimal gland primordia in E15 . 5 Le-cre; Pea3-/-; Ermfl/fl; Er81fl/fl ( hereafter referred to as Pea3 TKO ) embryos were noticeably smaller in size compared to the control ( Fig 1J and 1M , dotted lines ) . As reflected by TUNEL staining , this was consistent with an increase in apoptosis seen in the lacrimal gland primordia ( Fig 1K and 1N , arrows ) . Analysis of the malformed gland marked by GFP expression additionally showed that both duct elongation and branching were severely compromised at the post-natal P1 stage ( Fig 1L and 1O , arrows ) . In contrast , the lacrimal gland phenotype was considerably less severe in mice carrying at least one normal copy of Pea3 ( Fig 1P ) , indicating the importance of Pea3 gene . Of note , unlike Mek and Erk KO that displayed complete lacrimal gland aplasia , Pea3 TKO still presented with residual lacrimal glands . These results suggest that Pea3 transcription factors mediate some but not all of MAPK-dependent processes in lacrimal gland development . In order to decipher the gene regulatory network of Pea3 transcription factors , E14 . 5 lacrimal gland epithelial tissue from control ( Le-Cre ) and mutant ( Pea3 TKO ) mouse embryos were micro-dissected using laser capture microscopy and subjected to RNA-seq ( Fig 2A , n = 3 per condition ) . Unsupervised clustering analysis of the normalized data revealed that control and mutant samples were separated into two distinctive groups and that data from individual samples within each group were highly correlated ( Fig 2B , r = 0 . 8 ) , indicating the robustness of the obtained results . Gene ontology analysis showed that biological processes such as protein degradation , ECM interaction , glycosaminoglycan biosynthesis and cell adhesions are significantly downregulated in Pea3 TKO mutants ( Fig 2C ) , which is in line with the previous findings that proteoglycans and ECM proteins play important roles in lacrimal gland development [5 , 8 , 17 , 18] . In addition , PI3K and Ras pathways were also impaired in Pea3 TKO mutants , suggesting that downstream effectors of FGF signaling may also be compromised . To validate this idea , we compared our dataset with the previously published result from the Fgfr2 conditional knockout [8] . Gene set enrichment analysis ( GSEA ) revealed that there was indeed a significant overlap in downregulated genes between Pea3 TKO and Fgfr2 mutants ( NES = -6 . 8 , p = 0 . 01 ) ( Fig 2D ) [20] . Taken together , these results are consistent with the notion that the Pea3 family of genes act downstream of the FGF signaling cascade . Further analysis revealed that Pea3 transcription factors were uniquely positioned to fine tune the FGF signaling outcome . First , Pea3 transcription factors promoted their own expressions in the lacrimal gland bud , as Pea3 , Erm and Er81 transcripts were reduced in Pea3 TKO RNA-seq dataset ( Fig 3A ) . Second , expression of heparan sulphate biosynthetic enzymes Ext1 , Hs3st and Hs6st was also down regulated ( Fig 3A ) . Since heparan sulphate proteoglycans are known to act as co-receptors for Fgf10 , this was expected to dampen the positive feedback mechanism of FGF signaling . Third , Pea3 transcription factors were required for the expression of Dusp6 and Spry4 ( Fig 3A ) , which are both inhibitors of Ras-MAPK signaling . Reevaluating the available ChIP-seq data from the human LoVo and GIST48 cancer cell lines [21 , 22] , we found that all of the above negative and positive feedback genes could be bound by either PEA3 , ERM or ER81 in their promoter ( within 5000 bp of the transcriptional start site ) and/or enhancer regions ( beyond 5000 bp upstream or downstream to the promoter site ) ( Fig 3A and 3B ) . Many of the ChIP-seq peaks for PEA3 proteins overlapped with H3K4Me1 , H3K4Me3 and DNAse I sensitivity sites , signifying an open chromatin conformation in these regions ( Fig 3B ) . Since Erm was the most highly expressed Pea3 transcription factor during lacrimal gland development ( Fig 1B ) , we searched for the putative Erm binding sites in the corresponding mouse genomic regions using TRANSFAC database ( S1 Fig ) . By chromatin immunoprecipitation , we confirmed that Erm protein indeed bound to the Ext1 , Dusp6 , Col2a1 and Mmp2 loci in P4 lacrimal gland cells ( S1 Fig ) . In addition , RNA in situ hybridization experiments confirmed that Pea3 , Erm , Er81 and Dusp6 genes were down regulated specifically in the Pea3 TKO lacrimal gland primordia ( Fig 3C ) . These data show that Pea3 transcription factors play a central role in modulating the levels of FGF signaling by regulating the positive and negative feedback loops involved in the fine tuning of this pathway . The Sox family of transcription factors Sox9 and Sox10 have been previously identified as downstream targets of FGF signaling important for lacrimal gland development [8] . The expression levels of Sox10 were severely diminished in Pea3 TKO mutants , whereas the reduction in Sox9 expression was less dramatic ( Fig 3A and 3C ) . Interestingly , the ChIP-seq analysis suggested that the promoter of SOX9 but not that of SOX10 harbored direct binding sites for PEA3 factors ( Fig 3A ) . Sox9 was previously shown to regulate the expression of extracellular matrix related genes Col2a1 , Col9a1 , Mia1 and MMP2 , which is consistent with the dynamic remodeling of the extracellular matrix during lacrimal gland development [8] . Interestingly , these genes were also occupied by PEA3 transcription factors in their promoter/enhancer regions in GIST48 and LoVo cells , with their expressions being down regulated in Pea3 TKO mutants ( Fig 3A–3C ) . Therefore , by controlling both Sox9 and its downstream targets , Pea3 transcription factors activate a feedforward mechanism in regulating lacrimal gland development . Strikingly , the transcriptome analysis additionally revealed that many keratin genes were upregulated in Pea3 TKO mutants ( Fig 4A ) . This result was especially unexpected because the keratins that were ectopically expressed are typically found in the cutaneous epithelium during embryonic development , rather than in the lacrimal gland . This led us to hypothesize that there was a shift in cell identity from the lacrimal gland fate to the epidermal-like fate in the absence of Pea3 genes . To test this idea , we performed GSEA of differentially upregulated genes in Pea3 TKO mutants compared to the published gene expression datasets of E14 . 5 mouse embryonic skin [23] . This analysis showed that the transcriptome of the Pea3 TKO lacrimal gland primordia was significantly enriched in genes prevalent in the epidermis ( Fig 4B , NES = 11 . 99 , p<0 . 001 ) and hair follicle placode ( NES = 9 . 0 , p<0 . 01 ) . In contrast , no significant similarities were observed when compared with the dermal condensates , skin fibroblast , melanocyte or Schwann cells . We next examined a set of genes that displayed nested expressions from the epidermis , to the conjunctiva to the lacrimal gland . At E14 . 5 , Krt14 was mostly restricted to the epidermis , and Krt5 and Sfn were only present in the skin epidermis and the conjunctival epithelium , whereas Krt7 expression was expanded into the stalk region of the lacrimal gland but excluded from the bud ( Fig 4C ) . In the Pea3 TKO mutant , all these genes were expressed in the lacrimal gland primordia . These data indicated that Pea3 proteins prevented the lacrimal gland progenitors from adopting the epidermal fate . To further understand the molecular mechanism of Pea3 mutant defects , we sought to determine the most differentially regulated genes in our dataset . For this analysis , the gene expression changes depicted by Log2 ( fold change ) were plotted on the x-axis against the corresponding statistical significance depicted by -Log10 ( p-value ) on the y-axis ( Fig 5A ) . Apart from the aforementioned FGF-responsive genes Spry4 , Dusp6 , Col2a1 , Col9a1 , Sox9 and Sox10 , transcription factors Six1 and Six2 also emerged as significantly downregulated genes in Pea3 TKO mutants . Importantly , both SIX1 and SIX2 loci in GIST48 and LoVo cells displayed significant ChIP-seq peaks for PEA3 , Erm and ER81 in open chromatin conformations marked by histone H3K4Me1 and H3K4Me3 methylations and DNase I sensitivity , suggesting they could be direct targets of PEA3 transcription factors ( Fig 5B ) . Indeed , in situ hybridization for Six1/ Six2 revealed that their expressions were significantly reduced in E14 . 5 Pea3 TKO lacrimal glands ( Fig 5C , dotted lines ) and abolished in Le-Cre; Fgfr2fl/fl mutants ( Fig 5C , arrows ) . Therefore , Six1 and Six2 are transcriptional targets of Pea3 and FGF signaling in the lacrimal gland epithelium . While a Six1 deletion has been shown to affect lacrimal gland duct elongation and branching [24] , a Six2 mutant phenotype hasn’t previously been reported . We examined lacrimal gland development in Six2 knockout embryos at E15 . 5 , but did not observe any gross abnormalities ( S2 Fig ) . This could be due to compensation by Six1 during lacrimal gland development . To test this idea , we used siRNAs against Six1 and Six2 genes , which resulted in significant down regulation of their expressions in a cell based assay ( Fig 5D ) . In the ex-vivo lacrimal gland culture , exogenous Fgf10 induced significant growth of the E17 . 5 lacrimal gland primordia , which was dampened by siSix1 but not by scrambled siRNA ( Fig 5E and 5F ) . Although siSix2 did not display any effect , combined application of siSix1 and siSix2 led to significant reduction in the size of lacrimal gland buds induced by Fgf10 . These results show that Six1 and Six2 act synergistically to regulate lacrimal gland development . Although Pea3 proteins generally function as transcriptional activators , they can also act as repressors in certain contexts [25] , thus we examined genes upregulated in the Pea3 TKO mutants . Notably , pathway analysis revealed activation of the Notch signaling pathway reflected by an increase in expression of the ligand Jag1 , receptors Notch1 , Notch 2 and Notch3 , downstream target Hes1 and a reduced expression of Lunatic fringe ( Lfng ) ( Fig 6A ) . This was further confirmed by GSEA of Notch signaling genes in the Pea3 TKO transcriptome ( Fig 6B ) . Indeed , RNA in situ hybridization showed that Jag1 mRNA was normally restricted to the surface ectoderm and conjunctiva at E14 . 5 , but in the Pea3 TKO mutants , Jag1 transcripts were ectopically expressed in the lacrimal gland primordia , with its translated protein form being prominently induced in the same area ( Fig 6C ) . This was in sharp contrast to Lfng , a gene that was readily detectable in the control lacrimal gland with its expression being significantly reduced in the Pea3 TKO mutants ( Fig 6C ) . In line with these findings , Pea3 TKO mutant lacrimal gland primordia displayed readily detectable staining patterns of Notch1 intracellular domain ( Notch1-ICD ) , demonstrating that Notch signaling was aberrantly activated . After establishing the misplaced activation of Notch signaling in the lacrimal gland , we subsequently investigated the functional significance of its activation in this developing tissue . Lfng is a glycosyl transferase that prevents Jag1-mediated Notch signaling in a context dependent manner [26–28] . Consistent with its negative role in Notch signaling , genetic ablation of Lfng resulted in a moderate increase in Notch1-ICD staining in the tip of the E14 . 5 lacrimal gland ( S3A–S3D Fig ) . Interestingly , lacrimal gland size was reduced in P10 Lfng knockout pups , suggesting that the loss of Lfng expression likely contributed to the Pea3 TKO lacrimal gland phenotype ( S3E and S3F Fig ) . Nevertheless , the Lfng knockout did not fully activate Notch signaling to the extent seen in the Pea3 TKO mutants . This prompted us to directly express the Notch1 intracellular domain in the developing lacrimal gland using the Cre-inducible R26-N1CD allele . In E14 . 5 Le-Cre; R26-N1CD embryos , expressions of Six1 and Six2 were lost , but the lacrimal gland progenitor cell markers Pax6 and E-cadherin were retained ( Fig 7A and 7B , dotted lines ) . The downstream targets of FGF signaling such as Sox10 , Pea3 , Erm and Dusp6 were also downregulated ( Fig 7A ) . Interestingly , Jag1 was upregulated in the fornix of the conjunctiva where the lacrimal gland progenitors resided , suggesting that Notch signaling acted in an auto-stimulatory loop to increase Jag1 expression ( Fig 7B , dotted lines ) . At P1 , no lacrimal gland was found in Le-Cre; R26-N1CD embryos ( Fig 7B , n = 10 ) , demonstrating that aberrant activation of Notch was deleterious to lacrimal gland development .
FGF signaling plays an instructive role in lacrimal gland development , controlling its fate determination and morphogenesis . Mediated by the canonical Ras-MAPK pathway , FGF signaling induces expression of Pea3 transcription factors during the formation of both the epithelial and mesenchymal compartments of the lacrimal gland [18 , 29] . In this study , we showed that Pea3 transcription factors were necessary to establish the identity of the lacrimal gland epithelium , turning it away from epidermal and conjunctival cell fates ( Fig 7C ) . This was further attributed to the loss of Six1 and Six2 during lacrimal gland development , leading to both the disruption of duct elongation and branching morphogenesis . In addition , we found that Pea3 transcription factors inhibited the Notch signaling pathway which , when activated , prevents the expression of Six and Sox genes and causes the abortion of lacrimal gland induction . Collectively , our data demonstrate that Pea3 transcription factors control the expression profiles of key genes involved in the promotion of lacrimal gland identity and morphogenesis . The regulatory mechanisms controlling the expression levels of Six1 and Six2 are not well understood . Six1 deficiencies cause defects in organs that include the inner ear and kidney , both of which also develop through an epithelial-mesenchymal interaction like that which occurs in the lacrimal gland . However , contrary to what we observed in the lacrimal gland , analyses of inner ear development showed that Pea3 negatively regulated the pre-placodal genes Six1 and Eya2 , and Six1 acted upstream of Jag1 in the Notch signaling pathway [30 , 31] . In kidney development , both Six1 and Six2 are expressed in the cap mesenchymal area where they are required for the ureteric budding and branching process [32–34] . Although Pea and Erm transcription factors are present in both the ureteric bud and the mesonephric mesenchyme , they are only required in the epithelial compartment to mediate Ret signaling [35] . SIX1/Six1 have been previously implicated in lacrimal gland development in humans and in mice . A heterozygous missense mutation in the SIX1 gene causes autosomal dominant lacrimal gland stenosis whereas Six1 knockout mouse embryos displayed small lacrimal glands with duct elongation and branching defects [24] . Our RNA-seq analysis showed that Six2 was expressed at 5 . 6 folds higher than Six1 in the developing lacrimal gland epithelium , but surprisingly , Six2 null mutant embryos did not exhibit any lacrimal gland phenotype . This was likely due to compensation by Six1 , as our explant culture experiments showed that knockdown of both Six1 and Six2 synergistically disrupted branching morphogenesis of the lacrimal gland . We further showed that Six1 and Six2 were controlled by Pea3 transcription factors downstream of FGF signaling in the lacrimal gland epithelium . Thus , Six1 and Six2 genes are novel targets of Pea3 transcription factors in regulating lacrimal gland morphogenesis . Although Notch activity is important for maintaining the postnatal homeostasis of the lacrimal gland [36] , its temporal requirement during development has yet to be established . We have shown that Pea3 transcription factors prevent ectopic activation of Notch signaling during lacrimal gland induction . Analysis of our RNA-seq data for the modulators of Notch signaling showed that Lfng was the only Fringe family gene expressed abundantly in the lacrimal gland and its expression was significantly downregulated in the Pea3 TKO mutants . Lfng is a glycosyltransferase that adds O-linked fucose residues to the extracellular domain of the Notch receptor in order to modulate its ligand binding [37] . Lfng has been shown to potentiate the Dll-mediated but inhibit the Jag1-mediated Notch1 signaling pathways [26–28] . During sensory hair cell development in the inner ear , Lfng co-expresses with Jag1 and , when mutated in mice , partially rescues the Jag2 knockout phenotype [38 , 39] . During lacrimal gland development , Pea3 transcription factors may turn on the expression of Lfng to down regulate Jag1-mediated Notch signaling . This model was supported by the down regulation of Lfng levels in the absence of Pea3 transcription factors , and increased Notch1-ICD staining with reduced size of the lacrimal gland in the Lfng knockout . Lfng was not the only component of Notch signaling targeted by the Pea3 transcription factors in the lacrimal gland . In fact , the expression levels of Jag1 , Notch and Hes1 were all elevated in the Pea3 TKO mutants , resulting in a much higher level of Notch1-ICD staining compared to that seen in the Lfng mutants . To replicate such strong activation of Notch signaling , we directly expressed Notch1-ICD in the ocular surface , resulting in loss of both the Six and Sox genes and abrogation of lacrimal gland induction . These results highlight the importance of inhibiting aberrant premature Notch signaling during lacrimal gland development . Our study revealed a previously underappreciated FGF signaling network systematized by the Pea3 transcription factors targeting Sox , Six and Notch signaling pathways during development of the lacrimal gland . We also showed that Pea3 transcription factors not only directly promote the expression of heparan sulfates involved in potentiating FGF signaling but also activate expression of the inhibitory factors Sprouty4 and Dusp6 . We would like to suggest that by inducing both positive and negative feedback loops the Pea3 family of proteins may amplify the transcription response to low levels of FGF signaling but dampen the response to strong FGF signals . This non-linear transcriptional response mechanism can stabilize the FGF signaling network output given a wide range of FGF signal input , buffering the developmental system in face of environmental perturbations .
The animal experiments were approved by Columbia University Institutional Animal Care and Use Committee ( Protocol number: AAAR0429 ) . Mice carrying Erk1-/- , Erk2flox , Lfng-/- , Mek1flox and Mek2-/- alleles were bred and genotyped as described [28 , 40 , 41] . We obtained Er81flox mice from Dr . Silvia Arber ( University of Basel , Basel , Switzerland ) , Pea3-/- and Ermflox mice from Dr . Xin Sun ( University of California at San Diego , San Diego , CA ) , Fgfr2flox from Dr . David Ornitz ( Washington University Medical School , St Louis , MO ) and Le-Cre mice from Dr . Ruth Ashery-Padan ( Tel Aviv University , Tel Aviv , Israel ) . [15 , 19 , 42 , 43] . Rosa-N1-ICDflox/+ mice were obtained from Jackson lab ( Stock # 008159 ) . Animals were maintained in a mixed genetic background . Lacrimal gland growth and morphology were identical in Le-Cre and Le-Cre;Pea3+/-;Ermflox/+;Er81flox/+ mice , which were used as controls throughout the conducted experiments . Mice were housed in specific pathogen free ( SPF ) facility that employed a 12-hour light-dark cycle and were given standard mouse feed . RNA in situ hybridization was performed as previously described [44] . Briefly , the mouse embryos were harvested , fixed overnight in 4% PFA , equilibrated in 30% sucrose and cryo-frozen in OCT . On the day of the experiment , OCT blocks were sectioned at 10 μm , hybridized with the diluted probe at 68°C overnight in a wet chamber and moistened with solution containing 50% Formamide and 1X Salt ( 0 . 2M NaCl , 10mM Tris , 5mM NaH2PO4 , 5mM Na2HPO4 , 5mM EDTA ) . The probe was diluted at 1:200–500 in a pre-warmed hybridization buffer and incubated at 70°C for at least 10 minutes . On the next day , slides were washed 3X in wash buffer ( 1X SSC ( 150mM NaCl , 15mM Sodium citrate , pH 7 ) , 50% Formamide ) at 68°C . After cooling , slides were washed 2X with MABT ( 100mM maleic acid , 150mM NaCl , pH 7 . 5 , 0 . 1% Tween 20 ) and incubated at room temperature for 30 min . Slides were then blocked with 20% Sheep serum in MABT for 1 hour , followed by an overnight incubation with anti-DIG antibody ( 1:1500 ) at 4°C . On the next day , slides were washed 4-5X with MABT and 2X with alkaline phosphatase buffer . For color development , slides were covered with BM purple substrate and incubated at room temperature for 4–24 hrs . The following probes were used: Pea3 , Erm5 ( from Dr . Bridget Hogan , Duke University Medical Center , Durham , NC , USA ) , Er81 ( from Dr . Gord Fishell , New York University Medical Center , New York , NY , USA ) , Jag1 ( from Dr . Doris Wu , National Institute on Deafness and Other Communication Disorders , National Institutes of Health , Bethesda , MD ) , Lfng ( from Dr . Andy Groves , Baylor College of Medicine , Houston , TX ) , Six1 ( from Dr . Bernice Morrow , Albert Einstein College of Medicine , New York , NY , USA ) , Six2 ( from Dr . Thomas Caroll , UT Southwestern Medical center , Dallas , TX , USA ) , Sox10 ( from Dr . Anthony Firulli , Indiana University School of Medicine , Indianapolis , IN , USA ) , Dusp6 ( full length cDNA IMAGE clone: 3491528 , Open Biosystems , Huntsville , AL , USA ) , Sfn and Krt7 ( full length cDNA IMAGE clone: 184592 and 40614 , the DNA Resource Core , Harvard Medical School , Boston , MA , USA ) . For immunohistochemistry of paraffin samples , sections were deparaffinized and rehydrated by serial treatment with histosol followed by decreasing percentages of ethanol solutions [44 , 45] . For cryosections , sections were briefly washed with PBS to remove OCT . Antigen retrieval was performed with microwave boiling in citrate buffer ( 10 mM sodium citrate , pH 6 . 0 ) for 1–2 minutes followed by heating for 10 minutes at a low power setting . Sections were then washed with PBS and blocked with 5% NGS/0 . 1% Triton in PBS . Primary antibody incubation was performed overnight at 4°C in a humid chamber followed by incubation with fluorescent-conjugated secondary antibodies for 1 hour at room temperature in the dark . For signal amplification , HRP-conjugated secondary antibodies were used , followed by washing and equilibration with TNT buffer . The slides were then incubated with Tyramide reagent for 10 minutes , washed with TNT buffer , stained with DAPI and mounted with anti-fade reagent , 0 . 2% NPG , 90% glycerol in 1X PBS . The following primary antibodies were used: Pax6 ( PRB-278P ) and Krt14 ( PRB-155P ) ( both from Covance , Berkeley , CA , USA ) , Ecad ( U3254 , Sigma , St Louis , Missouri , USA ) , Jag1 ( sc-8303 , H-114 , Santa Cruz Biotechnology , Santa Cruz , CA , USA ) , Krt 5 ( 905901 , Biolegend , San Diego , CA , USA ) , N1-ICD ( #4147 , Cell signaling Technology , Boston , MA , USA ) 3T3/HeLa cells were cultured in Dulbecco's Modified Eagle's Medium supplemented with 10% fetal bovine serum and 1% penicillin/streptomycin ( Invitrogen ) at 37°C . For the Six1 knockdown , transient transfection of Six1 siRNA ( s73792 , Ambion , Carlsbad , CA ) was performed in 3T3 cells . Total RNA from 3T3 cells was extracted using the MiniRNA Plus kit ( Qiagen , Hilden , Germany ) and converted to cDNA using the High-Capacity cDNA Reverse Transcription Kit ( Applied Biosystems , Foster City , CA ) . Quantitative-PCR was performed using the PCR SYBR green 2X master mix ( Invitrogen , Carlsbad , CA ) in the StepOne plus Real time PCR instrument [46] . For the Six2 knockdown , transient transfection of Six2 cDNA ( clone TCM1304 , Transomic , Huntsville , AL ) was performed with Lipofectamine 3000 ( cat#L3000015 , Invitrogen , Carlsbad , CA ) according to the manufacturer's instruction . After 18 hours , cells were transfected with Six2 siRNA ( Silencer Select , s73794 , Ambion , Carlsbad , CA ) or scrambled siRNA with a final concentration of 20 nM using RNAi Max ( cat#13778150 , Invitrogen , Carlsbad , CA ) according to the manufacturer's instruction . siRNA silencing was conducted a second time after 8 hours . Cells were collected for Quantitative-PCR analysis following the Six2 cDNA overexpression for 48 hours and the Six2 siRNA knockdown for 24 hours . The primer sequences used were: Six1: 5’- ATGCTGCCGTCGTTTGGTT -3’ , 5’-CCTTGAGCACGCTCTCGTT -3’ , Six2: 5’- CACCTCCACAAGAATGAAAGCG-3’ , 5’-CTCCGCCTCGATGTAGTGC -3’ , Gapdh: 5’-AGGTCGGTGTGAACGGATTTG-3’ , 5’-TGTAGACCATGTAGTTGAGGTCA-3’ . P4 old lacrimal glands collected from 40 mouse pups were incubated with 1ml trypsin for 5 minutes and pipetted a few times to dissociate into single cells . 4ml DMEM+10% FBS was added to neutralize the trypsin before addition of 270μl formaldehyde ( 37% ) in 10ml of DMEM containing 10% FBS to fix the cells with shaking for 10 minutes . The cross linking was stopped by addition of DMEM with 10% FBS and 0 . 125M glycine for 5 minutes . After washed with cold 1xPBS twice ( 5 minutes each ) and centrifuged in 3000 rpm for 3 minutes , the cells were collected and re-suspended in 1ml of ChIP lysis buffer ( 10mM Tris-Cl , pH8 , 85mM KCl , 0 . 5% NP-40 , 5nM EDTA , 0 . 25% Triton; RIPA- 1% Triton , 150mM NaCl , 0 . 1% SDS , 0 . 1% Na-Deoxycholate , 10mM Tris-Cl , pH8 , 5mM EDTA ) with 1X protease inhibitor and kept in rocker at 4°C for 10 minutes . The cells were spun at 3K rpm , re-suspended with 1ml of RIPA buffer with 1X protease inhibitor before being sonicated with the power 1 second “on” , 2 second “off” for 8 minutes and spun in 15000 rpm for 10 minutes at 4°C . Pre-cleared by incubating with 45 μl agarose beads for 2 hrs at 4°C , the supernatant was spun at 3K rpm and incubated overnight with 1μg of antibody for 1mg of protein at 4°C , followed by 20 μl protein G bead for 2 hrs . The beads were washed with RIPA , Wash buffer A ( 50mM HEPES , pH7 . 9 , 500mM NaCl , 1mM EDTA , 1% Triton , 0 . 1% Na-deoxycholate , 0 . 1% SDS ) , wash buffer B ( 20mM Tris-Cl , pH8 , 1mM EDTA , 250 mM LiCl , 0 . 5% NP-40 , 0 . 5% Na-deoxycholate ) and TE buffer twice respectively ( 5 min each wash ) . After they were spun at 2000 rpm , the collected beads were incubated with 480μl elution buffer ( 1% SDS , 30mM Tris-Cl ( pH8 ) , 15mM EDTA , 200mM NaCl ) at 50°C overnight before adding the same volume of phenol:chloroform and centrifuging at 15000 rpm for 5 minutes . The supernatant was mixed with 2X ethanol ( 100% ) at -80°C for 30 minutes , recovered to RT and centrifuged at 15000 rpm at 4°C for 15 minutes . The pellet was washed with 70% ethanol and the air dried DNA was dissolved in 15μl of distilled water for PCR reaction . The mouse monoclonal antibody against Erm was from Proteintech ( Catalog number: 66657-1-Ig ) . The primers used are: CAGCGACTGGAATGAGAACA and GCTGGAACAGGTTGTGTTGA for Dusp6 , ACTTGGGACTGCCACACTG and AACAACCCCCTCCCTTCTAA for Col2a1 , TACGATGATGACCGGAAGTG and AGGTTGTTCCAGGTCAGGTG for Mmp2 , AGTCCCGCTTGATACCTTGA and GTGGCTTTCTCGCTGTCTTT for Ext1 . The lacrimal glands from E16 . 5–17 . 5 embryos were harvested and gently transferred onto filter paper ( 0 . 45 um ) in 35 mm low bottom dishes ( ibidi , Martinsried , Germany ) in medium ( DMEM , 5%FBS , 400ng/ml Fgf10 , 250ng/ml Heparin , 1X ITS , P/S ) containing either scrambled , Cy3-labeled negative control ( AM4621 , Invitrogen , Carlsbad , CA ) , Six1 ( s73792 ) or Six2 ( s73794 ) siRNA . Lipoefectamine-siRNA complexes were prepared in Optimem medium as per the manufacturer’s instructions . To test the genetic redundancy , 45nM of scrambled siRNA , 15nM Six1 + 30nM scrambled siRNA , 30nM Six2 +15nM scrambled siRNA and 15nM Six1 + 30nM Six2 siRNA were used . 10μl of matrigel and medium in a 1:1 ratio was added on top of each gland . The glands were cultured for 24–48 hrs at 37°C . Laser capture microdissection and RNA sequencing were performed as previously described [29] . The RNAseq data is available at the GEO repository under accession number GSE114509 . Unsupervised clustering analysis was performed in MATLAB using the Clustergram function . We determined interquartile ranges of the gene expression levels in all samples and the top 200 genes were plotted . GSEA was performed using MATLAB implementation of the same method as described [20] . KEGG pathway enrichment analysis and functional annotation was performed in DAVID . For the functional annotation of downregulated genes , a list of 476 genes was used for the analysis based on cutoff points for the normal expression levels ( > 50 units ) , Log2 ( fold change ) ( <-1 ) and p-values ( <0 . 05 ) . Volcano plots representing Log2 ( p-value ) vs Log2 ( fold change ) were plotted in MATLAB . -Log2 ( p-value ) > 50 were set to 50 in order to avoid the scaling issues in the plot . ChIP-seq analysis was performed using MACS [47] . SRA files of ETV1 ( ER81 ) , ETV4 ( PEA3 ) and ETV5 ( ERM ) ChIP-seq data were retrieved from the GEO database [21 , 22] . SRA files were converted to a Fastq format using sratoolkit , followed by mapping of the sequence reads on the genome ( hg18 ) to generate a SAM file . Peak calling was done using MACS ( using default parameters ) . The mapped ChIP-seq file was visualized on the human reference genome assembly ( hg18 ) using the UCSC genome browser . The Erm binding sites in the mouse genome were scanned using MATCH algorithm based on TRANSFAC database . | FGF signaling regulates cell fate decision by inducing genome-wide changes in gene expression . We identified Pea3 family transcription factors as the key effectors of FGF signaling in reprograming the epithelia transcriptome . Pea3 factors control both the feedback and feedforward circuities of FGF signaling in lacrimal gland development . They also activate specific expression of Six and Sox family genes and suppress aberrant activation of Notch signaling . In the absence of Pea3 genes , the lacrimal gland progenitors become epidermal-like in their gene expression patterns . The study of Pea3 function resolves the long standing conundrum of how FGF induces the lacrimal gland fate , providing direction for regenerating the lacrimal gland to treat dry eye diseases . |
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Cutaneous leishmaniasis ( CL ) is the most frequent form of leishmaniasis , with 0 . 7 to 1 . 2 million cases per year globally . However , the burden of CL is poorly documented in some regions . We carried out this review to synthesize knowledge on the epidemiological burden of CL in sub-Saharan Africa . We systematically searched PubMed , CABI Global health , Africa Index Medicus databases for publications on CL and its burden . There were no restrictions on language/publication date . Case series with less than ten patients , species identification studies , reviews , non-human , and non-CL focused studies were excluded . Findings were extracted and described . The review was conducted following PRISMA guidelines; the protocol was registered in PROSPERO ( 42016036272 ) . From 289 identified records , 54 met eligibility criteria and were included in the synthesis . CL was reported from 13 of the 48 sub-Saharan African countries ( 3 eastern , nine western and one from southern Africa ) . More than half of the records ( 30/54; 56% ) were from western Africa , notably Senegal , Burkina Faso and Mali . All studies were observational: 29 were descriptive case series ( total 13 , 257 cases ) , and 24 followed a cross-sectional design . The majority ( 78% ) of the studies were carried out before the year 2000 . Forty-two studies mentioned the parasite species , but was either assumed or attributed on the historical account . Regional differences in clinical manifestations were reported . We found high variability across methodologies , leading to difficulties to compare or combine data . The prevalence in hospital settings among suspected cases ranged between 0 . 1 and 14 . 2% . At the community level , CL prevalence varied widely between studies . Outbreaks of thousands of cases occurred in Ethiopia , Ghana , and Sudan . Polymorphism of CL in HIV-infected people is a concern . Key information gaps in CL burden here include population-based CL prevalence/incidence , risk factors , and its socio-economic burden . The evidence on CL epidemiology in sub-Saharan Africa is scanty . The CL frequency and severity are poorly identified . There is a need for population-based studies to define the CL burden better . Endemic countries should consider research and action to improve burden estimation and essential control measures including diagnosis and treatment capacity .
Cutaneous leishmaniasis ( CL ) is the most common clinical manifestation of leishmaniasis , a parasitic neglected tropical disease ( NTD ) [1] . Caused by an obligate intracellular protozoa from the Leishmania species and transmitted by the bite of Phlebotomine sand flies , the clinical presentations of CL include localized skin nodules ( often called oriental sores ) , diffuse non-ulcerated papules , dry or wet ulcers , and , in the mucocutaneous form , extensive mucosal destruction of nose , mouth , and throat . Transmission of CL may involve animal reservoir hosts ( e . g . , rodents , hyraxes ) in zoonotic foci , while anthroponotic CL ( where humans are the main parasite reservoir ) occurs in urban or periurban settings [2] . Environmental changes in rural contexts such as agricultural activities , irrigation , migration , and urbanization may increase the exposure risk for humans and result in epidemics . Likewise , outbreaks in densely populated cities or settlements have occurred , especially in conflict-affected zones such as Afghanistan or Syria [3 , 4] , in refugee camps and contexts of large-scale forced migration of populations . Globally , the World Health Organization ( WHO ) considers CL as endemic in 20 countries in the New World ( South and Central America ) and in 67 countries in the Old World ( southern Europe , Africa , the Middle East , parts of southwest Asia ) [5] . Between 700 , 000 to 1 , 200 , 000 CL cases are estimated to occur annually worldwide , with >70% of cases in 2014 reported from Afghanistan , Algeria , Brazil , Colombia , Costa Rica , Ethiopia , the Islamic Republic of Iran , Peru , Sudan and the Syrian Arab Republic [5 , 6] . Multiple parasite species cause CL: in the Old World , these are L . major , L . aethiopica , L . tropica , and , rarely , the viscerotropic L . donovani ( in Sudan ) , resembling similar a phenomenon more known for L . infantum [7–10] . Though CL is often considered self-healing , the duration varies for different species and can take months , or years [11] . Due to the clinical and epidemiological diversity in CL , its geographic clustering and lack of reliable surveillance data , estimating the CL burden are challenging [12] . The most widely used measure of disease burden known as the Disability Adjusted Life Year ( DALY ) combines estimated prevalence , incidence , and mortality , with an assigned disability weight for each disease [13] . However , the disability weights are defined using different approaches with regards to the expert panel composition , health state description , and valuation methods [14 , 15] . The specific stigma and psychosocial distress generated by a non-fatal condition are often overlooked [16 , 17] , although the social impact of CL is potentially severe and has been well-documented [18 , 19] . Moreover , in sub-Saharan Africa ( SSA ) , not only the disability but also the number of CL cases is largely underestimated . A recent global burden analysis listed 19 countries in SSA in the top 50 high burden countries [20] . The passive epidemiological surveillance system that prevails in these countries leads to the patchy data from this region . According to WHO , only Sudan and Ethiopia reported cases of CL [21] . The objective measures of burden such as prevalence and incidence of CL are scarce in this region , making it hard to advocate for funding and resources to tackle the disease . Whereas attention has been given to CL in Northern Africa ( Algeria , Libya , Morocco , Tunisia , Egypt ) and the Middle East [22–24] , data for sub-Saharan Africa is critically lacking , particularly in countries where CL is not a notifiable disease . This study focuses on SSA because it is a blind spot on the CL epidemiological burden map and the overall picture of what has been documented on CL is not known . We undertook a systematic review of the literature to synthesize current knowledge on CL burden in SSA .
We searched the following electronic databases: National Library of Medicine through Pubmed , Cochrane Register , Web of Science , CABGlobal Health , African Index Medicus and Google Scholar . We did an initial keyword search and subsequent searches based on Medical Subject Headings ( MeSH ) with various combinations of search terms “cutaneous leishman*” AND “Africa , South of the Sahara” ( which also included “Africa , Western”; “Africa , Eastern”; and “Africa , Southern” ) OR “Leishmaniasis , cutaneous” OR “Leishmaniasis , diffuse cutaneous” OR “Leishmaniasis , mucocutaneous” AND each individual sub-Saharan countries . The World Bank classification was used to define sub-Saharan African countries and to group them according to the region ( i . e . , southern , eastern , western , and middle Africa- see Box 1 ) . No language restrictions were set for searches , while we limited the publication date until 31 May 2018 . We hand-searched the reference lists of all recovered studies for additional references . We also explored and summarized information from the Global Health Observatory for leishmaniasis maintained by WHO for CL . We included studies if they are reporting primary data that help to determine the burden of CL in countries in SSA . The burden is defined as elements of 1 ) severity of the problem ( clinical , disability , case fatality , … ) in human patients; 2 ) frequency ( prevalence , incidence , … ) and 3 ) economic cost ( from patient , societal or health system perspective ) . We excluded animals or vector studies , studies on pathogenesis , immunology , histopathology , or on Leishmania species only , studies on diagnostic tests or treatment for CL and cases of Post Kala Azar Dermal Leishmaniasis ( PKDL ) –skin sequelae of VL . Case reports and case series of fewer than ten patients were also excluded . Sub-Saharan Africa as the main geographical interest refers to the settings where the studies were performed/conducted . Reviews about CL in a specific country or region without original data were excluded . The systematic review was conducted in line with PRISMA guidelines [25 , 26] . The review protocol was registered in PROSPERO , an international prospective register of systematic reviews , in July 2016 , number 42016036272 [27] . We selected the articles in a two-step process . In a first stage , titles and abstracts of all retrieved records were independently reviewed by two investigators ( TS and KV ) . In a second stage , the selected full-text articles were again reviewed ( by TS , KV , and a third person ) for eligibility . When full-text articles were excluded , the reason for exclusion was registered and reported . Any discordances were resolved through discussion or seeking consensus with a third investigator ( MB ) .
The data were extracted in parallel by two independent readers , using a specific data form , including information on the published record ( year , author ) , setting ( country ) , aim , study design , and main outcomes . We sought data on prevalence or incidence of CL among patients in health facilities and the community; demographic and clinical characteristics of CL patients , and the association between CL and other morbidities , notably Human Immunodeficiency Virus ( HIV ) . We attempted to use the STROBE checklist ( for reporting epidemiological studies ) to assess the ‘risk of bias , ’ but could not continue due to a large number of historical studies that are not in line with current reporting standards . The data analysis thus resulted in a narrative , qualitative synthesis of the included studies .
The flow diagram in Fig 1 shows the selection process: we identified 340 published articles , and after removing duplicates , we screened the title and abstracts of 289 articles , and exclude 184 . The full-text articles of the remaining 105 were assessed for eligibility , after which a further 51 were excluded . The remaining 54 articles were included . ( See Supporting Information 1 for all the included studies and the key information ) . The studies were published between 1955 and 2016; with only 12 ( 22% ) after 2010 . The studies were conducted in 13 out of the 48 countries in Sub-Saharan Africa: in eastern Africa ( Ethiopia , Kenya , Sudan ) , western Africa ( Burkina Faso , Cameroon , Chad , Ghana , Guinea , Niger , Nigeria , Mali , Senegal ) and southern Africa ( pre-independent Namibia ) . More than half of the studies were from western Africa ( 30/54 ) , notably Senegal ( 6 ) , Burkina Faso ( 5 ) and Mali ( 5 ) . Twenty-three studies studied CL in the community ( including three among school-children ) , and 28 used data collected in health facilities ( including 18 dermatology specialized services ) . The remaining three studies were mixed . All 54 studies were observational: 29 ( 54% ) were descriptive case series ( numbering a total of 13 , 257 cases ) , and 25 ( 46% ) followed a cross-sectional design , usually survey with various tools employed such as clinical screening or questionnaires . In eastern Africa , CL has been known for more than a century , with the first indigenous CL case recorded in 1911 in Sudan [28] . In Ethiopia , CL has been known since 1913 , and diffuse CL ( DCL ) clinical form was documented in 1960 in the highlands [29] . The first report of L . aethiopica as a distinct taxonomic entity was published in 1978 [30 , 31] , and since then , the species has also been found in the mountainous region of Kenya [32] . L . tropica was later reported from certain areas in Kenya during the 1990s , and since then considered to have a more restricted distribution than L . major [33 , 34] . In western Africa , only L . major has been thought to circulate in this region . The oldest case reports of CL come from Niger in 1911 [35] , then from Nigeria in 1924 , and from Senegal in 1933 [36] . Later more cases were reported from Cameroon , Mali , Mauritania , Burkina Faso and Guinea [37 , 38] . During the first half of the 20th century , the colonial medical officers documented sporadic case reports from an area that later became recognized as the ‘CL belt’ [38] . Several comprehensive ecological and epidemiological studies took place in suspected hyperendemic foci in Senegal [39–42] , Mali and Niger [43] . Current Namibia ( previously South West Africa ) , reported dozens of CL cases in the 1970s [44] , but the disease was not considered as a public health problem by the authorities [45] . Twelve studies ( Table 1 ) reported prevalence estimated by the Leishmanin Skin Test ( LST ) —also known as Montenegro test—to detect exposure to the parasites in CL foci . Through intradermal injection of Leishmania antigens , the induration is being read 48–72 hours later as a demonstration of a delayed type hypersensitivity reaction , much like a tuberculin skin test [11] . LST does not differentiate between past and present infection and not species specific , yet it is often used as a marker for cellular immunity against CL [46] . These studies were conducted at the community level in CL foci , and have shown fluctuation over time ( Table 1 ) . Changes from 4% to 91% in LST positivity rate were observed in the same villages following an outbreak in Sudan [47 , 48] . High variability across foci within one country has also been reported , for example in Ethiopia: in Ocholo , 57% of school children without CL lesions were LST positive [49] , while another study in the central-Ethiopian Rift Valley , LST positivity was maximum 5% . A study conducted in two neighboring villages in central Mali also demonstrated high variability: prevalence of Leishmania infection in Kemena was 45% , with the incidence of 19% and 17%; higher than Sougoula with 20% , 6% and 6% for the same years [50] . Reasons for these discrepancies are not known but possibly linked with hyper-clustering of reservoirs and vectors , leading to different intensity of peridomestic transmissions in Kemena [50] . A 2014 study from Mali complemented LST surveys with PCR and finger prick blood sample to measure antibody levels to sand fly saliva in endemic districts [54] . The results showed uneven prevalence of LST positivity across three different climatic areas ( 49 . 9% , 24 . 9% and 2 . 6% in Diema , Kolokani , and Kolondieba respectively ) , linked with north-south declining vector density . PCR was used to confirm L . major as the causative agent . LST positivity was also shown to be correlated to higher levels of antibodies to sand fly salivary proteins [54] . Across the studies , a consistent finding is that the proportion of positive LST increased with age and areas where CL transmission is active , at least a third of the population have had exposure to the Leishmania parasite [37 , 43 , 47–51 , 54–56] . Twenty-one studies reported estimates of CL prevalence or incidence; five were using medical records from hospitals , and the remaining were population estimates obtained through active screening for CL lesions and scars at the community level . All diagnosis was based on clinical examination . Though additional confirmatory methods ( microscopy/smear , histology , culture in NNN or combination of these ) were mentioned in all studies but two , it is unclear whether these were used in some or all or none of the patients . Among the five studies that were hospital-based , two used the number of dermatology consultations as the denominator , and the CL cases proportion found is 2% in Ouagadougou , Burkina Faso [57] and 14% in Addis , Ethiopia [58] . If suspected cases were to be denominator to calculate the CL cases proportion , they were found to be 78% ( 251/320 ) in Mali [59] and 93% ( 74/80 ) in Burkina Faso [60] . In most of the studies in the community , the prevalence of active CL was less than 5% . In endemic areas , the frequency of CL scars usually exceeds that of CL active lesions , except in a few special settings ( Table 2 ) . In Utut , Rift Valley in Kenya , a higher lesion versus scar rate ( 50% vs . 18% ) in migrant charcoal workers suggested a non-immune population’s encounter with the disease in an area where transmission occurs [34] . Also during an outbreak in a new focus in Silti , Ethiopia , the frequency of CL lesions was considerably more than that of CL scars [63] . In Sudan , 36% of the community were found to harbor active lesions during an outbreak [68] . To complement the findings from published studies , we also examined the data from the country official reporting system to WHO . The system record data from 1996 onwards , but clearly there are missing data ( Fig 2A and 2B ) . The absolute number of CL cases reported from eastern Africa is always higher than from western Africa , with Sudan bearing most of the burden . In western Africa , the number of cases reported from different countries is highly variable , and recurrent outbreaks were occurring in a 5–7 years cycle [74] . The increased cases in Ghana during 2002–2003 was prominent , yet there was a vacuum between 2007 and 2010 , and cases were reported again starting in 2011 . Other countries contribute little , with <100 cases per year ( Nigeria , Senegal ) . No data was reported from this region during 2015–2017 [75] . The majority ( n = 28 ) of the included records are clinical case series based on medical files from dermatology clinics or hospitals as the main data source . These studies describe a cohort of CL patients over a certain period , ranging from two to nine years . Chronologically , 10 studies reported CL cases in periods before 1980 [41 , 45 , 47 , 52 , 74 , 76–80] , 11 described patient groups observed between 1980–2000 [35 , 57 , 59 , 67 , 69 , 81–87] , and seven between 2000 and 2013 [58 , 60 , 88–92] . Hospitals reported that CL patients mainly came from surrounding areas or outside the cities or capital , such as Dakar , Senegal [74 , 88 , 93] or Niamey , Niger [84] . Eighteen studies report cases seen in specialized dermatology services . The proportion of CL cases among patients seen in those dermatology clinics is consistently less than 5% [59 , 69 , 94] . In the context of an outbreak , CL patients who seek care in specialized services represent only the tip of an iceberg , as shown in Burkina Faso ( further described below ) . Between 1999 and 2005 , a total of 7444 cases were recorded from various health centers in the capital Ouagadougou [95 , 96] , but during the same period , the dermatology hospital had only seen 251 CL cases [57] . Diagnosis in all the case series is obtained through clinical examination and smears or histopathology . In Chad , a hospital close to the Sudanese border reported a very high proportion of CL confirmed cases ( 580 out of 680 cases between 2008–2012 ) [89] . Three countries have published studies on CL outbreaks: Sudan , Ethiopia , and Ghana . The first ever epidemics in Sudan were reported in 1976–1977 along the Nile , in Shendi-Atbara north of Khartoum [68] , while the second and third outbreaks occurred in 1985 and 1986–1987 , respectively [97] . The last epidemic in Sudan was in Tuti island , and it affected at least 10 , 000 people in 7 months . Underestimation is likely mandatory reporting only started after the epidemic reached its peak [86] . People of both sexes , all age groups and all socio-economic classes were affected , which is suggestive of a disease ravaging in a non-immune population . The causal parasite was L . major LON-1 [98] and the outbreak was attributed to various factors such as immigration from west Sudan , the heavy rainfall in the year of the outbreak after a long period of drought—which led to increase in sandfly density as well as the rodent reservoir population—and waning of herd immunity of migrants from CL endemic areas in western Sudan ( Sayda el-Safi , personal communication ) . In Ethiopia , a CL outbreak occurred in 2005 in a district 150 km south of Addis . A survey then established an overall prevalence of 4 . 8% ( 92/1907 ) , and 1 in 5 cases had mucocutaneous lesions [63] . In Ghana , an outbreak of localized skin lesion consistent with CL occurred in Ho municipality , Volta region in 2003 [90] . The usual triggers of CL epidemics such as intrusion of humans into vector habitat through deforestation , road construction , wars or migration were not at work here . Previously , only one CL case had been reported from the country in 1999 , although the arid , Sahelian area of northern Ghana is considered to be part of the West African CL belt . Through passive case detection ( with biopsy as a confirmatory diagnosis ) with medical records review and active case finding , it was estimated that there were about 8876 CL cases between 2002 and 2003 in Ghana ( Fig 2A ) . All age groups were affected , and since then CL is considered endemic in this area . A study in the same district later found 60% parasite-confirmed cases among active CL suspects ( 41/68 ) . A phylogenetic analysis identified this Ghanaian parasite as new member of Leishmania enriettii complex , a possible new subgenus of pathogenic human Leishmania parasites [99] . Thirty-two studies described the clinical presentations of CL lesions . The most commonly used categories of the lesions are as followed: the localized CL or LCL , otherwise known as the classic oriental sore , refers to the lesion at the site of sand fly bites that may get ulcerated . LCL may appear as dry , papular forms with crust , or the wet , ulcerative forms with indurated edges . LCL can be singular or multifocal . When the nodules are multiple and nonulcerative , this is typically called a diffuse CL or DCL . In Sudan , mucosal leishmaniasis is described as lesion ( s ) that involves destructive mucosal inflammation which does not always start with a cutaneous lesion . This differs from New World mucocutaneous leishmaniasis ( MCL ) , which refers to a metastatic dissemination to the mucosal tissues starting from a distal cutaneous lesion [52 , 100] . Bacterial superinfection is common along with pain , itchiness , fever and the secondary inflammation often complicates clinical diagnosis [11 , 101] . The diagnosis documented in the medical files are often missing . A dermatology hospital in Addis , Ethiopia reported that among 234 confirmed CL cases , only 22% were categorized—consisting of 9% DCL , 10% MCL and 3% LCL [58] . The higher proportion of complicated or atypical lesions are frequently reported from teaching hospitals or specialized services . This includes sporotrichoid CL with painless subcutaneous nodules along the lymphatic vessels in Sudan [80 , 87] , or the diffuse CL in Ethiopia , which appear pseudo-lepromatous and can result in fungating or tumor-like lesions [52 , 80] . In the majority of the studies , the natural history of the lesions is only briefly described ( n = 51 ) . The duration between the first bite to lesion formation for LCL varied between 3–12 weeks [62 , 90] . Although CL can heal spontaneously , this seems to be dependent on the reported parasite species: L . major heals within approximately 2 to 12 months and L . tropica within 15 months , with a terminal scar appearing after about 24 months [11] . The description of diffuse CL caused by L . aethiopica suggests that it presents initially with nodules which do not heal or ulcerate but can metastasize widely [76] and are known to be very difficult to treat . In the case of DCL , spontaneous cure almost never happens . Mucocutaneous leishmaniasis is rare in Africa , but cases have been reported from Sudan and Ethiopia [52 , 80 , 100] . The lesions tend to be infiltrative and result in chronic edematous inflammation involving the lips , nose , buccal mucosa and larynx are . With regard to the locations of CL lesions , there appears to be a regional difference . CL lesions from eastern Africa are mostly found on the head ( i . e . , face including cheek , nose , forehead , ears , lips ) and less on the arms , legs or trunk , while from western Africa the highest proportion of lesions are on the upper and lower extremities . Amongst the 42 studies reporting the sex ratio of the patients ( Fig 3 ) , only 12 recorded more females than males affected [49 , 50 , 56 , 63 , 70 , 72 , 82 , 95 , 102] while the remaining described male preponderance , either due to hypothesized occupational exposure or males’ easier access to seek care in a health facility . Thirty-six out of the 54 studies reported the age of the CL cases: people of all ages are affected . However , when stratification according to age was reported , there is a broad tendency towards younger age groups ( between 10–30 years old . CL and HIV co-morbidities has been described in Burkina Faso [57 , 60 , 103] , Cameroon [70] , Mali [59] , and Ethiopia [91] , while sporadic cases have also been reported from Guinea , Ghana , Senegal , Nigeria , Ivory Coast and Sudan . Burkina Faso has recorded 13 . 5% ( 10/74 ) HIV positivity in a cohort of CL patients in 2000 , and another cohort of 32 CL/HIV patients was described in 2003–2004 [60 , 103] . Six out of 10 DCL cases in Ouagadougou were co-infected with HIV [57] . In Bamako , Mali , the prevalence of HIV among CL patients was 2 . 4% [59] . In Tigray , Ethiopia , a study reported an HIV prevalence of 5 . 6% , which increased to 8% two years later in 167 CL patients [92 , 104] . The only study reporting CL/HIV prevalence in the community was done in Cameroon in 2008 . Here , a total of 32 466 subjects were clinically screened , and amongst 146 active CL patients , seven ( 4 . 8% ) tested positive for HIV-1 and/or HIV-2 [70] . The consistent finding is that the clinical forms of CL are more diverse and complex in HIV co-infected patients , posing significant challenges in diagnosis and treatment . The lesions tend to be more severe: there are reports of infiltrative , leprosy-like , diffuse , psoriasis-like , verrucous , sporotrichoid , and angiomatous or Kaposi-like . Patients are more likely to have more than one lesion and more than one clinical forms [103] . Also , the time to lesion healing was longer in immunosuppressed individuals [70] , and particularly in atypical and severe CL patients with poor response to treatment [91] .
The epidemiological burden of cutaneous leishmaniasis in sub-Saharan Africa appears to be poorly documented . There is a paucity of robust evidence on prevalence and incidence on CL in this region . The diversity of CL epidemiological characteristics in endemic countries is not yet fully investigated . Nevertheless , the burden of CL morbidity remains important and most likely to be underestimated . Surveillance and mapping should be improved to mitigate outbreak risk and address dual co-infection with HIV . The current fragmented knowledge should be approached regionally , and awareness must be raised . In addition to population-based studies that better define the CL burden in sub-Saharan Africa , health systems should consider studies and action to improve CL essential diagnosis and care . | Cutaneous leishmaniasis ( CL ) is the most common form of this group of parasitic diseases , transmitted by sandflies . In sub-Saharan Africa , its extent of the problem is unknown , while elsewhere its disfigurement and stigma may cause a severe impact . This study systematically searched the literature to find evidence on the epidemiological data on human CL in this part of the world . Historically , CL has been present for decades in both western and eastern Africa , but unfortunately , in the last decades , the data are irregular and patchy . The estimated burden , relying on detected cases , may only capture part of the true number of cases . This article shows that there is insufficient evidence to have accurate figures; the diversity of the disease , along with poor surveillance have resulted in unprecedented CL outbreaks in the past . Many knowledge gaps remain , and we highlight the importance of improving the current fragmented knowledge by increasing commitments to tackle CL and conduct better population studies . CL in sub-Saharan Africa appears to be a blind spot and should not remain so . |
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A tantalizing question in cellular physiology is whether the cellular state and environmental conditions can be inferred by the expression signature of an organism . To investigate this relationship , we created an extensive normalized gene expression compendium for the bacterium Escherichia coli that was further enriched with meta-information through an iterative learning procedure . We then constructed an ensemble method to predict environmental and cellular state , including strain , growth phase , medium , oxygen level , antibiotic and carbon source presence . Results show that gene expression is an excellent predictor of environmental structure , with multi-class ensemble models achieving balanced accuracy between 70 . 0% ( ±3 . 5% ) to 98 . 3% ( ±2 . 3% ) for the various characteristics . Interestingly , this performance can be significantly boosted when environmental and strain characteristics are simultaneously considered , as a composite classifier that captures the inter-dependencies of three characteristics ( medium , phase and strain ) achieved 10 . 6% ( ±1 . 0% ) higher performance than any individual models . Contrary to expectations , only 59% of the top informative genes were also identified as differentially expressed under the respective conditions . Functional analysis of the respective genetic signatures implicates a wide spectrum of Gene Ontology terms and KEGG pathways with condition-specific information content , including iron transport , transferases , and enterobactin synthesis . Further experimental phenotypic-to-genotypic mapping that we conducted for knock-out mutants argues for the information content of top-ranked genes . This work demonstrates the degree at which genome-scale transcriptional information can be predictive of latent , heterogeneous and seemingly disparate phenotypic and environmental characteristics , with far-reaching applications .
Genome-scale transcriptional profiling has become a standard and relatively inexpensive way to identify the overall cellular state and condition-specific cellular responses to external stimuli . For instance , different sets of genes are known to be active in each growth phase and medium [1] , while strain polymorphisms can result in a remarkably diverse transcriptional repertoire [2 , 3] . Similarly , it is known that bacterial organisms undergoing rapid adaptations to varying environments , such as heat-shock and osmotic stress , produce differential expression profiles that are indicative of the corresponding stress [4–9] . Genome-wide transcriptional profiling can be thought of as a complex representation of all cellular functions and states , with a wealth of multiplexed information that , if decoded efficiently , can provide a fast and quite accurate all-encompassing snapshot of the cell and its environment . Despite its obvious correlation with various physiological and cellular states , we lack a clear understanding of the information content related to the manifold phenotypes that can be extracted from the genome-scale transcription profiles . Until now , a significant obstacle was the absence of sufficient transcriptional data to support the training of multi-feature and multi-label classifiers . Indeed , after aggregating all high-throughput transcriptional data that is currently available for E . coli , the most well-studied model microbe , we are still limited to a few thousands microarray or RNA-Seq experiments that cover more than 30 strains , a dozen different media and a multitude of other genetic ( knock-out , over-expressions , re-wirings ) , or environmental ( carbon limitation , chemicals , abiotic factors ) perturbations . Although this collection has already increased by an order of magnitude from the roughly two hundred genome-wide transcriptional profiles that we had eight years ago , it is still an inadequate sampling of the relevant experimental space . In addition , since these experiments have been performed in different technological platforms ( e . g . Affymetrix E . coli Genome 2 . 0 , Affymetrix E . coli Antisense ) and technologies ( e . g . microarrays vs . RNA-Seq ) , in different labs and under different environmental conditions , appropriate normalization schemes are both of paramount importance and with an added complexity . As such , efficient training of machine learning methods is hindered due to data complexity , compatibility and the curse of dimensionality that plagues datasets with thousands of features ( genes ) but only a few samples ( conditions ) . The application of high-dimensional prediction algorithms has been widespread in biology ranging from gene function prediction [10–12] , disease risk estimation from inherited variants [13] , and network inference [14–18] , but the vast majority of these studies are confined to the use of transcriptional data on pathological , pharmacological and clinical predictions [19–25] . Interestingly , a Saccharomyces cerevisiae study that involved tens of data samples was able to predict growth rates [26] , while a multi-class stressor prediction in rice used five hundred transcription profiles [27] . More recently , a probabilistic human tissue and cell type predictor was built based solely on gene expression profiles [28] . In this work , we investigate how well we can predict cellular and environmental state from genome-wide expression , using known gene expression profiles as our only training data . We report the optimal number of features for each classification task , what these features are , and all relevant pathways . To achieve this , we have extended , normalized and annotated a compendium that was compiled recently [29] to incorporate all published high-quality Affymetrix microarray and RNA-Seq datasets in E . coli ( 2258 samples in total , Fig . 1A ) . This E . coli Gene Expression Compendium ( EcoGEC ) , consists of publicly available data that were curated from online public databases such as GEO [30] , ArrayExpress [31] , SRA [32] , SMD [33] , M3D [34] and PortEco [35] . To increase the compatibility among the various arrays , we adjusted batch-effects across data from different sources and devised a statistical normalization scheme that significantly removed biases ( see Methods; Fig . 1B , Table 1 ) . Concomitantly , we developed an iterative learning procedure to impute unannotated or mis-labeled data and used it to increase the quality of the resulting datasets ( Fig . 2A ) . By applying four different machine-learning algorithms on the EcoGEC compendium ( Fig . 2B ) , we predicted six different organism and environmental variables from gene expression profiles related to medium , growth phase , strain , aerobic conditions , antibiotics and carbon sources present ( Fig . 2C ) . Functional , network and mechanistic analysis of the highly-informative features provide a comprehensive map of the implicated genes and pathways .
We first investigated how many genes are required to achieve optimal performance and the minimum number of genes with near-optimal performance , defined as 2% reduction from the optimal balanced accuracy . As shown in Fig . 3A , in most cases the cumulative information content is asymptotically approaching a maximal value within a few hundred genes . The balanced accuracy profile of the different predictors spans a large spectrum of behaviors , from profiles that are optimal early on , such as in the case of the medium classifier where the 150 first genes are sufficient for accurate classification , to profiles that rise slowly , as in the case of the composite classifier , which is defined as the model that classify classifies 3 characteristics of medium , phase , strain altogether . In general , however , our results show that the subset of genes that is needed to achieve high balanced classification accuracy is neither a handful of biomarkers , nor a large gene set , with all cases achieving near-optimal performance with 100 to 400 genes . In the most extreme case of the composite classifier , a near-optimal balanced accuracy ( 70 . 26% ) can be achieved with less than 400 genes , which is close to its maximum performance ( 71 . 55% ) that is achieved when considering all 4166 genes . To investigate the relationship of data size with classification performance , we systematically reduced the dataset , keeping a balanced class/label distribution . Our results argue that although there is an expected reduction in classification performance , as the dataset is progressively reduced by up to 75% , the method is quite robust with an average reduction of 6% classification performance per quartile reduction in data size ( S1 Table ) . In all cases , the classification performance is significantly higher than the balanced baseline ( Mann-Whitney-Wilkoxon test , P < 2 . 398 × 10−3 ) , with the balanced accuracy of all classifiers ranging between 69 . 95% ( ±3 . 52% ) to 98 . 27% ( ±2 . 32% ) ( Fig . 3B , S2 Table ) . For predicting the growth phase , we first imputed any unannotated phase information , which accounted for 34% of the compendium . We used a learning approach in which missing data is inferred iteratively . This preprocessing step was found to substantially increase the classification performance when evaluated across all classification tasks by an average of 7 . 3% and as much as 22% in some cases ( S1 Fig . , S2A Fig . , S2C Fig . , S3 Table , S4 Table ) . Interestingly , by following this approach , we were able to infer the characteristics from 90 . 6% of the unannotated phase data ( S2B Fig . ) . The iterative learning method does not significantly decrease the MI levels that are observed when compared to those obtained from the original dataset and the gene ranking is mostly preserved ( S5 Table , Kendall tau rank correlation: τ = 0 . 714 , P < 2 . 2 10−16 ) . The simultaneous prediction of all seven characteristics of a sample using seven individual classifiers yields an accuracy of 84 . 21% ( ±1 . 39% ) ( Fig . 3C ) . To create the necessary training set for the simultaneous prediction of three characteristics ( medium , phase and strain ) , we had to reduce the amount of classes to 13 due to insufficient data ( see Methods ) . Interestingly , the composite classifier that simultaneously selects one of the 13 classes , has an increased accuracy ( 71 . 55% ± 3 . 07% ) to that of individual classifiers on the same class types ( 61 . 23% ± 2 . 33% ) and it is significantly higher than the baseline ( 37% and 7 . 14% for balanced and imbalanced baseline accuracy , respectively ) . Altogether , the results suggest that multiple environmental and cellular features of an organism can be precisely predicted from a set of individual classifiers , by using a small , targeted gene set . Table 2 and S6 Table contain the contingency tables of each classifier and Fig . 3D depicts the corresponding ROC and PR curves [36] . The overall AUC of the ROC curves exceeds 0 . 82 , except in the case of stationary phase ( 0 . 71 ) . This result is likely due to the high noise level and low sampling size for that class , which dilutes discriminatory features between the mid/late exponential and stationary phases . In the contingency table of the composite classifier ( Table 2 ) , the lowest classification case was observed in the case of “Others” ( 58/179 samples ) . This is expected , since that class corresponds to samples that either are missing data or represent classes that have low sample sizes and are grouped together . Next , we investigated which genes have the highest information content and the respective pathways they belong to . The decrease of mutual information in ranked genes follows an inverse logarithmic relationship ( Fig . 4A and S5 Table ) . For each classifier , we selected the gene subset that accounts for the top 10% of the mutual information content of all genes , yielding feature sets that range from 49 to 136 genes . The overlap among classifiers is substantial: 141 out of a total 715 informative genes ( 19 . 7% ) are present in two or more different classifiers ( Fig . 4B ) . Functional enrichment analysis of the most informative genes reveals a rich repertoire of biological processes where their differential enrichment is discriminative of each specific class ( Fig . 5 , S7 Table ) . Not surprisingly , in the case of the aerobic respiration classifier enriched functional categories include cellular respiration ( P < 3 . 1 × 10−4 ) . Similarly , for phase and strain classifier , organic acid biosynthesis ( P < 2 . 7 × 10−4 ) and nitrogen biosynthesis ( P < 1 . 2 × 10−3 ) are up-regulated , respectively . Genes that are related to carbohydrate metabolism ( P < 6 . 1 × 10−7 ) are noticeably most informative to classify different carbon sources as well as strains . Some functional characteristics were statistically significant across multiple classifiers , including cell wall/peptidoglycan ( P < 2 . 7 × 10−7 ) and ATP-binding ( P < 1 . 5 × 10−8 ) , hydrolases ( P < 9 . 1 × 10−6 ) , membrane ( P < 4 . 1 × 10−6 ) , ribosome ( P < 2 . 2 × 10−7 ) and transport ( P < 4 . 2 × 10−6 ) ( Fig . 4C ) . The global pathway map in Fig . 5 depicts that most informative genes that were found to belong in five pathway groups: biosynthesis , signal transduction , degradation , transporter and central metabolism . For the composite classification of medium , strain and phase , relevant pathways are implicated with signal transduction , degradation , and transport ( Fig . 5A ) . Moreover , genes for phase classification are enriched in biosynthesis ( P < 4 . 3 × 10−7 ) which is in agreement with previous studies that report the prevalence of phase-dependent transcriptional regulation in a variety of biosynthetic processes [37–39] . Fig . 5B provides a more detailed view of the regional network involved in biosynthesis and transport , highlighting the pathways that would be most informative to classify various bacterial characteristics . Highly informative genes involved in specific pathways ( e . g . glutamate biosynthesis I , histidine , purine , and pyrimidine biosynthesis and glycerol-3-phophate/glycerol phophodiester ABC transporter ) have a crucial role from a functional network perspective , either by being a hub or their first-order neighbors in an identical pathway group . The analysis of the most informative genes for the media classifier reveals 14 genes encoding for membrane transporters and 7 involved in nitrogen metabolism ( Fig . 4 , S7A Table , S1 Text ) . From this set , five are implicated in amino acid transportation and synthesis ( gltK , gltJ , gltL , dppF , glnD ) . Different media contain different amounts of amino acids and nutrients required for bacterial growth so the activation of their biosynthesis is expected to be an informative feature about the media where bacteria are growing . Another 3 genes are involved in the enterobactin synthesis ( entA , entE , fepA ) , a siderophore that has been very recently revealed to be related to the growth of E . coli in M9 [40] . Over the course of the growth curve , the metabolic pathways change in order to optimize the use of the available nutrients and to ensure survival under stress conditions . The major transcriptional regulator for the entry into stationary phase is RpoS and , as expected , it is present in the set of genes informative for growth phase , along with several genes belonging to its regulon like dnaK , clpx , hemL , dps , rpsK , hfq , rplA , crr , rpsE and gapA [41] . In this set of genes , there are also genes already described to be differentially expressed in stationary phase , like hpf , crr and sspA [42–44] . In addition , ribosomal proteins ( rpsL , rpsQ , rpsE , rplA , rplT , rpmJ , rrsG ) are also implicated to be phase-dependent , which is in agreement with previous reports [45] . In the case of the strain classifier , the analysis displays a wide variety of genes involved in different pathways and cellular processes . Different strains have evolved differentially from their common ancestor and , hence , have developed different regulatory pathways for various processes including carbon assimilation , degradation , and membrane formation . All informative genes for the medium classifier ( S7A Table ) are included at the top 10% informative genes of the composite classifier with all remaining genes being part of metabolic processes ( S7D Table ) . Environmental perturbations , such as carbon source and oxygen abundance , give rise to informative genes that are specific to those cellular processes ( S7F Table and S7G Table , respectively ) . In the case of oxygen , GO analysis reveals 8 genes involved in the respiratory process , 4 in aerobic respiration ( sucA , acnB , nuoJ , cyoE ) and another 4 in fermentation ( hycC , hycE , hycF , fhlA ) . For carbon source prediction , we can find 15 proteins associated with membrane formation , with 6 of them described transporters ( atpC , kgtP , rhtB , lptG , malF , malG ) . In addition , 5 differentially expressed genes involved in carbohydrate metabolism also stand out ( malS , kgtP , malF , malG , pta ) . Regarding antibiotics , we have tested Norfloxacin , which functions by inhibiting DNA gyrase . Unexpectedly , in its informative gene list we cannot find any gene related to DNA repair or SOS response ( S7E Table ) , possibly because these genes are involved also in other environmental conditions and are not antibiotic-specific . Most of the genes that reveal the presence of Ampicillin are membrane proteins and cell wall proteins which is in agreement with its function as cell membrane inhibitor ( S7H Table ) , including the membrane protein porin ( ompF ) that is known to bind ampicillin [46] . Interestingly , a substantial subset of the informative genes that were selected as features were not differentially expressed in the respective samples ( S2 Table ) . A closer look at those genes , which range from 70% to 18% of the corresponding feature set , reveals that they indeed take part in processes that are characteristic of the respective environmental conditions . For instance , the oxygen classifier contains as features genes that are involved in both aerobic ( cyoD , nuoK , sucD , sucC and cyoB ) and anaerobic respiration ( hycB , menF , nuoK , nfsA , hypA ) , although these genes would not be selected if we ranked based on differential expression . Similarly , in carbon source classification this set includes 11 genes involved in carbohydrate catabolic processes ( dkgB , araG , gatZ , fbaA , malE , murQ , ascF ) and 6 in cellular polysaccharide metabolic processes ( kdsA , kdsD , waaC , waaP , rfaZ , rfa ) . The 24 transporters used for the medium classification , the 5 genes involved in translation for phase classification and 72 membrane proteins that are contained in the antibiotic feature set are indeed expected to be informative in the respective classification task , despite not being in the top differentially expressed genes . The results obtained in this study can be used to decipher novel , condition-specific gene functions . To assess whether biological function can be predicted by targeted experimentation of classifier-specific informative features , we selected one gene with high MI for carbon source classification ( ppiD ) and another gene that is highly ranked for classification between aerobic and anaerobic respiration ( ldcC ) . The MI of each gene is only high in the classifier of interest and not in the rest ( S8 Table ) . We then tested knock-out mutants [47] in their respective conditions . As such , both the ppiD and ldcC mutants and the wild type strain were grown in M9 supplemented with three different carbon sources: glucose , glycerol and lactate . The ldcC mutant functions as a negative control in the case of carbon source classification since this mutation is expected to have no effect on medium determination . Indeed , the results ( S3 Fig . , S12 Table ) show that ΔppiD growth is impaired in the presence of the three sugars ( t – test , P < 0 . 03 ) while growth with the ldcC mutant remains similar to the WT demonstrating the involvement of ppiD in the use of different carbon sources ( t – test , P > 0 . 07 ) . ppiD has been described as a membrane-anchored chaperone [48] but its specific function has not been discovered . Our result suggest that this protein is involved in sugar metabolism , possibly related to folding activity of membrane sugar transporters . Growth curves for knockout replicates of the top five informative genes for different carbon sources , as well as the growth curves for the genes related to aerobic growth genes ( as negative control ) , are shown in S4 Fig . . As expected , growth deficits were more pronounced in the first set in both glycerol and lactate ( t – test , P < 0 . 006 and P < 0 . 008 , respectively ) . We performed a similar experiment where the three strains ( WT , ΔppiD and ΔldcC ) were grown in M9 with glucose in aerobic and anaerobic conditions , in order to assess the influence of the ldcC mutation in these conditions . Here , the ppiD mutant serves as the negative control and the ldcC mutation is indeed informative of the aerobic conditions , although the difference is not as pronounced as in the case of carbon source classification ( P < 0 . 029 for ppiD; P > 0 . 080 for ldcC ) . A closer look at the MI values show that the informative genes for aerobic respiration are two orders of magnitude lower than those for medium , which suggests that information content is dispersed among a number of genes .
How much information regarding the life and the present environmental context can be inferred from the global transcription profile of an organism ? To address this question , we constructed an extensive , annotated gene expression compendium , where we trained Bayesian models for seven distinct classification tasks . Our models achieved high classification performance that was robust on the number of genes that were used as informative features . Our work demonstrates that bacterial transcriptomes embody rich information regarding the organism and the environment that it inhabits . Recent work demonstrates the power of such datasets to identify data-driven ontologies and rethink the definition of biological processes within them [49] . More importantly , multiple characteristics of an organism can be accurately predicted using a set of character-specific classifiers , suggesting practical advantages of this approach over limited datasets . Transcriptional activity is not the sole feature type that conveys predictive information regarding environmental conditions and an organism’s characteristics . Like eukaryotes , epigenetic signals regulate transcriptional activity in bacteria , for example , by altering DNA methylation states to control the binding of proteins to DNA [50] . Single-molecule real-time ( SMRT ) sequencing technology has been recently applied to reading of genome-scale methylation states in a pathogenic E . coli [51] and the technology would provide higher-resolution of molecular information of bacteria , enabling fine-scale predictive characterization based on it . Other features related to the genome-scale metabolic state , proteomic biomarkers and cell morphology can be incorporated to increase the predictive capacity of any given classifier . Similarly , while the six characteristics that we evaluated here are fundamental in their role and indicative of global processes , there are several other environmental and organismal characteristics , such as other abiotic factors or other microbial species in the same environment , which can be predicted from these features . Multiple characteristics of an organism are interrelated , implying its heterologous transcriptional landscapes in different combinations of phenotypic conditions . These complex dependencies in phenome are not readily analyzable even in the compilation of thousands of publicly available transcriptome profiles as the experimental conditions in published data are often disproportionate , typically skewed in favorable settings ( e . g . MG1655 strain over LB medium ) , which produces small sample sets or even empty sets in combinatorial conditions . Indeed , the results on composite classification argues that with the current omics dataset compilation , it is not feasible to explore many of the strain , phase , medium combinations , as we have sufficient data for only 13 classes , out of a total of 48 possible classes ( 4 for each of medium , phase , strain ) . Interestingly , the performance of the composite multi-class classifier performs significantly better for the overall classification of these characteristics , than an aggregate of individual classifiers for phenotypes , demonstrating large interdependencies across different conditions . By looking at the top informative genes in two classifiers , we demonstrated the involvement of the ppiD in the utilization of different carbon sources . Further analysis involves the use of over/under-expressed copies and protein-protein assays to discover quantitative associations and interaction partners . By analyzing the expression levels of the genes in the phase classifier that are not predictable using RT-PCR and transcriptional fusions we can find out novel regulation when growth phase changes from exponential to stationary . Another potential application is in the case of the antibiotics Ampicillin and Norfloxacin where this analysis can be used to identify implicated pathways in lethal and non-lethal concentrations . In recent years , the capacity of microorganisms to sense and act upon environmental stimuli [52] has sparked renewed interest due to its diverse applications in preventive medicine and synthetic biology [53 , 54] . These studies shed light on the adaptive behavior of cells under environmental temporal stimuli [55–57] and on the decomposition of promoter activity in complex conditions [58] . Our work here is the first that attempts to identify and comprehensively interpret the capacity of the transcriptome for characterizing a manifold of environmental conditions using the consensus of multiple statistical learning algorithms . Aside from its intellectual merit , the presented work can help building classifiers and selecting features in a number of practical applications . Detection and characterization of microbes are of great importance in many clinical , environmental , industrial , and agricultural application [59] . Data are increasingly become available for the adoption of such classification techniques since high-throughput methods have been recently applied at low cost . From battlefields to agricultural crop management , inexpensive sequencing transforms the landscape of what is possible in a timely , inexpensive manner . Our work paves the way towards the use of high-throughput expression datasets to a broad range of applications including detection and characterization of the environmental conditions and bacterial population that are important for clinical , environmental , industrial , and agricultural applications . Without loss of generality , this work can be described as a data-driven approach to “bacterial forensics” , i . e . the extraction of environmental knowledge from large-scale phenotypic bacterial data , and it can have far-reaching applications in environments that would be challenging to investigate otherwise .
We downloaded 83 RNA-Seq E . coli transcriptional profiles from 17 different GEO entries [30] that correspond to 8 strains , LB and MOPS media in wild-type ( WT ) , gene knock-outs ( KOs ) , double KOs and environmental perturbations . When bedGraph format was used in the data , gene expression level was measured in RPKM using the bgrQuantifier program that is part of the RSEQ tool [60] . For other formats such as wig , we first converted them into bedGraph . We filtered out samples where the environmental information was not known , which led to 64 samples for further analysis . Data were converted to log2 scale and performed quantile-normalization using MATLAB . The resulting RNA-Seq dataset was composed of 64 samples of 4725 genes . We integrated the RNA-Seq dataset ( 64 samples ) to the E . coli Microarray Compendium ( EcoMAC ) that consists of 2198 microarrays of 4189 genes for which raw files were downloaded and normalized by RMA ( robust multichip average ) method [29] . The integrated EcoGEC dataset consists of 2262 samples and 4166 genes ( Fig . 1A , S13 Table , S14 Table ) . Although integrative analysis of multiple microarray gene expression ( MAGE ) datasets allows to distill the maximum relevant biological information from genomic datasets , the unwanted variation , so-called batch-effects arising from data merged from difference sources has been a major challenge to impede such effort [61] . To adjust the non-biological experimental variation with the consideration of large number of datasets with a few samples , we used ComBat that is developed under Bayesian framework and is known to be robust to outliers in small sample sizes [62] . In the process of adjustment , we took into account experimental conditions as covariates to prevent loss of biological variations . Prior to building a prediction model , we transformed the adjusted gene expression data into categorical values ( under-expressed , UE; wild-type , WT; over-expressed , OE ) in order to deal with biases arising from combining different platforms and improve the classification accuracy [63] . We first measured the log2 Fold Change ( FC ) of gene expression with respect to the WT expression for each gene . WT samples were identified from experiments that didn’t undergo genetic and environmental perturbations from the three platforms ( 7 for Affymetrix E . coli Antisense Genome Array , 6 for Affymetrix E . coli Genome 2 . 0 Array , and 6 for RNA-Seq ) . log2 Fold Change ( FC ) was separately measured for each platform by comparing the mean of WT data . Using transformed data , we estimated a normal distribution N ( μ , σ2 ) for each gene and finally converted each log2 FC gene value into one of the 3 categorical values by measuring deviation from the mean ( UE when gij < μi – σi; WT when μi – σi ≤ gij ≤ μi + σi; OE when μi + σi < gij; gij is the log2 FC for gene i in sample j , μi is mean of gene i and σi is standard deviation of gene i ) . The platform-specific categorization of gene expression effectively removes platform biases ( Fig . 1B ) . The large fraction of unannotated phase data in the compendium hinders the maximum utilization of such resource . Missing phase information was imputed by iterative learning approach in which prediction model for growth phase is trained using the annotated phase data and inferred data in previous iteration until prediction of unknown data finally reaches at convergence ( S1 Fig . ) . In each iteration , the experiments that were unannotated ab initio were repeatedly inferred . Inference is based on consensus-based approach of four machine learning methods described above . Re-labeled phase information accompanying with annotated data is used for training the consensus model in next iteration . This procedure is halted once the similarity of phase labels between consecutive iterations is convergent when the similarity of phase labels between consecutive iterations converges ( change in fraction < ξ , where ξ = 0 . 01 here ) . Although the use of inferred labels through iterative learning demonstrates an increased performance , compared with the prediction using known labels only ( S2 Table ) , we report the performance for phase prediction using annotated labels only throughout the manuscript . To investigate the accuracy ( balanced ) of inference of unannotated data , we performed the simulation study for each classifier by randomly masking 30% of total labels of each class . First , the accuracy of inferred annotation after iterative learning is measured by comparing with real labels before and after iterative learning ( S2 Table ) . Then we further evaluate iterative learning for by changing the percentage of unannotated labels ( 2% , 5% , 10% , 20% ) in the total data ( S2 Fig . and S4 Table ) . A label is assigned for each of the seven classification characteristics ( two for antibiotics; Ampicillin and Norfloxacin ) . We have identified 4 classes for medium ( LB , M9 , MOPS , others ) , 3 classes for phase ( early-exponential , mid/late-exponential , stationary ) having both annotated and predicted data , and 4 classes for strain ( MG1655 , BW25113 , EMG2 , others ) . A class of “others” was added that corresponds to conditions that are unclear or scare in quantity . Classification of the strain , medium , and growth conditions can be integrated also as a multi-class problem . We synthesized a new predictor variable called composite by combining values of 3 characteristics . From the 48 possible classes ( combination of 4 labels for medium , 3 labels for phase , 4 labels for strain ) , only 13 combinations have enough data ( more than 5 samples ) for training , hence we have encompass all other labels with insufficient data under the label “others” , resulting in a total of 13 classes ( Table 2 ) . We use Naïve Bayes ( NB; [64] , Decision Trees ( DT , [65] ) , K-nearest-neighbors ( KNN , [66] ) and Support Vector Machines ( SVM , [67] ) to construct a consensus classification scheme [68] . The class label assigned is the one with the highest number of votes . The predictive power is assessed through Receiver-Operator Characteristic ( ROC ) and Precision-Recall ( PR ) curves [36] . For multi-class problems , such as in the case of medium , phase and strain classification , we built ROC/PR curves in a one-versus-rest ( OVR ) approach . The leave one batch out cross validation was conducted to verify model performance while removing batch effects . For this , each batch is left out for testing and the rest of data is then used for training . This procedure is iterated until all batches in the dataset are tested . For carbon source , phase , and composite classifier , the profiles having early-exponential phase or acetate are studied in a single project so inevitably , we had to rely on the batch-uncontrolled cross-validation . The classifier performances with and without batch control are compared in S11 Table . As the high imbalance of class distribution is observable in the dataset as shown in Table 1 , creating inflated baseline , we show the classifier performance for the original dataset as well as for the dataset with balanced class distribution . Mutual information is a stochastic measure of dependence [69] and it has been widely applied in feature selection in order to find an informative subset for model training [70] . In our work , each of the eight models were trained with the top k-ranked genes based on their mutual information ( MI ) to the label where MI is measured by I ( X;Y ) =∑∑p ( x , y ) log ( p ( x , y ) /p ( x ) p ( y ) ) Where x is the gene selected and y is the predictor variable . This process is iteratively repeated by increasing k with an interval of 10 and the exception of start ( 10 ) and end points ( all genes ) . Basically , the selection procedure of k features are performed in training data only and k showing the highest performance is selected for testing . All the analyses in this study other than the cross-validation of model used the features selected from the complete data . The most informative genes are selected by measuring the mutual information ( in bits ) for each of the characteristic variables and then selecting the top 10% genes based on their information content . These top informative genes are then used for finding shared genes across different classifiers ( Fig . 4B ) and for network analysis ( Fig . 5 ) . For functional enrichment analysis , we use all selected genes that optimize the classifier performance . Associated functional annotations for the set of selected genes for each of the classifiers are found by DAVID [71] . Various annotations including Gene Ontology terms , KEGG pathways , and InterPro protein domains are investigated . Among them , the 6 most statistically significant terms ( P < 3 . 7 10−4 ) for each classifier are displayed in Fig . 4 . Global map of genetic interactions for E . coli is reconstructed from [72] with pathway modules that functionally cluster genes based on the Pathway Ontology and transporter complexes curated in EcoCyc [73] . Pathway diagrams were re-plotted from the KEGG database [74] . In addition to DAVID , we have performed a GSEA analysis [75] where each gene is ranked by its mutual information ( S9 Table ) . We have also compared the results to those obtained by DAVID and provide this comparison in S10 Table . On average , 80 . 5% of DAVID results that correspond to the feature set at optimal classification performance are in the GSEA enriched terms . Growth curves of the WT , ΔppiD and ΔldcC were performed in M9 complemented with 0 . 4% of glucose , glycerol and sodium lactate . For growth curves , the starter cultures of all strains were grown and therefore adapted ( B7–9 generations ) to M9 glucose for 12 hours at 37C . Cultures were started at OD600 of 0 . 004 . OD600 was measured every 10 minutes on a Tecan Plate Reader . Two independent replicate growth tests were performed for each strain . For the anaerobic and aerobic growth curves bacteria were grown in M9 supplemented with glucose at 37C without shaking . The anaerobic growth was made in an anaerobic chamber where media was inserted 2 days prior to the experiment to extract all the oxygen present in the media . Samples were taken at 2 , 8 and 24 hours through a spectrophotometer ( S12 Table ) . For consensus-based prediction using four different classifiers , we used the Statistics Toolbox in MATLAB . For the multi-class SVM , one-versus-rest ( OVR ) approach was used in which for each class , a binary classifier is built for the class label and the rest . Each binary SVM was built using Gaussian Radial Basis Function ( RBF ) kernel and the default sigma factor of 1 was used . For soft margin , C parameter showing best performance was selected in the range of 0 . 5 to 4 in the training phase . For KNN , K was set to one in knnsearch . For decision tree and naïve Bayes , the default settings in ClassificationTree and NaiveBayes were used , respectively . The code used in this study including the imputation by iterative learning and the consensus-based prediction that allows users to reproduce the results is freely available on gitHub ( https://github . com/minseven/mForensics . git ) . | The transcriptional profile of an organism contains clues about the environmental context in which it has evolved and currently lives , its behavior and cellular state . It is yet unclear , however , how much information can be efficiently extracted and how it can be used to classify new samples with respect to their environmental and genetic characteristics . Here , we have constructed an extensive transcriptome compendium of Escherichia coli that we have further enriched via an iterative learning approach . We then apply an ensemble of various machine learning algorithms to infer environmental and cellular information such as strain , growth phase , medium , oxygen level , antibiotic and carbon source . Functional analysis of the most informative genes provides mechanistic insights and palpable hypotheses regarding their role in each environmental or genetic context . Our work argues that genome-scale gene expression can be a multi-purpose marker for identifying latent , heterogeneous cellular and environmental states and that optimal classification can be achieved with a feature set of a couple hundred genes that might not necessarily have the most pronounced differential expression in the respective conditions . |
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The type III secretion system is an essential component for virulence in many Gram-negative bacteria . Though components of the secretion system apparatus are conserved , its substrates—effector proteins—are not . We have used a novel computational approach to confidently identify new secreted effectors by integrating protein sequence-based features , including evolutionary measures such as the pattern of homologs in a range of other organisms , G+C content , amino acid composition , and the N-terminal 30 residues of the protein sequence . The method was trained on known effectors from the plant pathogen Pseudomonas syringae and validated on a set of effectors from the animal pathogen Salmonella enterica serovar Typhimurium ( S . Typhimurium ) after eliminating effectors with detectable sequence similarity . We show that this approach can predict known secreted effectors with high specificity and sensitivity . Furthermore , by considering a large set of effectors from multiple organisms , we computationally identify a common putative secretion signal in the N-terminal 20 residues of secreted effectors . This signal can be used to discriminate 46 out of 68 total known effectors from both organisms , suggesting that it is a real , shared signal applicable to many type III secreted effectors . We use the method to make novel predictions of secreted effectors in S . Typhimurium , some of which have been experimentally validated . We also apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis , identifying the majority of known secreted proteins in addition to providing a number of novel predictions . This approach provides a new way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal .
Gram-negative bacteria are a major cause of many human diseases and , due to the emergence of antibiotic resistance , development of new means to combat their infection is a goal of the world health organization ( WHO ) and other international health organizations [1] . Pathogenic bacteria express a large number of proteins associated with virulence some of which are secreted into the host milieu and interfere with normal host cell functions or immune response . Since many virulence factors allow the survival of pathogens under very specific infectious conditions they represent attractive targets for alternative therapies relative to current strategies , which aim to kill all bacteria and thus efficiently drive the emergence of antibiotic resistance and increase the host susceptibility to other infections by eliminating the normal flora [2] . The type III secretion system in Gram-negative bacteria forms the interface between the pathogen and its host [3] , [4] . Electron microscopy has revealed that the secretion machinery forms a needle-like structure that spans the inner and outer bacterial membrane [4]–[6] and allows injection of protein effectors directly into the cytoplasm of the eukaryotic host cell [7] . Each bacterial species has a repertoire of effector proteins which enact the virulence program of the bacteria by directly interacting with host cell pathways [7] . Though some of the genes that comprise the secretion machinery are well-conserved between species [8] , [9] , sequences of virulence effectors are diverse and the identity and nature of their signal sequences , target protein ( s ) in the secretion complex , and methods of regulation are poorly understood [4] . While carboxy terminal sequences can be important , as a general rule secreted proteins are targeted to their cognate apparatus by a signal that is encoded in the N-terminal region of the protein or alternatively the 5′ end of the mRNA sequence , and provides a sequence-based signature for the system [10] , [11] . To understand the type III secretion system and catalog its full complement of secreted substrates it is necessary to identify this secretion signal [4] , [12]–[14] . Elucidation of the mechanism by which effectors are targeted to be secreted will provide valuable insight into the virulence program of many Gram-negative bacteria . Effectors generally have two N-terminal domains that are important secretion . Residues 1–25 contain a region thought to be a secretion signal but that is highly variable in sequence [15] and , at least in some cases , highly tolerant of mutations [16] , [17] . For some effectors this region has been shown to be both necessary and sufficient for secretion [10] , [16] , [18] , [19] . However , no sequence motifs or common patterns have been identified that can be used to accurately predict type III secreted substrates . In addition , some effectors contain a chaperone binding domain that spans residues 25–100 [12] . Chaperones are necessary to stabilize some effectors , to maintain them in an unfolded state prior to secretion , and to expose the secretion signal sequence itself [4] , [12] . It has been proposed that the N-terminal secretion signal is an ‘ancestral’ flagellar targeting signal and that the chaperone-binding domain and chaperone itself may in some cases target the effector to a specific secretion apparatus [19] . In this study we chose to analyze type III secreted effectors and their putative secretion signals in three organisms: S . Typhimurium , P . syringae , and Chlamydia trachomatis . Though all three organisms are Gram-negative pathogens with type III secretion systems , they differ in host range , evolutionary history [20] , and lifestyles . Phylogenetic analyses of core components of the type III secretion systems also suggests that though they originated from a common ancestor , the secretion system from each of the organisms in this study falls into a different distinct group [21] , [22] . Both S . Typhimurium and P . syringae have extensively characterized repertoires of type III secreted effectors [23] , [24] , which provide a sufficient number of examples for rigorous training and evaluation of a computational learning approach such as ours . C . trachomatis was chosen as an important pathogen , which has a relatively poorly defined , set of secreted effectors , and thus represents a good target for computational predictions [25] . Proteins secreted through the type III secretion system are highly variable in sequence . Though there are related families of effectors [26] , [27] , a significant number have no detectable sequence similarity to any other known effectors . Approaches based on sequence similarity , G+C content , genomic location within horizontally transferred regions of the chromosome , regulation by known virulence regulators , fusion to enzymatic or epitope tags , and homology between diverse pathogenic organisms have all been used to identify effectors with limited success [26] , [28]–[35] . Most recently , a proteomic approach was used to greatly expand the estimated number of secreted effectors in pathogenic E . coli 0157:H7 [36] . This finding indicates that there are likely to be a large number of unknown effectors in type III secretion system-containing bacteria , even in well-studied organisms like S . Typhimurium and P . syringae . General features of the protein sequence have also been used to the same end , focused on the N-terminal secretion signal . In P . syringae the amino acid biases and patterns in the N-terminal secretion signal were used to identify novel effectors [30] , [34] , [37] . Detection of common promoter elements has also been used to identify novel effectors in P . syringae [32] , but this approach is limited to known and detectable motifs . To date there have been neither systematic predictive studies of type III secretion system effectors nor a general strategy to identify proteins that are targeted to the type III secretion system . We use a novel computational approach to identify secreted effectors based on sequence analysis and to delineate and define a putative N-terminal secretion signal common to the majority of type III secreted effectors . Our method , the SVM-based Identification and Evaluation of Virulence Effectors ( SIEVE ) , is trained on a set of known examples of secreted effectors based on sequence-derived information and then used to provide accurate predictions of secreted effectors in evolutionarily distinct bacteria . We show that SIEVE can identify known secreted effectors very well with simultaneous specificity and sensitivity of greater than 88% for prediction of effectors when trained on one species and tested on the other , in the absence of detectable sequence similarity between effectors in the two sets . A considerable strength of our findings comes from the fact that we considered a large number of different sequences from effectors in multiple organisms . Previously this has only been used for detection of sequence homology between effectors using traditional approaches [36] . Our novel analyses allowed us to detect the presence of a protein-encoded secretion signal in the N-terminal 16–20 residues of the majority of type III secreted effectors examined . Though variable in sequence , we define the most important residues for this secretion signal across multiple organisms . Finally , we use a model trained on the effectors from S . Typhimurium and P . syringae to suggest new candidates for type III secretion in S . Typhimurium and in C . trachomatis , the most common cause of female infertility in the US [38] .
We chose to target S . Typhimurium and P . syringae for our initial analysis because they have been well studied , especially in regard to type III secreted effectors , providing enough well-validated examples to train and evaluate our methods . C . trachomatis was chosen as a target for novel predictions because of the difficulties associated with studying it experimentally and its corresponding lack of well-validated secretion substrates . Salmonella infection is a major public health problem with three million cases of infection per year in the U . S . alone [39] . With the recent emergence of untreatable , multi-drug resistant strains such as phage type DT104 [40] the public health threat has become greater . Genome sequences were obtained from the NCBI database for S . Typhimurium LT2 ( AE006468 ) and associated virulence plasmid ( AE006471 ) . A set of 36 S . Typhimurium proteins reported to be type III secreted effectors was compiled from the literature ( Table 1; see also [23] ) . P . syringae strains have a broad host range in plants and cause a variety of diseases and is an important model system in plant pathology . Numerous studies of the secreted effector repertoires in P . syringae have been published [30] , [32] , [34] , [37] , [41] , [42] . This makes it an attractive model organism for testing methods to predict secreted effectors . We used the genome sequence from NCBI for P . syringae pathovar phaseolicola ( NC_005773 ) and a set of 32 P . sryingae type III secreted effectors was downloaded from the Pseudomonas-Plant Interaction website ( http://www . pseudomonas-syringae . org/ ) hypersensitive response and pathogenicity ( Hrp ) outer protein ( hop ) virulence protein database . C . trachomatis is an obligate intracellular pathogen infecting humans and causes a variety of sexually transmitted diseases [43] , as well as trachoma , a leading cause of preventable blindness worldwide [44] . The Chlamydiae infect a wide range of vertebrates and free-living amoebae and are a considered to be only distantly related to the Proteobacteria [22] . Though the genome sequence of C . trachomatis revealed the presence of a type III secretion system [45] , research on this system and its effectors has lagged due to difficulty cultivating this genetically intractable , obligate intracellular pathogen [14] . We obtained the genome sequence of C . trachomatis ( AE001273 ) from the NCBI database . SIEVE predictions for all proteins in these organisms as well as Shigella flexneri , Yersinia pestis and Vibrio cholerae , is available as Table S5 . To accurately determine the performance of SIEVE across organisms , all effectors in P . syringae that had any level of sequence similarity detectable by BLAST [46] to any effector in S . Typhimurium were removed . This reduced the number of effectors used in P . syringae from 32 to 29 , eliminating HopAN1 , HopAJ1 and HopAJ2 from consideration . BLAST was executed with default parameters meaning that sequence matches with expectation values worse than 2 . 0 were not reported . This process provides a conservative group of non-redundant effectors , ensuring that the performance results we report are not based on sequence similarity . Support vector machines ( SVM ) are a class of computational algorithms for classification [47] , [48] . Essentially , they can learn patterns based on known members of a class of protein sequences ( positive examples ) and the corresponding protein sequences , which are not members of that class ( negative examples ) . This process is referred to as “training” the algorithm and results in a computational “model” . The model can then be used to classify a different set of known examples to evaluate the performance of the model or can be applied to a set of unknown sequences to provide novel predictions . Information from each example sequence is used to train the model and the particular types of information chosen are referred to as the “features” of the model . For training the SVM in SIEVE we chose to use known secreted effectors as positive examples and proteins that have not been identified as effectors , i . e . the remainder of the proteins in the organism , as negative examples . The true set of negative examples is actually unknown; in fact we show that a number of the proteins in our negative example set are secreted but had not been identified during compilation of our initial positive example set . This fact means that the performance we report using SIEVE is a conservative , lower bound estimate , since it contains an unknown number of misclassified false-positive predictions ( i . e . real secreted effectors that have not yet been discovered ) . Features are the different sequence characteristics used as input to the SVM . The SVM uses the features to learn the difference between the positive and negative examples . Five sets of features were chosen for SIEVE based on their known or suspected distributions in secreted effectors: evolutionary conservation of the protein sequence ( CONS ) , a phylogenetic profile of sequence similarity to 54 other genomes ( PHYL; Table S1 ) , nucleotide composition of the cognate gene ( GC ) [35] , amino acid composition ( AA ) [17] , [41] , [49] , [50] , and finally the sequence of the N-terminal 30 residues of the protein sequence ( SEQ ) [30] , [50] . To determine the most important features for classification we used an iterative process known as recursive feature elimination ( RFE ) that successively eliminates features with low impact on the overall performance of the model . We used the SVM software suite Gist [51] to perform all training , testing and evaluation of different models . Except where noted ( e . g . Figure S1 ) , we used a radial basis function kernel with a width of 0 . 5 and an optimized ratio of negative to positive examples ( Figure S2 ) for SIEVE classification . See Text S1 for further details on machine-learning methods and the evaluation approaches used . To evaluate the performance of the method we used measures of sensitivity , the number of predictions that were correctly predicted as true positives divided by the number of all positive examples ( TP/ ( TP+FN ) ) , and specificity , the number of predictions that were correctly predicted as true negatives divided by the number of all negative examples ( TN/ ( FP+TN ) ) . We also used a common measure of performance for classification tasks , the receiver operating characteristic ( ROC ) curve that is produced by plotting the sensitivity of the method versus specificity [52] . The area under a ROC curve ( AUC ) is 1 when all examples ( positive and negative ) are classified correctly and is 0 . 5 when classification is random .
Bioinformatics approaches have been used to identify secreted effectors in a variety of organisms with some success [30] , [34] , [36] , [37] . However , the approaches described in these studies are focused on predicting effectors in a single organism and do not generalize to prediction in other organisms or are based on homology with known effectors . Accordingly , we wanted to test the ability of these methods in predicting secreted effectors in S . Typhimurium . We first examined the ability of SecretomeP [53] , a program which identifies non-classically secreted proteins generally in Gram-negative bacteria ( http://www . cbs . dtu . dk/services/SecretomeP/ ) . SecretomeP identified 12 of 36 known effectors in S . Typhimurium to be secreted , but also identified over 400 non-type III secreted proteins , yielding an overall precision of less than 3% for type III secreted substrates . This is not surprising since the method is trained on proteins secreted by a number of different systems , and is not designed to specifically identify type III secreted effectors . We next tested the ability of two simple measures to discriminate secreted effectors; the G+C content of the associated gene and the number of number of polar residues [34] in the N-terminal 30 amino acids of the protein . Plotting the sensitivity of this method versus its specificity gives the receiver operator characteristic ( ROC ) curve , which provides a summary of the performance of a method to classify things into two categories . Surprisingly , we found that the G+C content gave performance of 0 . 89 ( as judged by ROC analysis ) to discriminate secreted effectors from other proteins in S . Typhimurium . However , even with this performance the top 5 true positive predictions could be discriminated with a precision of only about 6% ( i . e . with 81 false positive predictions ) so the level of precision possible using this measure alone was also low . Additionally , we found that G+C content gave an ROC of 0 . 73 for prediction of P . syringae effectors indicating that it cannot be used to identify all effectors with the same confidence . The observed performance of G+C content in S . Typhimurium may be due to the fact that most effectors are located in horizontally transferred pathogenicity islands or islets , such as SPI-1 and SPI-2 [54] , [55] . Amino acid biases were largely uninformative for predicting effectors but the count of serine residues in the N-terminal 100 residues gave an ROC of 0 . 73 . This is consistent with previous observations of amino acid biases , including serine , in the N-terminal regions of effectors [24] , [41] . One previously published study that identified secreted effectors in P . syringae based in part on bioinformatics techniques [30] defined two sequence motifs . Secreted effectors were predicted by first searching for these two motifs then applying several other heuristic rules ( e . g . sequences shorter than 150 residues were screened out ) . We applied these same set of criteria to S . Typhimurium proteins and found that they could correctly identify only two of the known secreted effectors out of a total of 52 predictions ( 4% precision ) . This shows that these patterns while accurate on P . syringae are not applicable to S . Typhimurium . Another recent study used BLAST-determined sequence similarity between secreted effectors in different organisms to identify novel secreted effectors in Escherichia coli O157:H7 [36] . Though this approach is applicable to identification of secreted effectors in other organisms , it is based on detectable sequence similarity between known effectors , which is a significant limitation . The performance of the BLAST-based approach ( see Text S1 ) was 0 . 79 for prediction of known effectors in S . Typhimurium . Nearly one-third of the known effectors in S . Typhimurium showed no detectable sequence similarity to any of the effectors in the compiled list of all known effectors and thus could not be identified by this approach . Our results from applying these previously described methods for identification showed that though G+C content alone was surprisingly effective at predicting secreted effectors , its precision was too low to provide very useful predictions . Likewise , sequence patterns developed in P . syringae and more general amino acid composition biases provide limited discrimination . Finally , BLAST similarity to known secreted effectors in other organisms provided reasonable discrimination , but this approach identified only those secreted effectors that have been identified in another organism . We found that existing computational methods to identify secreted effectors were somewhat effective in different ways when applied to known effectors in S . typhimurium . We therefore wanted to see if the integration of some of the data underlying these approaches could be used for more accurate prediction of secreted effectors . With this in mind we developed an approach to integrate genomic sequence information using computational techniques from data integration and machine learning techniques ( the SVM-based Identification and Evaluation of Virulence Effectors or SIEVE ) . Similar methods have been used successfully for various classification tasks using biological sequences [56]–[66] . These methods use a set of known training examples to classify novel examples based on a set of features derived from the gene and/or protein sequences . We chose to integrate several features , using numeric values derived from analysis of the protein sequence , that have been directly or indirectly suggested to be important in discrimination of secreted effectors by previous studies from a number of organisms [17] , [30] , [35] , [41] , [49] , [50] . These include the G+C content ( GC ) and general amino acid biases ( AA ) , shown to have predictive value individually ( see above ) as well as evolutionary relationships ( EVOL and PHYL ) . Finally , we included the N-terminal sequence of proteins ( SEQ ) to allow the method to learn sequence patterns or biases that might be predictive of secreted effectors . The features used by the method are described in detail in Text S1 . To assess the ability of SIEVE to identify novel secreted effectors we trained a SIEVE model on the set of effectors from one organism then evaluated the methods performance on a set of effectors from a different organism that were not used in the training process . We examined the performance of a SIEVE model trained on P . syringae proteins and evaluated on S . Typhimurium proteins ( PSY to STM ) and the reverse experiment of SIEVE trained on S . Typhimurium proteins and evaluated on P . syringae proteins ( STM to PSY ) . These results show that the SIEVE approach performs very well at classification in terms of both specificity and sensitivity ( Figure 1 ) . At a sensitivity of 90% , i . e . 33 S . Typhimurium effectors and 26 P . syringae effectors , the specificity of the method is 88% when used to predict S . Typhimurium effectors ( PSY to STM model ) and 87% when applied to P . syringae effectors ( STM to PSY model ) . The performance ( ROC ) values for classification were 0 . 95 and 0 . 96 , respectively . These results indicate that our approach to integration of the chosen sequence-based features using a non-linear classification method accurately predicts type III secreted effectors between distantly related organisms . This suggests that there may be a set of features that are shared between effectors in both organisms , a hypothesis that we tested next . Several studies have highlighted the importance of a short region in the N-termini of effectors in secretion [18] , [67] , [68] . This region , thought to be between 10 and 50 amino acids in length , has sometimes been referred to as the secretion signal , though it does not contain any recognizable sequence pattern . Because our models included N-terminal sequence information we wanted to determine the length of sequence that provided the maximum discriminatory power for classification . We therefore examined the effect of including sequences of different lengths in both models to provide accurate discrimination of effectors . We trained models with the other types of features ( EVOL , GC , AA and PHYL ) using the N-terminal 0 to 40 residues as the SEQ feature set . A total of 10 models for each sequence length were trained using randomly selected negative examples and the mean performance ( i . e . ROC ) was calculated . The results for the S . Typhimurium signal ( PSY to STM model ) and the P . syringae signal ( STM to PSY model ) are shown in Figure 2A . Both models show an increase in performance from the baseline value ( which includes no SEQ features ) reaching a maximum when the length of the sequence reaches 29 or 31 amino acid residues , respectively . Additional sequence information beyond this length does not improve the ability of the model to classify effectors in the opposite organism . We next determined the sequence length that provides the majority of the information for each model , i . e . what is the length of sequence beyond which adding more residues to the model fails to improve performance significantly ? This analysis is shown in Figure 2B and was performed by calculating the difference in performance between the maximum performance for that model and performance for each sequence length and dividing this number by the standard error for that performance . In this analysis values that are less than 2 . 0 represent insignificant differences , for which the standard error would begin to overlap from the two values . According to the plot in Figure 2B the maximum significant length for the N-terminal sequence was determined to be 21 and 16 for S . Typhimurium ( PSY to STM model ) and P . syringae ( STM to PSY model ) effectors , respectively . These lengths agree generally with previously determined estimates of the length of the secretion signal [4] , [12] , [16] , [18] , [24] , [67] , [68] and indicate that a significant amount of information is shared between effectors across organisms in their N-terminal 30 residues , with most of the information residing in the first 16–20 residues . These results further support the hypothesis that there is a significant , sequence-based secretion signal in the N-termini of effectors which is not possible to detect using traditional alignment methods such as BLAST . Based on the success of our models at accurately identifying secreted effectors from sequence information we examined the hypothesis that this region contains a hidden sequence motif , possibly derived from an ancient ancestor [19] . To determine the most important sequence-derived features for the classification task in each of the models we used a recursive feature elimination approach ( see Text S1 for details ) . We found that a minimal set of 88 ( out of a total of 711 ) features retained the ability to accurately classify secreted effectors ( Figure S4 ) . The features that are most important for accurate classification include the evolutionary conservation feature ( CONS ) and G+C content ( GC ) , as well as several phylogenetic profile ( PHYL ) features ( see Text S1 ) and a number of specific sequence biases that span the 30 residue putative secretion signal discussed below . The models both contained a set of significantly important residues . These residues , shown in Figure 3 , represent those positions and residue types that the models found to be most important for classification . They form two weak sequence motifs , which are detectable by SIEVE in comparison to the background the N-terminal sequences from all other non-secreted proteins in the organism . The most significant sequence features that are shared between the two models are also shown in Figure 3 with a grey background . This indicates that the secretion signal from both organisms are more likely to have an isoleucine at position 3 , an asparagine at position 5 , a serine or glycine at position 8 , and a serine at position 9 , in addition to several other shared biases . The concentration of shared important features in the N-terminal 10 residues agrees with results from the sequence length analysis ( Figure 2 ) showing that the greatest gains in classification performance are from this region . The sequence motifs obtained here are consistent with a number of previous observations . They are rich in polar residues , especially serines , and have few charged residues , as observed in P . syringae [24] , [41] . The sequence patterns previously derived from P . syringae effectors [30] are almost completely consistent with the sequence biases from our models . Though , as we showed , these patterns are ineffective at accurately discriminating effectors in S . Typhimurium . Finally , it was shown that all proteins bearing synthetic secretion signals with the pattern MxIISSxS , among others , were highly secreted in Yersinia pestis [17] , which agrees well with the pattern identified for S . Typhimurium . Our results support the existence of a conserved , though highly variable , secretion signal encoded in the N-terminal 16–20 residues of type III secreted effectors . The important residues do not form a classic sequence motif but rather can be thought of as significant residue tendencies of the secretion signal . This type of secretion signal has been found in other secretion systems , most notably the Sec system in bacteria [69] . In the Sec system no specific sequence motif for secretion exists but a pattern of charged residues and a hydrophobic domain allows accurate detection of secreted substrates [70] . Collectively these results represent a large number of hypotheses that can be tested , for instance using mutagenesis and secretion assays , that will further elucidate the nature of the secretion signal and can help refine the models presented here . The lack of a classical sequence motif for secretion is expected from the historical failure of traditional sequence motif identification methods to identify type III secretion signals . It may also partly explain the observation that the N-terminal sequence shows considerable plasticity and yet can be functional [4] , [16] . We provide the unaligned N-terminal sequences of the effectors used in this study and show their agreement with the sequence tendencies presented in Figure 3 as Table S4 . We next wanted to test if SIEVE could generate useful predictions of novel type III secreted effectors in a well-characterized bacteria . Accordingly , we generated a ranked list of predictions by combining results from two applicable models ( PSY to STM and STM to STM , see Text S1 ) in S . Typhimurium . We show a selection of the highest scoring ∼2% of the predictions in Table 2 , and the remainder of these predictions are available as Table S2 . To help biologists interpret the scores associated with each prediction we calculated a confidence range for novel predictions based on a conservative set of positive and negative examples ( those described here ) and a “generous” set . The generous set uses a set of negative examples that limited to those proteins with well-defined functions . This process is described in Text S1 ( Figure S3 ) and is used to provide useful hypotheses for experimental validation . Investigating the proteins in Table 2 , we found evidence that the SIEVE predictions identify proteins that are likely to be secreted . The SIEVE results for S . Typhimurium contain two highly confident predictions ( SpvD and SpvC ) , which are in an operon that is co-regulated with SPI-2 and contains SpvB , which is a known effector . Though SpvC was not included in our positive example set a recent publication has identified it as being a secreted effector [71] . Although there was evidence that SpvD was secreted into the supernatant [72] , these results did not show that it was a type III secreted effector and so SpvD was also not included in our positive example set . SpvD is the prediction with the highest score providing further evidence that it is a secreted effector . The prediction list also includes three proteins for which the cognate gene is regulated by the PhoP/Q two-component regulatory system [73]–[75] , envF and pagDK . PhoP/Q is induced in acidic and Mg2+-poor medium and within the macrophage phagosome [76]–[78] . We used a CyaA fusion assay to show that PagD is secreted in macrophages ( L . Crosa and F . H . unpublished results ) , further validating that the approach is useful for predicting secreted effectors . Finally , the ZirS protein was identified by SIEVE . Interestingly , this protein was recently found to be the secreted protein from a novel two-partner secretion system , ZirTS [79] . Though ZirS is thought to have a cleaved signal peptide directing it through the inner membrane our findings suggest that the targeting signal for ZirS may be similar to that of the type III secretion system . In total , four of our novel predictions have been shown to be secreted experimentally . We are currently validating other predictions . Since many of our novel predictions do not have functional annotations and have not been experimentally investigated individually , we assessed the general role of proteins predicted to be secreted by SIEVE in virulence by one or more negative selection studies designed to detect genes essential for virulence in vivo [80]–[83] . From this analysis we found a greater than 2-fold enrichment of predictions implicated in one or more negative selection study in the predictions with scores in the top 10% relative to those in the remaining 90% ( p value 1e-28; using a two-tailed Student's t-test ) . It is important to note that many of the known S . Typhimurium effectors ( 10 of 37 ) were not identified in any of the original negative selection experiments most likely due to functional redundancy as well as specifics of the virulence assay employed in terms of different hosts and/or cell types . So the fact that some of our predictions are not found on these lists does not mean that they are not important in virulence . Rather , predictions that are known to be essential in virulence represent high-priority targets for future investigation . Two classes of genes identified appear to be false positive predictions . Several components involved in the biosynthesis of lipopolysaccharide ( LPS ) and O-antigen are identified by SIEVE . Since the complex directing biosynthesis and transport of LPS occurs at the inner membrane [84] , it is possible that components of this system use a targeting signal that is similar to type III secreted effectors . Several plasmid-encoded conjugative transfer proteins are also identified by SIEVE; TraJ , TraM , and TraS . The conjugative transfer system transfers a nucleoprotein complex during mating pair formation [85] . The TraM and TraJ proteins are associated with the relaxosome [86] , the protein complex that binds DNA and readies it for transport through the associated type IV secretion system [85] and TraS is an outer membrane protein involved in the entry exclusion ( Eex ) system . It is possible that components of the type IV secretion system may share some similarity with the type III system that allows them to be identified by SIEVE . SIEVE predicted components from three different functional groups to contain secretion signals , type III secretion system substrates , type IV secretion system-associated complexes and LPS biosynthesis proteins . Each of these are targeted to the cytoplasmic face of the inner membrane , either to be secreted or to form a functional complex . Our findings imply that diverse mechanisms of membrane targeting may share common features that direct targeting . Though they have different mechanisms , the types III and IV secretion systems share the common function of transporting virulence factors into host cells . The similarity between these two systems is supported by the observation that some type IV secreted effectors in Legionella pneumophila can be identified using SIEVE trained on type III secreted effectors from S . Typhimurium ( J . M . unpublished results ) . As can be seen in Table 2 , a number of other interesting predictions are made by SIEVE . However , the value of the SIEVE approach is demonstrated in that 74 of the predictions ( 82% ) have unknown or poorly described functions . Of these proteins 19 have been implicated in virulence by at least one of the negative selection studies , providing a reasonable starting point for experimental investigation . Finally , we examined the ability of SIEVE to provide useful predictions of type III secreted effectors for an organism that is difficult to study . We trained SIEVE on the positive and negative examples from both S . Typhimurium and P . syringae and applied the model to the C . trachomatis genome . Examining the list of top 10% of predictions ( Table 3 ) from C . trachomatis showed that a number of these proteins have been demonstrated to be secreted ( bold type ) by various experimental methods or predicted to be secreted by other computational approaches . Because it is complicated to work with both in terms of culturing and genetic manipulation [14] , [22] , a number of studies have been performed to identify candidate effectors by expression in heterologous systems or in cell culture systems [87]–[90] . Several of these studies have identified candidate effectors by their localization in the host cell [90]–[92] . During infection Chlamydia resides in a specialized cytoplasmic vacuole , also called an inclusion . Thus proteins that are localized to the inclusion body membrane , as well as those that are present in the cytoplasm are thought to be secreted through the type III secretion system . A recent study investigated 50 Chlamydial proteins believed to be localized to the inclusion membrane based on previous experimental or predictive studies [90] . Twenty-two of these proteins were determined to be inclusion localized , and 12 of these appear on our high-confidence list . Also , none of the 7 proteins found to be not secreted by this study were predicted by SIEVE . A family of several phospholipase D-like proteins predicted by SIEVE have also been implicated in pathogenesis , though have not been shown to be secreted and/or localized to the inclusion body [93] . Finally , two polymorphic membrane protein ( Pmp ) -like proteins , Pls1 and Pls2 , were found to be localized to the inclusion membrane [92] . However , their secretion was not blocked by a type III secretion system inhibitor , suggesting that they are secreted by a novel mechanism . Our findings suggest that , similar to the ZirS protein identified in S . Typhimurium , the secretion signals for Pls1 and Pls2 are related to the type III secretion signal . A number of other proteins on our list were shown to be secreted by heterologous expression systems . One large scale study in Shigella flexneri [89] used a reporter system to identify 18 candidate secreted substrates , 7 of which are on our high confidence list . Other experiments identified TARP ( CT456 ) [94] and CT847 [95] as secreted proteins , also showing that they were localized to the host cell during infection . Finally , our confident predictions include 8 proteins predicted to be secreted by a previous computational analysis [25] , but not yet experimentally validated . Again , a large number of the predictions are hypothetical proteins with no annotation providing a specific and confident set of candidates for further study . We also examined the known or predicted effectors that were not in the top 10% of predictions ( Table S3 ) . These included 21 proteins known to be secreted , but eight of these ( including IncA ) were in the top 30% of SIEVE predictions . It is important to note that some of the experimental methods used to identify secreted proteins , such as secretion in a heterologous system [89] , are merely suggestive that the protein is secreted C . trachomatis . Therefore this list is likely to be both incomplete and contain a number of false positives . In total , 24 of the 86 top SIEVE predictions ( 28% ) are known secreted effectors , have been shown to be localized to the inclusion membrane or cytoplasm of the host , or have been shown to be secreted in a heterologous expression system . This is in contrast to the 21 of 788 ( 3% ) of these proteins in the remaining 90% of the genome . We determined the performance of the method in C . trachomatis as 0 . 89 , though this is a conservative estimate of since it is likely that this list is incomplete and may contain false positives . These results show that our method , trained on proteins from other organisms , can provide useful predictions for other bacteria . Identification of the secretion signal that allows proteins to be targeted for secretion is of paramount importance for understanding any secretion system [69] . The type III secretion system is essential for virulence in a number of pathogenic bacteria and has been well studied in terms of its regulation , structural organization and secreted substrates [4] , [8] , [9] , [12] . Despite extensive investigation the nature and even existence of a secretion signal for substrates of the type III secretion system remains a debated topic [4] . Though the N-terminal region of a number of substrates has been shown to be necessary and , in some cases , sufficient , for secretion [16] , [18] , there is no clear sequence motif that is common to substrates , even those from the same bacteria . Several alternative hypotheses have been presented to explain this observation: that a cryptic amino acid sequence serves as the signal by adopting an unstructured or flexibly structured conformation; that the secretion signal is encoded by the mRNA and is not directly dependent on the protein sequence; or that targeting is accomplished by chaperone proteins that specifically bind the substrates [4] . There is evidence for each of these hypotheses indicating that targeting may be a complex and multifaceted process . Using an in silico approach , we provide evidence that the protein sequence in the N-terminal 30 residues of the majority of known substrates from two bacteria provides enough information to allow accurate classification by a machine-learning algorithm . We also show that there are significant sequence biases in this region , some of which are shared between organisms , but these are not identifiable by traditional sequence analysis methods . These findings indicate that there is a secretion signal encoded by the N-terminal 30 residues of type III secreted substrates and provide a number of testable hypotheses regarding this putative signal . Our results do not disprove the alternatives ( that the signal is encoded by mRNA or resides on the chaperones ) but indicate that a majority of the secreted substrates from S . Typhimurium and P . syringae have a protein-encoded , N-terminal secretion signal . Most of the core components of the type III secretion system are conserved between species [22] , [96] and several lines of evidence indicate that the targeting mechanisms employed by the system may also be conserved . The first is that type III secretion systems can export proteins encoding secretion signals from other bacteria [97]–[99] . The second is that a recently discovered class of type III secretion inhibitor can block secretion in Y . pestis , C . trachomatis and S . Typhimurium [2] , [100]–[103] , though the mechanism of inhibition is unclear [103] . Finally , it has been shown from available structures of effectors bound to their cognate chaperones that the structure of this interaction is conserved across species [104] . These observations have important implications for development of new antibacterial agents . Our findings support a model of the type III secretion process that includes a targeting mechanism that is conserved between organisms . Despite our identification of some generally conserved features between the two targeting signals the lack of a well-defined targeting sequence leaves open the questions of what features of the secretion signal are recognized by the targeting system . The importance of particular residues as determined by our analysis suggests that there are underlying conserved structural requirements which form the basis for recognition by the secretion apparatus but can be encoded by a large range of different sequences . The results we have presented show only that there is a significant amount of shared information between secreted effectors , especially in the N-terminal sequences . We propose that this shared information represents a biological function , that of type III secretion , and that the sequence patterns identified are functionally important in terms of targeting the proteins to the secretion apparatus . Further experimental investigation , for example mutational analyses based on these predictions , is necessary to validate this hypothesis . Also , we have presented a large number of high-confidence predictions of novel secreted effectors , a number of which have experimental evidence strongly suggesting that they are secreted substrates . Again , further experimental investigation of these predictions will allow refinement of the approaches described . The results presented here support previous findings that diverse bacteria have similar type III secretion system targeting signals [97]–[99] . We have described a novel computational method for data integration and classification to discriminate secreted effectors from the type III secretion system in two evolutionarily distinct organisms . The excellent performance of this method on discrimination of secreted effectors shows that the groups of effectors from the two organisms are similar based on a number of sequence-derived characteristics , despite the lack of detectable sequence similarity between them . Using the models produced by SIEVE we identified a set of conserved sequence biases that define a putative , common secretion signal for type III secreted effectors . This approach is a novel and effective way to identify secreted effectors in a broad range of pathogenic bacteria and provides valuable insight into the nature of the type III secretion signal . | Pathogenic bacteria release a number of different proteins that function to interfere with host defenses and allow bacterial invasion , persistence , and replication in the host . In many bacterial pathogens , the type III secretion system is used to inject these virulence factors directly to the cytoplasm of the host cell . The secreted proteins do not have well-conserved sequences and do not have any kind of common identifiable signal sequence to target them for secretion . This makes it very difficult to identify secreted proteins of this kind without experimental investigation , as can be done in other secretion systems . In this study , we develop a computational approach to detect secreted virulence factors from genomic protein sequences . We use this method to compare the N-terminal regions of proteins from S . Typhimurium and a plant pathogen , P . syringae , and show that this approach is the most effective method of computational identification of type III secreted proteins to date . We further use this approach to identify a sequence pattern in these proteins that presumably helps direct virulence proteins to the type III secretion apparatus . We provide novel predictions of secreted proteins in these two organisms , as well as in the human pathogen C . trachomatis . Better understanding of secreted virulence factors in pathogens will lead to new ways of combating important infectious diseases and provide understanding of the complex interaction between pathogen and host . |
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The clinical studies were approved by the Human Ethics Committee at Lund University ( approval numbers LU106-02 , LU236-99 and Clinical Trial Registration RTP-A2003 , International Committee of Medical Journal Editors , www . clinicaltrials . gov ) .
Acute cystitis is rapidly becoming a therapeutic enigma , as antibiotic resistance is reducing the options to a minimum [1–4] . Fortunately , new insights are now making it possible to explore immune response modifiers as alternatives to antibiotics . Acute cystitis occurs predominantly in girls and women with normal urinary tracts and at least 60% of all females will report an episode during their lifetime [5–7] . The recurrence rate is high , especially in a subset of patients , where severe , often recurrent cystitis episodes may cause chronic tissue damage and negatively impact the quality of life [8] . In addition , acute cystitis patients pose a highly significant challenge to the health care system . This study addresses if immunotherapy might be a relevant complement to antibiotics , in this patient group . The urinary bladder mucosa is often exposed to bacteria but does not always retaliate with full force . In patients with acute cystitis , infection triggers a rapid and potent innate immune and inflammatory response in the bladder mucosa and clinical symptoms include pain , urgency and frequency of urination [9–12] . The molecular basis of these symptoms is not well understood , but bacterial interactions with the bladder epithelium have been shown to create inflammatory cascades [13–15] , which also involve adjacent mucosal cells , such as mast cells and macrophages [16–20] . In asymptomatic carriers , the mucosa is exposed to bacteria of lower virulence and the mucosa remains fairly unresponsive , despite the presence of large numbers of bacteria in the lumen [21–24] . Asymptomatic bacteriuria ( ABU ) strains have evolved a mechanism to avoid elimination by the innate immune defense , through effects on RNA polymerase II and inhibition of host gene expression [22 , 25] . It is therefore challenging to understand , at the molecular level , how a state of exaggerated mucosal inflammation can be generated specifically in acute cystitis patients . The specific molecular interactions that drive the transition from a homeostatic innate immune response to bladder disease remain unclear . This study examined how innate immune response genes influence the outcome of bladder infection and the pathogenesis of acute cystitis . We identify acute cystitis as an IL-1β-driven , hyper-inflammatory disease [26 , 27] , possibly related to other hyper-inflammatory disorders [28 , 29] . Consistent with such a role , Il1b-/- mice were protected from infection and pathology . In contrast Asc-/- and Nlrp3-/- mice developed progressive IL-1β-driven bladder inflammation and severe pathology , caused by a new , non-canonical IL-1β processing mechanism , involving the metalloproteinase MMP-7 . We also identified the inflammasome constituents ASC ( Apoptosis-associated speck-like protein containing a CARD ) and NLRP-3 ( NACHT , LRR and PYD domains-containing protein 3 ) as negative regulators of MMP7 , explaining why MMP-7 is overexpressed in the mucosa of Asc-/- and Nlrp3-/- mice and the resulting state of IL-1β hyper-activation . Using IL-1β and MMP-7 as targets for immunotherapy , we succeeded in protecting susceptible Asc-/- mice against acute cystitis , confirming the potential of immunotherapy for this indication .
To address how infection creates a hyper-inflammatory state in patients with acute cystitis , we first infected the human bladder epithelial cell line HTB-9 in vitro and quantified inflammatory mediators in cell supernatants . We detected an increase in IL-1β secretion , four hours after infection with acute cystitis ( CY ) strains CY-17 , CY-92 , CY-132 or the uropathogenic Escherichia coli strain CFT073 ( P < 0 . 001 , compared to uninfected cells , two-tailed unpaired t-test ) . In contrast , the IL-1β response was low in cells infected with the ABU strain E . coli 83972 , indicating a virulence-association ( Fig 1A ) . IL-1β secretion was not detected in kidney epithelial cell supernatants after infection with the same strains , suggesting specificity for the bladder epithelium ( Fig 1B ) . Western blot analysis confirmed that mature IL-1β was present in the supernatants of the infected HTB-9 cells ( 4 hours ) , as well as unprocessed pro-IL-1β and N-terminal fragment ( Fig 1C ) . A rapid increase in IL-1β staining intensity was observed by confocal microscopy , in cells infected for one hour with 105 CFU/ml of CY-17 , CY-92 and CFT073 compared to uninfected cells or cells infected with the ABU strain ( Fig 1D and 1E , two-tailed unpaired t-test ) . This increase in cellular IL-1β levels was confirmed by Western blot analysis of whole cell extracts ( Fig 1F and 1G ) . At this time ( 1 hour ) , low levels of pro-IL-1β were detected . There was no significant reduction in cell viability after one ( ≥ 95% viable ) or four hours ( ≥ 90% viable ) , as quantified by PrestoBlue staining and no evidence of pyroptosis after one hour , when the increase in cellular IL-1β levels was detected ( S1 Fig ) . The presence of unprocessed pro-IL-1β in the 4 hour supernatant might be due to secretion of unprocessed IL-1β via exosomes [30] . To address if IL-1β activation is a characteristic of acute cystitis strains , we infected human bladder epithelial cells with an epidemiologically defined collection of pediatric acute cystitis isolates ( n = 67 , [31 , 32] ) . The majority of these strains ( 85% ) triggered an IL-1β response > 5 pg/ml and 64% of those triggered a high response ( 40–1 , 000 pg/ml , Fig 1H ) . We also examined a collection of pediatric ABU strains ( n = 62 , [31 , 33] ) , which was obtained by screening infants and children in the same geographic area for bacteriuria in the absence of urinary tract infection ( UTI ) symptoms . In contrast to the CY strains , most of the ABU strains did not trigger a strong IL-1β response ( 61% < 5 pg/ml ) , resulting in significantly higher mean IL-1β concentrations in supernatants of bladder cells infected with the CY strains than the ABU strains ( 121 . 8 and 32 . 4 pg/ml respectively , P < 0 . 001 , Fig 1H ) . To address if the secretion of IL-1β was influenced by bacterial hemolysin [19 , 20] , IL-1β concentrations were examined as a function of hemolytic activity in 40 CY and 38 ABU strains ( Fig 1I ) . There was no significant difference in IL-1β response between hemolysin positive and negative strains ( P = 0 . 07 , Mann Whitney unpaired test ) . In the cystitis subset , a significant difference between hemolysin positive and negative strains was observed , however ( P = 0 . 01 , Mann Whitney unpaired test ) , suggesting that hemolytic cell lysis may contribute to the IL-1β activating virulent phenotype of the acute cystitis strains , for example by assisting the release of IL-1β from cells infected with hemolysin-producing strains . The results suggest that the majority of acute cystitis strains activate an IL-1β response in human bladder epithelial cells . As IL-1β is processed by the inflammasome , we subsequently examined if mice with intact or defective inflammasome function develop acute cystitis . We infected mice with genetic defects affecting the NLRP-3 inflammasome: NLRP-3 deficient mice ( Nlrp3-/- [34] ) or ASC deficient mice ( Asc-/- [35] ) . In addition , Il1b-/- [36] and Casp1-/- [37] mice were used and C57BL/6 WT mice were included as controls ( Fig 2 ) . For sample sizes and number of experiments , please see each figure legend and an overview in S1 Table . The mice were infected by intravesical inoculation with E . coli strains that triggered high IL-1β responses in human bladder epithelial cells , in vitro ( CFT073 , CY-17 or CY-92 ) . Infected bladders were evaluated macroscopically , at sacrifice after 7 days and assigned a gross pathology score , defined by size , edema and hyperemia . Tissue pathology was further evaluated by hematoxylin and eosin ( H&E ) staining and immunohistochemistry of frozen tissue sections , and a histo-pathology score was assigned to each mouse . Histology was scored , independently , by two experienced researchers . The analysis was not blinded . Infection kinetics was followed in urine samples obtained after 6 and 24 hours , 3 and 7 days . Major , genotype-specific differences in bladder inflammation and pathology were detected after seven days ( Fig 2 ) . Two disease end points were distinguished . 1 . Severe , progressive cystitis in mice lacking ASC or NLRP-3 , resembling chronic human disease . 2 . A moderate , self-limiting form of acute cystitis in C57BL/6WT mice with intact inflammasome function , resembling sporadic human cystitis . Infected Asc-/- and Nlrp3-/- mice developed severe , acute cystitis with enlarged bladders , edema and hyperemia compared to uninfected bladders ( Fig 2A and S2A Fig ) . By histology , most bladders from Asc-/- mice showed extensive loss of tissue structure , defined by a pronounced mucosal and submucosal edema , with disappearance of the tissue folds that characterizes the healthy mucosa . In addition , a massive inflammatory cell infiltrate was present , along the mucosal border and in the submucosa ( 10/14 mice , 71% ) . Similar tissue destruction was observed in bladders from Nlrp3-/- mice ( 7/11 mice , 64% ) , ( Fig 2B ) . Mean gross bladder pathology score of infected Asc-/- and Nlrp3-/- mice were 7 . 9 and 7 . 2 , respectively ( Fig 2C ) . Bladder pathology was accompanied by high bacterial counts in urine and bladder tissue ( Fig 2D–2F ) . In Asc-/- mice , the neutrophil influx accelerated until day 7 , indicating a loss of homeostatic control and progression to chronic inflammation . Infiltrating bacteria and neutrophil aggregates or micro-abscesses were detected in the mucosa of Asc-/- and Nlrp3-/- mice , with extensive sloughing of epithelial cells into the lumen . Bacteria were mainly localized along the mucosal surface , with no evidence of bacterial invasion ( Fig 2G ) . This hyper-inflammatory phenotype was also observed after infection of Asc-/- mice with the acute cystitis strains CY-92 and CY-17 , which triggered high IL-1β responses in vitro ( S3A–S3D Fig ) . In contrast , there was no disease phenotype in Asc-/- mice infected with the ABU strain E . coli 83972 or in C57BL/6 WT mice after 24 hours or 7 days ( S3E Fig ) . There was no evidence of kidney involvement or pathology in mice infected with CY-92 and CY-17 , despite positive bacterial cultures from renal tissues . In contrast , C57BL/6 WT mice infected with CFT073 showed moderate macroscopic evidence of acute cystitis including a small increase in size , edema and hyperemia compared to uninfected controls ( Fig 2A and 2C , mean pathology score 1 . 5 ) . The low level of edema was confirmed by histology , with no evidence of tissue damage ( Fig 2B ) . Infection was accompanied by an increase in urine neutrophil numbers ( Fig 2D ) and bacterial numbers reached a peak after 24 hours and then declined ( Fig 2E and 2F ) . By immunohistochemistry , bacterial staining was weak and very few neutrophils were detected in the bladder mucosa ( Fig 2G ) . Il1b-/- mice showed an even more attenuated phenotype after infection with CFT073 , consistent with a key role of IL-1β for bladder inflammation and pathology compared to uninfected bladders ( Fig 2A and S2A Fig ) . There was no macroscopic evidence of acute cystitis ( Fig 2A and 2C , mean pathology score 0 . 7 ) and bladder tissue morphology remained intact ( Fig 2B , mean histo-pathology score 0 . 9 , P = 0 . 003 compared to C57BL/6 WT mice ) . Il1b-/- mice had fewer infiltrating neutrophils and lower bacterial counts than the C57BL/6 WT mice on day seven ( P < 0 . 001 and P < 0 . 05 ) , ( Fig 2D–2F ) . Furthermore , Casp1-/- mice , which have a functional IL-1β deficiency due to defective IL-1β processing and secretion [37] , did not develop acute cystitis ( S4A–S4D Fig ) . The bladders were enlarged and hyperemic , but there was no evidence of inflammatory changes or tissue damage . Casp1-/- mice showed reduced IL-1β secretion and tissue retention of IL-1β ( S4E–S4G Fig ) . As a result , IL-1β dependent gene expression was low and Casp1-/- mice showed a lack of inflammation in bladder tissues . Neutrophils and bacteria were present in urine but did not accumulate in the tissues and the mucosal morphology was intact ( S4 Fig ) . Infection was accompanied by strong mucosal IL-1β staining in bladder tissue sections in C57BL/6 WT mice , Asc-/- and Nlrp3-/- mice after 24 hours ( Fig 2H ) . Staining was mainly epithelial and was not seen in Il1b-/- mice or uninfected C57BL/6 WT mice . In parallel with the epithelial staining IL-1β was detected by ELISA in the urine of infected Asc-/- and Nlrp3-/- mice , with lower levels in C57BL/6 WT mice . By Western blot analysis , bands of approximately 36 and 18 kDa were detected ( S2B Fig ) . These studies identify genetic determinants of host susceptibility to acute cystitis . Asc and Nlrp3 were defined as key resistance determinants and IL-1β activation as a crucial step in the pathogenesis of acute cystitis . To define the mechanism of bladder pathology , we extracted total bladder RNA from infected Asc-/- and Nlrp3-/- mice with the highest pathology score after seven days and from C57BL/6 WT and Il1b-/- mice , with low pathology scores ( Experiments 1 and 2 in S1 Table ) and from uninfected bladders . The RNA was amplified , hybridized onto Mouse Genome array strips , washed , stained and scanned using the GeneAtlas system . Significantly altered genes were identified , by comparing infected- to uninfected mice of the same genetic background ( P-values < 0 . 05 and absolute fold change > 1 . 41 ) and sorted by relative expression using 2-way ANOVA [38] . Heat-maps were constructed by Gitools 2 . 1 . 1 software and differentially expressed genes and regulated pathways were analyzed by Ingenuity Pathway Analysis software ( see Materials and Methods ) . We identified a set of strongly upregulated genes in Asc-/- and Nlrp3-/- mice with the highest bladder pathology score , but not in C57BL/6 WT mice or Il1b-/- mice ( 2 , 228 specifically regulated genes ) . The heat map in Fig 3A illustrates the similarities in gene expression between 5/7 Asc-/- and 2/5 Nlrp3-/- mice analyzed by this technology . Those mice also had high and comparable histology scores , defined by evaluation of the H&E-stained bladder tissue sections from the corresponding mice . To further understand the disease process , we identified the most strongly upregulated genes in these mice . Genes with a FC > 100 included metalloproteinase Mmp7 , the neutrophil and monocyte chemoattractants Cxcl6 and Cxcl3 , the genes encoding calprotectin S100a8 and a9 and the stefin gene Stfa1 ( Fig 3B and S2 Table ) . By analysis of top-scoring canonical pathways , these genes were shown to control granulocyte and leucocyte diapedesis and signaling , acute phase responses including IL-6 and IL-1β signaling , IL-1R expression and NF-κB-signaling and dendritic cell maturation ( S5A Fig ) . These genes and pathways were not significantly regulated in C57BL/6 WT or Il1b-/- mice , supporting a disease association . The activation of inflammasome-genes and IL-1β-dependent gene network was analyzed , using Qiagen’s list of 84 key inflammasome genes ( Fig 3C ) . In the Asc-/- and Nlrp3-/- mice with a high histo-pathology score , Il1b expression was activated ( FC 10–41 ) , as were IL-1β-dependent and inflammasome-related genes including Cxcl1 , Cxcl3 and Il33 ( FC 5–200 ) , ( Fig 3C ) . These genes and pathways were not significantly regulated in C57BL/6 WT or Il1b-/- mice ( Fig 3C ) . Il1a expression was activated in Asc-/- and Nlrp3-/- mice with bladder pathology , but not among the 40 top-regulated genes ( FC 1 . 6–8 . 7 ) . Il18 , Casp11 and inflammasome-related NLRP genes were not transcriptionally regulated ( S2 Table ) . The results identify IL-1β driven pro-inflammatory genes that are activated , exclusively in Asc-/- and Nlrp3-/- mice with severe bladder pathology . This response was not detected in the kidneys of infected C57BL/6 WT mice ( S5B Fig ) . The Mmp7 gene , which encodes the matrix metalloproteinase ( MMP ) -7 [39] was strongly upregulated in Asc-/- and Nlrp3-/- mice with a high histo-pathology score ( Fig 3B ) . MMP-7 expression was therefore examined as a function of the histo-pathology score ( Fig 4A ) . In Asc-/- and Nlrp3-/- mice , Mmp7 expression showed a clear association to the overall bladder tissue pathology score and was not regulated in the Il1b-/- or C57BL/6 WT mice ( Fig 4A ) . High MMP-7 protein expression was confirmed , by immunohistochemistry , in bladder tissue sections from Asc-/- and Nlrp3-/- mice ( Fig 4B ) . Importantly , staining was exclusively epithelial , with shedding of MMP-7 positive cells into the bladder lumen . Epithelial MMP-7 activation was detected as early as 24 hours after infection and importantly , MMP-7 showed no detectable co-localization with neutrophils in the mucosa or sub-mucosa ( S6 Fig ) To further evaluate the involvement of MMP-7 in acute cystitis , we infected Mmp7-/- mice [40] with CFT073 and used Asc-/- mice as disease controls . Consistent with their intact inflammasome function , Mmp7-/- mice developed transient cystitis similar to C57BL/6 WT mice ( Fig 4C ) . A moderate mucosal IL-1β response was observed by immunohistochemistry ( Fig 4C ) . IL-1β levels in urine ( Fig 4C ) and IL-1β-dependent gene expression was comparable to that in WT mice , with expression of Ccl5 , Nlrc5 , Irf1 , Ctsb , Birc3 , and MyD88 . Thus , Mmp7 did not drive pathology in mice with intact ASC or NLRP-3 function . To address if MMP-7 cleaves IL-1β , we exposed recombinant GST-tagged pro-IL-1β to recombinant active MMP-7 in vitro and detected proteolytic fragments by Western blot , using IL-1β specific antibodies ( Fig 4D and S7A Fig ) . Kinetic analysis detected a time-dependent cleavage of IL-1β with a reduction in full-length protein from 10 to 60 minutes ( Fig 4D ) . Using antibodies with higher affinity for the mature IL-1β , a band of 18 kDa was detected corresponding in size to the recombinant , active and mature IL-1β control . With increasing time , a band of 16 kDa was also observed ( Fig 4D ) . Using the same experimental set up , we observed that ASC was degraded by MMP-7 over time ( S7B Fig ) while recombinant NLRP-3 was not cleaved by MMP-7 and therefore served as a negative control for unspecific effects of the enzyme ( S7C Fig ) . To address if the cleaved IL-1β fragments were biologically active , reaction mixtures containing pro-IL-1β and MMP-7 were collected after 30 minutes , when the mature product was detected by Western blot ( Fig 4D ) . Human bladder epithelial cells were stimulated with the reaction mixture for one hour , and IL-1β activity was quantified , by measuring the prostaglandin E2 ( PGE2 ) response [41] . The 30 minutes reaction mixture activated a dose-dependent PGE2 response but recombinant MMP-7 and pro-IL-1β ( 280 and 840 ng/ml ) alone had no effect ( Fig 4E ) . The results identify a new , MMP-7-dependent mechanism of pro-IL-1β processing in Asc-/- and Nlrp3-/- mice . To understand the mechanism of increased MMP-7 expression in infected Asc-/- and Nlrp3-/- mice , we examined if ASC and/or NLRP-3 may act as negative regulators of MMP7 expression . After infection of human bladder epithelial cells , with CY-17 and CY-92 , we detected a significant increase in MMP-7 staining ( confocal microscopy , Fig 5A and 5B ) . In contrast , ASC staining was reduced after infection with the virulent strains ( P < 0 . 001 ) and NLRP-3 showed a weaker staining ( P < 0 . 01 ) . The MMP-7 response to infection and the decrease in ASC and NLRP-3 levels were confirmed by Western blot analysis ( Fig 5C ) . ASC or NLRP-3 expression was subsequently inhibited by transfection of human bladder epithelial cells with ASC- or NLRP3- specific siRNAs and the effects on MMP-7 expression were examined by confocal imaging ( Fig 5D and S8A Fig ) . MMP-7 expression increased drastically in transfected and infected cells , where the expression of ASC or NLRP3 had been inhibited , but not in cells transfected with negative control siRNA ( Fig 5D ) . Inhibition efficiency of ASC and NLRP-3 expression by specific siRNAs was confirmed by Western blot analysis . Infection of the cells with CY-17 caused a further decrease in ASC and NLRP-3 staining ( Fig 5E , quantified in S8B Fig ) . Two protein bands where detected , one of 24 kDa , corresponding to the common ASC variant and one of 20 kDa ( ASC-b ) , corresponding to an ASC variant that enhances IL-1β secretion in human promyelocytic leukemia cells ( HL60 ) [42] ( Fig 5E ) . To address if infection with cystitis strains modifies the interaction of ASC and NLRP-3 in cells , co-immunoprecipitation was performed . ASC was shown to pull down NLRP-3 in nuclear extracts of uninfected cells but after infection , a reduction in ASC/NLRP-3 interaction was detected suggesting that a loss of ASC/NLRP-3 interaction in the nuclear compartment accompanies MMP7 activation ( S8C Fig ) . To determine if ASC and NLRP-3 interact with the MMP7 promoter , DNA fragments spanning the entire promoter were used as probes in electrophoretic mobility shift assays ( EMSA ) ( S9A Fig ) . A DNA fragment of 259 bp , adjacent to the transcription start site ( P1 , position -18/-276 ) was shown to interact with a nuclear protein extract from infected bladder cells , resulting in a significant band shift ( Fig 5F and 5G ) . Specificity for ASC and NLRP-3 was confirmed by competition with specific antibodies ( Fig 5G ) . In the absence of nuclear extract , the probe formed a single low molecular weight band , serving as a negative control . To confirm that ASC binds directly to the MMP7 promoter , recombinant ASC protein was incubated with the 259 bp DNA sequence and examined by EMSA . Strong dose-dependent binding of ASC to MMP7 promoter DNA was detected as a band shift , which was competitively inhibited by specific antibodies but not by the IgG isotype control ( Fig 5H ) . Other MMP7 promoter sequences did not interact with ASC or NLRP-3 in this assay ( S9A and S9B Fig ) . The results suggesting that NLRP-3 and ASC act as negative regulators of MMP7 expression and identify an ASC binding site in MMP7 promoter DNA , adjacent to the transcription start site . To address if IL-1β serves as target for immunomodulatory therapy , we selected the most susceptible genotype ( Asc-/- mice ) for treatment with the IL-1 receptor antagonist ( IL-1RA ) Anakinra . A dose of 1 mg per mouse in 100 μl of PBS was given intra-peritoneally , 30 minutes before infection and daily after infection with E . coli CFT073 ( Fig 6A ) . This dose was selected based on previous studies in murine models [43] . A dramatic therapeutic effect was observed compared to infected Asc-/- control mice . By macroscopic evaluation , the extent of edema , hyperemia and enlargement was reduced , resulting in a significantly lower pathology score ( P < 0 . 001 ) , ( Fig 6B and 6C ) . By histology , a reduced inflammatory response was seen in the bladders of treated mice and mucosal pathology was inhibited compared to untreated controls that developed extensive bladder pathology ( Fig 6D ) . Mucosal neutrophil infiltration , which accompanies pathology , was prevented and urine neutrophil numbers were low ( Fig 6E ) . As a control for unspecific effects of Anakinra on the bacteria , CFT073 was grown in Luria-Bertani with or without 500 ng/ml of IL-1RA for 10 hours . No difference in bacterial growth rate was detected ( S10 Fig ) . We subsequently treated susceptible Asc-/- mice with the matrix metalloproteinase inhibitor ( MMPI ) Batimastat . The MMPI was given 30 minutes before infection and on days 0–2 and 4–6 after infection ( 0 . 5 mg in 100 μl of PBS i . p . , Fig 6A ) . The MMPI had a significant protective effect ( Fig 6B–6D ) , detected by macroscopic evaluation , resulting in a reduced pathology score ( P = 0 . 002 ) . By histology neutrophil infiltration was reduced ( Fig 6D ) . As in the IL-1RA-treated mice , bacterial numbers remained elevated ( Fig 6E ) . Batimastat ( 250 ng/ml ) did not affect bacterial growth in vitro for up to 10 hours . No difference in bacterial growth rate was detected ( S10 Fig ) . As Batimastat is a broad metalloproteinase inhibitor , unspecific effects on other proteases might occur . Proteases inhibited by Batimastat other than MMP-7 , were not transcriptionally regulated in any of the mice with acute cystitis or controls . MMP-15 , which is not susceptible to Batimastat , was weakly activated in mice with bladder pathology ( FC 2 . 0 ) . These findings suggest that the therapeutic effect of Batimastat reflects inhibition of MMP-7 . The results confirm the importance of IL-1β and MMP-7 for the pathogenesis of acute cystitis and identify these molecules as functional targets for immunomodulatory therapy . Bacterial counts remained elevated in the IL-1RA and the MMPI treated mice in the absence of inflammation , suggesting that the treated Asc-/- mice might develop a condition more like asymptomatic bacteriuria than acute cystitis ( Fig 6E ) . To examine the human relevance of the findings in the murine UTI model , we collected urine samples from patients with acute cystitis or ABU and quantified the IL-1β and MMP-7 levels , by ELISA ( Fig 7 ) . Samples from patients with sporadic episodes of acute cystitis were collected at the time of diagnosis , defined by a positive dipstick , dysuria , urgency and frequency of urination but no fever ( n = 9 ) . Samples were also obtained from patients with ABU ( n = 161 ) , who carried the prototype ABU strain E . coli 83972 , following therapeutic inoculation [21] . The patients with ABU participated in a prospective study of E . coli 83972-inoculation with detailed monthly collection of symptom scores and urine samples . There were 20 patients with low symptom scores and 161 urine samples were obtained from this group . We found elevated concentrations of IL-1β in patients with acute cystitis compared to the asymptomatic patient group ( Fig 7A–7C ) resulting in a means of 264 . 5 pg/ml and 1 . 5 pg/ml , respectively ( P < 0 . 001 ) . In addition , all the patients with acute cystitis had positive MMP-7 levels , above the detection limit of 0 . 15 ng/ml ( Fig 7D ) . In a subset of 28 ABU urine samples , the mean MMP-7 concentration was low , resulting in mean concentrations of 15 . 4 ng/ml and 4 . 3 ng/ml , respectively ( P < 0 . 001 ) . The results show that patients with acute cystitis have more elevated concentrations of IL-1β and MMP-7 in urine , than patients with ABU , identifying IL-1β and MMP-7 as potential biomarkers of acute cystitis .
Symptoms and disease are the price we pay for an efficient host defense against infection . As innate immune effectors are activated to clear tissues of bacteria , they may also cause inflammation , symptoms and tissue damage , especially if innate immune control is compromised . This is exemplified here by acute cystitis , which is a common , mostly self-limiting infection except in a subset of patients , who develop severe , recurrent infections , suggesting increased susceptibility . This study proposes a new , genetic basis of susceptibility , exemplified by the disease phenotype in Asc-/- or Nlrp3-/- mice or resistance in Il1b-/- mice that were protected from infection . The transition of the bladder mucosa from a homeostatic innate immune response to acute disease reflects the molecular control of IL-1β processing , through inflammasome-dependent or non-canonical mechanisms ( Fig 8 ) , [44–46] . The findings suggest that acute cystitis might resemble hyper-inflammatory disorders [28 , 47 , 48] , where therapeutic efficacy of IL-1β inhibitors has been documented [49 , 50] . The results provide a molecular context for acute cystitis and for the susceptibility to acute cystitis in patients with severe and chronic disease . The severity of acute cystitis was clearly influenced by bacterial virulence as the acute cystitis strains activated IL-1β more efficiently than ABU strains . This comparison was especially valid , as the CY and ABU strains were isolated from the same pediatric population and geographic area , from children who either developed symptoms or were screened for asymptomatic carriage , by collection and culture of urine samples [31 , 33] . The mechanism of IL-1β activation by the acute cystitis strains remains unclear , however . Schaale et al . have studied the IL-1β response of macrophages infected with UPEC strains CFT073 or UTI89 and reported that IL-1β activation and secretion is hemolysin-dependent in murine macrophages but not in human [20] . Consistent with their studies , we saw high IL-1β responses to virulent and hemolysin positive CY strains , but we did not detect a direct association with hemolysin production , suggesting that additional features control the induction and secretion of IL-1β . Schaale at al . also pointed to the diversity among different UPEC strains , showing that some are able to boost the inflammasome while others may escape detection by not activating IL-1β . Nagamatsu et al . examined hemolysin and the IL-1β response to UTI89 , by inactivating a two-component signal transduction system . The mutant induced significantly higher IL-1β responses than the WT strain , in a hemolysin-dependent manner [19] . In addition , pyroptosis was linked to the presence of hemolysin , through activation of Caspase-1 and Caspase-4 . In the present study , bacterial determinants of pathology were not identified but the CY isolates are being subjected to whole-genome sequence analysis for this purpose . Paradoxically , Il1b-/- mice were resistant to infection , unlike the invasive enteropathogens Salmonella and Shigella , which are lethal for Il1b-/- mice [51 , 52] . Internalization of uro-pathogens by cells in the bladder mucosa has been extensively studied and intracellular communities have been highlighted as a niche for bacterial persistence [53–55] . Specific signaling pathways involved in bacterial uptake by bladder epithelial cells include the ubiquitin-proteasome machinery [56] , and especially type 1 fimbriae have been identified as essential ligands [57] . As Il1b-/- mice did not develop infection or mucosal inflammation , and proinflammatory genes were not expressed , the IL-1β response may help render the bladder mucosa susceptible to infection , possibly by enhancing bacterial growth [58] or tissue invasion . The ability to activate IL-1β production in host cells may therefore be a key to bacterial virulence , as suggested by the epidemiologic survey of strains used in the present study . The findings add MMP-7 to the list of metalloproteinases ( MMP-2 , MMP-3 and MMP-9 ) that cleave pro-IL-1β or degrade IL-1β in other cell types [59] . MMP-7 has also been shown to process and modulate the activity of anti-bacterial peptides produced by the Paneth cells in the mouse small intestine [60] , where cryptdins played a protective role during Salmonella typhimurium- [61] or Chlamydia trachomatis infections [62] . In that model , pro-inflammatory effects of MMP-7 were also detected in the intestinal mucosa [63] . ASC and NLRP-3 have recently been identified as transcriptional regulators of innate immune responses . ASC forms a complex with NF-κB and modifies NF-κB-dependent gene expression [64] . NLRP-3 is involved in the TH2 cell differentiation program and facilitates the binding of IRF-4 to DNA [65] . Here , we identify ASC and NLRP-3 as negative regulators of MMP7 transcription , based on 1 ) inverse regulation of MMP-7 with ASC and NLRP-3 in cells infected with CY-17; 2 ) strongly upregulated MMP7 expression after transfection of human cells with specific siRNAs against ASC and NLRP3; 3 ) identification of a specific MMP7 promoter DNA sequence , to which nuclear proteins from infected cells bind; 4 ) inhibition by antibodies to ASC or NLRP-3 of the interaction between nuclear proteins and the MMP7 promoter; 5 ) binding of recombinant ASC to the MMP7 promoter . As HDAC6 was recently found to interact with NLRP-3 and to modulate its inflammasome function [66] , we speculate that NLRP-3 may act as a co-repressor by binding to ASC and recruiting histone deacetylase ( HDAC ) to the MMP7 promoter , thereby generating a tight chromatin structure refractory to transcription . This was supported by evidence of a direct interaction between ASC and NLRP-3 in the nuclei of CY-17 infected cells . Future studies are required to address in greater detail the regulation of MMP7 expression by ASC and NLRP-3 . Acute cystitis is a handicap , socially and emotionally but despite its prevalence and importance for patients and society , acute cystitis is a poorly understood disease [6 , 67] . Social and behavioral factors have been emphasized as a cause of recurrent infections and until recently , therapeutic options have included a variety of shorter or longer antibiotic regimens , many of which have been discontinued , due to resistance development . It comes as no surprise , that this highly painful condition has been the focus of various interventions in addition to antibiotic therapy . Deliberate establishment of competitive microflora has shown promising clinical effects [21 , 22] but novel , therapeutic approaches are needed in this large patient group . In this study , we show that acute cystitis is amenable to IL-1 receptor inhibition and/or MMP blockade . As IL-1RA is in clinical use , short-term immunotherapy might be a realistic option as an adjunct to antibiotics in acute cystitis patients . The identified molecular disease determinants may also be helpful to address the unmet need for diagnostic tools in this patient group . The frequency of genetic variants , such as ASC mutations , and their relevance to disease would be an interesting focus of prospective clinical studies .
Cystitis ( CY ) and asymptomatic bacteriuria ( ABU ) strains were prospectively isolated during a study of childhood UTI in Göteborg , Sweden , using standard microbiological techniques [32 , 33] . The strain collection has been extensively studied and characterized with fimbrial genotype and phenotype , virulence factor expression , OKH antigen profiles and multilocus enzyme typing [31 , 68] . The hemolytic activity was assessed with blood agar plates , where the hemolytic zone surrounding the central stab of bacteria is recorded . The phenotype has been compared to the hly genotype and found to be a very close fit . The UPEC strain , E . coli CFT073 ( O6:K2:H1 ) [69] and the ABU strain E . coli 83972 ( OR:K5:H- ) [25] have been extensively characterized , including whole genome sequencing and were used as positive or negative controls . Bacteria were cultured on tryptic soy agar ( TSA , 16 h , 37°C ) , harvested in phosphate-buffered saline ( PBS , pH 7 . 2 ) and diluted as appropriate in RPMI without FCS . In the screen of IL-1β responses to acute cystitis or ABU strains , cells were exposed to 108 CFU/ml of bacteria with Gentamicin for 4 hours . In remaining experiments , cells were exposed to 105 CFU/ml for 1 hour or 4 hours without antibiotics . Overnight static cultures of E . coli CFT073 , CY-17 or CY-92 or 83972 in Luria-Bertani ( LB ) broth were used for experimental infection . Human bladder grade II carcinoma cells ( 5637 , ATCC# HTB-9 ) were cultured , to 70–80% confluency on 8-well chamber Permanox slides ( 6x104 cells/well ) , in 6-well plates ( 6x105 cells/well ) or 96-well plates ( 5x104 cells/well ) , ( all from Thermo Scientific ) in RPMI-1640 supplemented with 1 mM sodium pyruvate , 1 mM non-essential amino acids and 10% heat-inactivated FBS ( PAA ) at 37°C with 5% CO2 . Gentamicin ( 50 μg/ml ) was from GE Healthcare . HTB-9 cells in 96-well plates were infected for 1h or 4h . PrestoBlue ( Invitrogen , A13262 ) was added to each well to a final concentration of 10% . After 20 min of incubation at 37°C , total well fluorescence was measured using a microplate reader Infinite F200 ( Tecan ) , with 585 nm excitation and 620 nm emission filters . IL-1β concentrations in filtered supernatants ( Syringe Filter w/0 . 2 μm PES , VWR ) from cells infected with 108 CFU/ml ( with Gentamicin , 4 hours ) were determined by Immulite 1000 ( Siemens ) and IL-1β concentrations in cell supernatants or urine by Human or Mouse IL-1β/IL-1F2 DuoSet ELISA kits ( all from R&D Systems ) . Urine MMP-7 levels were quantified with Human total MMP-7 Immunoassay Quantikine ELISA ( R&D Systems ) . Cells were infected , fixed ( 3 . 7% formaldehyde , 10 min ) , permeabilized ( 0 . 25% Triton X-100 , 5% FBS , 15 min ) , blocked ( 5% FBS , 1h at RT ) , incubated with primary antibodies in 5% FBS overnight at 4°C ( anti-IL-1 beta , 1:100 , ab9722; anti-MMP7 , 1:25 , ab4044 , all Abcam; anti-ASC , 1:50 , sc-22514-R , Santa Cruz; anti-NLRP3/Cryo-2 , 1:100 , AG-20B-0014-C100 , Adipogen ) and appropriate secondary antibody ( Alexa Fluor 488 goat anti-rabbit IgG , A-11034 , or goat anti-mouse IgG , A-11001; Life Technologies ) , ( 1h at RT ) . After nuclear staining ( DRAQ5 , Abcam ) , slides were mounted ( Fluoromount , Sigma-Aldrich ) , imaged by laser-scanning confocal microscopy ( LSM510 META confocal microscope , Carl Zeiss ) and quantified by ImageJ software 1 . 46r ( NIH ) . Cells were lysed with RIPA lysis buffer , supplemented with protease and phosphatase inhibitors ( both from Roche Diagnostics ) and fractionated using the NE-PER Nuclear and Cytoplasmic extraction reagents ( Thermo Scientific ) . Supernatants were filtered and concentrated by trichloroacetic acid precipitation , followed by aceton desiccation . Proteins were run on SDS-PAGE ( 4–12% Bis-Tris gels , Invitrogen ) , blotted onto PVDF membranes ( GE Healthcare ) blocked with 5% bovine serum albumin ( BSA ) or non-fat dry milk ( NFDM ) , incubated with primary antibody: rabbit anti-IL-1 beta ( 1:2 , 500 in 5% NFDM , ab9722 , Abcam ) , rabbit anti-ASC ( 1:200 in 5% BSA , sc-22514-R , Santa Cruz ) , mouse anti-NLRP3/NALP3 ( 1:1 , 000 in 5% milk , Cryo-2 , Adipogen ) or rabbit anti-MMP7 ( 1:200 in 5% BSA , ab4044 , Abcam ) , washed with PBS tween 0 . 1% and incubated with secondary antibodies in 5% NFDM ( goat anti rabbit-HRP or goat anti-mouse-HRP , Cell Signaling ) . Bands were imaged using ECL Plus detection reagent ( GE Health Care ) and quantified using ImageJ . GAPDH ( 1:1 , 000 , sc-25778 , Santa Cruz ) was used as loading control . Nuclear fractions , extracted as described in Western blotting , were incubated with rabbit anti-ASC antibody ( sc-22514-R , Santa Cruz , 1 μg/ml ) overnight and complexes were collected with magnetic Dynabeads Protein G ( Life technologies ) , analyzed by SDS-PAGE with rabbit anti-ASC and mouse anti-NLRP3 ( Cryo-2 , Adipogen ) primary antibodies ( 1:200–1:1 , 000 , 5% BSA ) , followed by secondary anti-rabbit ( Cell Signaling ) or anti-mouse ( DAKO ) antibodies ( 1:4 , 000 , 5% NFDM ) . Total RNA was extracted from murine bladders or kidneys in RLT buffer with 1% β-Mercaptoethanol after disruption in a tissue homogenizer ( TissueLyser LT , Qiagen ) using Precellys Lysing kits ( Bertin Technologies ) , with the RNeasy Mini Kit ( Qiagen ) , 100 ng of RNA was amplified using GeneChip 3´IVT Express Kit , 6 μg of fragmented and labeled aRNA was hybridized onto Mouse Genome 430 PM array strips for 16 hours at 45°C , washed , stained and scanned using the Geneatlas system ( all Affymetrix ) . All samples passed the internal quality controls included in the array strips ( signal intensity by signal to noise ratio; hybridization and labeling controls; sample quality by GAPDH signal and 3’-5’ ratio < 3 ) . Transcriptomic data was normalized using Robust Multi Average implemented in the Partek Express Software ( Partek ) [70 , 71] . Fold change was calculated by comparing infected ( 7 days ) to uninfected mice of the same genetic background . Significantly altered genes were sorted by relative expression ( 2-way ANOVA model using Method of Moments , P-values < 0 . 05 and absolute fold change > 1 . 41 ) [38] . Heat-maps were constructed by Gitools 2 . 1 . 1 software . Differentially expressed genes and regulated pathways were analyzed by Ingenuity Pathway Analysis software ( IPA , Ingenuity Systems , Qiagen ) . Qiagen’s list of 84 key inflammasome genes was selected for analysis . The microarray data are available in the NCBI’s Gene Expression Omnibus repository ( accession number GSE86096 ) . Recombinant human IL-1β , NLRP-3 or PYCARD ( ASC ) ( 280 ng , H00003553-P02 , H00114548-P01 or H00029108-P01 , Abnova ) were incubated with recombinant active human MMP-7 ( 0 . 035U , #444270 Merck Millipore ) in MMP reaction buffer ( 20 mM Tris , pH 7 . 6 , 5 mM CaCl2 , 0 . 1 M NaCl ) at 37°C until stopped with 100 mM DDT . Fragments were detected by Western blot , using rabbit anti-IL-1 beta ( 1:2 000 , ab9722 , Abcam ) , rabbit anti-ASC ( 1:1 000 , p9522-75 , US Biological ) and rabbit anti-NLRP3 ( 1:500 , sc-66846 , Santa Cruz ) . HTB-9 cells were treated with the products of the in vitro proteolysis of pro-IL-1β by MMP-7 at different concentrations or with pro-IL-1β or MMP-7 alone , serving as negative controls . Prostaglandin E2 ( PGE2 ) concentrations were measured in filtered supernatants ( Syringe Filter w/0 . 2 μm PES , VWR ) by ELISA ( R&D systems ) . HTB-9 cells were transfected with PYCARD/ASC and NLRP3 specific siRNAs ( 0 . 09 μM , FlexiTube GeneSolution , #GS29108 and #GS114548 , Qiagen ) or with AllStars Negative Control siRNA ( #SI03650318 , Qiagen ) using the HiPerFect Transfection Reagent ( #301705 , Qiagen ) for 17 hours , then infected . Transfection efficiency was assessed by Western blotting . MMP7 promoter and promoter flanks were amplified in 10 different fragments by PCR using 15 ng of total human genomic DNA . For forward and reverse primers ( http://primer3 . ut . ee/ ) , see S3 Table . Thermal cycling conditions were as follows: 95°C for 2 min , 35 cycles ( 95°C for 30 s , 60°C for 30 s and 72°C for 40 s ) and 72°C for 5 min . Amplified DNA sequences from the MMP7 promoter were used as probes and labeled with GelGreen ( Biotium ) . Each reaction contained 3–5 μg of DNA probe with , 5 μg of nuclear extract from infected HTB-9 cells , or 0 . 2–0 . 65 μg recombinant ASC ( Abnova , H00029108-P01 ) or NLRP-3 ( Abnova , H00114548-P01 ) in binding buffer ( 100 mM Tris , 500 mM NaCl and 10 mM DTT , pH 7 ) . For the band shift competition assay , 0 . 5–1 μg of rabbit anti-ASC ( Santa Cruz , sc-22514-R , ) or 0 . 5 μg of rabbit anti-NLRP3 ( Cryo-2 , Adipogen ) antibodies were used . Binding reactions were incubated at 15°C for 30 min , loaded onto a 6% non-denaturing , non-reducing polyacrylamide gel and ran in a 50 mM Tris ( pH 7 ) , 0 . 38 M glycine , and 2 mM EDTA buffer at 100 V for 2–3 hours . Mouse IgG2A isotype control ( R&D Systems , MAB003 ) was used as negative control antibody . Gels were imaged using the Bio-RAD ChemiDoc system . Mice were bred and housed in the specific pathogen-free MIG animal facilities ( Lund , Sweden ) with free access to food and water . Female C57BL/6 mice or Il1b-/- [36] , Nlrp3-/- [34] Asc-/- [35] , Casp1-/- [37] , Mmp7-/- [40] mice were used at 9–15 weeks of age . The Il1b-/- mice have recently been shown to be functionally defective for IL-1α [72] . The Casp1-/- mice were also deficient for Caspase-11 [73] . Nlrp3-/- and Asc-/- mice were from Jürg Tschopp's laboratory , Department of Biochemistry , University of Lausanne and Institute for Arthritis Research ( aIAR ) . Mmp7-/- and Casp1-/- mice were purchased from The Jackson Laboratories , USA . Mice were intravesically infected under Isofluorane anesthesia ( 108 CFU in 0 . 1 ml ) , through a soft polyethylene catheter ( outer diameter 0 . 61 mm; Clay Adams ) . Animals were sacrificed under anesthesia; bladders and kidneys were aseptically removed and macroscopic pathology was documented by photography . Tissues were fixed with 4% paraformaldehyde or frozen for sectioning and RNA extraction . Viable counts in homogenized tissues ( Stomacher 80 , Seward Medical ) were determined on TSA ( 37°C , overnight ) . Urine samples were collected prior to and at regular times after infection and quantitatively cultured . Neutrophils in uncentrifuged urine were counted , using a hemocytometer . Gross pathology was scored based on the macroscopic appearance of the bladders at sacrifice . The score was based on edema , hyperemia and size , on a scale of 0–10 , where 0 is unchanged compared to the uninfected controls and 10 is most edematous , most hyperemic and largest size . Tissues were embedded in O . C . T . compound ( VWR ) and 5-μm-thick fresh cryosections on positively charged microscope slides ( Superfrost/Plus; Thermo Scientific ) were fixed with 4% paraformaldehyde or acetone-methanol ( 1:1 v/v ) . For H&E or immunohistochemistry , sections were blocked and permeabilized ( 0 . 2% Triton X-100 , 5% goat normal serum ( DAKO ) or 1% BSA ( Sigma ) , stained ( anti-neutrophil antibody [NIMP-R14] ( ab2557 , Abcam ) , polyclonal E . coli antibody ( 1:100 , NB200-579 , Novus Biologicals ) , anti-IL-1 beta ( 1:50 , ab9722 , Abcam ) or anti-MMP-7 ( 1:100 , ab4044 , Abcam ) , all rabbit antibodies ) . Alexa 488 anti-rat IgG or anti-rabbit IgG and Alexa 568 anti-rabbit IgG ( A-21210 , A-11001 and A-11011 , Life Technologies ) were secondary antibodies and nuclei were counterstained with DAPI ( 0 . 05 mM , Sigma-Aldrich ) . Imaging was by fluorescence microscopy ( AX60 , Olympus Optical ) . Richard-Allan Scientific Signature Series Hematoxylin 7211 and Eosin-Y 7111 ( Thermo Scientific ) were used to counterstain the tissue sections . Histology was scored using H&E stained bladder sections . The score was based on neutrophil infiltration , tissue architecture and epithelial thickness on a scale of 0–10 , where 0 is unchanged compared to uninfected controls and 10 the highest neutrophil infiltration , most destroyed tissue architecture and maximum epithelial thickness . The IL-1 receptor antagonist , Anakinra ( Kineret , SOBI ) or the broad-spectrum MMP inhibitor , Batimastat ( ab142087 , Abcam ) were injected intraperitoneally ( i . p . ) as described in Fig 6A . Urine samples from patients with sporadic acute cystitis were obtained at two primary care clinics in Lund , Sweden . A diagnosis of acute cystitis was based on a urine dipstick analysis positive for bacteria and symptoms from the lower urinary tract , including frequency , dysuria and suprapubic pain . Midstream urine specimens were obtained at the time of diagnosis . Patients with ABU were included in a placebo-controlled study of asymptomatic bacteriuria , following intravesical inoculation with E . coli 83972 [21] . Briefly , E . coli 83972 bacteriuria was established by intravesical inoculation ( 105 CFU/ml in saline ) , daily for three days and the outcome was measured as the total number of UTIs during an optimal period of 12 months followed by a cross over to a similar period without E . coli 83972 bacteriuria . Urine samples were obtained for cytokine analysis during E . coli 83972 bacteriuria , with negative symptom scores [21] . ELISA results , fluorescence intensity , Pathology , bacterial numbers and neutrophil responses were analyzed by unpaired two-tailed t-test or Mann-Whitney test after assessment of normality with the d’Agostino & Pearson omnibus normality test . Significance was accepted at P < 0 . 05 ( * ) , P < 0 . 01 ( ** ) or P < 0 . 001 ( *** ) . For animal numbers , see S1 Table . Data was examined using Prism ( v . 6 . 02 , GraphPad ) . Experimental infections were approved by the Malmö/Lund Animal Experimental Ethics Committee at the Lund District Court in Sweden ( approval number M44-13 ) . All animal care and protocols were governed by the European Parlement and Council Directive 2010/63/EU , the Swedish Animal Welfare Act ( Djurskyddslag 1988:534 ) , the Swedish Animal Welfare Ordinance ( Djurskyddsförordning 1988:539 ) and Institutional Animal Care and Use Committee ( IACUC ) guidelines . Experiments were reported according to the ARRIVE guidelines . The clinical studies were approved by the Human Ethics Committee at Lund University ( approval numbers LU106-02 , LU236-99 and Clinical Trial Registration RTP-A2003 , International Committee of Medical Journal Editors , www . clinicaltrials . gov ) . Patients gave their informed written consent . | Infections continue to threaten human health as pathogenic organisms outsmart available therapies with remarkable genetic versatility . Fortunately , microbial versatility is matched by the flexibility of the host immune system which provide a rich source of novel therapeutic concepts . Emerging therapeutic solutions include substances that strengthen the immune system rather than killing the bacteria directly . Selectivity is a concern , however , as boosting of the antibacterial immune response may cause collateral tissue damage . This study addresses how the host response to urinary bladder infection causes acute cystitis and how this response can be attenuated in patients who suffer from this very common condition . We identify the cytokine Interleukin-1 beta ( IL-1β ) as a key immune response determinant in acute cystitis and successfully treat mice with severe acute cystitis by inhibiting IL-1β or the enzyme MMP-7 that processes IL-1β to its active form . Finally , we detect elevated levels of these molecules in urine samples from patients with cystitis , suggesting clinical relevance and a potential role of IL-1β and MMP-7 both as therapeutic targets and as biomarkers of infection . These findings provide a much needed , molecular framework for the pathogenesis and treatment of acute cystitis . |
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Misfolded proteins in transgenic models of conformational diseases interfere with proteostasis machinery and compromise the function of many structurally and functionally unrelated metastable proteins . This collateral damage to cellular proteins has been termed 'bystander' mechanism . How a single misfolded protein overwhelms the proteostasis , and how broadly-expressed mutant proteins cause cell type-selective phenotypes in disease are open questions . We tested the gain-of-function mechanism of a R37C folding mutation in an endogenous IGF-like C . elegans protein DAF-28 . DAF-28 ( R37C ) is broadly expressed , but only causes dysfunction in one specific neuron , ASI , leading to a distinct developmental phenotype . We find that this phenotype is caused by selective disruption of normal biogenesis of an unrelated endogenous protein , DAF-7/TGF-β . The combined deficiency of DAF-28 and DAF-7 biogenesis , but not of DAF-28 alone , explains the gain-of-function phenotype—deficient pro-growth signaling by the ASI neuron . Using functional , fluorescently-tagged protein , we find that , in animals with mutant DAF-28/IGF , the wild-type DAF-7/TGF-β is mislocalized to and accumulates in the proximal axon of the ASI neuron . Activation of two different branches of the unfolded protein response can modulate both the developmental phenotype and DAF-7 mislocalization in DAF-28 ( R37C ) animals , but appear to act through divergent mechanisms . Our finding that bystander targeting of TGF-β explains the phenotype caused by a folding mutation in an IGF-like protein suggests that , in conformational diseases , bystander misfolding may specify the distinct phenotypes caused by different folding mutations .
Cellular and organismal functions depend critically on the correct folding and intracellular targeting of proteins , and folding mutations are associated with many human pathologies , including neurodegenerative diseases and some forms of diabetes and cancer [1] . In addition to directly impairing the function of the affected protein , folding mutations can exhibit toxic-gain-of-function properties [2] . The mechanistic understanding of what links a specific toxic-gain-of-function mutation to the resulting disease phenotype is still very limited . One of the proposed mechanisms is global disruption of cellular folding environment , initiated by titration of chaperones , degradation machinery , and other proteostasis components by the disease-associated proteins [3–7] . We have previously shown that ectopic expression of disease proteins in C . elegans causes misfolding and loss of function of unrelated chaperone-dependent or metastable proteins in the same cell , which , in turn , drives the toxic phenotypes [5 , 8] . This collateral damage to normal cellular proteins by a gain-of-function mutant protein has been termed 'bystander' mechanism [9 , 10] and has also been shown in response to the high amyloid burden in Alzheimer's disease model as well as in other disease models [11–13] . How a single misfolded protein overwhelms the proteostasis and which cellular proteins are subject to this bystander effect are open questions . The latter question is particularly important for understanding how a broadly- or ubiquitously-expressed mutant protein can cause cell-specific dysfunction in disease . Finally , in many models of disease , mutant proteins are ectopically ( over ) expressed . Because such proteins may engage the proteostasis machinery differently than endogenous mutant proteins , it is important to ask whether the bystander effect can contribute to gain-of-function mechanisms exerted by endogenous mutant proteins expressed in their cognate cellular environment . These questions about the bystander effect are particularly relevant in the endoplasmic reticulum ( ER ) , where a single misfolded protein can cause folding stress and cellular dysfunction even though many other itinerant proteins in the ER are in the process of folding and assembly and , thus , are non-native [14–16] . Here , we ask how a folding mutation in the endogenous C . elegans insulin/IGF-like protein DAF-28 [17] affects folding or maturation of other unrelated proteins in the secretory pathway and probe the molecular events underlying the cell-selective phenotypic outcome of this mutation . We use the DAF-28 ( R37C ) mutation to test the bystander mechanism for three reasons . First , it causes folding stress in the ER , as seen by induction of the unfolded protein response ( UPR ) [18] . Second , IGF proteins , the mammalian counterparts of DAF-28 , are strictly dependent on the molecular chaperone GRP94 for their folding and secretion [19 , 20] , indicating a strong engagement of this family of proteins with the ER proteostasis machinery . Third , DAF-28 ( R37C ) mutant animals exhibit a specific and quantifiable developmental phenotype called dauer diapause resulting from dysfunction of a single chemosensory neuron ( ASI ) , despite expression of the mutant DAF-28 protein in nine different tissues [21] . DAF-28 ( R37C ) mutant protein is encoded by a semi-dominant sa191 allele and causes inappropriate dauer entry [22] . In C . elegans , exposure of first larval stage animals ( L1 ) to adverse conditions , such as crowding , limited food , and elevated temperature , triggers a switch from reproductive growth to an alternative stress-resistant developmental stage known as dauer diapause [23] . The decision to continue reproductive development or to enter dauer is specified by secretion of the insulin/IGF-like protein DAF-28 ( referred to here as IGF-like ) and the TGF-β protein DAF-7 from the ASI sensory neurons [17 , 24–26] ( Fig 1A ) . The ASI neuron is the main source of the DAF-7/TGF-β in dauer signaling [27] . Loss of daf-7 results in partial activation of the dauer state even under growth-promoting conditions , and in a complete dauer entry under sensitizing conditions , such as elevated temperature [28] . In contrast , deletion of daf-28 does not cause dauer signaling due to redundancy with other insulin/IGF-like proteins [29 , 30] , consistent with the gain-of-function for sa191 allele . Overexpression of the wild-type DAF-28 or other insulin/IGF-like proteins ( INS-4 or INS-6 ) can rescue dauer induction in sa191 animals [17] , suggesting that the mutant DAF-28 ( R37C ) protein may have a dominant-negative effect on the wild-type pro-growth IGF-like proteins . The R37C substitution is in a predicted RXXR proteolytic cleavage site of DAF-28 [17] . In mammalian insulins and IGFs , proteolytic processing in Golgi or secretory vesicles follows their normal folding and disulfide bond formation in the ER . Thus , the phenotype of R37C mutation may be due to misprocessing of DAF-28 . However , DAF-28 with a different mutation in the same residue , R37A , does not cause a gain-of-function and is partially active [17] . Similarly , arginine substitutions in the KR cleavage site in human insulin—R89L , R89P , or R89H—result in a protein that is misprocessed but non-toxic and efficiently secreted , causing hyperproinsulinemia; however , mutation to a cysteine at the same residue—R89C—results in a severely misfolded protein and causes permanent neonatal diabetes mellitus ( PNDM ) [31 , 32] . These observations argue against misprocessing as the cause for the gain-of-function toxicity of DAF-28 ( R37C ) . Interestingly , many of the insulin folding mutations that cause PNDM and the mutant INS-gene-induced diabetes of youth ( MIDY ) generate unpaired cysteines [32 , 33] . Similarly , a disulfide bond-disrupting C ( A7 ) Y mutation in the Ins2 gene of Akita mouse , a diabetes model , is a toxic-gain-of-function mutation and results in insulin misfolding , induction of UPR , and eventual death of insulin-producing beta cells [34 , 35] . Conversely , an engineered insulin mutant carrying the Akita mutation but lacking all cysteines does not interfere with the wild-type insulin , despite being severely misfolded [33] . Thus , in addition to the general danger of having unpaired cysteines in the oxidizing environment of the secretory pathway , the insulin/IGF fold may be particularly sensitive to these mutations due to the topologically complex arrangement of three ( four in DAF-28 ) disulfide bonds in a small protein . Abnormal UPR induction is thought to be one of the mechanisms by which misfolded secretory proteins cause cellular dysfunction in many proteinopathies [36 , 37] . In some cases , such as in PNDM/MIDY , affected cells produce large amounts of the mutant protein , which triggers the UPR [38] . However , when the mutant protein represents only a small fraction of the non-native species in the ER , the mechanism of UPR induction is less clear , as it is not well understood what effect misfolding of one non-abundant protein has on the biogenesis of other proteins in the same compartment . Here , we find that the R37C folding mutation in the broadly-expressed IGF-like protein DAF-28 induces defects in the protein biogenesis of the endogenous DAF-7/TGF-β expressed in the ASI neuron . The combined deficiency in DAF-28 and DAF-7 biogenesis , but not in DAF-28 alone , explains the gain-of-function phenotype of the DAF-28 ( R37C ) mutation—deficient pro-growth signaling by the ASI neuron . This toxic effect can be modulated by ER chaperones but is observed prior to the ASI-specific UPR induction , indicating that a targeted defect in secretory protein biogenesis , rather than global ER stress , is a triggering event . Using a functional , fluorescently-tagged reporter , we find that the wild-type DAF-7 is normally localized to the sensory dendrite . However , in animals with misfolded DAF-28/IGF , DAF-7/TGF-β becomes mislocalized and accumulates in the proximal axon of the ASI neuron . The finding that bystander targeting of TGF-β explains the phenotype of a folding mutation in an IGF-like protein suggests that cellular context ( i . e . the cell-specific composition of the proteome ) may determine the distinct phenotypic outcomes of the different folding mutations implicated in conformational diseases .
daf-28 ( sa191 ) mutants expressing DAF-28 ( R37C ) protein have fully penetrant dauer arrest at elevated temperature of 25°C [22] . We have previously shown that growth at 25°C induces misfolding of temperature-sensitive and chaperone-dependent ( metastable ) proteins in C . elegans [5] , reflecting increased burden on the proteostasis machinery . Growth at 25°C also leads to changes in stress and longevity pathways and to altered fecundity , which may , in turn , affect proteostasis [39–41] . Elevated temperature itself is highly sensitizing to dauer entry , and a further 2°C increase can cause dauer entry even in wild-type animals [42] . We tested whether sa191 gain-of-function phenotype is still present at permissive temperatures . A majority of sa191 animals raised under growth-promoting conditions—20°C , abundant food , and low population density—abnormally entered the pre-dauer developmental stage L2d ( Fig 1B and 1C ) , indicating deficient pro-growth signaling by the ASI neuron ( Fig 1A ) [22] . As previously reported , the entry to L2d was transient , with many animals resuming the normal development within hours and some progressing to dauer before resuming the normal development . To circumvent this variability , we scored development of sa191 animals at 20 or 15°C at 65–66 or 90–91 hours post-gastrula , respectively . At these time points , most of the wild-type animals become reproductive adults ( green , Fig 1B and 1C ) and any time spent in L2d and/or dauer stages is reflected as mild or severe developmental delay ( yellow/red , Fig 1B and 1C ) . Unlike wild-type , 69±16% of sa191 animals raised at normal growth temperature ( 20°C ) were mildly delayed , and 29±15% were severely delayed as either early L4 larvae , dauers or pre-dauers/L2ds ( Fig 1C ) . By contrast , only 6±1% animals with daf-28 loss-of-function allele tm2308 were mildly delayed and 3±4% severely delayed . At low growth temperature ( 15°C ) , 98±1% of daf-28 ( sa191 ) animals were still mildly delayed ( Fig 1C ) , showing that DAF-28 ( R37C ) mutation exerts its gain-of-function properties even at low growth temperature . Entry into the L2d stage results from activation of some but not all of the converging dauer signals , such as decreased signaling from DAF-7/TGF-β [27] . DAF-7 is secreted from the ASI neurons and functions in parallel to DAF-28 ( Fig 1A ) . Indeed , most animals carrying the e1372 loss-of-function allele of daf-7 entered L2d stage at 20°C , resulting in growth delay at 65–66 hours ( Fig 1C ) . In this respect , the R37C gain-of-function mutation in DAF-28/IGF protein behaves as a phenocopy of the loss-of-function mutation of DAF-7/TGF-β . Thus , we asked whether DAF-7 protein was functional in daf-28 ( sa191 ) mutants . We used an established DAF-7 activity reporter—a cuIs5 transgene expressing GFP in the pharynx from a SMAD-dependent promoter ( SMAD::GFP ) . Reporter fluorescence is bright only when DAF-7 is secreted and is attenuated when DAF-7 secretion is low . At the late L1 larval stage , when DAF-7 and DAF-28 secretion determines the development vs . dauer decision , wild-type animals exhibited bright reporter GFP fluorescence ( Fig 2A and 2B ) . In contrast , many daf-28 ( sa191 ) animals had decreased GFP fluorescence ( Fig 2A and 2B ) , indicating decreased DAF-7 activity . The decrease in fluorescence was variable: some sa191 animals were comparable to daf-7 loss-of-function animals , while others showed intermediate to wild-type levels . Decreased DAF-7 activity in daf-28 ( sa191 ) animals could due to be its transcriptional downregulation . However , daf-7 expression does not depend on insulin signaling in C . elegans , and we did not detect a decrease in expression of its transcriptional reporter in the ASI neuron of sa191 animals ( [17] and S1A Fig ) . We asked whether the variability in SMAD-dependent GFP fluorescence , the proxy for DAF-7 activity , was related to the variability in the sa191 dauer phenotype . We separated the daf-28 ( sa191 ) ;cuIs5 animals by eye into ‘bright’ and ‘dim’ populations at the end of the L1 larval stage and scored their development . Indeed , we found that daf-28 ( sa191 ) mutants with ‘dim’ SMAD-dependent GFP fluorescence had a higher percentage of severely delayed animals ( 24% v . 9% ) than those with ‘bright’ fluorescence ( Fig 2C ) . To test whether decreased DAF-7 function contributes directly to the dauer phenotype in daf-28 ( sa191 ) mutants , we asked if over-expression of DAF-7 could rescue this phenotype . We used two independently generated transgenes . The first , adEx2202 , expresses daf-7 cDNA in the ASI neurons under the control of the gpa-4 promoter ( ASI::DAF-7 ) [43] . When crossed into daf-28 ( sa191 ) mutants , the ASI::DAF-7 transgene completely rescued the severe developmental delay , but not the mild delay ( Fig 2D ) . Thus , the adEx2202 transgene prevented the persistence of the L2d partial dauer stage in sa191 animals , or their commitment to dauer , but did not completely rescue the deficient pro-growth signaling . Second , we constructed a mCherry::DAF-7 fusion protein expressed from its cognate daf-7 promoter , using a strategy previously used to generate functionally-tagged TGF-β proteins—Dpp in Drosophila and DBL-1 in C . elegans ( S1B Fig ) [44 , 45] . mCherry was chosen because it lacks cysteines and , thus , would not be expected to interfere with oxidative folding of DAF-7 , which contains a disulfide bond-rich cysteine knot domain . The mCherry::DAF-7 protein was functional , as it efficiently rescued both characteristic phenotypes of the daf-7 ( e1372 ) loss-of-function allele—developmental delay/L2d entry in early larvae ( Fig 2E ) and the egg retention/dark intestine phenotype in adults ( >99% ) . Surprisingly , the dauer phenotype was also rescued in non-transgenic e1372 progeny of transgenic parents ( Fig 2E , N-Tg sib . ) , but only in the first generation , suggesting it was due to a maternal contribution . When expressed in daf-28 ( sa191 ) animals , the functional mCherry::DAF-7 protein rescued their severe developmental delay from 67% to 5±3% and supported normal reproductive development in the majority of animals ( Fig 2D ) . Interestingly , the L2d/dauer phenotype was again rescued in the first-generation non-transgenic sa191 progeny , indicating that the gain-of-function phenotype of DAF-28 ( R37C ) mutation could be rescued through maternal contribution of DAF-7 . Since the ASI-restricted adEx2202 transgene did not rescue the dauer phenotype in the first-generation non-transgenic siblings ( N-Tg . Sib . v . ASI::DAF-7 , Fig 2D ) , the maternal rescue may depend on the non-ASI expression of DAF-7 , perhaps due to the promoter or intronic elements present in our mCherry::DAF-7 transgene . Alternatively , the timing and/or strength of the gpa-4 promoter in the adEx2202 transgene may not be sufficient to see this rescue . Overexpression of DAF-7 could be rescuing the dauer entry in sa191 animals non-specifically , by simply increasing pro-growth signaling over the dauer induction threshold . If so , it should also decrease the abnormal dauer entry caused by genetic loss of all pro-growth insulin/IGF signaling . Deletion of daf-28 alone is not sufficient to induce dauer ( [29] and Fig 1C ) . However , a ZM7963 strain with a combined deletion of daf-28 , ins-4 , ins-5 , and ins-6 ( designated as 4xDel ) has a constitutive dauer phenotype at 20°C . Importantly , the dauer phenotype of this strain can be rescued by overexpression of DAF-28 alone , or by INS-4 or INS-6 , due to the redundancy between deleted insulin/IGF proteins [30] . In contrast to its strong rescue of daf-28 ( sa191 ) , mCherry::DAF-7 had no effect on the dauer induction in the 4xDel strain ( Fig 2D ) , indicating that it only rescues the specific defect caused by the sa191 mutation . Taken together , our results suggest that decreased availability of secreted DAF-7 protein underlies the gain-of-function mechanism of dauer induction in animals carrying DAF-28 ( R37C ) mutation . How could a putative folding mutation in DAF-28/IGF protein disrupt DAF-7/TGF-β activity ? Since both proteins need to be secreted to signal reproductive development , and since UPR induction specifically in the ASI neuron has been implicated in the sa191 dauer phenotype [18] , we wanted to test two possible mechanisms: ( 1 ) the ASI-specific UPR , induced by the misfolded DAF-28 protein , leads to global ASI neuron dysfunction during growth vs . dauer decision and ( 2 ) the bystander effect , i . e . a targeted collateral damage to the endogenous cellular proteins , including DAF-7 protein , from the misfolded DAF-28 ( R37C ) . Since overactive UPR can cause generalized cellular dysfunction and degeneration and interfere with development [46 , 47] , we first asked if there is UPR induction in the ASI neurons of daf-28 ( sa191 ) animals at the time when the growth vs . dauer decision is made—late L1/early L2 larval stages . To visualize UPR in individual cells , we used a phsp-4::GFP transgene , which has been shown to be a specific and sensitive reporter of UPR in C . elegans [48] . In wild type animals , the UPR reporter is weakly induced in the intestine and seam cells , and is undetectable in neurons ( Fig 3A and 3B ) . Surprisingly , we did not observe a specific or strong induction of UPR in the ASI neurons of sa191 animals at L1/L2 stages . Instead , we observed similar induction of the UPR reporter in several different head neurons of L2 animals , and no reporter induction in most L1 animals ( Fig 3C , 3D and 3E ) . This was not due to lack of sensitivity , as we easily detected reporter expression in non-ASI cells of same animals , including seam cells and intestine ( Fig 3C and 3D ) . Induction of the UPR reporter became more pronounced in the ASI neuron in older animals , eventually becoming the predominant source of GFP fluorescence ( L3 , Fig 3F ) . In C . elegans , deletion of PERK increases hsp-4 expression [49] , and we observed a striking upregulation of the UPR reporter in the intestine of unstressed pek-1 ( - ) animals ( Fig 3G ) . Compared to its intensity in cells of pek-1 ( - ) animals , the UPR reporter induction in the head neurons of daf-28 ( sa191 ) L1/L2 animals ( Fig 3C–3F ) was very weak . We also noted that deletion of pek-1 , that is known to rescue dauer phenotype of sa191 animals [18] , did not eliminate ER stress in neurons of L2 sa191 animals ( Fig 3H and 3I ) . The observed UPR reporter induction in intestine and seam cells of daf-28 ( sa191 ) animals could reflect misfolding of the DAF-28 ( R37C ) mutant protein in these cells . In addition to the ASI and other head neurons , daf-28 is expressed in eight other tissues , including pharynx , hypodermis , ventral nerve cord , intestine and several reproductive tissues [21] . To examine possible dysfunction of these cells , we assayed adult body size as indicator of intestinal and pharyngeal function , brood size for dysfunction in daf-28-expressing reproductive cells ( vulva muscles , gonad sheath cells , or distal tip cell ) , and swimming as proxy for gross dysfunction of ventral nerve cord . We found no significant differences between wild-type animals and daf-28 ( sa191 ) mutants for these phenotypes ( Fig 3J–3L ) . Previous reports also found no other deficiencies specific to the sa191 allele beyond dauer induction [22] . Finally , as noted by Li et . al . [17] , the highly transient nature of the L2d/dauer entry in sa191 animals indicates normal function of the ASJ neuron that regulates exit from dauer , despite expression of DAF-28 ( R37C ) protein . Thus , expression of the mutant DAF-28 ( R37C ) protein and its activation of UPR in cells and tissues other than the ASI neuron are not sufficient to cause cellular dysfunction . Importantly , the DAF-7 secretion defect in sa191 animals was observed already in the late L1 stage ( Fig 2A and 2B ) , prior to the strong and cell-specific UPR induction in the ASI neuron of older animals . Together , our data argue against the UPR being the initiating factor for the global ASI dysfunction and for the molecular events leading to the gain-of-function toxicity of DAF-28 ( R37C ) protein . If the R37C mutation indeed causes misfolding , its phenotypic expression would be expected to depend on ER chaperones and folding environment . In C . elegans , XBP-1 is a UPR transcription factor that is activated through a conserved splicing mechanism in response to the folding stress in the ER and , thus , upregulates expression of the ER chaperones and proteostasis components [48] . Importantly , transgenic expression of the active , spliced protein ( XBP-1s ) upregulates ER chaperone levels without causing ER stress [50] . We found that pan-neuronal expression of XBP-1s strongly rescued the gain-of-function dauer phenotype of sa191 animals , decreasing the number of severely delayed animals from 29±18% to 3±3% ( Fig 4A ) . This rescue , however , became less robust over several generations . This could be due to a silencing of the xbp-1s-expressing transgene in this genetic background , or could suggest a complex genetic interaction between the chronic folding stress and the constitutive XBP-1s activity . To ask whether the observed dauer rescue was related to improved DAF-7 activity , we measured the SMAD-GFP reporter fluorescence . Spliced XBP-1 efficiently rescued the decrease in SMAD::GFP fluorescence in sa191 animals ( Fig 4B ) . Of note , the positive effect of XBP-1s on the SMAD::GFP fluorescence did not attenuate over generations . Conversely , deletion of ER chaperones would be expected to exacerbate the phenotypes caused by misfolding of DAF-28 ( R37C ) , as it decreases the cell's ability to deal with misfolded proteins . To test this , we targeted HSP-4 , a stress-inducible form of the major HSP-70 ER chaperone BiP in C . elegans [48 , 49] . hsp-4 is expressed in only few tissues at basal conditions , and animals with hsp-4 ( gk514 ) deletion appear wild-type in the absence of applied stress . Deletion of hsp-4 resulted in increase in severely delayed animals from 29±18% to 83±6% , with majority of these being in L2d/dauer stages ( Fig 4A ) . Together , these data show that the developmental phenotype of sa191 allele can be modulated by molecular chaperones , consistent with the idea that misfolding of DAF-28 ( R37C ) contributes to its gain-of-function . Under acute ER stress conditions , activation of the PERK/eIF2α branch of the UPR allows for cellular recovery via transient attenuation of translation [51 , 52] . However , if translational attenuation was present during early development , it could lead to insufficient production of DAF-28 and DAF-7 proteins and , thus , the dauer phenotype of animals with misfolded DAF-28 ( R37C ) . Indeed , at 25°C , activation of PERK/eIF2α branch of the UPR specifically in the ASI neurons contributes to the dauer phenotype of daf-28 ( sa191 ) mutants [18] . Although we did not detect a decrease in pdaf-7::GFP fluorescence in the ASI neurons of L1-L2 daf-28 ( sa191 ) larvae ( S1A Fig ) , the GFP reporter may not be sensitive enough to detect translational attenuation . Thus , we asked whether elimination of PERK signaling was able to rescue the sa191 phenotype under our growth conditions . Deletion of pek-1 indeed partially rescued the gain-of-function phenotype in daf-28 ( sa191 ) mutants at 20°C ( Fig 4C ) when the animals were grown at a low density . However , we noticed that many daf-28 ( sa191 ) ;pek-1 ( - ) animals entered the L2d stage even at permissive temperature and in the presence of abundant food when plates became crowded ( Fig 4D–4H and S2 Fig ) . Since increased population density , and subsequent increased pheromone signaling , is one of the environmental inputs into the growth vs . dauer decision , loss of PERK may be selectively affecting specific aspects of the dauer signaling in sa191 animals . Thus , we wanted to ask whether dauer rescue by deletion of pek-1 is mediated by the rescue of DAF-7 activity , similar to what we found with spliced XBP-1 . We were unable to directly assess SMAD::GFP activity due to difficulty with crosses . Instead , we asked whether rescue by DAF-7 overexpression is also sensitive to the population density and found that , unlike deletion of pek-1 , mCherry::DAF-7 rescues the dauer phenotype of sa191 animals even at high density ( Fig 4D ) . Thus , rescue of the sa191 dauer phenotype by pek-1 deletion may not depend on increase in DAF-7 activity . Overall , out data show that the phenotypic expression of DAF-28 ( R37C ) mutation depends on the ER folding environment , and that the DAF-7 activity defect in these animals may be differentially affected by the two branches of the UPR . Since we found that UPR does not trigger the dauer signaling in daf-28 ( sa191 ) animals , we tested the second proposed mechanism—bystander misfolding of endogenous cellular proteins . We considered two potential targets: other pro-growth insulin/IGF-like proteins and DAF-7 . To examine the effect of mutant DAF-28 ( R37C ) on wild-type insulin/IGF-like proteins , we generated a wild-type DAF-28::mCherry and followed its localization in wild-type and sa191 animals . The DAF-28::mCherry expression followed the reported expression pattern of pdaf-28::GFP transcriptional reporter , with fluorescent protein detected in head and tail neurons , pharynx , hypodermis , and other tissues ( Fig 5A and 5B ) . DAF-28::mCherry was efficiently secreted upon expression in L1 stage , as detected by its uptake by endocytic scavenger cells , coelomocytes [53] ( Fig 5C and 5D ) . Interestingly , compared to the DAF-28::mCherry fusion protein , the previously described DAF-28::GFP protein [17 , 54] was not efficiently secreted , as judged by coelomocyte fluorescence , and instead appeared to accumulate in neuronal cell bodies ( Fig 5C and 5D ) . As discussed below , this may be due to misfolding of the GFP moiety . To verify the functionality of the DAF-28::mCherry fusion protein , we crossed it into the 4xDel strain . Despite mosaic expression , DAF-28::mCherry protein efficiently rescued the dauer phenotype of the 4xDel strain , showing that this protein is functional ( Fig 5E ) . Confocal imaging showed that DAF-28::mCherry protein is predominantly found in a punctate pattern reminiscent of the secretory vesicles in mammalian cells expressing insulin or IGF , with some puncta found in a regularly spaced pattern adjacent to the ASI axons ( Fig 5F ) . Inactivation of unc-104 , the C . elegans homologue of the axonal kinesin KIF1A , eliminated these axon-adjacent puncta ( Fig 5G ) , suggesting that they represent either local accumulation of DAF-28::mCherry protein secreted from the ASI axon , or protein present in axons of other neurons expressing daf-28 . The orientation of the ASI cell body , axon and dendrite is shown in Fig 5H . Misfolded insulin with unpaired cysteines can exert a dominant-negative effect on the wild-type insulin [33 , 55] . To determine whether interference with biogenesis of wild-type insulin/IGF-like proteins contributes to the gain-of-function of sa191 allele , we crossed sa191 animals to those expressing the functional DAF-28::mCherry protein . DAF-28::mCherry was still secreted , and we did not detect major redistribution of the fluorescent signal to the cell bodies as would be expected if the protein was retained in the ER ( Fig 6A ) . We did detect minor alterations in the ASI axon-adjacent punctate pattern of wild-type DAF-28::mCherry in these animals ( Fig 6A and 6B ) , which became less regular . Next , we generated a DAF-28 ( R37C ) ::mCherry transgenic strain . We found that this transgene phenocopied the endogenous sa191 mutation , as it caused severe developmental delay in the daf-28 ( tm2308 ) deletion background ( Fig 6C ) . Imaging revealed dramatic differences in localization of DAF-28 ( R37C ) ::mCherry mutant protein as compared to wild-type . First , the protein appeared to strongly accumulate in the pharyngeal muscle and , to a lesser extent , in hypodermal tissue in the head , two tissues known to express daf-28 ( Fig 6D , 6E and 6F ) . DAF-28 ( R37C ) ::mCherry also appeared to accumulate in the ASI proximal axons , forming large aggregate-like puncta ( Fig 6E–6H , block arrows ) . Unlike the puncta seen with the wild-type DAF-28::mCherry protein , these puncta were contained within the neuronal processes ( Fig 6F and 6G ) . Strikingly , these puncta created voids in the fluorescence of soluble cytosolic GFP expressed in the ASI neurons , suggesting that the transgenic mutant protein disrupts axonal architecture of these cells ( Fig 6H ) . Surprisingly , at least some of the DAF-28 ( R37C ) ::mCherry mutant protein was secreted , as evidenced by its presence in coelomocytes as early as L1 stage ( Fig 6D , cc ) . Our data thus show that endogenous DAF-28 ( R37C ) protein causes mild axonal defects , while overexpression of the DAF-28 ( R37C ) ::mCherry transgene , in addition to its own mistrafficking and accumulation , causes significant disruption of the ASI axons . We took advantage of the mCherry-tagged DAF-28 ( R37C ) to ask if the R37C mutation was indeed causing oxidative misfolding of this IGF-like protein . Misfolded MIDY insulins are known to engage in abnormal disulfide-linked protein complexes , resulting in loss of their detection as discrete species under non-reducing conditions [33] . DAF-28 ( R37C ) ::mCherry protein similarly did not resolve into any predominant bands under non-reducing conditions , while treating the worm lysates with reducing reagents produced a single band of expected size , approximately 37 kDa ( Fig 6I , white arrow ) . As control , mCherry protein alone resolved into a single band under both reducing and non-reducing conditions ( Fig 6I , black arrows ) . Although this is consistent with DAF-28 ( R37C ) ::mCherry protein being abnormally engaged in intermolecular disulfide-linked protein complexes , we could not unambiguously conclude this , since our ability to detect the wild-type DAF-28::mCherry protein , which , unlike the mutant , does not accumulate in any tissues , was not reliable ( S3 Fig ) . Finally , we examined the functionality and localization of the DAF-28::GFP protein , since we have found that it is not efficiently secreted ( Fig 5C and 5D ) . DAF-28::GFP protein exhibited a strong toxic gain-of-function phenotype , causing severe developmental delay in the daf-28 ( tm2308 ) deletion background ( Fig 6C ) . Moreover , DAF-28::GFP accumulated in both the cell body and axons of the ASI and ASJ neurons , eventually filling the cell bodies ( Fig 6J–6L ) . This is consistent with misfolding and ER retention of a GFP-tagged secretory protein , since GFP is known to undergo oxidative misfolding in the ER of mammalian cells due to the presence of two buried cysteine residues [56 , 57] . Our data so far indicate that neither of the two most straightforward mechanisms—UPR induction or dominant-negative effect on wild-type insulin/IGF-like proteins—completely explain the gain-of-function phenotype caused by DAF-28 ( R37C ) protein . As we observed defects in daf-7 signaling in sa191 animals ( Fig 2 ) , we next asked whether localization or secretion of our functional mCherry::DAF-7 protein was affected by the presence of the endogenous DAF-28 ( R37C ) protein . In the wild-type background , mCherry::DAF-7 fluorescence was detected in neurons as well as in the pharyngeal muscles ( Fig 7A–7E ) . In neurons , we observed punctate fluorescence in the cell bodies and in the area posterior to the ASI cell body ( Fig 7A , 7D and 7E , stars and arrowheads , respectively ) . In contrast to DAF-28::mCherry , which was present in axons but excluded from dendrites , mCherry::DAF-7 protein was mainly detected along the dendrites of the ASI neurons ( Fig 7A–7E , block arrows ) . Only rare small puncta were noted in the proximal axons ( Fig 7E , arrow ) . Consistent with dendritic trafficking , we observed a large accumulation of the fluorescent signal surrounding the base of the ASI sensory cilia located at the distal end of the dendrite ( Fig 7A , 7C and 7D , square brackets ) . Although the maturation and trafficking of DAF-7/TGF-β have not been characterized in C . elegans , the signal outside the cilia could represent locally secreted mCherry::DAF-7 protein . To test this , we used a transgene that expresses active caspase specifically in the ASI neurons [58] . Activation of apoptosis in the ASI neurons collapsed both the ASI cell body- and posterior-localized fluorescent signal and strongly decreased the accumulation of mCherry fluorescence near cilia ( Fig 7F–7I ) , confirming that they all represent the mCherry::DAF-7 protein secreted from the ASI neuron . ASI apoptosis did not eliminate the pharyngeal mCherry fluorescence ( Fig 7H ) , suggesting that DAF-7 protein may also be expressed in the pharynx . When placed in the background of the daf-28 ( sa191 ) mutation , mCherry::DAF-7 protein did not accumulate in the ASI cell bodies ( Fig 8A–8F ) , and we did not detect any severe defects in the dendritic targeting ( Fig 8B ) . Strikingly , unlike in a wild-type background , mCherry::DAF-7 protein accumulated in the proximal regions of the ASI axons in sa191 mutant animals ( Fig 8A–8H ) . This mistargeting of the mCherry::DAF-7 protein was evident already in the L1 ( Fig 8A and 8C ) and early L2 ( Fig 8D and 8E ) larval stages , which is the time the DAF-7 function in sa191 animals is compromised ( Fig 2A and 2B ) . This was not due to a generic trafficking defect , as localization of an unrelated secretory protein ChannelRhodopsin-2::YFP [59] to dendrites and axons was not affected by sa191 ( Fig 8I and 8J ) , and the shape of ASI cilia , which depends on the cellular trafficking , was also normal ( Fig 8B and 8J , bottom panel ) . Thus , the mistargeting of DAF-7 to the axon of the ASI neuron is a specific molecular consequence of the expression of misfolded DAF-28 ( R37C ) in the same cell , and may reflect the molecular events underlying the gain-of-function mechanism in sa191 animals . To quantify the mistargeting , we scored L1 or L2 animals with one or both of the mCherry-positive ASI neurons visualized in their entirety . In daf-28 ( sa191 ) mutant animals , 5 out of 9 ASI neurons examined had accumulated mCherry::DAF-7 protein in their axons , while 0 out of 8 ASI neurons in animals with wild-type daf-28 had such axonal accumulation . This was not simply due to the daf-28 ( sa191 ) background being sensitizing to dauer entry , since we did not detect ( 0 out of 6 ) axonal mistargeting of mCherry::DAF-7 protein in the equally sensitized daf-7 ( e1372 ) animals . The accumulation of mCherry::DAF-7 in the ASI axon and in the pharynx was even more prominent in older daf-28 ( sa191 ) mutant animals ( Fig 8F–8H , L4 animal shown ) . Based on these data , the folding mutation in the endogenous DAF-28/IGF protein leads to aberrant localization and accumulation of the wild-type bystander DAF-7/TGF-β protein in the axons of affected neurons . If the ectopic mCherry::DAF-7 protein is mislocalized in the ASI neurons of sa191 animals , how does it rescue the dauer phenotype ? First , it is possible that overexpression of mCherry::DAF-7 protein results in the UPR induction and increased chaperone expression in the ASI neuron . We consider it unlikely , as we did not detect significant induction of the UPR reporter in the ASI neurons of L1-L2 animals expressing mCherry::DAF-7 ( Fig 9A and 9B ) . It is also possible that the protein fraction that is still correctly localized to the ASI dendrite or secreted from its cell body is sufficient to produce the necessary pro-growth signaling . However , since we obtained much stronger rescue with the protein expressed from pdaf-7 promoter than with ASI-specific expression ( Fig 2D ) , we asked whether DAF-7 protein may be expressed in cells other than the ASI neuron and other DAF-28-expressing cells , or at an earlier time than the mutant DAF-28 ( R37C ) protein . Indeed , the SMAD::GFP reporter activity is clearly detectable in the developing pharynx already in the early embryonic stages ( Fig 9C , comma-stage embryo ) . We detected expression of the established daf-7 promoter-GFP transcriptional reporter ksIs2 in multiple developing neurons in comma-stage embryos , as well as in several neurons in 3-fold embryos ( Fig 9D–9G ) . Importantly , we detected accumulation of secreted mCherry::DAF-7 protein in the extraembryonic fluid at the same embryonic stages as the SMAD::GFP fluorescence ( Fig 9E , comma stage shown ) , suggesting that DAF-7 activity may have a physiological role in the early embryos . We also detected pharyngeal expression and localization to coelomocytes in 3-fold embryos ( Fig 9F and 9G ) . However , the earliest we were able to detect DAF-28::mCherry protein was in the coelomocytes of 2 . 5-fold stage embryos ( Fig 9H and 9I ) . Thus , the rescue of daf-28 ( sa191 ) dauer phenotype by the overexpressed mCherry::DAF-7 protein could be due to its secretion from cells other than the ASI neurons , or due to its expression prior to the onset of DAF-28 ( R37C ) expression . Finally , we asked whether expression of spliced XBP-1 and deletion of pek-1 rescued the daf-28 ( sa191 ) gain-of-function dauer phenotype by relieving the mislocalization of DAF-7 protein in the ASI neurons . Introduction of spliced XBP-1 into mCherry::DAF-7;daf-28 ( sa191 ) animals significantly reduced the mislocalization of mCherry::DAF-7 protein ( Fig 10A and 10B ) . Of 14 axons examined , we found only two , in the same animal , with significant axonal localization , and additional two with intermediate phenotype . Together with the rescue of SMAD::GFP induction in sa191 animals ( Fig 4B ) , these data suggest that expression of spliced XBP-1 indeed rescues the bystander targeting of DAF-7 by the mutant DAF-28 . In contrast to the spliced XBP-1 , introduction of pek-1 deletion allele into mCherry::DAF-7;daf-28 ( sa191 ) animals not only did not rescue , but appeared to enhance the DAF-7 mislocalization ( Fig 10C and 10D ) . The ASI proximal axons , and even some of their synaptic regions , appeared filled with red fluorescence ( Fig 10D ) . Strikingly , mCherry::DAF-7 protein showed massive accumulation in the ASI neuronal cell bodies in these animals ( Fig 10D , star ) . These data agree with the previous conclusion from Fig 4D that pek-1 ( - ) may rescue the dauer phenotype of daf-28 ( sa191 ) animals by mechanism other than increasing DAF-7 activity . Therefore , although both UPR branches tested here are able to modulate the gain-of-function phenotype of the folding mutation in DAF-28 , they appear to lead to vastly different molecular outcomes on the bystander target protein , DAF-7 .
Our data support oxidative misfolding of the mutant DAF-28 protein as the most proximal cause . As expected for a folding mutation [60] , altering the overall folding capacity of the ER modulated the penetrance of the dauer phenotype caused by the endogenous DAF-28 ( R37C ) mutation , while the transgenic mutant protein accumulated in tissues and exhibited aggregation-like behavior . Interestingly , as judged by its uptake into coelomocytes , at least some of the DAF-28 ( R37C ) ::mCherry mutant protein was secreted . ER quality control mechanisms typically prevent secretion of misfolded or non-native proteins . However , classical amyloid diseases are associated with secretion of destabilized amyloidogenic proteins , and increasing the stringency of the ER quality control by activation of the UPR transcription factor ATF6 selectively decreases their secretion [61] . The mechanism by which some of the DAF-28 ( R37C ) ::mCherry protein evades ER quality control is unclear . One possibility is that incorrect disulfide bond pairing in a small insulin/IGF-like mutant protein may result in an alternative structure that , while non-native , may present as a globular compact protein . Indeed , mammalian IGF is known to form two alternative stable conformations in vitro , only one of which has the correct disulfide bond arrangement and is active [62] , and a non-native mammalian mini-proinsulin has been shown to bypass ER quality control and be secreted in yeast [63] . Misfolded insulin , encoded by PNDM/MIDY mutations , can affect secretion of the wild-type insulin from the same cell [32 , 33] . On the other hand , Rajan et . al . [55] found that some insulin mutants , including PNDM-associated R89C , do not interfere with secretion of the wild-type insulin . Both R89C human insulin mutation and the C . elegans R37C mutation in DAF-28 introduce an unpaired cysteine while also disrupting the proteolytic processing site . Interestingly , similar to our DAF-28 ( R37C ) ::mCherry , the R89C mutant insulin was partially targeted to secretory granules and secreted , even though it strongly activated ER stress response and expression of the pro-apoptotic protein CHOP [55] . We did not detect ER retention of the wild-type DAF-28::mCherry protein in animals with sa191 mutation and observed only mild alterations in its axonal localization . A detailed biochemical characterization will be necessary to fully understand the molecular consequences that expression of DAF-28 ( R37C ) has on either wild-type DAF-28 or other insulin/IGF-like proteins . Our second observation is that , while activation of UPR plays a complex role in the phenotypic outcome of the sa191 mutation , it is unlikely to be its triggering mechanism . At the time sa191 mutation exerts its phenotypic effect ( L1/early L2 stages ) , the UPR reporter activity in the ASI neurons was either not induced , or was similar to that in several other neurons and much weaker than its induction in cells of animals carrying pek-1 deletion . Overexpression of the spliced XBP-1 showed that forced activation of this adaptive UPR branch can attenuate dauer signaling in sa191 animals , by suppressing the bystander effects of the mutant DAF-28 on localization and/or activity of DAF-7 . Deletion of pek-1 , on the other hand , prevented commitment to dauer by sa191 animals under non-stressful growth conditions , as was reported at high temperature [18] . Thus , unlike activation of IRE1/XBP1 branch of UPR , activation of PERK actually enhances the dauer phenotype of sa191 animals . However , about 20% of pek-1-deficient sa191 animals still abnormally entered the pre-dauer L2d stage at higher population densities ( Fig 4D ) , indicating that translational silencing or any transcriptional outcomes of the PERK activation did not , by themselves , initiate the molecular events leading to sa191 gain-of-function phenotype . These observations are inconsistent with the UPR being the trigger for ASI-specific dysfunction . The UPR appears to modulate the strength of signaling of TGF-β and insulin/IGF-like pathways , by affecting the balance of the folding environment in the ER and possibly impacting on translation . We suggest that , in context of the endogenous sa191 mutation , activation of the PERK/eIF2α pathway could amplify the initial trigger , which we infer to be the interference with the normal biogenesis of DAF-7/TGF-β by the folding mutant of DAF-28/IGF . However , PERK could also exert its effects through a yet-unknown , ASI- or DAF-7-independent mechanism , since we found that , despite its ability to decrease dauer phenotype , deletion of pek-1 enhanced the mislocalization of the transgenic mCherry::DAF-7 protein in animals with DAF-28 ( R37C ) mutation , and caused its massive intracellular accumulation in the ASI neurons . This was reminiscent of the findings that the PERK-deficient pancreatic β-cells exhibit severe disruption of ER-Golgi anterograde trafficking and disruption of the Golgi complex , while , paradoxically , decrease of PERK gene dosage ameliorated the progression of the Akita mouse expressing the gain-of-function insulin mutant to overt diabetes; the latter was proposed to be due to decrease degradation of the wild type insulin [64] . Our findings highlights the complexity of the UPR effects and underscore the need to further understand the role of UPR signaling under chronic , physiological , stress conditions . The selective targeting of DAF-7/TGF-β protein biogenesis , in particular its mislocalization to the proximal axon , is an intriguing finding . As previously noted , sa191 animals phenotypically resemble animals with deficient daf-7 signaling [17] , and we found that decreased DAF-7 function explains the sa191 gain-of-function dauer phenotype . However , because transcription of daf-7 does not depend on insulin signaling , the mechanism by which DAF-7 activity might be affected in these animals was unclear . Our data suggest the following model for the bystander targeting of DAF-7 protein and for its phenotypic outcome in sa191 animals ( Fig 11 ) . First , misfolded DAF-28 ( R37C ) protein in sa191 animals may titrate a component of the ER/Golgi proteostasis machinery that is required for efficient folding or maturation of the endogenous DAF-7 protein . This could be a chaperone required for productive folding by both DAF-7 and wild-type DAF-28 . Alternatively , DAF-7 may only share such a chaperone with the misfolded and not with wild-type DAF-28 . The latter is more likely , as we did not detect a strong effect of sa191 mutation on the wild-type DAF-28 . Because of the role of the unpaired cysteine , and the knotted arrangement of the disulfide bonds in DAF-7 , a potential candidate for this competition may be an oxidoreductase [65 , 66] . Another example of a potential candidate could be a folding chaperone with restricted client repertoire . For example , mammalian IGF proteins are completely dependent on the selective ER chaperone GRP94 for folding and secretion [16 , 19 , 20] . The resulting defects in the folding or maturation of the DAF-7 protein lead to its striking mislocalization and accumulation in the proximal axon of the ASI neuron . Axonal trafficking defects are common in neurodegeneration , yet , how soluble cargo is selectively sorted to the vesicles destined to axons vs . dendrites is not understood . It is possible that non-native species of DAF-7 , generated in the presence of misfolded DAF-28 ( R37C ) , interact abnormally with the sorting machinery , resulting in mistrafficking . The combined outcome of these defects in DAF-7 folding , processing , and/or trafficking , together with decreased function of the mutant DAF-28 ( R37C ) protein , result in decreased pro-growth signaling , despite the normal perception of the food signal ( Fig 11B ) . Decreased daf-7 activity , in turn , affects transcription of pro-growth insulin/IGF genes [17 , 67] , further reducing the pro-growth signaling . This balance of growth vs . dauer signals may result in activation of the dauer program , but may not be sufficient for the full commitment to dauer , as many sa191 animals that enter the L2d stage quickly resume reproductive growth . We suggest that PERK signaling is necessary for the dauer commitment of sa191 animals because it provides amplification of the signal , perhaps through translational attenuation ( Fig 11B ) . If the defect in DAF-7 protein biogenesis in sa191 animals is mediated by titration of a necessary chaperone by misfolded DAF-28 ( R37C ) , how can overexpression of DAF-7 rescue this defect ? Overexpression of a protein under conditions of chaperone insufficiency would further increase the folding stress in the ER; yet , we observe a strong rescue of the sa191 dauer phenotype by overexpressed DAF-7 ( Fig 2D ) . Our data suggest several explanations . First , daf-7 is thought to be expressed predominantly in the ASI and , in certain conditions , the ASJ and a subset of mechanosensory neurons [68] . However , we find that in embryos and just-hatched L1 larvae , daf-7 is expressed in multiple neurons . If these neurons do not express daf-28 ( sa191 ) , they may be a source of native , functional secreted DAF-7 transgenic protein . Second , we observe expression and secretion of mCherry::DAF-7 protein already in the early stage embryos and throughout subsequent embryonic development , while DAF-28::mCherry is expressed later . Similarly , the ASI-specific gpa-4 promoter in ASI::DAF-7 transgene is known to be active in early embryos [69] . Thus , transgenic mCherry::DAF-7 may be secreted prior to the disruptive effects of the endogenous DAF-28 ( R37C ) on its biogenesis . This interpretation is supported by the maternal rescue of dauer signaling by the transgenic DAF-7 , which suggests that the presence of this protein only in the embryo , without further expression in larval stages , is sufficient to prevent dauer induction . What accounts for the specificity of the toxic effect of the misfolded DAF-28 ( R37C ) protein , which appears to only target a dauer-promoting function in the ASI neuron despite being expressed in many other cells ? The most trivial possibility is that the mutant DAF-28 protein may be expressed at higher levels in the ASI neuron than in other cells in the early larval stages , although this is not supported by transcriptional reporters . Second , the proteostasis component ( s ) required for both DAF-28 ( R37C ) and DAF-7 protein biogenesis may only be limiting in the ASI neuron but present in a sufficient amount to buffer the misfolded DAF-28 mutant in other cells . In this case , the observed lack of generic protein mislocalization and of dysfunction or degeneration of the ASI neuron would also imply that the requirement for this chaperone is not as strong for other proteins expressed in this cell as it is for DAF-7 . An intriguing possibility is that the ASI neuron could , due to a yet unknown intrinsic property , be selectively sensitive to misfolding in the ER . Mammalian Purkinje cells , for example , are selectively sensitive to mutations in several broadly or ubiquitously expressed proteins , which cause ER stress and toxicity in Purkinje cells but not in other neuronal cell types [70–72] . Alternatively , sensitivity of dauer activation to ER stress , rather than selective sensitivity of the ASI neuron , could be responsible for the ASI-specific dauer phenotype , for example due to being tuned to small changes in the rate of secretion of DAF-28 and DAF-7 . Indeed , DAF-28 and DAF-7 cooperate to activate a growth-promoting feed-forward loop , while a decrease in DAF-7 feeds back to attenuate daf-28 transcription ( Fig 11 ) . Several observations , including some that were previously unexplained , support these two possibilities . First , a highly-expressed pdaf-7::GFP transcriptional reporter saIs8 was previously observed to abnormally induce dauer entry for unknown reasons [27] . Examination of its sequence suggests that it may be prone to misfolding since , in addition to the DAF-7 promoter , this reporter contains an N-terminal fragment of the DAF-7 protein , including ER signal sequence and most of its LAP , fused to a GFP moiety . In mammalian TGF-β , the LAP forms extensive hydrophobic contacts with the C-terminal cysteine-knot hormone domain [73] , which is deleted in this reporter . Misfolding of this reporter in the ER of the ASI neuron may explain the observed dauer induction . Second , we find that a DAF-28::GFP fusion protein induces a strong gain-of-function dauer phenotype in daf-28 deficient background , likely due to the oxidative misfolding of the GFP and the accumulation of the misfolded fusion protein in the ER of the ASI ( and ASJ ) neurons . Finally , it was previously reported that crossing DAF-28::GFP transgene into a daf-8 ( m85 ) mutant background unexpectedly resulted in irrecoverable dauer-constitutive phenotype [74] . DAF-8 is a pro-growth R-Smad transcription factor in the DAF-7/TGF-β signaling pathway . The sensitivity of the ASI neuron , and/or of the dauer signaling pathway , to protein misfolding in the ER may explain the dauer phenotype in all three of these examples . Our data suggest that DAF-7/TGF-β protein is a sensitive and selective target of the bystander effect caused by misfolded DAF-28/IGF . It will be very important to understand what makes a particular protein a target for bystander misfolding [75] , and whether different proteins are targeted by distinct misfolded species . Although global disruption of proteostasis and induction of stress responses are common to the toxic effects of misfolded proteins , there are examples of specific bystander targeting . In Drosophila , expression of human insulin bearing the Akita C96Y mutation results in ER stress and cellular dysfunction . While this mutant protein caused a general degeneration phenotype in the eye , its expression in the wing imaginal discs resulted in phenocopies of Notch and crossveinless mutations [76 , 77] . Interestingly , a targeted deletion of the ER thiol oxidase Ero1L specifically in the wing also caused a phenocopy of Notch , by inducing misfolding of Notch protein while not affecting other secretory proteins [78] . Our data suggest that a global vs . targeted bystander mechanism of a given folding mutation may depend on ( 1 ) the nature of the misfolded species it produces , ( 2 ) the identity , client repertoire , and availability of chaperones that bind these misfolded species , and , importantly , ( 3 ) the presence in the same cell of susceptible bystander targets controlling sensitive cellular or organismal processes . Identifying these potential bystander targets and the restricted chaperones could be instrumental in understanding—and protecting against—the specific toxic phenotypes in protein misfolding diseases .
Standard methods were used for worm culture and genetics [79] . Animals were synchronized by picking gastrula-stage embryos , or by hypochlorite treatment for FACS analysis . The following strains were obtained from the Caenorhabditis Genetics Center ( CGC ) : JT191 ( daf-28 ( sa191 ) V ) , TY3862 ( daf-7 ( e1372 ) III;cuIs5[pmyo-2C::GFP + pRF4 ( rol-6 ( su1006 ) ) ] ) , VC1099 ( hsp-4 ( gk514 ) II ) , RB545 ( pek-1 ( ok275 ) X ) , SJ4005 ( zcIs4[phsp-4::GFP]V ) , PD4792 ( mIs11[pmyo-2::GFP + ppes-10::GFP + gut-promoter::GFP]IV ) , HT2099 ( unc-119 ( ed3 ) III;wwEx85[pdaf-28::GFP] ) , FK181 ( ksIs2[pdaf-7::GFP + rol-6 ( su1006 ) ] ) , NM440 ( unc-104 ( e1265 ) II;jsIs1[pSB120 ( snb-1::GFP ) ;pRF4 ( rol-6 ( su1006 ) ) ] ) . N2AM ( wild-type ) is a subclone of N2Bristol from Morimoto Lab . DAF-28::GFP strain ( svIs69 array ) was from Naredi Lab ( U . of Gothenburg ) . Strain 2308 ( daf-28 ( tm2308 ) V ) was from the National BioResource Project ( Japan ) . ZM7963 ( hpDf761II;daf-28 ( tm2308 ) V ) and XL153 ( ntIs27[psra-6::ChR2::YFP , punc-122::dsRed] ) were a gift from Fang-Yen Lab ( UPenn ) . Crosses were conducted using phenotypic or fluorescent chromosomal markers ( http://www . wormbuilder . org/ ) . Strains were confirmed by PCR and restriction digest or sequencing . Transgenes were injected by Knudra Transgenics ( USA ) as 20ng/μL plasmid DNA and 80ng/μL sonicated salmon sperm DNA . All PCR products were verified by sequencing . Restriction sites were introduced into PCR primers . A 2 . 6 kb NaeI/XbaI fragment containing the daf-7 promoter region and first 23 residues of the DAF-7 protein , and a 1278bp SacI/EagI fragment from amino acid 23 to stop codon were amplified from N2 genomic DNA . XbaI/SacI worm mCherry minus the stop codon was amplified from pCFJ104 ( Addgene #19328 ) . The three fragments were assembled to exchange the pmyo-3::mCherry in pCFJ104 . The 703bp PstI/XbaI fragment containing coding region of daf-28 , and the 2 . 0 kb SphI/PstI daf-28 promoter region [21] were amplified from N2 genomic DNA . The 934bp XbaI/SacI worm mCherry and the 873bp SacI/PvuII unc-54 3’UTR fragments were amplified from pCFJ104 . Fragments were assembled in pMCS5 plasmid . The coding region of daf-28 was amplified from daf-28 ( sa191 ) genomic DNA as PstI/XbaI fragment and exchanged with the wild-type coding region of drxEx21 . Animals were grown on fresh plates seeded with OP50 E . coli at 20°C under non-crowded/non-contaminated conditions for at least 2 generations prior to embryo picking , to avoid effects on dauer entry [80] . 20–40 YA animals were allowed to lay embryos for 24 hours at 20°C . For transgenic rescue assays , only transgenic ( parent ) animals were picked , so that the non-transgenic animals among their progeny were siblings to the transgenic ones . From these , 100–200 gastrula-stage embryos were picked onto new plates and allowed to develop for 65–66 hours at 20°C . Animals with embryos present in uteri were scored as reproductive adults; YA or late L4 stages ( based on gonad development ) were scored as mildly delayed; and early L4 or earlier stages ( mainly L2d and/or dauers ) as severely delayed . Dauer larvae were radially constricted , lacked pharyngeal pumping , and had visibly constricted pharynxes [80] . L2d larvae were radially constricted to a lesser extent than dauers , had a uniformly dark intestine ( Fig 4F and 4H ) [80] and exhibited slow pharyngeal pumping . All developmental assays were repeated at least three times , raw data are in the Supplemental Data Table . For SMAD-reporter/development correlation , larvae were separated at ~35 hours post-gastrula into ‘bright’ and ‘dim’ populations based on GFP fluorescence viewed through stereo microscope , and allowed to develop for an additional 30 hours . Because of the separation , animals experienced drop in population density resulting in slight increase in reproductive development in sa191 . GFP intensity was measured in L1/early L2 animals using BioSorter ( Union Biometrica , USA ) for three strains carrying the cuIs5 transgene , and non-transgenic N2AM . Data analysis was performed by FlowJo software . An initial gate was set as the measurement of length ( time of flight ) versus absorbance ( extinction ) to distinguish larvae from debris . All animals with GFP intensity values higher than maximum detected in the non-transgenic N2AM strain were included in analysis . 50 or 100 L2 larvae in 30μL of water were flash-frozen in liquid nitrogen . 15μL of reducing or non-reducing sample buffer was added and samples incubated at 85–95°C for 10 minutes . Protein amounts were verified by Ponceau stain of membranes . Anti-RFP ( 5F8 , ChromoTek , Germany ) was used to detect DAF-28 ( R37C ) ::mCherry . JT191 and TU3401 strains were used as negative and positive controls . For the blot in S3 Fig , full plates were collected and frozen in aliquots . Worms were lysed by mechanical disruption , as described in [8] , treated with reducing/non-reducing sample buffer , and further processed as above . 10 L4 larvae ( total 150 animals/strain ) were picked into 25μL of M9 solution on a glass slide and acclimated for 1 minute . One minute movies were taken and analyzed using wrMTrck ImageJ plug-in ( Dr . Jesper Pedersen , http://www . phage . dk/plugins/wrmtrck . html ) . Pharyngeal GFP marker was introduced into daf-28 ( sa191 ) animals from PD4792 strain by crossing . PD4792 was used as control . 5 L4 larvae per plate ( total 30 animals/strain ) were plated and allowed to lay progeny at 20°C . Animals were transferred to new plates every 24-hours until egg-laying ceased . Number of progeny was quantified by counting fluorescent pharynxes using ImageJ . 50 L4 larvae of each strain were plated on seeded plates and incubated for 24 hours at 20°C . 30 adult animals were immobilized on glass slides using 20mM sodium azide , and imaged at 50X magnification on Leica M205FA . Microscale was included in the images as a ruler . Body length was measured from the tip of the nose to the tip of the tail with ImageJ . Confocal: animals were mounted on 2% agar pads with azide and imaged with Zeiss LSM700 microscope at Cell Imaging Center , Drexel University , using 1 . 4NA 63x oil objective . 12 bit confocal stacks were reconstructed in ImageJ as 3D projections , and where indicated overlaid on single plane DIC images . Stereo: animals were mounted as above , or immobilized by chilling on plates . Imaging was performed with Leica M205FA microscope and Hamamatsu Orca R2 camera , keeping magnification and intensity of fluorescent sources ( Chroma PhotoFluor 2 ) constant within experiment . All Chi-square , ANOVA , and t-test analyses were performed using Prism software ( GraphPad , USA ) . ANOVA was followed by multiple comparisons post-test , as indicated in Figure legend . α and significance levels are also indicated . For developmental assays , at least three replicates were used , the raw data including the number of animals per replicate are in Supplemental Data Table . | Correct protein folding and localization ensures cellular health . Dedicated proteostasis machinery assists in protein folding and protects against misfolding . Yet , folding mutations cause many conformational diseases , including neurodegenerative diseases and certain types of diabetes and cancer . Misfolded disease-related proteins interfere with proteostasis machinery , causing global misfolding in the cell . How this global mechanism leads to the specific phenotypes in different conformational diseases is unknown . Moreover , mutant misfolded proteins that only damage specific cell-types in disease often lose this cell-selectivity when overexpressed in genetic models . Here we use an endogenous folding mutation in a C . elegans secreted IGF-like protein , DAF-28 , that causes dysfunction in one neuron and a specific developmental phenotype , despite expression in many cells . We find that misfolding of mutant DAF-28 causes mislocalization and defective function of another , wild-type growth factor that is expressed in the affected neuron , the TGF-β protein DAF-7 . Decrease in DAF-7 function explains the observed developmental phenotype . This targeting of the bystander protein DAF-7 by the misfolded mutant DAF-28 is specific and is not caused by the global stress . Our data suggest that rather than global effects , it is the selective targeting of specific susceptible bystander proteins that defines the specific phenotypes in conformational diseases . |
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Many filamentous organisms , such as fungi , grow by tip-extension and by forming new branches behind the tips . A similar growth mode occurs in filamentous bacteria , including the genus Streptomyces , although here our mechanistic understanding has been very limited . The Streptomyces protein DivIVA is a critical determinant of hyphal growth and localizes in foci at hyphal tips and sites of future branch development . However , how such foci form was previously unknown . Here , we show experimentally that DivIVA focus-formation involves a novel mechanism in which new DivIVA foci break off from existing tip-foci , bypassing the need for initial nucleation or de novo branch-site selection . We develop a mathematical model for DivIVA-dependent growth and branching , involving DivIVA focus-formation by tip-focus splitting , focus growth , and the initiation of new branches at a critical focus size . We quantitatively fit our model to the experimentally-measured tip-to-branch and branch-to-branch length distributions . The model predicts a particular bimodal tip-to-branch distribution results from tip-focus splitting , a prediction we confirm experimentally . Our work provides mechanistic understanding of a novel mode of hyphal growth regulation that may be widely employed .
The ability to break symmetry and establish an axis of polarity is crucial for the function and development of almost all cell types . In bacteria , such symmetry-breaking is often mediated by cytoskeletal elements inside the cell that direct new cell wall synthesis . Many rod-shaped bacteria ( including Escherichia coli , Bacillus subtilis and Caulobacter crescentus ) grow solely through the isotropic insertion of new cell wall material throughout the length of the lateral walls [1] , [2] . Here , cell wall growth is directed by MreB , the bacterial ortholog of eukaryotic actin [3]–[6] , whereas cell division is mediated by the bacterial tubulin ortholog , FtsZ . In these rod-shaped bacteria , polarity systems are required to identify and differentiate cell poles that remain inert during cell elongation . However , many other organisms enlarge by hyphal growth , a strategy that has proved successful for the exploitation of soil and other environments . Hyphal growth has evolved independently in both eukaryotic and prokaryotic microbes , including fungi and Gram-positive bacteria of the genus Streptomyces . This mode of growth depends on pronounced cellular polarity and the specific localization of cell envelope assembly to one cell pole in order to achieve tip extension . New sites of growth arise by hyphal branching , which requires the re-orientation of cellular polarity and the de novo establishment of new zones of cell wall synthesis from which lateral branches emerge . The result is a mycelial network in which the regulation of branching largely determines the morphology and behaviour of the mycelium as it spreads through the environment . However , the general principles that control such cellular branching have remained unknown . Here we report a novel mechanistic basis for branch-site selection in the mycelial actinomycete bacterium Streptomyces coelicolor . Since all hyphal bacteria are actinomycetes , this mechanism is likely to be widely relevant in this important phylum of bacteria , which account for the majority of commercial antibiotics . Tip extension and hyphal branching in Streptomyces are independent of both MreB and FtsZ , and depend instead on the coiled-coil cytoskeletal-like protein DivIVA [7] , [8] . A functional DivIVA-EGFP fusion localizes to tips and marks new branch points well before visible lateral outgrowth [9] , [10] . Deletion of divIVA is lethal , whereas overexpression leads to greatly increased numbers of DivIVA foci along the lateral wall and de novo cell wall outgrowth at these foci [8]–[10] . These data suggest that DivIVA can direct cell polarity and recruit the machinery for cell wall synthesis . Additional cytoskeletal components may also be involved ( for example , Scy [11] ) , together forming a tip-organizing complex . However , regardless of whether there are additional components , we can use DivIVA-EGFP as a marker to monitor the dynamics of the tip-organizing complex as a whole . The branch-site selection mechanism that localises DivIVA to new sites along the lateral wall , from which branches subsequently emerge , was previously unknown . We therefore used the DivIVA-EGFP fusion to monitor the dynamics of the tip-organizing complex in S . coelicolor by live cell time-lapse imaging . These experiments revealed that the new DivIVA foci that initiate lateral branches arise predominantly by a novel tip focus-splitting mechanism that bypasses the necessity for initial nucleation or site-selection . In order to gain a deeper and more rigorous understanding of the regulation of hyphal branching , we then quantified hyphal branching patterns from still images , and developed a mathematical model of the DivIVA dynamics . As we will see , the model demonstrates that a remarkably simple tip-focus splitting mechanism is capable of quantitatively explaining all of our experimental branching pattern data , a result which is far from intuitive . Moreover , the model makes explicit predictions that we have experimentally verified . Intriguingly , a similar splitting mechanism has recently been reported in hyphal growth in fungi ( Neurospora crassa ) [12] , raising the possibility that this simple mechanism may be widely applicable .
Our previous studies have shown that DivIVA foci are always present at new branch points before outgrowth occurs [9] , [10] . However , the origin of such DivIVA foci and the factors that determine their localisation have remained unclear [8] . To further understand the branching process , we have therefore studied more carefully how such foci are formed and traced their origin from time-lapse images . These experiments revealed that new small foci often arise from existing DivIVA foci at hyphal tips , by a process where a small cluster of DivIVA separates from the tip-focus and is left on the membrane just behind the tip . An example is shown in Figure 1 ( see Video S1 for a movie of this figure ) . At around 12–18 minutes the focus of DivIVA at the tip splits and leaves behind a small focus on the adjacent membrane . As the tip continues to extend , the new focus remains fixed in place on the membrane and grows in size and intensity . In between 42 and 48 minutes a new branch is formed at the position of the new focus . Tip-focus splitting is only seen to occur from foci associated with extending tips; foci which have not yet initiated a branch , such as the smaller focus between 12 and 36 minutes in Figure 1 , do not undergo splitting . We traced the origin of 52 nascent branches in time-lapse images and found that 42 of them ( 81% ) were accounted for by tip-focus splitting events . Since only sufficiently large and intense DivIVA-EGFP foci are visible above the background fluorescence , some foci cannot be traced to their point of creation , and so this is likely to be an underestimate of the real proportion of branching arising from tip-focus splitting [10] . Thus , tip-focus splitting , rather than other potential mechanisms , such as spontaneous nucleation , appears to be the predominant method for focus initiation in wild-type cells . In order to quantitatively understand Streptomyces branch-site selection , we have measured two categories of distances from still images: the distance between the tip and the points where branches emerge , and the spacing between the branches themselves . Unlike the branch spacing , the tip-to-branch distance is not fixed: as the hyphae extend in length , the tip-to-branch distances increase . To avoid this difficulty we use our measurements to work out the tip-to-branch distance at the moment when the new branches appear , as discussed in Materials and Methods . Unless care is taken when measuring the distributions from still images , it is easy to introduce biases that uncontrollably skew the data . For example , if only branching events relatively close to hyphal tips can be measured ( as is inevitably the case for Streptomyces where individual hyphae cannot be traced into the dense mycelial clumps from which they emerge ) , then long branch-to-branch distances will never be recorded , even if they occur . As explained in Materials and Methods , we control for this effect by introducing a protocol so that all measured hyphae have effectively the same length , a distance we call the trim length . This is achieved by discarding hyphae which are shorter than the trim length and trimming those which are longer . This protocol does not eliminate measurement bias , but rather controls the bias so that our experimental measurements are unambiguous and can be precisely compared with data generated by our mathematical model ( see below ) . The measured tip-to-branch and branch-to-branch distributions with a trim are shown in Figure 2 . The tip-to-branch distribution has two distinct peaks , one between and one at ( Figure 2A ) . This might suggest that two distinct mechanisms are involved in producing new branches . Surprisingly , however , our later analysis will show that a single mechanism can account for both peaks . We assume that DivIVA foci , either on their own or as part of a tip-organizing complex , assemble the cell wall synthesis machinery to both extend hyphae and form new branches . Most new DivIVA foci do not immediately initiate a new branch ( Figure 1 ) . We assume this is a result of the small starting sizes of most foci . Foci must instead grow in size by accumulating DivIVA molecules from the cytoplasm until they contain enough molecules to initiate a new branch . To understand where new branches emerge we must therefore understand how the number of molecules , , in a focus changes with time . We will refer to this number as the tip-focus size . We consider simple cooperative binding where the rate of DivIVA molecules joining a focus is linearly dependent on both the cytoplasmic DivIVA density , , and the focus size , ( alternative growth rules are considered in Supporting Text S1 , but these alternatives give qualitatively similar results , with no better fit to the experimental data ) . Thus we have , where is a parameter independent of and . Although , in the minimal model , we assume foci never lose DivIVA molecules , including this process again makes little or no difference ( see Supporting Text S1 ) . We also assume that the cytoplasmic DivIVA density appearing in the above equation is the same for all foci ( this assumption is justified by our full simulations , see Supporting Text S1 ) . Thus we can replace by the single parameter , which we call the binding parameter , and consider . We assume that a focus starts with molecules and must reach molecules before it can form a branch . We can easily solve the above equation for to find the time taken , , for this growth from to . With an extension speed for established tips , the distance behind the tip where a branch appears is ( 1 ) By comparing images like Figure 1 at 12 and 42 minutes , we estimate a typical value for as between and , so that , to a rough approximation , . The absolute value of is difficult to determine , but since the fluorescence of a typical DivIVA focus is not dissimilar to that of an FtsZ ring , and since an FtsZ ring contains on the order of 10 , 000 molecules [13] , we take to be of a similar order of magnitude . The growth speed of an established tip , , is measured from time lapse images to be about . Due to the trimming issues discussed above , measuring a typical value for is not straightforward . In particular , using the average of a trimmed distribution , such as that in Figure 1A , will not give a good estimate . However , as explained in Materials and Methods , by studying the distributions over a range of trims , we estimate a value of about under the growth conditions used , which implies that should be about . ( See Figure S10 for a schematic of the colony morphology for different values of . ) Streptomyces produces branches at a range of distances behind tips , producing a distribution of tip-to-branch distances . In our model , this is due to fluctuations in the parameters in Eq . ( 1 ) . Note that , although we vary these parameters , we do not model the growth of foci themselves stochastically ( instead using a deterministic differential equation ) due to the large number ( thousands ) of molecules involved . Each binding event will itself be stochastic but the overall process involving many thousands of such binding events will be well described deterministically . So far we have been concerned with how the number of molecules in a pre-existing focus changes with time . We have not yet discussed the mechanism by which new foci are formed , the tip-focus splitting mechanism . Furthermore , after a tip-focus has undergone splitting , we are interested in the length of time before the focus can split again , which , after both foci have initiated new branches , will translate into the distance between branches . It is important to emphasise that , whereas the growth of foci controls the tip-to-branch distribution , it is the focus-splitting rules that control the branch-to-branch distribution . The simplest assumption that could be made would be that the focus-splitting probability per unit time is constant , independent of when the tip-focus last split . This would describe a Poisson process and so imply an exponential distribution for the branch-to-branch distribution . However , as Figure 1B shows , for distances smaller than the branch-to-branch histogram is not described by a decaying exponential: these shorter distances are measured much less frequently than implied by a Poisson distribution . This suppression of short branch-to-branch distances shows that focus-splitting events are not independent of each other: a tip-focus that has just split is less likely to immediately split again . One potential explanation is that the probability of tip-focus splitting depends on the tip-focus size , such that smaller tip-foci are less likely to split . For this reason we implement a minimum tip-focus size ( a critical mass ) , , below which the tip-focus cannot split , with some constant focus-splitting probability per unit time , characterised by the parameter , for all tip-foci above . Splitting events cause the tip-focus to decrease in size and so , in some instances , such a splitting will cause the tip-focus size to drop below . In that case , only after the tip-focus has absorbed more DivIVA from the cytoplasm will it have sufficient size to split again . This time delay effectively reduces the number of short branch-to-branch distances . Although it is difficult to analyse tip-focus splitting analytically , it is useful to note that , in the limit where is very large ( compared to ) , the branch-to-branch distance , , is given by ( 2 ) a result which follows in a very similar way to Eq . ( 1 ) . In order to compare the minimal model with the experimental data , we developed a simulation which grows Streptomyces hyphae , implements tip-focus splitting and focus growth , performs the trim to the required length , and extracts the distributions ( see Materials and Methods ) . We used the parameters listed in Table 1 with , , the mean initial focus size , and the mean focus size for branch initiation inferred from experiments ( see above ) , and with the standard deviations in and , that is and , and fitted to the experimentally determined tip-to-branch and branch-to-branch distributions at trim . We find that variations in just and are sufficient to fit all the measured distributions . For simplicity we take and to follow independent truncated Gaussian distributions , where the truncation ensures that and are always positive . This is required since Gaussian distributions assign non-zero probabilities to all values , whereas biologically foci cannot contain fewer than zero molecules . The means ( and ) and standard deviations ( and ) are those for the truncated distributions , rather than the full Gaussians . However , as shown in Supporting Text S1 , other distributions do not qualitatively change our results . In our fitting , it was not immediately clear whether should be larger or smaller than . Note that although we allow the possibility that is less than in the model , this does not mean that foci can split before they have initiated branches; DivIVA foci have only been observed to split when they are associated with a growing tip . However , smaller than would imply that newly formed branches cannot normally produce their own branches until the tip-focus has grown further to size . This in turn results in a gap between where a branch emerges from its parent hypha and the position of its first offshoot . We measured this distribution of distances and found no evidence for such a gap ( see Supporting Text S1 and Figure S2 ) , which implies that is equal to ( or smaller than ) . In our model we choose , although smaller values of make little qualitative difference . As shown in Figure 2 , there is excellent agreement between the minimal model fits and the experimental data . For the trimmed tip-to-branch distributions , our model is sufficiently simple that this distribution can be calculated analytically ( see Supporting Text S1 ) without recourse to simulations . The analytic prediction is also shown in Figure 2A and agrees extremely well with the simulation data , as expected . Note that the reason the tip-to-branch distribution drops to zero at is a consequence of the trimming protocol rather than any inherent property of Streptomyces . We chose a trim as a trade-off between distribution width and amount of data , but it is also possible to compare the model and the experimental data at other trims . Figures S8 and S9 show that there is also good agreement at trims of and . We have checked that the tip-to-branch and branch-to-branch distributions generated by the minimal model are robust to changes in all the parameters in Table 1 . Further , we tested that adding fluctuations in the tip growth speed , , and the on-rate parameter , , also do not qualitatively change these distributions ( see Supporting Text S1 ) . There is little to be gained by also considering fluctuations in since the stochastic nature of tip-focus splitting is already included via , the tip-focus splitting parameter . One of the most striking features of the experimentally measured tip-to-branch distribution , Figure 2A , is the peak at small distances . Naïvely it may be thought that a novel tip-focus splitting mechanism is required to account for this peak . However , our model predicts that this peak can be simply explained without additional assumptions . Since most new foci must attract more DivIVA molecules before they can initiate a new branch , the distributions of and must be such that most new foci start with fewer than molecules . However , there is a small tail to the distributions that causes a few foci to have above , i . e . when they are formed these foci already have enough DivIVA molecules to initiate branch outgrowth . These foci will cause branching almost as soon as they are formed , very close to zero distance from the tip . We have directly observed such events and an example is shown in Figure 3 ( see Video S2 for a movie of this figure ) . Furthermore , we also measured the total intensity of newly-produced foci from time-lapse images: from cases where the new branch appears next to the tip and from normal tip-focus splitting events when the new branch appears much further back . In the first case the average intensity is almost three times greater than in the second case , supporting the hypothesis that events where the branch appears next to the tip correspond to the initial focus size , , being much greater than average . The entire weight of the distribution with will give effectively zero tip-to-branch distances , which then naturally explains the peak at the origin in Figure 2A . Consequently , our model predicts that if the distribution is analysed with bins of smaller width , then the peak at the origin will become even more dramatic . After reanalysing the measured data , this prediction is strikingly confirmed , as shown in Figure 4 . Although the peak in the bin matches well , the agreement is not perfect in the range . However , we believe this feature is an unavoidable artifact of how the data is analysed: the tip growth speed cannot be measured directly from still images , rather only the distribution of speeds is known , which necessarily slightly smears the data ( see Materials and Methods and Supporting Text S1 ) . It has been shown that the DivIVA orthologue in B . subtilis preferentially assembles on negatively-curved membranes , and this appears to be an important factor in targeting of the B . subtilis protein to cell poles and septation sites [14] , [15] . Similarly , in Streptomyces , a preference for branches to emerge on the outer side of curved hyphae has been reported [10] , which suggests , for example , that for tips that bend to the left , foci are more likely to form on the right inner membrane . Although the mechanism by which this occurs is not yet fully understood , it is possible to ask how such an effect impacts our model . To do so we developed and simulated a more detailed computational model ( see Supporting Text S1 ) , which implements hyphal growth in two-dimensional space . At each time step in the simulation , the direction of tip growth is randomly varied by a small amount , such that over sufficiently long distances ( a few ) , memory of the previous growth direction is lost . We postulate that tip-foci with sizes above can split only when the local curvature near the tip is sufficiently high . Hence the earlier focus-splitting parameter , , is understood as an effective parameter that can be replaced by growth direction variation and a curvature threshold . However , it is worth noting that if curvature is the origin of , it must be quite a sensitive effect since during growth the mean curvature near the tip only changes by about . The full model ( see Supporting Text S1 for full details and parameters ) produces colony dynamics that match well with the wild-type phenotype ( for example , see Videos S3 and S4 ) . In particular , the tip-to-branch and branch-to-branch distributions are practically identical to the minimal model , thereby justifying our earlier simplifying assumptions . Since DivIVA is an essential protein , it cannot be completely removed . However , we can consider mild underexpression and various levels of overexpression . We first consider heavy overexpression . Previous work has examined hyphal morphology when divIVA was overexpressed in preformed hyphae to approximately twenty-five times its usual level [9] , [10] . Such overexpression resulted in increased levels of cytoplasmic DivIVA , swollen hyphal tips and lateral hyperbranching . Interestingly , after inducing increased DivIVA production , many of the new branches developed well behind the tip positions at the moment of induction . This observation is unexpected since , in the minimal model , foci can only be produced from the splitting of tip-foci . It is possible that these new branches are due to foci that were already present at the time of induction but that were too small to be seen , and that overexpression subsequently caused them to develop into branches much more rapidly than normal . However , if this explanation were correct , wild-type Streptomyces would form many branches hundreds of microns behind the tips , a strategy which would be very inefficient in terms of nutrition acquisition . For this reason , we favour an alternative explanation , namely that these new branches arise from a separate mechanism of focus formation: spontaneous nucleation . In this process , due to the stochastic dynamics of molecules within the cytoplasm , occasionally a sufficient number of DivIVA molecules come together on the membrane and spontaneously form a cluster . As is standard for nucleation dynamics [16] , and as we confirmed by stochastic simulations , for cytoplasmic DivIVA densities below some threshold , the probability of spontaneous nucleation ( involving the near simultaneous binding of multiple DivIVA molecules to overcome a nucleation barrier ) is close to zero . Above this threshold , however , we find that the rate of nucleation rises approximately linearly with increasing cytoplasmic density . We assume that for the parameters chosen in Table 1 , the DivIVA concentrations during wild-type growth fall well below this threshold and hence spontaneous nucleation does not occur . However , at 25-fold overexpression , this threshold is exceeded . In this latter case , we implemented spontaneous nucleation in our full model in the simplest possible way , by having a probability per unit length and time for spontaneously creating a new focus on the membrane , with a linear increase in nucleation probability with increasing cytoplasmic density above the threshold ( see Supporting Text S1 for full details and parameters ) . We were then able to produce simulated colony dynamics which successfully matched the observed phenotype of 25-fold overexpression ( for example , see Video S5 ) . In addition to heavy overexpression , we can also consider mild under- and overexpression . It was observed in [9] that underexpression seems to reduce the average tip-to-branch distance . It is important to realise that a change in DivIVA expression will probably not only affect the binding parameter ( since , with the cytoplasmic DivIVA density and a constant ) , but also the tip growth speed . This is because DivIVA is a critical component of the tip-organizing complex , which is present at all growing tips , and which is presumably important for tip extension . Since and are unlikely to depend strongly on DivIVA levels , Eq . ( 1 ) shows that it is actually the ratio which controls the average tip-to-branch distance . When DivIVA is underexpressed it is likely that both and decrease . Since in this case the average tip-to-branch distance decreases , this result suggests that proportionally decreases by more than . In the case of overexpression will increase . However , it is less likely that will also increase . This is because the tip-organizing complex , which is responsible for tip extension , is likely to consist of many components , of which DivIVA is only one . Unless other components in addition to DivIVA are overexpressed , the effect on tip growth speed could be small , with remaining approximately constant . Thus we predict that mild overexpression of DivIVA will reduce and so decrease the average tip-to-branch distance . If this is the case , then both mild under- and overexpression of DivIVA will reduce the average tip-to-branch distance , with wild-type levels corresponding to the longest tip-to-branch distance .
Streptomycetes , like other bacteria , lack the motor proteins , vesicle transport systems , and polarisome components that are fundamental in eukaryotic cell biology . Thus , tip extension in Streptomyces is likely to be simpler than in , for example , filamentous fungi . Given that a complex of polarity proteins ( including DivIVA ) must presumably first gather at future branch sites , understanding branch-site selection in filamentous bacteria involves understanding where , when and how these proteins cluster together in sufficiently large groups . One surprising feature of wild-type Streptomyces is that this clustering of polarity proteins is not a random , spontaneous process . Rather , we have shown that new branch sites are predominantly created from the tips of previous branches , by a tip-focus splitting mechanism . One important question concerns the benefit of producing foci , and hence branches , by tip-focus splitting rather than spontaneous nucleation . One possibility is that this provides a more efficient method of acquiring nutrients . Spontaneous nucleation will produce new branches at positions well behind the tips . This outcome would be suboptimal since regions far behind the tips are likely to have already been well-exploited , with few remaining nutrients . Tip-focus splitting , on the other hand , only generates new foci at tips and so biases branching towards the growing ends of hyphae , where nutrients are still more plentiful . Another potential advantage is that tip-focus splitting allows for a greater level of control over exactly where branching occurs . Unlike spontaneous nucleation where branches can appear anywhere , tip-focus splitting produces branches with an average tip-to-branch distance determined by parameters such as the initial tip-focus size and the binding parameter . By modifying these parameters , it is possible to respond to external stimuli . For example , under conditions when branching further from the tip would be favourable , we speculate that this could be achieved by modifying DivIVA ( or other proteins that affect its assembly ) so that the binding parameter is decreased ( this would correspond to a shift from the morphology shown in Figure S10B to that in Figure S10A ) . The morphology of branching organisms can be characterized by both the distance from the tip that new branches appear and the inter-branch distance . Counter-intuitively , our model shows that these distances are controlled by rather different processes . The tip-to-branch distance is governed by how long it takes new foci to gather enough molecules to initiate a new branch . This is related to the initial focus size , , the size at which a new branch is initiated , , the tip growth speed , , and the binding parameter , . In contrast , the branch-to-branch distance is governed by how often foci are formed ( how long foci take to develop into branches is now irrelevant ) . This is dependent on a partly overlapping , but nevertheless distinct set of parameters: the minimum tip-focus size for splitting , , the initial focus size , , the tip growth speed , , the binding parameter , , and the tip-focus splitting parameter , . We have focused on the control of branching during vegetative growth . However , there is a parallel question about how the first germ tube emerges from a spore . By imaging germinating spores expressing functional divIVA-EGFP , it has been shown that , exactly as in vegetative growth , a focus of DivIVA is first observed on the spore envelope , which then grows in size before initiating the first branch [9] . It is interesting to inquire how this first focus is formed . It is clear that the tip-focus splitting mechanism cannot be responsible since there are no previous DivIVA foci from which the first focus could arise . It is possible that other proteins , such as SsgA [17] , aid DivIVA focus formation during spore germination . However , there is another possibility , that the spontaneous nucleation mechanism which plays a role when DivIVA is heavily overexpressed , is also responsible for the first DivIVA focus in a spore . If this is the case , then the DivIVA concentration within a spore would have to first rise high enough to overcome the nucleation barrier , an effect which may well be testable . In fungi , branching also occurs at the cellular level and involves establishment of new cell poles at which apical growth will occur [18] . An apical cluster of vesicles and cytoskeletal elements named the Spitzenkörper has a prominent role in fungal tip extension . During branching , a new Spitzenkörper structure is established at the nascent branch tip , aided by proteins that direct cell polarity , cytoskeletal reorganisation , vesicle transport , and exo- and endocytosis ( for reviews , see e . g . [18]–[21] ) . One of the components that appears to be involved in branch site selection prior to assembly of the Spitzenkörper structure is the protein complex termed the polarisome . Homologs of the budding yeast polarisome component Spa2p have been detected at hyphal tips in several fungi , and intriguingly , in Neurospora crassa , small foci of SPA-2-GFP were observed to detach from the major SPA-2 assemblies at elongating hyphal tips and subsequently give rise to new lateral branches [12] . This observation strongly suggests that , in addition to streptomycetes , tip-focus splitting mechanisms are also involved in the establishment of new hyphal branches in filamentous fungi . Streptomycetes appear to regulate hyphal growth and branching in a simple way . Indeed , we have found that a remarkably simple model can quantitatively explain the statistical properties of the entire hyphal network . Even the bimodal nature of the tip-to-branch distribution originates from a single mechanism of forming new foci , combined with variation in the parameter values . It is tempting to speculate that tip-focus splitting might be used by many filamentous organisms amongst fungi and Actinobacteria . In fact , focus splitting could turn out to be a general mechanism in situations where discrete foci must be generated in a growing organism .
S . coelicolor A3 ( 2 ) strains M600 ( ) , M145 ( ) and K112 [] , which produces DivIVA-EGFP , were pregerminated and cultivated at in YEME medium [22] . Hyphae were prepared for microscopy as described previously [9] . Samples were observed through a DIC 63× objective of a Nikon Eclipse 800 microscope equipped with a Pixera ProES600 camera and still images were taken with Pixera software and processed with ImageJ ( National Institute of Health USA ) . Live cell time-lapse microscopy was performed essentially as described in [10] . In brief , hyphae of S . coelicolor strains were grown on 1% agarose pads with Oxoid antibiotic medium no . 3 . Pads were sealed to the bottom by an oxygen-permeable Lumox Biofoil 25 membrane ( Greiner Bio-One ) and to the top by a coverslip . Samples were incubated at to and observed using a Zeiss Axio Imager Z1 microscope , a 9100-02 EM-CCD camera ( Hamamatsu Photonics ) , and Volocity 3DM software ( Improvision ) . Images were captured every 6 minutes , processed by Volocity and analysed using ImageJ . Still images do not normally capture the exact instant at which a new branch emerges . To find the tip-to-branch distance at the moment the branch emerged , we measure the length of the new branch , calculate how long it has been growing for , and determine where the tip was when the new branch emerged . The calculation incorporates an initial speed for new branch growth of about half that of established branches , increasing linearly in time until full speed is reached after about ninety minutes ( see Figure S1 ) . For details see the Supporting Text S1 . When measuring tip-to-branch and branch-to-branch distances from still images , it is important to control biases that artificially skew the data . For example , as an extreme case , if the measured hyphae segments were all less than in length , it would then be impossible to measure any branch-to-branch distance greater than . To control this problem we use the following protocol . Before any measurements are performed , all hyphae must be trimmed to some fixed length : any hyphae shorter than this are discarded and , for those which are longer , only the segment within a distance of the tip is included in the data set . The effect of trimming is to ensure that all measured hyphae are effectively of length . As a consequence , both the tip-to-branch and branch-to-branch distributions explicitly depend on the trimming length . Estimating the average tip-to-branch distance from still images is complicated by the need to impose the trimming protocol on all measured data . The true average tip-to-branch distance is the average tip-to-branch distance at infinite trim . Distributions at progressively smaller trims have progressively smaller average tip-to-branch distances . The largest trim that we have a reasonable amount of data for is , with an average tip-to-branch distance of . It is not obvious that this trim is sufficiently high to give a good estimate of the true average tip-to-branch distance . However , by fitting the full distributions at , and trims and extrapolating to infinite trim , this is seen to be a good approximation to the true average . We give details of the minimal model simulation here; details of the full model simulation can be found in Supporting Text S1 . We simulate the growth of a single hypha starting with a single DivIVA focus at the tip ( initially of size ) and keeping track of where branches appear . At each time step ( ) , the hypha length is increased by , the tip-focus is increased in size according to , and the tip-focus splitting rules are implemented ( i . e . a tip-focus above has a probability of splitting ) . If a new focus is created then its initial and final sizes , and , are chosen at random from truncated normal distributions , after which Eq . ( 1 ) gives the tip-to-branch distance . After the hypha has grown to sufficient length ( we grow the hypha to twice the trim length in order to effectively randomise the initial conditions ) , the tip-to-branch and branch-to-branch distances are measured if they satisfy the trimming protocol with trim , i . e . tip-to-branch distances are recorded only if the branch appears within a distance of the tip , and branch-to-branch distances are recorded only if both branches are within a distance of the tip . | Amongst the great variety of shapes that organisms assume , many grow in a filamentous manner and develop at least partly into a network of branches . Examples include plant roots , fungi and some bacteria . Whereas the mechanisms of filamentous growth are partially understood in fungi , the same cannot be said in filamentous bacteria , where our knowledge of hyphal growth regulation is very limited . To rectify this we have studied the bacteria Streptomyces , which are an excellent model for all hyphal bacteria . The protein DivIVA is known to play a critical role in controlling filamentous growth in Streptomyces , forming large foci at branch tips and smaller foci that mark sites of future branch outgrowth . However , until now nothing was known about how these foci first appear . We have shown experimentally that new foci appear via a novel mechanism , whereby existing tip-foci split into two clusters . The larger cluster remains at the growing tip , while the smaller cluster fixes onto the adjacent lateral membrane , where it grows in size , eventually initiating a new branch . By mathematically modelling how DivIVA foci grow , we show how this one simple mechanism of focus formation can quantitatively capture the statistical properties of the entire hyphal branching network . |
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GATA transcription factors are highly conserved among eukaryotes and play roles in transcription of genes implicated in cancer progression and hematopoiesis . However , although their consensus binding sites have been well defined in vitro , the in vivo selectivity for recognition by GATA factors remains poorly characterized . Using ChIP-Seq , we identified the Dal80 GATA factor targets in yeast . Our data reveal Dal80 binding to a large set of promoters , sometimes independently of GATA sites , correlating with nitrogen- and/or Dal80-sensitive gene expression . Strikingly , Dal80 was also detected across the body of promoter-bound genes , correlating with high expression . Mechanistic single-gene experiments showed that Dal80 spreading across gene bodies requires active transcription . Consistently , Dal80 co-immunoprecipitated with the initiating and post-initiation forms of RNA Polymerase II . Our work suggests that GATA factors could play dual , synergistic roles during transcription initiation and post-initiation steps , promoting efficient remodeling of the gene expression program in response to environmental changes .
In eukaryotes , gene transcription by RNA polymerase II ( Pol II ) is initiated by the binding of specific transcription factors to double-stranded DNA . The yeast transcription factors target regulatory regions called UAS or URS ( for Upstream Activating/Repressing Sequences ) , generally directly adjacent to the core promoter . The generated regulatory signals converge at the core promoter where they permit the regulation of Pol II recruitment via the ‘TATA box-binding protein’ and associated general transcription factors [1 , 2] . The transcription factor binding sites are usually short sequences ranging from 8 to 20 bp [3] . They are most often similar but generally not identical , differing by some nucleotides from one another [3] , making it sometimes difficult to predict whether a given UAS will function as such in vivo . GATA factors constitute a family of transcription factors highly conserved among eukaryotes and characterized by the presence of one or two DNA binding domains which consists of four cysteines ( fitting the consensus sequence CX2CX17-18CX2C ) coordinating a zinc ion followed by a basic carboxy-terminal tail [4] . While vertebrate GATA factors possess two adjacent homologous zinc fingers , fungal ones contain only one single zinc finger , being most closely related to the C-terminal vertebrate zinc finger [5 , 6] , which is the one responsible for determining the binding specificity of GATA-1 , the founding member of the GATA factor family [7] . The specificity of GATA factor binding has been thoroughly characterized in yeast [8–10] and metazoans [11–18] . In addition , structure determinations of protein-DNA complexes , first for GATA-1 [4] , then for its fungal orthologue AreA [19] , allowed for the identification of the subtle determinants of DNA specificity for GATA factors . Notably , the conserved DNA binding domain of GATA factors was reported to bind to consensus sequences ( corresponding to GATAA ( G ) or GATTAG for the yeast GATA factors described hereafter ) , as shown in various organisms using direct or indirect methods [4 , 19–22] . These consensus sequences are accordingly referred to as GATA motifs . Since its discovery 40 years ago in chicken cells , the family of GATA factors was extended in human cells and represents master regulators of hematopoiesis and cancer [23] . However , although approximately 7 million GATA motifs can be found in the human genome , the GATA factors occupy only 0 . 1–1% of them . Conversely , other regions are occupied by GATA factors despite lacking the consensus motif [24 , 25] . Consistently , even if most GATA factors bind to core GATA sequences , peculiar specificities have been reported for the flanking bases as well as for the fourth base of the GATA core element [26–29] . These studies revealed an elevated flexibility in the recognition sites for vertebrate and fungal GATA factors , much greater than previously anticipated , making the search for GATA sites and their enrichment in GATA-regulated genes tedious and unproductive . In addition , GATA factors can swap among them for the same motif and switch from active or repressive transcriptional activity . All these observations developed the main paradigm shift of how GATA factors are recruited and reside on the chromatin [30 , 31] . In yeast , the family of GATA transcription factors contains over 10 members [32] . Four of them are implicated in the regulation of Nitrogen Catabolite Repression ( NCR ) -sensitive genes , the expression of which is repressed in the presence of a preferred nitrogen source ( glutamine , asparagine , ammonia ) and derepressed when only poor nitrogen sources ( e . g . proline , leucine , urea ) are available [10] . The key GATA factors involved in NCR signaling are two activators ( Gln3 and Gat1/Nil1 ) and two repressors ( Gzf3/Nil2/Deh1 and Dal80/Uga43 ) [33–38] . In a perfect feedback loop , the expression of DAL80 and GAT1 is also NCR-sensitive , which implies cross- and autogenous regulations of the GATA factors in the NCR mechanisms [38–41] . Under nitrogen limitation , expression of DAL80 is highly induced [35] , and Dal80 enters the nucleus where it competes with the two GATA activators for the same binding sites [20 , 39 , 42] . Although initially described as being active under nitrogen abundance [37 , 38] , the Gzf3 repressor also localizes to NCR-sensitive promoters in conditions of activation [40] . The sequence conservation among the four yeast NCR GATA factors is remarkable and the residues involved in contacts with the DNA , thus specificity determination , are 100% conserved . In this respect , the binding sites of Dal80 on target DNA are likely to be recognized also by Gln3 , Gat1 and Gzf3 [28] . In vitro , the Gln3 and Gat1 activators bind to single GATA sequences , presumably as monomers [43] , like their orthologous vertebrate counterparts , while Dal80 was found to bind to two GATA sequences , 15–35 bp apart , in a preferred tail-to-tail orientation or to a lower extent in a head-to-tail configuration [9 , 20 , 39 , 44] . In vivo , GATA factor binding site recognition also appears to require repeated GATA motifs within promoters , as shown for the NCR-sensitive DAL5 promoter [45–47] . This led to the actual fuzzy definition of UASNTR , consisting in two GATA sites located close to one another to present a binding platform for GATA factors [45–47] . Finally , in some cases , the existence of auxiliary promoter sequences was shown to compensate single GATA site , allowing for transcriptional activation [48] , although this was never as efficient as additional GATA sites [49] . The antagonistic role of Dal80 also requires multiple GATA sites [39 , 42] , and inactivation of one of the four GATA sites of the UGA4 promoter results in the loss of the Dal80-repressive activity while affecting moderately Gln3- and Gat1- activation capacity [20] . In summary , although NCR-sensitive genes are recognized to contain at least one GATA site , and often more , a precise definition of the minimal element required for binding and transcriptional regulation is still lacking . In yeast , genome-wide ChIP analyses have allowed gaining insights into the GATA factor gene network through the identification of direct targets [50–53] . However , these studies were not performed in activating conditions , when all GATA factors are expressed , localized in the nucleus and active , so that the current list of GATA factor targets are likely to be underestimated . On another hand , bioinformatic analyses have shown that , since GATA sequences are short , they can be found almost everywhere throughout the genome . Therefore , based on the sole criteria of the presence of repeated GATA sequences in yeast promoters , a third of the yeast genes could hypothetically be NCR regulator targets [54] . However , such GATA motif repetitions have been found in the promoter of 91 genes , inducible by GATA activators in absence of a good nitrogen source , supposed to be directly targeted by the GATA activators [55] . Nevertheless , the functionality of these hypothetical UAS still needs to be directly demonstrated in vivo [1] . Here , we provide the first genome-wide identification of Dal80 targets in yeast , in physiological conditions where Dal80 is fully expressed and active . Using a ChIP-Seq approach combined to a bioinformatic peak-calling procedure , we defined the exhaustive set of Dal80-bound promoters , which turned out to be much larger than anticipated . Our data indicate that at some promoters , Dal80 recruitment occurs independently of GATA sites . Strikingly , Dal80 was also detected across the body of a subset of genes bound at the promoter , globally correlating with high and Dal80-sensitive expression . Mechanistic single-gene experiments confirmed the Dal80 binding profiles , further indicating that Dal80 spreading across gene bodies requires active transcription . Finally , co-immunoprecipitation experiments revealed that Dal80 physically interacts with active form of Pol II .
In order to determine the genome-wide occupancy of a GATA factor in yeast , our rationale was to choose Dal80 as it is known to be highly expressed in derepressing conditions and forms chromosome foci when tagged by GFP [56] . We grew yeast cells in proline-containing medium and performed a ChIP-Seq analysis using a Dal80-Myc13-tagged strain and the isogenic untagged strain , as a control ( Fig 1A ) , after ensuring that the Myc13-tagged form of Dal80 was functional ( S1A Fig ) . Dal80-bound regions were then identified using a peak-calling algorithm ( see Material & Methods ) . A promoter was defined as bound by Dal80 on the basis of a >75% overlap of the -100 to -350 region ( relative to the downstream ORF start site ) by a peak ( Fig 1B ) . We chose to use as the reference coordinate the translation initiation codon rather than the transcription start site ( TSS ) since the latter has not been accurately defined for all genes . Then , our arbitrary definition of the promoter as the -350 to -100 region relative to the ATG codon was based on the distribution of the TSS-ATG distance for genes with an annotated TSS ( median and average distance = 58 and 107 bp , respectively; see S1B Fig ) . Strikingly , Dal80 was found to bind to 1269 gene promoters ( Fig 1C and 1D and S1 Table ) . This number , corresponding to 22% of all protein-coding gene promoters , is much higher than anticipated given the roughly hundred target genes generally cited for the GATA transcriptional activators Gat1 and Gln3 [55 , 57] , presumably sharing binding sites with Dal80 . However , we noted that some peaks ( 221 ) overlapped several promoters ( 471 ) , mainly of divergent genes ( 442 ) , as shown in Fig 1E for an illustrative example . Despite it is possible that in such cases , only one of the two divergent promoters is targeted by Dal80 , the number of in vivo Dal80 target sites we identified here has been extensively extended from what was acknowledged so far . Among the genes showing Dal80 binding at their promoter , we noticed a significant enrichment for cytoplasmic translation genes , as well as genes involved in small molecule biosyntheses , including amino acids ( S2 Table ) . Before our work , very few studies have investigated the transcriptional targets of Dal80 in vivo in conditions of nitrogen deprivation . One of them , based on mini-arrays [58] , identified 19 Dal80-regulated genes , all of which have been isolated in our ChIP-Seq analysis ( highlighted in orange in column B of S3 Table ) . As expected given the similarity between binding sites of Dal80 and the other nitrogen-regulated GATA factors , other genes related to previous nitrogen regulation screens [55 , 57–64] are also significantly enriched within our list: 103 of the 205 previously identified nitrogen-regulated genes have been identified in our ChIP-Seq analysis using Dal80 as the bait , which is much more than expected by chance ( P<0 . 001 , Chi-square test; S3 Table , column B ) . Surprisingly , analysis of GATA site occurrence over Dal80-bound and unbound promoters revealed no difference between the two classes , 48 . 2% and 51 . 3% of Dal80-bound and unbound promoters containing at least two GATA sites , respectively ( Fig 1F ) . Likewise , we observed no major difference between the Dal80-bound and unbound promoters in respect of the GATA sites spacing ( S1C Fig ) and orientation ( S1D Fig ) preferences defined in vitro for Dal80 binding [9] . Intriguingly , 20% of Dal80-bound promoters do not contain any GATA site ( Fig 1F ) , indicating that Dal80 recruitment can also occur independently of the presence of consensus GATA sites ( see S1B Fig for visualization of Dal80 recruitment to a GATA-less promoter ) . In summary , our ChIP-Seq analysis revealed that Dal80 binds to a set of promoters larger than previously expected , targeting biosynthetic functions and protein synthesis in addition to nitrogen catabolite repression . We asked whether Dal80-binding to promoters could be associated to regulation of gene expression by the nitrogen source and/or Dal80 . We therefore performed RNA-seq in wild-type cells grown in glutamine- and proline-containing medium , and in dal80Δ cells grown in proline-containing medium . Firstly , we identified 1682 ( 30% ) genes differentially expressed ( fold-change ≥2 or ≤0 . 5 , P ≤0 . 01 ) in wild-type cells according to the nitrogen source provided ( Fig 2A ) , including 754 genes upregulated ( NCR-sensitive ) and 928 downregulated ( revNCR-sensitive ) in proline-containing medium ( see lists in S4 Table ) . Consistent with previous reports , DAL80 was found in our set of NCR-sensitive genes ( S4 Table ) , showing very low expression in glutamine-containing medium and strong derepression in proline ( S2A Fig ) . More globally , 97 of the 205 genes previously identified as NCR-sensitive were also found in our list ( P<0 . 0001 , Chi-square test; S4 Table ) . In parallel , we identified 546 genes showing significantly altered expression ( fold-change ≥2 or ≤0 . 5 , P ≤0 . 01 ) in proline-grown dal80Δ cells compared to wild type ( Fig 2B; S5 Table ) . In agreement with the previously described repressive activity of Dal80 [35] , 232 genes are indeed negatively regulated by Dal80 ( up in dal80Δ; red dots in Fig 2B ) . Unexpectedly , 314 genes are positively regulated by Dal80 ( down in dal80Δ; blue dots in Fig 2B ) . This is the first in vivo global indication suggesting a positive function for Dal80 in gene expression . The Dal80-repressed group was enriched for genes involved in small molecule catabolic processes ( S6 Table ) , while the Dal80-activated genes were mostly involved in amino acid biosynthesis ( S7 Table ) . Again , we noticed an overlap between Dal80-regulated genes and nitrogen regulated genes that were identified in other screens: 86 of the 205 previously identified nitrogen-regulated genes have been identified as Dal80-regulated , which is much more than expected by chance ( P<0 . 0001 , Chi-square test; column D of S3 Table ) . Globally , we observed a significant correlation between Dal80-sensivity and regulation by the nitrogen source ( P<0 . 00001 , Chi-square test; Fig 2C; see also S2B Fig ) . Indeed , there are more NCR-sensitive Dal80-activated and Dal80–repressed genes than expected in case of independence ( Fig 2C; see also S2B Fig ) . Similarly , the number of revNCR-sensitive Dal80-repressed genes is also significantly higher than expected by chance ( Fig 2C; see also S2B Fig ) . In contrast , the number of revNCR-sensitive Dal80-activated genes is significantly lower than expected by chance ( Fig 2C; see also S2B Fig ) , indicating a negative correlation in this case . This observation is consistent with the DAL80 gene itself being NCR-sensitive , so that the Dal80-activated genes can only be activated when DAL80 is expressed . More importantly , Dal80 recruitment to promoters significantly correlated with nitrogen- and Dal80-sensitivity . In fact , nitrogen-regulated expression and Dal80-binding are not independent , as NCR-sensitive ( 212 ) and especially revNCR-sensitive ( 325 ) genes are significantly enriched in Dal80-bound genes ( P<0 . 00001 , Chi-square test; Fig 2D; see also S2C Fig ) . We also observed a significant correlation between Dal80-sensitive gene expression and Dal80 recruitment at the promoter: 211/546 of Dal80-regulated genes were bound by Dal80 , including 120/314 Dal80-activated and 91 Dal80-repressed genes , which again is much more than expected by chance ( P<0 . 00001 , Chi-square test; Fig 2E; see also S2D Fig ) . Fig 2F shows an illustrative example of an NCR-sensitive , Dal80-activated gene ( UGA3 ) , the promoter of which is bound by Dal80 ( Fig 1E ) . S3A Fig shows the RNA-Seq signals for another NCR-sensitive , Dal80-repressed and Dal80-bound gene ( MEP2 ) , correlating with Pol II occupancy levels ( S3B Fig ) . In summary , there is a significant correlation between Dal80 recruitment to the promoter of genes and a regulation by the nitrogen source and/or Dal80 at the RNA level , indicating that Dal80 recruitment to promoters is physiologically relevant . More specifically , we identified a subset of 211 Dal80-bound genes that are regulated by Dal80 ( S3 Table ) , and that are therefore a robust class of direct Dal80 targets . The metagene analysis described above revealed that the genes bound by Dal80 at the promoter also display a signal along the gene body , although this intragenic signal remains globally lower than in the promoter-proximal region ( Fig 1D ) . This observation prompted us to investigate the possibility that Dal80 also occupies the gene body , at least for a subset of genes . We identified 189 genes showing Dal80 intragenic occupancy , according to a >75% overlap of the ORF by a Dal80-Myc13 peak ( Fig 3A and 3B ) . Among them , 144 ( 76% ) were also bound at the promoter ( Fig 3B ) . On the other hand , 45 genes showing Dal80 intragenic binding were not bound at the promoter ( Fig 3B ) . Hence , we distinguished four classes of genes ( S8 Table ) : ( i ) those bound by Dal80 at the promoter only ( “P” class; Fig 3C; S8 Table , column C ) , ( ii ) those showing both promoter and intragenic binding ( “P&O” class; Fig 3D; S8 Table , column E ) , ( iii ) those bound across the ORF only ( “O” class; Fig 3E; S8 Table , column D ) , ( iv ) the unbound genes ( Fig 3F ) . Interestingly , we noted that the global Dal80-Myc13 signal at the promoter was higher for the “P&O” class in comparison to the “P” class ( Fig 3C and 3D ) . Most of the genes of the “O” class are not Dal80-sensitive ( 40/45; S8 Table , column J ) . Furthermore , a substantial fraction of them correspond to small dubious ORFs , close to or even overlapping an adjacent Dal80-bound gene promoter . In these cases , the limited resolution of the ChIP-Seq technique , combined to the small size of these genes , might have allowed them to pass the filters we used to identify Dal80 intragenic binding . Overall , these observations suggest that the existence of the “O” class is likely to be physiologically irrelevant . Therefore , this class will not be further considered in our study . In conclusion , we identified a subset of genes showing intragenic Dal80 occupancy , in most cases correlating with a strong Dal80 recruitment at the promoter . We asked whether Dal80 occupancy across gene bodies correlates with nitrogen-regulated gene expression and Dal80-sensitivity . We observed that nitrogen-regulated genes ( NCR and revNCR; Fig 4A; see also S4A Fig ) and Dal80-regulated genes ( Dal80-activated and -repressed; Fig 4B; see also S4B Fig ) were significantly more represented in the P&O class compared to the Dal80-unbound class . Strikingly , we also observed that the genes of the P&O class are more expressed than the unbound genes ( P < 2 . 2e-16 , Wilcoxon rank-sum test; Fig 4C ) but also than the P-bound genes ( P = 1 . 3e-14 , Wilcoxon rank-sum test; Fig 4C ) . However , it should be noted that a fraction of P-bound and unbound genes are expressed to higher levels than genes of the “P&O” class ( S4C and S4D Fig ) , indicating that high expression does not always imply intragenic Dal80 occupancy . Together with the observation that genes of the “P&O” class globally showed higher Dal80-Myc13 ChIP-Seq signal at the promoter than those of the “P” class ( Fig 3C and 3D ) , our results indicate that Dal80 occupancy across gene bodies correlates with a stronger recruitment at the promoter and higher expression in proline-containing medium . This raises the question of the specificity of the intragenic signal observed by ChIP-Seq . Indeed , for several proteins , unspecific ChIP signals have been detected across the body of a subset of highly expressed Pol II- and Pol III-dependent genes , referred to as ‘hyper-ChIPable’ loci [65–67] . We asked whether genes of our P&O class have been previously identified as ‘hyper-ChIPable’ ( S9 Table , column G ) . This comparison indicated that 48/1125 of the P-bound genes and 27/144 of the P&O genes match with hyper-ChIPable loci ( S4E and S4F Fig; see also S9 Table , columns H-I ) , suggesting that for a minority of cases , the intragenic Dal80 signal could be due to the ‘hyper-ChIPability’ of the locus and therefore be non-specific . However , since these ‘hyper-ChIPable’ loci were defined under growth conditions that are different from those used in our study ( growth in rich medium vs proline-containing synthetic medium ) , we aimed to get a more robust control for the specificity of Dal80 within gene bodies . Our rationale was to evaluate how similar and/or specific two close GATA factors could share/distinguish this “so called” artefactual hyper-ChIPability property . We performed a similar ChIP-Seq analysis using another GATA factor , the Gat1 activator [68] , using the same conditions and following the same experimental procedure as described above ( Figs 1A , 1B & 3A ) . Interestingly , 83 . 2% ( 936/1125 ) of the promoters bound by Dal80 were also bound by Gat1 ( S4G Fig; S9 Table , column E ) , reinforcing the accuracy of the extended list of novel GATA-bound genes in yeast . Strikingly , the proportion of common targets among the P&O class dramatically decreased , 55% ( 79/144 ) of the genes bound by Dal80 at the promoter and across the gene body also showing promoter and intragenic binding for Gat1 ( S4H Fig; S9 Table , column F ) . Importantly however , 65/144 P&O for Dal80 do not display intragenic binding for Gat1 ( S4H Fig; S9 Table , column F ) , although Gat1 is recruited to the promoter of 57 of them . Thus , we can define a subset of 57 genes showing a specific intragenic occupancy of Dal80 , while both Dal80 and Gat1 are recruited to their promoters similarly . As an illustrative striking example , Fig 4D shows a snapshot of the ChIP-Seq signals across MEP2 , a well-characterized NCR-sensitive gene , the promoter of which is bound by the two GATA factors , but only Dal80 is found within the gene body . To summarize , Dal80 occupancy across the gene body correlates with high expression levels . In a substantial proportion of cases , intragenic occupancy was found to be specific for Dal80 , as another GATA factor also recruited to the promoter in the same experimental conditions was not detected within the gene body . In order to validate our genome-wide observations and get additional mechanistic insights into the molecular bases of Dal80 occupancy across the body of highly expressed genes , we characterized the binding profile of Dal80 along the ammonium permease-coding gene MEP2 , an NCR-sensitive gene of the “P&O” class ( see Fig 4D ) . ChIP experiments followed by qPCR confirmed that Dal80 binds not only the promoter , but also across the coding region of MEP2 in proline-grown cells ( Fig 5A and 5B ) . No signal was observed in glutamine-grown cells ( Fig 5B ) , indicating that Dal80 recruitment only occurs when it is expressed ( S2A Fig ) . To determine whether Dal80 intragenic occupancy is mediated by nascent RNA binding during transcription , we performed a similar ChIP experiment on the MEP2 gene , treating the chromatin with RNase before the immunoprecipitation . Our results show no significant change of the Dal80-Myc13 signal across MEP2 upon RNAse treatment of the chromatin extracts before the immunoprecipitation ( Fig 5C ) , indicating that Dal80 occupancy across the gene body does not depend on RNA . Since genes of the Dal80 “P&O” class are globally highly expressed , we asked whether active transcription is a prerequisite for Dal80 binding across the ORF . Our strategy was to select an NCR gene for which Dal80 is bound at the promoter when repressed and then monitor Dal80 occupancy once the gene is activated . Our RNA- and ChIP-Seq data allowed us to isolate the UGA4 locus , another well-characterized NCR-sensitive gene , bound by Dal80 at the promoter ( Fig 6A; see snapshot in S5A Fig ) . UGA4 expression is induced by GABA ( γ-aminobutyric acid ) and is strongly repressed by Dal80 in the absence of the inducer [69] . To derepress UGA4 without inducer , a Dal80-specific deletion in the C-terminal leucine zipper domain was generated , impairing Dal80 repressive activity without affecting its binding capacity [34 , 44] . Indeed , in the Dal80ΔLZ-Myc13 strain ( Fig 6B ) , the steady-state level of UGA4 mRNA ( S5B Fig ) and Pol II occupancy ( S5C Fig ) both increased to derepressed levels in non-inducing conditions , like in a dal80Δ strain . Strikingly , in these conditions , full-length Dal80-Myc13 binding was restricted to the UGA4 promoter ( Fig 6A; see also S5A Fig ) , while Dal80ΔLZ-Myc13 binding was detected at the promoter and across the body of UGA4 ( Fig 6A ) . Interestingly , the leucine zipper of Dal80 and consequently , its dimerization , needed for UGA4 repression , were not required for its localization across the UGA4 gene body . Importantly , these results confirm that promoter binding is not sufficient to confer intragenic binding , but suggest that transcription activation is required . Altogether , these observations prompted the important mechanistic question of how Dal80 can be localized to gene bodies upon transcription activation . In order to test if the presence of an NCR-sensitive promoter could confer intragenic Dal80 binding across the body of a non-NCR-sensitive gene , we placed the URA3 ORF under the control of different promoters bound or not by Dal80: the MEP2 and TDH3 promoters as P&O representative , the ALD6 promoter for the P class and the VMA1 promoter , which is not bound by Dal80 ( Fig 7A ) . When driven by PMEP2 , the expression of URA3 becomes NCR-sensitive and followed wild-type MEP2 expression ( S6 Fig ) , correlating with Pol II recruitment over the URA3 ORF ( Fig 7B ) . In these conditions , we observed Dal80-Myc13 binding at the promoter of MEP2 and also across URA3 ( Fig 7C ) . Similarly for PTDH3-URA3 construct , Dal80 also was relocalized within the URA3 ORF , although to a lesser extent . Importantly , Dal80 binding was not detected across URA3 when it was expressed from its native locus , under the control of its promoter ( Fig 7C ) or under the control of the Dal80-bound PALD6 or unbound PVMA1 ( Fig 7C ) , reinforcing the idea that those promoters fail to carry sufficient information for Dal80 to occupy the URA3 ORF . Among the obvious characteristics , we noticed that Pol II occupancy is higher within those P&O URA3 genes than the P only , suggesting that transcription strength might be a key determinant for Dal80 localization across the ORF . Interestingly , among the P&O fusions ( MEP2 and TDH3 ) , we noted a difference in Dal80 binding levels to the adjacent URA3 ORF , while those of Pol II remain similar across the two coding regions , suggesting that Pol II level might not be the only factor that control Dal80 occupancy . In conclusion , these results show that for the same URA3 sequence , the Dal80 occupancy displays distinct features depending only on the promoter characteristics to be classified as P , P&O or unbound , reflecting transcriptional strength . We propose that Dal80 presence within the ORF could be attributed to a spreading mechanism , controlled by Pol II complex and Dal80-promoter recognition capacity . These results exclude strongly DNA motif ( s ) as a main determinant for Dal80 spreading into ORF but rather raise the question of the direct implication of Pol II itself . To test the hypothesis that the active Pol II complex could be responsible for Dal80 spreading beyond Dal80-bound promoters , we assessed the effect of rapid inactivation of Pol II using the thermosensitive rpb1-1 strain [70 , 71] . We analyzed Dal80-Myc13 binding along MEP2 in WT and rpb1-1 cells . When rpb1-1 cells were shifted at 37°C for 1h , MEP2 mRNA and Pol II levels showed a 2-fold ( S7A Fig ) and >10-fold decrease ( S7B Fig ) , respectively , reflecting the expected transcription shut-down when rpb1-1 cells are shifted in non-permissive conditions . In the same conditions , we observed a significant >5-fold reduction of Dal80-Myc13 levels across the MEP2 ORF , while the binding at the promoter was not affected ( Fig 8A ) . This result reinforces the idea that Dal80 spreading across the body of NCR-sensitive genes is strongly correlated to an active Pol II . To get insights into the mechanism by which Dal80 associates to actively transcribed gene bodies , we tested whether it physically interacts with the transcriptionally engaged form of Pol II ( Fig 8B ) . Total protein extracts from Dal80-Myc13 cells were immunoprecipitated with antibodies directed against the Pol II CTD and its phospho-forms Ser2P and Ser5P , respectively characteristic of elongating and initiating Pol II forms . All three antibodies enabled effective immunoprecipitation , whereas no antibody and nonspecific antibody controls generated a lower or no signal at all . Thus , Dal80 would physically interact with phosphoforms of the Pol III , suggesting a strong association with Pol II engaged in active transcription from initiating to elongating polymerase . Together , our data indicate that Dal80 spreading across the body of NCR-sensitive genes depends on active transcription and that Dal80 interacts with the transcriptionally active forms of Pol II , supporting a model where Dal80 spreading across the body of highly expressed , NCR-sensitive genes might be the result of Dal80-Pol II association at post-initiation transcription phases .
Eukaryotic GATA factors belong to an important family of DNA binding proteins involved in development and response to environmental changes in multicellular and unicellular organisms , respectively . In yeast , four GATA factors are involved in Nitrogen Catabolite Repression ( NCR ) , controlling gene expression in response to nitrogen source availability . One of them , the Dal80 repressor , itself NCR-sensitive , acts to modulate the intensity of NCR responses . Over the past decade , a number of studies have screened the genome aiming at gathering an inventory of genes regulated by the nitrogen source . Although >500 genes have been shown to be differentially expressed upon change of the nitrogen source [57 , 64] , the list of NCR-sensitive genes was reduced to about 100 , based on their sensitivity to GATA factors [55 , 57 , 60 , 63] , suggesting that the number of Dal80 targets would be situated in that range . Here , using ChIP-Seq , we identified 1269 Dal80-bound promoters , which considerably extends the list of potential Dal80 targets . In fact , the number of Dal80-bound promoters could even have been greater . Indeed , the GATA consensus binding site is rather simple and short , so that in yeast , a total number of 10 , 000 putative binding sites can be found in all protein-coding gene promoters , 2930 promoters having at least two GATA sites , which is thought to be a prerequisite for in vivo binding and function of the GATA factors . The difference between the number of promoters with ≥2 GATA sites and the number of Dal80-bound promoters suggests the existence of a selectivity for Dal80 recruitment . This selectivity could rely on promoter architecture and/or chromatin structure , conditioning the requirement for auxiliary DNA binding factors that would stabilize Dal80 at some promoters . Moreover , although we observed a significant correlation between Dal80 binding and regulation , the expression of most of the Dal80-bound genes was not affected in a dal80Δ mutant strain . Again , Dal80-dependence for transcribing these genes , as well as their NCR sensitivity , could require the presence of yet unknown cofactors which are not produced or inactive under the tested growth conditions . In mammals , GATA factors also display an extraordinary complexity in the relationships between binding and expression regulation . Like Dal80 , GATA-1 and GATA-2 only occupy a small subset of their abundant binding motif throughout the genome , and the presence of the conserved binding site is insufficient to cause GATA-dependent regulation in most instances [72] . GATA-1 binding kinetics , stoichiometry and heterogeneous complex formations , conditioned by composite promoter architecture , influence its transcriptional activity and hence diversify gene expression profiles [72] . Given the high conservation at the amino acid level between the DNA binding domains of the four yeast NCR GATA factors , it is likely that they all recognize identical sequences ( GATAA , GATAAG or GATTAG ) . This consensus has been largely validated in the past using gene reporter experiments , mutational analyses and in vitro binding experiments on naked DNA . Nonetheless , of the 1269 bound promoters , 48% contained at least two GATA sites , a proportion that is not different from that observed among unbound promoters , and the amount of GATA sites per promoter was not different between the two groups either . In addition , Dal80 recruitment was found to occur independently of the presence of GATA sites in 20% of Dal80-bound promoters , as also previously observed in mammalian cells [24 , 73] . Future experiments will be required to decipher how Dal80 can be recruited to these GATA-less promoters . Among the different possibilities is a recruitment of Dal80 by degenerated GATA motifs . In this regard , we identified 5 degenerated GATA motifs within a 70 bp window corresponding to the peak of Dal80 binding signal at the promoter of the GATA-less , Dal80-sensitive gene ALD6 ( see S1E Fig ) . However , it also has to be noted that upon tolerance of only one mismatch within the GATA consensus , multiple degenerated motifs are detected in every yeast promoter . Unexpectedly , although Dal80 has always been described as a repressor , we identified 314 genes that are positively regulated by Dal80 ( their expression is significantly decreased upon Dal80 deletion; S5 Table ) . These genes are significantly enriched in amino acid biosynthetic processes , resembling the amino acid starvation response mediated by the Gcn4 transcriptional activator . Interestingly , the promoter of 122/314 Dal80-activated genes contain Gcn4-binding sites ( S5 Table ) , and this group of 314 Dal80-activated genes is significantly enriched for genes regulated by the General Amino Acid Control ( GAAC; YeastMine Gene List , Publication Enrichment , P<1 . 6e-13 ) , through the Gcn4 activator . Interconnections between NCR and GAAC have already been demonstrated , mostly at the level of nitrogen catabolism control: 1-a large number of non-preferential nitrogen sources leads to increased transcription of GAAC targets [57]; and 2- Gcn4 contributes , with Gln3 , to the expression of some but not all NCR-sensitive genes [74 , 75] . However , this is the first time that evidence are provided indicating a positive role for Dal80 at the level biosynthetic gene expression . The most striking and unexpected finding of this work is the observation that Dal80 also occupied the body of a subset of genes . Dal80 binding at the promoter and spreading across the body of the 144 genes of the “P&O” class correlated with high expression levels and sensitivity to Dal80 . It has been previously reported that at some loci , referred to as ‘hyper-ChIPable’ , high expression levels might induce artefactual detection of DNA-binding factors across gene bodies [65] . However , in the context of this work , several observations argue for a specific association of Dal80 with gene bodies , at least for a subset of genes . Firstly , a considerable fraction of genes of the “P” class show similar or even higher expression levels than genes of the “P&O” class ( S4C and S4D Fig ) , indicating that high expression does not always induce spreading of Dal80 across the gene body . Secondly , only 27 of the genes of our “P&O” class have been previously defined as ‘hyper-ChIPable’ ( S9 Table , column I ) , even if the conclusion should be taken with caution as the two sets of experiments were performed upon very distinct physiological conditions . Thirdly , and more importantly , a similar ChIP-Seq analysis performed under the same experimental conditions using another GATA factor ( the Gat1 activator ) allowed us to define a subset of 57 genes that are specifically and only bound by Dal80 across their body , while both Dal80 and Gat1 are recruited to their promoter ( see Fig 4D and S4H Fig ) . Thus , although we cannot exclude that in few cases , the signals for Dal80 across the intragenic region could still depend on the hyper-ChIPability of the locus , we propose that for the majority of “P&O” genes , the intragenic association of Dal80 is specific and biologically relevant . This is further supported by the observation that Dal80-sensitive ( -activated and–repressed ) genes are statistically more enriched within the “P&O” class , compared to the “P” class ( Fig 4B ) . However , the causality relationship between Dal80 intragenic binding and high expression levels in derepressing conditions ( proline ) remains unclear to date . The observations we made at the genome-wide level were experimentally confirmed using ChIP experiments , at the level of single well-characterized NCR-sensitive genes . Promoter binding appears to be required but not sufficient . Indeed , the inactivation of Pol II-dependent transcription correlates with decreased intragenic binding ( and vice versa ) , further indicating that Dal80 spreading across gene bodies depends on active transcription . Consistently , we detected a physical interaction between Dal80 and transcriptionally active forms of Pol II . Together , our data lead us to propose a model where Dal80 could travel from the promoter of highly expressed , NCR-sensitive genes through the gene body by accompanying the elongating Pol II complex ( Fig 9 ) . However , it is also possible that Dal80 spreading across gene bodies is determined , but yet temporally distinct , from the passage of the elongating Pol II . For instance , chromatin marks deposited upon Pol II passage could favor Dal80 intragenic binding afterwards . Additional investigations will be required to define which domain of Dal80 is responsible for the interaction with the transcription machinery , to determine whether there is any causal relationship between Dal80 intragenic binding and high expression levels , and to decipher the potential role of Dal80 during active transcription . In this respect , we propose that the leucine zipper domain is not involved . Whereas the binding of elongation factors across gene bodies has been thoroughly documented [76] , it has also been described for some specific transcription factors . For example , Gal4 was reported to bind to its consensus DNA target within the ACC1 ORF , but the authors concluded that the observed transcriptional repression of the ACC1 gene was most likely resulting from random GAL4 binding “noise” over the genome , thus having no physiological explanation for this ORF-bound transcription factor [77] . Likewise , Gcn4 was detected across the PHO8 ORF , with concomitant recruitment of the SAGA complex , but without any impact on gene expression [78] . More recently , binding of the Gcn4 transcription factor to its consensus site at some ORFs , when located in proximity of the transcriptional start site , was found to play a consistent role in controlling embedded cryptic promoters in yeast , thereby affecting Gcn4-dependent transcription of some genes [79] . A recent study has identified CTD phosphorylation of Pol II as a hub that optimizes transcriptome changes to adequately balance optimal growth and stress tolerance responses [80] . The addition of nitrogen to nitrogen-limited cells rapidly results in the transient overproduction of transcripts required for protein translation ( stimulated growth ) whereas accelerated mRNA degradation favours rapid clearing of the most abundant transcripts , like those involved in high affinity permease production , that are highly expressed NCR-sensitive genes , for example [64] . The involvement of the Nrd1-Nab3-Sen1 ( NNS ) and TRAMP complexes in these regulatory responses has been envisioned very recently [81 , 82]; deadenylation , decapping and exonuclease mutants display impaired GAP1 mRNA clearance upon nitrogen upshift [83] . Thus , a possible role of Dal80 ( and possibly of the other GATA factors ) binding along highly expressed genes could be to transmit nutritional signals to elongation-related processes , like histone modification , chromatin remodelling [84 , 85] , mRNA export/processing [86] or roadblock termination [87] . Interestingly , in human cells , GATA factors are also reported to occupy non-canonical sites within the genome , further reinforcing that they can be recruited to the chromatin independently of their motif [24 , 73] . In addition , 43% of the GATA1 peaks were collected among exon , introns and 3’UTR of coding genes in human erythroleukemia cells [73] . It is tempting to hypothesize that GATA factors could have a dual or synergistic role during transcription , i . e . recruiting/stabilizing the PIC complex as for any classical transcription factor in the promoter/enhancer regions and promoting competent transcription at a post initiation step interacting with the RNAPII .
Experiments were conducted using S . cerevisiae strains of the FY genetic background . The strains used are listed in S10 Table . Dal80 and Gat1 were tagged with 13 copies of the c-myc epitope ( Myc13 ) as described [88] using primers listed in S10 and S11 Tables . The PMEP2-URA3 allele in strains FV806-808 , and PTDH3-URA3 , PVMA1-URA3 , PALD6-URA3 alleles in strains FV1105-1107 , respectively , were created by amplification of the URA3 gene using the same strategy , with primers listed in S10 and S11 Tables . Cultures were grown at 29°C to mid-log phase ( A660nm = 0 . 5 ) in YNB ( without amino acids or ammonia ) minimal medium containing the indicated nitrogen source at a 0 . 1% final concentration , glucose ( 3% ) and the appropriate supplements ( 20 μg/ml uracil , histidine and tryptophan ) to cover auxotrophic requirements . Cell extracts and chromatin immunoprecipitations were conducted as described [40] using primers listed in S11 Table . The cells ( 100 ml cultures grown to an absorbance ( A660 nm = 0 . 6 ) corresponding to 6 × 106 cells/ml ) were treated with 1% formaldehyde for 30 min at 25°C and mixed by orbital shaking . Glycine was then added to a final concentration of 500 mM and incubation continued for 5 min . The cells were collected , washed once with cold 10 mM Tris-HCl , pH 8 , washed once with cold FA-SDS buffer ( 50 mM HEPES-KOH , pH 7 . 5 , 150 mM NaCl , 1 mM EDTA , 1% Triton X-100 , 0 . 1% sodium deoxycholate , 0 . 1% SDS , 1 mM phenylmethylsulfonyl fluoride ) , and resuspended in 1 ml of cold FA-SDS buffer . An equal volume of glass beads ( 0 . 5 mm in diameter ) was added , and the cells were disrupted by vortexing for 30 min in a cold room . The lysate was diluted into 4 ml of FA-SDS buffer , and the glass beads were discarded . The cross-linked chromatin was then pelleted by centrifugation ( 17 , 000 × g for 35 min ) , washed for 60 min with FA-SDS buffer , resuspended in 1 . 6 ml of FA-SDS buffer for 15 min at 4°C , and sonicated three times for 30 s . each ( Bioruptor , Diagenode ) , giving fragments with an average size of 250–300 bp . Finally , the sample was clarified by centrifugation at 14 , 000 × g for 30 min and diluted 4-fold in FA-SDS buffer , and aliquots of the resultant chromatin containing solution were stored at –80°C . Pol II and Myc13-tagged proteins were immunoprecipitated by incubating 100 μl of the chromatin containing solution for 180 min at 4°C with 2 μl of mouse anti-Pol II and anti-Myc antibodies , respectively ( SCBT CTD4H8 or SC-40 , respectively ) prebound to 10 μl of Dynabeads Pan Mouse IgG ( Dynal ) according to the manufacturer's instructions . Immune complexes were washed six times in FA-SDS buffer and recovered by treating with 50 μl of Pronase Buffer ( 25 mM Tris , pH 7 . 5 , 5 mM EDTA , 0 . 5% SDS ) at 65°C with agitation . Input ( IN ) and immunoprecipitated ( IP ) fractions were then subjected to Pronase treatment ( 0 . 5 mg/ml; Roche Applied Science ) for 60 min at 37°C , and formaldehyde cross-links were reversed by incubating the eluates overnight at 65°C . Finally , the samples were treated with RNase ( 50 μg/ml ) for 60 min at 37°C . DNA from the IP fractions was purified using the High Pure PCR Product Purification Kit ( Roche Applied Science ) and eluted in 50 μl of 20 mM Tris buffer , pH 8 . IN fractions were boiled 10 min and diluted 500-fold with no further purification prior to quantitative PCR analysis . Quantitative RT-PCR was performed as described previously [40] using primers listed in S11 Table . Total RNA was extracted from 4-ml cultures and cDNA was generated from 100 to 500 ng of total RNA using a RevertAid H Minus first-strand cDNA synthesis kit with oligo ( dT ) 18 primers from Fermentas using the manufacturer's recommended protocol . cDNAs were subsequently quantified by RT-PCR using the Maxima SYBR green qPCR master mix from Fermentas . Cultures ( 100 ml ) were harvested , washed once in 50 mM Tris , pH 8 , and resuspended in 1ml of buffer ( 50 mM Tris , pH 8 , 150 mM NaCl , 5 mM EDTA , 0 . 05% NP-40 , 1 mM phenylmethylsulfonyl fluoride , and complete protease inhibitor cocktail tablets [Roche] ) . Lysis was performed by shaking with 425–600 μm acid-washed glass beads ( Sigma ) on an IKA Vibrax VXR orbital shaker at maximum speed for 30 min at 4°C . Cell debris and glass beads were removed by centrifugation . Immunoprecipitation was performed by incubating 200 μl of total cell extracts with 20 μl of Dynabeads PAN mouse immunoglobulin G ( Invitrogen ) that were preincubated with anti-HA ( SCBT , SC-7392 ) , anti-CTD ( SCBT , CTD4H8 ) , anti-Ser2P ( BioLegend , H5 ) or anti-Ser5P ( BioLegend , H14 ) antibodies and 20 μl of 1% phosphate-buffered saline-bovine serum albumin for 2 h under orbital shaking ( 800 rpm ) at 30°C . Immune complexes were washed three times in lysis buffer , eluted by boiling in sodium dodecyl sulfate ( SDS ) sample buffer , and loaded on SDS-polyacrylamide gel for anti-Myc Western blotting . ChIP-Seq analysis was performed from two biological replicates of proline-grown 25T0b ( no tag ) , FV078 ( DAL80-MYC13 ) and FV034 ( GAT1-MYC13 ) cells . Lysis and chromatin extraction was as described above . The average fragment length of sonicated fragment was 300–350 bp . For each condition , libraries were prepared from 10 ng of “input” or “IP” DNA using the TruSeq ChIP Sample Preparation Kit ( Illumina ) . Single-read sequencing ( 50 nt ) of the libraries was performed on a HiSeq 2500 sequencer . Reads were uniquely mapped to the S . cerevisiae S288C reference genome using Bowtie2 v2 . 1 . 0 [89] , with a tolerance of 1 mismatch in seed alignment . Tags densities were normalized on the total number of uniquely reads mapped . Dal80- and Gat1-bound regions were identified through a peak-calling procedure using version 2 . 0 . 9 of MACS [90] , with a minimum false discovery rate ( FDR ) of 0 . 001 . For each strain and condition , total RNA was extracted from two biological replicates using standard hot phenol procedure , ethanol-precipitated , resuspended in nuclease-free H2O ( Ambion ) and quantified using a NanoDrop 2000c spectrophotometer . Ribosomal RNAs were depleted from 1 μg of total RNA using the RiboMinus Eukaryote v2 Kit ( Life Technologies ) . After concentration using the Ribominus Concentration Module ( Life Technologies ) , rRNA-depleted RNA was quantified using the Qubit RNA HS Assay kit ( Life Technologies ) . In parallel , rRNA depletion efficiency and integrity of both total and rRNA-depleted RNA were checked by analysis in a RNA 6000 Pico chip , in a 2100 bioanalyzer ( Agilent ) . Strand-specific total RNA-Seq libraries were prepared from 125 ng of rRNA-depleted RNA using the TruSeq Stranded Total RNA Sample Preparation Kit ( Illumina ) , following manufacturer’s instructions . Paired-end sequencing ( 2 x 50 nt ) of the libraries was performed on a HiSeq 2500 sequencer . Sequenced reads were mapped to the reference genome using version 2 . 0 . 6 of TopHat [91] , as described [92] . Tags densities were normalized on the total number of reads uniquely mapped on ORFs . Differential expression analysis was performed using DESeq [93] . Differentially expressed genes were identified on the basis of a fold-change ≥2 and a P-value ≤0 . 01 . Statistical details can be found in the corresponding figure legends . Error bars correspond to standard error . Statistical significance tests were carried out using the Student’s t test when indicated . Sequence data can be accessed at the NCBI Gene Expression Omnibus using accession numbers GSE86307 and GSE86325 . Genome browsers for visualization of processed ChIP-Seq and RNA-Seq data are accessible at http://vm-gb . curie . fr/dal80 . Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact , Isabelle Georis ( igeoris@ulb . ac . be ) . Bioinformatics and genome wide dataset requests could also be addressed to antonin . morillon@curie . fr for rapid processing . | GATA transcription factors are highly conserved among eukaryotes and play key roles in cancer progression and hematopoiesis . In budding yeast , four GATA transcription factors are involved in the response to the quality of nitrogen supply . Here , we have determined the whole genome binding profile of the Dal80 GATA factor , and revealed that it also associates with the body of promoter-bound genes . The observation that intragenic spreading correlates with high expression levels and exquisite Dal80 sensitivity suggests that GATA factors could play other , unexpected roles at post-initiation stages in eukaryotes . |
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Dengue is the most extensively spread mosquito-borne disease; endemic in more than 100 countries . Information about dengue disease burden , its prevalence , incidence and geographic distribution is critical in planning appropriate control measures against dengue fever . We conducted a systematic review and meta-analysis of dengue fever in India We searched for studies published until 2017 reporting the incidence , the prevalence or case fatality of dengue in India . Our primary outcomes were ( a ) prevalence of laboratory confirmed dengue infection among clinically suspected patients , ( b ) seroprevalence in the general population and ( c ) case fatality ratio among laboratory confirmed dengue patients . We used binomial–normal mixed effects regression model to estimate the pooled proportion of dengue infections . Forest plots were used to display pooled estimates . The metafor package of R software was used to conduct meta-analysis . Of the 2285 identified articles on dengue , we included 233 in the analysis wherein 180 reported prevalence of laboratory confirmed dengue infection , seven reported seroprevalence as evidenced by IgG or neutralizing antibodies against dengue and 77 reported case fatality . The overall estimate of the prevalence of laboratory confirmed dengue infection among clinically suspected patients was 38 . 3% ( 95% CI: 34 . 8%–41 . 8% ) . The pooled estimate of dengue seroprevalence in the general population and CFR among laboratory confirmed patients was 56 . 9% ( 95% CI: 37 . 5–74 . 4 ) and 2 . 6% ( 95% CI: 2–3 . 4 ) respectively . There was significant heterogeneity in reported outcomes ( p-values<0 . 001 ) . Identified gaps in the understanding of dengue epidemiology in India emphasize the need to initiate community-based cohort studies representing different geographic regions to generate reliable estimates of age-specific incidence of dengue and studies to generate dengue seroprevalence data in the country .
Dengue is the most extensively spread mosquito-borne disease , transmitted by infected mosquitoes of Aedes species . Dengue infection in humans results from four dengue virus serotypes ( DEN-1 , DEN-2 , DEN-3 , and DEN-4 ) of Flavivirus genus . As per the WHO 1997 classification , symptomatic dengue virus infection has been classified into dengue fever ( DF ) , dengue haemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) . The revised WHO classification of 2009 categorizes dengue patients according to different levels of severity as dengue without warning signs , dengue with warning signs ( abdominal pain , persistent vomiting , fluid accumulation , mucosal bleeding , lethargy , liver enlargement , increasing haematocrit with decreasing platelets ) and severe dengue [1 , 2 , 3] . Dengue fever is endemic in more than 100 countries with most cases reported from the Americas , South-East Asia and Western Pacific regions of WHO [1] . In India , dengue is endemic in almost all states and is the leading cause of hospitalization . Dengue fever had a predominant urban distribution a few decades earlier , but is now also reported from peri-urban as well as rural areas [4 , 5] . Surveillance for dengue fever in India is conducted through a network of more than 600 sentinel hospitals under the National Vector Borne Disease Control Program ( NVBDCP ) [6] , Integrated Disease Surveillance Program ( IDSP ) [7] and a network of 52 Virus Research and Diagnostic Laboratories ( VRDL ) established by Department of Health Research [8] . In 2010 , an estimated 33 million cases had occurred in the country [9] . During 2016 , the NVBDCP reported more than 100 , 000 laboratory confirmed cases of dengue [6] . It is therefore possible that dengue disease burden is grossly under-estimated in India . High dengue disease burden and frequent outbreaks result in a serious drain on country’s economy and stress on the health systems . In India , case detection , case management , and vector control are the main strategies for prevention and control of dengue virus transmission [6] . A new dengue vaccine is now available and several vaccines are in the process of development [10 , 11 , 12] . Information about dengue disease burden , its prevalence , incidence and geographic distribution is necessary in decisions on appropriate utilization of existing and emerging prevention and control strategies . With this background , we conducted a systematic review and meta-analysis to estimate the disease burden of dengue fever in India . We also reviewed serotype distribution of dengue viruses in circulation , and estimated case fatality ratios as well as proportion of secondary infections .
This systematic review is registered in PROSPERO ( Reg . No . CRD 42017065625 ) . We searched Medline ( PubMed ) , Cochrane Central , WHOLIS , Scopus , Science Direct , Ovid , Google Scholar , POPLINE , Cost-Effectiveness Analysis ( CEA ) Registry and Paediatric Economic Database Evaluation ( PEDE ) databases for articles published up to 2017 . The main search terms included incidence , prevalence , number of reported cases , mortality , disease burden , cost of illness , or economic burden of dengue in India . The complete search strategy is described in S1 Appendix . Back referencing of included studies in bibliography was also done to identify additional studies . The search results were initially imported to Zotero software ( Version 4 . 0 . 29 . 5 ) and duplicate records were removed . During title screening , we examined relevant studies from various databases . Our inclusion criterion was studies reporting dengue infection in India , not restricted to setting , design , purpose and population . Titles thus selected were subjected to abstract screening . Studies were considered eligible for further examination in full text if their abstracts reported incidence , prevalence , number of reported cases , mortality or the burden of dengue fever anywhere in India . Studies reporting complications of dengue , serotype details of dengue virus as well as seroprevalence of dengue were also included . Using a pre-designed data extraction form , two reviewers extracted details from selected studies independently . The data , which differed between the reviewers , were resolved by consensus . Information about the year of publication , study setting ( hospital/laboratory based , or community-based ) , study location , study period , laboratory investigations , number of suspected patients tested and positives , age distribution of cases , and details of dengue serotypes were abstracted ( S1 Dataset ) . The primary outcome measures of interest were ( a ) prevalence ( proportion ) of laboratory confirmed dengue infection among clinically suspected patients in hospital/laboratory based or community-based studies , ( b ) seroprevalence of dengue in the general population and ( c ) case fatality ratio among laboratory confirmed dengue patients . The diagnosis of acute dengue infection among the clinically suspected patients was based on any of the following laboratory criteria: ( a ) detection of non-structural protein-1 ( NS1 ) antigen , ( b ) Immunoglobulin M ( IgM ) antibodies against dengue virus ( c ) haemagglutination inhibition ( HI ) antibodies against dengue virus , ( d ) Real-time polymerase chain reaction ( RT-PCR ) positivity or ( e ) virus isolation . Seroprevalence of dengue was based on detection of IgG or neutralizing antibodies against dengue virus . Studies providing prevalence ( proportion ) of laboratory confirmed dengue infection among clinically suspected patients were classified into ( a ) hospital/laboratory-based surveillance studies and ( b ) outbreak investigations or hospital/laboratory-based surveillance studies when the outbreak was ongoing in the area , as mentioned in the original research paper . Studies regarding outbreak investigations considered an increase in number of reported cases of febrile illness in a geographical area , as the criteria for defining an outbreak . The outbreak investigations included one or more of the following activities: active search for case-patients in the community , calculation of attack rates for suspected case-patients , confirmation of aetiology and entomological investigations . For the case fatality ratio , the numerator included reported number of deaths due to dengue and denominator as laboratory confirmed dengue patients . Our secondary outcomes of interest were the following: ( a ) proportion of primary and secondary infections among the laboratory confirmed dengue patients . This classification was made based on the information about dengue serology provided in the paper . Primary dengue infection was defined as acute infection , as indicated by qualitative detection of NS1 antigen , and/or IgM or HI antibodies or RT-PCR positivity and absence of IgG antibodies against dengue virus . A case of acute infection as defined above , in presence of IgG antibodies , was considered as secondary dengue infection [2 , 13 , 14] . Some of the studies used the ratio of IgG to IgM antibodies as the criteria for differentiating primary and secondary infections [14]; ( b ) distribution of predominant and co-circulating dengue virus serotypes; ( c ) proportion of severe dengue infections based on WHO 1997 or WHO 2009 criteria [1 , 2] . The category of severe dengue infection included patients with DHF and DSS as per the WHO 1997 classification as well as severe dengue infections classified as per the WHO 2009 classification and ( d ) cost of illness , which included reported direct and indirect costs associated with dengue hospitalization . The risk of bias was assessed using a modified Joanna Briggs Institute ( JBI ) appraisal checklist for studies reporting prevalence data [15] and essential items listed in the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) checklist [16] . The criteria for assessing bias primarily included methods for selecting participants , methods for laboratory testing , and outcome variables ( Supplementary file S2 Appendix ) . We conducted quantitative synthesis to derive meta-estimates of primary and secondary outcomes ( severity of disease and primary/ secondary infections ) and qualitative synthesis to describe the serotype distribution and economic burden due to dengue . We followed Meta-analysis of Observational Studies in Epidemiology ( MOOSE ) guidelines [17] . For each study , primary outcomes ( prevalence of acute infection , seroprevalence and CFR ) were summarized as proportion and their 95% confidence intervals were computed . We used logit and inverse logit transformations for variance stabilization of proportions [18] . Binomial–Normal mixed effects regression model was used to estimate the pooled proportion of dengue infections . Forest plots were used to display pooled estimates . Heterogeneity was tested using likelihood ratio test . Funnel plots with logit prevalence on x-axis and standard errors on y-axis and Egger’s test were used to evaluate publication bias . Independent variables potentially associated with the prevalence of laboratory confirmed dengue were included as fixed-effects in univariate and multivariate binomial meta-regression models . P <0 . 05 was considered statistically significant . Sensitivity analysis was carried out by leaving out one study at a time in the order of publication to check for consistency of pooled estimates . Analyses were performed in the R statistical programming language using the ‘metafor’ package [19 , 20] .
The search strategy initially identified 2 , 285 articles from different databases . After removal of duplicates , 1 , 259 articles were considered for title and abstract screening . Seven hundred and forty-six articles were excluded for reasons provided in Fig 1 . Thus , 513 articles were found to be eligible for full-text review . After the review of full-text articles , 233 studies were included for the analysis [21–253] . The details of the studies included in the review are provided in the PRISMA flowchart ( Fig 1 ) . None of the studies reported incidence of dengue fever . Funnel plots and Egger’s test revealed no publication bias in the estimates of dengue prevalence in hospital-based surveillance studies , hospital-based surveillance studies during outbreaks and outbreak investigations . CFR estimates , however , showed a significant publication bias , and studies with high prevalence were more likely to be published . In the sensitivity analysis , the estimated pooled proportions were found to be consistent for all study outcomes . ( S3 Appendix )
The present study has estimated the burden of dengue fever based on published literature from India spanning over five decades . Most of the published literature included in the analysis were hospital/ laboratory-based surveillance studies or reports of dengue outbreak investigations . Additionally the published data from VRDL network has been included in the analysis [65 , 96] . The data from the other two nationally representative surveillance platforms could not be used for the analysis because surveillance data from NVBDCP only reports the number of laboratory confirmed dengue cases , while the IDSP data is not available in the public domain . There was no community-based epidemiological study reporting the incidence of dengue fever . Our analysis revealed that among the clinically suspected dengue fever patients , the estimated prevalence of laboratory-confirmed dengue infection was 38% . The burden of dengue was also variable in studies conducted in different settings . Our findings indicated that most of the laboratory confirmed dengue cases in India occurred in young adults . Dengue positivity was higher between the months of August and November , corresponding to monsoon and post-monsoon season in most states in India . In the meta-regression , studies that had used WHO/NVBDCP case definitions and the hospital based studies conducted during outbreaks or studies reporting outbreaks were more likely to have laboratory confirmation of dengue . The odds of laboratory confirmation were also higher among studies conducted during the period of 2011 to 2017 , as compared to studies conducted prior to the year 2000 . Information about seroprevalence of dengue in the general population is a useful indicator for measuring endemicity of dengue fever . The dengue vaccine ( CYD-TDV ) manufactured by Sanofi Pasteur has been introduced in two sub-national programs in Philippines and Brazil [254] and it has been suggested that vaccine acts by boosting the naturally acquired immunity [255] . WHO SAGE conditionally recommends the use of this vaccine for areas in which dengue is highly endemic as defined by seroprevalence in the population targeted for vaccination [12 , 256] . The results of the two vaccine trials and mathematical modelling suggest that optimal benefits of vaccination if seroprevalence in the age group targeted for vaccination was in the range of ≥70% [255 , 256] . In 2018 , WHO revised the recommendation from population sero-prevalence criteria to pre-vaccination screening strategy [257] . The pooled estimate based on the seven studies conducted in India indicated a dengue seroprevalence of 57% . However , this estimated seroprevalence is not representative of the country , as these studies were conducted only in 12 Indian states , and some had used a convenience sampling method [201] . The computed pooled estimate of case fatality due to dengue in India was 2 . 6% with a high variability in the reported CFRs . The CFR estimated in our study was higher than the estimate of 1 . 14% ( 95% CI: 0 . 82–1 . 58 ) reported in the meta-analysis of 77 studies conducted globally; in the 69 studies which adopted WHO 1997 dengue case classification , the pooled CFR was 1 . 1% ( 0 . 8–1 . 6 ) while the pooled CFR for 8 studies which used the WHO 2009 case definition , the pooled CFR was 1 . 6% ( 95% CI: 0 . 64–4 . 0 ) [258] . Higher CFR observed in our analysis could be due to smaller sample sizes as 14 of the 35 studies that reported CFR of 2 . 6 or higher had a sample size of 100 or less , while in the remaining 21 studies the denominator ranging between 101 and 400 . Also , we only considered laboratory confirmed dengue cases in the denominator for the calculation of CFR . As per the NVBDCP surveillance data , a total of 683 , 545 dengue cases and 2 , 576 deaths were reported in India during 2009–2017 giving a CFR of 0 . 38% [6] . The lower CFR estimates from NVBDCP data could probably be on account of under-reporting of deaths due to dengue , or inclusion of higher number of mild cases in the denominator [259] . As per the NVBDCP surveillance data , an average of 28 , 227 dengue cases and 154 deaths were reported annually during 2009–2012 . The number of dengue cases reported increased thereafter , with an average of 100 , 690 cases per year during 2013–2017 . However , the reported number of deaths did not increase proportionately . The information about severity of dengue cases is not available from NVBDCP surveillance data . The published studies from India indicated circulation of all the four-dengue serotypes , with DEN-2 and DEN-3 being the more commonly reported serotypes . Two third of the studies reported circulation of more than one serotype . Co-circulation of multiple serotypes was particularly evident from the published studies in Delhi . More than two third ( 16/19 ) studies from Delhi reported circulation of more than one serotype; and most of the studies conducted in the last 10 years identified co-circulation of more than one serotype [Table 3] . Our review also revealed that more than two-fifth of the laboratory confirmed infections were secondary dengue infections and nearly one-fourth of the cases were severe in nature . Circulation of numerous dengue serotypes is known to increase the probability of secondary infection , leading to a higher risk of severe dengue disease [260] . Our systematic review has certain limitations . First , our study included only peer-reviewed literature from selected databases and we excluded grey literature which may have provided additional data . Second , most of the studies on disease burden were hospital-based , with no community-based studies estimating incidence . Hospital-based studies do not provide any information about the community level transmission as hospitalization is a function of health-seeking behaviour of the population . In absence of the information about health seeking behaviour provided in these studies , we estimated the prevalence of dengue using number of patients tested in the hospitals as the denominator . Third , the hospital-based studies used varying case definitions and laboratory tests to confirm dengue infection . Fourth , information about the type of health facility ( public or private ) , or residential status of patients ( urban or rural ) , and age was not uniformly reported and hence we did not estimate the dengue prevalence by these variables . In conclusion , the findings of our systematic review indicate that dengue continues to be an important public health problem in India , as evidenced by the high proportion of dengue positivity , severity and case fatality as well as co-circulation of multiple dengue virus serotypes . Our review also identified certain research gaps in the understanding on dengue epidemiology in the country . There is a need to initiate well planned community-based cohort studies representing different geographic regions of the country in order to generate reliable estimates of age-specific incidence of dengue fever in India . As such studies are cost intensive , a national level survey to estimate age-stratified dengue seroprevalence rates could be an alternative . Such estimates could be used to derive the relative proportions of primary and secondary infections using mathematical models [261] . Well planned studies in different geographic settings are also needed to generate reliable data about economic burden from India . Although the existing dengue surveillance platforms of NVBDCP , IDSP and VRDL are generating data about dengue disease burden , these systems could be strengthened to also generate data about dengue serotypes , severity , and primary and secondary infection from India . | Dengue fever , an extensively spread mosquito-borne disease , is endemic in more than 100 countries . Information about dengue disease burden , its prevalence and incidence and geographic distribution is necessary to guide in planning appropriate control measures including the dengue vaccine that has recently been licensed in a few countries . We performed a systematic review and meta-analysis of published studies in India on dengue . The overall estimate of the prevalence of laboratory confirmed dengue infection based on testing of more than 200 , 000 clinically suspected patients from 180 Indian studies was 38 . 3% . The pooled estimate of dengue seroprevalence in the general population and CFR among laboratory confirmed dengue patients was 56 . 9% and 2 . 6% respectively . There were no community-based studies reporting incidence of dengue . Our review also identified certain knowledge gaps about dengue epidemiology in the country . Identified gaps in the understanding of dengue epidemiology in India emphasize the need to initiate community-based cohort studies representing different geographic regions to generate reliable estimates of age-specific incidence of dengue and studies to generate dengue seroprevalence data in the country . |
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DEF-like and GLO-like class B floral homeotic genes encode closely related MADS-domain transcription factors that act as developmental switches involved in specifying the identity of petals and stamens during flower development . Class B gene function requires transcriptional upregulation by an autoregulatory loop that depends on obligate heterodimerization of DEF-like and GLO-like proteins . Because switch-like behavior of gene expression can be displayed by single genes already , the functional relevance of this complex circuitry has remained enigmatic . On the basis of a stochastic in silico model of class B gene and protein interactions , we suggest that obligate heterodimerization of class B floral homeotic proteins is not simply the result of neutral drift but enhanced the robustness of cell-fate organ identity decisions in the presence of stochastic noise . This finding strongly corroborates the view that the appearance of this regulatory mechanism during angiosperm phylogeny led to a canalization of flower development and evolution .
Depending on the nature of the interactions of their constituents , gene regulatory circuits can display a variety of dynamical behaviors ranging from simple steady states , to switching and multistability , to oscillations . Temporal or spatial patterning during development requires activation of genes at a particular time or position , respectively , and the inhibition in the remaining time or part . Regulatory genes involved in such processes often show a switch-like temporal or spatial dynamics , which requires a direct or indirect positive non-linear feedback of the genes on their own expression , e . g . via dimers of their own product [1] . Switch-like behavior can be displayed by a single gene [2] , [3] , but many gene regulatory switches have a more complex structure . Due to the small number of molecules involved , these switches are inherently stochastic and their behavior under noisy conditions can strongly depend on their genetic architecture [4]–[6] . In some cases the complex regulatory interactions have been quite well documented , but the functional implications of the corresponding regulatory circuitry have remained enigmatic . A good case in point is provided by some floral homeotic ( or organ identity ) genes from model plants such as Arabidopsis thaliana ( thale cress; henceforth termed Arabidopsis ) and Antirrhinum majus ( snapdragon; henceforth called Antirrhinum ) . Floral homeotic genes act as developmental switches involved in specifying organ identity during flower development . According to the ‘ABC model’ , three classes of floral organ identity ( or homeotic ) genes act in a combinatorial way to specify the identity of four types of floral organs , with class A genes specifying sepals in the first floral whorl , A+B petals in the second whorl , B+C stamens ( male reproductive organs ) in the third whorl , and C alone carpels ( female organs ) in the fourth floral whorl [7] . The combinatorial genetic interaction of floral homeotic genes may involve the formation of multimeric transcription factor complexes that also include class E ( or SEPALLATA ) proteins , as outlined by the ‘floral quartet’ model [8] . In Antirrhinum , there are two different class B genes termed DEFICIENS ( DEF ) and GLOBOSA ( GLO ) . In Arabidopsis these genes are represented by APETALA3 ( AP3 ) , the putative orthologue of DEF , and PISTILLATA ( PI ) , the putative GLO orthologue . For simplicity , we will refer to DEF-like and GLO-like genes from here on . DEF-like and GLO-like genes represent paralogous gene clades that originated by the duplication of a class B gene precursor 200–300 million years ago [9] , [10] . All class B genes identified so far , like most other floral homeotic genes , belong to the family of MADS-box genes , encoding MADS-domain transcription factors [11] , [12] . Mutant phenotypes reveal that DEF-like and GLO-like genes are essential for the development of petals and stamens , since def and glo loss-of-function mutants all produce flowers with petals converted into sepals and stamens transformed into carpels [13]–[17] . When co-expressed in the context of a flower , DEF and GLO are not only required , but even sufficient for specifying petal and stamen identity , as revealed by transgenic studies ( e . g . , [18] ) . Induction and stable maintenance of switch-gene expression are typically two independent processes , depending on a transient external signal and autoregulation , respectively [19] . Whenever a transient activating signal is above a threshold , the gene activity switches from the OFF- to the ON-state . The signal is required only for initiation , but not for maintenance of gene activity . Due to the autoregulation , the gene's response becomes in a wide range independent of the exact strength of the input signal . During later stages of flower development ( in Arabidopsis from stage 5 on ) , mRNA of DEF- and GLO-like genes is detected only in whorls 2 and 3 [15] , [16] . This is so because upregulation and maintenance of class B gene expression in Arabidopsis and Antirrhinum during later stages of flower development depends on both DEF and GLO , due to an autoregulatory loop involving these proteins ( Figure 1C ) . The proteins encoded by class B genes of Arabidopsis and Antirrhinum are stable and functional in the cell only as heterodimers , i . e . , DEF-GLO complexes , because both nuclear localization and sequence-specific DNA-binding depend on obligate heterodimerization [19] , [20] . Class B protein heterodimers bind to specific cis-regulatory DNA sequence elements termed ‘CArG-boxes’ ( consensus 5′-CC ( A/T ) 6GG-3′ ) . Except PI , the promoter regions of all class B genes of Arabidopsis and Antirrhinum contain CArG-boxes that are involved in positively regulating class B gene expression [21]–[23] . These data , together with the total functional interdependence of the two class B gene paralogues , strongly corroborate the hypothesis that positive autoregulatory control of class B genes involves heterodimers of class B proteins that bind to CArG-boxes in the promoters of class B genes ( Figure 1C ) [14] . Since PI lacks CArG-boxes in a minimal promoter region , the autoregulatory feedback may work indirectly in this case [23] , [24] . Obligate heterodimerization of their encoded products involved in positive autoregulation explains why DEF-like and GLO-like genes are functionally non-redundant and totally interdependent . This raises the question as to how and why such a regulatory system originated in evolution . Studies on the interaction of class B protein orthologues from diverse gymnosperms and angiosperms suggested that , following a gene duplication within the class B gene clade , obligate heterodimerization evolved in two steps from homodimerization via facultative heterodimerization [25] . Meanwhile obligate heterodimerization of DEF-like with GLO-like proteins has also been observed outside of the eudicots Arabidopsis and Antirrhinum in diverse groups of monocots , suggesting that it originated quite early or several times independently during angiosperm evolution [26] . So why then did obligate heterodimerization evolve ? In principle , it could represent a neutral change in protein-protein interactions that occurred by random genetic drift [25] . This cannot be excluded at the moment , but for several reasons , it appears not very likely . Even though obligate heterodimerization originated early or several times independently within class B proteins , it did not occur in any other class of floral homeotic proteins , suggesting some kind of functional specificity . Moreover , it occurs within evolutionary especially ‘successful’ ( e . g . , species-rich ) groups of angiosperms , suggesting that it might provide some selective advantage . Winter et al . [25] suggested that obligate heterodimerization in combination with autoregulation may have provided a selective advantage because of the fixation of class B gene expression patterns and thus the spatial domain of the floral homeotic B-function within the flower during evolution . Mutational changes in the promoter region of only one class B gene that expand the gene's expression domain may leave the late and functionally especially relevant expression domain of the class B genes unchanged , because expression of the other partner would be missing in the ectopic expression domain . Only parallel changes in both types of class B genes , which are much less likely than changes in single genes , could lead to ectopic expression of the B-function under the assumption of obligate heterodimerization and strong autoregulation . Thus obligate heterodimerization may have evolved in parallel , or even as a prerequisite , of the canalization of floral development and thus standardization of floral structure in some groups of flowering plants [25] . Amending this ‘evolutionary’ explanation of obligate heterodimerization , we put forward and test a set of stochastic in silico models of class B gene and protein interactions as shown in Figure 1 , thus testing the hypothesis that obligate heterodimerization also provides advantages during development by providing robustness against wrong cell-fate decisions caused by stochastic noise . The models enabled us to study the influence of noise in isolation from other factors , and allowed the comparison of three major stages in the envisioned path of evolutionary transitions ( Figure 1 ) : ( A ) One ancestral gene positively regulates its transcription via a homodimer of its own gene product; ( B ) Two genes positively regulate their transcription via homo- and heterodimers of both types of products; this very likely represents the situation directly after duplication of the ancestral gene; ( C ) Obligate heterodimerization of the two products for regulation , i . e . , the situation in extant Arabidopsis and Antirrhinum . Since only a small number of individual transcription factors is actually in the nucleus at any time [24] , [25] , stochastic fluctuations play a large role in the behavior of gene regulatory circuits , and may have an influence on their evolutionary dynamics [5] , [27] . Each model consists of a set of reactions for transcription factor binding , transcription , dimerization , and decay ( Table S1 ) , where translation is modeled in one step together with dimerization for efficiency ( details in Methods section ) . In turn , each reaction is associated with a propensity function ( Tables S2 and S3 ) , which yields the probability of an occurrence of that reaction in a time step . Using the Gillespie algorithm [28] , the exact order and timing of reactions is then stochastically determined , based on the propensities . To model transient activation of the circuits , we simulate an inflow of activating molecules ( summarizing all different activating transcription factors other than DEF/GLO that act on the respective genes ) over 50 minutes of simulated time . After this time , the inflow is switched off and the system equilibrates , i . e . , reaches a state in which no change occurs except for stochastic fluctuations ( always reached after 72 hours of simulated time ) . If at this point gene product dimers are still present , the circuit is considered as active ( full expression ) , otherwise it is inactive ( no expression of class B genes ) . Linear stability analysis of the corresponding differential equation system reveals that both the active and the inactive state constitute stable fixed points in all three systems , with an unstable fixed point in between ( data not shown ) .
The activation of the DEF and GLO genes depends on a temporally limited concerted action of many more genes and proteins besides the class B genes themselves , which have been described from an evo-devo perspective [12] and by mathematical modeling [29] . To keep the focus on the self-regulation of the genetic switch , we summarize these in one common or two distinct activators for both genes , respectively . In the first experiment we used a common regulator to temporally activate both genes , and investigated the switching behavior of the three circuits with regard to the number of available activatory input molecules . Looking at the probability of reaching full expression ( Figure 2A ) , the most probable state in the one-gene circuit switches from no steady-state expression ( resulting in a non-class B cell identity ) to full expression ( class B , i . e . , petal or stamen cell ) at approximately 10 input molecules . Gene duplication without further mutational changes leads to a 3 times lower switching threshold ( Figure 2A ) , which may entail a drastically increased zone of class B gene expression in the flower . Mutations leading to obligate heterodimerization again increase the activation threshold to the previous level , thus restoring the class B gene expression region ( Figure 2A ) . Therefore , in contrast to the facultative heterodimerization circuit , obligate heterodimerization results in the same switching threshold and thus the same domain of expression as just one autoregulatory gene . This result is in contradiction to an intuitive expectation that two genes can produce twice as many dimers as a single gene . With obligate heterodimerization , however , the heterodimers assemble from translated products of one DEF and one GLO mRNA intermediate , while the homodimer in the one-gene system is produced from two translated proteins of the same type . Because mRNA is not used up in translation , this leads to equal production rates for the heterodimer in the obligate heterodimerization system and the homodimer in the one-gene system . To look at the robustness of the switching decision against stochastic noise , we calculated the decision uncertainty ( binary entropy ) , thus more uncertainty implies less robustness . Focusing on the two circuits with identical expression domains , this uncertainty is nearly equal in the first and third circuit for small numbers of activatory input molecules , until the peak of uncertainty is reached . In contrast , the probability for a decision against class B gene mediated cell identity despite large numbers of activatory input molecules is significantly higher in the one-gene circuit than in the circuit with obligate heterodimerization . With 60 activatory molecules , the probability for such a ‘false negative’ in the former circuit is still 10% , while the latter one achieves nearly 100% correct decisions under our conditions ( Figure 2B ) . Hence , comparing one autoregulatory class B gene with the circuit after duplication and reduction to obligate heterodimerization , our model suggests that an important difference lies in the response to larger numbers of activatory molecules , where the latter system exhibits a clearly reduced tendency to switch off by mistake . This is explained by the fact that although the circuit needs both DEF-like and GLO-like proteins to sustain activation , its two pools of gene products provide a buffer to temporary stochastic failure of one of the two genes . This is especially important during the initial phase of activation , where circuits that are supposed to lock themselves into permanent expression are susceptible to a run of ‘bad luck’ , i . e . , the supposedly-active genes are inactive over a longer period of time . Obligate heterodimerization of gene products therefore provides a way to gain robustness against wrong cell identity decisions while retaining the original expression domain of one autoregulatory gene . Even though the mechanisms of the initial activation of DEF-like and GLO-like genes appear to be quite similar , they are very likely not identical [23] , since the initial expression patterns of DEF- and GLO-like genes are slightly different . In Arabidopsis flowers at an early developmental stage 3 , AP3 ( DEF-like ) is expressed in the organ primordia of whorls 2 and 3 , but also in parts of whorl 1 , while PI ( GLO-like ) is expressed in whorls 2–4 at the same stage [15] , [16] . In contrast , the AP3 orthologue DEF is expressed weakly in the organ primordia of whorl 4 ( carpels ) and very weakly in those of whorl 1 ( sepals ) , while the PI orthologue GLO is expressed in sepal but not carpel primordia of early stages during Antirrhinum flower development [14] , [19] . To investigate the consequences of independent input into both genes , we explored a model setting in which the DEF-like and the GLO-like gene are activated independently by two input signals . Our experiments showed that immediately after gene duplication , the mode of integration represents a logical ‘OR’ , meaning that both inputs can independently switch on the circuit ( Figure 3A ) . In this case , each input has the role of the one input present before duplication . After the transition to obligate heterodimerization , a logic ‘AND’ function is achieved ( Figure 3B ) , thus both inputs are needed for activation . In conclusion , we are providing here , to the best of our knowledge , the first rationale , developmental genetic explanation for the intricate design of a genetic switch controlling class B floral homeotic gene expression in core eudicots , involving obligate heterodimerization and positive autoregulatory feedback of two duplicate genes or their protein products , respectively . The increased robustness against unwanted deactivation by chance found in case of obligate heterodimerization strongly suggests that this mechanism has a distinct advantage when the number of available regulatory molecules is small , leading to less cells of wrong identity in a floral organ and therefore to sharper organ identity transitions . It should be noted that since the mathematical model applies to any system with obligate heterodimerization and positive feedback , the conclusions drawn here also transfer to any such system . However , to the best of our knowledge , the phenomenon of obligate heterodimerization together with positive feedback seems quite rare in genetic regulation outside of flower development , potentially due to the high cost of maintaining this system together with a strong dependence of the predicted fitness gain on external factors that might be specific for the situation depicted here . In the standard ABC model , class A and C genes are mutually antagonistic [7] , [30] , while class B genes have no floral homeotic ‘repressor’ , possibly explaining the class-specific need for sharpened expression domains and thus obligate heterodimerization , which is not found in the other two gene classes . However , Zhao et al recently reported that the antagonistic expression of class A and class C genes is involved in defining the expression domain of class B genes in Arabidopsis [31] , suggesting that our observation may not be sufficient to explain the obligate heterodimerization of class B proteins . Taking a different perspective , the evolution of a regulatory ‘AND’ function out of an ‘OR’ function may have provided the plant with a more stringent control of the class B floral homeotic genes depending on different induction signals . The fact that there must be different inputs into DEF- and GLO-like genes is obvious from gene expression studies ( see above ) , but its functional importance may have escaped the attention of previous investigations because of the coordinate upregulation and functional importance of DEF- and GLO-like genes in the second and third floral whorl . Our results suggest that identifying these different induction pathways , and clarifying their molecular mechanisms ( e . g . , trans-acting factors and cis-regulatory DNA motifs in DEF-like and GLO-like genes being involved ) would enable an important step forward in understanding class B floral homeotic gene function in flowering plants . The functional implication of these different input signals , and hence also of our hypothesis , could be tested by transgenic experiments . For example , Arabidopsis class B gene mutants in which both the AP3 and the PI gene have been brought under the control of the AP3 or the PI promoter rather than every gene under its own promoter ( as in the wild-type ) should affect the spatial or temporal development of petals or stamens , or both . Transgenic plants mutated at the pi locus ( pi-1 ) in which wild-type PI is expressed under control of the AP3 promoter ( 5D3 ) have already been reported [32] . These plants were used only as control for other experiments and have therefore not been described in much detail concerning the traits of interest here . However , it is clear that the 5D3::PI pi-1/pi-1 plants do not just show petals in the second floral whorl and stamens in the third floral whorl , as wild-type plants do; rather , they frequently develop sepal/petal mosaics in the second whorl , and mosaic organs or even carpels in the third whorl . These observations support our hypothesis concerning the functional importance of different induction pathways controlling the expression of DEF- and GLO-like genes for a proper development of organ identity in whorls two and three . More detailed analyses should be done to better understand how exactly the transgenic plants deviate from wild-type plants , and why . In addition , complementary transgenic studies in which AP3 is expressed under control of the PI gene promoter ( pPI ) should be performed in order to determine whether the pPI::AP3 ap3/ap3 plants have also developmental defects . The construction of a transgenic plant with switched promoters ( i . e . , pAP3::PI pPI::AP3 ap3/ap3 pi/pi ) would also be of great interest . Due to the apparently symmetric roles of AP3 and PI , one might speculate that this phenotype shows less deviation from the wild type than the transgenic plants with both genes under the control of a single promoter . If the origin of obligate heterodimerization of class B proteins during evolution provided some plants with selective advantages , one may expect that this had an impact on the molecular evolution of these proteins , which indeed seems to be the case . Class B floral homeotic proteins are MIKC-type MADS-domain proteins characterized by a defined domain structure , including a MADS ( M ) , Intervening ( I ) , Keratin-like ( K ) and a C-terminal ( C ) domain [11] , [12] . The K-domain mediates heterodimerization of GLO- and DEF-like proteins and has been postulated to fold into three amphipatic α-helices termed K1 , K2 and K3 [33] . In accordance with the expectations mentioned above , phylogenetic data indicate that after the duplication leading to DEF-like and GLO-like gene lineages , positive selection acted on the sections of these genes encoding the K-domain [34] . Intriguingly , one site under positive selection [34] is in a subdomain of K1 ( “position 97-102” according to ref . [33] ) proposed to be critical for heterodimerization specificity of DEF- and GLO-like proteins , as revealed by yeast two-hybrid analyses [33] . Given that the duplicates resulting from one homodimerizing protein would be capable of homo- as well as heterodimerization , our results suggest that positive selection should have enforced the loss of the homodimerization ability , since our model with duplicated class B genes and obligatory heterodimerization implies a sharper switching characteristic and a more constrained domain of class B gene expression than the one with facultative heterodimerization . It has been proposed that within the subdomain of K1 mentioned above , the interaction of Glu-97 in PI and Arg-102 in AP3 facilitates specific heterodimerization between AP3 and PI and prevents formation of homodimers [33] . For these sites , however , positive selection has not been detected [34] . Clearly , the relationships between the molecular evolution and biophysical interactions of DEF- and GLO-like proteins deserve more detailed studies in the future . All in all , our findings strongly support the view that the unexpected complexity of the floral homeotic gene switch considered here was not simply produced by random genetic drift but evolved because it provided the plant with a clear selective advantage . This might have led to the establishment of this regulatory motif in a whole range of plant species . In line with this notion , it is intriguing that at least some basal angiosperms do not have sharp , but ‘fading borders’ of expression of orthologues of DEF-like and GLO-like genes as well as gradual transitions in organ identity [35] . This underlines the hypothesis [25] that the mechanism described here improves developmental robustness and thus helped to canalize the development and hence also the evolution of flowers within angiosperm evolution .
The model investigated in this work is simulated using the Gillespie algorithm [28] , implemented as a C++ function linked to MATLAB ( The MathWorks , Inc . 2008 ) . This method , which simulates an exact instance of the stochastic master equation , explicitly accounts for each reaction event and thus represents stochastic effects in full detail . A list of all modelled reactions is given in Table S1 , and the full model is shown in Figure S2 . Transcription factor binding and unbinding are simple reaction processes , where we assume that exactly one functional copy of both DEF and GLO genes are available . For simplicity , we assume that only activated DNA is transcribed; however , experiments with basal transcription rates have led to qualitatively similar results . The decision to model translation and dimerization in one step was taken to simplify the model while keeping the focus on transcriptional rather than translational regulation . This entails that we only model DEF and GLO mRNA and the dimerized proteins , but not the single DEF and GLO proteins . The slight loss of accuracy here has been unavoidable , as we needed to keep the model computationally tractable for the large numbers of replicated experiments . All constituents of the model decay with a linear rate . For details on all kinetic rate constants , see the Text S1 and Tables S1–S3 . We conducted 10 , 000 experiments for each parameter combination . The different types of regulation are achieved by enabling or disabling the binding and activation of one type of gene by either a transcription factor homodimer produced by itself , a heterodimer of the products of both genes , or a homodimer of the proteins encoded by the other gene . Concerning initial activation , the class B genes are regulated by a number of ( possibly interacting ) transcription factors , some of which are still unknown . Since the aim of this contribution is to investigate the effect of autoregulation on gene activity , we summarize the effects of all upstream transcription factors in two specific input factors , IDEF and IGLO , and a common input factor , IC . As developmental switches , the B-genes are transiently activated by their inputs , which are switched off after activation . Depending on the level of gene activity reached by that time , this activity either stays high or decays to a low value again , corresponding to on- and off-states of the genes . To model the transient activation , an inflow of ( on average ) N activatory molecules ( of type IDEF , IGLO or IC , respectively ) over a period of T minutes was simulated . After time T , the inflow is switched off and the system is left alone , reaching steady state . Figure S1 shows example time courses for all three modes of regulation considered here . All three systems investigated in this work represent autoactivatory circuits , which are used by the plant to establish the expression ( ON-state ) or non-expression ( OFF-state ) of homeotic genes in certain floral whorls . Therefore , a decision has to be made , depending on the number of activatory input molecules initially coming into the system . For low numbers of input molecules , the decision should be ‘OFF’ , for higher numbers it should be ‘ON’ . To measure the uncertainty of this decision , we use the binary entropy function . Let X be a random variable that takes value 1 with probability p , value 0 with probability 1−p , i . e . , a Bernoulli trial . The entropy of X is defined asIn our case , X taking value 1 means that the system reaches ON-state , value 0 means OFF-state . Repeating the simulation 10 , 000 times , we compute the probability p for each specific number N of activatory input molecules IC ( Figure 2A ) . Using the formula above , this translates to the binary entropy , or decision uncertainty ( Figure 2B ) . Alternative approaches which could potentially lead to additional insights into the functionality of the DEF-GLO system include the application of control theory [36] or an analytical calculation of the first and second stochastic moments , which should confirm the experimental results in this paper . | The development of organs , their position , and boundaries in multicellular organisms are defined by genes that can sustain their own activation over long periods of time , termed genetic switches . A good case in point is provided by the genetic machinery controlling the development of flowers in higher plants . In Arabidopsis thaliana and other plants , a particular class of these genes—DEF-like and GLO-like floral homeotic genes—regulates the development of petals and stamens . These genes are self-activating via a heterodimer of their protein products , making the activity of each one of them fully bound to the activity of the other one . The reason for their total functional interdependence has long remained unclear , as the expression of both genes is jointly controlled by shared transcription factors in addition to the heterodimer . In principle , one gene alone could provide their switching functionality . In this study , we use computer modeling to show that the obligate heterodimerization mechanism found in DEF- and GLO-like genes reduces the susceptibility of the genetic switch to failure caused by stochastic noise . This would have provided the system an evolutionary advantage over a single gene with the same functionality . |
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Inferior olivary activity causes both short-term and long-term changes in cerebellar output underlying motor performance and motor learning . Many of its neurons engage in coherent subthreshold oscillations and are extensively coupled via gap junctions . Studies in reduced preparations suggest that these properties promote rhythmic , synchronized output . However , the interaction of these properties with torrential synaptic inputs in awake behaving animals is not well understood . Here we combine electrophysiological recordings in awake mice with a realistic tissue-scale computational model of the inferior olive to study the relative impact of intrinsic and extrinsic mechanisms governing its activity . Our data and model suggest that if subthreshold oscillations are present in the awake state , the period of these oscillations will be transient and variable . Accordingly , by using different temporal patterns of sensory stimulation , we found that complex spike rhythmicity was readily evoked but limited to short intervals of no more than a few hundred milliseconds and that the periodicity of this rhythmic activity was not fixed but dynamically related to the synaptic input to the inferior olive as well as to motor output . In contrast , in the long-term , the average olivary spiking activity was not affected by the strength and duration of the sensory stimulation , while the level of gap junctional coupling determined the stiffness of the rhythmic activity in the olivary network during its dynamic response to sensory modulation . Thus , interactions between intrinsic properties and extrinsic inputs can explain the variations of spiking activity of olivary neurons , providing a temporal framework for the creation of both the short-term and long-term changes in cerebellar output .
A multitude of behavioral studies leave little doubt that the olivo-cerebellar system organizes appropriate timing in motor behavior [1–3] , perceptual function [4–6] and motor learning [7–10] . Furthermore , the role of the inferior olive in motor function is evinced in ( permanent and transient ) clinical manifestations , such as tremors , resulting from olivary lesions and deficits [11–16] . Although the consequences of olivary dysfunctions are rather clear , the network dynamics producing functional behavior are controversial . At the core of the controversy is the question whether inferior olive cells are oscillating during the awake state and whether these oscillations affect the timing of the inferior olivary output [17–19] . The inferior olive is the sole source of the climbing fibers , the activity of which dictates complex spike firing by cerebellar Purkinje cells ( for review , see [20] ) . Climbing fiber activity is essential for motor coordination , as it contributes to both initiation and learning of movements [8 , 10 , 21–26] , and it may also be involved in sensory processing and regulating more cognitive tasks [27–30] . Understanding the systemic consequences of inferior olivary spiking is therefore of great importance . The dendritic spines of inferior olivary neurons are grouped in glomeruli , in which they are coupled by numerous gap junctions [10 , 31–33] , which broadcast the activity state of olivary neurons . Due to their specific set of conductances [34–40] , the neurons of the inferior olive can produce subthreshold oscillations ( STOs ) [41–43] . The occurrence of STOs does not require gap junctions per se [44] , but the gap junctions appear to affect the amplitude of STOs and engage larger networks in synchronous oscillation [10 , 16 , 42] . Both experimental and theoretical studies have demonstrated that STOs may mediate phase-dependent gating where the phase of the STO helps to determine whether excitatory input can or cannot evoke a spike [45 , 46] . Indeed , whole cell recordings of olivary neurons in the anesthetized preparation indicate that their STOs can contribute to the firing rhythm [42 , 43] and extracellular recordings of Purkinje cells in the cerebellar cortex under anesthesia often show periods of complex spike firing around the typical olivary rhythm of 10 Hz [17 , 47–49] . However , several attempts to capture clues to these putative oscillations in the absence of anesthesia have , so far , returned empty handed [19 , 50] . It has been shown that in the anesthetized state both the amplitude and phase of the STOs can be altered by synaptic inputs [10] . Inhibitory inputs to the inferior olive originate in the cerebellar nuclei and have broadly distributed terminals onto compact sets of olivary cells [51–53] . Excitatory terminals predominantly originate in the spinal cord and lower brainstem , mainly carrying sensory information , and in the nuclei of the meso-diencephalic junction in the higher brainstem , carrying higher-order input from the cerebral cortex ( Fig 1A ) [15 , 54 , 55] . In addition , the inferior olive receives modulating , depolarizing , level-setting inputs from areas like the raphe nuclei [55] . Unlike most other brain regions , the inferior olive is virtually devoid of interneurons [56 , 57] . Thus , the long-range projections to the inferior olive in conjunction with presumed STOs and gap junctions jointly determine the activity pattern of the complex spikes in Purkinje cells . How these factors contribute to functional dynamics of the inferior olive in awake mammals remains to be elucidated . Here , we combine recordings in awake mice–in the presence and absence of gap junctions–with network simulations using a novel inferior olivary model to study the functional relevance of STOs in terms of resonant spikes . We are led to propose a view of inferior olivary function that is more consistent with the interplay between STOs , gap junctions and inputs to the inferior olive . Rather than acting as a strictly periodic metronome , the inferior olive appears more adequately described as a quasiperiodic ratchet , where cycles with variable short-lasting periods erase long-term phase dependencies .
To study the conditions for , and consequences of , rhythmic activity of the inferior olive , we made single-unit recordings of cerebellar Purkinje cells in lobules crus 1 and crus 2 ( n = 52 cells in 16 awake mice ) in the presence and absence of short-duration ( 30 ms ) whisker air puff stimulation ( Fig 1B and 1C ) . In the absence of sensory stimulation , the complex spikes of 35% of the Purkinje cells ( 18 out of 52 ) showed rhythmic activity ( Fig 2A–2C; S1 Fig ) with a median frequency of 8 . 5 Hz ( inter-quartile range ( IQR ) : 4 . 7–11 . 9 Hz ) . Upon sensory stimulation , 46 out of the 52 cells ( 88% ) showed statistically significant complex spike responses . Of these , 31 ( 67% ) had sensory-induced rhythmicity ( Fig 2D–2F ) , which was a significantly larger proportion than during spontaneous behavior ( p = 0 . 002; Fisher’s exact test ) . The median frequency of the oscillatory activity following stimulation was 9 . 1 Hz ( IQR: 7 . 9–13 . 3 Hz ) . Hence , the preferred frequencies in the presence and absence of sensory stimulation were similar ( p = 0 . 22; Wilcoxon rank sum test ) ( Figs 2C , 2F and 3 ) . The duration of the enhanced rhythmicity following stimulation was relatively short in that it lasted no more than 250 ms . With our stringent Z-score criterion ( >3 ) , only a single neuron showed 3 consecutive significant peaks in the peri-stimulus time histogram ( PSTH ) . The minimum inter-complex spike interval ( ICSI ) across cells was around 50 ms , putatively representing the refractory period . We conclude that complex spikes also display rhythmicity in awake behaving mice , and that sensory stimulation can amplify these resonances in periods of a few hundred milliseconds , even though stimulation is not required for the occurrence of rhythmicity per se . The pattern of rhythmic complex spike responses that was apparent for a couple of hundred milliseconds after a particular air puff stimulus repeated itself in a stable manner across the 1 , 000 trials ( applied at 0 . 25 Hz ) during which we recorded ( Fig 4A and 4B ) . For example , the level of rhythmicity of the first 100 trials was not significantly different from that during the last 100 trials ( comparing spike counts in first PSTH peak , p = 0 . 824; χ2 test , or latency to first spike , p = 0 . 727 , t test ) . This strongly indicates that there is–in a substantial fraction of the Purkinje cells–persistent oscillatory gating of the probability for complex spikes after a sensory stimulus resulting in time intervals ( “windows of opportunity” ) during which complex spikes preferentially occur ( Fig 4B–4E ) . These windows of opportunity become even more apparent when sorting the trials on the basis of response latency: the first complex spikes with a long latency following the stimulus align with the second spikes of the short latency responses . Similarly , there are trials during which complex spikes appear only at the third cycle ( Fig 4C , seen as a steeper rise around trial no . 650 ) . The occurrence of spikes during later cycles , not predicated on prior spikes , argues against refractory periods or rebound spiking as the sole explanations for such rhythmic firing [58] and highlight the putative existence of network-wide coherent oscillations . Since sensory stimulation of the whiskers can trigger a reflexive whisker protraction [59–61] and complex spike firing is known to correlate with the amplitude of this protraction [61] , we examined the relation between periodic complex spike firing and whisker protraction . To this end , we further analyzed an existing dataset of simultaneously recorded Purkinje cells and whisker movements during 0 . 5 Hz air puff stimulation of the ipsilateral whisker pad . In line with our previous findings [61] , trials during which a single complex spike occurred within 100 ms of whisker pad stimulation had on average a slightly , but significantly stronger protraction ( from 6 . 1 ± 5 . 4° to 6 . 8 ± 5 . 3° ( medians ± IQR ) , n = 35 Purkinje cells , p = 0 . 033 , Wilcoxon-matched pairs test after Benjamini-Hochberg correction; S2A–S2C Fig ) . Our new analysis revealed that also the occurrence of a second complex spike was correlated with a stronger whisker protraction . This could be observed as a second period of increased protraction during trials with two complex spikes . When compared to the increase in trials with a single complex spike , this second protraction was highly significant ( p < 0 . 001 , Wilcoxon-matched pairs test after Benjamini-Hochberg correction; S2D Fig ) . The second complex spike was unlikely a mere reflection of stronger protraction following the first complex spike , as there was no difference in whisker protraction between the trials that had a complex spike during the first , but not the second 100 ms after stimulus onset , and the trials with the opposite pattern ( a complex spike during the second , but not the first 100 ms; p = 0 . 980 , Wilcoxon-matched pairs test after Benjamini-Hochberg correction ) . The rhythmic firing pattern of complex spikes was thus reflected in the behavioral output of mice . The existence of windows of opportunity for complex spike activity is compatible with the assumption of an underlying STO , and cannot solely be explained by rebound activity without invoking circuit-wide extrinsic mechanisms . To test the implications of assuming olivary STOs , we proceeded to reproduce a detailed network with a tissue-scale computer model of the inferior olive neuropil . The model is constituted by 200 biophysically plausible model cells [40 , 46 , 62] embedded in a topographically arranged 3D-grid ( Fig 5A–5C ) . It has the scale of a sheet of olivary neurons of about 10% of the murine principal olivary nucleus ( cf . [63] ) . The model was designed to test hypotheses about the interaction between intrinsic parameters of olivary neurons , such as STOs and gap junctional coupling , and extrinsic parameters including synaptic inputs during the generation of complex spike patterns . Each neuron in the model is composed of a somatic , an axonal and a dendritic compartment , each endowed with a particular set of conductances , including a somatic low threshold Ca2+ channel ( Cav3 . 1; T-type ) , a dendritic high threshold Ca2+ channel ( Cav2 . 1; P/Q-type ) and a dendritic Ca2+-activated K+ channel , chiefly regulating STO amplitudes , while a somatic HCN channel partially determines the STO period ( Fig 5B; see also Methods ) . The dendrites of each neuron are connected to the dendrites of , on average , eight nearby neighbors ( within a radius of three nodes in the grid , representing a patch of about 400 μm x 400 μm of the murine inferior olive ) , simulating anisotropic and local gap junctional coupling ( Fig 5C ) . As the inferior olive itself , our model has boundaries which have impact on local connectivity characteristics , such as the clustering coefficient , though these did not have significant impact on the average firing rate between edge and center cells ( p = 0 . 812 , comparing edge and center cells , Kolmogorov-Smirnov test; S3 Fig ) . The coupling coefficient between model cells varied between 0 and 10% , as reported for experimental data [45 , 64 , 65] . Sensory input was implemented as excitatory synaptic input , simulating the whisker signals originating from the sensory trigeminal nuclei that were synchronously delivered to a subset of model neurons . Additionally , a “contextual input” was implemented as a combination of inhibitory feedback from the cerebellar nuclei and a level setting modulating input ( Figs 1A and 5A ) . This contextual input is modeled after an Ohrstein-Uhlenbeck process , essentially a random exploration with a decay parameter that imposes a well-defined mean yet with controllable temporal correlations ( see Methods ) . The amplitude of the contextual input drives the firing rate of the model neurons , which we set around 1 Hz ( S4 Fig ) , corresponding to what has been observed in vivo [28 , 43 , 66] . Thus , our model network recapitulates at least part of the neural behavior observed in vivo due to biophysically plausible settings of intrinsic conductances , gap junctional coupling and synaptic inputs . Whether a model neuron at rest displays STOs or not is largely determined by its channel conductances . Activation of somatic T-type Ca2+ channels can trigger dendritic Ca2+-dependent K+ channels that can induce Ih , which in turn can again activate T-type Ca2+ channels , and so forth . This cyclic pattern can cause STOs that could occasionally produce spikes ( Fig 5B ) . In our model , the conductance parameters were randomized ( within limits , see Methods ) so as to obtain an approximate 1:3 ratio of oscillating to non-oscillating cells ( S5 Fig ) guided by proportions observed in vivo [43] . Sensitivity analysis with smaller ratios ( down to 1:5 ) did not qualitatively alter the results ( data and analyses scripts are available online in https://osf . io/9hmpy/ ) . In the absence of contextual input , model neurons were relatively silent , but when triggered by sensory input , as occurred in our behavioral data ( Figs 2 and 4 ) , STOs synchronized by gap junctions would occur for two or three cycles ( Fig 6A and 6B ) . Our network model confirms that gap junctional coupling can broaden the distribution of STO frequencies and that even non-oscillating cells may , when coupled , collectively act as oscillators ( S6 Fig ) [67] . Adding contextual input to the model network can lead to more spontaneous spiking in between two sensory stimuli . Compared to the situation in the absence of contextual input , the STOs are much less prominent and the post-spike reverberation is even shorter ( Fig 6C ) . Accordingly , despite the significant levels of correlation in the contextual input ( 10% ) , the periods between oscillations are more variable due to the interaction of the noisy current and the phase response properties of the network . In addition , in the presence of contextual input our model could readily reproduce the appearance of preferred time windows for spiking upon sensory stimulation as observed in vivo ( Fig 6D–6F , cf . Fig 4 ) . This was particularly true for the model cells that directly received sensory inputs ( Fig 6G–6H ) . Moreover , the observed rhythmicity in model cells as observed in their STO activity was in tune with that of the auto-correlogram ( Fig 6D–6I ) in that the timing of the STOs and that of the spiking were closely correlated ( cf . Fig 5B ) . It should be noted though that model cells adjacent to cells directly receiving sensory input showed only a minor effect of stimulation . Thus , even though the gap junction currents in the model were chosen as the ceiling physiological value for the coupling coefficient ( ≤10% ) [45 , 67] , these currents alone were not enough to trigger spikes in neighboring cells . Both directly stimulated model cells and those receiving only contextual input exhibited phase preferences , seen in the spike-triggered membrane potential average as well as in the spike-triggered average of the input currents ( Fig 6G–6I ) . Spike-triggered averages of membrane potentials for any cell showed depolarization followed by hyperpolarization . In contrast , trials in which no spike was generated showed a depolarization just before the occurrence of the input . Similarly , the average of the input showed a long-lived phase preference , not only for a hyperpolarization before the spike , but also a preference for a depolarization in the previous peak of the STO , more than 100 ms earlier . These results are in line in vitro experiments under dynamic clamp and noisy input [68 , 69] . Likewise , the model indicates that for short durations STOs can induce clear phase dependencies for spiking , which fades under the variation of period durations dependent on the trial-specific contextual input ( as seen in our data ) . Depolarizing sensory input delivered onto a subset of the model cells can reset the STO phase in oscillatory cells and create resonant transients in others ( Fig 7A and 7B; see also S7 Fig on the appearance of rebound firing ) . If a second stimulus is delivered during this short-lasting transient , the response probability is increased . As in most cells with resonant short-lasting dynamics , inputs delivered during different phases can cause phase advances or delays . Hyperpolarization advanced the phase between 0−π and delayed the phase between π−2π , whereas depolarization had roughly the opposite effect , in addition to phase advancements with spikes in later cycles between π−2π ( Fig 7C–7E ) . Thus , there is a mutual influence of synaptic inputs and STOs on periodicity . While STOs can lead to phase-dependent gating , synaptic input can either modulate or reset the phase of the STOs , generating variable periods that range between 40–160 ms for the chosen amplitude of the contextual input ( Fig 8; S6 Fig ) . The only means of settling the question about the prevalence of STOs in awake and behaving mice would be intracellular recordings of inferior olivary neurons , which remains a daunting experimental challenge . We therefore looked for a less invasive method that could read out , from indirect and infrequent complex spikes , the presence or absence of STOs . We have developed one such paradigm inspired by auditory studies [70 , 71] using a rhythmic gallop stimulus that we first applied to the network model ( Fig 7F ) . In the gallop paradigm , stimuli are applied in quick succession with alternating intervals , comparable to the putative period of the underlying oscillation . Enough stimuli should be applied such that after multiple presentations the stimuli sample a uniform distribution of phases . In the context of auditory stimuli , the standard gallop experiment involves different tones and is used to test perceptual separation of auditory streams . Such rhythmic stimuli can help indicate resonances or physical limitations of the system , and distinguish across possible models for this separation ( such as in neural resonance theory [71] ) . One possible mechanism of auditory stream separation is an underlying oscillatory process which resets in certain phases and is less responsive in others . According to the in vitro inferior olive literature [41–43] this behavior is to be expected , and hence , such a stimulus can help distinguish underlying processes . If spikes are modulated by an oscillatory process , the presence of spikes on a short interval should be able to predict , in the next interval , the absence or presence of spikes . Indeed , if the underlying process producing spikes has oscillatory components and a relatively stable period , the probability of spikes in each interval is systematically different , which would appear as asymmetric ratios of response in the different intervals ( Fig 7F ) . This can be inspected as the length of the empty and filled vertical bars representing ratio of probabilities of spiking for long or short stimulation intervals ( Fig 7G ) . Thus , if the period of the STO rhythm would be regular and cause phase-dependent gating , complex spike responses following each stimulus interval are expected to show preferences for the short or long window of stimulation; these preferences were indeed observed ( Fig 7G , left ) . However , this clear phase dependency only appears in the noiseless model scenario . After adding a moderate amount of contextual input , this dependency washes off , rendering the responses in the two windows more symmetric ( Fig 7G , right ) , with only a few cells ( 5/200 ) displaying significant ratio differences ( tested against bootstrap with shuffled spikes ) . In line with the experimental in vivo data ( e . g . , Figs 2 and 4 ) , the olivary spike rhythmicity in the network model was steadily present over longer periods , and for a wide range of contextual input parameters ( S5 Fig ) . In addition , it also comprised , as in the experimental data , variations in frequency and amplitude during shorter epochs ( Figs 8 and S6 ) . Analysis of the network parameters indicates that these latter variations in oscillatory behavior can be readily understood by their sensitivity to both the amplitude ( parameter 'sigma' ) and kinetics ( parameter 'tau' , temporal decay ) of the contextual input . Indeed , because of the underlying Ornstein-Uhlenbeck process , the generation of contextual input converges to a specified mean and standard deviation , but in short intervals the statistics including the average network STO frequency can drift considerably ( Figs 6C and 8 ) . Since relatively small differences in oscillation parameters such as frequency can accumulate , they can swiftly overrule longer-term dependencies created by periodically resetting stimuli , as an analysis of phase distributions shows ( Fig 9 ) . Thus , based upon the similar outcomes of the network model and in vivo experiments , we are led to propose that ( 1 ) the STOs in the inferior olive may well contribute to the continuous generation of short-lasting patterns of complex spikes in awake behaving animals , and that ( 2 ) the synaptic input to the inferior olive may modify the main parameters of these STOs . Note that in the absence of input , periodic rhythmic behavior should be the default behavior of oscillating cells . Thus , in all likelihood , even if the inferior olive oscillates endemically , sustained but variable input should induce highly contextual spike responses to variable periods and render the olivary responses quasiperiodic , rather than regularly periodic as observed in reduced preparations . In line with in vivo whole cell recordings made under anesthesia [10 , 43] our awake data support the possibility that the moment of spiking may be related to the phase of olivary STOs , especially during the period of several hundred ms following stimulation ( Figs 2 and 4 ) . As discussed above , a gallop stimulus would expose such an oscillatory process underlying the response probabilities . Four idealized scenarios about the expected results can be constructed , as follows: first , one can start with a complete absence of STOs , which would result in a response probability unrelated to stimulus intervals; second , it could be that there were STOs , but no phase-dependent firing ( to be expected if the STO amplitude is small ) , which would also lead to complex spike firing irrespective of stimulus intervals; third , there could be STOs , but each stimulus would evoke a phase-reset , which again , would not lead to interval dependencies; and fourth , there could be STOs in combination with phase-dependent gating , which would result in a clear dependency of complex spike firing on the previous interval length ( Fig 7G , left panel ) . It should be noted that the large majority of studies on inferior olivary physiology , especially in reduced preparations , found evidence for the fourth situation ( STOs + phase-dependent gating ) [41 , 43 , 67] . To study whether phase-dependent gating in conjunction with an underlying oscillatory process could shape complex spike response timing in vivo we applied both a 250 vs . 400 ms and a 250 vs . 300 ms gallop stimulation using air puffs to the whiskers . Using only trials with a CS in the previous trial to calculate the ratio of responses ( 'conditional firing' ) a slight bias could be observed in the 250 vs . 400 ms paradigm ( Fig 10A ) and to a lesser extent in the 250 vs . 300 ms ( Fig 10B ) . Analysis including all trials ( 'non-conditional' ) is included in S8 Fig and shows no significant bias for any of the cells tested . Hence , our in vivo data are in line with the results from the network model subjected to synaptic noise , and show that the timing of complex spike responses to sensory stimulation is biased but not strongly determined by STOs . Our experimental data provided evidence for phase-dependent complex spike firing during brief intervals , but gallop stimulation did not expose a strong impact of STOs on complex spike response probabilities . Therefore , we sought an alternative approach to study the impact–if any–of STOs on complex spike firing in vivo . We reasoned that , if a sensory stimulus triggers a complex spike response with a certain probability , higher stimulation frequency should result in a proportional increase in complex spike firing . In particular , stimulus frequencies that would be in phase with the underlying STO would be expected to show signs of resonance and result in disproportionally increased complex spike firing . However , over periods of tens of seconds the complex spike frequency was resilient to varying the stimulus frequency between 1 and 4 Hz ( linear regression = -0 . 02; R2 = 0 . 1 ) ( Fig 10C ) and did not show signs of resonance with any of the stimulus frequencies , as there were no frequencies at which the complex spike firing was substantially increased . Only a very high rate of sensory stimulation ( 10 Hz ) , commensurable with the average duration of windows of opportunity , could induce a mild increase in complex spike firing frequency , albeit at the cost of a highly reduced response probability ( average increase: 71 ± 64% corresponding to an average increase from 1 . 12 Hz to 1 . 92 Hz; n = 5; p < 0 . 05; paired t test ) . This examination indicates that the average complex spike frequency is robust and stiff to modulation over longer time periods , imposing a hard limit on the frequency with which complex spikes can respond to sensory stimuli , confirming recent reports on complex spike homeostasis [72] . As stimulus triggered resonances were not observed at any of the stimulation frequencies , we turned to a more sensitive measure for the detection of oscillatory components in complex spike firing . We developed a statistical model that extrapolated from frequencies inferred through inter-complex spike intervals and stimulus triggered histograms ( Fig 11A and 11B ) . We reasoned that phase-dependent gating would imply that the interval between the last complex spike before and the first one after sensory stimulation aligns to the preferred frequency . In contrast , if sensory stimulation would typically evoke a phase reset , as suggested by our network model ( Fig 7 ) , no such relation would be found . The method was applied only to Purkinje cells with highly rhythmic complex spike firing . For each of those , we calculated their preferred frequency in the absence ( Fig 11C ) or presence of sensory stimulation ( Fig 11D ) . We used that frequency to construct statistical models representing idealized extremes of phase-dependent ( oscillatory ) and -independent ( uniform ) responses . For the oscillatory component we employed an oscillatory gating model , where the timing of the first complex spike after stimulus onset would be in-phase with the ongoing oscillation . This model was contrasted to a linear response model in which sensory stimulus could evoke a complex spike independent of the moment of the last complex spike before that stimulus , apart from a refractory period . For each Purkinje cell , we compared the distribution of the intervals between the last complex spike before and the first complex spike after stimulus onset with the predicted distributions based on the linear model , the oscillatory model and nine intermediate models , mixing linear and oscillatory components with different relative weights ( Fig 11A–11E ) . For the two extreme models as well as for the nine intermediate models we calculated a goodness-of-fit per Purkinje cell . Overall , when using these relatively long periods ( 300 ms ) , the linear model was superior to the oscillatory model , although a contribution of the oscillatory model could often improve the goodness-of-fit ( Fig 11F–11H ) . Despite the apparent failure of the oscillatory model to fit the data , the data did show an oscillatory profile for many of the cells ( Fig 11E ) . This lends support to our observations that short-lived , but reliable , oscillations are apparent in complex spike timing , although they have little impact on the timing or probability of sensory triggered CS responses . Apart from the STOs , extensive gap junctional coupling between dendrites is a second defining feature of the cyto-architecture of the inferior olive [31 , 33 , 73] . Absence of these gap junctions leads to relatively mild , but present deficits in reflex-like behavior and learning thereof [10 , 74] . We analyzed the inter-complex spike interval times in Purkinje cells of mutant mice that lack the Gjd2 ( Cx36 ) protein and are hence unable to form functional gap junctions in their inferior olive [69] . In line with the predictions made by our network model ( Figs 5H and S6 ) , the absence of gap junctions did not quench rhythmic complex spike firing during spontaneous activity ( Fig 12A ) . In fact , the fraction of Purkinje cells showing significant rhythmicity in the Gjd2 KO mice was larger than that in the wild-type littermates ( Gjd2 KO: 38 out of 65 Purkinje cells ( 58% ) vs . WT: 15 out of 46 Purkinje cells ( 33% ) ; p = 0 . 0118; Fisher’s exact test ) , with their average rhythmicity being significantly stronger ( p = 0 . 003; Kolmogorov-Smirnov test ) , measured by Z-scores of side peaks ( Fig 12B ) . Indeed , the variation in oscillatory frequencies across Purkinje cells of the mutants was significantly less than that in their wild-type littermates in that the latency to peak times per Purkinje cell were less variable ( p = 0 . 0431; Mann-Whitney test; Fig 12C ) . This latter finding is at first sight contradictory to our findings in the network model , where we show that gap junctions promote more uniform firing rates through increased synchrony between neurons ( Fig 5H ) . These simulations were run in the absence of synaptic input , though . Addition of contextual input also creates more variability in the wild type cells ( S6B Fig ) . As the lack of gap junctions increases cell excitability [10 , 69] , it is likely that synaptic input has a larger impact in the absence of gap junctions , leaving less room for inter-cell heterogeneity . Overall , removal of gap junctions affected the temporal and spatial dynamics by increasing the stereotypical rhythmicity of complex spike firing . We made paired recordings of Purkinje cells in awake mice to study the temporal relations of their complex spikes during spontaneous activity . The cell pairs were recorded with two electrodes randomly placed in a grid of 8 x 4 , with 300 μm between electrode centers . For each pair of simultaneously recorded Purkinje cells , we made a cross-correlogram . The median number of complex spikes in the reference cell used for these cross-correlograms was 827 ( range: 74–2174 ) . Cell pairs showed coherent activity in that they could show a central peak and/or a side peak in their cross-correlogram ( Fig 13A–13C ) . The side peaks could appear at different latencies , similar to the range observed in auto-correlograms of single Purkinje cells ( cf . Fig 2B ) . Moreover , Purkinje cell pairs that did not produce signs of synchronous spiking in the center peak could still produce an “echo” in the side peak after 50–150 ms . Counter-intuitively , cross-correlograms of Purkinje cell pairs of the wild type mice showed less often a significant center peak than those of Gjd2 KOs ( WT: 51 out of 96 pairs ( 53%; N = 4 mice ) ; Gjd2 KO: 44 out of 61 pairs ( 72%; N = 7 mice ) ; p = 0 . 0305; Fisher’s exact test ) . In line with the more stereotypic firing observed in single cells in the absence of gap junctions ( Fig 12 ) , the strength of the center peak was on average enhanced in the mutants ( Z-scores of significant center peaks ( median ± IQR ) : WT: 3 . 47 ± 1 . 82; Gjd2 KO: 5 . 75 ± 5 . 58; p = 0 . 0002; Mann-Whitney test ) ( Fig 13D–13F ) . Instead , the side peak of Gjd2 KO Purkinje cell pairs was not stronger than that of WTs ( Z-scores of significant side peaks ( median ± IQR ) : WT: 3 . 01 ± 0 . 89; Gjd2 KO: 3 . 04 ± 1 . 52; p = 0 . 194; Mann-Whitney test ) , leading to a lower ratio between center and side peak ( mean ± SEM: WT: 90 . 70 ± 5 . 17%; Gjd2 KO: 72 . 86 ± 6 . 52%; p = 0 . 036; t = 2 . 143; df = 67; t test ) ( Fig 13E and 13F ) . Interestingly , the occurrence of side peaks in Purkinje cell pairs was unidirectional in approximately half the cell pairs ( WT: 47 out of 82 pairs with at least one side peak ( 57% ) ; Gjd2 KO: 25 out of 47 pairs ( 53% ) ; p = 0 . 714; Fisher’s exact test ) , which means that one of the neurons of a pair was leading the other , but not vice versa . As this was consistent in the Gjd2 KO as well as the WT Purkinje cells , these data could reflect traveling waves across the inferior olive , which , however , must have extrinsic sources [44 , 75] . Thus , the paired recordings are compatible with the findings highlighted above in that the presence of coupling can affect the coherence of STOs for short periods up to a few hundred milliseconds , while leaving the window for later correlated events open .
During spontaneous activity , Purkinje cells generally fire a complex spike roughly once a second , but this frequency can be increased to about 10 Hz by systemically applying drugs , like harmaline , which directly affect conductances mediating STOs in the inferior olive [41 , 81] . Since these drugs also induce tremorgenic movements beating at similar frequencies , it has been proposed that the inferior olive may serve as a temporal framework for motor coordination [11 , 82] . This oscillatory firing behavior of the olivary neurons may mirror limb resonant properties and act as an inverse controller , for example by dampening the dynamics of the muscles involved [83] . In line with previous recordings [22–24 , 28 , 61 , 72 , 76 , 78 , 84] , the current data indicate that only a small fraction of Purkinje neurons respond to sensory stimulation with a complex spike response probability larger than 50% . This probability falls substantially with increasing frequency of stimulation , as the overall spike frequency only marginally increases to high frequency stimulation . Even after applying different temporal patterns of sensory stimulation for longer epochs , we observed no substantial deviation from the stereotypic 1 Hz firing rate . Moreover , it should be noted that even if the frequency of underlying oscillations has bearing on the pattern of responses of the gallop stimuli , conditional dependencies should be expected for most STO frequencies , unless the ratio of the interval of gallop and the STO period has no remainder . Given the seemingly consistent frequencies predicted by PSTH's and autocorrelograms of single cells ( Figs 2 and 3 , but also seen in cross correlograms , as in Fig 13 ) , we chose gallop intervals with periods commensurate with a representative frequency of 8 Hz , each of which should sample different phases in the oscillation . If at all present , we should have observed conditional dependencies on at least a few cells . In our study , complex spikes remain as unpredictable as ever . Thus , regulatory mechanisms keep the complex spike rate relatively stable over longer time periods [72] . No resonance is exhibited , irrespective of an enduring powerful sensory stimulus in a variety of frequencies . Save few exceptions , the presence of a complex spike in an interval is compensated by the absence in another . Thus , it looks as if the complex spikes rearrange themselves in time in order to keep close to its proverbial 1 Hz frequency . It remains to be shown to what extent the mechanisms involved are intrinsic ( cell-dependent ) and/or extrinsic ( network-dependent ) . A possible candidate for setting the overall level of excitability through intrinsic mechanisms is given by Ca2+-activated Cl− channels , which are prominently expressed in olivary neurons along with Ca2+-dependent BK and SK K+ channels [85 , 86] . In addition , the olivo-cerebellar module itself could partly impose this regulation [86–88] . Indeed , the long-term dynamics within the closed olivo-cortico-nuclear loop may well exert homeostatic control , given that increases in complex spikes lead to enhanced inhibition of the inferior olive via the cerebellar nuclei [20] . The impact of such a network mechanism may even be more prominent when changes in synchrony are taken into account [89] . We propose that a closed-loop experiment conducted while imaging from a wide field , producing stimulation as a function of the degree of complex spike synchrony , could tease out conditional complex spike probabilities . Increasing our capability of predicting complex spikes is instrumental to elucidate the control of inferior olivary firing . The existence of temporal windows of opportunity for complex spike responses following sensory stimulation highlighted a potential impact of STOs on conditional complex spike gating [10 , 41 , 43 , 90] . Indeed , autocorrelogram peaks correlated well with interspike intervals following stimulation , arguing for an underlying rhythm . Complex spikes could appear in a particular window even when they were not preceded by a complex spike in a previous window during a single trial , arguing against a prominent role of refractory periods in creating rhythmic complex spike responses . Comparing actual firing patterns with statistical models mixing linear or oscillatory interval distributions indicated a potential impact of oscillations . The mild impact of the oscillatory component on explaining the data may in part depend on the assumption that cells have a well-defined frequency . In other words , a variable rebound time could offset the phase response by a couple of milliseconds , reducing the contribution of the oscillatory model , though phase preferences due to prior spikes may still occur ( i . e . , Fig 11E ) . Our biophysical model suggests that fluctuating inputs , such as those mediating inhibition from the cerebellar nuclei or those relaying depolarizing modulation from the raphe nuclei [91 , 92] , may induce variations in the oscillation period on a cycle-by-cycle basis ( Figs 8 and 9 ) . As these contextual inputs are absent or suppressed in decerebrate or anesthetized preparations , as well as in vitro , they may also explain why many earlier studies systematically encountered cells with well-defined STO frequencies [10 , 41 , 43 , 45 , 47 , 79 , 93 , 94] . In the network model , in which we mimicked the contextual input as an Ornstein-Uhlenbeck process with local variations but no long-term drifts of the mean [95] , the results agree well with the experimental observations in terms of synchronous firing , phase shifts , cross-correlogram peaks and side peaks , as well as overall firing frequency . Indeed , the absence of resonant responses over longer time windows and the inconsistency of individual olivary cells to fire on every trial or cycle indicate that the STOs are not regularly periodic , but rather quasiperiodic , while still being synchronous . Even though several lines of evidence suggest a role for STOs ( see above ) , we did not observe an unequivocal , significant conditional dependence of complex spikes in the gallop paradigm , as expected by a noiseless model . How can a system with rhythmic responses at least partially fail to be phase modulated by such periodic stimuli ? An attractive alternative explanation for rhythmicity might be the occurrence of high-threshold Cav2 . 1 P/Q-type Ca2+ channel-dependent rebound spikes ( S7 Fig ) [12 , 62] . If impulse-like input to the olive can evoke a spike , and if this spike produces a rebound spike some tens of milliseconds later , this could explain the alignment between the PSTHs and cross-correlograms . However , this argument cannot explain stimulus triggered spikes at the second or third window of opportunity , without an earlier spike as observed in Fig 4 . As the occurrence of the rebound spike is predicated on a prior spike , a spike in the second or third window without a prior spike cannot be explained by the rebound spiking phenomenon , at least not within the same cell . In other words , the spikes happening exclusively in the second ( or third ) window of opportunity cannot be the result of a previous spike in the same cell , unless there is a shared rhythm in the network . It is also conceivable that strong hyperpolarization that is synchronized with the complex spike rhythm could promote reverberating firing , but this is an extrinsic mechanism , discussed below . As they stand , our findings do not support the idea that the post-spike hyperpolarization is a prerequisite for the complex spike pattern observed . Multiple windows of opportunity could , according to our model , be enhanced by transient oscillations induced by resets relayed by gap junctions to the local olivary circuit . Apart from the almost complete absence of interneurons , the presence of STOs and the exclusive projection to the cerebellum , the abundance of dendro-dendritic gap junctions is another defining feature of the inferior olive . The absence of these gap junctions does not lead to gross motor deficits , but prevents proper acquisition and execution of more challenging tasks [10 , 16 , 74] , which is in line with the relatively minor impact found on complex spike activity in Gjd2 KO mice . At first sight , the effects of deleting gap junctions seem counterintuitive . Synchronous and rhythmic patterns are exacerbated , rather than diminished by the loss of gap junctions . However , the side peak of the auto-correlogram is significantly squashed , indicating that the gap junctions have a role in the increased coherence of the upcoming oscillation . Gap junctions do not only facilitate synchronization of coupled neurons , they also lower their excitability by increasing the membrane resistance [69] . Together , this results in less direct coupling , observed as reduced synchrony of direct neighbors [16 , 96] , and increased responsiveness to synaptic input . This leads to more long-range coherence and as a consequence gap junction networks may act as a “noise filter” , promoting short-range quorum-voting on phase ( a term coined by Winfree [97] ) . This effect is visible in our model as spikes are most likely to occur when excitation follows inhibition ( Fig 6H ) . This is in line with the finding that complex spikes of nearby Purkinje cells have a preference to fire together [72 , 98 , 99] . This concept also agrees with the possibility that coupled olivary neurons may control movements by dampening the dynamics of the muscles involved at an appropriate level [83 , 100] , as both the resonances and movement oscillations increase shortly after sensory stimulation in Gjd2 KO mice [16] . Network resonances are a pervasive feature of brain circuits and they can be induced by subthreshold oscillations of particular cell types [101 , 102] . In addition to the autochthonous dynamics of the inferior olive , reverberating loops through the circuit could help explain some features of complex spike firing , including the occurrence of complex spike doublets and side peaks in cross-correlograms . Such phenomena could be explained by "network echoes" , where complex spikes in one cycle would induce complex spikes in the next cycle [87 , 103 , 104] . The most obvious candidate loop to produce is that via the cerebellum and the nuclei of the meso-diencephalic junction [55 , 105] . The output of the inferior olive is mainly directed via exceptionally strong synapses to the Purkinje cells [106] . These Purkinje cells in turn inhibit neurons of the cerebellar nuclei that can show rebound firing after a period of inhibition [88 , 107] . This rebound activity can excite the inferior olive again via a disynaptic connection via the nuclei of the meso-diencephalic junction . While an isolated complex spike is unlikely to evoke such a rebound activity , a larger group of Purkinje cells could be successful in doing so [20 , 107 , 108] . The travel time for this loop ( around 50–100 ms ) has been indirectly assessed in the awake preparation [8 , 10 , 26] , and corresponds to the latency of the rebound firing in the cerebellar nuclei under anesthesia [87 , 88 , 107 , 109] . This implies that the travel times for the entire loop would be in the same order as found for the preferred frequencies of complex spike firing . Other , more elaborate loops involving for instance the forebrain may also exist [110] and could play an additional role in shaping complex spike patterns . A putative impact of reverberating loops on rebound activity could be a network phenomenon , as the impact of an isolated complex spike may not be sufficient to trigger this loop . This is in line with the reduced “echo” in the cross-correlograms of the Gjd2 KO mice and enhanced doublets following lesions of the nucleo-olivary tract as occurs in olivary hypertrophy [111] . Taken together , rebound spiking , STOs and reverberating loops all seem to promote in a cooperative manner complex spike rhythmicity at a time scale of about 200 ms . Through modeling , we found that not only the state of the inferior olivary oscillations determines which inputs are transmitted , but that these inputs also determine the state of the network . Thus , inputs from both the cerebellum and the cerebrum determine the probability of complex spike responses on a cycle-by-cycle basis providing a quasiperiodic framework to align synchronous groups . This sharply contrasts with a view in which the inferior olive is a clock with regular periodicity . A circuit-wide understanding of cerebellar resonances on the basis of such a mechanism could open a novel pathway to explore the cerebellar gating by other brain regions . The combination of delayed gap junctions and delayed inhibition , as found in the olivo-cerebellar loop [104 , 112] , can affect oscillatory behavior [113] . The interplay between STOs and delayed inhibition is therefore also relevant for other neural circuitries , for instance for creating filter settings for the perception of sounds with specific oscillatory properties [114–116] or orchestrating rhythmic movements as shown in the present study ( see also [117] ) . Well-coordinated movement sequences are not timed rigidly; they must be enacted flexibly and contextually . In order to catch a ball , or a prey , or to perform any other appropriately timed movement , it is essential to fine-tune the duration and onsets of multiple coordinated output systems . An inferior olive that responds contextually to time varying input by advancing and delaying cycles does not act as a rigid clock or metronome , but more contextually , as a ratchet-pole system , with the frequency of 'clicks' of the ratchet reflecting the recent history of applied torque . The properties we have encountered in this study are consistent with a 'ratchet-like' dynamics for the inferior olive , which integrates time-varying stimulus in a phase-dependent manner . According to this view , the inferior olive responds to all inputs ( sensory and otherwise ) , by producing phase changes that are informative about the recent history of input , and dictate the appearance of coherent complex spike waves arriving at the cerebellar cortex .
All experimental procedures were approved a priori by an independent animal ethical committee ( DEC-Consult , Soest , The Netherlands ) as required under Dutch law . Experiments were performed on 16 adult ( 9 males and 7 females of 25 ± 14 weeks old ) homozygous Gjd2tm1Kwi ( Gjd2 KO , formerly known as Cx36 KO mice [10] ) mice which were compared to 15 wild-type littermates ( 8 males and 7 females of 26 ± 13 weeks old; means ± sd ) . The generation of these mice has been described previously [118] . The data described in S2 Fig originated from previously published recordings in 35 wild-type mice [61] . All mice had a C57BL6/J background . The mice received a magnetic pedestal that was attached to the skull above bregma using Optibond adhesive ( Kerr Corporation , Orange , CA ) and a craniotomy of the occipital bone above lobules crus 1 and crus 2 . The surgery was performed under isoflurane anesthesia ( 2–4% V/V in O2 ) . Post-surgical pain was treated with 5 mg/kg carprofen ( “Rimadyl” , Pfizer , New York , NY ) and 1 μg lidocaine ( Braun , Meisingen , Germany ) . Mice were habituated during 2 daily sessions of 30–60 min . Extracellular recordings of Purkinje cells were made in the cerebellar lobules crus 1 and 2 of awake mice as described previously [28] . Briefly , an 8 x 4 matrix of quartz-platinum electrodes ( 2–4 MΩ; Thomas Recording , Giessen , Germany ) was used to make recordings that were amplified and digitized at 24 kHz using an RZ2 BioAmp processor ( Tucker-Davis Technologies , Alachua , FL ) . The signals were analyzed offline with SpikeTrain ( Neurasmus , Rotterdam , The Netherlands ) using a digital band-pass filter ( 30–6 , 000 Hz ) . Complex spikes were recognized based on their waveform consisting of an initial spike followed by one or more spikelets . A recording was accepted as that of a single Purkinje cell when a discernible pause of at least 8 ms in simple spike firing followed the complex spikes and when the complex spikes were of similar shape and amplitude throughout the recording . Sensory stimulation was applied as air puffs of 20 psi and 25 ms duration directed at the whisker pad ipsilateral to the side of recording . The stimuli were given in trains of 100 or 360 pulses either at regular or alternating intervals . During a recording , trains with different stimulus intervals were played in a random sequence . Whisker videos were made from above using a bright LED panel as backlight ( λ = 640 nm ) at a frame rate of 1 , 000 Hz ( 480 x 500 pixels using an A504k camera from Basler Vision Technologies , Ahrensburg , Germany ) . The whiskers were not trimmed or cut . Whisker movements were tracked offline as described previously [119] using a method based on the BIOTACT Whisker Tracking Tool [120] . We used the average angle of all trackable large facial whiskers for further quantification of whisker behavior . Of each Purkinje cell we computed the probability density function ( PDF ) of both its complex spike autocorrelogram and its distribution of intervals between consecutive complex spikes ( inter-complex spike intervals ( ICSIs ) ) . PDFs were calculated with an Epanechnikov kernel ( with finite support ) with a width of 10 ms . In order to exclude stimulus-induced alterations in complex spike firing , complex spikes detected between 20 and 200 ms after a stimulus were omitted from this phase of the analysis . PDFs were calculated from 0 up till 500 ms . The peak in the ICSI PDF was considered as the “preferred ICSI interval” and its strength was expressed as the Z-score by dividing the peak value by the standard deviation of the PDF . To understand the impact of Purkinje cell with little or no preference for specific ICSI intervals on the analysis , we chose to look both at the Purkinje cells with high and low Z-scores . Thus , we grouped Purkinje cells into high and low level Z-scores , using a threshold of 3 . Air puff stimulations frequently triggered double complex spike response peaks , suggestive of an underlying inferior olivary oscillation . For further analysis of the conditional responses , an estimate of the putative inferior olivary frequency was derived from the interval between these two response peaks . First , it was established for each Purkinje cell whether two peaks were present in the PSTH . To this end , we set a threshold for each of these two peaks . For the first peak , this was calculated by reshuffling the ICSIs over the recording followed by calculating a stimulus-triggered pseudo-PSTH , repeating this procedure 10 , 000 times and selecting the 99% upper-bound . We considered the first response peak to be significant if it crossed the upper-bound uninterruptedly for at least 10 ms . Since the second response peak typically is much smaller than the first one , we calculated a new threshold for the second peak by excluding the time-window for the first response peak . This window was set from the time of the stimulus until where the response probability drops to the average response frequency , the response frequency as expected if stimuli do not trigger complex spikes , following the significant ‘first’ responsive peak . In 5 out of 98 Purkinje cells , the PDF of the response rate between clear peaks remained above the average response rate , in which case we used the time point where the amplitude drop in the PDF was more than twice the difference between upper bound and average response probability . The rest of the bootstrap method was identical to that for the first response peak . Only peaks up to 0 . 5 s after the stimulus were included in the population analysis . In order to test whether the phase of the inferior olivary oscillations affected the complex spike response probability , we compared the complex spike intervals over an Air puff for each stimulus that triggered a complex spike . To this end , we analyzed the recordings of 25 Purkinje cells ( 10 WT and 15 Gjd2 KO ) that were measured previously in crus 1 and crus 2 of awake , adult mice . We included only Purkinje cells that displayed clear oscillatory complex spike firing indicated by the display of a secondary complex spike response peak , as evaluated according to the bootstrap method described above , and/or significant peaks in the ICSI histogram . Only stable recordings covering at least 500 stimuli at frequencies below 1 Hz were considered for this analysis . For each recording we compared two idealized statistical models of the observed ICSI distributions: an oscillatory model showing phase-dependent spiking and stable olivary oscillation frequencies and a uniform model lacking phase-dependencies . For the oscillatory model , we created complex spike probability functions for the pre-stimulus interval ( -300 to 0 ms ) based on the oscillatory period established either for the ICSI distribution or from the interval between the two complex spike response peaks . We fitted a sine wave with the observed frequency , having its peak at the moment of the first complex spike in the stimulus response window ( 20–200 ms after the air puff ) and derived spike probability levels during the pre-stimulus interval from these fits , with the trough representing zero probability . Frequency and amplitude of every cycle were kept constant for the whole recording . In the uniform model , we calculated the pre-stimulus spiking probability with a uniform distribution based on the complex spike frequency of each Purkinje cell . We did include a refractory period , being the shortest ICSI observed for each recording , to reflect the inability of consecutive complex spikes to occur with a very short time interval . Refractory periods were comparable between mutants and wild types; 49 ± 15 ms for Gjd2 KO cells and 50 ± 20 ms for WT cells . Subsequently , we constructed compound fits consisting of linear summations of the two models . One extreme was the oscillatory model and the other the uniform model and we considered nine intermediate combinations ( e . g . , 0 . 3 x the oscillatory model + 0 . 7 x the uniform model ) . Every compound fit was run for 10 , 000 times . The goodness of fit was computed as the absolute differences of every single run of the model with the actual ICSI distribution . The model networks used here are comprised of a topographical grid of 200 coupled cells , in a 10x10x2 lattice arrangement , which may resemble an area of about 400 μm x 400 μm of the inferior olive , for instance , the rostral portion of the dorsal lamella of the principle olive of the mouse . It is available online at https://github . com/MRIO/OliveTree , branch 'Warnaar' . For instructions on how to run the model and reproduce analysis , check README_Warnaar . txt' . Each cell within these networks was modelled according to the single cell model described in [46] , which is an elaboration of a previous model [62] with an added axon and modified fast sodium channel . Equations are provided in the appendix of that publication at ( https://doi . org/10 . 1371/journal . pcbi . 1002814 . s002 ) , and can be checked in the MATLAB functions IOcell and createDefaultNeurons in the codebase . The model includes three compartments ( soma , dendrite , axon hillock ) with 12 conductances . In addition to the ionic mechanisms , the dendrite of the model cell has a Ca2+ concentration state variable , which is related to the intrusion through the Cav2 . 1 channels . The main ionic conductances responsible for the oscillation are the somatic T-type Ca2+ and the Ca2+-activated K+ ( SK ) channels present in the dendritic compartment . The crucial parameters governing the emergence of subthreshold oscillations are randomized , reflecting the experimental facts that about one third of the cells oscillate endemically ( in vivo ) with intrinsic variations in oscillatory frequencies [43] . The behavior of the STO of the model cells in our network as a function of their parameters , for models with and without gap junctions , are included in S5 and S6 Figs . Cell parameters are found in S1 Table . Connectivity is created with the function 'createW . m' in the MATLAB codebase . Briefly , cells within a specified radius of each other were connected according to a probability function such as to ensure the specified mean degree in the network ( n = 8 ) , chosen to resemble the observed connectivity distributions reported in the literature . The connectivity parameters ( distance and average connection probability ) were chosen to match experimental values ( radius ≤ 120 μm ) and average connection probability ( ~8 neighbors ) . The procedure to obtain connectivity is as follows . First , pairwise distances between all cells are calculated . Then , a binary adjacency matrix is created by thresholding those distances within a specified radius . Thereafter , we assign a random number between 0 and 1 to each link from a uniform distribution . Finally , this matrix is made binary by comparing each entry with a probability so that the average number of connections approximates a given mean connectivity . This binary adjacency matrix is then multiplied by the mean gap junction conductance parameter . Finally , gap junction conductance values are then randomized by a uniform jittering of the conductance by 10% of their original value . The conductance of gap junctions was normalized with a saturating factor by difference of potential between the neighboring cells , according to [62] based on findings from [121] with the following function: gc¯ ( ∆V ) =gc ( 0 . 8e ( −∆V2/100 ) +0 . 2 ) FORMULA 1 Where ΔV is the voltage difference between the connected cells , gc is the nominal coupling and gc¯ is the effective coupling . Two inputs are given to the model , one emulating the sensory input from whisker pad stimulation and the other representing a stimulus-independent background reflecting diverse excitatory and inhibitory inputs to the inferior olive . The latter consisted of a continuous stochastic process with known mean and standard deviation with a relaxation parameter following the Ornstein-Uhlenbeck process [95] , succinctly described underneath . Only one subset of cells in the center of the network ( 40% of the cells in a mask spanning a radius of 3 cells from the center of the network ) representing efferent arborization , receives the “sensory input” , with “sensory” currents being delivered to the soma of modelled cells ( gAMPA = 0 . 15 mS/cm2 ) . “Sensory input” was modeled according to O’Donnel et al . [122] . The mask is represented in Fig 5A . The cells of the inferior olivary network most likely share input sources due to overlapping arborizations of efferent projections [123] . To represent both shared and independent input , we have modeled the current source in each cell as having an independent process and a shared process , with a mix parameter ( alpha ) of 10% input correlation shared by all the cells in the network . This level of correlation leads to a coherent background oscillation in the cells of the network , which is exacerbated in the presence of gap junctions ( S5 Fig ) . Ornstein-Uhlenbeck ( OU ) is a noise process that ensures that the mean current delivered is well behaved and that the integral of delivered current over time converges to a constant value [95] . The OU current is a good approximation for synaptic inputs originating in a large number of uncorrelated sources , where synaptic events are generated randomly and each event decays with a given rate ( τ ) . We use a recursive implementation according to the following recursive formula: ηi ( t+1 ) =ηi ( n , t ) *exp ( −δ/τ ) + ( 1/τ ) ( μ−ηi ( t ) ) +σ*√δ*ξi FORMULA 2 Where ηi ( t ) is the noise amplitude of neuron i at time t . The noise process is parameterized by τ , σ , μ where τ represents the synaptic decay time constant , δ is the integration step time for our forward Euler integrator , σ is the standard deviation of the noise process and μ is its mean . The random draw from a Gaussian distribution at every time step is represented by ξi . Neurons in the inferior olive receive broad arborizations , leading to input correlations across nearby neurons . In our model this is represented via a mixture of an independent process for each neuron nindependent and a shared process , nall , common to all the neurons in the network , parameterized by a mixing parameter α , called 'noise correlation': ni ( t ) =α*nindependent+ ( 1−α ) *nall ( t ) FORMULA 3 Simulation results throughout the article come from simulations with noise use an α where neurons share 10% of their noise input . Reported results are qualitatively robust to changes in this value ( S5 Fig ) . To examine the dependence of network dynamics on the characteristics of the incoming input , we computed the 200 neuron network sweeping a grid of the main input parameters ( τ , σ , μ , α ) of the Ornstein-Uhlenbeck noise process . The network response in terms of STO frequency , population firing rates , proportion of firing neurons was analyzed with respect to a grid of input parameters . For comparability of statistics and reproducibility of results , all results displayed in this article were obtained from a single random seed . We have tested the network with multiple seeds and the results are qualitatively indistinguishable . The parameters of the Ornstein-Uhlenbeck process were tuned such that the network emulating the wildtype network ( with gap junctions , WT ) produced an average frequency of 1 Hz and more than 95% of the model cells fire at least once every 5 seconds ( the parameter space for the network responses including STO , population firing rate and proportion of cells that fire within 3s is found in S5B Fig ) . The parameters to achieve these criteria are dependent on the total leak through the gap junctions . There are multiple methods to compensate the absent leak in the gapless network . In the present case , the network without gap junctions has been tuned to produce the same firing frequency as the network with gaps by increasing the membrane leak currents from 0 . 010 to 0 . 013 mS/cm2 . This results in a similar excitability but slightly lower STO frequency in the “mutant” . The average firing rate behavior of the network shows a linear relationship with the standard deviation of the OU process ( S4 Fig ) . For the present network with balanced connectivity and a single gap conductance of 0 . 04 mS/cm2 , the Ornstein-Uhlenbeck parameters are ( τ , σ , μ , α ) , μ = −0 . 6 pA/cm2 , σ = 0 . 6 pA/cm2 and τ = 20 ms . τ is a decay parameter that represents the synaptic decay times expected for olivary inputs , in this case chosen to emulate dendritic GABA according to Devor and Yarom [124] . Both synchrony and instantaneous frequency were estimated on the basis of a novel phase transformation of the membrane potential , which is more robust than the standard Hilbert transform , and can produce a linear phase response to the non-linear shape of the subthreshold oscillations [125] . This transformation improves the estimation of the momentary phase and compensates for the fact that ionic mechanisms induce different rates of membrane potential change at different phases of the oscillation . This phases analysis was conducted with the DAMOCO toolbox [126] . From the instantaneous phase , the instantaneous frequency is simply the inverse of the first order finite difference of phase . Synchrony across cells is estimated with the Kuramoto order parameter ( K ) : K ( t ) =|1N∑ei ( Ψ ( t ) −ϕn ( t ) ) | FORMULA 4 Where ϕn is the phase of each neuron , N is the number of neurons and Ψ is the phase average of all oscillators . To estimate a phase response curve of the stimulated neurons , first a “sensory stimulus” is delivered at a phase known to produce an action potential ( and resetting ) . The location of the first peak after stimulation is recorded . Subsequently , eight more simulations receive another stimulus , with same parameters as the resetting stimulus , but at different phases ( at incremental intervals of 2π/8 ) . The effect of that stimulation ( delay or advance ) on the next peak is recorded as a phase delta . Results are plotted in Fig 7A–7E . | Activity of the inferior olive , transmitted via climbing fibers to the cerebellum , regulates initiation and amplitude of movements , signals unexpected sensory feedback , and directs cerebellar learning . It is characterized by widespread subthreshold oscillations and synchronization promoted by strong electrotonic coupling . In brain slices , subthreshold oscillations gate which inputs can be transmitted by inferior olivary neurons and which will not—dependent on the phase of the oscillation . We tested whether the subthreshold oscillations had a measurable impact on temporal patterning of climbing fiber activity in intact , awake mice . We did so by recording neural activity of the postsynaptic Purkinje cells , in which complex spike firing faithfully represents climbing fiber activity . For short intervals ( <300 ms ) many Purkinje cells showed spontaneously rhythmic complex spike activity . However , our experiments designed to evoke conditional responses indicated that complex spikes are not predominantly predicated on stimulus history . Our realistic network model of the inferior olive explains the experimental observations via continuous phase modulations of the subthreshold oscillations under the influence of synaptic fluctuations . We conclude that complex spike activity emerges from a quasiperiodic rhythm that is stabilized by electrotonic coupling between its dendrites , yet dynamically influenced by the status of their synaptic inputs . |
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We have mapped a protein interaction network of human homologs of proteins that modify longevity in invertebrate species . This network is derived from a proteome-scale human protein interaction Core Network generated through unbiased high-throughput yeast two-hybrid searches . The longevity network is composed of 175 human homologs of proteins known to confer increased longevity through loss of function in yeast , nematode , or fly , and 2 , 163 additional human proteins that interact with these homologs . Overall , the network consists of 3 , 271 binary interactions among 2 , 338 unique proteins . A comparison of the average node degree of the human longevity homologs with random sets of proteins in the Core Network indicates that human homologs of longevity proteins are highly connected hubs with a mean node degree of 18 . 8 partners . Shortest path length analysis shows that proteins in this network are significantly more connected than would be expected by chance . To examine the relationship of this network to human aging phenotypes , we compared the genes encoding longevity network proteins to genes known to be changed transcriptionally during aging in human muscle . In the case of both the longevity protein homologs and their interactors , we observed enrichments for differentially expressed genes in the network . To determine whether homologs of human longevity interacting proteins can modulate life span in invertebrates , homologs of 18 human FRAP1 interacting proteins showing significant changes in human aging muscle were tested for effects on nematode life span using RNAi . Of 18 genes tested , 33% extended life span when knocked-down in Caenorhabditis elegans . These observations indicate that a broad class of longevity genes identified in invertebrate models of aging have relevance to human aging . They also indicate that the longevity protein interaction network presented here is enriched for novel conserved longevity proteins .
Genetic modulation of life span is ultimately mediated through proteins , and the mechanisms that allow this control must necessarily involve the interaction of multiple proteins . As a biological pathway , aging is a pleiotropic process , and many of the proteins identified as influencing this process have a proportionate pleiotropy of function . Modulations of the levels in a single protein have been found that provide robust increases in life-span for an organism [1] , [2] , but contributions from many genes are expected to dictate longevity in all organisms . This idea is supported by an investigation of yeast protein-protein interaction networks that found that proteins related to aging have a significantly higher connectivity than expected by chance [3] . Similarly , a second group found that their computational model suggested aging genes have more connections in interaction networks , and that this may be useful in identifying new aging genes [4] . Therefore , a useful way to identify novel genes with roles that affect life span is to identify their gene product's interactions with known aging-associated proteins . A role for protein interactions in processes is most apparent at the level of protein complexes that assemble to carry out a particular function . Likewise , protein interactions that mediate signaling cascades demonstrate how interactions functionally translate into a biological pathway . Indeed , biological processes are built of hierarchical protein-protein interaction assemblies that together carry out the overall physiological process . Therefore , the identification of interactions that a protein participates in can be an informative way to pursue an understanding of the protein's function . A common method for identifying protein interactions is the yeast two-hybrid system ( Y2H ) , which uses the interaction of two proteins to reconstitute a transcription factor that then activates expression of a reporter gene [5] . An important development in the Y2H approach was the introduction of the screening of libraries of potential interacting proteins [6] . This development made it possible to identify novel protein interactions . Novel interactions impart a suggested role in a physiological process for proteins based on the established involvement of their interaction partner in that process . Recently , high throughput approaches have expanded this idea to a systems-scale level: investigators can identify the network of interactions that occur among a large set of proteins , and from this infer the relationships of those proteins in , as well as their contribution to , the system . Such an approach has been used to interrogate the protein interaction networks that underlie model organisms [6]–[12] , human cells [13] , [14] , and organisms responsible for infectious diseases [15]–[17] . Biological processes such as vulval development in nematodes [18] , and familial neurodegenerative diseases [19]–[21] have also been the subject of large-scale Y2H interaction mapping . From these studies , many hypotheses for new participants in biological pathways have emerged . The results from high-throughput protein interaction studies are known to contain false-positive ( i . e . biologically irrelevant ) interactions intermingled with the biologically relevant interactions . Independent large-scale studies of the same system may not necessarily distinguish the two [22] , although detection of an interaction in more than one study is strong evidence for the authenticity of the interaction . An additional approach to address interaction validity is to use features of the network itself to provide evidence for the physiological relevance of the identified interactions . Protein interaction networks behave as scale-free networks , and the resultant properties such as path length and clustering features can be mined with bioinformatic methods to evaluate the properties of a given interaction within the network [23] , [24] . Comparisons with other phenotypic data can provide further support . An observation of similar regulation using gene expression analysis has been used to establish confidence in protein interactions by a number of groups [8] , [11] , [15] , [25] , [26] . Shared gene ontology annotations [27] can also be used to identify characteristics of proteins that support the link ( s ) suggested by the interactions [15] .
We performed a comprehensive survey of the published literature on the genetics of aging as studied in model systems ( yeast , fly , nematode and mouse ) and identified 363 genes that have been reported to increase life span when mutated . Most of these genes were curated in the SAGE KE Genes/Interventions Database ( http://sageke . sciencemag . org ) . The remainder were culled from published large-scale genetic screens for longevity phenotypes [28]–[32] . In order to characterize these longevity genes/proteins in the context of a human protein interaction network we sought to analyze their protein interaction partners in a large human protein interaction database . We have used high-throughput yeast two-hybrid methods to construct a large network comprised of 114 , 689 unique binary interactions between fragments of human proteins . This network was generated using results from ∼345 , 000 individual yeast two hybrid screens . Aspects of the Prolexys human protein interaction network and methods used to generate it have been described previously [15] , [21] , [33] . The 114 , 689 interaction network was filtered to create a Core Network with 70 , 358 unique binary interactions between protein fragments representing 10 , 425 unique genes curated as NCBI RefSeq entries . The Core Network was generated by removal of “sticky” proteins identified using a K-means clustering method [15] . Exclusion of bait proteins with >87 interactions and prey proteins with >231 interactions resulted in removal of 44 , 331 interactions and 855 nodes ( i . e . unique genes ) from the unfiltered network . Figure 1A shows a log-log graph of node degree distributions of the unfiltered network ( black circles ) and the Core Network ( red circles ) . The fact that the degree distribution appears as a straight line on a logarithmic plot indicates that the Core Network is scale-free [23] , [34] . This Core Network was queried to determine the interaction properties of human protein homologous to proteins experimentally implicated regulation of life span . A masked version of the complete Core Network is shown in Table S6 . The majority of genes and proteins identified as having a role in modulation of life span were discovered in yeast , fly and nematode . We therefore identified the human orthologs and homologs of these invertebrate longevity genes according to definitions used in NCBI's Homologene ( http://www . ncbi . nlm . nih . gov/entrez/query . fcgi ? DB=homologene ) and the Karolinska Institute's Inparanoid Database ( http://inparanoid . cgb . ki . se/ ) . Of the 363 invertebrate longevity genes identified , 252 have human homologs and 175 of these homologs are represented in the Core Network of the Prolexys protein interaction database ( Table S1 ) . The proteins encoded by the 175 human homologs of invertebrate longevity genes were observed to interact with 2 , 163 additional human proteins in the yeast two-hybrid assays . This longevity protein interaction network ultimately consists of a total of 3 , 271 binary connections between the 2 , 338 proteins ( Table S2 ) . When the longevity network was derived from the Core Network it was immediately apparent that the longevity homologs were unusually highly connected with an average node degree of 18 . 8 and a median node degree of 7 . 0 ( see Table S1 for individual node degrees ) . These values are notably higher that average and median node degrees of 13 . 5 and 5 . 0 observed for the entire Core Network ( Table S6 ) . Figure 1B shows a box plot comparing the distribution of node degrees for the 175 human longevity protein homologs and the Core Network from which the longevity sub-graph was derived . This indicates that human homologs of longevity proteins comprise a group of highly connected hubs in the Core Network . The increase in the median node degree for the longevity proteins indicates that this distribution is not due to the effect of outliers . A path length analysis was performed to determine whether the network of longevity protein homologs were more closely connected to each other than would be expected by chance . Figure 2A shows the average mean shortest path length in 1 , 000 sets of 175 proteins selected at random from the Core Network is 4 . 61 as compared to 4 . 15 for the longevity network ( p = 0 . 004 ) . This result is consistent the prediction that proteins with shared functions ( in this case the modification of life span ) are more likely to be closely associated in the network than would be expected by chance . To determine whether this path length difference is a trivial result of the high average node degree in the longevity network , we performed a path length analysis using networks with randomized connections . In order to do this , the edges in the Core Network of 70 , 358 binary interactions were randomly reassigned while preserving the node degree of each individual protein . The average path length between the longevity protein homologs present in 100 randomized core interactomes was then determined . As shown in Figure 2B , we found that the average shortest distance between any two longevity proteins ( 4 . 15 ) is significantly less than the average distance of 4 . 73 ( ±0 . 13 ) between these proteins in the 100 networks with randomly assigned interactions ( p<0 . 01 ) . This result shows that the decreased path length observed in the longevity network is not simply a feature of high node degrees but is in fact dependent on the connections between the specific interacting proteins included in the longevity network . The 2 , 163 human proteins that interact with the invertebrate longevity homologs are not known to be involved directly in aging or longevity phenotypes . However , because of their ability to bind directly to known longevity proteins in the yeast two-hybrid assay , these can be considered as candidate longevity proteins . To validate potential roles for the interacting proteins in human longevity we looked for evidence that the expression of genes encoding these proteins might be changed during the aging process . To do this , we compared the network to DNA microarray datasets comparing gene expression in human skeletal muscle from cohorts of young and old healthy volunteers [35] . In this microarray study , skeletal ( vastus lateralis ) muscle biopsies from healthy older and younger adult men and women were compared using gene expression profiling . After quantile normalization , the number of genes significantly differentially expressed with age was determined by performing , on a probe-by-probe basis , 24 , 354 two-sample t-tests . To control the family-wise error rate ( FWER ) , the significant genes were chosen at 5% using Holm's step-down method . FWER was used to insure a low probability of any false positives among this list . Using a false discovery rate cut-off of 5% , a large number of genes were found to be differentially expressed as a consequence of age [35] . To integrate the longevity interactome with the gene expression data , we asked whether any of the genes encoding longevity proteins or their interactors ( “1° interactors” ) were significantly changed in the transcript profiles from old vs . young human cohorts . Of the 175 longevity proteins , 169 were represented on the microarray used in this study by 210 probes . We determined how many of the 210 probes had a significant association of expression and age using analyses based on loess normalized intensities converted to log scale . HOPACH ( Hierarchical Ordered Partitioning and Collapsing Hybrid ) was then used to cluster the resulting genes and generate plots of similarly expressed genes . This analysis identified 54 of the 210 probes ( 52 of 169 unique genes ) as being differentially expressed between the old and young cohorts ( FDR q-value<0 . 05 ) . The differentially expressed aging gene homologs are listed in Table 1 . To see whether this was unusual , we included an additional test to determine whether this set of probes is more enriched in genes associated with age than one would expect by pure chance . We drew randomly from the original list of genes probes ( 24 , 354 probes genes ) 210 at a time and for each of these random draws , examined the number of genes probes significantly associated with age at the same level of significance . However , among only 1 of the 1 , 000 random draws we performed , did that many or more significant genes probes come up , implying a significant enrichment among this set ( p-value = 0 . 001; see Figure 3B ) . A permutation test for all 236 gene longevity gene homologs present on the microarray ( represented by 291 probes ) is shown in Figure 3A . We found among the longevity gene homologs ( regardless of whether they were present in the interaction network data ) , 66 out of 291 probes were significantly associated with age . However , among only 4 of the 1000 random draws we performed , did that many significant genes come up , implying a significant enrichment among this set as well ( p = 0 . 004 ) . We next evaluated the 2 , 507 probes that correspond to genes encoding 2 , 036 of the 2 , 163 1° interactors in the longevity interactome network . We repeated the analyses described above for the longevity proteins . Among the 1° interactors , 611 out of the 2 , 507 probes ( 581 of 2 , 036 genes ) were significantly associated with age . In 1 , 000 random draws of 2 , 507 probes , none contained 611 ( or more ) significant genes , demonstrating significant enrichment among the set of 1° interactors ( p<0 . 001; see Figure 3C ) . These statistical analyses clearly demonstrate that genes encoding human homologs of invertebrate longevity genes and genes encoding their interacting proteins are highly enriched among genes with a statistically significant change in expression between young and old muscle tissue in human . This result is somewhat surprising in that these genes are derived primarily from experiments done in invertebrate models , and thus one might not expect a priori to see age-dependent changes in expression levels in human tissue . Two preliminary conclusions are suggested by these observations: 1 ) longevity genes discovered in invertebrate models are likely to play some roles in human longevity and 2 ) cells and tissues appear to modulate expression levels of such longevity genes during the aging process in human . A list of human homologs of invertebrate aging genes and genes encoding interacting proteins that show significant expression changes in aging human muscle are shown in Table S3 . Figure 4 shows a subnetwork of the longevity interactome . This subnetwork includes only those genes whose expression is significantly changed in the aging microarray data . This subnetwork contains 339 interactions among 325 proteins , roughly 10% of the interactions in the larger network . We consider proteins in this network to be of high interest for further studies . An example of one group of interest is FRAP1 ( mTOR ) and its interacting proteins . FRAP1 has total of 63 interacting protein interactions in the longevity network . FRAP1 has a well-established role in longevity , with loss of function mutations in the FRAP1 orthologs in both nematodes [36] and yeast [30] , [32] leading to increased life span . Our results suggest that FRAP1 may also have a role in human longevity . Human FRAP1 interacts with 63 proteins that have not previously been shown to be involved in longevity . Some of these have functions that are consistent with known FRAP1 functions of FRAP1 , e . g . an interaction with RPS27 , a component of the small ribosomal subunit may be related to the function of FRAP1 in translational control; similarly , nuclear import of FRAP1 is necessary for signaling through S6K and an interaction with TPR supports the idea that mTOR associates with the nuclear pore [37] . Interestingly , mRNA levels for 24 of the 63 partners ( 38% ) of FRAP1 are significantly different between young and old patient samples . Proteins that can interact with FRAP1 are thus frequently expressed differentially with age in human . FRAP1 interacting proteins that show significant changes in gene expression during aging in human muscle are shown in Table 2 . To determine whether there is a relationship between protein interaction and a correlation in gene expression between protein pairs in this network , we compared the distribution of both negative and positive gene expression correlations with binary interactions . Figure 5 shows the distribution of gene expression correlations for the experimentally derived longevity network as compared to simulated networks of genes with randomly assigned binary connections . Both positively and negatively co-regulated protein pairs are enriched in the longevity interaction network relative to that observed in randomized networks . This observation supports the idea that interacting proteins are transcriptionally co-regulated [38] . A list of the binary pairs with significant age-dependent transcriptional co-regulation is shown in Table S4 . In order to test the hypothesis that interacting partners of human longevity homologs might themselves be longevity proteins we tested a group of these for effects on life span in C . elegans . The 24 FRAP1 interacting proteins with significant gene expression changes in aging human muscle are listed in Table 2 . Of these 18 were tested for their ability to modulate life span in C elegans using RNAi mediated knock-down ( six of 24 were not tested because reagents were not available in our RNAi library ) . Wild-type N2 C . elegans were fed E . coli expressing double-stranded RNA corresponding to genes encoding 18 FRAP1 interacting proteins and life spans were determined in two independent experiments . Of the 18 genes tested in this way , six reproducibly extended the life span of C . elegans by >10% ( Figure 6 ) . These genes are listed in Table 3 . The gene showing the greatest effect on life span after RNAi treatment is RPS27 . Knock-down of rps-27 expression in nematode resulted in 50% and 44% increases in life span in two independent experiments . Mammalian RPS27 encodes a zinc finger-containing protein component of the 40S ribosomal subunit [39] . Several studies have established that TOR signaling can modulate life span in yeast [30] , [32] and fly [40] . It has been demonstrated further that inhibition of translation can also extend life span indicating that loss-of-function in TOR signaling modulates aging through an effect on rates of translation [41]–[43] . Since RPS27 is a component of the ribosome and interacts with FRAP1 ( Tor ) , it is likely that the life span extension seen in the rps-27 knock-down is due to an effect on rates of translation either through TOR signaling , direct effects on ribosome structure , or a combination of the two . The fact that 33% of the candidates tested had a significant effect on life span extension is noteworthy . Previous genome wide screens in C . elegans using RNAi have reported that less than 1% of the nematode genome may encode genes that can extend life span when knocked-down [28] , [29] .
We present here a large protein interaction network comprised of human homologs of genes known to influence longevity in invertebrate systems and their interacting proteins . To compile this list of homologs , we selected genes that confer increased life span when mutated , deleted or knocked down in yeast , flies or nematodes . The longevity homolog sub-network ( 3 , 271 interactions ) is derived from a much larger Core Network ( 70 , 358 interactions ) that was generated in an unbiased fashion using a random high throughput yeast two hybrid process . The Core Network was generated from larger network after removal of sticky proteins with very high node degrees [15] , [21] , [33] . Analysis of the human longevity interactome presented here show that the 175 human longevity homologs are more closely connected that would be expected by chance , with a mean path length of 4 . 15 as compared to and average of 4 . 61 in the Core Network . Another striking feature of human homologs of invertebrate longevity proteins is their exceptionally high average node degree of 18 . 8 ( as compared to an average of 13 . 5 in the Core Network ) . This observation indicates that human longevity protein homologs may function as hub proteins in the human interactome [44] , [45] . The fact that longevity proteins are hubs may be indicative of their having a central role in cellular function . They may also function as nodes that connect and/or integrate functionally diverse cellular components and systems . It is interesting to consider the possibility that knock-down of these longevity genes may extend life span through a mechanism that involves uncoupling connections between cellular components of diverse function . A striking conclusion of this study is dramatic degree of enrichment for genes encoding network proteins among genes that are transcriptionally modulated during aging in human muscle tissue . This correlation indicates that the network is enriched for proteins involved in human aging . This conclusion is consistent with the observation that human proteins interacting with the longevity homolog FRAP1 can increase life span when knocked-down in C . elegans . Overall these results provide evidence that the broad class of longevity proteins identified in invertebrates have a conserved role in processes of human aging and longevity .
Complementary DNA was generated from poly ( A ) + RNA isolated from multiple human tissues ( including adult brain , fetal brain and liver ) and inserted between the Gal4 transcriptional activation domain and the Schizosaccharomyces pombe URA4 coding region of pOAD . 102 ( prey plasmid ) or the Gal4 DNA-binding domain and the S . cerevisiae MET2 coding region of pOBD . 111 ( bait plasmid ) . Yeast transformed with bait or prey plasmids were plated on medium lacking uracil ( prey ) or methionine ( bait ) to select for transformants expressing the markers fused to the cDNA inserts . Additional information about the plasmids , yeast strains and library construction can be found in Supplementary Information . The two-hybrid expression plasmids , pOBD . 111 and pOAD . 102 used in this study have been described [15] . pOBD . 111 and pOAD . 102 are modifications of pOBD and pOAD [46] . The bait and prey yeast strains used were respectively , R2HMet ( MATα ura3-52 ade2-101 trp1-901 leu2-3 , his3-200 met2Δ::hisG gal4Δ gal80Δ ) and BK100 ( MATa ura3-52 ade2-101 trp1-901 leu2-3 , 112 his3-200 gal4Δ gal80Δ GAL2-ADE2 LYS2::GAL1-HIS3 met2::GAL7-lacZ ) , a derivative of PJ69-4A [47] . Bait and prey cDNA libraries were made using poly ( A ) + RNA prepared from human tissues ( see Table S5 ) by random primed cDNA synthesis followed by the PCR addition of yeast recombination tails . Both bait and prey cDNAs are cloned as a double fusion between the two-hybrid domain on the 5′ end of the insert and an ORF-selection marker on the 3′ end . Specifically , bait cDNA inserts were cloned between the GAL4 DNA binding domain and the TRP1 or MET2 coding regions , and prey inserts between the GAL4 transcriptional activation domain and URA3 [15] . These cDNAs were then cloned into linearized expression vectors by recombination in yeast [46] . Yeast transformed with bait were plated on medium lacking tryptophan or methionine to select for in-frame TRP1 or MET2 fusions , respectively , and prey were selected without uracil for in-frame URA3 fusions . Y2H screens were performed in 96-well plates by mating in each well 5×106 cells of a yeast clone expressing a single bait with 5×106 clonally diverse cells from a prey library . After mating overnight , the Matings were plated using a Genesis Workstation 150 liquid handling robot ( Tecan ) onto medium that selected simultaneously for the mating event , the expression of the ORF-selection markers , and the activity of the metabolic reporter genes , ADE2 and HIS3 . Yeast that grew on this selection medium ( “positives” ) were counted and transferred into liquid medium in a 96-well format using a MegaPix colony picking robot ( Genetix ) . A maximum of 48 colonies per mating were picked . Searches that yielded more than 200 positives ( ∼2% of all searches ) were considered to result from bait plasmids that activated transcription in the absence of specific protein-protein interactions , and were not analyzed further . Cloned inserts were amplified from plasmid PCR . Liquid cultures grown from positive yeast colonies were used as templates in PCR reactions that amplified either both bait and prey cDNA inserts , or prey inserts only in screens in which the baits had been sequenced before the matings . The PCR reactions were assembled in 384-well format using the Genesis Workstation 150 or a custom built ( Zymark ) PCR workstation that included a SciClone ALH 500 liquid handling robot ( Zymark ) . PCR amplification took place in Primus-HT thermocyclers ( MWG Biotech ) . The amplicons served as templates in DNA sequencing reactions . Identities of insert fragments were established by querying against the NCBI RefSeq database . The Y2H protein-protein interaction database is the result of two distinct workflow modes referred to as random and directed . In the random mode individual bait clones are picked randomly from a library and mated with a library of prey cDNAs . Directed searches , on the other hand , are matings of prey libraries with a single intentionally constructed bait cDNA clone whose identity is known a priori . In random searches , moreover , the identity of the bait is discovered – depending , again , on a particular workflow – either before or after the mating has been performed . The alternatives are to sequence both the bait and prey from Y2H positives ( called positive-derived sequence ) or to sequence the bait plasmid before mating ( called pre-sequencing ) requiring only the prey to be sequenced from positive diploids . All Y2H search data and DNA sequences used to determine interaction pairs reported in this study are included in Table S5 . A total of 363 genes that had been reported to increase life span when mutated yeast , fly , nematode and mouse species were compiled from SAGE KE and the published literature . We then screened for their respective clusters in Homologene and Inparanoid databases . The human genes among those clusters were deemed to be the orthologs of the respective invertebrate genes . Any additional human paralogs were also taken into consideration . The 363 invertebrate genes have homology to genes had human ortholog/paralog which resulted in a total of 252 human genes . k-means clustering ( k = 2 ) was applied sequentially to prey and baits in the core protein interaction database to define two populations of genes based on their number of partners [15] . Those interactions involving genes ( i . e . baits with >87 interactions and preys with >231 interactions ) were deemed promiscuous by this analysis and removed from the final dataset . The remaining interactions were referred to as the “Core Network” . The unfiltered core interactome had a total of 120 , 779 interactions involving 11 , 327 genes curated as NCBI Gene entries . The Core Network after filtering comprised of 71 , 814 interactions from 10 , 430 genes . The aging interactome reported here includes only interactions from the Core Network . To establish the basis for suitable null hypotheses , the process of deriving subnetworks from the large interaction network was performed 1000 times with sets of 175 genes randomly selected from one of two sources: 1 ) any gene contained in the Y2H PPI database or 2 ) genes in either Homologene or InParanoid having homologs of C . elegans , D . melanogaster or S . cerevisiae . Because the latter set corresponds to genes conserved from phylogenetically distant organisms it is referred to as “ancient . ” In each iteration of the process , the 175 genes were used to query the Y2H PPI database and create subnetworks in a manner otherwise identical to that of the procedure for longevity homologs . The mean shortest path length between any two aging genes in the actual longevity network was calculated . We simulated the Core Network 100 times , by rewiring the edges , preserving the node degree of each protein . The aging related human genes were then screened through 100 randomized networks , to generate 100 simulated longevity networks . We then calculated the mean shortest path length between any two aging genes in the 100 randomized networks . A one sided t-test was used to compare mean shortest path lengths of the experimentally derived data to those of 100 randomizations . No background correction was performed given the very low levels of background intensity , however we performed loess normalization [48] on the entire set of probes to account for differences in the distribution of intensities among arrays . To select the genes that are differentially expressed with regards to age among the probes that matched our set of longevity proteins we performed , gene by gene , simple two-sample t-tests and used the Benjamini-Hochberg procedure [49] to derive adjusted q-values for the list of genes ranked by statistical significance . After deriving the number of significantly differentially expressed genes ( based on an FDR cut-off of 5% ) , we wished to determine if this set of probes was significantly enriched with genes whose expression changes related to age , which motivated a permutation test to find whether the identification of a gene is related to life span extension was independent of differential expression with regards to the microarray data on muscle tissue in old and young subjects . We simply performed a large number of permutations on the longevity protein label for the total set of probes , each permutation randomly designated genes as either longevity protein genes or not and then among this random set , we performed the same procedure to find the number of significantly differentially expressed genes . After 1000 permutations , we have 1000 randomly generated numbers of significantly differentially expressed genes and we can compare our observed number to this null distribution to find the p-value of the test that these genes ( related to life extension ) or unrelated to age in human muscle . We performed an identical analysis for the 1° interactor genes . To examine whether probes for genes encoding binary interaction pairs had more evidence of co-regulation in the microarray data , we examined correlation of log2 expression of probes of pairs of genes that were 1 ) connected directly and randomly chosen equal number of pairs of probes for pairs of genes unconnected in the network from the total list of probes on the Illumina array . For genes connected in the interactome represented by more than one probe , the correlation of all relevant pairs of probes were estimated ( i . e . , if there were 3 probes in one gene matched with 2 probes in another , this generated a total of 6 correlations ) . The purpose of this was to determine whether genes connected in the interactome were more related in expression than randomly drawn pairs of genes . Animals were grown on NGM agar plates seeded with OP50 E . coli at 20°C . RNAi bacteria strains were cultured as previously described [50] . Wild-type N2 animals at the late L4 larval stage were fed with E . coli expressing different double-stranded RNAs and incubated at 25°C for life span experiments . 5-fluorodeoxyuridine ( 0 . 05 mg/ml ) was added onto plates during the reproductive phase to eliminate progeny . Animals were transferred onto fresh plates every 3–6 days . The first day of adulthood is Day 1 in survival curves . Animals were scored as alive , dead or lost every other day . Animals that did not move in response to touching were scored as dead . Animals that died from causes other than aging , such as sticking to the plate walls , internal hatching or bursting in the vulval region , were scored as lost . In all life span assays , E . coli carrying the empty RNAi vector L4440 was fed to animals as controls . Statistical analyses were performed using the Prism 4 software ( Graphpad Software , Inc . , San Diego , CA , USA ) . Kaplan–Meier survival curves were plotted for each life span experiment and p values were calculated using the log-rank test [50] . | Studies of longevity in model organisms such as baker's yeast , roundworm , and fruit fly have clearly demonstrated that a diverse array of genetic mutations can result in increased life span . In fact , large-scale genetic screens have identified hundreds of genes that when mutated , knocked down , or deleted will significantly enhance longevity in these organisms . Despite great progress in understanding genetic and genomic determinants of life span in model organisms , the general relevance of invertebrate longevity genes to human aging and longevity has yet to be fully established . In this study , we show that human homologs of invertebrate longevity genes change in their expression levels during aging in human tissue . We also show that human genes encoding proteins that interact with human longevity homolog proteins are also changed in expression during human aging . These observations taken together indicate that the broad patterns underlying genetic control of life span in invertebrates is highly relevant to human aging and longevity . We also present a collection of novel candidate genes and proteins that may influence human life span . |
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Proteins perform their function or interact with partners by exchanging between conformational substates on a wide range of spatiotemporal scales . Structurally characterizing these exchanges is challenging , both experimentally and computationally . Large , diffusional motions are often on timescales that are difficult to access with molecular dynamics simulations , especially for large proteins and their complexes . The low frequency modes of normal mode analysis ( NMA ) report on molecular fluctuations associated with biological activity . However , NMA is limited to a second order expansion about a minimum of the potential energy function , which limits opportunities to observe diffusional motions . By contrast , kino-geometric conformational sampling ( KGS ) permits large perturbations while maintaining the exact geometry of explicit conformational constraints , such as hydrogen bonds . Here , we extend KGS and show that a conformational ensemble of the α subunit Gαs of heterotrimeric stimulatory protein Gs exhibits structural features implicated in its activation pathway . Activation of protein Gs by G protein-coupled receptors ( GPCRs ) is associated with GDP release and large conformational changes of its α-helical domain . Our method reveals a coupled α-helical domain opening motion while , simultaneously , Gαs helix α5 samples an activated conformation . These motions are moderated in the activated state . The motion centers on a dynamic hub near the nucleotide-binding site of Gαs , and radiates to helix α4 . We find that comparative NMA-based ensembles underestimate the amplitudes of the motion . Additionally , the ensembles fall short in predicting the accepted direction of the full activation pathway . Taken together , our findings suggest that nullspace sampling with explicit , holonomic constraints yields ensembles that illuminate molecular mechanisms involved in GDP release and protein Gs activation , and further establish conformational coupling between key structural elements of Gαs .
G protein-coupled receptors ( GPCRs ) mediate a large variety of physiological events throughout the body by activating intracellular signal transduction pathways [1] . Signaling molecules , such as hormones and neurotransmitters , can induce conformational changes in GPCRs , which allow it to complex with intracellular protein partners such as heterotrimeric guanine nucleotide-binding protein G . β2 Adrenergic Receptor ( β2AR ) , a so-called class A receptor , initiates activation of stimulatory protein Gs mainly through interactions with the latter’s α subunit ( Gαs ) . Upon activation , Gs interacts with effector proteins in the cell which , ultimately , leads to a cellular response . However , a precise characterization of the activation mechanism of Gs has remained elusive [2] . Molecular dynamics ( MD ) simulations can structurally characterize the dynamics of biomolecules in great detail [3] . However , as increasingly sophisticated experimental techniques yield ever bigger molecular systems and complexes , the computational demands to ensure adequate sampling of the conformational landscape often require highly specialized hardware and algorithms [4] . In parallel , time-independent or non-deterministic sampling-based algorithms together with simplified macromolecular representations have also led to tremendous insights . Conformational sampling with CONCOORD has provided seeds for subsequent MD simulations to overcome large energy barriers in the characterization of recognition dynamics of ubiquitin [5 , 6] . Rapid exploration of conformational space in internal coordinates with a traditional mechanical force field via a biased Monte Carlo approach [7] accurately predicted agonist binding modes for GPCRs [8] . Exhaustive sampling has predicted ensembles of low-energy conformers for GPCRs associated with ligand binding and activation [9] . Rosetta-based sampling and energy analysis provided a structural basis for rhodopsin-mediated GDP release from Gi , a inhibitory protein highly related to Gs [10] . Vibrational modes of a biomolecule are well-approximated with a so-called Elastic Network Model ( ENM ) , in which non-bonded interactions are replaced with a harmonic pseudo-potential [11] . Analysis of ENMs with NMA , which relies on a Hamiltonian in which the kinetic energy is also quadratic , yields the equations of motion around a minimum of the potential energy of the system . While low-frequency modes are generally associated with biological activity , the second order approximation underlying NMA limits its ability to access conformational substates and observe larger , diffusional motions . Nonetheless , NMAs are enormously successful and have , for instance , proposed GPCR activation mechanisms [12] . Combined with short MD trajectories NMA also predicted a molecular mechanism for GDP release from Gi [13] . Kinogeometric sampling ( KGS ) treats a biomolecule as a branched polymer , with rotatable bonds as degrees of freedom ( DoFs ) and non-covalent ( hydrogen ) bonds as distance constraints [14–16] . Hydrogen bonds define nested , closed loops that require coordinated changes of DoFs to avoid breaking the bonds . Kinogeometric sampling maps structural perturbations onto a subspace of conformation space that accounts for the reduced flexibility of these closed loops . This procedure intrinsically favors certain directions on the conformational landscape , namely those that avoid , collectively , native hydrogen bond dissociation . Additionally , representing biomolecular systems with fewer DoFs enables better exploration of conformation space and , ultimately , allows fitting sparse experimental data sets while reducing the risk of overfitting . Distance constraints from hydrogen bonds can completely rigidify substructures of biomolecules . For instance , an α-helix is often rigidified owing to its backbone hydrogen bonding network . Kinogeometric and similar sampling-with-constraint techniques have relied on combinatorial constraint counting to explicitly identify rigid substructures in the molecule that result from the hydrogen bonds [17] . Perturbing a molecular conformation with constraints generally required breaking constraints and subsequently reclosing them [18] , or iteratively refitting the perturbed conformation and the rigid substructures [19] . Here , we extend our kinogeometric computational techniques by abandoning explicit constraint counting to proteins . Our procedure efficiently samples conformational degrees of freedom in a lower-dimensional subspace in which instantaneous distance constraints are preserved exactly [20] . The advantage of our method is that a single , exact mathematical analysis both provides constraint satisfaction and infinitesimal , coordinated directions of motion for the degrees of freedom of the protein . It naturally couples motions throughout the protein by many interconnected and interdependent cycles , making few additional assumptions on interactions . As a result , collective motions emerge which deform the protein along preferred dimensions . We apply our algorithm to compute a broad conformational distribution of the inactive and active states of the α subunit of free ( i . e . not receptor-bound ) , apo ( i . e . nucleotide-free ) Gαs . We demonstrate that our ensemble identifies detailed molecular mechanisms implicated in domain opening and activation of protein Gs . We compare the findings to an ensemble obtained with a state-of-the art torsional ENM . An ENM representation with torsional degrees of freedom is conceptually similar to our approach , and is known to better represent protein conformational changes than Cartesian ENMs [21 , 22] . We selected an implementation , the iMC module of iMod , that is capable of generating large domain motions by sampling along low-frequency normal modes [23] .
The linear , branched structure of proteins naturally forms a kinematic linkage , i . e . a chain with rigid groups of atoms , or rigid bodies , as links and rotatable bonds or degrees of freedom ( DoF ) , as revolute joints . The DoFs are the backbone torsion angles ( ϕ and ψ ) and the side-chain torsion angles ( χi ) . Bond lengths , bond angles and the peptide torsion angle ω are assumed fixed at their initial values in this study . Rigid bodies are the largest sets of atoms in a protein without internal , rotational degrees of freedom ( S1 Fig ) . We initially set each atom or group of double-bonded atoms as a rigid body . The rigid bodies of atoms connected by a non-rotatable covalent bond are merged . Hydrogen atoms are explicitly included in the model . A vector q ∈ 𝕊n , q = ( q1 , … , qn ) T completely specifies a conformation for a molecule with n rotational degrees-of-freedom . We represent the kinematic linkage as a rooted , directed spanning tree , i . e . an acyclic graph G = ( V , E ) that connects all vertices V such that each one , except the root , has only one incoming , directed edge E . Vertices Vi , i = 1 , …B represent rigid bodies , and edges Ej , j = 1 , … , n represent DoFs . Hydrogen bonds are encoded as distance constraints , resulting in closed loops or so-called kinematic cycles in G ( Fig 1 ) . A cycle-closing hydrogen bond connects two subtrees propagating from a common ancestor rigid body Vc ( Fig 1 ) . To avoid hydrogen bond dissociation , a perturbation Δq should leave the positions of the hydrogen bond donor atom h and acceptor atom A unchanged with respect to a local coordinate frame placed at A and h . We denote the DoFs subject to constraints as cycle DoFs . For each cycle i = 1…m , we can define endpoint maps f : 𝕊 k ↦ ℝ 3 , x h , AL , R = fh , AL , R ( q ) , which map the ncycle DoFs of the molecular conformation q to the hydrogen bond acceptor A and donor h positions xh , A , along the left ( L ) or right ( R ) sub trees stemming from Vc . The six holonomic closure constraints fhL ( q ) −fhR ( q ) =0 , fAL ( q ) −fAR ( q ) =0 ( 1 ) define a constraint manifold 𝓜 , which is in general ( ncycle − 5m ) -dimensional . Motions on 𝓜 result in coordinated changes to DoFs that satisfy the distance constraints , and thus maintain hydrogen bonds . However , such motions are difficult to calculate since the constraint manifold is complex . We approximate the manifold locally by its tangent space Tq𝓜 at q . Differentiating Eq ( 1 ) yields ddt ( fhL ( q ) −fhR ( q ) ) = ( dfhLdq−dfhRdq ) q˙=0 , ddt ( fAL ( q ) −fAR ( q ) ) = ( dfALdq−dfARdq ) q˙=0 , ( 2 ) which we can rewrite as J q . = 0 . The 6m × ncycle Jacobian matrix , J , gives the instantaneous relationship between the cycle degrees of freedom and the end-point positions and orientations . Entries of the Jacobian matrix are efficiently computed as J ij = u j × ( r - r O j ) , where u is a unit vector along DoF j , r denotes the position of the donor or acceptor atom of the cycle-closing bond , and rOj denotes the position of the tail atom of DoF j . Perturbing a molecular conformation with any vector selected from a sufficiently small neighborhood of the origin in the null-space of J , i . e . Ker ( J ) = {q ∈ 𝕊n:Jq = 0} will maintain hydrogen-bond distances . The right-singular vectors of the singular value decomposition J = U Σ VT form a basis , N , of the null-space of the Jacobian . Note that N is orthonormal , and that NNT is the orthogonal projection onto Ker ( J ) . A null-space perturbation projects a trial-vector Δq onto the null-space , ΔqTq𝓜 = NNTΔq . Previous sampling-with-constraint procedures relied on an elegant combinatorial pebble game algorithm [17] to identify exactly all rigid and flexible substructures in the molecule [15 , 24] . The pebble game algorithm , originally developed for 2D network glasses and later validated for 3D molecular graphs by the molecular conjecture [25] , explicitly counts constraints and degrees of freedom . Our projection method does not require constraint counting , recognizing that the subset of rigidified degrees of freedom Vrigid span the nullspace of the projection matrix Ker ( NNT ) in our method: V rigid = { q : NN T q = 0 } Note that Ker ( NNT ) never requires explicit computation in our method . Mapping a trial move Δq onto Ker ( J ) by NNTΔq naturally projects out the rigidified DoFs . In addition to cycle DoFs , proteins generally have free DoFs that are not part of any closed loop and , therefore , not subject to constraints . Note that the designation free or cycle DoF is independent of the choice of the root R . Bond lengths and angles are assumed fixed in our kinematic representation , representing bonded energy terms . Non-bonded van der Waals interactions are represented by a hard-sphere , repulsive potential that is scaled for each atom type . We use an efficient grid-indexing method for detecting clashes [26] . While no explicit dihedral energy term is present , disallowed dihedral combinations are avoided by clashes . To validate our algorithm , we selected the three proteins with the largest RMSD between apo and holo conformations from a data set curated for predicting apo conformations from holo conformations [27] . Hydrogen bonds shared between apo and holo conformations were included as constraints . The domains were determined as follows: L-Leucine binding protein ( leub ) domain 1 residues 1–119 and 251–327 , domain 2 residues 120–250 and 328–345; Osmo protection protein ( osmo ) domain 1 residues 6–109 and 213–275 , domain 2 110–212 . Alginate binding protein ( algi ) domain 1 residues 1–133 and 310–400 , domain 2 residues 134–309 and 401–490 . For each holo conformation , 20 , 000 random samples were generated with exploration radius of 8Å for leub , 6Å for osmo and 10Å for algi , see the section KGS sampling below for details . To analyze the results , the centers of mass of the holo domains were first aligned with the z-axis of the laboratory coordinate system . Domain 1 in each sample in the conformational ensemble was aligned with domain 1 of the holo conformation before the zenith and azimuth angle of domain 2 of the sample were calculated [28] . We used the ligand-free ( PDB 2ZIJ ) and bound ( PDB 1BB5 ) crystal structures of human lysozyme as starting conformations . We made the L96A mutation to the bound structure to match the wild-type sequence of the ligand-free conformation [29] . Hydrogen bonds shared between the starting conformations were included as constraints . We generated 20 , 000 random samples with an exploration radius of 4Å . To analyze the results , ensemble conformations were aligned to the backbone heavy atoms of the bound structure . The breathing angle θ was computed from the centers of mass of the CA atoms from three protein regions [29] . The RMSD of the CA atoms of secondary structure elements from the bound structure was computed for each ensemble [29] . The angle θ and RMSD were binned in 0 . 5 degrees and 0 . 1Å to calculate ‘free-energy’ landscapes of these reaction coordinates . KGS takes as input a constraint file , which allows users to identify which distance constraints to maintain . In this study , hydrogen bonds belonging to our modeled Linkers I and II were removed . In both systems , the intersection of the sets of hydrogen bonds for active and inactive states was retained . Eventually , KGS sampling of both states was performed with 130 hydrogen bonds in total . A structural representative for activated apo Gαs was extracted from the crystal structure of β2AR:Gs complex with PDB id 3sn6 [30] and inactive apo Gαs was obtained from 1azt [31] . The crystal structure of the inactive state of Gαs had three residue gaps: 1 − 34 , 70 − 86 , 391 − 402 . Residues 70 − 86 ( Linker I , Fig 2 ) were added by Xpleo [16] and subsequently refined in Coot [32] . Finally , the structure was truncated to include residues 35 to 391 ( 357 residues ) . The crystal structure of active Gαs had four residue gaps: 1 − 8 , 60 − 87 , 203 − 204 , 256 − 262 . Residues 60 − 87 were built by Xpleo , 203 − 204 were added in Coot , 254 − 265 were copied from the inactive structure of Gαs after alignment , and the sequence was also truncated to include residues from 35 to 391 . The α subunit of Gs consists of a Ras-like domain and an α-helical ( AH ) domain ( Fig 2 ) . The Ras-domain is about 260 residues , which is connected to the AH-domain ( about 112 residues ) by two linkers . The long α1–αA linker I , spans residues 65 to 88 , and a shorter αF–β2 linker II spans residues 200 to 206 ( Fig 2 ) . These structures were then parametrized by the CHARMM27 all-atom force field [33] including the CMAP correction [34] and solvated in an octahedral unit cell with 19 , 737 TIP3 water molecules and electrostatically neutralized by 22 Na and 12 Cl ions ( concentration of 0 . 05 M and no ions within 6Å of any protein atom ) for a total of 65 , 000 atoms . The resulting system was minimized with Gromacs 4 . 6 . 3 [35 , 36] by a series of steepest descent and conjugate gradient algorithms by gradually reducing constraints on the protein atoms . The minimized structures of active and inactive apo Gαs served as the input models for the sampling algorithms . The Gs α subunit was represented by 1769 rigid bodies and 1768 directed edges corresponding to the dihedral DoFs ϕ , ψ , and χi . While any rigid body in the molecule can serve as the root R , we set R as the first rigid body at the N-terminus of the molecule . There were 767 cycle DoFs in the system . To ensure rapid and broad diffusion of the sampled ensemble , the sampling protocol inspired by Rapidly-exploring Random Trees of previous work was used [14 , 15 , 37] , which we briefly summarize . The sampling pool was initialized with the minimized conformations of active or inactive apo Gαs qinit . We generated a pool of 20 , 000 samples in an exploration sphere of fixed radius ( 20Å RMSD ) from qinit , which was subdivided into shells 𝓢i , i ∈ {1 , … , 100} of width 0 . 2Å , as follows . At each sampling step , a shell 𝓢k was selected at random from the subset of shells containing at least one conformation . Next , an entirely random conformation qrandom was generated . The conformation that was RMSD-closest to qrandom in 𝓢k was selected as qseed , and qrandom was discarded . A random perturbation Δq to qseed was proposed , that was then projected onto the constraint manifold and applied to qseed to obtain a new conformation qnew , i . e . qnew = qseed+NNTΔq . If qnew did not contain clashes , it was added to the pool in the shell corresponding to its RMSD from qinit , else it was discarded . The exploration radius and shell width are adjustable parameters . The combination of values selected for this study were found to balance broad exploration and uniform coverage . The collision factor that scales VdW radii during collision detection was set to 0 . 75 . The maximum rotation of a DoF was scaled to 0 . 29 degrees , which was found to reflect a good balance between fast divergence from initial structure and a high acceptance ratio . To test if the sampling trajectories had converged , we additionally generated a conformational distribution of 50 , 000 samples around the inactive and active states . All analyses are based on 20 , 000 samples , unless otherwise stated . We carried out ENM normal modes vibrational analysis ( NMA ) in internal coordinates ( IC ) with the software package iMOD [23] . After first obtaining the IC normal modes for each structure with the iMODE tool , we generated a conformational ensemble of 20 , 000 samples with the default NMA Monte Carlo sampling procedure enabled by the iMC module [23] . We obtained the first 20 normal modes by using all default settings , except enabling χ dihedral angles as DoFs to better agree with the KGS DoFs . By default iMC selects from the 5 lowest frequency modes for a Monte Carlo step . S2 Fig . displays all the modes . We used coarse-grained all heavy-atom representation and a sigmoid function pairwise interaction potential with default parameterization . We scaled the parameter a ( ’linear factor to scale motion’ ) ten-fold to better match the amplitude of domain motions suggested by experimental measurements . Further increasing the scaling did not lead to better agreement . To examine if a sigmoid function potential possibly over-constrained the system , we also sampled using a coarse-grained , CA-only representation with an essential dynamics ( ED ) potential function . A scale factor of a = 10 agreed with experimental data , but led to distortions in the models . ( S3 Fig ) . Thus , to enable a direct , one-to-one comparison between KGS and ENM , we selected an all-heavy atom , sigmoid function representation for iMC with amplitudes scaled by a = 10 , notwithstanding its slightly overconstrained model . We additionally generated conformational ensembles with the distance-restraint based sampling procedure CONCOORD [38] . We used the default , heavy-atom CONCOORD settings for structure and distance bounds generation with OPLS-AA parameters . We used near-default parameters for sampling , using the following command line: disco -on disco . pdb -n 20000 -i 2500 -viol 1 . -bump . The software is implemented in C++ . Calculations were performed on a single , 2 . 6GHz Intel processor core . Average time to obtain an accepted conformation was 8 . 9s , at an average acceptance ratio of 30% . Depending on the size of the molecule , computations take from several hours to a few days . No attempts were yet made to optimize the code . The performance limiting step is currently the repeated ( 𝓞 ( n2 ) ) calculation of RMSD within shells to ensure broad sampling . The shell width balances performance with broad diffusion . The RMSD calculation would be trivially replaced by more modern algorithms that are two orders of magnitude faster [39] . Our SVD calculation is optionally GPU-accelerated . The software and sampling trajectories are available from http://smb . slac . stanford . edu/~vdbedem .
To validate our algorithm , we computed conformational distributions for three two-domain protein crystal structures that were determined in both holo and apo conformations . For each protein , the domains open , re-orient and conformationally adjust upon adopting the apo conformation . We observed conformational distributions directed along holo-apo pathways . Starting from the holo conformation , we found that conformational ensembles on the constraint manifold defined by interconnected cycles were highly biased toward the apo conformation ( Fig 3a ) . Polar plots of the distribution of zenith ( θ ) and azimuth ( ϕ ) angles of relative positions of the centers of mass of the two domains reveal domain opening and collective , reorientating motions toward the apo conformation . No conformational pathways connecting the holo substate to the apo substate were observed , but it is unknown if ligand-free holo-apo conformational interconversion occurs for these proteins in solution . Additionally , sampling limitations or steric barriers between the states can prevent end-to-end pathways . Reaching sparsely populated , ‘excited’ substates often demand additional ( experimental ) restraints on conformational sampling techniques [14 , 27 , 29] . We furthermore tested whether conformational distributions owing to collective motions on the constraint manifold can accord with free energy landscapes observed in solution . Apo human lysozyme displays large breathing motions , characterized by the angle θ between the α and β domains . The free-energy landscape for the reaction coordinates θ and RMSD to the holo crystal structure of apo and holo ( triNAG-bound ) human lysozyme was recently characterized from replica-averaged , RDC-restrained molecular dynamics simulations [29] . While the free energy of apo lysozyme has a single minimum , the holo state revealed a second , sparsely populated ‘unlocked’ state centered on ( 49° , 1 . 5Å ) in addition to the main ‘locked’ state around ( 58° , 0 . 9Å ) ( Fig 3b ) . The holo protein is capable of sampling a wider range of θ angles than the apo structure , presumably to facilitate product release . Our conformational distributions starting from the ( ligand-free ) holo and apo structures revealed surprisingly similar conformational distributions compared to those from RDC-restrained simulations ( Fig 3b , left panel ) . The holo distribution samples more broadly , and more towards closed conformations ( smaller θ angles ) than the apo distribution , in agreement with the free-energy landscape observed from RDC restrained simulations . Additionally , weak local maxima were observed in the holo distribution , corresponding to the ‘locked’ and ‘unlocked’ state ( Fig 3b , right panel ) . The unlocked state corresponds to a sparsely populated , intermediate state , which was validated experimentally . Thus , collective motions on the constraint manifold enable quick diffusion away from the initial state along biologically-relevant directions that map the conformational landscape of the protein . β2AR can form a complex with heterotrimeric stimulatory protein Gαsβγ[40] . While the precise mechanism of protein Gs-activation remains poorly understood , interaction with the activated receptor is incidental with the dissociation of GDP and the βγ subunits [41] . Gαs , which binds GTP after the release of GDP , subsequently interacts with many effector proteins in the cell . It is hypothesized that its profound conformational flexibility plays a crucial role in signal modulation [42] . The activated ( nucleotide-free ) state of Gαs involves a large motion of the AH-domain with respect to the stable Ras-domain [43] . Additionally , the α5-helix of Gαs translates and rotates upward to interact with the cytoplasmic core of the receptor . The domain opening purportedly facilitates the release of GDP . The β6–α5 loop , which binds the purine ring of GDP , and the β6 strand also change conformation ( Fig 2 ) . The large distance separating the crystal structures of the active and inactive states suggests that Gαs can access many different conformations [2 , 42 , 44] . However , structurally characterizing and determining the sequence of events in the activation pathway by experimental means has proved challenging . Simulations suggest a coupled motion between the AH-domain and helix α5[13 , 45] . Additionally , the opening angle of the AH-domain upon activation is the subject of intense debate . Several lines of evidence suggest that crystal lattice formation may have played a role in selecting an extreme opening angle for the AH-domain [10 , 44] . Distance distributions obtained in solution indicate that the conformational variability of the AH-domain of Gi protein in complex with rhodopsin is more limited than that observed in the crystal structure of β2AR:Gs [10] . We first examined the conformational variability of the AH-domain between the active and inactive states with the methods KGS , iMC , and CONCOORD . The RMS deviations for KGS samples starting from the inactive state of the AH-domain of Gαs was 13 . 5Å , while for the active state it was 5 . 8Å ( Fig 4a ) . For iMC , the observed values were 5 . 2Å and 11 . 1Å , and for CONCOORD 9 . 7Å and 15 . 8Å ( Fig 4b ) . In addition , all three methods identify large motions of helix α5 concurrent with the domain motions . The maximum opening angle Θmax between the two domains was 27 . 2 degrees ( Fig 5 and S4 Fig , 37 . 9 degrees for 50 , 000 samples ) for the inactive state KGS ensemble , compared to 91 degrees for the activated crystal structure ( Fig 2 ) . iMC reported a maximum opening angle for the inactive AH-domain of around 9 . 9 degrees ( Fig 5 , left panel ) . The CONCOORD conformational ensemble reported a range of 15 − 20 degrees of an opening angle around the active state , and 18 degrees around the inactive state ( Fig 5 , right panel ) . Both iMC and CONCOORD sample with nearly uniformly fixed radius around the active starting conformation , which is rationalized by their reliance on an equilibrium conformation ( Fig 5 ) . In contrast , KGS , by design of its RRT-based sampling avoiding steric collisions , mimics a trajectory diffusing out of the starting conformation . KGS sampling of the activated state exhibited a slowing rate of change , while the opening angle of the inactive state still appeared to increase slightly at 20 , 000 samples , leveling of at 50 , 000 samples ( S4 Fig ) . The lack of full convergence did not appreciably change the conformational distributions , but can moderately limit interpreting the ensemble as a collection of exchanging conformational substates . Interestingly , while KGS sampling of the active conformation initially exhibits greater conformational diversity away from the inactive state , later samples are directed more towards the inactive state . The KGS ensemble for free , apo Gαs compares very well with the RMSD and opening angle reported from experimental observations in solution . Double Electron-Electron Resonance ( DEER ) spectroscopy measurements suggest that the average displacement of the apo AH-domain of Gi protein complexed with rhodopsin is 15Å [10] . From the nine models of receptor-bound Gi conformations reporting on the DEER observations , we measured an equivalent average opening angle Θ of 25 . 5 degrees ( Θmax = 48 . 8 degrees ) after alignment to the Gαs Ras-domain . The KGS ensemble suggests that ligand-free Gαs is structurally and evolutionary designed to access a broad range of opening angles . However , a set of discrete samples connecting the inactive with the active state of Gαs ( Fig 4a and 4b ) was not observed . The sample acceptance ratio in KGS , i . e . samples not rejected owing to collisions between atoms , also differed substantially between the inactive and active states ( 35% vs 15% ) . These findings could signify a steep conformational barrier between the inactive and active crystal structures between 40 to 80 degrees of domain opening angle . For instance , in the activated state of β2AR:Gαs , the α1-helix of the Ras-domain is partially melted to accommodate the large motion . In the remainder , we focus on a direct comparison of the directional conformational variability of KGS and iMC since these methods are conceptually most alike . We calculated a distribution of angles between the mean displacements of Cα atoms of the Gα AH-domain for the two ensembles ( Fig 6a and S5 Fig ) . The mean displacement is the vector connecting the center of mass of all AH-domain Cα atoms of the initial structure to the averaged center of mass of the ensemble , after alignment to the stable part of the Ras-domain . The angles of mean displacement for the AH-domain are visualized in Fig 6 . The angles of mean displacements did not align but were significantly shifted for both the active ( 57 . 6 degrees , Fig 6a yellow bar ) and inactive state ( 70 . 2 degrees , Fig 6a gray bar ) . The long tails for the angle distributions , in particular for the inactive state , identify a significant number of residues for which the angle differ by more than 90 degrees . Thus , large-amplitude motions of Gαs are described differently by the two procedures . The conformational distributions from KGS starting from the inactive form of Gαs aligns with the proposed activation mechanism of the β2AR:Gαs after GDP release . The direction of motion for the KGS inactive ensemble corresponds to a domain opening motion in the viewing plane , with a small component orthogonal to the viewing plane ( Fig 6b ) . The iMC motion is nearly orthogonal to the viewing plane , resulting in a transverse ‘rocking’ motion , with a moderate component downward towards a domain opening motion . Floquet and coworkers observed a similar , pivoting motion for the AH domain around the αA helix , which is implicated in GDP release , from Cartesian NMA with the CHARMM27 force field for protein Gi [13 , 46] . The size of the vectors reflects the difference in RMSD amplitude of the ensembles . For the active state both methods have a significant component orthogonal to the viewing plane . For neither method the main displacement in the active state appears to be along the activation pathway , signifying that additional mechanisms , such as GTP hydrolysis , likely play a key role in Gαs . The direction of mean displacement for iMC is nearly identical for the inactive and active ensembles . A possible explanation is that local structural changes in the AH-domain between the active and inactive state are modest , leaving interactions defined by the ENM largely unchanged between the states . Receptor-induced conformational changes in helix α5 are believed to contribute to GDP release [10 , 47] . Concomitant with activation , helix α5 undergoes a rotation and translation towards β6 . The magnitude and direction of these fluctuations in the KGS ensemble are striking , coinciding with those observed in MD simulations [45] . Fig 6c shows the view looking towards the cytoplasm from the receptor core . The top panel shows the α5 helix in its active conformation , and the bottom panel in its inactive conformation . The distribution of magnitudes and directions of the KGS displacement vectors along the helix in the inactive state ( bottom panel ) correspond remarkably well to a translation and rotation along a path to reach the active state ( top panel ) . RMS amplitudes of 8 . 3Å and 7 . 9 Å were observed for α5-helix in the KGS ensemble of the active and inactive states . By contrast , the iMC displacement vectors are slightly smaller in magnitude in the inactive state ( indicated by RMS spread of 1 . 4Å and 3 . 6 Å ) and have a component nearly opposite to the activation pathway . Note that while in general normal mode vectors indicate undirected displacement , our displacement vectors were calculated directly from the ensembles . Next , we analyzed displacements at the residue level for both domains of Gαs . Fig 7 ( S5 Fig ) shows the normalized magnitude of the mean CA atom displacement vectors of the ensembles . Each displacement vector was calculated as the average RMSD vector of all conformations after alignment to the stable part of the Ras-domain ( as above ) , and normalized within the angle values of its own ensemble . Mean displacements for the KGS and iMC sampled Gαs ensemble exhibit a clear pattern; they are larger for the AH-domain and vanishingly small for the Ras-domain in both the active and inactive state . The coordinated perturbations of DoFs by KGS resulted in intra-domain displacements shared by groups of contiguous residues . Three regions of the AH-domain separately display collective features indicated by elevated mean displacements , corresponding to the C-terminus of αA and αB , αC and αD , and αE and αF . A remarkably similar pattern is observed for the iMC ensemble . Helices A − D are located towards the outer radius of the rotation of the AH-domain , explaining the elevated levels of mean displacement in both active and inactive state ( Fig 2b ) . Surprisingly , their relative orientation remains well-preserved despite a sparse inter-secondary structure hydrogen-bond network in the AH-domain . The pattern of displacements is similar for the active and inactive state . Analysis at the residue level reveals key details suggesting collective motion . In the Ras-domain , helix α5 shows a large displacement , exceeding the mean displacement values of the Ras-domain ( Fig 7 , right-most shaded bands ) . The growth in amplitude towards the C-terminus is characteristic for the rotational motion we observed in the previous section . Interestingly , the single , unique feature standing out in an otherwise flat Ras-domain is elevated displacement for helix α4 and loop αG–α4 ( residues 320–340 ) in both active and inactive state ( Fig 7 ) . Fig 6d shows the motion of α4 and the adjacent loop . Helices αG and α4 are implicated in GDP release . Similar motions were observed using Cartesian NMA with the CHARMM27 force field [13] in protein Gi . Strikingly , both the α5 and motions of α4 and the adjacent loop are absent in the iMC Gs active ensemble , but both are present in KGS . This strongly suggests these motions are conformationally coupled , but possibly shifted to higher modes in iMC . The mean displacements up to residue number 80 suggest anti-correlated motions in iMC and KGS in the active state ( Fig 7 , top ) . The mean displacement reported by iMC is elevated owing to restraints between the BC loop in the AH domain and ( truncated ) helix α1 . This results in collective motions of the β1-strand with the highly mobile AH domain . The amplitude of the iMC motions is likely overestimated , as it leads to significant distortions of the β-sheet in the Ras domain ( S6 Fig ) . Similarly , the proximity of Linker I to helix F leads to collective motions in iMC . The absence of explicit constraints , i . e , hydrogen bonds in Linker I suppresses collective motions in KGS . While the precise nature of Linker I motions remains unclear , the absence of well-defined electron density in the crystal suggests this loop is highly mobile . To examine the origin of collective motion , we analyzed the distribution of the DoFs in the conformational ensembles . We observed key differences between the two methods in the spatial distribution of flexibility throughout the protein . The mean RMSF for free and cycle DoFs are summarized in Table 1 . Cycle DoFs are uniformly distributed throughout the protein . In KGS , 43 . 4% of total DoFs are cycle DoFs and of those 41% are rigidified , indicated by vanishing RMSF for cycle DoFs ( Fig 8 ) . These DoFs are contained in the null space of the projection matrix NNT . Rigidified cycle DoFs correspond largely to secondary structure elements , where DoFs are overconstrained by short or overlapping cycles . Free DoFs have larger RMSF than cycle DoFs ( Table 1 ) . If rigidified DoFs are excluded from the RMSF , a modest reduction of 20 . 5% in flexibility from free to cycle DoFs is observed . By contrast , while iMC does not define free or cycle DoFs , we observed a reversed flexibility trend compared to the corresponding DoFs in KGS–the cycle DoFs are 1 . 8 times more flexible than free DoFs for iMC . One possible contributing factor to this somewhat paradoxical find is that normal modes are obtained from eigenvectors of the Hessian matrix defined by the potential function . Free DoFs , like those in surface side-chains , are , on average , subject to fewer restraints , and thus less likely to contribute to major modes . The magnitude of a trial move is scaled by the eigenvalues of the modes , and more constrained areas may thus dominate the size of the move . We also observed that large parts of the Ras-domain do not show elevated RMSF with iMC ( Fig 8 ) , signifying that many vibrational frequencies rather than a single mode dominate structural changes for this domain . iMC locates elevated flexibility mainly in loop residues ( Fig 8 ) . Linkers I and II stand out , as well as the β6–α5 loop . Note that the backbone DoFs for LI and LII are cycle DoFs owing to hydrogen bonds between , for instance , β1 and β2 . By contrast , elevated variability in KGS is concentrated less in loop areas , and distributed more uniformly throughout the protein . The magnitude of helix α5 RMSD spread is nearly identical in the two states . However , small , motional differences in specific helical sub-regions can signify different functionalities . Significant flexibility towards the C-terminal part of the helix would enhance α5-helix conformational selectivity for inactive-like conformers , while a more active-like conformation would promote specificity through small-scale deformations near the N-terminal part of the helix . For the inactive state we observe elevated variability in the KGS ensemble from the C-terminus of α4 , through β6 , up to the N-terminus of α5 ( Fig 8a , dashed rectangle I ) . The C-terminus of helix α5 and the α4–β6 loop interact with the receptor . A conserved TCAV motif in the β6–α5 loop binds the GDP guanine ring . Helix α5 and strand β6 transmit receptor-induced conformational changes to facilitate GDP release [40] . KGS elevated variability is present in the inactive state , but moderated in the activated state and shifted away from the β6 strand . The magnitude of variability is reduced from inactive to active state for both sampling techniques , suggesting that smaller changes dominate this area in the active state . This interpretation is supported by iMC motions , where elevated variability shifts from β6 to the N-terminus of α5 upon activation . A heat map of conformational changes reveals a hotspot of highly elevated flexibility near the GDP binding pocket in the inactive state ( Fig 8b , bottom left circled ) . Similarities with a heat map obtained from peptide amide hydrogen-deuterium exchange mass spectrometry ( DXMS ) experiments , which report on exchange rates of amide hydrogens , are striking [48] . While the increased exchange rates established general sensitivity to GDP release , our nucleotide-free analysis suggests that increase in dynamics or disordering of this segment is , at least partly , attributable to motion of helix α5 and the AH domain . We also observed conformational coupling of the N-terminus of helix α5 to α1 , and the adjacent β1–α1 loop ( P-loop ) , which binds the nucleotide phosphate ( Fig 8a , dashed rectangle II and circled in inactive state ) . How the elevated flexibility is further coupled is illustrated in Fig 8b , left panels . Coupling in the GDP binding pocket extends to include helix α1 , helix αF , Linker II ( SW I ) , and the N-terminus of αE . Functional , conformational coupling is revealed to a lesser extent by iMC ( Fig 7b , right panels ) . In particular , the close coupling around the GDP binding pocket appears absent , and elevated flexibility is mostly located in loop residues . For iMC , variability of αE is shifted towards the C-terminal end of helix αD .
Proteins interconvert between functional , often sparsely populated conformational substates at a multitude of spatiotemporal scales to perform their function and interact with other biomolecules [49–51] . Understanding how these substates probe the conformational landscape and how they are coupled through collective motions can provide insights into molecular mechanisms and protein function [52 , 53] . Our conformational sampling algorithm maps small random perturbations , highly suggestive of equilibrium fluctuations , onto a constraint manifold that is defined by the hydrogen bonding network . Our new method does not require explicitly calculating rigid substructures of the protein . Instead , DoFs are subject to coordinated motion on the constraint manifold , and DoFs in isostatic or overconstrained substructures are intrinsically rigidified . Cycle DoFs contribute significantly to the distribution of the resulting conformational ensemble . Cycle DoFs make up nearly half of the DoFs , are distributed throughout the molecule , and their RMSF is only moderately reduced compared to free DoFs . The coordinated motion and distribution of cycle DoFs can potentially provide new information about mechanisms of conformational coupling . Compared to iMC , we observed motions with larger amplitudes , but both methods were in agreement with accepted mechanisms . We were better able to distinguish molecular mechanisms , and locate the origin of conformational flexibility . Important rotational DoFs stand out , and are , surprisingly , located not just in loops to accommodate inter domain motion . Results for Linker I and ( activated ) loop residues 254–265 should be interpreted with care , as experimental evidence to support their initial conformation is limited . Conformational coupling in the iMC ensemble was less pronounced , and sometimes more difficult to distinguish owing to higher modes or reduced motional amplitudes . The limited range of motion of the iMC sampling procedure likely results from the assumption of harmonic vibrations around equilibrium positions in the ENM . Large deviations break the underlying assumptions and would perturb the topology of the initial conformation–drawbacks that KGS intrinsically avoids . In contrast to the KGS distributions , the RMSD for the inactive state are reduced compared to the active state . The interface between the AH-domain and Ras-domain is subject to restraints imposed by the ENM , which limits the amplitude of the motion along the activation pathway from the inactive state . Nonetheless , while the active state sampled ensemble exhibits a larger RMSD than the inactive state , an overall reduced amplitude with respect to KGS was observed . Interdomain ENM restraints in the direction of the activation pathway alone do not explain the reduced RMSD . We observed a KGS ensemble along a pathway associated with activation for the α sub-unit of protein Gs . Conformational interconversions can occur through a myriad of alternative transition pathways . Computationally probing a multi-state conformational landscape through extensive MD simulations to obtain a probable minimum free energy pathway is often prohibitively expensive . In addition , sampling is generally affected by limitations and imperfections of the force fields [54] . At the expense of highly accurate energy estimates , our method efficiently explores the conformational space accessible to a protein while it maintains exactly covalent and hydrogen bond geometry , and avoids steric clashes . Nonetheless , interpretation of the ensemble as a collection of exchanging conformational substates would require long sampling trajectories to satisfy ergodic properties . Our method illuminates coupled intra- and interdomain motions , complementary to rigid-body domain sampling and subsequent loop rebuilding [10] . Paired with sophisticated MD simulations or energy relaxation protocols [55 , 56] our conformational ensemble can , for instance , serve as starting points for detailed transition path sampling . An exceptionally striking feature of our KGS ensemble is the magnitude of fluctuation of helix α5 concurrent with the Gαs AH-domain motion . These coupled motions point to a potential molecular mechanism of concomitant , structural changes between two remote sites implicated in the release of GDP upon activation . There is increasing experimental evidence to support this mechanism , which was first predicted by computational means for protein Gi by Floquet and coworkers [13] . Their NMA-based analysis of GDP-bound Gi identified a motion for the AH-domain that pivots on the long axis of the αA helix . Surprisingly , this transverse motion qualitatively agrees with our nucleotide-free iMC analysis . The similarity of nucleotide-free and nucleotide-bound motions is likely owed to ENM interactions between the Ras and AH domain in iMC , which mimic interactions of the nucleotide with each domain . Essential Dynamics Analysis ( EDA ) of AH domain motions upon ejection of GDP on the phosphate side from selected nanosecond time scale Targeted Molecular Dynamics trajectories furthermore revealed close agreement with motions from NMA analysis [46] . The transverse motion likely plays a key role in GDP release and Gi activation at nanosecond time scales . By contrast , whereas the AH domain motion for Gαs observed from KGS analysis also exhibits the small transverse component , it is mainly directed along a domain opening trajectory in agreement with DEER measurements , potentially additionally identifying longer , micro- to millisecond time-scales motions . While it is speculative to join analyses from two different proteins , these observations do suggest an activation mechanism whereby a transverse ‘rocking’ motion facilitates or results from GDP release , which in turn leads to a domain opening motion . For inactive , apo Gαs , elevated mobility is centered on a hotspot near the GDP binding pocket , extending to the N-terminus of helix α5 , α1 , and the adjacent P-loop . The mobile helix α4 is conformationally coupled to the hub through β6 . Previous studies established that a conformational ensemble obtained by maintaining hydrogen bonds through iteratively refitting rigid substructures agrees well with MD simulations [57] . In our method , the coordinated motions on the constraint manifold resulting from hydrogen bond encode ‘natural’ modes of deformation . However , these coupled motions and broad diffusion by a carefully selected sampling strategy come at the expense of a greatly simplified energy function . It allows our method to overcome high-energy barriers , but can lead to conformations with high physical energies . Thus , care should be taken in interpreting individual ensemble members prior to extensive energy minimization . Hydrogen bonding networks enforce collective motions that couple conformational substates implicated in GDP release . Our results highlight that in addition to stabilizing tertiary structure , hydrogen bonding networks mediate molecular mechanisms and dynamics . Indeed , evidence is emerging that hydrogen bonds mediate longe-range , correlated motions [58] . Our nullspace sampling procedure with explicit , holonomic constraints can relate motion to function by revealing molecular mechanisms . It enables researchers to formulate testable hypotheses about networks of residues that facilitate motions implicated in GDP release and AH-domain motion . In addition , our procedure could be augmented with intra-molecular distance constraints obtained from experimental data . | Multi-cellular physiology is an emergent property , which depends critically on inter-cellular signaling pathways . Transmembrane G protein-coupled receptors ( GPCRs ) mediate a large variety of physiological events throughout the body , such as vision or cardiovascular regulation . It is thus no surprise that GPCRs are targeted by more than one third of all FDA-approved drugs . Molecules such as hormones and neurotransmitters transmit messages to cells via GPCRs complexed to cytosolic heterotrimeric G proteins . G proteins , upon activation , interact with other molecules to trigger a cellular response . Despite an increasing amount of structural data , the precise conformational dynamics and activation mechanism of G proteins remain poorly understood . The size of the multi-protein complexes and the time scales at which conformational changes occur hinder adequate sampling of the conformational landscape with molecular dynamics simulations . Here , we extend and use an efficient , robotics-inspired conformational sampling procedure to probe the conformational landscape of protein G during activation . Our procedure reveals coupled , molecular mechanisms of the activation pathway , which are absent in a comparative analysis with normal modes . Our exciting results can ultimately lead to modulation of biological activity by drug design or fine-tuning of conformational heterogeneity . |
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Mutations in the retinoblastoma tumor suppressor gene ( rb1 ) cause both sporadic and familial forms of childhood retinoblastoma . Despite its clinical relevance , the roles of rb1 during normal retinotectal development and function are not well understood . We have identified mutations in the zebrafish space cadet locus that lead to a premature truncation of the rb1 gene , identical to known mutations in sporadic and familial forms of retinoblastoma . In wild-type embryos , axons of early born retinal ganglion cells ( RGC ) pioneer the retinotectal tract to guide later born RGC axons . In rb1 deficient embryos , these early born RGCs show a delay in cell cycle exit , causing a transient deficit of differentiated RGCs . As a result , later born mutant RGC axons initially fail to exit the retina , resulting in optic nerve hypoplasia . A significant fraction of mutant RGC axons eventually exit the retina , but then frequently project to the incorrect optic tectum . Although rb1 mutants eventually establish basic retinotectal connectivity , behavioral analysis reveals that mutants exhibit deficits in distinct , visually guided behaviors . Thus , our analysis of zebrafish rb1 mutants reveals a previously unknown yet critical role for rb1 during retinotectal tract development and visual function .
Biallelic mutations in the retinoblastoma susceptibility gene rb1 are causal for intraocular childhood retinoblastomas . Rb1 is a member of a gene family that consists of three members , p105/Rb1 , p107/Rb-like1 , and p130/Rb-like2 , collectively known as “pocket proteins” [1] . The activity of these proteins is controlled , in part , by cyclin/cyclin-dependant kinase complexes . Upon activation , Rb proteins bind to an array of proteins , including members of the E2F family of transcription factors to execute a range of cellular functions , including cell cycle exit , terminal differentiation , and cortical cell migration [2] . In humans , germline or somatic mutations occur throughout the 180 kb genomic region spanning the rb1 gene , including its promoter region , exons , and intronic essential splice sites , resulting in bilateral or unilateral retinoblastomas within the first 2 years of life [3] , [4] . Given its clinical relevance , the role of Rb1 during embryonic development and during tumor suppression has been studied intensely , mostly using mouse models [5] . Rb1 is expressed ubiquitously during murine development , postnatally , and continues to be expressed in adults . Embryos harboring non-conditional Rb1 knockout alleles exhibit ectopic proliferation and apoptosis throughout the nervous system and die prenatally at embryonic day 14 . 5 [6] , [7] , [8] . Embryos with conditional loss of Rb1 in the retina display ectopic division and considerable apoptosis of retinal transition cells starting at E10 [9] , [10] , [11] , [12] . Retinas in these animals contain reduced numbers of rods , bipolar cells , and RGCs , yielding a retina with a thin outer nuclear layer and a hypoplastic optic nerve . However , the etiology of optic nerve hypoplasia and if/how Rb1 functions in RGC axonal guidance has not been examined . Similarly , electroretinogram recordings from Rb1 deficient mouse retinas have revealed reduced photoreceptor to bipolar to amacrine signal transmission [10] , yet the behavioral consequences have not been examined . Here , we report that zebrafish space cadet mutants carry a rb1 mutation found in cases of sporadic and familial human retinoblastoma [13] , [14] , [15] , [16] . In zebrafish rb1 mutants , RGC precursors show delayed exit from the cell cycle and hence a delay in the generation of early-born , postmitotic RGCs , whose axons are critical for pioneering the retinotectal tract . This delay leads to RGC intrinsic axon guidance defects , aberrant retinotectal connectivity , and deficits in phototactic behaviors . Together , this work describes a novel model for understanding the developmental role of rb1 and reveals a previously unknown role for rb1 in the formation of the retinotectal tract .
We previously identified two mutant space cadet alleles , based on abnormal startle response behavior to acoustic or tactile stimuli [17] , [18] . Using recombination mapping , we mapped the space cadette226a allele to a 1 . 1 cM interval on chromosome 21 between single nucleotide polymorphic markers in the myo5b locus ( 20 recombinants/2688 meioses ) , and in the ncam1 locus ( 12 recombinants/2688 meioses ) , respectively ( Figure 1A ) . This genomic interval contains several annotated genes , including rb1 , lpar6 , and cystlr2 , which have retained syntenic positional conservation between humans , mice and zebrafish ( Figure 1B ) . Sequencing of rb1 cDNAs isolated from spcte226a larvae revealed the presence of 4 nucleotides inserted between exon 19 and exon 20 . Subsequent sequencing of genomic DNA isolated from spcte226a larvae confirmed a single nucleotide change in the splice donor sequence of intron 19 ( nt1912+1: G to A; Figure 1C ) . This generates a cryptic splice site donor , resulting in the 4 base pair insertion into the rb1 mRNA . This 4 base pair insertion causes a premature stop codon in exon 20 , predicted to truncate the protein at amino acid 677 , thereby severely truncating the B domain and cyclin domain essential for Rb1 function ( Figure 1D ) [15] . Interestingly , identical mutations have been reported in human patients with familial and sporadic forms of retinoblastoma [13] , [14] , [15] , [16] . The zebrafish rb1 gene is 67% similar ( 52% identical-based on amino acid sequence ) to the mouse and human rb1 . The Rb1 protein consists of an A and B domain forming Rb1's binding “pocket” , and a cyclin binding domain ( Figure 1D ) , and shows 81% amino acid similarity ( 66% identical ) between zebrafish and mammalian rb1 in these critical domains . Thus , sequence homology , syntenic conservation , and cDNA sequence analysis provide compelling evidence that space cadet phenotypes are caused by an rb1 gene mutation known to cause retinoblastoma . Sequence analysis of the second mutant space cadetty85d allele did not reveal any changes in the coding sequence or in any of the splice donor and acceptor sites , suggesting that this allele is caused by a regulatory mutation in the rb1 locus . Importantly , analyses of spcte226a and spcty85d mutants revealed no significant differences with regards to the strength of the phenotypes examined below ( Table 1 ) . From here on , we will refer to the space cadet gene as rb1 and describe anatomical and behavioral defects in the rb1te226a allele . During zebrafish embryogenesis , the earliest born RGCs begin extending axons at 32 hpf , cross the ventral midline of the diencephalon to form the optic chiasm at 36 hpf , and project dorsally to the contralateral optic tectum by 48 h to form a retinotectal pathway critical for mediating visually guided behaviors by 120 hpf [19] . We previously reported that 120 hpf stage rb1 mutants display wild type like retinal lamination and expression of terminal RGC cell differentiation markers , but exhibit various RGC axonal pathfinding defects [18] . To determine the temporal onset and spatial site of RGC pathfinding errors in rb1 mutant embryos , we used the ath5:gfp transgenic line expressed in RGCs to examine the development of the retinotectal trajectory [20] . Importantly , between 28–96 hpf we did not detect a difference in the intensity of GFP fluorescence in the retinas of rb1 mutant compared to wild type retinas ( Figure S1 and data not shown ) . At 36 hpf wild type RGCs have exited the retina and pioneered across the ventral midline to form an optic chiasm ( n = 40 , Figure 2A ) . In contrast , only 13% ( n = 30 ) of rb1 mutant retinas had RGC axons that exited the retina ( Figure 2B , 2C ) , suggesting that the loss of rb1 function causes a delay in the initial outgrowth of RGC axons from the retina . At 48 hpf the optic nerve in rb1 mutants was significantly thinner , with a mean diameter of 3 . 04 µm ( n = 18 ) , compared to the thicker optic nerves in wild type siblings ( 13 . 76 µm , n = 22; Figure 2D–2F ) . At 72 and 96 hpf , when wild type RGC axons have reached and innervated the optic tectum , rb1 mutant tecta show a significant reduction in RGC axon tectal innervation ( Figure 2G–2L; see Methods for quantification details ) . Together , these results reveal that rb1 mutants exhibit a delay in RGC axonal outgrowth , leading to a delay in optic nerve development , and reduced innervation of the optic tectum . To determine whether the identified mutation in rb1te226a is causative of the delay in retinotectal development , we injected wild type rb1 mRNA into one-cell stage rb1te226a mutants and examined optic nerve diameter at 48 hpf . Microinjection of wild type rb1 mRNA restored optic nerve diameter in rb1 deficient mutants in a dose dependent manner , demonstrating that mutations in zebrafish rb1 cause RGC outgrowth defects ( Figure 3A–3C , 3E ) . To determine if and to which extent the mutant rb1te226a allele has retained biological activity , we examined the ability of rb1te226a mRNA to rescue retinotectal development in rb1te226a mutants ( Figure 3D–3E ) . Injection of rb1te226a mRNA failed to significantly increase optic nerve diameter in rb1te226a mutants , suggesting that the rb1te226a protein product has very limited , if any , functionality . However , we cannot exclude the possibility that the mutant phenotype is ameliorated by maternal rb1 mRNA and/or protein deposition . Finally , we asked whether Rb1 functions within RGCs for their axons to exit from the retina and enter the retinotectal path . Because zebrafish rb1 is expressed ubiquitously throughout development ( Figure 4A–4B ) , we generated chimeric embryos by transplanting cells at the blastula stage between rb1 mutant and wild type embryos , and then examined their ability to exit from the retina ( Figure 4C ) . A significant fraction of axons from genotypically mutant rb1 RGCs transplanted into wild type hosts failed to exit from the retina ( 19% of retinas showed failure of transplanted rb1 mutant RGC axons to exit , n = 31 , Figure 4E–4F ) , consistent with the low but significant frequency of rb1 mutant retinas in which we observed a complete failure of RGCs to exit from the eye ( 11%; see below ) . Conversely , 100% of rb1 mutant retinas showed exit of axons from transplanted wild type RGCs ( n = 69 , Figure 4D ) . Thus , during zebrafish development rb1 acts RGC autonomously for axons to exit the retina and to form the optic nerve . Given the RGC intrinsic defects observed in rb1 mutants , we next wanted to determine the primary defect leading to the delay of RGC axons to exit from the retina . Rb1 canonically functions to regulate cell cycle checkpoints , promoting cell cycle exit and differentiation of progenitors and suppressing cell cycle re-entry of differentiated cells [2] . In the retina , rb1 has been shown to promote the exit of retinal progenitor cells from the cell cycle into the various postmitotic cell types that populate the retinal lamina [9] , [10] , [11] , [12] . To examine rb1 deficient retinas for cell cycle defects , we labeled wild type and rb1 mutant retinas for M-phase positive nuclei with an anti-phosphohistone-H3 antibody ( anti-pH3 ) during the initial phase of RGC birth and axon outgrowth , between 28 and 36 hpf . During this time window , premitotic ath5 positive retinal progenitors divide , with one daughter becoming a postmitotic RGC and the other maintaining its progenitor potency to give rise to other retinal cell types that become postmitotic at later stages of development [21] . Although the total number of M-phase positive increased with time between 28 and 36 hpf in rb1 mutant and wild type retinas , we observed fewer M-phase positive nuclei in rb1 deficient retinas , compared to wild type retinas , at each time point examined ( Figure 5 ) . One possibility is that the reduction of M-phase retinal precursors in rb1 deficient retinas is due to increased cell death . Indeed , compared to wild type retinas , rb1 deficient retinas showed a slight , but significant increase of TUNEL positive nuclei between 28 and 36 hpf ( Figure S1 ) . Importantly though , comparing the increased number of TUNEL positive nuclei to the decreased number of pH3 positive nuclei in rb1 mutants at 28 , 32 , and 36 hpf revealed that apoptosis accounts for only 18–26% of the observed reduction in M-phase positive retinal precursors in rb1 mutant retinas at each time point examined . This suggests that in rb1 mutant retinas cell death contributes only partially to the deficiency of M-phase positive nuclei ( Figure S1G ) . Thus , the reduction in M-phase retinal precursors in rb1 mutant retinas suggests a prolonged terminal cell cycle for the retinal precursors , which need to exit their final cycle to become the earliest population of postmitotic RGCs . To determine if loss of rb1 function indeed causes an initial delay in the presence of postmitoic , differentiated RGCs , we examined expression of isl2b-gfp , one of the earliest transgenic markers indicative for postmitotic RGCs [22] . We found that in wild type retinas postmitotic , differentiating RGCs marked by isl2b-gfp expression emerged first at 32 hpf , increased significantly in their abundance by 36 hpf , and by 48 hpf isl2b-gfp positive RGCs were densely packed throughout the ganglion cell layer ( Figure 6A , 6C , 6E , n = 25 , 16 , and 29 , respectively ) . In contrast , isl2b-gfp positive RGCs were present in only 13% of rb1 deficient retinas at 32 hpf ( n = 23 ) . Because of the cytoplasmic localization of the GFP signal and the density at which RGCs normally populate the ganglion cell layer , it is difficult to determine the total number of isl2b-gfp positive RGCs . Nonetheless , semi-quantitative analysis revealed that by 36 hpf , isl2b-gfp positive RGCs were present in 90% of rb1 mutant retinas ( n = 29 ) ; however , their distribution with the retina was more similar to that of younger wild type retinas at 32 hpf ( Figure 6B , 6D ) . By 48 hpf , all rb1 mutant retinas harbored isl2b-gfp neurons ( n = 32 ) , although differentiation still appeared to lag in 81% ( n = 32 ) of the rb1 mutant retinas compared to the more densely packed ganglion cell layer in wild type retinas ( n = 29 , Figure 6E–6F ) . Despite the reduced number of RGCs present at 48 hpf , rb1 mutant isl2b-gfp positive RGCs express DM-GRASP , a late marker of RGC differentiation , demonstrating that mutant RGCs were fully differentiated once becoming postmitotic ( Figure S2 ) . Importantly , the number of ath5-gfp positive RGC precursors was unaffected in rb1 mutants ( Figure S1 and data not shown ) . Thus , the rb1 deficiency causes a delay in the transition of RGC precursors to postmitotic RGCs , but not in the specification of RGC precursors . Aside from the reduced population of early born RGCs , rb1 mutant retinas appear grossly normal , and at 120 hpf , show proper lamination by each retinal cell type [18] . Although we did not determine whether birth dating of other retinal cell types is affected in rb1 mutant retinas , netrin-positive exit glial cells and Muller glia cells are present in appropriate numbers and location , indistinguishable from wild-type retinas ( Figure S2 ) . Taken together , these results suggest that a delay in cell cycle exit by rb1 deficient RGC precursors leads to a transient reduction in the early born postmitotic RGCs without consequence to the gross morphology and overall cellular landscape of the rb1 mutant retina . The early born RGCs are located within the central retina and pioneer the retinotectal tract to the contralateral optic tectum [22] . In the absence of the early pioneering RGC axons , the axons of later born , more peripherally located RGCs fail to exit the eye and project aberrantly within the retina [22] . Given the reduced number of these early born , central RGCs in rb1 mutants , we sought to determine whether peripheral RGC axon trajectories were affected . For this , we labeled small groups of RGCs in the anterior peripheral retina with DiO ( green ) and in the posterior peripheral retina with DiI ( red , Figure 7A–7F ) . In 120 hpf larvae wild type larvae , all labeled axons from anterior and posterior RGCs fasciculated shortly after sprouting from their soma and extended as an axon bundle , forming a path directly toward the retinal exit point ( n = 83 , Figure 7A–7B , 7D–7E ) . In contrast , 91% ( n = 65 ) of rb1 deficient retinas harbored a significant subset of axons that had extended aberrantly throughout the retina and failed to exit ( Figure 7C , 7F ) . These results demonstrate that the delayed differentiation of the early born RGCs in rb1 mutants impairs the ability of later born RGC axons to exit the retina . The delayed cell cycle exit and differentiation of pioneering RGCs lacking rb1 may also affect axon navigation by later born RGC axons at key choice points: the ventral midline of the diencephalon and/or the optic tectum . To examine these possibilities , we filled the RGC layer of the left and right eyes of wild type and rb1 mutant larvae with either DiI or DiO , respectively ( Figure 7G ) . In wild type siblings , 99% of dye filled optic nerves projected to their appropriate contralateral tectum ( n = 946 , Figure 7H ) . In contrast , rb1 mutant optic nerves displayed a variety of phenotypes . The majority of rb1 deficient optic nerves were significantly thinner than their wild type counterparts ( 37% , n = 663 , Figure 7I–7J , 7L ) , consistent with what we observed in with ath5:gfp ( Figure 2 ) . In a significant portion of rb1 deficient optic nerves , 17% , RGCs projected to both the contralateral but also to the ipsilateral tectum , indicative of midline pathfinding defects ( n = 663 , Figure 7K–7L ) . Focal DiI/DiO labeling of RGC axons arising from the anterior and posterior retina revealed that retinotopic mapping , a function of retinal cell body location [23] , remains intact in rb1 mutants despite the aberrant pathfinding en route to the optic tectum ( Figure S3 ) . Finally , in 11% of rb1 mutant retinas , there was a complete failure of RGCs to exit from the eye , even at 120 hpf ( Figure 7J ) . Taken together , these results suggest that rb1 deficient RGC axons make intraretinal and midline pathfinding errors , leading to reduced and incorrect tectal innervation . By 120 hpf , zebrafish larvae perform an array of sensorimotor behaviors , including responses to visual stimulation . For example , changes in visual field illumination , such as the sudden absence of light or a shift from uniform to focal illumination , elicit specific , stereotyped turning behaviors [24] , [25] . We first examined the ability of rb1 mutant larvae to perform positive phototaxis , defined as navigating toward a target light source that is presented after extinguishing the pre-adapted uniform light field [25] . Positive phototactic navigation is characterized by larvae first turning towards the target light source and then swimming forward towards the target . As previously reported , when presented with a target light source wild type larvae facing away from the light target show significant initiation of turns , which are preferentially biased towards the light target ( Figure 8A–8B ) . Once facing the target , wild type larvae initiate forward scoot swims ( Figure 8C ) . In contrast , turn initiation in rb1 mutant larvae facing away from the light target was dramatically reduced ( Figure 8A ) . On the few occasions when they initiated a turn , turning direction was unbiased with respect to the light target ( Figure 8B ) . Moreover , rb1 mutants facing the light target did not show an increase in forward scoot swim initiation above baseline ( Figure 8C ) . To further determine whether rb1 mutants respond to more extreme changes in illumination , we examined their ability to perform an O-bend response to a visual dark flash stimulus , a sudden extinction of light [24] . Again , compared to their wild type siblings , rb1 mutants displayed a minimal O-bend response to dark flash stimulation ( Figure 8D ) . Despite their impaired visual responses , rb1 mutants showed no difference in the spontaneous initiation of turning or swimming behaviors compared to wild type siblings ( Figure 8D ) . Importantly , the kinematic parameters of spontaneously occurring turning and swimming movements were indistinguishable between rb1 mutants and their wild type siblings , demonstrating that the neural circuits required for initiation and execution of turning behaviors are largely intact in rb1 mutants . Together , these results demonstrate that rb1 mutants exhibit visual deficits .
Children with biallelic germline or sporadic inactivation of rb1 are likely to form ocular tumors during early childhood . Initially , the retinas of affected individual show an otherwise grossly normal morphology . In contrast , even conditional rb1 knockout mouse models exhibit ectopic proliferation and cell death leading to significant morphological defects throughout affected retinas . We find that inactivation of the zebrafish rb1 gene through a rb1 causing mutation results in mutant retinas that display very limited signs of cell death , with differentiated retinal cell types that are properly laminated , similarly to childhood retinas lacking rb1 . Thus , the fairly ‘normal’ retinal landscape of zebrafish rb1te226a mutants provided us with a unique opportunity to investigate if and how rb1 is required to establish the retinotectal projection . Our analysis reveals a RGC autonomous requirement for rb1 in regulating RGC axon pathfinding within the retina and at presumptive choice points en route to the optic tectum . Moreover , we demonstrate that zebrafish rb1te226a mutants exhibit deficits in visually guided behaviors , suggesting that the retinotectal path defects in rb1 mutants may be sufficient to impair vision . Together , this work reveals a novel role for rb1 in the establishment of RGC axon projections during development and establishes a unique model for understanding the developmental and tumor suppressor roles of the rb1 gene . Zebrafish rb1te226a mutants harbor a human retinoblastoma causing rb1 gene mutation . The mutant protein is truncated in the B-domain and lacks the cyclin-binding domain , reducing Rb1's capacity to form a ‘pocket’ , and reducing its capacity for phosphorylation by cyclin dependent kinases [2] , [26] . Consistent with the notion that the rb1te226a mutant allele is largely non-functional , mRNA over-expression in rb1 mutants does not ameliorate the rb1 mutant phenotype . Despite the absence of biological activity of the truncated rb1te226a protein , mutant zebrafish show a significantly milder retinal phenotype compared to conditional or even germline rb1 mouse knockouts [6] , [7] , [8] , [9] , [10] , [11] , [12] . One possible explanation is the strong maternal contribution of rb1 in zebrafish ( Figure 4A ) , which may suppress phenotypic expressivity at early stages of development . Consistent with this idea , formation of the initial scaffold of axon tracts during the first day of development appears unaffected in rb1te226a mutants , yet visual and hindbrain pathways that develop after the first day of development show defects [18] . In humans the rb1 nt1960+1 mutation , which is identical to the zebrafish rb1te226a mutation , causes ocular tumors [13] , [14] , [15] , [16] , raising the possibility that zebrafish rb1 mutants might also develop tumors as juveniles . However , rb1 mutants fail to inflate a functional swim bladder , and die ∼7 days post fertilization , precluding the analysis of ocular tumors in juveniles . Although wild type rb1 mRNA injection rescues the early RGC and retinotectal defects through 48 hpf , these transiently rescued rb1te226a mutants do not survive beyond 7 days of development , indicating that rb1 plays a critical role after the injected mRNA has been degraded . Establishing stable , inducible rb1 transgenic lines to rescue developmental deficits will therefore be required to monitor juvenile and adult zebrafish for retinal tumors . In rb1te226a mutants , a significant subset of RGC axons fail to exit the retina , and many of the exiting axons then project incorrectly to the ipsilateral tectum , revealing a previously unrecognized requirement for rb1 in regulating axon pathfinding . One possible explanation for the RGC guidance defects is that in rb1te226a mutant RGCs the expression of guidance factors might be disrupted . Interestingly , cortical cell migration was shown to be dependent on rb1 regulated neogenin expression [27] , suggesting that rb1 deficient RGC axons might lack guidance factors required to navigate towards the retinal exit point and properly cross the ventral midline . To investigate this possibility , we performed microarray gene expression analysis of the retina and brains of 32 hpf rb1te226a mutants ( MAW and MG , unpublished ) . However , this approach did not reveal a significant change in the expression levels of neogenin or other known axon guidance genes . Although it remains possible that rb1 regulates expression of un-identified guidance factors , it is more likely that rb1 regulates axon pathfinding indirectly by ensuring the timely exit of RGC precursors from the cell cycle and hence the appropriate temporal appearance of differentiated RGCs . In fact , genetic ablation of the earliest born RGCs prevents the formation of the retinotectal tract [22] . This suggests that RGC birth order imprints a critical hierarchical pathfinding role on RGC axons , such that axons from the earliest born RGCs pioneer the retinotectal tract that later born RGC axons will follow [22] . The delayed onset of RGC birth in rb1te226a mutants may therefore reduce the population of pioneering RGCs present during a restricted window of environmentally expressed guidance factors . Zebrafish rb1te226a mutants display deficits in the acoustic startle response [17] , [18] and in visually guided behaviors , reflecting the importance of rb1 function for the development of neural circuitry underlying behavior . The deficits in startle behavior are due to defects in a small subset of hindbrain neurons , the spiral fiber neurons [18] , and giving the results presented here , it is tempting to speculate that rb1 plays a similar role for the transition of these neurons from precursors to postmitotic neurons . Unfortunately , markers that follow the development of spiral fiber neurons are not available , precluding such analysis . Therefore , we focused on the well-characterized development of RGCs . Although we demonstrate a defect in the early development of these cells and their axonal connectivity , we cannot exclude the possibility that zebrafish rb1 mutants exhibit defects in the development and/or function of other retinal cell types , and that these defects contribute to the deficits in visual behaviors we observe . Future analysis of transgenic lines expressing the wild type Rb1 gene in individual retinal cell types will reveal which cell type ( s ) and connections are causative of the visual deficit . In summary , we report a zebrafish mutant carrying a human disease causing rb1 mutation , which reveals novel roles of rb1 in regulating RGC axon pathfinding and visually guided motor behavior . Furthermore , these mutants provide a non-murine vertebrate model of rb1 and offer new potential for identifying the elusive retinoblastoma cell of origin and further insight into the developmental role of rb1 .
All experiments were conducted according to an Animal Protocol fully approved by the University of Pennsylvania Institutional Animal Care and Use Committee ( IACUC ) on January 27 , 2011 , protocol number 803446 . Veterinary care is under the supervision of the University Laboratory Animal Resources ( ULAR ) of the University of Pennsylvania . The zebrafish ( Danio rerio ) strain used in this study was the spcte226a allele ( now referred to as rb1te226a ) of space cadet [17] , [18] , maintained on a mixed TLF and Tubingen background . The rb1te226a allele was also crossed into the ath5:gfp and isl2b:gfp transgenic backgrounds for RGC analysis [20] , [22] . rb1te226a+/−;ath5:gfp+/− or rb1te226a+/−;isl2b:gfp+/− adults were always crossed with rb1te226a+/−;TLF adults to ensure that rb1te226a embryos analyzed for GFP-expressing RGCs were hemizygous for GFP . Throughout the manuscript , rb1−/− , “rb1 deficient” , and rb1 mutant refers to rb1te226a homozygotes . The other space cadet allele spcty85d [17] was only used where mentioned . Embryos were collected in the morning , maintained on a 14/10 hour light/dark cycle at 28°C , and staged as described previously [28] . Larvae were raised in 6 cm plastic Petri dishes at a density of 20–30 per 7 mL in E3 medium ( 5 mM NaCl , 0 . 17 m mM KCl , 0 . 33 mM CaCl2 , 0 . 33 mM MgSO4 ) with medium changes at 48 hpf ( hours post fertilization ) and 96 hpf . Behavioral experiments were conducted on 120 hpf larvae . A three generation mapping cross between rb1te226a heterozygous and WIK fish was generated , and pools of 25 F2 mutant and F2 sibling 5 dpf larvae were collected in the F2 generation and used for bulk segregant mapping ( see Table 2 for simple sequence length and single nucleotide polymorphic markers ) . Mutant larvae were identified by performing successive , unilateral C-bends to acoustic or tactile stimulation [17] , [18] . To identify the mutation , cDNA was prepared following total mRNA extraction from 5 dpf larvae as previously described [29] . rb1 cDNA was amplified with primers ( rb1:1–6 , Table 2 ) designed against overlapping regions of the rb1 reference sequence ( Ensembl ) with the following RT-PCR conditions: 94°C for 3 min and then 40 cycles of 94°C for 45 sec , 57°C for 1 min , and 70°C for 1 min . Products were gel purified and cloned into the pCR2 . 1-TOPO-TA vector for sequencing . After detecting a frameshift and 4 nucleotide addition to the end of exon 19 in rb1te226a cDNA clones , gDNA was isolated from 5 dpf larvae , and intron 19 was amplified with the rb1:8 primers , using identical PCR conditions to those described above . For rb1 RNA injection , cDNA was prepared from genotyped homozygous wild type or rb1te226a mutant 5 dpf larvae ( dCAPS protocol , see below ) and amplified with the rb1:FL primers ( similar conditions as above , except extension time increased to 3 min ) , which includes the coding region of rb1 , and cloned into the pCS2+ vector . Wild type rb1 and rb1te226a mRNA was prepared using the mMessage mMachine kit ( Ambion , NY ) and injected at the 1-cell stage at doses ranging from 1–100 picograms . Embryos injected with 20 or greater picograms of rb1 mRNA showed gross morphological abnormalities and necrosis , whereas embryos injected with 10 picograms or less appeared morphologically normal . To genotype rb1te226a embryos , we developed a dCAPS assay [30] using the dCAPS program ( http://helix . wustl . edu/dcaps/dcaps . html ) to design appropriate primers ( Table 2 ) . After gDNA isolation , PCR was performed as described above . The PCR product is then digested with SspI ( New England Biolabs , Ipswich , MA ) , cleaving the rb1te226a allele and producing a 120 bp fragment that can be distinguished from the 150 bp wild type allele on a 3% agarose gel containing 1 . 5% Metaphor agarose ( Lonza , Rockland , ME ) . All genotyping , except for BrdU labeled embryos , was performed following immunolabeling experiments . For immunostaining , embryos were fixed in 4% paraformaldehyde ( PFA ) overnight at 4°C , permeabilized with 1 mg/mL collagenase , and blocked for 1 hour with 5% normal goat serum in 0 . 1 M phosphate buffer . Embryos were then incubated in the primary antibodies anti-GFP ( 1∶200 mouse JL8 , Clontech , Mountain View , CA or 1∶500 rabbit , Invitrogen , Carlsbad , CA ) , anti-phosphohistone-H3 ( Millipore , Charlottesville , VA ) , 1∶100 anti-BrdU ( Roche , Branchburg , NJ ) , and/or 1∶50 A2-J-22 polyclonal antisera ( recognizes carbonic anhydrase II , kindly provided by Dr . P . Linser ) overnight at 4°C in blocking solution , washed out , and then detected by the addition of AlexaFluor488 or AlexaFluor594 conjugated secondary antibodies ( 1∶500 , Invitrogen , Carlsbad , CA ) . TUNEL assay was performed as previously described [31] using Apoptag Peroxidase In Situ Apoptosis Detection Kit ( Chemicon , Temecula , CA ) . After staining , samples were mounted in DAPI containing Vectashield ( Vector Labs , Burlingame , CA ) . Images were acquired with a Zeiss 710 confocal laser scanning microscope ( LSM 710 ) using ZEN2010 software . For in situ hybridization , digoxygenin-UTP labeled antisense riboprobes for rb1 were synthesized and hydrolyzed from the full length rb1 cDNA construct [32] . Whole-mount in situ hybridization was performed as described previously [33] . Images were acquired with a Zeiss Axioskop compound microscope . For RT-PCR based expression analysis , the rb1:FL and B-actin primers ( Table 2 ) were run against cDNA prepared from total mRNA extracted from 25 embryos/larva at each stage . 120 hpf larvae were anesthetized ( 0 . 01% Tricaine ) and fixed in 4% paraformaldehyde at 4°C overnight . Larvae were removed from fix , washed briefly in phosphate buffered saline ( PBS ) , and mounted dorsal side up for whole retinal injection or laterally for discreet RGC labeling on glass microscope slides in a bed of 1 . 5% agarose . To label all RGCs , the vitreal space of each eye was filled with either of the fluorescent lipophilic dyes DiI ( red ) or DiO ( green ) ( Molecular Probes , Eugene , OR ) dissolved in 1% chloroform , using a WPI PV820 picopump injector fitted with a glass micropipette . For discreet labeling , a small region of the exposed eye was labeled with pulses of DiI/DiO dissolved in 0 . 5% dimethylformamide . Injected larvae were kept moist with PBS and incubated overnight at room temperature in a humidity chamber in darkness . Larvae were then examined for phenotype analysis using a Zeiss Axioplan compound fluorescent microscope . Eyes were carefully removed from selected representative larvae , which were then remounted on coverslips in agarose for imaging . Images were recorded using a Zeiss 510 confocal laser scanning microscope ( LSM510 ) and Zeiss LSM510 analytic software . For transplant direction wild type donor into space cadet host , wild type transgenic Tg ( ath5:gfp ) and rb1te226a heterozygous fish were used to generate wild type GFP expressing donor embryos and non-GFP expressing rb1te226a mutant embryos , respectively . For transplant direction space cadet donor into wild type host , rb1te226a; Tg ( ath5:gfp ) double heterozygotes and either TU or TLF strain wild type mating pairs were used to generate rb1te226 GFP expressing donor embryos and non-GFP expressing wild type embryos , respectively . Once the appropriate donor-host embryos were collected , embryos were immediately placed in E3 medium and kept at room temperature . Donor embryos were pressure injected into the yolk sac at the 1–2 cell stage with the lineage tracer tetramethylrhodamine dextran , 3 Kd , 5% w/v ( Molecular Probes , Eugene , OR ) dissolved in 0 . 2 M KCL and filter sterilized . Donor and host embryos were then incubated at 28 . 5°C in E3 medium in darkness to grow synchronously to the 1000 cell stage . Embryos were then transferred into room temperature complete E2 medium ( E2 ) to retard growth , and dechorionated using Pronase ( 1∶50 in E2 of 30 mg/ml stock , Roche ) in glass 60 mm petri dishes . Dechorionated embryos were washed extensively with E2 , transferred using a fire polished glass Pasteur pipette into individual wells in a transplantation dish containing E2 , and properly oriented . Transplantation needles were made using #1BBL No Fil borosilicate glass pipettes ( WPI ) , pulled to produce fine tips in a P87 pipette puller ( Sutter Instruments , Novato , CA ) , broken at various diameter openings , and polished using a microforge . Needles were then inserted into a standard pipette holder connected to a modified manual injection apparatus , and mounted in a micromanipulator arm for precision control . Thirty to fifty blastomeres were carefully removed from the donor embryo using the transplantation pipette/manual injector apparatus , and transferred into the adjacent host embryo at the apex of the animal pole ( eye/nose region ) . Operated embryos were maintained in the transplantation dish wells in E2 at 28 . 5°C in darkness following transplantation , and were allowed to develop undisturbed until epiboly completed . Embryos were then transferred from the transplantation wells/dish into either separate 1 . 5% agarose coated 60 mm plastic Petri dishes for donors and hosts , or 1 . 5% agarose coated wells in 12-well tissue culture plates as host-donor pairs , depending on the direction of the transplant , and incubated at 28 . 5°C for five days . The later was necessary in order to correctly identify rb1te226a donors from each donor-host pair , as the motility phenotype does not manifest itself until 120 hpf . 120 hpf larvae were screened for the presence of GFP expressing RGC clones using a Leica MZFIII fluorescence stereomicroscope , and further analyzed for misprojecting RGC axons using a Zeiss axioplan compound fluorescence microscope . Host larvae suspected of containing misprojecting RGC axons ( ie , not exiting the eye , or midline defects ) were then fixed and stained with anti-GFP antibody as described above , and imaged using a Zeiss LSM510 microscope and Zeiss LSM510 analytic software . Confocal z-stacks were of sufficient depth ( 150–220 µm ) to insure optic nerves were not inadvertently missed . Confocal stacks were processed into maximum and/or summation intensity projections using ImageJ for quantification . We used the full width half maximum algorithm to calculate optic nerve diameter from maximum intensity projections of GFP-labeled retinal ganglion cell axons . Tectal innervation was determined by making 20 µm summation projections of GFP labeled tecta , tracing the area of the labeled tectum to determine the Raw Integrated Density ( RID ) per µm2 , and subtracting the RID/µm2 of an unlabeled , background region . TUNEL and anti-pH3 labeled nuclei were counted from 30 µm stacks using Volocity ( PerkinElmer , Waltham , MA ) , with individual cells distinguished by fluorescent intensity and size . Statistical analysis was performed on all data using the Graphpad prism software ( www . graphpad . com ) . Behavioral experiments were performed on 120 hpf larvae and analyzed with the FLOTE software package as previously described [25] , [34] , [35] . rb1te226a and wild type siblings were identified based on acoustic startle behavior [17] , [18] and then grouped by phenotype for visual behavior testing in 6 cM petri dishes at a density of 12 fish per dish . For all behavioral experiments , N = 48 rb1te226a and 48 wild type sibling larvae . For phototaxis experiments , video recordings were triggered every 500 msec , with each recording covering a 400 msec time window , for a total duration of 4 sec of recorded behavior . Each group of 12 larva were subjected to 3 rounds of phototaxis testing , with 3 min between trials . Orientation of larvae to target light was determined at the beginning of each 400 ms recording as previously described [25] , such that the behavior of each larva was tested multiple times and in different orientations with respect to the target light . Therefore , the N for Figure 8A and 8C ranged from 75 to 547 for wild type siblings and 136 to 311 for rb1te226a larvae . In Figure 8B , the N ranged from 39 to 106 for wild type siblings and 13–33 for rb1te226a larvae . For dark flash response experiments , N = 4 groups of 12 larvae . Spontaneous behavior was analyzed on individually housed larvae on a 4×4 grid array . | Before an organism can execute necessary behavioral responses to environmental stimuli , the underlying neural circuits that regulate these behaviors must be precisely wired during embryonic development . A properly wired neural circuit is the product of a sophisticated collaboration of multiple genetic pathways that orchestrate cell type specification , the extension and growth of the cell processes that connect each circuit component , and the refinement of these connections . In an unbiased genetic screen designed to identify the genes required for proper circuit formation in developing zebrafish embryos , we identified a human disease causing mutation in the retinoblastoma-1 ( rb1 ) gene that disrupts the formation of the zebrafish visual circuit . rb1 canonically functions to regulate the cell cycle , and when mutated the loss of rb1-mediated cell cycle control elicits childhood ocular tumor formation . Genetic models of rb1 have been developed to study the developmental role of rb1 in the retina; however , ectopic cell proliferation and death within the retina have largely precluded the ability to evaluate the formation and integrity of neural circuits connecting the retina with the brain . In this study , through genetic and cellular analysis of a zebrafish rb1 mutant , we reveal a novel role for rb1 in regulating the establishment and functionality of the visual circuitry . |
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Lipophosphoglycan ( LPG ) is the major surface glycoconjugate of Leishmania protozoan and has an important biological role in host-parasite interactions both in the midgut epithelium of the sand fly vector and in the vertebrate macrophages . Canine leishmaniasis ( CanL ) is a chronic infectious disease predominantly caused by Leishmania infantum . An early and accurate immunodiagnosis of the disease is crucial for veterinary clinical practice and for disease control . In this work , we evaluated L . infantum LPG as an antigen in an indirect enzyme-linked immunosorbent assay ( ELISA ) for CanL immunodiagnosis ( LPG-ELISA ) by testing serum samples from 97 naturally infected dogs with diverse clinical presentations ranging from subclinical infection to severe disease , as evaluated by veterinarian infectologists . Serum samples from healthy dogs from non-endemic areas ( n = 68 ) and from dogs with other infectious diseases ( n = 64 ) were used as controls for assay validation . The performance of the LPG-ELISA was compared with that of an ELISA using the soluble fraction of L . infantum total lysate antigen ( TLA ) . LPG-ELISA presented a superior performance in comparison to TLA-ELISA , with 91 . 5% sensitivity , 98 . 5% specificity and 99 . 7% accuracy . A distinguishing feature of the LPG-ELISA compared to the TLA-ELISA was its higher ability to identify subclinical infection in clinically healthy dogs , in addition to the absence of cross-reactivity with other canine infectious diseases . Finally , LPG-ELISA was compared to TR DPP visceral canine leishmaniasis test , the immunochromatographic test recommended by the Brazilian Ministry of Agriculture . LPG-ELISA exhibited higher values of specificity ( 98 . 5% versus 93 . 1% ) and sensitivity ( 91 . 5% versus 90 . 6% ) compared to TR DPP . In conclusion , L . infantum-derived LPG was recognized by antibodies elicited during CanL in different infection stages and was shown to be a suitable antigen for specific clinical settings of veterinary diagnosis and for public health usage .
Leishmaniasis comprehend a complex of chronic zoonotic diseases caused by intracellular protozoa from the Leishmania genus . The etiologic agent of visceral leishmaniasis in India and East Africa is Leishmania donovani , while the disease is predominantly caused by Leishmania infantum in the Middle East , central Asia , Mediterranean countries and the Americas [1] . In Latin America and Mediterranean countries , dogs ( Canis familiaris ) with canine leishmaniasis ( CanL ) are the main sources of L . infantum infection for the invertebrate sand fly vector [2] . Leishmania has a great ability to evade the host immune system . To survive in the host’s mononuclear phagocytic cells , this parasite developed biochemical and morphological adaptations , and glycoconjugates are the main molecules involved in these processes [3] . The most studied Leishmania glycoconjugate is the lipophosphoglycan ( LPG ) , a dense glycocalix covering the promastigote’s surface and flagellum [4] . Its carbohydrate motif shares similarities with the proteophosphoglycans ( PPGs ) found in the intracellular amastigote stage , which causes disease in vertebrate hosts [5 , 6] . LPGs mediate several mechanisms that are essential to parasite virulence , both in the vertebrate and invertebrate host , such as immunomodulation and attachment to the sand fly midgut , respectively [7 , 8] . Several immunodiagnostic tests for CanL have been developed to detect specific antibodies against Leishmania . In Brazil , the Health Ministry recommends an immunochromatographic assay based on a recombinant rK28 antigen of L . infantum ( TR DPP -CVL ) combined with an indirect ELISA based on L . major crude total antigen ( EIE ) as criteria for the culling of seropositive dogs in surveillance and control programs for visceral leishmaniasis [9] . Although those tests present good sensitivity in the detection of diseased dogs ( “symptomatic dogs” , as called by some authors ) , they cannot distinguish susceptible and resistant dogs and are quite insensitive for the detection of subclinical infection in clinically healthy dogs ( also called by some authors as “asymptomatic dogs” ) . Thus , assays with better predictive values are still needed [10 , 11] . Moreover , many serologic assays can present false-positive results due to cross reactions with other species of Leishmania such as L . braziliensis [12] , and with other infectious agents that are common in dogs , including Trypanosoma cruzi [13] , Ehrlichia canis , Babesia canis [14 , 15] . The search for an ideal Leishmania antigen for CanL immunodiagnosis has focused on proteins and peptides , most of which are found in proteomic studies [16 , 17] . However , disadvantages of the use of such molecules include the time-demanding and high-cost processes and the low stability of the synthetized compounds . The use of glycoconjugates as antigens has not been fully explored [18 , 19] , but the ability of LPG to stimulate high antibody titers in human hosts has also been demonstrated [20] . Moreover , scientific evidence of the high stability of these molecules and a high yield of the purification procedures [21] suggests that they can be advantageous for immunotests . Thus , since Leishmania-derived glycoconjugates have immunomodulatory and immunogenic properties and has been described as having a similar structure in L . infantum isolates from different continents [22] , phosphoglycans ( PGs ) could be promising candidates for the immunodiagnosis of the infection . In this context , the present study was designed to evaluate the application of LPG as an antigen for CanL immunodiagnosis .
The present study was approved by the Committee on Ethical Use of Experimental Animals of the School of Veterinary Medicine of the Federal University of Bahia ( n . 023/2013 ) . All procedures involving animals were conducted according to the guidelines of the Brazilian Council of Animal Experimentation ( CONCEA ) and strictly followed the Brazilian law for “Procedures for the Scientific Use of Animals” ( 11 . 794/2008 ) . Serum samples from dogs with confirmed diagnosis of L . infantum infection as well as samples from non-endemic areas for leishmaniasis were used to standardize the ELISAs . Ninety-seven dogs were diagnosed as infected by L . infantum DNA detection by PCR in blood , skin biopsy or aspirates from lymph node , bone marrow or spleen; parasitological assays were carried out using tissue aspirates . L . infantum-infected dogs were used as positive controls in this study . Among the selected positive dogs , 10 were clinically healthy by means of physical and clinical pathology exams , and 87 presented a variable range of disease , from mild to severe CanL ( Table 1 ) . Sixty-eight canine serum samples from non-endemic areas were included as negative controls , and those dogs should not have history of traveling to endemic areas; dogs from non-endemic areas were evaluated by the same molecular and parasitological tests as those for the positive control dogs ( Table 1 ) . In addition , sera from 30 dogs from an endemic area , presenting negative results in molecular and parasitological assays , were included in the study . The exclusion criteria were lack of confirmation of L . infantum infection by molecular or parasitological methods or diagnosis of any coinfections . All dogs also underwent blood and urine sampling for hemogram , serum biochemistry tests and urinalysis . Samples for parasitological and molecular diagnostic exams of the studied dogs were obtained under sedation with acepromazine ( 0 . 1 mg/kg; intravenous ) . Fine needle aspirates were obtained as previously described from the spleen [23] , bone marrow and/or lymph nodes and immediately processed or kept at -20°C . Sera from dogs with infections other than by L . infantum were kindly provided by colleagues from other research institutions and used for specificity assay determinations . Thirteen of those samples were from dogs infected with L . braziliensis . Twenty samples were from T . cruzi experimentally infected dogs and collected in the acute and chronic phases of disease . Thirty-one samples from dogs with other pathogens , whose infections were confirmed by molecular or parasitological tests , were obtained from non-endemic L . infantum areas ( Table 1 ) . All of the 97 dogs naturally infected by L . infantum underwent a clinical evaluation by physical examination and clinical pathology for CanL severity staging as proposed by Solano-Gallego et al . [24 , 25] . Accordingly , infected dogs were characterized into Stage 1 –clinically healthy parasitized dogs ( n = 10 ) ; Stage 2 –dogs with mild clinical disease ( n = 23 ) ; Stage 3 –dogs with severe disease ( n = 56 ) ; and Stage 4 –dogs presenting very severe clinical disease ( n = 68 ) . Hemograms were carried out using an automatized counter ( Sysmex pocH-100iVDiff , Roche ) , and serum biochemistry analysis was performed using automatized equipment and commercial kits ( Wiener ) for the quantification of urea , creatinine , total protein , albumin , and urinary protein and creatinine for calculating the urinary protein/creatinine ratio . Cytological examinations were performed in smears of freshly obtained fine needle aspirates of bone marrow and/or lymph nodes . Smears were stained with a modified Romanowsky staining rapid test kit ( Panótico Rápido , Laborclin , Brazil ) and analyzed under optical microscopy . A positive cytological result was given by the finding of amastigote forms within cells . For isolation of Leishmania sp . in culture medium , freshly obtained spleen aspirates were inoculated into biphasic Novy-MacNeal-Nicolle ( NNN ) medium with Schneider’s liquid phase ( Sigma-Aldrich Inc . , USA ) supplemented with 20% bovine fetal serum ( Gibco , USA ) and 50 μg/mL gentamicin , as standardized previously [23 , 26] . The cultures were kept in a BOD chamber at 23°C and examined weekly under optical microscopy for 30 days . Positive culture results were identified by the visualization of moving promastigote forms . Molecular diagnosis was performed to confirm that the natural infection of the studied dogs was in fact due to L . infantum by detecting the parasite’s DNA in their biological samples . Thus , genomic DNA was extracted from Leishmania cultures isolated from the dogs as well as from blood samples or spleen , bone marrow or lymph node fine needle aspirates , using a commercial kit ( Wizard Genomic DNA Purification Kit , Invitrogen Life , Brazil ) , following the manufacturer’s recommendations . Purified DNA was then tested by the PCR methodology proposed by Lachaud et al . [27] using the primers RV1 ( forward; 5’-CTTTTCTGGTCCCGCGGGTAGG-3’ ) and RV2 ( reverse; 5’-CCACCTGGCCTATTTTACACCA-3’ ) , which amplify L . infantum kinetoplast DNA . Promastigotes of L . infantum ( MCAN/BR/89/Ba-262 ) were washed in PBS and centrifuged at 2 , 100 x g . The pellet was mixed with 2 . 5 mL of a CHCl3/MeOH ( 3:2 ) solution and 0 . 5 mL of 4 mM MgCl2 . The mixture underwent sonication and centrifugation , and this procedure was repeated twice . The resulting pellet was treated with 3 . 0 mL of CHCl3/MeOH/H20 ( 10:10:3 ) and 0 . 5 mL of CHCl3/MeOH ( 1:1 ) . For the LPG extraction , 2 . 5 mL of ESOAK ( water/ethanol/ethyl ether/pyridine/NH4OH 15:15:5:1:0 . 017 ) was added to the resulting pellet , followed by sonication and centrifugation . The supernatant containing LPG was then evaporated with nitrogen and resuspended in 1 mL of CH3COOH 0 . 1 N/NaCl 0 . 1 N . This final solution was applied to a phenyl-sepharosis column ( BIO-RAD #731–1550 ) . After inoculating the solution containing LPG , the column was washed with six volumes of CH3COOH 0 . 1 N/NaCl 0 . 1 . After one additional wash with 1 mL of CH3COOH 0 . 1 N and one wash with 1 mL of ddH20 , 4 mL of ESOAK was used for LPG elution . The eluate was then evaporated with nitrogen and resuspended in 1 mL of ultrapure water . To confirm the presence of LPG in the final eluate , the CA7AE monoclonal antibody was used for western blotting as reported previously [22] . L . infantum cultures ( MCAN/BR/89/Ba-262 ) were expanded in biphasic NNN-Schneider medium ( Sigma-Aldrich Inc . , USA ) supplemented with 20% fetal bovine serum and 50 μg/mL gentamicin and kept in a BOD chamber at 23°C until reaching stationary phase . The culture was then centrifuged at 3 , 000 x g for 10 minutes and the parasite pellets washed three times with sterile PBS before storage at -80°C . For antigen purification , the parasite pellets were submitted to three cycles of ultrasound at 4 Hz for one minute and then centrifuged at 14 , 000 x g for 10 minutes at 4°C . The supernatant was then collected , and the protein concentration determined by the bicinchoninic acid method ( Thermo Fisher , Waltham , MS ) . Western blotting was carried out to assess the recognition of LPG by antibodies from L . infantum-infected dogs . A purified LPG solution was submitted to an electrophoretic run on a 12 . 5% polyacrylamide gel ( SDS-PAGE ) and then transferred to nitrocellulose membranes . After a blocking step with 10% casein in PBS pH 7 . 4 for 16 hours at -4°C , the membranes were washed three times with PBS with 0 . 05% Tween 20 ( PBST ) and incubated with pool of control sera ( diluted 1: 1 , 000 in PBST with 5% casein ) for two hours under agitation at 23° C . After three more washes with PBST , the membranes were incubated with a horseradish peroxidase-conjugated anti-dog IgG antibody ( Bethyl , USA ) under agitation for one hour . After three more washes with PBST , the membranes were incubated with an enzyme substrate and chromogen solution ( 4-chloro-1-naphtol and hydrogen peroxide ) for ten minutes , and the reaction was stopped with ultrapure water . The ELISAs were standardized by a checkerboard titration method . Different antigen ( LPG and TLA ) concentrations and different dilutions of the control pooled sera and of the anti-canine IgG antibody conjugated to horseradish peroxidase were tested . The optimal assay conditions were determined by the higher ratio value between the positive and negative pool’s OD readings . The positive and negative serum pools consisted of an equal quantity of ten negative or ten positive control serum samples , as described in Table 1 . The ELISA was developed on high-binding flat bottom polystyrene microplates ( Parker Elmer , Waltham USA ) , which were sensitized with LPG or TLA diluted in 100 μL carbonate/bicarbonate buffer pH 9 . 6 per well at 4°C for 14 hours . The plates were then washed two times with PBS 0 . 05% Tween 20 ( PBST ) blocked with casein 10% in PBS and incubated at 37°C for three hours . After four more washes with PBST , 100 μL of serum samples diluted in PBST with 5% of casein were added in duplicate to the wells and incubated at 37°C for one hour . The plates were washed six times with PBST , and the anti-canine IgG horseradish peroxidase antibody ( Bethyl , USA ) diluted in PBST with 0 . 5% casein was added and incubated for one hour at 37°C . After six washes , the reaction was developed with a solution containing ortophenylenediamine ( OPD ) and peroxide hydrogen , diluted in citrate buffer pH 4 . 3 and interrupted with H2SO4 4N . The OD readings were obtained using an ELISA plate reader ( BIO-RAD , USA ) . The positive and negative controls that were used for the LPG-ELISA standardization and the serum samples from dogs naturally infected with L . braziliensis or experimentally infected with T . cruzi were tested by the TR DPP-CVL test ( Bio-Manguinhos , Rio de Janeiro , Brazil ) . This test consists in a validated dual-path immunochromatographic platform using the rK28 recombinant protein as antigen , and is currently recommended as the official screening test by the Brazilian Ministry of Agriculture [28] . All the samples were tested as recommended by the manufacturer . The ELISA cut-off was defined as the mean OD of negative serum samples plus three standard deviations [29] . The calculations of specificity , sensitivity , negative and positive predictive values ( NPV and PPV ) were based on the number of positive ( for L . infantum or other infections ) and negative control serum samples that presented positive or negative results in each ELISA [30] . A receiver operating characteristics ( ROC ) curve was obtained for each ELISA using SPSS v . 12 . 0 . software ( IBM , USA ) , and the accuracy was defined as the area under the curve ( AUC ) of each ROC curve . The agreement between ELISA results and parasitology/molecular positivity for L . infantum infection was calculated using the Kappa ( K ) index with the following classification: 0 –no concordance; 0 to 0 . 19 –very low correlation; 0 . 20 to 0 . 40 –weak correlation; 0 . 40 to 0 . 59 –moderate concordance; 0 . 60 to 0 . 79 –substantial concordance; 0 . 80–1 . 00 –high concordance [31] . The calculation of repeatability and reproducibility was based on the OD readings obtained in assays carried out by three different technicians in three different days ( reproducibility ) or in twenty repetitions performed by the same technician at the same time ( repeatability ) . The results for these two parameters were expressed as the percentage ( % ) of coherent results according to the serum pool infectivity status .
Immunoblot analysis with pooled sera from L . infantum naturally infected dogs revealed immunoreactivity against the LPG antigen . The pool of positive sera recognized the expected LPG molecule , displaying the expected smear characteristic of glycoconjugates ( Fig 1 ) . There was no reaction of the transferred LPG to the nitrocellulose membrane with the negative canine pooled sera , sera from dogs in the acute ( AP ) and chronic ( CP ) phases of an experimental infection with T . cruzi , and naturally infected with L . braziliensis ( Lb ) . For the evaluation of LPG as a serodiagnostic antigen candidate for L . infantum infection , positive canine sera from endemic areas for CanL and negative canine sera from non-endemic areas for the disease were individually tested by ELISA . Each sample was tested against purified LPG and crude extract from L . infantum . First , all ELISA procedures were optimized with regard to antigen concentrations , control sera and conjugated antibody dilutions by checkerboard titration . LPG-ELISA tests revealed that 0 . 5 μg/mL of antigen and serum and conjugated antibody dilutions of 1: 400 and 1: 10 , 000 , respectively , produced the best resolution between optical density ( OD ) readings of the positive and negative sera pools . Similarly , optimal conditions for the TLA-ELISA reaction were achieved using 8 . 0 μg/mL of antigen and serum and conjugated antibody dilutions of 1: 800 and 1: 10 , 000 , respectively . These conditions achieved a positive: negative OD reading ratio for the serum sample pools of 17 . 99 for the LPG-ELISA and 13 . 5 for the TLA-ELISA . Ninety-seven positive serum samples and 68 negative control serum samples were used to evaluate the performance of the LPG antigen in the serological assays ( Fig 2 ) . The cut-off point , calculated by the mean OD of the negative samples plus three standard deviations , was determined for the LPG- and TLA-ELISA as 0 . 251 and 0 . 288 , respectively . Both ELISAs identified only one of the negative control samples as positive . Regarding false-negative results , the LPG-ELISA identified nine samples from infected dogs as negative ( S1 Table ) , with OD readings below the established cut-off point , while the TLA-ELISA presented 17 false-negative results . As shown in Table 2 , the diagnostic specificity of the LPG-ELISA was determined as 98 . 5% , equal to that achieved by the TLA-ELISA ( 98 . 5% ) . However , the LPG-ELISA sensitivity value ( 91 . 5% ) was superior to that obtained by the TLA-ELISA ( 85 . 0% ) . Both assays were very stable , as they presented high repeatability ( 97 . 5% for the LPG-ELISA and 96 . 1% for the TLA-ELISA ) and reproducibility ( 99 . 7% for the LPG-ELISA and 95 . 1% for the TLA-ELISA ) . The predictive values , which express the possibility of a sample with a specific result being identified with that exact profile , were similar in both assays when considering the positive predictive value ( PPV– 98 . 9% ) , but the negative predictive value ( NPV ) was higher for the LPG-ELISA ( 89 . 3% ) than for the TLA-ELISA ( 84 . 8% ) . The accuracy of each test was calculated using the area under the ROC curve ( Fig 3 ) , and this parameter was also higher for the LPG-ELISA ( 99 . 7% ) than for the TLA-ELISA ( 98 . 6% ) . When serum samples taken from dogs from an endemic area and presenting negative results in parasitological and molecular assays were tested by LPG-ELISA , as an example of local cut-off development and application in an endemic area , a 0 . 310 cut-off could be established . This result made the LPG-ELISA sensitivity drop to 84 . 6% , considering 15 false-negative results in a total of 97 positive samples tested ( S1 Fig ) . When serum samples from dogs with confirmed infection by T . cruzi or L . braziliensis ( with or without clinical signs of disease ) were tested , no positive reactions could be seen by either LPG- or TLA-ELISA ( Fig 4 ) . However , when sera from dogs with confirmed infection by Hepatozoon sp . , Ehrlichia sp . , Babesia sp . or Anaplasma spp . were tested , the TLA-ELISA presented two positive cross reacting results ( 2/11–18 . 18% ) , one for a dog that was infected with Ehrlichia sp . and one for a Babesia sp . -infected dog ( Fig 5 ) . Regarding the ELISA tests carried out with serum samples from dogs with subclinical infection by L . infantum , the LPG-ELISA identified 9/10 ( 90% ) as positive , while just one sample ( 1/9 ) was detected as positive by the TLA-ELISA ( Fig 6 ) . Both assays presented similar performances regarding animals in stages 2 , 3 and 4 of the disease . The TR DPP-CVL immunochromatographic assay presented 10 false negative and 05 false positive results , while LPG-ELISA had 09 false negatives and 01 false positives . These results conferred a 93 . 1% specificity and 90 . 6% sensitivity for TR DPP , and LPG-ELISA consequently had higher validation parameters ( 98 . 5% specificity and 91 . 5% sensitivity ) . Regarding dogs with subclinical L . infantum infection , TR DPP-CVL was able to correctly diagnose as positive 06 dogs , and LPG-ELISA presented 09 positive results . Interestingly , the cross-reactions in samples from dogs experimentally infected with T . cruzi and in the acute phase of the infection were significant ( 60% positive results ) , and 03 of 13 animals that had a natural infection by L . braziliensis were positive when using TR DPP-CVL . None of the T . cruzi or L . braziliensis infected animals gave positive results for LPG-ELISA ( Table 3 ) .
There is still a demand for a commercially available immunoassay to diagnose CanL that is accurate enough to meet the requirements for good preventive veterinary medicine and public health actions . False-positive results can lead to the unnecessary treatment or even culling of non-infected dogs . On the other hand , tests that are not sensitive enough to detect infected subclinical dogs can lead to ineffective control of the disease , since these dogs would not be cared for and might act as reservoirs [32] . For this reason , in Brazil the official protocol recommended by the Ministry of Agriculture for the diagnosis of CanL is based on the application of two tests , a screening immunochromatographic assay with the recombinant rK28 protein as antigen ( TR DPP-CVL ) , and a confirmatory ELISA with L . major total antigen ( EIE ) . The need for two tests makes the control of the disease expensive and time-consuming . In this context , our proposal was to evaluate a non-protein LPG glycoconjugate as an antigen for the ELISA test , since these molecules are immunogenic , highly stable and broadly expressed by L . infantum . The use of glycoconjugates as antigens for the development of vaccines or in immunodiagnostic assays has been reported [33–35] . L . infantum LPG has been previously characterized according to the number of sugars branching off the repeat unit motif; here , we chose type I LPG from Ba262 L . infantum strain , originally isolated from a dog . In the present study , our data show that LPG ( and perhaps its PG motif ) induced a significant humoral response , expressed by the intense recognition of this molecule by specific antibodies from infected dogs , while the negative controls did not . Supporting our reasoning , anti-L . infantum LPG antibodies have been previously found in human patients from areas endemic for leishmaniasis [33 , 20] . Interestingly , these studies demonstrated that antibodies to L . infantum LPG were detected in patients without history of disease , which indicates subclinical infection . The present LPG-ELISA exhibited 98 . 5% specificity and 91 . 5% sensitivity , while the TLA-ELISA , despite having the same specificity , achieved only 85 . 0% sensitivity . The accuracy levels of the LPG-ELISA and TLA-ELISA were 99 . 7% and 98 . 5% , respectively . A similar result was found by de Arruda et al . [38] , who described a validation of two ELISAs produced by the governmental Brazilian Bio-Manguinhos , one of them using a L . infantum TLA as antigen , and the other one that became the official EIE for CanL control in the country . The authors described 91 . 85% specificity , 83 . 75% sensitivity and 91 . 7% accuracy for the L . major antigen ( EIE ) and 89 . 80% specificity , 82 . 69% sensitivity and 89 . 3% accuracy for the L . infantum [36] . Comparatively , the LPG-ELISA in our study was able to identify more infected dogs with fewer false-negative results since it presented higher sensitivity than that described for the EIE . Moreover , the literature reports similar specificity ( 92% ) and sensitivity ( 92% ) values for a LPG-ELISA in the diagnosis of human infection by L . infantum in African and Mediterranean countries [33 , 20] . The LPG-ELISA reported herein also shows a relevant capacity of recognizing an antibody response to L . infantum in healthy dogs with subclinical infection . The difficult diagnosis of subclinically infected dogs has been one of the reasons for several studies on the immunodiagnostics of CanL , which have focused on the use of peptides and proteins developed in genomic , bioinformatics or proteomic studies to improve the sensitivity of serological tests for CanL [37] . In fact , Faria et al . [16] , using multiepitope synthetic proteins , were able to identify subclinically infected dogs more effectively ( 80% ) than the Brazilian official tests EIE ( 0% ) and TR DPP-CVL ( 10% ) . Moreover , ELISAs with different recombinant proteins used as antigens presented positivities ranging from 23% to 65% , depending on the stage of the disease [16] . Mendes et al . [38] found 98% sensitivity and 99% specificity in an ELISA using a synthetic bi-epitope peptide , but their study did not mention the clinical staging of the dogs . One of the consequences of such variable specificities and sensitivities of different assays for CanL immunodiagnosis is that the seroprevalence in endemic areas can present marked fluctuations depending on the chosen assay [10] . In this study , we were able to include samples from 10 subclinically infected dogs that presented no alterations at the clinical examination and ancillary clinical pathology tests made by veterinarian infectologists . The LPG used in the present study has been previously isolated and described as a highly stable molecule [36] . This high stability explains why the LPG-ELISA presented 97 . 5% repeatability and 99 . 7% reproducibility in the present study . These findings suggest that the LPG-ELISA is expected to present the same result for a given sample when tested several times by the same operator in one specific laboratory and likely even when tested by different laboratories and different operators . De Arruda et al . [36] pointed to the fact that an assay for CanL immunodiagnosis must have high reproducibility in order to be broadly used as a reference assay . A later study reported a positive predictive value ( PPV ) of 27 . 8% and a negative predictive value ( NPV ) of 99 . 1% when using a L . infantum TLA and 29 . 6% PPV and 99 . 3% NPV with a L . major TLA [39] . Our results show higher values for PPV and NPV , indicating that the LPG-ELISA can be used as a reliable tool for epidemiologic surveys . In addition , the procedure to purify L . infantum LPG has been shown to result in a high yield [21] . Such characteristics make the present L . infantum LPG a molecule that can be easily obtained and purified and that is capable of allowing a long validity span to commercial immunodiagnostic kits . One of the procedures adopted by some countries for CanL control is the culling of Leishmania-infected dogs . However , in the human-dog interaction , dogs should not be considered irrelevant , since there is a strong link between owners and their companion animals , and their killing can cause intense emotional damage [40] , and no scientific evidence supports that this procedure is effective in reducing the incidence of visceral leishmaniasis [41] . Taking into consideration that the LPG-ELISA presented a high capacity of diagnosis with minor cross reactivity , owing to its high specificity value , one can conclude that this assay will produce a very low frequency of false-positive results . Accordingly , considerable unnecessary culls of noninfected dogs can be avoided . The occurrence of cross-reactions with other pathogens that are endemic to the same areas as Leishmania is another problem that hinders the proper diagnosis of CanL [42] . The LPG-ELISA did not result in any cross-reactivity in the serum samples from dogs infected by L . braziliensis , T . cruzi , Babesia sp . , Ehrlichia sp . or Hepatozoon sp . that were tested in this study , while the TLA-ELISA presented cross reactivity for Ehrlichia sp . ( 18 . 2% - 2/11 ) and Babesia sp . ( 12 . 5% - 1/8 ) . Recently , Carvalho et al . [15] proposed an ELISA using a L . infantum LiHypA recombinant antigen , which conferred 100% sensitivity and 99% specificity for CanL diagnosis , but it exhibited cross reactivity with B . canis . A strategy used by various authors to avoid false-positive results is to standardize the serological test by highly diluting the serum samples , but such a procedure can reduce the sensitivity of the method . The standardization of our LPG- ELISA protocol included a broad range of serum dilutions and resulted in an assay that was free of cross reactions and false-positive results . To improve specificity and reduce false-positive results due to cross-reactions , numerous studies have focused on the use of recombinant proteins as antigens in immunoassays for CanL . Two of those antigens , rK39 and rk26 L . infantum-derived recombinant proteins , were produced with the purpose of being purer antigens , thus containing a less diversity of epitopes that could promote reactions with antibodies produced in dogs infected with other pathogens [43 , 44] . However , a study described that the use of rK39 and rK26 did not promote a significant increase in the specificity or sensitivity of serological assays when compared to L . infantum total antigen [45] . Comparing rK39 and rK26 with another recombinant antigen , rA2 , in ELISA tests , Porrozzi et al . [46] found that , even though rA2 increased the test’s specificity , it still presented cross-reactivity with sera from Leptospira interrogans-infected dogs , while rK26 and rK39 presented cross-reactivity with sera from dogs infected with L . braziliensis . Conversely , the present LPG-ELISA showed high specificity and sensitivity , and no cross-reactions were observed when testing serum samples from dogs infected with other infectious agents , even those with L . braziliensis . The absence of cross reactivity by the sera from dogs infected with L . braziliensis in the LPG-ELISA could be because L . braziliensis expresses 10 to 20-fold less LPG than the other Leishmania species [21] . In this sense , the immune system of L . braziliensis-infected dogs would be less exposed to LPG interactions; consequently , there would be less immunogenic activation and weaker antibody responses to this specific molecule . Nevertheless , it should be mentioned that L . braziliensis LPG is very similar to L . infantum type 1 LPG [27] . One of the most notable results of the present work is that L . infantum-infected dogs presenting no clinical signs of the disease , herein classified as having subclinical infection , were expressively identified as positive by the LPG-ELISA ( 90% - 9/10 ) . In contrast , the TLA-ELISA diagnosed only one of those ten dogs as positive ( 10% ) . The proper diagnosis of subclinically infected dogs is very important for the control of leishmaniasis in endemic areas , since these dogs can remain untreated or unfollowed . Susceptible subclinically infected dogs can develop active infection , representing a source of infection for other dogs and humans . Such dogs , even with a recent infection , produce specific antibodies in high levels , before developing clinical signs and would also be characterized as having subclinical infection [47] . Leishmania-specific humoral responses in susceptible dogs are characterized by high levels of specific immunoglobulins [48] . However , such abundant immunoglobulins are non-protective and are instead associated with active infection , intense disease development and presentation of clinical signs in dogs [47 , 48] . On the other hand , resistant dogs remain clinically healthy and have a small or frequently undetectable parasite load; however , they can present fluctuating and low levels of anti-Leishmania immunoglobulins [46] . Indeed , the immunodiagnosis of dogs with unapparent infection is a difficult task and several assays have failed to be sensitive enough to detect infection in such dogs . By using rK39 as an antigen in ELISA tests , De Lima et al . [49] found that the assay was 100% specific for L . infantum diagnosis in clinically diseased dogs , but it failed to detect nine dogs with subclinical infection . Similarly , De Carvalho et al . [50] showed that 18 . 7% of dogs that were PCR-positive for L . infantum DNA were diagnosed negative in the official two-test Brazilian protocol . Another study showed that the rLiHypA antigen provided 100% detection of subclinically infected dogs , but the test’s accuracy was impaired by its cross-reaction with babesiosis [15] . In a recent study , Figueiredo et al . [51] evaluated the performance of the previously validated TR DPP-CVL in samples taken from 1446 dogs , having found a sensitivity of 89% and a specificity of 70% , and observed positive results in only 75% of the subclinically infected ( “asymptomatic” ) dogs . We found a similar result in the present study , where only six out of ten ( 60% ) dogs with subclinical L . infantum infection tested positive in the TR DPP-CVL , while the LPG-ELISA was able to detect nine of those dogs ( 90% ) . Even though we have compared two assays using different platforms , our results can suggest that a similar immunochromatographic assay using L . infantum LPG as antigen might as well result in a very sensitive and specific diagnostic test . In addition , an interesting finding was that 60% of dogs that were in the acute phase of an experimental infection with T . cruzi presented positive results in the TR DPP-CVL , but the same dogs became negative when in the chronic phase of the trypanosomiasis . A possible explanation for this result is that the TR DPP-CVL uses the Staphylococcal protein A conjugated to colloidal gold as a development reagent . It has been described that protein A has affinity not only for IgG but also for IgM [52]; in this way , large amounts of IgM produced after the experimental infection with T . cruzi might have been a source of cross-reaction in the TR DPP-CVL . It is noteworthy that low affinity IgM produced by the B1 subpopulation of B-lymphocytes can be related to cross reactions [53] . When serum samples taken from endemic area’s negative dogs were used to determine a regional cut-off , the sensitivity of LPG-ELISA dropped to 84 . 6% , since the employment of a higher cut-off led to the occurrence of 15 false-negative results among a total of 97 samples . These results express an example from a local situation and represent a methodology used by many clinical laboratories to establish regional cut-offs . However such an approach should not be applied to the overall evaluation of the present LPG-ELISA´s parameters standardization . Indeed , samples from an endemic area cannot be trusted as truly negative due to the fact that infected dogs are not always positive in parasitological or molecular assays , since the parasite may be present in other tissues than the ones that were sampled [23] . In this way , the use of negative samples from non-endemic areas represented the overall capacity of the present LPG-ELISA to discriminate between truly infected or non-infected dogs . In conclusion , L . infantum-derived LPG presented high efficacy in the detection of specific immunoglobulins in dogs infected with the parasite when used in an ELISA-based diagnostic test . The LPG-ELISA showed no cross-reactions with sera from dogs infected with other pathogens and was able to identify 90% of the samples from dogs with subclinical infection . Because of the high stability of the LPG molecule and its high yield and simple and cheap purification methodology , it is a promising molecule for a highly accurate immunodiagnosis of CanL . | Canine leishmaniasis ( CanL ) caused by Leishmania infantum is a zoonotic disease with high importance for the public health of several countries . L . infantum-infected dogs can be a domestic reservoir of the protozoan parasite for sand flies , which transmit it from dogs to humans during their blood meals . It is important to diagnose and treat infected dogs as early as possible , so the dissemination of CanL and the transmission to humans can be controlled . The currently commercially available assays present problems , such as cross-reaction with other canine diseases and lack of sensitivity in the detection of dogs that do not present clinical signs of disease . In this work , we tested lipophosphoglycan ( LPG ) , a molecule abundantly found in the protozoan´s surface , as an antigen in an immunodiagnostic platform . We were able to see that the assay using LPG is highly sensitive and specific , showed no cross-reaction with other canine infectious diseases and successfully identified infected dogs with no signs of disease . |
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Toxoplasma gondii resides in an intracellular compartment ( parasitophorous vacuole ) that excludes transmembrane molecules required for endosome - lysosome recruitment . Thus , the parasite survives by avoiding lysosomal degradation . However , autophagy can re-route the parasitophorous vacuole to the lysosomes and cause parasite killing . This raises the possibility that T . gondii may deploy a strategy to prevent autophagic targeting to maintain the non-fusogenic nature of the vacuole . We report that T . gondii activated EGFR in endothelial cells , retinal pigment epithelial cells and microglia . Blockade of EGFR or its downstream molecule , Akt , caused targeting of the parasite by LC3+ structures , vacuole-lysosomal fusion , lysosomal degradation and killing of the parasite that were dependent on the autophagy proteins Atg7 and Beclin 1 . Disassembly of GPCR or inhibition of metalloproteinases did not prevent EGFR-Akt activation . T . gondii micronemal proteins ( MICs ) containing EGF domains ( EGF-MICs; MIC3 and MIC6 ) appeared to promote EGFR activation . Parasites defective in EGF-MICs ( MIC1 ko , deficient in MIC1 and secretion of MIC6; MIC3 ko , deficient in MIC3; and MIC1-3 ko , deficient in MIC1 , MIC3 and secretion of MIC6 ) caused impaired EGFR-Akt activation and recombinant EGF-MICs ( MIC3 and MIC6 ) caused EGFR-Akt activation . In cells treated with autophagy stimulators ( CD154 , rapamycin ) EGFR signaling inhibited LC3 accumulation around the parasite . Moreover , increased LC3 accumulation and parasite killing were noted in CD154-activated cells infected with MIC1-3 ko parasites . Finally , recombinant MIC3 and MIC6 inhibited parasite killing triggered by CD154 particularly against MIC1-3 ko parasites . Thus , our findings identified EGFR activation as a strategy used by T . gondii to maintain the non-fusogenic nature of the parasitophorous vacuole and suggest that EGF-MICs have a novel role in affecting signaling in host cells to promote parasite survival .
Toxoplasma gondii is an obligate intracellular protozoan parasite that infects around a third of the human population worldwide . T . gondii is of clinical importance because it causes encephalitis in immunocompromised individuals and retino-choroiditis in immunocompetent and immunosuppressed patients . T . gondii can also cause congenital infection that may result in cerebral and ocular disease . Tachyzoites of T . gondii infect virtually any nucleated cell through active invasion . This process is dependent on the parasite actin-myosin motor and sequential secretion of proteins from micronemes and rhoptries , specialized organelles present in the apical end of the parasite [1] . Once secreted , T . gondii micronemal proteins ( MICs ) are expressed at the parasite surface membrane and they interact with host cell receptors [2] . MICs contain adhesive domains such as type I thrombospondin repeats , apple domains , EGF repeats and integrin A domains [3] , [4] . The connection between transmembrane MICs to the actin-myosin motor ( glideosome ) of the parasite together with the binding of host cell receptors by MICs is considered to enable the organism to penetrate host cells [5] , [6] . Following the release of MICs , rhoptries secrete rhoptry neck proteins ( RONs ) that are critical for the formation of a structure called the moving junction ( MJ ) [7] , [8] . The MJ anchors the parasite to the host cell while the parasite penetrates it . The MJ is also believed to function as a sieve that excludes host type I transmembrane proteins from entering the PV membrane ( PVM ) [8] , [9] . The end result is the formation of a parasitophorous vacuole that is devoid of host proteins required for recruitment of endosomes and lysosomes [10] . T . gondii cannot withstand the lysosomal environment . Thus , the non-fusogenic nature of the PV is critical since it allows the parasite to survive and replicate . The immune system can deprive the parasite from this niche by disrupting the PVM through the effects of IFN-γ/Immunity related GTPases ( IRG ) [11] , [12] and by making the PV fusogenic through the effects of CD40 ligation [13]–[15] . CD40 re-routes the PV to the lysosomes through the autophagy machinery [13]–[15] . Autophagy is a conserved cellular mechanism of lysosomal degradation . During autophagy , portions of the cytosol or organelles are encircled by an isolation membrane [16] . The expansion of the isolation membrane results in the formation of a double membrane structure called autophagosome that delivers its contents to the lysosomes for degradation [16] . Autophagy is recognized as a mechanism stimulated by innate and adaptive immune mechanisms to degrade numerous intracellular pathogens [17] . However , various bacteria and viruses have evolved mechanisms to prevent autophagic degradation by targeting autophagy proteins to avoid recognition by the autophagy machinery or prevent the initiation and maturation of autophagosomes [18]–[24] . Much less is known regarding whether pathogens manipulate signaling cascades that regulate autophagy to prevent their degradation . HIV-1 envelope can activate the negative regulator of autophagy mTOR and it has been proposed that this would prevent lysosomal degradation of virions [25] . Bioinformatic analysis of human THP-1 cells infected with Mycobacterium tuberculosis suggested that the pathogen activates host signaling cascades that impair autophagy [26] . The highly successful nature of T . gondii as a pathogen together with evidence that the autophagy pathway can trigger lysosomal killing of the pathogen raise the possibility that T . gondii prevents autophagic targeting of the PV to maintain the non-fusogenic nature of the PV . Moreover , approximately 25–35% of various CD40+ cells subjected to CD40 ligation are unable to kill T . gondii further suggesting that the parasite may utilize mechanism ( s ) to prevent induction of autophagic killing . Here we report that maintenance of the non-fusogenic nature of the PV requires T . gondii-induced activation of EGFR-Akt , a signaling cascade that prevents autophagy protein-dependent vacuole-lysosomal fusion , lysosomal degradation and killing of the parasite . Blockade of EGFR-Akt may prove of therapeutic benefit for toxoplasmosis since it is sufficient to induce killing of the parasite without the need for immune-induced activation of host cells .
We determined whether Akt is quickly activated by T . gondii during infection of various non-hematopoietic cells . Activation of Akt is a multistep process where phosphorylation of Serine 473 results in full activation of the molecule [27] . Primary human brain microvascular endothelial cells ( HBMEC ) were infected with either type I ( RH ) or type II ( ME49 ) strains of T . gondii under conditions that caused synchronized infection . T . gondii infection resulted in an enhanced phosphorylation of Akt Serine 473 as assessed by immunoblot ( Figure 1A ) . Similar results were obtained with a mouse endothelial cell line mHEVc ( Figure 1B ) . T . gondii also caused Akt phosphorylation in a human retinal pigment epithelial ( RPE ) cell line , an effect that decreased at later time points post-infection ( Figure 1C ) . We assessed whether viable parasites are required to induce activation of Akt . HBMEC were challenged with live or killed parasites followed by determination of Akt activation . Viable but not killed tachyzoites induced Akt phosphorylation ( Figure 1D ) . Activation of phosphatidylinositol 3-kinase ( PI3K ) with resulting production of phosphatidylinositol 3 , 4 , 5 trisphosphate ( PIP3 ) production is a major trigger of Akt activation [28] . The amino-terminal pleckstrin homology ( PH ) domain of Akt mediates recruitment of this molecule to plasma membrane containing increased PI ( 3 , 4 , 5 ) P3 or PI ( 3 , 4 ) P2 [29] . Indeed , the PH domain of Akt fused to GFP ( PH-Akt-GFP ) has been used as a probe to examine sites of PIP3 accumulation [30] . HBMEC were transiently transfected with a plasmid encoding PH-Akt-GFP followed by challenge with RH T . gondii that express cytoplasmic RFP ( T . gondii-RFP ) . T . gondii-infected cells exhibited accumulation of PH-Akt-GFP around the parasite ( Figure 1E ) . To examine the role of PI3K in this process , HBMEC were incubated with or without LY294002 , a specific PI3K inhibitor , followed by challenge with the parasite . LY294002 did not affect the percentage of infected cells ( not shown ) . Accumulation of PH-Akt-GFP around T . gondii was ablated by LY294002 ( p<0 . 01 ) ( Figure 1E ) . Moreover , incubation with LY294002 impaired the upregulation of Akt phosphorylation induced by T . gondii , especially in the earlier time points post-infection ( Figure 1F ) . Similarly , Akt phosphorylation during T . gondii infection was impaired in HBMEC transfected with siRNA against the PI3K catalytic subunit p110α ( Figure 1G ) . Taken together , these findings indicate that T . gondii induces rapid Akt activation in non-hematopoietic cells in a manner that is dependent on PI3K . We performed studies to investigate whether blockade of Akt signaling promotes killing of T . gondii . HBMEC were incubated with or without Akt inhibitor IV followed by challenge with T . gondii . The percentage of infected cells at 2 hours and 24 hours post-challenge were determined . Akt inhibitor IV did not impair the percentage of infected cells at 2 h ( Figure 2A ) . However , treatment with Akt inhibitor IV markedly reduced the percentage of infected cells at 24 h ( p<0 . 01 ) ( Figure 2A ) . Changes in the percentage of infected cells were not due to preferential cell loss in Akt inhibitor IV-treated cells since cell densities as determined with an eyepiece grid were similar in all experimental groups and inhibition of Akt did not induce a detectable increase in apoptosis of T . gondii-infected cells ( not shown ) . Akt inhibitor IV not only induced a significant decrease in the numbers of parasites per 100 HBMEC at 24 h but it also caused a profound reduction in the numbers of T . gondii-containing vacuoles per 100 HBMEC ( p<0 . 01 ) ( Figure 2A , Figure S1A ) . Similar results were obtained with mouse endothelial cells ( mHEVc; Figure 2A , Figure S1A ) and human RPE cells ( p<0 . 01 ) ( Figure 2A , Figure S1A ) . Not only pharmacologic inhibition of Akt but also Akt knockdown in HBMEC reduced the parasite load and the number of T . gondii-containing vacuoles ( p<0 . 01 ) ( Figure 2B , Figure S1A ) . The vacuoles that persisted after Akt knockdown had similar numbers of parasites as those from control cells ( Figure S1B ) . These results indicate that blockade of Akt caused parasite killing . T . gondii infection causes Akt activation in macrophages [31] . Similar to endothelial and epithelial cells , treatment with Akt inhibitor IV caused anti-T . gondii activity in the mouse macrophage line RAW 264 . 7 and in mouse microglia line BV-2 ( p<0 . 01 ) ( Figure 2C and not shown ) . These findings revealed an important role of Akt activation in promoting survival of T . gondii within host cells . T . gondii survives within mammalian cells by avoiding delivery of the lysosomal contents into the parasitophorous vacuole [32]–[34] . Akt is a negative regulator of autophagy [35] , a cellular mechanism that results in lysosomal degradation and killing of T . gondii [13]–[15] . First , we examined T . gondii-infected cells after Akt inhibition to determine the distribution of LC3 , a protein associated with the autophagosome membrane . mHEVc-LC3-EGFP cells were treated with or without Akt inhibitor IV and challenged with T . gondii-RFP . Akt inhibitor IV led to significant accumulation of LC3 around the parasite ( p<0 . 01 ) ( Figure 2D ) . Electron microscopy studies were performed since a double membrane isolation membrane that encircles portions of cytoplasm or organelles is formed during autophagy [16] . Indeed , a double membrane structure was noted around the parasitophorous vacuole membrane in HBMEC treated with Akt inhibitor IV ( Figure 2E ) . Next , we examined the effects of Akt inhibition on the distribution of the late endosomal/lysosomal molecule LAMP-1 . Endothelial cells were incubated with or without Akt inhibitor IV , challenged with T . gondii-YFP followed by staining with anti-LAMP-1 mAb . Treatment with Akt inhibitor IV resulted in a remarkable increase in the percentage of parasites surrounded by LAMP-1 ( p<0 . 01 ) ( Figure 2F ) . To explore whether the killing of T . gondii during inhibition of Akt is dependent on the autophagy machinery , we examined the effects of knockdown of the autophagy proteins Beclin 1 or Atg7 on T . gondii survival . Transfection with Beclin 1 siRNA or Atg7 siRNA effectively diminished expression of Beclin 1 or Atg7 respectively ( Figure 2G , H ) . Endothelial cells transfected with Beclin1 siRNA or Atg7 siRNA were incubated with or without Akt inhibitor and challenged with T . gondii . Cells transfected with Beclin1 siRNA ( Figure 2G ) or Atg7 siRNA ( Figure 2H ) were unable to control the parasite in the presence of the Akt inhibitor IV . Since autophagosomes deliver their contents to lysosomes for degradation , we examined the role of lysosomal degradation in killing of T . gondii utilizing the lysosomal protease inhibitors leupeptin and pepstatin . mHEVc and RPE cells were treated with or without Akt inhibitor IV and infected with T . gondii . 1 h post infection cells were treated with or without leupeptin plus pepstatin . Lysosomal protease inhibitors impaired the anti-T . gondii activity induced by Akt inhibition ( p<0 . 05 ) ( Figure 2I and not shown ) . Finally , the anti-T . gondii activity induced by Akt inhibitor IV in mouse microglia and human RPE cells was impaired by 3-methyl adenine , an inhibitor of autophagy ( p<0 . 05 ) ( Figure 2J and not shown ) . Taken together , these results indicate that T . gondii-induced Akt activation is critical to promote parasite survival because it prevents killing of T . gondii dependent on the autophagy pathway and lysosomal protease activity . Akt activation classically occurs downstream of cell membrane receptors that include growth factor receptors , G protein-coupled receptor ( GPCR ) and TLR [36] . To examine the role of GPCR in Akt activation in non-hematopoietic cells , HBMEC were incubated with or without Pertussis toxin ( PTx ) , an inhibitor of GPCR signaling , followed by challenge with T . gondii tachyzoites . PTx did not affect the initial percentage of infected cells ( data not shown ) . Incubation with PTx decreased basal Akt phosphorylation . However , PTx did not prevent the increased Akt phosphorylation induced by T . gondii ( Figure 3A ) indicating that T . gondii can activate Akt independently of GPCR signaling . In contrast , PTx inhibited Akt activation induced by lysophosphatidic acid ( LPA ) , a GPCR ligand [37] ( Figure 3A ) . To examine the potential role of TLR signaling in Akt , MyD88 was knocked-down in HBMEC using siRNA . Knockdown of MyD88 did not affect T . gondii-induced Akt activation ( Figure 3B ) . In contrast , as assessed by FACS , the ICAM-1 upregulation induced by LPS ( 1 µg/ml ) in HBMEC was inhibited in cells transfected with MyD88 siRNA compared to those transfected with control siRNA ( cMFI: Control siRNA = 10 , 682±1 , 053; MyD88 siRNA = 3 , 250±527; p<0 . 05 ) . These studies indicate that GPCR and TLR are unlikely to play a major role in Akt phosphorylation induced by T . gondii in non-hematopoietic cells . Relevant to the possibility of activation of growth factor receptors during T . gondii-host cell interaction is the fact that host cell invasion by T . gondii requires the secretion of parasite micronemal proteins ( MICs ) with the potential to activate such receptors [38] . MICs exist as multiprotein complexes , the most important being MIC1/4/6 , MIC3/8 , MIC2/M2AP , and a complex of the microneme protein TgAMA1 with rhoptry neck proteins RON2/RON4/RON6/RON8 [39]–[41] . MIC3 , MIC6 and MIC8 have multiple domains with homology to EGF [42] and are therefore termed EGF-MICs . As an initial experiment , we examined whether T . gondii induces autophosphorylation at 2 major tyrosine residues of EGFR ( 1068 and 1148 ) . HBMEC were incubated with RH T . gondii tachyzoites followed by determination of EGFR phosphorylation by immunoblot . T . gondii induced activation of EGFR , as indicated by phosphorylation of tyrosine residue 1068 ( Figure 4A ) . Moreover , the parasite caused phosphorylation of tyrosine residue 1148 , a site that appears to be phosphorylated only by ligand binding to EGFR [43] ( Figure 4A ) . Similar results were found using the ME49 strain of T . gondii ( not shown ) . Immunoblot analysis revealed that EGFR activation occurred in HBMEC upon challenge with viable but not killed parasites ( Figure 4B ) . EGFR autophosphorylation was not only observed in endothelial cells but also in human RPE cells and mouse microglia incubated with T . gondii ( Figure 4C , 4D ) . Thus , T . gondii causes EGFR activation in various mammalian cells . Next , we examined whether EGFR signaling is involved in activation of Akt triggered by T . gondii . Endothelial cells were transiently transfected with a plasmid that encodes either control siRNA or EGFR siRNA followed by challenge with T . gondii . The efficiency of EGFR knockdown was confirmed by immunoblot ( Figure 5A ) . EGFR knockdown ablated the ability of T . gondii to induce activation of Akt at all time points tested ( Figure 5A ) . Next , we explored the role of EGFR signaling on Akt activation in professional phagocytes . Mouse microglia were treated with vehicle or AG1478 , a pharmacological inhibitor of EGFR kinase activity , followed by challenge with T . gondii . Inhibition of EGFR kinase activity ablated parasite-induced Akt activation in mouse microglia ( Figure 5B ) . We assessed whether EGFR activation affects T . gondii survival within host cells . HBMEC were treated with vehicle or AG1478 followed by challenge with T . gondii . While AG1478 did not affect the percentage of infected cells at 2 h , AG1478 caused a marked reduction in the percentage of infected cells 24 h post-challenge ( p<0 . 05 ) ( Figure 6A ) . In addition , there was a significant reduction in the numbers of parasites per 100 endothelial cells ( p<0 . 01 ) ( Figure 6A ) . Similar results were obtained whether HBMEC or human retinal endothelial cells were infected with RH or ME49 strains of T . gondii ( not shown ) . The role of EGFR in affecting parasite survival was confirmed with a genetic approach since knockdown of EGFR in human RPE cells resulted in enhanced killing of T . gondii ( p<0 . 01 ) ( Figure 6B ) . Similar to the studies of blockade of Akt , inhibition of EGFR signaling not only reduced the percentages of infected cells but also caused a reduction in the numbers of vacuoles per 100 cells without affecting the numbers of parasites in the vacuoles that persisted after EGFR blockade ( not shown ) . The effects of EGFR signaling inhibition were not restricted to non-hematopoietic cells since mouse bone marrow-derived macrophages also acquired anti-T . gondii activity when treated with AG1478 ( p<0 . 05 ) ( Figure 6C ) . To further explore the role of EGFR in the survival of T . gondii , we took a reverse approach and infected parental CHO cells , known to be EGFR null [44] , and CHO cells expressing human EGFR ( CHO-EGFR ) . A reduction in the percentage of infected cells and a reduction in parasite load at 24 h were observed in parental CHO cells compared to CHO-EGFR cells ( p<0 . 05 ) ( Figure 6D ) . These findings revealed an important role of EGFR in promoting Akt activation and T . gondii survival within host cells . We investigated whether T . gondii killing induced by inhibition of EGFR is dependent on autophagy proteins . Knockdown of EGFR in mHEVc cells or treatment of these cells with AG1478 resulted in an enhanced accumulation of LC3 and LAMP-1 around the parasite ( p<0 . 05 ) ( Figure 6E and 6F ) . Moreover , silencing of Beclin 1 or Atg7 prevented induction of anti-T . gondii activity in endothelial cells subjected to EGFR knock-down or treated with AG1478 ( p<0 . 01 ) ( Figure 6G and 6H ) . Taken together , activation of EGFR signaling promoted survival of T . gondii within host cells by inhibiting autophagy protein-dependent killing of the parasite . EGFR ligands exist as precursors transmembrane proteins that are shed from the plasma membrane by members of the ADAM ( a disintegrin and metalloprotease ) family of zinc-dependent metalloproteases [45] . This results in an autocrine or paracrine EGFR activation , a phenomenon that explains how proteins such GPCR activate EGFR [45] . We explored whether EGFR activation triggered by T . gondii could be due to this mechanism of autocrine/paracrine signaling . HBMEC were treated with GM6001 , a broad spectrum ADAM inhibitor , followed by challenge with T . gondii . GM6001 did not affect the percentage of infected cells ( data not shown ) and did not prevent the ability of T . gondii to induce EGFR activation ( Figure 7A ) . Moreover , EGFR phosphorylation after T . gondii infection took place despite incubation with PTx ( Figure 7B ) . These findings suggest that ADAM- and GPCR-dependent EGFR activation do not play a major role in EGFR phosphorylation induced by T . gondii . As stated above , MIC3 , MIC6 , MIC8 have multiple domains with homology to EGF [42] . MIC7 and MIC9 also express EGF-like domains but these MICs have poor or no expression in tachyzoites [42] . We examined the effect of deficiency of MICs on the ability to induce activation of EGFR and Akt . HBMEC were infected with wild type ( WT ) , MIC1 ko ( lacks MIC1 , resulting in deficient secretion of MIC6 [46] ) , MIC3 ko ( lacks MIC3 ) , MIC1-3 ko ( lacks MIC6 secretion and MIC3 ) parasites followed by determination of EGFR and Akt activation . These MIC ko parasites still express MIC8 ( MIC8 deficiency results in parasites that are unable to infect mammalian cells ) . The multiplicity of infection was adjusted so that the initial percentages of infected HBMEC were similar for all strains of the parasite ( Figure 8A ) . Compared to WT T . gondii , MIC1 ko and MIC3 ko parasites caused a partial reduction in EGFR and Akt phosphorylation ( p<0 . 05 ) ( Figure 8B , 8C ) . MIC1-3 ko parasites caused further decrease in EGFR and Akt phosphorylation compared to MIC1 ko and MIC3 ko parasites ( p<0 . 05 ) ( Figure 8B , 8C ) . However , even in cells infected with MIC1-3 ko parasites the reduction in EGFR and Akt phosphorylation was not complete . MIC1-3 ko parasites still express MIC8 , a molecule that has EGF-like domains . We used conditional MIC8 knockout T . gondii previously generated using a tetracycline-inducible system to explore the potential role of MIC8 in signal activation [47] . Incubation of these parasites with anhydrotetracycline ( ATc ) results in almost complete ablation of MIC8 [47] . Parasites previously grown in the absence or presence of ATc were incubated with HBMEC . We could not detect an appreciable decrease in Akt phosphorylation in cells exposed to MIC8 deficient parasites ( Figure S2 ) . To further explore the role of MICs in the activation of EGFR and Akt , HBMEC were incubated with Pichia pastoris-derived MIC3 . Although the EGF-like domains alone do not appear to promote the adhesion of MIC3 to mammalian cells [48] , it was still possible that MIC3 could cause EGFR and Akt activation . Indeed , compared to recombinant MIC4 ( a control that does not express EGF-like domains ) incubation with recombinant MIC3 caused enhanced phosphorylation of EGFR and Akt in HBMEC ( Figure 8D , 8E ) . Moreover , incubation with E . coli-derived MIC6 but not M2AP caused EGFR-Akt phosphorylation ( Figure 8D , 8E ) . This response was unlikely to be mediated by LPS since M2AP and MIC6 preparations had similar concentrations of LPS ( 12 ng/ml and 12 . 4 ng/ml respectively ) . In addition , LPS at concentrations between 10–1 , 000 ng/ml failed to induce EGFR phosphorylation in HBMEC ( not shown ) . Taken together , EGF-MICs ( MIC3 and MIC6 ) can induce EGFR-Akt activation and parasites deficient on these MICs have diminished capacity to activate EGFR and Akt . Cells stimulated with CD154 ( CD40 ligand ) exhibit accumulation of LC3 around T . gondii and killing that is dependent on autophagy proteins [13]–[15] . We examined whether targeting of the parasite by LC3+ structures in CD154-treated cells can be affected by EGFR signaling . Endothelial cells were treated with or without CD154 followed by challenge with T . gondii in the presence or absence of EGF . EGF did not affect the initial percentage of infected cells ( not shown ) . As previously reported [15] , CD154 caused accumulation of LC3 around T . gondii ( Figure 9A ) . Targeting of parasites by LC3+ structures was inhibited in cells that were exposed to EGF ( p<0 . 05 ) ( Figure 9A ) , The effect of EGF was specific since addition of AG1478 to cells treated with EGF restored LC3 accumulation around T . gondii ( Figure 9A ) . Similar results were obtained using rapamycin , a well-described stimulator of autophagy ( Figure 9B ) . Next , we explored the role of MICs on the distribution of LC3+ structures in endothelial cells treated with CD154 . Endothelial cells were treated with or without CD154 followed by challenge with WT , MIC1 ko , MIC3 ko , MIC1-3 ko and their respective complemented parasites . Infection with MIC1 ko , MIC3 ko or MIC1-3 ko parasites induces a partial decrease in EGFR-Akt activation ( see Figures 8B , 8C ) . Indeed , in control endothelial cells ( no CD154 treatment ) there were no differences in the low level LC3 accumulation around the parasites ( Figure 9C ) . After treatment with CD154 , enhanced accumulation of LC3 around the parasites was similar in endothelial cells infected with WT , MIC1 ko or MIC3 ko parasites ( Figure 9C ) . In contrast , cells infected with MIC1-3 ko parasites ( the strain that was the weakest inducer of EGFR-Akt activation ) exhibited a significant further increase in LC3 accumulation ( p<0 . 05 ) ( Figure 9C ) . These results were specific because the phenotype was lost in the complemented strain of T . gondii ( MIC1-3 ko+MIC1-3 ) ( Figure 9C ) . Examination of the parasite load revealed that the loads of MIC1 ko , MIC3 ko and MIC1-3 ko parasites were not significantly different from those of WT parasites in control endothelial cells ( no CD154 treatment ) ( Figure 9D ) . When cells were treated with CD154 , MIC1-3 ko T . gondii displayed increased susceptibility to CD154-induced anti-T . gondii activity ( p<0 . 05 ) ( Figure 9D ) . Similar to the studies of LC3 expression , the phenotype of MIC1-3 ko parasites was lost in the complemented strain ( MIC1-3 ko+MIC1-3 ) ( Figure 9D ) . Next , we examined whether increased killing of MIC1-3 ko parasites was observed in cells treated with another autophagy inducer ( rapamycin ) or in cells treated with IFN-γ , a cytokine that triggers anti-T . gondii activity independently of autophagic degradation [13]–[15] . Similar to CD154-stimulated cells , MIC1-3 ko parasites were more susceptible to rapamycin-induced killing ( p<0 . 05 ) ( Figure 9E ) . Moreover , in contrast to the results obtained with CD154-stimualtion , anti-T . gondii activity induced by IFN-γ/TNF-α was similar in all parasite strains tested including MIC1-3 ko T . gondii ( Figure 9F ) . Finally , we explored the effects of recombinant MICs on CD154-induced killing of MIC1-3 ko T . gondii . In initial experiments , recombinant MICs did not affect the load of T . gondii in non-activated ( control ) endothelial cells or cells treated with IFN-γ/TNF-α ( not shown ) . Next , control or CD154-activated endothelial cells were challenged with WT or MIC1-3 ko parasites in the presence of absence of recombinant MICs . Whereas treatment of endothelial cells with MIC4 and M2AP did not affect the load of WT or MIC1-3 ko parasites in CD154-activated cells , treatment with MIC3 or MIC6 inhibited CD154-induced T . gondii activity ( p<0 . 05 ) ( Figure 9G ) . Moreover , the phenotype of MIC1-3 ko parasites of increased susceptibility to CD154-mediated anti-T . gondii activity was lost in the presence of either MIC3 or MIC6 since the loads of WT and MIC1-3 ko parasites were no longer different in cells treated with these EGF-MICs ( Figure 9G ) . Taken together , our findings indicate that EGFR , MIC3 and MIC6 negatively regulate autophagic killing of T . gondii .
Avoidance of lysosomal degradation is pivotal for the survival of numerous intracellular pathogens including T . gondii . Our studies indicate that , in addition to exclusion of type I transmembrane proteins from the PVM , T . gondii also activates EGFR-Akt signaling in the host cell to prevent targeting of the parasite by LC3+ structures and pathogen killing that is dependent on autophagy proteins and lysosomal protease activity . Thus , these studies identified EGFR-Akt signaling as a pathway critical for pathogen survival . In addition , they suggest that EGF-MICs may be involved in pathogen virulence not only by allowing parasite invasion of host cells but also by activating host cell signaling that counter-regulates autophagy . Various bacteria and viruses encode virulence factors that impair the function of autophagy proteins and as a result , avoid their degradation via the autophagy pathway [18]–[24] . It has been suggested that HIV-1 and M . tuberculosis may prevent autophagic degradation by affecting signaling cascades that regulate the autophagy pathway [25] , [26] . Our studies indicate that indeed a pathogen can act at the level of a regulatory pathway to avoid its degradation by the autophagy machinery . Relevant to our findings is the report that HIV-1 tat impairs autophagy by stimulating counter-regulatory cascades ( Akt and STAT3 ) , although these studies did not examine whether these pathways would prevent lysosomal degradation of the virions [49] . Our studies indicate that T . gondii-induced EGFR activation is a major event upstream of Akt phosphorylation in endothelial and RPE cells , a finding consistent with the important role of EGFR and other growth factor receptors as activators of Akt signaling [36] , [50] . PI3K is a classical link between growth factor receptors and Akt activation . However , in contrast inhibition of EGFR signaling , the effect of PI3K inhibition on Akt activation appeared to be more transient . These findings may be explained by the fact that , besides PI3K , there are additional activators of Akt that might be engaged by growth factor receptors [51] . T . gondii has been reported to activate Akt in macrophages , a phenomenon that was inhibited by PTx [31] . Our studies indicate that EGFR also contributes to Akt activation in macrophages/microglia since the parasite caused EGFR autophosphorylation and inhibition of EGFR signaling impaired parasite-induced Akt activation . Moreover , not only activation of Akt but also activation of EGFR in endothelial cells , RPE cells and macrophages/microglia prevented killing of T . gondii dependent on autophagy proteins and lysosomal enzymes . The fact that Akt activation has been linked to inhibition of apoptosis of T . gondii-infected cells [31] raises the possibility that parasite-induced EGFR - Akt signaling may not only promote parasite survival by preserving the non-fusogenic nature of the PV but also by avoiding death of infected cells subjected to pro-apoptotic signals . While EGFR is a central mediator of Akt activation in the early stages after T . gondii , Akt phosphorylation has recently been reported at 24 h post-infection with the parasite [52] . This raises the possibility that T . gondii may also activate Akt through additional mechanisms besides parasite engagement of EGFR . Although T . gondii causes EGFR - Akt activation and these signaling molecules have been shown to inhibit autophagy [35] , [53] , [54] , T . gondii does not appear to prevent autophagosome formation in infected cells . Indeed , large LC3+ structures were readily detected within infected cells during early stages post-infection ( see Figure 2D ) , a finding previously reported in host cells at 24 h post-infection [55] . Moreover , there is no decrease in the levels of LC3 II ( the lipidated form of LC3 that associates with the autophagosome membrane ) during the early stages of infection ( Muniz-Feliciano and Subauste , unpublished observations ) . In fact , T . gondii has been reported to increase LC3 II levels and autophagosome formation in host cells at 24 h post-infection , presumably as an attempt to gain access to nutrients [55] . Our studies indicate that while global autophagy did not appear to be inhibited by T . gondii , engagement of EGFR impaired targeting of the PV by LC3+ structures . Future studies that identify how autophagosomes target the PV will likely shed light on the molecular mechanism by which EGFR - Akt diminish autophagic targeting of the parasite . Various pathogens can target EGFR . Pseudomonas aeruginosa and Helicobacter pylori can cause EGFR phosphorylation that is mediated by the release of membrane-bound EGF ligands and transactivation of EGFR [56] , [57] . Klebsiella pneumonia causes EGFR activation that appears to be dependent on bacterial capsule polysaccharide engagement of TLR4 and subsequent Src-dependent EGFR activation [58] . In addition , proteins from oncogenic viruses activate EGFR to mediate transformation [59] . Much less is known on whether microbial products can directly engage and activate EGFR . It has been suggested that H . influenza may activate EGFR through the presence of bacterial-derived molecules with EGF-like properties [60] . Uptake of Influenza A virus causes EGFR activation , a process that may be dependent on multivalent binding of hemagglutinin to sialic acids present on EGFR or ganglioside GM1 leading to aggregation of rafts , clustering of EGFR and its activation [61] . Our studies suggest that EGF-MICs play a role in mediating EGFR-Akt activation of host cells and prevention of parasite killing since: recombinant EGF-MICs ( MIC3 and MIC6 ) induce EGFR-Akt activation while MICs that lack EGF domains do not cause appreciable phosphorylation of EGFR and Akt; EGFR signaling inhibits LC3 accumulation around T . gondii; parasites deficient in 2 EGF-MICs ( MIC3 and MIC6: MIC1-3 ko parasites ) cause markedly impaired EGFR-Akt activation and exhibit increased encasing by LC3+ structures as well as killing in cells treated with autophagy stimulators; MIC3 and MIC6 impair parasite killing mediated by the autophagy pathway . It was interesting to note that MIC1-3 ko parasites are not targeted by LC3+ structures and are not more likely to be killed in unstimulated cells despite the markedly weakened EGFR-Akt signaling . MIC1-3 ko parasites only display increased susceptibility to autophagic targeting and killing when autophagy is stimulated by CD154 or rapamycin . Of relevance to our findings , other studies support the existence of signaling thresholds that need to be achieved in order for autophagy to take place [62] , [63] . For example , in Drosophila both the Ret-like receptor tyrosine kinase Stitcher ( Stit ) and insulin receptor ( InR ) are required for cell growth and proliferation through the PI3K-I/TORC1 pathway in the wing disc [63] . A decrease in either Stit or InR signaling diminishes TORC1 activity and suppresses growth [63] . However , this decrease in TORC1 activity is not sufficient to trigger autophagy in the wing [63] . Autophagy only takes place when both Stit and InR are impaired [63] . It was proposed that the simultaneous inactivation of Stit and InR reduces PI3K-I activity and TORC1 signaling below a critically low level at which autophagy in the wing can no longer be prevented [63] . Given that the EGFR-Akt pathway inhibits autophagy by regulating TORC1 activity , a similar phenomenon could be at play in the case of T . gondii infection . The reduction in EGFR-Akt observed in cells infected with MIC1 ko or MIC3 ko parasites does not translate in increased autophagic killing of these parasites either in unstimulated cells or in cells treated with stimulators of autophagy . The further reduction in EGFR-Akt signaling observed in cells infected with MIC1-3 ko may still be sufficient to prevent autophagic killing in unstimulated cells but results in enhanced killing in cells treated with autophagy stimulators . Finally , further inhibition of EGFR-Akt signaling ( by genetic or pharmacological approaches ) triggers autophagic targeting of T . gondii even in unstimulated cells . Thus , our studies suggest that the effects of MIC deficiency on the levels of EGFR-Akt activation likely explain the differences in outcome observed after infection . Taken together , in addition to being key for invasion of host cells , EGF-MICs ( MIC3 and MIC6 ) contribute to the induction of a signaling cascade within these cells that is required to avoid lysosomal degradation of the parasite . While MIC1-3 ko parasite exhibited a marked defect in EGFR-Akt activation in host cells , phosphorylation of these molecules still took place . Although we cannot rule out a role of MIC8 in activation of this cascade , it appears that the residual ability of MIC1-3 ko parasites to activate EGFR-Akt may not be explained by their expression of MIC8 ( an EGF-MIC ) . Conditional MIC8 ko parasites did not exhibit a noticeable defect in signal activation in host cells . These findings are likely explained by the fact that MIC8 ko parasites do not exhibit defects in attachment to host cells and they secrete MICs [47] . The presence of an additional mechanism of EGFR-Akt activation that normally cooperates with MIC-dependent EGFR signaling may explain why MIC1-3 ko T . gondii have residual capacity to activate the EGFR-Akt pathways . T . gondii is very successful as a pathogen and utilizes various strategies to manipulate host cell signaling to ensure its survival [64]–[67] . Here we report that the parasite activates EGFR - Akt to maintain the non-fusogenic nature of PV a process that appears to be dependent at least in part on EGF-MICs . These findings may be of therapeutic relevance since various inhibitors of EGFR are being used for treatment of cancer . The fact that EGFR inhibition induced parasite killing in cells not treated with immune activators , raises the possibility that this approach may be effective even in immunocompromised hosts .
Primary human brain microvascular endothelial cells ( HBMEC ) were obtained from ScienCell Research Laboratories ( Carlsbad , CA ) and cultured in fibronectin-coated tissue culture flasks and basal medium supplemented with Endothelial Cell Growth Supplement ( ECGS ) and 5% fetal bovine serum ( FBS ) all from ScienCell . The mouse high endothelial venule cell line ( mHEVc ) ( gift from Joan Cook-Mills , Northwestern University , Chicago , IL ) and mHEVc cells stably expressing LC3-EGFP construct ( mHEVc-LC3-EGFP ) or hmCD40 plus LC3-EGFP ( hmCD40 mHEVc-LC3-EGFP ) [15] were cultured in DMEM plus 10% FBS ( HyClone; Logan , UT ) . A human RPE cell line ( ARPE-19; American Type Culture Collection , Manassas , VA ) , a mouse macrophage line ( RAW 264 . 7 ) and mouse microglia line ( BV-2 ) were cultured in DMEM plus 10% FBS . Mouse bone marrow-derived macrophages were obtained as described and cultured in DMEM plus 30% L929-conditioned medium , 10% FBS and 5% horse serum [68] . Parental Chinese Hamster Ovary ( CHO ) cells and CHO cells expressing human EGFR ( CHO-EGFR ) were cultured in MEM plus 10% FBS . Experiments were conducted using tachyzoites of the RH strain of T . gondii ( Type I strain ) , RH that express cytoplasmic YFP [69] or cytoplasmic DsRed ( RFP ) [69] , tachyzoites of the ME49 ( type II strain ) , transgenic parasites deficient in micronemal proteins MIC1 ko , MIC3 ko , MIC1-3 ko and the complemented strains ( MIC1ko+MIC1 , MIC3 ko+MIC3 and MIC1-3 ko+MIC1-3; gift from Maryse Lebrun , Universite de Montpellier 2 , France ) [39] , as well as conditional MIC8 ko parasites ( gift from Markus Meissner , University of Glasgow ) . Parasites were maintained in human foreskin fibroblasts following standard procedures [70] . In order to deplete MIC8 , conditional MIC8 ko parasites were cultured in HFF in the presence of anhydrotetracycline ( 1 µg/ml ) for 48 h . T . gondii tachyzoites were killed by incubation in 1% paraformaldehyde in PBS . A potassium buffer shift was used to synchronize T . gondii invasion of serum-starved ( 0 . 1% FBS ) mammalian cells as described [71] . Briefly , freshly egressed tachyzoites were resuspended in Endo buffer and incubated with cells for 20 minutes at 37°C . The Endo buffer was replaced for a low-potassium permissive medium to allow parasite invasion . In certain experiments , mammalian cells were incubated with Akt inhibitor IV ( 1 . 25 µM; EMD Millipore , Billerica , MA ) , PI3K inhibitor ( LY294002; 20 µM; Sigma-Aldrich; St . Louis , MO ) , EGFR inhibitor ( AG1478; 1 µM; EMD Millipore ) , a broad spectrum ADAM inhibitor ( GM6001; 10 µM; EMD Millipore ) ( all 1 h prior to challenge with T . gondii ) , Pertussis Toxin ( PTx; 100 ng/ml; EMD Millipore; 4 h prior to challenge ) , leupeptin ( 10 µM; EMD Millipore ) and pepstatin ( 10 µM; EMD Millipore; both 1 h after challenge with T . gondii ) , 3-methyl adenine ( 3MA; 10 mM; Sigma Chemical ) and rapamycin ( 1 µM; EMD Milipore; both 2 h after challenge with T . gondii ) or vehicle . To induce CD40 signaling , mHEVc cells were treated with cell-free supernatants containing either multimeric human CD154 or a non-functional CD154 mutant [72] ( T147N; both obtained from Dr . Richard Kornbluth , Univ . of California San Diego , current address Multimeric Biotherapeutics Inc . , La Jolla , CA ) for 18 h at 37°C as previously described [73] prior to challenge with parasites . Monolayers were fixed at indicated time points and stained with Diff-Quick ( Dade Diagnostics , Aguada , Puerto Rico ) . The percentage of infected cells , the numbers of tachyzoites and vacuoles per 100 cells as well as the numbers of parasites per vacuole were determined by light microscopy by counting at least 200 cells or 200 vacuoles per monolayer as previously described [15] . For expression of MIC3 and MIC4 in P . pastoris , amplified DNA fragments were cloned into a pPICZα A vector ( Invitrogen; Carlsbad , CA ) . The pPICZα A vector contains the S . cervisiae α-factor secretion signal that allows for the secretion of folded proteins from P . pastoris . Cells were grown in BMGY media , washed and resuspended in BMMY media for an initial OD600 of 20–40 . The culture was then incubated in a 28°C incubator with vigorous shaking . The culture was then grown for 1–5 days depending on the optimal period of expression . Inhibition of glycosylation during culture required the addition of 20 µg/ml of tunicamycin . The supernatant is then passed through a HiTrap Q HF Column ( GE Healthcare; Little Chalfont , UK ) . The eluted fraction was buffer exchanged into nickel-column binding buffer ( 50 mM Tric-HCl , pH 8 . 0 , 50 mM NaCl ) . If needed further protein purification was achieved by a further nickel affinity step and gel filtration . M2AP and MIC6 encompassing residues 87 to 197 ( including EGF2 and EGF3 motifs ) were generated using a pET32 Xa/LIC plasmid ( Novagen , EMD Millipore ) in the Origami ( D3 ) Escherichia coli strain ( Stratagene ) [74] , [75] . Expressions of the fusion protein was induced by adding 1 mM IPTG and harvested after overnight culture at 18°C . The cells were collected by centrifugation and lysed by French Press . The fusion protein incorporating a hexahistidine tag was purified by bench top chromatography using a nickel-nitrilotriacetic acid resin ( QIAGEN ) . The fusion partner of protein was cleaved by factor Xa and removed by an additional chromatography step and the factor Xa was removed by agarose resins ( Novagen ) . Protein samples were concentrated to 0 . 5 mM in 50 mM NaCl , 50 mM potassium phosphate and 5% D2O at pH 5 . 8 . Endotoxin concentrations were similar in MIC3 and MIC4 as well as in M2AP and MIC6 . Cells were transiently transfected with a plasmid that encodes Akt-PH-GFP [76] , human PI3K p110α siRNA [77] , human Akt siRNA [78] , mouse Beclin1 siRNA [79] , mouse Atg7 siRNA [79] , human MyD88 siRNA [80] , human EGFR siRNA [81] or control siRNA ( Dharmacon ) using Lipofectamine 2000 ( Invitrogen ) or an Amaxa nucleofector as described [13] , [15] . siRNA against mouse EGFR was synthesized using siRNA construction kit ( Ambion ) [82] following manufacturer's recommendation and used for mouse EGFR knock-down after transfection using Lipofectamine 2000 . To assess for LC3 accumulation around the parasite , mHEVc-LC3-EGFP cells were cultured with or without Akt inhibitor IV or transfected with either control siRNA or EGFR siRNA or treated with or without EGF ( 50 ng/ml; PeproTech ) . Monolayers were challenged with RH T . gondii that express cytoplasmic RFP ( T . gondii-RFP ) . Five hours post-challenge , monolayers were fixed with 4% paraformaldehyde , slides were mounted using Fluoromount G and assessed for LC3-EGFP accumulation around T . gondii as described [13] , [15] . In certain experiments , hmCD40 mHEVc-LC3-EGFP cells treated with or without CD154 were infected with WT , MIC1 ko , MIC1 ko+MIC1 , MIC3 ko , MIC3 ko+MIC3 , MIC1-3 ko or MIC1-3 ko+MIC1-3 tachyzoites . Monolayers were fixed with 4% paraformaldehyde , permeabilized with 0 . 1% Triton X-100 and incubated with rabbit anti-T . gondii Ab ( BioGenex; San Ramon , CA ) for 30 minutes . Monolayers were then washed with PBS and incubated for 1 h at room temperature with goat anti-rabbit Alexa 568-conjugated secondary antibody ( Jackson ImmunoResearch Laboratories Inc . , West Grove , PA ) . HBMEC transfected with a plasmid encoding PH-Akt-GFP were cultured with or without LY294002 followed by challenge with T . gondii-RFP . Distribution of PH-Akt-GFP was examined 5 min . post-challenge . In certain experiments , endothelial cells were treated with either Akt inhibitor IV or AG1478 and challenged with T . gondii-YFP were fixed with 4% paraformaldehyde at 8 h post-infection , permeabilized with ice-cold methanol . Monolayers were incubated overnight at 4°C with either mouse anti-human LAMP-1 or rat anti-mouse LAMP-1 ( all from Developmental Studies Hybridoma Bank; Iowa City , IA ) . Monolayers were washed with PBS plus 1% BSA , then incubated for 1 h at room temperature with Alexa 568-conjugated secondary antibodies ( Jackson ImmunoResearch Laboratories Inc . ) . Specificity of staining was determined by incubating monolayers with secondary antibody alone . Slides were analyzed using a Leica DMI 6000 B automated microscope equipped for epifluorescence microscopy . Experimental groups had triplicate samples and at least 100 cells per sample were counted . Endothelial cells were seeded onto a sterilized Aclar Embedding Film ( Electron Microscopy Sciences , PA ) and incubated with or without T . gondii tachyzoites in the presence of Akt inhibitor IV or vehicle . At 5 h post-challenge , the Aclar sheets with their attached cells were fixed as described [83] . After a soak in acidified uranyl acetate , the specimen was dehydrated in ethanol , passed through propylene oxide , and embedded in Poly/Bed ( Polysciences , PA ) . Sections were cut in a horizontal plane parallel to that of the Aclar film to provide panoramic views of the endothelial cells . Thin sections were stained with acidified uranyl acetate in 50% methanol followed by triple lead stain of Sato . These sections were examined in a JEOL 1200 EX electron microscope ( Tokyo , Japan ) . Cells were lysed in buffer supplemented with protease and phosphatase inhibitors ( Cell Signaling ) . Equal amounts of protein were subjected to either 7 . 5% or 10% SDS-PAGE ( Bio-Rad ) and transferred to PVDF membranes . Membranes were probed with either antibody to total Akt ( Cell Signaling ) , phospho-Ser473 Akt ( Cell Signaling ) , total EGFR ( Santa Cruz Biotechnology ) , phospho-tyrosine 1068 EGFR ( Invitrogen ) , phospho-tyrosine 1148 EGFR ( Cell Signaling ) , Atg7 ( Cell Signaling ) , Beclin 1 ( BD Biosciences ) , PI3K p110α ( Cell Signaling ) or MyD88 ( Cell Signaling ) followed by incubation with secondary antibody conjugated to horseradish peroxidase ( Santa Cruz Biotechnology ) . Bands were visualized by using enhanced chemiluminescence kit ( Pierce Bioscience ) . Intensities of phospho-Akt and phospho-EGFR were calculated using ImageJ ( NIH ) and normalized against total Akt and total EGFR respectively . Results from pooled experiments were analyzed for statistical significance using 2-tailed Student's t test and ANOVA ( InStat version 3 . 0 , GraphPad; La Jolla , CA ) . Differences were considered statistically significant when P was<0 . 05 . | Toxoplasma gondii resides in a parasitophorous vacuole that excludes transmembrane proteins required for recruitment of endosomes and lysosomes and thus , does not follow the path of classical lysosomal degradation . However , the non-fusogenic nature of the vacuole can be reverted when autophagy , a pathway to lysosomal degradation , is upregulated through the immune system or pharmacologically . Maintenance of the non-fusogenic nature of the vacuole is central to parasite survival . Thus , in addition to preventing degradation through a classical lysosomal pathway , T . gondii may also deploy strategies to prevent constitutive levels of autophagy from targeting the pathogen and causing its lysosomal degradation . We report that T . gondii accomplishes this task by causing EGFR activation in host cells . In cells that were not subjected to immune or pharmacologic upregulation of autophagy , blockade of EGFR resulted in parasite encasing by structures that expressed the autophagy protein LC3 , vacuole-lysosomal fusion and autophagy protein-dependent killing of the parasite . Moreover , EGFR signaling also impaired targeting of the parasite by LC3+ structures in cells treated with stimulators of autophagy . Studies with T . gondii deficient in EGF domain containing-micronemal proteins ( EGF-MICs ) and recombinant EGF-MICs support the concept that these parasite adhesins contribute to EGFR activation . |
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Parasitic helminths establish chronic infections in mammalian hosts . Helminth/Plasmodium co-infections occur frequently in endemic areas . However , it is unclear whether Plasmodium infections compromise anti-helminth immunity , contributing to the chronicity of infection . Immunity to Plasmodium or helminths requires divergent CD4+ T cell-driven responses , dominated by IFNγ or IL-4 , respectively . Recent literature has indicated that Th cells , including Th2 cells , have phenotypic plasticity with the ability to produce non-lineage associated cytokines . Whether such plasticity occurs during co-infection is unclear . In this study , we observed reduced anti-helminth Th2 cell responses and compromised anti-helminth immunity during Heligmosomoides polygyrus and Plasmodium chabaudi co-infection . Using newly established triple cytokine reporter mice ( Il4gfpIfngyfpIl17aFP635 ) , we demonstrated that Il4gfp+ Th2 cells purified from in vitro cultures or isolated ex vivo from helminth-infected mice up-regulated IFNγ following adoptive transfer into Rag1–/– mice infected with P . chabaudi . Functionally , Th2 cells that up-regulated IFNγ were transcriptionally re-wired and protected recipient mice from high parasitemia . Mechanistically , TCR stimulation and responsiveness to IL-12 and IFNγ , but not type I IFN , was required for optimal IFNγ production by Th2 cells . Finally , blockade of IL-12 and IFNγ during co-infection partially preserved anti-helminth Th2 responses . In summary , this study demonstrates that Th2 cells retain substantial plasticity with the ability to produce IFNγ during Plasmodium infection . Consequently , co-infection with Plasmodium spp . may contribute to the chronicity of helminth infection by reducing anti-helminth Th2 cells and converting them into IFNγ-secreting cells .
Infections with Plasmodium and helminths are extremely common , each contributing to substantial morbidity in affected populations [1–3] . Additionally , co-infections with Plasmodium species and intestinal helminths occur frequently in co-endemic areas [4 , 5] . The impact of co-infection on disease burden , pathogenesis , resistance to infection and immunity is complex and poorly understood . The vast majority of reported co-infection studies have focused on the impact of helminth infection on Plasmodium-associated responses , identifying altered anti-malarial immune responses or malaria-associated pathology during helminth co-infection [6–11] . However , the specific impact of Plasmodium infection on anti-helminth immunity has not been well characterized . Experimental murine models of helminth and Plasmodium co-infections have been established , however these have also mainly focused on how concomitant helminth infection affects Plasmodium immunity and pathology [11–16] , with much less focus on how Plasmodium infection impacts helminth-associated type 2 responses . Murine models of intestinal helminth infections have delineated a clear role for Th2-directed immune responses for proficient immunity . In particular , infection with the natural murine helminth , Heligmosomoides polygyrus , results in a chronic infection with the induction of a polarized type 2 response , characterized by IL-4-producing Th2 cells , alternative activation of macrophages and elevated IgE , closely mimicking human helminthiasis . Following anthelmintic treatment , Th2 cell-dependent immunity protects mice from re-infection ( reviewed in [17 , 18] ) . In contrast , acute blood-stage infection with the rodent malaria parasite , Plasmodium chabaudi chabaudi ( AS ) , results in polyclonal lymphocyte activation with a strongly polarized Th1 response [19] . Disease is associated with a spectrum of immunopathologies including splenomegaly and anemia [20–22] with peak parasitemia occurring 7–9 days post-infection [23] . These well-studied experimental systems , modeling human disease , provide appropriate tools to dissect the immune responses during co-infection . There is a large body of literature describing the antagonistic relationship between Th1 and Th2 cell differentiation . In vitro-based studies have clearly established that under Th1 and Th2 polarizing conditions , differentiated cells become more fixed in their phenotype with increasing rounds of cell division , losing their ability to convert to alternative phenotypes [24 , 25] . Mechanistically , T-bet and GATA-3 , transcription factors required to promote Th1 and Th2 differentiation , respectively , inhibit differentiation of the opposing phenotype [26 , 27] . Despite this clear antagonistic relationship , IL-4+IFNγ+ and T-bet+GATA-3+ Th cells are readily observed in vivo [28 , 29] , and several studies have established that Th subsets retain flexibility in their ability to produce non-lineage-specific cytokines [30–32] . Indeed , recent studies challenging the fate-lineage dogma demonstrated that antigen-restricted TCR transgenic Th2 cells co-produced IFNγ and IL-4 following LCMV infection [33 , 34] . In light of these new data , it is possible that Th cell conversion occurs during co-infection , altering immunity to one or both pathogens or contributing to the chronicity of helminth infection . In this study , we observed that Plasmodium and helminth co-infection led to a reduction of helminth-elicited Il4gfp+ Th2 cells and compromised anti-helminth immunity . We hypothesized that helminth-elicited Th2 cells were being converted into IFNγ-secreting Th1 cells during Plasmodium co-infection , as pressure to control both pathogens was placed on the Th cell population . To test this hypothesis , we generated triple cytokine reporter mice to accurately purify and identify Il4gfp , Ifngyfp and Il17aFP635-expressing cells to determine whether Th2 cells had the ability to change their phenotype . We observed that Il4-expressing Th2 cells could readily produce IFNγ following adoptive transfer in Rag–/–recipients , and these cells reduced severe parasitemia during acute P . chabaudi infection . Conversion of Th2 cells was dependent upon IL-12 and IFNγ-signaling , and blockade of these cytokines during co-infection preserved the Th2 response . Overall , this study provides fresh insight into the functional relationship between IFNγ- and IL-4-producing Th cells during co-infection and indicates that limiting acute Th1 responses may preserve Th2-mediated anti-helminth immunity .
To assess the impact of concomitant Plasmodium infection on the development of Th2 responses , we infected mice with H . polygyrus and 6 days later with 105 P . chabaudi-infected red blood cells ( Fig 1A ) . To accurately identify simultaneous transcription of Th1 ( Ifng ) , Th2 ( Il4 ) and Th17 ( Il17a ) lineage-defining genes , we generated a triple cytokine reporter mouse ( Il4gfpIfngyfpIl17aCreR26FP635 ) using existing and new fluorescent cytokine reporter mouse strains [35–37] ( S1 Fig ) . Following infection with L3 larvae of the intestinal helminth , H . polygyrus , we observed a significant expansion of Il4gfp+ CD4+ Th2 cells in the mesenteric lymph nodes 14 days post-infection . Co-infected mice had significantly reduced numbers of Il4gfp+ CD4+ Th2 cells in the mesenteric lymph nodes ( Fig 1B ) as well as a reduction in serum IgE ( Fig 1C ) and decreased expression of the alternative macrophage activation marker , Retnla ( Relmα ) in the gut ( S2 Fig ) . These data indicated that helminth-elicited Th2 cells and Th2-driven immune responses were compromised during Plasmodium co-infection . The reduced Il4gfp+ cells in the mesenteric lymph nodes correlated with an increase in Ifngyfp+ cells in the spleen during co-infection . Very few Il17aFP635+ cells were induced in this model ( S2 Fig ) . Following the resolution of acute malarial parasitemia , Th2 cell numbers in the mesenteric lymph nodes and serum IgE returned to levels observed in mice infected with H . polygyrus only ( S2 Fig ) . H . polygyrus establishes a chronic infection in wild type C57BL/6 mice . However , treating mice with anthelmintics kills adult parasites and allows a protective memory Th2 response to develop . Upon re-infection , mice expel worms in a CD4+ T cell- and IL-4-dependent manner [38 , 39] . Following the observation that P . chabaudi infection compromised Th2 cell responses ( Fig 1B ) , we tested whether P . chabaudi infection would impact Th2-dependent anti-helminth immunity . We infected wild type mice with H . polygyrus , treated mice with the anthelmintic , pyrantel pamoate , and then infected mice with P . chabaudi 7-days prior to re-infection with H . polygyrus ( Fig 1D ) . Although H . polygyrus-specific IgG1 levels were comparable between groups of mice ( S2 Fig ) , P . chabaudi-infected mice that had been given a secondary H . polygyrus challenge infection had significantly more adult worms in the intestinal lumen ( Fig 1E ) , indicating that Plasmodium infection compromised proficient anti-helminth immunity . It has become clear in recent years that lineage-committed CD4+ T cells retain a degree of plasticity , with the ability to convert between phenotypes [30] . Plasmodium infection elicits a polyclonal expansion of lymphocytes and IFNγ-secreting T cells [21 , 22] . We therefore hypothesized that the loss of Il4gfp+ Th2 cells in the mesenteric lymph nodes and the increase in Ifngyfp+ cells in the spleen during H . polygyrus and P . chabaudi co-infection was due to conversion of Th2 cells to an IFNγ-producing Th1-like phenotype . To test whether Th2 cells could produce IFNγ during P . chabaudi infection , we FACS-purified CD4+TCRβ+Il4gfp+Ifngyfp–Il17aFP365– Th2 cells from 2-week in vitro cultures ( S1 Fig ) , adoptively transferred them into Rag1–/–mice and infected the recipient mice with P . chabaudi . Cytokine expression in the transferred cells was analyzed in the spleen at day 8 post-infection ( Fig 2A ) . Transferred Th2 cells ( Il4gfp+Ifngyfp–Il17aFP365– ) almost completely lost expression of Il4gfp and , comparable to naïve T cells , expanded with approximately 80% of cells expressing Ifngyfp ( Fig 2B ) . Il17aFP635+ cells were barely detectable ( <1% ) following Plasmodium infection , in line with previous data [21 , 22 , 40] . IFNγ protein was also detectable in the serum of mice that received either naive CD4+ T cells or purified Th2 cells , but not in P . chabaudi-infected Rag1–/–mice that received no T cells , indicating that serum IFNγ was T cell-dependent ( Fig 2C ) . Thin blood smears from recipient mice identified that following infection of Rag1–/–mice , very high parasitemia is observed ( Fig 2D ) . The adoptive transfer of naïve T cells to Rag1–/–mice significantly reduced the high parasitemia , confirming an important T cell-dependent role in the control of high parasitemia during acute infection . This system permitted us to test whether Th2 cells , which had converted into IFNγ+ cells , could also control high parasitemia following acute infection . Indeed , adoptive transfer of Th2 cells also significantly reduced parasitemia ( Fig 2D ) , suggesting a functional loss of hemoglobin and severe anemia were also prevented in Rag1–/–mice given Th2 cells ( Fig 2E and 2F ) . Although Th2 cells up-regulated IFNγ in uninfected recipient Rag1–/–mice , significantly greater expansion of these converted cells occurred in P . chabaudi infected recipient mice ( S3 Fig ) . These data demonstrate that purified Il4-expressing Th2 cells were capable of producing IFNγ and could protect mice during acute P . chabaudi infection , similar to naive CD4+ T cells . Finally , to determine whether Th2 cells had the capacity to produce non-lineage cytokines in another model system , we infected Rag1–/–recipient mice with Candida albicans ( S4 Fig ) . At day 6 post C . albicans infection , transferred Il4gfp+ Th2 cells had lost Il4 expression and up-regulated IFNγ , similar to P . chabaudi infection . Interestingly , transferred Th2 cells did not up-regulate IL-17a , unlike naïve controls ( S4 Fig ) . We next asked whether Th2 cells that had down-regulated Il4gfp and expressed Ifngyfp retained the ability to re-express Th2-associated cytokines . We transferred Il4gfp+ Th2 cells into Rag1–/–mice and infected recipient mice with P . chabaudi , as in Fig 2A . At day 8 post-infection with P . chabaudi , we sorted CD4+TCRβ+ Ifngyfp+Il4gfp-Il17aFP635– cells from the spleens of recipient mice ( Fig 2G ) . Converted cells were then cultured in vitro with IL-4 and TCR stimulation . As expected , Ifngyfp+ cells that were previously either naïve or Il4gfp+ secreted IFNγ protein ( Fig 2H ) , validating the fidelity of the transcriptional reporter system . However , only Ifngyfp+ cells that were previously Il4gfp+ secreted the Th2-associated cytokines IL-13 and IL-5 ( Fig 2I ) , indicating that converted cells were indeed plastic , retaining the ability to produce Th2 cytokines . To identify the degree of transcriptional re-wiring of the converted cells in this model , we performed RNA sequencing on Th2 cells ( Il4gfp+ ) , converted Th2 cells ( Il4gfp+ → Ifngyfp+Il4gfp- ) , naïve CD4+ T cells , and Th1 cells ( naïve → Ifngyfp+Il4gfp– ) , using the same sorting strategy as in Fig 2G . Comparing the transcriptome of all significantly differentially regulated genes ( p<0 . 05 , >2-fold relative to naive T cells ) between the populations , we identified that converted cells had adopted a transcriptional profile very similar to Th1 cells ( Fig 3A and 3B , S1 Table ) with the majority of differentially regulated genes common with Th1 cells , while retaining some transcriptional similarity with their Th2 origin . Converted cells expressed Ifng , Tnf , Il2 and Il10 and largely lost expression of Il4 and Il6 , in comparison to the Th2 controls ( Fig 3C ) . Similarly , the transcriptional machinery in converted cells resembled Th1 cells with elevated Tbx21 ( Tbet ) and Eomes and low expression of Th2-associated transcription factors Gata3 and Nfil3 ( Fig 3D ) . To identify putative mechanistic pathways responsible for Th2 cell conversion , we used an upstream pathways algorithm to predict factors that may contribute to the observed transcriptional profile ( Ingenuity Pathways Analysis ) . This analysis identified canonical Th1 differentiation factors including IL-12 , IFNγ and type 1 IFN as potential upstream factors contributing to the observed transcriptional profile in converted cells ( Fig 3E ) . Furthermore , converted cells expressed Il12rb1 , Il12rb2 , Ifngr1 and Ifnar1 ( Fig 3F ) . In summary , converted Th2 cells had undergone significant re-wiring , closely resembling Th1 cells . When T cells undergo expansion in lymphopenic environments a population of rapidly dividing cells up-regulate CD44 and IFNγ [41–43] . To test whether conversion of Th2 cells into IFNγ-expressing cells could occur in a CD4+ T cell replete mouse , we transferred purified Th2 cells or naïve CD4+ T cells into OTII Rag1–/–mice [44] , which have CD4+ T cells specific only for OVA peptide . We infected recipient mice with P . chabaudi and analyzed donor and host cells at day 8 post-infection ( Fig 4A ) . Purified Th2 cells transferred into CD4+ OTII Rag1–/–mice , similar to Th2 cells transferred into Rag1–/–mice , produced IFNγ and down-regulated IL-4 ( Fig 4B and 4C ) , contributing to elevated levels of serum IFNγ ( Fig 4D ) . In contrast , host OVA-specific CD4+ T cells did not produce IFNγ following Plasmodium infection ( Fig 4C ) . Thus , Th2 cell conversion was not dependent on lymphopenia . Given that Th cells require both TCR stimulation and cytokine-mediated signaling for differentiation , it was conceivable that pre-activated Th2 cells in this system would only require a second cytokine receptor-mediated signal to up-regulate IFNγ , without the need for any additional TCR stimulation . We took two independent approaches to test whether TCR engagement was required for Th2 cells to produce IFNγ . First , we generated and FACS-purified TCR-restricted Th2 cells from OTII Rag1–/–mice crossed with Il4gfp reporter mice . We then transferred these OVA-specific Il4gfp+ Th2 cells into Rag1–/–recipients ( devoid of OVA ) and infected recipient mice with P . chabaudi ( Fig 5A ) . Unlike polyclonal Il4gfp+ Th2 cells that lost expression of Il4gfp and produced IFNγ , antigen-restricted OTII Il4gfp+ Th2 cells retained expression of Il4gfp and failed to produce IFNγ ( Fig 5B ) . Furthermore , IFNγ was not detectable in the serum of mice that received OVA-specific Il4gfp+ Th2 cells ( Fig 5C ) . Functionally , the failure to produce IFNγ correlated with significantly higher parasitemia , comparable to mice that received no T cells ( Fig 5D ) . These data indicate that TCR signaling was required for the functional conversion of Th2 cells into IFNγ-secreting cells . To verify the requirement of TCR-signaling for conversion , we transferred purified Il4gfp+Ifngyfp–Il17aFP365– Th2 cells into Rag1–/–recipient mice which were also deficient in MHC Class II and therefore unable to present antigens to Il4gfp+ Th2 cells . Recipient mice were infected with P . chabaudi , and transferred cells were analyzed at day 8 post-infection ( Fig 5E ) . As before , Il4gfp+ Th2 cells transferred into MHC Class II-sufficient Rag1–/–recipient mice down-regulated Il4gfp and up-regulated Ifngyfp . However , Il4gfp+ Th2 cells transferred to MHC Class II-deficient Rag1–/–recipient mice remained Il4gfp+ , did not express Ifngyfp ( Fig 5F ) and failed to reduce severe parasitemia ( Fig 5H ) . IFNγ was also undetectable in the serum ( Fig 5G ) . Taken together , these two experimental systems demonstrate that conversion of Th2 cells in this model requires TCR engagement . It has been shown previously that type I IFN signaling was required for IFNγ production from LCMV-specific TCR transgenic Th2 cells [34] . We had also observed that type 1 IFN was a candidate cytokine that could contribute to the transcriptional profile of converted Th2 cells ( Fig 3E ) . We therefore tested the requirement for type 1 IFN signaling by crossing Ifnar–/–mice with Il4gfp reporter mice . FACS purified Il4gfp+Ifnar–/–or Il4gfp+Ifnar+/+ Th2 cells were transferred to Rag1–/–recipient mice , subsequently infected with P . chabaudi and analyzed at day 8 post-infection ( Fig 6A ) . Both type I IFN responsive and unresponsive Th2 cells were capable of up-regulating IFNγ ( Fig 6B and 6C ) , contributing to serum IFNγ levels ( Fig 6D ) . Furthermore , type I IFN responsive and unresponsive Th2 cells afforded similar protection from high parasitemia ( Fig 6E ) , and prevented a loss in hemoglobin and red blood cells ( Fig 6F ) . Thus , type I IFN signaling was dispensable for IFNγ production from ex-Th2 cells and for controlling high parasitemia . From our RNA-Seq analysis we also identified that the canonical Th1 differentiating cytokines , IL-12 and IFNγ , may be responsible for the transcriptional profile observed in our converted cells ( Fig 3E ) . We first tested whether Th2 cells were responsive to IL-12 by measuring the phosphorylation of STAT4 following exposure to IL-12 . Supporting previous studies [45–47] , neither naïve CD4+ T cells nor sorted Il4gfp+ Th2 cells phosphorylated STAT4 in response to IL-12 ( Fig 7A and 7B; Pre- transfer ) . We then sorted transferred cells from naïve CD4+ T cell or Il4gfp+ Th2 cell recipient Rag1–/–mice 2 weeks post-transfer and found that both populations were responsive to IL-12 ( Fig 7A and 7B; Post-transfer ) . Thus , it was possible that IL-12 was promoting IFNγ expression in Th2 cells following P . chabaudi infection . We tested the role of IL-12 by transferring naïve or Il4gfp+ Th2 cells to Rag1–/–mice and blocking IL-12 prior to and after P . chabaudi infection ( Fig 7C ) . Blocking IL-12 reduced expression of Ifngyfp in naïve T cells ( reduced from 78 . 9% to 52 . 61% ) ; however , IL-12 blockade did not substantially alter the frequency of Ifngyfp+ cells derived from Th2 cells . Instead , IL-12 blockade maintained expression of Il4gfp+ in the Th2 population , with significantly larger Il4gfp+ and Il4gfp+Ifngyfp+ populations ( Fig 7D–7F ) . These data indicate that in this system IL-12 down-regulated Il4gfp expression , but was not required for IFNγ from Th2 cells . Furthermore , neutralization of IL-12 did not impact parasitemia ( Fig 7G ) . We next tested whether IFNγ , which contributes to Th1 differentiation [48] , was required for IFNγ expression by Th2 cells . To do this , we blocked IFNγ , IL-12 , or both IFNγ and IL-12 throughout the experiment ( Fig 8A ) . Blockade of IFNγ or IL-12 alone did not have a major impact on IFNγ production by Th2 cells ( Fig 8B ) . As above , IL-12 blockade preserved Il4gfp expression in a population of Th2 cells ( Fig 8C ) . However , blockade of both IFNγ and IL-12 led to a >50% reduction in IFNγ-expressing cells deriving from Th2 cells ( from 66 . 7%±1 . 5% IFNγ+ cells to 31 . 6%±3 . 4% IFNγ+ cells , Fig 8B ) , indicating that both IL-12 and IFNγ were required for optimal conversion of Th2 cells into IFNγ-secreting cells during Plasmodium infection . Despite a 50% reduction in IFNγ-secreting cells following IL-12 and IFNγ blockade , the remaining ~30% of IFNγ+ cells were sufficient to prevent high parasitemia ( S5 Fig ) . Finally , we translated these new observations back into a co-infection scenario , as presented in Fig 1 , and tested whether helminth-induced Th2 cells had the capacity to up-regulate IFNγ in a co-infection scenario . First , we purified ex vivo Il4gfp+Ifngyfp–Il17aFP635– Th2 cells from d14 H . polygyrus-infected mice and transferred them into day 14 H . polygyrus-infected Rag1–/–mice . Recipient mice were then co-infected with P . chabaudi and the transferred cells were analyzed at day 8 post P . chabaudi infection ( Fig 9A ) . Similar to in vitro-derived Th2 cells , H . polygyrus-derived Th2 cells down-regulated Il4gfp and up-regulated Ifngyfp , albeit to a slightly lesser extent than naïve T cells ( Fig 9B ) . Re-stimulation of lymph node cells with H . polygyrus antigen and IL-4 led to the secretion of IL-5 and IL-13 from mice given H . polygyrus Th2 cells , but not from mice given naïve T cells ( Fig 9C ) . These data suggested that despite a high degree of conversion to IFNγ-secreting cells , cells retained antigen-associated cytokine secretion . To more accurately determine whether converted cells retained the capacity to produce Th2 cytokines in an antigen-specific manner , we sorted Th2 cells , or naïve cells , that had converted into Ifngyfp+ cells from recipient mice and restimulated them in vitro with H . polygyrus antigen or P . chabaudi infected red blood cells ( iRBC ) . Ifngyfp+ cells , which were previously naïve or Il4gfp+ Th2 cells , produced IFNγ when co-cultured with irradiated APCs , supporting the cytokine reporter expression ( Fig 9D ) . iRBCs further stimulated more IFNγ from naive T cells , but not from Th2 cells , suggesting that either ex vivo Th2 cells were not responding to malarial antigens , or that they were already secreting IFNγ at capacity . In addition , ex vivo H . polygyrus elicited Th2 cells which had down-regulated Il4gfp and up-regulated Ifngyfp produced IL-5 in response to H . polygyrus antigen , suggesting that converted cells retained antigen specificity and plasticity in this model ( Fig 9D ) . Finally , we tested whether the factors promoting IFNγ in the adoptive transfer model , IL-12 and IFNγ ( Fig 8B ) , were responsible for the loss of Th2 cells and type-2 immunity during H . polygyrus and P . chabaudi co-infection . To do this , we infected wild type mice with H . polygyrus and at six days post-infection , mice were co-infected with P . chabaudi with or without blocking antibodies to IL-12 and IFNγ ( Fig 10A ) . Blockade of IL-12 and IFNγ preserved Il4gfp+ Th2 cells in co-infected mice ( Fig 10B ) and maintained elevated levels of helminth-induced type-2-associated IgE ( Fig 10C ) . However , despite preserving Th2 cells and IgE , proficient anti-helminth immunity was not fully restored in mice given blocking antibodies ( S6 Fig ) . Thus , IL-12 and IFNγ play a major role compromising Th2 responses during helminth/ Plasmodium co-infection , but additional factors also contribute to compromised anti-helminth immunity during co-infection .
In this study , we identified that Plasmodium infection significantly reduced CD4+ Th2 cells during co-infection with H . polygyrus and that anti-helminth immunity was compromised during co-infection . Mechanistically , we found that Il4gfp+Ifngyfp–Il17aFP635– Th2 cells , purified from novel triple cytokine reporter mice , converted to IFNγ-secreting cells , contributing significantly to anti-Plasmodium immunity . IFNγ production by Th2 cells was dependent on TCR , IL-12 , and IFNγ signaling , all of which contributed to the transcriptional re-programming of Th2 cells . Finally , we found that blockade of IL-12 and IFNγ during Plasmodium and helminth co-infection preserved Th2 responses and IgE production , but was insufficient to fully restore anti-helminth immunity . There is a large body of literature describing the prevalence of helminth and Plasmodium co-infection in human populations [4 , 5 , 8 , 11 , 49 , 50] , and mouse models [16 , 51] , with the majority of studies focusing on the impact of helminth infections on anti-Plasmodium responses . Relatively few have focused on how parasite-elicited Th2 responses are affected during Plasmodium co-infection . Our data show that IL-4-expressing Th2 cells , serum IgE , and functional parasite expulsion are reduced during co-infection ( Fig 1 ) . This is in line with previous reports , including reduced schistosome-specific IL-4 and IL-5 in Plasmodium and schistosome co-infected individuals [52] and suppressed IL-4 responses during H . polygyrus and Plasmodium yeolii co-infection [53] . Reduced type-2 responses [54] and Th2-mediated immunopathology have also been observed in schistosome and Plasmodium co-infected mice [55] , consistent with the notion that anti-helminth associated Th2 responses are compromised during Plasmodium co-infection . However , these studies did not offer mechanistic insight as to how this reduction in type-2 immunity might occur and importantly how type-2 immunity might be preserved during co-infection . In this study , we focused on the impact of co-infection on CD4+ T cells , which are a critical cell type for immunity to H . polygyrus and contribute significantly to anti-malarial immunity [56] . For our studies , we developed a triple cytokine reporter mouse ( Il4gfpIfngyfpIl17aFP635 , S1 Fig ) , which had several important advantages . These mice allowed the determination of T cell phenotype ex vivo without the need for re-stimulation , as well as the ability to obtain highly purified populations of Il4gfp+Ifngyfp–Il17aFP635– Th2 cells , which were not expressing other lineage-associated cytokines[29] . Adoptive transfer of these cells allowed us to accurately determine whether purified Th2 cells changed their phenotype , and finally , simultaneous cytokine reporters allowed us to test whether any conversion was reversible and truly plastic . To this end , we observed that highly-purified Il4gfp+Ifngyfp–Il17aFP635– Th2 cells , either generated in vitro for two weeks ( Fig 2 ) or isolated ex vivo from H . polygyrus-infected mice ( Fig 9 ) , were able to produce IFNγ during Plasmodium infection in Rag1–/–mice . This phenomenon is in line with several previous observations 1 ) identifying that in vitro generated LCMV-specific TCR transgenic Th2 cells could express both IFNγ and IL-4 [34] , 2 ) a ‘bi-functional’ population of Tbet+ GATA3+ cells are generated following H . polygyrus infection [29] and 3 ) the Tbx21 locus ( encoding T-bet ) has bivalent epigenetic histone modifications in Th2 cells [57] suggesting Th2 cells retain some flexibility . We observed expression of Ifng , Tbx21 , Klrg1 , Gzmb , Gzmc in converted Th2 cells , while maintaining low levels of Il4 transcription ( Fig 3 , S1 Table ) and the ability to produce IL-5 and IL-13 ( Fig 2 ) . This suggested that converted cells were possibly poly-functional . Whether they are similar to ‘bi-functional’ cells [29] is unclear . Helmby observed exacerbated liver pathology with significantly increased IFNγ and mortality during H . polygyrus and Plasmodium co-infection [58] . Whether Th2 cells converted to IFNγ-secreting cells , contributing to aggravated liver pathology in their study was unclear . Similarly , Th2 cells that up-regulate IL-17 during airway allergen challenge in mice contribute to more severe airway pathology [59] , and allergic patients have a greater frequency of IFNγ-secreting cells [60] . Indeed , polyfunctional T cells , which secrete multiple cytokines , correlate with greater protection following vaccination [61] , contribute to severe inflammatory syndromes in humans [62] and mice [37] and have greater anti-tumor activity [63] . Thus , understanding the mechanisms of Th cell conversion and the generation of polyfunctional T cells may provide important insight into immunity and immunopathology . Interestingly , in our model of C . albicans , in vitro polarized Th2 cells were unable to produce IL-17a , unlike naïve cells ( S4 Fig ) , suggesting that there is either an important relationship between Th2 and Th1 cells , or that the transcriptional machinery required for IL-17 production is more tightly regulated than for IFNγ . To identify mechanistic pathways contributing to Th2 cell conversion , we employed RNA-Seq analysis of Th1 cells ( Ifngyfp+ ) , Th2 cells ( Il4gfp+ ) and Th2 cells that had up-regulated IFNγ ( Ifngyfp+Il4gfp– ) . We identified a high degree of transcriptional similarity between Th1 cells and converted cells , extending significantly beyond cytokine expression . For example , Th1 and converted Ifngyfp+Il4gfp–cells , but not Th2 cells , had similar transcript abundance encoding for several enzymes ( Bace2 , Cdc25c , Cd38 Chst11 , Dusp5 , Gzmb , Gzmc and Gzmk , Gstt1 , Pdcd1 , Ptpn5 , Spag5 , Troap ) , chemokine receptors ( Cxcr3 , Cmklr1 , Cx3cr1 and Ccr5 ) , ion channels ( Cacna1l and Ttyh2 ) , kinases ( Stk32c , Ttbk1 , Ttk , Ltk , Cdk1 , Pbk , Ccnb1 in addition to many other kinases ) , nuclear receptors ( Nr4a2 , Ahr ) , miRNAs ( miR-142 , miR-155 and miR-Let7d ) and transcriptional regulators ( Rai14 , E2f7 , Gas7 , Cdkn2b , E2f8 , Klf12 , Runx2 and Eomes ) . Significantly , Th1 cells use a feed-forward regulatory circuit involving Tbx21 ( Tbet ) and Runx3 for maximal IFNγ production and silencing of Il4 [64] . In our study , both Th1 cells and converted Th2 cells which had lost Il4 and up-regulated Ifng , had elevated Runx3 and Tbet , suggesting that this feed-forward loop was transcriptionally active , supporting optimal IFNγ production in converted cells . Whether the epigenetic landscape of converted cells matched that of their Th1 counterparts is of great interest , as converted Th2 cells retained the capacity to produce Th2-associated IL-5 and IL-13 ( Fig 2 ) in an antigen-specific manner ( Fig 9D ) . Previous studies have indicated that Th1 cells have the capacity to up-regulate Th2-associated features in vivo following helminth infection [65] . In our hands , naïve T cells which had up-regulated IFNγ+ in vivo following Plasmodium infection did not have the capacity to secrete IL-5 or IL-13 when re-stimulated in vitro with anti-CD3/28 and IL-4 . Whether there are specific in vivo factors which more readily support T cell plasticity is currently unclear . We would hypothesize that in vitro generated or ex vivo H . polygyrus Th2 cells had bivalent methylation marks in the Il5 and Il13 locus allowing re-expression of these genes following the appropriate activating signal . Supporting this , converted Th2 cells retained some Th2-associated features , including elevated expression of Gfi1 , Il4 and Il33r , which may provide the appropriate machinery to re-activate Th2-associated genes , reminiscent of their Th2 past ( Fig 3 and S1 Table ) . Using an upstream analysis algorithm ( Ingenuity Pathways Analysis ) with our transcriptional data sets we identified IL-12 , IFNγ and to a lesser extent type 1 IFN , as putative factors that could contribute to the observed transcriptional profile of converted cells . This supports a recent study that identified the requirement of Tbet and Stat4 for IFNγ expression in memory Th2 cells [66] . In our study , unlike previous studies , type I IFN signalling in Th2 cells was dispensable for IFNγ production from converted Th2 cells in vivo ( Fig 6 ) [34] . Blocking IL-12 or IFNγ alone did not impact the frequency of converted IFNγ+ cells from transferred Th2 cells ( Figs 7 and 8 ) . These data are in agreement with a previous study that found restoring IL-12 responsiveness in Th2 cells , through ectopic expression of IL-12Rβ2 , was insufficient to convert Th2 cells into IFNγ-secreting cells [67] . However , in our model , anti-IL-12 treatment alone preserved IL-4 expression in a sub-population of transferred cells ( Figs 7 and 8 ) . Blockade of both IFNγ and IL-12 substantially reduced IFNγ+ cells deriving from Th2 cells , suggesting that an IL-12-STAT4 signaling pathway down-regulated IL-4 , while an IFNγ / STAT-1 / T-bet pathway was required for optimal IFNγ expression , in accordance with canonical Th1-inducing conditions for naive T cells [68] . While we found that blockade of these cytokines reduced IFNγ+ cells , there was no change in control of parasitemia ( S5 Fig ) . We speculate that this is due to the incomplete loss of conversion , with the remaining IFNγ being sufficient to control levels of parasitemia . TCR stimulation was essential for in vitro-derived Th2 cells to produce IFNγ ( Fig 5 ) and ex vivo H . polygyrus-elicited Th2 cells required H . polygyrus-infected recipient mice to survive and up-regulate IFNγ . Thus , with sufficient TCR signaling , a change in the local cytokine milieu may be sufficient to re-program Th cells . During helminth and Plasmodium co-infection , either cross-reactive antigens or microflora-derived signals may provide the necessary first TCR signal [69–71] . Alternatively the broad polyclonal activation of non-specific T cells during Plasmodium infection may be sufficient [21 , 22 , 72] . Although TCR engagement , IL-12 and IFNγ were required for optimal conversion of Th2 cells into IFNγ-secreting cells , it is possible that other factors also contribute to conversion , including IL-27 , which can induce expression of Tbet , and IL-18 , which can induce IFNγ production [73 , 74] . In conclusion , we have shown that IL-12 and IFNγ suppressed Th2 responses during H . polygyrus and P . chabaudi co-infection . Mechanistically , we identified that TCR engagement with IL-12 and IFNγ signaling converted in vitro-generated Th2 cells into IFNγ-producing cells during P . chabaudi infection . Importantly , although blocking IL-12 and IFNγ during co-infection did not retain fulminant anti-helminth immunity , it did preserve Th2 cell numbers and serum IgE , highlighting a novel mechanistic pathway of how Plasmodium infection negatively impacts anti-helminth Th2 responses . Overall , our studies indicate that Plasmodium infection can negatively impact anti-helminth responses , that Th2 cells retain substantial plasticity in the context of Plasmodium infection , and that this plasticity may play a role in the reduced Th2 response during co-infection .
All mice were bred and maintained under specific pathogen-free conditions at the National Institute for Medical Research . Strains used included: C57BL/6 , Ifngyfp [36] , Il4gfp[35] , C57BL/6 Rag1–/–[75] , MhcII–/– ( B6 . 129-H-2<dlAb1-Ea ) [76] crossed with Rag1–/–at NIMR [77] , OTII Rag1–/– ( B6 . Cg ( Tcrαβ ) 425Cbn/J ) [78] , OTII Il4gfp Rag1–/– ( OTII Rag1–/–crossed with Il4gfp at NIMR ) , and Ifnar–/–Il4gfp ( Ifnar–/–[79] crossed with Il4gfp at NIMR ) . Triple cytokine reporter mice ( Il4gfpIl17CreIfngyfpR26FP635 ) were established by crossing Il4gfp/gfpIl17Cre/Cre[37] mice with Ifngyfp/+R26FP635/FP635 mice , producing Il4gfp/+Il17Cre/+Ifngyfp/+R26FP635/+ . The generation of R26FP635 reporter mice will be presented in detail elsewhere ( JB and AP , manuscript in preparation ) . Briefly , R26FP635 mice were generated by inserting the coding sequences of the red fluorescent protein FP635 [80] into the pROSA26 targeting vector downstream of a loxP-flanked neomycin resistance cassette containing three transcriptional stop signals by homologous recombination . R26FP635 reporter mice in this study were backcrossed to C57BL/6 for more than 8 generations . Mice were infected by oral gavage with 200 infective stage 3 ( L3 ) Heligmosomoides polygyrus larvae , diluted in water . The anthelmintic drug pyrantel pamoate ( Sigma , 5mg/dose in water ) was given orally on two consecutive days . Infections with Plasmodium chabaudi chabaudi ( AS ) were performed by i . p . injection of 105 parasitized red blood cells . Parasitemia was measured by blinded counting of Giemsa-stained blood smears . Anemia and hemoglobin were measured by diluting blood in Krebs buffered saline with 0 . 2% glucose and with 100 IU/mL heparin and measured using Vetscan ( Abaxis-VetScan HM5 Hematology ) . Infections with Candida albicans were performed by i . v . injection of 105 yeast forms . Cell sorting was performed using a FACS Aria II ( BD Biosciences ) , MoFlo XDP ( Beckman Coulter ) , or Influx ( BD Biosciences ) cell sorter . To prepare cells for sorting , CD4+ cells were first positively selected using MACS CD4 beads and magnetic columns ( Miltenyi Biotec ) . Cell suspensions were then stained for 25 minutes with antibodies in PBS with 1% FCS . To prepare for sorting , stained cells were diluted in phenol-red free IMDM ( Gibco ) ( with 1% FCS , 2mM EDTA ( Invitrogen ) , 100 U/mL Penicillin and 100 μg/mL Streptomycin ( Gibco ) , 8 mM L-glutamine ( Gibco ) , and 0 . 05 mM 2-mercaptoethanol ( Gibco ) ) . Propidium iodide ( PI ) was used to determine cell viability in sorting experiments . Intracellular cytokine staining ( ICS ) was performed following 6 hours of re-stimulation with 50ng/mL phorbol 12-myristate 13-acetate ( PMA , Promega ) and 1 μg/mL ionomycin ( Sigma ) and BD Golgi Stop and BD Golgi Plug ( diluted 1:1000 , BD Biosciences ) . Following surface stain , cells were incubated with eBioscience Fixation/Permeabilization buffer for 25 minutes followed by 25 minutes in Permeabilization buffer ( eBioscience ) , and incubation with antibodies in Permeabilization buffer for a further 30 minutes . For flow cytometry analysis , cells were analyzed using a BD LSRII ( BD Biosciences ) and data were analyzed using FlowJo software ( Version 7 . 6 . 5 , Treestar Inc ) . In all cases using triple cytokine reporter mice , cells from wild type , Ifngyfp or Il4gfp single cytokine reporter mice were used as controls to set gates to differentiate yfp and gfp . Antibodies used include: CD4 ( efluor450 and PE-Cy7 , RM4-5 , eBioscience ) , CD25 ( Fitc , 7D4 , BD Pharmingen ) , CD44 ( Fitc , Percpcy5 . 5 , and APC , IM7 , eBioscience ) , CD45 . 1 ( PE-Cy7 and APC , A20 , eBioscience ) , IFNγ ( Pacific Blue , XMG1 . 2 , Biolegend ) , IL4 ( PE , 11B11 , eBioscience ) , pSTAT4 ( Alexa Fluor 647 , BDPhosflow ) , TCRβ ( APC , H57-597 , eBioscience ) and GFP ( Alexafluor647 , FM264G , BioLegend ) . Staining was performed in presence of FcR Blocking Reagent ( Miltenyi Biotec ) . In analysis experiments , viability was determined using the Molecular Probes Live/Dead Fixable Blue Dead Cell Stain Kit ( Life Technologies ) . For phospho-STAT staining , sorted cells were resuspended into serum-free media and incubated at 37 degrees for 20 minutes , followed by incubation with 10 ng/mL IL-12 ( R&D ) for 15 minutes . Cells were then fixed for 10 minutes at 37 degrees with prewarmed BD Phosflow Lyse/Fix Buffer , washed , permeabilized with BD Phosflow Perm Buffer III for 30 minutes on ice , washed , and stained for 1 hour with antibodies in PBS for FACS analysis . Naive CD4+ T cells were sorted from spleens as CD4+TCRβ+CD44–CD25–Il4gfp–PI− ( Il4gfp reporter ) or CD4+TCRβ+CD44–CD25–Il4gfp-Ifngyfp–Il17aFP635–PI− ( triple reporter ) . Th2 cells were cultured for 2 weeks from splenic CD4+ cells in vitro with 10 ng/mL IL-4 ( R&D ) , 5 ng/mL IL-2 ( R&D ) , 10 μg/mL anti-IFNγ ( XMG1 . 2 , BioXcell ) , and Mouse T-Activator CD3/CD28 Dynabeads ( Life Technologies ) in IMDM with 10% FCS . Th2 cells were sorted as CD4+TCRβ+Il4gfp+PI− ( Il4gfp reporter ) or CD4+TCRβ+Il4gfp+Ifngyfp–Il17aFP635–PI− ( triple reporter ) . For each experiment , 0 . 2x106 to 1x106 cells were adoptively transferred i . v . into recipient C57BL/6 Rag1–/- mice . Blocking antibodies diluted in PBS ( anti-IFNγ , XMG1 . 2 , anti-IL12p40 C17 . 8 , BioXcell ) were used at 0 . 4 or 0 . 5 mg/ dose . Sorted cells were cultured in 96 well round bottom plates in various conditions . Where indicated , antigen presenting cells were spleens depleted of CD4+ cells by MACS magnetic separation ( Miltenyi Biotec ) and irradiated ( 3000 rads ) . H . polygyrus antigen was isolated by homogenization of cleaned adult worms in PBS . IFNγ , IL-5 , and IL-13 were measured using DuoSet ELISA kits , according to the manufacturer’s instructions ( R&D ) . Total IgE ELISA was performed by coating with Purified Rat Anti-Mouse IgE ( R35-72 , BD Pharmingen ) at 2 μg/mL overnight , followed by overnight incubation with serum and standard ( Purified Mouse IgE , k isotype Standard , BD Pharmingen ) , and detection with Biotin Rat Anti-Mouse IgE at 1 μg/mL ( R35-118 , BD Pharmingen ) , Streptavidin HRP at 1:000 ( BD Pharmingen ) and ABTS One Component HRP Microwell Substrate ( SurModics ) . H . polygyrus-specific IgG1 was detected by coating plates with 5 μg/mL H . polygyrus antigen overnight , followed by overnight incubation with serially diluted serum and detection with Biotin Rat Anti-Mouse IgG1 ( Invitrogen ) and streptavidin and ABTS , as above . RNA was isolated from cells or tissue using RNeasy Mini Kit according to manufacturer’s instructions ( Qiagen ) . For qRT-PCR of small intestine-derived RNA , 1 cm sections of tissue were harvested and stored in RNAlater ( Sigma ) before homogenisation and RNA extraction using RNeasy Mini Kit ( Qiagen ) . cDNA was reverse transcribed from RNA using QuantiTect Reverse Transcription Kit ( Qiagen ) according to the manufacturer’s instructions . qRT-PCR analysis was performed using Power SYBR Green PCR master mix ( Applied Biosystems ) on an ABI Prism 7900HT Sequence Detection System ( Applied Biosystems ) . Relative quantities of mRNA were determined by the comparative threshold cycle method as described by Applied Biosystems for the ABI Prism 7700/7900HT Sequence Detection Systems using the following primers; Hprt Fwd: 5’-GCCCTTGACTATAATGAGTACTTCAGG-3’ and Rvs: 5’-TTCAACTTGCGCTCATCTTAGG-3’; Retnla Fwd: 5’-CCCTCCACTGTAACGAAGACTC-3’ and Rvs: 5’-CACACCCAGTAGCAGTCATCC-3’; Chil3: Fwd: 5’- CATGAGCAAGACTTGCGTGAC-3’ and Rvs: 5’-GGTCCAAACTTCCATCCTCCA-3’; Arg1 Fwd: 5’- GGAAAGCCAATGAAGAGCTG -3’ and Rvs: 5’- GCTTCCAACTGCCAGACTGT -3’ . RNA-seq libraries were constructed using the TruSeq RNA Sample Preparation Kit V2 according to manufacturer’s instructions ( Illumina ) . Libraries were sequenced using the HiSeq 2500 System ( Illumina ) . The raw Illumina reads were analyzed as follows . First , the data quality was analyzed using FastQC ( www . bioinformatics . babraham . ac . uk/projects/fastqc ) . Low quality bases were trimmed using Trimmomatic [81] , and the read pairs which passed the trimming quality filters were aligned to mm10 ( Ensembl version 75 ) using Tophat2 [82] . Counts were determined using htseq_count [83] . Normalisation and statistical analysis was performed using edgeR [84] . Statistically significant genes with FDR < 0 . 05 are reported . Significantly differentially expressed genes were uploaded into Ingenuity Pathways Analysis ( IPA ) and subjected to upstream analysis to identify factors that could have contributed to the transcriptional profile observed in converted Th2 cells . Data sets were compared by Mann Whitney test using GraphPad Prism ( V . 5 . 0 ) . Differences were considered significant at *P ≤ 0 . 05 . All animal experiments were carried out following United Kingdom Home Office regulations ( project license 80/2506 ) and were approved by UK National Institute for Medical Research Ethical Review Panel . | Approximately a third of the world’s population is burdened with chronic intestinal parasitic helminth infections , causing significant morbidities . Identifying the factors that contribute to the chronicity of infection is therefore essential . Co-infection with other pathogens , which is extremely common in helminth endemic areas , may contribute to the chronicity of helminth infections . In this study , we used a mouse model to test whether the immune responses to an intestinal helminth were impaired following malaria co-infection . These two pathogens induce very different immune responses , which , until recently , were thought to be opposing and non-interchangeable . This study identified that the immune cells required for anti-helminth responses are capable of changing their phenotype and providing protection against malaria . By identifying and blocking the factors that drive this change in phenotype , we can preserve anti-helminth immune responses during co-infection . Our studies provide fresh insight into how immune responses are altered during helminth and malaria co-infection . |
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During its development , the parasite Schistosoma mansoni is exposed to different environments and undergoes many morphological and physiological transformations as a result of profound changes in gene expression . Characterization of proteins involved in the regulation of these processes is of importance for the understanding of schistosome biology . Proteins containing zinc finger motifs usually participate in regulatory processes and are considered the major class of transcription factors in eukaryotes . It has already been shown , by EMSA ( Eletrophoretic Mobility Shift Assay ) , that SmZF1 , a S . mansoni zinc finger ( ZF ) protein , specifically binds both DNA and RNA oligonucleotides . This suggests that this protein might act as a transcription factor in the parasite . In this study we extended the characterization of SmZF1 by determining its subcellular localization and by verifying its ability to regulate gene transcription . We performed immunohistochemistry assays using adult male and female worms , cercariae and schistosomula to analyze the distribution pattern of SmZF1 and verified that the protein is mainly detected in the cells nuclei of all tested life cycle stages except for adult female worms . Also , SmZF1 was heterologously expressed in mammalian COS-7 cells to produce the recombinant protein YFP-SmZF1 , which was mainly detected in the nucleus of the cells by confocal microscopy and Western blot assays . To evaluate the ability of this protein to regulate gene transcription , cells expressing YFP-SmZF1 were tested in a luciferase reporter system . In this system , the luciferase gene is downstream of a minimal promoter , upstream of which a DNA region containing four copies of the SmZF1 putative best binding site ( D1-3DNA ) was inserted . SmZF1 increased the reporter gene transcription by two fold ( p≤0 . 003 ) only when its specific binding site was present . Taken together , these results strongly support the hypothesis that SmZF1 acts as a transcription factor in S . mansoni .
Schistosomiasis is a disease caused by trematode worms , mainly Schistosoma mansoni , S . haematobium and S . japonicum . According to World Health Organization , this parasitic disease affects 200 million people throughout the world [1] . Although the level of schistosome-associated morbidity is unclear , some recent studies have demonstrated that the illness is a more serious problem than it was previously thought to be [2] , [3] . Therefore , emphasis should be focused on mechanisms that could not only prevent , but also cure schistosomiasis . A useful approach to fight the disease should include infrastructure and educational components , as well as the development of vaccines and new drugs [4] . Luckily we are living a special moment , with the recent publication of both S . mansoni [5] and S . japonicum [6] genomes , which will bring to the scientific community an enormous amount of data to be mined in the search for new therapeutic targets and vaccine development . Lastly , additional effort should also be dedicated to studies regarding the biology and development of the parasite . During its life cycle , S . mansoni is exposed to different environmental conditions: water , intermediate molluscan host , and a definitive vertebrate host . As a consequence , this parasite suffers many transformations in its morphology and physiology , and , as such , represents an interesting but challenging biological system to investigate gene regulation processes [7]–[9] . A variety of publications have focused on the identification and characterization of S . mansoni stage- , tissue- and sex-specific/abundant proteins and their coding genes [10]–[14] , which may uncover hidden aspects of parasite biology and thus provide useful leads for the development of novel intervention strategies [7] . In a primary analysis of the S . mansoni transcriptome , Verjovski-Almeida and colleagues suggested that the number of differentially expressed genes could reach as many as 1000 for each stage [15] . In more recent publications , in which analyses of gene expression were carried out using microarray , SAGE ( Serial Analysis of Gene Expression ) and proteomic experiments , the authors confirmed a number of sex- and stage-specific , differentially expressed genes [8] , [16]–[26] . In order to better understand the transcriptional regulation of S . mansoni genes , it is necessary to identify new transcription factors , coactivators/corepressors and chromatin remodeling factors that control this molecular process , along with regulatory elements in the promoter region of genes [9] . Several efforts to describe new transcription factors in this parasite have been made [27]–[31] , but given the complexity of its life cycle there are still many components to be discovered and characterized . Zinc finger motifs are found in several proteins amongst eukaryotic organisms and are key proteins for transcription regulation [32]–[34] . SmZF1 is a S . mansoni 19 kDa protein ( GenBank accession number AAG38587 ) containing three C2H2 type zinc finger motifs . Its cDNA was casually isolated from an immune screening of a S . mansoni adult worm lambda gt11 expression library using an anti-tegumental serum . The transcript coding for SmZF1 was also detected by PCR amplification in egg , cercaria , schistosomulum and adult worm cDNA libraries , suggesting that the protein is essential for metabolism during different stages of the parasite life cycle [35] . In a previous work , we used a recombinant SmZF1 protein in EMSA experiments to investigate its binding capacity/specificity for DNA and RNA oligonucleotides . SmZF1 was found to bind both double and single-stranded DNA , as well as RNA oligonucleotides , but with about 10-fold lower affinity . Although we noticed that SmZF1 recognized DNA and RNA oligonucleotides not containing putative target sites , the protein bound preferentially to the ones containing the sequence 5′-CGAGGGAGT-3′ ( oligonucleotide D1-3DNA ) . Furthermore , unrelated oligonucleotides were not able to abolish this interaction . Taken together , these initial results suggested that SmZF1 may act as a putative transcription factor in S . mansoni [36] . In order to better characterize the biological function of the SmZF1 protein , in this study we proposed to: ( i ) verify the subcellular localization of SmZF1 in the cells of S . mansoni , as well as in mammalian COS-7 cells expressing a recombinant YFP ( Yellow Fluorescent Protein ) -SmZF1 protein; ( ii ) test the ability of SmZF1 to activate or repress gene transcription . The results described herein define SmZF1 as a S . mansoni nuclear protein capable of activating gene transcription .
In order to obtain anti-SmZF1 antibodies , the MBP ( Maltose Binding Protein ) portion of a MBP-SmZF1 recombinant protein [36] was cleaved using Factor Xa protease ( New England Biolabs , Ipswitch , MA , USA ) . The cleavage reaction was carried out for 48 h at 4°C in a 1∶25 enzyme: protein proportion . After digestion and fractionation by electrophoresis , a Coomassie blue-stained protein band ( 450 µg ) , representing the SmZF1 portion of the recombinant protein was excised from a 10% SDS-PAGE , homogenized with PBS ( Phosphate Buffered Saline – 130 mM NaCl , 2 mM KCl , 8 mM Na2HPO4 , 1 mM KH2PO4 ) , then emulsified with Complete Freund Adjuvant and used for the primary intramuscular injection into a rabbit or with Incomplete Freund Adjuvant for the two subsequent boosts ( 15 and 30 days after the first immunization ) . Pre-immune serum was obtained before the first immunization and rabbit serum containing anti-SmZF1 antibodies was collected 15 days after the third immunization . S . mansoni adult worms used in this study were recovered from perfused mice . Lung-stage schistosomula were prepared according to Harrop and Wilson [37] . Cercariae were obtained from Biomphalaria glabrata by exposing the infected snails to light for 2 h to induce shedding of parasites . Sections of Omnifix ( AnCon Genetics Inc . , Melville , NY , USA ) fixed , paraffin-embedded adult male or female worms were deparaffinized using xylol , hydrated with an ethanol series , washed in PBS and then incubated in a blocking solution ( 0 . 05% Tween 20 , 1% w/v BSA ( Bovine Serum Albumin ) in PBS pH 7 . 2 ) overnight at 4°C . Samples were reacted for 1 h with either the anti-SmZF1 or a control , pre-immune rabbit serum , both diluted 1∶30 in 10x diluted blocking solution . Sections were then washed in PBS and reacted for 1 h with a 1∶400 diluted goat anti-rabbit IgG-Cy-5 conjugate ( Jackson Immunoresearch Laboratories Inc . , West Grove , PA , USA ) in 10x diluted blocking solution , which also contained Alexa Fluor 488 phalloidin ( Invitrogen , Carlsbad , CA , USA ) diluted 1∶100 to stain actin microfilaments ( except for adult male worms ) . Afterwards , samples were washed , incubated for 10 min with 1∶3000 diluted propidium iodide ( Sigma-Aldrich , St . Louis , MO , USA ) in 10x diluted blocking solution to stain nuclei and then washed with PBS . For experiments using cercariae and lung-stage schistosomula , a whole-mount protocol was chosen . Omnifix fixed cercariae were treated with a permeabilizing solution ( 0 . 1% Triton X-100 , 1% w/v BSA and 0 . 1% w/v sodium azide in PBS pH 7 . 4 ) for 3 h at 4°C under constant agitation . Subsequent immunostaining steps used the same solution and condition . Samples were incubated overnight with the anti-SmZF1 antibody diluted 1∶90 , washed several times and reacted for 4 h with the goat anti-rabbit IgG-Cy-5 conjugate diluted 1∶1200 in solution containing Alexa Fluor 488 phalloidin ( 1∶500 ) . The cercariae were then incubated for 20 min with propidium iodide diluted 1∶6000 and washed once more . The schistosomulum immunohistochemistry assays were carried out as with cercaria , with the following modifications: lung stage schistosomula were treated with permeabilizing solution overnight and then incubated with the anti-SmZF1 antibody ( 1∶90 ) for 2 h . The secondary antibody was used at a 1∶1000 dilution , and the phalloidin at a 1∶100 dilution for 2 h . Samples ( adult male and female worms , schistosomula and cercariae ) were prepared with a mounting solution ( 90% glycerol , 10% tris-HCl 1 M , pH 8 . 0 ) and the fluorescence images were captured with a Carl Zeiss LSM 510 META confocal microscope using a 63x oil-immersion objective lens in the Center of Electron Microscopy ( CEMEL-ICB/UFMG ) . Images were analyzed with Zeiss LSM Image Browser software and edited with Adobe Photoshop CS . All research protocols involving mice used in the course of this study were reviewed and approved by the local Ethics Committee on Animal Care at Universidade Federal de Minas Gerais ( CETEA – UFMG N° 023/05 ) . Adult worms recovered from perfused mice were manually separated and pooled according to their sex . Total RNA of both male and female worms was extracted using Trizol reagent ( Invitrogen ) and treated with DNase using Ilustra RNAspin Mini RNA Isolation Kit ( GE Healthcare , Waukesha , WI , USA ) according to the manufacturer's instructions . RNA was then quantified using a NanoDrop Spectrophotomer ND-1000 ( Thermo Scientific , Waltham , MA , USA ) . cDNA was synthesized using 0 . 3 to 1 . 0 µg total RNA and Superscript III First-Strand Synthesis SuperMix for qRT-PCR ( Invitrogen ) according to the manufacturer's protocol . For q-PCR reactions , the primers SmZF1_real2_forw ( 5′–ACTTCTCTCAGAAATCCAGCCT–3′ ) and SmZF1_real2_rev ( 5′–TGGAGAGGATTATACAATCTGGTT–3′ ) were used at a 600 nM initial concentration . The S . mansoni glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) gene ( primers GAPDH_forw 5′–TCGTTGAGTCTACTGGAGTCTTTACG–3′ and GAPDH_rev 5′–AATATGAGCCTGAGCTTTATCAATGG–3′ ) was used as an endogenous control in order to normalize relative amounts of total RNA . GAPDH primers were used at a 900 nM initial concentration . The amplicon sizes were 96 bp and 65 bp for SmZF1 and GAPDH , respectively . q-PCR reaction mixtures consisting of 2 . 5 µl of cDNA , 12 . 5 µl of Power SYBR Green PCR Master Mix ( Applied Biosystems , Foster City , CA ) and 5 µl of each primer in a total volume of 25 µl were added to 48-Well Optical Reaction Plates for amplification and quantification in a StepOne Real-Time PCR System ( Applied Biosystems ) . Each q-PCR run was performed with two internal controls in order to assess both potential genomic DNA contaminations ( i . e . , no reverse transcriptase added in the cDNA synthesis ) and purity of the reagents used ( i . e . , no cDNA added ) . Dissociation curve standard analyses were performed at the end of each assay to certify the specific amplifying of targets . For each set of primers , both male and female conditions ( including negative controls ) were run in three technical replicates . The experiment was repeated two times ( biological replicates ) and the delta-delta Ct method [38] was used in order to make a relative quantification comparing male and female transcript levels . Due to the nonparametric distribution of data , statistical analysis of delta-delta Ct values was performed using the Mann-Whitney U-test with significance set at P<0 . 05 . The SmZF1 cDNA was PCR amplified in a reaction mixture prepared in a 50 µL final volume containing 25 ng of template DNA , 0 . 2 pmol µL−1 of each primer ( SmZF1-start-Xba: 5′–CAGTCTAGAACTTTAACTATGGAATT-3′ and SmZF1-stop-Apa: 5′-CAGGGGCCCCATCCGGAAAGGCTTGAGA-3′ , or SmZF1-start-Sac: 5′-CAGGAGCTCACTTTAACTATGGAATT-3′ and SmZF1-stop-Hind: 5′-CAGAAGCTTCATCCGGAAAGGCTTGAGA-3′ ) , 200 mM dNTPs and 5 U of Taq DNA polymerase ( Phoneutria , Belo Horizonte , MG , Brazil ) in the appropriate buffer ( 50 mM KCl , 10 mM Tris-HCl pH 8 . 4 , 0 . 1% Triton X-100 , 1 . 5 mM MgCl2 ) . The fragments obtained were double-digested with XbaI and ApaI or SacI and HindIII restriction enzymes ( New England Biolabs ) and purified using a Wizard SV Gel and PCR Clean-up System ( Promega , Madison , WI , USA ) following the manufacturer's instructions . The fragments were then inserted , respectively , into the commercial vectors pCDNA4/TO/myc-His ( Invitrogen ) or pEYFP-c1 ( Clontech , Mountain View , CA , USA ) , generating the constructions pCDNA4-SmZF1 and pEYFP-SmZF1 , which express the recombinant proteins SmZF1-myc tag and YFP-SmZF1 , respectively . In addition , the viral thymidine kinase ( tk ) promoter region was inserted ( NheI/BglII ) into the commercial vector pGL3-basic ( Promega ) , generating the vector pGL3-tk-luc , with the luciferase ( luc ) reporter gene under control of the thymidine kinase promoter . Subsequently , an oligonucleotide containing four repetitions of the putative SmZF1 DNA binding site , D1-3DNA [36] , was inserted ( KpnI/NheI ) upstream of the minimal tk promoter , producing the vector pGL3-zf-tk-luc . The oligonucleotide sequence was as follows: 5′-CAGGAAACAGCTATGACCGGCGAGGGAGTGATCGGCGAGGGAGTGATCGGCGAGGGAGTGATCGGCGAGGGAGTGTCGTGACTGGGAAAACCCTGGCG-3′ ( specific binding sites D1-3DNA are indicated in bold ) . Ligation products were used to transform the E . coli DH5a strain and the rescued plasmids were sequenced using 10 pmol of appropriate primers ( for constructions based on pCDNA4/TO/myc-His: CMV-fow 5′-CGCAAATGGGCGGTAGGCGTG–3′ and BGH-rev 5′-TAGAAGGCACAGTCGAGG–3′ , for constructions based on pEYFP-c1: YFP-fow 5′-TTTTGCTCACAGGTTCT–3′ and YFP-rev 5′-GCCGTAGGTGGCATCGCC–3′ , for constructions based on pGL3-basic: GLprimer2 5′-CTTTATGTTTTTGGCGTCTTCCA-3′ and RVprimer3 5′-CTAGCAAAATAGGCTGTCCC-3′ ) , 4 µL of DYEnamic ET Dye Terminator Kit – MegaBACE ( GE Healthcare ) and 300 ng of DNA . The sequencing products were analyzed in the MegaBACE 1000 DNA Sequencer ( GE Healthcare ) . The above plasmid constructs were used either to transfect or co-transfect COS-7 cells using Lipofectamine™ 2000 Transfection Reagent ( Invitrogen ) , according to the manufacturer's protocol . COS-7 cells were maintained at 37°C , 5% CO2 in Dulbecco's modified Eagle's medium ( Invitrogen ) supplemented with 10% fetal bovine serum and 1% glutamine ( Invitrogen ) . The plasmids pEYFP-c1 ( control ) or pEYFP-SmZF1 were transfected ( as above ) into COS-7 cells for transient protein expression studies . Forty-eight hours after transfection the culture medium was carefully removed and cells were fixed ( 15 min ) with 3% paraformaldehyde in PBS , washed and then quenched using PBS plus 10 mM NH4Cl ( 10 min ) . Cells were washed three times with PBS and incubated for 7 min with 0 . 1% Triton X-100 . After another wash in PBS , COS-7 cells nuclei were stained ( 4 min ) with 5 µL of 1 mM Hoechst 33342 dye ( Sigma-Aldrich ) . The fluorescence was directly observed using a confocal microscope ( Carl Zeiss LSM 510 META , 200x ) equipped with a Photometrics Quantix CCD camera controlled by MetaMorph imaging software ( MDS Analytical Technologies , Downingtown , PA , USA ) . For Western blot assays , COS-7 cells ( 0 . 5×106 ) transfected either with pCDNA4-SmZF1 or pEYFP-SmZF1 and control cells transfected either with pEYFP or pCDNA were washed and resuspended in 200 µL of cold TNE ( 150 mM NaCl , 50 mM Tris-HCl pH 7 . 5 and 1 mM ethylenediaminetetraacetic acid ( EDTA ) ) . A 50 µL aliquot of cells was centrifuged ( 700 g , 4 min , 4°C ) and the pellet resuspended in 50 µL of 2x SDS gel-loading buffer ( 100 mM Tris-HCl pH 6 . 8 , 200 mM dithiothreitol , 4% SDS , 0 . 2% bromophenol blue , 20% glycerol ) and boiled for 5 min , generating the total extract . The remaining 150 µL of cells was centrifuged ( 700 g , 4 min , 4°C ) and the pellet resuspended in 40 µL of lysis buffer ( 10 mM Tris-HCl pH 7 . 5 , 10 mM NaCl , 2 mM MgCl2 , 1 mM phenylmethylsulphonylfluoride ( PMSF ) , one dissolved tablet of Complete Protease Inhibitor Cocktail ( Roche , Basel , Switzerland ) , 1 mM Na3VO4 and 1 mM NaF ) plus 100 µL of 1% Nonidet P-40 ( Sigma-Aldrich ) in 50 mM Tris-HCl pH 7 . 5 . Samples were incubated in an ice bath for 10 min and centrifuged ( 700 g , 4 min , 4°C ) . Ninety-five microliters of 5x SDS gel-loading buffer was added to the supernatant , which was boiled for 5 min , generating the cytoplasmic fraction . The pellet was washed twice with cold TNE , centrifuged ( 700 g , 4 min , 4°C ) , resuspended in 50 µL of 2x SDS gel-loading buffer and boiled for 15 min , generating the nuclear fraction . COS-7 total , cytoplasmic and nuclear extracts , normalized at equal volume percentage , were separated using 10% SDS-PAGE and blotted ( 2 h , 20 mA ) onto nitrocellulose membranes ( Whatman GmbH , Dassel , Germany ) using a semi-dry blot system ( GE healthcare ) . Antibody reactions were performed as described by Koritschoner and colleagues [39] . Briefly , membranes were blocked overnight in TBS ( 25 mM Tris-HCl pH 7 . 4 , 137 mM NaCl , 5 mM KCl , 0 . 6 mM Na2HPO4 , 0 . 7 mM CaCl2 , 0 . 5 mM MgCl2 ) plus 1 mM EDTA , 1 mM Na3VO4 , 0 . 05% Tween-20 and 3% BSA followed by two washes with 100 mM Tris-HCl pH 8 . 0 , 200 mM NaCl , 0 . 2% Tween-20 ( wash buffer ) . Samples were reacted with anti-myc , anti-GFP or anti-c-erbB-2 ( 1∶1000 ) peroxidase conjugated antibodies ( BD Biosciences , Franklin Lakes , NJ , USA ) in blocking buffer for 1 h . Subsequently , blots were washed and developed with ECL enhanced chemiluminescence reagents ( GE Healthcare ) and exposed to X-ray film . The exclusively cytoplasmic protein c-erbB-2 was used as a quality control for extracts . For the electrophoretic mobility shift assay ( EMSA ) , 20 pmol of the D1-3DNA oligonucleotide ( 5′-CGAGGGAGT-3′ ) was incubated with 1 µg of the total extract of COS-7 cells transfected with plasmids pEYFP-c1 ( control ) or pEYFP-SmZF1 . Extracts were produced as follows: cells ( 0 . 5×106 ) were washed in PBS and resuspended in 100 µL of TDGK solution ( 20 mM Tris-HCl pH 7 . 5 , 2 mM dithiothreitol , 400 mM KCl , 5 µg/ml leupeptin , 5 µg/ml aprotinin , 20% glycerol , 0 . 5 mM PMSF , 1 mM Na3VO4 ) . Samples were maintained on ice for 30 min , centrifuged ( 15000 g , 20 min , 4°C ) and then the supernatant was collected . Protein concentrations were measured and normalized as previously described [36] . The extract/DNA binding reactions were carried out in a final volume of 15 µL of binding solution ( 4 mM Tris-HCl pH 8 . 0 , 40 mM NaCl , 1 mM ZnSO4 , 4 mM MgCl2 , 5% glycerol ) for 15 min at 4°C . For supershift reactions , the DNA/extracts mixture was incubated , as above , with 1 µL of anti-GFP or 2 µL of anti-SmZF1 antibodies . After incubations , samples were fractionated in a 4% non-denaturing polyacrylamide gel in TBE buffer ( 89 mM Tris-borate pH 8 . 0 , 2 mM EDTA ) , at a constant 25 mA at 4°C , to separate the bound complex from the free oligonucleotides . The resulting gels were stained with VISTRA Green DNA specific dye ( GE Healthcare ) , according to the manufacturer's protocol . Plasmid DNA co-transfections of COS-7 cells were carried out in 24-well plates ( Corning Inc . , Corning , NY , USA ) . The day before transfection , 8×104 COS-7 cells were plated in 0 . 5 ml of medium/well . For each well , 2 µl of LipofectamineTM 2000 Transfection Reagent were mixed with 1 . 2 µg of the plasmid DNA of interest and 300 ng of TK-Renilla reporter plasmid in serum-free Opti-MEM ( Invitrogen ) to allow the formation of DNA-LipofectamineTM 2000 Transfection Reagent complexes . The complexes were added to the respective wells and mixed by gently rocking the plate back and forth . Cells were incubated in a 5% CO2 incubator at 37°C for 48 h and then lysed with 60 µl of reporter lysis buffer ( Promega ) . Luciferase activity ( Relative Light Units – RLU ) was assayed with 20 µl of lysate and 80 µl of luciferase assay reagent ( Promega ) in a TD20/20 luminometer ( Promega ) using a 10 s measurement period . Each transfection was performed in triplicate . Transfection efficiency was normalized to TK-Renilla luciferase reporter plasmid . Statistical analysis of the data was carried out with Minitab Version 1 . 4 using Student's t test with Welch's correction . Only p values<0 . 05 were considered as significant .
SmZF1 ( GenBank accession AF316828 ) was initially identified during a screen of an adult worm S . mansoni cDNA library [35] . Although it has also been detected in cDNA libraries of other developmental stages of this parasite ( i . e . , egg , 3 h schistosomulum and cercaria ) , the biological function of the protein coded by this gene remains to be elucidated . The SmZF1 protein contains three C2H2-type zinc finger motifs and binds specific DNA oligonucleotides , as do similar nuclear proteins involved in gene transcriptional regulation [35] , [36] . Therefore , to investigate whether SmFZ1 is present in the nucleus , where it could act as a transcription factor , we decided to verify its subcellular localization at diverse S . mansoni life stages . We carried out in situ immunohistochemistry experiments using an anti-SmZF1 antibody on S . mansoni collected at various stages during its life cycle . Western blot assays using the recombinant SmZF1 protein previously separated from its MBP portion , as well as fractionated extracts form adult worms revealed that this polyclonal antibody is specific to SmZF1 ( Supporting information , Figure S1 ) . The immunohistochemistry assays showed that SmZF1 protein localizes in the cells nuclei of adult male worms ( Figures 1A–D ) , cercariae ( Figures 1K–N ) and lung stage schistosomula ( Figures 1P–S ) . Although we have performed three different experiments in which we analyzed various paraffin sections of female adult worms , the protein could not be detected in this stage using this technique ( Figures 1F–I and Supporting information , Figure S2 ) . No SmZF1 staining was observed in the negative controls ( Figures 1E , J , O , T ) in which only the rabbit pre-immune serum was used . These results suggest that SmZF1 is a S . mansoni protein present in the nuclei of cells from diverse developmental stages where it may act as a transcription factor . Plus , SmZF1 expression might be sex-specific since it could not be detected in adult female worms . We were unable to confirm the results from the immunohistochemistry experiments showing differences in expression of SmZF1 between male and female by Western blot , since nuclear protein extraction from single sex pooled S . mansoni worms did not provide sufficient material necessary for SmZF1 detection . Therefore , we decided to verify gene expression by comparing the transcript levels between adult male and female worms . Total RNA extraction was performed in separate pools of male or female worms and q-PCR analyses were carried out using primers specifically designed for SmZF1 amplification . We detected no difference in SmZF1 expression ( p = 0 . 22 ) between male and female worms when comparing the amplification profile , indicating that the SmZF1 mRNA is equally present in both genders ( data not shown ) . These results suggest that although the SmZF1 gene is transcribed in female worms , a post-transcriptional regulatory mechanism could be occurring to block SmZF1 protein production in adult female worms . After demonstrating the nuclear localization of SmZF1 in S . mansoni cells , the next step in the protein characterization was to heterologously express it in a mammalian system to test its ability to activate the transcription of a reporter gene . To accomplish this , we initially transfected COS-7 cells with the pEYFP-SmZF1 construction and forty-eight hours after transfection , we verified the presence of the YFP-SmZF1 recombinant protein mainly in the cells nuclei using fluorescence microscopy . However , a low level of fluorescent staining remained in the cytoplasm . In some cases , the protein was also visualized as fibrous material in the perinuclear region , probably associated with the cytoskeleton or Golgi complex . The YFP protein ( negative control ) was visualized diffusely distributed throughout the cells area ( Figure 2A ) . Since part of the fusion protein still remained in the cytoplasm of the cells , a second construction lacking YFP ( SmZF1-myc tag ) was used to confirm the SmZF1 nuclear localization in mammalian cells . Western blot assays using equal amounts of total , cytoplasmic and nuclear extracts of COS-7 cells expressing the proteins YFP , YFP-SmZF1 or SmZF1-myc tag were performed . Fractions were analyzed using either anti-GFP ( which also recognizes YFP ) or anti-myc antibodies ( Figure 2B ) . The results corroborated those obtained by fluorescence microscopy ( Figure 2A ) , showing that YFP-SmZF1 is present in both nuclear and cytoplasmic extracts , with a slight enrichment of the protein in the nuclear extract ( Figure 2B ) . However , the recombinant protein SmZF1-myc tag is only present in the nuclear COS-7 extract , suggesting that YFP may be interfering in the transport of the fusion protein to the nucleus . The quality of the fractionation was confirmed by the localization of the cytoplasmic protein c-erbB-2 in the total and cytoplasmic fractions only ( Figure 2B ) . In previous experiments using purified recombinant SmZF1 protein expressed in bacteria , we demonstrated the nucleic acid binding ability and specificity of SmZF1 , its preference for DNA as compared to RNA , and its putative best DNA binding sequence ( D1-3DNA ) [36] . To verify whether the recombinant protein YFP-SmZF1 expressed in mammalian COS-7 cells was able to interact with D1-3DNA binding site in a manner comparable to its recombinant prokaryotic counterpart , EMSA assays were performed . Total extracts of COS-7 cells transfected with either pEYFP-c1 or pEYFP-SmZF1 , expressing YFP or YFP-SmZF1 , respectively , were incubated with the D1-3DNA oligonucleotide . To confirm the SmZF1/D1-3DNA interaction , supershift assays using anti-GFP and anti-SmZF1 antibodies were also performed . Extracts of cells expressing the YFP-SmZF1 recombinant protein were able to shift the oligonucleotide migration in the gel ( Figure 3 , lane 5 ) . Additionally , both anti-GFP and anti-SmZF1 antibodies were able to supershift D1-3DNA migration , confirming that the YFP-SmZF1 protein was responsible for the oligonucleotide binding ( Figure 3 , lanes 6 and 7 ) . Extracts of cells expressing only the YFP protein ( Figure 3 , lanes 2–4 ) , as well as anti-GFP and anti-SmZF1 antibodies ( Figure 3 , lanes 8 and 9 ) , were not able to shift the D1-3DNA migration . Although new vectors which will allow transfection of schistosome cells are under development [40]–[42] , it is still not possible to continuously cultivate schistosome cells lineages in vitro . Accordingly , some authors describe the use of mammalian cells to study aspects of S . mansoni gene regulation processes , such as testing transcription factor activities or mapping promoter regions of genes [28] , [43] , [44] . Thus , a luciferase system assay in COS-7 mammalian cells expressing YFP-SmZF1 fusion protein was used here to test SmZF1ability to regulate gene transcription . COS-7 cells co-transfected with the expression vector pEYFP-SmZF1 and the construction pGL3-zf-tk-luc , which contains four repetitions of the SmZF1 D1-3DNA binding site and a thymidine kinase minimal promoter upstream of the luciferase coding gene , were able to increase gene transcription by 2-fold ( p≤0 . 003 ) when compared to negative controls , using the Student's t test ( Figure 4 ) . These results suggested that SmZF1 positively affects the transcriptional activity of the minimal thymidine kinase promoter in COS-7 cells .
Schistosomiais is one of 13 neglected tropical diseases that together affect 1 billion people worldwide . The disease is considered the second most socioeconomically devastating parasitic disease , the first being malaria [45] . According to Chirac and Torreele , in the past 30 years the number of drugs which target these neglected diseases is about 1% of all the new chemical entities commercialized by the pharmaceutical industry [46] . S . mansoni presents a variety of interesting biological regulatory processes , such as transcriptional control , which can be used to allow its adaptation to the diverse biotic and abiotic environments [8] . Description of genes expressed in a stage- or sex-specific manner may help to elucidate the events used by the parasite to deal with these potentially adverse conditions . In turn , this information may also help to develop suitable vaccines and chemotherapeutic drugs against this organism [7] . As stated in the recent and high quality review on schistosome genomics by Han and colleagues [47] , some potential drug targets should include proteins involved in DNA replication , transcription and repair systems . This suggestion is also corroborated by a chemogenomics screening approach described as part of the up-to-date S . mansoni genomic analysis , in which the authors used a strategy to find significant matches between parasite proteins and proteins known to be targets for drugs in humans and human pathogens . That study revealed 26 putative S . mansoni protein targets and their potential drugs . Of these 26 targets , three proteins are involved in DNA metabolism and two others are involved in chromatin modification ( histone deacetylase 1 and 3 ) [5] . These two examples emphasize the importance of nuclear proteins as potential drug targets . According to the authors of the S . mansoni transcriptome project [48] , 2 . 4% of the categorized ESTs ( Expressed Sequence Tags ) under the Molecular Function in Gene Ontology ( GO ) encode transcriptional regulators . A search for conserved domains using the Pfam database in a subset of those transcripts showed that 5% of them consist of zinc fingers of the C2H2 group [48] . Moreover , most of the 15 Pfam domains found were from proteins involved in either intercellular communication or transcriptional regulation . These findings reinforce the importance of this class of regulatory proteins for S . mansoni biology . In addition , using the SAGE approach , Ojopi and colleagues found that 9 . 7% of the most abundant genes ( genes containing more than 500 tags ) from S . mansoni adult worms comprise those from the nucleic acid binding GO functional category [49] . The present study defines the SmZF1 protein as a S . mansoni transcription factor . SmZF1 is a C2H2 zinc finger protein able to specifically bind to RNA and DNA , but with higher affinity for DNA molecules . Its transcript was identified in the cercaria , egg , schistosomulum and adult worm stages , suggesting its importance as a regulatory protein [35] , [36] . To define SmZF1 activity as a transcription factor , we first verified its subcellular localization , since this class of proteins is preferentially located or able to go to the cell nucleus , this import being a central step to regulate gene transcription [50] , [51] . In silico analyses of the SmZF1 amino acid sequence did not predict any classical potential nuclear localization signal ( NLS ) , but did reveal positively charged amino acids within the zinc finger motifs [35] . It has been demonstrated that zinc finger motifs are sufficient and sometimes essential for nuclear localization of ZF proteins , even without any canonical NLS detected in their amino acid sequences [51] , [52] . Moreover , it is well known that small proteins ( <40 Kda ) , like SmZF1 , are sometimes able to passively diffuse into the nucleus [50] . Immunohistochemical analysis of the diverse parasite developmental stages demonstrated that SmZF1 was indeed localized in the nucleus of S . mansoni cercariae , schistosomula and adult male worms . This confirms previous results obtained by SmZF1 cDNA amplification [35] and reinforces our hypothesis that the protein is a transcription factor . An unexpected result was the lack of detection of SmZF1 protein in adult female worms when assayed by this technique . This differs from available transcriptome data , given the existence of one EST sequence ( GenBank accession number BF936884 ) derived from an adult female worm cDNA library presenting 99% identity with SmZF1 . Also , studies using oligonucleotide microarrays in which the SmZF1 sequence was spotted on the slide did not reveal this transcript as being differentially expressed between adult male and female worms [16] , [20] . Based on these observations , we performed q-PCR experiments to analyze the SmZF1 mRNA expression . We were not able to detect differences in the levels of SmZF1 transcripts between adult male and female worms , indicating that the SmZF1 gene is being equally transcribed in adult female as it is in adult male worms . The fact that SmZF1 protein was not detected in adult female worms by immunofluorescence experiments suggests that a post-transcriptional mechanism regulates the gene . It is important to note that , apparently , SmZF1 mRNA levels are low in all parasite life cycle stages , as demonstrated by the number of ESTs matching SmZF1 cDNA present at dbEST ( Table S1 ) . Since the SmZF1 protein is highly abundant at the various stages , as verified by immunohistochemistry assays ( except for the female adult worm ) , it can be hypothesized that the protein has a long half life and that the few existing mRNAs may possess a high translational rate . However , this picture might be different for female adult worms , in which the transcript could be less translated or translated in a non-efficient way . As a second hypothesis , the protein in females may present a higher turnover . Future experiments need to be done in order to clarify these points . In a recent study concerning S . japonicum , Liu and colleagues analyzed data obtained using either transcriptome or proteome approaches and found several genes with no direct correlation in their expression when comparing these two techniques [53] . The authors explained this fact by limitations in sensitivity of the proteomic technologies they employed , but also highlighted that some transcripts may be relatively stable , persisting throughout several stages and being translated in a shorter window . This could contribute to the discrepancy between the proteomic and transcriptomic data [53] . According to Hokke and colleagues , investigating proteins differentially associated with each sex could reveal important clues concerning the formation of sexually mature schistosomes and consequently leading to the description of novel chemotherapeutic targets acting in the maturation process [54] . Recently , different groups have used a myriad of approaches to describe schistosome genes expressed in a gender- or stage-enriched/specific fashion , emphasizing the importance of identification and characterization of proteins that may be controlling the transcription of these genes [8] , [16]–[26] . Moreover , the sex-specific presence of a protein potentially capable of regulating the expression of a large number of other genes , as in the case of SmZF1 , becomes undoubtedly important in this context . One molecule , SmLIMPETin appears to modulate gene expression in S . mansoni [55] . SmLIMPETin gene is less expressed in sexually mature adult females when compared to sexually immature adult females and sexually mature and immature adult males [55] . These observations suggest that the sex-specific expression of a transcription factor may be a common feature involved in the maintenance of this parasite life cycle . The ability of SmZF1 to activate/repress transcription of a luciferase reporter gene in a cellular context was assessed using COS-7 cells . The first step was to confirm the expression , localization and activity of the fusion protein YFP-SmZF1 , used for the assay . YFP-SmZF1 was clearly visualized in the COS-7 cells nuclei using fluorescent microscopy; however , the protein was also visualized as fibrous filaments dispersed at the perinuclear region , probably associated with the cells cytoskeleton . Furthermore , Western blot assays showed the preferential nuclear localization for YFP-SmZF1 , although it was also detected to a lesser degree in the cytoplasmic extract fraction . One possible explanation for this finding is that the YFP portion of the fusion protein , considering its larger size , is interfering with the efficiency of its transport to the nucleus . Conversely , recombinant SmZF1-myc tag , a smaller protein , is detected exclusively in the nuclear portion of COS-7 cells . The second step was to verify the protein activity , i . e . , if the recombinant protein YFP-SmZF1 was able to bind to its target DNA . EMSA assays were performed using total COS-7 extracts incubated with the putative SmZF1 best binding sequence , D1-3DNA . The experiments showed that cell extracts expressing the YFP-SmZF1 recombinant protein retarded the migration of D1-3DNA in the gel . When anti-GFP or anti-SmZF1 antibodies were added to the extract-DNA samples , a supershift was observed , confirming the binding of YFP-SmZF1 to its target . The transcriptional activity of SmZF1 was further tested using a luciferase reporter system . The results showed a 2-fold increase on the luciferase gene expression in COS-7 cells co-transfected with pGL3-zf-tk-luc and pEYFP-SmZF1 . The small but significant increase in the luciferase gene expression observed might be due to the absence , in COS-7 cells , of additional proteins that are important for the proper arrangement of the transcriptional complex into the promoter region . Supporting this hypothesis , Emami and colleagues found that a species-specific interaction between TFIID and Sp1 was essential for transcriptional activation , thus suggesting a difference in transcriptional machinery between vertebrates and invertebrates [56] . As for SmZF1 , if a binding partner was present , the increase in the transcriptional activation would probably be much more substantial . A similar scenario has been reported for the protein SmNR1 from S . mansoni . In a recent work , Wu and colleagues demonstrated that the SmNR1 protein alone is able to activate the transcription of a reporter gene in COS-7 cells , but when another protein already known to interact with it ( SmRXR1 ) is present , this activation increases approximately 2-fold [28] . In order to better characterize SmZF1 action as a transcription factor , future experiments designed to detect the protein binding partners will be necessary . In addition to the DNA/RNA specific binding ability of SmZF1 [36] , the evidence of its nuclear localization , as well as its capacity to activate gene transcription , strongly suggest that SmZF1 is a S . mansoni transcriptional regulator . Additional experiments aimed at determining SmZF1 biological role are being performed . Recently our group used RNAi to conduct an in vitro phenotypic screening of 32 S . mansoni genes , including SmZF1 , known to be expressed at the sporocyst stage [57] . In this study , miracidia were cultivated in vitro , transformed into sporocysts in the presence of specific dsRNAs and observed during 7 days , in order to evaluate phenotypic changes . The treatment of the S . mansoni larvae with SmZF1-dsRNA induced a reduction of 30% on the SmZF1 transcript levels , when assayed by q-PCR . This modest reduction on the transcript levels was accompanied by a shortening at the sporocyst length in two out of three independent experiments , when compared to a negative control in which a GFP-dsRNA was used . These results show that , even with a small reduction at the transcript levels the parasite phenotype was altered , demonstrating the importance of the SmZF1 gene expression for the parasite larval stage . We believe that the significance of these findings can be extended for the other life cycle stages . | Schistosomes are parasites that exhibit a complex life cycle during which they progress through many morphological and physiological transformations . These transformations are likely accompanied by alterations in gene expression , making genetic regulation important for parasite development . Here we describe a Schistosoma mansoni protein ( SmZF1 ) that may act as a parasite transcription factor . These factors are key proteins for gene regulation . We have previously demonstrated that SmZF1 is able to bind DNA and that its mRNA is present at different stages during the parasite life cycle . In this study we aimed to define if this protein can function as a transcription factor in S . mansoni . SmZF1 was detected in the nucleus of adult male worms , cercariae and schistosomula cells . It was not , however , observed in female cells , suggesting it to be gender specific . We used mammalian cells expressing recombinant SmZF1 to analyze if SmZF1 protein is able to activate/repress gene transcription and demonstrated that it increased the expression of a reporter gene by two-fold . The results obtained confirm SmZF1 as a S . mansoni transcription factor . |
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To improve our knowledge on the epidemiological status of African trypanosomiasis , better tools are required to monitor Trypanosome genotypes circulating in both mammalian hosts and tsetse fly vectors . This is important in determining the diversity of Trypanosomes and understanding how environmental factors and control efforts affect Trypanosome evolution . We present a single test approach for molecular detection of different Trypanosome species and subspecies using newly designed primers to amplify the Internal Transcribed Spacer 1 region of ribosomal RNA genes , coupled to Illumina sequencing of the amplicons . The protocol is based on Illumina’s widely used 16s bacterial metagenomic analysis procedure that makes use of multiplex PCR and dual indexing . Results from analysis of wild tsetse flies collected from Zambia and Zimbabwe show that conventional methods for Trypanosome species detection based on band size comparisons on gels is not always able to accurately distinguish between T . vivax and T . godfreyi . Additionally , this approach shows increased sensitivity in the detection of Trypanosomes at species level with the exception of the Trypanozoon subgenus . We identified subspecies of T . congolense , T . simiae , T . vivax , and T . godfreyi without the need for additional tests . Results show T . congolense Kilifi subspecies is more closely related to T . simiae than to other T . congolense subspecies . This agrees with previous studies using satellite DNA and 18s RNA analysis . While current classification does not list any subspecies for T . godfreyi , we observed two distinct clusters for these species . Interestingly , sequences matching T . congolense Tsavo ( now classified as T . simiae Tsavo ) clusters distinctly from other T . simiae Tsavo sequences suggesting the Nannomonas group is more divergent than currently thought thus the need for better classification criteria . This method presents a simple but comprehensive way of identification of Trypanosome species and subspecies-specific using one PCR assay for molecular epidemiology of trypanosomes .
Human African trypanosomiasis ( HAT ) or sleeping sickness is classified as a neglected tropical disease by WHO , that is endemic in sub-Sahara Africa . HAT affects impoverished rural areas of sub-Saharan Africa , where it coexists with animal trypanosomiasis constituting a major health and economic burden [1] . The disease is caused by protozoan parasites of the genus Trypanosoma , it is transmitted by the bite of blood-sucking tsetse flies ( Diptera , genus Glossina ) . The human disease is caused by Trypanosoma brucei rhodesiense and Trypanosoma brucei gambiense , causing an acute and chronic disease in humans respectively [2] . T . b . rhodesiense is found in East Africa and transmitted by Glossina morsitans , while T . b gambiense is distributed in West Africa and is mainly transmitted by Glossina pallidipes [3–5] . Uganda is the only country that both forms of the disease occur with the potential for overlapping infections [6] . According to WHO , the incidence of sleeping sickness has fallen over the years , from 10 , 388 cases reported in 2008 to 2 , 804 cases reported in 2015 [7] . However , WHO estimates the number of actual cases to be below 20 , 000 [8] . This decrease is attributed to improved case detection and treatment and vector management [9] . Despite this decreased incidence , it is estimated that up to 70 million people distributed over 1 . 5 million km2 remain at risk of contracting the disease [10] . Besides , African animal trypanosomiasis ( AAT ) is one of the biggest constraints to livestock production and a threat to food security in sub-Saharan Africa . The parasites T . congolense ( Savannah ) and T . vivax are considered the most important animal Trypanosomes due to their predominant distribution in sub-Saharan Africa and their economic impact [11] . They cause pathogenic infections in cattle ( Nagana ) and also infect sheep , goats , pigs , horses , and dogs , while T . brucei brucei ( and T . brucei rhodesiense ) is pathogenic to camels , horses , and dogs , but causes mild or no clinical disease cattle , sheep , goats and pigs [12–14] . T . simiae causes a fatal disease in pigs and mild disease in sheep and goats . T . godfreyi shows a chronic , occasionally fatal disease in pigs experimentally [15 , 16] . T . evansi was originally found to infect camels but it is present in dromedaries , horses , and other equines as well as in a wide range of animals causing Surra disease , while T . equiperdum causes dourine in equines [17] . Three species ( T , evansi , T . vivax , and T . equiperdum ) are independent of the tsetse fly vector and thus distributed outside Africa [18 , 19] . Their transmission is either mechanically , for T . evansi and T . vivax , or sexually for , T . equiperdum . T . vivax can be transmitted cyclically by Glossina spp . and mechanically and therefore can found in both tsetse-infested and tsetse-free areas [20] . Given that Trypanosome parasites are maintained in wild and domestic animals as reservoirs , this complicates control measures . Morphological methods have limited ability to distinguish between Trypanosome species due to the existence of trypanosomes sharing developmental sites , and mixed and immature infections . Thus , molecular methods are used for species identification . Identification of Trypanosome species and subspecies is important to interrogate aspects such as what contribution different species/subspecies make to livestock disease and , are species/subspecies differences responsible for assumed “strain” differences in drug response among others . The ribosomal RNA sequence region harboring internal transcribed spacer sequences have been used to identify Trypanosome species in hosts and vectors . Epidemiological and screening studies rely on polymerase chain reaction ( PCR ) to amplify the internal transcribed spacer 1 ( ITS1 ) region of ribosomal genes to analyze Trypanosome species diversity [16–19] . This locus located between the 18s and 5 . 8s ribosomal subunit genes which are about 100–200 copies [21] and is widely used to identify Trypanosome species based on amplicon size in [22] a gel . However , ITS1 PCR coupled with viewing products on agarose gels fails to distinguish some species/genotypes such as T . simiae and T . simiae Tsavo . Another limitation with ITS1 PCR is the sensitivity of detection , showing bias in detection of some Trypanosome species over others [23 , 24] . Some are prone to non-specific amplification particularly in bovine blood samples [25] . To address some of the problems that ITS PCR method poses , fluorescent fragment length barcoding ( FFLB ) method has been developed for Trypanosome species detection [26] . FFLB is based on length variation in regions of the 18s and 28s ribosomal RNA gene region . Fluorescently tagged primers , designed in conserved regions of the 18s and 28s ribosomal RNA genes , are used to amplify fragments with inter-species size variation , and sizes are determined accurately using an automated DNA sequencer . FFLB has been shown to be more sensitive in the identification of Trypanosome species and subspecies and has the capacity to detect new species through identification of unique barcodes [27 , 28] . However , the method requires the use of four different PCR reactions per sample . A major problem with identification of Trypanosome species with the use of ribosomal RNA genes is that they cannot be used to distinguish between Trypanozoon species ( T . brucei brucei , T . brucei rhodesiense , T . brucei gambiense , T . evansi , and T . equiperdum ) [22 , 26 , 29 , 30] . Currently , Trypanozoon subspecies are identified by specific PCR [31–34] and microsatellites markers [32 , 35 , 36] . When dealing with a large number of samples either for tsetse fly or animal infection prevalence studies , undertaking multiple PCRs for each sample is an expensive and a laborious undertaking . Most often PCR amplicons are sequenced to confirm species identification usually through capillary sequencing . Recently , next-generation sequencing ( NGS ) has been established as a well-established method for profiling bacterial and fungal , communities . Among the many advantages , NGS provides a higher sensitivity to detect low-frequency variants , the lower limit of detection of DNA , higher throughput with sample multiplexing and comprehensive coverage among others . With the exception of Plasmodium in mosquitoes , relatively few studies have applied this technology in the diagnostics of protozoal infections [37 , 38] . It is therefore suited in the analysis of the genetic diversity of Trypanosome genotypes which is a composite aspect of understanding anthropogenic disturbance that may change repertoires of trypanosomes infecting human and livestock [39] .
For this study , we analyzed tsetse fly samples from three different groups collected at three different locations ( Fig 1 ) at different times . The first group was used for the initial analysis and to validate our method and consisted of 188 tsetse flies collected from the area around Hurungwe Game reserve in Zimbabwe between March and April 2014 . The second group was included in our final analysis to expand Trypanosome species spectrum and diversity and consisted of 200 tsetse flies from Rufunsa area ( Zambia ) near Lower Zambezi National park ( surrounding farms and villages ) collected in November and December 2013 ) . For these samples , information on tsetse fly species and sex was not available . The third group comprised of 85 flies caught in Zambia; on the border between Kafue National park and public settlement area , collected in June 2017 . For this group , flies were sorted according to sex and their species identity determined morphologically . Flies from all three groups had been collected using either custom-made mobile traps attached on a slow-moving vehicle ( Kafue and Rufunsa groups ) or Epsilon traps ( Hurungwe group ) . Individual flies were preserved in separate tubes containing silica gel ready for crushing and DNA extraction . All flies analyzed in this study were caught on public land . DNA extraction from all tsetse fly samples analyzed in this study was done by following a protocol adopted for extraction of DNA from crushed tsetse fly samples . Briefly , dried flies in tubes containing stainless beads were transferred to a smashing machine and crushed at 3 , 000 rpm for 45 sec . DNA from crushed flies was isolated using the DNA Isolation kit for mammalian blood ( Roche USA ) as per the manufacturer’s protocol with the slight modification suggested for extraction of DNA from Buffy coat , where Red blood cell lysis step is bypassed . This allows lysis of all cells in the solution at once including trypanosomes using the white cell lysis buffer . The DNA sample was stored at -80°C until analysis . The following sequences were retrieved from NCBI , Trypanosoma brucei ( JX910378 , JX910373 , JN673391 , FJ712717 , AF306777 , AF306774 , AF306771 and AB742530 ) , Trypanosoma vivax ( JN673394 , KC196703 and TVU22316 ) , Trypanosoma congolense ( JN673389 , TCU22319 , TCU22318 , TCU22317 and TCU22315 ) , Trypanosoma simiae ( JN673387 and TSU22320 ) , Trypanosoma godfreyi ( JN673385 ) Trypanosoma evansi ( D89527 ) , Trypanosoma otospermophili ( AB175625 ) , and Trypanosoma grosi ( AB175624 ) . They were aligned in Geneious 9 . 1 . 5 software ( Biomatters Ltd , Auckland , New Zealand ) using MAFFT multiple aligner with default settings and ITS1 region identified by comparing annotations and terminal regions of 18s and 1 . 5s ribosomal RNA regions . Pairs of primers flanking the ITS1 region were picked manually based on the consensus of bases in the alignment flanking the ITS1 region . Manual editing was done on the final primer pair that was chosen , to improve the range of Trypanosome species and subspecies . We used Primer-BLAST ( https://www . ncbi . nlm . nih . gov/tools/primer-blast ) to confirm that the primers would amplify the target species , check the species range and the melting temperature . The final pair comprised our new primers named Amplification of ITS ( AITS ) forward ( AITSF ) and reverse ( AITSR ) .
In silico evaluation of the primers showed that our newly designed primer pair ( AITSF/AITSR ) had a broad range similar to previously developed ITS1/ITS2 primer set [41] while the range of the CF/BR primer set , previously developed to detect pathogenic Trypanosomes [24] was confined to the pathogenic ( S1 Table ) . We evaluated the sensitivity of newly designed AITSF/AITSR primers to amplify ITS1 region of different Trypanosome species in comparison to commonly used ITS1 primers; CF/BR primers . PCR was performed on pGEMT-easy plasmid DNA containing ITS1 inserts from different Trypanosome species at different dilutions . Our evaluation was based on the visual sight of bands in a gel ( the conventional method of analysis ) . Our results showed that AITSF/AITSR primers were slightly more sensitive in the detection of T . brucei , T . simiae and T . congolense ( S1 Fig ) . AITSF/AITSR primers could detect 103 T . brucei , T . simiae , T . vivax and T . congolense and T . godfreyi ITS1 copies while CF/BR primers could detect 103 T . godfreyi and T . vivax ITS1 copies , 104 T . simiae and T . congolense ITS1 copies and 105 T . brucei ITS1 copies . Trypanosomes have about 115 copies of ribosomal RNA genes [21] . Reads generated from amplicon sequencing were of relatively good quality . Apart from those from Zimbabwe , more than 90% of the reads passed quality filtering in all samples ( Table 2 ) . The no . of ASVs generated in replicate runs was slightly different indicating slightly different detection sensitivities in the replicate PCR runs . Only the forward read was retained for downstream analysis in reads that did not merge due to either amplicon being longer than 600 b . p or due to low-quality bases in the overlap bases . This did not affect the final identification of reads as shown by the simulated data results described later . We analyzed the Rufunsa samples in replicates and compared the results . Both replicates had similar results in regard to individual Trypanosome species detection per sample seen in the gel image analysis ( Fig 3A ) as well as amplicon read analysis ( Fig 3B ) . The outcome of detection for each of the Trypanosome species and subspecies in replicate runs was comparable and the Fischer’s exact test confirmed that there was no significant difference ( P<0 . 05 ) in the number of positive detections in replicate runs ( S2 Table ) . Simulation of data generated from Trypanosome sequences downloaded from NCBI and analyzed using the AMPtk ( amplicon toolkit ) pipeline ( version 1 . 2 . 4 ) ( https://github . com/nextgenusfs/amptk ) showed that amplicon sequence variants ( ASVs ) generated by the pipeline as primary units of representing sequence diversity , were more accurate in correctly inferring the diversity sequences compared to operational taxonomic units ( OTUs ) derived from clustering sequences at 97% identity ( S3 Table ) . The specificity and precision of distinguishing between individual sequences of the same Trypanosome species are reflected by the number of ASVs or OTUs representing each of the different species . For example , only one OTU was generated for all three Trypanosoma theileri sequences , and three OTUs were generated for seven Trypanosoma simiae sequences , while the number of ASVs generated in each case represented each sequence accurately . The simulated data results indicated that read analysis using the AMPtk pipeline and ASVs instead of OTUs was suitable for sensitive identification of Trypanosome reads . By comparing gel images after PCR and sequence data , it was observed that the sensitivity of detection of Trypanosome DNA was increased by sequencing . Samples with bands that were barely visible after the 1st PCR became visible after the 2nd PCR and were confirmed as positive after sequencing ( Fig 4A ) . It was also observed that some T . godfreyi and T . vivax amplicon bands were of a relatively similar size and it was difficult to distinguish the two by gel analysis alone ( Fig 4B ) . From this example , sample no . 10 has an ITS amplicon size of about 400 b . p similar to that of sample no . 6 and 8 . Sequence analysis showed that the band in sample no . 10 was identified as T . vivax while bands observed in sample no . 6 and 8 were identified as T . godfreyi despite their similar sizes . Mixed and single infections with multiple and single bands respectively were observed and confirmed by amplicon sequence analysis . Results for the second PCR using dual-index primers showed consistency with those of the first PCR . There were no bands visible outside the expected range indicating the absence of non-specific amplification in both PCR steps . The 1st PCR amplicons were slightly longer than expected sizes due to the adapter sequences ( approx . 80 bp ) added to the primer , therefore the bands observed corresponded to T . congolense ( Kilifi/Forest and Savannah ) ; 650–800 b . p , T . brucei; 520–540 bp , T . simiae; 440–500 bp , T . godfreyi; 320–400 bp , and T . vivax; 290–400 bp . The accuracy in distinguishing between Trypanosome species and subspecies was analyzed by phylogenetic analysis of ASV sequences and their species identity allocated by BLAST . ASVs were named after the area of collection of the sample they originated from , ASV number allocated during analysis , accession number and the taxonomic name of their respective top hit BLAST subject sequence . Phylogenetic analysis of all ASVs obtained from this study showed that ASVs named after same Trypanosome species clustered together regardless of sample collection location . Sub-clustering into different subspecies of the same species was also observed ( Fig 5 ) . The Nannomonas subgenus showed the highest diversity of sub-clustering where T . simiae clustered into two main subspecies; T . simiae and T . simiae Tsavo . Two T . simiae Tsavo II ASVs from Kafue , with 91% and 97% identity to T . congolense Tsavo ( Accession number U22318 ) recently reviewed and classified as T . simiae Tsavo [49 , 50] clustered distinctly from the rest of the T . simiae Tsavo I ASVs . T . congolense ASVs showed the highest diversity and clustered into three main subspecies; Kilifi , Riverine/Forest , and Savannah . T . congolense Savannah represented the most diversity in all the ASVs analyzed from all the samples . T . congolense Kilifi clustered separately and far from T . congolense Savannah and Riverine/Forest subspecies . T . godfreyi showed sub-clustering into two main subspecies while T . vivax ( belonging to the Dutonella subgenus ) also clustered into two subspecies . It was expected that the Trypanozoon subgenus ( T . brucei/T . evansi ) did not show any distinct sub-clustering . The prevalence of Trypanosome infection in tsetse flies caught in the Rufunsa area , Zambia , was 25 . 6% , that of in the Kafue area , also Zambia , 28 . 2% , while that of the Hurungwe area , Zimbabwe , was 47 . 3% . Flies caught in Rufunsa had the highest prevalence of T . congolense while those from Kafue had the highest prevalence of T . godfreyi ( Table 3 ) . The highest prevalence of T . brucei/ T . evansi was recorded in flies caught in Hurungwe . We did not detect any T . brucei/ T . evansi from flies collected in Kafue . Mixed infections were predominant in flies caught in Rufunsa and Hurungwe while flies caught in Kafue were predominantly infected with T . godfreyi ( Fig 6 ) . Only tsetse flies from the Kafue region were sorted by sex during collection and we observed that the infection rate in female flies ( 38 . 6% ) was more than twice that of male flies ( 17 . 1% ) . Additionally , we did not detect T . congolense and T . vivax infections in male flies . Flies caught in Hurungwe did not have single infections with T . congolense or T . godfreyi .
This study reports a new and versatile approach for detection of Trypanosome DNA in multiple samples with high sensitivity and precision than conventional PCR-gel approach . We have established that conventional ITS PCR gel analysis is not an accurate way of determining the prevalence of Trypanosome species infections since identification of species by band size is inaccurate and may lead to misidentification of some Trypanosome species . Our new approach is sensitive at the subspecies level and has a high capacity to process large amounts of samples in one run ( approximately a 700 samples mixed library ) owing to the high repertoire of Illumina dual indexing primers . However , we did not see any unique clusters that could distinguish between the Trypanozoon subspecies which are of high priority because 1 ) they cause HAT ( T . b rhodesiense and T . gambiense ) and 2 ) their distribution is not restricted to Africa ( T . evansi and T . equiperdum ) . However , we did identify two clusters of T . vivax . This is important since T . vivax is distributed outside Africa since it can be transmitted both cyclically by tsetse flies and also mechanically . Failure to distinguish between Trypanozoon subspecies was expected since the ribosomal RNA genes are highly conserved in this subgenus and cannot be able to tell apart the subspecies [29 , 30] . Moreover , a study based on genome-wide SNP analysis of 56 Trypanozoon genomes , including eight T . evansi and four T . equiperdum has revealed extensively similar genomes [51] . A single molecular test able to distinguish between members of the Trypanozoon subspecies is yet to be developed thus , subspecies specific based tests remain obligatory for their identification . As part of this work , we have also developed new primers that show high sensitivity to T . brucei compared to conventional primers and cover a wider range of the Trypanosoma genus . With our approach , it is now possible to identify species and subspecies of Trypanosomes by sequence analysis on individual samples as opposed to pooled samples for a large dataset which allows for the detection of new isolates . It is also possible to make a better inference of the Trypanosome species circulating in an area . This approach is practical and , with the decreasing cost of next-generation sequencing , cost-effective way to monitor large field samples of all kinds . They can , therefore , be utilized in a wide range of samples from vectors and hosts and the analysis of new Trypanosome species . The results obtained in this study indicate that T . vivax and T . godfreyi have very similarly sized ITS1 amplicons making it difficult to identify one from the other based solely on gel band sizes . Sequencing and clustering of the reads effectively address this issue . Phylogenetic analysis shows several interesting population substructures in the cases of T . simiae and T . congolense . Within the T . congolense clade , Savannah and Riverine/Forest subspecies show more sequence similarity while the Kilifi type shows more divergence . This agrees with a previous study that found T . congolense Savannah and Riverine/Forest had 71% similarity in satellite DNA sequence [52] and that the Kilifi subspecies was as divergent from other T . congolense subspecies [53] . The clustering of T . congolense Kilifi close to T . simiae species than other T . congolense subspecies is quite interesting in that an earlier study had identified a new T . congolense Tsavo strain ( Accession number U22318 ) [54] which has been classified as T . simiae Tsavo [55] . We identified two ASVs from Kafue area ( classified as T . simiae Tsavo II in this study ) that had 91% and 97% identity to the U22318 T . congolense Tsavo sequence and that clustered with T . simiae Tsavo rather than other T . congolense species sequences supporting the T . simiae Tsavo classification . However , they cluster separately from the other T . simiae Tsavo ASVs , suggesting that they may have a divergent genotype . Perhaps there is a complex relationship between T . congolense and T . simiae species yet to be identified . Prevalence of Trypanosome infection in caught tsetse flies differed in the sampled areas with single and mixed infection being detected in flies caught agreeing with previous studies [37 , 56 , 57] . This may be an important factor in the exchange of information between species . We also observed that the infection rate of female tsetse flies was more than twice that of male flies . This result is in contrast to dissection data from the Tinde experiment where male Glossina morsitans centralis had a salivary gland infection rate ( 5 . 4% ) more than twice that of females ( 2 . 1% ) [58] . However , our results agree with other studies on Glossina morsitans , reporting high infection rates in female flies compared to males [59 , 60] . More research is needed to find out the role of sex and infection rate differences between the different Glossina species in both laboratory and wild caught flies . To conclude , our results imply that with this approach , it is possible to detect and distinguish between different Trypanosome species and subspecies accurately ( with the exception of Trypanozoon subgenus ) and therefore infer prevalence of infection more precisely using a single test without having to undertake satellite DNA analysis that requires species-specific primers . This is made possible by deep sequencing which enables resolution at a single nucleotide level . This high resolution at sub-cluster level utilizing only the ITS1 region has not been shown before thus a practical and sensitive barcoding of African trypanosomes . Using our approach , it is thus possible to distinguish T . godfreyi from T . vivax , as well as highlight finer subpopulation structures within the T . simiae and T . congolense clades that raise interesting questions regarding their classification . It is highly likely that there are genomic and taxonomic differences between T . vivax , T . godfreyi and T . congolense subspecies that need to be studied . This could provide answers on the evolution of Trypanosomes such as; what contribution do these Trypanosome subspecies make to livestock disease ? Are these genotypes responsible for assumed “strain” differences in drug response ? Can these new genotypes be correlated with the old morphological criteria and species designations ? Do these “strains” have the potential of evolving to new subspecies that could pose new risks ? There is a need for more studies to catch up with the molecular taxonomy to answer these questions . | Tsetse flies are central actors in the transmission of Trypanosomes to vertebrate hosts . Therefore , detection of Trypanosomes in the tsetse flies is important for understanding the epidemiology of African trypanosomiasis as a component of new control or surveillance strategies . We have developed a method that combines multiplex PCR and next-generation sequencing for the detection of different Trypanosome species and subspecies . Similar to the widely used bacterial metagenomic analysis protocol , this method uses a modular , two-step PCR process followed by sequencing of all amplicons in a single run , making sequencing of amplicons more efficient and cost-effective when dealing with large sample sizes . As part of this approach , we designed novel Internal Transcribed Spacer 1 primers optimized for short read sequencing and have slightly better sensitivity than conventional primers . Taxonomic identification of amplicons is based on BLAST searches against the constantly updated NCBI’s nt database . Our approach is more accurate than traditional gel-based analyses which are prone to misidentification of species . It is also able to discriminate between subspecies of T . congolense , T . simiae , T . vivax , and T . godfreyi species . This method has the potential to provide new insights into the epidemiology of different Trypanosome genotypes and the discovery of new ones . |
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Kaposi's sarcoma ( KS ) is caused by infection with Kaposi's sarcoma-associated herpesvirus ( KSHV ) . The virus expresses unique microRNAs ( miRNAs ) , but the targets and functions of these miRNAs are not completely understood . In order to identify human targets of viral miRNAs , we measured protein expression changes caused by multiple KSHV miRNAs using pulsed stable labeling with amino acids in cell culture ( pSILAC ) in primary endothelial cells . This led to the identification of multiple human genes that are repressed at the protein level , but not at the miRNA level . Further analysis also identified that KSHV miRNAs can modulate activity or expression of upstream regulatory factors , resulting in suppressed activation of a protein involved in leukocyte recruitment ( ICAM1 ) following lysophosphatidic acid treatment , as well as up-regulation of a pro-angiogenic protein ( HIF1α ) , and up-regulation of a protein involved in stimulating angiogenesis ( HMOX1 ) . This study aids in our understanding of miRNA mechanisms of repression and miRNA contributions to viral pathogenesis .
At our current understanding , the herpesvirus family is the only viral family expressing multiple miRNAs . Kaposi's sarcoma-associated herpesvirus ( human herpesvirus 8 ) expresses 12 pre-miRNAs [1] , [2] , [3] , [4] . These miRNAs are encoded in the latency locus of the KSHV genome and all KSHV miRNAs are expressed during latency . This discovery presented the possibility that KSHV expresses miRNAs to modulate host gene expression by a mechanism that would avoid generating additional viral proteins , which could be detected by the host immune system . Although many groups have been successful in detecting viral miRNA expression , our understanding of the functions of the viral miRNAs has been limited due to the small number of validated miRNA target genes . Previously identified human targets include thrombospondin [4] , BACH-1 [5] , [6] , BCL-2 associated factor [7] , MICB [8] , musculoaponeurotic fibrosarcoma oncogene homolog [9] , IκBα [10] , Rbl2 [11] , p21 [12] , caspase 3 [13] , TWEAKR [14] , TGFβR2 [15] , and other targets . These targets represent host genes involved in angiogenesis , transcription regulation , immune evasion , NF-κB regulation , epigenetic modifications , apoptosis and cell cycle regulation . Recently , a number of other host targets have been identified by purifying RNA-induced silencing complexes and analyzing associated nucleic acids [16] [17] , [18] in primary effusion cell lines , which represents a recent addition to the technologies used to identify miRNA targets . Gene expression studies to discover targets repressed by viral miRNAs in primary endothelial cells have been limited . Previous methods for miRNA target prediction include measuring changes at the mRNA level in response to miRNAs using microarrays and bioinformatic methods to search for limited sequence complementarity [4] , [7] . The human targets of miRNAs that will be detected depend on the expression profiling methods utilized and the mechanisms of miRNA-mediated repression [19] . If a miRNA is inhibiting gene expression by stimulating deadenylation and destabilization of the mRNA target , then gene expression microarrays can be successful in identifying targets . However , miRNAs may repress gene expression of some targets by inhibiting translation and mRNA expression profiling may miss miRNA targets that are repressed at the protein level , but not at the mRNA level . One method to detect these types of targets is by measuring changes in protein expression in the presence of specific miRNAs . Stable isotope labeling of amino acids in cell culture ( SILAC ) coupled with tandem mass spectrometry has been used recently to study the effects of miRNAs on protein expression [20] , [21] , [22] , [23] . In this report , the pulsed SILAC method was employed to focus on changes in newly translated proteins in the presence of KSHV miRNAs . Here , we report the discovery of human targets of viral miRNAs using this technology in primary human endothelial cells , a relevant cell type for KSHV infection . We found that specific miRNAs can inhibit expression of a protein involved in immune response and can stimulate expression of two proteins known to stimulate angiogenesis ( a key hallmark of Kaposi's sarcoma ) .
Since sixteen miRNAs were introduced into HUVECs simultaneously during the SILAC assay , the analysis of potential miRNA targets and protein expression was complex , even though these experiments were biologically relevant to the expression of all miRNAs during normal viral infection . Bioinformatic programs are commonly used to identify complementary sequences between miRNAs and their potential targets . We used TargetScan [25] to search for seed-matching sequences in the 3′ untranslated regions ( UTRs ) of transcripts corresponding to proteins that were identified in the SILAC analysis . An initial analysis of genes included in both the SILAC and TargetScan datasets separated the genes into two sets , one with at least one TargetScan site ( 847 genes ) and another set of corresponding transcripts which did not have any TargetScan sites ( 424 genes ) ( Figure 2A ) . This revealed that the fraction of proteins containing at least one predicted miRNA target site ( in the corresponding transcript's 3′UTR ) was larger in the set of proteins that were strongly repressed ( Figure 2B ) . Approximately 60% of proteins that were not repressed ( log2>0 ) had at least one seed-matching site in their corresponding 3′UTR , suggesting an over 60% false positive rate of detection using seed matching alone . However , those proteins whose transcripts have seed-matching sites tend to have lower expression in the presence of KSHV miRNAs , as do the proteins from mRNAs with multiple seed-matching sites ( Figure 2C–D ) . Repressed proteins detected in the SILAC analysis can represent direct targets of KSHV miRNAs , as well as indirect targets . In order to determine if these repressed genes are directly targeted by KSHV miRNAs , we chose six genes based only on protein expression changes to test in standard 3′UTR luciferase reporter assays . Using full 3′ UTRs , we determined that all six of the 3′UTR luciferase reporters tested ( GRB2 , ROCK2 , STAT3 , HMGCS1 , TSPAN3 , AKAP9 ) are significantly inhibited by at least one KSHV miRNA ( Figure 3A ) , but TSPAN3 repression was the weakest of the six 3′UTRs tested . Interestingly , GRB2 was also recently described as a target of KSHV miRNAs [17] . Additionally , we mapped the specific site targeted by a KSHV miRNA for two of these targets , ROCK2 and HMGCS1 ( Figure 3B–C ) . Luciferase reporters shown in Figure 3A contained 3′UTRs downstream of a firefly luciferase gene and reporters shown in Figure 3B–C had 3′UTRs downstream of a renilla luciferase gene . Different transcription rates , half lives of luciferase enzymes , and cloned 3′UTR context may have been responsible for certain variations in the repression of the same 3′UTR in different reporter plasmids . The mutation of predicted sites significantly relieved miRNA-mediated repression for both miRNA targets ( Figure 3B–C ) . Together , these results suggest the 3′UTRs of these six genes identified in the SILAC screen contain sequences targeted directly by KSHV miRNAs . Using two-color quantitative Western blotting , we assayed sixteen mature miRNAs for their ability to modulate endogenous protein expression of four ( of the six ) luciferase-validated target genes in primary endothelial cells . All four proteins tested , GRB2 , ROCK2 , STAT3 ( alpha and beta isoforms ) and HMGCS1 , were inhibited significantly by at least one miRNA ( Figure 4A ) . Furthermore , the protein expression from the majority of the individual genes tested was inhibited significantly by multiple miRNAs . For example , GRB2 protein expression was repressed by miR-K4-3p , -K4-5p , and -K9* . We observed an overall correlation between the miRNAs that repress the 3′UTR reporter and the miRNAs that decrease the steady-state levels of endogenous protein . This supports the pulsed SILAC strategy as a method of discovering miRNA targets . It is also important to determine target protein expression levels in the context of viral infection . We observed significant repression of four miRNA targets , including a particularly robust inhibition of HMGCS1 in de novo infected HUVECs compared with mock infected cells ( Figure 4B ) . The repression of HMGCS1 protein after infection was similar to the protein expression changes in the pSILAC data ( Figure 1E ) and cells transfected with miR-K11 mimics ( Figure 4A ) . Repression after de novo infection validates that these targets are repressed in the context of physiological levels of viral miRNAs during infection . An additional use of the proteomic data is to address the question of how miRNAs repress gene expression . Whether miRNA-induced gene expression changes are reflected primarily at the mRNA or the protein level may lead to a better understanding of miRNA repression mechanisms . Using the same transfected cells from the proteomic screening , we also analyzed the mRNA expression profiles using microarrays ( Figure 4C , Table S1 ) . All of the protein expression changes in Figure 1D were combined with mRNA expression changes from microarray analysis and plotted in Figure 4C . The protein and mRNA expression changes of the six newly validated miRNA targets were analyzed and for all six of these target genes the changes at the protein level were more pronounced than at the mRNA level ( Figure 4C ) . These findings justified the additional focus on protein expression changes to predict miRNA targets , which may be missed by solely measuring changes at the mRNA level ( depending on the mRNA expression change cutoff values used ) . Identifying potential miRNA targets is an initial step to elucidate the functions of KSHV miRNAs . One of the validated miRNA targets , Rho-associated , coiled-coil containing protein kinase 2 ( ROCK2 ) has been shown to be largely responsible for lysophosphatidic acid ( LPA ) -induced intercellular adhesion molecule 1 ( ICAM1 ) expression in HUVECs [26] . ICAM1 is essential for the recruitment and transmigration of leukocytes to sites of inflammation [27] . Therefore , we hypothesized that KSHV miRNA-mediated knockdown of ROCK2 would contribute to the decrease of ICAM1 expression induced by LPA as part of a host immune evasion strategy during latency . HUVECs were transfected with individual or combinations of KSHV miRNAs or siRNAs targeting ROCK2 , treated with LPA , and harvested at 48 h post-transfection . The whole cell lysates were analyzed for relative changes in ROCK2 and ICAM1 protein expression by quantitative Western blot analysis . In LPA-treated cells , ROCK2 protein was sufficiently repressed by both miR-K4-3p and siROCK2 , but not reproducibly by miR-K10a . We observed an average 6-fold increase of ICAM1 protein expression upon treatment with LPA ( data not shown ) . While there was a significant decrease in ICAM1 protein expression from LPA-treated cells also transfected with miR-K4-3p or siROCK2 , there was a much more robust repression of ICAM1 expression by miR-K10a transfection ( Figure 5A ) . Based on these results , we hypothesized that miR-K10a represses ICAM1 up-regulation through a ROCK2-independent mechanism . It was known that STAT3 can activate ICAM1 expression [28] , [29] , [30] and LPA treatment induces STAT3 phosphorylation [31] . We confirmed an increase in phospho-STAT3 ( Tyr705 ) using Western blot analysis upon LPA treatment and found decreased levels of phospho-STAT3 ( Tyr705 ) in the presence of miR-K10a ( Figure 5B ) . While repression of total STAT3 protein levels with miR-K10a transfection in the absence of LPA was variable , STAT3 protein levels were repressed in LPA-treated cells upon transfection with miR-K10a mimics compared to control mimics . TargetScan analysis found three potential miR-K10a binding sites in the STAT3 3′UTR ( Table S2 ) , and luciferase assays with the STAT3 3′UTR confirmed direct repression by miR-K10a ( Figure 5C ) . This suggested a potential role of STAT3 in the repression of ICAM1 in LPA-treated endothelial cells that is independent of ROCK2 . Additionally , we observed strong repression of ICAM1 after de novo KSHV infection in HUVECs ( Figure 5D ) . To determine if KSHV miRNAs play a role in this repression , HUVECs were transfected with miRNA inhibitors to miR-K4-3p and miR-K10a , then infected with KSHV , and analyzed for ICAM1 protein expression three days after infection . ICAM1 protein expression is modestly elevated ( likely due to incomplete inhibition of target miRNAs ) in HUVECs transfected with miR-K4-3p and miR-K10a inhibitors ( Figure 5E ) . Together , these results show that KSHV miRNAs decrease LPA-stimulated ICAM1 expression and are at least partially responsible for ICAM1 repression during KSHV infection in primary endothelial cells , which could potentially minimize recruitment of leukocytes to areas of KSHV infection . Our initial focus was to identify direct miRNA target genes by focusing on genes that were repressed in the presence of the viral miRNAs . However , we were intrigued by the increased protein production of heme oxygenase 1 ( HMOX1 , log2 = 2 . 03 ) and biliverdin reductase ( BLVRA , log2 = 1 . 99 ) in the presence of KSHV miRNAs . These proteins are important factors in oxidative stress and heme metabolism [32] . HMOX1 protein was previously described to be upregulated upon infection with KSHV [33] . Because miRNAs usually work through suppressing gene expression , these results suggested that some KSHV miRNAs may work through modulating protein expression of factors regulating HMOX1 and BLVRA protein expression . An analysis of promoters corresponding to the up-regulated proteins ( top 5% ) revealed that HIF1α binding sites were enriched in this set of up-regulated genes ( p-value = 0 . 0005 ) . Closer inspection revealed both HMOX1 and BLVRA are transcriptional targets of HIF1α [34] , [35] . We sought to determine if specific miRNAs could influence HIF1α expression or activity . Inducing hypoxia in 293 cells ( also HUVECs , data not shown ) with the addition of a hypoxia mimic , cobalt chloride ( data not shown ) , or inducing hypoxia with incubation in 1% oxygen , showed that miR-K7 can induce a 5-fold activation of endogenous HIF1α protein levels ( Figure 6A ) . We also observed that miR-K7 can increase HIF1α transcriptional activity through assays using a HIF-responsive luciferase reporter ( Figure 6B ) . Quantitative PCR data did not detect a significant change in HIF1α mRNA levels ( Figure 6C ) , suggesting transcription rates are not affected by miR-K7 . HIF1α protein is constitutively produced , but destroyed in cells growing in normoxic conditions . We suspected that miR-K7 might increase HIF1α protein levels by repressing an inhibitor of HIF1α protein expression . We investigated the changes in protein expression of four inhibitors of HIF1α , including hypoxia-inducible factor 1-alpha inhibitor ( HIF1AN ) , egl nine homolog 1 ( PHD2/EGLN1 ) , von Hippel-Lindau tumor suppressor ( VHL ) , and tumor protein p53 ( TP53 ) , but we did not detect significant changes ( Figure 6D ) . However , another protein , ring-box 1/E3 ubiquitin protein ligase ( RBX1 ) , has been shown to mediate ubiquitination and degradation of HIF1α [36] . Protein levels of RBX1 were modestly repressed in hypoxic cells transfected with miR-K7 mimic compared to the negative control miRNA mimic ( Figure 6D ) . It was unknown if RBX1 is a direct target of miR-K7 , but RBX1 was found in miRNA target detection screens ( CLIP assays ) in KSHV-infected cells [17] , [18] . These data suggested that RBX1 may play a partial role in miR-K7 upregulation of HIF1α protein levels during hypoxia , but it remains likely that up-regulation of HIF1α is due to changes in expression of multiple genes that remain to be determined . Taken together , these results suggest miR-K7 may repress additional inhibitors of HIF1α protein expression . In normoxia , HMOX1 protein expression was not induced by miR-K7 ( Figure 6E ) . Furthermore , the increase in HMOX1 protein expression detected in the SILAC analysis ( in normoxia ) was likely not due to increased HIF1α protein levels , but rather repression of a repressor of HMOX1 . In addition to positive regulation by HIF1α , HMOX1 was also known to be repressed by BTB and CNC homology 1 , basic leucine zipper transcription factor 1 ( BACH1 ) which is a known target of miR-K11 [5] , [6] . Under normoxia and miR-K11 expression , we observed an expected repression of BACH1 and a robust 4 . 5-fold activation of HMOX1 protein expression ( Figure 6E ) . These results suggest up-regulation of HMOX1 by miR-K11 is achieved by repression of BACH1 during normoxia . In addition to determining the roles of miRNAs through the study of individual target genes , the analysis of predicted target gene functions could highlight cellular pathways and biological processes that miRNAs regulate during infection . Furthermore , repressed gene expression could be the result of direct or indirect consequences of miRNAs , but both classes of targets may influence KSHV-infected cells . Analysis of the biological processes enriched in the most repressed ( five percent ) proteins showed that many of these repressed proteins are involved in translation , cytoskeleton , cell cycle , chromatin modification and angiogenesis ( Figure 7 ) . While it is currently unknown how many of these repressed proteins are direct miRNA targets , this analysis points to certain cellular functions important to KSHV pathogenesis that KSHV miRNAs are targeting , directly or indirectly .
In order to understand miRNA functions , it is critical to identify their targets , so we can increase our knowledge of cellular pathways that are important for infection and pathogenesis . Genome-wide studies have been conducted analyzing the Argonaute-associated mRNAs ( CLIP assays ) in B cells [16] , [17] , [18] , and the microarray and proteomic screening for miRNA-induced gene expression changes in primary endothelial cells from this report represent a complimentary dataset for elucidating viral miRNA functions . Indeed , integration of miRNA targets from CLIP methods and other expression studies will continue to be useful for identifying miRNA target sites , as well as >those CLIP hits that are repressed at the mRNA and/or protein level . Compared with other approaches to discover miRNA targets , current mass spectrometry methods are able to query a lower number of gene products . Despite this limitation , this current study has identified repression of multiple novel and previously validated miRNA targets ( THBS1 , GRB2 ) . Additionally , gene expression studies can reveal direct and indirect miRNA targets , both of which are important for virus-host interactions . By inspecting gene expression changes at both the mRNA and protein level , we have demonstrated that multiple miRNA targets are likely missed using microarrays since the miRNA target may only be repressed at the level of translation . This finding is relevant given the conflicting reports about the predominant mechanism and order of repression mechanisms [37] that are utilized by miRNAs to modulate gene expression , whether that be mRNA level repression [38] or translation inhibition [39] , [40] . In this study , validated miRNA targets AKAP9 , STAT3 , and GRB2 proteins were significantly repressed , but microarray results indicated mRNA levels were not reduced in the presence of KSHV miRNA mimics . The protein SH3-domain GRB2-like endophilin B1 ( SH3GLB1 ) was the second most inhibited protein , but the mRNA levels were relatively unchanged ( log2 0 . 03 ) . Interestingly , previous reports have shown that SH3GLB1 functions as a tumor suppressor and pro-apoptotic factor [41] , [42] . Given our findings , this proteomic method is clearly an important start to discover novel miRNA targets . Furthermore , we have also shown novel functions of viral miRNAs involved in cellular pathways important to KSHV pathogenesis , including ICAM1 repression , HMOX1 up-regulation and HIF1α up-regulation . Previous studies have indicated that ROCK2 is involved in a pro-inflammatory pathway induced by lysophosphatidic acid ( LPA ) that results in the up-regulation of intercellular adhesion molecule 1 ( ICAM1 ) on the surface of endothelial cells [26] . ICAM1 binds with lymphocyte function-associated antigen 1 ( LFA-1 ) and leads to the recruitment and transmigration of leukocytes . Interestingly , ICAM1 is downregulated from the cell surface and degraded through a well-described mechanism by the KSHV lytic protein , K5 , which can cause a decrease in the recruitment of helper T cells [27] , [43] , [44] . Furthermore , a previous study [45] and this report have also shown a decrease in ICAM1 expression during latent de novo infection of endothelial cells . We discovered that KSHV miRNAs , miR-K10a and miR-K4-3p , repress ICAM1 expression after induction by LPA , likely through ROCK2 and STAT3-associated pathways . Our data indicate that miR-K10a may be inhibiting LPA induction of ICAM1 by multiple mechanisms . First , the repression of a direct or indirect miRNA target of miR-K10a may be partially responsible for the decrease in LPA-induced STAT3 phosphorylation . HITS-CLIP data [18] showed the kinase PTK2B/FAK as a hit for miR-K10a alone , and , interestingly , PTK2B/FAK is thought to be responsible for phosphorylation of STAT3 in LPA-treated cells [31] . Although STAT3 protein levels can be repressed by miR-K6-5p , unlike miR-K10a , it is not predicted to target the kinase ( PTK2B/FAK ) and it remains to be determined if miR-K6-5p can repress LPA-activation of ICAM1 . Second , miR-K10a may directly inhibit STAT3α total protein levels in LPA-treated cells , as suggested by the results from the STAT3 3′UTR luciferase assays with miR-K10a . While others [45] have shown that low levels of the KSHV protein K5 can still down-regulate ICAM1 expression , we believe it is likely that during latent infection , the inhibition of ICAM1 is also due to the viral miRNAs , miR-K4-3p and miR-K10a . However , further studies are required to further elucidate the contributions of viral protein and viral miRNA-mediated repression of ICAM1 . HIF1α can activate transcription of VEGF and other factors involved in angiogenesis [46] , which raises the possibility that KSHV miRNAs may influence the angiogenic environment in KSHV-infected endothelial cells . Since miR-K7 increases HIF1α protein levels , but did not inhibit some major repressors of HIF1α ( Figure 6 ) , this suggests miR-K7 is working through an alternative pathway . We also observed a modest decrease in RBX1 when HIF1α is upregulated and the combined data suggest that there may be an underappreciated mechanism regulating HIF1α protein levels . Others have reported an increase in HIF1α activity with KSHV infection [47] , [48] , [49] . This increased activity is likely due to contributions from both viral proteins and viral miRNAs . Interestingly , analysis using MetaCore software reveals human genes involved in translation initiation are enriched in the proteins repressed by KSHV miRNAs in endothelial cells . This class of translation initiation genes was also enriched in predicted miRNA targets from both KSHV and EBV miRNAs in co-infected latent BC1 cells [17] , [50] . By contrast , lytic viral infections have been known to repress host translation inhibition [51] and others report that translation is activated upon KSHV lytic reactivation [52] . Together , these results suggest KSHV may play a complex role in influencing translation during latency and lytic infection . This investigation into HIF1α regulation by miRNAs was raised by the fact that the HIF1α transcriptional target heme oxygenase ( HMOX1 ) is strongly upregulated in this proteomic screen and in KSHV infected cells in a previous report [33] . It was also found that increased HMOX1 activity stimulated proliferation of KSHV-infected endothelial cells [33] . Both heme oxygenase I ( HMOX1 ) and bilverdin reductase ( BLVRA ) are strongly up-regulated in the presence of KSHV miRNAs in our study , and both of these gene products can protect endothelial cells from oxidative stress [53] . This also suggests certain KSHV miRNAs may protect cells from oxidative stress , by inhibiting BACH1 from repressing HMOX1 expression . Increased HMOX1 activity also correlates with increased angiogenesis [54] , [55] , [56] , [57] . Taken together , KSHV miRNA induction of HMOX1 can potentially protect cells from oxidative stress and increase proliferation and angiogenesis . In summary , the SILAC method revealed miRNA targets and discovered ways in which KSHV miRNAs can influence proliferation , angiogenesis , and immune evasion . More in-depth studies are needed to fully understand the significance of selected human genes targeted for repression by viral miRNAs .
293 cells were maintained in Dulbecco's modified Eagle's medium ( DMEM ) containing 10% fetal bovine serum ( FBS ) and 1× penicillin and streptomycin ( Pen Strep ) glutamine solution ( Gibco ) . Primary human umbilical vein endothelial cells ( HUVECs; Lonza ) were maintained in EGM-2 ( Lonza ) for up to five passages . Locked nucleic acids were from Exiqon . Synthetic KSHV miRNA mimics and a non-targeting miRNA ( control ) were from Ambion ( Sequences in Supplemental Information ) . HUVECs were seeded at 2×105 cells/well in a 6-well plate , transfected by using 1 . 5 µl/well DharmaFECT 1 reagent ( Dharmacon ) and 10 nM KSHV miRNA , and harvested at 48 h posttransfection ( hpt ) . ON-TARGETplus SMARTpool small interfering RNAs ( siRNAs ) targeting ROCK2 and an ON-TARGETplus nontargeting pool were obtained from Dharmacon . For ICAM1 experiments , cells were then serum starved overnight in basal media ( EBM-2 ) with 25% EGM-2 and , 40 hours post-transfection , treated with LPA ( 50 µM , Enzo ) for 8 hours . Cells were harvested at 48 hr . post-transfection and lysed in RIPA . For SILAC experiments , HUVECs were transfected ( total miRNA concentration was 10 nM ) in T75 flasks for 6 hr . and then split into new flask with medium-heavy ( with 13C6-L-arginine and D4-L-lysine ) or heavy ( 13C615N4 L-arginine and 13C615N2 L-lysine ) SILAC media as described [20] , except the media was also supplemented with endothelial growth factors ( Bulletkit , Lonza ) . After 30 hr . post-transfection , cells were harvested from flasks , counted , and equal number of cells from each condition were combined and frozen . Frozen cell pellet containing equal amount of control ( neg miRNA ) and experimental ( KSHV miRNAs ) cells were suspended in 100 µl of 25 mM ammonium bicarbonate buffer ( pH 8 . 4 ) . The cells were lysed by brief sonication and the proteins were denatured by heating the protein lysate at 95°C for 5 min . Protein concentration was estimated using standard BCA assay ( Pierce ) and the lysate was subjected to trypsin ( enzyme to protein ratio 1∶100 ) digestion overnight at 37°C . The tryptic digest was lyophilized and reconstituted in 25% ACN/0 . 1% FA ( 100 µl ) and fractionated using strong cation exchange ( SCX ) liquid chromatography into 96 fractions . The fractions were pooled on the basis of the intensity profile into 45 fractions , vacuum dried and reconstituted in 12 µL of 0 . 1% formic acid prior to nano-flow reversed-phase liquid chromatography mass spectrometry analysis . NanoRPLC–MS/MS analysis was performed using an Agilent 1100 nanoflow LC system coupled with hybrid linear ion trap-fourier transform ion cyclotron resonance ( LIT-FTICR ) mass spectrometer ( LTQ FT Ultra ) ( ThermoElectron , San Jose , CA ) . The system was connected to a 75 µm i . d . ×360 mm o . d . ×10 cm long fused silica microcapillary column ( Polymicro Technologies , Phoenix , AZ ) packed in-house with 5 µm , 300 Å pore size C-18 silica-bonded stationary RP particles ( Vydac , Hysperia , CA ) . The LC mobile phase A was 0 . 1% formic acid in water and B was 0 . 1% formic acid in acetonitrile . After the peptide sample injection , gradient elution was performed under the following conditions: 2% B at 500 nL/min in 30 min; a linear increase of 2–42% B at 250 nL/min in 40 min; 42–98% B at 250 nL/min in 10 min; and 98% at 500 nL/min for 18 min . The LIT-FTICR-MS was operated in the profile mode with 50 , 000 resolution for FTMS scans and followed by the data-dependent MS/MS scans where the seven most abundant peptide molecular ions in each FTMS scan were sequentially selected for collision-induced dissociation ( CID ) using a normalized collision energy of 35% . Dynamic exclusion was applied to minimize repeated selection of peptides previously selected for CID . The capillary temperature and electrospray voltage were set to 160°C and 1 . 7 kV , respectively . The raw LC-MS/MS data obtained from FT-LTQ was analyzed by MaxQuant ( version 1 . 0 . 13 . 13 ) for peptide identification and quantification . MS/MS peak list from individual RAW files were generated using the Quant module of the MaxQuant software and protein identification was performed using MASCOT against a decoy human database . Oxidation of methionine was searched as a variable modification . The false discovery rate was set at 1% for peptide and protein identification . Peptide peak intensities were used to determine the relative abundance ratio of “heavy” labeled proteins to “medium” labeled proteins . Unlabeled peptides were not used for further analysis . The ratio of “heavy” to “medium” proteins represents the fold change values reported ( Figure 1D-E , Tables S1 , S2 ) . Raw data files from pSILAC from both technical replicates were combined and then processed in MaxQuant to improve the coverage and the number of peptides found per protein . The Spearman correlation coefficient between protein expression changes for the two biological replicates is 0 . 51 ( and 0 . 43 Pearson correlation coefficient ) . The Spearman correlation coefficient between mRNA expression changes for the two biological replicates is 0 . 54 ( and 0 . 57 Pearson correlation coefficient ) . Due to the limited amount of sample obtained from the primary cells , equal amount of heavy ( H ) and medium ( M ) labeled cells were mixed prior to processing of the samples . To verify that there was no labeling bias , an MA plot ( M = log2 ( H ) −log2 ( M ) , A = ½ ( log2 ( H ) +log2 ( M ) ) was performed followed by Lowess curve analysis on the transformed data . Figure S4 shows that the Lowess regression line is almost straight around zero horizontal line , demonstrating no labeling bias in the H and M labeling . RNA was purified using Tri reagent ( Ambion ) and RNA quality was determined using a Bioanalyzer 2100 ( Agilent ) . Agilent arrays were performed and analyzed using Agilent Feature Extraction Software and Genespring GX as previously [7] . HUVEC microarray data was deposited to NCBI GEO database , accession number GSE43640 . Total cell protein was harvested from cell pellets by using RIPA lysis buffer ( Sigma ) supplemented with 1× Halt protease and phosphatase inhibitor cocktail ( Thermo Scientific ) . Cells were lysed on ice for 10 min , and cell debris was removed by centrifugation at 13 , 000 rpm for 10 min . Nuclear extracts for HIF1α blots were prepared using NE-PER ( Pierce ) . The Li-Cor Odyssey system was used for the detection and quantitation of protein bands . The following primary antibodies were used: rabbit anti-TWEAKR ( 4403 , Cell Signaling ) , rabbit anti-BCLAF1 ( Bethyl ) , goat anti-BACH1 ( SC-14700 , Santa Cruz ) , mouse anti-GAPDH ( sigma ) , anti-STAT3 ( 9132S , Cell Signaling ) , rabbit anti-HMGCS1 ( sc-33829 , Santa Cruz ) , rabbit anti-GRB2 ( 3972S , Cell Signaling ) , rabbit anti-ICAM1 ( 4915 , Cell Signaling ) , rabbit anti-ROCK2 ( sc-5561 , Santa Cruz ) , mouse anti-HIF1α ( NB100-105 , Novus ) and mouse anti-actin ( AC-74 , catalog number A5316; Sigma ) antibodies . The following secondary antibodies conjugated to infrared ( IR ) fluorescing dyes were obtained from Li-Cor: goat anti-rabbit antibody IR800CW , goat anti-mouse antibody IR680 , and goat anti-mouse antibody IR800CW . Protein band intensities were calculated and background corrected using ImageStudio ( Li-Cor ) . Results are normalized to actin levels , relative to levels in mock-infected or negative-control miRNA conditions . Full-length 3′UTR assays were performed as previously described [58] . Assays in Figure 3A used 3′UTR firefly luciferase reporters and were contransfected with a control renilla luciferase reporter under the control of a thymidine kinase promoter . Assays in Figure 3B–C used 3′UTRs cloned into a dual luciferase reporter . The 3′UTRs were cloned downstream of the renilla luciferase gene reporter . Luciferase values were normalized to an internal luciferase reporter and to parental vectors lacking cloned 3′UTRs . The hypoxia-inducible factor ( HIF ) luciferase reporter has five HIF-responsive elements in the promoter upstream of firefly luciferase reporter gene ( Panomics ) . Mutations of the predicted miRNA binding sites within the 3′UTRs of ROCK2 and HMGCS1 were performed as previously described [58] using the following primers and their reverse compliments: 5′-GCAGGCCTGCAAATACTGGCACAGAAATATAATCATACACCTTATTAACGGTGA-3′ for HMGCS1 and 5′-CTATGAAAGCAGTCATTATTCAAGGTGATCGTAAAGATCCAGTGAAAACAAGACTGAAATAT-3 for ROCK2 mut1 and 5′-TTACGCAGGACATTCTTGCCGTAAAGACATGATCCCAGATAAGTGTGTGT-3′ for ROCK2 mut2 . HUVECs were transfected as in SILAC experiments ( mixture of 16 mimics , total concentration of mimics was 10 nM ) . Each Ago2 immunoprecipitation was performed from individual T75 flasks using an Ago2 antibody ( 20 µl per 1 ml immunoprecipitation of diluted lysate , Cell Signaling #2897 ) , and the Magna RIP System ( Millipore ) . Purified RNA was subjected to TaqMan MicroRNA Reverse Transcription Kit . Mature miRNA levels were determined using Taqman MicroRNA Assays and viral miRNAs were normalized to human miR-21 levels using the ΔΔCt method . Note uninfected and untransfected HUVECs ( control ) had no detectable miR-K12-1 , but displayed average threshold cycles of 35 for miR-K12-7 and 37 cycles for miR-K12-11 . Uniprot IDs from pSILAC data , Agilent microarray probe IDs , and TargetScan v5 . 0 Refseq IDs were mapped to Ensembl gene IDs . Data integration was performed using Ensembl gene IDs . TargetScan sites “8mer” , “7mer-m8” , and “7mer-1A” were included , but 6mer sites were not included . Additional seed matching information is provided in Table S2 . Data similar to Table S2 was used to calculate the number of seed matching sites per 3′UTR . The empirical cumulative distribution function ( ecdf ) was performed using R ( http://CRAN . R-project . org/ ) . Promoter analysis of hypoxia-inducible factor responsive elements was performed using ExPlain ( Biobase ) using up-regulated ( top 5% ) proteins compared to a control set of genes normally expressed in HUVECs . At least three biological replicates were used for each analysis and the mean and standard deviation were used in T-tests . Changes were considered statistically significant when P<0 . 05 . ENSG00000134318 ENSG00000168610 ENSG00000140391 ENSG00000127914 ENSG00000112972 ENSG00000177885 | Kaposi's sarcoma-associated herpesvirus is the virus associated with multiple proliferative disorders , including Kaposi's sarcoma , primary effusion lymphoma and multicentric Castleman's disease . This virus expresses small nucleic acids ( with sequences distinct from other organisms ) , called microRNAs , that can limit expression of specific genes . Currently , we only know a few validated targets of these viral microRNAs and the mechanisms of microRNA-mediated repression are still being actively debated . We used a method to look at protein expression changes induced by these viral microRNAs to better understand microRNA targets and functions . The method we describe here found microRNA targets that are missed by other approaches . In addition to identifying previous microRNA targets and discovering new microRNA targets , we found the function of specific viral microRNAs to be associated with immune evasion and the expansion of blood vessel networks , a hallmark of Kaposi's sarcoma . The results may be a resource for those studying microRNAs from other organisms , and furthermore , the microRNA functions described provide mechanistic insight into viral pathogenesis and immune evasion . |
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Aging and longevity are considered to be highly complex genetic traits . In order to gain insight into aging as a polygenic trait , we employed an outbred Saccharomyces cerevisiae model , generated by crossing a vineyard strain RM11 and a laboratory strain S288c , to identify quantitative trait loci that control chronological lifespan . Among the major loci that regulate chronological lifespan in this cross , one genetic linkage was found to be congruent with a previously mapped locus that controls telomere length variation . We found that a single nucleotide polymorphism in BUL2 , encoding a component of an ubiquitin ligase complex involved in trafficking of amino acid permeases , controls chronological lifespan and telomere length as well as amino acid uptake . Cellular amino acid availability changes conferred by the BUL2 polymorphism alter telomere length by modulating activity of a transcription factor Gln3 . Among the GLN3 transcriptional targets relevant to this phenotype , we identified Wtm1 , whose upregulation promotes nuclear retention of ribonucleotide reductase ( RNR ) components and inhibits the assembly of the RNR enzyme complex during S-phase . Inhibition of RNR is one of the mechanisms by which Gln3 modulates telomere length . Identification of a polymorphism in BUL2 in this outbred yeast population revealed a link among cellular amino acid availability , chronological lifespan , and telomere length control .
The observation that dietary restriction promotes longevity in organisms ranging from yeast to primates raises the expectation that molecular mechanisms mediating this lifespan extension may also be shared among species . In support of the idea that related genetic circuitry controls aging in different species are the findings that genetic or pharmacological modulations of the conserved nutrient responsive pathways , such as target of rapamycin ( TOR ) [1] or insulin-like-growth factor ( IGF-1 ) [2] , increase lifespan in a wide range of species including mammals . The budding yeast Saccharomyces cerevisiae has become a popular model for studying the genetic and molecular basis for variation in lifespan . Two different forms of aging have been studied in yeast . Replicative lifespan ( RLS ) is defined by the number of daughter cells that are generated by a budding mother cell whereas chronological lifespan ( CLS ) is defined as the ability of yeast cells to survive in stationary phase as judged by the their capability to reenter the cell cycle after nutrients are reintroduced [3] , [4] . The two types of aging in yeast are thought to have their counterparts in mammals as the aging of dividing stem cells or the aging of non-dividing cells such as neurons or muscle cells , respectively . In addition to replicative and chronological aging , mutant yeast cells dividing in the absence of telomerase components exhibit loss of viability [5] similarl to replicative senescence of human fibroblasts in culture [6] . Recent epidemiological studies of human populations demonstrated a correlation between reduced leukocyte telomere length and overall mortality [7] , suggesting a link between telomere maintenance and organismal aging . Furthermore , life stress has been shown to influence leukocyte telomere length [8] , establishing a role for environmental stress in telomere stability . Little is known about how these processes connect , though twin studies suggest that both telomere length regulation and longevity in humans have a strong genetic component [9] , [10] . Most of what we have learned about telomere maintenance mechanisms and the genetics of aging comes from model organisms where the effects of the single gene changes can be examined independently from other genetic alterations . However , because natural populations are genetically diverse , differences in aging and telomere maintenance are more likely to result from the integration of effects of polymorphisms at multiple loci . In order to gain insight into telomere maintenance in genetically diverse populations , we have previously employed an outbred yeast model consisting of 122 haploid progeny derived by a cross of vineyard RM11-1a ( RM ) and laboratory S288c yeast ( BY ) [11] . Parental strains differ at 0 . 5% of their nucleotides and the progeny have been genotyped at >3000 markers , allowing for quantitative trait locus ( QTL ) mapping . In a previous telomere length study , we identified several loci that control telomere variation in this cross [12] . In this study , we used the same outbred model to explore chronological aging as a complex trait . During the course of these studies , we found that that one of the loci that controls chronological lifespan is identical to a major locus found to control telomere length , suggesting a previously unrecognized link between the two yeast aging-related phenotypes . This was an intriguing finding because changes in telomere length are linked to DNA replication , while chronological aging occurs in non-dividing cells . Furthermore , the two phenotypes were regulated in opposite directions by this locus: strains that inherited the vineyard allele had shorter telomeres and longer lifespans . We found that a single amino acid substitution in Bul2 , a component of an ubiquitin ligase complex which polyubiquitylates amino acid permeases and regulates their presence at the cell membrane , controls cellular amino acid availability and is responsible for the variation in both telomere length and CLS . We also elucidated a pathway by which decreased cellular amino acid uptake conferred by the BUL2 polymorphism and the consequent inhibition of nutrient-responsive TOR1 signaling lead to reduced telomere length .
To determine chronological lifespan of the 122 haploid progeny ( segregants ) from the RM/BY cross , strains were grown in YPD medium in 96-well plates to stationary phase , where cells maintain metabolic activity but cease mitotic division . Chronological lifespan ( CLS ) studies are often done in synthetic media , where yeast lifespans can be analyzed in a few weeks [13] . Because of the observation that the use of synthetic medium in CLS studies exposes cells to lifespan-limiting acidification [14] , we decided to carry out segregant CLS analysis in YPD where acidification of the media during culture outgrowth is not a problem . After intervals of approximately 30 days , we harvested 1 µL of each stationary phase culture , spotted culture dilutions on YPD plates , and determined viability of cultures as the ratio of microcolonies after 24 hours of growth to the total cell number plated . We found excellent correlation ( R = 0 . 98 ) between the cell viability determined using our microcolony method and the viability measured using colony forming ability ( Figure S1 ) . The vast majority of cultures were found to be fully viable after the initial interval of 5 days in stationary phase ( Figure 1A ) . Further along in stationary phase , segregant culture viabilities decreased to an average of 70% after 31 days ( range 30–91% ) , 35% after 59 days ( range 11–62% ) and 20% after 100 days ( range 0 . 5–40% ) . The observed viability distributions of the chronologically aged segregant strains displayed several interesting features . First , the variation in viability between segregants was continuous , suggesting that multiple genetic loci control survival among the segregants . Second , we observed that the parental strains' phenotypes are in the middle of the range . Such transgressive segregation , in which the segregant progeny exhibit more extreme phenotypes than either parental strain , suggests the presence of compensatory genetic loci within both the RM and BY parental backgrounds . Finally , the rank of the segregant viabilities was not static , as illustrated by the changing order of the parental strains over time , which suggests that different genes are responsible for early and late viabilities . We used genome-wide linkage analysis to identify the loci ( QTL ) responsible for the variation in chronological lifespan . Each segregant strain has been characterized for BY or RM inheritance at 2 , 956 polymorphic markers across the genome [11] . Using genome-wide linkage analysis , phenotype distributions can be compared between segregants that inherit the BY or RM sequence at each locus . A significant difference between the two distributions establishes a linkage between the trait of interest and the genomic sequence near the tested polymorphic marker . We found that stationary phase survival is linked to several genetic loci , consistent with the observed continuous range in viability ( Figure 1B–1D , Table S2 ) . We also noticed that the strength of linkage of the mapped loci changes with time . The chromosome 13 linkage , for instance , has LOD scores >3 . 5 at 31 and 59 days , yet it has no role in controlling viability after 100 days in culture . On the other hand , the chromosome 14 linkage had the opposite temporal pattern: not significant at day 31 yet has LOD scores >3 . 5 at day 59 and 100 . The alteration of the relative importance of different loci at different time points suggests that cells depend on different cellular processes during early and late stages of chronological lifespan . Comparison between the genome scan for loci that control chronological lifespan and our previous analysis for loci that control telomere length ( Figure 1E ) revealed that the strongest linkage for chronological lifespan at day 31 ( chromosome 13 locus ) is congruent with a previously identified locus that controls telomere length [12] . The segregant strains which inherited the RM allele of chromosome 13 locus had longer CLS ( 65% versus 56% viability at 30 days ) and shorter telomeres ( 261 bp versus 286 bp ) compared to strains which inherited the BY allele of the locus . In order to determine whether other mutants with short or long telomeres exhibit either reciprocal effects or alterations in CLS in general , we examined a panel of deletion mutants known to have telomere length alterations and found no correlation between telomere length changes and CLS ( Figure S2 ) . Likewise , a more general comparison of CLS and telomere length , using data from the recent global CLS study [15] and our previous telomere length screen [12] , did not reveal any correlation between telomere length and CLS ( Figure S2 ) . While we found no general correlation between telomere length and CLS , the striking overlap of genetic linkage between telomere length and chronological aging in this cross led us to hypothesize that these two traits are both controlled by a common polymorphism and that identifying the responsible gene may reveal an unexpected link between telomere maintenance and chronological aging . Among the polymorphisms in the mapped region , we identified one in the coding region of BUL2 , a gene encoding a component of the Rsp5p E3-ubiquitin ligase complex involved in amino acid permease sorting . During growth in the presence of rich nitrogen sources , high affinity amino acid permeases , such as the general amino acid permease GAP1 and the proline transporter PUT4 , are polyubiquitylated by a complex consisting of Bul1 , Bul2 and Rps5 , which specifies vacuolar-targeting of permeases for degradation [16] , [17] . Cellular amino acid permease activity can be monitored using the toxic proline analogue ADCB , which is transported across the cell membrane via nitrogen-regulated PUT4 and GAP1 [18] . We found that the parental RM and BY strains exhibit a striking difference in ADCB sensitivity when grown with a rich nitrogen source ( Figure 2A ) . Consistent with higher permease activity and amino acid intake relative to the RM strain , the BY strain was not able to grow at concentrations of ADCB that were non-toxic to the RM strain . Genome-wide linkage analysis of ADCB sensitivity in the segregants demonstrates that the BUL2-containing locus underlies the parental differences in permease activity ( Figure 2B ) . The BY strain carries a single Leu883Phe substitution relative to the RM version of Bul2 , which is conserved among many fungal homologs ( Figure 2C ) and all but three of the sequenced S . cerevisiae strains ( F883L is present in S288c and the two baking isolates YS2 and YS9 ) [19] . Engineering the RM allele of BUL2 into the BY strain restored ADCB resistance , whereas substitution of the BY BUL2 allele into the RM strain resulted in ADCB sensitivity ( Figure 2D ) . These findings indicate that the BY BUL2 Phe883Leu polymorphism confers a loss of Bul2 function , similar to that of a bul2Δ mutant , and increases permease activity and amino acid uptake . We next evaluated whether the same BUL2 polymorphism that controls cellular permease activity also mediates chronological lifespan and telomere length variation . The replacement of BUL2 in the BY parental strain with the RM allele led to an increase in chronological lifespan ( from 55% to 65% viable cells at 30 days in YPD medium ) , which was similar in magnitude to the increase in chronological lifespan conferred by the RM BUL2 allele in the segregants ( Figure 3A , 3B ) . Conversely , the replacement of the RM BUL2 allele with the BY BUL2 allele in the RM parental strain decreased chronological lifespan ( 67% versus 62% ) after 30 days . We next examined the effect of BUL2 alleles on the time-dependant viability curves in both laboratory and vineyard background using the synthetic media that is commonly used for CLS studies . ( In order to minimize the viability reduction due to media acidification , we used buffered SC medium [14] ) . Consistent with previous reports , we observed that CLS is shortened in SC medium compared to YPD , however , restoration of BUL2 function using RM BUL2 allele in the laboratory strain extended chronological life span even more robustly than we have observed in YPD ( Figure 3C ) . BUL2 replacement in the vineyard strain with the hypomorphic BUL2BY allele shortened CLS and deletion of BUL2 led to further reduction in CLS ( Figure 3D ) , which parallels the effect of BUL2 allele replacement and BUL2 deletion on cellular permease activity in the vineyard strain , judged by increased ADCB sensitivity in the BUL2BY alleles and BUL2 deletion ( Figure 2D ) . The effects of BUL2 allele replacement on CLS results were also confirmed using standard colony formation metrics [13] . These findings demonstrate that the BUL2 polymorphism controls variation of chronological lifespan in the RM/BY cross . The average telomere length in the segregants that contain the BY allele of BUL2 was 286 bp , which is 25 bp longer than the telomere length average of segregants that contain the RM allele ( 261 bp ) ( Figure 4A ) . Therefore , if BUL2 is the responsible polymorphism for telomere length alteration , then the BUL2 allele replacement in the RM parental strain is expected to create a 25 bp increase in telomere length , while the allele replacement in the BY strain would have a modest telomere length reduction . We found that allele replacement of BUL2 in both parental strains led to alterations in telomere length as predicted by the segregant analysis: telomeres were found to be longer in the RM strains with BUL2 replaced by the BY allele and telomeres were shorter in the BY strains containing the RM BUL2 allele replacement ( Figure 4B ) . As expected from the segregant analysis , the effect of allele replacement was modest , but also consistent and reproducible , as shown by analysis of several independent strains . Deletion of BUL2 lengthened telomeres in the RM background , but had no effect in the BY background ( Figure 4C ) . These results demonstrate that the leucine residue substitution present in the BY parent creates loss of Bul2 function , leading to higher activity of amino acid permeases on cell membranes , reduced chronological lifespan , and increased telomere length . Reduced availability of cellular nitrogen and amino acids conferred by the restoration of Bul2 function is expected to reduce the activity of the nutrient sensitive TOR1 kinase . Since the region containing the BUL2 locus had been previously identified as a regulatory hotspot that controls abundance of many transcripts in this cross [20] , we evaluated whether these transcriptional alterations could be mediated by alterations in TOR1 activity . Consistent with this possibility , we found that the set of genes overexpressed in strains containing BUL2RM significantly overlaps with genes that were found to be overexpressed in response to amino acid deprivation ( p = 1 . 1×10−8 ) and rapamycin ( p = 1 . 2×10−3 ) ( Figure 5A , Table S3 ) [21] , known inhibitors of TOR1 activity [22] , [23] . Because reduction of TOR1 signaling has been shown to extend chronological lifespan [24] , [25] , the viability gain in chronological aging assays conferred by the restoration of Bul2 function can be explained by reduced activity of the nutrient responsive TOR pathway . Could the same gene network be mediating telomere length alterations conferred by BUL2 function ? To investigate this possibility , we re-examined data from our previous genome-wide telomere length screen [12] , focusing on deletion mutants of genes in the nitrogen signaling circuit . We reasoned that such mutants would likely affect telomere length through the same mechanism as BUL2 , thus we might gain insight into BUL2's mechanism of action on telomere length from known modes of action through these other nitrogen-signaling mutants . Among the mutants in genes involved in nitrogen signaling , we found that cells lacking TOR1 have modest reduction in telomere length and that cells lacking URE2 have strikingly short telomeres ( Figure 5B ) . In rich nitrogen environments , Ure2 binds to the transcriptional activator Gln3 and inactivates it through cytoplasmic sequestration [26] , [27] . Upon encountering nitrogen-limiting environments , Gln3 is released from its complex with Ure2 and translocates to the nucleus to upregulate nitrogen catabolite responses [28] . The short telomere phenotype in ure2Δ mutants is mediated by Gln3 , as we found that the deletion of GLN3 restored the short telomere lengths in ure2Δ cells back to wildtype lengths ( Figure 5C ) . We hypothesized that the reduced nitrogen availability occurring in cells with functional Bul2 ( i . e . the RM allele ) leads to increased Gln3 transcriptional activity and shorter telomeres . In order to evaluate whether transcriptional alterations previously mapped to the region containing the BUL2 locus [20] could be mediated by Gln3 , we compared the set of genes that are upregulated by the RM BUL2 allele with the genes that are upregulated in response to URE2 deletion . Of the 19 transcripts that are significantly upregulated in strains with the RM BUL2 allele , 10 transcripts were found to be overexpressed in our transcript array analysis of ure2Δ cells ( of which there were 208 transcripts ) , including known direct Gln3 targets such as BAT2 and DIP5 ( Figure 5A , Table S4 ) ( p = 8 . 5×10−11 ) [29] . These findings , along with previous reports which link loss of Bul2 to decreased Gln3 nuclear localization [30] , support a model in which restoration of Bul2 function leads to decreased cellular nitrogen availability , thereby promoting Gln3 transcriptional activity and reduction of telomere length . Could Bul2's effect on telomere length be mediated by Gln3 ? To address this question , we examined the effect of the BUL2 allele replacement in cells lacking GLN3 . We found that neither did the RM BUL2 allele in the BY gln3Δ strain shorten telomeres , nor did the BY allele replacement increase telomere length in the RM gln3Δ strain ( Figure 5D ) . The requirement of Gln3 for BUL2 allele-induced telomere alterations supports the idea that BUL2 telomere length changes are mediated by modulation of Gln3 transcriptional activity . These findings , along with previous reports which link loss of Bul2 to decreased Gln3 nuclear localization [30] , support a model in which restoration of Bul2 function leads to decreased cellular nitrogen availability , thereby promoting Gln3 transcriptional activity and reduction of telomere length . In order to determine the relationship of the telomere maintenance defect caused by the deletion of URE2 to other pathways that participate in telomere maintenance , we compared telomere lengths of ure2Δ single mutants and double mutants that were ure2Δ and deficient in either DNA damage signaling ( tel1Δ ) , telomerase ( tlc1Δ ) , or telomere-capping ( yku70Δ ) functions . The ure2Δ cells showed synthetic telomere length phenotypes with the yku70Δ , tel1Δ , and tlc1Δ mutants ( Figure S3 ) , suggesting that Ure2's effect on telomere maintenance acts independently from pathways involved in telomere extension , telomere-capping , and TEL1-mediated DNA damage signaling . Our previous study of telomere maintenance genes identified a significant subset of mutants involved in nucleotide biosynthesis as having altered telomere length [12] . For instance , loss of the ribonucleotide reductase large subunit RNR1 results in telomere shortening on par with loss of YKU70 or TEL1 . Since nitrogen availability dictates growth , we speculated that mimicry of nitrogen starvation created by increased nuclear Gln3 would induce cells to conserve nitrogen and restrict nucleotide synthesis , and this in turn would cause shortening of telomeres . We first examined transcript levels in ure2Δ cells , anticipating reductions in nucleotide biosynthesis gene expression , but found only modest decreases in RNR1 and other nucleotide genes unlikely to account for the magnitude of telomere shortening in ure2Δ mutants . However , among the upregulated genes in ure2Δ cells , we found a strong increase in expression of Wtm1 , an inhibitor of ribonucleotide reductase . Wtm1 protein levels were found to be almost 5-fold higher in ure2Δ cells compared to wildtype ( Figure 6A ) . In addition , allele replacement with BUL2RM in the BY background gave rise to a 50% increase in Wtm1 , while in the vineyards strain the replacement of BUL2 with the hypomorphic BUL2BY and BUL2 deletion decreased the Wtm1 protein level by 40% and 80% respectively ( Figure 6A ) . The ribonucleotide reductase complex assembles during S-phase and consists of large Rnr1 subunits and the two small subunits Rnr2 and Rnr4 . Unlike Rnr1 , which is always cytoplasmic , Rnr2 and Rnr4 are localized in the nucleus during G1 and translocate to the cytoplasm during S-phase [31] . This process is controlled by Dif1 , which promotes nuclear import , and Wtm1 , which anchors the small subunits Rnr2 and Rnr4 in the nucleus [32] , [33] . Based on our observation that Wtm1 expression increases in ure2Δ cells , we hypothesized that ure2Δ cells have increased nuclear retention of the small subunits Rnr2 and Rnr4 . As previously observed , we found that Rnr4-GFP is nuclear during G1 and cytoplasmic during S-phase in wildtype cells ( Figure 6B , 6C ) . While Rnr4-GFP is appropriately nuclear in ure2Δ cells during G1 , 56% of ure2Δ cells retain Rnr4-GFP in the nucleus during S-phase . We determined that this aberrant nuclear Rnr4 localization in ure2Δ is dependent on Wtm1 since ure2Δwtm1Δ double mutants have completely restored cytoplasmic localization of Rnr4-GFP . Rescue by WTM1 deletion is not merely due to loss of nuclear Rnr4 localization: more than 50% of wtm1Δ cells still maintain nuclear localization of Rnr4-GFP in G1 ( Figure 6C ) . Examination of strains with different BUL2 alleles revealed that alteration of Bul2 function has a small but reproducible effect on S-phase Rnr4-GFP localization ( Figure 6D ) . Both RM BUL2BY and RM bul2Δ strains had 2 . 6% of S-phase cells with nuclear Rnr4-GFP , which is a significant decrease from the 5 . 6% seen in the RM wildtype strain . The fraction of cells with nuclear Rnr4-GFP increases from 8 . 0% in BY wildtype to 10 . 3% in the BY strain with the RM allele of BUL2 and decreases to 5 . 6% of S-phase cells in the BY bul2Δ strain . These results suggest that cells with decreased TOR signaling , such as in ure2Δ mutants and cells with BUL2RM , form fewer ribonucleotide reductase complexes during S-phase due to increased Wtm1 expression . We then investigated whether deletion of WTM1 would rescue the ure2Δ telomere length shortening . Telomere length comparison of ure2Δ and ure2Δwtm1Δ mutants reveals that deletion of WTM1 partially rescues telomere shortening due to loss of URE2 ( Figure 7A ) . Along the same lines , we found that deletion of the Rnr1 inhibitor SML1 [34] also abrogates the ure2Δ short telomere length defect ( Figure 7B ) . These findings support our hypothesis that the shortened telomeres in ure2Δ cells are due , at least in part , to limitation of ribonucleotide reductase activity .
Examination of quantitative trait loci that regulate chronological aging and telomere length in the progeny from a cross between the laboratory strain S288c and a vineyard strain , RM11-1a , led to identification of a polymorphism in BUL2 which alters trafficking of amino acid permeases and cellular amino acid import . Loss of Bul2 function , conferred by the laboratory allele of the gene , initiates a cascade of events outlined in Figure 8 that centers on TOR , a nutrient-responsive protein kinase previously implicated in CLS control . Our study defines a novel downstream role for TOR signaling in the regulation DNA replication and telomere maintenance through Gln3-mediated assembly of ribonucleotide reductase during S-phase . Amino acids are powerful activators of TOR signaling not only in yeast , but also in multicellular eukaryotes . For Drosophila melanogaster larvae , amino acid deprivation inhibits TOR activity and leads to growth inhibition and reduced body size [35] . Similarly , Caenorhabditis elegans lacking the intestinal amino acid transporter pep-2 also have reduced body size [36] . Increasing evidence suggests that reduced intake of amino acids , which consequently reduces TOR activity , may be a key component of life-extending dietary interventions . Lifespan extension granted by dietary restriction in D . melanogaster was abolished by re-addition of amino acids [37] . Additionally , dietary reduction of a single essential amino acid , either tryptophan or methionine , was sufficient to confer lifespan extension in both mice and rats [38]–[40] . While dietary restriction studies in S . cerevisiae typically involve glucose restriction , our finding that restoration of Bul2 function and resulting reduction of cellular amino acid import extends CLS supports the idea that amino acid-mediated regulation of TOR signaling controls longevity . While several of the upstream molecular events that control TOR activity , such as growth factors and energy status , are understood in great detail [41] , we only have rudimentary knowledge of how cells sense amino acid sufficiency and transmit this signal to TOR . TOR forms two separate complexes: the rapamycin-sensitive TOR complex 1 ( TORC1 ) , which regulates growth , ribosome biogenesis , translation and lifespan , and the rapamycin-insensitive TOR complex 2 ( TORC2 ) involved in actin cytoskeleton organization and cell wall integrity [42] . Recent studies in mammalian cells have identified several components that are required for TOR activation by amino acids , including Rag GTPase heterodimers involved in the recruitment of TORC1 complex to the lysosomal membrane compartment [43] . In addition to their roles as activators of TOR , the S . cerevisiae Rag GTPase orthologs Gtr1 and Gtr2 [23] are also implicated in the retrieval of Gap1 and other high affinity amino acid permeases from the vacuolar trafficking pathway [44] , thus promoting their localization to the plasma membrane . Because the retrieval of Gap1 from the vacuolar targeting pathway is regulated by amino acid availability ( discussed below ) , these findings raise the possibility that the related amino acid-responsive pathway that controls TOR also controls recycling of high affinity transporters to the cell membrane . In contrast to the majority of the 23 amino acid permeases in yeast , which are constitutively expressed and import specific amino-acids with low affinity , high affinity permeases such as the general amino acid permease Gap1 and proline permease Put4 are highly expressed only during nitrogen limitation [16] , [45] , [46] . Gap1 and its related class of permeases have a high capacity for amino acid transport and are thought to scavenge amino acids for use as a source of nitrogen . Intracellular sorting is one of the mechanisms by which the quality of available nitrogen controls the presence of high affinity permeases at the cell membrane: during growth with a good nitrogen source such as ammonium , glutamate and glutamine , Gap1 is sorted to the vacuole for degradation [16] . When cellular nitrogen and amino acids levels are low , Gap1 is sorted to the plasma membrane . A complex consisting of Rps5 , Bul1 and Bul2 ubiquitylates Gap1 and specifies its sorting to the multivesicular endosome . From the endosome , Gap1 can be targeted either to the vacuole or trafficked to the plasma membrane depending of the amino acid availability [47] . The amino acid-regulated step in this process appears to be Gap1 retrieval from the endosome rather than Gap1 ubiquitylation . Nevertheless , ubiquitylation is a prerequisite for controlling Gap1 localization because in its absence , Gap1 never reaches the endosome and is constitutively targeted to the plasma membrane . Therefore , loss of Bul2 function , such as in cells with the BY allele of BUL2 , results in non-discriminatory import of amino acids and greater intracellular amino acid and nitrogen availability . Our finding that the common laboratory strain S288c carries a loss-of-function mutation in BUL2 , subsequently leading to indiscriminant amino acid uptake , is important for future studies that exploit yeast as a model for amino acid sufficiency and TOR signaling . Specifically , such studies should include strains with wild-type BUL2; for example , they could employ the allele substitution strains described here . The mutation in BUL2 adds to the list of genetic alterations in the standard laboratory strain that are not representative of other members of the species such as loss of function changes in AMN1 [48] and MKT1 [49] . Similar to the control of Gap1 , mammalian growth factor receptors are also regulated by ubiquitin-mediated trafficking . While yeast cells detect cellular resources directly through their import via permeases , multicellular organisms rely on growth factors such as IGF-1 , which also stimulates TOR activity through Akt-Tsc-Rheb signaling , to coordinate nutrient availability with growth [1] . Cell surface localization of the IGF-1 receptor ( IGF-1R ) has been shown to depend on ubiquitylation by Nedd4 , a homolog of the catalytic Rsp5 subunit of the Rsp5/Bul1/Bul2 ubiquitin ligase [50] . It is intriguing that Nedd4−/+ mice have reduced IGF-1 receptors on the cell surface and phenotypes consistent with reduced IGF-1 signaling , including decreased body size [51] , raising the possibility that they may share increased longevity with other IGF-1-related dwarf mice . Reduced amino acid import in cells with functional Bul2 inhibits TORC1 activity , consistent with our observation of increased activity of TOR-inhibited transcription factor GLN3 in cells containing the RM BUL2 allele compared with cells which have the BY allele of BUL2 . ( In favorable nitrogen conditions , high TORC1 activity sequesters Gln3 in the cytoplasm . ) Reduced TOR activity has been previously shown to extend both chronological and replicative lifespan in yeast [24] , [25] , [52] . Because reduced TOR activity extends lifespan also in higher eukaryotes [53]–[55] , there is great interest in understanding the downstream events that mediate this effect . Several mechanisms by which nutrients and TOR inhibition promotes CLS in yeast have been proposed , including reduced accumulation of acetate and/or acidification of culture media [14] , promotion of respiration and autophagy [56] , [57] , and increased activity of stationary phase and stress-responsive transcription factors [25] . CLS experiments are often carried out in synthetic media which is complicated by significant media acidification due to release of organic acids during fermentation ( the initial media pH of 4 . 2 decreases to <3 after cells reach stationary phase ) . A combination of acidic pH and high concentration of acetate in the media has been linked to reduction of cell viability [14] . Because our chronological aging assays are performed in rich media ( YPD ) , which has an initial pH of 6 . 0 that reduces only to 5 . 8 after cells reach stationary phase , or buffered synthetic media , acetate toxicity is an unlikely mechanism for CLS modulation in our study . A study by Bonawitz et al . linked reduction in TOR activity to increased cellular respiratory capacity [56] . While translation is generally inhibited by reduced TOR activity , Bonawitz et al . found that translation of mitochondrial proteins was enhanced and led to increased respiration during growth in glucose . Respiration becomes increasingly important for maintaining energy supplies and viability as cells transition from fermentative growth to stationary phase . The importance of respiration during the stationary phase transition is supported by the findings of two recent genome-wide studies that identified respiratory deficient mutants among those with the shortest CLS [15] , [57] . In the same studies , mutants defective in autophagy , another process stimulated by TOR inhibition , were also found to have short CLS . These observations suggest that autophagy and respiration constitute important mediators by which reduced TOR activity promotes CLS . The inhibition of TOR that occurs in cells during the post-diauxic shift and preparation for stationary phase also elicits specific transcriptional responses that are essential for maintaining viability during quiescence [25] . One target of TOR is the Rim15 protein kinase that translates nutrient limitation signals from TOR , as well as Ras/PKA and Sch9 , into upregulation of cellular responses necessary for survival in stationary phase [58] . Similarly to Gln3 , Rim15 is phosphorylated by the nutrient-sensing kinases and retained in the cytoplasm , but upon nutrient deprivation , dephosphorylated Rim15 translocates to the nucleus to activate transcription factors Gis1 and Msn2/4 , which upregulate genes necessary for post-diauxic shift [59] and stress response respectively [60] , [61] . Deletion of either RIM15 or its target transcription factors shortens CLS and abolishes benefits conferred by caloric restriction or mutations in Tor/Ras/Sch9 that mimic calorie restriction [25] . Since Rim15 and Gln3 are both directly regulated by TOR through cytoplasmic sequestration , we predicted that Gln3 , like Rim15 , would be essential for proper stationary phase transition and survival . In support of this idea , we have found that deletion of GLN3 in the vineyard strain dramatically shortens CLS ( Figure S4 ) and that alteration of Bul2 function did not affect CLS in gln3Δ mutants . However , consistent with previous reports [24] , [25] , we found that deletion of GLN3 in the laboratory strain increased CLS . The paradoxical increase in CLS in response to GLN3 deletion in the laboratory strain is in opposition to the CLS detriment conferred by the loss of function of other transcription factors such as Msn2/4 or Gis1 which are , similarly to Gln3 , upregulated during starvation . Furthermore , the opposing effect of GLN3 deletion in the laboratory and vineyard strains makes it difficult to determine the precise role of GLN3 as a mediator of CLS alterations in the cascade of events initiated by the BUL2 polymorphism . Serving as a central link between nutrient availability and growth , TORC1 regulates many cellular processes including ribosome biogenesis , protein translation , autophagy and respiration [1] . During the examination of how telomere maintenance is affected by amino acid import , we discovered that ribonucleotide reductase ( RNR ) complex assembly during S-phase is modulated by the TOR-responsive transcription factor Gln3 , defining a novel downstream role for TOR in DNA replication . We found that increased Gln3 activity , conferred by the deletion of URE2 , upregulates Wtm1 , which , in turn , promotes nuclear retention of the small RNR4 subunit in the nucleus . Deletion of WTM1 restores cytoplasmic localization of the small subunits and partially rescues the telomere length defect of ure2Δ cells . TORC1 inhibition by rapamycin was previously associated with genotoxic stress sensitivity and inability to maintain high Rnr1 and Rnr3 levels in response to DNA damage [62] . Using telomere length as a phenotype , we have uncovered a role of TORC1-responsive transcription factor GLN3 in modulation of RNR assembly during S-phase in response to cellular amino acid availability . TOR-mediated control of DNA replication adds further to TORC1's role in coordinating nutrient availability , growth and cell division . What is the relevance of our observation to mammalian and human aging ? Both dietary restriction and inhibition of TOR activity have been linked to lifespan extension in mice [40] , [55] . At the same time , epidemiological studies in humans have found an association between longevity and long telomeres [9] , [10] . Because our study demonstrates that dietary restriction and consequent reduction in TOR activity lead to reduction of telomere length , it will be important to determine whether reduced signaling in response to dietary restriction through this highly conserved nutrient and growth related pathway also reduces telomere length in mammals .
Experiments were carried out using standard YPD media ( 2% glucose , 1% yeast extract , 2% peptone ) unless otherwise noted ( ie . ADCB assays ) . The strains used in this study , listed in Table S1 , are from either the S288c ( BY ) or RM11-1A ( RM ) S . cerevisiae backgrounds . The segregant library has been previously described [11] , except that AMN1 has been deleted in each of the segregants to facilitate single cell viability analysis . ( The RM allele of AMN1 confers clumpiness , which precludes single cell analysis , whereas the S288c allele of AMN1 was previously shown to create a loss of AMN1 function [48] ) . Gene deletion mutants were either from yeast ORF deletion collection or were created using standard PCR transformation methods . For allele replacement , we PCR-cloned a fragment containing 1 kb of the 3′ end of BUL2 and 1 kb BUL2 downstream sequence from either the BY or RM strain using a 5′ primer with an XhoI site ( 5′- GGCTCGAGGATTGATGATACCGCCAGCCAATCACC ) and a 3′ primer with a HindIII site ( 3′- GGCCAAGCTTGCGGGAAAAAGGCCAAACTCTACG ) . These fragments were inserted between the XhoI and HindIII sites in pRS406 , a vector containing URA3 . We used site-directed mutagenesis ( QuikChange II kit , Stratagene ) to introduce the L883F polymorphism into the BY BUL2 vector . Allele replacement strains were generated using the “pop-in/pop-out” gene replacement method with the linearized BUL2 vector [63] . BUL2 allele replacement strains were first screened by sensitivity to ADCB and then PCR-sequenced to confirm the desired BUL2 polymorphisms . For each strain , 1 µL of saturated culture was inoculated into 150 µL of YPD ( 2% glucose ) or buffered synthetic complete media [14] in 96-well plates . Plates were then incubated for 2 days at 30°C , at which point they were foil-sealed to prevent evaporation and kept at 30°C for the remaining time . Strains were examined in triplicate . To assay viability , 1 µl of each resuspended culture was harvested , diluted in water , spotted onto solid YPD media , and incubated for 24 hours at 30°C . Microcolonies and cells that had not divided were counted using a microscope , with the total number of events ( n>200 for each culture ) used as the denominator to determine viability percentage . Additionally , colony formation unit ( CFU ) assays was used to determine viability in select RM and BY strains . Comparison between CFU and microcolony values obtained show that the two assays are highly correlative ( R = 0 . 98 ) ( Figure S1 ) . Genome-wide linkage analysis of segregant data was performed using the publicly available R/qtl software . Effects of RM/BY allele inheritance in the segregants were examined using R ( box plots ) and Excel ( student's t-test ) . Initial ADCB toxicity assays were carried out using 25 µg/mL ADCB ( L-Azetidine-2-Carboxylic Acid , Sigma-Aldrich ) dissolved in SD media ( 1 . 9 g YNB , 0 . 5% ( NH4 ) 2SO4 , 2% dextrose ) supplemented with leucine ( 80 µg/mL ) , lysine ( 60 µg/mL ) , and uracil ( 20 µg/mL ) to compensate for the auxotrophies present in the segregant library . Cells were inoculated into 150 µL media in 96-well plate and incubated at 30°C . Segregant growth in ADCB was quantified using absorbance at OD660 after 17 hours in 30°C . BUL2 allele replacement spot assays were carried out on solid SD media of the same composition with 25 µg/mL ADCB . Genomic DNA was harvested from saturated 3 mL cultures using a phenol∶chloroform DNA extraction . Telomere lengths were evaluated as described in Gatbonton et al . [12]: genomic DNA was digested overnight with XhoI , resolved by gel electrophoresis ( 0 . 5% TBE , 0 . 9% agarose gel , run for 360 V•hr ) and transferred to Hybond-N membrane . Terminal restriction fragments containing telomeres were visualized using 32P-labeled probes amplified from the Y′ subtelomeric sequence . Total RNA was harvested from 20 mL logarithmic phase cultures in biological triplicate using the hot phenol method previously described by Schmitt et al . [64] . Three competitive hybridizations for each experimental group ( ure2Δ versus wildtype ) were performed using three separate cultures , and the log2 of the expression ratio was calculated for every ORF . To assess the intrinsic variation of expression levels for different ORFs , wildtype versus pooled wildtype hybridizations were performed using three separate cultures . Arrays used were spotted oligo probe arrays generated by the Fred Hutchinson Cancer Research Center Genomics Resource . Probability of overlap with BUL2RM-upregulated transcripts was calculated using the binomial probability formula . Yeast whole cell extracts from 5 mL logarithmic phase cultures were harvested using the NaOH protein extraction method previously used by Thaminy et al . [65] and Kushnirov [66] . Proteins were resolved using SDS-PAGE ( 10% polyacrylamide gel , 120 V for 90 minutes ) and transferred to a nitrocellulose membrane . Proteins of interest were probed with antibodies against actin ( 1∶1000 dilution , Neomarkers ) or HA ( 1∶5000 dilution , Covance ) and visualized using HRP-conjugated IgG antibodies ( 1∶1000 , Vector Laboratories ) . Wtm1 blot intensity was quantified using ImageJ and normalized to actin intensity . The Rnr4-GFP strain was obtained from the commercially available Invitrogen/UCSF GFP-tagged collection and genes were deleted using standard PCR transformation protocols . Cells from logarithmic phase cultures were harvested and fixed using paraformaldehyde , as previously described by Biggins et al . [67] . To visualize nuclei , fixed cells were incubated with 1 µg/mL DAPI for 1 hour , washed once and resuspended in sorbitol . Cells were sonicated before visualization and scoring . At least 200 events for both S-phase and G1 cells were scored for wildtype , ure2Δ , wtm1Δ and ure2Δwtm1Δ strains . At least 500 S-phase cells were scored for RM and BY BUL2 allele strains . Images were captured using a Nikon E800 fluorescence microscope . | Dietary restriction promotes longevity in many species , ranging from yeast to primates , and delays aging-related pathologies including cancer in rodent models . There is considerable interest in understanding how nutrient limitation mediates these beneficial effects . Much of what we have learned about the genetics of aging comes from studying isogenic model organisms , where the effects of single gene changes can be examined independently of other genetic alterations . In order to explore a broader spectrum of genetic variation and to gain insight into aging-related phenotypes as polygenic traits , we analyzed the chronological lifespan of 122 S . cerevisiae strains derived from a cross between laboratory and vineyard yeast strains . The major genetic locus controlling chronological lifespan was found to be identical to a previously mapped locus that controls telomere length . Identification of the responsible polymorphism in BUL2 , a gene involved in controlling amino acid permeases , allowed us to establish a previously unrecognized link among cellular amino acid intake , chronological aging , and telomere maintenance . While human epidemiological studies have linked shortened telomeres with increased mortality , it is unclear how these processes are connected . Our results suggest that , in yeast , reduced amino acid uptake and consequent reduced nutrient signaling extend chronological lifespan but reduce telomere length . |
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We report the identification , functional expression , purification , reconstitution and electrophysiological characterization of a novel cation channel ( TcCat ) from Trypanosoma cruzi , the etiologic agent of Chagas disease . This channel is potassium permeable and shows inward rectification in the presence of magnesium . Western blot analyses with specific antibodies indicated that the protein is expressed in the three main life cycle stages of the parasite . Surprisingly , the parasites have the unprecedented ability to rapidly change the localization of the channel when they are exposed to different environmental stresses . TcCat rapidly translocates to the tip of the flagellum when trypomastigotes are submitted to acidic pH , to the plasma membrane when epimastigotes are submitted to hyperosmotic stress , and to the cell surface when amastigotes are released to the extracellular medium . Pharmacological block of TcCat activity also resulted in alterations in the trypomastigotes ability to respond to hyperosmotic stress . We also demonstrate the feasibility of purifying and reconstituting a functional ion channel from T . cruzi after recombinant expression in bacteria . The peculiar characteristics of TcCat could be important for the development of specific inhibitors with therapeutic potential against trypanosomes .
Trypanosoma cruzi is a unicellular parasitic eukaryote and the etiologic agent of Chagas disease , which currently affects millions of people in North , Central and South America , and is becoming frequently diagnosed in non-endemic countries [1] , [2] . T . cruzi has a complex life cycle involving insect and mammalian hosts and different morphological and functional stages: epimastigotes and metacyclictrypomastigotes in the insect vector , and intracellular amastigotes and bloodstream trypomastigotes in the mammalian host . During its life cycle , the parasite finds extreme fluctuations in environmental conditions to which it must adapt in order to survive . A wide range of ionic concentrations , osmolarities and pHs are major challenges to cope with when it transits through the vector gut to the excreta , and from this highly concentrated environment to the interstitial fluid of the mammalian host . Particularly , the concentration of K+in the vector can vary between 40 to 358 mM depending on the feeding cycles of the insect [3] , and from 5 to 140 mM between the extra and intracellular environments of the mammalian stages . In previous studies [4] , [5] we demonstrated that a plasma membrane H+-ATPase is the major regulator of intracellular pH ( pHi ) in all stages of T . cruzi . However , in contrast to epimastigotes , whose pHi is not affected by extracellular cations [4] , trypomastigotes possess a cation-dependent pHi control . We proposed [5] that , as occurs in plants [6] , [7] and other protists [8] , in these trypomastigote stages an inward rectifier K+ channel functions in K+ uptake dissipating the plasma membrane potential ( Vm ) generated by the H+-ATPase thereby increasing its efficiency . This putative channel could be blocked bythe addition of Cs2+ or Ba2+ [5] . The plasma membrane H+-ATPase also plays a significant role in the regulation of Vm in all stages of T . cruzi [9] . In contrast to epimastigotes the Vm of trypomastigotes is markedly sensitive to extracellular Na+ and K+ . In support of the presence of a K+permeable channel , the Vm is hyperpolarized by K+-free buffer in trypomastigotes [9] . Interestingly , trypomastigotes are able to maintain a negative Vm in a K+-rich buffer at acidic pH , conditions that they encounter when they enter the parasitophorous vacuole [9] . This is differentfrommammalian cells , which are usually depolarized by either acidic or high extracellular K+ concentrations . Amastigotes , in contrast , appear to be impermeable to K+ in agreement with the high intracellular K+ environment in which they live [9] . The marked differences in the regulation of Vμ in trypomastigotes as compared to amastigotes suggest that during transformation to amastigotes , trypomastigotes undergo significant changes in their ion transport mechanisms . However , the nature of these changes and the molecular identity of K+permeable pathways are unknown . K+ channels are members of one of the largest and most diverse families of membrane proteins , widely described from bacteria to humans [10]–[12] . Their roles include plasma membrane potential maintenance , pHi and cell volume regulation , excitability , organogenesis and cell death [13]–[16] . From the structural point of view , they can be divided into two main groups: channels containing six transmembrane domains , including in this category voltage-dependent K+ channels and calcium-activated K+ channels [17] , and channels with only two transmembrane domains , such as the inward rectifier K+channels ( Kir channels ) [18] and the widely described bacterial channel KcsA [10] . As a general rule , a functional K+ channel is formed by interaction of four pore-forming subunits interacting through a conserved tetramerization domain . Association with other proteins , interaction with surrounding lipids and post-translational modifications generate a functional diversity that exceeds the predictions based solely on the number of identified genes [17] . High yield recombinant expression and purification of functional ion channels has been technically very difficult and restricted to prokaryotic channels until recently [19] . In this work we demonstrate the feasibility of purifying a functional cation channel from T . cruzi after recombinant expression in bacteria . We report the molecular and electrophysiological characteristics of this inwardly rectifying K+permeable channel and the changes occurring in its localization during the parasitetransformation into different developmental stages . Our results indicate that T . cruzi has the unexpected ability to change the localization of this cation channel to adapt to different environments to which it is exposed in its different developmental stages .
We searched for K+channels in the TriTryp database ( http://tritrypdb . org/tritrypdb/ ) and found two genes encoding for putative voltage-dependent K+channels in T . cruzi ( Tc00 . 1047053511301 . 140 and Tc00 . 1047053507213 . 30 ) . The sequences showed 98% identity between them and likely correspond to alleles of the same gene ( TcCat ) . The orthologous identified in T . brucei ( Tb927 . 10 . 16170 ) and L . major ( LmjF19 . 1620 ) shared 64% and 55% amino acid identity respectively ( Fig . S1A ) . Structural analysis ( TopPred ) ( Text S1 ) predicted two transmembrane domains between amino acids 77–97 and 169–189 and a tetramerization domain at position 5–73 ( Pfam02214 ) that is the only region with similarity to other K+channels like Kv4 . 3 ( Fig . S1B ) . The ORF predicts a 297 amino acid protein with an apparent molecular weight of 34 kDa . No significant identity was found with well-characterized bacterial channels like KcsA or with mammalian ( Kir channels ) and bacterial inward-rectifiers ( Fig . S1C ) . Interestingly , no conserved K+ channel signature sequence [T-X-G-Y ( F ) -G] [20] was identified in TcCat , raising the question of the ion selectivity of this channel . Other important features of TcCat are the presence of longer mode 2 interacting phospho-motif for 14-3-3 proteins at positions 128–134 ( RHALTIT ) , putative phosphorylation sites at serines 103 , 190 , 214 and 248 and N-glycosylation sites at positions 181 ( NGTA ) , 228 ( NFTF ) and 286 ( NSTR ) , that can be relevant for the regulation of the activity and the interaction with other proteins . TcCat localization was analysed by indirect immunofluorescence using affinity-purified antibodies against the recombinant protein . In trypomastigotes , the channel has a clearly defined punctuate pattern along the flagellum ( Fig . 1A ) . In epimastigotes ( Fig . 1B ) , TcCat also has a peripheral punctuated pattern with some apparently intracellular labeling . To further evaluate whether the punctuate localization could be due to labeling of patches of plasma membrane and not intracellular vesicles we performed immunolocalization in permeabilized and non-permeabilized cells . In both trypomastigotes and epimastigotes TcCat was detected , at least in part , exposed to the cell surface ( Fig . S2 ) . In amastigotes that were spontaneously released to the supernatant of infected L6E9 myoblasts the channel showed plasma membrane localization ( Fig . 1C ) . However , in intracellular amastigotes TcKCat seems to be confined to a spot that could be the remaining short flagellum ( Fig . 1D ) . This change in localization is consistent with a role of TcCat in K+ uptake , which would become less important in the intracellular environment rich in K+ . In agreement with a developmental regulation of TcCat expression , labeling decreased considerably in metacyclictrypomastigotes ( Fig . 1E ) . Immunoelectron microscopy analysis confirmed the patched distribution of TcCat in tissue culture-derived trypomastigotes along the flagellar attachment zone ( Figs . 1F and 1G ) . The association of ion channels in clusters has been described previously [21]–[23] and seems to be related with preferential targeting to specific membrane lipid microdomains or lipid rafts , which are known to be more abundant in the flagellar membrane of trypanosomes [24] . Expression of TcCat was verified by western blot analysis ( Fig . 1H ) confirming the presence of the channel in all three stages of the parasite . The native protein detected in the parasites has an apparent molecular mass of 43 kDa , slightly higher than that predicted by the ORF . This difference could be due to post-translational modifications as can be expected from the presence of several putative phosphorylation and N-glycosylation sites . Densitometry analysis using α-tubulin as a loading control as well as Coomassie blue staining ( Fig . S3 ) indicated that the level of expression is similar in trypomastigotes , amastigotes and epimastigotes ( Fig . 1I ) , as suggested by IFA . We evaluated the change in the localization of TcCat by IFA during the differentiation in vivo . At 5 h post-infection of mammalian cells , TcCat is already detected at a single intracellular spot both in parasites with trypomastigote-like morphology ( Fig . 2A , yellow arrows ) and in rounded amastigote-like cells ( Fig . 2B , red arrows ) . At 24 and 48 h post-infection ( Figs . 2C and 2D ) TcCat remains intracellular in the replicating amastigotes , close to the flagellar pocket . In extracellular trypomastigotes , 96 h post-infection , TcCat was always localized at the plasma membrane ( Fig . 1A ) . We studied the expression of TcCat during the differentiation of trypomastigotes to amastigotes in vitro . At 30 min after induction of differentiation in vitro at pH 5 . 0 , staining with antibodies against TcCat was at the tip of the flagellum ( Fig . 2E ) , and this single spot labeling was maintained even when the cells rounded up to transform into amastigotes ( Figs . 2F–H ) . Potassium uptake defective S . cerevisiae mutants ( Δtrk1 , Δtrk2 , Δtok1 ) were used to investigate the K+ influx ability of TcCat ( see Text S1 ) . These mutants depend on high extracellular K+ concentration for their growth as they only have the non-specific cation uptake mechanism , termed NSC1 , for growth [25] . Mutants were kept in defined medium ( SC ura- ) supplemented with 100 mMKCl , pH 5 . 8 . TcCat expression was induced by switching the carbon source and the channel was rapidly detected on the yeast surface by immunofluorescence analysis with anti-TcCat antibodies ( Fig . 3B ) . After 2 h ( Fig . 3C ) , yeasts were collected by centrifugation and placed in standard SC ura-medium without KCl . Otherwise the presence of high K+ concentration was toxic upon induction of TcCat expression . The channel was expressed on the yeast surface for up to 72 h at high levels , although , at 24 h , some labeling could be observed in the periphery of the yeast vacuole , probably due to recycling or degradation ( Fig . 3D ) . Control cells were transformed with empty vector pYES2 . A monoclonal antibody against the 69-kDa subunit of the vacuolar H+-ATPase was used as a control of proper permeabilization ( Fig . 3A–D ) . TcCat expression in complemented yeasts was also verified by western blot analysis using anti-TcCat antibodies ( Fig . 3E ) . Two bands were detected , one that corresponds to the predicted molecular weight of the protein product ( about 35 kDa ) and a second band of approximately 45 kDa similar to that of the native protein in the parasites ( Fig . 1H ) , suggesting that post-translational modifications also occurred in yeasts . Functional complementation and restoration of the normal growth phenotype was achieved when culturing the mutant yeast in serial dilutions in SC ura-galactose agar plates without addition of KCl . Under these conditions , mutants transfected with vector alone ( MpYES2 ) were not able to grow when diluted to 10−1 or more , while mutants complemented with TcCat ( MC ) showed no significant difference in growth as compared with wild type yeast ( WT ) ( Fig . 3F ) . The results indicate that TcCat is indeed a K+ conductive pathway able to functionally complement a heterologous system . The activity of TcCat was detected in patches excised from cell-size giant liposomes ( inside-out configuration ) containing the purified recombinant protein ( Text S1 , Figs . S4 and S5 , and Table S1 ) . Currents from liposomes containing only asolectin were recorded as control ( Fig . S6A , black squares ) , showing a significant lower level compared with currents from liposomes containing purified TcCat ( Fig . S6A , red circles ) . Currents were recorded under symmetrical conditions in the absence of Mg2+ , unless stated otherwise , with bath and pipette solutions containing 140 mMKCl , 10 mMHepes-K , pH 7 . 4 . Single channel currents were observed when an increasing voltage-pulse protocol between −80 to +80 mV was applied ( Fig . 4A ) . The current-potential relationship for the single channel was not linear in the presence of Mg2+ , as expected for an inward rectifier channel . The chord conductance ( γ ) ( Fig . 4B , open circles ) calculated under symmetrical KCl in the absence of Mg2+was 77±4 pS and 59±2 pS at −80 and +80 mV , respectively ( n = 14 ) indicating a slight intrinsic rectification . Although no significant reduction in the current was observed at positive potentials in the presence of Mg2+ , a significant increase in the inward current was evident at negative potentials in the presence of 1 mM MgCl2 in the bath solution ( Fig . 4B , black squares ) , with unitary conductances of 122±7 pS and 56±3 pS at −80 and +80 mV , respectively ( n = 13 ) . These results suggest that the mechanism of blockage by Mg2+ is different from the one described for inward rectifier K+ channels . The unitary level of current was frequently observed in clusters , as shown in Fig . 4C where at least two channels could be detected , opening and closing independently . The histograms showncorrespond to the unitary current of one or two channels at the indicated voltages ( Fig . 4C ) . This recorded activity agrees well with the localization in patches described above . Important variations in the open probability were observed in recordings from different days . When 14 independent experiments were analyzed , the open probability was not significantly sensitive to voltage , with values of 0 . 26±0 . 04 and 0 . 2±0 . 04 at −80 and +80 mV , respectively ( Fig . 4D ) . The cationic nature of the TcCat conductive properties was verified applying a voltage-ramp protocol from −80 to +80 mV under symmetrical conditions ( Fig . 5A , black line ) or replacing the bath solution for a non-permeantcation ( 140 mM NMDG-Cl , 10 mMHepes-K , pH 7 . 4 ( Fig . 5A , gray line ) . A shift in the reversal potential of the current ( ΔVrev ) was observed from 0 mV to −54±6 mV ( n = 4 ) , close to the theoretical Vrev calculated for K+ under those conditions ( −70 mV ) . Replacement of the bath solution for buffered 340 mMKCl induced a ΔVrev of +39±1 mV ( n = 5 ) with a theoretical calculated Vrev of +22 mV . These results suggest that TcCat preferentially permeates K+ . To calculate the selectivity for cations over anions of TcCat , we applied similar voltage ramp protocols but replacing the bath solution for a non-permeant anion ( 140 mM K-gluconate , 10 mMHepes-K , pH 7 . 4 ) . A ΔVrev of −8 . 7±0 . 4 ( n = 10 ) was measured under asymmetrical conditions ( Fig . 5A , red line ) . Based on the bi-ionic equation ( see Equations under Text S1 ) , the calculated permeability ratio for K+ over Cl− was 5 . 9±0 . 5 ( n = 10 ) , indicating a preferential cation permeability but with weak selectivity filter , in agreement with the sequence data . In order to study TcCat selectivity for monovalent cations , a voltage-ramp protocol was applied under symmetrical condition for K+ ( Fig . 5B , black line ) or replacing the bath solution for 140 mMXCl , X being different cations . Under bi-ionic conditions , the shift in the Vrev indicates the relative permeability of X+ respect to K+ ( Fig . 5B ) . The permeability sequence obtained was: K+>Cs+>NH4+>Rb+>Na+>Li+>NMDG+ ( Fig . 5C ) that corresponds to Eisenman sequence IIIa [26] . This represents a selectivity of about 2 . 5 for K+ over Na+ , which may indicate that TcCat is a potential conductive pathway for both physiological ions . This biophysical characterization shows that TcCat is , indeed , a channel permeable to K+ and that shows inward rectification . In the presence of Mg2+ , the unitary conductance is , as expected , higher at negative than at positive potentials and the open probability is not voltage-dependent . The effect of divalent cations was evaluated by adding controlled concentrations of Ba2+ , Ca2+or Mg2+to the bath solution . Fig . 6A shows that in the presence of 10 mM BaCl2 ( red line ) or 10 mM CaCl2 ( green line ) a significant decrease in the total current can be observed compared with the control ( black line ) . No important shift in the Vrev was recorded , indicating the low permeability for these ions . A consistent decrease in the total current was observed when a voltage-step protocol was applied ( Fig . 6B ) in the presence of lower concentrations of the divalent cations , with a more remarkable effect for Mg2+ ( blue inverted triangles ) . A concentration-dependent effect was observed for Ba2+ and Ca2+ when applying a voltage-step protocol in the presence of controlled concentrations of both ions ( Fig . S6 C and D ) . The effect of Ba2+ over the leak through asolectin vesicles was evaluated comparing the normalized current in the presence of different concentrations of the divalent ion on empty liposomes ( Fig . S6B , black circles ) or liposomes containing TcCat ( Fig . S6B , black squares ) . In the presence of 1 mM BaCl2 the residual current at −80 mV ( Fig . S6B , upper panel ) is about 55% in empty liposomes while it is close to 30% in liposomes containing TcCat , indicating that a percentage of the current through the channel is sensitive to the presence of the divalent cation . Similar results are obtained at +80 mV ( Fig . S6B , lower panel ) . Based on the dose-dependent blockage , the calculated inhibition constants ( Ki ) for Ba2+ were ( in mM ) : 0 . 54±0 . 08 and 0 . 63±0 . 06 at −80 and +80 mV , respectively , with no significant dependency on the applied voltage ( n = 3 independent experiments ) . The blockage by Ca2+ required higher concentration , with calculated Kis ( in mM ) of 5 . 2±0 . 3 and 4 . 5±0 . 2 at −80 and +80 mV , respectively . In all cases , a residual current was observed , even at 10 mM divalent cation concentration , indicating a leaking current or a partial blockage ( Fig . S6D and F ) . Conventional K+ channel blockers were also tested ( Fig . 6C ) , with no significant effect for tetraethylammmonium ( TEA ) up to 10 mM ( n = 3 ) and a 50% reduction in the total current for 4-aminopyridine ( 4-AP ) at 1 µM ( n = 4 ) . Importantly , a blockage effect was observed in the presence of the anti-TcCat antibody when added to the bath solution at a concentration of 0 . 12 µg/µl and at holding potentials of −80 and +80 mV ( Fig . 6D ) . No significant effect was observed when the same concentration of pre-immune sera was applied to the preparations ( Fig . 6D , pre-immune ) . As mentioned before , an inward-rectifier K+ channel seems to be involved in the maintenance of plasma membrane potential , intracellular pH and osmoregulation [5] , [9] . To assess the role of TcCat on some of these processes we evaluated the localization of the protein in T . cruzi epimastigotes and trypomastigotes under osmotic stress . Under isosmotic conditions , the channel is localized at the plasma membrane , in a punctuate pattern , with some intracellular staining more evident in epimastigotes ( Fig . 7A and B , Iso ) . When epimastigotes were placed under hyperosmotic stress , maintaining the same ionic concentrations , TcCat almost completely translocated to the plasma membrane ( Fig . 7A , Hyper ) . Remarkably , in trypomastigotes , after 30 sec under hyperosmotic stress , TcCat disappeared from the cell surface of the parasites ( Fig . 7B , Hyper ) . No intracellular accumulation of the protein was observed suggesting that the protein is released to the extracellular medium , probably by shedding mechanisms previously described for other T . cruzi surface proteins [27] . To prove this hypothesis , the supernatant of parasites under different osmotic conditions were precipitated and evaluated by western blot analysis . In trypomastigotes under hyperosmotic stress TcCat was detected in the supernatants ( Fig . S7A ) . That was not the case for trypomastigotes under isosmotic or hyposmotic conditions ( Fig . S7A ) or for epimastigotes under similar treatments ( Fig . S7B ) . Parasites overexpressing GFP were used as a control to rule out lysis of the parasites as a mechanism of release of TcCat ( Fig . S7A ) . Addition of Ba2+ or 4-AP , at concentrations that inhibit TcCat activity , could prevent the change in the localization of the protein in trypomastigotes ( Fig . 7C ) suggesting that the mechanism of elimination is linked to the sensing of the K+ concentration . No differences were observed in TcCat localization when the parasites were under hyposmotic stress ( Fig . 7A and B , Hypo ) . Morphologically , the typical response toosmotic stress can be detected in the parasites , with a more accentuated change in the shape of epimastigotes compared with trypomastigotes ( Fig . 7A and 7B ) . Although the osmotic stress response is sensitive to tubulin de-polymerizing agents [28] , TcCat translocation was not blocked or modified by treatment with trifluralin ( 500 µM ) or chloralin ( 10 µM ) ( Fig . S8 ) . The potential role of TcCat in osmoregulation was further evaluated in the parasites under osmotic stress . Interestingly , the shrinkage of trypomastigotes under hyperosmotic stress was significantly reduced in the presence of 1 mM BaCl2 ( Fig . 7D , open triangles ) or 100 µM 4-AP ( Fig . 7D , black squares ) as compared to the control ( Fig . 7D , open diamonds ) suggesting a role of K+ influx through TcCat during cell volume decrease .
In T . cruzi [9] as well as in T . brucei [29]–[31] and other protists like Toxoplasma gondii [32] and Pneumocystis carinii [33] , the membrane potential is not dependent on K+ but rather is proton driven . The permeability to K+in the plasma membrane is not predominant and it varies depending on the developmental stage of the parasite . We postulated previously that an inward rectifier K+ channel was involved in K+ uptake in T . cruzi trypomastigotes , dissipating the membrane potential generated by a plasma membrane located H+-ATPase and thereby facilitating its function in controlling pHi [5] . In this work , we demonstrate that a gene encoding a protein with sequence homology to K+ channels is present in the T . cruzi genome ( TcCat ) and can complement yeast deficient in K+transporters , providing genetic evidence that it encodes a functional K+channel . In addition , we demonstrate by patch clamping giant liposomes containing recombinant TcCat that this channel has several distinct characteristics that differentiates it from mammalian inward rectifier K+ ( Kir ) channels . Furthermore , this channel has the unexpected ability of translocating to different cellular compartments in response to environmental stress . Examination of TcCat indicates that the only well conserved sequence of the protein compared with other K+ channels is its tetramerization domain , which is probably the reason why it was first annotated as a voltage-dependent K+ channel ( TriTrypDB . org ) . The same domain is present in all voltage-dependent ion channels , including K+ , Na+and Ca2+channels . BLASTP analysis of TcCat shows as only significant hits voltage-dependent K+channels from different organisms ( e . g . E value: 3−08 to 6−06 for Xenopus ) . We need to consider that the overall sequence identity between ion channels is low . ClustalW alignments show that the identity between KcsA and H . sapiens Kv4 . 3 ( both voltage-dependent K+channels ) is only 23% . Same analysis indicates that TcCat and H . sapiens Kv4 . 1 , and Kv4 . 3 are 19 and 20% identical , respectively . When inward-rectifier K+channels are compared , similar results are found , with 20% identity between H . sapiens Kir2 . 1 and KirBac1 . 1 and 23% identity between Kir2 . 1 and KirBac2 . 1 . Moreover , when the comparison is established considering other evolutionary distant organisms , like C . elegans , the identity values obtained are always close to 20% . For other type of channels , like Na+and Ca2+channels , the conservation is even lower , underscoring the relevance of functional validation to establish the function of putative ion channels . TcCat lacks the conserved K+ channel signature sequence [T-X-G-Y ( F ) -G] [20] , which is compatible with the relatively low selectivity of the channel for K+ over Na+ and the possibility that TcCat can potentially transport both ions although the relative permeability ratio PNa/PK is lower than the values previously reported for other non-selective cation channels [34] , [35] . This is in agreement with results showing that either K+ or Na+ ( at high concentrations ) are important for pHi control in trypomastigotes under acidic conditions [5] . Unfortunately , there are no structural data identifying amino acids that form the pore in non-selective cation channels and the sequence TLESW , recently identified as the selectivity filter for Na+channels [36] , is not present in TcCat . The extracellular pore-forming region of TcCat has a short sequence ( TFGADG in TcCat and TYGADG in TbCat and LmCat , Fig . S1 ) characterized by the presence of negatively charged amino acids , and a glycine residue , which could be involved in K+ selectivity , as occurs in HKT transporters from plants [37] and bacteria [38] , and that is also conserved in the TbKHT1 K+ transporter of T . brucei [39] . This selectivity filter would be more similar to the low conserved pores described for some bacterial KirBac [40] or with the mutation in the pore of Kv3 . 1 in weaver mice that turns it into a non-selective cation channel [41] , [42] . The presence of a distinct selectivity filter in TcCat could be important for the development of specific inhibitors with therapeutic potential against trypanosomes . In bloodstream trypomastigotes TcCat is exposed to the cell surface making it accessible to blockade by pharmacologic agents . Previous investigators have used yeast strains carrying trk1Δ and trk2Δ deletions for complementation with inward-rectifying K+ channels from a variety of organisms [43]–[45] . However , it has been indicated that many inwardly rectifying K+ channels ( as occurs with TcCat ) are inhibited by high concentrations of external divalent cations and that to analyze heterologously expressed K+ channels in yeast it is desirable to reduce the concentration of external divalent cations . These conditions , however , favor the activity of the non-specific uptake system NSCI [46] . On the other hand , growth at the low pH required for mutants carrying the trk1 , trk2 , and tok1 deletions used in this work inactivates NSC1 [25] . The complemented mutants obtained in this work will therefore provide a versatile genetic system for further studies of the assembly and composition of TcCat . There is no previous electrophysiological description of the biophysical properties of ion channels in trypanosomatids . There are several limitations for the characterization of ion channels in motile unicellular organisms . Small size , irregular shape and active motility represent a problem for direct recording . The presence of a strong subpellicularcytoskeleton beneath the plasma membrane makes extremely hard to excise the patch and obtain a seal of suitable quality for single-channel recordings . The alternative of a cell-attached configuration is limited by the noise that the motility of the parasite introduces . We failed in many attempts to do direct patch-clamp in the parasites . Methods that decrease the motility like low temperature or use of actin-depolymerizing agents were considered but it can also be argued that they change the physiological conditions of the cell making the results obtained subject to discussion . Based on these facts we decided to use a reconstituted system in giant liposomes for ion channel characterization that has been extensively validated [19] , [35] , [47]–[58] . Although the mechanism by which the proteins are inserted in the liposomes is unknown , the orientation in which this occurs is not random . Reconstitution of acetylcholine receptors [47] , glycine receptors [48] , glutamate receptors [49] , KcsA [52] , [59] and KirBac1 . 1 [57] indicate that the proteins are oriented “right-side out” , meaning with the intracellular side facing the bath . The direction of the rectification observed for TcCat ( Fig . 4B ) suggests that this channel is also oriented with the intracellular side facing the bath . The low conservation of the structure , particularly the sequence of the selectivity filter , can explain the functional differences observed in TcCat compared with other cation channels . Characterization of K+channels , although detailed and exhaustive in some cases , is mainly limited to bacterial ( KcsA ) and mammalian channels , with some particular cases of model organisms like Drosophila and C . elegans . In Fasciola hepatica [60]and Dictyosteliumdiscoideum [61] the presence of K+ channels with relative permeability ratio PK/PCl of 5 and 7 , respectively , have been previously reported , suggesting that the selectivity of some channels in these organisms is not as high as for bacterial or mammalian K+channels . Electrophysiological characterization of TcCat by patch clamping of giant liposomes indicates characteristics of K+ permeable channel with inward rectification characterized by non-linear current potential relationship for the single channel conductance . Therectification is weak , similar to what has been reported for KirBac1 . 1 and Kir1 . 1 [20] , [57] . TcCat unitary conductance is significantly higher at negative than at positive potentials only in the presence of Mg2+ , with no important differences in the outward currents suggesting a different mechanism of TcCat blockage by Mg2+ . Although the residue responsible for the rectification in mammalian Kir channels ( 171D ) is conserved in TcCat ( Fig . S1C ) , it is followed by a positively charged amino acid ( 171His ) , which could potentially interfere with the binding of Mg2+ to the aspartic acid residue . Site-directed mutagenesis studies are in our future plans to address this and other structural properties . Overall , the open probability did not show significant voltage-dependency . We have to mention that important differences were observed with different preparations . This could be explained by variations in the way that channels associate into clusters when reconstituted in liposomes . This type of behavior has been previously reported when purified KcsA was recorded in giant liposomes ( 52 ) . TcCat also differs from other inward rectifier K+ channels in its low selectivity for K+ over Na+ , suggesting that it can transport either cation , and in its pharmacology . Blockers most commonly used for Kir channels are Ba2+ and Cs+ while TEA and 4-AP are known as inhibitors of Kv channels but have little effect on Kir channels [20] . TcCat was much less sensitive to Ba2+ than classical Kir channels ( Ki 540–630 µM as compared to 13–390 µM for Kir2 . x channels , [20] ) and was insensitive to Cs+ , when assayed in giant liposomes . Cs+ , however , was as effective as Ba2+ in decreasing pHi of intact trypomastigotes [5] . TcCat is not permeable to Ca2+ , even more it can be blocked by it at similar concentration that we have previously reported as inhibitory for non-selective cation channels from T . cruzi epimastigotes membranes [35] . Although Ca2+usually does not block inward-rectifier channels , structural and functional differences observed in TcCat make it a non-canonical inward rectifier . KirBac1 . 1 is also a weak rectifier K+channel that has been demonstrated to show several atypical behaviors compared with mammalian Kirs like inhibition by Ba2+and Ca2+ [62] , polyamine insensitivity [57] and blockage ( instead of activation ) by PIP2 [63] . In addition , 4-AP had inhibitory effect on TcCat total current at relatively low concentrations ( 1 µM ) . It has been reported that although free-living prokaryotes have recognizable K+ channel genes , most but not all , parasitic prokaryotes have no K+ channel genes since they live in the K+-rich environment of their host cells [64] . The eukaryote T . cruzi , which has both intracellular and extracellular stages has solved the problem of having a K+ channel while in a K+-rich environment by sequestering it to an intracellular location in intracellular amastigotes . This sequestration starts very early during differentiation of trypomastigotes upon acid pH-stimulation . Interestingly , if the amastigotes are released as such to the extracellular medium , poor in K+ , the channel reappears at the surface of the cells . Translocation of the channel to the surface also occurs in epimastigotes submitted to hyperosmotic stress suggesting a role for this channel in the recovery from this type of stress . Although a number of mechanisms are involved in the control of localization of K+channels in other cells [20] , there is no precedent for the rapid translocation of TcCat that occurs when the cells are submitted to acidic pH ( trypomastigotes ) , hyperosmotic ( epimastigotes ) , or extracellular stress ( amastigotes ) . Rapid phosphorylation/dephosphorylation changes could be involved in this translocation , as occurs with Kv4 . 2 [65] and ROMK [66] . The two-pore K+ channels K2P 3 . 1 and K2P 9 . 1 cell surface destination is also dependent on phosphorylation which regulates the interaction with 14 . 3 . 3 proteins [67] . Ιν αδδιτον , the localization of TcCat in the flagellar membrane of trypomastigotes suggests a role for this channel in the modulation of flagellar motility and sensing . In this regard , K+ channels are required to modulate the motility of ciliates and sperm cells [68] and Ca2+ channels located in the flagellar membrane of T . brucei are important for flagellar attachment and intracellular signaling [69] . In conclusion , we identified and characterized , at the molecular and biochemical levels , an novel inward-rectifier cation channel in T . cruzi with electrophysiological characteristics different from other Kir channels and that has the surprising ability to change its cellular localization when cells are exposed to different environmental stresses . In addition we have obtained yeast mutants that will provide a useful genetic system for studies of the assembly and composition of the channel and we demonstrated the feasibility of purifying a functional ion channel from T . cruzi after recombinant expression in bacteria .
The entire open reading frame of TcCat was amplified by PCR with the primers 5′-CGGGATCCATGAGAAGGCGGGCCGTC-3′ and 3′-AACTGCAGTTAATGCGCTCTCCATATGTC -5′ introducing restriction sites for BamHI and PstI ( underlined ) . PCR product was cloned into pGEM-T easy ( Promega ) and verified by automated sequencing . Cloned product was digested with restriction enzymes and ligated into pQE80L ( Qiagen ) expression vector . Expression of the recombinant protein in E . coli pLysS strain was induced with 0 . 5 mM isopropyl-β-D-thiogalactopyranoside ( IPTG ) overnight at 37°C . His-tagged recombinant protein was purified under denaturing conditions with His-Bind cartridges ( Novagen ) . Purified product was separated by SDS-PAGE , stained with Coomasie blue and the corresponding band was excised from the gel and used as immunogen to obtain a rabbit polyclonal antibody against TcCat at CocalicoBiologicals , Inc ( Reamstown , PA ) . For immunofluorescence microscopy , parasites were fixed in PBS , pH 7 . 4 , with 4% paraformaldehyde , adhered to poly-lysine coverslips , and permeabilized for 3 min with PBS , pH 7 . 4 , containing 0 . 3% Triton X-100 . Permeabilized cells were treated for 30 min at room temperature with 50 mM NH4Cl and blocked overnight with 3% BSA in PBS pH 8 . 0 . Purified polyclonal antibody against TcCat ( dilution 1∶250 ) was incubated for 1 h at room temperature . Goat α-mouse and goat α-rabbit Alexa conjugated secondary antibodies ( 1∶2 , 000 ) were incubated for 1 h at room temperature . DNA-containing organelles were stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( 5 µg/ml ) . For TcCat immunolocalization in intracellular amastigotes , the cells were grown in coverslips and fixed at different times post-infection in cold methanol for 20 min . Immunolocalization in non-permeabilized parasites was done as described omitting the permeabilization step . A monoclonal antibody against T . brucei phosphate pyruvate dikinase ( PPDK ) ( glycosomal marker ) ( a gift from Frédéric Bringaud , Université Bordeaux Segalen , France ) was used as a permeabilization control . Differential interference contrast ( DIC ) and direct fluorescence images were obtained by using an Olympus IX-71 inverted fluorescence microscope with a PhotometrixCoolSnapHQ charge-coupled device camera driven by Delta Vision softWoRx3 . 5 . 1 ( Applied Precision , Issaquah , WA ) . This same software was used to deconvolve and process the final images . The figures were built by using Adobe Photoshop 10 . 0 . 1 ( Adobe System , Inc . , San Jose , CA ) . For western blot analysis , T . cruzi epimastigotes , amastigotes and trypomastigotes were collected by centrifugation at 1 , 600× g for 10 min , washed twice in PBS , pH 7 . 4 , and resuspended in modified RIPA buffer ( 150 mM NaCl , 20 mM Tris-Cl pH 7 . 5 , 1 mM EDTA , 1% SDS and 0 . 1% Triton X-100 ) containing protease inhibitor cocktail ( 2 mM EDTA , 2 mMphenylmethylsulfonyl fluoride ( PMSF ) , 2 mMtosylphenylalanylchloromethyl ketone ( TPCK ) , 0 . 1 mMtrans-epoxysuccinyl-L-leucylamido ( 4-guanidino ) butane ( E64 ) and Sigma P8340 protease inhibitor cocktail , 1∶250 ) . Total homogenate of each sample were separated by SDS-PAGE . Proteins were transferred onto nitrocellulose membranes and blocked overnight with 5% nonfat dry milk in PBS-0 . 1% Tween 20 ( PBS-T ) . Blotting was done with α-TcCat ( 1∶5 , 000 ) and goat α-rabbit horseradish peroxidase conjugated antibodies ( 1∶20 , 000 ) for 1 h at room temperature and developed with ECL reagent . Membranes were stripped with 62 . 5 mMTris-HCl , pH 6 . 8 , 2% SDS , 1% β-mercaptoethanol at 50°C for 30 min , extensively washed in PBS-T and incubated with monoclonal α-tubulin ( Sigma ) and goat α-mouse horseradish peroxidase conjugated antibodies ( 1∶10 , 000 ) as a loading control . Densitometric analysis of 4 independent experiments was performed with Alfa-Imager software . T . cruzi trypomastigotes and epimastigotes at log phase of growth ( 3 days ) were collected at 1 , 600× g for 5 min , washed twice in PBS and resuspended in isosmotic buffer ( 64 mMNaCl , 4 mMKCl , 1 . 8 mM CaCl2 , 0 . 53 mM MgCl2 , 5 . 5 mM glucose , 150 mM D-mannitol , 5 mMHepes-Na , pH 7 . 4 , 282 mosmol/L ) at a cell density of 1×108/ml . Aliquots of 5×106 parasites were placed in tubes and 500 µl of either hyposmotic ( 64 mMNaCl , 4 mMKCl , 1 . 8 mM CaCl2 , 0 . 53 mM MgCl2 , 5 . 5 mM glucose , 50 mM D-mannitol , 5 mMHepes-Na , pH 7 . 4 , 177 mosmol/L ) or hyperosmotic buffer ( 64 mMNaCl , 4 mMKCl , 1 . 8 mM CaCl2 , 0 . 53 mM MgCl2 , 5 . 5 mM glucose , 500 mM D-mannitol , 5 mMHepes-Na , pH 7 . 4 , 650 mosmol/L ) were added . TcCat blockers were added at the indicated concentrations to the corresponding buffers . Cells were fixed at different times after osmotic stress by adding same volume of 8% paraformaldehyde in PBS , pH 7 . 4 , and immunofluorescence analysis was performed as described before . Relative cell volume changes after osmotic stress were measured by light scattering method [71] . Briefly , the cells were washed twice in PBS and resuspended at a density of 4×108 cells/ml in isosmotic buffer . Aliquots of 4×108 parasites were distributed in 96 well plates and an appropriate volume of hyperosmotic buffer was added to reach a final osmolarity of 650 mosmol/L . Absorbance at 550 nm was monitored every 10 sec for 12 min . The results were normalized respect to the value of a 3 min pre-reading under isosmotic conditions . To measure TcCat release after hyperosmotic stress , trypomastigotes and epimastigotes under osmotic stress were collected by centrifugation ( 1 , 600× g for 10 min ) after 2 min of treatment . Supernatants were precipitated with 10% trichloroacetic acid for 1 h on ice . Precipitated proteins were collected by high-speed centrifugation ( 20 , 000× g for 20 min ) , washed and evaluated by western-blot analysis using anti-TcCat . Anti-tubulin was used as control to show that no parasite material was present in the supernatant , other than the released protein . Parasites overexpressing GFP were used as a control to rule-out lysis of the cells as a mechanism of release of TcCat ( Fig . S7A ) . | The use of high-resolution electrophysiological techniques to study ion channels has provided a large amount of information on functional aspects of these important membrane proteins . However , the study of ion channels in unicellular eukaryotes has been limited to detection of ion conductances in large cells , gene identification studies , and pharmacological treatments to investigate the potential presence of different ion channels . In this paper we report the first identification , functional expression , purification , reconstitution , and electrophysiological characterization with single-molecule resolution of a novel cation channel ( TcCat ) from Trypanosoma cruzi . This is a novel channel that shares little sequence and functional similarities to other potassium channels and its peculiar characteristics could be important for the development of specific inhibitors with therapeutic potential against trypanosomiasis . Surprisingly , the parasites have the unprecedented ability to rapidly change the localization of the channel when they are exposed to different environmental stresses . We demonstrated the feasibility of purifying and reconstituting a functional ion channel from T . cruzi after recombinant expression in bacteria . In addition , we obtained yeast mutants that will provide a useful genetic system for studies of the assembly and composition of the channel . |
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Next-generation sequencing has made possible the detection of rare variant ( RV ) associations with quantitative traits ( QT ) . Due to high sequencing cost , many studies can only sequence a modest number of selected samples with extreme QT . Therefore association testing in individual studies can be underpowered . Besides the primary trait , many clinically important secondary traits are often measured . It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits . However , analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled . Some methods exist for analyzing secondary traits in selected samples , where some burden tests can be implemented . However p-values can only be evaluated analytically via asymptotic approximations , which may not be accurate . Additionally , potentially more powerful sequence kernel association tests , variable selection-based methods , and burden tests that require permutations cannot be incorporated . To overcome these limitations , we developed a unified method for analyzing secondary trait associations with RVs ( STAR ) in selected samples , incorporating all RV tests . Statistical significance can be evaluated either through permutations or analytically . STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations . It also enables jointly analyzing multiple cohorts ascertained under different study designs , which greatly boosts power . The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies . STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced . A significant association between LDLR and systolic blood pressure was identified , which is supported by pharmacogenetic studies . In summary , for sequencing studies , STAR is an important tool for detecting secondary-trait RV associations .
Next-generation sequencing has already revolutionized the study of complex traits , and made possible the detection of rare variant associations . Many sequence based association studies are currently being performed , some of which have already lead to the discovery of associations with clinically important traits , such as lipids levels [1] , age related macular degeneration [2] , inflammatory bowel disease [3] , blood pressure [4] , body mass index [5] , etc . In particular , there is increasing interest to detect associations with quantitative traits ( QT ) . It has been suggested that complex traits can be due to multiple variants with small effects , and are quantitative in nature [6] , [7] . Mapping multiple quantitative traits may help elucidate the etiology of complex traits , reducing sample heterogeneity [8] , dissecting gene pleiotropy and refine the definition of complex diseases [7] , [9] , [10] . For example , recent studies of type 2 diabetes have been focused on multiple related QTs , such as fasting glucose levels [11] , insulin resistance levels [11] , and c-reactive proteins [12] . Many quantitative traits are usually measured in different studies as secondary outcomes . It is of great interest to combine multiple studies for detecting associations with commonly measured primary or secondary traits . For example , the National Heart Lung and Blood Institute-Exome Sequencing Project ( NHLBI-ESP ) is studying a variety of different phenotypes and employed both extreme and random sampling . In order to improve power , all samples with the phenotype of interest measured are jointly analyzed . Specifically , the association analysis of low density lipoprotein cholesterol ( LDL ) levels is performed by combining several studies which include a well-phenotyped random population cohort , selected samples with extreme LDL levels as well as individuals with extreme body mass index ( BMI ) . Many methods have been developed for detecting rare variant associations [13]–[19] . These methods are all based upon aggregating multiple rare variants across a genetic region , which is usually a gene . Compared to analyzing each variant individually , these region based tests can be more powerful . However , rare variants that are involved in complex trait etiologies usually only have moderate effect sizes , and their aggregated frequencies across a genetic region can still be low . It is therefore necessary to sequence and analyze a large number of samples in order to have adequate power to detect associations . Although next generation sequencing is much more cost effective than Sanger sequencing , it is still expensive to generate high read depth data for large numbers of samples . Given cost constraints , in order to improve power , many studies sequence samples with extreme QT rather than the entire cohort . The selective sampling study design produces challenges for analyzing secondary traits . Without properly accounting for the sample ascertainment mechanisms , type I errors for detecting secondary trait associations may be inflated [20] , [21] . This is because due to the correlations between the primary and secondary traits , mean values for the secondary traits will be different between individuals with primary trait from opposite extreme tails . Additionally if the primary trait is associated with a gene region , the cumulative variant frequencies will also be different . Therefore spurious association can be created by ascertainment . The bias for the naïve analysis of secondary trait is demonstrated both theoretically and empirically in this article . Several methods exist for detecting secondary trait associations in selected samples . For example , a retrospective likelihood method was developed for mapping secondary phenotypes using regression models ( SPREG ) in case control studies [20] . It was subsequently extended in an empirical Bayesian framework [22] , which utilizes genotype information from cases for rare diseases and can be more powerful than the retrospective likelihood method . However , both methods are not directly applicable to the studies where the primary trait is quantitative and extreme sampling is implemented . We previously developed a method for detecting multiple ( secondary ) trait associations ( MTA ) in selected samples , which jointly models multiple phenotype associations and sampling ascertainment status [21] . MTA can be used to analyze data from studies with known sampling mechanisms , e . g . case control , and extreme sampling designs . It incorporates several rare variant association tests , whose statistical significance can be evaluated analytically e . g . the combined multivariate and collapsing ( CMC ) [16] , and Gene- or Region-based Analysis of Variants of Intermediate and Low frequency ( GRANVIL ) [18] . Weighted sum statistics [14] , [23] can also be incorporated , if the weights that are assigned to each variant site are not dependent on the trait . One major advantage of using MTA is that cohorts ascertained under different sampling schemes can be combined for detecting associations with commonly measured traits . These studies can be targeted at the same or different primary traits . By combining data from different studies , much larger sample sizes can be analyzed and the power to detect associations can be greatly improved [21] . However none of these methods for detecting secondary associations incorporate sequence kernel association test ( SKAT ) , a powerful variance component score test based method . This method can be more powerful when causal variants have bidirectional effects and/or a large proportion of the variants within gene region are non-causal . Standard permutation algorithms cannot be applied to obtain empirical p-value . This is because when the primary and secondary traits are correlated and the genetic region is associated with the primary trait , neither the secondary trait residuals nor the locus genotypes are interchangeable under the null hypothesis . Therefore , the statistical significance can only be evaluated via asymptotic approximations , which has several notable limitations: 1 . ) Due to the low frequency of rare variants , asymptotic approximation for some tests may be violated , which can lead to either inflated type I error or loss of power . 2 . ) For some rare variant association methods , the analytical distribution for the test statistics is unknown and therefore the statistical significance has to be evaluated empirically . These rare variant tests that require evaluating p-values via permutation are often more powerful than the methods implemented in MTA , e . g . CMC or GRANVIL . It is therefore desirable that these tests can be applied to analyze secondary traits . To overcome the limitations of existing methods , a unified model was developed to detect secondary trait associations using selected samples . In the samples with extreme primary quantitative traits , through re-parameterizing the likelihood functions , interchangeable residuals for the secondary traits can be obtained under the null hypothesis . The residuals are approximately independent , and normally distributed . We proved theoretically that the analysis of secondary trait associations can be equivalently implemented by analyzing the correlation between the secondary trait residuals and the gene/genetic region . Therefore , any rare variant association test that can analyze QT in random population based studies can be incorporated in STAR . In addition , multiple cohorts can be jointly analyzed through conventional mega-analysis methods that use individual participant data or meta-analysis methods that use summary level statistics . A variety of popular rare variant tests have been implemented in the STAR framework and the power to detect secondary trait associations was evaluated . Specifically , we considered the weighted sum statistic ( WSS ) [14] , [23] , sequence kernel association tests ( SKAT ) [17] , and variable threshold test ( VT ) [24] . Additionally the kernel based adaptive cluster test ( KBAC ) [15] , which was originally developed for analyzing binary disorders , was extended to analyze quantitative traits and incorporated in STAR for detecting associations with secondary phenotypes ( Text S1 ) . The performances for these methods were compared using extensive simulation studies . Genetic data were simulated under a realistic population genetic model as described by Kryukov et al [25] , which incorporates both demographic change and purifying selections . Phenotypes were simulated based upon parameters estimated from clinically important complex traits . It is demonstrated that under a broad variety of phenotype models , the power for detecting secondary trait associations can be greatly improved through the use of more powerful rare variant tests that are incorporated in STAR . There does not exist a method that is consistently the most powerful , and the power difference between top performing methods is generally small . When the effects of causal variants are unidirectional , the VT test outperforms other methods in most scenarios . When there are variants with effects in opposite directions or only a small proportion of the variants are causal , SKAT can be more powerful than alternative methods in many settings . The STAR method was also used to analyze a published sequence dataset from the SardiNIA project [1] , where nine genes were sequenced in 256 individuals with extreme LDL levels ( individuals taking lipid-lowering therapies were not considered for the analysis ) . In the original article by Sanna et al [1] , the authors focused on detecting associations with the primary trait LDL , and did not consider analyzing other metabolic and lipids traits . In this article , the analysis was extended to detect associations with other clinically important traits , which include high density lipoprotein cholesterol ( HDL ) , total cholesterol level ( TCL ) , triglyceride ( TG ) , insulin levels ( INSULIN ) , BMI , systolic and diastolic blood pressure ( SysBP and DiasBP ) . One association was identified between LDLR and SysBP , which is statistically significant after applying a Bonferroni correction for testing multiple genes and traits . This association has strong biological support from pharmacogenetics studies [26] . These findings provide new insight on the etiology for the LDLR gene , and established the importance of our method in sequence based association studies . An R-package , STARSEQ which implements the STAR method is available through the Comprehensive R Archive Network ( CRAN ) at http://cran . r-project . org/web/packages/STARSEQ/ . Additional companion softwares are deposited at http://code . google . com/p/starseq/ .
Under the null hypothesis of no gene/secondary trait associations , following the MTA framework , a multivariate generalized linear model can be implemented to estimate nuisance parameters [21] . The link functions for the mean parameters of the two traits satisfy ( 1 ) In the above model , and are covariates , such as age or sex . The residual terms for the secondary traits , i . e . are correlated with the primary trait , and not interchangeable under the null hypothesis , i . e . ( 2 ) It was previously shown via simulations that naïve inferences for secondary trait associations , which ignore sample ascertainment mechanisms , can be biased [20] , [21] . It can also be proved theoretically that due to extreme sampling on the primary trait , spurious associations can be created between the gene locus and secondary trait ( Text S2 , Figures S1 and S2 ) . Without adjusting for the sample ascertainment mechanisms , the biases in the secondary trait effects will increase linearly with respect to the trait residual correlation and approximately linearly with respect to the primary trait effects when the magnitude of primary trait effects is small . Using this theoretical framework , we also evaluated some standard adjustment methods . e . g . 1 . ) Separately analyzing individuals with high and low extreme primary traits , and then combining the results via meta-analysis . or 2 . ) Incorporating an indicator to denote whether an individual has a high or low extreme primary trait as a covariate , and perform linear regression analysis using the entire sample . We proved theoretically that these methods will not eliminate the bias in the association analysis of secondary traits , and type I error will still be inflated after the adjustment . In order to obtain unbiased results , sampling schemes have to be properly modeled . Ascertainment corrected likelihood can be used , which jointly models sample ascertainment status and genotype/phenotype association , i . e . ( 3 ) The likelihood model can be used for both trait dependent sampling and population based random sampling . We showed analytically that the secondary trait effects can be consistently inferred under the ascertainment corrected likelihood model . Details for the likelihood specification can be found in ( Text S3 ) . The likelihood function in equation ( 1 ) needs to be re-parameterized in order to facilitate deriving the SKAT statistics and performing permutations . It is clear that ( 4 ) The conditional probability satisfies ( 5 ) Instead of estimating the variance and correlation coefficients for , the following parameters are estimated , i . e . . As is shown in ( Text S3 ) , the Jacobian for the re-parameterization , i . e . is non-degenerate and the re-parameterization is one to one and invertible . Therefore , an equivalent mean model can be fitted under the null hypothesis , i . e . ( 6 ) Practical issues for fitting the model are discussed in ( Text S4 ) . In this model , the residual errors and for the primary and secondary traits are uncorrelated . In particular , the residual errors after re-parameterization are interchangeable under the null hypothesis . Burden tests , such as CMC and WSS , aggregate multiple rare variants across a genetic region and analyze them jointly . The following model can be used to obtain score statistics for a burden test: ( 7 ) In formula ( 7 ) , is the genotype coding for the locus multi-site genotypes . Examples include the weighted sum coding [14] , i . e . , where each variant site is assigned a weight and the weighted genotypes are aggregated . For some rare variant association tests such as KBAC [15] , the genotype coding can also depend on the QT , i . e . . Formula 7 can be used for detecting single variant associations as well , where is the coding for single variant genotype . Score tests can be formally constructed from the joint likelihood for testing the null hypothesis of no gene/secondary trait associations , i . e . . If the samples are ascertained based upon only the primary trait , score tests can be equivalently constructed from the conditional likelihood , i . e . . This is because the joint likelihood can be factorized , i . e . ( 8 ) When the samples are ascertained based upon the primary trait , the distribution of conditional on is independent of the ascertainment status , i . e . In addition , the term does not contain the parameter of interest . The score function thus takes the form ( 9 ) where , , and are maximum likelihood estimates under the null hypothesis . It is clear from formula ( 9 ) that is proportional to the covariance between the secondary trait residuals and the locus genotype coding . Given that , , and are consistent estimators under the null hypothesis , by Slutsky's theorem , the residuals for the secondary trait i . e . are approximately normally distributed and interchangeable under the null hypothesis . Therefore , the analysis of rare variant secondary trait associations can be implemented by analyzing the correlation between the corrected residuals and the locus genotype coding . Standard permutation algorithms can be implemented by shuffling the residuals under the null hypothesis . In our STARSEQ package , we also provide flexible tools for calculating the adjusted secondary trait residuals , which can be analyzed by any user specified rare variant association test . Using similar ideas , we show in ( Text S5 ) , that the extended SKAT statistic in STAR has the form ( 10 ) where is the kernel function used to compare two multi-site genotypes and , and is the estimated mean secondary trait value under the null model . P-values for the extended SKAT method can be obtained either analytically or via permutation . The KBAC test was previously developed for the analysis of binary trait associations [15] . It is extended to analyze rare variant QT associations in studies using randomly ascertained samples or samples with extreme traits . The extended KBAC method has also been incorporated in STAR for analyzing secondary trait associations . The details for the extensions are given in ( Text S1 ) . Type I error and power were evaluated for the following rare variant association tests that were extended in STAR , i . e . CMC , KBAC , WSS , SKAT and VT . Genetic data were generated according to a four parameter demographic model for Europeans [25] , [27] . In addition , purifying selection is also modeled , which influences the variant site frequency spectrum . Among the variants with selection coefficients >10−4 , 50% are randomly chosen to be causal for the primary trait , and another 50% of the variants are independently chosen to be causal for the secondary trait . The set of causal variants for the primary trait are denoted by and that for the secondary trait are denoted by . Variants belonging to the intersection of and modulate both the primary and secondary phenotypes . QTs were simulated according to the following bivariate normal distribution: ( 11 ) The magnitudes of the causal variant effects are assumed to be inversely correlated with the minor allele frequencies ( MAF ) , i . e . For a special case when , the magnitude for the effects of all rare causal variants is constant , i . e . . In the simulations , we considered models where 1 . ) 2 . ) and 3 . ) and . For each set of parameter values of and , we evaluated the power for different rare variant association tests when 1 . ) all causal variants have effects in the same direction or 2 . ) 80% of the variants increase the mean secondary trait value while the remaining 20% decrease the mean secondary trait value . In the simulations , the primary and secondary traits are assumed to be positively ( or negatively ) correlated with coefficients ( or ) . For the evaluation of type I errors , datasets were simulated with . Data for selective sampling studies are simulated , where for each dataset , a total of 5 , 000 individuals with extreme primary trait values are selected from a cohort of 100 , 000 individuals . Two-sided alternative hypothesis was tested for each method . Although p-values for CMC and SKAT can be obtained analytically , they can either be conservative or anti-conservative [23] . In order to calibrate the distribution of p-values , we evaluated the statistical significance of all methods empirically using 5 , 000 permutations . The power and type-I errors for each method were obtained using 10 , 000 replicates for a significance level of . As a comparison to STAR , type I error for linear regression analysis was also evaluated , where sample ascertainment mechanisms were ignored . In order to illustrate the application of STAR for combining multiple cohorts , a meta-analysis of three studies was simulated . The primary trait for each study is different and a common secondary trait is measured for all studies . In the first study , the gene region is associated with the primary trait , and causal variants have an effect of −0 . 5 . The correlation between the primary and secondary traits is 0 . 6 . In the second study , the primary trait is associated with the gene region , and causal variants have an effect of 0 . 25 . The primary and secondary traits are correlated with coefficient 0 . 4 . In the third study , the gene region is not associated with the primary trait , and the correlation between the primary and secondary traits is −0 . 2 . In each study , a different pool of 50 , 000 samples was simulated and 2 , 500 individuals with extreme primary trait were selected and analyzed for association . P-values for all rare variant tests in each study were obtained based upon 5 , 000 permutations . Meta-analysis is performed by combining Z-score statistics , which are transformed from p-values and weighted by the square root of the sample sizes in each study [28] . In order to evaluate type I errors , data were simulated under the assumption that the secondary trait effects for all variants were 0 . The empirical distribution of p-values was obtained using 10 , 000 replicates . For evaluating power , two scenarios were considered , i . e . ( A ) causal variants have an unidirectional effect of 0 . 5; ( B ) causal variants have bidirectional effects , where 80% of the causal variants have effect 0 . 5 and the other 20% of the causal variants have effect −0 . 5 . The power for analyzing each individual study and meta-analysis was evaluated using 10 , 000 replicates under a significance level of α = 0 . 05 . Association analyses were performed for the nine genes that were sequenced from the SardiNIA project [1] . First , coding regions of four genes , APOB , B3GA4 , LDLR and PCSK9 were tested for associations with the eight metabolic QTs . The genes APOC1 , APOC2 , APOE , B4GA4 contain no variants with MAF<1% , and SORT1 contains only 1 rare variant site . Gene-based association analysis was not performed for these five genes . Among the 256 individuals , 33 were taking blood pressure ( BP ) lowering medications; and their BP levels were adjusted by adding 10 mm Hg to their SysBP and 5 mm Hg to DiasBP levels [29] . Following the same strategy as the initial LDL analysis [1] , residuals for each trait were obtained and quantile-normalized after adjusting for age , age×age and sex in the entire SardiNIA cohort . The normalized residuals of the 256 samples were analyzed for associations with the four genes , i . e . APOB , B3GA4 , LDLR and PCSK9 . The five rare variant association tests incorporated in STAR were used to analyze the data . In addition to the secondary traits , the associations with the primary trait ( i . e . LDL levels ) were also analyzed . For the rare variant tests that use fixed MAF thresholds ( i . e . CMC , WSS , KBAC and SKAT ) , variants with MAF<1% were analyzed . For VT test , variants with MAF<5% were used in the analysis . The secondary traits were also analyzed using standard linear regression that ignores the ascertainment mechanism , as a comparison to the analysis using the STAR method .
Type I error for STAR was investigated when 1 . ) the gene region is neither associated with the primary trait nor the secondary trait . 2 . ) the gene region is associated only with the primary trait but not the secondary trait . The quantile-quantile plots of empirical p-values and their theoretical expectations are displayed for different rare variant tests . It can be seen that all tests incorporated in the STAR method have well controlled type I error . The p-values for the five tests are slightly conservative even when permutation is used to evaluate significance . This can occur when either the aggregate variant frequencies are low or the sample size is not sufficiently large . For example , when the primary trait effect is and residual correlation is , the type I errors for CMC , WSS , KBAC , VT and SKAT are respectively 0 . 048 , 0 . 046 , 0 . 042 , 0 . 045 and 0 . 047 ( Figure 1 ) . As a comparison , we also evaluated type I errors of linear regression analysis that ignores sample ascertainment mechanisms . When the gene region is not associated with the primary trait , type I errors for all rare variant tests are well controlled . However , if the gene region is also associated with the primary trait , the distribution of p-values under the null hypothesis is highly skewed and the type I errors for all tests are seriously inflated ( Figure S3 ) , which is concordant with our theoretical expectations . The power of detecting secondary trait associations was compared for a variety of rare variant tests ( Figure 2; Figure 3; and Figures S4 , S5 , S6 , S7 ) . Compared to the CMC method , the extended SKAT , WSS , KBAC and VT methods in STAR can be more powerful under a broad variety of models . For example , when the primary trait is associated with the gene region with effect and trait residual correlation is , if causal variants have fixed unidirectional secondary trait effect , i . e . , the power for WSS , KBAC and VT tests are respectively 73 . 5% , 74 . 1% and 78 . 1% , which all have greater power than the CMC ( 71 . 5% ) ( Figure 2 ) . If the secondary trait effects are bidirectional , the power for the VT ( 50 . 6% ) and SKAT ( 54 . 3% ) tests are much higher than that of the CMC ( 41 . 4% ) , and the power for KBAC ( 44 . 1% ) is also slightly greater than the power for the CMC ( Figure 3 ) . VT can be more powerful than methods that use fixed variant frequency threshold , when the secondary trait effects are unidirectional . This is because using a fixed variant frequency threshold may result in the inclusion of higher frequency non-causal variants or the exclusion of more frequent causal variants from the analyses . For example , when the primary trait effect is and trait residual correlation is , if the secondary trait effects are unidirectional and fixed with , the power for VT is 78 . 1% , which is considerably higher than the power for the CMC ( 71 . 5% ) ( Figure 2 ) . However , the difference in power between the best performing methods is small . For instance , when the primary trait effect is and trait residual correlation is , if the secondary trait effects are unidirectional and variable with and , the power for VT is 85 . 9% , which is only 0 . 3% and 2 . 6% higher than the power for the WSS and KBAC . The variance component score test SKAT is less powerful than burden tests when causal variant effects are unidirectional . For example , when , , and the causal variant effects are unidirectional with , the power for SKAT is 53 . 1% , which is 24 . 3% lower than VT and 21 . 6% lower than KBAC ( Figure 2 ) . However , when the causal variant secondary trait effects are bidirectional , SKAT is among the most powerful methods . For instance , if the magnitudes of the causal variant effects are inversely correlated with MAFs , when , and , the power for SKAT is 63 . 2% , which is much greater than the power for CMC ( 49 . 2% ) , WSS ( 51 . 0% ) , and KBAC ( 54 . 4% ) and slightly higher than the power for VT ( 60 . 3% ) ( Figure S5 ) . When the gene region is associated with both the primary and secondary traits , the power to detect secondary trait associations can be greater than when the gene region is only associated with the secondary trait . This is because variants with pleiotropic effects can be more enriched through extreme sampling . For example , when secondary trait effects are , and residual correlation is , if the gene region is not associated with the primary trait , the power for CMC , WSS , KBAC , VT and SKAT are respectively 61 . 9% , 61 . 4% , 64 . 7% , 67 . 7% and 49 . 7% ( Figure 2 ) . However , if the gene region is also associated with primary trait with effect , the power for the five tests increases to 65 . 3% , 63 . 3% , 67 . 7% , 70 . 1% and 53 . 1% respectively ( Figure 2 ) . Therefore , the power for detecting secondary trait associations can also be increased through sequencing samples with extreme primary trait values . The power and type I errors for STAR were evaluated for a simulated meta-analysis of three studies . As shown in ( Figure S8 ) , the empirical p-values and their theoretical expectations are well aligned on the quantile-quantile plot . Under a significance level of α = 0 . 05 , the type I errors for the five rare variant tests are CMC ( 0 . 051 ) , WSS ( 0 . 049 ) , KBAC ( 0 . 049 ) , VT ( 0 . 047 ) , SKAT ( 0 . 051 ) , which are well controlled . Due to the small sample size that is used , the type I errors for analyzing each individual study can still be slightly conservative . For example , in study 1 , where causal variant effect for the primary trait is −0 . 5 and the correlation between the primary and secondary traits is 0 . 6 , the type I errors for the five tests are respectively: CMC ( 0 . 046 ) WSS ( 0 . 044 ) , KBAC ( 0 . 046 ) , VT ( 0 . 047 ) and SKAT ( 0 . 045 ) . We also evaluated the power of the STAR method under the alternative hypothesis ( Figure S9 ) . It can be seen that the power for meta-analysis is always higher than the power for each individual study , which highlights the benefit of combining multiple studies to detect associations with commonly measured traits . Sequence data from the SardiNIA project were analyzed to detect associations with multiple lipids and metabolic traits . First , association analyses were carried-out for the primary trait LDL levels ( Table 1 ) . In the original article by Sanna et al [1] , extreme LDL values were dichotomized and association analyses were performed by comparing variant carrier frequencies between individuals sampled from opposite ends of the trait distribution . Only APOB was found to be nominally significantly associated with LDL ( p-value 0 . 03 ) . When QT values are analyzed instead of the dichotomized trait and more powerful association methods are used , the power to detect associations with the primary trait can be increased . For the association with APOB , the p-values for the five tests are , , , , , . Additionally a significant association with LDLR that was not previously detected was also observed ( , , , , ) . Among the tests that were used to analyze the association between LDLR and LDL , VT gives the smallest p-value . On the other hand , for the association with APOB , the score statistics from VT are maximized at the same MAF threshold as used by the other tests ( i . e . 1% ) . In this case , the CMC test gives the most significant p-value . Next we analyzed secondary trait associations with the four genes , i . e . APOB , B3GA4 , LDLR and PCSK9 ( Table 2 ) . One significant association , i . e . the association between LDLR and SysBP , is identified by CMC , WSS and KBAC after applying a Bonferroni correction for testing multiple genes and traits . The p-values for VT and SKAT are also nominally significant ( , , , , ) . The score statistics in VT are maximized at the MAF cutoff 1% . In this scenario , the p-value of VT is not as significant as that of CMC , WSS and KBAC , because the burden test statistics are not increased at alternative frequency thresholds and a penalty for multiple testing is paid . It is interesting to note that LDLR is associated with both the primary trait LDL and the secondary trait SysBP , which are correlated with a coefficient of 0 . 145 ( Table S1 ) . It is possible that a portion of the rare variants in LDLR have pleiotropic effects and are enriched in the dataset via selective sampling on the primary trait , which increase the power for detecting secondary trait associations . We also compared the analysis using STAR and standard linear regressions ( Table S2 ) . Due to the small sample sizes that are used , we did not observe an excess of false positive signals for the naïve linear regression analysis . However , we noted that for the association between LDLR and SysBP , the p-values from STAR are smaller . In addition , for the associations between LDLR , PCSK9 and TCL that were previously implicated in genome-wide association studies [30] , the p-values from STAR are also more significant .
In this article , we present a likelihood model which can be used to analyze secondary trait associations in selected samples . The method corrects for the bias in the distribution of the secondary traits induced by selective sampling . All rare variant association analysis methods can be extended within the STAR framework . STAR makes it possible to apply more powerful rare variant association tests for the analysis of secondary trait and allows jointly analyzing cohorts that were ascertained for different primary traits . The power for detecting associations with secondary traits can be greatly enhanced . In addition to performing gene-based association analysis , the STAR method and STARSEQ software can also be applied to detect single variant associations ( data not shown ) . Currently , many sequence based genetic studies are being performed to detect associations with complex traits . Due to the high cost of sequencing , the sample sizes for many of these studies are small . It was previously shown that in order to have sufficient power ( e . g . >80% ) to detect association with rare variants in an exome-wide study , in some cases it is necessary to sequence at least 10 , 000 samples with extreme traits from a cohort of 100 , 000 [25] . However , both the cohort size and the cost of sequencing exceed the capacity of most studies . Therefore to increase power it is important that multiple studies can be jointly analyzed . The STAR method is particularly useful , since cohorts that are ascertained for diverse primary traits using different study designs can be jointly analyzed . Previously CMC and GRANVIL tests were extended for analyzing secondary traits with p-value being evaluated analytically [21] . Many of the rare variant association methods implemented in STAR can be more powerful than CMC and GRANVIL . In fact , despite being computationally efficient , CMC and GRANVIL can be underpowered when a large portion of the variants in the gene region are non-causal or when the genetic effects of causal variants are bidirectional . Other methods such as SKAT may perform better in these scenarios . In addition , through assigning weights to different variant sites , variants that are potentially causal can be assigned higher weights , which can help to distinguish causal from non-causal variants . When variable selection based methods are used , the set of variants where the Z-score statistics are maximized can be selected and tested . These methods can be more robust against the inclusion of non-causal variant in the analysis , and also potentially be more powerful than CMC and GRANVIL methods even after adjusting for multiple comparisons . Permutation algorithm is often a necessary ingredient for rare variant association tests . Even if asymptotic approximations exist for some rare variant association tests such as SKAT and CMC , they may not be accurate and type I errors may be inflated or deflated [23] . This is because the asymptotic distribution for the test statistic can be affected by the number of rare variant sites and variant site frequency spectrums [17] , [23] . In practice , there can be considerable variation in the number of variant sites and frequencies within a gene region [31] . It is possible that an asymptotically valid test has inflated or deflated type I errors when genetic regions with only a few variant alleles are analyzed . Therefore , when a significant analytical p-value is obtained , it is necessary to empirically confirm the result using permutation . Under the STAR framework , we compared the power of several rare variant tests for analyzing secondary traits in selected samples . It is clear from our comparisons that when causal variants have unidirectional effects , burden tests perform better than SKAT . However , when variants with effects in opposite directions are present , SKAT can be more powerful than burden test based methods . Given that the goal of the article is to introduce a method for analyzing secondary traits in selected samples , rather than to compare different rare variant tests , our simulations are not as comprehensive as some existing reviews , such as Basu and Pan [32] and Ladouceur et al [33] . However , based upon the simulation scenarios that we considered , it is clear that the power for detecting associations can be greatly improved through the STAR model . In addition , our conclusions for comparing multiple rare variant tests are also compatible with the comprehensive reviews , in that there is not a consistently most powerful rare variant test and the difference in power between top performing methods is usually small . In addition to the simulation experiments , it is also important to examine and compare the performance of different methods in large scale sequencing studies , such as the NHLBI-ESP . In the analysis of the SardiNIA dataset , we adjusted the blood pressure for individuals undergoing antihypertensive therapy . The rank of sample blood pressure traits was only slightly changed after the adjustment . Given that we quantile-normalized the trait prior to the association analysis , the impact of the adjustment on the result is very minimal . In order to evaluate the robustness of the results , we also analyzed the associations with blood pressure when no adjustments were made , and the results are very similar ( data not shown ) . A significant association was identified between rare variants in LDLR and secondary trait SysBP , where carriers of rare variants in the LDLR gene tend to have lower SysBP levels . In fact , the LDLR gene has also been shown to be strongly associated with reductions of SysBP among the patients that receive atenolol , an antihypertensive drug [26] . These discoveries imply that variants in the LDLR gene may influence the etiology of SysBP . LDLR is potentially an important gene target for blood pressure treatment . In order to replicate signals [22] that were found in the SardiNIA cohort , many current large scale sequencing studies can be considered , such as the NHLBI-ESP etc . In addition to replicating associations , there is also great scientific interest in exploring whether rare causal variants identified in a founder population are identical to those from out-bred populations [34] . With the large scale application of next generation sequencing to study complex traits , samples from many existing cohorts will be sequenced . There can be insufficient power for analyzing associations in each individual study . It would be highly beneficial if samples from multiple cohorts can be combined for analyzing commonly measured traits . STAR is thus important and will greatly accelerate the process of identifying genes involved in complex trait etiology . | Next-generation sequencing has greatly expanded our ability to identify missing heritability due to rare variants . In order to increase the power to detect associations , one desirable study design is to combine samples from multiple cohorts for mapping commonly measured traits . However , many current studies sequence selected samples ( e . g . samples with extreme QT ) , which can bias the analysis of secondary traits , unless the sampling ascertainment mechanisms are properly adjusted . We developed a unified method for detecting secondary trait associations with rare variants ( STAR ) in selected and random samples , which can flexibly incorporate all rare variant association tests and allow joint analysis of multiple cohorts ascertained under different study designs . We demonstrate via simulations that STAR greatly boosts the power for detecting secondary trait associations . As an application of STAR , a dataset from the SardiNIA project was analyzed , where DNA samples from well-phenotyped individuals with extreme low-density lipoprotein levels were sequenced . LDLR was identified to be significantly associated with systolic blood pressure , which is supported by a previous pharmacogenetics study . In conclusion , STAR is an important tool for sequence-based association studies . |
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Effective immunotherapies for HIV are needed . Drug therapies are life-long with significant toxicities . Dendritic-cell based immunotherapy approaches are promising but impractical for widespread use . A simple immunotherapy , reinfusing fresh autologous blood cells exposed to overlapping SIV peptides for 1 hour ex vivo , was assessed for the control of SIVmac251 replication in 36 pigtail macaques . An initial set of four immunizations was administered under antiretroviral cover and a booster set of three immunizations administered 6 months later . Vaccinated animals were randomized to receive Gag peptides alone or peptides spanning all nine SIV proteins . High-level , SIV-specific CD4 and CD8 T-cell immunity was induced following immunization , both during antiretroviral cover and without . Virus levels were durably ∼10-fold lower for 1 year in immunized animals compared to controls , and a significant delay in AIDS-related mortality resulted . Broader immunity resulted following immunizations with peptides spanning all nine SIV proteins , but the responses to Gag were weaker in comparison to animals only immunized with Gag . No difference in viral outcome occurred in animals immunized with all SIV proteins compared to animals immunized against Gag alone . Peptide-pulsed blood cells are an immunogenic and effective immunotherapy in SIV-infected macaques . Our results suggest Gag alone is an effective antigen for T-cell immunotherapy . Fresh blood cells pulsed with overlapping Gag peptides is proceeding into trials in HIV-infected humans .
Several attempts at immunotherapy of HIV using more conventional vaccines have thus far been poorly immunogenic and weakly efficacious in human trials [1] , [2] , [3] , [4] . The use of professional antigen-presenting cells such as dendritic cells to deliver HIV immunotherapies has shown strong immunogenicity efficacy in macaques and pilot humans studies but is limited to highly specialized facilities [5] , [6] , [7] . A simple intermittent immunotherapy that reduces the need for long-term antiretroviral therapy ( ART ) would be a quantum advance in treating HIV . We recently reported the robust T-cell immunogenicity of treating unfractionated whole blood or peripheral blood mononuclear cells ( PBMC ) with overlapping peptides of SIV , HIV-1 or hepatitis C virus in outbred pigtail monkeys [8] , [9] . We termed this simple immunotherapy OPAL ( Overlapping Peptide-pulsed Autologous ceLls ) . This technique is attractive since there is no prolonged ex vivo culture of antigen-presenting cells , robust CD4 and CD8 T-cell responses to both structural and regulatory proteins can be induced , and peptide antigens are simple to manufacture to high purity . This study assessed whether OPAL vaccination improves the outcome of SIV-infected monkeys . Considerable debate exists regarding the most effective antigens to target for T-cell based therapeutic HIV vaccination . It has been widely believed that broader immunity to multiple proteins would be more efficacious [10] , [11] . In contrast , recent studies highlight the effectiveness of Gag-specific T cell immunity in comparison to T cell immunity to other antigens . We therefore also assessed whether narrower responses induced only to SIV Gag are as effective as more broadly targeting all 9 SIV proteins .
Juvenile pigtail macaques ( Macaca nemestrina ) free from Simian retrovirus type D were studied in protocols approved by institutional animal ethics committees and cared for in accordance with Australian National Health and Medical Research Council guidelines . All pigtail macaques were typed for MHC class I alleles by reference strand mediated conformational analysis and the presence of Mane-A*10 confirmed by sequence specific primer PCR as described [12] , [13] . 36 macaques were injected intravenously with 40 tissue culture infectious doses of SIVmac251 ( kindly provided by R . Pal , Advanced Biosciences , Kensington , MD ) as described previously [14] , [15] and randomized into 3 groups of 12 animals ( OPAL-Gag , OPAL-All , Controls ) 3 weeks later . Randomization was stratified for peak SIV viral load at week 2 , weight , gender and the MHC I gene Mane-A*10 ( which is known to enhance immune control of SIV ) [15] . Animals received subcutaneous injections of dual anti-retroviral therapy with tenofovir and emtricitibine ( kindly donated by Gilead , Foster City , CA; both 30 mg/kg/animal ) for 7 weeks from week 3: daily from weeks 3–5 post-infection and three times per week from weeks 6–10 . This dual ART controls viremia in the majority of SIV-infected macaques [16] , [17] , [18] , [19] , [20] . Two animal groups ( OPAL-Gag and OPAL-All ) were immunized with OPAL immunotherapy using PBMC as previously described [8] . Briefly , peripheral blood mononuclear cells ( PBMC ) were isolated over Ficoll-paque from 18 ml of blood ( anticoagulated with Na+-Heparain ) . All isolated PBMC ( on average 24 million cells ) were suspended in 0 . 5 ml of normal saline to which either a pool of 125 SIVmac239 Gag peptides or 823 peptides spanning all SIVmac239 proteins ( Gag , Pol , Env , Nef , Vif , Tat , Rev , Vpr , Vpx ) were added at 10 µg/ml of each peptide within the pool . Peptides were 15mers overlapping by 11 amino acids at >80% purity kindly provided by the NIH AIDS reagent repository program ( catalog numbers 6204 , 6443 , 6883 , 6448-50 , 6407 , 8762 , 6205 ) . To pool the peptides , each 1 mg vial of lyophilised 15mer peptide was solubilized in 10–50 µl of pure DMSO and added together . The concentration of the SIV Gag and All peptide pools was 629 and 72 µg/ml/peptide respectively , although each peptide was pulsed onto cells at 10 µg/ml regardless of vaccine type . The peptide-pulsed PBMC were held for 1 hr in a 37°C waterbath , gently vortexed every 15 minutes and then , without washing , reinfused IV into the autologous animal . Peptide concentrations and timing of incubation were adapted from effective stimulation of T cell responses in vitro . Control macaques did not receive vaccine treatment . This was done since ( a ) we had not previously observed any significant VL changes with non HIV/SIV peptide sets ( [8] , [9] and unpublished data ) , ( b ) reinfusion of blood cells pulsed with irrelevant sets of peptides would result in some level of immune activation and drive higher viral loads in controls , artificially magnifying any reductions in the active treatment groups , ( c ) reinfusion of control peptide pulsed cells might have obscured any unexpected safety problems of the procedure . SIV-specific CD4 and CD8 T-cell immune responses were analysed by expression of intracellular IFNγ as previously described [21] . Briefly , 200 µl whole blood was incubated at 37°C with 1 µg/ml/peptide overlapping 15mer SIV peptide pools ( described above ) or DMSO alone and the co-stimulatory antibodies anti-CD28 and anti-CD49d ( BD Biosciences/Pharmingen San Diego CA ) and Brefeldin A ( 10 µg/ml , Sigma ) for 6 h . Anti-CD3-PE , anti-CD4-FITC and anti-CD8-PerCP ( BD , clones SP34 , M-T477 and SK1 respectively ) antibodies were added for 30 min . Red blood cells were lysed ( FACS lysing solution , BD ) and the remaining leukocytes permeabilized ( FACS Permeabilizing Solution 2 , BD ) and incubated with anti-human IFNγ-APC antibody ( BD , clone B27 ) prior to fixation and acquisition ( LSRII , BD ) . Acquisition data were analyzed using Flowjo version 6 . 3 . 2 ( Tree Star , Ashland , OR ) . The percentage of antigen-specific gated lymphocytes expressing IFNγ was assessed in both CD3+CD4+ and CD3+CD8+ lymphocyte subsets . Responses to the immunodominant SIV Gag CD8 T-cell epitope KP9 in Mane-A*10+ animals were assessed by a Mane-A*10/KP9 tetramer as described [13] . Total peripheral CD4 T-cells were measured as a proportion of lymphocytes by flow cytometry on fresh blood . Plasma SIV RNA was quantitated by real time PCR on 140 µl of plasma at the University of Melbourne ( lower limit of quantitation 3 . 1 log10 copies/ml ) at all time-points using a TaqMan probe as previously described [21] , [22] and , to validate these results with a more sensitive assay , on pelleted virions from 1 . 0 mL of plasma at the National Cancer Institute ( lower limit of quantitation 1 . 5 log10 copies/ml ) as previously described [23] . The primary endpoint was the reduction in plasma SIV RNA in OPAL-immunized animals compared to controls by time-weighted area-under-the-curve ( TWAUC ) for 10 weeks following withdrawal of ART ( i . e . samples from weeks 12 to 20 ) . This summary statistical approach is recommended for studies such as these involving serial measurements [24] . We compared both active treatment groups ( OPAL-Gag and OPAL-All ) to controls separately and together . The primary analysis was restricted to animals that controlled viremia on the ART at week 10 ( VL<3 . 1 log10 copies/ml ) , since control of VL is an important predictor of the ability of animals to respond to immunotherapies [8] , [25] . A pre-planned secondary virologic endpoint was studying all live animals adjusting for both VL at the end of ART ( week 10 ) and Mane-A*10 status . Group comparisons used two-sample t-tests for continuous data , and Fisher's exact test for binary data . Survival analyses utilised Cox-regression analyses . Prior to initiating the study , we estimated the standard deviation of the return of VL after treatment interruption would be approximately 0 . 8 log10 copies of SIV RNA/mL plasma [5] , [16] , [17] , [18] , [19] , [20] . In this intensive study we estimated that 2 of the 12 monkeys within a group may have confounding problems such as incomplete response to ART or death from acute SIV infection . A 10 control vs 10 active treatment comparison yields 80% power ( p = 0 . 05 ) to detect a 1 . 0 log10 difference in TWAUC VL over the first 10 weeks . An estimated comparison of 10 control vs all 20 actively treated animals ( OPAL-Gag plus OPAL-All ) gave 80% power to detect differences of 0 . 87 log10 copies/ml VL reduction . This study was conducted according to a pre-written protocol using Good Laboratory Practice Standards from the Australian Therapeutic Goods Administration as a guide . Protocol deviations were minor and did not affect the results of the study . Partial data audits during the study did not raise any concerns about the study conduct .
OPAL immunotherapy was studied in SIV-infected pigtail macaques receiving ART . Pigtail macaques have at least an equivalently pathogenic course of SIV infection as alternate rhesus macaque models [14] , [26] . Thirty-six macaques were infected with SIVmac251 and 3 weeks later treatment with the antiretrovirals tenofovir and emtricitabine for 7 weeks was initiated . The animals were randomly allocated to 3 groups stratified by peak plasma SIV viral load ( VL ) , Mane-A*10 status ( an MHC class I gene that improves VL in SIV-infected pigtail macaques [15] ) , weight and gender . Macaques were immunized 4 times under the cover of antiretroviral therapy ( weeks 4 , 6 , 8 , 10 ) with autologous fresh PBMC mixed for 1 hour ex vivo with 10 µg/ml/peptide of either 125 overlapping SIV Gag 15mer peptides only ( OPAL-Gag ) , 823 SIV 15mer peptides spanning all 9 SIV proteins ( OPAL-All ) or un-immunized . The macaques were initially followed for 26 weeks after ceasing ART on week 10 . All 36 macaques became infected following SIVmac251 exposure and had a mean peak VL of 7 . 1 log10 copies/ml ( Table S1 ) . Prior to vaccination , 4 animals died during acute SIV infection with diarrhoea , dehydration , lethargy , anorexia and weight loss . The vaccinations were well tolerated , with no differences in mean weights , haematology parameters , or clinical observations in OPAL immunized animals compared to controls ( data not shown ) . There was striking SIV-specific CD4+ and CD8+ T-cell immunogenicity after the course of vaccination in the OPAL immunized animals . Mean Gag-specific CD4 and CD8 T-cell responses 2 weeks after the final immunization were 3 . 0% and 1 . 9% of all CD4 and CD8 T cells respectively in the OPAL-Gag group . Mean Gag-specific CD4 and CD8 T-cell responses 2 weeks after the final immunization were 0 . 84% and 0 . 37% in the OPAL-All group and 0 . 15% and 0 . 29% in controls ( Fig . 1A , B ) . The Gag-specific T cells in the OPAL-All immunized animals , but not control or OPAL-Gag only immunized animals , also had elevated T-cell responses to all other SIV proteins . Mean Env , Pol and combined regulatory protein-specific CD4/CD8 responses were 2 . 5%/11 . 8% , 0 . 8%/0 . 3% and 1 . 5%/2 . 4% respectively in the OPAL-All group compared to ≤0 . 4% for all CD4/8 responses to non-Gag antigens in control and OPAL-Gag groups ( Fig . 1C , D and Fig . 2 ) . The kinetics of induction of non-Gag CD4 and CD8 T cell responses in the OPAL-All group was similar for induction of Gag-specific T cell immunity . Stronger CD8 T-cell responses to non-Gag proteins correlated with reduced CD8 T-cell responses to Gag ( Fig . 1E ) . Thus , although a larger number of SIV proteins were recognized in the OPAL-All immunized animals , Gag responses were reduced in comparison to only immunizing with Gag peptides . Although the short linear peptides were primarily used to induce T cell immunity , we also studied serial plasma samples for SIV-specific antibodies . All animals seroconverted following SIV infection , as shown by Western Blot ( Fig . 3A ) . No significant enhancement of Gag or Env antibody responses occurred with the OPAL vaccinations ( Fig . 3B , C ) . There was a dip in mean Gag antibody responses during the period of ART in all groups consistent with reduced viral antigen during this period . In addition to the lack of difference in mean Gag ( p26 ) or Env ( gp36 ) responses shown in Figure 3B and 3C , there were also no significant different antibody responses to p16 , p68 , gp125 and gp140 across the vaccine groups ( not shown ) . The 7-week period of ART controlled VL to below 3 . 1 log10 copies/ml in 26 of the remaining 32 animals by week 10 ( Table S1 ) . The pre-defined ( per-protocol ) primary VL endpoint analyses was performed on animals controlling viremia on ART ( 26 animals ) . The 6 animals that failed to control viremia on ART had higher peak VLs at week 2 ( mean±SD of 7 . 74±0 . 33 compared to 6 . 94±0 . 52 for animals controlling viremia on ART , p<0 . 001 ) and higher VL following ART withdrawal ( 5 . 98±0 . 53 vs 4 . 28±0 . 90 , p<0 . 001 ) . Control of VL is likely to be important in achieving optimal results from immunotherapy of infected macaques [8] , [20] . The primary endpoint comparison of VL between combined OPAL-All and OPAL-Gag treatment groups in the 10 weeks after ART withdrawal was 0 . 5 log10 copies/ml lower than controls ( p = 0 . 084 , Fig 4 , Table 1 ) . Each vaccination group ( OPAL-All and OPAL-Gag ) had very similar reductions in VL . By 6 months after ART withdrawal , the mean difference in VL between control and OPAL-immunized groups was 0 . 93 log10 copies/ml ( p = 0 . 028 , Table 1 ) . As a secondary endpoint , we also analysed all 32 remaining animals by adjusting for VL control on ART and Mane-A*10 status . There was a significant difference in VL between controls and vaccinated macaques with these analyses at both 10 and 26 weeks off ART ( p = 0 . 050 , 0 . 016 respectively , Table 1 ) . To confirm the virologic findings using a sensitive independent VL assay , frozen plasma ( 1 ml ) from study week 32 was shipped to the National Cancer Institute ( NCI ) in Maryland , USA . Drs M Piatak and J Lifson kindly analysed the samples for SIV RNA blindly using an assay with a limit of quantitation of 1 . 5 log10 copies/ml ( Table S1 ) [23] . The University of Melbourne and NCI assays were tightly correlated ( r = 0 . 97 , p<0 . 001 ) and showed an almost identical mean reduction in viremia in vaccinees compared to controls at this time ( 0 . 82 vs 0 . 88 log10 copies/ml respectively ) . To further assess the durability of SIV control and prevention of disease with OPAL immunotherapy , we re-boosted all 32 animals in the same randomized groups 3 times with the identical procedure ( at week 36 , 39 , 42 ) without ART cover and followed the animals for an additional 6 months . Despite the lack of ART cover , SIV-specific T cell immunity was dramatically enhanced in immunized animals 2 weeks after the last vaccination , similarly to the primary vaccination ( Figs 1 , 2 ) . The T cell responses to Gag were again highest in the OPAL-Gag group with broader responses in the OPAL All group . The pattern of enhancement of T cell immunity was similar for the first and second vaccination sets ( Figs 1 , 2 ) . We again sampled plasma for viral load every 3–6 weeks . To account for the death of animals from AIDS , we used a “last observation carried forward” analysis for missing VL data . Significant viral control was maintained throughout the follow up period of just over 1 year off ART ( Fig 4A , Table 1 ) . In animals which controlled VL on ART , there was a mean 0 . 98 log10 copies/ml difference between controls and vaccinees 54 weeks after coming off ART ( p = 0 . 019 for time-weighted analysis ) . Twelve of the remaining 32 animals developed incipient AIDS and were euthanised during the extended follow up . All 6 animals that did not control viremia on ART required euthanasia . Of the 6 euthanised animals which did control viremia on ART , 5 were in the control group and one in the OPAL-Gag group . OPAL immunotherapy resulted in a survival benefit , analysing either the 26 animals that controlled viremia on ART ( p = 0 . 053 , Fig 4B , Table 1 ) or all 32 animals , adjusted for Mane-A*10 status and control of viremia on ART ( p = 0 . 02 , Table 1 ) .
In summary , OPAL immunotherapy , either using overlapping Gag SIV peptides or peptides spanning the whole SIV proteome was highly immunogenic and resulted in significantly lower viral loads and a survival benefit compared to unvaccinated controls . The virologic efficacy in OPAL-immunized macaques was durable for 12 months after ART cessation . Our findings on OPAL immunotherapy were observed despite the virulent SIVmac251-pigtail model studied [14] and provide strong proof-of-principle for the promise of this immunotherapy technique . The OPAL immunotherapy approach is simpler than many other cellular immunotherapies , particularly the use of dendritic cells . The use of DNA , CTLA-4 blockade and viral vector based approaches are also now showing some promise in macaque studies [17] , [27] , although such approaches have not yet been translated into human studies . This study added peptides to PBMC , however we have shown an even simpler technique , adding peptides to whole blood is also highly immunogenic , a technique that will be more widely applicable ( [8] and unpublished studies ) . This is one of the largest therapeutic SIV vaccine studies yet reported . Although it may have been ideal to have studied irrelevant peptide-pulsed autologous cells as an additional control group , we were concerned that this may have magnified the therapeutic effect or obscured any safety concerns . In the end , the vaccination process was both safe and effective . How well the findings on OPAL immunotherapy translate to humans with acute HIV-1 infection will be determined by clinical trials . Virus-specific CD4 T cells are typically very weak in HIV-infected humans or SIV-infected macaques; dramatic enhancement of these cells were induced by OPAL immunotherapy and this may underlie its efficacy [28] . We measured IFNγ-producing T cells in this study since we had not developed polyfunctional ICS assays prior to initiating the study . However , recent cross-sectional polyfunctional ICS assays suggests OPAL immunotherapy can also induce T cells capable of also expressing the cytokines TNFα and IL-2 , the chemokine MIP1β and the degranulation marker CD107a ( unpublished data ) . A ∼1 . 0 log10 reduction in VL would result in a substantial delay in progressive HIV disease in humans and allow a reasonable time period without the requirement to reintroduce ART [29] if these findings are confirmed in human trials . Both the control and vaccinated macaques were treated with ART early in this study ( 3 weeks after infection ) , which alone can be associated with a transiently improved outcome in humans [30] . None-the-less , a massive loss of CD4+ T cells in the gut occurs within 2 weeks of infection [31] . Although it may be challenging to identify humans within 3 weeks of infection , this is when HIV-1 subjects typically present with acute infection . The durable control of viremia exhibited by the vaccinated animals is interesting and consistent with other recent macaque studies [27] , suggesting the need for re-immunization may not be substantial . We cannot attribute the durable control of viremia to the second set of immunizations; there was only a marginal , non-significant , increase in the difference in VL between OPAL vaccinees and controls before and after the second immunization series . Further studies are required to address the timing and benefit of ART cover during boosting immunizations with OPAL immunotherapy . Control of viremia was similar for the OPAL-Gag and OPAL-All groups . Gag-specific CD4 and CD8 T-cell responses in OPAL-Gag animals 5 . 1- and 3 . 5-fold greater than those in the OPAL-All animals , despite an identical dose of Gag overlapping peptides . This suggests antigenic competition between peptides from Gag and the other SIV proteins . Inducing immunodominant non-Gag T-cell responses by multi-protein HIV vaccines may limit the development of Gag-specific T-cell responses [21] . A large human cohort study demonstrated Gag-specific T-cell responses were the most effective in controlling HIV viremia [32] . Useful subdominant T cell responses may be particularly susceptible to dominant non-Gag T cell responses [33] , [34] . The utility , if any , of inducing T-cell responses to non-Gag proteins ( i . e . excluding Gag peptides from the vaccine antigens ) can be addressed in future studies of this flexible vaccine technology . Therapeutic HIV vaccines may not need to aim for maximally broad multi-protein HIV-specific immunity . OPAL immunotherapy with Gag peptides is proceeding into initial trials in HIV-infected humans . Additional peptides can readily be added into standard consensus strains mixes to cover common strain or subtype variations between strains with this technology [35] . Additional technologies such as toggling variable amino acids peptides may provide further T cell immunogenicity with this general technology [36] . Immunotherapy with peptides delivered onto fresh blood may have potential applicability for other chronic viral diseases such as hepatitis C virus infection and some cancers such as melanoma [37] . | Effective immunotherapies for HIV are needed . We assessed a simple technique , reinfusion of fresh blood cells incubating with overlapping SIV peptides ( Overlapping Peptide-pulsed Autologous ceLls , OPAL ) , in 36 randomly allocated SIV-infected monkeys . We analyzed this therapy for the stimulation of immunity , control of virus levels , and prevention of AIDS . The OPAL immunotherapy was safe and stimulated remarkable levels of T-cell immunity . Levels of virus in vaccinated monkeys were 10-fold lower than in controls , and this was durable for over 1 year after the initial vaccinations . The immunotherapy resulted in fewer deaths from AIDS . We conclude this is a promising immunotherapy technique . Trials in HIV-infected humans of OPAL therapy are planned . |
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Microbial infection during various stages of human development produces widely different clinical outcomes , yet the links between age-related changes in the immune compartment and functional immunity remain unclear . The ability of the immune system to respond to specific antigens and mediate protection in early life is closely correlated with the level of diversification of lymphocyte antigen receptors . We have previously shown that the neonatal primary CD8+ T cell response to replication competent virus is significantly constricted compared to the adult response . In the present study , we have analyzed the subsequent formation of neonatal memory CD8+ T cells and their response to secondary infectious challenge . In particular , we asked whether the less diverse CD8+ T cell clonotypes that are elicited by neonatal vaccination with replication competent virus are ‘locked-in’ to the adult memory T cell , and thus may compromise the strength of adult immunity . Here we report that neonatal memory CD8+ T cells mediate poor recall responses compared to adults and are comprised of a repertoire of lower avidity T cells . During a later infectious challenge the neonatal memory CD8+ T cells compete poorly with the fully diverse repertoire of naïve adult CD8+ T cells and are outgrown by the adult primary response . This has important implications for the timing of vaccination in early life .
The immune system of neonates is generally characterized as immature and more susceptible to infections with various pathogens [1]–[3] . Many of the most debilitating infections are inflicted by intracellular pathogens that are either vertically transmitted or acquired very early on in life ( e . g . HIV , CMV , EBV , TB , HSV ) . Although CD8+ T cells are considered the key players in combating these intracellular pathogens , their capacity to provide protective immunity in neonates is still poorly understood . Importantly , since the timing of infection in some cases affects the subsequent pathogen load and pathogenesis of infection , we wished to understand whether early exposure to infection or vaccination compromises the later ability to control infection as an adult . The ability of CD8+ T cells to mount a protective response to new pathogens is dependent upon the presence of a broad repertoire of T cells of appropriate immune functionality [4] , [5] . Diversification of the repertoire is developmentally regulated and the neonatal T cell repertoire in mice is restricted not only by the reduced number of T cells that are present , but also by the number of unique antigen receptors that are able to be produced . Diversity of T-cell receptor ( TCR ) usage is accomplished by multiple mechanisms during T-cell maturation in the thymus [6] . Somatic recombination of germline segments identified as variable ( V ) , diversity ( D ) , and joining ( J ) segments by Rag-1 and Rag-2 proteins results in an enormous number of T cells with distinct antigen binding domains . Further diversification is accomplished by nibbling or loss of germline-encoded nucleotides and the addition of complementary template-dependent ( P ) and random template-independent ( N ) nucleotide additions at the junctions between these germline segments prior to ligation [7] . The addition of N regions between germline-encoded segments is mediated entirely by terminal deoxynucleotidyl transferase ( TdT ) and it has been estimated that 90–95% of the diversity of the T cell repertoire is attributed to this critical step [8] . The expression of TdT is likely to have a significant impact on both the quantity and quality of TCR clonotypes that are able to respond to various pathogens at different stages of development . In mice , TdT is not upregulated in the thymus until 4–5 days after birth , with significant nucleotide additions being observed at day 8 [9] , [10] . Thus , in the first week of life we would still expect much of the peripheral T cell repertoire to be comprised of clonotypes that have not been sculpted by TdT and thus be devoid of N-additions . Indeed , we recently have characterized the TCRβ repertoire of CD8+ T cells responding to the immunodominant HSV-1 epitope , gB498–505/Kb ( gB-8p ) in neonate [11] and adult mice [12] and showed that the gB-8p TCRβ repertoire in neonatal mice is severely restricted and comprised of more germline sequence-rich clonotypes [11] . This restricted TCR repertoire in neonates may have direct effects on the ability of primary neonatal CD8+ T cells to respond to antigen , as well as indirect effects on their ability to transition into the long-lived memory pool . The key question we wished to examine in this report is whether the primary CD8+ T cell response in neonates induces a memory pool of sufficient diversity to later mount a robust secondary response to infection , or whether neonatal infections ‘lock-in’ a poor memory CD8+ T cell population that exhibits impaired recall responses in later life . Here , we demonstrate how the developmental stage of the host at the time of vaccination or primary infection can alter the composition of the long-lived memory CD8+ T cell pool , as well as their ability to respond to subsequent infections .
In this report , we aimed to compare antiviral memory CD8+ T cells that were generated in either neonatal or adult stages of development . Over 90% of the CD8+ T cell response in HSV-1-infected C57BL/6 mice is directed against a single Kb-restricted immunodominant epitope in the glycoprotein B ( denoted gB-8p ) [13] . To compare the expansion of naïve and memory gB-8p CD8+ T cells in neonatal ( 7-day old ) and adult mice ( 8–12 week-old ) , both age groups were acutely infected with vaccinia virus expressing the gB-8p peptide ( VACV-gB , i . p . ) and challenged 6–8 weeks later with HSV-1 ( i . p . ) . In this way , we were able to mimic neonatal vaccinations with live viral vectors and preferentially prime the neonatal T cells that were available in early life . However , smaller numbers of peripheral T cells are present in neonatal mice compared to adults [14] , [15] . Therefore , as in our previous study of the neonatal primary response [11] , the dose of VACV-gB was normalized in neonates ( 2×101 PFU/mouse ) and adults ( 2×105 PFU/mouse ) by titrating VACV-gB doses down to the least amount of virus that was required to elicit a comparable relative frequency ( ∼10% ) of gB-8p CD8+ T cells at the peak of the response ( Fig . 1A ) . This difference in viral dose is necessary as an adult dose is lethal to neonates and adult mice administered a neonatal dose clear the virus too rapidly to allow the detection of antigen-specific CD8+ T cells . In addition , as primary vaccinia virus infection is intended to mimic vaccination at different ages , we note that a decreased dose of immunogen is routinely administered to children for a variety of human vaccine protocols . While the total number of gB-8p CD8+ T cells was much higher in adults at the peak of the primary infection ( due to increased cellularity ) , similar levels of gB-8p-specific memory T cells were observed in neonatal- and adult-immunized mice by 6 weeks post-infection ( Fig . 1B ) . All mice were then challenged with 1×106 pfu of HSV-1 ( i . p . ) , and we observed a secondary CD8+ T cell response that was comparable between neonate and adult-vaccinated mice ( Fig . 1B ) . We recently have compared the clonal composition of the gB-8p-specific TCRβ repertoires involved in the primary CD8+ T cell responses to VACV-gB in neonatal and adult mice [11] . The gB-8p-specific TCRβ repertoires in neonatal mice were found to have the same basic features , in terms of gene usage biases and CDR3β amino acid motif , as in adult mice . However , the significantly less diverse gB-8p-specific Vβ10+ TCRβ repertoires of neonatal mice were predominantly comprised of shorter germline-gene-encoded CDR3β sequences . This published data on the primary response to vaccination in adults and neonates was used as a baseline for comparison with the secondary responses following challenge . To determine whether the less-developed T cell repertoires involved in the immune responses to neonatal vaccination are ‘locked-in’ to secondary responses to infection , we examined the clonotypic composition of gB-8p-specific TCRβ repertoires involved in secondary CD8+ T cell responses to HSV-1 infection in adult mice that had been previously vaccinated with VACV-gB either as neonates or as adults . The same Vβ10 gene usage bias associated with primary CD8+ T cell responses to the gB-8p epitope was also observed in both neonatal-vaccinated and adult-vaccinated mice at the peak of the secondary immune responses ( Fig . 2A ) . However , the large inter-mouse variability in Vβ10 gene usage observed in the primary responses [11] in neonatal mice was substantially reduced in the secondary immune responses . Single-cell sequencing was then used to examine in greater depth the composition of the gB-8p-specific Vβ10+ CD8+ TCRβ repertoires involved in the secondary immune responses . The gB-8p-specific Vβ10+ TCRβ repertoire data are summarized in Table 1 and representative repertoires are shown in Fig . S1 , and the gB-8p-specific Vβ10+ CD8+ TCRβ repertoire characteristics quantitatively compared in Fig . S2 . It is important to mention that while the primary and secondary responses were elicited by two different pathogens to avoid antibody interference , we previously showed that the clonotypic composition of gB-8p-specific Vβ10+ CD8+ cells in the primary response is similar among a wide range of infections , including VACV-gB and HSV1 [16] . In adult mice , the general characteristics of the gB-8p-specific Vβ10+ CD8+ TCRβ repertoires associated with primary responses were largely preserved in the secondary immune responses ( Fig . S2 ) . In neonatal mice , we have previously reported that the primary response contains a restricted repertoire of T cells that is largely germline encoded [11] . Thus , we expected the secondary response in neonatal-vaccinated mice should also comprise a more restricted subset of these cells . Surprisingly , the secondary response in neonatal-vaccinated mice was significantly more diverse and showed a much higher proportion of TCRβ clonotypes requiring N-additions than the previously reported neonatal primary response [11] ( Fig . 2B , C ) . However , despite this diversification of the gB-8p-specific Vβ10+ TCRβ repertoires between primary neonatal responses and secondary responses , TCRβ clonotype diversity remained significantly reduced compared to mice that were primed as adults ( Fig . 2C ) . These results suggest that priming neonatal mice leads to a recall response of intermediate diversity between the neonatal primary response and the ‘normal’ adult secondary response . Thus , we investigated what mechanisms contribute to this partial “locking-in” of the immature neonatal repertoire during secondary responses later in life . Since we observe a significant change in the gB-8p-specific CD8+ TCRβ repertoire between the neonatal primary and secondary responses , we set out to identify when this diversification occurred . Firstly , it seems possible that only a subset of the neonatal primary response contributes to the secondary response , and that these cells are selectively the more ‘adult-like’ clonotypes . This selection for adult-like clonotypes could occur either during the contraction phase from the primary response , or during the expansion phase from the memory response to the secondary response . To investigate this , we first analyzed the neonatal resting memory compartment ( Fig . S2 ) . Although there was a trend for a more diverse ( Fig . S2K , L ) and less germline encoded ( Fig . S2J ) TCRβ repertoire in the resting memory population compared with the neonatal primary response , this was not sufficient to explain our observations for the secondary responses in neonatal-vaccinated mice . A key question is to what extent memory neonatal gB-8p CD8+ T cells participate in a secondary immune response ? That is , we would expect neonatal gB-8p memory T cells to be present at higher numbers than adult naive gB-8p T cells , and therefore dominate the recall response . Alternatively , it is possible that the less diverse neonatal T cell clonotypes will be impaired in their ability to compete with a fully developed adult naïve T cell repertoire and thus be underrepresented in the response to a secondary challenge . To examine these possibilities , we adoptively transferred equal numbers of neonatal or adult gB-8p memory CD8+ T cells into different congenic recipient mice ( CD45 . 1 ) followed by HSV-1 challenge . This allowed us to distinguish between the expansion of naïve ( CD45 . 1 ) and memory ( CD45 . 2 ) T cells during the secondary immune response . Donor neonatal and adult gB-8p memory T cells were evaluated at the peak of the recall response following HSV-1 infection , and the proportion of the response contributed by neonatal or adult memory cells was assessed . The magnitudes of the overall response were comparable between the recipients of neonatal memory and adult memory T cells ( Fig . 3A ) . However , approximately 3 fold fewer neonatal memory T cells were observed at the peak of the recall response compared to the adult memory T cells ( Fig . 3B ) . In repeat experiments , we also bled recipient mice at 4 and 6 days post-infection and observed a greater contribution by adult memory CD8+ T cells to the overall response at both time points ( Fig . S3 ) . Importantly , these differences were not statistically significant until day 6 , suggesting that neonatal memory CD8+ T cell become activated but exhibit an impaired ability to expand and compete in the adult mouse . The reduced contribution of neonatal memory cells compared with adult memory cells in the secondary response raised the possibility that the majority of cells in the secondary recall response in neonatally-vaccinated mice may actually be primary adult CD8+ T cells , rather than neonatal memory cells . To investigate this , we first looked at the clonotypic differences between donor secondary neonate gB-8p-specific memory and the recipient primary adult gB-8p-specific effector CD8+ T cell populations at 6 days post-infection . Although a significantly smaller proportion of the secondary neonatal gB-8p-specific memory T cells used the Vβ10 gene compared with the primary adult gB-8p-specific effector T cells , Vβ10 gene usage was prevalent in most mice ( Fig . 4A ) . Single cell sorting and sequencing of the gB-8p-specific Vβ10+ TCRβ clonotypes for these two populations ( Table 1; Fig . S1 C , D ) revealed that a significantly higher proportion of secondary neonate memory TCRβ clonotypes required no nucleotide additions ( Fig . 4B ) and the secondary neonate memory TCRβ repertoires were significantly less diverse compared with the primary adult gB-8p-specific effector Vβ10+ TCRβ repertoires in the same mouse ( Fig . 4C ) . Furthermore , we verified that the features of the secondary neonate gB-8p-specific memory Vβ10+ TCRβ repertoires were indicative of the resting memory population following VACV-gB infection in neonatal mice ( Fig . S2 G–R ) . To summarize , when we tracked the fate of adoptively transferred neonatal memory cells in the recall response in adult congenic recipients , these neonatal memory TCRβ repertoires maintained similar features to the neonatal memory population . However , the other major contributor to the responding population were the adult primary cells . When we separately analyzed the repertoire of the adult cells contributing to the response to the secondary challenge , we found that they resembled the normal adult secondary response , and were comprised of a significantly higher proportion of TCRβ clonotypes with N-additions , and were significantly more diverse than the neonatal memory cells in the same response . This suggests that the observed ‘diversification’ of the secondary response in neonatal-vaccinated mice arose not because the neonatal repertoire itself was altered , but because the neonatal recall response was so poor , that it was outcompeted by the adult primary response . Therefore , the combination of the narrow neonatal memory and diverse adult primary repertoires led to the observed intermediate level of TCRβ repertoire diversity in the secondary responses in neonatal vaccinated mice . Given the less diverse TCR repertoire and poor recall responses exhibited by neonatal memory T cells , we next questioned whether this might be mediated by a neonatal T cell pool is insufficiently broad to select high avidity memory gB-8p CD8+ T cells . This is important since high avidity T cells have been shown to respond more vigorously to infection and kill infected cells faster than low avidity T cells [17] . Our hypothesis was that the responding pool of gB-8p CD8+ T cells in neonates will not include as many ‘best-fit’ T cells and will exhibit much lower TCR avidity than adult gB-8p CD8+ T cells . To test this , TCR:pMHC disassociation kinetics were assessed between neonate and adult memory gB-8p CD8+ T cells . During the steady-state , resting memory phase , neonatal gB-8p CD8+ T cells demonstrated much faster pMHC:TCR off-rates compared to adults ( Fig . 5 ) . Collectively , these data suggest that the less diverse neonatal gB-8p memory repertoire undergoes poor recall responses due to lower proportion of high-avidity CD8+ T cells in the memory compartment . To validate and broaden the significance of our results , we next asked whether other types of infection also give rise to neonatal memory CD8+ T cells with poor recall efficacy . For these experiments , we infected neonatal and adult mice with an attenuated strain of Listeria monocytogenes that expresses the gB-8p peptide ( denoted ActA LM-gB ) . This strain lacks a gene that is required for mobility and is incapable of infecting nearby cells , allowing us to better control for variations in the availability and abundance of antigen and challenge both age groups with the same dose . At six weeks post-infection , we co-transferred equal numbers of neonatal ( CD45 . 2 ) and adult ( CD45 . 1 ) gB-specific memory CD8+ T cells into congenically marked recipients ( Thy1 . 1 ) , which were subsequently infected with HSV-1 ( 1×106 pfu , i . p . ) . By transferring memory CD8+ T cells from adult or neonatal primed donors into the same host , we could rule out potential environmental differences arising during the response . Importantly , the percentage of neonatal and adult gB-specific memory CD8+ T cells were found to be similar prior to infection , with neonatal donor cells slightly outnumbering adults ( 68 . 8%±2 . 3 vs 30 . 6%±2 . 3 ) . At the peak of the response , spleens were harvested and the ratio of neonatal to adult memory CD8+ T cells was calculated . Consistent with our results following vaccinia infection , we again observed a greater contribution of adult memory CD8+ T cells to the secondary response ( Fig . 6A ) . Importantly , this analysis measured the total donor response by IFNg production , and thus included both Vβ10+ and Vβ10- gB-8p specific CD8+ T cells . These findings suggest that limited recall responses are likely a common feature among neonatal memory CD8+ T cells , regardless of how they are initially primed . Despite the fact that neonatal vaccinated mice exhibited reduced TCR diversity , TCR avidity and lower recall efficiency , a critical remaining question was whether these differences resulted in impaired immune protection . To address this , we compared the ability of neonatal and adult memory CD8+ T cells to clear a high dose infection of recombinant Listeria monocytogenes expressing gB-8p ( Lm-gB ) . We choose to challenge mice with LM-gB instead of HSV-1 for these studies because well-defined sites of infection can be more easily monitored . Neonatal and adult mice were again vaccinated with ActA-/- LM-gB ( 1×106 cfu , i . p . ) and CD8+ T cells were allowed to transition into the memory phase . At 6 weeks post-infection , we transferred the same number of neonatal or adult gB-specific CD8+ T cells into separate recipient mice and challenged both groups with wt LM-gB ( 5×104 cfu , i . v . ) . Three days later , the livers of recipient mice were homogenized and the bacterial loads were examined . As shown in Fig . 6B , adult memory CD8+ T cells reduced the bacterial load 3 fold compared to an equivalent number of neonatal memory CD8+ T cells . Indeed , the neonatal memory CD8+ T cells showed no better bacterial control than naïve adult cells ( which we have shown contribute significantly to the response in the presence of a neonatal memory response ) . This data suggests that a suboptimal recall response exhibited by neonatal memory CD8+ T cells could result in reduced immune protection .
The focus of this report was to determine how the composition and responsiveness of the memory CD8+ T cell pool is altered by neonatal vaccination or infection that occurs prior to the diversification of the CD8+ T cell repertoire . Our results demonstrate that the restricted neonatal T cell memory pool induced by early vaccination is in fact comprised of fewer clonotypes that are also of lower avidity than the adult response . However , while vaccination early in life recruits many of these ‘less fit’ clonotypes and allows them to persist in the memory CD8+ T cell compartment , these neonatal memory clonotypes are not efficiently recruited into the proliferative recall response to secondary challenge . In the absence of a strong neonatal memory response mediating early viral control , a robust adult primary response is generated , which ultimately comes to dominate the neonatal memory population . Despite this contribution from the adult repertoire , the secondary response in mice vaccinated as neonates remained significantly restricted , in terms of the diversity of TCR clonotypes , compared with secondary responses in adult-vaccinated mice . These observations describe a situation that is mechanistically similar to the phenomenon of ‘original antigenic sin’ , in which prior infections with a related pathogen can “trap” the immune system into responding with less efficient memory clonotypes . However , in this case , the same pathogen may “trap” less efficient clonotypes into the immune reserve simply by priming these T cells during the early stages of development . One of the most interesting findings of our present study was that there is significantly more recruitment of adult naïve clonotypes into the neonatal secondary response than the adult secondary response . Thus , when neonatal memory CD8+ T cells are faced with competition from a fully-developed adult naïve T cell repertoire , they prove inferior and make a smaller contribution to the overall memory response . Previous reports indicate that new naïve T cells seed the periphery at a relatively constant rate of 1–2×106 cells/day and the number of splenic recent thymic emigrants reach a peak at ∼6 weeks of age [18] . These naïve clonotypes will have also been sculpted by TdT , which should allow for significantly greater opportunities to generate high avidity gB-8p-specific CD8+ T cells . Indeed , our data suggest that more ‘best-fit’ gB-8p-specific CD8+ T cells exist in the adult naïve pool at 6–7 weeks of age compared to those available in the neonate memory pool . A number of recent reports have examined the role of TdT in generating robust anti-viral CD8+ T cell immunity to acute infections . Mansour Haeryfar et al . showed that the overall magnitude and breadth of the CD8+ T cell responses to influenza and vaccinia virus were reduced in TdT-/- mice and the hierarchy of immunodominant epitopes was altered [19] . The authors proposed that the reshuffling of immunodominant determinants was due to the loss of high affinity clones for some ( but not all ) viral determinants . In support of this , Kedzierska et . al . showed that the avidity of influenza-specific CD8+ T cells was lower in TdT-/- mice for the NP366 epitope , where the response is public and clonotypically restricted , but not for the PA224 epitope , which elicits a more private and diverse TCR repertoire [20] . Ruckwardt et al . [21] recently reported differences between neonate and adult CD8+ T cell responses to respiratory syncytial virus infection with respect to TCR diversity , functional avidity , precursor frequency and epitope immunodominance hierarchy . However , in terms of the latter , this study suggests that the shifting epitope immunodominance is not associated with TdT . Together , these studies indicate that the relative role of TdT in promoting optimal anti-viral CD8+ T cell immunity may ultimately depend upon the clonal complexity of the T cell response against specific viral determinants being examined . Although the neonatal repertoire is also comprised of TCRs generated in the absence of TdT , it is important to mention that the neonatal repertoire is potentially even less diverse than adult TdT-/- mice due to lower numbers of T-cells in the neonatal periphery . Based on previous estimates , we would expect ∼2×106 different TCRs ( with an average clone size of 10 ) in adult wild-type mice and ∼1–2×105 different TCRs ( with an average clone size of 100 ) in adult TdT-/- mice [8] , [22] , [23] . However , there are 10–100 times fewer CD8+ T cells in 7-day old neonatal mice compared to adult mice [24] , [25] . Therefore we expect the neonatal repertoire to consist of only a small fraction of the total T cell repertoire that is available in adult TdT-/- mice . In our report , we elected to prime neonates and adult mice with either an acute virus ( VACV-gB ) or an attenuated bacterial strain ( ActA- LM-gB ) so that we could more closely mimic vaccinations and clearly delineate effector and memory CD8+ T cell responses . However , generating sufficiently broad CD8+ T cell repertoires may be even more beneficial in the context of chronic and persistent pathogens . Many of these chronic pathogens ( e . g . HIV , HCV ) are able to evade the immune response by undergoing a high rate of mutation . Thus , not surprisingly , one key correlate of immune protection against these chronic viral pathogens is diversity in TCR usage [26]–[29] . Responding with a larger number of distinct clonotypes that can recognize multiple epitopes on these pathogens as well as a diverse range of epitope variants has been shown to provide better protection against immune escape . While this has not been rigorously examined in neonatal mice , our prediction would be that the diminished neonatal repertoire would be significantly impaired in limiting the emergence of viral escape mutants . These results suggest that there are long-term consequences for vaccinations or infections that occur prior to the diversification and maturation of the adult immune system . However , it is important to mention that our results do not rule out the possibility that other developmental factors may contribute to poor neonatal immunity . In this report , we have used TCR analysis as a tool to track neonatal clonotypes , since phenotypic markers alone cannot be used to reliably distinguish naïve and memory CD8+ T cells [30] . Our analysis shows that neonatal clonotypes transition into the adult memory pool , but undergo a limited recall response and confer reduced immunity against a secondary challenge . In regard to these challenge experiments , it is important to point out that pathogen clearance was examined in recipient mice following adoptive transfer of either neonatal or adult memory CD8+ T cells . Thus , only a fraction of the total memory pool is participating in the secondary response . This is an important point since other studies have shown that clearance is dependent upon the number of CD8+ T cells that are adoptively transferred into recipient mice prior to challenge [31] . Under limiting conditions , we observed a statistically significant difference in the ability of neonatal and adult memory CD8+ T cells to clear infection . Importantly , this data does not necessarily indicate that neonatal memory CD8+ T cells are incapable of responding ( Fig . 6A clearly shows some contribution of neonatal memory CD8+ T cells to the recall response ) , but rather that they are less functional compared to adults at the level that was examined . The degree of immune protection by neonatal memory CD8+ T cells will likely vary with the number and type of memory cells that are generated [32] . Given that neonatal T cell clonotypes do in fact gain access to the memory pool in adults , it is now imperative that we fully understand how neonatal memory CD8+ T cells differ than adult memory CD8+ T cells at the cellular and molecular level . These studies would be especially important to consider in the context of tissue resident memory T cells , or the long-lived population of T cells that remain detached at the peripheral sites of initial pathogen encounter ( i . e . lung , skin , gut , etc ) . Knowledge from these studies will provide us with a solid platform to understand how infections early in life may impact the development of T-cell mediated diseases in adulthood , and also to guide rational design of vaccines that can be safely administered to neonates .
C57BL/6 ( H-2b ) and B6-LY5 . 2/Cr ( H-2b ) mice were purchased from NCI ( Frederick , MD ) and Thy1 . 1 mice ( B6 . PL-Thy1a/CyJ ) were obtained from The Jackson Laboratory ( Bar Harbor , Maine ) . All mice were maintained under pathogen-free conditions in the animal facility at either the University of Arizona or Cornell University . Pregnant mice were individually housed and monitored daily for births . Neonatal mice were used at 7 days of age . Adult mice were obtained from commercial vendors and used at 2–3 months of age . All animal experiments were conducted by guidelines set by the University of Arizona Institutional Animal Care and Use Committee ( IACUC ) , under the University of Arizona approved animal protocol #08-059 , and in accordance with the U . S . Animal Welfare Act . Recombinant vaccinia virus ( VACV ) expressing the MHC class I-restricted CTL epitope HSV gB498–505 ( SSIEFARL , gB-8p in the text ) , designated VACV-gB , was generously provided by Dr . S . S . Tevethia ( Pennsylvania State University of College Medicine , PA ) . VACV-gB viral stocks were propagated and quantified in 143B cells . HSV-1 strain 17 was obtained from Dr . D . J . McGeoch ( University of Glasgow , Scotland , U . K . ) , cloned as a syn+ variant and tittered on Vero cells in our laboratory as previously described [33] , [34] . Neonatal and adult mice were intraperitoneally infected with either 2×101 or 2×105 PFU , respectively . Recombinant strains of Listeria monocytogenes expressing the gB-8p epitope , designated Lm-gB or ΔActA Lm-gB , were provided by Dr . Sing Sing Way ( Cincinnati Children's Hospital Medical Center , OH ) and have been previously described [35] . Prior to infection , the bacteria were grown to log phase ( OD600 0 . 1 ) , and mice were either immunized with ΔActA Lm-gB ( 1×106 CFU , i . p . ) or challenged with LM-gB ( 5×104 CFU i . v . ) in 100 ul of PBS . The gB-8p:Kb tetramer was obtained from the National Institutes of Health Tetramer Core Facility ( Emory University , Atlanta , GA ) . mAbs anti-CD8α ( clone 53–6 . 7 ) , anti-CD4 ( RM4-5 ) , anti-CD11a ( 2D7 ) , anti-Vβ10 ( B21 . 5 ) , anti-Vβ8 ( F23 . 1 ) , anti-CD45 . 2 ( 104 ) were purchased from commercial sources . FCM data was acquired on the custom-made FACS LSRII instrument equipped with four lasers , using Diva software ( BD Biosciences ) , and analysis was performed using FloJo software ( Treestar ) . To evaluate the degree of TCR avidity , the relative off-rates were determined by a tetramer decay assay . For this , splenocytes were stained with anti-CD8α and gB-8p:Kb tetramer for 1 hour at 4°C . These cells were then washed and incubated in the presence of saturating amounts of anti-Kb antibody ( AF6; Biolegend ) at room temperature to prevent rebinding . At various times , cells were removed , placed in fixation buffer and the amount of gB-8p:Kb tetramer remaining on the surface was quantified by flow cytometry . These measurements were expressed as a percentage relative to tetramer staining at time t = 0 . Splenocytes were harvested at indicated timepoints following infection . CD8+ T cells were isolated using positive immunomagnetic selection ( Miltenyi Biotec , Auburn , CA ) and CD8+CD4-gB-8p:Kb+ Vβ10+ lymphocytes were individually sorted using the FACSAria cell sorter system ( BD Biosciences ) as previously described [12] , [35] . Control wells without sorted cells were included on every plate to identify any possible contamination . cDNA synthesis , PCR amplification and sequencing of individual Vβ10 transcripts were performed exactly as previously described [12] , [35] . The gB-8p-specific CD8+ TCRβ repertoires were characterized by sequentially aligning each TCRβ sequence with the Vβ10 ( TRBV4 in IMGT nomenclature ) gene , followed by the best-match Jβ gene and the best-match Dβ gene . This analysis was done using the IMGT reference alleles for the Mus musculus TRB genes [36] . The CDR3β sequence was then identified between , and inclusive of , the conserved cysteine in the Vβ-region and the conserved phenylalanine in the Jβ-region . The diversities of the CD8+ TCRβ repertoires specific for the gB-8p-epitope in each mouse were evaluated using two different measures of diversity , the number of different TCRβ amino acid sequence clonotypes and Simpson's diversity index [37] . Simpson's diversity index accounts for both the variety of amino acid sequence clonotypes and their clone sizes , and ranges in value from 0 ( minimal diversity ) to 1 ( maximal diversity ) . To account for differences in the sizes of the TCRβ repertoire samples , TCRβ repertoire diversity was estimated as the median value of 10 , 000 random draws of subsamples of 48 TCRβ sequences from the total TCRβ repertoire sample [37] . The diversity analysis was performed using Matlab ( The Mathworks , Natick , MA ) . To evaluate immune protection , 5×104 CFU of wt Lm-gB was administered intravenously as previously described [38] . On day 3 post-infection , livers were harvested into sterile PBS and weighed . Tissues were homogenized mechanically using a Tissue-Tearor electric homogenizer ( BioSpec Products , Bartlesville , OK ) . Serial dilutions were made in sterile PBS and plated onto BHI agar . Plates were incubated overnight at 37°C . The log10 CFU/g of tissue was calculated as: log10 [ ( CFU/dilution factor ) × ( organ weight+homogenate volume ) /organ weight ) ] . TCRβ repertoire features of the endogenous CD8+ T cell responses were compared using a Mann-Whitney test for all pairwise comparisons between age/infection groups , with Bonferroni correction for multiple pairwise comparisons . TCRβ repertoire features of the recipient and adoptively transferred CD8+ T cell populations were compared using a Wilcoxon text . For the tetramer decay assay results , exponential decay rates for individual mice were compared between neonatal and adult CD8+ T cells using a Mann-Whitney test . All statistical analyses were performed using GraphPad Prism software ( GraphPad Software Inc , San Diego , CA ) . | Newborns typically have a heightened sensitivity to infectious diseases , the reasons for which are not yet well understood . One contributing factor is the limited diversity of lymphocyte receptors early in life to recognize antigen and control infection . We have previously shown that antigen-specific CD8+ T cell repertoires are significantly constricted in neonates compared with adults . In this study , we addressed the question of whether the developmental stage of the host at the time of vaccination influences the composition of the memory CD8+ T cell repertoire and its ability to mount a robust response to subsequent infections . We observed that the antigen-specific T cell repertoires elicited in the context of an acute neonatal infection , that are less diverse and comprised of lower-avidity T cells , are partially ‘locked-in’ to the adult memory T cell repertoire . However , in the face of a secondary infectious challenge , naïve adult T cells outcompete the lower avidity neonatal memory T cells and raise the diversity of the overall CD8+ T cell response . These results have potential implications for the design of vaccines to be administered in early life . |
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A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities . The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease . The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis ( CL ) can be very challenging due to the many inference networks between large sets of host and vector species , with considerable heterogeneity in disease patterns in space and time . One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic , climatic and environmental factors at two different scales , in the Neotropical moist forest biome ( Amazonian basin and surrounding forest ecosystems ) and in the surrounding region of French Guiana . With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases , we obtained risk maps with high statistical support . The predominantly identified human CL risk areas are those where the human impact on the environment is significant , associated with less contributory climatic and ecological factors . For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human , although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America .
Vector-borne diseases that threaten one-third of the world's population are driven by intertwined socio-economic and environmental factors , such as climate change and modifications of ecosystems through deforestation , conversion of natural habitats to man-made ecosystems and extended urbanisation [1] . To understand these disease agent dynamics , it is necessary to determine ( 1 ) the geographic area and associated ecological conditions where the transmission cycle could likely occur , with the infected vectors and host reservoirs , ( 2 ) the risk factors that promote transmission to humans and ( 3 ) the human communities that are the most exposed to infection hazards on a local scale [1–3] . Landscape ecology may contribute to the knowledge of the influence of biotic and abiotic factors on the presence and dynamics of the vectors and host reservoirs [4] . It also favours the development of spatial models of risk prediction at a relevant geographic scale [5] , which finds its theoretical and more practical extensions within the new pathogeography paradigm [6] . These spatial models theoretically make it possible to reveal the geographical areas where the transmission rate of the disease risk is predicted to be the highest by identifying the environmental , climatic and socio-economic risk factors that may expose the most vulnerable individuals and populations to microbial hazards and threats [7 , 8] . These models may summarise the concept of risk in epidemiology underlying the notions of hazard , exposure and vulnerability . Hazard represents at least the occurrence and distribution of the microbial agent under scrutiny in a geographical area as well as the distribution of vectors , hosts and their interaction . Exposure is related to the probability of an individual or a community being exposed to microbial hazard through recreational or occupational activities . Vulnerability represents the individual and group conditions that make humans more sensitive to infection , e . g . , genetic susceptibility or malnourished people [9] . Within the last decade or so , ecological niche models ( ENMs ) have been proposed in landscape epidemiology to explore the relationships between the potential distribution of vectors or host species reservoirs and environmental variables [10] . The ENMs are used to circumvent gaps in knowledge of species distribution and are based on the occurrence of a species and relevant environmental variables for identifying the most favourable habitats for the establishment and survival of the species of interest [11] . Then they project the relationships over a geographical area to identify non-surveyed areas where there are favourable environmental conditions , and which are propitious for the development and spread of this species . Applied to hosts [12] and vectors [7] of pathogens , it has been possible to better understand the complex influences of spatial heterogeneity and environmental variation on the distributions of species involved in the disease agent transmission cycle , often interpreted as the more likely distribution of the disease agent and hence the disease [13] . Within this framework , the vector-borne disease models show that at larger scales , vectors presence is correlated with climatic and non-climatic factors , with these abiotic factors having a strong influence on vector species range delineation , i . e . , the limits of distributional ranges towards more northern areas [7 , 14] . The influence of anthropic pressures on the environment plays a significant role at more local geographic scales and can unbalance the complex interactions between hosts , vectors and disease agents [15 , 16] . To properly identify the set of biotic and abiotic conditions suitable for disease maintenance and dispersal , the BAM ( biotic , abiotic , movement ) framework was proposed [17] . Biotic and abiotic conditions are based on transmission pathways between host and vector communities and shape the geographic and ecologic distributions of the parasite . The movement summarise limitations , accessibility and possible barriers for spreading opportunities . As such , ENMs applied to vector or reservoir-borne infectious diseases may be confounded to the hazards component part in disease risk calculation . This theoretical framework may help to choose the candidate biotic and abiotic variables and the scales at which all these components must be tested to best fit with the biological model . However , relevant movement may be complicated to model . Today , the development of risk maps for ( zoonotic ) vector-borne diseases remains difficult for two reasons . First , creating a risk map requires considering the notions of hazard , exposure and vulnerability , in addition to choosing the explanatory variables using the BAM framework . Indeed , the likelihood of contact and contamination between human and host-vectors can vary considerably from one region to another , depending on biodiversity and landscape management programs , education level , health surveillance and control , living conditions , economic resources , etc . [16] . Some anthropogenic variables such as the human footprint ( HFP ) , deforestation , urban expansion and poverty [18] allow studying the vulnerability of human communities . Second , for disease systems with multi-host species and/or multi-vector species [19] it may be unrealistic to model all the actors in systems of such diversified communities of vectors and hosts [20 , 21] . Identifying explanatory variables and modelling the occurrence of recognised vectors and/or hosts may miss important parts of the infectious disease system , leading to conflicting issues when suitable areas for disease agent establishment are expected to be considered as epidemiologic risks [22–24] . An alternative approach may be to focus on the occurrence of human cases , considering that disease records indicate the circulation of the pathogen , whatever hosts and vectors , including secondary ones , are involved in the disease agent’s life cycle [5 , 6] . In disease ecology , in the past decade these models relying on human case have shown relevance in identifying more favourable areas for diseases occurrence and risk prediction [25 , 26] . Thus , species distribution modelling ( SDM ) with human cases and climatic , environmental and anthropogenic variables may be useful in identifying the different factors influencing the complex disease transmission cycle such as for cutaneous leishmaniasis ( CL ) . CL is caused by a protozoan parasite of the genus Leishmania with a complex life cycle involving multiple phlebotomines and mammal species acting as natural vectors and reservoirs , respectively , for the parasite [27 , 28] . In Meso and Southern Americas , 940 , 396 new cases of cutaneous ( CL ) and mucosal leishmaniasis were reported by 17 endemic countries from 2001 to 2017 [29] . American cutaneous leishmaniasis is widespread in the Amazonian Basin and throughout the Neotropical rainforest biome , a region with high biodiversity , and caused by several Leishmaniinae species [30–35] . Within Amazonia , the different Leishmania species have a more focal distribution due to their transmission cycles associated with specific ranges of the host reservoirs and vectors [2] . Further , transmission cycles are mainly sylvatic , although urbanisation processes have been reported in some South American countries such as Colombia [34 , 36] . The sylvatic cycle occurs in forested environments and the rural/domestic cycle occurs mainly in forested-associated human settlements by intra-domiciliary transmission . At the infection focus ( a given area where transmission occurred ) , all components of the cycle must be brought together . Risk models aim to correlate these infection foci with human activities to define the areas that are at high eco-epidemiological risk of infection for humans . However , for leishmaniasis Vélez et al . ( 2017 ) [2] pointed out that the limit of these infection foci was complex to define due to ( 1 ) the high diversity of phlebotomine species and the numerous host species involved in the disease life cycle , ( 2 ) the diversity of Leishmania species , ( 3 ) the complexity of confirming phlebotomine species as vectors and wild mammalians as hosts and ( 4 ) the challenge of diagnosing human cases with clinical forms of leishmaniasis . Further , the large geographic extent of the disease and disease agent cycles that may operate in space induce many complex ecological interactions [36] and add uncertainty on the place of infections , which is problematic when models are based on the geolocation of human cases . Last , major anthropogenic disturbances in the Amazonian region impact complex networks of species communities in forest ecosystems; land uses and modifications of the natural habitats are recognised critical factors affecting the mammals and the phlebotomine community's abundance and density [37] . Previous studies have used SDM to map CL occurrence with human cases as input data based on the boosted regression tree ( BRT ) [14 , 38] and regression Bayesian modelling [39] showed that climatic parameters acted as the most important predictors of CL distribution at the scale of the South American continent [14 , 38] and in Brazil only [39] . However , beyond the climatic influence , the level of anthropogenic pressure can act at a finer local scale to influence the disease distribution cycle [40 , 41] . The aim of the present study was to map the risk of CL based at two different scales in the Amazonian forest and surrounding Neotropical moist forest ecosystems . This geographic area allows working at higher spatial resolution than previously published studies , controlling the influence of bioclimatic factors previously identified as disease occurrence drivers [14 , 38 , 39] and likely highlighting a putative role of more local bioecological drivers . We used maximum entropy implemented with the MaxEnt software [42] , based on a presence-background ENM , identifying non-linear responses of CL cases to different fine-resolution biotic and abiotic variables at both the Amazonian and French Guianan scales . These two models were run independently and are not assumed to validate each other , but instead are expected to show the extent to which the geographic grain influences the relative importance of contributory variables for the spatial prediction of the disease risk . We used only the official human CL epidemiological records as input data to predict the risk of leishmaniasis occurrence . The cases were geolocated in the health centres , resulting in uncertainty as to the contamination area and geography-biased case reports for this sylvatic disease . To stay within the BAM reasoning framework , we attempted to adapt the model to the real ecological conditions of the CL cycle . To reflect the most likely places of contamination and properly handle the field realities , we randomly distributed the occurrence of cases outside urban centres . By eliminating areas where one is unlikely to find autochthonous CL cases , we succeeded in integrating the movement ( M ) of the BAM framework . Several redistribution methods made it possible to control the sampling biases related to the uncertainty of case geolocation . The novelty of this work was its redistribution of the occurrences of the disease cases , testing several CL case distribution methodologies , to approach the ecological characteristics of the disease as closely as possible .
For the Amazonian model , we used a total of 149 , 368 human CL cases referenced in 1415 localities from Brazil , Colombia and French Guiana . These case records were predominantly located in the same large Neotropical moist forest biome that encompasses the Amazonian basin , the Guiana shield , and north-west forests of South America ( Fig 1 ) . For Brazil , 75 , 441 CL cases , reported from 2007 to 2015 , spread across 444 localities in the Amazonian states of Acre , Rondônia , Tocantins , Pará , Roraima , Amapá , Mato Grosso and Amazonas were obtained from the Secretaria de Vigilância em Saúde-SVS ( Secretary of Surveillance in Health ) from the Brazilian Ministry of Health . The data were validated by the Technical Group of Leishmaniasis , the Coordenação Geral de Doenças Transmissíveis ( CGDT ) , the Departamento de Vigilância de Doenças Transmissíveis ( DEVIT ) and by the Secretaria de Vigilância em Saúde ( SVS ) of the Ministério da Saúde . Input data for CL for these states were the place of infection at the municipality scale . In Colombia , 73 , 479 cases were spread across 882 localities in all the 32 departments of Colombia from 2007 to 2015 . Colombian data were extracted from the SIVIGILA ( National Public Health Surveillance System ) website , which gathers cases of the various diseases that require mandatory reporting . CL data were validated by the Grupo de Investigaciones Microbiológicas-UR ( GIMUR ) from Universidad del Rosario , as reported elsewhere [43] . In French Guiana the 448 cases distributed in 89 localities come from patients in consultation for suspected leishmaniasis at the LHUPM ( Laboratoire Hospitalo-Universitaire de Parasitologie et Mycologie ) and in the country’s different health centres , between 2008 and 2015 . We chose not to include cases from Venezuela , Suriname , Guyana , Bolivia and Peru , because we had no access to official cases coming from health centres that could be considered as non-biased public data . We report a geospatial analysis of CL data . For Colombia and Brazil , the data were readily obtained from existing public access databases ( Colombia: SIVIGILA , and Brazil: SINAN ) . For French Guiana , we report the cases from the database already published in Simon et al . ( 2017 ) [45] . For all data , the information that identifies the patient was anonymised in the databases and there is no need for ethical considerations . All data were processed in ArcGis 10 . 4 [46] . All variables were used at the resolution of 30 arc-seconds ( ~1 km2 ) for the Amazonian and French Guiana models . Geographic variables available at another resolution and vectorized variables were resampled at 30 arc-seconds using the nearest neighbour joining method , implemented with ArcGis 10 . 4 . The bioclimatic , environmental and anthropogenic variables are given in Table 1 , with their initial resolution . In total , 26 variables were used for the Amazonian model including 19 bioclimatic variables from WorldClim2 , three anthropogenic variables with the population density , the human poverty and the human footprint ( HFP ) and four environmental variables: the biomass aboveground , elevation , forest canopy height and species richness in mammals . For French Guiana , we used the same 19 bioclimatic variables that for the Amazonian model , plus a cloud cover variable . The same environmental variables were used as for the Amazon model with in addition , the percentage of the cell covered by high forest , the distance to river courses , the distance to forest edge and the distance to a relief at least of 500 meters . However , we did not have the species richness variable in mammals for this last model . Two anthropogenic variables were used , the density of tracks and road network and HFP; we used a specific HFP developed for French Guiana , which has a higher level of detail and a more recent update than for the Amazonian HFP variable . The detailed information and sources of the variables used for both models is available in supplementary method ( S1 Method ) .
Method 2 of the distribution of the points led to the best AUC score ( 0 . 842; 95th ranked AUC value for null model = 0 . 5073 ) ( S1 Table ) . The five variables explaining the probability of occurrence of CL cases best were , human population density ( 30 . 8% of the contribution ) , HFP ( 30 . 2% ) , Bioclim 4 ( seasonal temperature; 18 . 9% ) , mammalian species richness ( 13 . 8% ) and aboveground biomass ( 6 . 3% ) . For the jackknife test the variable with the highest gain when used alone was population density , which therefore appears to contain the most useful information by itself ( S1 Fig ) . The variable that most decreases the gain when it is omitted is Bioclim 4 , which therefore appears to have the most information that is not present in the other four variables ( S1 Fig ) . The likelihood of occurrences does not vary whatever the population density ( Fig 2A ) . The likelihood of occurrence increases sharply to a HFP value of about 50 , then decreases sharply ( Fig 2B ) . This decrease can be attributed to our method of distributing case occurrences for high HFP values , excluding the more anthropised areas and large urban centres in the Amazon ( values above 51 ) where transmission of CL is unlikely to occur given the ecology of the CL transmission cycles . The likelihood of case occurrence decreases rapidly as the seasonal temperature variation ( Bioclim 4 ) increases ( Fig 2C ) . The likelihood of occurrence of cases with mammal species richness looks like a bell-shaped curve: it abruptly increases near 110 species , since low-richness areas indicate either non-forested habitats , where CL does not occur , or disturbed forest habitats; the occurrence then decreases for the highest mammal richness values , those associated with very remote , species-rich and restricted Amazonian regions where , at least , no CL human cases are reported ( Fig 2D ) . Concerning the aboveground biomass , the likelihood of case occurrence is stable , then decreases over a very small interval of the variable , between 200 and 250 tons/ha , and finally increases when the values of the variable increase . The predicted risk map is driven mainly by population density and HFP , showing disturbed forest areas and large nuclei of human populations as foci potentially at risk for leishmaniasis transmission to human populations living in these contexts . The north-northwest of South America , mainly Venezuela , and the south-eastern part of the Amazon basin , notably near the south of the delta area , appear as the most at-risk areas for leishmaniasis transmission according to the explanatory variables retained in the models ( Fig 3 ) . The model with distribution method 2 had the best AUC ( 0 . 885 , null model = 0 . 5491 ) ( S1 Table ) . The best AUC score was obtained with four explanatory variables that included two climatic variables ( Bioclim 2 and 16; mean diurnal range of temperature and the precipitation of the wettest quarter , respectively ) , one anthropogenic variable ( HFP ) and one environmental variable ( distance to forest relief ) , with overall the most significant contribution being HFP with 70 . 1% of the total explanation . The jackknife test training shows that the explanatory variable with greatest gain when used alone and that decreases the gain the most when omitted is HFP . Jackknife analysis was performed to test the importance of each of the variables retained . Bioclim variables 2 and 16 contributed 9 . 2% and 15 . 4% of the total explanation , respectively . The last variable distance to a relief of at least 500 m seemed to contribute very little to the model ( 5 . 3% ) , but the jackknife test showed a decrease in AUC when the variable was not present in the training and the test ( S2 Fig ) . The likelihood of occurrence increases with HFP until 35–40 and then it decreases according to a bell-shaped curve . This decrease is directly related to the point distribution of method 2 since areas with HFP > 40 were excluded from contamination areas ( Fig 4A ) . For Bioclim 16 , the likelihood of occurrence slightly increases with precipitation of the wettest quarter , indicating that the occurrence of cases increases monotonically during the rainy season in this region ( Fig 4B ) and then drops for the highest values of precipitation of the wettest quarter . The response of the mean diurnal range variable ( Bioclim 2 ) shows that the likelihood of occurrence slightly decreases as the temperature amplitude increases and then sharply rises to reach a plateau for the highest values of Bioclim 2 ( Fig 4C ) . When the amplitude is the highest , there is a sharp increase in the likelihood of cases occurring , as explained by several cases of CL in the eastern part of the French Guiana region . The response curve of the distance to relief of at least 500 m variable shows that occurrence is high at 500 m and then drops off rapidly and increases gradually at lower altitudes ( Fig 4D ) . The risk map shows that prediction for CL transmission is higher where the HFP index is high , i . e . anthropogenic activities ( hunting , logging , development of activities and housing at edges ) are most common ( Fig 5 ) .
At the beginning of this study , the set of initial variables tested was large enough to encompass all the ecological complexity of the CL life cycle . In agreement with previous studies using human CL occurrence data [14 , 38 , 39] , the variable contributing most to the Amazonian model were two anthropogenic variables , i . e . population density and HFP , followed by seasonal temperature , mammal species richness and aboveground biomass . At French Guiana scale , the variables explaining the greatest number of cases were HFP , followed by precipitation in the wettest quarter ( Bioclim 2 ) and the mean diurnal range of temperature ( Bioclim 16 ) . At the Amazonian large scale , the presence of four biotic variables with wild mammal species richness , population density , HFP and aboveground biomass show the likelihood of increased case occurrence when all these parameters also increase . Several studies have shown that changes in human activities with landscape management in rural areas may affect the population dynamics and distribution of phlebotomine species in Amazonia [41 , 55 , 56] . The response of the seasonal temperature indicates that CL cases are more likely to occur in geographical areas with the least amplitude in seasonal variation . This is not surprising and lends support to the absence of CL cases in the Andes Mountains , with their present unfavourable meteorological and ecological conditions for phlebotomine vectors [33] . Although this observation is ecologically consistent for a large-scale study , it does not add information on climatic factors favouring the risk in the Amazonia biome . Here , unlike the results of previous studies [14 , 38] , the contribution of rainfall remained below 5% , probably because the model is run in the same biome where precipitation has no significant impact on the risk of CL transmission . Wild mammal species richness and aboveground biomass are reminders that the involvement of mammalian hosts and the ecology of the vector are also important biotics drivers to be considered in assessing the risk of CL [57] . Interestingly , in French Guiana , the likelihood of case occurrence is also mainly driven by the biotic HFP variable , with cases increasing as HFP rises . Although environmental policies in this region are very protective [58] , pressures on forest ecosystems have changed over the last few decades . Today , 86 . 2% of CL cases reported are due to L . guyanensis whose the life cycle is mainly sylvatic , but an increase in cases due to L . braziliensis has been observed in recent years [45] . The ecology of L . braziliensis has been assimilated with disturbed and peri-domestic forest habitats in several parts of Amazonia [37] . For this model , the HFP biotic variable probably provided a better account for anthropogenic modification on the environment given its finer resolution and more up-to-date data than those used for the entire Amazonian region [59] . For French Guiana , we observed a probability of an increase in CL case occurrence when the precipitation of the wettest quarter and mean diurnal range increased , confirming the importance of these climate variables in the Amazon basin regardless of the scale chosen . Indeed , in French Guiana a large majority of cases are in the north-east region where precipitation and mean diurnal temperature variations are the greatest . This increase can potentially be explained by the climatic conditions , which are more favourable for vector proliferation , and by the more extensive anthropogenic activities related to the forest [59] . For the response curve of the variable representing the distance to a relief of more than 500 m , the probability of cases occurring is higher on the 500-m reliefs and when one moves away from these reliefs . This result may reflect the high biological diversity of phlebotomine species with different altitudinal distributions as we observed in many regions of Southern America . Ready et al . [60] showed the presence of Psychodopygus wellcomei , the main vector of L . ( V . ) braziliensis in Amazonia , at altitudes over 500 m and then the sharp drop in the probability of occurrence of CL cases and its consistent increase can reflect the ecological requirement of vectors in French Guiana . The risk map obtained for the Amazonian model is relatively similar to the at-risk areas highlighted by a previous study at the South American scale [14] . However , it differs from the map obtained by Purse et al . [38] where the entire Amazon basin was found at risk . In the present study , the AUC , omission test and the null model suggest that the predictions are reliable . The predominantly identified risk areas are described where the human impact on the environment is substantial , i . e . , close to urban centres and along roads and rivers where human populations are concentrated . Venezuela , north-east of Brazil , and northern Bolivia emerge as potential at-risk areas while no case of CL in this region was used in the model . The data currently available on CL indicate that cases have been identified in these areas [61] , although they are not being included as input data , suggesting that the model did not make a significant commission error . In Colombia , the states in the south-east did not come out as a potentially high-risk area . This result seems to contradict the recent study conducted by Herrera et al . [43] , which indicated that these states had the highest incidence and number of cases in the country . This failure may be explained by the limit of spatial ENM when working with quantitative data . Despite a very high number of reported CL cases in this region , the number of cities and the population density remain very low . However , ENMs handle quantitative data such as prevalence , because the information is retained at the pixel scale and whatever the number of cases in one pixel , it is saturated with the first reported case . Despite our procedure to create a buffer zone to randomly disperse cases , cases and substantially increase the number of available pixels to distribute the cases , the model still gives greater importance to areas where the spatial occurrence of cases is widely distributed . For French Guiana , this is the first study to propose a high-resolution risk map based on precisely geolocalised cases . For this European territory , high-risk areas are located where the anthropogenic pressures on habitats are the strongest . A risk zone appears on the map in the west of this region despite the absence of cases , suggesting under-reported and/or under-diagnosed cases . French Guiana is a region where deforestation , hunting , forestry activities , and legal and illegal gold panning have increased in recent years [62] . This information , collected on the importance of the influence of human activities in increasing the risk of this disease , as well as the numerous studies carried out on the possible anthropisation of the vector cycle as shown in Colombia [34] and Manaus , Brazil [63] , suggest that human activities in the rainforest in the Amazon and French Guiana could promote a peri-domestication of the CL disease cycle . Also , throughout Amazonia , people could be infected in peri-urban forest fragments with great canopy cover , which is essential for maintenance of the Leishmania vector/reservoir species diversity and abundance [64–66] . The methodology of this study is based on satellite imagery and correlative analyses , but it remains a visual assessment . It also excludes that the cycles could occur in anthropised and highly disturbed habitats . Indeed , in Colombia CL is linked to the urban cycle [34] and in the largest Amazonian cities such as Belém , CL is associated with small forest fragments surrounded by an urban area and where ( phlebotomine ) putative vectors may sustain [64] . Consequently , it may be interesting to retain relatively high values of HFP in order not to completely obscure the likelihood of local peri-domestication of CL . Another limitation of our study is that some areas of the Amazon biome are not considered at risk while we do know the existence of CL cases , as in Peru and Bolivia . Heterogeneity in the availability of our data increases the models’ omission rate , but we favoured data that were reliable and retrieved directly from the public health database for each country . Unfortunately , it was possible to find this kind of data for only two countries , i . e . , Colombia and Brazil , and for the French Guiana region . We also attempted to obtain the most updated variables for the Amazonian model , but some are not updated over the period when the cases occurred , so the environmental data are not necessarily concomitant with the case occurrence period . In addition , we are aware that the models are highly dependent on the input variables and spatial scaling , so risk maps produced with large-scale data and models should not be extrapolated for more restricted geographical areas; risk maps are first context- and space-dependent . Modelling a parasite system that is based on several species of hosts and reservoirs requires considering relevant biotic and abiotic variables summarising the ecological conditions in which the transmission cycle takes place . For this complex issue , the BAM diagram may help to select the variables and the scale of study . Finally , for both models ( Amazonia and French Guiana ) this study highlighted the importance of considering the anthropogenic drivers for risk assessment . This conclusion differs from that proposed by Pigott and collaborators , [14] who argued that climatic conditions were the main driver of CL case distribution in South America . The adequate choice of the spatial scale under scrutiny , in accordance with the variables explored , can be a major determinant in the discrepancy that we observed between Pigott et al . and our present results . Therefore , risk mapping should not be made without considering variables representing the vulnerability of human individuals and communities to the disease and further add to the importance of an appropriate scaling when designing ENM studies [50 , 67] . Generally , coarse-scale studies appear to favour the importance of climatic variables in explaining infectious disease presence and spatial distribution [68] . This pattern has already been referred to as Eltonian Noise Hypothesis [69] which assumes that local biological interactions or microhabitat biotic conditions required by a specific parasite cycle should not affect niche estimates at coarse scales [19] . Many studies have attempted to make future projections of climate change on vector-borne diseases to determine the factors favouring disease emergence and to predict the dispersal of infectious disease agents . For diseases whose transmission cycles are confined to restricted geographic areas , it is likely that the small-scale human impact firstly may influence spatial expansion or regression of these diseases . With the methodological framework proposed here and with fine-scale and updated variables on anthropogenic disturbances , ENMs remain a valuable tool to determine local factors that are the drivers of parasite transmission and may help relevant decision-making by health authorities . Every ENM study that uses risk modelling should target the proper scale based on these elements . This statement can be extended most particularly to the Leishmania ecological system . In French Guiana , the CL system is mainly represented by L . guyanensis and Nyssomyia umbratilis with Xenarthran species acting as major host reservoirs [31 , 45] , while this cannot be identical for other pan-Amazon regions with other species involved in the cycle [34 , 70] . The relevance of developing future models of CL risks with only climatic variables is questionable . Indeed , it is likely that the policy and economic decisions with their cascading impacts on poverty , hygiene , war , displacement of populations , etc . , and short-term local planning strategies [71] will have a more direct and immediate impact on biodiversity and their interactions with disease components . This is particularly true in regions where the expected climatic variations will remain low compared to the impact of microclimates created , for example , by the creation of hydroelectric dams [40] , the burden of extensive agriculture [72] or the effects of edge habitats [73] . These anthropogenic factors will remain extremely difficult to control in the future and will continue to challenge the relevance of predictive models , whatever the ongoing methodological improvements and the quality of the data used as independent variables in models . | Cutaneous leishmaniasis is a vector-borne zoonotic disease with a complex transmission cycle that includes many parasite , vector and host species . This disease continues to pose public health problems worldwide despite the measures put in place . In recent years , methodological tools commonly used in ecology , called ecological niche prediction models , have made it possible to determine the environmental and anthropogenic variables that may be favourable to the presence of the host and vector species communities involved in the cycle and therefore to the presence of certain disease agents . The use of these models , based on the presence of human cases of the disease , can overcome some of the uncertainties concerning the diversity of the vectors and the potential hosts involved in the transmission cycle . This approach of health ecology combining ecology and epidemiology could provide new insights into understanding the cycle of disease transmission and the influence of environmental factors and thus improve the prediction of disease emergence and epidemics . It can be applied to various vector-borne diseases whose transmission cycles are still poorly understood and for which studies classically carried out in epidemiology have not prevented disease progression . |
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Natural killer T ( NKT ) cell development depends on recognition of self-glycolipids via their semi-invariant Vα14i-TCR . However , to what extent TCR-mediated signals determine identity and function of mature NKT cells remains incompletely understood . To address this issue , we developed a mouse strain allowing conditional Vα14i-TCR expression from within the endogenous Tcrα locus . We demonstrate that naïve T cells are activated upon replacement of their endogenous TCR repertoire with Vα14i-restricted TCRs , but they do not differentiate into NKT cells . On the other hand , induced TCR ablation on mature NKT cells did not affect their lineage identity , homeostasis , or innate rapid cytokine secretion abilities . We therefore propose that peripheral NKT cells become unresponsive to and thus are independent of their autoreactive TCR .
Natural Killer T ( NKT ) cells represent a subset of T cells in mice and humans that express NK cell markers and recognize a small class of glycolipid ( auto- ) antigens [1] , [2] . Most mouse NKT cells express an invariant Vα14-Jα18 ( Vα14i ) TCRα rearrangement ( Vα24-Jα18 in humans ) . In principle , all TCRβ-chains are able to pair with this Vα14i-TCR chain [3] . However , the selection of NKT cells by endogenous glycolipids presented by the monomorphic MHC class I-like CD1d induces a strong bias towards TCRs containing Vβ8 , Vβ7 , or Vβ2 [1] , [3] , which is abrogated in the absence of selection [3] , [4] . Recently , crystallographic analysis demonstrated a conserved binding mode of the NKT cell TCR to various glycolipids , where only germline-encoded residues were in direct antigen contact , reminiscent of innate pattern-recognition receptors [5] . Moreover , several observations suggest that this receptor is inherently auto-reactive [1] , [2] and thereby determines NKT cell identity and influences their function . The expression of several inhibitory NK cell receptors on NKT cells was suggested to control their self-reactivity and avoid autoimmune activation [6] , [7] . During development in the thymus , the few T cells expressing a Vα14i-TCR are selected upon recognition of self-lipids on double-positive thymocytes . Although several good candidates have been put forward [8]–[10] , the exact nature of the selecting glycolipids remains controversial . Homotypic interactions involving the SLAM family ( SLAMf ) receptors 1 and 6 are additionally required for NKT cell differentiation [11] . Auto-reactive activation during thymic selection is thought to induce a substantially stronger TCR stimulus in comparison to that during the development of conventional T cells [12] , [13] . As a consequence , expression of the transcription factors Egr1 and Egr2 is strongly increased [13] , which in turn directly induce PLZF , the key transcription factor controlling NKT cell differentiation , migration , and functions [13] . Interestingly , the homeostatic proliferation of NKT cells after adoptive transfer was similar in CD1d-deficient and wild-type mice , indicating that this process is mostly cytokine-driven and does not depend on continued TCR-mediated self-lipid-recognition [14] , [15] . However , as the transferred cells contained CD1d , a role for antigen could not be completely excluded . In addition , tonic antigen-independent TCR signals might contribute to NKT cell maintenance and phenotype . During immune responses , NKT cell activation depends mostly on two parameters: engagement of the TCR and the presence of proinflammatory cytokines released from antigen-presenting cells activated by innate immune pathways such as toll-like receptor ( TLR ) signals . Lipids derived from different bacteria [16]–[19] were shown to directly activate mouse and human NKT cells in a TLR- and IL-12-independent manner , and NKT cells are required for productive immune responses against these pathogens . NKT cells can also be activated indirectly through cytokines such as IL-12 , IL-18 , or type I interferons ( IFNs ) [20] . However , it remains controversial whether , depending on the strength of the cytokine signal , weak responses to self-antigens presented by CD1d are an additional obligate requirement . In one study , CD1d-dependent signals were found to be necessary for full NKT cell activation in response to all tested pathogens [20] . In contrast , others reported that IL-12-dependent NKT cell activation after LPS injection [21] or MCMV infection [22] is independent of either foreign or self-glycolipid antigen presentation by CD1d . Upon activation , the most distinguishing feature of NKT cells is their ability to rapidly produce and secrete large amounts of cytokines ( Th1 and Th2 cytokines , among others ) . Their fast , effector-like response could be based on steady-state expression of cytokine mRNA in mice [23] , [24] that was suggested to be a consequence of tonic self-reactive activation [2] . Recently , it was reported that human NKT cells do not constitutively express cytokine mRNAs . Instead , rapid cytokine-induced innate IFNγ production by NKT cells was suggested to rely on obligate continuous recognition of self-lipids , which retains histone acetylation patterns at the IFNG locus that favor transcription [25] . Another characteristic feature of NKT cells , their surface marker expression reminiscent of memory or recently activated T cells , was also connected to their inherent autoreactivity [2] . To thoroughly address the open questions regarding the nature and importance of TCR signaling for NKT cells , we generated a novel mouse model that allowed us to study the extent of Vα14i-TCR-mediated auto-antigen recognition in the periphery and its relevance for NKT cell identity . Furthermore , we monitored the fate of NKT cells after TCR ablation . Our results prove the inherent self-reactivity of the NKT cell TCR and demonstrate that although essential for positive selection , tonic TCR signaling is not required for NKT cell homeostasis , lineage identity , and rapid cytokine secretion .
In order to produce large numbers of NKT cells in a physiological manner and to manipulate the expression of the semi-invariant Vα14i-TCR in a conditional fashion , we generated Vα14iStopF knock-in mice . To this end we cloned a productive Vα14-Jα18 rearrangement , including the Vα14 leader exon , intron and 1 . 8 kb of upstream regulatory sequence , and 0 . 2 kb intronic sequence downstream of Jα18 . These elements were inserted by homologous recombination 3′ of Jα1 upstream of the Cα constant region of the Tcrα locus ( Figure 1A ) . Expression of putative upstream rearrangements is aborted by four SV40 polyA sites at the 5′ end of the construct , and expression of Vα14i is rendered conditional through a loxP-flanked STOP cassette . We obtained over 80% ( 271 of 325 ) homologous recombinant ES cell clones during gene targeting , indicating an unusually high targeting efficiency of our construct ( Figure S1A ) . The development of conventional T and NKT cells , identified by staining with mouse CD1d-PBS57-tetramers ( tetramer+ ) , occurs unperturbed in Vα14iStopF/wt heterozygous mice . In homozygous Vα14iStopF/F mice , T cell development is abolished due to transcriptional termination of TCRα expression before the Cα exons ( Figure 1B ) . We bred Vα14iStopF to CD4-Cre mice , in order to express the inserted Vα14i-chain in double-positive thymocytes , mimicking the physiological timing of TCRα-chain rearrangement and expression [26] , [27] . On average 23 times more thymic and 43 times more splenic NKT cells were generated in these , compared to wild-type mice ( Figures 1B and 2A–E ) . Around 9% of the tetramer+ T cells in CD4-Cre Vα14iStopF/wt mice expressed the CD8 co-receptor ( over 80% as CD8αβ heterodimer; Figures 1C and S1B , C ) , which is also expressed by some human NKT cells , but normally not in mice [28] . The proportions of CD4− CD8− double negative ( DN ) and CD4+ cells were comparable between transgenic ( tg ) and wild-type NKT cells ( Figure 1C ) . Furthermore , the tgNKT cells were largely comparable to wild-type NKT cells with respect to Vβ-chain bias ( Figure 1D ) and surface phenotype ( Figure 1E ) . Finally , we found that NKT cells from CD4-Cre Vα14iStopF/wt animals expressed the critical transcription factors promyelocytic leukemia zinc finger ( PLZF ) , GATA binding protein 3 ( GATA-3 ) , and T-helper-inducing POZ/Krüppel-like factor ( Th-POK ) ( Figure 1F ) [28] , [29] . Interestingly , we also detected a substantial proportion of the recently described NKT17 subset in the transgenic animals . These DN NK1 . 1− NKT cells express the transcription factor ROR-γt and were shown to produce the cytokine IL-17 upon activation ( Figure 1F ) [29] , [30] . Premature TCRα expression leads to aberrant T cell development in transgenic mouse models [26] , [27] . To directly compare the consequence of premature to CD4-Cre-mediated timely Vα14i-TCRα-chain expression in our knock-in approach , we bred our mice to a germline Cre-deleter strain ( Nestin-Cre ) [31] . Compared to CD4-Cre-induced Vα14i-TCRα-chain expression , premature expression in Cre-deleter Vα14iStopF/wt led to significantly reduced numbers of NKT cells in thymus and spleen , especially of CD4+ NKT cells ( Figure 2A–C ) . In addition , we found reduced thymocyte counts and a significant increase of most likely lineage-“confused” DN ( CD4− CD8− ) tetramer-negative T cells ( Figure 2D , E ) . In fact Cre-deleter Vα14iStopF/wt mice strongly resemble the “first generation” Vα11 promoter-driven ( Vα11p ) Vα14i transgenic mice in these respects ( Table S1 ) [32] . Moreover , in Cre-deleter Vα14iStopF/wt mice , we observed increased proportions of Vβ9- , Vβ10- , and Vβ14-containing Vα14i-TCRs , which can recognize α-GalCer-loaded tetramers , but most likely not endogenous self-glycolipids [3] , [4] , pointing to perturbed positive selection ( Figure 2F ) . CD4-Cre Vα14iStopF/wt mice produce more NKT cells than any of the previously reported models , including mice with a Vα14i allele derived from a NKT cell nuclear transplantation experiment [11] , [32]–[35] . A comparison of different Vα14i-transgenic models demonstrates that both the correct timing and endogenous control of TCR expression control favor NKT cell development ( Table S1 ) . Our analyses therefore showed that physiological timing of Vα14i-TCRα-expression at endogenous levels in CD4-Cre Vα14iStopF/wt mice contributes to the production of large numbers of correctly selected , bona fide NKT cells . To test the functionality of our transgenic NKT cells , we injected CD4-Cre Vα14iStopF/wt mice with the NKT cell ligand α-Galactosylceramide ( α-GalCer ) and determined their cytokine production directly ex vivo . The transgenic NKT cells were able to mount a rapid and robust cytokine response . Although a reduced proportion of transgenic NKT cells responded , in absolute cell numbers there was a 6–10-fold increase compared to wild-type NKT cells ( Figure 3A ) . We did not observe significant steady-state cytokine production by transgenic or control NKT cells , and we detected only minor increases in cytokine levels in the serum of some of these mice ( Figure S1D ) . Since cytokine production also varies with NKT cell maturation , we analyzed NKT cell development in CD4-Cre Vα14iStopF/wt mice in more detail . This revealed a strong bias toward immature fractions in the thymus , due to the dramatic increase in NKT cell progenitors . In the periphery , 20% of NKT cells fully matured , as judged by the expression of NK1 . 1 and other NK cell markers ( Figure 3B , C ) . This view is further supported by the reduced proportion of CD69 and T-bet-expressing NKT cells in CD4-Cre Vα14iStopF/wt compared to wild-type mice ( Figure 3D ) . The expression of both CD69 and T-bet strongly correlated with NK1 . 1 surface levels ( Figure S1E , F ) . This also explains the higher intracellular PLZF expression in CD4+ and DN NKT cells of CD4-Cre Vα14iStopF/wt animals in comparison to control animals ( Figure 1F ) , as it was shown that PLZF expression is downregulated during NKT cell development [36] . Reduced maturation seems to be a common feature in mice with overabundance of NKT cells ( Figure S1G and Table S1 ) [33] . Indeed , a comparison of different Vα14i-tg mice suggests that independently of the total number of NKT cells generated , the size of the homeostatic niche for mature NKT cells appears to be around two million cells ( Table S1 ) . IL-15 is critical for the final maturation of NKT cells [37] and together with IL-7 required for their peripheral maintenance [14] , [38] . NKT cells compete with NK cells for these resources [38] . The halved number of NK cells in CD4-Cre Vα14iStopF/wt mice ( Figure 3E ) suggests that the availability of these and maybe other cytokines might be insufficient due to the dramatically increased NKT cell numbers . The fact that a similar effect was observed in Vα11p-Vα14itg mice ( Figure 3E ) underscores this notion . These results let us conclude that while large amounts of NKT cells can be produced in mice , depending on the mode of Vα14i expression , the number of fully mature NKT cells is restricted by homeostatic constraints , some of which are shared with NK cells . The strong self-lipid-induced TCR stimulus that early NKT cell progenitors receive in the thymus can be visualized through high GFP expression under the control of the Nur77 gene locus , reporting TCR signal strength [12] . However , the subsequent loss of GFP in mature NKT cells suggests that these cells are either not exposed to or not responsive to self-antigens . In order to answer this question and to study NKT cell TCR-autoreactivity in the periphery , we investigated the consequences of Vα14i-TCR signals for conventional naïve T cells . We wondered whether Vα14i-TCR expression on naïve T cells , lacking inhibitory receptors and generally a NKT cell “identity” , would lead to activation upon ( self- ) lipid recognition and what cellular fate ( s ) are elicited by such activation . To this end , we generated mice enabling us to exchange the endogenous TCR-repertoire present on naïve peripheral T cells for a Vα14i-restricted TCR repertoire . The induction of Cre expression in Mx-Cre CαF/Vα14iStopF mice inactivates the CαF allele and simultaneously turns on the Vα14iStopF allele , leading to substitution of endogenous TCRα-chains with the Vα14i TCRα-chain ( Figure 4A ) . As mentioned above , the Vα14i-chain can pair with all TCRβ-chains [3] , although only Vβ2- , Vβ7- , and Vβ8-containing Vα14i-TCRs can recognize endogenous lipids such as iGb3 [3] , [4] . Since TCRs containing one of these Vβ-chains constitute approximately 30% of the CD4+ and CD8+ peripheral T cell pool ( Figure 1D and unpublished data ) , we predicted that our genetic switch experiment should generate sufficient numbers of T cells able to recognize self-lipids . In Mx-Cre transgenic mice , Cre expression can be induced through injection of dsRNA , such as poly ( I:C ) [39] . However , low-level “leaky” recombination occurs also in absence of an inducer [39] , [40] , leading to increased numbers of tetramer+ T cells in naive Mx-Cre CαF/Vα14iStopF mice ( Figure S2A ) . Therefore , splenocytes were depleted of tetramer+ T cells by magnetic cell separation ( MACS , Figure S2A ) , and 20×106 purified cells were injected intravenously ( i . v . ) into recipient animals lacking conventional αβ T cells and NKT cells ( Cα−/− or Vα14iStopF/F ) . After cells were allowed to engraft for 2 wk , the TCR switch was induced by poly ( I:C ) injection . Importantly , except for a short-term activation of the immune system , poly ( I:C ) injection in Mx-Cre mice per se has no significant long-lasting effect on peripheral conventional T cells [40] , [41] or on the number and phenotype of NKT cells ( unpublished data ) . To definitely exclude any effect of poly ( I:C ) injection on our results , we waited 2–4 mo before analyzing the animals after the induced TCR switch . We found significant numbers of tetramer+ CD4+ and CD8+ T cells as a result of this switch experiment ( Figure 4B–E ) . “Unloaded” tetramers did not stain these cells , demonstrating that they were not reactive against CD1d itself ( Figure S2B ) . The TCR-switched tetramer+ T cells were predominantly enriched in cells expressing Vβ-chains that are associated with high avidity auto-antigen binding: Vβ2 , Vβ8 . 1/8 . 2 , and Vβ7 ( Figure 4D , E ) [3] , [4] , [42] . The exceptions were CD8+ TCR-switched tetramer+ T cells , in which Vβ7-expressing cells were not enriched . The bias toward tetramer+ CD8+ T cells ( Figure 4C ) is most likely due to more efficient Mx-Cre-mediated recombination in these cells [40] . Animals containing TCR-switched tetramer+ T cells , but not controls , displayed splenomegaly ( Figure 5A , B ) , characterized by increased numbers of macrophages/monocytes , neutrophils , and Ter119+ erythroid progenitor cells , suggesting an inflammatory state ( Figure 5C–E ) . In line with these findings , we could detect elevated serum TNF in more than half of these mice ( Figure 5F ) . Elevated levels of other cytokines , such as IL-2 , IL-4 , IL-5 , IL-6 , IL-10 , IL-17 , and IFN-γ , were not found in the sera of these mice ( unpublished data ) . Interestingly , we found that 6 ( highlighted in red throughout the figure ) of 17 spleens containing TCR-switched T cells were almost completely devoid of B cells ( Figure 5G ) as well as dendritic cells ( DCs , Figure 5H ) , which present lipid antigens to NKT cells via CD1d [1] . Furthermore , tetramer- “conventional” T cells were also strongly reduced in these animals ( unpublished data ) . Together , these results suggest that induced expression of the Vα14i-TCR on conventional naïve T cells causes sterile inflammation , possibly due to autoimmune activation . The appearance of tetramer+ cells displaying a Vβ bias similar to antigen-selected NKT cells , together with signs of inflammation upon TCR switch and the absence of CD1d-expressing B cell and DCs in some cases , suggested auto-antigen-mediated activation of TCR-switched cells . To verify that the newly assembled Vα14i-TCR on conventional T cells is functional , we injected recipients of Mx-Cre CαF/Vα14iStopF and control cells with α-GalCer or PBS 2 mo after switch induction . Ninety minutes after α-GalCer , but not PBS , injection , CD4+ and CD8+ tetramer+ T cells produced IFN-γ and TNF ( Figure 6A ) , demonstrating the functionality of the newly assembled Vα14i-TCR . In comparison to NKT cells from wild-type or CD4-Cre Vα14iStopF/wt animals , a smaller proportion of tetramer+ T cells produced cytokines ( Figures 6A and S2C ) . Tetramer+ TCR-switched T cells could also be activated in vitro through α-GalCer-pulsed A20 cells overexpressing CD1d ( unpublished data ) [43] . To study the consequences of Vα14i-TCR expression on tetramer+ TCR-switched T cells in more detail , we analyzed their surface phenotype and transcription factor expression . Absence of NK cell markers ( Figures 6B and S2D ) and PLZF expression ( Figure 6C ) indicated that the Vα14i-TCR signals are not sufficient to induce NKT cell differentiation of mature conventional T cells . However , the TCR-switched tetramer+ T cells expressed significantly higher levels of Egr2 in comparison to tetramer− T cells in the same animals ( Figure 6D ) , suggesting that the switched cells receive stronger TCR signals [13] . TCR-switched T cells showed further signs of cellular activation , as they expressed elevated levels of CD69 ( Figure 6E ) . Interestingly , these T cells displayed also significantly increased surface levels of PD-1 , LAG-3 , and less frequently , BTLA and TIM-3 , which is typical of exhausted/anergic cells ( Figure 6F–H and unpublished data ) [44] , [45] . To test whether exhaustion/anergy of tetramer+ TCR-switched T cells prevented a more dramatic form of autoimmune inflammation , we injected mice with PD-L1 and PD-L2 blocking or control antibodies twice a week for 4 consecutive weeks , starting 2 d before switch induction . The administration of these blocking antibodies has previously been shown to efficiently prevent anergy induction of conventional T as well as NKT cells , and to partially reverse the exhaustion of CD8+ T cells [44] , [45] . However , we did not observe any dramatic differences in spleen weight or cellularity , or signs of increased inflammation , between animals receiving PD-L blocking or control antibodies ( unpublished data ) . In response to PD-1 blockade , other inhibitory receptors such as LAG-3 , BTLA , or TIM-3 might control the TCR-switched T cells . Taken together , our results showed that expression of the Vα14i-TCR on mature conventional T cells is not sufficient to induce a NKT cell differentiation program . Still , it is likely that Vα14i-TCR signals induce auto-antigen-mediated activation , possibly to the point of exhaustion . We therefore present strong evidence that the Vα14i-TCR can constitutively recognize self-lipids in the naïve steady state situation in vivo . The evidence for autoreactivity of the Vα14i-TCR on mature peripheral T cells raised the old but still not completely resolved question whether and to what extent interactions with self-lipid-presenting APCs are required for NKT cell maintenance , cellular identity , and function . In order to evaluate the importance of constitutive TCR expression and signaling for NKT cells directly in vivo and for long periods of time , we ablated the TCR on mature T cells using poly ( I:C ) injection of Mx-Cre CαF/F mice [40] . Two weeks after induced Cre-mediated recombination , around 30% of CD4 and 65% of CD8 T cells had lost functional TCR expression in these mice ( Figure 7A and [40] ) . To unambiguously identify TCR-deficient NKT cells , we developed a robust staining strategy based on CD4 , NK1 . 1 , CD5 , and CD62L expression ( Figure S3A ) . This limited us to CD4+ NKT cells , but our staining identified over 50% of the total NKT cell populations in thymus and spleen ( Figure S3B ) . Around 65% of the thus identified NKT cells had lost TCR surface expression 2 wk after Cre induction ( Figure 7A , B ) . Due to complete Cre-mediated recombination in lymphoid progenitors , T cell development is blocked at the double positive stage in Mx-Cre CαF/F mice after induction of Cre [40] . This allowed us to study the T cell decay in the absence of cellular efflux from the thymus . In agreement with previous studies [40] , [46] , we found that loss of the TCR leads to decay of naïve CD4+ CD44low and memory/effector-like CD4+ CD44high T cells with a half-life of 40 d and 297 d , respectively ( Figure 7C , D ) . Interestingly , we observed essentially no decay of receptor-less NKT cells , with a calculated half-life of 322 d ( Figure 7E ) , and could find significant numbers of TCR-deficient NKT cells even 45 wk after TCR deletion ( unpublished data ) . To evaluate the role of TCR signals during in situ homeostatic proliferation , we administered BrdU for 4 wk via the drinking water , starting 2 wk after induced TCR ablation . Naïve CD4+ CD44low as well as CD4+ CD44high memory/effector-like T cells showed significantly decreased BrdU incorporation in TCR-deficient compared to TCR-expressing cells ( Figure 7F , G ) . In contrast , TCR ablation did not affect NKT cell proliferation ( Figure 7F , G ) . Interestingly , the BrdU incorporation was identical in TCR-deficient CD4+ CD44high T and NKT cells , indicating that in the absence of TCR signals the cytokine-driven expansion of CD4+ CD44high memory/effector-like T and NKT cells is similar ( Figure 7F , G ) . Our results therefore indicate that long-term in situ NKT cell homeostasis is completely independent of TCR-induced signals . In absence of de novo T cell generation , we found elevated Egr2 expression in mature thymic , but not splenic , NKT cells compared to DP thymocytes and CD4+ T cells , respectively ( Figure 8A ) . This indicates that NKT cells receive stronger TCR signals in the thymus , which is supported by the decreased Egr2 expression of mature thymic TCR-deficient NKT cells ( Figure 8A ) . Surprisingly , in mature NKT cells in thymus and spleen , expression of the TCR-signal-induced key transcription factor PLZF is completely unaffected by TCR ablation ( Figure 8B ) . In order to more generally evaluate to what extent NKT cell TCR-expression is required for the maintenance of characteristic lineage-specific gene expression ( resembling recently activated T cells ) , we extensively analyzed the cell-surface phenotype of NKT cells 6 wk after TCR ablation . Of all the analyzed markers , the only significant changes that we observed on splenic NKT cells upon TCR ablation were downregulation of NK1 . 1 , CD4 , CD5 , and ICOS ( Figures 8C , D and S3C–E ) . NK1 . 1 expression was also reduced in thymic TCR-deficient NKT cells , in addition to CXCR6 expression ( unpublished data ) . CD5 and ICOS expression were also reduced in TCR-deficient splenic naïve as well as CD62Llow CD4+ T cells ( Figure S3C , D ) . CD4 was upregulated on TCR-deficient CD4+ naïve , but downregulated on NKT and CD4+ CD44high T cells ( Figure S3E ) . Strikingly , all other cell surface markers characteristic for the NKT cell lineage , among them the transcription factors PLZF , GATA-3 , T-bet , and Th-POK , as well as many cell surface markers whose expression is also induced upon TCR engagement , remained largely unaffected by loss of the NKT cell TCR ( Figure 8D ) . Treatment of mice with LPS , a cell wall component of gram-negative bacteria , leads to release of IFN-γ by NKT cells via stimulation with IL-12 and IL-18 produced by innate immune cells . This does not require acute TCR engagement [21] . However , it has been proposed that the ability of NKT cells to rapidly release IFN-γ in this context critically requires continuous weak TCR activation in the steady state [25] . We therefore analyzed IFN-γ release of TCR+ and TCR- NKT cells after in vivo injection of LPS , α-GalCer , and PBS ( Figure 9A , B ) . As expected , Egr2 expression could only be detected in NKT cells that were activated through their TCR ( Figure 9A ) . Accordingly , 90 min after α-GalCer injection , the majority of TCR+ NKT cells , but virtually none of the TCR- NKT cells or the CD4+ conventional T cells , produced IFN-γ protein ( Figure 9B ) . Interestingly , NKT cell activation through LPS injection in vivo was able to induce similar IFN-γ production by TCR- NKT cells in comparison to their TCR+ counterparts ( Figure 9B ) . Our results thus clearly demonstrate that homeostasis and key features defining the nature of NKT cells , namely the unique activated cell-surface phenotype and the innate capacity for instant production of IFN-γ , do not require continuous auto-antigen recognition in the mouse .
The elucidation of NKT cell function and their intriguing semi-invariant TCR benefited enormously from Vα14i-TCR transgenic mouse models [11] , [32] , [47] , [48] . Over the last years , it became increasingly clear that premature expression of transgenic TCRα chains , including Vα14i [11] , [32] , leads to various unwanted side-effects such as impaired β-selection and the generation of large numbers of DN T cells both in the periphery and in the thymus [26] , [27] . This drawback affects even TCR alleles generated through nuclear transfer of mature NKT cells [33] . For that reason , Baldwin et al . developed a system in which a transgenic CAGGS-promoter-driven TCRα-chain is expressed upon CD4-Cre-mediated excision of a loxP-flanked STOP cassette , mimicking the physiologic expression time point [26] . Likewise , Griewank and colleagues expressed the Vα14i-TCR under direct control of CD4 promoter and enhancer sequences [11] . These are clear improvements , but carry the inbuilt caveats of the respective heterologous expression construct . For example , it has been shown that a large proportion of activated mature T cells loses expression from such transgenic CD4 promoter enhancer constructs [49] . Here , we present a novel approach , in which the expression of the transgenic Vα14i-TCRα-chain , and in the future any other TCRα-chain of interest , can be initiated via CD4-Cre at the DP stage in the thymus , and is under endogenous control of the Tcrα locus throughout the lifespan of the cell . In these mice , large numbers of bona fide CD4+ and DN NKT cells were generated . The reduced proportions of fully mature stage 3 NKT cells ( NK1 . 1+ , CD69high , T-bet+ ) , as well as the reduced numbers of NK cells , are most likely a consequence of limiting amounts of common differentiation and maintenance factors , such as IL-15 [14] , [37] , [50] . In addition , attenuated TCR-signaling due to increased competition for self-antigen/CD1d-complexes might delay the full maturation of NKT cells in the transgenic animals . TCR signals have been proposed to play a role in the initiation of CD69 expression on NKT cells , as well as in the induction of IL-2Rβ , the β-chain of the IL-2 and IL-15 receptors [13] . Moreover , we observed the generation of tetramer+ CD8+ T cells . CD8+ NKT cells are found in the human , but not in wild-type mice . CD8 expression on Vα14i NKT cells does not interfere with negative selection , avidity for antigen presented by CD1d , or NKT cell function [28] . Instead , it was proposed that the absence of CD8+ NKT cells in the mouse is due to the constitutive expression of the transcription factor Th-Pok in all CD4+ as well as DN NKT cells [28] . Th-Pok has been shown to be crucial for the maturation and function of NKT cells , and directly represses CD8 expression [28] . This scenario fits well with the fact that the CD8+ tetramer+ T cells in the CD4-Cre Vα14iStopF/wt ( as well as in the Vα11p-Vα14itg animals ) did not express Th-Pok . These cells also lack many other characteristic features of NKT cells , including PLZF expression . Therefore , we refer to them as tetramer+ CD8+ T cells . Given the faithful recapitulation of endogenous TCRα-chain expression timing and strength in our knock-in mice , combined with the extremely high homologous recombination efficiency , we believe that our strategy should prove useful for the generation of further novel TCR-transgenic mouse models . By replacing RAG-mediated Vα14 to Jα18 recombination with Cre-mediated activation of Vα14i expression in CD4-Cre Vα14iStopF/wt mice , we can directly couple conditional gain or loss of gene function with Vα14i-TCR expression in NKT cells . NKT cell-specific gene targeting in mice with physiological NKT cell numbers could be achieved through the generation of mixed bone marrow chimeras with Jα18−/− bone marrow , which cannot give rise to Vα14i-NKT cells . Our studies were designed to elucidate whether or to what extent the expression of the autoreactive semi-invariant TCR would activate a peripheral mature naïve conventional T cell , convert it into an NKT cell , or induce gene expression typical of NKT cells . We took advantage of the conditional nature of the Vα14i-TCR knock-in transgene for a TCR switch experiment on conventional peripheral T cells . Naïve CD4+ T cells inherit a high plasticity [51] . Depending on TCR signaling strength and cytokine environment , they can differentiate in various subsets in periphery . This differentiation includes the induction of specific transcription factors , namely T-bet ( Th1 ) , GATA-3 ( Th2 ) , ROR-γt ( Th17 ) , and FoxP3 ( peripherally derived regulatory T cells ) . For NKT cells , it is believed that strong TCR signaling , together with homotypic interactions involving the SLAM family ( SLAMf ) receptors 1 and 6 , ultimately leads to PLZF induction during thymic development [11] , [13] . DP thymocytes , presenting auto-antigen via CD1d and also expressing SLAMf members , are crucial for thymic NKT cell selection [11] . These SLAMf receptors are expressed on peripheral lymphocytes in comparable levels to double positive thymocytes ( www . immgen . org ) . Therefore , lymphocytes , especially marginal zone B cells , which express CD1d to a similar level as DP thymocytes , should be able to present antigen and SLAMf-mediated co-stimulation , to naïve conventional T cells with a newly expressed Vα14i-TCR on their surface . The elevated levels of the TCR-induced transcription factor Egr2 in switched tetramer+ T cells suggest that they receive an ( auto- ) antigenic signal . This finding is in principle in agreement with our finding that tetramer+ TCR-switched T cells are enriched in cells that express Vβ2- and Vβ8 . 1-/8 . 2-containing Vα14i-TCRs . These TCRs were shown to have the highest avidity for NKT cell antigens [3] . Furthermore , Vβ7-containing Vα14i-TCRs were shown to be favored when endogenous ligand concentration are suboptimal in CD1d+/− mice [42] . In fact , in CD4+ tetramer+ TCR-switched T cells the relative enrichment for Vβ7-expressing cells was slightly higher than for Vβ2- and Vβ8 . 1-/8 . 2-expressing cells ( unpublished data ) . However , the interpretation that this advantage is due to antigenic selection is at odds with the fact that Vβ7-expressing cells are not enriched in tetramer+ TCR-switched CD8+ T cells . We currently have no satisfactory explanation for this discrepancy . Both CD4+ and CD8+ Vα14i-TCR-expressing conventional T cells show features of activation and exhaustion/anergy , but do not develop into NKT cells , judged by absent PLZF and NK cell marker expression . This indicates that either mature T cells have lost the ability to enter the NKT cell lineage , the peripheral Vα14i-TCR signal is not strong enough , or as yet unidentified components of the thymic microenvironment are required to induce an NKT cell fate . Indeed , the high Egr2 expression of mature NKT cells that matured in the periphery and migrated back to the thymus ( Figure 8A ) suggests that stronger self-antigens are presented at this location . Interestingly , unlike TCR-switched tetramer+ T cells , Egr2 expression in mature splenic NKT cells was similar to that of conventional mature CD4+ T cells . Our data therefore suggest that in the periphery , the Vα14i-TCR can recognize self-lipids , but maturing NKT cells undergo a developmental program that prevents an auto-reactive inflammatory response . At this point , we cannot exclude the possibility that the observed cellular activation was antigen-independent . The fact that the internal control cells , the co-transferred tetramer− T cells , show no or significantly less signs of activation strongly argues for an involvement of antigen recognition or tonic signaling by the Vα14i-TCR . It also remains possible that the transient immune activation caused by the poly ( I:C ) administration contributes to the observed phenotypes . In all likelihood , this contribution is small , as we never observed any significant immune activation , not to mention loss of CD1d-expressing antigen-presenting B cells and dendritic cells , in Mx-Cre CαF/wt control mice that received poly ( I:C ) . Despite these caveats , our results clearly show that under our experimental conditions , Vα14i-TCR expression on conventional naïve T cells leads to their activation and general immune deregulation . These findings seemed to support notions that NKT cell maintenance [52] , their activated surface phenotype , and especially their rapid cytokine expression abilities might depend on constant antigen recognition [25] . However , by ablating the TCR on mature NKT cells in situ , we unequivocally demonstrated that long-term mouse NKT cell homeostasis and gene expression are nearly completely independent of TCR signals . In this regard , they are similar to memory T and B cells , which can maintain their numbers , identity , and functional capabilities in the absence of antigen [53] , [54] . Our results are hard to reconcile with a recent report suggesting that NKT cell maintenance requires lipid presentation by B cells [52] . While there might be some differences between mouse and man , a more likely scenario is that the observations of Bosma et al . reflect rather acute local activation than true homeostatic requirements . Most of the known functions of NKT cells critically depend on their ability to rapidly secrete large amounts of many different immune-modulatory cytokines shortly after their activation . Still , it is not fully understood how NKT cell activation is triggered in different disease settings , and especially to what extent signaling in response to TCR-mediated recognition of antigens versus activation by proinflammatory cytokines contributes to this . Various studies reported that CD1d-dependent signals were required for full NKT activation in vitro [19] , [20] , [55] , although most of them contained the caveat of potentially incomplete blockade of CD1d function by blocking antibodies . Our experiments , in line with a recent report [21] , show that even in the complete absence of TCR signaling for 4 wk , NKT cells can be robustly activated in vivo to produce IFN-γ upon LPS injection in similar amounts as their TCR+ counterparts . Thus , we demonstrate that in mouse NKT cells continuous steady-state TCR-signaling is not required to maintain the Ifng locus in a transcriptionally active state , as recently proposed for human NKT cells [25] . Therefore , our results clearly demonstrate that cellular identity and critical functional abilities of mature NKT cells , such as steady-state proliferation and innate cytokine secretion ability , although initially instructed by strong TCR signals , do not require further antigen recognition through their TCR . Collectively , our data strongly support the view that Vα14i-TCR expression on developing NKT cells triggers a program that makes them unresponsive to peripheral self-antigens , which can continuously be recognized by their auto-reactive TCR . NKT cells are extremely potent immune-modulatory cells that upon activation can instantly secrete a large array of cytokines . Although they are selected by high affinity to auto-antigens , similar to regulatory T cells , they are not mainly suppressive cells . Therefore , it seems plausible that NKT cells are rendered “blind” to peripheral auto-antigens , rather than depend on continuous stimulation by self-lipids to maintain their cellular identity and innate functions . By keeping their activated state independent of self-antigen recognition , NKT cells can stay poised to secrete immune-activating cytokines while minimizing the risk of causing damage to self during normal physiology . On the other hand , the presence of the auto-reactive Vα14i-TCR serves to detect pathogenic states when a stronger signal is generated by the enhanced presentation of potentially more potent self-antigens or foreign lipids .
To generate Vα14iStopF mice , B6 ES cells ( Artemis ) were transfected , cultured , and selected as previously described for Bruce 4 ES cells [56] . Mx-Cre [39] , CαF [40] , CD4-Cre [57] , Nestin-Cre [31] , Vα11p-Vα14i-tg [32] , and Vα14iStopF mice were kept on a C57BL/6 genetic background . As we did not observe any differences between CD4-Cre and Vα14iStopF/wt mice in NKT cell biology , they were sometimes grouped together as controls . Mice were housed in the specific pathogen-free animal facility of the MPIB . All animal procedures were approved by the Regierung of Oberbayern . At the age of 6–8 wk ( or 2 wk after cell transfer for the TCR switch experiment ) , animals were given a single i . p . injection ( 400 µg ) of poly ( I:C ) ( Amersham ) . All mice were analyzed 6–8 wk after injection , unless otherwise indicated . Single-cell suspensions were prepared and stained with monoclonal antibodies: B220 ( clone RA3-6B2 ) , BTLA ( 8F4 ) , CD11c ( N418 ) , CD122 ( TM-b1 ) , CD127 ( A7R34 ) , CD160 ( eBioCNX46-3 ) , CD25 ( PC61 . 5 ) , CD28 ( 37 . 51 ) , CD38 ( 90 ) , CD39 ( 24DMS1 ) , CD4 ( RM4-5 ) , CD44 ( IM7 ) , CD45RB ( C363 . 16A ) , CD5 ( 53-7 . 3 ) , CD62L ( MEL-14 ) , CD69 ( H1 . 2-F3 ) , CD8α ( 53-6 . 7 ) , CD8β ( H35-17 . 2 ) , CD95 ( 15A7 ) , DX5 ( DX5 ) , Egr2 ( erongr2 ) , GATA-3 ( TWAJ ) , Gr1 ( RB6-8C5 ) , ICOS ( 7E . 17G9 ) , IL-4 ( 11B11 ) , IL-13 ( eBio13A ) , IL-17A ( eBio17B7 ) , IFN-γ ( XMG1 . 2 ) , LAG-3 ( eBioC9B7W ) , LFA-1 ( M17/4 ) , Ly49A/D ( eBio12A8 ) , Ly49C/I ( 14B11 ) , Ly49G2 ( eBio4D11 ) , Mac1 ( M1/70 ) , NKG2A ( 16A11 ) , NKG2D ( CX5 ) , NK1 . 1 ( PK136 ) , PD-1 ( J43 ) , ROR-γt ( AFKJS-9 ) , T-bet ( eBio4B10 ) , TCRβ ( H57-597 ) , Ter119 ( TER-119 ) , Th-POK ( 2POK ) , and TNF ( MP6-XT22 ) ( all from eBioscience ) . SiglecF ( E50-2440 ) was from BD . TCRβ chains were stained with the mouse Vβ TCR screening panel ( BD ) . PLZF antibody and the CXCL16-Fc fusion were generous gifts from Derek Sant'Angelo and Mehrdad Matloubian , respectively . mCD1d-tetramers were provided by the NIH tetramer core facility . For intracellular transcription factor stainings , cells were fixed and permeabilized with the FoxP3 staining kit ( eBioscience ) . For intracellular cytokine stainings , mice were injected i . v . in the tail vein with 40 µg of LPS ( Sigma ) or 2 µg αGalCer ( Funakoshi ) in a total volume of 200 µl PBS . Afterwards , cells were treated according to manufacturer's instructions with the Cytofix/Cytoperm kit ( BD ) . For multiplex measurement of cytokines in the serum , we used the mouse Th1/Th2 10plex Cytomix kit according to manufacturer's instructions ( eBioscience ) . Samples were acquired on a FACSCanto2 ( BD ) machine , and analyzed with FlowJo software ( Treestar ) . The heat map was generated using perseus ( part of the MaxQuant software [58] ) . Mice were fed with 0 . 5 mg/ml BrdU ( Sigma ) in the drinking water for 4 consecutive weeks . Directly afterwards , BrdU incorporation was analyzed with a BrdU Flow Kit ( BD ) . Serum TNF levels were determined by ELISA as recommended by the manufacturer ( BD ) . RNA was isolated ( QIAGEN RNeasy Micro Kit ) and reverse transcribed ( Promega ) for quantitative real-time polymerase chain reaction ( PCR ) using probes and primers from the Universal Probe Library ( Roche Diagnostics ) according to the manufacturer's instructions . Statistical analysis of the results was performed by one-way ANOVA followed by Tukey's test , or by student t test , in Prism software ( GraphPad ) . The p values are presented in figure legends where a statistically significant difference was found . | Immune system natural killer T ( NKT ) cells help to protect against certain strains of bacteria and viruses , and suppress the development of autoimmune diseases and cancer . However , NKT cells are also central mediators of allergic responses . The recognition of one's own glycolipid antigens ( self-glycolipids ) in the thymus via the unique Vα14i T cell receptor , Vα14i-TCR , triggers the NKT cell developmental program , which differs considerably from that of conventional T cells . We generated a mouse model to investigate whether the Vα14i-TCR on mature NKT cells constantly recognizes self-glycolipids and to assess whether this TCR is required for survival and continued NKT cell identity . Switching the peptide-recognizing TCR of a mature conventional T cell to a glycolipid-recognizing Vα14i-TCR led to activation of the T cells , indicating that this TCR is also autoreactive on peripheral T cells or can signal autonomously . But TCR ablation did not affect the half-life , characteristic gene expression or innate functions of mature NKT cells . Therefore , the inherently autoreactive Vα14i-TCR is dispensable for the functions of mature peripheral NKT cells after instructing thymic NKT cell development . Thus the Vα14i-TCR serves a similar function to pattern-recognition receptors , in mediating immune recognition of foreign invasion or diseased cells . |
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Identifying the forces that drive proteins to misfold and aggregate , rather than to fold into their functional states , is fundamental to our understanding of living systems and to our ability to combat protein deposition disorders such as Alzheimer's disease and the spongiform encephalopathies . We report here the finding that the balance between hydrophobic and hydrogen bonding interactions is different for proteins in the processes of folding to their native states and misfolding to the alternative amyloid structures . We find that the minima of the protein free energy landscape for folding and misfolding tend to be respectively dominated by hydrophobic and by hydrogen bonding interactions . These results characterise the nature of the interactions that determine the competition between folding and misfolding of proteins by revealing that the stability of native proteins is primarily determined by hydrophobic interactions between side-chains , while the stability of amyloid fibrils depends more on backbone intermolecular hydrogen bonding interactions .
Defining the rules of protein folding , a process by which a sequence of amino acids self-assembles into a specific functional conformation , is one of the great challenges in molecular biology [1]–[3] . In addition , deciphering the causes of misfolding , which can often result in the formation of -sheet rich aggregates , is crucial for understanding the molecular origin of highly debilitating conditions such as Alzheimer's and Parkinson's diseases and type II diabetes [4] . Major advances in establishing the interactions that drive the folding process have been made by analysing the structures in the Protein Data Bank ( PDB ) , and particularly by examining the frequency with which contacts between the different types of amino acid residues occur [5] . In this statistical approach , interaction free energies are derived from the probability , , of two amino acids of types and being in contact in a representative set of protein structures using the Boltzmann relation . This operation defines a matrix that lists the free energies of interaction between amino acid pairs . One of the most studied matrices of this type has been reported by Miyazawa and Jernigan [5] . Three distinct analyses of this matrix ( Fig . 1A ) have all revealed that residue-water interactions play a dominant role in protein folding [6]–[8] . More recently , the same statistical potential method has been used to investigate aggregation of soluble proteins into the amyloid state , now recognised as a generic , alternative , stable and highly organised type of protein structure [3] . A method for predicting the stability of amyloid structure ( PASTA ) [9] extracts the propensities ( ) of two residues found on neighbouring strands in parallel or antiparallel -sheets in a representative set of PDB structures . The resulting parallel strand and antiparallel strand interaction free energy matrices ( referred to here as “parallel” and “antiparallel” respectively ) are shown in Fig . 1B and 1C . Owing to the absence of a large number of solved atomic resolution amyloid fibril structures in the PDB , the central assumption of the PASTA approach is that the side-chain interactions found in the -sheets of globular proteins are the same as those stabilising -sheets in the core of amyloid fibrils [9] . This assumption is supported by the observation that the PASTA matrices are highly successful at predicting the portions of a polypeptide sequence that stabilise the core regions of experimentally determined amyloid fibrils and the intra-sheet registry of the -sheets [9] . We therefore treat the PASTA matrices as statistical potentials for the parallel and antiparallel -sheets found in the core of amyloid fibrils [9] . In this work we carry out a comparative analysis of the interaction matrices for folding and amyloid formation , in order to reveal the nature of the interactions that drive these two processes , and to provide fundamental insight into the competition between them . Our results indicate that the balance between hydrophobic and hydrogen bonding interactions is inverted in these two processes .
The contact approximation for the effective Hamiltonian , , used to describe a system of polypeptide chains usually takes the form ( 1 ) where is the residue type at position along the polypeptide chain , is the position of residue and is a function reflecting the fact that two amino acids interact with free energy when they are in spatial proximity to each other [10] . For random heteropolymers , the pairwise contact free energies can be approximated as a set of 210 independent random variables ( i . e . the 210 independent elements in a symmetric matrix ) . For the MJ matrix , a plot with the axes running from hydrophobic ( C , F , L , W , V , I , M , Y , A , P , black ) [11] to hydrophilic ( H , G , N , T , S , R , Q , D , K , E , magenta ) [11] residue types reveals three large blocks of hydrophobic interactions ( Fig . 1A ) . The most stabilising interactions are hydrophobic-hydrophobic ( Fig . 1A , top left corner , blue ) , followed by hydrophobic-polar ( Fig . 1A , bottom left corner and top right corner , yellow/green ) and polar-polar interactions ( Fig . 1A , bottom right corner , red ) . On closer inspection , analysis of these interactions in the form of a histogram shows that the distribution of contact free energies determined from the Miyazawa-Jernigan ( MJ ) matrix ( Fig . 1D ) can be represented as the sum of three Gaussian terms corresponding to hydrophobic-hydrophobic ( H-H ) , hydrophobic-polar ( H-P ) and polar-polar ( P-P ) contacts [6] ( Fig . 1D ) . This interpretation implies that globular proteins are stabilised mainly by side-chain hydrophobic interactions [6] since the sum of all H-H , H-P and P-P contacts captures the overall distribution of contact free energies extremely well ( Fig . 1D ) . In contrast to the MJ matrix , contour maps of the parallel and antiparallel -sheet contact matrices of the type characteristic of amyloid fibrils [4] show highly destabilising contact free energies between all Pro-X pairs ( Fig . 1B , C , proline row , proline column , red/yellow ) . Since proline cannot form inter-molecular backbone hydrogen bonds this observation suggests that the stabilisation of -sheets arises mainly from the dominance of backbone hydrogen bonding , with hydrophobic interactions ( Fig . 1B , C , top left corner , blue ) playing a secondary role . Furthermore , plots showing the distribution of the contact free energies from parallel and antiparallel -sheets ( Fig . 1E , F ) of the type found in amyloid structures [4] indicate , unlike the situation for native folds described above , a single narrow Gaussian distribution for polar and non-polar contacts alike . This result , combined with the significance of the destabilising Pro-X contacts , is consistent with the view that a major role in protein aggregation into amyloid fibrils is played by backbone hydrogen bonding interactions [12]–[14] , which are “generic” [3] to any polypeptide chain , although sequence-dependent effects are also important to modulate the propensity of specific peptides and proteins [15]–[17] . The difference in these probability distributions arises because we are examining the contact free energies that define the protein folding and misfolding free energy minima via the MJ and PASTA matrices respectively . It is clear that the possible number of ways of forming a given contact between amino acids and is greater in globular proteins than in fibrillar aggregates as the area of Ramachandran space available to -sheets ( 13 . 3% of the total space ) is much smaller than that accessible to native proteins . In addition , the type of amino acid and specific sequence patterns have varying degrees of globularity [18] or aggregation propensity [16] with certain amino acids , notably proline , appearing much more frequently in globular proteins than in the core region of amyloid fibrils [9] . To investigate the consequences of these differences in the conformational spaces relevant to folding and misfolding we consider the constrained sampling of the protein Hamiltonian over a subspace of conformational space , which is given formally by ( 2 ) where is the partition function sampled over the subspace . Interaction parameters to describe the folding process are usually defined by considering a subspace that includes the regions of conformational space corresponding to the native states of globular proteins [19] . By contrast , interaction parameters to describe the aggregation process are defined for a subspace that includes only the regions of conformational space corresponding to -sheet rich structures such as -helices or amyloid fibrils [19] . While the Hamiltonian , , is invariant , the space over which it is integrated will vary depending on the region of conformational space that is being explored . In our case , this leads to distinct “effective” Hamiltonians for the protein folding and misfolding minima; these Hamiltonians have the same general form as Eq . [1] but have different amino acid interaction matrices , according to Eq . [2] , depending on which process is involved . We thus conclude that there could be differences in the various effective energy terms stabilising globular proteins and amyloid fibrils and that such differences can be described by giving different weights to hydrophobicity and hydrogen bonding interactions in the two states . In this view , hydrophobicity and hydrogen bonding do not represent fundamental interactions but effective ones , which result from constrained sampling procedures such as those defined by Eq . [2] . We decomposed the MJ and PASTA matrices into a combination of the HP ( Hydrophobic-Polar ) model [11] and a backbone hydrogen bonding model in which all amino acids , except for proline , are capable of forming backbone hydrogen bonds ( by analogy , we term this the HB model ) . These two-body interactions are described by three interaction matrices , , and , with the following properties: if and are both hydrophobic residues and topological neighbours , and otherwise; if either or is a hydrophobic residue , and are topological neighbours , and otherwise; if and can both form backbone hydrogen bonds and are topological neighbours , otherwise . As a first approximation , we initially fit the MJ and PASTA matrices to an equation of the form: ( 3 ) where is the matrix of interest , , and are the weightings of the , and matrices , respectively , and is a constant ( the solvent-solvent interaction parameter ) [8] . The normalisation constant shifts the elements of the MJ and PASTA matrices along the free energy axis thus allowing comparison of , and between different matrices . It is used to set the free energy of forming a polar-polar contact , , to zero and all other weightings are measured relative to this reference , i . e . and measure the additional free energy of forming hydrophobic contacts and the free energy gained through hydrogen bond formation . Importantly , the adjustment of to give a non-zero free energy has no effect on the ratios of to listed in Table 1 . The weightings ( Table 1 ) should be , and are , approximately equal to the free energy of a single hydrogen bond ( 2 . 5 [20] ) . This simple decomposition given by Eq . [3] gives very good agreement with the MJ ( correlation coefficient 0 . 87 ) and parallel matrices ( correlation coefficient 0 . 77 ) and good agreement with the antiparallel matrix ( correlation coefficient 0 . 69 , or 0 . 70 if disulfide bonds are taken into account ) . This coarse-grained HP-HB model is therefore a good approximation to the original matrices , and can thus provide insight into the relative importance of the hydrophobicity and hydrogen bonding terms for the different types of structures ( Table 1 ) . Since , and are all binary matrices , it is straightforward to quantify the marginal effect of each of the regressors in our general linear model from the values of their coefficients , and . For the MJ matrix , the ratio of to is ( Table 1 ) indicating that for protein folding the hydrophobic term is twice as important as the hydrogen bonding term . This ratio was corroborated by decomposing three recent pairwise contact potentials for the native states of globular proteins [21]–[23] which gave a similar result ( values are 0 . 4 [21] , 0 . 7 [22] , 0 . 73 [23] and on average ) . This finding is in agreement with previous work suggesting that the HP model captures the essence of protein folding [11] . Nevertheless , hydrogen bonding does play an important role in protein folding since highly polar sequences can fold to form -helices , and “side-chain only” molecular dynamics simulations fail to capture crucial aspects of protein folding [24] . Indeed , protein folding simulations have shown that it is necessary to include a mainchain-mainchain hydrogen bonding term in order to obtain secondary structure [25] . For protein misfolding and amyloid formation , the ratio of to for both PASTA matrices is ( Table 1 ) suggesting that backbone-only hydrogen bonding is about 50% more important in stabilising amyloid fibrils than hydrophobic interactions . To demonstrate the robustness of this result , we tested the sensitivity of the ratio to the Pro-X elements of the PASTA matrices and calculated that the high values of the Pro-X side-chain interaction free energies in the parallel and antiparallel matrices would have to be reduced by 4 or 5-fold respectively to achieve the same ratio of found in the MJ matrix . Given that the side-chain interaction free energies are derived from the Boltzmann relation , and that the high Pro-X interaction free energies reflect the infrequent occurrence of proline residues in -sheets , a reduction of this magnitude would translate into a much greater number of Pro-X contacts being detected in the -sheets of the PDB dataset used by the authors of PASTA [9] . The increased weighting of the matrix relative to the matrix in the decomposition of the PASTA matrices shows that the destabilising effect of proline is more disruptive to the hydrogen bonded -sheet structure than to the native fold of globular proteins in which proline has evolved to play an important structural , and stabilising , role e . g . in Pro-induced -turns [26] . This result underscores the importance of sequence-independent hydrogen bonding in defining the amyloid structure . This “generic” view [12] is consistent with the observation that even hydrophilic and homopolymeric sequences of amino acids can form amyloid fibrils [13] . However , the amino acid sequences of individual peptides and proteins influence their specific propensity to aggregate [16] , [17] , and to form self-complementary side-chain packing interfaces between adjacent -sheets in the fibrils [15] , [27] , [28] . We also note that in the -sheets of globular proteins , the effects of backbone hydrogen bonding tends to be averaged out in Eq . ( 2 ) by the presence of other secondary structure motifs ( -helices , -turns and coil ) . A number of controls were performed to confirm that the ratio of to is inverted between folded globular proteins and amyloid fibrils . Firstly , the value of is only slightly affected by considering amino acids such as Proline and Alanine to be hydrophilic rather than hydrophobic . In our initial classification of hydrophobic and hydrophilic residues [11] , the ratios between the hydrogen bonding and hydrophobic terms , , are 0 . 48 , 1 . 59 and 1 . 39 for the MJ , parallel and antiparallel PASTA matrices respectively ( Table 1 ) . By considering proline residues to be hydrophilic , rather than hydrophobic , the ratios become 0 . 55 , 1 . 78 and 1 . 66 for the MJ , parallel and antiparallel PASTA matrices respectively . Furthermore , if we adopt the partitioning suggested by Li , et al . [6] in which both proline and alanine residues are considered to be hydrophilic rather than hydrophobic , the ratios become 0 . 61 , 2 . 14 and 2 . 27 for the MJ , parallel and antiparallel PASTA matrices respectively . This analysis shows that the ratio is inverted between the MJ and PASTA matrices using the most common classifications of amino acids into hydrophilic and hydrophobic sets . We also note that the MJ matrix is calculated by using the quasi-chemical approximation in which protein residues are assumed to be in equilibrium with the solvent . By considering water to be the reference state , all residue-residue interactions are attractive and so all elements of the MJ matrix are negative . By ignoring chain connectivity , it has been argued that this “connectivity effect” introduces a bias into the MJ matrix . However , a knowledge-based pair potential for describing amino acid interactions in the native folds of globular proteins developed by Skolnick , et al . [21] , which we refer to as the SJKG matrix , explicitly includes effects due to chain connectivity . Skolnick , et al . [21] conclude that ignoring chain connectivity does not introduce errors and that the quasi-chemical approximation is sufficient for extracting statistical potentials such as the MJ matrix . By virtue of using native reference states , the SJKG matrix has both positive and negative side-chain interaction free energies and is similar in this way to the PASTA matrices ( Fig . 1B , C ) . The SJKG matrix also has a mean free energy of approximately zero ( 0 . 08 ) like the PASTA matrices ( 0 . 51 and 0 . 13 for parallel and antiparallel respectively , Fig . 1B , C ) . However , like the MJ matrix , the SJKG is a statistical potential for the native folds of globular proteins and when we decompose this matrix using the HP-HB model we get a ratio of to of 0 . 4 , which is almost identical to the ratio found for the MJ matrix . Thus , this result strengthens our findings as the hydrophobicity term , , is even more dominant than the hydrogen bonding term , , in the decomposition of the SJKG matrix than in the MJ matrix ( ratios of 2 . 50 and 2 . 08 respectively ) . In addition , the comparison of the value of the normalisation constant ( 0 . 94 ) with the values of the and terms ( 0 . 49 and 1 . 24 , respectively ) in the HP-HB decomposition of the SJKG matrix confirms that the value of does not affect the ratio of for native proteins and that this ratio is reversed between folded globular proteins and amyloid fibrils . From the contour maps ( Fig . 1A , B , C ) and the histograms of contact free energies ( Fig . 1D , E , F ) it is clear that the free energy of forming hydrophobic-polar ( H-P ) side-chain contacts is stabilising for globular proteins although not nearly as important in the simple formation of -sheets . Thus , for protein folding we find that where is the free energy of forming a polar-polar contact and is not stabilising ( ) and and are the free energies of forming hydrophobic-polar contacts and hydrophobic-hydrophobic contacts respectively . These weightings are in excellent agreement with a modified form of the HP model [29] ( in the present study compared to 2 . 3 in the modified HP model [29] ) and so validate its use in protein folding simulations . The inclusion of the HP term in Eq . [3] has only a marginal effect on the regression to the parallel or antiparallel matrices as demonstrated by the relatively small coefficient 0 . 2 ( Table 1 ) . This result suggests that the segregation of hydrophobic and polar residues is not very important in -sheet formation and could lead to solvent exposed non-polar side-chains in prefibrillar aggregates , a feature that has been suggested to be closely linked to cytotoxicity [30] . The minor effect of the HP term is also in accord with our finding that hydrophobic interactions play a less significant role than inter-molecular hydrogen bonding in stabilising amyloid fibrils and again supports the idea that peptides and proteins are prone to forming amyloid structures irrespective of sequence [12] , [13] , although the relative propensities to form such structures will vary with sequence [16] , [27] . Previous analyses of the MJ matrix shows that two-body interactions are not sufficient to capture all of the details of the 210 independent amino acid interactions that describe the variety of native protein structures [6]–[8] . A one-body term , , describing the individual properties of each amino acid , is also required . Adding this additional term to our previous free energy expression Eq . [3] gives ( 4 ) The application of this equation to the MJ , parallel and antiparallel matrices gives correlation coefficients of 0 . 99 , 0 . 90 and 0 . 90 respectively ( Fig . 2A , B , C ) . This expression , therefore , describes the original data extremely well and suggests that the diverse and complex interactions stabilising both the native and fibrillar states are amenable to a low-dimensional representation using simple two-body and one-body terms [6]–[8] . It is remarkable that the same approach can be used to decompose both the MJ and PASTA matrices , indicating that the underlying interactions are the same but that the balance is different , and leads to a clear demarcation of the thermodynamic minima of the native and amyloid states of the protein free energy landscape . The three sets of 20 one-body parameters , , that are derived from the MJ , parallel and antiparallel matrices are listed in Table 2 . Previous work has shown that one-body components of the MJ matrix , known as q-values , are closely related to the interactions governing secondary structure formation [6] . We find that our equivalent one-body potentials , MJ ( Table 2 ) , correlate extremely well with ( correlation coefficient of 0 . 98 , Fig . 3A ) , and are numerically almost identical to this previously published q-scale ( Table 2 , column 4 ) provided that the hydrophobic and hydrophilic q-values are separated and have their respective mean values subtracted from each non-polar and polar element . This procedure removes an average hydrophobic penalty for non-polar residues ( +1 . 45 ) and an average hydrophilic gain for polar residues ( −0 . 07 ) . This residue-specific hydrophobic ( hydrophilic ) cost ( gain ) can be interpreted as an average free energy cost of placing in water the surface of a given residue plus the gain of attractive dipolar interaction between the residue concerned and water , with polar residues being more favourable than non-polar residues [7] . This effect is even more apparent in the simpler case of the one-body components of the parallel and antiparallel PASTA matrices ( Table 2 , parallel and antiparallel respectively ) . When existing parallel and antiparallel -sheet propensity scales [31] are converted into free energies ( Table 2 , column 5 and 6 respectively ) , grouped into polar and non-polar terms and then separately shifted to have zero mean , thus removing the average hydrophobic ( hydrophilic ) cost ( gain ) to water of forming a sheet ( the values are +0 . 32 ( −0 . 51 ) and +0 . 34 ( −0 . 25 ) for parallel and antiparallel -sheets respectively ) , the remainder correlates extremely well with ( correlation coefficients of 0 . 96 and 0 . 97 for parallel and antiparallel -sheets respectively , Fig . 3B , C ) , and is numerically almost identical to the one-body potentials of the parallel and antiparallel matrices ( parallel and antiparallel respectively , Table 2 ) . This result suggests that the one-body free energy components of the MJ , parallel and antiparallel matrices are given by ( 5 ) where represents the free energy to form hydrogen bonded secondary structure and is an average free energy of solvation . Hence , we suggest that the one-body free energy terms , , correspond to a stabilisation of the native or fibrillar state through a competition between hydrophilicity and the formation of hydrogen bonded secondary structure . The HP-HB-SS ( HP-HB-secondary structure ) model described above suggests therefore that both the globular and amyloid states of proteins are stabilised by hydrophobic interactions , hydrogen bonding and the formation of secondary structure , and that there is a common form for the effective Hamiltonian , , describing both protein folding and misfolding , given by the substitution of Eq . [4] into Eq . [1] ( 6 ) The two-body terms in the effective Hamiltonian are , and , which correspond to the relative strengths of hydrophobic interactions and hydrogen bonding , and take the values given in Table 1 . The effective energy function is further modulated by the additive residue specific terms ( Table 2 ) , which correspond to the free energy of secondary structure formation plus a free energy of solvation . It is important to note that there is a loss of translational and rotational entropy on going from native to fibrillar states [32] which we do not consider here . This loss of entropy would be expected to stabilise the native state in a sequence- and conformation-independent manner and would add a native-biasing term to the effective energy function given in Eq . [6] . Although the general form of the effective Hamiltonian is the same for protein folding and misfolding , the variables , , and are different for these two processes , with the result that the minima in the two cases will occur at different positions in conformational space . Fibrillar aggregates represent a well-defined region of the wider protein folding landscape characterised by the pervasiveness of generic intermolecular hydrogen bonding [12] . Since the Hamiltonian maps the sequence space on to the structure space , as the weights , and change so too does the shape of the resulting structure . The dominance of the collapse-inducing hydrophobic force in protein folding leads to a globular tertiary structure , with hydrophobic residues buried in the core and largely polar residues on the surface of the protein [33] . However , when unidirectional inter-molecular hydrogen bonding is in the ascendancy , the result is ordered protein self-association into elongated , rigid , rod-like aggregates [14] . By decomposing the MJ and PASTA matrices into two-body and one-body components , we have effectively decoupled the two-body non-local interactions from the one-body , local interactions entangled in these statistical potentials . This approach enables us to analyse quantitatively the relative importance of local and non-local interactions in determining the folding and misfolding of proteins . It is clear from Tables 1 and 2 that the magnitude of the non-local ( tertiary ) interactions are significantly greater than the local ( secondary ) interactions in stabilising the native protein or fibrillar aggregate . This result indicates that nonlocal inter-residue interactions are the major determinant of secondary structure in the HP-HB-SS model . This finding is in excellent agreement with a large body of experimental [34] and computational analyses [35] , which demonstrates that the sequence patterns of polar and non-polar amino acids dominate their intrinsic secondary structure propensities in determining the secondary structure motifs of a globular protein [36] or amyloid fibril [37] . Our prediction that hydrophobic patterning and sequence independent hydrogen bonding is more important than residue-specific identity in shaping secondary and tertiary structure helps explain why a wide variety of amino acid sequences can encode the same basic protein fold [38] . It is also consistent with the mutational robustness of functional proteins , which typically only fail to fold correctly following several mutations of individual amino acids [39] . In addition , globular proteins have evolved to mitigate against the non-local effect of polar/nonpolar periodicity by deliberately spurning alternating hydrophobic patterns which program amino acid sequences to form amphiphilic -sheets and amyloid fibrils [40] . This is further evidence that tertiary interactions overwhelm the intrinsic propensities of individual amino acids in real proteins , which agrees with our analysis . The mathematical form of the effective Hamiltonian of Eq . [6] describing protein folding and misfolding is analogous to that of a spin glass model in which competition between conflicting interactions leads to a rugged free energy landscape [41] . Apart from topological frustration , which arises due to chain connectivity , the three sources of energetic frustration in the HP-HB-SS model stem from the competition between intramolecular collapse and intermolecular self-association , the contest between frustrating nonlocal interactions and , finally , the inability to satisfy simultaneously all local secondary structure preferences . As discussed earlier , in our model the relative strengths of the hydrophobicity to hydrogen bonding terms governs the dichotomy between folding and misfolding ( Table 1 ) . The conflicting optimisation factors imposed by hydrophobic clustering , maximal backbone hydrogen bonding and the segregation of hydrophobic and polar residues prevent the native state or fibrillar aggregate from energetically satisfying all of these inter-residue interactions . Finally , since non-local interactions predominantly determine globular [36] and fibrillar protein structures [37] , there is an additional source of mismatch between the secondary structure motifs encoded by the hydrophobic patterning of the amino acid sequence as a whole and the secondary structure propensities of the individual amino acids . This intricate interplay of competing interactions gives rise to multiple local minima in the effective energy function of Eq . [6] but , in accordance with the principle of minimal frustration [2] , the sequence of a protein has evolved to reduce the number of alternative minima as much as possible and to have its native state as the global minimum of the protein folding free energy landscape [2] , [3] . However , the ruggedness of the folding free energy landscape increases the likelihood that excited native-like states exist , which may be transiently populated via thermal fluctuations , thus potentially leading to amyloid formation even under physiological conditions [42] . Moreover , frustration in the protein misfolding free energy landscape can lead to amyloid fibril polymorphs with different physical and biological properties [43] . Lowering the discordance between non-local ( Table 1 ) and local ( Table 2 ) interactions leads to more stable and cooperative native protein folds [35] , [44] , and has implications for the de novo design of proteins [44] and amyloid fibrils [45] , [46] . Indeed , knowledge of the residue-specific one-body terms ( Table 2 ) , and the understanding that they correspond to the free energy of secondary structure formation once a solvation free energy is taken into account , may aid in the rational design of globular folds through mutational screening of regions known to be critical for aggregation .
The present work indicates that there are common intermolecular forces stabilizing both globular and fibrillar states of proteins , but that a different balance of these forces results in either folding or misfolding to non-functional and potentially toxic aggregates . This situation occurs as the competing processes of protein folding and misfolding are finely tuned in terms of their free energies . Upon folding , the protein minimises the free energy of the protein-water system by clustering hydrophobic groups and forming intramolecular hydrogen bonds in the globular interior . By contrast , upon aggregation into amyloid fibrils , the formation of an extensive intermolecular hydrogen bonding network compensates for any exposure of hydrophobic groups to water that results from the fibrillar structure of the aggregated state . It has been found in molecular dynamics simulations that the correct balance between hydrophobicity and hydrogen bonding must be attained for proteins to fold correctly or to self-assemble into the alternative well-defined amyloid structure rather than into amorphous aggregates [19] , [47] . For example , if hydrophobicity is too dominant , then an amorphous cluster of residues with few native contacts can be formed rather than a correctly folded protein [19] . Interestingly , these simulations suggest that hydrogen bonding is more than twice as important as hydrophobicity for aggregation into amyloid fibrils [19] , [48] , and that hydrophobicity is approximately twice as important as hydrogen bonding for protein folding [19] , findings that are in close agreement with those reported by the analysis in the present paper . Recent experimental evidence supports this interpretation of protein folding and misfolding . It has been found that the substitution of backbone ester groups for the amide linkage does not significantly affect the structure of native proteins [49] , suggesting that the folded core is mainly stabilised by hydrophobic interactions . Similar experiments for protein aggregation , however , reveal that peptides with removed backbone amide groups have a much reduced propensity to form ordered aggregates [50]; indeed such species are being explored as potential therapeutic inhibitors of amyloid fibril growth [51] . In addition , the large elastic modulus of amyloid fibrils stems mainly from generic inter-backbone hydrogen bonding indicating that this is a dominant interaction defining the amyloid state [14] . The weights , and are functions of physical [52] , [53] and chemical [54]–[56] parameters . Hydrophobic attraction , , and hydrogen bond interaction strength , , are both strongly environment-dependent intermolecular forces and vary in a complex manner as externally driven parameters such as temperature , pH , ionic strength and denaturant concentration are changed [32] . Despite the complicated nature of these interactions , experiments show that at low concentration , denaturants increase the monomer-monomer dissociation energy approximately linearly [54] . This suggests that the monomer-monomer association energy is a linear decreasing function of denaturant concentration under mildly denaturing conditions . In keeping with our model , we speculate that at low denaturant concentrations , is large , thereby promoting the native state by increasing residue-residue hydrophobic attraction , whereas at higher denaturant concentrations the lowering of leads to destabilisation of the hydrophobic core of the native structure , making intermolecular association much more likely [57] . Our analysis suggests that there is an optimal balance between hydrophobicity and hydrogen bonding for protein folding and a significant redistribution of these intermolecular forces for amyloid formation . Such a shift in balance can be seen as a jump between free energy landscape minima , and could occur , for example , at a critical concentration [58] , or pH [55] , or at a temperature sufficiently high to overcome kinetic barriers between the native and amyloid minima [46] . Overall , however , this balance appears to be very finely tuned for both protein folding and misfolding , and it is interesting to speculate on the role of this delicate balance of forces within the cell . It has been suggested that proteins have evolved to be expressed intra-cellularly at levels in the region of the critical concentration for aggregation [58] . While a plentiful abundance of a given protein in the cell optimises its function , being on the verge of insolubility leaves proteins susceptible to environmental changes and prone to aggregation [59] . Our findings are consistent with this hypothesis [58] , since elevated protein levels increase the likelihood of intermolecular as opposed to intramolecular interactions , and suggest that a precarious balance between hydrogen bonding and hydrophobic forces dictates whether peptides and proteins adopt normal or aberrant biological roles . In conclusion , we have reported an interpretation of statistical potentials for protein folding [5] and misfolding [9] by expressing them in terms of a model containing specific terms for hydrogen bonding and hydrophobicity . This approach has enabled us to describe complex and diverse interactions using specific values of three distinct two-body terms and intrinsic secondary structure propensities . We have explained the significance of each of these terms and derived a physically meaningful common form of effective Hamiltonian for both protein folding and amyloid formation . This approach suggests that while hydrophobicity , hydrogen bonding and the formation of secondary structure are important to both processes , the balance between hydrophobicity and hydrogen bonding is remarkably different in the two regimes . Our central finding is that the stabilities of correctly folded proteins are dominated by side-chain hydrophobic interactions and that amyloid fibrils are stabilised mainly by sequence-independent intermolecular hydrogen bonding . We have also quantified the relative importance of local and non-local interactions in determining the structure and stability of proteins in both their globular and fibrillar forms and find that inter-residue interactions are more influential than secondary structure propensities in shaping the final native or amyloid fold . This result shows that , in accordance with the principle of minimal frustration [2] , natural proteins have evolved to maintain a low ratio of local-to-non-local interaction strengths , thereby minimising the effect of a potent source of frustration and ensuring cooperative and stable folding [35] , [44] . In summary , we have found that the conflict between protein folding and misfolding is governed by the contest between a side-chain-driven hydrophobic collapse and a backbone-driven self-association . The almost infinite variety of outcomes of such a conflict gives rise to the rich and diverse behaviour exhibited by proteins and the resulting balance between health and disease .
The weights of the two-body terms , , , , and the constant , , were determined by performing multiple regression in MATLAB . The twenty one-body terms , , were determined by performing a simulated annealing minimisation in MATLAB . | In order to carry out their biological functions , most proteins fold into well-defined conformations known as native states . Failure to fold , or to remain folded correctly , may result in misfolding and aggregation , which are processes associated with a wide range of highly debilitating , and so far incurable , human conditions that include Alzheimer's and Parkinson's diseases and type II diabetes . In our work we investigate the nature of the fundamental interactions that are responsible for the folding and misfolding behaviour of proteins , finding that interactions between protein side-chains play a major role in stabilising native states , whilst backbone hydrogen bonding interactions are key in determining the stability of amyloid fibrils . |
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Multilocus analysis of single nucleotide polymorphism haplotypes is a promising approach to dissecting the genetic basis of complex diseases . We propose a coalescent-based model for association mapping that potentially increases the power to detect disease-susceptibility variants in genetic association studies . The approach uses Bayesian partition modelling to cluster haplotypes with similar disease risks by exploiting evolutionary information . We focus on candidate gene regions with densely spaced markers and model chromosomal segments in high linkage disequilibrium therein assuming a perfect phylogeny . To make this assumption more realistic , we split the chromosomal region of interest into sub-regions or windows of high linkage disequilibrium . The haplotype space is then partitioned into disjoint clusters , within which the phenotype–haplotype association is assumed to be the same . For example , in case-control studies , we expect chromosomal segments bearing the causal variant on a common ancestral background to be more frequent among cases than controls , giving rise to two separate haplotype clusters . The novelty of our approach arises from the fact that the distance used for clustering haplotypes has an evolutionary interpretation , as haplotypes are clustered according to the time to their most recent common ancestor . Our approach is fully Bayesian and we develop a Markov Chain Monte Carlo algorithm to sample efficiently over the space of possible partitions . We compare the proposed approach to both single-marker analyses and recently proposed multi-marker methods and show that the Bayesian partition modelling performs similarly in localizing the causal allele while yielding lower false-positive rates . Also , the method is computationally quicker than other multi-marker approaches . We present an application to real genotype data from the CYP2D6 gene region , which has a confirmed role in drug metabolism , where we succeed in mapping the location of the susceptibility variant within a small error .
Genetic association studies have emerged as a powerful tool for dissecting the genetic contribution to complex , common diseases . Their main goal is to identify inter-individual genetic variants , mostly single nucleotide polymorphisms ( SNPs ) , which show the strongest association with the phenotype of interest , either because they are causal or , more likely , statistically correlated or in linkage disequilibrium ( LD ) with an unobserved causal variant ( s ) . Univariate analyses that test each marker for association with the phenotype can be inefficient , as they do not take into account the patterns of LD among markers as opposed to multi-marker or haplotype-based approaches . Haplotype-based analyses are promising and their use is supported by results from recent studies that suggest that the human genome consists of block-like regions of ancestrally conserved chromosomal segments , whose boundaries are defined by recombination hotspots [1–3] . The main difficulty with a haplotype-based approach is that , for a large number of SNPs , there may be many haplotypes , usually a few common and several rare ones . One solution is to model all rare haplotypes as a single “exposure” group , but this approach could lead to loss of information . An alternative approach to sensibly reducing the number of haplotypes considered is to cluster structurally “similar” haplotypes , as they are more likely to carry the same susceptibility allele and therefore have similar associated risk [4] . The rationale behind this approach is that haplotypes that inherit a causal mutation , e . g . , case haplotypes for a dichotomous trait , tend to also inherit alleles at markers nearby due to LD . Therefore , case haplotypes are expected to be more similar around the causal locus compared to control haplotypes . Hence , similar haplotypes are grouped together in homogeneous clusters , within which disease risk is assumed constant [4] . A key issue with such haplotype clustering methods is the choice of the metric used to determine how similar one haplotype is to another . The similarity metric can be , for example , the proportion of SNPs at which two haplotypes are the same [5] , or it can exploit the ancestral relationships of haplotypes by adopting the notion that if the causal mutation has occurred only once then haplotypes share a common ancestry at that point [6–11] . One recently proposed clustering method is that of Waldron et al . [11] . They modify the ideas of Molitor et al . [8] by looking for only one cluster with the highest disease risk haplotypes , and by modifying the similarity score to account for population allele frequencies and to allow allele mismatches . In particular , Waldron et al . [11] define a hypothetical ancestral haplotype ( namely the cluster centre ) from which the members of the cluster are thought to have descended , and they measure the similarity of the centre with each observed haplotype around a putative causal locus . The similarity metric is calculated for all windows containing the putative location . Each window score is the sum of the SNP scores and the final score for each unique haplotype is taken to be the maximum window score . The window with the maximum score is the part of the ancestral haplotype that the haplotype inherited . The cluster is defined to consist of all haplotypes whose similarity score exceeds some threshold . The ancestral haplotype , the causal locus , the penalty parameter for allele mismatches , and the threshold are random variables and are updated with a Markov Chain Monte Carlo ( MCMC ) algorithm . Clustering approaches can be thought of as an empirical approximation to the more formal coalescent approach , which is promising for LD mapping [12] , as the coalescent is more likely to infer a better approximation to the evolutionary history of mutations of a set of haplotypes . In fact , the genealogy of a sample of haplotypes contains the patterns of genetic diversity of the distinct haplotypes , with putative disease mutations embedded within . Several approaches based on the coalescent have been developed for fine-scale mapping [13 , 14] . However , most of these methods are effective only for a small number of markers and individuals . The coalescent assumes that the variation in haplotypes can be described only by their mutational history . However , to approximate the shared ancestry among haplotypes more accurately , a fine-mapping approach may need to account for recombination . This can be achieved using methods that consider ancestral recombination graphs ( ARG ) [15 , 16] , but their computational complexity is still high . In this paper , we propose a Bayesian partition model [17] to cluster haplotypes according to their associated level of risk by exploiting evolutionary information . The method is computationally fast and can handle large datasets with many markers and/or subjects . Bayesian partition models have been used in genetic association studies by Seaman et al . [18] for highly polymorphic candidate genes and by Molitor et al . [8] and Morris [19 , 20] for candidate genes or small candidate regions . We focus on candidate gene regions with densely spaced markers and assume that a perfect phylogeny holds over short chromosomal lengths in the region . The perfect phylogeny assumption implies that each SNP has arisen as a result of a single ancestral mutation . Recombination , parallel mutations , or back mutations can cause the perfect phylogeny assumption to be violated . The distance used for the clustering method has an evolutionary interpretation , as sequences are clustered together depending on the time to their most recent common ancestor in the genealogy . In particular , we proceed by splitting the chromosomal region of interest into sub-regions or windows where the perfect phylogeny assumption holds . Focusing on case-control studies , at each step of the MCMC algorithm we select a window , i . e . , a perfect phylogeny , and we then partition the haplotype space into disjoint clusters on the basis of the relative ages of the markers in the selected window . Each cluster is then assigned a specific risk . Potentially , haplotypes can be clustered on the basis of any tree and each SNP has , a priori , a positive probability to be a cluster centre . The number and centres of the clusters are both assumed unknown , a priori . Our approach is fully Bayesian and we obtain posterior samples of quantities of interest , sampling over the space of possible partitions . We are particularly interested in the posterior probability of each SNP being a cluster , since high values correspond to markers or locations where case and control haplotypes are best separated , suggesting the presence of a disease susceptibility variant in the region . We assess the performance of the proposed method in a simulation study by comparing it with single locus analysis; to the haplotype-based method of Waldron et al . [11] , as implemented in the software HAPCLUSTER; and to the ARG-based method of Minichiello and Durbin [16] , implemented in the software Margarita . We consider various simulation scenarios differing in genetic relative risk , minor allele frequency of the causal allele , number of cases and controls , disease model , marker density , and recombination rate . Results indicate that the proposed method performs similarly in localizing the causal allele while yielding lower false-positive rates . Also , the method is computationally faster than other multi-marker approaches . We also apply the proposed method to real genotype data from the CYP2D6 gene region , which has been shown to be associated with drug metabolism [21] , and we succeed in mapping the location of the susceptibility variant within a small error .
We investigated the performance of the proposed method using simulated case-control data under different scenarios . Results were compared to those obtained from the univariate Fisher's exact test of association at each SNP maker and those using the HAPCLUSTER algorithm [11] . The ARG-based Margarita [16] was only run on the default scenario as defined below because of computational time constraints . We choose HAPCLUSTER as a representative of alternative haplotype-clustering methods since Waldron et al . [11] found ( in simulation studies ) that it performs better than other similar methods such as BLADE [7] and DHSMAP [6] . They also found that their distance metric outperformed those of Durrant et al . [10] and Yu et al . [9] . An alternative ARG-based method is that of Zöllner and Pritchard [15] . However , in trial runs we found that it is not computationally feasible for such an extensive simulation study . We used the software FREGENE [22] to simulate two pools of 20 , 000 haplotypes , corresponding to a uniform or variable recombination rate , spanning a 1-Mb chromosomal region . The population with constant recombination rate was simulated from the simple Wright-Fisher model with recombination and mutation rate equal to 2 . 3 × 10−8 and 1 . 1 × 10−8 per site per generation , respectively . The second population was simulated with recombination hotspots . We assumed that 60% of all recombination events take place in recombination hotspots , which occur on average every 200 kb and are 2 kb in length . Also , 1% of the genome was assumed to consist of hotspots . The recombination rate within hotspots was 6 . 56 × 10−7 per site per generation , and 4 . 44 × 10−9 between hotspots [22] . The mutation rate was 2 . 3 × 10−8 per site per generation . To reflect ascertainment bias , we draw markers from the set of SNPs having minor allele frequency ( MAF ) larger than 1% . From these markers , 1 , 000 ( or 340 depending in the SNP density chosen ) SNPs were selected with probability proportional to p ( 1 − p ) , where p is the allele frequency of a marker in the sample , to reflect an extra ascertainment bias towards markers with two common alleles and to give 1- ( or 3- ) kb average SNP density . A causative locus was then selected at random with allele frequency between p − 0 . 005 and p + 0 . 005 , where p was in a range between 0 . 02 and 0 . 3 . Then , for each pair of randomly sampled haplotypes , the case/control status was assigned according to either an additive or dominant disease model for the genotypes at the causal site assuming a disease prevalence K equal to 1% while the genetic relative risk of the heterozygote genetic relative risk ( GRR[Aa] ) varied between 1 . 2 and 2 . 4 . Specifically , if fi is the penetrance function given i copies of the causal allele , i = 0 , 1 , 2 , and GRR ( Aa ) = r = f1/f0 , then following the liability model used in Tzeng [23] and assuming HWE , we have f0 = K/ ( 1 − 2p + 2pr ) and f2 = 2rf0 − f0 for an additive disease model , and f0 = K/ ( 1 − 2p + 2pr + p2 − rp2 ) and f2 = f1 for a dominant one . Pairs of haplotypes were sampled with replacement from the 20 , 000 haplotypes until N cases and N controls were obtained . Thus , each case ( control ) individual contributed two case ( control ) haplotypes to the analysis . The sample size of cases and controls N also varied between 200 and 2 , 000 . Next , we removed the causal allele from the dataset and , using the algorithm described in Materials and Methods , we found the perfect phylogenies in the dataset . The average number of gene trees was 200 and the average number of SNPs in a gene tree was four . Using the SEQ2TR and the TREEPIC software of Griffiths [24] , we obtained the relative ages of the mutations in the different phylogenies . We assumed a Beta ( 1 , 1 ) prior for the haplotype risks implying that , a priori , each observed haplotype has a 0 . 5 risk of disease . The MCMC algorithm was run for 100 , 000 iterations with a burn-in of 10 , 000 iterations for 50 datasets across different combinations of the simulation parameters . We define the “default” scenario as that corresponding to having N = 1 , 000 cases and controls simulated with variable recombination rate under an additive disease model with 1 . 6 GRR ( Aa ) , a SNP density of 1 kb , and a causal allele with 5% MAF . The computing time for a dataset of 1 , 000 markers and 4 , 000 haplotypes was approximately 23 min ( 14 min to construct the phylogenies and 9 min to run the algorithm ) on an Intel Xeon 3 . 40GHz processor with 2 Gb of memory . The corresponding computing time for HAPCLUSTER was 24 min . Note that while HAPCLUSTER is written in C++ , the proposed method is implemented in R . As mentioned earlier , we compare the results from Margarita only under the default simulation scenario . To run Margarita on a single dataset of 1 , 000 markers and 4 , 000 haplotypes , we split the data into overlapping windows of 200 markers and then run the algorithm separately on each window , as suggested by Minichiello ( personal communication ) . This resulted in five windows for a single dataset . Each window took 15–16 h to run with 10 , 000 permutations on 100 ARGs on a high computing cluster of 2 . 66GHz Xeon 5150 CPUs , making an exhaustive comparison of the two approaches impractical . An R package [25] called BETA ( Bayesian Evolutionary Tree based Association analysis ) implementing the method described in this article is available upon request from IT ( ioanna . tachmazidou03@ic . ac . uk ) . The results from a single simulated dataset under the default scenario are shown in Figure 1 , where the dot on the x-axis indicates the position of the single susceptibility mutation . For the proposed method , the marginal posterior probability of association , i . e . , the probability of each SNP being a cluster centre , and the Bayes factor in favour of association at each marker are shown . We also report the ( log ) p-values from Margarita and Fisher's exact test , and the posterior density of location from HAPCLUSTER . The estimate of the causal mutation is based on the marker with the minimum p-value ( when using the single locus test and Margarita ) , the maximum Bayes factor ( BETA ) , or the mode of the posterior distribution of location ( HAPCLUSTER ) . For this dataset , all methods identified a marker within 10 kb of the true causal allele except for HAPCLUSTER ( 502-kb distance ) . The association signal is however notably clearer under the proposed method . The same dataset contained 208 perfect phylogenies and Table 1 reports the posterior probability and Bayes factor of a tree carrying the causal locus , in which the numbers in brackets is the tree ( all remaining trees had posterior probability less than 0 . 015 ) . The true causal allele was embedded within tree 7 with marker S43 the closest to it . The posterior mode of the distribution for the number of clusters was two , including the “null” cluster ( explained in the “Bayesian partition model” section of Materials and Methods ) , and SNP S58 , which belonged to tree 10 , had the highest marginal posterior probability of being a cluster centre . All marginal probabilities larger than 0 . 01 and corresponding Bayes factors are given in Table 2 . The physically closest SNP to the true susceptibility allele in the table is S47 , also embedded within tree 7 . Figure 2 shows the perfect phylogeny with the highest posterior probability of containing the susceptibility allele ( tree 10 ) , together with the case and control multiplicities of each unique haplotype in the tree . The proposed approach is not limited to the case of a single variant in a single candidate region . Figure 3 shows the results from a simulated dataset in which two liability alleles in separate regions ( possibly corresponding to two separate candidate regions ) contribute independently to disease susceptibility . The results reported correspond to genotype data for 200 cases and controls simulated assuming a variable recombination rate , an additive disease model , SNP density of 1 kb , MAF of causal alleles of 10–15% , and GRR ( Aa ) = 3 . In total there were 184 perfect phylogenies with the two liability alleles belonging to trees 2 and 183 . In Figure 3 , the dots on the x-axis indicate the positions of the two susceptibility mutations . For this particular dataset , the Bayesian model appears to perform better than the single locus analysis , both in terms of location error and in reducing noisy associations . Trees 1 and 179 had the highest posterior probability of carrying the causal alleles , and the posterior mode of the distribution for the number of clusters was three , including the “null” cluster ( see “Bayesian partition model” section of Materials and Methods ) . The distances between the loci with the highest marginal posterior probabilities of being cluster centres and the true locations of the susceptibility alleles were 4 kb and 19 kb , while the corresponding distances for the SNPs with the two smallest p-values were 13 kb and 36 kb . The advantage of the proposed method is likely due to the fact that we fully exploit the LD information around the causal alleles , incorporating the evolutionary information through the perfect phylogeny assumption . Tables 3–6 report the distances from the true location of the liability variant , together with its standard error for our method , HAPCLUSTER , and the single locus Fisher's exact tests under the different simulation scenarios . In each case , results shown are averages over 50 simulated datasets . The location of the causal allele is estimated by the SNP with the minimum p-value for Fisher's exact test and Margarita , by the posterior mode using a kernel density estimate for HAPCLUSTER , and by the SNP with the maximum Bayes factor or marginal posterior probability of being a cluster centre for the proposed model . For BETA , we report results both when the number of clusters is random and when it is fixed at two . Although the former assumption is more flexible , fixing the number of clusters to two is more sensible if , a priori , one expects only one causal mutation in the candidate region . Also , in the latter case , results are more directly comparable with those from HAPCLUSTER , which assumes only two clusters . In each table , the results under the default scenario are shown for ease of comparison . The average distance from the true location for Margarita over 50 replicates under the default simulation scenario is reported in Table 7 , in which PERM p-value is the markerwise p-value calculated by permutation , EVD p-value is the markerwise p-value calculated by fitting an extreme value distribution , and EXP p-value is the experimentwise p-value calculated by permutation , as given by Margarita . Overall , there are no significant differences among the methods in terms of localization error . Figures S1–S14 show typical outputs from each of the methods considered under the default scenario . Note that for BETA , results are from the general version with a random number of clusters , as for all graphs shown . In the Supporting Information , we also report results of performance comparison over 100 datasets simulated under alternative scenarios ( separated in Tables S1 and S2 depending on the MAF of the causal SNP ) . Similarly , there were no major differences in the distribution of the distances of the estimated and true location of the susceptibility allele for the different methods . Figure 4 plots the cumulative probability that the identified location is within some distance from the true location , over the 50 replicates and the default scenario . For reasonable location errors , the methods perform equally , with HAPCLUSTER possibly showing a slight advantage . On the other hand , the advantage of the proposed approach is evident when considering the number of false-positive associations over replicates , as well as the clarity in association signals . To quantify the latter , we consider a window around the causal SNP and calculate the average number of significant associations within that window across the 50 replicates . Results are shown in Figure 5 . For BETA and single-marker tests , we report results from using two different significance thresholds , namely a Bayes factor in favour of association larger than or equal to 10 or 150 ( corresponding to a strong or decisive signal , [26] ) or a p-value smaller than or equal to 0 . 05 or the Bonferroni-adjusted value ( 0 . 05 divided by the number of markers in each dataset ) , respectively . For Margarita , we consider the markerwise p-values calculated by permutation , while “Margarita Bonferroni” and “Margarita corrected” correspond to p-values corrected for multiple testing using Bonferroni and permutation , respectively . Results for HAPCLUSTER are not reported , as this software does not provide markerwise estimates of measures of association . The average number of associations found by BETA with a threshold of 10 for the Bayes factor remains stable as the distance increases and is lower than that given by all other methods apart from the single-marker Fisher's exact test results using a conservative Bonferroni adjustment . The latter , however , still yields a noisier signal than BETA under the more stringent threshold of 150 for the Bayes factor ( bottom two lines in Figure 5 ) . To compare the power of the different methods , we define a window of 100 kb on either side of the causative allele and calculate the proportion of the 50 replicates yielding a significant association within the window , as in Minichiello and Durbin [16] . The significance of a signal is assessed using the rules described in the previous paragraph . Figure 6 shows the probability of detecting a significant association within 100 kb of the causal SNP under various scenarios and over the 50 replicates . In each plot , we vary a simulation parameter along the x-axis while assuming default values for the remaining ones . As mentioned earlier , Margarita was run only for the default scenario . We were unable to obtain results from HAPCLUSTER , as this method does not give markerwise measures of association . From the results in Figure 6 , BETA using the strong rule has more power than both the single locus approach and Margarita ( default scenario only ) with multiplicity-corrected results by permutation , and slightly less power than plain Margarita . Uncorrected single locus test is the most powerful approach , having , however , the worst performance in terms of false positives . As noted earlier , an advantage of the proposed approach is the ability to remove much of the noisy associations . To investigate this further , we calculated the false positive rate from BETA and compared the results with the analogous quantity for Margarita and the univariate analysis . Specifically , given threshold p-values for Margarita and Fisher's exact test and threshold Bayes factors for BETA , we defined as false positives Mfp , those markers with smaller p-values or larger Bayes factors lying outside a window of 100 kb either side of the causal site [27] . For each dataset with M markers , the false positive rate is then Mfp/M . Figure 7 shows the mean false positive rates over the replicates and for different scenarios . The threshold values for the Bayes factors and the p-values are the same as in the previous analyses . For Margarita , the three points correspond to the default scenario and markerwise p-values calculated by permutation or experimentwise p-values calculated by permutation . The false-positive rates for BETA are very low under all simulation scenarios . Under the default scenario , BETA controls the false positives much better than Margarita . Results for HAPCLUSTER are not reported , since , as mentioned earlier , the method does not provide markerwise measures of association . Note that the choice of a 100-kb window is arbitrary; a 200-kb window was also used ( unpublished data ) , which did not alter the conclusions about false positives . Finally , we constructed 50 datasets under a null model of no disease association , and we calculated the false-positive rate . For the univariate analysis , this was 4 . 048% ( p-value ≤0 . 05 ) and 0 when using Bonferroni correction , while BETA resulted in a false-positive rate of 0 . 138% ( Bayes factor ≥10 ) and 0 . 002% ( Bayes factor ≥150 ) . Therefore , the proposed model appears to be reliable in confirming association . The CYP2D6 gene on Chromosome 22q13 has a known role in drug metabolism , with multiple polymorphisms of CYP2D6 gene causing a recessive poor drug metaboliser phenotype . Hosking et al . [21] genotyped 1 , 018 individuals at 32 SNP markers across a 890-kb region flanking the CYP2D6 gene . From the 1 , 018 individuals , 41 were predicted to have the poor metaboliser phenotype and were thus treated as cases . This dataset has been used by Morris et al . [28] , Maniatis et al . [29] , Waldron et al . [11] , and Verzilli et al . [27] to test their proposed LD mapping methods . Hosking et al . [21] reported a 390-kb region of significance around CYP2D6 , Morris et al . [28] gave a 95% posterior confidence interval of 185 kb , and Maniatis et al . [29] yielded a 172-kb support interval , while Waldron et al . [11] and Verzilli et al . [27] refined it to 160 kb and 79 kb , respectively . We used PHASE [30] to resolve ambiguous haplotype pairs for each individual . The pair for each individual was chosen at random according to the posterior probability of the haplotype pair provided by PHASE , and the resulting dataset was analysed as phase-known haplotype data . To investigate the effect of phase uncertainty , we repeated the above procedure ten times to obtain ten independent datasets , and ran the proposed method separately on each of these datasets . Since the average SNP density for this dataset is 30 kb , we used a geometric prior distribution on the number of SNPs of each tree with parameter p equal to 0 . 98 ( see “Model specification” section of Materials and Methods ) . An interpretation of this approach is that there is prior belief that the causal allele lies in a single-marker tree . Moreover , we fixed the number of clusters to be two , i . e . , we expect a single causal location . Each dataset consisted of 26 perfect phylogenies except for one that had 27 . Most of the datasets resulted in 23 trees that contained a single SNP , two trees with two SNPs , and one tree with five SNPs . In all analyses , only gene trees 17 , 18 , 19 , and 20 resulted in a non-zero posterior probability of carrying the liability allele ( with an average of 0 . 75 , 0 . 08 , 0 . 15 , and 0 . 02 , respectively ) . Table 8 reports the marginal posterior probabilities and Bayes factors of each SNP being a cluster centre ( averaged over the ten analyses ) , and Figure 8 shows p-values from Fisher's exact test for single-marker disease association , the marginal posterior probability of association and the Bayes factor in favour of association at each marker ( again averaged over the ten analyses ) , where the vertical line on the x-axis indicates the location of CYP2D6 . The results suggest strong evidence that marker 19 at 550 kb is the closest marker to gene CYP2D6 ( at 525 . 3 kb ) , which leads to a location error of 24 . 7 kb . All ten analyses resulted in the same 95% credible interval of 119 kb . The same credible interval was given from all ten datasets analyzed by the general BETA version ( where the number of clusters is random ) . In this case , in all ten imputed datasets , the posterior mode of the distribution for the number of clusters was two , including the “null” cluster ( explained in the “Bayesian partition model” section of the Materials and Methods ) . The credible interval obtained by BETA compares favourably with the supporting intervals reported by other authors mentioned above .
We have presented a Bayesian method to perform an evolution-based association analysis using haplotype data . Haplotype data capture the genetic variation among individuals in a population , and their use in genetic association studies can potentially increase the power to locate susceptibility variants [31] . Our approach is based on the construction of rooted gene trees over small genetic regions . Although gene trees do not represent the exact history of haplotypes , they offer a sensible and computationally efficient approximation of the ancestry of a sample of chromosomes . The proposed algorithm is particularly suited for densely genotyped regions and can be applied to the analysis of single candidate genes , multiple candidate genes , or larger candidate regions . The performance of the proposed method has been compared with single-locus analyses and with recently proposed multi-locus methods in simulation studies . Results indicate that BETA performs similarly in localizing a causal allele , but leads to lower false-positive results . Moreover , it offers computational advantages over alternative multi-marker methods . In an application to real data from the CYP2D6 region , we are able to map the location of a susceptibility variant within a small error . The proposed model is flexible and computationally efficient . It makes no assumptions about the disease model and allows modelling of multiple putative variants . Moreover , it can be easily extended to handle a continuous phenotype , and work is ongoing to apply it to genetic association studies with a survival outcome . We have also presented a simplified version of the proposed method , in which we restrict the number of clusters to two , which is equivalent to looking for a single marker that best separates cases from controls . This is appropriate when we suspect a single susceptibility allele in the region of interest . In this way , we remove some variability , since we fix one of the parameters , leading to improved performance . Although , in general , the version of BETA with a random number of clusters is more flexible and realistic , we recommend using both versions and comparing the results . The incorporation of environmental covariates in the model could be made possible by assuming , for instance , a cluster-specific probit regression . However , this is likely to be computationally demanding . Moreover , in our presentation of the method , we have assumed that the haplotypes are inferred from the genotypes with certainty . Although haplotype reconstruction is more reliable with dense markers and regions of strong LD , phase uncertainty ideally should be incorporated into the analysis . For instance , a fairer comparison with univariate analysis should probably involve simulating genotypes and then running our method on estimated haplotypes . Haplotype reconstruction programs , such as PHASE [30] , output the posterior probabilities of haplotype pairs for each genotype , and we could randomly select the haplotype pair of an individual according to these posterior probabilities . This is the approach we used in the application to the real data from the CYP2D6 region . Alternatively , we could add a further Metropolis-Hastings ( M-H ) step and sample from the different haplotype reconstructions and perform the rest of the analysis ( as described in Materials and Methods ) given the chosen phase .
Over small genomic regions , where LD is strong and recombination is low , it is reasonable to assume that haplotypes have evolved according to a perfect phylogeny [24] . Assuming nonrecurrent point mutations ( in which case the infinitely-many-sites model holds ) , we can construct a unique tree that describes the mutation history of a sample of haplotypes . The tree is a representation of the haplotype data and it is useful to think of the haplotype data as a tree , because the causal variant is embedded within the coalescent process describing the genealogy of the haplotypes under study [32] . Consider , for example , the incidence matrix for the haplotype data reported in Table 9 . Columns correspond to 12 diallelic SNPs and rows identify the unique haplotypes and we assume there are 800 haplotypes in total . Alleles at each SNP position are coded as 0 for the major allele ( i . e . , the most frequent in the population ) and 1 for the minor allele . Data are compatible with a rooted phylogeny if and only if , for any two SNPs ( or columns ) in the incidence matrix , the pattern of ( 01 , 10 , 11 ) is not present . An explanation of this constraint is that since the infinitely-many-sites model does not allow for back or recurrent mutation , the only way for these three gametic types to exist in the sample is for at least one recombination event to have occurred between the two sites [33] . Therefore , the use of the perfect phylogeny model requires both observations of little or no recombination in DNA segments [1–3 , 34] , and the infinitely-many-sites assumption of population genetics . It is possible to construct a gene tree when the perfect phylogeny condition is true for all pairs of SNPs of a study sample using , for example , Gusfield's algorithm [35 , 24] . Figure 9 shows the gene tree for the haplotypes in Table 9 . The nodes in the tree correspond to mutations that have generated the segregating sites and the gene tree is rooted at the haplotype with all major alleles . Mutations are ordered on the tree according to their relative age . If the causal mutation is embedded between SNPs 1 and 7 , all descendant haplotypes of that lineage will inherit it and , therefore , we expect that most case haplotypes are among the 308 haplotypes that correspond to the first three branches of the tree ( first three lines of Table 9 ) . Thus , in the region of the disease locus , a sample of case haplotypes tend to have a more-recent shared ancestry than do control haplotypes , because many of them share a recent disease mutation . Note , however , that sporadic cases due to phenocopies , dominance , and epistasis introduce substantial noise in the phenotype–haplotype relationship , which influences the relative frequencies of nonpenetrant case haplotypes carried by unaffected controls and control haplotypes carried by affected cases . The proposed method can be applied to a single candidate region , multiple candidate regions , and to fine-scale mapping . Recent studies suggest that recombination events occur preferentially outside genes [34 , 36] . Thus , in the case of single or multiple candidate regions we assume that each gene lies in a region of high LD . Within each region , then , we assume , as described above , a coalescent model of evolution and the infinitely-many-sites model and represent each gene with a separate tree . For fine-scale mapping , the chromosomal segment can be divided into a number of gene trees with boundaries determined by loci in which the perfect phylogeny assumption is violated . Details of how this is achieved are given in the following section . A rooted perfect phylogeny ( PP ) assumption poses the constraint that , for any two SNPs in the incidence matrix , not all three combinations ( 01 , 10 , 11 ) exist . Recombination and back or parallel mutation leads to the possible existence of all three combinations . We have developed an R routine based on the algorithm of Lenhard [37] that scans a chromosomal region consisting of m markers and splits the region into sub-regions that satisfy the PP condition . In particular , starting from SNP S1 , it checks the PP condition between SNPs S1 and S2 . If the condition is true , it checks the condition between pairs S1 and S3 , and S2 and S3 . If the condition is not valid for SNPs S 1 and S 3 , then SNPs S 1 and S 2 form a gene tree and the procedure is repeated starting from SNP S 3 . The same happens if the condition is valid for S 1 and S 3 , but not for S 2 and S 3 . If the condition is true for both pairs , the algorithm checks the PP assumption pairwise between SNPs S 4 and S 1– S 3 . Generally , if the pattern of ( 01 , 10 , 11 ) is identified between SNPs Si and Sj ( for every i < j ) , but not identified for any pairs between Sk1 and Sk2 ( for i ≤ k1 < k2 < j ) , then SNPs Si − Sj − 1 form a perfect phylogeny , and the procedure is repeated starting from SNP Sj . However , note that this algorithm leads to only one of the possible tree configurations for the chromosomal region under study , since using different SNPs as a starting point may result in different tree configurations . As mentioned earlier , the proposed method splits the haplotype space into disjoint clusters on the basis of haplotype similarity , with the number of clusters unknown , a priori . To measure the closeness of one haplotype to another , we adopt a distance that has an evolutionary interpretation , with sequences sharing a cluster depending on the time to their most recent common ancestor . Thus , the distance metric is based on the relative ages of the mutations in the sample or on the order with which the mutations have arisen in the haplotype sample , which is provided by the topology of the gene tree . Any SNP set selected as cluster centres can therefore be time ordered , and we assign haplotypes to clusters according to the relative ages of the centres . Suppose , for example , that SNPs 4 , 5 , and 7 of Figure 9 are selected as cluster centres . SNP 7 is older than SNP 5 , and SNP 4 is on a different branch , implying that a haplotype carrying mutation 4 cannot carry mutation 5 or 7 . Starting with SNP 5 , we assign the haplotypes that correspond to the first two branches of the tree ( namely , the first two haplotypes in Table 9 ) as members of this cluster . The only member of the cluster with SNP 7 as centre is the third haplotype , because , although the first two haplotypes carry mutation 7 , they have been already allocated to a cluster . The seventh , eighth , and ninth haplotypes are allocated to a separate cluster with centre SNP 4 , and all remaining haplotypes are assigned to a hypothetical “null” cluster , which can be interpreted as a baseline risk group . Therefore , the choice of the centres defines the way that haplotypes are assigned to their clusters . Given the centres , every haplotype is deterministically allocated to the cluster with the closest centre , using the metric above . Haplotypes within each cluster have cluster-specific risks of disease , which are assumed to be exchangeable and to come from some simple distribution . As mentioned earlier , this is intended to capture the fact that haplotypes that are similar to each other in the region of a putative causal mutation are likely to be associated with similar risks of disease . An MCMC algorithm is developed to obtain posterior samples of quantities of interest , averaging over the space of possible partitions . In particular , we are interested in the posterior distribution of the number of clusters and the posterior probability that each SNP is chosen as a cluster centre . For example , in the extreme scenario of a fully penetrant variant that is among the set of typed markers , we expect a high posterior probability of having only two clusters , namely , the cluster with the causal variant as cluster centre and the “null” cluster . For simplicity , let us first consider the case in which the haplotype data form a single perfect phylogeny , as in Table 9 . Assume that the haplotype space is currently partitioned into nc = nclust + 1 independent clusters ( nc includes the “null” cluster , while nclust is the number of SNPs selected as cluster centres ) . A convenient approach to parameterising the space of possible partitions is to introduce an indicator vector γ , with γ = ( γ1 , …γnSNP ) , with γk in {0 , 1} , k = 1 , … , nSNP , such that γk = 1 if the kth SNP is selected as cluster centre and γk = 0 otherwise , where nSNP is the number of SNPs in the dataset . That is , there is a one-to-one map from the space of possible partitions to the sample space of γ . Next , yij in {0 , 1} is the disease status indicator of haplotype i = 1 , … , nj in cluster j = 1 , … , nc . The vector of responses for cluster j is denoted by Dj = ( y1j , y2j , …ynjj ) and let D = {Dj , j = 1 , … , nc} . Each yij is assumed to have a Bernoulli distribution with parameter θj , the disease risk associated with cluster j . The Bayesian formulation is completed by specifying priors on the parameters θ and γ . We assume a uniform prior on γ , i . e . , the probability of each cluster configuration is equal to 1/2nSNP . Note that this induces a probability distribution on the number of cluster centres; the probability of having nclust cluster centres is equal to . Cluster-specific risks are then given a conjugate Beta distribution with parameters α and β . This choice of prior distributions leads to computational advantages . In particular , the posterior distribution of γ is proportional to the product of its prior distribution and the marginal probability of the data where the latter is available analytically as where Γ denotes the Gamma function and Θ = . Similarly , the full conditional distribution of the risk parameters is readily sampled from , as it is available in closed form In the case of ntr perfect phylogenies or trees ( i . e . , when we split the regions into separate windows or where we consider more than one candidate region ) , an extra layer is added in the hierarchy of the model , since the partition γ is now conditional on the tree T selected to cluster haplotypes . In particular , we specify a uniform prior on the trees , so that , a priori , each tree is equally likely to contain the putative mutation ( recall that the underlying rationale is to exploit between marker LD around a putative causal variant , independent of the extent of LD or number of markers corresponding to each tree ) . The joint prior distribution of a gene tree T and a partition γ is given by where denotes the number of segregating sites in gene tree T . Details of the proposed MCMC algorithm are given later on . Note that instead of assuming a uniform prior on the trees , we could use a more informative prior distribution . For example , if the average marker density is large , we would expect recombination to break the perfect phylogeny condition frequently , resulting in several trees with a small number of SNPs and a few trees with a larger number of SNPs . In this case , it might be more appropriate to use a prior distribution that favours trees with a small number of markers , such as the geometric distribution . Upon convergence , from the posterior sample of partitions we obtain the posterior probability that the causal mutation is embedded in the ancestry of each of the gene trees . The mean and standard deviation of the posterior risk associated with each unique haplotype in the sample are also obtained . Furthermore , we estimate the Bayes factor in favour of association at each marker , which is given by the ratio of the posterior odds to prior odds [26] . The prior of each SNP being a cluster centre is evaluated by simulation using Equation 3 . Finally , we use the location of the SNP with the highest marginal posterior probability of being a cluster centre as an estimate of the location of the susceptibility allele . For the proposed method , it is straightforward to construct credible intervals for the estimated location of the causative SNP . At each iteration of the MCMC algorithm , we obtain an estimate of the causal location by averaging the locations of the markers currently selected as cluster centres . Thus , upon convergence , we obtain a posterior distribution of locations , from which a credible interval can be constructed . Under the default simulation scenario , in 43 out of the 50 replicates , the 95% credible interval contained the true causal locus . Figure 10 shows the posterior densities of the putative location of the causative variants , together with 95% credible intervals for two datasets simulated with 1 . 8 and 2 . 4 GRR ( Aa ) , with all other simulation parameters set at their default values . The credible intervals are 150 and 15 kb wide , respectively . To assess the sensitivity of the results to prior specification , we assigned Gamma ( 10 , 10 ) hyperpriors to the parameters α and β of the Beta prior on disease risks . We then ran the model for 100 different datasets simulated with variable recombination rate , additive disease model with GRR ( Aa ) 2 , SNP density 1 kb , 200 cases and controls , MAF 5% , and MAF of the causal SNP 5%–7% , and obtained an average distance of 274 . 66 kb ( 22 . 98 kb standard error ) , compared to an average distance of 237 . 49 kb ( 25 . 17 kb standard error ) of the standard model . As expected , in this case , the average distance was higher than before , since we allowed for more sources of uncertainty . However , both models resulted in a similar average number of clusters . Considering the case of a single perfect phylogeny , we use a M-H step to sample from the full conditional distribution of the vector γ given the data . Namely , we consider two possible moves in the partition space: ( 1 ) Birth step: adding a cluster centre . ( 2 ) Death step: deleting a cluster centre . Each move entails selecting a SNP at random , and proposing to change , if the current γi = 0 ( birth ) or otherwise . Thus , the proposal distribution q ( γ*|γ ) is simply 1/nSNP . Given the cluster centres , the observed haplotypes are deterministically allocated to the haplotype clusters according to the time in which they share a common ancestor in the genealogy with the cluster centres ( as described earlier ) . Since we assume a conjugate Beta distribution for θ , the acceptance probability simplifies to min ( 1 , Bayes factor ( γ* , γ ) ) = min ( 1 , p ( D|γ* , α , β ) /p ( D|γ , α , β ) ) , where the marginal probability is calculated using Equation 1 . In the case of ntr perfect phylogenies , we need an extra MCMC step in which we sample the tree containing the putative mutation . At each MCMC iteration , we now have two M-H steps: ( 1 ) Change partition step: sample a new partition from the posterior distribution of the number of clusters and the cluster centres without changing the current gene tree . ( 2 ) Update tree: sample a new tree and a new partition from their joint posterior distribution . The first M-H step is the same as the one used in the case of a single gene . For the second M-H step , assuming a uniform prior on the trees , the joint prior distribution of a gene tree T and a partition γ is given by Equation 3 . In particular , we first sample a tree from the ntr possible trees with probability 1/ntr , and then each SNP in the tree has a 0 . 5 probability of being a cluster centre . Therefore , the proposal move in the tree and the partition space is equal to Equation 3 . This leads to an acceptance probability for the second M-H sampler that again only involves the Bayes factor in favour of the proposed partition over the current partition . In summary , the MCMC algorithm is: | Genetic association studies offer great promise in dissecting the genetic contribution to complex diseases . The underlying idea of such studies is to search for genetic variants along the genome that appear to be associated with a trait of interest , e . g . , disease status for a binary trait . One then proceeds by genotyping unrelated individuals at several marker sites , searching for positions where single markers or combinations of multiple markers on the paternally and maternally inherited chromosomes ( or haplotypes ) appear to discriminate among affected and unaffected individuals , flagging genomic regions that may harbour disease susceptibility variants . The statistical analysis of such studies , however , poses several challenges , such as multiplicity and false-positives issue , due to the large number of markers considered . Focusing on case-control studies , we present a novel evolution-based Bayesian partition model that clusters haplotypes with similar disease risks . The novelty of this approach lies in the use of perfect phylogenies , which offers a sensible and computationally efficient approximation of the ancestry of a sample of chromosomes . We show that the incorporation of phylogenetic information leads to low false-positive rates , while our model fitting offers computational advantages over similar recently proposed coalescent-based haplotype clustering methods . |
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Cytochrome P450 enzymes are found in all life forms . P450s play an important role in drug metabolism , and have potential uses as biocatalysts . Human P450s are membrane-bound proteins . However , the interactions between P450s and their membrane environment are not well-understood . To date , all P450 crystal structures have been obtained from engineered proteins , from which the transmembrane helix was absent . A significant number of computational studies have been performed on P450s , but the majority of these have been performed on the solubilised forms of P450s . Here we present a multiscale approach for modelling P450s , spanning from coarse-grained and atomistic molecular dynamics simulations to reaction modelling using hybrid quantum mechanics/molecular mechanics ( QM/MM ) methods . To our knowledge , this is the first application of such an integrated multiscale approach to modelling of a membrane-bound enzyme . We have applied this protocol to a key human P450 involved in drug metabolism: CYP3A4 . A biologically realistic model of CYP3A4 , complete with its transmembrane helix and a membrane , has been constructed and characterised . The dynamics of this complex have been studied , and the oxidation of the anticoagulant R-warfarin has been modelled in the active site . Calculations have also been performed on the soluble form of the enzyme in aqueous solution . Important differences are observed between the membrane and solution systems , most notably for the gating residues and channels that control access to the active site . The protocol that we describe here is applicable to other membrane-bound enzymes .
Molecular simulation methods are widely used to study membrane proteins . [1] An advantage of these methods is that the protein of interest can be studied in an approximately native environment . A limitation is imposed by the spatial and temporal scales accessible at a single level of description . In many cases , it may be useful or necessary to investigate levels of detail ranging from protein-lipid interactions on long timescales , to chemical reactions and electronic structure . This type of application is particularly well exemplified by the cytochrome P450 enzymes , where the questions of adverse drug interactions and of substrate access are still not well-understood . However , multiscale modelling is extending the scope of computational methods to overcome this limitation and further the understanding of biological processes . For example , coarse-grained molecular dynamics simulations , in combination with atomistic simulations ( CG/AT ) , allow the study of larger systems for longer timescales than those accessible by atomistic simulations alone . [2] Similarly , hybrid quantum mechanics/molecular mechanics ( QM/MM ) methods enable the calculation of reaction mechanisms in enzymes to high accuracy , whilst explicitly including the effects of the surrounding enzyme and solvent environment . [3] In order to model the reactions of enzymes in large biological assemblies , multiscale methods , which span the range from CG through AT up to the QM/MM level , will allow us to answer these important questions in chemical biology . The framework that we introduce here allows these questions to be answered . Simulations of membrane proteins provide an example application of where multiscale techniques can help us to understand the relationship between structure and function in biological molecules . Membrane proteins are involved in many biological process , such as transport , signalling , and enzymatic activity . The cytochrome P450 enzymes ( P450s ) are members of the latter category and perform a variety of functions , such as steroid synthesis and drug metabolism . [4] Whilst P450s in prokaryotes are soluble proteins ( such as CYP101 and CYP102A1 ) , eukaryotic P450s are membrane-bound . Binding to the membrane occurs via an N-terminal transmembrane α helix , and enables the protein to be oriented close to its redox partner protein , which in the case of human P450 isoforms is cytochrome P450 reductase . [5] , [6] The redox partner protein is required in order to supply the two electrons that are necessary for the reaction cycle . Significant challenges are posed by the modelling of drug metabolism by P450s , particularly in predicting metabolite formation . Simulation methods , such as docking , molecular dynamics , and QM/MM modelling have been shown to be useful in rationalising the selectivity of oxidation of substrates . [7]–[9] However , other important factors , such as substrate access ( via the membrane ) and binding to the active site , must be considered for a more complete perspective on how drugs are metabolised . To date , all of the human P450 structures that have been determined by X-ray crystallography are for truncated enzymes , from which the transmembrane helix has been deleted , in order to increase the solubility of the protein . [10]–[12] However , typical P450 substrates involved in drug metabolism are hydrophobic . Indeed , it is the oxidation of such substrates by P450s that increases the solubility of hydrophobic drug molecules to aid excretion . It has been suggested that the entrance of the substrate into the active site of the drug metabolising P450s occurs via the membrane . [13] Hence , it follows that to study the entrance of substrates into human P450s , the membrane environment should be included . Unless P450s can be crystallized in their membrane-bound form , modelling approaches are thus required . In the present study we focus on drug metabolism , but similar approaches will also be applicable to e . g . biocatalysis and signalling . Atomistic MD simulations of membrane-bound CYP3A4 have been performed previously using a lipid bilayer consisting of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) . [14] The protein was placed into the bilayer at a depth consistent with the Orientations of Proteins in Membranes ( OPM ) database . [15] . A stable pathway was found , linking the active site and membrane through the region between the B-C loop and the beta domain . The presence of the membrane was found to affect the opening and closing of the tunnels that link the active site to the surrounding environment . Membrane-bound CYP3A4 has also been simulated using a simplified model of a lipid bilayer to enhance lipid mobility . [16] The authors observed spontaneous binding and insertion of the globular domain of the enzyme into the membrane mimetic during 50 ns unbiased simulations . The transmembrane helix was not present in the majority of the membrane-bound simulations . Membrane binding was found to induce conformational changes on the protein at the membrane interface , causing changes to the active site access channels , compared to the crystal structure and a single simulation performed in solution . In the same study , experimental measurements of the heme tilt angle were performed for CYP3A4 bound to a nanodisc membrane . The average tilt angle was measured as 60° , which is in good agreement with the previously measured angles for CYP17A1 ( 47–63° ) and CYP21A2 ( 38–78° ) . [17] The tilt angle calculated from the simulations of CYP3A4 was in the range 70–80° . Coarse-grained molecular dynamics ( CGMD ) simulations are widely exploited for building models of protein:membrane systems . [18] CGMD allows the orientation of a protein within a membrane to be determined , whilst taking into account specific interactions between the lipid headgroups and hydrophobic tails . Conversely , these interactions are not taken into account in methods where the membrane is treated as a hydrophobic ‘slab’ ( such as OPM ) . CGMD simulations can be used to generate configurations for conversion to atomistic models , for subsequent study using atomistic molecular dynamics simulations . [2] This type of methodology has been applied previously to CYP2C9 . [19] A model of the transmembrane helix was constructed and incorporated into a lipid bilayer via a CGMD self assembly simulation . The protein was subsequently attached to the transmembrane helix and further CGMD simulations were performed . It was necessary to perform these two steps separately , because in test simulations of the self-assembly of the bilayer around the complete protein and transmembrane helix , the bilayer did not assemble around the transmembrane helix . The resultant CG protein:membrane model was used as the basis of atomistic molecular dynamics simulations . The presence of the membrane was found to have a limited effect on the conformational flexibility of the protein , and was localised to the regions that made contact with the lipid bilayer . The authors found more than one possible orientation of the CYP2C9 in the membrane , differing by the conformation of the FG loop . The membrane-bound CYP2C9 remained in closed or almost-closed conformations throughout the simulations , however , motions were observed that corresponded to the opening and closing of tunnels from the enzyme active site . Whilst previous studies have shown that the membrane has an effect on the dynamics and active site channels of P450s , it is currently not known whether binding has an effect on the electronic structure of the key intermediates in the catalytic cycle , specifically the iron-oxo complex , Compound I ( Cpd I ) . [20] Cpd I is widely accepted to be the active oxidizing species in P450s . Any changes in Cpd I may result in differences in reactivity ( and selectivity ) between membrane-bound P450s and those in solution . In order to study electronic structure and chemical reactivity , a computational method is required that treats electrons explicitly , such as quantum mechanics ( QM ) . In large systems , such as enzymes , it is convenient to partition a model system into QM and molecular mechanical ( MM ) subsystems , in order to make the calculation computationally feasible . The QM region comprises a region of sufficient size to model the chemical reaction of interest , typically of around 50–100 atoms . QM/MM calculations have been used to study many enzymatic reactions , including those in P450s . [8] , [21]–[24] Such calculations have aided in the characterisation of intermediates in the catalytic cycle , and have provided mechanistic information concerning various oxidative reactions . The choice of QM method is an important one , and often a compromise between speed and accuracy is necessary . Density functional theory methods ( in particular the B3LYP functional ) have been identified as an effective compromise between speed and accuracy for calculations on Cpd I . [21] , [24]–[28] In previous work , it has been shown that including an empirical dispersion correction in DFT calculations improves the accuracy of calculated barriers to oxidation , relative to those where the correction is not included . [29] , [30] Numerous studies of P450 mechanisms have been performed using gas phase QM models and QM/MM calculations . [24] , [28] , [31] Whilst gas phase calculations provide useful insight into the reactivity of P450s , inclusion of the local protein environment ( e . g . using a QM/MM approach ) provides a more realistic model and improves agreement with experiment . [8] , [21] For example , the electronic structure of Cpd I has been found to be sensitive to its local environment: the distribution of unpaired electron spin density differs between vacuum and QM/MM models . [25] Similarly , when calculating activation energy barriers for hydroxylation and epoxidation in P450s , better agreement with experiment is observed when using QM/MM , compared to QM calculations performed on a gas phase model system . [21] It is not currently known whether further extension of the size of MM part of the QM/MM model to include the transmembrane helix and membrane , would further improve the accuracy of P450 calculations . The heme in P450s is relatively far away from the membrane and hence any electrostatic effects of the membrane on the heme are unlikely to be significant . However , MD simulations of membrane-bound P450s have suggested that the dynamics of P450s are influenced by the presence/absence of membrane . [16] , [32] Therefore , it is conceivable that subtle changes of the protein environment surrounding Cpd I may occur due to the presence of the membrane . This study is the first application of a simulation pipeline that spans the range from CGMD , to atomistic MD simulations , to QM/MM reaction modelling . Other integrated workflows are currently being developed for making better predictions of P450-related toxicity . [33] In the current work , we have studied CYP3A4 , as it is responsible for the metabolism of the majority of therapeutic drugs , and is relatively well-studied in terms of its membrane localization and orientation , as described above . [14] , [16] We have chosen to model the substrate R-warfarin ( Fig . 1 ) , a widely used anticoagulant drug with a narrow therapeutic index due to drug-drug interactions , and a known substrate of CYP3A4 . [34] R-warfarin undergoes aliphatic hydroxylation at C10 . [34] The hydroxylation of S-warfarin in CYP2C9 has been studied previously by QM/MM using a truncated enzyme model in the absence of membrane and transmembrane helix . [8] In that work it was shown that QM/MM calculations are capable of rationalising the regioselectivity of oxidation by P450s , and were better at doing so than performing calculations using gas phase models consisting solely of Cpd I and substrate . We find that the presence of the membrane significantly affects the gating residues that control access to the active site of the protein . However , the presence of the membrane does not seem to influence residues surrounding the active oxidising species , and hence the reactivity of this species is unchanged . This finding provides an important validation for the use of engineered P450s as models of their in vivo counterparts .
Coarse-grained MD simulations were used in two stages to generate a stable model of the membrane-bound form of the protein in a simple model of the endoplasmic reticulum ( POPC/POPE ) lipid bilayer . The first set of simulations was used to determine the relative orientations of the transmembrane ( TM ) and globular/soluble domains . This is a necessary requirement of using CGMD simulations where the secondary structure is predefined ( usually based on the original atomistic structure ) , and tertiary structure is generally maintained by using an elastic network . CGMD simulations were performed , in which the restraints between the TM and globular domains were relaxed ( see Fig . S1 and Methods ) , allowing the protein to equilibrate while the membrane self-assembled . In each case , the TM domain inserted into the membrane . The most representative model , based on structural clustering of this first set of trajectories , was then used as the starting point for further CGMD simulations , in which the globular/TM orientation and secondary structure was restrained . In each of the second set of CGMD simulations , the TM domain adopted a transmembrane orientation . In agreement with simulations of CYP3A4 with a HMMM membrane model , [16] the A-anchor ( residues 44–47 ) was found to insert most deeply into the hydrophobic region of the membrane , followed closely by the G′-helix . The F′-helix formed hydrophobic contacts with the lipid tail groups , but to a lesser extent than observed previously [16] ( Fig . S2 ) . The bilayer was observed to thin to accommodate the A-anchor and G′ region , and thicken on the opposite side near the F′ and FG loop ( Fig . 2 ) . Specific lipid binding sites were observed in the A anchor region . Preliminary simulations of the globular domain alone interacting with the membrane led to symmetrical thickening of the bilayer ( unpublished data ) , implying that the presence of the trans-membrane helix affects deformation of the bilayer and , therefore potentially , substrate access . A comparison of the averaged lipid headgroup positions from these simulations with the predicted membrane-bound orientation from the OPM database is revealing ( Fig . S3 ) . There is a ∼20° difference in the orientation of the membrane normal relative to the protein in the two cases . However , close to the interaction surface of the protein , the membrane is distorted in the CGMD simulations , such that the interacting regions agree well between simulation and the OPM model based on experimental data . The orientation of the globular domain may be fully defined in terms of its depth of insertion in the membrane , as well as by choosing two orthogonal vectors and and measuring the angle between the membrane normal and each of these ( and respectively ) . [16] . The vectors and are defined as follows: connects one helical turn in each of helix C and helix F ( the midpoints of the atoms of residues 137–141 and 207–211 , respectively ) . vi is orthogonal to , and connects the centres of the first and last helical turns in helix I ( midpoints of Cα atoms of residues 292–296 and 321–325 , respectively ) . These properties are observed to equilibrate within the first 30 ns and converge over the 4 simulations ( ° , ° ) . The unbiased CGMD simulations therefore lead to a single , conserved orientation in the the membrane . The configuration generated from the CGMD simulations was converted to an atomistic model ( see Methods ) for further simulations , described in the sections that follow . Four different model systems were simulated with atomistic MD: the membrane-bound and soluble forms of CYP3A4 , both with and without R-warfarin present in the active site ( details of the setup for all models is provided in the Methods section ) . Three 50 ns simulations were performed for each system . Unlike the coarse-grained methods , atomistic simulation allows tertiary structure change and so a degree of conformational relaxation was expected , and is indeed important in allowing conformational changes associated with membrane binding . Some initial relaxation occurs , with the orientation of the protein converged by the final 20 ns of simulations . Hence only the last 20 ns of each simulation was used for analysis . The root mean square deviation ( RMSD ) of the backbone alpha carbon atoms from their average positions gives an overall indication of the stability of each system during a simulation . The RMSD is very similar between the membrane and solution simulations , with values ca . 1 . 6 Å and ca . 2 . 1 Å , respectively . The orientation of the protein in the bilayer generated using this unbiased simulation approach may be compared to calculated orientations of the heme group of CYP3A4 from linear dichroism experiments [16] as discussed in the Introduction . The average heme tilt angle calculated from atomistic simulations is ° . This compares to the experimental value ( calculated for CYP3A4 bound to a nanodisc ) of 60 and previous simulation value of 72° . [16] There are no notable losses of secondary structure in any of the membrane or solution simulations ( see Fig . S4 ) . The multiscale CG/AT approach therefore provides a well-equilibrated starting point for further simulation . As mentioned in the introduction , QM/MM calculations have not been previously performed on membrane-bound proteins . In the following section , QM/MM calculations of the membrane-bound model of CYP3A4 have been performed . The effect of membrane-binding on the electronic structure of the active oxidizing species ( Compound I ) has been investigated , as well as the energy barrier to the hydrogen abstraction from R-warfarin by Compound I . Starting structures for QM/MM calculations were obtained from the atomistic MD simulations described above , and truncated using the protocol described in the Methods section .
Multiscale modelling is recognised as a central goal in molecular simulation of complex biological systems . [50] Efficient and practical protocols and methods for realistic multiscale modelling have the potential to contribute to many important problems in biology . Here , we have demonstrated a practical multiscale simulation approach in an application to a key system involved in drug metabolism . CGMD simulations provided an unbiased method to generate an equilibrated model of the membrane-bound CYP3A4 . A significant degree of distortion of the lipid bilayer was observed in the region in contact with the protein . Thus it is clear that the planar model representation of the lipid bilayer used in the OPM method is something of a simplification . Consideration of the non-planar properties of the membrane is expected to play a role in the mode of substrate interaction with the protein . The CG model of a protein:membrane complex was successfully converted to a atomistic model , using an automated procedure that is applicable to other proteins and is compatible with other forcefields . A stable protein:membrane complex was obtained , in which the location and orientation of the protein in the lipid bilayer stabilized within a relatively short simulation timescale . Comparison of the atomistic simulations of the membrane-bound model with the corresponding solubilized form of the enzyme showed that the binding of the enzyme to the membrane has a limited effect on the overall protein flexibility . However , significant differences are observed in the behaviour of the reactant and product channels between the membrane-bound and solution models . This agrees well with previous multiscale simulations of CYP2C9 . [19] The atomistic simulations of CYP3A4 with R-warfarin revealed interactions that appear important to substrate-binding . Arg212 was found to interact with the substrate , and the gate formed by the sidechains of Phe108 and Phe304 were found to influence the orientation of warfarin in the active site . Additionally , the opening and closing of the 2b access channel was found to influence the mobility of warfarin in the active site . The effect of membrane-binding on the residues surrounding the proximal side of the heme has been found to be minimal and , as a result of this , the local electrostatic environment surrounding the QM region is effectively the same in the membrane-bound and solution simulations . Hence , the electronic structure of Cpd I is not affected by the presence of the membrane , nor are the barriers to hydrogen abstraction . The finding is important , because it supports the use of engineered P450s as models of their membrane-bound counterparts . Further studies could investigate the roles of different lipids on activity . Human drug-metabolizing P450s , including CYP3A4 , have been identified as being responsible for adverse drug reactions due to drug-drug interactions . One possible cause of drug-drug interactions is competitive binding of substrates to P450 isoforms . Multiple substrate binding to CYP3A4 has been studied both experimentally [51]–[53] and theoretically [54] , [55] . The large active site cavity of CYP3A4 can easily accommodate more than one substrate molecule simultaneously . Whilst we have not studied this phenomenon in detail here , a calculation was performed in which a second molecule of R-warfarin was docked into the active site of CYP3A4 when a first R-warfarin molecule was already present ( see Fig . S15 ) . We observed from this preliminary docking calculation that more than one molecule of R-warfarin can indeed be accommodated at any one time . The multiscale approach outlined here should be useful in future studies to investigate the entire reaction cycle of CYPs . Firstly , the entrance of substrate into the active site through the membrane may be simulated . This process is perhaps best suited to accelerated simulations methods e . g . a metadynamics approach , [56] because ( for models of this size ) such an event is beyond the timescale of atomistic MD simulations at present . The steps leading to the formation of Cpd I and hydroxylation of the substrate could be modelled with QM/MM . Finally , the exit of the product may also be investigated , following a conversion back to a CG model following the QM/MM calculations . As larger simulations become more computationally feasible , it will be possible to include more components , e . g . including the reductase protein in the simulation , however , this will require further evidence for orientations . QM/MM calculations may also be used to refine CG and atomistic MD parameters , particularly those used for substrates . Empirical valence bond parameters [57] for this reaction might also be calculated , fitted to the QM/MM calculated barriers , enabling free energy barriers to be calculated , and the effects of point mutations on the barrier to be investigated .
The protocol for building a protein:membrane model is outlined in Fig . 8 . The process starts with the selection of an X-ray crystal structure . The 1TQN crystal structure was used in this study . [12] Missing residues were added ( residues 282-285 and the transmembrane helix ) by homology modelling using the MODELLER program [58] . For the simulation models containing substrate , R-warfarin was docked into the active site using AUTODOCK VINA . [59] The Arg212 side chain , which has been identified as important in ligand binding in CYP3A4 , points into the active site in the 1TQN structure . This is in contrast to the 1W0G structure , [40] where the residue points away from the heme . Hence , this residue was treated as flexible during the docking calculations . Nine docking poses were located during the docking calculation . All of these placed the substrate in the active site and possessed similar calculated binding affinites , ranging between −9 . 0 and −8 . 5 kcal/mol . The docking pose in which C10 was located closest to the heme Fe ( pose 8 , see Fig . S16 ) was selected for simulation . Each model was converted into a course-grained model using the make_cg_martini . pl script , which prepares input for a GROMACS simulation using the MARTINI 2 . 1 CG forcefield . [60]–[63] Lipids were added to the simulation box in random orientation using the genbox utility in GROMACS , according the composition present in the natural environment of the enzyme . In the present study , 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine ( POPC ) and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine ( POPE ) were used in an approximated 3:1 ratio . These are the main components of the endoplasmic reticulum membrane , according to the OPM database . [15] CG water beads and ions ( [NaCl] = 0 . 15 M , excess charge neutralized ) were also added to the simulation box at this stage . 5000 steps of steepest descent energy minimization are performed before the initial CG self-assembly simulation . The CG simulations were performed at 310 K and 1 atm , using Berendsen temperature and pressure coupling ( semiisotropic ) . A timestep of 20 fs was used and each simulation was 1 μs in length . During the CG simulation , the lipid bilayer forms around the transmembrane helix . Usually in a CG simulation , elastic constraints are applied to the protein beads , in order to preserve the tertiary structure of the protein . Because the exact orientation of the transmembrane helix relative to the rest of protein is unknown , it was necessary to relax some of the elastic constraints , particularly around the region where the transmembrane helix is attached to the protein . CG simulations were performed using GROMACS 4 . 6 . [64]–[67] An additional CG simulation was then performed to sample different conformations around this region of the protein . Once a suitable protein:membrane complex was obtained , the conversion from CG model to atomistic resolution was performed using the previously defined CG2AT protocol . [2] During this protocol , the lipids are first converted from a CG to AT representation using a fragment library based approach , and undergo energy minimisation , before the protein undergoes a similar transformation . The substrate was not present during the CG simulations and was introduced to the atomistic representation of the protein by superposition with the initial docked pose prior the conversion to CG . The atomistic model generated from the previous step was then aligned during conversion ( automated within the cg2at script ) . Atomistic MD simulations were performed using NAMD version 2 . 9 . [68] The CHARMM27 force field with CMAP corrections was used for protein atoms . [69] , [70] The CHARMM36 force field [71] was used for lipids and the CHARMM implementation of the TIP3P model used for water ( with Lennard-Jones parameters for the hydrogen atoms ) . [72] Parameters for Cpd I and warfarin were obtained from previous work . [8] , [25] The structures obtained from the CG2AT conversion were energy minimized using 6000 steps of conjugate gradient minimization for all hydrogen atoms , followed by 6000 steps for all hydrogen and solvent atoms . The water molecules were then heated to 300 K and equilibrated for 20 ps in an NVT ensemble . The positions of all atoms were then energy minimized by 10000 steps of CG minimization . The system was heated to 300 K over 20 ps before equilibration of 1000 ps with constant temperature , pressure and area in the xy plane . It was necessary to include this extra step in order to maintain the shape of the membrane . The Nose-Hoover piston [73] and Langevin temperature control [74] were used during equilibration . Three production MD simulations of 50 ns were obtained for each system , started from heating stages with different initial velocities . The production MD simulations were run in the absence of a constant area constraint . A time step of 2 fs was used and bonds to hydrogen atoms were constrained with the SHAKE algorithm . The solution MD systems were solvated using the SOLVATE plugin in VMD . [75] During initial calculations the protein was solvated such that there was 12 Å of water surrounding the entire protein . Due to the shape of the protein it was found that this led to the protein interacting with its periodic image . Hence , the soluble simulations were repeated in a cubic box of water with the length of the longest side from the initial calculations ( see Table S1 for box dimensions ) . Convergence is a vital issue in MD simulations , which is why several independent CGMD simulations and 3 repeats of each 50 ns atomistic simulation were performed . This is generally regarded as better than running a single longer simulation . [76] It is difficult to prove convergence , but important aspects do need to be converged , e . g . the independent CGMD simulations led to a converged orientation of the protein within the bilayer . The properties that we have focused on in this work are the dynamic properties of the substrate channels , the reorganisation of protein near the protein-lipid interface , the active site volume and the behaviour of Arg212 . By using data from multiple independent simulations , we are able to observe variation in these properties , within the timescales simulated , that are consistent with previous computational studies . We hence decided only to analyze the portions of the atomistic simulations where ( in the case of the membrane-bound models ) the orientation of the protein in the membrane ceased to vary to an appreciable extent ( and was closest to the experimentally determined value ) . For this reason , only the last 20 ns of each atomistic simulation was used for analysis purposes . QM/MM calculations were performed using the QoMMMa program . [77] The QM part of the calculations was performed in Jaguar version 5 . 5 . [78] The QM region consisted of the substrate ( where applicable ) , iron-oxo porphyrin ( without heme substituents ) and the proximal cysteine ( modelled as SCH2 ) . Valences of atoms at the QM/MM boundary were satisfied by using the link atom approach . [79] The B3LYP density functional was used [80]–[82] along with the LACVP basis set for iron [83] and 6–31G for all other atoms . [84] The quartet spin state of Cpd I was modelled with a restricted open-shell approach . An empirical dispersion correction [85] was applied in all QM calculations , as this was found to be important in previous work . [29 , 29] For consistency with the atomistic MD simulations , the CHARMM27 force field [69] , [70] was used for the MM part of the QM/MM calculations using Tinker version 6 . 1 . Structures for QM/MM calculations of the electronic structure of Cpd I were obtained from the last 20 ns of each MD unrestrained MD simulation at 5 ns intervals . Structures for reaction profiles were obtained from restrained MD simulations that were performed as follows . A structure was selected from the unrestrained simulations in which the distance between the Cpd I oxygen and C10 was at the minimum distance . A restrained energy minimization ( 10000 steps of CG ) was then performed on this structure , with an harmonic restraint centred on 1 . 2 Å applied to the O-H5 distance ( corresponding to the Cpd I oxygen and hydrogen attached to C10 ) with a force constant of 100 kcal/mol/Å2 . The system was then heated to 300 K over 20 ps and equilibrated for 180 ps with the same restraint present . A production MD simulation was then performed of 10 ns , again with the same restraint on the O-H distance . Structures were truncated prior to QM/MM calculation such that all water molecules and ions at a distance greater than 5 Å of any protein atom were removed . In the membrane-bound structures , the membrane was excluded from the QM/MM calculations . All residues that contained at least one atom that was within 5 Å of any atom of Cpd I or substrate were included in the QM/MM energy minimization , while all other atoms were fixed to their initial positions . Reaction energy profiles were generated by performing QM/MM energy minimization with a restraint corresponding to the O-H5 distance ( force constant 1000 kcal/mol/Å2 ) . Structures were first optimized with a reaction coordinate value of 1 . 8 Å and then in increasing and decreasing intervals of 0 . 1 Å to 3 . 1 and 0 . 9 Å . This approach has been used previously for modelling the hydroxylation of S-warfarin in CYP2C9 . [8] All of the scripts used in this work are available to download at http://ccpforge . cse . rl . ac . uk/ | A significant amount of information about how enzymes and other proteins function has been obtained from computer simulations . Often , the size of the system that is required to provide a sufficiently realistic model places limitations on both the timescale of the simulation , and the level of detail that can be studied . Computational approaches that utilise more than one type of method ( so-called ‘multiscale methods’ ) allow the size of system , and timescale of study , to be increased . Membrane-bound proteins , such as cytochrome P450 enzymes ( P450s ) , are an example of where multiscale simulations can be used . P450s are important in drug metabolism , and are known to be involved in adverse drug reactions . Access of substrate to the active site of these enzymes through the membrane is not well-understood . In the present work , a simulation pipeline is presented that leads through the construction and refinement of a realistic protein:membrane system by molecular dynamics simulations to reaction modelling . An important drug-metabolising P450 , CYP3A4 , is used as an example , together with the anticoagulant drug R-warfarin . Our investigations reveal that a membrane-bound model is required to fully capture the gating mechanisms and substrate ingress/egress channels . The simulation protocol described here is transferrable to other membrane-bound proteins . |
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The 2015-16 Zika virus pandemic originating in Latin America led to predictions of a catastrophic global spread of the disease . Since the current outbreak began in Brazil in May 2015 local transmission of Zika has been reported in over 60 countries and territories , with over 750 thousand confirmed and suspected cases . As a result of its range expansion attention has focused on possible modes of transmission , of which the arthropod vector-based disease spread cycle involving Aedes species is believed to be the most important . Additional causes of concern are the emerging new links between Zika disease and Guillain-Barre Syndrome ( GBS ) , and a once rare congenital disease , microcephaly . Like dengue and chikungunya , the geographic establishment of Zika is thought to be limited by the occurrence of its principal vector mosquito species , Ae . aegypti and , possibly , Ae . albopictus . While Ae . albopictus populations are more widely established than those of Ae . aegypti , the relative competence of these species as a Zika vector is unknown . The analysis reported here presents a global risk model that considers the role of each vector species independently , and quantifies the potential spreading risk of Zika into new regions . Six scenarios are evaluated which vary in the weight assigned to Ae . albopictus as a possible spreading vector . The scenarios are bounded by the extreme assumptions that spread is driven by air travel and Ae . aegypti presence alone and spread driven equally by both species . For each scenario destination cities at highest risk of Zika outbreaks are prioritized , as are source cities in affected regions . Finally , intercontinental air travel routes that pose the highest risk for Zika spread are also ranked . The results are compared between scenarios . Results from the analysis reveal that if Ae . aegypti is the only competent Zika vector , then risk is geographically limited; in North America mainly to Florida and Texas . However , if Ae . albopictus proves to be a competent vector of Zika , which does not yet appear to be the case , then there is risk of local establishment in all American regions including Canada and Chile , much of Western Europe , Australia , New Zealand , as well as South and East Asia , with a substantial increase in risk to Asia due to the more recent local establishment of Zika in Singapore .
In May 2015 , a Zika disease outbreak originated in Brazil , and by October 5 , 2016 , local transmission of the Zika virus had been reported in over 60 countries and territories , with over 750 thousand estimated cases [1] . The World Health Organization ( WHO ) previously predicted that the virus would establish itself in all countries in the Americas except Canada and Chile [2] , and with few exceptions this scenario has proved true . Travel-imported cases have also been increasingly reported throughout the United States , as well as in Australia , New Zealand , Canada , Western Europe , and China [3] , representing the first time Zika has been reported in many of these western countries . The Zika virus was first isolated in 1947 in a sentinel monkey in the Zika forest of Uganda [4] which gave the virus its name . It was first found in humans in 1952 [5] . However , only 14 human cases were documented prior to 2007 [6] , and these were limited to small isolated epidemics in equatorial Africa and tropical Asia [6] . Since the 1950s the virus has spread eastwards from Africa through Asia and the Pacific , culminating with the 2015-16 outbreak in Latin America [7 , 8] . The first documented recent outbreak of Zika disease occurred on Yap Island in the Federated States of Micronesia in the North Pacific in 2007 with less than 200 acknowledged cases [6] . In 2013 another outbreak occurred in French Polynesia , with around 28 , 000 suspected cases , at which point Zika began to be generally recognized as a re-emerging infectious disease [9] . The outbreak subsequently spread from French Polynesia to other Pacific Islands including New Caledonia , Cook Island , and Easter Island , where autochthonous transmission cycles were established [10] . Travel–imported cases were also documented in Japan [11] , Germany [12] and Norway [10] , among other regions . The recent outbreak in Latin America began in Brazil , with the first documented Zika case reported in May , 2015 [13] , although phylogenetic analyses of virus RNA sequences suggest that the virus was introduced into the Americas between May and December 2013 [14] . The virus quickly spread from Brazil throughout Latin America; by February 2016 , an estimated 31 , 555 cases were identified in Colombia alone [2] . Historically , Zika infection has been associated with mild symptoms typically resembling and milder than those of related arboviruses such as dengue and chikungunya; many cases of infection show no symptoms at all . However , the recent outbreaks in French Polynesia and Latin America have been associated with much more serious clinical manifestations of the virus . In Brazil and French Polynesia , a link between Zika and a rare congenital disease , microcephaly , has been identified [15–22] . Additionally , Guillain-Barre Syndrome ( GBS ) has been reported in patients infected with Zika , firstly in the 2013 French Polynesia outbreak [23] , and since in greater numbers in Brazil , El Salvador , Venezuela , Colombia , and Suriname [22] . The unprecedented size of the outbreak , rate of spread , and potential links with microcephaly and GBS prompted the WHO to declare the current Zika virus outbreak a public health emergency of international concern [24] . Zika now joins a list of arboviral diseases such as dengue and chikungunya that are being increasingly reported in new parts of the world , all likely introduced through global transport systems such as passenger air travel and maritime freight [14 , 25] . Geographic spread of the virus occurs when infected travelers travel from affected regions to ones without local establishment of the disease , but in which the known and suspected vector species have established populations . Like dengue and chikungunya , Zika is known to be spread by Aedes aegypti; it is also strongly suspected to be spread by Aedes albopictus . While vectorial competence of Ae . aegypti is well established [26–29] , and it is now confirmed to be the primary vector in spreading Zika [30–32] , the capacity of Ae . albopictus as a secondary vector for spreading Zika is still unclear . There is evidence of the potential role of Ae . albopictus [33 , 34] , however , there is limited and conflicting quantitative estimate of its efficiency [35 , 36] . Jupille et . al . [35] found that both Ae . aegypti from Madeira Island and Ae . albopictus from France were able to transmit the Zika virus , however Ae . albopictus from France was found to be less suitable to sustain local transmission . Chouin-Carneiro et . al . [36] observed high infection but low transmission rates for both Ae . aegypti and Ae . albopictus , while WHO [31] notes the vector competence for both species is similar , but Ae . albopictus is considered to have lower vector capacity than Ae . aegypti . The outcomes from these studies suggests both species are capable of Zika transmission , while also highlighting the uncertainty in the role that Ae . albopictus may play in transmitting , spreading , and helping to maintain the virus in many areas of the world . Further , potential virus adaptation to new vectors , as demonstrated in the case of Chikungunya in La Reunion [37 , 38] , introduces additional uncertainties . The uncertainty surrounding the vectorial competence of different Aedes species in spreading Zika serves as the main motivation for the present analysis . This study explicitly addresses the differences in the potential geographical risk of Zika spread and local disease cycle establishment if Ae . aegypti is the sole competent vector versus if both Ae . aegypti and Ae . albopictus are competent for this purpose . Scenarios which further vary in the relative capacity of Ae . albopictus as a secondary vector are also considered . As noted earlier , available evidence indicates that the two species differ in their vectorial competence . Moreover , the two species also vary widely in their present geographic distribution: Ae . aegypti is mainly present in wet tropical regions , while Ae . albopictus , a much better disperser , has a wider global presence in temperate regions , including the northern United States and parts of Canada , southern regions of the Americas including Chile , parts of Western Australia and East Asia . The analysis presented here is global and carried out at the finest resolution ( 1 arc-minute ) that was permitted by the available data . Some preliminary findings were reported earlier [39] but the methodology was not described; all the analyses have been expanded and updated here and expectations from those preliminary findings were used to validate conclusions from the analysis using “back-testing” . Several recent studies have mapped the potential spread of Zika into new regions [40–43] . These studies differ from the present one in either the assumptions made about the competence of potential vector species , in the spatial resolution or geographical extent of the study areas used , or in the methodological tools that were used . Monaghan et . al . [42] simulated Ae . aegypti and Ae . albopictus mosquito abundance based on meteorological models , and overlaid the results with travel and socioeconomic factors to estimate the cities in the United States with the highest expected cases of travel-imported Zika . Nah et . al . [43] presented a global risk model for Zika importation which used survival analysis and publicly available epidemiological and air travel data to predict the risk of importation and local transmission of Zika at the country level . In one study , Bogoch et . al . [40] presented an air travel-based risk map of Zika spread from Brazil into the rest of the Americas , and in another study modeled risk posed to Africa and the Asia Pacific region [41] . Both works [40 , 41] implicitly assumed Zika to be equally efficiently spread by both Ae . aegypti and Ae . albopictus , and all studies only considered airline travelers departing the Americas . However , on August 28 , 2016 local Zika spread was confirmed in Singapore , and autochthonous Zika transmission has since been reported across multiple community clusters [44] . With Singapore serving as a new potential source of Zika infected travelers , a substantially higher risk is posed to South and South-east Asia , where Aedes mosquito populations are well established , and Zika and dengue are endemic . The present analysis extends previous work by presenting a global risk analysis based on a new mathematical framework to estimate Zika importation and establishment risk at a city level based on the most recent state of the outbreak , and accounting for uncertainties regarding the vectorial competence of Ae . albopictus . The risk analysis reported in this paper considers six scenarios , A , B , C , D , E and F , respectively , which vary in their assumed relative capacity of Ae . albopictus compared to Ae . aegypti , as a secondary spreading vector of Zika . The scenarios are bounded by two extreme assumptions; in Scenario A spread is assumed to be driven by Ae . aegypti presence alone , while in Scenario F spread is driven by Ae . aegypti and Ae . albopictus presence equally . In Scenarios B through E spread is driven predominately by Ae . aegypti presence with Ae . albopictus presence playing a lesser role . These scenarios are further described in the Materials and Methods section . Besides air travel data , this work utilizes ecological vector habitat suitability models for Ae . aegyti and Ae . albopictus previously developed to analyze the role of air travel in the risk of geographical spread of dengue [45–47] . Those models are relevant to the risk of Zika spread because the same two vector species are implicated with one difference: while Ae . aegypti is known to be an efficient vector for both diseases , in the case of dengue Ae . albopictus is also known to be a competent but less efficient vector , whereas in the case of Zika it is a likely vector but its relative competence is unknown . Thus , the focus of this analysis will be on four questions: The analysis reported here only considers potential vectorial transmission of Zika . It ignores other modes of transmission that have been reported including sexual transmission [22] and congenital transmission [22] .
The following protocol was used for a scenario specific risk analysis . Six scenarios are considered , A–F , which vary in the assumed relative capacity of Ae . albopictus as a spreading vector of Zika . The six scenarios are bounded by Scenario A , where spread is assumed driven by air travel and Ae . aegypti presence alone , and Scenario F , where spread is assumed to be driven by air travel and both species equally . Scenario B , C , D and E represent cases where Ae . albopictus plays a secondary role to Ae . aegypti . Specifically , the relative capacity of Ae . albopictus compared with Ae . aegypti ranges from 10% to 75% across these scenarios . Assigned weights are used in the six scenarios to represent the relative capacity , and are w = 0 , 0 . 10 , 0 . 25 , 0 . 5 , 0 . 75 and 1 for Scenarios A–F , respectively . In Scenario A the assigned weight is 0 , representing the case where Ae . albopictus has no capacity to spread Zika , while in Scenario F the assigned weight is equal to 1 . 0 , representing equal capacity for the two species . The range of weights is selected to demonstrate the variability in the risk posed to or from a particular location as a function of the relative capacity of Ae . albopictus to transmit Zika . Because Ae . aegypti has been confirmed as the primary spreading vector of Zika , the sensitivity analysis is more focused on the lower relative capacities of Ae . albopictus , which is suspected to play a much lesser role . Given these six scenarios , the protocol consists of seven stages: A separate analysis was conducted to evaluate the performance of the risk model . The protocol described above was re-implemented , wherein the set of source airports , S , was defined as those in areas with autochthonous Zika transmission as of February 15 , 2016 rather than October 5 , 2016 [39] . Between February 15 and October 5 , 2016 , 29 new countries and territories were added to the CDC list of affected regions . The ranking and relative risk quantified by the proposed model for each scenario for these 29 countries is presented and discussed . This analysis also serves to identify the sceanrio most consistent with the observed outbreak , and thus the role played by the secondary spreading vector , Ae . albopictus . The proposed risk model is based on data from the global air traffic network and species distribution models for the principle spreading vectors species .
The destination risk model results are illustrated in Fig 1 . The top 100 cities to which Zika may be imported from affected regions for scenarios A , C , D , E and F are shown . The results for Scenario B are too hard to distinguish from A and C in the figure , so it is left out . The size of the circle represents the estimated expected relative risk posed to each city , with the color indicating the scenario . For those cities which are served by more than one international airport , the relative risk for all airports which serve the given city are aggregated . Solid dark red indicates the risk from Ae . aegypti alone , i . e . , Scenario A , and the color of the circles lightens progressively from Scenario A to Scenario F . All risk values are computed using eq ( 3 ) , for their respective scenarios . S1 Table contains the list of the top 100 at risk destination cities included in the map for all six scenarios , including their corresponding rank , relative risk , and designated country . To gain a better understanding of the risk posed by outgoing travelers , the risk posed by each city in a known affected region for exporting infected travelers is also assessed . The top 100 origin cities in the affected regions likely to export Zika to new regions are listed in S2 Table , including their corresponding rank , relative risk , and designated country . Similarly to the destination risk , the relative risk at the city level is aggregated over all airports which serve a given city . S3 and S4 Tables further breaks down the previous results to identify those routes which carry the most risk into and out of cities , and include the top 100 highest risk origin-destination city travel pairs for Scenario A and F , respectively . Finally , in regard to validation , the model was run for each scenario using the set of sources as of February 15 , 2016 along with travel data for February 2015 . The destination risk results were aggregated to the country level , ranked and compared with the actual set of 29 counties/territories that were added to the CDC list of confirmed affected regions between February 15th , 2016 and October 5 , 2016 . Results from the back-testing analysis for each scenario are presented in S5 and S6 Tables . S5 Table lists the set of 29 Countries with local Zika transmission confirmed between February 15 and October 5 and the relative ranking for each of those countries computed for each of the six scenarios . S6 Table lists the top 29 countries at risk for the six scenarios . The results reveal Scenario A identified more of the 29 countries in it’s top 29 ranking than the other three scenarios , however all scenarios identified at least 15 of the 29 , in their top 29 . A more detailed discussion of these results will be presented as part of the Discussion below .
As the Zika outbreak continues to progress , the number of countries with local transmission is increasing , and this was especially the case during the first half of 2016 . The results presented thus far serve as projected relative risk estimates for each city , and can be used to identify the locations most likely to see imported cases followed by local outbreaks in the near future . However , in an attempt to evaluate the model’s ability to accurately identify the regions most likely to experience future outbreaks , as well as identify the level of contribution of Ae . albopictus in the outbreak , we implemented the model using the state of the outbreak in February 15 , 2016 ( to define the set of high risk sources ) , and compared the model outcomes across all scenarios with the actual set of regions later confirmed to be infected . ( These earlier results were partially noted in [39] ) . In fact , all six scenarios ranked Miami , Florida as the top at-risk destination by a significant margin , and in late July , 2016 the first autochthonous Zika cycle in the United States was reported to have been established in the Miami , Florida region . Between February 15th , 2016 and October 5 , 2016 29 new counties or territories were added to the CDC list of confirmed affected regions . These countries are listed in S5 Table . For each of the six scenarios considered , a country level ranking was computed by aggregating the incoming risk across all cities in a given country , and ranking the countries accordingly . These country-level results from the back-testing are presented in S5 with their respective ranking , alongside the list of new countries added to the CDC list during that time . All six scenarios identified at least 15 of the 29 countries in their respective top ranked 29 . However , Scenario A outperformed the other five scenarios , with 21 of the top ranked 29 countries accounted for . As the assumed relative capacity of Ae . albopictus increased , the number of top ranked countries matching the 29 confirmed affected countries decreased . This result suggest that Scenario A , which only accounts for Ae . aegypti presence , is the most accurate model for identifying the regions most likely to experience local establishment in the future . However , it is important to recognize the discrepancy in the rankings across scenarios highlights an important factor; when comparing the performance of the different scenarios it is important to distinguish between risk of importation and risk of local establishment , the later of which we are comparing the results with . In the five scenarios which account for the additional presence of Ae . albopictus , an increasing number of countries identified as high risk ( these are listed in S6 Table ) are in more developed regions , compared with those countries identified by Scenario A . This discrepancy is because suitable habitats for Ae . albopictus expand much further north and south of the equator when compared with Ae . aegypti , therefore many cities in Europe , as well as Japan , Australia , New Zealand , and major cities in the northern U . S . are at substantially increased risk of Zika establishment only if Ae . albopictus is a capable spreading vector . While these locations , critically , have established vector populations and have experienced a high number of imported cases [3] , with the sole exception of Miami , they did not lead to local establishment , likely due to the resources available for local mosquito control and surveillance . Therefore , until the capacity , or lack there of , of Ae . albopictus is confirmed , the cities identified at highest risk in all Scenarios should continue to be subject to a high level of surveillance . Finally , the country level risk predictions in [43] are also consistent with the outcomes of this study . After aggregating the city level relative risks to the country level , the United States and Argentina were identified to be at highest risk in the present study . Nah et . al . [43] ( who excluded the U . S . ) also identified Argentina to be at highest risk of Zika importation , followed by Portugal , Uruguay , Spain , and Peru , which are also among our top ranked countries across the scenarios . While many of the same countries were identified to be at high risk by both models , discrepancies in the rankings exists for various reasons . Firstly , Nah et . al . [43] estimated the risks of importation and local transmission separately , while our model combines the two within a single risk modeling framework . Secondly , the present study was conducted at a later state in the outbreak when more countries were confirmed to have local transmission; these countries are listed in [43] as at-risk of importation , while in the present study they are considered to pose additional risk . Additionally , the present study is conducted at the city level instead of the country level , and due to the more spatially disaggregate analysis , the results can not be directly compared . Although the methodologies vary substantially between these studies , the consistency among the model results serves to further validate the present study . The preliminary findings did not identify the Federated States of Micronesia or the Marshall Islands as high at-risk destinations of any rank , which highlights one of the potential limitations of this analysis that will be explicitly discussed below . This work takes a major step towards improving our understanding of the spreading risk posed by Zika , however there are six limitations of this analysis , including persistent uncertainties regarding epidemiological parameter estimates , which must be noted here and addressed in future research: Results from this analysis highlight the substantial geographic and quantitative increase in global risk posed as a function of the relative capacity of Ae . albopictus as a secondary spreading vector of Zika , and reveal the set of cities at greatest risk of Zika importation and establishment . The results from the back-testing suggest that the geographic spread of Zika is driven primarily by Ae . aegypti , which is consistent with other studies [30–32] . However , the results from the different scenarios also highlight the increased risk posed to new parts of the world , specifically the U . S . and Europe , if Ae . albopictus were to become a more capable spreading vector . To control the spread of Zika geographically , local surveillance and control efforts are required in both known affected regions and at-risk regions yet to report cases . This is true for locations with reported travel-imported cases that have yet to see locally established cases . As the Zika outbreak continues to spread internationally , so does the uncertainty surrounding the local transmission mechanisms and clinical manifestations of the disease . The possibility of direct human-to-human Zika transmission demands further immediate investigation , and the link between Zika and microcephaly and GBS are of vital concern . Lastly , the uncertainty associated with Zika risk is further compounded based on the implications from the analysis presented here which shows that the vector competence of Ae . albopictus relative to Ae . aegypti demands further investigation . This goal can only be achieved through a combination of field studies to collect a representative variety of strains of these vectors followed by laboratory studies of virus transmission . | Between 1952 , when the Zika virus was first found in humans , and 2007 Zika disease outbreaks were limited to small isolated epidemics in equatorial Africa and tropical Asia . However , the recent outbreak , which began in Brazil in May 2015 , resulted over 750 thousand estimated cases and confirmed local transmission in more than 60 countries by October , 2016 . Like dengue and chikungunya , Zika is spread by Aedes aegypti mosquitoes and possibly , other species including Aedes albopictus . Geographic spread of the virus occurs when infected travelers travel from affected regions to ones without an established local Zika disease cycle , but in which the known and potential vector species have established populations . We estimate the risk of Zika importation and establishment into new regions using air travel data and ecological vector habitat suitability models for Ae . aegypti and Ae . albopictus . Given the uncertainties surrounding the vectorial competence of Aedes mosquitoes , we compare the geographic risk profiles when spread is driven by air travel and Ae . aegypti presence alone , with spread driven by air travel and both species . We conclude that there is a much higher global risk of Zika spread under the latter scenario , although it is the least likely . |
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Sex chromosomes evolve distinctive types of chromatin from a pair of ancestral autosomes that are usually euchromatic . In Drosophila , the dosage-compensated X becomes enriched for hyperactive chromatin in males ( mediated by H4K16ac ) , while the Y chromosome acquires silencing heterochromatin ( enriched for H3K9me2/3 ) . Drosophila autosomes are typically mostly euchromatic but the small dot chromosome has evolved a heterochromatin-like milieu ( enriched for H3K9me2/3 ) that permits the normal expression of dot-linked genes , but which is different from typical pericentric heterochromatin . In Drosophila busckii , the dot chromosomes have fused to the ancestral sex chromosomes , creating a pair of ‘neo-sex’ chromosomes . Here we collect genomic , transcriptomic and epigenomic data from D . busckii , to investigate the evolutionary trajectory of sex chromosomes from a largely heterochromatic ancestor . We show that the neo-sex chromosomes formed <1 million years ago , but nearly 60% of neo-Y linked genes have already become non-functional . Expression levels are generally lower for the neo-Y alleles relative to their neo-X homologs , and the silencing heterochromatin mark H3K9me2 , but not H3K9me3 , is significantly enriched on silenced neo-Y genes . Despite rampant neo-Y degeneration , we find that the neo-X is deficient for the canonical histone modification mark of dosage compensation ( H4K16ac ) , relative to autosomes or the compensated ancestral X chromosome , possibly reflecting constraints imposed on evolving hyperactive chromatin in an originally heterochromatic environment . Yet , neo-X genes are transcriptionally more active in males , relative to females , suggesting the evolution of incipient dosage compensation on the neo-X . Our data show that Y degeneration proceeds quickly after sex chromosomes become established through genomic and epigenetic changes , and are consistent with the idea that the evolution of sex-linked chromatin is influenced by its ancestral configuration .
Sex chromosomes have originated independently many times from ordinary autosomes in both plants and animals [1] . A common feature of heteromorphic sex chromosomes is that while X chromosomes maintain most of their ancestral genes , Y chromosomes often degenerate due to their lack of recombination , with only few functional genes remaining ( for a recent review see [2] ) . The loss of gene function is often accompanied by an accumulation of repetitive DNA on ancient Y chromosomes , and a switch of chromatin structure from euchromatin to genetically inert heterochromatin [2 , 3] . Loss and silencing of Y-linked genes drives the evolution of dosage compensation on the X chromosome , which is often mediated by chromosome-wide epigenetic modifications . Drosophila males , for example , acquire a hyperactive chromatin conformation of their single X , while one of the two X’s in female mammals becomes heterochromatic [4 , 5] . Studies of young sex chromosomes have improved our understanding of the genomic and epigenomic mechanisms driving the divergence between X and Y [6–9] . Neo-sex chromosomes of Drosophila are formed by chromosomal fusions between the ancestral sex chromosomes and ordinary autosomes . The neo-Y , which is the autosome that became linked to the Y , entirely lacks recombination since it is transmitted through males only , which in Drosophila do not undergo meiotic recombination . Consistent with theoretical predictions that selection is ineffective on non-recombining chromosomes [10] , neo-Y chromosomes in several Drosophila taxa have undergone chromosome-wide degeneration , and the extent of gene loss roughly corresponds to the age of the neo-Y . In particular , the very recently formed neo-Y of D . albomicans ( <0 . 1 million year old ) still contains most of its protein coding genes with <2% being putatively non-functional [11] , but a large fraction of neo-Y genes ( roughly 40% ) are down-regulated [9] , suggesting that transcriptional silencing might be initiating Y degeneration . The older neo-Y chromosome of D . miranda ( 1 . 5 million years old ) has acquired stop codons and frame-shift mutations in almost half of its genes , shows a dramatic accumulation of transposable elements ( between 30–50% of its DNA is composed of TEs ) [12 , 13] , and most neo-Y genes are expressed at a lower level than their neo-X homologs [11] . These changes at the DNA sequence level are accompanied by a global change in chromatin structure , and the D . miranda neo-Y is adopting a heterochromatic appearance marked by histone H3 lysine 9 di-methylation ( H3K9me2 ) [3] . The neo-X of D . miranda , in contrast , has maintained most of its ancestral genes but is evolving partial dosage compensation , by co-opting the canonical dosage-compensation machinery of Drosophila ( the MSL-complex ) . This complex is targeted to the ancestral X of Drosophila species , and up-regulates gene expression through changes of the chromatin conformation of the X , mediated by histone H4 lysine 16 acetylation ( H4K16ac ) [14 , 15] . The neo-sex chromosome shared by members of the D . pseudoobscura species group was formed about 15 million years ago , and has evolved the typical properties of old sex chromosomes: the neo-Y is completely degenerate and heterochromatic , while the neo-X is fully dosage compensated by the MSL machinery [3 , 16] . Well-studied neo-sex chromosome systems are all derived from euchromatic autosomes , and studying a neo-sex chromosome that originated from an autosome with some features similar to heterochromatin may allow a more general understanding of the evolutionary principles of chromatin formation on sex chromosomes . Here , we collect data on the genome , transcriptome and epigenome of D . busckii , a species with a poorly characterized neo-sex chromosome derived by a fusion ( and supposedly followed by a pericentric inversion on the X ) between the ancestral sex chromosomes and the “heterochromatic” dot chromosome ( Fig 1A ) [17 , 18] . The age , and the extent of sequence , expression and epigenetic divergence of the neo-sex chromosomes of D . busckii are unknown , but the dot chromosome has an unusual evolutionary history and a unique chromatin structure . It was a sex chromosome in an ancestor of higher Diptera , and only reverted to an autosomal inheritance in the ancestor of the Drosophilidae family [19 , 20] . Studies on the assembled distal arm ( ~1 . 2Mb ) of the D . melanogaster dot chromosome have revealed several features that distinguish it from other autosomes: it has a very low recombination rate and a high repeat content [21–23] , harbors less than 100 genes [24] that have low codon usage bias[25] and which show evidence of reduced levels of positive and purifying selection [26] . Genes on the dot chromosome are embedded into a unique heterochromatin-like milieu that is regulated differently from canonical pericentric heterochromatin [21 , 27] . Both dot-linked genes and genes located in pericentric heterochromatin are enriched for the ‘silencing’ histone marks H3K9me2 and H3K9me3 and the heterochromatin protein HP1a relative to euchromatin , but show a depletion of these marks at the transcriptional start sites of active genes . In addition , expression of dot-linked genes ( but not genes in pericentric heterochromatin ) requires binding of the chromosomal protein Painting of Fourth ( POF ) and the histone methyltransferase EGG , and the gene bodies of transcribed genes show an enrichment of the histone modification H3K9me3 ( but not H3K9me2 ) that is not observed at active genes located in pericentromeric heterochromatin . Genes on the dot chromosome that are not expressed and repetitive regions on the dot chromosome probably adopt a more general POF/EGG independent mechanism of heterochromatin packaging that is shared with pericentromeric regions [28] . Intriguingly , in three Drosophila species including D . busckii , POF was found to bind the X chromosome specifically in males [29] . This mimics the localization of the MSL complex , the canonical dosage compensation machinery of Drosophila , but unlike in other Drosophila species , immunostaining to polytene chromosomes detected no binding of the MSL complex on the X chromosome of D . busckii [16 , 29] . The phylogenetic position of D . busckii is uncertain , and some early studies placed it as a sister to all other Drosophila species [16 , 30] . These findings , together with the discovery that the dot was actually the ancestral sex chromosome in Diptera led to the hypothesis that D . busckii might harbor a more ancestral mechanism of dosage compensation mediated by POF [31] , which may have been derived from a dosage compensation system in an ancestor of Drosophilidae where the dot was the X chromosome [19] . Here , we collect DNA sequence , transcriptome and chromatin data characteristic of dosage compensation and heterochromatin together with immunostaining of polytene chromosomes , to characterize the formation of a sex chromosome from a heterochromatic ancestor , and also to disentangle the relationship between POF and MSL .
We sequenced the D . busckii female genome to an extremely high sequencing coverage ( >150 fold , S1 Table ) with libraries spanning a gradient of insert sizes ( up to 10kb ) to produce a highly continuous de novo assembly ( scaffold N50: 946kb , average scaffold size: 60 . 8kb ) with a total assembled length of 152 . 7Mb . Orthologous Drosophila chromosomes show high conservation ( >95% ) in their gene content [32] , and we assign the chromosomal locations of D . busckii genome scaffolds based on their alignments with D . melanogaster chromosomes . 89% of the sequences could be assigned to individual linkage groups , and we further tested our chromosomal assignments by sequencing the male genome . The ancestral X chromosome is hemizygous in males , and mapped male read depth is indeed only half of the female read depth along the entire X chromosome , while read depths are very similar between sexes on autosomes ( median log10 coverage value of male vs . female: 3 . 50 vs . 3 . 46; P>0 . 05 , Wilcoxon test ) ( Fig 1B ) . Interestingly , coverage in both sexes is also very similar along the dot chromosome and only slightly reduced in males ( median of male vs . female: 3 . 42 vs . 3 . 44 ) , implying that the neo-X and neo-Y still share considerable sequence homology . This suggests that the age of the neo-sex system of D . busckii is younger than that of D . miranda , which shows significantly reduced male read depth ( by about 25% ) along its neo-sex chromosome due to neo-X/Y divergence [11] . We annotate 13 . 1% of the assembled genome as consisting of repetitive elements , with the dot chromosome containing the highest repeat content ( 17 . 3% ) among all chromosomes . We also produce transcriptomes of male and female D . busckii third instar larvae and adults , and integrated them during gene annotation . A total of 12 , 648 protein-coding genes were annotated using D . melanogaster proteins as query , 11 , 859 ( 93 . 6% ) of which have one-to-one D . melanogaster orthologs . We find a higher proportion of annotated genes actively expressed in male than in female ( 69 . 4% vs . 53 . 8% ) with a normalized expression level RPKM ( average RNA-seq reads per kilobase of gene per million fragments mapped ) higher than 5 , and also a generally lower male expression level on the X chromosome relative to autosomes ( Wilcoxon test , P<0 . 05 , S1 Fig ) , in both developmental stages . These patterns are consistent with sex-biased expression patterns found in D . melanogaster [33 , 34] , and a similar ‘demasculinization’ found on the X chromosomes in other Diptera [19 , 20] . The phylogenetic relationship of D . busckii within the Drosophila genus is unclear . Some studies placed it as a sister to all other Drosophila species [16 , 30] , while others put it within the Drosophila subgenus [35] . This uncertainty in the phylogenetic position of D . busckii could have resulted from the small number of genes that were previously investigated , and we use whole-genome sequence alignments of representative Drosophila species and other Drosophilidae , to generate a phylogenomic tree . Our alignments include D . melanogaster , D . pseudoobscura and D . willistoni from the Sophophora subgenus; D . albomicans [11] , D . grimshawi and D . virilis from the Drosophila subgenus , D . busckii and two recently sequenced Diptera species within the Drosophilidae family: Scaptodrosophila lebanonensis [36] and Phortica variegata [19] as outgroups to the Drosophila genus [35–37] . In total , we aligned CDS sequences of 6189 orthologous genes spanning a total of 19 . 1Mb from each species and acquired a consensus tree with high bootstrapping values ( Fig 2 ) . D . busckii consistently clusters with the Drosophila subgenus species ( D . albomicans , D . grimshawi and D . virilis ) rather than being placed at the base of all Drosophila . This phylogenetic analysis suggests that D . busckii is not a member of an early divergent Drosophila lineage , but originated within the Drosophila subgenus . We assembled and mapped a total of 1 . 17Mb ( with 6 . 9% of the sequence as gaps ) of dot chromosome sequence in D . busckii , in comparison to 1 . 35Mb of assembled dot sequence in D . melanogaster . The D . busckii dot chromosome overall shows more than 10 times higher levels of heterozygosity ( 1 . 56 SNPs per 100bp on average ) in male than in female , predominantly due to nucleotide sequence divergence between the neo-X and neo-Y chromosomes ( Fig 1B ) . The median level of pairwise divergence at silent sites between neo-X and neo-Y alleles is 0 . 84% , which is about 3 times lower than synonymous divergence between neo-sex-linked genes of D . miranda ( 2 . 8% ) [11] . Assuming a mutation rate of 5 x 10−9 per bp ( as estimated in D . melanogaster ) [38] and 10 generations a year , this indicates that the D . busckii neo-sex chromosomes originated only about 850 , 000 years ( 0 . 85 MY ) ago . Note that while the fixation of ancestral polymorphisms can contribute to the neo-X/Y divergence , the low level of silent site diversity on the dot [23] implies that ancestral polymorphism is expected to have very limited impact on our estimate of the age of the neo-sex chromosomes of D . busckii . The recent formation of the D . busckii neo-sex chromosome is consistent with the similar level of read depth observed between sexes along the neo-X chromosome , suggesting this system is still at an initial stage of differentiation ( Fig 1B ) . We annotate a total of 86 neo-sex linked genes ( vs . 80 protein-coding genes on the D . melanogaster dot chromosome , see notes in Materials and Methods ) , all of which show the same level of read depth between sexes ( S2 Fig ) . Thus , unlike on the older neo-Y chromosome of D . miranda [11] , none of the protein-coding genes has yet been deleted from the neo-Y of D . busckii . However , we find male-specific SNPs or indels ( i . e . , mutations on the neo-Y ) that cause premature stop codons and/or frameshift mutations in 50 neo-sex linked genes , implying that there is a large number of genes on the neo-Y that supposedly have lost their normal functions ( Fig 3A ) . The proportion of putative non-functional genes ( 58 . 2% ) is much higher on the neo-Y of D . busckii than on that of D . miranda ( 34 . 2% ) [11] . This is unexpected , since there has been less time for degeneration on the younger neo-Y chromosome of D . busckii . In addition , the much smaller size of the dot chromosome predicts weaker effects of Hill-Robertson interference [10 , 39] and thus a lower rate of degeneration on the D . busckii neo-Y . However , simulation results have shown that the effects of interference asymptote quite fast with the number of genes [40] . Several other factors could help to explain the large fraction of non-functional genes on the recently formed neo-Y of D . busckii . First , genes located on the dot generally show lower levels of evolutionary constraint [41 , 42] . Consistent with reduced levels of purifying selection on dot-linked genes , we find that the neo-X alleles show a significantly lower level of codon usage bias than genes on autosomes and the X chromosome ( Wilcoxon test , P<0 . 05; S3 Fig ) . Note that it is possible that selection for optimal codon usage has become more efficient for dot-linked genes on the neo-X since the dot/X fusion , which may have placed them within a more highly recombining environment , as has been observed for D . willistoni [43] . In this case , ancestral levels of codon usage bias may have been even lower for dot-linked genes . Further , the median rate of protein evolution ( as measured by the ratio of nonsynonymous vs . synonymous substitutions using PAML ) at the ancestral branch before the neo-X/Y divergence is higher than that of other autosomes ( median Ka/Ks = 0 . 082 vs . 0 . 075 ) , and non-functional genes show a higher ancestral rate of protein evolution than genes with a functional copy on the neo-Y ( median Ka/Ks = 0 . 086 vs . 0 . 068; S4 Fig ) . Although both differences are not statistically significant , probably due to the low number of genes on the dot chromosome , these results are consistent with the idea that genes under lower selective constraints are becoming pseudogenized more quickly on a degenerating neo-Y , as observed on the neo-Y of D . miranda [11 , 44] . In addition , the gene content of the dot chromosome appears feminized / demasculinized , that is , dot genes in Drosophila and in other Diptera species are over-expressed in ovaries , and under-expressed in testis [19] . Genes with female function are under less purifying selection on the male-limited neo-Y chromosome , which may contribute to accelerated rates of pseudogenization . Neo-X homologs of neo-Y genes that are functional are expressed at a significantly higher level in both male larvae ( S5B Fig ) and adults ( Fig 3B ) than neo-X homologs of neo-Y genes that have become pseudogenized ( Wilcoxon test , P = 0 . 0087 ) . This indicates that the loss of functional Y-linked genes preferentially starts from lowly-expressed genes with less selective constraints , consistent with our findings on the neo-Y of D . miranda [44] . Finally , hemizygosity of dot-linked genes is generally tolerated in D . melanogaster [42] , and null mutations at dot-linked genes may have a negligible effect on fitness if heterozygous . Thus , lower levels of evolutionary constraints , an excess of female-biased genes , and general haplosufficiency of dot genes may contribute to their rapid degeneration on the neo-Y of D . busckii . In addition to functional decay in protein coding sequences , we also found a chromosome-wide expression bias for neo-sex linked genes ( Fig 3C ) : 75 genes ( 88% ) display significantly higher expression from the neo-X chromosome relative to the neo-Y in male adults ( Fisher’s exact test , P<0 . 05 , see Methods ) , and a similar pattern was observed in male larvae ( S3A Fig ) . Putative pseudogenes on the neo-Y tend to show a slightly more severe ( but not statistically significant ) expression bias than functional genes ( median log2 ratio of neo-X vs . neo-Y expression: 1 . 80 vs . 1 . 71; Wilcoxon test , P = 0 . 41 ) . This chromosome-wide expression bias for neo-sex linked genes could be caused by down-regulation of neo-Y alleles and/or up-regulation of neo-X alleles ( i . e . , dosage compensation ) . Although many genes ( 77 . 9% ) show a similar level of expression for male ( with neo-X/Y gene expression levels combined ) and female ( less than 1 . 5 fold difference; Fig 3D ) , genes with lower relative expression from the neo-Y tend to be more female-biased ( Fig 3E , blue line , Spearman’s rank correlation coefficient: -0 . 47 , P = 1 . 04e-5 ) . This suggests that neo-X-biased expression is partly due to down-regulation of neo-Y linked genes . The single neo-X chromosome in males is transcribed at a higher level than a single neo-X chromosome in females ( Fig 3F ) , which suggests that some form of dosage compensation has evolved on the neo-X . However , there is no significant correlation between down-regulation of neo-Y genes ( i . e . neo-X vs . neo-Y expression bias ) , and up-regulation of neo-X genes in males ( i . e . expression of the neo-X in males vs . females , Fig 3E , orange line; F-statistic P>0 . 05 ) . This may suggests that dosage compensation is not gene-specific , but could also reflect a lack of statistical power due to the low number of genes on the dot . The neo-Y chromosome of D . miranda has become partially heterochromatic within 1 . 5 million years . It is enriched for the silencing histone modification H3K9me2 relative to the neo-X and other chromosomes [3] , and expression of neo-Y genes is down-regulated chromosome-wide . To investigate whether an accumulation of silencing histone marks may cause down-regulation of neo-Y linked gene expression in D . busckii , we obtained ChIP-seq profiles of both H3K9me2 and H3K9me3 from male larvae . The two histone modification marks are strongly correlated with each other and HP1a in pericentric heterochromatin , but have distinctive distributions on the dot chromosome of D . melanogaster: H3K9me3 shows an unusual correlation with POF over actively transcribed gene bodies , while H3K9me2 strongly associates with silenced genes [27 , 45] . We analyzed the distribution of H3K9me2 and H3K9me3 at active and silent genes ( expression status defined from S1 Fig ) , and find that both marks are significantly enriched on the dot chromosomes of D . busckii relative to autosomes ( Wilcoxon test , P<0 . 05; see Methods , Fig 4A and 4D ) . H3K9me3 shows a similar level of enrichment between the neo-Y and the neo-X ( Wilcoxon test , P>0 . 05 , Fig 4D ) , and enrichment tends to be higher at active relative to silent genes on both the neo-X and neo-Y ( Wilcoxon test P>0 . 05; Fig 4D–4F ) . In contrast , H3K9me2 levels are significantly increased at neo-Y genes relative to their neo-X homologs ( Wilcoxon test , P = 0 . 000637 , Fig 4A ) , particularly on those that are transcriptionally silenced ( Wilcoxon test , P = 0 . 000381 , Fig 4A–4C ) , and non-functional neo-Y genes show a significant increase in H3K9me2 binding relative to their neo-X homologs ( Wilcoxon test , P = 0 . 0001494; S6 Fig ) . The H3K9me2 enrichment level of silent neo-Y genes is higher than that of active neo-Y genes ( median value: 0 . 79 vs . 0 . 47 , Wilcoxon test P = 0 . 089 , Fig 4A ) , and the enrichment level of H3K9me2 , but not H3K9me3 , is negatively correlated with the gene expression level of neo-Y but not neo-X alleles ( S7 Fig , Spearman’s rank correlation coefficient -0 . 23 , P = 0 . 04 ) . We further analyzed metagene enrichment profiles , and find both H3K9me2 and H3K9me3 to be enriched at gene bodies relative to their flanking regions . The increase of H3K9me2 enrichment on silent neo-Y genes is not restricted to gene bodies but extends into flanking regions as well ( Fig 4C ) . These results suggest that down-regulation of neo-Y gene expression may be caused by H3K9me2 modification , but it is also possible that some genes are first silenced through mutations in their regulatory region , and then preferentially become targeted by H3K9me2 . Overall , our results provide robust evidence that the neo-Y chromosome of D . busckii is becoming more heterochromatic , mediated by H3K9me2 enrichment , which further contributes to the degeneration of neo-Y genes . Most genes on the ancestral X of D . busckii are expressed at similar levels in males and females , i . e . they are dosage compensated ( S8 Fig ) . The molecular mechanism of dosage compensation in D . busckii has been unclear , and in situ hybridization experiments to polytene chromosomes to stain for components of the MSL machinery , using antibodies derived from D . melanogaster , have previously failed to identify MSL binding on the ancestral X chromosome of D . busckii [16] . Instead , an antibody designed against the POF protein in D . melanogaster was found to coat the entire X chromosome of D . busckii in males only [29] , and to co-localize with H4K16ac , a histone marker for dosage compensation in Drosophila [46] . This has led to the proposal that D . busckii does not utilize the MSL machinery to compensate its X chromosome , but instead is using a regulatory mechanism that involves POF [47] . However , it is unclear whether the MSL antibodies tested are just too diverged to produce a reliable hybridization signal , or if MSL-dependent dosage compensation is indeed absent in D . busckii . To evaluate the mechanism of dosage compensation in D . busckii , we utilized both bioinformatics and experimental approaches . First , we annotated the intact open reading frames and gene expression patterns of the key MSL complex proteins and non-coding RNAs , as well as the POF protein and a duplicated copy of POF found in D . busckii . Transcriptome profiling revealed that MSL-2 , POF , roX-1 and roX-2 non-coding RNA all exhibit male-biased expression patterns ( S9 Fig ) , similar to their orthologs in D . melanogaster . We further performed immunostaining with a new D . melanogaster MSL-2 antibody , and find weak but male-specific staining of the X chromosome in D . busckii ( Fig 5A ) . In D . melanogaster , the MSL complex catalyzes the deposition of the activating histone mark H4K16ac , and ChIP-seq profiling in D . busckii clearly reveals that H4K16ac is significantly enriched on the ancestral male X relative to autosomes and the neo-sex chromosomes ( Wilcoxon test , P<2 . 2e-16 , Fig 5B ) . This is consistent with MSL-dependent dosage compensation in D . busckii , and orthologous X-linked genes show a significant correlation in their enrichment levels of H4K16ac between larvae samples of D . busckii and D . melanogaster ( Spearman’s rank correlation coefficient: 0 . 36 , P<2 . 2e-16; Fig 5C ) , suggesting that a similar set of genes is being targeted by the dosage compensation complex on the X in both species . Finally , our metagene analysis of the H4K16ac mark reveals a distinctive 3’ bias specifically over active X-linked gene bodies ( Fig 5D ) , consistent with the pattern mediated by the MSL complex in D . melanogaster [46 , 48] . Taken together , these results suggest that D . busckii shares the same mechanism of dosage compensation for the ancestral X chromosome as D . melanogaster , despite their distant phylogenetic relationship ( Fig 2 ) and their different sex chromosome karyotype . Degeneration and down-regulation of neo-Y genes should select for the acquisition of dosage compensation on the D . busckii neo-X . If the MSL-complex were co-opted on the neo-X in D . busckii to achieve dosage compensation , we would expect similar enrichment of H4K16ac along the neo-X in males . Instead , we find that neo-X linked genes are significantly depleted for H4K16ac relative to autosomes ( Wilcoxon test , P<2 . 28e-13 ) ( Fig 5B ) , similar to the H4K16ac depletion patterns on the dot in D . melanogaster ( S10 Fig ) . This indicates a lack of MSL-dependent dosage compensation on the D . busckii neo-X chromosome , in contrast to other neo-sex chromosome systems where a substantial fraction of the neo-Y has become pseudogenized [3 , 16] . Instead , it suggests that an ancestrally repressive chromatin structure , as is the case for the dot , may severely constrain the evolution of hyperactive chromatin , despite rampant Y degeneration .
We have performed a detailed investigation of the genomic and epigenomic evolution of the young neo-sex chromosomes of D . busckii . All previously studied neo-sex chromosome systems are derived from euchromatic autosomes , but the D . busckii neo-sex chromosome originated from the dot chromosome and its unique , more heterochromatic conformation is probably dictating its unusual patterns of chromatin evolution . We found that both the neo-X and neo-Y chromosome are enriched for both H3K9me2/3 relative to other chromosomes , but only H3K9me2 was reported to have a silencing function on the heterochromatic dot chromosome in D . melanogaster [45] . Consistent with the idea that increased heterochromatin formation may contribute to the observed down-regulation of neo-Y gene expression ( Fig 3B ) , we find that H3K9me2 is enriched at silenced neo-Y linked genes relative to their neo-X homologs , and these genes also tend to become pseudogenized more quickly on the neo-Y . This is consistent with our results in D . miranda , and suggests that genes on the neo-Y under lower selective constraints are more likely to become heterochromatic and non-functional early on [3 , 44] . In contrast , the H3K9me3 mark is not associated with silent chromatin on the dot chromosome of D . melanogaster , and instead enriched along actively transcribed genes on the dot chromosome [45] . We found that neo-Y linked genes show a similar level of H3K9me3 enrichment relative to their neo-X homologs , and no difference between active and silenced genes , suggesting that H3K9me3 does not contribute significantly to expression differences between the neo-X and neo-Y of D . busckii . One important caveat in the above analysis is that we can only measure relative expression or histone modification changes on the neo-sex chromosomes , but cannot distinguish whether those changes occurred on the neo-X or neo-Y . It is formally possible that the neo-X has evolved reduced levels of H3K9me2 ( but not H3K9me3 ) , relative to the neo-Y . No close relatives of D . busckii that lack the neo-sex chromosome fusion are known , preventing us from directly distinguishing between those possibilities . Two chromosome-wide regulatory systems have been characterized in D . melanogaster: one that is mediated by the MSL complex and that targets the male X chromosome; and the other that is mediated by POF and that targets the dot chromosome in both sexes . POF has been shown to bind the nascent RNA of actively transcribed genes on the dot chromosome , and increases levels of expression of these genes [49] . Since some studies placed D . busckii as a sister to all other Drosophila species , an apparent lack of MSL-binding to the X chromosome [29] has led to the intriguing hypothesis that POF may represent an ancestral dosage compensation system . However , our phylogenomic analysis demonstrates that D . busckii in fact belongs to the Drosophila subgenus ( Fig 2 ) , and we show that MSL-dependent dosage compensation appears to be conserved in D . busckii . The MSL complex is present in D . busckii males and its components show similar male-biased expression patterns as found in D . melanogaster ( S9 Fig ) , it binds the X chromosome of D . busckii males ( Fig 5A ) , and the H4K16ac dosage compensation mark is found along actively transcribed X-genes in D . busckii ( Fig 5B ) . This calls for a re-examination of the proposed role of POF in dosage compensation in D . busckii [29] . Despite clear evidence for dosage compensation of the ancestral X chromosome of D . busckii by the MSL complex , we found no signs of MSL-mediated dosage compensation on its neo-X . Rampant neo-Y degeneration ( i . e . almost 60% of neo-Y genes have frameshift mutations or stop codons ) should in principle select for the evolution of dosage compensation on the neo-X of D . busckii . Indeed , in other Drosophila species with neo-sex chromosomes , dosage compensation was found to evolve rapidly after their formation and degeneration of neo-Y genes , by co-opting the ancestral MSL machinery . In D . miranda , the neo-X chromosome has evolved partial dosage compensation through the acquisition of novel MSL-binding sites that recruit the MSL-complex to the neo-X [14 , 15] , and MSL-binding was found to be associated with H4K16ac enrichment . Even older neo-X chromosomes , like the one shared by members of the D . pseudoobscura subgroup , have evolved full MSL-mediated dosage compensation [3 , 16] . In contrast , we did not detect any enrichment of the H4K16ac modification on the neo-X of D . busckii . This is probably due to the younger age of the D . busckii neo-X chromosome , the fact that the dot chromosome contains only few genes and flies with a single copy of the dot chromosome are fully viable in D . melanogaster ( due to compensation mediated by POF [50] ) , and/or the difficulty of evolving a hyper-active chromatin structure for dosage compensation from an ancestrally more heterochromatic background . Our previous work in D . miranda showed that dosage compensation preferentially evolves in chromatin regions that are ancestrally active [3] , probably due to an antagonism between forming repressive , condensed heterochromatin and hyperactive , open chromatin resulting in dosage compensation [50] . Despite down-regulation of neo-Y genes and a lack of MSL-mediated dosage compensation of neo-X genes , we find that transcription of the single neo-X chromosome in males is not simply half that in females , and neo-sex linked genes do not exhibit strong sex-biased expression patterns . This suggests that the down-regulation of neo-Y linked genes is either at least partially compensated by transcriptional buffering mechanism [47] , which may play an important role during early sex chromosome differentiation , before the establishment of global dosage compensation on young X chromosomes . Alternatively , a POF-mediated regulatory mechanism might compensate for reduced gene dose of neo-Y linked genes . It will be of great interest to further investigate the evolutionary and functional relationship between these two chromosome-wide compensatory mechanisms that have been described in Drosophila .
An iso-female line of D . busckii provided by J . Larsson and originally caught in Tallinn , Estonia in the year 2000 was used for this study . About 50 virgin adult male and female were used for genomic DNA extraction using Puregene Core Kit A ( Qiagen , Inc ) . Total RNA from about 50 larvae and virgin adult flies of each sex were extracted by RNAeasy Mini Kit ( Qiagen , Inc ) . Library preparation and genomic or poly-A selected transcriptome sequencing were then performed at Beijing Genomic Institute or UC Berkeley Sequencing facility following the standard Illumina protocol . We sequenced the libraries by paired-end sequencing with 90bp read length for all the RNA-seq libraries and most of the genomic libraries , and 50bp for long-insert libraries . Female DNA was sequenced to very high coverage ( 172 fold , S1 Table ) for de novo assembly of a reference genome , and male DNA was sequenced to medium coverage ( 27 fold ) . We assembled the reference genome by ALLPATHS-LG [51] with standard parameters . The output scaffold sequences were aligned to D . melanogaster chromosomal sequences ( v5 . 46 ) downloaded from FlyBase by LASTZ ( http://www . bx . psu . edu/~rsharris/lastz/ ) using a nucleotide matrix for distant species comparison . Alignment results were filtered using a cutoff of at least 30% of the entire scaffold aligned with 50% sequence identity . We wrote customized perl scripts to build pseudo-chromosomal sequences of D . busckii . We further used RepeatMasker and RepeatModeler ( http://www . repeatmasker . org ) to annotate the repeat content of the D . busckii genome . RNA-seq reads from both sexes were separately aligned to the chromosome sequences of D . busckii by tophat [52] . The alignments were then provided to cufflinks [53] for transcriptome annotation . We integrated the annotation results from cufflinks and used a non-redundant protein sequence set of D . melanogaster ( v5 . 46 ) to annotate the D . busckii genome using the MAKER pipeline [54] . We annotated 79 out of 80 dot-linked D . melanogaster protein-coding genes in the D . busckii genome . 71 of them are located on the dot chromosome of D . busckii , and the remaining 8 genes are located on the X chromosome or other autosomes , including 4 genes whose D . virilis orthologs also map to other chromosomes [55] . The additional 15 genes annotated on the D . busckii dot chromosome are either predicted by a combination of RNA-seq evidence and de novo open reading frame annotation , or have a D . melanogaster ortholog located on another chromosome . We compared the distributions of normalized expression level ( measured by Reads Per Kilobase per Million , RPKM ) in gene regions and intergenic regions , and used the value where the two distributions separate as a cutoff ( log10 RPKM = 0 . 65; S1 Fig ) to define genes that are transcriptionally active or not . We analyzed the codon usage bias of all annotated D . busckii genes by CodonW ( http://codonw . sourceforge . net/ ) . We used the standard GATK pipeline [56] for calling SNPs in male and female DNA samples . In brief , sequencing reads were aligned to the D . busckii genome with bowtie2 [57] and PCR duplicate reads were removed using the Picard tool ( http://broadinstittute . github . io/picard ) . We used UnifiedGenotyper for calling variants , and discarded SNPs/indels with low qualities ( Quality<30 ) , low coverage ( Depth<5 ) , strand biases or clustering patterns for initial SNP filtering . To account for the different sequencing coverage of the male and female samples , we further plot the distributions of variant qualities of male and female SNPs to determine a different variant quality cutoff for the second round of filtering . We identified a total of 16977 heterozygous SNP sites from the male sample and only 496 female heterozygous sites on the dot chromosome . After excluding the sites that are shared by both sexes , we used the quality-filtered male-specific SNPs/indels as the putative fixed neo-X/Y divergence sites , and introduced the alternative nucleotides to the reference neo-X genome to produce the reference genomic sequence of the neo-Y chromosome . Note that only individuals from a single inbred line were sequenced; this means that some of the fixed differences between the neo-X and neo-Y are not actually fixed in the population but may be segregating on either chromosome . Based on the female-specific heterozygous sites , we estimated that only 1 . 5% of the divergence sites maybe derived from segregating polymorphic sites . We then used GeneWise [58] and annotated the non-functional genes of the neo-Y using the proteins annotated from the female reference genome as query . To analyze neo-X and neo-Y allele-specific gene expression and histone profiles ( see below ) , we aligned the male RNA-seq or ChIP-seq reads against the female reference genome and specifically collected reads that overlapped the male-specific SNP sites . These reads encompass informative neo-X/Y divergence sites , and we used customized perl scripts to assign their linkage to either the neo-X or neo-Y , dependent on whether the SNP is male-specific or not . To correct for potential mapping biases , we normalized the count of RNA-seq reads against the DNA-seq reads from males , whose ratios between neo-X and neo-Y alleles are expected to be 1 . To test the significance of biased gene expression between neo-X/Y alleles , we used Fisher’s tests with the allelic-specific DNA-seq read count and allelic-specific RNA-seq read count of the neo-X or neo-Y allele for the 2×2 table . This should account for potential mapping biases of neo-X and neo-Y derived reads , and their ratio is expected to be similar between neo-X/Y alleles if they are transcribing at a similar level . Since the enrichment of ChIP-seq profiles is calculated by normalizing against the input DNA-seq control , we did not do any further correction . When comparing the binding level between the neo-X/Y alleles or different chromosomes , we calculated the ratio of aligned read numbers of ChIP experiment vs . input DNA control , spanning the gene body and 1 . 5 kb flanking regions . We collected CDS sequences from D . pseudoobscura ( v3 . 1 ) , D . virilis ( v1 . 2 ) , D . willistoni ( v1 . 3 ) , D . grimshawi ( v1 . 3 ) and D . albomicans from FlyBase , and two Diptera species Scaptodrosophila lebanonensis and zoophilic fruitfly ( Phortica variegata ) whose genomes have been recently produced in our lab [19 , 36] . Orthologous relationships of genes between species were determined through reciprocal BLAST or precomputed annotation from FlyBase . We aligned all the orthologous sequences for the same gene by translatorX [59] , a program that performs codon-based nucleotide sequence alignment and removed low-quality alignment regions by Gblock [60] . The alignments were then concatenated and provided to RAxML for constructing maximum-likelihood trees with the GTRCAT algorithm , with P . variegate assigned as an outgroup to all Drosophila species . We bootstrapped the tree 1 , 000 times and calculated confidence values for each node as described in the manual of RAxML [61] . Branch-specific evolutionary rates were calculated for the resulting high-confidence tree using the PAML package [62] . To collect data for as many genes as possible , we only used D . melanogaster and D . virilis as outgroups of D . busckii in the input tree . We calculated lineage specific synonymous or nonsynonymous substitution rates using codeml under the ‘free-ratio’ model , which assumes each phylogenetic branch has a different rate of evolution . Polytene chromosomes were dissected from male third instar larvae and processed for immunostaining with primary MSL-2 antibody ( Santa Cruz Biotechnology , sc-32458 , dilution ratio: 1:10 , room temperature , overnight ) and secondary fluorescence antibody Alexa Fluor 555 Dye ( Life Technologies , room temperature , 2 hours ) . Approximately 5g of male third instar larvae were used for chromatin extraction . Chromatin was cross-linked with formaldehyde and sheared by sonication . Chromatin pull-down with IgG agarose beads ( Sigma , A2909 ) was performed as described previously [63] . We used the following antibodies for ChIP-seq experiments: ( 1 ) H3K9me3 ( Abcam ab8898; 3 μl/IP ) ( 2 ) anti-H4K16ac ( Millipore 07–329; 5 μl/IP ) ( 3 ) H3K9me2 ( Abcam ab1220; 3μl/IP ) . Immunoprecipitated and input DNAs were purified and processed according to the standard paired-end Solexa library preparation protocol . Paired-end 100-bp DNA sequencing was performed on the Illumina Genome Analyzer located at UC Berkeley Vincent J . Coates Genomic Sequencing Facility . ChIP-seq and input control reads were aligned to the D . busckii genome by bowtie2 [57] . The resulting alignments were filtered using a cutoff for mapping quality higher than 30 , and provided to MACS [64] to call peaks of enrichment along the chromosomes . We use MEME [65] to identify targeting sequence motifs within peak regions . For metagene analyses , we first determine a cutoff to define ‘bound’ or ‘unbound’ states of certain chromatin marks within each scaled bin of genes or flanking regions , by comparing the distribution of their normalized enrichment levels between chromosomes ( S11 Fig ) . Then for each bin , we calculated the average bound level across all the studied genes , after dividing them into different groups of chromosomes and active/silent genes . ChIP-seq data of male D . melanogaster is downloaded from NCBI SRA database ( accession#: PRJEB3015 ) [66] and orthologous relationship between D . busckii and D . melanogaster genes was determined using reciprocally best BLAST searches . | DNA is packaged with proteins into two general types of chromatin: the transcriptionally active euchromatin and repressive heterochromatin . Sex chromosomes typically evolve from a pair of euchromatic autosomes . The Y chromosome of Drosophila is gene poor and almost entirely heterochromatic; the X chromosome , in contrast , has evolved a hyperactive euchromatin structure and globally up-regulates its gene expression , to compensate for loss of activity from the homologous genes on the Y chromosome . The evolutionary trajectory along which sex chromosomes evolve such opposite types of chromatin configurations remains unclear , as most sex chromosomes are ancient and no longer contain signatures of their transitions . Here we investigate a pair of unusual young sex chromosomes ( termed ‘neo-Y’ and ‘neo-X’ chromosomes ) in D . busckii , which formed through fusions of a largely heterochromatic autosome ( the ‘dot chromosome’ ) to the ancestral sex chromosomes . We show that nearly 60% of the neo-Y genes have already become non-functional within only 1 million years of evolution . Gene expression is lower on the neo-Y than on the neo-X , which is associated with a higher level of binding of a silencing heterochromatin mark . The neo-X , on the other hand , shows no evidence of evolving hyperactive chromatin for dosage compensation . Our results show that the Y chromosome can degenerate quickly , but the tempo and mode of chromatin evolution on the sex chromosomes may be constrained by the ancestral chromatin configuration . |
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Herpesviruses persist indefinitely in their host through complex and poorly defined interactions that mediate latent , chronic or productive states of infection . Human cytomegalovirus ( CMV or HCMV ) , a ubiquitous β-herpesvirus , coordinates the expression of two viral genes , UL135 and UL138 , which have opposing roles in regulating viral replication . UL135 promotes reactivation from latency and virus replication , in part , by overcoming replication-suppressive effects of UL138 . The mechanism by which UL135 and UL138 oppose one another is not known . We identified viral and host proteins interacting with UL138 protein ( pUL138 ) to begin to define the mechanisms by which pUL135 and pUL138 function . We show that pUL135 and pUL138 regulate the viral cycle by targeting that same receptor tyrosine kinase ( RTK ) epidermal growth factor receptor ( EGFR ) . EGFR is a major homeostatic regulator involved in cellular proliferation , differentiation , and survival , making it an ideal target for viral manipulation during infection . pUL135 promotes internalization and turnover of EGFR from the cell surface , whereas pUL138 preserves surface expression and activation of EGFR . We show that activated EGFR is sequestered within the infection-induced , juxtanuclear viral assembly compartment and is unresponsive to stress . Intriguingly , these findings suggest that CMV insulates active EGFR in the cell and that pUL135 and pUL138 function to fine-tune EGFR levels at the cell surface to allow the infected cell to respond to extracellular cues . Consistent with the role of pUL135 in promoting replication , inhibition of EGFR or the downstream phosphoinositide 3-kinase ( PI3K ) favors reactivation from latency and replication . We propose a model whereby pUL135 and pUL138 together with EGFR comprise a molecular switch that regulates states of latency and replication in HCMV infection by regulating EGFR trafficking to fine tune EGFR signaling .
Human cytomegalovirus ( CMV ) , a β-herpesvirus ubiquitous in the world’s population , has adapted many trade-offs for its persistence . CMV replicates to low titers and causes minimal cytopathology , such that the primary infection is typically unapparent . CMV , like all herpesviruses , persists in the host through the establishment of latent state and chronic states [1] . During latency , CMV genomes are maintained in the infected cell with little to no viral gene expression and no virus replication . Given the commitment of T-cell immunity to CMV infection [2] , CMV likely reactivates subclinically with high frequency . However , in an immune incompetent host , including solid organ or stem cell transplant recipients , CMV reactivation remains a major cause of morbidity and mortality [3] . There is no CMV vaccine , and current antivirals fail to target the latent virus . Understanding the mechanistic basis of latency is critical to developing strategies to target latent virus . The ULb’ region of the CMV genome is conserved between human , chimpanzee and rhesus macaque CMV strains , but is completely lacking from rat , mouse and guinea pig strains , suggesting that this region represents an adaptation of the virus to the primate host [4] . The ULb’ region is lost upon serial passage of the virus in fibroblasts [5] , resulting in viruses with higher replicative capacity but more restricted tropism . It is suspected that the estimated 20 open reading frames encoded by the ULb’ region are required for infection and persistence in the host . The UL133-UL138 locus , termed UL133/8 , is encoded within the ULb’ region and encodes four genes: UL133 , UL135 , UL136 and UL138 . These genes have important functions for replication in vascular endothelial cells [6 , 7] and differentially regulate latency and reactivation in CD34+ hematopoietic progenitor cells ( HPCs ) [4 , 8–10] , a site of CMV latency . Antagonism between UL135 and UL138 highlights the complex interplay between proteins encoded by the UL133/8 locus in regulating levels of replication . UL138 suppresses virus replication and promotes latency in CD34+ HPCs [4 , 8 , 11] . By contrast , UL135 promotes de novo replication from transfected viral genomes when UL138 is expressed and is required for reactivation from latency in CD34+ HPCs . Thus , UL135 functions , in part , by overcoming the suppressive effects of UL138 [10] . These studies suggest the existence of a genetic switch regulating states of infection; however , the mechanism by which UL135 and UL138 regulate infection states is unknown . In this study , we demonstrate that UL138 and UL135 proteins ( pUL138 and pUL135 ) antagonize one another by targeting EGFR . EGFR is a powerful host target as it regulates cellular proliferation , differentiation , angiogenesis and survival [12] . While pUL138 potentiates EGFR signaling by enhancing cell surface levels , pUL135 diminishes EGFR signaling by promoting its turnover . The opposing dual targeting of EGFR by pUL135 and pUL138 suggests that modulation of receptor tyrosine kinase ( RTK ) trafficking and signaling underlies , at least in part , the transition of the virus into and out of latency . Indeed inhibition of EGFR or downstream PI3K favors viral replication and stimulates reactivation of UL135-mutant viruses in CD34+ HPCs . These studies define a genetic switch regulating viral replication and latency in the host .
We identified host interacting proteins by mass spectrometry following the immunoprecipitation of flag epitope-tagged pUL138 ( IP-MS/MS ) in the context of infection to begin to understand the mechanisms by which UL138 suppresses viral replication . Top-ranking co-precipitating proteins based on peptide count and coverage are shown in Fig 1A . IP-MS/MS peptides and data are provided for these candidates in S1 Table . EGFR was a particularly interesting candidate because it sits at the center of a network of related pUL138-host interactions as determined by STRING and NCBI analysis , which are listed in Fig 1A . Indeed , this was the only large network that emerged from the 128 interactions identified . Work by others has demonstrated interactions between pUL138 and two other receptors , TNFR [13 , 14] and MRP-1 [15] . Our study confirmed the interaction with MRP-1 ( Fig 1A ) . We confirmed the interaction between pUL138 and EGFR with a reciprocal pull-down , immunoprecipitating EGFR in cells transiently expressing Myc- tagged fusion proteins , pUL135MYC , pUL138MYC , and pUL37MYC . Consistent with the IP-MS/MS results , pUL138MYC co-precipitated with EGFR ( Fig 1B ) . Intriguingly , we also detected an interaction between pUL135MYC and EGFR . These results suggest that both pUL138 and pUL135 interact with EGFR in the absence of other viral factors . The interaction between pUL138 or pUL135 with EGFR is not the result of the myc epitope tag because pUL37MYC did not co-immunoprecipitate with EGFR . In the context of infection , we confirmed interaction between EGFR and pUL135 or pUL138 , but not another myc-tagged protein from the UL133/8 locus , pUL133MYC ( Fig 1C ) . Viruses containing disruptions to prevent expression of UL138 ( UL138STOP ) or UL135 and UL138 ( UL135/8STOP ) serve as controls . These experiments indicate that both pUL135 and pUL138 interact with EGFR when expressed alone and in the context of infection . The interaction of both pUL135 and pUL138 with EGFR is intriguing given the antagonistic relationship between UL135 and UL138 in the context of infection [10] . Additionally , the IP-MS/MS screen indicated an interaction between pUL135 and pUL138 , which confirms previous interactions studies ( Fig 1A ) [9] . To further investigate a requirement of EGFR for the interaction between pUL135 and pUL138 , we overexpressed both pUL135V5 and pUL138MYC in HEK-293 cells , which express little to no EGFR [16] . Immunoprecipitation of pUL135 ( pUL135V5 ) using an antibody to the V5 tag co-precipitated pUL138MYC ( Fig 1D ) . This pull down is reciprocal to that of the IP-MS/MS experiment where pUL138FLAG was pulled down ( Fig 1A ) . The co-precipitation of pUL138 with pUL135 in HEK-293 cells suggests that the interaction between pUL135 and pUL138 does not require EGFR . Further work is required to define the domains of pUL135 , pUL138 , and EGFR required for interaction . EGFR signaling may be modulated during viral infection in a number of ways , including phosphorylation , ubiquitination , and trafficking . pUL135 and pUL138 are membrane-associated proteins; pUL135 is localized at the Golgi , cell surface and cytoskeleton [4 , 17] , whereas pUL138 is at the Golgi [4 , 8] . Because pUL138 has been shown to alter MRP-1 and TNFR at the cell surface [13–15] , we wanted to determine if EGFR surface levels were altered during infection . EGFR was reduced by ~70% on the surface of WT-infected cells relative to mock-infected cells ( p-value ≤ 0 . 001 ) ( Fig 2A ) . This reduction is in part due to the reported transcriptional downregulation of EGFR during virus replication [18 , 19] . Relative to WT infection , disruption of UL135 increased EGFR surface levels by 29% ( p-value ≤ 0 . 05 ) , while disruption of UL138 decreased EGFR surface levels by 22% ( p-value ≤ 0 . 05 ) ( Fig 2B ) . These data indicate an opposing role for pUL135 and pUL138 in modulating surface levels of EGFR . We next asked if pUL135 and pUL138 affected cell surface levels of EGFR when expressed outside the context of viral infection ( Fig 2C ) . UL135 overexpression reduced EGFR surface levels compared to the control ( Ratio of mean fluorescent intensities , MFI , 1 . 7±0 . 17 ) , but UL138 alone had no affect ( Ratio of MFI , 1±0 . 28 ) . This suggests that pUL135 alone stimulates reduction of EGFR levels at the cell surface , whereas pUL138 requires additional viral or infection-induced factors to stimulate EGFR expression at the cell surface . By contrast , UL138 expression alone upregulates surface expression of TNFR1 [13 , 14] and confirmed in S1 Fig , suggesting that UL138 requires other infection-specific factors for the regulation of EGFR , but not TNFR1 . The reduced surface expression of EGFR might reflect a role for pUL135 in stimulating EGFR turnover . We examined total cellular levels of EGFR in the context of infection with WT , UL135STOP or UL138STOP ( Fig 2D and 2E ) . All three viral infections showed a statistically significant decrease in the total EGFR relative to the mock control , reflecting the infection-mediated transcriptional downregulation of EGFR [19] . Relative to mock infection , WT and UL138STOP infection decreased EGFR levels by 70–75% ( p-value≤0 . 001 ) , with neither being statistically different from each other . In UL135STOP infection , total EGFR levels were 50% ( p-value≤0 . 001 ) reduced relative to mock-infected cells , but 50% increased relative to WT infection ( p-value = 0 . 0013 ) . These results suggest that UL135 stimulates the turnover of EGFR during CMV infection , while UL138 has no affect on total EGFR levels . To assess whether or not UL135 turnover of EGFR might represent global modulation of receptor degradation , we also analyzed two other RTKs , platelet-derived growth factor receptor α ( PDGFRα ) and vesicular endothelial growth factor receptor 2 ( VEGFR2 ) , as well as the serine-threonine kinase transforming growth factor β receptor 1 ( TGFβR1 ) . We chose PDGFRα and VEGFR2 because they activate similar downstream signaling pathways to EGFR , while TGFβR1 was chosen because it is trafficked similarly to EGFR [20 , 21] . As previously published [22] , PDGFRα and VEGFR2 were down regulated during WT infection ( p-value ≤0 . 01 for all infections ) , but their levels are not affected by UL135 or UL138 ( Fig 2D and 2E ) . Therefore , while CMV infection affects multiple RTKs during infection , UL135 and UL138 appear to have some specificity for EGFR . TGFβR1 levels were largely unaffected by infection and not affected by either UL138 or UL135 . These results suggest some specificity of pUL135 and pUL138 to EGFR . Changes to EGFR surface levels during infection may reflect functions of pUL135 and pUL138 in altering the internalization or recycling of EGFR-containing vesicles . To investigate these possibilities , we monitored changes in EGFR surface levels over time following an EGF pulse . As expected , uninfected cells rapidly internalized EGFR following stimulation with EGF; EGFR reached the lowest surface levels , approximately 52% of the initial levels ( zero minute time point ) , by 25 minutes ( Fig 3A ) . Approximately 90% of the initial EGFR surface levels were recovered by 90 minutes . As would be expected from our findings in Fig 2A , EGFR surface levels were decreased at the zero time point during WT virus infection . Further , EGFR trafficking was severely diminished in WT-infected cells ( Fig 3A ) . The WT-infected cell data is shown on an expanded scale in Fig 3B to better illustrate the trafficking pattern of EGFR . In WT-infected cells , EGFR was maximally internalized by 25–30 minutes ( 63% of initial levels ) post pulse . In contrast to uninfected cells , EGFR internalization was accompanied by oscillation in surface levels between 1 and 30 minutes . The oscillation observed between 10 and 30 minutes in WT or UL135STOP infection is a point for further investigation , but may reflect a destabilization of EGFR internalization , rapid recycling back to the cell surface [23] , or rapid trafficking of an internal pool of EGFR . Maximal restoration of EGFR surface levels was not observed until 120 minutes post stimulation in WT infected cells . While the increased surface levels at 120 minutes might reflect delayed recycling , this interpretation is confounded by the possibility that at least some portion of the surface levels at this time are contributed by new synthesis of EGFR . These results indicate that CMV infection alters internalization and recycling of EGFR . To determine the contribution of UL135 and UL138 to EGFR trafficking during infection , we compared UL135STOP or UL138STOP infection to WT infection ( Fig 3C , expanded in 3D and 3E ) . Prior to the EGF pulse , EGFR surface levels were increased or decreased in UL135STOP or UL138STOP infection relative to that of WT-infected cells , as anticipated from our analysis of surface levels ( Fig 2B ) . The internalization of EGFR following EGF stimulation in UL135STOP infection reflected that of WT infection at the early time points ( Fig 3C–3E ) , marked by early oscillation in EGFR surface levels . EGFR levels reached their lowest levels by 10 minutes ( 52% of initial levels ) . However , unlike WT infection , EGFR surface levels were restored to 85% of initial levels by 60 minutes in UL135STOP infection . The kinetics of EGFR trafficking back to the surface during UL135STOP infection exceeded the kinetics observed in uninfected cells ( 60 vs . 90 min ) . In UL138STOP infection , maximal internalization was achieved by 1 minute and only 24% of the initial levels were internalized . Notably , the oscillation of surface EGFR observed in WT and UL135STOP infection ( Fig 3C ) was lost in the absence of UL138 . EGFR levels did not vary more than 2% between 1 to 30 minutes post EGF pulse ( Fig 3C; 0–60 minutes expanded in Fig 3D and 3E ) . Eighty-eight percent of the EGFR surface levels were restored by 60 minutes ( Fig 3C–3E ) . These studies reveal distinct roles for pUL135 and pUL138 in modulating EGFR trafficking . The differences in EGFR trafficking are summarized by plotting EGFR surface levels ( relative to levels prior to EGF stimulation in each infection ) at 1 , 10 , 25 and 60 minutes ( Fig 3F ) . We conclude that ( i ) EGFR trafficking is impeded by CMV infection , ( ii ) UL135 impedes recovery of EGFR at the cell surface , and ( iii ) pUL138 impedes internalization or stimulates rapid recovery of EGFR to the cell surface during early times post EGF . These data further support a role for pUL135 in stimulating turnover of EGFR , while revealing a role for pUL138 in modulating EGFR recycling or trafficking to the cell surface from early endosomes . Ligand binding induces homo- or heterodimerization of EGFR , and is coupled to the auto- or Src-mediated phosphorylation of a number of sites in the cytosolic tail of EGFR . Autophosphorylation of tyrosine 1068 ( Y1068 ) is an indicator of EGFR activity and is required for binding to the SH2 domain of the growth factor receptor-bound protein 2 ( Grb2 ) [24] . We analyzed phosphorylation of Y1068 following an EGF pulse in the context of infection with or without pUL135 and pUL138 ( Fig 4A and 4B ) . Phosphorylation was induced by EGF stimulation to a similar level in uninfected and WT infected cells when normalized for total EGFR levels , as indicated by pY1068 or pY . However , phosphorylation of Y1068 increased by approximately 20% during UL135STOP infection ( p-value = 0 . 002 ) relative to the WT infection . While not statistically significant , Y1068 phosphorylation tended to decrease in UL138STOP infection relative to WT infection . Using the same blots we also analyzed total tyrosine phosphorylation ( pY ) on EGFR . Again , we detected a 20% increase in pY during UL135STOP infection ( p-value = 0 . 047 ) . However , during UL138STOP infection pY staining of EGFR was decreased by 60% ( p-value = 0 . 015 ) , suggesting that pUL138 maintains EGFR signaling during infection , but not necessarily through Y1068 ( Fig 4B ) . These results indicate a role for pUL135 in attenuating EGFR signaling whereas pUL138 functions to maintain it . Further work will be important to determine how pUL135 and pUL138 may affect specific phosphorylation sites on EGFR and how these specifically affect EGFR activity in infection . Due to the altered trafficking and activation of EGFR , we analyzed the subcellular distribution of EGFR in the context of infection . In uninfected , serum-starved cells EGFR is predominantly localized to the cell surface in an inactive state . Accordingly , phosphorylation of Y1068 is low in these cells ( Fig 4C , top row ) . The addition of serum-containing media stimulated the phosphorylation of EGFR and its localization into cytoplasmic vesicles ( Fig 4C , second row ) . Strikingly , EGFR was predominantly localized to a juxtanuclear compartment in both serum-starved and fed infected cells ( Fig 4C , bottom 2 rows ) . Activated EGFR ( pY1068 ) was detected predominantly in the juxtanuclear compartment irrespective of the serum-starved or–fed state , suggesting that viral infection sequesters and sustains EGFR activity even under serum stress . We next wanted to determine if the activated EGFR present at the juxtanuclear compartment in infected cells represented EGFR sequestered following its synthesis or trafficked from the cell surface . We labeled serum-starved fibroblasts with EGF ligand conjugated to Alexa Fluor-647 ( EGF-647 ) . Twenty-minutes following a temperature shift , internalization of EGF-647 and EGFR were detected in uninfected and infected cells ( Fig 4D ) . However , in infected cells , EGF-647 and EGFR were localized to the juxtanuclear compartment , indicating that the juxtanuclear EGFR is trafficked from the cell surface . The juxtanuclear localization of EGFR resembles the viral assembly compartment ( VAC ) , a virus-induced reorganization of endo- and exocytic membranes that functions in capsid tegumentation and envelopment [25–27] . We sought to determine if EGFR was localized to the VAC and define the EGFR-containing vesicles . We analyzed Y1068 localization with the cis-Golgi marker , GM130 ( Fig 5A ) , and the viral tegument protein , pp28 ( Fig 5B ) , both established markers for the VAC . EGFR localized in the region with GM130 and pp28 , although the staining was not co-incident . We have previously reported that pUL135 and pUL138 also localize to the VAC during infection [4 , 8]; however , we did not observe any difference in the localization of pY1068 EGFR between WT , UL135STOP or UL138STOP infections ( S2 Fig ) . To define the EGFR-containing vesicles in the VAC , we analyzed the co-localization of EGFR with a number of Rab proteins that serve as endocytic vesicle markers . Rab proteins are small GTPases that modulate distinct membrane trafficking events . Rab 5 is localized to sorting endosomes and mediates fusion of early and late endosomes [28] . Rab11 marks the endocytic recycling compartment ( ERC ) and the trans-Golgi network and is involved in late endocytic recycling events . Typically , the association of EGFR with Rab 5 vesicles is transient and not observed at steady state , consistent with our findings in uninfected cells ( Fig 6A and 6C ) . EGFR is not typically sorted to the Rab 11-positive ERC under normal growth , but , EGFR has been observed to recycle in Rab 11 vesicles in states of stress , drug treatment or in immortalized cells [29 , 30] . Accordingly , the colocalization of EGFR with Rab 11 in uninfected cells was minimal ( Fig 6B and 6D ) . However , the association of Rab5 ( Fig 6A and 6C ) and Rab11 ( Fig 6B and 6D ) with EGFR was increased in the context of CMV infection . The extent of Rab 5 or Rab 11 co-localization with EGFR was quantitated by two methods: Rab coincidence with EGFR and Pearsons correlations using the Image J Mosaic suite Squassh workflow [31 , 32] . While there was a significant increase in the association of Rab 5 or 11 and EGFR between uninfected and infected cells ( Fig 6C and 6D ) , we did not observe a statistically significant change between WT and mutant virus infections . Similar results were obtained with Pearson correlations . The coincidence of Rab 5 or Rab 11 with cytosolic EGFR has a Pearson correlation of 0 . 1 in uninfected cells , which rose to ≥0 . 3 in all infection conditions analyzed . Defining differences in vesicle association between WT and mutant viruses will likely require dynamic assays that follow a pulse of EGF over time , similar to those in Fig 3 . The discrete association of EGFR with Rab5 and Rab11 vesicles in the context of infection suggests that CMV induces the accumulation of EGFR in vesicles poised for its recycling during CMV infection . While EGFR and PI3K activation has been shown to be important for entry of HCMV into fibroblasts and monocytes [33–35] , nothing is know about the role of EGFR throughout infection . Based on our observation that UL135 , an activator of replication , induced the turnover of EGFR and EGFR is transcriptionally downregulated during replication in fibroblasts [18 , 19] , we hypothesized that reduced EGFR levels and activity in the context of viral infection promoted virus replication . To determine a role for EGFR and its downstream phosphatidylinositol 3-kinase ( PI3K ) signaling in virus replication , we analyzed CMV replication over time in the presence or absence of the EGFR kinase inhibitor , AG1478 ( Fig 7A ) or the PI3K inhibitor LY294002 ( Fig 7B ) . So as not to interfere with viral entry , inhibitors were not applied to cells until after viral entry . Inhibition of EGFR increased replication in fibroblasts by 5-fold at 6 dpi ( p-value<0 . 01 ) and maintained a statistically significant increase at 8 dpi ( p-value<0 . 05 ) relative to vehicle control ( Fig 7A ) . The PI3K inhibitor enhanced replication by 6-fold ( Fig 7B ) at 6 dpi ( p-value<0 . 01 ) and 13-fold by at 8 dpi ( p-value<0 . 01 ) . These data demonstrate that inhibition of EGFR and PI3K activity enhances CMV replication . Loss of UL138 results in a virus that is unable to establish or maintain a latent infection in CD34+ HPCs and instead replicates productively . UL135 functions , in part , to overcome UL138-mediated suppression and loss of UL135 results in a failure to reactivate from latency [10] . These findings demonstrate an antagonism between UL138 and UL135 that governs entry into and exit from latency . Given the antagonistic regulation of EGFR by pUL135 and pUL138 defined here , we hypothesized that the opposing functions of pUL138 and pUL135 in regulating EGFR impacts states of latency and reactivation in CD34+ HPCs . As surface expression of EGFR is not well established in CD34+ HPCs , we first investigated EGFR surface levels in the context of WT , UL135STOP and UL138STOP virus infection using EGF-647 . We verified the specificity and sensitivity of this assay using human embryonic kidney-293 cells , which express little to no EGFR [23] . HEK-293 cells transfected with a plasmid expressing EGFR and labeled with EGF-647 increased fluorescent signal 2500-fold compared to cells not expressing EGFR ( S3A Fig ) . In contrast , a fluorescently-conjugated EGFR antibody increased staining by only 800-fold relative to the control , indicating increased sensitivity of EGF-647 ligand for detecting surface levels of EGFR . In CD34+ HPCs , detection of EGFR at the cell surface was similarly enhanced 5-fold by EGF-647 ligand ( S3B Fig ) , while the fluorescently conjugated antibody to EGFR was unable to detect EGFR on the surface of CD34+ HPCs . In contrast to infection in fibroblasts ( Fig 2 ) , EGFR surface levels were increased 1 dpi in WT-infected CD34+ HPCs relative to uninfected CD34+ HPCs ( Fig 8A ) . UL135STOP infection resulted in increased levels of EGFR relative to WT infection , consistent with our findings in fibroblasts . Surface levels of EGFR in UL138STOP did not change significantly relative to WT infection on uninfected cells . The alterations in surface expression of EGFR were transient and were not observed at 4 and 8 dpi time points . These results suggest that pUL135 and pUL138 alter surface EGFR expression early in infection of CD34+ HPCs . To determine if EGFR activity mpacts CMV latency or reactivation from latency , we chemically inhibited EGFR signaling in infected CD34+ HPCs . Pure populations of infected ( GFP+ ) CD34+ HPCs were seeded into long-term bone marrow culture in the presence of the AG1478 EGFR inhibitor or a DMSO vehicle control . After 10 days in culture , the cells or an equivalent cell lysate were seeded by limiting dilution in a cytokine-rich media to promote reactivation on monolayers of permissive fibroblasts . The infectious centers detected in the cell lysate reflect the virus produced prior to reactivation and serves as a “pre-reactivation” control . The frequency of infectious centers produced in CD34+ HPCs treated with AG1478 are represented as the fold change in infectious centers relative to the cells treated with vehicle control ( Fig 8B ) . Inhibition of EGFR in WT infection resulted in a 3-fold and 6-fold greater frequencies of infectious centers produced prior to reactivation and following reactivation , respectively . While EGFR inhibition did not enhance UL135STOP replication in unstimulated CD34+ HPCs ( Pre-reactivation ) , inhibition enhanced the frequency of reactivation of UL135STOP by 3-fold . This result indicates that the inhibition of EGFR in combination with a reactivation stimulus partially complements the defect in reactivation of the UL135STOP virus [10] . In the case of UL138STOP , which replicates in the absence of a reactivation stimulus , inhibition of EGFR did not significantly increase infectious centers formation either prior to or following reactivation . Similar to AG1478 , another EGFR kinase inhibitor , Gefitinib , increased the frequency of infectious centers formation in WT and UL135STOP infection , but not UL138STOP ( S4 Fig ) . We also analyzed the effect of PI3K inhibition on the WT infection in CD34+ HPCs . Treatment of cells with PI3K inhibitor LY294002 increased infectious centers production in WT infection 3-fold in combination with a reactivation stimulus ( Fig 8C ) . While the PI3K enhanced infectious centers formation in unstimulated cells 2-fold , this difference was not statistically significant . From these results we conclude that the inhibition of EGFR or downstream PI3K enhances reactivation in CD34+ HPCs . These results reveal a role for EGFR and PI3K in maintaining latency during CMV infection in CD34+ HPCs .
Herpesviruses have evolved complex interactions with the host to achieve lifelong persistence . To avoid elimination , herpesviruses masterfully evade intrinsic , innate and adaptive defenses to infection . Although less well defined , herpesviruses also modulate epigenetic silencing and homeostatic signaling in the host cell to create an optimal environment for persistence . EGFR is a major homeostatic regulator of cell proliferation , differentiation , adhesion/migration , survival [36 , 37] , and most recently , innate signaling [38 , 39] and DNA repair [40] . As such , EGFR represents a potentially powerful target for viral manipulation during infection with complex DNA viruses . Many viruses target EGFR: RNA viruses ( e . g . , rhinovirus , RSV , influenza and measles ) induce EGFR [41–43] , while DNA viruses ( e . g . , adenovirus , CMV and HSV ) typically inhibit EGFR [19 , 44–46] during their replicative cycles . EGFR has been reported to be an entry receptor for CMV [33] . In the context of HCMV replication , EGFR is transcriptionally downregulated at early times during a productive infection due to the induction of Wilms’ Tumor Factor 1 [18 , 19] , a known transcriptional repressor of EGFR . As a result , CMV infection decreases the responsiveness of infected fibroblasts to external stimulation [18] . These observations were made using laboratory strains of CMV , indicating that ULb’ region genes are not required for the transcriptional downregulation of EGFR . EGFR signaling has also been shown to be critical for viral entry into monocytes and for monocyte survival and migration [34 , 47] . In the context of infection in endothelial cells , a proposed site of CMV persistence , CMV binding to EGFR and β1 and β2 integrins induces increased EC proliferation , motility and capillary tube formation indicative of an angiogenic response [48] . Taken together , these studies implicate EGFR as a major host regulator of infection contributing to CMV persistence in broad contexts of infection . The mechanisms by which and to what end viruses target EGFR are largely undefined . Our study defines EGFR as a target of viral manipulation that impacts CMV latency ( Fig 9 ) . Further , we have identified two antagonistic viral proteins , pUL135 and pUL138 , that target EGFR with opposing effects . The opposing regulation of a single host target by two viral proteins defines a molecular switch to allow the virus to sense and respond to changes in the cellular environment . While likely an over simplification , pUL138 promotes latency by maintaining EGFR surface levels and activity in infected cells , while pUL135 mediates the turnover of EGFR from the cell surface for viral replication and reactivation ( Fig 9 ) . This opposing regulation may result in a more sensitized state in CD34+ HPCs that is poised to respond to changes in the host , while EGFR signaling in fibroblasts is more insulated . Heightened EGFR levels at the cell surface may contribute to the establishment of latency by modulating cell survival , differentiation or innate signaling . Sustained levels of EGFR may be less important in the context of virus replication because the virus expresses genes to prevent cell death , evade the immune response , or promote protein synthesis . The regulation of EGFR during CMV infection likely reflects a fine-tuning of host signaling , not an all or none event . EGFR activates several major downstream pathways , including mitogen-activated protein kinase ( MAPK ) / extracellular signal-regulated kinase ( ERK ) , PI3K , phospholipase C gamma/protein kinase C ( PLCγ/PKC ) , and signal transducer and activator of transcription ( STAT ) . Some activities downstream of EGFR are required for replication and reactivation , including that of the mammalian target of rapamycin , mTOR [49–51] . Therefore , it is possible that CMV suppresses EGFR for reactivation , while selectively maintaining activity of specific downstream pathways , such as mTOR . More work is required to understand how CMV regulates specific activities of EGFR and its downstream pathways and how this regulation impacts latency and reactivation . Our studies demonstrate that EGFR activity and that of downstream PI3K activity negatively impact virus yields in fibroblasts ( Fig 7 ) and reactivation in CD34+ HPCs ( Fig 8 ) . In apparent contradiction , PI3K was reported to promote HCMV replication in fibroblasts [35] . However , cells were pre-treated with PI3K inhibitors prior to infection and the inhibitor was replenished throughout infection in these studies . Therefore , the requirement of PI3K for CMV replication reported in these studies cannot be separated from its requirement for viral entry [33] , which was discovered years after these initial studies . These studies also used laboratory strains of HCMV at very high MOI , which may also impact the apparent differences between our findings . Future work will define the specific aspects EGFR function that benefit viral latency or hinder virus replication . Localization of activated EGFR to the VAC was an unexpected result ( Figs 4C and 4D and 5A and 5B ) . The VAC has been considered a primary site of virion maturation where the virion acquires a full complement of tegument and an envelope [25–27] . Further , the presence of active EGFR at the VAC even when cells were under the stress of a starved state suggests that CMV insulates EGFR from host feedback mechanisms . Consistent with our findings , CMV infection has previously been shown to circumvent cellular stress responses , such as amino acid deprivation , and relocalize activated mTOR to the VAC [52] . Transferrin has also been shown to localize within the VAC during infection [25 , 53] and its recycling has been shown to be suppressed by CMV microRNAs targeting host secretory factors [54] . Sequestration of host signaling factors in the VAC suggests a role for the VAC as a “viral signaling compartment” that insulates host signaling molecules or sustain virus-prescribed levels of signaling ( Fig 9 ) . pUL135- and pUL138-mediated control of EGFR may underlie the ability of the virus to sense and respond to changes in extracellular environment . It will be interesting to understand how this putative role of the VAC relates to the maturation of virus particles . While the VAC has not been studied in the context of latency in CD34+ HPCs , we have described roles of pUL135 and pUL136 in regulating formation of the VAC and viral maturation in endothelial cells , a site of chronic infection in the host [7] . The defects in VAC formation and virion maturation in the absence of UL135 or UL136 expression may reflect a primary role of these proteins in modulating membrane and protein trafficking in the host to manipulate cellular signaling and homeostasis , and in turn affect the outcome of infection . The VAC is comprised of many host secretory membranes , including those important to endocytic sorting and recycling [25 , 53] . Consistent with our findings , previous studies have shown that Rab11 and transferrin localize to the VAC [25 , 53] and that Rab5 is partially localized to the VAC [55] . The steady state association of EGFR with Rab5 and Rab11 vesicles in the context of infection ( Fig 6 ) was surprising and indicates that HCMV infection has altered host trafficking . In uninfected cells , EGFR transiently passes through Rab5 vesicles , but is not known to traffic to the Rab11-marked ERC except under exceptional conditions of cellular stress associated with drug treatment , cancer or immortalizaion [28–30] . EGFR is commonly used a marker for trafficking through an early endosomal/lysosomal route exclusive of the ERC . Therefore , the increased association of EGFR with Rab 5 vesicles may indicate an increased transit time through this compartment and the induced association with Rab11 vesicles may indicate a redirection of trafficking that reflects a state of stress . In studies exploring the ERC in infection , Britt and colleagues reported that EGF did not localize to the VAC [53] . While the reason for the discrepancy between these findings and the current study is not known , one intriguing possibility is that UL/b’ proteins are required for EGFR localization to the VAC . While we have not observed a role for pUL135 or pUL138 in trafficking EGFR to the VAC ( S2 Fig ) , other ULb’-coded proteins may be important for re-orienting trafficking in the cell . Another possibility is that localization of EGF ligand to the VAC is transient . Britt and colleagues exposed cells to EGF ligand for 2 hours prior to fixation , whereas we examined cells fixed at 20 minutes following exposure to ligand . Therefore , our studies may demonstrate trafficking of EGF to the VAC that then exits the VAC by 2 hours . Kinetic studies will be required to define how CMV infection affects vesicle trafficking . In addition to its role in regulating surface levels of EGFR in infection , pUL138 has been shown to reduce MRP-1 and enhances TNFR1 levels at the cell surface , although the significance of these changes to latency is not known [13–15] . As TNFα stimulates CMV reactivation [56–58] , increased surface levels of TNFR1 may poise cells for reactivation . The mechanism by which pUL138 differentially regulates surface protein expression is not known . In contrast to our findings for EGFR , pUL135 overexpression did not affect surface expression of TNFR1 [13] . Therefore , the partnership between pUL138 and pUL135 in regulating EGFR may not extend to all proteins targeted by pUL138 . Moreover , while CMV alters the levels of many proteins [59] , the effect of pUL135 and pUL138 on EGFR appears to be specific , as they did not affect total levels of similar RTKs , including PDGFRα and VEGFR2 , or a receptor trafficked similarly to EGFR , TGFβR1 . The role of pUL135 reorganization of the actin cytoskeleton and reduced focal adhesion is mediated by an interaction with the Abelson interacting protein-1 ( Abi-1 ) , which reduces natural killer cell recognition and killing [17] . Notably , Abi-1 contains Src homology 3 ( SH3 ) domains that mediate its interaction with the Cbl E3 ubiquitin ligase and impact endocytosis and signaling [60] . Intriguingly , Abi1 regulates the phosphorylation and Cbl-mediated turnover of EGFR , EGF-induced Ras , ERK and PI3K pathways [60–62] . The role of the Abi-1-pUL135 interaction in pUL135-mediated turnover of EGFR is not yet defined . pUL135 was also recently demonstrated to contribute to the turnover of the Rho-associated kinase , Rock1 , that regulates cytoskeleton [63] . This result suggests that pUL135 functions in targeting other cellular proteins for turnover . A role pUL135-mediated turnover of Rock1 in latency or reactivation has not yet been determined . Oncomodulatory properties have been attributed to CMV in the case of glioblastoma [64] . EGFR is driver of a number of cancers [37] , and is overexpressed in ~54% of glioblastomas [65] . Thus , pUL138-mediated stimulation of surface expression in latently infected cells raises the possibility that viral regulation of EGFR contributes to the oncomodulatory properties of CMV . While the increase in EGFR expression induced by CMV infection of CD34+ HPCs in vitro was transient ( Fig 8A ) , this may reflect limitation of CD34+ culture . Natural oscillations between states of latency and subclinical reactivation in the host might result in heightened or sustained EGFR expression . UL138 expression is a marker for CMV sero-positivity [11] and , as such , is likely expressed throughout the persistence of the virus . More broadly , RTKs and PI3K signaling have been shown to play key roles in herpesviral latency . PI3K stimulation through nerve growth factor-binding to the RTK , TrkA , is required for the maintenance of HSV-1 latency in neurons [66] . The HSV-1 tegument protein VP11/12 activates PI3K to stimulate phosphorylation of Akt; however , other as yet unidentified viral proteins are also involved [67] . Similar to the role of pUL138 in stimulating surface expression of EGFR and promoting viral latency , latency membrane protein-1 ( LMP-1 ) of EBV enhances expression and activity of EGFR [68 , 69] . LMP-1 activates EGFR , STAT3 , and ERK pathways through PKCδ [70] , while LMP-2A constitutively activates PI3K and Akt pathways [71 , 72] and stabilizes β-catenin [71] . The PI3K/Akt pathway also maintains latency in murine herpesvirus γ-68 replication and Kaposi’s sarcoma-associated herpesvirus ( KSHV ) infection [73] . The K1 glycoprotein of KSHV activates Akt downstream of PI3K to enhance cell survival [74] . Our work identifying opposing viral regulators of EGFR offers a powerful path towards defining the mechanistic underpinnings and the significance of viral-mediated control of EGFR and its downstream signaling pathways to viral infection . Understanding the complex interplay between herpesviruses and host signaling at the molecular level is important for defining the mechanistic basis of viral latency and persistence . This work defines a molecular switch that regulates latent and replicative states through the modulation of EGFR . The regulation of EGFR at the cell surface provides a novel means by which the virus may sense and respond to changes in the cellular environment to enter into , maintain or exit the latent state . There is no vaccine for CMV and CMV antiviral therapies are limited in their ability to control CMV because they do not target latently infected cells . Our work identifies potential targets for the design of novel antiviral strategies aimed at latent reservoirs .
Primary lung fibroblasts ( MRC-5;ATCC ) , HEK-293T/17 ( ATCC ) , Sl/Sl ( Stem Cell Technologies ) , M2-10B4 ( Stem Cell Technology ) , and CD34+ HPCs were maintained as previously described [75] . Human CD34+ HPCs were isolated , as previously described [75] , from de-identified bone marrow and cord blood samples obtained from the University Medical Center at the University of Arizona , in accordance with our Institutional Review Board . TB40/E WT , UL135STOP¸and UL138STOP are described in Umashankar et al . 2014a . TB40/E UL1383xFLAG was described by Petrucelli et al . 2012b . TB40/E UL133MYC was created by a similar fashion as was previously described for FIX-UL133MYC [76] . All Primer sequences can be found in Table 1 . pCIG-UL135MYC is previously described [75] . pCIG-UL135V5 was created by amplifying UL135V5 with UL135-FWD and UL135-V5-REV primers . The product was digested with NheI and BamHI , gel purified , and ligated into pCIG to create pCIG-UL135V5 . UL138 was amplified from TB40/E with XbaI-UL138-Fwd and UL138-myc-NheI-Rev primers and ligated between XbaI and NheI digestion sites of pCIG . pCIG-UL37MYC was created by digesting pcDNA3-UL37MYC ( Victor Goldmacher; Immunogen ) with HindIII and XbaI , blunting the UL37MYC fragment with Klenow , and ligating into the EcoRI site of pCIG . HA-tagged ubiquitin ( HAUb ) polypeptide sequence was subcloned from pCMV-HAUb into pCIG via NheI and EcoRI sites . EGFR was amplified from pCIG-EGFRGFP using pCIG Fwd and EGFR 3xFlag Rev . EGFR3xFLAG product was then digested with NheI and ligated in pCIG expression vector that was digested with BamHI , blunted with Klenow , and redigested with NheI . Lentiviruses were created by cotransfecting HEK293T/17 cells with pCIG vectors , pMD2 . G , and psPAX2 ( Addgene #12259 and 12260; Trono Lab ) using polyethylenimine ( Polysciences ) and collecting supernatants 48h later . To identify pUL138 interacting partners , fibroblasts were infected at an MOI of 3 with TB40/E-UL1383XFLAG . Interacting proteins were isolated by cryogenic cell lysis at 48hpi and rapid immunoaffinity purification as previously described [77] . Briefly , protein complexes were isolated by immunoprecipitation for 1 hr . using anti-FLAG antibody conjugated to Dynabeads ( ThermoFisher Scientific ) . Proteins were eluted , dried and resuspended in SDS-loading buffer . Samples were alkylated with iodoacetamide and separated by SDS 10% PAGE . The entire lane was cut into sections and subjected to in-gel tryptic digestion . The isolated tryptic peptides were analyzed by LC-MS/MS using an ESI-LTQ XL mass spectrometer ( ThermoScientific ) . Peptide identification was carried out using SEQUEST with a global false discovery rate of 5% . The experimental design and a detailed description of antibodies used for flow cytometry are provided in S1 Text and Table 2 , respectively . Fibroblasts were infected with WT , UL135STOP , or UL138STOP virus at 1 MOI . Alternatively , cells were lentivirally transduced with 1 MOI of HAUb , UL135MYC , or UL138MYC . Cells were fixed with 2% paraformaldehyde in PBS for 30min and washed with excess PBS . For surface level experiments in fibroblasts , cells were stained with Brillant Violet 421-conjugated ms α-EGFR or APC-conjugated ms α-TNFR1 for 30min at 4°C in FACS buffer ( PBS with 0 . 5% FBS ) . Samples were washed with excess FACS buffer to remove unbound antibody . Samples were washed with excess FACS buffer to remove unbound antibody . For trafficking experiments , fibroblasts were stimulated with 10nM EGF on ice for 30 minutes prior to staining for EGFR . Cells were washed with PBS and complete media warmed to 37°C was added to each sample . Samples were incubated at 37°C for 1-180min , washed with ice-cold PBS , and trypsinized on ice . Cells were processed as described above . Detection of low level EGFR surface expression was optimized using HEK293T/17 cells ( do not express endogenous EGFR ) untransfected or transfected with pCIG-EGFR3xFLAG . 48h post transfection , cells were collected by trypsinization and stained using either Brilliant Violet 421 conjugated ms α-EGFR or Alexa Fluor 647 conjugate EGF ligand . Cells were analyzed using FACS to determine sensitivity and specificity ( S2 Fig ) . For surface level experiments in CD34+ HPCs , infected ( GFP+ ) HPCs were sorted using PE conjugated ms α-CD34 at 24 hpi ( FACSAria , BD Bioscience Immunocytometry Systems ) . Cells maintained in LTBMC were labeled with Alexa Fluor 647-conjugated EGF at 1 , 4 , and 8dpi and fixed with 2% paraformaldehyde in PBS prior to FACS analysis . All samples were analyzed using a BD LSRII equipped with FACSDiva Software ( BD Bioscience Immunocytometry Systems ) and FlowJo software . Infected or lentivirus-transduced fibroblasts were lysed in TNEN lysis buffer ( 1% NP-40 , 150 mM NaCl , 5 mM EDTA , 20 mM Tris-HCl pH 7 . 5 , 10 mM sodium fluoride , 10 mM n-ethylmaleimide , 50 μM ammonia molybdate , 2 mM sodium orthovanadate with 1x HALT protease and phosphatase inhibitor cocktails ( ThermoFisher ) ) . EGFR was precipitated from 1mg of lysate , as determined using Pierce BCA assay kit ( Thermo Fisher ) , using ms α-EGFR antibody ( Ab13; Table 2 ) and Pierce Scientific protein G magnetic beads ( Thermo Fisher ) . IP samples were washed with TNEN wash buffer ( 0 . 5% NP-40 , 150 mM NaCl , 1 mM EDTA , 50 mM Tris-HCl pH 7 . 5 , 10 mM sodium fluoride , 50 μM ammonia molybdate , 2 mM sodium orthovanadate ) then resuspended in SDS-Page loading buffer with DTT for immunoblot analysis . Transfected HEK293T/17 cells were lysed using the above protocol . Immunoprecipitations were performed with 200 μg of protein lysate using ms α-V5 antibody . 20% of the lysates were analyzed to control for protein expression and should not be considered as a quantitative measure of relative protein levels . For analysis of phosphorylation , infected cells were serum starved for 24h prior to lysis . At 48hpi , cells were incubated with complete media containing10nM EGF on ice for 30min and then prewarmed complete media at 37°C for 15min . Immunoprecipitations were performed as described above with 500μg of protein . Immunoprecipitation samples or 50μg of lysate were separated by electrophoresis on 10% Tris-Bis SDS-PAGE gel and either NuPage MES or MOPS buffers . Gels were transferred onto PVDF membrane ( EMD Millipore ) using NuPage Transfer buffer with 0 . 05% SDS and 20% methanol at 50V for 3h . Blots incubated with TBS-BT ( Tris buffered saline with 0 . 25% BSA and 0 . 1% Tween20 ) for 30min prior to antibody staining . Rb α-EGFR ( D38B1 ) , chk α-Myc , chk α-V5 , and ms α-phosphotyrosine ( 4G10 ) were incubated with the blots with 5% non-fat milk , 0 . 25% bovine serum albumin , and 0 . 05% sodium azide . In contrast , Ms α-IE1/2 ( 3H4 ) , rb α-phospho EGFR ( pY1068 ) , rb α-PDGFRα , and rb α-VEGFR2 were incubated with the blots in TBS with 0 . 1% Tween20 , 5% BSA , and 0 . 01% Sodium azide , and ms α-Tubulin in TBS-BT . Blots were incubated with fluorescent secondary antibodies and imaged and quantitated using a Li-Cor Odyssey infrared system . Antibodies and sources are defined in Table 2 . For serum starvation condition , cells washed twice with PBS at 30hpi and incubated with serum-free media for 18h . Where specified , fibroblasts cells were transduced with 1 MOI of EGFR3xFLAG lentivirus for 20h prior to CMV infection . Samples were processed as previously described and stained with antibodies ( Table 2 ) [75] . All images were obtained using a DeltaVision RT inverted Deconvolution microscope . Representative single plane images were adjusted for brightness and contrast . Colocalization analysis was conducted using the Squassh workflow in the MosaicSuite for Image J and FiJi [31 , 32] . Confluent fibroblasts were infected with 0 . 5 MOI of WT virus and were treated with DMSO , 5μM AG1478 ( Caymen Chemical ) , or 20μM LY294002 ( Cell Signaling ) at 18 hpi . Virus present in cells and media were quantified by the TCID50 as described previously [10] . An infectious centers assay was used to quantitate infectious centers produced by CD34+ HPCs , as described previously [75] . CD34+ HPCs were treated with 10 μM AG1478 , 10μM Gefitinib ( Cell Signaling ) , 20 μM LY294002 or DMSO for vehicle control . | Cytomegalovirus , a herpesvirus , persists in its host through complex interactions that mediate latent , chronic or productive states of infection . Defining the mechanistic basis viral persistence is important for defining the costs and possible benefits of viral persistence and to mitigate pathologies associated with reactivation . We have identified two genes , UL135 and UL138 , with opposing roles in regulating states of latency and replication . UL135 promotes replication and reactivation from latency , in part , by overcoming suppressive effects of UL138 . Intriguingly , pUL135 and pUL138 regulate the viral cycle by targeting the same receptor tyrosine kinase , epidermal growth factor receptor ( EGFR ) . EGFR is a major homeostatic regulator controlling cellular proliferation , differentiation , and survival , making it an ideal target for viruses to manipulate during infection . We show that CMV insulates and regulates EGFR levels and activity by modulating its trafficking . This work defines a molecular switch that regulates latent and replicative states of infection through the modulation of host trafficking and signaling pathways . The regulation of EGFR at the cell surface provides a novel means by which the virus may sense and respond to changes in the host environment to enter into or exit the latent state . |
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It has not previously been possible to live image the earliest interactions between the host environment and oncogene-transformed cells as they initiate formation of cancers within an organism . Here we take advantage of the translucency of zebrafish larvae to observe the host innate immune cell response as oncogene-transformed melanoblasts and goblet cells multiply within the larval skin . Our studies indicate activation of leukocytes at very early stages in larvae carrying a transformed cell burden . Locally , we see recruitment of neutrophils and macrophages by 48 h post-fertilization , when transformed cells are still only singletons or doublets , and soon after this we see intimate associations between immune and transformed cells and frequent examples of cytoplasmic tethers linking the two cell types , as well as engulfment of transformed cells by both neutrophils and macrophages . We show that a major component of the signal drawing inflammatory cells to oncogenic HRASG12V-transformed cells is H2O2 , which is also a key damage cue responsible for recruiting neutrophils to a wound . Our short-term blocking experiments show that preventing recruitment of immune cells at these early stages results in reduced growth of transformed cell clones and suggests that immune cells may provide a source of trophic support to the transformed cells just as they do at a site of tissue repair . These parallels between the inflammatory responses to transformed cells and to wounds reinforce the suggestion by others that cancers resemble non-healing wounds .
Cancers originate from one or a few clones of transformed cells that gain a growth advantage over neighboring normal cells , which , in turn , enables them to invade the host microenvironment and form a tumor [1] , [2] . Decades of research using various murine tumor models , as well as analysis of human clinical tumor samples , has revealed how activation of various oncogenes and/or loss of tumor suppressor gene function , can intrinsically confer a growth advantage on transformed cells [1] , [2] . However , it is now clear that many host-derived cellular and molecular components can also influence this cellular transformation [3]–[5] . In particular , there is considerable evidence that the host immune system plays a pivotal role in several conflicting aspects of cancer initiation and progression , both as a key player in immune elimination , to “find and destroy” transformed cells [6]–[8] , and as an active assistant that may aid expansion and metastatic spread of a tumor [4] , [9] , [10] . Inflammation is a crucial function of the innate immune system that protects host tissues against dangerous insults that are detrimental to tissue homeostasis , including wound damage and pathogen invasion [11] . Acute inflammation , as triggered by wounding , is a rapid and self-limiting process: chemical mediators are induced in a tightly regulated sequence , and innate immune cells move in and out of the affected area , destroying infectious agents , and delivering growth factors and other signals that aid in repairing the damaged tissue [12] . However , when innate immunity goes awry , inflammation does not always resolve , and it is believed that chronic , “smouldering , ” and often subclinical inflammation can be the root cause of many human pathologies , including cancer [13]–[16] . Because of difficulties in predicting when and where transformed cells may arise in an organism , very little is currently known about the earliest events whereby host tissues respond to somatic cell transformation prior to the emergence of any sign of malignant progression . When does the host first recognize transformed cells , and how do they interact ? Answering this question begs a model that allows easy live , in vivo observation of these events . Recently , the zebrafish , Dano rerio , has emerged as a new model organism in studies of cancer biology , with many molecular and cellular components that operate during tumorigenesis in mammals seemingly conserved in fish [17]–[19] . The translucency of zebrafish larvae makes it possible for us to observe how the transformed cells interact with various cellular components of their host environment in vivo , and at high resolution . During zebrafish development , innate immune cell types emerge as early as 15 h post-fertilization ( hpf ) [20] , [21] , and become competent to respond to infections and wounds from around 22 hpf [22] , [23] . However , it is believed that mature adaptive immune cells emerge significantly later , not before at least 1 wk of larval life [24] , [25] . This provides a further advantage , since it enables us to tease apart the contribution solely from innate immune cells to the earliest events of tumorigenesis , without complications from cross talk with the adaptive immune system . In this study we have used oncogenic forms of Ras and Src to induce transformation of cells that reside within the larval skin . Our in vivo , live imaging movies indicate that these transformed cells activate and recruit host leukocytes from very early stages of development . We show that this initial recruitment of leukocytes to transformed cells is , at least in part , triggered by local synthesis of H2O2 . This parallels the early damage signals that first draw innate immune cells towards wounds [26] , [27] . However , instead of subsequent shutdown and resolution of the inflammatory response , as occurs following the acute wound response , the inflammatory response to transformed cells appears to amplify and progress towards that of a chronic inflammatory state as seen in chronic non-healing wounds . Inhibiting the generation of H2O2 prevents leukocyte recruitment towards wounds in zebrafish larvae , and we have found that it also prevents leukocyte recruitment towards transformed cells . Inhibition of leukocyte recruitment leads to a reduction in the growth of neoplastic cells , indicating an early trophic function for innate immune cells . Together , our data indicate homologies , at both the cellular and molecular levels , between the transformed-cell-induced host innate immune response and a wound inflammatory response .
To investigate potential early interactions between transformed cells and the host immune system , we used transgenic zebrafish lines that have fluorescently labeled leukocytes . Several such zebrafish lines have now been generated and characterized [31]–[33] . We first injected the mitfa:V12RAS-mitfa:mCherry construct into one-cell-stage Tg ( BACmpo:eGFP ) i114 ( hereafter referred to as MPO:GFP ) embryos with eGFP-tagged neutrophils [31] , and followed sporadic V12RAS expression in melanocytes and their precursors , melanoblasts . We observed a significant enrichment of eGFP+ cells adjacent to mCherry+ transformed cells ( 26/28 larvae with ectopic clones ) ( Figure 1A ) at 4 dpf . A similar association was not seen after injection of mitfa:mCherry control plasmid ( Video S1C ) . Our live imaging of V12RAS+mCherry+ melanocyte clones in MPO:GFP larvae revealed that MPO+ cells mount an active inflammatory response towards even very few of these V12RAS+ cells and are able to engulf fragments of them ( Figure 1B; Video S1A ) . These data indicate that V12RAS-transformed melanocytes induce a host inflammatory response from the very earliest stages . In order to address whether such an inflammatory response is unique to RAS-induced transformation , or whether this is a more general host response to oncogene-induced cell transformations , we have used a v-Src model that has recently been used to study the interaction of transformed cells and their normal epithelial neighbors in zebrafish larval skin [34] . We use the same pBR-Tol2-UAS-GAP43-GFP-SC-v-Src construct to induce clones of v-Src-transformed cells that co-express GAP43-eGFP in Tg ( LysC:DsRed ) embryos . All the resulting embryos that have clones of v-Src over-expressing cells show active recruitment of leukocytes to these clones , both when live imaged to visualize neutrophil attraction ( Figure 1Ci; Video S2B ) , and in fixed specimens immunostained with an antibody against the pan-leukocyte marker , L-plastin ( Figure 1I ) , which reveals all leukocytes . Again , there is no such recruitment of immune cells to fluorescent cells after injection of GAP43-eGFP control plasmid ( Figure 1Cii; Video S2A ) . Next , we wanted to determine whether this response extended beyond the melanocyte lineage . We continued to focus on skin as a model tissue by studying expression of V12RAS+ cells in another cell population that resides in the epidermis and asked whether these cells too could trigger a similar innate immune response . The zebrafish kita promoter drives gene expression in melanocytes and a subpopulation of mucus-secreting cells of zebrafish larvae from 24 hpf ( kita expression pattern [35] ) . In zebrafish skin , mucus-secreting cells are scattered throughout the epidermis as individuals and are analogous to goblet cells that have the potential to give rise to rare carcinoid tumors in humans [36] . In the double transgenic line generated by crossing the Et ( kita:GalTA4 , UAS:mCherry ) hzm1 ( hereafter referred to as kita:GalTA4 ) driver line [37] with the Tg ( UAS:eGFP-H-RASV12 ) io6 reporter line ( hereafter referred to as UAS:V12RASeGFP ) [38] , larval offspring express oncogenic V12RASeGFP fusion protein in kita-expressing cells . The membrane localization of the V12RASeGFP fusion protein provided significant optical advantages for our subsequent live imaging studies . Mucus-secreting cells expressing V12RAS exhibit abnormal overgrowth , as indicated by increased BrdU incorporation , in comparison to control eGFP-expressing cells ( Figure 1J and 1K ) and gradually form multicellular clumps within the epidermis of the larvae . To examine how host leukocytes react to these transformed cells , we crossed the Tg ( kita:Gal4 , UAS:V12RASeGFP ) double transgenic fish ( hereafter referred to as the V12RAS line ) with Tg ( LysC:DsRed ) [33] fish to obtain embryos in which lysozyme C positive ( LysC+ ) leukoctyes are labeled by DsRed ( red ) and V12RAS-transformed cells are labeled by eGFP ( green ) . Tg ( kita:GalTA4 , UAS:eGFP ) fish ( hereafter referred to as the eGFP line ) were crossed with Tg ( LysC:DsRed ) fish to obtain control larvae that have eGFP-labeled , but otherwise normal , goblet cells , as well as DsRed-labeled LysC+ cells . The live images of control eGFP larvae versus V12RAS+ larvae at 4 dpf ( Figure 1D and 1E ) show that there are increased numbers of LysC:DsRed+ cells distributed within the trunk skin of larvae with transformed cells ( Figure 1D , 1E [dotted box] , and 1L ) , indicating that leukocytes are activated by the presence of V12RAS+ cells in the epidermis . To assess the responsiveness of all of the host innate immune cells , beyond just those that are LysC+ ( largely neutrophils ) , we stained larvae with an antibody against L-plastin , and , again , we observed a marked increase in recruited cells , this time including not only neutrophils but also macrophages , into the skin of the trunk region ( Figure 1F , 1G , and 1M ) . Moreover , we found that leukocytes were often associated with V12RAS+ cells in these larvae ( Figure 1H ) . A study in Drosophila has reported that tumors can systemically stimulate proliferation of the host hematopoietic lineage [39] . To determine whether this is the case in our zebrafish model with the presence of V12RAS+ cells , we counted the number of LysC:DsRed+ cells in V12RAS+ larvae compared with in their V12RAS+ siblings by fluorescence-activated cell sorting and found no overall increase of LysC+ cells in V12RAS+ larvae ( data not shown ) . However , we did see reduced numbers of LysC:DsRed+ cells in the hematopoietic tissue in V12RAS+ larvae ( Figure 1Eii ) , where most larval LysC+ cells normally reside at this developmental stage ( Figure 1Dii ) [33] , suggesting that these cells are drawn to the transformed cells in the skin . Together these observations indicate that V12RAS+ transformed cells induce an inflammatory environment that draws leukocytes to the skin and , moreover , that transformed cells also direct local recruitment of leukocytes towards them . To determine whether this inflammatory response is reflected by up-regulation of pro-inflammatory markers , we performed reverse transcription PCR ( RT-PCR ) on larvae carrying a transformed goblet cell burden , and indeed we see increased levels of tnfα , cxcl1 , il8 , ifn1 , and il1β in these larvae ( Figure S1A ) . Moreover , if we look by quantitative PCR ( qPCR ) at levels of two of these genes , il1β and cxcl1 , we see their clear induction in larvae where V12RAS expression is induced by heat shock only 6 h before RNA extraction , suggesting that pro-inflammatory gene expression is a rapid response to V12RAS expression ( Figure S1B ) . It is well established that tissue damage induces an inflammatory response and that innate immune cells are rapidly recruited to any wound site [12] . Live imaging studies using leukocyte reporter transgenic larval zebrafish have enabled detailed cell behavioral analysis of this in vivo recruitment of innate immune cells to wounds [32] , [40] , as well as the subsequent resolution of these cells , which occurs over a similar time course to that seen in mammalian systems . It has not been previously reported how innate immune cells behave when they first confront transformed cells growing in the host environment . Our investigations began by comparing the response of LysC+ cells to V12RAS+ cells as they expand within normal flank epidermis , versus their response to a laser wound made in a similar region of the larval skin ( Figure 2 ) . In unwounded eGFP control 4-dpf larvae , LysC:DsRed+ cells migrate along the horizontal myoseptum ( Figure 2A , 2D , and 2G; Video S3A ) as though following a chemotactic guidance cue , with a highly polarized pathway ( meandering index [MI] = 0 . 93±0 . 049 , mean ± standard error of the mean ) . Laser wounds made to the trunk region in control larvae ( without transformed cells ) trigger an inflammatory response of similar intensity and duration to that previously reported for wounds made to the fin [32] , [40] . An average of 12 . 6±0 . 819 ( n = 10 ) LysC:DsRed+ cells are drawn to the wound , away from their normal patrolling route within 30 min of wounding ( Figure 2B , 2E , 2H , and 2J; Video S3B ) . At early time points after wounding , the retention time for individual neutrophils averages about 60 min , but this decreases as the wound heals ( Figure 3I ) . Subsequently , cells leave the wound ( Figure 3D–3F; Video S4 ) , mirroring the previously reported retrograde migration of neutrophils from wound back to the blood vessel they initially came from [32] . In equivalent stage larvae with clones of V12RAS+ cells growing in the epidermis , we observe many more LysC:DsRed+ cells in the flank region than in control larvae ( Figure 2C and 2J ) . LysC:DsRed+ cells are distracted from their normal patrolling path and turn towards where the V12RAS+ cells are distributed ( Figure 2F and 2I; Video S3C ) . Once in the V12RAS+-cell-enriched area , LysC:DsRed+ cells change their direction more frequently , approximating the behavior of neutrophils at a wound ( Figure 2K , MI = 0 . 55±0 . 072 ) , as a consequence of moving between individual V12RAS+ cell clones . They often make contact with V12RAS+ cells and slow down in their proximity and then migrate away again toward another V12RAS+ cell clone ( Figure 2F and 2I; Video S3C ) ; the total footprint of leukocytes almost entirely covers the locations of V12RAS+ cells ( Figure 2F ) . The random walk behavior exhibited by LysC:DsRed+ cells within V12RAS+ cell territory , and their retrograde migration to and from individual V12RAS+ clones is similar to that of LysC:DsRed+ cells responding to a wound ( Figure 2E; Video S3B ) . Our data indicate that transformed cell clones growing at early stages in the epidermis induce an inflammatory response and that leukocytes are recruited to the vicinity of transformed cells in a similar manner to that seen when innate immune cells sense , and are drawn towards , wounds . We wondered whether the similar behaviors of leukocytes toward transformed cells and wounds might be reflected in molecular markers known to be associated with the changing phenotype of leukocytes at wounds [41] . In situ hybridization studies reveal that some L-plastin+ cells express the “alternatively activated” macrophage ( M2 ) marker , arginase1 [42] ( Figure S1C ) , but in similar specimens , immunohistochemistry reveals that there are also some L-plastin+ cells expressing “classically activated” macrophage ( M1 ) markers such as TNFα ( Figure S1D ) . This heterogeneity of transformed-cell-associated leukocytes is similar to the phenotypes of wound macrophage populations [41] . To capture the earliest possible recognition of V12RAS+ cells by leukocytes we crossed Tg ( kita:GalTA4 , UAS:V12RASeGFP ) fish with Tg ( fli:eGFP ) in which GFP is expressed in all early myeloid lineages [43] , prior even to the onset of MPO:GFP or LysC:DsRed expression . These transgenic fish have previously been used to study how primitive macrophages are recruited to wounds [22] . Time-lapse movies revealed that the first leukocyte recruitment to V12RAS+ cells generally occurs as early as 55–60 hpf , when they are single cells , and no later than when they become two-cell clones ( we refer to this as the “initial stage” in Figure 3A ) . Fli:eGFP+ cells migrating in the proximity of V12RAS+ cells appear to actively change direction , as indicated by their low MI ( 0 . 46±0 . 073 ) ( Figure 3A and 3G; Video S5A ) , and move toward individual V12RAS+ cells; they occasionally pause at one clone but never for more than 10 min before moving on ( Figure 3A; Video S5A ) . The retention time of those Fli:eGFP+ cells that slow down and contact V12RAS+ cells is short-lived , averaging 4 . 23±0 . 43 min ( Figure 3A and 3H; Video S5A ) . To assess how the host inflammatory response changes as tumor expansion proceeds , we crossed V12RAS+/Fli:eGFP+ fish with Tg ( LysC:DsRed ) and chose larvae at 4–5 dpf that had already developed more than 15 V12RAS+ clones within the skin of one side of the flank , but with no more than 15 cells in any one individual clone . These clones we refer to as “early developmental” stage clones ( Figure 3B; Video S5B ) . Our time-lapse movies revealed that the most significant change in inflammatory cell behavior as transformed cell clones expand is a prolonged retention time ( 11 . 15±1 . 69 min ) at any one clone ( Figure 3H ) . LysC+ cells in V12RAS+ clone territory at the “early developmental” stage continue to actively move between individual V12RAS+ clones ( Figure 3B; Video S5B ) . The MI of these cells remains similar ( MI = 0 . 55±0 . 057 ) to that in response to the “initial stage” V12RAS+ clones ( Figure 3G ) . Finally , we examined larvae that exhibited more than 30 V12RAS+ clones , with at least one clone on the flank of each fish consisting of more than 30 V12RAS+ cells ( defined in this study as “late developmental” stage , which generally occurred when larvae were between 6 and 8 dpf ) , to analyze the host response to more advanced transformed cell groups ( Figure 3C ) . At this late stage , LysC+ cells exhibit considerably greater retention time after contacting V12RAS+ cells ( Figure 3H , retention time = 58 . 81±10 . 08 min; Video S5C ) . They are often associated with one V12RAS+ clone for long periods , and actively interact with individual cells within this clone ( Figure 3C; Video S5C ) . Tracking analysis also showed a greatly reduced meandering index ( Figure 3G; MI = 0 . 28±0 . 034 ) . Our comparison of the progression of the V12RAS+-clone-induced immune response with that of a standard wound inflammatory response ( Figure 3D–3F and 3I; Video S4 ) indicates that leukocytes initially respond to the V12RAS+ transformed cells in a way that is similar to the response to an acute wound but that this response escalates with the expansion of the V12RAS+ clone until it eventually progresses to a chronic inflammation state , similar to that observed in chronic wounds , or as previously described in a zebrafish model of chronic skin damage/inflammation [44] , [45] . The contribution of various inflammatory cell lineages toward tumorigenesis is still a matter of considerable investigation [9] , [46] , [47] . In the mammal , various immune cell lineages have been found associated with the tumor microenvironment , and the composition of these leukocytes as well as their individual phenotypical changes are thought to be important in determining whether inflammation contributes to tumor elimination or tumor promotion [7] , [46] , [47] . We wanted to determine whether we could distinguish various innate immune cell lineages from our live imaging movies and determine how they each respond to V12RAS+ cells in the zebrafish larvae . By restricting our live imaging studies to within 9 dpf , we avoid the mature adaptive immune functions that could interfere with our analysis of the innate immune response [48] . We used Tg ( fli:eGFP ) to reveal all of the myeloid lineages in the early larva , and , using it in combination with LysC:DsRed , and MPO:GFP transgenes , we can distinguish four types of differently expressing leukocytes ( Figure 4A; Video S6 ) . In agreement with studies from other groups we observed a population of leukocytes expressing MPOlowLysC− , termed “tissue residential macrophages” [49] residing in the skin ( Video S6A ) ; these cells have a distinct elongated morphology and squeeze themselves between other cells . It is commonly accepted that the MPOhigh population are neutrophils [20] , [49] , [50] . We found these cells also to have a high level of LysC:DsRed expression . Time-lapse movies show that neutrophils migrate with distinct polarity , often with a broad leading edge and narrow rear and that they migrate rapidly ( Video S6 ) . Amongst LysC+ cells there is another distinct population that are MPO− , which can be distinguished in time-lapse movies by their smaller size , lower level of LysC:DsRed signal , and higher migration speed compared to the MPOhighLysC+ population . These cells migrate largely in a neutrophil-like manner ( Figure 4A; Video S6B ) . Our time-lapse movies also capture another leukocyte population that are Fli:eGFP+ but also negative for both LysC and MPO ( Figure 4A; Video S6C ) . These cells are smaller , less than a quarter of the volume of a neutrophil , yet they migrate with similar speed to neutrophils ( Figure 4A; Video S6C ) , in a “neutrophil-like” manner . To examine the response of these cells to tissue damage we made a small wound in the caudal fin with a tungsten needle . Our time-lapse movies indicate that all of these various leukocytes are recruited to a wound ( Figure 4Bi; Video S7A ) . We describe , above , how LysC+ cells are activated and recruited towards V12RAS+ cells . We next used Tg ( LysC:DsRed , fli:eGFP , kita:GalTA4 , UAS:V12RASeGFP ) larvae to test whether the other subtypes of leukocytes were also activated during the transformed-cell-induced inflammatory response . Our time-lapse movies show that , just as for the wound-triggered inflammatory response , all of these leukocytes appear to be activated and drawn to V12RAS+ cells in the host skin ( Figure 4Bii; Video S7B ) . We looked in more detail at the interactions that take place between V12RAS+ cells and recruited leukocytes . Once recruited , leukocytes actively exhibit dynamic and intimate contacts with V12RAS+ cells . Both transformed cells and leukocytes polarize and extend filopodial and lamellipodial protrusions towards one another ( Figure 5A; Video S8 ) . Frequently , leukocytes make extensive contact with V12RAS+ cells for prolonged periods of time , often more than 2 h , seemingly probing the V12RAS+ cells , and this behavior becomes more common as the V12RAS+ clones expand in number . When a leukocyte moves away from a V12RAS+ cell , it generally leaves a “tether” linking leukocyte and V12RAS+ cell; these tethers can be largely transformed-cell-derived , or occasionally have equal contributions from immune cell and transformed cell ( Figure 5B and 5C; Video S9 ) . Occasionally , a leukocyte appears to be dragged back to the V12RAS+ cell by this tether between the two cells ( Video S9 ) . Activated innate immune cells are known to engulf both cell debris and invading pathogens at sites of tissue damage and infection [23] . In our time-lapse movies we observe active phagocytosis by all of the LysC+ cells and tissue residential macrophages in the presence of V12RAS+ clones . Perhaps unsurprisingly , the different leukocyte lineages engulf in different ways . LysC+ cells ( neutrophils ) appear to extend membrane protrusions and break off small fragments of V12RAS+ cells ( Figure 5D; Video S10 ) , whereas macrophages are able to deform themselves to engulf a whole cell ( Figure 5E and 5F ) . The consequences of these two phagocytic modes is most clearly indicated by L-plastin staining of tissues , which reveals that cells labeled by L-plastin , but not MPO:GFP ( i . e . macrophages ) , contain larger aggregates of engulfed RFP-tagged ( V12RAS+ ) material resembling intact cells , while MPO+ cells ( neutrophils ) contain only smaller fragments of engulfed V12RAS+ cells ( Figure 5G and 5H ) . To test whether immune cells were not simply engulfing transformed cells because the latter were undergoing apoptosis , we stained for the cleaved ( active ) form of caspase 3 and observed no indication of increased apoptotic cells within these early transformed cell populations or the immune cells drawn to them ( data not shown ) . Our time-lapse observations suggest that early recruitment of innate immune cells towards V12RAS-transformed cells in the larval epidermis may be similar to the wound inflammatory response . We next wanted to know how V12RAS+ transformed cells might attract the attention of host innate immune cells , and whether there were parallels between the two types of inflammatory triggers—wounding and the presence of transformed cells . A recent study showed that H2O2 is released after wounding of zebrafish larvae and that the consequent gradient of H2O2 is required for leukocyte recruitment towards the wound [26] . We wanted to test whether V12RAS+ cell growth in the epidermis might utilize the same signal to activate immune cells . We soaked larvae in the H2O2-specific probe acetyl-pentafluorobenzene sulphonyl fluorescein ( which is converted to its fluorescent form when exposed to H2O2 ) [51] for 30 min prior to imaging . The efficiency of this reporter dye was confirmed by imaging the transiently increased levels of H2O2 at wound edges of control larvae ( Figure 6Ai; [26] ) . We observed slightly increased levels of fluorescent signal throughout V12RAS+ larval flank skin as compared with control larvae ( Figure 6Aii and 6Aiii ) . But more dramatically , our time-lapse studies revealed stochastic and transient H2O2 production in the vicinity of V12RAS+ cells and their immediately neighboring normal cells ( Figure 6B–6D; Video S11 ) , and this transient increase of H2O2 signal generally appears to precede the recruitment of several LysC:DsRed+ cells toward the V12RAS+ clone ( Figure 6B–6D; Video S11 ) . These data indicate that H2O2 could play a role in V12RAS+-induced leukocyte recruitment . To complement this real time approach to imaging H2O2 flux in the neighborhood of transformed cells , we also utilized an immuno-spin trap technique that reports a history of ROS exposure by accumulation of 5 , 5-dimethyl-l-pyrroline N-oxide ( DMPO ) protein adduct wherever protein-centered radicals are generated [52]; incubation of larvae in medium containing DMPO for 6 h and subsequent immunostaining for DMPO indicates a “mist” of ROS modification around all V12RAS+ cells but none adjacent to control eGFP cells ( Figure 6E and 6F ) . To test whether H2O2 is indeed required for leukocyte recruitment to transformed cells , we used a pan-NADPH oxidase inhibitor , diphenyleneiodonium chloride ( DPI ) , which has previously been shown to block H2O2 production in wounded zebrafish larvae , and thus prevent leukocyte wound recruitment [26] . At the same concentration that blocks leukocyte migration toward a wound , we see reduced numbers of LysC:DsRed+ cells migrating into the epidermis of DPI-treated V12RAS+ larvae compared with control larvae ( compare Figure 6H and 6I; quantification in Figure 6L; Video S12A and S12B ) ; LysC:DsRed+ cells that migrate along the horizontal myoseptum no longer change their direction to migrate towards the V12RAS+ cells , as they do in untreated larvae ( compare Figure 6E and 6F; quantification in Figure 6J; Video S12A and S12B ) . H2O2 can be generated either directly by specialist NADPH oxidases ( NOXes ) or converted by superoxide dismutase ( SOD ) from superoxide ( O2− ) that has been generated by other NOXes [53] . Treating 4-dpf V12RAS+ larvae with the SOD inhibitor diethyldithiocarbamate ( DDC ) [54] will drive increased superoxide anion ( O2− ) levels and reduce any H2O2 generated via SOD [53] , and while we see increased numbers of neutrophils in the skin after this treatment ( Figure 6H , 6J , and 6L ) , we see no change in their recruitment to V12RAS+ clones ( Figure 6H , 6J , and 6M; Video S12A and S12C ) . These data suggest that SOD conversion of O2− to H2O2 is not required for transformed cell recruitment of leukocytes . In zebrafish larvae , the sole NOX that can generate H2O2 independently of SOD is DUOX , and morpholino knockdown of DUOX has been shown to prevent recruitment of neutrophils to wounds [26] . We used morpholinos to knock down zebrafish DUOX by blocking pre-mRNA splicing and showed by RT-PCR that this knockdown extended at least until 5 dpf ( Figure S2 ) . Immuno-spin trap analysis of such morphant embryos revealed a much reduced “mist” of ROS modified proteins adjacent to V12RAS+ cells ( Figure 6G ) , indicating H2O2 generation had successfully been blocked . Analysis showed that leukocyte recruitment to V12RAS+ clones in DUOX morphant larvae appeared similar to that in DPI-treated larvae , with a much reduced number of LysC:DsRed+ cells seen migrating within the epidermis and very few of these cells passing close to V12RAS+ cells ( Figure 6K; Video S12D ) . For both DPI and DUOX morpholino treatments , leukocytes otherwise exhibit entirely normal motility; for example , their migration along the horizontal myoseptum is unperturbed . Together these data indicate that where V12RAS+ cells are growing in the host epidermis , increased H2O2 production is the primary signal drawing leukocytes to the locale of these transformed cells , just as is the case for recruitment of inflammatory cells to wounds . But these experiments alone do not reveal whether it is the transformed cells themselves or their immediate disturbed neighbors that generate the H2O2 signal . To determine the cellular source of the H2O2 signal , we undertook a series of transplantation experiments to generate chimeric DUOX morphant/wild-type [WT] larvae ( Figure 7 ) . In a pair of complementary experiments we transplanted cells from embryos previously injected with hsp:V12RASeGFP into embryos expressing LysC:DsRed , where either the donor or the host embryo had been previously injected with DUOX morpholinos . In this way we generated larvae with green transformed cells and red neutrophils in which either the transformed cells or their neighbors were deficient in generating H2O2 via DUOX ( Figure 7B and 7C; Video S13B and S13C ) . We found that , in both cases , neutrophils were drawn to the vicinity of transformed cells in numbers that were not significantly different from one another ( although less than for positive control hsp:V12RASeGFP-injected larvae—Figure 7D–7G; Video S13 ) , suggesting that both V12RAS over-expressing cells and neighboring host epithelial cells use DUOX to generate H2O2 that recruits leukocytes . LysC:DsRed embryos with transplanted control GAP43-eGFP-expressing cells showed no sign of immune cell recruitment to eGFP+ cells ( Video S14 ) . We observed , however , that in those chimeric embryos where transformed cells were deficient in DUOX , leukocytes , whilst drawn towards the clone , tended to skirt around the transformed cells , rather than over them as in the converse or positive control experiments ( Figure 7D–7F; Video S13 ) . Having revealed that innate immune cells are actively recruited from very early stages to sites of oncogene-transformed cells , and that they extensively interact with them , we sought to test the outcome of these interactions by preventing contact between these cells . We have done this in three complementary ways . First , we used morpholino knockdown of pu . 1 , as previously described [55] , to transiently block the differentiation of myeloid lineage cells; in such larvae , myeloid cells are delayed from appearance until 3 dpf ( [55] and our own observations ) . This approach directly tests the requirement of innate immune cells for growth of transformed cell clones . Second , we used a chronic exposure to the pan-NOX inhibitor DPI ( 36–60 hpf ) . And , third , we again generated DUOX morphants to specifically block the attractant signal . The latter two strategies carry the proviso that they may also influence V12RAS+ cell proliferation directly because of potential ROS requirements for RAS-transformed cell growth [56] , [57] . In all three of these treatments that prevent recruitment of immune cells to the transformed cells , we see a consequent reduction in numbers of V12RAS+ cells ( Figure 8A–8G ) , suggesting a significant trophic role for host innate immune cells during the earliest stages of transformed cell development . To test whether this reduction in numbers of transformed cells in the absence of immune cell recruitment is a consequence of increased apoptosis or reduced proliferative index , or both , we have immunostained for the cleaved ( active ) form of caspase 3 , which indicates almost no apoptosis in either pu . 1 morphant or control transformed cells over this period ( data not shown ) , but we do see a significant reduction in the proliferative index of transformed cells , as revealed by 6-h BrdU incorporation rate , from 76% in control V12RAS+ larvae ( n = 21 ) to 59% in pu . 1 V12RAS+ morphants ( n = 33 ) ( Figure 8H ) , suggesting that the immune cell trophic signal operates by enhancing cell proliferation .
What is immediately clear from our studies is the very early onset of an innate immune response toward transformed cells , almost as soon as the oncogenic transformation has commenced in otherwise normal tissue . Two distinct transformed lineages ( melanocytes and goblet cells ) are seen to recruit both neutrophils and macrophages ( and potentially other leukocytic lineages ) at stages as early as when clones consist of only singletons or doublets . These data indicate that oncogene-transformed cells , even as individuals , can emit cues that , directly or indirectly , trigger a host innate immune cell inflammatory response . Many clinical studies show that pro-inflammatory factors are elevated in the serum of cancer patients [62] . Studies in mouse models have also shown that oncogene-induced release of pro-inflammatory cytokines can act as a paracrine signal that might promote an inflammatory microenvironment in the surrounding tumor stroma , which in turn drives tumor growth [62] , [63] . We have also found in our V12RAS-driven zebrafish melanoma model that many pro-inflammatory factors are up-regulated in established tumor tissues ( P . Walker , M . Jones , S . He , C . Michailidou , N . Haud , et al . , unpublished data ) . Importantly , we also see up-regulation of pro-inflammatory factors in the early stage larvae , even before establishment of a mature tumor , suggesting that pro-inflammatory factors may play some role in regulating the initial phase of an oncogene-driven inflammatory response by paracrine mechanisms . An immediate question that arises from our in vivo observation concerns the nature of the signal ( s ) that direct leukocyte recruitment . Our data suggest that the key signal regulating leukocyte activation and recruitment towards transformed cells is DUOX-mediated H2O2 production , which is also the immediate damage signal that attracts neutrophils to wounds [26] . Studies in mammalian airway epithelium also suggest a regulatory role for enhanced H2O2 production in the host innate immune response to diverse bacterial triggers [64] . Thus , enhanced production of H2O2 might be a universal mechanism used by the host to sense the disruption of tissue homeostasis by various insults and to activate the appropriate innate immune response . The current data indicate that in this instance , DUOX is the major contributor to the synthesis of H2O2 at sites where V12RAS-transformed cells are dividing but clearly other NOXes may also contribute and , indeed , may play more significant roles where other cell lineages or transforming oncogenes are involved . Our transplantation experiments show that both oncogene-transformed cells and their disrupted neighbors contribute to the signal that attracts immune cells to these sites . It has previously been shown that oncogenic RAS-transformed cells suffer ROS stress [65] , but what is not clear is how individual malignant cells disrupt tissue homeostasis in order to trigger otherwise healthy , epithelial neighbors to do likewise . Perhaps , the upstream signal that directs DUOX activation in these cells is the same as that which triggers H2O2 synthesis by wound edge epithelial cells and will turn out to be a generic , rapid response to tissue disruption . In a wound scenario , any NOXes must already be expressed so that tissue damage merely triggers their activation . However , there is evidence that colon cancer cells up-regulate NOX1 [66] , and so it is possible that DUOX is up-regulated by transformed cells in our fish model also , although our qPCR data would suggest that this is not the case ( data not shown ) , and recent studies indicate that DUOX is constitutively expressed in zebrafish larval mucosal epithelia , including epidermis [67] . Also entirely unclear is how leukocytes might read the chemotactic H2O2 signal or some downstream consequence of it . There is no known receptor for H2O2 , but potentially H2O2 gradients might be “read” via intracellular sensors such as phosphatases that subsequently regulate the chemotactic response . Indeed , one such phosphatase , PTEN , which is known to have a pivotal role in cell migration , is modulated by exposure to H2O2 [68] , and other phosphatases clearly have an impact on growth factor signaling and might alter the chemotactic response in this way also [69] . Alternatively , there may be other extracellular ( or intracellular ) substrates that H2O2 modifies in some way to convert them into chemotactic factors or other modulators of cell motility [53] , [70] , and the genetic tractability of zebrafish offers good opportunities to uncover such mechanisms . Aside from a shared molecular mechanism that mobilizes host innate immune cells towards these “early tumors” and to wounds , our live imaging observations offer further support for , and refinement of , the well-established doctrine suggesting that “tumors are wounds that do not heal” [13] , [71] . While the earliest recruitment of leukocytes resembles that of an acute healing wound , as transformed clone size increases , so the time spent by leukocytes with the clone also increases until their behavior more resembles that of a chronic skin lesion where inflammation does not resolve [44] , [45] . This prolonged time spent in the neighborhood of transformed cells may lead—as well as to the changes in dynamic cell behavior we describe here—to phenotypic changes in leukocytes . Indeed , we see some leukocytes expressing the M2 marker , arginase1 , which indicates a more trophic function for those cells at the transformed cell site . The molecular nature of such trophic signals is not revealed by our studies , but clearly such transitions may significantly influence tumor progression , and the translucent zebrafish offers potential opportunities to live image these events in situ , using appropriate reporter fish lines . It has been shown in mouse models that tumor-recruited bone-marrow-derived cells are a heterogeneous population composed of many subtypes of leukocytes distinguished by their surface markers , but there is no definitive association between the presence of any individual subtypes and a particular outcome of tumor progression [47] . Definitive markers for cataloging the zebrafish larval myeloid cell subtypes are still subject to refinement . However , by combining several transgenic reporter fish lines for different myeloid lineage marker genes and cell morphology , our observations reveal four different subtypes of leukocyte that show a range of behaviors during their recruitment to , and interaction with , V12RAS+ cells , and suggest that multiple leukocyte lineages can participate in the earliest inflammatory response to transformed cells . Our data reveal transformed cell material in both neutrophils and macrophages , but macrophages appear by far the more significant phagocytes of these two leukocyte lineages . The high-resolution live imaging also enables us to observe the structures that transiently form between innate immune cells and transformed cells . For example , we see tethers linking leukocytes and transformed cells , and we speculate that these may play a “holding” role , enabling leukocytes to maintain a long-term association with transformed cells . Similar tethers , termed nanotubes , have been previously reported to play important roles in immune cell-cell communication in vitro [72] , and the same may be true for these links between immune cells and transformed cells in vivo . For the first time , to our knowledge , we have been able to observe the behaviors of various innate immune cell lineages as they interact with these developing transformed cell clones , and , using several complementary strategies , we go on to demonstrate that these interactions have significant impact , because without them , we see reduced proliferation and , as a consequence , fewer transformed cells , at least in the early stages we have examined . This demonstration of a trophic role is all the more dramatic because of our observation that when recruited to a site where transformed cells are growing , neutrophils and macrophages are able to phagocytose some of these cells , so there appear to be competing clearance and trophic roles for innate immune cells at these earliest stages of tumor initiation . However , at least , in simple terms of the numbers of surviving transformed cells , we show that the presence of innate immune cells at the lesion site is beneficial to early transformed cell growth . Our current function testing studies are acute experiments , but other approaches may enable longer term blocking of recruitment of innate immune cells , or more permanent genetic deletion of various leukocyte lineages , which will allow us to dissect precisely the contribution of each lineage to tumor initiation , clearance , and trophic support , from these early initiation stages all the way through to full-blown cancer . The in vivo , live observations extend our current understanding of the earliest surveillance steps whereby innate immune cells sense the presence of small numbers of oncogene-transformed cells in otherwise normal tissue . Besides providing opportunities to live image these early interactions , our zebrafish model also enables us to study the signals that regulate leukocyte recruitment to these oncogene-transformed cell precursors of tumors , and we reveal that H2O2 is a convergent signal for initiating the host inflammatory response to both oncogene-transformed cells and to wounds , thus further extending the parallels between cancers and healing wounds .
Adult zebrafish ( Danio rerio ) were maintained and crossed as previously described [73] . Strains included Lon AB ( obtained from Robert Kelsh , University of Bath ) ; Tg ( UAS:eGFP-H-RASV12 ) io6 [38] , Et ( kita:GalTA4 , UAS:mCherry ) hzm1 [37] , Tg ( fli:eGFP ) [43] , Tg ( LysC:DsRed ) [33] , and Tg ( BACmpo:eGFP ) i114 [31] , and embryos were maintained and staged according to standard protocols [73] . Sterile laser wounding was performed as previously described [22] with adaptation for 5-dpf larvae . In brief , zebrafish embryos at 5 dpf were anesthetized in 0 . 3% Danieau's solution containing 0 . 1 mg/ml Tricaine ( ethyl 3-aminobenzoate , Sigma ) . Instead of targeting the laser beam to the yolk , wounds were made to flank skin within the area illustrated in Figure 2 . Fin wounds were made as previously described [40] , using tungsten needles ( Fine Science Tools ) on 4-dpf embryos . For our transient V12RAS- and v-Src-induced cell transformation experiments , embryo injections were performed according to the published protocol [29] , [34] . In brief , for RAS transformation , LysC:DsRed embryos were co-injected with 1 nl of plasmid containing mitfa:V12RAS-mitfa:mCherry ( 25 ng/µl ) and 10 U I-SceI meganuclease at one-cell-stage . Embryos containing mCherry+ melanocytes clones were selected at 48 hpf for further live imaging analysis . For v-Src-induced cell transformation pBR-Tol2-UAS-GAP43-eGFP-SC-v-Src , pCS2-Gal4FF ( 25 ng/µl ) and Tol2 capped RNA ( 100 ng/µl ) were co-injected into one-cell-stage LysC:DsRed embryos at 2 nl per embryo . Equivalent amounts of pBR-Tol2-UAS-GAP43-GFP , pCS2-Gal4FF and Tol2 capped RNA mix were used for control injections . After injection , embryos developed in normal conditions , and at 3 dpf embryos with similar GFP+ cell clones were selected for live imaging . All the morpholinos were obtained from GeneTools . Splicing block morpholino against zebrafish DUOX ( 5′-AGTGAATTAGAGAAATGCACCTTTT-3′ ) , 0 . 4 mM , or standard control morpholino , 0 . 4 mM , was injected together with 0 . 2 mM p53 morpholino ( 5′-GCGCCATTGCTTTGCAAGAATTG-3′ ) into one-cell-stage embryos , as previously described [26] . We injected morpholino against zebrafish pu . 1 ( 5′-GATATACTGATACTCCATTGGTGGT-3′ ) as previously described [55] . For all RNA preparations we used phenol-chloroform extraction ( TRIzol , Invitrogen ) , and cDNA template was generated using SuperScript VILO cDNA synthesis kit ( Invitrogen ) . For morphotyping of embryos/larvae , RNA was prepared from 3-dpf , 5-dpf , control and DUOX morphant larvae . Knockdown efficiency was confirmed using the following primers: DUOX forward , 5′-ACACATGTGACTTCATATCCAG-3′ , and DUOX reverse , 5′-ATTATTAACTCATCCACATCCAG-3′ . DUOX-morpholino-mediated splice perturbation produced a 39-bp in-frame deletion within the peroxidase-like domain of DUOX as described before ( Figure S2 ) [26] . For RT-PCR of pro-inflammation genes , total RNA was prepared from pools of five each of RAS+ larvae and their RAS− siblings at 5dpf . The primers used were as described previously [74]: il8F 5′-TGTGTTATTGTTTTCCTGGCATTTC-3′ , il8R 5′-GCGACAGCGTGGATCTACAG-3′; cxcl1F 5′-GGCATTCACACCCAAAGCG-3′ , cxcl1R 5′-GCGAGCACGATTCACGAGAG-3′; ifn1F 5′-TCTTAATACACGCAAAGATGAGAACT-3′ , ifn1R 5′-GTCAGGACTAAAAACTTCAC-3′; tnfαF 5′-GCGCTTTTCTGAATCCTACG-3′ , tnfαR 5′-TGCCCAGTCTGTCTCCTTCT-3′; il1βF 5′-GCCTGTGTGTTTGGGAATCT-3′ , il1β R 5′-TGATAAACCAACCGGGACAT-3′; zfefαF 5′-CTGGTTCAAGGGATGGAAGA-3′ , zfefαR 5′-GAGACTCGTGGTGCATCTCA-3′ . For qPCR of V12RAS-associated pro-inflammation genes we heat shocked ( 39°C for 30 min ) 5-dpf WT and Tg ( hsp70l:eGFP-HRAS_G12V ) io3 larvae . After 6 h , total RNA was extracted from pools of 60 larvae , and qPCR was performed with LightCycler 480 II thermocycler ( Roche ) using the SYBR Green I Master kit ( Roche ) . The conditions for the reactions were as follows: 10 min at 95°C , followed by 40 cycles of 15 s at 95°C and 60 s at 60°C . For each sample , expression levels were analyzed using Q-Gene software , which expresses data as mean normalized expression . All the genes were normalized to expression of β-actin as an endogenous control . The plot is a mean of three different samples . Blastomere transplantation experiments were performed according to standard procedure [73] . In brief , embryos were injected at the one cell stage with pTol2-hsp:V12RASeGFP ( 25 ng/ul ) +Tol2 capped RNA ( 100 ng/ul ) with and without DUOX morpholino+p53 morpholino , and used as donors . Negative control donors were prepared by injecting pBR-Tol2-UAS-GAP43-eGFP+Gal4FF capped RNA+Tol2 capped RNA . Approximately 50 donor cells were transplanted into stage-matched WT LysC:DsRed host embryos or DUOX-morpholino-injected LysC:DsRed host embryos ( Figure 7A ) . Transplantations were carried out between 4 hpf and 5 hpf . After transplantation , healthy chimeric embryos were left to develop in Danieau's solution containing antibiotic Pen/Step in the 28 . 5°C incubator until 20 hpf , when all the chimeric embryos were heat shocked at 37°C for 1 h to induce expression of the V12RAS transgene . The chimeric larvae were repeatedly heat shocked for 1 h every 8 h . At 3 dpf , larvae were selected that had clones of superficial GFP-expressing cells for live imaging studies ( as described below ) . To generate positive mosaic control larvae , we injected 1 nl of pTol2-hsp:V12RASeGFP ( 25 ng/ul ) +Tol2 capped RNA ( 100 ng/ul ) into a single cell of stage 16- to 32-cell Tg ( LysC:DsRed ) embryos . Whole mount fluorescent in situ hybridization was performed as described before [75] , with some modification . To generate a zebrafish arginase1 probe we first cloned the gene from pooled zebrafish cDNA into pCRII-TOPO ( Invitrogen ) , using the primers Arg1F 5′-ATGATGAAGATGAAGAGCCTTAGCG-3′ , Arg1R: 5′-TTATGGATTTGGCATTTTGTAATCTGGG-3′ . The pCRII-zfArg1 construct was linearized using BamHI , and T7 polymerase was used to generate DIG-labeled ( Roche ) probe . V12RAS+ larvae , 5 dpf , and their RAS− siblings were used for in situ analysis . Hybridization and post-hybridization washes of 5-dpf larvae were carried out at 68°C and the signal developed using a Fluorescein-Tyramide TSAplus kit ( PerkinElmer ) according to the user manual , with some modification according to published protocol [75] . After the in situ signal had developed , samples were re-fixed in 4% PFA for 30 min then washed in PBST for 3× 10 min before carrying out subsequent immunostaining to reveal leukocytes and RAS+ cells . Immunostaining for L-plastin was performed as previously described [40]; in brief , embryos/larvae were fixed in 4% PFA plus 0 . 2% Triton X-100 at room temperature for 2 h prior to rinsing , blocking , and incubation with rabbit polyclonal anti-L-plastin antibody ( 1∶500 ) overnight at 4°C . Other primary antibodies used in this study were mouse anti-TNFα ( 1∶10 ) ( ab1793 , Abcam ) and mouse anti-RAS ( 1∶100 ) ( #610001 , BD Transduction Laboratories ) . Subsequently , either a Cy3- or Cy5-conjugated secondary antibody ( Jackson Laboratories ) was used to reveal primary antibody localization . For analysis of leukocyte recruitment , embryos were segregated into RAS+ and RAS− groups , and L-plastin+ cells in the same flank skin region were counted for each embryo using a Zeiss Axiophot microscope . For BrdU labeling , we incubated 50-hpf larvae in 10 mM BrdU in E3 fish water for 6 h . Following fixation in 4% PFA , larvae were treated with 2 N HCl for 20 min , before they were processed for immunofluorescence with anti-BrdU antibody ( Sigma ) . Cy3- or Cy5-conjugated secondary antibody was used to reveal BrdU uptake in GFP+ ( i . e . , V12RASeGFP or control eGFP-expressing ) cells in either pu . 1 morphant or control larvae . For all of our live imaging studies , larvae were mounted on their sides in 1 . 5% low melting agarose ( Sigma ) , in a glass-bottomed dish , filled with 0 . 3% Danieau's solution containing 0 . 01 mg/ml Tricaine . The climate chamber covering the microscope stage was set at 28°C . Images were collected using a Leica SP5-AOBS confocal laser scanning microscope attached to a Leica DM I6000 inverted epifluorescence microscope with a 63× glycerol lens . Videos were recorded at 1 frame/min and were exported from Volocity as QuickTime movies using the Sornson3 video compressor to play at 6 frames/s . 3-D reconstruction was performed in Volocity before export . In order to investigate leukocyte recruitment in the absence of H2O2 , larvae were pre-incubated in 0 . 3% Danieau's solution supplemented with 100 µM DPI ( Sigma ) in 1% DMSO , 60 min before and throughout the imaging period . DDC ( Sigma ) was used at 100 µM , and again embryos were treated 60 min before and throughout the imaging period . H2O2 imaging using a live cell fluorescein dye was performed as previously described [26]; in brief , 4-dpf RAS+ larvae and their control siblings were loaded for 60 min with 50 µM acetyl-pentafluorobenzene sulphonyl fluorescein ( Calbiochem Merck ) in 1% DMSO in 0 . 3% Danieau's solution and imaged as above . To test the dye efficiency , control caudal fin incisional wounds were imaged in WT larvae . Zebrafish larvae with V12RASeGFP+cells or control eGFP+ cells , and V12RASeGFP+/DUOX morphants were incubated in Danieau's medium containing 200 mM DMPO ( Enzo Life Sciences ) from 48 hpf to 56 hpf , then fixed in 4% PFA overnight at 4°C . Larvae were subsequently processed for immunostaining using rabbit anti-DMPO ( 1∶500 ) ( ALX-210-530-R050 , Enzo Life Sciences ) , as described above for standard primary antibodies , in order to reveal ROS exposure . Cy3-conjugated secondary antibody ( Jackson Laboratory ) was used to reveal the primary antibody . All the time-lapse movie quantification and tracking analysis was done using Volocity 4 . 0 . 4 or 5 . 0 . 2 ( PerkinElmer-Improvision ) . Individual LysC:DsRed+ cells were identified using automatic “find objects use intensity” and “color the object” functions in Volocity for all the time points to generate a “footprint map . ” The final “footprint map” was manually corrected by de-selecting the green fluorescent RAS+ cells . Cell size was calculated using the “find objects use intensity” function in Volocity . Cell tracks were generated using the “manual track the object” function in Volocity , which automatically calculates the average cell speed and MI ( MI = D/T , where D is the shortest linear distance between the start and end point of the migration path and T is the total distance traveled by the cell ) . MI reflects a cell's directionality , with the highest MI = 1 , indicating highly directional migration . Each migrating cell was tracked as long as it could be distinguished from other migrating cells and only if it had been present in the movie for more than ten time points . Cell tracks were then overlaid onto individual images from movies . “Retention time” of leukocytes in the region of RAS+ cells was defined as the duration of time that a leukocyte exhibited direct contact with a RAS+ cell . “Retention time” for a wound response was defined as the duration of time that a LysC:DsRed+ cell stayed within the central wound area ( Figure 2B insert highlights this zone ) . Embryos at the one cell stage were injected with control , DUOX+p53 , and pu . 1 morpholinos at 0 . 2–0 . 8 mM as described above . A complementary pharmacological approach involved incubation with 50 µm of the NOX inhibitor DPI from 36 hpf to 60 hpf—higher concentrations or longer treatment times with this drug disrupt embryo development , and so are not suitable for our clone analysis ( data not shown ) . At 3 dpf for the morphant studies and 60 hpf for the DPI-treated embryos , larvae were fixed in 4% PFA for 1 h at room temperature , washed briefly in PBS , and mounted laterally to count numbers of V12RASeGFP+ cells in flank skin by comparison to controls . All the data were analyzed ( Prism 4 . 1 , GraphPad Software ) using either an unpaired two-tailed Student's t test or Mann Whitney U test for comparisons between two groups , and one-way ANOVA with appropriate post-test adjustment for multiple group comparisons . | The translucency of zebrafish larvae allows us to live image the earliest dynamic interactions between host innate immune cells and oncogene-transformed cell clones as they first establish themselves as the precursors of full-blown cancer . These early associations manifest via cytoplasmic tethers between an immune cell and a transformed cell , and occasional phagocytic engulfment . Immune cells are first attracted to transformed cells at surprisingly early stages , before transformed cells have had a chance to form clones and are thus still singletons or doublets . We show that the key attractant is hydrogen peroxide ( H2O2 ) , which was also recently shown to be the essential early damage signal responsible for drawing neutrophils to wounds . Tissue transplantation experiments allow us to test which cells are responsible for generating the H2O2 attractant , and we show that both transformed cells and their otherwise healthy neighbors contribute . Blocking H2O2 synthesis , either pharmacologically or by morpholino-mediated knockdown of DUOX , the enzyme responsible for H2O2 synthesis in larval skin , very significantly reduces the numbers of neutrophils and macrophages drawn to transformed cell clones , and this results in reduced numbers of transformed cells , suggesting that innate immune cells play a trophic and/or support role in early transformed cell growth . |
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KSHV is the causative agent of Kaposi sarcoma ( KS ) , a spindle-shaped endothelial cell neoplasm accompanied by an inflammatory infiltrate . To evaluate the role of KSHV vFLIP in the pathogenesis of KS , we constructed mice with inducible expression of vFLIP in endothelial cells . Abnormal cells with endothelial marker expression and fusiform appearance were observed in several tissues reminiscent of the spindle cells found in KS . Serum cytokines displayed a profound perturbation similar to that described in KSHV inflammatory cytokine syndrome ( KICS ) , a recently described clinical condition characterized by elevated IL6 and IL10 . An increased myeloid component with suppressive immune phenotype was found , which may contribute to functional changes in the microenvironment and cellular heterogeneity as observed in KS . These mice represent the first in vivo demonstration that vFLIP is capable of inducing vascular abnormalities and changes in host microenvironment with important implications for understanding the pathogenesis and treating KSHV-associated diseases .
Kaposi sarcoma herpesvirus ( KSHV ) , also called human herpersvirus 8 ( HHV-8 ) , one of the most recently discovered human oncoviruses [1] , displays tropism for different cell types and a dual oncogenic role , both in lymphomagenesis and vascular oncogenesis . KSHV is specifically associated with Kaposi sarcoma ( KS ) and two B-cell lymphoproliferative diseases , namely primary effusion lymphoma ( PEL ) and a large subset of cases of multicentric Castleman’s disease ( MCD ) [1–3] . KSHV is also associated with KSHV inflammatory cytokine syndrome ( KICS ) , a newly described clinical condition characterized by systemic illness , poor prognosis , elevated KSHV titers , increased levels of viral IL6 and IL10 comparable to those seen in KSHV–MCD but lacking the characteristic lymphadenopathy of KSHV–MCD [4 , 5] , and KSHV-associated hemophagocytic syndrome ( VAHS ) , an extremely rare syndrome reported in immunocompromised patients with MCD and markedly elevated levels of serum human IL6 [6] . KSHV has been found associated also with POEMS syndrome , a rare multisystemic nosological entity characterized by polyneuropathy , organomegaly ( particularly cardiomyopathy ) , endocrinopathy , monoclonal gammopathy and skin lesions [7]; however , a role for KSHV in this disease is controversial , and POEMS may be part of the spectrum of the inflammatory abnormalities seen in MCD , whether KSHV-associated or not . Similarly to other related herpesviruses , there is dependency on latency for transformation , although this dogma encountered exceptions and has been subjected to debate [8–11] . KSHV genes regulating viral genomic persistence and capable of inducing cellular transformation are transcribed during latency ( i . e . , LANA , v-cyclin , vFLIP ) , and the KSHV mode of infection is predominantly latent in KSHV-induced tumors [12] . Experimental data indicate a role for the viral FLICE-inhibitory protein ( vFLIP ) in KSHV pathogenesis , as it is a latent gene capable of activating NF-κB [13 , 14] , a hallmark cellular pathway constitutively active in PEL and indispensable for the maintenance of lymphoma cell survival [15–17] . FLIP proteins are a group of cellular and viral proteins identified as inhibitors of death-receptor ( DR ) -induced apoptosis [18 , 19] . They contain two death effector domains ( DED ) capable of inhibiting DED-DED interactions between FAS-associated protein with death domain ( FADD ) and pro-caspase 8 and 10 within the death-inducing signaling complex ( DISC ) responsible for DR-induced apoptosis [20] . Based on the homology of KSHV vFLIP with cFLIP proteins , it has been thought that vFLIP becomes part of the DISC , preventing the recruitment and processing of procaspase 8 and , thereby , FAS-induced apoptosis [19] , although there is little experimental evidence supporting this direct role in apoptosis inhibition . Nonetheless , it is clear is that vFLIP directly binds to IκB kinase ( IKK ) γ , inducing IKKα/β phosphorylation , IκBα degradation and p100 cleavage , resulting in the activation of both the classical and alternative NF-κB pathways [13 , 14 , 21] . Another established function of vFLIP is inhibition of cell death by blocking autophagy [22] . Several groups have developed mice expressing vFLIP in B-cells [23–25] . Among these , our group used a Cre-Lox recombination approach to express vFLIP in all B-cells and specifically in germinal center B-cells , confirming its role in lymphomagenesis and defining the in vivo immunological functions of vFLIP as an abrogator of germinal center formation and immunoglobulin ( Ig ) maturation [23] . Tumors occurring in mice expressing vFLIP in B cells retain major features of PEL , namely B-cell origin , as formally demonstrated by the presence of monoclonal Ig gene rearrangements , and remodeling of BCR with downregulation of B-cell markers , including CD19 and lambda . However , these tumors were also characterized by expression of histiocytic/dendritic cell ( DC ) antigens , consistent with transdifferentiation from B-cells into the myeloid lineage , without excluding a coexisting paracrine effect on the surrounding myeloid cells [23] . Notably , KS lesions are characterized by the presence of inflammatory cells , including numerous histiocytes [26] . Thus , induction of myeloid cell proliferation by vFLIP could be part of the cellular events and microenvironment alterations that occur during KS pathogenesis . The role of vFLIP in vascular oncogenesis is suggested by the in vitro observations that vFLIP induces spindle cell morphology and expression of inflammatory cytokines in endothelial cells and phosphorylation of STAT1 and STAT2|[27–29] . Both spindling and a proinflammatory microenvironment are key features of KS , defined as a chronic inflammation-associated malignancy due to the presence of spindle-shaped endothelial cells , slit-like neovascular structures , and abnormal vascular spaces with extravasation of red blood cells , as well as variable quantities of infiltrating inflammatory cells and secretion of angiogenic and inflammatory cytokines such as VEGF , PDGF , bFGF , TGFβ , IL1β , IL6 and INFγ [30] . However , the role of vFLIP in the initiation of KSHV-related vascular pathogenesis , if any , is largely unknown . A substantial number of studies have indicated that the cell of origin of KS spindle cells is of endothelial origin as these cells express both blood ( e . g . , CD34 ) and lymphatic ( e . g . , VEGFR3 , podoplasmin , LYVE-1 , Prox1 ) endothelial cell markers ( BEC , LEC ) [31–34] and display a gene signature that falls in between the two cell types , albeit closer to LEC [35] . KSHV can infect both BECs and LECs and is capable of reprogramming their transcriptomes to make BECs more alike LECs and viceversa [35–37] . Therefore , to address the role of vFLIP in vascular oncogenesis , we generated mice that express vFLIP under the control of VE-Cadherin promoter , which has been reported to be active in both BECs and LECs [38] . These transgenic ( TG ) mice showed systemic endothelial alterations with increased spindle-like cells and changes in serum cytokines , reminiscent of certain features of KS and KICS . We also observed remodeling of myeloid differentiation toward cell types known to have implications in host microenvironment , tumor immune evasion , angiogenesis and vascular lesion development .
We generated mice expressing vFLIP in endothelial cells by using a recombinant inducible system . Previously generated conditional mice for vFLIP ( ROSA26 . vFLIP knock-in mice ) [23] were bred with mice expressing cre recombinase in the form of a fusion protein with the estrogen receptor under the transcriptional control of VE-Cadherin promoter ( Cdh5 ( PAC ) . creERT2 mice ) [39] , thus resulting in vFLIP expression in endothelial cells upon tamoxifen treatment ( Fig . 1A ) . Before generating ROSA26 . vFLIP;Cdh5 ( PAC ) . creERT2 TG mice , we tried to constitutively express vFLIP in endothelial cells by crossing ROSA26 . vFLIP mice with mice expressing cre recombinase under the control of Tie2 promoter . However , embryonic lethality was observed , suggesting that constitutive expression of vFLIP is detrimental for embryogenesis and incompatible with life . Instead , the inducible ROSA26 . vFLIP;Cdh5 ( PAC ) . creERT2 TG mice ( carrying both cre and vFLIP ) were born at the expected Mendelian frequency and were indistinguishable from their wild-type ( WT ) littermate controls ( carrying only vFLIP ) in terms of fertility and developmental features . Expression of vFLIP was evaluated in 2–3 month-old mice , approximately one month after intra-peritoneal ( i . p . ) injection of tamoxifen in both TG and littermate control mice . vFLIP expression was detected at the RNA and protein level in lung , spleen , liver and heart ( Fig . 1B ) . The level of vFLIP expression was assessed by quantitative real-time RT-PCR in lung , spleen , liver and heart derived from both TG and controls mice , as well as in BC3 PEL cell line and primary KS with lymph node involvement ( Fig . 1C ) . As expected , the highest level of expression was observed in BC3 , where all the cells harbor KSHV multiple copies of the viral genome . The splenic fraction derived from B-cell-specific TG mice show also high level of vFLIP expression , comparable with BC3 , and this reflects with the high percentage of B-cells in the spleen and the fact that vFLIP expression is controlled by a strong promoter ( i . e . , CD19 ) . Instead , the endothelial-specific TG mice express lower levels of vFLIP , although comparable with vFLIP expression seen in primary KS . This is consistent with the percentage of endothelial cells in the organs analyzed , which is lower than the percentage of splenic B-cells . Since antibodies to vFLIP are not adequate for immunohistochemistry or flow cytometry , we monitored transgene expression using antibodies to EGFP , which is expressed in a common transcript with vFLIP due to the insertion of an IRES between the two gene sequences ( Fig . 1A ) . EGFP was detected by immunohistochemistry in cells lining vascular spaces and with the morphologic appearance of endothelial cells in different organs , including intestine ( S1A Fig . ) ; these cells were also positive for the endothelial marker CD34 . While B-cell-specific TG mice expressed vFLIP in the splenic B-zone as expected , the endothelial-specific TG mice expressed vFLIP in the vascularized interfollicular area ( S1B Fig . ) . The endothelial identity of transgenic cells and the endothelial specificity of vFLIP expression was confirmed by flow cytometry performed in the heart , where EGFP was expressed in the vast majority ( 70 . 8% ± 1 . 4% ) , of endothelial cells defined as CD45−CD11b−CD31+ ( Fig . 1D , middle panel ) , but not in splenic B-cells ( Fig . 1E , middle panel ) . Conversely , B-cell specific vFLIP TG mice expressed EGFP in splenic B-cells ( Fig . 1E , right panel ) , but not in endothelial cells ( Fig . 1D , right panel ) . Taken together , these data showed that vFLIP had the expected pattern of expression restricted to endothelial cells . Virtually all organs and tissues were affected by pathological changes ultimately related to endothelial dysregulation . Numerous elongated cells frequently lining poorly formed vascular spaces was diffusely found throughout several organs , but most notably in the myocardial parenchyma of TG mice . In the heart , these endothelial cells lined the capillaries surrounding individual myofibers , but also proliferated into the parenchyma , expressed vFLIP and many retained endothelial markers ( CD34 and/or CD31 ) and expressed Ki67 ( Fig . 2A ) . Similar findings with the presence of spindle cells , and plump endothelial cells lining vascular spaces , were found in several organs including skeletal muscles ( Fig . 2B ) , brown fat ( Fig . 2C ) and brain ( S2 Fig . ) . These proliferating endothelial cells do not express the lymphatic marker PROX1 , in spite of successful staining of lymphatic endothelial cells in sites where these normally occur including skin , intestines and splenic red pulp ( S3A-S3C Fig . ) . An abnormal perineurial proliferation of endothelial-like cells was found in several tissues in the TG mice , including perirenal capsule , diaphragm muscle , salivary gland , pancreas , but not in the controls ( Fig . 3 ) . The abnormalities observed in the pancreas prompted us to check for signs of endocrinopathy ( e . g . , diabetes ) ; serum glucose levels were slightly increased , but the difference was not significant ( Fig . 3 ) . On the abdominal side of diaphragm and in the peripancreatic region , few nerve bundles were also surrounded by hyperplastic perineurial cells , mixed inflammatory cells , lymphocytes and plasma cells and inflammation extended to the adjacent connective tissue . Chronic inflammation , documented with the presence of mixed cell infiltrate of neutrophils , lymphocytes , plasma cells and histiocytes , was found in several tissues , including the peritoneum ( Fig . 4A ) , meninges ( Fig . 4A ) , kidney and skeletal muscle . Both kidneys showed subcapsular areas with numerous spindle cells ( Fig . 3 ) , and the perirenal fat was infiltrated by neutrophils , lymphocytes , plasma cells and histiocytes . Extra-medullary hematopoiesis , with both erythroid and myeloid hyperplasia , was present in the spleen and liver . Peripheral blood analysis showed that TG mice have left-shift ( i . e . , high metamyelocytes and bands with normal neutrophil count ) , suggesting a demand for neutrophils that exceeded their production and release , a scenario usually seen in case of chronic inflammation at different anatomic sites as observed in our TG mice . The mice were viable after tamoxifen administration , but starting as early as few weeks after induction they developed the pathological abnormalities here described and by the age of 3–4 months more than 60% of mice had died ( Fig . 4B ) as result of a systemic illness that comprised myocardial , meningeal , skeletal muscular , peritoneal and perineurial pathological changes . Although i ) the pattern of cytokine perturbation indicates the existence of M2-type polarization , which eventually favors immune suppression and tumor immune evasion rather than autoimmunity , [ii ) vFLIP does not appear to be a particularly immunogenic protein and iii ) KSHV , in general , has developed a wide array of strategies to evade the host immune responses , the mice were not exposed to the transgene during their embryonic development and , thus , they could have theoretically developed immune response toward vFLIP , resulting in a pseudo-autoimmunity that could partially account for the pathological findings and the poor mouse overall survival . Thus , we assessed the presence of a humoral immune response against vFLIP by immunoblotting , but no cross-reactivity was found between a pool of mouse sera isolated from seven TG mice and whole cell lysates derived from lung , spleen , liver and heart of both TG and control mice ( Fig . 4C ) . In vitro ectopic expression of vFLIP in either endothelial or B-cells has been shown to confer a myeloid-prone gene expression profile with production of cytokines that have potential tropism for myeloid cells [28 , 40] . To assess whether in vivo expression of vFLIP is capable of exerting similar effects , a panel of fourteen cytokines and growth factors ( IL10 , IL6 , INFγ , IL1β , IL12p70 , TNF , IL4 , IL2 , IL13 , GM-CSF , Phospho Stat1 , RANTES , IL12/IL23p40 , MCP1 ) was tested in serum samples collected from mice one month after vFLIP induction by tamoxifen ( Fig . 5 ) . We used a flow cytometry bead-based assay , which provides quantitative data and is linear within a large range of concentration ( from 30 fg/ml to 200000 fg/ml ) ( S4 Fig . ) . Compared to control mice , vFLIP TG mice showed increase of IL10 , IL6 , IL2 , IL13 , INFγ , TNF , MCP1 and RANTES . These findings are in line with in vitro data on gene expression profiling obtained in PEL and endothelial cells that ascribed to vFLIP the ability to activate the expression of several cytokines and growth factors potentially implicated in remodeling of the tumor microenvironment by myeloid cells [28 , 40] . Noteworthy , the systemic illness with poor prognosis and the profound changes in cytokines profile , particularly with increased IL6 and IL10 , are aspects similar to those described for MCD and KICS . To gain insights into the mechanism and consequences of the cytokine storm and further assess the effect of transgene expression in vivo , myeloid differentiation was analyzed by flow cytometry with particular emphasis at the cell subsets that could be influenced by or responsible for the observed cytokine perturbation . A large increase in number of CD45+CD11b+Gr1+/− cells was found in lung , spleen , liver and heart , both in endothelial and B-cell specific vFLIP TG mice ( Fig . 6A ) . A more detailed analysis revealed that the myeloid subpopulation preferentially expanded was Ly6G+Ly6Cint ( Fig . 6B ) . These cells were large ( FSChigh ) , have high granularity ( SSChigh ) and expressed high levels of Gr1 , therefore they likely represent granulocytic myeloid derived suppressor cells ( MDSCs ) ( also called polymorphonuclear-MDSCs , PMN-MDSCs ) , as opposed to monocytic-MDSCs ( Ly6GintLy6C+ ) that lack granularity and express lower level of Gr1 [41–43] . Moreover , Ly6G−Ly6C− cell population , which was expanded only in lung and heart , represented cells that did not express Gr1 , were smaller , had no granularity and potentially represent tumor associated macrophages ( TAM ) or DCs based on immunophenotype , although a functional characterization would be necessary to confirm this . Considering that the endothelial cells are a component of the hematopoietic niches , we compared bone marrow from control and TG mice , but no abnormalities in myeloid or lymphoid hematopoiesis were found that could be ascribed to the expression of vFLIP in endothelial cells . The tumor microenvironment has been shown to be deeply affected by myeloid cells , including CD11b+Gr1+ cells , which are able to produce soluble factors , such as Bv8 , that influence angiogenesis , extracellular matrix remodeling , anti-VEGF resistance and mobilization of additional myeloid cells toward premetastatic sites [44] . Therefore , we checked whether the expanded myeloid cell subpopulations were differentially expressing any these factors . No differences were observed in TG versus WT mice in the levels of expression of Bv8 , VEGF and MMP9 , indicating that these cell subsets exert their function in vFLIP-mediated pathogenesis through different mechanisms .
In this study , we have investigated the effect of inducible recombinant vFLIP expression in endothelial cells to model KSHV-associated vascular pathogenesis as observed in KS . Mice developed pathological abnormalities with systemic changes and appearance of elongated spindle-like endothelial cells , mimicking aspects of KS and other KSHV-associated diseases . Mice developed a profound proinflammatory phenotype with perturbation of serum cytokines , similarly to KICS , as well as expansion of myeloid cells , which unveiled a key role of vFLIP in initiating a cascade of events that lead to changes in host microenvironment , ultimately favoring tumor immune evasion , angiogenesis and tumor progression during KSHV pathogenesis . Given the evidence that KSHV can infect both BECs and LECs [35–37] , and vFLIP induces spindling of endothelial cells in vitro [27] , we tested the hypothesis that in vivo expression of vFLIP in endothelial cells would lead to the development of KS-like disease . Mice developed vascular abnormalities with the presence of spindle cells expressing endothelial antigens in virtually all organs , but , unexpectedly , not in the skin , which is the most common location for KS in humans . While the reasons for this finding are unclear , other viral genes are likely to contribute to the many aspects of KSHV pathogenesis in humans in the context of natural infection , including specific organ involvement [45] . Nevertheless , the endothelial-specific vFLIP TG mice generated showed a proliferation of spindle cells , and a proinflammatory phenotype , indicating that this characteristic of KS can be induced by vFLIP alone . In this setting , vFLIP induces expression of cytokines including those that can result in formation of autocrine loops . For example , there is increased production of IL2 , and the IL2 receptor alpha chain is upregulated by NF-κB [46] , which is turn is activated by vFLIP . Similarly , there is an increase of TNF production in the vFLIP TG mice , and the TNF receptor ( CD120B ) molecule is induced by NF-κB [47] , which in turn can further activate the NF-κB pathway creating a positive regulatory loop . However , we did not obtain complete KS phenotype , so it is likely that cooperation with other KSHV proteins ( e . g . , vGPCR , LANA , vCyclin , vIL-6 , K1 ) and/or noncoding transcripts ( e . g . , miR 17–92 , miR K12-7 ) , which are co-expressed in KS and relevant for vascular tumorigenesis , are required for full pathogenesis [48–52] . In this regard , previously reported TG mice for vGPCR and vCyclin also failed to fully recapitulate KSHV-associated vascular diseases although tumorigenic properties of the viral products were otherwise demonstrated [48 , 49 , 53–58] . Expression of multiple viral products has been achieved in B-cells using the latency locus under the control of the native viral promoter , but specific expression in endothelial cells has not been assessed [24] . Our mouse model contrasts with previous TG models of KSHV-encoded genes in the extent of a proinflammatory phenotype . The severity and systemic nature of the endothelial changes were reminiscent of certain features of the POEMS syndrome [7] . Although the etiopathogenesis of this syndrome is still largely unknown , a role for KSHV has been suggested by few studies . First , there is frequent association with KSHV-associated MCD and angioma formation . Second , in POEMS syndrome there is overproduction of proinflammatory cytokines , including TNFα , IL1β , IL10 , IL6 , VEGF [59] , similarly to what is observed in MCD and KICS , suggesting that these three clinical entities partially overlap . Moreover , POEMS is characterized by the presence of monoclonal Ig , usually IgG or IgA with lambda light chain , and KSHV encodes for viral IL6 that is functionally active on human myeloma cells [60] . KSHV was found in the lymphoid cells of MCD , as well as in the microvenular hemangioma , the pathognomonic endothelial lesion , positive for CD34 , CD31 , LYVE-7 and Prox-1 , that characterizes this syndrome [61–64] . However , other studies failed KSHV detection in this syndrome [65 , 66] . Similarities of POEMS with ROSA26 . vFLIP;Cdh5 ( PAC ) . creERT2 TG mice included: i ) neuropathic symptoms , which in mice are likely related to hyperplasia of the perineurium around nerve bundles in spinal nerve roots , ganglion and skeletal muscle , ii ) systemic presence of elongated endothelial cells , particularly in the heart , which is also increased in size , reminiscent of organomegaly seen in POEMS iii ) proneness to develop endocrinopathy ( e . g . , diabetes ) , as suggested by increased glycemic levels observed in TG mice , and iv ) overproduction of proinflammatory cytokines , including TNFα , IL10 , IL6 . While the association between POEMS and KSHV remains controversial , a role for KSHV in KICS is well-established and the cytokine storm observed in the vFLIP TG mice is very reminiscent of that seen in this syndrome [4 , 5] . We also observed remodeling of myeloid differentiation with expansion of CD11b+Gr1+Ly6G+Ly6C+/− cells , phenotypically corresponding to granulocytic myeloid derived suppressor cells ( MDSCs ) . Under physiological conditions , immature myeloid cells from the bone marrow differentiate into granulocytes , macrophages or dendritic cells ( Fig . 7 ) . Tumors are capable of secreting several factors in the tumor microenvironment responsible for changes in myeloid differentiation that ultimately can favor tumor immune evasion , angiogenesis and tumor progression . M1 toward M2 polarization is favored by increase in IL10 and reduction in IL12 , which lead to reduced Th1 activity and tumor immune evasion , along with angiogenesis and tumor promotion . The main myeloid subpopulations responsible for these effects in tumors are TAM , MDSC , and suppressive DC ( Fig . 7 ) . Aberrant CD11b+Gr1+ myeloid cells have also been found in the mouse placenta , where most likely exert immune suppressive and angiogenetic functions to promote immune tolerance and growth of the developing embryo [67] . Mouse MDSCs consist of two major subsets: granulocytic CD11+Ly6G+Ly6Clow cells and monocytic CD11b+Ly6G+/−Ly6Chigh cells ( M-MDSCs ) , which differ in their immunosuppressive mechanisms [43 , 68] . MDSCs derive from the bone marrow hematopoietic precursors due to the altering of myelopoiesis by chronic inflammatory mediators [69] , such as STAT1 and NF-κB , signaling pathways known to be vFLIP targets ( 56 , 59 ) . MDSCs exert their immunosuppressive functions primarily by inhibiting antitumor T-cell function . Moreover , MDSCs are able to secrete angiogenic factors , matrix metalloproteinases and cytokines promoting neoangiogenesis and tumor growth and skewing immune responses towards protumoral Th2-type with activation of Tregs . Thus , MDSCs play a central role in the development of immunosuppressive tumor microenvironment [43] , as also emphasized by the fact that functionally active tumor-specific CD8+ T-cells can develop anergy or undergo apoptosis when adoptively transferred into a microenvironment containing MDSCs; moreover , depletion of MDSCs restore CD8+ T cell function , thus confirming their role in induction and maintenance of host immunosuppression [41] . The cooperation between chronic inflammation and myeloid cell expansion is particularly relevant . In our vFLIP TG mice there is evidence of chronic inflammation at different anatomic sites , sustained also by left-shift in myeloid differentiation . Moreover , vFLIP transcriptome , as defined by in vitro gene expression profiling of both vFLIP-expressing endothelial cells and PEL [28 , 40] , highlights the fact that vFLIP activates several proinflammatory cytokines directly implicated in tumor microenvironment and remodeling of myeloid cells , particularly IL4 , IL10 , IL6 , IL13 , TGFβ , CCL5/RANTES , IL2 , IL1β , G-CSF , similar to those seen in our in vivo data . The myeloid phenotype observed in our vFLIP TG mice , with expansion of phenotypically bona fide granulocytic-MDSCs , is the first demonstration that vFLIP exerts in vivo induction and remodeling of myeloid differentiation with changes in critical components of the microenvironment toward a proinflammatory , angiogenic and immunosuppressive effect . The aberrant myeloid differentiation seems to be a consequence of vFLIP-mediated perturbation of cytokine profiles; once the microenvironment is polarized toward M2 , development of MDSCs rather than Th1 activity is favored . In turn , MDSCs , through the upregulation of molecules such as VEGF , Bv8 and MMP9 , can favor angiogenesis , tumor progression and tumor immune evasion ( Fig . 7 ) . Additional studies are necessary to dissect whether this cytokine storm is produced by myeloid cells or , alternatively , by the endothelial cells with the myeloid cells being a target of this cytokine overproduction . However , the myeloid phenotype with expansion of CD11b+Gr1+cells was observed in both endothelial and B-cell specific vFLIP TG mice , therefore it likely represents myeloid cells chemotactically recruited by the ectopic expression of vFLIP in either cell type , which , in turn , precedes the expression of cytokines known to have tropism for myeloid cells . In addition to vFLIP’s ability to impair GC formation and Ig maturation , this change in cytokine profile with remodeling of myeloid differentiation might represent a novel mechanism developed by KSHV to achieve immune evasion by altering the microenvironment to prevent immune recognition of KSHV-infected cells . Considering that Th1-type responses promote cellular immunity against intracellular pathogens and tumors , particularly meaningful is the evidence that KSHV as oncovirus has developed mechanisms to induce Th2 polarization and sabotage host immunity through manipulation of the microenvironment . Interestingly , also KSHV miR-K12-7 induces the expression of IL6 and IL10 [70] , which by inhibiting DC maturation protect PEL from host immune recognition [71] and simultaneously act as independent growth factors for these cells [72 , 73] . It is likely that myeloid differentiation is also perturbed in KSHV-infected individuals , with M2 polarization and impairment of Th1 activity . Although there is need for prospective studies on myeloid cells in KSHV-infected patients , quantitative and functional defects of peripheral blood DC and monocytes with reduced IL12 and increased IL10 were reported as becoming even more pronounced in advanced stages of KS [74] . Moreover , KSHV-specific CTLs are very rare in patients who progress to KS , supporting the role of Th1 immune responses in controlling KSHV replication and transformation [75] . Finally , the cytokine profile from patients with KSHV-associated disease further sustains the hypothesis based on our in vivo finding that vFLIP-induced M2 polarization of the microenvironment ( with increased IL10 , IL13 , IL4 , INFγ and reduction in IL12 ) is critical for KSHV pathogenesis . KSHV is associated with KS in which tumor identity has been made extremely puzzling by the presence of a rich myeloid component , as well as KICS and MCD , both associated with inflammatory cytokines . Our findings suggest this phenomenon is a result of vFLIP-driven remodeling of the microenvironment through a paracrine effect due to the secretion of myeloid-stimulating factors from vFLIP-expressing endothelial or B-cells ( Fig . 7 ) . Most macrophages in KS lesions do not contain KSHV , largely favoring a paracrine effect , although rare cells co-express LANA and histiocytic antigens [76] . In conclusion , we have revealed a previously unknown function for vFLIP in inducing in vivo expansion of the myeloid compartment with the emergence of a cellular component of immunosuppressive phenotype . This has important implications for the pathogenesis of KSHV-associated malignancies that invariably display a rich myeloid inflammatory infiltrate , which remains poorly characterized . The profound myeloid phenotype induced by vFLIP supports the key role vFLIP has in contributing to host immune dysfunction with development of tumor immune evasion during KSHV pathogenesis . The high-level coordination between cellular and soluble components seen in these mice provide a model to test inhibitors of vFLIP or other immunotherapeutic approaches targeting the microenvironment as potential anticancer agents for KSHV-associated diseases .
To generate mice expressing the transgene in an endothelial-cell specific manner , homozygous ROSA26 . vFLIP TG mice [23] were crossed with heterozygous Cdh5 ( PAC ) . creERT2 knock-in mice [39] of C57BL/6 genetic background; therefore , all experimental mice were on 129/Sv-C57BL/6 genetic background and age-matched littermates were used as controls . Genotyping was performed by PCR analysis on mouse tail DNA . All mice were housed , bred and studied according to the guidelines of Institutional Animal Care and Use Committee at Cornell University . Mice were monitored for pathological changes weekly and sacrificed when visibly ill , according to approved protocols . Statistical analysis of event-free survival was performed by GraphPad Prism v . 5 ( San Diego , CA , USA ) using Kaplan-Meier cumulative survival curve and the log-rank test to evaluate statistical significance . Lung , spleen , liver and heart were isolated during autopsy and promptly processed to obtain a single cell suspension using collagenase A and DNaseI treatment . RNA extraction , RT-PCR and quantitative RT-PCR were performed using standard protocols as detailed in Supporting Methods ( S1 Methods ) . Total protein extracts were prepared from lung , spleen , liver and heart using RIPA buffer , gel electrophoresed on 12% SDS-PAGE gel , transferred to a polyvinylindene difluoride membrane ( Millipore ) and immunostained according to standard methods using anti-FLAG ( M2; Sigma ) and anti-β-actin ( Sigma ) antibodies . Single-cell suspensions prepared from lung , spleen , liver and heart were stained using standard procedures with a panel of fluorescent-labeled antibodies ( see S1 Methods ) . 7AAD was used for the exclusion of dead cells . Data were acquired on LSRII or Aria flow cytometer ( Becton Dickinson ) and analyzed using FlowJo software ( Tree Star ) . Four μm thick formalin-fixed , paraffin-embedded sections were stained for H&E or immunostained with the following antibodies: anti-EGFP ( Abcam ) and anti-CD34 ( MEC14 . 7; Abcam ) . Mice 8–12 weeks of age were subjected to i . p . injection with 0 . 2 ml of tamoxifen ( 150 mg ) ( Sigma ) , dissolved in a mixture of 90% corn oil ( Sigma ) and 10% ethanol ( Sigma ) , and analyzed after 30–45 days . Transgene expression was assessed as early as 1 week after induction and remained constitutive over time . To determine the concentration of a panel of fourteen serum cytokines , a flow cytometry bead-based assay was used , which exploits particle with discrete fluorescence intensities to detect soluble analytes at very low concentrations . GM-CSF , Phospho Stat1 , RANTES , IL12/IL23p40 and MCP1 were quantified using BD Cytometric Bead Array ( CBA ) Mouse/Rat Soluble Protein Master Buffer Kit , while for the detection of IL10 , IL6 , INFγ , IL1b , IL12p70 , TNF , IL4 , IL2 , IL13 BD CBA Mouse Enhanced Sensitivity Master Buffer Kit was used . Each capture bead has a distinct fluorescence and is coated with a capture antibody specific for a soluble protein . The bead populations are resolved in two fluorescence channels of a flow cytometry , and each bead population is given an alphanumeric position indicating its position relative to other beads . Beads with different position can be combined to create multiplex assay and analyze multiple proteins from a single sample . After incubation of the capture beads with analytes and detection reagent , the PE mean fluorescence intensity ( MFI ) of the complex was measured and readings within the assay linear range were used to calculate the serum cytokines concentrations against cytokines standard curve for each analyte ( Becton Dickinson ) . Statistical significance , defined as P<0 . 05 , was assessed by two-tailed unpaired Student’s t-test . | Kaposi’s sarcoma ( KS ) is the most common cancer in men infected with HIV , and also among the most frequent malignancies in Sub-Equatorial Africa . KS is a tumor of endothelial cell origin that is caused by infection with a gamma-herpesvirus , called KS herpesvirus ( KSHV ) or human herpesvirus 8 ( HHV-8 ) . KSHV vFLIP is a viral oncoprotein expressed during latent infection . We report here the generation and characterization of mice expressing KSHV vFLIP in an inducible manner in endothelial cells . Transgenic mice showed: 1 ) systemic endothelial abnormalities , with the presence of fusiform cells reminiscent of the spindle cells found in KS , 2 ) development of a profound perturbation in serum cytokines , reminiscent of the cytokine storm characteristic of KSHV-associated cytokine syndrome ( KICS ) , and 3 ) remodeling of myeloid differentiation with expansion of myeloid cells displaying a suppressive immunophenotype that potentially favors host immune evasion , angiogenesis and tumor progression . This is the first example of significant changes in myeloid differentiation , vascular abnormalities and cytokine perturbation entirely initiated by ectopic expression of a single viral gene , making this mouse model a useful system to dissect the mechanisms viruses use to manipulate the host microenvironment culminating in sabotage of immunity and development of vascular lesions . |
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The interaction between follicular T helper cells ( TFH ) and B cells in the lymph nodes and spleen has a major impact on the development of antigen-specific B cell responses during infection or vaccination . Recent studies described a functional equivalent of these cells among circulating CD4 T cells , referred to as peripheral TFH cells . Here , we characterize the phenotype and in vitro B cell helper activity of peripheral TFH populations , as well as the effect of HIV infection on these populations . In co-culture experiments we confirmed CXCR5+ cells from HIV-uninfected donors provide help to B cells and more specifically , we identified a CCR7highCXCR5highCCR6highPD-1high CD4 T cell population that secretes IL-21 and enhances isotype-switched immunoglobulin production . This population is significantly decreased in treatment-naïve , HIV-infected individuals and can be recovered after anti-retroviral therapy . We found impaired immunoglobulin production in co-cultures from HIV-infected individuals and found no correlation between the frequency of peripheral TFH cells and memory B cells , or with neutralization activity in untreated HIV infection in our cohort . Furthermore , we found that within the peripheral TFH population , the expression level of TFH-associated genes more closely resembles a memory , non-TFH population , as opposed to a TFH population . Overall , our data identify a heterogeneous population of circulating CD4 T cells that provides in vitro help to B cells , and challenges the origin of these cells as memory TFH cells .
Follicular helper CD4 T cells ( TFH ) are crucial for the development of antigen-specific B cells within germinal centers ( GC ) . TFH cells interact through co-stimulatory receptors and provide essential soluble factors ( i . e . IL-4 , IL-21 ) to promote the survival , isotype switching and selection of high affinity memory B cells [1] . Phenotypic and gene signature analysis has revealed a highly conserved molecular profile of TFH cells in humans , non-human primates ( NHP ) and mice , which is characterized by increased expression of Bcl-6 , CXCR5 , PD-1 , ICOS and decreased expression of CCR7 [2]–[4] . Human TFH cells exhibit a polarized cytokine profile characterized by compromised production of TH1 cytokines and increased secretion of IL-4 , IL-10 and IL-21 [5] . Although IL-21 is characterized as a “hallmark” cytokine of TFH cells , other THelper subsets produce this cytokine [6] . The origin and differentiation of TFH is unclear , as previous studies found TFH cells can derive from TH1 or TH2 cells , or independently of other CD4 lineages [7]–[9] . However , it is well established that the transcription factor Bcl-6 regulates several molecules involved in TFH development ( i . e . PD-1 , IL-21R , CXCR5 ) [10] , [11] . Similarly , the fate of TFH , particularly those in the germinal center ( GC-TFH ) , following the effector phase of the immune response is unclear . We have recently shown that NHP GC-TFH display compromised in vivo cell cycling and are prone to in vitro cell death [4] . Other studies have shown that TFH can form a memory pool found in anatomical sites outside the lymph nodes [12] . Hence , TFH cells may adopt a “central memory” phenotype or undergo cell death after the effector phase [13] . In humans , a circulating CD4 T cell population characterized by high CXCR5 expression can provide in vitro help for B cell isotype switching and shares functional characteristics with TFH cells [14] . It was proposed that these circulating cells , termed “peripheral TFH” ( pTFH ) could represent the memory counterparts of TFH outside the lymphoid organs . Further investigation is needed to establish a direct relationship between TFH cells and pTFH cells . It is becoming increasingly important to understand the interplay between CD4 T cells and B cells during HIV infection , specifically with relation to the generation of broadly neutralizing antibodies . Chronic HIV/SIV infection results in profound changes in CD4 T cell dynamics in lymph nodes characterized by TFH accumulation and increased ability of non-TFH to egress the lymph node [4] , [15] . How this impacts upon the dynamics of pTFH is unknown . Elucidating the biology and dynamics of pTFH , and their ability to provide B cell help may be important for our understanding of TFH memory formation during chronic infection , as well as the establishment of immune correlates reflecting the interactions between CD4 T cells and B cells within secondary lymphoid organs . This is of particular interest for monitoring clinical studies where the B cell arm of the immune system is under investigation [16] . Here we define , detect , quantify and characterize peripheral CD4 T cell populations that support B cell differentiation . We show that particular circulating CD4 T cell populations with distinct cytokine profiles have the capacity to help B cells in vitro . We further show that the frequencies of pTFH populations are significantly compromised during chronic HIV infection but can recover with antiretroviral treatment ( ART ) , although in vitro immunoglobulin production from HIV-infected subjects both on and off ART is reduced compared to healthy subjects . Furthermore , gene expression analysis of pTFH cells and CD4 T cells in tonsil tissue suggest pTFH cells are most closely related to a non-TFH memory population within secondary lymphoid organs . Overall , our data challenge the relationship between pTFH cells and TFH memory cells .
Previous studies defined a population of circulating CD4 T cells that express CXCR5 , promote the differentiation of naïve B cells and induce immunoglobulin secretion in vitro [14] , [17] . We further defined CXCR5high CD4 T cells from blood , analyzed their cytokine production and determined their ability to promote B cell differentiation in vitro . CXCR5high CD4 T cells were found predominantly within the CD27highCD45ROhigh CD4 T cell population ( hereafter referred to as central memory ( CM ) ) , in agreement with previous studies [17] . The majority of the CXCR5high CD4 T cell population also expressed CCR7 and we found the CCR7highCXCR5high population represented 6 . 5+/−2 . 8% ( mean+/−S . D . ) of total CD4 T cells in healthy subjects ( Figure 1A ) . The majority of CXCR5high cells expressed CD150 . We further analyzed these cells based on expression of CCR6 , which was previously used in combination with CXCR3 to define a pTFH subset that promotes IgG and IgA production [14] , and PD-1 . CCR7highCXCR5highCCR6high cells represented 1 . 2+/−0 . 9% of total CD4 T cells and a minority of these cells were PD-1high . To analyze the ability of these populations to promote B cell differentiation , naïve and CM CD4 T cells from HIV-uninfected individuals were sorted based on expression of CCR7 , CXCR5 , CD150 , CCR6 and PD-1 ( Figure 1A ) , and cultured with autologous naïve B cells ( CD19+CD27−IgD+ ) as previously described [14] , [18] in the presence of staphylococcal enterotoxin B ( SEB ) . Notably , our sorted naïve B cell population did not express isotype-switched immunoglobulin ( Figure S1A ) and culture conditions that lacked SEB did not induce immunoglobulin production ( data not shown ) . Naïve and CM CCR7low CD4 T cells failed to promote B cell differentiation and immunoglobulin production whereas CM CCR7highCXCR5low cells induced limited production of IgM , IgG1 and IgG3 compared to the CCR7highCXCR5high populations ( Figure 1B ) . The CCR7highCXCR5highCCR6highPD-1high population induced the greatest production of IgG1 , IgG3 and IgA compared to the CXCR5low population . Prior studies defined pTFH cells based on surface expression of CXCR5 , CCR6 and the lack of CXCR3 expression [14] . We found that the greatest help for immunoglobulin production was from CXCR5highCCR6high cell populations and , within those , from the PD-1high cells . We did not eliminate a small population of CXCR3+ cells in order to avoid removing a larger population of CXCR5highCCR6high cells that induce B cell differentiation ( Figure S1B ) . The cytokine profile of pTFH populations shared characteristics with other Thelper subsets , including TH1 , TH17 and Treg cells . Supernatant from the CXCR5highCCR6highPD-1low coculture contained the greatest quantities of TNF-α , IL-2 , and IL-17 compared to the CXCR5highCCR6lowPD-1high coculture ( Figure 1C ) . Notably , the CXCR5highCCR6highPD-1high population , which promoted the greatest production of IgG1 , IgG3 and IgA , showed the greatest IL-21 production , although at low levels . Overall , CXCR5high CD4 T cell populations induced B cell immunoglobulin production , although the CXCR5highCCR6highPD-1high population did so most efficiently . However , this population is not characteristic of a TFH population found in secondary lymphoid organs , as coculture supernatants included a broad array of cytokines characteristic of TFH cells and multiple other Thelper subsets . To determine the impact of HIV on pTFH populations , we compared pTFH cells from HIV-uninfected subjects and treatment-naïve HIV-infected subjects ( Table S1 ) as a frequency of total CD4 cells . Irrespective of how pTFH cells were defined , there was a significant decrease in the pTFH population from HIV-infected subjects compared to HIV-uninfected subjects ( Figure 2A ) . Subjects with CD4 counts greater than 200 had significantly lower pTFH populations , while subjects with CD4 counts less than 200 had the lowest frequency of all phenotypically defined pTFH populations . However , when we defined the CCR6highPD-1high population as a subset of the CXCR5high population , the frequency of the CCR6highPD-1high population increased in subjects with CD4 counts less than 200 ( Figure S2A ) . The increase in PD-1high cells was likely due to immune activation in HIV infection , as we observed increases in the frequency of both PD-1high and ICOShigh cells within the CXCR5high population , with the greatest increases seen in samples with CD4 counts less than 200 ( Figure S2A ) . We also observed a positive trend between CXCR5highPD-1high cells and serum concentrations of soluble CD14 . ( Figure S2A ) . For 10 HIV-infected individuals on whom we had longitudinal samples , we observed a loss of pTFH populations as a frequency of total CD4 T cells over 36 to 48 months ( Figure 2B ) . However , the frequency of PD-1high , ICOShigh and CCR6highPD-1high cells within the CXCR5high population remained stable ( Figure S2B ) . Next , we investigated the impact of ART on the frequency of pTFH within total CD4 T cells . Longitudinal analysis on samples from before and after 24 and 48 weeks of ART revealed a recovery of pTFH populations ( Figure 2C ) . However , the frequency of PD-1high , ICOShigh and CCR6highPD-1high cells remained stable within the CXCR5high population ( Figure S2C ) . Overall , HIV infection causes a loss of pTFH cells and ART promotes the recovery of these populations . To investigate the impact of HIV on the ability of pTFH cells to support B cell differentiation , we performed co-culture experiments with pTFH cells from HIV-infected subjects . We focused on the CXCR5highCCR6high population that included both PD-1high and PD-1low cells due to limited cell numbers in HIV-infected subjects . Similar to previous results , the CXCR5highCCR6high population from HIV-uninfected subjects supported significantly more immunoglobulin production compared to the CXCR5low population . ( Figure 3A ) . However , for HIV-infected subjects we observed less overall immunoglobulin production when CXCR5highCCR6high CD4 T cells were co-cultivated with naïve B cells . Furthermore , in viremic subjects , we observed increased IgM and IgG1 production in co-culture supernatants from the CXCR5low population , compared to HIV-uninfected subjects . Similar to HIV-uninfected subjects , we found that pTFH cells from HIV-infected subjects produced a broad spectrum of cytokines ( Figure S3A ) . Our data raise the possibility that some pTFH cells exhibit a CXCR5low phenotype in HIV infection . This phenotype could be due the down regulation of CXCR5 on pTFH cells , or indicate the existence of a unique CXCR5low pTFH population in chronic HIV infection . In order to distinguish these two possibilities , we investigated whether CXCL-13 impacts CXCR5 expression on CD4 T cells . We found that incubation of HIV-uninfected PBMC with CXCL-13 led to a decrease in frequency of CXCR5-positive CD4 T cells , presumably due to the internalization of CXCR5 ( Figure 3B ) . Furthermore , in HIV infection we found that viral load positively correlated with CXCL-13 levels and negatively correlated with the frequency of CXCR5-positive CD4 T cells ( Figure 3C ) . However , we did not observe a direct correlation between CXCL13 levels and the frequency of CXCR5-positive CD4 T cells . Importantly , we also found that in vitro infection of CXCR5-expressing CD4 T cells did not impact CXCR5 surface expression ( Figure S3B ) . Therefore , our data support the possibility that in untreated HIV infected individuals , increased levels of CXCL-13 could effect CXCR5 surface expression on pTFH cells . TFH-dependent B cell differentiation requires IL-21 . To characterize directly cytokine production from pTFH cells from HIV-uninfected and HIV-infected subjects , we performed intracellular cytokine staining after ex vivo SEB stimulation . In addition to surface markers used to define pTFH cells , we detected CD154 , IFN-γ , IL-2 , IL-17 and IL-21 ( Figure 4A ) . In HIV-uninfected individuals , a minority of CD154-positive , cytokine-positive cells express a CCR7high phenotype ( 10 . 1% of IFN-γ positive cells; 28% of IL-2-positive cells; 19 . 4% of IL-17-positive cells and 17 . 9% of IL-21-positive cells ) , while a gradual reduction of cytokine production was found in further differentiated cells based on CXCR5 and CCR6 expression ( Figure 4B ) . However , for all of the cytokines detected , we observed a population of cells that were CCR7highCXCR5highCCR6high , including IL-21-producing cells . Overall , we determined that a mean of 4 . 5% of CD154-positive IL-21-positive cells were CCR7highCXCR5highCCR6high ( Figure 4B ) . However , this pTFH population also produced IFN-γ , IL-2 and IL-17 ( 0 . 8% of IFN-γ positive cells; 9 . 0% of IL-2-positive cells and 7 . 1% of IL-17-positive cells ) . Next , we analyzed cytokine production from HIV-infected subjects off-treatment . Overall , we observed a loss of cytokine-producing cells from the CCR7high population and a general shift towards the CXCR5lowCCR6low population ( Figure 4A ) . Thus , we observed a loss of CCR7highCXCR5highCCR6high pTFH cells that produce IL-2 , IL-17 and IL-21 ( Figure 3B; IL-2: 9 . 0% for HIV-negative vs 2 . 0% for HIV-positive; IL-17: 7 . 1% for HIV-negative vs 2 . 2% for HIV-positive; IL-21: 4 . 5% for HIV-negative vs 1 . 1% for HIV-positive ) . To analyze HIV-specific cells , PBMC were stimulated with Gag peptide pools and analyzed for cytokine expression . Very few IL-2-positive and IL-17-positive cells were detected within the CM compartment ( data not shown ) . Gag-specific IFN-γ and IL-21-producing cells were detected , however , compared to SEB-stimulation fewer HIV-specific cells expressed CCR7 ( 4 . 4% vs 10 . 7% of IFN-γ positive cells; 3 . 5% vs 11 . 9% of IL-21-positive cells for Gag and SEB stimulation , respectively ) . A majority of HIV-specific cells were not CCR7highCXCR5highCCR6high ( Figure 4C; 0 . 4% of IFN-γ positive cells and 0 . 9% of IL-21-positive cells were CCR7highCXCR5highCCR6high ) . Overall , we observed IL-21 production from the CCR7highCXCR5highCCR6high pTFH population , although we detected the most IL-21 in non-pTFH cells , which were CCR7low and CXCR5low . In addition to IL-21 , the CCR7highCXCR5highCCR6high pTFH population produced IL-2 and IL-17 , cytokines characteristic of TH1 and TH17 cells , respectively . However , from HIV-infected individuals we observed a loss of CCR7highCXCR5highCCR6high cells making IL-2 , IL-17 and IL-21 . Previous studies have described a relationship between the frequency of peripheral CXCR5high cells and memory B cells and antibody titers with vaccination [16] . Therefore , we analyzed the relationship between the frequency of pTFH cells and IgG-positive memory B cells in PBMC from HIV-infected individuals . We found no significant correlation between the frequency of pTFH cells and IgG-positive B cells ( Figure 5A ) . Similarly , we failed to detect a relationship between the frequency of pTFH and HIV-1 Env-specific antibody titers or total plasma IgG levels ( data not shown ) . It has also been reported that PD-1high CD4 T cells in blood are associated with cross-clade neutralizing antibody responses during HIV infection [19] and these PD-1high CD4 T cells may represent a population of pTFH cells . Thus , the relationship between pTFH cells and neutralization activity was analyzed using HIV-infected samples classified as good neutralizers ( median ID50>100 ) or poor neutralizers ( median ID50<100 ) [20] . Irrespective of how pTFH cells were defined , we failed to find any relationship between neutralization activity and pTFH cells ( Figure 5B ) . While pTFH cells induce B cell differentiation and immunoglobulin secretion in vitro , the relationship between pTFH and TFH cells in secondary lymphoid organs remains unclear . Our in vitro coculture studies indicated the greatest isotype-switched immunoglobulin production was elicited from B cells cocultivated with CXCR5highCCR6high pTFH cells ( Figure 1B ) . Therefore , we investigated the expression of CCR6 on TFH ( CXCR5highPD-1high ) and non-TFH ( CXCR5lowPD-1low ) tonsil cells to determine if the CXCR5highCCR6high pTFH population is related to TFH cells within secondary lymphoid organs ( Figure 6A ) . The lowest frequency of CCR6high cells was found within the CXCR5highPD-1high compartment ( 1 . 5% of CXCR5highPD-1high cells ) and the greatest frequency of CCR6high cells within the non-TFH compartment ( 9% of CXCR5lowPD-1low cells; Figure 6B ) . Similarly , RNA sequence data from the CXCR5highCCR6highPD-1high pTFH population more closely resembles a memory , non-TFH CD4 T cell population from the tonsil ( CM CD57lowPD-1dimCCR7highCCR5lowCXCR4low ) as compared to the non-germinal center TFH population ( CM CD57lowPD-1highCCR7lowCXCR5high ) or the GC-TFH population ( CM PD-1highCD57high; Figure 6C ) . In agreement with previous reports [5] , [17] , tonsil TFH populations expressed higher levels of BCL6 , IL-21 , and CXCL13 , and lower levels of PRDM1 and S1PR1 compared to the non-TFH memory population . The pTFH population from HIV-uninfected individuals expressed comparable levels of S1PR1 and PRDM1 to the non-TFH memory population in the tonsil ( Figure 6 ) . We also observed lower transcript levels of MAF , BCL6 , IL-21 , and CXCL-13 in the pTFH population compared to tonsillar TFH populations . Importantly , MAF protein expression was highest in the CCR6highPD-1high pTFH population compared to other peripheral populations , although still lower than tonsillar TFH cells . ( Figure 6D ) . For many of the selected genes , pTFH cells from HIV-infected subjects were comparable to pTFH from HIV-uninfected individuals , however , we observed greater transcript levels of activation molecules such as ICOS and CD69 . Additionally , the levels of IL-21 were decreased in pTFH cells from HIV-infected individuals , supporting earlier results ( Figure 4B ) . Collectively , our data suggest the pTFH population characterized as CXCR5highCCR6high most closely resembles a non-TFH memory population in the tonsil .
The development and nature of human TFH memory cells following an effector immune response are not known . The ability to define a population of memory TFH cells in PBMC ( pTFH ) would help inform our understanding of CD4 T cell dynamics within lymphoid tissue during vaccination or infection . Studies of chronic infection may be helpful in this regard [21] . Whether the accumulation of TFH cells during chronic infection [4] , [15] impacts the TFH memory population is of particular interest , especially if memory TFH cells migrate between lymphoid organs and peripheral tissues . Recent studies [14] , [16] have suggested that circulating CXCR5high CD4 T cells may represent the peripheral counterparts of TFH cells . However , the relationship between pTFH and TFH cells within secondary lymphoid organs remains unclear . Therefore , it is of great relevance to determine if pTFH cells originate from GC-TFH cells and represent a memory TFH population , reflect a precursor population that differentiates into GC-TFH upon re-exposure to antigen , or both . Our studies begin to address these issues by further defining pTFH cells , comparing pTFH cells to tonsillar TFH cells , and analyzing the effect of HIV on these cells . In concordance with previous studies , we showed that circulating CXCR5high CD4 T cells support B cell differentiation in vitro [14] , [17] . A majority of the CXCR5high cells expressed CD150 , and while CD150 was used for gating in the co-culture assays , we found it did not impact the loss of pTFH cells or effect our results with respect to loss of pTFH cells , recovery with ART or lack of association with B cell or antibody responses ( data not shown ) . However , within the CXCR5high population the expression of CCR6 and PD-1 did further define pTFH populations with differential abilities for naïve B cell help and isotype switching . Thus , pTFH cell populations support both the activation and maturation of naïve B cells , and immunoglobulin isotype switching . Correspondingly , the individual pTFH populations produced cytokines associated with B cell maturation and survival , such as IL-21 [22] , IL-2 [23] and IL-17 [24] , in contrast to TFH cells within secondary lymphoid tissue , which display a limited cytokine profile that includes IL-4 , IL-10 and IL-21 , but compromised production of IL-2 and IL-17 [4] . Whether these pTFH populations represent different stages of TFH memory development or originate from separate CD4 T cell populations within lymphoid tissue [25] is still unclear . In order to better understand the relationship between TFH and pTFH cells , we compared gene expression levels between pTFH and tonsillar CD4 T cell populations and focused on genes important for TFH differentiation , migration , and function . We found that the pTFH population with the greatest B cell helper function most closely resembled a CM , non-TFH CD4 T cell subset within the tonsil . While our studies do not directly address the relationship between GC-TFH in lymph nodes and circulating CD4 T cells from the same patients , our data challenge whether pTFH are memory TFH cells . A recent study reported that germinal center TFH cells in mice migrate throughout the follicle , but generally do not leave the follicle to enter the blood [26] . While it is conceivable that pTFH cells represent a very minor population of TFH cells that exit the follicle , it is also possible that pTFH cells are reflective of a precursor TFH population that exits the lymphoid organ and enters the circulation before entering the follicle . However , while we find the CXCR5highCCR6highPD-1high pTFH population does not resemble a memory TFH population , Locci and colleagues found a CXCR5+CXCR3-PD-1+ pTFH subset that functionally and transcriptionally resembles a memory TFH population [27] . A recent study in mice reported that memory TFH cells have reduced mRNA expression of TFH markers such as Bcl6 , IL-21 , ICOS and PD-1 compared to the effector TFH population [28] , indicating the expression of these molecules may change depending on the phase of infection . Therefore , further investigation of pTFH subsets and their relationship to memory and effector populations at multiple stages of infection is needed . pTFH and naïve B cell co-cultures from HIV-infected subjects produced fewer immunoglobulins compared to co-cultures from HIV-uninfected subjects . The observed defect in immunoglobulin production is likely due to impaired pTFH help to B cells instead of B cell dysfunction , as co-cultures included naïve B cells rather than memory B cells that exhibit abnormalities in HIV infection [29] . Furthermore , while co-culture supernatants from HIV-infected subjects demonstrated a heterogeneous cytokine profile , similar to HIV-uninfected subjects , intracellular cytokine staining showed that fewer CCR7highCXCR5highCCR6high pTFH cells produced IL-2 , IL-17 and IL-21 in chronic HIV infection compared to HIV-uninfected individuals . Furthermore , gene expression analysis of HIV-infected pTFH revealed fewer IL-21 and IL-4 transcripts , although the overall levels of cytokine transcripts were low . Recent studies have shown TFH cells within secondary lymphoid organs accumulate in some donors or animals during chronic HIV/SIV infection and that TFH accumulation is associated with GC B cell expansion and increased serum immunoglobulin concentrations [4] , [22] , [30] . In contrast to TFH cells , our studies revealed pTFH cells consistently decrease in chronic HIV infection , with disease progression resulting in a greater reduction of these compartments within the total CD4 T cell population . However , it should be noted that we were unable to analyze TFH cells within secondary lymphoid organs from these subjects and therefore we are unable to directly compare the frequency of pTFH cells and TFH cells from the same individual . The differences between the increase in TFH cells and decrease in pTFH cells may be due to differences in disease state ( i . e . early vs late infection ) or represent a steady state of TFH cells trafficking between the lymphoid tissue and the blood . The decreased frequency of pTFH in the blood may indicate impaired ability of TFH to exit the lymph node in chronic HIV infection where the tissue architecture is not intact . Alternatively , the decreased frequency of pTFH in the blood may be a result of pTFH trafficking to secondary lymphoid organs . In agreement with previous studies [14] , [17] , we found a majority of CXCR5high cells express CCR7 , and it has previously been suggested that pTFH cells migrate to secondary lymphoid organs upon infection due their expression of CCR7 and CD62L [14] . A confounding factor with regard to how we interpret the decrease in pTFH cells is that we also found a reduction in the surface expression of CXCR5 on CD4 T cells in chronic HIV infection , which may result from increased sera levels of CXCL-13 [31] , [32] . Furthermore , our co-culture data indicate that CXCR5low CD4 T cells from viremic subjects can induce some B cell differentiation . These data support the possibility that in chronic HIV infection , a subset of functional pTFH cells may be phenotypically defined as CXCR5low . Additionally , it should be noted that analysis of cellular subsets within the CXCR5high population in chronic HIV infection revealed the frequency of CCR6highPD-1high cells increased . These results are consistent with a state of generalized immune activation , as we also observed increased surface expression of ICOS on CXCR5high and CXCR5highPD-1high cells , and a positive association between the frequency of PD-1high cells within the CXCR5high population and serum concentrations of soluble CD14 [33] . Similarly , gene expression analysis indicated increased transcript levels of activation markers , such as ICOS and CD69 within the pTFH population during HIV infection . Overall , these data emphasize the difficulty in defining pTFH cells in chronic HIV infection and understanding the relationship between pTFH cells and TFH cells . The uncertain definition of pTFH cells in HIV infection may provide an explanation as to why we were unable to identify correlations between pTFH populations and circulating IgG-positive memory B cells , or between pTFH cells and HIV-specific IgG ( data not shown ) . Furthermore , we found no correlation between the frequency of pTFH and the neutralization activity of a well-characterized cohort of HIV-infected donors [20] . However , the absence of a correlation between pTFH cells and circulating HIV Env-specific IgG may also be explained by the lack of a time-dependent association ( early vs . late infection ) between TFH and pTFH cells , or indicate that the generation of IgG and broadly neutralizing antibodies is regulated by parameters other than pTFH , confounded by T-cell independent antibody production commonly observed in HIV infection [34] or generalized immune activation . Thus , our data challenge the application of the pTFH population as a surrogate of GC TFH-B cell interactions in chronic HIV infection . While our studies did not find a correlation between pTFH cells and neutralizing antibodies , several recent studies , each with a different definition of pTFH cells , have reported an association with antibody responses during vaccination , infection or autoimmune disease [27] , [35]–[37] . Therefore , further studies are needed to establish the association between pTFH subsets and the generation of neutralizing antibodies , especially in HIV infection . Overall , our data indicate that a range of circulating CD4 T cell populations can provide B cell help , possibly through differential secretion of soluble factors and/or cell-cell contact interactions [17] , [35] and that HIV infection results in loss of these cells over time , but with relative increases within the CXCR5high compartment which may be explained by immune activation . Furthermore , we did not find any association between pTFH and measures of B cell function such as HIV neutralization breadth/potency , HIV-specific IgG , or total IgG , suggesting application of this population as a surrogate of GC TFH-B cell interactions during HIV infection may be limited . A better understanding of the differentiation process and the developmental relationship between pTFH subsets and lymph node TFH cells is critical for the establishment of reliable peripheral blood CD4 T cell correlates for monitoring infection- or vaccine-associated B cell responses .
Signed informed consent was obtained in accordance with the Declaration of Helsinki and approved by the appropriate Institutional Review Board . Tonsil cells were acquired from anonymized discarded pathologic specimens from Children's National Medical Center ( CNMC ) under the auspices of the Basic Science Core of the District of Columbia Developmental Center for AIDS Research . The CNMC Institutional Review Board determined that study of anonymized discarded tonsils did not constitute ‘human subjects research . ’ Fresh HIV-uninfected peripheral blood mononuclear cells ( PBMC ) were obtained from individuals participating in the NIH research apheresis program . Fresh HIV-infected blood was obtained from the Vaccine Research Center Clinic or Drexel University College of Medicine . Frozen HIV-infected PBMC were obtained from three study populations ( Table S1 ) . For untreated HIV infection , cells were obtained from volunteers who participated in a therapeutic vaccination trial ( no efficacy was observed ) conducted in the 1990's prior to the advent of combination antiretroviral therapy ( cART ) [38] . The second study population consisted of donors from a cohort used to identify individuals with HIV broadly neutralizing antibodies [20] . To study the effect of cART , we obtained PBMC from HIV-infected donors participating in AIDS Clinical Trials Group study A5142 prior to initiation of cART and 24 and 48 weeks post-therapy [39] , [40] . PBMC and tonsil cells were cultured in RPMI 1640 supplemented with 10% fetal bovine serum , 2 mM L-glutamine , 100 U/mL penicillin and 100 µg/mL streptomycin ( Invitrogen ) . Directly conjugated antibodies were acquired from the following: ( 1 ) BD Biosciences: CD3-H7APC , CXCR5-Alexa488 ( RF8B2 ) , CCR7-Alexa700 , IgG-APC , IFN-γ-Alexa700 and IL-21-Alexa647 ( 3A3-N2 . 1 ) ( 2 ) Beckman Coulter: CD45RO-ECD and CD27-PC5 ( 3 ) Biolegend: CCR7-BV421 , CCR6-PE ( TG7/CCR6 ) , CCR6-Alexa647 ( TG7/CCR6 ) , CD20-BV570 , CD150-PE , IL-2-BV605 , IL-17a-Cy5 . 5PerCP and CD154-Cy5PE ( 4 ) Invitrogen: CD4-Cy5 . 5PE , CD27-QD655 , CD27-QD605 and CD19-PacBlue ( 5 ) Southern Biotech: IgD-FITC and IgD-PE ( 6 ) eBioscience: cmaf-eFluor660 ( sym0F1 ) , CXCR5-PerCP-efluor710 ( MU5UBEE ) . Biotinylated anti-PD-1 was from R&D and streptavidin-Cy7PE ( or QD655 ) was from Molecular Probes . The following antibodies were conjugated in our lab: CD19-QD705 and CD57-QD565 . Quantum dots and Aqua amine viability dye were obtained from Invitrogen . Co-culture experiments were performed with freshly isolated PBMC . 5×104 CD4 T cell populations were sorted based on expression of CCR7 , CXCR5 , CD150 , CCR6 and PD-1 and cultured with 5×104 autologous naïve B cells ( 1∶1 ratio ) in the presence of SEB ( 0 . 5 µg/ml ) . Supernatants harvested on Day 2 were analyzed for cytokines using Luminex technology ( Milliplex MAP Kit , HTH17MAG-14K , Millipore ) . The lower limit of detection ( LOD ) was set at the lowest concentration on the standard curve and values below the LOD were counted as zero . Supernatants collected on Day 12 were analyzed for immunoglobulins ( Milliplex MAP Kit , HGAMMAG-301K ) . Some supernatants exceeded the saturation limit of the standard curves for IgM and IgG3 . These values were included in the analysis and quantified as being equivalent to the highest determined concentration . Soluble CD14 and CXCL-13 ( R&D Systems ) were measured in plasma or sera from HIV-infection patients according to the manufacturer's instructions . Freshly isolated PBMCs were incubated with recombinant human CXCL-13 ( R&D Systems ) at 37°C or 4°C and analyzed for CXCR5 surface expression by FACS . CD4 T cell populations were sorted from uninfected PBMC ( n = 5 ) , HIV-infected PBMC ( n = 5 ) and uninfected human tonsils ( n = 4 ) based on expression of CCR7 , CXCR5 , CD150 , CCR6 and PD-1 for PBMC and CD57 , PD-1 , CCR7 , CXCR5 , CCR5 and CXCR4 for tonsils . Total RNA was purified from sorted cell populations and treated with DNAse I ( Ambion ) to minimize genomic DNA contamination . Polyadenylated RNA was isolated using Oligo-dT Dynabeads ( Life Technologies ) , chemically fragmented , and used to construct barcoded Illumina Truseq libraries . Libraries were size-selected , quantified , pooled , size-selected and quantified again , and clustered on an Illumina Truseq Paired-End Flowcell v3 . The flowcell was sequenced on an Illumina HiSeq 2000 in a 2×75-base paired-end , indexed run . Adaptor sequence was trimmed from the raw sequencing reads using Trimmomatic . The trimmed sequencing reads were subsequently aligned to the human genome ( hg19 ) using TopHat 2 . Differential expression testing was done using Cufflinks 2 and visualization of differential expression was done using the R package cummerbund . Accession numbers of the selected genes are shown in Supporting Table S2 . Neutralization activity of patient sera was determined against 20 viral isolates using a TZM-bl neutralization assay as previously described [20] . Freshly isolated PBMCs were stimulated with PHA ( 10 µg/ml ) . After 12 hours stimulation , CXCR5high cells were sorted by FACS Aria based on surface molecule expression and infected by a multiplicity of infection ( MOI ) of 0 . 01 with either HIV NL-E or HIV NLAD8-E [41] . The infected cells were cultured in the presence of 50 U/ml recombinant human interleukin-2 ( R&D ) for 5 days and analyzed for CXCR5 expression by FACS . Experimental variables were analyzed using the nonparametric Mann-Whitney U test , the Wilcoxon matched-pairs signed rank test or the Friedman test with Dunn's multiple comparison post-test . Correlation analysis was performed using the nonparametric Spearman test . Error bars depict mean+SEM in all bar graphs shown . The GraphPad Prism statistical analysis program ( GraphPad Software , version 5 . 0 ) was used throughout . | Follicular T helper cells ( TFH ) interact with B cells within germinal centers of lymphoid tissue to promote the survival , isotype switching and generation of high affinity memory B cells and plasma cells . Recently , a population of circulating CD4 T cells that shares phenotypic and functional characteristics with TFH cells , named peripheral TFH cells , has been identified . The relationship between peripheral TFH cells in the blood and TFH cells within the lymphoid tissue remains unclear , and whether or not peripheral TFH cells can provide insight into T cell and B cell dynamics within lymphoid tissue during infection or vaccination is not understood . Here we characterize peripheral TFH cells and show that unlike TFH cells , peripheral TFH cells secrete a diverse array of cytokines and decrease , rather than increase , during chronic HIV infection . Furthermore , we did not observe a relationship between peripheral TFH cells and memory B cells , or with the production of neutralizing antibodies to HIV . Overall , our data indicate that while peripheral TFH cells share some characteristics with TFH cells , they may not represent a good surrogate to study T cell and B cell dynamics within lymphoid tissue . |
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Transstadial transmission of tick-borne rickettsiae has been well documented . Few studies , however , have evaluated the role of transovarial transmission of tick-borne rickettsiae , particularly in nature within the host-vector ecosystem . This cross-sectional study aimed to understand the role of transovarial transmission of tick-borne rickettsiae among feeding ticks at different life stages . Tick eggs laid by engorged wild-caught adult female ticks were pooled and tested for Rickettsia spp . and Anaplasma/Ehrlichia spp . using molecular techniques , while adult fed ticks were tested individually . Additionally , larval and nymphal ticks were collected in the wild from small mammals , pooled and tested for Rickettsia spp . and Anaplasma/Ehrlichia spp . There were 38 fed adult and 618 larvae/nymphs ( 60 pools total ) Dermacentor spp . ticks collected from livestock and rodents . All individual adult ticks and tick pools were positive for Rickettsia spp . While none of the larvae/nymphs were positive for Anaplasma/Ehrlichia spp . , two adult fed ticks were positive . Rickettsia spp . DNA was detected in 91% ( 30/33 ) of the pooled eggs tested , and one pool of eggs tested positive for Anaplasma/Ehrlichia spp . Sequencing data revealed Rickettsia spp . shared ≥99% identity with R . raoultii ompA . Anaplasma/Ehrlichia spp . shared ≥89% identity with A . ovis 16S ribosomal RNA . This study identified potential transovarial transmission of Rickettsia spp . and Anaplasma spp . among D . nuttalli ticks . Additional studies are needed to further assess the proportion of transovarial transmission occurring in nature to better understand the burden and disease ecology of tick-borne rickettsiae in Mongolia .
While significant effort has been directed to study tick-borne rickettsiae , they continue to be a global public health threat . Mongolia is a country known for its rich nomadic and pastoral culture , with populations of people who work very closely with their livestock in environments that are often densely populated with ticks . Additionally , ecotourism is a rapidly growing industry in Mongolia , placing international visitors at risk of exposure to tick-borne rickettsiae [1 , 2] . This public health challenge is further complicated by a limited knowledge and understanding of tick and tick-borne rickettsiae ecology within Mongolia [2–4] . The mobility of ticks is restricted to questing and travelling via feeding on animals and humans [5] . Tick-borne rickettsiae typically undergo transstadial transmission before being vectored by a competent tick host . However , depending on the tick species and the type of tick-borne rickettsiae , transovarial transmission may also occur [6] . Research related to transovarial transmission has been particularly limited within the Asian and Eurasian regions of the world . Several tick-borne rickettsiae surveillance and case studies have been conducted throughout China , Russia and Mongolia , which have tested humans [7–10] , livestock [11–15] , wildlife [16–19] , and ticks [20–28] . However , most of these studies focused exclusively on ticks in their adult life stage , either fed or unfed . Few studies have examined the larval and nymphal stages of ticks in the Eurasian environment . Larval and nymphal life stages of ticks are of special interest in regard to exposure risk , as their small size can lead to less readily detectable feeding on human hosts [29–31] . Tick-borne rickettsiae of most concern in the Asian and Eurasian regions of the world are Rickettsia spp . [21 , 24] , Anaplasma spp . [12 , 14 , 16 , 25 , 27 , 32] , and Ehrlichia spp . [17 , 25] . These rickettsiae have been associated with small mammal reservoirs [6 , 17 , 19] . Collectively , the objectives of this study were to further investigate the life cycle of tick-borne rickettsiae in locally occurring ticks; to examine the propensity of certain tick-borne rickettsiae to undergo potential transovarial transmission; and to evaluate the infection prevalence of tick-borne rickettsiae infections from early life stage ticks throughout the Northern Mongolia region .
Handling procedures for livestock were conducted by trained veterinary staff prior to this study during animal care and were in accordance with the Mongolian Institute of Veterinary Medicine , Ulaanbaatar , Mongolia . Verbal consent was obtained from livestock owners at time of tick collection . Female adult fed ticks were collected from livestock at time of veterinary care of livestock and kept alive at room temperature in storage containers at the Laboratory of Arachno-Entomology and Protozoolgy , Institute of Veterinary Medicine in Ulaanbaatar , Mongolia . Moist cotton was placed near the ventilation of the containers to replicate environmental humidity conditions . Once female ticks laid eggs ( between 2–7 days of incubation ) , both adult ticks and eggs were stored separately at -80°C until DNA extraction was performed . The whole egg clutch was pooled and tested from each adult female tick . Mass of egg clutches ranged from 10 to 410 milligrams . Eggs and adult ticks were briefly rinsed with 70% ethanol in sterile 1 mL vials to remove contamination and then air dried on a sterile dish in preparation for processing [33 , 34] . Trapping and handling procedures for small mammals were approved by the Duke University Institutional Animal Care and Use Committee ( #A086-16-04 ) in accordance with the Mongolian Institute of Veterinary Medicine , Ulaanbaatar , Mongolia . At each location , live Tomahawk and Sherman traps were placed near holes where there were signs of recent small mammal habitation . All captured small mammals were sedated with ketamine ( 50 mg/Kg ) and inspected for ticks . Ticks were stored in 70% ethanol at room temperature . Specimens were taxonomically identified to genus for larvae and nymphs and species for adults by a trained entomologist . Ticks were air dried and pooled based on life stage ( larvae range 1–15; nymphs range 1–5 ) , small mammal host , sampling location , and tick genus . Pools ( n = 60 ) were stored at -20°C in new sterile 1 mL vials before genomic DNA was extracted . All ticks and eggs were ground using a sterile pre-chilled mortar and pestle with 500 μL of sterile PBS and 50 mg sterile sand for friction [35] . Contents were then centrifuged in a 1 . 5 mL vial at 9 , 500 g for 5 minutes . Supernatant was pipetted from the sand deposit , inserted into a new vial and stored at -20°C . Genomic DNA was extracted from tick supernatant using TIANamp Genomic DNA Kit ( Tiangen Biotech ( Beijing ) Co . , LTD , Beijing , China ) and tested for molecular detection of Rickettsia spp . targeting the citrate synthase gene ( gltA ) [36] and the outer-membrane protein gene ( ompA ) [37] , as previously described ( Table 1 ) . For the molecular detection of Anaplasma spp . and Ehrlichia spp . , the 16S rRNA gene [17] was targeted as previously described ( Table 1 ) . Gel electrophoresis was used to evaluate amplified products using 1% ( w/v ) agarose gels stained with ethidium bromide at 120 V . Gels were analyzed using the Gel Doc EZ System ( Bio-Rad , Hercules , California ) with ultra-violet illumination . A representative subset of positive amplicons were selected and directly sequenced using Sanger sequencing ( Eton Biosciences , Inc . , NC , USA ) . Sequencing results were then compared against the NCBI nucleotide database using the Standard Nucleotide BLAST application ( http://www . ncbi . nlm . nih . gov/BLAST/ ) for species identification . Rickettsia spp . gltA and ompA sequences were used as confirmation of amplified Rickettsia spp . samples and Anaplasma/Ehrlichia spp . 16S rRNA sequences were used as confirmation of amplified Anaplasma spp . Anaplasma/Ehrlichia spp . , and Rickettsia spp . sequences were structured for phylogenetic relatedness using Molecular Evolutionary Genetics Analysis ( MEGA ) software , version 7 . 0 . Engorged tick infection status was compared to corresponding oviposited egg infection status for PCR-positive Rickettsia spp . and Anaplasma/Ehrlichia spp . samples , as well as sequence data . Transovarial transmission was considered to have occurred when the corresponding female tick and egg mass were both PCR positive . Statistical analyses , including two-way frequencies with measures of association , were conducted using STATA 14 . 1 ( StataCorp , College Station , TX ) .
All individual adult ticks and larval/nymphal tick pools were PCR-positive for Rickettsia spp . Subsequent PCR testing of paired eggs resulted in 91% ( 30/33 ) PCR positive among tick egg pools for Rickettsia spp . Sequencing data revealed Rickettsia spp . shared ≥99% identity with R . raoultii ompA ( Accession numbers MH234455 and MH234456 ) shown in the phylogenetic analysis ( Fig 4 ) . A majority ( 23/32 ) of gltA sequences shared ≥99% identity with R . raoultii ( Accession numbers MH208721 and MH208722 ) shown in the phylogenetic analysis ( Fig 5 ) , however 9/32 sequences were considered inconclusive , falling between 84%-95% identity with R . raoultii . Of the 38 engorged adult ticks collected , two ticks ( 5% ) were PCR-positive for Anaplasma/Ehrlichia spp . , while none of the larval/nymphal pools were PCR-positive for Anaplasma/Ehrlichia spp . Additionally , one pool of eggs laid by an Anaplasma/Ehrlichia spp . -positive engorged adult female tick , was also found to be PCR-positive for Anaplasma/Ehrlichia spp . All PCR-positive Anaplasma/Ehrlichia spp . ticks and the positive egg clutch were further examined using a sequencing approach to identify the infecting rickettsial species . Sequencing results indicated that the Anaplasma/Ehrlichia spp . positive egg clutch and corresponding engorged adult female tick shared 99% identity ( accession number MG461482 ) and the other engorged adult female tick shared 89% ( accession number MG461483 ) identity with the A . ovis 16S ribosomal RNA gene . Both Anaplasma spp . sequences are shown in the phylogenetic analysis ( Fig 6 ) .
Like many tick pool studies , it is difficult to determine the exact prevalence of disease using this approach . Due to the nature of the maximum likelihood estimation calculation , the proportion of infected ticks with maximum likelihood of being Rickettsia spp . infected within tick pools cannot be calculated if 100% of sample pools are positive [59] . Additionally , due to the pooling of tick eggs , this study was unable to determine a more precise proportion of transovarial transmission from an infected female tick to at least one progeny . Though data suggest that transovarial transmission for R . raoultii did occur , we were unable to determine how many progeny were infected . Additionally , by only screening infection status of egg mass , we are unable to discuss if infected larvae will hatch . Furthermore , Rickettsia spp . PCR primers have been shown to cross-react with Anaplasma spp . and Ehrlichia spp . However , this study also utilized a general screening assay for Anaplasma/Ehrlichia spp . and confirmation by sequencing , which allowed for greater confidence in the Rickettsia spp . PCR assay . The indication that D . nuttalli ticks can serve as reservoirs for R . raoultii may warrants additional evaluation of transovarial and transstadial transmission of R . raoultii . Studies should focus on assessing tick eggs , either in smaller egg pools or individually , to determine the proportion of transovarial transmission as well as transstadial transmission for R . raoultii in eggs entering larval life stage , and larvae entering nymphal stages in the natural foci of Mongolia . Additionally , the testing of larvae from animal hosts and the environment should be further examined , preferably testing individual ticks instead of tick pools . Also , this report has identified a potentially novel transovarial transmission of A . ovis . Further investigation would be needed to determine the efficiency and prevalence of transovarial transmission of this rickettsiae . | In this study , we evaluate the probability or likelihood that tick-borne rickettsiae might be transmitted vertically from wild engorged adult female ticks collected throughout the Northern region of Mongolia during the summer of 2016 . While significant effort has been directed to study tick-borne rickettsiae , this public health challenge is complicated by the limited knowledge and understanding of tick and tick-borne rickettsiae ecology within Mongolia . Tick-borne rickettsiae of concern to humans and animals in this region of the world are Rickettsia spp . , Anaplasma spp . , and Ehrlichia spp . Using molecular techniques , we detected rickettsiae among all Dermacentor spp . tick life stages and demonstrated potential vertical transmission of Rickettsia spp . , and Anaplasma spp . among wild engorged adult female Dermacentor nuttalli ticks . We believe our findings provide important information regarding the ecology of key rickettsiae associated with tick-borne disease in Mongolia . |
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Sepsis is a consequence of systemic bacterial infections leading to hyper activation of immune cells by bacterial products resulting in enhanced release of mediators of inflammation . Endotoxin ( LPS ) is a major component of the outer membrane of Gram negative bacteria and a critical factor in pathogenesis of sepsis . Development of antagonists that inhibit the storm of inflammatory molecules by blocking Toll like receptors ( TLR ) has been the main stay of research efforts . We report here that a filarial glycoprotein binds to murine macrophages and human monocytes through TLR4 and activates them through alternate pathway and in the process inhibits LPS mediated classical activation which leads to inflammation associated with endotoxemia . The active component of the nematode glycoprotein mediating alternate activation of macrophages was found to be a carbohydrate residue , Chitohexaose . Murine macrophages and human monocytes up regulated Arginase-1 and released high levels of IL-10 when incubated with chitohexaose . Macrophages of C3H/HeJ mice ( non-responsive to LPS ) failed to get activated by chitohexaose suggesting that a functional TLR4 is critical for alternate activation of macrophages also . Chitohexaose inhibited LPS induced production of inflammatory molecules TNF-α , IL-1β and IL-6 by macropahges in vitro and in vivo in mice . Intraperitoneal injection of chitohexaose completely protected mice against endotoxemia when challenged with a lethal dose of LPS . Furthermore , Chitohexaose was found to reverse LPS induced endotoxemia in mice even 6/24/48 hrs after its onset . Monocytes of subjects with active filarial infection displayed characteristic alternate activation markers and were refractory to LPS mediated inflammatory activation suggesting an interesting possibility of subjects with filarial infections being less prone to develop of endotoxemia . These observations that innate activation of alternate pathway of macrophages by chtx through TLR4 has offered novel opportunities to cell biologists to study two mutually exclusive activation pathways of macrophages being mediated through a single receptor .
Sepsis and septic shock , one of the most common causes of admission in intensive care units results in death of nearly 3 , 50 , 000 people every year only in US and Europe [1] , [2] . The disease is a consequence of systemic bacterial infections that stimulates mediators of inflammation due to hyper activation of phagocytes . Immune cells express pattern recognition receptors ( PRRs ) which recognize immunostimulatory microbial products called PAMPs ( pathogen associated molecular pattern ) and trigger production of inflammatory mediators which assist the host in elimination of infectious agents [3] , [4]; however hyper induction of such mediators by dysregulated innate immune cells leads to sepsis and septic shock [5] , [6] . In sepsis caused by Gram-negative bacteria , endotoxin ( LPS ) activates the immune system through TLR4 and induces activation of macrophages that produce inflammatory mediators [7] , [8] . TLR4 is the signaling receptor for LPS but doesn't directly bind to LPS [9]–[11] . LPS forms a complex with LPS binding protein and CD14 which in turn delivers LPS to MD2 and LPS-MD2 complex activates through TLR4 resulting in dimerization of TLR4 [3] and initiate the signaling process for production of cytokines and other critical molecules needed for hyper-inflammation associated with endotoxemia/sepsis [12] . While effective use of antibiotics has resulted in improved prognosis of sepsis , deterioration of clinical symptoms and mortality has been attributed to persistent inflammatory cascade . Neutralization of inflammation is considered essential for preventing severe consequences of sepsis [13] , [14] . Thus developments of antagonists that block either activation through TLRs or downstream signaling pathways that inhibit the storm of inflammatory molecules are widely pursued by several investigators . Antibodies to TLR4 [15] , [16] , TLR4/MD2 [17] complex or LPS analogs [18] , [19] have been tested in animal models for their efficacy to protect against enditoxemia/Gram-negative sepsis although only LPS analogues have been undergone clinical trials . Nitrate salts [20] , 5c , an inhibitor of sphingosine kinase [21] , oxidized phospholipid [22] , [23] molecules have also offered promising results . Despite all these attempts very few candidate molecules have reached the level of clinical trials . A very recent report of one of the clinical trial for a promising agent was found to be ineffective ( http://clinicaltrials . pharmaceutical-business-review . com/news/eisai-eritoran-fails-to-meet-primary-endpoint-in-phase-iii-trial-250111 ) . In this study we report a novel mechanism that blocks endotoxemia by an approach fundamentally different from those documented so far . We demonstrate that a low molecular weight chito-oligosaccharide , chitohexaose ( chtx ) delicately balances the storm of inflammation induced by LPS while concurrently activating macrophages into a non inflammatory alternate pathway through TLR4 . Administration of chtx protected mice from endotoxemia prophylactically as well as therapeutically . The study also offered evidence for induction of two diverse activation pathways of macrophages through a single receptor , TLR4 . The stimulating ligand appears to determine the activation phenotype viz; classical pathway by LPS and alternate pathway by chtx . We stumbled on these findings while searching for the elusive innate receptors for nematodes .
Our initial studies were designed to identify an innate receptor on murine or human phagocytes that recognize nematodes . Biotinylated somatic extracts of adult stage parasites of Setaria digitata ( FAg ) and Brugia pahangi bound to surface of human monocytes as well as to murine bone marrow macrophages significantly more than the lymphocytes ( Figure 1 A–D ) . Specificity of biotinylated FAg reacting to monocytes was confirmed by competitive inhibition with unlabeled FAg ( Figure 1 B ) . C . elegans , a non-pathogenic nematode also contained a component binding to human monocytes and murine macrophages ( Figure 1C , D ) . A glycoprotein ( AgW ) affinity purified using WGA-Sepharose column ( Figure S1A ) was found to be the active component ( Figure 1E , F ) . Since another filarial glycoprotein ES-62 has been previously reported to interact with macrophage surface through TLR4 [24] , we tested such a possibility in our system . AgW as well as FAg competitively inhibited reactivity of antibodies to TLR4 on surface of murine bone marrow macrophages ( Figure 2A , Figure S 1 B , C , Figure S 2 A , B , C ) and on human monocytes ( Figure 2B , Figure S 1D , E , Figure S 2 D , E , F ) . We sought direct proof by performing a novel solid phase immunoassay developed by us . Soluble TLR4 present in membrane lysates of human PBMCs ( Figure 2C ) and murine bone marrow cells ( Figure S 1F ) reacted with FAg/AgW bound to solid phase . Human and murine TLR4 reacted with extracts of other nematodes also viz; Nippostrongylus brasiliensis , Heligomosomoides polygyrus and Caenorhabditis elegans ( Figure 2C , Figure S 1F ) suggesting the presence of conserved TLR4 binding components in nematodes . Enhanced binding of labelled FAg to jurkat cells over expressing TLR4 further confirmed its ability to interact with TLR4 ( Figure 2D , E ) . Since immunomodulatory properties of helminth products have been shown to depend on glycan moiety [25] we examined the possible role of carbohydrates in FAg-TLR4 interaction described above . Deglycosylated ( Figure S 3A ) or chitinase treated ( Figure S 3B ) FAg failed to competitively inhibit interaction of labeled FAg with monocyte surface suggesting the involvement of carbohydrate residues in such interactions . Susceptibility to chitinase also indicated the role played by chitin or it's oligomers in FAg interacting with TLR4 . This was tested by competitive inhibition using chitin oligomers . Binding of AgW to human monocytes was inhibited by chitosugars of varying size , longer the chain length higher was the degree of inhibition ( Figure 3A ) . Further , the hexasaccharide , chitohexaose ( chtx ) also inhibited reactivity of soluble TLR4 to AgW on solid phase in a dose dependant manner ( Figure 3B ) . These results suggested involvement of chitosugar residues present in FAg/AgW in interaction with TLR4 . In silico analysis using crystal structure of TLR4 and 3D structure of chtx ( http://pubchem . ncbi . nlm . nih . gov/summary/summary . cgi ? cid=197182&loc=ec_rcs ) also indicated significant affinity between the two molecules ( Figure 3C , Figure S 3C ) . Based on these observations further biological characterization were carried out using chtx as described below . The above findings that chtx residues present in FAg or AgW binds to TLR4 opened up the possibility of using the small molecular weight chito-oligosaccharide as a potential TLR4 antagonist to block LPS mediated inflammatory responses . BMDM of normal mice and normal human PBMCs were stimulated with LPS or chtx for 48 hrs and levels of TNF-α , IL-1β , IL-6 and nitrites were quantified in culture supernatants . LPS stimulated significant production of inflammatory mediators while chtx failed to do so in both murine and human systems ( Figure 4 A–E ) . Induction of genes for production of inflammatory molecules by LPS and failure to up regulate such genes by chtx was confirmed by Q-PCR also ( Figure 4F ) . Chtx on the other hand significantly inhibited LPS mediated inflammatory activation of mononuclear cells in both the systems ( Figure 4 A–E ) . We then tested induction of reactive oxygen species ( ROS ) by LPS and chtx . Murine BMDMs upon stimulation with LPS significantly up regulated ROS while chtx failed to do so . Rather chtx significantly inhibited LPS induced up-regulation of ROS . ( Figure 4G ) This observation is significant in the context of a recent report suggesting that induction of inflammatory cytokines is dependent on up regulation of ROS [26] . For in vivo validation of the above observations , BALB/c mice were administered a lethal dose of LPS with and without chtx . Chtx significantly inhibited LPS induced increase in plasma levels of TNF-α , IL-1β , IL-6 and nitrites ( Figure 5 A , B ) while increasing IL-10 levels ( Figure 5B ) . More critically , chtx significantly blocked LPS induced mortality of C57BL/6 mice ( Figure 5C ) suggesting that it can be used as a potential antagonist to block adverse biological consequences of endotoxemia in vivo . Administration of chtx 6 , 24 and 48 hrs after onset of endotoxemia was also effective in blocking mortality of mice suggesting its potential as a therapeutic agent ( Figure 5 D ) . BALB/c mice which are more susceptible to LPS induced endotoxemia than C57BL/6 mice were also protected by chtx ( Figure 5E ) . Alternate activation of macrophages has been well documented in helminth infections [27] . Presence of abundant chitin in helminthes [28] and activation of chitinase family proteins like AMCase , Ym-1 and chitinase-3 during helminth infections [29] suggests that chitin breakdown products from helminthes could be responsible for alternate activation of macrophages . We explored this possibility by stimulating human PBMCs and murine BMDM with chtx . Ym-1 , Arginase-1 and IL-10 ( Figure 6 A–D , Figure S 4 ) were up-regulated by murine BMDM upon stimulation with chtx . Similarly , human monocytes released IL-10 ( Figure 6 E ) and up-regulated intracellular Arginase activity confirming induction of alternate activation of mononuclear cells by chtx in vitro ( Figure 6 D , E ) . The potential of chtx to induce alternate activation of macrophages in vivo was also addressed . BALB/c mice were intraperitoneally administered with chtx or LPS and after 90 minutes the peritoneal cells were harvested and expression of Ym-1 and Arginase-1 in CD14+ve cells were analyzed . Chtx up regulate both Ym-1 and Arginase-1 while such an up-regulation was not observed in LPS administered mice peritoneal macrophages ( Figure S 4 G , H ) . Further , in vivo alternate activation of macrophages by chtx even after onset of endotoxemia was demonstrated . Up-regulation of Ym-1 and Arginase-1 was observed in peritoneal macrophages collected 90 minutes post chtx administration in mice with ongoing endotoxemia . It was observed that chtx induce alternate activation of macrophages even after onset of endotoxemia ( Figure 6 F , G ) . Chitin and chitin breakdown fragments have been reported extensively to activate macrophages but the pathway of activation and receptors involved remain contradictory [30] . The above described results suggested that chtx activates macrophages through alternate pathway using TLR4 . We sought direct biological proof by stimulating BMDMs of C3H/HeJ ( TLR4 mutant mice ) [31] and C3H/OuJ ( wild type mice ) with chtx and LPS and canonical markers of both alternate as well as classical macrophage activation were scored . As expected , LPS induced release of inflammatory cytokines by BMDMs of wild type mice and not by cells of mutant C3H/HeJ mice ( Figure 7 A , B ) . Chtx on the other hand failed to induce classical activation markers viz; TNF-α , IL-1β and nitrite ( Figure 7 A , B ) but up-regulated expression of alternate activation markers viz; Ym-1 ( Figure 7 C , E ) and Arginase-1 ( Figure 7 D , F ) in wild type mice and not in mutant mice . From these observations we make two broad conclusions: a ) classical and alternate macrophage activation could be mediated using the same receptor , TLR4 by LPS and chtx respectively and b ) the mutation in TLR4 gene that results in substitution of proline to histidine in its intracellular domain [32] plays a critical role in both classical as well as alternate activation pathways in macrophages . Finally we addressed the significance of these observations in human lymphatic filariasis . The expression of TLR4 was significantly low on monocytes of infected subjects positive for CFA ( Circulating Filarial Antigen ) in comparison to endemic controls ( negative for CFA ) ( Figure 8A ) . Binding of labeled FAg to monocytes was also significantly less in infected subjects in comparison to endemic controls ( Figure 8B , C ) however when monocytes of infected subjects were incubated in vitro at 37°C for 4 hrs binding of labeled FAg as well as expression of TLR4 were comparable to endemic controls ( Figure 8D , E ) . We interpret these findings to imply that circulating filarial antigens saturate TLR4 on monocytes which get recycled when incubated in culture thus exposing surface TLR4 to bind FAg or to react with anti-TLR4 ( Figure 8E ) . Further , LPS induced inflammatory molecules by PBMCs of infected subjects was significantly decreased in comparison to controls as shown by TNF-α , IL-1β and IL-6 ( Figure S 5A–C ) levels in culture supernatants suggesting that monocytes of subjects with filarial infections are less prone for activation by LPS possibly due to saturation of TLR4 with circulating filarial antigens . This issue was further addressed by incubating monocytes of infected individuals with heterologous ( negative for CFA ) normal plasma and FBS and stimulated with LPS or with LPS+FAg and inflammatory molecules like TNF-α , IL-1β and IL-6 were quantified in culture supernatants . LPS induced inflammatory cytokines by monocytes of infected individuals are comparable with monocytes of endemic controls when incubated with normal heterologous plasma or with FBS suggesting that filarial antigen in infected plasma saturate TLR4 on monocytes thus blocking activation by LPS ( Figure 8 F , G , H ) . Expression of CD23 , CD163 , and CD206 canonical markers of alternate macrophage activation [33] were significantly up-regulated on circulating monocytes of infected subjects in comparison to uninfected controls ( Figure 8 A ) .
Three novel issues stand out from the results being reported in this communication-a ) that a small molecular weight carbohydrate , chtx activates macrophages to a non-inflammatory phenotype through TLR4 and in doing so functions as an LPS antagonist and blocks induction of inflammatory mediators by LPS in vitro ( both in murine macrophages and in human monocytes ) and endotoxemia in vivo , b ) that two diverse pathways of activation of macrophages could be operational by two different ligands viz; LPS and chtx using a single receptor TLR4 on the host cell and finally c ) that glycoproteins of filarial nematodes with chtx as a constituent could be saturating TLR4 on circulating monocytes in infected subjects rendering them refractory to LPS induced inflammatory activation . We have provided evidence for direct binding of AgW to TLR4 by flow cytometry and consequent activation of alternate pathway of monocytes/macrophages . We have also shown by in silico analysis and solid phase immunoassay that chtx also binds toTLR4 and that the phenotype of activation pathway by chtx is similar to that of AgW . It is critical to note that unlike AgW , native LPS does not bind to TLR4 - several investigators have provided convincing evidence to suggest that native LPS does not bind directly to TLR4 but it activates macrophages by forming a complex with LPS binding protein ( LBP ) and CD14 and this complex delivers LPS to MD2 which activates macrophages through TLR4 [9]–[11] . When viewed in this context our findings on direct binding of AgW to TLR4 and activation of macrophages by alternate pathway is fundamentally different from macrophage activation by LPS which does so without directly binding to TLR4 in its native form . Benefits of inhibition of TLR4 activation has been documented in several experimental models of lethal shock . Anti-CD14 antibodies in rabbits , primates and humans [34]–[36] , anti-TLR2 antibodies [37] and antibodies to TLR4 [15] or TLR4/MD2 complex [17] have been tested with a high degree of success . Synthetic LPS antagonists such as Eritoran and Tak-242 have been tested in experimental models of endotoxic shock and also in human disease [19] but Eritoran has been recently reported to be ineffective in phase-III clinical trial ( http://clinicaltrials . pharmaceutical-business-review . com/news/eisai-eritoran-fails-to-meet-primary-endpoint-in-phase-iii-trial-250111 ) . Molecules involved in downstream signaling of TLR such as platelet-activating factor [38] oxidized phospholipids [22] , [23] , nitrate salts [20] , 5c [21] have also been tested with a degree of success . Potential of antibodies to TNF-α , IL-1RA , TNF-α soluble receptors and anti-bradykinin have also tested [39] but it has been observed that treatment with such TLR inhibitors interfere with innate immunity of host against infection and consequently increasing the risk of shock and mortality [39] , [40] , [41] . In this context the results of this study offers significant promise- a non immunogenic inexpensive small molecular weight chito-oligosaccharide can be used as an LPS antagonist . Based on in vitro demonstration of alternate activation of murine macrophages and human monocytes and in vivo activation of murine macrophages into alternate phenotype by chtx leading to inhibition of LPS mediated induction of inflammatory molecules ( such as IL1β , TNF-α , IL-6 etc ) by chtx we conclude that the small molecular weight carbohydrate induces alternate activation of macrophages in vivo and mediates protection against endotoxemia . Thus Chtx appears to protect against endotoxemia by two mechanisms - a ) it competitively inhibits LPS induced activation by binding to TLR4 and/or b ) it activates macrophages by alternate anti-inflammatory pathway . Generation of such an activation appears to have many advantages since alternately activated macrophages are reported to be endotoxin resistant [42] , [43] with increased phagocytic activity [44] and enhanced expression of scavenger receptors and proangiogenic factors [45] make them assist in tissue repair and resolution of inflammation [46] . We are currently testing if monocytes of sepsis patients can be re-programmed to non-inflammatory state by chtx . LNFP-III a complex carbohydrate moiety of S . mansoni , thioredoxin peroxidase of F . hepatica and migration inhibition factor ( MIF ) of B . malayi [47] , [48] are other molecules of helminth origin reported to induce alternate activation of macrophages but the host receptor through which such activation is mediated is still largely unknown . The current study demonstrating the role of TLR4 in chtx induced alternate activation of macrophages has offered insights into the issue of induction of alternate activation by helminth products . Although there have been many studies examining TLR signaling in response to pathogens [49]–[51] fewer studies have examined interaction of multicellular helminth parasites with TLRs on monocytes or macrophages . Helminth products such as excretory secretory ( ES ) product of Necator americana or OV-Asp-1 of Onchocerca volvulus [52] , [53] are known to interact with cells of the innate immune system but the receptor associated with this interaction has not been elucidated . In the present study a filarial glycoprotein designated as AgW has been demonstrated to bind directly to TLR4 . These results are in broad agreement with demonstration of ES-62 , a filarial glycoprotein interacting with TLR4 [24] . Apart from identifying TLR4 as a receptor for a helminth carbohydrate , the current study will be of crucial interest to cell biologists since TLR4 appears to function as a common receptor for both classical as well as alternate activation of macrophages and the nature of ligand determining the phenotype . This clearly offers scope for acquiring insights into molecular events involved in mutually exclusive activation pathways of macrophages . Human filariasis is characterized by chronic persistence of circulating filarial antigens ( CFA ) for several years . TLR4 on monocytes in infected subjects appear to be saturated in vivo with CFA since labeled FAg bound poorly bound to monocytes ex vivo and normal binding of FAg to TLR4 could be achieved by allowing antigen saturated TLR4s to recycle in vitro . The findings reported here suggest that CFA in plasma seem to remain bound to TLR4 on monocyte surface in infected subjects and contribute to sustenance of their alternate activated state . The following observations indicate that inherent defect in monocytes of infected subjects do not contribute to their failure to get activated by LPS: 1 . Monocytes of infected subjects when incubated with infected ( autologous ) plasma , the response to LPS was significantly diminished ( as shown by decreased TNF-α , IL-1β and IL-6 levels in culture supernatants ) when compared with response of same monocytes incubated with FBS or normal plasma; 2 . Monocytes of infected subjects cultured with normal plasma respond poorly to LPS when FAg was added exogenously; 3 . FAg significantly inhibited release of TNF-α , IL-1β and IL-6 by normal monocytes when cultured with autologous plasma and stimulated with LPS; 4 . When monocytes of infected subjects are incubated with FBS or with normal human plasma , the cells get activated well to LPS as shown by higher levels of inflammatory cytokines in supernatants - TNF-α , IL-1β and IL-6 levels are comparable to those observed in normal monocyte cultured with normal ( autologous ) plasma . While our observations that circulating monocytes in filariasis infected subjects display alternate activation markers are similar to an earlier report [54] diminished induction of mediators of inflammation such as TNF-α , IL-1β and IL-6 by LPS treated monocytes of filariasis infected subjects is a novel observation . The possibility that subjects with filarial infections will be regulating hyper inflammation associated with bacterial infections and thus offering protection against endotoxemia associated with sepsis needs further investigation . These findings also suggest interesting evolutionary issues on co-infection of humans with nematodes and gram negative bacteria and their pathogenesis . It is tempting to propose that increasing incidence of sepsis/septic shock in developed countries over the last 100 years ( 2 ) could have been due to eradication of helminth infections , a scenario similar to increased incidence of allergies and autoimmune diseases in developed countries as a consequence of elimination of infectious disease as proposed in ‘Hygiene Hypothesis’ .
Institutional Animal Ethics Committee of Institute of Life Sciences “approved” all the protocols followed for experiments conducted using mice . The study was carried out in strict accordance with the recommendations of the Committee for Prevention of Cruelty and safety of experiments with animals ( CPCSEA ) a regulatory body of Government of India that supervises Care and Use of Laboratory experimentation through their nominees in the Institutional animal ethics committee . Adult Setaria digitata worms from peritoneal cavities were collected from the abattoir attached to a local zoo after obtaining approval from zoo authorities . The animals are slaughtered in the abattoir regularly for feeding wild cats and no animals were slaughtered specifically for the purpose of our study . The study on human filariasis was approved by Institutional Human Ethics Committee of Institute of Life Sciences which operates under the guidance of regulations of Indian Council of Medical Research . Written informed consents were obtained from each of the normal control volunteers , filariasis infected persons and/or their legal guardians before collection of blood samples . Peritoneal dwelling adult female filarial parasites ( Setaria digitata ) were collected from cattle in a local abattoir , attached to the local zoological park at Nandankanan , Bhubaneswar after obtaining necessary approval from zoo authorities . The worms were transported to the laboratory in Dolbecco's Modified Eagles Medium ( DMEM ) ( Sigma ) pH 7 . 00 containing antibiotics [Penicillin Streptomycin solution 1 ml/100 ml of medium] ( Sigma ) and 1% glucose ( Hi-media ) . Aqueous extracts of S . digitata ( designated as FAg ) was prepared by homogenization followed by ultrasonication and the aqueous extract of adult worms was biotinylated using appropriate derivatives of biotin i . e . N-hydroxysuccinamide derivatives ( Sigma ) suitable for protein labeling . One milligram of FAg was passed through WGA-Sepharose ( Sigma ) coloumn and the unbound proteins were washed by passing PBS and glycoproteins bound to WGA were eluted by Glycine-HCl buffer ( pH 3 . 6 ) . The pH of the elutes was adjusted using 0 . 1 M NaOH and dialysed against PBS . Protein concentration of the elute was estimated and stored at −20°C for further use . BALB/c , C57BL/6 , C3H/OuJ and C3H/HeJ mice were obtained from National Institute of Immunology , New Delhi which was originally imported from Jackson laboratories , Germany . Breeding and maintenance were done at the animal facility at Institute of Life Sciences , Bhubaneswar , India . 8–10 weeks old animals were used for this study . Institutional animal ethics committee of Institute of Life Sciences , Bhubaneswar approval was obtained for all the investigations conducted in mice . Mouse bone marrow cells were collected from femoral shafts by flushing with 3 ml . of cold sterile DMEM ( Sigma ) supplemented with 20 mM of L-Glutamine ( ICN ) , antibiotics ( 1 ml penicillin and streptomycin/100 ml of medium ) ( Sigma ) containing 10% FBS . The cell suspension was passed through a sieve to remove large clumps . The cell suspension was washed 2–3 times with sterile DMEM and adjusted to 0 . 5×106 cells/well and cultured in 24 well plates . After 8–10 hrs incubation at 37°C non adherent cells were removed by washing with sterile DMEM and the adherent cells ( more than 96% positive for CD14 ( Figure S-4 and S-6 indicating high purity ) were stimulated with LPS ( Sigma , 055:B5 L-2880 ) with or without FAg or chtx ( Dextra Lab ) at 10 µg/ml concentration . After 48 hrs the supernatants were aspirated , frozen at −80°c and used later for estimation of cytokine levels . The adherent cells were removed using chilled medium and analysed for intracellular Arginase activity by calorimetric assay and intracellular staining using antibodies to Ym-1 and Arginase-1 staining and scored by flowcytometry . 8–10 weeks old BALB/c and C57BL/6 mice were intraperitoneally injected with 15 mg/Kg body wt and 60 mg/Kg body wt . of LPS respectively with and without FAg ( 100 µg ) or AgW ( 50 µg ) or chtx ( 250 µg ) and observed for mortality a period over 168 hrs . These doses were determined by prior titration and the lowest concentration effective in vivo was chosen for experimentation . For analysis of levels of cytokines mice were sacrificed 2 hrs after challenge with LPS or LPS with FAg ( 100 µg ) or LPS with chitohexaose ( 250 µg ) . Blood was collected in heparinised tubes by heart puncture and clear plasma was isolated by centrifugation at 5000 g for 10 minutes and analysed for presence of TNF-α , IL-1β , nitrite and IL-10 as described below . Human PBMCs were isolated from heparinised venous blood samples by density gradient centrifugation method using Histopaque ( Sigma ) . Briefly , the heparinised blood was layered on LSM medium gently in the ratio of 1∶1 and subjected to centrifugation at 100 g for 30 minutes . The white layer representing PBMCs was aspirated out gently and transferred aseptically into sterile centrifuge tubes . The suspension of cells was then washed and cultured in sterile DMEM supplemented with 20 mM of L-Glutamine ( ICN ) , 10% of autologus plasma/FBS and antibiotics ( 1 ml penicillin and streptomycin/100 ml of medium ) ( Sigma ) . The no . of cells was adjusted 0 . 5×106 cells/well in 24 well plates . After 8–10 hrs incubation at 37°C non adherent cells were removed by flushing with sterile DMEM and the adherent cells were stimulated with LPS , FAg or chtx , LPS along with FAg and LPS along with chtx at 10 µg/ml concentration for 48 hrs . after which the supernatants were removed and used for cytokine estimation . The adherent cells were removed and analyzed for intracellular Arginase activity by calorimetric assay as described below . For study of recycling of the receptor , the PBMCs were resuspended in DMEM containing 0 . 1% BSA and incubated at 37°C for 4 hrs . Then cells were incubated with biotinylated FAg at 4°C for 30 minutes followed by staining with streptavidin-FITC and analyzed by FACS . Supernatants from human monocytes or mouse BMDM cultures as well as mice plasma samples were analysed for levels of TNF-α , IL-1β , IL-6 and IL-10 by a sandwich ELISA according to manufacturers instruction using commercially available ELISA kits ( e-Biosciences ) . Human PBMCs or mouse BMDM ( 1×106/ml ) were stained for 30 minutes at 4°C with fluorescence labeled antibodies specific to CD14 , TLR4 ( e Biosciences ) mixed with and without FAg or AgW along with relevant isotype controls . Human cells were also stained with antibodies to CD23 , CD163 , and CD206 ( Santacruz Biotech . ) . The cells were thoroughly washed to remove the unbound antibodies and analysed by FACS ( BD FACS caliber ) . For intracellular Arginase-1 and YM-1 staining , cells were permeabilised with 1× FACS permeabilising solution ( BD biosciences ) and then incubated with rabbit antibodies to mouse YM-1 ( Stemcell technologies ) or goat anti-mouse Arginase-1 antibodies ( Santacruz Biotech . ) followed by staining with anti-rabbit IgG-FITC ( Sigma ) and anti-goat IgG-PE ( Santacruz Biotech . ) respectively and analysed by FACS . Appropriate isotype controls/conjugate controls were used for all flowcytometric assays . Human PBMCs or mouse ( BALB/c ) bone marrow cells ( 2×106 ) were incubated with 2 ml of cell lysis solution ( Sigma ) and a cocktail of protease inhibitors ( Sigma ) for one hr . at 4°C and then ultra-sonicated . The supernatant was collected by centrifuging at 1500 g for 10 minutes and stored for further use . ELISA plates ( Nunc maxisorp ) were coated with 1 µg of PBS extracts of S . digitata , AgW , N . brasiliensis , H . polygyrus or mock BSA , Phosphorylcholine coupled to BSA , GlcNaC-BSA , Mannose-BSA or LPS . After blocking with 1% skimmed milk-PBS ( Hi-media ) , human PBMC lysates or mouse ( BALB/c ) bone marrow cell lysates were incubated for 2 hr at 37°C . The plate was thoroughly washed and were incubated with rabbit anti-human and rabbit anti-mouse TLR4 ( e Biosciences ) respectively . The binding of anti-human and anti-mouse TLR4 was detected by using peroxidase conjugated anti-rabbit IgG ( Sigma ) . The enzyme activity was measured using OPD ( Sigma ) . The intracellular accumulation of ROS was determined using the fluorescent probe ( 2 , 7 , Dichloro Dihydro Fluorescein Diacetate ) H2-DCFDA as described previously [56] . Nitrite level in culture supernatants of BMDM and intracellular Arginase activity of both human monocytes and BMDM lysates were quantified as described elsewhere [57] . Arginase activity was measured in cell lysates . Briefly , cells were lysed using 50 µl of 0 . 1% Triton X-100 . 5 µg Pepstatin and 5 µg aprotinin were used as protease inhibitors during lysis . This mixture was incubated for 30 min at room temperature . 50 µl of 10 mM MnCl2 and 50 mM Tris-HCl were added to lysed cells to activate the enzyme by heating for 10 min at 56°C . Then 25 µl of 0 . 5 M L-arginine , pH 9 . 7 was added and incubated at 37°C for 45 min . The reaction was stopped with 400 µl of H2SO4 ( 96% ) /H3PO4 ( 85% ) /H2O ( 1/3/7 , v/v/v ) . 25 µl of α-isonitrosopropiophenone ( dissolved in 100% ethanol ) was added to the mixture followed by heating at 95°C for 45 min and urea concentration was measured at 540 nm . The X-ray structure of the extracellular domain of TLR4 ( PDB code: 3FXI ) [58] in complex with MD-2 is available . The X-ray structure of the TLR4-MD-2 complex ( 3FXI ) was downloaded from the PDB data base . The TLR4 structure from this complex was extracted and used for docking with chtx . The chemical structure of chtx molecule was extracted from pubchem database ( http://pubchem . ncbi . nlm . nih . gov ) . Structure of the chtx was retrieved into two-dimensional MDL/SDF format and three dimensional coordinates were generated using the ACCELRYS DS modelling 2 . 5 ( Accelrys Inc . San Diego , CA 92121 , USA ) software suite . The missing hydrogen of the structure was fixed and subjected to energy minimization . All energy minimization were carried out using the conjugate gradient method of CHARM force field using the ACCELRYS DS modelling 2 . 5 ( Accelrys Inc . San Diego , CA 92121 , USA ) software suite . Docking studies were carried out using Genetic Optimization for Ligand Docking ( GOLD ) software , version 4 . 1 . 1 ( Cambridge Crystallographic Data Centre , Cambridge , UK ) . The number of run was set to 100 in the standard default settings . The standard default settings , consisting of population size-100 , selection pressure-1 . 1 , niche size-2 , migrate-10 , cross over-95 , number of operations-1 , 00 , 000 number of docking 10 were adopted for GOLD docking . For ligand- protein binding , 10 docking conformations ( poses ) were tested and the best GOLD score were considered for further analysis . The ligand showing maximum interactions with the protein were plotted using the program LIGPLOT . The active site was predicted by using Q-site finder [59] . Total RNA was isolated from stimulated cells using RNAeasy columns from Qiagen , as per the manufacturer's instructions . 1 µg of total RNA from each sample was treated with DNAse I ( Ambion Inc . ) . Synthesis of cDNA was performed by using First Strand Synthesis kit and the Superscript III Reverse Transcriptase ( Invitrogen ) , according to the manufacturer's instructions . All real-time PCR experiments were performed in ABI prism 7900 HT sequence detection system ( ABI ) as described earlier [60] . The PCR conditions were as follows: 95°C for 10 min , 95°C for 15 sec , 58°C for 30 sec and 72°C for 30 sec for 40 cycles . The primers used for each gene are listed in ( Table S 1 ) . Primers were used at a concentration between 1 and 5 pmoles per reaction . All the reactions were analyzed using the software ( SDS 2 . 3 ) provided with the instrument . The relative expression of the genes was calculated by using 2-ΔΔCt formula using GAPDH as a normalizer . The values reported are the mean of two biological replicates . The standard deviation from the mean is shown as error bars in each group . Statistical significance among experimental groups was analyzed by the unpaired Student's t-test using Graph pad prism software ( Prism-5 ) . | Sepsis is one of the leading causes of death contributing to mortality as high as 54 percent in intensive care units across the world . Hyper inflammation induced by bacteria or bacterial products through Toll like receptors leads to sepsis and hence current approaches are directed towards blockade such receptors . While many such candidate antagonists have shown promise they also result in induction of inappropriate innate immune responses thus increasing risk of development of shock leading to death . In this study we describe a novel approach to treat endotoxemia associated with sepsis , fundamentally different from other reports . Chitohexaose a small molecular weight polysaccharide by virtue of its ability to bind to active sites of TLR4 inhibited LPS induced production of inflammatory mediators by murine macrophages and human monocytes . Administration of chitohexaose with LPS blocked endotoxemia leading to mortality of mice . More significantly , Chitohexaose reversed inflammation and protected mice even 24/48 hrs after onset of endotoxemia . Apart from competitively inhibiting LPS induced inflammation chitohexaose also activated alternate pathway of macrophages . Such macrophages are known to display increased phagocytic activity , are resistant to LPS induced activation and associated with resolution of inflammation and tissue repair . |
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The transcription factor ATF2 has been shown to attenuate melanoma susceptibility to apoptosis and to promote its ability to form tumors in xenograft models . To directly assess ATF2's role in melanoma development , we crossed a mouse melanoma model ( NrasQ61K::Ink4a−/− ) with mice expressing a transcriptionally inactive form of ATF2 in melanocytes . In contrast to 7/21 of the NrasQ61K::Ink4a−/− mice , only 1/21 mice expressing mutant ATF2 in melanocytes developed melanoma . Gene expression profiling identified higher MITF expression in primary melanocytes expressing transcriptionally inactive ATF2 . MITF downregulation by ATF2 was confirmed in the skin of Atf2−/− mice , in primary human melanocytes , and in 50% of human melanoma cell lines . Inhibition of MITF transcription by MITF was shown to be mediated by ATF2-JunB–dependent suppression of SOX10 transcription . Remarkably , oncogenic BRAF ( V600E ) –dependent focus formation of melanocytes on soft agar was inhibited by ATF2 knockdown and partially rescued upon shMITF co-expression . On melanoma tissue microarrays , a high nuclear ATF2 to MITF ratio in primary specimens was associated with metastatic disease and poor prognosis . Our findings establish the importance of transcriptionally active ATF2 in melanoma development through fine-tuning of MITF expression .
Malignant melanoma is one of the most highly invasive and metastatic tumors [1] , and its incidence has been increasing at a higher rate than other cancers in recent years [2] . Significant advances in understanding melanoma biology have been made over the past few years , thanks to identification of genetic changes along the MAPK signaling pathway . Those include mutations in BRAF , NRAS , KIT and GNAQ , all of which result in a constitutively active MAPK pathway [3]–[5] . Consequently , corresponding transcription factor targets such as microphthalmia-associated transcription factor ( MITF ) [6] , AP2 [7] , and C-JUN [8] and its heterodimeric partner ATF2 [9] are activated and induce changes in cellular growth , motility and resistance to external stress [10] , [11] . In addition , constitutively active MAPK/ERK causes rewiring of other signaling pathways [4] . Among examples of rewired signaling is upregulation of C-JUN expression and activity [8] , which potentiates other pathways , including PDK1 , AKT and PKC , and plays a critical role in melanoma development [12] . Activating transcription factor 2 ( ATF2 ) , a member of the bZIP family , is activated by stress kinases including JNK and p38 and is implicated in transcriptional regulation of immediate early genes regulating stress and DNA damage responses [13]–[15] and expression of cell cycle control proteins [16] . To activate transcription , ATF2 heterodimerizes with bZIP proteins , including C-JUN and CREB [17] , [18] , both of which are constitutively upregulated in melanomas [8] . ATF2 is also implicated in the DNA damage response through phosphorylation by ATM/ATR [19] . Knock-in mice expressing a form of ATF2 that cannot be phosphorylated by ATM are more susceptible to tumor development [20] . Nuclear localization of ATF2 in melanoma tumor cells is associated with poor prognosis [21] , likely due to transcriptional activity of constitutively active ATF2 . Indeed , expression of transcriptionally inactive ATF2 or peptides that attenuate endogenous ATF2 activity inhibits melanoma development and progression in xenograft models [22]–[26] . These studies suggest that ATF2 is required for melanoma development and progression . The transcription factor MITF has been shown to play a central role in melanocyte biology and in melanoma progression [27] , [28] . Yet , the role of MITF in early stages of melanoma development remains largely unexplored . Factors controlling MITF transcription have been well documented and include transcriptional activators , such as SOX10 , CREB , PAX3 , lymphoid enhancer-binding factor 1 ( LEF1 , also known as TCF ) , onecut domain 2 ( ONECUT-2 ) and MITF itself [29]–[33] , as well as factors that repress MITF transcription , including BRN2 and FOXD3 [34] , [35] . In addition , MITF is subject to several post translational modifications which affect its availability and activity , including acetylation , sumoylation and ubiquitination [27] , [28] . To directly assess the importance of ATF2 in melanoma development , we employed a mouse melanoma model in which ATF2 is selectively inactivated in melanocytes . We demonstrate that melanoma development is markedly attenuated in mice expressing a transcriptionally inactive form of ATF2 in melanocytes . Surprisingly , ATF2 control of melanoma development was mediated , in part , through its negative regulation of SOX10 and consequently of MITF transcription . Inhibition of ATF2 abolished mutant BRAF-expressing melanocytes' ability to form foci on soft agar , which was partially rescued when expression of MITF was attenuated . The significance of these findings is underscored by our observation of human melanoma tumors , in which high ratio of nuclear ATF2 to MITF expression was associated with poor prognosis . These findings identify a novel mechanism underlying melanocyte transformation and melanoma development .
Global Atf2 knockout in mice leads to early post-natal death [36] . Therefore , the Cre-loxP system was utilized to disrupt Atf2 in melanocytes . Deletion of its DNA binding domain and a portion of the leucine zipper motif results in a transcriptionally inactive form of ATF2 ( Figure 1a; [36] ) . To generate loss-of-function mutants , we established mice that would allow CRE-dependent deletion of these domains . Mice homozygous for the loxP-flanked ( floxed ) Atf2 gene ( Atf2f/f ) were born at the expected Mendelian ratios and presented no apparent abnormalities . In addition , in several tissues analyzed , ATF2 expression levels were comparable between WT and Atf2f/f mice ( data not shown ) . To elucidate the role of ATF2 in melanoma , Atf2f/f mice were crossed with mice harboring a 4-hydroxytamoxifen ( OHT ) -inducible Cre recombinase-estrogen receptor fusion transgene under the control of the melanocyte-specific tyrosinase promoter , designated Tyr::CreER ( T2 ) . Upon administration of OHT , we predicted that CRE-mediated recombination would be induced in a spatially and temporally controlled manner in embryonic melanoblasts , melanocytes , and in putative melanocyte stem cells [37] . The resulting Atf2f/f/Tyr-CreER ( T2 ) mice , designated melanocyte-deleted ( md ) Atf2md ) , indeed expressed the gene encoding the ATF2 transcriptional mutant in melanocytes . Immunoblot analysis of ATF2 protein confirmed that melanocytes prepared from wild-type TyrCre+::Atf2+/+::Nras+::Ink4a−/− ( WT ) mice express a 70 kDa band corresponding to full length ATF2 , whereas melanocytes of TyrCre+::Atf2md::Nras+::Ink4a−/− mice express only a 55 kDa band , corresponding to the size of ATF2 lacking the DNA binding and leucine zipper domains ( Figure 1b ) . To address the role of ATF2 in de novo melanoma formation Tyr::CreER::NrasQ61K::Ink4a−/− ( KO of exon 2–3 of Cdkn2a locus , which encodes for both p16Ink4a and p19Arf; [38] ) mice , which develop spontaneous melanoma ( Lynda Chin , unpublished observations ) , were crossed with Atf2md mice . Similar to findings reported by Ackermann et al . [39] , mutant N-Ras/Ink4a−/− mice developed melanoma within 8–12 weeks with metastatic lesions often seen in the lymph nodes . However , the incidence of melanoma was lower in Tyr::CreER::NrasQ61K::Ink4a−/− mice used in the present study ( 50% penetrance , of which 50% of the tumors were confirmed to be melanoma ) , probably because expression of mutant NRAS was induced only after birth , as opposed to activation of NRAS during embryogenesis , as reported in [39] ) . Thus , Atf2md::N-RasQ61K::Ink4a−/− mice were used to assess changes in melanoma incidence in the absence of functional ATF2 over a period of up to 8 months . In all cases , mouse skin was treated with Tamoxifen within 3–5 days after birth to inactivate ATF2 ( Figure 1b ) and with doxycycline in their drinking water to induce expression of the NRAS mutant transgene ( See Materials and Methods for details; Figure 1c ) . In the control group ( Tyr::CreER::Atf2+/+::NrasQ61K::Ink4a−/− ) , 11/21 mice ( 52% ) developed tumors within 8–16 weeks ( Table 1 ) . In ATF2 heterozygotes ( Tyr::CreER::Atf2−/+::NrasQ61K::Ink4a−/− ) , 18/44 mice ( 41% ) developed tumors within 8–16 weeks , and in the Tyr::CreER::Atf2md::NrasQ61K::Ink4a−/− group only 3 of 21 animals ( 15% ) developed tumors within 24–36 weeks ( Figure 1d and Table 1 ) . To evaluate tumor type , we examined melanoma markers including DCT and S100 in all tumors ( Figure 1e , Figure S1 ) . This analysis identified 55–63% of tumors as melanomas in both the Atf2+/+ ( 7/11 ) and Atf2+/− ( 10/18 ) groups ( Table 2 ) . Only one of the three tumors observed in the Atf2md group was identified as a melanoma . Kaplan Meier curve did not reveal significant differences in survival among the different genotypes , probably since this study was primarily designed to follow tumor incidence . Common to all genotypes , most tumors that were not identified as melanomas were fibrosarcomas and lymphomas , consistent with previous reports [38] . These data suggest that transcriptionally active ATF2 is required for melanoma development in the NrasQ61K::Ink4a−/− mouse melanoma model . To assess the mechanism underlying ATF2's contribution to melanoma development , we conducted gene profiling array analysis of primary melanocytes prepared from Tyr::Cre+::Atf2+/+::NrasQ61K::Ink4a−/− and Tyr::Cre+::Atf2md::NrasQ61K::Ink4a−/− mice . Analysis was limited to melanocytes , since , as reported above , only one melanoma formed in the ATF2 mutant group . In all cases , ATF2 was inactivated and NRAS was induced in culture within 48h of plating cells , as monitored by western blots ( Figure 1b , 1c and data not shown ) . Melanocytes were enriched , and immunostaining with appropriate markers confirmed that samples were free of keratinocytes and fibroblasts ( data not shown; see Materials and Methods for details ) . RNA was prepared from cultures and two biological and technical replicates were used for data analysis . As shown in Table 3 , among transcripts differentially expressed in ATF2 WT and mutant cultures were several factors that play an important role in melanocyte pigmentation , including Mitf , Silver , Tyrp1 and Dct . qPCR analysis , performed on independently prepared RNA samples from melanocytes expressing WT ( Tyr::Cre+::Atf2+/+::NrasQ61K::Ink4a−/− ) or mutant ATF2 ( Tyr::Cre+::Atf2md::NrasQ61K::Ink4a−/− ) , confirmed altered expression of pigmentation genes ( Table 3 ) . These data provide the initial indication that ATF2 negatively regulates Mitf and several other important pigmentation genes . As the pigmentation genes identified in this array are known to be regulated by MITF [27] , we focused on regulation of MITF by ATF2 . To confirm that ATF2 negatively regulates Mitf expression , we assessed MITF transcription in primary mouse melanocytes harboring WT ( Tyr::Cre−::Atf2+/+ ) or mutant ( Tyr::Cre+::Atf2md ) forms of ATF2 . RNA prepared from whole skin of these mice ( 3 mice per group ) was subjected qPCR analysis . Significantly , Mitf expression was inversely correlated to the presence of functional ATF2; samples obtained from ATF2 mutant skin exhibited a greater than 2-fold increase in MITF expression compared with those obtained from WT ATF2 mice ( Figure 2a ) . Likewise , we found that genes transcriptionally regulated by MITF , such as Dct , Silver and Tyrp1 , were upregulated in the skin of mutant ATF2 mice ( Figure 2a ) . The degree of altered expression of pigmentation genes was less pronounced in whole skin samples than in cultured melanocytes ( Table 3 ) , probably due to confounding effects of in vitro cell culture . To confirm the qPCR data , we performed immunostaining of skin tissue samples obtained from 4 days old WT or ATF2 mutant mice and observed increased MITF expression in melanocytes from Atf2md mice relative to their WT counterparts ( Figure 2b ) . Quantification of MITF staining revealed an approximate 2-fold increase in nuclear MITF expression in Atf2md compared to WT mice ( Figure 2c ) . Of note , the level of S100 staining in the hair matrix was markedly reduced in the skin of Atf2md mice . At a later time point ( 2 weeks ) representing an advanced stage of melanocyte development , S100 staining was similar in both genotypes , while MITF expression remained upregulated in Atf2md mice ( not shown ) . In all , these data confirm our initial observations in primary mouse melanocytes that MITF levels are elevated in ATF2 mutant-expressing cells . Additional assessment was performed in melan-Ink4a-Arf1 melanocytes , a line derived from black Ink4a-Arf null mice [40] , and in primary human melanocytes . In both , ATF2 expression was inhibited by viral infection with the corresponding mouse or human shRNA ( shATF2 ) . Infection of either primary human ( Figure 3a ) or melan-Ink4a-Arf1 melanocytes ( Figure 3b ) with shATF2 markedly increased MITF transcription and protein expression ( Figure 3a , 3b ) . These findings show that loss of transcriptionally active ATF2 allows higher expression of MITF and strongly suggest that ATF2 negatively regulates MITF expression in melanocytes . Given that ATF2 negatively regulates MITF in melanocytes of mouse and human tissues and in related melanocyte cell lines , we asked whether ATF2 also regulates MITF in human melanoma cells . Initially , we assessed changes in MITF expression in six human melanoma lines harboring oncogenic mutations in BRAF or NRAS , and in which ATF2 expression was effectively inhibited by corresponding shRNA ( shATF2 ) . In all cases , shRNA specificity was confirmed using three independent sequences ( data not shown ) . Surprisingly , the six melanoma lines fell into two classes based on distinct patterns of regulation of MITF by ATF2 ( Table 4 ) . The first class comprised four of the six melanoma cultures ( 1205Lu , WM35 , WM793 and WM1361 ) , in which MITF expression was elevated 3–6-fold following inhibition of ATF2 expression ( Figure 3c , S2a ) . Conversely , a second class of cells , including MeWo and 501Mel cells , exhibited decreased MITF expression after ATF2 knockdown ( KD ) , suggesting positive regulation of MITF by ATF2 ( Figure 3d , S2b ) . Notably , this latter group showed high levels of basal MITF expression [41] , [42] , suggesting that regulation of MITF expression in these cells differs mechanistically from that of the first group . Further , in response to stress ( UV or hypoxia ) the MeWo and 501Mel lines further reduced MITF expression ( Figure 3d and data not shown ) , providing further evidence for differential regulation of MITF in these cells both prior to and in response to stress stimuli . Additional analyses were performed , employing 12 more melanoma cell lines . Inhibition of ATF2 expression revealed that 4/12 exhibited increase in MITF expression , while 6/12 decreased MITF expression . Two of the 12 lines did not exhibit change in MITF expression following ATF2 KD ( Table 4 , S5 ) . Collectively , out of 18 melanoma lines we found that 8 ( 44% ) retained similar negative regulation of MITF by ATF2 as observed in the melanocytes . However , another 8 ( 44% ) exhibited positive regulation of MITF by ATF2 , pointing to a transcriptional switch that occurred in the course of melanocyte transformation . MITF was not affected by altered ATF2 expression in 2/18 cell lines ( Table 4 , Figure S5 ) . In all , in about 50% of the melanoma cell lines ATF2 elicits negative regulation of MITF , similar to what was seen in human and mouse melanocytes . MITF transcription is regulated by complex positive and negative cues [27] . For instance , while CREB and SOX10 positively regulate MITF , BRN2 and FOXD3 have been shown to downregulate MITF expression [29] , [30] , [34] , [35] . Hence we used melanocytes and representative melanoma lines to assess mechanisms underlying positive or negative regulation of MITF . Infection of the human melanocyte line Hermes 3A with shATF2 effectively inhibited ATF2 expression , upregulated Mitf transcription and increased transcription of SOX10 and FOXD3 ( from 7- to 10-fold ) and to a lesser extent of Pax3 and Brn2 ( from 1 . 5- to 2-fold ) ( Figure 4a , S3a ) . Similarly , inhibition of ATF2 transcription in human melanoma 1361 cells increased SOX10 and FOXD3 transcription , albeit , to a lesser degree ( 3- and 1 . 5-fold , respectively ) compared with human melanocytes ( Figure S3b ) . Neither BRN2 nor PAX3 transcription was elevated in melanoma cells in which ATF2 expression was inhibited ( Figure S3a ) . These observations suggest a role for ATF2 in FOXD3- and SOX10-mediated regulation of MITF transcription in melanocytes and melanoma cells . To assess the possible role of FOXD3 in regulation of MITF we inhibited FOXD3 expression in melanocytes expressing control shRNA and shATF2 . Inhibition of FOXD3 expression increased SOX10 transcription and protein expression , albeit to lower levels compared with inhibition of ATF2 expression ( Figure S4 ) . Concomitant increase of MITF RNA and protein levels was also lower , compared with that seen upon inhibition of ATF2 expression . Notably , inhibition of both ATF2 and FOXD3 resulted in additive increase of SOX10 and MITF ( Figure S4 ) . These data suggest that FOXD3 may also contribute to negative regulation of MITF in melanocytes , independent of ATF2 . Since inhibition of FOXD3 elicited a less pronounced effect compared with ATF2 , and since the effect appeared ATF2-independent and furthermore did not appear to mediate similar changes in human melanoma cells ( Figure S3b and data not shown ) , we focused on assessment of direct mechanisms underlying ATF2 effect on MITF transcription . To this end we first analyzed MITF promoter sequences for ATF2/CRE elements ( Cyclic AMP response element ) , which can be targeted by ATF2 , as well as sequences recognized by BRN2 and SOX10 using a luciferase reporter construct ( MITF-Luc ) [43] . Using either a wild-type ( WT ) construct or one in which the BRN2 site was mutated , we observed increased luciferase activity following inhibition of ATF2 transcription in WM1361 melanoma ( Figure 4b ) , as well as in LU1205 and WM35 melanoma cells ( data not shown ) . The relative increase in luciferase activity following ATF2 inhibition was equivalent in both constructs , suggesting that an ATF2 effect is not mediated by BRN2 ( Figure 4b , left panel ) . Similarly , MITF transcriptional activities were altered to a similar degree following inactivation of the CRE element ( Figure 4b , right panel ) , suggesting that ATF2 down-regulation of the MITF promoter is indirect . We therefore assessed whether SOX10 , which positively regulates MITF and whose transcription markedly increases in melanocytes and melanoma cells in which ATF2 expression is inhibited ( Figure 4a , S3b ) , may mediate ATF2 effect on MITF transcription . Analysis of a MITF-Luc construct harboring a mutant SOX10 binding site revealed that ATF2 inhibition no longer elicited increased MITF transcription in human melanocytes or in melanoma cells ( Figure 4c ) . In agreement , inhibition of SOX10 expression by corresponding siRNA attenuated the increase in MITF transcription seen in shATF2-expressing human melanocytes ( Figure 4d ) or melanoma cells ( Figure 4e ) . These results suggest that ATF2 regulation of MITF transcription is mediated by SOX10 . In agreement , chromatin immunoprecipitation ( ChIP ) assays confirmed increased binding of SOX10 to the MITF promoter in melanoma cells expressing shATF2 ( Figure 5a ) . A putative response element for AP1 ( which can serve as an ATF2 response element through ATF2 heterodimerization with JUN family members; [9] ) has been identified in upstream regions of the Sox10 promoter [44] . We examined potential ATF2 binding to this element by ChIP and found that endogenous ATF2 , but not ATFa , binds to that AP1 sequence ( −4797–4791 ) in both human melanocytes and melanoma cells ( Figure 5b ) . We next set to identify ATF2 heterodimeric partner , which could mediate negative regulation of SOX10 transcription . Among members of the JUN family implicated in transcriptional silencing is JunB . Thus , further assessment was performed to determine if JunB functions as an ATF2 heterodimerization partner to regulate SOX10 transcription through the AP1 site . ChIP analysis confirmed that JunB binds to the AP1 site found in SOX10 promoter sequences ( Figure 5c ) . To confirm a possible role for JunB in regulating MITF transcription we asked whether expression of TAM67 , a negative regulator of Jun family members , could attenuate the binding and transcriptional activities elicited by JunB . Expression of TAM67 indeed reduced the degree of ATF2 and JunB binding to the AP1 site on SOX10 promoter . Further , KD of ATF2 expression abolished binding of both ATF2 and JunB to the AP1 site on the SOX10 promoter ( Figure 5c ) . These data confirm the presence of ATF2-JunB complex on Sox10 promoter and suggest that ATF2 recruits JunB for binding to the AP1 site on SOX10 promoter . To assess the role of JunB on SOX10 transcription we have monitored changes in Sox10 expression at the protein and RNA levels . Expression of TAM67 caused increased expression of SOX10 in both human melanoma ( ∼2 folds; Figure 5d ) and melanocytes ( ∼3 folds; Figure 5e ) , indicating some relief of JunB inhibition . Co-expression of TAM67 with Jun B attenuated this increase , reducing the level of Sox10 expression to basal levels ( Figure 5d , 5e ) . Over-expression of JunB , but not Jun D , effectively inhibited Sox10 expression in both the melanoma and melanocytes cells ( Figure 5d , 5e ) . These data suggest that JunB mediates inhibition of Sox10 expression . To further reveal the role of ATF2 in this inhibition , we assessed the effect of JunB on Sox10 expression in cells expressing control shRNA or shATF2 . While ectopic expression of JunB reduced the expression of Sox10 in control shRNA-expressing cells , such decrease was no longer seen in cells expressing shATF2 ( Figure 5f ) . Collectively , these findings suggest that ATF2 , in concert with JunB , is responsible for inhibition of Sox10 expression . We next assessed the effect of ATF2 on SOX10 and MITF expression in 12 additional human melanoma cell lines . In all cases cells were infected with shATF2 and changes in SOX10 and MITF were monitored at the level of RNA . Notably , about 4/12 melanoma lines revealed increase in both SOX10 and MITF expression upon KD of ATF2 ( Figure S5 , Table 4 ) . In contrast , 6/12 melanoma lines revealed decrease in MITF expression , of which 5 also shown decrease in SOX10 expression , pointing to positive regulation of SOX10 and MITF in these melanoma cells . In two out of the 12 melanoma lines ATF2 affected SOX10 but not MITF transcription ( Figure S5 ) . Overall , our cohort of 18 melanoma lines revealed that about 50% of the melanomas retained negative regulation of MITF by ATF2 , as seen in the melanocytes ( primary and cell lines ) ( Table 4 ) . To further assess whether ATF2 regulation of MITF is SOX10-dependent in melanocytes and melanoma cells , we coexpressed SOX10 in shATF2-expressing cells . As seen in earlier analysis , inhibition of ATF2 expression caused increase in MITF transcription in the human melanocytes and 4 melanoma cell lines , ( WM1361 , WM793 , LU1205 , WM35; Figure S6 ) . Notably , the melanocytes and 2/4 melanoma cell lines revealed ATF2 effect on MITF expression is SOX10-dependent ( WM1361 , WM793; Figure S6 ) . Two of the four melanoma cell lines did not reveal increased SOX10 expression , although they retained increased MITF expression , upon inhibition of ATF2 ( Lu1205 , WM35; Figure S6 ) . These findings confirm that while in melanocytes , expression of SOX10 and MITF is negatively regulated by ATF2 , this mechanism is conserved in approximately half of melanomas surveyed . Along these lines , the two melanoma lines ( MeWo and 501 Mel ) that exhibit positive regulation of MITF by ATF2 also exhibited positive regulation of SOX10 by ATF2 ( Figure S7 ) . Inhibition of ATF2 expression reduced SOX10 and MITF RNA and protein levels ( Figure S7a–c ) . In order to determine whether JunB lost its ability to elicit negative regulation of SOX10 and MITF in melanoma cells where ATF2 no longer inhibited SOX10 or MITF expression , we transfected those cell lines with TAM67 and JunB alone and in combination . In these cells , whereas TAM67 effectively attenuated Sox10 and MITF expression , JunB did not alter expression of these genes , suggesting that positive regulation of MITF and SOX10 by ATF2 depends on other members of the Jun family of transcription factors ( Figure S7d ) . Conversely , TAM67 or JunB had no effect on melanoma cells in which ATF2 inhibits MITF independently of SOX10 , suggesting that in these cases , ATF2 likely cooperates with transcription factors other than JunB to elicit negative regulation of SOX10 and MITF ( Figure S7d ) . Consistent with this observation , ChIP assay confirmed ATF2 and CREB , but not JunB , binding to the Sox10 promoter in these cells ( Figure S7e ) . These findings suggest that changes in ATF2 heterodimeric partner ( from JunB to CREB ) are likely to cause the switch from negative to positive regulation of SOX10 , and in turn , MITF ( see below ) . The possibility that altered expression of JunB may account for ATF2 positive or negative regulation of Sox10 and MITF were excluded , as no clear correlation between JunB expression and the ability of ATF2 to elicit negative regulation of Sox10/MITF were seen ( Figure S7f ) . Among response elements potentially required to upregulate MITF transcription is the CRE element , which is implicated in CREB-mediated upregulation of MITF transcription [45] . Although transcriptional activity from a CRE mutant MITF promoter was lower compared to the WT promoter ( 30% ) , it was no longer responsive to inhibition of ATF2 expression in the MeWo cells ( Figure S8a ) . Pull-down assays using biotin-tagged MITF promoter sequences harboring the CRE identified ATF2 and CREB as CRE-bound proteins in MeWo melanoma cells ( Figure S8b ) . In agreement , ChIP analysis confirmed occupancy of the CRE site on MITF promoter by ATF2 ( Figure S8c ) . These findings are consistent with the fact that ATF2 heterodimerizes with CREB [9] and with a report that p38/MAPK14 ( which phosphorylates ATF2 ) plays an important role in MITF transcription dependent on the CRE site [46] . These results establish that ATF2-dependent activation of MITF transcription in these melanoma cells is mediated through the CRE site , likely in cooperation with CREB . Notably , MeWo and 501Mel lines are known to express high MITF levels compared to other melanoma lines [41] , [42] , suggesting these cells harbor distinct mechanisms that preclude negative regulation of MITF by ATF2 . To determine whether the contribution of ATF2 to melanocyte transformation and development is MITF-dependent , we assessed melanocytes' ability to grow and form colonies in soft agar , which is indicative of their transformed potential . Expression of mutant BRAFV600E in immortal melanocytes is reportedly sufficient for growth on soft agar [47] . Thus we infected melan-Ink4a-Arf1 melanocytes with mutant BRAF ( Figure 6a ) and confirmed their ability to form colonies in soft agar . Mutant BRAF expression effectively caused formation of about 1000 colonies per 5000 cells ( Figure 6b , 6c ) . In contrast , melanocytes infected with BRAF600E and with shATF2 formed on average about 20 colonies , indicative of loss of tumorigenicity ( Figure 6b , 6c , 6d ) and consistent with our initial observation that the number of melanoma tumors significantly decreases in the absence of transcriptionally functional ATF2 ( Tables 1–2 ) . To determine the importance of MITF at this early stage of melanocyte transformation we inhibited MITF expression ( using shRNA ) in melanocytes expressing mutant BRAF alone or mutant BRAF+shATF2 . Significantly , inhibition of MITF expression decreased the number of BRAF-induced foci ( from 1000 to about 100 per well ) . Over-expression of MITF in BRAF-expressing melanocytes also inhibited focus formation , to a degree similar to that seen following inhibition of MITF expression ( Figure 6b , 6c , 6d ) . This observation implies that effective inhibition or overexpression of MITF attenuates melanocyte transformation , consistent with previous reports ( 52 ) . Remarkably , inhibition of MITF expression in melanocytes expressing both mutant BRAF and shATF2 rescued , at least partially , melanocytes' ability to form foci on soft agar ( 400 compared with 20 seen in shATF2 cells; Figure 6b , 6c , 6d ) . These findings suggest that inhibition of MITF expression in melanocytes lacking ATF2 expression can promote transformation . That MITF inhibition in melanocytes expressing ATF2 WT can attenuate their ability to form foci on soft agar is attributable to the relative expression of MITF RNA and protein in each condition ( Figure 6d ) . MITF expression levels in ATF2 KD cells increased 7 . 5-fold compared with control BRAF-expressing melanocytes . Inhibition of MITF expression in ATF2 KD melanocytes reduced MITF expression 2 . 5-fold relative to controls , whereas MITF KD alone resulted in lower MITF expression ( 5-fold; Figure 6d ) . Thus , complete abrogation of MITF expression attenuates melanocyte transformation , whereas low to moderate levels of MITF expression are sufficient to promote growth on soft agar . Higher MITF expression levels , as seen in ATF2 KD cells , result in a total loss of melanocytes' ability to form foci on soft agar . These findings are in line with the proposed rheostat model in which medium levels of MITF are optimal for growth and melanoma development [48] and in agreement with our observations in a mouse melanoma model . We next assessed whether inhibition of melanocyte growth on soft agar by altered ATF2 and/or MITF expression can be attributed to decreased proliferation or increased apoptosis . Inhibition of ATF2 expression caused notable accumulation of cells in G2 ( 60% ) , with significant cell death induction ( 22% ) compared to controls ( 4% ) , ( Figure 6e , 6f ) . Interestingly , such altered cell cycle distribution and cell death rate were associated with a significant increase in MITF protein levels ( Figure 6d ) . In contrast , inhibition of MITF expression did not significantly induce cell death ( 6 . 5% ) but resulted in fewer cells in G2/M-phase and more cells in G1 , compared with inhibition of ATF2 alone . These observations suggest that MITF inhibition is sufficient to reduce the rate of cell cycle progression through G2/M phase and that inhibited growth of BRAF600E-expressing melanocytes on soft agar may be attributed to abrogation of distinct cell cycle-regulatory mechanisms . Combined inhibition of ATF2 and MITF restored cell cycle distribution to that seen in control melanocytes , and reduced cell death from 22 . 4% to 12 . 9% . Of interest , MITF overexpression promoted a similar degree of cell death ( 11 . 4% ) without altering cell cycle distribution , similar to combined inhibition of ATF2 and MITF ( Figure 6e , 6f ) . Together , these observations suggest that simultaneous inhibition of ATF2 and MITF averts cell cycle abrogation induced when expression of either of these factors is perturbed individually , further substantiating regulation of MITF by ATF2 . The availability of a melanoma TMA , consisting of over 500 melanoma samples and in which expression of both ATF2 and MITF in the same tumors had been measured enabled us to assess possible associations between ATF2 and MITF and their correlation with survival and other clinical and pathological factors . Our earlier studies revealed that ATF2 subcellular localization in tumors is significantly correlated with prognosis: nuclear localization , reflecting constitutively active ATF2 , was associated with metastasizing tumors and poor outcome [7] . Here we quantitated immunofluorescent staining of TMAs for MITF and ATF2 by employing our automated , quantitative ( AQUA ) method . To normalize ATF2 and MITF levels , expression of each of the two proteins in individual patients was divided by the median expression level of the respective protein in all patients , and the nuclear ATF2/MITF ratio was calculated and log-transformed . By ANOVA analysis , the ratio was higher in metastatic than in primary specimens ( t value = 2 . 823 , P = 0 . 0051 ) , as shown in Figure 7a . No association was found between nuclear ATF2/MITF ratio and disease-specific survival among patients with metastatic melanoma ( not shown ) . Significantly , a high nuclear ATF2/MITF ratio in primary melanoma specimens was associated with decreased 10-year disease-specific survival ( P = 0 . 0014; Figure 7b ) . On Cox multivariable analysis , this association with survival was independent of patient age , Breslow thickness or the presence or absence of ulceration ( data not shown ) . Nuclear ATF2 alone in primary specimens was associated with poor survival , but to a lesser degree than the ratio of nuclear ATF2/MITF ( P = 0 . 0118 for ATF2 as a single discriminator versus P = 0 . 0014 for the ratio of nuclear ATF2/MITF ) . Nuclear MITF as a single discriminator was not a significant predictor of survival ( P = 0 . 185 ) , as was reported previously using immunohistochemistry [49] . These observations suggest that active ( nuclear ) ATF2 in melanoma can suppress MITF expression , and that this phenomenon is associated with poor prognosis .
Identifying mechanisms underlying early phases of melanocyte transformation and melanoma development is central to understanding the etiology of this devastating tumor , as well as for developing novel treatment approaches . Previous studies indicate the presence of mutant BRAF in melanocytic lesions , as well as its effect on pigment gene expression [6] , [50] , [51] . The present study enhances our understanding of early events contributing to melanoma development . We demonstrate that loss of a transcriptionally active form of ATF2 in melanocytes inhibits melanoma development in an Nras/Ink4a model . Our quest to understand mechanisms underlying ATF2 activity in this process led us to identify an important role for ATF2 regulation of MITF , an important regulator of melanocyte biogenesis and a factor implicated in melanoma progression [49] . Surprisingly , ATF2 negatively regulated MITF expression in mouse and human melanocytes , suggesting that ATF2 transcriptional activities limit MITF expression . We demonstrate that such negative regulation is elicited through downregulation of SOX10 by ATF2 , in cooperation with JunB . A putative AP1 response element has been identified in SOX10 promoter sequences and ChIP analysis of this domain showed ATF2 and JunB binding . Overexpression of JunB efficiently suppressed SOX10 expression in an ATF2-dependent manner and inhibition of Jun transcriptional activities phenocopied the effect of shATF2 , suggesting that negative regulation of SOX10 by ATF2 is direct , and is mediated in cooperation with JunB . Importantly , ATF2-dependent negative regulation of Sox10 and consequently of MITF seen in melanocytes , but only in about 50% of the 18 melanoma cell lines studied here . Correspondingly , JunB , which is required for ATF2-dependent inhibition of Sox10 transcription , is no longer found on the promoter of SOX10 in melanoma cells ( i . e . 501Mel ) that exhibit positive regulation by ATF2 . Rather , CREB and ATF2 are found on SOX10 and MITF promoters , pointing to a switch in ATF2 heterodimeric partners to enable positive regulation of these genes . Notably , melanoma cell lines that exhibit positive regulation of SOX10 and MITF by ATF2 , also show high basal levels of MITF expression [41] , [42] , suggesting that additional genetic or epigenetic changes distinguish these lines from melanocytes and the other melanoma lines in which ATF2 elicits negative regulation of MITF . Notably , ATF2 control of MITF expression affected the ability of BRAF600E-expressing melanocytes to exhibit transformed phenotype in culture , monitored by their ability to grow on soft agar . Inhibition of ATF2 abolished soft agar growth of BRAF600E-expressing melanocytes , which was partially rescued upon KD of MITF . Interestingly , both the over expression or the KD of MITF resulted in inhibition of melanocytes ability to grow on soft agar , substantiating the notion that a fine balance of MITF expression must be maintained in order to ensure its contribution to cellular proliferation and transformation . We propose that excessively low or high MITF levels block melanocyte transformation , whereas intermediate levels allow transformation to occur . Overall , our observations demonstrate that ATF2 plays an important role in fine-tuning those levels and support the rheostat model proposed for MITF's role in melanoma development and progression [48] . Of importance , ATF2 and MITF affect the ability of BRAF600E-expressing melanocytes to grow on soft agar via distinct mechanisms . Whereas specific inhibition of ATF2 causes both accumulation of cells in G2 and induction of cell death , specific alteration of MITF protein levels—particularly depletion—significantly affects cell proliferation and inhibit growth on soft agar by non-lethally slowing cell cycle progression at G2/M . These observations are consistent with a report from Wellbrock and Marais [52] , who showed that altered MITF expression inhibits melanocyte proliferation . Importantly , inhibiting MITF expression in ATF2 KD melanocytes was sufficient to partially rescue melanocyte growth on soft agar . While supportive of our finding in the Nras::Ink4a mouse melanoma model , where expression of transcriptionally inactive ATF2 inhibits melanoma formation , these observations provide the foundation for a model in which ATF2 inhibition causes increased MITF levels and concomitant inhibition of melanocyte growth , possible induction of cell death and delayed development . The latter is suggested by IHC analysis of mouse skin from ATF2md mice , which shows notably reduced S100 staining indicative of delayed melanocyte development: ATF2 KO melanocytes appear to represent anagen stage IV , whereas WT represent anagen stage VI . This delay was seen at the 4- but not the 14-day time point , suggesting that an ATF2 effect might be limited to a specific subpopulation or phase of melanocyte development . The early ( 4 day ) time point is within the time frame that allows induction of melanoma development by UV-irradiation of c-Met or H-Ras mutant mice [53] . It is therefore plausible that timely control of MITF expression by ATF2 determines melanocyte susceptibility to transformation . Our analysis of genes whose expression is altered by ATF2 KD in melanocytes identified a cluster of pigmentation genes , many reportedly regulated by MITF [6] , [54] . Therefore , changes in TYRP1 , DCT and SILVER expression could be attributed to altered MITF expression . However , initial analysis points to a more complex mechanism since ( i ) the degree of changes in expression of these genes was often greater than that seen for MITF and ( ii ) expression of some pigmentation genes was found to be independent of MITF in some melanoma and melanocyte cultures . Hence , further studies are required to address mechanisms underlying ATF2 regulation of these pigmentation genes and the significance of such regulation to melanocyte transformation and melanoma development . While our present studies focused on the ATF2-MITF axis , it is expected that additional ATF2-regulated genes contribute to melanoma development [12] . In agreement , our earlier studies using both human and mouse melanoma lines demonstrate that inhibition of ATF2 effectively inhibits tumorigenesis and blocks metastasis [22]–[26] . Important for ATF2 function is its subcellular localization . While findings presented here position ATF2 as an oncogene functioning in melanocyte transformation and melanoma development , earlier studies from our laboratory and others suggest that in keratinocytes and mammary glands , ATF2 elicits a tumor suppressor function [55] , [56] . Of interest , assessing the localization of ATF2 in the melanoma cell lines studied here revealed that all express nuclear ATF2 . Interestingly , in most cases the nuclear staining revealed a punctate staining , resembling the localization of ATF2 to DNA repair foci following DNA damage ( Figure S9 ) . A possible link between the presence of ATF2 in repair foci in most melanoma cells points to the possible presence of activated DNA damage response which may be associated with genomic instability [19] , [20]—aspects that will be explored in future studies . Significantly , the appearance of nuclear ATF2 is correlated with poor prognosis in melanoma , whereas melanomas that exhibit cytosolic ATF2 exhibit a better survival . Notably , cytosolic ATF2 is primarily seen in non-malignant skin tumors [55] . Here we demonstrate that high nuclear ATF2/MITF ratios are associated with poor prognosis in primary melanomas , but not with metastatic melanomas . The latter finding attests for the important role ATF2 plays to control MITF expression in the early phase of melanocyte transformation and melanoma development . Overall , using the mutant Nras/Ink4a melanoma model we provide genetic evidence for a central role for ATF2 in melanoma development . We demonstrate that in the absence of transcriptionally active ATF2 , melanoma formation is largely inhibited . Furthermore , our data point to an unexpected role of ATF2 in fine-tuning of MITF transcription through regulation of its positive regulator SOX10 . Mouse melanoma models and in vitro transformation studies indicate that this newly identified regulatory pathway is required for early phases of melanocyte transformation . Given that ATF2 affects activity of the oncogenes N-Ras ( mouse model ) and BRAF ( melanocyte growth on soft agar ) ; we expect that ATF2 play significant roles in melanomas that carry either of these mutations .
Research involving human participants has been approved by the institutional review board at Yale University ( where the TMA was prepared and analyzed ) . All animal work has been conducted according to relevant national and international guidelines in accordance with recommendations of the Weatherall report and approved by the IACUC committee at SBMRI . Mice bearing a conditional allele for mutant ATF2 in which the DNA binding domain and part of the leucine zipper domain were deleted , were generated as previously described [36] , [55] . To study the function of ATF2 in melanocytes , we utilized the Cre-loxP system for disruption of the ATF2 gene in melanocytes [37] . The Tyr::CreER::Atf2md mice and their littermate controls ( WT ) were of FVB/129P2/OlaHsd ( TyrCreERT mice were FVB , ATF2fl/fl were 129P2/OlaHsd ) and N-Ras/Ink4a−/− mice were C57Bl/6/129SvJ . For melanoma studies we have used Tyr::CreER::NrasQ61K::Ink4a−/− mice ( developed at HMS by LC ) following their cross with the Tyr::CreER::Atf2md mice . Skin specimens were fixed in neutral buffered formalin solution and processed for paraffin embedding . Skin sections ( 5 µm in thickness ) were prepared and deparaffinized using xylene . For MITF , DCT and S100 immunostaining , tissue sections were incubated in DAKO antigen retrieval solution , for 20 min in a boiling bath , followed by treatment with 3% hydrogen peroxide for 20 min . Antibodies against MITF ( 1∶100 from Sigma ) , DCT ( 1∶500 , kind gift from Dr . Vincent Hearing ) and S100 ( 1∶100 , DAKOCytomation; Carpinteria , CA ) were allowed to react with tissue sections at 4°C overnight . Biotinylated anti-rabbit IgG was allowed to react for 30 min at room temperature and diaminobenzidineor Nova Red were used for the color reaction . Hematoxylin was used for counterstaining . The control sections were treated with normal mouse serum or normal rabbit serum instead of each antibody . Immortalized human melanocytes Hermes 3A which has hTERT ( puro ) and CDK4 ( neo ) expression [57] were grown in RPMI 1640 medium containing Fetal Bovine Serum ( FBS , 10% ) , 12-O-tetradecanoyl-phorbol-13-acetate ( TPA , 200 nM , Sigma , St . Louis , MO ) , Cholera toxin ( 200 pM , Sigma ) , human stem cell factor ( 10 ng/ml , R&D systems , Minneapolis , MA ) , and endothelin 1 ( 10 nM , Bachem Bioscience Inc . , Torrance , CA ) . Primary human melanocytes ( NEM-LP; Invitrogen ) were grown in medium 254 and HMGS ( Cascade Biologics ) . Mouse melanocytes ( melan-Ink4a-Arf1 ) were grown as for immortalized human melanocytes excluding human stem cell factor and endothelin . Melanoma cell lines were grown in DMEM medium supplemented with 10% FBS and penicillin/streptomycin ( P/S; Cellgro ) . Melanoma cell lines used in this study LU1205 , WM793 , 501MEL , WM35 , WM1361 , MeWO ( kind gift from Meenhard Herlyn ) , UACC903 were maintained in DMEM medium supplemented with 10% FBS and Penicillin/Streptomycin . Melanoma cell lines SbCl2 , WM9 , WM4 , WM1650 , A2068 , WM1366 , WM3629 , WM1552 , SKMEL2 , SKMEL5 , and SKMEL8 were maintained in RPMI medium supplemented with 10% FBS and Penicillin/Streptomycin . Primary melanocytes cultures were prepared from mice carrying the Atf2 WT or mutant genotypes and N-Ras/Ink4a−/− as follows . Dorsal-lateral skin was removed from one day-old pups , disinfected with 70% ethanol for 1 min and then washed at least twice with sterile PBS . The skin was submerged in 1× Trypsin/EDTA overnight at 4°C and next day , the skin was placed in a Petri dish with mouse melanocyte culture medium ( described below ) . The epidermis and sheared tissue was removed and discarded with forceps . The tissue was transferred to 15 ml centrifuge tubes and vortexed vigorously until solution becomes cloudy ( 1–2 min ) . The cell suspension was transferred to tissue culture flasks . After 3 days , melanocyte growth medium containing 0 . 8 µg/ml geneticin ( Sigma-Aldrich ) was added to eliminate contaminating fibroblasts ( melanocytes are resistant to such treatment ) . Geneticin-containing medium was removed and replaced with fresh media after 1 day . Media was changed twice a week . Primary mouse melanocytes were grown in F-12 media ( Invitrogen ) containing 20% L-15 media ( Invitrogen ) , 4% of FBS and Horse serum ( Invitrogen ) , Penicillin ( 100 units ) and streptomycin ( 50 µg ) antibiotics , db-cAMP ( 40 µM , Sigma-Aldrich ) , 12-O-tetradecanoyl-phorbol-13-acetate ( TPA , 50 ng/ml , Sigma-Aldrich ) , alpha-Melanocyte stimulating hormone ( α-MSH , 80 nM , Sigma-Aldrich ) , Fungizone ( 2 . 5 µg/ml , Sigma-Aldrich ) and melanocyte growth supplement ( Invitrogen ) . Primary melanocytes were treated with 4-OHT ( 10 µM ) for 8h followed by addition of doxycycline ( 2 µg/ml ) for 24h to inactivate ATF2 and induce expression of N-Ras . ATF2-specific shRNA clones were obtained from Open Biosystems ( catalog no . RHS4533 ) . Five different shRNA were obtained and tested for their efficiency of KD . Clone TRCN0000013714 was more efficient in inhibiting ATF2 in human cell lines while clone TRCN0000013713 was more efficient for knocking down mouse ATF2 . For subsequent experiments we used the respective shATF2 clone depending on human or mouse cell lines . We also tested 3 different clones for KD of ATF2 to rule out any off target effect ( Data not shown ) . siRNA control ( cat # 4611 ) and three SOX10-specific siRNA oligonucleotides were obtained from Ambion ( cat # 4392420 ) . Four FOXD3 specific siRNA were obtained from Dharmacon ( Cat # J-009152-06 -07 , -08 , -09 ) . These siRNAs were pooled together in equimolar ratio for transient transfection . An MITF specific shRNA , and MITF promoter luciferase constructs ( WT and mutant CRE-Luc constructs ) were obtained from Dr . David Fisher [58] . pGL3 vectors containing wild-type and BRN2-site-mutated MITF promoters were obtained from Dr . Colin Goding [34] . pGL3 vectors containing wild-type and SOX10-site-mutated MITF promoters were obtained from Dr . Michel Goossens [59] . Retroviral vectors encoding a fusion protein consisting of full length human BRAF and BRAFV600E linked to the T1 form of the human estrogen receptor hormone-binding domain were generously provided by Dr . Martin McMahon [60] . SOX10 expression vector obtained from Dr . Alexey Terskikh , RSV-JunB , RSV-JunD were obtained from Dr . Michael Karin and pBabe-Flag-TAM67 from Dr . Michael Birrer . Antibodies against SOX10 and CREB ( sc-1734 and sc-186 respectively ) were from Santa Cruz Biotechnologies; antibodies against ATF2 , pERK and ERK ( catalogue # 9226 , 4337 and 4695 respectively ) were obtained from Cell Signaling; antibodies against MITF ( C5 ) were purchased from Cell Lab vision . Protein extract ( 40–60 µg ) preparation and western blot analysis were done as described previously [8] . Specific bands were detected using fluorescent-labeled secondary antibodies ( Invitrogen , Carlsbad , CA ) and analyzed using an Odyssey Infrared Scanner ( Li-COR Biosciences ) . β-Actin antibody was used for monitoring loading . Human melanoma and melanocytes were grown in coverslips , fixed ( 4% paraformaldehyde and 2% sucrose in 1×PBS ) , and then permeabilized and blocked ( 0 . 4% Triton X-100 and 2% BSA in 1×PBS ) at room temperature . The cells were then washed ( 0 . 2% Triton X-100 and 0 . 2% BSA in 1×PBS ) and incubated overnight at 4°C with monoclonal anti-rabbit antibody against ATF2 ( 20F1 , 1∶100 ) , followed by five washes and then subsequent incubation at room temperature for 2 h with anti-rabbit IgG ( Invitrogen , 1∶300 ) and Phalloidin ( Molecular Probes , 1∶1000 ) . DNA was counterstained with 4 , 6-diamidino-2-phenylindole ( DAPI; Vector Laboratories ) containing mounting medium . Skin samples were collected from the backs of mice and immediately fixed with Z-fix , processed , and embedded in paraffin . Paraffin sections were routinely stained by H&E . Dewaxed tissue sections ( 4 . 0–5 . 0 µm ) were immunostained using rabbit polyclonal antibodies to MITF ( Sigma-Aldrich ) , S100 ( S100B; DAKOCytomation; Carpinteria , CA ) , and DCT ( αPEP8 , kindly provided by Dr . Vincent Hearing ) . Application of the primary antibody was followed by incubation with goat anti-rabbit polymer-based EnVision-HRP-enzyme conjugate ( DakoCytomation ) . DAB ( DakoCytomation ) or SG-Vector ( Vector Lab , Inc . ; Burlingame , CA ) chromogens were applied , yielding brown ( DAB ) and black ( SG ) colors , respectively . Quantitative analysis was performed as described previously [61] . Briefly , all slides were scanned at an absolute magnification of 400× [resolution of 0 . 25 µm/pixel ( 100 , 000 pix/in . ) ] using the Aperio ScanScope CS system ( Aperio Technologies; Vista , CA ) . The acquired digital images representing whole tissue sections were analyzed applying the Spectrum Analysis algorithm package and ImageScope analysis software ( version 9; Aperio Technologies , Inc . ) to quantify IHC and histochemical stainings . These algorithms make use of a color deconvolution method [62] to separate stains . Algorithm parameters were set to achieve concordance with manual scoring on a number of high-power fields , including intensity thresholds for positivity and parameters that control cell segmentation using the nuclear algorithm . Primary melanocytes were treated with 4-OHT and Doxycycline before isolation of total RNA . 500 ng of total RNA was used for synthesis of biotin-labeled cRNA using an RNA amplification kit ( Ambion ) . The biotinylated cRNA is labeled by incubation with streptavidin-Cy3 to generate probe for hybridization with the Mouse-6 Expression BeadChip ( Illumina MOUSE-6_V1_1_11234304_A ) that represents 46 . 6K mouse gene transcripts . We analyzed the BeadChips using the manufacturers BeadArray Reader and collected primary data using the supplied Scanner software . Data analysis was done as follows . First , expression intensities were calculated for each gene probed on the array for all hybridizations using illumina's BeadStudio 3 . 0 software . Second , intensity values were quality controlled and normalized: quality control was carried out by using the BeadStudio detection P-value set to <0 . 01 as a cutoff . This removed genes which were never detected in the arrays . All the arrays were then normalized using the cubic spline routine from the BeadStudio 3 . 0 software . This procedure accounted for any variation in hybridization intensity between the individual arrays . Finally , these normalized data were analyzed for differentially expressed genes . The groups of 2 biological and 2 technical replicates were described to the BeadStudio 3 . 0 software and significantly differentially expressed genes were determined on the basis of the difference changes in expression level ( Illumina DiffScor>60 or DiffScore<−60 ) and expression difference p-value<0 . 01 . Microarray data are available under accession number GSE23860 . Human embryonic kidney 293T cells were transfected with corresponding retro- or lentiviral shRNA constructs ( 10 µg ) , Gag-pol ( 5 µg ) and ENV expression vectors ( 10 µg ) by calcium phosphate transfection into 10 cm plates and supernatant was collected after 48 hours to obtain viral particles . 2 million melanocytes and melanoma cells in 10 cm plates were infected with 5 ml of viral supernatant along with 5 ml of medium in the presence of 8 µg/ml polybrene . The virus was replaced with fresh media after 8 hours of infection . After two days , puromycin ( 1 . 5 µg/ml ) was used to select cells for 3 days . For human and mouse melanocytes the media was changed to DMEM containing 10% FBS 24 h prior to harvesting cells . 50 nM duplexes of scrambled and SOX10- or FOXD3- specific siRNA were transfected into human melanocytes and WM1361 melanoma cells ( 2 million cells per transfection ) by Nucleofection using Amaxa reagents ( NHEM-Neo Nucleofector and Solution R respectively ) for SOX10 or FOXD3 knock down . Over 90% of the cells transduced were able to resist drug selection , indicating efficient infection of the respective genes . GFP was also used to monitor efficiency of infection , confirming >90% GFP expression by fluorescence microscopy . Quantitative PCR was performed as described earlier [8] . Total RNA was isolated using an RNeasy mini kit ( Sigma , St . Louis , MO ) and reverse transcribed using a high cDNA capacity reverse transcription kit ( Applied Biosystems , Foster City , CA ) following the manufacturer's instructions . Specific primers ( Valuegene , San Diego , CA ) used for PCR were as follows: Human ATF2 , forward: tgtggccagcgttttaccaa , reverse: tgatgtgggctgtgcagttt . , human MITF , forward: aaaccccaccaagtaccaca , reverse: acatggcaagctcaggac . , human SOX10 , forward: caa gtaccagcccaggcggc , reverse: gggtgccggtggtccaagtg . , human FOXD3 , forward: gcgacgggctggaagag , reverse: gctgtccgtgatggggtgcc . , human PAX3 , forward: ggaactggagcgtgcttttg , reverse: ggcggttgctaaaccagac . , human BRN2 , forward: gaaagagcgagcgaggaga , reverse: caggctgtagtggttagacg . , mouse MITF , forward: agatttgagatgctcatcccc , reverse: gatgcgtgatgtcatactgga , mouse TYRP1 , forward: ccctagcctatatctccctttt , reverse: taccatcgtggggataatggc . , mouse DCT , forward: gtcctccactcttttacagacg , reverse: attcggttgtgaccaatgggt , mouse Silver , forward: tgacggtggaccctgcccat , reverse: agctttgcgtggcccgtagc . The reaction mixture was denatured at 95°C for 10 min , followed by 40 cycles of 95°C for 15s , annealing at 60°C for 30s and extension at 72°C for 30s . Reactions were performed using the SYBR Green qPCR reagent ( Invitrogen ) and run on an MX3000P qPCR machine ( Stratagene , La Jolla , CA ) . The specificity of the products was verified by melting curve analysis and agarose gels . The amount of the target transcript was related to that of a reference gene ( Cyclophilin A for both human and mouse ) by the Ct method . Each sample was assayed at least in triplicate and was reproduced at least three times . Chromatin immunoprecipitation was performed using the Magna-Chip ( Upstate ) according to the manufacturer's instructions . Control shRNA and ATF2 knocked down WM1361 cells ( one 10 cm plate for each , 80% confluent ) were fixed in 37% formaldehyde and sheared chromatin was immunoprecipitated and subjected to PCR for 32 cycles . The following primers corresponding to the MITF promoter , spanning the SOX10 binding site were used , forward: gcagtcggaagtggcag , reverse: caactcactgtcagatcaa . Antibodies against Sox10 and CREB ( sc-1734 and sc-186 respectively ) were from Santa Cruz Biotechnologies . IgG control , and glyceraldehyde-3-phosphate dehydrogenase oligonucleotides were provided by the kit . Antibody against ATF2 ( sc-6233 ) , JunB ( sc-8051 ) , JunD ( sc-74 ) were obtained from Santa Cruz . Antibodies against ATFa were provided generated by Nic Jones . For Sox10 promoter , the following primers spanning AP-1 binding site were used; forward: cccagtgctggcctaatagc , reverse: cacccttgatatccccaagtga . MeWo , WM35 , WM1361 , Lu1205 cells in six-well plates were transiently transfected with 0 . 5 µg of reporter plasmid containing WT or CRE mutant , BRN2 mutant or SOX10 mutant MITF promoter and 0 . 1 µg of pSV-β-Galactosidase ( Promega , San Luis Obispo , CA ) using Lipofectamine 2000 reagent ( Invitrogen ) . Human melanocytes ( 2 million ) were transfected with 2 µg of reporter plasmid containing WT or SOX10 mutant MITF promoter and 0 . 3 µg of pSV-β-Galactosidase using Amaxa reagent ( NHEM-Neo nucleofector kit , Lonza ) according to the manufacturer's protocol . Cell lysates were prepared from cells after 48 h . Luciferase activity was measured using the Luciferase assay system ( Promega ) in a luminometer and normalized to β-galactosidase activity . The data were normalized to β-galactosidase and represent the mean and SD of assays performed in triplicate . All experiments were performed a minimum of 3 times . Melan-Ink4a-Arf1 cells were transduced with a retroviral vector expressing BRAFV600E:ERT1 and selected with puromycin for 3 days . These cells were treated with 200 nM of estrogen receptor antagonist ICI 182780 ( ICI , Tocris Bioscience ) to induce expression of BRAFV600E . After one day , these cells were transduced with a lentiviral vector expressing either shATF2 or shMITF separately , or in combination . Colony formation was carried out as described by Franken et al . [63] . Briefly , 5 , 000 cells were plated into each well of a 6-well plate , and cells were grown in mouse melanocyte media containing ICI and puromycin ( 1 . 5 µg/ml ) for 3 weeks until colonies became visible . The colonies were stained with P-Iodonitrotetrazolium Violet ( 1 mg/ml Sigma , St . Louis , MO ) . This experiment was performed in triplicate and reproduced 2 times . Genomic DNA was isolated from tail tissue was subjected to PCR resulting in amplification of a 549 bp DNA fragment for Atf2 floxed and a 485 bp DNA fragment for wild type mice . PCR conditions included one cycle at 95°C for 3 min; and 30 cycles of 94°C/30 sec , 55°C/30 sec and 72°C/1 min and one cycle at 72°C for 5 min . Primers used for PCR reactions were forward: caatccactgccatggcctt , reverse: tcagataaagccaagtcgaatctgg . MeWo cells were left untreated or treated with 20 mJ/cm2 of UV-B for 1 h . The cells were lysed using lysis buffer containing 1% Triton-100 and incubated with 4 µg of biotin-labeled MITF promoter spanning the CRE site oligo ( 5′-gaaaaaaaagcatgacgtcaagccaggggg-3′ ) in the presence of poly- ( dI-dC ) ( 20 µg/ml ) for 2h at 4°c . The oligo-bound proteins were captured using streptavidin-agarose ( Invitrogen ) for 1 h incubation , followed by extensive washes with washing buffer ( 20 mm HEPES , 150 mm NaCl , 20% glycerol , 0 . 5 mm EDTA , and 1% Triton-100 ) and analyzed using SDS-PAGE and western blots . To evaluate the cell cycle index of Melan-Ink4a-Arf1 cells stably overexpressing BRAFV600E:ERT1 alone or in combination with shRNA to the genes indicated in Results , cells were plated in media containing ICI and puromycin ( 1 . 5 µg/ml ) at 2×106 cells per 10 cm plate O/N . Cells were labeled with 10 µM of 5-bromo-2-deoxyuridine ( BrdU; Sigma Chemical Co . ) , for an hour . Cells were then washed , fixed , and stained with anti-BrdU mAbs and propidium Iodide ( BD Biosciences , San Jose , CA ) according to the manufacturer's protocol , and analyzed on a BD FACSCanto machine . Cell cycle phase was analyzed using the Mod Fit LT v . 2 program ( Verity Software , Topsham , ME ) . In a separate experiment the cells were stained with Annexin V-APC and 7-AAD ( BD Pharmingen , San Diego , CA ) according to manufacturer's protocol , to enable analysis of early apoptosis and cell death . Cells were treated under hypoxia ( 1% O2 ) for indicated time points using a hypoxia chamber ( In Vivo 400; Ruskin Technologies Ltd , Bridgend , UK ) . Mice were treated with 4-Hydroxytamoxifen ( 25 mg/ml in DMSO ) by swabbing the entire body ( excluding the head ) on days 1–3 after birth . On day 4 the pups were placed under UVB light source ( FL-15E; 320 nm ) and exposed to 20 µW/cm2 for 22 seconds . Ninety minutes after UVB treatment mice were sacrificed and entire skin was removed and processed . Tissue microarrays were constructed as previously described [21] . The arrays included a series of 192 sequentially collected primary melanomas and 299 metastatic melanomas . Slides were stained for automated , quantitative analysis ( AQUA ) for ATF2 and MITF as previously published [49] , [64] . The AQUA scores for the two markers were obtained from the AQUAmine database ( www . tissuearray . org ) . | Understanding mechanisms underlying early stages in melanoma development is of major interest and importance . Recent studies indicate a role for MITF , a master regulator of melanocyte development and biogenesis , in melanoma progression . Here we demonstrate that the transcription factor ATF2 negatively regulates MITF transcription in melanocytes and in about 50% of melanoma cell lines . Increased MITF expression , seen upon inhibition of ATF2 , effectively attenuated the ability of BRAFV600E-expressing melanocytes to exhibit a transformed phenotype , an effect partially rescued when MITF expression was also blocked . Significantly , the development of melanoma in mice carrying genetic changes seen in human tumors was inhibited upon inactivation of ATF2 in melanocytes . Melanocytes from mice lacking active ATF2 expressed increased levels of MITF , confirming that ATF2 negatively regulates MITF and implicating this newly discovered regulatory link in melanoma development . Primary melanoma specimens that exhibit a high nuclear ATF2-to-MITF ratio were found to be associated with metastatic disease and poor prognosis , further substantiating the significance of MITF control by ATF2 . In all , these findings provide genetic evidence for the role of ATF2 in melanoma development and indicate an ATF2 function in fine-tuning MITF expression , which is central to understanding MITF control at the early phases of melanocyte transformation . |
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Kaposi's sarcoma associated herpesvirus ( KSHV ) is etiologically associated with endothelial Kaposi's sarcoma ( KS ) and B-cell proliferative primary effusion lymphoma ( PEL ) , common malignancies seen in immunocompromised HIV-1 infected patients . The progression of these cancers occurs by the proliferation of cells latently infected with KSHV , which is highly dependent on autocrine and paracrine factors secreted from the infected cells . Glutamate and glutamate receptors have emerged as key regulators of intracellular signaling pathways and cell proliferation . However , whether they play any role in the pathological changes associated with virus induced oncogenesis is not known . Here , we report the first systematic study of the role of glutamate and its metabotropic glutamate receptor 1 ( mGluR1 ) in KSHV infected cell proliferation . Our studies show increased glutamate secretion and glutaminase expression during de novo KSHV infection of endothelial cells as well as in KSHV latently infected endothelial and B-cells . Increased mGluR1 expression was detected in KSHV infected KS and PEL tissue sections . Increased c-Myc and glutaminase expression in the infected cells was mediated by KSHV latency associated nuclear antigen 1 ( LANA-1 ) . In addition , mGluR1 expression regulating host RE-1 silencing transcription factor/neuron restrictive silencer factor ( REST/NRSF ) was retained in the cytoplasm of infected cells . KSHV latent protein Kaposin A was also involved in the over expression of mGluR1 by interacting with REST in the cytoplasm of infected cells and by regulating the phosphorylation of REST and interaction with β-TRCP for ubiquitination . Colocalization of Kaposin A with REST was also observed in KS and PEL tissue samples . KSHV infected cell proliferation was significantly inhibited by glutamate release inhibitor and mGluR1 antagonists . These studies demonstrated that elevated glutamate secretion and mGluR1 expression play a role in KSHV induced cell proliferation and suggest that targeting glutamate and mGluR1 is an attractive therapeutic strategy to effectively control the KSHV associated malignancies .
Kaposi's sarcoma-associated herpesvirus or human herpesvirus-8 ( KSHV/HHV-8 ) infection is etiologically associated with Kaposi's sarcoma ( KS ) , a vascular endothelial tumor , and two B-cell lymphoproliferative diseases , primary effusion lymphoma ( PEL ) or body-cavity based lymphoma ( BCBL ) and multicentric Castleman's disease [1] , [2] , [3] . These cancers occur more frequently in the setting of immunosuppression , including HIV-1 infected patients , and develop from cells latently infected with KSHV . In vivo KSHV has a broad tropism and viral genome and transcripts are detected in a variety of cells such as B cells , endothelial cells , monocytes , keratinocytes , and epithelial cells [4] , [5] . Latent KSHV DNA is present in vascular endothelial and spindle cells of KS lesions , associated with expression of latency associated ORF73 ( LANA-1 ) , ORF72 ( v-cyclin D ) , K13 ( v-FLIP ) , and K12 ( Kaposin ) genes and microRNAs [5] . Cell lines with B cell characteristics , such as BC-1 , BC-3 , BCBL-1 , HBL-6 and JSC have been established from PEL tumors [4] , [5] . In PEL cells , in addition to the above set of latent genes , the K10 . 5 ( LANA-2 ) gene is also expressed [4] , [5] . About 1–3% of PEL cells spontaneously enter the lytic cycle and virus induced from these cells by chemicals serve as the source of virus . Multiple genome copies of both KSHV and EBV exist in latent form in BC-1 , HBL-6 and JSC cells while BCBL-1 and BC-3 cells carry only the KSHV genome [4] , [5] . KSHV infects a wide variety of human cell types in vitro , including fibroblasts , keratinocytes , B cells , endothelial , and epithelial cells [4] , [5] , [6] . Following infection , KSHV establishes latency within the target cells and the expression of the viral latent ORF71 , ORF72 , ORF73 , and K13 genes continues to maintain latency [7] . In addition , host genes required for regulating apoptosis , signal induction , cell cycle regulation , inflammatory response , and angiogenesis are also highly upregulated in the latently infected cells [8] . Studies have linked the expression of KSHV latency genes ORF71 ( v-FLIP ) , -72 ( v-Cyclin ) , -73 ( LANA-1 ) and K12 ( Kaposin A ) to the oncogenic activity of latently infected cells [9] , [10] , [11] . These genes induce the oncogenic potential of KSHV by increasing proliferative potential , growth and chromosome instability as well as by preventing apoptosis of the infected cells [9] , [10] , [11] . A hallmark of KSHV associated cancers is the excessive secretion of cytokines and growth factors [12] , [13] , [14] . Modulation by viral proteins and virally induced cellular proteins promote the secretion of autocrine and paracrine cytokines and growth factors leading into the proliferation , survival , and growth of the latently infected cells [12] , [13] , [15] , [16] , [17] , [18] . However , the mechanism behind KSHV induced cancer progression is not completely understood . Glutamate is a major excitatory neurotransmitter in the mammalian brain . It also plays a central role in several cellular functions , including cell survival and death by its interaction with receptors [19] . Glutamate released into the extracellular space binds and activates two classes of cell surface receptors , ionotropic ( iGluRs ) and G protein-coupled metabotropic glutamate receptors ( mGluRs ) . There are three groups of mGluRs , and group I mGluRs have been extensively studied in relation to cell survival and death . Group I consists of mGluR1 and mGluR5 subtypes ( mGluR1/5 ) which are coupled to Gαq/11 proteins . Agonist stimulation of group I mGluRs activate PLC , which results in the activation of PKC , PKC dependent pathways , and ERK1/2 [20] , [21] , [22] . The group I mGluRs are distributed in a variety of non-neuronal cells including human B and microvascular dermal endothelial cells , the natural target cells of KSHV [23] , [24] , [25] . However , the involvement of excess glutamate secretion and glutamate receptor expression in cell proliferation is an unexplored area of research in the KSHV oncogenesis field . This current study was undertaken with a rationale that identifying and defining the role of glutamate in KSHV biology will lead into targeted specific treatments for KSHV-associated malignancies . Our studies demonstrate that KSHV infection induces the secretion of glutamate and expression of mGluR1 receptor , and increased mGluR1 expression was detected in KS and PEL tissue sections . Most notably , glutamate secretion and mGluR1 activation in KSHV latently infected cells occurred through two independent pathways regulated by two individual viral latent proteins , LANA-1 and Kaposin A . Our data highlight how KSHV LANA-1 and Kaposin A proteins contribute to the generation of glutamate , activation of mGluR1 , and strongly suggest the possibility of exploiting the glutamatergic system for the therapeutic intervention of KSHV dependent cancers .
To determine the role of glutamate in KSHV infection , we first evaluated the secretion of glutamate during de novo KSHV infection of primary human microvascular dermal endothelial cells ( HMVEC-d ) . Kinetics of glutamate secretion showed that KSHV infection robustly increased glutamate release as early as 8 h post-infection ( p . i . ) which continued to increase throughout the 5 d p . i . observation period ( Figure 1A ) . In contrast , when the cells were infected with replication defective UV treated KSHV for 5d , there was no significant difference in glutamate secretion between uninfected and UV-KSHV infected cells , ( Fig . 1A ) suggesting that viral gene expression is required for the increased secretion of glutamate . To determine whether the secretion of glutamate is specifically induced by KSHV , cells were infected with KSHV pre-incubated with heparin ( Hep-KSHV ) , which is known to block the binding and entry of KSHV to the target cells [26] . In contrast to the untreated virus , heparin treated virus ( Hep-KSHV ) considerably reduced the secretion of glutamate ( Figure 1A ) . This suggested that KSHV entry and infection is required for the increased secretion of glutamate . KS is an endothelial tumor , whereas PEL is of B-cell origin [2] , [27] , [28] . The telomerase immortalized endothelial cell line ( TIVE ) latently infected with KSHV ( TIVE-LTC ) , and the PEL derived B-cell line BCBL-1 are well-established in vitro models to study KS and PEL , respectively [2] , [29] . In addition , BJAB-KSHV , a Burkitt's lymphoma B-cell line carrying latent KSHV DNA , has also been used as an additional model for studying KSHV pathogenesis [30] . To test whether the process of glutamate generation is relevant to KS , we measured the secretion of glutamate in KSHV TIVE-LTC cells as well as in uninfected control TIVE cells . Similar to de novo KSHV infection , higher levels of glutamate release were observed in KSHV ( + ) TIVE-LTC cells than in KSHV ( − ) TIVE cells ( Figure 1B ) . When the association of glutamate to PEL was assessed , high levels of glutamate release were observed in KSHV ( + ) BCBL-1 and BJAB-KSHV cells compared to the KSHV ( − ) B-cell line BJAB ( Figure 1C ) . To elucidate the mechanisms of glutamate generation in the infected cells , we next determined the expression of glutaminase , the major enzyme responsible for glutamate production [31] . Compared to the uninfected cells , a time dependent increase in glutaminase expression was observed during 8 h , 24 h , 48 h and 5 d of de novo infection of primary HMVEC-d cells by KSHV ( Figure 1D , lanes 1–6 ) . In contrast , at 5 d p . i . with UV-KSHV , no significant difference in glutaminase expression from uninfected cells was observed ( Figure 1D , lanes 1 and 7 ) . These results demonstrated that the increased glutamate secretion is linked with increased glutaminase expression in the infected cells . This link was further confirmed by the detection of a higher level of glutaminase expression in the latently infected TIVE-LTC ( 2 . 8 fold ) , BJAB-KSHV ( 2 . 5 fold ) , and BCBL-1 cells ( 3 . 4 fold ) than in their respective uninfected control TIVE and BJAB cells ( Figure 1E , lanes 1–5 ) . To further investigate the role of glutaminase in glutamate secretion , we used a glutaminase specific inhibitor , L-DON ( 6-diazo-5-oxo-norleucine ) [32] . Cells were treated with L-DON at a concentration of 500 µM and 1 mM and the supernatants were analyzed for glutamate release . We found that 500 µM of L-DON inhibited glutamate secretion by >50% and 1 mM of L-DON by >65% . Dose dependent inhibition of glutamate secretion in L-DON treated cells strongly suggested that glutaminase is the major enzyme that contributes to the generation of excess glutamate in KSHV infected cells ( Figure S1A ) . L-DON had no significant cytotoxicity on BJAB cells at 500 µM and 1 mM concentrations ( data not shown ) . KSHV latency-associated ORF73 gene product LANA-1 has been shown to induce c-Myc expression [33] . Since c-Myc has also been shown to activate the expression of glutaminase [34] , we hypothesized that the increased glutamate secretion observed in KSHV infected cells could be mediated by LANA-1 through its c-Myc activation , which in turn stimulates the expression of glutaminase . To test this hypothesis , when BJAB cells were transduced with lentivirus constructs of LANA-1 , we observed increased secretion of glutamate in LANA-1 transduced cells compared to vector alone ( Figure 2A ) . We also observed ∼2-fold increase in c-Myc and glutaminase protein expression in LANA-1 transduced cells ( Figure 2B ) . To support our finding that LANA-1 mediated c-Myc activation is directly involved in glutaminase expression and glutamate secretion , we used lentiviruses encoding shRNAs to knock down c-Myc in BJAB cells over expressing LANA-1 . As shown in figure S1B , LANA-1 over expression induced the secretion of glutamate , and this induction was abolished by the knockdown of c-Myc ( Figure S1B ) . Since no considerable increase in glutamate release was observed in the absence of c-Myc in LANA-1 expressing cells ( Figure S1B ) , these results suggested that LANA-1 mediated c-Myc activation is required for glutamate release . To confirm the functional relationship of c-Myc expression with glutaminase expression in KSHV infected cells , we transduced TIVE-LTC cells and BCBL-1 cells with c-Myc and control shRNA lentiviral vectors . A significant reduction in glutaminase expression was observed in c-Myc knockdown TIVE-LTC cells ( 61% ) and BCBL-1 cells ( 67% ) compared to control shRNA transduced cells ( Figures 2C and D ) . These results suggested that LANA-1 mediated c-Myc activation plays a crucial role in the expression of glutaminase and glutamate secretion in cells latently infected with KSHV . Among the several types of glutamate receptors , mGluR1 is considered an oncogenic protein due to its ability to regulate the functions related to cancer cell proliferation [35] , [36] . Hence , we theorized that the biological effect of glutamate in latent KSHV induced oncogenesis may be mediated through the expression of mGluR1 receptors . To test this , we first determined mGluR1 expression by RT-PCR in primary endothelial cells infected for 5 d with KSHV and UV-KSHV . Compared to uninfected cells , KSHV infection increased the expression of mGluR1 ( Figure 3A ) . In contrast , UV treated virus had no significant effect on mGluR1 expression ( Figure 3A ) and suggested that sustained mGluR1 receptor expression probably depended upon KSHV gene expression . When the relative expression levels for the mGluR1 receptor in KSHV latent TIVE-LTC , BJAB-KSHV and BCBL-1 cells as well as control BJAB and TIVE cells were determined by RT-PCR , upregulation of mGluR1 in both KSHV ( + ) TIVE-LTC cells and BCBL-1 cells was observed compared to uninfected TIVE and BJAB cells ( Figure 3B ) . Western blot ( Figure 3C ) and immunoprecipitation analysis ( Figure S2A ) confirmed the higher levels of mGluR1 protein in de novo KSHV infected primary cells compared to the uninfected and UV-KSHV infected cells . Similarly , high levels of mGluR1 expression were also observed in BJAB-KSHV , BCBL-1 and TIVE-LTC cell lines by Western blots ( Figure 3D ) and by immunoprecipitation analysis ( Figure S2B ) . The expression of mGluR1 in KSHV infected primary cells and latent cells was also examined by immunofluorescence assay ( IFA ) . Increased mGluR1 staining was detected in LANA-1 expressing spindle shaped HMVEC-d cells infected with KSHV ( Figure 3E ) , as well as in TIVE-LTC , BJAB-KSHV and BCBL-1 cells compared to their respective uninfected controls ( Figures 3F and G ) . These results clearly demonstrated that KSHV infection results in the increased mGluR1 expression in latently infected cells . To verify the pathological association of mGluR1 in KSHV associated cancers , we immunostained normal as well as KSHV infected KS and PEL tissues by dual labeled IFA for mGluR1 and KSHV LANA-1 as a marker for infection . Strong positive immunostaining for both mGluR1 and LANA-1 were detected in the spindle shaped endothelial cells of KS tissue ( Figure 4A ) , and in the stomach PEL ( Figure 4B ) samples . In contrast , only a basal level of mGluR1 was detected in control normal skin and stomach samples ( Figures 4A and B ) . These results clearly demonstrated the in vivo association of increased mGluR1 expression with KSHV infection . The expression of mGluR1 in non-neuronal cells is regulated by RE-1 silencing transcription factor/neuron restrictive silencer factor ( REST/NRSF ) [37] . Binding of REST to a DNA recognition sequence called the neuron restrictive silencer elements ( NRSE or RE-1 ) repress the expression of neuronal genes such as mGluR1 in non-neuronal cells [37] , [38] , [39] . To analyze whether REST expression plays any role in mGluR1 expression in KSHV infected cells , we determined the expression of REST mRNA and protein . real-time RT-PCR analysis of REST revealed similar levels of REST expression in both uninfected and KSHV infected latent cells ( Figures S3A and B ) . However , REST protein expression determination by Western blots showed 55% , 72% and 42% reduction in BJAB-KSHV , BCBL-1 and TIVE-LTC cells , respectively , compared to the respective controls ( Figures S3C and D ) . This suggested that REST expression in KSHV infected cells is probably modulated at the post-transcriptional level . To decipher the mechanism regulating REST expression at the post-translational level , we first determined the subcellular localization of REST in TIVE and TIVE-LTC cells by IFA . In the uninfected TIVE cells , REST was highly expressed and was predominantly localized in the nucleus ( Figure 5A ) . In contrast , REST distribution was markedly decreased in the nucleus of TIVE-LTC cells and was predominantly localized to the cytoplasm ( Figure 5A ) . A similar cytoplasmic relocalization of REST was observed in almost all KSHV infected BJAB-KSHV and BCBL-1 cells compared to the uninfected BJAB cells where it was exclusively localized in the nucleus ( Figure 5B ) . Western blot analysis of cytoplasmic and nuclear fractions of the KSHV positive cell lines confirmed that REST localization is significantly decreased in the nucleus ( Figures 5C and D ) with a concomitant increase in the cytoplasm of infected cells , whereas it was undetectable in the cytoplasm of uninfected TIVE and BJAB cells ( Figures 5C and D , lane 1 ) . Interestingly , analyses of REST in the cytoplasmic fractions from the infected cells showed a small shift in molecular weight in both TIVE-LTC ( Figure 5C , lane 2 ) and BCBL-1 cells ( Figure 5D , lane 3 ) . We reasoned that this small shift in band size could be due to phosphorylation of REST , which is known to result in migration differences on SDS-PAGE . To determine whether the shifted band detected in the cytoplasm is indeed the phosphorylated form of REST , we first treated the cytoplasmic extracts from TIVE-LTC cells with lambda phosphatase or with lambda phosphatase and phosphatase inhibitor , and then the extracts were Western blotted . Treatment with lambda phosphatase resulted in the disappearance of the modified band , suggesting that the shift in band size was due to phosphorylation ( Figure S4A ) . It has been reported that serine phosphorylation of REST in the conserved phosphodegron motif promotes recognition by the E3 ubiquitin ligase β-TRCP and ubiquitination [40] , [41] . As our data suggested an unexpected decrease of REST in the infected cells , we next asked whether the phosphorylation of REST in the cytoplasm was followed by its phosphorylation-dependent ubiquitination . To examine this regulatory role , we first verified the serine phosphorylation of REST in the cytoplasm of infected cells by immunoprecipitating with phosphoserine antibody and Western blotting with REST antibody . Consistent with the Western blot results ( Figures 5C and D ) , a significant level of serine phosphorylation of REST was detected in KSHV-infected TIVE-LTC , BJAB-KSHV and BCBL-1 cells compared to a very low level of phosphorylation in KSHV-negative TIVE and BJAB cells ( Figure 6A ) . We next determined whether β-TRCP could be associated with phosphorylated REST in the cytoplasm of infected cells . Immunoprecipitation of cytoplasmic extracts of BJAB , BJAB-KSHV and BCBL-1 cells with REST and Western blots with anti-β-TRCP antibodies showed increased interaction of REST with β-TRCP in the infected cells , whereas it was barely detectable in uninfected cells ( Figure 6B ) . We next determined whether REST degradation occurs in the cytoplasm of KSHV-infected cells . Analysis of cytosolic fractions from TIVE-LTC , BJAB-KSHV and BCBL-1 cells by immunoprecipitation with REST and Western blots for polyubiquitin revealed a higher level of ubiquitination in TIVE-LTC , BJAB-KSHV and BCBL-1 cells compared with TIVE and BJAB cells displaying lower levels of ubiquitination ( Figure 6C ) . Thus , the ubiquitination levels of REST correlated with REST phosphorylation and the association of REST with β-TRCP in the cytoplasm . In order to confirm that the ubiquitin proteasome system is involved in the degradation of REST in KSHV infected cells , BCBL-1 and TIVE-LTC cells were treated with the proteasome inhibitor MG132 , and the cell lysates were Western blotted for REST . As shown in figure 6D , compared to the untreated cells ( lanes 3 , 5 , and 7 ) , MG132 treatment increased the protein level of REST in the infected BJAB-KSHV , BCBL-1 , and TIVE-LTC cells ( lanes 4 , 6 , and 8 ) . However , MG132 treatment had no significant effect on the REST protein level in uninfected BJAB cells ( lanes 1 and 2 ) . This result further supported our finding that the degradation of REST observed in the infected cells was probably mediated by the ubiquitin proteasome pathway . Since REST was more localized in the cytoplasm of latently infected cells , we hypothesized that latent KSHV protein ( s ) in the infected cells binds and sequesters REST in the cytoplasm , which in turn leads to overexpression of the mGluR1 gene . To determine the identity of the KSHV latent protein responsible for this , BJAB cells were transduced with the lentiviral constructs of KSHV latent ORF71 , -72 , -73 , and Kaposin A genes , expression levels assessed by real-time PCR ( Figure S4B ) , and mGluR1 level analyzed by Western blot . ORF K12 or Kaposin A transduction led to a robust increase in mGluR1 expression in BJAB cells , indicating the involvement of Kaposin A in the regulation of mGluR1 expression , whereas the other latent genes did not significantly induce the expression of mGluR1 ( Figure 7A ) . mGluR1 expression in Kaposin A transduced BJAB cells was further confirmed by immunoprecipitation experiments ( Figure S4C ) . Transduction efficiencies were determined by control lentiviral GFP expression ( Figure S4D ) . We also observed higher levels of mGluR1 protein expression in primary HMVEC-d cells transduced with Kaposin A which further demonstrated the Kaposin A dependency of mGluR1 expression ( Figure 7B ) . To determine whether Kaposin A is responsible for the observed cytoplasmic relocalization of REST in the infected cells , we transduced HMVEC-d cells with a lentiviral Kaposin A construct ( ORF K12 ) and localization was determined by IFA using anti-Kaposin A antibodies . This analysis revealed that a major portion of endogenous REST was translocated into the cytoplasm and colocalized with Kaposin A in the transduced cells ( Figure 7C ) . To verify that REST binds to Kaposin A in the cytoplasm of KSHV infected cells , we immunoprecipitated REST from cytoplasmic fractions of both uninfected BJAB and KSHV-infected BCBL-1 cells and then Western blotted with anti-Kaposin A antibodies , which detected specific bands of Kaposin A at approximately 16–18 kDa ( Figure 8A ) . BCBL-1 cell lysates used as positive control also identified 16–18-kDa immunoreactive bands of Kaposin A in the infected cells ( Figure 8A ) . The predicted molecular weight of Kaposin A is 6-kDa; however , WB analyses often detect specific bands of about 16–18-kDa and above [42] , [43] , [44] . Similar immunoprecipitation analysis using TIVE and TIVE-LTC cells also revealed that REST interacts with Kaposin A in the infected cell cytoplasm ( Figure 8B ) . We also observed the colocalization of REST and Kaposin A in the cytoplasm of TIVE-LTC ( Figure 8C ) and BCBL-1 cells ( Figure 8D ) . To further verify the physical interaction of REST with Kaposin A , we co-transduced 293T cells with Kaposin A and retroviral FLAG tagged REST and the cytoplasmic and nuclear lysates were immunoprecipitated with Kaposin A and Western blotted with anti-FLAG antibodies . This co-immunoprecipitation experiment demonstrated the ability of Kaposin A to interact with REST in the cytoplasm , but not in the nucleus ( Figure 8E ) . We next examined the staining pattern and colocalization of REST and Kaposin A in KS and PEL patient samples and in normal tissues by immunofluorescence analysis . As shown in Figures 9A and B , strong nuclear staining of REST was observed in normal skin tissues as well as in normal stomach tissues . In contrast , cytoplasmic localization of REST and notable colocalization with Kaposin A were observed in the endothelial cells of KS as well as in the cells of PEL tissues , presumably the B cells . Together , these results suggested that Kaposin A expression regulates mGluR1 expression through interaction with REST in the cytoplasm of KSHV infected cells . As shown in figure 6B and C , phosphorylated REST interacts with β-TRCP and promotes the ubiquitination and degradation of REST in the cytoplasm of infected cells . It has previously been reported that REST has a degron motif and the phosphorylation of REST at serine 1024 , 1027 , and 1030 of the degron motif is required for the interaction of REST with β-TRCP during oncogenic transformation [41] . Because Kaposin A is a protein involved in transformation of infected cells [45] , [46] , we postulated that Kaposin A binding with REST phosphorylates REST at the 1024 , 1027 , and 1030 residues , leading to the interaction with β-TRCP and ubiquitination of REST . To investigate this , we co-transduced 293T cells with Kaposin A and FLAG REST-WT or FLAG-REST triple mutant ( where all three phosphodegron residues are mutated-FLAG-REST-S1024/1027/1030A ) , cytoplasmic fractions immunoprecipitated with anti-phosphoserine antibodies and Western blotted with anti-FLAG antibodies . A significant level of serine phosphorylated REST was detected in Kaposin A and FLAG REST-WT transduced cells ( Figure 10A , upper panel ) . In contrast , the serine phosphorylation of REST was severely impaired in Kaposin A and FLAG-REST triple mutant transduced cells suggesting that Kaposin A mediates REST phosphorylation in its phosphodegron sites . As Kaposin A is involved in REST phosphorylation in the conserved degron sites , we next determined whether phosphorylated REST binds to endogenous β-TRCP . As shown in Figure 10A , middle panel , immunoprecipitation with FLAG and Western blot with β-TRCP showed a markedly increased interaction of REST with endogenous β-TRCP in Kaposin A and FLAG REST-WT transduced cells , whereas no interaction was observed in Kaposin A and FLAG-REST triple mutant transduced cells . These data demonstrated that blocking Kaposin A mediated phosphorylation of REST weakens its association with endogenous β-TRCP . To further investigate which specific degron site is phosphorylated by Kaposin A , we transiently transduced 293T cells with lentiviral Kaposin A and 48 h after transduction , the cells were transfected with pCMV-FLAG-REST WT plasmid or pCMV-FLAG-REST individually mutated at serine 1024 , ( pCMV-FLAG-REST-S1024A ) , 1027 ( pCMV-FLAG-REST-S1027A ) , or 1030 ( pCMV-FLAG-REST-S1030A ) . Cell lysates were immunoprecipitated using anti-phosphoserine antibodies followed by Western blotting with anti-FLAG antibodies or vice versa . As shown in Figure 10B , the serine 1027 mutant completely abolished the capacity for phosphorylation ( Figure 10B , lane 5 , first and second panel ) . However , the serine 1024 and 1030 mutants had no effect on phosphorylation compared to wild type REST ( Figure 10B , lane 4 and 6 , first and second panel ) , indicating that the phosphorylation of these two sites are not directly mediated by Kaposin A . The defect in REST phosphorylation in mutant serine 1027 suggested that Kaposin A initially phosphorylates REST on serine 1027 . Phosphorylation on 1027 may provide the signal to phosphorylate the other degron residues . In order to determine whether the serine 1027 induced phosphorylation is responsible for REST ubiquitination , the cell lysates immunoprecipitated with anti-FLAG antibody were analyzed by Western blotting with an anti-polyubiquitin antibody . Consistent with the increased phosphorylation , the ubiquitination was markedly increased in wild type REST , as well as in serine 1024 and 1030 mutants transfected cells ( Figure 10B , lanes 3 , 4 , and 6 , third panel ) . In contrast , the phosphorylation defective mutant 1027 failed to induce ubiquitination ( Figure 10B , lane 5 , third panel ) . These results suggest that serine 1027 mediated phosphorylation is required for the ubiquitination of REST . We also observed that the phosphorylation defective mutant FLAG-REST-S1027A stabilized REST ( Figure 10B , lane 5 , fourth panel ) . The observed reduction of REST in FLAG-REST WT and FLAG-REST-S1024A and -S1030A ( Figure 10B , lanes 3 , 4 and 6 , fourth panel ) , after Kaposin A stimulation may be due to the degradation of phosphorylated REST at the 1027 residue . Taken together , our studies demonstrated that Kaposin A regulates REST phosphorylation in the conserved phosphodegron motif which enhances the ubiquitination of REST and thus reduces the level of REST . To further verify the role of Kaposin A in REST phosphorylation , 293T cells transduced with vector alone or Kaposin A were transfected with FLAG-REST S1027 or FLAG-REST WT first and then transfected with control or Kaposin A specific siRNA . After 48 h post transfection , levels of REST phosphorylation were assessed by immunoprecipitating with anti-FLAG antibody followed by Western blotting with anti-phosphoserine antibody . We observed that compared to control siRNA transfected cells , Kaposin A siRNA transfected cells abolished the phosphorylation and degradation of REST in REST WT transfected cells ( Figure 10C , lane 4 and 5 , first and second panel ) . Kaposin A specific siRNA efficiently knocked down the expression of Kaposin A in the transduced cells ( Figure 10C , lane 5 , third panel ) . As expected , cells transfected with phosphorylation defective mutant REST-S1027 had no effect on phosphorylation ( Figure 10C , lane 3 , first panel ) . These data confirm that Kaposin A is essential for the phosphorylation of REST . We next focused on the biological response of glutamate release and binding to its receptors . We postulated that the glutamate released by infected cells binds to mGluR1 permitting cellular signaling and the proliferation of glutamate secreting infected cells . To determine the effects of glutamate and mGluR1 on cell proliferation , primary HMVEC-d cells infected with KSHV for 3 d were cultured for 2 d in the presence or absence of glutamate release inhibitor riluzole , and mGluR1 antagonists A841720 and Bay 36-7620 , pulsed with BrdU for 2 h and BrdU incorporation determined by IFA . As shown in Figure 11A , HMVEC-d cells infected with KSHV for 5 d showed a much higher rate of proliferation than the uninfected cells . This increased proliferation of HMVEC-d cells was significantly reduced by exposure to riluzole , A841720 and Bay 36-7620 ( Figure 11A ) . These results were also confirmed by BrdU cell proliferation ELISA ( Figures S5A and B ) . We further tested the involvement of riluzole , A841720 , and Bay 36-7620 in TIVE and TIVE-LTC , BJAB and BCBL-1 cell proliferation by BrdU cell proliferation ELISA . No treatment and vehicle treatment were used as controls . As shown in Figure 11B , treatment with riluzole , A841720 and Bay 36-7620 showed a concentration dependent decrease in the proliferation of both TIVE-LTC and BCBL-1 cells ( Figures 11B and C ) . Due to the absence or low level of expression of mGluR1 receptors , only a minimal effect was observed in the proliferation of uninfected TIVE and BJAB cells ( Figures S5C and D ) . To further confirm the effect of inhibitors on cell proliferation , cells treated with riluzole , A841720 or Bay 36-7620 were monitored using a vibrant MTT cell proliferation assay kit . Riluzole , A841720 , and Bay 36-7620 caused a 60–70% decrease in cell growth compared to the untreated control ( Fig . 11 D and E ) . Next , we confirmed the role of mGluR1 on the proliferation of infected cells by using mGluR1 shRNA . TIVE- LTC cells transduced with mGluR1 shRNA or control shRNA were assayed for BrdU incorporation . Compared to control shRNA cells , mGluR1-shRNA significantly reduced the proliferation of TIVE-LTC cells ( Fig . 11G ) , indicating that mGluR1 plays a key role in the proliferation of KSHV infected cells . Collectively , these results suggested that riluzole and mGluR1 antagonists suppressed the binding of glutamate to the receptors of infected cells and thereby arresting the activation of receptors by glutamate leading into the proliferation of KSHV infected cells .
Glutamate release along with autocrine and paracrine glutamate receptor signaling has been demonstrated to accelerate cell proliferation and tumor progression [47] , [48] . During the latent phase of KSHV infection , the cytokines and growth factors released into the extracellular milieu play significant roles in the long term proliferation , survival , and maintenance of the infected cells which probably results in KSHV associated malignancies [8] , [12] , [13] , [15] , [16] , [17] , [18] , [49] . Our comprehensive studies demonstrating the increased secretion of glutamate into the cytokine milieu in response to KSHV infection suggest that glutamate could be acting as an autocrine and paracrine growth factor during KSHV induced oncogenesis . Secretion of glutamate occurs in uninfected and infected cells , with comparatively low levels in uninfected cells . We have demonstrated that KSHV infection and appropriate viral gene expression are critical for the generation and release of glutamate in the infected cells . As the viral genome persists in a latent state in the infected cells , the expression level of the latent genes may affect glutamate secretion . Our current study clearly suggested a mechanism whereby the latent ORF73 gene expression affect the stability of c-Myc activation and the depletion of which resulted in reduced glutaminase expression and glutamate secretion . This implies that the level of infection and consistent expression of viral genes are required for the continued secretion of glutamate . Our studies also show that KSHV infected cells induced the highest levels of glutaminase expression and caused a moderate increase in glutamate release . This difference could be attributable to glutamate transporters and the uptake of glutamate into cells . The glutamate taken up by the cells is converted into glutamine via the glutamine synthetase pathway [50] . Since there are several evidences to indicate that glutamate uptake and its enzymatic conversion are significant steps to maintain extracellular glutamate concentration [50] , [51] , [52] , it is possible that expression or functional impairment of glutamate transporters may also be involved in the maintenance of extracellular glutamate levels in the infected cells . c-Myc has numerous significant effects on cancer cell metabolism by modifying expression of proteins involved in metabolic pathways [53] . It is known to stimulate increased expression of its target proteins and glutaminase expression by transcriptional repression of mir23a/b in cancer cells [34] . The increased c-Myc activity may also significantly alter the metabolism of glutamine in the infected cells . These changes in glutamine metabolism may profoundly influence the synthesis of molecules involved in growth and survival of infected cells . Although increased glutaminolysis is a supplementary source of energy and may provide significant benefits in terms of the survival of the infected cells , they require additional factors for the induction of cell proliferation or transformation . Thus , while the role of increased metabolism and the components involved in metabolism remains to be determined , it is clear from our study that the secreted glutamate is being used to activate mGluR1 which contribute to the proliferation of infected cells . Interestingly , we report that KSHV infected cells also upregulate the expression of glutamate receptor mGluR1 , which in turn results in increased proliferation as a result of glutamate binding to mGluR1 in the infected cells . Enhanced expression of mGluR1 , and the intracellular signaling pathway activated by mGluR1 , has the ability to induce cell proliferation and oncogenic transformation [35] , [36] . Our data provide evidence that mGluR1 is upregulated in in vitro latently infected cells and in vivo patient samples . Mechanistically , mGluR1 overexpression involves relocalization of REST from the nucleus to the cytoplasm and loss of REST expression in the infected cells . Decreased REST expression , relocalization of REST , and degradation of REST are possible adaptations to antagonize REST-mediated effects to accomplish the overexpression of mGluR1 [39] , [54] , [55] . A remarkable difference in the pattern of REST localization observed in the infected cells indicates that mGluR1 expression may be regulated via the relocalization of REST . Translocation of REST to the cytoplasm relieves the NRSE or RE1 mediated transcriptional repression in the promoter regions of mGluR1 and upregulates its transcription ( Figure 12 ) . Another one of our major findings is that the KSHV latent protein Kaposin A is responsible for cytoplasmic relocalization of REST and mGluR1 activation ( Figure 12 ) . Kaposin A mediated oncogenesis has been demonstrated in vitro in Rat3 fibroblasts and in nude mice [45] , [46] . Previous studies have suggested that Kaposin A regulates oncogenesis by influencing the phosphorylation of signaling molecules involved in cellular processes , such as cell proliferation and gene transcription [42] , [46] . Our findings suggest that sequestration of REST in the cytoplasm by Kaposin A modulates phosphorylation-dependent ubiquitination of REST by altering the phosphorylation status of REST ( Figure 12 ) . Kaposin A regulates REST phosphorylation at the specific degron sites which are essential for binding to β-TRCP and degradation of REST during oncogenic transformation . Thus , the downregulation of REST , which is seen in actively proliferating cancer cells [56] , [57] , might be involved in the regulation of mGluR1 and in cellular transformation during KSHV induced cancer development . Since Kaposin A does not have a known protein kinase domain , how Kaposin A binding to REST induces phosphorylation of REST needs to be elucidated . Several mechanisms are possible to account for the phosphorylation of REST by Kaposin A . Kaposin A has been reported to phosphorylate a number of kinases involved in cell proliferation [46] . Therefore , it is possible that Kaposin A may couple through one of these kinases for the activation of REST and recruitment of β-TRCP . It is also possible that the interaction of Kaposin A with REST may induce the phosphorylation of REST by allowing a conformational change . These modifications would create a favorable molecular environment for the cross talk between REST and β-TRCP . In addition to Kaposin A mediated mGluR1 expression , we observed that over expression of LANA-1 also lead to a slight increase in mGluR1 expression . This increase in mGluR1 expression may be a result of an alteration in the N-terminal repressor domain of REST on the mGluR1 promoter . The N-terminal repression domain of REST represses target gene expression by recruiting the transcriptional corepressor mSin3 and then forming a complex with histone deacetylase ( HDAC ) [58] , [59] . Since LANA-1 has already been shown to be associated with mSin3 co-repressor as well as with HDAC [60] , it is expected that LANA-1 may be able to bypass REST mediated repression by sequestration of the mSin3/HDAC complex which results in the expression of mGluR1 genes . It is also known that mSin3/HDAC regulated repression is not sufficient for complete transcriptional repression of REST target genes [58] , [59] , [61] . Therefore , the slight induction of mGluR1 expression in LANA-1 expressing cells could be due to the partial derepression of REST target genes by LANA-1 . Glutamate receptor antagonists and glutamate release inhibitors were shown to be effective in suppressing the proliferation of non-neuronal cancer cells [62] , [63] . Identification of the activity of glutamate and mGluR1 in glioma and melanoma development has been the rational approach for testing glutamate release inhibitors talampanel and riluzole in clinical trials for the treatment of glioma and melanoma , respectively [64] , [65] . Our functional data shows that the increased production of glutamate and expression of mGluR1 in response to KSHV infection promotes the proliferation of infected cells . Several studies have demonstrated that the signaling pathways activated by mGluR1 contribute to the proliferation and survival of cancer cells [22] , [66] . Further studies are essential to determine the role of glutamate and mGluR1 activity in signal induction , viral gene expression , and viral genome maintenance in cells latently infected with KSHV . The blocking effect of riluzole , and the mGluR1 antagonists on proliferation of KSHV infected cells suggests that these molecules could potentially be used for the treatment of KSHV associated malignancies by directly targeting the glutamatergic system in the infected cells .
Primary human dermal microvascular endothelial cells ( HMVEC-d cells CC-2543 ) were purchased from Clonetics , Walkersville , MD . KSHV negative B-lymphoma cell line BJAB , and the KSHV latently infected B-cell line BCBL-1 , were obtained from ATCC . BJAB-KSHV ( KSHV–GFP recombinant virus in BJAB ) was a gift from Dr . Blossom Damania ( University of North Carolina , Chapel Hill ) . TIVE ( telomerase-immortalized vein endothelial cell line ) and TIVE LTC cells ( TIVE cells carrying KSHV in a latent state ) were a gift from Dr . Rolf Renne ( University of Florida ) . These cell lines were maintained as described previously [67] . Induction of the KSHV lytic cycle with TPA in BCBL-1 cells , and KSHV purification procedures have been previously described [68] . UV-treated replication-defective KSHV was prepared by exposing the purified virus stock to UV light ( 365 nm ) for 20 min at a 10-cm distance . KSHV DNA was extracted from live KSHV and UV-treated KSHV , and the copies were quantitated by real-time DNA PCR using primers amplifying the KSHV ORF73 gene as described previously [7] . Unless stated otherwise , primary cells were infected with KSHV at 50 MOI ( multiplicity of infection ) per cell at 37°C . Rabbit anti-mGluR1 and β-TRCP antibodies as well as mouse anti-BrdU antibodies were from Cell Signaling , Beverly , MA . Mouse anti-glutaminase and rabbit anti-mGluR1 and -TATA binding protein ( TBP ) antibodies were from Abcam , Cambridge , MA . Mouse anti-tubulin and β-actin antibodies were from Sigma , St . Louis , MA . Mouse anti-c-Myc ( 9E10 ) and REST antibodies were from Santa Cruz , Santa Cruz , CA . Rat anti-Kaposin A/C and mouse anti-polyubiquitin antibodies were from Millipore , Temecula , CA . Mouse anti-ORF73 antibodies were generated in Dr . Chandran's laboratory . Anti-rabbit and anti-mouse antibodies linked to horseradish peroxidase were from KPL Inc . , Gaithersburg , Md . Alexa 488 and 594 conjugated secondary antibodies were from Invitrogen . Protein A and G–Sepharose CL-4B beads were from Amersham Pharmacia Biotech , Piscataway , NJ . Lambda phosphatase ( λPPase ) , and L-DON ( 6-diazo-5-oxo-norleucine ) were from Santa Cruz . Riluzole , A841720 and Bay 36-7620 were from Tocris Bioscience , Minneapolis , MN . Plasmids encoding FLAG-tagged human REST wild-type and site-specific REST mutant plasmids ( pCMV-FLAG-REST-S1024A , pCMV-FLAG-REST-S1027A , pCMV-FLAG-REST-S1030A ) , wild type FLAG-REST and triple mutant FLAG-REST-S1024/1027/1030A cloned into retroviral vector pQCXIN were provided by Dr . Stephen Elledge [41] ( Harvard Medical School ) . Lentiviral constructs of KSHV ORF71 ( vFLIP ) , ORF72 ( vCyclinD ) , ORF73 ( LANA-1 ) and ORFK12 ( Kaposin A ) were obtained from Dr . Chris Boshoff at the UCL Cancer Institute [69] . A plasmid encoding c-Myc shRNA sequence ( plasmid #29435 ) was from Addgene . Transfection was performed using 5 µg of plasmid DNA and lipofectamine 2000 ( Invitrogen ) as per the manufacturer's instructions . Lentivirus was produced by transfection with a four-plasmid system , as previously described [70] . Briefly , 293T cells were transiently transfected with lentiviral constructs and the plasmid packaging system ( Gag-Pol , Rev and VSV-G ) , the supernatants were collected , and filtered . Infections were carried out by incubating the virus preparation with cells in the presence of polybrene . The infection efficiency was estimated by analyzing GFP-expressing lentiviral vectors as positive controls . The expression levels of transduced viral genes were assessed by real-time PCR . For mGluR1 knockdown , lentiviruses encoding mGluR1 shRNA or control shRNA were purchased from Santa Cruz Biotechnology . TIVE-LTC cells were transduced with control lentivirus shRNA and mGluR1 lentivirus shRNA according to the manufacturer's instructions and selected by puromycin hydrochloride . An equal number of uninfected and infected cells were used for the experiments . Supernatants harvested at different times were centrifuged and glutamate levels were determined in 96-well plates by using a glutamate assay kit as per the manufacturer ( Biovision , Mountain View , CA ) . The concentration of glutamate was determined by measuring the absorbance at 450 nm with a microplate reader . Total RNA was isolated with TRIzol Reagent ( Invitrogen ) and treated with DNase I ( Ambion ) at 37°C for 30 min . Reverse transcription was performed using a High-Capacity cDNA reverse transcription kit ( Applied Biosystems ) . Regular PCR for mGluR1 was performed using 5 µl of the synthesized cDNA using appropriate forward and reverse primers as described by Choi et al [71] . PCR primers were as follows: mGluR1 5′-GTGGTTTGATGAGAAAGGAG-3′ ( forward ) and 5′-GTTGCTCCACTCAAGATAGC-3 ( reverse ) . β-actin 5′-GCTCACCATGGATGATGATATCGCC-3′ ( forward ) and 5′GGATGCCTCTCTTGCTCTGGGCCTC-3′ ( reverse ) . Quantitative real time-PCR was performed with SYBR Green and an ABI prism 7000 sequence detection system ( Applied Biosystems , Foster City , CA ) . The comparative Ct method was used to quantitate gene expression relative to the uninfected control . The following primer set was used: REST ( forward 5′-GAGGAGGAGGGCTGTTTACC-3′; reverse 5′-TCACAGCAGCTGCCATTTAC-3′ ) . Primers used for qRT-PCR of viral genes: ORF71 ( forward 5′-AGGTTAACGTTTCCCCTGTTAGC-3′; reverse , 5′-AGCAGGTCGCGCAAGAG-3′ ) , ORF72 ( forward-5′-AGCTGCGCCACGAAGCAGTCA-3′; reverse , 5′-CAGGTTCTCCCATCGACGA-3′ ) , ORF73 ( forward 5′-CGCGAATACCGCTATGTACTCA-3′; reverse 5′-GGAACGCGCCTCATACGA-3′ ) , Kaposin A ( forward 5′ GGATAGAGGCTTAACGGTGTTT-3′; reverse 5′-CAGACAAACGAGTGGTGGTATC-3′ ) . A pool of two siRNAs synthesized by Integrated DNA technologies ( IDT ) were used to knockdown Kaposin A . siRNA sequences were as follows: siRNA1- 5′-r ( UUGCAACUCGUGUCCUGAAUGCUACGG ) -3′ , siRNA2-5′- r ( CCACAAACACCGUUAAGCCUCUAUCCA ) -3′ . Cells were transfected with siRNA at 100 pmol ( 50 pmol each ) using siLentFect ( Biorad ) according to the manufacturer's instructions . Cell lysates were collected at 48 h post-siRNA transfection for immunoprecipitation and Western blot analysis . Formalin-fixed , paraffin-embedded tissue samples from healthy subjects and patients with KS and primary effusion lymphoma were obtained from the ACSR ( AIDS and Cancer Specimen Resource ) . Sections were deparaffinized with HistoChoice clearing reagent and rehydrated through ethanol to water . For antigen retrieval , the sections were microwaved in 1 mmol/l EDTA ( pH 8 . 0 ) for 15 min , permeabilized with 0 . 5% Triton X-100 for 5 min , and then blocked with blocking solution ( Image-iT FX signal enhancer-Invitrogen ) for 20′ at RT . Immunostaining was performed using anti-mGluR1 and anti-mouse LANA-1 antibodies , followed by Alexa-488 and Alexa-594 conjugated secondary antibodies . Nuclei were stained with 4′ , 6-diamidino-2-phenylindole ( DAPI ) ( Molecular Probes , Invitrogen ) , and stained cells were viewed under a fluorescence microscope with a 20× objective and the Nikon Metamorph digital imaging system . Cells grown on 8 well chamber slides ( Nalge Nunc International ) were fixed with 4% paraformaldehyde for 15 min , permeabilized with 0 . 2% Triton X-100 , and blocked with Image-iT FX signal enhancer ( Invitrogen ) for 20 min . Cells were then incubated with primary antibodies against the specific proteins and subsequently stained with Alexa 488 or 594 conjugated secondary antibodies . Cells were mounted in mounting medium containing DAPI . Images were acquired using a Nikon 80i fluorescent microscope equipped with a Metamorph digital imaging system . Cells were lysed in RIPA buffer containing 15 mM NaCl , 1 mM MgCl2 , 1 mM MnCl2 , 2 mM CaCl2 , 2 mM phenylmethylsulfonyl fluoride , and protease inhibitor mixture ( Sigma ) . The cell lysates were centrifuged at 13 , 000× g for 20 min at 4°C . Samples mixed with sample buffer containing β-mercaptoethanol , heated at 95°C for 5 min , and separated by SDS PAGE . The protein samples were then Western blotted with the indicated primary antibodies followed by incubation with species-specific HRP-conjugated secondary antibodies . Immunoreactive bands were visualized by enhanced chemiluminescence ( Pierce , Rockford , IL ) according to the manufacturer's instructions . To determine the fold change , blots were scanned , and quantified by densitometric analysis ( Alpha Innotech Corporation , San Leonardo , CA ) and normalized with respect to the amount of β-actin . For immunoprecipitations , 300–500 µg of cell lysates prepared in RIPA buffer or in NP-40 buffer were incubated with the appropriate primary antibody for 4–8 h with end-over-end rotation at 4°C , and the precipitated proteins captured by Protein A or G-Sepharose . The samples were Western blotted with specific primary and secondary antibodies . Statistical significance was calculated using a two tailed Student's t-test . P<0 . 05 was considered significant . | Kaposi's sarcoma associated herpesvirus ( KSHV ) , prevalent in immunosuppressed HIV infected individuals and transplant recipients , is etiologically associated with cancers such as endothelial Kaposi's sarcoma ( KS ) and B-cell primary effusion lymphoma ( PEL ) . Both KS and PEL develop from the unlimited proliferation of KSHV infected cells . Increased secretion of various host cytokines and growth factors , and the activation of their corresponding receptors , are shown to be contributing to the proliferation of KSHV latently infected cells . Glutamate , a neurotransmitter , is also involved in several cellular events including cell proliferation . In the present study , we report that KSHV-infected latent cells induce the secretion of glutamate and activation of metabotropic glutamate receptor 1 ( mGluR1 ) , and KSHV latency associated LANA-1 and Kaposin A proteins are involved in glutaminase and mGluR1 expression . Our functional analysis showed that elevated secretion of glutamate and mGluR1 activation is linked to increased proliferation of KSHV infected cells and glutamate release inhibitor and glutamate receptor antagonists blocked the proliferation of KSHV infected cells . These studies show that proliferation of cancer cells latently infected with KSHV in part depends upon glutamate and glutamate receptor and therefore could potentially be used as therapeutic targets for the control and elimination of KSHV associated cancers . |
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A large proportion of functional sequence within mammalian genomes falls outside protein-coding exons and can be transcribed into long RNAs . However , the roles in mammalian biology of long noncoding RNA ( lncRNA ) are not well understood . Few lncRNAs have experimentally determined roles , with some of these being lineage-specific . Determining the extent by which transcription of lncRNA loci is retained or lost across multiple evolutionary lineages is essential if we are to understand their contribution to mammalian biology and to lineage-specific traits . Here , we experimentally investigated the conservation of lncRNA expression among closely related rodent species , allowing the evolution of DNA sequence to be uncoupled from evolution of transcript expression . We generated total RNA ( RNAseq ) and H3K4me3-bound ( ChIPseq ) DNA data , and combined both to construct catalogues of transcripts expressed in the adult liver of Mus musculus domesticus ( C57BL/6J ) , Mus musculus castaneus , and Rattus norvegicus . We estimated the rate of transcriptional turnover of lncRNAs and investigated the effects of their lineage-specific birth or death . LncRNA transcription showed considerably greater gain and loss during rodent evolution , compared with protein-coding genes . Nucleotide substitution rates were found to mirror the in vivo transcriptional conservation of intergenic lncRNAs between rodents: only the sequences of noncoding loci with conserved transcription were constrained . Finally , we found that lineage-specific intergenic lncRNAs appear to be associated with modestly elevated expression of genomically neighbouring protein-coding genes . Our findings show that nearly half of intergenic lncRNA loci have been gained or lost since the last common ancestor of mouse and rat , and they predict that such rapid transcriptional turnover contributes to the evolution of tissue- and lineage-specific gene expression .
The mammalian transcriptome has recently been shown to be surprisingly diverse in its extent and encoded functions [1]–[3] , much of which are noncoding RNAs ( ncRNAs ) as they are not translated into proteins . The ability to sequence the entire transcriptome in an unbiased manner has not only allowed more complete characterization of well described and highly abundant noncoding RNAs with known function , such as transfer RNAs , small nuclear RNAs , small nucleolar RNAs and ribosomal RNAs , but have also revealed additional ncRNA species . For example , a number of long ncRNAs ( lncRNAs ) larger than 200 nucleotides ( nt ) have been discovered [2] , [4] , [5] . Many lncRNA loci are intergenic , when transcription occurs wholly within the genomic intervals between two adjacent protein-coding genes [6] . Some lncRNAs can be transcribed divergently from a neighbouring protein-coding transcript using identical or almost identical transcriptional initiation complexes [6] . In addition , lncRNAs overlapping with protein-coding genes can be transcribed from either strand [6]–[8] . Although the precise roles of many lncRNAs remain unknown , in general they are thought to act in transcriptional regulation [6] , [9] , [10] . LncRNAs can regulate gene expression programs through a variety of mechanisms , including interactions with chromatin remodelling complexes or transcription factors [11] . Consistent with a cis-regulatory role , co-expression of intergenic lncRNA loci with their neighbouring protein-coding genes has been observed [12] , [13] and a number of intergenic lncRNAs have demonstrated roles in regulating the expression of genes in their genomic vicinity [9] . Some intergenic lncRNAs appear to regulate the expression of both neighbouring and distal genes [14] , [15] . Indeed , many intergenic lncRNAs have been experimentally demonstrated to have roles in regulating transcription of distally located targets , in trans [16] . Nevertheless , the exact proportion and the distinguishing features of cis- and trans-acting intergenic lncRNAs remain unknown . If lncRNAs' functional roles are conserved it is expected that their loci should be evolutionarily preserved . Indeed , the transcripts and promoters of mammalian intergenic lncRNAs exhibit signatures of selective constraint: their promoters are highly conserved across vertebrates [2] and they have accumulated fewer substitutions than neighbouring putative neutral sequence [17] , [18] . However little is yet known of the evolutionary persistence of lncRNA transcription . Generally the loss and gain of functional noncoding sequence can occur rapidly , with approximately half of all functional ancestral nucleotides predicted to have been gained or lost in mouse or rat since their common ancestor [19] . Other noncoding RNAs , in particular tRNAs , have been shown to exhibit rapid turnover of their transcribed loci , despite conservation of their function [20] . Turnover of regulatory elements underlies species-specific transcriptional evolution and may be associated with phenotypic changes [21] . Only a small minority of intergenic lncRNAs in mouse or human were found to have transcribed orthologous sequences in the other species [22] , [23] . This might reflect turnover of transcribed loci , or it might imply that intergenic lncRNAs , which are often lowly expressed and tissue specific [6] , [9] , [18] , [23] , have transcribed orthologous sequences that remain undetected . Indeed , analysis of the transcription of three intergenic lncRNA loci across homologous regions of the mammalian and avian brain revealed that some intergenic lncRNAs can have conserved expression patterns [24] . To resolve the extent of lncRNA transcriptional turnover it is important to undertake a careful comparison of lncRNA transcription in homogeneous and homologous tissues . Achieving this in closely related species also allows the distinction of transcriptional turnover from DNA sequence turnover and furthermore might reveal otherwise unexpected mechanisms of regulatory divergence . Here we experimentally and computationally explored the genetic structure and function of lncRNA loci in matched tissues from three closely related rodent species , Mus musculus domesticus ( C57BL/6J ) , Mus musculus castaneus and Rattus norvegicus .
We identified transcripts expressed in the liver of three young adult male Mus musculus domesticus ( inbred strain C57BL/6J termed hereafter Mmus ) individuals by directional , stranded ribosomal RNA ( rRNA ) -depleted transcriptome sequencing ( total RNAseq ) ( Figure 1A ) ( see Materials and Methods ) . Data from three independent biological replicates were pooled . About 80% of sequencing reads were mapped [25] to the reference Mmus ( mm9 ) genome and liver gene expression was detectable for 61% of all UTRs and coding exons annotated in the mouse genome ( coverage: 66% ) . We found that a substantial fraction of sequencing reads map to unannotated , likely noncoding , loci consistent with previous results [26] . Using our total transcriptome sequencing data we assembled de novo 56917 transcripts [27] expressed in the Mmus liver ( Figure 1A ) . As a consequence of the short-read single end nature of our data , our transcripts can be fragmented due to incomplete coverage of the full-length cDNA . To identify independent transcripts , we performed genome-wide chromatin immunoprecipitation followed by sequencing ( ChIPseq ) against trimethylation of lysine 4 of histone H3 ( H3K4me3 ) , which marks the beginning of actively transcribed genes [28] and identified enriched regions [29] ( Figure 1A ) ( see Materials and Methods ) . We intersected the genomic locations of 18303 H3K4me3 enriched regions with the predicted 5′ end of our RNAseq-defined Mmus transcripts longer than 200 bases in length , thereby predicting 8915 distinct transcription start sites ( TSSs ) ( Figure 1A ) . As found in previous studies , we identified a limited number of protein-coding genes that exhibited evidence of bidirectional transcription at their TSS ( Figure S1 , Table S10 ) [30] . Most of these transcribed regions are likely noncoding and are not further addressed in our study except when supported by a de novo assembled noncoding transcript [31] . Similarly , we identified transcripts that were either intergenic ( n = 388 ) or intragenic ( n = 8527 ) based on their overlap with Mmus protein-coding gene annotations ( Figure 1A ) ( see Materials and Methods ) . Intergenic transcripts lacking protein-coding potential [32] were annotated as long intergenic ncRNAs ( intergenic lncRNAs ) ( n = 316 , Table S3 ) . Next we defined transcribed loci as clusters of one or more transcripts with overlapping exonic or intronic nucleotides . From 293 of these loci only intergenic lncRNA transcripts were expressed ( Table S3 and S4 ) . The vast majority ( n = 233 ) of these intergenic lncRNA loci have no overlap with intergenic lncRNAs annotated in the mouse genome by Ensembl ( build 64 ) , demonstrating that current mouse intergenic lncRNA catalogues are largely incomplete [18] . Mmus liver intergenic lncRNAs transcripts were significantly ( two-tailed Mann-Whitney test , typically p<1×10−4 ) found to be: ( i ) more lowly expressed , ( ii ) shorter and ( iii ) to have fewer exons than their protein-coding transcript counterparts ( Table S2 ) consistent with previous reports [23] , [33] . The second group of 7289 intragenic loci comprises 8527 transcripts overlapping protein-coding genes ( Ensembl build 60 , Table S3 and S4 ) . Forty-nine loci have overlapping antisense RNAs transcribed from the opposite strand and marked by separate H3K4me3 enriched regions indicating independent transcriptional initiation ( Table S9 ) . Examples in this category include the constitutively expressed noncoding RNA Kcnq1ot1 [34] . Most protein-coding genes are expressed in multiple tissues [35] . In contrast , lncRNA expression tends to be spatially and temporally restricted [6] , [18] , [23] , [36] . We validated the expression of 15 randomly selected liver expressed intergenic lncRNA transcripts by quantitative PCR ( RT-qPCR ) in seven Mmus adult tissues ( Figure 1B ) and nine intragenic antisense lncRNA transcripts by strand specific RT-qPCR [8] in four adult tissues ( Figure S2D ) . These tissues were chosen because they show different degrees of cell type complexity and biological functionality . We found that the large majority of the tested intergenic and intragenic antisense lncRNA transcripts are predominately expressed in liver . Large changes in gene expression are observed during tissue development [37] . In order to identify whether the intergenic lncRNAs we identified are developmentally regulated during hepatocyte differentiation , we measured the abundance of representative lncRNAs by RT-qPCR at embryonic stages E10 , E12 , E14 and E18 and adult stage P62 . Our data showed that lncRNAs are also extremely specific to the adult developmental stage of liver . In summary , the intergenic lncRNAs we identify are specifically expressed in nutritionally unstressed adult liver ( Figure 1C ) . Sequence comparison of mouse intergenic lncRNAs and their human and rat orthologous sequence have shown that these transcripts tend to be constrained , an evolutionary hallmark of functionality , albeit at much lower levels than protein-coding genes [17] , [18] . However little is yet known about transcriptional turnover of lncRNA during evolution . To address the transcriptional turnover of lncRNAs , we explored their transcription across three rodents . In addition to Mmus , we studied transcript expression in the adult liver of a closely related mouse Mus musculus castaneus ( CAST/EiJ termed Mcas ) and in the rat ( Rattus norvegicus , termed Rnor ) ( Figure 2 ) . The two mouse subspecies , Mmus and Mcas , diverged about one million years ago ( MYA ) and last shared a common ancestor with Rnor about 13 to 19 MYA [38] ( Figure 2A ) . These differences in species separation across evolutionary time allowed us to take two snapshots of transcriptional turnover during rodent evolution , using the closest wild-derived mouse species ( Mcas ) to Mmus that is commercially available and Rnor as the evolutionary nearest rodent species with a well-annotated genome . Similar to the characterization of transcripts in Mmus liver , we performed RNAseq and H3K4me3 ChIPseq experiments in Mcas and Rnor , and identified 158 and 605 intergenic lncRNAs respectively ( Tables S1 , S5 , S6 , S7 , S8 ) . The observed difference between the numbers of annotated intergenic lncRNA loci across the three rodents ( 293 , 158 and 605 for Mmus , Mcas and Rnor , respectively ) can be either due to experimental bias or underlying biology . To test the contribution of the difference in read number of each species RNAseq library ( Table S1 ) , we reassembled transcripts in Mmus and Rnor after randomly selecting from Mmus and Rnor libraries the same number of reads as Mcas , our smallest library ( Table S1 ) . For each species we repeated this procedure 10 times . By comparing the numbers of intergenic lncRNAs in Mmus or Rnor that overlapped a transcript from these recreated libraries , we found that the differences in numbers of lncRNAs between mice ( Mmus and Mcas ) species are mostly due to the depth of sequencing . After adjusting the read number of the Mmus RNAseq library to the Mcas RNAseq library , we identified a mean of 154 intergenic lncRNA loci ( standard deviation = 3 . 4 ) for Mmus , a similar number to the one assembled in Mcas ( n = 158 ) , suggesting that the difference in the number of lncRNA loci is due to an experimental bias . In contrast , in Rnor , using the same number of sequencing reads the reduction approach afforded a mean of 284 intergenic lncRNA loci ( standard deviation = 5 . 9 ) . This number corresponds to a 80% rise over the 158 Mcas intergenic lncRNA loci and indicates that there is an increase of liver lncRNA loci in the rat lineage . We next considered if during rodent evolution lncRNA loci were conserved in their transcription in a similar manner to protein-coding genes . We defined transcriptional turnover as instances of genomic loci for which syntenic sequence is conserved between two or more species yet transcription of this conserved sequence is not . To determine conservation of transcribed loci , we combined H3K4me3 peaks with RNA sequencing reads overlapping ( by more than 1 bp ) the syntenic regions to create a stringent set of conserved loci ( see Materials and Methods ) . These loci show evidence of both transcriptional initiation and transcript formation . Owing to the availability of its larger number of publicly available genome wide resources , such as spatial and temporal expression patterns [39] , we anchored our analysis on Mmus . To allow differentiation between sequence and transcriptional turnover we only considered Mmus loci that have aligned orthologous sequence in the rat genome [intergenic lncRNA loci n = 268 ( 91 . 5% ) , protein-coding loci n = 6723 ( 92 . 2% ) ] . We then classified mouse loci according to their transcriptional conservation into three classes: those specific to Mmus , if evidence of expression was found only in Mmus; those conserved in Mus genus , when evidence of transcription was found in Mmus and Mcas but not in Rnor; and , those conserved across these rodents , when expression evidence was found in Mmus , Mcas and Rnor ( Figure 2A , Table S4 ) . Our definition does not explicitly take into account conservation of exon-intron structure . Globally , H3K4me3 and RNAseq signals were grouped according to our classification ( Figure 2B–2C ) . In order to confirm that the observed differences were not solely a consequence of biases introduced by sequencing depth , we validated our interspecies comparisons by semi-quantitative RT-PCR in independent biological replicates from adult livers of Mmus , Mcas and Rnor for 24 intergenic lncRNA transcripts from four categories ( rodent conserved , Mus genus conserved , Mmus-specific , and Rnor-specific , Figure S3 ) . These RT-PCR results confirmed that our global approach accurately identifies species- and lineage-specific intergenic lncRNAs . Turnover of transcription is considerably more frequent for intergenic lncRNA loci than for protein-coding genes in the rodent liver ( Figure 2D ) . A significantly smaller fraction of intergenic lncRNA than protein-coding loci exhibit conserved transcription across rodents [intergenic lncRNA loci n = 160 ( 59 . 7% ) , protein-coding loci n = 6169 ( 91 . 7% ) , two-tailed Fisher's exact test , p<10−3] . Conversely , a significantly higher proportion of intergenic lncRNA than protein-coding loci are specific to the Mmus lineage [intergenic lncRNA loci n = 30 ( 11 . 2% ) , protein-coding loci n = 75 ( 1 . 1% ) , two-tailed Fisher's exact test , p<10−3] . The difference in sequencing depth between the three species influenced the number of annotated intergenic lncRNAs . To account for this effect and provide a more conservative estimate of transcriptional conservation we considered the set of intragenic and lncRNA loci that were assembled after adjusting the Mmus and Rnor RNAseq library sizes to that of Mcas ( see Materials and Methods ) . Intragenic and intergenic lncRNA loci were annotated as previously . We considered a Mmus locus to have conserved expression if it had an overlapping H3K4me3 peak and an overlapping transcript ( >1 bp ) . As previously , we found protein-coding gene loci to be more often conserved in rodents ( 1326/2415 , 55% ) than intergenic lncRNA loci ( 31/110 , 28% , two-tailed Fisher's exact test , p<10−3 ) . Next we aimed to gain initial insights into the conservation of exon-intron structures of Mmus intergenic lncRNAs . For mouse intergenic lncRNAs and protein-coding loci whose transcription was conserved in rat ( 160 and 6169 loci , respectively ) we compared the coverage by RNAseq reads of mouse exonic nucleotides in the rat orthologous regions . We found that rodent conserved protein-coding transcripts have a significantly higher coverage ( median 78% ) than intergenic lncRNA ( median 47% , two-tailed Mann-Whitney test , p<2×10−16 , Figure S4 ) . This observation can be a consequence of lower coverage of low abundance transcripts and/or lower conservation of exon-intron structure for intergenic lncRNAs . Similarly , we observed that the transcriptional conservation of noncoding transcripts that overlap protein-coding genes in antisense orientation also showed a rapid decay across rodent evolution . Only 36% of the Mus conserved intragenic antisense transcripts are expressed in Rnor ( Figure S2 ) . These results indicate that the large majority of ncRNAs are conserved in the Mus genus but not in the evolutionarily further distant species Rnor . The apparent low conservation of intragenic antisense transcription is consistent with previous conservation analysis [33] . To investigate transcriptional turnover of intergenic lncRNAs beyond the rodent lineage , we used publicly available polyA+ transcriptome sequencing data for the adult human liver ( Human BodyMap 2 . 0 RNAseq data ) . Rodents and human shared a common ancestor over 90 MYA [40] . We considered in this analysis only Mmus transcripts whose expression was supported by at least one overlapping polyA+ sequencing read [41] . We found that the majority of mouse intergenic lncRNA loci overlap polyA+ reads ( 273/293 loci ) , suggesting that few intergenic lncRNA loci assembled here transcribe only non-polyadenylated transcripts . We discarded 1368 ( 18 . 8% ) protein-coding and 159 ( 58 . 2% ) intergenic lncRNA loci in Mmus that lack an apparent orthologous sequence in the human or rat genome [42] . As observed for the rodent lineage , a significantly smaller fraction of Mmus intergenic lncRNA than protein-coding genes orthologous in humans are expressed in the liver [intergenic lncRNA loci ( n = 76 , 56 . 7% ) , protein-coding loci ( n = 5689 , 96 . 1% ) , two-tailed Fisher's exact test , p<10−3 ) ( Figure S5 ) . Our data indicate that the fraction of liver transcribed mouse intergenic lncRNAs expressed in the orthologous region of the human genome is two-fold higher ( two-tailed Fisher's exact test , p<10−3 ) than prior estimates [22] , which supports the use of homologous tissue types to investigate levels of transcriptional conservation of tissue specific transcripts , such as intergenic lncRNAs . We conclude that rapid turnover of intergenic lncRNAs is not restricted to the rodent lineage , but is widespread among eutherian mammals . Next we examined how sequence constraint reflects transcriptional conservation of intergenic lncRNA and protein-coding loci . For each transcript we considered its most 5′ nucleotide to correspond to the transcriptional start site and defined its promoter as the 400 nucleotides upstream of this site . We compared the mouse-rat nucleotide substitution rate for intergenic lncRNA loci ( dloci ) and promoters ( dpromoter ) , to rates for genomically neighbouring and non-overlapping ancestral repeats [ARs ( dAR ) ] with matched G+C content [18] , [43] . ARs are transposable element-derived sequences that were present in the last common ancestor of human and mouse; most of these sequences have been observed to evolve neutrally and hence provide reliable proxies for local neutral mutation rates [44] . We first confirmed that Mmus liver-expressed intergenic lncRNA loci accumulated mutations at a significantly slower rate than adjacent neutral sequence ( Figure S6A ) ( dloci = 0 . 148 , dAR = 0 . 164 , two-tailed Mann-Whitney test , p<3×10−7 ) . In line with this observation , long sequence segments that have preferentially purged insertions or deletions in Mmus and Rnor lineages were 1 . 6-fold enriched in intergenic lncRNA transcription over expected levels ( permutation test , p<10−3 ) [44] . As previously reported [12] , [18] the sequences of intergenic lncRNA loci evolve more rapidly than those of full-length protein-coding loci ( Figure S6B ) ( dloci/dAR = 0 . 902; protein-coding dloci/dAR = 0 . 857; two-tailed Mann-Whitney test , p<2×10−3 ) . Additionally , the putative core promoters of intergenic lncRNAs accumulated significantly more substitutions than those of protein-coding genes ( Figure S6C ) ( intergenic lncRNA dpromoter/dAR = 0 . 843; protein-coding dpromoter/dAR = 0 . 746 , two-tailed Mann-Whitney test , p<2×10−5 ) . The discrepancy between this result and published findings [2] is likely due to the incompleteness of lncRNA transcripts' 5′ ends and thus to incomplete delineation of lncRNA promoter sequences . To determine whether loss of transcription is associated with loss of sequence constraint , we compared Mmus to Rnor nucleotide substitution rates between two groups of intergenic lncRNAs: those specific to the Mus genus ( Mmus and Mcas ) and those conserved among these rodents ( Mmus , Mcas and Rnor ) . Rodent conserved intergenic lncRNA loci show evidence for purifying selection on both transcribed ( two-tailed Mann-Whitney test , p<4×10−10 ) ( Figure 3A ) and putative promoter sequences ( two-tailed Mann-Whitney test , p<3×10−12 ) ( Figure 3B ) . Intergenic lncRNA loci transcribed in the Mus genus but not in Rnor , exhibit no constraint in transcribed regions ( two-tailed Mann-Whitney test , p>0 . 2 ) ( Figure 3A ) . Mus genus-conserved putative core promoters accumulated significantly fewer substitutions than neighbouring putatively neutral sequence ( median dprom = 0 . 151 and dAR = 0 . 165 , two-tailed Mann-Whitney test , p<5×10−3 ) suggesting they evolved under purifying selection ( Figure 3B ) . Negative selective pressure was significantly higher on the promoters of loci with rodent conserved transcription than on promoter sequence with Mus genus-specific transcription ( rodent conserved median dprom/dAR = 0 . 783 , Mus genus-specific median dprom/dAR = 0 . 901 , two-tailed Mann-Whitney test , p<7×10−3 ) . We asked whether the observed low degree of sequence constraint on intergenic lncRNA loci , relative to protein-coding genes , was due to rapid transcriptional turnover of a subset of intergenic lncRNAs . To test this , we compared Mmus to Rnor nucleotide substitution rates for the transcribed sequences ( including exons and introns ) between the subset of intergenic lncRNA loci exhibiting conserved expression in the rodent liver ( n = 160 ) with the corresponding set of protein-coding genes ( n = 6641 ) and found no significant difference ( intergenic lncRNA dloci/dAR = 0 . 827 , protein-coding dloci/dAR = 0 . 842 two-tailed Mann-Whitney test , p>0 . 58 ) ( Figure S7A ) . For loci conserved in rodents , nucleotide substitution rates of intronic and exonic sequence were compared between Mmus and Rnor . Introns ( dintron ) of protein-coding genes and intergenic lncRNAs evolved at comparable rates ( intergenic lncRNA dintron/dAR = 0 . 959 , protein-coding dintron/dAR = 0 . 986 , two-tailed Mann-Whitney test , p>0 . 28 ) ( Figure S7C ) . In contrast , protein-coding gene exons evolve under strong purifying selection ( intergenic lncRNA dexon/dAR = 0 . 805 , protein-coding dexon/dAR = 0 . 484 , two-tailed Mann-Whitney test , p<10−15 ) ( Figure S7B ) likely to ensure the maintenance of their coding potential during evolution . Our results therefore indicate that intergenic lncRNA loci that were gained or lost in recent Mus evolution evolved neutrally between mouse and rat . Conversely , rodent conserved intergenic lncRNAs have accumulated fewer substitutions than neighbouring neutral sequence indicating that conservation of transcription is reflected in sequence constraint . Mammalian intergenic lncRNA loci and their genomically adjacent protein-coding genes show a significant tendency to exhibit similar spatiotemporal expression profiles [12] , [13] , [15] , [23] , [45] . We found intergenic lncRNA transcription in liver occurs significantly more frequently near to protein-coding genes that are expressed in the liver [39] than expected by chance ( see Materials and Methods; 1 . 6-fold; permutation test , p<5×10−3 ) . Complementary results were obtained using Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) tissue annotation categories ( Figure S8 ) [46] . About 30% of the protein-coding genes closer to intergenic lncRNA loci were classified as liver expressed ( p<3×10−5 ) . We considered whether lineage-specific transcription of intergenic lncRNAs might associate with the expression level of genomically adjacent protein-coding genes ( see Materials and Methods ) . If intergenic lncRNAs have no effect on nearby protein-coding gene expression , then lineage-specific differences in gene expression of genes should be unaffected by whether a neighbouring intergenic lncRNA locus is transcribed . The existence of relatively large numbers of lineage-specific intergenic lncRNAs in mouse and rat permitted this hypothesis to be tested using Mmus and Rnor . Two additional reasons that we specifically analysed the intergenic lncRNAs identified in these two species were ( i ) the high quality of the genome annotations , relative to Mcas , and ( ii ) the existence of other published datasets that permitted further validation of our results [20] . First , we normalised gene expression for Mmus and Rnor RNAseq data ( see Materials and Methods , Figure S9A ) and validated the fold-difference on 17 selected protein-coding mRNA by RT-qPCR ( Figure S9C and S9D ) . In order to obtain a baseline for transcriptional variation between species from this normalised set , we first estimated the fold difference in liver expression between 230 Mmus housekeeping protein-coding genes [47] and their one-to-one orthologous genes in Rnor ( median fold-difference in expression = 0 . 020 , see Materials and Methods ) . Next , we identified the closest protein-coding gene for each conserved or lineage-specific Mmus or Rnor intergenic lncRNA . We selected the intergenic lncRNA loci whose neighbouring protein-coding genes had annotated [48] one-to-one orthologs in the second species ( Table S9 ) . We found that the expression levels of the genes whose nearest intergenic lncRNA locus showed conserved expression between rodents ( n = 148 ) were similar to housekeeping gene levels ( median fold-difference = −0 . 035 , two-tailed Mann-Whitney test , p>0 . 36 ) ( Figure 4 , Table S12 ) . We then asked whether gene expression levels alter when a nearby intergenic lncRNA is gained or lost in one species . In contrast to the conserved situation above , we found that those protein-coding genes A nearest to lineage-specific intergenic lncRNA loci ( n = 137 ) tended to be expressed at a higher level , with a median increase in gene expression of approximately 25% ( median fold-difference = 0 . 212 , two-tailed Mann-Whitney test , p<0 . 005 ) ( Figure 4 , Table S12 ) . We repeated this analysis and confirmed this result using an independent dataset [20] . We found that the median expression levels of protein-coding gene loci adjacent to lineage-specific intergenic lncRNA loci were significantly higher than those of protein-coding gene loci near conserved intergenic lncRNA loci ( two-tailed Mann-Whitney test , p<7×10−5 ) ( Figure S9B , Figure S10 ) . Transcription increased for half ( 50% ) of those protein-coding genes lying adjacent to lineage-specific intergenic lncRNA loci , when assessed using either total RNA or mRNA expression; in contrast , less than a third ( 29% ) of protein-coding genes near conserved intergenic lncRNA loci show consistent increased expression in both datasets ( two tailed Fisher's exact test , p<0 . 05 , Figure S11 ) , suggesting that in some cases gain or loss of intergenic lncRNAs may influence the expression levels of neighbouring genes . We next investigated if some relative orientations of lineage-specific lncRNA transcription were more frequently associated with increased expression of the most proximal protein-coding gene . We divided lineage-specific intergenic lncRNA and protein-coding gene pairs into three classes ( Figure S12A ) : tandem ( 48 gene pairs ) if transcription occurred in the same orientation , divergent ( 71 gene pairs ) , or convergent ( 17 gene pairs ) if transcription occurred in opposite directions either diverging or converging , respectively . All three relative genomic arrangements are associated with increased expression of the closest protein-coding genes . Both tandem and convergent orientations are associated with significantly increased expression at the 5% level while divergent orientation is significant at the 10% level ( p<0 . 08 , Figure S12B ) . We considered a number of possible interpretations for this apparent association of lineage-specific intergenic lncRNAs with increased transcription of nearby protein-coding genes . The increased gene expression could be either ( i ) due to regional modifications to the genome that co-ordinately influence all coding and noncoding loci [49] or ( ii ) correlated with the transcription of the proximal intergenic lncRNA locus [13] , [15] . A key distinguishing feature between these two mechanisms is whether lineage-specific expression of intergenic lncRNAs is associated with regional increases in transcription . To test this , we identified the next most proximal protein-coding gene B , beyond its closest protein-coding gene A ( Figure 4A ) . Genes duplicated in tandem often share regulatory elements and , as a consequence , exhibit similar expression patterns [50] . To account for this evolutionary bias , we excluded 17 protein-coding genes B that were annotated [48] as protein-coding gene A paralogs ( see Materials and Methods ) . In contrast to the observed lineage-specific effects on protein-coding genes A , the expression levels of protein-coding genes B were not significantly affected ( two-tailed Mann-Whitney test , p>0 . 7 ) by either conserved ( median fold-difference = 0 . 078 ) or lineage-specific ( median fold-difference = −0 . 088 ) intergenic lncRNA transcription ( Figure 4 , Table S13 ) . We next tested whether similar results might be obtained for lineage-specific protein-coding genes . We used the previously identified set of Mus-genus lineage-specific expressed protein-coding genes . We identified genes A′ as the closest protein-coding genes to these loci , protein-coding A′ ( Figure S13 ) . We excluded paralogous protein-coding gene pairs and considered only protein-coding genes A′ with a one-to-one ortholog in rat ( 89 genes ) . Transcription levels of nearby genes appear unaffected by the presence of lineage-specific protein-coding gene transcription in the genomic vicinity ( median fold-difference = 0 . 052 , two-tailed Mann-Whitney test , p>0 . 4 ) ( Figure S13 ) . As an additional control , we compared the densities of chromatin boundary elements ( CCCTC-binding factor [CTCF]-bound sites ) and DNase I hypersensitivity sites in the intergenic regions between ( i ) the lineage-specifically expressed intergenic lncRNA locus and its neighboring protein-coding gene A and ( ii ) protein-coding genes B , using data from previous studies [51] , [52] . We found no significant differences between these densities ( permutation test p>0 . 2 ) . The association between lineage-specific lncRNA transcription and increased expression levels of neighbouring protein-coding genes might depend on the distance between their transcriptional start sites ( TSSs ) . The median distance of the TSS of a lineage-specifically expressed intergenic lncRNA with its closest protein-coding gene is 22 kb . However , no significant correlation was observed between this distance and the median fold difference in expression for protein-coding genes measured between mouse and rat ( Pearson correlation , R = −0 . 03 , p = 0 . 76 , Figure S14 ) . Our comparison of matched tissues in two species thus revealed that birth or death of intergenic lncRNAs is associated with changes in transcription of proximal protein-coding genes .
Previous studies have indicated that 12 to 15% of lncRNAs are conserved between human and mouse , based on comparison of EST and cDNA datasets from disparate experimental designs [22] , [23] . Our matched interspecies data are perhaps better suited to establish experimentally the rate of lncRNA turnover . The use of mouse and rat , being closely related species , minimises the effects of genomic sequence divergence , thus better uncoupling sequence and transcriptional changes . Transcription of noncoding loci is more frequently gained or lost than transcription of protein-coding genes; between 28% and 61% of intergenic and antisense lncRNAs , respectively are specific to the Mus genus . We expect similar turnover will be found in most cell types of various developmental stages given that liver is a typical somatic tissue [53] . The transience of intergenic lncRNA transcription is mirrored by changes to selective pressures acting on their sequences . Our results are consistent with purifying selection acting on transcribed intergenic lncRNA loci , and with no selection acting on untranscribed orthologous sequence in other species . This coupling of transcriptional conservation with sequence constraint suggests that conserved intergenic lncRNA loci are biologically significant in rodents . The expression levels of intergenic lncRNAs and their genomically neighbouring protein-coding genes have previously been shown to be positively correlated [12] , [13] . We find that species-specific transcription of intergenic lncRNAs correlates with elevated expression of neighbouring protein-coding genes . The increased transcription observed among neighbouring genes is unique to intergenic lncRNAs , and seems unlikely to be due to local changes in chromatin environment . If the intergenic lncRNAs in other tissues and species behave similarly , intergenic lncRNAs could contribute substantially to lineage-specific and tissue-specific evolution of gene expression . The rapid turnover we observed in lncRNA transcription strongly resembles what was recently reported for transcription factor binding events [54]–[56] , tRNA transcription [20] and functional regulatory sequences in general [19] . For instance , between 10 to 20% of transcription factor binding events overlap between human and mouse liver [56] , which is similar in scale to what we now find for intergenic lncRNAs . These parallels suggest that rapid evolution is a general feature of noncoding regulatory mechanisms . It was recently proposed that intergenic lncRNAs have minimal impact on the transcriptional regulation of their neighbouring protein-coding genes [16] , [23] . By exploiting the rapid birth and death of noncoding RNAs , we revealed that intergenic lncRNAs could contribute to lineage-specific changes in the expression levels of neighbouring protein-coding genes . Our data do not preclude distal regulatory roles , which might be lineage-specific , for some or all intergenic lncRNAs we investigate . It will now be crucial to understand how intergenic lncRNAs evolve and to unravel the molecular mechanisms underlying lineage-specific gene expression changes associated with intergenic lncRNAs .
ChIPseq , RNAseq , and RT-PCR experiments were performed on liver material isolated from three rodents: Mus musculus domesticus ( Mmus ) , Mus musculus castaneus ( Mcas ) , and Rattus norvegicus ( Rnor ) . Each ChIPseq and RNAseq experiments were performed on at least two independent biological replicates from different animals . Mmus and Mcas ( male adults , 10 weeks old ) were obtained from the Cambridge Research Institute . Rnor ( male adults , 9 weeks old ) were obtained from Charles River . All tissues were either treated post-mortem with 1% formaldehyde for ChIP experiments or flash-frozen in liquid N2 for RNA experiments . The investigation was approved by the ethics committee and followed the Cambridge Research Institute guidelines for the use of animals in experimental studies under Home Office license PPL 80/2197 . ChIP sequencing experiments were performed as described previously [57] using H3K4me3 antibody ( CMA304 ) [58] . In brief , the immunoprecipitated DNA was end-repaired , A-tailed , ligated to the sequencing adapters , amplified by 18 cycles of PCR and size selected ( 200–300 bp ) . For RNA-sequencing library preparation , total RNA was extracted using Qiazol reagents ( Qiagen ) and DNase-treated ( Turbo DNase , Ambion ) . Ribosomal RNA was depleted from total RNA using RiboMinus ( Invitrogen ) . RNA was reversed transcribed and converted into double-stranded cDNA ( SuperScript cDNA synthesis kit , Invitrogen ) , sheared by sonication followed by paired end adapter ( Illumina ) ligation and prior to PCR amplification cDNA was UNG-treated to maintain strand-specificity [59] . After passing quality control on a Bioanalyzer 1000 DNA chip ( Agilent ) libraries were sequenced on the Illumina Genome Analyzer II ( single-ended ) and post-processed using the standard GA pipeline software v1 . 4 ( Illumina ) . H3K4me3 ChIPseq and associated input DNA control ChIPseq reads were aligned to the corresponding reference genomes ( mm9 for Mus musculus domesticus and Mus musculus castaneus; Rn4 for Rattus norvegicus ) using MAQ version 0 . 7 . 1 ( default parameters ) [60] . Reads mapping to multiple genomic locations were discarded . Genomic regions enriched over matching input DNA control were defined using MACS version 1 . 3 . 7 . 1 using the default parameters [61] . Comparative analysis was carried out using the Galaxy web tool [62] . Total RNA sequencing reads were mapped with Tophat ( version 1 . 3 . 0 ) [25] , using default parameters . A file containing the mapped coordinates of mouse and rat ESTs and mRNA mapped coordinates ( downloaded from UCSC on the 11th March 2011 ) was provided to facilitate total RNA read mapping across splice junction for Mmus and Mcas , and Rnor respectively . Reads mapping to rRNA , tRNA and mtRNA were masked and the remainder were used to assemble transcripts de novo using Cufflinks ( version 1 . 3 . 0 ) [27] . We filtered out transcripts smaller than 200 nucleotides ( nt ) and without an H3K4me3 peak overlapping their predicted transcriptional start site ( TSS ) . Transcripts overlapping protein-coding gene annotations ( by one or more base pair ) from RefSeq , Ensembl ( build 60 ) [48] and UCSC were annotated as intragenic . To discriminate between unannotated protein-coding and putatively noncoding transcripts we estimated the coding potential of all intergenic transcripts using the coding potential calculator ( CPC ) [32] . We annotated all transcripts with a coding potential less than 0 as intergenic long noncoding RNAs ( intergenic lncRNAs ) . The 400 nt region upstream of the 5′ end ( TSS ) of each intergenic lncRNA or protein-coding transcript was annotated as a putative promoter . Transcribed loci were defined as non-overlapping regions with one or more transcripts that can contain overlapping exonic or intronic nucleotides . Loci containing only transcripts predicted to be intergenic lncRNAs were annotated as intergenic lncRNA loci . The remainder were annotated as protein-coding loci . For the identification of antisense transcripts from the Cufflinks output file ( n = 56917 ) , we first identified 2383 transcripts overlapping protein-coding genes in antisense orientation in Mmus . This number included four types of ambiguous cases that were systematically removed: ( i ) annotated protein-coding transcripts ( removing 1816 transcripts ) , ( ii ) antisense transcripts lacking an H3K4me3 peak independent from the TSS of overlapping protein-coding gene ( removing 324 transcripts ) , ( iii ) transcripts lacking H3K4me3 marks at their 5′ end , and ( iv ) mapping assembly artefacts , revealed by visual inspection ( collectively removing 90 transcripts ) . Taking all of these cases into consideration , 49 loci ( or 153 antisense transcripts ) were annotated in Mmus . A similar procedure was conducted in Mcas and Rnor , revealing 66 loci in total . To identify lncRNAs deriving from bidirectional transcription at TSSs of protein-coding genes , we subtracted divergently transcribed protein-coding genes from our list of actively transcribed protein-coding genes . The TSSs of gene loci are spanned by one H3K4me3 peak and the evidence of divergent transcription is represented by RNAseq reads mapping in opposite directions . We identified divergent reads within an 1 kb window of a protein-coding gene's annotated TSS ( Ensembl , build 60 ) [30] . Heatmaps and transcription start site aggregation plots were constructed using seqMINER [63] . To account for the difference in RNAseq library size between the three rodent species ( Table S1 ) Mmus and Rnor transcripts were assembled using the same number of reads in Mcas library , the smallest RNAseq library . Reads were randomly selected without replacement and transcripts reassembles using Cufflinks and annotated as described above . RT-PCR analysis of lncRNAs was performed by reverse transcription of 10 µg of DNase-treated total RNA according to the manufacturer's protocols using 200 U SuperScript-II Reverse Transcriptase ( Invitrogen Corporation ) , 0 . 5 µg oligo ( dT ) and 0 . 5 µg random primers or 1 µg gene-specific primers ( see Table S11 ) . Negative controls were included in RT reactions . The cDNAs were then treated with RNase H at 37°C for 1 hour . Each PCR reaction typically contained 25 ng of cDNA , 5 pmol of the gene-specific primers ( Table S11 ) , 10 µL PCR Master Mix ( Bioline ) , and 2 µL of the diluted cDNAs in a total volume of 20 µL . Reactions were carried out in triplicate in ABI 7900HT Fast Real-Time PCR system at the optimal temperature , as defined by provider instructions . The significance of genome-wide associations between intergenic lncRNAs and their neighbouring protein-coding genes was assessed using Genome Association Tool ( GAT ) ( Heger et al . , in preparation ) . GAT compares the observed number of overlapping nucleotides between a set of segments with particular annotations to what would be expected from random placement of these segments . Expected densities are obtained using a randomisation procedure that accounts for G+C content and chromosome specific biases . A previous version of GAT was used in [9] , [18] . This tool infers associations between intergenic lncRNA loci ( segments ) across the following annotation sets: ( I ) mouse-to-rat indel purified segments [44] and ( II ) liver-expressed protein-coding gene territories ( Average Difference values >200 ) [39] . A protein-coding gene territory is defined as the genomic region containing all nucleotides that are closer to the gene than they are to its most proximal up- and downstream protein-coding genes , as described elsewhere [9] , [18] . As a second tool , we used the gene functional classification tool Database for Annotation , Visualization , and Integrated Discovery ( DAVID ) ( default parameters: count = 2 and ease = 0 . 1 ) [46] to explore the enrichment of tissue gene expression . Regions of the mouse and rat genome that are enriched in CTFC binding were obtained from [51] . DNase hypersensity sites ( DHS ) in the mouse adult liver were obtained from [52] . Only male and sex independent DHS peaks that were either annotated as being robust and standard were considered in this analysis . GAT ( Heger et al . , in preparation ) was used to test the observed density of these two class of regulatory elements in the intergenic region between lineage-specific intergenic lncRNAs and protein coding gene A ( Figure 4 ) to what would be expected based on their distribution across the intergenic regions between lineage-specific intergenic lncRNA and protein-coding gene B ( Figure 4 ) . Orthologous regions between Mmus and Rnor were identified using whole genome pairwise alignments [42] . An intergenic lncRNA locus was considered to be expressed in another species when its orthologous ( between Mus species and Rnor ) or equivalent ( between Mmus and Mcas ) position had an overlapping ( >1 bp ) H3K4me3 peak and one or more overlapping RNAseq reads . Due to the lack of H3K4me3 data for human , overlap ( >1 bp ) by one or more RNAseq reads in the orthologous human location was considered sufficient evidence for transcriptional conservation of an Mmus locus in human sequence . Only Mmus loci whose transcription was supported by one or more polyA+ selected sequencing read [41] were considered in this analysis . Identical criteria were used to determine the conservation of antisense lncRNA loci . An antisense lncRNA locus was judged to be expressed in another species when its orthologous position had an overlapping ( >1 bp ) H3K4me3 peak and one or more overlapping RNAseq reads in opposite orientations . We visually inspected these calls on 66 loci across the three rodent species . Nucleotide constraint between Mmus and Rnor locus , exon , intron or putative promoter was estimated as described previously [18] . Pairwise substitution rates between Mmus and Rnor genomic regions were estimated using BASEML from the PAML package with the REV substitution model [64] . The substitution rate of the region of interest was compared to the rate observed for non-overlapping adjacent ( <500 kb ) ancestral repeats ( inserted before the primate and rodent split ) with similar G+C content [18] . Mmus and Rnor protein-coding transcript annotations were downloaded from Ensembl ( build 60 , http://www . ensembl . org/index . html ) and used to define a set of constitutive exons for each gene . To account for differences in size of constitutively expressed portions of Mmus and Rnor genes , the total number of overlapping reads per nucleotide in Rnor was adjusted to what would be expected if the sequence in Rnor had the same length as that observed in Mmus . The expression of a gene in Rnor or Mmus is proportional to the sum of reads mapped to their exons divided by their combined length . To allow comparison of gene expression between species , read counts were normalized using TMM ( edgeR package ) [65] . Briefly , to estimate the normalised library size for each species , it was assumed that 60% of expressed genes were transcribed at similar levels in the two species . Other cut-offs ( 50% and 70% ) yielded similar results . The normalised Mmus and Rnor library size was used to calculate the expression level ( as total number of fragments per kb of sequence per million reads mapped , FPKM ) of each gene in each species . Each intergenic lncRNA locus was paired with its genomically closest protein-coding gene . Only pairs whose protein-coding genes had one-to-one orthologs between Mmus and Rnor were considered . The fold difference in expression levels of protein-coding genes associated with lineage-specific ( Mus-genus or Rnor-specific ) or rodent conserved expression was estimated between [6] the same direction . To calculate the fold difference in expression for each housekeeping gene between Mmus and Rnor species X and Y were randomly assigned . Fold expression differences for protein-coding genes B or A′ ( Figure 4 , Figure S12 ) were calculated in a similar manner . Apart from permutation tests all other statistical analysis were performed using the R package [66] . RNAseq and H3K4me3 ChIPseq sequencing data are available from ArrayExpress under accession number E-MTAB-867 . Additional mRNAseq data used was E-MTAB-424 . | The best-understood portion of mammalian genomes contains genes transcribed into RNAs , which are subsequently translated into proteins . These genes are generally under high selective pressure and deeply conserved between species . Recent publications have revealed novel classes of genes , which are also transcribed into RNA but are not subsequently translated into proteins . One such novel class are long noncoding RNA ( lncRNA ) . LncRNA loci are controlled in a similar manner to protein-coding genes , yet are more often expressed tissue-specifically , and their conservation and function ( s ) are mostly unknown . Previous reports suggest that lncRNAs can affect the expression of nearby protein-coding genes or act at a distance to control broader biological processes . Also , lncRNA sequence is poorly conserved between mammals compared with protein-coding genes , but how rapidly their transcription evolves , particularly between closely related species , remains unknown . By comparing lncRNA expression between homologous tissues in two species of mouse and in rat , we discovered that lncRNA genes are “born” or “die” more rapidly than protein-coding genes and that this rapid evolution impacts the expression levels of nearby coding genes . This local regulation of gene expression reveals a functional role for the rapid evolution of lncRNAs , which may contribute to biological differences between species . |
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The Unfolded Protein Response ( UPR ) maintains homeostasis in the endoplasmic reticulum ( ER ) and defends against ER stress , an underlying factor in various human diseases . During the UPR , numerous genes are activated that sustain and protect the ER . These responses are known to involve the canonical UPR transcription factors XBP1 , ATF4 , and ATF6 . Here , we show in C . elegans that the conserved stress defense factor SKN-1/Nrf plays a central and essential role in the transcriptional UPR . While SKN-1/Nrf has a well-established function in protection against oxidative and xenobiotic stress , we find that it also mobilizes an overlapping but distinct response to ER stress . SKN-1/Nrf is regulated by the UPR , directly controls UPR signaling and transcription factor genes , binds to common downstream targets with XBP-1 and ATF-6 , and is present at the ER . SKN-1/Nrf is also essential for resistance to ER stress , including reductive stress . Remarkably , SKN-1/Nrf-mediated responses to oxidative stress depend upon signaling from the ER . We conclude that SKN-1/Nrf plays a critical role in the UPR , but orchestrates a distinct oxidative stress response that is licensed by ER signaling . Regulatory integration through SKN-1/Nrf may coordinate ER and cytoplasmic homeostasis .
The endoplasmic reticulum ( ER ) is responsible for multiple functions in protein synthesis and processing , lipid metabolism , xeno/endobiotic detoxification , and Ca2+ storage ( reviewed in [1] , [2] ) . The ER forms a continuous structure with the nuclear envelope and maintains extensive contact with mitochondria [3] , [4] . Consequently , the ER is well positioned to sense and respond to changes in the cellular environment . All secretory and membrane-bound proteins are synthesized in the rough ER , a process that is highly regulated so that only properly folded and modified proteins are released to the Golgi [1] , [2] , [5] , [6] . Maturation and folding of these proteins involves glycosylation and formation of appropriate Cys-Cys crosslinks . When its protein folding capacity is exceeded ( ER stress ) , the ER protects itself through the Unfolded Protein Response ( UPR ) ( Figure S1A ) [2] , [5] , [6] . This signaling and transcription program decreases protein translation , expands ER size and folding capacity , and directs misfolded proteins to be degraded in the cytosol . The UPR functions continuously to maintain ER homeostasis , but is amplified and diversified under ER stress conditions [5] , [7]–[10] . In response to severe ER stress , the UPR promotes ER absorption through autophagy and ultimately may induce cell death . ER stress and the UPR have been implicated in many human diseases , including diabetes , inflammatory disease , neurodegenerative disease , secretory cell malignancies , and other cancers [6] , [11] , [12] . The canonical metazoan UPR is orchestrated by three major ER transmembrane signaling proteins ( IRE1 , PERK , and ATF6 ) , and three bZIP-family transcription factors ( XBP1 , ATF4 , and cleaved ATF6 ) ( Figure S1A ) [2] , [5] , [6] . The most ancient of these transmembrane proteins , IRE1 , is a cytoplasmic endoribonuclease and kinase that senses unfolded proteins in the ER . In response to ER stress , the IRE1 RNAse initiates cytoplasmic splicing of the mRNA encoding XBP1 , the transcription factor that is most central to the UPR . The IRE1 kinase contributes to ER homeostasis by regulating the IRE-1 endonuclease activity , and transmits signals through JNK , p38 , and other pathways . The kinase PERK phosphorylates the translation initiation factor eIF2α , thereby globally decreasing translation . This reduces the ER protein-folding load , but also favors translation of mRNAs that encode protective proteins , including ATF4 . ATF6 resides in the ER membrane but is transported to the Golgi and cleaved in response to ER stress . The activation status of these transmembrane proteins is influenced by their interactions with the ER chaperone BiP ( HSP-3/-4 in C . elegans ) . The ER lumen maintains an oxidative environment , in contrast to the cytoplasm , because the ER enzyme systems that form disulfide bonds generate reactive oxygen species ( ROS ) [1] , [13] , [14] . Accordingly , ER stress may eventually lead to cellular oxidative stress and activation of oxidative stress defense genes [15] . Metazoan oxidative and xenobiotic stress responses are orchestrated mainly by the Nrf bZIP-family transcription factors ( Nrf1 , 2 , 3 in mammals ) . Nrf-family proteins regulate genes involved in various small molecule detoxification processes , including glutathione biosynthesis and conjugation , and have been implicated in longevity assurance in invertebrates and mammals [16]–[21] . These transcription factors have recently been shown to function in proteasome regulation , stem cell maintenance , and metabolism , suggesting that they may control a wider range of processes than previously realized [22]–[26] . It has been reported that mammalian Nrf1 and Nrf3 associate with the ER membrane and nuclear envelope [27]–[30] , and that Nrf2 is phosphorylated by PERK [31] , [32] . While these last observations are intriguing , it is unknown whether Nrf-family proteins might actually be involved in ER stress defenses , either through mobilizing an oxidative stress response or participating in the UPR itself . The nematode C . elegans has been a valuable system for investigating how Nrf proteins function and are regulated in vivo , because of its advantages for employing genetics to elucidate regulatory networks , and performing whole-organism analyses of stress resistance and survival . The C . elegans Nrf ortholog SKN-1 plays a critical role in resistance to oxidative and xenobiotic stress , and in various pathways that extend lifespan [16] , [17] , [19] , [23] , [33] . Here we describe a comprehensive analysis of whether SKN-1 might be involved in the UPR . We found that under ER stress conditions SKN-1 directly activates many genes involved in ER function , including canonical ER signaling and transcription factors that in turn induce skn-1 transcription . Importantly , this response is distinct from that which SKN-1 mobilizes under oxidative stress conditions . SKN-1 is required for resistance to ER stress , including reductive stress , a surprising finding given the importance of SKN-1 for oxidative stress defense . Unexpectedly , UPR signaling is needed for SKN-1 to mobilize an oxidative stress response , suggesting that the ER has a licensing and possibly sensing role during oxidative and xenobiotic stress responses .
Several observations led us to investigate whether SKN-1/Nrf might be involved in ER stress defenses . Expression profiling that we performed in C . elegans under normal and oxidative stress conditions suggested that SKN-1 regulates a number of genes that are involved in UPR or ER functions [21] . These included atf-5 ( UPR transcription factor ATF4 ) , ckb-4 ( choline kinase ) , pcp-2 ( prolyl carboxypeptidase ) , and many genes encoding xenobiotic metabolism enzymes that localize to the smooth ER ( Table S1 ) . Moreover , a genome-wide Chromatin Immunoprecipitation ( ChIP ) analysis of C . elegans L1 stage larvae ( MOD-ENCODE ) [34] detected binding of transgenically expressed SKN-1 at the predicted regulatory regions of numerous genes involved in UPR- or ER processes , including UPR signaling and transcription ( ire-1 , xbp-1 , pek-1 , and atf-6 ) , Ca++ signaling , and protein folding and degradation ( Table S1 ) . To investigate whether SKN-1 might be involved in the UPR , we first used quantitative ( q ) RT-PCR to investigate whether it is needed for expression of representative ER stress-induced or ER maintenance genes , many of which are predicted to be SKN-1 targets ( Table S1 ) . In these initial gene expression studies we induced ER stress by treating C . elegans with the N-linked glycosylation inhibitor tunicamycin ( TM ) , at a concentration that readily induces the UPR but does not cause detectable toxicity ( 5 µg/ml , Figure S1B ) [15] . TM treatment resulted in skn-1-dependent upregulation of numerous canonical or predicted UPR- or ER-related genes ( Figures 1A and 1B , Table S1 ) . skn-1 was also required for the basal expression of psd-1 , R05G6 . 7 , and cnb-1 , even though these genes were not activated by TM ( Figures 1A and 1B ) . TM-induced ER stress also upregulated two direct SKN-1 targets that are involved in glutathione metabolism ( gcs-1 and gst-4 ) [19] in a skn-1–dependent manner , and transgenic reporter analysis detected gcs-1 activation in the intestine , the C . elegans counterpart to the gut , liver , and adipose tissue ( Figures 1C and 1D ) . Importantly , however , ER stress did not activate various other genes that are typically induced by SKN-1 under oxidative stress conditions ( Figure S1C ) . Taken together , the data indicate that SKN-1 mediates a response to ER stress , but also that this response does not correspond simply to its oxidative stress defense function . To investigate whether SKN-1 activates genes directly during ER stress , we used ChIP to detect endogenous SKN-1 and markers of transcription activity at pcp-2 , atf-5 , and gst-4 , each of which is flanked by SKN-1 binding sites and upregulated by oxidative and ER stress in a skn-1-dependent manner [21] ( Figures 1B and 1C ) . SKN-1 was readily recruited to these genes in response to either TM-induced ER stress or Arsenite ( AS ) -induced oxidative stress ( Figures 2A , 2E , 2I , and S2A-S2C ) . During transcription , RNA Polymerase II ( Pol II ) is phosphorylated on Ser 2 of its C-terminal domain ( CTD ) repeat ( P-Ser2 ) [35] . At each gene we examined , ER stress increased Ser 2 phosphorylation levels ( Figures 2B , 2F , and 2J ) . Also consistent with transcriptional activation , at these loci ER stress increased acetylation of Histone H3 , another marker of transcription activity [36] , but reduced overall Histone H3 occupancy ( Figures 2C , 2D , 2G , 2H , 2K , and 2L ) . Taken together , our findings suggest that SKN-1 directly activates a major transcriptional response to ER stress . We next investigated whether SKN-1 might regulate expression of core UPR signaling and transcription factors , as predicted by the MOD-ENCODE data [34] . XBP-1 is central to the UPR , and in mammals it controls transcription of other core UPR genes ( atf4/atf-5 , and BiP/hsp-4 ) along with many downstream genes [6] , [37] . During the UPR , xbp-1 expression is regulated at the level of transcription , as well as through cytoplasmic splicing of its mRNA by the IRE-1 endoribonuclease ( Figure S1A ) [5] , [6] . The spliced form of the xbp-1 mRNA ( xbp-1s ) encodes the transcriptionally active form of XBP-1 ( XBP-1s ) . When SKN-1 was lacking , ER stress failed to induce accumulation of each xbp-1 mRNA form and , remarkably , decreased the ratio of xbp-1s to the unspliced xbp-1 form ( xbp-1u ) ( Figures 3A , 3B , and S3A ) . The xbp-1 locus includes a predicted SKN-1 binding site ( not shown ) , and ChIP results indicated that endogenous SKN-1 accumulates at the xbp-1 site of transcription in response to ER stress ( Figure 3C ) . This evidence that SKN-1 directly regulates xbp-1 could account for the reduction in total xbp-1 mRNA , but not the apparent effect of SKN-1 on xbp-1 splicing . A plausible explanation is that lack of SKN-1 also reduced basal and ER stress-induced expression of ire-1 ( Figures 3D and 3E ) . Moreover , we observed that SKN-1 is recruited to the ire-1 locus in response to ER stress ( Figure 3F ) , consistent with MOD-ENCODE evidence that ire-1 may be a SKN-1 target [34] . SKN-1 was also required for expression of other core UPR genes . Mutation or RNAi knockdown of skn-1 prevented ER stress-induced expression of the unfolded protein chaperone and sensor HSP-4 ( BiP ) ( Figure S1A ) ( Figures 3G , S3B , and S3C ) . Binding of SKN-1 at hsp-4 was not detected in the MOD-ENCODE study of L1 larvae [34] , but our ChIP evidence indicated that both SKN-1 and XBP-1 bind directly to the hsp-4 locus ( Figures S3D and S3E ) , which includes predicted SKN-1 binding sites ( not shown ) . SKN-1 similarly contributed to expression of the core UPR factors pek-1 and atf-6 ( Figures 3D and 3E ) . Our evidence that SKN-1 is important for transcriptional induction of core UPR signaling and regulatory factors predicts that it should be important for C . elegans survival under ER stress conditions . Treatment with TM at a 7-fold higher concentration ( 35 µg/ml ) than is sufficient to induce the UPR impaired the survival of skn-1 mutants but not wild type animals ( Figure 3H and Table S2 ) . We conclude that SKN-1 plays a critical role in the UPR through its direct transcriptional regulation of core UPR factors , along with many downstream genes . We next examined whether expression of skn-1 itself is increased when the ER becomes stressed , and whether various conditions that cause ER stress affect SKN-1 activity . Treatment with TM increased the levels of multiple mRNA species that encode SKN-1 isoforms ( Figure 4A and S4A ) . In addition , non-lethal treatment with either the Ca++ pump inhibitor thapsigargin ( Thap ) or the proteasome inhibitor Bortezomib upregulated transcription of skn-1 , and various SKN-1-regulated genes ( Figures 1 , 4A , and S4B–S4C ) . Finally , knockdown of either the ER chaperone hsp-4 or the UPR transcription factor atf-6 resulted in transcriptional upregulation of skn-1 and many of its ER stress targets in the absence of drug treatment , presumably because of an elevated level of ER stress ( Figures 4A , S4D and S4E ) . We conclude that skn-1 transcription and activity are increased in response to a variety of conditions that are associated with ER stress . An important hallmark of the UPR is a decrease in the overall levels of translation [5] , [6] . This relieves stress on the ER , and allows translation of atf4 and other protective genes to be maintained or even increased . We investigated whether SKN-1 translation is similarly “spared” under ER stress conditions . Supporting this idea , TM treatment increased SKN-1 protein levels , a trend that was observed in Western and IP-Western analyses of whole animals with two specific SKN-1 antibodies ( Figures 4B and S4F–S4I ) . Based upon its size , this approximately 85 kD SKN-1 species is likely to represent SKN-1a , the largest SKN-1 isoform . While this size is larger than the expected SKN-1a MW of 70 kD , SKN-1 is phosphorylated and predicted to be glycosylated , as is characteristic of Nrf1 and Nrf3 ( not shown ) [17] , [28] , [38]–[40] . Our finding that SKN-1 protein levels are increased by ER stress is consistent with earlier evidence that SKN-1 translation seemed to be preserved when translation initiation was inhibited [41] . Prolonged ER stress leads to accumulation of reactive oxygen species ( ROS ) and induction of an oxidative stress response [15] , [42] , making it important to determine whether ER stress treatments might activate SKN-1 simply through a secondary response to oxidative stress . Arguing against this interpretation , even though SKN-1 is well known to defend against oxidative stress , we found that reductive ER stress also induced a SKN-1-dependent response . The reducing agent dithiothreitol ( DTT ) initiates the UPR through reduction of Cys-Cys bonds in the ER [43] . DTT treatment resulted in transcriptional induction of skn-1 and many of its target genes , and increased SKN-1 protein levels ( Figures 4C and S4J ) . SKN-1 appeared to be required for its downstream targets to be activated by DTT-induced reductive stress ( Fig . S4K ) , and knockdown of either skn-1 or hsp-4 rendered C . elegans comparably sensitive to reductive stress from DTT ( Figure S4L and Table S3 ) . Another way to reduce oxidation in the ER is through inhibiting expression of the oxidase ERO-1 , which promotes Cys-Cys crosslinking [43] . ero-1 RNAi decreases ROS levels , initiates the UPR , and extends lifespan [15] . As observed with DTT , ero-1 RNAi transcriptionally activated skn-1 and several of its downstream targets ( Figure 4D ) . Additional lines of evidence support the idea that SKN-1 acts in the UPR independently of its role in oxidative stress defense . Many genes that are activated by SKN-1 under oxidative stress conditions were not upregulated by ER stress ( Figures S1C and S4M ) . Oxidative stress from AS treatment induced the SKN-1::GFP ( green fluorescent protein ) fusion to accumulate to high levels in intestinal nuclei , as previously described ( Inoue , et al . , 2005 ) , but this did not occur in response to ER stress ( Figure S4N ) . Finally , we did not observe increased levels of oxidized proteins under conditions of TM-induced ER stress ( Figure S4O ) . Taken together , the data show that ER stress directs SKN-1 to activate a specific set of its target genes independently of any secondary oxidative stress response . If ER signaling pathways regulate SKN-1 , then key UPR signaling and transcription factors should be required for ER stress to activate SKN-1 and its target genes . Accordingly , RNAi or mutation of ire-1 , atf-5 , pek-1 , or hsp-4 essentially prevented ER stress from inducing transcription of skn-1 and several of its target genes ( Figure 5A ) . Knockdown of xbp-1 under control conditions increased background expression of some SKN-1 isoforms and target genes ( skn-1b , pcp-2 , gst-4 , hsp-4 ) , possibly because ER stress was increased , but also interfered with ER stress-induced activation of several of these genes ( skn-1a , pcp-2 , gcs-1 , hsp-4 ) ( Figure S5A ) . RNAi against ire-1 , which is essential for XBP-1s expression [5] , [6] , also blocked TM-induced accumulation of SKN-1 , Pol II , or P-Ser2 at the gst-4 , pcp-2 , and atf-5 loci ( Figures 5B–5E , S5B and S5C ) . Knockdown of hsp-4 or pek-1 had a similar effect ( Figure S5D–S5G ) . The evidence indicates that , in general , core UPR factors are required for ER stress to upregulate expression of SKN-1 and its target genes . The most straightforward mechanism through which ER stress could increase skn-1 transcription is through the direct regulation of skn-1 by one or more of the canonical UPR transcription factors . During the UPR , downstream gene transcription is controlled largely by XBP1 and ATF4 , which may regulate each other directly , with ATF-6 playing a more specialized role [8] , [15] , [37] . The skn-1 locus contains possible XBP-1 and ATF-6/XBP-1 binding elements ( not shown ) , and genome-wide ChIP studies suggest that mammalian Nrf3 may be a direct XBP1 target [37] . We determined that XBP-1 binds within the skn-1 locus in response to ER stress , suggesting direct regulation ( Figure 5F ) , a remarkable parallel to the direct regulation of xbp-1 by SKN-1 ( Figure 3C ) . Moreover , ATF-6 was also recruited to the skn-1 locus in response to ER stress ( Figure 5G ) . In mammals , XBP-1 may regulate its own expression [37] . Our ChIP analysis indicated that SKN-1 also binds to its own locus with ER stress ( Figure 5H ) , suggesting that SKN-1 , XBP-1 , and ATF-6 together regulate skn-1 transcription . ER stress also resulted in XBP-1 and ATF-6 recruitment to the direct SKN-1 targets pcp-2 and gst-4 ( Figures S5H–S5K ) . Together , the evidence suggests that SKN-1 , XBP-1 , and ATF-6 may function together to regulate several downstream genes . We conclude that SKN-1 is transcriptionally integrated into the UPR , in which it functions upstream , downstream , and in parallel to the known core UPR transcription factors . The mammalian SKN-1 orthologs Nrf1 and Nrf3 have been detected in association with the ER ( see Introduction ) , raising the question of whether this might also be true for a proportion of SKN-1 . Consistent with this idea , Nrf1 and the SKN-1a isoform each contain a predicted transmembrane domain [27] ( Figure S6A ) . To investigate whether SKN-1 might be present at the ER , we asked whether it might be detected in association with the ER-resident chaperone BiP ( HSP-3/-4 ) ( Figure S1A ) . We performed co-immunoprecipitation ( IP ) analyses of intact worms that had been crosslinked with formaldehyde as in our ChIP experiments . These conditions capture direct and indirect in vivo interactions that occur within approximately 2 Å , and allow for high-stringency detergent and salt-based washings that minimize non-specific binding [44] , [45] . Under both normal and ER stress conditions , association between HSP-4 and SKN-1 was readily detected by high-stringency IP performed in either direction ( Figure 6A and 6B ) . As in Figure 4B , the size of this SKN-1 species suggested that it may correspond to SKN-1a . The data suggest that some SKN-1 may be produced at the ER and might remain associated with this organelle . Given that BiP has been found in other cellular locations besides the ER [46] , we also investigated whether SKN-1 is present in a cellular fraction that is enriched for the ER ( Figure S6B ) . SKN-1 was readily detectable in an ER fraction that included HSP-4 , but not the cytoplasmic protein GAPDH ( Figures 6C and 6D ) . The interaction between endogenous SKN-1 and HSP-4 was confirmed within this ER fraction by a co-IP that was performed without crosslinking ( Figure 6E ) . Together , our findings suggest that the association of SKN-1/Nrf proteins with the ER is evolutionarily conserved . Our finding that UPR factors are required for SKN-1 activity to be increased under ER stress conditions raised a related question: might UPR-related mechanisms also be involved in SKN-1 responses to oxidative stress ? Surprisingly , we found that RNAi or mutation of core UPR signaling and transcription factors ( atf-5 , pek-1 , ire-1 , hsp-4 and xbp-1 ) impaired oxidative stress ( AS ) -induced activation of several SKN-1 target genes , including skn-1 itself ( Figures 7A , 7C , and S7A ) . Similarly , ire-1 RNAi attenuated activation of the gcs-1::GFP reporter in the intestine ( Figure S7B ) . This impairment of the oxidative stress response is particularly striking because ire-1 RNAi actually increased oxidized protein levels , in contrast to the mild AS treatment conditions used for gene expression analyses ( Figure S4O ) . Importantly , oxidative stress from AS did not simply activate the canonical UPR . Many SKN-1-regulated genes that were induced by oxidative stress were not upregulated by ER stress , and vice-versa ( Figures S1C , S4M , and S7C ) . This shows that SKN-1 mobilizes distinct transcriptional responses to oxidative and ER stress , even if these responses overlap to an extent . Moreover , AS primarily increased accumulation of the unspliced xbp-1 mRNA form ( xbp-1u ) , in striking contrast to the increase in xbp-1s levels that is characteristic of ER stress ( Figures 3A and 7C ) . Treatment with the oxidative stressor tert-butyl hydrogen peroxide ( tBOOH ) induces a SKN-1-dependent response that overlaps with the AS response , but includes SKN-1-independent activation of many genes that are otherwise SKN-1-dependent [21] . Knockdown of ire-1 or hsp-4 inhibited tBOOH from upregulating skn-1 and some SKN-1 targets ( Figure 7B ) , but did not eliminate activation of other genes ( gcs-1 , sdz-8 , and gst-10; not shown ) . The data suggest that core UPR factors are needed for SKN-1 to function properly under oxidative stress conditions , in addition to the setting of ER stress . The extensive regulatory integration that exists among UPR transcription factors , as described by others and in this study ( Figures 7A , 7B , and S7A ) [8] , [15] , [37] , could explain why multiple UPR-associated signaling and transcription factors are needed for skn-1 expression to be increased in response to oxidative stress . However , we considered that the UPR might also influence SKN-1 regulation at a post-translational level . In the C . elegans intestine SKN-1 is predominantly cytoplasmic under normal conditions , but accumulates in nuclei in response to oxidative stress from AS treatment [38] . This nuclear accumulation was dramatically reduced in animals that had been exposed to ire-1 RNAi ( Figure S7D ) . The presence of SKN-1 in intestinal nuclei is dependent upon its phosphorylation by the p38 kinase , which is activated by oxidative stress [23] , [38] , [47] . The IRE-1 kinase activity transmits signals through the JNK and p38 MAPK pathways [6] , [48]–[50] , and we determined that ire-1 knockdown largely prevented the increase in p38 signaling that occurs in response to oxidative stress ( Figures 7D and S7D ) . Taken together , these data suggest that IRE-1 is required for oxidative stress to activate SKN-1 post-translationally . If UPR signaling and transcription factors are required for SKN-1 to mobilize appropriate oxidative stress responses , then oxidative stress sensitivity should be increased when these canonical UPR factors are lacking . Accordingly , RNAi or mutation of these genes significantly increased sensitivity to oxidative stress from exposure to AS , paraquat , or t-BOOH ( Figures 7E , S7E , and S7F; Table S4 ) . We conclude that signaling from the ER is required for SKN-1 to respond to oxidative stress , and therefore that UPR-mediated regulation of SKN-1 plays a central role in the homeostatic integration of ER and oxidative stress responses .
It is well-established that the canonical UPR transcription factors XBP1 , ATF4 , and ATF6 control overlapping sets of downstream genes and processes [5] , [6] , but much less is known about how their responses to ER stress might be integrated with other mechanisms that maintain cellular stress defense and homeostasis . We have determined that the oxidative/xenobiotic stress response regulator SKN-1/Nrf functions as a fourth major UPR transcription factor in C . elegans . Without SKN-1 , ER stress failed to increase the expression of core UPR signaling and transcription factors , many of which are regulated directly by SKN-1 ( ire-1 , xbp-1 , atf-5 , and hsp-4; Figures 1 , 2 , 3 and S3 ) . It was particularly striking that SKN-1 was disproportionally required for production of spliced xbp-1 mRNA ( xbp-1s ) , presumably because of its importance for IRE-1 expression ( Figures 3D–F ) . SKN-1 was also needed for ER stress to upregulate numerous genes that are known or predicted to be involved in various ER- or UPR-related processes , including ER homeostasis ( ero-1 , pdi-2 ) , chaperone-mediated protein folding ( hsp-3 , hsp-4 , dnj-28 , T05E11 . 3 ( HSP-90/GRP94 ) ) , autophagy ( lgg-1 , lgg-3 ) , calcium homeostasis ( sca-1 , crt-1 ) , ER membrane integrity ( ckb-4 ) , and a pathway that defends against ER stress when the canonical UPR is blocked ( abu-8 , abu-11 [51] ) ( Figure 1 , 3G and Table S1 ) . Together , our data indicate that SKN-1 regulates transcription of essentially the entire core UPR apparatus and many downstream ER stress defense genes in vivo . We were surprised to find that SKN-1 was so broadly important for UPR transcription events . A trivial explanation for our findings would be that skn-1 mutants did not need to induce the UPR robustly because they were resistant to ER stress . This explanation was ruled out , however , by our finding that skn-1 mutants are actually sensitized to ER stress from diverse sources ( Figures 3H and S4L ) . Importantly , our ChIP studies and MOD-ENCODE data [34] indicate that SKN-1 controls many core and downstream UPR genes directly by binding to their promoters ( Figures 2 , 3 , and S3E , Table S1 ) . We also found that ER stress induces SKN-1 , XBP-1 , and ATF-6 to bind promoters directly to regulate many of the same genes , including skn-1 itself ( Figures 5 , S3 , and S5 ) . In addition , under ER stress conditions , UPR signaling increased levels of skn-1 mRNA and protein ( Figures 4 and S4 ) , indicating that SKN-1 is controlled by the UPR and is an active participant in this response . Together , our data reveal that a remarkable degree of regulatory and functional integration exists between SKN-1 and the three canonical UPR transcription factors ( Figures 7F and S1A ) . Although ER stress increases skn-1-dependent transcription and SKN-1 occupancy at several downstream gene promoters , it did not detectably alter the overall levels of SKN-1 in intestinal nuclei , at least as indicated by levels of a transgenic GFP fusion protein ( Figure S4N ) . While this might seem paradoxical , we observed a similar situation with reduced TORC1 signaling [19] . Under conditions of low TORC1 activity SKN-1 target genes were activated in a skn-1-dependent manner , and this was accompanied by increased SKN-1 binding to their promoters , but not by an obvious increase in the bulk levels of SKN-1 in nuclei . Our finding that SKN-1 binds to downstream UPR genes together with other UPR transcription factors suggests a paradigm that could explain this phenomenon . If SKN-1 binds cooperatively with UPR factors or other co-regulators to some of its targets , this could shift the binding equilibrium to allow those targets to be activated by SKN-1 that is already present in the nucleus , without it being necessary to “flood” the nucleus with higher levels of SKN-1 . This scheme might be important for fine-tuning of SKN-1 downstream functions , and for allowing SKN-1 to activate different targets in different situations , as we have observed in this study . In performing these analyses , we were mindful of the concern that the involvement of SKN-1 in the UPR might derive from its possible role in a secondary oxidative stress response . Several lines of evidence argued against this interpretation . For example , the direct involvement of SKN-1 in regulating multiple core UPR signaling and transcription factors during the UPR ( Figures 3 and S3 ) is not consistent with its UPR functions deriving simply from a secondary oxidative stress response . Moreover , under our ER stress conditions SKN-1 was required for accumulation of the spliced form of the xbp-1 mRNA , whereas oxidative stress increased levels of the unspliced xbp-1 message ( Figures 3A , 3B , and 7C ) . It was particularly striking that SKN-1 defended against reductive ER stresses ( Figures 4C , 4D , S4J , S4K , and S4L ) , given the extensively described role of SKN-1/Nrf proteins in oxidative stress responses . These last observations indicated that SKN-1 defends against ER stress per se , and not only against oxidative conditions . Importantly , ER stress and the UPR directed SKN-1 to activate some of its target genes that are induced by oxidative stress , but not others ( Figure S1C and S4M ) . On the other hand , many genes that SKN-1 activated under ER stress conditions were not induced by oxidative stress ( Figure S7C ) . Taken together , the data show that SKN-1 does not simply activate oxidative stress defenses in the context of ER stress , but orchestrates a specific transcriptional ER stress response that is integrated into the broader UPR . Our finding that SKN-1 mobilizes overlapping but distinct responses to ER and oxidative stress defines a new function for this surprisingly versatile transcription factor . It also supports our model that SKN-1/Nrf proteins do not control the same genes under all circumstances , but instead induce protective responses that are customized to the challenge at hand [19] , [26] . The idea that SKN-1 works together with canonical UPR transcription factors at downstream genes may provide a model for understanding how particular SKN-1 functions can be mobilized under different conditions , if these proteins and other SKN-1 “partners” guide its activities . Consistent with reports that Nrf1 and Nrf3 are present at the ER [27]–[30] , we found that some SKN-1 also localizes to the ER . We detected association between SKN-1 and the ER chaperone HSP-3/4 ( BiP ) in crosslinking analyses of intact animals , the presence of SKN-1 within an ER fraction , and association between SKN-1 and HSP-3/4 within that fraction ( Figure 6 and S6 ) . Each of these experiments involved analysis of endogenous proteins . These strategies would have detected either direct or indirect interactions , so they do not demonstrate that SKN-1 binds directly to HSP-3/4 ( BiP ) , but they do show that these proteins reside very close to each other at the ER . Apparently , association between SKN-1/Nrf proteins and the ER is evolutionarily conserved . The example of ATF-6 , which is activated through cleavage in the Golgi ( Figure S1A ) , predicts that ER-associated SKN-1 might have a signaling function in which it is cleaved in response to ER stress . However , the relative instability of SKN-1 and the presence of smaller isoforms have so far confounded the resolution of this question ( not shown ) . We recently determined that some SKN-1 also localizes to mitochondria and that SKN-1 can promote a starvation-like state when overexpressed , a function that also appears to be conserved in Nrf proteins [26] . Given the extensive communication between the ER and mitochondria [4] , [52] , our results suggest that SKN-1/Nrf might respond directly to the status of each of these organelles . Consistent with this notion , SKN-1 is required for expression of the C . elegans ortholog of mitofusin ( fzo-1 ) ( Figure 1A ) , which mediates mitochondrial fusion and mitochondria-ER interactions [4] . Taken together , our findings show that processes controlled by SKN-1/Nrf proteins are critical for ER stress defense and homeostasis , and that SKN-1 is extensively intertwined with the UPR in vivo . While differences could exist between C . elegans and mammals with respect to regulatory networks , the extent of the functional interactions we have observed predicts that mammalian Nrf proteins are likely to play an important role in the UPR that is distinct from their familiar function in oxidative stress responses . Perhaps our most surprising finding was that core UPR signaling and transcription factors were required for SKN-1 to mount a transcriptional response to oxidative stress ( Figures 7 and S7 ) . Cooperative interactions between SKN-1 and UPR transcription factors could account for some of these findings , through their effects on SKN-1 expression , but it was striking that ire-1 was needed for AS to induce SKN-1 nuclear accumulation , a phenomenon that does not occur under ER stress conditions ( Figures S4N and S7D ) . Moreover , ire-1 was required for the AS-induced p38 signal that is needed for SKN-1 to be present in nuclei ( Figure 7D ) . These last findings indicate that IRE-1 affects the oxidative stress response at a step upstream of SKN-1 . One speculative possibility for further investigation is that the IRE-1 kinase activity might be needed to initiate the oxidative stress-induced p38 signal . Together , our data show that signaling from the ER is required to “license” the oxidative/xenobiotic stress response , and suggest that the ER might function in effect as a stress sensor . This importance of the UPR for SKN-1 activity may have implications for our understanding of aging and longevity assurance . SKN-1/Nrf not only defends against resistance to various stresses , but is also important in pathways that affect longevity , including insulin-like , TORC1 , and TORC2 signaling , and dietary restriction [16] , [17] , [19] , [20] . IRE-1 and XBP-1 have each been implicated in longevity [53] , [54] , making it important to determine the extent to which these UPR-based mechanisms might influence aging through regulation of SKN-1/Nrf and its functions . Why would such extensive integration have arisen , in which SKN-1/Nrf is essential for the UPR , and signaling from the ER is needed for SKN-1/Nrf activities that are distinct from the UPR ( Figure 7F ) ? SKN-1/Nrf controls cellular processes that profoundly influence the ER . Its target genes drive synthesis of glutathione , the major redox buffer within the ER , and encode many endobiotic and xenobiotic metabolism enzymes that reside on or within the smooth ER ( Table S1 ) [20] , [21] , [55] . Under some circumstances SKN-1/Nrf also regulates proteasome expression and activity , and numerous chaperone genes [20] , [21] , [23]–[25] . One possibility is that the influence of SKN-1 could attune the UPR to events taking place in the cytoplasm . It might be advantageous to mount a robust transcriptional UPR if the cytoplasm is under duress , for example , and to moderate the UPR when cytoplasmic stress is low . Under these conditions , SKN-1 activity would be relatively high and low , respectively . SKN-1 activity is also comparatively low when translation rates are high [19] , [23] . If the ER becomes stressed under growth conditions it might be useful to limit the transcriptional UPR initially , because a reduction in translation rates might largely suffice to restore homeostasis . Again , under these conditions low SKN-1 activity could act as a brake on the transcriptional UPR . With respect to the oxidative/xenobiotic stress response , it could be important for the ER to have a “vote” on its intensity , given the profound influence of SKN-1/Nrf on cellular redox status and resources devoted to the ER . It seems likely , therefore , that the ER not only manages its own homeostasis , but through SKN-1/Nrf has a broader impact on cellular stress defense networks that is likely to be critical in their normal and pathological functions .
For each condition studied , RNA was extracted from approximately 100 µl of packed mixed-stage worms that were collected in M9 at the indicated time point . To induce UPR-associated gene expression , at day three of adulthood worms were treated with 5 µg/ml TM ( Sigma ) for 16 hours [15] , or at day four with 5 mM DTT ( Sigma ) [54] for two hours , 5 µM thapsigargin ( Enzo ) [56] for two hours , or 5 µM Bortezomib ( proteasome inhibitor , LC Labs ) for six hours ( similar to published C . elegans MG132 proteasome inhibitor treatment [57] ) . In each case , these treatments were non-lethal . For arsenite ( AS ) and tBOOH exposure , up to 100 µl of packed worms were collected and nutated in 5 mM AS or 12 mM tBOOH for 1 hour ( a non-lethal duration ) . Each of these treatments was performed in a volume of 1 ml , and was followed by pelleting . RNA was analyzed by qRT-PCR as described , with values normalized to an internal standard curve for each amplicon [19] , [44] . The same treatment conditions were used for ChIP experiments . Expression or nuclear accumulation of transgenic GFP proteins was scored as “low , ” “medium , ” or “high” essentially as published [19] , or were quantified using ImageJ 1 . 45S . ChIP was performed essentially as described [19] , [44] . 2 ml of packed mixed-stage worms were crosslinked with formaldehyde at room temperature for 20 minutes . After quenching , lysis , and determination of protein concentration , 1 mg/ml samples were frozen as aliquots at −80°C . The resolution of the assay was approximately 250–500 bp [44] . The monoclonal antibody FC4 [58] was used for SKN-1 ChIP experiments , as in previous ChIP analyses [19] . Other antibodies are described in the Supplemental Experimental Procedures . Analyses of intergenic regions and control genes ( not shown ) indicated that average signals of 14% , 11% , 26% , 4% , 11% , 7% , and 8% represent thresholds for specific presence of SKN-1 , Pol II , PSer2 , and H3-AcK56 , XBP-1 , ATF-6 , and Histone H3 respectively . Worms from five confluent 20 cm2 plates were collected in M9 with or without TM treatment ( 5 µg/ml ) for 16 hours , in order to generate 2× 1 ml of packed mixed-stage animals . Worms were sonicated 3× for 20 seconds in homogenization buffer ( supplied by IMGENEX kit , supplemented with HDAC inhibitors , protease inhibitors , phosphatase inhibitors , and MG132 ) with the Branson midiprobe 4900 Sonifer before fractionation with the IMGENEX Endoplasmic Reticulum Enrichment Kit ( Cat No . 10088K ) [59] . Mitochondrial and ER fractions were washed 3× with 1 ml PBS and resuspended in 400 µl PBS ( supplemented with HDAC , protease , and phosphatase inhibitors and MG132 ) . Up to 100 µl of the ER or cytoplasmic fractions were used for each IP . Controls for a polyclonal rabbit antiserum raised against SKN-1c ( JDC7 , referred to as pSKN-1 ) are shown in Figures S4F–S4J . HSP-3/4/BiP was detected with either C-terminal Drosophila Hsc3 [60] ( Figures 6A and 6B ) or N-terminal human BiP antibody ( Sigma et21 ) [61] , [62] ( Figures 6C and 6E ) . Note that both BiP antibodies recognized the same 75 kD band . ATF-6 ( Abcam ab11909 ) , Tubulin ( Sigma #9026 ) , and GAPDH ( Santa Cruz sc25778 ) antibodies were also used . Phosphorylated p38 was detected using an antibody from Cell Signaling T180/Y182 as described previously [23] . For Western blotting , antibodies were used at the following dilution: 1∶200 FC4 monoclonal αSKN-1 , 1∶200 polyclonal αSKN-1 , 1∶1000 αPol II , and 1∶1000 for αHsc3 . All other antibodies were used at manufacturer's recommended concentrations . For IPs , the indicated antibodies ( 50 µl FC4 monoclonal αSKN-1 or polyclonal αSKN-1 , 10 µl Hsc3 ( BiP ) or 20 µl BiP ( Sigma ) ) and pre-blocked Salmon Sperm DNA/Protein A beads ( Zymed ) were added to lysates or samples from the fractionation described above . The final volume was brought to 500 µl in 1× PIC , 1× PMSF , and 1∶1000 MG132 diluted in 1× PBS . Samples were nutated overnight at 4°C and washed three times for 5 minutes at 4°C the next day with NP-40 wash buffer . Beads were spun down at 3000 rpm and resuspended in 4× SDS Laemmli Buffer . Samples were boiled for 15 minutes with 20 µl β-mercaptoethanol and 50 µl 4× SDS Laemmli . Samples were loaded ( 50 µl each ) onto NuPAGE Novex Bis-Tris 10% Gels . Pierce ECL or Femto Western Blotting Substrate was used for detection . Other methods are available in Text S1 ( Supplementary Materials and Methods ) . | Proteins that are placed in membranes or secreted are produced in a cellular structure called the endoplasmic reticulum ( ER ) . An accumulation of misfolded proteins in the ER contributes to many disease states , including diabetes and neurodegeneration . The ER protects against a toxic buildup of misfolded proteins by activating the unfolded protein response ( UPR ) , which maintains ER homeostasis by slowing protein synthesis and enhancing ER functions such as protein folding and degradation . Many of these processes are controlled by three canonical ER/UPR gene regulatory factors . Here we identify the gene regulator SKN-1/Nrf as also playing a critical role in the UPR . SKN-1/Nrf is well known for its functions in oxidative stress defense and longevity . We now report that SKN-1/Nrf mobilizes an ER stress gene network that is distinct from its oxidative stress response , and includes regulation of other central UPR factors . Surprisingly , we also find that ER- and UPR-associated mechanisms are needed to “license” SKN-1/Nrf to defend against oxidative stresses . Our findings show that UPR and oxidative stress defense mechanisms are integrated through SKN-1/Nrf , and suggest that this integration may help maintain a healthy balance between ER and cytoplasmic functions , and stress defenses . |
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Tumorigenesis is a multi-step process in which normal cells transform into malignant tumors following the accumulation of genetic mutations that enable them to evade the growth control checkpoints that would normally suppress their growth or result in apoptosis . It is therefore important to identify those combinations of mutations that collaborate in cancer development and progression . DNA copy number alterations ( CNAs ) are one of the ways in which cancer genes are deregulated in tumor cells . We hypothesized that synergistic interactions between cancer genes might be identified by looking for regions of co-occurring gain and/or loss . To this end we developed a scoring framework to separate truly co-occurring aberrations from passenger mutations and dominant single signals present in the data . The resulting regions of high co-occurrence can be investigated for between-region functional interactions . Analysis of high-resolution DNA copy number data from a panel of 95 hematological tumor cell lines correctly identified co-occurring recombinations at the T-cell receptor and immunoglobulin loci in T- and B-cell malignancies , respectively , showing that we can recover truly co-occurring genomic alterations . In addition , our analysis revealed networks of co-occurring genomic losses and gains that are enriched for cancer genes . These networks are also highly enriched for functional relationships between genes . We further examine sub-networks of these networks , core networks , which contain many known cancer genes . The core network for co-occurring DNA losses we find seems to be independent of the canonical cancer genes within the network . Our findings suggest that large-scale , low-intensity copy number alterations may be an important feature of cancer development or maintenance by affecting gene dosage of a large interconnected network of functionally related genes .
Tumor development is generally thought to be a process in which healthy cells transform into malignant tumor cells through the step-wise acquisition of oncogenic alterations [1] , [2] . This implies that certain changes have to occur together for effective oncogenic transformation of a normal cell . There are a multitude of ( epi- ) genetic lesions that cause deregulated expression of oncogenes and tumor suppressor genes . Co-operative deregulation of cancer genes has indeed been observed in several different settings . Retroviral insertional mutagenesis screens in mice have shown preferential co-mutation of specific combinations of genes within the same tumor [3] . Likewise , in a study where a thousand individual tumors were screened for mutations in 17 different oncogenes , preferential co-mutation of the PIK3CA and KRAS genes was observed [4] . Besides single basepair mutations or retroviral integrations , the activity of genes can also be perturbed by DNA copy number alterations that arise as a result of genomic instability , which is frequently observed in tumor cells [1] . Whether genomic instability is important for tumor initiation is controversial , but its contribution to tumor progression is undisputed [5] , [6] . Loss of DNA is a mechanism for the tumor to eliminate copies of tumor suppressor genes , which prevent cancer formation . Conversely , DNA copy number gain or amplification may lead to activation of oncogenes that promote tumor development . We aimed to find genomic regions of gains and losses that are preferentially gained or lost together . We could subsequently link genes that lie in co-occurring regions to each other , allowing us to find functional interactions that reveal the mechanisms underlying tumor development . DNA copy number alterations ( CNAs ) may be measured on microarray platforms [7] . Array-based comparative genomic hybridization ( aCGH ) of differentially labeled tumor and normal ( 2n ) DNA is performed on oligonucleotide- or Bacterial Artificial Chromosome ( BAC ) based microarray platforms . For each probe on the microarray , the ratio of signal intensities of tumor versus normal DNA is a measure of the relative DNA copy number of the corresponding genomic region in the tumor sample . Platforms designed to identify single nucleotide polymorphisms ( SNPs ) can also infer CNAs by comparing the raw probe intensity values measured in a tumor sample with a reference sample . In order to extract those DNA copy number aberrations that preferentially occur together , we developed an analysis framework . The basic premise of our analysis is to define a pair-wise score for any given pair of genomic locations present in the dataset . This scoring index will only be high if both genomic locations are recurrently aberrated in multiple independent samples within the tumor panel , and if they co-vary similarly over the different samples ( Figure 1 ) . Using a Gaussian kernel convolution method we look for aggregates of high scores in the 2D genomic pair-wise space ( Figure 2 ) . The top peaks in the convolved score matrix can be mapped back to two distinct co-mutated genomic locations . The genes that reside in these genomic locations can then be functionally related to each other . The raw data consist of non-discrete measurements of the average DNA copy number of the population of cells present in the measured sample . The signal consists of a measurement of a heterogeneous population of tumor cells , which may contain many populations potentially carrying different mutations and copy number alterations , as well as normal ( diploid ) cells . To reduce heterogeneity as much as possible we choose to analyze a collection of hematopoietic tumor cell lines , which on a per-sample basis can be considered clonal . There were several other reasons for analyzing this particular dataset . First , it is a high resolution dataset of well-characterized , clinically relevant samples . Although these samples are cell lines , they are widely used as a model system for the diseases from which they have been derived . Second , this collection of samples includes cell lines derived from T- and B-cell leukemias carrying rearranged T-cell receptor and immunoglobulin loci , respectively . We therefore should be able to separate these two distinct lymphoid malignancies based on co-occurring DNA copy number losses at the T-cell receptor and immunoglobulin loci . During T- and B-cell development , these loci undergo DNA recombination and gene deletion in a process known as V ( D ) J-recombination . The human genome contains three specific T-cell receptor loci ( alpha/delta , beta and gamma ) on two different chromosomes that determine their variability . B-cells have three different loci ( IgG kappa , IgG lambda and the IgG heavy chain ) on three different chromosomes that undergo recombination to generate a diverse repertoire of immunoglobulins . Since T- and B-cells only undergo recombination of their respective loci after lineage commitment , it is unlikely that T-cell receptor loci are recombined in B-cells and vice-versa . If our approach is successful at finding co-occurring losses , it should identify the co-occurring rearrangements at the T-cell receptor alpha/delta and beta/gamma loci in T-cell leukemias . Similarly , we should be able to pick up co-occurring losses at the IgG kappa , lambda and heavy chain loci in B-cell malignancies .
A classic example of finding associations in a large ( binary ) dataset is association rule mining . Identification of cooperating events in continuous data requires a different approach than binary association rule mining . First we developed a method to score for co-occurrence between two continuous measurements ( Figure 1 ) . We then applied this score in a framework that is able to find co-occurrences in genome-wide measurements . This framework is shown in Figure 2 and is detailed in the Materials and Methods . DNA copy number measurements at two different genomic loci can be visualized in a 2D space , with each axis representing measurements at a certain genomic locus . A point in this space represents a sample in which both loci were measured . Figure 1a shows four hypothetical combinations of measurements . We sought to score for co-occurring high or low values in the DNA copy number data; in other words , regions that display similar patterns of large-amplitude amplification and deletion across the tumor set . This situation is shown in the third panel Figure 1a . The other panels show other potential situations that may arise when comparing two continuous measurements . To score for co-occurring gains all negative values are set to zero ( Figure 1b ) . To score for co-occurring losses all positive values need to be set to zero and the absolute values of the measurements need to be used . We use the covariance of the two measurements to score for co-occurring loci . This score only rewards a high value to a truly co-occurring and co-varying pairs of measurements ( Figure 1c , right panel ) . However , a high covariance alone is not sufficient , since it is possible that a high covariance occurs while at least one of the loci never reaches a high amplitude ( see Figures 1e and 1f ) . For this reason we multiply the covariance score with the sum of the individual valued in each sample . This method of scoring only rewards a high value to a co-varying pair of measurements with a large aberration amplitude across the tumors ( Figure 1c , right panel ) . The co-occurrence scores can be computed for every pair of genomic loci ( Figure 2c ) . By performing a two-dimensional Gaussian kernel convolution on these scores in the co-occurrence space we can take local neighborhood effects into account . This operation is performed for different kernel widths in order to capture scale dependent effects , resulting in a Convolved Co-occurrence Matrix ( CCM ) as shown in Figure 2d . High values in this matrix represent candidate co-occurring regions in the data . A peak in the CCM can be mapped back to two specific loci , the size of which is determined by the σ parameter of the Gaussian function used to convolve the score matrix ( Figure 2e ) . The genes that are located in the loci associated with a peak in the CCM are subsequently investigated . We examined both enrichment for known cancer genes in these gene lists and we investigated functional relationships between the genes derived from the two loci ( Figure 2f ) . Additional details can be found in the Materials and Methods section . We ran our analyses on the aCGH profiles of 95 hematological tumor cell lines analyzed on the Affymetrix Genome-Wide Human SNP Array 6 . 0 . See the supplemental data ( Dataset S1 ) for a list of the cell lines that were analyzed . The data was generated by the Cancer Genome Project ( Wellcome Trust Sanger Institute , Hinxton , UK ) . We employed three scale parameters: 2Mb ( σ = 1/3 Mb ) , 10Mb ( σ = 5/3 Mb ) and 20Mb ( σ = 10/3 Mb ) . In the remainder of this text we will refer to these as Scales 2 , 10 and 20 . These scales roughly determine the size of the aberrant regions we expect to find . By employing a small , medium and large scale we maximize the chance of detecting co-occurring changes of all possible sizes . To remain conservative we limited our primary analysis to the top 50 peaks in the Convolved Co-occurrence Matrix ( CCM ) for each of the scales and each of the comparisons ( gain-gain , loss-loss , loss-gain ) . This resulted in 9 top-50 lists of co-occurring regions retrieved from this dataset . A substantial fraction of the 95 cell lines are derived from T- or B-cell lymphomas with functionally rearranged T-cell receptor or IgG genes . We therefore expected to identify co-occurring losses at the T-cell receptor alpha/delta and beta/gamma loci in the T-cell leukemias . Similarly , our method should identify co-occurring losses at the IgG kappa , lambda and heavy chain loci in B-cell malignancies . Because the recombination loci for both the T-cell receptor and the IgG genes are both relatively small ( in the 1Mb range ) we expected to retrieve these co-occurring losses in the small ( 2 Mb ) scale analyses . Since we disregarded co-occurrences on the same chromosome we expected to find five co-occurring losses . Indeed , four of the five expected co-occurring losses are present in the top 50 peaks of the Scale 2 analyses ( Table 1 ) . Figure 3 shows two examples from the top 50 lists of co-occurring loci . The separation of T- and B-cell lines is immediately apparent . T-cell lines are strongly associated with losses in the T-cell receptor loci . A large subset of B-cell lines are associated with losses in the IgG loci . However , a subset of the B-cell lines is not associated with any loss of these loci . In this particular subset of lines the IgG loci seem to be gained . It is known that the IgG loci are favorite partners for oncogenic translocations [8] . Whether this is the cause of the amplification of these loci is not known . While the recovery of the V ( D ) J-related recombination loci as co-occurring losses serves as a positive control for our analysis approach , we are mainly interested in identifying cooperating genes or regions that might play a role in cancer . To see whether the locations we recover are linked to this disease , we analyzed whether the co-occurring genomic loci are enriched for genes known to play a role in cancer . As a reference gene set we used the Cancer Gene Census list [9] . The results of this analysis are shown in Table 2 . As can be seen , the co-lost loci are mainly enriched for tumor suppressor genes , and the gain-gain regions for oncogenes . Since one expects loss of tumor suppressors and gain of oncogenes , this is a logical result , further increasing our confidence that our approach identifies truly relevant genomic loci . While finding enrichment for cancer genes is an encouraging result , this does not explain the possible cooperation between two loci . We expect that the co-occurring loss of two regions points to a functional relationship between the constituents of the genomic loci . A co-occurrence between two genomic regions can point to many different kinds of interactions between the genes residing in both regions , e . g . biochemical interactions of the protein products or functional collaboration of two cancer genes in tumorigenesis . We therefore decided to employ interaction data to shed further light on the genes present in the co-occurring regions . We translated the co-occurring pairs of genomic loci to pairs of gene sets , and we investigated the functional relationships of their protein products using the STRING database [10] ( version 8 . 1 ) . The STRING model weighs functional associations between genes based on several different sources of evidence , among which: biochemical interaction , joint presence in a pathway , high-throughput interaction experiments , text mining and interactions of homologs in other species . To find a functional relationship between two co-occurring regions we looked for a direct interaction in the STRING database between the two gene-sets defined by our co-occurrence analysis . To determine whether the number of observed interactions is significant , we compared the number of direct interactions found between genes located in the top 50 co-occurring regions to a set of randomly chosen pairs of genomic loci . The metric we used to determine significance is the ratio between the number of interacting genes and the total number of genes found on the genomic loci . A p-value for enrichment for direct interactions was calculated using a two-tailed Fisher's exact test . Results are shown in Figure 4 . As can be seen , the only analysis that resulted in an enrichment of functional interactions is Scale 20 , for all three situations . We found no enrichment for interacting protein coding genes on Scale 2 ( not shown ) and Scale 10 . Since we evaluated gene sets in a window one-third the size of the analysis-scale we may be under-estimating the size of the co-occurring loci and the larger Scale 20 actually captures the size of the aberrations best . In order to keep control of the complexity , we considered in our co-occurrence analysis only radially symmetric kernels , i . e . Gaussian kernels with diagonal , equal variance covariance matrices . This implies that asymmetric co-occurring regions – where a small locus co-occurs with a large locus – will not be optimally detected . Since an asymmetric co-occurring region typically consists of a series of symmetric co-occurring regions detected on a smaller scale ( just like a rectangle can be constructed from a collection of squares ) , we set out to construct larger co-occurring regions from the results of the smaller scales using a hierarchical clustering approach . For details see Supplemental Figure S1 . Briefly , we collected the loci involved in the top 500 co-occurrences of the Scale 2 analysis . This resulted in 1000 genomic loci . For each pair of loci , we calculated the genomic distance in base pairs . The distance between two loci on different chromosome arms was set to a default high value ( 1 * 108 ) . This resulted in a 1000×1000 distance matrix . On this distance matrix we performed single linkage hierarchical clustering . The resulting dendrogram was cut at 1 * 107 bp ( 5 kernel widths ) . The resulting clusters are unique genomic loci and were represented as nodes in a graph . Clusters were then linked if a co-occurrence was found between individual loci of different clusters . These links are represented as edges in a graph . The result of the clustering analysis is shown in Figure 5 . As can be seen in Figure 5 we were able to construct a network of co-occurring copy number changes for the gain-gain , loss-loss and gain-loss situations . As expected , the gain-gain and loss-loss networks show enrichment for oncogenes and tumor suppressor genes , respectively . The gain-loss network only shows enrichment for tumor suppressors . The percentage of genes involved in functional interactions between the nodes that are linked in the graph vastly exceeds the functional interaction enrichment found in the single scale 20 Mb analyses . At least 11% of the genes present in the genomic locations - represented by the nodes in the graphs - have high confidence ( >0 . 9 ) annotated functional interactions along the edges as revealed by STRING analysis . The thickness of the edges in the graphs shown in Figure 5 indicates how often a co-occurrence was found in the top 500 of the Scale 2 analysis . Several edges were strongly supported by co-occurrences in the top 500 . These strongly supported edges were always associated with loci that were ranked high in the co-occurrence list ( as indicated by node size ) . The nodes that are associated with these highly represented edges seem to form an important subgraph . To reveal these subgraphs , we removed all edges supported by less than 5% of the top 500 co-occurrences . For brevity and simplicity we only consider the gain-gain and loss-loss networks . This resulted in the two core networks shown in Figures 6 and 7 . The edge thickness of the gain-gain core network shown in Figure 6 represents the number of functional interactions found using the STRING database between genes that map within the loci described by the nodes . To determine the common denominator among the interacting genes , we employed Ingenuity Pathway Analysis ( IPA; Ingenuity Systems ) to perform a functional enrichment analysis on all genes residing within the gain-gain core network . This revealed strong enrichment for processes involved in cancer ( Figure 6b ) . From Figure 6a it is immediately apparent that most of the functional interactions are found between 1q and 7p/q . If we remove the 1q node from the entire network described in Figure 5 the enrichment for functional interaction drops dramatically ( Figure 6c ) . Therefore , we hypothesize that the co-occurring gain between 1q and 7p/q is the most important effect in the gain-gain analysis in this dataset . This is strengthened by the fact that almost all known oncogenes within the entire network map to 1q , 7p or 7q ( Figure 6a ) . The well-studied canonical oncogene MYC maps to 8q and is not a determining hub in the gene interaction network as constructed by STRING . The loss-loss core network is shown schematically in Figure 7a . A loss of approximately 18 megabases on chromosome 17p appears to be a central hub , which is co-lost with several other genomic loci . These loci show a very high enrichment of genes that interact with 17p , and of the six loci , four contain multiple known tumor suppressor genes . A functional enrichment analysis of all genes residing on loci co-lost with 17p , reveals many cancer-related processes ( Figure 7b ) , suggesting that the interacting genes are most likely also the cancer-relevant genes . If we remove 17p from the original network we see a large decrease in the percentage of genes involved in functional interactions ( Figure 7c ) confirming the importance of 17p in the loss-loss network . One of the most intensively studied cancer genes , TP53 , resides in the 17p locus . Furthermore , the canonical cancer gene RB1 and the CDKN2A/B locus are present in two of its co-lost regions . Since these are well known tumor suppressors , and therefore the subject of thousands of research papers , they might constitute the bulk of the functional relationships in our analysis . To test this hypothesis , we excluded these four genes and repeated the interaction analysis of the core network . As can be seen in Figure 7c , the enrichment is only slightly lower without the canonical genes , suggesting that the functional relationship between the co-occurring losses on 17p and the other loci are driven by other genes . We investigated the remaining 113 interactors for any interesting interactions that might be a target of this collection of co-occurring losses . Within the total network of interactors we found a sub-network centered on the nuclear co-repressor NCOR1 ( TRAC1 ) ( Figure 7d ) . This interaction network included – besides NCOR1 – the peroxisome proliferator-activated receptor alpha ( PPARA ) , the MAPK pathway suppressor GPS2 , the nuclear co-activator ( and known tumor-suppressor ) p300 and a gene of unknown function , CBFA2T3 . All interactions found are based on physical binding and co-occurrence in Pubmed abstracts . To see whether we could find more information regarding the putative tumor suppressor function of the different interactors , we tested if we could corroborate our findings with data from a large retroviral insertional mutagenesis ( IM ) screen where hematopoietic tumors were induced through Murine Leukemia virus ( MuLV ) infection of wild-type mice or Trp53 or p19-ARF deficient mice [11] . An illustration of the retroviral insertions sites near Cbfa2t3 is shown in Figure 7d . Although Cbfa2t3 was not flagged as a common integration site , several viral integrations near this gene were found . Remarkably , two individual tumors harbored a bi-allelic integration near the transcription start site of Cbfa2t3 , suggesting functional inactivation of this candidate tumor suppressor gene . Indeed , bi-allelic integration is thought to be a hallmark of tumor suppressor genes in IM screens [12] . Given that we find this sub-network of interactors in a co-occurring network of DNA copy number losses and the recovery of inactivating insertions in a retroviral IM screen , we conclude that this network might be a putative tumor suppressor network .
Several studies have investigated concerted copy number changes in aCGH data . In studies on lung cancer [13] and ovarian cancer [14] the authors performed a post-hoc co-occurrence analysis on genomic locations that were found to be significantly altered in a one-dimensional analysis . A more integrated effort to analyze relations between CNAs in brain cancer was published recently [15] . Although this study scores systematically for co-aberration , it is limited in resolution as it employs cytobands as the genomic unit within which aberrations are scored . Cytobands are relatively arbitrarily determined entities and are quite heterogeneous in size . Furthermore this approach is dependant on converting the continuous-valued copy number data to discrete copy number calls . This results in loss of important information since it removes the possibility of weighting the intensity of a CNA . In contrast , our approach is able to correct for unequal probe distances , enabling us to perform our analysis on a very high ( 20 kbp ) resolution . In addition , our scoring method not only incorporates the sign of the copy number change , but also its intensity and the concomitant CNAs within the immediate genomic neighborhood . The output of our analysis does not include a measure of significance . Constructing a background distribution based on permutations of the DNA copy number data would mean re-running our analysis thousands of times , a task which remains computationally infeasible at this stage . Furthermore , the multiple-testing problem would have to be properly addressed , given that the number of tests is the square of the number of grid points in the 2D space . Due to the complexity of the analysis procedure ( minimum operation and kernel smoothing ) the definition of an analytical null distribution has remained elusive . Therefore , we have chosen to work with top n results , residing in the extremes of the results distribution , thus minimizing the chance of including false positives . The top n lists allowed us to generate workable results which we have validated extensively with other sources of evidence . While we were able to use a distributed computing solution for our analysis , we were fortunate to have the required computational architecture at our disposal . Since the problem basically consists of repeating the same action many times it could be well-suited to software optimization or a hardware based solution where the most time-consuming actions are handled by a dedicated processing unit . When looking for areas in the 2D pair-wise space highly enriched for co-occurrence scores we convolve this space with a 2D-Gaussian kernel . The sigma parameter of this function is a representation of the size of the aberrations we expect to recover . Currently we make the implicit assumption that the co-occurring aberrations have the same size by using a symmetric kernel for the convolution . This could be relieved by allowing for an asymmetrical ( ellipsoid ) Gaussian kernel for all combinations of scales used . Clearly , this comes at the cost of increased computational complexity . Here we resolve this issue by concatenation of the results obtained in a small scale . In this way we can recover co-occurring losses of different sizes that give a better enrichment for functional interactions when combined with the single peaks obtained in a higher scale analysis . In our analysis of a set of cell lines derived from hematological malignancies we found enrichment of cancer related genes and functional interactions in co-occurring DNA copy number changes . Our results suggest that tumorigenesis requires elimination of multiple gatekeeper genes and gain of multiple oncogenes as demonstrated by the presence of many functional interactions between the loci in the gain-gain and loss-loss core networks . Haploinsufficiency is a well known characteristic of several tumor suppressor genes , where simple reduction of gene dosage by loss of gene copies at the DNA level can already promote oncogenic transformation [16] . It is conceivable that changes in gene dosage of multiple interconnected genes involved in cancer-related processes such as cell cycle , DNA repair and signaling can also weaken a cells defense against uncontrolled cell proliferation . In this case , heterozygous loss or gain of large genomic regions , such as the ones identified in this study , might effectively sensitize cells to become tumorigenic . We show that the 17p loss and its co-lost regions are highly enriched for functional relationships , which are not fully explained by the presence of the TP53 gene , often thought to be the single target of this deletion [17]–[19] . Although TP53 is no doubt an important target of the DNA copy number loss , our analysis indicates that the concomitant loss of other genes near TP53 , as well as co-occurring losses on the other genomic loci may together account for the full tumorigenic effect . Loss of the loci on 17p , 9p , 9q , 13q , 16q and 22q has been reported previously for several types of hematological malignancies represented in our dataset [20]–[23] . The picture that emerges from this analysis of collaborative aberrations is that many of the reported losses collaborate with the frequently occurring 17p loss as a central hub . We don't recover co-occurring losses among the spoke loci in the core network . This could suggest that the non-17p regions form subsets of co-occurring losses with 17p , whose interconnections themselves do not occur frequently enough in the top 500 co-occurring losses we investigated . Not all of the gene-gene interactions defined by the 17p network involve the well-known canonical cancer genes TP53 , RB1 and CDKN2A ( INK4a/ARF ) . We found one sub-network of genes around NCOR1 which might be an example of other tumor suppressor genes that are affected by the concerted loss of these genomic loci . The hub of this interaction network , NCOR1 , is a well-known transcriptional co-repressor that associates in a ligand-independent manner with nuclear receptors [24] . It is responsible , together with the closely related factor SMRT , for recruitment of HDAC proteins to the DNA to induce transcriptional silencing . Its role in cancer is not well-established . NCOR1 null mice die in early embryogenesis [25] . A dominant-negative mutant of NCOR1 is known to increase proliferation in hepatocytes [26] and more recently it has been shown that NCOR1 decreases AKT phosphorylation , thus countering its pro-survival signal [27] . It would seem that specific loss - or at least decrease in gene dosage of NCOR1 - might increase proliferation and promote survival . All interactions between NCOR1 and its partner genes ( PPARA , GPS2 and CBFA2T3 ) have been based on co-occurrence in PubMed abstract and true physical binding [28]–[31] . CBFA2T3 is a close relative of ETO , which is a target of the recurrent AML1-ETO translocation that occurs in acute myeloid leukemia . It has been shown that the fusion gene AML1-ETO actually interferes with the CBFA2T3-NCOR1 interaction , and that its oncogenic effect derives from that inhibition [31] . In a retroviral insertional mutagenesis screen in mice , Cbfa2t3 is recurrently targeted by bi-allelic retroviral integrations , which are predicted to cause functional inactivation of Cbfa2t3 [11] . PPARA is a member of the Peroxisome proliferator-activated receptors , and has been implicated in hepatocellular carcinoma development in rodents [32] . Since other members of this family , such as PPARG , exhibit a tumor suppressor-like phenotype , it is possible that PPARA can act as a tumor suppressor in hematological malignancies . GPS2 is a known suppressor of JNK signaling [33] , which is one of the constituents of the MAP kinase signaling pathway . Deregulation of this pathway is a well-known phenomenon in cancer [34] . Taken together with the association between NCOR1 and the known tumor suppressor p300 , our data suggest a selective advantage for loss of multiple constituents that interact with NCOR1 since they all may have tumor suppressor-like activities . Many studies focus on a single hematological malignancy in which a single combination of aberrations might be important [19] , [35] , [36] . Since we examine a large panel of samples derived from many different hematological malignancies , our results might not specifically apply to any single type of lymphoma or leukemia . They might hint at more general processes that are important for the tumors to arise and maintain themselves . However , one should not forget that this analysis is based on a panel of cell lines , which may have adapted to in vitro tissue culture conditions by acquiring additional aberrations that are rarely found in real tumors in patients . Furthermore , given the fact that we examine copy number changes it might be worthwhile to analyze a highly genomically unstable tumor type , such as BRCA1/2-related breast cancer . We have developed a method for genome-wide analysis of collaborating DNA copy number changes and their corresponding networks . Using this approach we have identified a loss-loss network centered around a region on human chromosome 17p . This network is highly enriched for functional relationships and hints at a more complex system of tumor suppression in which many different genes are affected simultaneously to induce cancer . We show one example of a sub-network around the nuclear co-repressor NCOR1 that may be a novel network of tumor suppressor genes that are affected by the observed co-occurring losses . The observation that DNA copy number changes may affect gene dosage of larger numbers of cancer-relevant genes deviates from the classical view where mutations in a few ( 5–7 ) cancer genes lead to tumor development . Our data support the notion of cancer-related networks or pathways , where multiple collaborating genes are deregulated simultaneously to induce oncogenesis . Such a network view of oncogenesis is an important step towards developing effective drug targets because it increases the number of potential targets . However , this view also implies that multiple molecules need to be targeted simultaneously in order to achieve optimal therapy response and to reduce the risk of therapy resistance .
Datasets consisting of array-based copy number measurements are continuously increasing in size . If probe level interactions are evaluated , the analysis space is of dimensionality for probes on the genome . As a result , the analysis time and memory usage will also increase quadratically with the number of probes . Instead of a grid positioned at the genomic positions of the probes , we employ an equally spaced genomic grid as a basis for all subsequent steps . The distance between grid-points is a user-defined variable , and will determine the finest resolution of the outcome and computational efficiency . We have performed all analyses using a genomic grid with a grid spacing of 20 Kb . Given a genome of base pairs and a grid spacing of , this results in grid positions , with , where represents the integer part of the real number , . The grid positions can be represented in the following row vector: , where . Let the aCGH profile of the tumor be represented by the following row vector of probe measurements: , with being the number probes . Let the midpositions of the probes be located at . To employ the genomic grid we need to compute , for each aCGH array , the value of the aCGH profile on the grid points . We achieve this by performing , for the grid position , , a kernel-weighted regression of all probe values situated in the range , employing a triangular kernel centered at , with maximal amplitude of 1 and width of 2 . More specifically , the interpolated copy number aberration at the grid position is given by: ( 1 ) Here the set is the set of probe positions such that . The interpolated copy number profile of the tumor is represented by the row vector: . The complete dataset of tumors is refopresented by the matrix , where the probe values of the tumor constitute the row of matrix . Negative and positive log2 values respectively denote loss or gain of DNA in the test sample versus the reference sample . We regard both situations separately , which prevents the negative and positive values cancelling each other through summation later in the algorithm . We separate gains and losses by only retaining grid positions with positive values for the gains or negative values for the losses . The absolute values of the separated matrices are then used in the downstream steps . The remaining grid positions are set to zero . More specifically , the gains matrix , is given by , with ( 2 ) Similarly , the loss matrix , is given by with ( 3 ) Because we treat gains and losses separately we have four different co-occurrence situations to be considered given two loci on the genomic grid: i ) gain/gain , ii ) gain/loss , iii ) loss/loss and iv ) loss/gain . So , when evaluating the co-occurrence of loci and , we will evaluate the behavior of i ) columns and for gain/gain; ii ) columns and for gain/loss; iii ) columns and for loss/loss and iv ) columns and for loss/gain . ( Here is the column of matrix ) . All subsequently described steps will be performed for these four situations separately , where and will be employed as shorthand for the abovementioned column vectors of interpolated copy number values associated with genomic grid positions and , respectively . The first component of the co-occurrence score is the continuous variant of the AND Boolean logic function: the minimum operation . For two grid points , and , the sum across all tumors of the minimal probe value per tumor at and , , is calculated as follows: ( 4 ) These values are aggregated in a matrix , . If we only use the minimum as a scoring function , those grid positions that are ubiquitously aberrated will always receive a high score , regardless of the aberration pattern in the other grid position . Two regions that are aberrated ubiquitously in all tumors are undoubtly important to the tumor but they are not necessarily functionally related . They might be a hallmark of the particular disease under study , but show no direct functional interaction . To prevent these ubiquitously aberrated regions from dominating the analysis and to detect those regions that represent functional co-occurrences , we weigh the minimum score computed above with the covariance of the interpolated probe values at the two grid positions and , ( 5 ) where and are the expected values of the probe values at grid position and across tumors , respectively ( i . e . and ) . These values are aggregated in a matrix , . We combine both the minimum matrix and the co-variance matrix by element-wise multiplication to form the co-occurrence score matrix , , with ( 6 ) Since we believe the co-occurrence score to be a smooth variable , and since neighboring co-occurrence values can therefore be employed to reduce the noise locally , we convolve the co-occurrence score matrix with an isotropic 2D Gaussian kernel function . In practice this implies sampling the 2D Gaussian kernel function on a square grid consisting of × genomic grid positions and then performing the convolution of this sampled kernel function with the co-occurrence score matrix . The sampled isotropic 2D Gaussian function is defined as , with ( 7 ) The standard deviation of the isotropic Gaussian , , determines the scale of the analysis . Since the Gaussian quickly decays we set , allowing contributions from , convolving with a finite kernel with minimal loss in accuracy . The scale of an analysis is therefore defined as . The scales employed in this study are: 2 Mb ( = 1/3 Mb ) , 10Mb ( = 5/3 Mb ) and 20Mb ( = 10/3 Mb ) . Before the convolution step , we pad the co-occurrence matrix by mirroring the true data at each chromosome boundary and each centromere . By convolving the appropriately padded co-occurrence score matrix and the sampled 2D Gaussian function the Convolved Co-occurrence Matrix ( CCM ) is obtained: ( 8 ) with , as the convolution operator and ( 9 ) This matrix is a representation of the amount of co-occurrence between two locations on the genome . We calculate a CCM-matrix for each possible combination of chromosome-arms and for each of the four combinations of gains and losses listed above . With 39 unique chromosome arms in the human genome ( disregarding the p-arms of the acrocentric chromosomes and the sex chromosomes ) , three different scales and 4 triangular pair-wise matrices to evaluate ( loss-loss , gain-gain , gain-loss and loss-gain ) we compute 8892 different CCMs . To solve this problem computationally we used a large distributed computing cluster . Our choice of resolution of the genomic grid was bounded by the memory present on the nodes . We set to 20000 base pairs , which is the lowest value still allowing the largest chromosome-arm pair to be successfully computed on one computing node . For each CCM we determine the top N peaks for each combination of gains and losses . The nth peak represents two co-occurring loci , and , and the location of the peak is defined by two co-ordinates on the genomic grid: . For each locus , we define a region of interest of size centered on and , respectively . We define this small region of interest to only select regions that are very near to the actual peak . To investigate the co-occurrence for functional relationships , we extract , for each of the co-occurring loci , the genes present in the regions of interest . More specifically , we define , for loci and , the associated gene sets and , where ( 10 ) and ( 11 ) Where is the position of gene , which we chose to be the mid-position of the gene . The genesets were established by a BioMart query from the Ensembl database . We restricted ourselves to the bio type ‘protein_coding’ . The list of CGC genes was obtained from the CGC website ( http://www . sanger . ac . uk/genetics/CGP/Census/ ) . The reference list of all genes was retrieved from the Ensembl website , with a filter to keep only genes with bio-type = ‘protein_coding’ . This left 18840 genes . All CGC genes that could not be mapped back to the reference gene set were excluded . The CGC genes that were annotated as ‘recessive’ were used as the tumor-suppressor genes and ‘dominant’ as oncogenes . Enrichment for all CGC genes , the tumor-suppressor subgroup and the oncogene subgroup in the gene sets determined by the co-occurrence analysis was calculated using a Fisher's exact test . The set of pairs of interacting genes which are such that one gene is associated with locus and the other gene of the pair with locus is then defined as ( 12 ) Where represents the confidence of interaction , according to the STRING database , between genes gk and gl . We then determine all gene lists of interactors for the top N peaks of a given co-occurrence analysis , i . e . : ( 13 ) For each of the top N co-occurring loci , we also determine the total number of genes in the regions of interest of those loci . So , for loci and we define the set: ( 14 ) The total number of genes associated with the top N co-occurring loci is then given by ( 15 ) The interaction ratio , , is then defined as ( 16 ) where denotes the cardinality of set . As a control we randomly pick size-matched locations for all co-occurring regions in the top N and repeat the process for recovering interactions . For 100 randomly chosen co-occurring regions we calculate the resulting . A Fisher's exact test is then used to asses the significance of enrichment of versus . For all pairs of co-occurring loci , , present in the top N of an analysis , let the set of loci representing the first and second member of the co-occurrence locus be defined asandrespectively . Given that the pairs of genomic locations corresponding to the top N co-occurring loci are given bywe define the set of genomic locations loci involved in co-occurrences asFor each possible pair of locations in the genomic distance is aggregated in matrix : ( 18 ) Where is defined as: ( 19 ) We perform hierarchical clustering on matrix using single linkage hierarchical clustering . Leaf nodes are assigned to clusters using a distance cutoff of 107 bp ( 10Mb ) . Clusters are represented as nodes in a graph . Edges between nodes are drawn if any co-occurrence relationship is found between loci present in the nodes . The case we subjected to analysis was a dataset containing 105 cell-lines derived from hematological origin . The aCGH measurements were done on 1 . 8 million probe Affymetrix SNP 6 . 0 arrays . After data pre-processing we were left with 95 samples . These cell lines are a subset of the Cancer Genome Project cancer cell line project ( http://www . sanger . ac . uk/genetics/CGP/CellLines/ ) . A list of the cell lines included in this dataset can be found in Dataset S1 . | It is generally accepted that a normal cell has to acquire multiple mutations in order to become a malignant tumor cell . Considerable effort has been invested in finding single genes involved in tumor initiation and progression , but relatively little is known about the constellations of cancer genes that effectively collaborate in oncogenesis . In this study we focus on the identification of co-occurring DNA copy number alterations ( i . e . , gains and losses of pieces of DNA ) in a series of tumor samples . We describe an analysis method to identify DNA copy number mutations that specifically occur together by examining every possible pair of positions on the genome . We analyze a dataset of hematopoietic tumor cell lines , in which we define a network of specific DNA copy number mutations . The regions in this network contain several well-studied cancer related genes . Upon further investigation we find that the regions of DNA copy number alteration also contain large networks of functionally related genes that have not previously been linked to cancer formation . This might illuminate a novel role for these recurrent DNA copy number mutations in hematopoietic malignancies . |
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Mutations in human Gli-similar ( GLIS ) 3 protein cause neonatal diabetes . The GLIS3 gene region has also been identified as a susceptibility risk locus for both type 1 and type 2 diabetes . GLIS3 plays a role in the generation of pancreatic beta cells and in insulin gene expression , but there is no information on the role of this gene on beta cell viability and/or susceptibility to immune- and metabolic-induced stress . GLIS3 knockdown ( KD ) in INS-1E cells , primary FACS-purified rat beta cells , and human islet cells decreased expression of MafA , Ins2 , and Glut2 and inhibited glucose oxidation and insulin secretion , confirming the role of this transcription factor for the beta cell differentiated phenotype . GLIS3 KD increased beta cell apoptosis basally and sensitized the cells to death induced by pro-inflammatory cytokines ( interleukin 1β + interferon-γ ) or palmitate , agents that may contribute to beta cell loss in respectively type 1 and 2 diabetes . The increased cell death was due to activation of the intrinsic ( mitochondrial ) pathway of apoptosis , as indicated by cytochrome c release to the cytosol , Bax translocation to the mitochondria and activation of caspases 9 and 3 . Analysis of the pathways implicated in beta cell apoptosis following GLIS3 KD indicated modulation of alternative splicing of the pro-apoptotic BH3-only protein Bim , favouring expression of the pro-death variant BimS via inhibition of the splicing factor SRp55 . KD of Bim abrogated the pro-apoptotic effect of GLIS3 loss of function alone or in combination with cytokines or palmitate . The present data suggest that altered expression of the candidate gene GLIS3 may contribute to both type 1 and 2 type diabetes by favouring beta cell apoptosis . This is mediated by alternative splicing of the pro-apoptotic protein Bim and exacerbated formation of the most pro-apoptotic variant BimS .
The Kruppel-like zinc finger protein Gli-similar ( GLIS ) 3 plays a critical role in pancreatic development , and loss-of-function mutations in this transcription factor lead to a syndrome characterized by neonatal diabetes , hypothyroidism and other congenital dysfunctions [1] , [2] . Genome-wide association studies in large numbers of individuals with type 1 ( T1D ) or type 2 ( T2D ) diabetes indicated that common variants near GLIS3 gene are associated with both forms of diabetes [3]–[7] , making GLIS3 one of the few candidate genes for both T1D and T2D . It remains to be proven , however , that susceptibility alleles for T1D and T2D actually decrease expression of GLIS3 in pancreatic beta cells . GLIS3 is also implicated in the regulation of human fasting glucose and insulin [4] , [8] and glucose-stimulated insulin release [5] , suggesting a key role for the transcription factor in human beta cell development/function . GLIS3 deficient mice have a major decrease in beta cell mass and develop neonatal diabetes [9] , [10] . These mice also have decreased expression of several key transcription factors required for the endocrine development of the pancreas , i . e . Neurogenin3 , NeuroD1 , MafA and Pdx1 [9] , [10] . Moreover , conditional knockout of GLIS3 in adult mice causes defective insulin secretion and increase susceptibility to high fat diet-induced diabetes [11] . In vitro knockdown ( KD ) or overexpression of GLIS3 in rat insulinoma 832/13 cells showed that the transcription factor binds to a cis-acting element in the rat insulin 2 ( Ins2 ) , modulating its transcriptional activity [12] . GLIS3 also synergizes with the beta cell transcription factors Pdx1 , MafA and NeuroD1 , increasing insulin promoter activity , besides directly regulating the expression of MafA ( another important inducer of the insulin promoter ) [12] . These observations suggest that GLIS3 plays an important role for the development of mature pancreatic beta cells and for the transcription of its key hormone insulin . There is , however , little information on the role of GLIS3 in beta cell susceptibility to immune- or metabolic-induced apoptosis and little data on its impact on adult beta cells . Beta cell apoptosis contributes to the two main forms of diabetes [13] , [14] . Diabetes candidate genes expressed in beta cells may have a major impact on cell survival/function in T2D [15] , [16] , [17] and T1D [18]–[22] and in the local inflammatory responses leading to insulitis and chronic autoimmunity in T1D [19] , [21] , [23] . We have presently developed an in vitro model of GLIS3 deficiency in beta cells by using siRNAs targeting different regions of the GLIS3 mRNA . GLIS3 KD increased beta cell apoptosis under basal condition and sensitized cells to death induced by interleukin 1β ( IL-1β ) + interferon-γ ( IFN-γ ) or palmitate , agents that may contribute to beta cell loss in respectively T1D and T2D . This increase in apoptosis was secondary to the activation of the intrinsic pathway of apoptosis through alternative splicing of the pro-apoptotic BH3-only protein Bim at least in part via inhibition of the splicing factor SRp55 . The present data provide the first indication that a candidate gene for diabetes may modify alternative splicing and thus hamper beta cell survival .
GLIS3 KD in INS-1E cells ( Figure 1A–1E ) significantly decreased key transcription factors for the maintenance of the beta cell phenotype , namely MafA and Pdx1 , the glucose transporter Glut2 and INS2 . These observations were reproduced using a second siRNA targeting GLIS3 ( Figure S1A and S1B ) , and were confirmed in primary rat beta cells , where a 50% KD of GLIS3 led to a decrease in INS2 expression and a trend for decreased Glut2 expression ( Figure 1F–1H ) . These changes in gene expression by GLIS3 KD had a functional impact , with decreased basal and glucose-stimulated glucose metabolism and of glucose +/− forskolin-induced insulin release in INS-1E cells ( Figure 1K–1L ) and a 25% decrease in insulin accumulation in the medium of human islets transfected with GLIS3 siRNA , as compared to controls ( Figure 1J ) . We next evaluated whether GLIS3 KD affects beta cell viability under basal condition or following exposure to stress signals that may be relevant for type 1 diabetes , namely the pro-inflammatory cytokines IL-1β + IFN-γ or the viral by-product double stranded RNA ( dsRNA ) [18] , [19] , [21] , tested here as the synthetic analog PIC , or for type 2 diabetes , namely the free fatty acids oleate and palmitate [13] . GLIS3 KD by two independent siRNAs increased basal and cytokine-induced apoptosis in INS-1E cells ( Figure 2B , Figure S1C , Figure S2A and S2B ) . Importantly , GLIS3 KD by two independent siRNAs also augmented apoptosis in human islet cells , under both basal condition and following exposure to IL-1β + IFN-γ ( Figure 2C and 2D , Figure S1D and S1E ) . The KD of GLIS3 ( Figure 2E and 2G ) also sensitized INS-1E cells to apoptosis induced by PIC ( Figure 2F ) , oleate and palmitate ( Figure 2H ) . Thus , even a partial decrease in GLIS3 expression , as may be the case in some of the diabetes-predisposing gene polymorphisms , enhances beta cell sensitivity to basal , immune- or metabolic stress-induced apoptosis . In a mirror image of these experiments , GLIS3 overexpression using an adenoviral vector ( Figure S3A ) lead to increase MafA expression ( Figure S3B ) and decreased by >50% cytokine-induced apoptosis in INS-1E cells ( Figure S3C ) . Apoptosis secondary to GLIS3 KD and exposure to pro-inflammatory cytokines was mediated by the intrinsic ( mitochondrial ) pathway of apoptosis , as suggested by increased cleavage of caspases 9 and 3 ( Figure 3A; densitometry in Figure S2A and S2B ) , cytochrome c release to the cytosol ( Figure 3B; densitometry in Figure S2C ) and Bax translocation to the mitochondria ( Figure 3C ) . A possible mechanism for cytokine-induced apoptosis in beta cells is increased nitric oxide production and consequent endoplasmic reticulum ( ER ) stress and Chop activation [24] , [25] . GLIS3 KD , however , did not increase nitric oxide production ( Figure S4A ) or Chop expression ( Figure S4B ) , making it unlikely that these are relevant mechanisms for beta cell apoptosis following GLIS3 inhibition . Interestingly , GLIS3 KD led to a decrease in Chop expression under basal condition or at some time points following cytokine exposure . Beta cells express markers of ER stress even under basal condition , probably due to the high load on the ER caused by physiological and fluctuating insulin production [26] . It is conceivable that the decrease in Ins2 mRNA expression observed in GLIS3 KD cells ( Figure 1D ) contributes to the observed decrease in Chop expression . Beta cell survival is critically dependent on the balance between anti- and pro-apoptotic Bcl-2 proteins [27] . To examine whether GLIS3 modulates these proteins we measured expression of two key anti-apoptotic proteins , namely Bcl-2 and Bcl-xL . GLIS3 inhibition did not affect Bcl-2 and Bcl-xL expression under basal condition or following exposure to cytokines ( Figure 4 ) , and neither was there a change in a third anti-apoptotic protein , namely Mcl-1 ( data not shown ) . We next examined the pro-apoptotic BH3-only proteins DP5 and PUMA . These proteins have previously been shown to contribute to IL-1β + IFN-γ-mediated beta cell apoptosis [28] , [29] , but their expression was not increased by GLIS3 KD ( Figure S4C and S4D ) . If anything , there was a decrease in PUMA expression at some time points . Another important mediator of cytokine-induced beta cell apoptosis is the BH3-only protein Bim . Previous studies from our group have shown that STAT-1-induced Bim expression [30] , [31] and JNK-induced Bim phosphorylation on serine 65 [20] contribute to beta cell apoptosis . GLIS3 KD ( Figure 5A ) increased basal Bim mRNA expression and led to a mild increase in its expression following cytokine treatment at 2 and 8 h , with a decrease after 16 and 24 h ( Figure 5B ) . This was independent of STAT-1 activation , since GLIS3 KD did not modify total or phospho-STAT1 expression following exposure to IL-1β + IFN-γ for 0 . 25–24 h ( data not shown ) . Bim has three main isoforms generated by alternative splicing , namely BimEL , BimL , and BimS [32] . Western blot showed a preferential and nearly 2-fold increase in the expression of BimS in GLIS3 KD cells both before and after exposure to cytokines ( Figure 5C; the blots are quantified in Figure 5D ) . There was a less marked increase in BimEL and BimL at some of the time points following cytokine exposure ( Figure 5C; densitometry in Figure S5A and S5B ) . The BimS up-regulation seems to be secondary to GLIS3-modulated alternative splicing , since GLIS3 KD induced a nearly 2-fold increase in BimS mRNA expression basally and following cytokine exposure ( Figure 5E ) , with only a minor and transient increase in BimEL and BimL mRNA ( at 2–8 h of cytokine treatment ) which was followed by a significant decrease after 16 and 24 h ( Figure S5C and S5D ) . Importantly , these findings were reproduced in human islets , where KD of GLIS3 led to nearly 50% increase of BimS ( Figure 5F ) with no significant increase in the other two splice variants ( Figure S5E and S5F ) . In INS-1E cells exposed to palmitate , there was also a significant increase in BimS expression ( Figure 5G ) and less marked changes in BimEL and BimL ( Figure S5G and S5H ) . The mirror image was seen in gain-of-function experiments: adenoviral GLIS3 overexpression ( Figure S7A ) decreased BimS expression and caspase 3 cleavage ( Figure S7B ) . The decreased caspase 3 activation corroborates the finding that GLIS3 overexpression protects against cytokine-induced apoptosis ( Figure S3C ) , probably via inhibition of BimS ( Figure S7B ) . We have previously shown that this Bim siRNA markedly decreases expression of the three splice variants of Bim in cytokine-treated INS-1E cells [30] . In both INS-1E cells , primary beta cells and human islet cells Bim depletion by >50% ( P<0 . 05 ) ( Figure S6A , S6B and data not shown ) abrogated the basal increase in apoptosis observed following GLIS3 KD ( Figure 6A , 6B and 6C ) . Interestingly , while Bim depletion protected human islet cells against apoptosis ( Figure 6C ) , it failed to prevent the decrease in insulin secretion secondary to GLIS3 KD ( data not shown ) , indicating dissociation between the functional and pro-apoptotic effects of GLIS3 KD . Bim KD also partially prevented the increase in cell death induced by GLIS3 KD + cytokines ( Figure 6A , 6B and 6C ) . These observations were confirmed with a second siRNA ( Figure 6D ) that induced a preferential inhibition of BimS ( 71±4% inhibition of BimS , P<0 . 001 ) . To examine whether this beneficial effect of Bim KD was restricted to cytokines , we performed double KD for GLIS3 and Bim and then exposed the cells to palmitate ( Figure 6E ) . Palmitate treatment also preferentially increased expression of BimS in INS-1E cells ( Figure 5G , Figure S5G and S5H ) . Bim KD had only a minor protective effect against palmitate alone , in agreement with recent data suggesting that DP5 and PUMA are the main mediators of palmitate-induced beta cell apoptosis [33] , but it abrogated the additive effect of GLIS3 KD upon palmitate exposure , decreasing cell death to the levels observed with palmitate alone ( Figure 6E ) . To address the mechanisms by which GLIS3 affect Bim splicing , we examined the potential role of Pnn and SRp55 , two splicing factors described in other tissues as potential regulators of Bim splicing [34] , [35] and detected as present and modified by cytokines in human islets exposed to cytokines [21] . Pnn expression was not modified by GLIS3 KD ( data not shown ) . On the other hand , GLIS3 KD decreased protein expression of SRp55 in INS-1E cells ( Figure 7A ) ( 43%±8% inhibition of SRp55 protein expression , p<0 . 05 , n = 7 ) , while GLIS3 overexpression augmented SRp55 expression basally and following cytokine exposure ( Figure S7B ) . To assess the functional impact of decreased expression of SRp55 , we inhibited it with two specific siRNAs ( Figure 7B ) . After KD of SRp55 , there was a significant increase of BimS expression under both basal condition and following cytokine treatment ( Figure 7C ) . We next evaluated whether SRp55 KD affects beta cell viability and observed an increase in apoptosis under basal condition and following cytokine exposure ( Figure 7D ) indicating a relevant role of SRp55 in viability . Double KD of SRp55 and BimS ( 71%±4% inhibition of BimS , p<0 . 001 ) counteracted the increase in apoptosis caused by SRp55 KD ( Figure 7D ) , suggesting a role for this splicing regulator in the downstream effects of GLIS3 ( Figure 7E ) . cAMP generators have been previously shown to protect beta cells against both cytokine- and palmitate-induced apoptosis [36]–[39] , and we evaluated whether forskolin could prevent beta cell apoptosis following GLIS3 KD . Interestingly , forskolin nearly completely prevented the basal increase in apoptosis following GLIS3 KD ( Figure 8A ) , which was accompanied by a significant decrease in the expression of BimS but not BimEL or BimL ( Figure 8B and 8C ) . In cytokine-treated GLIS3 KD deficient cells forskolin induced only a mild and partial protection , which was paralleled by a progressive restoration of BimS expression ( Figure 8B and 8C ) .
Genome-wide association studies have allowed the identification of a large number of associations between specific loci and T1D or T2D . The mechanisms by which most of these candidate genes predispose to diabetes remain to be clarified . This emphasizes the need for detailed studies on the function of candidate genes in the key tissues involved in the development of diabetes . Taking into account the central role for beta cell failure in both T1D and T2D [13] , it is of particular relevance to clarify the potential impact of these “diabetes genes” on pancreatic beta cell dysfunction and death . There is little convincing genetic link between T1D and T2D to date [40]–[42] , with the possible exception of Latent Autoimmune Diabetes in Adult ( LADA ) , a particular form of diabetes that has been reported to share some susceptibility risk factors from both T1D and T2D [43] . To our knowledge the GLIS3 locus is the only one showing association with genome-wide significance for both T1D , T2D or glucose metabolism traits in non-diabetic subjects , adults or children and adolescents , and in population-based cohorts [3]–[8] . GLIS3 is the single gene located within the confidence interval of the region of association with T1D [3] , and the SNPs that have been reported to be associated with T1D , T2D and T2D-related traits are all in very strong linkage disequilibrium ( LD ) to each other ( pairwise correlation coefficient r2 of 0 . 95 to 1 . 0 between the strongest associated SNPs for the key studies [3] , [4] , [6] , [7] , [44] ) , supporting the hypothesis that a unique variant near GLIS3 may be responsible for all the reported associations with these common diabetes and related traits . Furthermore , a review of all the published genetic studies and available data on T1D , T2D and T2D-related traits indicated that the orientation of association is concordant between all these traits ( C . Julier , unpublished observations ) , with the same allele associated with increased risk of T1D , increased risk of T2D , increased fasting glucose , decreased fasting insulin level , decreased HOMA-B and glucose stimulated insulin release ( nominal P-values for association with these traits <10−3 [4] , [5] ) , suggesting the role of a shared mechanism between both forms of diabetes . Pancreatic islets from T2D patients have a nearly 50% decrease in GLIS3 mRNA expression as compared to islets obtained from non-diabetic subjects ( P<0 . 001; data re-calculated from [45] and confirmed by RT-PCR analysis of whole islets and FACS-purified human beta cells; Bugliani M , Marselli L and Marchetti P , unpublished data ) , but it remains to be determined whether this is a direct effect of the risk alleles on GLIS3 expression or secondary to chronic exposure to high glucose levels . Similarly , GLIS3 was found to be one of the most differentially expressed genes between beta cells from T2D and non-diabetic subjects [46] . The fact that recessive loss-of-function mutations in GLIS3 cause severe neonatal diabetes in humans [1] and in transgenic mouse models [9] , [10] , secondary to a major decrease in beta cell mass , suggests that this transcription factor is necessary for beta cell development and differentiation . Together , these genetic and functional observations indicate that GLIS3 itself is the susceptibility gene responsible for the observed associations with T1D , T2D and T2D-related traits . The region strongly associated with T1D as defined by Barrett et al . [3] maps to the 5′ region of the GLIS3 long transcript , which is pancreas and thyroid specific [1] and includes the first exons and corresponding promoter region . Of note , all the SNPs in LD with diabetes and associated SNPs are non-coding . This suggests that the responsible variant affects the regulation of GLIS3 expression in pancreatic beta cells , most likely through a reduction of GLIS3 expression predisposing to T1D and T2D . It is thus important to understand whether these milder phenotypes affect the resistance of adult beta cells to challenges provided by immune- , viral- or metabolic-mediated stress . These stresses may cross talk with candidate genes for T1D and T2D . Our present observations suggest that a relatively mild reduction of GLIS3 gene expression in beta cells by two independent siRNAs decreases expression of Pdx1 , MafA , Ins2 and Glut2 and inhibit glucose oxidation and glucose-induced insulin secretion . These findings are in line with evidence obtained in foetal , neonatal or adult mouse beta cells [9] , [11] , and suggest a key role for GLIS3 in maintaining the beta cell differentiated phenotype . Of particular interest in the context of diabetes is the observation that GLIS3 KD increases rat beta cell apoptosis under basal condition and sensitizes the cells to death induced by pro-inflammatory cytokines ( IL-1β + IFN-γ ) , the viral by-product dsRNA , and the free fatty acids oleate and palmitate , while GLIS3 up-regulation protects against cytokine-induced apoptosis ( present data ) . GLIS3 KD also increases apoptosis of human islet cells under both basal condition and following exposure to IL-1β + IFN-γ . This broad range of sensitization to pro-apoptotic stimuli by GLIS3 KD suggests that GLIS3 , besides contributing to maintain beta cell function , provides signals required for preservation of cell viability . In line with these observations , suppression of Pdx1 , a key transcription factor for the maintenance of the differentiated phenotype of beta cells , triggers beta cell death via dissipation of the mitochondrial inner membrane electrochemical gradient Deltapsi ( m ) [47] . GLIS3 KD also contributes to beta cell apoptosis via a mitochondrial phenomenon , namely triggering of the intrinsic pathway of apoptosis as a result of the activation of the BH3-only protein Bim ( see below ) . Decreased Pdx1 expression sensitizes pancreatic beta cells to ER stress [48] , but this is not the case for GLIS3 KD , as indicated by normal expression of Chop ( present findings ) and other ER stress markers ( data not shown ) . The increase in cell death in GLIS3 deficient cells is secondary to activation of the intrinsic pathway of apoptosis , as indicated by Cytochrome c release to the cytosol , Bax translocation to the mitochondria and activation of caspases 9 and 3 . A detailed analysis of the upstream pathways implicated in GLIS3 KD-induced beta cell apoptosis indicated modulation of alternative splicing of the pro-apoptotic BH3-only protein Bim , favouring expression of the most pro-apoptotic splice variant of Bim , namely BimS [49] , [50] . In agreement with these observations , Bim depletion abrogated the pro-apoptotic effects of GLIS3 KD alone or in combination with pro-inflammatory cytokines or palmitate . Bim can bind to and inhibit most anti-apoptotic Bcl-2 proteins , besides directly activating the pro-apoptotic protein Bax [51] . Importantly , Bim contributes to cytokine- [20] , [30] , virus- [23] and high glucose-induced [52] pancreatic beta cell apoptosis . Previous observations in pancreatic beta cells indicated that Bim can be regulated by cytokines at the transcriptional [30] , [53] or phosphorylation [20] level . The present study is the first to show regulation of Bim function in beta cells by changes in splicing . There are three main isoforms of Bim , namely BimEL , BimL , and BimS that are generated by alternative splicing [32] . BimEL and BimL have a binding site for the dynein light chain 1 which decreases their pro-apoptotic activity via sequestration to the cytoskeleton [32] , [54] , while BimS is free to exert its potent pro-apoptotic activity [49] , [50] . Alternative splicing affects more than 90% of human genes [55] . It generates enormous proteome diversity , and may have a major impact on cell survival , exposure of novel antigenic epitopes , alteration of surface location of antigens and posttranslational modifications . There is a growing interest in the role of alternative splicing in several autoimmune diseases [56] , but nearly nothing is known on its role in pancreatic beta cell dysfunction and death in diabetes . We have recently shown that beta cell exposure to pro-inflammatory cytokines modifies alternative splicing of hundreds of expressed genes and affects expression of more than 50 splicing-regulating proteins [21] , [57] . Palmitate also modifies alternative splicing of a different group of genes in human islets ( Cnop M , Sammeth M , Bottu G and Eizirik DL , unpublished data ) . The present observations provide the first indication that a candidate gene for diabetes may act by regulating alternative splicing . This effect of GLIS3 KD is mediated , at least in part , via down regulation of the splicing factor SRp55 ( Figure 7 ) . This was confirmed by the reverse experiment , i . e . GLIS3 overexpression induced SRp55 and prevented BimS production ( Figure S7B ) . In line with this , the inhibition of SRp55 led to an increase in BimS expression and beta cell apoptosis ( Figure 7 ) . These results suggest that GLIS3 regulates the expression of splicing factors and consequently the splicing of their target genes . It remains to be clarified whether this is a direct effect or a secondary phenomenon via downstream regulation of other genes . In conclusion , the present observations suggest that modifications in expression of the candidate gene GLIS3 may contribute to both T1D and T2D by favouring beta cell apoptosis . This takes place to a large extent via modified alternative splicing of the pro-apoptotic protein Bim . Additional studies are now required to characterize this new avenue for functional studies on candidate genes for diabetes , namely their cross-talk with alternative splicing and other processes regulating generation of gene/protein diversity .
Human islet collection and handling were approved by the local Ethical Committee in Pisa , Italy . Wistar rats were used according to the rules of the Belgian Regulations for Animal Care with approval of the Ethical Committee for Animal Experiments of the ULB . INS-1E cells ( kindly provided by C . Wollheim , Centre Medical Universitaire , Geneva , Switzerland ) at passages 60–72 were cultured in RPMI 1640 GlutaMAX-I medium , supplemented with 5% heat-inactivated foetal bovine serum ( FBS ) , 50 units/ml penicillin , 50 µg/ml streptomycin , 10 mM HEPES , 1 mM Na-pyruvate , and 50 µM 2-mercaptoethanol in a humidified atmosphere at 37°C and 5% CO2 . Isolated pancreatic islets of male Wistar rats ( Charles River Laboratories , Brussels , Belgium ) , housed following the guidelines of Belgian Regulations for Animal Care , were dispersed and beta cells purified by autofluorescence-activated cell sorting ( FACSAria , BD Bioscience , San Jose , CA , USA ) [58] , [59] . Beta cells ( 93±2% purity; n = 6 ) were cultured in Ham's F-10 medium containing 10 mM glucose , 2 mM glutamine , 50 µM 3-isobutyl-L-methylxanthine , 0 . 5% fatty acid-free bovine serum albumin ( BSA ) ( Roche , Indianapolis , IN , USA ) , 5% FBS , 50 units/ml penicillin , and 50 µg/ml streptomycin [59] . The same medium but without FBS was used during cytokine exposure . Human islet cells from 8 non-diabetic donors ( age 66±5 years , five men/three women , body mass index 25 . 7±0 . 9 Kg/m2 ) were isolated in Pisa , with the approval of the Ethics Committee of the University of Pisa . Islets were isolated by enzymatic digestion , and density-gradient purification [60] . They were then cultured in M199 medium containing 5 . 5 mM glucose and shipped to Brussels , Belgium within 1–5 days of isolation . After overnight recovery in Ham's F-10 containing 6 . 1 mM glucose , 10% FBS , 2 mM GlutaMAX , 50 µM 3-isobutyl-1-methylxanthine , 1% BSA , 50 U/ml penicillin and 50 µg/ml streptomycin , islets were dispersed , transfected with siCTL , siGLIS3 , siBim or siGLIS3/siBim and exposed or not to cytokines for 24 h . The same medium but without FBS was used during cytokine exposure . The percentage of beta cells in the dispersed islet preparations , as determined by immunohistochemistry for insulin [37] , was 48±6% . The siRNAs used in the study are described in Table S1 . The optimal concentration of siRNA used for cell transfection ( 30 nM ) was established previously [61] . Cells were transfected using the Lipofectamine RNAiMAX lipid reagent ( Invitrogen , Carlsbad , CA , USA ) as previously described [31] . Allstars Negative Control siRNA ( Qiagen , Venlo , the Netherlands ) was used as negative control ( siCTL ) . siCTL does not affect beta cell gene expression or insulin release , as compared with nontransfected cells [31] , [61] , [62] . Beta cells transfected with siRNAs were used for experiments 24–48 h after transfection . To express GLIS3 in insulin-secreting cells , we obtained from SIRION Biotech ( Munich , Germany ) a recombinant adenovirus comprising fragments of the mouse GLIS3 mRNA ( GenBank: NM_175459 ) . The murine GLIS3 coding region was amplified by PCR from cDNA clone BC167165 purchased from Source Bioscience ( Berlin , Germany ) and was cloned via Nhe1 and EcoRV into the shuttle vector pO6-A5-CMV to give pO6-A5-CMV-GLIS3 . The CMV-GLIS3-SV40-pA region of pO6-A5-CMV-GLIS3 was then transferred via recombination in a BAC vector containing the genome of a replication deficient Ad5-based vector deleted in E1/E3 genes . Presence and correctness of the GLIS3-ORF in the resulting BAC-vector BA5-CMV-GLIS3 was confirmed by DNA-sequencing . An adenovirus expressing the luciferase protein ( Ad-LUC ) was used as control [63] . INS-1E cells were infected as previously described [63] . The cytokine concentrations used were based on previous dose-response experiments performed by our group [64] , [65] and were 10 units/ml or 50 units/ml of recombinant human IL-1β for INS-1E cells or primary rat beta cells/human islet cells , respectively ( a kind gift from Dr . C . W . Reinolds , National Cancer Institute , Bethesda , MD-USA ) and 100 units/ml or 500 units/ml of recombinant rat IFN-γ for INS-1E cells and primary rat beta cells or 1000 units/ml of recombinant human IFN-γ for human islet cells ( R&D Systems , Abingdon , UK ) . Culture supernatants from cytokine-treated cells were collected for nitrite determination ( nitrite is a stable product of NO oxidation ) at OD540 nm using the Griess method . The synthetic dsRNA polyinosinic-polycytidylic acid ( PIC; Sigma , St Louis , LO , USA ) was used at the final concentration of 1 µg/ml [19] . Cellular transfection with PIC was made as described for siRNA , with the difference that Lipofectamine 2000 was used instead of Lipofectamine RNAiMAX [19] . Oleate and palmitate ( sodium salt , Sigma , Bornem , Belgium ) were dissolved in 90% ( vol . /vol . ) ethanol and diluted 1∶100 to a final concentration of 0 . 5 mM in the presence of 1% charcoal-absorbed BSA , corresponding to a free fatty acid/BSA ratio of 3 . 4 [66] , [67] . Forskolin was diluted in DMSO and used at final concentration of 20 µM ( Sigma ) . mRNA was extracted and reverse transcribed as described [59] . Expression of target genes was determined by real-time PCR using SYBR Green [59] , [68] and comparison with a standard curve [69] . Expression values were corrected by the housekeeping gene glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) for INS-1E and primary rat beta cells and β-actin for human islet cells . GAPDH or β-actin expression is not modified under the present experimental conditions [18] , [66] , [70] . Primer sequences are described in Table S2 . Primers for MafA , Pdx1 , INS2 , Glut2 , Chop , Dp5 and Puma were described previously [53] , [71] . D-[U-14C] glucose ( specific activity: 300 mCi/mM , concentration: 1 mCi/ml , Perkin Elmer , Waltham , MA , USA ) was used to evaluate glucose oxidation in control and GLIS3 KD cells exposed to different glucose concentrations as described [72] . The rate of glucose oxidation was expressed as pmol/120 min . 105 cells . For determination of insulin secretion , INS-1E cells were incubated for 1 h in glucose-free RPMI GlutaMAX-I medium and then incubated for 30 min in Krebs-Ringer solution . Cells were then exposed to 1 mM , 10 mM or 10 mM glucose with forskolin ( 20 µM ) for 30 min . Insulin was measured in the supernatant by the rat insulin ELISA kit ( Mercodia , Uppsala , Sweden ) . Results were normalized by the insulin content measured after cell lyses . Insulin accumulation in the medium of cultured human islets was measured by the human insulin ELISA kit ( Mercodia , Uppsala , Sweden ) . The percentage of viable , apoptotic and necrotic cells was determined following 15 min of incubation with 5 mg/ml of the DNA-binding dyes propidium iodide ( PI , Sigma ) and Hoechst 33342 ( HO , Sigma ) . This method is quantitative and has been validated for use in pancreatic beta cells and INS-1E cells by comparison with electron microscopy , caspase-3 activation and DNA laddering [18] , [59] , [66] , [73] , [74] . A minimum of 600 cells was counted in each experimental condition . Viability was evaluated by two independent observers , one of them unaware of sample identity . The agreement between findings obtained by the two observers was >90% . In some experiments apoptosis was confirmed by Western blot analysis of cleaved caspase-9 and -3 , cytoplasmic cytochrome c release and BAX translocation to the mitochondria . INS-1E cells were lysed in Laemmli buffer and equal amounts of total protein were heated at 100°C for 5 min , resolved by electrophoresis in 10–14% SDS-polyacrylamide gel and electro-blotted onto nitrocellulose membranes . Immunodetection was performed after overnight incubation with antibodies for cleaved caspase 9 and 3 ( Cell Signaling , Danvers , USA ) , Bcl-2 ( Cell Signaling , Danvers , USA ) , Bcl-xL antibody ( Cell Signaling , Danvers , USA ) , Bim and p-Bim antibodies ( Cell Signaling , Danvers , USA ) , SRp55 antibody ( LifeSpan Biosciences ) , STAT1 and p-STAT1 antibodies ( Cell Signaling ) . α-tubulin ( Cell Signaling ) was used as the loading control . Membranes were then exposed to 150 ng/ml secondary peroxidase-conjugated antibody ( anti IgG ( H+L ) -HRP , Invitrogen ) for 2 h at room temperature and visualized by chemiluminescence ( SuperSignal , Pierce Biotechnology , Rockford , IL , USA ) . Bands were detected by a LAS-3000 CCD camera ( Fujifilm , Tokyo , Japan ) . The densitometry of the bands was evaluated using the Aida Analysis software ( Raytest , Straubenhardt , Germany ) . For the assessment of cytochrome c release , INS-1E cells harvested in cold PBS were centrifuged ( 500 g for 2 min ) and resuspended with 50 µl lysis buffer ( 75 mM NaCl , 1 mM NaH2PO4 , 8 mM Na2PO4 , 250 mM sucrose , 21 µg/µl aprotinin , 1 mM PMSF and 0 . 8 µg/µl digitonin ) and vortexed for 30 s . After centrifugation ( 20 , 000 g for 1 min ) the supernatant was collected as the cytoplasmic fraction . The pellet was resuspended in 50 µl lysis buffer containing 8 µg/µl digitonin , centrifuged ( 1 min at 20 , 000 g ) and the supernatant collected as the mitochondrial fraction [23] , [37] . Equal amounts of proteins were used for Western-blotting with antibodies for cytochrome c ( BD Biosciences ) ( cytoplasmic protein ) , apoptosis-inducing factor ( AIF ) and cytochrome c oxidase ( COX IV ) ( mitochondrial proteins ) ( Cell Signaling ) . β-actin was used as the loading control . INS-1E cells were plated on polylysine-coated glass culture slides ( BD Biosciences ) . After transfection and treatment , cells were fixed for 15 min in 4% paraformaldehyde , washed with PBS and permeabilized in Triton X-100 0 . 1% for 5 min . Slides were then blocked using 5% goat serum and incubated overnight at 4°C with a Bax antibody ( Santa Cruz Biotechnology ) plus ATP synthase β antibody ( mitochondrial marker ) ( BD Biosciences ) . Cells were washed with PBS and incubated for 1 h with the appropriate Alexa fluor 488 or 555-conjugated antibodies ( Invitrogen ) . Cells were stained with Hoechst 33342 , mounted and photographed using fluorescence microscopy ( Axio Imager , Carl Zeiss , Zaventem , Belgium ) [62] . Data are presented as mean ± SEM . Comparisons were performed by two-tailed paired t-test or by ANOVA followed by paired t-test with Bonferroni correction , as adequate . A P value<0 . 05 was considered as statistically significant . | Pancreatic beta cell dysfunction and death is a central event in the pathogenesis of diabetes . Genome-wide association studies have identified a large number of associations between specific loci and the two main forms of diabetes , namely type 1 and type 2 diabetes , but the mechanisms by which these candidate genes predispose to diabetes remain to be clarified . The GLIS3 gene region has been identified as a susceptibility risk locus for both type 1 and type 2 diabetes—it is actually the only locus showing association with both forms of diabetes and the regulation of blood glucose . We show that decreased expression of GLIS3 may contribute to diabetes by favouring beta cell apoptosis . This is mediated by the mitochondrial pathway of apoptosis , activated via alternative splicing ( a process by which exons are joined in multiple ways , leading to the generation of several proteins by a single gene ) of the pro-apoptotic protein Bim , which favours formation of the most pro-apoptotic variant . The present data provides the first evidence that a susceptibility gene for diabetes may contribute to disease via regulation of alternative splicing of a pro-apoptotic gene in pancreatic beta cells . |
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The transmission dynamics of mosquito-vectored pathogens are , in part , mediated by mosquito host-feeding patterns . These patterns are elucidated using blood meal analysis , a collection of serological and molecular techniques that determine the taxonomic identities of the host animals from which blood meals are derived . Modern blood meal analyses rely on polymerase chain reaction ( PCR ) , DNA sequencing , and bioinformatic comparisons of blood meal DNA sequences to reference databases . Ideally , primers used in blood meal analysis PCRs amplify templates from a taxonomically diverse range of vertebrates , produce a short amplicon , and avoid co-amplification of non-target templates . Few primer sets that fit these requirements are available for the cytochrome c oxidase subunit I ( COI ) gene , the species identification marker with the highest taxonomic coverage in reference databases . Here , we present new primer sets designed to amplify fragments of the DNA barcoding region of the vertebrate COI gene , while avoiding co-amplification of mosquito templates , without multiplexed or nested PCR . Primers were validated using host vertebrate DNA templates from mosquito blood meals of known origin , representing all terrestrial vertebrate classes , and field-collected mosquito blood meals of unknown origin . We found that the primers were generally effective in amplifying vertebrate host , but not mosquito DNA templates . Applied to the sample of unknown mosquito blood meals , > 98% ( 60/61 ) of blood meals samples were reliably identified , demonstrating the feasibility of identifying mosquito hosts with the new primers . These primers are beneficial in that they can be used to amplify COI templates from a diverse range of vertebrate hosts using standard PCR , thereby streamlining the process of identifying the hosts of mosquitoes , and could be applied to next generation DNA sequencing and metabarcoding approaches .
Females of most mosquito species require a blood meal , taken from a host animal , in order to mature their eggs [1] . This requirement enables mosquitoes to vector disease-causing parasites and pathogens between their vertebrate host animals . Within an ecosystem , the host-use patterns of the mosquito community form a network that mediates the movement of mosquito-vectored parasites and pathogens between vertebrate hosts [2] . Determining the structure of these networks and the transmission dynamics of mosquito-vectored parasites and pathogens requires an understanding of the interactions between mosquito and vertebrate host communities . Such interactions define mosquito host-use patterns and are characterized through molecular or serological methods , collectively referred to as blood meal analysis , that determine the taxonomic origin of a mosquito blood meal [3 , 4] . Current blood meal analysis approaches are largely PCR- and Sanger sequencing-based . In general , DNA is extracted from a blood meal , a taxonomically informative fragment of host DNA is amplified by PCR and sequenced , and the resulting sequence is compared against a DNA sequence reference database [5] . For the barcoding region of the vertebrate cytochrome c oxidase subunit I gene ( COI ) , few primers are available that fit the needs of blood meal analysis applications . Those that are available are either specific to certain host classes [6 , 7 , 8] , limiting the ability to detect the full range of hosts , or involve nested amplification reactions [9 , 10] that require greater resource and effort investment . Similarly , primer sets , mostly vertebrate class-specific , are available for another DNA barcoding marker , cyt b [11–13] . However , taxonomic coverage for cyt b in publicly accessible databases is not as complete as COI . Amplification of vertebrate DNA templates from blood meal-derived DNA extracts requires PCR primers deliberately designed to fit the unique needs of blood meal analysis . Primers must account for the presence of abundant non-target DNA ( e . g . , mosquito , symbiont , parasite [14] ) , host templates that may be derived from species of any vertebrate class [3] , and the inherent degradation of target DNA templates due to digestion in the mosquito midgut . To address these factors , primer sets ideally ( 1 ) avoid co-amplification of non-vertebrate templates through vertebrate-mosquito priming site nucleotide mismatches , ( 2 ) amplify a universal range of vertebrate host taxa templates by annealing at sites that are conserved across vertebrate classes , and ( 3 ) produce a short amplicon . Balancing versatility across target taxa with avoidance of non-target co-amplification can be challenging , particularly for COI templates which may have limited conserved sites across target taxa [15] . Sequence variation between vertebrate taxa often requires the use of multiple degenerate sites on the primer sequence to achieve versatility . A degenerated primer sequence represents a population of distinct primer sequences that collectively cover the range of potential annealing site sequence combinations [16] . Increasing the number of degenerate bases in a primer sequence increases the number of unique primer sequences in the population . As a result , when primer sequences are highly degenerate ( i . e . , > 516-fold degeneracy ) , the likelihood of non-specific amplification increases [17 , 18] . Based on these considerations , we designed four degenerate primer oligonucleotides that can be paired to amplify a 244 , 395 , or 664 bp fragment of the vertebrate COI gene . The primers were validated in two experiments . The first ( Experiment 1 ) assessed primer versatility across vertebrate host classes and avoidance of mosquito template co-amplification using DNA extracted from a set of previously identified mosquito blood meals and unfed female and male mosquitoes . The second ( Experiment 2 ) investigated the efficacy of the new primers in identifying a set of unknown mosquito blood meals .
We designed two forward and two reverse primers intended to amplify templates on the barcoding region of the vertebrate COI gene [19] while excluding mosquito templates ( Table 1 ) . We downloaded COI sequences of 31 vertebrate host species belonging to the classes Amphibia , Aves , Mammalia , and Reptilia , and 12 mosquito species of the genera Aedes , Anopheles , Culex , Culiseta and Uranotaenia from the NCBI GenBank database [20] ( S1 Table ) . The 43 reference sequences were aligned using the bioinformatics software Geneious 8 . 9 . 1 [21] . The alignment was used to identify 20–25 bp sequences that were well-conserved across vertebrate taxa but included nucleotide mismatch positions between mosquito and vertebrate taxa ( vertebrate universal ) , or well-conserved across all taxa ( universal ) . Three potential primer sequences containing primer-mosquito annealing site mismatches and one well-conserved potential primer sequence were identified from the alignment . Oligonucleotide primers were designed for these sites with sequence variation among vertebrate host taxa represented by degenerate bases [22] . To minimize the level of degeneracy of the primers , we allowed for limited primer-vertebrate annealing site mismatches , and designed the primers so that when such mismatches could not be avoided , they were positioned towards the 5' end of , or internally within the primer sequence , where they would be least likely to impede amplification [23] . Two primers , a vertebrate universal forward primer ( Mod_RepCOI_F ) and a universal reverse primer ( Mod_RepCOI_R ) , were designed from an existing primer pair ( RepCOI_F and RepCOI_R ) that was originally created for DNA barcoding Madagascan reptiles [24] and has been used without modification in blood meal analysis targeting reptilian hosts [8] . The forward primer sequence and its position were modified to accommodate mosquito-vertebrate nucleotide mismatches toward the 3' end . Both forward and reverse primers were modified to improve versatility across vertebrate taxa . Two primers ( VertCOI_7194_F and VertCOI_7216_R ) were designed de novo at similar positions within the Mod_RepCOI amplicon . These four primers allow three primer combinations that produce an amplicon of 244 , 395 , or 664 bp ( Table 2 ) . We tested the ability of the three primer combinations to amplify a diverse range of vertebrate host class templates ( versatility ) , and to avoid the co-amplification of mosquito templates ( specificity ) . To assess the versatility and specificity of each primer combination , we compared the DNA concentration of PCR products derived from several template categories: vertebrate host class templates ( Amphibia , Aves , Mammalia , Reptilia ) , mosquito-only DNA templates , and no-DNA negative controls . For the vertebrate host class categories , we used a set of 93 previously identified mosquito blood meal DNA extracts ( S2 Table ) , each representing a unique vertebrate species ( one blood meal per species ) and together representing the vertebrate classes Amphibia ( 9 species ) , Aves ( 51 species ) , Mammalia ( 17 species ) , and Reptilia ( 16 species ) . This set of templates was selected to represent a wide range of vertebrate classes and species , from a larger set of blood meals field-collected in Florida , and identified using at least one of the primer combinations ( Table 2 ) or another vertebrate-specific COI primer set [10 , 24] . Sequencing trace files associated with the selected blood meal extracts contained unambiguous sequences , with no indication of the presence of DNA from multiple hosts ( electropherogram double peaks ) or degraded signal . All blood meals were fresh at the time of DNA preservation ( blood meal scored as BF1 or BF2 , as described in the Field and laboratory protocols section ) . Mosquito specimens from which the set of blood meal DNA templates were derived represented 17 species of the genera Aedes , Anopheles , Culex , Culiseta , Psorophora , Uranotaenia , and Wyeomyia ( S2 Table ) . The mosquito-only DNA template category consisted of 14 DNA extracts derived from unfed female or male mosquitoes , each a unique species . DNA preservation and extraction protocols for these 14 mosquito-only extracts were identical to those used for blood meals extracts . Each of the 93 vertebrate host templates , 14 mosquito-only DNA templates and four negative controls were used in three PCRs , each with one of the three primer combinations . Reactions were performed in 96-well PCR plates and all three PCRs per individual DNA extract were included on the same plate and thermocycler run . Amplification success was initially determined by ethidium bromide staining and gel electrophoresis of PCR products as described in the Field and laboratory protocols section . For each PCR product , the remaining volume was sent to the University of Florida , Interdisciplinary Center for Biotechnology Research ( ICBR ) for DNA quantification by Qubit fluorometer ( Thermo Fisher Scientific , Waltham , MA ) . To account for differences in amplicon length , Qubit DNA concentration readings ( ng/μl ) were used to calculate the DNA concentration in nM and nanomolar concentrations were used in statistical comparisons . For each primer combination , mean DNA concentration of PCR products for the categories Amphibia , Aves , Mammalia , Reptilia , mosquito and negative controls were compared . To investigate the reliability and feasibility of our primer sets , we performed a small-scale test using field-collected blood meal specimens . We used a hierarchical approach to PCR amplify vertebrate host templates from the set of unknown blood meal DNA extracts collected at a field site in Alachua County , Florida . Initially , each DNA extract was used in one PCR with the Mod_RepCOI_F + VertCOI_7216_R primer combination . Ethidium bromide staining and gel electrophoresis were used to determine if amplification was successful . If amplification was unsuccessful , the DNA extract was used in a second PCR with the VertCOI_7194_F + Mod_RepCOI_R primer combination . If this reaction was not successful , the DNA extract was then used in a final PCR with the Mod_RepCOI_F + Mod_RepCOI_R primer combination . If amplification failed in all three reactions , no further steps were taken . If amplification was successful , the DNA extract was not used in subsequent reactions , and the PCR product was sequenced . Products of all successful reactions were sent to Genewiz ( South Plainfield , NJ ) for DNA sequencing using Sanger sequencing on an ABI 3130 sequencer ( Applied Biosystems , Foster City , CA ) . Resulting sequence chromatograms were examined and edited for quality in the bioinformatics software Geneious Version 8 . 9 . 1 [21] . Edited sequences were submitted to the BOLD v . 4 Identification Engine [27] . A species-level taxonomic identity was assigned to a sequence if it was ≥ 98% similar [28] to a sequence referenced in the BOLD database or to an independently obtained reference sequence . In several cases , blood meal COI sequences submitted to BOLD did not meet this criterion , but were suspected to either represent a species not yet referenced in the BOLD database ( i . e . , Sylvilagus palustris; marsh rabbit ) or a species with unusually high intraspecific COI sequence divergence ( i . e . , Anolis carolinensis; green anole ) . In Florida , there are several distinct lineages of A . carolinensis that correspond with ancient island refugia [29 , 30] . None of the reference sequences currently in the BOLD database that include locality information represent the A . carolinensis lineage ( central Florida ) that occurs in the study region . For A . carolinensis and S . palustris , we independently obtained reference sequences for comparison against blood meal sequences . Extracted DNA from tissue of morphologically identified specimens collected in central Florida were provided for A . carolinensis ( University of Florida , Florida Museum of Natural History , Division of Herpetology; accession numbers 170869 and 170871 ) and S . palustris ( University of Florida , Florida Medical Entomology Laboratory , collected by Nathan Burkett-Cadena; IACUC protocol number 201408377 ) . From these DNA extracts , we generated reference sequences using the Mod_RepCOI_F + Mod_RepCOI_R primer combination ( 664 bp ) in PCR and sequencing , as described in the Field and laboratory protocols section . These reference sequences were aligned to blood meal sequences that did not meet the ≥ 98% similarity criterion using the NCBI Basic Local Alignment Search Tool ( BLAST ) for two sequences . If similarity was ≥ 98% , the corresponding taxonomic identity was assigned to the sequence .
Gel electrophoresis indicated that amplification success for all primer combinations was generally high for blood meal templates across the range of vertebrate host classes , and poor for templates that contained only mosquito DNA ( Table 3 ) . However , no primer combination successfully amplified all blood meal templates , and for each combination , there were a small number of dim bands or failed amplifications . No PCR products were detected in the no-DNA negative controls . Amplification failed for the majority of reactions that used DNA extracted from unfed female or male mosquitoes . Faint bands at the expected amplicon size were visible on the gel following electrophoresis for DNA templates derived from Aedes triseriatus ( all three primer combinations ) , Uranotaenia sapphirina ( two primer combinations ) , and Uranotaenia lowii ( one primer combination ) , indicating amplification of a low concentration PCR product . Otherwise , amplification of mosquito templates was undetected by gel electrophoresis . The results of Qubit fluorometer DNA concentration quantification of PCR products reflected those of gel electrophoresis . The majority of PCR products derived from blood meal templates contained DNA concentrations that were sufficient for Sanger sequencing ( >45 nM ) , although there were some exceptions ( S3 Table ) . Qubit DNA concentration quantification , like gel electrophoresis , indicated that there were a small number of failed reactions and low concentration PCR products among blood meal templates . Primer combination Mod_RepCOI_F + VertCOI_7216_R was the most effective at amplifying high concentration PCR products , and the DNA concentration of PCR products was >45 nM for all 93 vertebrate host templates tested ( S4 Table ) . However , an amplicon was not visible by gel electrophoresis in four cases . Comparatively , primer combinations VertCOI_7194_F + Mod_RepCOI_R and Mod_RepCOI_F + Mod_RepCOI_R each failed to produce >45 nM PCR products for nine and four vertebrate templates , respectively . DNA concentration of blood meal PCR products varied substantially across vertebrate host classes . The DNA concentration of PCR products from mosquito-only templates and no DNA negative controls was generally low . All negative controls resulted in products that contained a DNA concentration < 45 nM , likely representing primer interactions . In some cases ( e . g . , Ae . triseriatus , Ur . lowii , Ur . sapphirina ) , PCR products from mosquito-only templates had DNA concentrations > 45 nM ( S4 Table ) , but these concentrations were not consistently detectable by gel electrophoresis . Amplification success , as measured by the DNA concentration of PCR products , was significantly affected by primer combination , template and their interaction term ( Table 4 ) . Tukey’s HSD tests were used post hoc to compare the categories of template: the four host classes ( Mammalia , Aves , Reptilia , Amphibia ) , mosquito , and the negative controls , among primer combinations . For all primer combinations , the DNA concentration of mosquito template and negative control PCR products were not significantly different from each other . Both mosquito template and negative control products were significantly different from all vertebrate categories of the template variable for all primer combinations , with the exception of the Mammalia and Amphibia categories amplified with the Mod_RepCOI_F + Mod_RepCOI_R primer combination ( Fig 2 ) . These results suggest that in general , the primer combinations are effective in parsing host templates from mosquito templates . To better interpret differences between the amplification success of vertebrate templates amplified by each primer combination , we compared the mean DNA concentration of PCR products of the primer combinations in a one-way ANOVA with the mosquito template and negative control categories of the template variable omitted . Primer combination had a significant effect on amplification success ( F2 , 267 = 234 , 858 , P = <0 . 001 ) . Post hoc Tukey’s HSD tests ( P < 0 . 05 ) indicated that , with mosquito template and negative control categories omitted , mean DNA concentration was significantly higher for Mod_RepCOI_F + VertCOI_7216_R . No differences were detected between Mod_RepCOI_F + Mod_RepCOI_R and VertCOI_7194_F + Mod_RepCOI_R . Altogether , 61 blood-fed and two unfed female mosquitoes were collected at River Styx , Alachua County , Florida , USA on 28 April 2017 ( S4 Table ) , representing six species ( Table 5 ) . In the first PCR , using the primer combination Mod_RepCOI_F + VertCOI_7216_R , amplification of vertebrate COI templates was successful for 59 of 61 ( 96 . 7% ) blood meal DNA extracts . Amplification was not detected by gel electrophoresis of ethidium bromide-stained PCR products for the two negative control unfed females . Sanger sequencing reactions for successful PCRs resulted in high quality , unambiguous COI sequences that were each reliably matched to a vertebrate host species . In all cases , no indications of multiple host feedings were apparent in sequencing trace files . With the exception of mosquitoes that had fed on Anolis lizards and Sylvilagus rabbits , all sequences met the ≥ 98% similarity criterion for reliable identification when submitted to BOLD . Twenty-two Cx . territans ( 69% of individuals of this species ) and five Cs . melanura ( 83% ) had taken blood meals from hosts suspected to be A . carolinensis based on BOLD query results . Sequences from these individuals were close ( 93 to 96% similarity ) matches to A . carolinensis and Anolis porcatus ( Cuban green anole ) BOLD reference sequences , but did not meet the ≥ 98% similarity criterion to attribute species-level identifications to the samples . Of the two species , only A . carolinensis is known from northern/central Florida , while A . porcatus is potentially established in extreme southern Florida [36] . Two-sequence BLAST alignments to independently obtained A . carolinensis reference sequences resulted in ≥ 98% similarity in all cases . Host COI sequences from two Ae . infirmatus blood meals were ~88% similar to BOLD-referenced Sylvilagus audubonii ( desert cottontail ) sequences . Because two Sylvilagus species , Sylvilagus floridanus ( eastern cottontail ) and S . palustris are known from Florida , and the BOLD database currently only includes reference sequences for S . floridanus , we suspected that these blood meal-derived sequences represented S . palustris . Alignment of these sequences to independently obtained S . palustris reference sequences resulted in ≥ 98% similarity in both cases . Extracted DNA from two mosquito blood meals ( one Cx . territans and one Cq . perturbans ) did not amplify in the initial PCR using the primer combination Mod_RepCOI_F + VertCOI_7216_R . Both DNA extracts were used in a second reaction using the primer combination VertCOI_7194_F + Mod_RepCOI_R . Extracted DNA from the Cx . territans blood meal amplified and was identified as A . carolinensis . The amplification failure in the initial reaction for this blood meal , scored BF2 , may have been the result of a pipetting or protocol error , as its subsequent identification as A . carolinensis suggests that host DNA degradation or primer-annealing site mismatch were not the cause . A third PCR using the Mod_RepCOI_F + Mod_RepCOI_R combination failed to produce amplification of the remaining Cq . perturbans DNA extract . The Cq . perturbans blood meal may have failed because of an advanced stage of digestion ( BF3 ) , which correlates with degradation of host DNA templates .
The identification of generally universal COI primers for mosquito blood meal analysis that can be used in standard PCRs improves the ability to detect the full range of potential vertebrate hosts , reduces the resources and time required to make identifications , and streamlines the process of identifying the hosts of mosquitoes . We conclude that the new primers presented here are generally effective in amplifying a PCR product from a range of vertebrate host class DNA templates , but not mosquito DNA templates , and demonstrate their efficacy in identifying the host vertebrate species of unknown mosquito blood meals . These primers provide another option to the currently available COI primers for mosquito blood meal analysis , many of which are vertebrate class-specific , or otherwise require nested PCRs or primer cocktails [6–10] . Principal advantages of these new primers are that they amplify a fragment of the COI gene , are compatible with standard amplification reactions , vary in size and include a set producing a small ( 244 bp ) amplicon , and amplify templates across a range of vertebrate classes . Concomitantly , these primers have limitations and could be improved . Importantly , no individual primer combination produced PCR products that could be detected by gel electrophoresis for the full range of tested vertebrate host species . This suggests that when applied to field-collected samples of unknown origin , none of the primer combinations alone should be expected to independently assure the amplification of a PCR product , necessitating a hierarchical approach . One of the advantages of the COI gene in species identification is its rapid rate of evolution , and the resultant ability to distinguish between even recently diverged species [28] . Simultaneously , rapid rates of evolution complicate primer design for blood meal analysis by making it difficult to identify suitable priming sites consisting of sequences that are conserved across vertebrate taxa [15] , but mismatched with mosquito templates . In cases where primer-template mismatches prevent the extension of host templates , a hierarchical approach can solve this issue through the use of varied priming sites in secondary or tertiary reactions that are expected to improve the likelihood that the template can ultimately be amplified . Using the primer combinations presented here , there are several factors that should be considered when designing a hierarchical PCR strategy ( Table 6 ) , including amplicon length and versatility differences between primer combinations . Ideally , field-collected mosquito blood meals are fresh when collected and host DNA templates have undergone minimal digestive degradation . After a mosquito takes a blood meal , host DNA gradually degrades until becoming undetectable by PCR 30–72 h post-feeding [37–40] . As DNA degrades , strand breaks accumulate on template DNA molecules over time . As a result , shorter fragments persist for a longer period of time than long fragments , corresponding with an inverse relationship between amplification success and the size of the amplified template [41] . Under the assumption that shorter DNA fragments likewise persist for a greater duration in the mosquito midgut than longer fragments , we designed two primer combinations , VertCOI_7194_F + Mod_RepCOI_R and Mod_RepCOI_F + VertCOI_7216_R , that amplify relatively short fragments ( 395 and 244 bp , respectively ) of template DNA and are expected to be more effective than primer sets that amplify longer fragments when blood meals are well-digested . Concomitantly , these shorter amplicons contain less taxonomic information and sequence variation than longer amplicons , potentially making it difficult to distinguish between closely related species [42] . Sequences of the COI DNA barcode region are expected to produce inconclusive identifications ( 98–100% identity to more than one species ) for approximately 5% of taxa when the amplicon size is 250 bp [42] . In cases where the resulting sequences match multiple , closely-related sympatric host vertebrates , a subsequent PCR using a primer set producing a longer amplicon could be used to resolve ambiguity . Another issue in using shorter mitochondrial sequences for species identification is the potential for accidental amplification of nuclear mitochondrial pseudogenes ( NUMTs; fragments of mitochondrial DNA transposed to the nuclear genome ) [43] . Nuclear mitochondrial pseudogenes are most often <600 bp in length , usually <200 bp [44] , and may be co-amplified . Co-amplification of a NUMT is expected to result in a trace file with ambiguous nucleotide peaks , which may lead to the inability to identify a sequence . Such trace files should be carefully examined and interpreted , not only to recognize NUMTs , but also blood meals of mixed origin that can produce similar ambiguous peaks [10] . Issues related to NUMTs are likely to be resolved by re-amplifying and sequencing a sample with a primer combination that produces a longer amplicon . Experiment 2 tested the effectiveness of a hierarchical approach to blood meal identification using the newly designed primers on a small sample of field-collected mosquitoes . Although this sample was relatively limited in size and the number of mosquito and host species it represented , we were able to identify > 98% of the collected blood meals ( 60 of 61 blood meals ) . Contemporary literature records of mosquito blood meal identification success vary , but host identification success of ~50–80% of blood fed specimens are common [7 , 45–48] . While we cannot expect that the high rate of amplification and host identification we observed in our sample will necessarily translate to larger samples or samples representing greater host diversity , these results support the validation of these primer combinations as an effective means of identifying mosquito blood meals . In general , the mosquito-vertebrate host associations we determined in Experiment 2 reflect the known vertebrate host class associations of the collected mosquito species in North America [49–52] . However , our sample of Cs . melanura ( n = 6 ) , a typically ornithophilic mosquito and the primary vector of the Eastern equine encephalitis virus , a medically important Alphavirus , fed predominantly on the lizard A . carolinensis , and only one blood meal was derived from a bird . Previous research indicates that birds , particularly passerines , are dominant hosts for Cs . melanura . Much of the ecological research on Cs . melanura has taken place in the northern United States , beyond the range of A . carolinensis and other abundant lizard species , or has used molecular methods specific only to avian and mammalian hosts [53–55] . Our small sample size for Cs . melanura inhibits the ability to draw conclusions regarding general host-use patterns . However , this result reflects recent findings on Cs . melanura host-use patterns in Florida suggesting that Anolis lizards are important hosts for this mosquito in the state [56] , and highlights the importance of using blood meal analyses that are compatible with the full range of potential mosquito host animal classes so that unexpected host taxa are not missed . As molecular technologies advance and the costs of DNA sequencing decrease , next generation DNA sequencing is likely to be increasingly applied to examinations of vector blood meals . Technologies such as Illumina and 454 pyrosequencing have advantages over Sanger sequencing approaches to mosquito blood meal analysis [57] , and make feasible a metabarcoding or community sequencing approach to identifying pooled mosquito blood meals . Under such an approach , DNA extracted from mosquito blood meals could be pooled and sequenced in parallel , enabling the identification of large numbers of blood meals simultaneously and affordably , albeit without the ability to link a particular host species to an individual mosquito . Primer sets that target only a range of the potential vertebrate host species or certain genes ( e . g . , cyt b , 16S ribosomal RNA ) may not lead to accurate characterizations of mosquito host-use patterns because feedings on non-target or unanticipated hosts could be missed , or host species for which reference sequences do not yet exist would not be identified , respectively . Similarly , primer sets that require nested amplification reactions can produce biased results when used to sequence communities [58] . The primer sets presented here may be useful in community sequencing approaches to blood meal analysis , as they are generally vertebrate-universal and compatible with standard PCR . However , variation in the efficiency of amplification between host taxa may pose an issue , and further research is needed to investigate the suitability of these , or other primers to next generation and community sequencing blood meal analysis approaches . Future research should also consider the possibility that some mosquitoes are specialists of non-vertebrate hosts [59] , and strive to develop blood meal analysis methods that can detect the full range of animals that may be fed upon by mosquitoes . Mosquito blood meal analysis provides insight on the host-use patterns of mosquito communities , and by extension , the ecology and epidemiology of mosquito-vectored pathogens . The COI primers presented here amplify COI templates of a universal range of terrestrial vertebrate classes while avoiding co-amplification of mosquito templates , and provide an alternative to the currently available primer sets that target only particular vertebrate classes , or require nested or multiplexed PCR . These primers streamline the process of determining the hosts of mosquitoes through Sanger sequencing , and are candidates for the development of next generation sequencing or metabarcoding-based approaches to blood meal analysis . | Female mosquitoes take blood meals from diverse vertebrate hosts , including amphibians , birds , mammals , reptiles , and fishes . Mosquito species vary in their host-use patterns . Identifying which mosquitoes feed from which hosts is critical to understanding how mosquitoes transmit disease-causing pathogens between vertebrates . Host-use patterns are determined by blood meal analysis . In modern blood meal analysis , host DNA is extracted from a mosquito , amplified in a polymerase chain reaction ( PCR ) , sequenced , and identified . We designed new primers that selectively amplify a fragment of the DNA barcoding gene , cytochrome c oxidase subunit I ( COI ) , of vertebrates , while avoiding that of mosquitoes . We validated the new primers in experiments using previously identified mosquito blood meals and mosquito DNA , and demonstrated their efficacy in identifying unknown mosquito blood meals . We found that the primers are well-suited to mosquito blood meal analysis applications . They are universal across vertebrates , target the COI DNA barcoding gene , and are effective in standard amplification reactions . |
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Supported by recent computational studies , there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding ( NSC ) , an efficient population coding scheme based on dimensionality reduction and sparsity constraints . We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces , by some associative areas to conjunctively represent multiple behaviorally relevant variables , and possibly by the basal ganglia to coordinate movement . In addition , NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas . Although NSC might not apply to all brain areas ( for example , motor or executive function areas ) the success of NSC-based models , especially in sensory areas , warrants further investigation for neural correlates in other regions .
Brains face the fundamental challenge of extracting relevant information from high-dimensional external stimuli in order to form the neural basis that can guide an organism's behavior and its interaction with the world . To support complex patterns of behavior , populations of interconnected neurons must implement a rich repertoire of linear and nonlinear operations on their synaptic inputs that take into account context , experience , and anatomical constraints [1] . For example , anatomical bottlenecks often force the information stored in a large number of neurons to be compressed into an orders-of-magnitude-smaller population of downstream neurons [2–4] , such as storing information from 100 million photoreceptors in 1 million optic nerve fibers or resulting in a 10–10 , 000-fold convergence from cortex to the basal ganglia [3] . One potential approach to addressing this challenge is to reduce the number of signals required to transmit information in the network—for example , through sparse-coding schemes ( text in bold appear in the Glossary section ) , in which information is represented by the activity of a small proportion of neurons in a population [5–7] . A number of different definitions of sparsity can be found in the literature [8 , 9] , which can sometimes lead to controversy as to which codes can still be considered sparse [8] . An extreme example is the so-called local code , in which each unique event , or “context , ” is encoded by a single active neuron , or “grandmother cell” [10] ( illustrated in the left column of Fig 1A ) . Local codes not only suffer from low representational capacity , because they allow a population of N neurons to encode at most N contexts , but also require a large number of neurons to cover the space of possible contexts . On the other hand , a dense code represents each context by the combined activity of all neurons in the population ( Fig 1A , right column ) . In theory , dense codes lead to high representational capacity ( at M activity levels , allowing for MN contexts to be encoded ) , but they also suffer from neuronal cross talk because every neuron is involved in every context . Alternatively , sparse codes ( Fig 1A , center column ) can be described as a trade-off between the benefits and drawbacks of dense and local codes , in which each context is encoded by a different subset of neurons in the population . [5] . In general , sparse coding reduces the overall neural activity necessary to represent information . Another approach to address this challenge is to reduce the number of variables required to represent a particular input , stimulus , or task space , a process known as dimensionality reduction . Although responses of individual neurons are often complex and highly nonlinear , a population of neurons might share activity patterns because of individual neurons in the population not being independent of each other . Dimensionality reduction methods have proved useful in elucidating these shared activity patterns and thus effectively explaining population activity using a lower number of variables than there are neurons in the population ( for a recent review , see [14] ) . Neurons often encode several behaviorally relevant variables simultaneously [15–18] , allowing for multifaceted representations of high-dimensional stimulus spaces . For example , a population of neurons tasked with encoding human faces might opt to represent each individual face as a combination of a set of standard faces ( Fig 1B , left column ) . In such a holistic representation of faces [11] , each individual neuron would itself respond to a face as a whole ( i . e . , a face “template” ) without explicitly representing individual face components , and an arbitrary face could be represented by combining different face templates ( e . g . , by adding 10% of template 1 to 20% of template 2 and subtracting 30% of template 3 ) . On the other hand , faces can also be represented as a combination of individual face components , such as eyes , noses , and mouth , in what is known as a parts-based representation ( Fig 1B , right column ) [12 , 19] . Both approaches allow for representing arbitrary faces as a combination of neural activity but have drastically different consequences on the set of stimulus features each neuron responds to . Although visual information from the eyes , nose , and mouth would of course be included in a holistic face representation , that information would not be explicitly represented as structural units in their own right [11] . Linear combinations of holistic components often involve complex cancellations between positive and negative contributions and thus lack the intuitive meaning of adding parts to form a whole . In contrast , a parts-based representation allows for only nonsubtractive combinations of stimulus features [12] . Although the relevant stimulus dimensions are often not known a priori , several sophisticated mathematical techniques exist that allow us to discover these representations directly from experimental data [14 , 19–23] . In this article , we review evidence from experimental and theoretical studies suggesting that a number of neuronal responses can be understood as an emergent property of nonnegative sparse coding ( NSC ) , an efficient population coding scheme based on dimensionality reduction and sparsity constraints . In particular , we review evidence for NSC in sensory areas that efficiently encode external stimulus spaces , for associative areas to conjunctively represent multiple behaviorally relevant variables , and for the basal ganglia to coordinate movement .
The fundamental principle of efficient coding is that a sensory system is adjusted to the specific statistics of the natural environment from which it encodes and transmits information [24–27] . Efficiency , in this context , is an information-theoretic term that should not be confused with “minimizing energy expenditure . ” Instead , a sensory pathway is treated as a noisy communication channel , in which the goal is to maximize the rate at which information can be reliably transmitted by minimizing the redundancy between representational units . Early theories of efficient coding [24 , 25] were developed based on the visual system . Attneave [25] pointed out that there is a significant degree of redundancy in natural visual images because of correlations in both the spatial and temporal domains ( for a recent review , see [28] ) . For example , the luminance values of a pair of pixels separated by a fixed distance in a natural image are likely to be highly correlated ( Fig 2A ) . These statistical regularities constrain the images a visual system is likely to encounter to a tiny fraction of the set of all possible images . It was therefore argued that the visual system should not waste resources on processing arbitrary images but instead use statistical knowledge about its environment to represent the relevant input space as economically as possible . Extending this idea to the neural level , Barlow [24] proposed that the goal of early neurons in sensory processing is to transform raw visual inputs into an efficient representation such that as much information as possible can be extracted from them given limited neural resources . This efficient coding principle has been able to explain a wide variety of neuronal response properties in the early visual system , such as the center-surround structure of receptive fields ( RFs ) in the retina [30] , temporally decorrelated signals in the lateral geniculate nucleus ( LGN ) [31] , and the coding of natural scenes in the primary visual cortex ( V1 ) [9] . At the level of single neurons , efficient coding suggests that the information carried by a neuron's response can be maximized by using all response levels with equal frequency [29 , 32 , 33] . For example , in the case of a neuron representing a single input variable with a single output variable , information is maximized when the input–output function corresponds to the cumulative probability function for the different input levels [29] , as shown in Fig 2B . Note that this coding procedure amplifies inputs in proportion to their expected frequency of occurrence rather than reserving large portions of its dynamic range for improbable inputs [29 , 32] . On the other hand , if the input–output function sensitivity is chosen as too low , high levels of the stimulus feature will be indistinguishable as the response function saturates; if the sensitivity is set too high , low levels of the stimulus feature cannot drive responses [29] . At the level of neuronal populations , neural responses should be both decorrelated ( i . e . , independent from one another ) and sparse ( i . e . , involve only a small fraction of neurons in the population ) [27] . Taking these ideas a step further , Olshausen and Field [34] noted that natural images contain statistical dependencies beyond linear pairwise correlations among image pixels and argued that these higher-order correlations should be taken into account when developing an efficient code . Their goal was thus to find a linear coding strategy capable of reducing these higher-order forms of redundancy . Linear sparse coding is one such strategy , in which monochromatic images I ( x , y ) are described in terms of a linear superposition of a number of B basis functions , wb ( x , y ) : I ( x , y ) =∑b=1Bwb ( x , y ) hb , ( 1 ) where hb are stochastic coefficients that are different for each image [35 , 36] . Learning a sparse code for images thus involved determining the values of both wb ( x , y ) and hb for all b and ( x , y ) , given a sufficient number of observation of images , under the constraint that hb be sparse . In this context , hb was considered sparse if it took very small or very large ( absolute ) values more often than a Gaussian random variable would [36] . This sparsity constraint allowed for basis functions that were not needed to describe a given image structure to be weeded out . When Olshausen and Field applied linear sparse coding to natural images , they found that the emerging basis functions were qualitatively similar in form to RFs of simple cells in V1 [35 , 37] , thus giving empirically observed RFs an information-theoretic explanation . In this context , hb in Eq 1 corresponded to the ( signed ) activation value of a particular V1 neuron , and wb ( x , y ) were the connection weights ( or synaptic weights in an artificial neural network ) that were closely related to that neuron's RF . Sparsity , in this context , is an information-theoretic concept related to how efficiently and completely information is encoded with the basis functions described previously . Please note that this is different from empirical observations of brain areas being “sparsely” activated; that is , sparse population activity does not necessarily imply that a brain area implements a sparse-coding scheme . This confusion is fueled in part by the wide variety of definitions of sparsity used in the literature [8 , 38] . For example , even though sparse coding ( as a theoretical framework ) applied to natural images yields V1-like RFs , recent evidence suggests that neural activity in V1 might not be as sparsely activated as previously thought [39 , 40] . However , V1 still codes stimuli efficiently [40] . Olshausen and Field went on to show that the set of basis functions that best described V1 RFs was greater in number than the effective dimensionality of the input ( which they termed an overcomplete basis set ) [37] . It is worth noting that sparse coding with an overcomplete basis set is typically associated with an anatomical fan-out motif , such as expanding 1 million optic nerve fibers into more than 100 million V1 neurons or from a small number of mossy fibers to a 100-fold–larger number of granule cells in the cerebellum . However , as pointed out by Hoyer [41] , linear sparse coding falls short of providing a literal interpretation for V1 simple-cell behavior for two reasons: ( 1 ) every neuron could be either positively or negatively active , and ( 2 ) the input to the neural network was typically double-signed , whereas V1 neurons receive visual input from the LGN in the form of separated , nonnegative ON and OFF channels . In order to transform Olshausen and Field's sparse coding from a relatively abstract model of image representation into a biologically plausible model of early visual cortex processing , Hoyer [41 , 42] thus proposed to enforce both input signal and neuronal activation to be nonnegative ( though still allowing inhibitory connections ) . This seemingly simple change had remarkable consequences on the quality of the sensory representation: whereas elementary image features in the standard sparse-coding model could “cancel each other out” through subtractive interactions , enforcing nonnegativity ensured that features combined additively , much like the intuitive notion of combining parts to form a whole . The resulting parts-based representations resembled RFs in V1 much more closely than other holistic representations . These considerations led to the formulation of NSC in its current form . As a special case of linear sparse coding , NSC shares the same goal of accurately describing observed data as a superposition of a set of sparsely activated basis functions , as well as enforcing dimensionality reduction . In addition , NSC requires all basis functions and activation values ( i . e . , wb ( x , y ) and hb in Eq 1 ) to be nonnegative . However , NSC is more than just linear sparse coding with nonnegative weights . For example , whereas linear sparse coding typically uses a larger number of basis functions than there are dimensions in the input ( thus achieving dimensionality expansion ) , NSC makes use of nonnegative matrix factorization ( NMF ) to achieve dimensionality reduction . This has interesting implications for the kinds of basis functions that can be learned . Most prominently , the nonnegativity constraints used in NMF force the different basis functions to add up linearly , thus leading to the distinctive parts-based representations . Consider S observed stimuli or data samples , each composed of F observed feature values , such as a collection of S images I ( x , y ) s ( s∈[1 , … , S] ) from the previous example , each consisting of F different grayscale values . If we arrange the observed feature values of the s-th observation into a vector v→s ( i . e . , by flattening each observed image ) , and if we arrange all vectors into the columns of an F×S data matrix V , then linear decompositions describe these data as V≈WH , ( 2 ) where W is an F×B matrix that contains as its columns the B basis functions of the decomposition ( i . e . , the b-th column of W corresponding to wb ( x , y ) ∀x , y in Eq 1 ) , and H is a B×S matrix containing as its columns the activation values of each basis function for a particular input stimulus ( i . e . , the b-th column of H corresponding to hb ∀b in Eq 1 ) . The difference between V and WH is termed the reconstruction error . The goal of NSC is then to find a linear decomposition of V that minimizes the reconstruction error while guaranteeing that H is sparse . This can be achieved by minimizing the following cost function [42]: minW , H12‖V−WH‖2+λ∑ijf ( Hij ) , ( 3 ) subject to the constraints ∀ij:Wij≥0 , Hij≥0 , and ‖w→i‖=1 , where w→i denotes the i-th column of W . Here , the left-hand term describes the reconstruction error , whereas the right-hand term describes the sparsity of the decomposition . The trade-off between accurate reconstruction and sparsity is controlled by the parameter λ ( where λ≥0 ) , whereas the form of f defines how sparsity is measured ( a typical choice is the L1 norm on H ) . Analogous to efficient coding , Eq 3 forces prediction errors to be amplified in proportion to their expected frequency of occurrence because a more frequent event would show up more frequently in V . Hence , accounting for a rare observation at the expense of ignoring a more common one would result in an increased reconstruction error . In the case of λ = 0 , Eq 3 reduces to the squared-error version of NMF . Although NMF enforces all elements of W and H to be nonnegative , the resulting decomposition might not be sparse , depending on the number of basis functions B . In order to emphasize decompositions in which H is sparse , Eq 3 should be minimized with λ>0 [42] . Another open parameter is the number of basis functions , B , which controls the predictive power of the model and must be determined empirically . With a small number of basis functions , NSC is unlikely to achieve a low reconstruction error , be it in familiar contexts ( training data ) or in novel contexts ( held-out test data ) . In this case , the error depends on the systematic bias of the model , and the model is said to underfit the data ( left-hand side of Fig 3 ) . With increased model complexity , the model can learn subtle differences between different contexts with high accuracy , leading to a reduced bias ( training ) error . However , with increased complexity , the model is more likely to learn patterns between training contexts that arise either from underlying noise or from spurious correlations . As a result , the model will respond according to these learned patterns when a novel context is presented ( rather than according to the underlying actual relationships ) , in which case the model is said to overfit the data ( right-hand side of Fig 3 ) . Hence , the goal of a successful model is to find the ideal compromise in the bias–variance error trade-off [43] ( labeled “best model” in Fig 3 ) . Analogously to [35 , 37] , the basis functions obtained in NSC can be interpreted as the connection weights of a population of simulated neurons in an artificial neural network . In other words , under NSC , the number of basis functions B corresponds to the number of output neurons , and the response of the b-th model output neuron ( b∈[1 , … , B] ) to a particular input stimulus s , termed rbs , can be computed by feeding the dot product of that neuron's connection weights ( i . e . , the b-th column in W , w→b ) and a data vector ( i . e . , the s-th column in V , v→s ) to an activation function Θ: rbs=Θ ( w→b⋅v→s ) , ( 4 ) where “⋅” denotes the dot product . For example , the linear response of a model neuron can be calculated by setting Θ to the identity function Θ ( x ) = x . Note that the response of the model neuron to different stimuli s∈[1 , … , S] involves different columns of V but always relies on w→b . Thus , we can utilize W ( which must remain fixed once learned ) and Eq 4 to simulate a model neuron's response to arbitrary input stimuli by replacing the column in V with new input . This allows us to investigate the response properties of individual model neurons much in the same way that experimental neuroscientists study biological neurons . This is important because it means that NSC can be used to model neural activity in the brain , and the resulting activity patterns generated by NSC can be compared to and evaluated against experimental findings . It is important to note that the absence of negative weights in Eqs 2–4 does not preclude the modeling of inhibitory connections or even posit that inhibitory connections cannot participate in NSC . Rather , one important aspect of NSC is the parts-based , NMF-like decomposition of V; one way to achieve this is by enforcing nonnegativity constraints on W and H . Several studies have successfully incorporated inhibitory connections into their NSC-based models . One approach is to model them as nonnegative synaptic conductances . For example , Hoyer [41] used NSC to model V1 neurons as receiving input from both excitatory ON and inhibitory OFF cells in the LGN . Using prewhitened natural images , Hoyer sampled 12×12 pixel patches from the images and then separated positive and negative values into separate channels . Each image patch was thus represented by a 2×12×12 = 288 dimensional vector , each element of which mimicked the activity of an ON or OFF cell in response to the image patch . These vectors were then arranged into the columns of V . This procedure not only preserved the parts-based quality of the encoding but also allowed the modeling of the convergence of ON and OFF pathways . Another approach is to drop the nonnegativity constraint on W and thus effectively operate with both positive and negative synaptic weights . Only recently did it become clear that this approach was able to preserve the parts-based quality of the encoding ( as long as nonnegativity of H was enforced ) [44] , thus simplifying the construction of more complex network topologies .
The notion of parts-based object recognition is compatible with hierarchical models of vision , in which activation of simple features feeds into the activation of complex features [51] . There is a long history of debate as to whether humans detect faces based on their individual parts or as correctly arranged wholes ( for reviews , see [11 , 52 , 53] ) . The working hypothesis is that the brain might use holistic face information as an early gating mechanism to allow visual stimuli access to the face processing module but that most cortical circuitry relies on parts-based information [53] . Converging evidence from human imaging studies and primate physiology suggests that faces are processed in localized “patches” within IT [54] , where cells detect distinct constellations of face parts [55 , 56] , such as eyes [57] , and that whole faces can be recognized by taking linear combinations of neuronal activity across IT [19 , 58] . An influential paper by Lee and Seung [13] found that applying NMF to a database of face images yielded sparse , localized features that resembled parts of a face ( Fig 4A ) in a similar fashion to responses in area IT . In their case , NMF acted on an F×S data matrix V , whose rows corresponded to distinct features of the input ( e . g . , F different pixels of an image ) and whose columns corresponded to different stimuli or observations of those features ( e . g . , S different images ) . NMF was used to decompose the matrix into two reduced-rank matrices ( Fig 4 , inset ) whose linear combination could be weighted such that the product of W and H provided an accurate reconstruction of V ( see Eq 2 ) . A particular image , in this case encoded by F = 19×19 = 361 pixels could be accurately represented by a linear combination of a small number ( B = 49 ) of encoding variables or “basis images” ( Fig 4A ) . Such a representation is reminiscent to neural processing in IT , an area in the ventral visual “what” stream involved in encoding high-level object identity [58 , 59] , in which images of whole faces can be linearly reconstructed using responses of approximately 200 neurons that each respond to a certain set of physical facial features [19] . Interestingly , such a parts-based representation is not specific to face processing in IT; the same principle can be extended to body-selective regions in IT [60 , 61] . Although there seems to be a consensus that information-theoretic explanations are relevant when investigating early sensory areas , higher-order brain areas are often considered to be specialized for performing tasks ( e . g . , recognizing objects , making decisions , navigating an environment ) rather than the efficient encoding of information . It is therefore possible that the essential components of NSC might well be present in higher-order areas but , to date , have gone unnoticed . Because of its roots in efficient coding theories of natural image processing , NSC figures prominently in the vision neuroscience literature . For example , NMF-based models were able to reconstruct in vitro neuronal spike trains from the salamander retina [44 , 62] . By combining spike-triggered average with NMF , Liu and colleagues [44] were able to identify the subunit layout of retinal ganglion cells ( Fig 5 ) . This technique , termed spike-triggered NMF ( STNMF ) , involved applying NMF to the collection of those stimulus patterns contained in a spatiotemporal white-noise sequence that caused a given neuron to spike . Akin to common reverse-correlation analysis , the researchers averaged the collection of spike-eliciting stimulus segments to form the spike-triggered stimulus ensemble ( Fig 5A ) . STNMF then decomposed the ensemble of effective spike-triggered stimuli into a matrix W containing a set of modules ( or basis functions ) and a matrix H containing a set of hidden coefficients . Intuitively , the modules derived by STNMF should capture the subunit decomposition of the cell's RF because the spike-eliciting stimuli should have essential statistical structure imprinted on them by the subunits , such as correlations between pixel values [44] . And indeed , the identified modules corresponded to individual presynaptic bipolar cells , as verified by multielectrode array recordings with simultaneous recordings from individual bipolar cells through sharp microelectrodes [44] . This allowed the researchers to improve predictions about how ganglion cells respond to natural stimuli without the need to guess a specific model structure that may be constrained in terms of the size , shape , number , or nonlinearity of ganglion cell subunits . NSC has been extensively applied to early visual cortex , where it has successfully explained orientation and frequency tuning of simple and complex cells in V1 [41] as well as edge-like pooling of spatial frequency channels in V2 [63] , including RF properties such as end-stopping and contour integration [64] . These theoretical findings are in good agreement with a large body of research documenting the sensory response of V1 across animal models ( e . g . , [65–68] ) , although they are not without controversy . For example , one study [67] criticized that some of the early sparse-coding models generated RFs that looked like stereotyped edge detectors and did not capture the diversity of RF structure observed in cat and monkey V1 . However , by adjusting these models to limit the number of active neurons ( “hard” sparsity ) instead of limiting mean neuronal activity ( “soft” sparsity ) , Rehn and Sommer [69] were able to account for the diversity of shapes in biological RFs . Other researchers were concerned that the apparent sparse activation of V1 was an artifact of using simple artificial stimuli such as sinusoidal gratings and drifting bars , but Vinje and Gallant [9] were able to show that natural viewing conditions actually increased the sparsity of V1 activation . However , a number of recent studies suggest that responses are neither sparse nor low dimensional in V1 of the mouse [39 , 40] and monkey [70] . Using high-density electrophysiology , Stringer and colleagues [40] found that the response of more than 10 , 000 visual cortical neurons to 2 , 000 image stimuli is high dimensional . In monkey V1 , one needs to look at many principal components to decode natural images , and these principal components reflect contributions from most of the recorded neurons [70] . In addition , V1 neurons in the mouse might encode both visual stimuli and behavior in a mixed representation: a recent study found no separate sets of neurons encoding stimuli and behavioral variables , but each neuron multiplexed a unique combination of sensory and behavioral information [39] . These findings suggest that efficient coding might render an incomplete picture of sensory processing in V1 and that more studies are needed to reevaluate past findings . To this end , Stringer and colleagues [40] suggested that the population code of visual cortex might be determined by two constraints: efficiency , to make best use of the limited number of neurons , and smoothness , which allows similar stimuli to evoke similar responses . In summary , there is a large body of research showing that computational models based on efficient coding , such as NSC , can account for a variety of response properties in early visual cortex . Although methods like spike-triggered average [71] and dimensionality reduction [72] give us confidence that we have a good understanding of the sensory response in V1 , this understanding remains far from complete [73 , 74] and in fact might be missing a number of dimensions related to task , state , or behavior [39 , 40] . With the exception of face processing in IT [13 , 19] , NSC has yet to be applied to higher-order areas in the ventral visual pathway . The success of NSC in explaining V1 and V2 response properties suggests that it might be possible to extend the model to texture integration in V4 . Our group found evidence for NSC in the dorsal subregion of the medial superior temporal ( MSTd ) area [46] , which is part of the visual motion pathway in the dorsal visual stream . Neurons in MSTd respond to relatively large and complex patterns of retinal motion ( “optic flow” ) , owing to input from direction- and speed-selective neurons in the middle temporal ( MT ) area ( for a recent review , see [75] ) . Although MSTd had long been suspected to be involved in the analysis of self-motion , the complexity of neuronal response properties has made it difficult to experimentally investigate how neurons in MSTd might perform this function . When our group applied NMF to simulated neural activity patterns whose statistical properties resembled that of experimentally recorded MT neurons [46] , we found a sparse , parts-based representation of retinal flow ( Fig 4B ) similar to the parts-based representation of faces encountered by Lee and Seung [13] . The resulting “basis flow fields” showed a remarkable resemblance to RFs of MSTd neurons , as they responded to an intricate mixture of 3D translational and rotational flow components in a subset of the visual field . As a result , any flow field possibly to be encountered during self-movement through a 3D environment could be represented by only B = 64 simulated MSTd neurons , as compared with F = 9 , 000 simulated MT input neurons . This led to a sparse and parts-based population code in which any given stimulus could be represented by only a small number of simulated MSTd neurons [46] . Fig 6 shows the distribution of direction preferences of MSTd-like model units ( Fig 6A and 6B; [46] ) for rotation and translation , respectively . Each data point in the scatter plots specifies the preferred 3D direction of a model unit . Histograms along the boundaries show the marginal distributions of azimuth and elevation preferences . Not only did individual units match response properties of individual neurons in macaque MSTd [76] ) , but the model was able to recover statistical properties of the MSTd population as a whole , such as a relative overrepresentation of lateral headings . MSTd is known to encode a number of perceptual variables , such as the direction of travel ( heading ) and eye rotation velocity . During forward movement , retinal flow radiates out symmetrically from a single point , the focus of expansion ( FOE ) , from which heading can be inferred . However , instead of consisting of a set of distinct subpopulations , each specialized to encode a particular perceptual variable , MSTd has been found to consist of neurons that act more like basis functions , in which a majority of cells were involved in the simultaneous encoding of multiple perceptual variables ( Fig 6C ) . A similar picture emerged when we investigated the involvement of MSTd-like model units in the encoding of both heading and eye rotation velocity ( Fig 6C ) . Interestingly , the sparsity regime in which model MSTd achieved the lowest heading prediction error ( Fig 6D ) was also the regime in which MSTd-like model units reproduced a variety of known MSTd visual response properties ( for experimental details , refer to [46] ) . In contrast to findings about early visual cortex , this regime does not use an overcomplete basis set [35] , yet it can still be considered a sparse coding regime [8] because only a few MSTd-like model units were needed to recover the stimulus , and each model unit responded to a subset of stimuli ( see Fig 8C in [46] ) . Such an intermediary sparse code might be better suited ( as opposed to an overcomplete basis set ) for areas such as MSTd because the increased memory capacity of such a code might lead to compact and multifaceted encodings of various perceptual variables . Taken together , the computational modeling work on MSTd described previously suggests that NSC is not specific to primary sensory areas and may be observed in other downstream sensory regions . Analogous to early visual cortex , the auditory system is believed to decompose auditory signals into a set of elementary acoustic features [77] such that the complete acoustic waveform can be described by a sparse population code that operates near an information-theoretic optimum [77–79] . It is therefore not surprising that computational models based on NSC have been very successful at describing the spectro-temporal RF of neurons in the primary auditory cortex ( A1 ) [80 , 81] . Response properties of A1 neurons are well described by a spectrogram; they are often tuned to stimulus frequency but are rarely phase locked to oscillations of the sound waveform [82] . The cortical representation of auditory signals seems to not only be sparse but also rely on statistically independent acoustic features [83] . Similar to visual cortex , auditory cortex is hierarchically organized , with neurons in A1 responding to simple acoustic features of natural sounds and higher-order areas responding to more behaviorally relevant stimuli . The anterior superior temporal region of auditory cortex , for example , responds to categories of acoustic objects , such as sounds produced by voices and musical instruments [82] . An intriguing question for future modeling studies is therefore whether NSC can be extended to the next level of the auditory hierarchy: Would it be possible to construct more complex acoustic objects from a sparse , parts-based set of elementary , A1-like acoustic features ? And would the representation of such acoustic objects resemble neuronal responses in the anterior superior temporal region of auditory cortex ? Taken together , we suggest that auditory cortex is a good example for efficient and NSC-based coding in a sensory system other than the visual cortex , in which further study is warranted . The olfactory cortex is another nonvisual cortical area worth investigating for NSC-like responses . In contrast to most other sensory modalities , the basic perceptual dimensions of olfaction remain unclear . In particular , the olfactory modality is intrinsically high dimensional and lacks a simple , externally defined basis analogous to wavelength or pitch on which elemental odor stimuli can be quantitatively compared ( for a recent review , see [84] ) . Odors evoke complex responses in granule cells ( located in the olfactory bulb ) that evolve over hundreds of milliseconds [85] . Granule cells use a sparse combinatorial code to convey information about odor identity and concentration [86 , 87] . Downstream from the olfactory bulb , odors tend to activate a small but consistent proportion ( approximately 10% ) of cortical neurons in the piriform cortex [88] , which is thought to form odor object percepts [89 , 90] . Although piriform cortex is not topographically organized , a spatial structure can be discerned when examining the projections of output neurons , which are highly segregated and functionally specific . Whereas the anterior piriform cortex is associated with the encoding of odor identity and odor structure , the posterior piriform cortex is involved in associational aspects of odors , such as valence and similarity [89 , 91] . A compelling piece of evidence for NSC in the olfactory system was recently provided by Castro and colleagues [48]: In an effort to elucidate the dimensions along which perceptual space might be organized in the olfactory system , they applied NMF to a perceptual dataset built from 144 monomolecular odors , each represented by a 146-dimensional vector ( an “odor profile” ) . Each dimension in the odor profile corresponded to the rated applicability of a number of semantic labels , such as “sweet , ” “floral , ” and “heavy . ” By applying NMF to the odor profile , they showed that a set of 10 sparsely activated basis functions could accurately describe any odor in the dataset ( Fig 7A ) . Interestingly , NMF revealed a prominent block diagonal structure to the full matrix H ( Fig 7B ) , indicating that ( 1 ) a given odor tended to be characterized by a single prominent basis function , implying that the basis functions recovered by NMF were perceptually meaningful , and ( 2 ) all ten basis functions were being used approximately with equal frequency , implying that the basis functions recovered by NMF could span the space of behaviorally relevant odors . This suggests that a given odor percept may be considered an instance of one of several fundamental qualities . Furthermore , NMF recovered basis functions whose descriptors aligned with perceptual dimensions highlighted in several previous analyses of odor space , including but not limited to relative pleasantness ( e . g . , “fragrant , ” “sickening” ) and potential palatability ( “woody , resinous , ” “chemical , ” “sweet , ” and “lemon” ) . Odors clustered predominantly along these axes ( as illustrated in Fig 7C ) for three specific basis functions [48] . In summary , although sensory processing in the olfactory system remains an area of active research , there is evidence consistent with a sparse and parts-based encoding of odor identity and concentration . Only recently have NSC-based methods been employed to elucidate the neural code for olfaction . Future studies may provide additional supporting evidence . In early areas of primary somatosensory cortex ( S1 ) , a number of parallels can be drawn to sparse , reduced information processing observed in other primary sensory cortices . First , activity in rodent barrel cortex , a region of S1 that is a major target for somatosensory inputs from the whiskers via the thalamus , can be extremely sparse [92–94] , similar to activity in A1 . Consequently , sparse-coding models have successfully explained the response properties of individual neurons in rat barrel cortex ( e . g . , Hafner and colleagues [95] ) . Second , similar to V1 , neurons in primate areas 3b and 1 of S1 act like Gabor filters for tactile orientation [96 , 97] . The same is true for rat barrel cortex [98] . Third , similar to visual area MT , primate S1 contains a subpopulation of neurons that can infer the direction of tactile motion from a spatiotemporal pattern of activation across a 2D sensory sheet ( i . e . , the skin ) [99] . Specifically , neurons in area 1 of S1 tend to respond to plaid textures in the same fashion that MT neurons respond to visual plaids [99] . These findings suggest that much of what can be said about sparse and parts-based information processing in visual cortex also applies to S1 . One NSC-like model that has enjoyed success in explaining complex S1 rodent response properties is the rectified latent variable model ( RLVM ) , a combination of nonlinear dimensionality reduction with nonnegativity constraints . In an effort to elucidate the stimulus dimensions that individual S1 neurons respond to , Whiteway and Butts [100] applied RLVM to a two-photon imaging dataset of hundreds of simultaneously recorded neurons in mouse barrel cortex while the animal was performing a tactile discrimination task . Interestingly , they found basis functions that properly identified individual neurons . Similar to the recorded neuronal responses , these basis functions were closely related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task . Furthermore , RLVM achieved a lower reconstruction error than other linear dimensionality reduction techniques such as principal component analysis ( PCA ) , thus highlighting the benefit of using NMF-based decompositions over PCA to explain neural data . However , NSC has not been observed in nonhuman primate somatosensory cortex . Tactile information from various submodalities converges at later stages of monkey S1 [101 , 102] and is multiplexed across different time scales using both rate and spike timing codes [103] . These regions might represent different stages in the processing pipeline leading to form and texture perception [104] . Primate area 2 of S1 is known to integrate both tactile and proprioceptive stimuli; for example , some neurons respond only to active reaching movements , some respond only to passive movements ( e . g . , unexpected perturbations to the hand that generate passive limb displacements ) , and others respond to both [105] . These complex response properties may argue against a sparse and parts-based code in area 2 . Taken together , neurons in early somatosensory cortex respond to a small number of stimulus dimensions , not unlike to sensory neurons in early visual and auditory cortex . However , current evidence argues against NSC in higher areas of somatosensory cortex . The parallels to the visual system are striking though: area 1 , which resembles visual area MT by showing Gabor-like responses to tactile motion , feeds into area 2 , which resembles visual area MSTd by showing intermingling of responses to tactile and proprioceptive stimuli ( analogous to intermingling of visual and vestibular stimuli in MSTd ) . It is therefore not unthinkable that an NSC-like model that operates on neuronal inputs to area 2—constructed analogous to [46]—could reproduce some of these response properties . However , until the neuronal mechanisms underlying these complex response properties are better understood , one would have to conclude that NSC might not apply to later stages of somatosensory cortex . In our own work , we found evidence that NSC can explain response properties in RSC , an area important for navigation and spatial memory [106–108] . Neurons in the RSC conjunctively encode multiple variables related to the environment and one's position and movement within it ( e . g . , position , head direction , linear velocity , and angular velocity ) , allowing the representation of spatial features of the environment with respect to multiple reference frames [109] . Using a similar methodology to [46] , we applied NMF , with a sparsity constraint , to parameterized behavioral variables extracted from electrophyisiological recordings of RSC neurons in the rat [109] while the animal ran back and forth on a W-shaped track ( for experimental details , see Supporting information ) . We found a sparse and parts-based representation for behaviorally relevant variables such as the animal's position , head direction , and movement direction ( Fig 4C ) . Interestingly , model RSC neurons encoded these variables with respect to multiple frames of reference ( e . g . , head direction: allocentric reference frame , linear velocity: route-based reference frame ) . The dimensionality of the stimulus space was drastically reduced from F = 417 input neurons to a set of B = 30 model RSC neurons . The basis functions recovered by NMF were then used to generate simulated responses of model RSC neurons according to Eq 4 , and the simulated responses were compared with neuronal responses from the electrophysiological recordings . Interestingly , simulated neuronal activity could be classified into three broad categories , with remarkably similar population statistics to rat RSC: ( 1 ) responding to left and right turns on a specific position along the route , ( 2 ) responding to left and right turns regardless of the position along the route , and ( 3 ) exhibiting complex and robust firing patterns without turn sensitivity ( see Fig 8A and 8B as well as Supporting information ) . Taken together , this study suggests that neuronal population activity in RSC is consistent with NSC . This is an example that NSC can apply outside sensory cortex , even where responses have not traditionally been considered sparse or parts based . There is computational evidence for a reward-driven variant of NSC in the basal ganglia , a cluster of deep forebrain nuclei that are involved in the processing of motor , associative , and limbic information ( for recent reviews , see [3 , 110] ) . The basal ganglia network may be viewed as multiple parallel loops where cortical and subcortical projections interact with internal reentral loops , forming a complex network ideally designed for selecting and inhibiting simultaneously occurring events and signals ( for a recent review , see [111] ) . To achieve this function , the basal ganglia connect most cortical areas to the frontal cortex through a series of convergent and sparsely connected pathways [112] , in which signals from tens of millions of cortical neurons are projected onto a 10–10 , 000-fold smaller population of neurons in different subnuclei of the basal ganglia [3] . Similar to the convergence of 100 million photoreceptors onto 1 million optic nerve fibers in the retina , these highly convergent pathways from cortex to the basal ganglia suggest a potential role for dimensionality reduction . One possible model , termed the reinforcement-driven dimensionality reduction ( RDDR ) model , suggests that dimensionality reduction in the cortico-basal ganglia pathway is achieved via a combination of Hebbian and anti-Hebbian learning rules that are implemented by feedforward excitatory and lateral inhibitory connections [3 , 49] . These learning rules control the strength of synaptic weights in the network by altering the weight of a given synapse in proportion to the correlation between the firing rates of its presynaptic and postsynaptic neurons . In Hebbian learning , synaptic weights are strengthened given a positive correlation ( leading to a phenomenon referred to as long-term potentiation [LTP] ) , whereas synaptic weights are depressed if the firing rate correlation is negative ( leading to long-term depression [LTD] ) . On the other hand , in anti-Hebbian learning , which is typically applied to inhibitory connections , correlated activities are subjected to LTD , and uncorrelated activities are subjected to LTP . In order to implement dopamine-modulated Hebbian learning in this model , a reinforcement signal was used to dictate the level of dopamine in the circuit ( 1 for reward-related events , 0 for the absence of reward-related events , and negative values to simulate dopamine depletion ) [49] . The value of the reinforcement signal then determined the sign and magnitude of each synaptic weight change . In the RDDR model , a reinforcement signal corresponding to dopamine modulates the Hebbian learning rule of the feedforward projections , allowing the network to learn to extract input dimensions that are associated with reward activity while suppressing behaviorally irrelevant input dimensions . Whereas the original RDDR model was a neural network–based model for performing PCA [49] , later iterations incorporated nonnegativity constraints on the connection weights that effectively transformed the model into an NMF variant [3] . The model predicted that these lateral connections facilitated learning by shaping correlations between neurons in the corticostriatal projections using dopamine-modulated LTP and LTD , which has yet to be experimentally validated . In addition to suggesting a role for lateral connectivity in the basal ganglia , the RDDR model also advanced understanding of basal ganglia dysfunction in movement-related disorders such as Parkinson’s and Huntington’s disease . Previous studies had indicated that lesions to functionally healthy basal ganglia had minimal impact on behavior . Bar-Gad and colleagues [49] then suggested that this was an expected finding because of the network's ability to reorganize connections , whereas abnormal dopamine levels should significantly alter the reinforcement signal that controls the model's ability to discriminate behaviorally relevant input signals ( as in Parkinson disease ) . Accordingly , restoration of background dopamine levels via dopamine replacement therapy alleviates the symptoms , consistent with results of dopamine depletion and restoration in the model . In summary , NSC is a prime candidate to allow the basal ganglia to compress information in the cortico-basal ganglia pathway and extract input dimensions that are associated with reward activity . However , the complexity of the basal ganglia network has so far prohibited a deep scientific understanding of the multifaceted neural computations it performs .
We reviewed compelling evidence that a wide range of neuronal responses can be understood as an emergent property of efficient coding due to dimensionality reduction and sparsity constraints . In particular , NSC might be employed by sensory areas to efficiently encode external stimulus spaces , by some associative areas to conjunctively represent multiple behaviorally relevant variables , and possibly by the basal ganglia to coordinate movement . NSC is tightly connected to a number of unsupervised learning techniques , such as NMF ( a popular tool for high-dimensional data analysis [113] ) , k-means clustering ( an algorithm used to partition n observations into k clusters [114] ) , and independent component analysis ( ICA ) ( a computational method for separating a multivariate signal into additive , statistically independent subcomponents ) . Both NMF and ICA are capable of decomposing high-dimensional data into parts-based representations—in contrast to PCA , which usually results in holistic representations [13] . As originally noted by Hoyer [42] , if the fixed-norm constraint is placed on the rows of H instead of the columns of W , Eq 3 can be directly interpreted as the joint log-posterior of the basis functions and hidden components in the noisy ICA model [64] . Similarly , NSC is closely related to compressed sensing ( for a recent review , see [115] ) , and a recent study has even suggested to combine the two [116] . Compressed sensing posits that neurons might implement dimensionality reduction by randomly projecting patterns of activity into a lower-dimensional space—namely , by synaptically mapping N upstream neurons to a downstream region containing M<N neurons . Analogously , compressed sensing supports dimensionality expansion by projecting into a larger downstream area [115] . The theory of compressed sensing then provides the mathematical tools to reconstruct the original space from the random projections . NSC , ICA , and compressed sensing often make similar predictions that only slightly differ in the nature of the basis function representation necessary to achieve optimal reconstruction ( for details , please refer to the Discussion of [115] ) . For example , whereas ICA emphasizes the statistical independence of unmixed sources , and compressed sensing requires basis function to be “maximally incoherent”[115] , NSC does not make any such assumptions as long as the basis functions are nonnegative . There are several nonsensory areas that may demonstrate NSC . In this section we point to evidence that suggests this is the case but also discuss how sparse activity in these regions differs from NSC in sensory systems . We suggest that further studies should be carried out to assess the potential for NSC in these regions . In addition to the areas highlighted previously , the essential components of NSC might be present in other brain regions not traditionally associated with the efficient encoding of information . We offer three testable predictions of this theory: First , we suggest that a variety of neuronal response properties can be understood as an emergent property of efficient population coding based on dimensionality reduction . Depending on input stimulus and task complexity , we expect the dimensionality of the population code to be adjusted according to the bias–variance dilemma ( Fig 3 ) . This point of operation might differ across brain areas—for example , favoring neurons that respond to a small number of stimulus dimensions in V1 [35] but giving rise to mixed selectivity in higher-order brain areas such as MSTd [46] and RSC [50 , 164] . Second , we predict that parts-based representations can explain RFs of neurons in a variety of sensory and associative cortices , including but not limited to those brain areas discussed here . In agreement with the literature on basis function representations [18 , 159 , 160] , we expect parts-based representations to be prevalent in regions where neurons exhibit a range of tuning behaviors [46] , display mixed selectivity [165 , 166] , or encode information in multiple reference frames [50 , 109 , 164] . Third , where such representations occur , we expect the resulting neuronal population activity to be relatively sparse in order to encode information both accurately and efficiently . As noted previously , sparse codes offer a trade-off between dense codes ( in which every neuron is involved in every context , leading to great memory capacity but suffering from cross talk among neurons ) and local codes ( in which there is no interference but also no capacity for generalization ) .
In conclusion , there is increasing evidence that NSC can account for neuronal response properties in a number of sensory and associative cortices , as well as subcortical areas such as the basal ganglia . Although NSC might not apply to all brain areas—for example , motor or executive function areas—the success of NSC-based models , especially in sensory areas , warrants further investigation for neural correlates in other regions .
The software used to generate some of the data presented in Figs 1B , 2 and 6A is archived on Zenodo ( 10 . 5281/zenodo . 2641351 ) . The latest version is available on GitHub: https://github . com/mbeyeler/2019-nonnegative-sparse-coding . | Brains face the fundamental challenge of extracting relevant information from high-dimensional external stimuli in order to form the neural basis that can guide an organism's behavior and its interaction with the world . One potential approach to addressing this challenge is to reduce the number of variables required to represent a particular input space ( i . e . , dimensionality reduction ) . We review compelling evidence that a range of neuronal responses can be understood as an emergent property of nonnegative sparse coding ( NSC ) —a form of efficient population coding due to dimensionality reduction and sparsity constraints . |
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Schistosomiasis and HIV are both associated with kidney disease . Prevalence and factors associated with abnormal renal function among HIV-infected children in Africa compared to uninfected controls have not been well described in a schistosomiasis endemic area . This cross-sectional study was conducted at the Sekou Toure Regional Hospital HIV clinic in Mwanza , Tanzania . A total of 122 HIV-infected children and 122 HIV-uninfected siblings were consecutively enrolled . Fresh urine was obtained for measurement of albuminuria and Schistosoma circulating cathodic antigen . Blood was collected for measurement of serum creatinine . Estimated glomerular filtration rate ( eGFR ) was calculated using the modified Schwartz equation . Renal dysfunction was defined operationally as eGFR<60mL/min/1 . 73m2 and/or albuminuria>20mg/L in a single sample . Among 122 HIV-infected children , 61/122 ( 50 . 0% ) met our criteria for renal dysfunction: 54/122 ( 44 . 3% ) had albuminuria>20mg/L and 9/122 ( 7 . 4% ) had eGFR<60 . Among 122 HIV-uninfected children , 51/122 ( 41 . 8% ) met our criteria for renal dysfunction: 48/122 ( 39 . 3% ) had albuminuria>20mg/L and 6/122 ( 4 . 9% ) had eGFR<60 . Schistosomiasis was the only factor significantly associated with renal dysfunction by multivariable logistic regression ( OR = 2 . 51 , 95% CI 1 . 46–4 . 31 , p = 0 . 001 ) . A high prevalence of renal dysfunction exists among both HIV-infected Tanzanian children and their HIV-uninfected siblings . Schistosomiasis was strongly associated with renal dysfunction .
HIV remains common in sub-Saharan Africa ( SSA ) where 91% of HIV-infected children reside and 1 in every 20 adults is infected [1 , 2] . Kidney disease is an important complication in HIV-infected individuals and is associated with an increased risk of morbidity and mortality [3 , 4] . The prevalence of kidney disease among HIV-infected adults in high-income countries ranges from 5%–50% , and is most common in patients of African descent [4] . Among more than 300 HIV-infected adults starting ART at our own hospital in Tanzania , 70% had evidence of kidney disease [5 , 6] . Kidney disease among children with HIV is less well described [7] . In studies from SSA , the prevalence of markers of kidney disease among children with HIV varied greatly , ranging from 0–31 . 6% , depending on the methods used to evaluate the kidney [8–18] . In the few studies that included HIV-uninfected control subjects for comparison , the controls had less markers of kidney disease ( 0–6% versus 0–20 . 5% ) [8 , 9 , 13–15] . None of these studies determined both estimated glomerular filtration rate ( eGFR ) and albuminuria . For this reason , the total prevalence of kidney disease among HIV-infected children in SSA remains unknown , and it is difficult to know if the kidney disease observed among children in these studies was due to HIV infection itself , medication use , or other factors that might be common among African children . Therefore we conducted a cross-sectional study among HIV-infected children and their HIV-uninfected siblings in an area where kidney disease is common among HIV-infected adults . The objectives of our study were to determine the prevalence and correlates of renal dysfunction ( defined operationally as eGFR <60mL/min/1 . 73m2 and/or albuminuria >20mg/L in a single urine test ) and to compare the prevalence of renal dysfunction among HIV-infected and HIV-uninfected children . Our hypothesis was that the prevalence of undiagnosed renal dysfunction would be 30% and 5% among HIV-infected and uninfected children , respectively , and that active schistosome infection would be associated with renal dysfunction .
This cross-sectional study was completed between August—December 2013 in the outpatient HIV clinic at Sekou Toure Regional Hospital . Sekou Toure Hospital is located in the city of Mwanza , along the shore of Lake Victoria in northwestern Tanzania , and it serves a population of approximately 2 . 7 million people . The outpatient HIV clinic of Sekou Toure follows approximately 475 children who are referred to the clinic from the surrounding community-based voluntary counseling and testing centers in Mwanza . In our study , we enrolled HIV-infected children ages 2–12 years old who were attending the Sekou Toure HIV clinic . All of the mothers of these HIV-infected children were also HIV-infected , so we assume that these children were perinatally infected . The caretakers of all enrolled HIV-infected children were invited to bring uninfected siblings between the ages of 2–12 years for enrollment as controls . We tested siblings for HIV using the Determine HIV-1/2 rapid antibody test ( Alere Medical Co . , Ltd , Chiba , Japan ) as recommended by the Tanzanian National HIV Guidelines [19] . We excluded children with fever and those for whom urine samples could not be obtained . At least one parent or guardian for each child was interviewed . Both HIV-infected and uninfected children were examined . A structured questionnaire was used to collect demographic information , past medical history , and clinical symptoms . Additional information collected from HIV-infected children included a history of opportunistic infections , antiretroviral therapy ( ART ) status , and WHO clinical stage . In both groups ( HIV-infected and uninfected siblings ) , 4 milliliters of blood were drawn at the time of enrollment to measure the random blood glucose and serum creatinine as well as CD4+ T-cell counts for HIV-infected children . Clean , midstream urine samples were collected for most children , except for a small group of children ≤ 4 years old in whom urine bags were used for urine collection . Urine samples were collected between 8–10AM , after ≥2 hours of fasting . Children ≤4 years old who did not urinate within 1 hour were excluded in order to minimize the inconvenience to their mothers . In order to determine the species of Schistosoma and intensity of infection in this population , we additionally obtained 10mL of urine and fresh fecal samples on 40 consecutive children who were CCA positive . Random blood glucose was measured using a OneTouch® glucometer ( LifeScan , Inc . , Milpitas , California , USA ) . Fresh urine samples were tested immediately for albumin using a dipstick ( Micral B , Roche , Mannheim , Germany ) . Patients were considered to have albuminuria if the urine albumin concentration was above 20 mg/L , as per instructions provided by the manufacturer and according to our prior research [5 , 6 , 20] , and since a concentration of >20 mg/L has been demonstrated to correlate well with elevated albumin excretion rates by standard laboratory methods [21] . A urine dipstick ( Multistix 10SG , Siemens , USA ) was used to test for leukocyte esterase , hematuria , nitrates , glucose and ketones . The fresh urine sample was also tested using a circulating cathodic antigen ( CCA ) cassette test ( Rapid Medical Diagnostics , Pretoria , South Africa ) to detect active Schistosoma infection . The CCA test indicates active schistosome infection and can be positive in the urine during infection with either species of schistosomes that are endemic in Tanzania ( S . mansoni and S . haematobium ) , though its sensitivity is lower in S . haematobium [22–24] . CCA pointofcare testing is used widely and has been found to be more sensitive than the gold standard KatoKatz stool diagnosis of S . mansoni , particularly for lighter infections [25] . Following the manufacturer’s instructions , any visible line in the “test” area was considered positive . Line intensities were graded as “1” ( test line very faintly visible ) , “2” ( test line visible but lighter than control line ) , “3” ( test line equal to control line ) , and “4” ( test line darker than control line ) . For fecal samples , five slides were prepared using the Kato-Katz technique , as this has been shown to have a sensitivity comparable to examining stool specimens collected on different days [26] . Ten milliliters of fresh urine was filter concentrated and examined immediately by microscopy for both contamination and for S . haematobium . Intensity of infection was quantified as S . mansoni eggs per gram of stool and S . haematobium eggs per 10mL of urine . The laboratory personnel who performed these analyses were blinded to the HIV-status of the study subjects . Serum creatinine was measured using a COBAS Integra 400 Plus clinical chemistry machine ( Roche , Germany ) , calibrated by the Creatinine Jaffe 2 Method . An estimated glomerular filtration rate ( eGFR ) was calculated using the modified Schwartz equation as recommended by the Kidney Disease Improving Global Outcomes ( KDIGO ) guidelines [27] . Renal dysfunction was defined operationally as an eGFR ≤60 ml/min/1 . 73 m2 and/or albuminuria >20 mg/L on a single urine dipstick . The severity of renal dysfunction was classified as Stage 1 ( albuminuria + eGFR≥90 ) , Stage 2 ( albuminuria + eGFR 60–89 ) , Stage 3 ( eGFR 30–59 ) , Stage 4 ( eGFR 15–29 ) or Stage 5 ( eGFR<15 ) . Given the age of our study population ( 2 to 12 years ) , we defined malnutrition according to the BMI-for-age child growth standards from the WHO , defining severe malnutrition as a z-score of ≤-3 [28] . We also reported weight-for-age and height-for-age for those children <5 years old . The primary outcome was renal dysfunction as defined above . Based on the two-sample proportions Fisher’s exact test , we calculated that 122 children would be needed in each group to provide >95% power ( at p = 0 . 05 ) to detect the difference in prevalence of renal dysfunction between the two groups that we hypothesized ( 30% and 5% among HIV-infected and uninfected children respectively ) while also providing >80% power to show an association between schistosomiasis and renal dysfunction if the prevalence of schistosomiasis was 50% in the children with renal dysfunction and 25% in the children without renal dysfunction . Data was entered into Microsoft Excel 2010 and analyzed using STATA version 12 ( STATA Corporation , San Antonio , Texas ) . Categorical variables were described as proportions ( % ) , and continuous variables were described as medians [interquartile range] . Univariable logistic regression analysis was performed to determine which baseline characteristics were associated with renal dysfunction . All variables significantly associated with renal dysfunction by univariable analysis were subjected to a predetermined multivariable logistic regression model , which also automatically included the variables for age , gender , and HIV status . The validity of the multivariable logistic regression model was assessed using the likelihood ratio test and by assessing for interactions . In addition , the linearity assumption was checked for continuous variables by comparing models with these variables represented continuously versus categorically . After schistosomal infection was found to be associated with renal dysfunction , we decided that we should perform an additional univariable logistic regression analysis to determine which specific markers of renal dysfunction were associated with schistosomiasis . P values of less than 0 . 05 were considered statistically significant . Ethical approval for the study was obtained from the Research and Publications Committee of Bugando Medical Centre ( under whose jurisdiction Sekou Toure Regional Hospital falls ) , as well as the Institutional Review Board of Weill Cornell Medical College . Informed written consent was obtained from the parents and assent was obtained from children ≥8 years . The results of all tests were reported immediately to the clinician caring for the child for the sake of further management . All children with schistosomiasis were treated according to the Tanzanian National Guidelines with 40 mg/kg of praziquantel .
During the study period , 139 HIV-infected children were seen at the Sekou Toure HIV clinic , and 17 were excluded for the following reasons: 8 were not able to provide urine samples , 5 had an acute , febrile illness , and 4 parents did not consent . In the end , 122 HIV-infected children were enrolled . For all enrolled HIV-infected children , parents were invited to bring HIV-uninfected siblings between the ages of 2–12 years for enrollment as controls . A total of 132 siblings were screened , and 10 were excluded for the following reasons: 6 were not able to provide urine samples , and 4 had an acute , febrile illness . A total of 122 siblings were enrolled . The characteristics of excluded children did not differ between the 2 groups . Table 1 describes the baseline characteristics of the 2 groups . Among the 122 HIV-infected children , the median age was 8 years [4–11] and 63/122 ( 51 . 6% ) were female . Among the 122 HIV-uninfected children , the median age was 8 years [5–10] , and 55/122 ( 45 . 1% ) were female . Historical factors that were significantly different between the 2 groups included cough ( 26/122 [21 . 3%] vs . 12/122 [9 . 8%] , p = 0 . 01 ) , history of tuberculosis ( 10/122 [8 . 2%] vs . 1/122 [0 . 8%] , p = 0 . 005 ) , recurrent pneumonia ( 11/122 [9 . 0%] vs . 3/122 [2 . 5%] , p = 0 . 03 ) , and papular pruritic eruptions ( 12/122 [9 . 8%] vs . 2/122 [1 . 6%] , p = 0 . 01 ) . Physical exam factors that were significantly different between the groups were pallor ( 10/122 [8 . 2%] vs . 2/122 [1 . 6%] , p = 0 . 02 ) , thrush ( 4/122 [3 . 3%] vs . 0/122 [0%] , p = 0 . 04 ) and lymphadenopathy ( 23/122 [18 . 9%] vs . 3/122 [2 . 5%] p=<0 . 0001 ) . No children reported history of neurologic disease and none had signs of neurologic disease on physical examination . The BMI-for-age z-scores were similar in the 2 groups . For the children <5 years old , the weight-for-age and height-for-age z-scores were also similar in the 2 groups ( -0 . 76 [-1 . 57–0 . 05] vs . -0 . 48 [-1 . 12–0 . 21] , p = 0 . 47 and -1 . 58 [-2 . 16–0 . 13] vs . -1 . 33 [-2 . 54–0 . 49] , p = 0 . 47 respectively ) . Table 2 describes the renal dysfunction outcomes ( defined operationally as eGFR <60mL/min/1 . 73m2 and/or albuminuria >20mg/L in a single urine test ) . Among 122 HIV-infected children , 61/122 ( 50 . 0% ) met our criteria for renal dysfunction: 54 ( 44 . 3% ) had albuminuria , and 9 ( 7 . 4% ) had an eGFR <60 . Among 122 HIV-uninfected children , 51 ( 41 . 8% ) met our criteria for renal dysfunction: 48 ( 39 . 3% ) had albuminuria and 6 ( 4 . 9% ) had an eGFR < 60 . Table 3 shows the results of the univariable analysis for factors associated with renal dysfunction . In the univariable analysis only the presence of schistosomiasis was significantly associated with renal dysfunction ( OR = 2 . 51 , 95%CI 1 . 46–4 . 31 , p = 0 . 001 ) . Higher intensity of schistosomiasis was associated with higher prevalence of renal dysfunction ( OR = 1 . 3 ( 1+ ) vs . OR≈4 ( 2+/3+ ) , p for trend = 0 . 001 ) . By multivariable logistic regression analysis including schistosomiasis , age , sex and HIV status , schistosomiasis remained the only factor associated with renal dysfunction ( OR = 2 . 40 , 95%CI 1 . 37–4 . 17 , p = 0 . 002 ) . HIV infection was not significantly associated with renal dysfunction by either univariable or multivariable analysis . There was more renal dysfunction among children with higher WHO clinical stage , but this relationship was not statistically significant ( p = 0 . 23 ) . Of note , though , the association between schistosomiasis and renal dysfunction was somewhat stronger among HIV-infected children than their HIV-uninfected siblings ( OR = 3 . 04 , 95%CI 1 . 39–6 . 68 , p = 0 . 005 vs . OR = 2 . 08 , 95%CI 0 . 97–4 . 46 , p = 0 . 06 ) . Table 4 compares the prevalence of renal dysfunction among Schistosoma infected children and Schistosoma uninfected children regardless of HIV status . An eGFR less than 60 ml/min/1 . 73m2 was more common in the Schistosoma infected children ( 7 . 2% versus 5 . 6% , OR 2 . 61 , 95% CI 1 . 35–5 . 04 , p = 0 . 01 ) . There was also a higher prevalence of albuminuria among Schistosoma infected children ( 55 . 9% versus 34 . 4% , OR 2 . 92 , 95% CI 1 . 61–5 . 27 , p = 0 . 001 ) . The overall prevalence of renal dysfunction was 60 . 7% among Schistosoma infected children and 38 . 1% among uninfected children ( OR 2 . 51 , 95% CI 1 . 46–4 . 31 , p = 0 . 001 ) . None of the 40 subjects who provided urine for microscopy had eggs of S . haematobium detectable by microscopy . Of the 7 subjects from whom stool samples were available , 6/7 ( 85 . 7% ) had S . mansoni eggs detected , with concentrations ranging from 17 to 75 eggs per gram .
In our study , half of the HIV-infected children attending an HIV clinic in the Lake Zone of northwestern Tanzania had evidence of renal dysfunction ( defined operationally as eGFR <60mL/min/1 . 73m2 and/or albuminuria >20mg/L in a single urine test ) : 44 . 3% had albuminuria >20mg/L and 7 . 4% had an eGFR <60 ml/min/1 . 73 m2 . These rates are higher than those found in other studies from SSA . Four other countries in SSA ( Burkina Faso , Democratic Republic of Congo , Nigeria , and Zimbabwe ) have reported the prevalence of markers of kidney disease among HIV-infected children as ranging from 0–31 . 6% using methodologies similar to ours [8–18] . The reasons for the higher prevalence of renal dysfunction among HIV-infected children in the Lake Zone compared to prior studies in SSA is not known , but our findings are consistent with the findings among HIV-infected adults in our region [5 , 6] . Surprisingly , the prevalence of renal dysfunction was equally high among HIV-uninfected siblings . More than one-third of HIV-uninfected siblings had evidence of renal dysfunction: 39 . 3% had albuminuria >20 mg/L and 4 . 9% had eGFR<60 ml/min/1 . 73 m2 . In other studies among children in SSA that have included HIV-uninfected controls , the prevalence of markers of kidney disease in the control group has been low ( 0–6% ) [8 , 9 , 13–15] . The higher prevalence of renal dysfunction among HIV-uninfected children in our study is likely related to the unique nature of our control group . In order to minimize differences in socioeconomic factors , household exposures and genetics , we chose HIV-uninfected siblings as our control group , whereas prior studies have all used children from the pediatric outpatient clinics of the hospital in which the study was conducted as their controls [8 , 9 , 13–15] . The high prevalence of renal dysfunction that we observed among HIV-uninfected controls could therefore be related to either in utero HIV exposure or a high population prevalence of renal dysfunction . Because the dates of maternal HIV infection and viral suppression were not known , we could not confirm the HIV-exposure status among HIV-uninfected control siblings , but we suspect that most if not all were exposed since they were born within ~5 years of their HIV-infected sibling . HIV exposure , even in the absence of infection , has been associated with multiple abnormalities that may affect the development and function of the kidney in childhood [29–31] . On the other hand , the high prevalence of renal dysfunction among controls could also reflect a high community prevalence of renal dysfunction among children , possibly related to the known high prevalence of schistosomiasis . In order to investigate this possibility , we are currently planning a longitudinal study to examine a cohort of HIV-uninfected , unexposed school children in the Lake Zone . Schistosomiasis was strongly associated with renal dysfunction among HIV-infected and uninfected children ( OR = 2 . 51 , 95% CI 1 . 46–4 . 31 ) , and a higher intensity schistosome infection was associated with even more renal dysfunction ( p = 0 . 001 for trend ) . Approximately 46% of children with renal dysfunction had schistosomiasis by CCA testing compared to 25% of children without renal dysfunction . Multiple studies and epidemiological evidence have shown that S . haematobium infection may cause kidney disease ( particularly albuminuria ) in both adults and children [32–34] , but all of the 40 consecutive urine samples we tested were negative for S . haematobium , and <20% of CCA-positive subjects had the hematuria that is typical for this infection . The dominant species of schistosomiasis in our region , S . mansoni [35 , 36] , is also known to cause glomerulonephritis and kidney disease [37] . The prevalence of proteinuria among adult subjects infected with S . mansoni has been reported to be as high as 20% in Egypt [38 , 39] and 15% in Brazil [40 , 41] , and the severity and irreversibility of the disease has been demonstrated in several clinicopathologic and experimental studies [42–44] . The association between markers of kidney disease and S . mansoni in children , by contrast , has only been investigated in two small studies in SSA [45 , 46] which did not find an association between S . mansoni and overt proteinuria , but neither one of them investigated both eGFR and albuminuria as we have done . If further studies confirm the relationship between S . mansoni infection and kidney disease among children , schistosomiasis may become an important target for prevention of kidney disease in our population . Although schistosomiasis could explain a large proportion of the renal dysfunction in our study , 55% of children with renal dysfunction did not have evidence of current schistosomiasis . In these subjects multiple other factors may be contributing to their renal dysfunction such as acute glomerulonephritis , infections ( malaria , recurrent diarrhea ) , or genetic factors . The APOL1 gene mutation , for example , is known to be associated with renal disease in African populations [47] , younger age of onset of kidney disease [48] and faster decline in kidney function [49] . Further studies are needed to determine the factors other than schistosomiasis that might be contributing to the high prevalence of renal dysfunction that we observed . Kidney disease occurring at a young age may lead to end stage renal disease or other complications ( e . g . hypertension ) in adulthood; therefore targeted screening for early detection of kidney disease and treatment of reversible factors are high priorities . Many simple diagnostic tools such as light microscopy of the urine sediment are currently underutilized in screening efforts . In addition , the implementation of proven strategies for prevention and treatment , such as early antimicrobial therapy for severe infections and rapid correction of hypovolemic shock , must be accelerated . Our study has several limitations . First and foremost , this was a cross-sectional study and , therefore , neither confirmation of chronic kidney disease nor causality in the relationship between schistosomiasis and kidney disease could be examined . In addition , several gold standard investigations for HIV and kidney disease , such as quantitative HIV viral load testing , urine albumin-to-creatinine ratio , and kidney biopsies were not performed since they were not available in our region at the time of the study . Our operational definition of renal dysfunction may have resulted in information bias , with over diagnosis of kidney disease , and we are currently planning to assess our findings with a study using the standard KDIGO definition of chronic kidney disease . Also , the exclusion of children who could not produce urine samples , as well as the possibility that HIV-infected patients may have received previous praziquantel therapy could have caused some selection bias and underestimation of the population prevalences of renal dysfunction and schistosomiasis , respectively . In conclusion , our study identified a high prevalence of renal dysfunction ( defined operationally as eGFR <60mL/min/1 . 73m2 and/or albuminuria >20mg/L in a single urine test ) among HIV-infected Tanzanian children attending our pediatric HIV clinics . Almost 50% of both HIV-infected children and their siblings had renal dysfunction , and 6% had an eGFR <60 mL/min/1 . 73 m2 . Surprisingly , the prevalence of renal dysfunction among HIV-uninfected siblings was similar to the HIV-infected children . This may be related to either in utero HIV exposure or a high community-wide prevalence of renal dysfunction in children , and further studies are urgently needed to distinguish these possibilities . Schistosomiasis was strongly associated renal dysfunction in this population , and the predominant species of schistosomes in our region is S . mansoni ( not S . haematobium ) . Schistosomiasis may be an important target for prevention of kidney disease in children in sub-Saharan Africa . | Ninety percent of schistosomiasis occurs in sub-Saharan Africa , where 91% of HIV-infected children reside . Both schistosomiasis and HIV affect the kidney , but their respective effects on kidney disease in children are not well described . Our prior work in HIV-infected adults demonstrated a high prevalence of kidney disease , possibly worsened by schistosomiasis , but adults are less commonly and less heavily infected with schistosomiasis than children . Therefore , we sought to describe the prevalence and factors associated with renal dysfunction ( defined operationally as eGFR <60mL/min/1 . 73m2 and/or albuminuria >20mg/L in a single urine test ) among HIV-infected children and their uninfected siblings living in a schistosomiasis endemic area . We found that half of HIV-infected children and more than one third of HIV-uninfected children had renal dysfunction . Schistosomiasis was the only factor significantly associated with renal dysfunction , increasing odds of renal dysfunction by 2 . 5-fold . Nearly 50% of the renal dysfunction we observed in both groups could be explained by schistosomiasis . The strong association between schistosomiasis and renal dysfunction among both HIV-infected and uninfected children should remind clinicians to screen for schistosomiasis . It also ought to spur further prospective research to assess for causality and outcomes in the relationship between S . mansoni and kidney disease in children . |
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Next-generation sequencing ( NGS ) has the potential to transform the discovery of viruses causing unexplained acute febrile illness ( UAFI ) because it does not depend on culturing the pathogen or a priori knowledge of the pathogen’s nucleic acid sequence . More generally , it has the potential to elucidate the complete human virome , including viruses that cause no overt symptoms of disease , but may have unrecognized immunological or developmental consequences . We have used NGS to identify RNA viruses in the blood of 195 patients with UAFI and compared them with those found in 328 apparently healthy ( i . e . , no overt signs of illness ) control individuals , all from communities in southeastern Nigeria . Among UAFI patients , we identified the presence of nucleic acids from several well-characterized pathogenic viruses , such as HIV-1 , hepatitis , and Lassa virus . In our cohort of healthy individuals , however , we detected the nucleic acids of two novel rhabdoviruses . These viruses , which we call Ekpoma virus-1 ( EKV-1 ) and Ekpoma virus-2 ( EKV-2 ) , are highly divergent , with little identity to each other or other known viruses . The most closely related rhabdoviruses are members of the genus Tibrovirus and Bas-Congo virus ( BASV ) , which was recently identified in an individual with symptoms resembling hemorrhagic fever . Furthermore , by conducting a serosurvey of our study cohort , we find evidence for remarkably high exposure rates to the identified rhabdoviruses . The recent discoveries of novel rhabdoviruses by multiple research groups suggest that human infection with rhabdoviruses might be common . While the prevalence and clinical significance of these viruses are currently unknown , these viruses could have previously unrecognized impacts on human health; further research to understand the immunological and developmental impact of these viruses should be explored . More generally , the identification of similar novel viruses in individuals with and without overt symptoms of disease highlights the need for a broader understanding of the human virome as efforts for viral detection and discovery advance .
Viral discovery is rapidly advancing , driven by the advent of high-throughput technologies like next-generation sequencing ( NGS ) [1] . Applying NGS as a diagnostic tool holds the promise for vastly expanding our understanding of the spectrum of microbes infecting humans , as it does not require a priori knowledge of the pathogens present . It also has the potential to elucidate the spectrum of disease-causing viruses in patients with undiagnosed acute febrile illness ( UAFI ) , a common occurrence in health clinics around the world [2] . NGS can also serve to increase the power of surveillance systems to detect infrequent zoonotic transmissions that have the potential to become pandemics [3] . NGS has already been used successfully as both a diagnostic tool and a means to discover novel viruses associated with human disease [4–8] . Examples of these discoveries include novel arenaviruses [5] , phleboviruses [4] , and coronaviruses [8] . Recently a novel rhabdovirus , now referred to as Bas-Congo virus ( BASV ) , was identified in the blood of a patient from central Africa who was suspected of suffering from viral hemorrhagic fever [9] . However , a better understanding of the spectrum of viruses infecting humans is needed to fully realize the potential of NGS and differentiate between pathogenic and non-pathogenic viruses . This global problem is particularly acute in tropical regions throughout the world , where the burden of infectious disease remains high and the bloodstream virome of large numbers of apparently healthy individuals has not been characterized . Most studies of UAFI lack comparisons with apparently healthy individuals and rely on small-scale associations ( in some cases even a single patient sample ) without any statistical support or the ability to determine causality [7 , 9] . In this study we use high-throughput NGS to elucidate the spectrum of RNA viruses present in the blood of patients with UAFI in a population from southeastern Nigeria , using apparently healthy members of the same community for comparison . While we detected only known and common viral nucleic acid sequences in the UAFI patients , we were able to assemble full-length genomes of two novel , highly divergent rhabdoviruses from two apparently healthy individuals . We found that these viruses were similar to BASV and to viruses of the genus Tibrovirus . By conducting a serosurvey of our study cohort , we found that exposure to these novel viruses was unexpectedly high . Our findings suggest that human infection with certain types of rhabdoviruses may be common , and highlight the need for a broader understanding of the human virome as the use of NGS for microbial discovery advances .
Our study population consisted of men and women from all age groups and socioeconomic backgrounds living in and around Irrua , a modest-sized peri-urban village in southeastern Nigeria ( for further descriptions of the study population see S1 Table ) . As part of a partnership with the Irrua Specialist Teaching Hospital ( ISTH ) to study Lassa fever , we collected blood samples from suspected Lassa fever patients that tested negative for Lassa virus by reverse transcription PCR ( RT-PCR ) and subjected them to NGS ( S1 Table ) . We hypothesized that UAFI patients with symptoms resembling viral hemorrhagic fever could be infected with other pathogens that cause severe illness . We additionally collected samples from apparently healthy individuals ( i . e . , individuals whose temperature was in the normal range and did not have any overt symptoms of illness ) from the surrounding populations as part of the 1000 Genomes Project , and as part of a control population for our studies of Lassa fever . We performed collections of febrile cases and apparently healthy controls under approved IRB protocols in Nigeria ( Oyo State Ministry of Health , ISTH ) and the US ( Tulane University , Harvard University , Harvard School of Pubic Health , and the Broad Institute ) . All adult subjects provided informed consent , and a parent or guardian of any child participant ( aged under 18 years ) provided informed consent on their behalf . All children 7 and older additionally provided assent . Individuals provided written informed consent . If an individual was unable to read , a study staff read the document to the participant or guardian . The individual then provided a thumbprint , and the consent form was cosigned by the study staff as well as a witness . The use of thumbprints was specifically approved by the IRB granting institutions . We collected approximately 5–10 mL of venous blood in EDTA vacutainer tubes , centrifuged them to obtain the plasma from cellular fractions , and inactivated the plasma by adding buffer AVL ( Qiagen ) . We added carrier RNA to some of the samples as indicated in S2 Table . In the case of the apparently healthy controls , we collected an additional aliquot of ‘unadulterated’ plasma that was not inactivated with buffer AVL . We constructed RNA-seq libraries as previously described [10] . We prepared some of the libraries from extracted RNA for either single individuals ( referred to as singletons ) or from RNA pooled from several individuals ( referred to as pools ) ( S2 Table ) . We treated all samples with DNase . We primed RNA using random hexamers , or modified hexamers ( 5’-NNNNNNV-3’ from Integrated DNA Technologies ) if carrier RNA was present in the sample . We amplified the resulting libraries by PCR , pooled , and sequenced on an Illumina HiSeq 2500 according to the manufacturer’s specifications . Primers used for Sanger sequencing are listed in S3 Table . The raw data has been deposited to SRA under BioProject ID PRJNA271229 . We processed individual afebrile controls as described for UAFI samples; however , the method of pooling differed . We pooled and filtered unadulterated plasma ( without AVL ) samples and centrifuged them at 104 , 000 x g for 2 hours at 4°C . We resuspended the viral pellet in buffer and used it to construct libraries for sequencing . AVL denatures viral particles , thus preventing centrifugation of the particles . We have observed comparable results between samples inactivated by AVL and those that are not . We trimmed raw Illumina sequences consisting of 100 bp paired-end reads to remove bases from the ends of the reads with low quality scores , and discarded all reads shorter than 70 bp after quality trimming . We removed human and other contaminating reads using BMTagger ( NCBI ) , and removed duplicate reads and low complexity reads using PRINSEQ [11] . We assembled reads de novo using MetaVelvet [12] followed by Trinity [13] . We used contigs of at least 200 bp for BLASTn or BLASTx queries of the GenBank nucleotide ( NT ) or protein ( NR ) databases ( E-score cutoffs of 10-6 and 102 , respectively ) . In a parallel pipeline , we used individual reads for BLASTn or BLASTx queries of GenBank with the same E-score cutoff values . We performed taxonomic classification of assembled contigs and individual reads and visualized them using MEGAN 4 [14] . We considered samples to have a virus present if MEGAN 4 ‘min support’ was ≥5 and ‘min score’ was ≥50 . We assessed statistical significant differences in the distributions of viruses between UAFI samples and apparently healthy individuals using a two-tailed Fisher’s exact test with α<0 . 05 considered significant . We used quantitative real-time PCR to measure the number of Ekpoma viral RNA copies per milliliter of blood using the RNA-to-CT 1-Step Kit ( Applied Biosystems ) . The primers , which amplify an ~100bp region in the polymerase ( L ) gene , have the following sequences:: EKV-1: 5’-AAGAGTTGTTGGGATGGTCAGA-3’ ( forward ) and 5’- TGATTCTTGCTTCTCGCTCGAT-3’ ( reverse ) ; and EKV-2 primers: 5’-TGGCCAATTCCTTGGCTATCCCCT-3’ ( forward ) and 5’-TCCCGCCGGAGACATACATCTT-3’ ( reverse ) . We amplified PCR reactions on the ABI 7900 sequence detection system using the following cycling parameters: 30 minutes at 48°C , 10 minutes at 95°C , and 40 cycles of 15 seconds at 95°C and 1 minute at 60°C . A serial dilution of a synthetic DNA amplicon , which corresponds to the amplified region of the polymerase gene , was used to quantify the number of viral cDNA copies in the reaction . Human K562 RNA and RNA purified from the plasma of an afebrile individual ( 244M ) , were used as negative controls . We performed multiple sequence alignments of rhabdovirus nucleoprotein ( N ) , glycoprotein ( G ) , matrix ( M ) , phospoprotein ( P ) and polymerase ( L ) amino acid sequences using MAFFT v6 . 902b18 [15] with the following parameters:—localpair—maxiterate 1000—reorder—ep 0 . 123 before being trimmed using trimAl v1 . 419 [16] with the maximum likelihood specific parameter:-automated1 . We used PROTTEST [17] to identify rtREV+I+G [18] as the best evolutionary model and made maximum likelihood phylogenies with RAxML v7 . 3 . 0 [19] . Trees were bootstrapped using 500 pseudo-replicates . We also created trees using MrBayes v3 . 2 [20] . We first built trees using 46 rhabdovirus sequences and included parainfluenza virus-1 as an outgroup , to find the novirhabdoviruses as the likely root of the rhabdovirus tree , which has been previously described [21] . We then excluded parainfluenza virus-1 and built a tree using the 46 rhabdovirus sequences ( S6A Fig ) , which allowed us to select VSV as a likely outgroup for the tibroviruses and ephemeroviruses . Subsequent alignments and trees were then created using only the tibroviruses and ephemeroviruses , including EKV-1 , EKV-2 , and BASV , as well as VSV . We found that using parainfluenza virus-1 or the novirhabdoviruses as the root , gave the same tree topology . Relevant accession numbers can be found in S4 Table . We cloned His-tagged N genes from EKV-1 and EKV-2 into pET45B ( + ) and expressed them in E . coli . We lysed the cells in the presence of protease inhibitors and purified the proteins with HisPur Ni-NTA Spin Columns ( Thermo Scientific ) . We confirmed the purity of the proteins by Western Blot . We created ELISA plates by coating the EKV-1 and EKV-2 N proteins onto 96-well plates at 2μg/mL in carbonate-bicarbonate buffer overnight at 4°C . Human IgG specific to EKV-1 or EKV-2 was detected by ELISA as previously described [22] . We calculated cut-off values based on the mean of the US controls ( N = 137 ) plus three or five standard deviations .
We selected blood samples from 195 UAFI and 328 afebrile controls for RNA sequencing by Illumina NGS ( S1 Fig ) . We collected a number of demographic and clinical parameters ( S1 Table ) for each individual in our study . We successfully constructed 120 RNA-seq libraries from UAFI samples ( 94 singletons and 26 pools ) comprising a total of 195 individuals , and 58 RNA-seq libraries from afebrile apparently healthy control samples ( 34 singletons and 24 pools ) comprising a total of 328 individuals ( S5 Table ) . Illumina sequencing generated a total of 3 . 71 billion 100 base pair ( bp ) paired-end reads . We analyzed these samples using a bioinformatics and computational pipeline developed in our laboratory ( S2A Fig ) . After filtering out low-quality sequences , duplicates , human reads and common contaminants , less than 0 . 5% of the reads typically remained in each library ( S2B–D Fig ) . We examined the overall composition of reads identified in 94 singleton UAFI samples and in 34 apparently healthy singleton controls ( Fig . 1 ) . We found ~25% of the filtered reads returned no BLAST hit or were unable to be unequivocally assigned to the eukarotya , prokaryota or viral kingdoms . The majority of filtered reads in both UAFI and afebrile libraries were bacterial . The overall percentage of viral reads was similar between UAFI patients and afebrile controls ( 3 . 3% and 2 . 4% , respectively ) . The majority of viral reads were derived from three sources: human adenovirus C , phages , or GB virus C ( S6 Table and S1 Text ) . GB virus C , a non-pathogenic RNA virus [23] , was identified in 18% of UAFI singleton libraries and 12% of singleton healthy controls ( Fig . 1B and S3 Fig ) ; a higher percentage of pooled healthy controls contained GB virus C , possibly because each pool contained a greater number of individual samples compared to the UAFI pools . We identified several well-characterized pathogenic RNA viruses , including LASV , HIV-1 , hepatitis C and dengue virus in the UAFI patients ( Fig . 1B and S6 Table ) . We did not find any evidence for the presence of Ebola virus . LASV was the most frequent pathogenic virus observed in UAFI cases and the only virus statistically enriched in the UAFI as compared to the apparently healthy controls ( P-value = 0 . 002 , Fisher’s test; S3 Fig ) . Although samples were DNAse treated , we also detected several DNA viruses , including hepatitis B virus , herpesvirus 4 ( Epstein-Barr virus ) , herpesvirus 5 ( human cytomegalovirus ) , and herpesvirus 8 ( Kaposi’s sarcoma virus ) ( Fig . 1B and S6 Table ) . In two pools of RNA from afebrile individuals , we identified reads with distant relationships to previously identified rhabdoviruses . A PCR assay developed to identify the infected individual within each pool revealed two infected females aged 45 ( sample 13M ) and 19 ( sample 49C ) . We named the two viruses Ekpoma virus-1 ( EKV-1; from 13M ) and Ekpoma virus-2 ( EKV-2; from 49C ) because both individuals lived in Ekpoma , a village located about ten kilometers from ISTH . We assembled several long contiguous overlapping rhabdovirus sequences ( contigs ) ( Fig . 2A ) . From these contigs we synthesized virus-specific primers for EKV-1 and EKV-2 and used Sanger sequencing to confirm the results of Illumina sequencing and fill in missing parts of the genomes ( Fig . 2B ) . The combined sequencing produced two genomes of 12 , 659 bp ( EKV-1 ) and 12 , 674 bp ( EKV-2 ) ( GenBank accession numbers KP324827 and KP324828 ) . The coverage of EKV-1 ranged from 1–71x ( median 9x ) and the coverage of EKV-2 ranged from 1–29x ( median 8x; Fig . 2C ) . We did not find any additional samples that contained reads from these two novel rhabdoviruses . The Rhabdoviridae family includes at least eleven genera [24] . We found that the genomic organization of EKV-1 and EKV-2 , like BASV , is the same as members of the genus Tibrovirus ( S4 Fig ) . The viral genomes consist of the prototypical five open reading frames ( ORFs ) found in most rhabdoviruses ( N , P , M , G , and L ) as well as at least three additional ORFs of unknown function ( U1 to U3 ) [25] ( Fig . 2B ) . The latter three ORFs are also seen in other members of the genus Tibrovirus and their presence clearly distinguishes these viruses from the closely related genus Ephemerovirus . We found that the sequence identity among the Ekpoma viruses was low , ranging from 33 . 2–39 . 4% for the different ORFs at the protein level ( S4 Fig ) . The nucleoprotein and polymerase were the most highly conserved proteins ( S5 Fig ) , while U1–U3 were the most divergent . Overall , EKV-2 was more similar at the amino acid level to BASV ( 39 . 4% identity ) than it was to EKV-1 ( 35 . 1% identity ) . To determine the place of the Ekpoma viruses within the rhabdovirus phylogeny we constructed maximum likelihood and Bayesian trees for the major viral proteins . We found that EKV-1 and EKV-2 clustered with BASV , TIBV , and Coastal Plains virus ( Figs . 3A and S6 ) . We further found that EKV-1 is a closer evolutionary relative to TIBV than to EKV-2 or BASV . EKV-2 , in contrast , formed another branch with BASV ( Fig . 3A , B ) . Though these viruses were discovered in geographically distant locations , phylogenetic analyses suggest the presence of a distinct group of viruses in the Tibrovirus genus capable of human infection . Based on phylogenetic relationships , host range and genomic architecture , we propose that BASV , EKV-1 and EKV-2 should all be included within the genus Tibrovirus . To assess the level of human exposure to the novel rhabdoviruses , we developed enzyme-linked immunosorbent assays ( ELISAs ) to detect antibodies that recognized the N proteins of EKV-1 and EKV-2 . We performed a serosurvey for EKV-1 and EKV-2 on 457 samples and found that significantly more Nigerian individuals ( n = 320 ) had EKV-1- and EKV-2-specific antibodies than apparently healthy US controls ( n = 137; Fig . 3C; P-value < 0 . 0001 , Mann-Whitney test ) . Using conservative positivity cut-off values , we found that ~10% of Nigerian individuals show evidence of previous exposure to EKV-1 ( Table 1 and Fig . 3C ) . The seropositivity to EKV-2 was much higher , with ~50% of Nigerians showing evidence of previous exposure ( Table 1 and Fig . 3C ) . We did not observe any significant difference in the sex or age-range of the individuals with antibody titers to EKV-1 or EKV-2 ( S7 Fig ) . We cannot rule out that our assays do not cross-react with other similar rhabdoviruses , which could inflate the overall seroprevalence observed for the Ekpoma viruses; however , it should be noted that limited cross-reactivity was observed between EKV-1 and EKV-2 ( S8A Fig ) . While we found strong cross-reactivity between our assays for EKV-1 and rabies virus ( S8B Fig ) , the correlation between EKV-2 and rabies virus was much less pronounced ( S8C Fig ) . Importantly , when testing general cross-reactivity in our assays by comparing the ELISA results from the rhabdoviruses to that of LASV , we did not find any correlations ( S8D–F Fig ) . Acute infection with RNA viruses often produces high viral loads . To assess the level of viremia , we used quantitative real-time PCR to measure EKV-1 and EKV-2 viral copy number . We detected 4 . 5 million viral genome copies per milliliter of plasma in the individual infected with EKV-1 and 46 , 000 viral genome copies per milliliter of plasma in the individual infected with EKV-2 ( S9 Fig ) . These numbers , while informative , should be interpreted with caution , as sample degradation may have affected the number of viral copies detected . After the discovery of the two Ekpoma viruses , we sought to further determine the health of the infected individuals . Nearly two years after their initial blood draw , we conducted oral interviews with both individuals and collected convalescent serum samples . Both individuals tested negative for the two Ekpoma viruses by PCR upon testing of their convalescent samples ( S10 Fig ) ; however , using our ELISA assays , we found that they both had antibodies reacting with EKV-1 or EKV-2 , as expected ( S11 Fig ) . Notably , while both individuals had antibody titers at the time of infection and in the follow-up samples , the woman infected with EKV-2 showed lower titer in her follow-up sample , as compared to the original blood draw ( S11B Fig ) . The woman infected with EKV-1 could not recall any episode of febrile illness in the weeks or months following the collection of her initial blood sample . The woman infected with EKV-2 revealed that she suffered an episode of febrile illness two weeks after we collected her blood sample . She was admitted to the hospital where her illness was clinically diagnosed as malaria . While the individual’s illness resolved after anti-malarial treatment , we cannot confirm whether a malaria parasite was the causal agent . We attempted to isolate EKV-1 and EKV-2 by using plasma from the infected individuals to inoculate cultures of Vero E6 , BHK , C6/36 mosquito , LLC-MK2 , SW13 and biting midge ( Culicoides variipennis ) cell lines . We did not observe any evidence of viral cytopathic effects in these cultures , nor could we detect any virus by qPCR or electron microscopy . We also attempted to isolate the viruses by intracranial inoculation of newborn mice; however , we did not observe any signs of illness over 14 days . It is possible that the viruses may not be able to infect any of the tested cells or animals , however , potential sample degradation may have compromised the infectivity of viral particles .
We used high-throughput NGS to elucidate the spectrum of RNA viruses present in the blood of patients with UAFI in a population from southeastern Nigeria , using apparently healthy members of the same community for comparison . NGS has the advantage of being able to identify pathogens without culturing or a priori knowledge of the pathogen’s nucleic acid sequence . Despite the advantages of NGS , there are certain biases in our approach . First , the selection of blood limited our investigation to a single anatomical compartment . Many viruses cannot be detected in the blood ( e . g . , rabies virus which is strictly neurotropic ) . A complete understanding of a febrile or healthy person’s virome necessitates sequencing of all tissues in the body , which for practical reasons , is not possible . The ability to identify novel viruses is also limited to sequences that have some homology existing sequences in a public database . Highly divergent and truly novel pathogens may be missed by conventional BLAST searches . In our study , ~25% of filtered reads returned no BLAST hit or were unable to be unequivocally assigned to the eukaryotya , prokaryota or viral kingdoms . Despite these limitations however , we were able to identify EKV-1 and EKV-2 , both of which have only about 35% amino acid similarity to already known viruses . In our study we made an unexpected discovery of nucleic acid sequences suggestive of novel rhabdoviruses in our apparently healthy controls . The identified viruses , EKV-1 and EKV-2 , most closely resemble members of the genus Tibrovirus , and in particular BASV , based on genomic structure and phylogenic analyses . BASV was recently identified in an individual from central Africa displaying symptoms suggestive of viral hemorrhagic fever [9] . Despite detection in an apparently healthy individual , EKV-2 is the most closely related virus to BASV identified to date . Tibroviruses , including Tibrogargan , Coastal plains and Bivens Arm viruses , are transmitted by culicoidies insects and are known to cause subclinical infections in cattle and other ruminants [26] . Their amino acid sequence similarity to Tibrogargan and Coastal Plains viruses raises the possibility that they might be vector-borne [26–29] . If true , infection could be common in environments where biting insects are ubiquitous , like central and western Africa . Many rhabdoviruses have already been discovered in sub-Saharan Africa using conventional methods—mostly in insects and vertebrates ( Fig . 4 ) . Our results suggest many more remain to be discovered , and that a number of these may infect humans . Consistent with the potential for widespread and subclinical infection by rhabdoviruses , our serosurvey uncovered evidence for very high exposure to EKV-1 or EKV-2 , with nearly 50% of our apparently healthy cohort showing evidence of EKV-2 exposure . Despite this high rate , we did not detect any EKV-1 or EKV-2 nucleic acids in the UAFI patients . These results suggest that members of the genus Tibrovirus are unlikely to be common causes of viral hemorrhagic fever as has been suggested for BASV [9] . We attempted to isolate EKV-1 and EKV-2 , but were unsuccessful in our efforts . We speculate that sample handling may have caused degradation of viral particles . Alternatively , these novel viruses may not infect the common cell types we selected for culturing . Historically , isolating a virus from an infected individual is a necessary step for demonstrating the existence of the novel virus and that the patient was infected . However , as NGS becomes more common , it is likely that many new viruses will be identified that cannot easily be cultured . That does not mean these viruses cannot be studied biochemically or “recreated” in the laboratory . Parts of the virus can be synthesized de novo and incorporated into existing viral vectors . In some cases , the entire nucleic acid sequence of the virus can be synthesized de novo , introduced into cells , and potentially cultured . The recent discovery of three related rhabdoviruses—two in apparently healthy individuals ( this study ) and one in an acutely ill patient [9]—highlights the challenges of determining the true cause of unexplained illness . Many factors determine whether a particular virus will produce disease in the infected host , including genetic variation in the virus and the host , nutritional and immune status , and the presence of co-infections that may increase susceptibility to otherwise benign agents . Identifying the cause of disease becomes even more challenging since multiple microbes are present in a sample , including commensal bacteria and viruses . Proving disease causality is a centuries-old problem and identifying a potential pathogen is merely the first step in a long process . Researchers have recently proposed revisions to Koch’s postulates—the first framework for assessing causality—in light of advancing modern molecular techniques [30 , 31] to add rigor to the pursuit . Yet there are still a number of limitations to current studies . For many studies , investigators were only able to study a single patient sample [9] . Without sufficient numbers of samples from infected patients and matched apparently healthy individuals , it is impossible to interpret the clinical significance of a single virus detection . It remains possible that BASV produced an asymptomatic infection , like the control subjects infected with EKV-1 and -2 in our study , while the acute illness was actually due to another agent , like the rotavirus ( which the authors propose was a laboratory contaminant ) , or one of the many bacteria also present in the sample [9] . Of course , the true source of the infection could have been none of the microbes identified in the blood . Sampling of other tissues would be needed to rule out localized infections as the cause of disease . Regardless of whether infection with particular rhabdoviruses is symptomatic or not , the discovery of novel rhabdoviruses could be of importance to human health . Members of the Rhabdoviridae , such as lyssaviruses and vesiculoviruses , produce serious neurotropic disease in humans [32 , 33] . Others , such as vesicular stomatitis virus ( VSV ) , produce subtle neurotropic infections with few acute disease symptoms . BASV , like VSV , appears to have broad tissue tropism [34] and may infect similar cell types . Further studies are needed to determine if the novel rhabdoviruses discovered in this study produce neurotropic outcomes in humans similar to those of lyssaviruses and vesiculoviruses [35–37] . How should future studies using NGS tackle the issue of disease causality in these and other newly discovered microbes ? The most obvious approach involves finding a statistical association with the microbe in disease and non-disease states , similarly to what we show for LASV in this study ( S3 Fig ) . This requires collecting matched controls from either the patient or members of the community who do not have the disease . This approach faces its own challenges . If viral or host factors play a substantial role in disease outcome , it might necessitate large sample collections . Isolation of the pathogen and propagation in an animal model or tissue culture can provide valuable insights into its pathogenicity and effect on the host’s response to infection . The recent advent of NGS has the potential to transform the centuries-old pursuit of finding disease-causing pathogens and to elucidate the complete human virome . But in the process , it will be important to be cautious . As the vast majority of viruses studied over the past century have been those that cause disease , the large-scale sequencing of samples from vertebrates and insects will likely be biased towards identifying novel benign viruses rather than pathogenic ones . Although many newly discovered viruses may not cause overt symptoms of disease , they may have immunological and developmental consequences—perhaps by increasing susceptibility to other pathogens or affecting other aspects of human development . Pathogen discovery tools are evolving rapidly . Investigations that harness these new tools will likely identify a plethora of new viruses in humans , animals , and insects . Developing systems to assess causality , especially through the thorough sampling of non-disease-affected controls , will be critical to realizing the potential of NGS as a routine diagnostic tool . | Next-generation sequencing , a high-throughput method for sequencing DNA and RNA , has the potential to transform virus discovery because it does not depend on culturing the pathogen or a priori knowledge of the pathogen’s nucleic acid sequence . We used next-generation sequencing to identify RNA viruses present in the blood of patients with unexplained fever , as well as apparently healthy individuals in a peri-urban community in Nigeria . We found several well-characterized viruses in the blood of the febrile patients , including HIV-1 , hepatitis B and C , as well as Lassa virus . We also discovered two novel rhabdoviruses in the blood of two apparently healthy ( afebrile ) females , which we named Ekpoma virus-1 and Ekpoma virus-2 . Rhabdoviruses are distributed globally and include several human pathogens from the genera lyssavirus and vesiculovirus ( e . g . , rabies , Chandipura and vesicular stomatitis virus ) . The novel rhabdoviruses identified in this study are most similar to Bas-Congo virus , which was recently identified in an individual with an acute febrile illness . Furthermore , we demonstrate evidence of high levels of previous exposure to the two rhabdoviruses among our larger study population . Our results suggest that such rhabdovirus infections could be common , and may not necessarily cause overt disease . The identification of viral nucleic acid sequences in apparently healthy individuals highlights the need for a broader understanding of all viruses infecting humans as we increase efforts to identify viruses causing human disease . |
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Noise driven exploration of a brain network’s dynamic repertoire has been hypothesized to be causally involved in cognitive function , aging and neurodegeneration . The dynamic repertoire crucially depends on the network’s capacity to store patterns , as well as their stability . Here we systematically explore the capacity of networks derived from human connectomes to store attractor states , as well as various network mechanisms to control the brain’s dynamic repertoire . Using a deterministic graded response Hopfield model with connectome-based interactions , we reconstruct the system’s attractor space through a uniform sampling of the initial conditions . Large fixed-point attractor sets are obtained in the low temperature condition , with a bigger number of attractors than ever reported so far . Different variants of the initial model , including ( i ) a uniform activation threshold or ( ii ) a global negative feedback , produce a similarly robust multistability in a limited parameter range . A numerical analysis of the distribution of the attractors identifies spatially-segregated components , with a centro-medial core and several well-delineated regional patches . Those different modes share similarity with the fMRI independent components observed in the “resting state” condition . We demonstrate non-stationary behavior in noise-driven generalizations of the models , with different meta-stable attractors visited along the same time course . Only the model with a global dynamic density control is found to display robust and long-lasting non-stationarity with no tendency toward either overactivity or extinction . The best fit with empirical signals is observed at the edge of multistability , a parameter region that also corresponds to the highest entropy of the attractors .
The brain’s resting state activity shows large-scale fluctuating spatiotemporal patterns as observed in neuroelectric , neuromagnetic and hemodynamic brain imaging . Biswal and colleagues [1] demonstrated in their seminal work that co-activated brain regions maintain a high correlation of BOLD ( blood oxygen level dependent ) signal fluctuations at rest , identifying a resting-state network of functionally connected regions . Interestingly , these patterns show intermittent co-activations of more or less distant brain regions , which are known from task conditions . Deciphering the logic behind this organized activity is the subject of intense investigation . The observation that there are relatively consistent distributed patterns of activity during rest suggests that it might be possible to characterize network dynamics through a low-dimensional set of Resting State Network ( RSN ) patterns [2–11] , around which the dynamics is organized . Furthermore , recent experimental functional Magnetic Resonance Imaging ( fMRI ) studies demonstrated that the resting state dynamics is not stationary [12] in the sense that the set of functional correlations between brain areas , the so-called Functional Connectivity ( FC ) , changes on a time scale of tens of seconds to minutes . Although non-stationarity is not in conflict with a spatiotemporal organization around a low-dimensional set of RSN patterns , it certainly renders its interpretation more difficult . The non-stationary brain network dynamics was named Functional Connectivity Dynamics ( FCD ) [13] and shown to be also present in computational network models . These models are typically based on neural population models of the Wilson-Cowan type , which are coupled by a biologically realistic human connectivity matrix , the so-called Connectome , derived from diffusion weighted tensor imaging ( DTI ) . A necessary condition for the emergence of non-stationary FCD is a sufficiently strong nonlinearity in the network population models [13] , which then enables the network dynamics to generate brain activation states that cannot be linked trivially to its structural connectivity ( SC ) . These brain activation states are thus true consequences of the mutual presence of network connectivity and nonlinear dynamic interactions across network nodes . Ghosh et al [14] referred to the noise-driven stochastic process of transient activations of brain states as the exploration of the brain’s dynamic repertoire , emphasizing the potential functional relevance of these states . The number of states has been previously reported to be low ( 5 through 10 ) , but neither systematic analysis of its number and character , nor an empirical validation has been performed so far . Here we systematically analyze the capacity of a connectome-based network to store network patterns . The storage is accomplished through attractors , which represent regimes in state space that attract system trajectories as the network evolves in time . If the attracting regime is a point , we name it a fixed-point attractor . Our key hypothesis is that the capacity to generate transient fluctuating patterns over time stems from its ability to create a large number of multistable fixed point attractors . The set of attractors is directly linked to the observable variability in the fluctuating network signal in the presence of noise . Brain signal variability at rest has been proposed to be a good biomarker for various brain diseases , but in particular highlighted in studies of the aging brain [15–17] . For these reasons a systematic characterization of the range of attractors is critical and timely . To perform such systematic pattern identification and characterization , we adapt a deterministic variant of the spin-glass dynamics , called the Hopfield “graded neuronal response” model [18] . Related models such as the Brunel-Wang and Wong-Wang system have been used previously in connectome-based modeling [14 , 19 , 20] and are mostly constrained to multi-stable fixed-point dynamics and threshold behavior for an isolated network node . When connected in a network , these behaviors are changed and novel network states may emerge [13] . Conversely , the Hopfield network model has the advantage that some important dynamic quantifiers , especially relevant to signal complexity , can be computed analytically . More sophisticated neural population models have been developed showing complex oscillatory behaviors [21–25] and have been successfully applied to the exploration of mostly encephalographic large-scale network dynamics [26–29] . On these large spatial scales , time delays have been demonstrated to be critical for the emergent network oscillations [26 , 30] , which are known to be non-trivial to simulate computationally . Recent neuroinformatics platforms such as The Virtual Brain [31–34] aid in these efforts with the goal to enable the fusion of structural and functional empirical data for large-scale modeling purposes . However , modern computational neuroscience has also demonstrated the importance of a variety of mechanisms for neural network functioning beyond traditional excitatory and inhibitory coupling , as for instance diffusion of ions in the interstitial space or glial activity and astrocytes [35] . Such microscopic processes are modeled either with detailed biophysical models dependent on the neuronal membrane voltage dynamics , buffering by glial cells , and diffusion to the blood vessels or by more abstract models of so-called activator-inhibitor type . Our approach , however , limits the degrees of freedom and parameters of the Hopfield model . To absorb unconventional coupling mechanisms at least to some degrees , we generalize the traditional Hopfield model to include a total of three dynamic variants comprising dynamic threshold adaptation and mimicking effects of excitability changes and depolarization block [36 , 37] . Using a fine-grained connectome ( composed of up to one thousand nodes ) we construct three network models and systematically explore the range of their dynamic repertoires via attractor counts and bifurcation analyses . Then we characterize these dynamic repertoires functionally and validate them against empirical fMRI data . Finally , we scrutinize the degree of non-stationarity in each of the network models .
Here we systematically explore the capacity to store fixed-point attractors in connectome-based Hopfield networks . We consider three variants of the graded-response Hopfield model ( see Fig 1 ) . The different models are described in Eqs ( 1 ) , ( 2 ) and ( 3 ) – ( 5 ) and are referred to as the “Static and Local” ( SL ) threshold model ( equivalent to the traditional Hopfield network node ) , the “Static and Global” ( SG ) threshold model , and the “Dynamic and Global” ( DG ) threshold model . The two latter are variants of the first model ( SL ) testing different interplays between the excitatory and inhibitory influences . Two principal control parameters are considered hereafter: the gain G , representing the node excitability , and the scaling factor P , representing the ratio of excitation over inhibition . We initially present a detailed investigation of the SL model , its multistable behavior and bifurcations . We then extend the attractor analysis to all three models providing a detailed account of dependence of the attractor landscape on the gain G and the scaling factor P in the high-gain condition . Each attractor can be mapped on the cortical surface , and interpreted functionally: an attractor provides a set of nodes that are expected to “work together” , possibly reflecting an underlying brain function . These interaction effects are characterized in two ways: The first approach is via functional connectivity and matrix-to-matrix comparison , where we compute functional connectivity under variation of control parameters for the three network models and validate it against empirical data . The second approach is based upon a direct functional pattern-to pattern comparison . For the empirical data we use a set of resting-state functional MRI ( rs-fMRI ) time courses [45] with a total of 35 minutes recorded in two sessions per subject . In this section we present evidence for the non-stationary spatiotemporal dynamics in the three network models and highlight their differences . We first present typical noise driven time courses obtained in regions of high multistability of the three network models considered . Then we present a windowing approach to the estimation of the non-stationarity of their spatiotemporal dynamics , called the functional connectivity dynamics ( FCD ) . We use this metric to show correspondence between the real fMRI time courses and the simulated ones . We study in Fig 10 the temporal behavior of the noise-driven extension of the 3 network models ( Eqs ( 6 ) – ( 9 ) ) , in the parametric range where a prominent multistability is expected ( namely the regions of the parameter space dispaying the highest entropy—see Fig 5 ) . We separate the time scales of the activity and the thresholds , with τx = 10 ms and τθ = 80 ms . The thresholds dynamics is thus slower than the activation dynamics , making possible the emergence of more complex dynamics . We choose a moderate level of noise ( σx = σθ = 0 . 2 ) in order to have the attractor basins “close enough” to the ones obtained in the deterministic case . Each simulation lasts 10 seconds: in the first 2 seconds , the dynamics is deterministic; then the noise is turned on for the remaining time ( from 2 s to 10 s ) . For each dynamical system , 1000 initial conditions are chosen among the set of final attractors obtained after random sampling search ( see previous sections ) . Each gray line corresponds to the time course of the average activity for a different initial condition . The red line corresponds to a particular time course we picked up for its large temporal variability . The corresponding 998-nodes spatiotemporal time courses of the node activations are shown below in color code . The SL model ( see Fig 10a and 10b ) generates overlapping noisy trajectories forming a fuzzy cloud that covers almost the totality of the [0 , 1] interval , reflecting the strong variability of the average activities . The darker the gray , the stronger the density of trajectories . The two dark lines at the high and low levels of activity indicate a significant attraction toward the “Up” and “Down” states . Intermediate trajectories are however maintained , with random excursions around the varied intermediate patterns . The red time course represents a typical multistable trajectory . Several steps are observed that systematically push the trajectory toward stronger levels of activity , finally reaching the “Up” state and remaining on it . The spatio-temporal time course ( Fig 10b ) displays the progressive recruitment of more and more nodes , issuing a pattern with most of its nodes active . Other observations of temporally multistable trajectories confirm the systematic drift toward one of the two trivial attractors . Either by recruiting new nodes , the dynamics evolves toward the high activity state , or by progressive extinction , the dynamics evolves toward the low-activity regime with nodes displaying a noisy activity at low rates . Long-lasting temporal multistability appears thus difficult to implement in this model . Despite their number and variety , the intermediary attractors appear not stable enough to maintain a realistic switching activity in the long run . The simulations of the SG model ( see Fig 10c and 10d ) show a slightly different picture . Most of the initial conditions correspond to very sparse ( i . e . very local ) activation patterns . The introduction of noise at 2 seconds leads to a strong remapping of the activity , with strong contrast against the set of attractors obtained by sampling . In some cases , the dynamics converges toward the “Down” state and remains stuck in it ( lower line ) . In most of the cases , the average activity gradually increases toward an average activity between 0 . 2 and 0 . 5 . No tendency toward higher activities is observed , but rather different spatial patterns assembling and disassembling over time ( see Fig 10d ) , A strong core activity in the centro-medial cortex is observed ( cuneus , precuneus , paracentral lobule , posterior cingulate… ) , with a possible occipital and inferior parietal component , and a variable number of nodes activated in the rest of the cortex ( with an apparent tendency toward bilateral activation ) . The DG model shows a consistent multistable regime for various values of P ( see Fig 10e and 10f ) . The most temporally variable trajectories are obtained for P = 0 . 6 , which corresponds to a very sparse level of activation ( around 0 . 05 ) and to the leftmost limit of the multistability region ( see Fig 5 ) . Every initial condition results in an apparent low activity regime . When considering a particular trajectory ( red trajectory ) , small excursions to the “Down” activity are observed . When spatially displayed ( Fig 10f ) , distinct spatial patterns , with similar density , are visited during a single trajectory . Because of the low amplification , the activity is prone to fall to the “Down” state , which plays the role of a reset facilitating excursion toward a new attractor basin . As proposed in [13] , the FCD is measured by the change in Pearson correlation between time-shifted FC matrices in a session ( see Material and Methods ) . It is a synthetic measure of the underlying non-stationarity , which captures the global FC dynamics on the network level , but by construction overlooks non-stationary behavior between individual node pairs , when averaged across all node pairs . FCD thus quantifies the stationarity and persistence of the set of functional links as a whole and provides insight in the degree of the global functional organization of the network . A FCD matrix is computed for a single subject’s rs-fMRI signal and is compared in Fig 11 to two synthetic FCD matrices generated from the SL and the DG models . In the physiological FCD ( Fig 11a ) , each row shows a typical fluctuation of correlation across the time axis with regards to the temporal reference on the diagonal , with alternations of high ( ≃ 0 . 5 ) and low ( ≃ 0 . 3 ) correlations on large temporal intervals . Those alternations were also obtained in simulation in very specific parametric ranges on the SL and DG models . In the SL model , it is observed at the critical gain Gc = 12 . 6 . When computing the fMRI signals ( see Methods ) , long-lasting alternations between the “up” and “down” patterns are observed in the resulting FCD . However , the low correlations observed outside the diagonal ( ≃ 0 . 15 ) , reflect unrealistic changes in the FC organization across time . Similar FC variations are observed in DG model for a large span of P values ( P ∈ [0 . 5;2 . 5] ) . The most robust alternating behavior is obtained around the lowest bifurcation ( P = 0 . 75 , see Fig 11c ) . For moderate levels of noise , a more constant FC organization is maintained across time , with correlation values alternating between ≃ 0 . 3 and ≃ 0 . 6 . The analysis of the FCD thus shows that realistic ultra-slow alternations of invariant epochs of stationary FC ( resulting in checker board patterns ) can be observed in both models . The parameter ranges , for which these checker board patterns in the FCD appear , are however limited , near to a critical value in both cases ( i . e . G = 12 . 6 on the SL model and P = 0 . 75 in the SL model ) . The low recurrence of FC states observed in the SL dynamics moreover indicates a closer correspondence of the empirical data with the DG model . The general trend is thus a better realism of the DG model when comparing the simulated time courses with the real ones .
Our attractor sampling approach identifies the parametric range , under which a robust multistable behavior is obtained . Large attractors sets are obtained at the high gain ( low spin-glass temperature ) condition , every node having its activity either close to 0 or 1 . This large number of static attractors exceeds by several orders the size of attractor sets generally reported in connectome based simulations [20 , 42] . The number of attractors in a given network model may subserve the network capability to attain various functional configurations [49] , which has been termed the dynamic repertoire [14 , 50] . Previous works on the resting state dynamics that have explicitly extracted and counted network states have been typically concerned with equilibrium states of the network [13 , 20] . Here we have explored the network mechanisms influencing the network’s capacity to store patterns . Furthermore , we demonstrated that the number of attractors can be expanded by four orders of magnitude beyond the so far reported number of patterns , and that this attractor state can indeed be explored when driven by noise . The distinguishing feature of our model to other models is the local threshold setting , introducing a symmetry that helps the system to visit every possible stable configuration , as well as the formal separation of the model’s free parameters into a gain ( G ) controlling the node excitability and a scaling factor ( P ) controlling the excitatory/inhibitory ratio . Those two parameters were found to independently control the multistability , with a cascade of bifurcations of the Pitchfork type observed when controlling G , and a non-monotonic behavior , reminiscent of the more detailed mean-field models behavior [20] , when controlling P . We moreover show this strong multistability to be consistently obtained on different connectome datasets , while considerably weakened when using randomized versions of the initial connectomes . This finding supports the idea of an intrinsic small-world/scale-free structure of the connectome [38 , 51 , 52] , providing a support to the multistability , as already suggested by [42] . Clear spatially-segregated components are identified from the distribution of the attractors . Sets of principal modes are obtained , in different proportions , across the different models , using a specific “inclusion match” clustering analysis . The strongest complexity in number , variability and spatial extent is obtained on the original Hopfield graded-response ( SL ) model . Our clustering analysis reports specific large-scale networks , displaying a strong similarity with the independent components [2 , 4] or communities [8] obtained from fMRI resting state time course analysis . Some of them ( frontal pole core , centro-medial core , primary visual core ) were already identified in graph-theoretical approaches [38] , while others , like left and right ventral visual streams , or a left-lateralized parieto-frontal network , were not observed in the graph-theoretical approaches . In contrast , adaptive-threshold based variants of the Hopfield model ( SG and DG ) , although displaying sparser activity patterns , provide a lesser variability , with the centro-medial component dominating the samples , representing between 50 and 75% of the total attractor sets . This central activity is reminiscent of the default mode network [48] , although the orbitofrontal component remains absent . The other modes include a primary visual modality , a superior parietal element , a pre/post-central medial element , and a superior-temporal element . Although stemming from different principles , those different modes are similar to the “regional modules” identified in [38] . Attractor cross-correlation matrices were compared with the rs-fMRI-based functional connectivity matrices , also providing contrasted results when comparing the different models , with the SL model presenting the advantage of predicting both the intra-and inter-hemispheric functional connectivities . A first conclusion from these comparisons is the more flexible and more widespread-distributed attractor sets in the SL model , providing a better account of the large-scale structural organization than models having a tighter control of the nodes average activity . When noise is introduced into the network , the functional network dynamics becomes non-stationary , demonstrating epochs of invariant functional connectivity and transitions between them . Noise-driven exploration of the brain’s dynamic repertoire has been hypothesized to play an important role in the execution of cognitive functions and serve as a biomarker of aging [15] rendering the relation between network capacity and noise strength significant . Noise causes a large proportion of attractors to vanish and become invisible , leaving space to a much smaller attractor sets , including trivial attractors like the “Up” ( full brain activation ) and “Down” ( full brain deactivation ) sets . In the absence of density control , a “centrifugal” tendency toward either the “Up” or “Down” state is observed . Only the density control case ( DG model ) , imposing stable density across time , provides a condition where no tendency toward over-activation or extinction is observed . Only this central control of the average activity seems capable of maintaining the checker board pattern of FCD ( transitions between longer lasting epochs of invariant FC ) in a parametric range close to the bifurcation point . The existence of a central control in the brain is of course highly conjectural . In some studies the role of the thalamus or the claustrum is hypothesized to coordinate distant synchronized activities [53 , 54] . Nevertheless , our findings demonstrate that a dynamic feedback mechanism ( here through the dynamic adaptation of the thresholds ) has the capacity to significantly enhance the complexity of the time courses and qualitatively capture the non-stationary behavior of brain networks at rest . In conclusion , we propose a novel approach to large-scale brain simulation that encompasses the capability of generating large sets of spatially-distributed attractors , reminiscent of well-known resting state networks , and theoretically interpreting the parameters that control the dynamics . Apparent differences in the final distribution of activities are found in case of local or global control of the nodes activity . More precisely , physiologically plausible switching dynamics of the functional connectivity is obtained under a global control of the average activity ( DG model ) . The multistable behavior is obtained over a large parameter range , but the best fit with the ultra-slow functional connectivity dynamics , as observed in the rs-fMRI time courses , is found at the edge of multistability , a parameter region that also corresponds to the highest entropy of the attractors . Our work highlights the importance of the noise-free dynamics in analyzing the attractors’ landscape , for identifying high-multistability/high entropy parameter regions that both fit with the most physiological distributions of activity , and the most relevant time courses in the noisy condition .
To develop large-scale brain models we use a structural connectivity matrix , the Connectome , obtained from tractographic reconstruction of Diffusion Spectrum Imaging ( DSI ) data of five healthy subjects [38] . Diffusion MRI provides information on fiber orientations in vivo with anisotropic diffusion of water in the brain . The anisotropy is mainly caused by the barrier created by myelin sheath insulating neurons axon . 998 regions of interest ( ROI ) are extracted from a parcellation applied on a standard MRI . The resulting connectome is averaged from the most significant 10 , 000 connections of all five individual connectomes after a k-Core decomposition ( details are in [38] ) . The final Connectome is symmetric with positive weights . Null models are generated using a randomization algorithm ( Maslov and Sneppen [55] ) that preserves the degree of the node ( random switch of the non-null edges ) . We compute the functional connectivity for windowed portions of the rs-fMRI time series and then calculate the Pearson correlation for time-shifted FC matrices . The FCD is visualized as a correlation matrix over the time shifted values . Three models are considered: “Static and Local” ( SL ) threshold model ( equivalent to the traditional Hopfield network node ) , the “Static and Global” ( SG ) threshold model , and the “Dynamic and Global” ( DG ) threshold model . Time series of 15 minutes were computed on the SL and DG models at 998 nodes resolution . The signals were convolved with a Balloon/Windkessel kernel [56–58] , resulting in a BOLD time course with 0 . 5Hz resolution . Then a 1 minute sliding window is used to generate a time series of Pearson correlation values for each link , resulting in a N × N × T matrix , where T equals 14min ( total duration minus length of sliding time window ) . Second , a subset of n nodes is selected ( for instance the upper triangle of the matrix excluding the diagonal on Fig 11 ) . Third , a T × T matrix is calculated where each ( t1 , t2 ) couple reports the correlation among the two n-values vectors indexed at time t1 and t2 . This T × T matrix is referred to as the Functional Connectivity Dynamics matrix ( FCD ) . The connectome has a space-time structure ( structural links , length of tracts ) . The time delays via signal transmission ( equalling tract length divided by transmission speed ) are ignored , because we consider only fixed-point models at network nodes ( see for instance [20] ) . We use the connectome as the coupling in a recurrent network , which offers a natural support for fixed-point multistability similar to the spin-glass model [39] and subsequent studies on auto-associative memories extensively studied in the 80’s [59] . Hopfield’s seminal paper popularized this concept as a key element of associative memory , introducing the tools and ideas of statistical physics to neural networks modeling . Several studies demonstrated that the attractor stability decreases with the number of items stored in the network [40] and the maximal number of possible memories linearly grows with the number of neurons [60] , or with the number of synapses in the large dilution limit [61] . The extension of the initial model to graded neuronal responses [18] qualitatively provides similar properties [62] . The original Hopfield model [59] is a dynamical system with discrete time steps and composed of multiple nodes . The interactions between the nodes rely on a connectivity matrix built from a pre-existing set of prototypes . The update , inspired by the spin-glass [39] model ( used to describe magnetic properties of dilute alloys ) , is based on a random scan of nodes . The existence of a fixed point dynamics is guaranteed by a Lyapunov function . Hopfield gave conditions , under which every prototype is an attractor of the multistable network dynamics [59] . When the initial conditions are close enough to one of the prototypes , the dynamical system relaxes to the corresponding attractor . When the network dynamics relaxes into an attractor , then this process is interpreted as a retrieval of a stored memory . Various extensions of the initial model have been proposed . We employ the “graded-response” network model [18] , which has a similar Lyapunov function and offers a more intuitive physiological interpretation . This time-continuous version is similar to the Wilson-Cowan model [63] and belongs to the family of “neural mass” models where the activity of a node is interpreted as the collective activity of a set of neurons , typically their average firing rate . The dynamical system is then described by its N state variables x1 , … , xN . The connections between the nodes are described by a matrix W , whose entries are normalized , i . e . ||W|| = 1 . The matrix elements are taken from the connectome , where W = C/||C|| and C is the original connectome . Each node activity is determined by the weighted sum across the activity of all input nodes and experiences a linear decay ( see Eq ( 1 ) ) , where τx defines the time scale of the dynamics . The transfer function is a monotonic sigmoid with activation threshold θi bounded between 0 ( no output ) and 1 ( strong output ) ( see Eq ( 2 ) ) . τ x d x i d t = - x i + ∑ j = 1 N W i , j A j ( 1 ) A i = 1 2 ( 1 + tanh ( G ( P x i - θ i ) ) ) ( 2 ) where xi is the node potential , Ai the node output , W the connectivity matrix , θi the threshold , P the scaling factor , G the gain and τx the time constant . The activation threshold is an essential component of the network model . Classical Hopfield models use a different threshold on every node ( Static and Local ) ( Eq ( 3 ) ) . Note that here only the model with P = 1 exactly corresponds to the original Hopfield model , in which case the dynamical system is symmetric around Ai = 0 . 5 . Each node has an equal probability to be active or inactive ( provided its local weights sum is different than 0 ) . For P ≠ 1 , the activity is biased , either toward a higher proportion of nodes active for P > 1 , or a lesser proportion for P < 1 . We consider here several variants of the model , that is the case of static global threshold ( Static and Global—SG ) ( Eq ( 4 ) ) , and the case of dynamic global threshold ( Dynamic and Global—DG ) ( Eq ( 5 ) ) . θ i = 1 2 ∑ j = 1 N W i , j SL ( 3 ) θ = 1 2 N ∑ i = 1 N ∑ j = 1 N W i , j SG ( 4 ) τ θ d θ d t = - θ + ∑ i = 1 A i / N DG ( 5 ) The SG model ( Eq ( 4 ) ) is a natural simplification of the original Hopfield model . Replacing the local thresholds by a single one has strong implications , discarding the symmetry around the central 0 . 5 state . Each node continues to be active when a large enough proportion of its inputs is active . In this setting , as the weight averages are not balanced between the nodes , some nodes with greater ( resp . lower ) -than-average weight have a greater ( resp . lower ) probability to be active than the others . In order to keep similar parametric ranges as in the first model , the global threshold is calculated as the average over the local thresholds of ( Eq ( 3 ) ) . In the DG model , ( Eq ( 5 ) ) , the threshold changes over time as a function of the node activities . The general idea is to decrease the threshold when the nodes’ activity is too low , and to increase the threshold when the activity is too high . As such , the threshold participates dynamically in the process and exerts a regulatory influence on the dynamics , i . e . controlling the average level of activity ( in the spirit of e . g . [60] ) . The dynamic threshold could , for instance , be realized via a local population of inhibitory neurons ( with linear response ) . In this case , the scaling parameter P would represent the Excitatory/Inhibitory ( E/I ) ratio . P < 1 means that the inhibition dominates the excitation , and P > 1 means the opposite . Following Eq ( 3 ) , the dynamic threshold is calculated as the average over all nodes’ activity , representing a spatio-temporal average , i . e . the average proportion of active nodes in the system . From a biological standpoint , we interprete it as a global inhibitory node ( with linear response ) feeding back the average activity toward every excitatory node of the system ( see Fig 1 ) . We implement a stochastic generalization of the above networks via the following equations: τ x d x i d t = - x i + ∑ j = 1 W i , j A j + σ x η i ( t ) ( 6 ) and τ θ d θ i d t = - θ i + θ i 0 + σ θ ξ i ( t ) SL ( 7 ) τ θ d θ d t = - θ + θ 0 + σ θ ξ ( t ) SG ( 8 ) τ θ d θ d t = - θ + ∑ i = 1 A i / N + σ θ ξ ( t ) DG ( 9 ) where white Gaussian noise terms ηi and ξi are added linearly to the evolution equations with corresponding diffusion strengths σx and σθ , respectively . The deterministic computer simulations have been performed using the Euler discretization scheme with discrete steps of 0 . 1 ms . When not pointed out otherwise , the initial state is a random binary vector A0 , whose proportion of zeros and ones is set according to a density factor f0 . The initial conditions are set randomly according to a binomial draw with an expectation varying from 0 to 1 according to f0 . After initialization , the activity of the nodes is recurrently transmitted to the other nodes through the structural connectivity matrix . In the noiseless case , the dynamics is expected to relax on a stable attractor in short time . Let Ak ( 0 ) be the kth vector of initial conditions and Ak ( t ) the corresponding vector of activity at time t . The average activity at time t ( see Figs 2a , 10a , 10c and 10e ) is defined as A ¯ k ( t ) = 1 N ∑ i = 1 N A i k ( t ) . The state variable time constant is set to a fixed value τx = 10 ms . The threshold time constant τθ , mostly representing inhibitory influences , is considered slower and varied from 10 ms ( identical time scales ) to 80 ms ( time scales separation ) . The scaling factor P is a pivotal parameter interpreted as the system excitatory/inhibitory balance . Depending on the model , P varies from 0 ( no excitation ) to 20 ( strong excitation ) . The noise strengths σx and σθ are set within the interval [0 , 1] . In analogy with Hopfield’s neural network interpretation , we assume several attractors to be “stored” via the coupling matrix , here given by the connectome . We use the following reverse approach: given a particular connectivity matrix , we try to infer the set of prototypes embedded in it . To do so , we sample the initial conditions and randomly initialize the system with binary activation patterns ( namely active or inactive node ) . Since the number of initial configurations grows exponentially with the size , we only take a sample from a subset of possible initial states . Importantly , we consider different initial densities in order to better sample the state space , varying from 0 . 02 to 0 . 98 with 0 . 03 steps ( here 33 densities distributed linearly on the ]0 , 1[ interval—excluding 0 corresponding to a trivial solution for the continuous Hopfield model ) . In the DG model , the threshold time constant is set to the same value as the potential time constant . Then , for each density , we let n different initial conditions relax on their attractor . For each value of the parameter space to study , 33 × n random initialization are thus processed . In the particular case where the initial density is parametrized , n = 3 , 300 initializations are used on a given density parameters . The dynamics is stopped when the system reaches its equilibrium 〈 x ¯ ( t ) 〉 T - x ¯ ( t ) x ¯ ( t ) < ε or after 1 second if the equilibrium condition is not reached , where x ¯ ( t ) is the average potential over the nodes ( at time t ) , 〈 x ¯ ( t ) 〉 T is the temporal average of x ¯ ( t ) on the [t − T , t] temporal interval ( T = 100 ms ) , and ε a ( small ) constant ( 10−6 ) . Any final pattern of activity satisfying a double dissimilarity condition is saved , otherwise the cardinality of the better matching attractor set is incremented . The double dissimilarity condition considers both a Pearson correlation and a Euclidean similarity lower than 0 . 9 . The goal of this double dissimilarity condition is to discard the low-density patterns , possibly having a low correlation but a high Euclidian similarity , and the high density patterns possibly having a lower Euclidean similarity but being strongly correlated . A final set of m ≤ n attractors is obtained , each attractor being associated with its cardinality , representing the “width” of its attraction basin . sim cor ( x , y ) = ∑ i ( x i - x ¯ ) ( y i - y ¯ ) ( var ( x ) var ( y ) ) 1 / 2 sim eucl ( x , y ) = 1 1 + ( ∑ i ( x i - y i ) 2 ) 1 / 2 For large attractor sets we use clustering algorithms ( see Algorithm 1 ) to identify classes of attractors with spatial similarity . The clustering algorithm operates on binary patterns sets . A binary pattern is composed of active nodes ( that is activity > 0 . 5 ) and inactive nodes ( activity < 0 . 5 ) . Each binary pattern defines a set of active nodes A ( and a complementary set of inactive nodes A ¯ ) . For the purpose of extracting large-scale structural invariants , we use a specific “inclusion match” metric that indicates which proportion of a binary pattern A is included in a binary pattern B: incl ( A , B ) = { | A ∩ B | | A | if | A | > 0 0 if | A | = 0 Under this metric , patterns of variable size may share elements and are considered similar . For instance , if all the elements of the pattern A are included in the pattern B , the inclusion match is equal to 1 . Because the metric is non-symmetric , we employ the following modification sim incl ( A , B ) = max { incl ( A , B ) , incl ( B , A ) } . Algorithm 1 Clustering algorithm 1: parameter: k 2: initialize n sets: S ← {{x1} , … , {xn}} 3: simtest ← 1 4: while simtest > k do 5: E , F ← argmax ( E ′ , F ′ ) ∈ S × S sim ref ( E ′ , F ′ ) 6: simtest ← simref ( E , F ) 7: remove E and F from S 8: G ← E ∪ F 9: Add G to S 10: end while The similarity between two clusters E and F is the similarity between the reference patterns μE and μF of the two sets , i . e . simref ( E , F ) = simincl ( μE , μF ) . The reference patterns can be calculated in different ways ( e . g . the average pattern of the cluster , etc . ) . In the “double pass” clustering case , the algorithm 1 is applied twice to the same set of patterns . In the first pass , the reference patterns of the clusters are the patterns with the highest “inclusion score” , where the inclusion score is the sum of the inclusion matching with all the other patterns of the cluster . In the second pass , the reference patterns are the average binary patterns , gathering the nodes that are active in more than 50% of the patterns . The first pass results in a set of clusters whose constituents have a variable density , but share an essential “core” activity that is present in all the patterns of a cluster . The second pass is a smoothing pass that gathers together the clusters being similar on average ( where the final average patterns are then possibly composed of several “cores” ) . Consider each rs-fMRI pattern x as a set of 250 active nodes in a total of 1 , 000 nodes ( the 250 most active nodes in a cluster of observation vectors ) . Consider each simulation-based pattern as a set y of 100 active nodes in a total of 1 , 000 nodes ( the 100 most active nodes in a cluster of attractors ) . To test independence , we compare the match between x and y against a random set of 100 nodes z . If z is drawn independently from x , the expected match E ( | x ∩ z | | z | ) is 0 . 25 with variance 0 . 25 ( 1 - 0 . 25 ) 100 . Note m = | x ∩ y | | y | is the match between x and y . Then , under the Normal approximation , the Student t-value is ( m - 0 . 25 ) × 100 0 . 25 ( 1 - 0 . 25 ) , where t > 3 ( m > 0 . 37 ) denotes less than 0 . 1% chance for x and y to be independent . Simulated network data are downsampled with 100 Hz ( average values over 10 ms windows ) . Then the data are convolved with a Balloon/Windkessel kernel [56–58]: H ( t ) = exp ( - 0 . 5 t τ s ) sin ( t 1 τ f - 1 4 τ s T ) 1 τ f - 1 4 τ s T ( 10 ) with T = 10 , τs = 0 . 8 s , τf = 0 . 4 s . The resulting signal is finally downsampled at 0 . 5 Hz and detrended ( by suppressing the time-averaged value ) . Our modeling approach allows deriving analytically a potential function , from which many dynamic and stochastic properties of the network can be derived in a simple manner . We begin our discussion with the formulation of the Fokker-Planck equation and its solutions . As the resting state dynamics evolves , it traces out a trajectory in a M-dimensional state space . The M-dimensional state vector q ( t ) = ( …qi ( t ) … ) obeys the Langevin equation q ˙ ( t ) = K ( q ( t ) ) + F ( t ) where the rate of change of the state vector ( time derivative on the left ) depends on its deterministic influences K = ( …Ki ( t ) … ) that are non-linearly dependent on its current state and stochastic forces F = ( …Fi ( t ) … ) . Here we consider only δ-correlated fluctuating forces F , i . e . <Fi ( t ) Fj ( t′ ) > = Qi , j δ ( t − t′ ) . The probability density function f ( q , t ) defines the distribution realizations of trajectories in q-space and its dynamics is determined by the Fokker-Planck equation . f ˙ = - ∇ q { K f } - 1 2 ∑ k , l Q k , l ∂ 2 f ∂ q k ∂ q l ( 11 ) We can rewrite the Fokker-Planck equation as a continuity equation by means of the abbreviation j k = ( K k f - 1 2 ∑ l Q k , l ∂ f ∂ q l ) and obtain f ˙ = - ∇ q ∘ j , where the temporal change of the probability density f ( q ) is equal to the negative divergence of the probability current j = ( …jk… ) . For f ˙ = 0 , the stationary solution of the Fokker-Planck equation is time-independent . The stationary solution does not imply zero current , j = 0 , because closed probability flows may persist . The deterministic components may be expressed via a gradient dynamics as in the case of the SL and SG models ( q = x , M = N ) , then this allows us to determine an explicit stationary solution of the Fokker-Planck equation . In particular , when K k = - ∂ V ( x ) ∂ x k and the diffusion coefficients obey the condition Qk , l = δk , l Q , then the time independent probability density function reads: f ( x ) = n exp ( - 2 V ( x ) / Q ) ( 12 ) where n is the normalization coefficient , satisfying the natural boundary conditions that f ( x ) vanishes for |x| → ∞ . For our large-scale network , the potential function V ( x ) reads: V ( x ) = 1 2 ∑ i [ x i 2 - x i ∑ j W i , j - x i ∑ j ≠ i W i , j tanh ( G ( P x j - θ j ) ) - W i , i 1 G P ln cosh ( G ( P x i - θ i ) ) ] ( 13 ) The minima of the potential function V ( x ) determine the set of potential states that the large-scale brain network can occupy and thus defines its dynamical repertoire . The stationary time independent solution of the probability density allows characterizing the most likely paths to be taken in state space by identifying regions of high probability . This analytical approach turns out to be a major advantage as opposed to computational approaches , which would require time-consuming simulations of many realizations of trajectories . The following crucial distinction needs to be made here: what is commonly referred to as non-stationary brain dynamics at rest does generally not refer to a non-stationary dynamics of the probability density , but rather to the evolution of the trajectory in state space occupying certain subspaces for a finite time , followed by a rapid switch to occupy another subspace and dwell there for another characteristic time . The realized probability density is stationary and described by Eq ( 12 ) . Time-dependent solutions of the Fokker-Planck equation cannot be expressed analytically in general terms , but formulated at least locally around a given brain state ( minimum of V ( x ) ) , which allows the explicit computation of the time dependent moments such as the time-dependent mean around a brain state or its two-time correlation function [64] . For a large excitability G , the sigmoidal saturation function in our network model approximates a Heavyside function which approximates a reduced Ising-spin attractor model , which allows further analytical investigation as performed by Deco et al [42] . The model is then a network of stochastic binary units ( “spins” ) , where each unit si takes output value Ai = 1 with probability fi and value Ai = 0 with probability 1 − fi . For symmetric connectivity , the Boltzmann-Gibbs distribution giving the probability of finding the network in a specific state Aα can be expressed analytically by p α = e - β E α Z . ( 14 ) where Z is the partition function defined by Z = ∑ α e - β E α ( 15 ) and Eα is the energy function E α = - 1 2 ∑ i , j W i , j A i α A j α - ∑ i θ i A i α ( 16 ) The probability pα gives the probability of finding the configuration Aα . Therefore , in order to describe the attractor landscape of the spin network , we can characterize the existence and probability of each possible attractor ( here corresponding to a specific configuration Aα ) by the entropy of the system , which can be derived analytically , yielding: H = - ∑ α p α log p α = ∑ α β E α e - β E α Z + log Z ( 17 ) Deco et al . [42] computed explicitly different types of structural networks and investigated how the entropy of the Ising-spin network evolves as a function of connectivity . The higher the entropy is , the larger is the number of “ghost” attractors that efficiently structure the fluctuations of the system at the edge of the bifurcation . The empirical entropies ( shown in Figs 3 and 5 ) rely on the cardinality of the different sets of attractors obtained after sampling the initial condition space . Namely , for each attractor i , p ˜ i = n i n where ni is the cardinal of the ith attractor set and n is the number of samples . Then , ∀i: H ˜ = - ∑ i p ˜ i log p ˜ i ( 18 ) | Recent developments in non-invasive brain imaging allow reconstructing axonal tracts in the human brain and building realistic network models of the human brain . These models resemble brain systems in their network character and allow deciphering how different regions share signals and process information . Inspired by the metastable dynamics of the spin glass model in statistical physics , we systematically explore the brain network’s capacity to process information and investigate novel avenues how to enhance it . In particular , we study how the brain activates and switches between different functional networks across time . Such non-stationary behavior has been observed in human brain imaging data and hypothesized to be linked to information processsing . To shed light on the conditions under which large-scale brain network models exhibit such dynamics , we characterize the principal network patterns and confront them with modular structures observed both in graph theoretical analysis and resting-state functional Magnetic Resonance Imaging ( rs-fMRI ) . |
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Human immunodeficiency virus type 1 ( HIV-1 ) invades the central nervous system ( CNS ) shortly after systemic infection and can result in the subsequent development of HIV-1–associated dementia ( HAD ) in a subset of infected individuals . Genetically compartmentalized virus in the CNS is associated with HAD , suggesting autonomous viral replication as a factor in the disease process . We examined the source of compartmentalized HIV-1 in the CNS of subjects with HIV-1–associated neurological disease and in asymptomatic subjects who were initiating antiretroviral therapy . The heteroduplex tracking assay ( HTA ) , targeting the variable regions of env , was used to determine which HIV-1 genetic variants in the cerebrospinal fluid ( CSF ) were compartmentalized and which variants were shared with the blood plasma . We then measured the viral decay kinetics of individual variants after the initiation of antiretroviral therapy . Compartmentalized HIV-1 variants in the CSF of asymptomatic subjects decayed rapidly after the initiation of antiretroviral therapy , with a mean half-life of 1 . 57 days . Rapid viral decay was also measured for CSF-compartmentalized variants in four HAD subjects ( t1/2 mean = 2 . 27 days ) . However , slow viral decay was measured for CSF-compartmentalized variants from an additional four subjects with neurological disease ( t1/2 range = 9 . 85 days to no initial decay ) . The slow decay detected for CSF-compartmentalized variants was not associated with poor CNS drug penetration , drug resistant virus in the CSF , or the presence of X4 virus genotypes . We found that the slow decay measured for CSF-compartmentalized variants in subjects with neurological disease was correlated with low peripheral CD4 cell count and reduced CSF pleocytosis . We propose a model in which infiltrating macrophages replace CD4+ T cells as the primary source of productive viral replication in the CNS to maintain high viral loads in the CSF in a substantial subset of subjects with HAD .
Human immunodeficiency virus type 1 ( HIV-1 ) -associated dementia ( HAD ) is a severe neurological disease that affects a subset of HIV-1-infected individuals [1] , [2] . HIV-1 infection of the central nervous system ( CNS ) occurs shortly after peripheral infection , most likely through the trafficking of infected lymphocytes and monocytes across the blood-brain barrier ( BBB ) [3] , [4] . Once HIV-1 crosses the BBB it can infect perivascular macrophages and brain-resident microglia , and some studies have shown that neurotropic viruses preferentially infect macrophages [5]–[8] . HIV-1 may persist in the CNS during therapy due to the insufficient CNS penetration of some antiretroviral drugs [2] , [9]–[11] . HIV-1 variants have been detected at autopsy in the brains of HAD subjects , and these brain-derived variants are genetically distinct from virus detected in the peripheral blood [7] , [12]–[15] . A principal impediment to studying viral evolution in the CNS is that direct sampling of HIV-1 in brain tissue is usually possible only once , at biopsy or autopsy . To examine viral populations in the CNS over the course of HIV-1 infection we have relied upon repeated sampling of virus in the cerebrospinal fluid ( CSF ) . Previous studies have shown that virus detected in the CSF originates from both local CNS tissue and the peripheral blood [16]–[19] , indicating that the CSF may act as a site of mixing of virus present in the brain and the periphery . In addition , genetic compartmentalization has been reported between blood plasma and CSF viral variants [20]–[23] . We previously examined the cellular sources of HIV-1 in the CNS by utilizing the heteroduplex tracking assay ( HTA ) to measure viral decay rates in HIV-1-infected subjects initiating antiretroviral therapy [24] . In this study we reported that the subset of compartmentalized virus detected in the CSF of four asymptomatic subjects decayed rapidly after the initiation of therapy , suggesting that the compartmentalized virus is coming from a short-lived cell type , such as CD4+ T cells [24] . The population dynamics of systemic HIV-1 replication have been studied extensively [25]–[27] , but the extent of viral replication in specific cell types in the CNS over the course of disease is not yet known . The use of antiretroviral drugs to prevent HIV-1 infection of uninfected cells provides a tool for “viewing” the rate of decay for cell-free virus and virally-infected cells . HIV-1 decay in peripheral blood after the initiation of highly active antiretroviral therapy ( HAART ) occurs in at least two phases [25] , [27] . The first phase of decay is rapid and has been proposed to represent the turnover of cell-free virions and productively infected CD4+ T cells [25]–[28] . The second phase is slower and may reflect the decay of long-lived infected cells , possibly latently infected resting CD4+ T cells and cells of the monocyte lineage [25] , [27]–[29] , and the release of virions from follicular dendritic cells [28] , [30] . Recently , a study using the integrase inhibitor raltegravir reported altered HIV-1 decay kinetics and a reduction of the second viral decay phase [31] , suggesting integration as a rate limiting step of infection in a subset of cells . The implications of these data on measured viral decay rates remain to be clarified; however , the reduction in the second phase of HIV-1 decay may indicate that longer-lived HIV-1-infected cells contribute less to total viral load than previously thought , but it does not preclude the possibility that the second phase of HIV-1 decay may reflect the turnover of long-lived cells [31] , [32] . In this study , we characterized the lifespan of the cellular source of compartmentalized HIV-1 in the CNS of subjects with and without symptomatic neurological disease by calculating viral decay rates during the initiation of antiretroviral therapy . The heteroduplex tracking assay ( HTA ) [33] , [34] was used to distinguish between HIV-1 genetic variants in the CSF that were either compartmentalized to the CSF or equilibrated with the peripheral blood . HTA has been used in previous studies to differentiate between HIV-1 genetic variants in separate anatomical compartments [22] , [24] , [35] , [36] and HIV-1 evolutionary variants [37]–[42] , including drug resistance mutations [43] , [44] . The HTA is a useful tool for resolving and quantifying complex viral populations based on their genotype , and is able to detect HIV-1 variants that comprise as little as 1–3% of the total viral population . We targeted the variable regions of the env gene for HTA analysis of our subject population in order to resolve multiple HIV-1 genetic variants . In this study we confirm rapid viral decay in the CSF of asymptomatic subjects initiating HAART , and we report reduced rates of viral decay of compartmentalized virus in the CSF in a subset of neurologically symptomatic subjects initiating antiretroviral therapy . These results suggest a shift in the cell type that produces the bulk of the virus in the CSF late in disease as part of the process of viral pathogenesis in the CNS .
Our analysis included 11 asymptomatic subjects ( 7 new subjects , 4 subjects reported in [24] ) , 1 subject with minor cognitive motor disorder ( MCMD ) , and 7 subjects with HIV-1–associated dementia ( HAD; see Table 1 ) . In general , subjects with HAD have higher viral load in the CSF [17] , [45] , [46] and increased HIV-1 compartmentalization in the CSF [21] , [22] . To assess compartmentalization we measured the relative abundance of HIV-1 variants in the blood plasma and CSF as resolved by the heteroduplex tracking assay ( HTA ) , then calculated the percent difference values between the two viral populations ( see Table 2 ) . We found that the CSF and plasma viral populations were different for subjects with HIV-associated neurological disease ( average = 67% different; range = 36–88% different ) compared to the asymptomatic subjects ( average = 42% different; range = 10–78% different ) . This difference approached statistical significance in spite of the small sample size ( p = 0 . 054 using a two-tailed Mann-Whitney test ) , and this trend of increased viral compartmentalization in the CSF with HAD is consistent with the difference seen in a larger cross-sectional analysis [21] . We next used the HTA to follow differential decay of shared and compartmentalized variants when subjects initiated therapy . In this study , the subjects had an average reduction of 91% of the virus in the blood , and 88% of the virus in the CSF , over the period of sampling for HTA analysis ( Table 2 ) . The HTA is a useful tool for sampling complex viral populations , and is sensitive enough to detect minor variants within the population . We utilized HTAs targeting the hyper-variable regions V1/V2 and V4/V5 of the env gene to detect and measure the decay of individual HIV-1 variants in the cerebrospinal fluid and plasma of subjects initiating HAART . The HTA that was the most reproducible ( V1/V2 or V4/V5 ) was used for the final decay and half-life calculations . The half-lives for the different variants in the blood for four of these subjects have been reported previously [47] . The V1/V2 and V4/V5 HTA analyses for the seven new asymptomatic subjects revealed rapid HIV-1 decay for both compartmentalized and shared variants detected in the CSF ( see Figure 1 ) . The decay of individual variants was organized into two groups for half-life analysis: decay of CSF-compartmentalized variants and decay of variants shared between the blood and the CSF . The HTA gels for the longitudinal samples from the seven new asymptomatic subjects are shown in Figure 1A , and graphs representing the viral decay are shown in Figure 1B . In this analysis , viral variants that decay more slowly will make up an increasing percentage of the total viral population over the course of therapy . However , if all variants decay at the same rate then the relative percentages will remain the same over time . HIV-1 half-lives for plasma and CSF variants were calculated based on the slopes of the decay curves ( summarized in Table 2 ) . Based on data generated from the seven new asymptomatic subjects analyzed in this study , half-lives calculated for the total plasma viral load decay were short ( t1/2 mean = 1 . 46 days; t1/2 range = 0 . 58–2 . 27 days ) , and total CSF viral load half-lives were short ( t1/2 mean = 1 . 5 days; t1/2 range = 0 . 77–2 . 04 days ) . These half-lives are similar to the data reported for 4 asymptomatic subjects that were previously studied [24] . Although some asymptomatic subjects have large percent difference values between the blood and CSF viral populations , not all of the variants detected in the CSF met the criteria for compartmentalization . Viral variants in the CSF were considered compartmentalized if they were unique to the CSF or they were present in a substantially higher concentration in the CSF compared to the plasma . CSF-compartmentalized variants were detected in asymptomatic subjects 5005 , 4014 , and 4022 . To increase our sample size we included the half-life data from the four asymptomatic subjects reported in ref . [24] in our analysis of CSF-compartmentalized decay . Including these additional four subjects ( n = 7 total asymptomatic subjects with some compartmentalized virus: 3 new subjects and 4 previously reported subjects ) , we found that the half-lives for CSF-compartmentalized variants in these subjects were short , with a mean of 1 . 57 days ( t1/2 range = 0 . 75–2 . 75 days; see below ) . These data indicate that CSF-compartmentalized virus in asymptomatic subjects is most likely originating from a short-lived cell type , such as a CD4+ T cell . The reported half-life of a productively infected CD4+ T cell is approximately 2 days [28] , which coincides with our average measured half-life of 1 . 57 days in these subjects . We expanded our analysis of viral decay to HIV-1-infected subjects who were diagnosed with either MCMD or HAD to address the hypothesis that CSF-compartmentalized variants in these subjects originate from longer-lived cells . Viral decay in the CSF of eight subjects with neurological disease was analyzed using HTAs targeting the V1/V2 and V4/V5 regions of env . The HTA analyses for the eight subjects with HIV-associated neurological disease showed either rapid or slow viral decay among the subjects . The longitudinal HTA gels for each neurologically symptomatic subject are shown in Figure 2A , and the graphs of viral decay are shown in Figure 2B and 2C . Similar to the asymptomatic subject decay analysis , individual variants were grouped as either CSF-compartmentalized variants or variants shared between the blood and the CSF for the decay analysis . Total plasma viral load decay was rapid for all subjects with neurological disease , with a mean half-life of 2 . 11 days ( t1/2 range = 1 . 42–2 . 91 days; summarized in Table 2 and Figure 3 ) . We measured rapid viral decay for CSF-compartmentalized variants after the initiation of HAART for four subjects with HAD ( 4033 , 5003 , 7036 , 4051; t1/2 mean = 2 . 27 days; t1/2 range = 1 . 23–3 . 67 days; Figure 2B and Figure 3; summarized in Table 2 ) , similar to asymptomatic subjects . In contrast , prolonged viral decay was measured for CSF-compartmentalized variants for the other four subjects with neurological disease ( 4013 , 5002 , 4059 , 7115; t1/2 range = 9 . 85 days to no initial decay ) , with three subjects displaying biphasic decay . CSF-compartmentalized variants for subjects 4013 , 5002 , and 7115 displayed a biphasic decay ( see Figure 2C ) , where the first phase of viral decay was slow ( 4013 t1/2 = 28 . 5 days; 7115 t1/2 = no initial decay; 5002 t1/2 = no initial decay ) , and the second phase was faster ( 4013 t1/2 = 3 . 9 days; 7115 t1/2 = 6 . 4 days; 5002 t1/2 = 4 . 24 days ) . Figure 3 and Table 2 report the half-lives calculated for both phases of decay . Subject 4059 displayed only a slower decay rate for the CSF-compartmentalized variants ( t1/2 = 9 . 85 days ) . Total CSF viral load decay was similar to the decay rates measured for CSF-compartmentalized variants for all subjects with neurological disease . This is due to the fact that most of the virus in the CSF was compartmentalized in these HAD subjects . The decay of the small amounts of shared variants fluctuated in these subjects from decreasing with a rate similar to the virus in plasma to decreasing with a slow rate similar to that of the CSF-compartmentalized variants ( see Table 2 ) . For each CSF sample time point the CSF white blood cell ( WBC ) count was measured to determine if any subjects had CSF pleocytosis ( defined as >5 cells/µl; [48] , [49] ) . We found that all four subjects with rapid CSF-compartmentalized variant decay either had high CSF WBC levels at entry ( 4033 = 28 cells/µl; 5003 = 46 cells/µl; 7036 = 240 cells/µl; 4051 = 12 cells/µl ) , or the CSF WBC levels increased while on therapy . Conversely , the four subjects with neurological disease that displayed slower CSF-compartmentalized variant decay either had extremely low levels of CSF WBCs at entry ( 4013 = 10 cells/µl; 5002 = 66 cells/µl; 4059 = 1 cells/µl; 7115 = 12 cells/µl ) , or the CSF WBC levels decreased to low levels after the initiation of antiretroviral therapy . We examined the CSF WBC levels of these two groups in more detail by calculating the CSF WBC average for each subject from baseline through the first 14 days of antiretroviral therapy . The subjects with rapid CSF-compartmentalized variant decay had higher CSF WBC averages , while subjects with slower or biphasic CSF-compartmentalized variant decay had lower CSF WBC averages ( see Table 1 ) , and this difference was statistically significant ( p = 0 . 029 using a two-tailed Mann-Whitney test ) . It has been reported that HIV-1-infected subjects with CD4 counts below 50 cells/µl have reduced CSF pleocytosis [49] . We also examined whether the viral decay rates measured by HTA were correlated with the degree of immunodeficiency by analyzing CD4 counts for each group of subjects . The four subjects with rapid CSF-compartmentalized variant decay had significantly higher baseline CD4 counts ( see Table 1 ) compared to the four subjects with slower CSF-compartmentalized variant decay ( p = 0 . 006 using a two-tailed unpaired t-test ) . Thus , in subjects with HIV-1–associated neurological disease , viral decay rates are associated with the degree of immunodeficiency and CSF pleocytosis . We did not detect an association between CSF pleocytosis and rapid viral decay in the CSF for asymptomatic subjects . The CSF WBC average was calculated for each subject as stated above , and the range extended from 0 cells/µl up to 20 cells/µl ( Table 1 ) . All variants detected in the CSF of asymptomatic subjects decayed rapidly upon the initiation of antiretroviral therapy; however , we found that the presence of CSF-compartmentalized variants was associated with higher average CSF WBC levels . All four of the asymptomatic subjects that did not have compartmentalized virus had low average CSF WBC counts ( 4012 , 4030 , 4023 , 4021 ) , while the three asymptomatic subjects that had detectable CSF-compartmentalized variants also had higher average CSF WBC levels ( 5005 , 4022 , 4014; see Table 1 ) . Thus in the asymptomatic subjects the presence of pleocytosis may be associated with an early inflammatory response to increased levels of autonomously replicating virus . Some antiretroviral drugs have poor penetration into the CNS [50] . In order to determine whether the differential decay we detected by HTA was associated with poor CNS drug penetration , we calculated the CNS Penetration Effectiveness ( CPE ) rank [50] for the drug regimens that each of the 15 subjects were receiving at the time of sample collection ( see Table 1 ) . Drugs that have poor penetration into the CNS were assigned a rank of 0 , intermediate penetration was assigned a rank of 0 . 5 , and high penetration was assigned a rank of 1 [50] . The four subjects that showed a longer viral half-life by HTA analysis had CPE ranks ranging from 2 . 0 to 2 . 5 , while the other subjects that displayed rapid viral decay had CPE ranks from 1 . 5 ( 5 subjects ) to 3 . 5 ( 1 subject ) . All subjects with neurological disease had CPE ranks above 2 . 0 except for subject 5003 ( CPE rank = 1 . 5 ) . A previous study reported that CPE ranks below 2 . 0 were associated with a significant ( 88% ) increase in the ability to detect virus in the CSF , and higher CSF viral loads were associated with low CPE ranks [50] . All of the subjects with longer viral half-lives had CPE ranks of 2 . 0 or above , suggesting that the slower HIV-1 decay we detected by HTA was not associated with poor CNS drug penetration . Alternatively , there could be infected cells located in parenchymal compartments that are less accessible to drugs , but this seems unlikely because the virus still has access to the CSF . We also investigated the possibility that slower decay was a result of drug resistance mutations present in the viral population in the CSF . Drug resistance mutations were measured for CSF samples of subjects 4013 , 5002 , 4059 , and 7115 . The resistance test was conducted for time points after the initiation of drug selection to allow for enrichment of any potential drug resistant variants . Subjects 4013 , 5002 , and 4059 showed no evidence of resistance mutations in reverse transcriptase ( RT ) or protease that confers resistance to antiretroviral drugs ( data not shown ) . Subject 7115 had the resistance mutation K103N in RT , which confers resistance to non-nucleoside RT inhibitors ( NNRTI ) . However , at the time of this study , subject 7115 was not taking an NNRTI , and was instead on a drug regimen that included zidovudine , lamivudine , and lopinavir . Therefore , there is no evidence that drug resistance played a role in the slower viral decay detected by HTA in these four subjects . Using the biotin-V3 HTA procedure , we also examined whether slower viral decay was associated with V3 sequence differences . The biotin-HTA is a modification of the original HTA method that incorporates a biotin tag into the probe to allow direct sequencing of the query strand isolated from the gel [51] . This newly developed HTA procedure resolves minor variants in the gel , and then allows the recovery and sequence analysis of both major and minor HIV-1 V3 variants from complex viral populations [51] . Following V3 PCR amplification and HTA analysis , we excised the gel fragments containing the V3 heteroduplexes , purified the query DNA strand using streptavidin-coated magnetic Dynabeads® , and directly sequenced the subsequent V3 PCR products [51] . The migration patterns for the V3 heteroduplexes and the inferred V3 amino acid sequence obtained for the heteroduplex in each gel band are shown in Figure 4 . The biotin-V3 HTA procedure was conducted on plasma samples from all subjects at the first time point collected , and CSF samples were analyzed for subjects with HIV-associated dementia . No significant V3 sequence differences were detected between asymptomatic and symptomatic subjects , or between subjects with rapid versus slow decay by HTA ( Figure 4B ) . Only one subject ( 4014 ) had V3 sequences that were X4-like by the Position-Specific Scoring Matrix ( PSSM ) method [52] of predicting co-receptor usage based on genotype . We did note that two subjects with slower decay by HTA had compartmentalized V3 variants detected in the CSF viral population that were much more R5-like by sequence compared to the V3 sequence variants detected in the plasma viral population . However , R5-like V3 sequences were also detected in the CSF for HAD subjects with rapid viral decay , indicating that V3 sequence differences and co-receptor usage are not responsible for the differential decay detected by HTA .
There are several lines of evidence that support the idea that HIV-1 can replicate in the central nervous system ( CNS ) . HIV-1-infected macrophages and microglia have been detected in the brains of subjects with HIV-1–associated dementia ( HAD ) at autopsy [6] , [53] , [54] . In addition , genetically distinct HIV-1 variants , different from those in the peripheral blood , are seen in the CNS of subjects with HAD [7] , [12]–[15] . These inferences can be extended using CSF as a surrogate for the CNS where genetic compartmentalization can be detected when comparing blood and CSF viral variants [20]–[23] , and bulk virus in the CSF of subjects initiating HAART can decay with different kinetics compared to virus in the blood [16] , [19] , [55] . Furthermore , it appears that this independent replication is relevant , if not causal , of HIV-associated neuropathogenesis . The extent of compartmentalization in the CSF , as measured by the heteroduplex tracking assay , increases in subjects with HAD , suggesting more sustained autonomous replication is associated with the neurological disease state [21] , [22] . Also , slow decay of virus in the CSF compared to the blood is associated with subjects with neurological disease , especially HAD subjects , suggestive of virus being produced from a different cellular source [16] , [19] , [55] . In addition to viral genetic compartmentalization there are other markers of neuropathogenesis in HIV-1-infected individuals , such as CSF neopterin [56] , [57] , CSF light-chain neurofilament protein [57]–[59] , and CSF chemokine levels [60]–[64] . In the current work we have attempted to combine the observations of viral genetic compartmentalization and differential decay in subjects initiating HAART by comparing the rates of decay of variants shared between the CSF and the blood versus those variants that were compartmentalized in the CSF . The goal of this work was to examine the link between compartmentalized virus as a marker for autonomous replication in the CNS and the production of virus in the CNS by long-lived cells . We used heteroduplex tracking assays ( HTAs ) targeting the variable regions of env to identify CSF-compartmentalized variants and variants shared between the CSF and blood plasma , and then measured the viral decay kinetics of these two distinct classes of viral variants after the initiation of antiretroviral therapy for asymptomatic and neurologically symptomatic subjects . We found that plasma HIV-1 variants decayed rapidly for both neurologically asymptomatic and symptomatic subjects , indicating that short-lived cells , presumably activated CD4+ T cells , are the predominant source of virus in the periphery during all disease stages . Additionally , shared and compartmentalized variants in the CSF of seven asymptomatic subjects decayed rapidly , with a mean half-life of 1 . 35 and 1 . 57 days , respectively . These decay rates are consistent with our previous study of four asymptomatic subjects [24] . HIV-1 viral load decays in the peripheral blood with the same half-life as a productively infected CD4+ T cell ( approximately 2 days; [25] , [27] , [28] ) , so it is most likely that CSF-shared and compartmentalized virus in asymptomatic subjects is originating from a short-lived cell type , such as a CD4+ T cell . The level of HIV-1 compartmentalization in the CSF in these asymptomatic subjects varied , and we noted that there was a trend of increased CSF pleocytosis in the asymptomatic subjects with greater compartmentalization . We also examined HIV-1 decay in subjects with neurological disease that were starting HAART . Rapid viral decay was measured for CSF-compartmentalized variants after the initiation of HAART for four HAD subjects ( t1/2 mean = 2 . 27 days ) , while slow viral decay was measured for CSF-compartmentalized variants from the other four subjects with neurological disease ( t1/2 range = 9 . 85 days to no initial decay ) . It is known that HIV-1 may persist in the CNS during antiretroviral therapy due to insufficient CNS penetration of some antiretroviral drugs [2] , [9]–[11] . We determined that the slow decay detected for CSF-compartmentalized variants was not associated with poor CNS drug penetration , the presence of drug resistant virus in the CSF , or the detection of X4-like virus genotypes . It has been suggested that HIV-1 produced by long-lived cell lineages such as macrophages , microglia , and resting CD4+ T cells most likely decays with a half-life of 14 days or greater [27]–[29] . The longer half-lives we detected suggest that compartmentalized HIV-1 in the CSF of some neurologically symptomatic subjects may be originating from a long-lived cell type . While slower HIV-1 decay was detected for half of the subjects with neurological disease , compartmentalized variants in the CSF of some subjects decayed rapidly . Further analysis revealed that the differential decay measured for CSF-compartmentalized variants in subjects with neurological disease was correlated with the degree of CSF pleocytosis . Four of the eight subjects with HIV-associated neurological disease displayed rapid CSF-compartmentalized variant decay , and this was correlated with higher CSF WBC levels ( moderate to severe pleocytosis ) . The compartmentalized variants detected in the CSF of the four other subjects showed slow or biphasic decay after the initiation of HAART , and this was associated with lower CSF WBC levels ( no or mild pleocytosis ) . Additionally , the subjects with rapid CSF-compartmentalized variant decay had significantly higher CD4 counts than subjects with slow compartmentalized variant decay , indicating that subjects with slow decay of CSF-compartmentalized virus have increased immunodeficiency . We suggest that more profound immunodeficiency results in fewer lymphocytes trafficking into the CNS , which is consistent with the decreased CSF WBC counts for the subjects with slow decay . HIV-1 infection can be associated with CSF pleocytosis in neurologically symptomatic subjects , asymptomatic subjects , and individuals lacking any CNS opportunistic infections [48] . Additionally , some studies have shown that CSF WBC levels are correlated with CSF HIV-1 RNA concentrations [49] , [65] , [66] , and CSF pleocytosis has been shown to decrease after the initiation of antiretroviral therapy [48] . In this current study we found an association between the extent of immunodeficiency , CSF pleocytosis and rapid HIV-1 decay kinetics for compartmentalized variants in the CSF of neurologically symptomatic subjects , although the strength of the interpretation is somewhat limited by our small sample size . Taken together , we have developed a model of HIV-1 infection in the CNS in the context of neurological disease ( Figure 5 ) . The model has several features that incorporate viral genetic compartmentalization , CSF pleocytosis , and viral decay rates in the CSF as a measure of the virus-producing cell . First , the majority of the virus detected in the CSF of a subset of asymptomatic subjects is imported from the peripheral blood ( Figure 5A ) . HIV-1-infected CD4+ T cells in the peripheral blood release virus that is detectable in the blood plasma and the CSF and that decays rapidly upon the start of antiretroviral therapy , representing the relatively fast turnover of uninfected CD4+ T cells . HIV-1-infected CD4+ T cells in the peripheral blood can migrate from the periphery into the CNS and secrete virus in the CNS that is genetically similar to virus in the peripheral blood . No or only mild pleocytosis was detected for this group of asymptomatic subjects , and we suggest this represents minimal inflammation in the CNS . It is possible that some CNS HIV-1 variants are independently replicating at a low level in these asymptomatic subjects , but we were not able to detect these genetic variants above the background of virus recently imported from the periphery . In these subjects virus decays with the half life of peripheral T cells , the presumed source of the virus . A second pattern exists for the other asymptomatic subjects and also for a subset of the neurologically symptomatic subjects . There is increased compartmentalization of HIV-1 in this subset of asymptomatic subjects , and the majority of virus detected in the CSF is compartmentalized in HIV-1-infected individuals with severe neurological disease . In addition , both of these groups have increased pleocytosis . We found that CSF-compartmentalized variants decayed rapidly upon the initiation of antiretroviral therapy in these remaining asymptomatic subjects and in this subset of four subjects with HIV-1–associated dementia . It is possible that compartmentalized variants detected in these subjects are produced by long-lived cells in the CNS; however the majority of the compartmentalized virus is produced by a short-lived cell type . We propose that compartmentalized virus may be maintained by long-lived cells in the CNS and that this virus is amplified by short-lived trafficking CD4+ T cells to detectable levels in the CSF for asymptomatic subjects , and to high titers in the CSF of HAD subjects ( Figure 5B ) . The elevated level of pleocytosis is indicative of an inflammatory response , most likely to the autonomously replicating virus . Increased levels of CSF white blood cells may account for the influx of T cells that could be the source of the short-lived cells that are amplifying the compartmentalized virus . We would expect that most of the infiltrating T cells are HIV-specific , although some lymphocytes may be migrating into the CNS due to a general inflammatory environment . The asymptomatic subjects in this group have the hallmarks of viral pathogenesis associated with neurological disease and may be at risk for transition to HAD . Third , we detected slow decay of compartmentalized variants in the CSF for the four remaining subjects with neurological disease . These subjects shared the feature of viral genetic compartmentalization but did not show high levels of pleocytosis . Additionally , this subject group had the lowest blood CD4+ T cell counts ( Table 1 ) , indicating a state of increased immunodeficiency . We suggest that these subjects have more profound immunodeficiency , which would allow even more extensive viral replication and compartmentalization in the CNS ( Figure 5C ) . Increased immunodeficiency would result in reduced trafficking of CD4+ T cells into the CNS , so these cells would no longer be present to amplify virus from local CNS tissue , consistent with the reduced pleocytosis in this group . The slow decay rate of virus in the CSF in the absence of inflammatory cells suggests that compartmentalized HIV-1 in the CNS of these HAD subjects is originating from a long-lived cell type , such as perivascular macrophages and/or microglia in the CNS . Virus is unlikely to be coming from T cells that are persisting in the absence of immune-mediated killing since there is still rapid viral decay in the peripheral blood . The CSF viral loads of all four subjects displaying slow decay were high , similar to subjects with rapid viral decay , suggesting that a large amount of compartmentalized virus is being produced by longer-lived cells in the CNS . This may suggest that peripheral , uninfected monocytes may migrate into the brain parenchyma and differentiate into perivascular macrophages to levels that can sustain high viral loads in the CSF . An influx of monocytes into the CNS could also allow the entrance of peripherally-infected monocytes , which would explain the slower decay we detected for shared variants in the CSF of these subjects . Our studies support a model where increasing levels of autonomous viral replication in the CNS first induces an inflammatory state that then progresses to neurologic disease with increasing immunodeficiency . More profound immunodeficiency ultimately reveals long-lived cells that are able to maintain independent replication of virus in the CNS . Several env gene markers have been described in viral sequences taken at autopsy and linked to the ability of HIV-1 to infect macrophages [12] , [13] . The CSF provides an alternative window on these viral sequences where the evolution of the virus and its properties can be followed over time and into the disease state . Viral genetic compartmentalization and other markers of CNS inflammation could also play an important role in defining subjects at risk of progression to neuropathogenesis in the absence of therapeutic intervention .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The study was approved by the Institutional Review Board of the University of California at San Francisco . All subjects provided written informed consent for the collection of samples and subsequent analysis . The samples from study subjects used for variant decay analysis were collected during previous studies carried out at the University of California at San Francisco . All subjects used in this study were HIV-1-infected subjects that were initiating highly-active antiretroviral therapy . Subjects 4012 , 4013 , 4014 , 5002 , 5003 , and 5005 were recruited from a study examining antiretroviral therapy responses in the CSF , and are described in more detail in ref . [67] . Serial blood plasma and cerebrospinal fluid ( CSF ) samples were collected at baseline prior to the start of therapy and at varying intervals thereafter . Plasma and CSF HIV-1 RNA concentrations were determined using the Amplicor HIV Monitor kit ( Roche ) . CSF white blood cell counts were measured by routine methods in the San Francisco General Clinical Laboratory . Drug resistance mutations were analyzed for CSF samples of subjects 4013 , 5002 , 4059 , and 7115 using the TRUGENE® HIV-1 Genotyping Test Resistance Report using GuideLines™ Rules 12 . 0 ( Bayer HealthCare ) . Viral RNA isolation , RT–PCR , and HTA procedures were conducted as previously described [24] , [39]–[41] . Briefly , viral RNA was isolated from blood plasma and CSF samples ( 140 µl ) using the QIAmp Viral RNA kit ( Qiagen ) . Prior to RNA isolation , all CSF samples were centrifuged at 2 , 500 rpm for 5 minutes to remove any contaminating cellular debris . Samples with viral RNA levels less than 10 , 000 copies/ml were pelleted ( 0 . 5–1 . 0 ml ) by centrifugation at 25 , 000×g for 1 . 5 hours prior to RNA isolation to increase template number and improve sampling . Reverse transcription and PCR amplification of the V1/V2 , V3 , and V4/V5 regions of env were conducted with 5 µl of purified RNA ( from 60 µl column elution volume ) using primers that have been previously described for V1/V2 [39] , [41] , V3 [51]; and V4/V5 [41] and using the Qiagen One-Step RT-PCR kit ( Qiagen ) as per manufacturer's instructions . Heteroduplex annealing reactions were conducted as previously described [39] , [40] . The heteroduplexes were separated by 6% native polyacrylamide gel electrophoresis for V1/V2 and V4/V5 HTA [24] , [39] , and by 12% PAGE for biotin-V3 HTA [51] . The HTA probes used in these studies have been previously reported: V1/V2 Ba-L probe [39] , [41] , V1/V2 JRFL probe [39] , [41] , V4/V5 NL4-3 probe [24] , V4/V5 YU2 probe [41] , and the V3 Mut-1 probe [51] . The HTA gels were dried under vacuum , and bands were visualized by autoradiography . For the biotin-V3 HTA procedure , the desired labeled bands were excised from the dried gels , the DNA was purified from the gel , and the V3 sequence was obtained as previously described [51] . Duplicate RT-PCR products were analyzed by HTA for each sample to validate sampling and ensure reproducibility of the HTA pattern at each time point . Any time points where the HTA pattern between the two replicates differed significantly ( >20% ) were not used in the data analysis . Percent difference values between plasma and CSF viral populations were calculated as previously described [39] , [41] . The dried HTA gels were exposed to a PhosphorImager screen , and the relative abundance of each detected viral variant ( heteroduplex ) was calculated using ImageQuant software ( Molecular Dynamics ) . The variant RNA concentration was calculated by multiplying the relative abundance of each individual variant by the total HIV-1 RNA concentration for that sample . Variants in the CSF were considered compartmentalized by HTA if they were either unique to the CSF or if they had a substantially higher copy number in the CSF compared to the plasma . Compartmentalized variant half-lives were calculated using the time points when the viral load initially dropped after the start of antiretroviral therapy . | Infection of the central nervous system ( CNS ) with human immunodeficiency virus type 1 ( HIV-1 ) can lead to the development of HIV-1–associated dementia , a severe neurological disease that results in cognitive and motor impairment . Individuals that are chronically infected with HIV-1 sometimes display unique viral variants in their cerebrospinal fluid ( CSF ) that are not detected in the blood virus population , termed CSF-compartmentalized variants . The cell type that produces CSF-compartmentalized virus throughout the course of infection has not been determined . We used a sensitive assay to detect compartmentalized variants in the CSF of subjects with and without neurological disease , and then measured the decay kinetics of compartmentalized virus when subjects were starting antiretroviral therapy . We found that compartmentalized virus decays rapidly in asymptomatic subjects . Additionally , we detected differential decay ( i . e . rapid or slow ) in subjects with neurological disease , and this was associated with the number of white blood cells in the CSF . Our data supports a model of HIV-1 infection in the CNS where compartmentalized virus is produced by a long-lived cell type ( slow decay ) , and this virus can be amplified by short-lived cells ( rapid decay ) that traffic into the CNS , but is increasingly produced from long-lived cells in the immunodeficient state . |
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CLK-2/TEL2 is essential for viability from yeasts to vertebrates , but its essential functions remain ill defined . CLK-2/TEL2 was initially implicated in telomere length regulation in budding yeast , but work in Caenorhabditis elegans has uncovered a function in DNA damage response signalling . Subsequently , DNA damage signalling defects associated with CLK-2/TEL2 have been confirmed in yeast and human cells . The CLK-2/TEL2 interaction with the ATM and ATR DNA damage sensor kinases and its requirement for their stability led to the proposal that CLK-2/TEL2 mutants might phenocopy ATM and/or ATR depletion . We use C . elegans to dissect developmental and cell cycle related roles of CLK-2 . Temperature sensitive ( ts ) clk-2 mutants accumulate genomic instability and show a delay of embryonic cell cycle timing . This delay partially depends on the worm p53 homolog CEP-1 and is rescued by co-depletion of the DNA replication checkpoint proteins ATL-1 ( C . elegans ATR ) and CHK-1 . In addition , clk-2 ts mutants show a spindle orientation defect in the eight cell stages that lead to major cell fate transitions . clk-2 deletion worms progress through embryogenesis and larval development by maternal rescue but become sterile and halt germ cell cycle progression . Unlike ATL-1 depleted germ cells , clk-2–null germ cells do not accumulate DNA double-strand breaks . Rather , clk-2 mutant germ cells arrest with duplicated centrosomes but without mitotic spindles in an early prophase like stage . This germ cell cycle arrest does not depend on cep-1 , the DNA replication , or the spindle checkpoint . Our analysis shows that CLK-2 depletion does not phenocopy PIKK kinase depletion . Rather , we implicate CLK-2 in multiple developmental and cell cycle related processes and show that CLK-2 and ATR have antagonising functions during early C . elegans embryonic development .
CLK-2/TEL2 is a DNA damage checkpoint gene which is essential for viability in budding yeast , C . elegans and vertebrates . DNA damage checkpoints are essential for maintaining genome stability in response to DNA damage and act by coordinating DNA repair and by triggering a transient cell cycle arrest , or apoptosis of affected cells . The loading of a pair of highly conserved PI3 kinase-related kinases ( PIKKs ) , ATM and ATR , to sites of DNA damage acts at the apex of DNA damage response pathways [1] . These kinases have overlapping substrate specificity and phosphorylate multiple targets including the kinases Chk1 and Chk2 [2] , [3] . The first C . elegans clk-2 allele initially referred as rad-5 ( mn159 ) , was isolated in a screen for C . elegans mutants hypersensitive for ionizing irradiation [4] . C . elegans clk-2 temperature sensitive mutants are embryonic lethal at the restrictive temperature of 25°C [5]–[7] . However , the cause of this embryonic lethality is not known . At the “permissive temperature” of 20°C both known clk-2 temperature sensitive alleles lead to a slow growth phenotype that is particularly evident in the clk-2 ( qm37 ) allele , which also shows a reduction in cyclic behaviours such as pharyngeal pumping [5] , [6] . Furthermore , both alleles are defective in various DNA damage responses including DNA damage-induced germ cell apoptosis and cell cycle arrest when propagated at 20°C [5] , [6] . CLK-2/TEL2 has been implicated in S-phase regulation and DNA damage checkpoint responses in fission yeast [8] , [9] , and human CLK-2/TEL2 is required for the DNA replication checkpoint and for DNA crosslink repair [10] . Human and yeast CLK-2/TEL2 directly bind to all PI3K-related protein kinases ( PIKKs ) and are considered to be required for maintaining their stability [8] , [9] . Here we use the C . elegans experimental system to assess the essential functions of CLK-2 during development and cell cycle control . In worms cell cycle progression in early embryos occurs very rapidly , with alternating M and S phases and an apparent lack of gap phases [11] . The timing and pattern of cell division and differentiation is invariant and has been fully characterized [12] . Aberrant embryonic development can therefore be traced by cell lineage analysis and resolved at a cellular level [13] . A relatively high level of DNA damage is tolerated during rapid embryonic cell divisions , possibly as a result of natural selection that favours a rapid pace of replication at the expense of genome integrity [14] . Only high levels of DNA damage or replication failure lead to a DNA damage checkpoint-dependent slowing of cell cycle progression [14] . Interestingly , the DNA damage checkpoint is used during early embryogenesis to contribute to the asymmetry of the first zygotic cell division [15] . In contrast to this , cell proliferation is much slower in the C . elegans germline and DNA damage checkpoint signalling is much more sensitive [11] . The germline is the only proliferative tissue in adult worms . The gonad contains various germ cell types that are arranged in an ordered distal to proximal gradient of differentiation [16] , [17] . The distal end of the gonad is comprised of a mitotic stem cell compartment , which is followed by the transition zone where entry into meiotic prophase occurs . Proximal to the transition zone most germ cells are in meiotic pachytene and subsequently complete meiosis and concomitantly undergoing oogenesis in the proximal gonad . DNA replication failure and DNA double strand breaks lead to a prolonged cell cycle arrest of mitotic germ cells and to apoptosis of meiotic pachytene germ cells [18] . In this DNA damage response pathway CLK-2 and ATL-1 act as upstream DNA damage signalling molecules , while the worm p53 like gene cep-1 is only required for apoptosis [19] , [20] . Thus , CLK-2 and ATL-1 are part of sensitive germ cell DNA damage checkpoint pathways that ensure the faithful transmission of genetic information from one worm generation to the next . C . elegans clk-2 ts mutants show that CLK-2 is required for embryonic development [6] , [7] . As these mutants show an increased level of DNA damage in germ cells at the restrictive temperature , the embryonic lethality might be caused by the accumulation of DNA damage that ultimately may result in the death of the embryo [21] . Given that ATR stability depends on CLK-2 [8] , [9] , the depletion of CLK-2 might phenocopy the atl-1 ( worm ATR ) mutant phenotype , which is germline sterility associated with massive levels of DNA double strand breaks [22] . Furthermore , given that CLK-2 is required for the stability of all PIKKs clk-2 mutations might mimic the phenotype of depleting other PIKKs such as TOR-1 , implicated in nutrient sensing [23] and SMG1 , a kinase involved in nonsense-mediated mRNA decay [24] . Finally , loss of CLK-2 function might result in distinct developmental defects not directly predicted from failing to maintain normal levels of PIKKs or from potential DNA replication and/or DNA damage signalling defects . In this study , we assess the essential defects associated with clk-2 by analysing embryonic cell divisions by cell lineage analysis and by exploiting the C . elegans germline system . We show that clk-2 mutants exhibit defects in early embryonic development and in germline cell cycle progression . These phenotypes do not overlap with reported C . elegans PIKK deletion phenotypes .
We wished to determine why clk-2 mutants fail to complete embryogenesis . We therefore started our analysis by following the embryonic development of the two available recessive clk-2 thermosensitive ( ts ) mutants , mn159 and qm37 , ( Figure S1 ) by cell lineage analysis using 4D microscopy . Analysis of clk-2 mutant lineages at the restrictive temperature of 25°C revealed that asymmetric cell divisions occurred normally during the first three embryonic cell cycles as previously reported [5] , [6] but that cell division timing of all cells was delayed compared to wild type ( Figure 1A , B , Table S1 ) . This delay was more pronounced in clk-2 ( qm37 ) than in clk-2 ( mn159 ) ( Figure 1B , Table S1 ) . In the depicted recordings , the wild type embryo is at the 4-cell stage 11 min after cytokinesis of the P0 cell while the clk-2 ( mn159 ) embryo is about to reach the three cell stage with the AB cell approaching cytokinesis ( Figure 1C ) . The depicted clk-2 ( qm37 ) embryo is at the two cell stage with the AB blastomere just having undergone nuclear envelope breakdown ( Figure 1C ) . Thirty-one minutes after P0 cytokinesis wild type embryos are at the 8-cell stage while both clk-2 mutants are in the 6-cell stage . We next aimed to determine the cause of the cell cycle delay associated with clk-2 mutants . Given that clk-2 ( mn159 ) worms show increased DNA double strand breaks in the mitotic zone of the adult C . elegans germline at the restrictive temperature [22] , we reasoned that the cell cycle delay in clk-2 ( mn159 ) and ( qm37 ) embryos might be due to excessive DNA damage , potentially resulting from compromised DNA replication . We therefore tested whether RAD-51 foci , which are indicative of processed DNA double strand breaks or stalled replication forks [25] , accumulate in clk-2 embryos at the restrictive temperature . We indeed observed increased levels of RAD-51 foci in embryos examined between the 100 and 200 cell stage in both clk-2 ( mn159 ) ( 2 . 14±0 . 62 foci/nucleus n = 7 embryos ) and clk-2 ( qm37 ) ( 0 . 97±0 . 19 foci/nucleus n = 8 ) mutants compared to wild type ( 0 . 2±0 . 02 foci/nucleus n = 6 ) ( Figure 2A ) . These results indicate that clk-2 mutants display a delay in embryonic cell cycle timing and increased genomic instability . Given the delay in cell division timing and the accumulation of RAD-51 foci in clk-2 mutants , we asked if the delay is due to the activation of the DNA replication checkpoint . Previous studies showed that the ATL-1/CHK-1 checkpoint is needed for sensing replication failure in C . elegans embryos [15] . Furthermore , the ATL-1/CHK-1 checkpoint contributes to developmental asymmetry by being in part responsible for the DNA replication delay in the P1 cell . Co-depletion of atl-1 and chk-1 is needed to fully inactivate the DNA replication checkpoint [15] . We observed that upon atl-1/chk-1 depletion cell cycle timing is faster beyond the first embryonic cell division ( Figure 2B , Table S1 ) . We therefore conclude that the ATL-1/CHK-1 pathway acts in normal C . elegans early embryonic development to slow down cell cycle progression . As expected , atl-1/chk-1 ( RNAi ) rescued the prolonged cell cycle delay associated with depleting the DIV-1 DNA polymerase primase alpha-subunit [15] ( Figure 2B , Table S1 ) . Importantly , RNAi-mediated atl-1/chk-1 depletion largely rescued the delay in cell division timing associated with both clk-2 mutants ( Figure 2B , Table S1 ) . Our results thus indicate that clk-2 ( mn159 ) and clk-2 ( qm37 ) mutations result in increased DNA damage , which triggers the ATL-1/CHK-1 checkpoint . It has previously been shown that embryonic lethality associated with dut-1 ( RNAi ) , which leads to the misincorporation of uracil during DNA replication is partially rescued by clk-2 ( RNAi ) and chk-1 ( RNAi ) as well as by the clk-2 ( mn159 ) mutation [26] . These results hint towards a checkpoint function of CLK-2 in embryonic cell divisions . We therefore assessed if CLK-2 functions in DNA damage checkpoint signalling in embryos and asked if the cell cycle delay caused by div-1 ( RNAi ) depends on clk-2 . We found that the delay in S-phase progression of the P1 cell caused by div-1 ( RNAi ) is partially rescued by both clk-2 ts alleles ( Table S2 ) . These results suggest that CLK-2 has a checkpoint function in early embryos . However , the AB cell cycle delay is not rescued likely due to the above described cell cycle delay associated with clk-2 ts mutations . It was reported that CLK-2 and CEP-1 , the single C . elegans p53 homolog , cooperate in pathways leading to germ cell apoptosis upon treatment with ionizing irradiation ( IR ) [19] , [20] . cep-1 mutants are defective in IR induced apoptosis but are wild type for IR induced cell cycle arrest and DNA repair suggesting that CEP-1 acts downstream of CLK-2 in the DNA damage response pathway . Derry et al . also observed that a cep-1 deletion partially rescues the slow growth phenotype associated with clk-2 ( mn159 ) and clk-2 ( qm37 ) [27] . We first confirmed the reported partial rescue of the slow growth phenotype of clk-2 ( mn159 ) and ( qm37 ) by the cep-1 ( lg12501 ) deletion ( Figure S2A/B ) [27] . Given the rescue of the clk-2 slow growth phenotype by cep-1 we wondered if cep-1 ( lg12501 ) would suppress the embryonic cell cycle delay of clk-2 mutants . cep-1 ( lg12501 ) , which results in a slightly slower developmental rate compared to wild type , partially rescued the embryonic cell cycle delay associated with both clk-2 alleles ( Figure 2C , Table S1 ) . In contrast , the cell cycle delay in div-1 embryos was not rescued by cep-1 ( lg12501 ) ( Figure 2C , Table S1 ) . This may indicate that distinct DNA lesions occurring in clk-2 mutant embryos but not a general failure of DNA replication as it occurs in div-1 mutations leads to the activation of a cep-1 dependent checkpoint during early C . elegans embryogenesis . In addition , we found that clk-2 ( mn159 ) or ( qm37 ) ; cep-1 ( lg12501 ) double mutants develop to a later embryonic stage and often arrest in morphogenesis stage , with clear signs of tissue differentiation such as the formation of the pharynx or the appearance of gut granules . This late arrest never occurs in either clk-2 single mutant or atl-1/chk-1 ( RNAi ) depleted clk-2 embryos ( Figure 3A , B ) . Given the rescue of the clk-2 mutant cell cycle delay by a cep-1 deletion , we asked if CEP-1 might be modified in clk-2 mutant worms and assayed for changes in its abundance by western blotting . We found that the levels of CEP-1 protein were markedly increased in extracts prepared from synchronised adult clk-2 ( mn159 ) and clk-2 ( qm37 ) worms compared to wild type , indicating that the checkpoint triggered by clk-2 mutations leads to the accumulation of CEP-1 ( Figure 3C ) . This accumulation of CEP-1 likely results from increased CEP-1 in embryos . CEP-1 germline levels are not increased in clk-2 mutants ( data not shown ) and besides embryonic and germline expression CEP-1 is only expressed in very few cells in the pharynx [19] ( data not shown ) . In summary , we show that deleting cep-1 partially rescues the slow growth phenotype associated with clk-2 mutants and that CEP-1 accumulates in clk-2 mutants . It will be interesting to determine the mechanism of CEP-1 accumulation and if other embryonic defects also lead to CEP-1 accumulation . We speculated that there might also be phenotypes occurring in early clk-2 ( mn159 ) and clk-2 ( qm37 ) embryos that are not linked to the cell cycle delay of clk-2 mutants . Indeed , our lineage analysis revealed that 2 out of 7 clk-2 ( mn159 ) and 6 out of 12 clk-2 ( qm37 ) mutant embryos recorded at 25°C exhibit a distinct lineage defect ( Figure 4 ) . We found an abnormal spindle rotation of the ABar cell ( the anterior right granddaughter of the AB founder cell ) at the 8-cell stage in clk-2 mutants . In the six clk-2 ( qm37 ) embryos showing the abnormal spindle rotation ABar divided on average 48±6° off the a-p axis placing ABarp towards the ventral side of the embryo . In five wild-type embryos ABar divided on average by 54±14° off the a-p axis placing ABarp towards the dorsal side of the embryo . The ABar spindle in the six affected clk-2 ( qm37 ) embryos thus derived 102° from wild type . This abnormal rotation gives rise to mispositioned ABarp and ABara daughters at the 12 cell stage , bringing ABarp instead of ABara in contact to the MS blastomere ( Figure 4 , Videos S1 , S2 , and S3 ) . The MS blastomere emits an inductive signal which in wild type is part of the left versus right cell fate decision ( Figure 4 , arrows , Videos S1 , S2 , and S3 ) [28]–[30] . As a consequence cell fates of the early founder cells are changed in the clk-2 mutants , the ABara and ABarp blastomeres adopt the fates of their left counterparts , ABala and ABalp , respectively ( data not shown ) . This change in cell fate identity leads to embryonic death . A failure of the ABar blastomere to rotate the spindle properly can be taken as an indication that spindles are generally not polarised properly [31] , which is a hallmark of mutants in mom-2 ( wnt ) and mom-5 ( frizzled ) [32] . Future work will reveal , if clk-2 influences the Wnt pathway directly or if the observed clk-2 phenotype is independent of this pathway . To further assess potential developmental and cell proliferation-associated defects of clk-2 mutants , we analysed the germline of clk-2 mutants . clk-2 ts mutants are deficient in responding to DNA damaging agents [5] at the “permissive temperature” of 20°C and shifting clk-2 ( mn159 ) mutants to 25°C at the L4 stage leads to the accumulation of DNA damage in affected germ cells [22] . However , these studies were done with the clk-2 ts alleles . As it is not clear whether they act as null alleles at 25°C we analysed a clk-2 deletion allele . The clk-2 ( tm1528 ) deletion allele provided by the Japanese C . elegans knockout consortium lacks part of the 5′ region , the first three exons , and a part of the fourth exon ( Figure S1A ) . Western blotting with a CLK-2 specific antibody provided by Simon Boulton failed to detect any CLK-2 protein in clk-2 ( tm1528 ) worm extracts ( Figure S1B ) . We found that the major phenotype associated with the clk-2 ( tm1528 ) deletion mutant kept at 20°C is not embryonic lethality but germline sterility ( Figure 5A , see below ) and that the same phenotype occurs when the clk-2 ( tm1528 ) deletion mutant is kept at 25°C ( data not shown ) . The clk-2 ( tm1528 ) phenotype is recessive ( data not shown ) . Given that clk-2 ( tm1528 ) worms go through embryogenesis whereas clk-2 ( mn159 ) and ( qm37 ) worms arrest during embryogenesis at the restrictive temperature , we assume that clk-2 ( tm1528 ) worms are rescued by maternal contribution . To ascertain that the missing embryonic lethality of the clk-2 ( tm1528 ) mutant is indeed caused by the maternal supply we reviewed the phenotype of clk-2 ( mn159 ) and clk-2 ( qm37 ) worms by shifting those mutants to 25°C at the L1 stage . Under these conditions we found that clk-2 ( qm37 ) worms are 100% sterile similar to clk-2 ( tm1528 ) worms , while the weaker allele mn159 does not lead to sterility ( Figure 5A ) . Both ts alleles , as well as the deletion , lead to a protruding vulva phenotype ( pvl ) ( Figure 5A ) . This phenotype is often associated with sterile germlines and general problems in postembryonic cell cycle progression [33] . clk-2 ( qm37 ) and clk-2 ( tm1528 ) gonades are significantly smaller in size than those of wild type and clk-2 ( mn159 ) mutants and clk-2 ( qm37 ) and ( tm1528 ) gonads showed a dramatic reduction of germ cell numbers ( Figure 5B , Figure S3 ) . This reduction in germ cell numbers and germline sterility was also obtained upon clk-2 RNAi in the weaker clk-2 ( mn159 ) mutant , further indicating that the clk-2 ( qm37 ) and clk-2 ( tm1528 ) germline phenotypes represent the clk-2 null phenotype ( Figure 5B ) . These results are in contrast to a previous report which stated that no sterility of clk-2 ( qm37 ) germlines was observed [6] . The reduced germ cell number raised the question whether CLK-2 is required for germ cell proliferation or germ cell differentiation . To address this question we performed a time course analysis of germline development in wild type and clk-2 ( tm1528 ) worms . We found that both strains have similar numbers of germ cells up to the L4 stage at which point germlines of clk-2 ( tm1528 ) worms stop proliferating ( Figure 5C ) . To further assess if this phenotype is caused by a proliferation defect we took advantage of gld-2 ( q497 ) gld-1 ( q485 ) double mutants which have germlines that do not enter meiosis and are thus entirely proliferative . Comparing gld-2 ( q497 ) gld-1 ( q485 ) germlines to gld-2 ( q497 ) gld-1 ( q485 ) ; clk-2 ( tm1528 ) triple mutant germlines we found that germ cell numbers are dramatically reduced in the triple mutant indicating that clk-2 has a role in germ cell proliferation rather than in germ cell differentiation ( Figure 5D ) . In addition , clk-2 ( tm1528 ) and clk-2 ( qm37 ) germ cells are larger than wild type . This phenotype , which is reminiscent of arrested mitotic germ cells after ionizing irradiation , indicates that cells might stop cell division but continue with cellular growth [18] ( Figure 5B , arrowheads ) . In summary , our data suggest that CLK-2 is required for cell cycle progression in germ cells . Given that clk-2 mutations lead to a DNA damage checkpoint dependent delay of embryonic cell cycle progression ( Figure 2B ) and given that clk-2 ( mn159 ) germ cells showed elevated levels of RAD-51 foci indicative of faulty replication when shifted to the restrictive temperature at the L4 stage [22] , we suspected that the germ cell cycle arrest of the clk-2 ( tm1528 ) mutant might be due to the activation of the DNA damage checkpoint . We therefore examined if RAD-51 foci occur in the mitotic compartment of clk-2 ( tm1528 ) germ cells . To our surprise we found that like in wild type germ cells , RAD-51 was mainly localized in the cytoplasm of clk-2 ( tm1528 ) germ cells and did not form nuclear foci ( Figure 6A , Video S4 , Table 1 ) . In contrast , clk-2 ( mn159 ) shifted to the restrictive temperature of 25°C at the L1 or the L4 stage accumulated RAD-51 foci ( Figure 6A , Video S6 , Table 1 ) while clk-2 ( qm37 ) formed fewer foci ( Video S5 , Table 1 ) . Thus RAD-51 foci accumulate mostly in the weak clk-2 ( mn159 ) allele as reported previously [22] , while less foci formation is observed in clk-2 ( qm37 ) and only very few RAD-51 foci can be found in clk-2 ( tm1528 ) ( Table 1 ) . The defect in RAD-51 foci formation in clk-2 ( tm1528 ) might be due to a cell cycle arrest outside of S-phase or due to a failure to process DNA double strand breaks , which is needed for RAD-51 focus formation . To test whether DNA double stand break processing is defective in clk-2 ( tm1528 ) mutants we tested whether focus formation occurred after inducing DNA double strand breaks by exposing worms to ionizing irradiation . Irradiation-induced RAD-51 focus formation indicated that double strand break processing occurs normally in clk-2 ( tm1528 ) worms ( Figure 6B ) . Summing up , these results indicate that the clk-2 ( tm1528 ) deletion does not lead to excessive DNA damage and that CLK-2 is not needed for DNA double strand break processing . To further analyse the cell cycle arrest associated with CLK-2 depletion , we asked if clk-2 ( tm1528 ) germ cells arrest in a distinct cell cycle stage . To facilitate this analysis we first established G2 and M phase cell cycle markers . Prior to mitotic entry Cdk1 is kept inactive by Tyr-15 phosphorylation [34] , [35] . An antibody recognizing Tyr-15 phosphorylation of mammalian Cdk1 cross reacts with the corresponding phospho-epitope of C . elegans NCC-1/CDK-1 . Phospho-NCC-1/CDK-1 can be detected until late prophase in worm embryonic divisions [36] . To confirm that phospho-NCC-1 is indeed indicative of G2/M arrested germ cells , we irradiated wild type germlines and found that all germ cells arrested in G2 with high levels of NCC-1P-Tyr15 ( Figure 7A ) . We observed NCC-1 Tyr-15 phosphorylation in only few wild type and clk-2 ( mn159 ) mitotic germ cells but found that all clk-2 ( tm1528 ) cells and clk-2 ( qm37 ) cells showed high levels of NCC-1 Tyr-15 phosphorylation even in the absence of ionizing irradiation ( Figure 7A ) . The clk-2 prophase arrest phenotype might be caused by a direct prophase defect or alternatively by replication defects which could trigger a checkpoint-dependent late G2/M cell cycle arrest . To assess these possibilities , we depleted atl-1/chk-1 in clk-2 ( tm1528 ) worms . The efficiency of atl-1/chk-1 ( RNAi ) depletion was confirmed by observing germ cell micronuclei [22] and by the embryonic lethality of the progeny of RNAi depleted wild type worms ( data not shown ) . We found that all cells of clk-2 ( tm1528 ) atl-1/chk-1 ( RNAi ) germlines were NCC-1 Tyr-15 phosphorylation-positive ( Figure 7B ) . We therefore conclude that cell cycle arrest is unlikely to be mediated by activation of the ATL-1/CHK-1 DNA damage checkpoint ( Figure 7B ) . To further analyze the cell cycle stage of clk-2 ( tm1528 ) germ cells we also used antibodies against phosphorylated histone H3 ( P-H3 ) . In C . elegans P-H3 staining can be observed in cells from prophase/early prometaphase to late telophase [37] . When wild type gonads were stained with anti-P-H3 antibody only 2–5 nuclei per gonad arm were stained and all stained cells displayed a metaphase-like morphology . While we observed the same phenotype for clk-2 ( mn159 ) worms grown at 25°C , all germ cells were P-H3 positive in clk-2 ( qm37 ) worms propagated at 25°C and in clk-2 ( tm1528 ) worms ( Figure 7C ) . However , P-H3 positive cells did not show a metaphase-like morphology . Rather , in most nuclei chromosomes appear to be partially condensed but not aligned at the metaphase plate suggesting a prophase or very early pro-metaphase arrest . This arrest neither depends on atl-1/chk-1 ( Figure 7D ) , nor on cep-1 ( Figure S4 ) . Thus while cep-1 and atl-1/chk-1 are required for delaying cell cycle progression in clk-2 embryos , the germ cell cycle arrest observed in clk-2 ( tm1528 ) mutants does not depend on either of these genes . Given the early prophase arrest we also assessed centrosome behaviour in clk-2 ( tm1528 ) germ cells . Centrosome duplication occurs during S-phase and centrosomes split during late G2 phase . In prophase , centrosome maturation is an essential prerequisite for the assembly of the mitotic spindle , and centrosomes can be visualized through the accumulation of α and γ-tubulin ( for review see , [38] ) . Increased α-tubulin nucleation is followed by the formation of mitotic spindles [38] . When gonads were immunostained for γ-tubulin [39] to label centrosomes we found that centrosome duplication occurs normally in clk-2 ( tm1528 ) worms ( Figure 8A ) . Furthermore , double immunostaining for γ-tubulin and α-tubulin ( Figure 8B ) showed that several wild type germ cells exhibited accumulated α-tubulin , indicative of centrosome maturation and imminent spindle formation . In contrast , no α-tubulin accumulation and no spindle formation could be observed in clk-2 ( tm1528 ) germ cells , although germ cells with duplicated and separated centrosomes were present ( Figure 8B ) . These results raise the possibility that the prophase-like cell cycle arrest phenotype of clk-2 ( tm1528 ) germ cells might be due to the activation of the spindle assembly checkpoint , which responds to defects in spindle formation and kinetochore-microtubule attachment and blocks anaphase progression until correct bi-orientation has occurred [40] . We therefore tested if the RNAi depletion of the C . elegans MAD1 spindle checkpoint gene ortholog mdf-1 [41] rescues the cell cycle arrest phenotype observed in clk-2 ( tm1528 ) worms . Even though both wild type and clk-2 ( tm1528 ) strains displayed the typical previously described pre-meiotic like morphology of mdf-1 ( RNAi ) germ cells [41] ( Figure 8C ) , mdf-1 ( RNAi ) clk-2 ( tm1528 ) germ cells still uniformly stained P-H3 positive ( Figure 8C ) . In summary , our analysis of clk-2 germlines suggests that clk-2 is essential for cell proliferation and that cells deficient in CLK-2 arrest in prophase without forming a mitotic spindle . The CLK-2 cell cycle arrest phenotype is independent of DNA damage and spindle checkpoint activation .
It has recently been shown that CLK-2/TEL2 interacts with all PIKKs in budding and fission yeast as well as in mammals [8] , [9] , [42] , [43] . CLK-2/TEL2 depletion leads to reduced levels of PIKKs , and using CLK-2/TEL2 mouse knockout lines it was shown that the half life of PIKKs is reduced in those cell lines [9] . This finding together with the notion that PIKK dependent checkpoint signalling is reduced in cells lacking CLK-2/TEL2 led to the hypothesis that CLK-2/TEL2 might function in checkpoint signalling by regulating PIKK kinase levels . Given the conservation of the CLK-2 PIKK interaction it is likely that this interaction also occurs in C . elegans , albeit we could not confirm this since we were unable to generate specific CLK-2 and ATR antibodies suitable for immunoprecipitation from worm extracts ( data not shown ) . Nevertheless , our genetic results suggest that , at least in C . elegans , CLK-2 depletion does not phenocopy PIKK depletion phenotypes ( summarized in Table 2 ) . atl-1/ATR and clk-2 mutations have opposite phenotypes during embryonic development and a clk-2 deletion , unlike atl-1 depletion [22] , does not lead to mitotic germ cell catastrophe . Concerning ATM , this worm PIKK is primarily involved in responding to UV-induced DNA damage where , like CLK-2 it is required for UV-induced apoptosis [44] . Furthermore , an atm-1 deletion only shows weak defects in responding to ionizing irradiation [44] , unlike clk-2 ( qm37 ) and clk-2 ( mn159 ) point mutations . Similarly , clk-2 deleted worms do not resemble worms depleted for tor-1 , which arrest in the L3 larval stage and show concomitant gonadal and intestinal degradation [45] . It is possible that partial tor-1 depletion which results in a slow growth phenotype and enhanced longevity [46] , overlaps with the clk-2 ( qm37 ) phenotypes that include a slow growth and a relatively weak longevity phenotype [6] , [7] , [21] . However , the enhanced life span of clk-2 ( qm37 ) worms is rather weak and clk-2 ( tm1528 ) life span is dramatically reduced compared to wild type ( data not shown ) . Our evidence suggesting that CLK-2/TEL2 might not predominately act by regulating PIKK stability is also supported by recent evidence from the budding yeast system . While steady state levels of the budding yeast ATR homologue TEL1 are somewhat reduced in tel2-1 mutants , it was shown that TEL2 is required for the loading of TEL1 to sites of DNA damage [47] . In addition , the finding that TEL2 binding to the budding yeast MEC1 ATM like kinase is lost in tel2-1 mutants while MEC-1 remains functionally intact [48] , points towards the possibility that CLK-2 be able to regulate ATM and ATR PIKKs by mechanisms not directly related to TEL2 PIKK interaction . We observed that cell cycle progression in early clk-2 mutant embryos is generally delayed and is associated with DNA damage accumulation ( for summary see Table 2 ) . The clk-2 cell cycle delay is partially suppressed by atl-1/chk-1 and cep-1 deletion . These results are surprising in the light of previous reports suggesting that CLK-2 and ATL-1 might act together in C . elegans DNA damage response signalling in germ cells [22] . These two proteins might thus act in different pathways during C . elegans embryogenesis . Our results suggest that ATL-1 is active in clk-2 ts mutants . Thus even if there is a reduced level of ATL-1 protein in clk-2 mutant worms , enough ATL-1 is left to cause a cell cycle delay . In embryos depleted for DNA replication factors cell cycle progression is delayed starting from the very first cell cycle and upon division of the zygote the posterior daughter , referred to as the P1 cell , is particularly strongly affected [49] . This delay depends on the ATL-1/CHK-1 dependent DNA damage checkpoint . The relatively weak replication defect of CLK-2 could be due to partial loss of function in the clk-2 ( mn159 ) or ( qm37 ) point mutants or due to CLK-2 being required for faithful DNA replication rather than replication per se . Our genetic analysis implicates the C . elegans p53-like gene cep-1 in the cell cycle delay associated with clk-2 mutants during embryonic cell divisions . Interestingly , deleting cep-1 alleviates the cell cycle delay of clk-2 mutants but does not have an effect on the delay caused by div-1 mutants . Thus distinct DNA replication defects caused by div-1 and clk-2 depletion might lead to differential checkpoint activation . Our results implicate cep-1 in an embryonic DNA integrity checkpoint . Future studies will be required to address how cep-1 can slow embryonic cell cycle progression and which exact replication defects trigger CEP-1 accumulation . Despite a possible role of clk-2 in embryonic DNA replication , clk-2 ( tm1528 ) germ cells still undergo replication and do not display overt signs of genome instability . Analysis of clk-2 ( tm1528 ) deletion mutants reveals that these worms progress through embryogenesis due to maternal rescue but then halt cell cycle progression in the germline . This arrest is distinct from the cell cycle arrest induced by DNA damage and does not require the ATL-1/CHK-1 DNA damage checkpoint and CEP-1 . Similarly , this arrest does not require the spindle checkpoint . It will be interesting to assess if the cell cycle arrest is due to the requirement of clk-2 in G2 cell cycle progression or due to the activation of a further checkpoint such as the p38 stress activated checkpoint [50] . clk-2 ( tm1528 ) worms arrest in a phospho-histone H3 positive pro-metaphase like stage with partially condensed chromosomes , while DNA damage leads to a G2 arrest characterized by high levels of phosphorylated CDK-1 Tyr 15 and the absence of phosphorylated histone H3 in wild type worms . Interestingly , CDK-1 Tyr 15 is still phosphorylated in these arrested germ cells , indicating that these cells arrest with low CDK-1 activity . Thus our data suggest that there might be an uncoupling of mitotic events in clk-2 ( tm1528 ) germ cells . Clk2/Tel2 has also been shown to be required for cellular proliferation in mouse embryonic fibroblasts . The arrest after CLK-2/TEL2 depletion is not uniform in TEL2 deficient MEFs . These cells arrest with an increased proportion of cells with a 2N or 4N DNA content , and a reduced S and M phase index , and were reported to show a ‘senescence-like flattened morphology’ [8] . Thus CLK-2/TEL2 might have additional functions in mammalian cells that are not directly related to cell cycle regulation . Alternatively , a cell cycle regulatory function of CLK-2/TEL2 might not be uniformly needed in all cell types . Our analysis of clk-2 mutant phenotypes reveals distinct CLK-2 functions in embryonic cell cycle progression and in germ cell cycle progression . The clk-2 ( tm1528 ) null allele results in the most severe germline phenotype . At present we do not know if clk-2 ts alleles are completely inactive when shifted to the restrictive temperature during early embryonic cell cycle progression . Indeed , as is the case for the germ cell cycle arrest phenotype , a complete inhibition of clk-2 might result into earlier or more severe defects during embryonic cell divisions potentially resembling the clk-2 ( tm1528 ) germ cell cycle arrest phenotype . We extensively tried RNAi to completely inhibit clk-2 during embryogenesis using both RNAi injection and feeding procedures but never found a phenotype stronger than the phenotype of either clk-2 ts allele propagated at 25°C ( data not shown ) . clk-2 RNAi injections did not result in any phenotype [5] , and only the RNAi feeding construct introduced by the Nilsen laboratory worked for RNAi feeding . Only , when we analyzed clk-2 mutants kept at 25 . 5°C combined with clk-2 ( RNAi ) we observed more severe defects as seen in clk-2 ( qm37 ) and clk-2 ( mn159 ) mutants at the restrictive temperature . Under these conditions we observe a further delay of cell cycle progression ( particularly in the P lineage ) as compared to clk-2 ts mutants kept at the restrictive temperature ( Figure S5 ) . This delay appears as atl-1 independent . ATL-1 dependence was , however , difficult if not almost impossible to study due to severely abnormal cell divisions ( data not shown ) , that often resulted in cell divisions where only one daughter cell received an intact nucleus . Even without atl-1 ( RNAi ) treatment , nuclei often appeared as disorganized and at times fragmented under DIC optics ( Figure S5 ) , but we never observed uniform defects starting from the very first cell cycle , further complicating a detailed analysis ( data not shown ) . Obviously , these findings will raise the question as to how CLK-2 might affect early embryonic cell divisions , which will be the subject of further studies . These studies will however , require new clk-2 alleles as we currently can not rule out the possibility of off target effects associated with clk-2 RNAi that might unspecifically enhance clk-2 mutant defects . At present , we can only speculate if the developmental , cell cycle related and DNA damage response pathway defects associated with clk-2 mutations are due to a single molecular defect . We , indeed , favour an alternative model according to which CLK-2 affects multiple molecular processes . Our analysis which is based on an allelic series of clk-2 mutants with increasing strength clearly indentifies distinct functions associated with CLK-2 during embryonic and germ cell cycle progression as well as during embryonic development . It was recently shown that TEL2/CLK-2 belongs to the ARM repeat superfamily of structurally related proteins [47] ( Alexander Schleiffer , personal communication ) . Tandem ARM repeats fold together into a superhelical fold to form a surface for protein–protein interactions ( for review see , [51] , [52] ) . ARM repeat proteins are structurally related to proteins containing tandem HEAT motifs [51] . The demonstrated interactions between Tel2/CLK-2 and the HEAT repeat containing PIKKs suggests that TEL2/CLK-2 might act as an adaptor protein that impinges on multiple signalling pathways besides PIKKs through ARM/HEAT domain mediated protein-protein interactions . Our dissection of CLK-2 phenotypes in C . elegans is likely to stimulate future studies in mammalian cells addressing developmental and cell cycle-related functions of CLK-2/TEL-2 .
C . elegans strains were maintained at 20°C unless otherwise stated as described [53] . The following strains were used: clk-2 ( mn159 ) [5] , clk-2 ( qm37 ) [54] , cep-1 ( lg12501 ) [55] , gld-2 ( q497 ) gld-1 ( q485 ) ( gift of Tim Schedl ) , div-1 ( or148 ) [49] , clk-2 ( tm1528 ) was generated and kindly provided by Shohei Mitani . The clk-2 ( tm1528 ) deletion strain was backcrossed 5 times to reduce background mutations and balanced with hT2 [bli-4 ( e937 ) q418] by crossing to JK2689 [pop-1 ( q4645 ) dpy-5 ( e61 ) /hT2 [bli-4 ( e937 ) q418] to generate TG56 clk-2 ( tm1528 ) /hT2 [bli-4 ( e937 ) q418] . Further strains used were TG58 cep-1 ( lg12501 ) ; clk-2 ( qm37 ) , TG57 cep-1 ( lg12501 ) ; clk-2 ( mn159 ) , TG59 cep-1 ( lg12501 ) ; div-1 ( or148 ) , TG60 gld-2 ( q497 ) gld-1 ( q485 ) /hT2 [bli-4 ( e937 ) q418]; clk-2 ( tm1528 ) /hT2 [bli-4 ( e937 ) q418] . RNAi was performed by using the feeding procedure [56] . RNAi-expressing bacteria were seeded on NGM agar plates containing 3 mM IPTG and 50 µg/ml ampicillin , and worms were added as L4 larvae the following day . Animals were fed with bacteria carrying an empty L4440 feeding vector [57] or atl-1 , chk-1 [15] and mdf-1 feeding ( MRC geneservice ) constructs . Phenotypes were observed in F1 animals . F1 animals in the L4 stage were placed onto RNAi plates . F2 embryos were analysed after 24 h of incubation , and F1 animals were analysed after 48 h to observe germline phenotypes . Worms at the indicated time post-L1 were stained by DAPI using the following procedure . Animals were transferred to 100 µl M9 buffer and washed 3× with M9 buffer and resuspended in 1 ml 96% ethanol containing DAPI ( 200 ng/ml ) for 1 h and rehydrated with 1 ml M9 buffer for 1 h . Worms were transferred into 3 µl of mounting solution ( 90% glycerol , 20 mM Tris pH 8 . 0 , 1 mg/ml p-phenylenediamine ) and mounted on slides . Germ cells were identified by nuclear morphology according to DAPI staining . For the antibody staining , one day post-L4 adult gonads ( for clk-2 ( tm1528 ) 48 h post L4 ) were dissected in EBT ( 25 mM HEPES pH 7 . 4 , 0 . 118 M NaCl , 48 mM KCl , 2 mM CaCl2 , 2 mM MgCl2 , 0 . 1% Tween 20 ) on a slide coated with poly-lysine ( Sigma ) and freeze-cracked . The slides were transferred to −20°C cold methanol , for 5 minutes and washed three times in PBS for 10 minutes at RT . Slides were blocked for 30 minutes in 0 . 5% BSA in PBST ( PBS , 0 . 05% Triton-X100 ) and incubated overnight at 4°C with the primary antibody ( 1/1000 in 3% BSA in PBST ) . The next day , the gonads were washed three times in PBST each for 5 minutes at RT and incubated with the secondary antibody for 1 hour at room temperature . Gonads were washed three times in PBST each for 10 minutes and mounted with 5 µl mounting solution containing 0 . 5 µg/ml DAPI . Antibodies were used at the following dilutions: anti-α-tubulin antibody DM1A ( Sigma ) was used at 1/200 , anti-γ-tubulin 1/5000 ( gift of Carrie Cowen , IMP Vienna ) , anti PH3 1/400 ( Upstate ) , anti RAD-51 1/200 [25] , anti-Cdk1 1/100 ( pTyr15 , Calbiochem ) . Secondary antibodies used were anti-rabbit cy3 and anti-mouse FITC ( 1/1000 , Jackson ) . Methods for 4D-microscopy were described in [13] . Modifications of the 4D-microscope system are described in [31] . Embryos were recorded at 25°C and stacks of 25 DIC-images , viewing different focal planes of the developing embryo , were taken every 35 sec . The 4D-recordings were analysed using the SIMI Biocell program ( SIMI Reality Motion Systems , Unterschleissheim , Germany; http://www . simi . com ) [31] , [13] . Cell cycle timing was determined by measuring the time between the two mitotic divisions ( completion of cytokinesis ) . Deltavision microscopy was used to examine germlines using either a 60× or a 100× , UPlanSApo objective ( Olympus; NA 1 . 40 ) , Soft-WoRx software ( Applied Precision ) , and a CoolSnap HQ ( Photometrics ) OCD camera . Three-dimensional datasets were computationally deconvolved , and regions of interest were projected onto one dimension . Protein samples were resolved by SDS-PAGE analysis and transferred to polyvinylidine difluoride membrane ( PVDF , Millipore ) . Membranes were blocked in 5% powdered milk , diluted in PBS Tween , then probed with primary antibody diluted in blocking solution for 3 hours . Primary antibodies were anti-CEP-1 ( 1/100 [55] ) and anti-CLK-2 ( 1/1000 gift of S . Boulton ) . Antibody binding was detected using anti-rabbit or anti-goat IgG coupled to horse radish peroxidase ( Jackson ) and proteins were visualized using ECL ( Amersham ) and autoradiography . | PI3K-related protein kinases ( PIKKs ) ATM and ATR are essential upstream components of DNA damage signalling pathways , while TOR-1 acts as a nutrient sensor . CLK-2/TEL2 is a conserved gene initially implicated in budding yeast telomere length regulation and uncovered in the same genetic screen as the yeast TEL1 ATM like kinase . CLK-2/TEL2 was first implicated in DNA damage response signalling by C . elegans genetics , a function confirmed in yeast and human cells . In addition , CLK-2/TEL2 is essential for cellular and organismal survival from yeasts to vertebrates , but the essential phenotypes were not defined . A direct interaction between CLK-2/TEL2 and all PI3K-related protein kinases and the reduction of PIKK protein levels upon CLK-2/TEL2 depletion lead to the widely discussed notion that CLK-2/TEL2 mutants might phenocopy PIKK depletion phenotypes . We take advantage of embryonic lineage analysis and germline cytology to dissect developmental and cell cycle related functions of CLK-2 . CLK-2 depletion does not phenocopy PIKK kinase depletion . We rather link CLK-2 to multiple developmental and cell cycle related processes and show that CLK-2 and ATR have antagonising functions during early C . elegans embryonic development . Furthermore , we implicate CLK-2 in a distinct cell lineage decision and show that its depletion leads to a novel germline cell cycle arrest phenotype . |
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The TEAD family of transcription factors ( TEAD1-4 ) bind the MCAT element in the regulatory elements of both growth promoting and myogenic differentiation genes . Defining TEAD transcription factor function in myogenesis has proved elusive due to overlapping expression of family members and their functional redundancy . We show that silencing of either Tead1 , Tead2 or Tead4 did not effect primary myoblast ( PM ) differentiation , but that their simultaneous knockdown strongly impaired differentiation . In contrast , Tead1 or Tead4 silencing impaired C2C12 differentiation showing their different contributions in PMs and C2C12 cells . Chromatin immunoprecipitation identified enhancers associated with myogenic genes bound by combinations of Tead4 , Myod1 or Myog . Tead4 regulated distinct gene sets in C2C12 cells and PMs involving both activation of the myogenic program and repression of growth and signaling pathways . ChIP-seq from mature mouse muscle fibres in vivo identified a set of highly transcribed muscle cell-identity genes and sites bound by Tead1 and Tead4 . Although inactivation of Tead4 in mature muscle fibres caused no obvious phenotype under normal conditions , notexin-induced muscle regeneration was delayed in Tead4 mutants suggesting an important role in myogenic differentiation in vivo . By combining knockdown in cell models in vitro with Tead4 inactivation in muscle in vivo , we provide the first comprehensive description of the specific and redundant roles of Tead factors in myogenic differentiation .
The TEAD ( 1–4 ) transcription factors [1 , 2] bind to a consensus MCAT ( 5’-CATTCCA/T-3’ ) element , originally identified as the SV40 enhancer GT-II motif [3] [4–6] , through the evolutionarily conserved TEA/ATTS DNA binding domain [7 , 8] . Mammalian TEADs are widely expressed with prominent Tead1 and Tead4 expression in skeletal muscle , lung , heart and nervous system . Tead factors act as mediators of the Hippo signalling pathway interacting with the Yap and Wwtr1 ( Taz ) transcriptional co-activators to regulate proliferation , oncogenesis , stem cell maintenance and differentiation and control of organ size [9–14] . Teads also play an important role in skeletal , cardiac , and smooth muscle differentiation and physiology [15–18] . Tead4 is expressed in developing skeletal muscle in mouse embryos [2] and at later stages both Tead1 and Tead4 are co-expressed and co-localise to somites [19] . Blais et al . showed that Myod1 and Myog directly bind the Tead4 promoter and activate its expression during C2C12 cell differentiation in vitro [20] . Subsequently , we showed that stable shRNA-mediated Tead4 knockdown led to formation of shortened C2C12 myotubes [21] . ChIP-chip experiments in C2C12 cells overexpressing Flag-HA-Tead4 revealed that Tead4 occupied 867 promoters including Myog , and Cav3 . RNA-seq identified a set of genes down-regulated upon Tead4 knockdown amongst which are muscle structural and regulatory proteins . While our data described a role for Tead4 in activating muscle genes during differentiation , we also suggested that Tead4 may repress Ctgf and Ccnd1 expression contributing to cell cycle exit . However , we did not specifically address the role of other Teads in these cells . Here , we show by siRNA silencing that Tead factors are essential for primary myoblast ( PM ) differentiation , but that Tead1 , Tead2 or Tead4 play partially redundant roles . In contrast to C2C12 cells , silencing of Tead1 or Tead4 both impaired differentiation indicating a differential requirement for these factors in PMs and C2C12 cells . ChIP-seq of Tead1 and Tead4 identified their binding sites C2C12 cells and in mature muscle fibres . RNA-seq identified distinct but overlapping sets of genes deregulated by Tead silencing in C2C12 cells and PMs . Furthermore , somatic inactivation in muscle in vivo revealed an important role for Tead4 in muscle regneration . We therefore provide the first comprehensive study of the specific and redundant regulatory roles of Tead factors in myogenic differentiation .
We previously showed that Tead4 plays an essential role in C2C12 cell differentiation [21] . To extend the study of Tead4 function , we isolated primary myoblasts from 3–4 week-old C57BL/6 mice and differentiated them in vitro for 6 days . Quantitative RT-PCR analysis showed that Tead4 mRNA expression was strongly induced at days 3 and 6 during differentiation ( Fig 1A ) . Similarly , expression of Tead1 was also strongly induced , whereas Tead2 expression did not show strong variation and Tead3 was not significantly expressed in myoblasts . Immunoblots confirmed that Tead4 protein was strongly induced upon differentiation , whereas Tead1 protein was present in undifferentiated and differentiated cells ( S1A Fig ) . To address the function of Tead4 , control and Tead4 siRNAs were transfected 24 hours before the initiation of differentiation . Compared to control siRNA , siTead4 led to a potent reduction in Tead4 expression , but did not affect Myog and Myod1 levels ( Fig 1B ) . Staining of the transfected cells for myosin heavy chain ( hereafter Myh ) expression showed that Tead4 silencing did not impair myoblast differentiation as the Tead4-silenced cells formed long multinucleate myotubes ( Fig 1C and 1D ) . As Tead1 was also present in differentiating PMs , we investigated potential redundancy amongst the Teads . We used siRNAs to silence Tead1 , Tead2 or Tead4 and examined how this affected the expression of the other family members . Tead1 expression was strongly down-regulated by siTead1 , less so by siTead2 , but not by siTead4 ( Fig 1E and S1A Fig ) . Similarly , expression of Tead4 mRNA was not affected by silencing of Tead1 or Tead2 , but Tead4 protein levels were increased in undifferentiated cells when Tead1 was silenced . Expression of Tead2 was reduced in the undifferentiated state upon silencing of either Tead1 or Tead2 , but its expression during differentiation was minimally affected . Thus , expression of each of the Teads was rather independent of the others . As seen above , siTead4 silencing had little effect on differentiation ( Fig 1F ) . Similarly , Tead1 or Tead2 silencing had little effect on differentiation ( Fig 1F and 1G ) . Examination of gene expression showed nevertheless that Myh1 and Myh2 were reduced by each knockdown ( Fig 1H ) . In contrast , expression of Cav3 , a well-defined Tead4 target gene in C2C12 cells , and Myh7 was strongly and selectively diminished in siTead4 cells indicating a specific requirement for Tead4 at these genes that cannot be compensated by expression of the other Teads . We performed combinatorial silencing of Tead1 and Tead4 or of all three expressed Teads . The expression of the corresponding Tead mRNAs was reduced in all cases ( Fig 2A ) . In contrast to individual Tead1 and Tead4 knockdowns , their combinatorial knockdown had a potent effect on differentiation , with many cells expressing Myh , but no fusion and the prevalence of shorter myotubes ( Fig 2B and 2C ) . Moreover , a larger number of cells failed to initiate Myh expression . Similar observations were made using the Tead1 , Tead2 and Tead4 siRNA combination ( Fig 2B ) . Strongly reduced Myh1 , Myh2 , Myh7 and Tnni1 expression was seen in the siTead1/Tead2/Tead4 cells ( Fig 2D ) . Together , these observations showed that Teads play essential , but partially redundant functions in differentiating PMs . The redundant roles for Tead factors in differentiating PMs contrast with the critical role for Tead4 in C2C12 cells . To investigate this more closely , we performed single and combinatorial siRNA Tead knockdowns in C2C12 cells . As reported [21] , Tead4 was strongly induced during C2C12 cell differentiation ( Fig 3A ) and its induction was not diminished by Tead1 silencing , but was somewhat reduced by Tead2 silencing . Tead2 was also induced albeit less strongly than Tead1 or Tead4 , but its activation was strongly diminished by Tead1 or Tead4 silencing . Tead1 mRNA was induced during differentiation , but this was strongly reduced by siTead4 . In all situations , transfection with siRNAs against individual Teads or combinations of Teads had the potent and expected effects on their own expression . Tead1 or Tead4 silencing led to reduced myoblast fusion with the absence of longer and thicker fibres in favour of shorter less developed fibres ( Fig 3B and 3C ) . A similar , but less pronounced , effect was seen upon Tead2 silencing . Combinatorial Tead1/Tead4 silencing led to more dramatic effects with fewer and shorter fibres , while upon silencing of all three Teads few elongated myotubes were observed ( Fig 3B and 3C ) . These results revealed that normal expression of each Tead was essential for full differentiation and generation of long and thick fibres , and that Tead1 and Tead4 both strongly contributed to differentiation . Western blot analyses showed increased Tead4 protein levels in differentiated cell extracts ( S1B Fig ) . Tead1 on the other hand was decreased at day 6 in agreement with previous observations [22] . While Tead4 was increased in siTead1 cells , Tead1 was reduced in siTead4 cells . This highlights a difference with PMs where Tead1 was strongly induced even in the absence of Tead4 ( S1A Fig ) , whereas in C2C12 cells Tead4 is required for maximal Tead1 expression . Immunostaining showed Tead1 nuclear localisation in non-differentiated C2C12 cells , whereas Tead4 was present in both the nucleus and cytoplasm ( S1C Fig ) . At day 6 , Tead1 remained nuclear in cells that did not undergo differentiation , but was absent from differentiated myotube nuclei . In contrast , Tead4 expression was not detected in cells that did not undergo differentiation , but showed strong nuclear staining in myotubes . Strikingly , a comparison with PMs showed that Tead1 was strongly expressed in the nuclei of both myoblasts and myotubes , while Tead4 was both cytoplasmic and nuclear in myoblasts , but nuclear in myotubes ( S1D Fig ) . This observation could account for the differential requirement for Tead1 and Tead4 in PMs and C2C12 cells . In PMs , both proteins were nuclear in myotubes and can thus partially compensate for each other , whereas in C2C12 myotubes , Tead1 was down-regulated by siTead4 and absent from the nucleus and thus unable to compensate for loss of Tead4 . To understand how Tead1 and Tead4 regulate gene expression in C2C12 cells , we used ChIP-seq to profile their genomic occupancy . Chromatin was prepared before differentiation and after 6 days of differentiation and ChIP was performed with antibodies selective for either Tead4 or Tead1 . In undifferentiated cells , Tead4 occupied 2940 sites located mainly distant from the transcription start sites ( TSS ) ( Fig 4A and S1 Dataset ) . In differentiated cells , more than 8100 sites were occupied , the majority of which were again located distant from the TSS ( Fig 4B and S1 Dataset ) . Occupied sites in both undifferentiated and differentiated cells showed strong enrichment of the MCAT motif ( Fig 4C and 4D ) . Other motifs co-occurred with the MCAT motif at higher than expected frequency . In undifferentiated cells , enrichment in motifs for the AP1 ( Fos and Jun ) family was observed along with Runx1 that cooperates with AP1 and Myod1 to drive myoblast proliferation during muscle regeneration [23] ( Fig 4C ) . In differentiated cells , AP1 and Runx motifs were enriched , but additional motifs became prominent such as Ctcf , and Tcf3 and the E-Box ( Fig 4D ) . Comparison of sites in undifferentiated and differentiated cells indicated that occupancy of 1495 sites was lost during differentiation , whereas more than 6700 sites were gained and 1434 sites were occupied under both conditions ( Fig 4E ) . For example , Tead4 constitutively occupied sites upstream of the Ctgf and Ccnd1 genes , whereas occupancy of sites at the Acta1 locus is seen only during differentiation ( S2A Fig ) . Sites specifically occupied in undifferentiated and differentiated cells showed enrichment in AP1 motifs in the undifferentiated state , but enrichment of Myod1 , Myog , Tcf3 and Tcf12 that cooperates with Myod1 to promote myogenic differentiation [24] in differentiated cells ( Fig 4F ) . To assess co-localisation between Tead4 and transcription factors whose motifs were enriched at its binding sites , we compared the Tead4 profiles with those of Jun and Srf in C2C12 cells and Runx1 in primary myoblasts [25] [26] . Around 35% of sites bound by Tead4 in undifferentiated and differentiated cells overlapped with those bound by Jun ( S2B Fig ) . In contrast , there was little overlap with the sites preferentially bound in the differentiated state , where the frequency of co-occurring AP1 motifs was also reduced . Similarly , a strong binding overlap between Tead4 , Runx1 and Srf was seen involving sites occupied in undifferentiated and differentiated cells . A similar analysis of Tead1 identified 1400 bound sites in undifferentiated cells , enriched in MCAT , AP1 and Runx motifs ( S3A and S3B Fig ) . Nevertheless , in agreement with its absence from the differentiated cell nucleus , Tead1 occupancy was strongly reduced in the differentiated state with only 274 detected sites ( S3C Fig ) . Even at sites occupied in both conditions , Tead1 occupancy was reduced in the differentiated state ( S3D Fig ) . Thus , transition to the differentiated state involved a switch from Tead1 and Tead4 occupancy to predominantly Tead4 occupancy . In the undifferentiated state , Tead1 and Tead4 co-occupy more than 900 sites ( S3E Fig ) . A set of sites showed preferential occupancy by Tead1 , but only few sites showed exclusive Tead1 occupancy . Thus despite the fact that these two proteins bind identical sequences and that Tead1 occupancy was globally lower than Tead4 , a set of sites was preferentially occupied by Tead1 . As Tead4 regulated Tead1 expression during differentiation , we examined Tead4 occupancy at the Tead1 locus . Two constitutive Tead1/Tead4 occupied sites were observed , one upstream of the promoter of the longest isoform and a second upstream of an alternative promoter for a shorter isoform ( S4A Fig ) . During differentiation Tead1 occupancy diminished , but Tead4 occupancy was maintained suggesting that Tead4 directly regulates Tead1 . Integration of Tead1/4 ChIP-seq data with public data on histone modifications in undifferentiated and differentiated C2C12 cells revealed the presence of H3K27ac , a mark of active promoters and enhancers , at the Tead1 promoter in undifferentiated cells overlapping with the Tead1/4 occupied sites . Interestingly , upon differentiation , H3K27ac increased at the Tead1/4 occupied sites and new regions marked by H3K27ac appeared upstream of and overlapping with the alternative promoter . Moreover , integration with public ChIP-seq data indicated Myod1 and Myog [27] binding at the H3K27ac-enriched regions . These observations suggest that Tead4 cooperates with Myog and Myod1 to activate Tead1 expression during differentiation via constitutive and inducible enhancer elements , in agreement with Tead1 down-regulation in siTead4 knockdown C2C12 cells ( Fig 3A ) . At the Tead4 locus , Tead4 occupied a H3K27ac-marked site immediately upstream of its own promoter whose occupancy increased upon differentiation ( S4B Fig ) . In contrast , almost no Tead1 occupancy was seen . Upon differentiation , regions in the Tead4 gene body acquired H3K27ac , several which coincided with binding of Myod1 and Myog . This suggests that Tead4 positively regulates its own expression together with these factors that bind to differentiation-induced enhancer elements downstream of the Tead4 TSS . The above data show that at the Tead1 and Tead4 loci , enhancers binding Myog became activated during differentiation perhaps driving their expression . We tested this by performing siMyog silencing in C2C12 cells and in PMs . In both cell types , siMyog strongly inhibited differentiation ( S5A Fig ) . In C2C12 cells , Tead4 expression was reduced upon Myog silencing , while that of Tead1 was unaffected , and Ccnd1 expression was increased ( S5B Fig ) . Expression of Mef2c bound by both Myog and Tead4 ( S5C Fig ) was also strongly repressed . Hence , Myog is required for Tead4 , but not Tead1 , expression and differentiation . In a global analysis , we clustered Tead4-occupied sites in undifferentiated and differentiated cells with H3K4me3 a mark of active promoters , H3K4me1 , a mark of active and poised enhancers as well as H3K27ac . As few Tead4 sites localised at the TSS , only a limited overlap ( 280 of 2940 ) with H3K4me3 was observed ( Fig 5A and 5B ) . In contrast , 1698 Tead4 sites in undifferentiated cells showed strong association with H3K4me1 and/or H3K27ac defining a set of sites at active and poised enhancer elements . A similar situation was seen in differentiated cells where almost half were marked by H3K4me1 and H3K27ac and up to 1500 sites associated with H3K4me3 ( Fig 5A and 5B ) . Tead4 therefore occupied a set of functional regulatory elements in both undifferentiated and differentiated cells . A similar situation was seen for Tead1 in undifferentiated cells ( S6A and S6B Fig ) . Due to their low number , we did not analyse Tead1 sites in differentiated cells . To define the regulatory potential of Tead4 , we identified the genes closest to the Tead4-occupied sites associated with active chromatin marks . In undifferentiated cells , 1262 genes enriched in the ontology terms cell structure and motility , developmental processes , oncogenesis and cell cycle control were annotated ( Fig 5C ) . Interestingly , KEGG pathway analysis revealed that Tead4 ( and Tead1 , S6C Fig ) occupied sites associated with critical components of the Tgfβ ( Smad2 , 3 6 and 7 as well as Tgfb2 ) and Wnt-signalling ( Fzd1 , Fzd5 , Tcf7l2 ) pathways ( Fig 5C ) . In addition , several genes of the Hippo pathway such as Amotl1 , Amotl2 and Lats2 were also associated with Tead1/4 occupied sites . In differentiated cells , more than 2000 genes enriched in terms associated with developmental processes , muscle differentiation and contraction were annotated including the important regulatory genes Myod1 , Myog and Mef2a as well as numerous structural genes of the muscle fibre . At many sites , Tead4 binding and H3K27ac was either enriched or acquired de novo at these genes during differentiation ( S6D Fig ) . We analysed global co-localisation of Tead4 with Myod1- and Myog-occupied sites . In differentiated cells , more than 2000 Tead4 sites were co-occupied by all three factors ( S7A Fig and S2 Dataset ) . As Tead1 occupied sites essentially only in non-differentiated cells , a comparison with Myod1 and Myog-occupied sites in differentiated cells revealed only a limited overlap of around 50 sites ( S7B Fig ) . The Tead4-Myod1-Myog-occupied sites showed enrichment not only in the recognition motifs for these factors , but also for Tcf3 , Tcf12 , Runx , and Klf5 , whereas the AP1 family sites were less represented than in the overall Tead4 profile ( S7C Fig ) . We compared Tead1/4 occupancy with that of Mef2a , another myogenic factor for which a public data set is available in undifferentiated C2C12 cells [28] and identified a set of sites co-occupied with Tead1 and Tead4 ( S7E and S7F Fig ) . This analysis identified Tead4 sites closely associated with Myod1/Myog . Nevertheless , as shown above at the Tead4 and Mef2c loci , Tead4 may cooperate with Myod1/Myog to activate these genes despite more distant localization of the binding sites . We therefore defined genes associated with Tead4-occupied sites and compared them with genes associated with Myog/Myod1 sites to identify those potentially regulated by these factors despite binding more distantly spaced promoter and/or enhancer elements . A large majority of Tead4 associated genes was associated with Myog/Myod1 whose potential target genes also showed a strong overlap ( S7D Fig ) We used RNA-seq to investigate gene expression in differentiating PMs and C2C12 cells and how simultaneous Tead1 and Tead4 silencing affected these regulatory programs to impair differentiation . SiRNAs were transfected and RNA prepared after 24 hours ( day 0 ) before cells were moved to differentiation media and RNAs prepared 3 and 6 days later ( Fig 6A ) . Changes in expression in siTead1/4 compared to the siControl were quantified to identify genes showing a greater than Log2 fold change of 1 with adjusted p value <0 , 05 . In control C2C12 cells , 3137 genes were induced at day 3 and day 6 with respect to day 0 , while in PMs 3626 genes were induced of which 1845 genes were commonly induced in both cell types ( S8A Fig ) . Commonly regulated genes were associated with muscle differentiation . Similarly , 2375 genes were repressed during C2C12 cell differentiation and 2799 genes repressed in PMs with 1495 common to both cell types ( S8B Fig ) . Commonly repressed genes were associated with cell cycle , consistent with proliferation arrest during differentiation . Thus , similar but not identical , gene expression programs were activated and repressed during the differentiation of these two cell types . Analysis of the 5512 genes regulated during differentiation of siControl C2C12 cells identified genes with different expression profiles that could be summarised in 6 clusters ( S9A Fig ) . Genes in clusters 1 , 3 and 4 were down-regulated with different kinetics , while those in clusters 2 and 5 were up-regulated with different kinetics , and those in cluster 6 were transiently induced at day 3 . Further analyses showed that myogenic genes were amongst the most significantly up-regulated at days 3 and 6 , whereas cell cycle progression genes were strongly repressed ( S9B Fig ) . In addition , adipogenesis genes were also induced along with a metabolic switch involving increased expression of genes involved in oxidative phosphorylation . Following siTead1/4 silencing , up and down-regulated genes were seen at day 0 . The number of de-regulated genes increased at day 3 and diminished by day 6 ( Fig 6A ) . In total , 249 genes were up-regulated by siTead1/4 silencing between day 0–6 , while 549 were repressed ( Fig 6A and 6B and S3 Dataset ) . Analysis up-regulated genes at days 3 and 6 indicated strong enrichment in cell cycle , Notch , Wnt and Tgfβ signalling and epithelial to mesenchymal transition ( Fig 6C ) . In contrast , genes involved in myogenesis and oxidative phosphorylation were repressed . Hence , Tead1/4 contribute to activation of the myogenic differentiation program , but they also directly or indirectly repress growth promoting pathways leading to defective cell cycle arrest . To identify genes directly regulated by Tead4 , we determined those within 50 kb of Tead4 binding sites enriched specifically in differentiated cells . Around 4300 Tead4 occupied sites were associated with 4100 potential target genes showing expression in the RNA-seq data ( Fig 6D ) . A set of 172 down-regulated genes enriched in muscle differentiation functions was associated with Tead4 binding sites in differentiated cells ( Fig 6D ) . Similarly , a set of 107 up-regulated genes was associated with sites preferentially bound in the differentiated state . These genes were enriched in the cell cycle , Notch and Wnt signalling identified by the GSEA analyses . Hence , binding of Tead4 to these repressed genes during differentiation provided further evidence that Tead4 contributed to their repression . A similar analysis of differentiating PMs clustered gene expression ( S10A Fig ) in 6 kinetic classes and showed that differentiation was characterised by activation of genes involved in myogenesis , oxidative phosphorylation and adipogenesis , while cell cycle genes were repressed ( S10B Fig ) . Following siTead1/4 silencing , deregulated genes were observed at day 0 , increased at day 3 and then diminished at day 6 ( Fig 7A ) . In total , 563 genes were up-regulated between day 0–6 , while 377 were repressed ( Fig 7A and 7B and S4 Dataset ) . Down-regulated genes were associated with myogenesis , and oxidative phosphorylation , the hallmarks of differentiation , whereas up-regulated genes were enriched in angiogenesis and as seen in C2C12 cells in Wnt signalling ( Fig 7C ) . In PMs , up-regulation of cell cycle genes was observed , but the values were less significant reflecting the reduced proliferative capacity of PMs compared to C2C12 cells . Thus , Tead factors were essential to activate genes involved in PM differentiation , but also to repress Wnt signalling and signalling pathways like Tgfβ inhibiting PM differentiation . We compared genes de-regulated by siTead1/4 silencing in PMs and C2C12 cells . As the kinetics of their activation of repression may differ , we compared non-redundant lists of all genes deregulated between days 0–6 in each cell type . A set of 119 genes strongly enriched in muscle differentiation functions were commonly down-regulated ( S11A Fig ) . Strikingly however , a large set of 430 genes , again strongly enriched in muscle differentiation functions , was specifically down-regulated in C2C12 cells ( S5 Dataset ) . A smaller set of 258 genes was specifically down-regulated in PMs , but showed low enrichment in more diverse functions . Only 65 genes involved in signalling and proliferation were commonly up-regulated . However , a large set of almost 500 genes was specifically up-regulated in PMs ( S11B Fig ) . Remarkably , these genes showed enrichment in nervous system development and other neurogenesis functions ( S5 Dataset ) . Tead1/4 knockdown appeared to modify PM cell identity leading to the expression of neurogenesis genes , not normally expressed during PM differentiation . These results showed that Tead1/4 silencing had distinct effects on gene expression in PMs and C2C12 cells . We next addressed genome occupancy by Tead4 in mouse muscle in vivo . We developed a protocol to prepare chromatin from dissected hind-limb muscle ( see Materials and methods ) and performed ChIP-seq for Tead4 , H3K27ac and RNA Polymerase II ( Pol II ) . We analysed the Pol II ChIP-seq to determine whether the signal obtained reflected mainly Pol II occupancy in muscle or in contaminating non-muscle cells . More than 38000 Pol II peaks were identified most of which localised at the TSS ( Fig 8A ) . Transcribed genes can show high levels of promoter paused Pol II and low levels in the gene body or low pausing , but abundant elongating Pol II [29] . The second class often corresponds to tissue identity genes controlled by so-called “super enhancers” [30 , 31] . Analyses of the Pol II ChIP-seq data identified around 1000 genes with high levels of transcribing Pol II ( class A in Fig 8B ) , a second class ( B ) also with high Pol II in the transcribed regions and larger groups of genes ( C and D ) with high Pol II at the promoter , but lower levels in the gene body . Class A genes also showed high levels of H3K27ac throughout the gene body typical of what has been described at cell identify genes ( Fig 8B ) . Class A genes associated with high Pol II and H3K27ac were enriched in terms associated with muscle fibre ( Fig 8C ) . For example , the locus comprising Myh2 , 1 , 4 , 8 and 13 showed high Pol II density specifically over the Myh4 gene with much lower densities over the Myh1 and Myh2 genes , but no transcription of other myosin genes at this locus ( Fig 8D ) . These loci also showed extensive H3K27ac surrounding and throughout the gene body . The Pol II and H3K27ac ChIP-seq therefore identified a set of highly transcribed muscle identity genes confirming the signal comes predominantly from muscle cells . Of the 2220 identified Tead4 sites , 686 were associated with active promoters marked by Pol II and H3K27ac and enriched in muscle specific functions ( Fig 9A and S12A Fig ) . Genes associated with Tead4-bound sites showed enrichment in terms associated with muscle structural proteins ( Fig 9B ) . Aligning the muscle Tead4 ChIP-seq to the coordinates of the differentiated C2C12 cell peaks revealed 1558 sites with significant signal ( Fig 9C ) . Genes associated with these shared sites were enriched in muscle structural proteins . In the converse comparison using the top 2200 Tead4-bound sites in muscle as reference , 341 common peaks were identified ( Fig 9D ) . These comparisons revealed Tead4 sites in muscle that were not called amongst the 2200 high confidence sites , but although showing lower occupancy in muscle were shared with differentiated C2C12 cells . Tead4 therefore bound a distinct repertoire of sites in C2C12 cells and muscle and sites with high occupancy in muscle did not necessarily show high occupancy in C2C12 cells and vice-versa . We next performed ChIP-seq from muscle of mice in which Tead4 was specifically inactivated in fibres using the Hsa::Cre-ERT2 driver [32] . Mice with floxed Tead4 alleles were crossed to generate Hsa::Cre-ERT2::Tead4lox/lox animals . These mice were injected at 6–7 weeks with tamoxifen for 4 consecutive days and 3 weeks after injection Tead4 expression was strongly reduced in the tibialis anterior and gastrocnemius muscles showing efficient recombination ( Fig 9E ) . We performed Pol II , H3K27ac and Tead4 ChIP-seq from these Tead4musc-/- animals . Aside a small number of sites with signal in Tead4musc-/- animals , Tead4 binding was lost ( Fig 9F ) indicating that observed signal came almost exclusively from sites bound in muscle . Comparison of Pol II and H3K27ac profiles in wild-type and Tead4musc-/- muscle ( Fig 9G ) showed only minor changes in low intensity signals and hence that Tead4 loss did not affect global Pol II or H3K27ac distribution . For example , at the Acta1 locus Tead4 occupancy was lost in mutant muscle , whereas no change in Pol II and H3K27ac profiles was observed ( S13 Fig ) . Similarly , Tead4 occupied 3 sites at the Amolt2 locus in both C2C12 cells and wild-type muscle including an upstream enhancer site marked by H3K27ac ( S14 Fig ) . The shared sites co-localised with those occupied by Myod1 and Myog in C2C12 cells . In mutant muscle , Tead4 occupancy was lost , but no change for Pol II and H3K27ac was seen . In agreement with the unaltered Pol II and H3K27ac profiles , little change in expression of potential Tead4 target genes was seen in mutant muscle , only minor reductions in Myh2 and Myl2 expression were observed ( S15A and S15B Fig ) . Moreover , Tead4musc-/- animals did not show any marked phenotype in terms of muscle fibre size , muscle mass and grip strength ( S15C–S15E Fig ) . One potential explanation is redundancy with Tead1 . To investigate this possibility , we performed Tead1 ChIP-seq in muscle . Of the 358 sites identified , 188 were shared with Tead4 ( S12B Fig ) . Genes associated with Tead1-occupied sites were however enriched in muscle function . For example , prominent Tead1 and Tead4 occupancy was observed at the Acta1 and Amotl2 loci ( S13 and S14 Figs ) . Thus , redundancy with Tead1 may in part account for the lack of phenotype seen upon Tead4 inactivation . Alternatively , differentiating C2C12 cells and PMs represent a very different physiological state from mature differentiated fibres . A more comparable situation is muscle fibre regeneration . We therefore investigated the role of Tead4 in muscle fibre regeneration in vivo . We employed a protocol similar to that previously used to demonstrate the role of SRF in regeneration using the Hsa::Cre-ERT2 driver ( [33] and see Fig 10A ) . Muscle degeneration in Hsa::Cre-ERT2::Tead4lox/lox and Hsa::Cre-ERT2::Tead4+/+ animals was induced by notexin injection and Tead4 was inactivated in regenerating fibres by regular subsequent Tam injection ( Fig 10A ) . At 15 days after notexin injection , tibialis anterior mass was significantly lower in the Tead4musc-/- compared to the Tead4+/+ animals ( Fig 10B ) . Similarly , fibre cross-section area was significantly altered with more small fibres and less large fibres in Tead4musc-/- ( Fig 10C ) . Expression of several Tead4 target genes such as Myh1 and Myh2 , Ankrd2 , Lats2 and Amotl2 were all significantly reduced ( Fig 10D ) . Moreover , Tead1 and Myog expression were also reduced , whereas Ccnd1 expression was increased . Thus , the gene expression changes induced by Tead4 inactivation during notexin-induced regeneration were similar but not identical to those seen following siTead4 in PMs . At 30 days after notexin injection , tibialis anterior mass remained somewhat reduced in the mutant animals , but fibre cross-section area was comparable to that in control animals ( Fig 10E and 10F ) . Thus , Tead4 inactivation delayed the normal regeneration process .
Here we show the essential role of Tead factors in PM differentiation . While silencing of each individual Tead had little discernible effect at the cellular level , Tead4 silencing specifically affected expression of its direct targets Myh7 and Cav3 . Nevertheless , combinatorial Tead1/4 or Tead1/2/4 silencing strongly impaired PM differentiation with fewer cells initiating Myh expression and shorter myotubes . Functional redundancy may be explained by the persistent expression and nuclear localisation of Tead1 during differentiation of siTead4 PMs and vice-versa . In contrast , siTead4 silencing impaired C2C12 cell differentiation with formation of shorter myotubes . Individual siTead1 or siTead2 silencing also impaired differentiation , revealing differences in Tead contributions in PM and C2C12 cells . In C2C12 cells , Tead4 silencing diminished Tead1 and Tead2 expression . Indeed , Tead4 occupied Tead1 regulatory sequences to directly regulate its expression . In addition , while Tead1 and Tead4 were nuclear in differentiated PMs , Tead1 was absent from the differentiated C2C12 cell nuclei and therefore could not compensate Tead4 silencing . C2C12 cell differentiation is however impaired by siTead1 showing that it contributed to early events in this process . Differential contribution of Teads in the two cell types can thus be explained by differences in their regulation and intra-cellular localisation . Immunostaining detected Tead1 uniquely in the nucleus of non-differentiated C2C12 cells , whereas Tead4 expression was lower and distributed in both nucleus and cytoplasm . However , ChIP-seq showed higher genomic occupancy of Tead4 than Tead1 suggesting its preferentially recruitment to the non-differentiated cell genome . While it is possible that the ChIP-efficiency of the Tead4 antibody is higher than the Tead1 antibody , a set of sites showed preferential occupancy by Tead1 rather suggesting the overall lower binding of Tead1 cannot simply be explained by lower ChIP efficiency . Indeed , it has previously been reported that the Vgll2 cofactor induced during C2C12 cell differentiation inhibits Tead1 , but not Tead4 DNA binding [22] . Hence , it is possible that during differentiation Vgll2 acts to selectively inhibit Tead1 genomic binding leading either to its export from the nucleus and/or its reduced expression . In our previous study [21] , we performed ChIP in cells constitutively overexpressing tagged Tead4 . Despite constitutive Tead4 overexpression , we identified sites occupied only during differentiation consistent with their acquisition of H3K4me3 or H3K27ac . Others , exemplified by a site upstream of the Myog locus ( see S6D Fig ) , were occupied by exogenous , but not endogenous Tead4 in proliferating C2C12 cells . Thus , while Tead4 occupies many sites in undifferentiated C2C12 cells , there exists a subset of sites occupied only during differentiation irrespective of Tead4 expression levels , whereas others can be occupied in the undifferentiated state upon increased Tead4 expression . Tead4 genome occupancy is therefore not only regulated by its expression level , but also by changes in chromatin state during differentiation . Integration of Tead4 ChIP-seq data with that of chromatin modifications showed strong association of Tead4 occupied sites with active H3K27ac-marked regulatory elements in both undifferentiated and differentiated cells . Moreover , many sites showed Tead4/Myog/Myod1 co-occupancy . These observations reinforce the idea that Tead4 in particular and Teads in general may cooperate with Myod1 and Myog to regulate gene expression during differentiation . Myod1 orchestrates the activation of a compendium of muscle enhancer elements [25] [34] . The DNA sequences at these enhancers were enriched for the AP1 and Runx families , but not for the MCAT motif . Tead4-occupied sites in non-differentiated cells were enriched in AP1 and Runx motifs suggesting these factors collaborate to drive proliferation . Recently , sites occupied by Tead factors driving motility in cancer cells were identified and showed not only enrichment in AP1 motifs , but also many of the motifs that we found enriched at Tead4 sites in C2C12 cells [35] . In contrast , Tead4 sites preferentially occupied in differentiated cells showed little overlap with Jun , but better co-localisation with Runx and were enriched for E-boxes for Myod1/Myog consistent with their observed co-localisation . Nevertheless , although many Tead4 occupied sites are co-occupied by Myod1/Myog , these sites constitute only a smaller subset of a larger collection of Myod1/Myog sites explaining why the MCAT motif was not detected in the analyses of Blum et al . , [25] . RNA-seq showed that Tead1/4 drive distinct but overlapping gene expression programs in the two cell types . This partly reflects the different gene expression programs of PMs and C2C12 cells , but also suggests that Tead4 may occupy a different , but overlapping set of sites in these two cell types . Interestingly , a large number of muscle function genes are specifically down-regulated in C2C12 cells by Tead1/4 silencing . This may reflect the additional contribution of Mef2c that was down-regulated in C2C12 cells , but not PMs . More striking is the specific up-regulation of neuron-expressed genes in PMs suggesting that Tead1/4 silencing leads to altered cell identity . Nevertheless , many genes critical for fibre formation , contraction and neuromuscular junction are down-regulated by siTead1/4 in both cell types . Diminished expression of these genes contributes to the impaired differentiation observed . We previously suggested that in addition to activating muscle differentiation genes , Tead4 may also repress genes such as Ccnd1 and Ctgf that drive cell proliferation and whose expression is reduced upon differentiation [21] . While Tead4 binds genes like Ccnd1 before and during differentiation , we observed here sites preferentially bound in differentiated cells and associated with cell cycle such as E2f8 or Chek2 , or Wnt , Tgfβ and Notch signalling genes . These genes are normally repressed during differentiation , but were up-regulated after siTead1/4 silencing . Proper regulation of Wnt , Notch and Tgfβ signalling is essential for normal myogenic differentiation [36] [37] [38] and their mis-regulation impairs myogenesis and can lead to fibrosis [39] . For example , siTead1/4 silencing up-regulated its target genes Notch3 , and to a lesser extent Notch1 , and Dll1 ligand , accompanied by up-regulation of the Notch mediators Hey1 and Hey2 shown to inhibit myogenesis [40] . On the other hand , siTead1/4 silencing up-regulated its target gene Nkd1 , an antagonist of Wnt signalling [41] [42] that is normally required to promote differentiation . Thus , Tead4 plays a dual role during differentiation , not only activating the myogenic program , but also repressing cell cycle and signalling genes . Some discrepancies remain with our previous observations using shTead4 silencing in C2C12 cells . For example , shTead4 silencing strongly inhibited Myog expression , while this was not seen upon siTead4 silencing . This may reflect a fundamental difference in the two approaches . In the shRNA experiments , C2C12 myoblasts were infected , selected and Tead4 expression was silenced for up to 10 days before differentiation was initiated . As Tead4 occupies more than 2800 binding sites in proliferating myoblasts , it is likely that diminished Tead4 levels for several days prior to differentiation can affect activation of genes that are rapidly induced after differentiation . Extensive Tead4 genome occupancy in proliferating C2C12 cells may therefore play a critical role in establishing the proper chromatin state permissive for activation of genes during differentiation . Performing ChIP-seq directly from mature muscle fibres identified a set of a highly transcribed and H3K27ac-marked muscle cell identity genes and Tead4 and Tead1 bound sites . Tead1 and Tead4 occupied an overlapping set of sites that partially overlapped with those in C2C12 cells . Shared sites were strongly enriched at genes encoding muscle structural proteins and also at a smaller set of genes encoding signalling and cell cycle proteins . In particular , Tead1 and Tead4 occupied sites at genes of the Hippo signalling pathway like Lats2 and Amotl2 in C2C12 cells and in mature fibres . Similarly , in post-mitotic muscle , Tead4 occupied sites at the Ccnd1 and Ctgf loci that normally contribute to its proliferative function . This may reflect the known role of the Tead4-Yap1 axis in regulating muscle fibre size [13] . Despite the observed genomic occupancy , Tead4 inactivation in mature fibres had no marked effect on Pol II and H3K27ac distribution , led to only minor effects on target gene expression , and resulted in no evident phenotype . In contrast , Tead4 inactivation led to delayed muscle regeneration after notexin treatment . Significant reductions in muscle mass and fibre size were seen after 15 days , but by 30 days these parameters were comparable to those seen in control animals . In addition , Tead4 contributed to activation of muscle structural genes during regeneration-induced differentiation . The absence of phenotype in mature fibres is in agreement with a previous report where Tead4 was inactivated in post-implantation embryos [43] . Similarly , while Tead4 loss delayed regeneration , this process was not completely impaired , explaining the absence of a notable muscle phenotype seen in the study of Yagi et al . , although they did not specifically assay regeneration in their Tead4 mutant animals [43] . The results obtained with differentiating PMs in vitro and in muscle in vivo are all in accordance with strong redundancy between Tead1 and Tead4 in the myogenic process that minimises the effects seen upon loss of Tead4 alone . Nevertheless , our study defines for the first time the critical roles of these factors in myogenic differentiation .
Mice were kept in accordance with the institutional guidelines regarding the care and use of laboratory animals and in accordance with National Animal Care Guidelines ( European Commission directive 86/609/CEE; French decree no . 87–848 ) . All procedures were approved by the French national ethics committee . Intra-peritoneal injection of Tamoxifen ( 100μl of 1mg/ml ) for four consecutive days was performed on 6–7 week-old animals . After 3 weeks , animals were sacrificed , the tibialis anterior and gastrocnemius muscles were dissected and deletion of Tead4 was verified by PCR genotyping and RNA was prepared . For regeneration , notexin was injected in the tibialis anterior of mice previously treated with Tam . Four subsequent Tam injections were performed following notexin treatment to inactivate Tead4 in the newly forming fibres . Hind-limb grip strength was measured using a Bioseb Grip Strength Meter . Three consecutive readings were performed for each mouse within the same session and the mean value was recorded as the maximal grip strength for each mouse . Body weight was recorded using an electronic balance after sacrificing the mice . The tibialis anterior muscle was then dissected and its mass was measured . The tibialis anterior mass is represented as % of body weight . For fibre cross-section area measurements , transverse cryosections ( 8μm ) of mouse tibialis anterior muscle were stained with hematoxylin and eosin . Slides were scanned using NanoZoomer-XR Digital slide scanner ( Hamamatsu Photonics K . K . ) . Cross-section area was analyzed using the RoiManager plugin of Fiji image analysis software . The conditional Tead4 mutant allele was generated using a targeting construct where Tead4 exons 2 and 3 were flanked by two loxP sites . A neomycin resistance cassette ( PGK-Neo ) flanked by two Frt sites was inserted immediately downstream of the 5’ loxP site . The targeting vector based on pKOII contained a diphtheria toxin A ( DTA ) counter selection cassette to enrich for homologous recombination events . Homology arms were subcloned from cosmids MPMGcPO454Q2 and MPMGc121P0454Q01 ( The German Resource Center for Genome Research ) using the restriction enzymes NaeI and EcoRV ( 3 . 8 kb , short arm ) and EcoRV and KpnI ( 11 . 2kb , long arm ) . The Tead4 targeting construct was electroporated into V6 . 5 F1 hybrid embryonic stem ( ES ) cells [44] after linearization with NotI and subjected to G418 selection . Homologous recombination events in individual ES cell clones were detected by Southern blot analysis of XbaI digested DNA using a probe located outside of the homology arms and by PCR analysis using the following primers: ( 5’-loxP-FW ) AGTGCATGAGGCAAGAGGC , ( 5’-loxP-RV ) GCTCCTGGGACCATAGTTA; ( 3’-loxP-FW ) CAGGCCTCTCTCTGAGGTGA , ( 3’-loxP-RV ) ACTATGAGAGCCTCACAGGC . A positive clone was microinjected into C57BL/6 ( B6 ) blastocysts before transplantation into pseudopregnant foster mothers . Chimeric mice were mated to Flp-expressing transgenic mice to remove the neomycin resistance cassette by Flp-mediated recombination leaving behind a single Flp site and two loxP sites flanking exon 2 and 3 . These mice were the bred with previously described Hsa::CreERT2 mice [32] . C2C12 cells were grown in 20% foetal calf serum ( FCS ) containing DMEM medium and were differentiated for most experiments up to six days in 2% horse serum ( HS ) containing DMEM medium . Adult mouse primary myoblasts were isolated from C57BL/6 wild type 3–4 week-old mice and plated on matrigel-coated dishes . The primary myoblasts were grown in IMDM GLUTAMAX-I medium with 20% FCS and were differentiated in the same medium with 2% HS . The siRNA transfection experiments were performed as per the Lipofectamine RNAiMAX manufacturer’s protocol and cells were harvested at indicated time points of differentiation after the siRNA transfection . ON-TARGET-plus SMARTpool siRNAs for Tead1 , Tead2 and Tead4 knockdown and non-targeting siRNA were purchased from Dharmacon Inc . ( Chicago , Il . , USA ) . The siRNA experiments were performed at least in triplicates . Phase contrast images were taken at 4x magnification using the EVOS digital microscope . A list of all antibodies and primers used can be found in S6 Dataset . Whole cell extracts were prepared by the standard freeze-thaw technique using LSDB 500 buffer ( 500 mM KCl , 25 mM Tris at pH 7 . 9 , 10% glycerol , 0 . 05% NP-40 , 1 mM DTT , and protease inhibitor cocktail ) and Immunoblotting was performed by standard procedure . 1x105 cells were seeded on coverslips in 35mm dishes with matrigel for primary myoblasts and without matrigel for C2C12 cells and were transfected with siRNA 4 hours after seeding . Cells were refreshed 6 to 8 hours after the siRNA treatment and fixed on day 6 of differentiation with 4% formaldehyde for 10 mins . Cells were washed with PBS and permeabilized with 0 . 5% triton for 10 mins , washed twice with PBS-tween 0 . 2% and blocked with 5% BSA for 30 minutes . Cells were incubated with primary antibody overnight at 4°C followed by three PBS-tween 0 . 2% washes . Secondary antibody incubation was done for 30 minutes at room temperature . Cells were washed thrice with PBS-tween 0 , 2% and stained with DAPI . Coverslips were mounted on superfrost glass slides using Vectashield . Slides were visualised using an inverted fluorescence microscope at 10x magnification in all experiments . To quantify the fusion in double and triple knockdown experiments , we calculated the fusion index as the percentage of number of nuclei within the Myh-positive cells above total number of nuclei counted in a field . Nuclei in fields from three replicate experiments were counted and analysed by a two-tailed t-test . Note that Myh positive cells with only 3 nuclei were taken for the counting of the nuclei . Total RNA was extracted using the GenElute Mammalian Total RNA Miniprep Kit from Sigma . cDNA was prepared with using SuperScript II Reverse Transcriptase ( RT ) using the kit protocol and quantitative PCR was carried out with the SYBR Green I ( Qiagen ) and monitored using the Roche Lightcycler 480 . Primer sequences were designed using Primer3plus software and beta-actin was used as normalization control . Messenger-RNA-seq was performed essentially as described [45 , 46] . Sequence reads mapped to reference genome mm9/NCBI37 using Tophat [47] . Data normalization and quantification of gene expression was performed using the DESeq 2 Bioconductor package [48] . Significantly deregulated genes were selected using a log2 fold change >1 and <1 and adjusted p-value <0 , 05 . Gene ontology analyses were performed using the DAVID functional annotation clustering tool available at the website- https://david . ncifcrf . gov/ . For GSEA analyses , we used the mean of the log2 fold changes of the biological replicates as metric for the H Hallmark gene sets of the BROAD javaGSEA tool with 1 , 000 permutations and the canonical pathway ( cp ) subcollection of the C2 curated BROAD molecular signature gene-set collection . Chromatin immunoprecipitation from C2C12 cells was performed by standard procedures as previously described [45] [49] . For ChIP from mouse muscle , muscles harvested from hind limbs of three adult Hsa::Cre-ERT2::Tead4lox/lox mice with or without Tamoxifen injection were either snap frozen or immediately used for ChIP . The tissue was minced and quickly homogenised in cold hypotonic buffer with protease inhibitors using an Ultraturax homogeniser . The homogenised tissue lysate was fixed with 1% formaldehyde in fresh hypotonic buffer for 10 mins shaking at room temperature . Fixation was stopped by adding glycine to 0 . 15M concentration . Lysate was centrifuged at 3000rpm 5 mins at 4°C and the pellet was resuspended in fresh hypotonic buffer ( 25 mM HEPES , pH 7 . 8 , 1 . 5 mM MgCl2 , 10 mM KCl , and 0 . 1% NP-40 ) , supplemented with Protease Inhibitor Cocktail ( Roche , Basel , Switzerland ) . Lysate was filtered to eliminate debris and nuclei were collected using cell strainer of 70 μm pore size . The filtrate was centrifuged for 5 mins at 3000rpm to obtain a nuclear pellet that was resuspended and incubated 10 min at 4°C in sonication buffer ( EDTA 10mM , Tris-HCl , pH 8 . 0 , 50mM , SDS 1% with protease inhibitor cocktail and PMSF ) and then sonicated using Covaris sonicator for 20 to 25 mins . Lysate was then centrifuged for 15 mins at 11000g at 4°C to obtain the chromatin supernatant fraction that was the used for ChIP . ChIP-seq libraries were prepared and sequenced on an Illumina Hi-seq2500 as single-end 50-base reads . After sequencing , peak detection was performed using the MACS software [50] http://liulab . dfci . harvard . edu/MACS/ ) . Global clustering , meta-analyses and quantitative comparisons were performed using seqMINER and R ( http://www . r-project . org/ ) . Peaks were annotated with Homer ( http://homer . salk . edu/homer/ngs/annotation . html ) using a window of ±50 kb ( or as nearest gene ) relative to the transcription start site of RefSeq transcripts . De novo motif discovery was performed on the 200 base pairs surrounding the top 600 Tead1 and Tead4 peaks using MEME-ChIP . Motif enrichment analyses were performed using in house algorithms as described [49] . The public data for H3K27ac and H3K4me3 data were taken from the GEO accession GSE25308 [51] , Jun GSE37525 , Srf , GSM915168 , Runx , GSM1354734 . Myod1 and Myog ChIP-seq raw data were from GSE44824 [27] and re-analyzed in parallel to the Tead4 and Tead1 ChIP-seq data . The data in this paper have been assigned the accession number GSE82193 in the GEO database . | Aspects of muscle differentiation can be reproduced using the C2C12 cell line or primary myoblasts both of which can be differentiated to form myotubes in vitro . While the functions of recognised myogenic proteins such as Myogenin , Myod1 and MEF-family transcription factors in this process have been extensively studied , the role of the Tead factors has received only limited attention . Tead factors have well defined roles as mediators of Hippo signalling in promoting cell growth and oncogenic transformation , but are also involved in myogenic differentiation involving cell cycle arrest and activation of the myogenic gene expression program . Using integrative genomics and knockdowns in cell based models , we show that Tead factors are essential for differentiation of C2C12 cells and primary myoblasts , but make different contributions activating a distinct set of myogenesis genes in each cell type . We also developped effective chromatin immunoprecipitation from mature mouse muscle fibres allowing identification of highly transcribed muscle identify genes and identification of Tead1 and Tead4 occupied sites . Somatic inactivation in vivo revealed an important role for Tead4 in muscle fibre regeneration . The integration of genomics and loss of function in cell models in vitro and muscle in vivo provide the first comprehensive description Tead factors in myogenic differentiation . |
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To preserve genome integrity , the S-phase checkpoint senses damaged DNA or nucleotide depletion and when necessary , arrests replication progression and delays cell division . Previous studies , based on two pol2 mutants have suggested the involvement of DNA polymerase epsilon ( Pol ε ) in sensing DNA replication accuracy in Saccharomyces cerevisiae . Here we have studied the involvement of Pol ε in sensing proper progression of DNA replication , using a mutant in DPB2 , the gene coding for a non-catalytic subunit of Pol ε . Under genotoxic conditions , the dpb2-103 cells progress through S phase faster than wild-type cells . Moreover , the Nrm1-dependent branch of the checkpoint , which regulates the expression of many replication checkpoint genes , is impaired in dpb2-103 cells . Finally , deletion of DDC1 in the dpb2-103 mutant is lethal supporting a model of strand-specific activation of the replication checkpoint . This lethality is suppressed by NRM1 deletion . We postulate that improper activation of the Nrm1-branch may explain inefficient replication checkpoint activation in Pol ε mutants .
DNA integrity of living organisms is affected by perturbations that induce replication stress including nucleotide depletion or collision with lesions encountered in DNA exposed to alkylating agents [1] . Therefore , each cell must constantly monitor its genome integrity and coordinate DNA replication with cell division in order to avoid genetic instability [2] . Cell cycle checkpoints that monitor the accuracy of each phase of the cycle play crucial role in this control . The replication checkpoint monitors DNA duplication , and when activated , regulates transcription of specific genes , arrests replication progression , stabilizes replication forks , increases the dNTP pool , suppresses late-origin firing , delays cell division and finally restarts DNA synthesis after removal of replication stress [3–10] . It also prevents homologous recombination ( HR ) at double strand breaks ( DSB ) and stressed replication forks during S phase , presumably by blocking DNA ressection , to prevent genetic instability [11 , 12] . Checkpoint mechanisms encompass many proteins that act as sensors , mediators and effectors in a cascade of phosphorylation events [13] . In the first step , uncoupling of helicase and polymerase activities , unsynchronized leading and lagging strand replication or replication fork collapse result in accumulation of ssDNA [14 , 15] . After an activation threshold is reached [16] , large stretches of RPA-coated ssDNA recruit the apical protein kinase Mec1 bound to Ddc2 [17] . Then , the Ddc1 subunit of the 9-1-1 sensor checkpoint clamp ( Ddc1-Rad17-Mec3 in Saccharomyces cerevisiae ) is recruited to the ds-ssDNA junctions and activates the signaling network [18] . The checkpoint response is completely dependent on the 9-1-1 complex in G1 phase while in G2 Dpb11 is also involved in this process [19] . In the S-phase , multiple factors are needed to trigger checkpoint activation including Dna2 in addition to Ddc1 , Dpb11 [20–22] reviewed in [13 , 23] . It has been shown that a ddc1Δ dpb11-1 double mutant is partially defective in phosphorylation of the checkpoint effector kinase , Rad53 [20 , 24] , indicating that there is an additional S-phase checkpoint activation pathway . Since Dna2 is probably involved in this additional activation mechanism , in the triple dpb11Δ ddc1Δ dna2Δ mutant only negligible phosphorylation of Rad53 was detected [21] . Finally , there is also evidence that DNA polymerase epsilon ( Pol ε ) is involved in the 9-1-1 independent activation pathway ( Dpb11 recruitment to stalled replication forks ) [25] suggesting separation of replication stress sensors on the leading and lagging DNA strands [20 , 26] . Upon checkpoint activation , the phosphorylated signaling kinase Mec1 , transmits the signal to the downstream effector kinase Rad53 [27] . Its activation during replication stress is facilitated by checkpoint mediator protein Mrc1 [28 , 29] which promotes Mec1-Rad53 interactions [30] . Importantly , both Mec1 and Rad53 are essential genes in S . cerevisiae while not in Schizosaccharomyces pombe [31] . Rad53-dependent control of the replication stress response is divided into two branches: ( i ) the well-characterized Dun1-Crt1 pathway , also called DNA damage response ( DDR ) branch [32 , 33] , which mainly up-regulates the dNTP pool , and ( ii ) the Nrm1-MBF pathway , also called the G1/S cell cycle ( CC ) branch [34 , 35] , which up-regulates dozens of genes involved in many processes e . g . , TOS4 , TOS2 , MCD1 , CDC21 [36] . Pol ε is one of the major replicative polymerases that generally replicates the leading DNA strand while DNA polymerase delta ( Pol δ ) replicates the lagging strand [37–40] . Recently , an in vitro study of a reconstituted replisome has shown that Pol ε is targeted to the leading strand by the CMG complex ( Cdc45 , Mcm2-7 and GINS ) while Pol δ is targeted to the lagging strand by PCNA ( proliferating cell nuclear antigen ) [41] . Moreover , a chromatin immunoprecipitation based method ( eSPAN ) was used to demonstrate the same strand bias patterns of Pol δ and Pol ε [42] . Pol ε is composed of the catalytic Pol2 subunit and three non-catalytic subunits Dpb2 , Dpb3 and Dpb4 [43–45] , for review see [46 , 47] . Dpb3 and Dpb4 subunits are involved in stabilization of Pol ε interaction with DNA , and their deletion affects replication fidelity [48] . Pol2 and Dpb2 subunits are essential in yeast , although deletion of the N-terminal polymerase catalytic domain of Pol2 gives viable cells [49 , 50] . In contrast , its C-terminal half is necessary and sufficient to support growth and is involved in both interaction with the Dpb2 subunit and S-phase checkpoint activation [50–52] . The interaction of Dpb2 subunit with Psf1 , a subunit of the GINS complex , is important for the CMG complex assembly . Therefore , Dpb2 is involved in initiation of DNA replication but also links Pol ε to the CMG complex during elongation [53–57] . Finally , Pol ε –GINS interaction enables the preferential recruitment of Pol ε over Pol δ to the leading strand [41] . The dpb2 mutants isolated in our laboratory demonstrate temperature-sensitivity and an increased number of replication errors ( MMR-dependent mutator phenotype ) [58 , 59] . In these mutator strains , Pol ζ participates in DNA replication more often although the mutator phenotype of dpb2 mutants results not only from this error-prone TLS polymerase activity [60] . Moreover , these Dpb2 mutants are impaired in interaction with Pol2 and the GINS subunits Psf1 and Psf3 [56 , 58 , 61] which may result in increased participation of Pol δ on the leading strand and be partially responsible for the mutator phenotype [61] . In this work , we investigate the involvement of the Dpb2 subunit of Pol ε in triggering the response to replication stress . For this purpose , we use the dpb2-103 mutant carrying T342I S343F T345I P347S P348S substitutions , isolated in our laboratory [58] . We found that this mutant demonstrates phenotypes characteristic for replication checkpoint mutants . The dpb2-103 cells are sensitive to MMS ( methyl methanesulfonate ) and HU ( hydroxyurea ) , and fail to delay cell cycle progression when treated with these agents . Although , dpb2-103 cells undergo checkpoint-induced Rad53 phosphorylation , they cannot properly activate the Nrm1/MBF branch of downstream response . Finally , we observed a lethal effect of dpb2-103 mutation combined with ddc1Δ . We propose that the observed synergy suggests independent roles in checkpoint activation and that 9-1-1 may recognize damage on the lagging strand while dpb2 , as a subunit of Pol ε , acts on the leading strand .
Studies of the replication stress checkpoint have suggested the involvement of the catalytic subunit of DNA polymerase epsilon , Pol2 , in checkpoint activation [62 , 63] . Later , it was suggested that Dpb2 , the essential non-catalytic subunit of Pol ε interacts with Mrc1 , the checkpoint mediator , and that thus Dpb2 may also be involved in activation of the S-phase checkpoint through modulation of Pol2-Mrc1 interactions [64] . Dpb2 variants that contribute to a spontaneous mutator phenotype have been analyzed in our laboratory for many years [58–61] . Replication stress can be generated either by nucleotide depletion using HU or by blocking replication due to fork collision with MMS-generated DNA lesions , which are detected only during replication [1 , 65] . To determine whether Dpb2 protein is involved in proper execution of replication checkpoint , first we analyzed the sensitivity of yeast cells with the dpb2-103 allele to the genotoxic agent methyl methanesulfonate ( MMS ) or to hydroxyurea ( HU ) , the ribonucleotide reductase inhibitor ( Fig 1 ) . When compared to wild type cells , those with the dpb2-103 allele demonstrate increased sensitivity to both MMS and HU , although these cells were not as sensitive as the canonical S-phase checkpoint deficient mutant mec1-21 ( Fig 1A , 1B and 1C ) . Yeast cells challenged with genotoxic or replication stress activate the checkpoint and delay their cell cycle progression . Slowing down the progression through S-phase gives more time to complete perturbed DNA replication and may result from inhibition of dormant or late origin firing [1 , 3 , 47] or inhibition of replication elongation [66] . This delay can be observed by flow cytometry analysis of DNA synthesis progression in the population of yeast synchronized in G1 and released under specific conditions [9 , 67 , 68] . We synchronized dpb2-103 mutant yeast cells with α-factor and released them from G1 in the absence or presence of MMS or HU . Then , we performed a flow cytometry analysis of DNA content to monitor G1-S-G2 transitions . Under MMS treatment in minimal media at 23°C , dpb2-103 cells reached the 2C DNA content after 240 min while wild-type cells remained at the G1-S transition after the same time in the same conditions . ( Fig 2 ) . Under HU treatment dpb2-103 cells entered S-phase very slowly , whereas wild-type cells remained blocked in G1 phase . These results demonstrate that , similarly to the mec1-21 checkpoint mutant , dpb2-103 cells are defective in delaying cell cycle progression and DNA synthesis when challenged with replication stress . The checkpoint-induced delay of cell cycle progression in cells exposed to HU enables replication fork stabilization and DNA synthesis restart after release from replication stress . Therefore , we synchronized dpb2-103 cells in G1 , and released them in the presence of HU . After 90 minutes , we washed out HU and shifted cells into fresh medium . Unexpectedly , unlike the control strain mec1-21 cells , the dpb2-103 cells were able to restart DNA synthesis after release from HU ( S1 Fig ) . This result shows that the dpb2-103 mutant retains partial S-phase checkpoint activity , perhaps due to 9-1-1 checkpoint clamp sensing from the lagging strand . Previous work has suggested the involvement of the leading strand DNA polymerase ε in replication checkpoint activation [62 , 69] . At the same time , the 9-1-1 ( Ddc1-Rad17-Mec3 ) complex has been proposed to be involved in sensing lagging strand replicative stress [20] . If the 9-1-1 checkpoint clamp and Pol ε act in parallel , strand-specific pathways to induce the response to replication stress , one can expect reduced ability to induce the checkpoint in the double mutant . To test whether Dpb2 is the Pol ε subunit involved in inducing the leading strand pathway of the replication stress checkpoint , we decided to introduce a DDC1 deletion in the dpb2-103 cells . Interestingly , the attempts to substitute DDC1 with nourseothricin resistance cassette ( NAT1 ) were unsuccessful . Similarly , the dissection of tetrads obtained from a DPB2/dpb2-103 DDC1/ddc1Δ strain ( Fig 3A ) failed to generate dpb2-103 ddc1Δ cells , suggesting that the double mutant dpb2-103 ddc1Δ is inviable . In order to verify this , we attempted to introduce a DDC1 deletion into dpb2-103 cells carrying the pMJDPB2 plasmid [58] that provides Dpb2 protein . Transformants obtained in this experiment were cultured and serial dilutions were plated on YNBD medium and YNBD supplemented with 5-FOA to obtain plasmid-free clones . As expected , in contrast to the wild-type , dpb2-103 and ddc1Δ strains the dpb2-103 ddc1Δ cells became inviable after plasmid loss ( Fig 3B ) supporting the conclusion that the double mutant phenotype is lethal . The S-phase checkpoint induction deficiency of canonical mec1 or rad53 mutants is rescued by increasing dNTP formation by deletion of the ribonucleotide reductase inhibitor gene SML1 [4 , 70] . Therefore , we attempted to obtain dpb2-103 ddc1Δ sml1Δ cells through tetrad dissection of an appropriate heterozygous strain . However , after prolonged incubation , we obtained only small colonies of inviable double or triple mutants ( S2 Fig ) . These results demonstrate that the sml1Δ ( increased dNTP level ) does not rescue lethality of dpb2-103 ddc1Δ cells . Together , these results demonstrate synthetic lethality of the dpb2-103 mutation combined with deletion of the DDC1 gene . Checkpoint activation in the S-phase induces a cascade of phosphorylation events . To test the stage at which checkpoint activation fails in dpb2-103 cells , first we analyzed activation of the checkpoint kinase Rad53 . [27 , 71] . We compared the phosphorylation of Rad53 in dpb2-103 cells after MMS or HU treatment to the Rad53 status in checkpoint defective mec1-21 and pol2-12 cells . Migration of the phosphorylated form of Rad53 in polyacrylamide gels is retarded compared to unmodified Rad53 . In dpb2-103 cells , after MMS or HU treatment during 180 minutes , phosphorylated Rad53 was detected ( S3 Fig ) . As expected , in the checkpoint defective control strain mec1-21 , after either MMS or HU treatment no Rad53 phosphorylated form was observed . In pol2-12 cells Rad53 was phosphorylated after HU treatment and residual phosphorylation was observed under MMS-induced genotoxic stress ( S3 Fig ) . These observations suggest that although the dpb2-103 mutant seems to be impaired in S-phase checkpoint activation , the checkpoint kinase Rad53 is phosphorylated . However , it is not clear whether the protein is phosphorylated properly and the downstream signal amplified and propagated correctly . Rad53-dependent phosphorylation of Dun1 ( DNA-damage un-inducible ) up-regulates the dNTP pool , primarily through two mechanisms . First , Dun1 phosphorylates and thus inhibits the Crt1 ( constitutive RNR transcription ) repressor which regulates a small part of the checkpoint-dependent transcriptional response i . e . the RNR2 , RNR3 and RNR4 genes which encode subunits of the ribonucleotide reductase RNR [32 , 72] . Crt1 also represses the expression of gene HUG1 ( hydroxyurea and UV and gammaradiation induced ) whose product inhibits RNR through binding the Rnr2 subunit [73 , 74] . In parallel , Dun1 phosphorylates and promotes degradation of Sml1 , the inhibitor of RNR [4 , 75] Moreover , Dun1-dependent phosphorylation of Dif1 ( damage-regulated import facilitator 1 ) , results in inactivation of the nuclear import of the Rnr2 and Rnr4 subunits of RNR , resulting in their cytoplasmic localization [76 , 77] . Therefore , to test whether in the dpb2-103 cells the checkpoint was interrupted downstream of Rad53 , we analyzed the degradation of Sml1 and induction of RNR3 and HUG1 genes . The amount of the Sml1 protein was analyzed immunologically in extracts from cells treated with MMS for 60 or 120 minutes and compared with untreated cells . In wild-type cells , under genotoxic stress Sml1 is degraded after 60 minutes . Similar results were observed for dpb2-103 cells treated with MMS ( Fig 4A ) . Next , using quantitative RT-PCR we analyzed the expression of the ribonucleotide reductase gene RNR3 and gene HUG1 encoding the RNR inhibitor [73 , 74] . Expression of these two genes is upregulated by the Dun1-Crt1 branch of replication stress response . Wild-type or dpb2-103 cells were synchronized in G1 and released into S-phase in the presence of 200 mM HU . The amount of RNR3 or HUG1 transcripts was normalized to wild-type untreated cells . After 120 and 240 minutes of HU treatment , the RNR3 and HUG1 expression levels in dpb2-103 were similar to those observed in wild-type cells under the same treatment ( Fig 4B ) . It is noteworthy , that the RNR3 mRNA levels at the time of release from G1 and after 120 or 240 from release into S-phase were about 2-fold higher than in wild-type cells . In the control experiment , in mec1-21 cells , induction of RNR3 or HUG1 was not observed . These results demonstrate that the dpb2-103 cells activate the Dun1-Crt1 branch in response to HU or MMS induced replication stress . The replication checkpoint pathway downstream of Rad53 also encompasses a second branch , parallel to the Dun1/Crt1 , i . e . the Nrm1/MBF branch . The Nrm1 co-repressor ( negative regulator of MBF targets 1 ) together with the MBF ( MluI-binding factor ) repressor complex recognize the MCB ( MluI cell-cycle box ) DNA sequence in promoter regions of dozens of genes to repress transcription upon exit from G1 phase . Under replication stress , Rad53-mediated phosphorylation of the Nrm1 repressor prevents its binding to MBF promoters and allows upregulation of a set of MCB G1/S transition genes [34 , 35 , 78–83] . As a consequence , deletion of NRM1 and expression of MCB genes increases cell survival of checkpoint-deficient rad53Δ or mec1Δ yeast cells challenged with replication stress [78 , 79] . Therefore , we hypothesized that upregulation of G1/S transition genes would also rescue dpb2-103 sensitivity to replication stress . We saw that indeed nrm1Δ bypasses the sensitivity of dpb2-103 cells in the presence of HU or MMS ( Fig 5A ) . Interestingly , this was not the case for ddc1Δ cells–deletion of NRM1 does not rescue HU sensitivity resulting from DDC1 deletion ( Fig 5A ) . Flow cytometry shows that asynchronous dpb2-103 cells have perturbed cell cycle , i . e . lower 1C DNA content and slow progression through S-phase ( Figs 2 and 5B ) . Moreover , light scattering measurements indicate that dpb2-103 cells are larger than wild-type cells ( Fig 5C ) . This observation is confirmed by microscopic observations of dpb2-103 cells ( S4 Fig ) . Deletion of NRM1 in dpb2-103 cells partially suppressed these effects: the DNA content in dpb2-103 nrm1Δ cells shows higher 1C DNA content and lower proportion of S-phase cells ( Fig 5B ) . Moreover , forward scatter ( FSC ) measurement indicates a decrease in cell size of dpb2-103 mutants after NRM1 deletion ( Fig 5C ) . Together these results demonstrate that upregulation of MBF G1/S transition genes rescues several dpb2-103 phenotypes . To support the hypothesis that activation of G1/S transition genes ( the Nrm1/MBF branch ) is impaired in dpb2-103 cells under replication stress , we tested the induction of TOS2 , TOS4 , MCD1 and CDC21 MCB genes repressed by Nrm1 and upregulated during S phase to promote cellular tolerance to replication stress [34] . Tos4 contains an FHA ( ForkHead-associated ) domain which interacts with components of the HADAC ( histone deacetylase ) complex involved in the response to various environmental stresses including replication stress [84] . Mcd1 is a subunit of cohesion complex involved in sister chromatide cohesion and chromosome condensation [85] , Tos2 is involved in morphogenesis [86] , Cdc21 is a thymidylate synthase [87] . Wild type and dpb2-103 cells were synchronized in G1 and released into S-phase in the presence of 200 mM HU . The amount of TOS2 , TOS4 , MCD1 and CDC21 RNA was normalized to wild-type untreated cells synchronized in G1 ( time “0” ) ( Fig 6A and S1 Table ) . In parallel , cell cycle progression of these cells was monitored by flow cytometry analysis of DNA content ( Fig 6B ) demonstrating that both wild-type and dpb2-103 cells reached the S-phase 60–90 minutes after release from G1 . Interestingly , a difference between wild-type and dpb2-103 cells , in transcription of these genes can be observed even in normal growth conditions . In wild-type cells , expression of G1/S transition genes is upregulated after release from G1 block , reaches the maximum level after 60 minutes , and decreases after 90–120 minutes . In contrast , in dpb2-103 cells , G1/S transition transcripts are most abundant after 30 minutes of growth and reach the minimum after 60–90 minutes . More important , HU-generated replication stress induced elevated transcription of TOS2 , TOS4 , MCD1 and CDC21 genes in wild-type cells but not in dpb2-103 cells as observed at 90 and 120 minutes time points . ( Fig 6A ) . We conclude there is a defect in the Nrm1 branch of the checkpoint pathway . Positive effects of nrm1Δ on dpb2-103 survival , DNA content and cell size suggest that nrm1Δ may restore viability of dpb2-103 ddc1Δ cells . Therefore , we introduced nrm1Δ into dpb2-103 ddc1Δ pMJDPB2 cells and attempted to obtain plasmid-free cells on 5-FOA . Indeed , in contrast to dpb2-103 ddc1Δ , we were able to obtain viable dpb2-103 ddc1Δ nrm1Δ cells without the plasmid carrying the gene encoding WT Dpb2 ( Fig 6C ) . Similar results were obtained after tetrad dissection from a dpb2-103/DPB2 ddc1Δ/DDC1 nrm1Δ/ nrm1Δ diploid strain ( Fig 6D ) , demonstrating that the lethal effect of the dpb2-103 ddc1Δ is suppressed by derepression of genes that are up regulated in checkpoint proficient cells challenged with replication stress . This strengthens our conclusion that the Nrm1 pathway is affected in dpb2-103 and that Dpb2 and Ddc1 are involved in two separate branches of the checkpoint activation pathway .
Early studies of Pol ε suggested that its catalytic subunit , Pol2 , is involved in replication checkpoint activation . This function has been mapped to the essential C-terminal part of the protein as shown in temperature sensitive pol2-11 and pol2-12 mutants , which encode subunits lacking 31 or 27 C-terminal amino acids , respectively [62 , 88] . Besides replication perturbations , these mutants demonstrate a subset of checkpoint deficiency phenotypes including impaired DUN1 activation after MMS or HU treatment , low viability and elongated spindle formation after release from G1 synchronization into HU [62] . Therefore , such pol2 mutations allow entry into mitosis despite uncompleted DNA replication . The C-terminus of Pol2 is also involved in the interaction with Dpb2 , the second essential subunit of Pol ε [58 , 59 , 63] . This interaction is facilitated by cell-cycle dependent phosphorylation of Dpb2 by CDK in late G1 phase . Inactivation of phosphorylation sites of Dpb2 in the pol2-11 strain dramatically reduces its viability , demonstrating that the Dpb2-Pol2 interaction is essential [89] . Pol2 also interacts with Mrc1 the fork-associated protein that mediates the Mec1-dependent activation of Rad53 [64] . Moreover , mrc1Δ pol2-11 cells are inviable and overexpression of MRC1 rescues pol2-11 temperature sensitivity [64] . The Dpb2 subunit of Pol ε plays an essential role in maintaining the proper architecture of the replisome as it links the Pol ε with GINS and therefore the CMG helicase complex ( Cdc45 , Mcm2-7 and GINS ) through the interaction of the Dpb2 with the Psf1 and Psf3 GINS subunits [53 , 55–57 , 90] . Therefore , it is not surprising that increased amounts of Dpb2 [22] , or the four subunits of GINS [53] suppress the pol2-11 mutation . Interestingly , both pol2-11 and dpb2-103 mutations impair interaction between Pol2 and Dpb2 [58 , 59 , 63] . Consequently , mutations in DPB2 affecting proper interactions of Dpb2 with either Pol2 or GINS may disrupt the replisome integrity and influence correct activation of the DNA replication checkpoint on the leading strand . Therefore , the dpb2-103 mutant encoding Dpb2-103 which has impaired interaction not only with the catalytic subunit Pol2 but also with Psf1 and Psf3 subunits of the GINS complex ( S2 Table ) , was a good candidate for studies of DNA replication checkpoint-defective phenotypes . Using flow cytometry , we found that dpb2-103 mutant cells , similarly to mec1-21 mutant cells , failed to delay DNA replication after release from G1 block into MMS . ( Fig 2 ) . Checkpoint deficient mutant cells ( mec1 or rad53 ) have been shown previously to be unable to delay replication progression [68 , 91] . However , when compared to mec1-21 , dpb2-103 cells under MMS treatment progress through the S phase slowly and hardly reach the G2 phase , suggesting possible residual checkpoint activation ( Fig 2 ) . Another agent that induces replication stress , HU , slows DNA replication progression due to nucleotide depletion [8] resulting in elongating the time of origin firing [92] . Our flow cytometry experiments with G1 synchronized cells released into HU confirm that wild-type cells remain in early S even after 240 minutes . In contrast , the dpb2-103 mutant released from G1 synchronization into HU progressed through the S phase , albeit slowly , likely due to insufficient nucleotide precursors ( Fig 2 ) . One can therefore speculate that S phase progression of this mutant under HU treatment results from inefficient delay of DNA replication combined with alleviation of nucleotide depletion by the slight induction of RNR3 expression ( Fig 4B ) . This hypothesis is reinforced by the observation that when compared to the wild type strain , dpb2-103 cells progress very slowly through unperturbed S phase ( Fig 2 ) . Checkpoint-deficient cells such as mec1 mutants are also unable to resume DNA synthesis after transient HU treatment ( S1 Fig ) [20 , 31] . However , in our experiments , dpb2-103 cells resume DNA replication after the HU-generated block is removed in both permissive and restrictive temperature ( S1 Fig ) . This observation explains why dpb2-103 cells are less sensitive to these drugs when compared to the mec1 mutant ( Fig 1 ) . We also observed that similarly to pol2-11 and pol2-12 mutants [88] untreated dpb2-103 cells are larger , ( Fig 5C ) , and that DNA content in asynchronous cells indicates that the relative proportion of S phase dpb2-103 cells is higher when compared to DPB2 cells ( Fig 5B ) demonstrating cell cycle control perturbations . However , the relative amount of cells in S phase may also be due to replication perturbations resulting from impaired interactions both within Pol ε and between Pol ε and the CMG helicase complex [59 , 61] . Nonetheless , these observations reinforce the conclusion that the cell cycle control is perturbed in the dpb2-103 mutant most probably due to inefficient replication checkpoint activation . Because Dpb2 is a subunit of Pol ε , the leading strand polymerase [38 , 41] , it has been suggested that its role in sensing replication perturbations is oriented mainly toward the leading strand [20] . Then , the signal from the lagging strand would come from the 9-1–1 ( Ddc1-Rad17-Mec3 ) checkpoint clamp which is loaded specifically at the 5’ junctions of RPA-coated ssDNA and duplex DNA [93] . The existence of two parallel leading and lagging strand-specific checkpoint activation pathways would explain partial checkpoint activation in the pol2 mutants , the dpb2-103 or the ddc1Δ cells . Flow cytometry analysis of DNA content in ddc1Δ cells treated with MMS demonstrated progression through S phase similar to that observed in our study for dpb2-103 cells [18] , although none of these mutants abolishes Rad53 phosphorylation in response to MMS or HU treatment [20 , 71] ( S3 Fig ) . Consequently , it was suggested that in S . cerevisiae ddc1Δ cells the partial checkpoint activation is mediated by Dpb11 recruited to the replication fork by Pol ε in a 9-1-1 independent manner [20 , 25] . Therefore , it is not surprising that our experiments showed that the double dpb2-103 ddc1Δ mutant is lethal ( Fig 3 ) , supporting a model of separate sensing of replication stress on the two DNA strands and points out the involvement of Dpb2 in this process . The interpretation that the synthetic lethality of dpb2-103 and ddc1Δ results from the fact that replication defects in dpb2-103 cells can only be bypassed by a proficient replication checkpoint is also possible . However , given that the ddc1Δ mutant is only partially impaired in checkpoint activation and even combined with the dpb11-1 mutation retains low Rad53 activity that prevent replication fork breakdown this would not explain the synthetic lethality of dpb2-103 and ddc1Δ . Therefore , we favor the hypothesis of separation of replication problems sensing on leading and lagging strands ( Fig 7A ) . SML1 deletion can rescue mec1Δ or rad53Δ lethality , although it cannot restore checkpoint activation . However , after tetrad dissection of a heterozygous triple mutant we obtained colonies of very sick dpb2-103 ddc1Δ sml1Δ strains ( S2 Fig ) , what is in accordance with our observation that the Dun1/Crt1 pathway , which regulates Sml1 degradation is properly activated in dpb2-103 cells . It also demonstrates that the lethal effect of mec1Δ or rad53Δ mutations ( rescued by sml1Δ ) results from replication checkpoint activation/execution defects other than those occurring in dpb2-103 ddc1Δ cells . The involvement of Pol ε and Pol δ in the majority of replication of the leading and lagging DNA strands , respectively , is very well documented . However , it has been proposed recently , that Pol δ is the major replicase of both DNA strands [94] . However , mutation rate data obtained using Pol2 and Pol3 mutants that incorporate unique strand specific substitutions and studies of ribonucleotide incorporation into DNA by Pol ε and Pol δ as well as DNA in vitro studies [37 , 40 , 95 , 96] strongly support the model in which Pol ε acts as the major leading strand DNA polymerase . Moreover , the model in which Pol δ is the major replicase of both strands still locates Pol ε in association with the CMG complex on the leading strand with a role in correcting replication errors and proofreading rNMPs [94] . The replication checkpoint activation results in phosphorylation of the Rad53 effector kinase and subsequent cellular response to DNA synthesis problems . The best analyzed branch of this response is the Rad53-Dun1-dependent upregulation of dNTP pool [33 , 75] which normally limits DNA replication , but is upregulated 6- to 8-fold under replication stress to promote fork progression [8 , 97] . Interestingly , although the dpb2-103 mutant is impaired in correct response to replication stress , we detected Rad53 phosphorylation ( S3 Fig ) , degradation of the RNR inhibitors Sml1 and induction of expression of the RNR3 and HUG1 genes ( Fig 4 ) . We can therefore conclude that dpb2-103 cells activate the Dun1-Crt1 branch of replication stress response correctly ( Fig 7B ) . This suggests that Pol ε checkpoint mutants are partially proficient in activation of first steps of replication stress response although with modifications in the pattern of Rad53 phosphorylation . Such incomplete phosphorylation may be undetectable in the gel retardation assay of Rad53 which has been shown to contain multiple phosphorylation sites [98–100] . Alternatively , the Pol ε signal may act downstream of or independently of Rad53 in checkpoint activation . Importantly , these observations rule out the possibility that inefficiency of replication checkpoint activation in dpb2-103 cells results from the fact that the number of affected origins is not high enough to reach an activation threshold as demonstrated for the orc2-1 mutant defective in initiation of DNA replication and Rad53 phosphorylation under MMS treatment [16] . The results of our analysis of the second branch of Rad53-dependent response to replication stress , the Nrm1/MBF pathway , clarify the checkpoint-deficiency phenotypes of the dpb2-103 mutant . Rad53-dependent phosphorylation of the Nrm1 corepressor of MBF genes prevents its binding to MBF promoters in response to the S phase checkpoint [80] to activate expression of many genes involved in the replication stress response [36] . We found that NRM1 deletion suppresses the MMS and HU sensitivity of dpb2-103 cells ( Fig 5A ) and that dpb2-103 nrm1Δ cells partially rescue their cell size as well as their DNA content , demonstrating proficient progression through S phase ( Fig 5B and 5C ) . We also tested nrm1Δ dependent checkpoint-deficiency phenotypes rescue in pol2-12 cells . Our flow cytometry analysis shows , that the pol2-12 mutant demonstrates a defect in S-phase progression ( although less severe when compared to dpb2-103 cells ) , and that deletion of NRM1 restores proper DNA content ( S5 Fig ) . Moreover , NRM1 deletion alleviates pol2-12 HU and temperature sensitivity ( S5 Fig ) . These results suggest that the dpb2-103 and pol2-12 mutations in Pol ε may similarly affect the replication stress response . The observed nrm1Δ dependent rescue of dpb2-103 phenotypes during unperturbed growth can be explained by upregulation of Nrm1-regulated genes at the G1/S transition , which are prematurely downregulated in dpb2-103 cells , when compared to the wild-type cells ( Fig 6 ) . Moreover , we observed that expression of Nrm1-repressed genes TOS2 , TOS4 , MCD1 , CDC21 , which is upregulated in wild-type cells even after 90–120 minutes in response to replication stress , remain uninduced in dpb2-103 cells ( Fig 6 ) . Finally , the lack of Nrm1 repressor ( nrm1Δ ) partially rescued the synthetic lethality of dpb2-103 ddc1Δ cells; a similar lethality bypass by nrm1Δ has been observed for rad53Δ and mec1Δ mutants [78 , 79] . This demonstrates that checkpoint deficiencies in dpb2-103 cells are due mainly to impaired derepression of Nrm1-regulated genes ( Fig 7B ) . However , uncovering of the mechanism of Dpb2-dependent derepression of the Nrm1 branch of the replication stress response needs further investigation . The question remains whether the failure of dpb2-103 mutant to fully activate the replication checkpoint results from direct involvement of the Dpb2 in replication stress sensing / activation , protein stability changes , impaired phosphorylation or from defects in Pol ε association within the replisome . Indeed , mutations in dpb2-103 partially impair interaction of Dpb2 with the catalytic subunit Pol2 of Pol ε and strongly impair its interaction with the Psf1 subunit of GINS . However , it would be expected that the destabilization of Pol ε in the replication fork and possibly dissociation would induce replication checkpoint activation rather than abolish it . Therefore , we favor the hypothesis of direct involvement of Dpb2 in the replication stress response , which still needs further investigation .
S . cerevisiae strains listed in Table 1 . were grown in standard media [101] [102] . When nutrition selection was not required yeast complete medium YPD ( 1% bacto-yeast extract , 2% bacto-peptone , 2% glucose liquid or solidified with 2% bacto-agar ) was used . Yeast transformants were selected on YPD supplemented with appropriate antibiotics ( Hygromycin B 300 μ-ml or Nourseothricin 100 μg-ml ) . When necessary yeasts were selected for prototrophy on YNBD minimal medium ( 0 . 67% yeast nitrogen base without amino acids , 2% glucose , liquid or solidified with 2% bacto-agar ) supplemented with appropriate amino acids and nucleotides . For selection of URA3-plasmid-free cells , YNBD medium supplemented with 1 mg/ml 5-fluoroorotic acid ( 5-FOA ) was used [103] . Escherichia coli DH5α ( F- , gyrA96 , recA1 , relA1 , endA1 , thi1 , hsdR17 , supE44 , deoR , Δ ( lacZYA-argF ) U169 , [φ80Δ ( lacZ ) M15] ) cells were grown routinely at 37°C in L broth–liquid or solidified with 1 . 5% agar and supplemented when needed with ampicillin ( 100 μg-ml ) . For MMS sensitivity tests , yeast strains were grown in YPD medium until OD600 reached 0 , 6 , harvested and resuspended in 0 , 9% NaCl . Appropriate dilutions were plated on YNBD medium supplemented with MMS ( 0% , 0 , 01% , 0 , 02% or 0 , 03% ) . Colonies were counted after 5 days incubation at 23°C . For HU sensitivity tests , yeast strains were grown in YPD medium until OD600 reached 0 , 6 before adding HU to 200 mM final concentration . Samples were collected at indicated time points , washed with distilled water and plated on YNBD medium . Colonies were counted after 5 days incubation at 23°C . Yeasts were precultured overnight in YNBD medium at 23°C and appropriately diluted in YNBD medium to grow at 23°C until OD600 reached 0 , 4 . Cells were harvested , resuspended in fresh YNBD medium with the α-factor mating pheromone ( 4 mg/ml ) and grown for 2–3 hours at 23°C . Then to release them from α-factor , cells were harvested and washed three times with water . Next they were released from G1-arrest into fresh YNBD medium and incubated at 23°C . When necessary , cells were released from G1-arrest in YNBD medium containing 0 , 05% MMS or 200 mM HU . Samples were taken at indicated time points and fixed in 70% ethanol . Ethanol-fixed cells were harvested , washed and resuspended in 1 ml of sodium citrate ( 50 mM , pH 7 , 0 ) . After brief sonication they were treated with RNaseA ( 0 , 25 mg-ml ) at 50°C for 1 hour and with proteinase K ( 1 mg/ml ) for another hour at 50°C . Then , samples were diluted in sodium citrate containing propidium iodide ( 16 μg-ml ) and incubated overnight at 4°C . The DNA content was identified by measuring the propidium iodide fluorescence signal ( FL2 ) using Becton Dickinson FACSCalibur and the CellQuest software ( BD Bioscience ) . To evaluate the size of yeast cells , the forward scatter ( FSC ) was analyzed . Yeast cells were grown until OD600 reached 0 , 4–0 , 6 . Then , 0 , 05% MMS or 200 mM HU was added and cells were grown for 2 h . Cells were collected and prepared for SDS-PAGE as described previously [107] . For immunodetection , goat polyclonal anti-Rad53 antibody ( sc-6749 ) from Santa Cruz Biotechnology ) , rabbit polyclonal anti-Sml1 antibody ( AS10 847 ) from Agrisera and mouse monoclonal anti-actin antibody ( MAB1501 ) from Millipore were used . Total RNA was isolated using the Syngen Tissue RNA Mini Kit ( Syngen Biotech , POLAND ) as indicated in the manufacturer’s instruction . Reverse transcription was performed using the RevertAid™ First Strand cDNA Synthesis Kit ( ThermoFisher Scientific ) and Real-Time PCR was done using Real-Time 2xHS-PCR Master Mix SYBR ( A&A Biotechnology ) and LightCycler 480 ( Roche ) . Transcript levels were normalized to actin mRNA ( ACT1 ) . | The viability of living organisms depends on the integrity of their genomes . Each cell has to constantly monitor DNA replication and coordinate it with cell division to avoid genomic instability . This is achieved through pathways known as cell cycle checkpoints . Therefore , upon replication perturbation , DNA synthesis slows down and cell division is delayed . For that , a specific signal is induced and propagated through a mechanism that have already been identified but still need investigations . We have isolated a mutated form of Dpb2 , the essential subunit of DNA polymerase epsilon ( Pol ε ) holoenzyme . This mutated form of Pol ε impairs proper activation of the cellular response to replication stress . We show that yeast cells with mutations in the DPB2 gene fail to activate the Nrm1-regulated branch of the checkpoint , which controls numerous genes expressed in response to replication stress . Moreover , our results support the model of parallel activation of replication checkpoint from the leading and lagging DNA strands . This strongly suggests that Pol ε , the leading strand replicase , is involved in replication checkpoint activation from this strand . Our results contribute to the understanding of mechanisms of cellular response to replication stress , which are necessary to preserve genome stability . |
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The US2-11 region of human and rhesus cytomegalovirus encodes a conserved family of glycoproteins that inhibit MHC-I assembly with viral peptides , thus preventing cytotoxic T cell recognition . Since HCMV lacking US2-11 is no longer able to block assembly and transport of MHC-I , we examined whether this is also observed for RhCMV lacking the corresponding region . Unexpectedly , recombinant RhCMV lacking US2-11 was still able to inhibit MHC-I expression in infected fibroblasts , suggesting the presence of an additional MHC-I evasion mechanism . Progressive deletion analysis of RhCMV-specific genomic regions revealed that MHC-I expression is fully restored upon additional deletion of rh178 . The protein encoded by this RhCMV-specific open reading frame is anchored in the endoplasmic reticulum membrane . In the presence of rh178 , RhCMV prevented MHC-I heavy chain ( HC ) expression , but did not inhibit mRNA transcription or association of HC mRNA with translating ribosomes . Proteasome inhibitors stabilized a HC degradation intermediate in the absence of rh178 , but not in its presence , suggesting that rh178 prevents completion of HC translation . This interference was signal sequence-dependent since replacing the signal peptide with that of CD4 or murine HC rendered human HCs resistant to rh178 . We have identified an inhibitor of antigen presentation encoded by rhesus cytomegalovirus unique in both its lack of homology to any other known protein and in its mechanism of action . By preventing signal sequence-dependent HC translocation , rh178 acts prior to US2 , US3 and US11 which attack MHC-I proteins after protein synthesis is completed . Rh178 is the first viral protein known to interfere at this step of the MHC-I pathway , thus taking advantage of the conserved nature of HC leader peptides , and represents a new mechanism of translational interference .
Human cytomegalovirus ( HCMV ) is a widespread pathogen which is mostly asymptomatic in immune competent individuals , but pathogenic in the immune compromised such as post-transplant or AIDS patients [1] . Following primary infection , HCMV establishes a latent infection for life which is largely controlled by the cellular immune system . Immune control of HCMV requires enormous immunological resources with often more than 10% of the T cell pool being CMV-specific , a number that might further increase with age [2] . However , these immunological efforts are unable to eliminate the virus and do not prevent super-infection [3] . Thus , HCMV is a master in surviving in the face of a constant immunological onslaught . As one of the largest human viruses , with well over 200 open reading frames ( ORFs ) , HCMV uses only about a third of its coding potential for “essential” functions whereas the majority of its genes are non-essential for growth in vitro [4] , [5] . Many of these “non-essential” genes encode modulators of innate or adaptive immune responses including inhibitors of apoptosis , interferon-induction , T cell and NK cell recognition [6]–[9] . However , the importance of these immune modulators for viral pathogenesis and immune escape in vivo is not known since HCMV does not infect immunocompetent experimental animals . Such restricted species specificity is a hallmark of CMVs and , as a result , CMVs have co-evolved with their hosts [10] . Chimpanzee CMV is most closely related to HCMV [11] . However , chimpanzees are a protected species and unsuitable as an animal model . Although more distantly related to humans , rhesus macaques ( RM ) are readily available for experimentation . Sequence analysis of rhesus CMV ( RhCMV ) revealed that approximately 60% of the open reading frames ( ORFs ) are homologous to HCMV ORFs including most of the aforementioned immune modulators [12] , [13] . In order to study the importance of some of the immune regulatory functions in vivo , we have begun to characterize several of the conserved immune modulators of RhCMV . The US2-US11 genomic region of HCMV encodes multiple proteins that interfere with several MHC and MHC-like molecules . Among the best studied of these is the US6-family which contains four genes that inhibit MHC class I ( MHC-I ) -mediated antigen presentation to T cells: US2 , US3 , US6 and US11 [14]–[16] . These proteins are type I transmembrane glycoproteins that reside in the endoplasmic reticulum and show clear homology to each other and structural features resembling the IG-superfamily fold [17] . Despite these structural similarities , each protein interferes in its own unique way with the assembly of MHC-I with peptides at a post-translational level . Upon completion of heavy chain ( HC ) translation and translocation into the lumen of the ER , but prior to assembly with the light chain β2-microglobulin ( β2-m ) , US2 and US11 mediate the retro-translocation of MHC-I molecules to the cytosol [18] . There , the HC is deglycosylated by N-glycanase and degraded by the proteasome [19] . US6 inhibits peptide translocation by the TAP thus preventing the MHC-I heterodimers from obtaining viral peptides [16] . Finally , US3 prevents ER exit of peptide-loaded MHC-I molecules [15] , both by directly interacting with MHC-I molecules and by interfering with tapasin and protein-disulfide isomerase , both chaperones of the peptide loading complex [20] . We previously demonstrated that the US2-11 orthologues of RhCMV are also functionally equivalent in that Rh182 ( US2 ) and Rh189 ( US11 ) mediate proteasomal destruction of MHC-I , Rh183 ( US3 ) retains MHC-I and Rh185 ( US6 ) inhibits TAP [21] . Thus , it seemed likely that eliminating the genomic region spanning RhUS2-11 from RhCMV would restore MHC-I assembly and transport in RhCMV-infected cells as previously observed for US2-11-deleted HCMV [22] . Surprisingly however , we discovered that in addition to these conserved mechanisms , RhCMV contains an additional ORF , rh178 , that targets the MHC-I assembly pathway . Interestingly , this ORF does not display any homology to the US6 gene family and acts by a novel mechanism that operates post-transcriptionally , but prior to completion of translation/translocation .
Deletion of the genomic region encoding US2-US11 restores MHC-I expression in HCMV-infected cells [22] . To determine if deletion of the homologous region in the RhCMV genome would likewise restore MHC-I expression we created a recombinant RhCMV lacking RhUS2-11 using a RhCMV-derived bacterial artificial chromosome ( BAC ) ( Protocol S1 ) [23] . Similar to recombinant HCMV lacking US2-11 , a growth defect was not observed for ΔRhUS2-11 [22] . However , unlike US2-11-deleted HCMV , ΔRhUS2-11 retained some ability to reduce MHC-I steady state levels in infected TRFs as shown by immunoblot ( Fig . 1A ) . At 48 hours post-infection , MHC-I was markedly reduced in ΔRhUS2-11-infected TRFs . To determine whether the reduced steady state levels were due to interference with newly synthesized MHC-I , we immunoprecipitated MHC-I from radiolabeled TRFs infected with wild-type ( WT ) or ΔRhUS2-11 . When cells were labeled for one or two hours , we recovered dramatically less MHC-I from RhCMV-infected cells despite the use of polyclonal antiserum K455 recognizing all forms of MHC-I ( Fig . 1B ) [24] . Compared to WT there was an increase in HC recovery from ΔRhUS2-11-infected cells . Such residual HC was also observed in pulse-chase experiments , when ΔRhUS2-11-infected TRFs were pulsed for 10 min and chased from 30 min up to 90 min ( Fig . S1A ) . However , compared to mock-treated cells , radiolabeled HC was drastically reduced at all time points either during pulse or chase . In contrast to HC , expression of control proteins such as Transferrin-receptor or vimentin was unaffected in RhCMV-infected cells ( Fig . 1D ) . Also , we did not observe a general shut-off of host protein expression or a dramatic decrease of glycoprotein recovered with the lectin concanavalin A ( data not shown ) . Moreover , expression of the light chain β2-m was much less affected by RhCMV compared to HC , particularly in short pulse/chase experiments ( Fig . 1C ) . These data suggested that in addition to RhUS2-11 inhibiting MHC-I assembly , RhCMV specifically interferes with expression of HC . The residual HC recovered from ΔRhUS2-11-infected cells indicate that this viral inhibition of HC expression ( VIHCE ) was either incomplete or VIHCE did not equally affect all MHC-I alleles present in TRFs . Since only minimal amounts of HC are detectable during ΔRhUS2-11 infection , we wanted to examine if VIHCE caused rapid degradation of HCs . In cells infected with HCMV , HC is initially synthesized but then rapidly degraded as shown by pulse-chase ( Fig . 1C ) . This observation is consistent with previous reports and is due to the reverse translocation of MHC-I mediated by US2 and US11 followed by proteasomal destruction of MHC-I [19] . In contrast , during infection with both WT RhCMV and ΔRhUS2-11 only minimal amounts of HC were detectable after a 10-min radiolabel , and remained low during a 30-min chase ( Fig . 1C ) . Furthermore , during a radiolabel for only 1-min HC synthesis was markedly reduced during RhCMV infection ( Fig . S1B ) . To rule out that HC was not recovered due to epitope masking by a viral protein or because HC was in a complex with NP40-insoluble proteins , we lysed cells in SDS to disrupt protein complexes and denature the HC prior to IP . Using either a monoclonal antibody that recognizes only free HC ( HC-10 ) [25] or K455 , we were unable to recover increased amounts of HC under these conditions ( Fig . 1E ) . Taken together these data suggest that RhCMV either prevents complete HC synthesis or degrades HC prior to complete protein synthesis . Since co-translational degradation is mediated by proteasomes [26] we wanted to determine whether HC translation could be completed in the presence of proteasome inhibitors . TRFs were infected with ΔRhUS2-11 and treated with the proteasomal inhibitor MG132 . However , no significant increase in HC recovery was observed either when total MHC-I was immunoprecipitated with K455 from NP40-lysates or with HC-10 from SDS-lysates ( Fig . 1F ) . In contrast , HC was stabilized in cells transduced with Adenovirus expressing HCMV US11 . The proteasomal inhibitors Lactacystin and ZL3VS also failed to stabilize HC in ΔRhUS2-11-infected cells ( data not shown ) . Taken together these data strongly suggest that RhCMV inhibits expression of HC prior to or during polypeptide synthesis . Since this phenotype is observed in the absence of RhUS2-11 and is not present in HCMV , we further conclude that RhCMV contains one or more unique gene ( s ) encoding VIHCE . Since VIHCE seems to be specific to RhCMV , but absent from HCMV , we examined the RhCMV genome for potential candidate genes . The genomic region spanning ORFs Rh158 to rh180 , corresponding to the region between IE1/IE2 ( UL123/UL122 ) and US1 in HCMV , contains a large number of genes that are either specific to RhCMV or are homologous to genes frequently deleted in laboratory strains of HCMV [12] , [27] . To examine whether this region contains the VIHCE gene , we deleted Rh158–180 using the BAC-recombination strategy shown in Fig . 2A . Interestingly , Δ158–180 did not show any obvious growth defects despite such a large deletion ( data not shown ) . Moreover , pulse-chase labeling of Δ158–180-infected TRFs revealed initial synthesis of MHC-I followed by degradation ( Fig . 2B ) . This degradation could be inhibited by the proteasome inhibitor MG132 ( Fig . 2C ) . MG132 also stabilized a smaller , presumably deglycosylated , degradation intermediate ( * ) which is also observed in cells transfected with RhUS2 [21] . Thus , it seemed likely that Δ158–180 lacked VIHCE , and that in the absence of VIHCE HC was now degraded by the RhCMV homologues of US2 and US11 . To examine whether the combined deletion of RhUS2-11 and VIHCE would restore HC expression in RhCMV-infected cells , we created a recombinant lacking both Rh158–180 and RhUS2-11 ( Fig . 2A ) . As expected from the single deletions , the resulting double-deletion virus Δ158–180 , ΔRhUS2-11 did not display a growth defect in vitro ( not shown ) . When TRFs were infected with Δ158–180 , ΔRhUS2-11 , HC expression was similar to Mock-infected cells indicating that this recombinant virus no longer interfered with MHC-I expresson ( Fig . 2B ) . Taken together , these data indicate that the VIHCE gene is located within the Rh158–180 region of RhCMV . Furthermore , the fact that HC synthesis is observed in the absence of VIHCE supports our conclusion that VIHCE acts prior to the ER-associated degradation caused by the US2-US11 homologs . To identify the gene ( s ) coding for VIHCE we systematically deleted fragments of decreasing size within the Rh158–180 region in an iterative fashion ( Fig . 3A; Table S1 ) . We took advantage of the fact that HC is initially synthesized in cells infected with VIHCE-deleted virus but then degraded by US2 and US11 to distinguish between recombinants encoding or lacking VIHCE . Initially , viruses carrying deletions approximately spanning the left or right half of the Rh158–180 region were generated ( Fig . 3A ) . TRFs were infected with recombinants Δ158–168 and Δ167–180 and pulse-chase was performed . Since HC was expressed in TRFs infected with Δ167–180 and not in TRFs infected with Δ158–168 , we concluded that VIHCE was located in the Rh167–180 region . Similarly , HC was expressed in TRF infected with viruses Δ175–180 , Δ175–178 , Δ176–178 , and Δ177–178 , but not Δ167–174 , Δ179–180 , and Δ175–177 ( Fig . 3A ) . These data suggested that rh178 encodes VIHCE . The region encoding rh178 overlaps with several predicted ORFs and with a previously identified large intron of the US1-homologue Rh181 [28] ( Gene Bank Accession: AF474179 ) . To exactly determine the mRNAs encoding VIHCE we mapped the transcriptional start and stop sites of the rh178 ORF and generated additional , smaller deletions and point mutants within the rh178 coding region ( Figs . 3–4 ) . We performed 5′ and 3′ RACE as well as Northern blot analysis . Sequence analysis of the 5′ RACE product identified a transcription start site downstream of the originally predicted rh178 start codon ( Fig . 3D ) . The identified transcript is predicted to encode a shorter version of rh178 . 3′RACE and cDNA cloning further revealed additional splice products in this region: a shorter splice product lacking most of the rh178 protein encoding region ( rh178 . 4; Note that Rivailler et al . , ( 2006 ) have detailed additional predicted ORFs upstream of rh178 and denoted them rh178 . 1 , rh178 . 2 , and rh178 . 3 ) and the above mentioned large Rh181-transcript which does not contain rh178 since it is removed by splicing . These three transcripts share the same polyadenylation signal and 3′ terminus ( Fig . 3B ) . Northern blot analysis using the predicted rh178 coding region as probe revealed two transcripts ( Fig . 3C ) . A larger predominant transcript of approximately 1600bp corresponds to the expected size of rh178 . The smaller transcript may correspond to rh178 . 4 , a shortened rh178 , or an unidentified transcript of the opposite sense . These data confirm the expression of the rh178 transcript during infection and correct the prediction of its protein coding region . Kinetic analysis indicates that VIHCE is an early gene that is already expressed within 4 hours of infection ( Fig . S3 ) . To determine whether the protein encoded by rh178 is responsible for VIHCE , we created a frameshift in the 5′-end of the predicted coding region ( rh178FS ) ( Fig . 4A ) . Since the primer-directed mutagenesis strategy also caused deletion of a portion of the 5′-UTR we generated a control virus ( rh178FSCtrl ) containing the same modification of the predicted 5′-UTR of rh178 but no frameshift ( Fig . 4A ) . While rh178FSCtrl inhibited HC expression similar to WT ( Fig . 4B ) , HC was synthesized in rh178FS-infected TRFs ( Fig . 4C ) . Thus , VIHCE is mediated by the rh178-encoded protein . The rh178 protein ( Fig . 5A ) , with a molecular weight of approximately 24 kDa , does not display significant homology with non-RhCMV sequences in the genomic database . A stretch of highly hydrophobic amino-acids beginning at amino acid 14 is predicted to represent a non-cleaved amino-terminal signal anchor ( Fig 5B ) . Thus , the most likely topology for this protein is that of a type 1b transmembrane protein , i . e . a large cytoplasmic C-terminus following the signal-anchor . Immunofluorescence analysis of epitope-tagged rh178 indicates that the protein localizes to the ER , suggesting that rh178 is anchored in the ER-membrane ( Fig . 5E ) . To obtain better expression of rh178 for further analysis , we constructed replication-defective adenovirus vectors expressing either wild type rh178 ( Ad178 ) or HA-tagged rh178 ( Ad178-HA ) . While there is a predicted glycosylation site at position N101 , digestion of whole cell lysate from Ad178-HA transduced cells with peptide:N-Glycosidase F ( PNGase ) failed to cause a shift in rh178 migration , while a shift was seen with MHC-I HC ( Fig . 5C ) . Thus , rh178 does not appear to be glcosylated and this is a further indication that the C-terminus of rh178 is located in the cytosol . To determine if rh178 by itself was capable of VIHCE , we transduced TRFs and performed pulse-chase analysis . Cells transduced with Ad178 exhibited reduced expression of HCs while β2-m was unaffected ( Fig . 5D ) , similar to the HC inhibition observed in RhCMV-infected cells ( Fig . 1 ) . MHC-I HC in cells transduced with a control adenovirus vector , AdTrans , was not affected . Thus , rh178 is both necessary and sufficient for VIHCE . Our data suggest that VIHCE prevents expression of the majority of HCs prior to completion of protein synthesis . Residual , VIHCE-resistant HCs are eliminated by RhUS2-11 . The dramatic reduction of newly synthesized HC observed even in the presence of proteasome inhibitors further suggests that VIHCE either blocks transcription of HC mRNA , completion of HC protein synthesis , or causes HC degradation in a proteasome-independent manner . However , the levels of HC mRNA did not change upon RhCMV-infection as shown by Northern blot ( Fig . 6A ) and by quantitative RT-PCR ( data not shown ) . Additionally , the size of the HC mRNA was unaltered in RhCMV-infected cells suggesting that mRNA is not cleaved , alternative spliced or degraded by RhCMV . We further determined whether HC mRNA is polyadenylated and exported into the cytoplasm by isolating nuclear , cytoplasmic , and polyadenylated RNA fractions from infected cells . We did not observe a significant difference in any of these fractions compared to Mock-infected cells ( data not shown ) . These data indicate that HC mRNA transcription , poly-adenylation , splicing and export to the cytosol is not affected by RhCMV . To determine whether the association of HC mRNA with ribosomes is inhibited we analyzed the polyribosome distribution of HC mRNA [29] . When sucrose-gradient fractions from lysates of Mock-infected or RhCMV-infected TRFs were analyzed by Northen blots , HC mRNA sedimented to the polyribosome fractions 12 and 13 in both Mock- and RhCMV-infected cells ( Fig . 6B ) . Small shifts in polyribosome density were observed in RhCMV infection for both HC and GAPDH mRNA , suggesting virus infection causes a slight reduction of ribosomal occupancy on cellular transcripts . Therefore , it seems that VIHCE does not inhibit the association of polyribosomes with HC mRNA . While sedimentation to the polyribosome fraction indicates the association of HC mRNA with ribosomes , it was possible that the ribosomes were not active . In order to determine if the ribosomes associated with the HC mRNA are actively translating we incubated cells with puromycin . Puromycin is a polypeptide chain terminator that requires an active peptidyl transferase to cause ribosome dissociation from transcripts . A short ( 4 min ) incubation with puromycin caused a shift in the polyribosome profile of HC mRNA in both RhCMV and Mock-infected cells , indicating ribosome dissociation ( Fig . 6C ) . This result indicates that the ribosomes bound to the HC mRNA are actively translating and not simply stalled on the transcript . Taken together these data suggested that HC mRNA is transcribed normally in RhCMV-infected cells and that protein translation is not inhibited at the level of initiation or elongation . However , since full-length HC protein cannot be recovered it seems most likely that HC translation is not completed . Observations similar to VIHCE were reported for translation inhibition by microRNAs that bind to the 3′-UTR of target transcripts . Similar to VIHCE , mRNAs that are targeted by a given microRNA are found in an active polyribosomal complex but a translated polypeptide intermediate can not be recovered even in the presence of proteasome inhibitors [30] . To examine the possibility that VIHCE targets the 3′-UTR of HCs we tested the ability of VIHCE to block synthesis of HC with or without its native 3′-UTR . Since antibodies to rhesus HCs are not available , and VIHCE is able to block expression of human HCs ( Fig . 7A ) , we chose to examine VIHCE function on HLA-A3 . To determine whether the 3′-UTR was required for this inhibition we transiently expressed HLA-A3 with or without its native 3′-UTR in TRFs . Following transfection we infected cells with either RhCMV containing VIHCE ( ΔRhUS2-11 ) or RhCMV lacking VIHCE ( Δrh178 , ΔRhUS2-11 ) . Expression of both HLA-A3 carrying the native 3′-UTR and a heterologous vector-derived 3′-UTR sequence was reduced by VIHCE ( Fig . 7B ) . The 5′-UTR was vector-derived in both constructs . Therefore , we conclude that VIHCE does not target the UTRs of HC mRNA . Translation of type I transmembrane proteins such as HC is dependent upon an N-terminal signal peptide ( SP ) that mediates translocation across the ER membrane . Upon translation initiation , the SP is recognized by the signal-recognition particle ( SRP ) which binds to the SP and arrests translation . This is followed by docking of the translation complex to the SRP-receptor which aids the transfer of the ribosomal/mRNA/nascent polypeptide complex to the SEC61 translocon [31] . Translation then resumes and the nascent polypeptide chain is imported into the lumen of the ER . The fact that VIHCE requires the HC coding sequence suggested that the HC protein might be at least partially translated and that VIHCE acts on the nascent polypeptide . Compared to human HC , we observed that the murine MHC-I molecule H2-Kb was more resistant to VIHCE ( data not shown ) . We hypothesized that this resistance was encoded in the amino-terminus of H2-Kb , specifically the SP . To test this hypothesis we replaced the SP of HLA-A3 with that of H2-Kb . As a further control , we also introduced the SP of CD4 which is more divergent from the HLA-A3 SP ( Fig . 7C ) . In both instances we observed that expression of the chimeric protein was much less reduced by virus expressing VIHCE compared to native HLA-A3 . Remarkably , the SP of Kb is quite similar to that of HLA-A3 ( Fig . 7C ) yet HLA-A3 expression was restored to almost the same levels as observed for the CD4 SP ( Fig . 7D ) . Therefore , we conclude that the SP of primate MHC-I is required for VIHCE to inhibit HC translation . The fact that VIHCE requires the MHC-I SP further suggests that VIHCE interferes with SP-dependent translocation which would lead to translation arrest and rapid , co-translational destruction of the resulting protein fragments . We next examined if the MHC-I SP is sufficient for VIHCE recognition . To test this we created a chimeric CD4 molecule with the HLA-A3 signal peptide in place of the native CD4 signal peptide ( A3/CD4 ) . When either wild type CD4 or A3/CD4 was expressed in TRFs , neither molecule was significantly affected by the presence of VIHCE , whereas the endogenous MHC-I HC was decreased ( Fig . 7E , 7D ) . This indicates that while the MHC-I SP is necessary for recognition by VIHCE , it is not entirely sufficient .
We report here that the ORF rh178 of RhCMV encodes a novel immune modulatory function , viral inhibitor of heavy chain expression ( VIHCE ) , which prevents the translation of HC in a signal-peptide dependent , but not sufficient , manner . This finding is surprising because RhCMV additionally expresses the HCMV US2-US11 homologs that also interfere with MHC-I stability and assembly . The VIHCE-encoding rh178 is so far unique to RhCMV suggesting that rh178 represents an adaptation to the evolutionary pressure of the non-human primate MHC system . Our previous observations [21] suggested that the immune evasion mechanisms encoded by the US2-US11 region predate the separation of human and old-world non-human primates which is assumed to have taken place about 25 million years ago [32] . Recent sequence analysis of the MHC-I locus in RM revealed that the MHC-I has undergone a tremendous change since then . Whereas a typical human or ape haplotype contains “only” six active MHC-I genes , as many as 22 different MHC-I genes are expressed in rhesus . Moreover , the sequence divergence was estimated to be 10-fold higher and genes have been duplicated at an approximately three times greater rate than in humans [33] , [34] . Thus , it is conceivable that the additional MHC-I genes forced RhCMV to evolve additional countermeasures . It is known that polymorphic MHC-I proteins are differentially affected by US2 and US11 of HCMV [35] , [36] , although the exact rules of this discrimination still need to be determined . Moreover , each of the US6-family viral immune modulators interferes at a distinct step during the assembly cascade [37] . Allele-specificity has also been reported for MCMV which contains three genes [38] , unrelated to either the US6-family or VIHCE , and each of three MCMV-gene products interferes with a different step of MHC-I assembly [39] . Thus , it seems that CMVs optimize their interference mechanisms , both within a given organism by sequentially attacking MHC molecules during assembly and within a given population by broadening the allele-specificities of these attacks . This conclusion is also supported by our finding that RhCMV lacking either rh178 or RhUS2-11 only partially suppressed MHC-I assembly and transport compared to WT RhCMV . This is either due to differences in allele-specificity within a given animal or an incomplete elimination of all alleles . The finding that RhCMV has a larger number of gene products interfering with MHC-I assembly than either HCMV or MCMV thus correlates with the observation that RM have a larger number of active MHC-I alleles than either human or mouse . The extracellular domains of MHC-I , particularly the peptide-binding regions , are highly polymorphic and evolve rapidly . In contrast , the cleaved signal peptide is highly conserved among different MHC-I alleles including the RM MaMu and the human HLA genes [33] . Many signal peptides for MaMu-I , MaMu-3 and MaMu-A show less than 3 amino-acids difference to either HLA-A , B or C alleles and some MaMu-SPs are identical to HLA-SPs [40] . A possible reason for the high conservation of HLA signal peptide sequences is the fact that a conserved nona-peptide ( VMAPRTLLL in the HLA-A3 sequence ) is presented by the non-polymorphic HLA-E molecule to the negative signaling receptor CD94/NKG2A or C of NK cells [41] . This system seems to be conserved in RM , although some alleles start at the methionine within the peptide [33] . Interestingly , the SP of the HCMV UL40 glycoprotein contains this nona-peptide which is presented by HLA-E in HCMV-infected cells in a TAP-independent fashion [42] , [43] . By loading the decoy peptide onto HLA-E , HCMV is thought to prevent the “missing self” stimulation of NK cells by MHC-I downregulation . Importantly , this nona-peptide is also encoded within the SP of Rh67 of RhCMV which otherwise shares only 19% identity with UL40 [12] . Since VIHCE requires polypeptide sequence beyond the SP in MHC-I HCs , the Rh67 protein is likely resistant to VIHCE despite containing a similar SP sequence . The MHC-I SP mimic contained in UL40 sets precedence for CMV taking advantage of the highly conserved SP to escape the cellular immune response . Different from UL40 however , rh178 does not mimic the SP , but seems to rely at least in part on this conserved sequence to broadly eliminate HCs . VIHCE is clearly different from any other previously described immune modulatory mechanism since the ER-localized protein rh178 interferes with HC expression after the onset but prior to the completion of translation . One possible mechanism is that rh178 inhibits translation at a step that occurs after the SRP targets the nascent polypeptide/ribosomal complex to the ER membrane-localized SRP receptor . During this process , translation is arrested until SRP is released upon GTP hydrolysis and SEC61 binding [31] , [44] . A possible scenario is that rh178 interacts with the SRP/nascent polypeptide/ribosome complex at the ER-membrane thus prolonging translational arrest . Alternatively , rh178 could prevent this complex interaction with the SEC61 translocon in ER-membrane . Conceivably , rh178 could also interfere with the translocation of HC in a manner similar to cotransin , a small molecule translocation inhibitor , which specifically interferes with binding of certain SPs to a SEC61 subunit [45] . The ensuing translocational stalling results in co-translational degradation by the proteasome , a process that involves cytosolic chaperones [46] . For non-stop RNA it was recently also shown that translational arrest results in protein fragments that are rapidly degraded by the proteasome [47] . Therefore , it seems likely that HC translation intermediates are degraded by the proteasome despite the fact that we were unable to detect a degradation intermediate in the presence of proteasome inhibitors . Possible reasons why such breakdown products were not identified are their potentially small and heterogenous size and their extremely rapid degradation . HC-derived intermediates might also lack the epitopes recognized by the HC-specific antibodies used in this study . Targeted disruption of protein translation by a viral protein has so far not been described as an immune evasion strategy . However , it was recently shown that the microRNA miR-UL112 of HCMV inhibits the translation of MHC-I-related chain B ( MICB ) , a ligand for the activating NK cell receptor NKG2D [48] . Thus , CMVs seem to interfere at multiple levels and by multiple strategies with translation of immune stimulatory genes . The virus might thereby employ or mimic cellular pathways of translational or translocational regulation . Further elucidation of the molecular events of VIHCE might thus reveal previously unrecognized host cell mechanisms of translational and translocational control .
Telomerized rhesus fibroblasts ( TRFs ) [49] and telomerized human fibroblasts ( THFs ) were obtained from Jay Nelson and maintained in Dulbecco's modified eagle's medium ( DMEM ) with 10% fetal bovine serum , 100U/mL penicillin and 100ug/mL streptomycin . RhCMV strain 68 . 1 was obtained from Scott Wong [12] and propagated in TRFs . Recombinant RhCMVs were created as described in the supplemental methods using the RhCMV BAC obtained from Peter Barry [23] . Recombinant rh178 adenoviruses were created using the AdEasy vector system according to the manufacturers protocol ( Stratagene ) . Adenoviruses AdTrans and AdUS11 were obtained from David Johnson . HLA-A3 and CD4 constructs were expressed from a modified version of pCDNA3 . 1 ( - ) ( Invitrogen , Carlsbad , CA ) in which the CMV promoter was replaced with the EF1α promoter ( obtained from Jay Nelson ) to create pEF1α . HLA-A3 was obtained by PCR from Jurkat T-cell cDNA using the forward primer 5′ctggaattcatggccgtcatggcgccccgaac and the reverse primer 5′gtcggatcctcacactttacaagctgtgag to amplify the coding region only or the reverse primer 5′gtcggatccttaggaatcttctcc to include the 3′UTR . pEF1α expression plasmids were electroporated into TRFs using the AMAXA Nucleofector II ( AMAXA Biosystems , Gaithersburg , MD ) using cell line solution L and the T-030 program . 1e6-2e6 TRFs were resuspended in 100 µl AMAXA solution and 2 µg expression plasmid . After electroporation cells were recovered in 500 µl RPMI for 45min at 37°C , and then plated in prewarmed complete DMEM . Transfection efficiency was monitored with a GFP reporter and was consistently >90% . Infections with RhCMV were performed 24 hours after electroporation . Cells were starved for 30-min , except where noted , using DMEM without serum , methionine ( Met ) or cysteine ( Cys ) . Labeling was performed for indicated times using Pro-mix 35S-Met/Cys ( GE Healthcare ) at 400 µCi/mL . To chase the label , cells were washed 3× in phosphate buffered saline ( PBS ) followed by incubation at 37°C in DMEM with 10% FBS containing 90 µg/mL Met and 188 µg/mL Cys . For NP-40 lysis , cells were lysed for 30 minutes at 4°C in 1% NP-40 in PBS with complete protease-inhibitor cocktail ( Roche ) . For SDS lysis , cells were lysed for 10 minutes at 25°C in 0 . 6% SDS in PBS with complete protease-inhibitor cocktail , then diluted in 3× volume of 1 . 2% triton X-100 in PBS prior to immunoprecipitation . For glycosidase treatment , PNGase was obtained from NEB and used according to the manufacturers protocol after NP-40 lysis . Polyclonal sera K455 recognizes both chains of the MHC-I heterodimer , assembled and unassembled ( obtained from Per Peterson ) [24] . HC-10 only recognizes free MHC-I heavy chains [25] . HLA-A3 antibody was purified from the GAP A3 hybridoma , obtained from ATCC ( HB-122 ) . Antibodies to Calreticulin , Transferrin , Vimentin , HA and FLAG were obtained , respectively , from Stressgen ( Victoria , BC ) , Zymed ( S . San Francisco , CA ) , Biomeda ( Burlingame , CA ) , Santa Cruz , and Sigma . Human CD4 antibody ( AHS0412 ) was obtained from Invitrogen . Secondary Alexa Fluor-conjugated antibodies 594 goat anti-rabbit and 488 goat anti-mouse were obtained from Invitrogen . Approximately 5×106 TRFs were either Mock-infected or RhCMV-infected for 24 hours . Fresh media was placed on the cells for 45-minutes , and cells were placed on ice and washed 2× with cold PBS containing 0 . 1 mg/ml cycloheximide ( Sigma ) . All subsequent steps were performed at 4°C . Cells were lysed for 10 min using 600 µl of polysome lysis buffer ( 15mM Tris , pH 7 . 4 , 15mM MgCl2 , 0 . 3M NaCl , 1% Triton x-100 , 0 . 1 mg/mL cycloheximide , 1 mg/mL heparin ) . Lysates were cleared at 12 , 000× g for 10 min . The supernatant was layered onto the top of a 10–50% sucrose gradient composed of sucrose in polysome lysis buffer excluding Triton x-100 . The gradients were centrifuged at 35 , 000 rpm in a Sorvall SW-41 rotor for 3 hours . 750 µl fractions were collected from the top of the gradient . After adding 4 . 25ml of 5 . 65M guanidine HCl , each fraction was ethanol precipitated ( −20°C overnight ) . RNA was pelleted at 15 , 000× g for 30 min , washed with 70% ethanol , dried at 25°C , and resuspended in 400 µl RNAse-free water . RNA was then re-precipitated by adding 40 µl 0 . 3M sodium acetate and 900 µl 100% ethanol , washed with 70% ethanol and resuspended in 50 µl RNAse-free water . For Northern blotting , 10 µl of each fraction was separated on a denaturing 1% agarose gel containing 1× MESA ( Boston BioProducts , Worcester , MA ) and 3 . 7% formaldehyde and transferred to Immobilon-Ny+ nylon membrane ( Millipore ) by capillary blotting in 20×SSC . RNA was fixed by air drying at 25°C for 30 min and baking at 80°C for 2 hours . Radiolabeled probes were generated by random priming . After denaturing at 100°C for 10 min , the probe was chilled on ice and added to 5mL ExpressHyb hybridization solution ( Clontech ) for hybridization . Membranes were pre-hybridized for 30 min at 68°C followed by probe hybridization for 2 hours , rinsed and washed twice with 2× SSC , 0 . 05%SDS followed by two washes in 0 . 1× SSC , 0 . 1% SDS . Transfected cells were fixed with 3 . 7% formaldehyde for 40 minutes , washed twice with PBS , quenched with 50mM NH4Cl for 10 min , washed twice with PBS , and permeabilized with 0 . 1% Triton X-100 in PBS for 7 min prior to staining . Total RNA from TRFs infected with WT RhCMV ( or RhCMV lacking rh175–178 as a negative control ) for 24 hours was used . For 3′ RACE , cDNA was synthesized using an oligo-dT anchor ( 5′gaccggatccgaattcgtcgacttttttttttttttttv ) . PCR was performed from cDNA using a PCR anchor primer ( 5′-gaccggatccgaattcgtcgac ) and a gene specific primer . For 5′ RACE , cDNA was synthesized with a gene specific primer ( rh178 5′-catttgcatgcagctgtgcg ) . 10 µg cDNA was then treated with terminal deoxynucleotidyl transferase and 0 . 5mM dATP at 37°C for 30 min , followed by purification and PCR using a nested gene specific primer ( rh178 5′-gcgcgaaacacgcgtttgc ) and the oligo-dT anchor . | To avoid immune detection by cytotoxic T lymphocytes , viruses interfere with antigen presentation by major histocompatibility complex class I ( MHC-I ) molecules . We have discovered a unique cytomegaloviral protein that interferes with the biosynthesis of MHC-I heavy chains and was thus termed viral inhibitor of heavy chain expression ( VIHCE ) . We show that VIHCE does not affect transcription of MHC-I mRNA or the formation of poly-ribosomes . Surprisingly , however , very little MHC-I protein is detected , even when proteasomal protein degradation is inhibited , suggesting incomplete protein translation . Interestingly , VIHCE requires the proper MHC-I signal peptide , suggesting that CMV takes advantage of the high conservation of MHC-I signal peptides and interferes with protein translation by inhibiting signal sequence-dependent protein translocation . This is the first description of a viral protein that specifically targets the translation of a cellular immuno-stimulatory protein . |
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RNA silencing is an evolutionarily conserved sequence-specific gene-inactivation system that also functions as an antiviral mechanism in higher plants and insects . To overcome antiviral RNA silencing , viruses express silencing-suppressor proteins . These viral proteins can target one or more key points in the silencing machinery . Here we show that in Sweet potato mild mottle virus ( SPMMV , type member of the Ipomovirus genus , family Potyviridae ) , the role of silencing suppressor is played by the P1 protein ( the largest serine protease among all known potyvirids ) despite the presence in its genome of an HC-Pro protein , which , in potyviruses , acts as the suppressor . Using in vivo studies we have demonstrated that SPMMV P1 inhibits si/miRNA-programmed RISC activity . Inhibition of RISC activity occurs by binding P1 to mature high molecular weight RISC , as we have shown by immunoprecipitation . Our results revealed that P1 targets Argonaute1 ( AGO1 ) , the catalytic unit of RISC , and that suppressor/binding activities are localized at the N-terminal half of P1 . In this region three WG/GW motifs were found resembling the AGO-binding linear peptide motif conserved in metazoans and plants . Site-directed mutagenesis proved that these three motifs are absolutely required for both binding and suppression of AGO1 function . In contrast to other viral silencing suppressors analyzed so far P1 inhibits both existing and de novo formed AGO1 containing RISC complexes . Thus P1 represents a novel RNA silencing suppressor mechanism . The discovery of the molecular bases of P1 mediated silencing suppression may help to get better insight into the function and assembly of the poorly explored multiprotein containing RISC .
Most eukaryotes , including plants , make use of a well-conserved RNA silencing mechanism to regulate many essential biological processes , ranging from development and control of physiological activities , to responses to abiotic and biotic stress , in particular antiviral defense [1] , [2] . Antiviral defense in plants begins with the activity of RNase III type Dicer-Like ( DCL ) enzymes , which target viral RNAs [3] , [4] . Concerted action of the DCL4 , DCL2 , DCL3 and occasionally DCL1 enzymes results in the appearance of 21–24 nt small interfering RNAs ( siRNAs ) , the central components of the RNA silencing pathway [4] , [5] . These viral siRNAs subsequently loaded to endogenous AGO proteins , which are catalytic component of RNA-induced silencing complex ( RISC ) [6] , [7] . AGO1 and AGO7 are suggested to be involved in antiviral silencing [8] , [9] , [10] although previous study failed to detect viral siRNAs in tagged AtAGO1 [11] . It has been also shown that AGO7 favors less structured RNA targets , while AGO1 is capable of targeting viral RNAs with more compact structures [9] . AGO proteins are responsible for targeting RISC to viral genomes ( either RNA or DNA ) , and exert their action either through cleavage or inhibition of translation [12] . The RNA-dependent RNA polymerases ( RDRs ) of the host also play important roles in antiviral RNA silencing , being involved in production of secondary viral siRNA [13] , [14] , [15] , [16] , [17] , [18] . Viruses have evolved suppressors to counteract the RNA-silencing defense of the host [1] , [2] , [19] . The more than 35 viral silencing-suppressor families so far identified use different strategies to inhibit RNA silencing [2] , [20] . Sequestering siRNAs by siRNA-binding suppressors is a very common way to inhibit RISC assembly [21] , [22] , but other mechanisms have been described , such as inhibiting the biogenesis of 21 nt siRNA species [4] , [20] , [23] . Other suppressors inhibit RNA silencing through protein-protein interaction . The 2b protein of CMV strain Fny is suggested to inhibit RISC activity via physical interaction with the PAZ domain of the plant AGO1 protein [10] . Polerovirus P0 suppressor protein has been suggested to target PAZ domain of AGO1 and directing its degradation [24] , [25] . The Potyviridae is the largest family of plant RNA viruses; in most members , the single-stranded RNA genome is about 10 kb in size and encodes a single polyprotein that is processed into at least 9 mature proteins [26] ( Figure 1 ) . In the genus Potyvirus , the multifunctional HC-Pro ( helper component-proteinase ) was the first viral product to be recognized as a silencing suppressor [27] , [28] , [29] . The genome of Cucumber vein yellowing virus ( CVYV ) , genus Ipomovirus , family Potyviridae , lacks HC-Pro but contains two P1-type proteases [30] , properties shared by at least one other ipomovirus [31] . In CVYV , the second P1 cistron ( P1b ) was found to suppress RNA silencing [30] with a mode of action resembling that of the HC-Pro of potyviruses [32] . Interestingly , the type member of the genus Ipomovirus , Sweet potato mild mottle virus ( SPMMV ) , possesses an HC-Pro region and a single large P1 serine protease [33] . The peculiarities of the SPMMV genome that incorporates the largest P1 region among all known members of the family together with a typical HC-Pro region ( Figure 1 ) , prompted us to study how this virus might deal with the RNA-silencing machinery in its hosts . In the present study , we show that the large P1 protein of SPMMV possesses silencing-suppressor activity , while its HC-Pro protein does not , on its own . Using various reporter systems , we show that in vivo P1 inhibits target RNA cleavage mediated by RISC complexes loaded with either endogenous miRNA or with virus-derived siRNA . Moreover , suppression activity mapped to the N-terminal half of P1 , a region containing three WG/GW motifs that mimics AGO-binding linear peptide motif conserved both in metazoans and plants [34] , [35] . We have also determined that the WG/GW motifs at the very N-terminal end in P1 are required for AGO1 binding and for silencing-suppression , suggesting that P1 may use the conserved WG/GW motif binding surface of Ago proteins to inhibit RISC activity .
To investigate whether P1 and/or HC-Pro serve ( s ) as RNA silencing suppressor for SPMMV , we used the standard Agrobacterium coinfiltration assay [22] . The complete cistrons for P1 and HC-Pro were cloned into binary vectors and the resulting expression constructs were transferred into A . tumefaciens . Cultures of A . tumefaciens able to express GFP from a 35S-promoter GFP binary plasmid were mixed with cultures transformed with our SPMMV constructs before infiltration into Nicotiana benthamiana leaves . In this assay both fluorescence and RNA analysis identified SPMMV P1 , but not SPMMV HC-Pro , as the suppressor of RNA silencing ( Figure 2A ) . Weak fluorescence and low GFP mRNA levels were observed in patches infiltrated with the pBin61 empty vector ( negative control ) , and strong suppressor activity and increased GFP mRNA level were detected in patches infiltrated with a construct expressing the P1b of CVYV ( positive control ) [32] . Experiments designed to compare in parallel the suppression activity of SPMMV P1 with that of a suppressor from a potyvirus , the HC-Pro protein of Tobacco etch virus ( TEV ) were performed next . First , we checked in vitro if SPMMV proteins could bind either typical 21 nt ds siRNAs , or longer dsRNAs . Extracts of N . benthamiana leaves infiltrated with Agrobacterium strains expressing different suppressors were tested for siRNA binding with labeled 21 nt ds siRNA , and the complexes were resolved on a native gel . As expected , TEV HC-Pro bound ds siRNA , while SPMMV P1 and HC-Pro did not show any siRNA binding activity ( Figure 2B ) . The same extracts were then incubated with a labeled 49 nt dsRNA , and the putative complexes were analyzed on a native gel . In this case , formation of the expected RNA-protein complex only occurred between the 49 nt dsRNA and the Sigma3 protein of a Reovirus [38] used as positive control , but no complexes were detected in any of the other samples from constructs of P1 and HC-Pro of SPMMV ( Figure 2C ) . Next , we tested if P1 inhibits small RNA processing in leaves of transgenic N . benthamiana line 16C , expressing a GFP transgene , that were coinfiltrated with an Agrobacterium strain harboring a GFP inverted repeat ( GFP-IR ) construct . To this end , patches infiltrated with constructs expressing SPMMV P1 , SPMMV HC-Pro ( individual proteins ) , or SPMMV P1HC-Pro ( a construct containing both proteins in cis ) , or with TEV HC-Pro , were analyzed after 3 days for the presence of GFP mRNA and siRNAs by Northern blotting . No reductions in siRNA processing from the GFP-IR were observed in all expressed proteins ( Figure 2D ) , in contrast to the complete abolition observed in the positive control , which was the dsRNA-binding Sigma3 protein of Reovirus [38] . The 16c plants agroinfiltrated were also observed under UV light at 8 days after agroinfiltration to monitor the spreading of silencing signal . Importantly we found that the presence of SPMMV P1 did not abolish movement of the signal , and therefore silencing of the transgene around the agroinfiltrated area was observed ( Figure 2F ) . We also checked the capacity of the different viral proteins to inhibit in vivo 3′ modifications of small RNAs by the HEN1 methyltransferase [39] , [40] . We expressed P1 along with different silencing suppressor proteins , and the 3′ end methylation status of the mature and star strands of miR168 were then evaluated by oxidation and beta elimination followed by Northern blotting of total RNA samples . Consistently with our previous results [41] , TEV HC-Pro inhibited the 3′ methylation of both strands of the endogenous miR168 . However , SPMMV P1 and HC-Pro had no effect on HEN1 mediated 3′ modification . ( Figure 2 E ) . Our findings showed that in contrast to several other silencing suppressors SPMMV P1 does not interfere with the initial steps of the silencing pathway . Thus we hypothesized that it might compromise assembled RISC activity . Active RISC complexes are known to contain ss siRNA and are licensed to cleave the target RNA in a sequence specific manner [42] . Recently , we developed assays based on the transient expression of sensor constructs to test the effect of RNA-silencing suppressors on miRNA and siRNA loaded active RISC . Using these assays we have previously demonstrated that silencing suppressors with ds siRNA binding capacity such as the HC-Pro of potyviruses does not have any effect on miRNA and siRNA loaded RISCs in planta [22] , [43] . To determine whether SPMMV P1 might inhibit miRNA loaded RISC complexes , we agroinfiltrated GFP171 . 1 and GFP171 . 2 sensor constructs [44] with or without the viral suppressors . In these sensors a full complementary miR171 binding site was placed downstream of the STOP codon of GFP ORF allowing miR171-mediated silencing of the GFP171 . 1 mRNA , while GFP171 . 2 carried a mutant miR171 target site , which is refractory to miR171-driven RNA silencing [44] . In this experiment the control construct used was TEV HC-Pro . At two days postinfiltration , GFP fluorescence was evaluated under UV light , and then the infiltrated patches were used for RNA and protein isolation . Consistent with previous results , miR171-driven RNA silencing downregulated GFP171 . 1 , but not GFP171 . 2 at both the RNA and protein level . Strikingly , comparable GFP fluorescence and GFP mRNA and protein were detected in samples infiltrated with GFP171 . 1+P1 and GFP171 . 2+P1 , indicating that SPMMV P1 efficiently inhibited miR171 loaded active RISC complexes ( Figure 3 A , B ) . As expected for the control , TEV HC-Pro did not inhibit miR171 mediated degradation of GFP171 . 1 mRNA [22] . Next , we investigated if SPMMV P1 inhibits viral siRNA-loaded active RISC complexes . A previously described system which exploits N . benthamiana plants infected with Cymbidium ringspot virus ( CymRSV ) 19 Stop mutant ( Cym19S ) was used [45] . Cym19S , not expressing the ds siRNA binding silencing suppressor p19 , permits a strong RNA silencing response against the virus to be initiated and maintained by enabling viral siRNAs to be loaded into RISC complexes , leading to the recovery of the initially infected plant [45] , [46] . At 14–18 dpi of plants carrying Cym19S , the first systemic leaves showed recovery as a consequence of the remarkable amount of active RISCs loaded with siRNAs derived from the virus [22] . Messenger RNAs expressed from the sensor construct GFP-Cym , in which GFP ORF is fused with a ∼200 bp portion of the CymRSV , could be targeted and cleaved by RISC complexes containing Cym19S-derived siRNAs , while GPF-PoLV , in which GFP fused with a ∼200 bp region of Pothos latent virus , a virus unrelated to CymRSV , cannot be cleaved , and was used as a negative control [43] . Recovering leaves of Cym19S-infected plants were infiltrated with GFP-Cym and GFP-PoLV alone or with the indicated silencing suppressors . At 2 days post-agroinfiltration ( dpa ) , efficiency of RNA silencing was monitored by visual examination followed by Northern and Western blotting of RNA and protein samples isolated from infiltrated patches ( Figure 3C , D ) . When the agroinfiltration was performed only with sensors , Northern analysis using a GFP probe detected a shorter hybridizing band , diagnostic for RISC cleavage of the mRNA expressed from the GFP-Cym construct mediated by viral siRNA , while the hybridizing band remained intact in the case of the GFP-PoLV sensor . As expected , the control TEV HC-Pro was not competent to inhibit ss viral siRNA-loaded active RISC complexes , so the GFP-Cym sensor RNA was cleaved [22] . Remarkably , the Northern and Western analyses showed that GFP mRNA and protein levels were similar in GFP-Cym+P1 and in GFP-PoLV+P1 infiltrated samples , and no cleavage product of GFP was detected in the GFP-Cym+P1 infiltrated sample , suggesting that SPMMV P1 efficiently inhibited the slicing activity of the viral siRNA loaded RISC complexes ( Figure 3 C , D ) . The RISC complex is of high molecular weight ( >669 kD ) in animals [47] , [48] , contains the catalytic AGO protein , and has intrinsic small-RNA-dependent target cleavage activity . In plants such as N . benthamiana , transiently expressed or endogenous AGO1 protein co-fractionates in extracts with small RNAs , and can be found in at least two distinct complexes of above 669 kD and 158 kD [49] . In addition , it was reported that high molecular weight complexes containing viral siRNAs exhibited nuclease activity in vitro and preferentially targeted homologous viral sequences [50] . Having established that P1 inhibits active RISC , we hypothesized that inhibition of RISC requires physical interaction of P1 with AGO1 and small-RNA-containing complexes . To investigate this , we first tested whether P1 co-fractionates with AGO1 and small RNAs on a gel filtration column . N-terminally HA-tagged SPMMV P1 ( HA-P1 ) , 6×myc-tagged AGO1 of Arabidopsis thaliana ( myc-AGO1 ) [10] and GFP-IR were co-expressed in N . benthamiana leaves . At 3 dpi , extracts prepared from infiltrated leaves were fractionated on a Superdex 200HR column . Small RNAs were extracted from each fraction and analyzed by Northern blotting , and the AGO1 and P1 protein contents of fractions were monitored by Western blotting using antibodies raised against the HA and myc tags . Consistent with previous results , GFP siRNAs and miR159 were fractionated in two distinct complexes peaking at >669 and 158 kD , although they appeared in all fractions which also contained AGO1 ( Figure 4A ) . We experienced technical difficulty in separating the protein peaks , and this might reflect the disintegration of the large complexes during chromatography or more likely due to limited availability of the other RISC components . Despite these problems , the infiltrated myc-AGO1 co-fractionated clearly with small RNAs suggesting that GFP siRNAs had been loaded into the myc-AGO1-containing complexes . Interestingly , SPMMV P1 co-fractionated mainly with the 669 kD , but not with the smaller 158 kD myc-AGO1 and small RNA-containing complexes ( Figure 4 A ) . Next , we investigated if co-fractionation of P1 with myc-AGO1 and small RNAs was due to physical interaction . To test this , we agroinfiltrated HA-P1 with myc-AGO1 and GFP-IR . As a negative control , myc-AGO1 and GFP-IR were agroinfiltrated with HA-UPF1 , which is known not to be involved in RISC formation . At 3 dpi , extracts were prepared from infiltrated leaves , and HA-tagged proteins were immunoprecipitated ( IP ) with an anti-HA antibody . Inputs and eluates of IPs were tested for proteins by Western blotting and for GFP siRNA in Northern blots . The results showed that HA-P1 and HA-UPF1 were expressed at comparable levels and could be efficiently immunoprecipitated from extracts . Importantly , we found that myc-AGO1 coimmunoprecipitated with HA-P1 , but not with HA-UPF1 , confirming that the interaction between HA-P1 and myc-AGO1 is specific ( Figure 4B ) . Moreover , we found that GFP siRNAs , but not tRNA coimmunoprecipitated exclusively with HA-P1 and myc-AGO1 , strongly suggesting that P1 interacts with small RNA-loaded AGO1 . Taken together , we showed that siRNAs derived from GFP-IR became incorporated into myc-AGO1 , and that the P1 silencing suppressor specifically interacted with GFP siRNA-loaded myc-AGO1 . These results , along with earlier data proving that P1 inhibits si- and miRNA programmed RISC , suggest that the large complex ( 669 kD ) containing AGO1 and small RNAs corresponds to the plant RISC complex . P1 contains an extended N-terminal region and a protease domain at its C-terminal end , similar to the P1b of other ipomoviruses [30]; Text S1 ) . To determine the minimal region required for silencing suppression , we constructed P1 mutant truncated from the C-terminal end but retaining the first ( N-terminal ) 383 aa , designated as HA-P11-383 ( Figure 5A ) . To evaluate its silencing-suppressor activity , the mutant was co-expressed with GFP-171 . 1 in N . benthamiana plants . Visual examination under UV light and analysis of GFP-171 . 1 sensor RNA and GFP expression showed that HA-P11-383 was an effective silencing suppressor although lacking the entire C-terminal protease domain . Then , we checked the interaction between the deletion mutant of P1 and AtAGO1 . To test for interaction , we immunoprecipitated myc-AGO1 from extracts of infiltrated leaves expressing myc-AGO1 and GFP-IR with HA-P1 and HA-P11-383 . We used the pBIN61 empty vector as negative control . We found that HA-P11-383 interacted with myc-AGO1 as strongly as wt P1 . We concluded that P1 may be composed of two functional domains , the silencing suppressor domain is located at the N-terminal part , and the C-terminal part of P1 contains the protease domain . However , the protease activity of P1 was not analyzed . Our results showed that the N-terminal end of the P1 is required for Ago binding . Further inspection of the N-terminal end of P1 revealed repeating tryptophan-glycine/glycine-tryptophan residues ( WG/GW ) ( Figure 6A and Figure S2 ) , which are identical to the core amino acids of the WG/GW motifs recently found in Argonaute binding proteins , such as Tas3 and RNA Pol V ( El-Shami et al , 2007; Till et al , 2007 ) . Furthermore , analysis of amino acid composition revealed that the regions neighboring the WG/GW residues in P1 are rich in alanine , serine , glutamic acid , asparagine and aspartic acid , providing a context similar to that described for Tas3 and RNA Pol V proteins [35] , [51] . This observation prompted us to investigate the significance of the tryptophan residues at the N-terminal end of P1 in silencing suppressor activity . For this , we generated single , double and triple mutants of P1 by replacing tryptophan ( W ) by alanine ( A ) residue ( s ) at positions 15 , 101 and 131 , individually and in all double ( 3 ) and triple ( 1 ) combinations , by site-directed mutagenesis . Silencing-suppressor activity of the HA-tagged P1 single mutants was compared to the HA-P1wt , co-infiltrated with the GFP-171 . 1 sensor construct . Expression analysis of the GFP marker gene reflecting the strength of suppression of RNA silencing showed that suppressor activity of any of the single mutants was not reduced significantly , suggesting that presence of the remaining two tryptophan residues were sufficient to maintain the silencing suppressor activity of P1 ( data not shown ) . In contrast , the suppressor activities of double and triple mutants were greatly reduced . Consistently , wt P1 and the mutants were expressed at comparable level ( Figure 6 B , C ) . To test whether the WG/GW motifs are required for RNA silencing-suppression because they contribute to AGO1 binding , we tested the interactions between AtAGO1 and P1 double and triple mutants . Myc-AGO1 and GFP-IR were co-infiltrated with double and triple mutants of HA-P1 in line GFP16c/RDR6i N . benthamiana plants . As positive control , we used HA-P1wt and as negative control , myc-AGO1 and GFP-IR were infiltrated without P1 wt . At 3 dpi , extracts of infiltrated leaves were used to immunoprecipitate HA-tagged P1 wt and mutant proteins . Western analysis showed that HA-tagged proteins were expressed at comparable levels and were successfully immunoprecipitated . However , probing Western and Northern blots to detect myc-AGO1 protein and GFP siRNA derived from GFP-IR revealed that myc-AGO1 protein and GFP siRNAs were specifically co-immunoprecipitated with P1 wt , but not with any of the double or triple mutants of P1 ( Figure 6D ) . These results showed that changing at least two out of three tryptophan residues to alanine in the WG/GW motifs of P1 abolished its silencing suppressor activity . Moreover , our analysis showed that the ability of P1 to bind AGO1 depends on the presence of these motifs , suggesting a correlation between AGO1 binding and its activity as silencing suppressor . AGO1 of A . thaliana is involved both in the miRNA and the antiviral RNA silencing pathways [10] , [11] , [52] . Our results showed that P1 interacts with AGO1 to inhibit active miRNA and siRNAs loaded RISC . To get better insight into the mechanism of inhibition of RISC mediated by P1 , we performed immunoprecipitations with small RNA-loaded RISCs against P1 ( wild type ) and P1mut1-2-3 ( triple mutant ) -infiltrated leaf extracts . RNA samples from inputs and immunoprecipitates were probed for presence of two endogenous miRNAs ( Figure 7A ) . The results showed that mature miR159 and miR319 specifically co-immunoprecipitated with wt P1 , but not with P1mut1-2-3 . In addition , we found only mature miRNAs in the eluates of P1 immunoprecipitates , and the star strands for miR159 and mir319 could not be detected in inputs , or in eluates . Thus our results indicated that P1 interacts with endogenous RISC complexes loaded with single-stranded miRNAs ( Figure 7A ) . To test whether P1 interacts directly or indirectly with AGO1 we performed in vitro pull-down assays using recombinant MBP-AGO1366-1048 containing the PAZ-MID-PIWI domains and the 6×His-P11-383 N-terminal fragment of wt P1 . The results showed that MBP-AGO1366-1048 binds wt 6×His-P11-383 efficiently , while the triple mutant P11-383 was bound less strongly by AGO1 protein ( Figure 7B ) . This result strongly suggests a direct interaction between P1 and AGO1 proteins in vivo as well .
SPMMV is the only ipomovirus that has a typical potyvirid genome structure with P1 and HC-Pro regions in the C-terminal part of the polyprotein [33] . Other ipomoviruses with available complete sequences do not possess HC-Pro regions [31] , [54] , [55] . Despite the presence of an HC-Pro in SPMMV , we have found that the role of RNA-silencing suppressor is played by P1 . To better understand the molecular basis of P1-mediated silencing suppression , we analyzed the effect of P1 on different steps of the RNA silencing pathway . We found that P1 does not seem to interfere with the biogenesis of either transgene-derived siRNAs or endogenous miRNAs , since we observed that the accumulation of GFP-IR-derived siRNAs was mostly unaltered in the presence or absence of P1 ( Figure 2D ) . Similarly , accumulation of endogenous miRNAs and their 3′ methylation status were not influenced by the expression of P1 , in contrast to the well known TEV HC-Pro suppressor , which binds ds siRNA and miRNA intermediates and partially inhibits their 3′ methylation [22] , [41] ( Figure 2E ) . We also showed that P1 failed to bind short and long ds RNAs , in contrast to potyviral HC-Pro and the reovirus sigma3 , which efficiently bind ds siRNAs and long dsRNAs , respectively ( Figure 2B , C ) . Thus , this mechanism of silencing suppression seems to be unique among virus-encoded silencing suppressors identified so far . We tested the effect of P1 on miRNA- and viral siRNA-activated RISC complexes using GFP sensor constructs ( Figure 3 ) and it turned out that P1 efficiently inhibited both types of activated RISC in vivo . Moreover , we showed that transiently expressed AGO1 protein was found in a large ( >667kD ) and an approximately 158 kD protein complexes in size . Interestingly , P1 was found co-fractionating only with the large AGO1 containing complex with GFP siRNAs . This results distinguishes P1 from previously studied silencing suppressors , because it does not inhibit RISC assembly , like small RNA binding suppressors , nor inhibits RISC assembly by promoting degradation of AGO proteins , as it was found in the case of P0 protein of poleoviruses [25] , [49] , [53] . The 2b protein of CMV FNY strain was also shown to interact with AGO1 in vivo and in vitro and to inhibit RISC activity in vitro [10] . However , it is not known whether 2b prevents RISC assembly or inhibits siRNA loaded RISC by AGO1 binding . In addition , a recent report revealed that 2b proteins of Tomato aspermy virus ( TAV ) and the FNY strain of CMV bind 21nt ds small RNAs [56] , [57] , so it is not clear , whether 2b protein of cucumoviruses inhibits RNA silencing through siRNA binding , interacting with AGO1 or both . In contrast , P1 binds RISC by interacting the AGO1 subunit of RISC loaded with si- or miRNAs , as shown by our immunoprecipitation studies ( Figure 4 and 7 ) . Importantly , P1 interacted with AGO1 containing mature miRNAs but not their star strand; this adds support to our hypothesis that P1 interacts with the AGO1 component of active RISC complexes , and is in line with the efficient inhibition of GFP-sensor silencing by P1 . Our cumulative evidence strongly suggests that the P1 interaction with AGO1 is a direct physical interaction . Finally , using mutant P1 proteins with their silencing suppressor activity compromised/abolished , we obtained evidence that silencing suppression and AGO1 binding are linked . The WG/GW motifs located at N-terminal part of P1 strongly resemble the evolutionarily conserved GW linear peptide motifs shared by different silencing-related proteins used as “Ago hooks” to interact with Argonaute proteins [35] , [58] . Such WG/GW motifs have been described in proteins from different organisms , such as in the largest subunit NRPD1b of the RNA polymerase V in plants [51] , the P body-localized human protein GW182 [59] , [60] , [61] , and the Tas3 homologue of the GW182 RITS complex component in yeast [62] , [63] . All these proteins can interact with Argonaute proteins [35] . Recently , an RdDM effector KTF1 containing abundant WG/GW motifs and SPT5-like domains has also been identified as an AGO4 binding element [64] , [65] . Similarly to cellular WG/GW proteins , our analysis of P1 mutants indicates that the tryptophan residues are essential for interaction with AGO1 and are strictly required for silencing suppressor function ( Figure 6 ) . Recent results showed that the AGO-binding domains and the effector domain of GW182 paralogs map in different parts of the proteins [66] . Thus , the modular architecture of the WG/GW proteins that allowed the evolution of Ago-binding elements with positive effects on different RNA silencing pathways , like RNA Pol V , Tas3 , GW182 and KTF1 [35] , [51] , [64] , [65] , [66] , could have been mimicked by a viral protein , although in the case of P1 the effect is negative/suppressive . An attractive possibility to explain the negative effect exerted by P1 on the silencing machinery could be its capacity to outcompete essential AGO1 interacting components of RISC , although in plants these hypothetical AGO1 interactors have not been identified yet . Further experiments will be required to test this possibility and to identify which endogenous elements might be displaced by P1 . We can also postulate alternative explanations for the action of P1 . Since small RNA-dependent target cleavage by RISC requires base-pairing between the small RNA and the target RNA , the presence of P1 as an AGO1 interactor might result in covering the small RNA-binding groove of AGO1 , thus interfering with base-pairing between the small RNA and the target RNA . This latter possibility is really plausible , because precluding base-pairing between the target RNA and the small RNA would inhibit translation as well , and indeed the importance of translation inhibition in plants has recently been highlighted [12] . In agreement with this , our results with viral siRNA-loaded RISC complexes ( Figure 3C and D ) show that target cleavage activity did not always correlate with GFP expression ( compare lanes 4 and 8 in Figure 3 D ) ; this may indirectly indicate that translational inhibition is hampered by P1 in our system as well . The efficient binding of AGO1 , and inhibition of its function by P1 , shown by our experiments suggest that this suppressor might have evolved to bind AGO1 protein with high affinity to inhibit its function . Independently of its final mode of action during suppression , P1 is another example of the extraordinary adaptation of viruses , which are able to target highly conserved key elements of the antiviral silencing response , to be able to complete their infectious cycle . In our study we analyzed only AGO1 as P1 interactor , and we don't know whether P1 is able to interact with other plant AGO proteins . Interestingly , P1 failed to show any effect ( Peter Moffett , personal communication ) when assayed in an R gene-induced anti-viral response test that is dependent on AGO4-like but not AGO1-like activity [67] , suggesting that P1 is not able to target all AGOs . Our results can also help to explain the pathology of SPMMV , either alone or in synergism with other viruses . Generally speaking , defeat of the host RNA silencing response by a virus equipped with a silencing suppressor requires a high concentration of the suppressor in infected cells , above both the dissociation constant of the suppressor with its target and the intracellular concentration of the target molecule [68] . In SPMMV-infected cells , both existing and de novo assembled RISCs , including miRNA- and viral siRNA-loaded RISCs , should be considered as potential targets for P1 . At early stages of infection , the concentration of existing active RISC might be much greater than that of the de novo viral siRNA-loaded active RISC , and this would lead to the sequestration of P1 mainly by existing active RISC complexes . Consequently , we may hypothesize that the newly formed viral siRNA-loaded RISC could escape from suppression , resulting in low SPMMV titre , mild transient symptoms , and recovery of the plant . However , in cases where plants have additionally been infected with other viruses such as SPCSV , the initial antiviral response might be suppressed by the two-component silencing suppressor system of SPCSV [69] , [70] resulting in a much higher titre of SPMMV , which in turn might allow a high concentration of P1 in infected cells . P1 might then efficiently suppress RISCs loaded with both endogenous small RNAs and antiviral RNAs , which would then lead to the synergistic sweet potato disease . High accumulation of SPMMV has indeed been observed in mixed infection with SPCSV , resulting in a severe disease [71] . It is likely that symptom aggravation comes from the fact that both pathogens encode suppressors with complementary effects .
The African isolate SPMMV-130 was kindly provided by Jari Valkonen ( University of Helsinki , Finland ) in a sweet potato plant , and maintained in N . tabacum Xanthi plants . The complete P1 and HC-Pro regions of the virus were RT-PCR amplified from total nucleic acid extracts using primers 5′CCTCTAGAATGGGGAAATCCAAACTCACTTAC3′ and 5′GTCCCGGGTCAATAGAATTGTATCTGTTTAAGTTTACTAG3′ for P1 , and 5′CCTCTAGAATGGCAAGTTCTGTTGTACCCAATTTC3′ and 5′GTCCCGGGTCAACCAACCTTATAGGTTAACATCTCAC3′ for HC-Pro , and cloned , using the restriction sites highlighted in bold , into competent plasmids for sequencing . The two viral genes were cloned into pBIN-derived constructs for transient expression in N . benthamiana leaves . Variants incorporating the tagging element HA were also prepared . Plants were kept in a greenhouse at 22°C under a photoperiod of 12 h/12 h light/dark . Infiltration assays were performed on expanded N . benthamiana leaves of plants about 21 days old . N . benthamiana leaves were infiltrated essentially as previously described [46] . Agrobacterium strains harboring 35S-GFP , GPF-IR , GFP171 . 1 , GFP171 . 2 , GFP-Cym , GFP-PoLV and miR171c precursor were infiltrated with OD600 = 0 . 1 . SPMMV 35S-HC-Pro , HA-UPF1 [72] , 6×myc-AtAGO1 [10] and RNA-silencing suppressors such as 35S-P1 , 35S-P1HC-Pro , HA-P1 were infiltrated with OD600 = 0 . 2–0 . 3 . Infiltrated patches were used for total RNA and protein isolation , then analyzed by Northern and western blotting . Total RNA was isolated using TRIZOL reagent . RNA was analyzed on 37% formaldehyde containing agarose gels as described [46] . Small RNAs were analyzed on 12% arcylamide 8M urea gels . RNA isolation from column fractions also was described earlier [73] . Briefly , equal volume of 2×PK buffer was added to each fractions and Proteinase K at final concentration of 80ng/µl . Samples were incubated at 55°C for 15 min . Then , RNA was extracted by phenol-chlorophorm and precipitated with 2 , 5 volumes of ethanol . After recovering , RNA was resuspended in 50% formamide containing buffer and loaded on 12% arcylamide and 8M urea gels . The gels were blotted and hybridized with riboprobes to detect small RNAs or random primed DNA probes for conventional Northern blots . Total RNA samples were oxidized , ß-eliminated and detected as described in [41] . Briefly , a total of 10 µg total RNA was dissolved in 17 . 5 ml borax buffer , pH 8 . 6 , 50 mM boric acid and 2 . 5 ml 0 . 2 M sodium periodate was added . The reaction mixture was incubated for 10 min at room temperature in the dark and , after addition of 2µl of glycerol , incubation was repeated . The mixture was lyophilized , dissolved in 50 ml borax buffer , pH 9 . 5 ( 33 . 75 mM borax , 50 mM boric acid , pH adjusted by NaOH ) and incubated for 90 min at 45°C . RNA species were then separated on 12% denaturing PAGE blotted and hybridized using 32P labeled LNA oligonucleitide probes [74] as described above . Extracts for immunoprecipitation were prepared in IP buffer containing 30mM TRIS ( pH 7 . 5 ) , 150mM NaCl , 5 mM MgCl2 , 5mM DTT and 10% glycerol , then incubated for 1 hour at 4°C with beads containing anti-HA ( Roche ) or anti-myc antibody ( Sigma ) . The beads were then washed with IP buffer . Half of the eluates were used for RNA isolation as described [73] . Commercially available antibodies were used for detecting GFP ( Roche ) , HA-tag ( Roche ) , myc-tag ( Sigma ) , His-tag ( Amersham Biosciences ) MBP-tag ( Sigma ) . For AGO1 detection we used previously described anti-peptide antibody against N . benthamiana AGO1 [49] . Plant extracts for gel filtration were prepared in IP buffer and fractionation was carried out similarly as described earlier [73] . Briefly , 200 µl of extracts were loaded on the Superdex 200HR column and washed with IP buffer with 0 . 5 ml/min . 25 fractions were collected and after vortexing them for equilibration , each fraction were divided into two for RNA and protein isolation . RNA was isolated , as described above . Proteins were precipitated with 4 volumes of acetone and collected by centrifugation , then solubilized in 2×Laemmli buffer . Proteins were detected by western blotting . Site-directed mutagenesis was performed using the Quickchange site-directed mutagenesis kit ( Stratagene ) according the manufacturer's instructions to generate single , double and triple mutants with oligonucleotides listed in Text S1 . Deletion mutants were prepared by PCR using oligonucleotides P1-5′ 5′GGGGATCCCTAGAATGGGGAAATCCAAACTC3′ , and P1-383 5′GCGGATCCTCAATCATCAACTTGTGCGTTTAGGGA3′ . All mutants were verified by sequencing . GenBank accession numbers for new viral nucleotide sequence: GQ353374 and for complete SPMMV sequence: NC_003797 . | RNA silencing is an evolutionarily conserved sequence-specific gene-inactivation system that also functions as a major antiviral mechanism in higher plants and insects . Viral RNAs are processed by Dicer-like proteins into small interfering ( si ) RNAs , which trigger the RNA-induced silencing complex ( RISC ) assembly . Then siRNA loaded RISC inactivates cognate viral RNA . However , viral silencing suppressors evolved to counteract with RNA silencing targeting one or more key points in the silencing machinery . Here we show that in Sweet potato mild mottle virus , the role of silencing suppressor is played by P1 protein and it works by inhibiting si/miRNA-loaded RISC through targeting Argonaute 1 ( AGO1 ) . We confirmed using immunoprecipitation and in vitro binding assays that the interaction between P1 and small RNA loaded AGO1 is specific and direct . The suppression activity mapped to the N-terminal part of P1 containing three WG/GW motifs that resemble the AGO-binding linear peptide motif conserved in metazoans and plants . Site-directed mutagenesis proved that these three motifs are essential for both binding and suppression of AGO1 function . P1 protein is the only silencing suppressor identified so far that inhibits active RISC and this is the first demonstration of a WG/GW protein having negative effect on RNA silencing . |
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Despite intensive efforts using linkage and candidate gene approaches , the genetic etiology for the majority of families with a multi-generational breast cancer predisposition is unknown . In this study , we used whole-exome sequencing of thirty-three individuals from 15 breast cancer families to identify potential predisposing genes . Our analysis identified families with heterozygous , deleterious mutations in the DNA repair genes FANCC and BLM , which are responsible for the autosomal recessive disorders Fanconi Anemia and Bloom syndrome . In total , screening of all exons in these genes in 438 breast cancer families identified three with truncating mutations in FANCC and two with truncating mutations in BLM . Additional screening of FANCC mutation hotspot exons identified one pathogenic mutation among an additional 957 breast cancer families . Importantly , none of the deleterious mutations were identified among 464 healthy controls and are not reported in the 1 , 000 Genomes data . Given the rarity of Fanconi Anemia and Bloom syndrome disorders among Caucasian populations , the finding of multiple deleterious mutations in these critical DNA repair genes among high-risk breast cancer families is intriguing and suggestive of a predisposing role . Our data demonstrate the utility of intra-family exome-sequencing approaches to uncover cancer predisposition genes , but highlight the major challenge of definitively validating candidates where the incidence of sporadic disease is high , germline mutations are not fully penetrant , and individual predisposition genes may only account for a tiny proportion of breast cancer families .
Around one in six women who develop breast cancer has a first degree relative with the condition [1] . In the mid 1990s , a classical linkage approach identified germline mutations in two genes , BRCA1 and BRCA2 , which are associated with a high risk of developing both breast and ovarian cancer [2] , [3] . Although BRCA1 and BRCA2-specific genetic testing is rapidly evolving in the clinical setting , mutations in these genes are successful at explaining only around half of the dominant multi-case breast cancer only families [4] , and their contribution to the heritable risk of breast cancer has been estimated to be no more than around 20% of the total [5] , [6] . Importantly , the identification and management of individuals with high-risk breast cancer predisposition gene mutations is now well accepted in clinical practice . Although evidence-based risk management is only possible in a relatively small group of families , as it is limited by the identification of an underlying genetic mutation , the benefits for those individuals are well established [7] . Through a candidate gene approach , mutations in other high and moderate penetrance cancer-susceptibility genes have been identified in a further small proportion of families but the underlying etiology of the increased susceptibility to breast cancer in the majority of multi-case breast cancer families remains unknown . Recent advances in massively parallel sequencing technology have provided an agnostic means by which to efficiently identify germline mutations in individuals with inherited cancer syndromes at the individual family or cancer-specific level [8] , [9] . The aim of this study is to identify through a whole exome sequencing approach , the underlying familial predisposition to breast cancer in multiple multi-generational breast cancer families in whom no BRCA1 or BRCA2 mutation was identified ( BRCA1/2 negative families ) , and to assess the candidate genes identified by this means in a cohort of familial BRCA1/2 negative breast and ovarian cancer patients .
We performed intra-family exome sequence analysis of multiple affected relatives from 15 high-risk , trans-generational breast cancer families in whom full BRCA1 and BRCA2 mutation analysis had been performed and was uninformative in at least one breast cancer-affected family member ( Table 1 ) . Sequencing was performed on GAIIx or HiSeq instruments ( Illumina ) . The average read depth achieved for target regions was 83 . 19 and at least 80% ( average 89 . 12% ) of the capture target regions were covered by 10 or more sequence reads for all samples ( Table S1 ) . Following data filtering , an average of 35 overtly deleterious and 284 non-synonymous mutations were identified per individual ( Table S1 ) . To identify candidate predisposition genes we only considered those with overtly deleterious mutations that were shared by multiple affected relatives and/or were targeted in more than one family and further priority was given to genes with a role in mechanistically well-established breast cancer–associated DNA repair . A list of all overtly deleterious mutations identified in among the 33 individuals sequenced is provided in Table S2 . Two of the fifteen families were found to carry independent heterozygous truncating mutations in the Fanconi Anemia ( FA ) gene , FANCC . Neither family was reported to be of Ashkenazi Jewish ancestry and the mutations are different to those commonly reported among this ethnic group . Family 1 carried a novel nonsense mutation ( FANCC c . 535C>T , p . Arg179* ) that was present in the youngest affected individual ( breast cancer at age 37 ) and in her mother who had ovarian cancer at age 66 , but not in her breast cancer-affected sister who was diagnosed at age 46 ( Figure 1 ) . Family 2 was found to harbor a known pathogenic FA mutation ( FANCC c . 553C>T , p . Arg185* ) [10] which was present in two sisters who developed breast cancer aged 36 , and bilateral breast cancer aged 46 and 53 , respectively . A third family analyzed by exome sequencing was found to carry a heterozygous c . 1993C>T mutation in the BLM gene which is predicted to truncate the protein at codon 645 ( p . Gln645* ) . This known pathogenic Bloom syndrome mutation [11] co-segregated with cancer in the family ( Figure 1 ) , being present in all three sisters diagnosed with breast cancer aged 39 , 39 and 41 years respectively and absent in the two unaffected sisters . Although retrospective likelihood segregation analysis of these limited pedigrees did not reach significance ( see Text S1 ) , overall , co-segregation of FANCC and BLM mutations in these families appears consistent with that expected for moderately penetrant breast cancer alleles . Mutation analysis of all coding exons of FANCC and BLM was extended to the index cases from a further 438 BRCA1/2 negative breast cancer families ( from kConFab ) . This approach identified one further family with a heterozygous , known pathogenic FANCC mutation , ( c . 67delG , p . Asp23Ilefs*23 , rs104886459 ) [12] and one with a heterozygous pathogenic BLM mutation ( c . 2695C>T , p . Arg899* ) [11] . For FANCC , mutation hotspot exons 2 , 5 , 7 , 14 and 15 were screened in the index cases from an additional 957 BRCA1/2 uninformative breast cancer families attending familial cancer services ( including 561 obtained from the Peter MacCallum Cancer Centre Familial Cancer Centre and a further 396 from kConFab ) . One further family with a heterozygous FANCC c . 1661T>C ( p . Leu554Pro , rs104886458 ) missense variant , which is a functionally validated pathogenic FA mutation , was identified [13] . The index case in the FANCC c . 67delG family developed breast cancer at age 60 but independent clinical testing subsequently identified a deleterious mutation in BRCA2 ( c . 8297delC , p . Thr2766Asnfs*11 ) in other breast cancer-affected family members ( Figure 1 ) . Genotyping of both mutations within this family suggests that different individuals may carry risk conferred by one or both of these family mutations . The index case of the FANCC c . 1661T>C family developed bilateral breast cancer at age 44 and 55 , but DNA from other family members was not available for segregation analysis . All FANCC variants detected in index cases or controls are summarized in Table S3 . The index case of the BLM c . 2695C>T family developed breast cancer at age 33 but segregation analysis showed the mutation was inherited from her father rather than her mother whose reported family history of breast cancer had initiated their recruitment into kConFab ( Figure 1 ) . Interestingly , breast cancer was diagnosed much earlier in the index case compared to her maternal relatives ( 33 years versus 58 to 73 years ) possibly indicating a different genetic etiology . Unfortunately data regarding family history on the paternal side are limited . Neither the father nor the paternal grandparents were reported to have developed cancer but no further information regarding number or cancer status of other relatives is available . All BLM variants detected in index cases or controls are summarized in Table S4 . No pathogenic BLM mutations were detected in 464 healthy controls and none have been reported in the 1000 Genomes data ( 20100804 release , n = 1 , 092 ) [14] compared to 2/438 breast cancer families with BLM mutations . Likewise , no known pathogenic or overtly deleterious FANCC mutations were identified among the 464 controls or the 1000 Genomes data or among 654 healthy controls examined in an independent study [15] . The Exome Variant Server ( EVS ) , NHLBI Exome Sequencing Project , Seattle , WA , does report deleterious mutations in FANCC and BLM in 3/3 , 510 and 4/3 , 510 individuals of European decent , respectively . However , this cohort includes extreme tail sampling of traits relating to heart , lung and blood disorders . The latter group in particular may be expected to show enrichment for mutations in DNA repair machinery including FA genes . Excluding the Exome Variant Server frequency data , a total of 4/1 , 395 breast cancer families screened for all or at least the mutation hot spot exons carried overtly deleterious FANCC mutations compared to none among the combined control population ( n = 2 , 210 ) . While this is indicative that overtly deleterious mutation in FANCC and BLM are likely to be very rare in the population this must be considered a crude measure as the controls were drawn from diverse populations the majority of which were not matched to the index cases . However , it is possible that more families in our breast cancer family cohort may be explained by FANCC and BLM mutations since , for both genes , private non-synonymous variants were identified that are predicted to be damaging by in silico algorithms . One such variant , for which there was DNA available for segregation analysis , was FANCC p . Arg185Gln . This variant closely segregated with disease in this family , which included four female blood relatives with breast cancers diagnosed at ages 34 , 51 , 47 and 62 ( Figure 1 ) . The p . Arg185Gln variant was identified in 1/1 , 395 breast cancer families but not in any of 464 controls and has not been reported in the 1000 Genomes project or EVS database . Homozygous mutations in FANCC and BLM are responsible for FA ( complementation group C ) and Bloom syndrome , respectively , and individuals diagnosed with these syndromes have a high risk of cancer . Functionally , the FA and Bloom syndrome pathways play important roles in homologous recombination ( HR ) based repair of double-stranded DNA breaks [16] , [17] . Constitutional inactivating mutations in genes integral to error-free HR and responsible for FA have been clearly associated with an increased susceptibility to both breast and ovarian cancer [16] , and include the genes BRCA1 , BRCA2 ( FANCD1 ) , FANCN ( PALB2 ) , FANCJ ( BRIP1 ) , RAD51C ( FANCO ) and RAD51D . Thus , in addition to the direct genetic evidence that we have described here , FANCC and BLM are strong candidates for breast cancer susceptibility genes due to their role in the precise regulation of HR and some of its associated functions . Although there is limited data , heterozygous FANCC mutations have previously been linked to an increased incidence of breast and early onset pancreatic cancer [15] , [18] , [19] , however , no excess breast and ovarian cancer was observed among Ashkenazi Jews carrying the FANCC c . 711+4A>T mutation [20] . While another previous study failed to identify overtly pathogenic FANCC mutations in breast cancer , the study cohort size was small ( n = 88 ) [21] . In keeping with our data , two recurrent truncating mutations in the BLM gene were shown in a case control study to be associated with increased breast cancer risk in Russia [22] . Gruber et al reported an elevated risk of colorectal cancer in Ashkenazi Jews carrying the common BLMASH mutation and a non-significant excess of breast cancer [23] although a later study failed to confirm these findings [24] . Further to the germline mutations in FANCC and BLM , exome sequencing identified mutations in the breast cancer predisposition genes , PTEN and BRCA2 in an additional three of the original 15 families ( Figure S1 ) . The truncating PTEN mutation ( c . 217G>T , p . Glu73* ) was identified in only one branch of the family suggesting another susceptibility gene may explain the extended family history . Prior to this finding , the treating familial cancer centre reported no PTEN-associated clinical features within the family . In family 5 , exome sequencing identified a deleterious BRCA2 mutation ( c . 5722_5723delCT , p . Leu1908Argfs*2 , rs80359530 ) in two of the three family members tested ( Figure S1 ) . The mutation is present in a male diagnosed with breast cancer but not in the youngest affected female relative in the family , who had been offered the original clinical BRCA1 and BRCA2 mutation test in the clinic setting . Similarly in family 6 , exome sequencing identified a deleterious BRCA2 mutation ( c . 26delC , p . Pro9Glnfs*16 , rs80359343 ) in a female diagnosed with breast cancer at age 30 , but not in her cousin who was diagnosed at age 36 and was the only family member to have undergone full diagnostic BRCA1 and BRCA2 gene sequencing ( Figure S1 ) . These families are interesting in a clinical context since they were designated as unresolved on the basis of best clinical practice and demonstrate the need for targeted sequencing of all proven breast and ovarian cancer susceptibility genes to obtain maximum information in the clinical setting ( as previously demonstrated [25] ) . Our data also highlights the major challenge confounding genetic studies of common adult onset familial disease; the presence of ‘phenocopies’ in families with an inherited genetic predisposition and/or the convergence of pedigrees with different genetic causes ( e . g . PTEN family 4 ) . Among the remaining nine breast cancer families there were numerous genes that were recurrently targeted that warrant further investigation . It is noteworthy that in one family , one individual harbored a known FA pathogenic truncating mutation in FANCL . Mutation of this gene is responsible for a very small fraction of FA families and only three pathogenic mutations in FANCL are recorded in the Fanconi Anemia Mutation Database . In conclusion , we describe two potential breast cancer susceptibility genes FANCC and BLM both of which have functional roles in the regulation of HR . The heterozygous mutation carrier rate in Caucasians for these genes is extremely low ( for FANCC it is estimated at 1/3 , 000 [15] , whilst the carrier frequency of BLM mutations is unknown since the syndrome is exceedingly rare ) and notwithstanding the possibility of the “winners curse” [26] , the exome sequencing data is strongly suggestive that FANCC and BLM represent breast cancer predisposing genes . Together with the recently identified association of RAD51 paralogues with cancer predisposition [27] , [28] , our findings suggest that the number of unidentified moderate to high-risk susceptibility genes is very much larger than previously expected and the number of families explained by each gene is likely to be much less than 1% ( cf . RAD51C [27] , [29] ) . Consequently , providing definitive evidence for a causative role for novel breast cancer genes will be challenging and will require validation of rare mutations in thousands rather than hundreds of families . We predict that this will be a generic problem associated with identifying causative mutations in common diseases such as breast cancer and that validation rather than the technical exercise of exome sequencing is where the real challenge lies .
This study was approved by the Peter Mac Ethics Committee ( project numbers 09/62 and 11/50 ) . Informed consent was obtained from all participants . Fifteen high-risk breast cancer families with at least four cases of multi-generational breast cancer including at least one additional high-risk feature ( such as bilateral , early onset or male breast cancer , or ovarian cancer ) and at least two available blood specimens from breast cancer-affected individuals , were selected for whole exome sequencing from among approximately 800 BRCA1 and BRCA2 mutation negative families from the Kathleen Cunningham Foundation Consortium for Research into Familial Breast Cancer ( kConFab ) , which has been collecting biospecimens and clinical and epidemiological information from families recruited through Familial Cancer Centres in Australia and New Zealand since 1997 [30] . DNA from two or three breast cancer-affected individuals were obtained from each family for analysis ( as shown in Table 1 ) , at least one of whom had previously been screened for BRCA1 and BRCA2 mutations ( by sequencing of all coding exons and Multiplex Ligation-dependent Probe Amplification ) . Blood DNA from index cases from a further 834 mutation negative kConFab families and 561 mutation negative families obtained from the Peter MacCallum Cancer Centre Familial Cancer Centre were obtained for mutation analysis of candidate genes . Of those index cases obtained through the Familial Cancer Centre , individuals were breast cancer-affected , had a strong family history and been assessed for the probability of harboring a BRCA1 or BRCA2 mutations using BRCAPRO [31] and had been found on the basis of a verified family and personal history of having a 10% or greater probability . The index cases had undergone full diagnostic BRCA1/2 mutation search and no mutation was identified . However , it should be noted that the majority of these families did not fulfill the very stringent family history criteria that was required for recruitment to kConFab , the research cohort from which the families for the initial exome sequencing were taken [30] . Non-cancer control DNA samples were obtained from kConFab ( 226 age- and ethnicity-matched best friend controls ) and from the Princess Anne Hospital , UK ( 238 Caucasian female volunteers , as described previously [32] ) . DNA for candidate gene mutation analysis underwent whole genome amplification ( WGA ) using Repli-G Phi-mediated amplification system ( Qiagen ) prior to mutation analysis . 2–3 µg of DNA was fragmented to approximately 200 bp by sonication ( Covaris ) and used to prepare single- or paired-end libraries using the SPRIworks Fragment Library System I for Illumina Genome Analyzer on the SPRI-TE Nucleic Acid Extractor ( Beckman Coulter ) . Exome enrichment was performed using the NimbleGen Sequence Capture 2 . 1 M Exome Array , EZ Exome Library ( Roche NimbleGen ) or SureSelect Human All Exon version 2 or 50 Mb libraries ( Agilent Technologies ) according to the recommended protocols . Sequencing was performed on GAIIx or HiSeq instruments ( Illumina ) . Library preparation and sequencing details for each sample are provided in Table S1 . We did not observe any significant differences in performance of the different exome capture platforms . Paired-end sequence reads were aligned to the human genome ( hg19 assembly ) using the Burrows–Wheeler Aligner ( BWA ) program [33] . Local realignment around indels was performed using the Genome Analysis Tool Kit ( GATK ) software [34] . Subsequently , duplicate reads were removed using Picard and base quality score recalibration performed using GATK software . Single nucleotide variants ( SNVs ) and indels were identified using the GATK Unified Genotyper and variant quality score recalibration . Variants were annotated with information from Ensembl release 62 using Ensembl Perl Application Program Interface ( API ) including SNP Effect Predictor [35] , [36] . Single-end sequence reads were aligned as above except duplicate reads were flagged prior to base quality score recalibration and included in variant calling . Variants were first filtered for confident calls originating from bidirectional sequence reads using a quality threshold of ≥30 , read depth of ≥10 and allele frequency ≥0 . 15 . Prior to further filtering , variants were assessed for overtly deleterious mutation in known breast cancer associated genes [25] . Then , all variants present in the dbSNP database v132 , except those also reported in the public version of the Human Gene Mutation Database ( HGMD ) [37] were removed , as were all common variants detected in >10 out of 33 exomes . Next , variants with functionally deleterious consequences ( nonsense SNVs , frameshift indels , essential splice variants and complex indels ) were identified for evaluation [35] . Functionally deleterious variants were evaluated in each individual as well as pairwise between relatives . Primers flanking the BRCA2 , PTEN , FANCC and BLM mutations identified by whole exome sequence analysis were used to amplify germline DNA from affected index cases and all available relatives . The purified products were directly sequenced using BigDye terminator v3 . 1 chemistry on a 3130 Genetic Analyzer ( Applied Biosystems ) . High resolution melt ( HRM ) analysis was performed on duplicate PCR products amplified from 15 ng WGA DNA . Primer sequences and PCR conditions are provided in Table S5 . Melt analyses were performed on a LightCycler 480 Instrument using Gene Scanning Software ( Roche ) . Duplicate PCR products exhibiting variant DNA melt curves were Sanger sequenced to identify sequence variations . All novel sequence variants were confirmed by Sanger sequencing an independent PCR amplified from non-WGA DNA . The functional effect of missense variants were evaluated using in silico prediction tools SIFT and PolyPhen-2 [38] , [39] . The following GenBank reference sequences were used for variant annotation: FANCC , NM_000136 BLM , NM_000057; PTEN , NM_000314 and BRCA2 , NM_000059 . 1000 Genomes Browser , http://browser . 1000genomes . org/; Ensembl , http://www . ensembl . org/index . html; The Genome Analysis Toolkit , http://www . broadinstitute . org/gsa/wiki/index . php/The_Genome_Analysis_Toolkit; HGMD , http://www . hgmd . org/; Picard , http://picard . sourceforge . net; HGVS nomenclature for the description of sequence variants , http://www . hgvs . org/mutnomen/; NCBI SNP database , http://www . ncbi . nlm . nih . gov/projects/SNP/; The Fanconi Anemia Mutation Database , http://www . rockefeller . edu/fanconi/; BLMbase mutation registry , http://bioinf . uta . fi/BLMbase/; SIFT , http://sift . jcvi . org/; PolyPhen-2 , http://genetics . bwh . harvard . edu/pph2/ . Exome Variant Server , http://evs . gs . washington . edu/EVS/ . | Currently , we know that a woman who inherits a fault in one of two genes , BRCA1 or BRCA2 , has a high risk of developing both breast and ovarian cancer . However , such faults account for only half of all families with a strong family history of breast cancer . In this study , we planned to identify new genes that may be associated with an increased risk of developing breast cancer by looking for faults in every gene in the blood DNA of multiple women with breast cancer from large families with a strong family history of the condition over multiple generations . We can then track which gene fault is present in all the women with breast cancer in that family and in other families , but is not found in the women who did not develop breast cancer or have no family history . Using this approach , we identified faults in two genes , Fanconi C and Bloom helicase , in six families . Faults in these genes appear to increase the risk of developing breast cancer . Both these genes work in a similar way as BRCA1 and BRCA2 , and this highlights the importance of these functions in preventing breast cancer . Further studies need to be done to confirm our results . |
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Vinculin can interact with F-actin both in recruitment of actin filaments to the growing focal adhesions and also in capping of actin filaments to regulate actin dynamics . Using molecular dynamics , both interactions are simulated using different vinculin conformations . Vinculin is simulated either with only its vinculin tail domain ( Vt ) , with all residues in its closed conformation , with all residues in an open I conformation , and with all residues in an open II conformation . The open I conformation results from movement of domain 1 away from Vt; the open II conformation results from complete dissociation of Vt from the vinculin head domains . Simulation of vinculin binding along the actin filament showed that Vt alone can bind along the actin filaments , that vinculin in its closed conformation cannot bind along the actin filaments , and that vinculin in its open I conformation can bind along the actin filaments . The simulations confirm that movement of domain 1 away from Vt in formation of vinculin 1 is sufficient for allowing Vt to bind along the actin filament . Simulation of Vt capping actin filaments probe six possible bound structures and suggest that vinculin would cap actin filaments by interacting with both S1 and S3 of the barbed-end , using the surface of Vt normally occluded by D4 and nearby vinculin head domain residues . Simulation of D4 separation from Vt after D1 separation formed the open II conformation . Binding of open II vinculin to the barbed-end suggests this conformation allows for vinculin capping . Three binding sites on F-actin are suggested as regions that could link to vinculin . Vinculin is suggested to function as a variable switch at the focal adhesions . The conformation of vinculin and the precise F-actin binding conformation is dependent on the level of mechanical load on the focal adhesion .
The focal adhesion is a critical for cell-substrate adhesions [1] , [2] , necessary for Cell movement [3] , [4] , wound healing [5] , cancer cell metastasis [6] , , and other processes [8]–[10] . At the focal adhesion , actin filaments of the cellular cytoskeleton are linked to the extra-cellular membrane ( ECM ) of the substrate [5] . Once linked , both mechanical forces [11] originating from within the cell – such as myosin induced contraction of the actin filaments [12] during cell migration – can act on the ECM and the substrate , and , mechanical forces originating from outside the cell – such as flow induced cyclic stress in the case of endothelial cells [13] – can be transduced to the cellular machinery [14] . Formation of the focal adhesion involves linkage of focal adhesion proteins to ECM bound integrins [15]–[18] , linkage of focal adhesion proteins to each other [19] , [20] , and linkage of the ECM-focal adhesion complex to actin filaments [21]–[23] . The simplest focal adhesion complex would consist of a talin molecule bound to integrin via its head domain and bound to actin via its tail domain [24] , [25] . Talin has 11 cryptic binding sites for vinculin and activation of these binding sites along with subsequent recruitment of vinculin to the growing focal adhesion correlates with the strengthening of the focal adhesion [26] . Recruitment of vinculin would reinforce the focal adhesion as vinculin can crosslink an actin filament to the talin molecule [27] . This binding of the focal adhesion to actin filaments , by vinculin or other focal adhesion forming molecules , is a critical step in completing formation of a mechanical link between the cell and its substrate . The actin filament itself is composed of numerous individual actin subunits bound together to form a polar double-stranded filament [28] . Multiple filaments can be crosslinked by actin crosslinkers [29] , [30] . Both the actin subunits and the actin filament are polar , with a barbed-end ( + ) and a pointed-end ( − ) . In this paper , the actin subunit at the barbed-end is referred to as subunit n , with the next subunit towards the pointed-end referred to as subunit n-1 , and the subsequent subunit to n-1 is referred to as n-2 , and so on ( Figure 1 ) . Polymerization of the actin filament can occur at both ends of actin , but occurs with much higher efficiency at the barbed-end [31] . Each actin subunit has 4 subdomains: S1 , S2 , S3 , and S4 [28] . The S2 subdomain contains a DNase-I-binding loop ( D-loop ) that can interact with a neighboring actin subunit [28] . Recently , it was shown that polymerization of F-actin at the pointed-end is slower than polymerization at the barbed-end because of an interaction between the D-loop at n-1 with a hydrophobic patch of n-2 ( at the pointed-end ) [32] . n-1n-1 Vinculin is a globular protein much smaller than actin and with 5 helical domains: domain 1 ( D1 ) , domain 2 ( D2 ) , domain 3 ( D3 ) , domain 4 ( D4 ) , and the vinculin tail domain ( Vt ) [33] . D1–D4 together form the vinculin head domains . With Vt bound to each of the vinculin head domains , vinculin is considered to be in a closed conformation . Vt contains the likely binding sites for an actin filament [34] , while D1 contains the likely binding sites for talin [35] . In its closed conformation vinculin is unable to bind both F-actin at Vt and talin at D1 [36] , and the closed conformation is often referred to as the auto-inhibited conformation [37] . D1 of the vinculin head inhibits the linkage of Vt with F-actin . Several hypotheses have been explored concerning the mechanism of vinculin activation [38]–[40] . It is clear that a vinculin conformational change is necessary to allow for binding of vinculin to both F-actin and talin [41] . A recent computational study has proposed a conformational change that could activate vinculin: D1 of the vinculin head could move away from Vt and towards its talin binding partner [42] . The movement of D1 leading to vinculin activation could result from a force-induced stretching of vinculin [40] , [43] that would result from vinculin being recruited to mechanically stressed focal adhesions [43] . The computational studies capture one explanation for reinforcement of the linkage between a cell and its surface . Other studies examine additional possibilities such as force-responsive linkage to the substrate via syndecan-4 [44] . Vinculin recruitment to focal adhesions is made possible by activation of the vinculin binding sites ( VBS ) within the talin rod [45] , [46] . The VBS are hydrophobic in nature and buried within the helical structure of talin in the absence of force [47] . Initial computational investigation [48]–[50] and later experimental investigation [45] has demonstrated the exposure of the buried hydrophobic VBS only after force induced stretch of the talin rod . Once activated , the VBS can bind D1 of vinculin . Recent computational simulation suggests the interaction of D1 with VBS is completed , again , after force-induced activation of vinculin [51] . The focal complex serves to link ECM-bound integrin to actin filaments: force-induced activation of talin and activation of vinculin are effective only if the activated vinculin can bind along the actin filament and link it to the focal adhesion . Binding of vinculin to F-actin is the final step to complete this structure . Vinculin-actin binding is necessary for focal adhesions to be mechanically resilient [52] , and the interaction is crucial to the strengthening of the focal adhesion [53] . What features of the vinculin tail and F-actin allow for this crucial interaction ? Using a combination of experimental electron microscopy and computational protein docking methods Janssen et al . [36] have addressed the Vt interaction with F-actin . They suggest two patches of basic residues on the surface of Vt link with two patches of acidic residues on the surface of F-actin . One of the actin acidic patches is on the n-2 subunit and the other is on the n subunit . Their study also suggests that full-length vinculin in its closed conformation would be unable to bind along the actin filament as D1 would clash with regions of the actin filament . It is unclear dynamically how D1 would inhibit the link to F-actin , which of the acidic patches on F-actin are more critical to linking Vt , and whether the suggested conformation of activated vinculin [42] would allow for Vt to link these acidic patches . These issues are addressed in the first section on the binding of vinculin along the actin filament . It has also been suggested that interaction of Vt with F-actin can inhibit actin polymerization [54] . During cell movement actin filaments in the lamellipodia will polymerize at their barbed-ends [31] . The polymerization is involved in membrane protrusion at the leading edge of a migration cell [55] . One line of evidence supporting the notion that Vt can inhibit actin polymerization comes from studies of the bacterial effector IpaA [54] , [56] . IpaA can stop polymerization of actin filaments of its target cell and can even cause depolymerization of the actin filaments . The direct effect of IpaA is to activate vinculin for capping of F-actin at the barbed-end . Although the mechanisms of vinculin activation for F-actin capping by IpaA are not clear , it is suggested from these studies that vinculin can cap the actin filament and prevent its polymerization . Further lines of evidence for vinculin capping of actin filaments come from other studies showing that Vt ( isolated form other vinculin residues ) can catalyze G-actin nucleation through interaction with the barbed-end of G-actin [57] . Most recently , Le Clainche et . al . [58] have explored capping of F-actin by vinculin in vitro . They used pyrenyl-labeled actin fluorescence [59] , [60] to assay the polymerization of G-actin into F-actin before and after introduction of Vt in vitro . Introduction of Vt prevents polymerization of F-actin . Their results suggest that residues 1044–1066 of Vt are critical to capping of F-actin by vinculin . Several questions arise concerning this capping of actin filaments by vinculin that the second section of this study on the capping of F-actin will address: what is the structure of F-actin capped by Vt ? What residues and surface regions of the barbed-end are critical to interaction with Vt ? How favorable or stable are these interactions between the barbed-end of F-actin and Vt ? The interaction between vinculin and actin is of importance not only to efforts aimed at understanding focal adhesion formation via talin and vinculin , but also to efforts aimed at understanding the role of vinculin in regulating actin dynamics , or the role of vinculin in other cellular processes . A study by Wilins and Lin [61] established a role of vinculin in regulating actin dynamics , and more recently Huveneers et al . suggested vinculin to be involved in stabilizing force-dependent remodeling of endothelial cell-cell adhesions [62] . This study investigates both the interaction of vinculin along the actin filament and the capping interaction of vinculin with the barbed-end of F-actin . Molecular dynamics simulations are used to probe the interaction of vinculin along the actin filament using: ( a ) a structure of only Vt interacting with actin subunits n and n-1 , ( b ) a structure of vinculin is its closed conformation , and ( c ) a structure of vinculin in its suggested activated conformation [42] . Furthermore , a similar molecular dynamics approach is used to determine the likely structure , dynamics , and energetics of the interaction between Vt and the capping end of F-actin . Finally , computational techniques are used to evaluate an additional conformational change in full-length vinculin ( beyond the suggested activation of vinculin at D1 ) and explore the possibility of an interaction between the barbed-end of the actin filaments and vinculin in this second open structure .
Using pyrenyl-labeled actin to assay F-actin polymerization Le Clainche et al [58] demonstrated in vitro that Vt can effectively cap the barbed-end of actin . Capping of the actin filaments by vinculin has been demonstrated in cells affected by IpaA [71] . In such cells the capping of F-actin serves to depolymerize the actin filaments and make the cells compliant for Shigella invasion [54] . It is unclear if capping of actin filaments could play a role at sites of focal adhesions . It is also unclear if vinculin at focal adhesions is able to cap the actin filaments . A step towards clarifying both possibilities is to understand the nature of F-actin capping by vinculin . The vinculin tail residues implicated in F-actin capping reside in the C-terminus region and have also been implicated in interaction with the lipid membrane [72] . The last 21 residues of vinculin consist of a number of charged and basic residues that are predicted to readily interact with acidic residues at the actin barbed-end or on acidic phospholipids ( Figure 4A ) . In its closed conformation vinculin head domains occlude access to most of these residues . With Vt isolated from the vinculin head domains the last 21 residues could interact with the barbed-end of F-actin either through the surface of Vt that would be occluded by vinculin head domains , the occluded surface , or through the surface of Vt already exposed to solvent , the exposed surface of Vt . From the structure of Vt it is predicted that the occluded face of Vt would better link the barbed-end given the higher density of charged residues at this surface ( Figure 4A ) . Newly added actin subunits would interact with the barbed-end of F-actin . Examination of the F-actin structure predicts that the D-loop of subunit n interacts favorably with the interface between S1 and S3 of subunit n-2 ( Figure 1 ) . Specifically , residues 283–294 , 139 , 140 , 143 , 346 , 351 , and 374 of the barbed-end are implicating in stabilizing additional actin subunits by interacting with their D-loop structures [28] . Recent high resolution imaging of the actin filament pointed-end confirms the likely interaction between the D-loop of a newly polymerized actin monomer and the interface between S1 and S3 at the barbed-end of the actin filament [32] . Capping of F-actin by CapZ and other capping proteins prevents actin polymerization by occluding access to S1 or S3 [32] . Capping of F-actin by Vt would then likely result from interaction of Vt with S1 of the barbed-end , S3 of the barbed-end , or both subunits S1 and S3 of the barbed-end ( Figure 4B ) . The interaction of Vt with F-actin is evaluated using molecular dynamics . Vt is simulated initially oriented towards the barbed-end towards either ( A ) S1 only , ( B ) S3 only , or ( C ) towards S1 and S3 . Each orientation is simulated both with the exposed face of Vt initially oriented towards the barbed-end and with the occluded face of Vt initially oriented towards F-actin ( Table S1 ) . An additional conformational change is necessary for vinculin to be able to cap actin filaments . The simulations of Vt capping of actin filaments suggest that the occluded surface of Vt forms the most likely interaction with the interface between S1 and S3 . The occluded surface is normally in contact with D4 residues . Movement of D1 away from Vt , which was shown to be sufficient for allowing vinculin to bind along actin filaments , does not result in dissociation of Vt from D4 . The occluded surface of Vt , critical to F-actin capping , requires additional conformational changes in vinculin beyond D1 movement . Le-Clainche et al [58] , [78] also suggest a second vinculin conformational change is necessary to allow for vinculin capping of the actin-filaments . The interface between D4 and Vt has been implicated elsewhere as critical to Vt activation in general . Chen et al [78] describe a pincer-like mechanism to vinculin activation in which both the interface of D1 with Vt and the interface of D4 with Vt is disrupted to allow Vt to leave the pocket formed by the vinculin head domains and link with F-acitin . In another study , Cohen et al [65] demonstrate that both interactions between Vt and D1 and interactions between Vt and D4 are critical to maintaining an auto-inhibited conformation . The simulations in this study suggests that disruption of the key interactions between Vt and D1 coupled with movement of D1 away from Vt is sufficient to allow binding of vinculin along the actin filament . The interaction between Vt and D4 could however play a critical role in regulating Vt capping of actin filaments . The separation of D1 from Vt was simulated by assuming a cooperative activation mechanism and introducing stretch of vinculin to be consistent with that mechanism [42] . The source for D4 separation from Vt is less clear . If Vt separates from D4 at the focal adhesions then perhaps the movement of the actin filaments across the developing focal adhesion can supply induce separation of Vt from D4 . If however Vt separation from D4 is particular to cells affected by Shigella [71] , then D4 separation would result from the interaction with IpaA . Whatever the source allowing fro D4 separation , it is likely that D4 would separate after D1 separation . In either scenario , the magnitude of force that would be needed to induce a second conformational change is telling of how likely it is that a conformation shift would occur . Estimating the in vivo magnitude of force accurately is not possible with molecular dynamics . The time-scale of computationally feasible molecular dynamics simulations is orders of magnitude faster than the in vivo time-scale . Nevertheless , the conformational changes we suggest here can be informative . The interaction between vinculin and actin was explored in this paper using molecular dynamics simulations in three sections: first , the interaction of vinculin along the actin filament was investigated , then , the interaction of Vt with the barbed-end of the actin filament , and finally , the possible interaction between an open II vinculin conformation and the actin filament . Simulation of the interaction along the actin filament confirmed that although Vt can bind along the actin filament , full-length vinculin in its closed conformation is inhibited from binding along the filament . The open I conformation , previously suggested as a conformation of activated vinculin , was able to bind along the actin filament as Vt had , confirming that it is likely the structure of activated vinculin . Simulation of Vt interacting with the barbed-end of F-actin confirmed that Vt could indeed prevent polymerization of the actin filament . Vt binds to the barbed-end of F-actin similar to other capping proteins [74] and can prevent the association of a new actin subunit with S1 and S3 . Simulation of vinculin conformational changes beyond D1 separation and formation of the open I conformation revealed the possibility of an open II conformation . With the open II conformation vinculin could cap actin filaments , even at the focal adhesion . In general , evaluating binding modes between a large filament such as actin and a large protein such as vinculin is a computationally demanding endeavor . In addition to the computational challenge posed by the size of the proteins , the simulation time needed to allow for a binding event to occur can be prohibitive . In the context of these computational challenges , this study has simulated the binding events with the following strategy: ( 1 ) reduce the size of the binding proteins by limiting the number of actin monomers to include in the simulations , ( 2 ) reduce the simulation time by including an initial nudge of vinculin towards actin , and ( 3 ) reduce simulation time by placing vinculin within 15 Å of actin . This has allowed for simulation of binding , but the study is limited by not having repeat binding events to capture the true scholastic nature of the binding . An interesting scenario arises when considering the linkage of vinculin to actin at focal adhesions: vinculin is also linking to talin at focal adhesions , and so how are the three to be relatively oriented ? In what order would the two binding events occur ? The results from this study are insufficient to answer those questions accurately and additional simulations would be required to predict the binding mode of a talin-vinculin-actin complex . The results here suggest that steric limitations between talin and actin should govern the exact order of binding events , or exact binding modes that would be adopted . Putting together the results from all of these simulations , we can predict three potential regions of an actin filament that would bind vinculin ( Figure 10 ) . Consistently , the acidic residues in S1 – vinculin binding site A – were shown to be critical for an interaction between Vt and F-actin . These residues stabilized both the binding of vinculin along the actin filament and they were involved in stabilizing Vt capping of the actin filament . The surface between S1 and S3 of the barbed end – vinculin binding site B – was also consistently shown to stabilize Vt . Hydrophobic residues in this region would form hydrophobic cores with non-polar residues from Vt . Both basic and acidic residues in this region would form salt-bridges with their counterparts on Vt . The interactions between Vt and S1 that were highlighted by our simulations had previously been suggested to be involved in binding of vinculin along the actin filament , and the interactions between S1 and S3 were previously shown to be significant for capping of the actin filament . However , the third region on the surface of the actin filament that is suggested here to be involved in vinculin binding is novel: the residues in S3 of subunit n-2 – vinculin-binding site C . These residues can interact with charged residues from D1 of vinculin and in doing so contribute to further stabilizing the vinculin-actin linkage . With the presence of three binding regions we can predict that vinculin will differentially bind to each of the binding sites depending on the intensity of mechanical stress on the focal adhesion . It is possible that binding site A would interact with vinculin during vinculin activation , and would completely bind vinculin after activation . This interaction would require the least level of mechanical stress . Binding site C would bind vinculin after D1 is separated from Vt and can link to it . This would potentially require some level of mechanical stress . And binding of binding site B to vinculin would occur after transition of vinculin to the open II conformation . This would require the most level of mechanical stress . The exact binding interface between vinculin and F-actin , therefore , would be a function of the level of mechanical stress at the focal adhesion . Vinculin would be a variable switch at the focal adhesion , increasing its level of activation and F-actin binding depending on the level of mechanical stress at the focal adhesion . Previously , it was shown that formation of the vinculin open I conformation allows for the complete insertion of talin VBS in to D1 of vinculin [51] . We can now expand on those results and further state that after complete linking of vinculin to talin via D1 , and linking along the actin filament via Vt in the open I conformation , any additional forces from further movement or stress of the actin filament could induce an open II conformation ( Figure 11 ) . The formation of an open II conformation would then allow vinculin to remain linked to F-actin even as it moves . In this way vinculin could act both as a molecular clutch and as a variable switch at the focal adhesion . The predictions from this study are especially relevant to understanding focal adhesion structures . Focal adhesions play a role in numerous cell types and are especially involved in the processes of cell migration . The predictions from this study contribute towards understanding molecular mechanisms of cell migration via the focal adhesions . The question that remains unanswered after our simulations and analysis is whether the actin filaments can be capped at the focal adhesion . The simulations of vinculin in an open II conformation with the barbed-end suggested that F-actin can be capped , but what role would this play at the focal adhesion ? The understanding that vinculin is a variable switch is a testable and valuable prediction from the molecular dynamics simulation , but the capping of actin filaments at focal adhesions is really an unanswered question that is posed by our molecular dynamics simulations . Further investigations – both computational and experimental – are sought to address this question .
PDB ID 1ST6 was used to build a structure of full-length vinculin [66] . The missing proline rich linker region ( residues 843–877 ) was created via homology modeling using the SWISSMODEL toolkit [82] , as previously described [42] . The proline rich linker region is suggested to be flexible and therefore its structure not resolved . Inclusion of a homology model for the linker region in this study is justified as the linker region is not suggested to play a key role in the binding events , and the simulations should not be affected by inclusion of the linker homology model . Vt was built as residues 895–1066 from the full-length vinculin model . The structure of open full-length vinculin was taken from previously published simulation [42] . PDB ID 3LUE [66] was used to build a structure of F-actin . The 3LUE structure has α-actinin [83] CH domains bound to F-actin . The CH-domains are removed and only 3 of the F-actin subunits are used to build the F-actin structure . Complex of Vt bound to F-actin was build using Janssen et al [36] to orient Vt towards the two binding pockets along F-actin . Structure of full-length was build using the same Vt orientation but including the vinculin head domain residues . Vinculin was translated to be at least 15 Å away from F-actin . For simulation of Vt interaction with the barbed end , Vt was oriented either with the exposed or the occluded surface oriented towards Vt . Three arrangements of Vt with the barbed-end were simulated: Vt oriented towards S1 of the barbed-end of F-actin , Vt oriented towards S3 of the barbed-end of F-actin , Vt oriented towards both S1 and S3 of the barbed end . Vt was placed within 10 Å of the barbed-end in these structures . The initial orientation and setup of each simulation was random with respect to the exact pose of vinculin relative to actin; the distance from actin was imposed to be 10 Å and the face of vinculin ( exposed or occluded surface ) closest to actin was controlled , but the exact orientation pose of vinculin was chosen at random . It is possible the orientation pose of vinculin directly impacted the final binding mode , but given that the final binding orientation and pose of vinculin bound to actin was government by the mechanics of interaction , the final binding mode can be seen as more representative . For simulation with vinculin in the closed or the open II conformation , additional vinculin head residues are included with maintaining the Vt orientation . Each system was solvated with 12 Å of padding at each end of the simulation box . Simulations were carried out using the NAMD Scalable Molecular Dynamics program [84] , using an explicit solvent representation . Periodic boundary conditions were used along with a Langevin piston Nose-Hoover [85] mechanism for pressure control at 1 Atm . Constant temperature of 310K was maintained using a Langevin damping coefficient of 5/ps . Rigid bonds were enforced between hydrogen atoms and their bound larger atoms [86] . The CHARMm 27 force fields were used [87] , [88] . Simulation timesteps of 2 fs were used for all molecular dynamics . Each configuration was first minimized for 1000 steps using the conjugate gradient and line search algorithm implemented in NAMD [84] . Following minimization each configuration is simulated for at least 15 ns or until equilibration . All simulation results were visualized and analyzed using VMD [89] . For simulations of binding along F-actin the Vt , closed vinculin , and open I vinculin were initially nudged towards F-actin for less than 1 ns prior to simulation for 15 ns using a constant velocity pull on the center of mass of vinculin in a direction towards the center of mass of actin . Use of the nudge reduces the entropic barrier to binding . For simulation of Vt capping actin filaments no initial nudge is used and instead Vt is placed 5 Å closer to the barbed-end . Vt is smaller than full-length vinculin and can have faster translation , thus binding occurred even without an initial nudging force . The simulations are performed one time per setup . Additional simulations would allow for a statistical estimation of reproducibility , however , given computational limitations to running multiple 15 ns simulations , the present study is limited to a single simulation per setup . Umbrella sampling of D4 separation from Vt was carried out using GROMACS [90] . The umbrella sampling approach allows estimation of a free energy path along a reaction coordinate by estimation of the free energy difference between subsections of the path . The reaction coordinate was defined as the distance between the center of mass of D4 and the center of mass of Vt . Residues in D1 16 , 51 , 81 , and 115 were constrained with 1000 KJ/mol*nm2 to maintain an open I conformation throughout the simulation . Residues 926 , 958 , 988 , and 1031 of Vt were defined as the pull group and constrained along the reaction coordinate away from residues 730 , 760 , 794 , and 824 of D4 . An umbrella potential of 1000 KJ/mol*nm2 was used with a reference step of 0 . 2 Å in order to maximize umbrella overlap , allowing for an accurate estimation of the free energy path . The final potential of mean force was calculated using Grossfield's WHAM code [91] . | The interface between a cell and its substrate is strengthened by the formation of focal adhesions . In this study molecular dynamics simulations are used to explore the connectivity of one focal adhesion forming protein , vinculin , and the cytoskeletal filament , F-actin . The simulations demonstrate: ( 1 ) that vinculin can link along F-actin at these focal adhesions when it adopts an open conformation , ( 2 ) that the vinculin tail ( Vt ) can bind F-actin at its barbed-end preventing actin polymerization , ( 3 ) that vinculin can adopt two open conformations , and ( 4 ) that the second open conformation is necessary for vinculin to cap the actin filament . The results suggest that vinculin can act as a variable switch , changing its shape and the nature of its interaction with F-actin depending on the level of stress seen at a focal adhesion . Under the highest stress vinculin would adopt the open II conformation and link anywhere on F-actin , even its barbed-end . Under less stress vinculin could adopt the open I conformation and bind along F-actin . And under minimal stress vinculin could adopt its closed conformation . This variability allows for vinculin to truly function as the cell's mechanical reinforcing agent . |
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PIK3C2A is a class II member of the phosphoinositide 3-kinase ( PI3K ) family that catalyzes the phosphorylation of phosphatidylinositol ( PI ) into PI ( 3 ) P and the phosphorylation of PI ( 4 ) P into PI ( 3 , 4 ) P2 . At the cellular level , PIK3C2A is critical for the formation of cilia and for receptor mediated endocytosis , among other biological functions . We identified homozygous loss-of-function mutations in PIK3C2A in children from three independent consanguineous families with short stature , coarse facial features , cataracts with secondary glaucoma , multiple skeletal abnormalities , neurological manifestations , among other findings . Cellular studies of patient-derived fibroblasts found that they lacked PIK3C2A protein , had impaired cilia formation and function , and demonstrated reduced proliferative capacity . Collectively , the genetic and molecular data implicate mutations in PIK3C2A in a new Mendelian disorder of PI metabolism , thereby shedding light on the critical role of a class II PI3K in growth , vision , skeletal formation and neurological development . In particular , the considerable phenotypic overlap , yet distinct features , between this syndrome and Lowe’s syndrome , which is caused by mutations in the PI-5-phosphatase OCRL , highlight the key role of PI metabolizing enzymes in specific developmental processes and demonstrate the unique non-redundant functions of each enzyme . This discovery expands what is known about disorders of PI metabolism and helps unravel the role of PIK3C2A and class II PI3Ks in health and disease .
Identifying the genetic basis of diseases with Mendelian inheritance provides insight into gene function , susceptibility to disease , and can guide the development of new therapeutics . To date , ~50% of the genes underlying Mendelian phenotypes have yet to be discovered [1] . The disease genes that have been identified thus far have led to a better understanding of the pathophysiological pathways and to the development of medicinal products approved for the clinical treatment of such rare disorders [2] . Furthermore , technological advances in DNA sequencing have facilitated the identification of novel genetic mutations that result in rare Mendelian disorders [3 , 4] . We have applied these next-generation sequencing technologies to discover mutations in PIK3C2A that cause a newly identified genetic syndrome consisting of dysmorphic features , short stature , cataracts and skeletal abnormalities . PIK3C2A is a class II member of the phosphoinositide 3-kinase ( PI3K ) family of lipid kinases that catalyzes the phosphorylation of phosphatidylinositol ( PI ) [5] . PI3Ks are part of a larger regulatory network of kinases and phosphatases that act upon the hydroxyl groups on the inositol ring of PI to add or remove a phosphate group [6] . The combinatorial nature of phosphorylation at the -3 , -4 , and -5 position of the inositol ring gives rise to seven different PI species , termed polyphosphoinositides . Among these polyphosphoinositides , class II PI3Ks are generally thought to catalyze the phosphorylation of PI and/or PI ( 4 ) P to generate PI ( 3 ) P and PI ( 3 , 4 ) P2 , respectively [7] . PI ( 3 ) P , PI ( 3 , 4 ) P2 , and the other polyphosphoinositides each account for less than ~1% of the total phospholipid content of a cell [8] . However , despite their relatively low abundance , they play central roles in a broad array of signaling pathways and are central to the pathophysiology underlying cancer , metabolic disease , and host-pathogen interactions [6] . The functions of class II PI3Ks are poorly understood relative to many other kinases and phosphatases that regulate PI metabolism , in part because there was no causal link between any class II PI3K and a monogenic human disease . In contrast , a number of disorders of PI metabolism have previously been described that have provided invaluable insight into the physiological functions of specific PI metabolizing enzymes [9] . These include Charcot-Marie-Tooth type 4J ( FIG4 ) [10 , 11] , Centronuclear X-linked myopathy ( MTM1 ) [12] , and primary immunodeficiency ( PIK3CD ) [13 , 14] , among others . As just one example , detailed studies of FIG4 subsequent to its identification as a cause of Charcot-Marie-Tooth type 4J have revealed both genetic and physiological interactions with VAC14 and PIKFYVE , which together generate PI ( 3 , 5 ) P2 and are required for melanosome homeostasis , oligodendrocyte differentiation , and remyelination [15–18] . Collectively , the array of PI metabolism disorders is striking for its phenotypic diversity , affecting a wide range of organ systems including those described above as well as others that lead to neuromuscular , skeletal , renal , eye , growth , and immune disorders . The diversity of phenotypic manifestations resulting from PI metabolism defects highlights the lack of functional redundancy between genes that regulate nominally the same enzymatic transformation of PIs . PIK3C2A has previously been attributed a wide-range of biological functions including glucose transport , angiogenesis , Akt activation , endosomal trafficking , phagosome maturation , mitotic spindle organization , exocytosis , and autophagy [19–28] . In addition , PIK3C2A is critical for the formation and function of primary cilia [23 , 26] . However , as mentioned above , there is as yet no link between PIK3C2A or any class II PI3K and a Mendelian disorder . Here , we describe the evidence that homozygous loss-of-function mutations in PIK3C2A cause a novel syndromic disorder involving neurological , visual , skeletal , growth , and occasionally hearing impairments .
Five individuals between the ages of 8 and 21 from three unrelated consanguineous families were found by diagnostic analyses to have a similar constellation of clinical features including dysmorphic facial features , short stature , skeletal and neurological abnormalities , and cataracts ( Fig 1 , Table 1 , S1 Table ) . The dysmorphic facial features included coarse facies , low hairline , epicanthal folds , flat and broad nasal bridges , and retrognathia ( S1 Table ) . Skeletal findings included scoliosis , delayed bone age , diminished ossification of femoral heads , cervical lordosis , shortened fifth digits with mild metaphyseal dysplasia and clinodactyly , as well as dental findings such as broad maxilla incisors , narrow mandible teeth , and enamel defects ( Fig 1B and 1C , S1 Table , S1 Fig ) . Most of the affected individuals exhibited neurological involvement including developmental delay and stroke . This was first seen in individual I-II-2 when she recently started having seizures , with an EEG demonstrating sharp waves in the central areas of the right hemisphere and short sporadic generalized epileptic seizures . Her brain MRI showed a previous stroke in the right corpus striatum ( Fig 1E ) . Hematological studies were normal for hypercoagulability and platelet function ( S2 Table ) . In addition , brain MRI of patient II-II-3 showed multiple small frontal and periventricular lacunar infarcts ( S1E Fig ) . Unclear episodes of syncope also led to neurological investigations including EEG in individual III-II-2 , without any signs of epilepsy . Her brain MRI showed symmetrical structures and normal cerebrospinal fluid spaces but pronounced lesions of the white matter ( S1E Fig ) . Other recurrent features included hearing loss , secondary glaucoma , and nephrocalcinosis . In addition to the shared syndromic features described above in all three families , both affected daughters in Family I were diagnosed with congenital adrenal hyperplasia ( CAH ) , due to 17-alpha-hydroxylase deficiency , and were found to have a homozygous familial mutation: NM_000102 . 3:c . 286C>T; p . ( Arg96Trp ) in the CYP17A1 gene ( OMIM #202110 ) [29 , 30] . The affected individuals in Families II and III do not carry mutations in CYP17A1 or have CAH , suggesting the presence of two independent and unrelated conditions in Family I . The co-occurrence of multiple monogenic disorders is not uncommon among this highly consanguineous population [31] . To identify the genetic basis of this disorder , enzymatic assays related to the mucopolysaccharidosis subtypes MPS I , MPS IVA , MPS IVB , and MPSVI were tested in Families I and II and found to be normal . Enzymatic assays for mucolipidosis II/III were also normal and no pathogenic mutations were found in galactosamine-6-sulfate sulfatase ( GALNS ) in Family I . Additionally , since some of the features of patient II-II-3 were reminiscent of Noonan syndrome , Hennekam syndrome , and Aarskog-Scott syndrome , individual genes involved in these disorders were analyzed in Family II , but no pathogenic mutation was identified . In patient III-II-2 , Williams-Beuren syndrome was excluded in childhood . Additionally , direct molecular testing at presentation in adulthood excluded Leri-Weill syndrome , Alstrom disease , and mutations in FGFR3 . Given the negative results of targeted genetic testing , WES and CNV analysis was performed for the affected individuals from all three families . Five homozygous candidate variants were identified in Family I , including the CYP17A1 ( p . Arg96Trp ) mutation that is the cause of the CAH [29 , 30] , but is not known to cause the other phenotypes . The remaining four variants affected the genes ATF4 , DNAH14 , PLEKHA7 , and PIK3C2A ( S3 Table ) . In Family II , homozygous missense variants were identified in KIAA1549L , METAP1 , and PEX2 , in addition to a homozygous deletion in PIK3C2A that encompassed exons 1–24 out of 32 total exons ( S3 Table ) . The deletion was limited to PIK3C2A and did not affect the neighboring genes . Sequence analysis of Family III showed a homozygous missense variant in PTH2R , nonsense variant in DPRX , and splice site variant in PIK3C2A ( S3 Table ) . Sequencing analyses revealed that all affected family members in the Families I , II , and III were homozygous for predicted loss-of-function variants in PIK3C2A , and none of the unaffected family members were homozygous for the PIK3C2A variants ( Fig 1A and 1G ) . The initial link between these three families with rare mutations in PIK3C2A was made possible through the sharing of information via the GeneMatcher website [3] . The PIK3C2A deletion in Family II was confirmed by multiplex amplicon quantification ( S2A Fig ) . The single nucleotide PIK3C2A variants in Families I and III were confirmed by Sanger sequencing ( S2B and S2C Fig ) . In Family I , the nonsense mutation in PIK3C2A ( p . Tyr195* ) truncates 1 , 492 amino acids from a protein that is 1 , 686 amino acids . This is predicted to eliminate nearly all functional domains including the catalytic kinase domain , and is expected to trigger nonsense-mediated mRNA decay [25] . Accordingly , levels of PIK3C2A mRNA are significantly decreased in both heterozygous and homozygous individuals carrying the p . Tyr195* variant ( Fig 2A ) . The deletion in Family II eliminates the first 24 exons of the 32-exon PIK3C2A gene and is thus predicted to cause a loss of protein expression . This is consistent with a lack of PIK3C2A mRNA expression ( Fig 2B ) . The variant in PIK3C2A in Family III affects an essential splice site ( c . 1640+1G>T ) that leads to decreased mRNA levels ( Fig 2C ) . Deep sequencing of the RT-PCR products revealed 4 alternative transcripts in patient-derived lymphocytes ( p . [Asn483_Arg547delinsLys , Ala521Thrfs*4 , Ala521_Glu568del , and Arg547SerinsTyrIleIle*] ) of which the transcript encoding p . Asn483_Arg547delinsLys that skips both exons 5 and 6 was also observed in patient’s fibroblasts ( S3 Fig ) . Although this transcript remains in-frame , no PIK3C2A protein was detected by Western blotting ( Fig 2D and 2F ) . This is consistent with Families I and II , for which Western blotting also failed to detect any full-length PIK3C2A in fibroblasts from the affected homozygous children ( Fig 2E and 2F ) . Thus , all three PIK3C2A variants likely encode loss-of-function alleles . Importantly , among the 141 , 456 WES and whole genome sequences from control individuals in the Genome Aggregation Database ( gnomAD v2 . 1 ) [32] , none are homozygous for loss-of-function mutations in PIK3C2A , which is consistent with total PIK3C2A deficiency causing severe early onset disease . To test whether the observed loss-of-function mutations in PIK3C2A cause cellular phenotypes consistent with loss of PIK3C2A function , we examined PI metabolism , cilia formation and function , and cellular proliferation rates . PIK3C2A deficiency in the patient-derived fibroblasts decreased the levels of PI ( 3 , 4 ) P2 throughout the cell ( Fig 3A ) as well as decreased the levels of PI ( 3 ) P at the ciliary base ( Figs 3B and S4A ) . The reduction in PI ( 3 ) P at the ciliary base was associated with a reduction in ciliary length ( Fig 4A ) , although the percentage of ciliated cells was not altered ( Fig 4B ) . Additional cilia defects include a reduction in the levels of RAB11 at the ciliary base ( Figs 4C and S4B ) , which functions within a GTPase cascade culminating in the activation of RAB8 , which together with ARL13B selectively traffics ciliary proteins to the cilium [33] . Additionally , there was increased accumulation of IFT88 along the length of the cilium ( Figs 4D and S4C ) , which is a component of the intraflagellar transport sub-complex IFT-B , and is essential for the trafficking of ciliary protein cargoes along the axonemal microtubules [34 , 35] . Together , these findings are suggestive of defective trafficking of ciliary components . Finally , the proliferative capacity of PIK3C2A deficient cells was reduced relative to control cells ( Fig 5 ) . As PIK3C2A is a member of the class II PI3K family , we tested whether the expression of the other family members PIK3C2B and PIK3C2G were altered by PIK3C2A deficiency . The expression of PIK3C2G was not detected by qRT-PCR in either patient-derived or control primary fibroblasts . This is consistent with the relatively restricted expression pattern of this gene in the GTEx portal [36] , with expression largely limited to stomach , skin , liver , esophagus , mammary tissue , and kidney , but absent in fibroblast cells and most other tissues . In contrast , PIK3C2B expression was detected , with both mRNA and protein levels significantly increased in PIK3C2A deficient cells ( Fig 6A–6D ) . Downregulation of PIK3C2A using an inducible shRNA in HeLa cells also resulted in elevated levels of PIK3C2B ( Fig 6E ) . Together , these data are consistent with increased levels of PIK3C2B serving to partially compensate for PIK3C2A deficiency .
Here we describe the identification of three independent families with homozygous loss-of-function mutations in PIK3C2A resulting in a novel syndrome consisting of short stature , cataracts , secondary glaucoma , and skeletal abnormalities among other features . Patient-derived fibroblasts had decreased levels of PI ( 3 , 4 ) P2 and PI ( 3 ) P , shortening of the cilia and impaired ciliary protein localization , and reduced proliferation capacity . Thus , based on the loss-of-function mutations in PIK3C2A , the phenotypic overlap between the three independent families , and the patient-derived cellular data consistent with previous studies of PIK3C2A function , we conclude that loss-of-function mutations in PIK3C2A cause this novel syndrome . The identification of PIK3C2A loss-of-function mutations in humans represents the first mutations identified in any class II PI-3-kinase in a disorder with a Mendelian inheritance , and thus sheds light into the biological role of this poorly understood class of PI3Ks [7 , 37] . This is significant not only for understanding the role of PIK3C2A in rare monogenic disorders , but also the potential contribution of common variants in PIK3C2A in more genetically complex disorders . There are now numerous examples where severe mutations in a gene cause a rare Mendelian disorder , whereas more common variants in the same gene , with a less deleterious effect on protein function , are associated with polygenic human traits and disorders [38–40] . For example , severe mutations in PPARG cause monogenic lipodystrophy , whereas less severe variants are associated with complex polygenic forms of lipodystrophy [41 , 42] . In the case of PIK3C2A deficiency , the identification of various neurological features including developmental delay , selective mutism , and the brain abnormalities detected by MRI ( S1 Table ) may provide biological insight into the mechanisms underlying the association between common variants in PIK3C2A and schizophrenia [43–45] . Other monogenic disorders of phosphoinositide metabolism include Lowe’s syndrome and Joubert syndrome , which can be caused by mutations in the inositol polyphosphate 5-phosphatases OCRL and INPP5E , respectively [46] . All three of these disorders of PI metabolism affect some of the same organ systems , namely the brain , eye , and kidney . However , the phenotype associated with mutations in INPP5E is quite distinct , and includes cerebellar vermis hypo-dysplasia , coloboma , hypotonia , ataxia , and neonatal breathing dysregulation [47] . In contrast , the phenotypes associated with Lowe’s syndrome share many of the same features with PIK3C2A deficiency including congenital cataracts , secondary glaucoma , kidney defects , skeletal abnormalities , developmental delay , and short stature [9 , 48] . The enzyme defective in Lowe’s syndrome , OCRL , is functionally similar to PIK3C2A as well , as it is also required for membrane trafficking and ciliogenesis [49] . The similarities between Lowe’s syndrome and PIK3C2A deficiency suggest that similar defects in phosphatidylinositol metabolism may underlie both disorders . In addition to Lowe’s syndrome , there is partial overlap between PIK3C2A deficiency and yet other Mendelian disorders of PI metabolism such as the early-onset cataracts in patients with INPP5K deficiency [50 , 51] , demonstrating the importance of PI metabolism in lens development . The viability of humans with PIK3C2A deficiency is in stark contrast to mouse Pik3c2a knockout models that result in growth retardation by e8 . 5 and embryonic lethality between e10 . 5–11 . 5 due to vascular defects [20] . One potential explanation for this discrepancy is functional differences between human PIK3C2A and the mouse ortholog . However , the involvement of both human and mouse PIK3C2A in cilia formation , PI metabolism , and cellular proliferation suggests a high degree of functional conservation at the cellular level [26 , 28] . An alternate possibility is that the species viability differences associated with PIK3C2A deficiency result from altered compensation from other PI metabolizing enzymes . For instance , there are species-specific differences between humans and mice in the transcription and splicing of the OCRL homolog INPP5B that may uniquely contribute to PI metabolism in each species [52] . Alternately , PIK3C2B levels were significantly increased in human PIK3C2A deficient cells , including both patient-derived cells and HeLa cells surviving PIK3C2A deletion , suggesting that this may partially compensate for the lack of PIK3C2A in humans , although it remains to be determined whether a similar compensatory pathway exists in mice . It is intriguing that both PIK3C2A and OCRL have important roles in primary cilia formation [26 , 53 , 54] . Primary cilia are evolutionary conserved microtubule-derived cellular organelles that protrude from the surface of most mammalian cell types . Primary cilia formation is initiated by a cascade of processes involving the targeted trafficking and docking of Golgi-derived vesicles near the mother centriole . They play a pivotal role in a number of processes , such as left-right patterning during embryonic development , cell growth , and differentiation . Abnormal phosphatidylinositol metabolism results in ciliary dysfunction [55] , including loss of PIK3C2A that impairs ciliogenesis in mouse embryonic fibroblasts , likely due to defective trafficking of ciliary components [26] . The importance of primary cilia in embryonic development and tissue homeostasis has become evident over the two past decades , as a number of proteins which localize to the cilium harbor defects causing syndromic diseases , collectively known as ciliopathies [56 , 57] . Hallmark features of ciliopathies share many features with PIK3C2A deficiency and include skeletal abnormalities , progressive vision and hearing loss , mild to severe intellectual disabilities , polydactyly , and kidney phenotypes . Many of these disorders , including Bardet-Biedl Syndrome , Meckel Syndrome , and Joubert Syndrome are also associated with decreased cilium length [58] , as seen in PIK3C2A deficient cells . Ciliary length is a function of both axoneme elongation and cilium disassembly , and is molecularly regulated by intraflagellar protein transport , including the velocity of transport and cargo loading , as well as soluble tubulin levels and microtubule modifications [59 , 60] . As defects in intraflagellar protein transport were likely indicated by abnormal IFT88 localization along the length of the cilium in PIK3C2A deficient cells , this may represent a potential mechanism underlying the shortened cilium . Further work and the identification of additional patients with mutations in PIK3C2A will continue to improve our understanding of the genotype-phenotype correlation associated with PIK3C2A deficiency . However , the identification of the first patients with PIK3C2A deficiency establishes a role for PIK3C2A in neurological and skeletal development , as well as vision , and growth and implicates loss-of-function PIK3C2A mutations as a potentially new cause of a cilia-associated disease .
The study was approved by the Helsinki Ethics Committees of Rambam Health Care Campus ( #0038-14-RMB ) , the University Hospital Institutional Review Board for Case Western Reserve University ( #NHR-15-39 ) , the Ethics Committee of the Friedrich-Alexander University Erlangen-Nürnberg ( #164_15 B ) , and was in accordance with the regulations of the University Medical Center Groningen’s ethical committee . Written informed consent was obtained from all participants . Whole exome sequencing ( WES ) of two patients from Family I was performed using DNA ( 1μg ) extracted from whole blood and fragmented and enriched using the Truseq DNA PCR Free kit ( Illumina ) . Samples were sequenced on a HiSeq2500 ( Illumina ) with 2x100bp read length and analyzed as described [61] . Raw fastq files were mapped to the reference human genome GRCh37 using BWA [62] ( v . 0 . 7 . 12 ) . Duplicate reads were removed by Picard ( v . 1 . 119 ) and local realignment and base quality score recalibration was performed following the GATK pipeline [63] ( v . 3 . 3 ) . The average read depth was 98x ( I-II-1 ) and 117x ( I-II-2 ) . HaplotypeCaller was used to call SNPs and indels and variants were further annotated with Annovar [64] . Databases used in Annovar were RefSeq [65] , Exome Aggregation Consortium ( ExAC ) [32] ( v . exac03 ) , ClinVar [66] ( v . clinvar_20150330 ) and LJB database [67] ( v . ljb26_all ) . Exome variants in Family I were filtered out if they were not homozygous in both affected individuals , had a population allele frequency greater than 0 . 1% in either the ExAC database [32] or the Greater Middle East Variome Project [68] , and were not predicted to be deleterious by either SIFT [69] or Polyphen2 [70] . Whole exome sequencing was performed on the two affected individuals of Family II and both their parents essentially as previously described [71] . Target regions were enriched using the Agilent SureSelectXT Human All Exon 50Mb Kit . Whole-exome sequencing was performed on the Illumina HiSeq platform ( BGI Europe ) followed by data processing with BWA [62] ( read alignment ) and GATK [63] ( variant calling ) software packages . Variants were annotated using an in-house developed pipeline . Prioritization of variants was done by an in-house designed ‘variant interface’ and manual curation . The DNAs of Family III were enriched using the SureSelect Human All Exon Kit v6 ( Agilent ) and sequenced on an Illumina HiSeq 2500 ( Illumina ) . Alignment , variant calling , and annotation were performed as described [72] . The average read depth was 95x ( III-II-2 ) , 119x ( III-I-1 ) and 113x ( III-I-2 ) . Variants were selected that were covered by at least 10% of the average coverage of each exome and for which at least 5 novel alleles were detected from 2 or more callers . All modes of inheritance were analyzed [72] . Variants were prioritized based on a population frequency of 10−3 or below ( based on the ExAC database [32] and an in-house variant database ) , on the evolutionary conservation , and on the mutation severity prediction . All candidate variants in Families I , II , and III were confirmed by Sanger sequencing ( primers listed in S4 Table ) . Microarray analysis for CNV detection in Family I was performed using a HumanOmni5-Quad chip ( Illumina ) . SNP array raw data was mapped to the reference human genome GRCh37 and analyzed using GenomeStudio ( v . 2011/1 ) . Signal intensity files with Log R ratio and B-allele frequency were further analyzed with PennCNV [73] ( v . 2014/5/7 ) . In Family III the diagnostic chromosomal microarray analysis was performed with an Affymetrix CytoScan HD-Array and analyzed using Affymetrix Chromosome analysis Suite-Software , compared with the Database of Genomic Variants and 820 in house controls . All findings refer to UCSC Genome Browser on Human , February 2009 Assembly ( hg19 ) , Human Genome built 37 . CNV analysis on the WES data of Families II and III were performed using CoNIFER [74] . Variants were annotated using an in-house developed pipeline . Prioritization of variants was done by an in-house designed ‘variant interface’ and manual curation as described before [75] . Subsequent segregation analysis of the pathogenic CNV in Family II was performed with MAQ by using a targeted primer set with primers in exons 3 , 10 , 20 and 24 which are located within the deletion and exons 28 , 32 , 34 which are located outside of the deletion ( Multiplex Amplicon Quantification ( MAQ ) ; Multiplicom ) . Human dermal fibroblasts were obtained from sterile skin punches cultured in DMEM ( Dulbecco's Modified Eagle's Medium ) supplemented with 10–20% Fetal Calf Serum , 1% Sodium Pyruvate and 1% Penicillin and streptomycin ( P/S ) in 5% CO2 at 37°C . Control fibroblasts were obtained from healthy age-matched volunteers . Fibroblasts from passages 4–8 were used for the experiments . To measure cell proliferation , cells were detached using trypsin and counted with an Automated Cell Counter ( ThermoFisher ) . Cells ( n = 2500 ) were plated in triplicate in 96-well plates . Viability was measured at day 2 , 4 , 6 and 8 . Each measurement was normalized to day 0 ( measured the day after plating ) and expressed as a fold increase . Viability was assessed by using CellTiter-Glo Luminescent Cell Viability Assay ( Promega ) . Three independent experiments were performed . HeLa cells were infected with lentiviral particles containing pLKO-TET-PI3KC2A-shRNA or pLKO-TET-scramble-shRNA in six-well plates ( n = 50 , 000 cells ) . After two days , the medium containing lentiviral particles was replaced with DMEM 10% FBS , 1 . 5μg/ml puromycin . After 7 days of selection , cells were detached and 100 , 000 cells were plated in six-well plates in triplicate in the presence of doxycycline ( 0 . 5 , 1 and 2 μg/ml ) . Medium containing doxycycline was replaced every 48 hours . After 10 days of doxycycline treatment , cells were lysed and analysed by Western blot . Total RNA was purified from primary fibroblasts using the PureLink RNA purification kit ( ThermoFisher ) or RNAPure peqGOLD ( Peqlab ) . RNA was reverse transcribed into complementary DNA with random hexamer using a high-Capacity cDNA Reverse Transcription Kit ( ThermoFisher ) . RT-PCR from lymphocytes to detect exon-skipping in family III was performed using primers flanking exon 6 . The resulting product was sequenced on an Illumina HiSeq2500 ( Illumina ) to detect splicing variants with high sensitivity . Gene expression was quantified by SYBR Green real-time PCR using the CFX Connect Real-Time System ( BioRad ) . Primers used are detailed in S4 Table . Expression levels were calculated using the ΔΔCT method relative to GADPH . Protein was extracted from cultured primary fibroblast cells as described [76 , 77] . Extracts were quantified using the DC protein assay ( BioRad ) or the BCA method . Equal amounts of protein were separated by SDS-PAGE and electrotransferred onto polyvinylidene difluoride membranes ( Millipore ) . Membranes were blocked with TBST/5% fat-free dried milk and stained with antibodies as detailed in S5 Table . Secondary antibodies were goat anti-rabbit ( 1:5 , 000 , ThermoFisher #31460 ) goat anti-mouse ( 1:5 , 000 , ThermoFisher #31430 ) , goat anti-rabbit ( 1:2 , 000 , Dako #P0448 ) , and goat anti-mouse ( 1:2 , 000 , Dako #P0447 ) . Primary fibroblasts were grown on glass coverslips to approximately 80% - 90% confluency in DMEM + 10% FCS + 1% P/S , at which time the medium was replaced with DMEM without FCS for 48 hours to induce ciliogenesis . Cells were fixed in either methanol for 10 minutes at -20°C or 4% paraformaldehyde for 10 minutes at room temperature ( RT ) . Fixed cells were washed in PBS , and incubated with 10% normal goat serum , 1% bovine serum albumin in PBS for 1 hour at RT . If cells were fixed with paraformaldehyde , blocking solutions contained 0 . 5% Triton X-100 . Cells were incubated with primary antibody overnight at 4°C , washed in PBS , and incubated with secondary antibody including 4' , 6-diamidino-2-Phenylindole ( DAPI ) to stain nuclei for 1 hours at RT . Coverslips were mounted on glass slides with fluoromount ( Science Services ) and imaged on a confocal laser scanning system with a 63x objectives ( LSM 710 , Carl Zeiss MicroImaging ) . Primary antibodies are detailed in S5 Table . To induce ciliogenesis , cells were grown in DMEM with 0–0 . 2% FCS for 48 hours . Cells were washed in PBS , then fixed and permeabilized in ice-cold methanol for 5 minutes , followed by extensive washing with PBS . After blocking in 5% Bovine Serum Albumin , cells were incubated with primary antibodies for 1 . 5 hours at RT and extensively washed in PBS-T . Primary antibodies used for Centrin and ARL13B are detailed in S5 Table . To wash off the primary antibody , cells were extensively washed in PBS-T . Subsequently , cells were incubated with secondary antibodies , Alexa Flour 488 ( 1:800 , Invitrogen ) and Alexa Fluor 568 ( 1:800 , Invitrogen ) , for 45 min followed by washing with PBS-T . Finally , cells were shortly rinsed in ddH2O and samples were mounted using Vectashield with DAPI . Images were taken using an Axio Imager Z2 microscope with an Apotome ( Zeiss ) at 63x magnification . Cilia were measured manually using Fiji software taking the whole length of the cilium based on ARL13B staining . At least 300 cilia were measured per sample . Cilia lengths were pooled for 3 control cell lines and compare to 2 patient-derived samples ( II-II-2 and II-II-3 ) . Statistical significance was calculated using a Student t-test . PI ( 3 ) P at the ciliary base was detected in randomly chosen cells using the same exposure for each acquisition . A specific anti-PI ( 3 ) P antibody ( Echelo Z-P003 ) was used to quantify the PI ( 3 ) P by measuring the green fluorescent intensity around the ciliary base in a region with a diameter of 8 μm and a depth of 10 μm as previously illustrated and described [26] . | Identifying the genetic basis of rare disorders can provide insight into gene function , susceptibility to disease , guide the development of new therapeutics , improve opportunities for genetic counseling , and help clinicians evaluate and potentially treat complicated clinical presentations . However , it is estimated that the genetic basis of approximately one-half of all rare genetic disorders remains unknown . We describe one such rare disorder based on genetic and clinical evaluations of individuals from 3 unrelated consanguineous families with a similar constellation of features including short stature , coarse facial features , cataracts with secondary glaucoma , multiple skeletal abnormalities , neurological manifestations including stroke , among other findings . We discovered that these features were due to deficiency of the PIK3C2A enzyme . PIK3C2A is a class II member of the phosphoinositide 3-kinase ( PI3K ) family that catalyzes the phosphorylation of the lipids phosphatidylinositol ( PI ) into PI ( 3 ) P and the phosphorylation of PI ( 4 ) P into PI ( 3 , 4 ) P2 that are essential for a variety of cellular processes including cilia formation and vesicle trafficking . This syndrome is the first monogenic disorder caused by mutations in a class II PI3K family member and thus sheds new light on their role in human development . |
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Understanding how axon guidance receptors are activated by their extracellular ligands to regulate growth cone motility is critical to learning how proper wiring is established during development . Roundabout ( Robo ) is one such guidance receptor that mediates repulsion from its ligand Slit in both invertebrates and vertebrates . Here we show that endocytic trafficking of the Robo receptor in response to Slit-binding is necessary for its repulsive signaling output . Dose-dependent genetic interactions and in vitro Robo activation assays support a role for Clathrin-dependent endocytosis , and entry into both the early and late endosomes as positive regulators of Slit-Robo signaling . We identify two conserved motifs in Robo’s cytoplasmic domain that are required for its Clathrin-dependent endocytosis and activation in vitro; gain of function and genetic rescue experiments provide strong evidence that these trafficking events are required for Robo repulsive guidance activity in vivo . Our data support a model in which Robo’s ligand-dependent internalization from the cell surface to the late endosome is essential for receptor activation and proper repulsive guidance at the midline by allowing recruitment of the downstream effector Son of Sevenless in a spatially constrained endocytic trafficking compartment .
The complex wiring patterns of the adult central nervous system are established by the stepwise navigation of growth cones and migrating cells through a series of choice points during development . At each choice point , the complement of guidance receptors expressed on the growth cone’s plasma membrane determines which of the cues in the extracellular environment will inform the cell’s guidance decision as it navigates toward its eventual synaptic partner . Understanding how an individual growth cone deploys its guidance receptors to make specific guidance decisions is critical to learning how proper wiring is established in development . Roundabout ( Robo ) receptors comprise a family of highly conserved axon guidance receptors that mediate repulsion in response to their Slit ligands during neuronal development [1–4] . Robo receptors have also been implicated in genome-wide association studies with the pathogenesis of several human diseases including autism and schizophrenia [5 , 6] , and they are thought to be causatively linked to dyslexia and periventricular nodular heterotopia [7] , suggesting roles in guidance of more diverse axonal projections in the human cortex that are yet to be characterized . In both invertebrates and vertebrates , Slits serve as repulsive cues to their Robo receptors by demarcating regions into which axons cannot maintain their exploratory projections . In the case of the Drosophila embryonic ventral nerve cord ( VNC ) , Slit is expressed by midline glia , which creates a barrier for axonal projection for any growth cones expressing Robo at their surface [2 , 3] . In robo mutants normally ipsilaterally-projecting ( ipsilateral or post-crossing commissural ) axons ignore the presence of this repulsive cue and project into the midline and even circle there in namesake roundabouts [8] . Slit-mediated repulsive guidance can also instruct axonal projections by corralling fascicles into relative valleys of Slit expression- mouse callosal axons project between the indusium griseum and the glial wedge structures [9]- or by directing a 90° turn in bifurcating branches of sensory axons into the dorsal funiculus [10] . Analogously , there exists a relative valley in Slit expression in medio-lateral axis of the Drosophila VNC through which a sizeable set of longitudinal fascicles project [11] . The mechanism by which Slit triggers repulsion at the cellular level is not completely understood , but must involve an initial mis-projection into Slit-expressing regions in order to sense and then respond to the presence of the repulsive cue . One growth cone phenotype resulting from loss of Robo is defective filopodial retraction from the Slit-containing embryonic midline in Drosophila , resulting in stabilization of contralateral filopodial projections [12] . Similarly , loss of robo2 ( astray ) in zebrafish leads to abnormal stabilization of mis-projecting growth cones in the ventral forebrain , ‘errors’ that are normally corrected in wild-type [13] . The error-correction implicit in repulsive guidance from an initially adhesive protein-protein interaction requires some sort of physical severing which has been ascribed to juxtamembrane cleavage , endocytosis , or both [14–19] . Endocytosis in the growth cone has been implicated in the plasma membrane dynamics necessary for such responses as collapse[14 , 20 , 21] , or , when applied asymmetrically , turning [22–24] . Endocytosis has also been implicated in the control over the complement of guidance receptors expressed on the growth cone surface , thereby fine-tuning sensitivity to extracellular cues [25–27] . Endocytic trafficking of Robo by Commissureless has also been demonstrated to negatively regulate delivery to the growth cone surface [28 , 29] . Endocytic trafficking of guidance receptors might serve not only to control surface receptor levels , but also to gate their activation once inside the cell . Evidence for this idea comes from the correlation between a requirement for the RhoGEFs vav2 and vav3 in both Ephrin endocytosis and proper retinogeniculate axon targeting [14] , as well as the correlation between Rac activity in EphA receptor endocytosis and retinocollicular targeting [30] . Whether receptor endocytosis represents a general mechanism to control activation of repulsive guidance receptor signaling and whether the transit of internalized guidance receptors through distinct endocytic compartments is required for in vivo signaling is not known . In this study , we identify Clathrin-dependent endocytosis of the Robo receptor as an obligate step in receptor activation and repulsive signaling . We present evidence that it is trafficking through endocytic compartments—following ligand-binding on the surface of the cell—that is required for receptor activation . We identify—with subcellular resolution–the early and late endosomes as compartments from which Robo signals , and identify the sequence motifs in Robo’s C-terminus that are required for its Slit-dependent internalization . Finally , we show that Slit-dependent endocytosis is required for both in vitro recruitment of the Ras/Rho GEF Son of Sevenless ( Sos ) , a downstream effector of Robo repulsive signaling and for Robo-mediated midline repulsion in vivo .
Based on previous findings suggesting a role for endocytosis in modulating axon guidance receptor activity and signaling , we could envision at least two plausible models for how Robo receptor endocytosis might regulate axon repulsion . If endocytosis modulates the amount of Robo receptor on the surface of the growth cone , a reduction in receptor endocytosis would be predicted to lead to increased levels of surface receptor and more robust repulsive signaling . Alternatively , if Robo receptor endocytosis is an obligate step in receptor activation , preventing or reducing Robo endocytosis would result in impaired repulsive signaling . To test which , if either , of these functions endocytosis might contribute to Slit-Robo signaling , we first sought genetic evidence implicating endocytic trafficking in midline axon repulsion . We examined an ipsilateral subset of axons whose projection patterns depend on Robo’s repulsive response to Slit . In robo mutants the medial-most of the FasII-positive fascicles invariably collapse and circle at the midline . Reducing slit and robo gene dose by half in heterozygous slit , robo/+ embryos results in a partial loss of repulsion , which represents a sensitized background in which we can detect both suppressors and enhancers ( Fig 1 ) . We , and others , have used this sensitized genetic background to uncover additional genes that contribute to midline repulsion [31–34] . In addition to offering a sensitive readout for alterations in midline repulsion , this strategy allows us to detect dominant genetic interactions , which avoids potential complications from removing all endocytic gene function , which would be predicted to have broad and early developmental defects . We screened mutants in known regulators of endocytosis for genetic interactions with slit and robo , including mutations in genes involved in ( 1 ) Clathrin-dependent endocytosis- alpha-adaptin and endophilinA- , ( 2 ) entry into the early endosome–rab5- and ( 3 ) entry into the late endosome- rab7 . Removing one copy of α-adaptin and endophilinA- genes involved in cargo loading and formation of clathrin coated pits [35 , 36]–enhances the number of crossing errors compared to slit , robo/+ heterozygotes ( Fig 1B–1D ) . Removing one copy of either rab5 , which regulates entry into the early endosome , or rab7 , which regulates entry into the late endosome also enhances ectopic crossing ( Fig 1E and 1F ) . In order to corroborate these findings , we tested for genetic interactions between the mutant alleles of endocytic trafficking genes and slit in another , more restricted subset of axons ( Fig 2A ) . Just like the FasII+ subset of axons , the normally ipsilateral Apterous+ ( Ap ) axons are sensitive to partial loss of repulsion; a loss of one copy of slit alone induces ectopic crossing events in 11% of embryonic segments ( Fig 2B ) . Inhibiting Clathrin-dependent endocytosis in this sensitized background by removing one allele of α-adaptin or endophilinA enhances the frequency of ectopic crossing events ( Fig 2F ) . Removing one copy of rab5 or rab7 also enhances ectopic crossing errors . These genetic interactions suggest that trafficking from the plasma membrane , and into the early and late endosome positively regulate repulsive midline guidance . Together these observations are consistent with endocytosis contributing to receptor activation , as opposed to a modulation of surface levels available to bind Slit . To determine whether the endocytic trafficking events relevant for midline guidance are occurring in neurons , we mis-expressed Dominant-Negative ( DN ) transgenes to inhibit components of the endocytosis machinery in the Ap neurons . Ectopic expression of DN forms of shibire , Drosophila Dynamin , ( to block scission of invaginated Clathrin-coated pits [37 , 38] ) , Rab5 and Rab7 ( to prevent entry into early and late endosomes , respectively ) , but not Rab4 and Rab11 ( to prevent entry into the recycling endosome ) , results in enhancement of the ectopic crossing defects that are observed in slit heterozygotes ( Fig 2C–2F ) . These findings are consistent with a model in which endocytic trafficking in neurons is contributing to Slit-Robo mediated repulsion . Further , the ectopic crossing events caused by expressing ShiDN or Rab5DN in the Ap axons in slit heterozygotes are fully rescued by increasing signaling of the Robo pathway by co-expression of a wild type Robo transgene ( S1A Fig ) : an observation that is consistent with a specific requirement for endocytic regulation during Slit/Robo repulsion . Taken together , these data are consistent with a model in which endocytic trafficking from the plasma membrane into the early and late , but not the recycling endosome of neurons positively regulates Robo-mediated midline repulsion . However these interactions alone cannot distinguish between the possibilities of endocytosis positively regulating repulsion from the midline , or negatively regulating attraction to the midline . We directly tested the latter hypothesis by assaying whether reducing the dosage of endocytic trafficking genes could enhance the ectopic crossing errors induced by enhanced midline attraction resulting from ectopic expression of the attractive guidance receptor Frazzled [39] . We detect no statistically significant difference between the observed crossing frequency and the predicted percentage crossing frequency from an additive interaction ( S1B Fig ) , suggesting that endocytosis is not negatively regulating attractive guidance . These observations further support the interpretation that disrupting endocytosis is specifically affecting midline repulsion . Our genetic interaction data are consistent with endocytosis in neurons positively regulating Slit/Robo-mediated repulsive guidance , but they do not provide insight into the cell and molecular mechanism . In order to test whether this positive regulation of repulsive signaling is due to endocytosis of the Robo receptor itself , we assayed whether manipulations to Robo’s capacity to undergo endocytosis would affect its signaling . Using sequence alignment with known binding motifs to AP-2 , the Clathrin adaptor complex expressed specifically on the surface of cells , we identified two tyrosine-based motifs in Robo’s C-terminus that are both conserved in human Robo1 sequence and predicted to be required for loading of Robo into Clathrin-coated pits— ( 1 ) YLQY , of the type YXXФ [40] , and ( 2 ) YQAGL , like the tyrosine containing sorting signals in the epidermal growth factor receptor ( EGFR ) and L1/NgCAM [41 , 42] ( S2J Fig ) . If Robo’s trafficking through the endocytic pathway is required for its repulsive response to Slit binding , then we would predict that both reducing Shibire function , and disrupting Robo’s ability to be loaded into Clathrin-coated pits would disrupt Robo signaling . To explore these possibilities , we developed an in vitro system to determine whether endocytosis of the Robo receptor can occur in response to Slit , and whether this process contributes to receptor signaling . Drosophila embryonic cells transfected with Robo that are bath treated for 10 minutes with Slit-conditioned media ( CM ) exhibit a robust spreading behavior , forming elaborate branched structures ( Fig 3A ) . In contrast , cells transfected with Robo and treated with CM from cells expressing empty vector show no such response ( Figs 3 , S2B , S4A and S4B ) . We have quantified this spreading behavior in two ways- first , we compute the total area of each cells’ processes as a number of pixels , and , using representative cells , compute the Average Process Area as a function of transfected Robo and type of CM treatment ( histogram , Fig 3D ) . To characterize the branching of processes in Slit-treated cells , we also performed Sholl analyses to compute the complexity of individual cell’s process field as a function of its radius starting after the cell cortex . These analyses are graphically displayed as the average Sholl profile of many cells treated with Slit CM ( Fig 3D ) . To assay whether the observed process elaboration behavior is indeed a readout of Robo activation in response to Slit we tested the following negative control variants of Robo: 1 ) deletion of the ectodomain ( RoboΔEcto , S2A’ Fig ) , 2 ) deletion of the first immunoglobulin domain ( RoboΔIg1 , Fig 3B ) , the minimal region that interacts physically with Slit’s D2 domain [43–45] , or 3 ) Robo missing its entire C-terminus ( ΔC , Fig 3C ) , which is required for all signaling output [46] . Each of these mutated forms of Robo show a loss of process elaboration in response to Slit . We also noted that expression of Robo∆C results in variable increase in the size of the cell cortex even in the absence of Slit treatment; however , since the Slit-dependent branch elaboration that we observed and quantified is independent of effects on the cell cortex , we did not explore this phenomenon further . Robo that is missing its Conserved Cytoplasmic CC2 and CC3 motifs , required for binding of the downstream effectors Ena , Dock , Pak , SOS and therefore Rac activation [32 , 47 , 48] , also display impaired spreading behavior ( S2C Fig ) . These observations support the idea that Robo signaling in response to Slit binding is required for the Rac-dependent spreading behavior seen in WT Robo-expressing cells . Next we wanted to test for a role for Clathrin-dependent endocytosis in Robo’s ability to generate branched processes in response to Slit treatment . We find that inhibiting endocytosis directly by co-transfection with DN Shibire ( Fig 3E ) , or treatment with the Dynamin inhibitor Dynasore ( S2D Fig ) , reduces the complexity of processes generated in response to Slit , as does deleting entirely , or point mutating the tyrosine residues of either of the two putative AP-2 binding motifs in Robo’s C-terminus ( Figs 3F , 3G and S2F–S2H ) . Deleting both motifs at the same time also results in a smaller maximum radius of the process field ( Figs 3H and S2E ) , similar to deleting the entire C-terminus , suggesting that the two AP-2 interacting motifs are each required for , and additively contribute to , Robo signaling . The qualitative and quantitative similarity in the process morphology of Slit-treated cells where Robo endocytosis is prevented , either by global disruption ( Dynasore or DN Shibire ) or by specific Robo mutations , suggests a contribution of receptor internalization to Robo’s activation . In addition , we find that endocytic trafficking , beyond internalization from the surface , through the early and late endosome also positively regulate Slit-dependent process elaboration . Inhibiting entry to the early or late endosome by co-expression of DN-Rab5 ( Fig 3I ) , or DN-Rab7 ( Fig 3J ) , respectively , also reduces branching complexity in Robo expressing Slit CM-treated cells . These data are consistent with a requirement for Clathrin-dependent endocytosis of the Robo receptor and trafficking into the early and late endosome for Slit-dependent process branching and outgrowth . In order to assess whether Robo’s C-terminal putative AP-2 interaction motifs indeed disrupt ligand-dependent endocytosis we directly assayed for a change in surface Robo levels in response to Slit in the same in vitro system . Using pHluorin , a pH sensitive GFP tag , on Robo’s N-terminus to distinguish surface Robo from the Robo protein in the lower pH environment of most cytosolic compartments , we analyzed the Slit-dependent reduction in surface receptor levels in S2R+ cells ( Fig 4A–4F ) . In cells transfected with wild-type pHluorin–tagged Robo , there is a reduction in the fluorescence intensity of pHluorin in Slit-treated , as compared to control treated cells , which we quantified as a percent decrease in average signal intensity across many cells ( Fig 4A , 4B and 4G ) . This Slit-dependent decrease in surface signal is inhibited by deleting Robo’s C-terminus ( Fig 4C and 4D ) , suggesting a requirement for signaling in the Slit-dependent reduction in Robo surface levels . Evidence that our small deletions disrupt Clathrin-dependent endocytosis comes from the similarity of their effect on surface levels to the effect observed by inhibiting Shibire with the Dynamin inhibitor drug Dynasore [49] . In both cases the Slit-dependent decrease in surface Robo is prevented ( Figs 4E , 4F and S3 ) , consistent with Slit stimulating Clathrin-dependent endocytosis of Robo . Analyzing trends in the spatial distribution of surface Robo intensity with reference to anatomical structures reveals clues about the mechanism of Robo internalization and branch formation . Tips of S2R+ processes bear peaks in surface Robo signal ( closed arrowheads in Figs 4A and S3A ) , which is similar to Robo localization on the tips of filopodia in the developing fly embryo [3] and in primary Drosophila neuron cultures [50] . In the cells that have responded to Slit treatment by reducing their Robo surface levels , presumably by Clathrin-dependent internalization from the surface , process branch-points are marked by reduction in surface Robo levels ( open arrowhead in Fig 4B ) . When inhibiting endocytosis , Robo signal stays high on both the processes with enlarged diameters and in the branch points that do exist ( open arrowhead Fig 4F ) , likely due to lack of Slit-dependent internalization . The correlation between the absence of receptor internalization , either by globally inhibiting endocytosis with Dynasore ( S4A and S4B Fig ) , or by deleting or point-mutating AP-2 adaptor motifs in Robo’s C-terminus ( Figs 4E–4G and S3C–S3G ) , and decreased process elaboration ( Figs 3H and S3F–S3H ) suggests that Clathrin-dependent endocytosis of Robo is required for its signaling output . To test whether the link between endocytic trafficking and Robo signaling is also observed in vivo , we analyzed Robo distribution and midline guidance in the embryo . The endogenous expression pattern of Robo throughout the embryonic ventral nerve cord is characterized by commissural exclusion and longitudinal enrichment [3] . If endocytic trafficking of Robo is required for repulsive signaling , we would expect to see a correlation between Robo mislocalization and guidance errors in embryos with defective endocytic trafficking . In fact , when we induce guidance errors by manipulating entry into the early endosome by expressing DN-Rab5 ( asterisks , Fig 4H ) , we see mislocalization of Robo to the ectopically midline projecting segments of normally ipsilateral axons ( open arrowheads , Fig 4I ) . This correlation between Robo mislocalization and guidance errors is specific to endocytic trafficking manipulations; when we induce ectopic crossing events by overexpressing the Frazzled attractive guidance receptor ( asterisk , Fig 4J ) , we find no mislocalized Robo on the crossing portions of axons , despite the similar number of ectopic crossing events ( closed arrowhead , Fig 4L ) . Further , Robo missing its AP-2 adaptor motif is also mislocalized to the commissural segments ( open arrowheads Fig 4M ) of ectopically crossing axons ( asterisks , Fig 4L ) . Finally , Robo is mislocalized to the collapsed Ap axon fascicles in embryos deficient for Slit , and to the ectopically crossing portions of axons in slit , robo/+ double heterozygotes expressing Robo missing its Slit-binding domain ( S3H Fig ) . Taken together these data suggest that Slit stimulates endocytosis of the Robo receptor , and that this decrease in surface signal is required for receptor signaling in the receiving cell as evidenced by the reduction in process elaboration in S2R+ cells and midline guidance errors in vivo . If our receptor manipulations indeed disrupt endocytosis , then we would expect to observe an effect on the intracellular trafficking of internalized Robo in experiments where we track Robo’s C-terminus in vitro following Slit treatment . We find that not only do our C-terminal motif deletions inhibit the Slit-dependent removal of Robo from the surface , but they also reduce Slit-dependent colocalization of Robo with endogenous Rab5 , a marker of the early endosome . Immunostaining for Slit and Rab5 reveals colocalization between Slit and the early endosome in cell processes ( Fig 5A , 5B and 5P ) . In response to Slit treatment , we also observe an induction of colocalization between Robo and Rab5 , specifically in the varicosities and branchpoints of cell processes ( Fig 5C–5E ) , the same structures that showed Slit-dependent surface Robo turnover ( arrowheads in Figs 4B and 5B ) . We have quantified this response as the percentage change in Manders’ overlap coefficient between Slit and Control CM treatment ( Fig 5Q ) . Expression of DN-Shibire ( Fig 5F and 5G ) , or deletion of Robo’s AP-2-binding motifs ( Fig 5K and 5L ) , prevents the Slit-dependent recruitment of Rab5 in cell processes , resulting in less colocalization of Slit with Rab5 ( Fig 5P ) . There is a concomitant reduction in colocalization of Rab5 with the Robo C-terminal tag in the same endocytosis-deficient conditions ( Fig 5H–5O and 5Q ) . These data provide evidence that Slit stimulates the translocation of Robo to the early endosome , and that this process requires Clathrin-dependent endocytosis specifically from the surface of cells . If Robo endocytosis is required for downstream signaling , then we would predict that inhibiting Clathrin-dependent endocytosis of Robo may prevent the recruitment of Son of Sevenless , which has previously been shown to be recruited to Robo in response to Slit-treatment in mammalian cells [48] . First , we assayed the relative contribution of Sos to the spreading behavior in our in vitro activation assay by co-expressing Sos missing its RacGEF domain ( Fig 6B ) . This dominant-negative construct blocks the Slit-dependent spreading behavior so effectively that the morphology of these cells are indistinguishable from those expressing Robo missing its entire C-terminus ( Fig 6A ) , indicating that this in vitro activation assay depends on the ability of Sos to activate Rac . Having shown that Sos is required for Robo-dependent cell spreading , we sought to examine the capacity of Robo to direct the subcellular localization of endogenous Sos in response to Slit treatment . Extracting the feature of endogenous Sos fluorescence intensity in processes reveals an increase in signal in Slit CM ( Fig 6D ) over Control CM-treated Robo-expressing cells ( Fig 6C and 6I ) , consistent with recruitment of Sos to processes in response to Slit treatment . Not only is Sos required for process elaboration in response to Slit , and actively recruited into the processes in cells treated with Slit , but it also it is also localized to regions previously shown to carry hallmarks of endocytic activity ( reduction in surface receptor levels ( Fig 4B ) and receptor colocalization with an early endosomal marker ( Fig 5E ) ) . Peaks in endogenous Sos signal in Slit CM processes occur at varicosities and branchpoints ( arrowheads Fig 6D’ and 6F’ ) , the same structures that are enriched for markers of endocytic activity . Further evidence that Sos recruitment to processes depends on Slit binding comes from the observation that deleting the Ig1 domain or deleting the CC2 and CC3 domains also block Sos recruitment ( Fig 6E–6F’ ) . Finally , inhibiting Clathrin-dependent endocytosis also abrogates the increase in endogenous Sos signal intensity in Slit-CM- treated processes over Control CM-treated processes ( Fig 6G–6H’ ) , consistent with a model in which Sos recruitment depends on , and therefore occurs following , Clathrin-dependent endocytosis of the Robo receptor in response to Slit-binding . Next , to test whether Robo endocytosis is important for its activation in vivo , we assayed these Robo constructs that are defective in Clathrin-dependent endocytosis for their midline guidance activity . First , we overexpressed either wild-type or mutant Robo transgenes in an otherwise wild-type background in two ectopic repulsion assays . All of the transgenes that we used were tagged with an HA epitope , inserted in the same genomic site and were expressed at comparable levels based on immunostaining for their HA epitope tags ( Fig 7I–7K ) . Driving expression of wild-type Robo in all neurons ( Fig 7B ) is sufficient to signal repulsion so strongly that we see 76% of embryonic segments do not form commissures ( Fig 7L ) . In contrast , none of our endocytosis-defective deletion constructs are able to disrupt midline crossing when similarly expressed ( Figs 7C , 7D , 7L and S5E ) . We see a similar requirement for endocytosis motifs in a commissural subset of axons- the EW axons- whose projection pattern is imaged in Fig 7E with GFP and schematized on the right as a crossed fascicle . Overexpressing wild-type Robo specifically in this subset ( Fig 7F and 7I ) causes ectopic repulsion from the midline ( Fig 7M ) . In contrast , Robo missing its endocytosis motifs ( Figs 7G–7K , 7M , S5F and S5G ) does not cause ectopic repulsion , consistent with a requirement for endocytosis of the Robo receptor for its repulsive midline guidance activity in vivo . If these AP-2 interaction motifs are indeed required for repulsive signaling then one would predict that over-expressing them might compete with endogenous receptors for access to ligand , thereby acting as a dominant-negative for midline repulsion . Accordingly , in embryos with reduced Slit dosage , expressing a Robo transgene missing both its AP-2 motifs , like that missing its entire C-terminus , does inhibit midline repulsion causing ectopic crossing of the medial-most FasII fascicles ( S5B–S5D Fig ) . Finally , to further assess the in vivo repulsive function of these receptor variants , we compared the ability of wild-type versus endocytosis-deficient Robo transgenes to rescue the loss of repulsion defects in robo mutant embryos in two normally ipsilateral subsets of axons . The FasII-positive axons project in three ( Fig 8A ) , and the Ap axons project in one fascicle ( Fig 8F ) , on either side of the midline . In robo mutants the medial-most pair of FasII , and both Ap , fascicles collapse onto the midline ( Fig 8B and 8G ) . Adding back wild-type Robo transgene either in all neurons or specifically in the Ap subset ( Fig 8C and 8H ) is sufficient to restore the ipsilateral projection pattern of these axons . In contrast , expressing Robo transgenes missing the AP-2-binding motifs , either singly or together , cannot rescue the midline crossing errors in robo mutants when expressed in all neurons ( Fig 8D and 8E ) or specifically in the Ap ipsilateral subset ( Fig 8I and 8J ) , consistent with a requirement for Robo endocytosis in its repulsive guidance function in vivo . We note that deleting Robo’s AP-2 binding motifs more greatly impairs midline guidance activity than process outgrowth in our in vitro activation assay , suggesting a functional dissociation in the underlying mechanisms of S2R+ process outgrowth . In the future it will be interesting to determine the effect of these small receptor manipulations on dissociated Drosophila growth cone responses to Slit-binding .
In contexts other than axon guidance , endocytic trafficking has been demonstrated to contribute to receptor signaling by allowing receptor recruitment to specific subcellular compartments . In the case of Wingless [51] , Notch [52] , EGFR and PVR [53] and VEGFR2 [54] , receptor activation is regulated by entry into the early endosome in response to ligand-binding at the surface . Regulation of receptor activation by entry into endocytic compartments can occur by gating spatial access to downstream effectors encountered in signaling complexes–such as Rac or CDC42 in the early endosome [55 , 56] , and MEK1 in the late endosome [57] , reviewed in [58] . These observations lend precedent to a model in which endocytic trafficking gates Robo’s spatial access to downstream effectors , such as Sos . The subcellular localization pattern of Slit , Robo , Rab5 and Sos in our in vitro process elaboration assay support this model; Slit and Robo-C terminal tag demarcate- with their peaks in fluorescence intensity- varicosities and nascent branch points along processes at the 2’ early time point ( arrowheads in Figs 5B , 5E and S4B ) which at 10’ become annexes within branch points ( arrowhead in Figs 3A and S4D ) . Within these enlargements occur correlated valleys in surface Robo signal ( arrowhead , Fig 4B ) and peaks in markers of both early endosome , Rab5 ( arrowheads , Figs 4B , 4E and S4B–S4D ) and Sos ( arrowheads , Fig 6D ) . Taking the formation of branchpoints to be the readout of repulsive signaling in the process elaboration assay , we propose that Slit binds to Robo to induce recruitment of both Rab5 and Sos to create what become hubs of endocytosis activity within two minutes , a timepoint previously verified as required for Clathrin-dependent endocytosis in S2R+ cells and in growth cones [59 , 60] . In this model , Slit binding to the cell is instructing the spatial location of Robo internalization to the early endosome and recruitment of its downstream effector Sos . Consistent with this , when Clathrin-dependent endocytosis is inhibited , Slit binding is intact , but fails to induce the recruitment of Rab5 and therefore there is a correlation between loss of both translocation of Robo from the cell surface to the early endosome , and decreased Sos recruitment . Our data are consistent with a model in which endocytic trafficking is mechanistically contributing to Robo’s activation by fully or partially gating access to its downstream effector Sos . Evidence from the literature suggests that Sos recruitment might not occur exclusively at the surface of the cell as we had previously reported [31 , 48] , but also in closely apposed early or late endosomal compartments [61] . Sos encodes a Pleckstrin Homology ( PH ) Domain just C-terminal to the Dbl Homology ( DH ) domain that is required for both its RacGEF function in Slit/Robo midline guidance in the fly [48] , and for in vitro Robo activation ( Fig 6B ) . PH domains bind phosphoinositols ( PI ) of the plasma membrane or small GTPases , and are invariably found adjacent to DH domains , strongly suggesting a functional link between DH and PH activity . In the case of Sos the PH domain has been suggested to act as a mechanical switch to allow initiation of the RacGEF activity of the DH domain upon conversion of a bound PIP2 to PIP3 by PI3Kinase ( PI3K ) [62] . Phosphoinositides have also been linked to early endosome fusion; Rab5 actively recruits PI3K , which in turn is required for Rab5-mediated conversion of plasma membrane to early endosome [63 , 64] . It will be interesting to determine whether Sos activation downstream of Robo is gated by PI3K in concert with recruitment to the Rab5-positive early endosome , as this would provide a mechanism by which Robo activation requires Clathrin-mediated endocytosis and Rab5 activity . At first glance , the ability of Robo to induce elaboration and branching of cell processes in vitro may seem inconsistent with a repulsive output; however , our rescue and gain of function genetic data make a strong case that the signaling output that we observe in vitro is critical for repulsion in vivo . In addition , there is ample precedent for Slit/Robo signaling to induce branching in both in vitro and in vivo contexts . For sensory afferents that bifurcate and send collaterals into iterative segments of the spinal cord , uniform Slit treatment induces branching in vitro either in suspension cultures of Rat DRGs in collagen gels or bath application to rodent trigeminal neurons [4 , 65] . The branched morphology of the peripheral arbor of trigeminal projections to the eye requires Slits and Robos [10] . Interestingly , bath application of Slit is sufficient to induce Robo1-dependent growth and branching of dendritic fields of mouse cortical neurons [66] , similar to our observations of Slit-induced branching and process growth in S2R+ cells . Since Robo is enriched in growth cone filopodia it is likely that during active migration Robo-containing filopodia would mediate adhesive interactions with Slit in the extracellular matrix . Subsequent Slit-induced filopodial retraction likely requires more than the filopodial dynamics provided by Ena- a Robo effector that is known to localize to the distal tips of filopodia [67 , 68] , since Robo missing its CC2 domain is not fully deficient for midline repulsion [47] . A commonality between our in vitro activation assay and previous analyses of growth cone collapse in culture may suggest a possible mechansim . Filopodial contact of a sympathetic growth cone to a retinal neurite is sufficient to initiate an increased rate of growth cone movement- a rapid retraction of an actin-rich structure along the existing axon [69] , all while filopodia stay attached , suggesting the existence of a retrograde cue from the filopodial point of contact to more proximal growth cone structures . Similarly , fixed imaging analysis of S2R+ cells in our assay reveals that bath-treatment of Slit CM stimulates the extension and branching of processes over those observed in Control CM . Given that the process elaboration response we observe requires the RacGEF domain of Sos , it is likely that the increased rate of motility implicit in the growth upon Slit treatment is due to alterations in Rac-dependent actin dynamics . Since the process elaboration and branching behavior also requires endocytic trafficking from the cell surface to the late endosome , we can speculate that Robo endocytosis is required to direct the Sos-induced actin motility required for spreading in vitro . It is the same receptor manipulations that abrogate Clathrin-dependent endocytosis in vitro that lead to impaired repulsive signaling in vivo , strongly supporting the idea that Robo endocytosis is required for proper repulsive output in the growth cone , perhaps by allowing the actin-based motility that leads to filopodial retraction and growth cone repulsion . Sequence analysis reveals putative AP-2 binding motifs in human Robo1 ( S2J Fig ) , suggesting conservation of the mechanistic contribution of endocytosis to growth cone navigation all the way to humans , further strengthening the possibility of the importance of this trafficking event . Might Slit-binding trigger a similar endocytic trafficking cascade in a growth cone , thereby mobilizing Robo so that it could serve as the retrograde cue informing growth cone behavior from the tips of filopodia ? Evidence from others shows that Clathrin-dependent endocytosis exists in the right time and place to play such a role in guidance behavior . First , markers of endocytic compartments , including the early endosome , have been identified in the growth cone [70 , 71] . If endocytosis serves as a general mechanism for expanding the spatial range of an activated receptor after exposure to ligand on filopodial tips , then we would expect to see examples of correlation between guidance cues trafficking retrogradely and guidance behavior . Endocytosis of guidance molecules in the growth cone has been shown to be initiated both from the base of the growth cone central domain and from the tips of filopodia [23 , 72] . Retrograde movement of endocytic compartments has been reported in the growth cone and in the case of internalized L1CAM movement occurs at the rate of F-actin retrograde flow [73 , 74] , suggesting that endocytic trafficking could provide an effective spatial track from which a guidance cue might influence the cytoskeleton to affect growth cone behavior . The timing reported by others of endocytosis in the growth cone also shows correlation with the endocytic trafficking of Robo we characterize here in vitro . At the same two minute timepoint we report Slit induces Robo removal from S2R+ cell surface here , Sema-3A has affected both a reduction in Neuropilin-1 levels [21 , 60]–and growth cone collapse in the Xenopus RGC growth cone , albeit with different ligand concentrations [75] . Finally , Frizzled endocytosis in a migrating growth cone reveals a correlation between filopodial dynamics and Frizzled endocytosis [23] . It remains to be determined whether retrograde Robo movement from the tips of filopodia is required for repulsion in response to Slit . Finally , here we have addressed how an endocytic cascade positively contributes to signaling from the Robo receptor , effectively expanding the spatial range of activated receptor within the growth cone . While allowing exposure to the machinery within the growth cone beyond filopodial tips would be required for behaviors such as growth cone retraction or turning in response to filopodial contact with Slit , allowing a receptor to signal too far from the spatial origin of its cue might ultimately prove confusing to a growth cone . It will be interesting to learn if there is a process that serves to curtail signaling from an endocytosed and activated receptor .
The following Drosophila mutant alleles were used: roboGA285 , roboz1772 , robo5 , slit1 , slit2 , slite158 , endoAEP297 , endoA∆4 , endoA10 , ada1 , ada3 , rab52 , P[lacW]Rab5k08232 , P[EPgy2]Rab7EY10675 , rab7FRT82B ( knock-out ) . The following transgenes were used: P[UAS-Shi . K44A]4–1;UAS[shi . K44A]3–7 , P[UASp-YFP-Rab5 . S43N] , P[UASp-YFP-Rab7 . T22N]06 , P[UASp-YFP-Rab4 . S22N]37 , P[UASp-YFP-Rab11 . S25N]35 . The following transgenic flies were generated by BestGene Inc ( Chino Hills , CA ) using ΦC31-directed site-specific integration into landing sites at cytological position 86F8 ( controlling for expression level effects from chromosomal position ) : P[5xUAS-3xHA-Robo-6xmyc] , P[5xUAS-3xHA-Robo∆YLQY-6xmyc] , P[5xUAS-3xHA-Robo-1xmCherry] , P[5xUAS-3xHA-Robo∆YQAGL-1xmCherry] , P[5xUAS-3xHA-Robo∆YQAGL-6xmyc] , P[5xUAS-3xHA-Robo∆YLQY∆YQAGL -6xmyc] , P[10xUAS-3xHA-Robo∆Ig1] , P[10xUAS-3xHA-Robo∆C-6xmyc] , P[10xUAS-3xHA-Robo∆YLQY∆YQAGL-6xmyc] . Also used were the extant lines P[GAL4-elav . L]3 ( elav-GAL4 ) , egMZ360 ( eg-GAL4 ) , ap-GAL4 . Embryos were genotyped using balancer chromosomes carrying lacZ markers or by the presence of epitope-tagged transgenes . Control and Slit CM were boiled for 10’ in 2X SDS Loading Buffer . Proteins were resolved by SDS Page and transferred to nitrocellulose and incubated with anti-Slit-C ( C555-6D ) 1:100 overnight at 4°C in PBS/0 . 05% Tween-20/5% non-fat dry milk . Blots were incubated with HRP-conjugated anti-mouse secondary antibody for 1 hour at RT and signal was detected using ECL Prime ( Amersham ) . | The formation of sterotyped neuronal connections during embryonic development is essential for animal survival and behavior . In particular , establishing proper connectivity at the midline is critical for the orchestration of rhythmic behaviors . Conserved genetic programs that instruct axon guidance at the midline have been characterized in multiple model systems , but the signaling mechanisms underlying axon guidance are not well understood . Slits and Robos comprise conserved families of axon guidance cues and receptors that control midline guidance by preventing inappropriate midline crossing . Here , we identify a novel mechanism that is required for Robo receptor activation and Robo-dependent axon repulsion in vivo . Using a combination of molecular genetic and cell biological approaches , we define a role for Slit-dependent trafficking of Robo from the plasma membrane to the early and late endosomes that contributes to Robo activation and signaling . In previous work , endocytic trafficking has been shown to modulate axon guidance responses by regulating surface levels of guidance receptors . In contrast , our observations indicate that endocytosis of the Robo receptor itself is required for receptor activation and precedes the recruitment of a key downstream signaling effector to the Robo receptor cytoplasmic domain . |
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Japanese encephalitis ( JE ) is a flaviviral disease of public health concern in many parts of Asia . JE often occurs in large epidemics , has a high case-fatality ratio and , among survivors , frequently causes persistent neurological sequelae and mental disabilities . In 1997 , the Vietnamese government initiated immunization campaigns targeting all children aged 1–5 years . Three doses of a locally-produced , mouse brain-derived , inactivated JE vaccine ( MBV ) were given . This study aims at evaluating the effectiveness of Viet Nam's MBV . A matched case-control study was conducted in Northern Viet Nam . Cases were identified through an ongoing hospital-based surveillance . Each case was matched to four healthy controls for age , gender , and neighborhood . The vaccination history was ascertained through JE immunization logbooks maintained at local health centers . Thirty cases and 120 controls were enrolled . The effectiveness of the JE vaccine was 92 . 9% [95% CI: 66 . 6–98 . 5] . Confounding effects of other risk variables were not observed . Our results strongly suggest that the locally-produced JE-MBV given to 1–5 years old Vietnamese children was efficacious .
Japanese encephalitis ( JE ) is a mosquito-borne flaviviral disease endemic in many regions of Asia [1] . Culex tritaeniorhynchus , the principal mosquito vector of the JE virus ( JEV ) , preferentially breeds in rice fields [2] , [3] . Swine are potent amplifiers of the virus and exhibit rapidly after virus transmission considerable viral loads . Thus , Culex mosquitoes breeding in rice fields and feeding on swine , are critical ecological factors favoring JE transmission to humans in rural areas . Prior to the availability and introduction of vaccines , JE was a significant cause of mortality in the northern provinces of Viet Nam with an annual incidence of 5–15/100 , 000 [4] . Most JE infections ( 96%–99 . 9% ) are asymptomatic or present as a mild disease only with rather non-specific flu-like symptoms . However , among symptomatic patients who exhibit symptoms of encephalitis and/or serious neurologic infection , the case-fatality ratio can be as high as 10%–30% [5] , [6] . Among survivors , 30% to 50% of individuals suffer from chronic , severe neuropsychiatric disabilities [5]–[7] . Why only a small proportion of infected individuals experience severe disease is not clear . Reasons may include host genetic factors , but also virulence factors of differing virus strains . Children younger than 15 years are at the highest risk of infection and the incidence peaks at three to ten years of age [7] . JE infections efficiently induce protective immunity [8] and seroprevalence studies indicate almost universal exposure to the infection in endemic areas by adulthood [9] . Specific antiviral treatment for JE is not available [10] and care of patients strongly depends on supportive measures . Vaccination is the primary strategy for prevention of infection [1] and has been shown to dramatically reduce the disease incidence in South Korea [11] , [12] , Japan [13] , China [14] , [15] , Thailand [15] and Taiwan [16] . In Viet Nam , children receive three pediatric doses ( 0 . 5 ml/dose ) of a locally-produced , mouse brain-derived , inactivated JE vaccine ( MBV; Nakayama strain; VaBiotech , National Institute for Hygiene and Epidemiology ( NIHE ) , Ha Noi , Viet Nam ) with the first two doses at one year of age given at an interval of two weeks followed by a third , booster dose one year later . The production of the MBV in Viet Nam was initiated in 1989 and supported by technology transfer from Japan . Bridging studies suggest that the Vietnamese MBV has an immunogenicity similar to that of the Japanese vaccine , reaching nearly 100% immunogenicity in children after the application of two doses [1] . The vaccine was integrated into Viet Nam's national Extended Program of Immunizations ( EPI ) in selected districts of Ha Tay and Hai Phong provinces in 1997 . Hospital-based surveillance of all patients presenting with an acute encephalitis syndrome ( AES ) is ongoing in the Ha Tay and Hai Phong Provincial Hospitals in the North of Viet Nam . Two provincial hospitals in the Ha Tay Province as well as the National Pediatric Hospital in Ha Noi are used as referral hospitals for JE surveillance since January 1 , 2004 . These hospitals jointly account for approximately 95% of all cases of acute encephalitis notified in the Ha Tay Province . In Hai Phong province , cases are identified through the national AES surveillance system . Cerebrospinal fluid ( CSF ) is collected at admission . The presence of immunglobulin-M ( IgM ) antibodies to JEV ( anti-JEV ) in CSF is defined as one of the criteria for a JE diagnosis [17]–[19] . Initial testing of specimens is performed at the National Pediatric Hospital Laboratory and Laboratory of Ha Tay Preventive Medicine Center , and confirmatory testing of all specimens was done by sending coded samples to the Department of Virology , Armed Forces Research Institute of Medical Sciences ( AFRIMS ) , Bangkok , Thailand . The objective of the present study was to assess in a case-control design the effectiveness of this MBV JE vaccine .
The study was conducted according to ethical principles consistent with the International Guideline for Ethical Reviews of Epidemiologic Studies [20] . The Institutional Review Board of the International Vaccine Institute , Seoul , Republic of Korea , and the local ethical committee of NIHE , Ha Noi , Viet Nam approved the study and granted ethical clearance . Vaccination status' of participants were assessed from vaccination records of the health centers . During visits of the households of children that were aimed at assessing the distances of piggeries and ricefields from the houses , oral informed consent was obtained from parents/guardians of cases and controls and documented in the questionnaire . Oral consent was considered appropriate for the study and approved by both Ethical Review Boards . For this matched case-control study , patients with a confirmed diagnosis of JE and younger than 15 years of age were identified from the database of the surveillance hospitals from January 2004 to December 2007 . Older children were not recruited , as they could not have received JE vaccines through the national immunization program . Controls were chosen from the birth registry of all births in the health center of the village of the case and matched for gender , age ( +/− six months ) , and proximity of their house . Only after four controls had been selected from birth registry and agreed upon by the study team , the vaccination record books were opened and the individual vaccination status was assessed . JE vaccination is provided during an annual mass campaign and vaccination records are kept at the health centers . The houses of cases and controls were visited and as an indicator of potential exposure to infection and the distances to piggeries and rice fields were assessed by the study team after obtaining oral informed consent from the adult/guardian present . A Student's t-test for unequal distributions was used to compare the age distribution of cases and controls . A chi-square test was employed to compare cases and controls for the proportion of males , proportion living within ≤50 meters of a piggery and percent living ≤30 meters of rice fields . A matched conditional logistic regression model was employed to evaluate the protective effect of immunization . A single dependent ( JE disease ) and independent variable ( immunization status ) was entered into the model . Odds ratios ( OR ) , 95% confidence intervals ( CI ) , and p values were calculated from model parameters . The protective effect of immunization was assessed for residents receiving three or more doses relative to two or less doses . Vaccine effectiveness ( VE ) was calculated as VE = 1−OR×100 [21] . The threshold of statistical significance was p<0 . 05 . All statistical analyses were performed using the Stata v 10 . 0 software ( Stata Corp . , Texas , USA ) .
We identified 30 laboratory-confirmed cases of JE infection and these were matched to 120 controls . After selection of controls was completed , it became evident that two controls were chosen who had no information on their immunization status in the birth registries . These two controls were excluded from the calculation of vaccine effectiveness . As cases and controls were matched for age and gender , no significant differences were observed between cases and controls for these variables . Cases and controls resided equidistantly from rice fields and piggeries ( Table 1 ) . Only individuals who had received the full course ( three doses ) of JE vaccine were considered as vaccinated; two cases and two controls received one and two cases and six controls two doses of JE vaccine , respectively , and were considered as not vaccinated ( Table 2 ) . Among the 30 laboratory-confirmed cases , the proportion of cases vaccinated was 60 . 0% , compared to 86 . 4% among the 118 control individuals . Based on the matched analysis vaccine effectiveness reached 92 . 9% [95% CI: 66 . 6–98 . 5%] ( Table 2 ) .
Three previous trials have evaluated the effectiveness of the JE MBV . A randomized double-blinded study conducted in northern Thailand , using JE MBV produced in Thailand , yielded an overall effectiveness of 91% [95% CI: 70 . 0–97 . 0] [22] . Another trial in Taiwan evaluated a Taiwanese vaccine and revealed an effectiveness of approximately 85% when two or more doses were administered [23] . A case-control study in Thailand showed an effectiveness of 94 . 6% in children ≥18 years of age [24] . The present matched case-control study suggests that the MBV produced in Viet Nam yields results that are similar to those of the Thailand and Taiwan studies . Even though the MBV has an excellent efficacy , its usage was recently restricted after considerable safety concerns were raised . Severe reactions such as hypersensitivity , including generalized urticaria and angioedema , occurred at far higher rates than observed in other routine vaccinations [25] , [26] , [27] , [28] . Consequently , Japan no longer recommends JE MBV vaccination [29] , [30] and recently introduced a Vero cell-derived JE vaccine; other countries are currently in the process of phasing out the MBV ( e . g . , Thailand , Sri Lanka ) . Nevertheless , MBV safety issues have not led to any restrictions in its use in Viet Nam . However , Viet Nam is currently developing its own Vero cell-derived vaccine , which is foreseen to be available for use in 2014/2015 . The Vietnamese government has announced that it would eliminate clinical JE by 2015 . However , in contrast to poliomyelitis virus , for which humans are the only hosts , the JE virus is enzootic and is , therefore , unlikely to be entirely eradicated from the environment . A recent JE surveillance study has shown that , even in the virtual absence of manifest human disease after vaccination , JEV is still widespread among swine [31] , showing that active transmission is perpetuated and that protective immunity of humans through persistent vaccination is a key measure to preventing disease in humans [13] . Therefore , controlling clinical JE disease through vaccination would not impact on reducing or eradicating the circulation of the virus within the vectors and animal hosts as JE is not transmitted from person to person and JE vaccination does probably not confer herd immunity [32] . Depending on the potency of enzootic transmission and the age-specific risks of natural human infection , the age for primary vaccination differs between countries . Most countries give one to two booster doses after the initial three-dose regimen [1] . In contrast to Japan and Korea , where nation-wide changes in lifestyle have provided additional contributions to the control of JE [11] , [33] , rural areas in Viet Nam have so far largely remained agrarian . Rice paddies cover extensive geographic areas , and little changes only have occurred in the natural environment and living conditions . Even though the effectiveness of the vaccination program appears to be high , an annual average incidence of 3 . 4/100 , 000 was observed among children less than 10 years of age . The current JE immunization program may be improved by immunizing younger children ( 6–23 months of age ) who are at the highest risk of patent infection , and providing a booster dose at 3–5 years after initial immunization with the three-dose regimen , reducing waning immunity in immunized children [1] . An inherent problem of inactivated vaccines is that due to their low immunogenicity , multiple vaccinations are required in order to induce and maintain sustained levels of protective immunity . Following the initial three doses given at immunization , the effectiveness of the MBV declines over years [34] . Therefore , the introduction of one or more booster doses using the adult formulation to children at school age is recommended to ensure protective immunity against JE using MBV . | Japanese encephalitis ( JE ) is a disease caused by a flavivirus transmitted by mosquitoes . Although pigs and wild birds are main reservoirs of the disease , it is occasionally transmitted to humans . The majority of infections in humans are asymptomatic . In persons developing encephalitis , JE has a high case-fatality rate and , among survivors , JE frequently causes persistent neurological sequelae and mental disabilities . Therefore , it is a public health concern in many parts of Asia and many countries vaccinate against JE . Since 1997 , children in Vietnam are vaccinated in high risk areas and receive a locally-produced vaccine . This study is aimed at evaluating the effectiveness of the Vietnamese JE vaccine through a case-control study , in which 30 cases and 120 controls were enrolled . The effectiveness of the JE vaccine was 92 . 9% [95% CI: 66 . 6–98 . 5] , which suggests that the locally-produced JE vaccine given to 1–5 year old Vietnamese children was efficacious . |
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Gene expression analysis is generally performed on heterogeneous tissue samples consisting of multiple cell types . Current methods developed to separate heterogeneous gene expression rely on prior knowledge of the cell-type composition and/or signatures - these are not available in most public datasets . We present a novel method to identify the cell-type composition , signatures and proportions per sample without need for a-priori information . The method was successfully tested on controlled and semi-controlled datasets and performed as accurately as current methods that do require additional information . As such , this method enables the analysis of cell-type specific gene expression using existing large pools of publically available microarray datasets .
Gene-expression profiling of whole tissues is affected by the different cell types that exist in the tissue and their relative proportions . Thus , changes detected by differential expression analysis may reflect differences in the proportions of the cell-types between samples rather than an important mechanistic change in gene-expression . For example , the proportion of tumor cells in breast cancer biopsies were found to significantly affect expression profiles , where consideration of these proportions improved response prediction [1] . Therefore , profiling of heterogeneous tissues rather than sorted cell-types can greatly limit the conclusions derived from such analyses . Solutions for experimentally separating cell-types from heterogeneous tissues include laser-capture microdissection to isolate morphologically distinguishable cells and flow cell sorting to purify cell-types from a tissue . However , in addition to the time-consuming nature of these methods , they may result in insufficient quantities of RNA , where amplification steps may introduce artifacts to the gene expression data [2] . Single cell RNA sequencing is becoming feasible; however , experimental costs are high and few studies utilize this method on a large patient pool . To address this issue , several approaches to computationally separate expression profiles of heterogeneous tissues into their constituent cell-types along with their relative proportions per sample have been developed . Most approaches utilize a linear model that has been demonstrated to yield accurate expression estimates [3] , [4]; in this model the gene-expressions of each cell-type are added up to form a mixed expression , where each cell-type is weighted according to its relative proportion in the tissue . All currently existing separation methods require some a-priori information about the tissue analyzed , such as the number of cell-types and their relative proportions in the tissue [3] , [5] , or the number of cell-types , their identity and their purified gene expression [4] , [6]–[8] , or just the number of cell-types in the tissue [9]–[11] . A preliminary attempt to estimate the number of cell-types in the mixed data , but not their identities , has also been proposed [12] . However , most studies do not purify the different cell populations in the tissue , enumerate their proportions or verify their identity , rendering these methods inapplicable to separation of such heterogeneous gene-expression datasets . In this study , we have developed a novel approach to blindly separate heterogeneous gene-expression data , i . e . , without using any specific prior information regarding the analyzed dataset . In addition to separating the heterogeneous tissue to the individual gene expression profiles of its constituent cell-types and their relative proportions per sample , the algorithm described here performs an extra step of identifying the number of cell-types in the tissue and their identities . Compared to existing methods , the only a-priori information the algorithm requires is an initial guess of the cell-types that may exist in the analyzed tissue and purified reference signatures of these cell-types , which may be found in abundance in publically available databases . We have successfully tested our algorithm on three publically available databases in which all the conditions are controlled and on a publically available semi-controlled dataset with estimated cell-type proportions . To our knowledge , this method is the first that can practically be applied , in a “plug and play” fashion , to any existing dataset of heterogeneous tissue samples , in order to identify the cell-types in the samples , their identities , their proportions per sample and their separated gene-expression signatures without requiring any prior knowledge .
The proposed algorithm is based on a hyper-spectral imaging method developed by Piper et al . [13] . It is designed to identify the number of cell-types in heterogeneous tissue samples , their identities , their relative proportions per sample and separate their individual gene expression signatures . The proposed algorithm includes three parts ( see methods section for more details ) . In the first part , non-negative matrix factorization [13] is used to obtain an initial estimate of expression profiles for each cell-type . A rough initial estimate of the numbers and identities of the cell-types in the tissue is required . This estimate can include cell-types that may not exist in the tissue . However , if a true cell-type is not included in the initial estimate , then the algorithm will not detect this cell-type and there may be ambiguities in the resulting cell-type signatures and proportions . In addition , purified reference signatures are required for each of the cell-types included in the initial estimate . Such reference signatures may be found in abundance in the Gene Expression Omnibus ( GEO ) [14] and may be general , i . e . , not be disease , tissue , experiment or study-specific . In the second part of the algorithm , the true number of cell-types is estimated using the symmetric Kullback-Leibler divergence ( SKLD ) between each of the estimated cell-type profiles and the initial cell-type reference signatures , where the closest estimated profiles are then chosen as the final cell-types . SKLD , a measure used to calculate the difference between two probability distributions , is used here as a measure of distance , as we describe in the methods section under ( 5 ) . In the final part , the cell-type proportions are computed per sample , using the method of non-negative least squares ( NNLS ) , a method that solves matrix equations algebraically with an added constraint for non-negative elements , as we describe in the methods section under equation ( 3 ) . Additional adjustments , motivated by the application of the algorithm to gene-expression data include: ( a ) majority voting , where the final identity of the cell-types is chosen from the results of several algorithm runs with random initializations , and ( b ) usage of classes , where several input reference signatures may be grouped into one “class” of cell-type . These adjustments were added to the algorithm to improve separation capabilities of cell-types with similar signatures and increase the algorithm's robustness to noisy reference signatures ( for additional details see the methods section ) . We tested the algorithm on three publically available datasets in which known proportions of known cell types were mixed and their gene-expression was measured . The liver-brain-lung dataset includes samples of rat liver , brain and lung cell mixtures [3] . The purified cell-type reference signatures were collected from GEO and included rat liver , brain , lung , intestine , heart and granulosa cell gene-expression profiles from different studies ( see “microarray data” in methods section; Figure S1A ) . Although the mixed samples included only three cell-types , more than three cell-types were inputted to the algorithm as the initial number of cell-types to test the algorithm's ability to discern the correct number . The algorithm successfully identified three cell-types in the mixed samples and their correct identities , i . e . , liver , brain and lung . High correlations were found between the gene-expression profiles of each estimated cell-type to the profile of its corresponding purified cell-type taken from the same study ( Figure 1A ) , in addition to shortest SKLD distances ( Figure S1B ) . These correlations , obtained by our blind separation method , were in the range of the correlations reported in the Shen Orr et al . study where the number of cell-types , their identities and their proportions per sample were input to the algorithm , and even higher in the case of the lung cell-type [3] . High correlations were also obtained between the actual and estimated cell-type proportions ( Figure 2A ) , in addition to shortest SKLD distances ( Figure S1C ) . Sample-by-sample comparison of the estimated proportions of each cell-type shows that our algorithm is successful in reconstructing accurate proportions per cell-type per sample , with an average absolute error of 3 . 4%±2 . 3 ( Figure 3A ) . In addition , the resulting expression signatures had shorter SKLD distances and thus were closer to the original purified expression profiles compared to the input profiles , demonstrating that the algorithm successfully advanced the input signatures ( Figure S1D ) . Note that we use SKLD distances as the distance measure in results testing , as it is the measure used in the algorithm itself . The Heart-Brain dataset includes samples of heart and brain human cell mixtures [15] . Purified cell reference signatures were collected from GEO and included myocardial ( heart ) cells , brain cells from the entorhinal cortex and grey matter , oocytes and hepatocytes from different studies ( see “microarray data” in methods section; Figure S2A ) . We unified the two heart signatures obtained from different studies under the class “heart” and the two brain signatures obtained from different brain tissues under the class “brain” . The algorithm successfully identified the true cell-types , i . e . , heart and brain . The cortex brain cell-type was identified in all algorithm majority voting runs whereas the brain grey-matter cell-type was identified in only 20% of the majority voting runs , suggesting that the cells in the mixtures are most probably cortex cells or cells with a similar signature . The estimated cell-type expression profiles showed the highest correlations ( Figure 1B ) and shortest SKLD distances ( Figure S2B ) to their corresponding purified cell-types taken from the same study . High correlations ( Figure 2B ) and shortest SKLD distances ( Figure S2C ) between the estimated and known cell-type proportions were obtained , with a low average absolute error of 1 . 7%±1 . 85 ( Figure 3B ) . Finally , the resulting expression signatures were closer to the original purified expression profiles compared to the input profiles ( Figure S2D ) . To test separation of cell-types with similar signatures , we chose the T-B-Monocytes dataset , containing mixtures of T , Monocyte and two types of B cell lines [4] . Purified cell reference signatures collected from GEO included human immune cell lines of T-cells , B-cells , Monocytes , NK cells and epithelial cells ( see “microarray data” in methods section; Figure S3A ) . The algorithm successfully identified all three cell-types ( T , B , Monocytes ) and also successfully discerned between the two types of B cell-lines , yielding a total of four resulting cell-types – T Jurkat , B Raji , B IM-9 and Monocyte THP-1 cell lines . High correlations were obtained between the gene-expression profiles of each estimated cell-type to the profile of its corresponding purified cell-type taken from the same study ( Figure 1C ) and between the estimated and known cell-type proportions ( Figure 2C ) , in addition to shortest SKLD distances ( Figures S3B–C ) . The average error in cell-type proportions per-sample obtained by our blind separation method was 5 . 7%±3 . 3 ( Figure 3C ) , which is close to the error reported by the original study separating these same samples , where the number of cell-types , their identity and their gene expression profiles were given as an input [4] . In addition , the resulting expression signatures were closer to the original purified expression profiles compared to the input profiles ( Figure S3D ) . A lower average error in cell-type proportions per sample ( 3 . 67%±3 . 04 ) and higher correlations between the estimated and known cell-type specific gene-expression profiles were obtained in an algorithm run where the two B cell-line types were unified under the “B cells” class ( Figures S3E–I ) . In this run , where the goal was to separate between the different immune cell-types in the mixed samples , the algorithm successfully identified the three cell-types – T-cells , B-cells and Monocytes . We tested the algorithm on a semi-controlled dataset of prostate cancer in which cell-type proportions were estimated by a pathologist [7] . The cell-types in the analyzed tissue were carcinoma , benign ( BPHE ) and dilated ( DCAE ) epithelial and stromal cells . Purified cell signatures of prostate tumor cell lines , benign prostate cells , normal prostate epithelial cells , stroma surrounding invasive prostate tumors and normal stroma were collected from GEO ( see “microarray data” in methods section; Figure S4A ) . Data were available for the percentage of tumor cells in each sample , thus classes unifying the prostate cell-lines and the other cell-types under “tumor” and “other” , respectively , were used . High correlations were obtained between the pathologist's estimated cell-type proportions and the cell-type proportions estimated by the algorithm ( Figure 2D ) , with an average error per sample of 12 . 44%±12 . 41 ( Figures S4B–C ) . Compared to the results obtained in the controlled datasets , the cell-type proportions prediction error was higher in this case . This could be due to the fact that no specific signatures for BPHE and DCAE cells were found within the same/similar microarray platforms . Therefore , general signatures for stromal and epithelial cells were used , which may have decreased the prediction accuracy . However , it is more likely that the pathologist's estimations of cell-type proportions were not fully accurate . Indeed , the lowest error calculated between the actual and estimated cell-type proportions in that study was: 9 . 5% and 12 . 5% for tumor and stromal cells , respectively [7] , where our blind separation algorithm reached similar error rates with non-specific signatures .
Gene-expression analysis of whole tissues , which are heterogeneous in nature and consist of a mixture of several cell-types , are utilized extensively and are highly abundant in public repositories such as GEO [14] . However , it is now becoming clear that the identity , composition and profiles of individual cell-types are extremely important to the process of unraveling the biology of each cell-type population and the interplay between the populations in both healthy and disease states . Due to the expense and difficulties of separating them , only a limited amount of studies profile and analyze individual cell-types . More importantly , public repositories are replete with existing data of whole tissues including thousands of patients , treatments , tissues and cell-types . This rich trove of data is from experiments that may never be repeated using such large patient pool or experimental conditions . Our techniques can realize the great potential of these data , which contains much information about the constituent individual cell-types in heterogeneous tissues that , to date , have not been fully interrogated . Computational methods have been developed to allow the separation of heterogeneous tissues into their cell-type constituent profiles and/or relative proportions [3]–[12] . However , all currently existing separation methods require that the number of cell-types in the tissue , their identity , or their relative proportions in the analyzed tissue are known . Such information rarely exists , as most profiling studies do not purify the cell-types in the tissue , extract their proportions or verify their identity , rendering the existing separation methods non-usable for most existing datasets; Rather , these datasets are usable only in experiments designed in advance to allow for the separation technique . We have developed a separation method that requires no a-priori information about the tissue analyzed other than an initial rough estimate of the cell-types that may exist in the tissue samples analyzed . This is a reasonable input to ask for and relatively easy to find , as information regarding the composition of most tissues is readily available in the literature and public databases such as GEO are replete with many types of purified cell-types from various experiments . As our algorithm does not require the purified cell-type profiles to be disease , tissue or even study-specific , one can simply use any relevant purified profile as an input to the algorithm . These properties render our algorithm the only useful method to separate most publically available heterogeneous microarray datasets . We successfully applied our separation technique to three controlled datasets with known proportions and cell types [3] , [4] , [15] in addition to a semi-controlled dataset where cell-type proportions per sample were estimated by a pathologist [7] , to test the method on a dataset that resembles the heterogeneous datasets available in the literature rather than on datasets specifically engineered for separation . Our blind separation technique accurately extracted the relative cell-type proportions per sample and their separated gene-expression signatures and performed just as well , and in some cell-types even better , than other reported separation techniques that require different types of input information about the dataset analyzed to be available . Most importantly , our technique successfully identified the number of cell-types in the tissues analyzed and their identities . These features are not included in any of the reported separation techniques , and are in fact considered as an integral input for the usage of these techniques . It is these features that are mostly unavailable for publically available datasets , or any dataset in which they have not been experimentally identified . In addition , the cell-type populations and proportions in a tissue are not always consistent amongst different individuals , which renders the identification of those populations and their identities crucial . The algorithm's robustness to varying input signatures was demonstrated by using additional cell-type signatures that were not related to the analyzed tissue as input to each controlled dataset ( e . g . the intestine , heart and granulosa cell-types were input to the liver-brain-lung dataset ) . To address the algorithm's robustness to signatures of different qualities , signatures from different studies were used for the same cell-type and gathered under the same class ( e . g two T-cell Jurkat and B-cell Raji cell-lines from different studies were input to the T-B-Monocytes dataset ) . The algorithm identified the correct number of cell-types and their correct identities in all examples . In general , the algorithm performed better when separating cell-types that were very different from one another as in the heart-brain dataset , compared to cell-types that were very similar to each other such as in the T-B-Monocyte dataset . However , in the latter example , given that no a-priori data about the mixed tissue was provided , the algorithm still yielded accurate results . In particular , the algorithm identified all three cell-types ( T , B and Monocytes ) with an error that was close to that reported by the original study where the number of cell-types , their identity and their true gene-expression profiles were given as an input [4] . Moreover , the algorithm also successfully separated the two B cell-lines , cell-types with an almost identical gene expression . A comparison between the true purified signatures from the same study to the input signatures mined from GEO and the resulting signatures inferred by our algorithm showed that , in each of the datasets explored , the resulting signatures were always closer to the true signatures than the signatures from GEO , demonstrating that our algorithm successfully identifies the input signatures close to the true ones . Compared to existing algorithms , our algorithm yielded at least comparable results , and in some cases better results ( such as predicting the lung cell-type in the liver-brain-lung dataset ) . An important distinction is that our algorithm does not require the a-priori information required in existing algorithms and , in contrast with those algorithms , it is able to determine the number of cell-types in the heterogeneous tissue and their identities . To demonstrate the importance of this added capability , we compared the performance of our algorithm to an NNMF approach , without the cell-type determination step , which is initialized in the same manner as our algorithm ( Figures S5 , S6 ) . We also compared our algorithm's performance to that of a simple NNLS-based algorithm , used here as a bench-mark due to the fact that most existing algorithms are based on NNLS [3]–[12] , [16] ( Figure S7 , S8 ) . In both cases , even a small error in the guess of the number of cell-types ( e . g . guessing 4 cell-types instead of 3 ) deteriorates the performance of these existing algorithms , demonstrating that the cell-type determination step is crucial for good separation . This emphasizes the usefulness of our algorithm not only in situations where no a-priori information exists , but also in the more common scenarios where one has a good but not perfect guess of the cell composition with an error of at most one or two cell-types . In summary , our blind separation technique successfully identifies the cell-type composition in heterogeneous gene-expression data , and provides high-accuracy estimates of cell-type specific signatures and their relative proportions per sample . The only information the algorithm requires is an initial estimate of the cell-types that may exist in the tissue analyzed and their signatures , which can be easily found in public databases such as GEO . This method is especially advantageous for re-analyzing existing microarray data for which no additional information is available , allowing re-examination and extraction of information for individual cell-type populations while taking advantage of already-existing , large-scale microarray datasets .
The following linear model is widely used for separation of gene expression [3]–[12] , [16]: ( 1 ) Where Mij is the mixed expression matrix of gene i in sample j , is the separated cell-type specific gene-expression matrix of gene i in cell type k and is the matrix of relative proportion of cell type k in sample j; is the total number of cell-types in the tissue , m and n are the total number of genes and samples , respectively [9] . Studies based on the model in ( 1 ) have shown that separation of mixed data with known proportions yielded cell-type specific expression estimates that were highly correlated with the corresponding purified cell gene-expression [3] , [4] , rendering the linearity assumption acceptable . All currently existing approaches , whether they use the linear model or not , require some a-priori information about the tissue analyzed , such as the number of cell types , their identity or their relative proportions in each sample [3]–[12] , [16] . In this work , we are interested in estimating G and C , from the observation M , without explicit a-priory knowledge of the number of cell-types in the tissue , , or their identities ( note that we will use upper-case boldface letters to denote matrices and lower-case boldface letters to denote vectors ) . Rather , we consider a collection of cell-types representing all possible cell-types assumed to comprise the analyzed tissue . This is a hypothesis-testing problem , where each possible combination of cell-types is a hypothesis . Our objective is to choose the correct hypothesis , i . e . , to determine which cell-types exist in the analyzed tissue . Assume that T is a label of a cell-type . We begin with a collection of labels that contains the true composition of cell-types . Specifically , if the true composition of cell-types in a given tissue sample are labeled by , we require that for each , there exists , such that . This is a reasonable assumption from a biological point of view , since if the tissue type is known then in most cases the cell-types that may exist in that tissue are also known . Note that if the initial collection of cell-types does not include one of the true cell-types , then this specific cell-type , its expression signature and relative proportions per sample will not be detected by the algorithm and there may be ambiguities in the resulting cell-type signatures and proportions . After estimating the true hypothesis , we estimate the parameters under that hypothesis , i . e . the specific cell-type expression ( G ) and the relative proportion of each cell-type per sample ( C ) . The algorithm that we propose requires as input purified gene-expression reference signatures li for each cell-type label , where . The latter constraint is necessary to have a unique solution to ( 1 ) , i . e . unique matrices G and C , up to normalization and permutation , which satisfies the decomposition in ( 1 ) . These reference signatures need not be identical to the purified signatures that comprise the original columns of the matrix G but only need to be taken from the same cell-type . Note that these reference signatures may be acquired from a different experiment , lab or tissue and are found in abundance in gene expression repositories such as GEO [14] . Separation of gene-expression can be viewed as a special case of a more general class of problems known as Nonnegative Matrix Factorization ( NMF ) problems , defined as follows: given a nonnegative data matrix M , find the smallest dimension matrices G and C with non-negative entries such that ( 2 ) where G is referred to as an end-members matrix ( where end-members are classes of composing materials that make up the object M [13] ) , and C represents the relative proportions in which the end-members are mixed in M i . e , G's ith column represents the signature of the ith end-member , and C's kth entry represent the relative proportion of the kth end-member in the jth data vector . This is equivalent to writing ( 1 ) in a matrix form , where each data vector represents microarray measurements of sample j . Each cell-type is an end-member , where G's ith column represents the gene signature of the ith cell-type . The jth column of C represents the relative proportions of the cell-types ( whose signatures comprise the columns of G ) in sample j . If the number of cell-types is smaller than the number of samples , the dimensions of G and C are smaller than the dimension of M , and the problem in ( 1 ) is a special case of the problem in ( 2 ) . The algorithm proposed in this paper is an adaptation of an NMF algorithm by Piper et al . [13] that was originally designed for spectral analysis of space objects . Piper et al . studied the problem of identification and classification of space objects whose orbits are significantly distant ( e . g . , geosynchronous satellites ) or whose dimensions are small ( e . g . , nanosatellites ) from ground-based telescope spectral measurements . In their problem , an object is classified by determining the characteristics of the material that make up its spectral trace . Each data vector mj represents a spectral trace ( i . e . the spectral image ) of the jth object . G's ith column represents the spectral signature of the ith material in the object ( end-member ) . Piper et al . 's hyper-spectral analysis approach is useful for analysis of gene-expression microarrays due to the use of prior knowledge . Their method uses a stored set of laboratory-obtained spectral signatures of space object materials obtained in a different experiment to determine the number of end-members . These stored signatures are not necessarily identical to the underline signatures but are only close to them . This approach is very appealing for separation of gene-expression , since in most cases the purified cell-types are not separated and profiled separately in the same experiment . Furthermore , it is possible to obtain cell-type specific reference signatures and use them for any analysis involving similar cell types . Despite the similarity between the two NMF applications , i . e . gene-expression analysis and spectral analysis , extensions to Piper et al . 's algorithm designed for spectral analysis were needed for the gene-expression analysis , as described in the following section . The proposed algorithm includes three major parts . In the first part , we obtain an initial estimate of the matrix G using as the number of columns . In the second part we estimate the true number of cell-types , , their identities , and the cell-type expression signatures matrix G . In the final part we compute the cell-type proportions matrix C . A detailed description of these steps is given in the following . All microarray data was downloaded from GEO [14] as raw . CEL files and RMA normalized using R© package “affy” . The datasets and reference signatures used in each analysis were jointly quantile normalized using R© package “limma” . The following accession numbers were used for each dataset: ( 1 ) Liver-brain lung dataset [3] ( GSE19830 ) , with reference signatures of purified rat liver ( GSE8252 ) , brain ( GSE3428 ) , lung ( GSE16849 ) , intestine ( GSE16849 ) , heart ( GSE5085 ) and granulosa ( GSE13883 ) cells . All reference signatures were chosen from the same platform as the analyzed data - Affymetrix Rat Genome 230 2 . 0 Array . ( 2 ) Heart-brain dataset [15] , with reference signatures of purified human myocardial ( heart ) cells from two different studies ( GSE21610 , GSE29819 ) , brain cells from the entorhinal cortex ( GSE4757 ) grey matter ( GSE28146 ) , oocytes ( GSE12034 ) and hepatocyte ( GSE31264 ) . All reference signatures were from the same platform as the analyzed data - Human Genome U133 Plus 2 . 0 Array . ( 3 ) T-B-Monocytes dataset [4] ( GSE11058 ) , with reference signatures of purified T cell Jurkat ( GSE7508 , GSE30678 ) , Monocyte THP-1 ( GSE26868 ) , B cell Raji ( GSE12278 , GSE13210 ) and IM-9 ( GSE24147 ) , IMC-1 NK ( GSE19067 ) and MCF-10A epithelial ( GSE10196 ) cell-lines . All reference signatures were from the same platform as the analyzed data - Affymetrix Human Genome U133 Plus 2 . 0 Array . ( 4 ) Prostate cancer dataset [7] ( GSE17951 ) . The dataset included 154 patient samples with proportions of the tumor cells were available for 137 samples . Reference signatures included purified prostate tumor cell lines - DU145 , PC3 , CWR22Rv , LAPC4 , C42B , LNCaP ( GSE12348 ) , benign prostate cells ( GSE3325 ) , normal prostate epithelial cells ( GSE9951 ) , stroma surrounding invasive prostate primary tumors and normal stroma ( GSE26910 ) . All reference signatures and analyzed data were from two similar platforms - Affymetrix Human Genome U133A Array and U133 Plus 2 . 0 Array . To separate a heterogeneous tissue , the user should have some knowledge regarding the nature of the tissue that is being separated and its possible cell-type constituents . Purified signatures of the candidate cell-types may be found in public repositories such as GEO via a simple search for the required cell-type and species . The chosen signatures need not be from the same disease , tissue study or experiment as the heterogeneous tissue to be separated . In case there are many possible relevant options from different studies for a cell-type , one can input several signatures of the same cell-type to the algorithm and gather them under the same class . This was demonstrated in the heart-brain and T-B-Monocyte dataset examples . The limit in the total number of signatures used for all cell-types is the number of samples of the mixed tissue that is being separated , as explained under “Linear model for separation of gene-expression” . In case of uncertainty as to what cell-types constitute the tissue , one does not have to be precise and can over-guess by inputting many , even un-related , cell-types into the algorithm . Note that under-guessing the number of cell-types may cause ambiguities in the algorithm results , as explained above . Parameters concerning majority voting ( threshold , number of majority voting runs ) and classes were set according to the nature of the cell-type signatures in each dataset , based on trial and error and common sense . In the case of majority voting , the more the input reference signatures are similar ( such as in the T-B-Monocyte dataset [4] , see also Figure S3A ) , the more likely that the algorithm will be farther away from the global minimum and therefore it will be harder to converge to a minimum that is close to the global minimum . Indeed , we noticed that the algorithm performs better with a lower threshold ( i . e . , a lower percentage of the number of times this cell-type is chosen out of the number of total runs ) and a higher number of majority voting runs in such cases . In cases where the input reference signatures are less similar to each other ( such as in the liver-brain-lung dataset [3] , see also Figure S1A ) , less majority voting runs are needed to yield accurate results . For classes' parameters , we observed that the algorithm encounters difficulties in separating cell-types for which the input reference signatures are very similar . In such instances , one might consider unifying these signatures under one class ( where biologically relevant ) or seek reference signatures from a different source . Observation of the input reference signatures , e . g . by drawing their heatmaps ( Figures S1 , S2 , S3 , S4A ) , can provide an indication regarding which reference signatures are similar . The algorithm was run with the following parameters for each dataset: ( 1 ) liver-brain-lung dataset [3]: majority voting threshold = 70% , majority voting runs = 10 , classes = none . ( 2 ) Heart-brain dataset [15]: majority voting threshold = 70% , majority voting runs = 10 , classes = unifying the two brain and two heart cell types to the classes “brain” and “heart” , respectively . ( 3 ) T-B-Monocytes dataset [4]: majority voting threshold = 70% , majority voting runs = 20 , classes = unifying the two B cell line types to the class “B cells” . ( 4 ) Prostate cancer dataset [7]: majority voting threshold = 70% , majority voting runs = 10 , classes = unifying the 6 different prostate tumor cell lines to the class “tumor” and the epithelial and stromal cells to the class “other” . | Gene expression microarrays are widely used to uncover biological insights . Most microarray experiments profile whole tissues containing mixtures of multiple cell-types . As such , gene expression differences between samples may be due to different cellular compositions or biological differences , highly limiting the conclusions derived from the analysis . All current approaches to computationally separate the heterogeneous gene expression to individual cell-types require that the identity , relative amount of the cell-types in the tissue or their individual gene expression are known . Publically available microarray-based datasets , which include thousands of patient samples , do not usually measure this information , rendering existing separation methods unusable . We developed a novel approach to estimate the number of cell-types , identities , individual gene expression and relative proportions in heterogeneous tissues with no a-priori information except for an initial estimate of the cell-types in the tissue analyzed and general reference signatures of these cell-types that may be easily obtained from public databases . We successfully applied our method to microarray datasets , yielding highly accurate estimations , which often exceed the performance of separation methods that require prior information . Thus , our method can be accurately applied to any heterogeneous dataset , where re-examination and analysis of the individual cell-types in the heterogeneous tissue can aid in discovering new aspects regarding these diseases . |
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Refinement of the nervous system depends on selective removal of excessive axons/dendrites , a process known as pruning . Drosophila ddaC sensory neurons prune their larval dendrites via endo-lysosomal degradation of the L1-type cell adhesion molecule ( L1-CAM ) , Neuroglian ( Nrg ) . Here , we have identified a novel gene , pruning defect 1 ( prd1 ) , which governs dendrite pruning of ddaC neurons . We show that Prd1 colocalizes with the clathrin adaptor protein α-Adaptin ( α-Ada ) and the kinesin-3 immaculate connections ( Imac ) /Uncoordinated-104 ( Unc-104 ) in dendrites . Moreover , Prd1 physically associates with α-Ada and Imac , which are both critical for dendrite pruning . Prd1 , α-Ada , and Imac promote dendrite pruning via the regulation of endo-lysosomal degradation of Nrg . Importantly , genetic interactions among prd1 , α-adaptin , and imac indicate that they act in the same pathway to promote dendrite pruning . Our findings indicate that Prd1 , α-Ada , and Imac act together to regulate discrete distribution of α-Ada/clathrin puncta , facilitate endo-lysosomal degradation , and thereby promote dendrite pruning in sensory neurons .
Neuronal remodeling is a pivotal step in the formation of mature nervous systems during animal development . Developing neurons often outgrow superfluous axonal or dendritic branches at early developmental stages . Selective elimination of the unneeded branches without the death of parent neurons , referred to as pruning , is crucial for the refinement of neuronal circuits at late stages [1–3] . Neuronal pruning is a naturally occurring process in mammals and insects . In the central and peripheral nervous systems of mammals , many neurons often prune their unwanted or inappropriate neurites in order to establish proper and functional neuronal connections [4–6] . In insects , such as Drosophila , the nervous system is drastically remodeled during metamorphosis [7–9] . In the central nervous system , mushroom body ( MB ) γ neurons prune their larval axonal/dendritic branches and extend their adult-specific processes to be integrated into the adult brains prior to eclosion [10] . In the peripheral nervous system , dendritic arborization ( da ) neurons undergo either apoptosis or pruning during early metamorphosis . For example , in the dorsal cluster , class IV da neurons ( ddaC ) and class I da neurons ( ddaD and ddaE ) selectively eliminate their larval dendrites whereas their axons remain intact [11 , 12] , while class III da neurons ( ddaF ) are apoptotic [12] . The dendrite-specific pruning event involves the formation of swellings and retracting bulbs [12] , morphologically resembling the axon/dendrite degenerative process associated with brain injury and neurodegenerative diseases . Thus , understanding the mechanisms of developmental pruning would provide insight into neurodegeneration in pathological conditions . Dendrite pruning of Drosophila ddaC sensory neurons has emerged as an attractive paradigm to elucidate the molecular and cellular mechanisms of neuronal pruning . In response to a late larval pulse of the steroid-molting hormone 20-hydroxyecdysone ( ecdysone ) , the dendrites of ddaC neurons are severed at their proximal region at 5–8 h after puparium formation ( APF ) , and subsequently the detached dendrites are rapidly fragmented and undergo phagocytosis-mediated degradation by 16–18 h APF ( Fig 1A ) [11 , 12] . It has been well documented that ecdysone and its nuclear receptors are required to induce the expression of several major downstream targets to initiate dendrite pruning [13 , 14] . Clathrin-mediated endocytosis ( CME ) is a major entry route that regulates the surface expression of transmembrane proteins and turnover of lipid membrane in eukaryotic cells [15] . The process is mediated by the highly conserved heterotetrameric Adaptor protein-2 ( AP-2 ) protein complex composed of α , β , μ , and σ subunits [16] . AP-2 is a CME-specific clathrin-associated adaptor that recruits clathrin to the plasma membrane-specific lipid phosphatidylinositol 4 , 5-bisphosphate ( PtdIns ( 4 , 5 ) P2 ) , leading to the formation of clathrin-coated pits . The invaginated pits are cleaved by the small GTPase dynamin to form clathrin-coated endocytic vesicles . Newly formed endocytic vesicles can fuse with early endosomes , a process mediated by the key GTPase Rabaptin-5 ( Rab5 ) [17] . Among several downstream routes , early endosomes can mature into multivesicular bodies in an endosomal sorting complexes required for transport ( ESCRT ) -dependent manner and subsequently fuse with lysosomes to degrade their protein and membrane components , a process known as endo-lysosomal maturation and degradation pathway [18] . It has been reported that CME regulates axon growth/guidance , dendrite extension/branching , and synaptic vesicle trafficking in vertebrate and invertebrate neurons [19 , 20] . We and others have also reported that endocytosis as well as endo-lysosomal degradation pathway play critical roles in ddaC dendrite pruning and MB γ axon pruning in Drosophila [21–23] . Rab5 and ESCRT complexes , two key regulators of the endo-lysosomal degradation pathway , promote dendrite pruning in ddaC neurons by facilitating lysosomal degradation of the Drosophila L1-type cell adhesion molecule ( L1-CAM ) Neuroglian ( Nrg ) [23] . Nrg is drastically endocytosed and degraded in dendrites , axons , and soma of ddaC neurons prior to pruning [23] . A parallel study also showed that Rab5/dynamin-dependent endocytosis appears to predominantly occur at the proximal regions of dendrites , leading to dendritic thinning and compartmentalized Ca2+ transients in ddaC neurons [22] . In MB γ neurons , PI3K-cIII/dynamin-dependent endo-lysosomal degradation pathway down-regulates the Hedgehog receptor Patched to promote axon pruning [21] . These studies highlight a general requirement of endocytosis and endo-lysosomal degradation for regulating distinct modes of neuronal pruning . However , the regulatory mechanism that promotes endocytosis and endo-lysosomal degradation pathway during neuronal pruning remains poorly understood . Here , we identified the critical role of a novel Drosophila SKIP-related gene , pruning defect 1 ( prd1 ) , in regulating dendrite pruning of ddaC sensory neurons . Mammalian Salmonella induced filament A ( SifA ) and Kinesin-interacting protein ( SKIP , also known as PLEKHM2 ) was originally identified as a target of the Salmonella effector protein SifA [24] . In uninfected mammalian cells , SKIP regulates the distribution of late endosomes and lysosomes [25 , 26] . However , the in vivo roles of SKIP and its related homologues during animal development are unknown . We show that Prd1 colocalizes with the endocytic components α-Adaptin ( α-Ada ) and clathrin in the dendrites of ddaC neurons . It forms a protein complex with α-Ada , the α subunit of the AP-2 complex ( also known as AP-2α ) , but not with the endosomal GTPase Rab5 . Similar to Prd1 , α-Ada and other subunits of AP-2 complex are all required for dendrite pruning in sensory neurons . Moreover , we show that Prd1 colocalizes and associates with the kinesin-3 immaculate connections ( Imac ) /Uncoordinated-104 ( Unc-104 ) . Importantly , both imac mutants and dominant-negative constructs exhibited severe dendrite pruning defects in ddaC sensory neurons . Prd1 , α-Ada , and Imac facilitate endo-lysosomal degradation of Nrg prior to dendrite pruning . Furthermore , genetic interactions among prd1 , α-ada , and imac suggest that they participate in the same pathway to promote dendrite pruning . Thus , our data demonstrate that the Prd1/α-Ada/Imac pathway promotes dendrite pruning via regulating α-Ada/clathrin distribution and endo-lysosomal degradation of Nrg .
To identify novel players in dendrite pruning , we expressed a collection of RNA interference ( RNAi ) lines using a class IV da neuron driver pickpocket-Gal4 ( ppk-Gal4 ) to knock down gene function in ddaC neurons . Two independent RNAi lines , v108557 ( #1 ) and v40070 ( #2 ) , were isolated with dendrite pruning defects . Both of RNAi lines target against a novel gene , CG17360 , which we therefore named pruning defect 1 ( prd1 ) . RNAi knockdown of prd1 via ppk-Gal4 caused prominent dendrite pruning defects in ddaC neurons at 16 h APF ( v108557 , n = 15 , Fig 1C , 1I and 1J; v40070 , n = 21 , Fig 1I and 1J ) . In contrast , at the same time point , larval dendrites were completely removed in the control neurons ( n = 25; Fig 1B , 1I and 1J ) . prd1 encodes a previously uncharacterized protein with 1 , 354 amino acids , which contains a pleckstrin homology ( PH ) domain at its C-terminal portion ( S1A Fig ) . Database searches revealed that in the Drosophila genome , Prd1 is most closely related to mammalian SKIP/PLEKHM2 ( S1A Fig ) . They share amino acid sequence identity in their C-terminal portions , including their PH domains and the flanking regions ( S1A Fig ) . SKIP was reported to regulate endosomal/lysosomal distribution in mammalian cells [25 , 26] . However , the function of Drosophila Prd1 was completely unknown . To further verify the requirement of prd1 for dendrite pruning , we took advantage of a prd1M56 mutant allele that was previously generated via flippase ( FLP ) -mediated recombination between two flippase recognition target ( FRT ) -containing P-element insertions ( S1B Fig ) . It deletes the majority of the prd1 coding region ( aa329–1 , 354 ) ( S1B Fig ) and hence is a strong hypomorphic allele . Mutants hemizygous for prd1M56 and a small deletion Df ( 3R ) Exel7310 ( deleting the entire prd1 gene and its neighboring genes ) died at the pharate adult stage and exhibited prominent dendrite pruning defects in ddaC neurons at 16 h APF . A total of 96% of those hemizygous mutant neurons exhibited dendrite severing defects and retained their larval dendrites with the attachment to their cell bodies by 16 h APF ( n = 24; Fig 1D , 1I and 1J ) . These dendrite pruning defects in the mutant pupae hemizygous for prd1M56 and Df ( 3R ) Exel7310 were fully rescued by ectopic expression of full-length Prd1 ( n = 22; Fig 1E , 1I and 1J ) or Venus-tagged Prd1 ( n = 19; Fig 1F , 1I and 1J ) . By mosaic analysis with a repressible cell marker ( MARCM ) analyses in ddaC neurons , another allele , prd1PS1 ( S1B Fig ) , phenocopied prd1M56/Df ( 3R ) Exel7310 mutants . All prd1PS1 mutant ddaC neurons failed to prune away their larval dendrites ( n = 11; Fig 1G , S1D Fig ) and exhibited 91% of severing defects and 9% of fragmentation defects ( Fig 1I ) . The length of unpruned dendrites in prd1PS1 mutants is comparable to that in hemizygous mutants of prd1M56 and Df ( 3R ) Exel7310 ( Fig 1J ) . Importantly , ectopic expression of full-length Prd1 also fully rescued their dendrite pruning defects in prd1PS1 mutant ddaC clones ( n = 12; Fig 1J ) , confirming that the dendrite pruning defects in prd1PS1 mutant neurons are caused by loss of prd1 function . The number of the primary and secondary dendrites in prd1M56/Df ( 3R ) Exel7310 ( 20 . 6 ± 0 . 33 , n = 10; Fig 1D ) remained similar to that of the control at the white prepupal ( WP ) stage ( 21 . 7 ± 0 . 16 , n = 10; Fig 1B ) . prd1M56 MARCM ddaC clones showed slightly simplified dendrite arbors ( S1C Fig ) . In addition to ddaC neurons , wild-type ddaD/E sensory neurons also completely pruned away their larval dendrites by 20 h APF ( n = 12; S2A Fig ) . prd1M56 ddaD/E clones retained some of their larval dendrites attached to their soma ( 89% , n = 9; S2A Fig ) . Moreover , wild-type ddaF neurons are apoptotic during early metamorphosis . Interestingly , ddaF neurons derived from prd1M56 MARCM clones were eliminated ( n = 5; S2B Fig ) , similar to wild type ( n = 3; S2B Fig ) , suggesting that prd1 is dispensable for ddaF apoptosis . Taken together , Prd1 is cell-autonomously required for dendrite pruning but dispensable for neuronal apoptosis in sensory neurons during early metamorphosis . To understand the functions of Prd1 in dendrite pruning , we examined its subcellular localization in ddaC neurons . Several antibodies were raised against three different portions of Prd1 ( S3A Fig ) . Given that ddaC neurons are sandwiched between the epidermis and body wall muscles , endogenous Prd1 signals in the dendrites were masked by its ubiquitous expression in the surrounding tissues . We therefore generated the transgenes expressing Prd1 tagged with Venus fluorescent protein at its N-terminus ( Venus-Prd1 ) . The expression of Venus-Prd1 , which was detected by the anti-Prd1 antibody ( n = 11; S3B Fig ) , fully rescued dendrite pruning defects in prd1M56/Df ( 3R ) Exel7310 mutants ( Fig 1F , 1I and 1J ) , suggesting that Venus-Prd1 functionally substitutes for endogenous Prd1 . Venus-Prd1 was distributed in the soma , dendrites , and axons of ddaC neurons ( n = 11; Fig 2G ) . Importantly , Venus-Prd1 also localized as some discrete puncta along the dendrites ( Fig 2G ) . Mammalian SKIP/PLEKHM2 functions in the proper distribution of endosomes/lysosomes [25 , 26] . We then investigated whether Prd1-positive puncta represent endosomes or endocytic vesicles . To this end , we co-expressed Venus-Prd1 with various endocytic markers green fluorescent protein ( GFP ) -Rab5 , GFP-α-Ada , and GFP-Clathrin light chain ( Clc ) /monomeric red fluorescent protein ( mRFP ) -Clathrin heavy chain ( Chc ) in ddaC neurons . Interestingly , Venus-Prd1 primarily localized adjacent to GFP-Rab5 ( 89% , n = 123 puncta , open arrowheads ) ( insets ) and occasionally colocalized with GFP-Rab5 ( 11% , arrowheads ) in the dendrites ( Fig 2A ) . This result suggests that the majority of Prd1 puncta juxtapose to Rab5-positive early endosomes . Interestingly , Venus-Prd1 colocalized with GFP-α-Ada ( 88% , n = 116 puncta; Fig 2B , insets ) , GFP-Clc ( 89% , n = 102 puncta; Fig 2C , insets ) , and mRFP-Chc ( 93% , n = 88 puncta; Fig 2D , insets ) in the dendrites of ddaC neurons ( arrowheads ) . These data suggest that Prd1 may be a component of α-Ada/clathrin-positive structures . Moreover , in the dendrites of ddaC neurons , Venus-Prd1 puncta were also enriched with phospholipase C-δ-pleckstrin homology ( PLC-δ-PH ) -GFP ( 94% , n = 394 puncta , Fig 2E ) , a PtdIns ( 4 , 5 ) P2 sensor that indicates membrane regions with highly active endocytosis [27] . As a control , Venus-Prd1 puncta localized distinctly from the Golgi marker ADP ribosylation factor 79F fused with enhanced green fluorescent protein ( Arf79F-EGFP ) in the dendrites of ddaC neurons ( 81% , n = 194 puncta; S3C Fig , insets ) . Thus , Prd1 and α-Ada/clathrin colocalize at punctate spots in the dendrites where endocytosis appears to be highly active . We next examined whether Prd1 regulates α-Ada localization in the dendrites . When prd1 was knocked down via RNAi , GFP-α-Ada formed prominent aggregates in the dendrites of mutant neurons ( n = 11 , 45%; Fig 2F ) , compared with the control neurons ( n = 11 ) . Likewise , Venus-Prd1 often accumulated on several enlarged cellular aggregates in the dendrites of α-ada RNAi mutant neurons ( n = 11; Fig 2G , arrowheads ) , compared with the control neurons ( n = 11; Fig 2G ) . These results suggest that Prd1 and α-Ada are mutually required for their distributions in the dendrites . We previously reported that the expression of Rab5DN or Vacuolar protein sorting-associated protein 4 ( Vps4 ) DN led to robust accumulation of ubiquitinated protein on enlarged endosomes in ddaC neurons [23] . Interestingly , Venus-Prd1 signals were also enriched on enlarged ubiquitin-positive endosomes in Rab5DN- or Vps4DN-expressing ddaC neurons ( n = 12 and 12 , respectively; S4A and S4B Fig ) , in contrast to the control ( n = 8; S4A and S4B Fig ) . Similar to Prd1 , α-Ada and Clc also accumulated on the aberrant endosomes in Rab5DN ( n = 14 and 15 , respectively ) or Vps4DN ( n = 12 and 8 , respectively ) mutant ddaC neurons ( S4A and S4B Fig ) . As controls , overexpression of either Rab5DN or Vps4DN did not affect the distribution of mitochondria ( Mito-GFP; n = 13 and 5 , respectively ) , Golgi ( GM130; n = 10 and 3 , respectively ) , and endoplasmic reticulum ( ER ) ( KDEL; n = 4 and 5 , respectively ) in ddaC neurons ( S5 Fig ) . Collectively , Prd1 predominantly colocalizes with α-Ada/clathrin-positive puncta in the dendrites and its distribution requires the endocytic regulators α-Ada , Rab5 , and Vps4 . Given their close localization patterns , we next attempted to examine potential protein–protein interactions between Prd1 and α-Ada/Rab5 . To this end , we co-transfected S2 cells and conducted co-immunoprecipitation ( co-IP ) experiments . α-Ada was present specifically in the immune complex when Prd1 was immunoprecipitated using an anti-Myc antibody ( Fig 3A ) . Reciprocally , Prd1 was also co-immunoprecipitated in the α-Ada complex using an anti-Flag antibody ( Fig 3B ) . The β subunit of AP-2 is Bap ( β-Adaptin , also known as AP-2β or AP-1-2β ) , which is probably shared between Adaptor protein-1 ( AP-1 ) and AP-2 complexes in Drosophila [28] . Similar to the association with α-Ada , Prd1 also formed a complex with Bap in S2 cells co-transfected with Myc-Prd1 and Flag-Bap ( S6A and S6B Fig ) . In contrast , Prd1 did not present in the same immune complex with Rab5 in both directions of co-IP experiments ( S7A and S7B Fig ) . Thus , Prd1 forms a protein complex with α-Ada and Bap , rather than with Rab5 . To assess a functional link between α-Ada and Prd1 , we then investigated whether α-Ada , like Prd1 , plays a role in dendrite pruning . We first knocked down α-ada gene function in ddaC neurons by ppk-Gal4 driver via RNAi . Two α-ada RNAi lines , v15566 ( #1 ) and BL#32866 ( #2 ) , caused consistent dendrite pruning defects in ddaC neurons by 16 h APF ( n = 13 and 30 , respectively; S8 Fig ) . Second , using a previously reported null allele α-adaptin3 ( α-ada3 ) [29] , we generated its homozygous MARCM clones to verify its requirement for dendrite pruning . Importantly , all α-ada3 mutant ddaC clones exhibited strong dendrite pruning defects , including 89% of severing defect at 16 h APF ( n = 9; Fig 4B , 4G and 4H ) , in contrast to the control neurons ( n = 5; Fig 4A , 4G and 4H ) . α-ada3 mutant neurons also showed notable dendrite morphology defects and their dendrite arbors were simplified at the WP stage ( n = 9; Fig 4B ) , similar to that reported in α-ada RNAi knockdown [20] . These dendritic defects were fully rescued by the expression of GFP-α-Ada under the control of ppk-Gal4 ( n = 11; Fig 4C , 4G and 4H ) . Similarly , ddaD/E MARCM clones homozygous for α-ada3 also failed to prune their larval dendrites by 20 h APF ( 89% , n = 9; S9A Fig ) . ddaF neurons derived from α-ada3 mutants were eliminated ( n = 2; S9B Fig ) , similar to control neurons ( n = 3; S9B Fig ) . We observed that α-Ada was expressed in both ddaF and ddaC neurons of wild-type larvae ( n = 8; S9D Fig ) . Thus , α-Ada , like Prd1 , is required for dendrite pruning of sensory neurons but not for neuronal apoptosis . We next examined other AP-2 subunits for their potential involvement in dendrite pruning . We generated BapΔ1 , an imprecise excision allele for Bap ( S9C Fig ) that encodes the β subunit of the AP-2 complex . BapΔ1 deletes a small C-terminal part of the coding region ( aa861–921 ) as well as the whole 3′-UTR region , suggesting a hypomorphic allele . Mutant clones homozygous for BapΔ1 showed mild pruning defects ( n = 17; Fig 4D , 4G and 4H ) , probably because of a weak allele or perdurance of the wild-type protein in mutant clones . These pruning defects were fully rescued by the expression of an upstream activating sequence ( UAS ) -Bap transgene ( n = 9; Fig 4E , 4G and 4H ) . Importantly , AP-2μ , the μ subunit of AP-2 complex , is also important for ddaC dendrite pruning . ddaC clones homozygous for AP-2μNN20 , a null allele with the M1I mutation [30] , showed severe dendrite pruning defects with many larval dendrites attached ( n = 17; Fig 4F , 4G and 4H ) , to an extent similar to α-ada3 mutant clones . Moreover , we examined the potential requirement of AP-1 genes ( AP-1μ and AP-1γ ) for dendrite pruning using the loss-of-function allele AP-1μSHE-11 [31] and AP-1γB/AP-1γD mutants [32] . No dendrite pruning defects were observed in ddaC clones of AP-1μSHE-11 and AP-1γB/AP-1γD mutants ( n = 6 , 9 , and 4 , respectively; S10 Fig ) . Thus , these data suggest that Prd1 most likely regulates dendrite pruning via AP-2 but independently of AP-1 . Collectively , α-Ada and other AP-2 subunits are critical for dendrite pruning in sensory neurons , whereas α-Ada is dispensable for neuronal apoptosis . Therefore , α-Ada and Prd1 play similar roles in dendrite pruning but not in neuronal apoptosis during metamorphosis . Prd1-related mammalian SKIP/PLEKHM2 binds to kinesin-1 and activates it to regulate the distribution of endosomes/lysosomes [25 , 26] . To investigate whether Prd1 regulates the distribution of α-Ada puncta via a motor protein , we examined potential colocalization between Prd1 and various motor proteins , including kinesin-1 , 2 , and 3 and dynein . Venus-Prd1 was distributed in a punctate pattern , which is distinct from the GFP-tagged Kinesin heavy chain ( Khc-GFP ) puncta ( n = 11; S11A Fig ) , suggesting that unlike SKIP , Prd1 may function independently of kinesin-1 in Drosophila . Neither did Prd1 colocalize with Kinesin associated protein 3 ( Kap3 ) , a subunit of the heterotrimeric kinesin-2 motor ( n = 24; S11B Fig ) . Remarkably , when Venus-Prd1 was co-expressed with red fluorescent protein ( RFP ) -tagged Imac ( Imac-RFP ) , a key neuronal kinesin-3 , Prd1 fully colocalized with Imac-RFP in the dendrites ( arrowheads ) and soma of ddaC neurons ( 94% , n = 462 puncta; Fig 5A ) . Moreover , we did not observe a similar distribution pattern between Prd1 and Dynein light intermediate chain ( Dlic ) or the dynein regulator Lis1 ( n = 5 and 11 , respectively; S11C and S11D Fig ) . Thus , Prd1 appears to specifically colocalize with kinesin-3 , but not with kinesin-1 and 2 or dynein , in the dendrites of ddaC sensory neurons . Because Venus-Prd1 colocalized with GFP-α-Ada and Imac-RFP in ddaC neurons , respectively , we expected a colocalization of GFP-α-Ada with Imac-RFP . Surprisingly , GFP-α-Ada did not colocalize with Imac-RFP in the dendrites ( open arrowheads ) , although their puncta occasionally juxtaposed in the soma ( arrowheads ) ( 7% , n = 99 puncta; Fig 5B ) . One possible explanation is that under the above experimental condition , in which both GFP-α-Ada and Imac-RFP were overexpressed , the low level of endogenous Prd1 protein is insufficient to bring GFP-α-Ada and Imac-RFP together . Remarkably , when Venus-Prd1 was co-expressed with GFP-α-Ada and Imac-RFP in ddaC neurons , GFP-α-Ada colocalized with Venus-Prd1 and Imac-RFP ( 79% , n = 527 puncta; Fig 5C ) . This result indicates that Prd1 may mediate the association between α-Ada and Imac in ddaC neurons . Likewise , Venus-Prd1 also colocalized with GFP-α-Ada and Imac-RFP in the axons ( S12A–S12D Fig ) . By contrast , overexpressed Venus-Prd1 failed to affect Rab5 localization in ddaC neurons , which was juxtaposed with Imac/Prd1 puncta ( 91% , n = 207 puncta; Fig 5D , open arrowheads ) . Thus , our results support the conclusion that Prd1 mediates the association of α-Ada with Imac in ddaC sensory neurons . Because the role of Imac in dendrite pruning is unknown , we next explored whether knockdown or loss of imac function caused dendrite pruning defects . Two independent imac RNAi lines , v47171 ( #1 ) and v23465 ( #2 ) , when expressed in ddaC neurons via ppk-Gal4 , led to severe dendrite pruning defects in all ddaC neurons at 16 h APF ( n = 13 and 5 , respectively; Fig 6B , S14 Fig ) . In contrast , the control neurons showed normal dendrite pruning at 16 h APF ( n = 17; Fig 6A ) . Moreover , using the previously reported null allele imac170 [33] , we generated their ddaC mutant clones and analyzed their defects in dendrite pruning at 16 h APF . ddaC clones homozygous for imac170 exhibited strong dendrite pruning defects by 16 h APF , with full penetrance ( n = 6 , respectively; Fig 6C , 6G and 6H ) . Similar to that reported in a previous RNAi study [34] , we also observed severe dendrite arborization defects in imac RNAi neurons and mutant clones . Only major dendrites were still present in the vicinity of mutant ddaC soma at the WP stage ( n = 11 and 6 , respectively; Fig 6B and 6C ) . Both dendrite morphology and pruning defects in imac170 mutant neurons were fully rescued by the expression of Imac-RFP ( n = 10; Fig 6D , 6G and 6H ) , suggesting that Imac-RFP functionally substitutes for endogenous Imac . Thus , Imac is required for both pruning and growth of dendrites in ddaC neurons . Like nematode Unc-104 , Imac contains a motor domain , three coiled coil domains , a forkhead-associated domain , and a PH domain ( S13 Fig ) . We identified a conserved ATP-binding sequence ( GQTGAGKS ) within the motor domain of the Imac protein and generated two mutant forms , GQTGAEKS ( ImacG102E ) and GQTGAAAA ( ImacAAA ) ( S13 Fig ) , which were reported to disrupt the motor activity of kinesins and myosins in other organisms [35] . We found that both Drosophila ImacG102E and ImacAAA behaved as dominant-negative forms , because their overexpression phenocopied imac loss-of-function mutants in terms of both dendrite arborization and pruning defects ( n = 13 and 7 , respectively; S14 Fig compared to Fig 6B and 6C ) . These data support the conclusion that the kinesin motor activity of Imac plays a crucial role in dendrite arborization and pruning . To rule out the possibility that the imac-associated dendrite pruning defects are secondary to the initial dendrite arborization defect , we conducted the Gene-Switch experiments by inducing the expression of these two dominant-negative forms at the early third instar larval stage ( 72 h after egg laying [AEL] ) . The Gene-Switch manipulations enabled mutant ddaC neurons to arborize mature and complex larval dendrites , as shown at the WP stage ( n = 12 and n = 8 , respectively; Fig 6F , S15 Fig ) , similar to their respective controls ( n = 9; Fig 6E , S15 Fig ) . Importantly , dendrite pruning defects were consistently observed upon the expression of ImacG102E ( n = 20; Fig 6F ) or ImacAAA ( n = 22; S15 Fig ) in ddaC neurons via the Gene-Switch driver GSG2295-Gal4 at 16 h APF , in contrast to no pruning defect observed in either non-induced ( n = 23; Fig 6E ) or induced controls ( n = 12; S15 Fig ) . Thus , the Gene-Switch experiments highlight that imac-associated dendrite pruning defects are not a secondary effect of dendrite arborization defect . Taken together , multiple lines of genetic evidence demonstrate that the Drosophila kinesin-3 Imac is a crucial motor protein regulating dendrite pruning in ddaC sensory neurons . We next examined whether imac is important for the distributions of Prd1 , α-Ada , and clathrin in ddaC neurons . First , we examined the distribution of the endogenous Prd1 protein with an anti-Prd1 antibody in imac knockdown or mutant neurons . In contrast to weak punctate signals of Prd1 in the control soma ( n = 11; Fig 7A ) , Prd1 accumulated on several bright aggregates in either imac RNAi or imac170 mutant neurons ( n = 11 and 7 , respectively; Fig 7A ) , suggesting that Imac promotes discrete distribution of Prd1 puncta in ddaC neurons . To better visualize its distribution in the dendrites , we expressed Venus-Prd1 and compared its distributions in control and imac RNAi ddaC neurons . In imac RNAi ddaC neurons , Venus-Prd1 strongly accumulated on many aggregates in the dendrites ( #1 , n = 21; control , n = 10; Fig 7B ) . Similar to the endogenous Prd1 protein ( Fig 7A ) , Venus-Prd1 was also observed to form several large aggregates in the soma of imac RNAi neurons ( #1 , n = 21 , Fig 7B , insets; #2 , n = 10 , S16 Fig ) , compared to the control neurons ( n = 10; Fig 7B , S16 Fig ) . Likewise , using a previously reported anti-α-Ada antibody [29] , we observed that endogenous α-Ada also aggregated on several large puncta in imac RNAi or imac170 mutant soma ( n = 9 and 9 , respectively; control: n = 12; Fig 7C ) . When overexpressed in imac RNAi ddaC neurons ( #1 and #2 ) , GFP-α-Ada , like the endogenous protein , strongly accumulated on the aggregates ( n = 8 and 12 , respectively; S16 Fig ) . Moreover , mRFP-Chc also accumulated as more aggregates in the dendrites and soma of imac RNAi ddaC neurons ( n = 13 , Fig 7D ) , compared to its distribution with small discrete puncta in control RNAi neurons . In contrast , we did not observe any obvious aggregates of Venus-Prd1 and α-Ada in khc RNAi ddaC neurons ( n = 6 and 19 , respectively; S17 Fig ) . These findings indicate that the kinesin-3 Imac , rather than kinesin-1 , plays an important role in distributing Prd1 , α-Ada , and clathrin in the dendrites of ddaC neurons . Given that Imac regulates the distributions of α-Ada/clathrin puncta , we further examined whether loss of imac function impairs endosomal distribution and lysosomal degradation . In imac RNAi ddaC neurons , the Venus-Prd1 aggregates were enriched with the endosomal markers anti–hepatocyte growth factor-regulated tyrosine kinase substrate ( Hrs ) ( n = 6; S18A and S18D Fig ) and Rab5-GFP ( n = 8; S18B and S18D Fig ) in the dendrites and soma , suggesting that these Venus-Prd1 structures are aberrant endosomes . We next examined whether these aberrant endosomes can fuse with lysosomes to undergo protein degradation . To this end , we utilized the LysoTracker dye to label highly acidified lysosomal compartments that normally fuse with endosomes to degrade the ubiquitinated proteins . The LysoTracker dye labeled discrete lysosomes in control ddaC neurons ( n = 16; Fig 7E ) . However , the Lysotracker dye was not enriched on aberrant endosomes in imac RNAi mutant neurons ( n = 10; Fig 7E ) , suggesting a compromise in endosomal acidification and maturation . Consistently , ubiquitinated proteins , which normally exhibited weak punctate structures in control neurons ( n = 12 , Fig 7F ) , were strongly enriched on Venus-Prd1 aggregates in imac RNAi neurons ( n = 8; Fig 7F ) , suggesting impaired endo-lysosomal degradation . As controls , in imac RNAi ddaC neurons , the secretory vesicle marker Sec15 did not accumulate as aggregates ( n = 4; S18C and S18D Fig ) and the Golgi marker β1 , 4-galactosyltransferase ( GalT ) -GFP appeared to be normal in size and distribution in the soma and dendrites ( n = 6; S18E Fig ) . The L1-CAM Nrg is endocytosed and down-regulated via the endo-lysosomal degradation pathway , leading to dendrite pruning in ddaC neurons [23] . We next examined whether Prd1 , α-Ada , and Imac regulate endo-lysosomal degradation of Nrg before the onset of dendrite pruning . In wild-type ddaC neurons , Nrg protein levels were significantly decreased in the somas , dendrites , and axons ( n = 23; S19A and S19E Fig ) at 6 h APF . Importantly , the Nrg protein accumulated dramatically in the somas , dendrites , and axons of prd1M56 /Df ( 3R ) Exel7310 ( n = 23; S19B and S19E Fig ) , α-ada3 MARCM ( n = 13 , S19C and S19E Fig ) , or ImacG102E ( n = 9; S19D and S19E Fig ) ddaC neurons , similar to those in Rab5DN mutant neurons . These data indicate that Prd1 , α-Ada , and Imac are required to promote Nrg endo-lysosomal degradation prior to dendrite pruning . Moreover , the expression of an nrg RNAi line , which has been shown to efficiently knock down its protein [23] , significantly suppressed the pruning defects of prdM56/prd1PS2 ( n = 25; S20A Fig ) , α-ada3 MARCM ( n = 13 , S20B Fig ) , or imac RNAi ( n = 11; S20C Fig ) mutant ddaC neurons . Thus , Prd1 , α-Ada , and Imac act to promote dendrite pruning at least partly through global endo-lysosomal degradation of Nrg . Moreover , a previous study has reported that local endocytosis leads to the thinning of proximal dendrites and compartmentalized calcium transients [22] . We next investigated whether Prd1/Imac/Unc-104 regulates local calcium transients at 6 . 5 h APF . While compartmentalized Ca2+ transients were present in the vast majority of control neurons ( n = 21; S21 Fig ) , the percentage of ddaC neurons with Ca2+ transients at 6 h APF was drastically reduced in prd1M56/Df ( 3R ) Exel7310 ( n = 20 ) and ImacG102E ( n = 13 ) ddaC neurons ( S21 Fig ) . These data suggest that Prd1 and Imac are also required to regulate compartmentalized calcium transients before the onset of dendrite pruning . Taken together , Imac is required for proper distributions of Prd1 , α-Ada , and clathrin in the dendrites and facilitates lysosomal degradation of Nrg in ddaC sensory neurons . To further explore the mechanisms whereby Prd1 and Imac regulate dendrite pruning , we conducted co-IP experiments to assess their potential physical association . In S2 cells co-transfected with Myc-Prd1 and Imac-HA , Prd1 was pulled down when Imac was immunoprecipitated using an anti-HA antibody ( Fig 3C ) . Reciprocally , Imac was also co-immunoprecipitated in the Prd1 immune complex ( Fig 3D ) . These co-IP data , together with the colocalization results ( Fig 5A ) , strongly support a functional link between prd1 and imac during dendrite pruning . Moreover , in S2 cells co-transfected with Flag-α-Ada and Imac-HA , the association between Imac and α-Ada was not detectable ( Fig 3E ) . Importantly , when Myc-Prd1 was co-transfected with Flag-α-Ada and Imac-HA , Imac was co-immunoprecipated by α-Ada ( Fig 3F ) . Thus , α-Ada and Imac form a protein complex in the presence of Prd1 in S2 cells . As a control , consistent with distinct localizations of Prd1 and the kinesin-1 Khc , we did not observe their physical association in the reciprocal co-IP experiments ( S22A and S22B Fig ) . These data suggest the selectivity of the interaction between Prd1 and the kinesin-3 Imac . Furthermore , Imac was not co-immunoprecipitated with Rab5 in reciprocal co-IP experiments ( S22C and S22D Fig ) . Taken together , our biochemical and cell biological data imply that Prd1 might interact with Imac and recruit α-Ada to Imac in vivo to facilitate endo-lysosomal degradation in ddaC sensory neurons . To further strengthen the functional link among Prd1 , Imac , and α-Ada in dendrite pruning , we conducted various combinations of genetic interaction assays . At 16 h APF in ddaC neurons , while heterozygous imac170 or imac172 had no adverse effect on dendrite pruning ( n = 21 and 23 , respectively; Fig 8A and 8B ) , they significantly enhanced the pruning defects of prd1M56/Df ( 3R ) Exel7310 mutant neurons ( n = 33 and 25 , respectively; Fig 8A and 8B ) . More larval dendrite branches persisted in the vicinity of their soma than those in prd1M56/Df ( 3R ) Exel7310 mutants alone ( n = 33 and 18 , respectively; Fig 8A and 8B ) . By contrast , removal of one copy of mical or cullin-1 , which are known to regulate dendrite pruning by a distinct mechanism [13 , 36] , did not enhance the dendrite-pruning phenotypes of prd1M56/Df ( 3R ) Exel7310 mutants ( S23A and S23B Fig ) . Next , we explored the genetic interaction between prd1 and α-ada . While the heterozygous α-ada3 allele did not show pruning defects ( n = 18; Fig 8C ) , removal of one copy of α-ada ( α-ada3/+ ) significantly enhanced the pruning phenotypes of prd1M56/Df ( 3R ) Exel7310 mutants ( n = 24; Fig 8C ) . Moreover , one copy of prd1M56 allele ( prd1M56/+ ) , which did not show notable pruning defects in the wild-type background ( n = 22; Fig 8D ) , drastically enhanced the pruning phenotypes of α-ada RNAi mutants ( n = 30; Fig 8D ) . Finally , removal of one copy of α-ada ( α-ada3/+ ) , which showed no pruning defect in wild-type background ( n = 20 ) , also caused a significant enhancement of the dendrite pruning defects in imac RNAi ddaC neurons ( n = 23; Fig 8E ) . Thus , these genetic interaction results strongly support that Prd1 , α-Ada , and Imac act in the same genetic pathway to promote dendrite pruning . In summary , multiple lines of genetic , biochemical , and cell biological evidence support the model that Prd1 , α-Ada , and Imac act in the same pathway to regulate discrete distribution of α-Ada/clathrin puncta , facilitate endo-lysosomal degradation of Nrg , and thereby promote dendrite pruning in sensory neurons ( in S24 Fig ) .
Growing evidence indicates that mammalian SKIP is involved in proper distribution of endosomes and lysosomes in mammalian cells . In infected cells , SKIP interacts with kinesin-1 to recruit the motor on the bacteria’s replicative vacuole , leading to the formation and distribution of late endosomes or lysosomes [37] . In uninfected cells , SKIP binds to the small GTPase Arf-like GTPase 8 ( Arl8 ) through its RUN domain or to the late endosomal GTPase Rab9 via its PH domain to regulate lysosomal distribution in a kinesin-1-dependent fashion [25 , 26] . Moreover , a mutation in human PLEKHM2 caused aberrant accumulation of both early and late endosomes in patients’ fibroblast cells [38] . However , the physiological function of SKIP/PLEKHM2 remained unknown . In this study , we demonstrate that a previously uncharacterized Drosophila SKIP-related gene , prd1 , plays a critical role in dendrite pruning . Several lines of evidence support the notion that Prd1 regulates dendrite pruning via AP-2 complex . First , Prd1 colocalized with the AP-2 α subunit α-Ada and clathrin in the dendrites . Second , Prd1 physically associated with α-Ada but not Rab5 . Third , Prd1 and α-Ada are mutually required for their proper distributions in the dendrites of ddaC neurons . Fourth , Prd1 distribution also requires the endocytic regulators Rab5 or Vps4 . Prd1 , similar to clathrin and the clathrin adaptor AP-2 , was enriched on enlarged endosomes in Rab5DN and Vps4DN mutant neurons , in contrast to its discrete distribution in wild type . Moreover , both prd1 and α-ada are important for endo-lysosomal degradation of Nrg . Finally , they genetically interacted during dendrite pruning . Reduction of prd1 gene dose ( prd1M56/+ ) dominantly enhanced the pruning phenotypes of α-ada RNAi mutants . The functional link between Prd1 and α-Ada suggests that Prd1 and α-Ada act together to regulate endo-lysosomal degradation . Similar to Prd1 , in a previous elegant study , Numb-associated kinase ( Nak ) , a Drosophila Actin-related kinase ( Ark ) family member , was identified as another binding partner of the clathrin adaptor protein AP-2 [20] . Nak distributes clathrin puncta in higher-order dendrites to promote dendritic growth [20] . However , we did not observe prominent dendrite pruning defects using a null nak2 mutant ( n = 29 ) , suggesting that AP-2-associated Nak is dispensable for ddaC dendrite pruning . Thus , it is conceivable that Prd1 acts as a novel binding protein of AP-2 to facilitate endo-lysosomal degradation of Nrg in the dendrites and promote dendrite pruning in sensory neurons . Kinesin motor proteins transport intracellular cargos along microtubule tracks [39] . Imac/Unc-104 belongs to the evolutionarily conserved kinesin-3 family . Imac/Unc-104 was reported to regulate synapse formation and synaptic vesicle transport in axons in fly and worm [33 , 40–43] . In mammals , two Imac homologues , namely kinesin ( KIF ) 1A and KIF1Bβ , transport synaptic vesicle precursors in axons [44 , 45] . Here , we provided multiple lines of genetic evidence using RNAi knockdown , loss-of-function mutants , and dominant negative approaches , unambiguously demonstrating that Imac is required for dendrite pruning , consistent with a recent report on imac RNAi knockdown phenotypes in ddaC neurons [46] . More importantly , for the first time we provide mechanistic insight into how the Drosophila kinesin-3 Imac regulates the distribution of α-Ada/clathrin puncta and promotes dendrite pruning of sensory neurons . imac mutant ddaC neurons showed severe defects in dendrite pruning and initial dendrite arborization , resembling α-ada mutants . imac displayed significant genetic interaction with α-ada . Moreover , Imac appeared to colocalize with the clathrin adaptor α-Ada and its interacting protein Prd1 in the dendrites . Interestingly , in imac mutant neurons , clathrin , α-Ada , and Rab5/Hrs accumulated into numerous aberrant aggregates , suggesting aberrant endosomal formation/distribution . The aberrant aggregates were also rich in ubiquitin but lacked LysoTracker signals , indicative of defective endo-lysosomal degradation . Furthermore , the L1-CAM Nrg protein accumulated dramatically in the somas , dendrites , and axons of ImacG102E ddaC neurons ( this study ) , whereas Nrg was drastically degraded via endo-lysosomal degradation pathway in wild-type neurons [23] . Therefore , our study demonstrates a novel role of Imac in promoting endo-lysosomal maturation/degradation in sensory neurons . Similarly , Caenorhabditis elegans Unc-104 regulates autophagosome formation and maturation in neurons [47] . Therefore , it is possible that the primary role of imac is to facilitate the formation or fusion of early endosomes and thereby lysosome-mediated degradation of the L1-CAM Nrg during dendrite pruning . Our cell biological , biochemical , and genetic data reveal a functional link between Prd1 and the kinesin-3 Imac in dendrite pruning . First , we show that Prd1 colocalizes with Imac but not with kinesin-1/2 or dynein . Second , Imac is required for the normal distribution of Prd1 and endocytic components α-Ada/clathrin , presumably endocytic vesicles , in dendrites . Third , Prd1 forms a protein complex with α-Ada and Imac but not with Rab5 or kinesin-1 . Finally , prd1 and imac are required for dendrite pruning in the same genetic pathway . How does imac regulate endo-lysosomal degradation of Nrg to promote dendrite pruning ? Our previous findings demonstrated that Rab5-dependent endocytosis regulates dendrite pruning via endo-lysosomal degradation of the L1-CAM Nrg [23] . We show here that Imac colocalized with the clathrin adaptor α-Ada via Prd1 in the dendrites but not with Rab5 . Moreover , co-IP experiments suggested that Prd1 formed a protein complex with α-Ada but not with Rab5 . One possibility is that the Prd1/α-Ada/Imac pathway might function at the internalization step of endocytosis , preceding the function of Rab5 , to facilitate the formation/fusion of early endosomes and thereby endo-lysosomal maturation/degradation during dendrite pruning . We envision that upon the cleavage of clathrin-coated vesicles , Prd1 associates with the clathrin adaptor protein AP-2 to recruit the newly formed vesicles to the kinesin-3 Imac , which in turn delivers them for the formation and fusion of early endosomes; early endosomes undergo endo-lysosome maturation and degradation , leading to dendrite pruning . However , in the established model of CME , AP-2/clathrin-coated endocytic vesicles are uncoated after budding; clathrin and AP-2 are released back into the cytoplasm to participate in another round of CME . The possible speculation is that Imac might deliver the α-Ada/clathrin-coated vesicles before their uncoating . Alternatively , α-Ada/AP-2 might play a noncanonical role in regulating endo-lysosomal degradation after the formation of early endosomes . Growing studies have shown noncanonical functions of the adaptor proteins AP-1/AP-2 and clathrin in vitro . In cultured cells , clathrin or AP-1/AP-2-associated vesicles can be directly distributed by kinesin or dynein motors [48 , 49] . Moreover , noncanonical function of AP-2 has been also reported in a recent study , in which AP-2 acts as an adaptor that links autophagosomes to a dynein motor for proper vesicular distribution in cultured neurons [50] . Further investigations are required to distinguish these two possible mechanisms whereby the Prd1/α-Ada/Imac pathway regulates endo-lysosomal degradation of Nrg to promote dendrite pruning . In summary , we identified a novel protein Prd1 and two associated proteins , including the clathrin adaptor α-Ada and the kinesin-3 Imac , which all play crucial roles in regulating dendrite pruning of sensory neurons . Mechanistically , Prd1 , α-Ada , and Imac act in the same pathway to regulate discrete distribution of α-Ada/clathrin puncta , facilitate endo-lysosomal degradation of the L1-CAM Nrg , and thereby promote dendrite pruning in sensory neurons .
Fly strains used in this study include α-ada3 ( H . Jackle ) [29] , AP-2μNN20 ( D . Bilder ) [30] , Rab52 , UAS-Rab5DN , UAS-GFP-Rab5 ( M . Gonzalez-Gaitan ) [51] , imac170 , imac172 , UAS-Imac-RFP ( T . L . Schwarz ) [33] , UAS-Vps4DN ( H . Stenmark ) [52] , UAS-GFP-α-Ada ( B . Lu ) [53] , UAS-GFP-Clc , UAS-mRFP-Chc , Cul1Ex ( C . T . Chien ) [20 , 54] , mical15256 ( Yu lab ) [13] , SOP-flp ( #42 ) , UAS-Dlic::EGFP ( T . Uemura ) [55] , ppk-Gal4 on II and III chromosome ( Y . Jan ) [56] , UAS-mRFP-Lis1 ( A . Moore ) [57] , UAS-Kap3-RFP ( K . Ray ) [58] , UAS-Khc-GFP ( Yu lab ) , UAS-ImacG102E , UAS-ImacAAA , UAS-Venus-Prd1 , UAS-Prd1 , UAS-Bap ( this study ) , UAS-Sec15-GFP ( H . Bellen ) [59] , AP-1μSHE-11 ( R . L . Borgne ) [31] , and UAS-Arf79F-EGFP ( T . J . Harris ) [60] . The following stocks were obtained from Bloomington Stock Center ( BSC ) : Gal4109 ( 2 ) 80 , elav-Gal4 , ppk-CD4-tdGFP ( BL#35843 ) , GSG2295-Gal4 ( BL#40266 ) , CG17360M56 ( BL#37744 ) , Df ( 3R ) PS1 ( BL#37741 ) , Df ( 3R ) PS2 ( BL#37742 ) , Df ( 3R ) Exel7310 ( BL#7965 ) , α-ada RNAi #2 ( BL#32866 ) , khc RNAi ( BL#25898 ) , P{EPgy2}EY01200 ( BL#15065 ) , Dp ( 1;Y ) BSC140 ( BL#30461 ) , UAS-GalT-GFP ( BL#30902 ) , UAS-mito-HA-GFP ( BL#8442 ) , UAS-PLC-δ-PH-GFP ( BL#39693 ) , AP-1γB ( BL#57051 ) , AP-1γD ( BL#57052 ) , nrg RNAi ( BL#38215 ) , UAS-GCaMP3 ( BL#32116 ) , and UAS-GCaMP6 ( BL#42746 ) . The following stocks were obtained from Vienna Drosophila RNAi Centre ( VDRC ) : prd1 RNAi #1 ( v108557 ) , prd1 RNAi #2 ( v40070 ) , α-ada RNAi #1 ( v15566 ) , imac RNAi #1 ( v47171 ) , imac RNAi #2 ( v23465 ) , and control RNAi ( v36355 ) . prd1 and Bap full-length cDNAs were PCR from EST LP07755 and LP17054 ( DGRC ) into Topo Entry vector ( Life Tech , Carlsbad , CA ) . The GATEWAY pTW or pTVW vectors containing the respective fragment of the cDNAs were constructed by LR reaction ( Life Tech , Carlsbad , CA ) . The variants of Imac were generated by G102E and GKS102-104AAA site mutagenesis ( Agilent Tech ) using pUAST-imac-HA as a template , respectively . The respective cDNA fragments were amplified by PCR and subcloned into pTWH vector ( DGRC ) . The transgenic lines were established by the Bestgene . P{EPgy2}EY01200 P-element insertion flies were crossed with the fly strain carrying the Δ2–3 transposase to induce imprecise excision events . Nearly 600 independent lines were established based on a loss of the w+ marker . One of lethal lines was found to be rescued by the duplication line Dp ( 1;Y ) BSC140 . Subsequent genomic PCR and DNA sequencing analysis indicated that this mutant BapΔ1 harbors a 1 , 381-bp deletion . cDNA fragments corresponding to aa151–350 ( antigen 1 ) , aa300–575 ( antigen 2 ) , and aa 591–724 ( antigen 3 ) of prd1 isoform A were amplified from EST LP07755 by Expand High Fidelity PCR System ( Roche ) and verified by DNA sequencing . The resultant products were expressed by the GST expression vector ( pGEX4T-1 , Pharmacia ) . After protein purification , the purified protein was used to immunize various guinea pigs and rats to generate polyclonal antibodies against Prd1 . The specificity of the antibody was verified in ddaC neurons expressing both prd1 RNAi and imac RNAi lines . To image Drosophila da neurons at the wandering third instar ( wL3 ) or WP stage , larvae or pupae were first washed in PBS buffer briefly , followed by immersion with 90% glycerol . For imaging da neurons at 16 h APF , pupal cases were carefully removed before they were mounted with 90% glycerol . Dendrite images were acquired on Leica TSC SP2 . Subcellular localization images were acquired on Leica TCS SP8 STED 3× super-resolution microscope . MARCM analysis , dendrite imaging , and quantification were carried out as previously described [13] . ddaC or other da clones were selected and imaged at the WP stage according to their location and morphology . The ddaC or other da neurons were examined for dendrite pruning defects at 16 h or 20 h APF . Embryos of appropriate genotype were collected at 6-h intervals and reared on standard food to the early third instar larva stage . The larvae were transferred to the standard culture medium , which contains 240 μg/mL mifepristone ( Sigma Aldrich M8046 ) . Neither puparium formation onset nor adult eclosion was affected by RU486 treatment . White prepupae with appropriate genotype were picked up , subject to dissection and phenotypic analysis at 16 h APF . The following primary and secondary antibodies were used for immuno-histochemistry at the indicated dilution: rat anti-Prd1 ( 1:200 ) ( this study ) , rabbit anti-α-Ada ( 1:200 ) , guinea pig anti-Avl ( 1:500; Yu lab ) , guinea pig anti-Hrs ( 1:300 ) [61] , mouse anti-Ubiquitin ( 1:500; FK2 , Enzo Life Sciences ) , rabbit anti-GFP ( 1:500 , Invitrogen ) , mouse anti-GPF ( 1:500 , Yu lab ) , guinea pig anti-Sec15 ( 1:200 ) , rabbit anti-GM130 ( 1:200 , Abcam ) , mouse anti-KDEL ( 1:200; 10C3 , Abcam ) , mouse anti-Nrg ( 1:20 , BP104 , DSHB ) . Cy5-conjugated goat anti-HRP was used at 1:200 dilutions , Cy3 or fluorescein isothiocyanate ( FITC ) conjugated secondary antibodies were used at 1:500 dilutions , and Cy5 conjugated secondary antibodies were used at 1:200 dilutions . For immune-staining assays , wild-type and mutant pupae or larvae were dissected in cold PBS and fixed in 4% formaldehyde for 12 min . The samples within the same group of experiments were stained in the same tube and mounted in VectaShield mounting medium , and the samples were directly visualized by Leica TCS SP8 STED 3 × super-resolution microscope and processed in parallel . Data analysis and statistics were performed via Excel ( Microsoft ) , and Imaris software . Based on the intensity profiles , the localization patterns were divided into three categories: colocalization , adjacent localization , and non-colocalization , as shown in the representative images ( S25 Fig ) . Live confocal images of da neurons expressing mCD8-GFP under the control of ppk-Gal4 or elav-Gal4 were shown at WP , 16 h APF and 20 h APF . For wild-type or mutant ddaC neurons , the percentages of fragmentation defect and severing defect were quantified in a 275 μm × 275 μm region of the dorsal dendritic field , originating from the abdominal segments 2–5 . The severing defect was defined by the presence of dendrites that remain attached to the soma at 16 h APF , whereas dendrite fragmentation defect is referred to as the presence of dendrite branches near the ddaC territory that have been severed from their proximal parts at 16 h APF [11–13] . Total length of unpruned dendrites was measured in a 275 μm × 275 μm region of the dorsal dendritic field using ImageJ . The number of samples ( n ) in each group is shown on the bars . Statistical significance was determined using either two-tailed Student t test ( two samples ) or one-way ANOVA and Bonferroni test ( multiple samples ) ( *p < 0 . 05 , **p < 0 . 01 , ***p < 0 . 001 , n . s . , not significant ) . Error bars represent SEM . Dorsal is up in all images . S2 cell culture and western blotting were carried out as described below . Myc-Prd1 , Flag-α-Ada , Flag-Rab5 , Khc-Flag , and Imac-HA expression vectors were generated by Gateway cloning . S2 cells were cultured at 25°C in Express Five SFM ( Gibco ) medium supplemented with 1% L-glutamine . For transfection , S2 cells were plated at a density of 2 × 106 cells per 35-mm dish 1 d before transfection . After 24 h culture , around 0 . 1–0 . 5 μg of each expression plasmid was transfected into the S2 cells using Effectene Transfection Reagent ( Qiagen ) ; cells were harvested 48 h post-transfection . Transfected S2 cells were homogenized with lysis buffer ( 25 mM Tris pH 8/27 . 5 mM NaCl/20 mM KCl/25 mM sucrose/10 mM EDTA/10 mM EGTA/1 mM DTT/10% [v/v] glycerol/0 . 5% Nonidet P40 ) with protease inhibitors ( Complete , Boehringer; PMSF 10 mg/mL , sodium orthovanadate 10 mg/mL ) . The supernatants were used for IP with anti-Myc , anti-Flag , or anti-HA overnight at 4°C followed by incubation with protein A/G beads ( Pierce Chemical Co . ) for 2 h . Protein A/ G beads were washed four times using cold PBS . Bound proteins were separated by SDS-PAGE and analyzed by western blotting with anti-Myc , anti-Flag , and anti-HA HRP-conjugated antibody . Co-IP experiments were repeated three times . Calcium imaging was performed with Olympus FV3000 using 60× Oil lens . Calcium images from 6 . 5 h APF ddaC neurons were acquired for 400–500 frames at 1 frame per 1 . 8–2 . 25 s and analyzed using Metamorph ( Molecular Devices ) and ImageJ software . | During the maturation of the nervous system , some neurons can selectively eliminate their unnecessary connections , including dendrites and axons , to retain specific connections . In Drosophila , a class of sensory neurons lose all their larval dendrites during metamorphosis , when they transition from larvae to adults . We previously showed that these neurons prune their dendrites via lysosome-mediated degradation of a cell-adhesion protein , Neuroglian . In this paper , we identified a previously uncharacterized gene , pruning defect 1 ( prd1 ) , which plays an important role in dendrite pruning . We show that Prd1 is localized and complexed with α-Adaptin and Imac , two other proteins that are also essential for dendrite pruning . Moreover , Prd1 , α-Adaptin , and Imac act in a common pathway to promote dendrite pruning by down-regulating Neuroglian protein . Thus , our study highlights a mechanism whereby Prd1 , α-Adaptin , and Imac act together to regulate distribution of α-Adaptin/clathrin puncta , facilitate lysosome-dependent protein degradation , and thereby promote dendrite pruning in Drosophila sensory neurons . |
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Intrinsic immunity describes the set of recently discovered but poorly understood cellular mechanisms that specifically target viral pathogens . Their discovery derives in large part from intensive studies of HIV and SIV that revealed restriction factors acting at various stages of the retroviral life cycle . Recent studies indicate that some factors restrict both retroviruses and retrotransposons but surprisingly in ways that may differ . We screened known interferon-stimulated antiviral proteins previously untested for their effects on cell culture retrotransposition . Several factors , including BST2 , ISG20 , MAVS , MX2 , and ZAP , showed strong L1 inhibition . We focused on ZAP ( PARP13/ZC3HAV1 ) , a zinc-finger protein that targets viruses of several families , including Retroviridae , Tiloviridae , and Togaviridae , and show that ZAP expression also strongly restricts retrotransposition in cell culture through loss of L1 RNA and ribonucleoprotein particle integrity . Association of ZAP with the L1 ribonucleoprotein particle is supported by co-immunoprecipitation and co-localization with ORF1p in cytoplasmic stress granules . We also used mass spectrometry to determine the protein components of the ZAP interactome , and identified many proteins that directly interact and colocalize with ZAP , including MOV10 , an RNA helicase previously shown to suppress retrotransposons . The detection of a chaperonin complex , RNA degradation proteins , helicases , post-translational modifiers , and components of chromatin modifying complexes suggest mechanisms of ZAP anti-retroelement activity that function in the cytoplasm and perhaps also in the nucleus . The association of the ZAP ribonucleoprotein particle with many interferon-stimulated gene products indicates it may be a key player in the interferon response .
Host restriction factor proteins are part of the intrinsic immune system of the cell , forming an early line of defense against viral infection . Intrinsic immunity is triggered when viral RNAs are recognized by pattern-recognition receptors , such as Toll-like and retinoic acid-inducible gene ( RIG-I ) -like receptor family members , causing activation of an effector protein ( for example , IRF3 ) and the expression of interferon ( IFN ) and hundreds of IFN-stimulated genes ( ISGs ) . Many viral restriction factors are ISGs that function by diverse mechanisms against a wide range of viral pathogens . For example , Myxovirus ( influenza virus ) resistance 1 , interferon-inducible protein p78 ( mouse ) ( MX1 , also known as MXA ) ) and MX2 ( MXB ) are closely related members of the IFN-induced dynamin family of large GTPases . MX1 is a broad-spectrum inhibitor of many RNA and DNA viruses ( reviewed in [1] ) . IFN-induced transmembrane protein family members ( IFITM1/2/3 ) are also potent inhibitors of a range of viruses including HIV-1 , although their mechanisms of action are unclear ( [2]; reviewed in [3] ) . BST2 ( Tetherin ) is a type II transmembrane glycoprotein capable of trapping enveloped virions at the cell surface ( reviewed in [4] ) . RSAD2 ( Viperin ) is an endoplasmic reticulum-associated protein that inhibits many RNA and DNA viruses at multiple stages of the viral life cycle , and which may be involved in innate immune signaling ( reviewed in [5] ) . RNA helicases and IFIH1 interact with Mitochondrial antiviral signaling protein ( MAVS ) , a mitochondrial outer membrane protein , activating formation of the MAVS signalosome and upregulation of NF-κB and IRF3 signaling pathways [6] . ISG20 is a 3'-5' exoribonuclease that inhibits single-strand RNA viruses including HIV-1 [7] . The transcriptional regulator TRIM28 ( KAP1 ) also limits HIV integration by binding acetylated integrase and inducing its deacetylation by recruiting HDAC1 [8] . While the cell can be infected by a wide variety of viruses , unrestricted activity of endogenous retroelements also poses a threat to genome integrity and cell function . Long-terminal repeat ( LTR ) retrotransposons include the human endogenous retroviruses ( HERVs ) that comprise 8% of the human genome , although no HERVs capable of replication have been identified . However , increased HERV expression has been implicated in multiple sclerosis , lupus , amyotrophic lateral sclerosis , and autoimmune rheumatic disease , although whether the increase is the cause or effect of disease remains to be determined ( [9]; reviewed in [10–12] ) . Non-LTR retrotransposons comprise at least one-third of the human genome and remain an ongoing cause of disease [13 , 14] . LINE-1s ( L1s ) are the only active autonomous mobile DNA remaining in humans , and among ∼500 , 000 copies at least 100 remain potentially active for retrotransposition in any human individual [15 , 16] . L1s have also been responsible for the genomic insertion in trans of thousands of processed pseudogenes and a million SINEs ( Alus and SVAs ) . The current residual activity of human retrotransposons is the background that escapes a variety of mechanisms that have evolved to limit replication of mobile DNA . Phylogenetic analyses suggest that eukaryote non-LTR retrotransposons predate LTR retrotransposons , which in turn gave rise to the retroviruses through the acquisition of an envelope gene [17–19] . The ancient origin and interrelatedness of the major classes of retroelements predicts they will be subject to some of the same host restriction factors . On the other hand , significant differences between their modes of replication suggest that non-LTR retrotransposons and retroviruses could be affected by the same restriction factors in divergent ways . For example , Apolipoprotein B mRNA-editing enzyme , catalytic polypeptide-like 3 ( APOBEC3 ) family members , first shown to inhibit HIV by hypermutation of minus-strand DNA , were then found to potently inhibit retrotransposition but without obvious hypermutation , suggesting a unique pathway ( reviewed in [20 , 21] ) . Recently , it was proposed that APOBEC3A inhibits L1 retrotransposition by deaminating transiently exposed single-strand genomic DNA that flanks the site of L1 integration [22] . The Aicardi-Goutières syndrome ( AGS ) -related anti-retroviral protein SAM domain and HD domain 1 protein ( SAMHD1 ) inhibits retroviruses in non-dividing myeloid cells and resting CD4+ T cells by depleting dNTP levels . However , SAMHD1 also limits non-LTR retrotransposition in dividing cells , suggesting an inhibitory mechanism different from nucleotide depletion [23] . Other innate restriction factors also suppress retrotransposons . LINE-1 activity is upregulated in cells deficient for TREX1 , a gene associated with systemic lupus erythematosus ( SLE ) and , like SAMHD1 , with AGS [24] . RNaseL , a member of the IFN antiviral response to dsRNA , likely restricts retrotransposons in cell culture by cleavage of their mRNAs [25] . Finally , the RNA helicase MOV10 , previously reported to affect replication of several RNA viruses , also limits activity of all human retrotransposons [26–28] . MOV10 may target retroelement complexes for degradation , possibly by RNA-induced silencing complexes ( RISC ) . Clearly , studying factors that restrict both retrotransposons and retroviruses can inform both fields . Therefore , we overexpressed a panel of antiviral ISGs and assayed for their previously untested effects on LINE-1 retrotransposition in a cell culture assay . Several of these factors strongly inhibited retrotransposition of an L1 reporter construct . We report that the zinc finger antiviral protein ZAP ( also called Zinc finger CCCH-type , antiviral 1 , ZC3HAV1 , and Poly ( ADP-ribose ) polymerase 13 , PARP13 ) is a potent inhibitor of not only viruses but also retrotransposons . ZAP targets positive- and negative-strand RNA viruses of several families , including Retroviridae ( HIV-1 , MoLV , and XMRV ) , Filoviridae ( Ebola and Marburg ) , Togaviridae ( alpha- , sindbis , Semliki Forest , and Ross river viruses ) , and Hepadnaviridae ( hepatitis B ) [29] , and has been shown to inhibit the double-stranded DNA murine gammaherpesvirus [30] . However , restriction is not universal: ZAP fails to inhibit vesicular stomatitis , poliovirus , yellow fever , and herpes simplex I viruses [31] . The presence of orthologs in fish , birds and reptiles suggests that ZAP ( like MOV10 ) is ancient in origin [32 , 33] . L1 expresses a 6-kb bicistronic RNA that encodes a 40 kD RNA-binding protein ( ORF1p ) of essential but uncertain function for retrotransposition , and a 150 kD ORF2 protein with endonuclease and reverse transcriptase ( RT ) activities . In the cytoplasm , ORF1p and ORF2p preferentially bind their own encoding RNA in cis to form a functional ribonucleoprotein particle ( RNP ) [34 , 35] . We show that ZAP strongly restricts retrotransposition in cell culture with loss of L1 RNA and RNP integrity . Association of ZAP with the L1 RNP is supported by co-IP and co-localization with ORF1p in cytoplasmic granules . Mass spectrometry ( MS ) analyses of the ZAP proteome revealed proteins that bind ZAP , including MOV10 , and suggest possible mechanisms of ZAP-mediated retroelement restriction .
We asked if increased interferon limits L1 activity in a cell culture assay for retrotransposition . In this assay , an enhanced green fluorescent protein ( EGFP ) reporter gene cassette , interrupted by a backwards intron and inserted in opposite transcriptional orientation into the 3' UTR of L1-RP ( a highly active L1 [36] ) , is expressed only when the L1 transcript is spliced , reverse-transcribed , its cDNA inserted in the genome , and the EGFP reporter gene expressed from its own promoter [37 , 38] . The full-length L1 and reporter cassette are cloned in a modified version of pCEP4 vector ( Invitrogen ) lacking a cytomegalovirus ( CMV ) promoter . Therefore , expression of the L1 is driven by its own 5' UTR . We transfected HEK 293T cells with the L1-EGFP reporter ( 99-PUR-RPS-EGFP ) in the presence of increasing amounts of Universal Type I Interferon ( IFN ) alpha , and at 5 days post-transfection assayed for fluorescent ( i . e . retrotransposition-positive ) cells by flow cytometry . Addition of IFN reduced cell culture retrotransposition in a dose-dependent manner . At 1000 U IFN/ml , retrotransposition was over 90 percent less than non-treated controls , with a loss of cell viability of less than 20 percent as determined by trypan blue staining and the MultiTox-Fluor Multiplex Cytotoxicity Assay ( Promega ) ( Fig 1A , compare lanes 2 and 3 ) . Several interferon-inducible proteins are known to restrict not only viruses but also endogenous retroelements . To extend the list of these proteins , we next screened a panel of ISGs and restriction factors with known antiviral activities for effects of their expression on L1 retrotransposition ( Fig 1B , upper bar chart ) . To reveal differences of transfection efficiency , cytotoxicity or reporter gene expression caused by the test proteins , we followed the approach of Wei et al . [39] and in conjunction with the retrotransposition assays transfected CEP-EGFP , a vector that constitutively expresses EGFP , together with empty vector or test constructs . After 4 days we performed flow cytometry to assay for loss of green cells ( Fig 1B , lower bar chart ) . It should be noted that the CEP-EGFP assay is a snapshot of GFP fluorescence at a single time-point , while the retrotransposition assay measures accumulated EGFP insertions that occur over 5 days . Therefore , the MultiTox-Fluor Multiplex Cytotoxicity Assay was also used as a direct assay for cell toxicity ( assessed at Day 3 post-transfection ) . This dual-detection assay generates a ratio of live to dead cell readings , thereby normalizing for cell number ( Fig 1B , table ) . 293T cells were cotransfected with 99-PUR-RPS-EGFP and empty vector or tagged ISG cDNA constructs . With the exception of MOV10 , none of the proteins shown in Fig 1B had previously been tested for effects on cell culture retrotransposition . Six factors , MOV10 ( as previously reported [26–28] ) , BST2 , ISG20 , MAVS , MX2 , and ZAP , reduced plasmid-directed LINE1 retrotransposition in 293T cells by over 75 percent ( Fig 1B ) . While minimal toxicity was detected by the MultiTox-Fluor assay for all proteins , BST2 and MAVS reduced CEP-EGFP vector expression 44 and 50 percent , respectively . However , loss of CEP-EGFP signal only partly explains the strong inhibition of cell culture retrotransposition observed for these proteins ( 86 and over 98 percent , respectively ) . To assess specificity of BST2-induced reduction of retrotransposition , we coexpressed Vphu , a codon-optimized version of the HIV-1-encoded BST2 antagonist Vpu [40] . Vphu restored L1 retrotransposition over 2-fold ( 11 to 26 percent ) , an amount limited by the fact that overexpression of Vphu itself reduced retrotransposition to 37 percent ( Fig 1C ) . We also tested MX2 mutant proteins for their activity in the cell culture assay ( Fig 1D ) . The N-terminal 25 amino acids of MX2 encode a nuclear envelope targeting domain essential for HIV-1 inhibition . Conversely , while MX2 mutated at K131A fails to bind GTP , it still inhibits HIV-1 as if wild-type [41–44] . Contrary to the results for HIV-1 , deleting the first 25 residues of our MX2 construct failed to block its inhibition of retrotransposition , but the K131A mutant lost ability to restrict L1s . Because the K131A mutant is expressed at a lower level than the wild-type protein ( also noted by [45] ) , we increased the amount of mutant MX2 plasmid transfected fourfold but still failed to restore inhibition of retrotransposition to wild-type levels . Thus , the mechanisms by which MX2 restricts retroviruses and retrotransposons appear to differ in some aspects . Unfortunately , endogenous MX2 is expressed at detectable levels in none of the cell lines we use for our retrotransposition assay ( 293T , HeLa , or human embryonal carcinoma 2102Ep cells ) and is induced by IFN in HeLa cells only ( S1 Fig ) . Consequently , we could not assay endogenous MX2 for effect on retrotransposition , and we pursued MX2-related experiments no further . Finally , we tested for interaction of IGS factors with the L1 RNP ( Fig 1E ) . Expression of pc-L1-1FH , a construct containing L1-RP with a tandem FLAG-HA tag fused to the C-terminus of its ORF1 , permits immunoprecipitation ( IP ) of basal L1 RNP complexes [46] . pc-L1-1FH and V5 epitope-tagged ISG proteins were coexpressed in 293T cells , followed by their co-IP on α-FLAG agarose . TRIM28 and IFITM1 bound non-specifically to the affinity agarose , and their associations with L1 were inconclusive . MOV10 ( as previously reported [27] ) , MX1 , MX2 , and the short isoform of ZAP ( ZAP-S/PARP13 . 2 ) all co-immunoprecipitated with the L1 RNP . These interactions were lost with RNase treatment . We have not confirmed that these proteins directly bind L1 RNA . Rather , proteins might bind non-L1 RNAs or other multi-protein complexes captured within the L1 RNP . Moreover , an unknown amount of tagged ORF1p along with its bound partners , free in solution and not part of retrotransposition-competent RNPs , will have co-purified within immunoprecipitates . Only functional analyses of restriction factors associated with the L1 RNP will ascertain their relevance to L1 biology . Thus , we have expanded the growing list of IFN-stimulated cellular restriction factors that inhibit human retrotransposition . We decided to focus on the zinc finger protein ZAP for further investigation . ZAP ( PARP13 ) is a member of the poly ( ADP-ribose ) polymerase ( PARP ) family of 17 proteins , some of which are capable of poly ( ADP ) ribosylation ( pADPr ) of acceptor proteins using NAD+ as a substrate . Human ZAP is a predominantly cytoplasmic protein that exists in two alternatively spliced isoforms . Long isoform 1 ( ZAP-L ) possesses a defective C-terminal PARP-like domain that lacks a catalytic glutamate residue essential for pADPr ( Fig 2A ) [47 , 48] . This domain is absent in ZAP-S . It was previously demonstrated that the first 254 amino acids of rat ZAP ( NZAP ) , comprising only the four CCCH-type zinc fingers , were sufficient to induce loss of viral mRNA and severely inhibit infection [49] . The zinc finger domain is believed to mediate binding of ZAP to long ZAP-responsive element ( ZRE ) sequences within the RNAs of target viruses [29 , 50–52] . Hayakawa et al . [53] reported that ZAP-S , but not ZAP-L , is an ISG whose endogenous expression is increased by Type I interferon , a fact we confirm for 293T cells ( Fig 1A , bottom panel ) . We cotransfected 99-PUR-RPS-EGFP in 293T cells , together with epitope-tagged ZAP or empty vector , and assayed for fluorescent cells at 5 days post-transfection ( Fig 2A and 2B ) . Coexpressed human HA-tagged HA-ZAP-S and HA-ZAP-L , and SBP-PARP13 . 1 ( ZAP-L with an N-terminal streptavidin binding protein tag [54] ) caused a precipitous decrease in the number of retrotransposition-positive 293T cells relative to the empty vector control . L1 inhibition caused by HA-ZAP-S was greater than by V5-TEV-ZAP-S ( Figs 1B and 2B ) , and it is possible that the long tag of the latter construct partially reduced ZAP-S activity . In turn , decrease in retrotransposition was greater for HA-ZAP-L and SBP-PARP13 . 1 than HA-ZAP-S . ZAP-L inhibited retrotransposition in a dose-dependent manner , and even the lowest levels of ectopically expressed protein caused significant loss of L1 activity ( Fig 2C , lanes 3–6 ) . Our data are in line with previous observations that ZAP-L exerts stronger activity against Semliki Forest virus , Sindbis virus , and Moloney leukemia virus ( MoLV ) than ZAP-S [32 , 55] . This increased antiviral activity of ZAP-L has been attributed to S-farnesylation within the C-terminal PARP-like domain [56] . Overexpression of PARP1 , a nuclear protein , failed to inhibit retrotransposition ( Fig 2B , lane 7 ) . We next tested for the effect of ZAP's N-terminal zinc finger and predicted RNA-binding domain . Expressing only the first 256 residues of human ZAP ( HA-ZAP 1–256 ) reduced L1 retrotransposition to 13 percent of vector control ( Fig 2B , lane 5 ) . The analogous N-terminal domain of rat ZAP ( HA-NZAP ) reduced cell culture retrotransposition even more effectively ( to 2 . 5 percent , although , it should be noted that these truncated ZAP proteins were expressed at higher levels than ZAP-S or ZAP-L; Fig 2B , lower panel ) . ZAP constructions with deletions of the zinc finger domain ( constructs HA-ZAP-L 253–902 and SBP-PARP13 . 1ΔZnF , which lacks residues 77 to 223 ) or mutated for five residues considered important for RNA binding ( SBP-PARP13 . 1VYFHR ) inhibited retrotransposition to a lesser but still significant degree ( 40–65 percent of vector control; Fig 2B , lanes 6 , 9 and 10 ) . Similar results were seen when the retrotransposition assay was performed in HeLa cells ( S2A Fig ) . Together , our data show that the ZAP zinc finger domain alone is sufficient but not exclusive for L1 inhibition . Tagged ZAP constructs had minimal effect on the expression of cotransfected CEP-EGFP plasmid ( Fig 2B , right bar chart ) . As an additional test for cell toxicity , ZAP constructs or empty vector were cotransfected with a plasmid that constitutively expresses the blasticidin S-resistance gene ( bsr ) . Coexpression of HA-ZAP-S , HA-ZAP-L , or rat HA-NZAP did not reduce the number of bsr-expressing foci remaining after 10 days of selection with blasticidin ( S2B Fig ) . We next cotransfected ZAP with ORFeus-HS , a synthetic human LINE-1 construct tagged with the EGFP reporter cassette and containing codon-modified ORF1 and ORF2 sequences and a CMV promoter in place of the L1 5' UTR [57] . Even though ORFeus-HS contains little authentic LINE1 DNA sequence , its retrotransposition in 293T cells was decreased by coexpression of ZAP full-length or N-terminal truncated proteins in the same manner as human L1-RP ( S2C Fig ) . We also assayed ORFeus-Mm [58] , a modified version of L1spa , a mouse L1 element with low retrotransposition activity [59] , possessing both CMV promoter and mouse L1 5' UTR , and codon-optimized to boost its activity to a level similar to that of human L1-RP . ZAP also inhibited cell culture retrotransposition of ORFeus-Mm ( S2D Fig ) . Thus , ZAP suppression of L1s is neither DNA sequence- , promoter- , nor species-specific . We also determined by HIRT preparation plasmid recovery and subsequent PCR that expression of ZAP had no effect on the stability of 99-PUR-RPS-EGFP reporter plasmid DNA ( S2E Fig ) . We next asked whether endogenous ZAP inhibits cell culture L1 retrotransposition . We confirmed by Western blotting that two previously described siRNA sequences [60] efficiently depleted endogenous ZAP protein when transiently transfected in 293T cells ( Fig 2D ) . Depletion of ZAP enhanced L1 retrotransposition 2- to 3-fold compared with control siRNAs or mock-transfected cells . We also determined the effect of ZAP expression on retrotransposition of the non-autonomous Alu SINE retrotransposon . Using the assay of Dewannieux et al . [61] , we cotransfected in HeLA-HA cells an Alu reporter construct tagged with the neomycin phosphotransferase gene ( Alu-neoTet ) , an ORF2 construct ( pCEP-5′UTR-ORF2-No-Neo ) to drive retrotransposition , and ZAP constructs or empty vector . Expression of HA-ZAP-L , HA-ZAP-S , rat HA-NZAP , and MOV10 reduced the number of neomycin-resistant retrotransposition-positive colonies more than 80 percent compared with empty vector control . pCEP-5′UTR-ORF2-No-Neo consists of CMV promoter , the L1 5' UTR , and ORF2 sequences only , suggesting that L1 ORF1 or 3' UTR sequence is not essential for ZAP inhibition of retrotransposition . All human endogenous retroviruses are thought to be incapable of retrotransposition due to inactivating mutations . However , mouse intracisternal A particle ( IAP ) LTR retrotransposons actively retrotranspose and cause new mutations . Using an established cell culture assay [62] , we found that overexpression of HA-ZAP-L and V5-TEV-MOV10 strongly restricted insertion of neo-tagged IAP elements in HeLa-JVM cells ( Fig 2F ) . Inhibition by HA-ZAP-S and HA-ZAP 1–256 was less severe , consistent with our results for L1 retrotransposition . Thus , expression of ZAP inhibits not only retroviruses but also both LTR and non-LTR retrotransposons , including mobile DNA currently active in the human genome . We examined the subcellular distribution of ZAP and its association with the L1 RNP . When expressed from a full-length L1 construct , ORF1p is present as an RNP in cytoplasmic stress granules ( SGs ) together with L1 RNA , ORF2p , and many other RNA-binding proteins . Granules are also detected in cells that express endogenous ORF1p at high levels , and their formation is not dependent upon external stress applied to the cell [63–65] . Stress granules ( SGs ) are cytoplasmic aggregates that contain stalled 48S pre-initiation complexes and are induced by a range of stresses . P-bodies ( PBs ) are constitutively expressed cytoplasmic granules rich in factors of RNA decay , including those of RISC [66] . ORF1p granules generally do not overlap , but may juxtapose PBs [63] . Interestingly , we reported [64] , and others have confirmed [67] , that ORF2p is detected in only a minor subset of ORF1p-positive cells when the two proteins are coexpressed from an L1 construct . The reason for this is unknown . Both ZAP-L and ZAP-S have been reported to colocalize in the cytoplasm with markers of PBs and SGs [52 , 54 , 68 , 69] . Epitope-tagged ZAP-S , coexpressed with ORF1-GFP-L1-RP ( a construct with CMV promoter , ORF1 C-terminally tagged with EGFP and intact downstream L1 sequence [64] ) and detected by immunofluoresence ( IF ) of fixed 293T cells , strongly colocalizes with ORF1-GFP and SG marker proteins eIF3 ( Fig 3A ) and TIA-1 . Endogenous ZAP and ORF1p similarly colocalize in the cytoplasm of 293T and 2102Ep cells ( Fig 3B and 3C ) . To track L1 RNA in fixed cells we used construct 99-PUR-L1-RP-MS2-6X [64] , consisting of L1-RP tagged at its C-terminus with a tandem array of six 19-bp stem loop sequences that bind bacteriophage MS2 coat protein . This construct was cotransfected with GFP-tagged ZAP-S and its RNA was detected in fixed cells with a fluorescent in situ hybridization ( FISH ) probe to the MS2 repeats . ZAP-S colocalized with L1 RNA in cytoplasmic granules ( Fig 3D ) . As noted above , V5-TEV-tagged ZAP-S interacts with L1 RNPs in an RNA-dependent manner ( Fig 1D ) . HA-ZAP-L and endogenous full-length ZAP also co-IP with L1 RNPs expressed from pc-L1-1FH ( Fig 3E , upper and lower panels , respectively ) . Shorter ZAP products , presumed to be degradants , were not recovered in the immunoprecipates . Conversely , FLAG-tagged ZAP-L ( ZAP-L-FL ) coimmunoprecipitates untagged ORF1p expressed from a full-length L1 construct ( Fig 3F ) . Unexpectedly , despite the ability of the zinc-finger domain alone to strongly inhibit retrotransposons ( Fig 2B ) , we failed to co-IP human HA-ZAP 1–256 ( Fig 3G ) or rat HA-NZAP with pc-L1-1FH RNPs . Chen et al . [52] proposed that some residues in the zinc finger domain are involved in ZAP's interaction with protein factors rather than RNA . Perhaps an unknown non-L1 protein or RNA can recruit truncated ZAP to the L1 RNP , although this requires further investigation . In summary , L1 RNPs and ZAP associate and are directed to the same cytoplasmic compartments . We previously identified 96 proteins associated with the L1 ORF1p RNP and confirmed a subset of these by direct co-IP and subcellular colocalization experiments [46] . We wished to determine the ZAP protein interactome and identify its members that are shared with that of the L1 . We transfected ZAP-L-FL and empty vector in parallel in 293T cells and performed IP from whole cell lysates in the presence or absence of RNase . Purification was highly efficient with relatively few proteins identified in vector-only lanes ( Fig 4A ) . Following IP , complex samples were analyzed by tandem mass spectrometry ( MS ) . Excluding 36 ribosomal proteins and likely contaminants ( such as keratins ) , 78 proteins satisfied three criteria: 1 ) predicted by 3 or more peptides , 2 ) present in two independent replicate IPs , and 3 ) unique to ZAP-L-FL isolates . Eleven proteins were shared with the L1 ORF1p RNP interactome defined in our previous paper ( Table 1 , S1 Table ) . To confirm protein interactions , a subset of cDNAs of proteins identified by MS were subcloned with an N-terminal V5-TEV-epitope tag or were obtained as gifts . Following cotransfection in 293T cells , 18 of 23 proteins tested co-IPed with ZAP-L-FL on α-FLAG agarose ( Fig 4B ) . A majority of these interactions were resistant to RNase digestion , suggesting direct protein-protein binding . In contrast , our previous work characterizing the ORF1p interactome found almost all of 41 confirmed protein associations to be lost upon RNase treatment [46] . Our analyses identified most previously described ZAP-interacting proteins . We confirmed RNA-independent binding of ZAP and PPR2R1A ( PR65A ) [70] , a component of the protein phosphatase 2 ( PP2A ) complex , and show for the first time RNase-resistant binding of its catalytic subunit , PPP2CA , as well ( Fig 4B ) . Wang et al . [70] reported that knockdown of PPR2R1A by shRNAs reduced ZAP suppression of an MoLV reporter . It has been reported that several serine residues immediately downstream of the rat ZAP zinc-finger domain are phosphorylated by glycogen synthase kinase 3β ( GSK3β ) , and that overexpression of GSK3β reduces , and its inhibition increases ZAP activity against MoLV [71] . These data are all consistent with inhibition of ZAP antiviral function by phosphorylation . Our MS analyses of human ZAP protein sequence ( 65% coverage ) confirmed two phosphorylated residues ( S257 and S284 ) homologous to those detected by Sun et al . [71] in rats , and identified four additional phosphorylated sites ( S335 , T375 , S378 , S796 ) . However , a role for phosphorylation or PP2A-mediated dephosphorylation in modulating ZAP inhibition of retrotransposition remains to be determined . It has been proposed that ZAP recruits the 3'-5' exosome to degrade target viral RNAs in cytoplasmic granules [51 , 72] . We identified exosome component EXOSC8 ( RRP43 ) in ZAP RNP immunoprecipitates , although we could not confirm its direct binding with ZAP-L-FL ( consistent with [72] ) . According to previous reports , EXOSC5 ( RRP46 ) binds rat but not human ZAP [50 , 72] . However , when tested , we found EXOSC5 to bind weakly with human ZAP-L-FL in the presence or absence of RNase . We also identified as a strong ZAP interactor DHX30 , an RNA helicase believed to be recruited by ZAP to unwind viral RNAs and facilitate their exosome-mediated degradation in stress granules [69 , 73] . We confirmed colocalization of DHX30 with both ZAP-S and ORF1-GFP in cytoplasmic granules , and RNA-independent binding of DHX30 with ZAP-L-FL ( Figs 4B and 5D and S3A ) . In addition to the 3'-5' degradation exosome , 5′-3′ degradation enzymes , including 5'-3' Exoribonuclease 1 ( XRN1 ) and Poly ( A ) -Specific Ribonuclease ( PARN ) , have been reported to bind ZAP and augment antiviral function [51] . We detected the XRN1 paralog XRN2 in the ZAP interactome . XRN2 not being available , we tested XRN1 tagged with RFP and found it to colocalize with GFP-tagged ZAP-S in cytoplasmic granules ( Fig 5N ) . XRN1 is a known SG and PB component and also colocalizes with ORF1p-GFP in SGs [63 , 74] . We also discovered novel ZAP-associated proteins . In addition to PP2A complex members , ZAP RNPs contain several other proteins involved in post-translational modification ( PTM ) . TRIM25 , which functions as an E3 ubiquitin and ISG15 ligase and defends against viruses by mediating ubiquitination of RIG-1/DDX58 [75] , associates with ZAP in the presence of RNA ( Fig 4B ) . Even after RNase digestion , ZAP-L strongly binds two ubiquitin carboxyl-terminal peptidases , USP7 and USP9X . Our MS analyses predicted two ubiquitinated lysines ( K226 , K783 ) and the UbPred and CKSAA_UBSITE algorithms [76 , 77] predicted 6 additional sites of ubiquitination within ZAP-L . Perhaps ubiquitin peptidases associate with the ZAP RNP to enhance its anti-retroelement activity by limiting ubiquitin-mediated degradation , although that remains to be determined . While its own PARP domain is likely catalytically inactive , ZAP-L itself is ADP-ribosylated and known to recruit other PARPs that are capable of pADPr , such as PARP5 and PARP12 [68] . PARP1 is the founding member of the PARP family . We report for the first time the direct binding of ZAP and PARP1 independent of RNase digestion . It is not known if PARP1 ribosylates ZAP . Also identified within the ZAP RNP were almost all components of the cytosolic chaperonin-containing TCP1 ( CCT ) complex . We tested several CCT complex members and confirmed weak binding of CCT2 , but not CCT6B or CCT8 , with ZAP-L-FL ( Fig 4B ) . The CCT complex was first discovered for its critical role in the folding of actin and tubulin , and subsequently many other CCT binding proteins were reported [78 , 79] . This is the first report of the association of CCT and ZAP . CCT4 and CCT6 were also detected in the ORF1p interactome [46] . The ZAP interactome includes several canonical components of SGs ( FXR1 , FXR2 , G3BP1 , and ELAVL1 ) and PBs ( DDX6 ) . We confirmed the association of these proteins with ZAP by colocalization in cytoplasmic granules and/or co-IP ( Figs 4B and 5 ) . Indeed , 65 percent ( 12/18 ) of the proteins tested that directly immunoprecipitated with ZAP-L-FL ( Fig 4B ) also colocalized with GFP-tagged ZAP-S in cytoplasmic granules of 293T cells ( Fig 5 ) . To the best of our knowledge , CCT2 , CCT8 , PPP2R1A , TRIM25 , and USP9X have not previously been reported in granules ( Figs 5A , 5B , 5J , 5L and 5M ) . We screened test proteins for colocalization in cytoplasmic granules with ZAP-S rather than ZAP-L ( Fig 5 ) , and it is possible that additional ZAP-associated proteins , bound only by ZAP-L's PARP-like domain , escaped detection . While ZAP-L and ZAP-S colocalize in cells , it is of interest that overexpression of ZAP-L induces fewer and larger cytoplasmic aggregates , and apparently binds ZAP-S to cause its redistribution to these large foci when the two isoforms are coexpressed; ZAP is known to dimerize ( S3B Fig; [52] ) . Lee et al . [69] reported that murine ZAP-S colocalizes in the cytoplasm with markers of PBs and SGs , but not with markers of mitochondria , endosomes , peroxisomes , or lysosomes . On the other hand , Charron et al . [56] , found that C-terminal S-farnesylation of mouse ZAP-L caused its partial redistribution to lysosomes and late endosomes . Reasons for the differing patterns of ZAP-S and ZAP-L bear further investigation . We previously reported that endogenous L1 ORF1p and antiviral protein MOV10 associate in an RNA-dependent manner and colocalize in SGs of cells ( Fig 5O; [27] ) . We now show that MOV10 is also a component of the ZAP interactome . Recently , Gregersen et al . [80] also detected ZAP by SILAC ( stable isotope labeling by amino acids in cell culture ) analyses of MOV10-interacting proteins . Both endogenous and exogenously expressed ZAP and MOV10 co-IP in a manner partially resistant to RNase digestion ( Fig 4C ) . We could not detect binding of MOV10 with HA-ZAP-L 1–256 or rat HA-NZAP . ZAP and MOV10 proteins closely colocalize in cytoplasmic granules ( Fig 5I and 5P ) . In summary , we present for the first time a comprehensive analysis of the ZAP protein interactome . Most of its components we tested directly bound and/or colocalized in RNA granules with ZAP . ZAP may recruit many cellular proteins to these cytoplasmic structures ( or vice versa ) . Previously , we demonstrated that overexpression of MOV10 strongly reduces the steady state number of L1-encoded RNA and proteins in transfected cells , although the mechanism of this loss remained uncertain [64] . We similarly assayed the effects of ZAP on L1 expression . As with MOV10 , levels of ORF1 protein expressed from pc-L1-1FH were significantly reduced in L1 RNPs immunoprecipitated from cytoplasmic extracts in the presence of cotransfected ZAP-L and ZAP-S . ORF2 activity was almost undetectable in the LEAP assay for L1 reverse transcription ( Fig 6A , lanes 3 and 4; [81] . In whole cell lysates , ORF1p expressed from pc-L1-1FH was significantly lower in the presence of ZAP-S , ZAP-L , or MOV10 but not empty vector or an unrelated protein ( Fig 6B ) . This was not a general protein effect , as overexpression of ZAP did not affect levels of coexpressed EGFP or endogenous heat shock protein 90 ( Fig 6B ) , and was without obvious effect on global protein expression detected by Coomassie blue staining of cell lysates ( Fig 6A , lower panel ) . We next examined L1 RNA transcribed from construct 99-PUR-JM111-EGFP in the presence or absence of ZAP . This construct contains a mutation in L1 ORF1 that prevents genomic insertions . PCR primers flanked the intron of the EGFP reporter cassette allowing products amplified from spliced cDNA to be distinguished from those of contaminating plasmid DNA . Paralleling the loss of ORF1 protein , diminished levels of L1 RNA were detected by RT-PCR in whole cell lysates cotransfected with HA-ZAP-L HA-ZAP-S , or HA-ZAP 1–256 , but not with empty vector or an unrelated protein . Analysis of endogenous HSPA6 RNA showed no such effect ( Fig 6C ) . We failed to determine whether or not ZAP affects expression of endogenous L1s in the genome . The observed reduction of exogenous L1 RNA in the presence of ZAP protein is consistent with previous reports that ZAP inhibits infecting viruses by causing loss of their RNAs at a post-transcriptional step [29 , 49 , 51 , 72 , 82] .
Restriction factor proteins are part of the innate immune defense system of the cell , which often detects infection by receptors that recognize viral nucleic acids . Many of these factors target retroviruses , but some , such as ZAP , act against a wide range of viral families . In certain cell types expression of antiviral genes is induced by type I or type II interferons . We have expanded the list of IFN-stimulated genes that limit LINE-1 retrotransposition when overexpressed , including BST2 , ISG20 , MAVS , MX2 , and ZAP ( Fig 1B ) . Most dramatic was an almost complete loss of retrotransposition in the presence of MAVS protein , which is only partly explained by cytotoxicity . MAVS acts downstream of the RIG-I and IFIH1 cytoplasmic receptors for viral dsRNAs . Receptor activation causes multimerization of MAVS to trigger a signaling cascade and production of type I IFNs [83] . Recent evidence indicates that upon viral detection peroxisomal-localized MAVS rapidly induces interferon-independent expression of defense factors to provide short-term protection . Mitochondrial MAVS signaling occurs later in infection , triggering IFN expression and induction of ISGs , and sustaining the immune response [84] . Thus , the profound inhibition of retrotransposition caused by overexpressed MAVS is likely due to the combined action of a number of interferon-induced genes . ISG20 strongly inhibited L1 retrotransposition without obvious cytotoxicity caused by its overexpression . ISG20 is a 3'-5' exonuclease that inhibits replication of several human and animal RNA viruses , including HIV-1 [7 , 85 , 86] . Its mechanism of action is unknown , although one might assume that it degrades viral or retrotransposon RNA Inhibition of L1 retrotransposition by the dynamin-like GTPase MX2 , but not by its closely related paralog MX1 , parallels recent reports that MX1 , a broad-spectrum inhibitor of many RNA and DNA viruses including influenza virus , fails to inhibit HIV retrovirus . On the other hand , MX2 does not inhibit infuenza virus but inhibits multiple strains of HIV1 at a late post-entry step by targeting the viral capsid and preventing accumulation of viral cDNA in the nucleus . [41–43 , 45] . Our mutation analyses suggest that the mechanism of MX2 inhibition of L1 retrotransposition may differ in some aspects from retroviral inhibition , requiring GTP binding but not nuclear localization . Like MX1 , overexpression of TRIM28/KAP1 had little effect on retrotransposition ( Fig 1B ) . However , in mouse ES cells TRIM28 strongly silences expression of multiple classes of endogenous LTR retroelements and modestly suppresses L1s by recruiting chromatin-remodeling factors [87 , 88] . Thus , TRIM28 may inhibit human retrotransposition by epigenetic modification of endogenous L1 elements , but remain ineffective in our cell culture assay against L1s expressed from a plasmid . Importantly , we showed that transient expression of either the long or short isoform of a general protein inhibitor of viral infection , ZAP , potently restricts genomic insertion of both non-LTR and LTR retrotransposons . Furthermore , siRNA-mediated knockdown of endogenous ZAP increased L1 retrotransposition 2- to 3-fold in 293T cells . Association with the L1 complex is confirmed by close colocalization of ZAP protein with ORF1p and L1 RNA in cytoplasmic stress granules , and by the detection of ZAP in RNP particles captured by immunoprecipitation of a tagged L1 construct . The CCCH-type zinc finger domain of ZAP recognizes MoLV and other viral transcripts and induces their degradation by recruiting RNA decay proteins [50 , 51 , 69 , 72 , 73] . Selected cellular RNAs are also targeted , including the TRAIL receptor , TRAILR4 [54] . Our evidence suggests that RNA degradation is also a characteristic of ZAP-associated loss of retrotransposition . Levels of exogenously expressed L1 RNA and protein are reduced in cell lysates in the presence of ZAP ( Fig 6C ) . Loss of ZAP binding in the L1 RNP upon RNase treatment , and the fact that deletion of the zinc finger domain or mutation of residues considered important for its RNA binding significantly reduced inhibition of retrotransposition , suggests that ZAP binds the L1 RNA to promote loss of RNP integrity . However , we cannot exclude the possibility that ZAP binds some other RNA that itself is recruited to the L1 RNP . Many non-L1 RNA species have been found in association with the L1 ORF1p complex , including mRNAs , Alu , SVA , and small cytoplasmic and nuclear RNAs [46 , 89] . ZAP binds ZREs with a minimum known length of 500 bp , and no common motif or secondary structure has been found [29 , 50 , 51] . A detailed investigation of how ZAP binds L1 RNA is required . The fact that deletion or mutation of the zinc finger RNA-binding domain reduced but did not abolish ZAP inhibition of retrotransposition ( Fig 2B ) , suggests that a second RNA-binding domain may exist , or that protein-protein interactions are also important for retrotransposon inhibition . Our data cannot exclude the possibility that ZAP may also inhibit L1s at the protein level ( perhaps by binding L1 RNA to interfere with ORF translation ) . Effects of ZAP on viral translation have been described [31 , 51] . Inhibition of GSK3β phosphorylation increases ZAP's ability to repress target mRNA translation without increased mRNA degradation [71] . And recently , Zhu et al . [82] demonstrated that ZAP represses translation of HIV-1 and Sindbis virus reporter constructs independently of mRNA decay by directly binding translation initiation factor eIF4A and interfering with its interaction with eIF4G . MOV10 is an RNA helicase that also strongly inhibits retrotransposition in cell culture assays [26 , 27 , 90] . Li et al . [28] showed that overexpression of MOV10 strongly reduced levels of exogenously expressed IAP and L1 RNA at a post-transcriptional step , while inhibition of endogenous MOV10 increased L1 RNA . On the other hand , Lu et al . [90] found that MOV10 decreases IAP RT products but not levels of IAP RNA and protein . The facts that MOV10 and ZAP bind each other independently of RNA , colocalize in cytoplasmic granules , associate with the L1 RNP , and promote similar loss of L1 RNP integrity and retrotransposition in cells , suggest that the two proteins may function in a common pathway , a notion worthy of further investigation . In this study , we also present for the first time a detailed analysis of the ZAP protein interactome , confirming most of its previously known interacting proteins and identifying new member proteins whose association with the ZAP RNP is consistent with its known cellular functions . For example , close colocalization in cytoplasmic granules of many of these proteins with ZAP , including L1 ORF1p , is not surprising in light of ZAP's dual roles in the assembly of RNA granules and the control of microRNA silencing . While itself catalytically inactive , ZAP recruits other PARPs active for poly ( ADP ) -ribosylation which is critical for SG formation [68 , 91] . Furthermore , overexpression of ZAP causes loss of microRNA silencing by targeting Ago2 ( a binding partner of ZAP , although not one detected by our study ) for pADPr [91 , 92] . We detected several PTM-related proteins within the ZAP RNP , including protein phosphatase 2A complex members ( PPP2R1A/B and PPP2CA ) , two related deubiquitinating enzymes ( USP7 and USP9X ) , and TRIM25 ( an E3 ubiquitin/ISG15 ligase ) . In addition to pADPr [68] , Sun et al . [71] reported phosphorylation of ZAP by GSK3β , and Charron et al . [56] found that S-farnesylation at the C-terminus of ZAP-L enhanced its restriction of Sindbis virus . Thus , PTMs likely modulate ZAP antiviral activity and may explain why ZAP efficiently restricts some classes of virus but not others . We also identified epigenetic modifying enzymes in ZAP-L-FL immunoprecipitates ( Table 1 ) . RUVBL1 and RUVBL2 are conserved AAA+ protein ATPases present in histone acetyltransferase complexes NuA4 and Tip60 , chromatin remodelling complexes Ino80 and SWR-C , and the telomerase complex [93] . HDAC1 and CHD4 are components of the NuRD ( nucleosome remodeling and histone deacetylase ) complex , along with HP1 , SETDB1 and TRIM28 ( KAP1 ) . While we detected TRIM28 in ZAP immunprecipitates , it was also present in vector-only samples ( 11 vs 4 peptides , respectively ) and is excluded from Table 1 . TRIM28 is targeted to specific DNA sequences via its interactions with various zinc finger proteins , and mediates gene silencing by recruiting NuRD to target promoters [94 , 95] . Epigenetic modifications involving TRIM28 and SETDB1 have been implicated in silencing both endogenous retroviruses and retrotransposons , including multiple classes of LTR retroelements and L1s [88 , 89 , 96–100] . Reichman et al . [101] also showed in silico that HDAC1 silences LTR retrotransposons in mouse mES cells . A possible link between ZAP and epigenetic silencers deserves investigation . Although ZAP is predominantly a cytoplasmic protein it can also function in the nucleus [29 , 102] . Macdonald et al . [103] showed that an unknown IFN-induced factor ( s ) synergizes with ZAP to control viral infection . Karki et al [104] identified 16 IFN-stimulated genes that act synergistically with ZAP to reduce alphavirus infectivity , including IFIH1 and SAMHD1 . Furthermore , ZAP-S stimulates RIG-1-mediated production of Type I interferon [53] . It is remarkable that 34 of the 78 proteins of the ZAP interactome shown in Table 1 are identified in the Interferome v2 . 01 Database of about 2000 ISGs as being induced at least 2-fold by interferon in either mice or humans ( S1 Table; [105] ) . This is a four-fold enrichment of ISG proteins in the ZAP interactome over what would be expected by chance . Likely we would have detected an even greater number of ISG products within the ZAP-L-FL interactome if prior to IP we had first stimulated their expression with interferon . MX1 and , MX2 , for example , while not detected by our MS analyses of endogenous ZAP-interacting proteins , when overexpressed strongly bind ZAP-L-FL in a manner resistant to RNase treatment ( S5 Fig ) . The association of ZAP with many ISG products suggests it may be a key player in the interferon response . This idea is supported by a recent study showing that ZAP-L-depleted HeLa cells are strongly enriched for expression of genes in the interferon immune response pathway [54] . In addition to MOV10 , other IFN-stimulated proteins associated with the ZAP RNP complex also play roles in viral control . Overexpression of DHX30 , for example , strongly enhances expression of HIV-1 , while restricting its RNA packaging [106] . G3BP1 and G3BP2 regulate expression of ISGs known to have broad antiviral activity , and are required for an IFN response against dengue and yellow fever viruses [107] . Chaperone HSPA4 ( HSP70 ) inhibits viral gene expression and replication [108] . Its co-chaperone DNAJ ( HSP40 ) has been associated with both activation and inhibition of viruses , including HIV [109] . Ubiquitination by TRIM25 increases the ability of RIG-1 to initiate antiviral signaling by facilitating its interaction with MAVS . Influenza A virus nonstructural protein 1 specifically inhibits TRIM25-mediated RIG-I ubiquitination , thereby suppressing its antiviral activity [75 , 110] . USP7 was originally identified by its interaction with the HSV-1-encoded E3 ubiquitin ligase ICP0 [111] . PARP1 is required for efficient replication and integration of HIV-1 [112 , 113] , and has been implicated in repressing Epstein Bar virus , Kaposi's sarcoma-associated herpesvirus , and Drosophila retrotransposons [114–116] . RUVBL2 inhibits influenza virus replication , apparently by interfering with oligomerization of the viral nucleoprotein [117] . Future investigations of the associations of these proteins with the ZAP complex could yield new insights into antiviral restriction . As we showed in Fig 1A , application of Type I interferon represses retrotransposition in cell culture . While this paper was under review , another study was published showing that increased endogenous L1 expression in the testes of MOV10L-deficient mice , or transfection of L1 constructs in mouse embryonic fibroblasts , is marked by increased levels of INFβ [118] . MOV10L is a restriction factor that represses retroelements in the male germline [119] . Conversely , treatment of human cells with INFβ suppressed L1 replication . Functional loss of an anti-retroelement restriction factor can alter the normal metabolism of retrotransposon RNA or its reverse-transcribed cDNA with possible consequences for the organism . Such may be the case with Type I interferonopathies , which include AGS , SLE , spondyloenchondrodysplasia , and STING-associated vasculopathy [120 121] . Indeed , it has been proposed that misregulated nucleic acids , deriving from an as yet unknown endogenous source , but possibly retrotransposons , accumulate and are recognized by sensors , triggering an interferon response and causing Aicardi—Goutières Syndrome [122 , 123] . AGS is a severe Mendelian inflammatory disorder that affects particularly the brain and frequently causes death in childhood . The disease is characterized by progressive encephalopathy , psychomotor regression , and lesions of the skin , together with increased levels of Type I IFN in the cerebrospinal fluid and serum , and induction of ISGs detectable in peripheral blood . AGS is associated with mutations in seven genes , all involved with nucleic acid metabolism or signaling: TREX1 , RNASEH2 A/B/C , SAMHD1 , ADAR1 and IFIH1 [124–126] . Most of these genes also have been linked with the innate immune system that restricts retroviral infection and suppresses endogenous retrotransposons [23 , 123 , 127 , 128] . We predict mutations in additional anti-retroelement genes , perhaps even including ZAP or MOV10 , will be linked to inflammatory diseases involving interferon overexpression .
Constructs CEP-EGFP , pc-L1-1FH , pc-L1-RP , ORF1-GFP L1-RP , 99-PUR-L1-RP-MS2-6X , pCEP-5′UTR-ORF2-No-Neo , and XRN1-RFP have been described [63 , 64] . ZAP-L-FL with a C-terminal FLAG-tag , and HA-ZAP 1–256 and HA-ZAP 253–902 with N-terminal HA-tags were generated by PCR and cloned in pcDNA6 myc/hisB vector . Ultimate ORF cDNA clones ( Invitrogen ) were cloned with V5-epitope tags and tobacco etch virus ( TEV ) protease cleavage sites on their N-termini by shuttling them from pENTR221 vector into pcDNA3 . 1/nV5-DEST vector using Gateway Technology ( Invitrogen ) . Ultimate ORF Clone ID numbers are shown in S1 Table . Clones obtained as gifts included Alu-neoTet and IAP-neoTNF ( M . Dewannieux , Institut Gustave Roussy , Villejuif [61 , 62] ) , DHX30 ( v2 ) -HA and DHX30 ( v2 ) -RFP ( C . Liang , Lady Davis Institute-Jewish General Hospital , Montreal [106] ) , pcDNA3 . 1-V5-His-MOV10 ( Y . -H . Zheng , Michigan State University , East Lansing [129] ) , ORFeus-Mm ( WA-125 ) ( W . An , Washington State Univ . [130] ) , ORFeus-HS ( WA117 ) ( L . Dai , Johns Hopkins School of Medicine , Baltimore [57] ) , pCDNA3 . 1-V5-His full-length PARP1 ( J . Pascal , Thomas Jefferson Univ . , Philadelphia [131] ) , GFP-PARP13 . 2 ( GFP-ZAP-S; A Leung , Johns Hopkins School of Medicine [89] ) , SBP-PARP13 . 1 , SBP-PARP13 . 1ΔZnF and SBP-PARP13 . 1VYFHR ( P Chang , MIT , Cambridge [54] ) , pDest51-USP9X-V5 ( R . Hughes , Buck Institute for Research on Aging , Novato [132] ) , Myc-USP7 ( Y . Sheng , York University , Toronto [133] ) , pcDNA-Vphu ( a codon-optimized version of the native Vpu gene; NIH AIDS Reagent Program [40] ) , pCEP-5′UTR-ORF2-No-Neo ( J . L . García-Pérez , GENYO , Spain ) [134] ) , and HA-ZAP-L , HA-ZAP-S , and Rat HA-NZAP ( H . Malik , Fred Hutchinson Cancer Research Center , Seattle [32] ) . An altered amino acid ( M201K ) was restored to consensus in HA-ZAP-L , and the change was found to have no effect on L1 retrotransposition . pEasiLV-MCS MX2-Flag WT was obtained from M . Malim ( King's College , London [45] ) and the MX2 gene was amplified with C-terminal V5-tag by PCR and recloned in the vector pcDNA3 . The K131A mutant was generated by the Quikchange Site-Directed Mutagenesis method ( Agilent Technologies ) . siRNAs were generated by Sigma-Aldrich based on the following sense sequences: siCNT3 AUGUAUUGGCCUGUAUUAG[dT][dT] , siZC3HAV1-2 1435–1453 UUGGGUCAGCAUCAUCUGC[dT][dT] , siZC3HAV1-3 1637–1655 AUGUGCUCAAAGUCCGUCC[dT][dT] [60] , and siCNT4 UAAGGCUAUGAAGAGAUAC[dT][dT] . For MS sequence determination , HEK 293T cells were transfected in T75 flasks with 15 μg of ZAP-L-FL or pcDNA6 myc/hisB ( Invitrogen ) empty vector and expanded for approximately 45 hr , followed by whole cell lysate preparation by sonication . IP and sample recovery were as previously described [46] . Treatment of samples with 30 μg/ml DNase-free RNase ( Roche ) was in the absence of RNase inhibitors . MS sequencing and database analyses was performed by the Johns Hopkins Mass Spectrometry and Proteomics Facility as previously described [46] . For each co-IP , extracts from approximately 6×106 293T cells in T75 flasks transfected with ZAP-L-FL and test protein constructs were prepared in 750 μl of lysis buffer supplemented with protease , phosphatase , and RNase inhibitors , and immunoprecipitated as previously described [46] . Lysates containing test proteins of predominantly nuclear localization were sonicated . RNase-treated reactions contained 25 μg/ml RNase , DNase-free HC ( Roche ) and 25 μg/ml RNaseA ( Invitrogen ) and no RNase inhibitors . Human 2102Ep embryonal carcinoma cells ( a gift from P . K . Andrews , University of Sheffield ) , HeLa-HA and HeLa-JVM cells ( [135]; gifts from J . L . García-Pérez , GENYO , Spain ) , and HEK 293T cells ( ATCC ) were grown in Dulbecco’s modified Eagle’s medium with 10% FBS ( Hyclone ) , GlutaMax and Pen-Strep ( Invitrogen ) . Plasmid and siRNA transfections used FuGENE HD ( Promega ) and Lipofectamine RNAiMAX ( Life Technologies ) reagents , respectively . The EGFP L1 cell culture retrotransposition assay was conducted as previously described [27 , 38] . 2 . 5×105 HeLa or 293T cells/well were seeded in 6-well dishes . The following day , 1 . 0 μg of 99-PUR-RPS-EGFP , a plasmid containing L1-RP and the EGFP retrotransposition reporter cassette , was cotransfected with 0 . 5 μg of empty vector ( pcDNA3 or pcDNA6 myc/hisB , Invitrogen ) or test plasmid . All transfections were in quadruplicate wells . Five days post-transfection , cells having a retrotransposition event , and hence expressing EGFP , were assayed by flow cytometry . Gating exclusions were based on background fluorescence of plasmid 99-PUR-JM111-EGFP , an L1 construct containing two point mutations in ORF1 that abolish retrotransposition [37] . Within each experiment , results were normalized to fluorescence of 99-PUR-RPS-EGFP cotransfected with empty vector . The Alu retrotransposition assay was carried out essentially as described in Dewannieux et al . [61] . Retrotransposition construct Alu-neoTet was cotransfected in HeLa-HA cells with pcDNA6 myc/hisB empty vector or retrotransposition driver plasmid pCEP-5′UTR-ORF2-No-Neo , together with test plasmids . Eighteen hours post-transfection , HeLa-HA cells were expanded from six-well plates to T75 flasks , and three days later selection for retrotransposition events with 550 μg/ml of G418 was begun . After 15 days of selection , cells were fixed , stained with Giemsa , and colonies were counted . Similarly , 1 . 0 μg of the IAP element reporter plasmid , IAP-neoTNF [62] , was cotransfected with 0 . 5 μg empty vector or test plasmid in HeLa-JVM cells , selected with G418 , and colony numbers were counted . To reveal any differences in transfection efficiencies of test proteins or off-target effects on EGFP reporter expression , we followed the strategy of Wei et al . ( 39 ) . Each test plasmid ( 0 . 5 μg ) was co-transfected in quadruplicate wells of 12-well plates with CEP-EGFP ( 0 . 5 μg ) , a construct that constitutively expresses EGFP from a CMV promoter . Four days post-transfection , EGFP fluorescence was determined by flow cytometry , as previously described [46] . To determine potential cell toxicity caused by test proteins , 18 , 000 293T cells were seeded in 75 μl of Dulbecco’s modified Eagle’s complete medium in 96-well plates . The next day , transfection reactions prepared with 70 ng of test plasmid , 0 . 2 μl of Fugene HD and 25 μl of Opti-MEM Reduced Serum Medium ( Invitrogen ) were added to each well . After 3 or 4 days , a MultiTox-Fluor Multiplex Cytotoxicity Assay kit ( Promega ) was used to assay cell toxicity , as previously described [46] . To further test potential toxicity from expression of ZAP , we co-transfected in HeLa cells pcDNA6 myc/his B , a bsr expression vector , together with either empty vector ( pcDNA3 ) or ZAP expression constructs . On day 2 , cells were expanded to T75 flasks and selection with 2 μg/ml blasticidin was begun . After 12 days , cells were fixed , stained and colonies were counted . Cytotoxicity will reduce total colony counts compared with empty vector control ( S2B Fig ) . Commercial antibodies included mouse ( ms ) α-V5-tag ( Invitrogen ) , ms α-FLAG-tag ( Sigma ) , rabbit ( rb ) α-HA-tag ( C29F4 ) , rb α-HSP90 and rb α-Myc-tag ( 71D10 ) ( Cell Signaling Technology ) , goat ( gt ) α- anti-eIF3η ( N-20 ) , gt α-MX2 ( C-20 ) , ms α-SBP ( SB19-C4 ) , and gt α-TIA1 ( C-20 ) ( Santa Cruz Biotechnology ) , rb α-β-tubulin-2 ( Pierce ) , and rb α-MOV10 and rb α-ZC3HAV1 ( ProteinTech ) . Donkey Cy3- , Cy5- , DyLight 488- , or DyLight 549-conjugated , and HRP-conjugated secondary antibodies were from Jackson ImmunoResearch Laboratories . Purified polyclonal α-ORF1p ( AH40 . 1 ) and monoclonal α-ORF1p ( α-moORF1 ) antibodies were gifts from M . Singer ( Carnegie Institution of Washington [136] ) and K . Burns ( Johns Hopkins School of Medicine [137] ) , respectively . Western blotting , IF , and FISH were performed as described [63 , 64] . L1 ORF2p reverse transcriptase analysis followed the LEAP protocol [81] . Primers used were: 3′RACE adapter NV: GCGAGCACAGAATTAATACGACTCACTATAGGTTTTTTTTTTTTVN 3′RACE outer: GCGAGCACAGAATTAATACGACT bORF2-end2 , GATGAGTTCATATCCTTTGTAGGG The sequence of the antisense RNA-FISH probe Cy2-MS2 was , Cy3-GTCGACCTGCAGACATGGGTGATCCTCATGTTTTCTAGGCAATTA . Cells were lysed and their RNA initially extracted with Trizol ( Life Technologies ) , followed by further purification using an RNeasy Mini Kit ( Qiagen ) . Residual DNA was removed by Turbo DNA-free Kit DNase treatment ( Ambion ) , and cDNA was generated from the RNA using the SuperScript III First Strand Synthesis System ( Invitrogen ) and a polyT primer . Subsequent PCR used GoTaq DNA polymerase ( Promega ) . RT-PCR primers were: 1EGFPcass5P TGTTCTGCTGGTAGTGGTCG 2EGFPcass3P TATATCATGGCCGACAAGCAG , which span the intron of the 99-PUR-JM111-EGFP reporter cassette , and 13HSPA6for CAAAATGCAAGACAAGTGTCG 14HSPA6rev TTCTAGCTTTGGAGGGAAAG , which amplify HSPA6 ( Accession No . NM_002155 ) . | Retrotransposons are mobile DNA elements that duplicate themselves by a "copy and paste" mechanism using an RNA intermediate . They are insertional mutagens that have had profound effects on genome evolution , fostering DNA deletions , insertions and rearrangements , and altering gene expression . LINE-1 retrotransposons occupy 17% of human DNA , although it is believed that only about 100 remain competent for retrotransposition in any individual . The cell has evolved defenses restricting retrotransposition , involving in some cases interferon-stimulated genes ( ISGs ) that are part of the innate immune system that protects the cell from viral infections . We screened a panel of ISGs and found several to strongly limit retrotransposition in a cell culture assay . Our investigations increase understanding of how ZAP , an important restriction factor against positive- and negative-strand RNA and some DNA viruses , also interacts with human retrotransposons to prevent genome mutation . Microscopy and immunoprecipitation show a close association of ZAP protein with the L1 ribonucleoprotein particle , as well as MOV10 , an RNA helicase that also inhibits retrotransposons . A detailed examination of the ZAP protein interactome reveals many other ISGs that directly bind ZAP , and suggests new directions for exploring the mechanisms of ZAP-mediated anti-retroelement activity . |
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To establish correlates of human immunity to the live plague vaccine ( LPV ) , we analyzed parameters of cellular and antibody response to the plasminogen activator Pla of Y . pestis . This outer membrane protease is an essential virulence factor that is steadily expressed by Y . pestis . PBMCs and sera were obtained from a cohort of naïve ( n = 17 ) and LPV-vaccinated ( n = 34 ) donors . Anti-Pla antibodies of different classes and IgG subclasses were determined by ELISA and immunoblotting . The analysis of antibody response was complicated with a strong reactivity of Pla with normal human sera . The linear Pla B-cell epitopes were mapped using a library of 15-mer overlapping peptides . Twelve peptides that reacted specifically with sera of vaccinated donors were found together with a major cross-reacting peptide IPNISPDSFTVAAST located at the N-terminus . PBMCs were stimulated with recombinant Pla followed by proliferative analysis and cytokine profiling . The T-cell recall response was pronounced in vaccinees less than a year post-immunization , and became Th17-polarized over time after many rounds of vaccination . The Pla protein can serve as a biomarker of successful vaccination with LPV . The diagnostic use of Pla will require elimination of cross-reactive parts of the antigen .
Plague is known as a primary natural zoonosis but is an extremely deadly infection for humans . The disease is caused by Yersinia pestis , a gram-negative bacterium , which upon entry in the body of mammalian host is capable of establishing three major forms of plague: bubonic , septicemic , and pneumonic [1 , 2] . The plasminogen activator ( Pla ) of Y . pestis is an outer membrane protease involved in dissemination of Y . pestis into circulation , and is one of the major virulence determinants of this pathogen [3–5] . The Pla protein is the surface-exposed trans-membrane β-barrel protease of the Omptin family with homologs found among many bacteria across family Enterobacteriacea [6] . Nevertheless , only Pla can convert plasminogen to plasmin by limited proteolysis , and this activity was likely crucial for the increased lethality of Y . pestis that developed during the course of evolution [7–9] . Detectable levels of relevant antibodies to Pla ( anti-Pla Abs ) have been measured in the convalescent sera of human patients who survived plague infection , as well as in mice that survived experimental plague infection [10 , 11] . Moreover , anti-Pla Abs of IgG class were detected in the sera of animals and humans vaccinated with live plague vaccine ( LPV ) indicating immunogenicity of this outer membrane protein [12] . Immunization with purified recombinant Pla or its use in a DNA vaccine formulation provided no protection against plague in a murine model [13] . Nevertheless , partial protection was seen in mice and rats against strain of Y . pestis lacking capsular antigen F1 [14] . Besides the testing of Pla as a potential protective antigen for plague subunit vaccine formulation , there were attempts to use this outer membrane protein for immuno-diagnostic purposes . A panel of monoclonal antibodies ( MAbs ) to Pla was created to different epitopes that were either species-specific for Y . pestis or able to recognize other bacteria [15] . Similar studies resulted in selection of anti-Pla MAbs capable of detecting natural Y . pestis isolates , as well as modified strains of plague microbe like capsule-negative variants [16 , 17] . The live plague vaccine created almost a century ago is still widely used in the former Soviet Union and China to immunize plague researchers and people at risk living in plague endemic territories [12 , 18] . The advantage of the LPV over a killed plague vaccine is its ability to defend against all forms of plague , as well its ability to mimic to the plague infectious process to a certain extent , resulting in a robust protection [19] . However , this vaccine is not approved for human use in the Western countries due to the safety concerns [20] . Nevertheless , construction of rationally attenuated vaccine strains of Y . pestis has garnered attention in recent years [21] , especially because the LPVs can induce both humoral and cellular immunity against plague [22–24] . Therefore , a detailed study of human immunity elicited by LPV is beneficial for both understanding the mechanism underlying the immune response to this vaccine and for future evaluation of efficacy of the next generation of plague vaccines . In this study , we investigated antibody and cell-mediated immunity in individuals vaccinated with the live plague vaccine line EV NIIEG , which is a derivative of the well-known vaccine strain Y . pestis EV76 [12] . Here , the Pla protein was used as a model antigen , which we intended to utilize in the future as a tool for evaluation of vaccine efficacy of vaccination and as a marker of exposure to plague .
Each human volunteer provided written informed consent for blood donation . The patients in this manuscript have given written informed consent ( as outlined in the PLOS consent form ) to publication of their case details . This study was approved by the Human Bioethics Committee of the Saratov Scientific and Research Veterinary Institute . The Institutional Review Board ( IRB ) was registered with the Office for Human Research Protections ( OHRP ) , registration number IRB00008288 ( https://ohrp . cit . nih . gov/search/irbsearch . aspx ? styp=bsc ) . Sera from healthy 26–72 years old volunteers ( n = 34 , group A ) of both genders who received multiple annual immunizations ( 2–51 injections ) with the live plague vaccine line EV NIIEG ( LPV ) , as well as from healthy individuals ( n = 17 , group B ) who had no history of contact with either Y . pestis microbe or its antigens , were tested . We further divided group A of immunized donors into subgroups of recently vaccinated ( A-RV , less than one year post-vaccination , n = 13 ) and early vaccinated ( A-EV , more than one year post-vaccination , n = 21 ) . The vaccination was performed by intradermal immunization ( scarification ) , which is a standard way to immunize people with LPV in Russia [12] . This immunization was done to plague researchers in their respectful institutions , and was not performed by us . The sera were aliquoted and stored at -80°C . Peripheral blood mononuclear cells ( PBMCs ) were isolated from heparinized blood by density gradient centrifugation in Histopaque ( Sigma , St . Louis , MO ) according to standard protocol . Cells were cultured in DMEM/F12 medium containing 10% FBS and antibiotic-antimycotic supplement for six days with or without stimulatory agent in 96 well plates ( 105 cells per well ) . The Hig-Tag-labeled Y . pestis recombinant proteins were purified as described previously for the panel of five antigens [25] . The quality of purification was evaluated with the silver stained PAGE . Soluble antigens , such as F1 , were treated with AffiPrep Polymyxin resin ( BioRad , Herciles , CA ) to remove the traces of LPS , while partially soluble Pla was isolated in two steps . First , we isolated Pla-containing inclusion bodies , and then purified Pla using Ni2+-chromatography under denaturing conditions . The level of contaminating LPS was measured with QCL-1000 Chromogenic LAL Assay kit ( Fisher Scientific ) . Both antigens were essentially LPS-free , as the LPS contamination was below the sensitivity level of the kit ( 0 . 1–1 . 0 EU/ml ) . Unstimulated PBMCs served as negative controls , and Concanavalin A from Canavalia ensiformis Type IV-S ( ConA ) ( Sigma ) was used as a positive control . The proliferative response was measured in quadruplicate by detection of BrdU incorporation using Cell Proliferation ELISA , BrdU chemiluminescent kit ( Roche Applied Science , Indianapolis , IN ) according to manufacturer’s protocol . The chemiluminescence was measured by using a BioTek Synergy HT reader ( BioTek Instruments Inc . , Winooski , VT ) . The proliferative response was expressed as a stimulation index ( SI ) calculated by dividing the mean relative light units per second ( rlu/s ) obtained for the cultured cells with a stimulant by the rlu/s of non-stimulated wells . Culture supernatants were collected on day 5 and preserved at -80°C until further use . The levels of IFN-γ , TNF-α , IL-4 , IL-10 , and IL-17A were measured by using commercial ELISA kits ( Vector-Best , Cytokine , Russia ) according to the manufacturer’s instructions . The reaction was developed using streptavidin-horseradish peroxidase with the tetra-methyl benzidine chromogen ( TMB ) , and the optical density was measured at 450 nm . Immulon 2 HB plates ( Thermo Scientific , USA ) were coated overnight at 4°C with recombinant Pla at concentration 5 μg/ml dissolved in 0 . 1 M carbonate buffer , pH 9 . 5 with 8 M urea . The remaining binding sites were blocked with 20% Newborn Calf Serum ( Sigma ) in Phosphate Buffered Saline ( PBS ) . Each serum sample was two-fold serially diluted in the range of 1:50 to 1:800 . Goat anti-human IgG ( Fab-specific ) -peroxidase ( HRP ) antibody ( Sigma ) was used as secondary antibody . The reaction was developed with the TMB substrate ( Sigma ) . The bacterial suspension of LPV was used as a control coating antigen in ELISA . The titers were calculated as the last dilution giving values above the cut-off level that was the mean value of the blank wells ( sera without antigen ) . Human antibody isotyping was performed by immunoblotting technique using relevant commercial murine monoclonal subtyping IgG subclass antibodies ( IgG1 , IgG2 , IgG3 , and IgG4 ) , as well as anti-human IgA , IgM , and IgE class specific antibodies ( Rosmedbio Ltd . , St . -Petersburg , Russia ) . The recombinant Pla antigen was separated by 12 . 5% SDS-PAGE , transferred to a nitrocellulose membrane , incubated with serially diluted human sera , and then probed with corresponding anti-human MAbs . Goat anti-mouse IgG ( Fab-specific ) -HRP Ab ( Sigma ) was used as secondary antibody . The substrate was TMB for the membranes ( Sigma ) . The endpoints were determined visually with the signal considered positive when the intensity was twice over the background . B-cell immune-reactive epitope mapping of the target antigen was performed in ELISA by using a library of 61 peptides generated from the sequence for Pla of Y . pestis CO92 ( accession no . CAB53170 . 1 ) and consisting of 15-mer peptides overlapping by 10 amino acids ( S1 Table ) . Nunc Immobilizer , Amino Modules Plates ( Thermo Scientific ) were coated with 20 μg of individual peptides in 0 . 1 M carbonate buffer , pH 9 . 5 , overnight . ELISA was then performed as described above . The dilution of tested sera was 1:100 . The interpretation of data was performed as described in a previous study with a similar design [26 , 27] . Briefly , optical density ( OD ) values were read with a BioTek Synergy HT reader at a wavelength of 450 nm ( reference wavelength , 630 nm ) . A signal was assigned as positive when it reached the cutoff value of twice the background OD . The background OD was the mean of the lowest 50% of all OD values obtained with that particular serum . The wells containing no peptides were used as negative controls , and recombinant Pla was used as a positive control . GraphPad Prism 6 software was used for data handling , analysis , and graphic representation . Non-parametric tests , i . e . the Mann–Whitney test for continuous unpaired data and the Chi-square test or the Fisher’s exact test for dichotomous variables , were performed for statistical analysis . Associations were assessed using Spearman’s Rank Correlation coefficient . A P value <0 . 05 was considered statistically significant .
To assess the in vitro proliferative response , PBMCs isolated from study subjects were stimulated with 5 μg/ml Pla or 2 μg/ml F1 ( control antigen ) . The SI induced by Pla was noticeably higher than that obtained in response to the control F1 antigen in both relevant vaccinated groups , such as group A-RV and group A-EV ( p<0 . 05 , p<0 . 0001 , respectively ) , as well as in the group B of unvaccinated individuals ( p<0 . 01 ) ( Fig 1A ) . Although the proliferative response to Pla was pronounced , there was no significant difference between both A-RV and A-EV groups of vaccinees and control donors in the group B . Nevertheless , a moderate trend of slightly higher stimulation indexes was observed in the cohort of recently vaccinated individuals ( A-RV group ) compared with the donors in the A-EV group with the last vaccination occurring more than one year ago ( p = 0 . 117 ) . The in vitro proliferative response to Pla was accompanied by a marked but nonspecific ( p>0 . 05 ) release of a number of cytokines , such as IFN-γ , TNF-α , and IL-10 by PBMCs derived from donors of both vaccinated ( A-RV and A-EV ) and unvaccinated groups ( Fig 1 ) . Surprisingly , we found that production of IL-4 was significantly greater in group B than in vaccinated donors . Although there was no significant difference between stimulated and control PBMCs , the level of IL-4 was reduced by 3 . 4-fold in group A ( ARV and A-EV ) of vaccinated donors in comparison with group B of naïve donors ( Fig 1B ) . In contrast , PBMCs obtained from donors of the A-EV group , who received multiple immunizations in the past , responded to stimulation with Pla by 14 . 7-fold increase ( p<0 . 05 ) in making IL-17A over the naïve donors of the group B ( Fig 1A , S1 Fig ) . This remarkable contribution of IL-17A production from the donors of the A-EV group resulted in the overall significant difference between groups A and B vaccinated and naïve donors ( p = 0 . 043 ) , while there was no statistical significance for the group A-RV in this category ( p>0 . 05 ) . The observed IL-17A release may indicate that the immune response to LPV becomes Th17-polarized over time multiple rounds of vaccination . There was a significant modest negative correlation between number of immunizations ( r = -0 . 475 , p<0 . 05 ) and the IL-4 response in vaccinees , although corresponding correlation with post-vaccination time was negligible ( r = -0 . 196 , p>0 . 05 ) ( S2 Fig and S3 Fig ) . Also , there was a slight positive correlation in the levels of IFN-γ ( r = 0 . 018 , p = 0 . 943 ) , IL-17A ( r = 0 . 018 , p = 0 . 943 ) and TNF-α ( r = 0 . 229 , p = 0 . 361 ) , and negative correlation of IL-10 ( r = -0 . 297 , p = 0 . 231 ) , with the number of LPV injections . The increase in the level of IFN-γ ( r = 0 . 079 , p = 0 . 756 ) , IL-17A ( r = 0 . 147 , p = 0 . 561 ) , and IL-10 ( r = 0 . 116 , p = 0 . 646 ) but not TNF-α ( r = -0 . 126 , p = 0 . 620 ) may potentially correlate with the post-vaccination time ( S3 Fig ) , although all latter cases were not statistically significant ( p>0 . 05 ) . Overall , we found significant association for the IL-4 cytokine , whose levels decreased in donors after an increasing number of vaccinations . The serological immune response to Pla elicited by the LPV was investigated in the vaccinated donors of group A in comparison with the naïve donors of group B by ELISA . We detected IgG class Abs to Pla , with titers ranging from 1:50 to 1:400 , in approximately half of the group A individuals . Moreover , all recently vaccinated donors in the A-RV subgroup were found to be anti-Pla positive . To our surprise , there was a significant difference in both the titers and percent of positive individuals between the subgroups A-RV and A-EV . Donors that received multiple repetitive immunizations ( A-EV group ) displayed a suppression of the antibody response to Pla . This observation correlates with the negative association between the level of IL-4 and number of LPV immunizations . On the other hand , 100% of the sera collected from donors in the naïve control group B reacted with the Pla antigen and exhibited titers similar to those found in the vaccinated donors of group A-RV , indicating Pla cross-reactivity ( Fig 2 ) . We next determined the reactivity of the sera for anti-Pla antibody classes and IgG subclasses ( Fig 3 ) . Based on the ELISA results , we separated the group A donors into responders ( A-Res ) and non-responders ( A-Non ) . All responders of group A and the majority of positive donors of group B demonstrated immunoreactivity with the IgG1 subclass of immunoglobulins . Only a single individual in each A and B groups showed the reaction with IgG2 subclass ( Fig 3A ) . This one donor from the group A-Res was positive for both IgG1 and IgG2 types . Among donors of the A-Non subgroup the negative reaction was observed for anti-Pla Abs of IgG1 ( p<0 . 05 ) , IgG2 and IgG4 subclasses ( p>0 . 05 ) . Moreover , all vaccinees ( group A ) possessed anti-Pla Abs for IgG3 while naïve donors of the group B did not have Pla-specific Abs of this subclass ( p<0 . 01 ) . In contrast , anti-Pla Abs of the IgG4 subclass was found exclusively in the sera of the group B donors . In addition to Pla-specific IgG , we also detected anti-Pla Abs of the IgA class in the sera of vaccinees but not in naïve donors . This corresponded with the increased level of IL-17A released by PBMCs from the group A donors ( see Discussion ) . Also , we observed the presence of anti-Pla Abs of the IgM subclass in both A and B groups of donors ( Fig 3B ) . Finally , IgE class antibodies to Pla antigen were found in sera of about one third of the vaccinated donors , both A-Res and A-Non , and only in a single unvaccinated individual . This result may be indicative of the putative allergenic potential of this antigen and the LPV vaccine in general . A library of 61 overlapping peptides , each 15 amino acid residues in length ( offset by 5 residues at a time ) deduced from the entire Pla sequence was probed with 12 sera samples that exhibited the highest anti-Pla IgG titers determined by ELISA . This set included sera from eight and four donors of the vaccinated A and naïve control B groups , respectively . The results of the screening for each serum are shown in the S2 Table . We found that most of the reactive peptides interacted with Abs from both groups of donors . Nevertheless , the peptides 9 , 11 , 18 , 30 , 34 , 36 , 49 , 52 , 54 , 56 , 58 , and 60 were specific for the donors of the A group , while peptides 19 , 33 , 35 , 50 , and 61 belonged exclusively to the group B donors . The frequency of appearance of each peptide for donor groups A and B is illustrated on Fig 4 . Among the group A specific peptides , none reacted with Abs of all eight donors tested indicating the absence of the wide-range immunodominant linear epitope . Only peptide 52 reacted with 50% sera of vaccinated donors , while most of peptides reacted with one or two sera . Therefore , the formation of the antibody response to the Pla antigen with respect to the linear B epitopes might be donor-specific . The B group specific peptides appeared just once in each corresponding serum . Two peptides , number 6 and 24 , were of particular interest , because of their strong cross-reactivity with sera from both vaccinated and naïve donors . Peptide 6 showed the remarkable ability to interact with Abs from any donor of both groups , while peptide 24 reacted 100% with sera from naïve and 50% with sera from vaccinated donors ( Fig 4 ) . The existence of two broadly-reactive Pla epitopes may explain the ELISA results shown on Fig 2 in which sera of all naïve donors reacted with the entire Pla antigen immobilized in the wells of the microtiter plate .
In the current study , we investigated for the first time the T-cell recall response to the Pla antigen in human donors vaccinated with LPV . Our data indicate that the proliferative response of human PBMCs to Pla stimulus was strong in nature and even exceeded that induced by capsular F1 antigen , which is known for its pronounced immunogenic characteristics [18 , 19] . However , we did not detect a statistical difference in this respect between vaccinated and naïve control donors , suggesting a nonspecific reaction likely due to the presence of cross-reacting T-cell epitope ( s ) within the Pla antigen . Nevertheless , there was a moderate trend ( p = 0 . 117 ) in observing a slightly high stimulation index in recently vaccinated individuals ( less than one year post-immunization ) . Therefore , we speculate that the specific T-cell response to Pla did occur in this group of vaccinees; however , it was masked by the pronounced cross-reactivity . Also , we report the presence of Pla cross-reactive linear B-cell epitopes that resulted in a strong reaction with this antigen of sera from naïve donors in ELISA . This was not totally surprising to us , since we saw an indication of this antibody cross-reactivity in our previous studies after probing Pla antigen with the panel of monoclonal antibodies [15] , and also observed the Pla-reactive band on immunoblot with naïve human sera [25] . Interestingly , multiple vaccinations with LPV suppressed the antibody titers to Pla that were observed when recently ( A-RV ) and early ( A-EV ) groups of vaccinated donors were compared ( Fig 2 ) . This suppression of the antibody response to the Pla antigen is likely due to the development of a dominant immune response to other competing and more potent antigen ( s ) of the live Y . pestis vaccine that became enhanced overtime . If true , this may mean that multiple booster immunizations with LPV may select for the response to a few dominant antigens . These antigens may not even be protective while presenting a threat of developing an allergic reaction instead ( see IgE response in Fig 3B ) . The Pla protein is considered to be a good candidate for Y . pestis specific diagnostic antigen [15–17] that is expressed well at both ambient and mammalian host temperatures [28] . However , the observed Pla cross-reactivity may result in certain limitations on its use for diagnostic purposes . Therefore , we mapped the cross-reactive regions of Pla using a library of 15-mer overlapping peptides . Comparison of peptide-ELISA results with sera from eight vaccinated and four naïve donors revealed two major cross-reactive peptides , peptides 6 ( IPNISPDSFTVAAST ) and 24 ( TDHSSHPATNVNHAN ) . Peptide 6 showed a particularly strong reaction for all sera tested and far exceeded the signal from any other reactive peptide in the library . There were other reacting peptides common for vaccinated and naïve donors; however , they were random and reactive with only one or two sera per group . Importantly , we found 12 peptides that specifically reacted with sera of vaccinated individuals and did not react with sera from naïve donors . Among them , only peptide 52 ( TPNAKVFAEFTYSKY ) reacted with 50% of sera from vaccinees suggesting that this region can potentially contain an immunodominant linear B-cell epitope recognized by the immune system of humans with different genetic backgrounds . This region may represent a good candidate to test for the purpose of creating a novel plague peptide vaccine . We determined the distribution of Pla-reacting immunoglobulins within the IgM , IgA , and IgE classes , as well as IgG subclasses ( IgG1 , IgG2 , IgG3 , and IgG4 ) in the sera of vaccinated and naïve donors ( Fig 3 ) . The anti-Pla Abs of the IgM class were found in all donors tested . Generally , natural human IgM antibodies or autoantibodies play a role in maintaining the physiological homeostasis and preventing a wide range of different infections [29] . The presence of anti-Pla Abs of IgM class in naïve donors and those who received LPV immunization many years ago suggests that they derived from a constant stimulation of the immune system with cross-reacting antigens rather than from the LPV vaccination . The suspected candidates for these stimulants could be Pla-homologous proteins of the Omptin group found in many Enterobacteriaceae [6] . In contrast , vaccinated , but not naïve , donors contained anti-Pla Abs of IgA class ( p<0 . 05 ) suggesting their origination from LPV immunization by dermal scarification . The existence of Pla-specific IgA correlated with our observation of marked production of IL-17A found after stimulation of PBMCs of vaccinated donors with the Pla antigen ( Fig 1A ) , which was absent in the naïve group of donors . It was shown previously that vaccine-specific Th17 cells formed by parenteral immunization were involved in eliciting a long-term detectible level of secreted IgA [30] . Moreover , subcutaneous priming with recombinant antigen in a Th17-inducing adjuvant followed by boosting promoted high and sustained levels of IgA in the lungs . This response was proven to be associated with germinal center formation in the lung-draining lymph nodes [31] . This may comprehensively explain the high efficiency of LPV against both bubonic and pneumonic plague [12 , 18 , 24] . Overall , these immune response characteristics to Pla antigen suggest that Th17 polarization of the immunity to LPV can be beneficial to the host during infection [32] . The release of IL-17A in response to stimulation of PBMCs of immunized individuals could also serve as an indicative marker of successful vaccination with LPV . Nevertheless , we would like to speculate that the presence of Pla-specific antibodies of the IgE subclass in vaccinated donors only ( Fig 3B ) may highlight the danger of a vaccine-related trigger of an allergic response and autoimmune disease . Further studies are needed to shed light on this important issue . It was reported previously that human immunization with killed plague vaccine induced long-lasting and mixed Th1/Th2 responses that were more polarized towards Th1 [33] . In our study , slightly elevated production of IFN-γ and diminished IL-4 in response to stimulation with Pla in the group of recently vaccinated donors ( Fig 1B ) also points to a Th1-biased immune response after administration of the live vaccine . This observation is supported by detection of anti-Pla Abs of IgG1 and IgG3 , and absence of IgG4 subclasses in the sera of these donors [34–37] . In summary , we found that despite complications with cross-reactivity , human immunity elicited by LPV could be assessed based on analysis of the immune response to Pla antigen . Our analysis showed that LPV vaccination resulted in the response being skewed towards Th1 and Th17 , while production of IL-17A by PBMCs of immunized donors in response to Pla antigen stimulation could be a good indicator of the induced immunity . Additionally , we mapped cross-reacting linear B-epitope candidates within the Pla antigen that should be helpful in developing Pla-based diagnostics for Y . pestis . | Yersinia pestis , the causative agent of plague , has been recognized as one of the most devastating pathogen experienced by mankind . It remains endemic in many parts of the world , and is considered emerging pathogen . A live attenuated Y . pestis strain EV line NIIEG has been used for decades in the former Soviet Union for human vaccination and has proven effective against all forms of plague . We began characterizing the Y . pestis-specific antibody and T cell-mediated immune responses in people immunized with live plague vaccine . The long term goal of our research is to understand the protective mechanisms underlying immunity to plague in humans and to discover novel protective antigens for their incorporation into a subunit vaccine . Here , we describe our study on immune responses in vaccinees to one of the essential virulence factors of Y . pestis , namely Pla antigen . The results of the study shed light on the development of the optimal markers to assess the correlation with vaccine-induced protection . |
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Extrachromosomal genetic elements such as bacterial endosymbionts and plasmids generally exhibit AT-contents that are increased relative to their hosts’ DNA . The AT-bias of endosymbiotic genomes is commonly explained by neutral evolutionary processes such as a mutational bias towards increased A+T . Here we show experimentally that an increased AT-content of host-dependent elements can be selectively favoured . Manipulating the nucleotide composition of bacterial cells by introducing A+T-rich or G+C-rich plasmids , we demonstrate that cells containing GC-rich plasmids are less fit than cells containing AT-rich plasmids . Moreover , the cost of GC-rich elements could be compensated by providing precursors of G+C , but not of A+T , thus linking the observed fitness effects to the cytoplasmic availability of nucleotides . Accordingly , introducing AT-rich and GC-rich plasmids into other bacterial species with different genomic GC-contents revealed that the costs of G+C-rich plasmids decreased with an increasing GC-content of their host’s genomic DNA . Taken together , our work identifies selection as a strong evolutionary force that drives the genomes of intracellular genetic elements toward higher A+T contents .
Bacterial genomes exhibit a considerable amount of variation in their nucleotide composition ( G+C versus A+T ) , ranging from less than 13% to more than 75% GC [1 , 2] . Despite intense efforts during the past decades , the selective pressures determining the evolution and maintenance of this variation remain elusive [3] . A general pattern that emerged from sequencing the genomes of numerous taxa is that bacteria , whose survival obligately depends on a eukaryotic host ( i . e . endosymbionts ) , display genomic AT-contents that are significantly increased in comparison to the genomes of their free-living relatives as well as their hosts’ genomes [4] . Interestingly , intracellular genetic elements that permanently or transiently exist outside the bacterial chromosome , such as plasmids , viruses , phages , and insertion sequence ( IS ) elements , are also usually characterized by a significantly higher AT-content than the genome of their host [5 , 6] . Finding that the nucleotide composition of these very different elements is consistently biased in the same direction suggests similar evolutionary mechanisms operate to produce this pattern . While less attention has been paid to extrachromosomal genetic elements such as plasmids and bacteriophages , two main hypotheses have been put forward to explain the biased nucleotide composition of obligate intracellular bacteria: First , high AT-contents can result from increased levels of genetic drift and mutational bias [4 , 7 , 8] . Genetic drift is particularly strong , when the bacteria’s effective population sizes are small , which is generally the case in vertically transmitted , intracellular symbionts [4] . Since the majority of DNA modifications caused by oxygen radicals ( either from the environment or generated by endogenous cellular processes ) lead to mispairing of DNA bases , which mostly results in GC→AT transitions and G/C→T/A transversions [9] , in the long-run genetic drift is expected to increase the elements’ overall AT-content . On the other hand , it has been argued that the AT-bias of intracellular elements could be adaptive , and thus be favoured by natural selection [5] . The reasoning behind this idea is that both endosymbiotic bacteria and plasmids occupy the same ecological niche , i . e . the intracellular environment of a larger organism , and thus have access to metabolites in the host’s cytoplasm [10] . This includes all nucleotides and their biochemical precursors . For the host cell , ATP and UTP nucleotides are energetically less expensive to produce than GTP and CTP nucleotides [5] . Moreover , ATP is the main energy currency used in cells and , thus , the most abundant nucleotide [11 , 12] . Hence , a preferential uptake of A+T nucleotides by the respective intracellular element may impose a lower metabolic burden on its host than consumption of the more valuable G+C nucleotides . Strikingly , in both endosymbiotic bacteria and plasmids , selection tends to reduce the costs intracellular elements impose on their host ( e . g . by reducing the size and transcriptional activity of the element [10] ) . The reason for this is that hosts harbouring metabolically ‘costly’ intracellular elements display a lower fitness than hosts with metabolically ‘cheaper’ elements [13] . As a consequence , selection acts against less fit host-symbiont/ bacteria-plasmid combinations , thereby favouring hosts that harbour metabolically cheaper intracellular elements ( host-level selection ) . Accordingly , if AT-rich elements are metabolically ‘cheaper’ than GC-rich elements , hosts with more AT-rich elements should be selectively favoured . Until now , GC-content variation in endosymbiotic bacteria has mainly been studied using comparative approaches [14 , 15] . This is likely due to technical difficulties to experimentally disentangle and manipulate these complex and often obligate host-endosymbiont systems [16] . Although understandable from a methodological point of view , sequence comparisons can only reveal correlative relationships . To identify the underlying mechanistic causes , however , manipulative experiments are required that allow to rigorously scrutinize the focal hypothesis under controlled conditions . While the GC-content evolution in plasmids has also mainly be studied using comparative approaches ( [6 , 17 , 18] , but see [19] ) , plasmids and their bacterial hosts are highly tractable systems , in which a variety of different features can be experimentally manipulated [10] . Here we take advantage of the experimental tractability of plasmid-host interactions to unravel the evolutionary consequences resulting from a biased nucleotide composition of host-dependent , intracellular elements . Manipulating the plasmids’ GC-content allowed us to experimentally test the hypothesis that a higher demand for G+C nucleotides due to the presence of an extrachromosomal genetic element ( here: plasmid ) limits host growth . Our results show indeed that bacteria that contain a GC-rich plasmid , and thus have an increased demand for G+C nucleotides , were less fit than bacteria containing an AT-rich plasmid . Supplying cells that contained a GC-rich plasmid with G+C nucleosides restored the fitness of host cells , while no such effect was observed for cells with AT-rich plasmids or when A+T nucleotides were supplemented to both types of plasmid-containing cells . These findings suggest that the cytoplasmic availability of G+C nucleotides limited the growth of cells with GC-rich plasmids . Moreover , introducing plasmids into different bacterial species with an increasing genomic GC-content revealed that fitness costs imposed by GC-rich plasmids decreased with increasing GC-content of the host genome . Taken together , our results provide strong experimental evidence that the commonly observed increased AT-content of host-dependent elements can be selectively favoured .
To determine if differences in the nucleotide composition of an extrachromosomal genetic element affect the fitness of the corresponding host cell , plasmids with high or low GC-contents were introduced into Escherichia coli cells . For this , two plasmids served as a backbone , into which eight non-coding AT- or GC-rich sequences of 1 kb in size were introduced to alter the cells’ net GC-content ( Fig 1 ) . Sequences originated from eukaryotic DNA and were carefully selected , such that no genes or regulatory elements were present ( see Materials and methods ) . In this way , chances of inadvertent gene expression were minimized , which could have resulted in additional metabolic costs . All AT-rich and GC-rich sequences were individually introduced into two different plasmid backbones that strongly differ in terms of their genetic architecture ( i . e . origin of replication , copy number , and selectable marker ) . The resulting plasmid constructs ( i . e . eight AT-rich and eight GC-rich plasmids for each of the two backbones ) were used as replicates to rule out plasmid- or sequence-specific effects . This system allowed us to study the fitness consequences resulting from intracellular elements that differed in their genomic nucleotide composition in otherwise isogenic bacterial cells . To determine whether the plasmids’ nucleotide composition affected the host cell’s fitness , AT-rich ( i . e . cells harbouring AT-rich plasmids ) and GC-rich ( i . e . cells harbouring GC-rich plasmids ) E . coli cells were grown for 24 h in minimal medium and the growth kinetics of these cultures were determined spectrophotometrically . The results of this experiment revealed that GC-rich cells harbouring the plasmid pJet1 . 2/blunt ( hereafter: pJet ) grew significantly less well than the corresponding AT-rich cells: GC-rich cells displayed a significantly extended lag-phase , a decreased maximum growth rate , and reached a lower maximum cell density as compared to AT-rich cells ( Fig 2A–2C ) . Repeating the same experiments with E . coli strains that harboured the second plasmid backbone ( i . e . pBAV1kT5gfp [20] lacking T5gfp; hereafter: pBAV ) , corroborated these results . Again , cell populations containing GC-rich plasmids displayed a significantly extended lag-phase , a decreased maximum growth rate , and reached a significantly reduced maximum cell density relative to the cognate AT-rich cells ( Fig 2D–2F ) . Taken together , GC-rich cells grew significantly less well than AT-rich cells , indicating fitness costs resulted from the presence of GC-rich plasmids . Due to the design of the experiment , effects that might have been caused by the inserted sequences or the plasmid backbones used , could be ruled out as explanation . The copy number of plasmids is usually genetically determined by replication-control mechanisms that are encoded by the plasmid itself [21] . Nevertheless , the copy number of a single plasmid can vary , depending on the extent of metabolic costs it imposes on its host . For example , the copy number of a plasmid has been shown to decrease with increasing length or gene content of the accessory region [13] . Accordingly , if a GC-rich plasmid imposes a higher metabolic cost on its host than an AT-rich plasmid , it should also be present in a lower copy number than the less costly AT-rich plasmid . This hypothesis was tested by quantifying the copy numbers of both plasmids harbouring AT-rich or GC-rich inserts via quantitative PCR ( qPCR ) . Indeed , qPCR analyses revealed that the copy number of both plasmid backbones analysed were drastically reduced when plasmids contained GC-rich inserts relative to plasmids with AT-rich inserts ( Fig 3 ) . This pattern was consistently observed over the entire growth cycle of the focal cells ( S1 Fig ) . Together , these results further corroborate that GC-rich plasmids likely impose a higher metabolic burden on their host cells than AT-rich plasmids , thus resulting in a strongly reduced copy number . The decreased copy number of GC-rich plasmids relative to AT-rich plasmids could make the corresponding cells more susceptible to the antibiotic , which has been used in previous experiments to stabilize the focal plasmid . This effect could explain the reduced growth of GC-rich cells ( Fig 2 ) . To rule out this possibility , cells containing the AT-rich or GC-rich pJet or pBAV plasmids were grown with 100% , 50% , or 0% of the antibiotic concentration that had been used in the abovementioned growth experiment . Quantifying fitness-relevant growth parameters of the differentially treated cultures revealed that the previously observed growth difference between cells harbouring AT-rich and GC-rich plasmids was similarly detectable for the maximal growth rate ( S2A and S2B Fig ) and the duration of the lag phase ( S2C and S2D Fig ) , yet less strongly pronounced for the maximal optical density achieved ( S2E and S2F Fig ) . Only when cells of the antibiotic-free environment ( 0% ) were compared , no significant difference between AT-rich or GC-rich cells was detectable for the maximal optical density reached ( Independent-samples t-test , n = 8 ) . Moreover , we tested whether cells lost their plasmids at the end of the 24 h growth period and whether this rate of plasmid loss depended on the antibiotic concentration in the growth medium and/ or the GC-content of their plasmids . The resistance mechanisms that were used to stabilize both plasmids relied on the degradation of ampicillin and kanamycin , respectively . Hence , it is conceivable that towards the end of the growth period , antibiotic concentrations might have fallen below inhibitory levels , thus allowing cells to lose their plasmid . If GC-rich plasmids are costlier than AT-rich plasmids , the former should be lost more readily than the latter . To test this , cells from all treatments of the previous experiment were plated after 24 h of growth on agar plates that did or did not contain the respective antibiotic . Indeed , a lower proportion of cells that initially contained the GC-rich plasmids were able to grow on antibiotic-containing medium than cells with AT-rich plasmids ( S2G and S2H Fig ) , which clearly shows that GC-rich plasmids were lost much faster than AT-rich plasmids . This result can be explained by the higher costs of GC-rich plasmids , which lowered their copy number ( Fig 2 ) , thus increasing the risk for segregational loss . The preferential loss of GC-rich plasmids likely takes place during later stages of the exponential growth phase , since both lag phase and maximum growth rate , which are the first parameters measured over the time course of the experiment , revealed even in the absence of the antibiotic a significant difference between populations of cells containing AT-rich and GC-rich plasmids . In contrast , the maximum optical density reached under the same conditions did not differ significantly between AT-rich cells and GC-rich cells , which was most probably due to the loss of the GC-rich plasmids . Taken together , these results clarify that the reduced growth of GC-rich cells can neither be explained by the presence of the antibiotic , nor the concentration used . Genetic information , which is acquired via horizontal gene transfer , can cause significant fitness defects by interfering with existing regulatory networks [22] . Given that horizontally acquired genes tend to be more AT-rich than the genome of the new host [23] , bacteria use this deviation to recognize and silence newly acquired alien DNA . This is achieved via so-called xenogeneic silencing proteins that bind to DNA with a higher AT-content than the genome of the host and silence transcription emanating from these sequences [22] . The Histone-like Nucleoid Structuring ( H-NS ) protein is the best-characterised xenogeneic silencing protein , which is present in all γ-proteobacteria including E . coli . Theoretically , action of H-NS could have affected the observed fitness difference between cells containing AT-rich and GC-rich plasmids . To rule out this possibility , we deleted hns from the genome of E . coli , introduced all AT-rich and GC-rich pJet plasmids into the newly constructed mutant and repeated the previous growth experiment using the Δhns strain and E . coli wild type as host cells . The results of this experiment revealed no statistically significant effect of the presence of hns on the growth patterns caused by AT-rich and GC-rich plasmids ( S3 Fig ) . However , it is well-known that deletion of hns results in a severe growth defect that is rapidly recovered by compensatory mutations [24] . The fact that the Δhns strain used in our study did not show this reduced growth phenotype , suggests additional mutations arose also during our experiments . Unfortunately , the high rate , with which these mutations occur , impedes a clean evaluation of the effect of H-NS . As a consequence , the experimental results involving the Δhns strain ( S3 Fig ) have to be interpreted with caution . To determine whether the decreased growth of GC-rich cells also translates into a decreased competitive fitness relative to AT-rich cells , coculture experiments were performed with randomly chosen pairs of AT-rich and GC-rich strains . As expected , populations of GC-rich cells were readily outcompeted by AT-rich strains ( Fig 4 ) : In all 32 replicate populations , a strong decrease in the frequency of GC-rich cells relative to AT-rich cells was observed shortly after the onset of the experiment . Already after two days , GC-rich strains went extinct in 50% of all experimental populations . At the end of the experiment ( i . e . after eight days ) , GC-rich strains were present in only two out of 32 populations ( i . e . 6% ) , suggesting a strongly reduced fitness of GC-rich cells relative to AT-rich cells . Two main explanations can account for this pattern . First , the growth of GC-rich cells could have been limited by the availability of G+C nucleotides within cells , while the cells containing AT-rich plasmids were less strongly affected . This hypothesis would be in line with the idea that the molecular composition of extrachromosomal genetic elements is strongly affected by natural selection . Second , other properties of GC-rich DNA in general could limit the growth of cells containing GC-rich plasmids . For example , an increased stability of GC-rich DNA could raise the chances for stable secondary structures that might hamper DNA replication [25] and thus slow down growth . These two effects can be distinguished in a competition experiment between AT-rich and GC-rich cells , in which cocultures are supplemented with either A+T or G+C nucleotides . If the observed decrease in fitness of GC-rich cells was truly due to a lack of G+C nucleotides within cells , then providing GC-rich cells with G+C nucleotides should enhance their growth more than supplementation with A+T . In contrast , if another mechanism such as e . g . the formation of secondary structures applies , nucleotide supplementation should not differentially affect the growth of AT-rich and GC-rich cells . For this experiment , nucleosides instead of nucleotides were used , since E . coli can take up nucleosides , but not nucleotides [26] . When A+T nucleosides were externally provided , the survival rate of GC-rich cells was not significantly different from the one of the unsupplemented control group ( Fig 4 ) , indicating that these nucleosides do not generally limit cellular growth . When G+C nucleosides were added to the growing cultures , the decline of GC-rich strains observed within the first three days of the experiment was as fast as in the untreated control group . However , from day three onwards , GC-rich cells continued to survive in ~35% of the cocultures , which represents a significant increase over both the A+T-supplemented and the untreated control group ( Fig 4 ) . This finding indicates that the low availability of G+C nucleotides limited the growth of GC-rich cells , thereby corroborating the hypothesis that the availability of nucleotides in the host cytoplasm plays a key role in shaping the GC-content of extrachromosomal genetic elements . The finding that AT-rich plasmids impose a lower metabolic burden on host cells than GC-rich plasmids was obtained using E . coli as host , whose chromosome has a GC-content of ~ 50% . However , the intracellular availability of nucleotides likely depends on the base composition of the cell’s chromosome , because the biosynthetic machinery of a cell is expected to have evolved in a way that it produces the required building block metabolites in optimal amounts . As a consequence , G+C nucleotides should be more abundant than A+T nucleotides in species with high genomic GC-contents , thus rendering GC-rich plasmids less costly than AT-rich plasmids . In order to test this hypothesis , eight bacterial species with a genomic GC-content ranging from 40% GC ( Acinetobacter baylyi ADP1 ) to 68% GC ( Azospirillum brasilense Tarrand ) were transformed with one randomly chosen pair of AT-rich and GC-rich pBAV plasmids . In this context , it should be noted that the GC-rich plasmid was characterized by a net GC-content of 51% ( AT-plasmid: 33% GC ) and was thus enriched in GC-nucleotides when compared to the chromosomes of species with low to intermediate GC-contents . However , the GC-rich plasmid displayed a slightly lower GC-content when compared to the genome of GC-rich species such as Azospirillum brasilense and Xanthomonas campestris . Subsequently , fitness of different host cells carrying the GC-rich versus AT-rich plasmids was quantified spectrophometrically as before . Indeed , the results of these experiments revealed that the burden imposed by these two plasmids depended on the genomic GC-content of the bacterial host: when the host chromosome was more AT-rich , the fitness of cells containing the AT-rich plasmid was higher than the one of cells containing the GC-rich plasmid ( Fig 5 ) . In contrast , when host cells with a more GC-rich chromosome were considered , the fitness of cells containing the GC-rich plasmid was increased relative to cells containing the AT-rich plasmid ( Fig 5 ) . Thus , the molecular composition of the host’s genome strongly affected the fitness cost imposed by extrachromosomal genetic elements . In addition , plasmid copy number measurements revealed that AT-rich plasmids were generally more abundant in species with low and intermediate genomic GC-contents ( S4 Fig ) . However , in the two species with the highest GC-contents ( i . e . Azospirillum brasilense and Xanthomonas campestris ) , GC-rich plasmids were present in higher copy numbers than the AT-rich plasmids . Taken together , GC-rich plasmids imposed higher fitness costs than AT-rich plasmids on species with low to intermediate genomic GC-contents , while AT-rich plasmids were more costly for bacterial species with higher genomic GC-contents . The DNA of intracellular , host-dependent elements such as bacterial endosymbionts and plasmids is generally more AT-rich than the DNA of their host’s genome . In the case of bacterial endosymbionts , this pattern is commonly thought to be due to neutral evolutionary processes , such as genetic drift or a mutational bias . However , here we provide strong experimental evidence that selective advantages can contribute to this pattern . By experimentally manipulating the GC-content of plasmids and quantifying the resulting fitness consequences for the corresponding bacterial host , we show that the fitness cost of plasmids strongly depended on the nucleotide composition of both the plasmid and the host’s genome . Specifically , when the genome of the host cell was characterized by intermediate to high A+T contents , GC-rich plasmids were more costly ( Figs 2 and 5 ) and present in a lower copy number than AT-rich plasmids ( Fig 3 and S1 Fig ) . In contrast , when the host chromosome was enriched in G+C , GC-rich plasmids were less costly ( Fig 5 ) and present in a higher copy number than AT-rich plasmids ( S1 Fig ) . Supplementation experiments confirmed that the observed fitness effects were indeed due to limiting pools of the corresponding nucleotides and not resulting from the GC-content of the introduced sequences per-se ( Fig 4 ) . The continuous synthesis of nucleotides is crucial for DNA replication in all dividing cells . Under optimal conditions , E . coli cells can divide every 20 minutes , while the replication of the chromosome takes about 40 minutes [27] . To overcome this problem , multiple chromosomal copies are simultaneously generated , such that replication can keep up with the speed of cell division [28] . Interestingly , not the activity of the polymerase , but the nucleotide biosynthesis required for DNA replication seems to be limiting growth , as evidenced by the observation that the shortage of a single nucleotide drastically decreases growth [29] . Moreover , up-regulation of the ribonucleotide reductase ( RNR ) in yeast , which is responsible for the synthesis of dNTPs , increases the speed of the replication fork [30] . Both studies show that nucleotide synthesis is the rate-limiting step for DNA synthesis and hence also growth . By linking the availability of nucleotides to cellular fitness , these studies support the main findings reported here . Our results are consistent with an evolutionary scenario , in which the intracellular availability of nucleotides ( A+T versus G+C ) depends on the genomic nucleotide composition of the bacterial cell . Over evolutionary time , a bacterial cell should establish an equilibrium , in which the biochemical machinery that produces all four nucleotides is tailored to meet the cell’s requirements . This includes not only the nucleotides that are required for DNA replication , but also those that are needed to produce RNA , signalling molecules ( e . g . ppGpp , cAMP ) , or coenzymes ( e . g . ATP ) . In addition , energetic and stoichiometric parameters are likely to affect nucleotide production rates . For example , the biosynthetic cost to produce A+T nucleotides is less than the energy that is required to biosynthesize the same amounts of G+C [5] . An extrachromosomal genetic element that enters such a cellular system disturbs this equilibrium by withdrawing nucleotides from intracellular pools to enable its own replication . By doing so , plasmids ( and likely also intracellular bacterial endosymbionts ) incur a cost to the hosting cell that depends on both the nucleotide availability in the host’s cytoplasm and the amount and identity of nucleotides it consumes . In cells of Escherichia coli , whose genome is characterized by a mean AT-content of ~ 50% , ATP is the most abundant nucleotide ( 3 . 5 mM ATP , 2 . 0 mM UTP , 1 . 9 mM GTP , and 1 . 2 mM CTP under exponential growth [11] ) . This is likely because of the dual function of ATP , which is not only used for RNA and DNA synthesis , but also plays a key role for transferring energy within cells . Unfortunately , to the best of our knowledge , no other study exists to date that quantified cytoplasmic nucleotide concentrations in other bacterial species , especially those that feature higher genomic GC-contents . Nevertheless , it appears reasonable to assume that cells with a higher genomic GC-content should also have an increased demand for G+C nucleotides including both ribo- and deoxyribonucleotides for RNA- and DNA-biosynthesis , respectively . This would imply higher cytoplasmic G+C levels and thus render the consumption of G+C nucleotides by GC-rich plasmids potentially less detrimental than in host cells with high genomic AT-content and thus , low cytoplasmic G+C . The observation that plasmid copy numbers of AT-rich plasmids were higher in species with low to intermediate genomic GC-contents , but decreased in species with more GC-rich genomes ( S4 Fig ) , is in line with this hypothesis . Previous work that compared the GC-content of plasmids with the genomes of their corresponding bacterial and archaeal hosts revealed that on average , the GC-content of plasmids was lower than the GC-content of their host’s genome ( i . e . between 3% and 10%; see [5] and [6] ) . Moreover , also genes that have been acquired by horizontal gene transfer seem to be generally characterized by a lower G+C-content relative to the resident genome’s nucleotide composition [23] . Two fundamentally different processes could cause the observed increased AT-content of plasmids and horizontally acquired genes relative to the genome of their bacterial host . First , plasmids and genes with an increased relative AT-content might be more successful in establishing in new host cells via routes of horizontal gene transfer ( i . e . conjugation , transduction , transformation ) . In this case , the compositional difference between donor and recipient cell would act as a barrier that limits the horizontal transfer of genetic material [31] . Second , plasmids or horizontally transferred genes could evolve towards increased AT-contents relative to their host’s chromosome once being present in the new cell [32] . In either way , the results presented in this study can help to explain the AT-bias of horizontally acquired genes or plasmids . If A+T nucleotides are more abundant in the cytoplasm of bacterial hosts and/ or cheaper to produce than G+C nucleotides , a plasmid or a bacteriophage that is more AT-rich is more likely to successfully establish in the recipient cell , because of the reduced metabolic burden it imposes [5] . However , how would selection operate to favour extrachromosomal genetic elements with an increased AT-content ? In principle , selection can operate on two different levels . First , several intracellular elements that differ in their genomic composition can compete against each other . If selective advantages are sufficiently strong , elements with an increased AT-content should outcompete elements with a lower AT-content , thus resulting in a globally increased AT-content of the entire population of intracellular elements ( i . e . selection acting on the level of the extrachromosomal genetic element ) . Alternatively , selection can act on the level of the host ( i . e . host-level selection ) . In this case , host individuals that contain more AT-rich elements are evolutionarily fitter than hosts containing more GC-rich intracellular elements . As a consequence of competition , hosts that contain more AT-rich elements will survive and reproduce with a higher chance , thus favouring AT-rich elements on the level of the host population in the long-run . For the given experimental set-up , our results demonstrate that host-level selection can be strong and result in an almost complete elimination of GC-rich plasmids within a few days ( Fig 4 ) . Unfortunately , it was not possible to test whether AT-rich plasmids could outcompete GC-rich plasmids within a given host cell , as plasmids using the same mode of replication cannot coexist within the same cell ( i . e . when they belong to the same plasmid incompatibility group , see [33] ) . Our results do not only help to understand the GC-content variation in extrachromosomal genetic elements such as plasmids and viruses , but have also significant ramifications for endosymbiotic bacteria . Similar to the interaction between bacteria and their plasmids , host-dependent bacterial cells regularly feature genomes with drastically increased AT-contents relative to the DNA of their host cell . In addition , many bacterial endosymbionts have lost the genes for an autonomous biosynthesis of all four nucleotides [34 , 35] . Hence , to maintain a sufficient nucleotide-supply , cells require uptake mechanisms that allow them to import nucleotides from the host’s cytoplasm . Indeed , uptake systems for nucleotide triphosphates in intracellular bacteria have been previously identified for Rickettsia and Chlamydia [36 , 37] , which are also known to lack specific genes essential for nucleotide biosynthesis pathways . Bacterial endosymbionts [38] , and in fact the majority of prokaryotic and eukaryotic organisms [39 , 40] , display a characteristic mutational bias that generally increases the genomic AT-content . In addition , newly established endosymbionts sometimes show an unexpectedly large number of polymerase slippage events that preferentially eliminate G+C-rich repetitive sequences , thus also biasing the endosymbiont’s genome towards an increased A+T-content [41] . Finally , population bottlenecks that occur frequently when populations of bacterial endosymbionts are vertically transmitted from parent to offspring host , result in random assortment of bacterial genotypes that can lead to the fixation of AT-rich symbiont populations within hosts . In the early onset of an endosymbiotic interaction , all of the abovementioned processes are likely selectively neutral . However , at some point , host individuals that harbour symbionts with increased AT-contents will display a higher fitness than hosts that contain more GC-rich symbionts , particularly given the large number of endosymbiont cells in an individual host that amplify the costs associated with nucleotide requirements of the symbiont population . Due to this fitness difference , host-level selection should favour hosts with metabolically ‘cheap’ AT-rich symbionts . We thus believe that the evolution of AT-rich endosymbionts is likely a combination of both neutral processes such as mutational bias/ genetic drift and natural selection . Taken together , our results provide strong experimental support for the hypothesis that the availability of nucleotides represents a significant evolutionary force that shapes the base composition of host-dependent , extra-chromosomal elements such as plasmids and likely also endosymbiotic bacteria . This interpretation is at odds with the widely-held view of drift as being the sole explanation for the AT-bias observed in the genomes of host-restricted bacteria . While our study adds an important new facet to this on-going discussion , it is most likely a combination of multiple factors that determines the nucleobase composition of bacterial genomes .
Eight AT-rich and GC-rich stretches of 1 kb in size each were identified from the AT-rich genome of Arabidopsis thaliana Col-0 ( genome version TAIR9 v171 obtained from the Plant Genomic Database ) and GC-rich genome of Chlamydomonas reinhardtii wild type 137 C ( assembly and annotation v4 obtained from DOE Joint Genome Institute ) . Both annotated genomes were imported into Geneious ( version 6 . 1 . 8 , Biomatters , New Zealand ) [42] that was used to identify AT-rich and GC-rich DNA stretches , respectively . Sequences containing simple sequence repeats in a total length of more than 30 bp were excluded to avoid the possible formation of stem-loop structures . Importantly , sequences were selected such that the chances for promoters , start codons , ribosome binding sites , or other regulatory elements were minimized . Putative prokaryotic promoters were predicted using Softberry BPROM ( softberry . com , [43] ) , which revealed on average two to three promoter sites within each of the AT-rich sequences , whereas none was identified for the GC-rich sequences . The chances for putative promoter regions are higher for AT-rich DNA , since promoter regions are generally enriched in AT-content . Putative open reading frames ( ORFs ) were predicted using the Geneious Ver . 6 . 1 . 8 . ORF finder . No or few ATG-ORFs were predicted for both AT-rich and GC-rich sequences ( i . e . 0–4 per sequence ) , a slightly higher number of alternative ORFs starting with GTG and TGG were found for the GC-rich sequences , which is due to the higher GC-content . Finally , sequences were tested for the presence of ribosome binding sites ( RBS , i . e . Shine Dalgarno Sequences ) . No RBS sequences were present in the AT-rich sequences and only three out of the eight GC-rich sequences contained one or two putative RBS ( i . e . GC04 , 06 , and 08 ) . The RBS identified , however , were not in close proximity of any of the ATG start codons , thus rendering translational activities unlikely . In addition , neighbouring regions of the insert position on both plasmid backbones used ( i . e . a modified pJet and pBAV plasmid , see below ) were analysed for the same features . For pJet , no putative promoters , ATG , GTG , or TGG ORFs or RBS were found in close proximity of the AT-rich/ GC-rich inserts ( i . e . 600 bp up- and downstream ) . For pBAV , a single ~200 bp GTG ORF was detected ~350 bp upstream of the insert position ( within the aminoglycoside-3’-phosphotransferase resistance cassette ) . However , due to its small size , it did not overlap with the insert sequences . Taken together , only a small number of insert sequences contain DNA-elements required for transcription or translation . However , the chances for gene expression are minimal , since i ) not all required elements are present , ii ) they are in the wrong order , and iii ) eight completely different sequences were used per treatment group ( AT/GC ) . PCR primers ( S1 Table ) were designed using the software Primer 3 [44] and synthesized by Metabion International AG ( Martinsried , Germany ) . Genomic DNA of A . thaliana Col-0 was extracted following the method of Allen et al . [45] and of C . reinhardtii wild type 137 C using the protocol described by [46] . AT-rich DNA was amplified by PCR using Phusion HiFi Polymerase ( Fermentas/ Thermo Fisher Scientific , Waltham , Massachusetts , US ) following the manufacturer’s protocol . PCR program: 98 °C 1 min , 30x: 98 °C 15 s , Tm primer 15 s , 68 °C 40 s . Elongation temperatures were decreased to 68 °C according to Su et al . [47] , since no PCR product was observed at 72 °C . GC-rich DNA was amplified from C . reinhardtii using Kapa2G Robust Polymerase ( Peqlab; Erlangen , Germany ) following the manufacturer’s recommendations for GC-rich DNA . PCR program: 95 °C 5 min , 30x: 95 °C 15 s , Tm primer 5 s , 72 °C 40 s . PCR products were purified by gel electrophoresis ( 1% agarose ) using the NucleoSpin Extract II gel and PCR clean-up Kit ( Macherey-Nagel GmbH & Co . KG , Düren , Germany ) . Two plasmid backbones were used for the insertion of AT-rich and GC-rich DNA . The first backbone was pJet1 . 2/blunt ( Thermo Fisher Scientific ) , a commercially available , high copy number plasmid of small size . The plasmid carries a pMBI* origin of replication and encodes a beta-lactamase ( bla ) that confers resistance to ampicillin , which was used to select for plasmid-containing cells . AT-rich and GC-rich sequences were inserted into the blunt-end multiple cloning site of pJet using the pJet1 . 2/blunt Cloning Kit ( Thermo Fisher Scientific ) lacking the PlacUV5 promoter . Plasmids were transformed into chemically competent E . coli TOP10 cells ( Invitrogen , Thermo Fisher Scientific ) using the heat shock method [48] . Transformed colonies were screened for the respective insert using the Colony Fast-Screen Kit ( Epicentre; Madison , Wisconsin , USA ) following the manufacturer’s instructions . Plasmids of selected transformants were sequenced at MWG Eurofins ( Ebersberg , Germany ) . To validate the experimental results and exclude plasmid-specific effects , all AT-rich and GC-rich sequences were additionally inserted into a second , high copy number plasmid , a modified pBAV1kT5-gfp [20] ( ordered from Addgene https://www . addgene . org/; Cambridge , Massachusetts , US ) . This plasmid uses a different replication system ( i . e . repA-mediated replication ) and encodes a different selectable marker , aminoglycoside-3’-phosphotransferase ( aph ( 3’ ) ) , which confers resistance to the antibiotic kanamycin . The gene encoding the green fluorescent protein present on the plasmid was not needed for this study and hence removed by digesting the plasmid with NotI ( Thermo Fisher Scientific ) . Blunt ends were generated and clean-up was carried out as described above . AT-rich and GC-rich sequences were inserted into the same position . The resulting plasmids were transformed into chemically competent E . coli TOP10 cells . Transformants were sequenced in order to validate loss of the T5-gfp cassette and successful insertion of the AT-rich and GC-rich sequences . In the main text , the modified plasmid lacking T5-gfp is denoted as pBAV instead of pBAV1Kt5-gfp . All AT-rich plasmids were transformed into E . coli BW25113 Ara- [49] , whereas GC-rich plasmids were transformed into E . coli BW25113 Ara+ [50] , respectively , that were made chemically competent using the rubidium chloride method [48] . The Δara mutation renders the strain unable to catabolize arabinose . Both strains can be phenotypically distinguished when plated on tetrazolium arabinose indicator plates , on which E . coli BW25113 ( Ara+ ) forms white and BW25113 Δara ( Ara- ) red colonies [51 , 52] . The arabinose marker is selectively neutral under the cultivation conditions used in this study ( independent-samples t-test: P > 0 . 05 , n = 8 ) . This phenotypic marker was used to distinguish both strains when grown in coculture . E . coli BW25113 Δhns was derived from the Keio collection ( strain JW1225 , [49] ) and cured from the kanamycin-resistance cassette as described [53] . Subsequently , cells were made chemically competent as above and used to transform all AT- and GC-rich pJet plasmids . All experiments were performed in M9 minimal medium [54] , which was complemented with 2 mM MgSO4 , 0 . 1 mM CaCl2 , and 5 g l-1 Glucose ( Sigma , St . Louis , Missouri , USA ) . For pBAV-containing strains , 0 . 25% Casamino acids ( Sigma ) were added to promote growth . Precultures were prepared by streaking genotypes from glycerol stocks on Lysogeny Broth ( LB , Sigma ) agar plates ( Thermo Fisher Scientific ) , which were incubated overnight ( 16 h ) at 37 °C . Subsequently , single colonies were picked and inoculated into 0 . 8 ml of M9 medium in a 96-deepwell plate ( Eppendorf , Hamburg , Germany ) , which was then incubated overnight at 37 °C under shaking conditions . To ensure plasmid maintenance , 100 μg ml-1 ampicillin or 50 μg ml-1 kanamycin ( Sigma ) were always added to the culture media for pJet- and pBAV-harbouring strains , respectively . Next , optical densities ( OD determined at a wavelength of 600 nm ) of all precultures were measured in a microwell platereader ( Spectramax 250 , Molecular Devices; Sunnyvale , USA ) using a 96-well plate ( Nunc , Fisher Scientific GmbH; Schwerte , Germany ) with a culture volume of 200 μl . The OD600nm of each culture was adjusted to 0 . 001 . Growth kinetic assays were performed in the same instrument . Using a culture volume of 200 μl , growth was measured as absorbance at 600 nm every 5 min at 37 °C for 24 h . Cultures were shaken for 3 min after each and 15 s prior to each measurement . Fitness-related growth parameters ( i . e . lag phase , maximum growth rate , and maximal OD600nm ) were calculated using Magellan 7 . 1 SP 1 software ( Magellan Software GmbH; Dortmund , Germany ) . Growth experiments using E . coli BW25113 Δhns harbouring AT-rich ( i . e . pJet AT01-08 ) or GC-rich ( i . e . pJet GC01-08 ) plasmids were performed and analysed as described above . For 24 h-copy number experiments , four E . coli strains harbouring AT-rich ( i . e . AT01-04 ) and GC-rich plasmids ( i . e . GC01-04 ) were chosen . Cells were precultured as stated above and then inoculated in 200 ml Erlenmeyer flasks containing 20 ml M9 medium supplemented with 100 μg ml-1 ampicillin ( initial inoculum: OD600nm = 0 . 001 ) . Subsequently , the OD600nm was monitored in regular intervals . Samples for plasmid copy number determination were taken every 6 h and stored by adding the same volume of 40% w/v glycerol at -80°C until real time PCR measurements were performed . Plasmid copy numbers were determined using quantitative real-time PCR ( qPCR ) following a previously described method [55] . For this , monocultures of cells were harvested after 24 h of growth . Plasmid copy numbers were determined by calculating both the total number of chromosomal and plasmid copies in each sample . Chromosome copy numbers were determined using a primer pair targeting the single copy gene dxs ( 1-deoxy-D-xylulose-5-phosphate synthase ) . For total plasmid numbers per sample , primer pairs targeting the respective antibiotic resistance gene were used ( i . e . either bla on pJet or aph ( 3’ ) on pBAV; genes and primer details see S2 Table ) . Bacterial cultures from both monocultures were diluted ~1:100 and used for qPCR . QPCR was performed using the Brilliant III Ultra-Fast SYBR Green QPCR Master Mix ( Agilent Technologies; Santa Clara , US ) in a BioRad CFX96 thermocycler ( Hercules , California , USA ) according to the manufacturer’s instructions . PCR program: 10 min 95 °C , 40x: 30 s 95 °C , 20 s 61 °C , 30 s 72 °C . Standard curves were prepared by 10-fold dilutions of both isolated plasmids and bacterial cells ( R2 of all standard curves: > 0 . 99 ) . Plasmid numbers per ng plasmid DNA template were calculated using an online calculator ( http://cels . uri . edu/gsc/cndna . html , Andrew Staroscik , Genomics & Sequencing Center , University of Rhode Island , Kingston , Rhode Island , USA ) . Cell numbers of each standard curve sample were measured using a CyFlow Space flow cytometer ( Partec , Görlitz , Germany ) , for which cells were stained with SYBR Green ( Sigma ) following the manufacturer’s protocol . Average plasmid copy numbers per cell were calculated from the respective standard curves ( R2 of all standard curves > 0 . 99 ) by dividing total plasmid numbers by the total number of cells . In order to test whether the reduced growth of cells harbouring the GC-rich plasmids was due a higher susceptibility to the supplemented antibiotic , additional growth experiments were performed in the absence of the antibiotic . For this , all AT-rich and GC-rich plasmid-containing strains ( i . e . both pJet and pBAV ) were precultured in M9 medium containing the respective antibiotic as described above . After that , cultures were centrifuged at 9 . 000 rpm for 1 min . Supernatants were discarded and cells were washed twice with fresh M9 medium in order to ensure that cultures are free of any residual antibiotic . Next , cultures were diluted as described above and subjected to one of three treatments: M9 medium containing 100% , 50% , or 0% of the antibiotic ( i . e . 100 μg ml-1 , 50 μg ml-1 , and 0 μg ml-1 ampicillin , and 50 μg ml-1 , 25 μg ml-1 , and 0 μg ml-1 kanamycin , respectively ) . The standard concentration of antibiotics used ( i . e . 100% ) was chosen according to the provider’s recommendation ( i . e . pJet: Thermo Fisher Scientific , pBAV: [20] ) . Growth kinetics were performed and measured as described above . In order to test whether plasmids were still present in the antibiotic-free cultures after the experiment has been terminated , cells were plated on agar plates that did ( i . e . 50% of the standard concentration ) or did not contain antibiotics . By comparing the number of CFUs ( colony forming units ) on the antibiotic-containing plates relative to antibiotic-free plates , the rate of plasmid loss was determined . For coculture experiments , one AT-rich strain was paired with a GC-rich strain , respectively ( all harbouring pJet plasmids ) . Eight combinations were randomly chosen ( i . e . Fig 1: AT1-GC1 , AT2-GC2 , etc . ) and each combination was replicated 4 times ( n = 32 ) . To test if the decrease in growth of GC-rich cells can be explained by their increased demand for G+C nucleotides , cocultures were grown in one of three different media: ( 1 ) pure M9 minimal medium , ( 2 ) M9 medium supplemented with A+T-nucleosides , or ( 3 ) M9 medium supplemented with G+C-nucleosides . In this experiment , deoxyribonucleosides have been used instead of deoxyribonucleotides , because no nucleotide transport systems are known for E . coli , while two nucleoside transport systems ( i . e . NupG and NupC , ) have been described [26] . Either 2’-deoxy-adenosine and thymidine or 2’-deoxyguanosine and 2’-deoxycytidine ( Sigma ) were added to the growth medium at a final concentration of 100 μM per nucleoside . The OD600nm of all precultures was adjusted to 0 . 0005 , resulting in a final OD600nm of 0 . 001 after mixing of cocultures . Fitness experiments were performed in a 96-deepwell plate ( Nunc ) with a culture volume of 0 . 8 ml . Cocultures were incubated at 37 °C under shaking conditions ( 220 rpm ) . 0 . 8 μl ( 1:1 , 000 dilution ) of all cocultures were transferred daily into fresh medium for a total of eight days . Every day , serial dilutions of all cultures were plated on TA agar plates to distinguish Ara+ ( AT-rich ) and Ara- - strains ( GC-rich ) of E . coli BW25113 using the arabinose utilization marker as described above . One AT-rich and one GC-rich pBAV plasmid , i . e . pBAV-AT01 and pBAV-GC02 , were randomly chosen to be introduced into other bacterial species differing in their genomic GC-contents . PJet could not be used for this purpose as it does not replicate in species other than E . coli . In contrast , pBAV has been shown to be a broad host range plasmid replicating in many other bacterial species [20] . The following species were used: Acinetobacter baylyi ADP1 ( 40% GC ) , Serratia entomophila ( DSM 12358 ) ( 58% GC ) , Pseudomonas protegens ( 61% GC ) , Pseudomonas putida ( 62% GC ) , Arthrobacter aurescens ( DSM 20116 ) ( 61 . 5% GC ) , Xanthomonas campestris ( DSM 3586 ) ( 65% GC ) , and Azospirillum brasilense ( DSM 1690 ) ( 68% GC ) . All strains were tested to be Kanamycin ( 50 μg/ml ) -sensitive . Both plasmids were introduced in electrocompetent cells of the above-listed species using a MicroPulser Electroporator ( Bio-Rad , Hercules , California , US ) with the following settings: 25 μF , 200 mA , and 2 . 5 kV using 70 μl of electrocompetent cells and 100–150 ng plasmid DNA . Colonies obtained were grown in LB medium supplemented with 50 μg ml-1 kanamycin . Plasmid isolation was performed as described previously and plasmids were sequenced using plasmid-specific primers targeting the AT/GC-rich insert . All strains were grown in M9 minimal medium containing glucose , sucrose , and malate ( glucose and sucrose: 5 g l-1 each , malate: 2 g l-1 ) as carbon source , as well as 2 mM MgSO4 , 0 . 1 mM CaCl2 , 45 μM FeSO4 , 0 . 5 mg ml-1 NaMO4 , and 0 . 01 mg ml-1 Biotin ( Sigma ) at 28°C and 220 rpm for 24 h . Growth experiments were performed as mentioned above using a Spectramax plate reader . Plasmid copy numbers of different bacterial species were determined by measuring plasmid numbers via quantitative Real-Time PCR as described earlier . However , all cell numbers were quantified by Flow Cytometry instead of qPCR , since using dxs-specific primers did not result in DNA amplification in most of the species , either due to an altered sequence or absence of the corresponding gene . Thus , plasmid copy numbers were calculated as plasmid number per cell count ( see above ) . Experiments were performed using plasmids that contained one of eight different AT- or GC-rich inserts . To reduce the impact of sequence-specific effects , data were analysed by treating AT-rich and GC-rich strains as replicates . In monoculture experiments , fitness-relevant parameters of AT-rich and GC-rich cells were statistically compared by two-sample independent t-tests . In coculture experiments , survival rates of strains harbouring GC-rich plasmids were calculated by scoring the number of populations , in which the strains were present relative to the total number of populations within each treatment ( n = 32 ) . Strains were considered to be extinct , if the number of colony forming units decreased below 2% of the total CFU counts . Survival rates between treatments were compared by performing Wilcoxon signed ranks tests . False discovery rate ( FDR ) was applied to P-values to correct for multiple testing [56] . Linear regression analyses were performed to correlate growth parameters of species harbouring AT/GC-rich plasmids with the species’ GC-content . All statistical analyses were performed using SPSS Software ( version 17 . 0 , SPSS Inc . , Chicago , IL , USA ) and R Studio ( Boston , USA ) [57] . | Genomes of endosymbiotic bacteria are commonly more AT-rich than the ones of their free-living relatives . Interestingly , genomes of other intracellular elements like plasmids or bacteriophages also tend to be richer in AT than the genomes of their hosts . The AT-bias of endosymbiotic genomes is commonly explained by neutral evolutionary processes . However , since A+T nucleotides are both more abundant and energetically less expensive than G+C nucleotides , an alternative explanation is that selective advantages drive the nucleotide composition of intracellular elements . Here we provide strong experimental evidence that intracellular elements , whose genome is more AT-rich than the genome of the host , are selectively favoured on the host level . Thus , our results emphasize the importance of selection for shaping the DNA base composition of extrachromosomal genetic elements . |
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Cell regulatory circuits integrate diverse , and sometimes conflicting , environmental cues to generate appropriate , condition-dependent responses . Here , we elucidate the components and mechanisms driving a protein-directed RNA switch in the 3′UTR of vascular endothelial growth factor ( VEGF ) -A . We describe a novel HILDA ( hypoxia-inducible hnRNP L–DRBP76–hnRNP A2/B1 ) complex that coordinates a three-element RNA switch , enabling VEGFA mRNA translation during combined hypoxia and inflammation . In addition to binding the CA-rich element ( CARE ) , heterogeneous nuclear ribonucleoprotein ( hnRNP ) L regulates switch assembly and function . hnRNP L undergoes two previously unrecognized , condition-dependent posttranslational modifications: IFN-γ induces prolyl hydroxylation and von Hippel-Lindau ( VHL ) -mediated proteasomal degradation , whereas hypoxia stimulates hnRNP L phosphorylation at Tyr359 , inducing binding to hnRNP A2/B1 , which stabilizes the protein . Also , phospho-hnRNP L recruits DRBP76 ( double-stranded RNA binding protein 76 ) to the 3′UTR , where it binds an adjacent AU-rich stem-loop ( AUSL ) element , “flipping” the RNA switch by disrupting the GAIT ( interferon-gamma-activated inhibitor of translation ) element , preventing GAIT complex binding , and driving robust VEGFA mRNA translation . The signal-dependent , HILDA complex coordinates the function of a trio of neighboring RNA elements , thereby regulating translation of VEGFA and potentially other mRNA targets . The VEGFA RNA switch might function to ensure appropriate angiogenesis and tissue oxygenation during conflicting signals from combined inflammation and hypoxia . We propose the VEGFA RNA switch as an archetype for signal-activated , protein-directed , multi-element RNA switches that regulate posttranscriptional gene expression in complex environments .
Mammalian cells integrate diverse , and sometimes conflicting , environmental signals to generate appropriate , condition-dependent responses . Tissue myeloid cells are exposed to a plethora of stimulatory and inhibitory signals , and thus its integrated response is particularly complex . This task is made more problematical , and possibly more critical , in dynamic , pathological environments . Myeloid cell vascular endothelial growth factor ( VEGF ) -A is critical for blood vessel formation during development , wound-healing , and tumorigenesis [1] . Hypoxia is possibly the most potent agonist of VEGF-A expression , working at the levels of transcription , mRNA stabilization , and translation [2] , [3] . VEGF-A synthesis is induced in monocyte/macrophages activated by pro-inflammatory agonists , including interferon ( IFN ) -γ and bacterial lipopolysaccharide . Overproduction of VEGF-A can cause excessive neovascularization , blood vessel permeability , and enhanced leukocyte recruitment , all hallmarks of chronic inflammatory conditions , including cancer and atherosclerosis [4]–[6] . Agents that inhibit VEGF-A or its receptor have been applied clinically to successfully limit colorectal and renal cell carcinoma [7] . Positive and negative regulation of VEGF-A expression has been reported in human macrophages in multiple stressed conditions . We have shown that VEGF-A expression in myeloid cells is translationally repressed by the IFN-γ-triggered GAIT ( interferon-gamma-activated inhibitor of translation ) system [8] , [9] . Importantly , under certain pathological conditions , for example within the avascular cores of tumors and in the thickened intima of atherosclerotic lesions , macrophages are simultaneously exposed to both inflammatory cytokines and hypoxia that act concurrently in multiple pathophysiological scenarios to regulate gene expression . Treatment of human monocytic cells with IFN-γ induces the synthesis of VEGFA mRNA and protein for up to about 12 to 16 h . However , VEGF-A synthesis and secretion are suppressed about 16 h after IFN-γ treatment despite the presence of abundant VEGFA mRNA [10] . Translational silencing of VEGFA and other GAIT targets requires binding of the GAIT complex to its cognate GAIT element in the target mRNA 3′UTR [10] . The GAIT element is a defined 29-nt stem-loop with an internal bulge and unique sequence and structural features . The human GAIT complex is heterotetrameric containing glutamyl-prolyl-tRNA synthetase ( EPRS ) , ribosomal protein L13a , NS1-associated protein–1 , and glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) [11] , [12] . A C-terminus truncated form of EPRS , termed EPRSN1 , functions as a dominant-negative regulator of GAIT complex activity and maintains basal expression of VEGF-A [13] . RNA-binding proteins ( RBPs ) that regulate mRNA stability or translation generally recognize their target mRNAs through structural or sequence-specific elements in the 5′ or 3′UTRs of mature mRNAs . The activity of trans-acting RBPs can be modulated by dosage ( in turn regulated by synthesis rate and stability ) , cellular localization , posttranslational modification , noncoding RNAs , and interacting protein partners . Heteronuclear ribonucleoprotein ( hnRNP ) L is a key posttranscriptional regulator of VEGF-A expression . Human hnRNP L has three consensus RNA recognition motifs ( RRM ) [14] and binds CA-rich elements ( CARE ) in coding and noncoding regions of multiple transcripts [15] . hnRNP L contributes to pre-mRNA splicing [16] , mRNA nucleocytoplasmic transport [14] , internal ribosomal entry site-mediated translation [17] , translational repression [18] , and mRNA stabilization [19] . The molecular mechanisms by which signal transduction systems integrate multiple environmental cues into a binary response that determines gene expression remain largely unexplored . We have reported that hnRNP L operates a hypoxia-stimulated , binary conformational RNA switch that overrides IFN-γ-induced GAIT-mediated translational silencing of VEGFA mRNA in human monocytic U937 cells and in primary human peripheral blood monocytes ( PBMs ) [20] . The proposed switch permits high-level VEGF-A expression under combined inflammatory and hypoxic stress . Here we elucidate the molecular mechanism underlying the IFN-γ- and hypoxia-dependent regulatory RNA switch . The switching mechanism involves condition-dependent posttranslational modification and relocalization of hnRNP L , and subsequent formation of an hnRNP L-containing heterotrimeric complex that stabilizes the VEGFA HSR in a translation-competent conformation .
HnRNP L is an essential component of the RNA switch that blocks GAIT-mediated translational silencing of VEGF-A mRNA , and permits high-level expression of VEGF-A in myeloid cells in the presence of IFN-γ and hypoxia ( Figure S1 ) [20] . To determine whether hnRNP L is sufficient for RNA switch function , the activity of recombinant protein was determined by in vitro translation of luciferase reporter bearing the VEGFA HSR in a wheat germ extract system in the presence of active GAIT complex from IFN-γ-treated U937 cells ( Figure 1A ) . hnRNP L failed to overcome the translational repression suggesting that posttranslational modification of hnRNP L or additional protein factors may be required . Identical results were seen using a rabbit reticulocyte lysate system ( not shown ) . Hypoxia-dependent hnRNP L binding partners were determined by RNA affinity purification using a 30-nt , 5′-biotinylated , extended CARE ( CARE-E ) from the VEGFA HSR ( Figure 1B ) . To reduce nonspecific binding , lysates from U937 cells incubated under normoxic or hypoxic conditions were pre-cleared with an excess of 5′-biotinylated antisense CARE-E RNA in which CA pairs were mutated to GU . Cleared lysates were incubated with biotinylated , wild-type CARE-E RNA and μMAC magnetic streptavidin microbeads , and applied to a magnetic column . Bound proteins were eluted with salt solution , concentrated , and subjected to SDS-PAGE and Coomassie stain ( Figure 1C ) . Bands enriched in lysates from hypoxia-treated cells were subjected to mass spectrometric analysis , and peptides corresponding to hnRNP L , hnRNP A2/B1 , and DRBP76 ( nuclear factor 90 or interleukin enhancer binding factor 3 ) were identified ( Table S1 ) . Binding of the proteins to CARE RNA was confirmed by RNA affinity isolation and immunoblot analysis of lysates from hypoxia-treated U937 cells . A hypoxia-inducible complex of hnRNP L , DRBP76 , and hnRNP A2/B1 ( HILDA ) was shown to bind wild-type but not mutant antisense CARE RNA; substantially less binding of the three proteins to CARE RNA was observed in normoxic lysates ( Figure 1D ) . The formation of an RNA-binding heterotrimeric complex was investigated by co-immunoprecipitation ( IP ) . Lysates from U937 cells and primary human PBM treated with IFN-γ under normoxic or hypoxic conditions were subjected to IP with anti-hnRNP L antibody , and probed with hnRNP A2/B1- and DRBP76-specific antibodies ( Figure 1E , left panel ) . A hypoxia-dependent interaction of hnRNP L with hnRNP A2/B1 and DRBP76 was observed . The interaction between hnRNP L and hnRNP A2/B1 was RNA-independent as shown by the lack of an effect of RNase A treatment . However , the RNase diminished the interaction between hnRNP L and DRBP76 , suggesting that the hnRNP L-DRBP76 complex is stabilized by RNA . The expression levels of the three HILDA complex constituents were not altered by hypoxia exposure ( Figure 1E , right panel ) . In vitro GST-pulldown experiments showed that recombinant GST-hnRNP L directly interacted with recombinant hnRNP A2/B1 and DRBP76 ( Figure 1F , left panel ) . In a parallel experiment , GST-hnRNP A2/B1 was found to directly bind hnRNP L but not DRBP76 ( Figure 1F , right panel ) . hnRNP L contains an N-terminal glycine-rich domain , three RNA-binding motifs ( RRM1–3 ) , and a proline-rich linker domain connecting RRM2 and RRM3 ( Figure 1G , top ) . Domain mapping experiments revealed that hnRNP A2/B1 binds the proline-rich linker in hnRNP L ( Figure 1G , left ) . In contrast , the RRM3-containing , C-terminal domain of hnRNP L was the binding site for DRBP76 ( Figure 1G , right ) . EPRS and hnRNP L from IFN-γ-treated U937 cells , in either normoxia or hypoxia , bind in vitro synthesized VEGF-A HSR in a mutually exclusive manner [20] . To provide in vivo evidence of the VEGF-A switch , RNA from cells treated with IFN-γ in the presence of normoxia or hypoxia for 24 h were immunoprecipitated with anti-EPRS and -hnRNP L antibodies and subjected to qRT-PCR using transcript-specific primers . GAIT complex EPRS and HILDA complex hnRNP L recognized and bound VEGFA mRNA following stimulation by IFN-γ under normoxic and hypoxic conditions , respectively , consistent with previous results ( Figure 2A ) [20] . To determine whether hnRNP A2/B1 or DRBP76 are required for hnRNP L binding to VEGFA mRNA , lysates from cells treated with IFN-γ and hypoxia were subjected to ribonucleoprotein IP ( RIP ) using anti-hnRNP L antibody , coupled with RT-PCR . hnRNP L interacted with VEGFA mRNA in control transfected cells; however , the interaction was substantially reduced following siRNA-mediated depletion of either hnRNP A2/B1 or DRBP76 ( Figure 2B ) . Similarly , the interaction of hnRNP A2/B1 or DRBP76 with VEGFA mRNA required the presence of the other ( Figure S2 , left and center panels ) . Moreover , the interaction of hnRNP A2/B1 and DRBP76 with VEGFA mRNA was abolished following hnRNP L depletion by siRNA-mediated gene silencing ( Figure S2 , right panels ) , suggesting that HILDA binding to VEGFA mRNA requires integrity of the entire complex . To begin to understand the roles of the individual protein components in RNA switch activity , their binding sites within the HSR region were mapped by UV-crosslinking . Of the three proteins , only hnRNP L and DRBP76 directly bind the VEGFA HSR . Interestingly , the two interacting proteins bind different regions of the HSR , hnRNP L binds the CARE , whereas DRBP76 binds the AU-rich stem loop ( AUSL ) ( Figure 2C ) . The less robust binding to the individual ascending ( AUSL-A ) and descending ( AUSL-D ) regions of the AUSL suggests that DRBP76 stabilizes the double-stranded AUSL in a conformation that prevents formation of the GAIT element , which overlaps AUSL-A ( Figure 1B ) . We determined the specific DRBP76-binding region by constructing a series of mutations in either AUSL strand . Mutation of M2 ( U404UAUAU409 to AAUAUA ) , but not M1 ( A416AUAUA421 to UUAUAU ) , inactivated the RNA switch of the HSR-bearing reporter RNA , suggesting the upper stem-loop region of the AUSL is critical ( Figure 2D and Figure S3A , B ) . Differences in luciferase activities of the mutant forms were due largely to altered translation as shown by comparable firefly luciferase mRNA levels determined by semi-quantitative RT-PCR ( Figure 2D , insert ) ; renilla luciferase mRNA levels were essentially the same for all transfections ( not shown ) . Complementary covariant mutations ( M2–M3 , A381UAUAA386 to UAUAUU ) on the M3 strand opposing M2 were introduced in an attempt to restore function . However , the M2–M3 double mutant failed to recover RNA switch activity , possibly due to disruption of the GAIT element structure by M2 mutation . Thus , we further created complementary mutations of U358UAUAU363 to AAUAUA ( M4 ) to restore the GAIT element structure at the distal 6-bp stem region . RNA switch activity was partially restored in the M2–M3–M4 triple mutant , indicating the stem structure , not the sequence , is critical for DRBP76 activity in the RNA switch . As controls , individual M3 and M4 mutants lacked GAIT-mediated translational silencing activity and RNA switch function . In the VEGFA HSR , the CARE adjoins the GAIT element with not even a single nt separating them ( Figure S3A ) [20] . To determine the maximum distance between the elements that permits RNA switch activity , we inserted 5- to 25-nt poly ( C ) spacers between them in an HSR-bearing reporter . Spacers up to 15 nt permitted RNA switch activity , but 20- and 25-nt spacers were inhibitory ( Figure 2E and Figure S3A , C ) , consistent with a distance limit for an effective interaction between the binding proteins hnRNP L and DRBP76 . The insertions did not affect mRNA expression of FLuc ( Figure 2E , insert ) and RLuc ( not shown ) significantly , indicating that altered translation was responsible for differential Luc activity . Together these results suggest that whereas hnRNP L is responsible for target selectivity , DRBP76 , through binding a nearby stem-loop region , has primary responsibility for stabilizing the RNA form lacking the GAIT structural element , thereby suppressing GAIT complex-directed translational silencing ( Figure 2F ) . Knockdown of DRBP76 did not significantly alter VEGFA mRNA half-life , providing additional evidence that DRBP76 influences VEGF-A expression primarily at the level of translation ( Figure S3D ) . By knockdown and overexpression experiments , we previously reported that hnRNP L is essential for hypoxia-induced switch activity in U937 cells [20] . To test the requirement for the other HILDA components , DRBP76 and hnRNP A2/B1 , both were subjected to siRNA-mediated knock-down ( hnRNP L knock-down served as positive control ) ( Figure 3A , top ) . Cells were treated with IFN-γ and hypoxia for up to 24 h , and lysates tested for their effect on in vitro translation of an HSR-bearing reporter . As seen before , 24-h lysates from IFN-γ-treated normoxic cells inhibited translation of the reporter , but 24-h lysates from hypoxic cells were inactive ( Figure 3A , bottom ) . However , deletion of either DRBP76 or hnRNP A2/B1 dramatically impaired the hypoxia-driven RNA switch to an extent comparable to that of hnRNP L knockdown , and permitted GAIT complex-mediated translation inhibition by 24-h lysates ( Figure 3A , bottom ) . We investigated the effect of these lysates on endogenous gene expression . As before , hypoxia prevented IFN-γ-mediated inhibition of expression of VEGF-A observed at 24 h ( Figure 3B ) . However , siRNA-mediated knock-down of either DRBP76 or hnRNP A2/B1 restored translational inhibition of VEGF-A without significantly altering the steady-state level of VEGFA mRNA ( Figure 3B ) . Polysome profiling was done to verify that the effects on VEGF-A expression were due to altered translation . IFN-γ activation of the GAIT pathway inhibited VEGF-A mRNA translation-initiation [21] , and this inhibition was reversed by hypoxia [20] . Indeed , following IFN-γ treatment under hypoxia , knock-down of either hnRNP A2/B1 or DRBP76 induced a dramatic shift of endogenous VEGFA mRNA from translationally active polysome pools to translationally inactive free mRNP pools ( Figure 3C and Figure S4 ) . hnRNP L expression is markedly reduced in normoxic , IFN-γ-treated cells , thereby permitting GAIT complex binding to the VEGFA mRNA and transcript-specific translational silencing [20] . Semiquantitative RT-PCR ( Figure 4A ) and Northern blot analysis ( Figure S5 ) showed that hnRNP L mRNA expression is unaltered by either hypoxia or IFN-γ treatment for up to 24 h , and that altered hnRNP L expression must be posttranscriptional . hnRNP L half-life was measured in the presence of cycloheximide to inhibit protein synthesis . In nonstressed monocytic cells ( normoxia , no IFN-γ ) the half-life of hnRNP L is about 12 h ( Figure 4B and Figure S6A ) . The half-life of hnRNP L was shortened to about 4 h by IFN-γ treatment in normoxia; however , hypoxia suppressed the effect of IFN-γ , restoring the half-life to about 12 h ( Figure 4C and Figure S6B ) . As shown previously , the proteasome inhibitor MG132 blocked IFN-γ-mediated hnRNP L degradation , indicating an important role of the ubiquitin/proteasome pathway in regulating hnRNP L expression [20] . To investigate the mechanism underlying IFN-γ-induced hnRNP L degradation , hnRNP L ubiquitination was determined . IFN-γ treatment in the presence of MG132 induced accumulation of a high molecular weight form of hnRNP L consistent with ubiquitination ( Figure 4D ) . Expression of HA-ubiquitin and detection with anti-HA-tag antibody confirmed formation of high molecular weight , ubiquitinated hnRNP L , and exposure to hypoxia dramatically diminished hnRNP L ubiquitination ( Figure 4E ) . We considered the von Hippel-Lindau ( VHL ) -containing ubiquitin ligase complex as a candidate E3 ubiquitin-protein ligase because of its normoxia-dependent role in regulation . VHL specifically targets proteins , e . g . , hypoxia inducible factor ( HIF ) -1α tagged by O2-dependent prolyl hydroxylation [22] . VHL was shown to interact robustly with hnRNP L , but not with hnRNP A2/B1 or DRBP76 , in an IFN-γ-dependent manner ( Figure 4F ) . Also , siRNA-mediated knockdown of VHL markedly reduced hnRNP L polyubiquitination ( Figure 4G , left panel ) with MG132 treatment , and increased hnRNP L stability following IFN-γ treatment in absence of MG132 ( Figure 4G , right panel ) . However , overexpression of VHL did not affect the stability of hnRNP L or the assembly of the HILDA complex in hypoxia , suggesting that HILDA complex formation might contribute to protection of hnRNP L from VHL-mediated degradation ( Figure S7 ) . In an in vitro ubiquitination system reconstituted with exogenous E1 and E2 enzymes and E3 ubiquitin ligase pVHL derived from lysate of 8 h , IFN-γ-treated U937 cells in normoxia further confirmed robust polyubiquitination of hnRNP L ( Figure S8 ) . In contrast , cell lysate from hypoxia-treated U937 cells failed to modify hnRNP L . Similar results were obtained with primary human PBM ( not shown ) . These results suggest that proteasomal degradation of hnRNP L in U937 cells and in human PBM is mediated by IFN-γ-triggered ubiquitination by a VHL-containing E3 ubiquitin ligase . hnRNP L is primarily localized in the nucleus in human monocytic cells but substantially redistributes to the cytoplasm during hypoxia [23] . Fluorescence visualization verified hypoxia-driven cytoplasmic relocalization of hnRNP L , even in the presence of IFN-γ ( Figure 5A ) . Similar hypoxia-stimulated cytoplasmic relocalization of hnRNP L was observed in primary human PBM-derived macrophages induced by macrophage colony stimulating factor ( M-CSF ) ( Figure S9 ) . Immunoblot analysis of cytosolic and nuclear fractions from IFN-γ- and hypoxia-treated cells further confirmed hnRNP L translocation ( Figure 5B ) . Cellular localization of RBPs can be regulated by their phosphorylation state [24]–[26] . Metabolic labeling with 32P-orthophosphate showed that hypoxia induced robust phosphorylation of hnRNP L at 8 h , and the modification was stable for at least 24 h ( Figure 5C ) . Immunoblot analysis of hnRNP L immunoprecipitated from hypoxia-treated cells with phospho-specific antibodies revealed strong phosphorylation at Tyr , but not at Ser or Thr ( Figure 5D ) . A time course experiment showed modest hnRNP L Tyr-phosphorylation after 0 . 5 h of hypoxia and maximal phosphorylation after 4 h in U937 cells ( Figure 5E ) and in primary human PBM ( not shown ) . Immunoblot analysis with anti-pTyr antibody showed Tyr-phosphorylated hnRNP L was almost completely restricted to the cytoplasm in hypoxia-treated cells ( Figure 5F ) . To identify the hypoxia-induced phosphorylation site , hnRNP L was immunoprecipitated from lysates of hypoxia-treated cells , and phospho-sites detected by mass spectrometry . Total coverage with three protease treatments was 84% , but phosphorylation events were not detected ( Figure S10 ) . Endogenous hnRNP L in U937 cells was knocked down with siRNA targeting the 3′UTR , and cells transfected with cDNA constructs containing specific , site-directed Tyr-to-Ala mutations at residues in regions not covered by the mass spectrometry analysis . Among the five hnRNP L mutants tested , only Y359A was not phosphorylated in U937 monocytic cells ( Figure 5G ) and in human PBM ( not shown ) . Tyr359 , and the surrounding sequence , is evolutionarily conserved from frogs to humans ( Figure 5H ) , and has been identified as a phospho-site by high-throughput proteomic survey ( www . phosphosite . org ) in both mouse and human ( in addition to Tyr phosphorylation at positions 47 , 48 , 92 , 267 , 285 , 333 , 340 , 363 , 375 , 565 , 574 , and 576 ) . To determine the role of Tyr359 phosphorylation in hnRNP L localization , cells were transfected with c-Myc-tagged wild-type cDNA or , phospho-dead ( Y359A ) or phospho-mimetic ( Y359D ) mutants . Under normoxic conditions , wild-type hnRNP L is primarily localized in the nucleus , but also present in the cytoplasm , as observed previously [23] . In contrast , the Y359A mutant was exclusively in the nucleus , and the Y359D mutant was exclusively cytoplasmic ( Figure 5I ) . Similarly , following IFN-γ stimulation under hypoxia , the Y359A and Y359F hnRNP L mutants were exclusively localized in the nucleus ( Figure S11 ) . As a control for specificity , Tyr130 mutants did not partition with the Tyr359 mutants . Cells were transfected with c-Myc-tagged wild-type or mutant hnRNP L , immunoprecipitated with anti-c-Myc antibody , and probed with hnRNP A2/B1 antibody . Y359D exhibited much greater binding to hnRNP A2/B1 compared to wild-type or Y359A mutant hnRNP L ( Figure 5J ) . Remarkably , the Y359D mutant , but not the Y359A mutant or wild-type protein , was completely resistant to IFN-γ-stimulated degradation ( Figure 5K ) . Consistent with the cellular translocation of hnRNP L ( Figure 5A ) , Tyr phosphorylation was induced by IFN-γ treatment in hypoxia ( Figure S12 ) . In summary , hypoxia-inducible Tyr359 phosphorylation of hnRNP L facilitates its cytoplasmic relocalization and prevents its degradation . Because hnRNP A2/B1 does not bind the HSR directly , it is more likely involved in regulation of its binding partner hnRNP L , than in operating the RNA switch itself . We tested the possibility that hnRNP A2/B1 contributes to hypoxia-induced stabilization of hnRNP L . siRNA-mediated knockdown of hnRNP A2/B1 resulted in hnRNP L destabilization following IFN-γ treatment in hypoxia ( Figure 6A ) . In contrast , hnRNP A2/B1 knockdown did not induce DRBP76 degradation ( Figure 6B ) . Also , siRNA-mediated knockdown of DRBP76 did not affect hnRNP L stability ( Figure S13 ) . Interestingly , hnRNP L was subject to IFN-γ-dependent Pro hydroxylation as shown by IP followed by probing with anti-hydroxyproline antibody ( Figure 6C ) . Hypoxia prevented the IFN-γ-inducible prolyl hydroxylation of hnRNP L ( Figure 6D ) . Knockdown of hnRNP A2/B1 under hypoxic condition and in the presence of IFN-γ and MG132 restored marked Pro hydroxylation of hnRNP L after 24 h ( Figure 6E ) . Finally , co-IP with anti-hnRNP L antibody revealed that hypoxia induced hnRNP A2/B1 binding to hnRNP L , and completely blocked hnRNP L recognition by VHL ( Figure 6F ) . These results indicate that the major function of hnRNP A2/B1 in the heterotrimeric switch is to protect hnRNP L from IFN-γ-triggered prolyl hydroxylation , ubiquitination , and subsequent degradation . Treatment of U937 cells with prolyl hydroxylase ( PH ) inhibitors L-mimosine and dimethyloxalylglycine ( DMOG ) blocked prolyl hydroxylation of hnRNP L and caused marked stabilization of the protein in the presence of IFN-γ under normoxia ( Figure S14 ) . Co-IP and RNA-binding studies suggest a model in which the interaction between DRBP76 and hnRNP A2/B1 is indirect and facilitated by hnRNP L and VEGFA HSR RNA ( Figure 2F ) . We investigated the interactions in detail by in vitro reconstitution using recombinant proteins and in vitro–transcribed RNA . DRBP76 and hnRNP A2/B1 by themselves did not bind , nor did the addition of either hnRNP L or HSR RNA restore their interaction significantly ( Figure S15 ) . However , when both hnRNP L and HSR RNA were added , then a modest interaction between hnRNP A2/B1 and DRBP76 was detected . A much stronger interaction was observed when phospho-mimetic hnRNP L ( Y359D ) was added together with HSR RNA , but not nonspecific RNA , thereby reconstituting the entire HILDA complex in vitro . To investigate the sufficiency of hnRNP L , hnRNP A2/B1 , and DRBP76 in operating the RNA switch , we determined the regulatory activity of the purified proteins in vitro . Phospho-mimetic hnRNP L ( Y359D ) was used to facilitate interaction with hnRNP A2/B1 . The three proteins were pre-incubated in several combinations , and their effect on in vitro translation of an FLuc reporter bearing the VEGFA HSR element ( and RLuc control RNA ) was determined in a wheat germ extract in the presence of 35S-Met and cytosolic extracts from IFN-γ-treated U937 cells . hnRNP L ( Y359D ) by itself or with either hnRNP A2/B1 or DRBP76 , did not restore translation in the presence of lysates from cells treated with IFN-γ for 24 h ( Figure 6G ) . However , the three proteins together substantially overcame the translational inhibition . Substitution of wild-type hnRNP L for the phospho-mimetic was ineffective , suggesting the posttranslational modification is not only required for maintaining a high level of cytoplasmic hnRNP L , but also is required for HILDA complex assembly . As positive controls , lysates from cells treated for 24 h with or without IFN-γ in hypoxia could rescue translation of HSR-bearing FLuc . These results support the role of the heterotrimeric HILDA complex in operating the RNA conformational switch .
The combinatorial activity of pairs of nearby elements has become an area of increasing interest , particularly with the recent recognition that microRNA binding to targets can influence protein binding to nearby target RNA elements [27] . There are few cases in which pairs of protein-binding RNA elements dictate the response . In one well-studied example , a combinatorial code in which the number and position of two elements—namely , the cytoplasmic polyadenylation element and Pumilio-binding element—determine translational activation or repression in Xenopus oocytes [28] . However , there is a dearth of studies on the mechanisms by which nearby RNA elements , and their cognate binding factors , integrate disparate environmental signals to generate a binary response and regulate gene expression . In one known case , the leader sequence of the Mg2+ transporter gene mgtA of Salmonella enterica contains a Mg2+-sensing riboswitch and an 18-codon , proline- or hyperosmotic stress-sensing ORF that integrate distinct signals to generate the cell response; however , an interaction between the disparate elements was not observed [29] . In the case of the GAIT system , we have reported that hypoxia prevents GAIT complex binding to the VEGFA 3′UTR by a switch in the conformation of RNA that masks the GAIT structural element [20] by converting the element into the ascending half of a long , double-stranded stem-loop . The switch is initiated by hypoxia-stimulated binding of hnRNP L to a 3′UTR CARE directly adjacent to the GAIT element . In this report we define the components of a heterotrimeric complex that constitutes the RNA switch , their regulation by IFN-γ and hypoxia , and their specific functions in directing the VEGFA mRNA switch in human monocytic cells . The requirement for each of the components of the HILDA complex to drive the RNA switch was shown by knockdown experiments in cells , and their sufficiency shown by in vitro reconstitution . The HILDA complex has not been previously described , but its individual components are known to regulate distinct mRNA-related functions . DRBP76 was initially identified through its binding to double-stranded RNA and to protein kinase R ( PKR ) [30] . DRBP76 exhibits multiple RNA-related functions including regulation of transcription , mRNA stability [31] , and translation [32] . DRBP76 also binds the VEGFA HSR in hypoxic breast cancer cells , increasing mRNA stability and translation , but the binding region within the VEGFA HSR in these experiments was not determined [33] . The double-stranded RNA-binding property of DRBP76 is most likely the critical function it performs in the context of the HILDA complex , stabilizing the conformation featuring a long , double-stranded stem loop , and disrupting the structure of the GAIT element . hnRNP A2/B1 , like hnRNP L , participates in splicing of pre-mRNAs and in translational regulation [34] . hnRNP A2/B1 also serves as a molecular motor-powered transporter of select mRNAs bearing specific hnRNP A2/B1 response elements ( A2RE ) , for example , neurogranin , Arc , and calmodulin-dependent kinase II [35]–[37] . Cytosolic complexes containing heterodimeric hnRNPs have been shown to interact with specific target mRNAs . For example , hnRNP L and I form a complex that binds murine inducible nitric oxide synthase mRNA , and regulates its translation [38] . Interestingly , the same pair of hnRNPs found in the HILDA complex , hnRNP L and A2/B1 , interacts with the glucose transporter 1 ( Glut1 ) 3′UTR , inducing translational repression and mRNA instability [18] . However , an interaction between DRBP76 and A2/B1 has not been described . hnRNP L is a critical component of the HILDA complex because it is uniquely responsible for stimulus sensing as well as target recognition . Our results show that the steady-state level and cellular localization of hnRNP L in myeloid cells are regulated both by IFN-γ and by hypoxia . Under normoxic conditions hnRNP L is distributed between the cytoplasm and nucleus , the latter for execution of mRNA processing functions . IFN-γ induces prolyl hydroxylation of cytoplasmic hnRNP L and consequent rapid , VHL-mediated ubiquitination and proteasomal degradation ( Figure 7 ) . Near-complete cytoplasmic depletion of hnRNP L permits GAIT complex binding to the VEGFA GAIT element in the translationally silent conformer , resulting in low-level translation of VEGFA mRNA . Hypoxia induces phosphorylation of hnRNP L on Tyr359 , which increases cytoplasmic localization by restricting transport into the nucleus . Hypoxia-inducible phosphorylation suggests the activity of a nonreceptor Tyr kinase such as a member of the Src , Abl , Jak , Syk , or Fak families . The sequence surrounding the Tyr359 phosphorylation site ( pRRGPSR359YGPQYGHPPPPPPPP ) exhibits 100% conservation in humans , rodents , rabbits , and frogs , and provides insight into the identity of the proximal kinase . “YG” is a specific Src kinase substrate motif ( PhosphoMotif Finder ) , and the downstream polyproline motif is a binding site for SH3-containing proteins , including Src family kinases . hnRNP A2/B1 binds Tyr359-phosphorylated hnRNP L and blocks recognition by VHL-containing E3 ubiquitin ligase complex , thus permitting cytoplasmic accumulation . The precise kinetics and binding order have not been determined , but our results suggest that the phospho-hnRNP L and hnRNP A2/B1 recruit DRBP76 to form the heterotrimeric HILDA complex that binds the VEGFA CARE . The interaction is weakened by nuclease treatment , indicating that the binding of DRBP76 to other complex members is enhanced by its interaction with the long , AU-rich stem-loop within the VEGFA HSR . The HILDA complex stabilizes the translationally permissive conformer that masks the GAIT element , thus resulting in uninhibited translation of VEGFA mRNA , even in the presence of IFN-γ-induced GAIT complex . The tumor suppressor protein VHL is an essential , target-specific component of a multifunctional E3 ubiquitin ligase complex involved in protein degradation [39] . The best-known target of VHL is hypoxia inducible factor ( HIF ) -1α and -2α , transcription factors that stimulate expression of multiple hypoxia-inducible transcripts , including VEGFA mRNA . In normoxia , O2-dependent prolyl hydroxylation of HIF-1α triggers recognition by VHL and consequent degradation , thereby inhibiting expression of HIF-1α targets [40] . However , prolyl hydroxylation of HIF-1α is inhibited in hypoxia , thereby stabilizing HIF-1α and increasing target mRNA transcription . Other VHL targets have been identified in renal cell carcinoma cell lines; interestingly , several are downregulated by VHL [41]–[43] . hnRNP A2/B1 has been reported to be targeted by VHL [44] . However , we find hnRNP A2/B1 binding to hnRNP L prevents targeting by VHL in human monocytic cells . Possibly , cell-type specificity of targets and directionality of regulation—i . e . , up or down—are promoted by additional factors within the VHL-bearing E3-ubiquitin ligase complex . Proline hydroxylase inhibitors DMOG and L-mimosine both block hnRNP L prolyl hydroxylation and consequent degradation . Collagen prolyl-4-hydroxylase ( C-P4H ) is a candidate because it is induced by hypoxia [45] , [46] and hydroxylates and destabilizes another RBP , Argonaute 2 ( Ago2 ) [47] . Likewise , HIF prolyl hydroxylase ( HIF-PH ) is a candidate because it modifies HIF-1α for poly-ubiquitination by pVHL and proteasomal degradation [48] . Long , noncoding regions of mRNAs , because of their manifold protein- and RNA-binding elements , are potentially ideal for integration of multiple inputs into a single output—i . e . , gene expression . Because of their unusually long length , the 3′UTR , which averages almost 600 nt in human mRNAs versus about 150 nt for 5′UTRs , is a particularly attractive target for signal integration [49] . A plethora of examples of posttranscriptional regulation have been described in which RBPs are activated by environmental signals that alter their binding behavior , generally by posttranslational modification and complex formation [50] . In most known cases , RBPs or complexes interact one-to-one with preformed sequence or structural elements [50] , [51] . More recently , regulatory processes have been described in which signals alter the conformation of the RNA to modulate gene expression [52] . The VEGFA 3′UTR RNA switch features alternative interaction of distinct protein complexes in response to environmental signals , culminating in regulated gene expression . The CARE element is analagous to a riboswitch aptamer domain , and hnRNP L acts as a “responder/selector , ” responding to environmental cues and determining HILDA complex mRNA target specificity . The AUSL element determines the expression outcome: VEGF-A expression is high when the double-stranded conformation is bound by the HILDA complex , and expression is depressed when the GAIT complex binds the GAIT element in the alternate conformation ( Figure 7 ) . To our knowledge there are not any previous reports of 3-RNA element switches . Likewise , the integration of two different signals—i . e . , hypoxia and inflammatory cytokine—by the VEGFA RNA switch lacks precedent . The principles , protein constituents , and mechanisms utilized by the VEGFA switch might be applicable to distinct mRNA switches . One possibility is that the HILDA complex recognizes other transcripts with sequence and structural elements analogous to the VEGFA switch region—i . e . , CARE and GAIT elements nearby DRBP76-binding double-stranded RNA stretches . Cytoplasmic hnRNP L binds VEGF-A mRNA and other transcripts in multiple cell lines [18] , [19] , [38] , suggesting that the HILDA complex might direct additional RNA switches . More generally , distinct RBPs may replace hnRNP L as the “specificity factor , ” but likewise recruit DRBP76 to stabilize nearby stem-loop structures and drive formation of alternate regulatory conformers . High-throughput screening has identified at least two RBPs hnRNP A1 and FUS ( fused in sarcoma ) that bind DRBP76 and might direct alternate RNA switches [53] , [54] . Alternatively , other inhibitory factors ( microRNA or proteins ) might replace the GAIT complex to drive the hnRNP L-directed GAIT-independent RNA switches in more general sense . We speculate that the VEGFA switch is a founding member of signal-activated , protein-directed , RNA switches that regulate posttranscriptional gene expression in vertebrates , and similar switches might be widespread RNA sensors in multicellular animals .
Phospho-safe extraction buffer was from Novagen ( Madison , WI ) . Rabbit reticulocyte lysate , wheat germ extract , large-scale RNA production system-T7 , and dual luciferase reporter assay system were from Promega ( Madison , WI ) . Human IFN-γ was obtained from R&D Systems ( Minneapolis , MN ) . Human monocyte nucleofactor kit was from Lonza ( Switzerland ) . Reagents for protein purification , nuclear and cytosolic extraction , and immunoanalysis were from Pierce ( Rockford , IL ) . Primers , dNTP mix , TRIzol LS reagent , one-step RT-PCR system , and competent cells were from Invitrogen ( Carlsbad , CA ) . Protein A/G beads , anti-α-tubulin , anti-hnRNP A2/B1 , rabbit anti-hnRNP L , and anti-GAPDH antibodies were from Santa Cruz ( Santa Cruz , CA ) . Mouse monoclonal anti-hnRNP L antibody was from Novus ( Littleton , CO ) . Anti-HDAC1 and anti-β-actin antibodies were from Biovision ( Mountain View , CA ) . Anti-c-Myc , anti-HA , goat anti-rabbit/mouse IgG ( Alexa Fluor® 488 Conjugate ) , streptavidin-HRP , and anti-ubiquitin antibodies were from Cell Signalling Technology ( Danvers , MA ) . Anti-DRBP76 antibody was from Biorbyt ( Cambridge , UK ) . GST monoclonal antibody was from Thermo Scientific ( West Palm Beach , FL ) . Anti-VHL antibody was from GeneTex ( San Antonio , TX ) . Anti-hydroxyproline antibody was from Abcam ( Cambridge , MA ) . Anti-rabbit IgG , anti-mouse IgG , and random-primer labeling kit were from GE healthcare ( UK ) . Translation grade [35S]methionine was from NEN-Dupont ( Boston , MA ) , α-[32P]CTP was from PerkinElmer ( Boston , MA ) , and [32P]orthophosphoric acid was from MP Biomedicals ( Solon , OH ) . Actinomycin-D , DMOG , and L-Mimosine were from Sigma ( St . Louis , MO ) . In vitro ubiquitination assay kit and ubiquitin were from Biomol ( Plymouth Meeting , PA ) and Boston Biochem ( Cambridge , MA ) , respectively . Human U937 monocytic cells ( ATCC , Rockville , MD ) were cultured in RPMI 1640 medium containing 10% heat-inactivated fetal bovine serum ( FBS ) , 2 mM glutamine , and 100 U/ml of penicillin and streptomycin at 37°C and 5% CO2 . PBM from healthy clinical donors were isolated by leukapheresis and countercurrent centrifugal elutriation under a Cleveland Clinic Institutional Review Board–approved protocol that adhered to American Association of Blood Bank guidelines . For preparation of cytosolic extracts , the cells were incubated for 1 h in medium containing 0 . 5% FBS and then with ( or without ) IFN-γ ( 500 units/ml ) in presence of hypoxia ( 1% O2 ) for an additional 8 or 24 h . Cell lysates were prepared in Phosphosafe extraction buffer containing protease inhibitor cocktail . To knock down endogenous hnRNP L , DRBP76 , hnRNP A2/B1 , or VHL , U937 cells were transfected with appropriate concentration of ( 100–200 nM ) gene-specific siRNA or a scrambled control siRNA using human monocyte nucleofactor kit . hnRNP L siRNAs containing 3 oligomers targeting the 3′UTR or ORF were from Origene . siRNA against DRBP76 , hnRNP A2/B1 , and VHL were from Santa Cruz . The bacterial expression plasmid pRSET-hnRNP L was generated using pcDNA3-hnRNPL-c-Myc as template and cloned between BamHI and EcoRI restriction sites in the pRSET-A vector for expression and purification of His-tagged hnRNP L . HNRNPL ORF was subcloned into pGEX-4T-1 vector and the plasmid transformed into E . coli BL21 ( DE3 ) for expression and purification of GST-tagged hnRNP L . hnRNP L cDNA was subcloned into pcDNA3-c-Myc between BamHI and EcoRI restriction sites and expressed in human U937 cells as described [20] . The pcDNA3-based hnRNP L Tyr-to-Ala , -Asp , and -Phe mutants were prepared using GENEART Site-Directed Mutagenesis System ( Invitrogen ) according to the manufacturer's instructions . The mutation was confirmed by DNA sequencing . DRBP76 ORF was cloned into pET28-a vector between NdeI and EcoRI restriction sites . Expression of GST-tagged proteins was induced with 500 nM isopropyl-β-D-thiogalactopyranoside ( IPTG ) at 30°C for 6 h with 50 µg/ml ampicillin . Soluble protein was extracted and purified with B-PER GST purification kit ( Thermo Fisher ) . His-tagged DRBP76 was generated in vitro using rabbit reticulocyte lysate in vitro translation system ( Promega ) , and purified with MagneHis Protein Purification System ( Promega ) . His-tagged wild-type hnRNP L and phospho-mimetic hnRNP L were expressed in E . coli BL21 ( DE3 ) with IPTG induction and in rabbit reticulocyte lysate in vitro translation system , respectively , and purified with Ni-NTA resin ( Qiagen ) . Recombinant GST-hnRNP A2/B1 and hnRNP A2/B1 were from Novus Biologicals and Origene , respectively . S100 extracts ( 4 mg ) from U937 cells cultured in normoxia or hypoxia were pre-cleared by incubation for 30 min at 4°C with 2 µg 5-biotinylated , mutant antisense CARE-E RNA oligomer ( 5′-biotin-UCUGUGUGGGUGGGUGUAUGUAUGUAAAUA-3′ ) , added to 200 µl of μMACs magnetic streptavidin microbeads for 10 min , and applied to μMACS separator . The cleared lysate was incubated with 2 µg of 5′-biotinylated , wild-type CARE-E RNA oligomer ( 5′-biotin-AGACACACCCACCCACAUACAUACAUUUAU-3′ ) , and then with streptavidin microbeads and applied to μMACS separator as above . The column was rinsed with 100 µl protein equilibration buffer and twice with 100 µl of lysis buffer . The bound material was applied to the column and washed 4 times with 100 µl of lysis buffer to decrease nonspecific binding . 200 µl of buffer containing 300 mM NaCl was applied to the column to elute bound protein . The eluate was desalted and concentrated using Centrifugal Filter Unit ( Microcon YM-3K , Millipore , Billerica , MA ) . Eluates were subjected to SDS-PAGE and Coomassie stain . Bands enriched only in hypoxia-treated sample were trypsinized and peptides mapped by capillary column LC-tandem MS ( LTQ-linear ion trap MS system , ThermoFinnigan , San Jose , CA ) . The data were analyzed with Mascot using CID spectra to search the human reference sequence database . Matching spectra were verified by manual interpretation aided by additional searches using the Sequest and Blast . Most IP experiments were done with Co-Immunoprecipitation kit ( Pierce ) following the manufacturer's instruction to eliminate antibody contamination of IP products . For some IP experiments , traditional method was used . Cells were lysed in Phospho-safe extraction buffer , and 500 µl of cell lysate was combined with 50 µl protein A/G agarose beads ( 50% bead slurry ) and pre-cleared at 4°C for 60 min . The samples were centrifuged at 13 , 000 rpm for 10 min at 4°C and the supernatant added to 50 µl of protein A/G beads and 2 µg of antibody , and rotated for 4 h at 4°C . The beads were washed 5 times with 1 ml cold lysis buffer . Protein gel loading dye ( 100 µl ) was added , and the samples boiled and loaded onto the gel . To avoid interference from IgG , rabbit-derived secondary antibody was used against mouse-derived primary antibody . GST and GST-hnRNP L were generated from E . coli BL21 ( DE3 ) transformants containing pGEX-4T-1 and pGEX-4T-1-hnRNP L , respectively . Cells were sonicated and the supernatant collected after high-speed centrifugation . GST and GST-hnRNP L ( 1 µg of each ) were incubated separately with glutathione-agarose beads for 30 min . After washing the agarose beads 4 times with 1 ml of PBS , 1 µg of recombinant DRBP76 and hnRNP A2/B1 were diluted in binding buffer ( 20 mM HEPES , pH 7 . 5 , 200 mM KCl , 5 mM MgCl2 , 0 . 2% bovine serum albumin , 10% glycerol , 0 . 1% Nonidet P-40 , 1 mM phenylmethylsulfonyl fluoride , and complete protease inhibitor mixture ) , combined , and incubated at 4°C for 2 h . The agarose beads were washed 5 times with binding buffer ( without bovine serum albumin and glycerol ) , and bound protein eluted by boiling in SDS loading buffer . Cycloheximide ( 50 µg/ml ) was added to 8×106 U937 cells in 4 ml RPMI1640 medium . Cells were harvested and lysed . Immunoblot was done using anti-hnRNP L antibody and the band intensity quantified and normalized by the initial value at 0-h time point . In vitro reconstitution of hnRNP L ubiquitination was performed as described [55] . Purified His-tagged hnRNP L ( 0 . 5 µg ) was preincubated with U937 cell lysate , and then incubated with a mixture of E1 and E2 enzymes , biotin-ubiquitin , and cell lysate as a source of hnRNP L E3 ligase . Recombinant hnRNP L was immunoprecipitated with anti-His tag antibody , and biotin-ubiquitin was detected by blotting with streptavidin-HRP . The metabolic labeling assay was performed as described previously [12] . U937 cells ( 8×106 cells ) in 4 ml RPMI 1640 medium were collected by centrifugation , re-suspended in phosphate-free medium , and metabolically labeled with a 4-h pulse of 32P-orthophosphate . The cells were collected by centrifugation and lysed with Phospho-safe extraction buffer containing protease inhibitor cocktail . hnRNP L was immunoprecipitated from lysates using mouse anti-hnRNP L antibody and protein A/G-agarose in cell lysis buffer . Proteins were resolved by 12% SDS-PAGE , and the gel was dried and applied to Phospho-screen for determination of radiolabeling . In vitro transcribed , 32P-labeled full-length HSR or truncated HSR RNA ( 20 fmol ) was incubated for 30 min at 4°C with purified recombinant proteins ( 0 . 2 µg ) in 20 µl of buffer containing 20 mM HEPES ( pH 7 . 5 ) , 5 mM MgCl2 , 50 mM KCl , 1 mM DTT , protease inhibitor cocktail , 0 . 1% Triton X-100 , 0 . 1 mg/ml yeast total tRNA , 40 U RNasin , and 10% glycerol . The mixture was crosslinked by 15 min exposure to ultraviolet light ( 1 , 800 J/cm2 ) on ice in a UV crosslinker . The protein-RNA complex was incubated with 1 µl of RNase A for 20 min at 25°C . Samples were denatured in SDS-PAGE buffer under reducing conditions , and complexes analyzed by 10% SDS-PAGE and autoradiography . The RIP assay was performed as described previously [13] . Protein A/G beads ( 50 µl ) were incubated with 500 µl of cell lysate ( 4 mg protein ) for 1 h at 4°C with rotation to pre-clear . The cell lysate was centrifuged and the supernatant collected . Mouse anti-hnRNP L antibody ( 2 µg ) was added ( mouse pre-immune IgG was used as negative control ) and the mixture incubated at 4°C overnight with rotation . Protein A/G beads ( 50 µl ) were added and incubated at 4°C for 4 h . The beads were washed five times with 1 ml of lysis buffer with rotation at 4°C . Total immunoprecipitated RNA was extracted with Trizol . Total RNA from the lysate was extracted and used as a positive control for RT-PCR . Immunoprecipitated RNA ( 3 µl ) and 1 µg of total RNA were used in reverse transcriptase reaction and subsequent PCR with Taq DNA polymerase . The PCR reaction ( 5 out of 20 µl ) was visualized by 1 . 5% agarose gel . The primers for semi-quantitative RT-PCR were as follows: RT_βactin-f: 5′-ATGGATGATGATATCGCCGCG-3′; RT_βactin-r: 5′-CTAGAAGCATTTGCGGTGGAC-3′; RT_VEGF-f: 5′-ACAGAACGATCGATACAGAA-3′; RT_VEGF-r: 5′-AAAGATCATGCCAGAGTCTC-3′; RT_hnRNPL-f: 5′-GAGTCCCATCTGAGCAGGAA-3′; and RT_hnRNPL-r: 5′-CAATTTTATTGAAATGTGCC-3′ . Polysome profiling was done as described [13] . CHX ( 100 µg/ml ) was added to cells for 15 min and then collected and washed two times with CHX-containing , ice-cold PBS . 107 cells were suspended in 350 µl TMK lysis buffer and incubated on ice for 5 min . The lysates were centrifuged at 12 , 000 rpm for 10 min and the supernatants collected . RNase inhibitor ( 2 µl , 40 U/µl ) and CHX ( 50 µl , 100 µg/µl ) were added in 50 ml each of freshly prepared 10% and 50% sucrose gradient solutions just before use . Cytosolic lysates were loaded on the sucrose gradient and centrifuged at 29 , 000 rpm for 4 h , and 8 fractions of about 1 ml were collected and combined; light RNP , 40S , 60S , and 80S formed the translationally inactive pool , and heavy polysome fractions formed the translationally active pool . Total RNA was isolated from both combined fractions by extraction with Trizol reagent and purified by RNeasy minikit ( Qiagen , Valencia , CA ) following the manufacturer's procedure . The RNA was quantitated and purity determined by agarose formaldehyde gel , and used for real-time PCR analyses . Capped , poly ( A ) -tailed template mRNAs was prepared using mMESSAGE mMACHINE SP6 and T7 kits ( Ambion ) . Firefly-Luc-VEGFA GAIT element-poly ( A ) ( 200 ng ) and Renilla-Luc ( 200 ng ) reporter RNAs were incubated with U937 cytosolic lysates ( 500 ng of protein ) from IFN-γ-treated U937 cells in the presence of 35 µl of wheat germ extract or rabbit reticulocyte lysate , and [35S]methionine . The translation reactions were performed for 90 min at 30°C and resolved by SDS-PAGE ( 10% polyacrylamide ) and visualized by phosphorimaging . In some experiments , the FLuc and RLuc activity was measured by chemiluminescence using luminator . U937 cells were transiently transfected with 5 µg of wild-type or mutant pCD-FLuc-VEGFA HSR using human monocyte nucleofactor kit . RLuc-expressing vector pRL-SV40 ( 1 µg ) was co-transfected for normalizing transfection efficiency . After 12 h , transfected cells were incubated with IFN-γ under Nmx . or Hpx . for up to 24 h , lysed , and lysate luciferase activities were measured using a dual luciferase assay kit ( Promega ) . The primers for semiquantitative RT-PCR of FLuc were as follows: RT_FLuc-f: 5′-GCCTGAAGTCTCTGATTAAGT-3′; RT_FLuc-r: 5′-ACACCTGCGTCGAAGT-3′; RT-RLuc-f: 5′-TGATTCAGAAAAACATGCAG-3′; RT-RLuc-r: 5′-ATATTTGTAATGATCAAGTA-3′ . Immunostaining of hnRNP L was as described [23] . U937 cells ( 106 cells/ml ) in 12-well plates with glass cover slip at the bottom were incubated in hypoxia or normoxia for 24 h . Cells were centrifuged for 5 min at 2 , 500 rpm and washed twice with PBS and then with 4% paraformaldehyde fixing solution for 20 min . Cells were washed twice with PBS , and incubated with rabbit anti-hnRNP L polyclonal antibody ( Santa Cruz , 1∶40 ) in blocking solution ( 2% BSA , 0 . 1% Triton X100 in PBS ) at room temperature for 2 h . Cells were washed twice with PBS and centrifuged at 1 , 500 rpm for 5 min . Alexa Fluor 488 goat anti-rabbit secondary antibody ( Invitrogen ) was added ( 1∶50 ) with phalloidin ( 1∶50 ) in blocking solution for 1 h . Cells were washed with PBS three times . DAPI dye was mixed in the mounting solution and the slides imaged . | Many cells of our body , particularly cells such as monocyte/macrophages involved in host immunity , are exposed to diverse and constantly changing environments . These cells require mechanisms by which they can rapidly respond to multiple , sometimes conflicting , environmental cues to generate appropriate responses . The 3′ untranslated regions ( UTRs ) , i . e . the noncoding tail of messenger RNAs , often contain multiple protein- and RNA-binding elements , thereby making it an ideal setting for receiving multiple such environmental cues , which can then be integrated into a single response that regulates the gene's expression . Monocytic cells exposed to inflammation and hypoxia produce vascular endothelial growth factor ( VEGF ) -A , a critical factor in blood vessel formation . VEGF-A expression is regulated under these conditions via a complex regulatory mechanism that involves its 3′UTR . Here we show how this regulatory switch works . Inflammation induces formation of a four-protein complex that binds an RNA element present in the VEGFA 3′UTR and blocks translation . Hypoxia , however , triggers the assembly of a completely different three-protein complex ( termed “HILDA” ) that coordinates the function of three neighboring RNA elements to “flip” the RNA conformation in such a way that prevents the first complex from binding , thereby allowing VEGF-A expression . We hypothesize that the VEGFA switch might function to ensure appropriate angiogenesis and tissue oxygenation when cells are exposed to conflicting signals from combined inflammation and hypoxia conditions . |
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Rickettsial infections are a common cause of hospitalization in tropical settings , although early diagnosis is challenging in the rural locations where these infections are usually seen . This retrospective , clinical audit of microbiologically-confirmed cases of scrub typhus or spotted fever group ( SFG ) rickettsial infection between 1997 and 2016 was performed a tertiary referral hospital in tropical Australia . Clinical , laboratory and radiological findings at presentation were correlated with the patients’ subsequent clinical course . There were 135 locally-acquired cases ( 95 scrub typhus , 37 SFG , 3 undifferentiated ) . There were nine hospitalizations during the first 5 years of the study period and 81 in the last 5 years ( p for trend = 0 . 003 ) . Eighteen ( 13% ) of the 135 cases required ICU admission , all of whom were adults . A greater proportion of patients with SFG infection required ICU support ( 8/37 ( 22% ) compared with 10/95 ( 11% ) scrub typhus cases ) , although this difference did not reach statistical significance ( p = 0 . 10 ) . Three ( 8% ) of the 37 patients with SFG infection had severe disease ( 1 died , 2 developed permanent disability ) versus 0/95 scrub typhus patients ( p = 0 . 02 ) . Adults with a high admission qSOFA score ( ≥2 ) had an odds ratio ( OR ) of 19 ( 95% CI:4 . 8–74 . 5 ) for subsequent ICU admission ( p<0 . 001 ) ; adults with a high NEWS2 score ( ≥7 ) had an OR of 14 . 3 ( 95% CI:4 . 5–45 . 32 ) for ICU admission ( p<0 . 001 ) . A patient’s respiratory rate at presentation had strong prognostic utility: if an adult had an admission respiratory rate <22 breaths/minute , the negative predictive value for subsequent ICU admission was 95% ( 95% CI 88–99 ) . In the well-resourced Australian health system outcomes are excellent , but the local burden of rickettsial disease appears to be increasing and the clinical phenotype of SFG infections may be more severe than previously believed . Simple , clinical assessment on admission has prognostic utility and may be used to guide management .
Rickettsial infections are a common cause of hospitalization in tropical settings [1–4] , although early , definitive diagnosis is challenging in the rural locations where these infections are usually seen [5] . Antibiotic therapy is highly effective if started early in the disease course [6 , 7] , although anti-rickettsial agents have limited activity against other serious pathogens–including malaria and bacterial sepsis–that can have similar presentations [2 , 8] . The clinical manifestations of rickettsial disease range from a mild , self-limiting illness to life-threatening multi-organ failure , although there are surprisingly few series that report the diseases’ clinical findings in a detailed manner [8–11] . Identifying the features of a patient’s presentation that increase the likelihood of rickettsial infection would help clinicians decide when to add anti-rickettsial therapy to empirical regimens . Meanwhile , identifying the features associated with the development of life-threatening infection would help expedite transfer of the high-risk patient to referral centers where more advanced supportive care is available [11] . In Far North Queensland , in tropical Australia , acute undifferentiated fever is a common clinical presentation [12] . Scrub typhus and spotted fever group ( SFG ) rickettsial infections , namely Rickettsia australis ( Queensland tick typhus ) and Rickettsia honei should be considered in the differential diagnosis , although other locally endemic infections–including leptospirosis , melioidosis and Q fever–can have a very similar presentation [13–16] . Rickettsial diseases were common in the region in the mid-twentieth century [17 , 18] , but more recently only small case series have been published [10 , 19 , 20] . This might suggest that their incidence has declined in the region , but detailed study of the infections’ temporospatial epidemiology has been lacking . This twenty-year retrospective review was therefore performed to examine the issue more systematically . The study also examined the clinical and laboratory features of these infections that might be used to facilitate their diagnosis and to expedite the identification of the patients at risk of life-threatening disease .
Data were de-identified , entered into an electronic database ( Microsoft Excel 2016 , Microsoft , Redmond , WA , USA ) and analysed using statistical software ( Stata version 14 . 0 , StataCorp LLC , College Station , TX , USA ) . Univariate analysis was performed using the Kruskal-Wallis and chi-squared tests . Continuous variables with an area under the receiver operating characteristic ( AUROC ) curve of > 0 . 7 in univariate analysis were selected for multivariate analysis . These continuous variables were transformed into binary variables—using cut-offs based on common clinical usage—with multivariate analysis performed using backwards stepwise logistic regression . The Far North Queensland Human Research Ethics Committee provided ethical approval for the study ( HREC/17/QCH/66–1148 QA ) . As the data were retrospective and de-identified , the Committee waived the requirement for informed consent .
The number of patients admitted to Cairns Hospital with rickettsial infections increased during the study period ( all rickettsial infections ( p for trend = 0 . 003 ) , scrub typhus ( p for trend = 0 . 001 ) and SFG infection ( p for trend = 0 . 04 ) ) . There were nine hospitalizations during the first five years of the study period and 81 in the last five years . There was no observed seasonal trend in patient presentation: 69/135 ( 51% ) presented during the 6-month November-April wet season while 66/135 ( 49% ) presented during the May-October dry season . The number of serology requests in the Far North Queensland region increased during the study period ( 333 in 1998 to 523 in 2016 , p for trend = 0 . 01 ) , but the proportion of tests that were positive also increased ( 7/333 ( 2 . 1% ) in 1998 compared with 86/523 ( 16 . 4% ) in 2016 , p for trend = 0 . 02 ) . To address the possibility of improved diagnostic sensitivity of the serological testing , the last 7 years of the study period–when the same diagnostic test ( BioCell Diagnostics ) was used–was examined . The proportion of positive tests increased during this period from 13/505 ( 2 . 6% ) in 2009 to 86/523 ( 16 . 4% ) in 2016 , p for trend = 0 . 04 . The annual incidence of rickettsial infections–positive cases defined as a serological titre ≥ 256 –increased in the region from 3 . 2/100 , 000 in 1998 to 30 . 8/100 , 000 in 2016 ( p = 0 . 03 ) ( Fig 2 ) . Cases were widely dispersed across the region ( Fig 3 ) . SFG cases occurred as far north as Lockhart River on the Cape York Peninsula . Scrub typhus cases extended further north to the Torres Strait islands . The patients’ median ( interquartile range ( IQR ) ) age was 36 ( 24–52 ) years; 76 ( 56% ) were male , 15 ( 13% ) were children ( age < 16 ) . Only 24 ( 18% ) patients in the cohort had a significant comorbidity . An occupational or recreational risk for exposure was documented in 58 ( 43% ) patients . Fever was present in 130/135 ( 96% ) ( Table 1 ) . Headache was more common in those with scrub typhus than those with SFG infection ( 69/95 ( 73% ) versus 18/37 ( 49% ) , p = 0 . 009 ) . Rash occurred in 54/135 ( 40% ) and was more common in patients with SFG infection ( 22/37 ( 59% ) versus 31/95 ( 33% ) , p = 0 . 005 ) ( Fig 4 ) . An eschar was identified in 21/135 ( 15% ) and was more common in patients with scrub typhus ( 19/95 ( 20% ) versus 2/37 ( 5% ) , p = 0 . 04 ) . Thrombocytopenia , abnormal liver function tests and impaired renal function were common findings ( Table 2 ) . A chest x-ray ( CXR ) was performed in 91/135 ( 67% ) ; in those who had a CXR performed , abnormal findings were present in 22/63 ( 35% ) with scrub typhus , and 14/28 ( 50% ) with SFG infection ( Fig 4 ) . Eighteen ( 13% ) patients had a transthoracic echocardiogram . Small pericardial effusions were noted in 3 ( 2 scrub typhus cases , 1 SFG case ) ; no other echocardiographic abnormalities were detected . Rickettsial disease was included in admitting clinicians’ initial differential diagnosis in 97/135 ( 72% ) ; 76 ( 56% ) patients received an antibiotic with anti-rickettsial activity at presentation , while 102 ( 76% ) received an anti-rickettsial antibiotic at some point during their hospitalization . Of the 102 patients who received anti-rickettsial therapy , 99 ( 97% ) adhered to national guidelines [22] for duration and 94 ( 92% ) received therapy within 48 hours of their admission . Doxycycline was used in 93/102 ( 91% ) and azithromycin was used in 11/102 ( 16% ) . The median duration of hospitalization was 3 days ( IQR 1–6 days , range 0–95 days ) . Eighteen ( 13% ) of the 135 cases required ICU admission , all were adults . A greater proportion of patients with SFG infection required ICU support ( 8/37 ( 22% ) compared with 10/95 ( 11% ) scrub typhus cases ) , although this difference did not reach statistical significance ( p = 0 . 10 ) . Patients requiring ICU care were older , had more profound thrombocytopenia , greater liver function test derangement , greater renal impairment , a higher C-reactive protein ( CRP ) and more likely to have an abnormal CXR ( Table 3 ) . Patients requiring ICU care had experienced symptoms for a median ( IQR ) of 7 ( 6–10 ) days prior to presentation , whereas those patients not requiring ICU admission had experienced symptoms for 5 ( 2–8 ) days ( p = 0 . 06 ) . Every patient admitted to ICU received anti-rickettsial antibiotic therapy , in 14 ( 78% ) it was within the first 24 hours . There was one death , in a patient with SFG infection . A 55-year-old farmer , without significant co-morbidities , presented with a 6-day history of fever , headache and myalgia , after a possible tick bite two weeks earlier . He was hemodynamically unstable on presentation with acute kidney injury , elevated transaminases and laboratory evidence of disseminated intravascular coagulation ( DIC ) . He was transferred to ICU where despite vasopressor support , renal replacement therapy ( RRT ) , mechanical ventilation and antibiotic therapy ( initially with piperacillin-tazobactam and doxycycline , subsequently escalated to meropenem , vancomycin and azithromycin ) he progressed to multi-organ failure and died less than 48 hours after presentation . Two patients with SFG infection had disabling sequelae , developing digital ischemia requiring amputation ( Fig 4 ) . Both patients had evidence of purpura fulminans ( skin necrosis and DIC ) and both required RRT and mechanical ventilation for survival . Overall 3/37 ( 8% ) patients with SFG infection died or had permanent disability compared with 0/95 patients with scrub typhus ( p = 0 . 02 ) ( Table 4 ) . None of the 15 children required ICU admission . Among the 120 adults , there were 5 variables which , when determined on admission , had an AUROC > 0 . 7 in univariate analysis for predicting subsequent ICU admission: respiratory rate ( AUROC 0 . 87 , 95% CI:0 . 79–0 . 94 ) , CRP ( AUROC 0 . 82 , 95% CI:0 . 68–0 . 95 ) , plasma aspartate aminotransferase ( AUROC 0 . 82 , 95% CI:0 . 73–0 . 92 ) , plasma creatinine ( AUROC 0 . 75 , 95% CI: 0 . 62–0 . 88 ) and age ( AUROC 0 . 72 , 95% CI: 0 . 59–0 . 86 ) . Binary variables were created for these 5 continuous variables using reference ranges and common clinical usage ( Table 5 ) . In multivariate analysis , 2 of these variables–a respiratory rate ≥ 22 ( odds ratio ( OR ) : 13 . 2 ( 3 . 8–46 . 0 ) , p < 0 . 001 , and a plasma creatinine > 120 μmol/L ( OR ( 95% CI ) : 3 . 5 ( 95% CI 1 . 03–12 . 0 ) , p = 0 . 04 ) –were found to be independently predictive . If only the clinical variables of age and respiratory rate in adult patients were examined in multivariate analysis–the odds ratio of a respiratory rate ≥ 22 had an odds ratio ( OR ) for ICU admission of 11 . 6 ( 95% CI: 3 . 3–40 . 5 , p < 0 . 001 ) while an age ≥50 had an OR for ICU admission of 5 . 1 ( 95% CI:1 . 6–16 . 2 , p = 0 . 006 ) . If an adult patient was <50 years and had a respiratory rate of < 22 on presentation to hospital , there was a negative predictive value ( NPV ) for ICU admission of 97% ( 95% CI 89–100 ) . Meanwhile , if an adult ≥ 50 had a respiratory rate ≥ 22 on presentation to hospital , the positive predictive value ( PPV ) for ICU admission was 62% ( 95% CI 32–86 ) . A qSOFA score could be calculated in 117 adults: a high qSOFA score ( ≥ 2 ) was present in 12 ( 10% ) and had an OR of 19 ( 95% CI: 4 . 8–74 . 5 ) for ICU admission ( p < 0 . 001 ) . A NEWS2 score could be calculated in 119 adults , a high NEWS score ( ≥7 ) was present in 21 ( 18% ) and had an OR of 14 . 3 ( 95% CI:4 . 5–45 . 2 ) for ICU admission ( p < 0 . 001 ) . The NPV of a low qSOFA score ( <2 ) and a low NEWS2 score ( <7 ) for ICU admission were 91% ( 95% CI: 83–95 ) and 93% ( 95% CI: 86–97 ) respectively . The ability of a high qSOFA or high NEWS2 score to predict death/disability and specific organ dysfunction are presented in Table 6 .
This report highlights an increasing incidence of scrub typhus and SFG infections in tropical Australia . It also suggests that clinical manifestations of SFG infections may be more severe than previously believed . Although they are neglected diseases globally and in absolute terms , an uncommon cause of hospitalisation in tropical Australia , local clinicians appear to have a good awareness of the infections , which have an excellent prognosis when treated promptly in a well-resourced heath setting . Simple , bedside clinical assessment appears helpful in identifying the patient at high risk of subsequent deterioration and may be useful for clinicians managing these patients in resource-limited settings . | Rickettsial infections are a common cause of hospitalization in tropical settings , although early , definitive diagnosis is challenging in the rural and remote locations where they are usually seen . It is important to recognise rickettsial infections early in their disease course as they can lead to life-threatening multi-organ failure if specific anti-rickettsial antimicrobial therapy is not prescribed promptly . In tropical Australia , scrub typhus and spotted fever group ( SFG ) rickettsiae are the dominant rickettsial pathogens and this twenty-year retrospective series examines the clinical and laboratory findings which might facilitate their recognition . The study highlights the infections’ increasing local clinical burden and reports that over 20% of the SFG cases in the series required Intensive Care Unit ( ICU ) admission , suggesting that severe SFG disease may be more common than previously believed . Simple , clinical prediction scores—calculated at presentation—identified patients who would subsequently require ICU admission . Importantly , they were also able to identify patients at low risk of disease progression . These entirely clinical scores—which can be calculated rapidly at the bedside—have the potential to facilitate the management of patients with scrub typhus and SFG infection , particularly in resource-limited settings which have the greatest burden of disease . |
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Clostridium difficile is the etiological agent of antibiotic-associated diarrhoea ( AAD ) and pseudomembranous colitis in humans . The role of the surface layer proteins ( SLPs ) in this disease has not yet been fully explored . The aim of this study was to investigate a role for SLPs in the recognition of C . difficile and the subsequent activation of the immune system . Bone marrow derived dendritic cells ( DCs ) exposed to SLPs were assessed for production of inflammatory cytokines , expression of cell surface markers and their ability to generate T helper ( Th ) cell responses . DCs isolated from C3H/HeN and C3H/HeJ mice were used in order to examine whether SLPs are recognised by TLR4 . The role of TLR4 in infection was examined in TLR4-deficient mice . SLPs induced maturation of DCs characterised by production of IL-12 , TNFα and IL-10 and expression of MHC class II , CD40 , CD80 and CD86 . Furthermore , SLP-activated DCs generated Th cells producing IFNγ and IL-17 . SLPs were unable to activate DCs isolated from TLR4-mutant C3H/HeJ mice and failed to induce a subsequent Th cell response . TLR4−/− and Myd88−/− , but not TRIF−/− mice were more susceptible than wild-type mice to C . difficile infection . Furthermore , SLPs activated NFκB , but not IRF3 , downstream of TLR4 . Our results indicate that SLPs isolated from C . difficile can activate innate and adaptive immunity and that these effects are mediated by TLR4 , with TLR4 having a functional role in experimental C . difficile infection . This suggests an important role for SLPs in the recognition of C . difficile by the immune system .
Clostridium difficile is a Gram-positive spore-forming intestinal pathogen . It is the leading cause of nosocomial antibiotic-associated diarrhoea among hospital patients and in severe cases can cause pseudomembranous colitis and even death [1] , [2] . The pathogenesis of C . difficile has been attributed to the two major toxins that the bacterium produces [3] , [4]; however , there is currently limited information regarding the recognition of this pathogen by the immune system and the immune response elicited following exposure to this organism . This may be due to the fact that this organism does not produce lipopolysaccharide and therefore has been less well studied than other gastrointestinal pathogens . C . difficile , along with a number of other bacteria , expresses a paracrystalline surface protein array , termed an S-layer , composed of surface layer proteins ( SLPs ) [5] . Two surface layer proteins termed high molecular weight ( HMW ) and low molecular weight ( LMW ) SLPs , form a crystalline regular array that covers the surface of the bacterium . SLPs are known to have a role in binding of C . difficile in the gastrointestinal tract however they may also have other roles [6] . There is now clear evidence that these proteins are important components of C . difficile [7] , and S-layers have previously been described as virulence factors for other bacteria such as Campylobacter fetus and Aeromonas salmonicida [8] , [9] . Their location on the outer surface of the bacteria suggests that they may be involved in immune recognition of the pathogen . Pathogen recognition involves a group of pattern recognition receptors expressed on immune cells called toll-like receptors ( TLRs ) which allow cells of the innate immune system , such as dendritic cells ( DCs ) , to detect conserved patterns of molecules on pathogens [10] . Several studies have highlighted the importance of TLR4 in a number of bacterial infections . For example , the recognition of Mycobacterium tuberculosis , a Gram-positive bacterium , by TLR4 is critical for elimination of the pathogen and containment of the infection to the lungs [11] . Activation of TLR4 initiates downstream signalling which in turn activates nuclear factor kappa beta ( NF-κB ) and interferon regulatory factor 3 ( IRF3 ) via myeloid differentiation factor 88 ( MyD88 ) -dependant and -independent pathways , respectively [12] , [13] . Activation of the MyD88 dependant pathway is mainly an event initiated at the plasma membrane while induction of IRF via the MyD88-independent pathway is dependant on the endocyotosis of TLR4 and requires the presence of CD14 and subsequently TIR-domain-containing adapter-inducing interferon-β ( TRIF ) [14] , [15] . When triggered , TLRs induce strong immune and inflammatory responses , characterised by production of inflammatory cytokines and subsequent activation of T helper ( Th ) cells [16] . The maturation of DCs following activation is characterized by the production of cytokines and changes in the expression of cell surface markers . It is now well established that production of IL-12 promotes Th1 differentiation , IL-4 induces Th2 cells , while IL-23 , IL-6 and IL-1β production by DCs is important in generating Th17 cells [17] , [18] . The importance of Th1 and Th17 cells are well recognised in bacterial clearance [19] . In the present study we tested the hypothesis that SLPs isolated from C . difficile are important for recognition of the pathogen and examined whether recognition of SLPs was mediated by TLR4 . We report that SLPs induce DC maturation and have the ability to subsequently generate Th1 and Th17 responses via TLR4 . Furthermore , we provide evidence that SLPs activate NFκB , but not IRF3 , downstream of TLR4 . Finally , we show that TLR4 has a functional role in experimental C . difficile infection . This is the first study to report a mechanism of recognition of C . difficile by the innate immune system , and suggests that they are important for activating the immune system and subsequent clearance of the pathogen .
BALB/c mice , C3H/HeN and C3H/HeJ mice were purchased from Harlan ( U . K . ) and were used at 10–14 wk of age . TLR2-deficient ( −/− ) [20] , TLR4−/− [21] , MyD88−/− [22] and TRIF−/− [23] , all on a C57BL/6J background , were used in C . difficile infection studies . Animals were housed in a licensed bioresource facility ( Dublin City University or Trinity College Dublin ) and had ad libitum access to animal chow and water . All animal procedures were carried out in accordance with Department of Health and Children Ireland regulations and performed under animal license number B100/3250 . All animal protocols received ethical approval from the Trinity College Dublin Bioresources Ethics Committee . C . difficile infected animals were weighed daily and any mice that became moribund , <15% loss in body weight , were humanely killed . C . difficile ( PCR Ribotype 001; toxin A and B positive; clindamycin resistant; HPA UK reference R13537 , Anaerobe Reference Unit , Public Health Laboratory , University Hospital of Wales ) isolated from a patient with C . difficile-associated disease was used for preparation of SLPs as previously described [6] . Briefly , SLPs were purified from cultures grown anaerobically at 37°C in BHI/0 . 05% thioglycolate broth . Cultures were harvested and crude SLP extracts dialysed and applied to an anion exchange column attached to an AKTA FPLC system ( MonoQ HR 10/10 column , GE Healthcare ) . The pure SLPs were eluted with a linear gradient of 0–0 . 3 mol/L NaCl at a flow rate of 4 mL/min . Peak fractions corresponding to pure SLPs were analysed on 12% SDS–PAGE gels stained with Coomassie blue and assessed for LPS contamination using a Limulus amoebocyte lysate ( LAL ) assay . The individual SLPs ( high and low molecular weight ) were separated by chromatography under the same conditions , but with 8 M urea included in all buffers . The urea was then dialysed out . Additional fractions containing irrelevant proteins were also kept for comparison . Bone marrow-derived immature DCs ( BMDCs ) were prepared by culturing bone marrow cells obtained from the femurs and tibia of mice in RPMI 1640 medium with 10% fetal calf serum ( cRPMI ) supplemented with 10% supernatant from a GM-CSF-expressing cell line ( J558-GM-CSF ) . The cells were cultured at 37°C for 3 days , and the supernatant was carefully removed and replaced with fresh medium with 10% GM-CSF cell supernatant . On day 7 of culture , cells were collected , counted , and plated at 1×106/mL for experiments . BMDCs from C3H/HeN and C3H/HeJ mice were cultured and activated with ovalbumin ( OVA ) peptide ( 323–339; 5 µg/mL ) in the presence of either LPS ( 100 ng/mL ) or SLPs ( 20 µg/mL ) for 24 h . After 24 h , DCs were collected and washed twice in sterile PBS/2% FCS and irradiated with 40 Gy ( 4000 rads ) using a gamma irradiator with a Caesium-137 source . A final concentration of 2×105cells/mL were added to CD4+ T cells , isolated from the spleens of OVA transgenic D011 . 10 mice ( 2×106 cells/mL ) and incubated . On day 5 of co-culture , the supernatant was removed and frozen for cytokine analysis . Fresh medium was added , and the cells were incubated until day 7 and supernatants removed . Newly harvested OVA/SLP or OVA/LPS-activated DCs were added ( 2×105 cells/mL ) with recombinant murine IL-2 ( 10 U/mL; Becton Dickinson ) for the second round of T cell stimulation . At the end-point of the experiment ( day 10 ) , supernatants were removed and frozen for cytokine analysis . DCs were incubated with either SLP ( 20 µg/mL ) or LPS ( 100 ng/mL ) for 24 h . Culture supernatants from this experiment as well as the DC:T cell co-culture experiments were removed and stored at −80°C until analysis . TNF-α , IL-1β , IL-10 and IL-12p70 , IL-12p40 , IL-23 , IFNγ , IL-17 and IL-4 concentrations in cell culture supernatants were analysed by DuoSet ELISA kits ( R&D Systems ) , according to the manufacturer's instructions . DCs were cultured as previously described and incubated with either SLPs ( 20 µg/mL ) or LPS ( 100 ng/mL ) for 24 h . In some experiments a p38 inhibitor ( S8308; 20 µg/mL ) was used . Cells were then washed and used for immunofluorescence analysis . The expression of CD40 , CD80 , CD86 and MHCII was assessed using an anti-mouse CD11c ( Caltag ) , and CD40 , CD80 , CD86 and MHCII ( rat IgG2a , BD Biosciences ) and appropriately labelled isotype-matched antibodies . After incubation for 30 min at 4°C , cells were washed and immunofluorescence analysis was performed on a FACsCalibur ( BD Biosciences ) using Cell Quest software . Human HEK293-TLR4 , HEK293-MD2-CD14-TLR4 and HEK293T were transiently transfected using GeneJuice transfection reagent ( Novagen , Madison , WI ) according to the manufacturer's instructions with a total amount of 220 ng DNA per well comprising of 75 ng ISRE- ( Clontech , Palo Alto , CA ) or κB-luciferase plasmid , 30 ng Renilla-luciferase and empty pcDNA3 . 1 vector as filler DNA . 24 h after transfection , cells were stimulated with LPS ( 100 ng/mL ) or SLPs ( 0–100 µg/mL ) for 6 h before lysis . Firefly luciferase activity was assayed by the addition of 40 µl of luciferase assay mix to 20 µl of the lysed sample . Renilla-luciferase was read by the addition of 40 µl of a 1∶1000 dilution of Coelentrazine ( Argus Fine Chemicals ) in PBS . Luminescence was read using the Reporter microplate luminometer ( Turner Designs ) . The Renilla- luciferase plasmid was used to normalise for transfection efficiency in all experiments . C . difficile ( R13537 ) , described above , was grown on blood agar plates under anaerobic conditions at 37°C for 5 days to generate spores . Spore inoculum was prepared as described by Sambol et al . [24] , the spore concentration was determined by dilution plating onto blood agar plates and stock solutions of 5×106 spores ml−1 were stored at −80°C . TLR2−/− , TLR4−/− , MyD88−/− , TRIF−/− and wild-type mice , all on a C57BL/6J strain background , were infected with C . difficile using an antibiotic-induced model of mouse infection [25] . Mice were treated for 3 days with an antibiotic mixture of kanamycin ( 400 µg/ml ) , gentamicin ( 35 µg/ml ) , colistin ( 850 U/ml ) , metronidazole ( 215 µg/ml ) and vancomycin ( 45 µg/ml ) in the drinking water . Mice were subsequently given autoclaved water . On day 5 , mice were injected i . p . with clindamycin ( 10 mg/kg ) . Mice were infected with 103 C . difficile spores on day 6 by oral gavage . Initial studies determined infection with 103 spores of C . difficile R13537 caused mild transient weight loss and diarrhoea in wild-type C57BL/6J strain mice . Mice that were not treated with antibiotics were also challenged with C . difficile . Animals were weighed daily and monitored for overt disease , including diarrhoea . Moribund animals with >15% loss in body weight were humanely killed . The cecum was harvested from uninfected ( day 0 ) and infected mice at days 3 and 7 and the contents were removed for CFU counts . The cecum was fixed in 10% formaldehyde saline and paraffin sections were hematoxylin and eosin-stained . Evaluation of histopathology was performed as previously described [26] . Briefly slides were scored by two independent investigators , blinded to the study groups , on a 0–3 scale as follows; absence of inflammation and damage was scored 0 , while mild , moderate and severe inflammatory changes were scored 1 , 2 and 3 respectively . The severity of mucosal damage and inflammation was based on the levels of mucosal epithelial damage and erosion , cell inflammation of the lamina propria , crypt abscess formation as well as the incidence and severity of oedema . The contents of cecum were recovered from infected and uninfected mice , weighed and stored frozen . Each sample of cecum material was thawed and homogenised in 1 ml PBS ( pH 7 . 4 ) by vortex mixing in a 1 . 5 ml microcentrifuge tube . The suspension was serially diluted ( 10−1 to 10−4 ) and 50 µl of each dilution was spread in duplicate onto quadrants of Brazier's CCEY plates ( Lab M ) . Plates were incubated under anaerobic conditions at 37°C for 30 h . Colonies were counted and CFU/g determined for each sample . The anti-SLP IgG was measured as previously described [5] . Briefly plates were coated overnight with 2 µg/mL and blocked for 1 h with blocking buffer ( PBS containing 2% nonfat dry milk ) . Serum samples were diluted 1∶50 and further serial 10-fold dilutions of samples were made in antibody buffer ( blocking buffer containing 0 . 05% Tween 20 ) . Bound antibody was detected with HRP-conjugated anti-mouse IgG followed by TMB . Reactions were stopped with 1 M H2SO4 , and ODs were read at 450 nm . One-way analysis of variance ( ANOVA ) was used to determine significant differences between conditions . When this indicated significance ( p<0 . 05 ) , post-hoc Student-Newmann-Keul test analysis was used to determine which conditions were significantly different from each other .
Samples from all stages of the purification process were run on SDS-PAGE gels to demonstrate the purity of the SLPs . Figure 1 clearly shows the presence of multiple bands in the crude extract and only two bands with molecular masses of 42–48 kDa and 32–38 kDa following anion exchange chromatography . Furthermore , we also purified individual high molecular weight ( HMW ) and low molecular weight ( LMW ) proteins which were also seen as single bands at the correct molecular weight on SDS gels . In order to confirm that any activity by the SLPs was attributed to the protein and not a contaminant , we also examined irrelevant proteins which were purified in the same manner but were eluted in different fractions to those of the SLPs . In order to assess whether SLPs could activate DCs we examined their ability to induce TNFα production by these cells . The graph in Figure 1 shows that SLPs induce TNFα production by DCs . This is not seen with the individual LMW and HMW proteins or the irrelevant protein . LPS was used as a positive control . The ability of SLPs to induce cytokine secretion in DCs was found to be dose dependent ( Figure S2 ) . As SLPs and LPS induced DCs to produce a similar profile of cytokines , we examined whether SLPs also activated DCs via TLR4 . Given that the differentiation of naïve CD4+ T cells into Th subsets is determined in part by the cytokines produced by DCs upon activation [17] , we specifically examined the effects of SLPs on these cytokines . Incubation of BMDCs isolated from C3H/HeN with SLPs induced significant production of IL-12p70 ( Figure 2; p<0 . 001 ) , IL-23 ( Figure 2; p<0 . 001 ) and IL-10 , important for Th1 , Th17 and Tr1 responses respectively , and also significant levels of TNFα ( Figure 2; p<0 . 001 ) . Interestingly , there was no significant induction of IL-1β by SLPs . The effects of both LPS and SLPs on cytokine production were completely absent in BMDCs from C3H/HeJ mice , indicating that the activation of DCs by SLPs occurs via TLR4 . DC maturation is also characterized by increased expression of MHC class II , CD40 , CD80 and CD86 [27] , [28] . As SLPs activated DCs via TLR4 , we examined the effects of SLPs on these markers in the presence and absence of TLR4 . Figure 3 demonstrates that SLPs induce DC maturation in a similar manner to LPS , in cells isolated from C3H/HeN mice with increased expression of MHC II , CD40 , CD80 and CD86 . This was completely abrogated in DCs isolated from C3H/HeJ TLR4 mutant mice . Activation of TLR4 results in the subsequent phosphorylation and activation of p38 . In order to further confirm that SLP activated DCs via TLR4 we examined the ability of SLP to induce DC maturation in the presence of a p38 inhibitor . Figure 4 demonstrates that SLP is unable to induce upregulation of MHC II , CD40 , CD80 or CD86 in the presence of a p38 inhibitor . Furthermore , the effects of SLPs on DC maturation markers was also dose dependent ( Figure S3 ) . An important event for the initiation of adaptive immunity is the activation of Th cells by DCs [29] . The DC cytokine production and co-stimulatory marker expression are key to this process . We first wanted to determine whether SLPs could induce a Th1 or Th17 response , given the importance of these responses in bacterial clearance [30] , [31] . Furthermore , since our earlier data demonstrated that activation of DCs by SLPs involves TLR4 , we wanted to determine whether this was critical for generation of subsequent adaptive immune responses . DCs isolated from both C3H/HeN and C3H/HeJ mice were exposed to OVA peptide in the presence of either SLPs or LPS . These DCs were then co-cultured with CD4+ T cells purified from OVA transgenic mice . T cells were exposed to two rounds of activation with DCs and the Th response was characterised . DCs activated with LPS/OVA or SLP/OVA induced a mixed T helper cell response , with significant production of IL-17 , IL-4 and IFNγ on both Day 4 and Day 10 ( Figure 5; p<0 . 001 ) . The dominant response was the production of IL-17 . No response was generated by either LPS/OVA- or SLP/OVA-activated DCs isolated from C3H/HeJ mice . In order to confirm that SLPs activate TLR4 , we performed experiments in which human HEK293 cells were transiently transfected with TLR4 along with the TLR4 accessory proteins , MD2 and CD14 . Non-transfected HEK293 cells were used as a control . Two separate experiments were carried out using luciferase as a reporter gene for activation of the transcription factors NFκB or ISRE ( indicative of interferon regulatory factor 3 ( IRF3 ) activation ) . As expected , neither LPS nor SLP were able to activate ISRE or NFκB in HEK293 cells in the absence of the TLR4 receptor ( Figure 6A&B ) . Exposure of HEK293-TLR4-MD2-CD14 cells to LPS resulted in significant activation of ISRE and NFκB ( Figure 6C&D; p<0 . 001 ) . When increasing concentrations of SLPs were incubated with the HEK293-TLR4-MD2-CD14 cells , there was a dose-dependent activation of NFκB ( Figure 6D; p<0 . 05 , p<0 . 01 , p<0 . 001 ) , but no activation of ISRE ( Figure 6C ) . The lack of effect of SLPs on IRF3 was further confirmed by our observation that SLP did not induce IFNβ production by DCs ( Figure S4 ) . Given that SLP did not activate IRF3 , and that CD14 is important for the endocytosis of the TLR4 complex for subsequent activation of IRF3 [32] , we examined whether SLP required CD14 for activation of TLR4 . We show that LPS activated NF-κB in HEK293-TLR4-MD2-CD14 ( Figure 7C ) cells but not HEK293-TLR4 cells ( Figure 7E ) . In contrast , SLP significantly induced NF-κB in both HEK293-TLR4 ( Figure 7E ) and HEK293-TLR4-MD2-CD14 cells ( Figure 7C; p<0 . 001 ) , suggesting that SLP does not require CD14 for activation of NF-κB downstream of TLR4 . To formally validate the biological relevance of the in vitro cell culture data indicating that C . difficile SLPs interact with TLR4 , wild-type , TLR2−/− , TLR4−/− , MyD88−/− and TRIF−/− mice were infected with the C . difficile strain that SLPs were isolated from . A recently described model of C . difficile infection of antibiotic treated mice was used [25] . Following infection wild-type , TLR2−/− and TRIF−/− mice developed comparable diarrhoea and transient weight loss , which peaked at day 3 post-infection ( Figure 8A ) . In contrast , both TLR4−/− and MyD88−/− mice developed marked weight loss by day 1 , with significantly greater weight loss ( p<0 . 05−0 . 001 ) relative to other groups on days 1–7 ( Figure 8A ) , which was associated with severe diarrhoea . Due to severe morbidity and associated >15% weight loss , 1/7 and 2/7 of TLR4−/− and MyD88−/− groups were humanely killed on day 3 , respectively , with no deaths in wild-type , TLR2−/− or TRIF−/− mice . Consistent with the weigh loss data , both TLR4−/− and MyD88−/− mice had significantly ( p<0 . 05 ) higher numbers of C . difficile spores in the cecum on day 3 compared to wild-type , TLR2−/− and TRIF−/− ( Figure 8B ) . The cecum from TLR4−/− and MyD88−/− had marked inflammatory cell infiltrates with oedema and epithelial disruption on days 3 and 7 post infection , that was significantly ( p<0 . 05−0 . 01 ) greater than the mild inflammation in the cecum of infected wild-type , TLR2−/− or TRIF−/− mice ( Figure 8C , 8D ) . It was notable that uninfected TLR4−/− and MyD88−/− mice had evidence of mild cecal inflammation ( Figure 8C , 8D ) , which is relevant to the known role of TLR4 and MyD88 in basal intestinal homeostasis [33] , [34] . As conventional housed mice are not susceptible to C . difficile infection , we evaluated if the intestinal alterations in TLR4−/− and MyD88−/− mice rendered these mice innately more susceptible to infection . However , TLR4−/− and MyD88−/− mice , and also wild-type , TLR2−/− and TRIF−/− mice , were refractory to infection when exposed to C . difficile without any prior antibiotic treatment ( data not shown ) . These data confirm an in vivo functional role for TLR4 , and not TLR2 , in a MyD88 but not TRIF dependent pathway , in C . difficile infection of antibiotic-treated mice . In order to confirm that SLPs were recognised in the context of the whole bacterium and that TLR4 is necessary for their recognition , wildtype and TLR4−/− mice were infected as before with C . difficile and serum was collected 3 days post infection . Wildtype mice showed an increase in anti-SLP IgG 3 days after infection with C . difficile ( data not shown ) . Figure 9 demonstrates that TLR4−/− mice have no detectable anti-SLP IgG compared to wildtype controls on day 3 post infection .
The significant findings of this study are that SLPs isolated from C . difficile induce maturation of DCs and subsequent generation of T helper cell responses required for bacterial clearance via TLR4 . We also demonstrate the significance of TLR4 in murine infection with C . difficile , with TLR4−/− and MyD88−/− mice displaying a more severe infection than wild type . Interestingly , we found SLPs to activate NFκB but not IRF3 , downstream of TLR4 which correlated with the observation that TRIF−/− mice did not have increased susceptibility or severity of infection . This is the first study to demonstrate a role for TLR4 in infection associated with C . difficile and suggests an important role for SLPs in the generation of the immune response necessary for clearance of this bacterium . SLPs have previously been described as virulence factors for other bacterial infections such as Campylobacter fetus and Aeromonas salmonicida [8] , [9] . There is now significant evidence that SLPs isolated from C . difficile are important components of the pathogen . Specifically , passive immunisation of hamsters with antibodies to these proteins affects the course of C . difficile infection , resulting in prolonged survival of hamsters [6] . While this evidence indicates the importance of these proteins , the way in which they are recognised by and activate the immune system is not clear . Activation of DCs is characterized by the production of cytokines and increased expression of MHCII , as well as co-stimulatory molecules [27] , [29] . We demonstrate that SLPs induce DC maturation , characterised by production of IL-12p70 , TNFα , IL-23 , IL-6 , and increased expression of MHCII , CD40 , CD80 and CD86 . This agrees with some previously reported effects of SLPs [35] . Interestingly , while there are some similarities between the response elicited with SLP and LPS , SLP did not induce IL-1β production , demonstrating a distinct effect of SLPs and further confirming that potential contamination with LPS is not responsible for the effects observed with SLPs . Other evidence was provided by our observation that the effects of SLPs on DCs were not reversed in the presence of polymyxin B , known to bind LPS ( Figure S1 ) . We next conducted experiments in C3H/HeN and C3H/HeJ mice , and showed that the effects of SLPs on DC maturation were mediated through TLR4 . Furthermore , our experiments demonstrated that intact SLPs , containing both the HMW and LMW proteins , were required for DC activation . The significance of this data is two-fold; firstly they demonstrate that the HMW and LMW proteins may need to be associated in their complex for recognition by TLR4; while an additional experiment examining the HMW and LMW proteins after recomplexing would be advantageous , their tight association has been recently demonstrated by Fagan et al . [36] . Secondly , the lack of response to the separated proteins confirms that the effects we observed with SLPs could not possibly be attributed to any contaminating ligand . This is further supported by the fact that an irrelevant protein purified in the same way was unable to elicit these effects on DCs . A number of pathogen derived molecules have now been shown to activate DCs through TLR4 . For example , LPS from Bordetella pertussis and Salmonella enteritidis have been reported to induce TLR4-dependent DC maturation [27] , [37] . Given that the interaction of DC with T cells is required for activation of adaptive immunity , and since SLPs induced potent production of cytokines important in promoting Th1 and Th17 responses [17] , [18] , the data suggest that SLPs may be important in the generation of these responses . We clearly demonstrate that DCs activated with SLPs have the capacity to drive strong Th1 and Th17 responses characterised by production of IFNγ and IL-17 . Indeed the dominant cytokine produced was IL-17 . Not surprisingly , SLPs also induced a weak Th2 response , which concurs with studies demonstrating SLPs to induce an antibody response [6] , [7] . The importance of T helper cells and their cytokines IFNγ and IL-17 are well recognised in bacterial clearance . Both Scid mice ( deficient in T and B cells ) and nude mice ( deficient in T cells ) show high susceptibility to infection with Coxiella burnetii [19] . Another study employing IFNγ−/− mice demonstrated that infection with Bordetella pertussis was exacerbated in the absence of IFNγ [38] . Furthermore , inhibition of IL-17 with a neutralising antibody results in increased infection with Pneumocystis carinii [39] . Since SLPs induce a potent Th1 and Th17 response , our data suggest that they may be important in clearance of C . difficile and that TLR4 is required for this . Several studies have highlighted a role for TLR4 in bacterial clearance; for example activation of TLR4 by Klebsiella pneumoniae has been shown to be critical for induction of IL-17 , known to be important in host defence against bacterial infection [40] . Therefore , we examined whether clearance of C . difficile was impaired in mice without functional TLR4 . We clearly demonstrate that C . difficile infection is more severe in TLR4−/− and MyD88−/− mice with increased weight loss , mortalities , and number of C . difficile spores in the cecum . This suggests that TLR4 and MyD88-mediated signalling are important in the clearance of the bacterium . Furthermore , the lack of an IgG response to SLP in TLR4−/− mice suggests that the recognition of SLP plays a key role in this process . Our findings in MyD88−/− mice concurs with a recent study which showed a more severe intestinal disease following infection with C . difficile in these mice [41] . It is noteworthy that while TLR4−/− and MyD88−/− mice were relatively more susceptible to infection following an antibiotic treatment regime , however , without antibiotic treatment and thus having an intact intestinal microbiota they were resistant to C . difficile infection infected similar to immunocompetent C57BL/6J mice . Recently , Jordan et al demonstrated that mice deficient in functional TLR4 showed increased susceptibility to infection with Rickettsia conorii which was associated with decreased Th1 and Th17 responses [42] . Importantly , rickettsiae do not possess classical endotoxic LPS . Given that C . difficile is a Gram-positive bacterium lacking LPS , our findings that SLPs , the immunodominant antigen on the surface of this bacterium , can activate the innate immune response via TLR4 are particularly significant . Recognition and subsequent binding of LPS to TLR4 results in an intracellular cascade of events involving the adaptor molecules MyD88 , MyD88-like adaptor molecule ( Mal ) , TRIF and TRIF-related adaptor molecule ( TRAM ) , culminating in downstream activation of the transcription factors NFκB and IRF3 for production of pro-inflammatory cytokines and type I interferons , respectively [43] , [44] . In order to confirm that SLPs can indeed activate TLR4 , we examined whether they induced activation of NFκB and IRF3 in human HEK cells transfected with TLR4-MD2-CD14 . We demonstrate that SLPs activate NFκB in a dose dependent manner downstream of TLR4 , however they did not activate ISRE which is indicative of IRF3 activation . The significance of this data is two-fold; firstly , it raised the possibility that SLPs activated TLR4 independently of CD14; given that activation of IRF3 downstream of TLR4 requires endocytosis of the TLR4 complex and its subsequent association with TRIF and TRAM [15]; this finding explains why our experiments in HEK/TLR4 cells clearly show that SLP , but not LPS , activated NKκB in the absence of CD14 . This is further supported by our data showing that mice deficient in TRIF did not get a more severe infection and our data showing that SLP does not induce type 1 IFN in DCs ( Figure S4 ) . Secondly , these data further confirm that our purified SLPs were free from LPS contamination as LPS clearly activates ISRE . A recent report has highlighted the ability of some TLR4 ligands to selectively activate signalling pathways downstream of TLR4 . Specifically , the vaccine adjuvant monophosphoryl lipid A induces strong TRIF-associated responses but only very weak MyD88-associated responses , showing a clear preference for activation of downstream IRF3 [45] . Interestingly , as production of IFNβ ( downstream of IRF3 activation ) is essential for induction of endotoxic shock , the inability of SLPs to activate the ISRE/IRF3 pathway and subsequent IFNβ may explain why numerous groups that administer SLPs to mice do not report any toxicity [46] , [47] . The data presented in this study demonstrate that SLPs activate innate and adaptive immunity via a TLR4-dependent mechanism . Given that the responses activated are critical to bacterial clearance , we propose that recognition of SLPs by TLR4 is important for recognition of the pathogen and the subsequent generation of the appropriate immune response required for bacterial clearance . This is further evidenced by our finding that a more severe disease is present in TLR4−/− mice along with the absence of an antibody response to SLP , suggesting that recognition of SLPs by TLR4 may play a role in determining the outcome of infection . Furthermore , it is now well recognised that TLRs play a key role in host defence against intestinal pathogens and maintenance of tissue homeostasis in the gastrointestinal tract [34] , [48] . It is of great interest that the amino acid sequence of SLP is highly variable between serogroups of C . difficile [49] . It is possible that these sequence differences could affect the recognition of SLPs by the innate immune system and therefore may explain why some strains of C . difficile cause severe infection and a high frequency of recurrence and yet others are associated with minimal clinical symptoms and pathology . While there is currently no known correlation between SLP sequence and virulence other reports suggest that variability of these surface layer proteins may be an important mechanism to escape host defence [50] , [36] and warrants further investigation . | Clostridium difficile is the leading cause of antibiotic-associated diarrhoea among hospital patients and in severe cases can cause pseudomembranous colitis and even death . There is currently limited information regarding how this pathogen is recognised by the immune system and the key mechanisms necessary for clearance of the pathogen . C . difficile expresses a paracrystalline surface protein array , termed an S-layer , composed of surface layer proteins ( SLPs ) . Their location on the outer surface of the bacteria suggests that they may be involved in immune recognition of the pathogen . In this study we demonstrate that these SLPs are recognised by toll-like receptor 4 ( TLR4 ) . Activation of TLR4 by SLPs resulted in maturation of dendritic cells and subsequent activation of T helper cell responses which are known to be important in clearance of pathogens . Furthermore , using a murine model of C . difficile infection we show that mice display increased severity of infection in the absence of TLR4 . This is the first study to demonstrate a role for TLR4 in infection associated with C . difficile and suggests an important role for SLPs in the generation of the immune response necessary for clearance of this bacterium . |
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Adaptive radiation is the rapid origination of multiple species from a single ancestor as the result of concurrent adaptation to disparate environments . This fundamental evolutionary process is considered to be responsible for the genesis of a great portion of the diversity of life . Bacteria have evolved enormous biological diversity by exploiting an exceptional range of environments , yet diversification of bacteria via adaptive radiation has been documented in a few cases only and the underlying molecular mechanisms are largely unknown . Here we show a compelling example of adaptive radiation in pathogenic bacteria and reveal their genetic basis . Our evolutionary genomic analyses of the α-proteobacterial genus Bartonella uncover two parallel adaptive radiations within these host-restricted mammalian pathogens . We identify a horizontally-acquired protein secretion system , which has evolved to target specific bacterial effector proteins into host cells as the evolutionary key innovation triggering these parallel adaptive radiations . We show that the functional versatility and adaptive potential of the VirB type IV secretion system ( T4SS ) , and thereby translocated Bartonella effector proteins ( Beps ) , evolved in parallel in the two lineages prior to their radiations . Independent chromosomal fixation of the virB operon and consecutive rounds of lineage-specific bep gene duplications followed by their functional diversification characterize these parallel evolutionary trajectories . Whereas most Beps maintained their ancestral domain constitution , strikingly , a novel type of effector protein emerged convergently in both lineages . This resulted in similar arrays of host cell-targeted effector proteins in the two lineages of Bartonella as the basis of their independent radiation . The parallel molecular evolution of the VirB/Bep system displays a striking example of a key innovation involved in independent adaptive processes and the emergence of bacterial pathogens . Furthermore , our study highlights the remarkable evolvability of T4SSs and their effector proteins , explaining their broad application in bacterial interactions with the environment .
Adaptation to different ecological niches can lead to rapid diversification of a single ancestor into an array of distinct species or ecotypes . This process , called adaptive radiation , typically occurs after the arrival of a founding population in a novel environment with unoccupied ecological niches ( ‘ecological opportunity’ ) and/or by the acquisition of a novel trait ( ‘evolutionary key innovation’ ) allowing the exploitation of so far unapproachable niches [1] . Spectacular examples of adaptive radiation come from different metazoan lineages with the cichlid fishes of the East African Great Lakes and the Darwin finches on Galapagos Islands representing the most prominent examples [2] , [3] . Although known from a few cases only , bacterial lineages also underwent adaptive radiation - as documented in natural settings as well as in evolution experiments [4]–[6] . It remains a fundamental problem to biology to understand why and how certain lineages diversified; adaptive radiations , and in particular the genetic and genomic basis thereof , provide an ideal set-up to address this question [3] , [7] . One of the most fascinating aspects of adaptive radiation is the frequent occurrence of evolutionary parallelism resulting in independent adaptation to same ecological niches [8]–[10] . Such evolutionary parallelisms are excellent examples for the action of similar , yet independent , selective forces and , hence , for the key role of natural selection in evolution [11] . Furthermore , parallel adaptive radiations in a single group indicate the existence of traits conferring a high degree of adaptability allowing the group members to efficiently occupy distinct environments . Therefore , lineages that radiate in parallel are of great value to study the molecular basis of adaptation and their independent evolutionary trajectories [1] , [9] . This is of particular interest in case of host-adapted bacteria differentiating into divergent ecological niches and potentially resulting in the emergence of new pathogens . Species of the α-proteobacterial genus Bartonella are specifically adapted to distinct mammalian reservoir hosts where they cause intra-erythrocytic infections [12] . Different animal models revealed that Bartonella upon reservoir host infection colonizes a primary cellular niche from where the bacteria get seeded into the bloodstream adhering to and invading erythrocytes [13]–[15] . In most cases , infections of the reservoir host do not lead to disease symptoms suggesting a highly specific adaptation to the corresponding host niche . The transmission between host individuals is mediated by blood sucking arthropods . An integrative genome-wide analysis showed that most factors essential for Bartonella to colonize their mammalian reservoir hosts are found within the core genome of this genus [16] . This is not surprising as it reflects the common strategy used by divergently adapted species to colonize their hosts . However , this study also revealed that two type IV secretion systems ( T4SS ) , Trw and VirB , which are essential for host interaction at different stages of the infection cycle represent the few colonization factors exclusively found in the most species rich sub-lineages of bartonellae . It was assumed that the horizontal acquisition of these T4SS substantially refined the infection strategy of Bartonella facilitating concurrent adaptation to a wide range of different hosts [16] . The VirB T4SS translocates a cocktail of evolutionarily related effector proteins into host cells of the primary infection niche where they modulate various cellular processes [17]–[21] . The Trw T4SS is involved in the erythrocyte invasion by binding to the erythrocytic surface with its manifold variants of pilus subunits [22]–[24] . Here , we study the evolutionary relationship of Bartonella species adapted to distinct reservoir hosts and investigate the genetic mechanisms underlying adaptive radiation in different lineages . We uncover two parallel adaptive radiations in the genus Bartonella . Our genome-wide analysis revealed a remarkable evolutionary parallelism in the horizontally acquired VirB T4SS in the two radiating lineages . This parallelism is characterized at the molecular level by the lineage-specific chromosomal integration of the virB loci and the independent origination of versatile sets of effector proteins for the interaction with host cells . Providing an arsenal of host-subverting functions that can be efficiently modulated , the VirB T4SS thus seems to represent an evolutionary key innovation triggering the independent radiations of the two lineages . Our study provides detailed insights into the molecular mechanisms underlying parallel adaptive radiations in a bacterial pathogen . Furthermore , many of the diversified T4SS effector proteins carry a FIC domain recently shown to mediate ‘AMPylation’ , a lately recognized post-translational modification [25] , [26] . FIC domains are highly conserved in evolution and the diversified variants of the Bartonella effector proteins may display a suitable model to study their activity spectrum in the future .
To study the adaptive evolution of Bartonella on a genomic level , we aimed for a set of genome sequences from species adapted to distinct mammalian reservoir hosts . To this end , we included in our analysis the published genome sequences of Bartonella bacilliformis ( Bb ) , Bartonella grahamii ( Bg ) , Bartonella henselae ( Bh ) , Bartonella quintana ( Bq ) , and Bartonella tribocorum ( Bt ) [16] , [27] , [28] . These five species are adapted to human ( Bb and Bq ) , cat ( Bh ) , mouse ( Bg ) , and rat ( Bt ) . Further , we sequenced the complete genome of Bartonella clarridgeiae ( Bc ) and generated draft sequences of Bartonella schoenbuchensis ( Bs ) , Bartonella rochalimae ( Br ) , Bartonella sp . AR 15-3 ( BAR15 ) , and Bartonella sp . 1-1C ( B1-1C ) . Bs was selected as representative of a solely ruminant-infecting clade [16] . Bc , Br , BAR15 , and B1-1C were previously shown to be closely related [29]–[31] . However , they were isolated from different mammalian reservoir hosts and therefore display a suitable set of species to study adaptive processes on the genomic level . BAR15 and B1-1C were recently isolated from American red squirrel and rat , respectively [30] , [31] , whereas Br was predominantly recovered from canidae like dogs or foxes , and Bc from cats [32]–[34] . Genome sequencing by 454-pyrosequencing resulted in an average sequence coverage of >35x . The single chromosome of the completely assembled genome of Bc was found to be 1 , 522 , 743 bp in size and thus belongs to the smaller genomes of Bartonella ( Table 1 ) . The draft genomes of Br , BAR15 , B1-1C , and Bs consist of 13 to 19 contigs with total genome sizes similar to the one of Bc . On average , 99% of all 454-sequencing reads were assembled into the analyzed 13 to 19 contigs indicating that our draft genomes did not miss essential sequence data for subsequent analysis . Genomic features of the strains used in this study are summarized in Table 1 . We inferred a robust species tree of the genus Bartonella based on 478 core genome genes of the ten available Bartonella genomes sequenced ( Figure S1 ) . To exclude that recombination or horizontal gene transfer within this set of core genome genes was affecting our phylogenetic analysis , we reconstructed single gene trees of the entire data set and performed a recombination analysis using the GARD algorithm [35] . 471 of the 478 genes revealed the same overall topology as our genome-wide phylogeny with the two monophyletic clades of lineage 3 and lineage 4 ( see Figure 1 ) . Further , the GARD analysis detected significant recombination breakpoints with a p-value<0 . 01 in only two out of the 478 core genome genes . Together these analyses show that our genomic data set is suitable for inferring a consistent species tree . Based on available sequence information for the housekeeping genes rpoB , gltA , ribC , and groEL , we included most other Bartonella species in the analysis resulting in a so-called supertree phylogeny ( Figure 1 ) [36] . Just as the analysis based on the 478 core genome genes of the ten sequences species alone , this supertree revealed four major clades in the monophyletic bartonellae: ancestral lineage 1 represented by the highly virulent human pathogen Bb [37]; lineage 2 comprising of Bs and three other ruminant-infecting species; lineage 3 consisting of the closely related Bc , Br , BAR15 , and B1-1C; and the most species-rich lineage 4 with 13 species including Bg , Bh , Bq , and Bt ( Figure 1 ) . A phylogeny based on only the four housekeeping genes resulted in the same clustering of these taxa into the four different Bartonella lineages ( Figure S1 ) . In contrast to the ancestral lineage 1 , lineages 2 , 3 , and 4 are ramifying to different degrees comprising species isolated from various hosts . While the species of lineage 2 are limited to infect ruminants and have overlapping host range [38] , the diversification of lineage 3 and 4 seems to result from the specific adaptation to distinct mammalian hosts [12] . To substantiate the ecological divergence within these two lineages , we analyzed the genotype-host correlation of Bartonella isolates sampled from diverse mammals . Based on gltA sequences , this analysis revealed clustering of strains isolated from same or similar hosts in lineage 3 and 4 ( Figure S2 ) . Further support for the host specific adaptation of different Bartonella species comes from recently published laboratory infections [39] , [40] and from our own rat infection experiments with the strains of lineage 3 ( Figure S3 ) . It is to mention that some Bartonella species can incidentally be transmitted to other hosts like humans [12] . These so-called zoonotic Bartonella species do not cause intraerythrocytic bacteremia in the accidental human host reflecting the lack of specific adaptation . However , such accidental transmissions might facilitate the emergence of new specificity resulting in host switches and the origination of new species . In particular , several species of lineage 3 and 4 are known to display such zoonotic pathogens , whereas for lineage 1 or 2 to our knowledge no such case has been reported so far [12] , [29] . In summary , our genome-wide phylogenetic analysis shows that the sister lineages 3 and 4 have evolved by adaptive radiations into same or similar ecological niches ( i . e . hosts ) . Long internal branches separating the two lineages from each other and preceding the radiations are evidence for their independent occurrence ( Figure 1 ) . Due to the lack of calibration time points , the exact timing of these independent radiations cannot be deduced . However , the phylogenetic tree in Figure 1 might suggest that lineage 3 diversified more recently compared to lineage 4 . This is supported by the mean p-distances inferred for the sequenced taxa of these two lineages: lineage 3 = 0 . 07±0 . 0002 , lineage 4 = 0 . 12±0 . 0003 ( see also Table S1 ) . Two alternative explanations for the observed differences in lineage diversification could be ( i ) a sampling bias , i . e . the full diversity of lineage 3 was not captured or ( ii ) smaller population sizes for species of lineage 4 over lineage 3 leading to faster evolution at purifying sites . Significant differences in population size might be rather unlikely as Bartonella species are thought to share a common life style in their respective reservoir host . Whether sampling of Bartonella species in animal populations was exhaustive enough is difficult to assess . However , a newly discovered species would only change the coalescent point of a lineage if it would hold a more ancestral position than the already known species of this lineage . In contrast to these alternative hypothesis , epidemiological studies rather seem to support the scenario of a more ancient onset of the radiation in lineage 4: ( i ) lineage 4 comprises a much wider range of divergently adapted species and ( ii ) they represent the most frequently found Bartonella species in natural host populations [30] , [41] . In contrast , except for Bc , taxa of lineage 3 were only recently detected and so far only sampled at low prevalence [29]–[32] . The evolutionary parallelism of the radiating Bartonella lineages provides an ideal setting to study independent evolutionary processes linked to the adaptation to divergent niches . An important driving force for these adaptive radiations might be the presence of ecological opportunities . The niche of Bartonella in the mammalian reservoir host , the bloodstream , displays a privileged environment in which other resource-competing microbes are typically absent . Thus , the adoption of the characteristic intra-erythrocytic infection strategy together with the vector-borne transmission route might have enabled adaptive radiations of Bartonella by the specialization to different hosts . However , not all Bartonella lineages appear to have diversified to the same extent ( see Figure 1 ) suggesting that the availability of such an ecological opportunity alone is not sufficient to explain the pronounced radiation of lineages 3 and 4 . Supposedly , key innovations , i . e . lineage-specific traits underlying the adaptation to the mammalian host niches are responsible for the adaptive radiations . Potential adaptive traits would have to be involved in species-environment interactions , such as molecular factors responsible for causing bacteremia . Further , in analogy to the modulation of the adaptive traits in metazoan radiations [2] , [3] , any molecular factor used to exploit distinct environments in a specific manner should be divergent among niche-specialized species [1] . Molecular evolutionary analyses provide the means to identify divergent adaptive traits as the genes encoding them are expected to show signs of adaptive evolution , i . e . an excess of non-synonymous ( dn ) over synonymous ( ds ) substitutions as the result of positive selection . We performed a genome-wide natural selection analysis in the two radiating lineages to detect genes ( and therefore traits ) with divergent evolution . To this end , we analyzed all orthologous genes from the available genomes of the radiating lineage 3 ( Bc , Br , BAR15 , and B1-1C ) and lineage 4 ( Bh , Bg , Bq , and Bt ) for signs of adaptive sequence evolution by inferring the natural selection of orthologs by estimation of ω , the ratio of non-synonymous ( dn , amino acid change ) to synonymous ( ds , amino acid conservation ) substitution rates ( ω = dn/ds ) . Generally , ω<1 , ω = 1 , ω>1 represent purifying , neutral , and positive selection ( adaptive evolution ) , respectively [42] . We first calculated “gene-wide” dn/ds for all orthologous genes of the two lineages ( lineage 3: 1 , 097 genes , lineage 4: 1 , 091 genes ) . We excluded one gene from this analysis for each of the two lineages , because the GARD analysis detected statistically significant recombination breakpoints . As adaptive evolution is typically affecting only a few sites of a gene rather than the entire gene sequence [42] , we first looked for genes exhibiting an elevated value of ω≥0 . 25 over the entire gene length . This analysis revealed 133 ( 12% ) and 86 ( 8% ) genes in lineage 3 and lineage 4 , respectively , under relaxed purifying selection indicating signs of adaptive evolution ( Figure 2 ) . To have an additional measurement for adaptive evolution , we subjected our genomic data sets to a maximum likelihood analysis for the detection of site-specific positive selection . To this end , we used the CodeML module implemented in the PAML package . CodeML compares the likelihoods of different evolutionary models for each analyzed gene alignment . When comparing model M2a ( PositiveSelection ) vs . model M1a ( NearlyNeutral ) , we detected 62 or 34 genes for lineage 3 and 26 or 14 genes for lineage 4 harboring sites under positive selection with a p-value of <0 . 05 or <0 . 01 , respectively . A large fraction of these genes ( 29 for lineage 3 and 12 for lineage 4 ) exhibited also gene-wide dn/ds values ≥0 . 25 indicating them as good candidates for encoding adaptive traits ( Table 2 ) . Comprehensive lists of the genes identified to have dn/ds values ≥0 . 25 and/or exhibiting site-specific positive selection in the CodeML analysis are provided in Table S2 and Table S3 for lineage 3 and lineage 4 , respectively . As non-synonymous mutations accumulate over time , the higher number of genes identified for lineage 3 could be a further indication for its more recent radiation , or alternatively , the effect of larger population sizes compared to lineage 4 . Irrespectively , these findings render the dataset derived from lineage 3 to be more sensitive to the detection of adaptive sequence evolution ( Figure 2 , Table 2 ) . Interestingly , there was a marked number of genes with ω≥0 . 25 over the entire gene length that overlapped in both lineages ( Table 3 ) . Among those were genes encoding autotransporters , hemin-binding proteins , and different components of the VirB T4SS . They all constitute important host colonization factors [43]–[45] and thus are likely to display adaptive traits of Bartonella . For many of these genes , also our analysis of site-specific natural selection detected positive selection ( Table 3 ) . Most remarkably , all analyzed Bartonella effector protein ( bep ) genes of the VirB T4SS were among the genes with ω≥0 . 25 . Particularly in lineage 3 , they showed strong signs of adaptive evolution by exhibiting ω>0 . 4 over the entire gene length . Further , in eight out of nine analyzed bep genes of lineage 3 , we detected site-specific positive selection ( Table 3 ) . Being exclusively found in the radiating lineages and showing strong signs of adaptive evolution , the VirB system and its effector proteins , thus , fulfill the criteria of an evolutionary key innovation likely contributing to the parallel adaptive radiations of Bartonella . Autotransporters and hemin-binding proteins could represent further adaptive traits important for radiations in Bartonella . They exhibited strong positive selection in our analysis and are known to be important factors for host colonization . Further , their conservation throughout the genus Bartonella indicates an important role for the life style and infection strategy of this pathogen ( Table S2 , Table S3 ) . However , factors conserved in radiating and non-radiating lineages appear unlikely to represent specific key innovations , unless other factors , such as the absence of ecological opportunities or ecological separation , prevented certain lineages to radiate and to colonize more divergent niches . Importantly , our analysis revealed some lineage-specific colonization factors to carry signs of adaptive evolution . Among others , surface-exposed pilus-components of the Trw T4SS exclusively present in lineage 4 were found to exhibit elevated dn/ds values and sites under positive selection ( Table S3 ) . This is in agreement with previous studies and appears to reflect the adaptation of this putative adhesion factor to the erythrocytic surface of different host species [22] , [24] . As the Trw T4SS is only present in lineage 4 , it might have specifically contributed to the radiation of this most species-rich clade of Bartonella . Factors known to be important for colonization and exclusively present in lineage 3 were not identified by our analysis . We cannot exclude that the selective pressure imposed by the immune-system might have contributed to the adaptive evolution detected in our genome-wide analysis . It was previously reported that the arms race between host and pathogen can drive the diversification of secretion system- and effector protein-encoding genes [46] , [47] . However , in case of the Trw T4SS , recently published in vitro infections with erythrocytes isolated from different mammals demonstrated that the Trw-dependent binding and invasion of Bartonella is host-specific [22] . Although experimental data is not yet available , our data suggest that the VirB T4SS and its effector proteins evolved by similar mechanisms . Together with the previous finding that the VirB T4SSs belong to the few colonization factors specific to the radiating lineages [16] , this analysis reveals these horizontally acquired host interacting systems as potential key innovations facilitating adaptation to new hosts and therefore driving the radiations of Bartonella . To further assess the role of the VirB T4SS for the independent adaptive radiations of Bartonella , we compared the chromosomal organization of the VirB and effector protein-encoding genes of the two lineages . Remarkably , our analysis uncovered independent evolutionary scenarios for the chromosomal incorporation of this horizontally acquired trait . In the genomes of lineage 4 ( Bg , Bh , Bq , and Bt ) , the virB T4SS genes , virB2-virB11 and the coupling protein gene virD4 , are encoded at the same chromosomal location ( Figure 3 , Figure S4 ) . Also , the bep genes are encoded in this region . In contrast , the genome sequences of lineage 3 ( Bc , Br , BAR15 , and B1-1C ) revealed marked differences in organization , copy number , and chromosomal localization of the genes encoding the VirB T4SS . In the completely assembled genome of Bc , we found three copies of the virB2-virB10 genes encoded at two different chromosomal locations ( Figure 3 , Figure S4 ) . Two copies are encoded at the same locus and belong to inverted repeats of ∼10kb . They are separated by several bep genes and the gene virD4 . A third copy of the virB2-virB10 cluster including an additional bep gene is encoded in another genomic region highly conserved across different Bartonella lineages ( Figure 3 ) . The same chromosomal integration and amplification of the virB T4SS genes was found in the other three genomes of lineage 3 . However , in a common ancestor of Br and B1-1C , one of the three copies must have been partially deleted , as only virB2 , virB3 , and a remnant of the virB4 gene were found in the corresponding region of these two genomes ( Figure 3 , Figure S4 ) . Interestingly , the different copies of virB2-virB10 are identical to each other within one species , but divergent across different species indicating the presence of an intra-chromosomal homogenization process . The fact that duplicated components of another T4SS , Trw , also evolved in concert , and the finding of several other identical genes or gene clusters in different Bartonella genomes [24] suggests that sequence homogenization is a common mechanism in Bartonella to conserve paralogous gene copies . The inverted organization of the two virB T4SS gene clusters seems to result from a duplication event subsequent to the integration of a first copy . Evidence comes from a remnant of the glutamine synthetase I gene ( glnA ) flanking the entire locus at its upstream end . The full-length copy of this vertically inherited housekeeping gene is located directly downstream of the integration site ( Figure 3 , Figure S4 ) . In addition to the effector genes adjacently located to the virB genes ( as in lineage 4 ) , we found six additional loci encoding bep genes in lineage 3 ( Figure S4 ) . These effector genes are not entirely conserved throughout lineage 3 , and the existence of gene remnants provides evidence of their deterioration in certain species . Altogether , we identified 12 to 16 bep genes in lineage 3 , whereas only five to seven bep genes are present in lineage 4 . Incomplete synteny in the corresponding regions may hinder comparison between the two different lineages , however , no gene remnants could be found at the different integration sites across the two lineages . We cannot fully exclude that massive genomic recombination events resulted in the different chromosomal locations and the lineage-specific dissemination of the virB and bep genes . Yet , such a scenario appears unlikely , as the overall genomic backbone is largely conserved ( Figure 3 ) and the flanking regions of the virB T4SS integration sites do not encode vertically-inherited orthologs across the two lineages . Furthermore , the absence of mobile elements adjacent to the virB T4SS genes such as recombinases , transposases , or integrases is not supportive of an intra-chromosomal mobilization of this genomic locus . T4SS are ancestrally related to conjugation machineries [48] . Thus , the virB genes might have been transferred from a conjugative plasmid into the chromosome by independent events after the divergence of lineage 3 and lineage 4 . In Bg , a closely related T4SS , the Vbh , is encoded on a plasmid in addition to a chromosomally integrated copy [28] . This indicates that these horizontally acquired elements can be maintained on extra-chromosomal replicons within Bartonella from where they are integrated into the chromosome . Similarly , pathogenic Escherichia coli strains from different phylogenetic clades were shown to have evolved in parallel by the independent incorporation of virulence traits from mobile genetic elements [49] . As the chromosomal organization implicates different evolutionary histories of the VirB T4SS in the two radiating lineages , we investigated the relation among the effector proteins translocated by this secretion system . It was previously shown that the bep genes have evolved from a single ancestor by duplication , diversification , and reshuffling of domains resulting in modular gene architectures [17] . The C-terminal BID ( Bartonella intracellular delivery ) domain is shared by all Beps as it constitutes the secretion signal for the transport via the VirB T4SS . In their N-terminal part , Beps either harbor a FIC ( filamentation-induced by cAMP ) domain or repeats of additional BID domains ( Figure S5 ) . We assessed the evolutionary relationship among the bep genes by inferring phylogenetic trees on the basis of either the BID or the FIC domain , or the entire gene sequence . This revealed that the bep genes of lineage 3 and lineage 4 form two separate clades ( Figure 4 , Figure S6 ) . Apparently , consecutive rounds of lineage-specific duplications of an ancestral effector gene resulted in the parallel emergence of two distinct arsenals of bep genes . These duplication events preceded the adaptive radiation in both lineages as phylogenetic clusters of effector genes ( Bep clades in Figure 4 ) comprise positional orthologs present in all or a subset of the analyzed genomes of the corresponding lineage ( Figure S4 ) . Gene duplications frequently display the primary adaptive response after the acquisition of beneficial factors , because they occur at much higher frequency than other adaptive mutations [50] . This might have been the initial selective pressure for the independent amplification processes . However , in both lineages , the duplicated bep genes subsequently diversified by accumulating mutations as indicated by different branch lengths separating bep genes in Figure 4 . To analyze the sequence evolution during the parallel amplification and diversification processes , we used a branch test for positive selection . Since positive selection is not continuously acting during evolution , this analysis allows the detection of episodic adaptive evolution on single phylogenetic branches . We detected positive selection on many of the internal branches suggesting that subsequent to their duplication different Bep clades have undergone adaptive sequence evolution in both lineages ( Figure 4 , Figure S6 ) . For Bartonella , experimental studies showed that effector proteins exhibit distinct phenotypic properties on host cells indicating that the evolutionary diversification of the duplicated effectors was substantially driven by the acquisition of novel functions [18]–[21] . Not all branches exhibit dn/ds values >1 , though , suggesting episodic changes in the selection pressure acting on different effector gene copies . For example , functional redundancy of paralogous effector copies could have resulted in neutral drift , whereas conservation of an advantageous function might have led to purifying selection on certain branches . The basis for the functional versatility seems to lie in the adaptability of the domains encoded by bep genes . In case of the FIC domain , recently published work showed that this domain mediates a new post-translational modification by transferring an AMP moiety onto a target protein [25] . Proteins ‘AMPylated’ by FIC domains belong to the family of GTPases . The diversity of these targets and their numerous functions in cellular processes might allow the diversified Beps to target and subvert a variety of host cell functions by target-specific ‘AMPylation’ . The high degree of conservation of the FIC domain in different kingdoms of life provides further evidence for the remarkable versatility of this domain [51] . Interestingly , also the BID domain , constituting part of the translocation signal of the effector proteins , seems to be capable of adopting various functions in the host cell [17] . In case of BepA , it was shown that the BID domain is sufficient to mediate the anti-apoptotic property of this effector protein [18] . BID domains of other Beps with the same domain constitution as BepA do not exhibit this phenotype indicating specific adaptive modulation of this domain for BepA . This functional adaptability might also explain why certain effector genes carry more than one BID domain . At last , tandem-repeated tyrosine-phosphorylation motifs found in a subset of effector proteins confer another multifaceted molecular mechanism to modulate cellular processes . Phosphorylated effector proteins are thought to recruit cellular binding partners resulting in the formation of signaling scaffolds that interfere with specific host cell signaling pathways [21] . For several effector proteins of Bh ( lineage 4 ) , tyrosine phosphorylation by host cells has been reported and the targeted host interaction partners studied [17] , [21] . Beside Bartonella , a number of other pathogens , as E . coli ( EPEC ) , Helicobacter pylori , or Chlamydia trachomatis are using tyrosine-phosphorylation of effector proteins to modulate their hosts in very distinct ways demonstrating the versatility of this type of host subversion [21] . In Bartonella , the tyrosine-phosphorylated effector proteins seem to display an important functionality of the VirB-mediated host modulation as we found effector proteins of this type in both radiating lineages ( see below ) . Our analyses suggest that the domain structure of the ancestral effector gene consisted of an N-terminal FIC and a C-terminal BID domain ( FIC-BID ) . In both lineages , the FIC-BID domain structure displays the most abundant effector protein type . In lineage 3 , only effector genes of Bep clade 9 consist of domain architectures different than FIC-BID ( Figure S5 ) . The gene tree in Figure 4 shows that bep genes with the shortest evolutionary distance across the two lineages are the ones harboring the FIC-BID structure ( BepA clade and Bep clade 1 ) . bep genes with different domain architecture constitute more distantly related clades across the two lineages indicating that they derived by independent recombination from the ancestral domain structure . Furthermore , the distantly related Vbh T4SS of Bg and Bs encodes an effector protein consisting of the FIC-BID domain structure [28] . As mentioned above , it was shown for lineage 4 that some of the derived bep genes become phosphorylated by host cell kinases at conserved tandem-repeated tyrosine-phosphorylation motifs leading to the interference with specific host cell pathways [21] . Strikingly , we found that bep genes with derived domain architecture in lineage 3 also harbour regions with tandem-repeated tyrosine motifs ( Figure 5 , Figure S5 ) . In silico predictions of tyrosine-phosphorylation sites with three different programs [52]–[54] consistently revealed a high number of potentially phosphorylated motifs within these repetitive regions ( Figure 5 , Table S4 ) . We ectopically expressed these effector proteins in HEK293T cells and showed that they are indeed phosphorylated within eukaryotic cells by tyrosine kinases implicating their functional importance for host interaction ( Figure 5 ) . Interestingly , the motifs found in lineage 3 are clearly different from the ones present in lineage 4 and are also less conserved as depicted by their consensus sequences in Figure 5 . This suggests that the motifs in lineage 3 may generally be under weaker purifying selection than in lineage 4 , because they target either less conserved or even different pathways in their hosts . Further , the lower degree of conservation and the higher number of motifs per effector found in lineage 3 could also indicate that these proteins and particularly their motifs are under positive selection and evolved more recently than their equivalents in lineage 4 . Together with the fact that tandem-repeated phosphorylation motifs are only found in bep genes with derived domain architecture , our findings , thus , suggest parallel evolution of this class of effector proteins within the two radiating lineages . Whether similar pathways are targeted by these effectors in the two lineages remains unknown . Yet , the striking parallelism in the molecular evolution of this class of effector proteins indicates their central role in the VirB T4SS mediated host modulation by Bartonella . Emerging infectious diseases are frequently caused by zoonotic pathogens which are incidentally transmitted to humans from their reservoir niche ( e . g . other animal hosts ) . Therefore , the understanding of the mechanisms driving diversification of host-adapted bacteria in nature is of relevance for human health . In the present study , we explored the adaptive diversification of host-restricted bartonellae . Our genome-wide phylogeny revealed that two sister clades of this α-proteobacterial pathogen have evolved by parallel adaptive radiations ( lineage 3 and lineage 4 in Figure 1 ) . Both lineages comprise species adapted to same or similar reservoir hosts including zoonotic ( e . g . Bh and Br ) or human specific ( Bq ) pathogens . The more recent diversification of lineage 3 including the recently recognized incidental human pathogen Br [29] underlines the importance to study the molecular basis of such lineage diversifications . In line with the ‘ecological’ parallelism of their radiations , our comparative genomic analyses between lineage 3 and lineage 4 uncover striking evolutionary parallelisms at the molecular level of a likely key innovation - the VirB T4SS – essentially involved in the infection of the mammalian hosts . Chromosomal fixation of this horizontally transferred trait occurred by independent evolutionary events . In both lineages , the arsenal of effector proteins translocated via the VirB T4SS was shaped independently by gene duplications and positive selection of diversified gene copies . This amplification process mostly occurred before the onset of the radiations . Strikingly , beside the diversification of effector proteins encoding the evolutionary conserved ‘AMPylase’ domain ( FIC ) , both lineages have convergently evolved a novel effector class with derived domain structure and tandem-repeated tyrosine-phosphorylation motifs . By these evolutionary processes , large reservoirs of distinct biological functions were invented from a single ancestral effector gene . This functional versatility provides the framework for the adaptive potential of the VirB T4SS . Apparently , the plasticity of the underlying genomic loci seems to have favored the parallel occurrence of these adaptive processes in two distinct lineages , thereby essentially contributing to the parallel radiations of Bartonella .
Animals were handled in strict accordance with good animal practice as defined by the relevant European ( European standards of welfare for animals in research ) , national ( Information and guidelines for animal experiments and alternative methods , Federal Veterinary Office of Switzerland ) and/or local animal welfare bodies . Animal work was approved by the Veterinary Office of the Canton Basel City on June 2003 ( licence no . 1741 ) . Bc strain 73 [34] , B1-1C [31] , and Br strain ATCC BAA-1498 [29] were grown routinely for 3–5 days on tryptic soy agar containing 5% defibrinated sheep-blood in a water-saturated atmosphere with 5% CO2 at 35°C . BAR15 [30] and Bs strain R1 [55] were grown under the same conditions on heart infusion agar and Colombia base agar , respectively . Using the QIAGEN Genomic DNA Isolation kit ( Qiagen ) , DNA was isolated from bacteria grown from single colonies . For 454-sequencing , the DNA was prepared with an appropriate kit supplied by Roche Applied Science and sequenced on a Roche GS-FLX [56] . To assemble the reads , Newbler standard running parameters with ace file output were used . Newbler assemblies were considerably improved by linking overlapping contigs on the basis of the “_to” and “_from” information appended to the read name in the ace files . For the assemblies of Bc , BAR15 , B1-1C , Br , and Bs , we obtained a 454-sequence coverage of 35x , 37x , 39x , 39x , and 29x , respectively ( for details on 454-sequencing see Table S5 ) . Repeats were identified by analyzing the coverage of each Newbler contig . If the link between two contigs was ambiguous , PCR and long-range PCR were used to confirm contig joins . For the complete assembly of the Bc genome , a library of 35 kb inserts was generated using the CopyControl Fosmid Kit ( Epicentre ) . By end-sequencing of library clones with Sanger technology , 983 high-quality reads were obtained and mapped onto the 454-sequencing-based assembly . Remaining sequence gaps were closed by PCR . The final singular contig was fully covered by staggered fosmid clones indicating a correct assembly of the circular chromosome of Bc . Gene predictions of the genome of Bc and the draft genomes of Bs , Br , BAR15 , and B1-1 were performed using AMIGene software [57] . Automated functional gene annotation was conducted with the genome annotation system MaGe [58] . For orthologous genes , the annotation was adopted from the manually annotated genome of Bt [16] . Manual validation of the annotation was performed for the virB and bep genes . By using the “FusionFission” tool of MaGe [58] fragmented genes were identified and the corresponding sequences subsequently examined for 454-sequencing errors . After correcting these errors , the updated sequences were re-annotated as described above . The sequence data of the genome of Bc and the contigs of the draft genomes of Br , BAR15 , B1-1C , and Bs is stored on the web-based interface MaGe ( Bartonella2Scope , https://www . genoscope . cns . fr/agc/mage/bartonella2Scope ) and has been deposited in the EMBL Nucleotide Sequence Database under accession numbers FN645454–FN645524 . Phylogenetic trees were based on nucleotide sequence data . Alignments were generated on protein sequences with ClustalW [59] and back-translated into aligned DNA sequences using MEGA4 [60] . Tree topologies were calculated with maximum likelihood and Bayesian inference methods as implemented in the programs PAUP* [61] and MrBayes [62] , respectively . The genome-wide phylogeny of Bartonella was calculated on the basis of 478 orthologous genes of the ten sequenced Bartonella genomes and the genome of Brucella abortus ( bv . 1 str . 9-941 ) . Orthologs were determined by using the “PhyloProfile Synteny” tool of MaGe [58] with a threshold of 60% protein identity over at least 80% of the length of proteins being directional best hits of each other . The alignments of the 478 identified genes were concatenated resulting in a total of 515 , 751 aligned nucleotide sites . Tree topology and branch lengths were obtained by maximum likelihood analysis using the HKY85 model . Bootstrap support values were calculated for 100 replicates . For Bayesian inference , the program MrBayes [62] was run for one million iterations with standard parameters ( two runs with four heated Monte-Carlo Markov chains in parallel; number of substitutions = 6; burnin = 25% ) . For the Bartonella ingroup , single gene trees were calculated with maximum likelihood and tree topology congruency assessed with PAUP* . 471 of the 478 single gene trees revealed the same monophyletic clustering of the eight taxa into lineage 3 and lineage 4 as the genome-wide phylogeny . Further , we performed a recombination analysis for each of the 478 single gene alignments using the GARD algorithm as implemented in the HYPHY package [35] . The GARD analysis was run with the GTR model using a general discrete distribution with three rate classes . To identify statistical significant recombination breakpoints in our alignments , we used the Kishino-Hasegawa test as implemented in the GARDProcess . bf algorithm of the HYPHY package . To include non-sequenced Bartonella species in the genome-wide phylogeny , we used available sequence data from the gltA , groEL , ribC , and rpoB genes ( 7731 aligned sites ) . Trees were obtained as described above . MrBayes [62] was run for five million iterations . Branch lengths for tip branches of non-sequenced taxa are calculated on the basis of the four housekeeping genes . Branch lengths for tip branches of sequenced taxa and internal branches separating sequenced and non-sequenced taxa are based on the genomic data set . The maximum likelihood tree only based on the gltA , groEL , ribC , and rpoB genes was inferred as described for the genome-wide phylogeny . Bep gene trees were inferred from nucleotide alignments of either the most C-terminal BID domain including the C-terminus ( 948 sites ) , the FIC domain including the N-terminal extension ( 1 , 305 sites ) , or the entire bep sequence of genes harboring FIC domains ( 3 , 972 sites ) . To select an appropriate substitution model , the Akaike information criterion of Modeltest 3 . 7 [63] and MrModeltest 2 . 0 [64] was used for the maximum likelihood and Bayesian inference analysis , respectively . For the alignments based on the BID domain or the entire bep gene sequence , we obtained the GTR+G+I model with both programs . For the alignments based on the FIC domain , the TVM+I+G model ( Modeltest 3 . 7 ) and GTR+G+I model ( MrModeltest 2 . 0 ) were selected . Trees were inferred with the parameters provided by these models as described above . MrBayes [62] was run for one million iterations . The Neighbor-joining phylogeny of different Bartonella isolates in Figure S2 was inferred from a 242 nt segment of the gltA gene with the program MEGA4 [60] . Bootstrap values were calculated for 1 , 000 replicates . Based on the four available genomes , orthologous genes for each of the two lineages 3 and 4 were determined by using the “PhyloProfile Synteny” tool of MaGe [58] . The threshold was set to 30% protein identity over at least 60% of the length of proteins being directional best hits of each other . The same tool was used to detect genes without orthologs . By comparing these automatically identified orthologs and non-orthologs , genes present in neither of the two lists were detected and manually assigned to one of the two lists . Alignments were generated and a GARD recombination analysis conducted as described above . To obtain the average dn/ds value ( ω ) of each ortholog , the arithmetic mean of pair-wise dn/ds values ( calculated by the method of Yang and Nielsen implemented in PAML 4 . 1 [65] ) was used . Site tests of positive selection were performed with PAML 4 . 1 using the CodeML module [65] . To detect positive selection model M1a ( NearlyNeutral ) vs . model M2a ( PositiveSelection ) and model M7 ( beta ) vs . model M8 ( beta+ω ) were analyzed . PAUP* [61] was used to infer maximum likelihood trees for each set of orthologs . For the CodeML control file , standard parameters were used . The relative significance of model M2a ( PositiveSelection ) vs . model M1a ( NearlyNeutral ) and model M8 ( beta+ω ) vs . model M7 ( beta ) was assessed using likelihood-ratio-tests ( two degrees of freedom ) . Genes for which significant positive selection was detected were inspected for alignment errors potentially affecting the results of this analysis . If necessary , the alignments were manually modified and the CodeML analysis repeated . Phylogenetic branches were tested for positive selection by using the TestBranchDNDS . bf module implemented as standard analysis tool in HyPhy [66] . Ten weeks old female WISTAR rats obtained from RCC-Füllinsdorf were housed in an BSL2-animal facility for two weeks prior to infection allowing acclimatization . For inoculation , bacterial strains were grown as described above , harvested in phosphate-buffered saline ( PBS ) , and diluted to OD595 = 1 . Rats were anesthetized with a 2–3% Isuflurane/O2 mixture and infected with 10 µl of the bacterial suspension in the dermis of the right ear . Blood samples were taken at the tail vein and immediately mixed with PBS containing 3 . 8% sodium-citrate to avoid coagulation . After freezing to −70°C and subsequent thawing , undiluted and diluted blood samples were plated on tryptic soy agar and heart infusion agar containing 5% defibrinated sheep-blood . CFUs were counted after 8–12 days of growth . Nucleotide distances were calculated with the program MEGA4 [60] for the alignments based on the genome-wide dataset and the four housekeeping genes . The numbers of base substitutions per site from averaging over all sequence pairs within and between groups were calculated . Codon positions included were 1st , 2nd , and 3rd . All positions containing gaps and missing data were eliminated from the dataset ( Complete deletion option ) . To construct the plasmids pPE2002 and pPE2004 , bep genes BARCL_1034 ( Bc ) and BARRO_80017 ( Br ) were amplified from genomic DNA with primer pairs containing flanking BamHI/NotI sites: prPE453 ( ATAAGAATGCGGCCGCGATGAAAAC-CCATAACACTCCTG ) /prPE454 ( CGGG-ATCCTTAATGTGTTATAACCATCGTTC ) and prPE455 ( ATAAGAATGCGGCCGCG-ATGAATTTTGGAGAAAAGAAAAAAATG ) /prPE456 ( CGGGATCCTTAAATAGC-TACAGCTAACGATTTTTTC ) , respectively . PCR products were digested with the enzymes BamHI and NotI and ligated into the BamHI/NotI sites of the backbone of plasmid pAP013 ( kindly provided by Arto Pulliainen ) . The resulting constructs pPE2001 ( BARCL_1034 ) and pPE2003 ( BARRO_80017 ) were cut with NotI and ligated with a GFP fragment obtained from NotI digested pAP013 . The plasmid pPE2007 was constructed by cutting bepE of B . henselae from plasmid pRO1100 ( kindly provided by Rusudan Okujava ) with NotI and BamHI and ligating it into pAP013 . All plasmid DNA isolations and PCR purifications were performed with Macherey-Nagel and Promega columns according to manufacturer's instructions . The protocol for growth and transfection of HEK293T was performed as described previously [18] . 36 h after transfection , cells were incubated for 10 minutes with 10 ml Pervanadate medium ( 5 ml PBS containing 100 mM orthovanadate and 200 mM H2O2 , incubated for 10 min with 500 µl Catalase [2 mg/ml in PBS] before 45 ml M199 medium were added ) . After washing three times with 7 ml of PBS at room temperature , cells were scraped off and resuspended in 1 ml of ice-cold PBS containing 1 mM EDTA , 0 . 5 mM phenylmethylsulfonyl fluoride ( PMSF ) , 1 mM orthovanadate , 1 mM leupeptin , and 1 mM pepstatin and collected by centrifugation ( 3 , 000g at 4°C for 60 sec ) . The resulting pellet was lysed in 300 µl of ice cold modified RIPA buffer ( 50 mM Tris-HCl [pH 7 . 4] , 75 mM NaCl , 1 mM EDTA , 1 mM orthovanadate , 1 mM leupeptin , 1 mM pepstatin ) for 1 hour at 4°C . The lysate was centrifuged ( 16 , 000g at 4°C for 15 min ) and 12 µl of anti-HA-agarose ( Sigma ) added to the supernatant . After 150 min of incubation at 4°C on a slowly turning rotation shaker , the agarose was washed three times with 300 µl of modified RIPA buffer ( 3 , 000g for 10 sec ) . The affinity-gel pellet was then resuspended in 20 µl of modified RIPA buffer , 20 µl of SDS-sample buffer ( 2× ) were added , and the sample was heated for 5 min at 95°C . Proteins were separated on a 10% SDS-polyacrylamide gel , blotted on a nitrocellulose membrane ( Hybond-C Extra , Amersham Pharmacia ) , and examined for tyrosine phosphorylation by using monoclonal antibody 4G10 ( Millipore ) and anti-mouse IgG-horseradish peroxidase ( HRP ) afterwards . The HRP-conjugated antibody was visualized by enhanced chemiluminescence ( PerkinElmer ) . For visualization of the signal from GFP-fusion proteins , the membrane was subsequently incubated in 4% PBS-Tween containing 0 . 02% NaN3 and anti-GFP antibody ( Invitrogen ) , followed by incubation with anti-mouse IgG-HRP and visualization by enhanced chemiluminescence . | Adaptive radiation is the rapid origination of an array of species by the divergent colonization of disparate ecological niches . In the case of pathogenic bacteria , radiations can lead to the emergence of novel human pathogens . Being divergently adapted to a range of different mammalian hosts , including humans as reservoir or incidental hosts , the genus Bartonella represents a suitable model to study genomic mechanisms underpinning divergent adaptation of pathogens . Here we show that two distinct lineages of Bartonella have radiated in parallel , resulting in two arrays of evolutionary distinct species adapted to overlapping sets of mammalian hosts . Such parallelisms display excellent models to reveal insights into the genetic mechanisms underlying these independent evolutionary processes . Our genome-wide analysis identifies a striking evolutionary parallelism in a horizontally-acquired protein secretion system in the two lineages . The parallel evolutionary trajectory of this system in the two lineages is characterized by the convergent origination of a wide array of adaptive functions dedicated to the cellular interaction within the mammalian hosts . The parallel evolution of the two radiating lineages on the ecological as well as on the molecular level suggests that the horizontal acquisition and the functional diversification of the secretion system display an evolutionary key innovation underlying adaptive evolution . |
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The influence of genetic ancestry on Trypanosoma cruzi infection and Chagas disease outcomes is unknown . We used 370 , 539 Single Nucleotide Polymorphisms ( SNPs ) to examine the association between individual proportions of African , European and Native American genomic ancestry with T . cruzi infection and related outcomes in 1 , 341 participants ( aged ≥ 60 years ) of the Bambui ( Brazil ) population-based cohort study of aging . Potential confounding variables included sociodemographic characteristics and an array of health measures . The prevalence of T . cruzi infection was 37 . 5% and 56 . 3% of those infected had a major ECG abnormality . Baseline T . cruzi infection was correlated with higher levels of African and Native American ancestry , which in turn were strongly associated with poor socioeconomic circumstances . Cardiomyopathy in infected persons was not significantly associated with African or Native American ancestry levels . Infected persons with a major ECG abnormality were at increased risk of 15-year mortality relative to their counterparts with no such abnormalities ( adjusted hazard ratio = 1 . 80; 95% 1 . 41 , 2 . 32 ) . African and Native American ancestry levels had no significant effect modifying this association . Our findings indicate that African and Native American ancestry have no influence on the presence of major ECG abnormalities and had no influence on the ability of an ECG abnormality to predict mortality in older people infected with T . cruzi . In contrast , our results revealed a strong and independent association between prevalent T . cruzi infection and higher levels of African and Native American ancestry . Whether this association is a consequence of genetic background or differential exposure to infection remains to be determined .
Chagas disease ( ChD ) , which is caused by the protozoan Trypanosoma cruzi , affects approximately 5 . 7 million people in 21 Latin American countries [1] . ChD is known as a neglected tropical disease and is an emerging issue in North America and Europe [2–5] . ChD is autochthonous in South and Central America but T . cruzi infection has spread to other regions of the world primarily due to immigration of infected persons [2] , although there has been evidence of some locally-occurring infections in the United States [3] . Currently , at least 300 , 000 persons with T . cruzi infection live in the US [4] and at least 80 , 000 in Europe [5] . The disease is costly to individuals and society with estimates of over USD 100 million spent on treatments and over USD 800 million in lost productivity each year [6] . Up to one third of those infected with ChD may develop chronic heart abnormalities and other complications of which Chagas cardiomyopathy is the most severe and life-threatening form [7] . The presence of major electrocardiogram ( ECG ) abnormalities ( a diagnostic measure of Chagas cardiomyopathy ) doubles the risk for mortality in T . cruzi-infected elderly populations [8] . The influence of African and/or Native American ancestry on T . cruzi infection and/or ChD outcomes is unknown . The existence of an association is plausible for at least two reasons: first , familial aggregation of T . cruzi seropositivity and ECG abnormalities have been found in highly endemic areas , suggesting that genetic variation may play a role in susceptibility to infection as well as disease progression [9 , 10]; second , an earlier publication , using ethnoracial self-classification , reported greater prevalence of ECG abnormalities among Black middle-aged adults relative to their White counterparts [11] . Latin America is one of the most ethnoracially heterogeneous regions of the world [12] , and Brazil is the largest and the most populous ChD endemic country in the region . The current Brazilian population’s genetic makeup is the product of admixture between Amerindians , Europeans colonizers or immigrants , and African slaves [13] . Brazil received nearly 4 million slaves from Africa , about seven times more than the United States [14] . Thus , the Brazilian population provides an opportunity to assess the relationship between T . cruzi infection and its complications with genetic ancestry in admixed populations . The Bambui-Epigen Cohort Study of Aging is conducted in a well-defined population of older Brazilian adults living in a formerly ChD endemic area [15] . We examined for the first time the association between genome-wide proportions of genomic ancestry with T . cruzi infection and cardiomyopathy , taking into account an array of socioeconomic and health indicators that could confound such an association . Additionally , we examined whether genomic ancestry affects the prognostic value of major ECG abnormalities for 15-year mortality in T . cruzi-infected individuals .
The Bambui cohort study of aging is ongoing in Bambuí , a city of approximately 15 , 000 inhabitants in the state of Minas Gerais in Southeast Brazil , which is one of the oldest known endemic areas for ChD [16–18] . Detailed information on this cohort can be found elsewhere [15] . Briefly , the population eligible for the cohort consisted of all residents aged 60 years and over on 1 January 1997 ( 92% of the 1 , 742 inhabitants in this age group participated ) . Most participants had some degree of admixture between African , European and Native American genomic ancestry [19 , 20] . T . cruzi infection status was assessed by means of three different assays performed concurrently: a hemagglutination assay ( Biolab Merieux SA , Rio de Janeiro , Brazil ) and two enzyme-linked immunosorbent assays ( Abbott Laboratories , Inc . , North Chicago , Illinois; and Wiener Laboratories , Rosario , Argentina ) . Infection with T . cruzi was defined by seropositivity in all of the three examinations; seventeen persons had discordant results among the assays and were excluded from the analysis . As far as we could determine , none of the cohort participants had a history of use of antitrypanosomal medications , and none of the seropositive subjects reported such treatment over the ensuing decade during annual follow-up visits . Thus , the use of antitrypanosomal therapy was not considered in the present analysis . In addition , no cohort participant had received a cardiac transplant . At the baseline examination , a digitally recorded 12-lead ECG ( Hewlett Packard MI700A ) reading was obtained at rest . ECGs were analyzed at the ECG Reading Center ( EPICARE , Wake Forest University ) and classified using the Minnesota Code ( MC ) criteria [21 , 22] . Major ECG abnormalities were defined by the presence of at least one of the following: old ( MC 1 . 1 . x or 1 . 2 . x ) or possible myocardial infarction ( 1 . 3 . x and 4 . 1 . x , 4 . 2 , 5 . 1 , or 5 . 2 ) , complete intraventricular blocks ( MC 7 . 1 , 7 . 2 , 7 . 4 , or 7 . 8 ) , frequent supraventricular or ventricular premature beats ( MC 8 . 1 . x , except 8 . 1 . 4 ) , major isolated ST segment or T-wave abnormalities ( MC 4 . 1 . x , 4 . 2 , 5 . 1 or 5 . 2 ) , atrial fibrillation or flutter or supraventricular tachycardia ( MC 8 . 3 . x . or 8 . 4 . 2 ) , other major arrhythmias ( MC 8 . 2 . x , except 8 . 2 . 1 ) , major atrioventricular conduction abnormalities or pacemaker use ( MC 6 . 1 , 6 . 2 . x , 6 . 4 , 6 . 8 , 8 . 6 . 1 or 8 . 6 . 2 ) , major QTi prolongation ( >115% ) and left ventricular hypertrophy ( LVH ) ( MC 3 . 1 together with [4 . 1 . x , 4 . 2 , 5 . 1 , or 5 . 2] ) . Further details can be seen elsewhere [8] . Cohort participants were genotyped with the Omni 2 . 5M array ( Illumina , San Diego , California ) [13] . We performed ancestry inferences using the model-based method [23] , implemented in the Admixture software . First , we used 370 , 539 SNPs to estimate for each individual African , European and Native American tri-hybrid ancestry proportions , using 266 African , 262 European and 93 Native American individuals from public datasets as parental populations [13] . Further , we inferred a kinship coefficient for each pair of individuals , using the software Reap [24] , conditioning on tri-hybrid individual admixture proportions . We used complex networks to identify families from the matrix of pair-wise kinship coefficients [13] . In this approach , pairs of individuals ( i . e . families ) are related if they have a kinship coefficient >0 . 1 ( first and second-degree relatives ) . Given that Brazilians with African ancestry generally have a high proportion of East African genetic markers ( as opposed to markers of West African origin ) , relative to African Americans and those from the Caribbean [13 , 25 , 26] , we used 331 , 790 SNPs and the reference dataset “U” [13] to further divide total African ancestry into its two components: a Western-African/non Bantu and an Eastern African/Bantu , hereafter called Western African and Eastern African , respectively . The fact that many Bambuí residents are related could affect high-resolution inferences of biogeographic ancestry ( such as West- and East-African ) with the Admixture software . To overcome this limitation , we performed separate Admixture runs to infer West- and East- African ancestry components , avoiding the presence of related individuals in the same run . Further details on how genetic and ancestry analyses of the Bambui cohort population were performed can be found elsewhere [13 , 27] . Deaths that occurred between study enrollment in 1997 and December 31 , 2011 , were included in the present analysis . Deaths were reported by next of kin during the annual follow-up interview and verified through the Brazilian mortality information system . Death certificates were obtained for 95 . 7% of the participants who died . Deaths from any cause were considered in this analysis . Potential confounding variables included baseline sociodemographic characteristics ( age , sex , schooling , household income and father’s occupation ) and health measures ( current smoking , hypertension , diabetes , coronary heart disease , C-reactive protein and non-HDL cholesterol level ) . We categorized schooling into incomplete primary school ( <4 years ) and complete primary and higher ( 4 years and more ) . We categorized monthly household income per capita into equal or superior to the median value ( median = 1 . 5 Brazilian minimum wages or USD 180 in 1997 ) . Occupation of the study participant’s father ( as informed by cohort members ) was categorized into urban workers , landowners , manual rural workers and unknown . Current smokers were persons who had smoked at least 100 cigarettes during their lifetime and who still smoke . Body mass index ( BMI ) was defined as weight ( in kg ) divided by height ( in meters ) squared . Hypertension was defined by mean ( two out of three measures ) systolic blood pressure of ≥140 mmHg and/or diastolic pressure of ≥90 mmHg and/or treatment [28] . Diabetes mellitus was defined by fasting blood glucose ≥126mg/dL and/or treatment [29] . Coronary heart disease was defined by prior medical diagnosis of myocardial infarction and/or symptoms of angina pectoris [30] . High sensitivity C-Reactive Protein was measured by the CRP immunonephelometric method ( BNII , Dade Behring , Marburg , Germany ) . Blood fasting glucose and cholesterol were determined by using standard enzymatic methods ( Merck , Darmstadt , Germany ) . Non-HDL cholesterol was defined by total cholesterol level minus HDL cholesterol . Unadjusted analyses were based on Pearson´s chi square , oneway ANOVA and Kruskall Wallis tests to examine differences across frequencies , means and medians , respectively . Individual proportions of genomic ancestries were expressed as medians or divided into quintiles . Prevalence ratios ( PR ) estimated by multivariable Poisson regression [31] were computed to examine associations between ( i ) genomic ancestry in quintiles and T . cruzi infection and ( ii ) genome ancestry in quintiles and major ECG abnormality among persons infected with T . cruzi . Further , we used Cox proportional hazard models to implement an analysis restricted to persons infected with T . cruzi to assess the influence of each category of genomic ancestry on the risk of major ECG abnormalities and subsequent mortality . The above-mentioned statistical analyses were based on two models . First , prevalence and hazard ratios were adjusted for age ( continuous ) , sex , smoking , hypertension , diabetes , coronary heart disease ( all dichotomous variables ) plus body mass index , log-transformed C-reactive protein and non-HDL cholesterol ( as continuous measures ) . We then added schooling , monthly household income per capita , and father’s occupation to the previous models . Because 913 participants were first- or second-degree relatives , and excluding them would lead to loss of power and possible selection bias , we kept all related individuals in our analyses and used robust variance estimators in multivariate models to correct results for clustering by family structure . Finally , we examined separately the significance of the effect of multiplicative interactions between sex and genomic ancestry on each outcome by means of cross-product terms in Poisson and Cox proportional hazards regression models , respectively . Since there was no evidence of interaction with sex , the analyses were carried out for both men and women with sex included as a covariate . Separate analyses were performed for African , Native American and European genomic ancestries and further for Western African sub continental ancestry . Statistical analyses were conducted using STATA 13 . 0 statistical software ( Stata Corporation , College Station ) . The Bambui cohort study of aging was approved by the Institutional Review Board of the Oswaldo Cruz Foundation , Rio de Janeiro , Brazil . Genotyping was approved by Brazil’s national research ethics committee , as part of the Epigen-Brazil protocol ( CONEP , resolution 15895 ) . Written informed consent was obtained from all participants at baseline and at all follow-up interviews .
Of the 1 , 606 baseline cohort participants , 1 , 343 had complete information for all study variables and were included in the current analysis . As shown in Table 1 , the prevalence of T . cruzi infection was 37 . 6% ( n = 505 ) . At baseline , the mean age of participants was 68 . 8 years , 61 . 2% were women , and low schooling level ( <4 years ) largely predominated ( 64 . 1% ) . The median proportions of African , Native American and European genomic ancestries were 9 . 6% , 5 . 4% and 83 . 8% , respectively . The median proportion of Western African sub-continental ancestry relative to total African ancestry was 63 . 9% ( complementarily , the corresponding value for Eastern African ancestry was 36 . 1% ) . T . cruzi infected participants had significantly higher median individual proportions of African and Native American ancestries and significantly lower median European genomic ancestry . Other baseline characteristics of the study participants , by T . cruzi infection status , are presented in Table 1 . Table 2 presents median individual proportions of African , Native American and European genomic ancestries by baseline characteristics . Median African and Native American genomic ancestries were significantly higher ( and European ancestry was significantly lower ) among those with lower schooling and income levels , those whose fathers were manual workers or had an unknown occupation , as well as those with any major ECG abnormality or previous coronary heart disease . Median African ancestry was lower in those aged 69 years and over and in those with BMI under 25 kg/m2 . No significant associations with genomic ancestry were found for other study variables . Associations between the different genomic ancestries and T . cruzi infection are shown in Table 3 . There was a graded positive univariate association between T . cruzi infection with the proportion of African and Native American ancestry , and a graded negative relationship with a greater proportion of European ancestry ( p<0 . 001 for all ) . After adjustments for age , sex and health measures , persons at the intermediate and highest quintiles of African and Native American ancestry were significantly more likely to be infected with T . cruzi relative to their counterparts in the lowest quintiles . After further adjustments for socioeconomic indicators ( schooling , income and father’s occupation ) these associations were attenuated , but remained largely significant ( PR = 1 . 38; 95% CI 1 . 07 , 1 . 79 and PR = 1 . 74; 95% CI 1 . 37 , 2 . 35 for those at the intermediate and highest quintiles of African ancestry , respectively , and PR = 1 . 54; 95% CI 1 . 19 , 1 . 99 for those at the highest quintile of Native American ancestry ) . The opposite trend was found for European ancestry ( PR = 0 . 73; 95% CI 0 . 63 , 0 . 85 and PR = 0 . 54; 95%CI 0 . 41 , 0 . 70 , respectively ) . Among those infected , 56 . 4% had at least one major ECG abnormality ( 31 . 7% among the non-infected ) . As shown in Table 4 , in the bivariate analysis , a major ECG abnormality among infected persons was not found to be significantly ( p>0 . 05 ) associated with African , Native American or European ancestry levels . This absence of association remained in analyses adjusted for age , sex and health measures , as well as in analyses further adjusted for socioeconomic indicators . Over a 15 year follow-up period , 683 participants died and 109 ( 8 . 1% ) were lost to follow-up , leading to 14 , 680 person-years ( pyrs ) of observations ( 5 , 251 pyrs among the infected ) . The death rate was 46 . 4 per 1 , 000 pyrs ( 56 . 2 and 40 . 9 per 1 , 000 pyrs among T . cruzi infected and non-infected , respectively ) . As shown in Table 5 , persons infected with T . cruzi with any major ECG abnormality were at significantly increased risk of death , compared to their counterparts with no such abnormalities , independent of age , sex and other health measures ( HR = 1 . 83; 95% CI 1 . 44 , 2 . 34 ) . Further adjustments for socioeconomic indicators had little impact on this association ( HR = 1 . 78; 95% CI 1 . 39 , 2 . 28 ) . The association was consistent across different levels of African , Native American and European genomic ancestries . We found no evidence of statistically significant multiplicative interactions between African , Native American and European genome ancestry levels and major ECG abnormalities on mortality ( p>0 . 05 for all ) . As shown in Table 6 , a statistically significant association between Western African proportion and T . cruzi infection was found in bivariate analysis , but the association lost significance after adjustments for socio demographic characteristics and health measures . Furthermore , we did not find any evidence of an association between the above mentioned ancestry levels and the presence of major ECG abnormalities among people infected with T . cruzi in either univariate or multivariate analyses ( p>0 . 05 for both ) . Finally , as previous observed for global African ancestry , levels of Western African ancestry did not modify the association between a major ECG abnormality and subsequent mortality among infected subjects ( p value for interactions >0 . 05 ) .
The key findings of the current study are: first , T . cruzi infection was strongly correlated with both African and Native American ancestry—and conversely showed a negative correlation with European ancestry—and this association had a graded effect; second , cardiomyopathy in infected persons was not associated with either African or Native American or European ancestry levels; third , genomic ancestry had no significant effect modification on the prognostic value of major ECG abnormalities for mortality in T . cruzi infected older adults; fourth , Western African sub continental origin was not associated with either T . cruzi infection or related outcomes . The above-mentioned findings were independent of an array of sociodemographic and biological confounders . The association between T . cruzi infection and higher levels of African and Native American ancestry may result from genetic influence on susceptibility and/or greater exposure to infection in these groups during the life course . Our study population was born before 1940 , and this cohort has experienced dramatic political and social changes during their lifetimes . Brazil has transitioned from a low-income , primarily rural country in the mid-1950s , to one of the largest economies in the world , with 84% of the population living in urban areas by 2010 [32 , 33] . Chagas disease is related to poor socio-economic circumstances , mostly in early life . In endemic areas , the main source of infection is a bloodsucking triatomine insect that colonizes poor households . Most individuals in these areas acquire the infection before they reach 20 years of age [34] . Further , ethnoracial disparities in Brazil are remarkable . Persons of African origin are more likely to have lower income and education , to experience race-based discrimination , and to report worse health outcomes [14 , 35] . Native Americans experience sustained marginalization [36] . Our results are in agreement with these observations , revealing higher levels of African and Native American ancestry in those with lower schooling and family income levels , as well as those whose fathers were rural workers or had an unknown occupation ( which suggests a less prestigious occupational category ) . T . cruzi infection followed this trend , with higher prevalence associated with worse current ( measured by income ) and worse early socioeconomic circumstances ( educational attainment and father’s occupation ) . However , the association between higher levels of African and Native American ancestry with T . cruzi infection was attenuated , but still remained largely significant after adjustments for socioeconomic indicators , suggesting a possible independent effect of genomic ancestry . Despite this finding , it is important to emphasize that although we control for several important measures of current and early socioeconomic circumstances , they cannot completely account for the complexity of unfavorable trajectories of persons with higher levels of African and Native American ancestry in Brazilian society [14] . Thus , we cannot exclude the possibility that residual confounding may still account for the association between higher levels of African and Native American ancestry and prevalent T . cruzi infection in our analysis . The fact that analyses of subsequent complications ( cardiomyopathy ) showed no association with genomic ancestry further tempers any inference regarding a causal relationship between genetic ancestry and increased vulnerability to T . cruzi . Chronic Chagas cardiomyopathy is the most clinically relevant manifestation of the disease . It manifests as heart failure , arrhythmia , heart block , thromboembolism , stroke and sudden death [7 , 16] . The pathogenesis of chronic chagasic cardiomyopathy is not completely understood [37] , but inflammation caused by persistent parasitism of the heart tissue appears to play an important role [38 , 39] . Additionally , a recent genome-wide study ( GWAS ) identified suggestive single nucleotide polymorphisms ( SNPs ) that may impact the risk of progression to cardiomyopathy in seropositive persons [37] . Electrocardiography has been considered an important tool in the management of ChD patients [7] . Information on ECG findings among the elderly infected with ChD is scant , and very few studies in middle-aged or older adults have used core-lab readings using classifications developed by the internationally accepted Minnesota Code [8] . A previous study in the Bambui cohort showed that any major ECG abnormality ( classified by the Minnesota Code ) was strongly and independently associated with increased risk for 10-year mortality among T . cruzi infected older adults [8] . The results of the current analysis , based on an extended 15 year-follow-up , are in agreement with these findings . Additionally , we found no evidence of an association between African and Native American ancestries and major ECG abnormalities among T . cruzi infected persons . The absence of an association was consistent in bivariate analyses as well as those adjusted for an array of potential confounding factors . Furthermore , African , Native American and European ancestry showed no significant interactions affecting the ability of major ECG abnormalities to predict subsequent mortality . Strengths of this study include the large population-based cohort followed for an extended period , and minimal loss of participants to follow-up . Another major strength is the use of genome-wide measures of ancestry . Genomic ancestry does not change over time , while ethnoracial self-classification is prone to misclassification—particularly in admixed populations [14 , 19] . Another strength is the inclusion of several biological and non-biological risk factors in our analysis . However , one cannot exclude the possibility that there may be additional unmeasured factors , including unknown genetic factors that confound our results . The current study is , to our knowledge , the first investigation on the influence of African , Native American and European genomic ancestry on T . cruzi infection and related outcomes . Our findings indicate that African and Native American ancestry have no influence on the presence of major ECG abnormalities and had no influence on the ability of an ECG abnormality in predicting mortality in older people infected with T . cruzi . In contrast , our results revealed a strong positive association between prevalent T . cruzi infection with higher levels of African and Native American ancestry . Whether this association is a consequence of genetic background , differential exposure to infection , or a combination of both factors , remains to be determined . | Chagas disease ( ChD ) , which is caused by the protozoan Trypanosoma cruzi , affects approximately 8 million people worldwide . ChD is known as a neglected tropical disease . The disease is endemic in South and Central American countries , and is an emerging issue in North America and Europe . This study examined , for the first time , the association between genomic ancestry and T . cruzi infection , Chagasic cardiomyopathy and its ability to predict long term mortality . Our results show that persons with higher levels of African and Native American ancestries ( and the reverse for European ancestry ) are more likely to be infected with T . cruzi . However , genomic ancestry had no effect on either Chagasic cardiomyopathy or on its ability to predict mortality . Whether the association between T . cruzi infection and genomic ancestry is a consequence of genetic susceptibility or differential exposure to infection due to poor socioeconomic circumstances over the life course , remains to be determined . |
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Hypoxia-induced cell injury has been related to multiple pathological conditions . In order to render hypoxia-sensitive cells and tissues resistant to low O2 environment , in this current study , we used Drosophila melanogaster as a model to dissect the mechanisms underlying hypoxia-tolerance . A D . melanogaster strain that lives perpetually in an extremely low-oxygen environment ( 4% O2 , an oxygen level that is equivalent to that over about 4 , 000 m above Mt . Everest ) was generated through laboratory selection pressure using a continuing reduction of O2 over many generations . This phenotype is genetically stable since selected flies , after several generations in room air , survive at this low O2 level . Gene expression profiling showed striking differences between tolerant and naïve flies , in larvae and adults , both quantitatively and qualitatively . Up-regulated genes in the tolerant flies included signal transduction pathways ( e . g . , Notch and Toll/Imd pathways ) , but metabolic genes were remarkably down-regulated in the larvae . Furthermore , a different allelic frequency and enzymatic activity of the triose phosphate isomerase ( TPI ) was present in the tolerant versus naïve flies . The transcriptional suppressor , hairy , was up-regulated in the microarrays and its binding elements were present in the regulatory region of the specifically down-regulated metabolic genes but not others , and mutations in hairy significantly reduced hypoxia tolerance . We conclude that , the hypoxia-selected flies: ( a ) altered their gene expression and genetic code , and ( b ) coordinated their metabolic suppression , especially during development , with hairy acting as a metabolic switch , thus playing a crucial role in hypoxia-tolerance .
Mammalian tissues experience a reduction in oxygen delivery at high altitude or during certain disease states , such as myocardial infarction and stroke . In order to survive , cells , tissues and organisms have developed various strategies to adapt to such O2 limited condition . There are indeed major differences between different organisms and cells in their ability to survive reduced environmental O2 . For example , turtle neurons are very tolerant to low oxygen and can survive without O2 for hours and days [1] , [2] . In contrast , mammalian neurons are very sensitive to reduced oxygen and cannot survive for even minutes under similar conditions . However , the mechanisms underlying survival in such extreme hypoxic conditions are not clear at present , in spite of the fact that there have been a number of interesting observations in this regard in the past few decades . For instance , it has been demonstrated that a number of hypoxia-tolerant animals ( e . g . Pseudemys scripta and Crucian Carp ) reduce their O2 consumption during hypoxia in such a way to minimize the mismatch between O2 supply and demand [3]–[5] . Similar phenomena was also observed in Drosophila melanogaster [6] , [7] and in newborn mammals [8] , [9] . Many questions , however , remain unsolved . For instance , we do not have an adequate understanding of the mechanisms that are responsible for reducing metabolic rate during low O2 conditions; and similarly , the mechanisms that are responsible for coordinating the suppression of these metabolic processes are still largely unknown . In the early 1990s , we discovered that the fruit fly , Drosophila melanogaster , is tolerant to acute anoxia ( zero mmHg O2 ) . Flies can sustain such environment for a few hours without any evidence of injury [6] . Since a ) Drosophila has been demonstrated to be a powerful genetic model for human diseases [10]–[14] and b ) many biochemical and genetic pathways are highly conserved between Drosophila and mammals , we used Drosophila in the current study to explore the mechanisms underlying tolerance to long-term hypoxia . We first generated a Drosophila melanogaster strain that can live perpetually ( i . e . , from generation to generation ) in severe , normally lethal , hypoxic conditions . To better understand the mechanisms underlying this remarkable hypoxia tolerance , we used cDNA microarrays containing 13 , 061 predicted or known genes ( ∼90% genes in the genome ) to examine the differences in gene expression profiles between the hypoxia-selected ( AF ) and naïve control ( NF ) flies [15]–[17] . We performed these studies in both larvae and adults to determine gene expression as a function of development . Furthermore , we used a combination of bioinformatic , molecular and genetic strategies to investigate the role of specific genes in hypoxia tolerance .
Twenty-seven wild-type isogenic lines constituted the parental population that we used for long-term experimental selection of a Drosophila melanogaster strain . At baseline , there was significant variability in hypoxia tolerance among these 27 lines , as determined by eclosion rate under 5% O2 and recovery time from anoxic stupor [17] . In order to determine the level of O2 at which to initiate the long-term selection experiment , we performed a pilot study by culturing F1 embryos of the parental flies under different levels of hypoxia ( e . g . , 8% , 6% or 4% O2 ) ( Figure 1A ) . We found that their survival rate was reduced at 6% O2 , and no adult flies were actually obtained at 4% O2 . At 8% O2 , however , the majority of embryos ( >80% ) completed their development and reached the adult stage . Therefore , hypoxia selection was initiated at 8% O2 , an O2 level in which flies can develop throughout their life cycle . The O2 concentration was then gradually decreased by ∼1% every 3 to 5 generations to maintain the selection pressure ( except for the transition between 5% and 4% which necessitated a much longer time ) . By the 13th generation , flies were able to complete development and perpetually live in 5% O2; and by the 32nd generation , the AF flies could even live perpetually under a severer level ( 4% of O2 ) , a lethal condition for NF . We hypothesized that this is due to , at least partially , newly occurring mutations or recombination and selection of favorable alleles in the AF population . To test this hypothesis , a subset of embryos obtained from selected flies were collected and cultured under normoxia for several consecutive generations . After 8 generations in normoxia , AF were re-introduced into the lethal hypoxic environment ( 4% O2 ) , and again , the majority ( >80% ) of the flies completed their development and could be maintained in this extreme condition perpetually . This result strongly suggested that the hypoxia-tolerance in the selected flies is a heritable trait . Several remarkable phenotypic changes were observed in the hypoxia-selected flies ( AF ) . As described previously [17] , the AF flies have a shortened recovery time from anoxia-induced stupor [6] , consume more oxygen in hypoxia , and show a significant reduction in body weight and size . We also demonstrated that this reduction in size is due to the reduction in both cell number and cell size [17] . In the hypoxia chambers , at 5% O2 levels , adult flies had significantly decreased body weight: the decrease in male body weight was about 25% and was further reduced to about 40% under 4% O2 ( Figure 1C and 1D ) . Interestingly , the reduction in body weight and size were reversed to normal when the AF embryos were grown under normoxia . Life span was also studied in our selected and naïve flies . Hypoxia-selection did not affect lifespan ( Figure 1B ) . Global gene expression profiles were examined in hypoxia-selected Drosophila melanogaster in the 3rd instar larval stage and in adults using cDNA microarrays that contained 13 , 061 known or predicted genes of the Drosophila melanogaster genome [16] , [18] . After analyzing the data sets with a significant cutoff of >1 . 5 fold difference and a false discovery rate ( FDR ) of <0 . 05 [19] , 2749 genes ( 1534 up- and 1215 down-regulated ) were significantly altered in the larval stage , but only 138 genes ( ∼20 times less than those in larvae ) met this criteria ( 95 up- and 43 down-regulated ) in the adult . The complete list of differentially regulated genes is detailed in Tables S1 and S2 . Among them , 51 genes were found to be altered in both larval and adult stages with 23 up-regulated and 7 down-regulated genes ( Figure 2A ) . Interestingly , most of the commonly up-regulated genes encode proteins that are related to immunity , and the majority of the commonly down-regulated genes encode proteins that are related to metabolism . The significantly altered genes were further analyzed , based upon the Gene Ontological categorizations ( GO ) [20] , by GenMAPP and MAPPFinder [20] , [21] . Less than half of the differentially expressed genes ( ∼40% ) encode for proteins whose functions have not been characterized; the remaining encode proteins that are involved in numerous biological functions , such as development , metabolism , defense mechanisms and signal transduction ( Tables S3 and S4 ) . More than 30 biological processes were found to be altered in both larvae and adult and these were mostly related to either defense ( especially immune responses , p<0 . 05 ) or metabolism ( especially carbohydrate and peptide metabolism , p<0 . 05 ) ( Figure 2B ) . The most affected processes in larvae were related to metabolism ( 1044 genes , especially carbohydrate metabolism , 135 genes , p<0 . 01 ) . In addition , multiple components of signal transduction pathways were identified to be significantly altered in larvae and these included EGF , insulin , Notch , and Toll/Imd signaling pathways ( Figure 3 , p<0 . 05 ) . To confirm the changes obtained from microarrays , 10 differentially expressed genes were randomly chosen and their expression levels were determined by real-time qRT-PCR using specific primers in larval samples ( Table S5 ) . The microarray results and the real-time PCR gave similar trends ( r = 0 . 85 , Figure S1 ) . Significant gene changes were identified in the family of genes regulating cellular respiration in AF , especially in larvae . The majority of these changes consisted of a down- rather than an up-regulation of gene expression ( Table S3 ) . Besides one pyruvate kinase isoform ( CG12229 ) , most of the genes encoding glycolytic enzymes were dramatically down-regulated ( Figures 4A and 4B ) . Similarly , suppression was also found in the TCA cycle ( Figures 4A and 4C ) , lipid β-oxidation , and respiratory chain complex genes in larvae ( Figure 5 ) . As shown in Figures 5B and 5C , among the 50 measured genes encoding components of the respiratory chain , 33 genes were down-regulated , and only 3 genes were up-regulated . Interestingly , the suppression of these metabolic genes occurred only in the larvae and not in the adult fly ( Figure 5C ) . Since many genes encoding metabolic enzymes were significantly down-regulated , we asked whether such down-regulation was coordinated at a transcriptional level . Therefore , the GenomatixSuite ( GEMS ) software was used to identify transcription factor binding elements in defined cis-regulatory regions of the TCA cycle , glycolysis and lipid β-oxidation genes . The TCA cycle related genes were separated into two groups , one containing the significantly down-regulated genes ( down-regulated group , 16 genes ) and the other containing those that were not significantly altered or up-regulated genes ( reference group , 8 genes ) ( Table S6 ) . The binding elements of the Drosophila transcriptional suppressor hairy were present in the regulatory region of the down-regulated genes ( 15 of 16 cis-regulatory regions , 0 . 88/kb ) but not in the reference group ( 1 out of 7 cis-regulatory regions , 0 . 15/kb ) ( p<0 . 0001 , CHI-Test ) ( Figure 6A ) . Of particular interest , the expression level of hairy was significantly up-regulated in AF ( Figure 6B , Table S1 ) . This result suggested that hairy , a key transcriptional suppressor , reduced the expression of the TCA cycle genes . No such specific transcription factor binding elements were found in genes encoding glycolysis or β-oxidation enzymes . To confirm that hairy directly binds to the cis-regulatory regions of these candidate TCA cycle genes , chromatin immunoprecipitation ( ChIP ) assay was performed using a specific antibody to hairy in Drosophila Kc cells . The candidate hairy binding targets were tested using specific primers in both hypoxia and normoxia ( Table S5 ) . For a negative control , we included a cis-regulatory region of another TCA cycle gene ( i . e . , CG6629 ) that was up-regulated in AF and had no hairy binding elements detected . We found that hairy did bind to the cis-regulatory region of gene l ( 1 ) G0030 in hypoxia but not in normoxia . Similarly , hairy was also found to bind to the cis-regulatory region of gene SdhB under both hypoxic and normoxic conditions , and its binding activity was significantly higher in hypoxia than in normoxia . Such hypoxia-induced increase in hairy binding did not occur in CG12344 , a hairy target gene , which encodes for a non-metabolic gene ( i . e . , an isoform of GABA-A receptor ) . This result demonstrates that the down-regulated TCA cycle genes are direct targets of hairy , and hairy specifically suppresses their expression under hypoxia . Furthermore , such hypoxia-induced suppression of TCA cycle genes was abolished in the hairy loss-of-function mutants , h1 or h1j3 ( Figure 7A ) . To further evaluate the role of hairy in hypoxia tolerance , we determined the survival rate of these two hairy loss-of-function mutants at 6% of O2 . This mild level of O2 was used since it is sufficient to show differences between the mutants and controls . As shown in Figure 7B , both hairy loss-of-function mutants exhibited much lower survival rate ( p<0 . 0001 , CHI-Test ) as compared to controls , proving the contribution of hairy to hypoxia tolerance in flies . Since some of the current experiments suggested that hypoxia tolerance was a heritable trait in the AF population , we studied these flies further to determine the genetic basis of this heritability . Two types of experiments were performed . First , we examined the activity levels of 2 key glycolytic enzymes , triose phosphate isomerase ( TPI ) and pyruvate kinase ( PK ) in NF and AF . We argued that , if there was a genetic basis for the change in enzyme activity , such activity level would be altered not only in the hypoxia-cultured flies but also in those cultured in normoxia , i . e . , the enzyme activity would be different from that in NF , whether in hypoxia or normoxia . As shown in Figures 8A and 8B , the activity of TPI in AF was indeed reduced when these flies were cultured in either normoxia or hypoxia . When PK activity was assessed , however , AF had a higher level activity under hypoxia and stayed at the same level in normoxia without a significant increase as compared to NF . Second , the genomic locus encoding TPI was sequenced to determine whether there were any differences between AF and NF . We chose to sequence the TPI locus , because , unlike PK , TPI is encoded by a single gene . As expected , a number of significant polymorphic differences were identified in the AF population that included 3 SNPs ( −77T/C , −53A/G , −51A/T ) and 1 indel ( −74 to −66 ) in the cis-regulatory region , 1 synonymous SNP ( 1051A/G ) in the coding region , 1 SNP ( 1480A/G ) in the 3′-untranslated region , and 1 SNP ( 1662C/T ) in the downstream region ( Figure 8C , Table S7 ) , demonstrating significant genetic differences in the AF population ( p<0 . 001 , CHI-Test ) .
The current laboratory selection experiments have shown that the hypoxia-selected flies have garnered , with ongoing generations , a spectacular ability to survive extremely low O2 conditions . To put this ability in perspective , these flies live perpetually now at an oxygen level that is equivalent to that above Mount Everest by ∼4 , 000 meters . This phenotypic breakthrough occurred after 32 generations of selection . One of the most profound phenotypic abnormalities is a reduction in body size in the AF flies . This substantial decrease in body size was presumably of physiologic relevance , which is likely related to shorten the O2 diffusion distances for improved survival . It is very interesting to note that , though we show evidence of heritability of the hypoxia survival trait in the selected flies , the change of body size and weight did not seem to be heritable , as body size was reverted to normal , even after a single generation in room air . The goal of the current study was to determine the genetic and molecular basis of adaptation to long-term ( i . e . , over generations ) hypoxic environments . Since survival under long term hypoxia is a complex trait , we expected this adaptation to be controlled by a number of pathways and genes . Indeed , our microarray and genetic analyses have shown that the survival of both larvae and adult flies was accompanied by alterations in a number of major signaling pathways , such as EGF , Insulin , Notch and Toll/Imd pathways . In this regard , there are a number of interesting observations made from our studies . First , the number of significantly altered genes was much larger in larvae than in the adults . More than 20% of all measured genes were significantly altered in the larval samples whereas only ∼1% changed in the adults , using the same statistical criteria . This major difference between larvae and adults may have a number of explanations , including the fact that larval cells undergo rapid growth , proliferation and differentiation as compared to adults . Second , although adaptive changes were found in both larvae and adult flies , it is possible that these changes were induced through both genetic and/or epigenetic physiologic regulation . Since we found that a ) the hypoxia-selected flies could live for a number of generations in normoxia and survive again when introduced back in 4% hypoxia , b ) some glycolytic enzymes maintained their activity in the AF population at a lower ( or higher ) level not only during hypoxia but also in normoxia ( e . g . , TPI and PK ) , and c ) a number of polymorphic changes existed in AF as compared to NF , our data provided direct evidence for actual genetic alterations that were responsible for , at least in part , tolerance to severe hypoxia . Third , several signal transduction pathways were altered in the selected flies , i . e . , EGF , Insulin , Notch and Toll/Imd pathway , which have also been found to be activated by hypoxic stimuli in mammalian tissues/cells [22]–[28] . For example , we and others have shown that the Notch receptor and its targets were altered in mouse heart [29] and cultured cells [30] , [31] following chronic hypoxia . Such similarities in hypoxia responses demonstrated that some of the mechanisms underlying hypoxia-tolerance are conserved across species . Fourth , previous studies have demonstrated an enhancement of glycolytic activity as a major metabolic response in cells/tissues following acute ( in minutes or hours ) hypoxia , in contrast , our present data showed that many genes encoding glycolytic enzymes were down-regulated in the AF . Moreover , no significant lactate accumulation was found in the AF flies ( unpublished observations from our laboratory ) . Finally , although hypoxia-tolerance is a complex trait , a single gene alteration , such as that of hairy , can remarkably make a significant difference in terms of survival . For more than three decades , physiologists have debated the importance of down-regulation of metabolism in hypoxia tolerance in some tolerant animals such as the turtle . For example , Hochachka and others have argued that anoxia-tolerant organisms depress their metabolism in order to minimize the mismatch between supply and demand [32]–[34] . While this idea , based on metabolic data , is intuitively appealing , there is no information about how various metabolic enzymes could be coordinated in order to survive severe long lasting hypoxia . Our current work provides the first evidence showing that such coordination can be achieved at a transcriptional level by a seemingly metabolic switch , i . e . , hairy , which is a transcriptional suppressor . This notion is supported by a number of observations from current study: a ) expression of hairy was up-regulated in AF , b ) hairy-binding region was presented in the cis-regulatory regions of the down-regulated genes but not others , c ) the functional ChIP analysis led us to believe that the down-regulation of these metabolic genes is based on the changes of hairy , especially that its binding was not increased for other , non-metabolic target genes , e . g . , CG12344 , and d ) hairy loss-of-function mutations abolished the suppression on the expression of TCA cycle genes and significantly reduced hypoxia survival in Drosophila . This decreased survival in flies carrying the hairy mutation is particularly important since these experiments strongly demonstrate that hairy contributes to hypoxia tolerance through the regulation of TCA cycle enzymes . While it is possible that the decreased survival is not related to the role that hairy plays in metabolic regulation but due to an inherent weakness of the hairy mutants , this is much less likely because a ) we have studied in the past different mutation alleles in flies and they do not necessarily behave differently in hypoxia [6] , and b ) we chose two different mutants of hairy and they have similar effects . Another interesting observation in this work is related to the enzymatic activities of triose-phosphate isomerase ( TPI ) and pyruvate kinase ( PK ) in AF . We hypothesized that , if there is a genetic basis for the change in enzyme activity , we should be able to detect the alterations in AF flies that were cultured in both hypoxia and normoxia , and the enzyme activity should maintain different from those in NF , whether in hypoxia or normoxia . Indeed , as expected , we found that the expression level and enzymatic activity of TPI were significantly lower in AF than those in NF in both hypoxia and normoxia . In addition , we identified 7 polymorphic differences in the genomic locus of the TPI gene in AF which demonstrated a genetic modification of this locus in AF strain . In contrast , the activity of PK in AF did not differ from that of NF in normoxia , although its activity was significantly higher in AF under hypoxia ( Figure 8B ) . This result demonstrated that there was an inhibition of this enzyme in NF under hypoxia , and such hypoxia-induced inhibition was abolished in AF . On the surface , this result might weaken our argument , a potential explanation for such a difference between PK and TPI is that the regulation of PK is much more complicated . Indeed , there are 7 genes encoding PK in D . melanogaster . In AF , we found that 1 gene ( i . e . , Pyk ) was down-regulated and another gene ( i . e . , CG12229 ) was up-regulated . Therefore , in addition to possible genetic rearrangements , other factors may play a role to keep higher activity of PK in AF under hypoxia and to minimize the difference between AF and NF in normoxia . If the changes that led to enhanced survival in extremely low O2 levels in the AF population are genetic in nature , as we demonstrated in this work , it is important to note that the breakthroughs in survival to lower and lower O2 levels took place after a relatively small number of generations in the Drosophila . This relatively small number of generations was also found when previous investigators were studying other phenomena such as geotaxis [15] . If translated into human years , the 32 generations that were needed to alter the phenotype and genotype in Drosophila can be approximated to about a thousand years . Although changes in the DNA code with selection pressure could take a much longer period of time ( potentially thousands and millions of years in “Darwinian time” ) , this work demonstrated that such changes in DNA and phenotype could proceed at a much faster rate , presumably because of the trait itself or the selection pressure applied . In summary , we have generated a Drosophila melanogaster strain that is very tolerant to severe hypoxic conditions through long-term experimental selection . Several adaptive changes have been identified in the hypoxia-selected AF flies that include up-regulation of multiple signal transduction pathways , modulation of cellular respiration enzymes , and polymorphic differences in metabolic enzymes such as TPI . While we believe that multiple pathways contribute to hypoxia-tolerant trait in this Drosophila strain , we demonstrate that hairy-mediated metabolic suppression is an important one . The adaptive mechanisms identified in this hypoxia-tolerant Drosophila model may also play a crucial role in protecting mammals from hypoxia injury .
Twenty-seven isogenic Drosophila melanogaster lines ( kindly provided by Dr . Andrew Davis ) were used as parental stocks for the long-term hypoxia-selection experiment as described previously [17] . Briefly , embryos collected from the parental population were divided into 6 groups , 3 groups of them were subjected to long-term hypoxia-selection , and 3 other groups were maintained under normoxia as controls . Both hypoxia-selection and control experiments were performed in specially designed population chambers ( 26 cm×16 cm×16 cm ) . These chambers were connected to either O2 at certain concentrations ( balanced with N2 , for the hypoxia-selection experiments ) or to room air ( 21% O2 , for the control experiments ) . The humidity in the chambers was maintained by passing the gas through water prior to going into the chambers . The flow speed was monitored by 565 Glass Tube Flowmeter ( Concoa , Virginia Beach , VA ) , and the O2 level within the chamber was monitored with Diamond General 733 Clark Style Electrode ( Diamond General Development Corp . , Ann Arbor , MI ) . The selection was started at 8% O2 and this concentration was gradually decreased by 1% each 3 to 5 generations to keep the selection pressure . Embryos , 3rd instar larvae and adult flies were collected from each generation and stored at −80°C for analyses . The results presented in the current study were derived from expression arrays using larval and adult samples . The body weight of hypoxia-selected flies was determined at each generation . Male flies ( n = 100 ) from hypoxia or control chambers were collected , weighed and used as the index of body weight . The hairy loss-of-function mutants ( h1 and h1j3 ) were obtained from Drosophila Stock Center ( Bloomington , IN ) . The survival rate of these stocks in hypoxia was determined by culturing them in a 6% O2 environment . After 3 weeks in culture , the number of live adult flies and pupae were counted . The ratio between live adult flies to the total number of pupae was calculated and presented as survival rate . The statistical significance of survival between hairy mutants and controls was calculated by CHI-test . cDNA microarrays containing 13 , 061 known or predicted genes of the D . melanogaster genome were processed according to previous descriptions [16] , [35] . Nine larval samples from AF or NF , 6 adult samples from AF or NF were included in this analysis . Each larval sample contained a pool of 25 3rd instar larvae , and each adult sample contained 25 male and 25 female adult flies from each individual population . Total RNA was extracted from the samples using TRIzol ( Invitrogen , Carlsbad , CA ) followed by a clean-up with RNeasy kit ( Qiagen , Valencia , CA ) . Three µg of total RNA from each sample was amplified with an in vitro transcription-based strategy [13] . A common reference design was applied for the hybridizations , and the reference RNA sample was created using a balanced pool of 3rd instar larvae ( for the larval samples ) or adult flies ( for the adult samples ) from each parental line . This reference was chosen so that relative abundance of each transcript could be calculated individually , and the relative levels of each transcript among biological replicates could be compared . A total of 30 arrays were included in this analysis , and the hybridizations were done in different days using arrays printed from different batches . Microarray images were acquired by GenePix 4000 microarray scanner using GenePix Pro 3 microarray analysis software ( Axon Instruments , Sunnyvale , CA , USA ) . The statistical significance ( q-value , i . e . , false discover rate ( FDR ) ) and the ratio of the changes in expression was calculated using Significance Analysis of Microarray ( SAM ) software [19] following LOWESS normalization . The fold changes were presented as ratios , if up-regulated , or −1/ratio , if down-regulated . The gene ontology ( GO ) based analyses were performed using GenMAPP software [20] . The microarray data can be retrieved using access number GSE8803 in the GEO database at http://www . ncbi . nlm . nih . gov/geo . Semi-quantitative real-time RT-PCR was used to evaluate the result of microarrays and to determine the differences in expression levels of genes encoding TCA cycle enzymes between the hairy mutants and control . All specific primers were designed by Primer 3 software [36] and synthesized at ValueGene ( San Diego , CA ) ( Table S5 ) . First strand cDNA was synthesized using SuperScriptII reverse transcriptase and Oligo- ( dT ) primer . Real-time PCR amplification was performed using ABI Prism 7900HT Sequence Detection System ( Applied Biosystems , Foster City , CA ) . For each reaction , 10 µl of 2× SYBR green PCR master mix ( Applied Biosystems , Foster City , CA ) and 0 . 5 µM of both forward and reverse primers along with 100 ng of each appropriate cDNA samples were mixed ( total reaction volume: 20 µl ) . Melting curves were determined and the final products were isolated with 4% agarose gel to ensure specificity of the reaction . The relative expression level was calculated using 2−ΔΔCt method , as described previously [37]–[39] . Drosophila melanogaster β-actin was used as internal control . The final results were presented as fold change of AF over NF or hairy mutants over yw control . All experiments were done in triplicate . The enzymatic activity of Triose Phosphate Isomerase ( TPI ) and Pyruvate Kinase ( PK ) were determined as previously described with modifications [40] . Briefly , the assay samples were extracted from 3rd instar larvae cultured under either normoxic or hypoxic condition ( 4% O2 ) . 0 . 2 ml ( 100 mg wet weight/ml ) of isolation medium ( 0 . 25 M sucrose , 1 mM EDTA-K , 5 mM HEPES-Tris , pH 7 . 4 , with protease inhibitors ) was added and the suspension of larval tissue in isolation medium was transferred into a 2 ml all glass homogenizer . 10% ( v/v ) Triton X-100 was added to this suspension making the final concentration of Triton X-100 0 . 5% ( v/v ) . Then the tissue was homogenized with the B ( i . e . , tight ) pestle . Aliquots of this homogenate were employed for enzymatic activity measurements . The enzymatic activities of TPI or PK were determined using 10 , 20 , or 30 µl of the homogenate with proper substrates . The dynamic of color formation was recorded using Beckman Coulter DU800 Spectrophotometer at selected wave length for each substrate . The kinetic parameters of the reaction were calculated by curve fitting . The common transcription factor binding sites in the defined promoter regions of candidate genes were analyzed using GenomatixSuite ( Genomatix Software GmbH , Germany ) . The genes encoding TCA cycle or β-oxidation enzymes were separated into down-regulated or reference groups . The genome DNA sequences from 2000 bp upstream of the first transcription starting site ( TSS ) to 500 bp downstream of the last TSS of each gene was downloaded and used as cis-regulatory region of the gene ( Drosophila genome R5 . 2 ) [41] . These sequences were subjected to GEMS analysis to identify common transcription factor binding elements [42] . The statistical significance of the transcription factor binding element frequency in the AF and NF population was analyzed by CHI-test . Chromatin immunoprecipitation and PCR detection of hairy binding in the cis-regulatory regions of genes encoding TCA cycle enzymes were performed in cultured Drosophila Kc cells ( obtained from Dr . Amy Kiger , UCSD ) [43] , [44] . The Kc cells were treated with 0 . 5% O2 for 4 hours . About 106 cells were used in each ChIP experiment . Chromatin immunoprecipitation was carried out using ChIP assay kit ( Upstate , Temecula , CA ) according to the manufacture's instructions . The immunoprecipitation was performed overnight at 4°C with 2 µg of hairy antibody ( Abcam , Cambridge , MA ) . DNA fragments were purified with phenol:chloroform ( Invitrogen , Carlsbad , CA ) . For PCR , 2 µl of a 25 µl DNA extraction was amplified with specific primers ( Table S5 ) . Genomic DNA was extracted from 15 male AF or NF adult flies . The samples were ground in 400 µl of homogenate buffer ( 100 mM Tris/HCl , 100 mM EDTA , 100 mM NaCl and 0 . 5% SDS , pH7 . 5 ) and incubated at 65°C for 30 min . Genomic DNA was extracted by adding in 800 µl extraction solution ( 1 2 . 5 ( v/v ) of 5 M KAc and 6 M LiCl ) . After 15 min of centrifugation at 13 , 000 rpm , the supernatant was transferred into a new tube and the genomic DNA was precipitated by adding 600 µl of isopropanol . The DNA pellet was washed with 70% of ethanol and dissolved in TE buffer . The genomic locus encoding TPI gene was amplified by PCR using specific primers ( forward primer: GTTTAAGGTCCGCAGAGGTG , and reverse primer: ATTTTGGCAAGCCTGTTGAT ) . All the coding exons and intronic flanking regions were amplified by polymerase chain reaction ( PCR ) using the high fidelity proofreading DNA polymerase , Platinum Pfx DNA Polymerase ( Invitrogen , Carlsbad , CA ) . The PCR products were purified and cloned into pCR4-TOPO plasmid to create the plasmid library for TPI alleles of the AF and NF population . Cycle sequencing was performed on an ABI automated sequencer ( Applied Biosystems , Foster City , CA ) by Eton Biosciences , Inc . ( San Diego , CA ) . Ten clones from AF or NF TPI allele library were sequenced and compared by using ClustalW2 software [45] ( http://www . ebi . ac . uk/Tools/clustalw2/ ) and DnaSP 4 . 0 [46] . The statistical power for the comparison was calculated by GPower software 3 . 0 . 3 [47] . | Hypoxia-induced injury has been related to multiple pathological conditions . In order to render mammalian cells and tissues resistant to low O2 environment , we wished to first understand the mechanisms underlying hypoxia-tolerance in resistant animals . Therefore , we generated a D . melanogaster strain that is tolerant to severe hypoxic conditions through long-term experimental selection . Several adaptive changes were identified in the hypoxia-selected flies that included up-regulation of multiple signal transduction pathways ( such as Notch pathway , Insulin pathway , EGF receptor pathway , and Toll/Imd pathway ) , modulation of cellular respiration enzymes , and polymorphic differences in metabolic enzymes ( such as TPI ) . While we believe that multiple pathways contribute to the hypoxia-tolerant trait in this Drosophila strain , we demonstrate that hairy-mediated metabolic suppression is a critical mechanism for reducing the mismatch between supply and demand of O2 . |
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Pachyonychia congenita ( PC ) is a cutaneous disorder primarily characterized by nail dystrophy and painful palmoplantar keratoderma . PC is caused by mutations in KRT6A , KRT6B , KRT6C , KRT16 , and KRT17 , a set of keratin genes expressed in the nail bed , palmoplantar epidermis , oral mucosal epithelium , hair follicle and sweat gland . RNA-seq analysis revealed that all PC-associated keratins ( except for Krt6c that does exist in the mouse genome ) are expressed in the mouse enamel organ . We further demonstrated that these keratins are produced by ameloblasts and are incorporated into mature human enamel . Using genetic and intraoral examination data from 573 adults and 449 children , we identified several missense polymorphisms in KRT6A , KRT6B and KRT6C that lead to a higher risk for dental caries . Structural analysis of teeth from a PC patient carrying a p . Asn171Lys substitution in keratin-6a ( K6a ) revealed disruption of enamel rod sheaths resulting in altered rod shape and distribution . Finally , this PC-associated substitution as well as more frequent caries-associated SNPs , found in two of the KRT6 genes , that result in p . Ser143Asn substitution ( rs28538343 in KRT6B and rs151117600 in KRT6C ) , alter the assembly of K6 filaments in ameloblast-like cells . These results identify a new set of keratins involved in tooth enamel formation , distinguish novel susceptibility loci for tooth decay and reveal additional clinical features of pachyonychia congenita .
Tooth enamel is made of 96% hydroxyapatite minerals , which makes it the hardest tissue in the human body . Enamel is also the first compartment of the tooth to be attacked by dental caries , a chronic disease that affects 42% of children and 92% of adults , with various degrees of severity ( number of teeth and tooth surfaces affected ) in the general population . Dental caries is initiated at the surface of the tooth by bacteria metabolizing food residues and releasing acids that dissolve enamel minerals [1] . Even though dental caries is influenced by environmental and behavioral factors , there is clear evidence that susceptibility to caries is also driven by host genetic factors [1–3] , and genome-wide association studies ( GWASs ) have revealed genetic variants associated with increased susceptibility to tooth decay [4–8] . These genetic factors may influence the quality of dental tissues and ability to resist carious attacks , may impact other aspects of the oral environment such as the quality of the saliva , enamel pellicle and oral microbiome , and may differ between the primary and permanent dentitions [9] . Tooth enamel is produced in two phases [10]: first , a secretion phase during which enamel matrix proteins are secreted and deposited in a highly structured manner to form enamel rods; and second , a maturation phase during which most enamel matrix proteins are degraded to make space for the full expansion of hydroxyapatite minerals . After maturation , the enamel is left with only 1% of proteins that are abundant near the dentin-enamel junction ( DEJ ) and expand throughout the enamel as thin layers of enamel rod sheaths located at the interrod region [11] . This organic material has been shown to play a crucial role in the biomechanical properties of enamel [12 , 13] and in the resistance to caries [14 , 15] . Until recently , the exact composition of the insoluble fraction in this organic material had been uncharacterized , even though there was strong evidence that the highly crosslinked proteins present in mature enamel had biochemical properties similar to those of keratins [11 , 16–18] . We showed previously that the organic material in mature enamel is in part composed of epithelial hair keratins , and that missense mutations in KRT75 , previously linked to common hair disorders , were associated with increased susceptibility to dental caries [19] . In the present study , we focus on the presence in enamel of another set of keratins encoded by genes mutated in pachyonychia congenita ( PC ) , a cutaneous disorder characterized by nail dystrophy and painful palmoplantar keratoderma [20 , 21] . Additional features of this disease may include oral leukokeratosis , follicular keratosis , cysts , hyperhidrosis , and natal teeth . Some of these phenotypic traits are consistent with the expression pattern of the keratins involved . Herein we present novel findings that relate this set of keratins to the development of tooth enamel and to the susceptibility to tooth decay .
In a previous study , we determined through RNA-seq analysis that subsets of epithelial keratins were expressed in the enamel organ in mouse [19] . Of particular interest was the expression of Krt6a , Krt6b , Krt16 and Krt17 ( Fig 1A ) , a set of keratin genes encoding keratin-6a ( K6a ) , keratin-6b ( K6b ) , keratin-16 ( K16 ) and keratin-17 ( K17 ) , respectively , and in which mutations in humans lead to pachyonychia congenita ( PC-K6a , OMIM #615726; PC-K6b , OMIM #615728; PC-K16 , OMIM #167200; PC-K17 , OMIM #167210 ) , characterized by nail dystrophy and painful palmoplantar keratoderma [20 , 21] . In humans , the KRT6 family includes a third member ( KRT6C , encoding K6c ) , mutations in which have been associated with a milder form of PC with no/minor nail defects ( PC-K6c ) that was initially reported as palmoplantar keratoderma , non-epidermolytic , focal or diffuse ( PPKNEFD , OMIM #615735 ) . Immunohistochemical analysis revealed that K6 ( using an antibody recognizing all K6 proteins ) and K17 are produced by rodent ameloblasts but exhibit very distinct distributions ( Fig 1B and S1A Fig ) . K6 distribution is relatively diffuse in secretory ameloblasts ( Fig 1B ) while K17 forms characteristic keratin filaments that run throughout the ameloblasts and underlying tissues ( stratum intermedium and papillary layer ) ( S1A Fig ) . At the apex of the ameloblasts and outside the Tomes’ processes , highly specialized structures where the deposition of enamel is coordinated , K6 was detected primarily at the interrod region ( Fig 1B , magnification top panel ) . In the same area , K17 staining resulted in parallel transverse bands within the rods in addition to a longitudinal interrod distribution similar to that obtained with K6 staining ( S1A Fig , magnification top panel ) . These distributions indicate that K6 and K17 are both incorporated into the enamel matrix but with distinct patterns . To determine whether these keratins were part of the organic material present in mature human enamel , we performed immunohistochemical staining on polished sections of human third molars ( Fig 1C and S1B Fig ) . Consistent with its distribution near the apex of rodent ameloblasts , K6 was detected primarily where the enamel rod sheaths are located , at the periphery of the enamel rods ( Fig 1C ) . More intense staining was detected at regular intervals near the DEJ , along structures that are likely to correspond to enamel tufts , areas of higher accumulation of organic material ( Fig 1C , left panel ) . In addition to intense staining near the DEJ , K6 was detected throughout the thickness of enamel and restricted to the periphery of the rods ( Fig 1C , right panel ) . K16 and K17 were detected near the DEJ where they were not restricted to the interrod regions but also present in the core of the rods ( S1B Fig ) , a pattern consistent with the distribution of K17 near the apex of rodent ameloblasts . The restricted pattern of K6 distribution at the enamel rod sheaths was confirmed with two different antibodies , a polyclonal antibody raised in guinea-pig against the C-terminus of the protein and a monoclonal antibody raised in mouse against the N-terminus ( S2 Fig ) . These results indicate that PC-associated keratins are part of the organic material present in mature enamel but exhibit distinct distributions . In order to determine if the presence of K6a , K6b , K6c , K16 and K17 in mature human enamel had an impact on the susceptibility to tooth decay , we tested the association between SNPs in the genes encoding these keratins and three measures of dental caries experience assessed in the primary dentition of 449 children ( mixed European descent , 6–12 years ) and permanent dentition of 573 adults ( mixed European descent , 25–50 years ) . We focused our attention to common missense SNPs that occur at a sufficient frequency ( minor allele frequency > 1% ) allowing for statistical testing in our unselected population-based cohort . Three missense SNPs in KRT6A , eight in KRT6C , and seven in KRT6B responded to these criteria ( Table 1 ) . Across all 18 missense SNPs , seven SNPs showed nominal evidence of association ( p < 0 . 05 ) with at least one measure of dental caries experience in either adults or children , and the following five SNPs exhibited associations with all three measures of dental caries experience ( Table 1 ) : The SNPs identified in KRT6A and KRT6C were associated with increased caries experience in adults only . Among the missense polymorphisms identified in KRT6B , rs61746354 ( K6bY497C ) was associated with higher caries experience in children , while rs144860693 ( K6bG97R ) and rs28538343 ( K6bS143N ) were associated with higher caries experience in adults ( Table 1 ) . These results indicate that the effect of specific polymorphisms in keratin genes may differ across dentition ( primary vs . permanent ) . Only one common missense SNP in KRT16 , rs111383277 ( KRT16:c . 121C>T; K16p . Arg41Cys ) , was at a frequency higher than 1% , while none were found in KRT17 . rs111383277 did not show significant association with dental caries experience in the cohorts tested . Due to this limited number of common SNPs in KRT16 and KRT17 , we were not able to conclude on the potential implication of these two keratins in caries risk . Keratins are structured into three major domains with a central “rod” domain , directly involved in the dimerization and further assembly of keratin filaments , flanked by a “head” domain and a “tail” domain on the N-terminal side and C-terminal side , respectively ( Fig 1D ) . Interestingly , all the missense polymorphisms that showed significant association with higher caries experience in KRT6A , KRT6B and KRT6C result in amino acid substitutions in the head or tail domains , while all the mutations that have been associated so far with PC are located at the beginning or at the end of the rod domain ( Fig 1D ) . The KRT6B polymorphism associated with higher caries experience in children ( rs61746354 , K6bY497C ) is the only SNP that results in an amino acid substitution in the tail domain ( Fig 1D ) . The missense SNPs in KRT6 genes were present at various frequencies in the cohorts studied ( S1 Table ) . Moderate to high linkage disequilibrium ( R2 between 0 . 69 and 0 . 89 ) was observed between rs17845411 ( K6aN21S ) , rs151117600 ( K6cS143N ) , rs144860693 ( K6bG97R ) , and rs28538343 ( K6bS143N ) ( Fig 1E ) . Genotype frequencies and quantifications of caries experience per genotype group for the three missense SNPs identified in KRT6B are shown in Fig 2 . The frequencies of rs144860693 ( K6bG97R ) and rs28538343 ( K6bS143N ) , the two variants that exhibited the most significant association with dental caries risk in adults , are high in the cohorts studied ( Fig 2A and 2B; S1 Table ) . These two SNPs have a major impact on caries experience , with an estimated increase in the average number of carious tooth surfaces of 1 . 6 and 2 . 4 surfaces per copy of the risk allele , respectively ( Table 1 ) . These variants did not demonstrate a statistically significant effect on average caries experience in children ( Fig 2A and 2B ) . rs61746354 ( K6bY497C ) , the missense SNP in KRT6B that was associated with higher caries risk in children and occurs at a frequency higher that 4% in our cohorts ( S1 Table ) , was associated with an estimated 1-surface increase in the average number of carious tooth surfaces ( Table 1 and Fig 2C ) . Genotype frequencies and quantifications of caries experience per genotype for the other missense SNPs identified in KRT6A and KRT6C are shown in S3 Fig . Given that KRT6B harbors three missense SNPs showing significant association with caries experience , we wanted to further quantify the genetic relationship of missense variants in this gene on dental caries . To do so , we examined pairwise interactions between rs144860693 ( K6bG97R ) , rs28538343 ( K6bS143N ) and rs61746354 ( K6bY497C ) . Statistically significant interaction effects were observed between rs144860693 ( K6bG97R ) and rs28538343 ( K6bS143N ) on the number of surfaces with untreated decay ( DS ) model , between rs144860693 ( K6bG97R ) and rs61746354 ( K6bY497C ) , and rs28538343 ( K6bS143N ) and rs61746354 ( K6bY497C ) on the number of decayed , missing due to decay , and filled surfaces ( DMFS ) , adjusting for age , sex , and all the other SNPs in KRT6B ( S2 Table ) . Even though rs61746354 ( K6bY497C ) was associated with higher caries risk in children only , this SNP exhibited a significant statistical interaction effect with rs144860693 ( K6bG97R ) and rs28538343 ( K6bS143N ) in adults ( S2 Table ) . Therefore , the effect of the two SNPs that result in amino acid substitutions in the head domain of K6b on caries risk in adults may be influenced by the presence or absence of the p . Tyr497Cys substitution in the tail domain of the same keratin , a SNP that by itself is associated to higher caries risk only in children . The two SNPs resulting in p . Ser143Asn substitution in KRT6B and KRT6C ( rs28538343 and rs151117600 , respectively ) also exhibited statistically significant interaction effect on the number of surfaces with untreated decay ( DS ) in adults ( p-value = 0 . 044 ) . When focusing on the 4 SNPs that lead to higher caries risk in adults , we found a significant cumulative effect of the number of risk alleles on caries experience ( S4 Fig ) . Given that KRT75 is adjacent and phylogenetically related to the KRT6 genes in the human genome , we explored potential linkage disequilibrium and interaction effects between the KRT75 SNP previously shown to increase caries experience in adults [19] and the newly identified SNPs in KRT6 genes . The previously reported SNP rs2232387 ( K75A161T ) was not in linkage disequilibrium with any of the KRT6 SNPs ( S5 Fig ) and there was no statistical interaction ( all p-values >0 . 05 ) between the same SNPs . Altogether , our data support genetic association between SNPs in KRT6A , KRT6B and KRT6C and tooth decay risk , in a way that is dentition-specific , and with statistical interaction between various loci in these three genes . In order to assess how mutations in KRT6 genes may affect enamel structure , we analyzed third molars that were extracted from a PC patient who is heterozygous for the KRT6A:c . 513C>A transversion that results in p . Asn171Lys amino acid substitution ( K6aN171K ) ( Fig 3A ) . This patient is a white male who was 18 years old at the time his third molars were extracted , and is the member of a family in which the mutation in KRT6A was previously reported [22] . The patient experienced 20/20 nail dystrophy , very painful palmoplantar keratoderma , oral leukokeratosis , follicular keratosis , but did not have natal teeth . The overall shape and structure of the third molar enamel did not appear defective based on micro-computed tomography analysis ( Fig 3B ) . However , scanning electron microscopy analysis of polished sections of the teeth ( section plane transverse to the enamel rods ) revealed alteration of the distribution and shape of enamel rods when compared to third molars extracted from healthy patients ( Fig 3C ) . The insoluble organic material present in mature enamel can be isolated after full demineralization of a tooth in EDTA . When isolated from molars extracted from this PC patient , the insoluble material exhibited uneven alignment of the enamel rod sheaths that tended to form curls ( Fig 3D ) . To assess the effects of the K6aN171K mutant protein on K6 distribution in enamel , we performed immunohistochemical staining using anti-K6 antibody on polished sections of the patient’s tooth . K6 staining was still found to be stronger in the tuft areas near the DEJ ( Fig 3E ) . However , K6 distribution was no longer restricted to the interrod but could also be found as smaller rings or clumps within rods ( Fig 3E ) . These results indicate that this PC-causing mutation in KRT6A leads to improper incorporation of the K6a protein into enamel rod sheaths , which results in altered shape and arrangement of enamel rods . Missense mutations in keratins may affect their assembly , modify their subcellular localization and/or affect their interaction with keratin-associated proteins . Phosphorylation and glycosylation of the head and tail domain of intermediate filaments proteins have been shown to influence their interaction with other proteins and their subcellular localization [23 , 24] . When comparing the position of the SNPs we determined to be associated with increased caries experience and potential sites for post-translational modifications in K6 proteins , we observed that the p . Ser143Asn substitution ( rs28538343 in KRT6B and rs151117600 in KRT6C ) is immediately adjacent to an LLS/TPL consensus phosphorylation site that is highly conserved in type II keratins [24 , 25] , and within a potential N-linked glycosylation site ( Fig 4A and S6 Fig ) . Although it remains to be determined how K6 proteins interact with and are deposited into the enamel matrix in the context of a secretory stage ameloblast in vivo , we assessed the effect of the p . Ser143Asn substitution in K6 in a context in which the mutant protein is overexpressed in ameloblast-like cells ( ALC ) [26] . In this assay , we also analyzed the behavior of K6N171K mutant protein carried by the PC patient included in this study ( Fig 3 ) , a mutation located in the rod domain and known to have a severe effect on keratin filament assembly [27 , 28] . Given the high degree of sequence identity between K6 proteins ( S7 Fig ) and the fact that the mutations of interest are located in highly conserved regions ( Fig 4A ) , we used K6a as a model protein for this assay . We used site-directed mutagenesis to introduce the c . 428G>A transition ( results in p . Ser143Asn substitution ) and the c . 513C>A transversion ( results in p . Asn171Lys substitution ) into the KRT6A cDNA , and cloned the different isoforms ( KRT6AWT , KRT6Ac . 428G>A and KRT6Ac . 513C>A ) into a vector that allows for tetracycline inducible co-expression of KRT6A isoforms and GFP ( Fig 4B ) . These constructs were used to transfect ALC-TetON cells in which expression of KRT6A isoforms and GFP can be induced by addition of doxycycline to the culture medium ( Fig 4C ) . Immunohistochemical analysis using anti-K6 antibody was used to determine the distribution of K6a isoforms in ALC-TetON cells ( Fig 4D ) . While K6aWT formed thick and relatively short bundles of keratin filaments in ALC-TetON cells , K6aS143N tended to form a web of thinner filaments together with large aggregates ( Fig 4D ) . These large aggregates were not seen with K6a proteins harboring the p . Asn21Ser and p . Gly97Arg substitutions ( S8A and S8B Fig ) caused by the other SNPs that are associated with higher caries in adults ( rs17845411 and rs144860693 , respectively ) and are in partial linkage disequilibrium with the SNPs leading to p . Ser143Asn substitution ( rs28538343 in KRT6B and rs151117600 in KRT6C ) . These results suggest that the p . Ser143Asn substitution may contribute most significantly to the caries-prone phenotype in adults . The behavior of the K6aN171K mutant protein fused to a YFP tag has been previously studied in the context of human hepatoma PLC cells in which the mutant protein was shown to form aggregates primarily located in the cytoplasm [27 , 28] . In the context of ALC-TetON cells , K6aN171K formed aggregates that showed heightened accumulation in the nucleus ( Fig 4D ) . These results confirm a severe impairment of K6a assembly in PC patients with p . Asn171Lys substitution . The fact that the aggregates in PLC cells were mostly in the cytoplasm may reflect a cell-specific behavior of the mutant protein or may be due to the YFP tag that was fused to K6a in these experiments [27 , 28] . Given that the SNP leading to p . Tyr497Cys substitution in the tail domain of K6b ( rs61746354 ) is the only one we found associated with higher caries risk in children , we tested its effect on K6a assembly . Similarly to K6aS143N , the K6aY497C isoform tended to form large aggregates in ALC-TetON cells ( S8C Fig ) , which suggests that this SNP may be the cause of the caries-prone phenotype in children . Even though this substitution is not found near a potential posttranslational modification site , the presence of a new cysteine in the tail domain may result in the formation of disruptive disulfide bonds . Given the interaction effects measured between the SNPs that lead to the p . Ser143Asn and p . Tyr497Cys substitutions , we generated a DNA construct for the expression of a K6a protein that harbors both substitutions ( K6aS143N-Y497C ) . This double mutant tends to form aggregates to the same extent as the single mutant proteins ( S8D Fig ) . Taken together , these results confirm that the PC-associated p . Asn171Lys substitution results in profound impairment of K6a protein assembly , and reveal that the caries-associated p . Ser143Asn and p . Tyr497Cys substitutions in K6 proteins also affect the behavior of the proteins , when overexpressed in an ameloblast cell line .
The present report highlights the contribution of specific sets of keratins to the organic fraction of mature tooth enamel and demonstrates through genetic and analytical studies their crucial function in the formation of enamel and its resistance to decay . K75 , an epithelial hair keratin in which mutations have been associated with hair disorders , was the first keratin we investigated in this context [19] . K6 proteins , that are the focus of the present study , are expressed in epithelia that withstand particularly high levels of mechanical strain ( palmoplantar skin , oral epithelium ) as well as in the supporting layers of the hair follicle where their function is similar to that of K75 which is not expressed in palmoplantar epidermis and oral epithelium . Our findings demonstrate that , as K75 , K6 proteins play a crucial role in the enamel rod sheath and that mutations in the genes encoding these keratins may impair the stability of the organic structural component of mature enamel . We propose that , with their unique biochemical properties , K75 and K6 contribute to the toughness , elasticity and resistance to degradation of the enamel rod sheaths , which contributes to establishing proper shape and arrangement of enamel rods and enhances the biomechanical properties of tooth enamel [12 , 13] . Moreover , since it has long been suggested that the stability of the proteins in mature enamel influences the resistance of enamel to carious attack [14 , 15] , we propose that keratins contribute to the stability of enamel rod sheaths and therefore to the resistance of enamel to decay . Since K6 proteins are also expressed in the oral epithelium , and patients with PC may exhibit oral leukokeratosis , there could be a partial involvement of the oral cavity in the increased susceptibility to caries measured in this study . However , the fact that we found SNPs that lead to a higher number of caries in children and not in adults ( same oral cavity but different set of teeth ) strongly suggests that defects in the dental tissue itself are the major factor leading to this effect . The structure and chemical composition of tooth enamel is known to be different between primary and permanent teeth . Primary teeth exhibit thinner and whiter enamel with a smoother surface and higher content in calcium and phosphate when compared to permanent teeth [29 , 30] . Enamel from primary teeth also has a greater susceptibility to demineralization [31] . Moreover , it has been proposed that the genetic factors influencing dental caries differ between primary and permanent dentition [9] . However , there has been no study comparing the composition of the organic material present in the enamel from primary and permanent teeth . The dentition-specific effect we report here for SNPs in KRT6 genes , which we previously observed for two SNPs in KRT75 , with one affecting adults and the other one affecting children [19] , suggests that the combination and/or the mode of incorporation of these keratins in the enamel rod sheaths is different in primary and permanent teeth . Even though this is the first evidence of K6 proteins being incorporated into the enamel matrix , a previous yeast-two-hybrid study determined that K6 could interact with enamel matrix proteins such as amelogenin and tuftelin [32] . It is therefore likely that K6 proteins interact with enamel matrix proteins during the process of enamel secretion . However , the mode of incorporation of keratins into the enamel matrix remains to be elucidated . The interaction of keratins with other proteins is known to involve their head and tail domains rather than the rod domain through which heterodimerization of acidic and basic keratins is established . These interactions are regulated by posttranslational modifications such as phosphorylation and glycosylation [23 , 24] , and mutations impairing such modifications have been linked to skin diseases [33] , as well as diseases related to liver and pancreatic injury [34] . Interestingly , all the caries-associated missense SNPs we identified in KRT6 genes result in substitutions in the head and tail domains of the proteins , which suggests that they may affect their interaction with other proteins rather than their heterodimerization . We further demonstrate that the p . Ser143Asn substitution that may affect phosphorylation and/or glycosylation of the head domain of K6 proteins affects the behavior of K6A in the context of an ameloblast cell line , which suggests that this substitution found in both K6b and K6c may contribute most significantly to the caries-prone phenotype in adults . Functional studies will be required to elucidate the effects of the p . Ser143Asn substitution on the biochemical properties of K6 proteins , in particular on its ability to undergo posttranslational modifications that may affect interaction with enamel matrix proteins and incorporation into the enamel in vivo . Interestingly , in a recent clinical report , an isolated case of PC was proposed to be caused by de novo c . 428G>A mutation in KRT6A that leads to the p . Ser143Asn substitution [35] . This is so far the only report of PC-causing mutation outside of the rod domain . Based on its location in the head domain and on the high frequency of the same substitution in K6b and K6c , the p . Ser143Asn substitution in K6a is unlikely to be the sole cause for the PC phenotype in this patient . In epidermal tissues , K6 proteins ( Type II , basic or neutral ) form heterodimers with K16 or K17 ( Type I , acidic ) to assemble in larger polymeric structures . Interestingly , the subcellular distribution of K16 and K17 proteins is distinct from the distribution of K6 in the enamel organ , which suggests that they do not follow their canonical mode of assembly in this tissue . Due to the low number of frequent missense SNPs in KRT16 and KRT17 in our cohorts , the present study did not allow us to make any conclusion on the potential genetic association between variants in these two genes and dental caries experience . However , the striking difference in the way K16 and K17 proteins are incorporated into enamel suggests that their function in this tissue is distinct from that of K6 proteins . Based on the restricted localization of K16 and K17 near the DEJ and in the core of the enamel rods , these keratins may be involved in shock absorption and protection against fracture [13] rather than in the resistance to caries . Structural analysis of enamel from PC patients with mutations in KRT16 and KRT17 will help address this question . In conclusion , we show for the first time that ( i ) K6 proteins are incorporated into mature tooth enamel at the rod sheaths , ( ii ) SNPs in KRT6 genes are associated with increased susceptibility to dental caries , ( iii ) a PC patient with a mutation in KRT6A exhibits defects in enamel structure , and ( iv ) caries-associated p . Ser143Asn substitution in K6 proteins impairs proper protein interactions .
We thank the Pachyonychia Congenita Project and Ms . Holly Evans for providing us with clinical information and extracted third molars from a PC patient ( 20040468–1057496 ) , and the NIDCR dental clinic for providing extracted third molars from healthy patients ( NCT01805869 ) . For the COHRA study , written informed consent was provided by all adult participants , and verbal assent with parental written consent was provided by all child participants . All procedures , forms and protocols were approved by the Institutional Review Boards of the University of Pittsburgh and West Virginia University . Written informed consent was obtained from the pachyonychia congenita patient , as part of a research registry approved by an institutional review board that complies with all principles of the Helsinki Accord ( Western IRB study no . 20040468 ) . All animal work was approved by the NIAMS Animal Care and Use Committee . The Center for Oral Health Research in Appalachia ( COHRA ) study was initiated to investigate the community- , family- , and individual-level contributors to oral health [36] . Participants from rural counties of Pennsylvania and West Virginia were enrolled via a household-based recruitment strategy , with eligible households required to include at least one biological parent-child pair . All other members of eligible households were invited to participate without regard to biological or legal relationships , or oral health status . Written informed consent was provided by all adult participants , and verbal assent with parental written consent was provided by all child participants . All procedures , forms and protocols were approved by the Institutional Review Boards of the University of Pittsburgh and West Virginia University . Intra-oral examinations of all participants were performed by licensed dentists and/or research dental hygienists . Each surface of each tooth ( excluding third molars ) was examined for evidence of decay , from which dental caries indices were generated . Three measurements of caries experience were considered: ( 1 ) the number of surfaces with untreated decay ( DS/ds ) ; ( 2 ) the traditional DMFS/dfs indices which represent the number of decayed ( D/d ) , missing due to decay ( M ) , and filled ( F/f ) tooth surfaces ( S/s ) in the permanent ( DMFS ) and primary ( dfs ) dentitions; and ( 3 ) the partial DMFS and dfs indices limited to the molars and premolar pit and fissure surfaces which are at high risk of decay . DNA samples were collected via blood , saliva or buccal swab . Genotyping for approximately 600 , 000 polymorphisms was performed by the Center for Inherited Disease Research at Johns Hopkins University using the Illumina Human610-Quadv1_B BeadChip ( Illumina ) . Extensive data cleaning and quality assurance analyses were performed as previously reported [4] . Imputation of approximately 16 million unobserved genetic variants was performed using the 1000 Genome Project ( phase 1 June 2011 release ) as reference . In brief , pre-phasing of haplotypes was performed via SHAPEIT2 [37] and imputation was performed via IMPUTE2 [38] . Linear regression was used to test the association of dental caries experience with genetic polymorphisms under the additive genetic model while adjusting for age and sex . Pairwise SNP-by-SNP interaction effects were tested in the same modeling framework by including main effects of each SNP and their product term for selected variants within the same gene region , along with age and sex . Analyses were performed separately for dental caries in the permanent dentition of adults ( ages 25–50 years ) and the primary dentition of children ( ages 6–12 years ) . All analyses were limited to self-reported non-Hispanic whites ( mixed European descent ) ; self-reported race was consistent with genetically-determined ancestry . Analyses were performed using PLINK ( v1 . 9 ) ( http://www . cog-genomics . org/plink/1 . 9/ ) [39] and R ( R Foundation for Statistical Computing ) . Third molars were obtained from a patient involved in the International Pachyonychia Congenita Research Registry ( IPCRR ) , under the IRB Protocol number 20040468 and IRB Study number 1057496 . Third molars from healthy patients were obtained from the NIDCR OP-1 Dental Clinic that were collected under the IRB Protocol number NCT01805869 . RNA-seq analysis was performed as described previously [40] . Briefly , mandibles were dissected from P10 mice , transferred to RNAlater solution ( Life Technologies ) and stored at 4°C . Enamel organs were dissected from mandibles and homogenized in Trizol reagent ( Invitrogen ) using the Precellys 24 ( Bertin Technologies ) . Total RNA was extracted and further purified using the RNAeasy mini kit ( Qiagen ) . RNA-seq analysis was performed using the Mondrian SP kit ( Illumina ) and the Illumina HiSeq 2000 sequencing system . Rat mandibles at postnatal day 10 were fixed overnight at 4°C in 4% paraformaldehyde in 1X PBS , dehydrated and embedded in paraffin and 10 μm-thick sections were prepared . Immunohistochemical analysis was performed using a blocking solution containing 5% goat serum and 7 . 5% BlockHen II ( Aves Labs , Tigard , OR ) in 1X PBS . Enzymatic antigen retrieval was performed using Ultravision Trypsin ( Thermo Fisher Scientific , Waltham MA ) . Primary antibodies used: Guinea-pig anti-K6 ( Progen Biotechnik GmbH , Germany ) , guinea-pig anti-K17 ( Progen Biotechnik GmbH , Germany ) . Alexa-488 anti-guinea-pig ( Thermo Fisher Scientific , Waltham MA ) was used as secondary antibody . Images were acquired on a Leica LS5 confocal microscope ( Leica Microsystems Inc . , Buffalo Grove , IL ) . Ground , polished and etched human teeth were stained with guinea-pig anti-K6 ( Progen Biotechnik GmbH , Germany ) , mouse anti-K6 ( Abcam , Cambridge MA ) , guinea-pig anti-K16 ( Progen Biotechnik GmbH , Germany ) or guinea-pig anti-K17 ( Progen Biotechnik GmbH , Germany ) antibody . Alexa-488 anti-guinea-pig and Alexa-555 anti-mouse ( Thermo Fisher Scientific , Waltham MA ) were used as secondary antibodies . Ground , polished and etched human teeth were prepared for SEM as described previously [19] . Samples were fixed overnight at 4°C in 2% glutaraldehyde , 2% PFA in 0 . 1M phosphate buffer pH 7 . 4 and dehydrated through a series of 50% , 70% , 95% and 100% ethanol solutions . They were incubated for 10 min in hexamethyldisilazane , air-dried for 30 min , mounted on aluminum specimen mount stubs covered with conductive carbon adhesive tabs ( Electron Microscopy Sciences , Hatfield , PA ) , sputter-coated with gold and analyzed under a Field Emission Scanning Electron Microscope S4800 ( Hitashi , Toronto , Canada ) at 10 kV . Micro-CT analysis of fixed molars was performed as described previously [19] using the Skyscan 1172 desktop X-ray microfocus CT scanner and the following parameters: 0 . 5mm AI + 0 . 1mm Cu filters , 100 kV , 100 uA , 8 . 00 micron resolution , 0 . 4 degrees rotation step over 180 degrees ) . CTvox software ( Bruker microCT ) was used for 3D reconstruction . The cDNAs encoding the , K6aN21S , K6aG97R , K6aS143N , K6aY497C and K6aN171K mutant proteins were generated by site-directed mutagenesis using the QuikChange Site-directed Mutagenesis Kit ( Stratagene ) . The following primers were used: N21S-forward: GGGGTTTCAGTGCCAgCTCAGCCAGGC , N21S-reverse: GCCTGGCTGAGcTGGCACTGAAACCCC , G97R-forward: GGCTTTGGTGGCGCCaGGAGTGGATTGG , G97R-reverse: CCAATCCACTCCtGGCGCCACCAAAGCC , S143N-forward: GTCAACCAGAaTCTCCTGACTCCCCTC , S143N-reverse: GAGGGGAGTCAGGAGAtTCTGGTTGAC , Y497C-forward: CCGTCTCCAGTGGCTgTGGCGGTGCCAG , Y497C-reverse: CTGGCACCGCCAcAGCCACTGGAGACGG , N171K-forward: GATCAAGACCCTCAAaAACAAGTTTGCC , N171K-reverse: GGCAAACTTGTTtTTGAGGGTCTTGATC ( lower cases indicate the position of the mutations ) . The pBi4-GFP vector was used to simultaneously express the reporter protein EGFP with K6aWT , K6aN21S , K6aG97R , K6aS143N , K6aY497C , K6aS143N-Y497C , or K6aN171K under control of a unique tetracycline responsive element . Murine Ameloblast-like cells ( ALC ) were kindly provided by Dr . Sugiyama [26] and used to produce a tetracycline inducible ameloblast cell line . These cells were stably transfected with a prtTA2-M2/IRES-Neo plamid obtained after subcloning of the rtTA2-M2 cassette [41] into the pCMV-IRES-Neo ( Clontech ) . rtTA-M2 is a mutagenized form of rtTA that shows a lower basal activity and a higher sensitivity to doxycycline ( Dox ) than the original rtTA [41] . The presence of the IRES cassette before the neomycin ( Neo ) resistance gene allowed coexpression of the rtTA-M2 transactivator and the Neo resistance gene , increasing the chance to select clones that express sufficient amounts of the transactivator in Neo-resistant cells . Clones were isolated and functionality of the tet system was screened by transient transfection with pTRE2-luc ( expression of luciferase under the control of tetracycline responsive element ) . Cells were grown in the presence or absence of 2 ug/ml Dox and luciferase activity was estimated . One clone exhibiting a low basal activity of the transactivator in the absence of Dox and a strong induction in the presence of Dox ( +Dox/-Dox ratio ) was selected for subsequent experiments and named ALC-TetON . ALC-TetON cells were grown in Dulbecco’s modified Eagle’s medium ( 10% fetal bovine serum , 1% penicillin/streptomycin and 1 ug/ml G418 ) . For transfections , the cells were grown to at least 70% confluence . 2 million cells were used per transfection with either the pBi4-GFP/K6aWT , pBi4-GFP/K6aN21S , pBi4-GFP/K6aG97R , pBi4-GFP/K6aS143N , pBi4-GFP/K6aY497C , pBi4-GFP/K6aS143N-Y497C , or pBi4-GFP/K6aN171K plasmid and the pCMV-K16WT plasmid ( Amaxa Nucleofactor ) . Transfected cells were seeded on glass coverslips coated with 0 . 1% gelatin and immediately induced with 2ug/ml doxycycline . Twenty-four hours after induction , cells were washed three times in 1X PBS and fixed with 4% paraformaldehyde in PBS for 15 min at room temperature . A 5 min incubation in 0 . 2% Triton in 1X PBS was used to permeabilize the cells before blocking unspecific sites using 3% BSA in PBS for 1 h . Primary antibodies diluted in blocking solution were applied for 1 h . Primary antibody used: guinea-pig anti-K6 ( Progen ) . Secondary antibodies diluted in blocking solution were applied for 30 min . Secondary antibodies used: Alexa Fluor 555 anti-guinea pig IgG ( Life Technologies ) . Nuclei were stained using DAPI and coverslips were mounted on glass slides using Mowiol ( Calbiochem ) . Images were acquired using a LEICA Sp5 confocal microscope . | Tooth decay , more commonly known as dental cavities , is the most common chronic disease worldwide , both in children and in adults . It consists in the destruction of tooth enamel , the outer layer of the teeth , by acid-producing bacteria . Enamel is the hardest tissue in the body , comprised of 96% minerals . However , it contains a small fraction of proteins that is important for its resistance to mechanical stress and decay . Here we show that this protein fraction contains a set of structural proteins ( K6a , K6b , K6c , K16 and K17 ) that belong to the keratin family and are present specifically in the skin of the palms and soles , as well as in nails . We further show that common genetic mutations that affect the composition of these proteins lead to an increased number of cavities . Rare mutations in these keratins lead to a human disease called pachyonychia congenita ( PC ) and characterized by severe nail malformations and lesions in the skin of the palms and soles . Analysis of wisdom teeth from one of these patients showed that their enamel exhibited structural defects . These results demonstrate that these keratins are important components of tooth enamel and that common genetic variants in the genes that encode them influence tooth decay risk in the general population . |
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Cellular senescence involves epigenetic alteration , e . g . loss of H3K27me3 in Ink4a-Arf locus . Using mouse embryonic fibroblast ( MEF ) , we here analyzed transcription and epigenetic alteration during Ras-induced senescence on genome-wide scale by chromatin immunoprecipitation ( ChIP ) -sequencing and microarray . Bmp2 was the most activated secreted factor with H3K4me3 gain and H3K27me3 loss , whereas H3K4me3 loss and de novo formation of H3K27me3 occurred inversely in repression of nine genes , including two BMP-SMAD inhibitors Smad6 and Noggin . DNA methylation alteration unlikely occurred . Ras-activated cells senesced with nuclear accumulation of phosphorylated SMAD1/5/8 . Senescence was bypassed in Ras-activated cells when Bmp2/Smad1 signal was blocked by Bmp2 knockdown , Smad6 induction , or Noggin induction . Senescence was induced when recombinant BMP2 protein was added to Bmp2-knocked-down Ras-activated cells . Downstream Bmp2-Smad1 target genes were then analyzed genome-wide by ChIP-sequencing using anti-Smad1 antibody in MEF that was exposed to BMP2 . Smad1 target sites were enriched nearby transcription start sites of genes , which significantly correlated to upregulation by BMP2 stimulation . While Smad6 was one of Smad1 target genes to be upregulated by BMP2 exposure , Smad6 repression in Ras-activated cells with increased enrichment of Ezh2 and gain of H3K27me3 suggested epigenetic disruption of negative feedback by Polycomb . Among Smad1 target genes that were upregulated in Ras-activated cells without increased repressive mark , Parvb was found to contribute to growth inhibition as Parvb knockdown lead to escape from senescence . It was revealed through genome-wide analyses in this study that Bmp2-Smad1 signal and its regulation by harmonized epigenomic alteration play an important role in Ras-induced senescence .
Cellular senescence was first described as the limited replicative capacity of primary cells in culture [1] . Activated oncogenes can induce premature form of cellular senescence , and cells fall into irreversible arrest to block cellular proliferation [2] , [3] . In addition to cell death programs such as apoptosis and autophagy , oncogene-induced senescence is recognized as a potent barrier against oncogenic transformation , suppressing unscheduled proliferation of early neoplastic cells [4]–[7] . Replicative senescence and oncogene-induced senescence are known to comprise activation of tumor suppressor pathways including p16Ink4a-Rb and p19Arf ( p14ARF in human ) -p53 signaling cascades . Genetic and epigenetic inactivation of these genes in cancer supported their crucial roles in senescence as barriers to tumorigenesis [8] , [9] . Although the roles of RB and p53 signaling pathways in senescence are undisputed , it has become clear that other factors are also involved . Expression of secreted factors , or “senescence-messaging secretome” , has been proposed as an example of such mechanisms [10] , [11] . The induction of senescence required several secreted factors including members of Wnt , insulin , transforming growth factor-β , plasmin and interleukin signaling cascades [11] . Epigenetic mechanism is also suggested to play important roles in senescence . When human fibroblasts senesced , heterochromatic regions condensed to form senescence-associated heterochromatic foci , where regions with histone H3K9 trimethylation ( H3K9me3 ) gathered [12] , and were recently shown to restrain DNA damage response [13] . Expression of Jhdm1b , a demethylase specific for H3K36me2 , caused cell immortalization or leukemic transformation depending on its demethylase activity on p15Ink4b , and its knock down resulted in cellular senescence [14] , [15] . INK4A and ARF region in young cells was repressed by H3K27me3 imposed by the Polycomb Group proteins , and the repressive mark was lost during oncogene-induced senescence , resulting in expression of p16 and p19; the loss of repressive mark was also detected when mouse embryonic fibroblast ( MEF ) underwent stress-induced senescence around seven passages [16]–[19] . Jmjd3 , a histone demethylase for H3K27 , was found to be essential in senescence , and its knock down lead to escape from senescence sustaining repression of p16 by H3K27me3 [20] , [21] . In the previous studies , we comprehensively analyzed aberrant promoter DNA methylation in colorectal cancer and reported three distinct DNA methylation epigenotypes [22] , [23] . Distinct methylation epigenotypes significantly correlated to different oncogene mutation statuses , suggesting that epigenotypes of cancer might perhaps be requisite phenotype of aberrant methylation to escape from oncogene-induced senescence by inactivation of critical factors of senescence [23] , [24] . To gain insight in phenotype of critical gene inactivation in oncogene-mutation ( + ) cancer , we aim to clarify critical genes/signals/phenomena in oncogene-induced senescence in normal cells in this study . Here we perform genome-wide analyses of epigenetic and gene expression changes in Ras-indeced senescence using mouse embryonic fibroblasts ( Figure S1 ) . We show that Bmp2/Smad1 signal is critical in Ras-induced senescence , and is regulated by coordinated epigenomic alteration . We further examine downstream target genes of this critical signal on genome-wide scale , and show that the epigenomic regulation of the signal involves disruption of negative feedback loop , and that activated downstream targets actually include a gene to contribute to growth arrest .
To induce cellular senescence , mouse embryonic fibroblasts after two passages ( MEFp2 ) was infected with retrovirus of oncogenic Ras ( RasV12 ) with N-terminal FLAG tag and cultured through day 10 ( Figure S2A ) . RasV12-infected cells ( RasV12 cells ) showed significant increase in number of SA-βgal ( + ) cells , compared to MEFp2 , MEF passed three more times without infection ( MEFp5 ) , mock-infected cells ( Mock cells ) , and wild type Ras ( RasG12 ) -infected cells ( Figure 1A and Figure S2B ) . Global gene expression analysis was performed using expression array . In RasV12 cells on day 10 , 822 genes were upreglated and 735 genes downregulated , by >5-fold compared to MEFp2 ( Tables S1 , S2 ) . Gene annotation enrichment analysis suggested that genes related to secreted protein ( P = 1 . 8×10−19 ) , extracellular region ( P = 1 . 2×10−21 ) , and differentiation/development ( P = 3 . 8×10−10 ) , e . g . Bmp2 and Igfbp3 , were upregulated , supporting the importance of secreted factor expression in senescence . Genes related to cell cycle ( P = 7 . 2×10−22 ) such as Cdc6 and Mcm5 were enriched in downregulated genes , indicating growth arrest . Also genes related to secreted protein ( P = 7 . 9×10−18 ) and extracellular region ( P = 9 . 2×10−14 ) such as Bmp4 and Tgfb2 were enriched in downregulated genes , suggesting that dynamic control of secretome by activation and repression of secreted factors occurred during senescence . To analyze epigenomic gene regulation during Ras-induced senescence , we selected H3K4me3 as an active mark and H3K27me3 as a repressive mark , and mapped them by Chromatin immunoprecipitation ( ChIP ) -sequencing . As reported , H3K27me3 mark at p16Ink4a-p19Arf locus in MEFp2 was markedly lost in RasV12 cells ( Figure 1A ) . ChIP-sequencing of H3K4me3 showed concurrent gain of the active mark around p16 transcription start site ( TSS ) , which reflected increase of p16 expression in RasV12 ( Figure 1B ) . By quantitative ChIP-PCR , significant gain of H3K4me3 and loss of H3K27me3 were validated in RasV12 cells , compared to MEFp2 ( Figure 1C ) . Gain of H3K4me3 and loss of H3K27me3 were also detected at intermediate level in Mock and RasG12 cells ( Figure 1C ) . Expression of p16 was also partially increased in Mock and RasG12 cells , at the similar level to MEFp5 ( Figure 1B ) . These indicated that p16 expression could be induced partially by gain of H3K4me3 and H3K27me3 during passages , which was in agreement with the previous report of gradual H3K27me3 loss in stress-induced senescence during 5–7 passages [16] , [18] , but more marked alteration occurred at this locus in Ras-induced senescence . Enrichment of Ezh2 , a member of the Polycomb Group proteins , was also analyzed by ChIP-PCR , and it was significantly decreased around p16 TSS in RasV12 cells compared to MEFp2 ( Figure 1D ) . When analyzing distribution of 36-bp reads mapped around TSS of 20 , 232 genes , the mapped reads were enriched within ±2 kb of TSS , mainly ±1 kb of TSS ( Figure S3A ) , for both H3K4me3 and H3K27me3 . We counted mapped reads within a window of genomic region , so that the number of mapped reads per million reads within a window is regarded as epigenetic status of the center position of the window . Within ±2 kb from TSS of each gene , the maximum number of mapped reads per million reads in a window size of 300 bp ( H3K4me3 ) or 500 bp ( H3K27me3 ) was regarded as the epigenetic status of each gene . A wider window was necessary for H3K27me3 because distribution of H3K27me3 was rather wide than H3K4me3 ( Figure 1A and Figure S3A ) . The number of genes with repressive H3K27me3 mark was generally decreased in RasV12 cells ( Figure S3B ) , in agreement of the previous reports [20] , [21] that expression of Jmjd3 was increased during senescence , whereas expression of Ezh2 was decreased ( Figure S4 ) . It was expected that genes activated by losing H3K27me3 might exist other than p16 and p19 , because of the decrease of genes with H3K27me3 mark in RasV12 cells . Among 20 , 232 genes with epigenomic alteration analyzed , 16 , 793 genes were also analyzed for expression on array ( Figure S5 ) . For epigenetic status of H3K4me3 , 9 , 164 genes in MEFp2 and 8 , 841 genes in RasV12 showed >4 reads per million reads around TSS , and regarded as H3K4me3 ( + ) . Similarly , 7 , 140 and 7 , 354 genes respectively with <3 reads per million reads were regarded as H3K4me3 ( - ) . Markedly higher expression levels of H3K4me3 ( + ) genes than H3K4me3 ( - ) genes were confirmed by comparing the mean of expression levels ( Figure S5A ) . For H3K27me3 , 2 , 612 and 2 , 370 genes with >1 . 5 reads per million reads around TSS were regarded as H3K27me3 ( + ) , and 13 , 205 and 12 , 841 genes with <1 were as H3K27me3 ( - ) in this study . H3K27me3 ( + ) genes were markedly repressed than H3K27me3 ( - ) genes ( Figure S5B ) . Among 284 genes losing H3K27me3 in RasV12 cells , 30 genes losing H3K27me3 and gaining H3K4me3 simultaneously , like p16 , showed significant enrichment in upregulated genes among the 284 genes ( P = 0 . 000007 , Kolmogorov-Smirnov test , Figure 2A ) . Among the 30 genes ( listed in Table S3 ) , Bmp2 , a secreted factor for BMP/SMAD pathway , was found to be the most upregulated secreted factor and activated more than p16 ( Figure 2A ) . Interestingly , 110 genes modified bivalently in MEFp2 showed loss of H3K27me3 and sustained H3K4me3 mark in RasV12 cells , but did not show significant enrichment in upregulated genes ( P = 0 . 9 , Figure 2A ) . Not only genes with H3K27me3 loss , but also there were as many as 239 genes showing H3K27me3 gain in RasV12 cells . Nine genes gaining H3K27me3 and losing H3K4me3 simultaneously showed significant enrichment in downregulated genes ( P = 0 . 0004 , Figure 2B . Genes are listed in Table S4 ) . Very interestingly , two of the nine genes were Smad6 and Nog , inhibitors for BMP-SMAD pathway [25] . The majority , 189 of the 239 genes , had neither modification in MEFp2 with very low expression levels . These genes acquired de novo H3K27me3 mark in RasV12 cells , but did not show any more downregulation ( P = 1 , Figure 2B ) . Around TSS of Bmp2 , a secreted factor for BMP-SMAD pathway , loss of H3K27me3and gain of H3K4me3 were validated by quantitative ChIP-PCR ( Figure 3A , 3B ) . ChIP-PCR also showed that Ezh2 enrichment was significantly decreased around Bmp2 in RasV12 cells ( Figure S6 ) . ChIP-PCR showed that H3K4me3 and H3K27me3 levels in MEFp2 were sustained in Mock and RasG12 , but specifically altered in RasV12 cells ( Figure 3B ) . Quantitative RT-PCR showed very low level of Bmp2 expression in MEFp2 , Mock cells and RasG12 cells , but marked increase to 91 . 6-fold in RasV12 cells ( Figure 3C ) . Bmp2 activation thus occurred specifically in Ras-induced senescence , different from p16 that partially showed increased expression and histone methylation alteration during passages ( Figure 1C ) . Retrovirus to express shRNA against Bmp2 ( shBmp2 ) was infected together with RasV12 infection , to knock down Bmp2 to 0 . 04–0 . 08 fold on days 3 , 7 , and 10 ( Figure 3D ) . Bmp2-knocked-down RasV12 cells escaped from senescence with decreased number of SA-βgal ( + ) cells compared to RasV12 cells . While Smad1/5/8 is known to serve principally as substrates for BMP receptors [26] , western blotting analysis revealed phosphorylation of Smad1/5/8 in RasV12 cells ( Figure 3E ) . Decrease of Smad1/5/8 phosphorylation level was also shown in Bmp2-knocked-down RasV12 cells ( Figure 3E ) , and continual cell growth faster than Mock cells ( Figure 3F ) . To confirm that this escape from senescence was specifically due to Bmp2 knockdown , Bmp2-knocked-down RasV12 cells were cultured with recombinant BMP2 protein ( rBMP2 , R&D systems #355-BM ) at 0 , 20 and 200 ng/mL in culture medium with 10% serum . The cells showed increased number of SA-βgal ( + ) cells in dose-dependent manner , even to the level of RasV12 cells when rBMP2 was at 200 ng/mL ( Figure 3G , 3H ) . The level of Smad1/5/8 phosphorylation was increased when rBMP was added ( Figure 3I ) , and growth curve showed growth arrest similar to senescent RasV12 cells ( Figure 3F ) . These results indicated that Bmp2 upregulation plays an important role in Ras-induced senescence . There was no increase of SA-βgal ( + ) cells when Mock cells or MEF cells without infection were exposed to rBMP2 at 200 ng/mL , indicating that increase of BMP2 alone is not enough to induce cellular senescence ( Figure 3G ) . As for Smad6 , a specific inhibitor for BMP-SMAD pathway , gain of H3K27me3 and loss of H3K4me3 in RasV12 cells were found and validated by quantitative ChIP-PCR ( Figure 4A , 4B ) . H3K27me3 and H3K4me3 levels in MEFp2 were sustained in Mock and RasG12 cells , and altered specifically in RasV12 cells . This indicated that these alterations of histone methylation were not detected in stress-induced senescence during passages , but specifically occurred in Ras-induced senescence , like Bmp2 . Markedly decreased expression of Smad6 to 0 . 05-fold specifically in RasV12 cells was also validated by quantitative RT-PCR , while there was no repression of Smad6 during passages ( Figure 4C ) . Ezh2 enrichment was also analyzed by ChIP-PCR ( Figure 4D ) . This histone methyltransferase for H3K27 was significantly increased around TSS of Smad6 in RasV12 cells . It was indicated that Ezh2 was recruited to this de novo H3K27 trimethylation site , and that repression mechanism by de novo H3K27me3 was still active although Ezh2 expression level itself was downregulated during senescence , and Jmjd3 expression level was upregulated ( Figure S4A ) . Smad6 with N-terminal Myc tag was introduced to MEF by retroviral infection together with RasV12 virus , and their simultaneous expression was confirmed by cellular immunofluorescence ( Figure 4E and Figure S7 ) . Western blotting analysis and cellular immunofluorescence showed decrease of Smad1/5/8 phosphorylation in Smad6-introduced RasV12 cells compared to RasV12 cells ( Figure 4F , 4G ) . Smad6-introduced RasV12 cells showed decreased number of SA-βgal ( + ) cells compared to RasV12 cells ( Figure 4H and Figure S8 ) and showed continual cell growth faster than Mock cells or Smad6-introduced Mock cells ( Figure 4I ) . These data indicated that Smad6 repression was important in Ras-induced senescence . Nog , another inhibitor for BMP-SMAD pathway , was repressed to 0 . 06-fold in RasV12 cells also by losing H3K4me3 and gaining H3K27me3 ( Figure 5A , 5B ) . Introduction of Nog cDNA by retrovirus infection together with RasV12 resulted in its overexpression and escape from senescence ( Figure 5B–5D ) . To clarify whether Nog at the physiological expression level could inhibit cellular senescence , Nog-transgenic ( Nog-Tg ) mice under Krt19 promoter [27] was used next , since the transgene was expected not to be modified with de novo H3K27me3 . Krt19 was expressed in MEFp2 at much higher level compared to brain and testis , confirming that Krt19 promoter is active in MEF ( Figure S9 ) . Nog-Tg female mouse was crossed with C57B6 , to establish and pool Tg ( - ) and Tg ( + ) MEFs from embryos of the same mother . Tg ( + ) MEF showed Nog expression at similar level to wild type MEFp2 and Tg ( - ) MEF ( Figure 5E ) . While Tg ( - ) MEF showed Nog repression by RasV12 infection similarly to wild type MEF , Tg ( + ) MEF did not show Nog repression by RasV12 infection and showed continual growth faster than Tg ( - ) MEF ( Figure 5E–5G ) . These indicated that Nog repression was also important in Ras-induced senescence . It was reported that oncogenic Ras induces DNA methylation-mediated epigenetic inactivation in NIH3T3 cells [28] , and that EZH2 directly controls DNA methylation [29] , [30] . We therefore performed bisulfite sequencing to analyze DNA methylation statuses of 5′ regions of Smad6 and Bmp2 where increase or decrease of Ezh2 was confirmed ( Figure 4D , Figure S6 ) . There was no methylation alteration of these regions in RasV12 cells compared to MEFp2 ( Figure 6A ) . Also , Dnmt1 expression level was not altered during Ras-induced senescence ( Figure 6B ) . To gain insight whether oncogenic Ras induces DNA methylation-mediated inactivation in MEF on genome-wide scale , we performed methylated DNA immunoprecipitation ( MeDIP ) -seq in MEFp2 and RasV12 cells ( Figure 6C , 6D ) . Although MeDIP is reported to be not accurate to detect DNA methylation in low-CpG regions , it is powerful screening method to detect candidate methylation regions in high-CpG regions , e . g . promoter CpG islands [22] , [23] , [31] . Increase of methylation was detected only in three candidate genes , and the increase was considered as a noise in genome-wide analysis because the increase was not validated by bisulfite sequencing ( Figure 6D , 6E ) . Bisulfite sequencing was performed for five more genes which showed slight increase of methylation in MeDIP-seq , but there was no methylation alteration in RasV12 cells compared to MEFp2 ( Figure 6F ) . Human fibroblast IMR90 was infected with RasV12 retrovirus ( RasV12-IMR90 cells ) . It was confirmed by SA-βgal staining on day 7 that cells fell into premature senescence ( Figure S10A ) . Real-time RT-PCR showed that BMP2 expression was markedly increased to 145-fold in RasV12-IMR90 cells , while SMAD6 and NOG expressions were decreased to 0 . 32-fold and 0 . 15-fold , respectively ( Figure S10B ) . Nog was introduced in IMR90 by retroviral infection with RasV12 , and Nog-induced RasV12-IMR90 cells showed continual cellular growth ( Figure S10C ) , suggesting that BMP2-SMAD1 is also an effector program in human fibroblasts . Since Bmp2 upregulation , Smad6 repression , and Nog repression were shown to contribute to Ras-induced senescence , downstream target genes of Bmp2-Smad1 signal are further analyzed on genome-wide scale . Smad1 binding sites in MEF were analyzed by exposing MEF to rBMP and ChIP-sequencing using anti-Smad1 antibody ( Figure 7A and Figure S11 ) . Smad1 mostly bound to gene regions; 1 , 103 ( 75% ) out of 1 , 479 Smad1 binding sites were located within 10 kb from 20 , 232 RefSeq genes , and 818 sites ( 55% ) were within 5 kb from their TSS . Using GADEM ( http://www . niehs . nih . gov/research/resources/software/gadem/ ) [32] , GGGGCGGGGC was extracted as highly enriched motif within Smad1 binding region in both whole genomic and TSS regions ( Figure 6B , Figure S12 ) . Using DME ( http://rulai . cshl . edu/dme/ ) [33] , it was confirmed that very similar motifs e . g . GGGCGGGGC ( Figure 7B ) or GGGGCGGGGM ( Figure S13 ) were enriched . This was in good agreement with the canonical SMAD1-bound GC-rich elements [26] , [34] , [35] and the previous report that the sequence GGCGGGGC was enriched within Smad1/5 binding regions in ES cells and pulled down SMAD proteins [36] . Genes with Smad1 binding site at TSS regions were significantly enriched in active genes in MEF , especially in genes upregulated by rBMP exposure ( Figure 7C ) , suggesting that Smad1 binding correlates to gene upregulation . Smad1 target genes upregulated most by rBMP exposure included Smad6 , which was upregulated by 4 . 5-fold in MEF ( Figure 7D , 7E ) . These indicated that Bmp2/Smad1 signal in MEF could be controlled by negative feedback through Smad1 regulation on Smad6 . However , Smad6 was repressed in RasV12 cells by H3K27me3 , so when Bmp2-knocked-down RasV12 cells was exposed to rBMP2 , Smad6 level was still suppressed lower than the level in MEFp2 ( Figure 7E ) . Smad1 target genes repressed in RasV12 cells were not limited to Smad6 . H3K27me3 gain during Ras-induced senescence was detected in 50 Smad1 target genes , which were enriched in genes repressed in RasV12 cells , e . g . Atoh8 . Atoh8 was highly upregulated in BMP2 exposure , but repressed in RasV12 cells with decrease of H3K4me3 from 8 . 7 to 1 . 8 and increase of H3K27me3 mark from 1 . 0 to 1 . 8 ( Figure 7C , Figure 8A and 8B . Gene list is available in Table S5 ) . It was reported that Atoh8 was , like Id1 , suggested to be a direct target of BMP-SMAD signal [37] . On the contrary , Smad1 target genes without increased repressive mark were shown to keep upregulation . Among 838 Smad1 target genes , 581 with no increase of H3K27me3 , or 156 showing decrease of H3K27me3 , were significantly enriched in genes upregulated in RasV12 cells ( P = 0 . 01 and P = 0 . 004 , respectively , Figure 8A ) . If Bmp2/Smad1 signal is critical in senescence , the most upregulated target genes are expected to include genes with growth suppressor function . To choose such candidate genes , the most upregulated target genes were screened using promoter methylation data of our previous methylated DNA-immunoprecipitation ( MeDIP ) -chip analyses of human cancer cells [22] , [23] ( Table S6 ) , since such genes may possibly be frequently inactivated in human cancer . The most upregulated targets then included Parvb , which showed promoter methylation in human cancer cell lines HCT116 and DLD1 ( Table S6 ) . When MEF senesced , Parvb showed increase of H3K4me3 from 8 . 6 to 16 . 8 , and decrease of H3K27me3 from 1 . 0 to 0 . 6 ( Figure 8B ) . Real-time RT-PCR validated increase of Parvb expression in RasV12 cells , and also when exposed to rBMP2 ( Figure 8C ) . When Parvb was knocked down to 0 . 05-fold by shRNA , SA-βgal ( + ) cells were partially decreased and cells showed continual growth ( Figure 8D and Figure S14 ) . Western blot analysis showed decrease of Akt phosphorylation in exposure to a growth factor or serum when Parvb with C-terminal V5 tag was introduced in MEF ( Figure 8E ) .
In this study , we examined H3K4me3 and H3K27me3 marks for genome-wide analysis of epigenomic changes , revealing that activation of Bmp2-Smad1 signal is important in Ras-induced senescence and it is regulated by dynamic epigenomic alteration in coordinated manner . Different from p16 , H3K4me3 and H3K27me3 marks on Bmp2 was not altered during passage in cell culture , but specifically altered in RasV12 cells to induce its marked upregulation , leading to Smad1/5/8 phosphorylation and cellular senescence . Decrease of Ezh2 and increase of Jmjd3 were detected in RasV12 cells at similar levels to MEFp5 , Mock cells and RasG12 cells . This may contribute to partial increase of p16 expression in MEFp5 , Mock cells and RasG12 cells , and partial decrease of H3M27me3 mark on p16 in stress-induced senescence during passages as reported [16] , [18] . However , the alterations on p16 were more markedly detected in RasV12 cells , and the alterations on Bmp2 and Smad6 were specifically detected in Ras-induced senescence and did not occur during passages . It is noteworthy that de novo formation of H3K27me3 occurs on Smad6 in RasV12 cells in spite of general decrease of Ezh2 and increase of Jmjd3 . The mechanism how these epigenetic regulations are programmed is largely unknown , but one possible answer might be non-coding RNA [38] , [39] . PRC2 was reported to be recruited in trans to its target gene by virtue of its association with HOTAIR , a 2 . 2 kb non-coding RNA in the HOXC locus [40] . Oncogenic Ras inhibited expression of ANRIL ( antisense non-coding RNA in the INK4 locus ) ; ANRIL showed binding to CBX7 within PRC1 and SUZ12 in PRC2 , and was important in repressing the protein-coding genes of INK4b/ARF/INK4a locus in cis to regulate senescence [41] , [42] . Ezh2 recruitment was increased in Smad6 , and decreased in Bmp2 and p16 ( Figure 1D , Figure 4D , Figure S6 ) . It would be interesting to analyze whether any non-coding RNAs recruit PRC to Smad6 and Bmp2 in cis or trans , and their expression alterations contribute to epigenetic alterations of these genes during Ras-induced senescence . Gene repressions by other epigenetic mechanism than Polycomb , such as H3K9 methylation , would be interesting to be analyzed next . Human fibroblasts in senescence are reported to suppress DNA damage response by forming heterochromatic foci , where regions with methylated H3K9 gathered [12] . Amplification of SETDB1 , a methyltransferase for H3K9 , was recently reported to play an accelerating role in melanoma onset [13] , while knockout of Suv39h1 , another histone methyltransferase for H3K9 , caused escape from senescence of lymphocytes [43] , suggesting necessity of adequate control of H3K9 methylation . Genome-wide analyses of methylated H3K9 and other epigenomic marks as well would be helpful to obtain the whole picture of epigenomic alteration and its importance in senescence . As for DNA methylation , it was reported that oncogenic Ras induces DNA methylation-mediated epigenetic inactivation in NIH3T3 cells and that 28 responsible genes including DNMT1 are required for the methylation [28] . DNA methylation statuses at 5′ regions of Smad6 and Bmp2 were not altered , however , indicating that expression changes of these genes during senescence were not due to DNA methylation . Dnmt1 level was not altered in RasV12 cells , either . Increase of methylation was detected only in three candidate genes by MeDIP-seq analysis , and the increase was considered as a noise in genome-wide analysis because the increase was not validated by bisulfite sequencing . Five more genes were chosen for bisulfite sequencing , because Sfrp1 was reported to be methylated by oncogenic Ras in NIH3T3[28] , and four other genes were chosen randomly from genes with slight increase of methylation in MeDIP-seq . There was no methylation alteration in RasV12 cells compared to MEFp2 , either . Although MeDIP is not accurate to detect DNA methylation in low-CpG regions [22] , [31] , it was suggested that DNA methylation unlikely occurs in Ras-induced senescence , at least high-CpG regions e . g . promoter CpG islands . The discrepancy between the previous report of NIH3T3 and our MEF result may be because MEF falls into cellular arrest by oncogenic stress and there might be no time enough to induce DNA methylation alteration . In NIH3T3 , cells transform by oncogenic Ras , and may have time enough to acquire DNA methylation during continuing proliferation . Or , two independent cells , NIH3T3 ( ATCC #CRL-1658 ) and K-ras-transformed NIH3T3 ( ATCC #CRL-6361 ) , were compared in the previous NIH3T3 study [28] , so the result might be different if one NIH3T3 clone is analyzed at time courses before and after induction of activated Ras . As for BMP-SMAD signals , utilization of four BMP type 1 receptors depends on BMP ligands; BMP2 and BMP4 utilize BMPR1A and BMPR1B , BMP6 and BMP7 bind principally to ACVR1 , and BMP9 is a ligand for ACVRL1 and ACVR1 [26] . We reported that Smad6 inhibited BMPR1A/BMPR1B preferentially to ACVR1/ACVRL1 , and inhibited BMP2-induced Smad1/5 phosphorylation more prominently than BMP6-induced Smad1/5 phosphorylation [44] . This is in agreement with the current results that Smad6 could cause decreased phosphorylation of Smad1/5 and escape from senescence , and that coordination of Bmp2 upregulation and Smad6 repression was critical in Ras-induced senescence . Our genome-wide analysis showed that Smad6 was a Smad1 target gene that could be highly upregulated by exposure to BMP2 , but strongly repressed in RasV12 cells with de novo H3K27me3 mark . Previous reports showed that BMP-activated Smad1/5 activates Smad6 expression through interaction with the Smad6 promoter [45] , [46] . These suggested that Smad6 repression with de novo H3K27 methylation blocks negative feedback loop to sustain the effect of upregulated Bmp2 , i . e . activation of Bmp2-Smad1 signal in Ras-induced senescence . In other words , dynamic H3K27me3 alteration is suggested to repress selectively the genes which could negatively control senescent signal , and to activate selectively genes which could positively affect senescent signal ( Figure 9 ) . In fact , another BMP-SMAD inhibitor , Nog , was also repressed by increased H3K27me3 mark . While ChIP-seq analysis did not show Smad1 binding site around Nog TSS , Nog was also highly upregulated by rBMP2 exposure ( Figure 6C ) and repressed by increased H3K27me3 mark in RasV12 cells ( Figure 5A , 5B ) . This might suggest that Nog repression could also be a disruption of negative feedback loop , though Nog is not a direct downstream target of Bmp2-Smad1 . Parvb , which possessed Smad1 binding site around its TSS , was upregulated in exposure to BMP2 or in RasV12 cells , and its knock down lead to escape from senescence . While PARVA was reported to bind to integrin-linked kinase ( ILK ) and play a critical role in cell survival by promoting membrane recruitment of Akt and its activation by phosphorylation , PARVB was reported to compete PARVA in binding to ILK and reverse its oncogenic effect by repressing ILK kinase activity [47] , [48] . As PARVB introduction was reported to suppress cellular growth of breast cancer cells with decreased Akt phosphorylation [49] , [50] , Parvb introduction in MEF also decreased phosphorylation of Akt in exposure to a growth factor or serum ( Figure 7E ) . It was suggested that Parvb might be one of Bmp2-Smad1 target genes playing a positive role in growth inhibition , at least partly , and selectively and effectively activated through simultaneous inactivation of negative regulators . We chose Parvb on the assumption that candidate genes downstream of BMP-SMAD might be inactivated by DNA methylation in full-blown cancers , but other downstream genes that were not methylation target in analyzed cancer cell lines might also play a positive role in senescence . Aberrations in BMP-SMAD signal have been frequently reported in human cancer . Juvenile polyposis syndrome , an inherited syndrome with high risk of colorectal cancer , is caused by germline mutation of BMPR1A or SMAD4 [51] , and importance of BMP signal is supported by its mouse model with transgenic Nog expression or with Bmpr1a inactivation [52] , [53] . BMP2 expression was lost in microadenoma of familial adenomatous polyposis , while BMP2 was expressed in mature colonic epithelial cells , promoting apoptosis and differentiation and inhibiting proliferation [54] . Inactivation of BMPR1A , BMPR2 , and SMAD4 was frequently observed in sporadic colorectal cancer , correlating to loss of Smad1/5/8 phosphorylation [55] . Colon epithelial polyps were developed even by alteration of BMP pathway in the stromal microenvironment , using mice with conditional inactivation of Bmpr2 in the stroma [56] . About prognosis , Smad6 expression was reported to be elevated in 40% of non-small cell lung cancer , and correlated to poorer outcome [57] . BMP2 upregulation was reported in senescence of other cell types , such as vascular smooth muscle cells [58] . Considering frequent RAS gene mutation in cancer , e . g . colon ( ∼40% ) and non-small cell lung cancers ( ∼30% ) [59] , further experiments are to be performed to clarify which cell types Bmp2-Smad1 signal is critical in oncogene-induced senescence , and whether Bmp2-Smad1 signal and its target genes are disrupted in cancer with association to oncogene mutation .
MEF was established from 13 . 5 embryonic day embryos of C57/B6 as reported [60] . After cells were passed twice ( MEFp2 ) , cells were infected with retroviruses for 48 hours . Then cells were exposed to 4 µg/mL puromycin for selection during days 0–3 , and were passed on days 3 , 7 , and 10 . Human fibroblast IMR90 ( JCRB9054 ) was purchased from Health Science Research Resources Bank ( Osaka , Japan ) , and 2 µg/mL puromycin were used for selection after retrovirus infection . Total RNA was collected using TRIzol ( Invitrogen , Carlsbad , CA ) . This study was certified by Animal Ethics Committee in Tokyo University . Nog-Tg mice using keratin 19 gene promoter and mouse Nog cDNA were previously established [27] , and were crossed with wild type C57/B6 mice five times to obtain C57/B6 background . Nog-Tg female mouse was crossed with C57B6 , and Tg ( - ) and Tg ( + ) MEFs were established from 13 . 5 embryonic day embryos of the same mother . Each embryo was minced separately , and Tg ( - ) and Tg ( + ) MEFs were pooled after genotyping each MEF , and used for experiments . Retroviral vectors for Ras was constructed by cloning cDNAs for wild type HRAS ( RasG12 ) and mutated HRAS ( RasV12 ) by reverse-transcription PCR products from HMEC and SK-BR3 cell RNA , respectively , with N-terminal FLAG tag into pMX vector that contains puromycin resistance gene ( a kind gift from T . Kitamura ) . Mock pMX vector ( Mock ) , and vectors containing RasG12 and oncogenic RasV12 were transfected into plat-E packaging cells ( a kind gift from T . Kitamura ) using FuGENE 6 Transfection Reagent ( Roche , Germany ) to prepare retroviruses . Smad6 cDNA with N-terminal 6x Myc tag , Nog cDNA with C-terminal V5 tag , and Parvb cDNA with C-terminal V5 tag were also cloned into pMX vector . To knock down Bmp2 or Parvb , double strand oligonucleotide DNA to express small hairpin RNA against Bmp2 ( shBmp2 ) or Parvb ( shParvb ) , respectively , was cloned into RNAi-Ready pSIREN-RetroQ Vector ( Clontech , CA ) . Viral packaging for Smad6 , Nog , shBmp2 and shParvb retrovirus vectors was also done using plat-E cells . Retroviruses of RasV12 and Nog for human fibroblast were prepared using Retrovirus Packaging Kit Ampho ( #6161 , TaKaRa Bio Inc , Shiga , Japan ) . For genome-wide transcription analysis , GeneChip Mouse Genome 430 2 . 0 Array ( Affymetrix ) was used as described [61] . The GeneChip data were analyzed using the Affymetrix GeneChip Operating Software v1 . 3 by MAS5 algorithms , to obtain signal value ( GeneChip score ) for each probe . For global normalization , the average signal in an array was made equal to 100 . Gene annotation enrichment analysis was done at DAVID Bioinformatics Resources ( http://david . abcc . ncifcrf . gov/ ) . Array data is available at GEO datasets ( #GSE18125 ) . MEFp2 and infected cells at day 10 were cross-linked with 1% formaldehyde for 10 min and were prepared for ChIP . ChIP using anti-H3K4me3 ( ab8580 , abcam , rabbit polyclonal ) , H3K27me3 ( 07–142 , Upstate , rabbit polyclonal ) , or Ezh2 ( #39103 , Active Motif , rabbit polyclonal ) antibody was performed as described previously [62] . For ChIP using anti-Smad1 antibody ( BioMatrix , mouse monoclonal ) , MEFp2 cells were starved for 16 hours and exposed to rBMP2 protein ( #355-BM , R&D systems ) at 25 ng/mL in serum-free medium for 1 . 5 hours . Cells were cross-linked with 1 mM Disuccinimidyl Glutarate ( Thermo Scientific , Rockford , IL ) for 20 min and 1% formalin for 10 min , and ChIP was performed similarly . For MeDIP , genomic DNA of MEFp2 and RasV12 cells was fragmented by sonication , and immunoprecipitated by anti 5-methylcytocine monoclonal antibody ( kindly supplied by Dr . K . Watanabe , Toray Research Center , Inc . ) , as we previously reported[22] , [23] , [63] . MeDIPed sample and Input sample underwent MeDIP-PCR to check enrichment of methylated regions in MeDIPed sample . Sample preparation for ChIP- and MeDIP-sequencing was performed according to the manufacturer's instructions ( Illumina ) , and sequencing was performed using Solexa Genome Analyzer II [61] . 36-bp single end reads were mapped to the NCBI Build #36 ( UCSC mm8 ) reference mouse genome , using the Illumina pipeline software v1 . 4 . The numbers of uniquely mapped reads for MEFp2 were 10 , 845 , 082 ( H3K4me3 ) , 11 , 519 , 151 ( H3K27me3 ) , 9 , 663 , 324 ( DNA methylation ) and 5 , 688 , 804 ( Input ) , those for RasV12 cells were 13 , 246 , 871 ( H3K4me3 ) , 9 , 894 , 241 ( H3K27me3 ) , 11 , 319 , 506 ( DNA methylation ) and 6 , 126 , 206 ( Input ) , and that for Smad1 ChIP-sequencing was 9 , 417 , 307 . Window sizes of 300 bp for H3K4me3 , 500 bp for H3K27me3 , 500 bp for DNA methylation and 300 bp for Smad1 , were used to calculate the number of mapped reads per million reads at the center of the window . Sequencing data is also available ( #GSE18125 ) . Aliquots of protein were subjected to SDS/PAGE and were transferred to nitrocellulose , and the resulting immunoblots were visualized using Amersham ECL Plus ( GE Healthcare ) and LAS-3000 ( Fujifilm , Japan ) . Phosphorylated Smad1/5/8 was detected using antibody against phospho-Smad1/5/8 ( Cell Signaling ) as primary antibody , and green-fluorescent Alexa Fluor 488 dye-labeled anti-rabbit antibody ( Invitrogen ) as secondary antibody . RasV12 with N-terminal FLAG tag and Smad6 with N-terminal Myc tag were detected using antibody against FLAG ( F7425 , Sigma , rabbit polyclonal ) and Myc ( 9E10 , Santa Cruz , mouse monoclonal ) as primary antibody , respectively , and Alexa Fluor 594 anti-rabbit antibody and Alexa Fluor 488 anti-mouse antibody ( Invitrogen ) as secondary antibody . Photographs were taken with Biozero BZ-8100 ( KEYENCE , Osaka , Japan ) . MEFp2 and infected MEFs on day 10 , and infected IMR90 on day 7 underwent SA-βgal staining as previously described [64] . Infected MEFs were counted on days 3 , 7 , 10 , 14 , 17 , 21 using Countess automated cell counter ( Invitrogen ) and seeded at density of 1×105 cells/6-cm dish for every passage . Infected IMR90 were counted on days 4 , 8 , 12 and 16 similarly , and seeded at density of 2 . 5×105 cells/6-cm dish . Mean number of three dishes was calculated and used to draw growth curve . Real-time PCR was performed using iCycler Thermal Cycler ( Bio-Rad Laboratories ) as previously described [65] . The experiment was triplicated and mean and standard error were calculated and shown . Primer information is in Tables S7 and S8 . DNA methylation status was analyzed by bisulfite sequencing as previously described [65] . Briefly , 500 ng of genomic DNA of MEFp2 and RasV12 cells underwent bisulfite treatment , and were finally suspended in 20 µL of distilled water . For bisulfite sequencing , 1 µl was used as a template for PCR with primers common for methylated and unmethylated DNA sequences . The primers and PCR conditions are available at Table S9 . PCR products were cloned into pGEM-T Easy vector ( Promega ) , and 9–10 clones each were cycle-sequenced using T7 and Sp6 primers . | To avoid becoming cancer cells , cells have a barrier system to block cellular proliferation by falling into irreversible growth arrest , so-called cellular senescence . For future strategy of cancer treatment , it is important to understand how cancer occurs , and investigation of underlying mechanism in senescence can lead to clarification of carcinogenesis mechanism . Epigenetic mechanism including DNA methylation and histone modification may be important to regulate gene expressions properly in senescence . Here , taking advantage of recent technical and methodological advance of genome-wide analyses , we examine epigenome and gene expression alteration in senescence induced by Ras oncogene . We identify that Bmp2-Smad1 signal is critical . We further examine downstream target genes of this critical signal on a genome-wide scale . We show dynamic and coordinated H3K27me3 alteration , e . g . activation of Bmp2 by loss of H3K27me3 , repression of the signal inhibitors and the negative feedback loop by gain of H3K27me3 , and selective activation of downstream target genes that may contribute to growth arrest . Our findings are helpful in understanding the importance of epigenetic regulation and a critical signal in the physiological barrier system against oncogenic transformation and the importance of disruption of BMP-SMAD signal in cancer , and they may provide an idea how cancer with Ras mutation occurs . |
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Neuronal assemblies often exhibit stimulus-induced rhythmic activity in the gamma range ( 30–80 Hz ) , whose magnitude depends on the attentional load . This has led to the suggestion that gamma rhythms form dynamic communication channels across cortical areas processing the features of behaviorally relevant stimuli . Recently , attention has been linked to a normalization mechanism , in which the response of a neuron is suppressed ( normalized ) by the overall activity of a large pool of neighboring neurons . In this model , attention increases the excitatory drive received by the neuron , which in turn also increases the strength of normalization , thereby changing the balance of excitation and inhibition . Recent studies have shown that gamma power also depends on such excitatory–inhibitory interactions . Could modulation in gamma power during an attention task be a reflection of the changes in the underlying excitation–inhibition interactions ? By manipulating the normalization strength independent of attentional load in macaque monkeys , we show that gamma power increases with increasing normalization , even when the attentional load is fixed . Further , manipulations of attention that increase normalization increase gamma power , even when they decrease the firing rate . Thus , gamma rhythms could be a reflection of changes in the relative strengths of excitation and normalization rather than playing a functional role in communication or control .
Modulations in gamma rhythms have consistently been observed during high-level cognitive processes such as attention [1]–[5] , memory [6] , feature-binding [7] , [8] , or conscious perception [9] , leading to the suggestion that these rhythms play a functional role in high-level cognitive processing [7] , [10] . However , several studies have shown that the magnitude and center frequency of the gamma rhythm depend on stimulus features such as contrast [11]–[13] , orientation [14] , [15] , size [15] , [16] , and direction [12] , [17] , irrespective of the cognitive state , suggesting that gamma rhythms could be a reflection of basic cortical processes such as the interaction between excitation and inhibition [18] . Recent studies have suggested that selective attention , a high-level cognitive function often associated with gamma rhythms [1]–[5] , is mediated through a sensory mechanism called normalization [19] , [20] . Normalization is a form of gain control in which neuronal responses are reduced in proportion to the activity of a large pool of neighboring neurons [21] , [22] . In the normalization model of attention , attention increases the excitatory drive to a neuron processing the attended stimulus . However , the increased excitatory drive also increases the strength of the normalization pool . The relative increase in the strength of normalization compared to excitation depends on several factors , such as the stimulus size and the focus of attention [20] , [23] , as well as tuning properties of the normalization pool [24] , and these factors determine the overall effect of attention on the firing rate of the neuron . The normalization model of attention , as well as other models ( see Discussion ) , therefore predict that attention changes the relative strengths of excitation and inhibition . We hypothesized that the changes in gamma power observed with attention reflect the effect of attention on the underlying excitation and normalization strengths . In particular , we hypothesized that gamma power should increase with increasing normalization , even if attentional load is held fixed . We tested this hypothesis by recording single units and local field potentials ( LFPs ) from the middle temporal area ( MT ) of two macaque monkeys while they performed a task in which normalization and spatial attention were varied independently , and studying the effects of these manipulations on gamma power .
To manipulate the strength of normalization , we cued the monkeys to attend to a stimulus outside the receptive field of an MT neuron while presenting two stimuli inside the receptive field—one moving in the cell's preferred direction and the second in the opposite ( null ) direction ( “Normalization Protocol , ” Figure 1A ) . The addition of a null stimulus , which by itself produces little excitation , decreases the response produced by the preferred stimulus alone , a phenomenon that has been explained using normalization [21] , [22] . The addition of a null stimulus does not appreciably increase the excitatory drive received by the recorded neuron , but it increases the normalization strength considerably because other neurons in the normalization pool have different direction selectivities and therefore some neurons in the pool respond to the null stimulus also . Therefore , addition of a null stimulus increases normalization strength without any appreciable increase in excitation , and consequently decreases the firing rate . We manipulated normalization by varying the contrasts of the preferred and null stimuli inside the receptive field ( each could take one of three contrasts: 0% , 50% , or 100% ) while keeping the animal's attention directed away from the receptive field . We label each condition as PxNy , where x and y are the contrasts of the preferred and null stimuli . The stimuli were presented rapidly ( 200 ms ) with a short interstimulus interval ( 158–293 ms; Figure 1C ) , which made it unlikely that the animals could adjust their attention in response to the variable contrast of stimuli within the duration of the presentations . Figure 2A shows the average time-frequency power ( on a log scale ) of 96 recording sites in the area MT of two monkeys ( 55 from Monkey 1 and 41 from Monkey 2; results were similar and individually significant for the two monkeys and hence the data were pooled ) for the P100N0 condition ( a single stimulus at 100% contrast moving in the preferred direction ) . Time-frequency analysis was done using the Matching Pursuit algorithm , which provided sufficient resolution to resolve any oscillatory activity related to normalization/attention as well as transient activity due to fast stimulus presentation rates ( see Materials and Methods for details ) . Line noise and monitor refresh rate caused a sustained increase in power in the LFP , visible as two narrow horizontal lines at 60 and 75 Hz in Figure 2A . In addition , there was a prominent increase in power between 65 and 80 Hz starting around ∼100 ms after stimulus onset . Figure 2B shows the power spectrum ( on a log scale ) of the LFP , obtained by averaging the time-frequency power between 50 and 250 ms ( red trace ) . For comparison , we also include the power spectrum when no stimulus was presented ( P0N0 condition; orange trace ) and the “baseline” spectrum obtained by averaging the power between 100 and 0 ms before stimulus onset for all nine normalization conditions ( black trace ) . The baseline spectrum had slightly more power than the P0N0 spectrum ( black curve is slightly above orange ) , which was expected because the baseline period contained some residual activity from the previous stimulus . The localized increase in gamma power between 65 and 80 Hz was reflected as a “bump” in the P100N0 spectrum , which was missing in both baseline and P0N0 spectra . The gamma band increase observed between 65 and 80 Hz is not an artifact of the monitor refresh . Because the monitor refresh occurs at a fixed frequency , phase-locking of neurons to the monitor refresh rate is typically limited to a very narrow frequency band around the refresh rate , and in particular there is no evidence in the literature of such artifacts spreading to a broad frequency band . Further , even if the activity related to the monitor refresh rate varied with time ( because the stimulus changed with time ) , it would cause an amplitude modulation of the 75 Hz sinusoid . The Fourier Transform of an amplitude modulated sinusoid is equal to the convolution of the Fourier Transform of the sinusoid ( which produces a delta function at 75 Hz ) and the Fourier Transform of the amplitude modulation . This is simply the Fourier Transform of the amplitude modulation centered at 75 Hz . Irrespective of the type of amplitude modulation introduced by the time-varying stimulus , the spread should be symmetric around 75 Hz , which was not the case . For the P100N0 condition , the artifact related to monitor refresh rate was visible as a narrow peak at 75 Hz that was distinct from the gamma band increase ( the spectrum for the P100N0 condition around 75 Hz is enlarged in the inset ) . Further , gamma modulation was observed for the attention condition even when the stimulus conditions were identical ( see below ) , which rules out the monitor refresh rate–related noise as the sole source of gamma power . Although the use of Matching Pursuit resolved the line and monitor-related noise from ongoing oscillatory activity in the gamma band at high resolution , the results obtained using a traditional multitaper method [25] , [26] were comparable and showed a prominent increase in power in the gamma range ( Figure S1 ) . Figure 3A shows the average firing rates when a stimulus moving in the neuron's preferred direction was presented at 0% ( left ) , 50% ( middle ) , and 100% ( right ) contrast , together with a null stimulus at 0% ( red traces; lower preferred stimulus contrast is shown in a lighter shade ) , 50% ( green ) , and 100% ( blue ) contrast . As expected from normalization , addition of a null stimulus decreased the firing rates . Figure 3B shows the change in LFP power relative to a common baseline period ( Figure 2B , black trace ) for different pairings of preferred ( different columns ) and null contrasts ( different rows ) . Gamma rhythm was observed between 65 and 80 Hz , and its strength increased when a null stimulus was added ( first versus second/third row ) . This increase was specific to the gamma band—for example , power did not increase in the high-gamma band ( >80 Hz ) with increasing normalization ( Figure 3B , also see Figure 4B for comparison as a function of frequency ) . To study these effects in more detail , we plotted the power between 50 and 250 ms as a function of frequency ( Figure 4A ) as well as the gamma power ( between 65 and 80 Hz; excluding 74–76 Hz ) as a function of time ( Figure 4C ) , for all nine normalization conditions . Figure 4B and 4D show the change in power ( in dB ) between the P100N100 and P100N0 conditions as a function of frequency and time , respectively . In Figure 4B , the change was significant only in the gamma range and at very low frequencies ( which was due to differences in transient activity; see Figure 3B ) . The change in gamma power started ∼50 ms after stimulus onset and persisted throughout the duration of the stimulus ( Figure 4D ) . To quantify the effect of normalization , we computed the total power in the gamma range ( 65–80 Hz , excluding 74–76 Hz; the analysis window is indicated by a black box in the panels of Figure 3B ) and high-gamma range ( 80–135 Hz ) , for each normalization condition . Figure 5A shows the mean change in gamma power for different stimulus conditions relative to the P100N0 condition . Neurons in area MT typically have a low semi-saturation constant ( σ in Text S1 ) and tend to saturate even for contrasts much less than 100% [27] , so the results were similar for stimuli at 50% and 100% contrast ( gamma power was not significantly different between P50N0 and P100N0 conditions; difference: 1 . 7%±2 . 0% , p = 0 . 39 , N = 96 , t test ) . However , gamma power increased significantly when a null stimulus at 50% or 100% contrast was added to a preferred stimulus at 50% or 100% contrast: relative changes in gamma power from P100N0 condition for P50N50 , P50N100 , P100N50 , and P100N100 conditions were 11 . 1%±2 . 8% , 11 . 3%±3 . 0% , 19 . 6%±2 . 8% , and 18 . 8%±3 . 1% , respectively ( p = 1 . 6×10−4 , p = 2 . 9×10−4 , p = 2 . 9×10−10 and p = 3 . 2×10−8 , N = 96 , t test ) . When analyzed separately for the two monkeys , the corresponding values were 10 . 5%±3 . 4% , 12 . 6%±4 . 3% , 25 . 6%±3 . 8% , and 27 . 3%±4 . 5% for Monkey 1 ( p = 3 . 7×10−3 , p = 4 . 6×10−3 , p = 1 . 3×10−8 , and p = 1 . 0×10−7 , N = 55 , t test ) and 11 . 8%±4 . 7% , 9 . 5%±4 . 1% , 11 . 5%±3 . 7% , and 7 . 3%±3 . 5% for Monkey 2 ( p = 0 . 02 , p = 0 . 03 , p = 0 . 003 , and p = 0 . 04 , N = 41 , t test ) . On the other hand , the increases in high-gamma power ( Figure 5B ) for corresponding conditions were −0 . 4%±1 . 3% , −1 . 5%±1 . 4% , 3 . 4%±1 . 4% , and 2 . 5%±1 . 5% , respectively ( p = 0 . 76 , p = 0 . 3 , p = 0 . 02 , and p = 0 . 09 , N = 96 , t test ) . Thus , addition of a second stimulus inside the receptive field of a neuron , which increased normalization , increased the magnitude of the gamma rhythm even when attention was fixed outside the receptive field . However , increasing normalization had negligible effect at high-gamma frequencies . Similar results were obtained using the multitaper method . Relative changes in gamma power ( sum of power at 65 , 70 , and 80 Hz ) from P100N0 condition for P50N50 , P50N100 , P100N50 , and P100N100 conditions were 5 . 3%±2 . 4% , 8 . 0%±3 . 0% , 13 . 0%±2 . 7% , and 16 . 2%±3 . 4% , respectively ( p = 0 . 03 , p = 0 . 009 , p = 5 . 2×10−6 and p = 7 . 3×10−6 , N = 96 , t test ) . For high-gamma power , the corresponding values were −0 . 2%±1 . 4% , −2 . 4%±1 . 3% , 2 . 2%±1 . 5% , and 1 . 0%±1 . 5% , respectively ( p = 0 . 87 , p = 0 . 07 , p = 0 . 14 , and p = 0 . 50 , N = 96 , t test ) . Interestingly , while normalization is generally thought to be largely un-tuned for orientation [21] , [22] , the gamma rhythm was much stronger when a preferred stimulus was presented instead of a null stimulus ( compare P0N100 versus P100N0 in Figures 3B; both should involve the same normalization signal ) . This suggests that the gamma rhythm depends not only on the suppressive normalization signal , but on the incoming excitatory drive as well , and could be a resonant phenomenon arising from the excitation–inhibition interaction [13] , [18] , [28] , [29] . However , differences in the levels of excitation alone across stimulus conditions cannot explain these results , because changes in excitation modulate power in a broad frequency band including the high-gamma band ( see Discussion for more details ) . Next , we studied the effect of shifting the focus of attention under identical stimulus conditions ( Figure 1B , “Spatial Attention Protocol” ) . Figure 6A shows the average firing rates of the 96 neurons when two stimuli at 100% contrast moving in the preferred and null directions were presented inside the receptive field , while the animal focused on a stimulus outside the receptive field ( P100N100; dark blue trace ) or on the null ( P100N100Att; magenta ) or preferred ( P100AttN100; violet ) stimulus inside the receptive field . This attentional manipulation allowed us to dissociate the dependence of gamma power on normalization versus firing rate modulations . This is because the response of the neuron shifted toward the response elicited when the attended stimulus was presented alone , and therefore decreased when attending to null ( P100N100Att ) and increased when attending to preferred ( P100AttN100 ) compared to the P100N100 condition [30] , [31] . In contrast , the strength of normalization increased for both P100N100Att and P100AttN100 conditions ( compared to the P100N100 condition ) because attention was directed to a stimulus inside the receptive field instead of outside . This was indeed reflected in the gamma power , whose strength increased when attention was directed inside the receptive field for both the P100N100Att and P100AttN100 conditions ( Figure 6B; compare first versus second/third row ) . Figure 6C shows the normalized firing rate ( Firing ) , gamma power ( γ ) , and high-gamma power ( Hi-γ ) for the P100N100 , P100N100Att , and P100AttN100 conditions ( normalized with respect to P100N0 as before ) . The firing rate decreased by 28 . 6%±1 . 8% ( dark blue bar ) when a null stimulus was added to the receptive field and decreased by 37 . 1%±2 . 3% when attention was directed to that null stimulus ( magenta bar ) . Attention to the preferred stimulus largely counteracted the presence of the null stimulus , leaving a decrease of only 3 . 3%±2 . 6% from the preferred only stimulus ( violet bar ) . On the other hand , gamma power increased by 18 . 8%±3 . 1% when the null stimulus was added , 33 . 6%±4 . 8% when this null stimulus was attended , and 40 . 1%±4 . 3% when the preferred stimulus was attended ( all changes compared to the P100N0 condition ) . The increase of 12 . 9% in the gamma power from P100N100 to P100N100Att was highly significant ( p = 3 . 5×10−5 , N = 96 , t test ) . When analyzed separately , the increase was 9 . 0% ( p = 0 . 0017 , N = 55 , t test ) for Monkey 1 and 18 . 2% ( p = 0 . 005 , N = 41 , t test ) for Monkey 2 . The increase from P100N100Att to P100AttN100 was 8 . 1% for the pooled data ( p = 0 . 02 , N = 96 , t test ) , 4 . 4% for Monkey 1 ( p = 0 . 35 , N = 55 , t test ) , and 13 . 3% for Monkey 2 ( p = 0 . 04 , N = 41 , t test ) . Thus , manipulations of attention that increased normalization increased gamma power even when they decreased the firing rate , suggesting that the effects of attention on gamma power may be an indirect consequence of its direct effect on normalization . Unlike manipulations of normalization , manipulations of attention changed the power at non-gamma frequencies also . For example , power in the high-gamma range increased by 2 . 5%±1 . 5% when the null stimulus was added , 9 . 6%±3 . 1% when this null stimulus was attended , and 15 . 0%±1 . 8% when the preferred stimulus was attended ( Figure 6C , “Hi-γ” ) . The increases of 6 . 9% from P100N100 to P100N100Att and 4 . 9% from P100N100Att to P100AttN100 were both significant ( p = 0 . 03 and p = 0 . 02 , N = 96 , t test ) . To study the effect of attention at different frequencies in more detail , we plotted the power between 50 and 250 ms as a function of frequency ( Figure 6D; left column ) and the gamma power as a function of time ( Figure 6D , right column ) for different attention conditions . The top row shows the raw power , while the middle and bottom rows show the change in power for the P100N100Att versus P100N100 condition and P100AttN100 versus P100N100 conditions , respectively . Attention increased the power in a broad frequency band above 50 Hz and decreased power below 30 Hz ( left column , middle and bottom rows ) . As a function of time , gamma power was elevated throughout the duration of the trial irrespective of stimulus onset for the P100N100Att versus P100N100 condition ( middle row , right column ) , but showed a larger increase after stimulus onset for the P100AttN100 versus P100N100 condition ( bottom row , right column ) . Results obtained from multitaper analysis were very similar ( not shown ) . We observed a pronounced suppression at low frequencies ( <30 Hz ) with attention , as shown in Figure 6B and 6D . To study the effects of normalization and attention at low frequencies , we plotted the change in power from baseline for different normalization and attention conditions ( Figure 7A ) . From the time-frequency difference plots ( Figures 3B and 6B ) , two prominent features were observed at low frequencies . First , we observed an increase in power at ∼10 Hz at ∼100 ms , probably reflecting the stimulus-induced transient . Second , we observed a pronounced suppression in power between 20 and 30 Hz . Figure 7B shows the change in power ( from the P100N0 condition as before ) in the alpha ( 8–12 Hz; left panel ) and beta2 ( 20–30 Hz; right ) bands for different normalization and attention conditions . For the Normalization conditions ( from P0N0 through P100N100 ) , alpha power increased with the strength of normalization , probably because the stimulus-induced transient reflected the overall population activity that increased with increasing normalization ( Figure 3B ) . The beta2 band did not show any significant modulation with normalization ( Figure 7B , right panel ) . This can also be seen in Figure 3B , where the blue patches reflecting the beta2 decrease have approximately the same intensity . Even though this patch appears missing in the P0N0 condition , it is only because power at other frequencies changes by a similar proportion—that is , other frequencies also have a similar shade of blue , so the color contrast is not salient ( compare the orange trace in Figure 7A that has no dip in the beta2 range with other traces that show a prominent dip ) . On the other hand , attention decreased the power in both alpha and beta2 ranges ( Figures 6B and 7 ) , consistent with a large number of prior studies [5] , [12] , [32] , [33] . Finally , we studied whether the increase in gamma power due to attention can be explained through normalization on a neuron-by-neuron basis . Neurons in area MT have a variable change in firing rate when a null stimulus is added to a preferred stimulus in their receptive field—for some neurons , the firing rate decreases substantially , while for others there is hardly any decrease , which can be explained by the variability in the strength of the normalization ( the tuned normalization model is summarized in Text S1 ) [24] . The strength of normalization can be approximated as α = ( firing rate ( P100N0 ) /firing rate ( P100N100 ) ) −1 ( Text S1 ) . Previous studies have shown that α is strongly correlated with the overall attentional modulation in firing rates [measured as ( P100AttN100−P100N100Att ) / ( P100AttN100+P100N100Att ) ] [19] , [24] . We therefore studied whether α can also predict the attentional modulation in gamma power . Figure 8A plots the relationship between the increase in gamma power ( measured in dB ) when attention was directed to the preferred stimulus versus outside ( P100AttN100 versus P100N100 ) , as a function of the normalization strength ( α ) . Neurons demonstrating a stronger normalization signal ( α ) should show a greater attentional modulation in gamma power . However , these two parameters were not correlated ( ρ = 0 . 01 , p = 0 . 9 , Spearman Rank test ) . This is because gamma power depends not only on the strength of normalization but also on the strength of the incoming excitation , and attention increases both these quantities . This issue can be partially resolved by studying the correlation between α and the increase in gamma power when attention was directed to the null stimulus ( Figure 8B ) , because in this case attention increases the strength of normalization but does not substantially increase the strength of incoming excitation ( because the null stimulus produces almost no response in neurons in area MT ) . In this case , the increase in gamma power was weakly but significantly correlated with α ( ρ = 0 . 3 , p = 0 . 003 , N = 96 , Spearman Rank test ) , although the correlation did not reach significance for Monkey 1 when the analysis was done separately for each monkey ( Monkey 1: ρ = 0 . 21 , p = 0 . 13 , N = 55; Monkey 2: ρ = 0 . 37 , p = 0 . 02 , N = 41 , Spearman Rank test ) . Thus , changes in firing rates from a pure manipulation of normalization ( which were used to estimate α ) were a weak but significant predictor of the changes in gamma power during a manipulation of attention , but only when attention modulated the normalization strength alone . Differences between the effects of normalization and attention on the power spectrum are addressed in more detail in the Discussion .
Early models of attention such as the biased competition model [35]–[37] suggested that when multiple stimuli are presented inside the receptive field of a neuron , they activate different neural assemblies that compete for high-level representation , and attention biases the competition in favor of the attended stimulus . These models , however , fail to explain the effect of attention on neural responses when a single stimulus is present inside the receptive field [38] . Other types of models such as the flexible input gain model [23] , [39] operate by changing the relative weights of inputs into a neuron , without changing the rules by which these inputs are integrated together . The input gain model can explain the increase in firing rates observed when a single stimulus is presented , as well as the competitive behavior when multiple stimuli are presented [23] , [39] . In this model , the response of a neuron when a preferred and a null stimulus are both presented is given by RP , N = λ ( ( βPRP ) n+ ( βNRN ) n ) 1/n , where RP and RN are the responses when the preferred and null stimuli are presented alone , βP and βN are the attentional gains applied to each input , n incorporates nonlinear summation ( n = 1 for linear; n = infinity for winner-take-all ) , while λ is a scaling term . However , input gain or biased competition models cannot easily explain the decrease in firing rates when a null stimulus is attended if the null stimulus produces no response to begin with , which was the case in our dataset ( Figure 3A , left panel ) . Specifically , if RN = 0 , the input gain model reduces to RP , N = λβPRP , which cannot explain the decrease in firing rate observed when attention is directed to the null stimulus unless the scaling parameter λ changes with the direction of attention ( preferred versus null ) . The normalization model of attention ( Text S1 ) also acts by multiplying the inputs by a gain term and , in this regard , is similar to the input gain model . In addition , the responses are divided ( normalized ) by a term that depends on the null stimulus contrast and null attentional gain , even if the null stimulus produces no response . The normalization model can effectively change the scaling term of the gain model ( λ ) with changing attention , and therefore can explain a wider range of experimental results [19] , [20] , [24] . Several studies have shown that increasing the strength of incoming excitation increases the power in a broad frequency band above ∼30 Hz , including the gamma and high-gamma band , and this broad-band increase in power is correlated with the firing rate of the neural population near the microelectrode [40] , [41] . This is different from “band-limited” gamma rhythm that is often visible in the power spectrum as a distinct “bump” with a bandwidth of ∼20 Hz , which is sustained by a inhibitory network [28] , [42] , [43] , and may not be correlated with spiking activity [14] , [34] , [41] . Our results show that normalization increases band-limited gamma , while attention increases both excitation and normalization and therefore affects the power over a broader frequency range . Band-limited gamma may not always be observed during an attention task . For example , Khayat and colleagues [12] recorded from area MT of monkeys engaged in an attention task while presenting two random dot patterns—one moving in the null direction at 100% contrast paired with another moving in the preferred direction at varying contrasts , thus changing both excitation and normalization across stimulus conditions . The authors observed a broadband change in power in the gamma and high-gamma range , but no band-limited gamma . A similar spectral profile was observed in another recording from area MT where random dot patterns were used [17] . Indeed , most early studies that showed a salient band-limited gamma used one of two types of stimuli—gratings or oriented bars [44]–[46] . Most studies showing an effect of attention on band-limited gamma have also used either gratings or bars [1] , [4] , [5] , [32] , [47] . The absence of a prominent band-limited gamma rhythm in a demanding attention task [12] suggests that band-limited gamma may not play a functional role in attention and may not even be a fundamental marker of normalization or excitatory–inhibitory interactions . Instead , it could be a rhythm that is generated under special stimulus conditions and may reflect excitatory–inhibitory interactions within those restricted conditions . In this paper we have only considered a specific type of normalization , which is due to the addition of a nonoverlapping null stimulus inside the receptive field . Response suppression also occurs when an overlapping null stimulus is added to a preferred stimulus inside the receptive field , or when the stimulus size exceeds the classical receptive field ( surround suppression ) . Whether these forms of suppression involve the same normalization circuit is unclear . For example , although earlier models of suppression produced by overlapping orthogonal gratings were based on inhibition [21] , [22] , recent models have explained this suppression without inhibition ( for a review , see [48] ) . Consistent with this , a recent paper has shown that superimposing a null grating on a preferred grating decreases the gamma power in the primary visual cortex ( V1 ) , and surprisingly , also increases the gamma center frequency [49] . It is possible that superimposed and nonoverlapping orthogonal gratings produce suppression by different mechanisms , with only the latter requiring inhibition . Similarly , the presentation of a stimulus that is larger than the classical receptive field suppresses the response , although this manipulation increases the gamma power and decreases the gamma oscillation frequency in V1 [16] . The mechanism of surround suppression is unclear , with some studies showing an increase in incoming excitation and inhibition [50] and others showing the opposite effect [51] . Similarly , the cortical sites where normalization acts are also unclear . Earlier models assumed that normalization occurred simultaneously in multiple areas ( V1 and MT; [52] , [53] ) . However , properties of some types of opponent motion suppression differ between V1 and MT , which has been explained by a mechanism in which suppression arises in area MT [54] . On the other hand , responses of MT neurons that respond to the global motion of plaids ( but not to the constituent component motion ) were explained by a model where divisive normalization instead occurred in V1 [55] . Chalk and colleagues [5] have recently shown that gamma power decreases in area V1 with increasing attention , although under identical conditions gamma increases in V4 . The differences could be due to the ways normalization is implemented in different cortical areas ( see [5] for a more detailed discussion ) . In summary , the normalization signal that is involved in response suppression could be computed using different mechanisms , depending on the specific stimulus properties and cortical area . At present , it is unclear how universal the relationship between gamma and normalization described in this article is; that is , whether other forms of normalization would also modulate gamma power in a similar way . Similarly , although the stimulus configuration used in this article ( nonoverlapping orthogonal stimuli inside the receptive field ) is a common design used in several attention studies [24] , [30] , [31] , [35] , [37] , the relationship between attention and gamma when other forms of normalization may be operating remains an open question . In our data , manipulations of normalization strength affected only the gamma range ( and very low frequencies that likely reflected a stimulus transient ) . Attention , on the other hand , decreased power at low frequencies , consistent with prior studies [12] , [32] , [33] and increased power in the gamma and high-gamma ranges . As described above , a broadband increase in gamma and high-gamma power is correlated with the firing rate of the neural population near the microelectrode [40] , [41] . However , in this study we observed an increase in gamma and high-gamma power even when attention was directed to the null stimulus . This is at odds with a previous study where gamma and high-gamma power decreased , consistent with the decrease in firing rate [12] . There are several factors that may have contributed to this difference . First , Khayat and colleagues [12] measured gamma power 510–1 , 010 ms after stimulus onset , while we measured gamma power between 50 and 250 ms after stimulus onset . It is possible that stimulus onset excites the entire population transiently , before suppressive and attention-related mechanisms take over to modify the responses of the neural population . The effect would be a transient increase in overall firing followed by a reduction in firing of the population , which may explain why high-gamma power is high initially ( when we recorded ) but lower in the steady state ( when Khayat and colleagues recorded ) . Another factor may be the spatial spread of attention . As described earlier , high-gamma power depends on the firing rate of the overall population near the microelectrode , not just of the neuron being recorded from the microelectrode . Directing attention to the null stimulus inside the receptive field has two opposing effects: an increase in the firing rate of most neurons in the attended cortical region , and a reduction in the firing rate of neurons whose receptive fields contained both the preferred and null stimuli ( such as the neurons shown in Figure 6A ) . Depending on the focus of attention , the overall population activity could either increase or decrease . Importantly , the changes in high-gamma power with attention do not influence the main result of this article , which is the increase in band-limited gamma power with increasing normalization strength . Because the stimuli used by Khayat and colleagues did not produce a salient band-limited gamma rhythm ( see above ) , the results between the two studies cannot be compared directly . The lack of change in high-gamma power with increasing normalization strength ( Figures 3B and 5B ) can be explained similarly . A single stimulus activates a population of neurons , whose firing rate decreases when a second orthogonal stimulus is added ( due to normalization and surround suppression ) . However , the second stimulus also activates another population of neurons . The overall population firing recorded by the microelectrode depends on the stimulus size , the size of the receptive field , suppressive surround and normalization pool , as well as the cortical spread of the population activity that is picked up by the microelectrode . It is possible that the overall population firing rate did not change appreciably when a second stimulus was added in our normalization protocol , so that high-gamma power did not change . The gamma peak was observed between 65 and 80 Hz , a frequency range that is slightly above the traditional gamma range ( 30–60 Hz ) and that overlaps with the high-gamma band [41] , [56] . This could be due to the early time window for analysis ( because the stimulus presentation was for a short duration ) , because gamma peak frequency is higher after stimulus onset and decreases with time ( for example , see Figure 1H of [41] ) . This is also consistent with a previous report that showed gamma oscillations at ∼50 Hz when analysis was done at a late interval ( >300 ms ) but a peak at 65 Hz when analysis was done at an early period ( [1] , compare their Figure 1 versus 4 ) . In addition , gamma center frequency varies from subject to subject depending on the resting GABA concentration [57] , and also depends on stimulus parameters such as size [16] , [34] and contrast [13] . Although the center frequency of the gamma rhythm was relatively high , it could be dissociated from high-gamma activity ( related to population firing ) based on the spectral profile because gamma rhythm between 65 and 80 Hz had a distinct bump in the power spectrum while the high-gamma activity had a broadband profile with no distinct peak . Nonetheless , because the effect of spiking activity is detectable above ∼50 Hz in the LFP and becomes progressively more significant with increasing frequency [41] , the increase in gamma power due to attention could partly be due to the increase in the population firing rate . In addition , as discussed above , gamma power depends not only on suppressive normalization , but also on the strength of the incoming excitation , and its precise relation with excitation and inhibition is unknown . Consequently , the increases in gamma power due to attention and to normalization were not tightly correlated in our data ( unlike the tight correlation observed in firing rates as described in [19] , [24] ) . Only when attention was directed to the null stimulus , for which the increase in the incoming excitation was less ( although not zero , because the high-gamma power increased significantly ) , could we observe a weak correlation between attention and normalization ( Figure 8B ) . In summary , our study shows that changes in the strength of normalization , which occur during attentional modulation , can also change the gamma power , although the precise nature of the relationship between normalization and gamma remains to be established . Changes in gamma power in an attention task due to changes in the underlying normalization strength must be accounted for before a more advanced functional role for gamma in the formation of communication channels [3] , [10] or binding of stimulus features [7] , [8] can be unequivocally established .
All procedures related to animal subjects were approved by the Institutional Animal Care and Use Committee of Harvard Medical School . This study uses the same dataset as used by Ni and colleagues [24] . Data were collected from two male rhesus monkeys ( Macaca mulatta ) that weighed 8 and 12 kg . A scleral search coil and a head post were implanted under general anesthesia . After recovery , each animal was trained to do an orientation change detection task . The animal was required to hold its gaze within 1 . 0° from the center of a small fixation target while a series of drifting Gabor stimuli were flashed at three locations: two within the receptive field of the MT neuron being recorded and one at a symmetric location on the opposite side of the fixation point from the receptive field . All three Gabors were centered at the same eccentricity from the fixation point , and the Gabors were identical except for their contrast and drift direction . The two stimulus locations in the receptive field were separated by at least 5 times the SD of the Gabors ( mean Gabor SD , 0 . 45°; SD of Gabor SD , 0 . 04°; range , 0 . 42–0 . 50°; mean separation of Gabor centers , 4 . 2°; SD , 0 . 86°; range , 2 . 2–6 . 9° ) . The stimuli were presented on a gray background ( 42 cd/m2 ) , which had the same mean luminance with the Gabors , on a gamma-corrected video monitor ( 1024×768 pixels , 75 Hz refresh rate ) . The animal was cued to attend to one of the three locations in blocks of trials and to respond when a Gabor with a different orientation appeared there ( the target ) , ignoring any orientation changes at uncued locations ( distractors ) , which occurred with the same probability as changes at the cued location . The animal indicated its response by making a saccade directly to the target location within 100–600 ms of its appearance . Correct responses were rewarded with a drop of juice or water . The target location was cued by a yellow annulus at the beginning of each trial as well as by instruction trials . Instruction trials consisted of a series of Gabor stimuli that appeared in only one location . Two instruction trials were inserted each time the cued location changed . Gabors were presented synchronously in all three locations for 200 ms , with successive stimuli separated by periods with pseudorandom durations of 158–293 ms . During each presentation , one Gabor inside the receptive field moved in the preferred direction of the neuron , while the other Gabor inside the receptive field moved in the opposite ( null ) direction . The Gabor outside the receptive field moved in an orthogonal ( intermediate ) direction . The “Normalization” and “Spatial Attention” protocols differed in the location of the cue ( outside versus inside the receptive field ) and the number of contrasts used for each stimulus ( three versus two ) . For the Normalization protocol ( Figure 1A ) , the monkey attended to the stimulus outside the receptive field , and all Gabors could take one of three contrast values: 0% , 50% , or 100% ( the target stimulus had either 50% or 100% contrast ) . This created nine different stimulus conditions inside the receptive field , as shown in Figure 3 ( for each condition , we pooled data for the three different contrast levels for the Gabor outside the receptive field ) . For the Spatial Attention protocol ( Figure 1B ) , the monkey attended to one of the locations inside the receptive field ( which could have either the preferred or null stimulus in different presentations ) . All Gabors had either 0% or 100% contrast ( target stimulus always had 100% contrast ) . We only used the stimulus condition for which both the preferred and null stimuli inside the receptive field had 100% contrast because that configuration showed the largest effect of attention . The stimulus at a given location inside the receptive field could either be the preferred or null stimulus across presentations within the same trial ( Figure 1 ) . For a subset of data recorded from Monkey 1 ( 45 out of 68 neurons ) , the stimulus direction was fixed for a given location , so that the preferred stimulus always appeared in the bottom half of the receptive field while the null stimulus always appeared on top . The results shown in the article were similar for this modified version of the task; the data were pooled . The timing of the target appearance in each trial was selected from an exponential distribution ( flat hazard function for orientation change ) to encourage the animal to maintain constant vigilance throughout each trial . However , trials were truncated at 6 s if the target had not appeared ( ∼20% of trials ) , in which case the animal was rewarded for maintaining fixation up to that time . The orientation change was adjusted for each stimulus configuration using an adaptive staircase procedure ( QUEST; [58] ) to maintain a behavioral performance of 82% correct [hits/ ( hits+misses ) ; range , 57%–93%] across all target locations [the average orientation change for targets and distractors were 50±12° and 52±7° for Monkeys 1 and 2 ( mean±SD ) ] . Both monkeys had fast reaction times ( 245±13 and 195±7 ms; mean ± SD ) , which , coupled with the large attentional modulation observed in the firing rates , suggested that they were paying close attention to the stimuli . Recordings were made using glass-insulated Pt-Ir microelectrodes ( ∼1 MΩ at 1 kHz ) in area MT ( axis ∼22–40° from horizontal in a parasagittal plane ) . A guide tube and grid system [59] was used to penetrate the dura . Spikes and LFP were recorded simultaneously using a Multichannel Acquisition Processor system by Plexon Inc . with a head-stage with gain 20 ( Plexon Inc . HST/8o50-G20 ) . Signals were filtered between 250 Hz and 8 kHz , amplified and digitized at 40 kHz to obtain spike data . For the LFP , the signals were filtered between 0 . 7 and 170 Hz , amplified and digitized at 1 kHz . We used the FPAlign utility program provided by Plexon Inc . to correct for the filter induced time delays ( http://www . plexon . com/downloads ) . The headstage HST/8o50-G20 has low input impedance , which can lead to a voltage divider effect at low frequencies ( Figure 2B shows this effect at frequencies below ∼5 Hz ) [60] . This is unlikely to affect our results because this effect is much less prominent in the frequency range of interest ( 65–80 Hz ) and we always compared data across different stimulus conditions that had the same filter settings . Once a single unit was isolated , the receptive field location was estimated using a hand-controlled visual stimulus . Computer-controlled presentations of Gabor stimuli were used to measure tuning for direction ( eight directions ) and temporal frequency ( five frequencies ) while the animal performed a fixation task . The temporal frequency that produced the strongest response was used for all of the Gabors . The temporal frequency was rounded to a value that produced an integral number of cycles of drift during each stimulus presentation , so that the Gabors started and ended with odd spatial symmetry , such that the spatiotemporal integral of the luminance of each stimulus was the same as the background . Spatial frequency was set to one cycle per degree for all of the Gabors . The preferred Gabor was used to quantitatively map the receptive field ( three eccentricities and five polar angles ) while the animal performed a fixation task . The two stimulus locations within the receptive field were chosen to be at equal eccentricities from the fixation point and to give approximately equal responses , and the third location was 180° from the center point between the two receptive field locations , at an equal eccentricity from the fixation point as the other locations . Cells were included in the analysis if they were held for at least nine repetitions ( mean 41 repetitions ) of each stimulus/attention combination used in this article . The response for each condition was taken as the average rate of firing in a period 50–250 ms after stimulus onset . Target stimuli and stimuli presented with a distractor were excluded from analysis , as were stimuli that appeared after the target . Additionally , the first stimulus presentation in each trial was excluded from analysis to reduce variance arising from stronger responses to the start of a stimulus series . Instruction trials were excluded from data analysis . Spikes and LFP were collected from 68 sites from Monkey 1 and 50 from Monkey 2 . Out of these , 13 and 9 sites were discarded because either the LFP signal was too large and saturated frequently or was too weak ( <10 µV ) . The results were similar ( and individually significant ) for the two monkeys , and the gamma oscillations were also in the same frequency range; the data were pooled . Time-frequency analysis was performed using the Matching Pursuit algorithm [61] . Due to the rapid presentation of the stimuli ( duration of 200 ms with interstimulus interval of 158–293 ms ) , the LFP signal had transient activity associated with stimulus onset/offset . This required time-frequency analysis over short intervals ( i . e . , good temporal but poor spectral resolution ) . On the other hand , line noise at 60 Hz and the monitor refresh rate at 75 Hz produced signals at constant frequency ( 60 and 75 Hz ) , which were sustained for long periods ( Figure 2 ) . To represent such signals , time-frequency analysis should be done over long intervals ( to achieve good spectral resolution at an expense of temporal resolution ) . These requirements are difficult to fulfill using traditional signal processing techniques such as short-time Fourier Transform or multi-tapering , but can be addressed using multiscale analysis techniques such as Matching Pursuit [61] . In this method , we start with an overcomplete dictionary of Gabor functions that have a wide range of time-frequency resolutions , including delta functions and sinusoids . The functions that best represent the signal are chosen for representation using an iterative procedure [26] . In this article , Matching Pursuit analysis was done on 1-s-long LFP segments , so the line noise at 60 Hz and the weaker noise at the monitor refresh rate of 75 Hz were captured by sinusoidal functions , which had a spectral resolution of ∼1 Hz , resulting in sharp lines at 60 and 75 Hz ( Figure 2 ) . Although Matching Pursuit algorithm provides better resolution to resolve transient and sustained activity , the results obtained using the multitaper method were similar ( Figure S1 ) . For each site , first a common “baseline power spectrum” was computed by averaging the power between 100 to 0 ms before stimulus onset for all nine normalization conditions ( denoted by Baseline ( ω ) ; Figure 2B , black line ) . For Figure 3B and 6B , the time-frequency power spectra were normalized by this baseline power [10 . ( log ( Power ( t , ω ) −log ( Baseline ( ω ) ) ] . Note that all the plots were normalized by the same baseline power ( average of the baseline power obtained from the nine normalization conditions ) , which eliminates the possible effects of differences in baseline power across conditions . We showed changes in LFP power instead of raw power because LFP has a prominent “1/f” structure with more energy at low frequencies , which makes it difficult to observe any changes at higher frequencies in the raw time-frequency power spectra . Further , the difference spectra do not show the line and refresh-rate-related noise because this noise is present before stimulus onset also . The difference spectra were smoothed by averaging the power in every 4 time and frequency bins ( essentially downsampling by a factor of 4 in both dimensions ) . This smoothing was done only for better visual display; all the power versus frequency/time plots ( Figures 4 , 5D , and 7A ) as well as the power difference calculations ( Figures 5 , 6C , 7B , and 8 ) were done using raw data . The gamma power was computed by summing the power between 65 and 80 Hz , but excluding the monitor refresh rate ( between 74 and 76 Hz ) . Power from each condition was divided by the power for the P100N0 condition before averaging across neurons . High-gamma power was taken between 80 and 135 Hz because we observed a noise peak between 140 and 150 Hz , possibly arising from the stepper motor used to drive the microelectrodes when it was not moving , and the power above 150 Hz was attenuated by the low pass filter in the Plexon recording system . | Brain signals often show a stimulus-induced rhythm in the “gamma” band ( 30–80 Hz ) whose magnitude depends on attentional load , leading to suggestions that gamma rhythm plays a functional role in routing signals across cortical areas . However , gamma power also depends on simple stimulus features such as size or contrast , which suggests that gamma could arise from basic cortical processes involving excitation–inhibition interactions . One such process is divisive normalization , a mechanism that suppresses the response of a neuron by the overall activity of a large pool of neighboring neurons . Recent studies have shown that attention increases the strength of both excitation and normalization . We hypothesized that the increase in gamma power in an attention task is due to the effect of attention on excitation and normalization . By manipulating the normalization strength independent of attentional load in macaque monkeys , we show that gamma power increases with increasing normalization , even when attentional load is held fixed . Thus , gamma rhythms could be a reflection of changes in the relative strengths of excitation and normalization rather than playing a functional role in communication or control . |
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Positive-strand RNA virus infections can induce the stress-related unfolded protein response ( UPR ) in host cells . This study found that enterovirus A71 ( EVA71 ) utilizes host UDP-glucose glycoprotein glucosyltransferase 1 ( UGGT1 ) , a key endoplasmic reticulum protein ( ER ) involved in UPR , to enhance viral replication and virulence . EVA71 forms replication complexes ( RCs ) on cellular membranes that contain a mix of host and viral proteins to facilitate viral replication , but the components and processes involved in the assembly and function of RCs are not fully understood . Using EVA71 as a model , this study found that host UGGT1 and viral 3D polymerase co-precipitate along with other factors on membranous replication complexes to enhance viral replication . Increased UGGT1 levels elevated viral growth rates , while viral pathogenicity was observed to be lower in heterozygous knockout mice ( Uggt1 +/- mice ) . These findings provide important insight on the role of UPR and host UGGT1 in regulating RNA virus replication and pathogenicity .
Positive-strand RNA viruses are capable of infecting a wide range of hosts , ranging from algae to humans . The mechanism underlying this broad range of pathogenicity spanning different hosts and tissue types involves the use of cellular membranes for viral genomic RNA replication , which provides a number of key benefits . Membrane structures allow buildup of a high local concentration of viral proteins , while also serving as a protective screen against protease cleavage . Membranes can further provide a structural scaffold that facilitates the correct spatial organization of viral replication complex ( RC ) components , and RCs can also be protected by the membrane against host infection sensors or other defense mechanisms [1 , 2] . Different positive-strand RNA viruses utilize different cellular membranes , resulting in a variety of morphological alterations; however , the sequences and functional domains of key viral proteins involved in membrane utilization are quite conserved among these viruses , suggesting that there are common strategies for the incorporation of cellular membranes into viral RCs [3 , 4] . Picornaviruses are a family of small positive-strand RNA viruses that include several notorious animal and human pathogens , such as rhinoviruses , Coxsackie viruses , foot and mouth disease virus , hepatitis A virus , and enterovirus A71 ( EVA71 ) . EVA71 typically causes hand , foot , and mouth disease ( HFMD ) , which is generally regarded as a mild childhood illness [5]; however , not along after its initial isolation in California during 1969 [6] , several deadly EVA71 epidemics occurred in the 1970s [7–9] , and the virus has recently been associated with severe neurological complications , such as brain stem encephalitis and acute flaccid paralysis , in Asian infants and young children [10] . Several large HFMD outbreaks in the Asia-Pacific region have also occurred in recent years , including Malaysia , 2007 [11]; Taiwan , 1998 [12]; Singapore , 2000 [13]; Japan , 1997 and 2000 [14]; Shandong , China , 2007 [15]; and Fuyang , China , 2008 [16 , 17] . EVA71 genomic RNA is about 7 , 400 nucleotides ( nt ) long , and upon viral entry into host cells , the RNA genome is directly translated into one polyprotein , which is then cleaved by virus-specific proteases into structural and replication proteins . About 10 mature proteins and several other intermediate products are generated during this process , and these elements go on to perform many independent functions in the viral life cycle [18 , 19] . One non-structural protein that plays a key role in EVA71 replication is the 3D viral polymerase , which is encoded in the P3 viral genome region and is cleaved by viral proteases from the 3CD precursor proteinase after translation [20–22] . The 3D polymerase is an RNA-dependent RNA polymerase ( RdRp ) responsible for plus-strand and minus-strand viral RNA synthesis in viral RCs [23 , 24] . The first step in this process involves uridylylation of the small viral protein , VPg , in which two uridine monophosphate ( UMP ) molecules bind to the hydroxyl group of a tyrosine residue near the N-terminus of VPg via a reaction catalyzed by the viral 3D polymerase [25] . The 3D polymerase can also facilitate viral RNA chain elongation in viral RCs [26–29] , and is known to interact with several host proteins , including Sam68 [30] . During picornavirus infection , viral RNA replication occurs on the cytoplasmic surfaces of single-membrane vesicles derived from the endoplasmic reticulum ( ER ) , and the membranes can serve to accelerate RC assembly during positive-strand genomic RNA replication [31] . Viral proteins 2BC and 3A are known to be involved in viral RC formation , and these proteins contain hydrophobic domains that allow them to interact extensively with cellular membranes [32 , 33] . Viral protein 3A also plays an important role in membrane reorganization through its interactions with cellular proteins such as GBF1 , Arf1 , and PI4KIIIβ [34–37] . Other non-structural viral proteins are known to interfere with cellular membrane metabolism , and even rearrange subcellular organelles . Many viral and host proteins and lipids are involved in the membrane remodeling process induced by RCs , and the underlying mechanisms are complex and not well understood; for example , the 3D viral polymerase does not have obvious membrane binding sequences or properties , and its presence in RCs is therefore quite puzzling . To enhance the current understanding of RC components and viral RNA replication following picornavirus infection , we used EVA71 as a model to evaluate interactions between the 3D viral polymerase and host proteins after virus infection . Proteins associated with 3D polymerase were immunoprecipitated with an anti-3D monoclonal antibody , and results showed that the host protein , UDP-glucose glycoprotein glucosyltransferase 1 ( UGGT1 ) , associates with 3D polymerase . UGGT1 , also known as HUGT1 , is a soluble ER protein that selectively reglucosylates unfolded glycoproteins , thus providing quality control for proteins transported out of the ER . Viral infections drive the accumulation of unfolded and misfolded proteins in the ER [38] , and to reduce the adverse effects of such accumulation , the host cell utilizes a stress-related defense mechanism known as the unfolded protein response ( UPR ) to decrease the load of newly synthesized proteins within the ER and eliminate incorrectly folded proteins [39–41] . Alternatively , proteins possessing non-native structures are recognized by UGGT1 , reglucosylated , and targeted for chaperone rebinding and ER retention [42] . UGGT1 can also add glucose molecules to the N-linked glycans of non-glucosylated substrates that fail quality control tests , thereby supporting additional rounds of chaperone binding and ER retention [42–46] . It has been shown that the disruption of protein folding in the ER induces UGGT1 expression [47] . Importantly , during EVA71 infection , we found that UGGT1 expression levels increase , and UGGT1 also redeploys from the ER to the cytoplasm , where it acts as a positive regulator of viral RNA synthesis . The 3A viral protein was also shown to increase UGGT1 and 3D polymerase levels in the membrane fraction . We further observed that the pathogenicity of EVA71 infection decreased in heterozygous Uggt1 knockout mice . These findings shed light on the molecular processes driven by host UGGT1 and viral 3D protein association , and provide important insight on the relationship between virus pathogenicity and viral-host interactions .
To better understand how the EVA71 3D polymerase associates with host proteins and other components of the viral replication machinery at RCs , we sought to identify novel host factors that associate with the 3D polymerase in RCs . Accordingly , an anti-3D monoclonal antibody was used to perform immunoprecipitation assays , in order to purify proteins associating with the 3D polymerase in infected cells . We immunoprecipitated mock-infected and EVA71-infected cell lysates with this anti-3D monoclonal antibody , and subsequently identified seven major protein bands that appeared in the EVA71-infected lysates , but not in the mock-infected lysates ( Fig 1A ) . We excised the protein bands that specifically associated with the 3D polymerase as shown in Fig 1A , digested the excisions with trypsin , and subjected them to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry ( MALDI-TOF MS ) analysis . Table 1 presents these seven proteins and their accession numbers , as obtained from the US National Center for Biotechnology Information ( NCBI ) protein database ( Table 1 ) . The seven proteins included one viral protein , the EVA71 3CD polyprotein ( Fig 1A , band 5 ) , and six host proteins: UGGT1 , elongation factor 2 , interleukin enhancer binding factor 3 ( ILF3 ) , lamina-associated polypeptide 2 isoform alpha , T-complex protein 1 subunit theta , and eukaryotic translation initiation factor 3 . The identified peptide sequences accounted for 24% of the UGGT1 sequence ( Table 1 and S1 Fig ) . The ER protein , UGGT1 ( Fig 1A , band 1 ) , was selected for further investigation . We performed Western blot analysis to confirm that UGGT1 associated with the 3D polymerase following EVA71 infection . Co-IP experiments using EVA71-infected or mock-infected RD cell extracts were conducted , and the anti-3D antibody was able to immunoprecipitate UGGT1 ( Fig 1B , lanes 3 and 4 ) , while reciprocal co-IP experiments showed that the anti-UGGT1 antibody was also able to immunoprecipitate 3D polymerase in EVA71-infected cell lysates ( Fig 1B , lanes 7 and 8 ) . Viral capsid proteins were not observed in the UGGT1-viral protein complexes ( Fig 1B ) . These results provide evidence that UGGT1 interacts with the EVA71 viral 3D polymerase . Host cells can mobilize the UPR in an attempt to restrict viral infection , and UGGT1 is known to be a key UPR factor in the ER . To ascertain UGGT1 expression levels in EVA71-infected cells , we compared UGGT1 levels in mock-infected and infected cells . UGGT1 expression levels were found to increase upon viral infection ( Fig 1B and 1C , input lysate ) . It is known that the 3D viral polymerase associates with viral RNA , and to determine whether UGGT1 interaction with 3D polymerase was mediated by RNA , we examined the interaction between UGGT1 and viral genomic RNA . Treatment with RNase A prior to co-IP assays did not reduce UGGT1 interaction with the EVA71 3D polymerase , indicating that this interaction was not mediated by viral genomic RNA ( Fig 1C , lanes 5 and 6 ) . Host protein ILF3 is an RNA-binding protein that associates with the 3D viral polymerase ( Table 1 ) . Here , ILF3 served as a positive control for RNase A treatment . ILF3 association with the 3D viral polymerase was reduced after RNase A was applied prior to co-IP assays . Degradation of RNA was confirmed by RNA gel analysis ( Fig 1C ) . To clarify the components of the UGGT1-3D complex , we purified the membrane fraction of EVA71-infected cells , and performed an immunoprecipitation assay to identify other viral proteins involved . The results shown in Fig 1D indicate that the 3A viral protein was also present in the UGGT1-3D complex , and this provides evidence to support an indirect interaction dependent on other viral proteins between UGGT1 and the 3D polymerase . In addition , as the 3D viral polymerase is located in the nucleus and cytoplasm at different stages of viral replication , while UGGT1 is located predominantly in the ER , we therefore sought to examine how UGGT1 and the 3D polymerase colocalize intracellularly during EVA71 infection , using fluorescence confocal microscopy . In mock-infected cells , UGGT1 was predominantly localized in the cytoplasm ( Fig 1E , panel 4 ) , while in EVA71-infected cells , both the 3D polymerase and UGGT1 were localized in the cytoplasm at 6 h post-infection ( Fig 1E , panel 8 ) . Moreover , when an anti-dsRNA antibody was used to highlight the location of RCs in an immunofluorescence assay , staining results showed that UGGT1 associates with RCs in the cytoplasm ( Fig 1F ) . However , co-IP experiments conducted in uninfected cells co-expressing Flag-UGGT1 and HA-3D showed that anti-HA antibodies did not precipitate Flag-UGGT1 , nor did anti-Flag antibodies precipitate HA-3D . Anti-HA antibodies also did not co-immunoprecipitate endogenous UGGT1 from RD cells expressing HA-3D ( Fig 2 ) . These results show that UGGT1 can co-precipitate with EVA71 viral polymerases in RCs , and further indicate that viral infection is essential for UGGT1 co-purification with the EVA71 3D polymerase . Together , these findings confirm that upon viral infection , UGGT1 levels increase , and UGGT1 co-precipitates with 3D polymerase and other factors on membranous replication complexes . To ascertain the effect of UGGT1 on viral replication and propagation , we infected ( negative control ) NC or UGGT1 siRNA-transfected cells with a high titer of EVA71 ( MOI = 10 ) , and assessed 3D polymerase expression at 6 h post-infection by confocal microscopy . We first analyzed the effect of UGGT1 knockdown on siRNA-transfected cell viability . Cell viability was measured by a CellTiter-Glo Luminescent Cell Viability kit ( Promega ) , which quantitates the ATP generated in viable cells . The results presented in S2 Fig demonstrate that cell proliferation and viability were not significantly different between NC siRNA- and UGGT1 siRNA-transfected cells ( S2 Fig ) . In UGGT1 siRNA-treated cells , viral protein expression was significantly lower compared to NC siRNA-treated cells ( Fig 3A , panels 10 and 14 ) . These results suggest that UGGT1 plays a critical role in enhancing viral replication . To further evaluate the effects of UGGT1 on EVA71 replication rates , we treated RD cells with NC or UGGT1 siRNA , and then infected these cells with a high ( MOI = 10 ) or low ( MOI = 0 . 1 ) EVA71 titer . The plaque assay was used to detect viral yields at various timepoints post-infection . Viral replication rates were found to be lower in uggt1 knockdown cells as compared to NC siRNA-treated cells , regardless of MOI levels ( Fig 3B and 3C ) . These results support the hypothesis that UGGT1 is a positive regulator during EVA71 infection . We repeated this experiment in SF268 glioblastoma ( SF268 ) cells to determine whether the effects of UGGT1 on viral replication are specific to a given cell type . Results showed that viral replication rates were also lower in UGGT1 siRNA-treated SF268 cells , as compared to NC siRNA-treated cells ( S3A and S3B Fig ) . To avoid siRNA off-target effects , we used EVA71 to infect cells overexpressing UGGT1 , and subsequently measured virus yields at 4 , 6 , and 8 h post-infection . The results showed that viral replication increased when UGGT1 was overexpressed in infected cells ( Fig 3D , S3C and S3D Fig ) . We further evaluated the role of UGGT1 in enterovirus D68 ( EVD68 ) . EVD68 is classified in a different group as other more common enteroviruses , such as EVA71 and coxsackievirus A16 , but this does not mean it is less pathogenic: in a 2014 outbreak of EVD68 , a total of 1 , 152 people in United States were confirmed as having acute respiratory infections caused by EVD68 . It is important for emergency clinicians to recognize this viral illness , because it can lead to respiratory distress that requires hospitalization or , in some instances , intensive care [48] . After infecting NC or UGGT1 siRNA-treated cells with EVD68 , we evaluated viral replication rates , and observed that EVD68 viral titers were lower in UGGT1 siRNA-transfected cells as compared to NC siRNA-transfected cells ( S3E and S3F Fig ) . These results confirm that UGGT1 is a positive regulator of EVA71 and EVD68 replication , and suggest that it may be a commonly utilized host factor for viral replication in enteroviruses . To assess whether the enzymatic activity of UGGT1 is critical for viral replication , we generated UGGT1 variants lacking monoglucosylation activity ( UGGT1 ( mut ) ) . Previously , it was shown that UGGT1 enzymatic activity would be abolished after the elimination of monoglucosylation activity via mutation [47] . We overexpressed UGGT1 or UGGT1 ( mut ) in infected cells , and subsequent comparison of viral yields showed no significant difference ( S4 Fig ) , suggesting that UGGT1 enzymatic activity is not critical for viral replication . In light of this , we propose that UGGT1 may primarily act as a protein bridge to facilitate viral replication . To assess the role of UGGT1 in viral pathogenicity in vivo , we generated Uggt1 knockout mice from the KOMP-CSD ES cell resource [43] . The Uggt1 gene deletion destroys reglucosylation activity in cells and is embryonically lethal at day E13 in mice [45] . Homozygous Uggt1 knockout mice were embryonically lethal; however , heterozygous mice were viable , fertile , developed normally , and did not reveal any obvious phenotypic alterations up to adulthood ( Fig 4A ) . Heterozygous Uggt1 knockout mice expressed only 50–60% of UGGT1 as compared to their UGGT1 wild-type littermates ( Fig 4B ) . A mouse-adapted EVA71 strain with increased virulence in mice , MP4 , was generated after four serial passages of the parental EVA71 strain 4643 in mice [46] . To quantify EVA71 replication rates in wild-type or heterozygous Uggt1 knockout mice , the viral load in different mouse tissues on Day 3 after EVA71 infection was assessed . EVA71 was detected in the brain ( Fig 4C ) and muscle tissues ( Fig 4D ) , but the viral load in Uggt1 heterozygous knockout mice was significantly lower than that in wild-type mice . These results prompted us to investigate the virulence of EVA71 in Uggt1 heterozygous knockout mice . We challenged 10-day-old wild-type or heterozygous Uggt1 knockout mice with a 105 plaque-forming unit ( PFU ) /mouse dose of EVA71 strain MP4 . Wild-type mice displayed severe limb paralysis on Day 4 after infection , while heterozygous knockout mice only demonstrated mild limb paralysis ( Fig 4E ) . Infected wild-type mice began to die on Day 8 after infection , whereas heterozygous Uggt1 knockout mice began to die on Day 10; however , the 90% survival rate in infected heterozygous knockout mice was still significantly higher ( P < 0 . 001 ) than the 0% survival in wild-type mice ( Fig 4F ) . To further ascertain if UGGT1 plays a similarly important role in other virus families with regard to enhancing virulence and pathogenicity , we selected the Japanese Encephalitis Virus ( JEV ) from the family Flaviviridae to study the role of UGGT1 upon virus infection . First , we performed Western blot analysis to confirm the association between UGGT1 and the NS5 polymerase following JEV infection . Immunoprecipitation experiments using JEV-infected or mock-infected BHK-21 cell extracts were conducted , and the anti-UGGT1 antibody was able to immunoprecipitate NS5 polymerase only in JEV-infected cell lysate ( S5A Fig ) . To determine growth efficiency of the virus in mouse brains , an experiment was carried out in suckling mice by intracranial inoculation with 104 PFU/mouse of the T1P1 JEV strain . After 7 days post-infection , we collected suckling mice brain tissue , and performed a plaque assay to determine viral titers . The results indicated that UGGT1 was able to associate with JEV polymerase NS5 and enhance viral growth efficiency in suckling mice tissue ( S5B Fig ) . These results show that UGGT1 knockdown can reduce EVA71 and JEV virulence and improve disease outcome . The EVA71 life cycle comprises entry , viral mRNA translation , viral RNA synthesis , and virus assembly . To evaluate the biological significance of UGGT1 in EVA71 replication , we examined the effects of UGGT1 on EVA71 replication efficiency . NC and UGGT1 siRNA knockdown RD cells were transfected with EVA71-Luc replicon RNA , and cell firefly luciferase activity ( measured in relative light units , RLU ) was measured at 6 h post-transfection . In EVA71-Luc replicon RNA , the viral genome P1 region was replaced with a firefly luciferase reporter gene , and luciferase expression therefore reflected viral replication . In UGGT1 siRNA-treated cells , EVA71-Luc replicon luciferase activity was reduced to 55% of the activity in control cells ( Fig 5A ) . This could be due to loss of UGGT1 promotion of either viral mRNA translation or viral RNA replication . We therefore examined the effect of UGGT1 on EVA71 cap-independent translation first , using dicistronic and monocistronic IRES-mediated translation assays [49] . In the dicistronic translation assay , the first cistron ( Renilla luciferase , RLuc ) involved cap-dependent translation , while the second cistron ( Firefly luciferase , FLuc ) required EVA71 IRES-dependent translation . The ratio of FLuc expression to RLuc expression reflects IRES-mediated translation activity . We transfected RD cells with NC or UGGT1 siRNA , and a dicistronic reporter plasmid was then co-transfected . After 48 h post-transfection , cell lysates were collected and used to calculate the ratio of FLuc to RLuc . The results showed that dicistronic IRES activity in NC siRNA-treated cells was not significantly superior to the activity in UGGT1 siRNA-treated cells ( S6 Fig ) , and monocistronic IRES activity also showed no significant difference between NC and UGGT1 siRNA-treated cells ( Fig 5B ) . These results indicate that assisting viral mRNA translation is not the role played by UGGT1 in EVA71 infection . As UGGT1 can be co-purified with the 3D polymerase , we speculated that UGGT1 may facilitate EVA71 replication by enhancing viral RNA synthesis . To ascertain this , we first monitored viral RNA production in NC or UGGT1 siRNA-treated RD cells that were subsequently infected with EVA71 . Intracellular RNA was isolated at different intervals post-infection , and EVA71 viral RNA was measured using quantitative real-time reverse transcription polymerase chain reaction ( RT-PCR ) . Results showed that viral RNA production was 33% lower in UGGT1 siRNA-treated cells , as compared to NC siRNA-transfected cells ( Fig 5C ) . Viral RNA levels were further investigated in Uggt1 knockdown cells . EVA71 was used to infect cells , and viral RNA was extracted at various timepoints post-infection . Slot blot analysis , using specific RNA probes that recognize positive or negative sense EVA71 RNA , was used to monitor viral RNA synthesis . The results in Fig 5D show that levels of both positive and negative sense EVA71 RNA were lower in UGGT1 siRNA-treated cells than NC siRNA-treated cells; specifically , positive-strand RNA levels were reduced by 20% in UGGT1 siRNA-treated cells , while negative-strand RNA levels were reduced by 54% ( Fig 5D ) . These results suggest that UGGT1 likely acts to enhance viral RNA synthesis during EVA71 infection . UGGT1 is a key quality control factor and protein folding sensor of the ER . To determine the localization of UGGT1 in cells following EVA71 infection , we used an anti-calnexin ( CNX ) antibody to evaluate proteins located in the ER in an immunoprecipitation assay . CNX is a transmembrane protein on the ER . In the absence of infection , UGGT1 and CNX were shown to colocalize in the ER ( Fig 6A , panels 5 and 21 ) , but some UGGT1 began deploying out of the ER to colocalize with the 3D viral polymerase upon EVA71 infection ( Fig 6A , panels 20 and 22 ) , to the point where little UGGT1 remained in the ER with CNX . UGGT1 and CNX signals overlapped by more than 75% in mock-infected cells , but this overlap was reduced to less than 55% in EVA71-infected cells ( Fig 6B ) . We further performed subcellular fractionation to separate the cytosol and microsome fractions in mock- or EVA71-infected cell extracts . Microsomes are vesicle-like artifacts re-formed from pieces of the ER when cells are broken up in the laboratory , and can be separated from other cellular components by differential centrifugation . The cellular protein , calnexin , serves as a marker for the ER component in the microsome fraction . UGGT1 was predominantly located within the ER microsome in the mock cell lysates ( Fig 6C , lanes 3 and 5 ) ; however , during EVA71 infection , UGGT1 was found to deploy out of the ER microsome . The proportion of UGGT1 external to the ER rose from 11% to 37% upon viral infection ( Fig 6C , lane 3 and 4 ) . EVA71 infection induces the rearrangement of intracellular ER membranes into characteristic vesicles that assemble into viral RCs . According to Fig 1D and 1F , UGGT1 colocalizes at RCs in association with the 3D viral polymerase; however , the 3D polymerase does not possess obvious membrane-binding sequences or properties , and therefore it is unclear as to how it came to be present in RCs . In contrast , viral protein 3A contains hydrophobic domains and extensively interacts with the cellular membranes that form RCs , and thus can play an important role in membrane reorganization through its interactions with host cellular proteins . To investigate the effect of UGGT1 co-precipitation with the 3D polymerase upon RC formation , we transfected plasmids expressing viral protein 3A , 3AB , or 3D into cells , and performed the membrane protein fractionation assay . ER membranes in cells were modified by the expression of 3A and 3AB . Levels of UGGT1 and 3D polymerase in the membrane protein fraction of 3A and 3D co-expressing cells were higher than that from cells expressing 3D alone ( Fig 7A ) . This indicates that the presence of viral proteins 3A or 3AB can enhance levels of UGGT1 and the 3D viral polymerase in the membrane protein fraction . To observe the effect of viral protein 3A upon the enhancement of UGGT1 levels in the membrane protein fraction , we used only 3A- or 3AB-expressing cells in the fractionation assay . Results showed that expression of 3A or 3AB enhanced the amount of UGGT1 by 2 . 1- and 1 . 8-fold in the membrane protein fraction ( Fig 7B ) . To evaluate the effect of UGGT1 levels on the amount of 3D polymerase in the membrane protein fraction , we co-transfected 3A- or 3AB-expressing plasmids with pFLAG-3D into NC or UGGT1 siRNA-treated cells , and compared the amount of 3D polymerase in the membrane protein fractions . Fig 6C shows that the amount of 3D polymerase in Uggt1 knockdown cells decreased to just 90% ( 3A+3D ) or 30% ( 3AB+3D ) of levels in NC siRNA-treated cells ( Fig 7C ) . We further performed an experiment assessing 3D recruitment to cell membranes with UGGT1 knockdown in the absence of 3A or 3AB . It is well-documented in poliovirus experimental systems that 3D interacts with 3AB , and therefore it is important to ascertain whether UGGT1 directly recruits 3D , or if it merely facilitates 3D-3A interaction . In Fig 7D , the results indicated that the level of 3D recruitment to cell membranes was the same between NC siRNA- or UGGT1 siRNA-treated cells . These results show that UGGT1 can indirectly facilitate 3D-3A interactions ( Fig 7D ) , and demonstrate that although the 3A viral protein can act to enhance levels of UGGT1 and the 3D viral polymerase in membrane protein fractions , the presence of 3D is also partly dependent on UGGT1 . To the best of our understanding , UGGT1 is the first identified host protein that deploys from the ER to the cytosol following EVA71 infection , and our results indicate that UGGT1 acts to promote 3D viral polymerase levels in the viral protein 3A-associated membrane fraction , which in turn may enhance viral replication and increase viral titers .
Viral infection typically triggers an arms race between the virus and host cell . For example , host cells can induce UPR in the ER to restrict viral infection , but viruses can counter this by manipulating the UPR to facilitate viral propagation . In this study , we showed that expression of the key UPR factor , UGGT1 , not only increases upon viral infection , but UGGT1 interaction with the EVA71 3D polymerase also has positive effects on viral growth and pathogenicity as well . Immunoprecipitation assays and MALDI-TOF analysis results indicate that the 3D viral polymerase co-precipitates with UGGT1 during EVA71 infection ( Figs 1 and 2 ) , and this interaction promotes EVA71 replication ( Fig 3 ) . Furthermore , heterozygous uggt1 knockout mice demonstrated lower EVA71 pathogenicity than wild-type mice ( Fig 4 ) , and this may be due to reduction of positive- and negative-strand viral RNA synthesis in the absence of UGGT1 ( Fig 5 ) . We also noted that UGGT1 deploys from the ER to the cytosol upon EVA71 infection ( Fig 6 ) , where it enhances 3D polymerase levels in the membrane fraction involved in RC formation; this process is facilitated by viral protein 3A , which acts to enhance the amount of UGGT1 in the membrane fraction ( Fig 7 ) . Together , these results confirm that EVA71 can utilize the UPR host defense mechanism and the UPR factor UGGT1 to facilitate viral RNA synthesis and pathogenicity , via UGGT1 co-precipitation with the 3D viral polymerase at RCs . We used immunoprecipitation assays to identify seven proteins that co-precipitate with the 3D polymerase , and future research could include the evaluation of other 3D polymerase-interacting host proteins as to their involvement in EVA71 replication , particularly ILF3 ( Table 1 ) . ILF3 acts to facilitate double-stranded RNA-regulated gene expression at the post-transcriptional level [50 , 51] . In recent years , investigators have developed an increasing interest in ILF3 and its interaction with select viral proteins [52–54] . It is known that ILF3 interacts with the 3’ stem-loop structure of dengue RNA and serves as a positive regulator of dengue virus replication [55]; however , ILF3 is also known to inhibit influenza virus replication during the early phase of infection via direct interactions with viral nucleoproteins [56] . These findings suggest that ILF3 can play both positive and negative regulatory roles in different types of viral infections . There is currently no research on the role of ILF3 in the EVA71 life cycle , and therefore further investigation on the effects of ILF3 in this respect could have significant import . Incidentally , although other proteins known to associate with viral genome RNA were also identified in Table 1 , including elongation factor 2 and eukaryotic translation initiation factor 3 [57 , 58] , our results show that RNase A treatment did not reduce the co-precipitation between UGGT1 and the 3D viral polymerase ( Fig 1C ) , and indicate that viral genomic RNA does not mediate UGGT1-3D interaction . Further research showed that viral proteins 3C , 3AB , and 3A also co-purify with the 3D-UGGT1 complex , and may act to facilitate UGGT1 and 3D interaction . Previous studies have found that picornavirus RNA replication occurs on the cytoplasmic surfaces of double-membrane vesicles originating from the ER , Golgi , and lysosomes in infected cells [31 , 59–61] . Poliovirus-induced membrane vesicles have also been linked to intracellular vesicular traffic involving COPII-dependent vesicles [62] . A recent study showed that poliovirus enriches membranes with phosphatidylinositol-4 phosphate , and promotes RNA replication through the recruitment of relevant viral and cellular proteins [37] . Our findings were similar in that UGGT1 also distributes from the ER to the cytosol to co-localize with the 3D viral polymerase , and this may help to facilitate EVA71 RC formation . To our understanding , this is the first study to report on an ER protein deploying to the cytosol to co-localize with the 3D viral polymerase . However , further research is needed to determine the exact location of UGGT1 within the viral RC membrane structure , perhaps by using an electron microscope . This research could also include further examination of the effects of UGGT1 and 3D polymerase association on the membrane secretory pathway . In S5 Fig , we found that UGGT1 associated with JEV polymerase NS5 upon viral infection , and enhanced viral pathogenicity . However , the UGGT1 and NS5 interaction may be direct or indirect . In future , we will seek to perform additional experiments to detect other cellular or viral proteins involved in the UGGT1-NS5 complex . This research is expected to provide more information regarding the role of UGGT1 in flavivirus replication . Mice have two UGGT genes , Uggt1 and Uggt2 [47] , but only the Uggt1 gene product displays reglucosylation activity , and its deletion halts reglucosylation activity in cells [45] . However , the product of Uggt2 has no reglucosylation activity , and its function is unknown . When 80% of UGGT2 is replaced with the UGGT1 N-terminal substrate recognition domain , reglucosylation activity can be partly restored in vitro , demonstrating that the remaining 20% of the UGGT2 C-terminal region can serve as a functional glucosyltransferase [63] . To ascertain whether UGGT1 activity is required during viral replication , or whether UGGT1 merely acts as a protein bridge , we generated UGGT1 mutation variants lacking monoglucosylation activity , and subsequently performed UGGT1 overexpression experiments , with the results shown in S4 Fig . After comparing viral yields between cells in which UGGT1 or UGGT1 ( mut ) was overexpressed , we found that there was no significant difference , suggesting that the enzymatic activity of UGGT1 is not required to enhance viral growth . We therefore propose that UGGT1 may primarily serve as a protein bridge that facilitates viral replication . In summary , our results demonstrate that UGGT1 can co-precipitate with the 3D polymerase at EVA71 RCs to increase viral RNA replication . This is the first study to describe the deployment of an ER protein to the cytosol upon viral infection , and the interesting role of UGGT1 in EVA71 replication suggests that it may provide insight into the development of novel anti-EVA71 strategies . Investigators have already designed several small molecular drugs that target the 3D viral polymerase [64–67] , and thus it may be feasible to develop therapies that target either the interaction between 3D and UGGT1 , or between 3D and RCs . Ascertaining the functions of other cellular factors in positive-strand RNA virus replication could further facilitate the development of unique antiviral strategies , or perhaps allow the harnessing of these viral proteins for other applications .
All animal experiments were conducted in accordance with the policies and procedures set forth by the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All procedures were approved by the Institutional Animal Care and Use Committee of Chang Gung University , Taiwan ( IACUC approval number CGU15-017 ) . Human embryonal rhabdomyosarcoma ( RD; from American Type Culture Collection: CRL-1620 ) and human glioblastoma ( SF268; provided by Dr . Jim-Tong Horng lab at Chang Gung University , Taiwan ) cells were maintained in Dulbecco's Modified Eagle Medium ( DMEM; Gibco , Grand Island , NY ) supplemented with 10% fetal bovine serum ( FBS; Gibco ) , and cultured at 37°C in a 5% CO2 atmosphere . Hamster kidney fibroblast ( BHK-21; from American Type Culture Collection: CCL-10 ) cells were maintained in RPMI 1640 Medium ( Gibco , Grand Island , NY ) supplemented with 2% fetal bovine serum ( FBS; Gibco ) , and cultured at 37°C in a 5% CO2 atmosphere . For transfection studies , subconfluent ( 70% ) monolayer cultures were transiently transfected or cotransfected with the respective plasmids , using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) . Transfected cultures were incubated for a further 48 h before being used in pull-down or co-immunoprecipitation ( Co-IP ) assays . The pFLAG-UGGT1 plasmid was constructed by amplifying UGGT1 from RD cell total RNA by RT-PCR , using a UGGT1 primer ( 5′-ATAAGAATGCGGCCGCGGGCTGCAAGGGAGACGCGAG-3′ ) containing a NotI restriction enzyme cutting site , and another primer ( 5′-GCTCTAGATCATAATTCTTCACGTTTCT-3′ ) containing a XbaI restriction enzyme site . The derived UGGT1 sequence was then cloned into a p3xFLAG-Myc-CMV vector ( Sigma-Aldrich , Munich , Germany ) . Plasmid pHA-3D was constructed by amplifying the sequence encoding the 3D viral polymerase from the EVA71 full-length infectious cDNA clone via PCR , using an EVA71 primer ( 5′-CGGAATTCCGATGGGTGAGATCCAATGGAT-3′ ) containing a EcoRI restriction enzyme site and another primer ( 5′-ACCTCGAGATCACAATTCGAGCCAATTTC-3′ ) containing a XhoI restriction enzyme site . The derived sequence was then cloned into a pCMV-HA vector ( Clontech , Palo Alto , CA ) . A UGGT1 variant in which the amino acid residues critical to monoglucosylation activity , aa 1452–1457 , were deleted ( UGGT1 ( mut ) ) , was generated by two-step overlap PCR mutagenesis . Primers * ( 5’-CGAGTAATAACTTCTTTGTGGA-3’ ) and ( 5’-ATGAATCATGTTAAGATTTGAAAG-3’ ) were used to generate the 5’ fragment and primers ( 5’-CTTTCAAATCTTAACATGATTCAT-3’ ) and * ( 5’- GGAATTCCGGAGACAGATCA-3’ ) were used to generate the 3’ fragment . Primers designated by asterisks were then used to amplify the overlapping fragments for substitution , via Spe1 and EcoR1 sites , into the UGGT1 expression vector ( pFLAG-UGGT1 ) described above . The mutation was confirmed by DNA sequencing , and the resulting plasmid DNA was designated pFLAG-UGGT1 ( mut ) [47] . RD cells were infected with EVA71 ( strain 4643/TW/98 ) at a multiplicity of infection ( MOI ) of 10 and incubated for 6 h , prior to conducting immunoprecipitation or Co-IP assays . Infected cells were lysed in a buffer containing 50 mM Tris-HCl ( pH 7 . 4 ) , 150 mM sodium chloride ( NaCl ) , 1% Triton X-100 , and a protease inhibitor cocktail ( Roche , Mannheim , Germany ) . Cell lysates were precleared with mouse immunoglobulin G ( IgG ) agarose and incubated with a mouse anti-3D monoclonal antibody on ice for 2 h , after which 50 μL of protein G-Sepharose beads were added , and the mixtures were incubated at 4°C overnight . Proteins bound to the beads were eluted into a 1× sodium dodecyl sulfate ( SDS ) running buffer by heating at 95°C for 5 min . For RNase A treatment , 100 μL of RNase A in an RNase A working buffer ( 0 . 5 U ) was added before any antibodies , and the samples were incubated at 37°C for 25 min . Total degradation RNA was extracted using an RNeasy kit ( Qiagen , Chatsworth , CA ) , according to the manufacturer's recommendations , and gel analysis was conducted . For the JEV immunoprecipitation assay , BHK-21 cells were infected with JEV ( strain T1P1 ) and incubated for 24 h , prior to conducting the immunoprecipitation assay . Pull-down products containing eluted proteins were boiled , subjected to 8–16% sodium dodecyl sulfate polyacrylamide gel electrophoresis ( SDS-PAGE ) , and visualized by silver staining or Western blotting . Each protein band was excised , destained , reduced , alkylated , and digested with trypsin . To extract the polypeptides , gel particles were subjected twice to consecutive 20 mM sodium bicarbonate and 5% formic acid in 50% acetonitrile treatments . The supernatants were combined and lyophilized , and the dried polypeptides were recovered by adding 10 μL of 0 . 1% formic acid , followed by sonication for 1 min . The recovered polypeptides were further purified using a ZipTip C18 column ( Millipore , Billerica , MA ) , and eluted with acetonitrile to a final volume of 3 μL . Protein bands were excised and identified using in-gel trypsin digestion , then analyzed using a Bruker Ultraflex MALDI-TOF mass spectrometer ( Bremen , Germany ) . After removing the masses derived from the standards , trypsin , matrix proteins , and keratins , the monoisotopic mass lists for each protonated peptide were subjected to database searches , and mass lists were exported to the Biotool 2 . 0 software package to perform peptide mass fingerprinting , using the Mascot ( http://www . matrixscience . com ) algorithm scoring to identify proteins . RD cells were seeded on 20-mm coverslips to 60% confluency and infected with EVA71 ( strain 4643/TW/98 ) at an MOI of 10 . At various post-infection timepoints , cells were washed with phosphate-buffered saline ( PBS ) and fixed with 4% formaldehyde for 30 min at room temperature ( RT ) . Cells were then washed with PBS and permeabilized using 0 . 75% Triton X-100 for 5 min at RT , then washed again with PBS and incubated in blocking solution ( PBS containing 0 . 5% bovine serum albumin ) for 1 h at RT . Cells were then immunostained with an anti-double-strand RNA antibody , J2 ( diluted 1:200; Scicons , Szirák , Hungary ) ; an anti-3D antibody , clone 1 ( diluted 1:500; prepared in the lab from recombinant 3D protein ) ; an anti-UGGT1 antibody , K-16 ( diluted 1:200; Santa Cruz Biotechnology , Santa Cruz , CA ) ; and an anti-CNX ( calnexin ) antibody , H-70 ( diluted 1:400; Santa Cruz Biotechnology ) for 2 h at 37°C . After washing three times with PBS , cells were incubated with Alexa Fluor 568-conjugated donkey anti-goat IgG ( Invitrogen ) and Alexa Fluor 488 goat anti-mouse IgG ( Invitrogen ) for 1 h at RT . Cell nuclei were stained using Hoechst 33258 ( 1:500 dilution ) for 20 min , according to methods previously described [68] . The cells were then observed using a confocal laser-scanning microscope ( LSM 510 NLO; Zeiss , Jena , Germany ) . RD cells were cultured in six-well plates ( 2×105 cells/well ) for 24 h and then transfected with UGGT1 siRNA ( UGGT1-HSS183580; Invitrogen ) , using Lipofectamine 2000 according to the manufacturer’s protocol ( Invitrogen ) . The cell viability assay was performed using CellTiter-Glo Luminescent Cell Viability Assay ( Promega ) . For the viability assays , to quantitate ATP generated by metabolically active cells , negative control ( NC ) or UGGT1 siRNA transfected cells were plated in 96-well plates at 5 , 000 cells/well . Cells were lysed with CellTiter-Glo Luminescent Cell Viability Assay reagent ( Promega ) , and luminescence was read using the GloMax Explorer System according to the manufacturer's instructions . Following transfection with NC or UGGT1 siRNA for 2 days , RD cells were seeded onto 12-well plates and incubated for 24 h . Cells were then plated to six-well plates ( 6×105 cells/well ) and infected with EVA71 at an MOI of 1 . After 60 min absorption at 37°C , the cells were washed twice and supplemented with medium , then incubated at 37°C for the indicated time periods , after which intracellular RNA was extracted using an RNeasy kit ( Qiagen , Chatsworth , CA ) . Viral RNA was detected via quantitative real-time RT-PCR with a Roche RT-PCR kit and a Lightcycler LC480 apparatus . The oligonucleotide primers and the probe for detecting EVA71 RNA were designed by Verstrepen et al [69] . Each sample was assayed in triplicate , and experiments were independently performed three times . The obtained data were analyzed using Roche Lightcycler LC480 system software . EVA71 RNA yields were normalized to that of actin RNA . Slot blot analysis for detecting positive-strand and negative-strand viral RNA was performed as previously described [70] . Viral RNA was extracted and dissolved in a solution of formaldehyde and 20× SSC for 30 min at 60°C . The reaction was then loaded onto a nitrocellulose membrane in the slot blot manifold . After washing twice , the nitrocellulose membrane was removed , air dried , and UV crosslinked . Digoxigenin-labeled RNA probes of 100 ng , specific for the genome or anti-genome of EVA71 , were produced using a DIG Northern starter kit ( Roche ) . The hybridization and detection procedures were performed according to the manufacturer’s protocol . For EVA71-Luc replicon assays , RD cells were transfected with NC or UGGT1 siRNA . Three days after transfection , the EVA71-Luc replicon ( kindly provided by Dr . Craig E . Cameron ) was transfected into cells . After 6 h , cell lysates were collected , and luciferase expression levels were determined with the luciferase reporter assay ( Promega , Madison , WI ) according to the manufacturer’s instructions . For the dicistronic expression assay , RD cells were transfected with UGGT1 siRNA , and after 3 days , a dicistronic construct , pRHF-EVA71 , was cotransfected with UGGT1 siRNA to RD cells . After 2 days , cell lysates were prepared in a passive buffer ( Promega ) and examined for Renilla luciferase ( RLuc ) and Firefly luciferase ( FLuc ) activities with a Lumat LB 9507 bioluminometer ( EG&G Berthold , Wildbad , Germany ) , using dual-luciferase reporter assays ( Promega ) conducted in accordance with the manufacturer’s instructions . Pixel colocalization of different color channels in confocal images was analyzed using Image J software and the ColocalizeRGB and Area Calculator plugins . Cellular membrane fractions were collected using the Mem-PER Plus Membrane Protein Extraction Kit ( ThermoFisher Scientific , San Jose , CA ) , in accordance with the manufacturer’s instructions . Approximately 40 μg of membrane proteins were separated with 12% polyacrylamide gels for SDS-PAGE , and electroblotted onto polyvinylidene fluoride ( PVDF ) membranes ( BioRad , Richmond , CA ) . PVDF membranes were blocked for 2 h at RT in 5% milk-TBST ( 25 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 0 . 05% Tween 20 ) , and then stained with anti-3D antibody ( diluted 1:10 , 000 ) , anti-3A antibody ( diluted 1:1 , 000 ) , anti-UGGT1 antibody ( diluted 1:500 ) , anti-VP2 antibody , MAB979 ( diluted 1:2 , 000; MILLIPORE ) , anti-CNX ( calnexin ) antibody ( diluted 1:1 , 000 ) , and an anti-HSP90 ( heat shock protein 90 ) antibody , ADI-SPS-771 ( diluted 1:1 , 000; Enzo Life Sciences , Farmingdale , NY ) for 2 h at 37°C . Afterwards , the membranes were washed for four times with TBST , and incubated at RT for 1 h with a peroxidase-conjugated secondary antibody ( diluted 1:1 , 000 ) , after which Amersham ECL Prime ( GE Healthcare , Waukesha , WI ) was used for chemiluminescence detection , and the signal was recorded on X-ray films . RD cells were mock infected or infected with EVA71 at an MOI of 10 . The cells were harvested at 6 h post-infection . EVA71-infected cells were washed with PBS on ice , scraped into PBS , and collected by centrifugation for 5 min at 1 , 500 × g . Cell pellets were resuspended in hypotonic solution ( 42 mM KCl , 10 mM MgCl2 , 10 mM HEPES , and pH adjusted to 7 . 4 ) and homogenized using a cell cracker ( 8 . 020-mm internal diameter , 8 . 010-mm bead diameter; HGM , Heidelberg , Germany ) . The homogenates were subjected to centrifugation for 10 min at 7 , 000 × g , after which mitochondrial-rich pellet was removed . The supernatant was collected and the concentration adjusted to 1 μg/μL using a hypotonic solution , then centrifuged for 45 min at 55 , 000 rpm at 4°C . The resulting supernatant was collected and designated as the cytosolic fraction . The pellet was resuspended in lysis buffer ( 10 mM HEPES , pH 7 . 9 , 1 . 5 mM MgCl2 , 10 mM KCl , 0 . 5 mM DTT , 1 mg/mL leupeptin , 2 mg/mL aprotonin , 1 mg/mL pepstatin A , 0 . 5 mM PMSF , 10 mM β-glycerophosphate , 1 mM sodium vanadate , and 0 . 1% Triton X-100 ) to derive the microsome-rich fraction . Following transfection with either NC or UGGT1 siRNA for 2 days , RD and SF268 cells were seeded to 12-well plates and incubated for 24 h . The cells were then infected with EVA71 ( strain 4643/TW/98 ) or EVD68 ( strain CGMH/TW/14 ) at an MOI of 10 or 0 . 1 . The viruses were allowed to adsorb for 1 h at 37°C . At various timepoints post-infection , cell lysates and supernatants of the cell culture medium were collected to determine viral titers , using plaque assays . At the final time point , cell lysates were collected to measure UGGT1 expression levels . For plaque assays , virus stocks were serially diluted in PBS and allowed to adsorb onto confluent cells for 1 h at 37°C . The inoculum was then removed , and cells were washed twice with PBS and then covered with 3 mL of an agar medium . After 4 days of incubation , plaques were counted , and virus concentration was calculated as PFU/mL . The mouse strain used in this study was created from an ES cell clone ( KOMP ID: CSD66441 , EPD0550_1_E05 clone ) obtained from the Knockout Mouse Project ( KOMP ) Repository supported by the US National Center for Research Resources-National Institutes of Health ( NCRR-NIH ) , and which was generated by the CSD consortium for KOMP ( https://www . komp . org/ ) . The ES cell clone was used to generate chimeric mice . Germline transmitted animals were bred at the Transgenic Mouse Model Core Facility of the National Core Facility Program for Biotechnology ( NCFPB ) , Academia Sinica , Taiwan . Animal care and handling were approved by the Institutional Animal Care and Use Committee of Chang Gung University . Uggt1 heterozygous knockout mice were housed under specific-pathogen-free conditions in individual ventilated cages . Institutional guidelines for animal care and use were strictly followed . Mice were intraperitoneally administered with 105 PFU/mouse of EVA71 strain MP4 [46] , and were then monitored daily for pathological signs , and sacrificed at various times post-inoculation . The severity of central nervous system ( CNS ) syndromes was scored from 0 to 4 using the following criteria for scoring CNS diseases: 4 = death , 3 = paralysis of both hind legs , 2 = paralysis of one hind leg , 1 = jerky movement , and 0 = normal movement . In the JEV-infected suckling mice model , 1-day-old WT or Uggt1 heterozygous knockout mice were injected with 104 PFU/mouse of JEV strain T1P1 , and on Day 7 after infection , JEV was extracted from brain tissues and quantitated . The tissues and organs of EVA71-infected mice and JEV-infected mice were harvested and stored at −80°C , and homogenized in DMEM on ice using a Precellys 24 ( Bertin Technologies , Montigny , France ) homogenizer . Viral titers in the supernatants of clarified homogenates were determined by a plaque assay as described above , and expressed as virus titers ( PFU/ml ) . | Positive-strand RNA viruses are adept at hijacking host cell machinery to promote viral propagation , including the formation of RCs containing viral and host proteins on intracellular membranes to facilitate virion assembly and avoid detection by host defense mechanisms . However , the processes by which RCs are assembled , as well as the host proteins involved , have not been fully elucidated as yet . Here , we show that the endoplasmic reticulum ( ER ) protein UGGT1 , a key regulator of the UPR host defense mechanism , co-precipitates with the 3D polymerase of EVA71 to facilitate RC formation , enhance viral RNA synthesis , and promote viral replication . Knockout of Uggt1 reduced viral pathogenicity in animal studies . These findings highlight the role to which viruses can hijack key host proteins to promote viral replication , and may serve as the basis for the development of novel anti-viral strategies . |
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Rapid bone destruction often leads to permanent joint dysfunction in patients with septic arthritis , which is mainly caused by Staphylococcus aureus ( S . aureus ) . Staphylococcal cell wall components are known to induce joint inflammation and bone destruction . Here , we show that a single intra-articular injection of S . aureus lipoproteins ( Lpps ) into mouse knee joints induced chronic destructive macroscopic arthritis through TLR2 . Arthritis was characterized by rapid infiltration of neutrophils and monocytes . The arthritogenic effect was mediated mainly by macrophages/monocytes and partially via TNF-α but not by neutrophils . Surprisingly , a S . aureus mutant lacking Lpp diacylglyceryl transferase ( lgt ) caused more severe joint inflammation , which coincided with higher bacterial loads of the lgt mutant in local joints than those of its parental strain . Coinjection of pathogenic S . aureus LS-1 with staphylococcal Lpps into mouse knee joints caused improved bacterial elimination and diminished bone erosion . The protective effect of the Lpps was mediated by their lipid moiety and was fully dependent on TLR2 and neutrophils . The blocking of CXCR2 on neutrophils resulted in total abrogation of the protective effect of the Lpps . Our data demonstrate that S . aureus Lpps elicit innate immune responses , resulting in a double-edged effect . On the one hand , staphylococcal Lpps boost septic arthritis . On the other hand , Lpps act as adjuvants and activate innate immunity , which could be useful for combating infections with multiple drug-resistant strains .
Despite colonizing more than half of the human population at some stage during their life [1] , Staphylococcus aureus ( S . aureus ) is a highly pathogenic microorganism responsible for a broad range of infections in humans [2] . Septic arthritis , considered to be one of the most aggressive joint diseases , is most commonly caused by S . aureus [3] . In a mouse model , S . aureus induced severe bone destruction 5 days postinfection [4] . In patients , even after initiating immediate treatment , the joint damage caused by septic arthritis is often irreversible [5] , leading to permanent joint dysfunction in up to half of the patients [6] . Furthermore , the emergence of methicillin-resistant S . aureus ( MRSA ) has severely reduced the available treatment options [2 , 7] . To limit the immune response and reduce the risk of permanent joint destruction , a combination treatment of antibiotics with immunomodulatory therapy has been proposed [8 , 9] . However , there are potential dangers associated with such combination therapies as long as the challenge of antibiotic resistance remains [10 , 11] . Therefore , the identification of the bacterial components responsible for joint inflammation and destruction is key for the development of new therapies . Antibiotic-killed S . aureus is known to induce destructive arthritis , and the bacterial cell wall components are the culprits in this context [12] . Among the S . aureus cell wall components , lipoproteins ( Lpps ) are Toll-like receptor 2 ( TLR2 ) agonists and the main immune stimulators , while lipoteichoic acids are much less important [13–15] . Lipidation of Lpps is known to be crucial for virulence in murine S . aureus systemic infection [16] . Depending on the degree of acylation in the lipid moiety of the Lpps , different TLR2 receptor combinations are activated: triacylated Lpps are agonists of TLR2/TLR1 heterodimers , while diacylated Lpps are agonists of TLR2/TLR6 heterodimers [17 , 18] . Staphylococcal species differ in the length of the fatty acid in the N-acyl group of the lipid moiety , which has drastic effects on innate and adaptive immune stimulation [19] . In the present study , we hypothesized that staphylococcal Lpps are the main inducer of synovitis and joint destruction in S . aureus-induced septic arthritis . Indeed , a single intra-articular injection of S . aureus Lpps induced macroscopic , chronic and destructive arthritis , which was mediated by monocytes/macrophages . However , the Δlgt strain , an Lpp diacylglyceryl transferase ( lgt ) deletion mutant , caused more severe joint inflammation than its parental strain . This increased severity in joint inflammation was due to the better survival of the Δlgt strain , suggesting that a lack of Lpps induces immune evasion . Importantly , coinjection of S . aureus with staphylococcal Lpps in mouse knee joints resulted in radical elimination of bacteria and diminished bone erosion . This protective effect was mediated by the lipid moiety of the Lpps and was fully dependent on TLR2 and the recruitment of neutrophils .
We injected the purified staphylococcal Lpp Lpl1 intra-articularly ( i . a . ) into mouse knee joints . Lpl1 is a model Lpp derived from the νSaα-specific lipoprotein-like cluster ( lpl ) that exists in highly pathogenic and epidemic S . aureus strains [20] . One single injection of Lpl1 at a dose of 10 μg/knee caused macroscopic joint inflammation after 24 hours , and the inflammation lasted for at least 21 days ( Fig 1A and 1B ) . The arthritogenic effect of Lpl1 was dose-dependent—even a 100-fold lower dose ( 0 . 1 μg/knee ) induced synovitis ( Fig 1C and 1D ) . Importantly , severe bone erosions were observed on day 7 , and all Lpl1-injected joints had erosions on day 10 , as verified by microcomputed tomography ( μCT ) scans ( Fig 1E–1G ) . Histologically , the highly inflamed synovium , pannus formation , and severe bone destruction ( Fig 1H ) exhibited characteristics of the typical histopathological picture of S . aureus septic arthritis [21] . The minimal bone destruction-inducing dose of Lpl1 was much higher ( 10 μg/knee ) than the inflammation-inducing dose ( 0 . 1 μg/knee ) . To further understand whether the lipid- or protein-moiety of Lpl1 was responsible for the arthritogenic effect , Lpp lacking the lipid moiety Lpl1 ( -sp ) was compared to the intact Lpp , Lpl1 ( +sp ) . Lpl1 ( -sp ) completely lacked the capacity to induce arthritis ( Fig 1I ) , suggesting that the lipid moiety of staphylococcal Lpps is fully responsible for their arthritogenic properties . Indeed , in vitro splenocyte proliferation was induced by both Lpl1 ( +sp ) and Pam3CSK4 ( a synthetic lipopeptide mimicking the N-terminal lipid portion of Lpps ) but not by Lpl1 ( -sp ) ( Fig 1K ) . To compare the arthritogenic capacity of Lpps with other S . aureus components , we injected mice i . a . with the staphylococcal superantigen toxic shock syndrome toxin-1 ( TSST-1 ) and with the peptidoglycan ( PGN ) purified from a mutant strain lacking Lpp diacylglyceryl transferase [15] . Only very mild and transient knee joint swelling was observed on day 1 , and the swelling disappeared by day 3 in the knee joints injected with 10 μg of PGN ( Fig 1J ) . Interestingly , heat-treated Lpl1 and Pam3CSK4 preserved their arthritogenic capacity ( S1 Fig ) , strongly suggesting that the Lpl1 is heat-insensitive . To understand the cellular mechanism behind Lpp-induced arthritis , we further analyzed the immune cells present in the local synovium using flow cytometry one day after Lpl1 injection . Synovial tissues from Lpl1-injected knee joints of wild-type mice demonstrated higher numbers and frequencies of CD11b+F4/80+ cells ( monocytes/macrophages ) and CD11b+Gr1+F4/80- cells ( neutrophils ) than those of phosphate-buffered saline ( PBS ) -injected knee joints . Significantly decreased numbers of infiltrating monocytes/macrophages and neutrophils were observed in TLR2-deficient ( TLR2-/- ) mice ( Fig 2A–2C ) . No difference with regard to B- and T-cells was observed . These results suggest that TLR2 is highly important for neutrophil and monocyte recruitment into synovial tissue following Lpl1 injection . Next , to elucidate which immune cells were responsible for the onset of arthritis , mice depleted of monocytes/macrophages , neutrophils or T-cells were i . a . injected with 0 . 33 μg Lpl1 , and the severity of the histopathological synovitis was examined on day 3 . The depletion of synovial macrophages and infiltrating monocytes by clodronate liposomes significantly reduced the severity of the synovitis ( Fig 3A ) , whereas neutrophil depletion by Ly6G antibodies had no effect on synovitis severity ( Fig 3B ) . To investigate the role of T-cells , mice were simultaneously depleted of CD4 and CD8 T-cells by intraperitoneal injection of anti-CD4 and anti-CD8 antibodies . No notable difference regarding the severity of synovitis was observed between the groups ( Fig 3C ) . Additionally , CTLA-Ig treatment ( abatacept ) that blocks T-cell activation had no effect on synovitis development , suggesting that T-cells were not essential for acute Lpp-induced joint inflammation . Fig 3D represents a typical picture of the knee joint from mice depleted of monocytes/macrophages showing the absence of leukocyte infiltration . To examine which cells were responsible for causing bone erosion , a higher dose of Lpl1 ( 10 μg/knee ) was i . a . injected . In line with the data described above , depletion of macrophages significantly attenuated macroscopic inflammation ( Fig 3E ) and more strikingly , completely protected the joints from bone damage caused by Lpl1 ( Fig 3F and 3H ) . No difference was found regarding bone erosions between the controls and neutrophil-depleted mice ( Fig 3G and 3H ) . TNF-α and IL-1 , released by monocytes/macrophages , are known to play a crucial role in septic arthritis [10 , 12 , 22] . To study the role of these cytokines in Lpl1-induced synovitis , both anti-TNF treatment ( etanercept ) and anti-IL1 treatment ( anakinra ) [10 , 11] were used . Anti-TNF , but not anti-IL1 , treatment significantly reduced the synovitis severity ( Fig 3I ) compared to the severity in the PBS-treated controls , indicating that Lpl1-induced arthritis is partially mediated by TNF-α . Finally , to study whether joint destruction caused by Lpp , a major ligand for TLR2 [23 , 24] , is mediated through this receptor , TLR2-/- mice were i . a . injected with Lpl1 . The TLR2-/- mice barely developed clinical signs of arthritis ( Fig 3J ) , and no joint destruction was observed compared to that of the wild-type mice , of which 100% presented joint erosions ( Fig 3K ) . To understand the role of Lpps in S . aureus septic arthritis , both heat-killed and live S . aureus SA113 strains , as well as a deletion mutant of the SA113 strain lacking lgt , were inoculated into the mouse knee joints . The heat-killed SA113Δlgt mutant strain resulted in less joint swelling than the SA113 parental strain on days 1 and 3 ( Fig 4A ) . Surprisingly , the reverse phenomenon was observed when live bacteria were used . The SA113Δlgt mutant strain caused a significantly higher degree of joint swelling on days 3 and 7 than the arthritis caused by its parental strain ( Fig 4B ) . The discrepancy between the heat-killed and live S . aureus SA113Δlgt mutant strains was also observed with regard to IL-6 levels in the knee homogenates on day 3 . Higher levels of IL-6 were induced by the live SA113Δlgt mutant , and a tendency towards lower levels of IL-6 was induced by the heat-killed SA113Δlgt mutant compared to the parental SA113 strain ( Fig 4C ) , whereas no differences were seen with regard to TNF-α levels ( Fig 4D ) . This unexpected increase in joint swelling and IL-6 levels could be explained by the higher bacterial load found in the knee joints of the SA113Δlgt mutant-inoculated mice on day 3 ( Fig 4E ) . To further elucidate the mechanism whether SA113Δlgt mutant survived better in joints than its parental stain , we analyzed the cytotoxicity of those strains towards mouse splenocytes ( S2 Fig ) . No significant difference was observed between those two strains , demonstrating that SA113Δlgt mutant does not have enhanced killing capacity of immune cells compared to its parental strain . We speculate that SA113Δlgt mutant is more resistant to immune-mediated bacterial killing than its parental stain . We reasoned that this might be due to a lack of Lpps that trigger the immune response . To test the above hypothesis , live S . aureus SA113Δlgt mutants mixed with various concentrations of Lpl1 ( +sp ) , Lpl1 ( -sp ) , or PBS were i . a . injected into mouse knee joints . Strikingly , the bacterial load in the knee joints was dose-dependently reduced when SA113Δlgt mutant was simultaneously administered with Lpl1 ( +sp ) . For the Lpp dose of 6 . 5 μg/knee , only 1 out of 5 knee joints was positive for bacterial culture with very low bacterial counts ( 101 colony forming units ( CFUs ) /knee ) , whereas in the PBS controls , all knee joints had high CFU counts ( 105 CFU/knee ) on day 3 postinfection ( Fig 4F ) . In contrast , a comparable dose ( 5 μg/knee ) of PGN purified from the SA113Δlgt mutant completely lacked the capacity to induce an eradicating effect on the bacteria ( Fig 4G ) . Importantly , the bacterial elimination effect was completely abolished when Lpl1 ( -sp ) , lacking the lipid moiety , was used ( Fig 4H ) . Additionally , synthetic lipopeptides ( Pam2CSK4 and Pam3CSK4 ) exhibited similar bacterial elimination effects as the Lpps , indicating that the lipid moiety of Lpl1 is important for bacterial elimination ( Fig 4I ) . To provide the evidence to support our hypothesis that SA113Δlgt mutant arouses immune responses to a lesser extent than its parental strain , we i . a inoculated both bacterial strains into mouse knee joints and compared the knee sizes , CFU counts in joints , and the levels of neutrophil attracting chemokines ( KC and MIP-2 ) on day 1 postinfection . The joints inoculated with SA113 strain were significantly more swollen ( Fig 5A ) and tended to have lower bacterial counts ( Fig 5B ) compared to SA113Δlgt injected knees . However , higher levels of KC were detected in SA113 injected knees than SA113Δlgt injected knees ( Fig 5C ) . Similar trend was also found in MIP-2 levels ( Fig 5D ) . These results demonstrate that SA113Δlgt is less potent to induce neutrophil attracting chemokines in S . aureus septic arthritis compared with its parental strain . We further analyzed the effect of addition of exogenous Lpl1 to SA113Δlgt in the similar setting . Strikingly , addition of Lpl1 to SA113Δlgt resulted in increased joint swelling ( Fig 5E ) , decreased CFU counts ( Fig 5F ) , and higher levels of KC and MIP-2 ( Fig 5G and 5H ) in the knees on day 1 postinfection . To understand the mechanism by which S . aureus Lpps mediate bacterial killing , we first studied whether the Lpps possess bactericidal capacity . Our data suggest that neither Lpp nor lipopeptides had direct bactericidal effect since in vitro incubation of SA113Δlgt with Lpl1 or Pam3CSK4 did not affect bacterial proliferation or survival ( S3 Fig ) . Since Lpps are specific ligands for TLR2 [23 , 24] , we hypothesized that the protective effect of Lpps is mediated by TLR2 . Indeed , the bacterial elimination induced by Lpl1 was completely abolished in TLR2-/- mice ( Fig 6A ) , strongly suggesting that TLR2 is essential for Lpp-induced bacterial killing . Next , we studied the importance of monocytes/macrophages and neutrophils in the knee joints after Lpl1 injection . The knee joints of monocyte/macrophage-depleted mice injected with a mixture of Lpl1 and SA113Δlgt mutant exhibited significantly higher bacterial counts than the knee joints of the non-depleted mice ( Fig 6B ) . However , macrophage depletion failed to fully abolish the effect of Lpps on bacterial elimination , suggesting that other cell types also contributed to the effect . Importantly , the protective effect of the Lpps completely disappeared in the neutrophil-depleted mice ( Fig 6C ) , suggesting that Lpl1 elicits neutrophils to kill bacteria . We further studied whether Lpps boost the phagocytic capacities of phagocytes in vitro . Mouse peritoneal macrophages stimulated with Lpl1 were incubated with green fluorescent protein ( GFP ) -expressing S . aureus . The S . aureus internalization rates in macrophages were analyzed by flow cytometry imaging . As expected , the opsonization of bacteria with mouse sera resulted in a 3–4 times higher rate of phagocytosis . However , no notable differences were observed between the Lpl1-stimulated and non-stimulated groups ( S4 Fig ) . Furthermore , the whole blood killing assay was thereafter performed to verify whether SA113Δlgt mutant survived better in whole blood than its parental strain . No tangible difference was observed between groups ( S5 Fig ) . As Lpps induced TNF-α release by macrophages ( S6 Fig ) and anti-TNF treatment attenuated the severity of Lpp-induced arthritis ( Fig 3I ) , we posed the question of whether the bacterial eliminating effect of Lpp was TNF-dependent . Coinjection of Lpl1 and S . aureus SA113Δlgt mutant reduced bacterial loads in a similar manner in mice treated with anti-TNF drug as in mice receiving PBS treatment ( Fig 6D ) , suggesting that TNF is not involved in Lpl1-mediated bacterial killing . S . aureus Lpps are known to induce nitric oxide production by macrophages [25] , and the expression of inducible nitric oxide synthase ( iNOS ) is associated with protective immunity against bacterial infections [26] . We used an iNOS inhibitor ( 1400W ) to block iNOS activity in mice receiving a mixture of Lpl1 and the SA113Δlgt strain . No notable difference was observed between the PBS-treated and iNOS inhibitor-treated animals ( Fig 6D ) . Since macrophages , but not neutrophils , are present in the synovium of healthy joints and monocytes/macrophages were responsible for the Lpp-induced joint inflammation ( Fig 2 and 3 ) , we hypothesized that Lpl1 stimulates macrophages via TLR2 , resulting in the release of large amounts of chemokines that in turn recruit neutrophils to kill bacteria . Indeed , peritoneal macrophages from wild-type mice stimulated with Lpl1 exhibited a quick dose-dependent release of neutrophil chemoattractant MIP-2 and KC 4 hours after stimulation and the monocyte-attracting chemokine MCP-1 24 hours after stimulation , whereas macrophages from TLR2-/- mice displayed no such chemokine release ( Fig 7A–7C ) . Mouse splenocytes , mainly composed of B- and T-lymphocytes , produced neither MIP-2 nor KC upon Lpl1 stimulation at 4 hours nor MCP-1 at 24 hours in wild-type and TLR2-/- mice . After 24 hours of stimulation , TNF-α levels were elevated in the supernatants of both peritoneal macrophages and splenocytes from wild-type mice stimulated with Lpl1 ( +sp ) and Pam3CSK4 but not those stimulated with Lpl1 ( -sp ) ( S6 Fig ) . As expected , increased TNF-α was observed in only LPS-stimulated cells from TLR2-/- mice . To further elucidate the importance of neutrophils attracting chemokine release in the Lpp-induced protective effect , CXCR2 blocking antibodies were used to inhibit in vivo neutrophil chemotaxis . Indeed , CXCR2 blocking antibodies efficiently reduced the total number of infiltrating neutrophils in Lpl1-injected knee joints to 13% on day 3 . Importantly , decreasing the infiltrating neutrophils by CXCR2 blocking antibodies almost fully abrogated the effect of bacterial elimination induced by Lpl1 ( Fig 7D ) , strongly suggesting that the release of neutrophil-attracting chemokines upon Lpp stimulation is the key mechanism of Lpp-induced bacterial killing . We next investigated whether enhanced bacterial elimination induced by Lpps leads to a better clinical outcome in septic arthritis . Mice were inoculated i . a . with a mixture of Lpl1 and the S . aureus LS-1 strain that was originally isolated from a mouse that spontaneously developed septic arthritis [21] . Mice injected with a mixture of Lpl1 and LS-1 had less swelling in their knee joints on days 7 and 10 postinfection ( Fig 8A and 8B ) and less bacterial load in their joints than mice in the control group that received a mixture of PBS and LS-1 ( Fig 8C ) . Furthermore , these mice exhibited significantly less pronounced bone erosions with lower frequency than the mice in the control group ( Fig 8D–8F ) , suggesting that the bacterial eradication effect elicited by Lpl1 applies not only to the S . aureus mutant strain deficient in lipidation of prelipoproteins but also to the wildtype S . aureus strains .
Our results demonstrate the dual role of staphylococcal Lpp in S . aureus-induced septic arthritis . On the one hand , Lpps induce inflammatory reactions and joint destruction mediated by monocytes/macrophages . On the other hand , Lpps cause the quick release of chemokines and consequent neutrophil recruitment , resulting in efficient bacterial killing . Importantly , both the lipid moiety of Lpps and TLR2 were identified as the molecular structures responsible for this outcome . Postinfectious complications in septic arthritis , such as joint deformation and deleterious contractures , remain a major medical challenge . Exaggerated immune responses have been proposed as the cause of such complications [9 , 12] . There are many bacterial components of S . aureus that possess the capacity to induce joint inflammation [12 , 27 , 28] . However , it is still unclear which component is most important in real-life infections . To address this uncertainty , not only the capacity of immune stimulation but also the quantity of components expressed in a single bacterium should be taken into consideration . A single intra-articular injection of Lpp ( 10 μg/knee ) induced chronic macroscopic arthritis lasting for at least 3 weeks as well as severe joint destruction verified by both histopathological and radiological examinations , demonstrating that Lpp is an arthritogenic molecule . Lpps were highly potent since they exerted a strong immunostimulatory effect even at the nanogram level in vivo . More importantly , the mice injected with the SA113Δlgt mutant strain lacking lipidation displayed less severe joint swelling than the mice injected with the SA113 parental strain at the early time points before the bacterial proliferation of Δlgt mutants exceeded that of the parental strains . This finding strongly suggests that Lpps are one of the major arthritogenic bacterial components . However , we have to keep in mind that the Δlgt strain that lacks lipidation maintained the capacity to induce joint inflammation , indicating that Lpps are not the only molecule in S . aureus that cause joint inflammation . Although intra-articular injection of PGN ( 10 μg/knee ) induced only transient and milder joint swelling , the importance of PGN in arthritis induction cannot be ruled out since PGN is the most abundant molecule in S . aureus . The synergistic effect of PGN and lipopeptides in immune activation has been reported previously [29] . Our data compellingly demonstrate that both neutrophils and monocytes rapidly migrated into the joints injected with Lpps , and monocytes/macrophages were fully responsible for joint destruction in Lpp-induced arthritis . Focal bone destruction in autoimmune arthritis is due to excessive bone resorption resulting from osteoclast activation that is mediated by local expression of receptor activator of nuclear factor kappa-B ( RANKL ) that is higher than that of its decoy receptor osteoprotegerin ( OPG ) [30] . Osteoclasts not only exist inside of the bone but also can be derived from mature monocytes and macrophages when a suitable microenvironment is provided by bone marrow-derived stromal cells [31] , which might be the case in this study . In fact , monocytes/macrophages were shown to mediate bone erosion in arthritis induced by other S . aureus components , such as bacterial DNA [27] and peptidoglycan [28] , as well as antibiotic-killed S . aureus [12] , suggesting that monocytes/macrophages might be the most important immune cells that determine the progression of septic arthritis . Indeed , mice depleted of monocytes developed less severe septic arthritis and joint lesions despite decreased bacterial clearance and higher mortality [32] . Macrophages are a major source of many cytokines involved in the immune response . In autoimmune arthritic diseases , proinflammatory cytokines play an important pathogenetic role . In particular , TNF release by macrophages , fibroblasts and T-cells in inflamed synovial tissue leads to joint swelling and subsequent joint destruction [33] . Furthermore , a previous study showed that the blockade of TNF , but not IL-1 , resulted in a reduction of bone erosions in a murine TNF-driven arthritis model [34] . Previously , we have shown that antibiotic-killed S . aureus induces arthritis through the TNF receptor . In the present study , TNF also played an important role in Lpp-induced arthritis since a ) Lpps induced TNF release in both macrophages and splenocytes and b ) TNF inhibition , but not IL-1 inhibition , significantly reduced synovitis severity . Unexpectedly , the live Δlgt strain gave rise to significantly more severe joint inflammation , although the heat-killed Δlgt strain caused less synovitis . This result is due to the fact that the Δlgt strain is more tolerant to host immune-mediated bacterial killing than the parental strain , as the bacterial load of the SA113 parental strain in the knee joints was significantly lower than that of the Δlgt strain . This finding is in line with the previous report that revealed that a lack of S . aureus Lpps causes bacterial immune evasion and lethal infections with disseminated abscess formation in mice with systemic S . aureus infection [35] . The importance of the lipid moiety in bacterial elimination was further confirmed by our experiments when Lpps were coinjected with S . aureus into mouse knee joints . This situation is a perfect example of the dual sides of host responsiveness to bacterial infections—on one hand , the host response protects the host against bacteria , but on the other hand , the response sometimes increases the infection severity when danger signals trigger exaggerated host responses . How is the bacterial killing effect mediated by Lpps ? Lpps themselves had no direct effect on bacterial proliferation . Rather , the enhanced local immune response triggered by Lpps was responsible for the bacterial killing effect . Macrophages , neutrophils and natural killer ( NK ) cells are the most important immune cell types in innate immunity . NK cells can be activated and exert their biological functions upon stimulation by synthetic lipopeptides with sequences from Lpps of S . aureus [36] . Macrophages seem to play a partial role , as the indirect bacterial killing effect was diminished in mice depleted of macrophages/monocytes . This finding suggests that Lpps have the capacity to trigger the activation of macrophages to better control and eliminate bacteria . Macrophages are phagocytes that play an important role in S . aureus septic arthritis [32] . S . aureus Lpps are the major inducers of inducible nitric oxide synthase ( iNOS ) and nitric oxide ( NO ) production in mouse macrophages [25] . The production of nitric oxide is important in controlling bacterial infections [26 , 37] . However , treatment with an iNOS inhibitor had no effect on the bacterial eradication induced by Lpps , suggesting that NO production is not critical for Lpp-induced bacterial killing . Notably , no complete abrogating effect was observed as a result of macrophage depletion , which indicates the involvement of other immune cells . Neutrophils were the most predominant cell type that infiltrated the local joints upon Lpp stimulation . Additionally , the total abrogation of the bacterial killing effect by Lpps in neutrophil-depleted mice strongly suggests that neutrophils are fully responsible for the protective effect of Lpps . In healthy joints , there are a very limited number of neutrophils . For the neutrophils to reach the joint cavity and exert their biological function , neutrophil chemoattractants are needed . KC and MIP-2 are known major chemokines responsible for recruiting neutrophils , and both bind to CXCR2 [38] . Resident tissue macrophages have been shown to be the major source of neutrophil chemokines [39] . In the current study , macrophages , but not T- or B-cells , quickly released large amounts of neutrophil-recruiting chemokines upon Lpp stimulation , and such chemokine release was fully controlled by lipid moiety-TLR2 signaling . Indeed , a positive feedback loop may exist in Lpp-injected knee joints , as monocytes are recruited to the local joints where they probably respond to Lpp stimulation with chemokine release that leads to an additional influx of monocytes and neutrophils to the local inflammation site . Actually , the bacterial killing effect of phagocytes might also be increased by Lpps , as Lpps can directly activate neutrophils by upregulating CD11b/CD18 and enhancing the production of reactive oxygen species [40] . In recent years , immune therapy targeting negative regulators of immune activation ( immune checkpoints ) has been an exciting research area in drug development , with promising results achieved in patients with a variety of cancers [41] . Similarly , in infectious diseases , the activation of the immune system to eliminate invading bacteria has always been an ultimate goal to overcome the challenges caused by the rapid emergence of antibiotic-resistant bacteria . The development of a vaccine that prevents S . aureus infections is of interest . However , thus far , all attempts to develop active or passive immunization against S . aureus have failed , which might be due to the lack of a well-defined , single virulence factor in S . aureus [42] . Purified Lpp acted in a protective fashion by means of less radiological bone destruction after treatment with a mixture containing a known pathogenic staphylococcal strain , i . e . , LS-1 . Our results suggest that Lpps/lipid moieties might be used as a potential candidate for immune therapy to combat local S . aureus infections , e . g . , septic arthritis and osteomyelitis . In this case , the dose of Lpps should be carefully controlled to keep the local tissue damage and efficient bacterial killing effect in balance . In addition , a similar protective immune response might also be induced by staphylococcal Lpps to eradicate other invading pathogens , such as gram-negative bacteria or even antibiotic-resistant bacteria . In conclusion , intra-articular injection of staphylococcal Lpps induced chronic joint inflammation and bone erosion . The observed effect was mediated by macrophages via TLR2 signaling and partially involved TNF . Surprisingly , purified S . aureus Lpps strengthened the immune response , consequently reducing bacterial burden and attenuating bone destruction in septic arthritis . Our findings may pave the way for the development of a novel strategy to address the challenge of antibiotic resistance .
Mouse studies were reviewed and approved by the Ethics Committee of Animal Research of Gothenburg ( Ethical number 58–2015 ) . Mouse experiments were conducted in accordance with recommendations listed in the Swedish Board of Agriculture's regulations and recommendations on animal experiments . Female NMRI mice and C57Bl/6 wild-type mice of both sexes , aged 6–8 weeks , were purchased from Envigo ( Venray , Netherlands ) and Charles River Laboratories ( Sulzfeld , Germany ) , respectively . Toll-like receptor 2-deficient B6 . 129-Tlr2tm1Kir/J ( TLR2-/- ) mice of both sexes were purchased from The Jackson laboratory ( Bar Harbor , Maine , USA ) . All mice were housed in the animal facility of the Department of Rheumatology and Inflammation Research , University of Gothenburg . Mice were kept under standard temperature and light conditions and were fed laboratory chow and water ad libitum . The SA113 , SA113Δlgt mutant[15] , and LS-1 [43] S . aureus strains were prepared as described . The strains were stored at -70°C until use . Before the experiments , the bacterial solutions were thawed , washed with sterile PBS , and adjusted to the required concentration . For experiments with heat-killed bacteria , the SA113 and SA113Δlgt mutant S . aureus strains were heat-killed at 95°C for 45 min and thereafter adjusted to a similar concentration using optical density at 600 nm ( OD600 ) . To ensure that no bacteria survived , the bacterial suspensions were plated and cultured for 24 hours . No bacterial growth was detected . The preparation and purification of the S . aureus lipoproteins Lpl1 ( +sp ) and Lpl1 ( -sp ) were performed by Dr . Nguyen ( Microbial Genetics , University of Tübingen , Germany ) , as previously described [20] . Lpl1 ( +sp ) was isolated from the membrane fraction of S . aureus SA113 ( pTX30::lpl1-his ) , whereas Lpl ( -sp ) was isolated from the cytoplasmic fraction of S . aureus SA113Δlgt ( pTX30::lpl1 ( -sp ) -his ) . Both of these Lpl1-his proteins were purified via Ni-NTA affinity chromatography . For the enhancement of protein expression , the clones were first cultivated aerobically at 37°C in the absence of xylose ( BO-medium ) until OD578 nm ≈ 0 . 5 was reached and were thereafter continuously cultivated for 4 hours in the presence of 0 . 5% xylose to induce Lpl1 expression . The bacterial cells were harvested and washed twice with Tris buffer ( 20 mM Tris , 100 mM HCl , pH 8 . 0 ) . Then , the pellet was resuspended in Tris buffer containing a protease inhibitor tablet ( Merck , Darmstadt , Germany ) and lysostaphin ( 30 μg/ml ) and was incubated at 37°C for 2 hours to disrupt the cell wall . After the first ultracentrifugation ( 235 , 000 x g for 45 min at 4°C ) , the supernatant containing the cytoplasmic proteins was collected for the next purification step . For membrane fraction isolation , the pellet was subsequently dissolved overnight at 6°C with Tris buffer containing 2% Triton-X100 . After the second ultracentrifugation step , the supernatant containing membrane fragments was collected . The purification step was carried with Ni-NTA Superflow beads ( Qiagen , Germany ) . The Ni-NTA beads capturing Lpl1 proteins were washed four times with the first washing buffer ( Tris buffer containing 0 . 25% Triton X-100 and 20 mM imidazole ) , followed by washing 2 times with the second washing buffer ( Tris buffer containing 0 . 25% Triton X-100 and 40 mM imidazole ) . Finally , Lpl1 was eluted with Tris buffer containing 500 mM imidazole . The Lpl1 were concentrated via a centrifugal ultrafilter unit with a molecular mass cut-off of 10 kDa ( Sartorius AG , Göttingen , Germany ) . The concentrated samples of Lpl1 were dialyzed overnight at 6°C with Dulbecco’s PBS ( DPBS ) buffer ( Life Technologies , Darmstadt ) by a MWCO 6–8 kDa tube dialyzer ( Merck , Darmstadt ) and were subsequently lyophilized overnight . A total of 2 μg of lyophilized samples were dissolved in water and subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) to determine the purity and quantity of purified protein samples . The purified compounds of Lpl1 were stored at -70°C until use and were adjusted in PBS to the required concentration before each experiment . To study the arthritogenic properties of S . aureus Lpps , six sets of experiments were performed , and NMRI , C57Bl/6 wild-type or TLR2-/- mice were intra-articularly injected into the knee joint with one of the following compounds in 20 μl of PBS: 1 ) purified Lpl1 ( +sp ) or Lpl1 ( -sp ) S . aureus Lpps; 2 ) heat-killed SA113 or SA113Δlgt mutant S . aureus strains; 3 ) live SA113 or SA113Δlgt mutant S . aureus strains; 4 ) solutions containing mixtures of SA113Δlgt mutant with either Lpl1 ( +sp ) , Lpl1 ( -sp ) , PGN or two synthetic lipopeptides , Pam2CSK4 and Pam3CSK4 ( EMC , Tübingen , Germany ) ; or 5 ) a mixture of the LS-1 S . aureus strain with either Lpl1 ( +sp ) or LS-1 in PBS; or 6 ) heat-treated Lpl1 ( +sp ) or Pam3CSK4 or unheated Lpl1 ( +sp ) or Pam3CSK4 . For experiments with heat-treated purified compounds , Lpl1 ( +sp ) and Pam3CSK4 were heat-treated at 95°C for 45 min . The severity of the clinical arthritis was judged by measuring the difference between the diameters of the knee joints with a caliper every 2–3 days . Knee synovial tissue was collected from C57Bl/6 wild-type and TLR2-/- mice that received i . a . injection of Lpl1 ( +sp ) ( 5 μg/knee ) or PBS and was placed in RPMI medium ( Fisher Scientific ) . The tissue was resuspended in medium with DNase I ( Sigma-Aldrich ) and type IV collagenase ( Fisher Scientific ) and was incubated for 1 hour at 37°C . A single-cell suspension was obtained after the tissue was homogenized and passed through a 35 μm cell strainer ( Becton Dickinson ) . Synovial cells were then analyzed using the following antibodies: V450-conjugated anti-CD11b ( Becton Dickinson ) , allophycocyanin ( APC ) -conjugated anti-F4/80 ( BioLegend ) , PerCP-Cy5 . 5-conjugated anti–Gr-1 ( BioLegend ) , fluorescein isothiocyanate ( FITC ) -conjugated anti-CD3 ( BioLegend ) , and PerCP-conjugated anti-CD19 ( BioLegend ) . Cells were analyzed using a FACSverse flow cytometer ( Becton Dickinson ) . FlowJo version 10 . 1 software ( Tree Star , Ashland , USA ) was used to analyze the data . Clodronate liposomes ( Liposoma BV , Netherlands ) are known to function as selective eliminators of macrophages [44] . NMRI mice were i . a . injected in the knee joints with a volume of 20 μl of clodronate liposomes or PBS control liposomes ( Liposoma BV , Netherlands ) 1 day prior to challenge with Lpl1 or coinjections of SA113Δlgt mutant with either Lpl1 or PBS . The mice were also treated intravenously with 200 μl of clodronate liposomes or PBS control liposomes the day before the challenge and on days +1 , +3 , and +5 after the challenge . Anti-Ly6G ( clone 1A8; BioXCell ) , a specific monoclonal antibody ( mAb ) , is known to selectively deplete murine blood neutrophils [45] . NMRI mice were intraperitoneally ( i . p . ) injected with a dose of 400 μg of anti-Ly6G or isotype control ( clone 2A3; BioXCell ) in 200 μl of PBS/mouse the day before the challenge and on days +1 and , +4 after the challenge with Lpl1 or coinjections of SA113Δlgt mutant with either Lpl1 or PBS . CD4 and CD8 T-cells were depleted simultaneously using rat anti-mouse CD4 mAb ( clone GK1 . 5; BioXCell ) and rat anti-mouse CD8α ( clone 2 . 43; BioXCell ) mAb; a rat IgG2b isotype control ( clone LTF-2; BioXCell ) served as a control . NMRI mice were i . p . injected with a dose of 400 μg of each antibody in 200 μl of PBS/mouse the day before and the day after challenge with Lpl1 . The efficacy of cell depletion was verified by flow cytometry . The depletion was carried out as described 1 day prior to blood collection . Mouse blood was collected into heparin tubes , erythrocytes were depleted with lysis buffer ( 0 . 16 M NH4Cl , 0 . 13 mM EDTA , and 12 mM NaHCO3 ) , and samples were washed . The single-cell suspensions were then adjusted in FACS buffer to a density of 2x106 cells/mL and analyzed using the following antibodies: V450-conjugated anti-CD11b , PE-Cy7-conjugated anti-Ly6G , Per-CP-conjugated anti-CD4 and PE-conjugated anti-CD8 ( all from Becton Dickinson ) APC-conjugated anti-F4/80 ( BioLegend ) . The representative flow cytometry plots are presented in S7 Fig . For cell depletion protocols , 85 . 6% of monocytes ( CD11b+ , Ly6G- and F4/80+ white blood cells ) , 99 . 6% of neutrophils ( CD11b+ , Ly6G+ , F4/80- ) , 97 . 3% of CD11b- CD4+ T cells and 90% of CD11b- CD8+ T cells were depleted ( S7 Fig ) . Etanercept ( Enbrel; Wyeth Europa ) , a soluble TNF receptor , was used for the anti-TNF treatment because it fully inhibits the biological function of murine TNF [8 , 11] . Abatacept ( Orencia; Bristol-Myers Squibb ) , a fusion protein of CTLA4-Ig , was used to modulate the costimulation of T-cells in mice [11 , 46] . Anakinra ( Kineret; Amgen ) , an IL-1 receptor antagonist , was used to block murine IL-1 [10 , 47] . Etanercept ( 0 . 2 mg/mouse in 0 . 1 mL of PBS ) or abatacept ( 0 . 5 mg/mouse in 0 . 1 mL of PBS ) was given subcutaneously ( s . c . ) the day before and the day after challenge with Lpl1 . Anakinra ( 1 mg/mouse in 0 . 1 mL of PBS ) was given s . c . daily , starting from day -1 until day +2 after the challenge . PBS served as a control . A potent selective inhibitor of iNOS , 1400W , was used to block murine iNOS [48] . Etanercept ( 0 . 1 mg/mouse in 0 . 1 mL of PBS ) or 1400W ( 0 . 25 mg/mouse in 0 . 1 mL of PBS ) was s . c . injected into NMRI mice twice per day , starting on day -1 until day +2 after challenge with coinjections of SA113Δlgt mutant with either Lpl1 or PBS . PBS treatment served as a control for both groups . Monoclonal anti-mouse CXCR2 antibody ( clone 242216; R&D Systems ) was used to block murine CXCR2 [49] . NMRI mice were i . p . injected with either anti-CXCR2 or an isotype control antibody ( clone 2A3; BioXCell ) ( 75–95 μg/mouse in 0 . 2 mL of PBS ) the day before challenge with coinjections of SA113Δlgt mutant with either Lpl1 or PBS . Knee joints were homogenized with an Ultra Turrax T25 homogenizer ( IKA , Staufen , Germany ) . Then , the homogenate was diluted in PBS , spread on horse blood agar plates , and incubated for 24 hours at 37°C . Viable counts of bacteria were performed and quantified as CFUs . SA113Δlgt mutant bacteria ( 103 CFU/mL ) were incubated with 25 μg/mL of Lpl1 , 100 μg/mL of Pam3CSK4 , or PBS control in tryptic soy broth ( TSB ) medium . At specific time intervals ( 1 , 3 , 6 , and 24 hours ) , samples of the bacterial mixtures ( 100 μl ) were spread on horse blood agar plates . After incubation for 18 hours at 37°C , colonies were counted . The effect of exogenous Lpl1 and Pam3CSK4 on S . aureus growth was evaluated by comparing the number of CFUs in the PBS control and the Lpl1- or Pam3CSK4-treated staphylococcal cultures at the different time points . Blood samples from healthy NMRI mice ( n = 4 ) were collected into heparin-containing tubes . SA113 or SA113Δlgt mutant bacterial suspensions were prepared and added into the mouse blood to a final concentration of approximately 1x103 CFU/mL . The incubated mixtures were shaken at 300 rpm for 2 hours at 37°C . To determine bacterial viability in blood , aliquots were withdrawn after 0 , 30 , 60 and 120 minutes of incubation , and samples were plated onto horse blood agar plates . Bacterial survival was evaluated as a percentage of number of CFUs at different time points compared with the number of bacteria initially added to the whole blood . Splenocytes from healthy NMRI mice and peritoneal macrophages from C57Bl/6 and TLR2-/- mice were prepared under sterile conditions . To prepare the splenocyte culture , mouse spleens were aseptically removed and passed through a nylon mesh . To collect the peritoneal macrophages , peritoneal lavage was performed using 10 mL of ice cold PBS . Erythrocytes were depleted in both cultures by lysis in 0 . 83% ammonium chloride , and the remaining cells were washed in PBS . The single-cell suspensions were then adjusted in Iscove’s complete medium ( 10% fetal calf serum , 2 mM L-glutamine , 5x10-5 M mercaptoethanol and 50 μg/mL gentamicin ) to a density of 1 . 5x106 cells/mL for splenocytes and 5x105 cells/mL for macrophages . For the proliferation assay , the splenocytes were stimulated with purified Lpl1 ( +sp ) or Lpl1 ( -sp ) ( 20–200 ng/mL ) , Pam2CSK4 or Pam3CSK4 ( 40 ng/mL ) , and TSST-1 ( 100 ng/mL ) or Iscove's medium ( negative control ) . A total of 1 μl of Ci [3H]thymidine ( Amersham , Bucks , UK ) was added for incorporation 12 hours before the cells were harvested , and the proliferative response was read with a micro-β counter . For cytokine/chemokine analysis , the macrophages were stimulated with purified Lpl1 ( +sp ) or Lpl1 ( -sp ) ( 0 . 02–0 . 2 μg/mL ) , Pam3CSK4 ( 2–20 ng/mL ) , LPS ( 1 μg/mL ) , or Iscove's medium for 4 hours . The supernatants were saved for later analysis . Splenocytes ( 5x106 cells/mL ) from healthy NMRI mice ( n = 4 ) were incubated with either 5x106 CFU/mL ( multiplicity of infection [MOI] = 1 ) or 25x106 CFU/mL ( MOI = 5 ) of SA113 or SA113Δlgt mutant bacteria in Iscove’s complete medium for 6 hours at 37°C . Aliquots were collected at 0 . 5 , 1 , 3 , and 6 hours of incubation . LDH was measured in the supernatants with a cytotoxicity detection kit ( Roche Diagnostics GmbH , Mannheim , Germany ) , according to the manufacturer’s directions . Absorbance was measured at 490 nm , and the results show the percentage of maximal LDH release in relation to positive control ( splenocytes treated with Triton X-100 ) . Peritoneal macrophages from NMRI mice were adjusted in Kreb’s Ringers glucose ( KRG ) ( 1x106 cells/mL ) and stimulated with purified Lpl1 ( +sp ) ( 0 . 2 μg/mL ) or PBS at 37°C for 1 hour . To study whether Lpl1 impacts phagocytosis , green fluorescent protein ( GFP ) -expressing S . aureus in KRG was incubated with or without 20% mouse serum at 37°C for 30 min , mixed with the stimulated peritoneal macrophages , incubated at 37°C for 20 min and analyzed as previously described [50] . The levels of IL-6 , MIP-2 , KC and TNFα in the supernatants from the knee joint homogenates and the levels of KC , MIP-2 , MCP-1 and TNFα in the supernatants from peritoneal macrophage or splenocyte stimulation were quantified using DuoSet ELISA Kits ( R&D Systems , Abingdon , UK ) . The knee joints were fixed in 4% formaldehyde for 3 days and then transferred to PBS . Imaging of the knee joints was performed ex vivo with a Skyscan1176 μCT scanner ( Bruker , Antwerp , Belgium ) to detect bone destruction after the studies were completed . The μCT settings were adjusted to a voxel size of 18 μm , an aluminum filter of 0 . 2 mm , and an exposure time of 800 ms , and the scans were conducted at 45 kV/555 μA . The X-ray projections were obtained at 0 . 42° intervals with a scanning angular rotation of 180° . The projection images were reconstructed into three-dimensional images using NRECON software ( version 1 . 6 . 9 . 8; Bruker ) and analyzed with CT-Analyzer ( version 2 . 7 . 0; Bruker ) . After reconstruction , experienced observers ( M . M . and T . J . ) evaluated the extent of bone and cartilage destruction in a blinded manner on a grading scale from 0–3 as previously described [11] . After the μCT scanning , the joints were decalcified , embedded in paraffin and sectioned with a microtome . Tissue sections were thereafter stained with hematoxylin and eosin . All slides were coded and assessed under a microscope in a blinded manner by two observers ( T . J . and M . M . ) . The extent of synovitis and cartilage-bone destruction was judged as previously described [12] . Statistical significance was assessed using the Mann-Whitney U test and Fisher's exact test , as appropriate . The results are reported as the mean ± standard error of the mean ( SEM ) unless indicated otherwise . A p value <0 . 05 was considered statistically significant . Calculations were performed using GraphPad Prism version 7 . 0b software for Macintosh ( GraphPad software , La Jolla , CA , USA ) . | Rapid bone destruction often leads to permanent joint dysfunction in septic arthritis , which is mainly caused by S . aureus . Despite advances in the use of antibiotics , permanent reductions in joint function occur in up to 50% of patients , who may also need joint replacement surgery . Additional challenge is posed by increasing antibiotic resistance of S . aureus , causing significant clinical and economic consequences . Although the outcome is poor , the current treatments for septic arthritis remain unchanged since many decades . It is largely unknown which bacterial factors cause aggressive joint damage . Here , we show that a single intra-articular injection of S . aureus lipoproteins ( Lpps ) into mouse knee joints induced chronic destructive macroscopic arthritis , and the monocytes/macrophages were the main culprit . However , coinjection of pathogenic S . aureus with Lpps into mouse knee joints attenuated the disease . The protective effect of Lpps was mediated by their lipid moiety , TLR2 on the host cells , neutrophil chemokine release , and consequent neutrophil recruitment . Our finding might be used as a novel concept in the treatment of multidrug-resistant bacterial infections . |
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The interaction between an antibiotic and bacterium is not merely restricted to the drug and its direct target , rather antibiotic induced stress seems to resonate through the bacterium , creating selective pressures that drive the emergence of adaptive mutations not only in the direct target , but in genes involved in many different fundamental processes as well . Surprisingly , it has been shown that adaptive mutations do not necessarily have the same effect in all species , indicating that the genetic background influences how phenotypes are manifested . However , to what extent the genetic background affects the manner in which a bacterium experiences antibiotic stress , and how this stress is processed is unclear . Here we employ the genome-wide tool Tn-Seq to construct daptomycin-sensitivity profiles for two strains of the bacterial pathogen Streptococcus pneumoniae . Remarkably , over half of the genes that are important for dealing with antibiotic-induced stress in one strain are dispensable in another . By confirming over 100 genotype-phenotype relationships , probing potassium-loss , employing genetic interaction mapping as well as temporal gene-expression experiments we reveal genome-wide conditionally important/essential genes , we discover roles for genes with unknown function , and uncover parts of the antibiotic’s mode-of-action . Moreover , by mapping the underlying genomic network for two query genes we encounter little conservation in network connectivity between strains as well as profound differences in regulatory relationships . Our approach uniquely enables genome-wide fitness comparisons across strains , facilitating the discovery that antibiotic responses are complex events that can vary widely between strains , which suggests that in some cases the emergence of resistance could be strain specific and at least for species with a large pan-genome less predictable .
Bacteria evolve antibiotic resistance in response to selective pressures that emerge from the interaction between the antibiotic and the bacterium . Routes of escape often lead through ways that diminish the interaction with the direct target . For instance , escape from penicillin , whose direct target are the Penicillin Binding Proteins ( PBPs ) , can often be found in mutations in PBPs that decrease the affinity for the drug , or in functionally related genes that compensate for diminished function [1–4] . However , it has become clear that the relationship between a bacterium and an antibiotic reaches far beyond its direct target . Instead an antibiotic triggers a complex , multi-factorial process that may begin with the physical interaction between the drug and its target but quickly propagates into the involvement of a variety of processes that can include regulation , metabolism and/or energy generation [5–12] . These system-wide selective pressures could explain why clinical strains often contain multiple alterations that may contribute to resistance but are located in genes whose primary role is not resistance but rather are involved in fundamental bacterial processes [13–17] . The adaptive sequence space thus seems to lie well beyond the antibiotic’s direct target , which contributes to the complexity of determining and predicting how and where resistance evolves . Moreover , adaptive mutations can be species-specific . For instance , the direct target of fluoroquinolones in gram-negatives including Escherichia coli is DNA gyrase , while in gram positives such as Staphylococcus aureus it is topoisomerase IV , which may explain why mutations in gyrA such as S83L in E . coli can increase the MIC to fluoroquinolones while the equivalent mutation in gyrA in S . aureus does not necessarily have an effect on the MIC [18–23] . Additionally , some gyrA mutations are associated with a fitness cost in E . coli [24 , 25] , while a positive fitness effect can be observed in Campylobacter jejuni and Salmonella enterica [26 , 27] . These contrasting phenotypes suggest that the manner in which a bacterium experiences antibiotic-induced stress may differ depending on the genomic background and the underlying genomic network . While detailed insights into these factors could help in designing novel antimicrobial strategies , the importance of the genomic background and to which extent antibiotic-sensitivity and resistance depend on network architecture is currently unclear . In this study we use Streptococcus pneumoniae to explore the importance of the genomic background on antibiotic-sensitivity and the manner in which stress is experienced and processed . S . pneumoniae is a human nasopharyngeal commensal and respiratory pathogen . It triggers pneumococcal pneumonia , meningitis , and septicemia , which results in ~1 million deaths annually among children <5 years of age , and ~0 . 5 million among groups including the immunocompromised and the elderly ( >65 yrs . ) , making it one of the most important bacterial pathogens worldwide [28–30] . Although vaccination has been successful , we and others have shown that it does not result in complete protection , and that some groups , such as children with Sickle Cell Disease , remain especially vulnerable [31] . Antibiotics thus continue to be extremely important as a treatment option , especially in acute disease . However , as with almost any clinically important bacterial pathogen , the emergence of multidrug-resistant ( MDR ) strains is a global problem [32–37] and with 1 . 2 million drug-resistant pneumococcal infections annually in the US , and $96 million in excess medical costs , S . pneumoniae is a serious concern [30] . S . pneumoniae is one of several species for which the availability of complete bacterial genomes has demonstrated that a distinction can be made between its core-genome ( the pool of genes shared by all members of a species ) and pan-genome ( a species’ global gene repertoire ) [38–41] . On average two pneumococcal strains may differ by ~300 genes in their genomic content , i . e . the presence and absence of genes [42 , 43] , which highlights the genome’s plasticity to retain function in the presence of variation . Such plasticity is remarkable because no genomic element , gene , or pathway exists in a vacuum; rather they are connected through networks resulting in specific organismal properties [44 , 45] . A newly acquired element thus needs to be integrated thereby possibly affecting existing connections and creating new ones . Consequently , no two genomes may function in the same manner , potentially affecting phenotypes ranging from drug tolerance to virulence to evolutionary potential . By employing genome-wide approaches we , and others , have shown that it is possible to determine , upon exposure to an environmental perturbation , where stress in the bacterial genome is experienced [31 , 46–55] . Here we apply Tn-Seq , a tool for systems-level analysis of microorganisms , which combines transposon mutagenesis with massively parallel sequencing to determine genome-wide fitness in a single experiment . We develop daptomycin-sensitivity profiles for two strains of the bacterial pathogen S . pneumoniae . Although the exact mechanism of action of daptomycin is not completely clear it seems to insert itself into the membrane for which the presence of phosphatidylglycerol in the membrane is required . Following insertion , membrane structure and curvature may be distorted leading to cells with altered cell shapes . These distortions in the membrane at the site of daptomycin insertion may lead to leakage of ions and loss of membrane potential and local dysregulation of cell division and/or cell wall-biosynthesis [56–61] . The daptomycin-sensitivity profiles generated in this study illustrate how the antibiotic’s effects ripple through the organism and how the bacterium deals with this stress with a diverse set of genes from different functional categories and organizational levels including: cell-wall organization , membrane integrity and transport , control and regulation of fundamental processes , and metabolism . Surprisingly , the sensitivity profiles turn out to be highly strain-specific highlighted by over 50% of sensitivity-profile genes that increase antibiotic sensitivity in one strain but have no effect , or even decrease sensitivity in the other strain . We show that these differences are partially the result of a network architecture that is not well conserved , exemplified by strain-specific differences in Potassium ( K+ ) -release and ClpP functionality , as well as differences in regulatory relationships between genes from different organizational levels . Importantly , we present a generally applicable , and highly sensitive approach that enables comparisons of environment-induced fitness effects on a genome-wide scale and species-wide level .
A major goal of this study is to determine whether bacterial strains from the same species that differ in their genomic content , respond in an identical manner to antibiotic stress . On a species level the genome of S . pneumoniae can be divided up in a core genome consisting of ~1600 genes , and a pan genome of ~4000 genes , while a genome on average has approximately 2000 genes . We selected two strains , TIGR4 ( T4 ) and Taiwan-19F ( 19F ) ( S1 Fig ) , which can both cause invasive disease: T4 is a serotype 4 strain that was originally isolated from a patient from Norway with Invasive Pneumococcal Disease ( IPD ) [62 , 63] , while 19F is a multi-drug resistant ( MDR ) strain isolated from a patient with IPD from Taiwan [64 , 65] . With respect to genomic content the strains share 1711 genes , while T4 has 324 genes that are absent in 19F , and 19F has 204 genes that are absent in T4 . On average two pneumococcal strains may differ by ~15% in their genomic content , and thus the amount of variation between these two strains is representative for differences observed between strains within the species [42 , 43] . All genes were split into 17 functional categories and except for the number of genes with unknown function , both strains share a similar distribution over these categories ( S2 Fig; S1 Table ) . Both strains are differentially susceptible to different antibiotics , for instance 19F is approximately 25-fold less susceptible to penicillin than T4 , while they are equally sensitive to daptomycin ( Fig 1 ) . Because equal sensitivity creates the simplest opportunity to test whether strains use the same genes in dealing with antibiotic induced stress , daptomycin is used here . To identify in detail which genes in the genome are involved in dealing with daptomycin-stress we employed transposon insertion sequencing ( Tn-Seq ) , which enables high-throughput and accurate calculations of the growth rate for each possible gene-knockout in the genome [31 , 46 , 48 , 49] . Six independent transposon libraries , each consisting of ~10 , 000 mutants were created in T4 and were grown in the absence and presence of daptomycin at a concentration of 25 μg/ml , which moderately slows the growth rate by ~15% ( Fig 1 ) . For each condition reproducibility was determined by comparing fitness between different libraries , which in each case was high ( R2 = 0 . 78–0 . 89 ) . Fitness values for each insertion in each gene were averaged and genes with a significant antibiotic-specific response were visualized in a network with Cytoscape [66] and grouped according to their functional category ( Fig 2A; S2 Table ) . Previously we showed how such a visual network approach provides a detailed overview of how an environmental disturbance can affect a bacterium on multiple different levels [31 , 48] . The same is true for this network , demonstrating how a large variety of genes from different functional categories become important for the survival of T4 in the presence of daptomycin ( Fig 2A; S2 Table ) , which highlights several aspects of daptomycin’s modus operandi: 1 ) Our results show that any gene that affects peptidoglycan ( PG ) biosynthesis , stability , or regulation can , upon its removal , make the bacterium more susceptible to daptomycin . Even a decrease in PG acetylation ( mediated by SP1479 ) or a decrease in the speed and efficiency of Penicillin Binding Protein ( PBP ) folding , which has been shown to be mediated by PrsA in B . subtilis [67] ( prsA/SP0981 ) , increases susceptibility to daptomycin . Associations of daptomycin with the cell wall have also been shown in other bacteria: in B . subtilis , and S . aureus , daptomycin induces a cell wall stress response [59 , 68] , and mutations that increase cell wall thickening have been associated with resistance in S . aureus . [69 , 70]; 2 ) Lipo- and membrane proteins that provide structural support to the membrane become important in the presence of daptomycin , suggesting that the interaction of daptomycin with the membrane has a de-stabilizing effect on membrane integrity . The importance of the membrane anchored protease FtsH ( SP0013 ) , whose function includes proteolysis of aberrant membrane proteins and thereby influences membrane turnover [71 , 72] , further suggests that daptomycin negatively affects membrane-protein stability and thus membrane integrity . In B . subtilis , daptomycin preferentially interacts with regions of the membrane enriched in phosphatidylglycerol ( PhG ) [59] , it has been physically associated with sites of membrane distortion [56] and resistance is linked to the overall PhG content [73] , in S . aureus mutations in mprF increase daptomycin resistance by changing membrane lipid composition and charge [74–76] , while in Enterococci changes in cardiolipin synthesis can increase daptomycin resistance [77 , 78]; 3 ) A Trk-system ( SP0479-0480 ) , which mediates K+-uptake [79] , becomes important in T4 in the presence of daptomycin , which suggests that T4 suffers from daptomycin-induced K+-loss . Indeed , it is assumed that daptomycin triggers potassium loss through its interaction with the membrane [57 , 60 , 61] . Besides K+ , other ions may be leaking out as well [61] , or at least other ions become more important and may compensate for K+-loss , which is highlighted by the importance of several ion transport systems in our network including SP1623 ( annotated as a cation-transporter ) , which we previously associated with pH-homeostasis [48]; 4 ) The sensitivity profile highlights the importance of a diverse set of cell division , RNA and protein turnover , signaling and regulation , and metabolism genes , indicating that the antibiotic’s effects resonate throughout some of the most embedded systems in the bacterium . Importantly , these profiles can also suggest roles for genes with unknown or unclear functions in at least two ways: 1 ) A gene with unknown function adjacent to a gene with a defined function and a similar sensitivity suggests that the two genes are involved in the same process . For instance SP1730 and SP1731 are hypothetical genes and have a similar fitness as their regulatory neighbors SP1732 ( stkP ) and SP1733 ( phpP; the cognate phosphatase of stkP ) , which sense intracellular peptidoglycan and have regulatory control over cell-division [80] . 2 ) Genes with domains that suggest a function or association with a specific functional category are more likely to be correct if they fit within the sensitivity profile . For instance BLAST searches and protein domain predictions predict that both SP1505 and SP1720 are membrane proteins . These predictions fit well with the sensitivity profile where membrane proteins make up one of the most important categories . Moreover , with GFP-fusions we confirmed the localization of both proteins in the membrane . We expected to uncover highly similar sensitivity profiles due to the strains’ equivalent susceptibility to daptomycin . However , less than 50% of the responsive genes have a conserved phenotype between strains ( Fig 2B; S3 Table ) , and based on a Jaccard similarity index , the networks are significantly different ( J = 0 . 24 , p<0 . 05 ) [81–83] . Additionally , the overall distribution of functional gene categories is significantly different between strains ( two proportion exact test; Z = 5 . 83 , N1 = 52 N2 = 57 , p<0 . 01 ) . This means that both on the individual gene-level as well as the overall functional level there is little conservation between strains in the distribution of the type of genes that are important in dealing with daptomycin stress . However , genes and pathways interact with each other and responses could be more conserved on a global scale . Therefore we grouped functional categories to determine whether we could analytically track how an antibiotic interacts with a bacterium and thereby identify how a bacterium in first instance perceives a ( extracellular ) threat and subsequently how this threat is processed . To enable this , functional categories were combined into four hierarchical groups , or layers . The first layer combines categories that make up the first physical layer an antibiotic could interact with , which is the capsule and the cell wall represented by peptidoglycan genes . The second physical layer of interaction is represented by the membrane and consists of the categories membrane , lipoprotein and transporter genes . The third layer combines genes that control and orchestrate fundamental processes: cell division , DNA turnover , RNA turnover , protein turnover , transcription and translation and regulation . The fourth and last layer combines all metabolism genes including nucleotide , carbohydrate and amino acid metabolism . These four layers thus combine the physical location of the gene-product with its molecular function ( Fig 2 ) . For both strains the first layer is indeed the first point of interaction ( Fig 2A and 2B ) after which the membrane becomes the next obstacle . This second layer includes 21 responsive genes in T4 and 20 in 19F and even though only 25% of the transporter genes in this layer are conserved , ~70% of the membrane and lipoprotein genes are conserved between strains . Thus , at least for the part of the network that is important for membrane integrity the two strains seem to experience and process daptomycin stress in a similar fashion . Moreover , when a global analysis is performed , in which the gene-categories are first collapsed into the described four layers , and then the four layers are compared between the two strains , we no longer observe a dissimilar response ( two proportion exact test , Z = 2 . 85 , N1 = 52 N2 = 57 , p = 0 . 16 ) , indicating that although on the individual gene level there is little conservation , the global response is more similar . To confirm that the wide variety of genes involved in dealing with antibiotic stress , as well as the lack of phenotypic conservation is not limited to daptomycin we further performed Tn-Seq with an aminoglycoside , a glycopeptide and a fluoroquinolone , and in both T4 and 19F , which shows that also these three classes of antibiotics trigger stress that is processed with genes from a wide variety of categories ( Fig 3 ) . In addition , conservation of phenotypes , i . e . the genes that either strain uses to deal with antibiotic stress , shows , similar to daptomycin , a limited signature of conservation between ~40–50% ( Fig 3 ) . To validate the sensitivity profiles , and exclude that the lack of conservation comes from low confidence Tn-Seq data , we compared Tn-Seq fitness ( WTn-Seq ) to fitness obtained from individual growth curves and/or from 1x1 competition assays ( W1x1 ) , in which a deletion-mutant is competed against the wild type . Note that in all three of these cases fitness ( W ) is calculated as the growth rate thereby enabling direct comparisons . In total 34 deletion mutations in T4 and 19F were constructed and sixty-five genotype-phenotype relationships were validated in the presence and absence of daptomycin ( Fig 4A and 4B; Table 1 ) . This resulted in a strong correlation ( R2 = 0 . 87 ) , which is similar to correlations we achieved previously [48] and confirms high-confidence Tn-Seq fitness data . Therefore , even though the strains have the same susceptibility to daptomycin , belong to the same species and share ~85% of their genomic content , this suggests that the underlying genomic networks must be different , which makes the strains respond in a different manner to the same stress . To develop a better understanding of how the underlying networks differ between strains we first set out to determine the role of a Trk-K+-uptake system which the Tn-Seq data indicates is important in the presence of daptomycin in T4 ( WTrk1 = 0 . 82 ) , while it is dispensable in 19F ( WTrk1 = 0 . 98 ) . Ion homeostasis is an essential part of life and transport systems are mandatory for ion uptake and extrusion . Although in general only traces of potassium are available in the environment it is generally the most abundant cation in bacteria and plays an essential role in for instance the maintenance of internal pH , in membrane potential adjustment , it acts as second messenger for stress signaling and it is a regulatory element for transcription control [79] [84] . Tn-Seq data for T4 indicates that 8 transporters become important in the presence of daptomycin , of which two encode a single Trk-K+-uptake system ( Trk1: SP0479-SP0480; Fig 5A ) suggesting that T4 suffers K+-loss upon exposure to daptomycin and that this two-gene system is important in countering that loss . The importance of Trk1 in the presence of daptomycin was validated ( Fig 5B , Table 1 ) and is concentration dependent indicated by a further drop in fitness upon an increase in daptomycin ( Fig 5C ) . By adding additional K+ both the wt and the Trk1 mutant can be ( partially ) compensated , which shows that the system becomes less important when K+ is more abundant ( Fig 5D ) . Moreover , the mutant is also sensitive to valinomycin ( an ionophore that releases K+ ) confirming that the importance of Trk1 is indeed due to daptomycin induced K+-loss ( Fig 5E ) . Interestingly , S . pneumoniae T4 has an additional Trk-uptake system ( Trk2: SP0078-SP0079; Fig 5A ) that was not indicated by Tn-Seq ( there was no loss of fitness ) , which we indeed verified ( Fig 5F , Table 1 ) . To further investigate the role of the two Trk-systems in K+-homeostasis as well as their importance in dealing with daptomycin stress , internal K+-concentrations under different conditions were determined for the wild type and the two Trk-system mutants . Although under standard growth conditions there is no clear difference in fitness between the wt , and the two Trk-system deletion mutants , internal K+-concentrations do vary between strains: ΔTrk2 contains on average 7-fold less K+ , while ΔTrk1 contains on average 14-fold less K+ relative to the wt ( Fig 6A ) . Interestingly , ΔTrk1 becomes very unstable under standard growth conditions and after it reaches peak OD it settles at the bottom of the culture vial , indicating that at high OD it is highly susceptible to lysis . Temporal gene expression analysis ( with/without daptomycin ) did not reveal any differences between the different Trk-systems . However , upon exposure to daptomycin clear differences in the loss of K+ were observed between all three strains: K+ -loss for the wt is the greatest ( ~4 . 5 fold ) , it is intermediate for ΔTrk2 ( ~2 . 5 fold ) , while for ΔTrk1 the already low K+ concentration drops by another 1 . 5-fold ( Fig 6B ) . K+ -loss thus partially seems dependent on the K+ concentration in the cell , i . e . the higher the concentration the bigger the loss . However , even though ΔTrk1 has the smallest relative loss , its final concentration is still ~7 . 5 fold lower than the wild type ( Fig 6A ) , which is enough to drastically hamper its growth ( Figs 1 and 5B–5C ) . In contrast , ΔTrk2 only has a 2-fold lower K+-concentration than the wt , which is apparently not large enough to affect growth . These results show that there is a clear hierarchy in Trk-system importance; Trk1 being the most important K+-uptake system to control K+-homeostasis both during normal growth conditions as well as during exposure to daptomycin . The lack of phenotypic conservation for Trk1 with respect to daptomycin-sensitivity between T4 and 19F could be the result of a third K+-uptake system in 19F that creates redundancy in K+-control . Indeed , we found a strain-specific third system in 19F encoded by SPT1006 , which is annotated as a K+-ion channel ( Fig 7A ) . To determine whether there is any hierarchy among these three systems , single knockouts were created , as well as double knockouts for all three possible combinations . However , none of these six mutants made 19F more susceptible to daptomycin . Even the double knockout consisting of the strain-specific 19F K+-system ( SPT1006 ) and Trk1 did not affect sensitivity of 19F to daptomycin nor valinomycin ( Fig 7B–7H ) . Measuring internal K+-concentrations confirmed that daptomycin-induced release of K+ was similar for 19F-wt and the three single mutants ( S3 Fig ) . The double mutants showed more variation but none of them was as dramatic as the knockout for Trk1 in T4 ( Fig 6A–6D ) . This suggests that daptomycin-induced release of K+ is counteracted by all three 19F K+-uptake systems and that , in contrast to T4 , there is no hierarchy amongst the systems . Additionally , K+-concentrations are approximately 5-fold higher in wt-T4 compared to wt-19F and daptomycin may have less of an impact on K+-loss in 19F , while instead other ions may be leaking out as suggested by the importance of the manganese transporter in 19F ( Fig 2B; SP1649-1650 ) . Importantly , these data confirm the Tn-Seq data and the lack of conservation between T4 and 19F indicating that the two strains handle K+-stress differently , possibly due to differences in their underlying networks . We have previously shown that we can ( partially ) reconstruct underlying networks by generating a genetic interaction map ( GIM ) , which is accomplished by creating a transposon library in the background of a query strain ( i . e . a strain that carries a knockout of the gene of interest ) [46 , 48] . For T4 , ΔTrk1 was used as a query strain , while for 19F the double mutant ΔTrk1-ΔSPT1006 was used to compensate for the presence of the additional K+-ion channel . Significant response genes ( i . e . interactions that deviate from the multiplicative model; see methods section for definition ) were visualized in a network and grouped according to their functional category ( S4A and S4B Fig; S4 and S5 Tables ) . There seems to be little conservation in genetic interactions: the networks are significantly different ( J = 0 . 08 , p<0 . 001 ) , the overall distribution of functional gene categories is significantly different ( two proportion exact test , Z = 8 . 11 , N1 = 49 N2 = 34 , p<0 . 01 ) , as well as the global response ( two proportion exact test , Z = 4 . 38 , N1 = 49 N2 = 34 p<0 . 01 ) . One of the most obvious differences between the strains is found in the second layer , which includes 18 transporters in T4 , of which Trk1 and 2 are indicated as synthetically lethal , which we confirmed by our inability to construct this double mutant , five transporters have an aggravating interaction ( a lower fitness than expected from the multiplicative model ) , while 12 interactions are alleviating ( a higher fitness than expected from the multiplicative model ) . Moreover , a further 21 additional alleviating interactions are present between Trk1 and genes from all four layers . In contrast , there are only 5 overall conserved interactions between strains , including a single alleviating transporter interaction . Moreover , of the seven aggravating transporter interactions in 19F , six are annotated as involved in metal-ion transport , indicating that the manner in which 19F deals with daptomycin-induced loss of membrane-potential may be decentralized and spread across multiple different transporters . To confirm the genetic interactions and lack of conservation between strains we validated 21 genotype-phenotype relationships of which one ( ΔTrk1-ΔSP2195 ) had a significantly higher fitness than expected ( Fig 8A and 8B; Table 2 ) . The GIMs are thus robust , and give hints as to the type of relationships between interacting genes . For the metal-ion transporters in either map it seems relatively easy to explain why they interact with the query genes: the removal of the K+-homeostasis system ( s ) makes both strains sensitive to a further disturbance in the bacterium’s ion-mediated potential , and thus a loss of any transporter that is involved in retaining what is left of that potential will have a negative effect on fitness . In contrast , the alleviating interactions in T4 show that the removal of these genes has a positive effect on fitness . This effect could also be accomplished by transcriptionally repressing these genes , and thus this suggests that these genes are dysregulated in a T4-ΔTrk1 background . To test this hypothesis we picked 7 genes from the T4-ΔTrk1 network: 4 regulators and 3 transporters . Expression of these genes was followed in wt-T4 , wt-19F and the two query strains for 5 different time-points and in three independent experiments . In the query strain T4-ΔTrk1 the expression of six out of seven genes changed abruptly by approximately 4-fold between 30 and 45 minutes after addition of daptomycin ( Fig 9A and 9B ) , while T4-wt expression did not change for any of these genes , or changes were gradual over time ( Fig 9A and 9B ) . In 19F-wt and its query strain , expression changes for all genes were comparable ( Fig 9C and 9D ) , and fluctuations over time , except for the response regulator ciaR ( SP0798/TCS05 ) were within 2-fold . Even though the small changes in 19F could still be affecting the response , these results further show that the stress T4 and 19F experience is also processed in a different manner , seemingly with dysregulation in a select set of genes , including stress regulators such as ciaR and ctsR ( SP2195 ) , as a result . One could argue that the lack of conservation in the GIMs could be unique for the K+-transporters , which have a relatively straightforward function and may not be that deeply integrated into the organismal network . Therefore , we set out to construct GIMs for ClpP , a protease that plays a crucial role in the regulation of various cellular responses by controlling proteolysis . For instance ClpP has been shown to repress competence in B . subtilis and activate stress proteins by targeted degradation of the repressor CtsR , additionally it has been associated with cell division , sporulation and cell wall biosynthesis [72 , 85] . Thus , by definition , this conserved protease is , in its role as a protein turnover gene , deeply integrated into the organismal network . Tn-Seq analysis indicates , and individual growth curves confirm , that the role of ClpP in basic growth as well as its sensitivity to daptomycin is strain dependent: ΔclpP ( SP0746 ) in T4 only slightly affects growth and , surprisingly , decreases sensitivity to daptomycin ( Fig 10A and 10B; Table 1 ) , while in 19F ΔclpP substantially lowers the growth rate ( Fig 10C; Table 1 ) . The T4-specifc decrease in daptomycin-sensitivity seems specific for this antibiotic , since it has the opposite effect on gentamicin sensitivity , a protein synthesis inhibitor ( Fig 10D ) . Importantly , sensitivity to valinomycin is not affected in ΔclpP ( Fig 10E and 10F ) , and thus the change in daptomycin-sensitivity does not seem to be related to intracellular K+-concentrations , which further confirm that the effects of daptomycin reach beyond K+ . GIMs were constructed to determine ClpP connectivity within each strain as was done for the K+-systems ( S5A and S5B Fig; S6 and S7 Tables ) . 19 genotype-phenotype relationships were validated; two of those relationships ( SP0798-ΔclpP; SP0047-ΔclpP ) have a significantly different fitness compared to Tn-Seq data , but the phenotype is stronger than initially measured , which thus confirms the interaction ( Fig 8A and 8B; Table 2 ) . Also here , there is little conservation between strains , the GIMs are significantly different ( J = 0 . 06 , p<0 . 001 ) , the overall distribution of functional gene categories is significantly different ( two proportion exact test , Z = 10 . 49 , N1 = 23 N2 = 21 , p<0 . 01 ) , as well as the global response ( two proportion exact test , Z = 5 . 06 , N1 = 23 N2 = 21 , p<0 . 05 ) . For instance in T4 , ClpP interacts in an aggravating manner with genes that are located adjacent to ClpP on the genome as well as a large number of nuclear metabolism genes . This latter relationship indicates that ClpP has control over nucleotide metabolism in T4 , which has indeed been suggested for S . pneumoniae [86] as well as B . subtilis [87] , but here that relationship seems to become important during daptomycin stress . In 19F , we confirmed that this strong relationship is not present ( Table 2 ) , instead , the clearest pattern in 19F emerges from ClpP interacting with a set of genes of which several play a role during competence including two component system-12 ( TCS12 ) , the regulators stkP ( SP1732 ) and ciaR ( SP0798 ) , and comM ( SP1945 ) , a membrane protein that protects against lysins and fratricide [88–90] ( Fig 11A , S5B Fig ) . The importance of TCS12 suggests that ClpP has a repressive regulatory effect on this system; i . e . since the comD/E system that makes up TCS12 becomes important in the absence of ClpP this suggests that it is activated ( Fig 11A ) . To verify this , we determined expression of the two TCS12 genes , comC ( which is in an operon with TCS12 ) and comM which is located ~300 genes downstream of TCS12 . As predicted comD , E and C , were highly upregulated ( between 20–60 fold ) in 19F in the absence of ClpP ( Fig 11B ) while comM was upregulated approximately 12-fold . Because of the importance and upregulation of the ‘anti-lysis’ gene comM , we expected that Pmp23 ( SP1026 ) , a membrane protein that is associated with lysis through its possible role in peptidoglycan turnover [91–93] , and SP0650 , a membrane protein with possible hydrolase activity ( which are both present in the network; S5B Fig ) , would also be upregulated , and that ComM was possibly protecting against their actions . However , this hypothesis had to be rejected since the expression of pmp23 and SP0650 hardly changed in the absence of ClpP , which does not exclude that ClpP still has control over these genes through its protease activity . In contrast , relative expression of all 6 genes was unchanged in T4-wt and T4-ΔclpP ( Fig 11B ) , confirming that under the tested conditions there are no relevant interactions between these genes in the T4 background . These data thus show that even for a highly conserved gene , genetic interactions are not necessarily conserved , which can lead to responses that are largely strain dependent . It has become clear that the interaction between an antibiotic and bacterial cell is a complex , multi-factorial process that resonates through the organism requiring the involvement of a diverse set of fundamental processes to overcome the antibiotic-induced stress [5–8] . The selective pressures invoked by an antibiotic are thus not only felt by the direct target but are dispersed across many different layers . This distribution of stress thus expands the adaptive sequence space , which may explain why multiple genetic perturbations across different layers can combine to confer elevated levels of resistance [13–17] . Here , we develop daptomycin-sensitivity profiles showing in detail the genes that are important in coping with daptomycin-induced stress . By creating hierarchical layers , that partially represent physical barriers the antibiotic interacts with as well as fundamental processes that regulate and ensure all aspects of the bacterium’s life cycle , we identify where the antibiotic has its biggest impact . We believe that these types of analyses can be used to uncover the bacterium’s weakest-links in the presence of an antibiotic and thus identify novel targets that could work synergistically with existing drugs , while it also indicates where in the genome the bacterium may adapt to decrease its sensitivity to the stress . For instance , mutations in daptomycin adapted strains of S . aureus , B . subtilis and Enterococci have been observed in ClpP and other proteases , different regulatory genes and TCSs , capsule genes , transporters , nucleotide metabolism genes , peptidoglycan genes , lipoproteins and membrane genes [56 , 69 , 70 , 73–78 , 94–98] . Although many of these mutations have not ( yet ) been directly linked to higher resistance , we show here that they may indeed contribute to drug-sensitivity . Importantly , it turns out that the sensitivity profiles strongly depend on the genomic background , and that even within a species responses can be strain specific . We show that it is possible to at least partially dissect the underlying network of the response through constructing a GIM . By removing the query-gene , on which these maps are based , it is as if a protective layer is removed from the organism , thereby further exposing parts that become important in the presence of the stress , and at the same time revealing the type of dependencies that exist between genes , including regulatory relationships . However , by diving deeper into the response by means of these GIMs , we uncover more complexity and even less conservation across strains . Our approach thus reveals that an important part of antibiotic-induced stress is experienced and processed by S . pneumoniae in a strain dependent manner . Take this one step further and it implies that adaptation to an antibiotic will , at least partially , be strain dependent . And thus , this could be one of the reasons why it remains so difficult to predict the emergence of antibiotic resistance . This study provides , a clear approach as well as important arguments to not only construct antibiotic-sensitivity profiles for different antibiotics but also perform this across different bacterial species and strains . Such profiles in combination with in vitro and in vivo adaptation experiments could provide an important improvement in our ability to predict where in the genome mutations may arise that decrease susceptibility to an antibiotic and put the organism on the road towards full clinical-resistance .
Experiments were performed with S . pneumoniae strains TIGR4 ( NCBI Reference Sequence: NC_003028 . 3 ) and Taiwan-19F ( NC_012469 . 1 ) . TIGR4 is a serotype 4 strain that was originally isolated from a patient from Norway with Invasive Pneumococcal Disease ( IPD ) [62 , 63] , while 19F is a multi-drug resistant strain isolated from a patient with IPD from Taiwan [64 , 65] . All gene numbers in the tables and figures are according to the TIGR4 genome , except when it concerns a strain-specific gene , these are preceded by SP or SPT referring to a T4 or 19F gene respectively . PATRIC [99] and BLAST were used to compile S8 Table , which matches gene numbers between T4 and 19F and lists strain-specific genes for each genome . A gene is considered strain-specific if 70% of the sequence has less than 70% similarity with the other genome [42] . Single gene knockouts were constructed by replacing the coding sequence with a chloramphenicol and/or spectinomycin resistance cassette as described previously [46 , 48 , 100] . S . pneumoniae was grown on sheep’s blood agar plates or statically in semi-defined minimal media ( SDMM ) at pH 7 . 3 , which contains 70 μg/ml calcium to ensure activity of daptomycin , 20 mM glucose and 5 μl/ml Oxyrase ( Oxyrase , Inc ) , at 37°C in a 5% CO2 atmosphere [48] . Where appropriate , cultures and blood plates contained 4 μg/ml chloramphenicol ( Cm ) and/or 200 μg/ml Spectinomycin ( Spec ) . Library construction was performed as described with transposon Magellan6 , which lacks transcriptional terminators , therefore allowing for read-through transcription , and it diminishes polar effects [46 , 48 , 101 , 102] . Additionally , the mini-transposon contains stop codons in all three frames in either orientation when inserted into a coding sequence . Six independent transposon libraries were constructed in wt-T4 and wt-19F and in four query strains ( T4: ΔTrk1 , ΔClpP; 19F: ΔTrk1-SPT1006 , ΔClpP ) , and selection experiments were conducted in SDMM in the presence or absence of 25 μg/ml daptomycin , which in this environment moderately slows growth for both strains by ~15% ( Fig 1 ) . Sample preparation , Illumina sequencing and fitness calculations were done as described [31 , 46–49 , 101–103] . In short , for each insertion , fitness Wi , is calculated by comparing the fold expansion of the mutant relative to the rest of the population by using an equation that we specifically developed to have fitness represent the growth rate of a mutant [46 , 48 , 103] . All of the insertions in a specified region or gene are then used to calculate the average fitness and standard deviation of the gene knockout in question . The advantage of using this approach is that Wi now represents the actual growth rate per generation , which makes fitness independent of time and enables comparisons between conditions and strains . To determine whether fitness effects are significantly different between conditions or strains three requirements have to be fulfilled: 1 ) Wi is calculated from at least three data points , 2 ) the difference in fitness between conditions has to be larger than 10% ( thus Wi—Wj = < -0 . 10 or > 0 . 10 ) , and 3 ) the difference in fitness has to be significantly different in a one sample t-test with Bonferroni correction for multiple testing [46 , 48] . All significant fitness values were visualized in a network with Cytoscape [66] . Importantly , here , fitness ( Wi ) represents the actual growth rate per generation , which makes fitness independent of time and enables comparisons between conditions and strains . To determine whether the observed distributions in the antibiotic sensitivity profiles that are based on the functional categories or layers are different it is enough to show that , if the classes are grouped into 2 macro classes , the resulting distributions are different . To compare two 2-class distributions we use a two proportion exact test and we reject the equality hypothesis at a p-value ≤ 0 . 05 . We build these two macro classes such that the differences are as large as possible . Hence , when a test cannot distinguish between these two reduced distributions it indicates that the original , non-reduced , distributions are also similar . Genetic interactions are defined as a deviation from the multiplicative model , which states that if a strain deleted for gene i has a fitness Wi and a strain deleted for gene j has a fitness Wj , then the double mutant strain Wij is expected to have the fitness Wi x Wj [46 , 48] . Genetic interactions were determined for the four query strains and has generally more experimental noise [46 , 48] , therefore to minimize false positives , we set more stringent cut offs: 1 ) fitness needs to be composed of at least five data points; 2 ) expected and observed fitness have to deviate by at least 17 . 5% , and 3 ) significant interactions have to pass a student’s t-test with Bonferroni correction for multiple testing . An exponentially growing culture was washed and resuspended in TA buffer to an OD600 of ~0 . 3 , and a small amount of culture was plated on blood agar for enumeration . The external background potassium concentration was measured every 3 seconds for one minute at room temperature using the MI-442 K+-ion microelectrode and the MI-409 dip-type reference microelectrode ( Microelectrodes , Inc . , Bedford , NH ) . Note that: 1 ) longer measurements proved unnecessary as readings stabilized after several seconds , and 2 ) for every set of measurements the electrode was first calibrated with known concentrations of KCl to ensure a linear regression ( Vmeas = mlog10[K+] + z ) , where Vmeas is the average mV of 20 data points measured over one minute . In samples for which the effect of daptomycin on K+-loss was determined cells were exposed to daptomycin for 20 minutes after which they were washed and resuspended in TA-buffer . For each sample , the internal K+-concentration was determined in a second measurement after lysing all cells through boiling . External and internal K+ concentrations were calculated by converting Log10 [K+] into molar concentrations of K+ as described previously [104] . For 1x1 competitions two strains were mixed in a 1:1 ratio and grown for approximately 8 generations to late exponential growth phase . Fitness , representing the growth rate , was calculated through the same approach as Tn-Seq data above by determining the expansion of the competition over the experiment and by determining the ratios of the competing strains at the start and at the end of the competition by plating appropriate dilutions on blood agar plates with selective antibiotics [46 , 48] . Mutants were always competed against their background strain: strains with a single gene knockout were competed against the wild type strain , while double mutants were competed against the query strain . Each competition was performed no less than four times , while single strain growth was performed no less than three times in 96-well plates by taking OD600 measurements every half hour using a Tecan Infiniti Pro plate reader ( Tecan ) . Additionally , competition assays and single strain growth were performed in the absence and presence of varying concentrations of daptomycin to determine whether growth rates changed with increasing concentration according to expectations ( Fig 1 ) , which was always the case . Lastly , figures throughout the manuscript depict a typical growth curve for the specific condition or mutant , while Tables 1 and 2 list growth rates calculated over all experiments . RNA was isolated from cultures at different times using the Qiagen RNAeasy kit ( Qiagen ) . RNA was treated with the TURBO-DNAfree kit ( Ambion ) , after which cDNA was generated from 1 μg RNA with iScript complete kit ( BioRad ) and random hexamers . Quantitative PCR was performed using a BioRad MyiQ . Each sample was measured in both technical and biological triplicates , and samples were normalized against the 50S ribosomal gene SP2204 . Tn-Seq sequencing data is deposited at the Sequence Read Archive under BioProject PRJNA318012 . | While antibiotic resistant bacterial pathogens cause millions of deaths each year it remains largely unclear how a bacterium deals with antibiotic-induced stress and how this leads to the emergence of resistance . Moreover , many bacterial species are composed of strains whose genomes vary considerably , and while this variation may significantly affect phenotypes such as antibiotic sensitivity , its importance is unknown . Here we apply the method Tn-Seq , showing it is feasible to develop a detailed view of how a bacterium experiences antibiotic stress , while simultaneously determining the influence of the genomic-background . We show for two strains of the bacterial pathogen Streptococcus pneumoniae that , even though they experience the same stress triggered by daptomycin , they use a majority of different genes to withstand this stress , including genes important for integrity of the membrane , potassium uptake and protein turnover . Additionally , by untangling underlying genomic networks we unexpectedly expose large differences in genetic-interactions as well as transcriptional regulation . Our study provides not only a sensitive approach to untangle the influence of the genomic-background on phenotypes such as antibiotic sensitivity , but also highlights that this knowledge is instrumental in understanding how bacteria respond to environmental stress , which in turn influences the manner in which they evolve . |
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Chagas disease , caused by the protozoan parasite Trypanosoma cruzi , presents a variable clinical course , varying from asymptomatic to serious debilitating pathologies with cardiac , digestive or cardio-digestive impairment . Previous studies using two clonal T . cruzi populations , Col1 . 7G2 ( T . cruzi I ) and JG ( T . cruzi II ) demonstrated that there was a differential tissue distribution of these parasites during infection in BALB/c mice , with predominance of JG in the heart . To date little is known about the mechanisms that determine this tissue selection . Upon infection , host cells respond producing several factors , such as reactive oxygen species ( ROS ) , cytokines , among others . Herein and in agreement with previous data from the literature we show that JG presents a higher intracellular multiplication rate when compared to Col1 . 7G2 . We also showed that upon infection cardiomyocytes in culture may increase the production of oxidative species and its levels are higher in cultures infected with JG , which expresses lower levels of antioxidant enzymes . Interestingly , inhibition of oxidative stress severely interferes with the intracellular multiplication rate of JG . Additionally , upon H2O2-treatment increase in intracellular Ca2+ and oxidants were observed only in JG epimastigotes . Data presented herein suggests that JG and Col1 . 7G2 may sense extracellular oxidants in a distinct manner , which would then interfere differently with their intracellular development in cardiomyocytes .
Chagas disease , caused by the protozoan Trypanosoma cruzi , is an important health problem affecting about 6 to 7 million people worldwide [1] . Infection in man is defined by two distinct clinical phases . The acute phase , corresponding to the initial period of infection , is characterized by high parasitemia and tissue parasitism , followed by the chronic phase of the infection , which persists throughout the life of the host and is characterized by low tissue parasitism as well as parasitemia [2] . Chronic infection has a variable clinical course , ranging from asymptomatic cases ( indeterminate form ) , to severe clinical conditions with heart ( chagasic cardiomyopathy ) and / or digestive tract ( megacolon or megaesophagus ) maladies . In patients with cardiac and / or digestive disorders , symptoms may appear between 10 and 30 years after initial infection and are due to the persistence of parasites in specific tissues , such as cardiac and / or smooth muscle , with the development of an intense inflammatory process , deleterious to the organ ( reviewed by [3] ) . Chagas disease clinical variability is well known to depend not only on genetic factors of the parasite , whose population structure is quite variable , but also on genetic factors of the host [4–6] . Previous studies conducted by our group showed that distinct parasite populations are found in different organs of infected patients [7] , reinforcing data on the existence of a differential tissue tropism , probably related to the development of the diverse clinical forms [8–10] . Later , we studied this tissue tropism by performing mixed infections in BALB/c mice with two clonal populations of T . cruzi , Col1 . 7G2 ( T . cruzi I ) and JG ( T . cruzi II ) , and detection of parasites directly from infected tissues . A predominance of Col1 . 7G2 was found in the rectum , diaphragm , esophagus and blood while JG was predominant in the cardiac muscle [11] . Later , we showed that this tissue tropism could be influenced by the genetic background of the host , where mice with the same MHC haplotype presented the same selection profile of T . cruzi in different tissues [12 , 13] . In vitro studies using infection in cultures of cardiac explants or primary cardiomyocytes , with Col1 . 7G2 and JG , indicated that tissue selection occurs due to the direct interaction between parasite and host cell , without direct influence of the host immune system [13 , 14] . In these studies , a more accelerated and efficient intracellular development of JG with respect to clone Col1 . 7G2 was observed in explants and cultures of cardiomyocytes isolated from BALB/c , suggesting that not only invasion , but also and mainly intracellular multiplication is important to tissue selection . Additionally , it was shown that this behavior profile was dependent on the cell type studied [14] . These findings reinforce that not only the parasite , but also the host cell response to infection is involved in the differential tissue tropism of T . cruzi . However , the mechanisms that define this selection are still poorly understood . During cell infection , infective trypomastigotes adhere to the surface of the host cell , being internalized in parasitophorous vacuoles , formed by lysosomal membrane [15] . Trypomastigote later escape from the vacuole to the cytoplasm of the cell and turn into the amastigote replicative form , colonizing the host cell [16–18] . Thus , during cell infection parasite passes through different environments , which can directly or indirectly influence its behavior within the cell . Data from the literature show that infected cells are able to respond to infection by activating several genes , through the production of cytokines and reactive oxygen species ( ROS ) , which could interfere with parasite intracellular behavior [19–24] . Here we investigate how stress responses mediated by oxidants in cardiomyocyte may influence infection by T . cruzi clonal populations , JG and Col1 . 7G2 , interfering with their intracellular multiplication rates .
This study was carried out in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of the Brazilian National Council of Animal Experimentation ( http://www . cobea . org . br/ ) and Federal Law 11 . 794 ( October 8 , 2008 ) . The institutional Committee for Animal Ethics of UFMG approved all the procedures used in this study . ( CEUA/UFMG–Licenses 45/2009 and 261/2016 ) Two clonal populations of Trypanosoma cruzi were used , Col1 . 7G2 and JG , belonging to T . cruzi lineages I and II , respectively . JG strain was originally isolated in 1995 by Professor Eliane Lages-Silva ( UFTM ) from a chronic patient with megaesophagus . Col . 1 . 7G2 is a clone from Colombian strain , which was originally isolated by Federici in 1969 from a chronic patient with cardiac disorders . Both T . cruzi populations were previously analyzed and characterized as monoclonal , through the analysis of the eight microsatellite loci according to previously described methodology [25] . Epimastigote forms of Col1 . 7G2 and JG were maintained in LIT ( Liver Infusion Tryptose ) medium containing 20 mg/mL of hemin and supplemented with 10% Fetal Bovine Serum and 1% Penicillin/Streptomycin , in T-25 cm3 bottles , in a 28°C incubator , and subcultured every two days [26] . Tissue culture trypomastigotes ( TCTs ) from Col1 . 7G2 and JG were obtained from the supernatant of infected LLC-MK2 monolayers and purified as described previously [27] . Primary cultures of cardiomyocytes were prepared from hearts of BALB/c mice neonates ( 0–2 days ) , according to protocol previously described [28] . 2x105 purified cardiomyocytes were plated in each well of a 24 well pate , containing a 13 mm circular glass coverslips , and maintained in a 37°C CO2 incubator for 72 hours prior to infection . Alternatively , 5x104 cells were plated in each well of a XF-24 cell culture microplate ( Seahorse Bioscience ) and maintained in a 37°C CO2 incubator for 120 hours prior to infection . Cultures of human cardiomyocytes ( Pluricardio ) were prepared from the differentiation of induced pluripotent stem cells ( iPSC ) obtained from Pluricell Biotechnologies . Cells were thawed and plated after counting by trypan blue exclusion method at a confluence of 2x105 cells per well in a 24-well plate . Cell differentiation was performed according to the company’s procedure , in 37°C CO2 incubator . All reagents required for maintenance and differentiation of cultures ( extracellular matrix and culture media ) were provided by the company . Five days after plating , after complete cell differentiation , cultures were used for the infection experiments . Immortalized embryonic fibroblasts were originally isolated from C57BL/6 mouse embryos and spontaneously immortalized in culture [29] . These cells were maintained in culture by consecutive passages in 25 cm 3 flasks in DMEM ( GIBCO ) culture medium supplemented with 10% FBS and 1% antibiotic ( penicillin/streptomycin ) . For the infection assays , 8x104 cells were plated in each well of a 24-well plate containing a 13 mm circular glass coverslips and maintained at 37°C in a CO2 incubator . For some experiments , parasites ( epimastigote stage ) were previously treated with H2O2 . H2O2 solutions were prepared daily assuming an extinction coefficient of 81M-1 cm-1 at 230 nm . For this , epimastigotes were subjected to treatment with different concentrations of H2O2 ( 5 to 100 μM ) for 6 days when the number of parasites was counted . Since 30 μM of H2O2 was the highest concentration in which JG and Col1 . 7G2 growth was still observed we used this concentration to perform the experiments . Treatment was performed by incubating 107 epimastigotes in 1mL PBS , pH 7 . 3 in the absence ( control ) or presence of 30 μM H2O2 at 28°C , for 30 minutes . Parasites were then washed and re-suspended in specific medium according to the experiment to be performed . For some experiments , cardiomyocyte cultures were treated with catalase ( Catalase-PEG C4963—SIGMA ) at a final concentration of 40U/mL . Treatment was initiated 2 hours prior to infection and maintained during and after parasite exposure . Cardiomyocyte cultures pre-treated or not with catalase , as well as fibroblast cultures were exposed to purified JG or Col1 . 7G2 TCTs re-suspended in high-glucose DMEM at a multiplicity of infection ( MOI ) of 50 . The infection was performed for 40 minutes at 37°C . After infection , monolayers were washed at least four times with PBS and either fixed with 4% ( w/v ) paraformaldehyde in PBS ( 0h–to determine the rate of parasite invasion ) or re-incubated for 4 , 8 , 12 , 24 , 48 or 72 hours prior to fixation and processing for immunofluorescence assays ( to determine parasitophorous vacuole escape– 4 , 8 and 12 h or parasite intracellular multiplication rates– 24 , 48 and 72 h ) . Alternatively , after parasite exposure ( 40 minutes at 37°C ) , cultures were washed and re-incubated for 48 and 72 hours before ROS measurements . After treatment , infection and fixation , coverslips containing attached cells were washed with PBS , incubated for 20 minutes with PBS containing 2% ( w/v ) bovine serum albumin ( BSA ) ( Sigma-Aldrich ) and processed for an inside/outside immunofluorescence invasion assay as described previously [27] . Briefly , cells were fixed and extracellular parasites were immune-stained using a 1:500 dilution of rabbit anti-T . cruzi polyclonal antibodies in PBS containing 2% ( w/v ) BSA ( PBS/BSA ) followed by labeling with Alexa Fluor-546 conjugated anti-rabbit IgG antibody ( Invitrogen ) . For evaluation of parasitophorous vacuole escape , after extracellular parasite staining , cells were permeabilized using a solution containing 2% ( w/v ) BSA and 0 . 5% ( v/v ) saponin ( Sigma-Aldrich ) in PBS ( PBS/BSA/saponin ) for 20 minutes . Host cell lysosomes were then immunostained using a 1:50 dilution of rat anti-mouse LAMP-1 hybridoma supernatant ( 1D4B; Developmental Studies Hybridoma Bank , USA ) in PBS/BSA/saponin for 45 minutes followed by labeling with Alexa Fluor-488 conjugated anti-rat IgG antibody ( Invitrogen ) , as described previously [30] . Subsequently , DNA from both host cells and parasites were stained for 1 min with 10 μM of DAPI ( Sigma-Aldrich ) , mounted on glass slide and examined on an Olympus BX51 , Zeiss , Apotome or Nikon Eclipse Ti . A general measure of oxidant formation were performed using CM-H2DCFDA ( 5- ( and-6 ) -chloromethyl-2' , 7'-dichlorodihydrofluorescein diacetate , acetyl ester—Molecular Probes ) probe , which fluoresces upon oxidation . For this , cell cultures , 48 and 72 hours post infection with JG or Col1 . 7G2 , were washed once with PBS and exposed to CM-H2DCFDA at a final concentration of 10 μM in PBS . Immediately after the addition of CM-H2DCFDA , the plate was read on a Varioskan Flash ( Thermo Scientific ) at 37°C for monitoring the probe’s oxidization rate , with excitation and emission wavelengths of 485 and 520 nm , respectively . The data were analyzed using the program SkanIt Software 2 . 4 . 5 . The probe oxidation curves were used to calculate the slope and are expressed as Relative Fluorescence Units ( RFU ) /min . Neonatal cardiomyocyte cultures plated in an XF-24 well culture microplate ( Seahorse Bioscience ) were infected with TCTs from JG or Col1 . 7G2 , as described above for 48 hours . One hour before the experiment , media was replaced with unbuffered Dulbecco’s Modified Eagle Medium ( DMEM , pH 7 . 4 ) supplemented with 4 mM L-glutamine , 5 mM glucose and 10 mM Pyruvate ( Gibco ) . Olygomycin ( 5 μM , an inhibitor of ATP synthase ( complex V ) ) , Carbonyl Cyanide-p-trifluoromethoxyphenylhydrazone ( FCCP 5 μM , uncoupling agent ) and a mix of antimycin A ( AA , complex III inhibitor ) and rotenone ( Complex I inhibitor ) at a final concentration of 5 and 1 μM , respectively were injected sequentially through ports in the seahorse flux pack cartridges . Oxygen consumption rates ( OCR ) were analyzed for control and infected cardiomyocytes [31] . At least 5 replicates of each condition per plate and three independent replicates were analyzed . The non-mitochondrial oxygen consumption obtained after AA/Rotenone addition were subtracted to all OCR values . The mitochondrial respiratory control index ( RCI ) was calculated as the OCR value with FCCP divided by the OCR value with oligomycin ( FCCP/Oligomycin ) . For the identification and quantification of anti-oxidant enzymes produced by the different T . cruzi populations , 1x107 epimastigote forms previously incubated or not with 30 μM H2O2 ( 30 min ) were fixed with 3 . 7% ( v/v ) formaldehyde in PBS , centrifuged at 12 , 000 g at room temperature ( RT ) , resuspended in a solution containing 0 . 1% ( v/v ) Triton in PBS and incubated for 30 minutes at RT for permeabilization . After permeabilization , samples were centrifuged and incubated overnight at 4°C with a 1/100 dilution of each of the rabbit polyclonal antibodies raised towards the different antioxidant enzymes analyzed ( Ascorbate peroxidase , APX; Mitochondrial Peroxiredoxin , MPX; Trypanothione reductase , TR; Trypanothione synthetase , TS; mitochondrial iron superoxide dismutase A , FeSOD-A and cytosolic iron superoxide dismutase B , FeSOD-B ) , in PBS containing 0 . 1% ( w/v ) BSA and 0 . 5% ( v/v ) Tween ( PBS/BSA/Tween ) . After this , samples were centrifuged , washed in PBS/BSA/Tween and incubated for 90 minutes with Alexa-Fluor 488-labeled anti-rabbit IgG secondary antibody ( anti-APX , MPX , TR and TS ) or anti-mouse IgG ( anti-SODA and SODB ) diluted 1:100 in PBS/BSA/Tween . After incubation with the secondary antibody , the samples were centrifuged , washed with PBS/BSA/Tween , re-suspended in the same solution and read on BD FACSCan or BD FACSCalibur flow cytometer . Acquired data were analyzed using the BD CellQuest Pro 6 . 0 or FlowJo program . For intracellular calcium measurements , 5x107 parasites/mL were loaded with 5 μM fura-2AM at 28–30°C in fura buffer ( 116 mM NaC1 , 5 . 4 mM KC1 , 0 . 8 mM MgSO4 , 5 . 5 mM glucose , 1 mM CaC12 , and 50 mM Hepes , pH 7 . 0 ) . After 1h , cells were washed and re-suspended in PBS and exposed or not to H2O2-treatment as described earlier . Afterwards cells were washed once in PBS , re-suspended in fura buffer and fluorescence determined in 107 cells/mL in fura buffer in a Hitachi F2500 fluorescence spectrophotometer with continuous stirring ( excitation at 340 and 380 nm and emission at 510 nm ) [32 , 33] . For determination of epimastigote oxidant production in control and/or after H2O2 treatment , parasites ( 3 x108/mL ) were loaded in Krebs-Henseleit buffer ( KH buffer , 15 mM NaCO3 , 5 mM KCl , 120 mM NaCl , 0 . 7 mM Na2HPO4 , 1 . 5 mM NaH2PO4 ) at 28°C with 5 μM MitoSOX ( 3 , 8-phenanthridinediamine , 5- ( 6-triphenylphosphoniumhexyl ) -5 , 6-dihydro-6- phenyl , Molecular Probes ) . After 10 min of incubation with the probe , the cells were washed and re-suspended in KH buffer . Cells were then incubated with H2O2 for 30 min and washed , as described . The detection of oxidized MitoSOX ( oxMitoSOX ) in 5x107 cells/mL was performed in this buffer in the presence of 40 μM digitonin and 5 mM succinate to determine the production of these species by the mitochondrial respiratory chain . The fluorescence was detected using a Cytation 5 microplate reader with excitation and emission wavelengths of 510 and 580 nm , respectively [34] .
In order to confirm previous results obtained from primary embryonic cardiomyocyte cultures infected with JG or Col1 . 7G2 [14] , cultures obtained from neonatal BALB/c mice were submitted to infection with the same T . cruzi populations and invasion and intracellular multiplication rates were analyzed . A different behavior regarding cell invasion was observed . JG infection rate was in this case higher than Col1 . 7G2 . The number of infected cells was about 4 . 5 times higher for cultures exposed to JG , when compared to cultures infected with Col1 . 7G2 ( Fig 1A ) . Intracellular multiplication rates , on the other hand , were in accordance with previous results from Andrade and colleagues ( 2010 ) [14] . Seventy-two hours post infection we observed that the number of intracellular parasites in JG infected cultures were higher than the number of intracellular parasites in cultures infected with Col1 . 7G2 ( Fig 1B ) . 72 hours after exposure to the parasites , cultures infected with JG showed an approximately 2 . 5-fold increase in the number of intracellular parasites relative to those cultures infected with Col1 . 7G2 ( Fig 1B ) . Fig 1C shows representative images of the number of intracellular parasites in cardiomyocyte 72 hours post invasion with each of the parasite populations . These results indicate that independently of the invasion rate , JG shows a better intracellular development in these cells when compared to Col1 . 7G2 ( as noted by the slope of the curve ) . As it is known , when T . cruzi trypomastigotes invade the host cell it first resides in a parasitophorous vacuole , formed by lysosomal membrane , and later escapes from this vacuole falling into the host cell cytosol , where it transforms into the amastigote replicative form . Therefore the kinetics of parasitophorous vacuole escape could alter the transformation of internalized trypomastigotes into amastigote forms and consequently parasite intracellular development . In order to determine the kinetics of parasitophorous vacuole escape for JG and Col1 . 7G2 in primary neonatal cardiomyocyte cultures , infected cultures were washed and fixed at 0 , 4 , 8 or 12 hours after exposure to the parasites , as described . Parasites labeled with DAPI and lacking anti-T . cruzi antibody labeling were considered as intracellular parasites . To evaluate the proportion of intracellular parasites associated with the parasitophorous vacuole , the cells were also labeled with anti-LAMP-1 antibody , a protein present on the lysosomal membrane . Thus , intracellular parasites that co-localized with this marker were counted as inside the vacuole , the other intracellular parasites were considered free in the cytoplasm . The number of parasites associated with the lysosomal marker , LAMP-1 , shortly after ( 0h ) , 4 , 8 and 12 hours after cell exposure to parasites is shown in Fig 2 . No significant difference was observed in the kinetics of vacuole escape between JG and Col1 . 7G2 . Soon after the invasion , for both T . cruzi populations , around 50% of the internalized parasites are associated with LAMP ( Fig 2A ) . Four hours after parasite removal , the number of parasites associated with LAMP reaches 100% . This is due to the fact that the abundance of lysosomal markers associated to the vacuole increases in the first moments after the invasion , facilitating its visualization . Later , 8 to 12 hours post invasion , the number of parasites associated with LAMP starts to drop for both JG and Col1 . 7G2 infections , indicating that the parasites are escaping from the vacuole into the cytosol ( Fig 2A ) . Representative images of parasites inside ( 4 hours ) or outside the vacuole ( 12 hours ) , for both JG and Col1 . 7G2 , are shown in Fig 2B . In order to identify other possible factors that could account for the differential growth rate of JG and Col1 . 7G2 in neonatal cardiomyocytes , we evaluated the levels of production of oxidants in these cells upon infection with the two T . cruzi clonal populations . It is known that cardiomyocyte infection by the parasite can induce the production of ROS , which can modulate the intracellular development of the parasite [35 , 36] . Analysis of the oxidant levels produced upon infection with JG and Col1 . 7G2 in cardiomyocyte cultures was done using the CM-H2DCFDA probe added to infected cultures , as described in material and methods . When oxidized , CM-H2DCFDA fluoresces and the amount of fluorescence produced is an indirect measure of the cellular production of ROS . CM-H2DCFDA fluorescence was measured 48 or 72 hours post infection , the period corresponding to the intracellular multiplication phase of the parasite . Forty-eight hours post infection no significant difference in the amount of oxidized probe was observed for those cultures infected with Col1 . 7G2 , relative to the control ( uninfected cultures ) ( Fig 3A ) . On the other hand , at the same time the levels of CM-H2DCFDA oxidation were about 1 . 6 fold higher for JG infected cultures when compared to the control or cultures infected with Col1 . 7G2 , indicating increased oxidant production after infection with JG ( Fig 3A ) . At 72 hours , both JG and Col1 . 7G2 infected cultures showed significantly higher levels of CM-H2DCFDA oxidization when compared to control non-infected cultures . ( Fig 3A ) . However , the levels of oxidized CM-H2DCFDA in JG-infected cultures were still significantly higher than that observed for cultures infected with Col1 . 7G2 ( Fig 3A ) . It had been shown that oxidant production by T . cruzi infected cardiomyocytes could come from mitochondrial dysfunction [23] . Thus , we evaluated the mitochondrial function in cultures of primary mouse cardiomyocytes infected or not with trypomastigote forms of Col1 . 7G2 or JG . The respiratory control index ( RCI ) allows the evaluation of the mitochondrial capacity of substrate oxidation with low proton loss . Thus , the higher the RCI the lower is mitochondrial dysfunction and oxidant production . RCI measurements 48 hours post infection revealed greater mitochondrial impairment in cultures infected with JG . While cardiomyocytes infected with Col1 . 7G2 showed a small increase in the RCI when compared to control non-infected cultures , cardiomyocyte cultures infected with JG showed a significantly lower RCI when compared to control or Col1 . 7G2 infected cultures ( Fig 3B ) . These results are in agreement with the higher rates of CM-H2DCFDA probe oxidation , indicating greater mitochondrial dysfunction and consequently higher production of oxidizing species cardiomyocyte cultures infected with JG . To assess the ability of JG and Col1 . 7G2 to cope with ROS produced upon infection in cardiomyocytes , the basal levels of different parasite anti-oxidant enzymes were assayed . Polyclonal antibodies directed to each of the anti-oxidant enzymes were used to label the parasites and the amount of labeling was read in a flow cytometer . Fig 4A shows the histograms of fluorescence intensities obtained in the epimastigote stage , for each T . cruzi population , for the different enzymes . The higher the expression of the enzymes the higher the number of cells presenting high levels of fluorescence . APX , MPX and TS anti-oxidant enzymes were found in higher amounts in the epimastigote forms of Col1 . 7G2 when compared to the same forms of JG in control conditions ( Fig 4A ) . To determine if the profile of the antioxidant enzyme production by JG and Col1 . 7G2 would be the same after exposure of the parasites to oxidative stress , epimastigote forms from JG and Col1 . 7G2 were treated with H2O2 prior to the evaluation of enzyme expression . Even after exposure to the oxidant , a higher content of antioxidant enzymes was found for epimastigote forms of clone Col1 . 7G2 . In this condition , higher expression of MPX , Fe-SODA and TR were found in Col1 . 7G2 epimastigotes when compared to JG ( Fig 4B ) . Therefore , higher amounts of anti-oxidant enzyme was only observed for Col1 . 7G2 , never for JG , either before or after exposure to an oxidative environment , indicating that JG could be more susceptible to oxidative stress . The results obtained above show that JG induces more ROS in infections of BALB/c neonatal cardiomyocyte cultures and has less anti-oxidant enzymes contents when compared to Col1 . 7G2 . Nonetheless , intracellular multiplication of JG in these cells is faster when compared to Col1 . 7G2 . These data suggest that , ROS production and oxidative stress generated during infection in cardiomyocytes may trigger JG intracellular development . To test this hypothesis we decided to investigate whether decreasing reactive oxygen species , such as hydrogen peroxide ( H2O2 ) by treatment of cardiomyocyte cultures with catalase during T . cruzi infection , could interfere with the intracellular development of JG and or Col1 . 7G2 in these cells . For this , cultures of BALB/c neonatal cardiomyocytes were incubated or not with catalase , as described in the methodology . After treatment with catalase , a statistically significant decrease in the intracellular growth rate of JG was observed . JG infected cells had lower number of intracellular parasites along the course of infection when compared to non-treated cells ( Fig 5A and 5C ) . Seventy-two hours post infection , the number of JG intracellular parasites for cultures treated with catalase was around 1 . 5 times lower than that obtained for control non-treated cultures , indicating a poorer intracellular development in a less oxidative environment ( Fig 5A ) . On the other hand , treatment of cultures with catalase did not interfere with Col1 . 7G2 intracellular growth ( Fig 5B and 5C ) , since the number of intracellular parasites along the course of infection was very similar in both treated and catalase treated conditions . These results suggest that the oxidative stress generated by the infection plays an important role in stimulating the intracellular development of the JG strain . The data obtained for primary cultures of cardiomyocytes from BALB/c mice suggest that the oxidative stress generated during infection benefits the growth of JG in these cultures . In order to verify if this data could be reproduced in cardiomyocytes from a different source , we performed cultures of human cardiomyocytes obtained from induced pluripotent human stem cells . First , the cultures of human cardiomyocytes were submitted to the same infection methodology with Col1 . 7G2 and JG and the rates of invasion and intracellular multiplication were evaluated . In these cultures , the rate of invasion observed for the two T . cruzi clonal populations , JG and Col1 . 7G2 , was similar to the results previously obtained by Andrade et al . ( 2010 ) , where Col1 . 7G2 had a higher number of infected cells when compared to cultures infected with JG ( Fig 6A ) . With respect to parasite growth , 72 hours post-infection , JG-infected cultures showed higher intracellular proliferation rates ( 2 . 14 times ) when compared to cultures infected with Col1 . 7G2 ( Fig 6B ) , reproducing the results obtained by Andrade et al . ( 2010 ) [14] and data obtained here for BALB/c neonatal cardiomyocyte cultures ( Fig 1B ) . JG growth in human cardiomyocyte cultures was about 2 . 14 times greater than Col1 . 7G2 ( Fig 6B ) . Since the profile of JG and Col1 . 7G2 intracellular development in human cardiomyocytes reproduced the data obtained from infections in neonatal BALB/c cardiomyocyte cultures , we decided to investigate the induction of oxidants upon infection of these cells . Evaluation of oxidant production was also performed by incubation of cells with the probe CM-H2DCFDA , 48 hours post infection . Again , no significant difference was observed in the amount of oxidized probe for those cultures infected with Col1 . 7G2 , relative to the control ( uninfected cultures ) ( Fig 6C ) . On the other hand , at the same time a significantly higher amount of probe oxidation , about 1 . 52 fold higher , was observed for those cultures infected with JG , relative to the control or about 1 . 29 fold higher when compared to cultures infected with Col1 . 7G2 , also indicating higher production of ROS upon infection of these cells with JG ( Fig 6C ) . Additionally , we also investigated whether inhibition of oxidative stress would affect JG or Col1 . 7G2 infection in these cultures . While JG multiplied better than Col1 . 7G2 in non-treated cultures ( Fig 6B ) , its growth was lower than Col1 . 7G2 in catalase treated cultures ( Fig 6D ) . We also compared JG and Col1 . 7G2 intracellular growth obtained from experiments performed in human cardiomyocyte non-treated cultures ( Fig 6B ) with the new data obtained from the experiments performed with human cardiomyocytes treated with catalase ( Fig 6D ) , which are shown in Fig 6E . As observed , a decrease in JG intracellular growth , but not in Col1 . 7G2 is found upon catalase treatment . These results imply that JG is also more responsive than Col1 . 7G2 to the repressive effects of catalase when infecting human cardiomyocyte cultures and reinforce the idea that the oxidative stress generated by the infection plays an important role in the intracellular development of at least some T . cruzi strain . We next investigated whether infection in a different cell type , would alter JG and Col1 . 7G2 intracellular behavior . For this , we performed infections with JG and Col1 . 7G2 in immortalized mouse embryonic fibroblasts ( MEFs ) and evaluated parasite intracellular growth and oxidant production upon infection . Fig 7A shows the number of intracellular parasites per infected cell over a total period of 72 hours of infection in MEFs . As can be observed , there was no significant difference in the number of intracellular parasites between Col1 . 7G2 and JG in any of the analyzed points , being the growth curve of both T . cruzi populations similar to each other . In order to confirm that these cells did not respond to infection with oxidant generation , analysis of CM-H2DCFDA oxidization , 48 hours post infection with JG or Col1 . 7G2 , was performed . For both cultures no statistically significant difference was observed in the levels of oxidized CM-H2DCFDA among control non-infected cultures and those infected with Col1 . 7G2 or JG ( Fig 7B ) . The above results suggest that oxidative stress may play in JG strain a role in the intracellular development of the parasite and may , in specific situations , be beneficial to its intracellular development . In the latter , oxidative stress could work as a signal triggering parasite intracellular growth [36 , 37] . It has been shown in the literature that the increase in free intracellular Ca2+ levels in the cytoplasm of T . cruzi may represent an important signal , leading to an increase in the infective capacity of this parasite [33] . It has also recently been shown that the decrease of IP3 receptor expression in the parasite leads to a decrease not only in the infectivity , but also in parasite intracellular growth [38] . Thus , we decided to verify whether exposure of Col1 . 7G2 and JG to oxidative stress , by incubation with H2O2 , could induce calcium signals in these parasites . Baseline levels of intracellular calcium , before treatment with H2O2 ( control ) , were significantly higher for Col1 . 7G2 , when compared to JG ( Fig 8A ) . However , upon H2O2 treatment , only JG was capable of increasing the Ca2+ levels , as observed for Trypanosoma brucei [39] ( Fig 8A ) . Another important signaling molecule is superoxide radical ( O2•- ) . It has been shown that high levels O2•- is deleterious to cells , however in adequate concentrations O2•- may function as a stimulator of cell growth , as well as to inhibit apoptotic pathways [40 , 41] . Thus , we also investigated the influence of H2O2 treatment on O2•-/H2O2 levels in epimastigote forms of JG and Col1 . 7G2 following MitoSOX probe oxidation . Baseline levels of MitoSOX oxidation for Col1 . 7G2 were very low , significantly lower than those observed for JG ( Fig 8B ) . On the other hand , oxidant treatment of JG led to an increase in MitoSOX oxidation ( Fig 8B ) . The above result may indicate that oxidant treatment may , by some unknown mechanism , enhance parasite O2•- production in the JG T . cruzi strain . Both intracellular O2•- and or H2O2 may be in part , responsible for the cellular signaling that boost parasite proliferation .
One of the great questions regarding T . cruzi infection is what defines the development or not of serious clinical forms resulting from the infection . As mentioned previously , T . cruzi infection in humans has a very variable clinical course , in which infected individuals may be asymptomatic or even develop severe clinical symptoms , presenting cardiac , digestive or cardio-digestive disorders ( reviewed by [42] ) . Understanding the mechanisms involved with the pathogenesis of this disease is essential for better control of the infection . Evidence from the literature shows that this clinical variability is related to a differential tissue tropism of parasite populations , which depends directly on the parasite-host cell interaction , without direct interference of the immune system [11 , 13 , 43 , 44] . Cellular infection can be divided into two stages: invasion and intracellular multiplication . According to previous data from our group , intracellular multiplication seems to be of fundamental importance for the definition of this selection [14] . Studying the behavior of two clonal populations Col1 . 7G2 ( T . cruzi I ) and JG ( T . cruzi II ) of T . cruzi during infection in primary cultures of BALB/c embryonic cardiomyocytes it was observed that JG , a T . cruzi strain with strong tropism to BALB/c hearts , presented higher intracellular multiplication rates in these cells when compared to Col1 . 7G2 [11 , 14] . In order to investigate the factors influencing this differential intracellular behavior we used as a study model the in vitro infection of primary cultures of BALB/c cardiomyocytes with the same T . cruzi clonal populations used in the previous studies , JG and Col1 . 7G2 . However , this time the cardiomyocytes were isolated from neonatal mice . Regarding invasion rates , the data obtained here diverged from the data previously published by Andrade et al . ( 2010 ) [14] . This divergence may be related to the fact that , although from the same type of animal , the stage of differentiation of the cells was distinct from the work published earlier [14] . Cardiomyocytes isolated from neonatal mice may express distinct proteins from the ones obtained from mouse embryos , which could account for the differences in invasion rates observed for these two cells [45] . Nonetheless , in BALB/c neonatal cardiomyocytes , JG still presented a higher multiplication rate when compared to Col1 . 7G2 . These data reinforce the idea that the intracellular development of the parasite , as suggested before [14] , may be more important for the determination of T . cruzi tissue tropism than cellular invasion itself . This hypothesis is also supported by the data obtained here from JG and Col1 . 7G2 infections in human cardiomyocyte cultures derived from iPSCs . Upon infection in these cells , even though Col1 . 7G2 invasion rates were higher than JG , parasite intracellular growth was greater for JG infected cultures . Several factors could affect the intracellular development of T . cruzi , among them its intracellular traffic . During cell infection , T . cruzi uses the cell membrane repair mechanism to promote its internalization in non-professional phagocytic cells , forming a vacuole containing lysosomal markers and content [46 , 47] . The acidic content of the vacuole allows the parasite to gradually escape into the cytoplasm of the cell , where it completes its transformation into the amastigote form and initiates its intracellular multiplication [15 , 48–51] . It was possible that a faster escape could advance the transformation of the parasite into the amastigote form and thus allow it to start its multiplication sooner . In fact , trans-sialidase superexpressor parasites , which escape faster from their parasitophorous vacuoles , differentiate into the amastigote earlier than wild type parasites [52] . Our data showed that , there was no difference in the rate of parasitophorous vacuole escape between Col1 . 7G2 and JG . Therefore this could not account for the differences in parasite intracellular development observed in the cardiomyocytes . Data from the literature show that infected cells are able to respond to infection by activating several genes , which could interfere with the intracellular behavior of the parasite [19 , 20 , 22 , 24] . Thus , we decided to evaluate the response of the host cell to infection , trying to correlate this data with the intracellular development of T . cruzi . For this , we investigated whether the production of ROS upon infection could be responsible for the differential intracellular growth of JG and Col1 . 7G2 in the studied cardiomyocyte cultures . It had already been shown that infection of cardiomyocytes with T . cruzi leads to a disturbance in the membrane potential of the mitochondria generating ROS [23] . In fact , by using the CM-H2DCFDA probe , we observed an increase in ROS production in primary cultures of BALB/c and human cardiomyocytes infected with Col1 . 7G2 ( 72 hours ) or JG ( 48 and 72 hours ) post infection , when compared to control uninfected cultures . Additionally , for JG infected cultures , a higher amount of ROS was produced 48 hours post infection , indicating a faster and stronger response of cardiomyocytes infected to this clonal population . The high increase in ROS detected in BALB/c cardiomyocyte cultures 48 hours post infection with JG was likely generated by mitochondrial dysfunction as revealed by the analysis of the respiratory control index ( RCI ) in BALB/c cardiomyocyte infected cultures , although other sources cannot be ruled out . At this time post infection , cultures infected with JG presented a significant decrease in the RCI . The RCI is the best general measure of mitochondrial function in cell populations that have sufficiently active glycolysis to support metabolism , while mitochondrial function is manipulated [31] . Thus , a decrease in RCI does indicate mitochondrial dysfunction . These results are in agreement with previous data from the literature showing that upon infection with T . cuzi mitochondrial potential is disturbed , inducing ROS production [23] . On the other hand , we could not detect mitochondrial dysfunction in Col . 17G2 infected cardiomyocytes 48 hours post infection . In agreement with this , at this time post infection , we could also not detect an increase in ROS in Col1 . 7G2 infected cardiomyocytes , when compared to control non-infected cultures . We are not sure why Col1 . 7G2 did not cause changes in cardiomyocyte mitochondrial RCI 48 hours post infection , but it may have to do with differences in the strains used . It is well known that T . cruzi populations do vary in their behavior during infection in cells , which may account for changes in their ability to interfere with mitochondrial function . In fact , although lower than that observed upon JG infection , we did observe changes in the RCI 72 hours post infection . So it is possible that upon infection with this strain there is a delay in the induction of mitochondrial dysfunction and generation of oxidative stress . In fact , there is data in the literature showing that hearts of BALB/c mice infected with Colombian strain do present a significant increase in the oxidative stress [53] . T . cruzi has a sophisticated system of antioxidant defenses to protect parasite from oxidative stress [54] . Interestingly , for epimastigote forms , none of the anti-oxidant enzymes evaluated were more expressed in JG when compared to Col1 . 7G2 . In fact , when there was a difference in enzyme expression , the higher expression was found in Col1 . 7G2 . This was also the case for parasites that were previously exposed to oxidative stress by incubation with H2O2 , which had been already shown to induce an increase in anti-oxidant enzyme levels [55] . In fact , for Coll . 72G parasites previously exposed to H2O2 , we observed an increase in Fe-SODA and MPX levels , showing that H2O2 was effective in inducing an increase in enzyme expression . So far , JG induced more ROS production in cardiomyocytes and was likely to be more susceptible to this generated oxidative stress . ROS has been shown to have a dual effect on cells . Although data from the literature show that an increase in ROS can compromise the intracellular growth of several pathogens , including T . cruzi , the opposite has also shown to be true [56–61] . It has recently been demonstrated that the oxidative stress generated by T . cruzi infection can lead to an increase in the replication rate of this parasite [36 , 37] . One of the possible explanations for the increase in the rate of parasite replication upon induction of oxidative stress could be the bioavailability of iron for use by the parasite [36 , 62] . It is known that iron is important for several metabolic events , such as DNA replication , mitochondrial respiration and anti-oxidant defense [63] . Thus , although amastigote forms of the parasite have been shown to be capable of binding and importing transferrin [64] in the intracellular environment , the concentration of this protein is very low . It is possible that free iron is more easily acquired in this way and then contributes to a better adaptation of this parasite to the intracellular environment . In the case of trypanosomatids , superoxide dismutases , important anti-oxidant enzymes , are iron-dependent [65] . Alternatively , the presence of oxidative stress could generate specific signals that would contribute to a more adequate response of the parasite in the cell , stimulating its faster replication . In fact , Finzi et al . ( 2004 ) also showed that pre-treatment of T . cruzi epimastigote forms with low concentrations of H2O2 increased parasite proliferation [66] . Considering the above , in our case , ROS seems to be important to give JG advantage during infection in cardiomyocytes . This is reinforced by the fact that inhibition of ROS by incubation of mouse and human cardiomyocytes with catalase inhibits JG intracellular growth . In fact , the inhibition of JG growth seemed even more prominent in human cardiomyocyte cultures . However distinct batches of catalase were used in the two experiments , which could account for this difference . Nonetheless , these results imply that at least for some strains ROS may be involved in parasite intracellular growth . Additionally , this is also corroborated by the data obtained from infected fibroblasts , which in our experimental conditions do not produce ROS in response to infection . In these cells JG did not present any growth advantage when compared to Col1 . 7G2 . Previous studies have shown that a recombinant strain of T . cruzi , an E . coli MutT superexpressor ( an enzyme involved in DNA repair ) , is more efficient in cell colonization compared to wild type parasites . In that work it was suggested that 8-oxo-GMP , generated by degradation of 8-oxo-GTP by MutT , could serve as signal to produce parasites more adapted to the intracellular environment [67] . It has also been shown that low concentrations of ROS were sufficient to promote better infection in in vitro and in vivo experiments [37] . Overall , our results suggest that ROS may have , in some specific circumstances , a helpful role in T . cruzi cell proliferation in non-professional phagocytes , which had not been shown before . Recently , Vilar-Pereira and colleagues [53] have studied the role of antioxidants in cardiac function during T . cruzi infection in mice . For this they evaluated the production of ROS in the hearts of BALB/c mice infected with Colombian strain , by intravital microscopy at the chronic stage , before and after the treatment with different antioxidant agents . In this case , as mentioned before in this discussion , they found high amounts of oxidative stress in hearts of non-treated mice , possibly due to the fact that the experiments were performed in vivo at later time points , after several rounds of parasite infection . Additionally they showed electrical and mechanical dysfunction in these infected mice [53] . Treatment with different antioxidants was able to in fact improve hart function . However , they also demonstrated that only treatment with resveratrol was able to reduce parasite burden , but not treatment with the other antioxidant drugs . This is in contrast with previous findings from the same group showing that heart parasite burden is decreased in response to antioxidant treatment [36] . However in the latter they have used T . cruzi Y strain . Colombian and Y strain , such as the clone of Colombian strain ( Col1 . 7G2 ) and JG used in our study , belong to two different T . cruzi lineages , T . cruzi I and II , respectively . It is possible that parasites from different lineages do respond differently to ROS . In this case , for T . cruzi II strains ROS may not have an effect in controlling or signaling to this parasite and that the effect of resveratrol may be by another signaling pathway , while T . cruzi I strains would be responsive to ROS . Our data supports this hypothesis since treatment does affect the T . cruzi I population , but not T . cruzi II . Whether T . cruzi II strains do not really respond to ROS or whether this response depends on the amount or the type of response triggered by the ROS production still remains to be elucidated . There are several data in the literature showing that the presence of oxidative stress could generate signaling molecules . Here we show that parasite treatment with H2O2 , leads to an increase in intracellular levels of calcium and also probably in O2•- and/or H2O2 production in JG , but not in Col1 . 7G2 T . cruzi clone , reinforcing that JG is more responsive to ROS , at least in this condition . Both molecules have been shown to interfere with cell death and replication [41 , 68] . In relation to calcium levels , it has been shown that in epimastigotes the regulation of intracellular calcium is important for multiplication and metacyclogenesis [69] . In the literature it has also been shown that ROS are capable of increasing intracellular Ca2+ in parasites such as Trypanosoma brucei [39] . To the best of our knowledge this is the first time that it is shown that T . cruzi , in this case JG strain , can also respond to oxidative stress by altering intracellular calcium levels . In this case , the increase in calcium levels observed for JG was not sufficient for induction of cell death , but could be important for signaling some pathway related to cell proliferation . With respect to O2•- , there are reports showing that its increase may induce programmed cell death in T . cruzi and that parasites overexpressing mitochondrial Fe-SODA are more resistant [57] . However , there are reports in the literature showing that an increase in the concentration of this molecule may also signal for increased cell proliferation , as well as to work as an inhibitor of apoptotic pathways [40 , 41] . In Dictyostelium discoideum , for example , the overexpression of SOD , with consequent consumption of O2•- , leads to the inhibition of multicellular aggregates [41] . What would determine whether ROS is responsible for death or proliferation would certainly be related to the amount to which parasites are exposed and the ability of the parasite to sense and trigger the intracellular signaling . The data presented in this work suggest a mechanism responsible for the better development of JG , dependent on the parasite response to oxidant production , in cardiomyocyte cultures and may contribute to the understanding of the behavior of T . cruzi populations during infection in the host . | Chagas disease , caused by the protozoan parasite Trypanosoma cruzi , presents a variable clinical course , varying from asymptomatic to serious debilitating pathologies with cardiac , digestive or cardio-digestive impairment . It has been suggested that parasite differential tissue tropism is responsible for the development of the distinct clinical forms . Differences in parasite tissue tropism have been shown previously , using mixed infections in mice with two distinct T . cruzi populations , Col1 . 7G2 ( T . cruzi I ) and JG ( T . cruzi II ) . In these infections hearts were preferentially colonized by JG . Increased JG adaptation to cardiac muscle was later confirmed in infection studies using isolated cardiomyocytes , where it was shown that selection was dependent on parasite intracellular development . However the mechanisms that determined this differential parasite intracellular growth was not described . Here we investigated whether host cell response upon T . cruzi infection was able to modulate parasite multiplication rate inside cells . We showed that , upon infection , cardiomyocytes increase the production of oxidative species , especially in cultures infected with JG and inhibition of oxidative stress severely interfered with the intracellular multiplication rate of JG . Data obtained suggests that JG and Col1 . 7G2 may sense extracellular oxidants in a distinct manner , which would enable JG to develop better inside cardiomyocytes . |
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Genomic DNA copy-number alterations ( CNAs ) are associated with complex diseases , including cancer: CNAs are indeed related to tumoral grade , metastasis , and patient survival . CNAs discovered from array-based comparative genomic hybridization ( aCGH ) data have been instrumental in identifying disease-related genes and potential therapeutic targets . To be immediately useful in both clinical and basic research scenarios , aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question , “What is the probability that this gene/region has CNAs ? ” Current approaches fail , however , to meet these requirements . Here , we introduce reversible jump aCGH ( RJaCGH ) , a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging . RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis . RJaCGH outperforms alternative methods , and the performance difference is even larger with noisy data and highly variable interprobe distance , both commonly found features in aCGH data . Furthermore , our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data . In summary , we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases .
Available methods for the analysis of aCGH fail some or most of these requirements . Smoothing techniques [21 , 23–28] do not use interprobe distance , nor do they provide posterior estimates of the likely state of each probe/clone , and data from each chromosome are analyzed independently of each other . Hidden Markov models ( HMMs ) and related techniques offer a flexible modeling framework , and can provide probabilities of alteration [14–16] . Some HMM-based methods [16 , 19] , however , do not incorporate the distance between probes , assuming instead that interprobe distance is constant . In addition , most of them do not deal satisfactorily with the unknown number of hidden states ( the true number of states of copy number ) . Some methods fix in advance the number of hidden states to three [14 , 15] or four [16]: prespecification of the number of states has the consequence of jumbling all changes involving multiple gains into a single state with a common mean , which is biologically questionable [22] , especially as the resolution of the technology improves . Moreover , the identification of important genes for disease sometimes requires examining the amplitude of CNAs and not just their presence and location [1]; collapsing states into three or four , however , precludes examining in fine enough detail the amplitude of CNAs . A better approach would provide posterior probabilities of the number of states; using such a procedure over many different experiments will tell us whether three- or four-state models are a reasonable simplification . Of those methods that do not assume a fixed number of hidden states [18 , 19 , 22] , one of them [22] cannot be used for questions about the number of hidden states , or for breaking the data into more categories than gained/lost/no change , which are increasingly important questions with higher-resolution techniques and are needed for distinguishing regions of moderate copy gains from regions of large copy gains; see also above for relationship between amplitude of CNAs and presence of disease genes . The remaining two [18 , 19] fit HMMs for a range of number of states and then use Akaike information criterion ( AIC ) –based model selection , but AIC-based selection with HMMs has not been theoretically justified [29] and does not provide a probability of the likely number of states; moreover , selecting a single model leads to underestimation of the true variability in the data . These two methods , in addition , use a final clustering step of hidden states that introduces several ad hoc decisions . We have developed a method , reversible jump aCGH ( RJaCGH ) , that fulfills the three requirements above , and does not suffer from the limitations discussed for other methods . Our method is applicable to aCGH from platforms including ROMA , oligonucleotide aCGH ( oaCGH; including Agilent , NimbleGen , and many noncommercial , in-house oligonucleotide arrays ) , bacterial artificial chromosome ( BAC ) , and cDNA arrays [1 , 13] . We start our modeling by noting that , for a given chromosome or genome , the copy numbers of genomic DNA ( e . g . , 0 , 1 , 2 copies , . . . ) of different probes or segments are an unknown finite number . Thus , probes or segments could be classified into several groups with respect to their ( unknown ) copy number . In addition , as mentioned above , we expect that the copy number of a probe will be similar to the copy number of its closest neighbors , with that expected similarity decreasing when probes are farther apart . Finally , for a given copy number , the aCGH fluorescence ratios should be centered around a log2 value , with some random noise . We want to use the observed log-ratios to identify regions with altered copy number . The biological features of this model ( a finite number of unknown or hidden states that are indirectly measured , with states of close elements likely to be similar , and variable distances between probes ) can be modeled with a nonhomogeneous HMM [29] . To provide a direct estimate of the probability that a given probe or region has an altered copy number , we use a Bayesian model computed via Markov chain Monte Carlo ( MCMC ) . Since we do not know the true number of hidden states , we fit models with varying numbers of hidden states and , to allow for transdimensional moves between models with different numbers of states , we used reversible jump [30] . After running a large number of MCMC iterations , we can summarize the posterior probabilities . First , we obtain posterior probabilities for the number of states . Conditional on a given number of states , each model provides posterior distributions of the parameters of interest ( e . g . , means , variances , transition matrices ) . From the latter , we can obtain posterior probabilities that a probe is gained or lost . To obtain our final estimates , we incorporate the uncertainty in model selection by using Bayesian model averaging [17] , with estimates weighted by the posterior probability of each number of states , for the probabilities of probes being gained or lost . We call the complete statistical method RJaCGH ( from reversible jump–based analysis of aCGH data ) .
We applied RJaCGH and the best performing alternative methods ( based on two recent reviews [20 , 31] ) to the 500 simulated datasets of [31] ( see also Protocol S1 ) . These are data “ . . . simulated to emulate the complexity of real tumor profiles” and designed to become “ . . . a standard for systematic comparisons of computational segmentation approaches , ” [31] and are not data simulated under our own model . To assess the effect of variable interprobe distance , we randomly deleted data points ( see details in Protocol S1 ) so that each original simulated dataset gives rise to another four datasets with ( an average of ) 10% , 25% , 50% , and 65% of observations missing . The length of these gaps is modeled by a Poisson distribution , so larger percentages of missing data correspond to larger variability in interprobe distances . Results in Figure 1 ( see also Figure 1 in Protocol S1 ) show the excellent performance of RJaCGH , and how it outperforms alternative methods . Moreover , Figure 2 ( see also Figures 2 and 3 in Protocol S1 ) shows that the difference between RJaCGH and alternative approaches is accentuated when we consider jointly the effects of noise and variability in interprobe distance . Analysis using three other performance statistics ( false discovery rate , sensitivity , and specificity ) show the same overall patterns ( see Protocol S1 , Figures 2 and 3 ) : for some specific statistics , RJaCGH can be second ( but very close ) to another approach; this other approach , however , performs poorly with respect to the remaining statistics . This paper focuses on the statistical performance of the methods compared . In terms of speed , nevertheless , our approach is clearly the slowest one . We are currently working on improving the speed of the execution both by using more efficient algorithms and by using parallel computing . Similar results are obtained when applying these methods to a real dataset of nine cell lines [32] , and when comparing the predicted ploidy with the known ploidy ( see Protocol S1 , Figure 4 ) . Overall , therefore , there is strong evidence that RJaCGH is the best performing of the existing methods .
The excellent performance of RJaCGH is a result of the statistical method used , which is essentially a careful and rigorous development from first principles . We set out to obtain a method that allows us to seamlessly incorporate interprobe distances ( to allow usage over varied technological platforms ) , that makes no untenable assumptions about the true number of copy levels ( since this is likely to vary between datasets ) , that permits analysis at the chromosome and the genome level , and , finally , that returns posterior probabilities of alteration , because these posterior probabilities constitute the direct answer to the basic biomedical question ( “Is this gene likely to have an altered copy number ? ” ) . Based simply on our usage of interprobe distance , we should expect RJaCGH to perform better than all alternative approaches , with the possible exception of BioHMM [18] , as interprobe distance variability increases . Moreover , RJaCGH adapts to variable noise in the data , without the need for fine-tuning of parameters ( all results reported are obtained from the default settings of RJaCGH ) . As noted above , the relative advantage of RJaCGH increases as the interprobe variability increases and the noise in the data increases , which shows that our theoretical developments have practical consequences and emphasizes the importance of both accounting for interprobe distance and appropriately modeling variance in the data . In addition , we use Bayesian model averaging , which has been repeatedly shown [33] not only to account for uncertainty in model selection but also to lead to point estimators and predictions that minimize mean square error . On its own , our usage of Bayesian model averaging could be largely responsible for the better performance of RJaCGH over all other methods , even in the absence of interprobe distance variability and when there is low noise in the data ( left of Figure 1 , and left of bottom-row panels in Figure 2 ) . In addition , reversible jump allows us to consider a variety of models ( regarding number of states ) , and its birth and split moves are also beneficial for a more thorough exploration of the posterior probability ( within a model with a given number of states ) when the density is multimodal . Finally , our method , in contrast to other approaches ( e . g . , DNAcopy ) , can identify single-clone aberrations , which might be key for large-scale genomic deregulation if the single-clone aberrations affect certain specific genes or promoters; for example , the inability to detect single-gene alterations is shown to have an effect in a study of pancreatic adenocarcinoma [5] , where the loss of the SMAD4 tumor suppressor is undetected . In addition to features that can be compared with other methods , RJaCGH has two unique features that set it apart from most alternative approaches . First , the user can analyze data at either the genome or the chromosome level , thus addressing different types of questions . Some approaches ( e . g . , BioHMM , HMM , GLAD , DNAcopy ) allow us to perform genome-wide inferences , but they use essentially an ad hoc postprocessing of results of analysis that is conducted at the chromosome level . Finally , one of the main features of RJaCGH , its returning of posterior probabilities of CNAs , simply cannot be compared with most alternative methods as they do not provide this type of output . What most alternative approaches return are smoothed means , p-values , or a classification into states without any assessment of the uncertainty of this assignment to states . But a probability of alteration ( which RJaCGH returns ) is much easier to interpret and to use ( with possibly different thresholds depending on the type of research question ) , and is often the direct answer to the basic biomedical question . The few alternative approaches that return probabilities of alteration [14–16] all make the untenable assumption that the true number of biological states of alteration is three [14 , 15] or four [16] . Directly returning probabilities of alterations has profound consequences , both for current practices and for future developments . As argued above , these probabilities are the direct answer to the question “Does this gene have an altered copy number ? ”; p-values or smoothed means are not a direct ( and often not even an indirect ) answer to that question . In addition , the improvement in the resolution of aCGH techniques [2 , 13] is increasingly allowing for multiple assayed spots per gene . Probabilities of alteration for each spot can be combined to find the gene-level probability of alteration , a distinct advantage over smoothed means or p-values . For currently active research areas , the availability of rigorously obtained probabilities of alterations has far-reaching consequences , both in terms of the biological phenomena that can be exposed and as an avenue of further research . First , the availability of probabilities of alteration should improve the identification of regions with consistent alterations across samples [34 , 35] in a statistically rigorous way ( including , if needed , control of false discovery rate ) , and the detection of subgroups of samples according to recurrence patterns [4 , 35 , 36] . Critical disease genes are often located in CNAs that are recurrent across individuals and that have at least some high-amplitude changes [1 , 35 , 37] , and analysis of aCGH data has allowed us to identify subgroups , within established diseases , that could have therapeutic relevance ( e . g . , in glioblastoma [4] ) . Available methods use the assignment of each gene to one state ( equivalent to assuming that there is complete certainty in this assignment ) ; however , we would not want to give the same weight , when looking for minimal common regions , to a gene with a probability of being gained of 51% and to a gene with a probability of being gained of 90% , since this practice will lead to a coarser definition of boundaries and can even preclude the detection of some minimal regions altogether . The inherent limitations of methods that use a simple categorization into gain/loss/no change with an assumed 100% certainty have already been recognized by some of the developers of such methods [35] . Moreover , incorporation of amplitude of change , which might be a crucial feature of minimal common regions that harbor critical disease genes [1 , 5 , 35 , 37] , is not feasible with some methods [34] , but should be straightforward by combining posterior probabilities and posterior means of each state , as returned from RJaCGH . Second , posterior probabilities of being in a specific state , together with the estimated posterior mean of each state , can be used as the basis for a statistically rigorous and biologically sound approach for identifying breakpoints . At present , the identification of breakpoints depends completely on the resolution of the method , and does not allow us to combine the probability of membership in different states with the biological relevance of an estimated mean difference; however , the precise definition of boundaries and amplification maxima are important not only for the study of genomic copy numbers , but also for understanding the relationship between aCGH and expression data [38] . Third , the model of RJaCGH can be extended to provide rigorous downstream analysis of aCGH , including patient classification [1 , 31] and the integration of gene expression and proteomic data [12 , 31] . CNA data analysis , compared with mRNA expression data , can be performed on formalin-fixed paraffin-embedded material , and CNAs define key events that drive tumorigenesis , and thus are probably more valuable as prognostic markers and as predictors of treatment response [39 , 40] . Improved resolution of CNA data analysis , however , can be crucial in obtaining very valuable classifiers , as evidenced from the “almost success” of some studies attempting to differentiate BRCA2 from BRCA1 , BRCAX , and sporadic cases in breast tumors ( see discussion in [40] ) ; the finer resolution provided by probabilities and posterior mean estimates might be pivotal here . Incorporating expression and proteomic data , on the other hand , is the basis for the identification of copy-number changes that are significant in the development of disease [1 , 41 , 42] . Since changes in copy number are not always reflected in changes in expression [1 , 5] , analytical methods that provide finer resolution are crucial . Moreover , within a probabilistic framework it is possible to systematically and rigorously address questions of how CNAs in a given chromosome affect expression changes in genes located in other chromosomes , an increasingly important research question [43] . Finally , the posterior probabilities and means returned from aCGH can be considered as denoised [44 , 45] signals from the log2 aCGH ratios that reflect underlying copy number variation; as such , these are highly relevant to the recent studies on the relationship between copy-number variation and complex phenotypes [46 , 47] , which emphasize the importance of copy-number variation in genetic diversity and disease in humans .
We use a nonhomogeneous HMM with Gaussian emissions . We can either fit one model to all the chromosomes of an array , or we can fit a different model for each chromosome of an array . Let n be the number of probes , and k the number of different copy numbers in the collection of probes . Let Si be the true state ( copy number ) of the probe i: Si = {1 , … , k}i =1 , . . . , n . Let Yi be the relative copy number of the probe i , that is the log ratio of fluorescence intensities between tumor and control samples . Let di be the distance in bases between probe i and probe i + 1 . How distance is measured depends on the platform: distance can be the distance from the end of the spot to the start of the next , if the length of the spots is proportional to the length of the probe ( so we have the same information for every probe ) , or the distance between the midpoint of the spots , if the length of the spots is not proportional to the length of the probe . We normalize these distances between 0 and 1 to increase numerical stability ( with probes in adjacent bases with a scaled distance of 0 ) . We assume that {Si} follows a nonhomogeneous first-order Markov process , as: P ( Si = si | Si−1 = si−1 , Xi−1 = xi−1 ) = Biologically , we expect that , the probability of staying in the same hidden state , is a decreasing function of Xi−1 , so the dependence of the state of a probe onto the next one is lower the farther the probes are . We also expect that when the distance between two probes is maximal , the state of a probe should be independent from the state of its predecessor . Thus , we model the transition probabilities as: where β has the form: with all βij ≥ 0 ∀ i , j . Finally , conditioned on {Si} , {Yi} follows a Gaussian process: Similar approaches have been used before with nonhomogeneous HMM [48 , 49] . In our case , the transition matrix should fulfill the following biologically based properties: ( 1 ) the probability of remaining in the same hidden state should be a decreasing function of the distance between a probe and the previous probe; and ( 2 ) when the distance between two probes is maximal , the state of a probe should not be affected by the state of the previous probe . With the above parameterization , and since the diagonal of β is zero ( which is also needed for the parameters to be uniquely defined ) , the probability of remaining in the same state i is , a decreasing function of distance ( x ) . Moreover , as distances are scaled between 0 and 1 , when the distance between two probes is 1 , the probability of staying in the same state is 1 / k , where k is the number of states; therefore , when the distance is maximal , the state of a probe does not depend on the state of the previous probe . ( The value of this “maximal distance” beyond which two probes are considered independent is a parameter to the model , and can be adjusted taking into account the specific array characteristics ) . For computational reasons and modeling flexibility , we opted for Bayesian methods using MCMC . To fit models with varying number of hidden states , we used reversible jump . Suppose that we have a collection of K HMM models , and each of them has a number of k hidden states , from k = {1 , . . . , K} . Let θ ( k ) be the HMM associated to k , that is , θ ( k ) = {μ ( k ) , σ2 ( k ) , β ( k ) } . The prior distributions for the model are the usual ones in mixture problems [50]: p ( k ) is the prior for the number of hidden states with p ( k ) ~ U ( 1 , k ) , p ( θ ( k ) | k ) is the prior of the HMM conditioned to k , the number of hidden states with u ( k ) ~ N ( α , ϱ2 ) , where α and ϱ are the median and range of Yi; σ2 ( k ) ~ IG ( ka , g ) , where ka is 2 and g is ϱ2 ( Yi ) / 50; βk ) ~ Γ ( 1 , 1 ) . The likelihood of the model , L ( y; k , θ ( k ) ) can be computed by forward filtering [29] , so the joint distribution is p ( k ) p ( θ ( k ) |k ) L ( y; k , θ ( k ) ) . We can draw samples from the posterior distribution through a reversible jump MCMC ( RJMCMC ) algorithm [30] . In RJMCMC , we explore the posterior distribution of possible models , jumping not only within a model but also between models with a different number of parameters . To match the difference between degrees of freedom , some random numbers u with density P ( u ) are generated , so if we are in state x , the new one is proposed in a deterministic way x′ ( x , u ) . The reverse move is the inverse of that function: x ( x′ , u′ ) . This way , the usual Metropolis-Hastings acceptance probability can be computed [50]: where L ( y | x ) is the likelihood , p ( x ) are the priors , p ( u | x ) are the densities of the candidates , and the determinant of the Jacobian of the change of variable . We combine several Metropolis steps in a sweep [29 , 51] . ( 1 ) Update HMM of a model using a series of Metropolis-Hastings moves . ( We do not use Gibbs Sampler to avoid the hidden state sequence from becoming part of the state space of the sampler , so dimensionality is reduced and reaching convergence is easier ) . ( 2 ) Update model ( birth/death ) . When we have r states , a birth/death move is chosen with probabilities pbirth ( r ) and pdeath ( r ) ( these are 1/2 except in the cases when no movement of that type can be made , [e . g . , a death move when there is only one state] ) . If a birth move is selected , a new state is created from the prior distributions and accepted with probability If a death move is chosen , a random state is deleted with a probability inverse to Equation 4 . ( 3 ) Update model ( split/combine ) . A split/combine move is attempted with probabilities psplit ( r ) and pcombine ( r ) ( again , 1/2 except when a move cannot be made ) . If a split move is selected , an existing state i0 is split into two , i1 , i2: Split column Split row This move is accepted with probability The split move must follow the adjacency condition [50]: the resulting states must be closer between them than to any other existing ones . If a combine step is selected , the symmetric move is performed , and the inverse probability of acceptance is computed . The combination of birth and split moves makes it possible not only to visit models with a different number of parameters , but also to explore more thoroughly the posterior probability in the case of a parameter with a multimodal density . These moves are common ones [29 , 51] , but we have changed several aspects of their design to improve the probability of acceptance , which is the most difficult step in reversible jump [29 , 30 , 51] . We constrain the variance of every state so that it cannot be greater than the variance of the entire data . Also , we have added the adjacency condition mentioned before , and used centering proposals [52] . To prevent label-switching of states , we have ordered the states according to means after every iteration of the sweep [50] . We run the former algorithm a large number of times ( e . g . , 50 , 000 ) and , after discarding the first iterations as burn-in , we keep the last ( e . g . , 10 , 000 ) samples as observations from the joint distribution so that we can make inferences from it . For every model that has been visited , we obtain the posterior probabilities of the mean copy number of every state , the variance of the copy number of every state , and the function of transitions between hidden states . By counting the number of times that each model has been visited , we obtain an estimate of the posterior probability of each model ( i . e . , we avoid using Bayesian information criterion [BIC] or AIC ) . Then , applying the Viterbi algorithm [29] to every sample obtained from the MCMC , and , as this sample is a function of the HMM , we can obtain its posterior probability , something that usual Viterbi cannot . From the Viterbi paths for all the samples , we can then compute the posterior probability that a probe belongs to every state or the probability that a sequence of probes is in a given state . When obtaining posterior probabilities of copy-number change , we use Bayesian model averaging [17] over all models visited . Let Si be the lost , gained , no-change status of probe i , K the set of the models considered ( in our case , that would be HMMs with 1 , . . . , K number of states ) , Mk the model with k number of states , and Si | Mk the state of probe i according to model k . We compute the unconditional ( with respect to model selection ) probability for the probe i as: As in any MCMC approach , it is crucial to assess convergence of the sampler . We follow common practice [53] of running several chains in parallel . The convergence of the sampler depends strongly on the distribution of the candidates in Metropolis-Hastings . That is , for every iteration , a new value for the parameters is proposed from a distribution centered in their current values . The standard deviation of that distribution must be chosen in a way that samples explore all the parameter space . These standard deviations are not parameters of the model in the sense that different values give different fits , but values that can speed up convergence of the algorithm . The convergence of the posterior probability of the number of hidden states is reached when a large enough number of transdimensional moves is made . This number need not to be large if the likelihood is substantially higher in a particular model and data size is big enough . The birth and death moves only depend on the priors , but the split and combine moves depend also on their own design and the values of τμ and τβ ( see Equation 5 and Equation 7 ) . The priors chosen have been extensively tested in mixture models [50] . In addition , the priors and rest of the parameters have very little effect: even small CGH arrays contain thousands of points , so that the likelihood from the data dominates any prior . With the 2 , 500 simulated datasets analyzed , we have only needed to specify the number of burn-in—50 , 000—and to-keep samples—10 , 000 , and the number of chains—four , and in only nine cases was there evidence of nonconvergence , which was solved by rerunning the samplers . We have implemented RJaCGH using C ( for the sweep algorithm ) and R [54] , and all analysis and comparisons have been done in R . The code that implements RJaCGH is freely available from the usual Comprehensive R Archive Network ( CRAN ) repositories as package RJaCGH ( http://cran . r-project . org/src/contrib/Descriptions/RJaCGH . html ) or from the repository at Launchpad ( https://launchpad . net/rjacgh ) . All data and code used for this paper are also publicly and freely available ( see details in Protocol S1 ) . | As a consequence of problems during cell division , the number of copies of a gene in a chromosome can either increase or decrease . These copy-number alterations ( CNAs ) can play a crucial role in the emergence of complex multigenic diseases . For example , in cancer , amplification of oncogenes can drive tumor activation , and CNAs are associated with metastasis development and patient survival . Studies on the relationship between CNAs and disease have been recently fueled by the widespread use of array-based comparative genomic hybridization ( aCGH ) , a technique with much finer resolution than previous experimental approaches . Detection of CNAs from these data depends on methods of analysis that do not impose biologically unrealistic assumptions and that provide direct answers to fundamental research questions . We have developed a statistical method , using a Bayesian approach , that returns estimates of the probabilities of CNAs from aCGH data , the most direct and valuable answer to the key biological question: “What is the probability that this gene/region has an altered copy number ? ” The output of the method can therefore be immediately used in different settings from clinical to basic research scenarios , and is applicable over a wide variety of aCGH technologies . |
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Malaria in pregnancy is exquisitely aggressive , causing a range of adverse maternal and fetal outcomes prominently linked to Plasmodium-infected erythrocyte cytoadherence to fetal trophoblast . To elucidate the physiopathology of infected erythrocytes ( IE ) sequestration in the placenta we devised an experimental system for intravital placental examination of P . berghei-infected mice . BALB/c females were mated to C57Bl/6 CFP+ male mice and infected with GFP+ P . berghei IE , and at gestational day 18 , placentas were exposed for time-lapse imaging acquisition under two-photon microscopy . Real-time images and quantitative measurements revealed that trophoblast conformational changes transiently restrain blood flow in the mouse placental labyrinth . The complex dynamics of placental microcirculation promotes IE accumulation in maternal blood spaces with low blood flow and allows the establishment of stable IE-trophoblast contacts . Further , we show that the fate of sequestered IE includes engulfment by both macrophagic and trophoblastic fetal-derived cells . These findings reinforce the current paradigm that IE interact with the trophoblast and provide definitive evidence on two novel pathogenesis mechanisms: ( 1 ) trophoblast layer controls placental microcirculation promoting IE sequestration; and ( 2 ) fetal-derived placental cells engulf sequestered IE .
Infection with Plasmodium parasites during pregnancy is one of the leading causes of maternal and perinatal morbidity and mortality in malaria endemic areas and is particularly severe in regions of unstable transmission [1] . Women infected with Plasmodium falciparum experience a range of adverse pregnancy outcomes including abortions , stillbirths , premature delivery and low infant birth weight . Early descriptions of marked accumulation of infected erythrocytes ( IE ) in the placental intervillous spaces [2] are currently explained by the cytoadherence of P . falciparum-infected erythrocytes to low-sulfated chondroitin 4-sulfate ( C4S or CSA ) proteoglycan present predominantly in the intervillous space of the placenta [3] and on the syncytiotrophoblast lining [4] . Binding of infected cells to placental C4S proteoglycan requires interaction of P . falciparum erythrocyte membrane protein 1 ( PfEMP1 ) molecule , namely VAR2CSA expressed on the surface of IE [5] , [6] . The current placental malaria ( PM ) pathogenesis paradigm stipulates that accumulation of IE in human placenta elicits an inflammatory response [7]–[9] that is presumably responsible for pathological changes observed in the placental barrier ( interhaemal membrane ) , which ultimately has a negative impact on fetal growth and viability [10]–[12] . To a large extent , this pathogenesis model is based on seminal findings that correlate human placental pathology with in vitro IE adhesion properties [13] . It is still unclear whether placental microcirculation contributes to establishment of in vivo IE-throphoblast interactions . IE cytoadherence in the placenta is seen as a strategy of the parasite to circumvent host immunity and propagate the blood stage infection . Little is known of the fate of sequestered IE and the role of fetal components in the subsequent inflammatory response . Although mouse PM develops within a narrow time window of mouse pregnancy with reduced accumulation of P . berghei-IE as compared to human acute PM , pathological findings suggest that the disease involves a strong inflammatory response leading to extensive placental tissue remodeling [14] , [15] . P . berghei-IE display placental tissue adhesion that is partially dependent on placental extracellular C4S [14] , but it is uncertain whether placental sequestration in the mouse is dependent on IE surface antigens mimicking the var2CSA adhesion mechanisms . Observations in experimental mouse models of severe malaria might not have direct correlation to human disease [16] . It should be noted that mouse placental tissue organization and vasculature architecture show marked differences compared to the human placenta [17] . In addition , the exposure time to the parasite is reduced to a few days , conditioning the extent of inflammatory infiltration and immune response observed in mouse PM . Nevertheless , this experimental PM model provides an opportunity to evaluate in vivo placenta-IE interactions and track the fate of IE in the placenta , a research that is not amenable in the human placenta . We used intravital microscopy methods to visualize IE in the placental maternal blood space and analyze the dynamics of IE-placenta interactions . We observed that unexpected maternal blood flow dynamics in the placenta labyrinth favor stable contacts of IE with the fetal trophoblast layers and promote phagocytosis of IE by the fetal cells in the placenta .
The mouse model of placental malaria is based on infection of pregnant mice at gestational day ( G ) 13 with P . berghei ANKA [14] . Placenta observations were performed on G18 in the labyrinth – the inner region of the placenta composed of fetal vasculature and fetal-derived trophoblast layers , the cytotrophoblast and syncytiotrophoblast ( Figures 1A and 1B ) . Together with fetal endothelia , the trophoblast layers in the labyrinth provide a trichorial barrier between maternal and fetal blood circulation , the interhaemal membrane ( IM ) ( Figure 1C ) through which nutrients and oxygen are transferred both by diffusion and active transport [18] . In this model , IM thickening is a prominent consequence of P . berghei infection during pregnancy [14] , [19] ( Figure 1D ) and congenital infection does not occur , as infected erythrocytes ( IE ) are restricted to maternal circulation and were never observed in fetal blood vessels [14] ( Figure 1E ) . To discriminate fetal placental tissue , infected erythrocytes and maternal blood in the infected placenta with fluorescence microscopy we combined three separate tags: i ) a label for fetal-derived tissue by crossing BALB/c females with actin-CFP C57Bl/6 ( B6 . Cyan ) males; ii ) infection of pregnant mice with P . berghei GFP labeled parasites; iii ) a label for the maternal blood fluid by Dextran-Rhodamine injection ( when applicable ) ( Figure S1 ) . Using this experimental system we performed intravital placental imaging by means of a novel two-photon microscopy staging technique that allowed visualization of the placental labyrinth displaying CFP+ placental components of fetal origin ( except for fetal erythrocytes that were CFP− ) , GFP+ IE and Rhodamine+ maternal blood spaces . Identification of CFP+ fetal-derived tissue ( Figure 2A ) was found using intravital imaging of the placenta in the transversal plane . The movement of the blood cell mass in Rhodamine+ areas revealed that maternal blood flow was highly heterogeneous in the labyrinth ( Video S1 ) . Time-lapse imaging showed maternal blood spaces with high flow rates but also various regions with low flow rates or no perfusion as evidenced by an uneven Dextran-Rhodamine distribution , suggesting that infected placenta display local impairments in microcirculation and tissue perfusion . These heterogeneous blood flow patterns allowed identification of areas with different flow within one microscope field ( Figures 2B and 2C ) . At higher magnification , blood flow in the labyrinth showed unexpected dynamics . Within the intravital observation period ( 300 s ) , blood flow appeared to be interrupted by transient occlusion of the maternal blood space lumen . Figure 2D shows sequential images of a continuous 6 min image acquisition ( Video S2 ) where trophoblast ( in blue ) and maternal blood space ( black areas with GFP+ IE ) are readily identified . These images demonstrate that blood flow was interrupted after 2 minutes of acquisition as the blood space was occluded by trophoblast conformational changes . Strong co-localization of the 2 images at observation time-points 72 and 360 s indicates that the modifications occurred in the same focal plane and are highly dynamic ( Pearson's r = 0 . 84; Spearman's r = 0 . 86 - “Pearson-Spearman correlation colocalization” ( PSC ) test ) ( Figure 2E ) . Topological trophoblast changes could involve the cytotrophoblastic bridges ( “Coan-Burton bridges” ) that cross the maternal blood spaces ( Figure 2F ) , which are abundant during the last third of pregnancy , as recently described by Coan et al . [20] . Further , remodeling of maternal blood spaces appears to be transient and seems to operate by reversible constriction . This is illustrated by comparison of sequential images in which trophoblast undergo conformational changes leading to either the “sealing-off” of lumen or “opening up” of other blood spaces ( Figure 3A; Video S3 ) . Intermittent passage of individual infected cells within maternal blood spaces suggests that trophoblast can also operate by temporarily “constricting” and “relaxing” the lumen of maternal blood area , thereby controlling blood flow ( Figures 3B and 3C; Video S4 ) . We propose that trophoblast topological changes act as highly dynamic and reversible septa that transiently occlude the maternal blood spaces and deflect blood flow ( Figure 3C ) , presumably promoting fetal-maternal exchanges and contributing to heterogeneous maternal blood flow rate patterning across the labyrinth . Maternal blood flow patterns were evaluated in the labyrinth of non-infected placentas at late stage gestation ( G18 ) . Intravital imaging with a 5 min time-period at 600× magnification showed that Dextran-Rhodamine labeled maternal blood exhibited different flow rates ( Figure 4A; Video S5 ) . To quantify the relative blood flow in selected regions we used the Dextran-Rhodamine signal as a reference and analyzed the variance of mean pixel value ( MPV ) at all time-points in visually defined areas of low , intermediate and high flow ( Figure 4B ) . For this analysis we applied a custom imaging stabilization algorithm that minimizes background movements of the living tissue in the final resulting image sequences ( see materials and methods ) . As expected , areas of low blood flow showed decreased MPV standard deviation ( SD ) compared to high and intermediate flow areas due to a more constant and lower flow dynamics ( Figure 4C ) . Conversely , the intermediate flow areas had the highest MPV SD , which results from a more heterogeneous flow pattern across the acquisition time-period ( very often related to “stop and go” flow ) . On the other hand , in areas of high flow the MPV SD was increased compared to low flow and decreased compared to areas of intermediate flow as a result of a more constant but highly dynamic flow pattern . These observations confirm that maternal blood flow heterogeneity in the labyrinth is a physiological characteristic of the microcirculation in the mouse placenta . We evaluated whether IE movement in infected placentas paralleled blood flow heterogeneity as ascertained by labeling maternal blood with Dextran-Rhodamine ( Figure 5A and Video S6 ) . Sequential time-points revealed that IE movement was dictated by blood flow rate . In areas of high blood flow no stationary IE were observed whereas in low flow regions IE remained stationary . Moreover , compilation of images across the z axis confirmed the conserved positioning of IE in low flow areas ( as detected by single signals with high intensity ) contrasting with the multiple signals of lesser intensity observed in regions of high blood flow ( Figure 5B ) . As our observations suggested that blood flow rate impacted on the speed of IE travelling in the labyrinth we evaluated whether the IE burden within blood spaces differed according to flow rates . From Video S2 , we visually identified three different blood flow rates within the same microscopic field ( Figures 6A–C ) and counted the number of IE per area during an observation period of 6 min ( Figure 6D ) . Time-lapsed analysis showed that the number of IE ( events/mm2 ) was consistently higher in regions of lower blood flow rate ( RII ) . This was particularly apparent after blood space occlusion ( Figure 2D; Video S2 ) , indicating a preferential IE accumulation in this region compared to RI and RIII ( Figure 6D ) . Individual IE tracking was performed in RI and RIII during the entire observation period whereas in RII IE were followed during the first 75 s of acquisition – the period before blood space occlusion ( Figures 6E–G ) . In RI IE traveled at relatively high speed and could be observed during a period of 10–15 s ( Figure 6E ) whereas in an intermediate flow region ( RIII ) IE were observed during 30–40 s ( Figure 6G ) . In contrast , in the RII region IE were present for a longer period ( 70 s ) and velocity was significantly lower compared to RI and RIII ( Figure 6F and H ) . The average velocity of parasitized cells ( Figure 6H ) confirmed that blood flow rates were sharply distinct amongst these regions . Taken together these observations indicate that transient blood flow alterations in the labyrinth determine the accumulation of IE in areas of low but not high blood flow , possibly favoring IE interactions with the trophoblast layers . These data also suggest that the parasite takes advantage of the heterogeneous placental blood flow pattern to accumulate in the mouse placenta . Cytoadherence and sequestration of P . falciparum-IE in the human placenta via interaction with C4S on trophoblast surface was initially proposed by Fried and Duffy [13] as a key event in placental malaria pathogenesis . In vitro studies in PM mouse models also showed binding of P . berghei parasitized cells to non-infected placental tissue [14] , [19] . By examining intravital images of infected placentas , we observed distinct IE-trophoblast interaction patterns in low blood flow areas . Stable contacts were observed when IE freely travelled in the maternal blood space and encountered the trophoblast . In a typical case the IE experiences a sharply decreased velocity , possibly caused by a rolling contact with the trophoblast membrane , and subsequently attains a steady position . This position was maintained for at least 100 s , until the end of the observation period ( Figures 7A and 7D; Video S7 ) . In other cases IE remained stationary , seemingly nested in the trophoblast , suggesting intimate contact with the tissue while other infected cells travelled freely in the blood with velocities varying between 1 and 6 µm/s ( Figure 7B and 7E; Video S8 ) . These observations indicate that IE-trophoblast contacts resist the forces of the surrounding blood flow , suggesting strong attachment mechanisms that maintain the IE immobilized on trophoblast membrane . We also observed transient interactions when IE engaged in short-term contact with the trophoblast ( aprox . 80 s ) followed by disengagement ( Figure 7C and 7F; Video S9 ) , possibly representing a failed IE adhesion event . In many instances , images showed interactions occurring in cytotrophoblast discontinuities ( “holes” described by Coan et al . [18] ) , suggesting direct contacts of IE with the underlying trophoblast layers ( Figure 7B ) . By using the same imaging acquisition set-up , we observed that IE do not accumulate on , or interact with , blood vessel walls of the popliteal lymph node in an infected non-pregnant female ( Figure S2 and Video S10 ) , thereby validating that IE interact with the placental tissue in distinct manners as compare to peripheral vessels . This data demonstrates that the IE adhere to placental tissue and establish stable interactions that immobilize IE on the trophoblast layers in vivo and possibly elicit interhaemal membrane responses . It is assumed that sequestered IE burst and release infective merozoites , however the fate of stationary IE after attachment to the trophoblast has not yet been addressed . We repeatedly observed that immobilized IE are subjected to further interactions with placental tissue . Image sequences showed that IE were immobilized on the trophoblast for approximately 250 s , abruptly traversed into a neighbor maternal space , and subsequently remained stationary ( Figures 8A , 8B and Video S11 ) . This behavior was also observed in Video S6 ( see arrowheads ) . Furthermore , intravital imaging of highly infected placenta provided evidence that stationary IE are targeted by fetal phagocytic cells . Sequential images showed that CFP+ cells actively migrate and target IE that are then engulfed and presumably undergo intra-phagocytic destruction ( Figure 8C and Video S12 ) over a time period of approximately 5 minutes . Furthermore , analysis of infected placenta sections showed that fetal-derived macrophages ( Mac-1+ cells ) protrude into the maternal blood space ( Figure 8D ) . Both fetal-derived Mac-1+ and Mac-1− cells showed engulfed IE and contained parasite-derived material ( Figures 8E and F ) . Microscopic examination also suggested that the majority of cells containing parasite material did not express the myeloid Mac-1 marker . These observations suggest that fetal-derived cells in the labyrinth are actively involved in IE uptake ( Figures 8E and F ) , a finding that is consistent with previous reports indicating that murine placental macrophages and trophoblast lineage cells have phagocytic capacity and can ingest IE [21] , [22] . This study provides first ever evidence in the living animal that trophoblast conformational changes modulate blood flow in the mouse placental labyrinth promoting accumulation of infected cells and establishment of intimate IE-throphoblast contacts that lead to IE sequestration . These findings support the notion that P . berghei-IE adhere in vivo to the trophoblast and highlight the fact that fetal-derived placental cells play a role in the response to placental malaria .
Here we provide an intravital description of the Plasmodium-infected placenta in the mouse . Our findings reveal novel placental microcirculatory attributes that favor PM pathology and reinforce the current paradigm that IE establish intimate interactions with trophoblasts and elicit fetal-derived cellular responses in the placenta . Specifically , this study demonstrates that unique characteristics of placental microcirculation driven by trophoblast conformational changes favor intra-placental IE accumulation . Furthermore , we show that fetal-derived cells in the placenta are involved in phagocytosis of sequestered IE in vivo . Two-photon microscopy was used to unveil the microcirculatory dynamics in the infected placental labyrinth . The technical procedure for in vivo imaging requires anesthesia and surgical exposure of the placenta unit that may influence the organ hemodynamics . Nevertheless our observations are compelling in revealing that placental microcirculation is , at least partially , governed by the fetal-derived trophoblast conformational changes . The trophoblast topology imposes heterogeneous blood flow pattern across the placenta , which is evident in absence of infection . We visualized areas of transient low-level flow or stasis providing intravital evidence that blood flow rate in the placental microcirculation is not controlled by the maternal arterial blood pressure . We propose that transient constrictions of maternal blood spaces generated by reversible trophoblast conformational changes are at the basis of an exquisite mechanism to control blood flow warranting prolonged contact of maternal blood with the interhaemal membrane . It did not escape our attention that some dynamic conformational changes may involve the “Coan-Burton bridges” as these structures were observed and described in an ultra-structural study as cytotrophoblastic prolongations connecting separate sides of maternal blood space lumen [20] . Such bridges could originate transient blood space obliteration and control maternal blood flow as evidenced by our intravital observations and illustrated in Figure 3C . Nevertheless , without a detailed 3D description of the labyrinth architecture at microscopic scale it is difficult to envisage how this flow-control mechanism could be coordinated at placental organ level . Also , a full three-dimensional reconstruction of the placenta would provide further information about the impact of Plasmodium infection on microcirculation , correlating the alterations in placental haemodynamics with pathological findings . It is plausible that such hemodynamic alterations would lead to increased uterine and umbilical artery vascular resistance as reported in pregnant women with P . falciparum infection [23] , [24] . Rhodamine negative maternal blood areas that contained stationary IE were frequently observed , indicating impaired perfusion . The uneven pattern of Rhodamine distribution in these maternal blood spaces is compatible with blood flow interruption ( or low perfusion ) by fibrin deposition leading to clot formation and subsequent thrombosis . Significant fibrin deposits in the intervillous space have been documented in placental malaria [25]–[27] . In P . falciparum-infected placenta fibrin clots narrowed and plugged intervillous spaces [27] . Fibrin thrombi formation and hemorrhage were also observed in placentas of aborted mice that were infected at G0 with P . chabaudi [28] . A recent study showed that intrauterine growth restriction was associated with compromised maternal circulation displaying slow intervillous blood flow , intermittent stops in perfusion as well as unperfused regions [29] . In our experimental system we frequently observe fibrin deposition in histological sections ( Figure S3 ) that raises the possibility that micro-thrombotic events can lead to low perfusion and contribute to PM pathology . Our observations demonstrate that low blood flow favors accumulation of IE in maternal blood spaces and promotes the establishment of stable contacts with the trophoblast . Apart from the CS4 enriched environment , we propose that the presence of low and intermediate flow regions in the placental labyrinth favors IE sequestration . Nevertheless , we noted that the IE encounters with trophoblast structures did not always lead to stable contact since transient IE-trophoblast interactions were also observed . As opposed to in vitro binding assays , the intravital images suggest that IE adhesion to the trophoblast is not limited to passive receptor-ligand interaction . The trophoblast appears to actively react to the presence of IE at the margins of maternal blood spaces , as is the case of sequestered IE that were carried across neighboring maternal blood regions ( Figure 8A and Video S11 ) . The observations that stationary IE are targeted by fetal macrophages and trophoblast provide evidence for an active reaction to the presence of IE . Our study is in line with findings showing that fetal placenta cells such as the trophoblast [21] , [28] , [30]–[33] and placental macrophages [22] respond to parasite components . The exact mechanisms linking fetal-derived placental cellular response against the parasite to the pro-inflammatory environment and angiogenic impairments observed in infected placenta [34]–[36] are yet to be determined . Our findings lead us to propose that placental hemodynamics as well as trophoblast responses to sequestered IE contribute to PM pathogenesis in the mouse . Despite the marked differences in microanatomy of the mouse and human placenta it cannot be excluded that impairments in microcirculation and pro-inflammatory responses from fetal-derived placental tissue also play a role in human Plasmodium placental infection . Nevertheless , it should be noted that the mouse model here studied represents an aggressive and acute form of placental infection that is observed in only a fraction of pregnant women with malaria [37] .
All procedures involving laboratory mice were in accordance with national ( Portaria 1005/92 ) and European regulations ( European Directive 86/609/CEE ) on animal experimentation and welfare and were approved by the Instituto Gulbenkian de Ciência Ethics committee and the Direção-Geral de Veterinária is the Official National Entity that regulates the use of laboratory animals in Portugal . Eight to twelve week-old BALB/c female and B6-Tg ( CAG-ECFP ) ( B6-Cyan ) male mice were obtained from the Instituto Gulbenkian de Ciência animal facility . Mice were bred and maintained under specific-pathogen free ( SPF ) conditions . All infection experiments made use of P . berghei ANKA constitutively expressing green fluorescent protein under the eef-1 promoter ( ANKA-GFP ) [38] , [39] . Infection inocula consisted of infected erythrocyte preparations ( IE ) obtained after one in vivo passage of a frozen parasite stock that was injected intra-peritoneally in BALB/c mice and collected when parasitemia reached approximately 10% . BALB/c females were mated to B6-Cyan males to obtain placentas where fetal components expressed cyan fluorescent protein ( CFP ) . The day that the pair was separated was considered gestational day 1 ( G1 ) . Pregnancy was monitored every other day by weighing the females . Body weight gain of 3 to 4 g at G10 to G13 indicated successful fertilization . Pregnant mice were intravenously ( i . v . ) infected on G13 with 106 P . berghei ANKA-GFP IE [14] . Intravital imaging was performed on G18 . Intravital images of lymph-node blood vessels from non-pregnant females were acquired 7 days after infection with 106 ANKA-GFP+ IE . Placentas from infected and non-infected pregnant mice , sacrificed on G18 , were fixed in 10% formalin and embedded in paraffin . Non-consecutive 5 µm sections were stained with hematoxylin-eosin ( HE ) and examined under light microscope ( Leica DM LB2 , Leica Microsystems ) . Placentas from P . berghei-GFP+ infected BALB/c females that were mated to B6 . Cyan males were fixed overnight in 4% formalin/6% sucrose , embedded in Tissue Tek OCT compound ( Sakura ) , snap frozen in liquid nitrogen and cut in 7 µm-thick slices using a Leica 3050S cryostat ( Leica Microsystems , Germany ) . Sections were rehydrated for 10 min in PBS 1X , stained with rat anti-mouse Mac-1 ( M1/70 ) biotinilated antibody and developed with streptavidin-HRP Cy3 conjugate . All incubations were performed at room temperature with PBS1x/10% FCS/0 . 1% azide/5% mouse serum . Slides were mounted in 2 . 5% 1 , 4-Diazabicyclo ( 2 , 2 , 2 ) octane ( pH 8 . 6 ) in 90% glycerol in PBS and images were acquired in a DMRA2 Leica microscope ( Leica Microsystems ) using 63X and 100X objectives . Mice were anaesthetized with 150 mg ketamine and 12 mg xylazine per kg body weight and kept on a warm pad at 37°C . For placental imaging , an incision on the lower abdomen was performed and one feto-placental unit of the uterus exposed . Uterine membrane was gently incised and fetus and placenta liberated so that placenta could be exposed in its entirety whilst still attached to the uterus by the decidua , so not compromising tissue irrigation . The fetus was covered with gauze immersed in PBS to avoid drying . Mouse was restrained in a bespoke apparatus with a sliding lid and the placenta was immobilized with a clip to display the fetal side upwards . The placenta was stabilized for observation by covering with a metal platform with an orifice in the middle which holds a cover slip . Tissue hydration was assured by surrounding the tissue with 2% low melting agarose and temperature was monitored with a sensor placed in contact with the tissue . This procedure is described in more detail by Zenclussen et al [40] . Preparation of popliteal lymph nodes was performed as previously described [41] Briefly , hind legs were shaved , the animal restrained on a warm pad at 37°C and an incision on the back of the hind leg near the “K” shaped artery was performed . The lymph node was exposed and immobilized using a metal strap with a small orifice . For blood flow detection , mice were injected i . v . with 1 mg of Rhodamine B isothicyanate ( Dextran-Rhodamine ) ( Sigma-Aldrich ) diluted in PBS , immediately before imaging . A Praire Ultima two-photon microscope on an Olympus BX-51 base with x-y translation stage equipped with two sets of conventional galvanometer-based scanners , fitted with a 2P Coherent Chameleon Laser tuned to 900–910 nm was used throughout . Rhodamine signal was separated from both GFP and CFP fluorescence emission using a dichroic mirror of 565 nm . The GFP/CFP fluorescent protein signals were split by a 495 nm dichroic mirror . Filters used were 500–550 ( GFP ) , 435–485 ( CFP ) and 570–620 nm ( Rhodamine ) . Time-lapse imaging of a single focal plan was performed . Data were acquired using PraireView software and 2D T-series imaging was performed in 512×512 pixel size frame at a rate of 1 . 8 s/frame . Objectives used were 20X ( 1 . 0 NA 2 mm working distance ) and 60X ( 0 . 90 NA 2 mm working distance ) . Images were processed and data analyzed using Fiji/ImageJ 1 . 46a software ( http://pacific . mpi-cbg . de ) . Cell velocity and distance travelled by infected cells were calculated using the Fiji/ImageJ Manual Tracking plug-in . Due to endogenous motion , such as that caused by intestinal peristaltic movement , we performed a software-based post-processing step to stabilize the intravital image sequences over time in order to better quantify the blood flow hemodynamics inside the labyrinth . For this , we developed a custom software algorithm that is conceptually simple and efficient and that has the capability to stabilize a full 3D two-photon microscopy image sequence . In particular , our software ( to be published elsewhere ) performs a cross-correlation based image registration between two consecutive z-image stacks and provides the optimal displacement ( x , y , z ) , such that the global pixel overlap between these two image stacks is maximum . Simply stated , the image drift in all dimensions ( x , y , and z planes ) is minimized in order to achieve a stable and better movie quality . In this study , only 2D image acquisitions were performed . Nonetheless , we were able to use our software to stabilize these image sequences with the same principles described above . In non-infected placentas , areas were selected by visual inspection based on apparent flow rate . A constant region of interest ( ROI ) was defined and the mean pixel value ( MPV ) was calculated for each frame in the image sequence . Also , the standard deviation ( SD ) of the MPV/frame was calculated . In infected placentas , an output image was generated from the merging of all images along the z axis containing the maximum pixel values over all images in the stack . This image analysis procedure was performed using Fiji/ImajeJ 1 . 46a software . Data were presented as mean values +/− SEM . Unpaired t test or ANOVA with Tukey's or Dunnet's post-test were performed using the GraphPad Prism 4 . 0 software . Data were considered significant for p<0 . 05 . Pearson and Spearman correlation coefficients were calculated using a specific plug-in ( http://www . cpib . ac . uk/~afrench/coloc ) [42] of Fiji/ImageJ 1 . 46a image processing software . | Malaria in pregnancy is exquisitely aggressive , causing a range of adverse effects impacting maternal and fetal health . Many of those effects are thought to derive from placental sequestration of red blood cells infected with the malaria parasite ( Plasmodium falciparum ) eliciting a placental inflammatory response that impairs maternal-fetal exchanges . We developed an experimental system for intravital microscopy to directly observe the course of placental infection in a mouse model of pregnancy-associated malaria . We found that microcirculation in infected placentas showed areas of low blood flow that promote sequestration of infected red blood cells . Furthermore , we observed that sequestered infected red blood cells are targeted and phagocytosed by fetal-derived cells in the materno-fetal interface . This work provides the first ever in vivo evidence that unique placental microcirculatory features promote infected red blood cell sequestration , implying a vascular component in placental malaria pathogenesis . Moreover , we reinforce the notion that fetal-derived cells contribute to the placental response against sequestered infected red blood cells . |
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The nuo-6 and isp-1 genes of C . elegans encode , respectively , subunits of complex I and III of the mitochondrial respiratory chain . Partial loss-of-function mutations in these genes decrease electron transport and greatly increase the longevity of C . elegans by a mechanism that is distinct from that induced by reducing their level of expression by RNAi . Electron transport is a major source of the superoxide anion ( O⋅– ) , which in turn generates several types of toxic reactive oxygen species ( ROS ) , and aging is accompanied by increased oxidative stress , which is an imbalance between the generation and detoxification of ROS . These observations have suggested that the longevity of such mitochondrial mutants might result from a reduction in ROS generation , which would be consistent with the mitochondrial oxidative stress theory of aging . It is difficult to measure ROS directly in living animals , and this has held back progress in determining their function in aging . Here we have adapted a technique of flow cytometry to directly measure ROS levels in isolated mitochondria to show that the generation of superoxide is elevated in the nuo-6 and isp-1 mitochondrial mutants , although overall ROS levels are not , and oxidative stress is low . Furthermore , we show that this elevation is necessary and sufficient to increase longevity , as it is abolished by the antioxidants NAC and vitamin C , and phenocopied by mild treatment with the prooxidant paraquat . Furthermore , the absence of effect of NAC and the additivity of the effect of paraquat on a variety of long- and short-lived mutants suggest that the pathway triggered by mitochondrial superoxide is distinct from previously studied mechanisms , including insulin signaling , dietary restriction , ubiquinone deficiency , the hypoxic response , and hormesis . These findings are not consistent with the mitochondrial oxidative stress theory of aging . Instead they show that increased superoxide generation acts as a signal in young mutant animals to trigger changes of gene expression that prevent or attenuate the effects of subsequent aging . We propose that superoxide is generated as a protective signal in response to molecular damage sustained during wild-type aging as well . This model provides a new explanation for the well-documented correlation between ROS and the aged phenotype as a gradual increase of molecular damage during aging would trigger a gradually stronger ROS response .
Mitochondrial function has been linked to the aging process in a number of ways [1] . In particular , mitochondria are crucial in energy metabolism and as such have been implicated in the aging process by one of the very first theories of aging [2] , the rate-of-living theory of aging [3] , which suggested that the rate of aging is proportional to the rate of energy metabolism ( reviewed in [4] ) . Mitochondrial function in animals is also known to decline with age [5] , [6] , which , together with the finding that mitochondria are an important source of toxic reactive oxygen species ( ROS ) , has led to the oxidative stress ( or free radical ) theory of aging [7] , [8] . Two types of mutations that affect mitochondrial function have been found to affect the rate of aging in C . elegans , mutations that shorten lifespan , such as mev-1 [9] and gas-1 [10] , and mutations that lengthen lifespan , such as clk-1 [11] , isp-1 [12] , lrs-2 [13] , and nuo-6 [14] . lrs-2 encodes a mitochondrial leucyl-tRNA-synthetase , and its effect on the function of mitochondrial electron transport is likely relatively indirect , via partial impairment of mitochondrial translation . However , clk-1 encodes an enzyme necessary for the biosynthesis of ubiquinone , a lipid antioxidant and an electron transporter of the respiratory chain [15] , and mev-1 , gas-1 , isp-1 , and nuo-6 all encode subunits of mitochondrial respiratory complexes . On the strength of the oxidative stress theory of aging it has been suggested , and supported by a number of observations ( reviewed in [16] , [17] ) , that the mev-1 and gas-1 mutations reduce lifespan by increasing mitochondrial oxidative stress , and clk-1 , isp-1 , and nuo-6 increase lifespan by reducing it . In addition to genomic mutations that affect mitochondrial proteins , it has been found that knockdown by RNA interference of C . elegans genes that encode subunits of mitochondrial complexes , including isp-1 and nuo-6 , also prolongs lifespan [13] , [18] , [19] . Although the effect of RNAi on ETC subunits , which is conserved in Drosophila [20] , was initially believed to be similar to that of the mutations [21] , [22] , [23] , it was recently found that it is in fact distinct and separable [14] . A recent study analyzed patterns of gene expression in isp-1 mutants together with those in clk-1 and cyc-1 ( RNAi ) [23] and suggested that the overlap between these patterns could define the biochemical processes that underlie the effect of all interventions that impact mitochondria . However , our recent findings that isp-1 ( qm150 ) and isp-1 ( RNAi ) trigger fully separable mechanisms suggests that the overlapping gene expression changes identified by Cristina et al . [23] might not be sufficient to prolong lifespan . Rather some of the gene expression changes that are specific to each type of intervention are necessary for their effect on lifespan and can act additively . isp-1 mutants show a trend toward low levels of oxidative damage to proteins , increased expression of the cytoplasmic Cu/Zn superoxide dismutase ( SOD-1 ) and of the mitochondrial Mn superoxide dismutase ( SOD-2 ) [24] , and increased resistance to acute treatment with the prooxidant paraquat [14] . However , although knocking down the genes encoding the major superoxide dismutase by RNAi results in normal or elevated levels of oxidative damage , it had no effect on the lifespan of the mutants [24] , suggesting that the reduced oxidative damage found in isp-1 mutants is not responsible for their longevity . Furthermore , the notion that mitochondrial oxidative stress could be the cause of aging has recently been challenged by a number of studies in C . elegans [24] , [25] , [26] , [27] , [28] , in Drosophila [29] , and in mice ( reviewed in [30] ) . ROS are not just toxic metabolites that lead to oxidative stress but are also signaling molecules that are believed to be involved in a mitochondria-to-nucleus signaling pathway that could impact aging [1] , [31] , [32] , [33] . Interfering with mitochondrial function has the potential to alter the rate and/or the pattern of production of ROS by mitochondria , including in counter-intuitive ways . For example , reducing oxygen concentration increases ROS production by mitochondrial complex III in vertebrate cells [34] , [35] , and the knockout of sod-2 in C . elegans can lead to normal [25] or increased lifespan in spite of increased oxidative damage [26] . Here we examined ROS production by mitochondrial mutants and found that isp-1 and nuo-6 mutants have increased generation of the superoxide anion but not increased levels of other ROS and that this increase is necessary and sufficient for longevity , suggesting that superoxide triggers mechanisms that slow down aging , presumably at the level of gene expression .
To measure changes in mitochondrial ROS generation that could affect signaling , it is not adequate to measure the level of ROS damage , as a change in ROS damage levels can be brought about by changes in detoxification of ROS , in protein turnover , or in damage repair . However , it is notoriously difficult to directly visualize or measure ROS generation and ROS levels in intact organisms including in living worms . To overcome this difficulty we have adapted a technique originally developed for vertebrates that uses flow cytometry to sort isolated intact mitochondria and measure ROS levels with indicator dyes ( Figure S1 ) [37] . Mitochondria were extracted from worms by standard techniques and loaded with either one of two fluorescent indicator dyes , H2DCFDA , a dye that is sensitive to a variety of ROS but rather insensitive to superoxide [38] , [39] , and MitoSox , a dye that is exclusively sensitive to superoxide [40] . The prooxidant paraquat ( PQ ) induces mitochondrial superoxide generation [41] , and the antioxidant N-acetyl-cysteine ( NAC ) has an antioxidant effect on all types of ROS [42] , [43] . As expected , when purified mitochondria were treated with PQ , the fluorescence of both H2DCFDA and MitoSox increased , and the fluorescence of both decreased when treated with NAC ( Figures 1A , 1B , and S1B ) . One limitation of this technique is the need for a rather large amount of mitochondria . For example , a sufficient amount of worms is not readily obtained from worms treated by RNAi , and we have therefore focused on long-lived mutants only . We used the cytometry technique to determine the generation of mitochondrial superoxide and of overall mitochondrial ROS in a number of long-lived mutants . Both isp-1 and nuo-6 mutations did not affect H2DCFDA fluorescence ( overall ROS ) significantly , but both showed elevated MitoSox fluorescence ( superoxide ) ( Figure 1C and 1D ) . Mutants of four other genes ( clk-1 ( qm30 ) , eat-2 ( ad1116 ) , daf-2 ( e1370 ) , and sod-2 ( ok1030 ) ) were also tested ( Figure 1E and 1F ) . clk-1 mutants showed an elevation of overall ROS-associated fluorescence but not of superoxide-associated fluorescence . daf-2 mutants were most similar to the mitochondrial respiratory chain mutants with an elevation of superoxide-associated fluorescence but no significant elevation in overall ROS-associated fluorescence . Finally , eat-2 and sod-2 mutants showed no significant elevation in either signal but only a trend for low overall ROS in the case of eat-2 mutants and a trend for increased superoxide in the case of sod-2 mutants . The elevated MitoSox signal in isp-1 , nuo-6 , and daf-2 corresponds mostly to increased superoxide generation , as all three mutants are known for elevated levels of the mitochondrial SOD-2 and SOD-3 [12] , [14] , [24] , [44] , whose activity would prevent the accumulation of superoxide . Elevated superoxide detoxification , however , should not prevent measuring increased superoxide generation as superoxide is generated at prosthetic electron carriers such as ubiquinone in complex III [45] , [46] and FMN in complex I [47] , [48] , which are at least partially buried in the complexes . Thus a small molecular weight dye that has access to these sites can trap the superoxide before it has the opportunity to diffuse toward the SOD-2 and SOD-3 proteins . There is no increase in the H2DCFDA signal in these mutants likely because this dye is not particularly sensitive to superoxide [49] . It appears therefore that in the presence of efficient detoxification the level of overall ROS is not significantly increased by the increased superoxide generation that we observe . This is consistent with the finding that these mutants do not have increased oxidative damage [14] , [24] . sod-2 deletion mutants do not show a significant increase in the MitoSox signal ( Figure 1E and 1F ) , indicating that decreased detoxification does not lead to an easily measurable increase in this signal in purified mitochondria . The signal from H2DCFDA , a dye which has very broad sensitivity but is not very sensitive to superoxide [49] , is also unchanged , suggesting that , at least in isolated worm mitochondria , electron transport is not the main source of the type of ROS to which H2DCFDA dye is significantly sensitive . The level of superoxide generation in these mutants might also be kept moderately low because of their reduced electron transport [26] , although low electron transport could in principle also result in elevated superoxide as we have observed in isp-1 and nuo-6 mutants . clk-1 mutants have only a small deficit in electron transport [24] , [50] , [51] , in spite of a strongly altered content in quinones [51] , [52] , [53] , [54] . Indeed , while wild-type animals contain endogenously synthesized UQ9 as well as a small amount of dietary bacterial UQ8 , clk-1 mutants contain only the dietary ubiquinone and no UQ9 . Here we found that clk-1 mutants have normal superoxide generation but enhanced overall ROS levels , which suggests that the antioxidant function of UQ9 is a crucial sink for mitochondrial ROS , whose absence appears to lead to an increase of overall ROS even in the absence of increase superoxide generation . eat-2 mutants are long-lived because of reduced food intake ( dietary restriction ) [55] . Although dietary restriction has been found to impinge on mitochondrial function in other systems , no changes in mitochondrial superoxide and overall ROS signals were observed . To determine how the elevated superoxide affects the lifespan of mutants , we treated worms with 10 mM of NAC and scored their survival ( Figure 2 and Table 1 ) . The treatment had no effect on the survival of the wild type ( Figure 2A ) , which shows that it is not toxic for lifespan at the concentration used . However , NAC treatment fully abolished the increased longevity of nuo-6 and severely limited that of isp-1 ( Figure 2B and 2C ) . The lesser effect on isp-1 is consistent with the larger increase of superoxide in these mutants ( Figure 1D ) , given that the effect of NAC is gradual ( 1 mM has less effect than 8 mM , which has less than 10 mM; Table S1 ) . At high concentration ( >10–15 mM ) NAC can be deleterious even on the wild type , but at the concentration used ( 10 mM ) NAC had no effect on the apparent health of the mutants , whose overall aspect after treatment was indistinguishable from that of the untreated worms ( Figure S2A ) . We have also quantified several phenotypes , including defecation , swimming , brood size , and post-embryonic development , after NAC treatment of the wild type and of nuo-6 , which is the mutant that is most sensitive to NAC ( 10 mM NAC completely abolishes its increased longevity ) . Treatment with 1 mM vitamin C also significantly shortened the lifespan of both isp-1 and nuo-6 mutants without affecting the wild type ( Table S1 ) . Most effects of NAC were quite small ( Figure S2B–E ) , except on the post-embryonic development of the wild type ( Figure S2C ) . Furthermore , for defecation , brood size , and post-embryonic development , the effect of NAC on the mutant produced a change in the same direction as on the wild type but of a lesser extent . Only for swimming is the effect greater on the mutant . But the effect consists of swimming faster after NAC treatment and thus bringing the mutant phenotype closer to the wild-type . We conclude that there is little evidence of an indirect deleterious effect of NAC . NAC had only a moderate effect on the lifespan of the insulin-signaling daf-2 mutants ( Figure 2E ) , suggesting that only a small part of the increased longevity of these mutants requires elevated mitochondrial superoxide . However , NAC fully abolished the increased lifespan of sod-2 mutants ( Figure 2F ) , suggesting that , although increased generation of superoxide and other ROS as detected by our techniques were not significantly altered in these mutants , their increased lifespan depends on an elevation of superoxide or some other ROS . NAC did not shorten the lifespan of clk-1 mutants at 10 mM ( Figure 2D ) , or even at 15 mM ( Table S1 ) , indicating that ROS metabolism is relatively irrelevant to the aging phenotype of these mutants . The effect of NAC on the lifespan of eat-2 could not be scored because NAC treatment rendered the animals unable to lay their eggs and they died from internal hatching at a young age . The origin of this effect is unknown . We also could not score the effect of NAC on RNAi-treated worms because 10 mM NAC was excessively damaging to the dsRNA-producing bacterial strain ( HT115 ) . To determine whether an elevation in mitochondrial superoxide generation is sufficient to increase lifespan , we used the superoxide generator PQ . Treatment of C . elegans with high concentration of PQ ( >0 . 2 mM ) is severely deleterious . We thus first tested the ability of PQ to increase ROS damage in the animals at a very low concentration ( 0 . 1 mM ) . We found that this treatment indeed measurably increased the level of oxidative damage to proteins at the young adult stage as assessed by determination of protein carbonylation ( Figure 3A ) and increased the expression of both the main cytoplasmic ( SOD-1 ) and the main mitochondrial ( SOD-2 ) superoxide dismutases ( Figure 3B and 3C ) . We then tested whether PQ could increase the lifespan of the wild type at three different concentrations ( 0 . 05 , 0 . 1 , and 0 . 2 mM ) and found that at all three concentrations both the mean and maximum lifespan were increased , with a maximal effect at 0 . 1 mM ( Figures 3D and 4A , and Tables 1 and S1 ) . The effect of 0 . 2 mM was less pronounced than that of 0 . 1 mM and similar to that of 0 . 05 mM , likely because at 0 . 2 mM a toxic effect starts to balance the pro-longevity effect . The effect does not depend on the exact chemical structure of paraquat , as benzyl-viologen , a compound with similar activity as PQ but structurally different , also increases lifespan ( Table S1 ) . A small effect of the prooxidant juglone under different conditions has also been documented previously [56] . The effect did not depend on an effect of PQ on the E . coli ( OP50 ) food source , as the effect was also observed with heat-killed cells ( Table S1 ) . Finally , the effect was not confined to development or adulthood as PQ prolongs lifespan whether provided only during adult lifespan or only during development ( Table S1 ) . PQ treatment failed to significantly prolong the lifespan on nuo-6 and isp-1 mutants ( Figure 4B and 4C , and Tables 1 and S1 ) . This experiment is equivalent to genetic epistasis analysis and suggests that nuo-6 , isp-1 , and PQ increase lifespan by the same mechanism . It also suggests that the maximum level of lifespan extension that can be obtained by increasing mitochondrial superoxide generation is already reached in these two mutants and further increase of superoxide generation through PQ treatment cannot increase lifespan further . This was not due to a resistance of these mutants to PQ as 0 . 2 mM PQ shortened the lifespan of the two mutants ( Table S1 ) . sod-2 mutants , whose longevity is suppressed by NAC , are not as long-lived when untreated as wild type animals that are treated with PQ . However , treatment with PQ makes the sod-2 mutants live as long as wild type animals treated with PQ ( Figure 4G ) . This absence of additivity suggests that the longevity of sod-2 mutants is indeed due to a small increase in superoxide , as expected from the function of SOD-2 , and the suppressing effect of NAC on the mutant lifespan . In contrast to what we observed with nuo-6 , isp-1 , and sod-2 , PQ treatment dramatically enhanced the lifespan of clk-1 and eat-2 mutants , significantly beyond the longevity increases induced by the mutations alone or by PQ applied to the wild type ( Figure 4D and 4E , and Tables 1 and S1 ) . This indicates that the effects of these mutations and the effect of superoxide are mechanistically distinct and additive , as expected from the finding that clk-1 and eat-2 mutants did not show increased mitochondrial superoxide levels ( Figure 1F ) and that the lifespan of clk-1 mutants could not be shortened by NAC treatment ( Figure 2D ) . PQ treatment had only a small lifespan-lengthening effect on daf-2 ( Figure 4F , and Tables 1 and S1 ) , which is consistent with the finding that daf-2 mutants already show a substantial increase in superoxide generation . We sought to determine whether the mutations and the PQ treatment had other common effects on mitochondrial function that could be responsible for the increased lifespans , besides elevation of superoxide levels . Work in other systems has suggested that increased mitochondrial biogenesis could impact lifespan positively [57] , [58] , [59] , and mitochondrial defects in C . elegans have been found to stimulate mitochondrial biogenesis , resulting in a denser mitochondrial network [13] . We have examined mitochondrial density in the two mitochondrial mutants and in PQ-treated worms with Mitotracker Red , which is specific to mitochondria in mammalian cells [60] , [61] , which stains worms uniformly , and whose staining fully overlaps with that of mitochondrially targeted green fluorescent protein ( GFP ) ( Figure S3 ) . We found that isp-1 and nuo-6 display a denser mitochondrial network , as expected ( Figure 5 ) . However , this was not observed in wild type worms treated with PQ ( Figure 5 ) , indicating that the mechanism by which the superoxide triggers longevity does not require increased mitochondrial biogenesis . We also tested the effects of PQ and NAC treatment on oxygen consumption and ATP levels in the wild type and in the two mitochondrial mutants ( Figure S4 ) . NAC treatment increased oxygen consumption in the wild type and in the mutants . This result uncouples oxygen consumption from lifespan as NAC has no effect on the lifespan of the wild type , and its effect on the oxygen consumption of isp-1 mutants is larger than on that of nuo-6 mutants , although its effect on aging is smaller . PQ had an effect only on nuo-6 , and it was small . Thus the effect of PQ on oxygen consumption also did not mirror its effect on lifespan . For ATP levels the only effect observed was a reduction by PQ of the elevated ATP levels that are observed in nuo-6 mutants . daf-2 mutants have elevated superoxide levels , and they are sensitive to NAC ( lifespan shortening by 15% ) . However , the level of superoxide in daf-2 appears not to be sufficient for a maximal effect as these mutants remain somewhat sensitive to PQ ( lifespan lengthening by 9% ) . To further study how superoxide plays a role in the lifespan of daf-2 we studied genes that function downstream of daf-2 . At least three genes are known to be required for the full lifespan extension of daf-2 , that is , daf-16 , aak-2 , and hsf-1 [62] , [63] , [64] . If one of these genes were necessary for an activity that mediates the small effect of PQ on daf-2 mutants , PQ should not be able to prolong the lifespan of mutants of such a gene . In fact , however , we found that PQ prolonged the lifespan of all three mutants tested ( Table 1 ) . The lifespan increase upon PQ treatment of daf-16 ( 35% increase ) and aak-2 ( 29% increase ) is not as large as upon treatment of the wild type ( 58% increase ) . This suggests that part but not all of the lifespan increase determined by superoxide requires daf-16 and aak-2 . These findings are consistent with the observations that the lifespan extension provided by nuo-6 and daf-2 ( e1370 ) are only partially additive ( Table S1 ) , similarly to what was found previously for isp-1 and daf-2 [12] , and that elimination of daf-16 partially shortens the lifespan of isp-1 [12] . We also tested the sensitivity to PQ of mutants that are diagnostic of a variety of pathways of aging . In particular mutants of genes that , based on their known functions in C . elegans or that of their homologues in other systems , might encode the targets of superoxide signaling or be otherwise necessary for implementing superoxide signaling . The c-Jun N-terminal kinase 1 ( JNK-1 ) is involved in stress responses in vertebrate cells and is a positive regulator of DAF-16 that acts in parallel to the effect of daf-2 on daf-16 [65] . We treated jnk-1 ( gk7 ) mutants with PQ and obtained a particularly large lifespan increase ( Table 1 ) . Although it is not clear what activities lie upstream of jnk-1 in C . elegans nor whether it has other targets than daf-16 , its activity does not appear necessary for the effect of superoxide . The transcription factor SKN-1 defends against oxidative stress by mobilizing the conserved phase II detoxification response and can delay aging independently of DAF-16 [66] . Although PQ induces oxidative stress and induces enzymes that protect from oxidative stress ( Figure 3 ) , it was still able to prolong the lifespan of skn-1 ( zn67 ) mutants ( Table 1 ) , indicating that skn-1 does not act downstream of superoxide . wwp-1 encodes a conserved E3 ubiquitin ligase that is necessary for lifespan extension by dietary restriction [67] . Treatment of wwp-1 ( ok1102 ) with PQ prolonged lifespan of these mutants , which is consistent with our finding that PQ can considerably extend the lifespan of eat-2 mutants ( Figure 4E ) . This confirmed that the lifespan increase produced by the superoxide increase in mitochondrial mutants is distinct from the mechanisms that support the lifespan effects of dietary restriction [14] . hif-1 encodes a worm homologue of the vertebrate hypoxia inducible factor 1α ( HIF-1α ) , a transcription factor involved in a number of protective mechanisms . In C . elegans hif-1 is necessary for a lifespan pathway that involves proteolysis and that is distinct from insulin signaling [68] and has also been involved in the dietary restriction pathway [69] . In vertebrates HIF-1α is positively regulated by mitochondrial ROS [34] , [35] , which would make it an interesting candidate to mediate the effects of superoxide . However , PQ was fully capable of increasing the lifespan of the hif-1 mutants ( Table 1 ) . Several of the genes whose mutants remain sensitive to PQ , including daf-16 , have been involved in stress responses , including oxidative stress , yet they do not seem necessary for the effect of PQ . Similarly we have shown previously that although the expression of SOD-1 and SOD-2 are elevated in isp-1 ( qm150 ) mutants , the elevation is not necessary for the extended lifespan of these mutants [24] . nuo-6 ( qm200 ) mutants also show elevated SOD-1 and SOD-2 expression [14] , but this too is unnecessary for the longevity of the mutants , as RNAi against sod-1 an sod-2 , which we have shown to be efficient in reducing enzyme levels [24] , does not shorten the lifespan of nuo-6 mutants ( Figure S5 ) . We conclude that the mitochondrial mutants protect from an aspect of the aging process that has not yet been studied through mutants that affect stress . In addition , our observations suggest that the lifespan effect we observed is not hormetic , as neither superoxide-detoxifying enzymes , nor the regulatory factors that are involved in protection from oxidative stress , are crucially implicated .
We have shown previously that mutations in isp-1 and nuo-6 prolong lifespan by a common mechanism [14] . Using direct measurement of ROS and superoxide we find here that this mechanism involves an increase in mitochondrial superoxide generation that is necessary and sufficient for the longevity of these mutants . As ROS , including superoxide [70] , [71] , [72] , are known to be intracellular messengers , the increased superoxide might trigger a signal transduction pathway that ultimately results in changes in nuclear gene expression [23] . Superoxide is highly reactive and could trigger such a signal by modifying proteins in the mitochondria or in the nearby cytosol after having escaped from the mitochondria through an appropriate channel [73] , [74] . Although no superoxide sensor has yet been identified , a similar type of mechanism , in which a highly reactive , quickly diffusing , molecule modifies a signal transduction protein , has been evidenced for nitric oxide ( NO ) , which covalently and permanently modifies guanylyl cyclases . Similarly , hydrogen peroxide ( H2O2 ) , the product of superoxide dismutation , can inactivate phosphatases involved in signal transduction . Future work will aim at using forward and reverse genetic screens in C . elegans to uncover the molecular machinery that reacts to the superoxide signal , as well as the transcription factors that are needed to regulate nuclear gene expression in response to the pathway's activation . In addition , the pattern of gene expression that results in increased lifespan in these mutants could be defined very specifically by identifying changes in the gene expression patterns that are common to isp-1 , nuo-6 , and PQ treatment and that are suppressed by treatment with NAC . A number of studies in C . elegans have explored hormesis by treating animals with sub-lethal but clearly deleterious treatments for a short period of time and observing subsequent prolongation of lifespan [75] . These hormetic effects are different from what we have observed and describe here , as both the genetic mutations and the very low level PQ are present throughout life and as only a part of the effect we observe might require the insulin signaling pathway . Furthermore , although in nuo-6 and isp-1 mutants the expression levels of the superoxide dismutases SOD-1 and SOD-2 are elevated , likely in response to the elevated superoxide generation , and as one expects in the hormetic response , these elevations are not necessary for the lifespan prolongation of nuo-6 ( Figure S5 ) or isp-1 [24] . CLK-1 is a mitochondrial protein that is required for ubiquinone biosynthesis and its absence affects mitochondrial function [50] , although it could potentially affect many other processes as ubiquinone is found in all membranes . Furthermore , ubiquinone is both a prooxidant as co-factor in the respiratory chain and an anti-oxidant . Interestingly , the mechanism of lifespan prolongation induced by clk-1 appears to be entirely distinct from , but particularly synergistic with , that induced by elevated superoxide . Indeed , clk-1 mutants do not show elevated superoxide generation and are not affected by NAC . Furthermore , although double mutant combinations of clk-1 with nuo-6 and isp-1 are not viable ( unpublished data ) the lifespans of clk-1 mutants treated with PQ ( Figure 4D ) , or of sod-2;clk-1 mutants [26] , or of clk-1;daf-2 mutants [76] are much greater than expected from simple additivity of the effects of individual mutations or treatments . Studies in yeast [77] and in worms [78] have suggested that an increase in ROS from mitochondria might also be important in triggering the lifespan extension produced by glucose restriction . However , our results here with an eat-2 mutation , one of the ways in which global dietary restriction can be produced in worms , as well as with a wwp-1 and hif-1 , which may function downstream of dietary restriction , did not reveal an involvement of superoxide signaling , providing further evidence for a distinction between the mechanisms of glucose restriction and dietary restriction . It remains possible , however , that DR could lead to superoxide or ROS production when it is induced by other methods than the use of an eat-2 mutant , as it is well documented that different types of DR induce different molecular mechanisms [79] . One question that our current experiments do not address is whether the mitochondrial dysfunction in the mutants , or the effect of PQ , is necessary in every tissue in order to increase longevity . There are indications for both the insulin signaling pathway mutants [80] , [81] and dietary restriction [67] , [82] that the entire effect might be mediated by action in particular cells that influence the physiology of the whole organism . Similarly , the presence or absence of the germline is sufficient for a dramatic effect on lifespan [83] . For mitochondrial dysfunction the question could be addressed in the future by mosaic analysis and by purifying and analyzing mitochondria from specific tissues using our flow cytometry technique to purify mitochondria expressing GFP in a tissue-specific manner . The oxidative stress theory of aging has been one of the most acknowledged theories of aging for the simple reason of the strikingly good correlation between the levels of oxidative stress and the aged phenotype [8] . A number of recent results in worms and in mice , however , have suggested that oxidative stress cannot be the cause of aging [24] , [25] , [26] , [30] . Our findings suggest a conceptual framework for why oxidative stress and the aged phenotype are so tightly correlated [31] . In this model mitochondria , like the rest of the cell , sustain a variety of age-dependent insults ( not only and not even principally from oxidative stress ) that trigger an increase in superoxide , which acts as a signal that induces general protective and repair mechanisms . However , aging in most animals is clearly irreversible , indicating that the protective mechanisms , which must have evolved to control damage in young organisms , are unable to fully prevent the accumulation of age-related damage . Thus , as superoxide is a reactive molecule as well as a signal , and as age-dependent damage cannot be fully reversed , it is possible that at high ages the chronically elevated superoxide will participate in creating some of the damage itself . This could explain the strong tendency for aged animals to have high oxidative stress and high oxidative damage , although it does not imply that ROS cause aging or even that they are a major source of age-dependent damage . In this model , the nuo-6 and isp-1 mutations lead to increased longevity because they turn on the stress signal prematurely and thus slow down the entire process .
Eggs were placed on plates at 20°C and left for 1 h to hatch . Larvae that had hatched during that period were placed onto fresh plates and monitored once daily until death . The animals were transferred once daily while producing eggs to keep them separate from their progeny . Animals were scored as dead when they no longer responded with movement to light prodding on the head and tail . Missing worms and worms that have died because of internal hatching ( bagging ) were replaced from a backup group . Survival was scored every day . Drugs were added into NGM media from a high concentration stock solution ( 500 mM for NAC , 1 M for PQ , and 500 mM for vitamin C ) before pouring of the plates . Plates were made fresh each week . Gravid adult worms were transferred from normal NGM plates to drug plates and left to lay eggs for 3 h . With each transfer of worms a substantial amount of bacteria was also transferred onto the new plates . The progeny was then scored for different phenotypes . All dyes except MitoSox were diluted in DMSO at high concentration ( all at 5 mM except H2DCFDA , which is at 10 mM ) and frozen at −20°C as a stock . MitoSox was prepared fresh at 5 mM for each use . Before staining stocks were diluted in M9 buffer at a 1∶1000 dilution . Young adult worms were transferred into staining solution and stained for 20 min . Worms were mounted on a thick layer of half-dried agar pad on microscopic glass slides and then subjected to confocal microscopy ( Zeiss LSM 510 Meta ) . Pictures were taken by Zeiss LSM Imaging software and analyzed by Volocity V4 . 0 software . Five young adult worms ( 1st day of adulthood ) were placed into 0 . 25 µl M9 buffer in a 0 . 5 µl sealed chamber at 22°C . A fiber optical oxygen sensor ( AL300 FOXY probe from Ocean Optics ) was inserted into this chamber and oxygen partial pressure was monitored for 15 to 30 min . Oxygen consumption measured in this way was normalized to body volume . For this worms were photographed at each measurement day under a binocular microscope and their cross-section was calculated with ImageJ software . Worm volume was determined by the formula: volume ( nl ) = 1 . 849 • 10–7 ( nl/µm3 ) • area 1 . 5 ( µm3 ) [84] . After RNAi treatment , 100 young adult worms of each genotype were picked , lysed in 2× loading buffer , and subjected to electrophoresis in 12% SDS–polyacrylamide gels ( SDS–PAGE ) , and then blotted onto nitrocellulose membrane ( Bio-Rad ) . After applying primary antibody ( 1∶1000 , rabbit polyclonal antibody against worm SOD-1 or SOD-2 ) and secondary antibody ( 1∶10 , 000 mouse anti-rabbit IgG , Invitrogen ) , the membranes were incubated with the ECL plus detection reagent ( Amersham Biosciences ) and scanned using a Typhoon trio plus scanner . Band densities were analyzed by ImageQuant TL V2003 . 03 . For fluorescence activated cell sorting [37] , adult worms grown on large NGM plates were collected and washed 3 times with M9 buffer . Worms were then suspended in 5× isolation buffer ( 200 mM mannitol; 120 mM sucrose; 10 mM Tris; 1 mM EGTA; pH 7 . 4 ) and set on ice . Worms were broken up with a 5 ml glass-glass homogenizer and centrifuged at 600 g for 10 min , the supernatant was collected and re-centrifuged at 7 , 800 g for 10 min , and the pellet was washed once with isolation buffer and then suspended in isolation buffer and kept on ice . Different dyes were added from stocks into the analysis buffer ( 250 mM sucrose; 20 mM MOPS; 100 uM KPi; 0 . 5 mM MgCl2; 1 uM CsA pH 7 . 0 ) at a 1∶1000 dilution before staining . 100 µl of mitochondria was added to 900 µl of analysis buffer with dye and substrate and incubated for 1 h at room temperature . Mitochondria were recollected by 7 , 800 g centrifugation and then suspended in 500 µl analysis buffer . A FACSCalibur instrument equipped with a 488 nm Argon laser and a 635 nm red diode laser ( Becton Dickinson ) was used . Data from the experiments were analyzed using the CellQuest software ( Becton Dickinson ) . To exclude debris , samples were gated based on light-scattering properties in the SSC ( side scatter ) and FSC ( forward scatter ) modes , and 20 , 000 events per sample were collected , using the “low” setting for sample flow rate ( Figure S1 ) . 200 age-synchronized young adult worms were collected in M9 buffer and washed three times . Worm pellets were treated with three freeze/thaw cycles and boiled for 15 min to release ATP and destroy ATPase activity , and then spun at 4°C and 11 , 000 g for 10 min . ATP contents were measured with a kit ( Invitrogen , Carlsbad , California , USA; Cat: A22066 ) . The ATP content value was then normalized to the soluble protein level of the same preparation , measured with the protein assay from Bio-Rad . Mitotracker green ( Invitrogen M7514 ) stock concentration 5 mM; H2DCFDA ( Invitrogen D399 ) stock concentration 10 mM; Mitotracker red ( Invitrogen M7512 ) stock concentration 5 mM . | An unequivocal demonstration that mitochondria are important for lifespan comes from studies with the nematode Caenorhabditis elegans . Mutations in mitochondrial proteins such as ISP-1 and NUO-6 , which function directly in mitochondrial electron transport , lead to a dramatic increase in the lifespan of this organism . One theory proposes that toxicity of mitochondrial reactive oxygen species ( ROS ) is the cause of aging and predicts that the generation of the ROS superoxide should be low in these mutants . Here we have measured superoxide generation in these mutants and found that it is in fact elevated , rather than reduced . Furthermore , we found that this elevation is necessary and sufficient for longevity , as it is abolished by antioxidants and induced by mild treatment with oxidants . This suggests that superoxide can act as a signal triggering cellular changes that attenuate the effects of aging . This idea suggests a new model for the well-documented correlation between ROS and the aged phenotype . We propose that a gradual increase of molecular damage during aging triggers a concurrent , gradually intensifying , protective superoxide response . |
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Human African Trypanosomiasis is a devastating disease caused by the parasite Trypanosoma brucei . Trypanosomes live extracellularly in both the tsetse fly and the mammal . Trypanosome surface proteins can directly interact with the host environment , allowing parasites to effectively establish and maintain infections . Glycosylphosphatidylinositol ( GPI ) anchoring is a common posttranslational modification associated with eukaryotic surface proteins . In T . brucei , three GPI-anchored major surface proteins have been identified: variant surface glycoproteins ( VSGs ) , procyclic acidic repetitive protein ( PARP or procyclins ) , and brucei alanine rich proteins ( BARP ) . The objective of this study was to select genes encoding predicted GPI-anchored proteins with unknown function ( s ) from the T . brucei genome and characterize the expression profile of a subset during cyclical development in the tsetse and mammalian hosts . An initial in silico screen of putative T . brucei proteins by Big PI algorithm identified 163 predicted GPI-anchored proteins , 106 of which had no known functions . Application of a second GPI-anchor prediction algorithm ( FragAnchor ) , signal peptide and trans-membrane domain prediction software resulted in the identification of 25 putative hypothetical proteins . Eighty-one gene products with hypothetical functions were analyzed for stage-regulated expression using semi-quantitative RT-PCR . The expression of most of these genes were found to be upregulated in trypanosomes infecting tsetse salivary gland and proventriculus tissues , and 38% were specifically expressed only by parasites infecting salivary gland tissues . Transcripts for all of the genes specifically expressed in salivary glands were also detected in mammalian infective metacyclic trypomastigotes , suggesting a possible role for these putative proteins in invasion and/or establishment processes in the mammalian host . These results represent the first large-scale report of the differential expression of unknown genes encoding predicted T . brucei surface proteins during the complete developmental cycle . This knowledge may form the foundation for the development of future novel transmission blocking strategies against metacyclic parasites .
Sleeping Sickness , or Human African Trypanosomiasis ( HAT ) , is a fatal parasitic disease transmitted by the bite of an infected tsetse ( Glossina spp . ) fly . The disease agents are the extracellular protozoan parasites belonging to the Trypanosoma brucei species complex . It is estimated that 60 million people in 36 African nations are at risk for HAT . The same parasite species complex also infects animals causing nagana , an economically important disease of livestock in Africa . There are no mammalian vaccines for disease control and the drugs used for chemotherapy have major adverse effects , are difficult to administer and have decreased efficacy in light of the emergence of parasite drug resistance . A number of disease control strategies , mainly focused on vector control and treatment of infections , have been applied . These are often successful in the short term , although a sustainable long-term solution remains unidentified . African trypanosomes undergo multiple differentiation steps as they complete their life cycle in the challenging environments of the mammalian and invertebrate hosts . Trypanosomes circulating in the mammalian bloodstream ( bloodstream form , BSF ) are found as either long slender forms that perpetuate the infection in the mammal , or as short stumpy forms that are infective to the tsetse fly . In the mammalian host , BSF parasites evade the adaptive immune system by changing their surface coat molecules in a process known as antigenic variation [1] . Antigenic variation has effectively prevented the development of mammalian vaccines to date . In the tsetse flies , a strong immune response apparently clears the parasites in the majority ( over 95% ) of challenged tsetse [2] but those parasitized flies remain infected for their lifetime and contribute to disease transmission . Once acquired in an infected bloodmeal , trypanosomes undergo several stages of differentiation in the fly before they are transmissible to the mammalian host [3] . In the midgut , the stumpy BSF parasites differentiate to the procyclic form ( PF ) parasites . Although the majority of flies can clear parasite infections at this stage [2] , in flies where the PF cells survive , trypanosomes migrate anteriorly to the proventriculus , and differentiate initially into the mesocyclic trypomastigote , then long and short epimastigotes . It is thought that only the short epimastigotes can invade the salivary glands , where they attach and differentiate ultimately giving rise to the free mammalian infective metacyclic trypomastigotes ( MCF ) , which are transferred to the mammalian host in saliva as the infected fly blood feeds . Only the BSF and PF developmental stages of T . brucei can be maintained in culture in vitro . The remaining developmental stages of the parasite can only be maintained in tsetse , making the access to and evaluation of these life stages difficult . The genomes of several related kinetoplastid parasites have been published , including T . brucei brucei and Trypanosoma brucei gambiense [4]–[8] . Improved technologies such as RNA sequencing have identified over 1 , 000 new transcripts in T . b . brucei [9] . Particularly relevant for disease control tools are surface expressed proteins that interact with the host environment , and specifically with the host immune system . Protein features that are suggestive of surface expression are associated signal peptides , trans-membrane domains , and glycosylphosphatidylinositol ( GPI ) anchor attachment domains . Many GPI-anchored proteins in mammalian systems have been shown or predicted to have hydrolytic activity , or serve as receptors or adhesion molecules , while some are suggested to be involved in trans-membrane signaling or membrane trafficking [10] , [11] . The two well-studied GPI-anchored surface coat proteins of T . brucei are the variant surface glycoproteins ( VSGs ) and procyclins , expressed by the BSF and PF cells , respectively . The VSG coat of the BSF trypanosome allows the parasite to evade the adaptive immune response and therefore persist in the mammalian host . The procyclins were initially thought to shield PF parasites from the digestive enzymes of the fly midgut [12] , but procyclin-null mutant trypanosomes were subsequently found to be capable of infecting tsetse [13] . BARP , a third GPI-anchored surface protein family identified in T . brucei [14] is expressed by immature salivary gland stages [15] . Functional assessment of the BARP proteins have not yet been described , so it is unknown if trypanosome survival or maturation in the salivary gland environment would be influenced in their absence . The serum resistance associated protein ( SRA ) , which allows the survival of Trypanosoma brucei rhodesiense in the human host was recently determined to be GPI-anchored [16] , demonstrating the role of GPI-anchored proteins in the host-range of this pathogen . Additionally , a sub-unit of the transferrin receptor ( ESAG6 ) was also demonstrated to be GPI-anchored [17] , and work continues on the characterization of this molecule . Here , we report on the differential expression of transcripts corresponding to putative GPI anchored proteins with unknown functions in T . brucei . The selected genes were initially identified in silico using the Big PI and subsequently by the FragAnchor GPI-prediction algorithms . The signal peptide and trans-membrane domains of the putative proteins were also analyzed in silico . Gene expression data was obtained from parasites infecting the tsetse and mammalian hosts . We discuss the implications of the observed transcript expression profiles with regard to parasite survival and transmission processes , with consideration of the mammalian infective metacyclic trypomastigote .
This experiment was carried out in strict accordance with the recommendations in the Office of Laboratory Animal Welfare at the National Institutes of Health and the Yale University Institutional Animal Care and Use Committee . The experimental protocol was reviewed and approved by the Yale University Institutional Animal Care and Use Committee ( Protocol 2011-07266 ) . Genes encoding putative GPI anchor attachment domains were identified in silico and manually curated during the annotation of the first publicly available T . brucei brucei strain 927 genome sequence [4] . GPI anchor predictions were made by the consortium using the publicly available software Big-PI Predictor ( http://mendel . imp . ac . at/gpi/gpi_server . html ) [18] . The known GPI anchored protein families , such as VSG and procyclin , were removed from the resulting list . A second program , FragAnchor ( http://navet . ics . hawaii . edu/~fraganchor/NNHMM/NNHMM . html ) , was applied to genome annotation data from version 4 of the T . brucei genome [19] . Non-VSG , non-procyclin genes were categorized as hypothetical , hypothetical conserved , or annotated with known functions , according to the parameters set by the T . brucei genome consortium . Interpro domains associated to these genes were retrieved from TriTrypDB ( http://tritrypdb . org/tritrypdb/ ) . Gene products with less than 36% identity to a match in the public databases were considered hypothetical proteins , having no known function . When protein identity levels of 36% and higher to other hypothetical proteins were detected , the protein was considered hypothetical conserved . Hypothetical conserved genes , which were predicted to have homologs within the T . brucei genome , were further classified as hypothetical gene family members . Homology to other kinetoplastid species was determined using either existing data on the Sanger T . brucei website ( for Leishmania major and Trypanosoma cruzi , ( TriTryp ) ) , or by using the omniBLAST protein search function on the Sanger GeneDB website ( http://www . genedb . org ) . Signal peptide and cleavage site predictions were determined by SignalP ( http://www . cbs . dtu . dk/services/SignalP/ ) [20] . Trans-membrane predictions were made using DAS Software ( http://www . sbc . su . se/~miklos/DAS/ ) [21] . Predictions of glycosylation sites were performed using the NetNGlyc 1 . 0 ( http://www . cbs . dtu . dk/services/NetNGlyc/ ) and NetOGlyc Servers ( http://www . cbs . dtu . dk/services/NetOGlyc/ ) [22] . All prediction software is publically available on the internet . The parasite strains used were T . b . brucei RUMP 503 and T . b . rhodesiense YTAT 1 . 1 . For gene expression analysis , RNA was prepared from BSF T . b . rhodesiense expanded in rats . Trypanosomes were harvested from infected blood at peak parasitemia using DEAE cellulose chromatography [23] , [24] . For fly infections , BSF T . b . brucei expanded in rats were cryopreserved for subsequent use . Newly emerged male flies from the Glossina morsitans morsitans colony maintained in the Yale insectary received 2×106–2×107/mL T . b . brucei parasites in defibrinated bovine blood meal diet using an artificial membrane system [25] . After a single parasite challenge , flies were maintained on defibrinated bovine blood provided every other day . Flies were dissected after a minimum of 40 days post infection ( dpi ) and 72 hrs after their last blood meal . Salivary gland ( SG ) infection status was microscopically determined on a Zeiss Axiostar Plus light microscope at 400× . Infected SG , proventriculus ( PV ) and midgut ( MG ) tissues from the same flies were collected in Trizol , vortexed , and midguts were homogenized immediately . Metacyclic form ( MCF ) parasites were obtained by collecting the blood remaining on the feeding apparatus after flies with mature SG infections were fed . Blood was collected in PSG buffer ( 0 . 04 M Na2HPO4 2 H20 , 0 . 006 M NaH2PO4 2 H2O , 0 . 07 M NaCl , to pH 8 . 0 with 1 M H2PO4 ) , centrifuged 5 min . at 3000 rpm , and the pellet was resuspended in Trizol and stored at −20°C until RNA isolation . Total RNA was isolated from fly tissues and infected blood using Trizol extraction , according to manufacturer's instructions ( catalog no . 15596-026 , Invitrogen , California ) . Genomic DNA was removed by incubation with DNAse I , according to manufacturer's protocol ( catalog no . 04716728001 , Roche , Indiana ) . Reverse transcription was performed according to manufacturers instructions for oligo d ( T ) primed reactions ( SuperScript II Reverse Transcriptase , catalog no . 18064-014; RNaseOUT , catalog no . 10777-019 , Invitrogen , California ) . Nucleotide sequences for all experimental genes were obtained from the publicly available genome reference at the Sanger Institute ( http://www . genedb . org/Homepage/Tbruceibrucei927 ) . Primer sequences were identified by using the OligoPerfect™ Designer primer design tool ( http://tools . invitrogen . com/content . cfm ? pageid=9716 ) ( see Table S1 ) . All primer sets were used in a PCR amplification reaction with gDNA to confirm that they amplified a single gene fragment of the expected size . PCR amplification conditions were: 2 minutes hot start at 95°C , 32 cycles at ( 95°C for 45 s , 53°C for 45 s , 74°C for 1 min ) and 74°C for 6 min . Primers used to amplify procyclin transcripts were designed to recognize both EP and GPEET procyclin . The trypanosome structural gene alpha-tubulin was used for normalization of experimental cDNAs: trypanosome infected tsetse SG , PV , and MG , as well as BSF obtained from infected rats . Five and ten-fold serial dilutions of each cDNA pool were analyzed by PCR for the presence of alpha-tubulin transcripts . Cycling conditions were: 2 min at 95°C , 28 cycles at ( 95°C for 45 s , 53°C for 45 s , 74°C for 1 min ) and 74°C for 6 min . The PCR amplification products from the different cDNA dilutions were resolved on a 1% agarose gel , visualized on a KODAK Image Station 2000R and gel images were captured using the IS2000R Image Aquire Software ( Eastman Kodak Co , Rochester New York ) . The cDNA dilutions that resulted in PCR products of equal intensity from the different tissue samples were identified and all subsequent PCR reactions were performed using these cDNA template dilutions . All experimental reactions were performed using the cDNA templates prepared as described above at 32 and 36 cycles for each sample in duplicate . As controls , alpha-tubulin and BARP sequences were amplified at 32 and 36 cycle reactions , respectively . Primer sequences can be found in Table S1 . All amplification products were analyzed by electrophoresis and imaging as described above . Genes that resulted in no amplification products or that yielded multiple bands after amplification were excluded from further analysis ( Table S2 ) . Expression analysis was repeated for genes that yielded a product in only one tissue cDNA or for genes with unclear results due to low levels of expression . Gel images obtained from the 36 cycle reactions were used to obtain a semi-quantitative measurement of expression variation between different developmental samples . The values were normalized to the trypanosome alpha-tubulin control to account for variation between the four experimental tissue samples ( SG , PV , MG , and BSF ) and experimental runs . The adjusted expression values based on alpha-tubulin levels were used to categorize the expression profile of experimental genes . Based on these adjusted values , the fold change was calculated for the four developmental samples tested , for each gene yielding expression data . Where no expression could be detected , that transcript was classified as not detected ( nd ) . If expression in one tissue was at least 2-fold higher than any other tissue , that gene was classified as being specific to that tissue . Parasite gene expression was classified as preferential for a tissue ( or tissues ) when gene expression was detected but the levels were less than 2 fold higher than that detected in other tissues . Genes with expression levels too low to be confidently categorized , or with expression profiles not corresponding to any other category were classified as miscellaneous . Expression levels were classified as high , medium or low based on the adjusted net intensities of the most prominent band for the experimental gene . Net intensity values ≥501 were classified as high , 101–500 as medium , and 0–100 as low . All expression data are being submitted to TriTrypdb . org . To validate the expression profiles observed with semi-quantitative analysis , 5 genes were selected for quantitative RT-PCR ( qRT-PCR ) . Standard curves were developed for each gene using serial dilutions of plasmids containing cloned inserts . Each standard was used to calculate transcript numbers in the experimental cDNAs tested . qRT-PCR primers and cycling conditions are listed in Table S3 . All reactions were performed on an icycler iQ real time RT-PCR detection system ( Bio-Rad ) . Three independent biological replicates of infected SG , PV and MG tissues were used , with 2 technical replicates per sample . For comparison to the quantitative data , the semi-quantitative fold change data was evaluated based on the SG , PV , and MG data points . As no BSF samples were evaluated by qRT-PCR , the semi-quantitative data for the BSF parasites was excluded from this comparison . Alpha-tubulin levels were used for expression normalization . Values are represented as the mean fold change ( ±SEM ) and statistical significance was determined using a Student's t test implemented in Microsoft Excel software .
An in silico analysis of the T . b . brucei strain 927 genome data using the BigPI GPI-anchor prediction software identified 163 putative proteins with GPI anchor attachment motifs . Fifty-seven of these gene products had known or predicted functions such as BARP , GP63 , trans-sialidase and the procyclin-associated genes , and were excluded from further analysis ( Table S4 ) . The remaining 106 putative proteins were evaluated for the presence of conserved domains ( Table S5 ) . These putative products were further searched for glycosylation , signal peptide , and trans-membrane domains , and a second predictive algorithm for GPI-anchor attachment domain ( FragAnchor ) was applied ( Table S6 ) . Typical GPI anchored proteins are expected to have a signal peptide and no trans-membrane domains [26] . Our analysis reduced the initial 106 genes down to 25 genes , which were predicted to encode products with GPI-anchor attachment motifs ( Table 1 ) . Of the 25 highly probable GPI-anchored gene products with unknown functions , only Tb09 . 142 . 0410 was considered to be hypothetical , having no identified homologs . Two genes ( Tb927 . 4 . 3290 and Tb927 . 10 . 990 ) were shared only with Trypanosoma congolense , while five others were conserved at the level of the TriTryp genomes . Seventeen genes were identified to be members of larger gene families . Interestingly , these were not widely shared between related kinetoplastids . Only one family ( Tb927 . 8 . 930 and Tb927 . 8 . 950 ) had homologs outside of the T . brucei complex , and these were found in Trypanosoma vivax . The remaining gene family members were either detected only as repeated genes in the genome of T . b . brucei ( 9 ) , or as having homologs in the genome of T . b . gambiense ( 6 ) .
Here we report on the identification of T . brucei genes encoding predicted unknown surface proteins obtained via in silico GPI-anchor attachment signal sequence prediction analysis . Expression profiling analysis from mammalian and tsetse developmental stages indicate that transcripts for the majority of the hypothetical and hypothetical conserved proteins are expressed in parasites during their development in the tsetse salivary glands and proventriculus . Most notably , we identified 8 trypanosome genes specifically expressed in parasitized salivary glands , expression for all of which was also detected from mammalian infective MCF trypanosomes present in fly saliva . The results of this analysis give the first large-scale insight into stage-regulated expression of genes encoding putative hypothetical surface proteins during key developmental processes in the tsetse fly , and support the established paradigm of differential expression through development . Functional characterization of these unknown proteins , particularly expressed by metacyclics in saliva , ay lead the way to novel transmission blocking strategies in the mammalian host . Proteins with GPI posttranslational modification are typically expressed on the surface of eukaryotic parasites and have the potential to participate in important biological processes such as cell–cell interactions , signal transduction , endocytosis , complement regulation , and antigenic presentation [27] . In protozoan parasites , GPI anchored glycoconjugates extensively coat the plasma membrane and are involved in many aspects of host–parasite interactions , such as adhesion and invasion of host cells , modulation and evasion from host immune response [26] . As such , there is interest in identifying the surface proteins of the medically important kinetoplastids , as reported in L . ( V . ) braziliensis and T . cruzi where proteomic techniques were applied to capture this class of proteins [28]–[30] . Current knowledge of the VSGs and procyclins , two of the best characterized GPI-anchored surface proteins of T . brucei has demonstrated the importance of these proteins in trypanosome developmental processes . Further , GPI biosynthesis has also been implicated as a molecular target for development of new drugs against African sleeping sickness [31] , [32] . The availability of the T . brucei genome allows for postgenomic discoveries including screens for hallmark motifs such as GPI anchor attachment signals associated with surface proteins [26] . Several publically available programs can be used to predict post-translational modifications ( PTM ) such as glycosylation and GPI-anchor attachment , although a gold standard for prediction software remains to be found [33] . As a result , experimental validation of predicted features is always warranted . The quality of predictive algorithm outputs vary in response to several factors . In the case of GPI-anchor prediction , variables include the size of the motif recognized , quality of the underlying data used to test the algorithm , and correct application of learning procedures such as neural networks [34] , [35] . The ideal tool would have high sensitivity to detect true positives , with a low false prediction rate [33]–[35] . Also relevant is the biological context being considered , as a result there are algorithms specifically for protozoa , fungi , plants , etc [34] . As seen with our dataset , two algorithms can generate different results from the same dataset . In this work , FragAnchor agreed with most , but not all of those genes previously identified by a BigPI search specific for protozoa GPI anchor attachment domains . A similar outcome with these two programs was reported after testing both against known positive and negative control GPI-anchored protein datasets [34] , and against a dataset from the protozoan pathogen Plasmodium falciparum [19] . In both of these cases , although correct identification of true GPI-anchored proteins was high , the false positive rate was high as well . Conversely , another group found FragAnchor to be more accurate than BigPI , while maintaining the same false positive rate [35] , although limitations associated with the algorithm they employed for comparison make it difficult to draw clear conclusions [34] . With these challenges in mind , we opted for a conservative approach in the identification of putative GPI-anchored proteins by selecting only those genes encoding products that showed agreement between the two predictive programs . As the absence of predicted trans-membrane domains is necessary to support a prediction of GPI-anchoring [26] , we further excluded putative proteins bearing any predicted trans-membrane domains from expression analysis despite predictions of GPI-anchoring . While the presence of a GPI anchor attachment signal suggests cell surface membrane expression as mentioned earlier , there is evidence that both N- and O- glycosylation status directs nascent proteins to the apical region [35]–[37] . Like GPI anchor attachment sites , glycosylation sites can be predicted using in silico methodology . Importantly , while the presence of predicted glycosylation sites support the expectation of surface expression , the absence of glycosylation does not imply a lack of surface expression of a protein [38] . Fifty-six of the in silico-identified genes in the T . b . brucei genome had known or predicted functions in other closely related kinetoplastid parasites and were not pursued for further expression analysis . These included all members of the BARP family , and many genes with putative functions , such as GP-63 surface protease ( 5 copies ) , trans-sialidase ( 4 copies ) , procyclin associated gene 4 ( 2 copies ) , and numerous carrier or transporter proteins . Our aim was to identify unknown SG stage-regulated genes for downstream characterization and investigation as novel transmission blocking targets . Of the 163 non-procyclin , non-VSG coding genes that were identified as encoding GPI-anchor proteins using the BigPI prediction software , 104 were confirmed with FragAnchor . With regard to possible function of these gene products , 106/163 had no known functions . A search of the available whole genome sequence information from T . b . gambiense , L . major , T . cruzi , T . congolense and T . vivax indicated that about 21% ( 22/106 ) of the identified genes were unique to T . b . brucei . With regard to the 25 genes that met our criteria to be considered likely to encode predicted GPI-anchored proteins , 5 were conserved at the level of the TriTryp genomes , 10 were shared with other species of Trypanosoma , and 10 were unique to T . b . brucei . It is possible that the lack of homologs in these genomes reflects the different biology of the parasite species , although it is also possible that as genome annotations improve homologs may be revealed . While T . b . gambiense is more closely related to T . b . brucei than the other trypanosomatid species analyzed , its biology differs from T . b . brucei . It remains to be seen if the unique genes in T . b . brucei genome contribute to its differing epidemiology . The annotated whole genome sequence of T . b . rhodesiense is not yet available , however , the status of T . b . brucei specific genes in T . b . rhodesiense is of interest both from an evolutionary and epidemiological point of view . Gene expression profiling analysis showed that the majority of the 21 genes for which we detected transcripts , are expressed by trypanosome developmental stages present in the tsetse fly PV and SG tissues , while comparatively fewer are expressed by mammalian bloodstream forms and none in the MG . A similar trend was found in genes encoding proteins with less likelihood of GPI anchoring . Similarly , a proteomic analysis that identified GPI-anchored molecules in T . cruzi insect-stage epimastigote cultures also found the majority of the identified proteins to be novel [30] . In the case of T . brucei , obtaining sufficient epimastigote and metacyclic parasites from infected tsetse flies for functional analysis is difficult since these stages are unculturable in vitro . Confirmation of the corresponding stage-regulated protein expression is a necessary next step , and the resulting data may shed light on the roles of these products in parasite biology . Complex gene expression profiles for putative surface proteins in the proventricular and salivary gland stages of T . brucei may reflect the multiple discrete trypanosome developmental stages infecting these tissues , or heightened sensitivity of these trypanosomes to the tsetse or mammalian bite-site host environment . Unlike the SG and PV , far fewer unknown putative surface proteins were associated with the BSF and MG stages . This minimal detection of unknown transcripts in PF and BSF samples may be related to the abundant expression of known GPI-anchored major surface proteins in these stages- specifically the procyclins and VSGs , respectively . Interestingly , genes encoding 8 of the 21 putative GPI-anchored proteins were specifically upregulated by parasites infecting tsetse SG . Although trypanosomes undergo four distinct developmental steps in this tissue , only two GPI-anchored protein families have been demonstrated on the surface of any SG stages to date . The alanine-rich BARP proteins are expressed on epimastigotes attached to the salivary gland epithelium . Free metacyclics in saliva no longer express BARP , but have upregulated the metacyclic variant surface glycoproteins ( M-VSGs ) in advance of inoculation into the mammalian host [17] , [39] . The data presented here suggest a more complex series of events may be involved in the maturation of the SG-inhabiting trypanosome stages . Proteins specifically expressed on the immature SG stages might be involved in host-parasite interactions and as such could be targeted to prevent parasite maturation in the fly using genetic modification strategies in the tsetse host [40] . On the other hand , proteins expressed on the mature metacyclics may present novel vaccine targets for use in the vertebrate hosts . Importantly , transcripts corresponding to the SG stage-regulated genes were not detected in the bloodstream form stages . Since the mammalian infective metacyclic trypomastigote is suggested to be “pre-adapted” to life in the vertebrate host , one could expect these samples to share proteins . There are two potential explanations for this observation . First , many gene products associated with adaptation to the vertebrate environment are likely to be intracellular i . e . related to energy metabolism , and therefore not bearing GPI-anchor attachment domains . As a result , these genes are expected to have been excluded from the in silico screen applied here . Second , when an infective fly bites the vertebrate host , metacyclic parasites are detected for several days with the bloodstream forms being not apparent until nearly a week after the infective bite [41] , [42] . Thus it is possible that transitional metacyclics ( t-MCFs ) , i . e . those detected in vertebrate blood in the days immediately after an infective tsetse bite , but before differentiation to the BSF , may have a transcriptome that reflects the parasite adaptation process from the environment of invertebrate saliva to vertebrate blood . MCF trypanosomes , like malarial sporozoites , are the critical developmental stage of the parasite which gives rise to infection in the vertebrate host . While considerable effort has been mounted towards development of a sporozoite vaccine for the prevention of malaria , this has not been the case with the MCF of T . brucei . To date , VSGs have effectively thwarted all attempts at developing a vaccine against the mature BSF . It is thought that MCF parasites also express variable proteins ( M-VSGs ) , which would hamper vaccine development efforts targeting MCF . Our results suggest however that GPI-anchored surface protein repertoire of MCF may be more complex and different from the BSF forms than originally thought . The expression of the genes encoding putative surface proteins on the mammalian-infective stage suggests a complex interface of MCF and mammalian bite-site . In summary , the in silico and semi-quantitative gene expression analyses approach used here has allowed an important first look at the stage-regulated expression of genes encoding putative GPI-anchored proteins with no known functions in the human and animal pathogen T . brucei . The findings presented here suggest that the tsetse host-parasite interplay during differentiation may be quite complex . Most importantly , these results greatly increase our understanding of trypanosome biology at the point of transmission to the vertebrate host , and identify a number of putative invariant surface proteins , which could be investigated further for novel transmission blocking strategies . | Human African Trypanosomiasis ( HAT ) is a fatal disease caused by African trypanosomes and transmitted by an infected tsetse fly . Presently , there are no vaccines to prevent mammalian infections . Proteins expressed on the trypanosome surface can influence the host environment and allow for their transmission . Potentially accessible to the adaptive immune systems of vertebrate hosts , these proteins could serve as future vaccine targets . Identification and characterization of these currently unknown proteins can help us develop strategies to alter the host environment , making it inhospitable for the parasite , thereby reducing disease transmission . While there is extensive knowledge about trypanosome development in the mammalian host , less is known about the molecular events in the tsetse fly , particularly the salivary gland stages . We used an in silico approach to identify putative surface proteins from the known genome sequence of Trypanosoma brucei , and we describe the stage specific expression of these genes during development in the tsetse fly and mammalian host . Our findings show that a majority of unknown transcripts encoding predicted surface proteins are expressed by the parasites infecting tsetse salivary glands . These data will help focus future investigations into transmission-blocking approaches targeting the expressed antigens of trypanosomes infecting tsetse salivary glands . |
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In the lifecycle of microorganisms , prolonged starvation is prevalent and sustaining life during starvation periods is a vital task . In the literature , it is commonly assumed that survival kinetics of starving microbes follows exponential decay . This assumption , however , has not been rigorously tested . Currently , it is not clear under what circumstances this assumption is true . Also , it is not known when such survival kinetics deviates from exponential decay and if it deviates , what underlying mechanisms for the deviation are . Here , to address these issues , we quantitatively characterized dynamics of survival and death of starving E . coli cells . The results show that the assumption – starving cells die exponentially – is true only at high cell density . At low density , starving cells persevere for extended periods of time , before dying rapidly exponentially . Detailed analyses show intriguing quantitative characteristics of the density-dependent and biphasic survival kinetics , including that the period of the perseverance is inversely proportional to cell density . These characteristics further lead us to identification of key underlying processes relevant for the perseverance of starving cells . Then , using mathematical modeling , we show how these processes contribute to the density-dependent and biphasic survival kinetics observed . Importantly , our model reveals a thrifty strategy employed by bacteria , by which upon sensing impending depletion of a substrate , the limiting substrate is conserved and utilized later during starvation to delay cell death . These findings advance quantitative understanding of survival of microbes in oligotrophic environments and facilitate quantitative analysis and prediction of microbial dynamics in nature . Furthermore , they prompt revision of previous models used to analyze and predict population dynamics of microbes .
Under favorable growth conditions , microorganisms can grow rapidly . For example , E . coli cells can grow as fast as ∼ 20 min per doubling under ideal growth conditions . If this rate continues , a single E . coli bacterium can generate the mass of the earth in a couple of days . Clearly exponential growth cannot be sustained infinitely . Eventually , nutrients required for cell growth will be depleted and cells will be subject to long periods of starvation . Indeed , a survey suggests that ecosystems are dominated by starving microbes [1] . Due to their dominance , understanding quantitatively how starving microbes live and die is of great interest in various fields of microbiology , ranging from analyzing microbial population dynamics in soils to predicting the number of microbes in freshwater . However , our quantitative understanding of survival kinetics of starving microbes is poor . In textbooks , survival kinetics has been commonly assumed as simple first order kinetics , i . e . , exponential decay [2 , 3] . In the literature , this assumption has been widely used as a basis for analyzing and predicting microbial population dynamics , e . g . , see [4–6] . This assumption , however , has not been rigorously tested . Currently , it is not clear under what circumstances this assumption is true . Also , it is not known when such survival kinetics deviates from exponential decay and if it deviates , what underlying mechanisms for the deviation are . A large body of studies exists that characterizes starvation response at the molecular level; for example , see [7–10] for complex signaling pathways and gene regulations in response to starvation in proteobacteria . However , our molecular-level knowledge is still far from complete even for model systems such as E . coli . Also , much of our knowledge is qualitative and it is not clear to what degree these molecular processes affect cell survival . Thus , our molecular-level knowledge has not contributed to quantitative understanding of survival kinetics of starving cells . In recent years , quantitative and phenomenological characterization of cellular processes proved to be a powerful approach for deeper understanding of complex biological systems , e . g . , see [11–15] . In particular , it was shown that despite complex underlying molecular interactions , simple phenomenological laws governing cellular-level behaviors can exist and such laws greatly facilitate deeper understanding of underlying mechanisms [16] . In this work , using such phenomenology-to-mechanism approach , we rigorously characterized cell survival under starvation using E . coli as a model system . We show that survival kinetics of starving E . coli is biphasic and cell-density-dependent . Quantitative analyses reveal simple quantitative formulas governing the patterns , e . g . , the first and second kinetics are well described by exp ( -t2 ) and exp ( -t ) respectively , and the duration of the first kinetics is inversely proportional to cell density . ( The results show that the previous assumption—exponential decay of survival of starving cells—is true only at very high cell density . ) Next , using this knowledge as a guide , we identified key underlying processes for cell survival . Using mathematical modeling , we showed how these processes contribute to the intricate survival patterns observed .
Cells were grown in minimal media with glycerol as the sole carbon source ( see Materials and methods ) . As cells grow , glycerol is consumed and eventually exhausted , leading to the cessation of growth; we provided low enough amounts of glycerol to ensure that the growth is arrested as a result of the exhaustion of glycerol , not by other nutrient sources ( S1 Fig; see Materials and methods for the exact glycerol concentrations used ) . The cultures containing different amounts of glycerol in the medium initially result in different saturating densities of cells at the onset of the growth arrest ( S1A Fig ) . The onset of growth arrest defines the time zero ( S1B Fig ) . Afterwards , the number of colony-forming units , NCFU , was determined at various time points using a standard plate count method . We define the cells that grow on the agar plates and form colonies as viable . The temporal kinetics of NCFU in glycerol-exhausted cultures with 5 different cell densities is plotted in Fig . 1 ( see S2 Fig for the kinetics of other cell densities ) . For the cultures whose densities are higher than ∼108 cells/ml , NCFU follows a single phase exponential decay . The black dashed lines are plotted for a visual guide and its slope , −μ0 ( = −0 . 018 hr -1 ) , corresponds to the rate of cell death in these cultures . Note that NCFU of starving wild-type E . coli cells reported previously in the literature can be well approximated by a single-phase exponential decay [17–19] . In the cultures with lower densities , however , we see biphasic kinetics of NCFU ( see black diamonds and red circles in Fig . 1 and purple hexagons and green triangles in S2 Fig ) ; NCFU gradually decreases initially ( the first phase ) and eventually decreases exponentially at the rate of −μ 0 ( the second phase ) . The period of the first phase becomes more pronounced at lower cell density , prolonging cell survival . When we repeated this experiment using other carbon sources or using a different E . coli strain , we observed similar density-dependent biphasic kinetics of NCFU ( S3 Fig and S4 Fig ) . Previously , it was shown that cultures starved of nutrients for a long time yield mutants with increased fitness , called the growth advantage in stationary phase ( GASP ) phenotype [20 , 21] . The appearance of GASP mutants results in visible change in NCFU; NCFU initially decreasing at a constant rate reaches a plateau when GASP mutants appear . The timing of appearance of GASP mutants depends on the types of media and bacterial strains used [21]; for example , in Luria-Bertani ( LB ) media , they appeared within several days of growth cessation [21] while in other starvation experiments using minimal media , they appeared after ~ 30 days of starvation [22] . In our experiment , using minimal media , we performed experiments for ~ 12 days . During this time , we did not observe such transition in NCFU ( i . e . , from a rapid decrease to a plateau ) ; see Fig . 1 . This strongly suggests that GASP mutants have not appeared during our experiments . Also , when we repeated the experiment using cells from single colonies obtained from the cultures starved for 12 days , we observed that NCFU of these cells decreases very similarly as NCFU shown in Fig . 1 ( S5 Fig ) . Taken together , we conclude that GASP mutants have not appeared and played no role in the survival kinetics observed in our experiments . It is well known that microorganisms use extracellular signaling to sense the density of the populations and coordinate their behaviors accordingly [23]; they secrete extracellular signals and such signals accumulate in the medium , allowing cells to sense the density and regulate their behaviors accordingly . The cost and benefit of such extracellular signaling has been recently demonstrated quantitatively [24] . It is possible that the density-dependent kinetics of survival and death observed in Fig . 1 could be also mediated by such extracellular signaling; such ( potential ) signals may accumulate to high levels in high cell-density cultures , or in low cell-density cultures at a later time , triggering a rapid , exponential decay of NCFU . Note that the second case requires the signals to be stable at least for ∼100 hrs ( see Fig . 1 ) . To test this possibility , we repeated the experiment above using media designed to remove or concentrate these potential secreted factors , as described below . First , cells were grown as in the previous experiment . When the growth stopped due to glycerol exhaustion at a high density ( NCFU ≈ 7·108/ml ) , we washed the cells and re-suspended them in a fresh medium without glycerol; the fresh medium would not contain these secreted factors . In Fig . 2A , we see that NCFU of cells in the fresh carbon-free medium ( green triangles ) decreases exponentially at a similar rate as NCFU from the previous experiment ( solid blue squares; re-plotted from Fig . 1 ) , indicating that the lack of these secreted factors has little effect on the kinetics . Second , we reason that a spent medium in which cells were grown previously to a high density would contain high levels of the secreted signals . Because the signals should be stable ( discussed above ) , starving cells at low cell density in such a medium would exhibit a rapid , exponential decay of NCFU , similarly to that in high cell density . We prepared such a spent medium ( the old cells were removed from the medium ) , added a low amount of glycerol and a low number of exponentially-growing cells to the medium , and grew them . After the growth was arrested at a low density ( NCFU ≈ 9·106/ml ) due to the exhaustion of glycerol , we measured NCFU over time ( see Materials and methods ) . We observed that NCFU from the spent medium ( green inverse triangles in Fig . 2A ) follows the same biphasic pattern as NCFU of the culture with a similar density from Fig . 1 ( compare green inverse triangles and solid red circles; the solid red circles are re-plotted from Fig . 1 ) . Taken together , these results indicate that the density-dependent kinetics of cell survival is not due to extracellular signaling . Previously , it was known that the master regulator of the general stress response rpoS plays an important role for survival of E . coli cells under various environmental stresses [7–10] . To examine a role of rpoS in the observed kinetics , we repeated our experiment ( that yielded Fig . 1 ) using the ΔrpoS strain and plotted NCFU as open symbols in Fig . 2B and S6 Fig . For all the densities tested , NCFU of the ΔrpoS strain decreases exponentially at the rate of −μoΔrpoS ( = −0 . 035 hr -1; see the dotted lines ) . This is higher than that of the wild type strain , −μ o ( = −0 . 018 hr -1 ) , consistent with previous observation [18 , 25] . NCFU of the low cell density culture of the ΔrpoS strain ( open red circles in Fig . 2B ) exhibits a period of gradual decay before it decreases exponentially at the rate of −μ oΔrpoS ( brown region ) . However , the period is much shorter than the period of gradual decay for the wild type cells ( green region ) ; note that the exact determination of this period is discussed below and in Fig . 3 . This indicates that rpoS plays an important role for the wild type strain to maintain NCFU for extended periods of time in low cell density . To quantitatively analyze the survival kinetics of the wild type cells under starvation , we re-plotted the data in a manner that reveals the power law exponent of exponential functions; we denoted the number of colony-forming units at the time zero by N0 and plotted log ( N0 /NCFU ) against time in a log-log plot ( Fig . 3 and S7 Fig ) . For example , if the kinetics of survival follows a first-order kinetics , meaning NCFU∝exp ( −c1⋅t ) , ( 1 ) where c1 is a coefficient , the plot of log ( N0 /NCFU ) yields a straight line with a slope of 1 ( orange line in Fig . 3A ) . If the kinetics follows NCFU∝exp ( −c2⋅t2 ) , ( 2 ) where c2 is a coefficient , the plot yields a straight line with a slope of 2 ( cyan dashed line ) . For high cell-density cultures ( i . e . , N0 ≥ ∼108 cells/ml ) , the data follows a straight line with a slope of 1 ( Fig . 3B and S7A Fig ) , indicating the temporal kinetics of NCFU is well described by Eq . ( 1 ) , i . e . , exponential decay , as discussed above . When we fit the data ( in Fig . 1 and S2 Fig ) using Eq . ( 1 ) , we see c1 remains constant for different N0 ( navy left triangle , blue square and orange right triangle in Fig . 3G ) and c1 ≈ μo ( = 0 . 018 hr -1 ) . For lower cell-density cultures ( i . e . , N0 < ∼108 cells/ml ) , the slope is initially 2 ( green region in Fig . 3C and 3D , and S7C Fig—S7E Fig ) , but becomes 1 later , revealing the biphasic decay seen in Fig . 1 at low density . Thus , the first phase and second phase of the survival kinetics are well described by Eqs ( 2 ) and ( 1 ) respectively . In these figures , the time at which the transition from the slope 2 and slope 1 occurs is marked as T0 ( see arrows; it is the time point at which the two lines intersect ) . In Fig . 3E , we see T0−1 is linearly proportional to N0 . Alternatively , T0∝N0−1 , ( 3 ) indicating the period of the first phase becomes shorter as cell density increases . This suggests that we observe only the second phase of survival kinetics in high density ( i . e . , exponential decay in Fig . 1 ) , because T0 is small . To obtain coefficients in Eqs ( 1 ) and ( 2 ) for the low cell-density cultures , we fit the data in Fig . 1 and S2 Fig using the equations; the data in t < T0 and in t ≥ T0 are fitted using Eqs ( 2 ) and ( 1 ) respectively . We see that c2 increases linearly to N0 in Fig . 3F . Hence , c2 = c ⋅ N0 , where c is a constant . Also , we see that c1 remains constant for different N0 , and c1 ≈ μo in Fig . 3G . Thus , together with Eq . ( 3 ) , the temporal kinetics of NCFU is well described by NCFU={N0⋅exp ( −c⋅N0⋅t2 ) N1⋅exp ( −μ0⋅t ) if0≤t<T0ift≥T0 , ( 4 ) where N1 is set to make NCFU a continuous function , being equal to N0exp ( −c⋅N0⋅T02+μ0⋅T0 ) . The quantitative formula ( Eq . ( 4 ) ) reveals that the previous assumption—NCFU decreases exponentially under starvation—is valid only at high cell density . At low cell density , however , NCFU gradually decreases initially , before it decreases exponentially . The initial gradual decrease , well described by exp ( -t2 ) , is extended at lower density , resulting in prolonged survival of starving cells . What is the mechanistic basis of the prolonged survival that appears in the density-dependent manner ? Because the kinetics is significantly altered in the ΔrpoS strain ( Fig . 2B ) , we first considered known regulation of RpoS expression and its effects on cell survival . As cells grow and consume substrates , the concentration of substrates in the medium will decrease ( green line in Fig . 4A ) . When the concentration falls to the level reducing the rate of cell growth , the expression of RpoS is activated ( blue line; note that higher RpoS levels at lower substrate concentrations were previously established [26 , 27] ) . The RpoS expression subsequently leads to expression of other new genes ( i . e . , RpoS regulon ) and the expression of these genes protects cells from stress [7–10] . Importantly , this protection is expected to be density-independent , because RpoS expression itself is independent of cell density [26 , 27] . In Fig . 3G , we see that in the second phase of the survival kinetics , NCFU decreases at the rate of −μo ( = −0 . 018 hr -1 , dashed line ) independently of cell density . This is lower than the rate of decrease in the ΔrpoS strain , −μ oΔrpoS ( = −0 . 035 hr -1 , see Fig . 2B ) , suggesting that the protection lowers the rate of viability loss during the second phase independently of cell density . This protection , however , is not likely to be a major cause for the extension of the first phase at low density , because the extension is strongly dependent on cell density; see Fig . 1 and Fig . 3E . ( There are studies suggesting that RpoS expression may be possibly higher at higher cell density [28 , 29] . Even if this is true , it cannot account for our observation that the first phase is extended further at lower cell density . ) Next , we turn to another major effect of RpoS . It is well known that the expression of RpoS represses cell growth ( red line in Fig . 4A ) [30–32] . Currently , the molecular mechanism of the repression is not clear , although it was proposed that RpoS directly inhibits the uptake of nutrients [39] . Importantly , with this repression , a negative feedback loop among RpoS , substrate concentration and cell growth is formed as depicted in Fig . 4A ( blue and green lines were described above ) . In biological systems , negative feedback is frequently employed to achieve a homeostatic maintenance or a gradual change of a system , e . g . , see ref . [40 , 41] . The negative feedback loop suggested above may play a similar role , providing a mechanism for a gradual change of NCFU observed in the first phase of the survival kinetics ( Fig . 1 ) for the wild type cells in the following way . For biomass increase , cells consume substrates in the medium . As the substrate concentration in the medium decreases to low levels due to the consumption ( green line in Fig . 4A ) , the feedback loop would exert repression on biomass increase ( blue and red lines ) , and hence , the substrate consumption ( green line ) . The repression would be stronger as the substrate concentration in the medium is further reduced . Eventually , it will lead to cessation or near-cessation of the substrate uptake for biomass increase , and consequently , prevents cells from completely depleting the substrate in the medium . Indeed , when we measured glycerol concentration in the medium at the onset of growth cessation ( time zero in Fig . 1 ) , we see that the glycerol concentration is not zero , but in μM range ( see Materials and methods ) . This observation also agrees with previous studies [33–35]; in these studies , it is shown that as the substrate concentration S decreases , the growth rate λ decreases , but λ becomes zero at a non-zero substrate concentration . Denoting this concentration by S1 , this phenomenon is illustrated in Fig . 4B as λ = 0 at S = S1 > 0 . Note that this is contrary to a prediction from the Monod equation , a well-known kinetic equation describing the relation between λ and S [42] , which predicts λ = 0 when S = 0; see S2 equation . However , the Monod equation does not address the decrease in a population size during starvation ( because λ in the Monod equation is always greater than or equal to 0 ) , and is not applicable to our study . In fact , the Monod equation is a purely empirical formula based on curve fitting of experimental data ( see the description below S2 Equation ) . A great deal of studies show that the Monod equation does not describe the dynamics of change in a population size well at very low substrate concentrations; see ref . [36 , 43] for review and S1 Text for details . Importantly , even when the growth rate of a population is zero ( i . e . , λ = 0 at S = S1 in Fig . 4B ) , the substrate consumption rate is not zero [37 , 38 , 44 , 45]; also , see ref . [36] for review . These studies have shown that it requires continuous influx of the substrate into the medium to maintain the population size at a constant level , termed maintenance requirement . ( It was proposed that the substrate is used to fix chemical “wear and tear” of cell materials and fulfill other non-growth related functions . See [36 , 46] for detail . ) . If the influx rate of the substrate meets the maintenance requirement , the population size is maintained; in Fig . 4B , this occurs when S is kept at S1 by continuous influx of the substrate against the consumption of the substrate for the maintenance . If the influx rate is less than the level needed for the maintenance , the population size decreases ( λ < 0 , green region in Fig . 4B ) . The rate of the decrease is faster at a lower influx rate of the substrate and , with no influx , the rate of the decrease reaches its maximum , i . e . , λ ( 0 ) in Fig . 4B . In our experiments , after the onset of growth arrest ( time zero in S1B Fig ) , there is no additional influx of the substrate to the medium . But , as discussed above ( 3 paragraphs above ) , a certain amount of the substrate ( i . e . , S1 ) remains in the medium at the onset of growth arrest . This conserved substrate can be used for the maintenance , allowing cells to maintain their population size initially . Such usage will result in continuous decrease of S ( cyan line in Fig . 4C ) , leading to a gradual decrease of λ below 0 ( see λ < 0 when S < S1 in Fig . 4B ) . Consequently , the population size will gradually decrease , giving rise to the first phase of the biphasic decay ( cyan line in Fig . 4D ) . Eventually , the substrate is completely exhausted at T0 ( Fig . 4C ) , hence , S = 0 ( orange line in Fig . 4C ) . Thus , after T0 , NCFU will decrease at the constant rate of λ ( 0 ) , i . e . , exponential decay , giving rise to the second phase ( orange line in Fig . 4D ) . In this model , the density-dependence of the first phase in the survival kinetics arises because , for the culture with higher cell densities , the conserved substrate ( S1 ) will be consumed by more cells . Thus , it will be depleted faster for high cell density , leading to shorter periods of the first phase . Therefore , in our model , the density-dependent survival kinetics can be accounted for without invoking presence of ( unknown ) extracellular signaling molecules , agreeing with our observation in Fig . 2A . On a related note , there exists a study that shows a density-dependent response to bacterial survival under antibiotic treatment and such density dependence can be accounted for without invoking extracellular signaling [47] . To examine whether the biological processes described above can quantitatively account for the survival kinetics observed in our experiments , we constructed a mathematical model based on them . The details of our model are described in S1 text . Briefly , our model contains two key components , both of which are discussed above . The first component is the dependence of λ on S , which is plotted in Fig . 4B . Because we are particularly interested in the change of the population size when the substrate is nearly or completely exhausted , ( i . e . , S is close or equal to 0 ) , the dependence of λ on S can be approximated to the first order in our model ( see S3 Equation—S5 Equation ) . The second component of our model is the decrease of the substrate concentration due to the consumption for the maintenance ( Fig . 4C ) . Here , based on previous studies [37 , 38] , we assume the substrate consumption rate per cell is constant over time and the total consumption rate is proportional to cell density ( S6 Equation ) . Quantitative formulation of these processes straightforwardly leads to a mathematical solution equal to the empirical formulas; compare Eqs ( 3 ) and ( 4 ) , and S11 Equation and S12 Equation . The solution states that a ) the decay of NCFU is biphasic , exp ( −c ⋅ N0 ⋅ t2 ) decay followed by exp ( −μ⋅t ) decay , and b ) the time at which the transition occurs ( i . e . , T0 ) is inversely proportional to cell density . The solution contains two fitting parameters , μ and c . Representing the rate of a population decrease at the zero substrate concentration ( i . e . , λ ( 0 ) ; see the description below S4 Equation ) , the μ can be obtained from the rate of decrease of NCFU in the second phase of survival kinetics in Fig . 1 and S2 Fig . Hence , λ ( 0 ) = − μ ≈ − μ0 . Alternatively , the μ as well as c can be obtained by fitting the solution ( S11 Equation and S12 Equation ) to the data shown in Fig . 1 and S2 Fig . The result of the fit is plotted as lines in these figures , which yielded μ = μ o = 0 . 018 hr -1 and c = 4 . 7×10–12 ml·hr-2 ( λ ( 0 ) = −μ = −μ0 in Fig . 4B is based on this result ) . The fit shows that the model can quantitatively account for our data—such consistency is expected because the solution of our model is equal to the empirical formula . The lifecycle of bacteria consists of short periods of feast , intercepted by long periods of starvation [1] . Quantitative analysis of how cells persevere during starvation is the focus of this study . Our findings show that after the onset of starvation , in high density cultures the loss of viability begins immediately at a constant rate . However , in low density cultures , the viability is maintained for extended periods of time before it decreases at the same constant rate . Such density-dependent survival kinetics is mediated by the master regulator of the general stress response rpoS . Integration of previously known processes reveals a thrifty strategy of bacteria , by which upon sensing impending starvation , cells repress nutrient consumption for biomass increase and use the remaining nutrient in the environment to delay cell death . Mathematical modeling of these processes accurately accounts for the density-dependent , biphasic survival kinetics . The benefit of such thrifty behavior is obvious; it delays cell death . However , we note that such behavior has a cost in bacterial fitness because it diverts the limited nutrients away from cell growth , reducing the number of offspring . Thus , we expect the evolution of such behavior would depend on environmental conditions and be favored when the benefit outweighs the cost . The benefit is expected to depend on the length of starvation that cells routinely experience . If starvation periods are very short , the benefit of delaying death for long-term survival becomes negligible and may be outweighed by the cost . ( in such case , it is expected that rpoS mutants outcompete the wild type cells . ) The benefit would increase as the starvation periods become longer . Of course , if starvation periods are very long , it would lead to the emergence of GASP mutants [20 , 21] , which is outside of the scope of this study . Thus , we expect that the evolution of such behavior would be strongly dependent on the starvation periods cells routinely experience . Another factor to affect the evolution of such behavior would be spatial structure of the environments . In structured environments where cells grow clonally , such behavior would be beneficial . However , in homogenous environments where the nutrients conserved to delay cell death by one species could be accessed by other species of bacteria , such behavior would not be beneficial . In such case , it would be more advantageous to use up all the nutrients and the rpoS mutants may be more fit than the wild type cells . Obviously , conserving the limiting nutrients by the wild type cells is one form of cooperation and rpoS mutants may appear as cheaters . However , the mixed population of the wild type cells and rpoS mutants may get fragmented and disperse at some point , and new monoclonal populations of wild types cells and those of rpoS mutants will be formed . During starvation , the latter will die rapidly , while the former will survive longer ( Fig . 2B ) . As such , how often such population fragmentation occurs will affect the evolution; see the previous studies [48 , 49] that quantitatively examined how such fragmentation affects the evolution of cooperative behavior . These considerations , taken together , suggest that the evolution of the thrifty strategy observed in our study depends on various environmental factors , and it would be interesting , in future studies , to determine the dependence quantitatively . It is worth noting that in previous experiments , when cells were grown in the nutrient-limited chemostat where nutrient levels were artificially kept very low , rpoS mutants were frequently found [50 , 51] . The observation agrees with our argument in that at low nutrient levels , wild type would try to conserve the nutrients by not growing , while rpoS mutants will continue to grow . On a related note , we believe such studies would draw an interesting analogy with a recent work that characterized the conditions affecting evolution of spore-formation in spore-forming bacteria [52] . Some bacterial species , such as B . subtilis , form spores upon sensing nutrient limitation [53] . Spores are very resistant to stress , persisting through starvation for long periods of time . In the recent work [52] , it was shown that it is beneficial to initiate spore-formation before nutrients are completely depleted by biomass increase and , in some conditions , extracellular signaling may evolve to assist this process . The first finding is analogous to our findings in that upon sensing impending starvation , these cells take action for long-term survival before the nutrients are completely depleted . Also , the second finding has bearing on understanding why in the cells we studied ( E . coli ) , extracellular signaling was not evolved ( Fig . 2A ) . We believe our study will advance our understanding of starved bacteria , especially their starvation survival physiology . Because ecosystems are dominated by starving microbes [1] , our findings will facilitate deeper understanding of microbial population dynamics in microbial ecology and environmental sciences . We expect that such knowledge will have important implications in public health sectors [54]; e . g . , accurate prediction of how pathogens persevere in freshwater will lead to better understanding of how infectious diseases spread and developing better public health policies .
Escherichia coli wild-type K12 strain NCM3722 [55 , 56] was used in our experiment . To make the ΔrpoS strain , we purchased the deletion allele of ΔrpoS from Keio deletion collection [57] , transferred it to NCM3722 using P1 transduction [58] . N-C- minimal media [59] , supplemented with 20mM of NH4Cl and various concentrations of glycerol , were used for cell growth . The glycerol concentrations used were 5 mM , 1 mM , 0 . 7 mM , 0 . 5 mM , 0 . 3 mM , 0 . 2 mM , 0 . 15 mM , and 0 . 05 mM . Note that although glucose is a common carbon source for cell growth , we did not use glucose in our experiments because of bacterial Crabtree effect [60]; cells growing on glucose excrete acetate , and the excreted acetate is used as the cell density increases and the glucose level decreases . This would complicate our study to characterize the cell density dependence of NCFU decay . Cells were grown at 37°C with shaking at 250 r . p . m . in a water bath ( New Brunkswick Scientific ) . To monitor their growth , optical density ( OD600 ) was measured using a Genesys20 spectrophotometer ( Thermo-Fisher ) . When the OD600 values were too low for the measurement using a standard sample holder ( 16 . 100-Q-10/Z8 . 5 , Starna Cells Inc ) , a sample holder ( 18B-SOG-40 , Starna Cells Inc ) that is 4 times longer ( OD600×4 ) was used . Cells were first grown in LB broth for 4∼5 hrs ( seed culture ) , transferred to a N-C- minimal medium with 20 mM of glycerol and 20 mM of NH4Cl and grown overnight ( pre-culture ) . The next morning , the cells growing exponentially in the pre-culture were transferred to the media specified above ( experimental culture ) . The initial density of cells in the experimental culture was adjusted such that cells continued to grow exponentially for at least 4 more doublings in the experimental culture , before their growth stopped due to the depletion of glycerol ( S1 Fig ) . The experiment in which the effects of extracellular signals were tested ( Fig . 2A ) was performed in the following way . First , cells were grown in the minimal medium with 5 mM of glycerol . When their growth stopped at high density due to glycerol depletion ( NCFU ≈ 7·108/ml ) , we waited ∼7 hrs . Then , cells were spun down ( the supernatant was set aside ) , transferred to a carbon-free medium with 20 mM of NH4Cl . The volume of the carbon-free medium was adjusted in such a way that the initial cell density ( measured from OD600 ) matches that from the viability curve of blue squares in Fig . 1 . Then , their viability was measured afterwards ( green triangles in Fig . 2A ) . Then , to the supernatant obtained from the procedure above , we added 0 . 05 mM of glycerol , transferred exponentially growing cells into it ( NCFU ≈ 5·105/ml ) , and grew them until growth stopped due to glycerol depletion at low cell density ( NCFU ≈ 9·106/ml ) . Then , we measured their viability afterwards ( green inverse triangles in Fig . 2A ) . Please note that initial cell density was first estimated from the OD600 value of the culture ( with the knowledge that 1 OD600 corresponds to ∼109 cells/ml ) and later confirmed ( using the viability assay as described below ) . The viability was determined by counting the number of colony-forming-unit ( NCFU ) on LB agar plates . After platting , the plates were incubated at 37°C overnight before counting . NCFU did not change even if the plates were incubated for 3∼5 more days . Through serial dilutions , we ensured NCFU to be around 100∼200 per agar plate ( 100 × 15 mm petri dish ) . NCFU reported was averaged values of 3 replicate measurements . Each experiment was independently repeated 2∼4 times ( e . g . , see S8 Fig ) . Glycerol concentration in the medium was measured using Glycerol assay kit ( SigmaAldrich , F6428 ) as described in the manual , except the ratio between the medium and the agent was increased to 1 to 1 . 5 . In four independently repeated experiments , we observed that the glycerol concentration at the onset of growth arrest of glycerol-starved cultures was between 0 . 5∼2 μM . We note that this is below the range of quantitative measurement of the method employed , and absolute quantification of such low concentrations is very difficult . However , we always observed positive values . ( The measurement was calibrated using media with known glycerol concentrations . In this calibration , the medium without glycerol is used as the reference for zero glycerol concentration . ) | Long periods of starvation are common in the lifecycle of microorganisms . Textbooks routinely describe that during starvation periods , cells die at a constant rate , i . e . , exponential decay . The exponential decay of cell survival has been commonly assumed in the literature to analyze and predict population dynamics of microbes . Here , we show that this assumption is true only at high cell density . At low cell density , cells can persevere for extended periods of time , before dying at a constant rate . Quantitatively analyzing the kinetics , we uncover mathematical formulas governing the density-dependent , biphasic decay of cell survival . Using mathematical modeling , we further reveal key underlying processes responsible for the perseverance . Our model highlights a thrifty strategy of bacteria; upon sensing impending starvation , small amounts of nutrients are conserved and used to persevere during starvation periods . In addition to advancing our fundamental understanding of physiology of bacteria in nature , our study will facilitate the analysis and prediction of microbial dynamics in nature . We expect that our findings will have broad impacts . For example , our findings can be used to accurately predict how pathogens survive in natural environments , which will lead to better public health policies . |
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Distributed neural processing likely entails the capability of networks to reconfigure dynamically the directionality and strength of their functional connections . Yet , the neural mechanisms that may allow such dynamic routing of the information flow are not yet fully understood . We investigated the role of gamma band ( 50–80 Hz ) oscillations in transient modulations of communication among neural populations by using measures of direction-specific causal information transfer . We found that the local phase of gamma-band rhythmic activity exerted a stimulus-modulated and spatially-asymmetric directed effect on the firing rate of spatially separated populations within the primary visual cortex . The relationships between gamma phases at different sites ( phase shifts ) could be described as a stimulus-modulated gamma-band wave propagating along the spatial directions with the largest information transfer . We observed transient stimulus-related changes in the spatial configuration of phases ( compatible with changes in direction of gamma wave propagation ) accompanied by a relative increase of the amount of information flowing along the instantaneous direction of the gamma wave . These effects were specific to the gamma-band and suggest that the time-varying relationships between gamma phases at different locations mark , and possibly causally mediate , the dynamic reconfiguration of functional connections .
Visual cortical processing is highly distributed and likely depends crucially upon the cooperative interactions between different groups of neurons . Although there is an extensive knowledge about how individual neurons encode specific features of visual stimuli [1 , 2] , how groups of neurons cooperate to give rise to a coherent perception of naturalistic scenes is still largely unknown [3 , 4] . One factor governing interactions among groups of neurons is their pattern of anatomical connections . This pattern in the primate primary visual cortex ( V1 ) includes a local recurrent microcircuitry involving both inhibitory and excitatory neurons [5 , 6] , as well as a larger network of horizontal connections spreading over several millimeters [1 , 7–11] . Such recurrent connectivity likely serves as an anatomical substrate to establish transient dynamic patterns of functional connectivity [12–15] , allowing selective communication between the populations of neurons involved in visual function [16–19] . However , the physiological mechanisms that may transiently modulate the effective strength of any given connection are largely unknown . One possibility is that transient interactions between neuronal groups depend upon the relative phase of the synchronization of gamma-band oscillations within each group [20–23] . Despite the growing support for a role of the relative temporal alignment of gamma oscillations in mediating communication , many questions about how they may operate remain unsolved [24] . In particular , while previous evidence linked gamma oscillations to symmetrical interactions between populations through synchronization [25] , it is not known whether they can establish a directional communication within a brain area and modulate it transiently according to the needs of stimulus processing or the demands of the task . Finding a mechanism for dynamic routing of information is of major importance for understanding intracortical communication . One reason why the above questions have not yet been fully clarified is that most previous studies considered symmetric measures of correlation ( or synchronization ) between neural populations [26] , which cannot provide information about the direction of interaction . We thus introduce here nonlinear information theoretic tools to quantify directed communication and its modulation by the stimulus to assess the role of the phase of gamma oscillations in modulating dynamically the routing of information and to analyze how this routing relates to the spatiotemporal pattern of gamma phase [27 , 28] . We analyze spiking activity and Local Field Potentials ( LFPs ) simultaneously recorded from locations separated by up to a few millimeters in the V1 of macaques during the presentation of naturalistic color movies . These stimuli present spatially extended visual features varying over a wide range of ecologically relevant time scales and are therefore ideally suited to both dynamically coactivate groups of neurons processing different regions of the visual field and to investigate how interactions among active groups of neurons may be modulated by changes in stimuli . We found that the local phase of gamma-band rhythmic activity exerts a dynamic , stimulus-modulated spatially asymmetric effect on the firing rate of spatially separated populations within V1 in a way that strongly suggests that directional information transfer is mediated by propagation of gamma oscillations . Further , differences in gamma phase across sites are transiently modulated by the visual stimulus ( with propagation of waves from the sending site accentuated when the sending site is strongly stimulated by the visual stimulus in its receptive field [RF] ) , despite the absence of reliable locking of gamma phase to the stimulus at any individual site . Finally , transient changes of phase differences across sites ( or spatial phase shifts ) co-occurred with changes in the causal interactions exerted by gamma oscillations onto spiking activity at other sites . These findings suggest that the dynamical relationships between gamma phases at different locations mark , and possibly causally mediate , the dynamic reconfiguration of functional and effective network connections during information processing .
We recorded extracellular potentials in opercular V1 ( foveal and parafoveal representations ) of three anesthetized macaque monkeys with multiple electrodes positioned with a guide according to a 4x4 square grid with interelectrode spacing in the range 1–2 . 5 mm ( Fig 1A ) . From the extracellular potentials recorded at each electrode , we extracted two aspects of mesoscopic network activity . First , we extracted Multiple Unit Activity ( MUA ) by filtering the extracellular signal in the ( 1 , 000–3 , 000 Hz ) frequency range and computed the time-varying envelope of this oscillation . This signal is known to reflect the spike rate of neurons within 300 μm distance around the electrode tip [29] . The MUA was used here to measure the massed firing rate ( and thus the strength of local network activity ) at a given time and location . Second , we extracted LFPs by low-pass filtering the despiked extracellular potentials ( see Materials and Methods ) . Here , LFPs , which are known to provide a robust measure of network oscillations [30 , 31] , were used to measure the instantaneous phase and amplitude of network oscillations around the electrode location , using the Hilbert transform of the LFP band-passed signal ( S1 Methods ) . Activity was recorded both during binocular visual stimulation with 4–6 . 5 min long color Hollywood movie clips with 30 Hz frame rates ( with the same movie clip being presented over 30–120 repeated trials in the same session ) and during several 5 min long stretches without visual stimulation ( spontaneous activity ) . RFs were identified for each site using reverse correlation of the gamma power ( Materials and Methods , S1A and S1B Fig ) . RF distances for all electrode pairs were in the 0–4° range , and the majority of RF pairs had an overlap , with 46% of the data having a relative area overlap of 0 . 4 or less ( S1C and S1D Fig ) . To study neural tuning to visual features , we extracted visual features from the RFs ( primarily Orientation Activation—OA—and local Time Contrast—TC—see S1 Methods ) with computer algorithms . The correlation over time of visual features ( S1F Fig ) showed that , though most RFs had a positive correlation , due to their partial overlap , the visual features in different RFs were partly independent ( mean correlation 0 . 73 +/− 0 . 21 for TC , 0 . 4 +/− 0 . 30 for OA ) , thereby allowing some evaluation of how differences in visually-driven RF activation may modulate mesoscopic neural signals . The power spectrum of the changes of RF visual features over time ( S1E Fig ) showed that—in agreement with previous analyses of natural movies [32]—features varied slowly , with the most power in the low frequency range . Importantly , the properties of the presented movie imply that the gamma-band ( 50–80 Hz ) oscillations cannot simply reflect the entrainment from stimulus dynamics and must originate instead from neural interactions . Previous work showed that the LFP gamma-band was the one whose power carried more information about the movie stimulus ( [33] , see also S2B Fig ) , and whose power was proportionally more enhanced during movie stimulation with respect to spontaneous activity ( S3 Fig , see also [34–36] ) . To assess in which LFP frequency band visual stimulation elicited spatially organized oscillatory activity , we computed the spatial coherence of the multisite recordings [37] . This measure has been previously used to detect interesting spatiotemporal activity such as travelling waves [37 , 38] . We found ( Fig 1B and 1C ) that visual stimulation elicited a consistent increase of spatial coherence in all sessions ( t test; p < 0 . 001 ) only in the ( 50–80 Hz ) gamma-band , suggesting that sensory information elicited spatially organized oscillations in this band ( note the 60 Hz sharp coherence peaks correspond to harmonics of the 30 Hz frame rate of the movie and of the 60 Hz refresh rate of the monitor ) . Our aim was to investigate whether and how this stimulus-enhanced spatiotemporal coherent neural activity in the gamma-band mediates interactions and communication among neural populations at different locations . Relationships between gamma phases of different neural populations have been reported to influence mutual , symmetric , relationships between the firing of different neuronal populations [26 , 39] . To extend this understanding to the case of directed—rather than mutual—information exchanges , here we investigated whether the phase of gamma oscillations of a neural population has a directed effect onto the firing rate of other receiving populations . To address this question quantitatively , we used a direction-specific information theoretic analysis of the impact of phase on spiking activity at other sites during visual stimulation with natural movies . We measured whether the gamma phase of the sending population influences the spiking activity of the receiving population above and beyond what can be predicted by the past firing rate dynamics of the receiving population itself . That is , we computed the mutual information between the past gamma phase at a “sending” electrode and the spiking activity at a “receiving” electrode , conditioned upon the past spiking activity of the receiving population ( see illustration in Fig 2A ) . This quantity is called the Transfer Entropy ( TE ) from the gamma phase at the sending electrode to the spiking activity at the receiving electrode [28] . Significantly positive values of TE mean that the gamma phase of the sending population exerts a causal effect ( in the Wiener-Granger sense ) on variations in firing rate in the receiving location . Note that TE values are reported after subtracting out spurious amounts of causation due to effects including common visual stimulation that do not reflect genuine communication between sites and are Z-scored in SD units of this spurious magnitude of causation ( see S1 Methods and [40] ) . This TE analysis was performed for all available pairs of electrodes in each session , thereby running through all possible combinations of putative “sending” and “receiving” populations . Importantly , in this section and unless otherwise stated , we considered the overall information carried during the whole time of movie presentation . The analysis of how this is dynamically modulated during the presentation of the movie will be left for later sections . Mean values of TE across the entire dataset are reported in Fig 2B and show a high amount of TE ( with Z-scored values of the order of 8–10 significant at p < 10−7; t test ) from the gamma phase at the sending electrode to the spiking activity at the receiving electrode . TE values were significantly larger than zero ( using a t test and a False Discovery Rate control with q = . 05 ) for 84% of electrode pairs . Moreover , there was a more than 2-fold highly significant ( p < . 001; t test across pooled electrode pairs of all sessions ) increase in TE magnitude during movie stimulation with respect to spontaneous activity ( Fig 2B ) , suggesting these directed causal influences of phase relate to the processing of visual information . We then investigated whether gamma phase of the sending population has an effect on the firing of the receiving population that goes above and beyond the one exerted by the firing rate of the sending population . To quantify this , we computed the Lagged Conditional Information ( LCI ) , which is the mutual information between the past gamma phase at the sending electrode and the spiking activity at the receiving electrode , conditioned upon the past spiking activity of the sending population . The results ( S4 Fig ) showed a highly significant ( with Z-scored values of the order of 8–10 significant at p < 10−7; t test ) LCI , showing that the relationship between gamma phase of the sending population and the spiking activity of the receiving population cannot be accounted for by the relationship between spiking activity and gamma phase at the sending location . To further corroborate this conclusion , we computed how correlated across all pairs of electrodes were the values of TE from the gamma phase of the sending electrode to the spiking activity of the receiving electrode with the TE from the spiking activity of the sending electrode to the spiking activity of the receiving electrode . We found that there was a significant ( p < . 05 ) but very small and negative correlation ( Pearson ρ = − . 12 ) , again supporting the conclusion that gamma phase of a population exerts an effect on the firing rate of another receiving population that is largely different from that exerted by its firing rate . Given that TE is a directed and potentially asymmetric measure that can detect a leading direction of communication , we quantified the degree of spatial asymmetry in our information measures . We define the spatial asymmetry index as the ratio between the absolute value of the difference in TE in both directions and the maximal information in one of the two directions . This index takes values from 0 to 1 , with near-zero values indicating perfect symmetry and near-one values indicating prevalence of one-directional communication ( See S1 Methods ) . In our dataset , we found that ( Fig 2C ) both during spontaneous activity and movie stimulation , the average asymmetry index was large—in the range 0 . 6–0 . 7 , suggesting a prevalence of directed asymmetric effects of gamma phase on spiking activity of other sites over symmetric communications such as mutual interactions . Moreover , this asymmetry increased significantly during movie stimulation ( p < . 05; t test across pooled electrode pairs across sessions ) . We next investigated how these directed interactions depend on the distance between recording sites . Fig 2D reports the histogram of TE values of the pairs of electrodes across all sessions . The data were partitioned into four equipopulated ranges of interelectrode distances . While a larger number of high TE values could be observed at short distances ( <2 . 24 mm ) , several pairs with large interelectrode distances also exhibited large interactions . In addition , electrode pairs exhibiting very low values could be found at all distances , supporting that the TE is not a simple function of distance and arguing against that they could be ascribed to external artifacts or volume conduction . We further checked how asymmetry of causal interactions is influenced by distance by selecting pairs with both a sufficiently strong causal interaction ( we eliminated in each session the 20% of electrodes pairs with the lowest TE values ) and a direction of dominant causal interaction ( one direction of causation 10 times bigger than the opposite direction ) . We call the so-defined pairs “strongly asymmetric pairs” ( or in short asymmetric pairs ) . The distribution with respect to interelectrode distance of such strongly asymmetric pairs ( S1 Table ) shows that they are proportionally more frequent at larger distances , whereas “symmetric pairs” ( defined as pairs whose relative difference between TE values of both directions is less than 20% , also eliminating the 20% of electrodes pairs with the lowest TE values ) are proportionally more frequent at shorter distances . Finally , we studied how frequency-specific is the causal influence of the phase of the sending population onto the spiking activity of the receiving population . To do so , we band-passed the LFP into four other lower-frequency bands ( 2–4 Hz , 5–15 Hz , 15–30 Hz , and 30–50 Hz ) , we computed the instantaneous phase and repeated the same TE analysis for the phase of each band . Comparisons of results across bands ( Fig 2E ) show that , although lower frequency bands had a larger causal effect during spontaneous activity , during visual stimulation with Hollywood movies the highest information values were obtained for the gamma-band . This suggests that gamma-band has an important role in transmitting and routing across sites the information needed for stimulus processing . Overall , our information theoretic measures of interactions suggest that during stimulation with naturalistic movies , gamma phase of a primary visual cortical population exerts a genuine directed effect on the level of firing rate at other receiving locations within V1 , and that this effect goes beyond what may be due to the level of firing rate at the receiving or the sending location . Previous studies suggested that symmetric mutual interactions among neural populations depend on the phase relationships between the rhythmic activity of the interacting neural populations rather than on the phase of one population only [26] . In the light of these observations , we asked the following questions: does the directed causal effect of the phase of gamma oscillations of a neural population onto the activity of other receiving populations depend on the phase relationships of gamma oscillations at different sites ? If so , what are the specific phase relationships that correspond to larger causal effect of gamma phase on spiking activity at other locations ? To address these questions , we investigated the relationship between the spatiotemporal distribution of gamma phases and directed information transfer . We defined instantaneous phase shifts between two electrodes as the difference at each time point between the instantaneous gamma phases computed from the band-pass-filtered LFP at each electrode , and we quantified the circular mean across time of these phase shifts ( see S1 Methods ) . To relate phase shifts to our measure of information transfer , we used the convention of measuring them as the difference between the phase at the “sending” electrode and the phase at the “receiving” electrode ( the electrode at which the effects on MUA activity are considered ) , exactly as defined above when quantifying TE across sites . With this definition , a positive phase shift means that the oscillation in the sending electrode precedes the oscillation in the receiving electrode ( see Fig 3A for an illustration ) . In this section , we first considered the phase shift averaged over the entire time of movie presentation in all trials . ( The dynamical changes of phase shifts over the time of movie presentation and their relationship to dynamic changes of information transfer will be addressed in later sections ) . Most electrode pairs showed absolute values of movie-averaged phase shifts distributed between 0° and 80° ( S5 Fig ) . Moreover , 100% of the electrode pairs had a significantly nonuniform distribution over time of phase shifts ( p < 0 . 01; Rayleigh test ) , meaning that the phase relationships among all electrodes were not random . To test the relationship between phase differences and causal effects of gamma phases onto receiving sites , we computed the Spearman correlation between the movie-averaged phase shift of an electrode pair and the amount of causal effect ( TE ) exerted by gamma phase of the sending population onto spiking activity at the receiving electrode . The results ( Fig 3B ) show—consistently in all sessions—a significant ( p < 0 . 05 with Bonferroni correction ) positive Spearman correlation of phase shifts with TE . In other words , positive ( respectively negative ) phase differences corresponded to higher ( respectively lower ) values of TE between gamma phase at the sending location and spike rates at the receiving location . This is further illustrated by computing the histograms ( cumulated across sessions ) of the movie-averaged phase shifts for the above defined strongly asymmetric pairs . The result ( Fig 3C ) shows mostly positive phase differences . We found that the above-defined symmetric pairs had mean phase shifts distributed around the value 0° , corresponding to a zero-lag synchrony ( Fig 3C ) . A simple , yet accurate way to summarize these results is that the sign of the phase shifts indicates the dominant direction of interaction . In other words , causation predominantly flows from the location with the earlier phase to the location with the later phase . This pattern of phase differences is thus consistent with a simple compact description of the causal interactions as a propagating gamma wave . We finally checked whether the relationships between phase differences and the magnitude and direction of causal effect of phase of rhythmic activity were specific to the gamma-band . We performed the same correlation analysis on the phase of LFP bands of frequencies lower than the gamma range ( the 2–4 Hz , 5–15 Hz , and 16–50 Hz bands ) . As shown in S6 Fig , the correlations between the phase shifts and the TE between lower frequency phases and spiking activity were weaker ( and not significant in all sessions ) than the one found for the gamma phase , suggesting that the relationships between phase shifts and the magnitude and direction of causal effects of the phase of rhythmic activity were specific to the gamma-band . Given that phase shifts indicate the direction of causation in neural activity and that they can be described as travelling waves , it is tempting to speculate that these shifts are associated to a propagation of gamma oscillations along the horizontal connections of V1 . In the following , we investigated the extent to which these phase shifts are compatible with known physiological and anatomical properties of lateral connectivity . Propagation of waves across space and time can be investigated by analyzing the patterns of phase differences across electrodes [37 , 38 , 41] . As illustrated in an example from our data ( Fig 3D ) , if direction of gamma causation and gamma wave propagation are aligned , recording sites positioned along a line of prevalent TE flow ( i . e . , recordings sites that have asymmetric TE with their neighbors all pointing to a leading direction of causation ) should also have gamma-band time lags and phase shifts distributed according to their algebraic positions along the direction of propagation . We estimated from this pattern the putative speed of propagation . Since the speed of a propagating wave is inversely proportional to the spatial derivative of the phase along the direction of propagation , we estimated the spatial phase shifts values against the distance along the directions defined by strongly asymmetric pairs in each session ( assuming these pairs are prone to be oriented along the direction of propagation ) . For each single strongly asymmetric pair , designated as the “reference causal pair” , we studied the propagation along the line passing through both electrodes of this pair . We thus computed the phase shift between the receiving site of the considered reference causal pair ( for the leading direction of causation ) and all the other recording sites lying along the propagation axis defined by the line crossing both sites of the considered reference causal pair ( see S1 Methods and S7 Fig ) . The algebraic propagation distance corresponding to each measured phase shift values was computed by projecting the interelectrode distance over the axis defined by the reference causal pair . The origin of the x-axis indicates the position of the receiving site . When the receiving site was not achieving a minimum ( zero ) phase shift with respect to the other electrodes , but instead this minimum was achieved by the receiving site of another strongly asymmetric pair , this latter receiving site was chosen as the reference of the x-axis , such that the origin always indicates the final target of the wave propagation , and not an intermediate site lying on the propagation trajectory . The results for each reference causal pair are plotted on Fig 3E in gray . To estimate the spatial derivative of the phase , these data points were used to fit a spline regression model ( Fig 3E , in blue ) , showing a steep negative slope close to the origin and a progressive flattening further away from the origin . The decrease in the slope might reflect that the waves quickly attenuate as they propagate along the cortical surface; alternatively , it can possibly arise from interferences between travelling waves propagating on overlapping parts of this surface . To minimize those effects in the speed estimation , we estimate the speed of propagation when it is closest to its target receiving population located at the origin in our representation . We thus estimated the propagation speed from the spatial derivative of the phase where it is larger: at null interelectrode distance , using spline interpolation ( see S1 Methods ) , leading to an average propagation speed of 36 ± 4 cm/s ( mean ± bootstrap estimated SD ) . This propagation speed is similar in magnitude to the signal propagation speed along axons of excitatory horizontal connections reported in the literature [42–45] . We also investigated whether causal interactions were more prominent among pairs with similar orientation preference . For this , we first estimated the RF of each recording site by reverse correlation using the responses to the movie . We then estimated , by extracting the orientation content of each movie frame and correlating it with neural activity , the orientation tuning curve of multiunit spiking activity at each recording site ( see [46 , 47] and Materials and Methods ) . The Spearman correlation between orientation tuning similarity ( measured by covariance between the curves ) and TE , across all electrode pairs from all sessions was positive ( ρ = 0 . 24 , p < 0 . 01 ) . Since horizontal connections are more likely among populations with similar orientation preferences [11 , 48 , 49] , this finding is compatible with the hypothesis that gamma phase shifts may reflect causal interactions propagating along horizontal connections . The above findings demonstrated that the direction and strength of the causation exerted by gamma phase on spiking activity at other receiving sites correlates , on average , over long periods of dynamic visual stimulation , with the difference in gamma phase at both sites . It has been suggested that patterns of phase relationships may act as a dynamical gain factor that weights the effect of the anatomical connection infrastructure and therefore allows to modulate rapidly the strength of interactions among populations [23] . Evidence in support of this theory has been reported at the level of mutual symmetric interactions [26] . To understand whether this dynamical modulation may apply also to the case of directed interactions documented here , we asked whether changes in gamma phase shifts are related to changes in the stimulus , and whether they are accompanied by a readjustment of the magnitude of causal interactions among sites . In this section , we begin by studying whether gamma phase shifts can be dynamically and reliably modulated by the stimulus . An example of the time course over the movie presentation of the phase shift between two example electrodes in individual trials is shown in Fig 4A . To aid visualization , only 25 s of movie presentation were shown , and phase shifts were temporally smoothed by computing circular statistics over a 300 ms sliding window ( we chose a window length of 300 ms because it is a time scale with high power of stimulus variations in these natural movies [32 , 34 , 50] ) . For a given window , circular mean of the phase shift was encoded in Fig 4A by the hue , while the consistency of the shift was quantified by the Phase Locking Value ( PLV ) and encoded by the intensity ( see S1 Methods ) . Phase shifts were not constant in time , but varied in value and sign within each individual trial of the movie presentation ( Figs 4A and 5A ) . Notably , there were specific time points ( or scenes ) during the movie presentation in which either positive or negative phase differences were elicited reliably across trials ( see the red arrows in Fig 4A for examples of such periods ) , indicating that the dynamics of phase shifts is modulated by the properties of the visual stimulus . To systematically quantify the relationship between phase shifts and visual stimuli , we computed the mutual information that phase shifts carry about which scene of the movie was being presented . Mutual information is a principled and comprehensive way to quantify whether the considered neural response varies reliably across trials from scene to scene ( see Materials and Methods ) . In our experimental setting , it quantifies ( in units of bits ) how much the observation of a neural response reduces the uncertainty about which scene of the movie is shown . The information about the movie scenes carried by phase shifts ( averaged over all electrode pairs for each session ) is shown in Fig 4B . Information in phase shifts was significant in all sessions ( t test with False Discovery Rate control q = . 05 ) , meaning that phase shifts are indeed reliably modulated by the stimulus . In contrast , and consistently with previous reports [51] , the gamma phase of each individual electrode carried very little information about the movie ( Fig 4B ) . The fact that reliable modulation of phase shifts by the visual stimuli happens in absence of reliable modulations of the phase at each individual site means that the stimulus modulation of phase shifts cannot be explained by stimulus-evoked changes in the phase of individual sites . This suggests that stimulus-modulated spatial phase patterns reflect an emergent property of the relative dynamics and interactions between different cortical sites . We further checked whether information in phase shifts could be explained by the stimulus modulation of the gamma-band power in each individual electrode [34 , 52] . Although the gamma power in each site carried significant stimulus information in this dataset ( S2B Fig ) , the information in the joint observation of power at a given site and phase shifts with respect to another site was much higher in each session ( paired t test and False Discovery Rate correction with q = . 05 ) than information carried by power or by phase shifts alone ( Fig 4B ) . This means that the gamma phase shifts and gamma power at each site are modulated by the stimulus in a largely complementary way . Similarly , we found ( S2C Fig ) that the information in gamma phase shifts was also complementary to that of the firing rate from the same electrode ( note that under these stimulation and recording conditions , the local firing rate and gamma power are coupled quite tightly [34] ) . These findings imply that movie modulations of phase differences cannot be explained by modulations in local power or firing rate alone . To understand how frequency specific were these phase modulations , we computed sensory information for phases and phase shifts in the lower frequency bands , 2–4 Hz , 5–15 Hz , 16–50 Hz ( S2A Fig ) . Sensory information of phases in individual recording sites were on average larger or at least comparable to the information of phase shifts between sites at the same frequency , and were significant ( p < . 001 , t test w . r . t . bootstrapped values; Bonferroni-corrected ) in all sessions and frequencies , barring one single exception ( phase at 15–50Hz for c08nm1 ) . These results obtained for lower frequencies ( <50 Hz ) are in sharp contrast with results reported above for the gamma phase ( Fig 4B ) . Thus , the emergence of stimulus-dependent phase shifts between sites in absence of a stimulus modulation of phases at individual recording sites is specific to gamma-band oscillations . Results presented above showed the average phase differences computed over the entire period of movie stimulation strongly correlate with the dominant direction of interaction computed over the entire movie presentation . Concurrently , our mutual information analysis showed ( Fig 4 ) phase differences between recording sites can vary reliably across different movie scenes . A natural question is whether such dynamic stimulus-induced changes in phase differences lead to dynamic changes in the strength of directed interactions between neural populations . We addressed this question by studying , for each pair of electrodes , the relationship between changes over time of the phase shifts between the sites and the changes over time in causation ( measured as TE ) exerted by the gamma phase at one location to the spiking activity at another receiving location . We first individuated , for each pair of electrodes , blocks consisting of periods of dominant positive and negative phase shifts . The segmentation , illustrated in Fig 5A for an example pair of electrodes , was implemented as follows . We first computed the sign of phase difference for a given pair of electrodes at all time-points in each trial . Then we labelled time points with a majority of positive shifts across trials as positive phase shift points . Conversely , we labelled time points with a majority of negative phase shifts across trials as negative points . We then considered for further analysis only blocks made of continuous time segments with at least 300 ms of either entirely positive ( “positive blocks” ) or entirely negative ( “negative blocks” ) phase shift . We first considered the strongly asymmetric pairs defined in our previous analysis over the entire period of movie presentation . For simplicity , in reporting the results of this analysis , for each pair we ordered the electrodes according to the dominant direction of TE computed across all the movie presentation time . The distribution of phase for such an electrode pair is plotted on the right-hand side of Fig 5A . As illustrated by this example , and as shown above ( Fig 3C ) , almost all electrode pairs with strongly asymmetric causal interactions have an average phase lag aligned with the leading direction of causation , and thus had a positive average phase shift restricted to the 0–45° range . However , the time-varying phase shift in positive and negative blocks covered a broader range: across all recordings , 50% of the average phase shifts in a positive or negative block were contained between −20 to 105 degrees , suggesting that phase shift values in this range can modulate information transfer . For each electrode pair , we then computed TE between gamma phase at the sending location and spiking activity at the receiving location using only positive-phase or negative-phase blocks respectively . Due to their bias towards positive shifts , there was a larger total duration for positive than for negative phase blocks . To make the quantitative comparison of TE computed in this way as fair as possible , we randomly down-sampled the number of blocks such that approximately the same time length was used to compute TE in each condition . We then investigated whether the amount of TE ( and thus the strength of causation ) between gamma phase and spiking activity at a receiving location was stronger during blocks of positive phase . We considered separately the modulation with the sign of the phase shift of TE in either the leading or the weaker direction of causation ( Fig 5B ) . We found that the values of TE along the leading direction of causation were more than four times larger ( p < . 001; sign test ) when the phase lags were positive ( i . e . , consistent with the gamma wave propagating along to the leading direction ) than when the phase values were negative ( i . e . , consistent with the gamma wave propagating opposite to the leading direction ) . In contrast , we found that values of TE against the leading direction of causation were approximately twice larger ( p < . 05; sign test ) when the phase lags were negative ( i . e . , consistent with a gamma wave propagating against the leading direction ) than when the phase values were positive . In other words , when the phase shifts transiently point toward the direction of causation that is the leading one , on average over the experiment , then TE in the dominant direction is transiently enhanced and the one in the weaker direction is transiently suppressed . This effect is reversed when the phase shifts transiently point against the direction of causation that is the leading one on average . All in all , these results suggest not only the time-averaged phase shift points toward the overall dominant direction of communication across an entire experiment , but that transient stimulus-related changes in phase shifts are accompanied by a relative increase in causation strength along the spatial direction indicated by the sign of the phase shift . In other words , transient changes in the direction of propagation of the putative travelling gamma waves correspond to transient relative increases of information transfer in the direction of putative wave propagation . We finally studied the dynamic modulations of TE for electrode pairs that were classified above as having “symmetric interactions” , i . e . , for pairs of electrodes classified as having comparable TE values in both directions ( according to the criteria defined in previous section ) . For these symmetric electrode pairs , we found that the amount of causation was the same ( p > . 05; sign test ) for both positive and negative phase shifts ( Fig 5B ) . This suggests that for these pairs of sites their interactions remain “mutual” ( i . e . , symmetric ) independently of transient changes of phase shifts . The above analysis suggests that shifts in gamma phases , whose sign indicate the instantaneous direction of the gamma wave propagation and correlate with the TE , may modulate information transfer . A natural question is what kind of visual information about the stimulus is conveyed by these phase shifts . To address this question , we extracted various movie features estimated from the RFs of each channel ( see S1 Methods ) , and then we correlated these movie features both with MUA firing rate in the same channel and with gamma phase shifts in the corresponding strongly asymmetric channel pairs . ( see illustration in Fig 6A ) . We first considered the tuning to the local TC in the RF . This was the RF visual feature that correlated the most with the MUA firing rate in the same site , with an increase in TC leading to an increase of MUA firing rate ( Fig 6E ) . We found ( Fig 6D ) that both the TC at each individual site and its sum correlated positively with the phase shift in asymmetric pairs . Given that positive time shifts ( i . e . , time shifts along the overall prevalent direction of gamma wave propagation ) lead to relative increases of TE in the overall dominant direction , this positive correlation between TC and phase shifts suggests that larger firing rate activation due to increase of TC ( either in one of the RFs or in their sum ) leads the gamma wave to travel from the sending to the receiving site and TE to increase in the direction of propagation . Given that the TC in the two RFs is partly correlated ( S7 Fig ) , it is difficult to assess whether phase shifts are more modulated by the TC in one of the two RFs or by their sum . Interestingly , though , we found ( Fig 6D ) that the phase shift correlated positively with the difference between TC in the sending and the receiving site , which suggests that the direction of the travelling wave is influenced not only by the individual RF features but also by their combination ( Fig 6D ) , with gamma waves more likely to travel from the sending site when the sending RF is more activated than the receiving one . We repeated the analysis considering the OA in the RF , defined as the squared cosine of the difference between the orientation of the gradient in the RF and the preferred orientation of the corresponding channel . We found ( Fig 6D ) that OA modulated MUA firing rate and gamma phase shifts in a way similar to TC , although the correlation between OA and neural activity was overall much less strong than that of TC , and ( unlike for TC ) the correlation between difference of OA across RFs and phase shifts ( though positive ) did not reach statistical significance . To gain insights into the overall effect on wave propagation of all the various visual features entering the movie , we computed the correlation between the trial-averaged MUA firing rate ( a robust and general marker of the overall effectiveness of the visual stimulus drive at any given time point ) and the phase shifts . We found ( Fig 6F ) that phase shifts correlated positively and significantly with the MUA in each RF , as well as with the sum and the difference between MUA in the sending and receiving RFs . The strongest correlation was found to be with the MUA in the sending RF . This , together with the positive correlation found with the difference between MUA in the sending and receiving RFs ( Fig 6F ) , suggests that wave propagation from the sending RF and causation in the direction of the travelling wave is more likely when the sending RF contains one of its preferred features , and that the wave propagation is enhanced when the receiving RF is less activated by the stimulus . A simple interpretation of these results in terms of exchange of visual information is that the causal gamma waves propagate to communicate to nearby RFs the presence of their preferred feature , and that causation is particularly effective on the receiving site when the latter is not shown an optimal stimulus . In addition to RF features mentioned above , we also computed a more global visual feature: the optic flow in the movie [53] . An example of estimated optic flow for one movie frame is given in Fig 6B . The optic flow in the movie generates an apparent movement in the visual field of the observer . The distributions of resulting speeds for all stimuli are presented in S8 Fig . The median values for the apparent speed were in the range 2 . 12–5 . 6 deg/s . Assuming the scaling of the parafoveal representation of the visual field in V1 is approximately 0 . 35 deg/mm , a purely feedforward mapping of object motion on the cortical tissue would result in propagating speeds in the range 6–16 mm/s . These values are much smaller than the speed values estimated in our analysis ( approximately 360 mm/s ) . This suggests that optic flow generated by moving objects in the movie cannot generate patterns of gamma waves by simple feedforward entrainment to object motion . However , endogenously generated gamma waves might still play a role in the processing of this kind of motion . To test this hypothesis , we studied the relationship between the optic flow traversing the sending RF in the direction of the receiving RF , that we call “directed motion , ” and the phase shift in asymmetric pairs ( see illustration Fig 6B and S1 Methods ) . We found a positive correlation between the directed motion from the sending to the receiving RF and the gamma phase shift ( Fig 6C ) . This shows that gamma wave propagation is modulated by extra-RF visual properties and corroborates the view that the travelling waves may communicate the presence of information that is salient to the sending RF .
Gamma oscillations have been traditionally associated with symmetric interactions among neural populations , such as synchronization and coherence [23 , 54] . Such synchronized elements can naturally support important computations , such as tagging groups of neurons that participate in encoding of the same percept [17 , 55] . In our data , we found a proportion of site pairs exhibiting symmetric causal interactions accompanied by zero-lag synchronization between populations , supporting this view about the function of gamma oscillations . However , we also found a more prominent proportion of recording pairs exhibiting a systematic nonzero lag gamma phase shift among spatially separate sites , that were accompanied by direction specific , rather than symmetric , communication between the sites . Our finding that these directed communications and the accompanying gamma phase shifts can be dynamically modulated by the stimulus suggests an important potential function for the phase relationships among oscillatory properties of different networks: the dynamic routing of the directional flow of information according to the needs of stimulus processing . This function cannot be easily accommodated by mutual zero-lag synchronization . While more theoretical work is needed to understand the importance and computational abilities of these dynamic directed interactions [56 , 57] , it seems apparent that the simultaneous presence of functions such as dynamic one-directional routing and tagging of groups that process information together can only increase the range of sensory computations that can be implemented by the same anatomical network . Another interesting question for future theoretical research is to study the conditions under which realistic network models can reproduce such a rich and heterogeneous dynamics with both symmetric and directional communication . It remains a theoretical challenge to understand how both phenomena may coexist within the same primary cortical network . An important finding is that gamma phase shifts between two sites can reliably change with the stimulus , and do so accompanied by simultaneous changes in the communication between the sites , in absence of a stimulus modulation of gamma phase at either site . This suggests that spatial gamma phase shifts reflect an emergent cooperative property of cortical dynamics that cannot be accounted for by feedforward sensory influences on individual sites . It is thus tempting to hypothesize that dynamic gamma phase shifts mediate dynamic stimulus- and direction-specific communication across cortical sites . A computational advantage of this putative mechanism of network reconfiguration is that it can control the cooperation among neuronal groups partly independently from the individual stimuli in the RF of each site , allowing flexible changes in neural communication depending on e . g . , extra-RF sensory features and other contextual influences . Our findings are indeed consistent with theoretical work suggesting that ongoing trajectories of network state reconfigurations participate in the mechanisms of processing of complex stimuli , such as those used in our experiment [58] . Our experimental observation moreover parallels the predictions of a recent modeling study [57] , showing that modification of phase differences between interacting neural populations can lead to a rapid reconfiguration of their effective connectivity pattern . Although we set out to investigate the specific hypothesis that directed network communication is modulated by gamma-band activity during sensory processing [26] , we investigated a wide range of LFP frequencies . It is worth examining the implications of differences across frequencies in the results we found . TE causation values were significant for all bands , although the gamma-band had the largest causal effect during visual stimulation . Thus , our results support the notion that all bands can , in principle , be involved in information transfer . However , two key findings are specific to the gamma-band . First , a consistently significant and positive correlation between phase shifts and travelling waves ( Fig 3B and S6 Fig ) , together with consistent stimulus-induced spatial coherence increases ( Fig 1B ) is found only for the gamma phase . Thus , the relationship between causation and stimulus-related travelling waves is strongly supported only for the gamma-band . Second , for the gamma-band , we can safely conclude that the propagating waves originate from neural dynamics rather than from spatiotemporal correlations in the movie , because the latter are too slow to account for the propagation of information by gamma waves . In particular , the same cannot be said about low frequency LFPs . Indeed , the spectral power of the movie features is highest in the low frequency range in which LFPs carry visual information [34] . Our previous modeling of visual cortex [59] , as well as work on auditory cortex [60 , 61] , suggest that the visual information in low frequency LFPs reflects the entrainment to the slow dynamics of natural stimuli , implying that low frequency waves may reflect stimulus dynamics rather than neural dynamics . Great care should be taken when comparing the results obtained at different frequencies . On the one hand , the causal effect of lower frequency bands may be overemphasized with respect to that of the gamma-band , because lower frequency bands have often larger spatial coherence and likely capture the activity of neural populations of a larger size [31] . This caveat , while it does not affect the conclusion that gamma activity has the highest causal effect during visual stimulation , complicates the interpretation of the amount of causation across bands . On the other hand , a potentially larger spatial spread of lower frequency LFPs may penalize the ability to detect travelling waves at low frequencies , because it could compress the range of phase shifts attainable by low frequency oscillations ( though we note that in our data , [2–4 Hz] phase shifts spanned a range similar to that of gamma phase shifts ) . All in all , the caveats in comparisons across frequencies suggest that , although our results support the hypothesis of causal travelling waves in the gamma range , we cannot rule out that causal travelling waves might exist in other bands than gamma . Thus , while a long line of evidence links specifically gamma oscillations to the relative timing of interactions between local inhibitory and excitatory neurons [23 , 24 , 26 , 62–71] , our results cannot support that the neural mechanism associated to the generation of gamma band oscillations are also exclusively responsible for the modulation of directed information flow . The spatiotemporal pattern of gamma phases that we reported is consistent with the idea that gamma oscillations mediate direction-specific interactions by propagating along specific directions and over distances of several millimeters . Existence of functionally relevant travelling waves has been hypothesized in visual cortex [37 , 72] . An interesting question regards the possible anatomical substrate of this propagation of gamma-band activity . Our finding that sites with stronger causation tend to have similar orientation tuning and our estimation of propagation speed are compatible with the hypothesis that such interactions may travel along horizontal connections . The phase of gamma-band oscillations has been recently implicated in mechanisms for feedforward transmission of information across different areas in the visual cortical hierarchy [73 , 74] . Our results suggest that phase of gamma-band oscillations may also be involved in directional information transfers within a brain area , and that they may do so by modulating the propagation of information along lateral connections . Our data , however , do not speak on how this process may interact with top-down modulations of sensory processing from higher cortical areas; as such top-down contributions are likely to be minimal under the conditions of opiate anesthesia used for our data collection . We consider possible confounding factors in our measures of directed causal interactions between gamma phase and spiking activity at other sites . One possibility is that these relationships are not actually causal but they are rather due to cross-talk between signal at different locations or even to volume conduction . However , both the strong spatial asymmetry of the causal interactions that we observed and the finding that gamma-band interactions increase during stimulus presentation speak against such potential artifacts . Another eventuality is that the causal effect of gamma phase may only show up artificially because firing rate and gamma phase of the sending population are correlated either because of locking of spike times to genuine network oscillations [23] or because of a spike-shape bleed-through on the LFP trace [75 , 76] . In other words , the reported causal effects of gamma phase may actually capture the effect of the level of firing rate of the sending population . The finding that LCI values are significantly positive ( and thus that the same rate of the sending population may elicit a different firing rate in the receiving population depending on the gamma phase of the sending population ) , and the relatively small correlation between causation values exerted by spiking activity and gamma phase argue against this explanation . In addition , we note that we minimized spike bleed-through by removing the spike shapes prior to the LFP computation [76] . An important practical consequence of our results is that phase shifts across multichannel recordings can be taken as meaningful markers for dynamic functional connectivity in distributed networks . Such phase relationships are easier to compute and much less data intensive than detailed measures of functional connectivity such as TE , and have been used as markers of interactions among areas [77–79] . The results we presented in this article elucidate some of the neural information transmission mechanisms that may be captured by observation of relationships between phases of the oscillatory activity of spatially separated neural populations , and provide a simple way to interpret the time-resolved sign of these measures in terms of directionality of dynamic information flow . Our study suggests that the dynamical relationships between gamma phases at different locations mark , and possibly causally mediate , the dynamic reconfiguration of functional network connections .
The data analyzed here was recorded as part of previous studies [34 , 40] . Recordings were obtained from the visual cortex of adult rhesus monkeys ( Macaca mulatta ) using procedures described below . All procedures were approved by local authorities ( Regierungspräsidium Tübingen ) , were in full compliance with the guidelines of the European Community ( EUVD 86/609/EEC ) and were in concordance with the recommendations of the Weatherall report on the use of nonhuman primates in research . Extracellular potentials were recorded in the V1 of three anesthetized monkeys for a total of four recording sessions ( d04nm1 , d04nm2 , g97nm1 , c98nm1 ) , each performed on a different day . The experimental procedures and recording setup have been described elsewhere [34 , 40] . In each session , 6 to 11 tungsten electrodes were positioned according to a 4 x 4 square matrix ( minimal interelectrode distance varied from 1 to 2 . 5 mm ) . During each session , between 30 and 120 trials of stimulation with color movies ( duration ranging from 4 to 6 . 5 min ) were recorded , as well as 5–10 trials of spontaneous activity ( 5 min duration ) . Spiking activity of neurons in the vicinity of each electrode was measured by extracting the MUA signal by high-pass filtering the extracellular potential above 1 , 000 Hz and subsequent rectification . The MUA signal obtained in this way measures the massed firing rate of a group of neuron in the vicinity of the electrode [30] . We used this measure of spiking activity because its values can be modeled by continuous random variables and are thus better suited to our information theoretic measures of spike field relationships than signals based on spike detection [40] . We extracted the LFP as follows . We cleaned the extracellular signal from spike bleed-through following the methodology proposed in [76] and code available at http://apps . mni . mcgill . ca/research/cpack/lfpcode . zip ( we used multiunit spikes detected with a threshold of 3 SD for this purpose ) . We then down-sampled the extracellular signals ( originally sampled at 20 , 835 Hz ) to 1 , 000 Hz and low-pass filtered with a cutoff frequency of 100 Hz to obtain broad-band LFP . From this signal , we extracted band-specific LFPs by band-passing it using a zero lag 8th order Butterworth FIR filter in the specified frequency range ( most analysis was done using the 50–80 Hz gamma-band ) . We extracted instantaneous phase and power of the considered oscillatory band using Hilbert transforms as detailed in S1 Methods . To compute how different neurophysiological signals ( such as phase or power at individual sites , or phase differences among sites ) were modulated by the movie stimulus , we computed the Shannon Information ( abbreviated as Information in this paper ) between the set of stimuli S and the neural response R , defined as I ( S;R ) =H ( R ) −H ( R|S ) ( 1 ) where H stands for the Shannon entropy . The first term on the right hand side of Eq 1 is called the response entropy and quantifies the variability of the neural response R across all trials and stimuli , while the second term is called the noise entropy and quantifies the residual variability of R for a given stimulus S . Information thus measures the reduction of uncertainty on R when S is known [80] . To apply this approach to a complex , time-varying stimulus such as a naturalistic movie , following previous work [34 , 51 , 81] , we divided the movie presentation time into nonoverlapping “scenes . ” Scenes were 300 ms long except for individual phases , which are fast time-varying and thus studied using scenes of one oscillation period duration . We considered each such scene and the associated neural response as stimulus response-pairs in Eq 1 . Thus , we computed how much information the neurophysiological response carried about which movie scene was presented . As such , it is a meaningful measure of how well and reliably the neural response is modulated during the movie . Information was computed by first binning the responses into a number of equipopulated bins and then applying statistical corrections to remove the limited sampling bias , using the Information Breakdown toolbox [82] available at http://www . sicode . eu/results/software . html ( see S1 Methods for full details ) . All variables ( except individual phases which vary at shorter time scales and were thus evaluated at a single point at the center of each scene , scene length having the duration of one period of oscillation ) were smoothed using a 300 ms rectangular sliding time window and binned into four bins ( we kept a low number of bins because of the low number of trials ) . TE is an information-theoretic measure of the causal dependency between the time series of a putative cause X and the time series of a putative effect Y in the framework of Wiener-Granger causality , stating that a signal Y is causing X if the knowledge of the past of Y reduces the uncertainty about the future of X . TE , along with other causal measures derived from the Wiener-Granger principle such as Granger Causality , is a widely used tool to infer causal functional connectivity from brain recordings . In many cases , the network inferred from these techniques matches well the patterns of anatomical connectivity [83] although the functional causal measures are also sensitive to the effect of dynamical variables such as the state of the network nodes that cannot be captured by anatomical connectivity [84] . The uncertainty of Xt , the present value of X , is quantified by its entropy H ( Xt ) . Using this definition , TE compares the uncertainty of Xt given its past Xpast with the uncertainty of Xt given both its past and the past of Y . The difference of these quantities defines the TE T ( Y→X ) =H ( Xt|Xpast ) −H ( Xt|Xpast , Ypast ) ( 2 ) It can be shown [28 , 85] that the above equation corresponds to the mutual information between present activity of Y and past activity of X , conditioned on the past activity of Y: T ( Y→X ) =I ( Xt;Ypast|Xpast ) ( 3 ) In our calculations , we almost always computed TE between the time series Y of the gamma-band phase at a putative sending location and the time series X of the spiking activity at a putative receiving location . Although , in one control analysis we used the spiking activity of the sending location as signal Y . TE was computed by binning the responses into a number of equipopulated bins and then using statistical corrections to remove the limited sampling bias , using the Information Breakdown toolbox [82] available at http://www . sicode . eu/results/software . html . The routine for TE estimation itself is provided as supplementary material ( http://dx . doi . org/10 . 6084/m9 . figshare . 1460872 ) . Finally , TE values were Z scored to the bootstrapped values of TE that would be obtained in case of no causation between the time series only because of a common time history of sensory stimulation ( See S1 Methods for details ) . We note that TE is similar in concept to Granger causality [27] but has the additional advantage over Granger causality that ( being based on mutual information ) it captures all possible kinds of relationships between the variables . We estimated the RF location using reverse correlation [86] between the gamma power in each channel and movie luminance . To ease computation , movie frames were spatially smoothed using an average over a 6 x 6 sliding window and then spatially down-sampled by a factor of 4 . When using reverse correlation with natural stimuli , the obtained RF is likely to be larger than the true RF because of the spatial correlations in the stimulus [87 , 88] . To achieve better localization of RFs , we thus minimized large correlation between pixel luminance across the frames in some cases by removing the first one or two largest singular value decomposition ( SVD ) components of the spatiotemporal time course of the movie luminance . Correlation values were computed across time for each experiment between each pixel luminance and gamma power , using time series subsampled at 66Hz , and taking into account a time lag of 60ms between the stimulus and the neural response in V1 , matching the order of magnitude reported in previous literature [88 , 89] . Correlation values were then Z-scored across experiments with the same movie stimulus , and the resulting maps for each movie were averaged together to get a final correlation map for each electrode in each recording session . An example of such correlation map is shown in S1B Fig . The RF center was chosen as the pixel achieving the maximum of this map , the RF shape was assumed square with vertical and horizontal borders , while the RF size was the smallest one achieving below 75% of the maximum value of the map on its border . | Complex and flexible behavior likely results from the ability of groups of neurons in the brain to reconfigure dynamically the information flow across different brain areas , depending on what the brain is engaged in ( processing a stimulus or carrying out a task ) . Here , we investigate how oscillations of cortical activity in the gamma frequency range ( 50–80 Hz ) may influence dynamically the direction and strength of information flow across different groups of neurons . By recording neural activity and measuring information flow between multiple locations in visual cortex during the presentation of Hollywood movies , we found that the arrangement of the phase of gamma oscillations at different locations indicated the presence of waves propagating along the cortical tissue . These waves were observed to propagate along the direction with the maximal flow of information transmitted between neural populations . The gamma waves changed direction during presentation of different movie scenes , and when this happened , the strength of information flow in the direction of the gamma wave propagation was transiently reinforced . These findings suggest that the propagation of gamma oscillations may reconfigure dynamically the directional flow of cortical information during sensory processing . |
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Due to the lack of fossil evidence , the timescales of bacterial evolution are largely unknown . The speed with which genetic change accumulates in populations of pathogenic bacteria , however , is a key parameter that is crucial for understanding the emergence of traits such as increased virulence or antibiotic resistance , together with the forces driving pathogen spread . Methicillin-resistant Staphylococcus aureus ( MRSA ) is a common cause of hospital-acquired infections . We have investigated an MRSA strain ( ST225 ) that is highly prevalent in hospitals in Central Europe . By using mutation discovery at 269 genetic loci ( 118 , 804 basepairs ) within an international isolate collection , we ascertained extremely low diversity among European ST225 isolates , indicating that a recent population bottleneck had preceded the expansion of this clone . In contrast , US isolates were more divergent , suggesting they represent the ancestral population . While diversity was low , however , our results demonstrate that the short-term evolutionary rate in this natural population of MRSA resulted in the accumulation of measurable DNA sequence variation within two decades , which we could exploit to reconstruct its recent demographic history and the spatiotemporal dynamics of spread . By applying Bayesian coalescent methods on DNA sequences serially sampled through time , we estimated that ST225 had diverged since approximately 1990 ( 1987 to 1994 ) , and that expansion of the European clade began in 1995 ( 1991 to 1999 ) , several years before the new clone was recognized . Demographic analysis based on DNA sequence variation indicated a sharp increase of bacterial population size from 2001 to 2004 , which is concordant with the reported prevalence of this strain in several European countries . A detailed ancestry-based reconstruction of the spatiotemporal dispersal dynamics suggested a pattern of frequent transmission of the ST225 clone among hospitals within Central Europe . In addition , comparative genomics indicated complex bacteriophage dynamics .
Clinical microbiologists have frequently been astonished by the impressive capability of pathogenic bacteria to acquire novel traits such as antimicrobial resistance . However , the actual speed at which nucleotide substitutions , entire genes , or complex mobile genetic elements are gained and lost in bacterial populations has rarely been determined [1] , [2] , [3] , [4] . A measure of the real-time nucleotide substitution rate in natural populations of pathogenic bacteria would enable the dating of evolutionary events and the reconstruction of a pathogen's demographic history based on DNA sequence variation , which ultimately could provide fundamental insights into the forces driving pathogen emergence and spread [2] , [5] . Methicillin-resistant Staphylococcus aureus ( MRSA ) are a common cause of hospital-acquired infections , imposing a heavy burden on patients and health care resources [6] . The prevention and treatment of such infections has become increasingly difficult due to this bacterium's ability to acquire resistance against all classes of antibiotics . Staphylococcus aureus has long been known to cause local outbreaks and regional epidemics of hospital infections , where the causative strains – identified through bacterial typing – may spread both within and across hospital wards , and among different hospitals [7] . Contemporary typing of S . aureus is performed by using molecular techniques , including DNA macrorestriction ( pulsed field gel electrophoresis ) and DNA sequence-based methods . Among the latter , multilocus sequence typing ( MLST ) , which indexes variation at seven slowly evolving genetic loci , has been extremely useful to gain a basic understanding of the population structure of S . aureus [8] . While more than 1 , 400 MLST-based sequence types ( ST ) have been reported for S . aureus to date , most of this diversity is clustered in a limited number of clonal complexes [8] . The worldwide predominance of a few clonal lineages among MRSA has resulted in the conception that MRSA strains may spread globally very rapidly [9] , [10] . However , by investigating the diversity and phylogeography of one such clone ( ST5 ) in greater detail , we have recently detected considerable spatial subdivision among populations from different localities , indicating that the dispersal of this clone over long distances happens rarely in comparison to the frequency at which novel MRSA arise through acquisition of the genetic methicillin-resistance island SCCmec [11] . In the present study , we have investigated the evolutionary history of an MRSA strain that recently emerged in Central Europe . By MLST , this strain is identified as sequence type ST225 ( allelic profile , 1-4-1-4-12-25-10 ) , which is a single locus variant of ST5 , the presumed ancestor of clonal complex CC5 [8] . While ST225 had been discovered first among isolates collected during the 1990s in the USA [8] , [12] , it was not detected in any European country before the year 2000 [13] , [14] , [15] , [16] , [17] , [18] . Since 2001 , however , its reported proportional abundance in Germany increased very rapidly [14] , and it was also reported from hospitals in neighboring countries [19] , [20] . Hence , this strain has a demonstrated ability to spread rapidly and to become predominant in the hospital environment , thereby replacing other MRSA strains that heretofore had been established for years [14] . At the same time , ST225 seems almost entirely restricted to the hospital environment , since it has not been reported from asymptomatic S . aureus carriage outside of hospitals and it is very rarely found among isolates from community-associated MRSA infections; in the latter , sporadic cases , close contacts to hospital patients or staff could not be excluded [21] , [22] . We analyzed an international sample of MRSA type ST225 sequenced at 118 , 804 basepairs per isolate . Based on serial , time-structured samples of DNA sequences , we observed the accumulation of genetic diversity over a few years . By using coalescent ( i . e . , genealogy-based ) methods , we calculated divergence times and reconstructed the pathogen's past demography . Our results are consistent with a scenario of a recent reduction in population size that has caused losses of genetic variation , and a subsequent population expansion of ST225 within Central Europe .
Isolates affiliated to ST225 – including both , MRSA and methicillin-susceptible S . aureus ( MSSA ) – display very limited genotypic and phenotypic variability based on contemporary , molecular typing techniques and antimicrobial resistance ( Table S2 ) . We used denaturing high-perfomance liquid chromatography ( dHPLC ) to screen for sequence polymorphisms at 269 genetic loci ( predominantly randomly chosen housekeeping genes ) from each of 73 S . aureus isolates ( Tables S2 , S3 ) . Genome fragments investigated were scattered along the S . aureus chromosome and altogether comprised 4 . 2% ( 118 , 804 basepairs ) of the genome ( Table S3 ) . Polymorphisms were ascertained through subsequent sequence analysis ( Table S4a ) . All isolates belonged to sequence type ST225 or a single locus variant thereof ( ST710 ) and had been isolated between 1994 and 2007 in the USA , the Czech Republic , Denmark , Switzerland , and Germany ( Table S2 ) . These analyses revealed 48 bi-allelic polymorphisms ( BiPs; i . e . , polymorphic sites at which exactly two alleles were observed ) , including 11 synonymous base substitutions in protein-coding regions , 26 non-synonymous substitutions , 10 substitutions in intergenic regions , and one insertion of a single nucleotide ( Tables S4a , S5 ) . The nucleotide diversity , π ( the average number of nucleotide differences per site between sequences from two isolates ) , was 0 . 00001 for coding regions and 0 . 00003 for non-coding regions ( Table S1 ) . This level of diversity is extremely low; in a similar study on a global sample of S . aureus sequence type ST5 ( the founder of clonal complex CC5 ) , we recently discovered ten-fold higher diversity in both , protein-coding and intergenic regions [11] . In 70 ST225 isolates from Europe , we found 41 BiPs , which corresponds to 0 . 6 BiPs per isolate or 28 differences between any two 2 . 8 Mbp genomes . A similar level of divergence was recently reported for community-associated MRSA strain ‘USA300’ , which , on average , displayed 35 differences between any two out of eight re-sequenced genomes [23] . The dN/dS value ( the ratio of changes at non-synonymous sites to changes at synonymous sites ) for protein-coding genes in ST225 was 0 . 77 , hence , similar to the value found for ST5 [11] . This high proportion of non-synonymous substitutions is unlikely to represent a signal of selective pressures , but is a consequence of the dynamics of short-term evolution ( i . e . , evolution which occurs within a few years , see below ) [11] , [24] , [25] . The 48 BiPs enabled the discrimination of 36 haplotypes ( i . e . , unique combinations of BiP alleles ) among the 73 isolates investigated ( Table S2 ) . There were only five parsimony informative sites ( where derived alleles occurred in >1 haplotype ) , and four of these were found in isolates from the USA . Consequently , most of the variation was unique to individual haplotypes , and little phylogenetic structure was discerned among European ST225 isolates ( Figure 1 ) . The minimum spanning tree based on these BiPs shows a star-like radiation that is rooted at a hypothetical node representing the most recent common ancestor of ST225 and the JH strain ( ST105; Figure 1a ) . This ancestor is affiliated to lineage ST5-K within the ST5 radiation ( Figure 1b ) . It carries a number of derived alleles ( listed in Table S4b ) that distinguish it from ST5 haplotypes , in agreement with the previous presumption that ST5 was the ancestral genotype within the clonal complex CC5 [8] . All ST225 MRSA isolates that we have investigated , including those from the US , carry a unique 997-basepair deletion in their SCCmec cassettes , which encompasses a 0 . 3-kb open reading frame ( N315-SA0035 ) and the adjacent direct repeat unit ( dru ) locus . Deletions of the dru locus have rarely been reported [26] , [27] . The presence of this characteristic feature in SCCmec indicates that the most recent common ancestor of the ST225 radiation had already been methicillin-resistant , which suggests that the entire radiation is younger than a few decades . The same dru deletion was present in the genome of the closely related JH strain ( ST105 , represented by isolates JH1 and JH9 [28] , Figure 1 ) , indicating it also existed in the common ancestor of ST225 and ST105 , which , hence , already was methicillin-resistant . In addition , we found identical recombinase ( ccrB ) and helicase ( cch ) gene sequences in SCCmec from all ST225 MRSA isolates and from the JH genome ( not shown ) , supporting the notion of a common origin . The dru deletion in international isolates also indicates a history of long-distance dissemination of MRSA , since sequence identity in this region would be unlikely if SCCmec elements had been imported repeatedly into locally endemic , methicillin-susceptible ST225 strains . Notably , our methicillin-susceptible isolates could not be distinguished from MRSA based on BiPs ( Table S2 ) , lending support to the presumption that they represent strains that have lost methicillin resistance together with parts of their SCCmec elements . Three of these MSSA carried SCCmec remnants in their chromosomes which we detected by PCR and sequencing , including the region with the dru deletion ( Table S2 ) . Even those isolates with no detectable traces of SCCmec may be secondary MSSA , however , since spontaneous , precise excision of SCCmec from the staphylococcal chromosome has been reported [29] , [30] . There are several arguments why our American isolates of ST225 represent the ancestral population of the European clade . First , US ST225 isolates have been observed as early as 1994 ( Table S2 ) , whereas this clone was not encountered before 2000 in Europe . Second , considerable genetic diversity is observed among US isolates even from a single federal state ( Wisconsin ) , with seven SNPs including four parsimony informative sites observed in only three isolates ( Figure 1a ) . This is in stark contrast with the extremely low genetic diversity in European isolates , which suggests a recent population bottleneck ( i . e . , a brief reduction in population size ) associated with the introduction of ST225 into Europe . A population bottleneck occurs , for example , when a small number of individuals founds a new population ( ‘founder effect’ ) , and may result in a significant loss of genetic variation . Third , American ST225 carry a spa sequence ( spa type t002 ) that is presumably ancestral to spa from European ST225 ( t003 , t045 , t456 , t1107; Tables S2a , S2b ) ; the latter spa sequences may have arisen from t002 through deletions of individual repeat units , a frequent phenomenon during DNA replication , whereas the opposite ( regain of unique repeats ) appears less likely . Spa type t002 was also previously considered ancestral to other spa types based on the presence of a large number of single-repeat variants [31] . Finally , the ST225 radiation branches off from the ST5-K lineage ( Figure 1b ) , to which the majority of ST5 isolates from the USA had been affiliated as reported in our previous study [11] . Taken together , we conclude that ST225 evolved from an MRSA that already carried the dru deletion in its SCCmec element . The novel clone spread to Europe somewhat later , where it rapidly became highly prevalent . The hypothesis of a single transmission event from the US is further supported by the low diversity and the monophyletic structure of the European ST225 radiation ( Figure 1 ) . However , current data do not preclude the existence of an ancestral ST225 population outside the US , although no such isolate has been observed so far . A plot of genetic distance from a common ancestor against sampling time gave a first indication of a measurable accumulation of DNA sequence variation over the sampling time interval ( Figures 2a , 2b ) . Such sets of temporally spaced molecular sequences with a statistically significant number of genetic differences can be used to simultaneously estimate divergence times , temporal changes of population size , and nucleotide substitution rates by applying suitable statistical methods [32] . Based on the sequence variation ascertained , we calculated the age ( divergence time ) of ST225 by applying a Bayesian coalescent method of phylogenetic inference that incorporated a strict molecular clock model [33] . The relaxed molecular clock model was ruled out as it yielded a posterior distribution of clock rates showing negligible variation ( with the standard deviation abutting zero ) , and was not statistically supported ( likelihood ratio test , P = 0 . 99 ) . Based on our dataset of 73 sequences , the most recent common ancestor of ST225 was estimated to 1990 ( 95% confidence intervals , 1987 to 1994 ) ( Table 1 ) . The age of the American ST225 clade coincides with the age of the entire ST225 radiation , and the European clade was estimated to have diverged since 1995 ( 95% confidence intervals , 1991 to 1999 ) ( Table 1 ) . Alternative tree priors ( i . e . , prior probability distributions ) for the Bayesian analysis resulted in very similar time spans ( Table 1 ) . Sampling from the prior distribution , in contrast , resulted in hugely inflated divergence times ( Table 1 ) , suggesting our results are not mere artefacts reflecting the priors . While it may seem surprising that the little sequence variation discovered may suffice to calculate divergence times with such tight confidence intervals , a test based on random permutation of sampling times across isolates resulted in much older dates and much larger credible intervals ( Figure 3 ) , indicating our age calculations were based on a genuine signal in the data [34] . The Bayesian skyline plot indicates a very sharp increase of the effective population size starting in 2001 , with strong growth continuing for about three years and levelling off thereafter ( Figure 4a ) . This demographic expansion , including the timing of events , is in full agreement with our observation of ST225 abundance in Central Europe ( Figure 4b ) . This scenario is also consistent with a rampant expansion of the clone after its trans-Atlantic spread . The skyline plot ( Figure 4a ) was not unduly affected by heterogeneity in sample size per year , as indicated by the analyses of ten random subsamples of sequences from each year ( Figure S1 ) . However , we cannot exclude that population growth may have been more stochastic during the 1990s than is suggested by the current skyline plot ( Figure 4a ) . To gain more detailed insights into the population structure during this time period , it would be particularly useful to investigate additional American ST225 isolates collected between 1990 and today , which are unfortunately not available at present . The composition of our sample seems to reflect the worldwide population structure of ST225 quite well , since many thousands of MRSA isolates have been genotyped to date in many countries , but no ST225 has ever been found outside Central Europe or the US . In a recent survey based on MLST typing of over 2 , 000 MRSA isolates sampled from Wisconsin , we did not find a single additional ST225 isolate ( unpublished results of SKS ) . To probe the abundance of ST225 in Germany during the 1990s , we randomly chose 200 isolates from 1997 from the culture archive of the German national reference center for staphylococci and characterized them by spa typing and MLST . None of them was affiliated to ST225 , suggesting that , at the time , the strain had been either absent or very rare in Germany . The mean nucleotide substitution rate within ST225 was estimated at 2 . 0×10−6 substitutions per nucleotide site and year ( 95% confidence intervals , 1 . 2×10−6 to 2 . 9×10−6 ) ( Table 1 ) . This short-term evolutionary rate varied only slightly depending on clock model and choice of priors ( Table 1 ) , and was also largely confirmed by an alternative method based on a full likelihood model assuming a perfect star genealogy , which gave a rate of 1 . 1×10−6 ( 95% confidence intervals , 7 . 5×10−7 to 1 . 4×10−6 ) . Even higher upper limits of substitution rates in bacteria have previously been estimated for Neisseria gonorrhoeae ( 4 . 6×10−5; [2] ) , Helicobacter pylori ( 4 . 1×10−5; [4] ) , and Campylobacter jejuni ( 6 . 6×10−5; [3] ) . In contrast to S . aureus , however , these three species are characterized by extremely high rates of homologous recombination , and , hence , part of the polymorphisms observed might have resulted from recombination rather than mutation [2] , [3] , [4] . Therefore , those reported rates had been considered maximal estimates; in the case of H . pylori , 100-fold lower rates were equally likely [2] , [4] . Our rate for MRSA ST225 exceeds an evolutionary rate estimate that had been proposed for Escherichia coli in the past ( 3×10−8 substitutions per nucleotide site and year ) by almost two orders of magnitude [35] . That previous estimate had been based on a laboratory mutation rate of 10−10 per nucleotide site and generation , and the assumption of approximately 300 generations elapsing per year [35] . Mutation frequencies measured in vitro ( i . e . , the average fraction of individuals carrying a particular resistance mutation in a laboratory culture ) are very similar in E . coli and S . aureus [36] , [37] , suggesting comparable underlying mutation rates ( the probability of a mutation to occur in each generation ) . While ‘mutator’ strains with elevated mutation frequencies have been described , they seem to be uncommon among clinical isolates [38] . A mutator phenotype for ST225 is also not supported by a comparison of whole genome sequences from 04-02981 ( ST225 , accession number CP001844 , see below ) and related isolates , including N315 , JH1 and JH9 ( Figure S2 ) , and additional isolates ( our unpublished data ) . In the genome from 04-02981 , we detected no inactivating mutations in any genes involved in DNA replication fidelity , DNA repair mechanisms , or recombination , which are commonly associated with mutator phenotypes [38] , [39] . Instead , it seems likely that the massive clonal expansion of ST225 was associated with short bacterial generation times and frequent transmission to new hosts . During rapid demographic expansions , both genetic drift and natural selection will be reduced , thus leading to an increase in the number of mutations segregating in a population , at least transiently [40] . Our results pointing to a rapid clonal evolution of S . aureus suggest that other bacteria may evolve faster than previously acknowledged . It must be considered , however , that observed molecular clock rates are time-dependent [41] . Generally , clock rates decline from initial mutation rates to long-term substitution rates , because the majority of mutations get eliminated with time due to genetic drift and selection [41] . Such rate curves have not yet been determined for bacteria . However , our results imply that recent divergence times of bacteria were possibly overestimated with dating based on the molecular clock rate suggested by Guttman and Dykhuizen [35] , [42] , [43] . It will be interesting to investigate short-term evolutionary rates in additional clones of S . aureus and other bacterial species . The time dependency of these rates may be established by comparing radiations at different levels of divergence . Interestingly , the high rates of evolutionary change we found in MRSA caused the accumulation of DNA sequence variation within a few years , a feature that heretofore had been found only in highly recombinant ( panmictic ) gonococcus [2] and in rapidly evolving viruses [44] . Importantly , the time-structured sampling of DNA sequences within evolutionary timescales enables the application of sophisticated analytical methods , which opens up exciting prospects for investigations of the recent evolutionary history of bacterial pathogens , together with the forces that have shaped their spatial distribution . We have investigated ST225 isolates from four European countries ( Table S2 , Figure 1a ) by reconstructing the most likely ancestry path between isolates to reveal the spatiotemporal dynamic of ST225 spread by applying the SeqTrack algorithm [45] . Interestingly , our results indicate that multiple haplotypes have been introduced into several countries ( Figure 1a ) . Figure 5 represents the cumulative number of isolates from any location ( bubbles ) and the inferred ancestries ( arrows ) for successive time windows . Note that while Figure 5 represents the best-supported ancestry path given the sampled isolates , some ancestries might not correspond to actual transmission events , as the true ancestral population might not have been sampled . To avoid any overinterpretation of the results , we restrict our interpretation to the global pattern and some specific unambiguous features of the inferred ancestries . After initial seeding into Europe , ST225 was transmitted to other locations in Germany and to additional European countries ( the Czech Republic , Switzerland and Denmark ) . Some local ancestries ( i . e . , within the same city ) are characterized by a relatively large genetic differentiation ( Figure 5 , colored dots ) suggesting long-term persistence of ST225 within the same location . Another interesting feature of the reconstruction of the spatiotemporal dynamics of ST225 lies in the repeated transmission events between countries . For instance , one isolate from Denmark is assigned an ancestor from Germany with high likelihood ( same genotype ) at least three years after the first transmission from Germany to Denmark . The first transmission ( Figure 5 , time window 2004/01/22–2004/12/17 ) could be traced back epidemiologically to an index patient that had been transferred from a hospital in Germany into a hospital in Copenhagen , Denmark , in 2004 , where the carried MRSA strain ( haplotype H225-07; Table S2 , Figure 1a ) later caused an outbreak involving multiple patients and staff . Two additional isolates collected from the same hospital in 2006 and 2007 were affiliated to the same haplotype ( Table S2 ) , indicating the clone was still present three years after the initial outbreak . However , a second haplotype ( H225-01 ) was indicated to have been introduced from Germany into Denmark ( Figure 5 , time window 2007/02/08–2007/11/20 ) , and this is unlikely to be an artefact due to insufficient sampling within Denmark , as several local ancestry events were identified earlier within Denmark . The SeqTrack results indicated 15 transfers among different countries within Europe ( Figure 5 ) . Considering the low informative diversity discovered , the limited number of isolates and countries investigated , and the short time span since emergence of ST225 has started in Europe , this number of detected international transfers of clones is very high . It indicates that cross-border spread of MRSA between the countries considered must have occurred frequently , and , more generally , that the turnover of hospital-associated MRSA is quite rapid even within a larger geographic region ( Central Europe ) . Hence , the question arises how efficient geographic dissemination may be mediated . Abundant international travel will result in occasional hospitalization outside the country of residency , and potential subsequent cross-border patient transfers into the respective home countries . This route is exemplified by the introduction of haplotype H225-07 from Germany into Denmark , with the subsequent establishment of this clone in the hospital for several years . In addition , it is well documented that colonized health-care personnel may promote the spread of MRSA [46] . It is also possible that some spread of ST225 occurs outside of hospitals , even though the lack of community-associated isolates suggests the prevalence to be low [47] . Efficient containment of MRSA spread requires pro-active surveillance and eradication of colonization [46] , [48] . It is unclear at present , if the success of particular MRSA strains such as ST225 may be due to fortuitous stochastic events or adaptive genetic changes . To reveal any genetic traits that distinguish ST225 from other strains of MRSA and may enable its massive expansion within short time , we sequenced the genome from one representative isolate , MRSA 04-02981 ( haplotype H225-01 , sequence accession number CP001844 ) . We used both 454 ( Roche ) and Solexa ( Illumina ) technology , and closed the genome sequence by using long-PCR and Sanger-sequencing . The final genome sequence likely contains very few sequencing errors , if any , since the application of two independent sequencing approaches resulted in only six conflicting SNP calls . The genome from isolate 04-02981 was found to be co-linear with previously sequenced genomes from related isolates N315 ( ST5 ) and JH1 ( ST105 ) [28] , [49] . There was no indication for the presence of any plasmids in isolate 04-02981 . Base substitutions were distributed evenly among genes of different functional categories ( not shown ) . The effects that individual missense mutations may have on protein function are hard to predict in most cases . In the genomes from both , 04-02981 and the JH strain ( including isolates JH1 and JH9 ) , two open reading frames were truncated , one of which encodes an unknown , hypothetical protein ( N315-SAS092 ) and another ( N315-SA1092 ) encodes Smf , a protein that has been suspected to be associated with transformation competence . In addition , two open reading frames were uniquely truncated in the genome from 04-02981 , encoding an adhesion factor ( N315-SA1267 ) and the transcription regulator norG ( N315-SA0104 ) . The latter pseudogene initially appeared particularly interesting , because experimental disruption of this gene had been shown previously to result in a fourfold increase of in vitro resistance to beta-lactam antibiotics [50] . However , after applying a deletion-specific PCR ( Table S6 ) , we found that none of the other ST225 isolates in our collection had this deletion . Hence , truncation of norG is not a common trait of ST225 , but rather is an idiosyncrasy of isolate 04-02981 , which just happened to be the one we had chosen for genome sequencing . The genome of isolate 04-02981 contains a stretch of 44 kilobases of DNA that is inserted in a non-coding region downstream of the sufB gene ( N315-SA0778 ) , resulting in a duplication of the 67-basepair sequence upstream of the integration site . The inserted sequence is highly similar ( sequence identity , 99 . 5% ) to an as yet unnamed prophage previously found in the JH strain at the same genomic position [28] . It shares 50% or less overall sequence similarity to other phage genomes sequenced previously , including Φ11 from S . aureus NCTC8325 [28] . The prophage contains 68 predicted open reading frames , 19 of which encode proteins for basic phage functionality , and 49 of which have unknown functions . None of them has similarities to any known or presumed virulence factors . By using PCRs targeting five specific regions ( Table S6 ) , we detected the presence of this prophage in all European ST225 isolates investigated and in other isolates affiliated to lineage ST5-K , but not in any other ST5 strains ( Figure 1b ) . Thus , this particular prophage is specific to lineage ST5-K and its descendants , and we thus named it ΦSaST5K . Of note , prophage ΦSaST5K was not detected in any of our three ST225 isolates from the US , and , hence , it must have been lost by their common ancestor . There is a second phage – ΦN315 – in the genome of 04-02981 , which it shares with isolate N315 , an MRSA from Japan that is affiliated to lineage ST5-G [11] . In the JH strain , however , ΦN315 has been replaced apparently by another , dissimilar phage [28] , and JH1 and JH9 harbor two additional prophages that have as yet not been seen in any other sequenced S . aureus genomes ( Figure S2 , Table S8b ) . This comparison of only three closely related MRSA genomes already points to the existence of complex phage dynamics , with varying apparent half-lives of prophages in their respective bacterial host chromosomes . Our data indicates that several phages are associated to ST225 and its ancestral lineage , and may have played a role for its evolution . Bacteriophages have been suspected to promote the spread of pathogenic bacteria , by using various potential mechanisms . For example , phage genes may be directly implicated in immune evasion or virulence [51] , or indirectly by affecting in trans the activity of bacterial genes outside the prophage , which in turn may enhance transmission or affect other fitness-related traits [52] . Alternatively , phages may possibly impact on competition between strains of staphylococci by driving lysis of bacterial cells that do not carry a related lysogenic phage . We have shown that a strain of MRSA has accumulated measurable genetic change within an epidemiological timescale . The high short-term evolutionary rate in this MRSA enabled the estimation of divergence times and analyses of past changes in population size based on time-structured , serial DNA sequence samples , which heretofore had been possible only for highly recombinant gonococci and viruses . Moreover , ancestry reconstruction revealed the history of geographic spread of this MRSA at unprecedented detail . Confirmation of higher than expected short-term substitution rates in a wider range of bacterial pathogens , together with the tangible prospect of whole-genome sequences for large numbers of related isolates [53] , [54] could prefigure a golden age for bacterial epidemiology . Presumably , bacterial pathogens will soon be amenable to detailed investigation of their recent evolutionary history and spread . At the same time , abundant polymorphisms will be discovered that will be useful for bacterial typing in epidemiological surveillance [55] , [56] , [57] .
Sources and properties of 73 isolates of S . aureus are listed in Table S2a . Susceptibilty to antibiotics was tested by using the broth microdilution method according to the DIN58940 instructions [58] and bacterial typing was performed as described previously [31] . Draft genome sequences were generated and assembled commercially . 454 sequencing was performed on a GS FLX machine at 454/Roche in Branford , CT , USA , providing 32-fold average coverage of the staphylococcal chromosome and resulting in 42 initial contigs with >500 basepairs . Solexa sequencing was performed on a Genome Analyzer System at GATC in Konstanz , Germany , generating paired-end reads that were mapped onto the N315 genome sequence at 49-fold average coverage . Remaining gaps between contigs were closed by PCR using Hot Taq DNA polymerase ( Peqlab , Germany ) or long PCR using the Expand Long Template PCR System ( Roche ) , respectively , and subsequent Sanger sequencing ( primers in Table S7 ) . Comparisons of contigs and genomes were performed by using Kodon software ( Applied Maths , Belgium ) . After correcting sequences at contig ends and within repetitive elements , there were 468 sequence differences to N315 , including base substitutions , insertions , and deletions ( Tables S8A–S8D , Figure S2 ) . Sequence differences to N315 that were shared between ST225 and the JH strain were considered correct since matching data had been generated in an independent study [28] . For insertions in the sequenced genome , we relied on 454 data , since they could not be detected among Solexa reads mapped against the N315 genome ( Tables S8A–S8D ) . Gene annotation was performed automatically using the RAST server [59] and corrected manually using Kodon and Artemis software [60] . The annotated genome sequence from isolate 04-02981 was submitted to GenBank ( accession number CP001844 ) . Mutation discovery was performed as described previously [11] . PCR primers used for amplification and sequencing are listed in Table S3 . A minimum spanning tree based on BiPs was constructed with Bionumerics 5 . 1 . The ancestral node was determined by comparison to genome sequences from isolates N315 and JH1 . PCR amplification of regions including the dru deletion , the four-basepair deletion within norG , SCCmec remnants , and prophage-specific fragments , respectively , were performed by using Hot Taq DNA polymerase ( Peqlab , Germany ) according to the manufacturer's instructions and by using the primers listed in Table S6 . Based on an alignment of polymorphic sites in protein-coding sequences , a maximum likelihood tree was calculated by using Treefinder software ( available at www . treefinder . de ) , applying the HKY model of DNA substitution . Rooting of the tree and linear regression of root-to-tip distances against dates of first haplotype appearance was performed by using Path-O-Gen software ( available at http://tree . bio . ed . ac . uk/software/pathogen/ ) , and the significance of the correlation was determined with SigmaPlot 11 . 0 ( SPSS ) . To assess whether nucleotide substitution rates in protein-coding sequences departed significantly from expectations under a strict molecular clock , we used a likelihood ratio test , based on a comparison of likelihood scores for maximum-likelihood trees calculated by using PAUP , with and without a molecular clock enforced . The statistical significance of the difference between likelihood scores was determined by assuming a chi-square distribution and s-2 degrees of freedom , where s was the number of sequences [61] . Evolutionary rates , divergence times , and Bayesian skyline plots were computed with the BEAST software ( available at http://beast . bio . ed . ac . uk/ ) [62] , using the HKY model of nucleotide substitution and a strict clock model ( unless stated otherwise ) , with concatenated protein-coding sequences ( 108 , 261 basepairs ) dated based on the year of isolate sampling , and with 108 iterations after a burn-in phase of 106 iterations . Markov chain Monte Carlo samples from three independent analyses were combined for estimation of posteriors , resulting in effective sample size values greater than 1 , 000 for all parameters . Various prior sets were used as indicated ( Table 1 ) . To test if date estimates were unduly influenced by prior assumptions , analyses were re-run ( 5×107 iterations ) on each of five datasets generated by randomly switching sampling dates across isolates . To sample from the prior distributions , analyses were run on an empty alignment . Further , to test if the resulting Bayesian skyline plot was confounded by temporal variation in sample size , we generated and analysed ( 107 iterations ) a series of datasets by subsampling from time classes and randomly drawing four isolates from each year . For an alternative rate estimate , we used a full likelihood model assuming that demographic expansion was strong enough to result in a perfect star genealogy ( i . e . , without any coalescent events ) . To avoid violation of this assumption , we analysed protein-encoding loci ( 108 , 261 basepairs ) from 58 European isolates exclusively , including only one isolate from each haplotype , except for the ancestral haplotype H225-01 . Likelihood of the model for each locus was then given by the binomial probability of the number of mutations observed in all isolates , given the sum of the genealogical branch lengths for all isolates ( i . e . , date of isolate collection - date of expansion start ) and a substitution rate parameter per locus and per year . A point multilocus substitution rate estimate ( per nucleotide site and per year ) and its 95% confidence interval were inferred based on the product of the above-described likelihood function for all loci , considering that all loci had a specific number of sites , were independent , and had a single , constant mutation rate . The procedure was written in R [63] and is available upon request to R . Leblois . The SeqTrack algorithm [45] was used to reconstruct the most plausible scenario for the spatiotemporal spread of the ST225 clone . This new method has been developed to study the dispersal and transmission of emerging pathogens during disease outbreaks , such as the 2009 swine-origin influenza A/H1N1 pandemic [45] . SeqTrack reconstructs the most likely ancestries among sampled strains using their genotype and sampling dates . This method differs fundamentally from phylogenetics in that it does not attempt to infer hypothetical ( and unobserved ) common ancestors , but rather seeks to reconstruct ancestries directly from the sampled isolates . Because of the low level of genetic variability in ST225 ( most strains differ by a single nucleotide from each other ) , we used a maximum parsimony approach to infer ancestries . Thus , the most likely ancestry path was searched for by minimizing the number of mutations between ancestors and descendents . Whenever several strains were equally likely ancestors of the isolate under consideration , we retained the one that was geographically closest . All analyses were performed using the R software [63] . Raw genetic distances between isolates ( in terms of number of point mutations ) were computed using the ape package [64] . SeqTrack analysis was then run using the seqtrack function implemented in the adegenet package [65] . | Because fossils of bacteria do not exist or are morphologically indeterminate , the timescales of bacterial evolution are widely unknown . We have investigated the short-term evolution of a particular strain of methicillin-resistant Staphylococcus aureus ( MRSA ) , a notorious cause of hospital-associated infections . By comparing 118 kilobases of DNA from MRSA isolates that had been collected at different points in time , we demonstrate that this strain has accumulated measurable DNA sequence variation within two decades . Further , we exploited this sequence diversity to estimate the short-term evolutionary rate and to date divergence times without paleontological calibration , and to reconstruct the recent demographic expansion and spatial spread of this MRSA . |
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Combination antiretroviral therapy ( cART ) dramatically improves survival of HIV-infected patients , but lifelong treatment can ultimately result in cumulative toxicities and drug resistance , thus necessitating the development of new drugs with significantly improved pharmaceutical profiles . We recently found that the fusion inhibitor T-20 ( enfuvirtide ) -based lipopeptides possess dramatically increased anti-HIV activity . Herein , a group of novel lipopeptides were designed with different lengths of fatty acids , identifying a stearic acid-modified lipopeptide ( LP-80 ) with the most potent anti-HIV activity . It inhibited a large panel of divergent HIV subtypes with a mean IC50 in the extremely low picomolar range , being > 5 , 300-fold more active than T-20 and the neutralizing antibody VRC01 . It also sustained the potent activity against T-20-resistant mutants and exhibited very high therapeutic selectivity index . Pharmacokinetics of LP-80 in rats and monkeys verified its potent and long-acting anti-HIV activity . In the monkey , subcutaneous administration of 3 mg/kg LP-80 yielded serum concentrations of 1 , 147 ng/ml after injection 72 h and 9 ng/ml after injection 168 h ( 7 days ) , equivalent to 42 , 062- and 330-fold higher than the measured IC50 value . In SHIV infected rhesus macaques , a single low-dose LP-80 ( 3 mg/kg ) sharply reduced viral loads to below the limitation of detection , and twice-weekly monotherapy could maintain long-term viral suppression .
Six classes of anti-HIV drugs block different steps of the viral life cycle , including cell entry , reverse transcription , integration and virion maturation [1] . Highly active antiretroviral therapy ( HAART ) with multiple drugs in a combination can suppress the virus to below the limitation of detection , thus leading to profound reductions in morbidity and mortality associated with AIDS . Because of the lack of an effective vaccine , antiretroviral therapy has also been considered a vital strategy to control the HIV transmission . Different from other drugs that act after infection occurs , HIV entry inhibitors intercept the virus before it invades the target cells . Currently , there are two anti-HIV drugs targeting the entry process: while maraviroc binds to the coreceptor CCR5 thus being used to treat infections by CCR5-tropic HIV isolates , the peptide drug enfuvirtide ( T-20 ) acts by blocking the fusion between viral and cell membranes [2–4] . T-20 is effective in combination therapy , but it exhibits relatively weak anti-HIV activity , short half-life , and low genetic barrier to inducing drug resistance [5 , 6] , calling for new membrane fusion inhibitors with improved pharmaceutical profiles . Emerging studies demonstrate that lipid conjugation is a more efficient strategy for designing peptide inhibitors that target the viral fusion step [7–12] . So-called lipopeptides can anchor to the target cell membranes thereby raising the concentrations of the inhibitors at the viral entry site [7 , 11] . In sequence structure , T-20 has a C-terminal tryptophan-rich motif ( TRM ) , which is considered a membrane-binding domain ( LBD ) that can interact with the target cell membrane to confer antiviral activity [13–15] . By substituting the TRM of T-20 with C16 fatty acid ( palmitic acid ) , we previously generated the lipopeptide LP-40 , which showed significantly increased anti-HIV activity [16] . The potency of LP-40 could be dramatically improved by introducing the intrahelical salt-bridge-prone and HIV-2/SIV sequences , as evidenced by LP-50 and LP-52 that inhibited divergent HIV-1 isolates at very low picomolar ( pM ) concentrations [17 , 18] . It is known that the fatty acid length , polarity and bulkiness can critically determine the pharmacokinetic of a peptide inhibitor [12 , 13 , 19 , 20] . For example , a long-acting glucagon-like peptide-1 derivative for type 2 diabetes has been successfully developed by replacing C16 with C18 ( stearic acid ) : while C16-conjugated liraglutide requires an once-daily dosage , C18-conjugated semaglutide is used once-weekly [21 , 22] . In order to develop a more efficient HIV fusion inhibitor for clinical development , herein we generated and characterized a panel of new lipopeptides with various fatty acids , including C18 , C8 ( octanoic acid ) , C12 ( lauric acid ) , C20 ( arachidic acid ) , C22 ( docosanoic acid ) , and C24 ( lignoceric acid ) . It was found that C18-conjugated lipopeptide LP-80 had the most potent anti-HIV activity and showed a long-acting therapeutic efficacy in SHIV-infected rhesus monkey models .
We recently identified that the chimeric peptide P-52 is an ideal template for design of lipopeptide-based fusion inhibitors with high activities against HIV-1 , HIV-2 , and SIV isolates [17] . In this study , we focused on examining the significance of the fatty acid carbon chain length in the development of a more efficient anti-HIV drug . P-52 was conjugated with different lengths of fatty acids , resulting in a group of new lipopeptides termed LP-77 ( C24 ) , LP-78 ( C22 ) , LP-79 ( C20 ) , LP-80 ( C18 ) , LP-81 ( C12 ) , and LP-82 ( C8 ) . Then , the anti-HIV activities of diverse lipopeptides were determined with three functional approaches . As shown in Fig 1 , LP-80 exhibited the most potent activities in inhibiting HIV-1HXB2 Env-mediated cell-cell fusion , HIV-1NL4-3 pseudovirus-mediated single-cycle cell entry , and replication-competent HIV-1JRCSF-mediated infection , with mean 50% inhibitory concentrations ( IC50s ) of 12 . 61 , 1 . 62 , and 2 . 45 pM , respectively . In comparison , LP-52 and the lipopeptides with longer fatty acids ( LP-77 , LP-78 , and LP-79 ) showed relatively lower anti-HIV activities , and the lipopeptides with shorter fatty acids ( LP-81 and LP-82 ) had markedly decreased potencies . Furthermore , a group of truncated lipopeptides were produced by referring the sequence of C18-conjugated LP-80 , and their inhibitory activities were characterized . It was found that the N-terminally ( LP-88 , LP-89 , LP-90 ) or C-terminally ( LP-91 ) truncated inhibitors still sustained very high anti-HIV potencies , especially LP-90 ( 24-mer ) which inhibited HIV-1HXB2 , HIV-1NL4-3 , and HIV-1JRCSF with IC50s of 16 . 31 , 3 . 55 , and 5 . 4 pM , respectively . Consistent with our previous observation on C16-conjugated lipopeptides [17] , addition of an N-terminal lysine residue seemly exerted a negative effect on the activity of LP-89 . Truncation from both the N- and C-terminals of LP-80 resulted in LP-92 , which only had a 21-amino acid core sequence and showed greatly reduced antiviral activity; however , LP-92 remained a highly active HIV fusion inhibitor relative to T-20 . To further explore the structure and activity relationship ( SAR ) of fatty acid-conjugated lipopeptides , we used circular dichroism ( CD ) spectroscopy to analyze their secondary structures and thermostabilities . As shown in Fig 2 and Table 1 , C16 , C18 , C20 , C22 , and C24-conjugated lipopeptides ( LP-52 , LP-80 , LP-79 , LP-78 , and LP-77 ) had comparable α-helicity and thermostability , but both of which reduced in C8 and C12-conjugated lipopeptides ( LP-82 and LP-81 ) . Similarly , all the truncated C18-conjugated lipopeptides ( LP-88 ~ LP-92 ) showed reduced α-helicity and thermostability . Next , we analyzed the interactions of diverse lipopeptides with an NHR-derived target mimic peptide ( N39 ) . As shown Fig 3 and Table 1 , all the inhibitors interacted with N39 to display increased α-helical contents . As compared to C16-conjugated LP-52 , the longer fatty acids did not obviously affect the α-helicity and thermostability of the lipopeptides , as shown by LP-77 , LP-78 , LP-79 , and LP-80; in contrast , conjugation with the shorter fatty acids resulted in greatly decreased α-helical contents and Tm values , as shown by LP-81 and LP-82 . Consistently , all the truncated lipopeptides exhibited a markedly decreased binding stability either . As suggested by LP-90 and LP-91 , while the N-terminal WEQK motif critically determined the α-helicity , the C-terminal LEK motif was more important in the binding stability . Taken together , these results demonstrated that both the fatty acid length and amino acid sequence are critical determinants for the conformation and stability of lipopeptide inhibitors . As demonstrated above , the C18-conjuagted lipopeptide LP-80 had highest antiviral and target-binding activities . Similar to the C16-conjugated lipopeptides [17] , the N-terminally truncated version LP-90 manifested very high capacities either . Herein , we sought to validate the inhibitory activities of LP-80 and LP-90 with HIV-1 isolates possessing different genotypes and phenotypes . First , six replication-competent HIV-1 strains were applied . As shown in Table 2 , the control inhibitors T-20 and LP-52 inhibited six viruses with IC50s of 15 , 305 . 37 and 16 . 95 pM , respectively , whereas LP-80 and LP-90 had their IC50s of 7 . 09 and 23 . 71 pM , respectively . Furthermore , a large panel of pseudoviruses with their Envs derived from primary HIV-1 isolates were prepared and used to determine the antiviral activities of the inhibitors by a single-cycle infection assay . Apart from T-20 and LP-52 , the previously characterized fusion inhibitor C34-Chol and broadly neutralizing antibody VRC01 were also tested for comparison [7 , 23] . As shown in Table 3 , T-20 , LP-52 , LP-80 , LP-90 , C34-Chol , and VRC01 inhibited divergent HIV-1 subtypes with mean IC50s of 29 , 950 . 09 , 16 . 97 , 5 . 58 , 13 . 38 , 68 . 06 , and 33382 . 09 pM , respectively . Therefore , the antiviral activity of LP-80 was about 5367- , 3- , 12- , and 5982-folds higher than that of T-20 , LP-52 , C34-Chol , and VRC01 , respectively . To exploit the mechanism of action of LP-80 , we also performed the in vitro selection of HIV-1 mutants resistant to LP-80 . So far the experiment failed because it was difficult to passage the virus in the presence of LP-80 inhibitor; by contrast , the concentration of the control peptide T-20 could be easily raised to 10 , 000 nM . Alternatively , we prepared a panel of HIV-1NL4-3 Env-based T-20-resistant mutants and examined the inhibitory activities of LP-80 and LP-90 by the single-cycle infection assay . As shown in Table 4 , both LP-80 and LP-90 displayed markedly reduced activities against diverse HIV-1NL4-3 mutant viruses , verifying that they also targeted the NHR sites of gp41 that critically determined the cross-resistance . Despite this , LP-80 and LP-90 remained highly potent inhibitors of T-20-resistant mutants , which might reflect their relatively higher genetic barriers to inducing resistance . By comparing the fold IC50 changes by LP-80 and LP-90 , the data also verified the importance of the N-terminal WEQK motif of the newly-designed lipopeptide inhibitors in overcoming the T-20 resistance problem , consistent with our previous findings [17] . We previously reported that T-20 derivatives , such as LP-50 , LP-51 , and LP-52 , possess extremely low cytotoxicity and high genetic resistance barriers [17 , 18] . In this study , we also determined the cytotoxicity of LP-80 in three different cell lines and human peripheral blood mononuclear cells ( PBMCs ) . As shown in Fig 4 , LP-80 had a 50% cytotoxic concentration ( CC50 ) of 62 . 33 μM in TZM-bl cells , of 72 . 23 μM in HEK293T cells , of 218 . 37 μM in MT-4 cells , and of 37 . 37 μM in human PBMCs , which were comparable with that of T-20 and LP-52 . Considering their antiviral activities at very low picomolar concentrations , the presented results suggested again that fatty acid-based lipopeptide fusion inhibitors possess an extremely high therapeutic selectivity index ( CC50/IC50 ratio ) . To exploit the in vivo stability of LP-80 , we sought to investigate its pharmacokinetics . First , LP-80 was subcutaneously or intravenously injected into six rats at 6 mg/kg of body weight and its serum concentration was monitored with time . As shown in Fig 5A and Table 5 , the subcutaneously injected LP-80 achieved a mean maximum concentration ( Cmax ) of 7 , 647 ng/ml with a T1/2 of 6 . 28 h; the intravenously injected LP-80 achieved a mean serum Cmax of 53 , 259 ng/ml with a T1/2 of 6 . 04 h . Impressively , about 7 ng/ml of LP-80 were still detectable in the sera of rats after injection 72 h , which were 257-fold higher than the concentration corresponding to the mean IC50 value measured against ten replication-competent viruses ( 7 . 09pM , Table 1 ) . Next , we determined the pharmacokinetics of LP-80 in non-human primates . As shown in Fig 5B and Table 5 , LP-80 achieved a mean Cmax of 24 , 781 ng/ml with a T1/2 of 13 . 74 h when injected subcutaneously into six healthy rhesus monkeys at a concentration of 3 mg/kg . LP-80 was still detectable at a mean concentration of 1 , 147 ng/ml in the sera of monkeys after injection 72 h , which was 42 , 062-fold higher than the IC50 value 7 . 09 pM , and it sustained at 9 ng/ml after injection 168 h ( 7 days ) . To elucidate the relationship between the serum concentration and anti-HIV activity of LP-80 , we also measured the inhibitory activity of the monkey sera . As shown in Fig 5C , all the sera showed highly potent and long-lasting activities in inhibiting HIV-1NL4-3 infection , with the peak levels during 6–8 h after injection . In line with their concentrations , the sera could inhibit 50% virus infection with mean dilutions of 16 , 157-fold after 72 h , 1 , 980-fold after 96 h , 211-fold after 120 h and 31-fold after 168 h . Analyzed by Pearson Correlation Coefficient ( Fig 5D ) , the serum concentration of LP-80 was highly correlated with its ex vivo anti-HIV activity ( R2 = 0 . 9625 , P < 0 . 0001 ) . We further investigated the therapeutic efficacy of LP-80 in a nonhuman primate model . Five rhesus monkeys ( K1 to K5 ) were intravenously infected with SHIVSF162P3 over 6 months , by which time chronic infection had been stably established . The set point viral loads ranged from 4 . 17 to 5 . 38 log10 RNA copies/ml after 191 days of infection ( Fig 6 ) . Then the monkeys were subcutaneously treated with LP-80 for two rounds . First , LP-80 was injected at 3 mg/kg of body weight once daily for 2 weeks . As expected , the plasma viral loads in three monkeys ( K1 , K2 , and K4 ) precipitated below the assay detection limit ( 100 copies/ml ) at day 4 after the initiation of treatment , which was the first blood sampling time , while the plasma viral loads in other two monkeys ( K3 and K5 ) declined to below the detection limit 8 days after treatment , which was the second blood sampling time . To observe its last-acting activity , the same dose of LP-80 was administrated once every 4 days for 4 weeks . Encouragingly , the viral replication was fully controlled during the treatment period . As expected , the viral loads rebounded in all of the treated monkeys between 10 to 21 days after LP-80 was stopped . After a 3-month interruption of the treatment , the monkeys had been chronically infected with SHIVSF162P3 for 325 days and maintained a mean plasma viral load of 4 . 5 log10 RNA copies/ml ( ranging from 3 . 87 to 5 . 1 ) . A second round treatment was initiated to observe the efficacy of LP-80 in a long-term monotherapy , in which the LP-80 was subcutaneously used at 3 mg/kg twice weekly for 6 months . As shown in Fig 6 , one injection resulted in the viral loads below the detection limit in all of the five monkeys , which were determined at day 4 after the initiation of treatment , and no viral rebound was detected during a 6-month treatment . After LP-80 cessation , the virus rebounded in four monkeys ( K1 to K4 ) between 14 to 28 days; however , the viral load in the monkey K5 remained undetectable even after LP-80 was withdrawn 2 months . We also detected viral DNA ( vDNA ) levels in the PBMC samples of monkeys pre- and posttreatment . It was found that a mean vDNA load was rapidly declined from 3 . 7 to 2 . 3 log10 vDNA copies/μg total DNA after a 2-month treatment and sustained a low level during the treatment ( Fig 7 ) . Noticeably , the vDNA loads in four monkeys ( K1 , K2 , K4 , and K5 ) could reach undetectable levels with a detection limit of 100 copies/μg total DNA . Similar to the plasma vRNA , the PBMC vDNA rebounded in all of the monkeys after LP-80 was stopped . As analyzed by Pearson Correlation Coefficient , the mean vDNA and vRNA loads were significantly correlated ( R2 = 0 . 8587 , P = 0 . 0079 ) . During the treatment , LP-80 did not cause significant injection site reactions ( ISRs ) . No systemic toxicities were observed in the monkeys by monitoring their blood biochemical parameters and CD4+ and CD8+ T lymphocytes ( S1 Table and S1 Fig ) . Taken together , the results demonstrate that LP-80 exhibits an extremely potent and long-acting therapeutic efficacy and low cytotoxicity in SHIV-infected rhesus monkeys .
HIV infection requires membrane fusion between the virus and target cells , which is mediated by viral envelope ( Env ) glycoproteins gp120 and gp41 [24] . Binding of gp120 to cell receptors induces series conformational changes in Env complex and activates the fusogenic activity of gp41 . In a current model , the N-terminal fusion peptide of gp41 is firstly inserted into target cell membrane; then its C-terminal heptad repeat ( CHR ) folds antiparallelly into the interior hydrophobic grooves adopted by the N-terminal heptad repeat ( NHR ) coiled coils , leading a stable six-helix bundle ( 6-HB ) structure that drives membrane merger . The CHR-derived peptides , such as T-20 , can competitively bind to viral NHR thus blocking 6-HB formation and viral entry [25] . In the past decade , we have dedicated our efforts to define the structure and function of gp41 and to develop new HIV fusion inhibitors . As results , we identified several structural features critical for the functionality of gp41 , including the interhelical salt-bridges [26 , 27] , M-T hook structure [28–30] , and pocket-2 conformation [31]; we designed a group of inhibitors with significantly improved pharmaceutical profiles , including CP32M [32] , HP23 [33] , 2P23 [34] , LP-11 [9] , LP-19 [10] , LP-46 [35] , LP-50 and LP-51 [18] , and LP-52 [17] . We also focused on selecting HIV mutants resistant to fusion inhibitors and proposed several resistance modes [36–40] . Meanwhile , a large panel of crystal structures were determined for diverse fusion inhibitors , including CP32 [29] , CP32M [41] , SFT [42] , MT-C34 [28] , MT-SFT [43] , SC22EK and MTSC22EK [44] , SC29EK [37] , HP23L and LP-11 [45] , LP-40 [16] , LP-46 [35] , and very recently , T-20 [46] . Combined , our series data have provided important information for understanding the mechanisms of HIV gp41-dependent membrane fusion and facilitated our development for the current fusion inhibitors that exhibit extremely potent and long-acting anti-HIV activity , as exemplified by LP-80 and LP-90 . Although T-20 was discovered in the early 1990s and approved for clinical use in 2003 [3 , 47–49] , its mechanism of action and structural property remain elusive . For example , the inhibitory mode of T-20 was suggested to target the NHR helices , the CHR helices , the fusion peptide , and the transmembrane domain of gp41 and the coreceptor binding site of gp120 [15 , 47 , 48 , 50–56] . Therefore , we revisited T-20 by characterizing its structural and functional characteristics [16 , 46] . The crystal structures of T-20 and its lipopeptide derivative LP-40 did provide a molecular basis for elucidating the mode of action of T-20 and guide our inhibitor design . By integrating the strategies of sequence optimization and lipid conjugation , T-20 sequence-based lipopeptide derivatives exhibited extremely potent anti-HIV activity , with mean IC50 values in the very low picomolar range [17 , 18] . We recently reported the therapeutic efficacies of LP-50 and LP-51: they could sharply reduce viral loads to below the limitation of detection in acutely and chronically SHIV infected rhesus monkeys [18] . However , previous studies demonstrated the importance of fatty acid length , polarity , and bulkiness in improving the pharmacokinetics of peptide inhibitors [12 , 13 , 19 , 20] . Especially , we were inspired by success of the long-acting glucagon-like peptide-1 derivative semaglutide [21] . Thus , we focused on examining the effects of different fatty acids in the T-20 derivatives . As discovered , the fatty acid length does play crucial roles in the secondary structures of newly-designed HIV fusion inhibitors as well as their binding and inhibitory functions . As compared to C16-conjugated LP-52 and C18-conjugated LP-80 , C12-conjugated LP-81 and C8-conjugated LP-82 had an identical peptide sequence but they showed dramatically reduced activities . Unexpectedly , the fatty acids with a further extended length ( C20 , C22 , and C24 ) attenuated the anti-HIV ability of inhibitors , reflecting the complexity of the structure-activity relationship ( SAR ) of such lipopeptide-based fusion inhibitors . Antiretroviral therapy ( ART ) has been very successful in treating HIV infection , but it does not eradicate the virus . Individuals infected with HIV require lifelong treatment with multiple drugs , which ultimately causes severe adverse effects and drug resistance . Also , noncompliance to the daily drug regimen often results in failure of the treatment regimen . As the only viral membrane fusion inhibitor available for clinical use , T-20 has shown effectivity in combination therapy of HIV-1 infection; however , it requires frequent injections at a high dosage and easily induces drug resistance , which have largely limited its wide application . From these perspectives , the most exciting result for LP-80 is its extremely high , long-acting antiviral activity . As demonstrated by the in vivo study , twice-weekly monotherapy with low-dose LP-80 could efficiently suppress the viral replication to below the limitation of detection during a 6-month treatment protocol . Consistently , the pharmacokinetics of LP-80 in rhesus monkeys had approved its long-acting activity , which even suggested a dosage once-weekly . By contrast , our previous studies demonstrated that C16-modified lipopeptides , such as LP-11 , LP-19 , LP-50 , and LP-51 , have shorter in vivo half-lives and thus require to be used once-daily [9 , 10 , 18] . Herein , we would like highlight the significance of two additional results . First , as one monkey remained aviremic following treatment cessation , it is valuable to investigate the potential of LP-80 for functional cure of HIV infection . Second , as the viral DNA level in the PBMCs of the treated monkeys markedly decreased , an early and/or long-term use of LP-80 might reduce the size of HIV reservoir . Furthermore , the extremely high genetic barrier to inducing drug resistance and therapeutic selectivity index characterized by LP-80 also make it an ideal drug for clinical use .
HEK293T cells and human T cell line MT-4 were purchased from American type culture collection ( ATCC; Rockville , MD ) . The following reagents were obtained through the AIDS Reagent Program , Division of AIDS , NIAID , NIH: TZM-bl indicator cells , which stably express large amounts of CD4 and CCR5 , along with endogenously expressed CXCR4 , from John C . Kappes and Xiaoyun Wu; HL2/3 cells , which stably express high levels of Env , Gag , Tat , Rev , and Nef proteins of the integrated HIV-1 molecular clone HXB2/3gpt , from Barbara K . Felber and George N . Pavlakis; the Panel of Global HIV-1 Env Clones , which contains 12 envelope clones as reference strains representing the global AIDS epidemic , from David Montefiori; a panel of molecular clones for producing infectious HIV-1 isolates , including pNL4-3 from Malcolm Martin , pLAI . 2 from Keith Peden , pSG3 . 1 from Sajal Ghosh , Beatrice Hahn , and George Shaw , pYK-JRCSF from Irvin SY Chen and Yoshio Koyanagi , p89 . 6 from Ronald G . Collman . Peptides were synthesized on rink amide 4-methylbenzhydrylamine ( MBHA ) resin using a standard solid-phase 9-flurorenylmethoxycarbonyl ( FMOC ) method as described previously [9] . For fatty acid-based lipopeptides , the template peptide contain a lysine residue at the C-terminus with a 1- ( 4 , 4-dimethyl-2 , 6-dioxocyclohexylidene ) ethyl ( Dde ) side-chain-protecting group , enabling the conjugation of a fatty acid that requires a deprotection step in a solution of 2% hydrazinehydrate-N , N-dimethylformamide ( DMF ) . All peptides were acetylated at the N-terminus and amidated at the C-terminus . They were purified by reverse-phase high-performance liquid chromatography ( HPLC ) to more than 95% homogeneity and were characterized by mass spectrometry . The α-helicity and thermostability of lipopeptides in the absence or presence of a target mimic peptide ( N39 ) were determined by circular dichroism ( CD ) spectroscopy . CD spectra were acquired on a Jasco spectropolarimeter ( model J-815 ) using a 1 nm bandwidth with a 1 nm step resolution from 195 to 270 nm at room temperature . Spectra were corrected by subtracting a solvent blank . The α-helical content was calculated from the CD signal by dividing the mean residue ellipticity [θ] at 222 nm by the value expected for 100% helix formation ( -33 , 000 degree . cm2 . dmol-1 ) . Thermal denaturation was performed by monitoring the ellipticity change at 222 nm from 20°C to 98°C at a rate of 2°C/min , and Tm ( melting temperature ) was defined as the midpoint of the thermal unfolding transition . The inhibitory activities of inhibitors on HIV-1HXB2 Env-mediated cell-cell fusion were determined by a reporter gene assay based on the activation of an HIV LTR-driven luciferase cassette in TZM-bl cells ( target ) by HIV-1 tat from HL2/3 cells ( effector ) . Briefly , 1 x 104/well of TZM-bl cells were plated in 96-well plates and incubated at 37°C overnight . Then , 3 x 104/well of HL2/3 cells were cocultured with target cells for 6 h at 37°C in the presence or absence of an inhibitor at graded concentrations . Luciferase activity was measured using luciferase assay reagents and a Luminescence Counter ( Promega , Madison , Wisconsin , USA ) . The inhibitory activities of inhibitors against HIV-1 pseudoviruses and replication-competent isolates were measured as described previously [17] . Briefly , HIV-1 pseudoviruses were generated via the cotransfection of HEK293T cells with an Env-expressing plasmid and a backbone plasmid that encodes Env-defective , luciferase-expressing HIV-1 genome ( pSG3Δenv ) . Viral stocks of replication-competent HIV-1 isolates were generated by transfecting viral molecular clones into HEK293T cells . Culture supernatants were harvested at 48 h posttransfection , and 50% tissue culture infectious doses ( TCID50 ) in TZM-bl cells were determined . Inhibitors were prepared in 3-fold dilutions , mixed with 100 TCID50 of a virus , and then incubated for 1 h at room temperature . The mixture was added to TZM-bl cells ( 104/well ) , and the cells were incubated for additional 48 h at 37°C . Luciferase activity was measured as described above . The in vivo therapeutic efficacy of LP-80 was evaluated in SHIV-infected rhesus monkeys as described previously [10 , 18] . Five adult Chinese rhesus macaques ( K1 to K5 ) were screened to be negative for SIV , herpes B virus , and simian T-lymphotropic virus . A simian-human immunodeficiency virus ( SHIVSF162P3 ) was expanded on macaque peripheral blood mononuclear cells ( PBMCs ) , and the TCID50 was determined . Macaques were intravenously inoculated with 1 , 000 TCID50 of virus . When chronic SHIV infection had been stably established after 6 months , the monkeys were subcutaneously treated with LP-80 , which was dissolved in double-distilled and deionized water at 20 mg/ml . In the first round of treatment , LP-80 was injected at 3 mg/kg of body weight once daily for 2 weeks , followed by 3 mg/kg once every four days for 4 weeks . After a 3-month interruption , a second round of treatment was initiated with LP-80 used at 3 mg/kg , twice weekly for 6 months . Plasma viral loads ( vRNA ) were determined by a quantitative real-time reverse transcription-PCR ( qRT-PCR ) assay with the limit of detection at 100 copy equivalents of RNA per ml of plasma . To measure vDNA , total DNA extracted from the monkey PBMCs was used as input for quantitative PCR assay , and the vDNA copy numbers were estimated by comparison to a pGEM-SIV gag477 standard curve . Primers and probe used for both vRNA and vDNA were gag91 forward ( GCAGAGGAGGAAATTACCCAGTAC ) , gag91 reverse ( CAATTTTACCCAGGCATTTAATGTT ) , and pSHIVgag91-1 ( 5’- ( FAM ) -ACCTGCCATTAAGCCCGA— ( MGB ) -3’ ) . Triplicate test reactions were performed for each sample . To determine the pharmacokinetics of LP-80 in rats , LP-80 was subcutaneously or intravenously injected to six Sprague-Dawley rats with a dose of 6 mg/kg , serum samples of rats were harvested before injection ( 0 h ) and after injection ( 5 , 15 , 30 min , and 1 , 2 , 4 , 8 , 24 , 48 , 72 , 96 , 120 , 168 , 216 h ) . To determine the pharmacokinetics of LP-80 in non-human primates , LP-80 was subcutaneously injected to six Chinese rhesus macaques at 3 mg/kg , serum samples of macaques were harvested before injection ( 0 h ) and after injection ( 1 , 2 , 4 , 6 , 8 , 12 , 18 , 24 , 36 , 48 , 60 , 72 , 96 , 120 , 144 , 168 h ) . Serum concentrations of LP-80 were measured by liquid chromatography and mass spectroscopy ( LC-MS/MS ) , and pharmacokinetic parameters were derived via non-compartmental modeling . A single-cycle infection assay was performed to determine the inhibitory activity of the monkey sera on HIV-1NL4-3 pseudovirus . The 50% effective concentration was defined as the fold serum dilution that inhibited 50% of virus infection . The cytotoxicity of LP-80 on TZM-bl , MT-4 , HEK293T , and human PBMC was measured using a CellTiter 96 AQueous One Solution cell proliferation assay ( Promega ) . In brief , 50-μl volumes of LP-80 at graded concentrations were added to cells , which were seeded on a 96-well tissue culture plate ( 1 × 104 cells per well ) . After incubation at 37°C for 2 days , 20 μl of CellTiter 96 AQueous One solution reagent was pipetted into each well and further incubated at 37°C for 2 h . The absorbance was measured at 490 nm using a SpectraMax M5 microplate reader . The in vitro selection of LP-80 resistant HIV-1 mutants was conducted as described previously [39] . MT-4 cells were seeded at 1×104/well in RPMI 1640 medium containing 10% FBS on 12-well plates . HIV-1NL4-3 was used to infect the cells in the presence or absence of a diluted inhibitor ( LP-80 or T-20 ) . Cells were incubated at 37°C with 5% CO2 until an extensive cytopathic effect was observed . Culture supernatants were harvested and used for next passage on fresh MT-4 cells with a 2-fold increase in inhibitor concentrations . Human PBMCs for determining the cytotoxicity of LP-80 were obtained from a previously existing collection [17] , which was provided by the Beijing Red Cross Blood Center . A healthy blood donor for the sample was anonymized . Protocols for the use of animals were approved by the Institutional Animal Care and Use Committee ( IACUC ) at the Institute of Laboratory Animal Science , Chinese Academy of Medical Sciences ( No . ILAS-VL-2015-004 ) . To ensure personnel safety and animal welfare , the study of animals was conducted in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Institute of Laboratory Animal Science and the recommendations of the Weatherall report for the use of non-human primates in research ( http://www . acmedsci . ac . uk/more/news/the-use-of-non-human-primates-in-research/ ) . All monkeys were housed and fed in an Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) -accredited facility . All animals were anesthetized with ketamine hydrochloride ( 10 mg/kg ) prior to the procedures . The experiments were performed in bio-safety level 3 laboratory . | T-20 is the only clinically approved viral fusion inhibitor , which is used in combination therapy for HIV-1 infection; however , it exhibits relatively low antiviral activity and easily induces drug resistance . Here we report a lipopeptide fusion inhibitor termed LP-80 , which exhibits the most potent activity in inhibiting divergent HIV-1 subtypes . Especially , LP-80 has extremely potent and long-acting therapeutic efficacy with very low cytotoxicity , making it an ideal drug candidate for clinical use . Furthermore , LP-80 and its truncated versions can be used as important probes for exploiting the mechanisms of viral fusion and inhibition . |
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The fitness effect of mutations can be influenced by their interactions with the environment , other mutations , or both . Previously , we constructed 32 ( = 25 ) genotypes that comprise all possible combinations of the first five beneficial mutations to fix in a laboratory-evolved population of Escherichia coli . We found that ( i ) all five mutations were beneficial for the background on which they occurred; ( ii ) interactions between mutations drove a diminishing returns type epistasis , whereby epistasis became increasingly antagonistic as the expected fitness of a genotype increased; and ( iii ) the adaptive landscape revealed by the mutation combinations was smooth , having a single global fitness peak . Here we examine how the environment influences epistasis by determining the interactions between the same mutations in two alternative environments , selected from among 1 , 920 screened environments , that produced the largest increase or decrease in fitness of the most derived genotype . Some general features of the interactions were consistent: mutations tended to remain beneficial and the overall pattern of epistasis was of diminishing returns . Other features depended on the environment; in particular , several mutations were deleterious when added to specific genotypes , indicating the presence of antagonistic interactions that were absent in the original selection environment . Antagonism was not caused by consistent pleiotropic effects of individual mutations but rather by changing interactions between mutations . Our results demonstrate that understanding adaptation in changing environments will require consideration of the combined effect of epistasis and pleiotropy across environments .
The extent to which mutations interact with their genetic background ( epistasis ) and the role such interactions play in evolution is not well understood [1] , [2] . Initial expectations were that epistatic interactions , defined as non-additive interactions among mutations , were common , causing fitness landscapes to be rugged and limiting the number of selectively accessible mutational paths [3] . Although early work revealed few such interactions ( reviewed in [4] ) , more recent studies of defined combinations of mutations have revealed abundant epistasis in a range of systems [5]–[17] . Studies that focused on interactions among beneficial mutations have often found a tendency for antagonism [18]–[21] , which is consistent with epistasis having a predictable influence on the curvature of fitness peaks in constant environments [22] , [23] . In addition to interactions within a genome , mutations can also interact with the external environment [24]–[29] . Moreover , phenotypic plasticity and epistasis can combine so that the fate of a mutation depends on both the environment and its genetic background [27] , [30] . This kind of dependence can have important evolutionary consequences . For example , Wright considered how fluctuating conditions — most often population size , but also the external environment — could change the sign of epistatic interactions and allow populations to evolve along otherwise maladaptive paths [31]–[35] . Relatively few studies have examined how epistasis and plasticity combine to influence mutational effects . One study that did found that all of 18 transposon insertion mutations were affected by either epistasis or plasticity , with half being affected by both [28] . It remains unclear , however , how interactions between beneficial mutations , which might be expected to depend strongly on a particular selective environment , will be affected by changes in the external environment . Such an understanding is vital for addressing questions concerning the course of adaptation in fluctuating conditions . For example: can the magnitude and sign of epistasis change with the external environment ? If so , are there any overarching features of mutation interactions , e . g . , a tendency towards antagonism , that nevertheless remain consistent ? A few studies have begun to address these questions by examining how epistasis between pairs of mutations changes with genomic [36] , [37] and environmental [38] contexts . Here , we expand this investigation of how the external environment affects epistatic interactions between five beneficial mutations that fixed in one population of a long-term Escherichia coli evolution experiment [19] . We first screened the response of the ancestor and the evolved genotype having all five mutations over a total of 1 , 920 external environments . Next , we measured the fitness of a set of 32 ( = 25 ) strains comprising all mutation combinations in the two environments with the most extreme opposing plasticity . These measurements allowed us to isolate effects of epistasis ( GxG ) , interactions between mutations and the environment ( GxE ) , and interactions between epistasis and the environment ( GxGxE ) on the fitness of defined genotypes . More generally , we investigated how the adaptive landscape , and the indirect consequences of each mutational step , might change with the external environment .
To determine how phenotypic plasticity changes following an adaptive walk , we used Biolog plates to compare the respiration ( a measure of catabolic activity ) of the ancestor and the strain containing five beneficial mutations ( hereafter , rtsgp , where each letter indicates a mutation in a gene or gene region as follows: r = rbs , t = topA , s = spoT , g = glmUS and p = pykF ) in 1 , 920 different environments . On average , rtsgp exhibited enhanced respiration over the ancestor ( mean = 84 . 37±1 . 39 ( SEM ) compared to 72 . 45±1 . 14 ( SEM ) ; paired t-test , t1919 = 26 . 28 , P = <0 . 0001 ) with significant differences in 203 environments ( see Materials and Methods for criteria ) , involving 171 gains of function and 32 losses of function ( Table S1 ) . These environments contained 28 alternative carbon sources , 34 alternative nitrogen sources , five alternative phosphate sources , ten nutritional supplements , and 126 “stressors” including antibiotics and other potentially toxic chemicals . Providing a useful control , one of the carbon sources in which the rtsgp strain had decreased respiration was D-ribose , which was expected due to a large deletion of the rbs operon in this strain [39] . We confirmed that measured respiration changes reflected growth rate changes in eight of the environments ( six gains of function and two losses of function ) by direct growth comparisons ( Table S2 ) . We focused on two environments that revealed large differences in respiration between rtsgp and the ancestor to examine how genotype and environment interact to affect fitness . The largest relative increase in respiration was in the presence of EGTA , a Ca++/Mg++chelator [40] . The largest decrease was in the presence of guanazole , a ribonucleotide DP reductase inhibitor [41] . In direct fitness competitions comparing rtsgp to its ancestor , rtsgp was significantly more fit in the environment containing the original selection medium ( DM25 , a minimal salts medium supplemented with glucose ) supplemented with EGTA than in the environment containing the selection medium alone ( DM25+EGTA: fitness = 1 . 497±0 . 068 ( 95% CI ) ; DM25: 1 . 299±0 . 061 ( 95% CI ) , t9 = 4 . 973 , P = 0 . 0008 ) . By contrast , rtsgp was less fit in DM25 supplemented with guanazole than in the selection environment ( DM25+guanazole: fitness of 1 . 116±0 . 029 ( 95% CI ) ; DM25: 1 . 299±0 . 061 ( 95% CI ) , t9 = −5 . 779 , P = 0 . 0003 ) . To examine the underlying genetic basis of this phenotypic plasticity , we quantified the fitness effect of each individual mutation in all three environments . The relative fitness of three of the five mutations significantly depended on the external environment ( rbs: F2 , 11 = 1 . 100 , P = 0 . 367; topA: F2 , 8 = 15 . 506 , P = 0 . 002; spoT: F2 , 10 = 31 . 389 , P<0 . 0001; glmUS: F2 , 10 = 3 . 513 , P = 0 . 070; pykF: F2 , 10 = 149 . 730 , P<0 . 0001 ) ( Figure 1 ) . In summary , three of the individual mutations present in the rtsgp genotype produced effects that differed significantly across environments . The above results demonstrate that the effect of individual mutations depend on the environment ( i . e . , G×E ) . However , it is also possible that interactions between mutations depend on the environment ( i . e . , G×G×E ) , which would further influence the topology of the fitness landscape and make it much more difficult to predict the influence of environmental changes on evolutionary outcomes . To examine G×G×E we measured the fitness of all combinations of the five beneficial mutations in the two focal environments ( i . e . , the selection environment supplemented with EGTA and guanazole ) . Fitness of each of the 32 genotypes comprising each mutation combination was quantified in both novel environments and compared with prior findings in the original selection environment [19] ( Figure 2 ) . To get some overall indication of the influence of environment on mutation effects we compare the number of “selectively accessible” mutational paths connecting the ancestor and rtsgp [42] . Although a different set of beneficial mutations would presumably be followed in guanazole and EGTA environments , considering a common set of genotypes allows a direct comparison of the effect of environment in altering selection pressures as a result of GxE and GxGxE . Of the 120 ( = 5 ! ) paths connecting the ancestor and rtsgp , 86 had monotonically increasing fitness in the selection environment [19] . By contrast , only 43 paths in EGTA and 2 paths in guanazole are selectively accessible ( Figure 2 , Tables S3 , S4 ) . ( The small number of selectively accessible paths in guanazole reflects , in large part , that rts and not rtsgp was the most fit genotype ( Table S6 ) . ) In all , nine mutational steps became significantly deleterious , six in the EGTA environment and three in the guanazole environment ( Tables S3 and S4 ) , although only three of these steps in the EGTA environment remain significantly deleterious when we correct for multiple comparisons . Nevertheless , differences in the number of selectively accessible paths available in different environments clearly indicate that environment affects landscape topology and selective constraints . To further examine the patterns of epistasis in the novel environments we focused on the effect of epistasis in determining the fitness of individual genotypes ( Tables S5 , S6 ) . In the EGTA environment mean epistasis was slightly , but not significantly , negative ( mean absolute epistatic deviation , εm = −0 . 039±0 . 046 ( 95% C . I . ) , t25 = −1 . 740 , P = 0 . 094 ) ( Figure 3 ) . In the guanazole environment mean epistasis was significantly positive ( εm = 0 . 057±0 . 022 ( 95% C . I . ) , t25 = 5 . 303 , P<0 . 0001 ) ( Figure 3 ) . In total , 16 and 5 genotypes exhibited significant epistasis in EGTA and guanazole , respectively ( Tables S4 , S5 ) . Both environments also displayed markedly different effects of higher-order epistasis involving interactions between at least three mutations . In the EGTA environment , genotypes tended to be more fit than expected from the sum of the relevant lower-order interactions ( mean higher-order epistatic deviation = 0 . 229±0 . 191 ( 95% C . I . ) , t15 = 2 . 556 , P = 0 . 022 ) . The opposite effect was seen in the guanazole environment ( mean higher-order deviation = −0 . 247±0 . 196 ( 95% C . I . ) , t15 = 2 . 693 , P = 0 . 017 ) . Considering only the mean effect of epistasis can miss other underlying patterns . For example , we previously found that the strength of negative epistasis between the five beneficial mutations increased with the expected fitness of the genotype in the selection environment , despite a lack of any mean effect [19] . This pattern has been reported in several other studies [18] , [20] , [21] and is consistent with interactions between beneficial mutations acting to slow the rate of adaptation . In the guanazole environment we found the same negative correlation between epistasis and expected fitness that was seen in the selection environment ( r = −0 . 748 ) ( Figure 4 , Figure S1 , Table S5 and S6 ) . The same correlation was only weakly negative in the EGTA environment ( r = −0 . 281 ) ( Figure 4 and Figure S2 ) . We also evaluated whether interactions between mutations and the environment , either EGTA or guanazole , contributed significantly to the overall variation in fitness , and found significant interactions between mutations ( GxG ) , mutations and the environment ( GxE ) , and interactions of both ( GxGxE ) ( overall model: F63 , 261 = 58 . 439 , P<0 . 0001 , Table S7 , see Materials and Methods ) . Using variance partitioning , we determined that GxGxE interactions explained approximately 8% of the variance in fitness observed in our complete data set ( Table S8 ) . Correlations between epistasis and expected fitness could reflect a general trend but could also be leveraged by outlying fitness or epistatic effects of an individual mutation . To distinguish between these possibilities we performed a series of ANCOVA analyses to test whether the presence or absence of each focal mutation influenced the overall relationship between epistasis and expected fitness ( Figure S3 and S4 ) . Only the pykF mutation explained a significant portion of the variation in the relationship between epistasis and expected fitness in the guanazole environment ( Figure S3 ) . Genotypes with this mutation tended to be more fit while the negative correlation , consistent with diminishing returns epistasis , with or without this mutation was retained . In the EGTA environment , considering genotypes distinguished by the presence or absence of either topA or pykF mutations revealed their significant contributions to the overall pattern of epistasis ( Figure S4 ) . The topA mutation tended to effect epistasis so as to decrease fitness ( mean epistasis of genotypes with topA = −0 . 088 compared to mean epistasis of genotypes without = 0 . 042 , t24 = 3 . 272 , P = 0 . 003 ) whereas pykF altered epistasis to generally increase genotype fitness ( mean epistasis of genotypes with pykF = 0 . 007 compared to mean epistasis of genotypes without = −0 . 087 , t24 = −2 . 156 , P = 0 . 041 ) . Genotypes lacking the rbs mutation again displayed a strong negative correlation between epistasis and expected fitness ( r = −0 . 783 ) , but adding the rbs mutation weakened the negative association between epistasis and expected genotype fitness without changing mean epistasis among these genotypes ( Figure S4 , genotypes with rbs , r = 0 . 089 , compared to without P = 0 . 033; mean epistasis with rbs = 0 . 0001 compared to mean epistasis of genotypes without = −0 . 078 , t24 = −1 . 720 , P = 0 . 098 ) . We used a higher-throughput approach using overall population growth ( AUC , see Materials and Methods ) as a proxy for fitness to assay for epistasis in nine additional environments . Seven of these environments were not expected to interact with the five mutations based on the initial Biolog screen comparing the rtsgp and ancestral strains ( Table S1 , Materials and Methods ) . In each environment , the growth of each single mutant was compared with the ancestor , rtsgp , and a randomly selected double-mutant , gp ( Figure 5 ) . In seven of nine environments , growth of either rtsgp or gp differed significantly from additive expectations assuming no epistasis ( Figure 5 , Tables S9 , S10 ) . The nature of these interactions also changed with the environment . For example , gp was significantly less fit than expected in two environments and significantly more fit than expected in four environments ( Table S9 ) . In summary , the sign and magnitude of epistasis among generally beneficial mutations may vary widely even with relatively small changes in the external environment .
Recent theoretical work has applied population genetic models to empirically constructed fitness landscapes to make basic predictions about the likelihood of particular evolutionary outcomes [8] , [14] . These outcomes depend crucially on the shape of the fitness landscape , which is determined by the form and extent of epistatic interactions between mutations . How sensitive these interactions , and therefore the repeatability of evolutionary outcomes , are to environmental change remains uncertain . To address this point experimentally we analyzed a set of strains including all combinations of the first five beneficial mutations that fixed during the adaptation of a population of E . coli to a constant laboratory environment ( Table 1 , [19] ) . By measuring the fitness of these strains in contrasting environments we generated two new empirical fitness landscapes that reveal how epistasis may change with the environmental context . Comparing these landscapes to the one determined in the original selection environment , we found interactions between mutations and their environment to be both common and complex . Previous work has shown that the diet breadth of 12 E . coli populations , including the population that was the source of the mutations used in our experiments , declined substantially during long-term evolution in a constant environment with a single carbon source [39] , [43] , [44] . However , it is difficult to distinguish if this trend was caused by few mutations of strong pleiotropic effect or if the beneficial substitutions display antagonistic pleiotropy in general . In an effort to distinguish these explanations , one study specifically focused on pleiotropic effects of beneficial mutations in five different environments . Mutations that were beneficial in the selected environment tended to be beneficial in others , and although there were exceptions , limited antagonistic pleiotropy was observed [45] , [46] . Here , we also report limited antagonistic pleiotropy with five beneficial mutations with an increased sample size of 1 , 920 environments from our initial Biolog screen ( Table S1 ) . This result supports the inference that antagonistic effects may be limited to a subset of beneficial variation . Since both studies focused on a collection of beneficial mutations contributing to initial adaptation to a minimal glucose environment , we speculate that early adaptation may be characterized by niche expansion with limited cost [47] . Epistasis was frequent in all environments and generally followed a pattern of diminishing returns . Nevertheless , both the individual effects of mutations and their interactions were environmentally dependent , in several cases resulting in mutations changing from being beneficial to deleterious or neutral ( Figure 1 , Figure 4 , Figure S2 and S3 ) . Perhaps most strikingly , different numbers of paths to the rtsgp genotype were found in each environment , one of which featured a different global peak . Our results also suggest that selective constraints in fluctuating environments may depend on how the environment influences epistasis between contending adaptive alleles , and not just the pleiotropic effects of individual mutations alone ( Table S5 ) . For example , the topA and glmS mutations were more beneficial in the EGTA environment alone ( positive pleiotropy ) ( Figure 1 ) , but in combination the tg genotype was much less fit than expected ( Table S4 ) . Since the fitness of this genotype did not significantly deviate from expectations in the guanazole environment ( Table S5 ) , environmental effects on epistasis ( GxGxE ) were not predicted by GxE interactions . More broadly , these results indicate that variable fitness of a genotype under different conditions can arise from altered interactions among the alleles comprising that genotype and not from any single mutation . This conclusion is robust to fitness measurements in environments not found to effect rtsgp respiration , suggesting that it is not dependent on our initial focus on the two environments in which GxE was most extreme ( Figure 5 ) . These interactions may be especially important in determining evolutionary outcomes given initially rugged fitness landscapes [14] , [48] , [49] or in naturally variable environments . In one study [17] , a more beneficial allele was eventually outcompeted by a less fit allele because of epistatic limits to the adaptive path of the former allele . However , a different outcome may have occurred in a fluctuating or seasonal external environment . Given prevalent genotype-by-environment interactions , epistatic interactions producing low fitness intermediates could be alleviated in alternative environments and allow new combinations of alleles to overcome evolutionary dead-ends and rise to fixation . This process could represent a mechanism for maintaining conditional , yet beneficial , variation in the population [50]–[52] . As evidence , the three significantly maladaptive steps in the EGTA environment are alleviated by a shift to either the guanazole or the original selection environment ( Figure 2 ) . Fluctuating environments may therefore provide a solution to evolutionary dead-ends in an inherently rugged fitness landscape . In summary , the combination of phenotypic plasticity and epistasis can strongly influence how an organism adapts to a new environment . Although the five mutations examined here would not likely be the same favored in these new environments , our results demonstrate that epistatic interactions are not static and can determine which trajectories are selectively accessible during an adaptive walk in a fluctuating environment . As a result , the fate of a mutation depends on its individual effect , epistasis with preexisting mutations and on interactions with the prevailing environment . With growing opportunities to survey dynamics of many genotypes within evolving populations , studies of both inherent properties of individual alleles and effects of their interactions in multiple conditions would address how frequently pleiotropy and epistasis guide adaptive evolution .
Twelve populations of E . coli have been propagated for more than 50 , 000 generations in Davis Minimal ( DM ) medium supplemented with 25 µg/ml glucose ( DM25 ) in a long-term evolution experiment studying the dynamics and genetic basis of adaptation [43] , [53]–[59] . Mutations identified in one of these populations , as described previously , are studied here [39] , [57] , [60]–[62] . Other types of media used in this study include Tryptic soy ( Tsoy ) broth , tetrazolium-arabinose ( TA ) agar plates , and DM media supplemented with sugars other than glucose or with glucose and additional compounds . E . coli strains were grown in rich Tsoy liquid media overnight from −80°C freezer stocks . Aliquots of overnight culture were transferred to 10 mL DM25 media to precondition the cultures for 24 hours prior to growth curves or fitness assays . To identify external environments that interact with the five mutations ( Table 1 ) , the respiration of the rtsgp genotype was compared to the ancestor of the long-term evolution experiment , REL606 , using Biolog's Phenotypic Microarray Services in duplicate ( Biolog , Hayward CA ) . This method utilizes a high-throughput approach to compare respiration of two strains in 1 , 920 different environments , consisting of a variety of carbon , nitrogen , phosphorous and sulfur sources , differences in pH , and an assortment of chemical agents that target a variety of cellular processes . This approach uses the reduction of a tetrazolium dye as a terminal electron acceptor to assess respiratory activity . The amount of respiration was quantified by the extent of color production taking readings every 15 mins and graphed as a kinetic response curve . Incubation , recording and quality control analysis of PM plates 1–20 were performed by Biolog staff using an OmniLog instrument . Relative respiration in each environment was compared using the average height of the kinetic response curves ( h ) . The two strains were considered to have differential growth in an environment if h differed by more than 3 standard deviations of the means of h for both strains . Since differences in respiration do not necessarily reflect differences in growth or fitness , growth rates and in some cases relative fitness ( see below ) of the rtsgp strain was compared with the ancestral strain in a variety of these external environments to confirm that respiration was representative of growth or fitness . These follow-up growth rate assays were confirmatory and qualitative , not quantitative ( Table S2 ) . The fitness of each constructed strain was determined relative to the ancestor by direct competitions as described previously [53] . Briefly , competitions were typically carried out at 37°C in 10 mL of DM25 , the same medium used in the original long-term evolution experiment , in 50 mL flasks with 10 mL beakers as covers . For some competitions glucose was replaced with another carbon source ( β-methyl-D-glucoside ) or supplemented with another compound at various concentrations ( all others ) . These compounds and concentrations were as follows: 1 . 25% β-methyl-D-glucoside , 0 . 5 mM 3-0-β-D-galactopyranosyl-D-arabinose , 50 µM Ara-Ser , 3 mg/mL guanazole , 25 µg/ml EGTA , 100 µM Trp-Ser , 12 µg/mL piperacillin , 100 µM sodium orthovanadate , 32 µg/mL novobiocin , and 10 mM sodium nitrite . The constructed strains were competed against a marked Ara+ ancestral strain ( REL607 ) that is able to utilize the sugar arabinose . The arabinose utilization phenotype was found to be neutral in each of these competitions but allowed for the two different cell types to be easily distinguishable on TA agar plates . Competitors were pre-conditioned in the medium used for the competition for 24 hours prior to all competitions . Each competitor was then standardized based on OD600 values and added to the competition environment . Competitions were typically carried out for three days with a 1∶100 mixture transferred to fresh media every 24 hours . Since the fitness effect of some mutations was small , multiday fitness assays were used to amplify subtle advantages . Mixtures of competing strains were plated on TA agar at the start and end of each competition to determine fitness . Relative fitness ( w ) was calculated as the ratio of natural logarithms of realized growth by each competitor over three days of competition . Assays were typically carried out with five-fold replication and no less than three-fold replication . The fitness values of genotypes in the selective environment assayed in our lab were generally lower than previously reported in a study carried out at the University of Houston with these strains [19] . We do not know the reason for this discrepancy , though lab-specific differences in fitness effects , for example due to differences in water source , have been seen previously [63] . We also tested whether different preconditioning methods influenced the outcome of these fitness assays ( that is , preconditioning cultures in the original evolution environment ( DM25 ) or under competition conditions ) . We found no significant difference in the fitness of two genotypes , tp and sgp , when competed against the ancestor under either preconditioning method ( tp , F2 , 6 = 0 . 667 , P = 0 . 244; sgp , F2 , 6 = 0 . 047 , P = 0 . 258 ) . Notwithstanding the difference , relative features of the fitness landscape do not seem to have changed ( all five beneficial mutations remained beneficial in the selective environment ( DM25 ) ( Figure 1 ) ) . We note also that the analyses reported in this work generally consider the fitness effects of genotypes within a single environment or across two novel environments ( DM25 glucose supplemented with EGTA or guanazole ) used in the experiments carried out at the University of New Hampshire . Importantly , the key result observed in the dataset reported by Khan et al . [19] , that epistasis was negatively correlated with expected fitness , is also seen in the work presented here . Relative fitness , w , was calculated as described above based on the change in the relative density of strains in direct competition with one another . The terms that we use to describe and quantify epistasis were adopted from da Silva et al . [15] . The effect of the interactions among adaptive mutations on relative fitness was calculated as absolute epistasis: ( 1 ) where is the set of mutations , is the fitness of the genotype with the entire set of mutations , and is the relative fitness of a mutant with mutation from that set . The null model assumes no interactions and under this model the fitness of a combination of beneficial mutations is equal to the product of the fitness of those mutations individually . We refer to this null hypothesis as the expected fitness of any combination of mutations . Any significant difference between the observed and expected fitness of a genotype indicates the presence of epistatic interactions . Moreover , the sign of the absolute epistasis is important , suggesting either a negative or positive interaction on the fitness of the genotype . Genotypes consisting of more than two adaptive mutations were further analyzed for net higher-order epistatic interactions , defined as epistasis that occurs between three or more mutations that cannot be explained as the result of constituent lower-order interactions . As a result , net higher-order epistasis was calculated by subtracting the effect of lower-order interactions as shown in equation 2 , ( 2 ) where represents the number of mutations present and represents the fitness of a subset of the mutations present . We used this combination of methods to determine what types of interactions are most important in producing the observed phenotypes . Given the error inherent to calculations of expected fitness and hence , we used the method of error propagation to approximate the error of both parameters [64] . Since expected fitness of a particular genotype is equal to the product of the fitness of those mutations individually , the error ( ) is calculated from the sum of the relative errors of the individual mutations as shown in equation 3 , ( 3 ) where is the standard deviation of single-mutation fitnesses present . Since the uncertainty of ε depends on both and , the error of is the summation of the uncertainty of both as shown in equation 4 , ( 4 ) Epistasis was considered significant using a t-test with the t-statistic calculated as the ratio of the mean relative fitness to its standard deviation and the degrees of freedom based on the number of replicate assays to determine significance ( Table S5 and S6 ) . To identify potential epistatic interactions among the five beneficial mutations in different environments , growth over 24 hours was quantified for the constructed strains containing only one of the five beneficial mutations and compared to both the ancestral strain and the constructed strain containing all five mutations , rtsgp . Cells were grown in 200 µL of DM25 media in 96-well plates with 12 replicates per strain . Relative growth was quantified as AUC based on OD600 measured every 15 minutes for 24 hours , compared to the ancestor , REL606 , and averaged across replicates . Average relative growth of genotypes containing only a single mutation were then used to calculate an expected additive value for gp and rtsgp assuming no epistatic interactions between mutations . The error for expected values was approximated using the method of error propagation described above . Observed and expected relative growth for both gp and rtsgp was compared in each environment using a t-test with the t-statistic calculated as the ratio of the mean relative growth to its standard deviation and the degrees of freedom based on the number of replicate assays to determine significance ( Table S9 , S10 ) . | The fitness effect of beneficial mutations can depend on how they interact with their genetic and external environment . The form of these interactions is important because it can alter adaptive outcomes , selecting for or against certain combinations of beneficial mutations . Here , we examine how interactions between beneficial mutations favored during adaptation of a lab strain of Escherichia coli to one simple environment are altered when the strain is grown in two novel environments . We found that fitness effects were greatly influenced by both the genetic and external environments . In several instances a change in environment reversed the effect of a mutation from beneficial to deleterious or caused combinations of beneficial mutations to become deleterious . Our results suggest that a complex or fluctuating environment may favor combinations of mutations whose interactions may be less sensitive to external conditions . |
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As tourism is the mainstay of the Maldives’ economy , this country recognizes the importance of controlling mosquito-borne diseases in an environmentally responsible manner . This study sought to estimate the economic costs of dengue in this Small Island Developing State of 417 , 492 residents . The authors reviewed relevant available documents on dengue epidemiology and conducted site visits and interviews with public health offices , health centers , referral hospitals , health insurers , and drug distribution organizations . An average of 1 , 543 symptomatic dengue cases was reported annually from 2011 through 2016 . Intensive waste and water management on a resort island cost $1 . 60 per occupied room night . Local vector control programs on inhabited islands cost $35 . 93 for waste collection and $7 . 89 for household visits by community health workers per person per year . Ambulatory care for a dengue episode cost $49 . 87 at a health center , while inpatient episodes averaged $127 . 74 at a health center , $1 , 164 . 78 at a regional hospital , and $1 , 655 . 50 at a tertiary referral hospital . Overall , the cost of dengue illness in the Maldives in 2015 was $2 , 495 , 747 ( 0 . 06% of gross national income , GNI , or $6 . 10 per resident ) plus $1 , 338 , 141 ( 0 . 03% of GNI or $3 . 27 per resident ) for dengue surveillance . With tourism generating annual income of $898 and tax revenues of $119 per resident , results of an international analysis suggest that the risk of dengue lowers the country’s gross annual income by $110 per resident ( 95% confidence interval $50 to $160 ) and its annual tax receipts by $14 per resident ( 95% confidence interval $7 to $22 ) . Many innovative vector control efforts are affordable and could decrease future costs of dengue illness in the Maldives .
With approximately half of the world’s population at risk , dengue remains the most important mosquito-borne infection world-wide , [1 , 2] costing almost $9 billion globally per year for prevention and control . [3] The ecology of Small Island Developing States and territories ( SIDS ) , particularly with regards to temperature and precipitation , keeps dengue a continuing threat . [4] Outbreaks of dengue in SIDS can cause high burden , affecting the majority of island residents and overwhelming health systems . [5] SIDS are at risk of vector borne diseases as they are prone to natural disasters , often lack safe water supply , sanitation and waste management strategies , and their local governments have limited resources to implement effective vector control and manage outbreaks . [6] The Republic of Maldives ( the Maldives ) is a South Asian SIDS in the Indian Ocean made up of around 1 , 192 islands . Its 417 , 492 residents ( in 2015 ) lived on 187 inhabited islands ( island inhabited by national residents ) plus 126 resort islands , all grouped in 20 administrative atolls . [7] The capital city , Malé , is the most populous island . The Maldives ranks high in South East Asia in World Bank health indicators , [8] thanks to a well-developed public health system with a publicly funded health center on each inhabited island , a hospital for each atoll , and both public and private referral hospitals in Malé . Maldivians are enrolled in the country’s universal insurance system , Aasandha , which covers the cost of medical treatment , prescriptions , transfers , and if necessary , overseas care . Dengue was first reported in the Maldives in 1979 and became endemic in 2004 , when all atolls began reporting a high incidence of infections . [9] The Maldives’ Health Protection Agency ( HPA ) is responsible for dengue and vector surveillance and control , supporting personnel on each atoll and each inhabited island . As dengue is transmitted by Aedes mosquitoes , and in the absence of effective treatment or a public vaccination program , the main prevention strategy relies on controlling the vector population through integrated vector management . [10 , 11] Tourism is the major economic sector of the Maldives , with more than 1 . 2 million visitors arriving in 2014 , [12] mostly from Europe . To sustain this economy , the Maldives maintains its reputation as a “paradise island destination” by ensuring a clean environment with minimal risk of infectious diseases . The coefficient ( ± standard error ) of “dengue” in a regression on tourist arrivals was highly significant . These calculations suggest that the risk of dengue reduced the number of international tourist arrivals by 11 . 7% ( 95% confidence interval 5 . 3% to 18 . 0% ) compared to the expected number without such a risk . [13] As 9 out of 10 guests stay in self-contained resort islands , which generally practice comprehensive waste management and source reduction strategies , their risk of dengue infection is limited . Over the last decade , however , tourists increasingly visit and stay in guest houses on inhabited islands . Mosquito populations are abundant on these islands , increasing the risk of exposure to dengue virus . There are reports of tourists[14 , 15] or visiting workers[16] contracting dengue in the Maldives . Predominant vector breeding sites on inhabited islands stem from unmanaged waste and the presence of unprotected water storage containers . With support from the World Bank , the Maldives is expanding its national plan for waste management , [17] initiating a $17 million program to improve waste collection and management at regional and island levels . [18] In addition , health care workers on islands are trained to promote the safe storage of water and prevention of breeding sites to residents . Each island council is tasked with developing its own vector control strategies based on local needs to obtain national funding support . Waste management is important in the Maldives not only to mitigate the risk of Aedes-borne infections , but also to maintain the pristine environment underpinning the country’s tourist industry . While the literature documents several studies on preventing and controlling dengue , [3 , 19] economic studies on dengue specifically in SIDS or tourism-based economies are rare . To better understand the economic cost of dengue illness , control and preventive efforts and inform future control efforts , we undertook an economic analysis of dengue prevention and case management in this tourism-based economy .
The Maldives has a well-developed public health system with a publicly-funded health center on each inhabited island , a hospital for each atoll , and both public and private referral hospitals in Malé . Dengue can be treated at each of these health centers . Maldivians are also enrolled in the country’s universal insurance system , Aasandha . In addition , the State Trading Organization ( STO ) , a public company primarily owned by the Government of the Maldives , operates at least one public pharmacy in every inhabited island where Maldivians can obtain prescribed medicines . The country’s latest ( 2016 ) gross national income per capita was US $10 , 630 . [20] We visited health offices and facilities across the country’s health care spectrum in December 2016 . These comprised island health centers and local council offices ( on Haa Dhaalu [HDh . ] Hanimaadhoo , Kaafu [K . ] Dhiffushi and K . Maafushi ) , a resort island ( Thulhagiri ) , a regional hospital ( on HDh . Kulhudhuffushi ) and the Indira Gandhi Memorial Hospital ( IGMH ) , the country’s main referral hospital ( in Malé ) . The location of each of these sites is marked in Fig 1 . We further conferred with senior officials from the HPA , National Bureau of Statistics , Allied Health Insurance , Aasandha Health Insurance , the STO , the Maldives Association of Travel and Tour Operators , Ministry of Environment and Energy , and the Ministry of Tourism . We obtained aggregate statistical data on dengue cases from the Maldives HPA . Population data for atolls were obtained from the Maldives National Bureau of Statistics 2014 Census . The Maldives National Bureau of Statistics Housing and Household Characteristics Statistical Release 2014 provided data on waste disposal and water sources . To analyze these data , we classified atolls as “high risk” for waste disposal when they disposed of their waste “in open garbage sites” or “on the beach or in the bush” . We designated atolls as “high risk” for water storage when there was “rain water collection” or “presence of an open well . ” We analyzed the relationship between risk factors related to waste disposal and water sources on dengue by regressing the six-year average incidence rate on these risk factors . To ensure comparable observations , we excluded the outlier atolls South Ari and South Thiladhunmathi as well as the capital , Malé . This study adopted a bottom-up costing approach . First , all elements of the dengue control program were identified . Thereafter , data on resource utilization and unit costs of each resource were obtained or derived for 2016 . Total program costs were then derived from the sum of the product of resource utilization and unit costs for each element . Data collected included both capital and recurrent expenditures for dengue control activities . Buildings were assumed to have a 20-year useful lifespan as they were generally small , made of local materials , and faced constant sun and humidity . The one piece of equipment ( thermal fogger ) was assumed to have a 10-year useful life . We recorded data for resource use and costs at the district level in a matrix by line item and function . All items combine amortized capital costs ( e . g . , buildings and equipment ) and recurrent ( e . g . , utilities , fuel , and maintenance ) costs . Thereafter , costs for the line items were summed up to provide the total cost of dengue control activities for each district . Data on vector control activities and scenarios about hypothetical typical cases were collected through structured interviews with managers of health centers , hospitals , island councils , and a resort . Additional financial statements and database extracts were obtained from the STO , Aasandha and IGMH . The dengue fraction reflects the supervisors’ best estimate of the share of the relevant staff time devoted to dengue control activities . All data were entered into Microsoft Excel and the statistical software R for analysis . [21] Finally , we estimated the impact of dengue on the tourism sector using a regression coefficient from the international literature . [13] The study did not access or use any individual patient data .
The number of officially reported dengue cases by year from 2011 through 2016 was 2909 , 1083 , 680 , 775 , 1881 , and 1931 , respectively , with an annual average of 1 , 543 cases . Fig 2 depicts the incidence of dengue cases per 100 , 000 inhabitants of each atoll from 2011–2016 . Based on these numbers , the average number of dengue cases per month per island health center from 2011–2016 was 0 . 98 . The total resident population of the Maldives was 417 , 492 in 2016 . [20] Data from the Maldives provide suggestive evidence on the effectiveness of two source reduction strategies in controlling dengue: waste removal and water source management . Fig 3A and 3B depict dengue incidence nationally against means of waste disposal and water management , respectively . The trends suggest that for both higher percentage risky waste disposal ( p = 0 . 06 ) and higher percentage risky water management ( p = 0 . 08 ) are associated with elevated risk of dengue infection . Table 1 shows the estimated annual cost of dengue surveillance based on staffing needs at national , regional , atoll , and island levels . The surveillance costs measured relate to the staffing and information technology required to capture , analyze and disseminate epidemiological data . The total cost for dengue surveillance is $1 , 338 , 141 per year , or $3 . 27 per Maldivian resident per year . Local initiatives , like those on the island of Hdh . Hanimaadhoo ( inhabitants: 1 , 800 ) , demonstrated effective systems of source reduction–regular waste collection run by the local council . For a small fee , residents put out their waste and laborers gather it from public places . It is loaded into trucks and taken to a central place on the island for storage and , in the future , for recycling . The costs include laborers , supervision and vehicles and are described in Table 2 . This initiative costs US$35 . 93 per island resident per year . A source reduction initiative , addressing household water storage and related breeding sites , involved regular household visits ( approximately twice per year ) to show homeowners safe water storage practices and explaining other measures to reduce breeding sites . This includes application of larvicides; Bacillus thuringiensis israelensis or temephos ( from 2016 onwards ) provision of larvivorous fish for households with wells or fresh water holding bodies and instruction about sealing the pipe connections into water tanks to prevent mosquito entry . Table 2 details the cost of these initiatives , resulting in US$7 . 89 per island resident per year . The Maldives HPA currently recommends space spraying ( thermal fogging or using a mist blower ) as a measure only during an outbreak and in targeted priority locations . In a prior year , authorities on K . Dhiffushi conducted fogging to reduce adult mosquito populations . As shown in Table 2 , fogging cost US $0 . 15 per year per island resident . To reduce risks to guests and maintain the reputation of the resort as well as the country as a whole , resort islands engage in extensive vector control . The costs of these activities are described in Table 2 . Thulhagiri resort employed 2 full-time staff for waste reduction , breeding site identification and gutter cleaning for the sole purpose of mosquito control for 160 guests . Another 7 employees work full time to ensure clean facilities are maintained , of which 1/7th of their time is dedicated to cleaning potential vector breeding sources . With employees’ benefits including free accommodation and food , these vector control activities cost $1 . 60 per hotel-guest night . To project the cost of extending the local waste management and household visit programs nationally , we estimated that scaling could halve the per capita costs from $43 . 82 ( $35 . 93 for waste collection plus $7 . 89 for house visits ) to $21 . 91 . The resulting national cost would be $9 . 1 million annually . Similarly , the projected vector control costs for resort islands would be $11 . 2 million annually , based on 1 , 234 , 248 tourists staying for an average of 5 . 7 days in 2015 . [12] Table 3 first examines cost at the lowest level of the system , a health center , on the inhabited island of K . Dhiffushi . Two types of cases are considered: an ambulatory mild case of dengue , and a hospitalized case of dengue . An ambulatory case is estimated to consult the medical officer in the island health facility two times , be tested for NS1 antigen and receive a simple prescription , resulting in an overall economic cost of $49 . 87 per ambulatory dengue case . A hospitalized case is also estimated to consult the medical officer in the island health facility two times , be tested for NS1 antigen , receive prescriptions , and stay at the health facility for one night . This resulted in an overall economic cost of $127 . 74 per hospitalized dengue case . Aasandha payments for the product or service , where available , are shown as financial costs . Table 3 also analyzes costs at a regional hospital . The hospital had 50 beds with 60% occupancy 365 days per year generating 10 , 950 bed-day equivalents . For outpatient services , it had 76 , 026 annual outpatient visits ( about 255 per weekday ) , each generating 0 . 32 bed day equivalents , [22] or 25 , 480 bed day equivalents . In total , the hospital produced 36 , 430 bed day equivalents , of which 30% were generated by inpatient stays and 70% by outpatient visits . The estimated economic cost of one hospitalized dengue case is US$1 , 164 . 78 . Finally , Table 3 examines the economic cost of care at IGMH for 2015 . Assuming an average stay for a dengue patient of 4 days , the 2015 healthcare cost of a hospitalization within IGMH was $1 , 655 . 50 . The derivation of these costs is presented in Supporting Information file S1 Table . In addition to funding conventional services , Aasandha also covers certain specialized services needed because of the Maldives’ status as a SIDS with a small and dispersed population . Table 4 shows the quantities and costs of these services: sea evacuations , air evacuations , and overseas hospitalizations for 2015 . With only 140 evacuations in one year , the quantity of these specialized services is low , but their aggregate cost is substantial at $116 , 901 in 2015 . The most expensive cost was overseas hospitalization to Sri Lanka ( 89% ) and India ( 11% ) . The average cost of the sea evacuations ( $519 ) was almost as high as that for the air transfers ( $628 ) . Using epidemiological data , unit costs of hospitalization and ambulatory care from this study , and indirect costs of dengue cases infection derived from a systematic literature review , [3] we calculated the overall cost of dengue illness in the Maldives in 2015 , as shown in Table 5 . We estimated a total cost of $2 , 495 , 747 , of which 48% was direct costs , 5% for emergency evacuations , and 47% indirect costs . The Maldives’ largest economic sector , tourism , brought a gross income of US $375 million ( $898 per resident ) and tax receipts of US$50 million ( $119 per resident ) in 2014 . [12] Based on the aforementioned regression analysis , [13] the risk of dengue lowers the country’s gross annual income by $44 million ( $110 per resident , 95% confidence interval $50 to $160 ) and its annual tax receipts by $6 million ( $14 per resident , 95% confidence interval $7 to $22 ) .
To our knowledge , this is the first economic evaluation of dengue prevention and control in the Maldives , a SIDS actively working to reduce the burden of vector borne diseases . [23] Using data from 2015 , we found the cost of dengue in the Maldives to be $2 , 495 , 747 ( $6 . 10 per resident ) for dengue illness plus $1 , 338 , 141 ( $3 . 27 per resident ) for surveillance . The study results suggest that the economic costs through depressing tourism are substantially greater than the economic cost of illness . This paper describes four mosquito source reduction strategies seen in the Maldives: ( 1 ) waste collection by island councils on inhabited islands , ( 2 ) household visits by community health workers in inhabited islands , ( 3 ) thermal fogging of insecticides on inhabited islands and ( 4 ) intensive waste and water management on a resort island . The costs of these interventions varied based on the setting: inhabited islands spent from $0 . 15 to $35 . 93 per person per year , while a resort island invested $1 . 60 per hotel guest night . The projected cost of scaling the local waste management and household visit programs nationally was $9 . 1 million annually , similar to the estimated $11 . 2 million already spent protecting tourists on resort islands . Costs on resort islands are higher than those inhabited by regular residents due to the resort islands’ imperative to maintain a clean and safe environment for tourists , higher wages , and more comprehensive benefits for workers , while island councils on inhabited islands must allocate their resources across a range of services . Sharing best practices could likely reduce future costs compared to these projections . Locations such as the inhabited island of Hdh . Hanimaadhoo and the resort island of Thulhagiri operate model integrated vector management programs , providing tourists staying on these islands with the best possible protection from dengue . These tourists , however , frequently visit neighboring inhabited islands , thereby increasing the risk of infection . By sharing best practices between resort islands and other inhabited islands , both tourists and locals would enjoy greater dengue protection . Regular surveillance and monitoring would allow both resort islands and the HPA to evaluate and refine their efforts . As climate change likely increases the burden of vector borne diseases , [24] extending effective vector control to all islands will become more important in the future . Financial and economic costs of dengue case management varied depending on the location and case classification . For mild dengue cases presenting at island health centers , the cost was $49 . 87 per ambulatory case , and almost tripled to $127 . 74 for hospitalized cases . Case management of complicated dengue patients in regional or national hospitals significantly increased the cost to $1 , 164 . 78 and $1 , 655 . 50 per hospitalized case , respectively . This is more expensive than the closest neighboring island , Sri Lanka , where average costs per hospitalization are between US$196 to $866 for adult cases , depending on disease severity and treatment setting . [25] Medical fees for non-citizens are double those for citizens , and Aasandha does not cover non-citizens . For example , health staff on K . Maafushi island estimated that foreign workers faced an incidence of symptomatic dengue 30 times that of its Maldivian residents ( i . e . , 300 versus 10 per 100 , 000 population per year ) . Similarly in Singapore , migrant workers are more at risk to arbovirus infection . [26] Besides a higher risk of contracting dengue , foreign workers are often reported to delay treatment , so their risk of serious illness , and possibly death , is higher . Migrant workers in the Maldives often come from dengue endemic countries and may work on several islands during their stay , thereby increasing the risk of dengue transmission throughout the country . With its 187 inhabited islands , the Maldives necessarily relies on boats , seaplanes and airplanes for transportation . Although scheduled routes exist , medical transports frequently cannot wait and require ad-hoc emergency evacuations of patients from island health facilities to referral hospitals , including chartering a speed boat . In our study we found that despite a high number of evacuations , these contributed to only 7% of total costs . We found plane evacuations more economic than boats , likely due to difference in costs of purchasing a plane ticket on scheduled flights compared to the cost of chartering a speed boat . Hospitalizations of complicated dengue cases overseas in our study cost on average $1 , 225 . This is in line with the previously reported average cost of $1 , 470 to obtain treatment of any kind in overseas health facilities from the Maldives . [27] The aggregate cost of overseas hospitalizations for dengue in 2015 ( $15 , 926 ) , however , forms only a small part of the estimated $68 . 9 million spent on overseas hospitalization for all conditions for Maldivian citizens . [27] We estimated that each island health facility clinician sees on average of only one dengue case per month . Due to this low frequency and high turnover of clinicians on these islands , investing resources in strengthening clinical capacity for dengue would be relatively ineffective . Maldivian authorities have implemented a “dengue hotline” to allow inexperienced clinicians to seek advice from experts at IGMH . Similar health hotlines exist in the UK[28 , 29] and the USA , [30] which contribute to the triage and surveillance of cases . The current limitation of the hotline in the Maldives , however , is that calls are not screened , and island clinicians use the service for complicated dengue cases only . An expanded service could advise callers on interpretation of symptoms and test results to assess the likelihood of dengue , appropriate treatment to minimize risk , and decide appropriately on transfers . Based on international experience , we expect that an enhanced hotline would prove favorable in terms of better patient outcomes , less disruption to patients and families , and lower costs to the Maldivian health care system . We suggest that the Ministry of Health consider introducing an enhanced hotline phased in by atolls covered , monitoring hotline calls under both existing and enhanced phases to examine utilization , health outcomes , and costs of services and evacuations , and extending and refining the enhanced hotline based on lessons learned . This study is limited in that only a selected number of interventions were costed , and as such , may not reflect the costs seen on each island . Furthermore , this study did not address the burden of other important arboviruses , such as chikungunya and Zika , which also have been reported in the Maldives . [31 , 32] Including this in the analysis would likely increase the cost of case management , but not the cost of vector control activities , as the diseases share the same mosquito vector . In estimating the total cost of dengue in the Maldives , some of the data items were not available for the country and had to be derived from results in other countries . Using country-specific data for those items , if available , would strengthen the analysis . Based on the importance of the tourism sector the need to mitigate the potential impact of climate change , the Maldives introduced a “green tax , ” charging tourists on resort islands and guest houses $6 and $3 per person per night , respectively . While this tax revenue does not directly support the health sector , it is levied to strengthen solid waste management throughout the Maldives . By reducing plastic and other receptacles that are used by Aedes mosquitos to breed , this tax indirectly lowers the burden of dengue . Some additional transport or tourism tax could be established , if necessary , to fund the time , travel , and follow-up expenses needed to expand vector control services nationally . This study reinforces the economic rationale for investment in effective dengue control . | As tourism is the mainstay of the Maldives’ economy , this country recognizes the importance of controlling mosquito-borne diseases in an environmentally responsible manner . This study sought to estimate the economic costs of dengue in this Small Island Developing State of 417 , 492 residents with an annual average of 1 , 543 reported symptomatic dengue cases . Overall , the cost of dengue illness in the Maldives in 2015 was $3 million ( $6 . 10 per resident ) and surveillance cost an additional $1 million ( $3 . 27 per resident ) . The risk of dengue lowers the country’s gross annual income by $110 per resident and its annual tax receipts by $14 per resident . Rigorous elimination of debris on some resort islands demonstrates effective and environmentally sound vector control . Many innovative vector control efforts are affordable and could decrease future costs of dengue illness in the Maldives . |
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HIV superinfection ( reinfection ) has been reported in several settings , but no study has been designed and powered to rigorously compare its incidence to that of initial infection . Determining whether HIV infection reduces the risk of superinfection is critical to understanding whether an immune response to natural HIV infection is protective . This study compares the incidence of initial infection and superinfection in a prospective seroincident cohort of high-risk women in Mombasa , Kenya . A next-generation sequencing-based pipeline was developed to screen 129 women for superinfection . Longitudinal plasma samples at <6 months , >2 years and one intervening time after initial HIV infection were analyzed . Amplicons in three genome regions were sequenced and a median of 901 sequences obtained per gene per timepoint . Phylogenetic evidence of polyphyly , confirmed by pairwise distance analysis , defined superinfection . Superinfection timing was determined by sequencing virus from intervening timepoints . These data were combined with published data from 17 additional women in the same cohort , totaling 146 women screened . Twenty-one cases of superinfection were identified for an estimated incidence rate of 2 . 61 per 100 person-years ( pys ) . The incidence rate of initial infection among 1910 women in the same cohort was 5 . 75 per 100pys . Andersen-Gill proportional hazards models were used to compare incidences , adjusting for covariates known to influence HIV susceptibility in this cohort . Superinfection incidence was significantly lower than initial infection incidence , with a hazard ratio of 0 . 47 ( CI 0 . 29–0 . 75 , p = 0 . 0019 ) . This lower incidence of superinfection was only observed >6 months after initial infection . This is the first adequately powered study to report that HIV infection reduces the risk of reinfection , raising the possibility that immune responses to natural infection are partially protective . The observation that superinfection risk changes with time implies a window of protection that coincides with the maturation of HIV-specific immunity .
Development of a safe and effective prophylactic HIV vaccine remains enormously challenging , due to the virus's high diversity and our limited understanding of immune correlates of protection . While most effective vaccines are designed to mimic natural infection and protective immune responses to it , such a template for HIV vaccine design remains elusive , since sterilizing immune responses to natural infection have not been observed . A priority of HIV vaccine development is , therefore , to identify settings where natural infection elicits some immune functions desired in a vaccine . For example , HIV-infected individuals who spontaneously control viral replication have provided insights into immune mechanisms of HIV control [1] . However , models where the response , rather than delaying disease , prevents infection – the ultimate goal of a prophylactic vaccine – remain less well characterized . Studies of superinfection ( reinfection from a different partner ) provide a unique model in which to investigate the impact of pre-existing responses on susceptibility to infection by diverse circulating viral variants , which include multiple subtypes with up to 30% sequence variation . HIV superinfection has been reported in a number of settings [2]–[13] , implying that HIV acquisition can occur despite the immune response to initial infection . However , it remains an open question whether pre-existing infection affords some protection from superinfection , and individuals who do become superinfected are a select subset deficient in a particular aspect of immunity . Published estimates of superinfection incidence vary from no identified cases [1] , [14]–[16] to rates roughly similar to initial infection [2]–[13] , [17] , [18] . These discrepancies are largely explained by differences in participant inclusion criteria and study design . The studies that have directly compared initial and superinfection incidence have had limited statistical power due to cohort size [5] , [12] , [17] , [18] or number of cases of superinfection identified [3] , [8] . Additionally , methods used to identify superinfection have evolved . Superinfection is most reliably detected in longitudinal samples by the presence of a single viral clade initially followed by introduction of a second phylogenetically distinct clade [19] . Detection sensitivity is dependent on the number of genomic regions analyzed [12] , as well as sequencing depth [20] . Until recently , sequences were obtained by limiting dilution amplification and Sanger sequencing [5] , [6] , [12] , [17] , which limits detection to cases where the second virus is relatively abundant . The development of next generation sequencing ( NGS ) has enabled higher-throughput , deeper sequencing of large cohorts [20] , [21] . To date , the largest study to examine the rate of superinfection in a prospective seroincident cohort was a NGS screen by Redd et al . of 149 individuals in which 7 cases were identified [8] . No statistically significant difference was found between the incidences of initial infection and superinfection , though the relatively small number of cases may have resulted in limited statistical power . A greater number of cases was found in a high-risk cohort in Mombasa , Kenya , with 12 cases of 56 women screened [5] , [12] , [17] . However , this study used Sanger sequencing to sample ∼7 clones per sample , which could miss lower frequency variants , and was not powered to compare incidences . In the present study , we developed a NGS method for identification of superinfection , and used it to screen 129 women in the same Mombasa cohort , including those classified as singly infected in the prior study . We identified 9 additional cases of superinfection , for a total of 21 cases in this cohort . These combined data enabled comparison of the incidence rates of initial infection and superinfection .
In order to conduct a sensitive , high-throughput screen for superinfection in the Mombasa cohort , we developed a pipeline for amplification , next-generation sequencing ( NGS ) , data cleaning , and phylogenetic and sequence diversity analysis of longitudinal plasma RNA ( Fig . 1 ) . One-hundred thirty-two women met our selection criteria for the NGS superinfection screen , with a median follow-up time of 2046 days ( IQR 1265–2848 ) . We successfully amplified gag , pol and env at three timepoints in 115 women and at least two genomic regions in at least the first and last timepoints in 129 . The remaining 3 women were dropped from analysis . In total , ∼1 . 7 million raw sequencing reads were obtained , with ∼1 . 25 million passing quality filtering: a median of 901 per amplicon per sample . Women were considered putative superinfection cases if the posterior probability of monophyly supported single infection at the earliest studied timepoint followed by introduction of a distinct viral clade and increased viral diversity consistent with that seen in simulated dual infection ( Fig . 1e&f ) . Putative cases of superinfection were confirmed and their timing specified by analyzing intervening timepoints . Nine cases of superinfection were detected and their timing specified . One case of suspected dual infection was detected , in which two clades were detected at the earliest sample analyzed ( 60 days post-infection ( dpi ) ) and throughout infection ( data not shown ) . Example data from two cases of superinfection are summarized in Figures 2 and 3 . Initial screening of subject QD151 ( Fig . 2 ) showed monophyletic subtype A infection at 39 dpi and two subtype A clades in all three genes at 938 and 1701 dpi . In subsequent analysis of intervening timepoints the second clade was first detectable at 801 dpi ( Fig . 2a ) . At this time , pairwise distance increased sharply , for example in gag from 0 . 27% at 241 dpi , to 12 . 75% at 801 dpi ( Fig . 2b ) , into the range observed in simulated dual infections . These observations supported introduction of a second subtype A variant between 241 and 801 dpi . The initial clade was no longer detectable in pol at 1701 dpi , suggestive of a genomic recombination event ( Fig . 2c ) . Similarly , subject QB210 ( Fig . 3 ) showed initially monophyletic infection with a subtype A/D virus , followed by introduction of a subtype C/D virus at 163 dpi , evidenced by polyphyly and a shift in pairwise distance ( >10% ) in all 3 genes ( Fig . 3a and 3b ) . In intervening timepoints , the second variant could be detected in all genes at 163–170 dpi , but was undetectable in gag and pol after 170 dpi , indicating recombination ( Fig . 3c ) . Characteristics of the 9 new cases of superinfection are summarized in Table 1 and Figure S2 . In all but two cases the superinfecting variant was detected in all 3 amplicons in at least one timepoint . In all cases , the superinfecting variant was detected at multiple timepoints in at least one amplicon . In one case ( QC369 ) , the initial variant became undetectable in any amplicon following superinfection , suggesting it was replaced , to our detection limit , by the superinfecting variant . Both variants were detected at two timepoints each , the initial variant at 17 dpi and 28 dpi , and the superinfecting variant at 143 dpi and 451 dpi ( Fig . S2 ) , indicating this result was not due to contamination . Further , the possibility of sample mix-up was excluded by HLA-typing ( data not shown ) . As illustrated in Figures 2 , 3 and S2 , in the other 8 cases , variants were intermittently detected in different amplicons at different times , suggestive of genomic recombination and dynamic turnover of the circulating viral population . Combining the data here with those from previous studies in the Mombasa cohort [5] , [12] , [17] , a total of 146 women were examined for superinfection: 90 were tested using NGS , 39 using both NGS and Sanger sequencing , and 17 using only Sanger sequencing . Among the 39 women previously identified as singly infected by Sanger sequencing and tested by NGS here , no new cases of superinfection were identified , suggesting older methods were sensitive enough to detect superinfection . Twenty-one cases of superinfection were confirmed based on detection of the superinfecting virus in two or more samples . The timing windows of all 21 superinfection events are summarized in Figure 4 and Table S2 . The midpoint of the timing window of the 9 new cases ranged from 81 to 1041 dpi , with 6 occurring within the first year of infection . The window of superinfection events was defined to a median of within 127 days , with window sizes of 90 to 1253 days . Timing of all 21 cases ranged from 63 to 1895 dpi , defined to a median of within 146 days . We detected both inter-subtype and intra-subtype superinfections . In 6 of 9 cases identified by NGS , the superinfecting variant was the same subtype as the initial variant in every gene where both were detected . In all 9 cases , the variants were the same subtype in the env amplicon ( Table 1 ) . Among all 21 cases of superinfection ( Table S2 ) , the majority of superinfection events we detected were intrasubtype , regardless of genomic region: 53 . 8% were intrasubtype based on gag sequence , 62 . 5% based on pol , and 70 . 6% based on env . We further investigated the possibility of a bias in sequence similarity of superinfecting variants to initial variants by analyzing amino acid diversity . We compared the pairwise amino acid distance between initial and superinfecting variants within each superinfection case to the distance that would be expected by chance . The latter was modeled by simulated mixtures of sequences from all possible pairs of singly infected individuals in the Mombasa cohort ( Fig . 5 ) . Using NGS data from the 9 superinfection cases and 120 singly infected women screened here , we found no significant differences between the sequence similarity within superinfected individuals and that expected by chance ( Fig . 5a ) . Including Sanger sequencing data from the additional 12 superinfected women previously screened yielded a similar result ( Fig . 5b ) The incidence of superinfection among women who were screened was compared to the incidence of initial infection in the entire cohort at risk . Only incident HIV infections ( occurring after enrollment in the cohort ) were included . Fourteen women who were seronegative but HIV RNA positive at enrollment were excluded for this reason . Seven of these had been screened for superinfection , and one was found to be superinfected , which mirrors the frequency of superinfection observed in the entire group . The individual with evidence of dual infection at the earliest timepoint was also excluded , since we were unable to distinguish coinfection from superinfection . After exclusions , 1910 women were at risk of initial infection , contributing 5124 person-years , and 138 women were screened for superinfection , contributing 764py following first infection . There were 295 initial infections , giving a crude incidence rate of 5 . 7 per 100pys , and 20 superinfections , giving a crude incidence rate of 2 . 61 per 100 pys . The incidence of superinfection and initial infection over time is summarized in Figure 6 . We used Andersen-Gill proportional hazards analysis to generate a hazard ratio ( HR ) relating the incidence of superinfection to that of initial infection . The unadjusted HR for this comparison was 0 . 49 ( CI 0 . 31–0 . 76 , p = 0 . 0018 ) . Variables previously shown to influence HIV exposure risk in this cohort [22] , [23] were included as adjustments in the model ( summarized in Table 2 ) . These included self-reported sexual risk behavior , place of work , hormonal contraceptive use , genital tract infections , years in sexwork , age at first sex , total follow-up time in the cohort and calendar year . The HR for superinfection compared to initial infection , adjusted for these variables , was 0 . 47 ( CI 0 . 29–0 . 75 , p = 0 . 0019 ) . Since proportional hazards analysis is based on time to infection and the precision with which superinfection timing was determined varied between cases , we performed sensitivity analyses setting infection timing for all cases to the start or midpoint of the timing windows rather than the end , as done for the above analysis . In both of these analyses , significant differences in incidence were also observed: setting infection timing to the start of the windows , the adjusted HR was 0 . 33 ( CI 0 . 18–0 . 58 , p = 0 . 00012 ) ; using the window midpoints , the adjusted HR was 0 . 39 ( CI 0 . 23–0 . 63 , p = 0 . 00016 ) . We assessed whether the risk of superinfection varied with time since initial infection by dividing our data into infection events occurring early or late in follow-up and estimating the HR , as above , in each subset . We found that within the first 6 months at risk , the incidence rates of initial and superinfection did not differ significantly ( adjusted HR 0 . 73 , p = 0 . 51 ) , whereas after 6 months the rate of superinfection was lower than that of initial infection ( adjusted HR 0 . 40 , p = 0 . 0017 ) . A similar result was observed when considering events within or beyond one year at risk: within the first year , the incidence rates of initial and superinfection did not differ significantly ( adjusted HR 0 . 54 , p = 0 . 14 ) , but after one year the rate of superinfection was significantly lower ( adjusted HR 0 . 43 , p = 0 . 0059 ) . Sensitivity analyses setting infection time to the start and midpoint of the timing windows as above reproduced the same results ( data not shown ) . We noted that the previous screens in the cohort appeared to detect a higher frequency of superinfection than the NGS screen ( 12 cases of 56 women screened , compared with 9 cases of 90 ) , with a greater fraction of the events occurring later after initial infection ( Fig . 4 ) . Since the NGS screen spanned later years in the cohort than the previous studies , such a difference could be due to the known decline in infection risk in the cohort over calendar time [22] , [23] . However , the numbers of events are small when the datasets are considered separately and the difference both in superinfection incidence rate and post-infection timing between the two studies was not statistically significant ( data not shown ) .
In this study we used NGS to screen for superinfection in 129 high-risk women and identified 9 cases of superinfection . Combined with previous studies[5] , [12] , [17] , a total of 21 cases of superinfection were detected among 146 women screened in this cohort . There was a statistically significant difference between the incidence of superinfection ( 2 . 61 per 100pys ) and initial infection ( 5 . 75 per 100 pys ) , with a hazard ratio of 0 . 47 after adjusting for potential confounding factors . This suggests that HIV infection provides partial protection from subsequent infection . The relatively large size of this cohort and high number of superinfection cases enabled us to detect for the first time a statistically significant difference between the incidence of initial infection and superinfection . This possibility has been proposed previously , though the studies were not designed and/or powered to detect a difference [17] , [18] . In the largest incidence study prior to the present study , Redd et al . screened a comparable number of individuals ( 149 ) in a lower-risk cohort and identified 7 cases of superinfection . The incidence of superinfection was not found to differ significantly from initial infection , but there was a trend for lower incidence of superinfection when controlling for baseline sociodemographic differences between the groups at risk of initial and superinfection . Analysis of our data using the same methods as Redd et al . – Poisson regression with propensity score matching [8] – was consistent with the results of our Andersen-Gill analysis , showing a significant difference in incidence , with an estimated incidence ratio of 0 . 48 ( p = 0 . 011 ) comparing superinfection to initial infection . In addition to sample size , two strengths of our incidence analysis were our specification of infection timing to within a few months on average and our comparison of initial and superinfection risk within the same cohort . These enabled us to adjust for the same potential confounding factors in both the initial infection and the superinfection risk sets , using frequently collected time-varying covariate data . Particularly important , given the sequential nature of superinfection , was adjustment for calendar year to control for decline in infection risk in the cohort over time . The distributions of initial and superinfection events over calendar time were similar ( Fig . S3 ) , suggesting community-level changes over time did not severely bias our analysis . The ∼two-fold reduction we found in the incidence of superinfection has a number of possible interpretations . First , it may indicate that the adaptive immune response elicited by initial infection provides partial protection from second infection . If this were the case , superinfection might preferentially occur early in infection , before the response has matured [2] , [13] , [24] . In support of this idea , we found that , although superinfection occurred throughout the course of first infection , the incidence of superinfection was significantly lower than initial infection after the first 6 months of infection , but not earlier . This suggests that susceptibility to superinfection decreased over time , coincident with broadening and strengthening of HIV-specific immunity . Indeed , this has been suggested by two earlier studies , each documenting three cases of superinfection that occurred within the first year after initial infection [3] , [18] . If the difference in incidence we observed is due to a partially protective adaptive immune response , we would anticipate superinfection would preferentially occur with more distantly related viruses , more likely to escape the response . Using viral subtype and pairwise amino acid distance as surrogate measures of antigenic distance , our data provided no evidence of this effect . The majority of the 21 superinfection events we detected were intrasubtype , and the proportion of subtype A , C and D viral sequences was similar for the initial and superinfecting viruses , consistent with the subtype distribution in this cohort [25] . The pairwise distance between initial and superinfecting variants was no higher than the distribution of distances between random pairs of singly-infected individuals from the Mombasa cohort . This may potentially be explained by limited sample size or insufficient simultaneously circulating subtypes . It also may be that sequence relatedness is a poor indicator of susceptibility to the immune response or the genome regions we analyzed are not critical antigenic determinants of protection . Alternatively , it is possible that protective immune responses are not driving the protective effect we observed . Another potential explanation for the lower risk of superinfection is that HIV infection itself may reduce infection risk by depleting permissive target cells . On the other hand , chronic immune activation and immunodeficiency following HIV infection could increase susceptibility , potentially blunting protective effects [26] . Thus , there may be a complex interplay of biological factors impacting HIV risk in an HIV-positive individual . So far , studies of immune correlates of superinfection have yielded variable results – some suggesting neutralizing antibody deficits in superinfection [27] , [28] , while others , including studies in the Mombasa cohort , detected no differences in antibody [29] , [30] or cellular [31] responses . A major challenge in these studies has been the identification and analysis of large enough numbers of superinfection cases: the small sample sizes in studies to date ( three to twelve superinfected individuals ) would restrict detection to only very large effects . Small sample size is just one factor that has made detecting immune deficits associated with superinfection challenging and contributed to variable results among studies . There has also been variation among published studies in the control groups used for comparison , including the time at which the response was analyzed relative to the time of superinfection and initial infection . Given the dynamic nature of the immune response , sample timing could impact measures in both controls and cases . Furthermore , precision in the estimated timing of superinfection varies between studies , and between cases , providing an additional variable . Divergent findings between studies may also reflect differences in the assays used and subtleties in the immune parameters they capture . Our finding of lower risk of superinfection than initial infection provides greater impetus for larger-scale comprehensive analysis of multiple immune mechanisms , including both those analyzed in the smaller studies to date and , perhaps of more interest , those not characterized in prior studies . If the discrepancies in earlier studies reflect the fact that multiple immune parameters are at play , then examining a variety of immune responses in the same individuals in a larger cohort may be needed to define responses that contribute to HIV susceptibility following initial infection . Like all studies , the study presented here has a number of limitations . Firstly , while our screening methods are among the most sensitive developed , it remains possible that some cases of superinfection were missed . In particular , reinfection by the same source partner is not captured by any existing methods . Additionally , our specification of the timing of superinfection was limited by the samples available to us . While follow-up was generally frequent in this study population , there were six superinfection cases where sample availability limited our ability to define the time of superinfection to within a one-year period . This uncertainty in superinfection timing did not affect our findings , as we found that whether we assumed in the incidence analysis that the true timing of superinfection was at the start , midpoint or end of the timing window , the results indicated that the incidence of superinfection was significantly lower than that of initial infection . Finally , as in all observational studies , residual confounding of our incidence estimate by behavioral changes and sexual network-level factors not measured or accounted for in our analyses remains a possibility . However , the fact that we compared initial and superinfection risk within the same cohort and collected covariate data at frequent intervals enabled us to minimize this issue to an extent not possible in previous studies . This study provides the first robust evidence that HIV infection reduces the risk of subsequent infection . The underlying mechanism remains unclear , but this finding prompts exploration of correlates of protection from HIV in high-risk individuals who continue to be exposed after first infection . Furthermore , this study reinforces that superinfection occurs at a considerable rate , calling for studies of its impact on the clinical progression , transmission , and epidemiology of HIV .
The study was approved by the ethical review committees of the University of Nairobi , the University of Washington and the Fred Hutchinson Cancer Research Center . Written informed consent was obtained from all participants . Seronegative women in Mombasa , Kenya , attended monthly visits , at which clinical examinations , interviews and sample collection took place , as previously described [22] . Following seroconversion , sample collection took place quarterly . Individuals were selected for superinfection screening based on sample availability <6 months and >2 years post-initial HIV infection , and an approximately equally spaced intervening sample . Within these limitations , samples with maximal plasma viral load , >1000 copies/ml , and prior to initiation of antiretroviral therapy were selected . Thirty-nine of 44 women previously screened for superinfection by Sanger sequencing and identified as singly infected [5] , [12] , [17] were rescreened; the remaining 5 women did not have adequate samples available . HIV virions were isolated from heparinized plasma using the μMACS VitalVirus HIV Isolation kit ( Miltenyi Biotec ) and viral RNA extracted from 140–420 µl , depending on viral load , using the Qiamp viral RNA Mini kit ( Qiagen ) . Nested RT-PCR of ∼500 bp in gag , pol and env was conducted in duplicate ( see Table S1 ) . RNA input into each reaction was normalized to 3000 viral genomes according to plasma viral load , or the maximum possible where viral load was too low . RT-PCRs for the three genes were multiplexed . Nested PCR reactions were carried out separately for each region with primers containing adaptors for Roche 454 sequencing and a unique 8 bp barcode sequence to identify each sample . PCR products were purified using AMPure XP PCR purification beads ( Agencourt ) and quantified using the Qubit dsDNA HS assay ( Invitrogen ) . PCR products were sequenced on the Roche 454 GS-Junior or GS-FLX titanium platform . Where initial sequencing suggested superinfection ( see below ) , timing was inferred by sequencing intervening timepoints . Sequences are available upon request from the authors . 454 sequences were error-corrected using AmpliconNoise [32] . Chimeric sequences were identified and removed using UCHIME [33] . Cross-contamination between samples sequenced together and contamination by other lab samples was identified by all-against-all BLAST against a local database of published HIV sequences and sequences from the same sequencing run . Sequences with high identity hits to known laboratory stains or other samples from the same sequencing run were removed . Sequences with abundance <5 reads or 0 . 5% of the sample , whichever was higher , were excluded from further analyses as lower abundance variants were not reproducibly detected in repeated deeper sequencing of two selected samples where rare variants formed a distinct phylogenetic clade . An amplicon-specific profile HMM was created from an alignment of representative sequences from multiple subtypes . For each subject and amplicon , 20 reference sequences were selected by placing 454 reads on a tree of candidate reference sequences [34] and minimizing the average distance to the closest leaf [35] . These reference sequences , representatives from subtypes common to the region , and 454 reads were aligned to the HMM using hmmalign [36] and non-consensus columns removed . Any sequences <200 bp long after alignment and trimming were removed . We used BEAST [37] to calculate a posterior probability of monophyly for the sequences . A posterior sample of trees was obtained using a strict molecular clock , Bayesian Skyline Plot population model and the HKY substitution model . Each MCMC chain ran 20 million iterations , sampling every 2000 , discarding the initial 25% of samples as burn-in . Chains were assessed for convergence by examining effective sample size ( ESS ) and by visual inspection of traces of key parameters . A strict clock was used as poor mixing was frequently observed under relaxed clock models . BEAST runs with intermediate posterior probabilities ( 0 . 2–0 . 8 ) were manually examined for recombinant sequences and run again with putative recombinants removed . Pairwise distances were calculated for all sequence pairs under the TN93 model using APE [38] , reporting the maximum within-subject distance . For comparison , 95% confidence limits of pairwise distances were calculated for sequences from known single infections ( previously screened in [5] , [12] , [17] ) and simulated dual infections . Dual infections were simulated by combining all pairs of sequences from previously screened singly infected samples . Pairwise distances calculated from 454 sequences obtained in this study were compared to the upper bound of the 95% quantile of single infection distances , and the lower bound of the 95% quantile of simulated dual infection distances . This pipeline was validated and refined by processing monophyletic viral isolates , known mixtures of isolates , and known cases of superinfection detected by Sanger sequencing [17] . These methods were found to be sensitive enough to distinguish two subtype A isolates mixed at abundances of 5%∶95% genome copies in all three genomic regions , and at 1%∶99% in two of three genomic regions ( Fig . S1 ) . Sequences were aligned as for the phylogenetic analysis . Insertions relative to the reference alignment were removed , and sequences with <60% coverage or identified as recombinants between initial and superinfecting variants upon visual inspection were excluded . For each case of superinfection , viral sequences were annotated as the initial strain or the superinfecting strain . We calculated the mean Hamming distance between amino acid sequences of the superinfecting strain from the time of superinfection detection and sequences of the initial strain up to and including this time . In calculating the mean distance , each pairwise comparison was weighted using the product of the multiplicities of the two reads . To investigate whether these distances deviated from what would be expected by chance , an artificial set of mock superinfections was generated by combining sequences from singly infected individuals . All pairs of singly infected individuals screened by 454 sequencing were enumerated . In each pair , one individual was randomly chosen to be the source of the ‘initial’ virus in the simulated superinfection . A time of ‘superinfection’ was chosen randomly from the available sampled timepoints and sequences from all timepoints up to and including this time were used for analysis . The other individual in the pair acted as the source of the ‘superinfecting’ virus . A time of ‘transmission’ was chosen randomly from the available sampled timepoints and sequences from this timepoint were used . Mean distances within pairs were calculated as above . The analysis was repeated including gag and env Sanger sequences from previously published cases [5] , [12] , [17] , trimmed to the genome region amplified for NGS , and given unit weight . A two-sample Wilcoxon test was used to test for a difference between the distances observed in true superinfections and those simulated in mock superinfections . Statistical analysis was performed using R ( www . r-project . org ) . The incidences of initial and superinfection were compared by Andersen-Gill proportional hazards analysis . The predictor was inclusion in the screen for superinfection , modeled as a time-dependent variable , and the outcome was time to HIV infection ( initial and super ) . Timing of infection events for the incidence analysis was set to the study visit of their detection ( for initial infection events the visit after inferred infection timing; for superinfection events , the time at which the superinfecting virus was first detected ) . Individuals who were HIV infected but not screened for superinfection were censored after acquisition of initial infection . Individuals who became superinfected were censored after acquisition of superinfection . Individuals who were screened and not found to be superinfected were censored at the last timepoint screened . Since samples after initiation of antiretroviral treatment were excluded from superinfection screening , no follow-up after treatment initiation was included . The model was adjusted for time-varying variables at each visit: calendar year , age , years in sexwork , number of weekly sexual partners , number of weekly unprotected sex acts , hormonal contraceptive use in the prior 70 days and any genital tract infection in the prior 70 days ( bacterial vaginosis , cervicitis , genital ulcer disease , gonorrhea , trichomoniasis ) ; place of work and age at first sex recorded at enrollment; and total follow-up time in the study . Incidences of initial and superinfection were also estimated as described in [8] , using Poisson regression and propensity score matching to select a subset of women at risk of initial infection whose baseline risk profiles most closely matched those of women screened for superinfection . | HIV-infected individuals with continued exposure are at risk of acquiring a second infection , a process known as superinfection . Superinfection has been reported in various at-risk populations , but how frequently it occurs remains unclear . Determining the frequency of superinfection compared with initial infection can help clarify whether the immune response developed against HIV can protect from reinfection – critical information for understanding whether such responses should guide HIV vaccine design . In this study , we developed a sensitive high-throughput method to identify superinfection and used this to conduct a screen for superinfection in 146 women in a high-risk cohort . This enabled us to determine if first HIV infection affects the risk of second infection by comparing the incidence of superinfection in this group to the incidence of initial infection in 1910 women in the larger cohort . We found that the incidence of superinfection was approximately half that of initial infection after controlling for behavioral and clinical differences that might affect infection risk . These results suggest that the immune response elicited in natural HIV infection may provide partial protection against subsequent infection and indicate the setting of superinfection may shed light on the features of a protective immune response and inform vaccine design . |
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Previous studies have shown that stimulation of whole blood or peripheral blood mononuclear cells with bacterial virulence factors results in the sequestration of pro-coagulant microvesicles ( MVs ) . These particles explore their clotting activity via the extrinsic and intrinsic pathway of coagulation; however , their pathophysiological role in infectious diseases remains enigmatic . Here we describe that the interaction of pro-coagulant MVs with bacteria of the species Streptococcus pyogenes is part of the early immune response to the invading pathogen . As shown by negative staining electron microscopy and clotting assays , pro-coagulant MVs bind in the presence of plasma to the bacterial surface . Fibrinogen was identified as a linker that , through binding to the M1 protein of S . pyogenes , allows the opsonization of the bacteria by MVs . Surface plasmon resonance analysis revealed a strong interaction between pro-coagulant MVs and fibrinogen with a KD value in the nanomolar range . When performing a mass-spectrometry-based strategy to determine the protein quantity , a significant up-regulation of the fibrinogen-binding integrins CD18 and CD11b on pro-coagulant MVs was recorded . Finally we show that plasma clots induced by pro-coagulant MVs are able to prevent bacterial dissemination and possess antimicrobial activity . These findings were confirmed by in vivo experiments , as local treatment with pro-coagulant MVs dampens bacterial spreading to other organs and improved survival in an invasive streptococcal mouse model of infection . Taken together , our data implicate that pro-coagulant MVs play an important role in the early response of the innate immune system in infectious diseases .
Today it is generally accepted that coagulation is tightly interwoven with the innate immune system [1] . Both systems can act in a combined effort to sense and eradicate an infection in a highly sophisticated manner . Indeed , evolutionary studies suggest that fibrinogen has relatively recently acquired its function as a clotting factor because many fibrinogen-related proteins in invertebrates have an important role in defense processes , such as pathogen recognition , agglutination , and bacterial lysis , however , not in clotting [2] . This applies also to other members of the coagulation cascade , as sequence homology analyses in vertebrates revealed that many clotting factors share ancestry with complement proteases [3] . Together these results show that the vertebrate coagulation system has developed from evolutionary related cascades involved in innate immunity [4] . It is therefore tempting to speculate that coagulation has a yet underestimated function in the host defense to infection . The coagulation cascade can be broken down into an extrinsic ( tissue factor driven ) and intrinsic pathway ( contact activation ) . Both arms are initiated by limited proteolysis and are amplified in a snowball-like manner , eventually resulting in the generation of thrombin , which then initiates formation of a fibrin network [5] . The Gram-positive bacterium Streptococcus pyogenes is a major human pathogen that mainly causes local and self-limiting skin and throat infections . Infections can occasionally become invasive and develop into serious and life-threatening conditions such as streptococcal toxic shock syndrome ( STSS ) and necrotizing fasciitis . Notably , both conditions are associated with high morbidity and mortality ( for a review see [6] ) . The bacterium has evolved a variety of strategies to evoke activation of the coagulation cascade , involving for instance the induction of tissue factor on monocytes and endothelial cells by M proteins or an activation of the intrinsic pathway at the bacterial surface [7]–[9] . M proteins are streptococcal surface proteins and probably one of the best-known virulence determinants of this pathogen [10] . They can be released during infections [11] and act on monocytes to trigger cytokine induction and tissue factor up-regulation [8] , [12] . Recently we reported that soluble M protein triggers the release of pro-coagulant MVs from human peripheral blood mononuclear cells ( PBMCs ) . Once released from PBMCs these MVs can initiate coagulation by activating both pathways in a sequential mode of action [13] . Apart from PBMCs MVs can be secreted from almost all other human blood-born cells , and depending on their cell activation MVs can differ in their composition and function . Elevated levels of MVs have been related to pathological conditions such as bleeding and thrombotic disorders , cardiovascular diseases , cancer , and infectious diseases [14] . They form sphere-shaped structures , less than 1 µm of diameter and limited by a lipid bilayer . In contrast to their cell of origin , MVs from activated cells expose negatively charged phospholipids , mainly phosphatidylserine ( PS ) , on their outer membrane , which present a neo-exposed docking site for many plasma proteins including coagulation factors [15] . Despite an increasing knowledge on the role ( s ) of MVs in pathological processes e . g . as signaling molecules , in angiogenesis , and in initiation or propagation of coagulation and inflammation [14] , their function in infectious diseases is only poorly understood . In the present study we investigated whether pro-coagulant MVs are part of the innate immune response by exposing antimicrobial activity . To this end we performed a number of in vitro and in vivo experiments to show that pro-coagulant MVs not only efficiently prevent the proliferation of S . pyogenes bacteria within a formed clot , but also that application of human MVs in a subcutaneous murine infection model dampens bacterial spreading and improves survival .
PBMCs were isolated from human blood and stimulated with M1 protein as described in Methods . MVs were then purified as reported earlier [13] and the pro-coagulant activity of MVs was confirmed by measuring the clotting time ( data not shown ) . For subsequent binding studies , pro-coagulant MVs were tagged with gold-labeled annexin V and incubated with S . pyogenes bacteria in the presence of 1% plasma . Figure 1A depicts transmission electron micrographs at lower and higher magnification . At higher magnification the figure shows that pro-coagulant MVs are bound to the bacterial surface in the presence of plasma . To test whether the presence of MVs derived from other cells , interferes with the binding of pro-coagulant MVs from PBMCs , whole blood was stimulated with M1 protein . MVs were isolated and their binding to S . pyogenes was studied by transmission electron microscopy . Figure 1B ( upper panel ) shows that MVs isolated from M1 protein-activated blood bind to the bacterial surface . The origin of PBMC-derived MVs was confirmed by immunostaining with CD14 , also showing that activation of blood with M1 protein caused an increase in binding of monocyte-derived MVs ( Figure 1B , middle panel ) . To test whether the activation stage of the MVs contributes to binding , MVs were immunostained with an antibody against tissue factor . Figure 1B ( lower panel ) shows that only a few tissue factor-positive MVs were found attached to the bacteria , when MVs were isolated from non-stimulated blood . However , a more intensive antibody staining was recorded when MVs were recovered from M1 protein stimulated blood , showing that blood cell activation led to pro-coagulant MVs that bind to the bacterial surface . Based on these results we decided to use MVs isolated from PBMCs for all further experiments . MVs that were isolated from M1 protein stimulated PBMCs are therefore referred to as “pro-coagulant MVs” and from non-activated PBMCs as “ctrl . MVs” throughout the remaining part of this study . The interaction of MVs with S . pyogenes was further investigated by fluorescence microscopy . Pro-coagulant or ctrl . MVs were labeled with PKH26 ( red ) and incubated with S . pyogenes in human plasma . After a 30 minute incubation step , aggregates of MVs and bacteria ( DAPI-stained , blue ) were observed ( Figure S1 ) , similar to those described by Timár and colleagues [16] . The number of MV-bacterial aggregates that exceeded 10 µm was quantified ( Table 1 ) . The data show that both types of MVs bind and aggregate bacteria , but incubation with pro-coagulant MVs induced more and larger aggregates when compared with ctrl-MVs ( Table 1 ) . Next we tested whether opsonization of S . pyogenes with pro-coagulant MVs , renders the bacteria susceptible for clotting . To this end , S . pyogenes bacteria were pre-incubated with pro-coagulant MVs in the presence or absence of human plasma , washed thoroughly to remove non-bound MVs , and added to recalcified plasma . Under these experimental settings clotting occurred within 162 s as shown in figure 2A . If , however , bacteria were incubated with pro-coagulant MVs in the absence of human plasma , no clotting was observed within 300 s and likewise , incubation of bacteria with plasma in the absence of pro-coagulant MVs prevented clotting ( Figure 2A ) . Together the experiments imply that plasma protein ( s ) are required for the binding of pro-coagulant MVs to the bacteria and subsequent activation of clotting . Fibrinogen is a plausible candidate , as it is an abundant plasma protein and has high affinity for most streptococcal strains , including the AP1 strain , which was used in this study [9] . Therefore S . pyogenes bacteria were incubated with pro-coagulant MVs in the presence of normal or fibrinogen-depleted plasma , washed to remove non-bound MVs , and added to normal recalcified plasma . As before , when bacteria were pre-incubated with pro-coagulant MVs in the presence of normal plasma , clotting occurred within 169 s , while clotting was significant delayed ( 235 s ) when bacteria were pre-incubated with pro-coagulant MVs in fibrinogen-depleted plasma , prior re-calcification with normal plasma ( Figure 2B ) . Note that fibrinogen-depleted plasma was generated by defibrination and as fibrinogen was not completely removed ( 0 , 04 g/l are remaining ) , clotting was only delayed but not completely prevented . Previous work has demonstrated that M1 protein from S . pyogenes is the main fibrinogen receptor on the AP1 strain used in this study [17] . To test whether M1 protein is also the major fibrinogen binding protein that mediates the interaction between bacteria and MVs , we employed an isogenic AP1 mutant strain ( MC25 ) , which does not express M1 protein on its surface [18] . Wildtype AP1 and MC25 bacteria were pre-incubated with pro-coagulant MVs in the presence of human plasma , washed thoroughly to remove non-bound MVs , and added to recalcified plasma . As depicted in figure 2C , MC25 bacteria tagged with pro-coagulant MVs were not as potent to induce clot formation as AP1 bacteria . The number of MV-bacterial aggregates was quantified by fluorescence microscopy and also in these experiments we found that the MC25 strain was not as effective as the AP1 strain to form aggregates ( 5±2 vs . 49±11 ) in plasma when opsonized with pro-coagulant MVs . Finally we further investigated , whether other M proteins , either from the same serotype or from other serotypes , can recruit MVs to their surface . We therefore tested 14 clinical isolates , of which 5 were of the M1 type and 9 of other serotypes ( Figure S2A and B ) . When subjecting these strains to clotting assays we found that all serotypes had similar pro-coagulant activities as seen for the AP1 strain . Together the results show that the binding of pro-coagulant MVs to streptococci alters the bacterial surface from a non-coagulative to a pro-coagulative state . This interaction seems to be a common mechanism of group A streptococci , as also other serotypes explored similar clotting activities when incubated with pro-coagulant MVs . Moreover the data suggests that fibrinogen plays an important role in this chain of events . To study the role of fibrinogen as molecular bridge in more detail , surface plasmon resonance spectroscopy was employed . In a series of experiments we tested whether the activation state of MVs constitutes a regulatory mechanism that steers their affinity for fibrinogen . Sensor chips were coated with ctrl . or pro-coagulant MVs and probed with increasing concentration of fibrinogen . Though fibrinogen binding to both ctrl . MVs ( Figure 3A ) and pro-coagulant MVs ( Figure 3B ) was detected , determination of the association constants revealed that pro-coagulant MVs have a much higher affinity for fibrinogen than ctrl . MVs ( 0 . 019 nM vs . 3 . 3 µM , respectively ) as shown in figure 3C . The results from clotting experiments and fluorescent microscopy implicate an important role of M1 protein in binding pro-coagulant MVs ( see Figure 2C ) . To verify this conclusion we measured the interaction between M1 protein and pro-coagulant MVs , immobilized on a sensor chip , by surface plasmon resonance in the presence or absence of fibrinogen . Figures 3D+E illustrates that an interaction between M1 protein and the pro-coagulant MVs was only detectable when the chip was pre-incubated with fibrinogen , confirming fibrinogen's function as a bridging factor . In conclusion , the data show that MVs derived from activated cells expose additional binding sites for fibrinogen , which are required as docking sites for the streptococcal adhesion factor such as M1 protein or M proteins from other serotypes . In order to investigate how pro-coagulant MVs can up-regulate additional fibrinogen binding-sites , mass spectrometry analysis was used , which allows the identification and quantification of intracellular , membrane associated , and secreted proteins of MVs . With this approach a total number of 169 proteins , with a false discovery rate of 1% , was identified in non-stimulated and pro-coagulant MVs ( Table S1 ) . In ctrl . MVs , 57% of the proteins were cytosolic , 23% secreted , 12% membrane-associated , and 8% mitochondrial origin ( Figure S3 ) . This composition changed drastically in pro-coagulant MVs , as here an increase in secreted and membrane associated proteins was found ( 36% and 28% , respectively ) , while a decrease in cytoplasm and mitochondrial proteins to 35% and 1% was measured ( Figure S3 ) . We also noted a rise in the concentration of 34 proteins recovered from pro-coagulant MVs comparing to ctrl . MVs ( Table 2 ) . In particular , leucocyte elastase levels were dramatically up-regulated ( approximately 2500 times ) , but also higher levels of the fibrinogen-binding integrins CD18 ( 42 times ) and CD11b ( 7 . 8 times ) were noted . Another integrin , alpha-V/beta-3 , which is a receptor for a number of human proteins including fibronectin , laminin , and vitronectin were also found upregulated ( 2 . 9 times ) . Finally we noticed that proteins with antimicrobial functions such as lysozyme and neutrophil defensin 1 ( 3 . 7 times and 2 . 8 times , respectively ) were also enriched in pro-coagulant MVs . Taken together the determination of the protein content in ctrl . and pro-coagulant MVs by mass spectrometry analysis revealed that , apart from two fibrinogen-binding integrins , other proteins with an important role in the early immune response , are also up-regulated in pro-coagulant MVs . Recent studies support the concept that clot formation at the site of infection entraps bacteria in the fibrin network , which in turn prevents bacterial spreading , and promotes bacterial elimination [19] , [20] . Based on these reports , we speculated that MVs could also act as a clotting initiator that chains the bacteria within a formed clot . To prove this hypothesis , S . pyogenes were incubated in recalcified plasma followed by the addition of ctrl . MVs or pro-coagulant MVs . Artificial phospholipids with pro-coagulant activity ( PLs ) or tissue factor ( TF ) containing samples served as positive controls . Stable clots were formed when pro-coagulant MVs , PLs , or tissue factor were added to the bacteria/plasma mixture , while loose and less compact clots were generated when the bacteria/plasma mixture was incubated with buffer or ctrl . MVs ( not shown ) . The clot samples were covered with Tris-buffer containing 1% plasma and incubated for two or four hours at 37°C . Aliquots were collected from the supernatants and bacterial loads were determined . After two hours of incubation the number of released bacteria from plasma clots derived by pro-coagulant MVs was significantly decreased ( 9 . 7 times ) , when compared with the number found in the supernatants of samples incubated with ctrl . MVs ( Figure 4A ) . After the four-hour incubation , samples treated with buffer of ctrl . MVs contained high loads of streptococci . As seen before , incubation of bacteria in a plasma clot derived from pro-coagulant MVs prevented the escape of bacteria from the clots ( more than 12 times , comparing to ctrl-MVs ) and also PLs or tissue factor induced clots had a similar effect ( Figure 4B ) . These data demonstrate that bacteria are efficiently trapped and immobilized if they are opsonized with pro-coagulant MVs . It has recently been shown that activation of the coagulation cascade on the surface of S . pyogenes leads to an induction of antimicrobial activity [20] . To investigate whether antimicrobial activity is also seen when clotting is induced by pro-coagulant MVs , additional bacterial growth experiments were performed . Streptococci were mixed with plasma and clotting was initiated by adding pro-coagulant MVs , PLs , or tissue factor . Ctrl . MVs or buffer served as controls . After 30 min , clots were homogenized and bacterial loads determined . As seen in figure 5A , bacterial counts were significant reduced to 20–30% in samples treated with pro-coagulant MVs , PLs , or tissue factor , when compared with ctrl . MVs . Samples incubated with buffer only , served as a control ( 100% growth ) . Clot formation appears to be the critical moment in these experiments , since no reduction in bacterial growth was monitored when calcium was omitted and thus clotting prevented ( Figure 5B ) . Similar results , though not a complete reversion , were seen when recalcified samples were treated with a peptide ( Gly-Pro-Arg-Pro ) that prevents the polymerization of fibrin monomers ( Figure 5C ) [21] . To visualize the bacteria , samples were subjected to scanning electron microscopy ( Figure 5D–F ) . In the absence of pro-coagulant MVs , S . pyogenes bacteria appear as intact cocci when incubated for 30 min in plasma ( Figure 5D ) . In the next series of experiments , pro-coagulant MVs ( Figure 5E ) were added to the plasma bacteria mixture . Figure 5F illustrates that after activation with pro-coagulant MVs , bacteria were weaved in a fibrin network . It also appears that the morphology of bacteria was not significant compromised , as the bacterial cell membrane seems to be still intact ( Figure 5F ) . These images may indicate that the effect seen is of bacteriostatic nature rather than bactericidal , however , more experimental support is needed to prove this conclusion . Taken together the data suggest that pro-coagulant MVs are able to prevent bacterial spreading and impair bacterial proliferation inside a plasma clot . Recently we reported that pro-coagulant MVs from patients suffering from streptococcal sepsis are significant increased [13] . To test whether this can also be observed in an invasive animal model of streptococcal infection , mice were subcutaneously infected . The animals were sacrificed after three time points ( 10 , 24–30 , and 42–48 hours after infection ) and plasma samples were recovered by cardiac puncture . Figure 6A depicts that the TF content in the plasma samples was not significant raised 10 hours after infection , but was significantly increased at the later time points ( Figure 6A ) . Similar results were seen when measuring the concentrations of pro-coagulant MVs , though they already start to peak 10 hours after infection ( Figure 6B ) . Thus , the data show that the generation of pro-coagulant MVs is part of the host response to invasive infection with S . pyogenes . The role of MVs in systemic infectious diseases is currently not completely understood , but it has been speculated that elevated levels in the early phase of sepsis may have protective effects [22] . We therefore studied whether the local application of pro-coagulant MVs to the site of infection may improve the outcome of the disease . Three groups of mice were infected with S . pyogenes bacteria and were treated either with vehicle , ctrl . MVs or pro-coagulant MVs . While application of ctrl . MVs failed to improve survival as compared to control ( vehicle ) , treatment with pro-coagulant MVs significant prolonged survival time and decreased the mortality rate ( Figure 7A ) . The subcutaneous injection of pro-coagulant MVs also had an impact on the bacterial load in different organs of the infected mice . Mice received a subcutaneous injection of S . pyogenes bacteria and simultaneously a single dose of pro-coagulant MVs . Infected animals were sacrificed 18 hours after infection , and bacterial loads in the blood , liver , and spleen were determined . As depicted in figure 7B–D , treatment with pro-coagulant MVs resulted in decreased numbers of bacteria in all organs when compared with non-treated animals . These results are in line with previous conclusions and may indicate that pro-coagulant MVs are part of the early host defense to an infection at an early stage of the infectious disease progression .
Pro-coagulant MVs constitute one of the main reservoirs of blood-borne TF , which are released from monocytes , macrophages , or endothelial cells with inducible TF expression [23] and they are therefore considered to be key determinants of the hemostasis equilibration [24] . Notably , the number of pro-coagulant MVs can significantly increase in patients suffering from sepsis as reported by us and other groups [13] , [25] . These findings raise the question whether they are part of the host response to infection or rather contribute to systemic hemorrhagic complications , such as disseminated intravascular coagulation ( DIC ) in severely ill patients . Reid and Webster recently published a review article on the role of MVs in sepsis [22] . The authors conclude that MVs are beneficial at the early stage of sepsis as they can compensate for some of the host's systemic reactions [22] . Our findings support this notion , because local treatment with pro-coagulant MVs significantly prevents bacterial dissemination and improves survival . Moreover , activation of PMBCs is triggered by the binding of M1 protein to toll-like receptor 2 [12] , which suggests that formation and release of pro-coagulant MVs follows the principles of pattern recognition and are therefore part of the innate immune reaction . However , as seen for many other host defense mechanisms , the systemic induction of pro-coagulant MVs may contribute to severe complications , such as DIC . A better understanding of the molecular mechanisms that modulates the tightly regulated process may lead to the development of novel antimicrobial therapies with different modes of action that can be used for local treatment or in systemic complications . Microvascular thrombosis and the formation of a fibrin network can be considered as an efficient and early response of the host defense against bacteria that can lead to an immobilization of bacteria and thereby attenuates the spreading of the pathogen [19] , [20] , [26] . Our studies show that pro-coagulant MVs bind to S . pyogenes and that this interaction leads to an alteration of the bacterial surface into a pro-coagulative state . We found that fibrinogen is a docking molecule that attaches protein M1 , a streptococcal surface-bound adhesion factor , to pro-coagulant MVs . Subsequent mass spectroscopic analysis revealed an up-regulation of the fibrinogen binding integrins ( CD18 and CD11b , respectively ) at the surface of pro-coagulant MVs . This chain of events presents a plausible explanation as to how pro-coagulative MVs achieve their affinity for S . pyogenes . However , it cannot be ruled out that other proteins such as fibronectin , vitronectin or laminin are also involved [27]–[29] . Notably , many of these host adhesion factors are also receptors for other bacterial pathogens [30] . Thus their binding to pro-coagulant MVs may represent some kind of pattern recognition mechanism that allows the targeting of other microorganisms in a more general sense . Future work will show whether pro-coagulant MVs are also interacting with other bacterial species and whether this involves the recruitment of fibrinogen and/or other host adhesion proteins . Our results show that plasma clots that were induced by pro-coagulant MVs can immobilize S . pyogenes as efficiently as clots induced by tissue factor or artificial phospholipids . Importantly , clots formed in the presence of pro-coagulant MVs had antimicrobial activity against S . pyogenes , which could be explained by their cargo containing antimicrobial peptides and proteins . However , we noted that clots formed by the addition of tissue factor or artificial phospholipids , were also able to kill the entrapped bacteria . It therefore remains to be determined as to what extent the peptides/proteins with antimicrobial activity from pro-coagulant MVs contribute to bacterial killing , or if there are bactericidal substances generated during the activation of the coagulation cascade . The latter hypothesis is supported by recent findings that many coagulation factors contain a sequence at their carboxy-terminal part with an antimicrobial activity [31] , [32] . Taken together , our data show that activation of the coagulation cascade and the formation of a fibrin network are important mechanisms to prevent bacterial dissemination and proliferation . As pro-coagulant MVs are induced at an early stage during bacterial infection , their local interaction with bacteria can be considered as part of the early immune response .
The S . pyogenes strain AP1 ( 40/58 ) serotype M1 and its M1-derivate MC25 have been described previously [18] , [33] . All other S . pyogenes strains were clinical isolates from our strain collection that have been characterized by standard microbiological procedures . Bacteria were grown overnight in Todd-Hewitt broth ( THB; GIBCO ) at 37°C and 5% CO2 . M1 protein was purified from the supernatant of S . pyogenes MC25 , as previously described [18] . Artificial pro-coagulant phospholipids were from Rossix ( Sweden ) . Recombinant tissue factor and anti TF were from American Diagnostica ( Germany ) . Anti CD14 was from Dako ( Denmark ) . PBMC isolation , stimulation as well as MV purification were performed as described previously and used at concentration range from 50 to 150 MPs/µl [13] . S . pyogenes bacteria from 10 ml overnight culture were washed and resuspended in 1 ml 10 mM HEPES-buffer ( 2×109 CFU/ml ) . 150 µl bacteria and 30 µl MVs - in the presence or absence of 300 µl human plasma – were mixed and incubated for 30 min . at 37°C . Alternatively , fibrinogen depleted plasma ( Affinity Biologicals , Canada ) was used . Bacteria were washed 3 times with HEPES-buffer by centrifugation ( 1550× g for 10 min . ) , and finally resolved in HEPES-buffer . Clotting time was measured in a coagulometer ( Amelung ) after addition of reaction mixtures to recalcified normal human plasma . 100 µl recalcified normal plasma was mixed with 25 µl 2×105 CFU S . pyogenes bacteria , 25 µl MVs or pro-coagulant PLs ( 0 . 25 mM , Rossix ) , or 2 pM tissue factor ( American Diagnostica ) and incubated at 37°C for 5 min . The clots were covered with 10 mM Tris-buffer containing 1% plasma . At the indicated time points , 100 µl aliquots of the supernatant were plated onto blood agar in 10-fold serial dilutions and the number of bacteria was determined by counting colonies after 18 hours of incubation at 37°C . Plasma clots were produced as described above , covered with Tris-buffer containing 1% plasma and incubated at 37°C for 30 min . Alternatively , the tetrapeptide Gly-Pro-Arg-Pro ( Bachem ) was added to prevent clotting ( 1 . 5 mg/mL final concentration ) . After incubation clots were disrupted in a Ribolyser ( Hybaid , 30 sec at speed 4 . 0 ) and the homogenate was plated directly onto blood agar . The number of bacteria was determined by counting colonies after 18 hours of incubation at 37°C . MVs were labeled with the red fluorescence aliphatic chromophore PKH26 dye ( Sigma ) , which intercalate into lipid bilayers [34] . After labeling , MVs were washed and centrifuged as described [13] . 150 µl bacteria ( 2×109 CFU/ml ) and 30 µl labeled MVs were mixed in 300 µl human plasma and incubated for 30 min at 37°C . After incubation 10 µl of the mix was dropped onto a cover slide , counterstained with DAPI ( Invitrogen ) and visualised by a BX60 fluorescence microscope and 100×1 . 3 or 60×1 . 25 UplanFl objectives ( Olympus , Hamburg , Germany ) . Human proteins ( annexin V , anti CD14 AB , and anti TF AB ) were labeled with colloidal gold ( 15 and 5 nm in diameter , BBI International ) as described earlier [35] . MV/S . pyogenes preparations were mixed with gold-labeled 20 nM proteins for 20 min at room temperature and processed for negative staining [36] . Clots were fixed with 2 . 5% glutaraldehyde overnight . Samples were washed 2–3 times with 0 . 1 M sodium phosphate buffer ( pH 7 . 3 ) , dehydrated with a series of increasing ethanol concentrations ( 5 minutes in 30% , 5 minutes in 50% , 10 minutes in 70% , 10 minutes in 90% and two times 10 minutes in ethanol absolute ) , and dried with CO2 by critical point method with a Emitech dryer as outlined by the manufacturer . Dried samples were covered with gold to a 10 nm layer and scanned with a Zeiss DSM 960A electron microscope . The Actichrome TF activity assay kit ( American Diagnostica ) was used to quantify the TF pro-coagulant activity in the plasma samples [13] . The Coa-MP activity kit ( Coachrom Diagnostica ) was used according to the instructions of the manufactory , to measure the pro-coagulant activity of MVs in plasma [13] . Protein digestion was carried out as previously described [37] . The resulting peptide mixtures were concentrated using spin-columns from Harvard Apparatus using the manufactures' instructions . The hybrid Orbitrap-LTQ XL mass spectrometer ( Thermo Electron , Bremen , Germany ) was coupled online to a split-less Eksigent 2D NanoLC system ( Eksigent technologies , Dublin , CA , USA ) . Peptides were loaded with a constant flow rate of 10 µl/min onto a pre-column ( Zorbax 300SB-C18 5×0 . 3 mm , 5 µm , Agilent technologies , Wilmington , DE , USA ) and subsequently separated on a RP-LC analytical column ( Zorbax 300SB-C18 150 mm×75 µm , 3 . 5 µm , Agilent technologies ) with a flow rate of 350 nl/min . The peptides were eluted with a linear gradient from 95% solvent A ( 0 . 1% formic acid in water ) and 5% solvent B ( 0 . 1% formic acid in acetonitrile ) to 40% solvent B over 55 minutes . The mass spectrometer was operated in the data-dependent mode to automatically switch between Orbitrap-MS ( from m/z 400 to 2000 ) and LTQ-MS/MS acquisition . Four MS/MS spectra were acquired in the linear ion trap per each FT-MS scan which was acquired at 60 , 000 FWHM nominal resolution settings using the lock mass option ( m/z 445 . 120025 ) for internal calibration . The dynamic exclusion list was restricted to 500 entries using a repeat count of two with a repeat duration of 20 seconds and with a maximum retention period of 120 seconds . Precursor ion charge state screening was enabled to select for ions with at least two charges and rejecting ions with undetermined charge state . The normalized collision energy was set to 30% , and one microscan was acquired for each spectrum . The data analysis was performed as previously described [37] . Briefly , the MS2 spectra were searched through the X ! Tandem 2008-05-26 search engine [38] against the human protein database . The search was performed with semi-tryptic cleavage , specificity , 1 missed cleavages , mass tolerance of 25 ppm for the precursor ions and 0 . 5 Da for fragment ions , methionine oxidation as variable modification and cysteine carbamidomethylation as fixed modification . The database search results were further processed using the Trans-Proteomic pipeline , version 4 . 4 . 0 [39] . Real time biomolecular interaction was analyzed with a BIAcore3000 system ( Biosensor , La Jolla , CA ) using L1 sensor chips [40] . The L1 sensor chip comprises a carboxymethyl dextran hydrogel derivatized with lipophilic alkyl chain anchors to capture phospholipid vesicles . Experiments were performed at 25°C with 10 mM TRIS , 0 . 9% NaCl , pH 7 . 4 as running buffer and PBS ( pH 7 . 4 ) as immobilization buffer . MVs were coated onto the L1-sensor chip according to the manufacturer' s instructions . Briefly , the L1 chip surface was washed by 2×3 minute injections of 40 mM N-octyl-β-D-glucopyranoside ( Roth , Germany ) at a flow-rate of 10 µl/min . MVs in PBS were then injected over the sensor for 30 min at a flow-rate of 2 µl/min resulting in 2000–2200 response units ( RUs ) of ctrl or pro-coagulant MVs . To remove residual multilayer structures and loosely bound vesicles , a short pulse of 10 mM NaOH was applied . Subsequently , BSA ( 0 . 1 mg/ml , 5 min ) was added to block non-specific surface binding . The resulting bilayer linked to the chip surface was taken as a model MV-membrane surface for studying fibrinogen binding . Pure buffer solutions and the solution containing fibrinogen or M1 protein were applied at a flow rate of 10 µl/min . Following each cycle of analysis , the sensorchip was regenerated either with short pulses of 10 mM NaOH leaving the MV-lipid monolayer intact for additional interaction studies , or with 40 mM N-octyl-β-D-glucopyranoside stripping the MV-lipid layer from the surface in order to adsorb new MVs . The sensorgrams for each fibrinogen–MV bilayer interaction were analyzed by curve-fitting . Data from 5 concentrations were selected for statistic analysis . RUs of the running buffer were subtracted from the RUs of the sample solution . The data were analyzed using the Biaevaluation 3 . 0 software ( Biacore ) that offers various reaction models to perform complete kinetic analyses . The data from the BIAcore sensorgrams were fitted globally , and the heterogeneous ligand model resulted in optimum mathematical fits , reflected by low χ2 values ( <5 ) . The subcutaneous infection model with S . pyogenes AP1 strain were performed in female Balb/C mice as described previously [33] . This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Committee on the Ethics of Animal Experiments the Landesveterinär- und Lebensmitteluntersuchungsamt Rostock ( Permit Number: 7221 . 3-1 . 1-031/10 . ) . Statistical analysis was performed using GraphPad Prism , Version 4 . 00 . The P-value was determined by using the unpaired t-test ( comparison of 2 groups ) or the log-rank test ( comparison of survival curves ) . All samples were analyzed in triplicate and all experiments were performed at least three times , if not otherwise declared . The bars in the figures indicate standard deviation . | The coagulation system is much more than a passive bystander in our defense against exogenous microorganisms . Over the last years there has been a growing body of evidence pointing to an integral part of coagulation in innate immunity and a special focus has been on bacterial entrapment in a fibrin network . However , thus far , pro-coagulant MVs have not been discussed in this context , though it is known that their numbers can dramatically increase in many pathological conditions , including severe infectious diseases . In the present study we see a significant increase of pro-coagulant MVs in an invasive streptococcal mouse model , suggesting that their release is an immune response to the infection . We find that pro-coagulant MVs bind to Streptococcus pyogenes and promote clotting , entrapment , and killing of the bacteria in a fibrin network . As a proof of concept pro-coagulant MVs were applied as local treatment in the streptococcal infection model , and it was demonstrated that this led to a significantly improved survival in mice . |
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Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry . We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell , such as a disease phenotype . The method extends the Nested Effects Model of Markowetz et al . ( 2005 ) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes . The method also expands the network by attaching new genes at specific downstream points , providing candidates for subsequent perturbations to further characterize the pathway . We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions . We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks . We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway . The method predicts several genes with new roles in the invasiveness process . We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line . Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes .
Carcinogenesis involves a host of cell-cell communication breakdowns that include the loss of contact inhibition , an increased potential to proliferate , and the ability to invade and spread into foreign tissue [1] . The molecular events involved in this transformation are still poorly understood . New systematic methods are needed to infer the key events responsible for these disease processes . The ability to measure gene expression changes for the entire genome in the presence of molecular perturbations , such as specific gene knock-downs , provides a new opportunity to infer gene networks in a data-driven manner . Our goal is to identify the genetic mechanisms underlying a phenotype , such as cancer cell deregulation . We take a network-based approach to the problem , starting with a set of signaling genes or S-genes , known to act in a common pathway . The input to the method is a matrix in which gene expression has been measured under the knock-down of each of the S-genes . Genes exhibiting differential expression across the knock-downs , here referred to as effect genes or E-genes , are used to predict a set of interactions among the S-genes , and expand the pathway by identifying newly implicated frontier genes based on their expression changes . We hypothesize that using a structured model of the interactions among the S-genes will improve the identification of frontier genes for inclusion in the network for subsequent rounds of investigation . Previous approaches for pathway expansion have used methods based on expression correlations to a phenotype of interest . These methods search for genes with expression profiles that are highly correlated with a particular phenotype or disease state and have led to promising results [2]–[5] . Methods using Analysis of Variance [6] , false-discovery [7] , and non-parametric methods [8] also have been proposed . For example , one method is to measure the correlation of gene expression levels with an idealized vector representing the phenotype ( e . g . indicator variables with zeroes for disease and ones for lack of disease ) [9] . One disadvantage of these methods is that they make no explicit use of the known members of a pathway or how these members interact with each other . More recently , several approaches have demonstrated learning a structured model from perturbation experiments [10]–[13] . Approaches based on Bayesian Networks have also been proposed [11] , [12] . However , these approaches attempt to identify networks over the E-genes rather than the S-genes and therefore require many replicated microarray experiments to distinguish signal from noise . Instead , perturbing genes of interest and constructing networks from observations of downstream changes allows powerful interventional reasoning , as well as reconstruction of interactions not directly reflected in expression levels , such as phosphorylation . In one approach , Carter et al . ( 2007 ) [14] decompose the matrix of expression changes under single- and double-gene deletions to infer a transcriptional regulation network from which phenotypes and gene expression responses following knock-downs can be predicted . An alternative approach is the Nested Effects Model ( NEM ) of Markowetz et al . ( 2005 , 2007 ) [10] , [15] , which has been used to predict interactions , including non-transcriptional interactions . Rather than searching for genetic networks that explain observational data , as several Bayesian Network approaches have done [11] , [16] , NEMs are useful in situations in which perturbations have been carried out on a focused set of genes . In this case , NEMs assume the interest is in a finer description of the interactions among the silenced genes rather than identifying a network of unrestricted connections between potentially additional genes . The NEM approach takes as input a matrix of expression changes , X . A column of X corresponds to a single gene knock-down ( or knock-out ) of one S-gene; a row corresponds to the response of an E-gene to all of the knock-downs . The method searches for approximate subset relations among the expression changes of the E-genes to organize the S-genes into a network . To do this it assumes , for example , that S-gene A is above S-gene B if the set of E-genes that change under gene A's knock-down are an approximate superset of the effected genes under B's knock-down . The current NEM approach uses binary set membership relations to identify a network and thus the exact nature of interaction between S-genes ( e . g . activation or inhibition ) is not determined . However , an appreciable extent of inhibition occurs in real genetic networks . To estimate the amount of inhibition present in living cells , we estimated the proportion of genes up-regulated in deletion mutants relative to wild-type from a yeast knock-out compendium [17] . Over half of the genes had increasing expression changes across the deletion strains , consistent with a high degree of inhibitory interactions in the yeast genetic network ( see Figure S1 ) . Thus , the inability to distinguish between stimulatory and inhibitory interactions may be a critical shortcoming of current NEM approaches . To address this limitation , we developed a generalization of the NEM approach using a probabilistic graphical model called a factor graph that allows a broader set of S-gene interactions to be recovered from the secondary effects of E-gene expression . This paper offers three methodological contributions . First , we present a factor graph formulation called FG-NEM that allows for an efficient search over all possible NEM structures for a high-scoring model . Second , we show how FG-NEMs extend the NEM approach for expanding the network beyond the current set of S-genes . Third , we show that FG-NEMs can model a more general class of S-gene interactions than NEMs , which increases the accuracy of network identification over an approach that considers a more restricted set of interactions . We demonstrate the usefulness of FG-NEMs on both simulated and biologically relevant signaling networks that contain both inhibition and activation . We apply FG-NEMs to identify novel genes not previously implicated in colon cancer cell invasiveness . Finally , we experimentally test FG-NEM predictions and report that knock-downs of the top-scoring genes lead to a loss-of-invasion phenotype , validating the approach . Source code is available as an R library from our website: http://sysbio . soe . ucsc . edu/projects/fgnem .
Our goal is to automatically identify genetic interactions among a set of signaling genes from gene expression changes observed under their knock-down . The signaling genes represent a set of genes that prior experimental evidence suggests participate in a common pathway . To infer a network , we use an extension of the Nested Effect Model ( NEM ) introduced by Markowetz et al . ( 2005 ) [10] . The set of silenced genes are denoted as the set S ( or S-genes ) . An NEM is a probabilistic formulation that measures how well a directed graph of the S-genes is consistent with expression changes collected under the separate silencing of each S-gene ( i . e . only single knock-downs are considered in NEM ) . While the method can make use of either complete deletion mutants or genes that may be partially silenced , here we use the term knock-down to refer to either case . We denote the knock-down of S-gene A as ΔA . We also refer to a set of effect genes as the set E ( or E-genes ) , for which gene expression data is available . The expression of an E-gene e is assumed to be influenced by at most one S-gene . The key assumption of NEMs is the expression changes observed under ΔA are an approximate superset of the changes observed under ΔB if gene A acts upstream of gene B in a pathway . We use the shorthand A>B to represent this generic directed interaction . In addition to identifying A>B , the E-gene expression changes on the microarray can be used to infer the “sign” of the interaction , either activating or inhibiting . In our framework , we extend the interactions so that an upstream gene can have either an inhibitory or stimulatory effect on downstream genes . Figure 1A presents an example , similar to Fröhlich et al . ( 2008 ) [18] that motivates the use of signed interactions . E-genes E1 through E13 are listed from top to bottom according to where they are attached to the network . Depending on the connections of the S-genes to one another and to the E-genes , a disruption in an S-gene will cause E-genes to either increase or decrease in expression relative to wild-type . For example , E-gene E7 decreases under ΔB relative to wild-type because the wild-type activation by B is absent in the deletion . On the other hand , the expression of E10 also decreases under ΔB relative to wild-type but as a result of a different mechanism . In wild-type , E10 is expressed at a baseline level because its repressor , the product of gene D , is inhibited by B's product . However , in the B deletion , D is derepressed , leading to inhibition of E10 . This toy example illustrates that the disambiguation of inhibition and activation , both for S-gene interactions and E-gene attachments , make it possible to account for an expanded set of mechanisms leading to the observed expression changes . The E-gene expression changes are available in a data matrix X where each column gives the difference in expression of each E-gene under the deletion of a single S-gene relative to wild-type . X may also contain replicates in the form of repeated S-gene knock-downs . The entry XeAr represents e's expression change under the rth replicate of ΔA . Furthermore , we assume that an unknown expression “state” for each E-gene under each knock-down , determines its set of expression changes observed across the {XeAr} replicates in the microarray data . The matrix , Y , records a hidden state for each E-gene under each knock-down , where entry YeA is the state of E-gene e under ΔA . We allow the states to be ternary-valued {+1 , −1 , 0} representing whether e is up-regulated , down-regulated , or unchanged under ΔA relative to wild-type respectively . Nested effects models include two sets of parameters . The parameter set Φ records all pair-wise interactions among the S-genes and the parameter set Θ describes how each E-gene is attached to the network of S-genes . In the original NEM formulations [10] , [15] , [18] Φ is a binary matrix with entry φAB set to one if S-gene A acts above S-gene B and zero otherwise . If φAB = φBA = 1 then the S-genes are assumed to operate at an equivalent position in the pathway . Note that indirect interactions are also represented in Φ so that if φAB = 1 and φBC = 1 it implies φAC = 1 . A parsimonious network among the S-genes is solved for by computing the transitive reduction of Φ . To allow for both stimulatory and inhibitory interactions in our formulation , φAB can assume six possible values for each unique unordered S-gene pair {A , B} . We refer to these values as interaction modes . The possible values are: ( i ) A activates B , A→B; ( ii ) A inhibits B , A⊣B; ( iii ) A is equivalent to B , A = B; ( iv ) A does not interact with B , A≠B; ( v ) B activates A , B→A; and ( vi ) B inhibits A , B⊣A . Plotting the response of E-genes under ΔA and ΔB yields a scatter-plot that may provide a signature for the type of interaction between A and B . For example , Figure 1B shows a scatter-plot of gene expression changes from the Hughes et al . ( 2000 ) yeast knock-out compendium [17] for a pair of knock-outs of the well-known pheromone-response genes: ΔSTE12 and the ΔDIG1/DIG2 double knock-out . Comparing the scatter-plot for these pheromone-response genes to the patterns in Figure 1C , it can be seen to match the inhibitory interaction mode more closely than the other modes , which is consistent with DIG1/DIG2's known inhibition of STE12 . Figure 1C shows an example of the first four modes . Shaded regions denote consistent E-gene responses for each mode . An interaction mode determines a constraint on the observed E-gene expression changes . For example , plotting the expression changes of E-genes that act downstream of either A or B for the generic A>B interaction mode produces points in one of the seven shaded regions shown in Figure 1Cv . Figure 1Cii shows an example where the inhibitory interaction mode is the best match to the data because a higher number of E-gene changes fall within consistent regions ( filled circles in the figure ) . In this manner , genomewide expression changes detected on the microarrays can be used as quantitative phenotypes to identify a variety of interactions between pairs of S-genes . Note that two genes are equivalent if their knock-downs lead to significantly similar expression changes , which may predict , for example , that they form a complex . Figure 1C also illustrates the generic interaction mode A>B used in an unsigned version of our method . We compare FG-NEM results to two unsigned variants to estimate the change in predictive power as a function of the introduction of sign . In effect , both variants consider four interaction modes: ( i ) A>B; ( ii ) B>A; ( iii ) A≠B; and A = B . For comparison purposes , a predicted unsigned interaction was treated as activation . In the FG-NEM AVT variant , FG-NEM is run on the absolute value of the data . In the uFG-NEM method , we remove the component of FG-NEM which models induced expression , resulting in interaction modes where the top and right five regions are disallowed in all interaction modes . Our goal is to find a structure among the S-genes that provides a compact description of X . To find a network that best “fits” the data , we take a maximum a posteriori approach as in [15] , [18] jointly identify Φ and Θ that maximize the posterior: ( 1 ) ( 2 ) where we introduce the hidden E-gene states by summing over all possible configurations of the Y matrix . Applying Bayes' Rule and dropping P ( X ) , which is constant with respect to the maximization , we obtain: ( 3 ) ( 4 ) The approximation in the last step uses the assumption that any E-gene attachments are equally likely given a network structure; i . e . P ( Θ|Φ ) is assumed to be uniformly distributed and is ignored in our approach . P ( Φ ) represents a prior over S-gene networks . As in previous NEM formulations , we assume that each E-gene is attached to a single S-gene and that each E-gene observation vector across the knock-downs is independent of other E-gene observations . The maximization function can then be written: ( 5 ) ( 6 ) ( 7 ) where Xe and Ye are the row vectors of data and hidden states for E-gene e respectively , and θe records the attachment point information for E-gene e . After rearranging the products and sums , we introduce the shorthand Le to represent the likelihood of the data restricted only to E-gene e . Previous approaches decompose Le over the knock-downs , which assume the S-gene observations are independent given the network and attachments ( see [18] for an example of such a derivation ) . To facilitate scoring the expanded set of interaction modes mentioned earlier , we replace Le with a function proportional to Le , Le′ . Le′ is defined as a product of pair-wise S-gene terms: ( 8 ) where θeAB represents the attachment of E-gene e relative to the pair of S-genes A and B . Note that both θeAB and φAB are indexed by the unordered pair , {A , B} , so that φAB and φBA are references for the same variable . We refer to θeAB as e's local attachment which can take on five possible values from the set {A , −A , B , −B , 0} representing that e is either up- or down-regulated by A , attached and either up- or down-regulated by B , or not affected by either S-gene . φAB defines the mode of interaction between S-genes A and B . Assuming the replicates are independent given the E-gene states , P ( XeA | YeA ) can be written as a product over replicate terms: , where P ( XeAr | YeA ) is modeled with a Gaussian distribution having mean and standard deviation σ estimated from the data ( see Text S1 ) . Substituting Le′ for Le into Eq . ( 7 ) and distributing the maximization over attachment points , we obtain the maximizing function used in our approach: ( 9 ) The interaction factors P ( YeA , YeB | φAB , θeAB ) have a value of one if the E-gene e is attached to either A or B and e's state is consistent with the interaction mode between A and B . If e's state is inconsistent with the interaction and attachment , then the factor has value zero . While we used hard constraints to model consistent and inconsistent expression changes ( corresponding to the rigid boundaries of the regions drawn in Figure 1C ) , such constraints could be softened to use factors with belief potentials between zero and one . Note that , to simplify the example , the interaction modes in Figure 1C show defined regions . However , P ( XeA | YeA ) is modeled as a Gaussian distribution and therefore assigns non-zero probabilities over all possible expression values rather than classifying some as allowed and others disallowed ( i . e . probability zero ) . The prior over interactions , P ( Φ ) , can represent preferences over specific interactions in the S-gene graph , allowing the incorporation of biologically-motivated constraints to guide network search . For example , the interaction priors for genes in a common pathway or genes whose products have been detected to interact in protein-protein interaction screens could be set higher than the priors for arbitrary pairs of S-genes . In this study , we chose to test the approach both with and without external biological information . Without external biological information , the prior encodes a basic property of the S-gene graph: that it should exhibit transitivity to force pair-wise interaction modes to be consistent among all triples . Using transitivity , all paths between any two genes , A and B , are guaranteed to have the same overall effect; i . e . the product of the signs of individual links along different paths between A and B are equal . In order to preserve the transitivity of identified interaction modes , the prior is decomposed over interaction configurations into transitivity constraints on all triples of S-genes; i . e . : ( 10 ) where τ is zero if the triple of interactions are intransitive , and one if the interactions are transitive ( see Text S1 for full definition ) . Using transitivity constraints forces the search to find consistent models that best explain the observed changes . The transitivity constraint includes both the direction of interactions and the sign of interactions . As S-gene interactions are signed , the transitivity constraint forces the sign of the product of two edges to equal the sign of the third; e . g . if A⊣B and B⊣C , then A→C . A result of modeling transitivity is that a directed cycle of stimulatory interactions will also imply activation between any pair of S-genes in the cycle , in both directions . Therefore , the method clusters such S-genes into equivalence interactions . The product over ρ factors in Eq . ( 10 ) encode evidence from high-throughput assays , such as protein-protein binding and protein-DNA binding interactions ( see “Physical Structure Priors” in Text S1 ) . While network structures are constrained to reflect more intuitive models , the decomposition introduces interdependencies among the interactions , adding complexity to the search for high-scoring networks . Importantly , max-sum message passing in a factor graph [19] provides an efficient means for estimating highly probable S-gene configurations . We next describe how the problem is recoded into message-passing on a factor graph . The formulation above provides a definition of the objective function to be maximized but says nothing about how to search for a good network . The search space of networks is very large making exhaustive search [10] intractable for networks larger than five S-genes . To apply the method to larger networks , we require a fast , heuristic approach . Markowetz et al . ( 2007 ) introduced a bottom-up technique to infer an S-gene graph . They identify sub-graphs of S-genes ( pairs and triples ) and then merge the sub-graphs together into a final parsimonious graph . Fröhlich et al . ( 2008 ) [18] use hierarchical clustering to first identify modules , subsets of S-genes with correlated expression changes . Networks among the modules are exhaustively searched and a final network is identified by greedily introducing interactions across modules that increase the likelihood . Here , we introduce the use of a graphical model called a factor graph to represent all possible NEM structures simultaneously . The parameters that determine the S-gene interactions , Φ , are explicitly represented as variables in the factor graph . Identifying a high-scoring S-gene network is therefore converted to the task of identifying likely assignments of the Φ variables in the factor graph . A factor graph is a probabilistic graphical model whose likelihood function can be factorized into smaller terms ( factors ) representing local constraints or valuations on a set of random variables . Other graphical models , such as Bayesian networks and Markov random fields , have straightforward factor graph analogs . A factor graph can be represented as an undirected , bi-partite graph with two types of nodes: variables and factors . A variable is adjacent to a factor if the variable appears as an argument of the factor . Factor graphs generalize probability mass functions as the joint likelihood function requires no normalization and the factors need not be conditional probabilities . Each factor encodes a local constraint pertaining to a few variables . Figure 2 shows the factor graph representing the NEM for the example S-gene network from Figure 1A . Each random variable is represented by a circle and each conditional probability term in Eqs . ( 9–10 ) is represented by a square . The factor graph contains three types of variables . First , every unique unordered pair of S-genes {A , B} has a corresponding variable , φAB , that takes on values equal to one of the previously mentioned interaction modes ( Figure 2 , “S-Gene Interactions” level ) . Second , every E-gene-S-gene pair is associated with a variable , YeA for the hidden expression state of effect gene e under knock-down A , ( Figure 2 , “E-gene Expression State” level ) . Third , every observed expression value is associated with a continuous variable , XeAr , where r indexes over replications of ΔA ( Figure 2 , “E-gene Expression Observation” level ) . Figure 2 also shows the expression factors , interaction factors , and transitivity factors of Eqs . ( 9–10 ) . To validate the involvement of predicted invasiveness frontier genes , HT29 colon cancer cells were resuspended in DMEM medium containing 0 . 1% FBS and seeded into the top wells ( 2×105 per well ) containing individual Matrigel inserts ( BD Biosciences , San Jose , CA ) according to manufacturer's protocol . The lower wells were filled with 800 µl medium with 10% fetal bovine serum as chemoattractant . Six to ten hours following seeding , the cells in the upper wells were transfected with the appropriate shRNA-expressing pSuper constructs [25] using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) . Final concentration of pSuper constructs was 1 . 6 µg/ml . The transfected cells were incubated at 37°C for 48 hours before assaying for invasion . Media was aspirated from the top wells and non-invading cells were scraped from the upper side of the inserts with a cotton swab and invading cells on the lower side were fixed and stained using DiffQuick ( IMEB , Inc . San Marcos , CA ) . Total number of invading cells was counted for each insert using a light microscope . Invasion was assessed in quadruplicate and independently repeated at least five times . The shRNA-expressing portion of the construct was designed using the siRNA Selection Program of the Whitehead Institute for Biomedical Research ( http://jura . wi . mit . edu/bioc/siRNAext/ ) , synthesized by Invitrogen and subcloned into the XhoI and BamHI sites of pSuper plasmid . Sequences for shRNA constructs are available in the Text S1 . shRNA construct MYO1G targets the myosin 1G mRNA ( GenBank accession number NM _033054 ) . shRNA construct BMPR1A targets the bone morphogenetic protein receptor , type IA mRNA ( NM_004329 ) . shRNA construct COLEC12 targets the collectin sub-family member 12 mRNA ( NM_130386 ) . shRNA construct AA099748 targets an expressed sequence tag mRNA ( AA099748 ) . shRNA construct CAPN12 targets the calpain 12 mRNA ( NM_144691 ) . shRNA construct scrambled serves as a nonsense sequence negative control .
We hypothesized that an estimate of genetic pathway structure based on modeling observed expression changes could facilitate the identification of new pathway members . To test this , we evaluated the ability of FG-NEMs , uFG-NEMs , and TM to identify genes involved in a diverse set of pathways in S . cerevisae using the well-studied gene expression dataset from the Hughes et al . ( 2000 ) knock-out compendium elucidated by Rosetta [17] . This compendium contains whole-genome expression profiles of 276 yeast gene-deletion mutants and P values for differential gene expression . We applied the FG-NEM approach to a human colon cancer invasiveness network elucidated by Irby et al . ( 2005 ) [26] . In this work , the authors identified several “tiers” of genes implicated in the invasion process under the control of SRC kinase . Genes were included in a tier if their knock-downs were found to produce a significant drop in the invasive potential of HT29 colon cancer cells as defined by invasion through Matrigel . To identify additional genes involved in the invasion process , the authors measured gene expression under an RNA interference knock-down of each gene in the tier . Genes whose expression was lower in the knock-downs producing loss-of-invasiveness , and higher in knock-downs that did not produce loss-of-invasiveness , were considered candidates for inclusion in the next tier . In this fashion , each tier was formed by knocking-down each candidate gene and assaying for loss-of-invasion in Matrigel .
We applied FG-NEMs to discover a human signaling network among genes involved in colon cancer cell invasiveness . The method formalizes and extends analysis of genetic interactions using high-dimensional quantitative phenotype data in the form of gene expression changes observed under specific perturbations . It makes explicit use of the knock-downs of known members of a pathway to identify how the members interact with one another and for identifying new members . The method predicts several genes with new roles in the cancer invasiveness process , two of which were verified to act in the pathway based on an ex vivo invasion assay . Thus , the FG-NEM approach may be a powerful tool for inferring regulatory connections and for identifying new partners of genes known to operate in a process of interest . The application of structured causal models for pathway identification and expansion promises to greatly accelerate the discovery of genetic pathways from genetic knock-downs and other intervention-based experiments . | Biological processes are the result of the actions and interactions of many genes and the proteins that they encode . Our knowledge of interactions for many biological processes is limited , especially for cancer where genomic alterations may create entirely novel pathways not present in normal tissue . Perturbing gene expression ( for example , by deleting a gene ) has long been used as a tool in molecular biology to elucidate interactions but is very expensive and labor intensive . The search for new genes that may participate can be a daunting “fishing expedition . ” We have devised a tool that automatically infers interactions using high-throughput gene expression data . When a gene is silenced , it causes other genes to be switched on or off , which provide clues about the pathway ( s ) in which the gene acts . Our method uses the genomewide on/off states as a fingerprint to detect interactions among a set of silenced genes . We were able to elucidate a network of interactions for several genes implicated in metastatic colon cancer . Genes newly connected to the network were found to operate in cancer cell invasion in human cells , validating the approach . Thus , the method enables an efficient discovery of the networks that underlie biological processes such as carcinogenesis . |
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West Nile Virus ( WNV ) , an emerging and re-emerging RNA virus , is the leading source of arboviral encephalitic morbidity and mortality in the United States . WNV infections are acutely controlled by innate immunity in peripheral tissues outside of the central nervous system ( CNS ) but WNV can evade the actions of interferon ( IFN ) to facilitate CNS invasion , causing encephalitis , encephalomyelitis , and death . Recent studies indicate that STimulator of INterferon Gene ( STING ) , canonically known for initiating a type I IFN production and innate immune response to cytosolic DNA , is required for host defense against neurotropic RNA viruses . We evaluated the role of STING in host defense to control WNV infection and pathology in a murine model of infection . When challenged with WNV , STING knock out ( -/- ) mice displayed increased morbidity and mortality compared to wild type ( WT ) mice . Virologic analysis and assessment of STING activation revealed that STING signaling was not required for control of WNV in the spleen nor was WNV sufficient to mediate canonical STING activation in vitro . However , STING-/- mice exhibited a clear trend of increased viral load and virus dissemination in the CNS . We found that STING-/- mice exhibited increased and prolonged neurological signs compared to WT mice . Pathological examination revealed increased lesions , mononuclear cellular infiltration and neuronal death in the CNS of STING-/- mice , with sustained pathology after viral clearance . We found that STING was required in bone marrow derived macrophages for early control of WNV replication and innate immune activation . In vivo , STING-/- mice developed an aberrant T cell response in both the spleen and brain during WNV infection that linked with increased and sustained CNS pathology compared to WT mice . Our findings demonstrate that STING plays a critical role in immune programming for the control of neurotropic WNV infection and CNS disease .
Encephalitic Flavivirus infections , including West Nile virus ( WNV ) , are ongoing or emerging threats to global health [1–4] . In particular , WNV continues to re-emerge in the Americas , causing neuropathology and death in the most severe cases [3 , 5–7] . Since its emergence in the USA in 1999 , annual outbreaks of WNV are impacted with fluctuations in neurovirulence attributed to the circulating strain [4–6 , 8 , 9] . Morbidity and mortality are dramatically increased in years where the circulating strain has enhanced neurovirulence , highlighting the significance of understanding host-pathogen interactions that control neurotropism [5 , 10] . An analysis of CDC reports reveals that of all cases reported between 1999–2014 , 9% of neurovirulent cases result in death , in contrast to 0 . 5% of non-neurovirulent WNV cases . Factors that limit WNV neurovirulence are not well understood but are critical to restrict pathology associated with WNV infections [5] . WNV infection in humans most commonly manifests as an asymptomatic or mild febrile illness known as West Nile Fever ( WNF ) with symptoms that include headache , generalized weakness , rash , fever or myalgia , and in some cases vomiting , diarrhea , joint or eye pain [3 , 5–7 , 11–13] . While most patients displaying WNF generally display symptoms for days to weeks , in some cases persistent symptoms continue to impact quality of life and cognitive abilities rendering a chronic disease outcome to WNV infection [11] . More serious disease occurs if the virus crosses the blood brain barrier and progress to West Nile Neuroinvasive Disease ( WNND ) [7] . WNND disease symptoms include meningitis , encephalitis , myelitis marked with acute flaccid paralysis , gastric complications , tremors and Parkinson-like symptoms [7 , 11 , 14–18] . Patients with WNND can maintain symptoms for weeks to months , with persistent symptoms including chronic fatigue , functional cognitive disorders or neuropsychiatric disabilities and physiological complications , particularly those who exhibited acute flaccid paralysis symptoms during acute infection [7 , 11 , 18] . Currently no therapeutics or vaccines are available for treatment of WNV infection or neuropathogenesis . Thus , there remains a critical need to understand the virus-host interactions of WNV neurovirulence . Both the innate and adaptive immune response are required to clear WNV infection and restrict immune mediated pathology [19] . In humans , infection with WNV typically occurs through subcutaneous inoculation from the bite of an infected mosquito . A parallel form of infection using sub-cutaneous challenge of WNV in a mouse model has been shown to replicate the progression , tissue involvement , and pathology of WNV infection that occurs in humans [19–22] . In the mouse model , viral replication occurs at the subcutaneous site of entry followed by infection of the draining lymph node and splenic infection [19] . These processes first trigger innate immune activation in peripheral tissues outside of the central nervous system ( CNS ) through viral recognition by the RIG-I-like receptors to induce IRF3 activation and the production of types I and III interferon ( IFN ) [23–26] . Innate ( RLR ) immune defenses triggered by RLR signaling and IFN actions serve to restrict the tissue tropism of WNV and are essential for protection against neuroinvasion [19 , 23 , 24 , 27–34] . Type I and III IFN are essential to inform the innate and adaptive immune interface to balance development of effective immunity , protect the blood-brain barrier , and limit immune-related pathology in the CNS [19 , 23 , 24 , 35–39] . In particular , type I IFN-dependent cytokine and chemokine signaling cascades are essential for functional development of the cytotoxic CD8+ T cell response , as well as its regulatory T cell ( Tregs; FoxP3+ CD4+ T cells ) counterpart [24 , 36 , 37 , 39–42] . While CD8+ T cells are required for controlling both peripheral and CNS viral load , CD4+ T cells , specifically Tregs , are essential for preventing symptomatic disease in the CNS [40–43] . The adaptor protein , Stimulator of Interferon Genes ( STING ) , has also been implicated in host defense against WNV [44–46] . STING was first described as an essential defense mechanism against both RNA and DNA viruses [47 , 48] . Since then , STING has been recognized for its role in responding to cytoplasmic DNA and mediating subsequent innate immune activation and IFN production . However its role in the defense against RNA viruses is poorly understood [47–54] . Intriguingly , multiple RNA viruses , including dengue virus , yellow fever virus , hepatitis C virus and coronaviruses , direct viral evasion strategies to disrupt the STING signaling pathway , reflecting a likely role for STING in host defense against RNA viruses [52] . STING was found to be required for host defense during infection with influenza A virus , as well as dengue virus , a closely related flavivirus to WNV [55–57] . Additionally , during infection with related flavivuses including Japanese encephalitis virus ( JEV ) and Zika virus , STING deficiency led to increased neuropathology in vivo and in vitro , suggesting a critical role for STING in CNS defense [58 , 59] . The role for STING in the CNS has been implicated in multiple other neurodegenerative diseases including Aicardi-Goutières syndrome , sterile immune mediated CNS pathology and during chronic CNS diseases [14 , 16 , 60–66] . In this study , we investigated the hypothesis that STING plays a regulatory role in the immune response against WNV , thereby restricting viral neurotropism and neuropathology . We show that STING is essential for host defense against WNV in a mouse in vivo model of infection . Clinical and pathological analyses demonstrate a novel role for STING in conferring CNS defense against WNV in vivo . We found that tonic levels of type I IFN were decreased in STING-/- bone marrow derived macrophages ( BMDM ) and linked with increased susceptibility to WNV infection . Following infection , we observed heightened immune responses in vitro and in vivo concomitant with increased viral load . STING deficiency led to the development of an aberrant adaptive immune response , with decreased activation of CD8+ cells and T regulatory cells ( Tregs ) in the spleen , and decreased CD4+ T cell numbers resulting in an altered CD4/CD8 T cell ratio in the CNS coupled with CNS disease . Our observations imply an essential role for STING within the interface between the innate and adaptive immune responses for effective immune programming in the control of WNV infection and CNS disease .
Previous studies demonstrated that mice defective in STING signaling experienced increased mortality during WNV infection , yet the linkage of STING to immune response programming for defense against WNV has not been defined [46] . Using genetically knocked-out Tmem173 ( STING-/- ) mice [67] , we first performed a survival analysis to confirm the role of STING in host survival during WNV infection ( Fig 1A ) . C57B/6J ( B6 , WT ) and STING-/- mice were infected through subcutaneous virus challenge via foot-pad injection and monitored for 18 days post infection ( dpi ) . Mice were scored daily for morbidity , marked as loss in body weight ( Fig 1B ) and overall increased clinical score ( Fig 1C ) . Consistently , between 8–12 dpi , mice either met euthanasia criteria ( Terminal; T ) or went on to survive ( Survivors; S ) through 18 dpi ( study end-point ) ( Fig 1D ) . Using this model , we confirmed the occurrence of increased susceptibility to WNV infection in the complete absence of STING ( Figs 1A and S1A ) , similar to what was previously described in STINGgt/gt mice [46] . We also observed significantly increased clinical severity scores in the STING-/- mice that persisted until the study-endpoint , when WT mice had returned to a base-line clinical score ( Fig 1B and 1C ) . Additionally , we monitored mice daily for the duration of the experiment until they either met euthanasia criteria or at the study end-point , day 18 post infection . Results from each mouse were analyzed to determine if there were differences in clinical signs between WT and STING-/- mice . Notably , STING-/- mice displayed increased neurological signs of disease , characterized by loss of balance , reduced muscle tone and reflexes predominantly in the pelvic limbs and increased paresis and paralysis , implicating more severe damage to the hind-brain and spinal cord ( Fig 1E and 1F ) . In order to determine if there was a survivor bias in the clinical data , we retrospectively stratified the data into cohorts of mice that met euthanasia criteria ( Terminal; T ) or ones that survived until day 18 post-infection ( Survivors; S ) , the pre-determined study end-point ( Fig 1D , 1G and 1H ) . By doing so , we found that significant differences in body weight loss and clinical scores between WT and STING-/- mice were only observed in the Survivor cohort and not in the Terminal cohort . While there is an essential role for STING in host survival during acute infection ( Figs 1A and S1A ) , these data implicate an additional prolonged requirement for STING in both prevention and recovery from neurological pathology . When we examined CNS pathology , we found that in both WT and STING-/- mice , pathological scores were significantly increased in the spines of the Survivor cohort , with a trend toward increased scores in the brains and spines of the Terminal cohort ( Fig 1D , 1I and 1J ) . Intriguingly , while STING-/- Terminal mice displayed increased CNS pathology , WT mice that met Terminal criteria had unexpectedly low clinical scores , suggesting that they met euthanasia criteria for reasons independent of severe encephalitis . During necropsy , we observed that the gastro-intestinal ( GI ) tract of Terminal mice exhibited gross distension or other aberrant phenotypes including stool compaction , disintegration and in some cases severe reduction in size or collapse of the GI tract ( S1B Fig ) . Pathologic analysis confirmed that Terminal mice display increased GI pathology that included microbiome overgrowth and neuronal degeneration and loss in the myenteric ganglia , particularly in STING-/- ( S1C and S1D Fig ) . Previous studies have indicated that GI manifestations during WNV infections exist in both mice and humans , and are positively correlated to increased neurotropism and mortality [15–17 , 22] . This outcome may imply that WT mice are meeting euthanasia criteria following WNV infection due to severe GI disease rather than severe CNS involvement as previously thought . Further , these results demonstrate that STING plays a systemic role in host defense against WNV , with increased frequency of mortality and pathology occurring in the CNS and GI tract in STING-/ mice . Together , these results show an essential role for STING in host survival and neuropathological defense in the CNS during WNV infection . To determine if STING is required for viral control in the CNS , we challenged mice with WNV via footpad injection and examined tissue viral load at 4 dpi ( peak of peripheral viremia ) and 8 dpi ( peak of detectible virus in the CNS ) ( Fig 2A ) . Viral titer of macrodissected brains and extracted spinal cords were examined by plaque assay individually for each mouse in the cohort ( Fig 2A ) . As expected , virus was not detected at 4 dpi in the CNS but by 8 dpi virus was clearly detected in different CNS regions . Virus was not consistently found in the CNS of all mice nor in every tissue examined . There was however , a consistent trend toward increased numbers of infected mice with detectible virus in the CNS as well as increased viral titers in the CNS of STING-/- mice compared to WT . To determine if there was detectible virus in the brains of Terminal vs Survivor mice , tissues from retrospectively sorted mice utilized for pathological analysis ( Fig 1I and 1J ) were immunostained for the presence of WNV antigen ( Fig 2B ) . WNV foci were found in the brains of WT and STING-/- Terminal mice but were not apparent in WT or STING-/- Survivors , suggesting that either the virus had cleared or that surviving mice did not have CNS infection . Neuronal death was assessed by TUNEL stain in both WT and STING-/ Survivors . Here we observed enhanced neuronal apoptotic death in the STING-/- cohort , suggesting STING may have a direct or indirect role in neuronal defense in the CNS ( Fig 2C ) . In order to determine if STING is required for neuronal defense against WNV , primary cortical neurons were isolated and cultured , followed by infection with WNV to determine viral growth kinetics under conditions of single and multi-step growth ( Fig 2D ) . Surprisingly , no difference was detected between in WNV replication in WT and STING-/- primary cortical neurons ( Fig 2D ) . To determine if the actions of STING might be restricted to the CNS for WNV protection , we performed an intracranial virus inoculation bypassing the role of the peripheral immune response and physical barriers such as the blood-brain barrier to directly infect the brain with WNV ( Fig 2E ) . At 4 dpi , there was no difference in CNS viral load found in WT vs STING-/- mice nor was viral load different between STING-/- and WT mice . Taken together , our observations imply that the role of STING is not limited to mediating viral control in the CNS . It is possible that STING is therefore required in the development of a protective immune response in the periphery such that in the absence of STING the immune response is aberrantly programmed , leading to CNS immunopathology . Given that STING deficiency was associated with enhanced mortality ( see Fig 1 ) without a significant increase in CNS viral burden ( Fig 2 ) , we considered that STING deficiency could result in defective antiviral innate immune signaling and lead to loss of viral control in the periphery , thereby leading to enhanced morbidity and mortality . We first tested the role of STING in BMDMs , as macrophages are a tropic cell and key modulator of peripheral viral control during WNV infection ( Fig 3A ) [19] . As expected , WNV levels were significantly increased by 24 and 48 hours post inoculation ( hpi ) . Unexpectedly however , STING-/- BMDM had increased innate immune and inflammatory gene expression , including enhanced level of type I IFN expression during WNV infection ( Fig 3B ) . We then examined the spleens of infected mice to determine if there was an overall loss of viral control manifested as increased viral load over WT . As expected , virus was detected at 4 dpi in both WT and STING-/- . Surprisingly however , there was no difference in 4 dpi viral titers between WT and STING-/- , nor was there a sustained virologic response in STING-/- mice ( Fig 3C ) . These data indicate that peripheral loss of viral control does not occur in the absence of STING ( Fig 3C ) . Similarly , viral RNA was detected equally in spleens of infected WT and STING-/- mice at 4 dpi , but the virus was largely cleared from the spleen by 8 dpi ( Fig 3D ) . In the CNS however , we observed a trend toward increased viral RNA and innate immune gene expression at 8 dpi in WNV-infected STING-/- mice , similar to that observed in BMDM ( Fig 3A and 3D ) . These data were unexpected as we initially predicted that STING deficiency would reduce innate immune activation based on the known role of STING signaling in IFN induction . These data demonstrate that innate immune activation and the inflammatory response are exacerbated in both in vitro and in vivo STING deficient models , possibly culminating in enhanced immunopathology in STING-/- mice . The canonical STING sensing pathway is dependent on upstream recognition of DNA danger- or pathogen-associated molecular patterns ( DAMP , PAMP ) such as DNA viruses , cell-free or mitochondrial DNA , by cyclic GMP-AMP synthase ( cGAS ) . In mammals , cGAS binding to dsDNA activates its synthase activity to produce a cyclic di-nucleotide , cGAMP ( cyclic guanosine monophosphate-adenosine monophosphate ) , which binds to STING , initiating downstream activation of STING by phosphorylation , STING relocalization from diffuse cytosolic to punctate pattern , and subsequent induction of innate immune signaling and IFN production [47 , 48 , 53 , 68 , 69] . During RNA virus infections however , the role for STING defense has not been well-characterized . To evaluate the activation of STING during WNV infection , we utilized a recently described telomerase reverse transcriptase human foreskin fibroblasts ( HFF ) model to assess activation of endogenous STING by phosphorylation and relocalization from the cytoplasm to the perinuclear space during WNV infection [70] . Transfection of interferon-stimulated DNA ( ISD; calf-thymus DNA ) into HFFs initiated re-localization of STING as previously reported by 3hpi [48 , 70] . Intriguingly however , STING was not relocalized in WNV infected cells ( Fig 4A ) . It is possible that the kinetics of STING activation are different from ISD activation of STING as compared to WNV infection , so we performed a time course experiment to detect STING activation by phosphorylation status [71] , assessing a range of 1–24 hpi at MOI = 1 ( Fig 4B ) . Similar to what was observed by IFA , STING phosphorylation was not observed at any time point during WNV infection , although phosphorylated STAT1 and WNV protein was detected at 24 hpi , suggesting virus replication and innate immune signaling were occurring normally ( Fig 4B ) . To determine if activation was dependent on viral load , we infected HFF with a MOI = 1 and MOI = 10 of WNV , but also observed no STING activation as measured by phosphorylation ( Fig 4C ) . These data suggest that STING is not canonically activated during WNV infection in HFF cultures and reveals a potential non-canonical role for STING in host defense during infection with WNV . In order to determine if there was a systemic change in the innate immune profile in STING-/- mice , we examined the cytokine and chemokine profile in the serum of WT and STING-/- mice at the peak of peripheral viremia ( 4 dpi ) and CNS viral burden ( 8 dpi ) . We found that mock infected STING-/- mice had an increased basal production of multiple cytokines and chemokines at 4 dpi . We also observed significant increases in IL33 , IL4 , IL6 , IL15 , MCSF , Gro-alpha , while at 4 dpi IP-10 ( CXCL10 ) was decreased in STING-/- compared to WT mice ( S2 Fig ) . While these cytokines have multiple roles in immune modulation , a common role among them is in activation and recruitment of T cells . These data suggest that STING is required for regulation of immune cytokine and chemokines that program immune cell trafficking and actions during WNV , as has been shown for STING in cancer immunity and autoimmune signaling [53] . To determine if STING is required for proper programming of the T cell response during WNV infection , we examined splenic T cells from WT and STING-/- mice at 8 dpi , a time point when the adaptive immune response is established in WT mice [24] . We observed a reduction in the frequency of CD8+ T cells , along with a trend toward decreased numbers of T cells in the spleens of STING-/- mice compared to WT during WNV infection ( Fig 5B ) . Additionally , within the CD8+ T cell subset ( Fig 5C ) , there was a significant decrease in frequency of activated ( CD44+ ) and CXCR3+ T cells , and we observed a consistent trend of decrease in the frequency of WNV-specific CD8+ T cells in the spleens of STING-/- mice compared to WT , suggesting that STING is required for optimal anti-WNV CD8+ T cell responses . We also observed a significant increase in the frequency of CD4+ T cells in STING-/- mice ( Fig 5B ) , with a corresponding trend toward increased absolute cell numbers . While we observed a trend toward differences in the absolute number of most cell populations examined between WT and STING-/- mice , we found that significant differences most typically occurred in cell frequencies , suggesting that the balance of T cells subsets may be skewed in the absence of STING . In particular , we found skewing within the T regulatory cell ( FoxP3+ ) populations ( Fig 5E–5G ) , with significant deficits in Ki67+ , CD44+ and CD73+ Tregs , CD44 and CD73+ Tregs . These data suggest that STING is required for modulating T cell responses and T cell frequencies during WNV infection that lead to a protective rather than pathogenic outcome . Because of the heightened innate immune profile and aberrant programming of the T cell responses in spleens of STING-/- mice , we examined the CNS-specific T cell profile across mouse lines . Histological analyses revealed trends toward increases in CNS immune cellularity , both in the form of perivascular and parenchymal mononuclear infiltrate , suggesting the CNS pathology may be immune-mediated ( Fig 6A ) . We then performed a CD3 IHC stain in the brains of Survivors , we found increased clusters of CD3 infiltrate in the hind and mid-brain regions ( Fig 6B ) co-localized with robust lesions . In serial slices of the same tissues , we did not observe WNV staining by IHC in STING-/- Survivors ( Fig 2 ) , however we did observe continued gliosis , suggesting that a potential immunopathology may occur in the brain of STING-/- mice infected with WNV . Previous studies indicated that cellular infiltrate in the brain is predominantly comprised of CD3+ T cells during WNV infection [72] . Therefore , we characterized T cell responses of WT and STING-/- mice in the CNS on 4 dpi to examine baseline differences at 8 dpi when WNV and leukocytes are both present in the CNS ( Fig 6J ) . Lymphocyte and T cell responses in both mock and WNV-infected mice were comparable at 4 dpi , indicating that there was no gross difference in the CNS between WT and STING-/- mice ( Fig 6C and 6D ) . By 8 dpi however , we found statistically significant decreases in the frequency and numbers of CD4+ T cells in STING -/- mice ( Fig 6F ) . Although there was no difference in the total numbers of CD8+ T cells , there was a statistically significant increase in the frequency of CD8+ T cells in the CNS of STING-/- mice , likely due to overall trend of decreased numbers of lymphocytes in the brain ( Fig 6C–6E ) . By 8 dpi , these changes resulted in a significantly decreased CD4/CD8 ratio of T cells , indicating an imbalanced T cell response to WNV in the CNS of STING-/- mice ( Fig 6I ) . Of cells that made it to the brain by 8 dpi , no differences were found in the absolute number of activated ( CD44+ ) or WNV-specific ( NS4b Tetramer+ ) CD8+ T cells ( Fig 6G and 6H ) , FoxP3+CD25+CD4+ T cells ( Fig 6K ) in the brain . These data suggest that STING is not essential for recruitment of WNV-specific cytotoxic T cells in the CNS , however it may be required for balancing the cytotoxic vs immunosuppressive adaptive response . Furthermore , it is also possible that the enhanced recruitment of cells to the CNS is in response to damage caused by the virus , aberrant immune signaling , or both . This outcome would suggest that STING plays an essential role in modulating the balance between immunopathogenic and immunoprotective response in the CNS during WNV infection . The increase in clinical disease and pathological damage observed in the STING-/- versus WT mice , particularly in Survivors , could be due to an aberrant immune response resulting in CNS damage after initial viral insult . We found that CNS pathology in WT mice is largely restricted to the cortex and meninges , while STING-/- mice display increased pathology in the cerebellum and hind/mid brain regions in addition to the cortex and meninges ( Fig 7A and 7C ) . These data correlate with the increased CD3 staining observed by IHC in STING-/- mice ( Fig 6B ) , also noted as the same brain regions where WNV is often detected by IHC ( Fig 2B ) . These observations suggest that STING plays a role in directing or maintaining the T cell response to specific loci within the CNS or that initial viral infection led to increased recruitment of a localized adaptive immune response that resulted in immunopathology . Furthermore , pathology in the spine was more diffuse , suggesting that STING has a widespread protective role in the CNS during WNV infection ( Fig 7B and 7D ) . These observations led us to investigate if there was a localized polarization of microglia or infiltrating macrophages in CNS regions toward an M1 or M2 phenotype ( Fig 7E ) . Microglia have the highest levels of STING ( Tmem173 ) expression observed in any cell within the adult mouse [73 , 74] and it is possible that in the absence of STING , microglia are aberrantly polarized , enhancing immune-mediated pathology . To examine this possibility , we assessed the expression of M1 ( CXC1 and IL6 ) and M2 ( Pparg , Arg1 , Chil3 and Retnla 1 ) associated genes by RT-qPCR in different regions of the CNS . In WT mice , we found that CXCl1 ( marking an M1 phenotype ) was present in the brain stem by day 8 post infection , and Retnla 1 expression ( marking an M2 phenotype ) occurred in both the mock and 4 dpi tissues within the brain stem and sub-cortex ( containing the thalamus ) regions of the brain ( Fig 7E ) . This profile suggests that CNS homeostasis includes a localized M2 phenotype that is induced to a M1 phenotype in WT mice following WNV infection . In STING-/- mice however , we found a widespread increase in the M1 response gene expression ( marked by CXCL1 and IL6 ) with the highest expression observed in the brain stem and spinal cord . Simultaneously , there was also a corresponding increase in Pparg and Chil3 ( marking the M2 phenotype ) , with no clear difference in Arg1 expression and an overall trend toward decreased expression of Retnla . These observations reveal a widespread increase in both M1 associated genes , with altered regulation of the M2 associated genes in STING-/- mice , potentially resulting in aberrant balance of the M1 and M2 polarization in the CNS . To determine where in the CNS STING is actually localized and if this tissue localization overlaps with the location of the cellular infiltrate noted histopathologically or with expression of innate immune genes , we utilized the Allen Brain Institute database to search for STING ( Tmem173 ) localization in the mouse brain [75] . Within the brain , STING expression is found within the olfactory bulb , thalamus/midbrain , brainstem and cerebellum , as well as low levels throughout the cortex , overlapping areas that are affected most severely by WNV infection ( S3 ) [14 , 75] . These regions of brain affected correlate with the clinical signs we observed including loss of balance , tremors , and loss of motor function ( Figs 1E and 7C–7E ) . Furthermore , these areas of STING expression overlap with the brain regions where altered regulation of M1 or M2 gene expression were most readily observed , implicating a role for STING in polarization of either or both microglia and macrophages in the CNS . Cumulatively , these data suggest that STING has an essential role in maintaining immune response homeostasis and immune programming in initial defense against WNV infection . Without STING , immunopathology occurs , leading to exacerbated CNS disease and clinical sequelae .
Recent years have seen a marked increase in the global health threat presented by emerging and re-emerging encephalitic viruses , particularly those with increased neurotropism and neuropathology such as WNV [1 , 3 , 10 , 76 , 77] . Previous studies indicated an important role for STING in host survival during WNV infection [46] , however it is unclear what role STING plays in conferring host defense against RNA viruses [52 , 54] . Here , we demonstrate that STING is essential to prevent host morbidity and mortality during WNV infection where it plays a role in immune homeostasis and programming . However , STING is not canonically activated in vitro upon infection with WNV , revealing a novel function for STING during infection with RNA viruses . Furthermore , we show that STING is essential for host neuropathological defense against WNV through regulation of the innate-adaptive immune interface in vivo . We found that STING deficient mice exhibit increased mortality and morbidity including increased and sustained neurological clinical signs , particularly in mice that survive infection ( Fig 1 ) . These data were corroborated by pathological analysis , which also revealed distinct differences in CNS pathology . Intriguingly , there seems to be a stratification in clinical and pathological findings between the STING-/- mice that meet euthanasia criteria and those that go on to survive . Survivorship bias has been previously reported in the WNV model , with these data further implicating this bias as a critical factor to consider when performing time course vs . end-point experiments [78] . Unexpectedly , these studies also revealed that there was minimal CNS pathology in WT mice that met euthanasia criteria . It is typically assumed that mice meeting euthanasia criteria do so because of neuroinvasion and subsequent encephalitis . Our data instead indicates that both WT and STING-/- Terminal mice have severe gross GI abnormalities , with corroborating abnormalities by histopathology , which may be the proximate cause of morbundity and meeting euthanasia criteria ( S1 ) . GI complications during WNV have been previously described , however further study is necessary to understand the implications of GI pathology on WNV induced morbidity and mortality [15–17 , 79] . Recently it has been shown that during WNV infection causes delayed GI transit , dependent on infiltrating antiviral CD8+ T cells [80] . Furthermore , both in this model and in a lung model where STING exhibits a gain-of-function mutation , T cell-dependent chronic tissue damage occurs , supporting our findings that STING may play a broad and significant role in communicating between the innate and adaptive immune responses [80 , 81] . Together , these data demonstrate an essential neuroprotective role for STING during WNV infection , potentially through a cellular mediated mechanism instead of the canonical interferon antiviral function typically attributed to STING . WNV typically is cleared through development of an innate immune response and effective T cell immunity [19] . To prevent progression to neuroinvasion , both the innate and adaptive immune response are critical to control WNV viremia and prevent viral induced pathology [19–21 , 24 , 82 , 83] . Because the known function of STING is to initiate a type I IFN response to both PAMPs and DAMPs , we anticipated that the type I IFN response would be diminished both in vivo and in vitro explaining the increased viral loads . Surprisingly , we actually observed an increased inflammatory and antiviral innate immune response in STING-/- mice in the CNS during WNV infection . This same increase in the cytokine-chemokine response was also observed in BMDM ( Fig 3 ) and in serum of infected mice ( Fig 5 ) . These outcomes were highly unexpected as the most commonly described role for STING is known as initiating a type I IFN response [46–48 , 53 , 54] . In particular , STING was shown previously to facilitate the actions of the ELF4 transcription factor to promote type I IFN expression from WNV-infected cells wherein loss of STING associated with reduced IFN and ISG expression ( 49 ) . While we observed significant increases in IFN and ISG expression in BMDM lacking STING , it is likely that STING imparts cell type-specific actions for regulation of innate immune signaling , similar to other pathogen recognition receptors that govern innate immune signaling against WNV , likely explaining this discrepancy between studies [19] . It is also important to note that our studies employed STING-/- mice produced through classical gene targeting approach [48] while the previous study used STINGgt/gt mutant mice produced from N-ethyl-N-Nitrosourea mutagenesis and encoding a T596A point mutation of STING [84] , highlighting that genetic differences between mouse lines might impact findings . Importantly , both mouse lines exhibit increased susceptibility to lethal WNV infection , and together reveal expanded roles for STING in immune regulation during WNV infection . Our data also suggest that STING has a role in controlling WNV replication and tropism , as we found increased viral loads in BMDM , as well as a trend toward increased viral load in the CNS , particularly in the hindbrain regions , but not in the spleens of infected mice lacking STING ( Figs 2 and 3 ) . The trend toward increased virus in the CNS of STING-/- mice could either suggest increased susceptibility of the virus in the CNS , delayed clearance of the virus after entering the CNS , or possibly a combination of the two . Variation observed within strains could be the result of harvesting mice at set time points instead of following them until a determination if they would survive or meet euthanasia criteria , highlighting the potential import of survivorship bias within this model . It does not appear that the requirement for STING in viral control is restricted to neurons or the CNS , as no difference was observed in the viral load of STING-/- primary cortical neurons or intracranial infection ( Fig 2 ) . This outcome suggests that while there is a peripheral requirement for STING in conferring CNS protection , it is not due to complete inhibition of viral control in the periphery . Intriguingly , base-line expression of type I IFN and ISGs were significantly reduced in STING-/- BMDM compared to WT , but not other inflammatory genes ( Fig 3 ) . It is possible that this reduction in baseline IFN allows WNV to establish an earlier and more robust infection , that is later controlled by the RIG-I dependent antiviral response [23 , 34] . However , we favor that STING plays a role in innate immune homeostasis , as in its absence the control of the inflammatory response is lost ( Figs 4 and 7 ) , thus leading to immune-mediated pathology . This function for STING may explain why we had a trend but not significant increase in viral load in the CNS; it is possible that virus is able to establish a stronger infection in the CNS earlier on but is cleared through an exacerbated innate inflammatory and antiviral response in the absence of STING . Alternatively , it is possible that in the absence of STING clearance of the virus takes longer due to an ineffective immune response . Following either of these events subsequent T cell recruitment is likely , but in a manner that leads to enhanced immunopathology and lack of recovery from clinical illness . In addition to its role in mounting a type I IFN response to PAMPs and DAMPs , recent studies demonstrated an essential role for STING in developing antitumor T cell responses [53] . These studies suggested that dead and dying cells are phagocytosed by dendritic cells , which requires STING to present antigen and produce a type I IFN signaling cascade that informs and develops the adaptive immune response . This outcome could also implicate a requirement for STING in microglial-dependent phagocytosis of dead and dying cells , with subsequent STING-dependent polarization and release of soluble factors that effectively recruit and maintain a protective cellular response in the CNS . Upon examining the CNS of infected mice , particularly in STING-/- with ongoing signs , we observed increases in mononuclear cellular infiltrate , implicating possible immunopathology . Previous studies have shown that there is an essential requirement for both CD8+ and CD4+FoxP3+ ( Treg ) T cells to control WNV and prevent immunopathology [42 , 43 , 72] . CD8+ T cells in particular are essential for WNV clearance , however without an adequate Treg response or appropriate balance of CD4+ and CD8+ T cells an uncontrolled cytotoxic T cell response could result in immune mediated pathology . Examining the programming of the adaptive immune response in spleens ( Fig 5 ) we found that expression of Ki67 , CD44 and CD73 in splenic FoxP3+CD4+ Tregs were impaired , implicating a role for STING in the proliferation , activation and suppressive potential of Tregs . Upon examining the brains of mice at baseline ( 4 dpi ) and following infection ( 8 dpi ) , we observed no differences at baseline between WT and STING-/- mice , however the total CD4+ T cells and the CD4/CD8 ratio was significantly decreased in STING-/- mice , suggesting that there is a defective recruitment or maintenance of T cells in the brain ( Fig 6 ) . These data in combination with enhanced CNS pathology suggest that the cytotoxic effect of CD8+ T cells may not be controlled adequately in the absence of STING . It is also possible that increases in cellular response within the CNS recruit an enhanced protective cellular response as a result of viral damage or aberrant immune signaling . Consistent with this either of these options , we found that in STING-/- survivors there were large clusters of CD3+ cells ( Fig 6 ) as well as other cellular infiltrate ( Fig 7 ) in the same vicinity as we observed increased pathology and where STING is localized in the brain ( Fig 7 ) . Recently , a noncanonical STING-dependent signaling pathway was described where multiple cell types initiated an innate immune response following IL1b release in response to mitochondrial DNA release in the cytoplasm [70] . Furthermore , this STING-induced response to IL-1b was essential for the control of dengue virus infection , a flavivirus related to WNV [70] and that this response is linked with protection against WNV neurovirulence in vivo [70 , 83] . Thus , it is intriguing to speculate that noncanonical STING activation in response to proinflammatory cytokine signaling serves to direct immune programming that protects against viral neuroinvasion and CNS pathology during WNV infection . In summary , our study reveals that that STING is required for immune response programming to restrict WNV infection and neuropathogenesis .
All animal experiments were approved by the University of Washington Institutional Animal Care and Use Committee ( IACUC ) guidelines as per protocol #4158–05 and follow the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . Invasive infections and manipulations were performed under anesthesia and every effort was made to limit suffering . C57BL/6J ( WT ) and Tmem173-/- ( STING-/- ) mice were genotyped and bred under specific pathogen-free conditions in the animal facility at the University of Washington . STING-/- mice were gifted by the Stetson lab , who generated them as previously described [67] followed by speed congenics to bring them to a 99 . 4% C57BL/6J background . Additional C57BL/6J ( WT ) mice were purchased from Jackson Laboratories , Bar Harbor , ME . Both male and female mice , ages 8–11 weeks were represented in both the control and infected groups . Mice for primary cortical neurons ( WT and STING-/- ) were set up as timed breeders and embryos were harvested . Mice were monitored daily and assigned a clinical score to describe overall well-being and signs of hind-limb dysfunction ( paresis ) . Clinical scores ( CS ) of ( 0 ) without clinical signs , or ( 1–6 ) dependent on severity of clinical signs presented . CS = 1: ruffled fur , lethargic; no paresis; CS = 2: very mild to mild paresis ( in 1 or more hind limbs with minimal gait disturbance or limb-dysfunction ) ; CS = 3: frank paresis involving at least one hind limb and/or eye conjunctivitis; CS = 4: severe paresis and/or paresis in both hind-limbs; CS = 5: true paralysis; CS = 6: moribund . Additionally , mice were observed daily for the presence or absence of various specific signs . Each mouse was scored as either exhibiting the clinical sign ( YES = 1 ) or not , ( NO = 0 ) . Each sign was monitored through the duration of the experiment and the results were graphed as the average daily score/mouse . Results of clinical signs monitored represent the entire population until they reached euthanasia criteria , at which point the remaining mice continued to be scored until day 18 post infection or study end point . Clinical signs monitored daily include: Lethargy ( L ) , Ruffled fur/decreased of grooming , Hunched , Paresis/Paralysis ( any degree of severity ) , Tremors , Abdominal ( Ab ) distension/GI distress , Loss of Balance , Increased Reflex/Tone in limbs ( fore and/or hind ) and tail , Decreased Reflex/Tone in limbs and tail . The clinical scoring system incorporated signs based off of predicted involvement of different anatomical regions within the CNS and was created using modifications of various previously described scoring systems for experimental autoimmune encephalomyelitis [86–89] . Similar neuroanatomic regions were examined pathologically in an attempt to correlate clinical and neurological phenotype of disease . Subcutaneously-infected mice were monitored for 18 days post infection ( dpi ) . Euthanasia criteria was determined as a clinical score ≥ 5 for 2 or more consecutive days , or 20% loss in body weight . A clinical score of 6 ( moribund ) or respiratory distress resulted in immediate euthanasia . Mice meeting euthanasia criteria were identified as Terminal ( T ) and were euthanized by CO2 asphyxiation followed by cervical dislocation . Mice who did not meet euthanasia criteria were monitored until end point ( 18 dpi ) were identified as Survivors ( S ) . All remaining S mice were euthanized at the end of study ( 18 dpi ) as described above . Mice used for morbidity and mortality analysis were necropsied when meeting euthanasia ( T ) criteria , or study end ( S ) . After euthanasia by CO2 , a complete necropsy was performed and tissues were collected and immersion fixed in 10% neutral buffered formalin [90] . The head was removed and skull cap lifted , leaving the brain within the skull cavity during fixation . The spine was fixed in situ in order to preserve the mesenteric ganglia . Histological preparation hematoxylin and eosin ( H&E ) and immunohistochemical ( IHC ) staining was performed by the UW Histology and Imaging Core ( HIC ) and the Vanderbilt University Medical Center Translational Pathology Shared Resource ( TPSR ) . Primary pathological analysis was performed on the CNS ( brain and spine ) and gastrointestinal ( GI ) tract by a board-certified veterinary pathologist ( PMT ) ( Supplemental methods Table 1 ) . In the brain , the following changes were scored on a subjective 0–4 scale of increasing severity: perivascular inflammation , parenchymal inflammation , hemorrhage , neuronal necrosis , and meningitis . In the spinal cord the presence ( 1 ) or absence ( 0 ) of mononuclear inflammation was documented from 5 different sections of the spine ( C1-C5 , C6-T2 , T3-L3 , L4-S2 , S3 ) for a maximum score of 5 per mouse . For the enteric nervous system ( ENS ) , the degree of mononuclear cells present in the myenteric ganglia , extent to the changes and any secondary GI lesions such as dilation or mucosal change were scores on a on a subjective 0–4 scale of increasing severity . IHC staining of WNV ( VRL W1015 ) and CD3+ T cells ( MCA1477 AbD Serotec ) were performed by the UW Histology Core . VeroWHO ( European Collection of Authenticated Cell Cultures; ECACC ) cells were cultured in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 10% FBS , 1mM sodium pyruvate , 2mM L-glutamine , antibiotic/antimycotic solution and non-essential amino acids ( complete DMEM; cDMEM ) and split using 0 . 25% Trypsin following PBS wash . HFF cells were kindly gifted from Stetson Lab and were grown in cDMEM . Cells were split using 0 . 05% trypsin following PBS wash . Bone marrow was collected from STING-/- and WT mice and frozen in 10% DMSO/90% FBS . To generate bone marrow derived macrophages ( BMDM ) , bone marrow stocks were thawed , washed and resuspended in cDMEM containing [50 μM] BME and [40ng/mL] murine MCSF ( mMCSF ) . Cells were cultured for 7 days in non-TC coated plates , then scraped , washed with PBS and seeded at 1E6 cells/well in 12-well TC coated plates with cDMEM+BME+mMCSF . Cells were infected or transfected the next day . WNV-TX biological isolates ( 2002 ) were utilized for in vivo work , while WNV-TX ic ( infectious clone ) stocks were utilized for cell culture ( in vitro ) studies . Working stocks were propagated in Vero-E6 ( American Type Culture Collection; ATCC ) and titered by standard plaque assay on VeroWHO and BHK21 ( American Type Culture Collection; ATCC ) cells as previously described [24] . Single-use aliquots from the same viral stock lot were prepared and utilized for all experiments described here . Age and sex-matched 8–11 week old mice were anesthetized by isofluorane and inoculated subcutaneously ( s . c . ) in the right rear footpad with 100 PFU WNV-TX 2002 ( WNV-TX ) diluted in 40 uL PBS , administered via 1mL insulin needle . Mice were monitored daily for clinical score and loss of body weight . Euthanasia criteria was determined as a clinical score ≥ 5 for 2 or more consecutive days , or 20% loss in body weight . A clinical score of 6 ( moribund ) or significant respiratory distress resulted in immediate euthanasia . Mice were anesthetized with ketamine/xylazine , the top of the head was cleaned with EtOH , and the mouse was then restrained manually on a solid surface . The site of injection was approximately halfway between the eye and ear , and just off the midline , in the medial posterior region of the top of the skull . The injection was done with a 29G needle using a Hamilton syringe into the cerebral cortex . Following infection , mice were monitored for revival from anesthesia and monitored daily for clinical score and loss of body weight . Euthanasia criteria was determined as a clinical score ≥ 5 for 2 or more consecutive days , or 20% loss in body weight . A clinical score of 6 ( moribund ) or significant respiratory distress resulted in immediate euthanasia . To determine the viral load from in vivo tissue samples , mice were terminally anesthetized using ketamine/xylazine mixture followed by cardiac perfusion with 30–40 mL PBS . Kidney ( s ) and spleen were collected whole; brains were harvested and macrodissected into four anatomical regions , including the cerebellum , cortex , sub-cortex , and brainstem [91]; spinal cords were collected by perfusion with PBS . Tissues were harvested into 1 mL PBS on ice in Percelly’s tubes with ceramic beads . Following harvest , tissues were homogenized ( Percellys 24 ) 5500/1x 20s/5 min and centrifuged at 4°C/5 min/10k rpm . Supernatant was collected and analyzed by plaque assay on Vero-WHO cells ( 0 . 5% agarose overlay , 3% Neutral red counter stain after five days post inoculation; plaques counted 10-15h post staining ) . Cells were inoculated with WNV in serum-free media and the inoculum left for 1hr rocking at 37°C . Inoculum was removed , cells washed 1x and media replaced with cDMEM . At the indicated time-points , supernatant was collected for virologic and cytokine analysis; cells were treated with RIPA buffer for WB analysis ( or ) with RLT for total cellular RNA isolation . Primary cerebral cortical neuron cultures were generated from E15 WT and STING-/- embryos as previously described [92] and maintained in serum free Neurobasal-A medium ( Life Technologies 21103–049 ) with B27 supplement ( Gibco 17504–044 ) . Neuron cultures were used for virologic experiments after 7 days in vitro . Cortical neuron cultures were infected at MOI 0 . 001 with WNV-TX [32] . Multistep growth curve experiments were performed as described [93] and quantified via plaque assay using BHK21-15 cells . Mice were euthanized in an isoflurane chamber followed by cardiac perfusion with 30–40 mL PBS . Tissues were harvested; right kidney and spleen were collected whole; brains were harvested and macrodissected into four anatomical regions , including the cerebellum , cortex , sub-cortex , and brainstem [91]; spinal cords were collected via PBS perfusion . Tissues were harvested into 1 mL RNALater and stored at 4°C for a minimum of 1 week to stabilize the RNA . Tissues were removed from RNALater solution and transferred to 1 mL TRIreagent in Percelly’s tubes with ceramic beads at RT . Following harvest , tissues were homogenized in a Percelly’s homogenizer ( 5500/1x20s/5 min ) followed by centrifugation ( 4°C/10k rpm/5min ) . RNA isolated with the Ribopure kit from TRIreagent using per manufacturer’s instructions . cDNA was generated from 350 ng RNA using iSCRIPT kits with random primers per manufacturer’s instructions . Cellular and viral gene analysis was assessed by SYBR Green RT-qPCR using an ABI Viia7 and analyzed as the linear fold change ( 2^-dCT ) over a housekeeping gene ( GAPDH ) from WT mock infected sample or mouse ( Table 2 ) . Cells were harvested in RLT and total cellular RNA isolated for RT-qPCR analysis using Qishredders and the Quiagen RNeasy kit per the manufacturer instructions . cDNA was generated from 100 ng total RNA using the iSCRIPT kit per manufacturer instructions using their provided oligo ( dT ) and random primers . Cellular and viral genes were analyzed by SYBR Green RT-qPCR using an ABI ViiA7 . Primers for BMDM experiments described above . Protein extracts from cells were prepared in RIPA buffer . 7–15 ng protein lysate was analyzed by 4–20% gradient SDS-polyacrylamide gel electrophoresis by immunoblotting , using 5% BSA blocking buffer and nitrocellulose membranes . The following antibodies were utilized: WNV NS3 ( R&D BAF2907 ) , Actin ( C4; EMD MAB1501 ) , STAT1 ( CST 9172P ) , STING ( CST D2PZF ) , pSTAT1 ( Y701; CST 58D6 ) , pSTING ( CST D7C3S ) . 8E4 ( or ) 8x10^4 HFF cells were seeded onto glass coverslips in a 24-well plate . The following day , cells were infected with WNV at MOI = 1 or transfected with calf-thymus DNA ( ctDNA; ISD ) ( Thermo Fisher , Waltham , MA , USA ) at 3ug/ml final concentration using Lipofectamine 3000 and following the manufacturer’s protocol . 24h after WNV infection or 3h after ctDNA transfection cells were fixed with 4% paraformaldehyde for 15min at room temperature ( RT ) . Cells were permeabilized with 0 . 1% Triton X-100 for 5min at RT . After blocking the cells for 30min with 3% BSA in PBS , immunofluorescent staining was performed overnight at 4°C with the following primary antibodies: rabbit-anti-STING ( 1:100 , gifted by Glen Barber ) , mouse-anti-dsRNA ( J2 , 1:800 , Scicons , Budapest , Hungary ) . Nuclei were counterstained with 4' , 6-Diamidino-2-Phenylindole , Dihydrochloride ( DAPI , Thermo Fisher ) . Fluorophore coupled secondary antibodies ( Thermo Fisher ) were applied for 1h at RT . After washing with PBS samples were mounted onto glass slides using ProLong Gold ( Thermo Fisher ) . Images were acquired with a Nikon Eclipse Ti confocal microscope equipped with a 60x oil immersion objective using the Nikon confocal software . Insets were captured with 4x enlargement of 600x images . Images were merged and processed using the Nikon confocal analysis software ( Nikon , Melville , NY , USA ) . Mice were euthanized by isoflurane and perfused with 30-40mL PBS to ensure systemic removal of blood and residual intravascular leukocytes . Spleens were homogenized and single cell suspensions were treated with ACK lysis buffer to clear any remaining red blood cells , washed and resuspended in FACS buffer ( 1X PBS , 0 . 5% FBS ) . Cells were plated at 1E6 cells/well and stained for surface markers 15 minutes on ice . Cells were then fixed , permeabilized ( Foxp3 Fixation/Permeabilization Concentrate and Diluent , Ebioscience ) and stained intracellularly with antibodies for 30 minutes on ice . Flow cytometry was performed on a BD LSRII machine using BD FACSDiva software . Analysis was performed using FlowJo software . The following directly conjugated antibodies were used: B515-Foxp3 , B710-CXCR3 , G575-Ki67 , G610-CTA-4 , G666-CD127 , G780-KLRG1 , R660-NS4b Tet , R710-CD45 , R780-CD44 , UV395-CD8 , UV730-CD3 , V450-CD73 , V610-CD4 , V655-CD25 , V510-live/dead . Cells were counted by hemocytometer using trypan blue exclusion . Brains were harvested into RPMI and mechanically suspended using a 70uM strainer . Each brain suspension was added to hypertonic Percoll to create a 30% Percoll solution , vortexed then centrifuged at 1250 rpm for 30 minutes at 4°C . Following centrifugation , the supernatant was aspirated and cell pellet treated with ACK lysis buffer to remove any residual red blood cells . Cells were then washed and filtered through a 70um nylon mesh to remove residual debris and resuspended in FACS buffer . Cells were counted using beads during FACS analysis . Cells were plated at 1E6 cells/well and stained for surface markers 15 minutes on ice . Cells were then fixed and extracellularly stained with antibodies for 30 minutes on ice . Flow cytometry was performed on a BD LSRII machine using BD FACSDiva software . Analysis was performed using FlowJo software . The following directly conjugated antibodies were used for Fig 7C–7I: FITC-CD19 , PerCP-Cy5 . 5-CD103 , PE-CD3e , PE-Cy7-CD4 , APC-WNV Tetramer ( NS4b ) , BV421-CD8a , BV510-CD45 . 2 , BV786-CD44 ( or ) Fig 7K: V510-live/dead , R710-CD45 , UV730-CD3 , UV395-CD8 , V610-CD4 , V655-CD25 , B515-Foxp3 . | In recent years , outbreaks of emerging and re-emerging neuroinvasive West Nile virus ( WNV ) infection have brought about a critical need to understand host factors that restrict neuropathology and disease . WNV infection in humans typically is either asymptomatic or results in a mild febrile illness , but in some cases virus spreads to the central nervous ( CNS ) causing a more severe form of neuropathological disease . Previous studies established that both innate and adaptive immune responses are essential for controlling WNV disease and restricting the virus from the CNS . In this study , we examined the role of Stimulator of Interferon Genes ( STING ) in conferring host defense during WNV infection in a murine model . Our studies revealed that STING is essential for restricting pathology in the CNS during WNV infection . Further , STING is required for effective programming of the innate and adaptive immune response to WNV . In the absence of STING , aberrant immune development leads to ineffective viral clearance and immunopathology in the CNS . These studies uncover a critical and previously unidentified role for STING in the restriction of WNV that may have broader implications for a role in conferring host defense against RNA viruses . |
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The survival motor neuron ( SMN ) protein , the determining factor for spinal muscular atrophy ( SMA ) , is complexed with a group of proteins in human cells . Gemin3 is the only RNA helicase in the SMN complex . Here , we report the identification of Drosophila melanogaster Gemin3 and investigate its function in vivo . Like in vertebrates , Gemin3 physically interacts with SMN in Drosophila . Loss of function of gemin3 results in lethality at larval and/or prepupal stages . Before they die , gemin3 mutant larvae exhibit declined mobility and expanded neuromuscular junctions . Expression of a dominant-negative transgene and knockdown of Gemin3 in mesoderm cause lethality . A less severe Gemin3 disruption in developing muscles leads to flightless adults and flight muscle degeneration . Our findings suggest that Drosophila Gemin3 is required for larval development and motor function .
Spinal muscular atrophy ( SMA ) is an autosomal recessive disorder characterised by degeneration of spinal cord motor neurons , as well as progressive muscular weakness , dysphagia , dyspnoea , and in severe cases , death [1] , [2] . The majority of SMA patients harbour deletions or mutations in the survival motor neuron ( SMN1 ) gene , which encodes an RNA-binding protein , SMN . In mammalian cells , the SMN protein is stably complexed with a group of proteins including Gemin2 [3] , Gemin3 [4] , [5] , Gemin4 [6] , Gemin5 [7] , Gemin6 [8] , Gemin7 [9] , and Gemin8 [10] . Biochemical studies in vertebrate systems suggested that the SMN complex plays an essential role in small nuclear ribonucleoprotein ( snRNP ) assembly . The SMN complex binds directly to small nuclear RNAs ( snRNAs ) and ensures that a set of seven Sm or Sm-like ( Lsm ) proteins are assembled onto snRNAs [11] . Gemin3 , the only RNA helicase in the SMN complex , contains nine conserved motifs including the Asp-Glu-Ala-Asp motif ( or DEAD box in one-letter code ) . The RNA helicase activity of Gemin3 is ATP-dependent with a 5′ to 3′ direction [12] . RNAi-mediated knockdown studies indicated a role for Gemin3 in the assembly of snRNP complexes as an integral component of the macromolecular SMN complex [13] , [14] . Furthermore , a recent study demonstrated that intracellular Gemin3 proteolysis by a poliovirus-encoded proteinase led to reduced Sm core assembly activity in poliovirus-infected cells [14] . In addition to snRNP biogenesis , Gemin3 was also implicated in transcriptional and microRNA regulation . Gemin3 was originally isolated as a cellular factor that associates with the Epstein-Barr virus nuclear proteins EBNA2 and EBNA3C , which play a role in the transcriptional regulation of both latent viral and cellular genes [15] . The non-conserved C-terminal domain of Gemin3 has been shown to interact with and modulate a variety of cellular transcription factors including steroidogenic factor 1 [12] , [16] , early growth response protein 2 [17] , forkhead transcription factor FOXL2 [18] , and mitogenic Ets repressor METS [19] . Although the majority of Gemin3 and its associated protein , Gemin4 , are found in the SMN complex , a less abundant Gemin3-Gemin4 complex has been isolated from HeLa and neuronal cells . The Gemin3-Gemin4 complex contains Argonaute 2 and numerous microRNAs , co-sedimenting with polyribosomes [20]–[22] . Despite the detailed studies in vertebrate systems and a recent study in Drosophila culture cells [23] , the function of Gemin3 in Drosophila development remains elusive . Here we identify the orthologue of Gemin3 in Drosophila melanogaster and demonstrate that Drosophila Gemin3 , like its vertebrate counterpart , associates with SMN . Loss-of-function gemin3 mutants are lethal as third instar larvae and/or prepupae . Before they perish , gemin3 mutants exhibit dramatic loss of mobility and neuromuscular junction ( NMJ ) defects . Tissue-specific expression of a dominant-negative transgenic construct and RNAi studies suggest that the function of Gemin3 in mesoderm , particularly in muscles , is essential for animal survival . Furthermore , disruption of Gemin3 in muscles causes flight muscle degeneration and loss of flight . Thus our study demonstrates that Drosophila Gemin3 plays a critical role in development and motor function .
We carried BLAST searches of the Drosophila melanogaster genome using human and mouse Gemin3 sequences , and found that the DEAD/DEAH RNA helicase 1 ( Dhh1 ) or CG6539 is the putative Drosophila Gemin3 orthologue . This gene , renamed for the present studies as gemin3 , is located on the third chromosome in region 67E3 , and is composed of 2 exons separated by a short intron . The Drosophila melanogaster Gemin3 protein is composed of 1028 amino acids and shows 33% identity and 55% similarity ( BLASTP ) to the respective human orthologue ( Figure 1A , B ) . This level of conservation is quite similar to that observed between the Drosophila and human SMN , which have an overall identity and similarity of 31% and 49% , respectively . The N-termini of Gemin3 , in which all nine DEAD-box helicase motifs reside , are more conserved than the C-termini . A region in the middle ( 451–573aa ) of Drosophila melanogaster Gemin3 corresponds to the SMN-binding domain identified in higher eukaryotes [5] . Aiming to test whether the physical interaction between SMN and Gemin3 reported in higher eukaryotes [24] is conserved in Drosophila , a co-immunoprecipitation approach was pursued . We have generated a transgenic line expressing CFP::Gemin3 . The CFP::Gemin3 gene is functional as it can rescue gemin3 mutants , which we describe later . In extracts derived from CFP::Gemin3 transgenic larvae , anti-SMN antibodies co-immunoprecipitate CFP::Gemin3 ( Figure 2 ) . Two recessive lethal gemin3 alleles were identified: PBac{RB}e03688 ( gemin3W ) and P{PZ}Dhh1rL562 ( gemin3R ) . We used PCR to confirm that the transposon insertion site of the gemin3W allele is located at 92 nt upstream of the transcription start site ( Figure 3A; Figure S1 ) . Part of the 5′ and 3′ piggyBac ends in the gemin3W allele were found to have been lost during the insertion . In the gemin3R allele , the P element inserted at 108 nt downstream of the transcription start site ( Figure 3A; Figure S1 ) . Since the P{PZ}-element insert sequence generates several premature stop codons , gemin3R is hypothesised to be an amorph . Several studies were pursued to demonstrate that the recessive lethality of both transposon insertions is specific to gemin3 disruption , thereby confirming that gemin3 is an essential gene . First , complementation crosses revealed that both gemin3 alleles retain their recessive lethality in trans to each other and to a chromosomal deficiency that completely eliminates the gemin3 gene ( Df[3L]ED4457 ) . Second , a re-mobilisation screen of the P-element in the gemin3R allele , which is the only transposon that could be excised , recovered homozygous viable precise excision alleles or revertants . Third , both low ubiquitous gemin3 and CFP::gemin3 transgenic expression driven by 1032-GAL4 [25] rescued the lethality of gemin3R homozygotes and gemin3R/gemin3W transheterozygotes . However , neither of the above gemin3 transgenes can rescue the lethality of homozygous gemin3W , suggesting that a non-specific mutation may be causing the lethality associated with the gemin3W allele . Since the lethality observed in gemin3 heteroallelic mutants was specific to the loss of gemin3 , further analysis concentrated on this genotype . Expression of the CFP::gemin3 transgene under the control of tissue-specific drivers such as G7-GAL4 ( muscle ) , elav-GAL4 ( neuron ) , or the combination of both could not rescue the lethality of gemin3R homozygotes and gemin3R/gemin3W transheterozygotes , suggesting that animal survival also depends on the basal level of Gemin3 in tissues not covered by the expression of G7-GAL4 or elav-GAL4 drivers . Homozygous gemin3R mutants survive to the third instar larval stage , while the transheterozygotic gemin3R/gemin3W animals survive to the prepupal stage after both genotypes experience a prolonged wandering third instar larval stage . The expression of gemin3 at different developmental stages was compared by two-step RT-PCR . Essentially gemin3 mRNA was expressed at all developmental stages ( Figure 3B ) . Supporting the amorphic allele hypothesis , we observed that expression of gemin3 mRNA was dramatically reduced in transheterozygous animals throughout their entire larval life , whereas the housekeeping control Tat-binding protein-1 ( Tbp-1 ) transcripts remained detectable ( Figure 3B ) . Heterozygous gemin3R adults have approximately half of the gemin3 mRNA transcript as that in wild-type animals ( Figure 3B ) . Although showing no dramatic mobility changes throughout the first and second larval stages , the gemin3R/gemin3W transheterozygotes exhibit a significantly decreased contraction rate at the third instar larval stage ( Figure 4A and Video S1 ) . The puparium formed by gemin3 heteroallelic mutants exhibited failed eversion of the spiracles and a large axial ratio ( Figure 4B , C ) , the latter of which is most probably the result of a failure in body wall muscle contraction . Ubiquitous expression of the CFP::gemin3 transgene within this mutant background rescues the defects in mobility , spiracle eversion and abnormal axial ratio , confirming that the CFP::gemin3 transgene is functional and the above phenotypes exhibited by gemin3R/gemin3W transheterozygotes are specifically due to the disruption of Gemin3 function ( Figure 4A–C ) . Mobility failure is probably not secondary to compromised muscle structure since gemin3 mutant larval fillets have an ordered pattern of muscle fibres without obvious muscle losses . In addition , there are no gross defects in the sarcomeric organisation in the gemin3 mutants ( Figure 4D ) . The obvious larval contraction defects of the gemin3 transheterozygotic mutants directed the research focus on the larval neuromuscular junction ( NMJ ) . The present studies focus on the highly characterised type I NMJ innervating ventral longitudinal muscles 6 and 7 , and aim at unveiling the presence of any morphological abnormalities in a gemin3 mutant background . To this end , larval muscle fillets were dissected and double-labelled with anti-HRP antibodies , which allow visualisation of the neuronal membrane , and an antibody against Discs-large ( Dlg ) , a primarily postsynaptic scaffold protein localised to the subsynaptic reticulum that surrounds each bouton . Although no obvious motor neuron denervation was detected , gemin3 heteroallelic mutants exhibit an appreciative synaptic overgrowth before pupariation ( Figure 5A ) and a significantly increased synaptic area even when normalised to muscle size ( Figure 5B ) . Expression of a gemin3 transgene in a mutant or wild-type background resulted in an increase in both NMJ and muscle area ( data not shown ) . When normalized to muscle area , the NMJ area and branches in rescued gemin3 mutants restore to the wild-type range , whereas normalized NMJ area and branch numbers within single NMJs are significantly decreased when gemin3 was overexpressed ( Figure 5B , C ) . A truncated gemin3 transgene ( gemin3ΔN ) , which lacks 424 amino acid residues from the N-terminus of Drosophila melanogaster Gemin3 and hence lacks the helicase core ( Figure 3A ) , causes lethality on ubiquitous expression . Whilst highlighting the importance of the helicase domain to the function of Gemin3 , the N-terminal truncated Gemin3 isoform is hypothesized to be a dominant-negative mutant . We used various drivers to investigate the effect on animal survival when gemin3ΔN is expressed in various temporal and spatial expression patterns ( Table 1 ) . No dramatic effect is observed when gemin3ΔN is expressed at 25°C under the control of elav-GAL4 , nrv2-GAL4 , D42-GAL4 , OK6-GAL4 , mef2-GAL4 , or C57-GAL4 drivers ( Figure 6A ) . However , expression of gemin3ΔN at 25°C by Act5C-GAL4 , how-GAL4 or C179-GAL4 driver results in lethality , and that by the G7-GAL4 driver leads to a significant decrease in viability ( Figure 6A ) . When the temperature shifted to 29°C to allow for maximal GAL4 activity , expression of gemin3ΔN by Act5C-GAL4 , C179-GAL4 , how-GAL4 , or G7-GAL4 driver causes lethality , while that by mef2-GAL4 and C57-GAL4 drivers results in decreased viability ( Figure 6B ) . Co-expression of an extra full-length gemin3 transgene but not a control gene such as GFP with the gemin3ΔN transgene significantly alleviates the driver-associated lethality ( Figure 6 and data not shown ) . These experiments indicate that the lethality or low viability associated with the expression of gemin3ΔN in the mesoderm and larval muscles is specifically due to the disruption of Gemin3 function . To confirm the driver-specific lethality pattern induced by the gemin3ΔN transgene , several gemin3 RNAi transgenic flies were isolated and tested to establish whether lethality can be induced when gemin3 knockdown occurs ubiquitously throughout the entire organism . Two RNAi transgenes , gemin3dwejra and gemin3munxar , fit this criterion . Reducing gemin3 gene activity using elav-GAL4 , nrv2-GAL4 , or D42-GAL4 has no effect on fly viability ( Figure 7 ) . In contrast , Gemin3 knockdown at both 25°C and 29°C via C179-GAL4 resulted in lethality . The how-GAL4 driver gave a similar effect when the gemin3dwejra and gemin3munxar RNAi transgene was expressed at both temperatures or at a temperature of 29°C , respectively ( Figure 7 ) . The lethality induced by gemin3munxar could be rescued by co-expressing a functional gemin3 transgene , thus excluding the possibility that lethality is the result of ‘off-target’ effects ( Figure 7A , B ) . Knockdown of gemin3 in the mesoderm and larval somatic musculature results in lethality at the late pupal stage , that is , pharate adults enclosed in pupae fail to eclose . Animals expressing gemin3ΔN under the control of the how-GAL4 driver often lead to pupariation and puparia have increased axial ratios , similar to the defects exhibited by the gemin3R/gemin3W transheterozygotes . In addition , how-GAL4≫gemin3ΔN pupae exhibited several morphological abnormalities , including head eversion defects , short legs , and short wings , although segmentation of the abdomen and mature eye pigments appear normal ( Figure 8 ) . While they can walk and jump normally , eclosed flies with an mef2-GAL4-driven gemin3ΔN expression have a reduced ability to fly . In a flight assay , those flies show defective flight ability , similar to wild-type flies with clipped wings , which are flightless ( Figure 9A and Video S2 ) . The indirect flight muscles ( IFMs ) in mef2-GAL4≫gemin3ΔN flies are shrunken , resulting in increased spacing , and breakages are obvious between the muscle fibers . Frequently , large tears within the indirect flight muscles are observed in mef2-GAL4≫gemin3ΔN flies but not in wild-type flies ( Figure 9B ) .
Gemin3 or DP103 was first identified in mammalian culture cells through biochemical approaches [5] , [15] . The Gemin3 protein has three critical features . First , the N-terminus of Gemin3 contains multiple helicase motifs including a DEAD-box . Second , Gemin3 interacts with SMN in vitro and in vivo [24] . Third , the Gemin3 and SMN proteins have a similar subcellular localization pattern [5] , [26] . In Drosophila there are 29 DEAD-box RNA helicases [27] . Using human and mouse Gemin3 to BLAST the Drosophila melanogaster genome , CG6539 , previously identified as DEAD/DEAH RNA helicase 1 ( Dhh1 ) , is the top hit . In the N-terminus , CG6539 contains 9 conserved RNA helicase motifs including a DEAD-box . A segment in the middle of CG6539 , which corresponds to the SMN-binding domain in human Gemin3 , is less conserved . Moreover , co-immunoprecipitation experiments using Drosophila larval muscle extracts show that Gemin3 binds to SMN in vivo . We have also carried localization assays , which demonstrate that Gemin3 co-localizes with SMN in the cytoplasm and nucleus [28] ( RJC , KED , and JLL , unpublished data ) . Taken together , we feel confident that we have identified the Drosophila orthologue of vertebrate Gemin3 . Recently , an independent study by Fischer and colleagues also identified CG6539 as Drosophila Gemin3 through bioinformatic and biochemical approaches using Drosophila culture cells [23] . Both their study in Drosophila culture cells and this study in Drosophila tissues have shown that Gemin3 interacts with SMN , suggesting that Gemin3 is a bona fide component of the SMN complex in fruit flies , similar to that in vertebrate systems . In this study , we have multiple lines of evidence demonstrating that Drosophila Gemin3 is essential for animal development and survival . Firstly , homozygous loss of gemin3 through a specific transposon insert ( gemin3R ) or a transheterozygous combination of two transposon inserts which do not complement each other ( gemin3R/gemin3W ) results in lethality at the larval and/or prepupal stage . Secondly , a functional gemin3 transgene specifically rescues the lethality and developmental defects caused by loss of gemin3 . Thirdly , expression of a dominant-negative allele of gemin3 ( gemin3ΔN ) or Gemin3 knockdown by RNAi ubiquitously or even in a tissue-specific pattern results in lethality or reduced viability . Gemin3-null mutants have recently been described in the mouse [29] . Heterozygous gemin3 mutant mice are healthy and fertile , with minor defects in the female reproductive system , whereas homozygous gemin3 knockout in mice leads to death at the 2-cell embryonic stage [29] . Thus , the lethality caused by loss of Gemin3 in Drosophila is consistent with the findings in Gemin3-null mice . However , while Gemin3-null mice died at an early embryonic stage , gemin3 mutant flies exhibit delayed lethality , which probably results from maternal contribution of the gemin3 transcript . In a separate study in female ovaries , we observed severe defects in nurse cells and oocytes when gemin3 is disrupted in germline cells ( RJC , KED , and JLL , unpublished data ) . The earliest clues pointing towards a motor function were a progressive loss of mobility and consequent long and thin puparia when Gemin3 function is lost . Similar phenotypes have previously been observed in mutants with disrupted Mlp84B , a muscle sarcomeric protein [30] , or Tiggrin , an extracellular matrix ligand for the position-specific 2 integrins [31] . We also observe that gemin3 mutants have an overgrown NMJ though these could be a secondary response to the progressive loss of muscle power . The size ratio of NMJs to muscles is reduced when gemin3 is overexpressed raising the possibility that Gemin3 might also play a role in synaptic growth . The requirement of Gemin3 in mesoderm and larval muscles for adult viability suggests a function of Gemin3 at the post-synaptic side . Based on the tissue-specific phenotypes uncovered , such a function is critical for pupal metamorphic changes and flight muscles . However , another possible explanation is that an earlier and wider disruption of Gemin3 by mesodermal-related drivers is responsible for the lethality , while late and local disruption of Gemin3 by neuroectodermal-related drivers causes milder phenotypes . More studies on the expression details of Gemin3 in pre- and post-synaptic tissues would help to distinguish those views . Studies in vertebrate systems , in vitro and in vivo , have shown that Gemin3 directly binds to SMN [24] . A recent study in Drosophila culture cells [23] and this study in fly tissues confirm that the interaction between Gemin3 and SMN is conserved from fly to human . This study raises the possibility of a functional interaction between Gemin3 and SMN . Loss of gemin3 phenocopies the larval mobility phenotypes observed in smn mutants [32] . Strong Gemin3 disruption in mesoderm and muscles led to striking developmental defects during metamorphosis , similar to those reported on disruption of SMN in a similar expression pattern [33] . A less severe gemin3 disruption in the developing musculature results in viable but flightless adult flies , which have flight muscle degeneration , similar to the phenotype in a hypomorphic smn mutant [34] . We observed that gemin3 mutants exhibit an overgrown NMJ before puparation and overexpression of gemin3 leads to a significant decrease in NMJ area and branches relative to muscle size . Interestingly , two studies describe a range of NMJ phenotypes for smn mutants [32] , [35] . It is still not clear whether smn and gemin3 mutants have similar morphologic defects at the NMJ as the parameters and the segments used for NMJ analysis vary in different studies . Comparison of smn and gemin3 mutant NMJs with the same standard , as well as analysing the NMJ phenotype in smn and gemin3 double mutants would help to address this question . The motor defects unravelled on disruption of Gemin3 function in Drosophila are very intriguing in view of its association with SMN , and the possible link to SMA . More studies are necessary to clarify the roles of SMN-Gemin3 interaction in development , which may help us to understand the molecular mechanisms of the devastating neurodegenerative disorder SMA .
The y w stock was used as the wild-type control . Transposon insertion alleles gemin3R ( P{PZ}Dhh1rL562 ) and gemin3W ( PBac{RB}e03688 ) were obtained from the Bloomington Drosophila Stock Centre ( BDSC ) at Indiana University and the Exelixis collection at Harvard Medical School , respectively . Complementation tests , transposon remobilisation and rescue studies were carried out according to standard genetic crossing schemes . The RNAi transgenic constructs UAS-gemin3dwejra ( 49505 ) and UAS-gemin3munxar ( 49506 ) were obtained from the Vienna Drosophila RNAi Center and their generation was described in Dietzl et al . [36] . GAL4 lines used in this study included 1032-GAL4 , Act5C-GAL4 ( BDSC ) , elav-GAL4 ( BDSC ) , nrv2-GAL4 ( gift from Paul Salvaterra , City of Hope National Medical Center , Duarte , California , USA ) , D42-GAL4 ( BDSC ) , OK6-GAL4 ( gift from Cahir O'Kane , University of Cambridge , Cambridge , UK ) , C179-GAL4 ( BDSC ) , how-GAL4 ( BDSC ) , mef2-GAL4 ( gift from Barry Dickson , Research Institute of Molecular Pathology , Vienna , Austria ) , G7-GAL4 ( gift from Aaron DiAntonio , Washington University , St . Louis , Missouri , USA ) and C57-GAL4 ( gift from Vivian Budnik , University of Massachusetts , Worcester , Massachusetts , USA ) ; the spatial and temporal expression patterns are described in the Results . All stocks were cultured on standard molasses/maizemeal and agar medium in plastic vials or bottles at 25°C . For the generation of the P{CFP::gemin3} transgenic construct , the PCR-amplified full-length coding sequence of gemin3 was ligated into the KpnI and XbaI restriction sites of the pUAST vector . The NotI and KpnI restriction sites of the resulting recombinant vector were then used to insert the cyan fluorescent protein ( CFP ) coding portion of the pECFP-C1 vector ( BD Biosciences Clontech , Palo Alto , California , USA ) upstream of the gemin3 sequence . The P{UAS-gemin3} construct was produced by ligating the gemin3 cDNA ( Drosophila Genomics Resource Centre , Indiana University ) in the pUAST vector using the KpnI and NotI restriction sites . The generation of the P{UAS-gemin3ΔN} involved PCR-amplification of the C-terminus of gemin3 followed by ligation into the KpnI and XbaI restriction sites of the pUAST vector . In both cases , the ligation products were used to transform E . coli competent cells using standard protocols . Correct transformants were further propagated and their harbouring plasmids were purified ( Qiagen HiSpeed Plasmid Midi Kit , Qiagen Ltd . , West Sussex , UK ) prior to microinjection in y w embryos ( BestGene Inc . , Chino Hills , California , USA ) . RNA was first extracted using the RNeasy kit ( Qiagen Ltd . ) and then reverse transcribed into cDNA using the QuantiTect Reverse Transcription Kit ( Qiagen Ltd . ) following manufacturer's instructions . PCR amplification of mRNA transcripts was performed using primers specific to gemin3 ( forward: 5′-CACTGGCCAAAATGGATCTAA-3′ and reverse: 5′-GGCATTGCCTCAATGAGTTT-3′ ) and Tbp-1 ( forward: 5′-CACCGAAAAGATCAAGGTCAA-3′ and reverse: 5′-CTTTGTTGACTCCGACCAGA-3′ ) mRNAs . RT-PCR products were resolved by electrophoresis on a 1 . 7% agarose gel containing ethidium bromide and bands were visualized by ultraviolet light . Measurement of larval mobility involved placing age-matched larvae individually at the centre of a 0 . 7% agar plate and measuring the forward body wall contractions exhibited by each larva for 1 minute . Puparial axial ratios were calculated by dividing the length by the width of the puparia , both of which were measured from still images . Adult viability assays were conducted by crossing GAL4 driver stocks to lines harbouring knockdown or truncated gemin3 transgenes . A week following eclosion , adult flies were screened and counted . Adult viability was calculated as the percentage of the number of adult flies with the appropriate genotype divided by the expected number for the cross . The flight assay was done according to a modified protocol originally designed by Benzer [37] . In brief , a 1000 ml-graduated cylinder divided into 5 sectors was coated internally with mineral oil . Flies were introduced into the top of the cylinder through a funnel and the flies stuck in each sector were counted . The height flies stick in the cylinder is indicative of their flight capabilities . Protein A beads washed and suspended in protein lysis buffer ( 2× protein lysis buffer [50 mM Tris pH8 , 150 mM NaCl , 1 mM EDTA , and 1% v/v NP-40]+21× protease inhibitor cocktail [complete , Mini; Roche Diagnostics Ltd . ] ) were incubated with preimmune serum or an antigen-specific antibody , including rabbit anti-GFP ( Abcam plc . , Cambridge , UK ) and rabbit anti-SMN ( gift from Marcel van den Heuvel , University of Oxford ) . Sample lysates were prepared by dissecting body wall larval muscle fillets ( ∼30/IP ) into cold 1× PBS followed by grinding into cold 2× protein lysis buffer . Following pre-clearing , lysates were incubated with beads coated with the appropriate target antigen-specific antibody . The beads were then washed in lysis buffer , and mixed with 4× NuPAGE LDS Sample Buffer ( Invitrogen Ltd . , Paisley , UK ) , 10× NuPAGE Reducing Agent ( Invitrogen Ltd . ) and deionised water . The mixture was then heated at 70°C in order to dissociate the immunoprecipitated antigen and any other macromolecules bound to it , followed by a brief spin . The bead-free supernatant was loaded onto a 4–12% NuPAGE Novex Bis-Tris pre-cast gel ( Invitrogen Ltd . ) , resolved and probed for GFP according to standard Western blotting procedures . Larvae were dissected in 1× PBS , fixed in 4% paraformaldehyde in PBS and then washed in 1× PBS+0 . 1% ( v/v ) Triton X-100 ( PBT ) . The tissues were next subjected to overnight staining at 4°C by mouse anti-Discs large antibodies ( 1∶100; Developmental Studies Hybridoma Bank , University of Iowa , Iowa , USA ) . The next day , tissues were washed in PBT and stained for ∼2 hours at room temperature with anti-rabbit Alexa Fluor 488-conjugated secondary goat antibodies ( 1∶50 ) , and anti-HRP goat antibodies conjugated to TRITC ( 1∶50; Jackson ImmunoResearch Laboratories Inc , West Grove , Pennsylvania , USA ) . Samples were then counterstained with nuclear-staining Hoechst 33342 ( 1∶500 ) and Cy5-conjugated actin-binding phallodin ( 1∶200 ) and mounted in Vectashield medium ( Vector Laboratories Ltd . , Peterborough , UK ) prior to viewing with a Zeiss LSM 510 META confocal microscope . ImageJ software ( NIH ) was used to quantify branch number , NMJ area , and muscle area from z-projections of confocal image stacks capturing ventral longitudinal muscles 6 and 7 ( Segment A1 ) . NMJ area constituted the presynaptic region stained by the anti-HRP antibody whereas branch number calculates the number of arborisations containing at least two boutons within a single NMJ . Both NMJ area and branch numbers were normalised through dividing each by the total muscle area of ventral longitudinal muscles 6 and 7 . Adult flies were fixed overnight in 4% ( v/v ) paraformaldehyde+2 . 5% ( v/v ) glutaraldehyde+0 . 1 M phosphate buffer pH7 . 2 . The flies were then washed in 0 . 1 M phosphate buffer pH7 . 2 and post-fixed with 2% ( w/v ) osmium tetroxide for 2 hours at room temperature . Following a wash in water , the samples were subjected to a series of progressive dehydration steps in ethanol : water mixtures prior to embedding in Spurr's resin . Ultrathin sections were then made with a diamond knife , stained with Toluidine Blue and viewed under a light microscope . | The childhood disease spinal muscular atrophy ( SMA ) has a drastic impact on motor neurons and muscles . The cause has been linked to a deficiency in the survival motor neuron ( SMN ) protein . SMN interacts with various proteins termed Gemins to form the SMN complex , among which Gemin3 is the only one with an RNA unwinding activity . Here , we study the function of D . melanogaster Gemin3 in the context of development . The association of Gemin3 with SMN , which had been reported previously in humans , is conserved in flies . Loss of Gemin3 resulted in death at larval stages . Before they die , gemin3 mutant flies become sluggish and develop large synapses , which are the contacts between motor neurons and muscles . Disruption of Gemin3 in mesodermal tissues , especially muscles , causes development defects , degeneration of flight muscles , and flies that are unable to fly . This study demonstrates that Gemin3 plays a critical role in fruit fly development , especially in motor function , which raises the question of whether disruption of Gemin3 contributes to SMA . |
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